Comprehensive Energy Systems, vol.5 - Energy Management [5, 1 ed.] 978-0-12-814925-6

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5.1 Energy Auditing Amine Allouhi and Ali Boharb, Sidi Mohamed Ben Abdellah University, Fez, Morocco Rahman Saidur, Sunway University, Selangor, Malaysia and Lancaster University, Lancaster, United Kingdom Tarik Kousksou, University of Pau and Pays de l'Adour, Pau, France Abdelmajid Jamil, Sidi Mohamed Ben Abdellah University, Fez, Morocco r 2018 Elsevier Inc. All rights reserved.

5.1.1 5.1.2 5.1.2.1 5.1.2.2 5.1.2.2.1 5.1.2.2.2 5.1.2.2.3 5.1.2.3 5.1.2.3.1 5.1.2.3.2 5.1.2.3.3 5.1.2.4 5.1.2.4.1 5.1.2.4.2 5.1.2.4.3 5.1.2.4.4 5.1.3 5.1.3.1 5.1.3.1.1 5.1.3.1.2 5.1.3.2 5.1.3.2.1 5.1.3.2.2 5.1.3.2.3 5.1.3.2.4 5.1.3.2.5 5.1.3.2.6 5.1.3.2.7 5.1.3.2.8 5.1.3.3 5.1.3.3.1 5.1.3.3.2 5.1.3.3.3 5.1.3.3.4 5.1.4 5.1.4.1 5.1.4.1.1 5.1.4.1.2 5.1.4.1.3 5.1.4.1.4 5.1.4.2 5.1.4.3 5.1.4.3.1 5.1.4.3.2 5.1.4.3.3 5.1.4.3.4 5.1.4.4 5.1.4.5 5.1.5 5.1.5.1

Introduction Fundamentals of Energy Audit Definition Classification of Energy Audits Industrial energy audits Commercial energy audits Residential energy audits Energy Audit Levels ASHRAE Level 1 – walk-through analysis/preliminary audit ASHRAE Level 2 – energy survey and analysis ASHRAE Level 3 – detailed analysis of capital intensive modifications Energy Audit: General Procedure Preparation phase Execution phase Reporting phase Postaudit phase Instrumentation for Energy Auditing Measuring Electrical Parameters Electric energy meters Electrical network analyzers Internal Comfort Measurements Thermometers Infrared thermometers Anemometers Humidity measurements Carbon dioxide meter Lighting level measurements Heat flux meter Blower door test Other Measurements Combustion analyzers Speed measurements Leak detectors Flow meters Energy Efficiency Measures Building Envelope Thermal insulation Windows External shading Reduction of air infiltration Heating, Ventilating, and Air-Conditioning Systems Electrical Systems Transformers Quality of electrical energy Electrical motors Lighting systems Compressed-Air Installation Combustion Installations Case Study: Energy Auditing Introduction

Comprehensive Energy Systems, Volume 5

doi:10.1016/B978-0-12-809597-3.00503-4

3 3 3 3 3 4 4 4 5 5 5 5 6 6 7 7 7 7 7 8 9 9 10 10 10 11 11 12 13 14 14 14 15 15 16 16 16 18 18 18 19 20 20 21 22 22 24 25 25 25

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5.1.5.2 Methodology 5.1.5.2.1 Manufacturing process 5.1.5.2.2 Audit process 5.1.5.2.3 Data collection and measurements 5.1.5.2.4 Mathematical formulations 5.1.5.3 Analysis 5.1.5.3.1 Profile of monthly electricity use 5.1.5.3.1.1 Monthly electricity price 5.1.5.3.1.2 Subscribed power 5.1.5.3.2 Transformers 5.1.5.3.2.1 Energy losses in transformers 5.1.5.3.2.2 Analysis of energy quality in transformers 5.1.5.3.3 Energy-intensive motors 5.1.5.3.3.1 Motors without variable frequency drive 5.1.5.3.3.2 Motors equipped with variable frequency drive 5.1.5.3.4 Compressed-air installations 5.1.5.4 Actions Plan and Analysis 5.1.5.4.1 Action 1: revise the subscribed powers 5.1.5.4.2 Action 2: adopt high-efficiency transformers 5.1.5.4.3 Action 3: improving energy efficiency of electric motors 5.1.5.4.4 Action 4: install variable frequency drive at low-load motors 5.1.5.4.5 Action 5: installation of variable frequency drive at the compressed air compressor 5.1.5.4.6 Action 6: treatment of harmonic pollution 5.1.5.5 Conclusions 5.1.6 Closing Remarks References Relevant Websites

Nomenclature

PB PC PSP r SP TEC THDI THDV V

Increase due to a displacement power factor below 0.8, MAD Payback period, years Power cost, MAD Price of subscribed power, MAD/kW Reductive coefficient Subscribed power, MVA Total energy consumption, MWh Total harmonic current distortion, % Total harmonic voltage distortion, % Volume of the space to be heated, m3

Greek letters Z Efficiency Average seasonal efficiency of the heat ZREC recovery unit

l raca

Thermal conductivity, W/m K Volumetric heat capacity, Wh/m3 K

Abbreviations C Compressor CO Carbon monoxide Carbon dioxide CO2 SC Specific consumption DPF Displacement power factor h Hours HV High voltage HVAC Heating, ventilation and air conditioning

LED MAD Mot Mot-VFD MV RMS Tr VFD

Light-emitting diode Moroccan dirham Motor Motor with VFD Medium voltage Root mean square Transformer Variable frequency drive

AEP AES APmax CF EC Eee EPx Esrd FEPC

Average electricity price (excluding taxes), MAD/kWh Annual energy saving, MWh/year Monthly maximal active power, MW Consumption fee, MAD Energy consumed, MWh Efficiency rating of energy-efficient motor, % Electricity prices for the time slice x, MAD/kWh Standard motor efficiency rating, % Fee of the excess of subscribed power, MAD

IDPF

26 26 27 27 27 28 28 28 28 30 30 31 31 31 33 33 35 35 35 38 40 40 40 43 43 43 44

Energy Auditing

5.1.1

3

Introduction

Energy is used in the industrial, commercial, and residential sectors by various equipment, machineries, and processes. Worldwide, more than half of the total energy goes to the industrial sector for different industrial purposes. Other sectors also use a quite sizeable amount of energy for various purposes. However, sometimes energy is not used in the most efficient manner for the above-mentioned sectors. A huge amount of energy is lost or wasted, while used in many pieces of equipment and processes. Environmental concern in relation with the use of fossil based energy is another major global issue that needs to be considered seriously. In such a situation, energy audit is an approach that identifies losses, amount of energy used by various equipment and processes, where energy goes, major energy-using equipment, breakdown of energy consumption, and opportunities for improvement. This is fundamental information needed for energy efficiency improvement, policy development, energy cost analysis for company benefit/profit, identification of energy conservation measures, and mitigation techniques to reduce greenhouse gas emissions. It was reported in the literature [1] that a well-defined energy efficiency improvement strategy may reduce energy consumption by 70% for industrial process energy use. Comprehensive information for details of energy audit on energy efficiency improvement, policy development, and environmental analysis is needed for many developing countries, especially those with limited energy resources. Therefore, this chapter will serve the purpose of filling these gaps up to a certain extent. Energy audits vary depending on many factors, such as the structure type and size, scope and depth of the analysis and type of applications involved. Residential, commercial, and industrial energy audits at different levels of analysis are increasingly being conducted in several countries worldwide. The pattern of energy use for each category designates the type and amount of energy losses. While insulation issues, heating, ventilation, and air conditioning (HVAC) applications and lighting present the main energy conservation opportunities in the residential and commercial sectors, the opportunities are more related to other specific applications in the industrial sector. Various losses (i.e., iron losses and copper losses) take place in the transformer (Tr). Energy is lost due to operating conditions, such as high altitude, extreme temperature fluctuations, accentuated humidity, seismic activity, severe contamination, and unexpected voltage variations. These conditions cause even and odd harmonic and intermittent loading [2]. Sometimes electric motors (Mots) are oversized and this makes them inefficient. Consequently, this leads to huge wastage of electrical energy [3]. There are various losses taking place in Mots as well. Since electric Mots have diverse applications and consume a major share of total energy consumption (TEC), their efficiency improvement will be of great importance from the economic and environmental point of view. There are various technologies available to diminish energy consumption of electric Mots. Variable speed drive is one of the technologies that can adjust the speed required. This consequently reduces a huge amount of energy consumption [4,5]. About 10–30% of total energy is used by compressed air systems and it was reported that this form of energy is the least efficient since only 30% of it is useful and the remaining percentage is lost as heat, through leakage and inefficient usage [6]. Energy is lost in HVAC, lighting, combustion process, and other electric systems, as well. Therefore, an energy audit is necessary to identify various losses to improve the efficiency of energy-using equipment, machinery, and processes.

5.1.2

Fundamentals of Energy Audit

5.1.2.1

Definition

An energy audit can be defined as an inspection or survey analysis of energy flows in a structure, in a process or in a system, intended to reduce the amount of energy input without negatively affecting the outputs [7]. It is the primary phase in proposing possibilities to diminish energy expense and carbon footprints and therefore, is a key point in decision-making in the area of energy management. For an organization, energy audit helps to understand, quantify, and analyze its energy utilization. It permits to detect where waste takes place, identify the most critical points and discover opportunities where energy consumption can be reduced. Finally, by means of eco-efficient and feasible practices and energy conservation methods, overall energy efficiency of the organization will be improved and its energy bill will be reduced [8]. This activity has different names in different countries. In Europe the term energy audit is often used. In the United States, energy assessments are the most practically employed and in some cases the term energy survey or energy scan can be encountered.

5.1.2.2 5.1.2.2.1

Classification of Energy Audits Industrial energy audits

Industrial energy audits are considered among the most complex and large audits due to the great variety of equipment found in industries. Generally, there is an important similarity between industrial equipment and those found during commercial audits, such as air-conditioners, ventilating fans, water heaters, coolers and freezers, and lighting systems, which are generally used in both commercial and industrial organizations. What makes the difference in industrial facilities is the specialized equipment used. The most important task here is to understand how this specialized equipment works, what are the conditions affecting its operation and particularly how to make significant improvements to reduce its energy consumption.

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For this, data sources related to specialized equipment should be maintained in a library of information and the audit team must have access to additional information sources that are generally available at research organizations and equipment suppliers. Currently, some electric and gas utilities with sufficiently trained and experienced staff offer industrial audits to their customers. Practical changes, including sometimes new equipment acquisition are therefore proposed to minimize the energy costs for a particular production environment. In some countries, instead of offering energy audits to their consumers, they program financial incentives to install high efficiency lighting, Mots, boilers, and other equipment. In the United States, for example, the Information Center of Energy Efficiency & Renewable Energy at the Department Of Energy (DOE) provides technical assistance and guidance to all sizes of plants and customized energy efficiency consultation to small and medium-sized industries free of charge. Energy audits in industries are also unique because of the structure of energy billing that generally belongs to the large commercial or industrial rate category with an interruptible rate that gives much cheaper energy service. In the industrial sector, while conducting an energy audit, safety remains a paramount concern. Compared to residential or commercial energy audits, many risks resulting from contact with hot objects, electric shocks, hazardous materials, falling fragments, and drive belts need to be avoided. Therefore, personal protective equipment (PPE) including safety glasses and shoes, specialized gloves, hardhats, and reflective Jackets are highly required in such audits.

5.1.2.2.2

Commercial energy audits

Commercial energy audits differ largely from simple audits for small offices to very complex audits for multistory office buildings or giant business centers [9]. As stated before, there are many similarities between large commercial and industrial audits. Instead of focusing on the highly specialized equipment used in the production process of facilities, the most important point in commercial structures is the building envelope that should be carefully examined. Many envelope features, such as building materials, insulation products, windows, exterior doors, and skylight design and air-sealing can be regarded as potential opportunities for improving energy efficiency. Moreover, commercial facilities are characterized by excessive use of electrical energy and have the larger capacity equipment, such as air-conditioners, water heaters, cookers, refrigerators, and office equipment (computers, copy machines, and phones). Therefore, adoption of more efficient equipment and reuse of waste heat can also be identified as potential opportunities for reducing energy consumption. An important difference between industrial and commercial audits relies on the lighting application. Lighting in commercial structures is an energy-intensive usage and accounts for 50% or more of the total electric bill. Lighting quality and levels are very crucial in various commercial operations. The improvement must focus on suggesting new lighting options that permit high light levels, while reducing the wattage needed. In many countries worldwide, commercial buildings are billed for energy based on their size. In this sense, small commercial customers pay on the basis of a per energy unit, while large commercial customers pay according to a more complex billing structure involving various elements, such as energy, rate of energy use, power factor, time of day, season of year, power factor, and other elements.

5.1.2.2.3

Residential energy audits

There are many similarities between energy audits for large, multistory apartment buildings and commercial audits. However, in the case of single-family residences, the approach is generally simpler. Residential audits are usually used to identify cost-effective solutions to ameliorate the comfort and efficiency of buildings. In several cases, subject houses may qualify for energy efficiency grants from governments. Energy auditors mainly focus on the analysis of the envelope thermal performance or quantify air leakages and examine the energy use and efficiency of residential applications, such as heaters, air conditioners, water heater, lighting, and “plug loads.” The residential energy audit should start by obtaining past energy bills and analyzing them. Since user behavior greatly affects energy use in residential buildings, an interview of the homeowners to understand their patterns of use over time is generally required. Finally, a written report regrouping the improvements list together with costs, benefits, and simple payback periods (PBs) is presented to the owner. Despite the simplicity of energy audits in the residential sector, several barriers limit their widespread use in many parts of the world. The main barriers are the low public awareness and the financial constraints related to the costs of energy audits.

5.1.2.3

Energy Audit Levels

The type of energy audits varies depending on many factors including: ● ● ● ● ●

the the the the the

function, type, size, and configuration of the structure energy systems; depth to which the audit is required; project specifications confirmed by the client; scope and potentials proposed by the energy auditor; and level and magnitude of projected energy savings and cost reduction intended.

The exact results of energy audits are usually quite complex to be predicted and the efforts that will be deployed and their cost effectiveness are initially unknown. The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) has

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defined three progressive levels of audits. Each audit level relies on the previous level. Obviously, the comprehensiveness of the site assessment, the amount of data collected and the detail provided in the final audit increases with the audit complexity, but the potential of energy saving becomes higher. The ASHRAE energy audit levels are discussed below [10].

5.1.2.3.1

ASHRAE Level 1 – walk-through analysis/preliminary audit

The Level 1 audit, known also as “walk-through audit,” “simple audit,” or “screening audit,” is the basic starting point for energy conservation. It is typically used in commercial buildings and small or medium industrial sites where the energy-consuming systems are quite simple and the likely areas of potential energy-saving measures are known in advance. The ASHRAE Level 1 audit is geared toward defining the type and nature of energy systems, preliminarily analyzing the site's energy consumption and identifying the simplest and most cost-effective energy upgrade measures. On this basis, in this audit type, readily available data are mostly used for the analysis of energy use and performance of the structure. Limited measurements are carried out and extensive data collection is not required. A short report listing the findings and a basic economic analysis of the improvements is finally elaborated without necessarily providing detailed recommendations, except for very visible projects or operational faults. Despite the degree of simplicity of this type of audit, it must be conducted by an experienced auditor. The completion time of the "walk-through" audit is very limited and therefore the auditor has to make quick and correct and profitable decisions.

5.1.2.3.2

ASHRAE Level 2 – energy survey and analysis

The Level 2 energy audit begins with the findings of the Level 1 audit, and evaluates the energy systems of the audited structure in more detail to propose a wide range of potential energy efficiency improvements. The approach differs depending on the structure type (residential, commercial, or industrial structure), but in all cases, more detailed data and information are needed. Detailed measurements and data inventory are usually carried out and different energy systems are extensively examined. The energy efficiency measures (EEMs) for this audit type are not direct or obvious as in the case of Level 1 audit and generally necessitate higher investments. For residential buildings, the EEMs include the assessment of the building envelope, HVAC systems, lighting devices, domestic hot water production system and “plug loads.” In the industrial sector, the focus is mainly geared toward the assessment of highly specialized equipment, such as compressed air, Mots, and process machines. The first step in this type of audit is to deeply analyze the energy consumption; quantify base loads; identify energy-intensive applications, usage patterns, and seasonal variation; and determine energy costs. According to the audited structure, during the audit process, there should be detailed discussions with the building ownership or facility manager and facility operation and maintenance staff to identify potential problem areas, and clarify financial and nonfinancial targets of the program. A clear and concise report, including an action plan to improve the energy efficiency, general future performances, and economic metrics is written. A meeting is then planned with the client to clarify the audit results, prioritize EEMs and give ways to evaluate and implement them. Some of the proposed measures are low-cost or can be implemented quickly, resulting in a short PB. Other measures require higher investments or considerable changes. Here, the auditor should help in the decision-making process and define the first steps of the implementation phase.

5.1.2.3.3

ASHRAE Level 3 – detailed analysis of capital intensive modifications

Some of the system upgrades outlined by the Level 2 energy audit may require detailed analysis of possible capital-intensive modifications, including modeling and simulation [11]. This type of audit, which is sometimes called an investment grade audit (IGA), is intended to provide supplementary engineering accuracy for more costly capital projects where uncertainty is less permitted since investors often demand guaranteed savings. Therefore, the Level 3 audit involves a collection of data over a longer time period, an accurate modeling of EEMs, an estimation power/energy response, detailed design of construction documents, and detailed costing estimates. Typically, a scope of work and schematics are provided so that the contractors installing the measures understand exactly what is to be installed. Investment levels can vary from tens of thousands to tens of millions of dollars. Dynamic simulation software packages are often used to perform energy calculations in this audit type. In the case of industrial activities, data loggers typically will be employed to monitor operation modes of pumps and Mots, hourly temperatures and humidity variation of affected spaces, switching behavior, and other parameters. These data are then used to calibrate the computer model of the structure and its various energy systems. Future changes can be accurately simulated and the results are thoroughly validated, which give a strong support during the decision-making process.

5.1.2.4

Energy Audit: General Procedure

To conduct an energy audit, a systematic approach is required in which the depth of the data collection and analysis might be different depending on the level, scope, and objectives of the audit. Generally, there are four main phases, each of which has several steps. These phases and steps are reported in Fig. 1.

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(1) Audit preparation

(2) Audit execution

Defining audit level, criteria and scope

Data inventory and measurements

Selection of audit team

Analyzing energy use patterns

Setting objectives

Diagnosing energy systems

Planning the audit

Exploring and comparing opportunities

Data collection

Identifying potential EEMs

Preliminary analysis

Economical assessment

(3) Audit reporting

(4) Post-audit activities

Writing the energy audit report

Implementing EEMs

Communicating with the client

Checking performances

Prioritizing and decision-making support

Maintaining measures

Fig. 1 Energy audit procedure. EEMs, energy efficiency measures.

5.1.2.4.1

Preparation phase

Before starting the energy audit, the level, criteria, and scope against which the audit will be conducted should be defined. An energy audit team should be established to organize and manage the audit process, especially at the facility level where audit activities are more intense and often require various skills and capabilities. If needed, hiring outside experts is recommended to carry out a thorough audit. In this phase, it is also important to set objectives in harmony with site boundary, timeline, and staff involvement. As the audit process is generally complex, planning the activities to outline strategies and procedures is mandatory. The auditor may use checklists in order to conduct the work in a systematic and consistent way. Once these steps are performed, the data and information collection process can begin. Present and former energy use pattern, construction, and energy utilization of every building or unit should be characterized. These data and information can be found thanks to a well-structured and accurate questionnaire, which will be answered during the first meeting between the energy auditor and the client.

5.1.2.4.2

Execution phase

Energy audits can take from few weeks to several months to complete, depending on the site nature and complexity. The audit process starts at the utility meters where energy flows are identified and sources of energy coming into a building or facility are measured. Data inventory is established to characterize the use and occupancy of the audited structure. Analysis of energy patterns for specific plant departments or items of process equipment are investigated. The diagnosis operations lead to an identification of opportunities for energy efficiency improvements. These opportunities must be carefully assessed and compared to identify the most appropriate ones. Once the potential EEMs are selected, auditors conduct a cost–benefit analysis to evaluate their economic viability.

Energy Auditing 5.1.2.4.3

7

Reporting phase

In the final meeting, the energy auditor (or audit team) presents his or her conclusions explained in a well-structured format through an energy audit report. The report needs to be clear, concise and precise, providing suitable information to the potential readers. It starts with an explanation of the audit objectives, scope, and methodology and moves toward an overview of the audited facility or building. The body of the report includes the main audit findings (energy use and budget of the structure, main identified anomalies and current performances), a detailed description of recommended energy measures classified in terms of no cost/low cost, medium cost, and high investment cost along with the implementation costs, savings, and economic indicators. During the final meeting, an action plan for the implementation of the retained EEMs is proposed. Generally, the auditor prioritizes the potential, direct, and low-cost opportunities and provides support in the decision-making process.

5.1.2.4.4

Postaudit phase

In practice, the implementation of recommended improvements encounters several barriers. Hence, establishing a clear procedure to guarantee a favorable realization of these improvements is required. This procedure should clearly outline goals, saving targets, and responsibilities for the implementation. The implementation phase should be achieved in a participative context with the focus on a smooth communication, an increased awareness, and a profound motivation. Audited structures can assess the benefits of the implemented activities by comparing actual performances and consumptions to the established goals using energy data and measurements. The audit process is completed by suggestions to maintain the audit results and ensure the sustainability of energy efficiency improvement.

5.1.3

Instrumentation for Energy Auditing

The energy audit of energy use necessitates measurements; these measurements require the use of accurate, reliable, durable, easy to use, and relatively inexpensive instruments. For measuring and estimating the required parameters, it is imperative to utilize accurate and complete data monitored for a representative duration. In practice, however, complete data are rarely available. The auditor has to control periodically the operational and maintenance status of the instruments and assess their probable measuring error to ensure trustworthiness of measurements. The measuring activity using both portable and installed instruments generally occurs during the executing phase, providing instantaneous or short-term records of performance over a short time interval. Special care should be considered when extrapolating short-term measurements to longer-term results. In this case, it is advised to perform measurements during periods that are representative for each equipment operation. The parameters usually monitored during an energy audit may cover the following [12–14]: ● electrical measurements including: voltage (V), current intensity (A), power factor, active power (kW), apparent power (kVA), reactive power (kVAr), energy consumption (kWh), frequency (Hz), and harmonics; ● temperature, pressure, relative humidity, radiation, heat flow, air velocity, and luminance level; ● exhaust gazes emissions and contents in CO2, O2, CO, SOx, and NOx; ● liquid and gas fuel flows; and ● others, such as pH, noise and vibration, total dissolved solids (TDS), revolutions per minute (RPM). Auditors should always undertake measurements of these parameters with due regard for safety rules, especially when dealing with specific equipment or processes.

5.1.3.1

Measuring Electrical Parameters

For measuring the electrical parameters the following instrumentation is used.

5.1.3.1.1

Electric energy meters

These measuring devices for electric energy are portable, quite simple, and do not require any special skills. The display, with which it is possible to interact by means of buttons, can provide the following values: ● ● ● ● ● ●

instantaneous voltage; current intensity; instantaneous power absorbed by the equipment; power factor; energy consumed (EC) during a certain period; and resulting economic value of the EC.

The market proposes a wide range of these devices at accessible prices. The latest models are smart energy meters that provide more precise and exact measures with supplementary functionality, such as real-time reads, power outage notices, and power quality supervision (Fig. 2).

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Energy Auditing

Fig. 2 Energy meter manufactured by currentcost. Reproduced from Currentcost. Available from: www.currentcost.com [accessed 18.08.16].

Fig. 3 Electrical network analyzer connected to the main breaker. Reproduced from Techni-Too. Available from: www.techni-tool.com [accessed 20.08.16].

5.1.3.1.2

Electrical network analyzers

Electrical network analyzers measure simultaneously the instantaneous voltage, current, and power factor. Their utilization requires specific skills in electrical engineering. The network analyzer is equipped with a communication cable connected to a computer to transfer the data recorded (Fig. 3). The stored data are processed and analyzed by a software package. After a proper

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connection of this instrument to the electrical panel of machinery or the substation under diagnosis, measurement readings can be displayed on its screen and involve: ● ● ● ● ● ● ● ●

root mean square (RMS) values of AC voltages and currents peak values for voltages and currents power factor active, reactive power, and apparent power frequencies of 50 and 60 Hz electric networks K-factor in current and voltage distortion factor of current and voltage total harmonic distortion for current and voltage and other parameters

5.1.3.2

Internal Comfort Measurements

Six factors affect the thermal comfort of occupants. These factors can be distinguished into two classes: personal and environmental factors. The personal factors include the clothing insulation and the metabolic heat. The environmental factors include the following factors [15]: ● ● ● ●

air temperature; mean radiant temperature of walls; air speed; and relative humidity.

These parameters can be measured either instantaneously or continuously. The main devices used for this purpose are listed below.

5.1.3.2.1

Thermometers

Air, gas, fluid, and surface temperatures are frequently controlled in any audit to optimize energy performance and quantify heat losses of a system. Handled digital thermometers indicate the temperature value precisely on their display (Fig. 4). Some thermometers indicate a single measured value, while others are able to measure a series of temperatures that can be stored for being processed later. Before selecting a thermometer, it is important to consider a number of parameters including the measurement scale, accuracy, and durability, as well as cost and application type. The most usual types of thermometer sensors are: ● Thermocouples: these devices are very rugged, economical, and can function over a large temperature field. A thermocouple is constructed whenever two different metals come into contact, resulting in a low open circuit voltage at the contact point, which

Fig. 4 Handheld digital thermometer manufactured by Davis. Reproduced from Davis. Available from: www.davis.com [accessed 20.08.16].

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Energy Auditing

varies depending on the temperature (Seebeck effect). The most-used thermocouple is the nickel–chromium–nickel (NiCr-Ni) or types K. Thermocouples require frequent calibration. Their weak signal easily affected by industrial noise remains their main disadvantage. ● Resistance thermometer detectors (RTDs): considered among the most technologically advanced instruments, these detectors are based on the fact that, in the case of certain metals, the resistance increases as the temperature increases. They are powered by an external constant current whose voltage varies proportionally with the temperature measured. ● Negative thermal coefficient thermistors: similarly to RTDs, NTC thermistors use the variation of electrical resistance to measure the temperature. The difference is that, in the case of NTC thermistors, the resistance decreases proportionally with increases in temperature.

5.1.3.2.2

Infrared thermometers

This is a noncontact type measurement that when oriented toward a heat source directly displays the temperature value. This instrument is useful for measuring temperatures in furnaces, moving objects, corrosive surfaces, in vacuum reactor, or subjected to strong electromagnetic fields. Temperature sensitivity ranges available are typically from 40 to 700 1C. The operating principle is based on the temperature measurement by quantification of the radiative energy emitted in the infrared. Accurate measurements can be achieved by using infrared thermometers with a possibility of manually setting the values of environmental parameters, such as emissivity and air temperature (Fig. 5).

5.1.3.2.3

Anemometers

Air speed measurements are performed using anemometers. They are principally employed to measure air flow from HVAC systems. Two types of anemometers are shown in Fig. 6: ● Rotating vane (left side): this instrument consists of a lightweight, air-driven vane, which is connected by a gearing system. From the rotating frequency of the vane determined using a rev counter, air velocity is electronically measured. Some of the currently commercialized rotating valve anemometers have additional features including sampling and statistics functions and data logging capability. ● Hot wire (right side): the principle is dissimilar for this anemometer type. A fine wire is heated electrically and placed in the flow stream. As the electrical resistance of most metals varies with the metal temperature, a relationship can be obtained between the resistance of the wire and the flow speed. These instruments have the advantage of being used to measure air speed inside small ducts because of their small dimensions. Nevertheless, there are quite complex and relatively expensive.

5.1.3.2.4

Humidity measurements

Humidity measurements are usually required in the energy audit to evaluate the cooling load existing in a system or to quantify the amount of latent energy present in exhaust airflow. For this type of measurement, the following instruments are the commonly employed in audits: ● Psychrometer: it is based on two thermometers; the first is dry and the other is covered with a cotton cloth moistened with distilled water. Given dry and wet bulb temperatures and barometric pressure, air humidity can be deduced using a

Fig. 5 Portable infrared camera manufactured by EnnoLogic. Reproduced from EnnoLogic. Available from: www.ennologic.com [accessed 20.08.16].

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Fig. 6 Two types of anemometers manufactured by Testo. Reproduced from Tequipment. Available from: www.tequipment.net/Testo_0560-4170. html [accessed 20.08.16].

Fig. 7 A sling psychrometer with direct reading of relative humidity. Reproduced from Forestry-Suppliers. Available from: www.forestry-suppliers. com [accessed 05.09.16].

psychometric chart or table normally supplied with the instrument. Some types of this instrument allow a direct reading of humidity (Fig. 7). When air temperature is below 01C, this instrument cannot be used. Moreover, it requires frequent cleaning and cotton-cloth replacement. ● Electronic hygrometer (or a thermohygrometer), is a portable device that simultaneously measures air temperature and relative humidity. Advanced devices, with better accuracy, contain data logging capacity and universal serial bus (USB) cable and USB driver disk (Fig. 8). Modern devices based on various principles are also presently available and include capacitive, resistive, thermal and gravimetric hygrometers.

5.1.3.2.5

Carbon dioxide meter

Monitoring carbon dioxide levels is required in many applications including public areas, such as offices, classrooms, factories, hospitals, and hotels, and for industrial hygiene in some countries. Carbon dioxide meters are principally useful to get information for knowing if the ventilation system operates properly. Then, it will be possible to adequately adjust the air ventilation flow to meet the real needs. A carbon dioxide meter offering the possibility of measuring as well ambient temperature and relative humidity and with a user-programmable audible alarm is shown in Fig. 9.

5.1.3.2.6

Lighting level measurements

The purpose of the lighting is to ensure visual comfort. The auditor needs to monitor light levels in order to check the adaptability of the lighting system to standards and to evidence opportunities of energy efficiency improvements. Illuminance is a measure indicating how much luminous flux is spread over a given area. A light meter or luxmeter is the instrument used for this purpose. It operates by using a sensitive photocell that captures luminous energy (photons), which is converted into electrical energy. Once this current is known it is possible to determine the lux value of the light captured. Certain colors of light are more effective at producing

12

Energy Auditing

Fig. 8 Electronic hygrometer manufactured by Tecpel. Reproduced from Tecpel. Available from: www.tecpel.com [accessed 05.09.16].

Fig. 9 Carbon dioxide meter manufactured by Extech. Reproduced from Extech. Available from: www.extech.com [accessed 05.09.16].

electrons from the energy received by the photons. The light meters are therefore usually equipped with spectrum correction filters. Portable and inexpensive light meters are currently available in a variety of lux ranges with a typical accuracy of 715% (Fig. 10).

5.1.3.2.7

Heat flux meter

It is usual that the building auditor needs to determine the U-value of opaque envelopes of which the thermophysical properties are not known. A heat flux meter can be used to easily measure the U coefficient. Three temperature values are needed: the outside temperature, the surface temperature of the wall face, and the ambient air temperature. An accurate estimation of the U-value is quite complex since the boundary conditions are not stationary but vary continuously. Because of this transient behavior, the

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13

Fig. 10 Light meter manufactured by Digi Sense. Reproduced from Cole-Parmer. Available from: www.coleparmer.com [accessed 07.09.16].

Fig. 11 Utilization of the heat flux meter. Reproduced from Testo. Available from: www.testo.org [accessed 07.09.16].

measurement is not instantaneous and must be carried out during an appropriate period of observation of a minimum of 72 h and when the temperature gradient between outer and inner surfaces is significantly important (not less than 101C). As a consequence, such measurement is suitable to be performed during winter months. Some of the available heat flux meters have an integrated reading memory in which the measured data can be stored and subsequently evaluated on a PC using the appropriate software (Fig. 11).

5.1.3.2.8

Blower door test

The blower door test shown in Fig. 12 permits the measurement of air tightness of building envelopes. Due to unsealed windows, uncontrolled air flows resulting from the pressure difference between internal and external environments can increase building’s

14

Energy Auditing

Fig. 12 Blower door test. Reproduced from Homesnuggers. Available from: www.homesnuggers.com/what-is-blower-door-testing/ [accessed 10.09.16].

energy costs and affect indoor air quality. Blower door test is conducted by the energy auditor to determine a home's airtightness. It is accomplished by over or under pressurizing the inside of the audited space, by a fan that mounts into the frame of an exterior door. A manometer and flow meter are employed for monitoring the pressure difference and the airflow caused by the pressure difference from the building’s shell. As a first step, the auditor maintains a pressure difference of 50 Pa and localizes the leaks that are responsible for the greatest thermal losses for infiltration. The second step is executed by performing measurements for several pressure differences between 10 and 100 Pa. For each measurement, volumes of air that are lost through the points of permeability are recorded. Once the points positioned on a graph on a logarithmic representation, one can then perform a linear regression to know the leakage rate.

5.1.3.3 5.1.3.3.1

Other Measurements Combustion analyzers

Combustion controls are essential for boilers to check whether or not combustion takes place properly. Beyond the regulatory response, combustion analysis can guarantee the safety of the boiler in time, but also its energy performance and less pollution. The combustion testing is effectuated using combustion analyzers with in-built chemical cells, which give the possibility of determining the concentrations of the combustion products including CO2, CO, SOx, and NOx. This analysis also needs an identification of other parameters, such as the temperature of the fumes and the temperature of the environment. The test is carried as follows. First, the boiler should work for some time before making measurements, in order to attain its standard operating temperature. The sensor of the combustion analyzer is then inserted into the flue to perform the measurement. A low concentration of CO2 and O2 and little or no trace of CO are indicators of a good combustion. From the obtained results, it is possible to judge if the combustion takes place properly and if the combustion efficiency of the boiler is within the design values. Otherwise, an adequate calibration of the burner and/or cleaning of heat exchange surfaces of the boiler must be done. Modern combustion analyzers are lightweight and easy to use with full color graphic display and additional measurements (Fig. 13). These instruments are fully automatic, so that when the measurements set is taken, the concentrations of the above gazes together with the boiler efficiency can be displayed.

5.1.3.3.2

Speed measurements

In the industrial audit process, speed measurements are essential to monitor the performance of pulleys, ventilation shafts or Mots. Speed measurement can be performed with the aid of a tachometer or stroboscope (Fig. 14). A simple tachometer has a wheel that gets in contact with the rotating body. After a few seconds, the wheel speed becomes equal to that of the rotating body. The resulting speed is then displayed. Stroboscopes are more sophisticated and safer instruments because they perform the measurement without contact between the instrument and the object. A stroboscope emits a flashing light that is employed to measure the speed of cyclically moving objects. The flash rate must be first set to a value superior than the estimated speed of the object.

Energy Auditing

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Fig. 13 Combustion analyzer manufactured by Testo. Reproduced from Testo-Direct. Available from: www.testo-direct.com [accessed 10.09.16].

Fig. 14 Rotational speed measurements using a tachometer (left) and stroboscope (right). Reproduced from Atp-Instrumentation. Available from: www.atp-instrumentation.co.uk [accessed 10.09.16].

Afterwards, the flash rate is progressively reduced until the first single image appears. This is an indication that the stroboscope flash rate is the same as the rotational speed of the object. The resulting speed is shown on the display.

5.1.3.3.3

Leak detectors

Air leaks constitute an important part of wasted energy in compressed air systems and pipeline networks, which can negatively affect the productivity of industrial facilities. The simplest way to detect leaks is to apply soapy water to suspect areas. The most powerful way is by using an ultrasonic leak detector that identifies high frequency hissing sounds resulted from air leaks under various conditions, even when the plant is in full operation. An example of a handheld metered pistol type ultrasonic leak detector is shown in Fig. 15.

5.1.3.3.4

Flow meters

Usually, the energy auditor needs to have the flow rate value or quantity of a gas/liquid moving through a pipe. Airflow measurement devices can be used to identify problems with air flows in HVAC systems and other air sources. A great variety of flow meters using different principles are currently available. These include pressure-based meters, optical flow meters, mechanical flow meters, thermal mass flow meters, and electromagnetic, ultrasonic, and coriolis flow meters. In the audit exercise, the best instrument should effectively measure the flow rates for a wide variety of fluids. In this case, ultrasonic Doppler devices can be a

16

Energy Auditing

Fig. 15 Ultrasonic leak detector manufactured by Mitchell Instrument. Reproduced from Mitchel Linstrument. Available from: www. mitchellinstrument.com [accessed 12.09.16].

Fig. 16 Ultrasonic flow meter. Reproduced from bpress.cn. Available from: www.bpress.cn [accessed 12.09.16].

good choice (see Fig. 16). The device clamps onto the outside of a pipe and ensures noncontact and direct measurements of flow rate.

5.1.4

Energy Efficiency Measures

5.1.4.1

Building Envelope

A building envelope has the role of physically isolating the inside of the building from the outside environment. It serves as an external protection to enhance the quality and control the indoor conditions irrespective of transient outdoor conditions [16]. The building envelope consists of opaque and transparent parts. The opaque envelope covers walls, roofs, floors, and insulation and transparent envelope include windows, skylights, and glass doors. The effectiveness of the thermal envelope depends on the following points [17]: ● the insulation levels in the walls, ceiling, and ground, including factors, such as moisture condensation and thermal bridges that impact insulation performance; ● the thermal properties of windows and doors; and ● the rate of exchange of internal and external air, that in turn relies on the airtightness of the envelope and driving forces, such as wind, inside/outside temperature differences, and air pressure differences resulting from mechanical ventilation or air distribution systems.

5.1.4.1.1

Thermal insulation

Applying thermal insulation appropriately in the building envelope is the most effective method to increase the thermal resistance and decrease energy consumption for the cooling and heating of the internal space [18]. Insulation types can be categorized into four families depending on their material type as shown in Fig. 17 [19].

Energy Auditing

Cellular materials

Calcium silicate, bonded perlite, vermiculite, and ceramic products

Fibrous materials

Glass wool, rock wool, and slag wool

17

Inorganic

Fibrous materials

Cellulose, cotton, sheep wool, wood, pulp, cane, or synthetic fibers

Cellular materials

Cork, foamed rubber, polystyrene, polyethylene, polyurethane, polyisocyanurate, and other polymers

Organic Thermal insulation

Metallic/metalized reflective membranes Advanced material

Rolled foil (usually aluminum), reflective paint, reflective metal shingles, or foilfaced plywood sheathing

Transparent materials (aerogel), phase change materials

Fig. 17 Insulation materials. Reproduced from Papadopoulos AM. State of the art in thermal insulation materials and aims for future developments. Energy Build 2005;37(1):77–86.

Table 1

Some insulating materials and their thermal conductivity values

Insulating material

Thermal conductivity (W/m K)

Expanded polystyrene (EPS) Extruded polystyrene (XPS) Expanded polyurethane (PUR) Expanded toasted cork Wood fiber Fiberglass Mineral wool Expanded perlite Expanded clay Plaster insulation

0.034–0.048 0.032–0.036 0.024–0.034 0.040–0.045 0.040–0.060 0.037–0.048 0.037–0.045 0.066 0.09–0.12 0.040

Source: Reproduced from Dall'O G. Green energy audit of buildings. London: Springer; 2013.

If we denote UE and UR the U value of the wall before and after the insulation, the thermal resistance of the wall is increased by the amount: DR ¼

1 UR

1 UE

ð1Þ

On this basis, the insulation thickness can be determined using the next formula: s ¼ DR  l

ð2Þ

where l is the thermal conductivity of the insulating material expressed in W/m K. Table 1 shows a list of some insulating materials and their thermal conductivity values. The auditor needs to select the most appropriate retrofit technology and accordingly define the insulating material and the suitable thickness. Using the degree-days method, one can estimate the primary energy savings with Eq. (3):   24  r  ðUAÞE ðUAÞR DDðyset Þ ð3Þ Es ¼ Z The number 24 represents the daily operation hours of the system. If the operation is intermittent, a reductive coefficient ro1 is applied. Z is the average seasonal efficiency of the system used for heating or cooling. DD (yset) is the total degree days considering the set-point temperature. Degree-days data published in the ASHRAE handbook [20] can be used. Otherwise, the estimation can be done using some simplified methods as reported by Erbs et al. [21]. Hourly simulation using the appropriate software can be more accurate for the estimation of energy savings resulting from insulation.

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Energy Auditing

Fig. 18 Roller shades mounted on the interior part of a classroom. Reproduced from Innova-Solutions. Available from: www.innova-solutions.co. uk/news/important-lighting-classroom/ [accessed 18.09.16].

5.1.4.1.2

Windows

In spite of the fact that the transparent parts generally occupy a limited surface of the buildings’ facade, they have a strong effect on the building performance, as they influence every aspect of the building behavior by ensuring protection against the external environmental conditions (heat, cold, wind, noise), daylight, ventilation, as well as the view of outside [22]. The thermal effectiveness of windows can be considerably improved by adopting multiple glazing layers, low-conductivity gases (argon in particular) between glazing layers, low emissivity coatings on one or more glazing surfaces, and use of lowconductivity framing materials (such as extruded fiberglass). Glazing that reflects or absorbs a huge portion of the incident solar radiation minimizes solar heat gain by around 75%, hence reducing cooling loads. Despite these technical developments, the costs of glazing and windows has remained constant or even dropped in real terms [23]. The procedure for the assessment of the window performance is similar to the case of envelope insulation. The energy auditor can propose the replacement of glass or the window or application of an additional frame. It is important to note that increasing the thermal performance of the transparent surface can cause a reduction of the transparency, which decreases the solar gains. Accordingly, for a more precise evaluation of the potential energy savings that can be generated, a global energy balance carried out using simulation software must be required. Smart windows can be used to minimize energy consumption and enhance thermal and visual comfort principally by controlling the solar radiation penetrating into a building [24]. Recent smart windows use small light-absorbing microscopic particles known as suspended particle devices (SPD), or light valves to make it go from clear to dark in a short period of time. Another innovative option is the use of gasochromic windows that can dynamically respond to heating and cooling in different seasons. According to the simulation results reported by Feng et al. [25], the hot summer and cold winter locations are the most convenient for testing grounds using smart windows, and the reduction of HVAC loads in Shanghai is 28.4 and 11.5% when using GC smart window as alternatives to the single clear float glass and the colored absorbing double glass unit, respectively.

5.1.4.1.3

External shading

Shading devices prevent the penetration of solar radiation into the building in summer, while allowing the needed solar gains in winter, which leads to a better thermal comfort with significant energy savings. Moreover, they play an important role in managing visual environment [26], protecting the openings from atmospheric agent, and providing a sculptured skin for buildings [27]. However, these devices, if inappropriately designed and selected, can cause increased need for artificial light and prevent the healthy winter solar radiation. Shading devices can be classified into two main categories: fixed and mobile devices. Fixed devices include overhangs, horizontal/vertical louvers, and egg-crates. Mobile devices include Venetian blinds, vertical blinds, roller shades, and deciduous plants [28]. Mobile shading devices allow, manually or by automated systems, to control sunlight based on adaptation of solar radiation, indoor temperature, or illuminance level [29]. Generally, mobile devices generate higher yearly energy savings since they permit winter sun and block direct summer sun. Fig. 18 shows roller shades mounted on the interior part of a classroom for controllable solar energy gain and daylight.

5.1.4.1.4

Reduction of air infiltration

Air leaks affect the indoor air quality and human comfort. Also, they cause a significant increase of the energy consumption of buildings. According to Caffey [29], 40% of the heating/cooling load in houses is due to air infiltration. Preventing air leaks into

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and out of a building can reduce heating and cooling costs, increase comfort, and improve durability. In order to ameliorate the airtightness of the building envelope, various techniques can be applied including [30]: ● Calking: there is a multitude of products on the market (urethane, latex, and polyvinyl) that can be applied to seal various leaks by filling gaps in all kinds of structures. Calking can also prevent water damage inside and outside of the building when used around faucets, ceiling fixtures, water pipes, drains, and plumbing fixtures. ● Weather stripping: foam rubber with adhesive backing can be used to seal air leaks around movable building components, such as doors or operable windows. ● Air retarders: these systems consist of one or more air-impermeable components that can be used to seal air leakage pathways in roof and foundation junctions, window and door openings, control and expansion joints, masonry ties, piping, and other infiltrations through the wall body. Many approaches including self-adhered modified bituminous sheets, polyethylene sheets, and liquid-applied rubber can be applied. However, these techniques are not economically viable when applied to existing buildings. Generally, to insure the air changes in accordance with air quality requirements, these techniques should be performed together with the installation of a controlled mechanical ventilation system. To assess approximately the energy savings resulting from a reduction in air infiltration, the following relationship can be used:

Es ¼

24ðra ca V Þ½nE

nR ð1 ZH

ZREC ފDDH ðyset Þ

ð4Þ

where the raca product is the volumetric heat capacity of the air (equal to 0.34 Wh/m3 K), V the net volume of the space to be heated, nE and nR are, respectively, the measured air changes due to infiltration and the air changes generated by the installation of mechanical ventilation system, expressed in vol/h, and ZREC is the average seasonal efficiency of the heat recovery unit (if applicable).

5.1.4.2

Heating, Ventilating, and Air-Conditioning Systems

The HVAC systems control air temperature, flow, and humidity levels to allow a suitable indoor environment for human activity. HVAC systems account for a great part of the energy consumption in residential, commercial, and industrial structures [31]. Therefore, appropriate actions to increase heating and cooling efficiency will have an excellent impact on energy savings. Many parameters affect the energy use, efficiency, and cost of operation of any HVAC system. These parameters include the building design, its duty cycle, the type of occupancy, the type of HVAC equipment installed, and finally, climatic conditions to which the building is exposed. Generally, the overall efficiency of HVAC systems is obtained by multiplying the efficiencies of its various parts including the emission, regulation, distribution, and production systems. An exhaustive list of EEMs that the auditor can adopt to achieve energy savings in HVAC systems is given below: ● System maintenance is a simple but often neglected energy-saving opportunity. Dirty heat exchange surfaces, clogged filters, and inoperable or malfunctioning dampers are responsible for inappropriate operation of HVAC systems and their decreasing efficiency. The maintenance requirements must be executed annually following the manufacturer recommendations. ● Thermostat calibration and setback: calibration of the thermostat should be examined because of the high inaccuracy of these devices. Currently, there are “smart” thermostats equipped with microprocessors that can be managed to set back or set forward the temperature according to the time of day and day of week. Based on the results given in Ref. [32], it is shown that for every 11F of thermostat set back (heating) or set forward (cooling) during an 8-h period, there is an opportunity to save about 1% in annual heating or cooling energy costs. ● Equipment modification: in commercial buildings, there are some modifications, including the control, retrofit, and adopting of new designs that can be made to improve energy efficiency of HVAC systems [32]. The equipment concerned are fans, pumps, air-conditioning units, chillers, ducts, and dampers. The main energy saving strategies are summarized in Table 2. ● Economizer systems and enthalpy controllers: the economizer controls the amount of outside air to the mixed air duct to uphold a preset temperature in the mixed air plenum. If temperature is slightly above this set point, the compressor is shut off and the cooling demand is totally ensured by the outside air. When the air temperature is significantly high, minimum outside air will be introduced to the system. For better results, a second control called the enthalpy control can be added. In harmony with the outdoor thermostat, the outdoor air humidity is also measured. However, the cost of such systems is high. ● Heat recovery techniques: several techniques can be employed to recover heat from exhausts. The most important ones are rotary wheel, air-to-air heat exchanger, heat pipe, and coil run-around cycle. Mardiana-Idayu and Riffat [33] reported the efficiency ranges of these devices along with their advantages as presented in Table 3.

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Table 2

Main energy-saving strategies for heating, ventilating, and air-conditioning (HVAC) systems

Equipment

Modification Control

Fans

• • •

Pumps

•

Retrofit

Turning off large fan systems when relatively few people are in the building Stopping ventilation 30 min before the building closes Adopting advanced control techniques to respond to the necessary loads Performing adjustments of the number of pumps turning on to meet the necessary load conditions

• • • • •

Reduction in airflow Sizing the motor (Mot) exactly to the requirements Hanging pulleys to provide the desired speed Applying variable speed drives on Mots or controlled pitch fans Decrease operating flow rates by trimming the pump impeller or by using a throttle (pressure-reducing) valve

Conditioning unit

Chiller

New designs

•

Adopting variable air volume (VAV)

•

Using several pumps of different capacities Using variable speed drive pump Adopting more efficient units with high energy efficiency ratio (EER) Using chillers with load control feature Using the building exhaust air as source of heat for a heat pump Using double bundle condenser for a centralized system Adopting multistage reciprocating and screw-type compressors

• •

•

•

Maintaining as warm as a chilled water loop and as cold a condenser water loop as possible Utilizing lower temperature water from the cooling tower to dissipate heat

• • •

Provide cooling using the cooling tower when the wetbulb is low Turning off the chiller but using its refrigerant to transfer heat Installing a cold storage tank

• • • •

Ducts and dampers

• •

Table 3

Control pressure drops throughout the entire distribution system Eliminating pressure drops by changing fan pulleys to slow the fan and open the dampers fully

• • •

Adopting static pressure dampers Preventing leakages Improving insulation

Heat recovery techniques, efficiency, and advantages

Heat recovery

Typical efficiency

Advantages

Fixed-plate

50–80%

Heat pipe

45–55%

Rotary wheel Run-around

Above 80% 45–65%

Compact, highly efficient due to high heat transfer coefficient, no cross contamination, can be coupled with counter-current flow No moving parts, no external power requirements, high reliability, no cross contamination, compact, suitable for naturally ventilated building, fully reversible, easy cleaning High efficiency, capability of recovering sensible and latent heat Does not require the supply and exhaust air ducts to be located side by side, supply and exhaust duct can be physically separated, no cross contamination

Source: Reproduced from Mardiana-Idayu A, Riffat SB. Review on heat recovery technologies for building applications. Renew Sustain Energy Rev 2012;16(2):1241–55.

5.1.4.3 5.1.4.3.1

Electrical Systems Transformers

All industrial plants have a power Tr that converts the voltage coming from the electric network. Due to its continuous operation and long service life, a slight increase in its energy efficiency can result in significant energy savings over the years. Actions to improve the energy efficiency of a Tr can be summarized as follows:

•

Technology choice: in recent years, specialists have worked to reduce the losses of distribution in Trs and increase their yields. Trs based on amorphous metals are one of the most recent technologies. They have the capability of reducing losses

Energy Auditing

• • •

21

compared to the standard Trs to 60–70% [34]. Their life service is longer (about 30 years) and for the cost, they are 20–30% higher [35]. Global compensation of reactive energy: this can be done by the installation of a capacitor bank connected at the secondary of the Tr. This technique makes it possible to reduce the power demand, and reduces the joule losses at the secondary windings of the Tr. Aeration of the Tr substation: the ventilation of the station can be done naturally or by a ventilator, the goal is to dissipate by convection thermal loads produced by the total losses of the Tr in operation. We note that an increase in the operating temperature of the Tr adversely affects its efficiency and its lifetime [36]. Load rate optimization: the Tr operates on average with a load factor x (%) defined as: x¼

Preal Pn

ð5Þ

where Preal and Pn denote the real and nominal powers, respectively. It should be noted that the efficiency of a Tr varies as a function of the load factor (see Fig. 19). The optimum charge rate for a good Tr operation is between 30 and 50% [36]. Due to this particular behavior (Fig. 19), it is possible to opt for the following options if several Trs are present in the industrial plant:

• • •

if the overall electrical load is less than 40 to 50% of Pn, it is possible to achieve considerable energy savings by disconnecting one or more Trs in order to bring the load of the others close to the optimum factor; a parallel arrangement of several Trs can be adopted in order to balance their charge rates; and if the overall electrical load is greater than 75% of Pn, additional capacity bank must be installed.

5.1.4.3.2

Quality of electrical energy

Electricity suppliers must guarantee a balanced three-phase voltage for the customers and a stable frequency. Due to the instability of the electrical network, the above conditions are not always verified. In order to improve and ensure the quality of energy supplied by the electricity producer at the national level, a series of standards have emerged in recent years, allowing framing the acceptable limits of quality disturbing elements of electricity (EN 50160, IEC 61000, IEEE Std1159-1995, etc.). Electrical disturbances sometimes present additional high maintenance costs and can cause various anomalies, especially for industrial structures, such as production stoppages and premature aging of equipment [37]. Some of the perturbations of electrical energy quality are below:

•

Voltage amplitude: this is a crucial factor for the quality of electricity. It must be maintained within 710% of the nominal value. It should be noted that for a load with a voltage above its nominal voltage, it is possible to generate overconsumption of energy as the case for certain lamps [38]. For a voltage lower than the nominal voltage, it is possible to have an increase in the currents, such as, for example, the Mots, which can degrade the lifetime of these Mots and increase line losses.

99.00

Efficiency (%)

98.00 15 kVA 30 45 75 112.5 150 225 300 500 750 1000

97.00

96.00

95.00

94.00 10

20

30

40

50

60

70

80

90

100

Transformer load (%) Fig. 19 Variation of efficiency according to transformer (Tr) load rates. Reproduced from Harden KD. Optimizing energy efficiency standards for low voltage distribution transformers [Doctoral dissertation]. Wayne, IN: Purdue University Fort Wayne; 2011.

22

•

•

•

Energy Auditing

Voltage dips: a voltage dip is a sudden drop in the voltage at a point of the electrical network to a value (conventionally) between 90 and 10% with respect to the reference voltage followed by a restoration of this voltage after a short period of time. The main causes of voltage dips are short circuits affecting the electrical network or connected installations and the starting of high-power Mots. Voltage unbalance: the three-phase voltage system must be three-phase and balanced: the three voltages have the same amplitude and are out of phase by 7120 degree. When these quantities do not satisfy one of these conditions that concerns the phase and the amplitude, one speaks of an unbalanced three-phase system. Voltage imbalance has negative effects on the operation of equipment, especially for Mots. It generates reverse current components that cause mainly parasitic braking torques and overheating. Harmonic pollution: harmonic pollution is a serious disruptive phenomena of electrical energy quality that most industries and electricity around the world are suffering from [39]. Harmonic currents are additional and unnecessary currents caused by nonlinear loads, such as variable speed drives. These harmonic currents cause a nonsinusoidal periodic form of the total current, which disrupts the waveform of the voltage and generates technical problems, overconsumption, and degradation of network equipment [40]. The negative effects of harmonic pollution on electric Mots include (1) the creation of harmonic torques, which are superimposed on the fundamental torque and increase the mechanical vibrations of the Mot, which will increase its mechanical fatigue rapidly [41]; and (2) increase in operating temperature and engine losses [42,43].

The treatment of this harmonic pollution can be effectuated by the installation of antiharmonic filters (passive, active, or hybrid filters). The filtering must be carried out at the level of the pollutant loads in order to protect the other sensitive equipment. Harmonic treatment has the advantage of reducing the distortion of the internal electrical network, protection of equipment against the harmful effects of harmonic pollution, improving the power factor of the load, and extension of the equipment life service.

5.1.4.3.3

Electrical motors

Electric Mots consume between 30 and 70% of the total EC by an industry [8]. The most common practices to improve energy efficiency of Mots are listed below:

•

The use of high-efficiency Mots: compared to standard-efficiency Mots, they generate less heat, require less cooling, and consume less energy. This is a new generation of electric Mots that is manufactured primarily in the United States and Europe [8]. The annual energy saving (AES) resulting from the replacement of a standard-efficiency Mot with a high-efficiency Mot can be estimated using [44]:

AES ¼ ECmot



1 Estd

1 Eee



ð6Þ

where AES is the annual energy saving (kWh/year), ECmot is the annual EC by the Mot (kWh), Esrd represents the standard Mot efficiency rating (%), while Eee is the energy-efficient Mot efficiency rating (%).

•

• •

The use of variable frequency drive (VFD): a VFD is used to regulate the speed and torque of the Mot according to the variation of the load in order to improve the Mot energy efficiency. In fact, a reduction in the nominal speed via the VFD can lead to reduced energy consumption [5]. The use of VFDs also guarantees a progressive start of the Mot with a clear improvement of its power factor and an extension of its life service [5,45]. Reactive energy compensation: Mots are large consumers of reactive energy and especially when operating at a low loading or at no-loading modes. Thus, it will necessary to compensate this reactive power and correct the power factor by a battery of capacitors. This action is reserved only for energy-intensive industrial Mots [46]. Mot resizing: electric Mots are often oversized and rarely operate at full load. Field studies indicate that, on average, Mots operate at about 60% of their nominal capacity [45]. The efficiency of induction Mots is usually at its maximum around 75% of the full load and remains relatively flat up to 50% load. At less than 40% of the full load, an electric Mot does not operate under optimized conditions and the efficiency drops very rapidly. A correct resizing of Mots will enhance the efficiency and reduces line losses due to low power factor.

5.1.4.3.4

Lighting systems

Lighting systems are responsible for an important part of energy consumption in residential, commercial, and industrial structures. For example, office buildings consume about 30–50% of total energy to provide lighting. In industries, around 15% of energy is consumed by lighting systems in production units and administrative areas [47]. Several strategies can be employed to reduce energy consumption caused by lighting systems. The most important ones are the following: ● Use efficient lamps: replacing the existing lamps with equivalent lamps with better energy efficiency is one of the main actions that can be proposed in the audit report to reduce the lighting energy consumption [48]. Up to 94, 90, and 40% of electrical energy can be saved by changing the lighting devices from incandescent to light-emitting diode (LED), from halogen to LED, and from incandescent to halogen, respectively (Fig. 20).

Energy Auditing

23

100 90 Energy savings (%)

80 70 60 50 40 30 20 10 0 25

30

50

65

75

100

120

150

180

Power (W) Fig. 20 Energy savings potential for various lighting systems.

Fig. 21 Exploitation of natural lighting: zenithal lighting. Reproduced from lrc.rpi. Available from: www.lrc.rpi.edu/resources/newsroom/pr_story. asp?id=265#.WCMFZNLJzct [accessed 25.09.16].

● Lighting controls: important energy savings can be achieved by using lighting controls to automatically turn lights on and off as required. Some automatic lighting controls are described briefly below: ○ Occupancy sensors: they switch the lighting system only when the building is occupied. Two main categories exist in the market: ultrasonic (sound detectors) and infrared (motion/heat detectors). These devices should be located appropriately to detect occupants or occupant activity in all parts of the room. ○ Photosensor controls: these devices sense ambient light conditions to avoid outdoor lights from operating during daylight hours. Some LED nightlights possess this feature built in, which makes them a good option. ○ Dimmer controls: they change the light output of lamps with respect to the prevailing daylight level. If the daylight is not sufficient to achieve the required design illuminance, the indoor lighting level is topped-up by artificial lighting [49]. ○ Timer controls: the controlled lights are switched on and off based on a previously fixed schedule. Time-based control systems can provide substantial saving, especially in office building applications where the energy savings can vary between 10 and 40% [50]. ● Exploitation of natural lighting: maximizing the use of natural light can lead to considerable energy savings. Four concepts must be respected to introduce a day lighting strategy: ○ penetration: collection of natural light inside the building; ○ distribution: homogeneous diffusion of light into the spaces; ○ protect: adjusting via shading devices the sun's rays that enter to the building; ○ control: monitor light penetration by movable screens to avoid visual discomfort. In industries and commercial buildings, to reduce energy consumption resulted from artificial lighting, zenithal lighting, as depicted in Fig. 21, is usually used. Some smart and innovative approaches that are currently available in the market to capture and diffuse daylight in buildings consist of overhead roof light featuring a UV-resistance polycarbonate dome, and a standalone solar sensor that orients the reflective mirror to capture as much natural daylight as possible. This mirror diffuses light through the building using two prismatic lenses, throughout the day, and even when the sky is overcast (Fig. 22).

Energy Auditing

24

Fig. 22 Exploitation of natural lighting: reflective mirror. Reproduced from Ecodis. Available from: www.ecodis.fr [accessed 25.09.16].

•

Use of voltage stabilizer: such devices can be used to correct the voltage of the electrical power supply to ensure a constant and secure power supply to the lighting system and reduce the energy consumption [51].

5.1.4.4

Compressed-Air Installation

For most industrial installations, compressed air has become an indispensable fluid. Its electricity consumption represents between 1 and 25%, depending on the sector. The sectors where this equipment has the most influence on the electricity bill are metallic manufacturing and food processing. To conduct an energy survey concerning air-compressed installations, the auditor may use a network analyzer, manometer, tachometer, and ultrasonic leak detectors. The annual energy consumption by a compressed air system can be estimated using the following formula ECa

c

¼ Pload  hload þ Pno

load

 hno

load

ð7Þ

where ECa–c is the annual EC by the compressor (kWh), Pload is the measured compressor power under load (kW), Pno-load is the measured compressor power in the no-load mode (kW), and h is the number of operating hours for each mode (load and noload). The most common energy conservation measures used by auditors for the air-compressed installation are discussed below:

•

•

Repairing leaks: leaks are an important source of energy wastage in the industrial compressed air system, which can sometimes reach up to 20% of the total compressed air production capacity [52]. They can cause a drop in system pressure, making pneumatic tools work less efficiently, which negatively affects the production process [53,54]. Air leaks, in general, occur at the joints, hoses, flange connections, elbows, reduction sockets, valves, filters, and safety valves. Leak detection can be done in several ways, including research for noise sources, the use of soap and water solution, or the use of ultrasonic leak detector for inaccessible zones [55]. According to previous studies [56], about 20% of the energy can be saved if air-compressed leakage is repaired. Cooling of the absorbed air: the increase in the temperature of the outside air causes an increase in the compressor consumption ratio. As a general rule, an increase of air temperature of 31C generates an overconsumption of 1% of the energy of the compressor [57,58]. This energy loss can simply be saved by supplying the compressed air station with external air, especially during the cold season, when the difference between the outdoor and indoor temperature is significantly high. It is possible to install a duct connecting the outside to the inlet of the compressor, or to the entire compressed air station.

Under the usual operating conditions, the compression work of 1 m3 of air at a given pressure is proportional to the absolute temperature of the intake air. Therefore the energy saving estimate achieved by cooling the intake air to the compressor can be found using [59]: ESa

c

¼ Ecomp r

ð8Þ

where ESa–c is the resulting monthly savings of cooling the absorbed air by the compressor (kWh), Ecomp is monthly EC by the compressor (kWh), and r is the reduced work of the compressor after cooling the intake air and is given by: r¼

W1 W0 T1 T0 ¼ W1 T1 þ 273

ð9Þ

where W1 and W0 are the compressor work when the air is taken from the inside and outside, respectively (kW), and T1 and T0 are the average air temperatures for the interior and exterior, respectively.

Energy Auditing

25

Other opportunities to reduce energy consumption of compressors are the optimization of the pressure level to a minimum operational value [60,61], the use of compressed air storage to minimize the compressor operation during peak hours and maximize it during off-peak hours, and the reduction pressure losses throughout the air circuit.

5.1.4.5

Combustion Installations

Combustion plants are heating equipment or installations using the combustion of a fuel (including waste) to produce hot water or steam (boiler) or to heat up materials to very high temperatures allowing chemical transformations (furnace). Heat losses in such equipment generally originate from (1) weak insulation causing conduction and radiation heat transfers, (2) stack gas, (3) presence of moisture in fuel, (4) incomplete combustion, and (5) the purges of boilers. Energy efficiency improvement of combustion plants must take into account both the parameters of the process involved and the parameters of the combustion. The optimal energy management strategies for boilers and furnaces largely differ according to the industrial process they serve. Thus, even if they are numerous, techniques for saving energy are very specific to each major industrial sector. The most commonly used practices by auditors to reduce energy use in combustion plants are the following: ● Reducing combustion gas temperature: the lower the combustion temperature is, the better the energy efficiency. However, it is necessary to find a compromise with other requirements, such as operation above the dew point and temperature ranges for effective flue gas purification. ● Preheating the combustion air going to the burner: the air supplying the burner can be preheated using the exhaust gas stream via a heat exchanger placed in the exhaust stack [62]. This improves the combustion and the organic fuels are better dried. The boiler energy efficiency can thus increase by 3–5%. Recuperative and regenerative burners optimize this preheating by directly recovering the waste heat. ● Controlling excess air: effective control of excess combustion air (also known as O2 control) means adjusting burner airflow to meet fuel flow. This action is one of the most important ways for managing the energy efficiency and atmospheric emissions of a boiler system. Technically, several controls with various levels of costs and sophistication can be used, such as on/off and high/low controls, parallel controls, mechanical jackshaft controls, cross-limiting control, and automatic control [63]. ● Fuel selection: taking into account in particular the calorific value and the level of pollutants during the combustion of the fuels envisaged, a well-chosen choice makes it possible to reduce the excess air and to increase the energy efficiency of the combustion process. ● Boiler sizing and operation: the boiler efficiency drops sharply at low load (i.e., running so far from the rated capacity). It is therefore recommended to select boiler sizes to match varying demand. It is generally suitable to work with two small boilers instead of a larger one. At low loads, one boiler is sufficient. When the loads are at the highest values, two boilers must operate. ● Use combined heat and power generation: a combined heat and power (CHP) unit consists of prime mover, generally a gas turbine or piston engine driving an electric generator and a heat recovery steam generator. The main benefit of CHP units is the electric energy produced at high thermal efficiency. ● Maintaining the cleaning: most of the fuels leave a certain quantity of deposit on the fireside of the tubes (fouling). This deposit reduces heat transfer significantly, which impact negatively the equipment efficiency. Regular checking and cleaning is generally sufficient for small size boilers while soot blower systems (that clean the boiler during operation) are preferable for larger sizes. ● Other options: these include the following: improving the insulation and avoiding leakages, testing the boiler water periodically for the level of dissolved solids, reducing the boiler’s steam operating temperature and pressure at the minimum required and recovering heat losses through heat exchangers.

5.1.5 5.1.5.1

Case Study: Energy Auditing Introduction

The cement manufacturing industry is one of the world’s most energy-intensive processes with high levels of anthropogenic climate change emissions [64]. In 2012, the cement industry consumed about 8.5% of total industrial energy consumption and caused around 34% of the industrial direct CO2 emissions [65]. Energy consumption and related emission for the cement industry are projected to increase proportionally with the cement production that is in turn expected to increase by 0.8–1.2% per year as a consequence of increasing urbanization and explosion growth especially in the developing world [66]. Cement plants consume both electrical and thermal energy and use a variety of energy resources. The main fuel used is coal, although many other possible fuels are used, such as shredded municipal waste, industrial waste, and some biomass [67]. The amount of thermal and electrical energy used depends on the process type. The dry process consumes more electrical but much less thermal energy than the wet process. In industrialized countries, the primary energy consumption in a typical cement plant is about 75% fossil fuel and around 25% electrical energy using a dry process [45]. To increase their economic performance and reduce their environmental impact, cement plants should improve their energy efficiency. Several works in the literature have focused on the energy efficiency improvements within the cement industry. Hasanbeigi et al. [68] identified and analyzed 23 energy efficiency technologies and measures applicable to the processes in China’s cement industry. Estimations have shown that the total final energy saving potential resulting from these EEMs over 20 is equal to 30% of the total primary energy supply of Latin America or Middle East or

Energy Auditing

26

around 71% of primary energy supply of Brazil in 2007. The results of a thermal energy audit analysis investigated on the pyroprocessing unit a cement plant were reported by Kabir et al. [69]. The exhaust gases and kiln shell heat energy losses were the major causes of high energy consumption. Waste heat recovery steam generator (WHRSG) and secondary kiln shell were studied. Annual thermal energy savings and greenhouse gases (GHGs) emissions reduction of 42.88 MWh and 14.10% were achieved, respectively. Engin and Ari [70] presented the energy audit analysis of a dry type rotary kiln system working in a cement plant in Turkey. It was shown that up to 40% of the total input energy was being lost as a result of hot flue gas, cooler stack and kiln shell. Heat losses recovering were introduced showing approximately 15.6% of thermal energy conservation. In Morocco, the obligation of energy audits in industries exceeding a certain limit of annual energy consumption has been recently legislated in the frame of the Moroccan energy efficiency law. The cement industry is the second most energy-intensive sector, which points up the need to understand its potential for energy efficiency improvement. This case study presents the methodology followed and the predicted energy efficiency improvement through a detailed energy audit carried out in a cement plant located in Morocco.

5.1.5.2

Methodology

This section explains the general framework of the conducted energy audit and its process, instruments used, data collection and analysis, approaches used to estimate energy consumption, energy saving potential, and PBs when applying one of the proposed conservation energy measures.

5.1.5.2.1

Manufacturing process

The audited cement factory is located in Morocco and belongs to large-size industries. It was built in 1988 and produces clinker and cement from limestone based on a dry process. Three major stages can be distinguished in the cement manufacturing process. They are discussed briefly in what follows.

•

•

•

Stage 1: extraction and crushing of raw materials: the raw materials used in the manufacture of cement (calcium carbonate, silica, alumina, and iron ore) are in most cases extracted from limestone rock, chalk, shale, or clay. These raw materials are removed by extraction of quarries and blasting. These natural minerals are then mechanically crushed. At this point, other minerals are added to correct the chemical composition of cement. These minerals are waste or by-products of other industries, such as paper ash. The grinding produces a fine powder, called "raw concrete," which is then preheated and placed in a furnace where it is subjected to other operations. Stage 2: heating and grinding of cement raw: the kiln is the heart of the cement manufacturing process. Once in the rotary kiln, the cement raw meal is heated to about 15001C, which corresponds approximately to the temperature of molten lava. At this temperature, chemical reactions occur and result in the formation of the clinker, a substance containing hydraulic calcium silicates. To heat the material to a high temperature, it is necessary to produce a flame at 20001C mainly using fossil fuels. The kiln is tilted by three degrees with respect to the horizontal, allowing the material to pass through it over 20–30 min. On leaving the kiln, the clinker is cooled and stored, before being ground to produce cement. Step 3: cement grinding and expedition: a small amount of gypsum (3–5%) is added to the clinker to control the hardening of the cement. This mixture is then very finely ground to obtain "pure cement." During this phase, other minerals could be added in addition to the plaster. These natural or industrial additives are dosed to provide the cement specific properties. At the end, the cement is stored in silos before being shipped in bulk or in bags to the sites where it will be used. These machines consume different forms of energy during the cement manufacturing process. The plant uses two forms of energy sources:

• •

Electrical energy supplied by the electricity network. It is used to drive electrical Mots, pumps, compressor, fans, blowers, conveyors, air-conditioning chillers, and lighting. Thermal energy under the form of grounded coal coke that is prepared at the factory and a very small amount of heavy fuel oil no. 2 stored in tanks, which is used for restarting the rotary kiln after stops. Thermal energy is mostly used in the kiln and preheating systems.

Based on a preliminary assessment, an estimation of the specific energy consumption (SEC) per ton of produced cement for this industry has shown that there is a considerable potential of reducing energy consumption since when compared to other typical cement industries, the SEC is significantly high (Table 4). Table 4

Specific energy consumption of typical cement plants

Energy source

Current plant

Typical plants

Reference

Electrical Thermal

138 kWh/ton 5.2 GJ/ton

110–120 kWh 4–5 GJ/ton

[71] [72]

Energy Auditing

Table 5

27

List of measuring devices and their specifications

Measuring instrument

Number

Three-phase power analyzer recorder

4

Multimeter

4

Technical characteristics

• • • • • • • • • •

Color LCD 320  240 Standard: EN50160 Precision: 70.5% Voltage (single voltage: up to 480 V; phase-phase voltage: up to 600 V) Current up to 3000 A Frequency 40 to 70 Hz Active, reactive and apparent power per phase Active, reactive, consumed and produced energy Measurement for harmonic current and power up to 50th order

• • • • •

Maximal AC current: 2000 A Maximal DC current: 3000 A Voltages (AC and DC): up to 1000 V Measurements: powers (W, VAr, VA, DPF, PF) and total harmonic distortion (THDI, THDV)

Abbreviations: DPF, pisplacement power factor; PF, power factor; THDV, total harmonic voltage distortion.

5.1.5.2.2

Audit process

Before starting the energy audit for this industry, several preparations have been made. First, a meeting was held with the appropriate plant personnel familiar with the physical conditions and day-to-day operation of the facility. Specific questionnaires were prepared to make clearer the manufacturing process and its main characteristics. Moreover, some discussions were made to:

• • • • •

define the system boundaries; enumerate areas where auditors’ attention should be focused; estimate the number of days and the number of auditors to be assigned to perform the energy audit; prepare a schedule for in-site interventions; and list the types of data requested during the audit, including, production statistics, monthly energy bills, technical specification of equipments, and energy and material flow diagrams.

Later, audit teams consisting of qualified and experienced engineers were formed. A lead experienced auditor was selected for each team to guide and manage the audit process. Audit team members prepared and checked the calibration of measurement devices and other safety equipment. The audit work started with a quick-off meeting that took place in the meeting room of the administration of the cement plant and with the presence of the auditors, general manager, and the heads of the cement plant services. The objective was to explain the purpose of the mission, introduce an energy audit target, and appoint a monitoring committee that will establish communication with expert auditors, make easy access to services and equipments during the measurement phase, and provide any necessary information.

5.1.5.2.3

Data collection and measurements

During the audit in the facility, a technical team counted all the equipment involved in the production process and extracted useful information from files including specifications of each engine. Furthermore, operating hours per working day and total working days in a year were estimated based on a communication with the head of the production service. The most important data that have been collected during the preaudit phase are power rating, operation time of energyconsuming machineries, and power factor. The history of three years’ energy bills was also collected. Then actual operating data on the energy consumption and power factors were measured using the instruments listed in Table 5.

5.1.5.2.4

Mathematical formulations

The necessary formulations to understand the high voltage (HV) energy billing in Morocco and to estimate load factors, energy consumption, energy savings, emission reduction, and PBs are presented in this section. The analysis of monthly bills resulting from electrical energy use for the audited plant is based on the data of the year 2013. Large-size Moroccan industries are supplied with HV electricity by the National Electricity Office. The main characteristics of HV billing structure in Morocco are described below:

•

Consumption fee: the electricity price (EP) varies as a function of time (off-peak hours, normal, peak, super-peak). Time slices are set by the Moroccan electricity supplier and are summarized in Table 6.

Energy Auditing

28

Table 6

System of electricity pricing

Description

Off-peak hours

Normal hours

Peak hours

Super-peak hours

Period October–March April–September

(22–07 h) (23–07 h)

(07–17 h) (07–18 h)

(17–18 h) and (20–22 h) (18–19 h) and (21–23 h)

(18–20 h) (19–21 h)

0.5908

0.6419

0.7881 393.29

0.8652

Billing Electricity price (MAD/kWh) Price of subscribed power (PSP) (MAD/kW)

The consumption fee (CF) is determined by summing the product of electricity consumption during each time slice by the associated EP. Therefore, CF ¼ EPnormal  ECnormal þEPoff

peak

 ECoff

peak þEPpeak

 ECpeak þEPsuper

peak

 ECsuper

peak

ð10Þ

Thus, the average electricity price (AEP) can be found using the next expression: AEP ¼

•

ECnormal þ ECoff

CF peak þ ECpeak þ ECsuper

ð11Þ peak

Power cost and fee due to excess of subscribed power (FEPC): the power cost (PC) is calculated on a yearly basis and is billed on a monthly basis by one twelfth. It is given as



PSP SPsuper peak þ 0:8 SPpeak SPsuper peak þ 0:6 SPnormal SPpeak þ 0:4 SPoff peak SPnormal ð12Þ PC ¼ 12 where SPi is the subscribed power (kW) relative to time slice (i) and PSP is the price of subscribed power (MAD/kW). But, if during a particular month, the maximum power demand has exceeded the value of the SP, the positive difference between the two powers will be subject to an additional fee called the FEPC. The FEPC can be calculated using Eq. (13).

FEPC ¼ 1:5 

PSP  max APsuper 12

þ0:6 APmax normal

peak

SPsuper

peak



 þ 0:8 APmax peak


SPnormal þ 0:4 APmax off peak

SPoff

peak

SPpeak 

 ð13Þ

are, respectively, the SP in time slice i and the maximum active power (MW). In the previous equation, SPi and APmax i An optimal determination of the SP in such a way to minimize the sum PC þ FEPC will generate a financial gain for the industry. • Penalty for a displacement power factor below 0.8: on a monthly basis, when the average displacement power factor (DPF) is below 0.9, the industry pays a penalty to the electricity supplier. This penalty is computed considering that, for every hundredth of DPF below 0.9, the total amount of fees will be increased by 2%. Thus, IDPF ¼ 2ð0:9

DPFÞðCF þ PC þ FEPCÞ

ð14Þ

Generally, societies try to maintain the DPF to values close to 0.9, nevertheless, it is preferable to enhance it to 1 in order to minimize the demand of reactive power.

5.1.5.3 5.1.5.3.1

Analysis Profile of monthly electricity use

5.1.5.3.1.1 Monthly electricity price The main characteristics of monthly electricity usage for the year 2013 are presented in Table 7. By analyzing Table 7, it can be seen that the monthly exploitation rates vary significantly according to the month of the year. This will generate a variable EP as indicated in Fig. 23. 5.1.5.3.1.2 Subscribed power The SPs are paid as premium based on the required power and are defined as presented in Table 8: As can be seen, the actual total annual PC is 9.163 MMAD, whereas the FEPC is almost 0 indicating that maximum power demands did not exceed the SP during the year 2013. It is noted that the SP is significantly higher compared to the maximum power demand. Therefore, it is necessary to revise the SP. On one hand, the power demand is around 22 MW, and knowing that the total installed power is 45 MVA (40.5 MW for a DPF¼0.9), this indicates that the Tr works at around 50% loading. This information will be used to calculate the load losses of Trs in what follows.

Energy Auditing

Table 7 Year 2013

January February March April May June July August September October November December Total

29

Characteristics of the energy bill (year 2013) Exploitation rates (%)

Energy consumed (EC) (MWh) Normal hours

Offpeak hours

Peak hours

Super peak hours

Total

Normal hours

Offpeak hours

Peak hours

Super peak hours (%)

4468.7 4591.7 4307.3 4840.1 5129.4 4202.1 3857.6 4741.2 4409.9 3992.1 4317.4 3654.6 52512.1

4670.9 3925.5 4043.5 3857.3 4021.2 3443.0 3362.6 4083.6 3798.2 4178.5 4307.3 3534.8 47226.4

1606.6 1732.2 1416.2 1881.1 1403.7 1400.7 1314.3 1781.9 1657.4 990.7 877.6 838.8 16901.2

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 647.4 585.1 539.2 1771.6

10639.8 10249.4 9767.0 10567.8 10554.4 9036.8 8534.5 10606.7 9865.5 9808.7 10087.4 8558.9 118,276.8

42.0 44.8 44.1 45.8 48.6 46.5 45.2 44.7 44.7 40.7 42.8 42.7 44.4

43.9 38.3 41.4 36.5 38.1 38.1 39.4 38.5 38.5 42.6 42.7 41.3 39.9

15.1 16.9 14.5 17.8 13.3 15.5 15.4 16.8 16.8 10.1 8.7 9.8 14.3

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.6 5.8 6.3 1.5

Consumption fee (CF) (MAD)

AEP (MAD/ kWh)

6,894,164 6,631,762 6,269,876 6,868,181 6,774,600 5,835,341 5,498,626 6,860,285 6,380,872 6,372,046 6,513,973 5,561,826 76,538,015

0.648 0.647 0.642 0.650 0.642 0.646 0.644 0.647 0.647 0.650 0.646 0.650 0.647

Abbreviations: AEP, average electricity price; MAD, Moroccan dirham.

0.652 0.650

10,000.0

0.648 8000.0

0.646 0.644

6000.0

0.642

4000.0

0.640 2000.0

0.638 0.636 l Au y gu pt st em b O er ct N obe ov em r b D ec er em be r

ne

ay

Se

Ju

Ju

ril

M

ch

Ap

ar M

ar

br

Fe

nu

ua

y

ry

0.0 Ja

AEP (MAD/kWh)

Energy total consumption (MWh)

12,000.0

Fig. 23 Monthly values of total energy consumption (TEC) and average electricity price (AEP).

Table 8

Monthly power demands

Months

January February March April May June July August September October November December SP (MW) (SP-APmax) (MW) PC (MAD/year) FEPC (MAD/year)

APmax Peak hours (MW)

Normal hours (MW)

Off-peak hours (MW)

Super-peak hours (MW)

17.98 21.31 21.44 16.80 21.39 21.25 21.39 21.31 21.52 16.70 16.15 15.95 23.50 1.98 9,163,657 0

22.92 23.35 23.21 22.51 22.96 22.96 22.96 22.94 22.94 22.98 23.04 20.48 26.00 2.65

22.65 22.85 22.91 22.77 22.21 22.20 22.74 22.45 22.80 22.77 22.76 19.91 26.00 3.09

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 13.47 13.39 14.82 15.00 0.18

Abbreviations: APmax, maximum active power; FEPC, fee of the excess of subscribed power; MAD, Moroccan dirham; PC, power cost; SP, subscribed power.

30

Energy Auditing

Table 9

Characteristics and measurements performed for the transformers (Trs)

Trs

Power (MVA)

Primary voltage (kV)

Secondary voltage (kV)

Average load rate (%)

DPF

THDI (%)

THDV (%)

Tr 1 Tr 2 Tr 3 Tr 4 Tr 5 Tr 6 Tr 7 Tr 8 Tr 9 Tr 10 Tr 11 Tr 12 Tr 13 Tr 14 Tr 15 Tr 16 Tr 17 Tr 18 Tr 19 Tr 20 Tr 21 Tr 22 Total

2.5 1.6 1.6 1.25 1.25 1.2 1 1 1 1 0.8 0.8 0.8 0.63 0.63 0.63 0.63 0.5 0.4 0.4 0.4 0.25 20.270

11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11

0.73 0.4 0.4 0.4 0.4 0.69 0.4 0.4 0.4 0.4 0.73 0.66 0.4 0.4 0.69 0.4 0.4 0.7 0.4 0.4 0.4 0.4

62.2 59 52 55 63.06 54.8 52.3 47 23 62.5 61 24.5 44.7 52 40.7 17.9 44.9 51.5 53.4 54 54.4 67.4

0.84 0.948 0.94 0.94 0.944 0.89 0.91 0.85 0.94 0.758 0.97 0.84 0.89 0.85 0.960 0.78 0.81 0.91 0.880 0.95 0.86 0.85

14.6 8.1 7.2 14.6 12.1 18.7 9.2 7.2 17.9 6.4 22.4 33.2 18.7 5 15.6 25.6 4.3 11.5 19.0 14.6 27.3 3.2

3.2 4.4 2.1 5.1 4.6 2.3 2.1 2.4 2.1 1.7 4.5 5.1 4.7 1.5 4.2 1,5 1.2 3 1.4 4.1 3 1

Abbreviations: THDI, total harmonic current distortion; THDV, total harmonic voltage distortion.

Table 10

Estimated losses in transformers (Trs)

Power (MVA)

Number

No-load losses (kW)

Load losses at 50% loading (kW)

Total annual energy losses (MWh)

15.00 2.50 1.60 1.25 1.00 0.80 0.63 0.50 0.40 0.25 Total

3 1 2 3 4 3 4 1 3 1 25

21.56 5.8 2.3 2.9 2.5 2.2 1.8 1.56 1.3 0.9 42.82

36.81 6.5 3.88 3.88 3.15 2.7 2.2 1.85 1.55 1.06 63.58

1534.03 107.75 108.27 178.18 197.98 128.77 140.16 29.87 74.90 17.17 2517.08

5.1.5.3.2

Transformers

In the cement plant, seven Tr stations are present and include 25 Trs including 3 HV Trs (60 kV) supplying electricity to the other medium voltage (MV) Trs of (11 kV). Some of the MV Trs are connected in parallel and others do not. These Trs feed the general low voltage switch gear or departures of engines directly. The overall power of the HV/MV Trs is 45 MVA, the overall power of the MV/LV Trs is 20.27 MVA. The primary voltage is 11 kV, while the various secondary voltages are 230, 400, 690, 700, and 725 V. The network analyzers are used to measure the instantaneous variation of electrical parameters (voltage, amperage, active, apparent and reactive powers, power factor, harmonic currents, and voltages) and have allowed us to analyze the quality of power supplied and the level of pollution generated by electrical loads. Table 9 gives the average measured data over 24 h for each Tr. 5.1.5.3.2.1 Energy losses in transformers Two types of energy losses occur in Trs: load and no-load losses. No-load losses are constant and depend only on the Tr power. Load losses vary, however, in addition to the Tr power, according to the load rate, as well. Table 10 shows the total calculated energy losses of each Tr. These calculations were estimated for a load rate of 50% over 24 h operation. Total energy losses are estimated at 2517.08 MWh/year, representing 2.12% of the overall energy consumption of the plant. The total cost of these losses amount to 1628.54 kMAD/year.

Energy Auditing

1 34.2%

2 33.7%

31

3 33.0%

–

720A

3U 3V 3A L1

0

L2 L3

–720

RMS

THD

CF

max min

Fig. 24 Waveform of distorted current (Tr 9).

1 12.9%

2 13.0%

3 12.6%

–

640A

3U 3V 3A L1

0

L2 L3 –640

RMS

THD

CF

max min

Fig. 25 Waveform of distorted current (Tr 15).

5.1.5.3.2.2 Analysis of energy quality in transformers The analysis of power quality at the MV and LV Trs has allowed us to draw the following conclusions:

• • •

•

The Trs (Tr 9, Tr 12, Tr 16) are oversized compared to the power demands and have a low charge rate. It should be specified that the optimum charge rate for proper operation of the Tr must be between 30 and 50% [35]. It is recommended to analyze the possibility of eliminating Trs with low charge rates and allocate their respective charges on other Trs to reduce electrical losses. An electrical pollution is presented at several Trs, for example, the harmonic distortion exceeds 20% for a load rate of 61% in the case of Tr 11. This is mainly due to the presence of variable speed drives which are the main pollutants of the plant electrical network. The presence of harmonic currents circulating in the network is an important source of electrical losses and a permanent danger for all equipment. The treatment of harmonic currents is needed to protect Mots, Trs, and other equipment against the effects of harmonic currents. Figs. 24 and 25 show the instantaneous captions taken by the network analyzer for both Trs Tr 9 and Tr 15. The current wave of two Trs is completely distorted for the three phases. There is a possibility to improve further the DPF to a value close to 1.

5.1.5.3.3

Energy-intensive motors

Electric Mots consume between 30 and 70% of total consumption according to the industry type [73]. DC, asynchronous, synchronous, and stepper Mots are used in industry, but 80% of the Mots used are asynchronous Mots. In this energy audit, only energy-intensive Mots whose power exceeds 18 kW are treated. The following measurements were taken for Mots equipped with VFD and for Mots without VFD. 5.1.5.3.3.1 Motors without variable frequency drive The audited cement plant contains nearly 94 Mots whose power is greater than 18 kW and that are not equipped with VFD. These Mots belong to the IE1 energy class (according to the classification of IEC 60034-30). Table 11 presents the main operating characteristics of these Mots.

Energy Auditing

32

Table 11

List and measures of motors (Mots) (nonequipped with variable frequency drives (VFDs))

Motors

Nominal power (MW)

Charge rate (%)

Efficiency (%)

Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot

1.900 0.650 0.450 0.450 0.250 0.250 0.250 0.220 0.220 0.160 0.132 0.132 0.110 0.110 0.110 0.110 0.110 0.110 0.110 0.110 0.110 0.090 0.090 0.090 0.090 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.052 0.052 0.045 0.037 0.037 0.037 0.037 0.037 0.036 0.036 0.035

69.05 45.77 78.67 34.67 65.60 30.60 65.60 38.18 30.45 63.13 55.23 26.44 43.36 30.09 83.64 40.82 88.55 73.00 39.91 36.36 30.09 86.67 86.67 69.78 64.33 81.33 71.33 81.33 71.33 70.67 76.80 69.33 27.87 73.87 90.73 51.82 50.91 71.64 51.82 38.91 28.18 90.73 40.27 87.82 39.82 83.27 88.36 60.73 65.82 71.64 43.08 23.08 73.33 70.27 34.32 51.62 52.43 50.00 61.39 66.94 77.14

93.20 91.10 95.10 95.10 94.00 81.00 94.00 94.00 81.00 93.80 93.50 93.50 93.30 93.30 93.30 93.30 93.30 93.30 93.30 93.30 93.30 93.00 93.00 93.00 93.00 92.70 92.70 92.70 92.70 92.70 92.70 92.70 92.70 92.70 91.70 91.70 91.70 91.70 91.70 91.70 91.70 91.70 88.10 91.70 91.70 91.70 91.70 91.70 91.70 91.70 81.70 81.70 91.70 91.20 88.10 88.10 91.20 91.20 91.20 91.20 91.20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61

Operating hours (h) 8640 8640 8640 8640 8640 8640 8640 8640 8640 8640 8640 8640 8640 8640 8640 8640 8640 8640 8640 8640 8640 7920 7920 7920 7920 7920 7920 7920 8640 8640 8640 8640 8640 8640 7200 7200 7200 7200 8640 8640 8640 8640 8640 8640 8640 8640 8640 8640 7200 7200 7200 8640 8640 8640 8640 8640 8640 8640 7200 8640 8640

Annual energy consumption (MWh/year) 12,162.75 2821.51 3216.15 1417.29 1507.40 816.00 1507.40 772.09 714.67 930.32 673.64 322.50 441.72 306.52 851.96 415.79 901.97 743.61 406.53 370.42 306.52 664.26 664.26 534.81 493.08 521.17 457.09 521.17 498.64 493.98 536.85 484.66 194.80 516.35 391.80 223.77 219.85 309.36 268.53 201.63 146.04 470.16 217.23 455.08 206.34 431.53 457.91 314.70 284.23 309.36 197.41 126.90 310.93 246.32 124.55 187.31 183.79 175.26 174.47 228.32 255.79 (Continued )

Energy Auditing

Table 11

33

Continued

Motors

Nominal power (MW)

Charge rate (%)

Efficiency (%)

Operating hours (h)

Annual energy consumption (MWh/year)

Mot 62 Mot 63 Mot 64 Mot 65 Mot 66 Mot 67 Mot 68 Mot 69 Mot 70 Mot 71 Mot 72 Mot 73 Mot 74 Mot 75 Mot 76 Mot 77 Mot 78 Mot 79 Mot 80 Mot 81 Mot 82 Mot 83 Mot 84 Mot 85 Mot 86 Mot 87 Mot 88 Mot 89 Mot 90 Mot 91 Mot 92 Mot 93 Mot 94 Total

0.035 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.018 0.018 0.018 0.018 0.018 0.018 0.018 0.018 0.018 0.018 0.018 0.018 9.19

38.29 47.67 69.67 68.00 50.33 84.33 37.67 69.67 44.67 30.33 59.00 38.33 89.55 67.27 37.73 30.00 55.91 63.64 49.55 54.09 53.18 31.11 35.00 64.44 34.44 64.44 78.89 31.11 35.00 77.22 55.56 44.44 59.44 53.67

79.20 90.70 90.70 90.70 90.70 90.70 90.70 90.70 90.70 90.70 90.70 90.70 89.90 89.90 84.90 86.90 86.90 89.90 91.90 89.90 89.90 89.30 89.30 89.30 89.30 89.30 89.30 89.30 89.30 89.30 89.30 89.30 89.30 68.87

8640 5040 5040 4320 4320 8640 8640 8640 8640 8640 8640 8640 4320 8640 8640 8640 8640 8640 8640 8640 8640 4320 4320 4320 7920 7920 8640 4320 4320 7920 7920 8640 8640 750,960.00

146.18 79.46 116.14 97.16 71.92 241.01 107.64 199.09 127.65 86.69 168.61 109.55 94.67 142.24 84.47 65.62 122.29 134.55 102.48 114.37 112.44 27.09 30.48 56.12 54.99 102.88 137.39 27.09 30.48 123.28 88.69 77.40 103.53 48,690.07

From Table 11, it can be observed that several Mots operate at low charge levels (e.g., Mot 12, 33, 41, and 52). Therefore, installation of VFD for these electric Mots could be considered as a potential solution to reduce energy consumption. 5.1.5.3.3.2 Motors equipped with variable frequency drive Table 12 lists the main operation characteristics of Mots that are equipped with VFD. The following observations can be made:

• • • •

The level of THDI is very high for all of the treated Mots; it exceeds the limit of the recommended international standards (10%). Figs. 26–28 show instantaneous captions of the three phase currents for selected Mots. One can observe a distorted current waveform due to current harmonics of order 5 and 7 as depicted by the spectrum of harmonics in the two figures. The total harmonic voltage distortion (THDV) reached 9% for some Mots, disrupting voltage waveform for other equipment. The level of the power factor is very low for some Mots. The charge rate is generally within the standards.

5.1.5.3.4

Compressed-air installations

Energy consumption of compressed-air systems represents 6.16% of the overall consumption of the cement plant. The compressed-air installation has three production facilities with a working pressure of 7 bars. Compressed-air is used almost everywhere in the plant. Table 13 shows the technical specifications and operation characteristics of compressors. The number of operating hours in full-load and no-load modes were taken from the clock of each compressor. It is clearly observed that there is an inadequate management of the compressors operating hours. Several compressors often operate in no-load mode (see Table 13). Moreover, visual inspection of the air-compressed installation revealed that there is a lack of adequate drying of the air at the outlet of some compressors, which implies the presence of water in the circuits, causing:

• •

increased pressure losses and overconsumption; and corrosion of pipes and consequently, increased risk of leaks and increased maintenance cost.

Energy Auditing

34

Table 12

List and measures of motors (Mots) with variable frequency drive (VFD)

Item

Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Total

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Nominal power (MW)

Charge rate (%)

Efficiency (%)

THDI (%)

THDV (%)

Operating hours (h)

Annual energy consumption (MWh)

1.900 0.650 0.480 0.450 0.250 0.250 0.250 0.220 0.220 0.220 0.160 0.160 0.055 0.045 0.045 5.355

67.53 39.22 48.88 37.63 62.66 55.31 62.66 48.74 62.19 61.43 63.49 57.50 38.35 58.20 46.52

93.20 91.10 91.70 91.70 89.90 81.00 93.90 92.70 89.90 89.00 88.70 88.70 92.10 91.60 91.60

30.6 38.3 56.9 34.1 86 86.6 45.2 85.3 85.3 85.3 114 68.3 75.2 80 102.6

8.9 8.2 6.9 9.8 4 6,0 4.9 6.2 5.3 5.8 6.1 5.3 4.2 6.6 5.2

8640 8640 8640 8640 8640 8640 8640 8640 8640 8640 8640 8640 7200 7200 7200 125,280

11,895.41 2417.78 2210.70 1595.50 1505.42 1474.81 1441.30 999.47 1314.87 1311.97 989.45 896.14 164.90 205.85 164.54 28,588.12

Abbreviations: Mot-VFD, motor with variable frequency drive; THDI, Total harmonic current distortion; TDHV, total harmonic voltage distortion.

45.1%

1

44.3%

2

3

42.7% Ah 05

520A

0

–520

L2 L3 1

max min

CF

41.5% 101.8A

1

3

V

5

7

9 11 13 15 17 19 21 23 25

A

VA

+

–

U

Fig. 26 Waveform and spectrum of harmonic currents of Mot-VFD 7. CE, consumption fee; Mot-VFD, motor with variable frequency drive; RMS, root mean square; THD, total harmonic distortion.

1

74.2%

2

71.0%

3

Ah 05

75.0%

130 A

26.7A +176° 3U 3V 3A L1 L2 L3

0

CF

max min

3

27.1A +178°

62.8% 27.3A +170°

100

–,+ 3L L1 L2 L3

50

1

< t = 50ms l1 = +100 l2 = –107 l3 = –7 > THD

60.2%

%

–130 RMS

2

62.5%

1

V

3

5 A

7

9 11 13 15 17 19 21 23 25 >>> VA

U

–

+

Fig. 27 Waveform and spectrum of harmonic currents of Mot-VFD 13.CE, consumption fee; Mot-VFD, motor with variable frequency drive; RMS, root mean square; THD, total harmonic distortion.

Energy Auditing

1 8.8%

2

9.6%

3 9.0%

–

1040 V

600 A

1 34.5%

–

2

27.7%

3 26.2%

3U 3V 3A L1

3U 3V 3A

0

0

L1

35

L2 L3

L2 L3 –600

–1040

Fig. 28 Waveform of current and voltage of Mot-VFD 4. Mot-VFD, motor with variable frequency drive.

Table 13

Technical characteristics of the installed compressors

Facility

Compressor

Power (kW)

Flow rate (m3/h)

Total operating hours (h)

Full-load operation hours (h)

No-load operation hours (h)

Loading (%)

No. 1

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13

75 75 75 75 75 160 75 75 110 110 160 160 160

880.92 880.92 880.92 880.92 880.92 1695.6 880.92 880.92 1144.8 1144.9 1695.6 1695.6 1695.6

8458 8410 8572 8602 8569 8373 8655 8662 8652 8646 8574 8655 7699

5527 2986 2802 2686 7297 3435 3938 3644 3714 2291 5584 2686 4066

2931 5423 5770 5917 1273 4938 4717 5018 4938 6355 2991 5968 3633

65.35 35.51 32.69 31.22 85.15 41.02 45.50 42.07 42.93 26.50 65.12 31.04 52.81

No. 2

No. 3

Table 14 provides electrical measurements by a multimeter at the circuit breaker terminals of each compressor, which allows us to evaluate the electrical consumption and the specific consumption using Eqs. (7) and (15), respectively SC ¼

EC production

ð15Þ

The specific consumption of compressor varies from one compressor to another. On an average basis, it is around 136.04 Wh/ Nm3. The specific consumption of typical well-sized air-compressed installation varies between 85 and 130 Wh/Nm3 [74]. For some compressors the specific consumption far exceeds the reference value, and this is due to the low charge rate.

5.1.5.4

Actions Plan and Analysis

In this section, an action plan is proposed to improve energy efficiency and management in the cement plant. Energy saving potential of each energy efficiency project along with its financial viability are reported.

5.1.5.4.1

Action 1: revise the subscribed powers

Based on the analysis of electricity bills, it was noticed that SPs are much higher than the actual power demand of the cement plant. To optimize the annual power fee, it is recommended to revise SPs in a way to be in agreement with the power demand. For each time slot, new SP values were selected based on the maximum power required (see Table 15). Switching to SPs of 22, 23.5, 23, and 15 MW for peak, normal, off-peak, and super-peak hours, respectively, can generate a direct financial gain of 786.580 MAD/year. A request for revision the SPs should be directed to energy distributor.

5.1.5.4.2

Action 2: adopt high-efficiency transformers

Trs are the first input of electrical energy. Due to their continuous operation and long service, a slight improvement of their energy efficiency can generate significant economic and environmental benefits over the time.

36

Energy Auditing

Table 14

Electricity consumption and production of air compressors

Compressor

Pload (kW)

Pno-load (kW)

ECair (kWh/year)

Production (Nm3/year)

SC (Wh/Nm3)

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 Total

72.90 72.90 72.90 72.90 72.90 155.52 72.90 72.90 106.92 106.92 155.52 155.52 155.52 –

24.76 24.76 24.76 24.76 24.76 52.06 24.76 24.76 35.85 35.85 52.07 52.07 52.07 –

475,494.60 351,966.16 347,118.89 342,265.57 563,421.04 791,244.18 403,856.93 389,888.56 574,159.64 472,791.73 1,024,070.99 728,549.15 821,478.29 7,286,305.75

4,415,805.26 2,385,750.10 2,238,657.60 2,145,593.44 5,829,285.91 5,281,443.84 3,146,091.78 2,911,339.15 3,856,400.58 2,379,014.88 8,586,147.33 4,131,127.90 6,252,219.99 53,558,877.75

107.68 147.53 155.06 159.52 96.65 149.82 128.37 133.92 148.88 198.73 119.27 176.36 131.39 136.04

Abbreviations: SC, specific consumption; EC, energy consumed.

Table 15

New subscribed powers (SPs)

Current status

SP (MW) PF (MAD) SP (MW) PF (MAD)

Future changes Savings (MAD)

SPpeak

SPnormal

SPoff-peak

SPsuper-peak

23.5 9,163,657 22 8,377,077 786,580

26

26

15

23.5

23

15

Abbreviations: MAD, Moroccan dirham; PF, power factor.

Table 16

Energy saving and profitability achieved by installing amorphous transformers (Trs)

Trs

Power (MVA)

Loading (-)

Annual energy losses (MWh/year)

Price of conventional Tr (MAD)

Price of amorphous Tr (MAD)

Payback periods (PBs) (year)

Tr 1 Tr 2 Tr 3 Tr 4 Tr 5 Tr 6 Tr 7 Tr 8 Tr 9 Tr 10 Tr 11 Tr 12 Tr 13 Tr 14 Tr 15 Tr 16 Tr 17 Tr 18 Tr 19 Tr 20 Tr 21 Tr 22 Total

2.500 1.600 1.600 1.250 1.250 1.200 1.000 1.000 1.000 1.000 0.800 0.800 0.800 0.630 0.630 0.630 0.630 0.500 0.400 0.400 0.400 0.250 20.270

0.622 0.590 0.520 0.550 0.631 0.548 0.523 0.470 0.230 0.625 0.610 0.245 0.447 0.520 0.407 0.179 0.449 0.515 0.534 0.540 0.544 0.674

107.748 107.748 107.748 54.137 54.137 54.137 59.393 59.393 59.393 59.393 49.494 49.494 49.494 42.924 42.924 42.924 42.924 35.040 29.872 29.872 29.872 24.966 1193.024

300,000 192,000 192,000 150,000 150,000 144,000 120,000 120,000 120,000 120,000 96,000 96,000 96,000 75,600 75,600 75,600 75,600 60,000 48,000 48,000 48,000 30,000 2,432,400

390,000 249,600 249,600 195,000 195,000 187,200 156,000 156,000 156,000 156,000 124,800 124,800 124,800 98,280 98,280 98,280 98,280 78,000 62,400 62,400 62,400 39,000 3,162,120

2.610 1.671 1.671 2.598 2.598 2.494 1.894 1.894 1.894 1.894 1.818 1.818 1.818 1.651 1.651 1.651 1.651 1.605 1.506 1.506 1.506 1.127 1.911

Abbreviation: MAD, Moroccan dirham.

Amorphous metal-based Trs can save 60–70% of the energy losses compared with conventional Trs [34]. Moreover, they have a longer service life (30 years). The present cement plant has conventional Trs. A large majority of this equipment has reached the end of its life cycle. Therefore, it is proposed to change the current MV/LV conventional Trs by amorphous Trs.

Energy Auditing

Table 17 Mot

Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot

Energy savings resulting from using high energy efficiency motors (Mots) Nominal power (MW)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

37

1.9 0.65 0.45 0.45 0.25 0.25 0.25 0.22 0.22 0.16 0.132 0.132 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.09 0.09 0.09 0.09 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.075 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.052 0.052 0.045 0.037 0.037 0.037 0.037 0.037 0.036 0.036

ECmot (MWh)

Loading (%)

Esdr (-)

Eee (-)

12162.75 2821.51 3216.15 1417.29 1507.40 816.00 1507.40 772.09 714.67 930.32 673.64 322.50 441.72 306.52 851.96 415.79 901.97 743.61 406.53 370.42 306.52 664.26 664.26 534.81 493.08 521.17 457.09 521.17 498.64 493.98 536.85 484.66 194.80 516.35 391.80 223.77 219.85 309.36 268.53 201.63 146.04 470.16 217.23 455.08 206.34 431.53 457.91 314.70 284.23 309.36 197.41 126.90 310.93 246.32 124.55 187.31 183.79 175.26 174.47 228.32

69.05 45.77 78.67 34.67 65.60 30.60 65.60 38.18 30.45 63.13 55.23 26.44 43.36 30.09 83.64 40.82 88.55 73.00 39.91 36.36 30.09 86.67 86.67 69.78 64.33 81.33 71.33 81.33 71.33 70.67 76.80 69.33 27.87 73.87 90.73 51.82 50.91 71.64 51.82 38.91 28.18 90.73 40.27 87.82 39.82 83.27 88.36 60.73 65.82 71.64 43.08 23.08 73.33 70.27 34.32 51.62 52.43 50.00 61.39 66.94

0.93 0.91 0.95 0.95 0.94 0.81 0.94 0.94 0.81 0.94 0.94 0.94 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.88 0.92 0.92 0.92 0.92 0.92 0.92 0.92 0.82 0.82 0.92 0.91 0.88 0.88 0.91 0.91 0.91 0.91

0.95 0.93 0.97 0.97 0.96 0.83 0.96 0.96 0.83 0.96 0.96 0.96 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.94 0.93 0.90 0.90 0.93 0.93 0.93 0.93

AES (MWh/ year) 206.71 50.17 52.51 23.14 33.41 24.27 33.41 17.11 21.26 20.71 15.09 7.22 9.94 6.89 19.16 9.35 20.29 16.73 9.14 8.33 6.89 16.51 16.51 13.29 12.25 13.61 11.94 13.61 13.02 12.90 14.02 12.66 5.09 13.49 11.34 6.48 6.36 8.95 7.77 5.84 4.23 13.61 15.97 13.17 5.97 12.49 13.25 9.11 8.23 8.95 32.06 20.61 9.00 6.08 3.29 4.95 4.54 4.33 4.31 5.63

Price (MAD)

145,350.00 49,725.00 34,425.00 34,425.00 19,125.00 19,125.00 19,125.00 16,830.00 16,830.00 12,240.00 10,098.00 10,098.00 8415.00 8415.00 8415.00 8415.00 8415.00 8415.00 8415.00 8415.00 8415.00 6885.00 6885.00 6885.00 6885.00 5737.50 5737.50 5737.50 5737.50 5737.50 5737.50 5737.50 5737.50 5737.50 4207.50 4207.50 4207.50 4207.50 4207.50 4207.50 4207.50 4207.50 4207.50 4207.50 4207.50 4207.50 4207.50 4207.50 4207.50 4207.50 3978.00 3978.00 3442.50 2830.50 2830.50 2830.50 2830.50 2830.50 2754.00 2754.00

Payback period (PB) (years) 1.10 1.55 1.02 2.32 0.89 1.23 0.89 1.54 1.24 0.92 1.05 2.18 1.32 1.91 0.69 1.41 0.65 0.79 1.44 1.58 1.91 0.65 0.65 0.81 0.88 0.66 0.75 0.66 0.69 0.69 0.64 0.71 1.76 0.66 0.58 1.02 1.03 0.73 0.85 1.13 1.56 0.48 0.41 0.50 1.10 0.53 0.50 0.72 0.80 0.73 0.19 0.30 0.60 0.73 1.34 0.89 0.98 1.02 1.00 0.76 (Continued )

38

Energy Auditing

Table 17

Continued

Mot

Nominal power (MW)

ECmot (MWh)

Loading (%)

Esdr (-)

Eee (-)

AES (MWh/ year)

Price (MAD)

Payback period (PB) (years)

Mot 61 Mot 62 Mot 63 Mot 64 Mot 65 Mot 66 Mot 67 Mot 68 Mot 69 Mot 70 Mot 71 Mot 72 Mot 73 Mot 74 Mot 75 Mot 76 Mot 77 Mot 78 Mot 79 Mot 80 Mot 81 Mot 82 Mot 83 Mot 84 Mot 85 Mot 86 Mot 87 Mot 88 Mot 89 Mot 90 Mot 91 Mot 92 Mot 93 Mot 94 Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Total

0.035 0.035 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.018 0.018 0.018 0.018 0.018 0.018 0.018 0.018 0.018 0.018 0.018 0.018 1.9 0.65 0.48 0.45 0.25 0.25 0.25 0.22 0.22 0.22 0.16 0.16 0.055 0.045 0.045 14.544

255.79 146.18 79.46 116.14 97.16 71.92 241.01 107.64 199.09 127.65 86.69 168.61 109.55 94.67 142.24 84.47 65.62 122.29 134.55 102.48 114.37 112.44 27.09 30.48 56.12 54.99 102.88 137.39 27.09 30.48 123.28 88.69 77.40 103.53 11,895.41 2417.78 2210.70 1595.50 1505.42 1474.81 1441.30 999.47 1314.87 1311.97 989.45 896.14 164.90 205.85 164.54 77,278.19

77.14 38.29 47.67 69.67 68.00 50.33 84.33 37.67 69.67 44.67 30.33 59.00 38.33 89.55 67.27 37.73 30.00 55.91 63.64 49.55 54.09 53.18 31.11 35.00 64.44 34.44 64.44 78.89 31.11 35.00 77.22 55.56 44.44 59.44 67.53 39.22 38.88 42.07 32.66 45.31 62.66 47.83 60.37 61.43 63.49 55.19 56.53 49.42 45.41

0.91 0.79 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.90 0.90 0.85 0.87 0.87 0.90 0.92 0.90 0.90 0.89 0.89 0.89 0.89 0.89 0.89 0.89 0.89 0.89 0.89 0.89 0.89 0.93 0.91 0.92 0.92 0.90 0.81 0.94 0.93 0.90 0.89 0.89 0.89 0.92 0.92 0.92

0.93 0.81 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.93 0.92 0.92 0.87 0.89 0.89 0.92 0.94 0.92 0.92 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.91 0.95 0.93 0.93 0.94 0.92 0.83 0.96 0.95 0.92 0.91 0.91 0.91 0.94 0.94 0.94

6.31 4.77 1.98 2.90 2.42 1.79 6.01 2.69 4.97 3.18 2.16 4.21 2.73 2.29 3.44 2.29 1.70 3.17 3.26 2.38 2.77 2.72 0.66 0.75 1.38 1.35 2.52 3.37 0.66 0.75 3.02 2.18 1.90 2.54 202.16 42.99 38.80 37.14 36.44 43.87 32.01 22.77 31.83 32.40 24.60 22.28 3.81 4.80 3.84 1698.11

2677.50 2677.50 2295.00 2295.00 2295.00 2295.00 2295.00 2295.00 2295.00 2295.00 2295.00 2295.00 2295.00 1683.00 1683.00 1683.00 1683.00 1683.00 1683.00 1683.00 1683.00 1683.00 1377.00 1377.00 1377.00 1377.00 1377.00 1377.00 1377.00 1377.00 1377.00 1377.00 1377.00 1377.00 145,350.00 49,725.00 36,720.00 34,425.00 19,125.00 19,125.00 19,125.00 16,830.00 16,830.00 16,830.00 12,240.00 12,240.00 4207.50 3442.50 3442.50 1,112,616

0.66 0.88 1.81 1.24 1.48 2.00 0.60 1.34 0.72 1.13 1.66 0.85 1.31 1.15 0.76 1.15 1.55 0.83 0.81 1.11 0.95 0.97 3.24 2.88 1.56 1.60 0.85 0.64 3.24 2.88 0.71 0.99 1.13 0.85 1.12 1.81 1.48 1.45 0.82 0.68 0.93 1.15 0.83 0.81 0.78 0.86 1.73 1.12 1.40 1.02

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Abbreviations: AES, annual energy saving; EC, energy consumed, MAD, Moroccan dirham; Mot-VFD, motor with variable frequency drive.

Based on the assumption that the amorphous Tr can reduce 50% of energy losses, and that the cost of a new amorphous Tr is greater than 20–30% over the cost of purchasing a conventional Tr [35], Table 16 reports the energy saving potential and PB of this project.

5.1.5.4.3

Action 3: improving energy efficiency of electric motors

High efficiency Mots generate less heat and require a smaller and more energy-efficient cooling fan [75]. Generally, their cost is 10–25% higher than standard Mots [44]. Reachable AESs resulting from replacing standard efficient Mots with high-energy efficiency Mots can be estimated by using Eq. (6).

Energy Auditing

Table 18

Energy savings achieved by installing variable frequency drive (VFD) on motors (Mots)

Mots

Nominal power (MW)

Loading (%)

ECmot (MWh/an)

Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot

1.900 0.650 0.450 0.250 0.250 0.250 0.220 0.220 0.160 0.132 0.132 0.110 0.110 0.110 0.110 0.110 0.110 0.090 0.090 0.075 0.075 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.052 0.052 0.037 0.037 0.037 0.037 0.036 0.036 0.035 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.018 0.018 0.018 0.018

69.05 45.77 34.67 65.60 30.60 65.60 38.18 30.45 63.13 55.23 26.44 43.36 30.09 40.82 39.91 36.36 30.09 69.78 64.33 69.33 27.87 51.82 50.91 51.82 38.91 28.18 40.27 39.82 60.73 65.82 43.08 23.08 34.32 51.62 52.43 50.00 61.39 66.94 38.29 47.67 69.67 68.00 50.33 37.67 69.67 44.67 30.33 59.00 38.33 67.27 37.73 30.00 55.91 63.64 49.55 54.09 53.18 31.11 35.00 64.44 34.44

12,162.75 2821.51 1417.29 1507.40 816.00 1507.40 772.09 714.67 930.32 673.64 322.50 441.72 306.52 415.79 406.53 370.42 306.52 534.81 493.08 484.66 194.80 223.77 219.85 268.53 201.63 146.04 217.23 206.34 314.70 284.23 197.41 126.90 124.55 187.31 183.79 175.26 174.47 228.32 146.18 79.46 116.14 97.16 71.92 107.64 199.09 127.65 86.69 168.61 109.55 142.24 84.47 65.62 122.29 134.55 102.48 114.37 112.44 27.09 30.48 56.12 54.99

1 2 4 5 6 7 8 9 10 11 12 13 14 16 19 20 21 24 25 32 33 36 37 39 40 41 43 45 48 49 51 52 55 56 57 58 59 60 62 63 64 65 66 68 69 70 71 72 73 75 76 77 78 79 80 81 82 83 84 85 86

39

AES (MWh/year) 5351.609 1241.467 623.606 663.258 359.040 663.258 339.717 314.453 409.341 296.403 141.899 194.358 134.869 182.949 178.875 162.984 134.869 235.318 216.957 213.250 85.710 98.460 96.733 118.152 88.718 64.258 95.579 90.791 138.466 125.062 86.858 55.837 54.802 82.418 80.867 77.116 76.768 100.459 64.320 34.963 51.100 42.752 31.645 47.363 87.600 56.165 38.142 74.188 48.201 62.585 37.165 28.873 53.809 59.202 45.090 50.322 49.476 11.920 13.410 24.691 24.195

Price of VFD (MAD) 670,558 230,019 160,740 88,478 89,015 89,015 78,333 78,333 56,970 47,000 47,000 39,167 39,167 39,167 39,167 39,167 39,167 35,055 35,055 29,213 29,213 21,423 21,423 21,423 21,423 21,423 21,423 21,423 21,423 21,423 20,254 20,254 14,412 14,412 14,412 14,412 14,022 14,022 13,633 11,685 11,685 11,685 11,685 11,685 11,685 11,685 11,685 11,685 11,685 8778 8778 8778 8778 8778 8778 8778 8778 7182 7182 7182 7182

Payback period (PB) (years) 0.20 0.29 0.40 0.21 0.39 0.21 0.36 0.39 0.22 0.25 0.52 0.31 0.45 0.33 0.34 0.38 0.45 0.23 0.25 0.21 0.53 0.34 0.35 0.28 0.38 0.52 0.35 0.37 0.24 0.27 0.36 0.57 0.41 0.27 0.28 0.29 0.29 0.22 0.33 0.52 0.36 0.43 0.58 0.39 0.21 0.33 0.48 0.25 0.38 0.22 0.37 0.48 0.25 0.23 0.30 0.27 0.28 0.94 0.84 0.45 0.46 (Continued )

40

Energy Auditing

Table 18

Continued

Mots

Nominal power (MW)

Loading (%)

ECmot (MWh/an)

AES (MWh/year)

Price of VFD (MAD)

Payback period (PB) (years)

Mot 87 Mot 89 Mot 90 Mot 92 Mot 93 Mot 94 Total

0.018 0.018 0.018 0.018 0.018 0.018 7.114

64.44 31.11 35.00 55.56 44.44 59.44 32.078

102.88 27.09 30.48 88.69 77.40 103.53 33,868.046

45.267 11.920 13.410 39.024 34.057 45.551 14,901.940

7182 7182 7182 7182 7182 7182 2,590,522

0.25 0.94 0.84 0.29 0.33 0.25 0.27

Abbreviations: AES, annual energy saving; EC, energy consumed; MAD, Moroccan dirham.

Table 19

Energy savings and return time for installing variable frequency drive (VFD) for compressors

Compressors

Pno-load (kW)

No-load operation hours (h/year)

AES (MWh)

Price of VFD (MAD)

Payback period (PB) (years)

C1 C2 C3 C4 C6 C7 C8 C9 C10 C11 C12 C13 Total

24.76 24.76 24.76 24.76 52.06 24.76 24.76 35.85 35.85 52.07 52.07 52.07

2930.68 5423.35 5769.70 5916.68 4938.28 4716.91 5017.93 4937.84 6354.90 2990.71 5968.41 3633.14

72.56 134.27 142.84 146.48 257.11 116.78 124.23 177.01 227.81 155.71 310.75 189.16 2054.72

62,000 62,000 62,000 62,000 132,000 62,000 62,000 95,000 95,000 132,000 132,000 132,000 1,090,000

1.34 0.72 0.68 0.66 0.80 0.83 0.78 0.84 0.65 1.32 0.66 1.09 0.83

Abbreviations: AES, annual energy saving; MAD, Moroccan dirham.

The next table represents energy savings and financial indices resulting from replacing the current Mots with high-energy efficiency Mots. The annual savings obtained via this action are estimated to be 1698.11 MWh, which represents 2.2% of the total EC by electrical Mots. As indicated in Table 17, the total investment is estimated at 1,112,616 MAD and the payback time is 1.02 years.

5.1.5.4.4

Action 4: install variable frequency drive at low-load motors

This project proposes the installation of VFD in Mots whose charge rate is below 70%. The VFD will aim to reduce the Mot speed with 20% to adjust the power demand of the load. Energy saving potential for such action is 44% as indicated in earlier studies [5]. Table 18 summarizes the energy savings and PB for the studied Mots: A total energy savings of 14,901.940 MWh can be reached for an investment of 2,590,522 MAD. The payback is around 3 months, indicating that the project is economically viable.

5.1.5.4.5

Action 5: installation of variable frequency drive at the compressed air compressor

We recommend installing variable speed drives for compressors whose charge rate is below 80%. As a result, no-load operation hours and SEC of these compressors will be reduced. The total energy savings is about 2,054,720 kWh/year, representing a reduction of 28.2% compared to the initial consumption of the compressors (see Table 19). The investment is for the purchase and installation of VFD for the compressors is about 1.09 MMAD and the overall PB is 0.83 years.

5.1.5.4.6

Action 6: treatment of harmonic pollution

As already mentioned the plant uses several Mots equipped with VFD but without antiharmonic filters causing a high-level of distortion. Therefore, it is recommended to install antipassive harmonic filter in these Mots. As reported in actions 4 and 5, some additional VFDs were proposed to be used in the rest of the engines; the estimation has considered them as well. The achieved energy savings by installing passive filters is generally small compared to other measures, such as reducing line losses in cables, reducing the AP, or improving the DPF. Generally, the energy saving potential varies between 2 and 5% depending on the case. To give a first approximation of the profitability of this project, it is assumed that the potential of energy savings is 2%. In this case, the total energy saved is 1383.581 MWh, while the investment is about 4,287,205 MAD, resulting in a payback of 4.84 years (Table 20).

Energy Auditing

Table 20

Energy savings resulting from installing antiharmonic passive filters

Motors (Mots) Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot Mot

1 2 4 5 6 7 8 9 10 11 12 13 14 16 19 20 21 24 25 32 33 36 37 39 40 41 43 45 48 49 51 52 55 56 57 58 59 60 62 63 64 65 66 68 69 70 71 72 73 75 76 77 78 79 80 81 82 83 84 85 86

41

Nominal power (MW) 1.900 0.650 0.450 0.250 0.250 0.250 0.220 0.220 0.160 0.132 0.132 0.110 0.110 0.110 0.110 0.110 0.110 0.090 0.090 0.075 0.075 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.055 0.052 0.052 0.037 0.037 0.037 0.037 0.036 0.036 0.035 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.030 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.022 0.018 0.018 0.018 0.018

ECmot (MWh/an) 12,162.75 2821.51 1417.29 1507.40 816.00 1507.40 772.09 714.67 930.32 673.64 322.50 441.72 306.52 415.79 406.53 370.42 306.52 534.81 493.08 484.66 194.80 223.77 219.85 268.53 201.63 146.04 217.23 206.34 314.70 284.23 197.41 126.90 124.55 187.31 183.79 175.26 174.47 228.32 146.18 79.46 116.14 97.16 71.92 107.64 199.09 127.65 86.69 168.61 109.55 142.24 84.47 65.62 122.29 134.55 102.48 114.37 112.44 27.09 30.48 56.12 54.99

AES (MWh/years) 243.25 56.43 28.35 30.15 16.32 30.15 15.44 14.29 18.61 13.47 6.45 8.83 6.13 8.32 8.13 7.41 6.13 10.70 9.86 9.69 3.90 4.48 4.40 5.37 4.03 2.92 4.34 4.13 6.29 5.68 3.95 2.54 2.49 3.75 3.68 3.51 3.49 4.57 2.92 1.59 2.32 1.94 1.44 2.15 3.98 2.55 1.73 3.37 2.19 2.84 1.69 1.31 2.45 2.69 2.05 2.29 2.25 0.54 0.61 1.12 1.10

Price of filter (MAD) 442,700 154,050 107,550 78,000 78,000 78,000 73,260 73,260 57,920 47,784 47,784 40,810 40,810 40,810 40,810 40,810 40,810 34,200 34,200 32,925 32,925 24,695 24,695 24,695 24,695 24,695 24,695 24,695 24,695 24,695 23,348 23,348 18,833 18,833 18,833 18,833 18,324 18,324 17,815 15,570 15,570 15,570 15,570 15,570 15,570 15,570 15,570 15,570 15,570 11,462 11,462 11,462 11,462 11,462 11,462 11,462 11,462 4302 4302 4302 4302

Payback period (PB) (years) 2.84 4.27 5.93 4.04 7.47 4.04 7.41 8.01 4.86 5.54 11.58 7.22 10.40 7.67 7.84 8.61 10.40 5.00 5.42 5.31 13.20 8.62 8.78 7.18 9.57 13.21 8.88 9.35 6.13 6.79 9.24 14.37 11.81 7.85 8.01 8.39 8.21 6.27 9.52 15.31 10.47 12.52 16.91 11.30 6.11 9.53 14.03 7.21 11.10 6.30 10.60 13.65 7.32 6.66 8.74 7.83 7.96 12.41 11.03 5.99 6.11 (Continued )

42

Energy Auditing

Table 20

Continued

Motors (Mots)

Nominal power (MW)

ECmot (MWh/an)

AES (MWh/years)

Price of filter (MAD)

Payback period (PB) (years)

Mot 87 Mot 89 Mot 90 Mot 92 Mot 93 Mot 94 Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD Mot-VFD C1 C2 C3 C4 C6 C7 C8 C9 C10 C11 C12 C13 Total

0.018 0.018 0.018 0.018 0.018 0.018 1.900 0.650 0.480 0.450 0.250 0.250 0.250 0.220 0.220 0.220 0.160 0.160 0.055 0.045 0.045 0.075 0.075 0.075 0.075 0.160 0.075 0.075 0.110 0.110 0.160 0.160 0.160 13.779

102.88 27.09 30.48 88.69 77.40 103.53 11,895.41 2417.78 2210.70 1595.50 1505.42 1474.81 1441.30 999.47 1314.87 1311.97 989.45 896.14 164.90 205.85 164.55 475.49 351.97 347.12 342.27 791.24 403.86 389.89 574.16 472.79 1024.07 728.55 821.48 69,179.05

2.06 0.54 0.61 1.77 1.55 2.07 237.91 48.36 44.21 31.91 30.11 29.50 28.83 19.99 26.30 26.24 19.79 17.92 3.30 4.12 3.29 9.51 7.04 6.94 6.85 15.82 8.08 7.80 11.48 9.46 20.48 14.57 16.43 1383.581

4302 4302 4302 4302 4302 4302 442,700 154,050 107,550 107,550 78,000 78,000 78,000 78,000 78,000 78,000 57,920 57,920 24,695 24,695 24,695 32,925 32,925 32,925 32,925 57,920 32,925 32,925 40,810 40,810 57,920 57,920 57,920 4,287,205

3.27 12.41 11.03 3.79 4.34 3.25 2.91 4.98 3.80 5.27 4.05 4.13 4.23 6.10 4.63 4.64 4.57 5.05 11.70 9.37 11.73 5.41 7.31 7.41 7.52 5.72 6.37 6.60 5.55 6.74 4.42 6.21 5.51 4.84

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Abbreviations: AES, annual energy saving; EC, energy consumed; MAD, Moroccan dirham.

Table 21

Summary of the action plan

Projects

Project no 1: revise the subscribed powers (SPs) Project no 2: adopt high-efficiency transformers (Trs) Project no 3: improving energy efficiency of electric motors (Mots) Project no 4: install VFD at lowload Mots Project no 5: installation of VFD at the compressed air compressor Project no 6: treatment of harmonic pollution Total

Energy saving (MWh)

Cost of energy savings (MAD/year)

Emission reductions of CO2 (ton)

Investment (MAD)

Payback Period (PB) (years)

0.00

786,580

0.00

0

0.00

596.512

381,768

403.77

729,720

1.91

1698.110

1,086,790

1149.41

1,112,616

1.02

14,901.940

9,537,242

10,086.80

2,590,522

0.27

2054.72

1,315,021

1390.80

1,090,000

0.83

1383.581

885,492

936.52

4,287,205

4.84

20,634.863

13,992,892

9,810,063

0.70

13,967

Abbreviations: MAD, Moroccan dirham; VFD, variable frequency drive.

The PB is quite high because of the low energy saving potential. Nevertheless, as stated before there are many indirect benefits that can be reached by installing VFDs especially related to maintenance issues. Putting in practice the entire action plan, the cement plant will save 20,634.863 MWh, which is equivalent to an economic gain of 13,992,892 MAD. The resulting CO2 mitigation potential is about 13,967 t annually (Table 21).

Energy Auditing 5.1.5.5

43

Conclusions

This two-level energy audit concerned the analysis of the electrical energy consumption of a cement plant located in Morocco. A preliminary analysis of electricity bills and production data has identified an energy saving potential of 13.61–20.81%. The studied plant was installed 28 years ago, and the majority of its equipment (Mots, Trs) has reached the end of its service life. We have proposed in this study to change these facilities by other higher energy efficiency equipment: amorphous Trs and high efficiency Mots. The replacement can be executed gradually since the total investment is significantly high. The use of network analyzers has permitted the detection of a high level of harmonic pollution originally caused by VFD installed on some Mots. The harmonic distortion measured was outside the standards and represented a real danger for equipment sensitive to harmonics. It was proposed to install (for the Mots equipped with VFD) antipassive harmonic filters to improve the power quality and voltage supplied to the equipment. Applying the proposed action plan will contribute to 20,634.863 MWh/year energy savings for an investment of 9,810,063 MAD generating therefore an acceptable PB of 0.7 years.

5.1.6

Closing Remarks

This chapter discusses the general aspects related to energy auditing, presents the types and levels of energy audits, describes the methodology followed and measuring devices used, and summarizes the most important energy conservation measures commonly proposed by the auditors for various energy systems. Finally, a case study referring to the energy audit in an industrial facility has been reported, showing the audit procedure, diagnosis phase, and the action plan proposed by the audit team along with its financial feasibility.

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Energy Auditing Fuchs E, Masoum MA. Power quality in power systems and electrical machines. Burlington: Academic press; 2011. André Perrenoud et Marc Correvon. Rapport Wp12 VALIDATION D’UN ÉCONOMISEUR D’ÉNERGIEDANS LE DOMAINE DE L’ÉCLAIRAGE. Boharb A, Allouhi A, Saidur R, et al. Auditing and analysis of energy consumption of an industrial site in Morocco. Energy 2016;101:332–42. Frelin W. Impact de la pollution harmonique sur les matériels de réseau [Doctoral dissertation]. Université Paris Sud-Paris XI; 2009. Carvalho RJO. Dynamic performance of induction motor under nonsinusoidal conditions. In: Proceedings of the 10th international conference on harmonics and quality of power, vol. 1. IEEE; 2002. p. 122–26. Lin D, Batan T, Fuchs EF, Grady WM. Harmonic losses of single-phase induction motors under nonsinusoidal voltages. IEEE Trans Energy Convers 1996;11(2):273–86. Fuchs EF, Stensland T, Grady WM, Doyle M. Measurement of harmonic losses of pole transformers and single-phase induction motors. In: Industry applications society annual meeting, 1994, conference record of the 1994 IEEE, vol. 1. IEEE; 2000. p. 128–34. Garcia AGP, Szklo AS, Schaeffer R, McNeil MA. Energy-efficiency standards for electric motors in Brazilian industry. Energy Policy 2007;35(6):3424–39. Thirugnanasambandam M, Hasanuzzaman M, Saidur R, et al. Analysis of electrical motors load factors and energy savings in an Indian cement industry. Energy 2011;36 (7):4307–14. Jovanovic´ S, Gajic´ B, Mijailovic´ S. Reactive power compensation and loss reduction in large industrial enterprises. Int J Electr Power Energy Syst 1991;13(6):337–42. Kralikova R, Andrejiova M, Wessely E. Energy saving techniques and strategies for illumination in industry. Proc Eng 2015;100:187–95. Trifunovic J, Mikulovic J, Djurisic Z, Djuric M, Kostic M. Reductions in electricity consumption and power demand in case of the mass use of compact fluorescent lamps. Energy 2009;34(9):1355–63. Li DH, Cheung AC, Chow SK, Lee EW. Study of daylight data and lighting energy savings for atrium corridors with lighting dimming controls. Energy Build 2014;72:457–64. Ribinstein FM, Karayel M. The measured energy savings from two lighting control strategies. IEEE Trans Ind Appl 1984;5):1189–97. Boharb A, Allouhi A, Jamil A, et al. Analysis of the electrical energy consumption and energy audit of interior lighting for an industrial site in Morocco. In: 3rd International renewable and sustainable energy conference (IRSEC), IEEE; 2015. p. 1–6. Boharb A, Allouhi A, Saidur R, Kousksou T, Jamil A. Energy conservation potential of an energy audit within the pulp and paper industry in Morocco. J Clean Prod 2017;149:569–81. Kaya D, Phelan P, Chau D, Ibrahim Sarac H. Energy conservation in compressed‐air systems. Int J Energy Res 2002;26(9):837–49. Compressedairchallenge. Available from: www.compressedairchallenge.org/library/factsheets/factsheet07.pdf. Crespo RJ. Evaluation of energy usage in the chemical industry and effective measures to reduce energy losses [MS Thesis]. Mississippi State University; 2009. Radgen P. Efficiency through compressed air energy audits. In: Energy audit conference. Available from: www.audit06.fi; 2006. Dindorf R. Estimating potential energy savings in compressed air systems. Proc Eng 2012;39:204–11. Parekh PS. Investment-grade compressed air system audit, analysis, and upgrade in a pulp and paper mill. Bellevue, WA: UNCADE Inc; 2000. Saidur R, Rahim NA, Hasanuzzaman M. A review on compressed-air energy use and energy savings. Renew Sustain Energy Rev 2010;14(4):1135–53. D’Antonio M, Epstein G, Moray S, Schmidt C. Compressed air load reduction approaches and innovations. In: Proceedings of the 27th industrial energy pechnology conference; 2005. p. 10–13. Galitsky C. Energy efficiency improvement and cost saving opportunities for the vehicle assembly industry: an energy star guide for energy and plant managers. Berkeley, CA: Lawrence Berkeley National Laboratory; 2008. Heatpump. Available from: www.heatpump-reviews.com/heat-pump.html [accessed 22.09.16]. Canadian Industry Program for Energy Conservation. Boilers and heaters: improving energy efficiency. Ottawa, ON: Natural Resources Canada, Office of Energy-efficiency; 2001. Summerbell DL, Barlow CY, Cullen JM. Potential reduction of carbon emissions by performance improvement: a cement industry case study. J Clean Prod 2016;135:1327–39. Huang YH, Chang YL, Fleiter T. A critical analysis of energy efficiency improvement potentials in Taiwan's cement industry. Energy Policy 2016;96:14–26. International Energy Agency (IEA). World energy outlook; 2015. Paris: IEA. Gartner E. Industrially interesting approaches to “low-CO2” cements. Cem Concr Res 2004;34(9):1489–98. Hasanbeigi A, Morrow W, Masanet E, Sathaye J, Xu T. Energy efficiency improvement and CO2 emission reduction opportunities in the cement industry in China. Energy Policy 2013;57:287–97. Kabir G, Abubakar AI, El-Nafaty UA. Energy audit and conservation opportunities for pyroprocessing unit of a typical dry process cement plant. Energy 2010;35 (3):1237–43. Engin T, Ari V. Energy auditing and recovery for dry type cement rotary kiln systems: a case study. Energy Convers Manag 2005;46(4):551–62. Madlool NA, Saidur R, Hossain MS, Rahim NA. A critical review on energy use and savings in the cement industries. Renew Sustain Energy Rev 2011;15(4):2042–60. Ziya S, Zuhal O, Hikmet K. Mathematical modeling of heat recovery from a rotary kiln. Appl Therm Eng 2010;30:817–25. McKane A, Perry W, Aixian L, Tienan L, Williams R. Linking energy efficiency and ISO: creating a framework for sustainable industrial energy efficiency. Berkeley, CA: Lawrence Berkeley National Laboratory; 2005. Cipollone R. Carbon and energy saving markets in compressed air. In: IOP conference series: materials science and engineering, vol. 90, no. 1. IOP Publishing; 2015. p. 012085. Akbaba M. Energy conservation by using energy efficient electric motors. Appl Energy 1999;64(1):149–58.

Relevant Websites https://www.ashrae.org American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). https://www.iea.org International Energy Agency (IEA).

5.2 Energy Conservation Yas¸ar Demirel, University of Nebraska Lincoln, Lincoln, NE, United States r 2018 Elsevier Inc. All rights reserved.

5.2.1 5.2.2 5.2.2.1 5.2.2.2 5.2.2.3 5.2.2.4 5.2.2.5 5.2.2.6 5.2.3 5.2.3.1 5.2.3.2 5.2.3.3 5.2.4 5.2.4.1 5.2.4.2 5.2.4.3 5.2.4.4 5.2.4.5 5.2.4.6 5.2.4.6.1 5.2.4.6.2 5.2.4.7 5.2.5 5.2.5.1 5.2.5.1.1 5.2.5.1.2 5.2.5.1.3 5.2.5.1.4 5.2.5.1.5 5.2.5.1.6 5.2.5.1.7 5.2.5.1.8 5.2.5.1.9 5.2.5.1.10 5.2.5.1.11 5.2.5.1.12 5.2.5.1.13 5.2.5.1.14 5.2.5.1.15 5.2.5.1.16 5.2.5.1.17 5.2.5.2 5.2.5.2.1 5.2.5.2.2 5.2.5.2.3 5.2.5.2.4 5.2.5.2.5 5.2.5.3 5.2.5.3.1 5.2.5.3.2 5.2.5.3.3 5.2.5.3.4 5.2.5.3.5

Introduction Fundamentals Energy Efficiency Standards Comparison of Energy-Efficiency Standards Sustainability in Energy Systems Sustainability Metrics Energy-Water Nexus Sustainability Assessment Tools Systems and Applications The First Law of Thermodynamics and Heat Bernoulli Equation Electric Circuit Analysis and Assessment Energy Assessments Energy Analysis Energy Management Thermodynamic Analysis Distillation Column Systems Distillation Column Targeting Tool Thermal analysis Exergy loss profiles Energy Analyzer Sector Analyses Industrial Sector Energy usage Economic and technical saving potentials Iron/steel Chemicals/pharmaceuticals Pulp/paper Nonmetallic minerals Nonferrous metals Petroleum refineries Food/beverage Machinery Some possible energy conserving measures in industrial sector Improvements in power plants Energy conservation in the compression work Energy conservation in expansion by replacing a throttle valve with a turbine Energy conservation by using high-efficiency electric motors Energy saving opportunities in industrial sector Some barriers to energy conserving Agricultural Sector Farm vehicles Dairy operations Fertilizer use Greenhouse heating with long-term solar energy storage Energy self-assessment tools Transportation Sector Alternative fuel vehicles Alternative fuels Vehicles technology and energy efficiency Identifying future transport pathways Mobility model

Comprehensive Energy Systems, Volume 5

doi:10.1016/B978-0-12-809597-3.00505-8

47 48 48 48 48 49 50 50 50 51 51 52 52 52 53 53 53 54 55 55 56 57 57 57 58 58 59 59 59 60 60 61 61 62 62 63 65 66 67 67 67 68 68 68 68 68 70 71 71 72 72 72 73

45

46

Energy Conservation

5.2.5.4 Residential Sector 5.2.5.4.1 Energy conservation in home heating and cooling 5.2.5.4.2 Home heating by fossil fuels 5.2.5.4.3 Home heating by electricity 5.2.5.4.4 Home heating by solar systems 5.2.5.4.5 Home cooling 5.2.5.4.6 Commercial buildings 5.2.5.4.7 Accommodation and food service activities 5.2.5.4.8 Information and communications 5.2.5.4.9 Behavior and human dimensions of energy conservation 5.2.5.5 Case Studies for Energy Conservation Measures 5.2.5.5.1 ECMs in a crude oil refinery by thermodynamic analysis 5.2.5.5.1.1 Process description 5.2.5.5.1.2 Composite curve and heat exchanger network system 5.2.5.5.1.3 Column grand composite curves and exergy losses 5.2.5.5.1.3.1 Preflash column 5.2.5.5.1.3.2 Crude column 5.2.5.5.1.3.3 Vacuum distillation unit 5.2.5.5.1.3.4 Economic evaluation 5.2.5.5.2 Energy conservation measures in the back-end separation of an ethylene plant 5.2.5.5.2.1 Process description 5.2.5.5.2.2 Column grand composite curves and exergy loss profiles 5.2.5.5.2.2.1 Column 1 5.2.5.5.2.2.2 Column 2 5.2.5.5.2.2.3 Column 3 5.2.6 Results and Discussion 5.2.7 Future Directions 5.2.8 Closing Remarks References Relevant Websites

Nomenclature A Cp D Ex HD Hdef Hfeed HL HLmin HV HVmin k n_ N

Availability, kW Heat capacity, kJ kg 1 K 1 Distillate rate, kg h 1 Exergy, kJ kg 1 Distillate enthalpy, kJ kg 1 Enthalpy deficits, kJ kg 1 Feed enthalpy, kJ kg 1 Liquid enthalpy, kJ kg 1 Minimum enthalpy for liquid flow, kJ kg Vapor enthalpy, kJ kg 1 Minimum enthalpy for vapor flow, kJ kg Thermal conductivity kW m 1 K 1 Molar flow rate, mole h 1 Number of total stages

Abbreviations ACEEE American council for an energy efficient economy AFUE Annual fuel utilization efficiency AGO Automotive gas oil ASD Adjustable speed drive ASE Alliance to save energy BAU Business-as-usual

P qC qR R rp S T _s W 1

1

73 73 74 74 74 75 76 76 76 77 78 79 79 79 79 79 79 81 81 81 81 82 82 83 84 85 88 88 88 89

xD x F yF g Z r

Pressure, Pa Condenser duty, kW Reboiler duty, kW Gas constant, 8.314 kJ mole Compression ratio Entropy, kJ kg 1 K 1 Temperature, K Shaft work, kW Distillate liquid fraction Feed liquid fraction Feed vapor fraction Cp Cv 1 Efficiency Density, kg m 3

CAFE CEPCI CGCC CHP CNG COP CTT ECM

Corporate average fuel economy Chemical engineering plant cost index Column grand composite curve Combined heat and power Compressed natural gas Coefficient of performance Column targeting tool Energy conservation measures

1

K

1

Energy Conservation

EER EISA EES HEM HENS HRSG EU FCC GHG IAC ICE IEA IEED IEEM

5.2.1

Energy efficiency ratio Energy Independence and Security Act Energy efficiency standard High efficiency motors Heat exchanger network system Heat recovery steam generator European Union Fixed capital cost Greenhouse gas Industrial assessment center Internal combustion engines International Energy Agency Industrial energy efficiency database Industrial energy efficiency model

kTOE LVGO MBP MTOE NF NRCS RR TA TBP SEER USDA VDU VMT VSD

47

Kilo tonne of oil equivalent Light vacuum gas oil Management best practice Million tonnes of oil equivalent Feed stage National Resources Conservation Services Reflux ratio Thermodynamic analysis Technical best practice Seasonal energy efficiency ratio United States Department of Agriculture Vacuum distillation unit Vehicle miles traveled Variable speed drives

Introduction

Energy conservation mainly refers to reducing energy consumption and increasing efficiency in energy usage for the same useful energy output. For example, using less fuel is energy conservation, as is reducing the demand on a limited supply such as fossil fuels and replacing it with renewable energy. Energy recovery also may be a part of energy conservation through captured and hence reduced waste energy. Energy conservation may lead to increased security, financial gain, and environmental protection. For example, electric motors consume a considerable amount of electrical energy and operate at efficiencies between 70 and 90% [1]. Therefore, using an electric motor operating with higher efficiency will conserve energy throughout its useful life. When energy conservation facilitates the replacement of nonrenewable resources with renewable energy resources this is often the most economical solution to energy security. Energy conservation and consequently higher energy efficiencies across most sectors of the economy are becoming part of sustainable development [2,3]. Environmental degradation due to fossil energy consumption is becoming a universal consensus and has spurred movement toward post-carbon energy technology and energy conservation by most of the sectors [4]. Buildings use almost 35–40% of all energy consumed and are responsible for 30–40% of the GHG emissions [5]. Residential air-conditioning is a main factor to peak demand on the power grids. The power at these peak demands is very expensive. This is one of the reasons why the residential energy efficiency and conservation programs focus on space heating and cooling as well as water usage. Minimum energy performance standards (MEPS) for buildings and appliances have been adopted by many countries. Hot water from processes such as power plants and steel mills may be used for heating of homes and offices in the nearby area. Energy conservation through insulation or improved buildings may also help. Low temperature heat recovery would be more effective for a short distance from producers to consumers [6]. The industrial sector is a good candidate for energy assessment programs such as the DOE’s Industrial Assessment Centers (IACs) [7–9] and the Corporate Energy Management Team [8]. The goal is to control energy usage and cost, improve energy efficiency, and reduce greenhouse gas (GHG) emissions. Almost 93% of the total energy for transportation comes from petroleum hence the potential for GHG emission reduction would be significant even for a modest fuel demand reduction, especially for imported petroleum [6]. Energy recovery may lead to reducing the overall waste energy from a system. For example, a waste energy, mainly in the form sensible or latent heat, from a subsystem may be usable in another part of the same system. Therefore, energy recovery may be a part of energy conservation. There is a large potential for energy recovery in industries and utilities leading to reduced use of fossil fuels and hence GHG emission. Some examples of energy recovery practices are [10]:

• • • • • • •

energy and water recycling, heat recovery steam generator (HRSG), heat regenerative cyclone engine, thermal diode, thermoelectric modules, regenerative brake, used in electric cars and trains, where the part of kinetic energy is recovered and stored as chemical energy in a battery, and active pressure reduction systems, where the differential pressure in a pressurized fluid flow is recovered rather than converted to heat in a pressure reduction valve.

Reducing energy consumption through efficiency and conservation provides the most important and most economically strategic projects for transition to a sustainable energy system. This vision will require a combination of increased research and development on energy efficiency and policies that encourage conservation and use of high-efficiency systems. It will also require structural and behavior changes in residential and transportation systems such as replacing fossil fuels with renewable energy [11].

48

Energy Conservation

5.2.2

Fundamentals

To remain competitive most of the industrial processes depend on stable and affordable energy supply. Some of these industrial processes produce energy, while others use energy. Energy conservation in both types of the processes will increase thermal efficiency and reduce wasted energy. The next sections discuss some possible energy conservation measures (ECMs) through energy assessment and sustainability assessments [11,12].

5.2.2.1

Energy Efficiency Standards

Energy efficiency as a resource should be capable of yielding energy and demand savings that can displace supply-side resources including nonrenewable energy-based electricity. The US DOE has established an energy efficiency standard (EES) for more than 50 different products, appliances, and equipment including commercial, industrial, lighting products, and residential products [13]. The EES group produces technoeconomic and environmental analyses for these products:

• • • • • • • •

equipment price and markup, energy use, consumer life-cycle cost and payback period, shipment, national impact, which considers national energy savings and consumer net present value, emission impact, employment impact, and regulatory.

These analyses create standards that achieve maximum improvement in energy efficiency that are feasible and lead to considerable energy savings. Thermal efficiencies of residential furnaces and boilers are measured by annual fuel utilization efficiency (AFUE). AFUE is the ratio of heat output of the furnace or boiler to the total energy consumed by them over a typical year. AFUE does not account for the circulating air and combustion fan power consumptions and the heat losses of the distributing systems of duct or piping. An AFUE of 90% means that 90% of the energy in the fuel becomes heat for the home and the other 10% escapes up the chimney and elsewhere [1]. The energy efficiency ratio (EER) of a cooling device is the ratio of output cooling (in Btu h 1) to input electrical power (in Watt) at a given operating point. The efficiency of air conditioners is often rated by the seasonal energy efficiency ratio (SEER). The SEER rating of a unit is the cooling output in Btu during a typical cooling season divided by the total electric energy input in Watt h during the same period. The coefficient of performance (COP) is an instantaneous measure (i.e., a measure of power divided by power), whereas both EER and SEER are averaged over a duration of time. The time duration considered is several hours of constant conditions for EER, and a full year of typical meteorological and indoor conditions for SEER [1,14–17].

5.2.2.2

Comparison of Energy-Efficiency Standards

Energy-efficiency standards (EES) for residential appliances, equipment, and lighting have been adopted globally by developed and developing countries and are contributing considerably in achieving energy conservation. Many countries have mandatory minimum EESs and labeling programs beside voluntary standards [9,16]. EES for equipment and appliances are MEPS in most countries (Table 1). In Japan the Top Runner and in the United States the Energy Star approaches are common. Labeling clearly recognizes an efficiency grading system to make the difference in efficiency among products depending on whether the standard is for minimum efficiency or for the top runners. Energy Star programs are used in many countries. In the United States, Energy Star is a joint program of the US Environmental Protection Agency (EPA) and Department of Energy (DOE), launched in 2011 to help industry, business, and consumers to adopt energy saving products and practices, and reduce energy waste and GHG emissions. The Energy Star program (1) establishes specifications, testing procedures, and verifications requirements for various consumer appliances and commercial products; (2) combines research into residential energy use to promote energy-efficient homes; and (3) develops a commercial building energy asset rating program to assess building energy usage accurately.

5.2.2.3

Sustainability in Energy Systems

Energy is a global challenge since the ways we produce, convert, store, and use energy are changing the earth's climate and environment, hence the ways of human life as well as the next generation's future. Sustainable energy systems aim for inclusion of all three elements: environment, social, and economic (Fig. 1). Use of emerging renewable energy technologies (including solar, wind) and alternative ways of using traditional fossil and nuclear fuels are growing but are constrained by various factors including cost, infrastructure, public acceptance, and others [1,10]. To fully implement sustainable engineering solutions, engineers use the following principles in energy systems:

•

Integrate environmental impact assessment (EIA) tools.

Energy Conservation

Table 1

Labeling types for various energy standards

Country

Label

EU

Mandatory Voluntary

United States

Mandatory Voluntary Mandatory Voluntary Mandatory Voluntary Mandatory

Canada China Japan

49

Energy label – comparative Eco-label – endorsement Energy star – endorsement Energy guide – comparative Energy star – endorsement Energy guide – comparative Energy star – endorsement Energy level – comparative Energy conservation certification label – endorsement Energy saving label – comparative Energy saving label – endorsement Energy star – endorsement

Voluntary

Source: Reproduced from American Society of Heating Refrigerating and Air-Conditioning Engineers Inc., Method of testing for annual fuel utilization efficiency of residential central furnaces and boilers report No. BSR/ASHRAE Standard 103-1993R. Atlanta, GA. First Public Review; 2003 and Hirayama S, Nakagami H, Murakoshi C, Nakamura M, Mizutani S. International comparison of energy efficiency standard and labels: development process and implementation phase. In: 2008 ACEEE summer study on energy efficiency in buildings; 2008.

Social 1D Socio-ecologial

Socio-economic 3D

2D

Sustainable

2D

Eco-economic Environment 1D

Economic 2D

1D

Fig. 1 Sustainability elements with different dimensions.

• • • • • •

Conserve and improve natural ecosystems while protecting human health and well-being. Ensure that all material and energy inputs and outputs are as inherently safe and benign as possible. Minimize depletion of natural resources and waste. Develop and apply engineering solutions in line with local geography, aspirations, and cultures. Actively engage with communities and stakeholders. Use material and energy inputs that are renewable.

5.2.2.4

Sustainability Metrics

Suitable assessment tools are needed for the development of sustainable energy systems. Sustainability is maintaining or improving the material and social conditions for human health and the environment over time without exceeding the ecological capabilities that support them. The three dimensions of sustainability are economic, environmental, and societal. The 3D sustainability metrics are indicated by the intersection of all three of these dimensions. The following sustainability metrics can be used for the analysis of energy systems [18,19]:

• •

Material intensity as nonrenewable petroleum used/unit mass of product(s). Energy intensity as nonrenewable energy/unit mass of product(s).

50

Energy Conservation

Table 2 Carbon dioxide emission rates for various CO2 emission factor data sources and fuel sources Fuel sources

Natural gas Petroleum-coke Coal bituminous Coal anthracite Crude oil Bio gas

CO2 emission factor data sources, lb MMBtu US-EPA-Rule-E9-5711

EU-2007/589/EC

130.00 250.21 229.02 253.88 182.66 127.67

130.49 226.78 219.81 228.41 170.49 0

1

Source: Reproduced from Aspen Technology. Available from: http://www.aspentech.com/ products/aspen-plus.aspx; 2016 [accessed 06.07.16].

• •

Potential environmental impact as emissions (CO2 equivalent)/unit mass of product(s). Water intensity as fresh water used/unit mass of product(s).

The energy intensity metrics are estimated from the nonrenewable energy usage in most of the sectors. The metrics for potential environmental impact can be estimated from the carbon tracking option based on the US-EPA-Rule-E9-5711 of CO2 emission factor data source with an ultimate fuel source selected (see Table 2). With this standard an emission factor of 7.85 10 5 kg CO2 kJ 1, and CO2 energy source efficiency factor of 0.85 are used [13,20,21]. World energy-related CO2 emissions was 32.2 billion metric tons in 2012; it is predicted that the CO2 emissions will rise to 35.6 billion metric tons in 2020 and to 43.2 billion metric tons in 2040 in the IEO 2016 reference case [22]. This shows an increase of 34% over the projection period. Coal is responsible for 43% of total emissions in 2012; its share is projected to stabilize and then decline to 38% in 2040. The natural gas share of CO2 emissions was 20% in 2012, and increases over the projection to 26% of total fossil fuel emissions in 2040 [22,23].

5.2.2.5

Energy-Water Nexus

The energy-water nexus points out the relationship between them; energy is used to supply water, either for treatment or transport, while water is necessary to produce many forms of energy, as well as to generate steam or cooling medium. Nearly 15% of global freshwater withdrawals annually are required for extracting and processing fossil fuels and for generating electricity from various steam power plants. Conversely, disruptions in energy supply impact water treatment, production, and distribution, and hence water security. At the same time, the agro-food supply chain accounts for 30% of the world’s energy consumption and is the largest consumer of water resources, accounting for approximately 70% of all freshwater use. Vulnerabilities in water and energy supply pose critical risks for food security [24]. Programs for ECMs should include water conservation. Energy and water management guidelines are as follows: (1) prepare accurate information on cost and usage of energy and water, (2) maximize operating efficiency, and (3) incorporate the efficiency in capital investments. A life-cycle cost analysis may determine whether it is more advantageous to allocate capital up front to reduce energy or water use over the lifetime of the equipment [1]. In a wastewater treatment plant, biological treatment of sludge and wastewater produces a nutrient-rich biosolid that needs a strategy for management. This kind of strategy is becoming challenging because of the need for energy conservation and tighter control of GHG emissions [25,26].

5.2.2.6

Sustainability Assessment Tools

Some of the sustainability assessment tools [10,27] are life-cycle assessment (LCA) [1,28], EIA, life cycle costing (LCC), process energy analysis, social life cycle assessment, cost benefit analysis. These tools require high-level expertise and address one or two sustainability aspects. Sustainability assessment should (1) provide reliable information, (2) address a process’s context, (3) point out problem areas, (4) point out focused solutions, and (5) complete within limited tile and resources. The tools addressing three elements of sustainability include: (a) fuzzy-based sustainable manufacturing assessment model, (b) sustainable manufacturing map, (c) sustainable manufacturing indicators, (d) indicators for sustainable manufacturing, (e) integrated assessment of sustainable development, (f) a holistic and rapid sustainability assessment tool, (g) sustainable value stream mapping, and (h) sustainable domain value stream framework. Most of the tools require extensive reporting. Only a few tools are capable of identifying specific problems and solutions for them [27].

5.2.3

Systems and Applications

Energy conservation is a fundamental principle of science and there are primary equations that are used as a reformulation of the principles of conservation energy in various application in heat and thermodynamics, fluids, and electrical circuits.

Energy Conservation

51

Energy conservation can be applied in various sectors including industrial, transportation, domestic appliances, energy production and conversion, and energy storage. Some of these sectors are complex, interactive, and energy intensive systems, which may be subject to energy analyses to explore ECMs [11,12,29]. This study considers some possible ECMs for various energy intensive systems and applications including:

• • •

power production in Brayton and Rankine cycles, compression process, and home heating and cooling.

In the two case studies for energy conservation in the industrial sector, energy and exergy analyses of the separation process by distillation columns are considered in:

• •

crude oil refinery operation, and ethylene production plant.

5.2.3.1

The First Law of Thermodynamics and Heat

The first law of thermodynamics represents the energy conservation principle. According to the first law, the state function of internal energy U in a closed system is equal to the sum of the heat received by the system δq and the mechanical work δW performed on the system by the surroundings dU ¼ δq þ δW

ð1Þ

Changes of heat and work depend on the path of a change and are not state functions [1,30]. The symbol δ is used throughout the text to indicate differentials of path-dependent functions that are not state variables. A change in a state function U accompanying the transition of a system Hfrom one state to another is path independent and when the system returns to its original state, the integral of the change is zero dU ¼ 0. For a rigid body subject to both boundary and body forces, Newton’s second law of motion is P P ð2Þ Fs þ Fb ¼ 0

where Fs is the force vector acting on the surface of the rigid body and Fb is the body force associated with external fields, such as gravitational, inertial, coulombic, etc. If Fs causes the weight of a body to rise during the motion, we have dv ¼0 ð3Þ dt where dv/dt is the acceleration vector a (F¼ma). The mechanical work W associated with the movement of a rigid body is the scalar product of the net force and displacement vectors dl Fs

mg

m

δW ¼ Fs dl ¼ mgdl þ mvdv

ð4Þ

The total work on the weight between states 1 and 2 is W ¼ mg ðz2

z1 Þ þ

m 2 v 2 2

v12



ð5Þ

The first and second terms on the right are the difference in potential and kinetic energy, respectively. The sign convention adapted here assumes that heat transferred into the system from the surroundings is positive, and work transferred into the system (work done on the system) at which energy is transferred into the system from the surroundings is positive. In general, the term δW represents all different forms of work. Work is the product of an intensive variable and a differential of an extensive variable. For example, if the system is displaced by a distance dl under a force F, it performs the work of –Fdl. If –dNi moles of substance i with the chemical potential mi flow from the system to its surroundings, the chemical work of –mdNi occurs. Thus the total work becomes δW ¼

PdV þ Fdl þ cde þ

n X

mi dNi þ …:

ð6Þ

i¼1

Here –PdV refers to the sign convention recommending that work done on the system is positive as the compression work leads to –dV and positive work. Some other types of work interactions are surface deformation (sdA, where s is the surface tension and dA is the change in surface area), electric polarization, magnetic polarization, frictional, and stress–strain. For an open system, an additional contribution to the energy conservation due to the exchange of matter (dUm) occurs and with Eq. (6), we have dU ¼ δq þ δW þ dUm ¼ δq

PdV þ Fdl þ cde þ

n X

mi dNi þ …: þ dUm

ð7Þ

i¼1

5.2.3.2

Bernoulli Equation

The Bernoulli equation represents the energy conservation principle for steady state fluid flow systems. Considering the two points before and at the constriction, we have the conservation in pressure energy, kinetic energy, and potential energy in a steady state

52

Energy Conservation

flow expressed as 1 1 P1 þ rv12 þ rgh1 ¼ P2 þ rv22 þ rgh2 ð8Þ 2 2 The Bernoulli effect refers to qualitative behavior of fluids and states that lowering the area of fluid flow causes the increase in fluid flow velocity and decrease in pressure: A2oA1-v24v1 and P2oP1. This also describes the pressure as energy density F FI Energy ¼ ¼ ð9Þ A AI Volume where F and A are the force and area, and l is the distance. Hydrostatic pressure of a fluid is used as energy density together with kinetic and potential energy densities in the Bernoulli equation. On the other hand, osmotic pressure results from the energy density of solvent molecules in osmosis. P¼

5.2.3.3

Electric Circuit

Voltage is the electric potential energy per unit charge. Electric current flowing through conductors is directly proportional to the voltage applied as shown in Ohm’s law I ¼ V/R where I is the current, V is the voltage, and R is the resistance. The electric power P for a resistor in a direct current circuit is calculated as the product of applied voltage and the electric current P ¼ VI

ð10Þ

Electric power represents the energy conversion from electrical energy of the flowing charges to other forms, such as heat mechanical energy, or magnetic field. According to the voltage law, the voltage changes around any closed electric circuit loop must sum to zero regardless of the path. This law is the result of energy conservation. The current law states that the sum of the currents into any junction is equal to the sum of the currents out. ECMs are the natural outcome as the economic and environmental costs rise. Increasing the energy efficiency of energyintensive industrial sectors such as metal casting and chemical manufacturing is one important measure available. The industrial manufacturing sector is very complex and requires a careful approach for all the achievable ECMs. The metal casting sector engages in smelting and refining ferrous and nonferrous metals from ores or scrap recycled metals for producing metal products. Various types of melting furnaces, such as electric based or fuel-fired furnace, operate under different energy efficiencies. Some ECMs are [9,11,12]:

• • • • • • • •

Proper insulation and maintenance. Proper capacity and flow rate usages compatible with peak-demand energy costs. Waste heat recoveries through recuperative and regenerative heat exchangers. Combined heat and power (CHP) productions. Modeling for energy optimization to match the design parameters with the operating conditions Proper process and energy-intensive equipment controls. Improved process motors, multistage compressors, variable speed compressors, pumps, and fans. Reduction of the waste energy in buildings.

Chemical manufacturing involves diverse sectors for commodity productions with specific specifications such as fuels, polymers, pharmaceuticals, resins, fertilizers, and dyes, and needs excessive use of energy, causing excessive GHG emissions. The two levels for reducing the energy usage in industrial manufacturing are capital investment (selecting the best equipment) and operating cost (how the energy is used).

5.2.4 5.2.4.1

Analysis and Assessment Energy Assessments

Energy assessment identifies all the energy streams, and attempts to balance the total energy inputs with their use and optimize the facility’s energy costs. It quantifies energy usage according to its discrete functions and answers these questions: how much energy is used? Where is energy consumed? How is it used? How can we reduce cost/consumption? How to estimate losses/reduce losses? The main steps in energy assessments are [7,8]: 1. Identify the recent trends in energy consumption and cost from the earlier energy usage data. Plotting the monthly energy consumption versus production helps to develop energy performance indices (EPI). 2. Estimate the unit costs for all utilities and review the data and manufacturing processes that lead to ideas for ECMs, such as shutting off equipment, replacing equipment, or renegotiation of a supply contract. Sometimes overhead cost associated with energy usage may be much higher than the actual cost of energy. Average cost of electricity per kilowatt hour ¼ (consumption charge þ demand charge)/total kilowatt hour. 3. Compute energy intensity, which is the ratio of total consumption to per-unit amount of product. This helps create the EPIs for that month. The monthly values of EPIs will show if there is consistency or a need for further investigations if significant variations exist. 4. Evaluate utility supply options by reviewing energy supply contracts to uncover possible saving opportunities.

Energy Conservation

53

5. Prepare a utility balance that comes from annual energy consumption and monthly utility bills. Identify the process units with their percentages of energy consumption costs of operations. The data on unit efficiency can be used to identify some critical equipment in energy costs. 6. Review if there are ECMs previously identified but not implemented by reviewing the previous assessments. Select the participants from utilities, production, and maintenance for identifying missed saving opportunities. 7. Develop a detailed schedule to identify ECMs and for facility inspection. Arrange for energy and equipment surveys by outside parties. Survey steam traps, steam lines, compressed stream lines, insulation, refrigeration, cooling towers, lighting, and variable-frequency drives [15].

5.2.4.2

Energy Analysis

Energy analysis consists of (1) acquiring energy use and its cost, and (2) analyzing data to identify ECMs to reduce energy use and cost, and consequently increase efficiency. Based on the cost and operation type one needs to prioritize the ECMs. Some sources for this are (1) the US DOE’s Advanced Manufacturing Office, which helps analyze energy conservation opportunities in manufacturing [14] by developing clean energy products and technology; and (2) the US DOE’s Save Energy Now assessments, which focus on implementation of identified ECMs. Besides, the US Office of Energy Efficiency & Renewable Energy (EERE) develops technologies that increase the energy efficiency in the building, transportation, and industrial sectors by conserving energy. The teams of the US DOE IAC are university-based faculty and students and provide free energy assessment to small- and medium-sized manufacturers [14].

5.2.4.3

Energy Management

For effective energy management there are several basic steps, which are management commitment, data analysis, analysis of energy conservation options, implementation energy conservation options, and continued feedback and analysis. Data analysis could be easy with a common energy unit, such as Btu. Table 3 shows Btu equivalent of various energy sources. Energy management programs provide organizations with the information, tools, and assistance to reduce energy, water, and nonrenewable energy use, as well as GHG emissions. The program provides the organizations with annual performance data, use energy savings performance contracts, utility energy service contracts, and support with energy efficient products. Within this framework organizations may prepare sustainability/energy score cards. The program offers expertise regarding all levels of project and policy implementation to reduce energy intensity of operations [11,12]. In analyzing the energy cost one needs to consider avoided cost, seasonal averages, demand, and power factor, which is the ratio of real power (kW) to apparent power (kilovolt-amps, kVA). Apparent power is the amount of power provided to the facility. Reactive power is measured in kVAR and includes inductive loads (i.e., transformers, electric motors) that require current to create a magnetic field but do not produce work. Real power is the work consumed by the device [1].

5.2.4.4

Thermodynamic Analysis

The Gouy–Stodola theorem states that the lost available work is directly proportional to the rate of entropy production due to irreversibility in systems with exchange of energy and momentum within the system and at its boundaries. The design questions are how to design thermal systems to produce the least possible entropy production Sprod. For a heat and fluid flow steady state flow process, the rate of entropy production results from local irreversibilities by heat and viscous effects k m S_ prod ¼ 2 ð∇T Þ2 þ Θ T T Table 3

ð11Þ

Energy data for analysis

Unit energy source/unit

Btu equivalent

Kilowatt hour Therm Natural gas, cf3 Fuel oil #2, gal Fuel oil #4, gal Fuel oil #6, gal Propane, gal Coal, ton Boiler, Hp Hp Refrigeration, ton

3412 100,000 1000 140,000 144,000 152,000 91,600 28,000,000 9.81 kW 746 kW 12,000 Btu h

1

Source: Reproduced from Demirel Y. Energy: production, conversion, storage, conservation, and coupling. 2nd ed. London: Springer; 2016.

54

Energy Conservation

where k is the thermal conductivity, m is the viscosity, and Θ is the viscous dissipation function in s 2. Thermodynamic analyses (TA) include second law analysis, exergy analysis, pinch analysis, equipartition principle, Gibbs free energy minimization, thermoeconomics, exergoeconomics, and extended exergy analysis [30,31]. Thermodynamic analyses aim at identifying, quantifying, and minimizing irreversibilities in a system. Such analyses are of considerable interest when efficient energy conservation is important as the irreversibility is directly associated with efficiency. TA firstly assesses the thermodynamic performance of the current operation, secondly identifies the scope of improvements and retrofits to reduce the cost of operation by energy conservation, and thirdly assesses the thermodynamics and economic effectiveness of the retrofits. Second law analysis can identify the sources and quantity of entropy production in various processes in a system. Exergy analysis describes the maximum available work when a form of energy is converted reversibly to a reference system in equilibrium with the environmental conditions; hence, it can relate the impact of energy utilization to the environment. Pinch analysis can reduce the hot and cold utility requirements by integrating process heat streams. On the other hand, the equipartition principle states that a process would be optimum when the thermodynamic driving forces are uniformly distributed in space and time. The exergy method has been widely used for the analysis of industrial processes and energy production processes [31]. Exergy analysis is conducted to save the fixed and operational costs of mainly thermal engineering systems by conserving energy. Exergy analysis firstly identifies the processes that contribute to the total exergy loss. Secondly, it identifies the scope for optimization either by increasing the thermal efficiency or through reducing the irreversibility. The third step assesses the impact of the optimization on economics and sustainability. Thermodynamics, fluid mechanics, heat and mass transfer, kinetics, material properties, operational and design constraints, and geometry are required to establish the relationships between physical configuration and entropy production and to minimize entropy production. Exergoeconomics converts monetary expenses into equivalent exergy fluxes [31] and optimization of energy usage for a process is based on these exergy fluxes. Normally, in conducting an exergoeconomic balance, a system of simultaneous equations with a higher number of unknowns than equations is obtained, thus some additional equations and assumptions may be required [8–10]. Such an extended representation of exergy flow diagrams constitutes a substantial generalization of cumulative exergy consumption procedure, and may provide a coherent and consistent framework for including nonenergetic quantities like capital cost, labor cost, and environmental impact into an engineering optimization procedure.

5.2.4.5

Distillation Column Systems

Distillation column systems are highly energy-intensive processes [32]. In a distillation column, the heat supplied at a higher temperature source in the reboiler is discharged at a lower temperature in the condenser (Fig. 2). Assuming the column to be a

TC

Condenser

qC

Distillate

Feed

Bottoms qR TR Fig. 2 Distillation column as a heat engine between reboiler and condenser.

Reboiler

Energy Conservation

reversible heat engine, the network available from the thermal energy is    To Wheat ¼ qR 1 qC 1 TR

To TC



55

ð12Þ

where To is the ambient temperature, qR and qC are the reboiler and condenser duties, respectively. The minimum separation work Wmin required for separation is the net change in availability A Wmin ¼ DAs ¼ Aprod

Afeed

ð13Þ

where A¼ H ToS. The change of availability of separation is the difference between the work supplied by the heat and the work required for the separation of components within the feed stream, which contains the work lost due to irreversibilities DAs ¼ Wheat

Wts

ð14Þ

where Wts is the total work necessary for the separation. Minimizing the work lost due to irreversibility will minimize the total heat needed for separation. Efficiency based on the second law of thermodynamics is Zth ¼

Wmin Wmin þ Wlost

ð15Þ

For example, propylene–propane mixture is a close boiling mixture and requires a distillation column with a reflux ratio (RR) of 15.9 (close to minimum) and 200 equilibrium stages. The reboiler and condenser duties are 8274.72 kW and 8280.82 kW, respectively. The reference temperature is 294K. The lost work is To S_ prod ¼ 1902.58 kW. Availability analysis yields: P _ min ¼ _ ¼ 140:81 kW. The thermodynamic efficiency from Eq. (15) is Zth ¼ 0.0689 or 6.89%. The sources of lost work are W out nA [30,33]:

• • •

The work lost due to a high-pressure drop (as high as 10 psi) is considerable at the condenser and reboiler. The work lost due to heat transfer results from differences in temperature between the inlet streams of liquid and vapor on each stage, and is a large contributor to the total lost work. The lost work due to mixing, heat and mass transfer on the feed stages may be considerable depending on the feed stream conditions.

5.2.4.6

Distillation Column Targeting Tool

TA in this study is considered for distillation columns, which use highly energy-intensive processes to separate mixtures into their components [34,35]. For distillation columns, TA uses the column targeting tool (CTT), energy analyzer, and carbon tracking of the Aspen Plus simulator [21] to estimate the sustainability metrics of energy intensity and potential environmental impact. For an existing distillation column, TA identifies the scope of improvements for reducing the costs of energy and GHG emissions. The CTT is a retrofitting tool for lowering the cost through modified operating conditions, and providing insight into understanding tray/ packing capacity limitations in a distillation column. The CTT is based on the practical near-minimum thermodynamic condition representing a close to practical reversible column operation [33]. The CTT has the capabilities of thermal and hydraulic analyses that can help identify the targets for appropriate column modifications in order to (1) reduce thermal energy costs, (2) improve energy efficiency, and (3) reduce capital cost by improving thermodynamic driving forces [36].

5.2.4.6.1

Thermal analysis

Thermal analysis capability distributes reboiling and condensing loads over the temperature range of operation to help identify design targets for improvements in energy consumption and thermal efficiency. It produces column grand composite curves (CGCC) and exergy loss profiles [33,34]. The CGCCs represent the theoretical minimum heating and cooling requirements in the temperature range of separation. CGCCs show the inefficiencies introduced through column design and operation, such as mixing, pressure drops, multiple side-products, and side strippers. Using CGCC is significant because it is (1) a graphical tool to assess the current energy use and flow conditions of distillation operations, (2) based on the complex and rigorous stage-by-stage calculations, and (3) capable of leading to qualitative and quantitative assessments [36]. The user makes changes to column configurations and specifications until CGCCs and exergy profiles display actual operations closer to ideal operations. CGCCs can help identify the following targets for potential column modifications [33]: Feed location – if a feed is introduced too high up in the column, a sharp enthalpy change occurs on the condenser side on the stage-H CGCC plot; the feed stage should be moved down toward the reboiler. If a feed is introduced too low in the column, a sharp enthalpy change occurs on the reboiler side on the stage-H CGCC; the feed stage should be moved up toward the condenser. A more appropriate feed location may lead to considerable reductions in reboiler and condenser duties as well as stage exergy losses. Feed conditioning – if a feed is excessively subcooled, the stage-H CGCC plots show a sharp enthalpy change on the reboiler side, and the extent of this change determines the approximate feed heating duty required. If a feed is excessively overheated, the stage-H CGCC plots show a sharp enthalpy change on the condenser side, and the extent of this change determines the approximate feed cooling duty required. RR – the gap between the pinch point and ordinate suggests that the duties in the reboiler and condenser can be further reduced by reducing RR. This modification requires the change of total stages in a distillation column.

56

Energy Conservation

Side condensing or reboiling – if a significant area exists above the pinch, a side reboiler can be placed at a convenient temperature level. This allows heat supply to the column using a low-cost hot utility, hence lowering the overall operating costs. If a significant area exists below the pinch, a side condenser can be placed at a convenient temperature level. This allows heat removal from the column more effectively and by a cheaper cold utility. For estimation of the enthalpy deficits, the equations for equilibrium and operating lines are solved simultaneously at each stage for specified light key and heavy key components. Using the equilibrium compositions of light L and heavy H key components, the enthalpies for the minimum vapor and liquid flows are obtained and used in the enthalpy balances at each stage to determine the net enthalpy deficits [35]. Hdef ¼ HLmin Hdef ¼ HLmin

HVmin þ HD HVmin þ HD

ðbefore the feed stageÞ ðafter the feed stageÞ

Hfeed

ð16Þ ð17Þ

where HLmin and HVmin are the minimum enthalpy for liquid and vapor flows, respectively, HD is the distillate enthalpy, and Hfeed is the feed enthalpy. After adding the individual stage enthalpy deficits to the condenser duty, the enthalpy values are cascaded, and plotted in the CGCC. At the feed stage, mass and energy balances differ from an internal stage and the enthalpy deficit becomes          ð18Þ HV XD XF = YF XF Hdef ; F ¼ QC þ D HD þ HL XD YF = YF XF The values of YF ¼ XF may be obtained from an adiabatic flash for a single-phase feed, or from the constant relative volatility estimated with the converged compositions at the feed stage and feed quality. This procedure can be reformulated for different choices of the key components. In a CGCC, a pinch point near the feed stage occurs for nearly binary ideal mixtures. However, for nonideal multicomponent systems multiple pinches may exist in rectifying and stripping sections [37,38].

5.2.4.6.2

Exergy loss profiles

Exergy (Ex) is the maximum amount of work that may be performed theoretically by bringing a resource into equilibrium with its surroundings through a reversible process X Ex ¼ DH To DS þ ni Dmi ð19Þ

where H and S are the enthalpy and entropy, respectively, and To is the surroundings temperature, which is usually assumed as 298.15K, ni is the number of moles of species, and Dmi is the chemical potential difference of species i. In many thermal processes, the effect of chemical exergy due to chemical potential difference of species i is negligible, then the physical exergy balance for a steady state system becomes     X  X  _ 1 To þ W _ 1 To þ W _s _ s ¼ Ex _ loss _ þQ _ þQ ð20Þ mEx mEx Ts Ts out of into system system _ s is the shaft work. In general, the exergy loss profiles can be used to examine the degradation of _ is the mass flow rate, W where m accessible work due to (1) momentum loss (pressure driving force), (2) thermal loss (temperature driving force), and (3) chemical potential loss (mass transfer driving force) [31]. The exergy profiles are plotted as state-exergy loss. Exergy losses (destructions) represent inefficient use of available energy due to irreversibility, and should be reduced by suitable modifications. As the exergy loss increases, the net heat duty has to increase to enable the column to achieve its required separation task. Thermodynamic efficiency is estimated depending on the sign of the main goal; Eq. (21) for the negative main goal and Eq. (22) for the positive one ¼

Exmin Exmin Exloss

ð21Þ

ZðþÞExmin ¼

Exmin Exmin þ Exloss

ð22Þ

Zð

ÞEx min

The main goal is the minimum exergy loss [32,39]. Minimum exergy determined by calculating the difference between exergies of products and the feed streams X X _ _ Exmin ¼ mEx ð23Þ mEx out

in

_ is the mass flow rate. One of the most fundamental outcomes of classical engineering thermodynamics is the idea that the where m energy conversion efficiency of a closed-cycle heat engine, such as steam power generation, cannot exceed the Carnot efficiency. This limit corresponds to systems that operate reversibly. The difference between the actual and the Carnot work represents the lost work for a heat engine. Possible ECMs for some steady state flow processes are [29–31]:

•

Reduce irreversibilities associated with pressure drops, fluid friction, and stream-to-stream heat transfer due to temperature differences.

Energy Conservation

• •

57

Reduce waste-heat by designing a HRSG with a smaller stream-to-stream temperature difference, and/or reduce friction by designing a turbine with a higher efficiency. Select a cost-effective design that may result from a consideration of the trade-offs between possible reduction of exergy loss and potential increase in operating cost.

5.2.4.7

Energy Analyzer

The energy analyzer in the Aspen Plus design and simulation package improves the process heat integration through optimum heat exchanger network systems (HENS) for a specified temperature approach at the pinch point using the pinch analysis. In order to save energy and reduce capital cost a minimum number of heat exchangers NHx,min may be used in the process [30]. NHx;min ¼ NHs þ NCs þ NHU þ NCU

ð24Þ

1

where NHs and NCs are the number of available hot and cold streams, respectively, and NHU and NCU are the number of hot and cold utilities, respectively. An increase in DTmin causes higher energy and lower capital costs (a smaller heat exchanger area as seen in Fig. 3); for example, an increase of 5oC decreases the heat exchanger area by 11%, and increases the required minimum energy by about 9%. To find the optimum value of DTmin, the total annual cost is plotted against the energy cost. An optimum DTmin appears at the minimum total annual energy and capital costs. The optimum value for DTmin is generally in the range of 3–40oC depending on the type of processes [1,30]. Pinch analysis yields optimum energy integration of process heat and utilities by matching the hot and cold streams with a network of heat exchangers (see Fig. 4). The pinch point is the location of DTmin on the adjusted composite diagram where the hot and cold curves most closely approach to each other in temperature in a vertical direction. The overshoot of the hot composite curve represents the minimum cold utility (qc,min) required, and the overshoot of the cold composite curve represents the minimum hot utility (qh,min) required. In an optimum HENS, hot and cold streams can only exchange energy up to the pinch point, which is a minimum allowable temperature difference ∆Tmin leading to an optimum driving force for heat transfer [1,30]. Pinch analysis may also lead to optimum integration of evaporators, condensers, furnaces, and heat pumps by reducing the utility requirements. Pinch analysis is utilized widely in industry leading to considerable energy conservation.

5.2.5

Sector Analyses

Discussed in this section are possible ECMs for sectors including the industrial sector, transportation sector, agricultural sector, and home heating and cooling sector. Within the industrial sector, eight energy-intensive industrial sectors are considered. These are pulp/paper, iron/steel, nonmetallic mineral, chemical/pharmaceutical, nonferrous metal, petroleum refineries, food/beverage, and machinery. Following this, some of the illustrative examples for possible ECMs including power plants, compressors, and home appliances are discussed. The case studies include industrial processes of refinery operation and back end separation of an ethylene plant with ECMs and economic indicators.

5.2.5.1

Industrial Sector

The industrial sector is the main driver in the development of an increase in gross domestic product (GDP). It accounts for 80% of energy use and usually requires the use of natural gas, petroleum, and coal in furnaces and ovens as well as in chemical reactions, distillation and other processes needed to produce chemical compounds, plastic, steel, and other products [40,41]. Energy use by the industrial sector in the United States is around 32%, which is the largest energy-demanding sector compared with the Hot utility

Cost

Hot utility

Energy cost

Investment cost

Total cost

Capital cost

ΔTmin

Cold utility

Optimum

Hot utility

Cold utility ΔTmin Cold utility

(A)

ΔTmin

ΔT

Fig. 3 Optimum DTmin from energy cost and capital cost changes.

(B)

Operating cost

58

Energy Conservation

280 260 C2

Hot utility section

240

H1

220 200

C2

Pinch

180

T, °C

160 Hot composite

140

H1&H2

C2&C1

120 100 80 Cold composite

60 C1

40 H2

Cold utility section

20 0 0

5000

10,000

15,000

20,000

25,000

q, kW Fig. 4 Hot and cold composite curves for DTmin ¼201C using two hot (H) streams and two cold (C) streams.

residential, commercial, and transportation sectors. Manufacturing accounts for 85% of industrial energy use in processing food and materials like petroleum, iron ore, bauxite, wood, and other minerals, refining oils and gas, heat treating metal, assembling cars, and other processes. Industrial energy use also includes nonmanufacturing activities, like agriculture, construction, mining, and water and wastewater treatment facilities [23]. Worldwide industrial energy policies require the setting of quantitative targets to reduce energy use and GHG emissions for volume reduction targets, physical efficiency improvement targets, and economic intensity improvement targets. An overview by Rietbergen and Blok [42] includes approximately 50 different emission permit systems, voluntary or negotiated agreement schemes, and emission trading systems. Targets in industrial energy and climate policies are used in environmental permits, voluntary or negotiated agreements, and emission trading schemes. Volume targets aim at environmental outcomes with public relevance. Physical efficiency targets lead to environmental improvements with a high level of integrity [42].

5.2.5.1.1

Energy usage

The most significant energy use includes heating, accounting for approximately 179,000 KTOE-kilo tonnes of oil equivalent (B66% of total), and electrical energy, accounting for approximately 70,000 kTOE (B26% of total). Total final energy consumption in the eight sector groups within the EU was 272,487 kTOE in 2013. This accounts for 25% of total final energy consumption (1,103,813 kTOE) in 2013. The eight industrial sector groups analyzed in this study account for 98% of industrial final energy consumption (276,638 kTOE) in the EU. Table 4 provides a breakdown of the final energy consumed within the respective sector groups based on the temperature intervals of thermal processes, categorized into low (o2501C), medium (250–6001C), and high (46001C) [40]. Globally, renewable energy is the fastest-growing energy source increasing by an average 2.6%/year. Nuclear power is the world’s second fastest-growing energy source, with consumption increasing by 2.3%/year. Electricity is the world’s fastest-growing form of energy consumption. Fossil fuels still account for 78% of energy use in 2040. Natural gas is the fastest-growing fossil fuel and its consumption increases by 1.9%/year mainly in the electric power sector and in the industrial sector. This is because of rising oil prices, adaptation of more energy-efficient technologies with less GHG emissions, and switching away from liquid fuels. Coal is the world’s slowest-growing energy source, rising by 0.6%/year [22].

5.2.5.1.2

Economic and technical saving potentials

The energy supply chain includes electricity, steam, natural gas, coal, and other fuels. Energy is then processed using a variety of highly energy-intensive systems, including steam, process heating, and motor-driven equipment such as compressed air, pumps, and fans. The origin of energy efficiency programs traces back to the energy crisis in the 1970s, when the new concept of “energy

Energy Conservation

Table 4

59

Energy consumption in MTOE in the eight industrial sector groups with projections

Sector

Energy usage 2013

Share %

Energy usage 2015

Energy usage 2020

Energy usage 2025

Pulp/paper Iron/steel Nonmetallic mineral Chemical/pharmaceutical Nonferrous metal Petroleum refineries Food/beverage Machinery Total

34.26 50.81 34.25 51.48 9.38 44. 65 28.35 19.28 272.46

12.6 18.6 12.6 18.9 3.4 16.4 10.4 7.1 100

40.80 63.10 37.50 56.70 9.50 45.70 28.70 20.30 302.3

39.60 64.50 37.40 59.90 9.10 46.00 27.90 20.10 304.5

38.40 65.90 37.10 63.10 8.90 44.10 27.10 19.90 304.5

Note: MTOE, Million tonnes of oil equivalent. Source: Reproduced from Chan Y, Kantamaneni R. Study on energy efficiency and energy saving potential in industry from possible policy mechanisms contract No. ENER/C3/2012439/S12.666002. Available from: https://ec.europa.eu/energy/; 2015.

conservation” emerged to help customers cope with soaring energy prices. Over time, this led to the development of an expanded set of customer energy efficiency programs provided by electric and natural gas utilities. The American Council for an Energy Efficient Economy (ACEEE) [43], founded in 1980, has made energy efficiency an integral element of utility investments and operations. These utility energy efficiency programs have expanded fairly steadily over the years. In the 21st century, energy efficiency is regarded as an important utility sector resource that can also reduce GHG emissions, save money for customers, and generate jobs. In response to both economic concerns and climate change, governments have supported energy efficiency by conserving at the highest levels [43]. Understanding how energy is used and wasted can help plants identify areas of energy intensity and ways to implement ECMs. Crosscutting technologies such as combustion, distributed energy, fuel and feedstock flexibility, and nanomanufacturing are common to many industrial processes across multiple industries; even small improvements in efficiency in these industrial processes can yield large energy savings and reduce the carbon footprint. Opportunities also exist for companies to conserve energy in data centers, which consume large amounts of energy to run and maintain computer systems, servers, and associated highperformance components. Throughout the industrial manufacturing process, energy is lost due to equipment inefficiency and mechanical and thermal limitations. Optimizing the efficiency of these systems can result in significant energy and cost savings and reduced CO2 emissions. Business-as-usual (BAU) production trends and energy consumption trends through 2050 provides the basis for modeling to assess sectoral ECMs and their associated energy saving potential. This model consists of two phases, i.e., Phase 1: data collection and developing sector profiles with BAU energy consumption projections, and Phase 2: assessment of ECMs with barrier analysis and development of ECMs. This analysis may provide the industry with the potential barriers preventing the uptake of economically viable ECMs. To project and quantify the ECMs at a sectoral level, the industrial energy efficiency model (IEEM) was used. The Industrial Energy Efficiency Database (IEED) is a list of potential ECMs, both in terms of technical best practice (TBP) and management best practice (MBP). Each of these ECMs are applied in IEEM to generate the “technical potential” scenarios and the “economic potential” scenarios for each sector [40]. The technical potential scenarios estimate the level of energy consumption that would occur if all industrial processes are upgraded with ECMs that are technically feasible, regardless of any economic constraints. The economic potential scenario 1 estimates the level of energy consumption that would occur if all industrial processes are upgraded with ECMs that are economically feasible with a 2-year simple payback period, while the economic potential scenario 2 has a 5-year simple payback period at a predefined uptake rate and trend. The economic and technical potentials are projected up to 2050 (on a 5-year increment), based on the amount of savings achieved with reference to the sector-specific BAU projections [40].

5.2.5.1.3

Iron/steel

Provided EU steel makers remain competitive with sector specific ECMs implemented, production in this sector is assumed to increase through a continuous process of investments and restructuring, despite increases in energy costs (Table 5). Because of the limitations of emerging energy efficient technologies in steelmaking, energy intensity (process heating 75% and electrical energy 19% in 2013) for the sector is expected to improve only marginally for some time with increasing production trends.

5.2.5.1.4

Chemicals/pharmaceuticals

The chemicals/pharmaceuticals sector keeps expanding through 2050 and sales are projected to be nearly 60% greater than 2010 levels (Table 6). As a result, the sector’s energy consumption (process heating 58% and electrical energy 30% in 2013) is expected to increase despite continuously improving its energy intensity trends due to the limited potential for further improvement. The chemicals industry continues to consume large quantities of petroleum feedstock [22].

5.2.5.1.5

Pulp/paper

The production in the pulp and paper sector expands gradually through 2050 due to continued growing demand for consumer goods packaging. Although energy- and raw materials-intensive, the sector has a good track record in the improvements of its

60

Energy Conservation

Economic and technical potentials with energy intensity trends and energy conserving measures for the iron/steel sector

Table 5 Year

Final million tonnes of oil equivalent (MTOE)

Share %

2013 2030 2050

50.8

18.6 22.1 23.5

Business-as-usual (BAU) (MTOE year–1)

Economic potential 1 (MTOE)

Economic potential 2 (MTOE)

Technical potential (MTOE)

67.5 72.8

2.9 (4.3%) 6.2 (5.8%)

3.1 (4.6%) 6.8 (7.1%)

16.3 (24%) 18.9 (26%)

Note: Energy intensity trend: drastic improvements between 1970 and 1980 due to the switch to blast furnace/basic oxygen furnace and electric arc furnace production route, replacing the less efficient open-hearth production route. Sector specific ECMs: state-of-the-art power plant, coke dry quenching, basic oxygen furnace waste heat and gas recovery, continuous casting, scrap preheating, sinter plant waste heat recovery, optimized sinter pellet ratio (iron ore), top gas recovery turbine, stove waste gas heat recovery, state-of-the-art power plant. Source: Reproduced from Chan Y, Kantamaneni R. Study on energy efficiency and energy saving potential in industry from possible policy mechanisms contract No. ENER/C3/2012439/S12.666002. Available from: https://ec.europa.eu/energy/; 2015.

Table 6 Economic and technical potentials with energy intensity trends and energy conserving measures for the chemicals/ pharmaceuticals sector Year

Final million tonnes of oil equivalent (MTOE)

Share %

2013 2030 2050

51.4

18.9 21.7 25.9

Business-as-usual (BAU) (MTOE year 1)

Economic potential 1 (MTOE)

Economic potential 2 (MTOE)

Technical potential (MTOE)

66.4 80.1

2.6 (4.0%) 6.4 (5.8%)

3.2 (4.9%) 7.4 (7.1%)

16.5 (25%) 17.8 (22%)

Note: Energy intensity trend: reduced approximately 4%/year between 1990 and 2000, while dropped to 1%/year afterwards. Sector specific ECMs: distillation columns operational optimization, distillation column improved controls, improved energy efficiency of existing distillation column with retrofit, improved distillation column design, optimized heating in distillation column and preheating feed, improved reactor design, improved catalysts, optimized heating in furnace (cracking) and preheating feed, CHP for electricity generation, process optimization and improved process design, waste heat recovery, advanced process operation, membranes and other pharmaceutical process developments, novel separation processes (emerging), improved naphtha cracking technologies (emerging), more efficient low grade waste heat recovery technologies (emerging), interplant, process integration, interplant process integration. Source: Reproduced from Chan Y, Kantamaneni R. Study on energy efficiency and energy saving potential in industry from possible policy mechanisms contract No. ENER/C3/2012439/S12.666002. Available from: https://ec.europa.eu/energy/; 2015.

Economic and technical potentials with energy intensity trends and energy conserving measures for the pulp/paper sector

Table 7 Year

Final million tonnes of oil equivalent (MTOE)

Share %

2013 2030 2050

34.2

12.6 12.2 10.6

Business-as-usual (BAU) (MTOE year 1)

Economic Potential 1 (MTOE)

Economic Potential 2 (MTOE)

Technical Potential (MTOE)

37.3 32.9

1.1 (2.9%) 1.9 (5.8%)

1.4 (3.8%) 2.3 (7.1%)

7.2 (19%) 5.5 (17%)

Note: Energy intensity trend: steadily reducing. Sector specific ECMs: thermomechanical pulping (TMP) refiner heat recovery, efficient TMP refiner and pretreatment, efficient screening of recovered fibers, paper process heat recovery and integration, paper drying section shoe press, efficient paper process refiners, energy efficient vacuum systems for dewatering, thermo compressors, combined heat and power (CHP), heat recovery for the biomass and sludge drying process, heat recovery from radial blowers used in vacuum systems. Source: Reproduced from Chan Y, Kantamaneni R. Study on energy efficiency and energy saving potential in industry from possible policy mechanisms contract No. ENER/C3/2012439/S12.666002. Available from: https://ec.europa.eu/energy/; 2015.

energy intensity. This improvement trend is expected to continue as a growing number of integrated mills will further improve the sector’s energy intensity. As such, the energy consumption (process heating 59% and electrical energy 31% in 2013) is projected to decrease despite a gradual increase in production rates (Table 7).

5.2.5.1.6

Nonmetallic minerals

The nonmetallic minerals sector requires high capital costs and overall production will remain relatively flat through 2050 (Table 8). The ability to upgrade and improve energy efficiency is limited. Overall energy intensity for the sector will remain flat, resulting in a gradual decline in energy consumption trend (process heating 74% and electrical energy 17% in 2013).

5.2.5.1.7

Nonferrous metals

Production in the nonferrous metals sector will remain stagnant with no new EU investment in production capacity and the corresponding expansion of production capacity outside the EU (Table 9). Aluminum and copper production technologies have not gone through any drastic improvement in energy intensity. However, mainly because of recycling the used metals, the energy

Energy Conservation

61

Economic and technical potentials with energy intensity trends and energy conserving measures for the nonmetallic minerals sector

Table 8 Year

Final million tonnes of oil equivalent (MTOE)

Share %

2013 2030 2050

34.2

12.6 12.1 11.6

Business-as-usual (BAU) (MTOE year 1)

Economic potential 1 (MTOE)

Economic potential 2 (MTOE)

Technical potential (MTOE)

36.9 36.1

1.2 (3.3%) 2.4 (6.6%)

1.3 (3.6%) 2.6 (7.2%)

7.1 (19%) 6.3 (18%)

Note: Energy intensity trend: minimal improvements. Sector specific ECMs: replacement of furnace/kiln/dryer with optimized design, retrofit of furnace/kiln/dryer to improve design (wet to semidry process), increasing number of preheater stages in rotary kilns, recovery of excess heat from kilns cooling zone for increased preheating (other than rotary kilns), conversion to reciprocating grate cooler for clinker making in rotary kilns, using high efficiency equipment for grinding and other electrical uses, fuel substitution for more efficient thermal energy consumption, low temperature heat recovery for power generation, use of increasing levels of cullet for glassmaking, improved materials (substitutes) and product design for more efficient manufacturing, advanced oxyfuel combustion technologies (emerging), smart design and clustering of manufacturing facilities. Source: Reproduced from Chan Y, Kantamaneni R. Study on energy efficiency and energy saving potential in industry from possible policy mechanisms contract No. ENER/C3/2012439/S12.666002. Available from: https://ec.europa.eu/energy/; 2015.

Economic and technical potentials with energy intensity trends and energy conserving measures for the nonferrous metals sector

Table 9 Year

Final million tonnes of oil equivalent (MTOE)

Share %

2013 2030 2050

9.3

3.4 2.8 2.5

Business-as-usual (BAU) (MTOE year 1)

Economic potential 1 (MTOE)

Economic potential 2 (MTOE)

Technical potential (MTOE)

8.6 7.8

0.5 (5.5%) 0.9 (12%)

0.5 (3.8%) 1.0 (12.7%)

1.9 (22%) 1.6 (21%)

Note: Energy intensity trend: gradual reduction mainly due to higher aluminum recycling. Sector specific ECMs: optimized heating operating practices, waste heat recovery for preheating (combustion air and charge material, waste heat boiler for power generation, low temperature waste heat recovery, oxygen enrichment of combustion air, recovery and combustion of carbon monoxide, separate drying of concentrates, selection of optimal furnace design, improvements to alumina production from bauxite, prevention and minimization of salt slag, use clean scrap, increased recycling, inert anode technology (emerging)). Source: Reproduced from Chan Y, Kantamaneni R. Study on energy efficiency and energy saving potential in industry from possible policy mechanisms contract No. ENER/C3/2012439/S12.666002. Available from: https://ec.europa.eu/energy/; 2015.

Economic and technical potentials with energy intensity trends and energy conserving measures for the petroleum refineries sector

Table 10 Year

Final million tonnes of oil equivalent (MTOE)

Share %

2013 2030 2050

44.6

16.4 13.9 11.8

Business-as-usual (BAU) (MTOE year 1)

Economic potential 1 (MTOE)

Economic potential 2 (MTOE)

Technical potential (MTOE)

42.5 36.7

1.7 (4.0%) 3.1 (8.5%)

1.9 (4.5%) 3.5 (9.5%)

10.6 (25%) 8.3 (8.3%)

Note: Energy intensity trend: gradual improvements despite the decline in market. Sector specific ECMs: distillation columns operational optimization, improved EE of existing distillation column with retrofit, advanced distillation column designs, heat integration and waste heat recovery, combined heat and power (CHP) for electricity generation, integrated gasification combined cycle, power recovery using backpressure turbogenerator, advanced (predictive) process and maintenance control systems, interplant process integration, cogeneration using gas turbine exhaust gas as combustion air for heating furnace, progressive crude distillation, fouling mitigation in the crude distillation preheat train and fired heater, catalytic reforming: replace horizontal feed/effluent heat exchangers with vertical plate and frame exchanger, more efficient low grade waste heat recovery technologies (emerging), improved water treatment system operation and design, improved catalysts (emerging), novel hydrogen production technologies (emerging), novel desulfurization technologies (emerging). Source: Reproduced from Chan Y, Kantamaneni R. Study on energy efficiency and energy saving potential in industry from possible policy mechanisms contract No. ENER/C3/2012439/S12.666002. Available from: https://ec.europa.eu/energy/; 2015.

intensity keeps improving and the energy consumption (process heating 32% and electrical energy 57% in 2013) for the sector is expected to remain rather flat through 2050.

5.2.5.1.8

Petroleum refineries

Overall, production in the petroleum refineries sector is assumed to decline by 23% through 2050 due to a net decline in coke production, an increase in cheap imports (Table 10). Between 1992 and 2010, EU refiners have increased energy efficiency by 10%; however, the energy intensity (process heating 84% and electrical energy 7% in 2013) will increase slightly through 2030, as more energy-intensive processing is required to satisfy the increasing demand for lighter and lower sulfur products.

5.2.5.1.9

Food/beverage

The food and beverage industry is traditionally strong in the EU with growing consumer demand through 2050 with product innovation, resulting in continuous improved productivity. It is expected that the sector will continue its strong energy intensity

62

Energy Conservation

improvement trend through 2050, resulting in declining energy consumption (process heating 62%, electrical energy 34% and cooling 10% in 2013) even as production continues to grow (Table 11).

5.2.5.1.10

Machinery

Compared to other manufacturing sectors, the machinery sector is less energy intensive and energy consumption is linked to the manufacturing demands of the product specifications. Consequently, it is assumed that the sector will continue with its strong reduction trend in energy intensity (process heating 40% and electrical energy 53% in 2013) over the longer term, resulting in a relatively flat energy consumption as production continues to increase (Table 12).

5.2.5.1.11

Some possible energy conserving measures in industrial sector

There are a good range of economically viable ECMs to be implemented in the industrial sectors, which will help improving its energy intensity over a long period of time mainly because of overall market conditions, growth, and competitiveness. Most sectors have recorded increasing growth trends, apart from petroleum refineries and nonmetallic mineral sector groups. Table 13 presents generic improvement for ECMs for various energy intensive processes used within the industrial sectors evaluated over 230 ECMs and screened each ECM for economic viability based on a simple payback period [40]. Economic Potential Scenario 1 projects the energy consumption trend assuming the sector will implement applicable ECMs that satisfy a 2-year simple payback criteria, while Economic Potential Scenario 2 has a 5-year simple payback criteria, based on a predefined uptake rate and trend. The technical potential scenario displays the maximum energy saving potential that is technically feasible, regardless of the economic constraints on implementing these measures. The average industrial energy saving technical potential is approximately 20–23% of final energy consumption. This potential is based upon an immediate application of current ECMs available to the respective sector groups regardless of its economic viability. This reflects a 10–15% higher reduction potential than the economic potential scenarios. Further industrial sector energy reduction potential requires innovative technologies and research and development efforts. Market competitiveness is the strongest driver for energy efficiency solutions [40–42]. Table 14 provides a summary of the economical ECMs satisfying the 2- and 5-year simple payback period criteria along with its projected energy saving impact across the sector groups [40]. Energy saving technologies, such as use of high efficiency motors (HEMs), variable speed drives (VSDs), economizers, leak prevention, and reducing pressure drop are relatively very effective measures. Based on energy saving technologies, it has been found that a sizeable amount of electric energy, emissions, and utility bills can be saved by using these technologies in the industrial sectors. Payback periods for different energy savings measures have been identified and found to be economically viable in most cases [41,42]. Table 11

Economic and technical potentials with energy intensity trends and energy conserving measures for the food/beverage sector

Year

Final million tonnes of oil equivalent (MTOE)

Share %

2013 2030 2050

28.3

10.4 8.6 7.6

Business-as-usual (BAU) (MTOE year 1)

Economic potential 1 (MTOE)

Economic potential 2 (MTOE)

Technical potential (MTOE)

26.4 23.5

1.4 (5.2%) 2.4 (10.1%)

1.7 (6.5%) 3.2 (13.5%)

6.8 (26%) 5.7 (24%)

Note: Energy intensity trend: only gradual improvements. Sector specific ECMs: increased combined heat and power (CHP), adsorption chillers and trigeneration to meet cooling requirements, fuel switching, substitution of waste gases, optimized facility operating procedures, optimization of operating practices for cooking and baking, optimization of operating practices for distillation, drying and evaporation, optimization of operating practices for refrigeration, improved mechanical equipment efficiency, improved cleaning, washing, and sterilizing equipment efficiency, contact dryer for improved drying efficiency. Source: Reproduced from Chan Y, Kantamaneni R. Study on energy efficiency and energy saving potential in industry from possible policy mechanisms contract No. ENER/C3/2012439/S12.666002. Available from: https://ec.europa.eu/energy/; 2015.

Table 12

Economic and technical potentials with energy intensity trends and energy conserving measures for the machinery sector

Year

Final kilo tonne of oil equivalent (kTOE)

Share %

2013 2030 2050

19.2

7.1 6.4 6.1

Business-as-usual (BAU) (million tonnes of oil equivalent (MTOE) year 1)

Economic potential 1 (MTOE)

Economic potential 2 (MTOE)

Technical potential (MTOE)

19.8 19.0

1.0 (5.2%) 2.0 (10.5%)

1.3 (6.5%) 2.5 (13.3%)

5.3 (27%) 4.8 (25%)

Note: Energy intensity trend: inconclusive due to lack of data. Sector specific ECMs: high efficiency grinding (gw) for mechanical pulp, enzymatic pretreatment for tmp refiner, black liquor gasification, high efficiency process equipment (electrical), implement lean manufacturing system, optimized process redesign, optimized techniques for efficient equipment operation, high efficiency process equipment (thermal). Source: Reproduced from Chan Y, Kantamaneni R. Study on energy efficiency and energy saving potential in industry from possible policy mechanisms contract No. ENER/C3/2012-439/S12.666002. Available from: https://ec.europa.eu/energy/; 2015.

Energy Conservation

Table 13

63

Possible energy conserving measures

End use

Energy efficiency improvement opportunity description

Chillers/compressors

High efficiency chiller, optimized distribution system, floating head pressure controls, premium efficiency refrigeration control system, optimized chilled water temperature and/or optimized condenser temperature and pressure, preventative refrigeration/cooling system maintenance, variable speed drives (VSD) on chiller compressor Premium efficiency for adjustable speed drive (ASD) compressor, optimized distribution system, minimize operating air pressure, optimized sizing of compressor system, optimized sizes of air receiver tanks, compressor heat recovery, premium efficiency air dryer (compressors), replace compressed air use with mechanical or electrical, sequencing control, eliminate air leaks, preventative compressor maintenance, synchronous belts for air compressors, premium efficiency ASD compressor, compression ratio optimization (gas compressor), retrofit internal parts of existing centrifugal compressors Gas compressor right sizing, volume pocket adjustments, minimum cylinder clearance, compressor heat recovery, preventative compressor maintenance, synchronous belts for air or gas compressors Boiler right sizing, boiler load management, advanced boiler controls, high efficiency burner, economizer, process heat recovery to preheat makeup water, boiler combustion air preheat, blowdown heat recovery, automated blowdown control, condensate return, steam trap survey and repair, minimize deaerator vent losses, boiler water treatment, insulation, preventative boiler maintenance, steam trap survey and repair, condensate recovery, efficient boiler system, flue gas monitoring, heat exchanger maintenance and optimization Dryer: high efficiency burner, air curtains, exhaust gas heat recovery, insulation, advanced heating and process control, combustion optimization, preventative maintenance, furnace: maintenance, generic energy efficiency improvement opportunity description, high efficiency burner, exhaust gas heat recovery, insulation, advanced heating and process control, combustion optimization, preventative kiln maintenance, high efficiency burner (kiln), exhaust gas heat recovery (kiln), insulation (kiln), advanced heating and process control (kiln), combustion optimization (kiln), high efficiency burner (oven), air curtains (oven), exhaust gas heat recovery (oven), insulation (oven), advanced heating and process control (oven), combustion optimization (oven), preventative oven maintenance, flue gas monitoring (dryer), flue gas monitoring (furnace), flue gas monitoring (kiln), flue gas monitoring (oven) High/premium efficiency motors (pumps), impeller trimming (pump), optimization of pumping system, premium efficiency control with ASD (pumps), preventative pump maintenance preventative pump maintenance High/premium efficiency motors (fans), impeller trimming or inlet guide vanes, optimized duct design to improve efficiency, premium efficiency control, with ASD (fans), synchronous belts (fans), preventative fan maintenance Premium efficiency control with ASD motors High/premium efficiency motors, correctly sized motors, optimized motor control synchronous belts, preventative motor maintenance Optimization, premium efficiency and demand controlled ventilation control with variable speed drives (VSD) Seasonal temperature settings adjustments, ventilation heat recovery, automated temperature control, reduced temperature settings, destratification fans, warehouse loading dock seals, air curtains, preventative packaged HVAC maintenance High efficiency light fixtures, efficient lighting design, lighting controls: on/off timer settings, lighting controls: occupancy sensors, lighting control according to zones Submetering and interval metering, integrated control system, heat exchanger dry-type transformers, power factor correction, electricity demand management control system, process integration and pinch analysis, implementation of management best practices (MBPs) to support energy efficiency

Compressor for pneumatic systems

Compressor for process air or gas system Indirect heating (boilers)

Direct heating (ovens/kilns/dryers/ furnaces)

Pumps

Fans/blowers

Motors/other Machine drive Ventilation Packaged heating, ventilation and air conditioning (HVAC) Lighting System

Source: Reproduced from Chan Y, Kantamaneni R. Study on energy efficiency and energy saving potential in industry from possible policy mechanisms contract No. ENER/C3/2012439/S12.666002. Available from: https://ec.europa.eu/energy/; 2015.

5.2.5.1.12

Improvements in power plants

An increase in thermal efficiency may lead to using less fuel and minimize adverse environmental effects. Exergy analysis for a coalbased power and methanol system shows that polygeneration system may save 3.9% and 8.2% energy compared to the individual processes [44]. A conceptual trigeneration system proposes a the conventional gas turbine cycle for the high temperature heat addition while adopting the HRSG for process heat and vapor absorption refrigeration for the cold production. That exergy loss in a combustion chamber is significantly affected by the pressure ratio and turbine inlet temperature. Trigeneration with gas turbines can improve energy utilization. Introducing air refrigeration cycle for inlet air cooling provides considerable improvement in the performance. Inlet air cooling increases the energy and exergy efficiencies [44]. Various designs of power production cycles, reheat, regenerative, cogeneration, as well as combined cycles are often analyzed by exergy analysis for identifying the ECMs [30]. Such improved designs can meet the growing energy demand, and hence need to be optimized by identifying the irreversibility at each component. Energy and exergy analyses for a combined-cycle power plant by

64

Table 14

Energy Conservation

Summary of possible economic energy conserving measures across the sector groups

Energy conserving measures

Total energy saving potential by 2030 (%)

ECMs witho2 year payback period: Integrated control system Submetering and interval metering Flue gas monitoring (furnace and boiler) High efficiency burner (furnace) Exhaust gas heat recovery (furnace and kiln) Implementation of energy management systems Advanced heating and process control (furnace) Combustion optimization (furnace) Steam trap survey and repair Preventative furnace maintenance

17.3 13.8 8.3 8.1 5.0 4.9 4.6 3.8 1.9 1.6

ECMs with 2–5 year payback period: Premium efficiency controls with automatic speed drives (pumps, fans, and other motors) High efficiency nonpackaged HVAC equipment Advanced boiler control Process heat recovery to preheat makeup water Optimization of pumping system Use of radiant heat instead of convection heating Sequencing control Variable speed drives (VSD) for chiller compressor

5.7 1.3 0.9 0.9 0.6 0.3 0.2 0.1

Source: Reproduced from Chan Y, Kantamaneni R. Study on energy efficiency and energy saving potential in industry from possible policy mechanisms contract No. ENER/C3/2012439/S12.666002. Available from: https://ec.europa.eu/energy/; 2015.

Table 15 Process

Distribution of exergy losses in actual and ideal reheat Rankine cycles Actual reheat Rankine Exloss, kJ kg

2–3 3–4 4–5 5–6 6–1 Cycle

1

1,212.54 19.57 48.41 238.50 154.10 1,673.55

Ideal reheat Rankine %

Exloss, kJ kg

72.4 1.2 2.9 14.3 9.2

1,220.89 0 59.00 0 137.31 1,417.20

1

% 86.1 0 4.2 0 9.7 18

Source: Reproduced from Demirel Y. Nonequilibrium thermodynamics transport and rate processes in physical, chemical and biological systems. 3rd ed. Amsterdam: Elsevier; 2014.

using the data taken from its subunits can be used to analyze a complex energy system more thoroughly and to identify the potential for improving efficiency of the system. Table 15 compares the exergy losses of ideal and actual reheat Rankine cycle operations. The total exergy loss increases around 18% in the actual operation [30]. Thermodynamic analyses of Otto cycle (spark-ignition engines) with various gas mixtures as working fluids with variable temperature specific heats shows that the exergy loss during the combustion process decreases as the engine load or engine speed increases. Exergy analysis is also helping to understand the performance of hybrid power systems, such as gas turbine cycle with steam generation for methane conversion within solid oxide fuel cells [30]. In a Brayton cycle operating as gas-turbine engine, the temperature of the exhaust gas leaving the turbine T4 is often higher than the temperature of the gas leaving the compressor T2 as seen in Fig. 5. Therefore, the gas leaving the compressor can be heated in a regenerator by the hot exhaust gases. The regenerator is a counterflow heat exchanger to recover the waste heat, which is in some cases also known as a recuperator. The thermal efficiency of the Brayton cycle increases as a result of regeneration because the portion of energy of the exhaust gases is used to preheat the gas entering the combustion chamber. Thus, in turn, regeneration can reduce the fuel input required for the same network output from the cycle. The addition of a regenerator (operating without thermal losses) does not affect the network output of the cycle. A regenerator with higher effectiveness will conserve more fuel. The effectiveness e of the regenerator operating under adiabatic conditions is defined by

e¼



H5 H4

H2 H2



ð25Þ

65

Energy Conservation

Fuel

3

T

2

qin

Regenerator

5

Combustion 3

Wturb.out

2

W

4

Ws Compressor

Wcomp.in

Turbine

qout

1

4

1

(A)

(B)

S

Fresh air

Exhaust gasses

1′

1

qC

3 4 4′

(A)

2

qH

Temperature (T)

Temperature (T)

Fig. 5 (A) Simple Brayton cycle and (B) Brayton cycle with regeneration; the condition for regeneration is T44T2.

3′

x3 x3′

Entorpy (S)

qH

2

2′

1 qC

4

3 3′

Entorpy (S)

(B)

Fig. 6 Possible improvements in energy conversion efficiency in Rankine cycles: (A) decreasing the condenser pressure and (B) increasing the boiler temperature.

The regeneration is possible only when T4cT2. The effectiveness of most regenerators used in practical engine operations is below 0.85. The thermal efficiency is

Zth: regen: ¼ 1

  T1 ðrp Þðg T3

1Þ=g

ð26Þ

where rp is the compression ratio (P2/P1) and g ¼ Cp =Cv . Some of the possible modifications in operation of steam power plants for ECMs are [1,30]:

•

•

•

Increasing the efficiency of a Rankine cycle by reducing the condenser pressure. A considerable thermal efficiency increase is possible by reducing the condenser pressure. Fig. 6(A) shows the increased area representing power output after reducing the condenser pressure. However the quality of the discharged steam decreases, which is not desirable for the blades of the turbine. Increasing the efficiency of a Rankine cycle by increasing the boiler temperature. The thermal efficiency can be increased by increasing the boiler temperature (see Fig. 6(B)). The quality of the discharged steam also increases, which is desirable for the protection of the turbine blades. The thermal efficiency of a Carnot cycle operating between the temperature limits of Tmin and Tmax maximum efficiency is Zth,Carnot ¼ 1 (Tmin/Tmax). Increasing the efficiency of a Rankine cycle by increasing the boiler pressure. The thermal efficiency can be increased considerably by increasing the boiler pressure [30].

5.2.5.1.13

Energy conservation in the compression work

It is possible to conserve energy in the compression work by minimizing the friction, turbulence, heat transfer, and other losses. A practical way of energy conservation is to keep the specific volume of the gas small during the compression work. This is possible by maintaining the temperature of the inlet gas flow as the specific volume is proportional to temperature. Therefore, cooling the gas as it is compressed may reduce the cost of compression work in a multistage compression with intercooling as seen in Fig. 7. The gas is cooled to the initial temperature between the compression stages by passing the gas through a heat exchanger called the intercooler. Energy recovery by intercooling may be significant especially when a gas is to be compressed to very high pressure [1].

66

Energy Conservation

Work saved

P 2 P2

Px

Polytropic

Polytropic

Intercooling Isothermal P1

1

V Fig. 7 Energy conservation in the compression work by intercooling; the work saved appears between two polytropic compressions starting at the second stage with the pressure Px. Reproduced from Demirel Y. Energy: production, conversion, storage, conservation, and coupling. 2nd ed. London: Springer; 2016.

Cryogenic manufacturing plant data

Table 16 T, K

P, kPa

H, kJ kg

110 110 110 120 120 120

1000 2000 5000 1000 2000 5000

209.0 210.5 215.0 244.1 245.4 249.6

1

S, kJ kg 4.875 4.867 4.844 5.180 5.171 5.145

1

K

1

Cp, kJ kg

1

1

K

3.471 3.460 3.432 3.543 3.528 3.486

r, kg m

3

425.8 426.6 429.1 411.0 412.0 415.2

Source: Reproduced from Alhajji M, Demirel Y. Energy intensity and environmental impact metrics of the back-end separation of ethylene plant by thermodynamic analysis. Int J Energy Environ Eng 2016;7:45–59.

Fig. 7 shows the work saved varies with the value of intermediate pressure Px, which needs to be optimized. The total work input for a two-stage compression process is the sum of the work inputs for each stage, and estimated by "  # "  # gRT1 Px ðg 1Þ=g gRT1 P2 ðg 1Þ=g þ ð27Þ Wcomp ¼ MW ðg 1Þ P1 MW ðg 1Þ Px where g ¼ Cp/Cv, R is the gas constant, MW is the molecular weight. In Eq. (27), Px is the only variable. The optimum value of Px is obtained by differentiation of Eq. (27) with respect to Px and setting the resulting expression equal to zero. Then, the optimum value of Px becomes: Px ¼(P1P2)1/2. Therefore, energy conservation will be maximum, when the pressure ratio across each stage of the compressor is the same and compression work at each stage becomes identical Wcomp1 ¼ Wcomp2 "  # 2gRT1 Px ðg 1Þ=g Wcomp ¼ ð28Þ MW ðg 1Þ P1

5.2.5.1.14

Energy conservation in expansion by replacing a throttle valve with a turbine

Replacing the throttling valves with a turbine produces power and hence conserves electricity [45]. Consider a cryogenic manufacturing plant that handles liquid methane at 115K and 5000 kPa at a rate of 0.3 m3 s 1. In the plant a throttling valve reduces the pressure of liquid methane to 1000 kPa. A new process replaces the throttling valve with a turbine. Using the data in Table 16 for the properties of liquid methane we can estimate the power produced and the savings in electricity usage per year if the turbine operates 360 days per year with a unit cost of electricity at $0.1 kWh 1. Assumptions: steady state and reversible operation; adiabatic turbine, methane is ideal gas; kinetic and potential energies are negligible. P1 ¼ 5000 kPa;

T1 ¼ 115K;

Q1 ¼ 0:30 m3 s 1 ;

H1 ¼ 232:3 kJ kg

1

r1 ¼ 422:15 kg m 3 ;

;

P2 ¼ 1000 kPa; T1 ¼ 110K; H2 ¼ 209:0 kJ kg

1

Energy Conservation Unit cost of electricity ¼ 0:09 kWh 1 ; Mass flow rate : 422:15 kg m 3 ð0:3 m3 s 1 Þ ¼ 126:6 kg s _ out ¼ m _ ðH1 Power produced : W

5.2.5.1.15

H2 Þ ¼ 126:6 kg s 1 ð232:5

209:0ÞkJ kg

1

67

1

¼ 2949:8 kW

_ out Dt ¼ ð2949:8 kWÞð360Þð24Þh year 1 ¼ 25;486;099 kWh year Annual power production : W   Saving in electricity usage : 25;486;099 kWh year 1 0:09 kWh 1 Þ ¼ 2;548;099 year 1

1

Energy conservation by using high-efficiency electric motors

Practically all compressors are powered by electric motors, which cannot convert the electrical energy into mechanical energy completely. Electric motor efficiency is defined by _ comp W ZMotor ¼ ð29Þ _ elect W Motor efficiency varies between 70 and 96% [1]. The portion of electric energy that is not converted to mechanical power is converted to heat, which is mostly unusable. For example, assuming that no transmission losses occur:

• • •

A motor with an efficiency of 80% will draw an electrical power of 1/0.8 ¼1.25 kW for each killowatt of shaft power it delivers. If the motor is 95% efficient, it will draw 1/0.95¼ 1.05 kW only to deliver 1 kW of shaft work. Therefore between these two motors, electric power conservation is: 1:25ðhmotor ¼ 95%ÞkW21:05ðhmotor ¼ 95%Þ ¼ 0:20 kW: HEMs are more expensive but they save energy, which is estimated by [1].  _ elect:saved ¼ ðRated powerÞ ðLoad factorÞ 1 W Zstd

1 Zefficient



ð30Þ

where the rated power is the nominal power delivered at full load of the motor and listed on its label. Load factor is the fraction of _ elect:saved Þ (annual the rated power at which the motor normally operates. Annual saving is estimated by annual energy saving ¼ ðW operation hours). A compressor that operates at partial load causes the motor to operate less efficiently. The efficiency of the motor will increase with the load. Using the cold air for compressor intake lowers the compressor work and conserves energy.

5.2.5.1.16

Energy saving opportunities in industrial sector

In order to increase shareholder value and reduce expenses, the industrial sector has found energy efficiency investments to be an attractive avenue. As climate change awareness and mitigation strategies increase, it is likely that industry will increasingly prioritize energy efficiency as a critical solution to reducing GHG emissions (ACEEE). Workforce has emerged as a vital issue in the successful implementation of energy efficiency throughout all sectors. Financial incentives are also an important instrument for spurring investment in energy efficient technologies and services. The industrial sector engages in a variety of practices to conserve energy [46]; for example, energy assessment can determine where the energy efficiency opportunities exist in a plant. In addition, the DOE’s IACs and the US Department of Commerce’s National Institute of Standards and Technology’s Manufacturing Extension Partnerships program offer energy efficiency solutions and perform research into new ways to manufacture existing products using emerging and new advanced technologies for small to medium industrial firms [46]. The International Organization for Standardization (ISO) offers a voluntary standard that provides a framework for managing and improving energy performance. The industrial sector can implement the standard, which is called ISO 50001 [47]. A variety of best practices and equipment can help industrial plants conserve energy, such as CHP systems, which produce electricity from recovered heat that is given off in various processes, VSDs, and advanced sensors and controls [46]. The industrial sector can utilize for low-cost energy savings tools from utility energy efficiency programs [43]: 1. 2. 3. 4.

Industrial efficiency programs can achieve large energy savings at low cost. The dollars and cents of industrial efficiency program investment. Myths and facts about industrial opt-out provisions. Overview of large-customer self-direct options for energy efficiency programs.

5.2.5.1.17

Some barriers to energy conserving

Barriers to uptake of ECMs are not well understood. The barriers to energy efficiency and ECMs too often focus on a perspective external to the enterprise and create a situation where internal and external perspectives diverge (Table 17). Internal perspective may consist of many underevaluated behavioral elements, which can lead to irrational choices from an external perspective. By overlaying the energy saving potentials with the associated economic, organizational, and technical barriers, the following potential intervening measures could address these barriers and further encourage ECMs uptake [40]: 1. Improving energy efficiency through changes in how energy is managed, instead of replacing equipment. 2. Mandatory implementation of energy management systems for large energy intensive companies.

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Energy Conservation

Table 17

Some barriers to energy efficiency and energy saving opportunities

Origin

Area

Barriers

External

Market

Energy price distortion, low diffusion of information and technologies, market risks, difficulty in gathering external skills Lack of proper regulation, distortion in fiscal policies Lack of interest in energy efficiency, technology suppliers not updated, scarce communication skills Technical characteristics not adequate, high initial costs Scarce communication skills, distortion in energy policies, lack of interest in energy efficiency Cost for investing capital availability, difficulty in identifying the quality of the investments Low capital availability, hidden costs, intervention related risks Lack of interest in energy efficiency, other priorities, inertia, imperfect evaluation criteria, lack of sharing the objectives, low status of energy efficiency, divergent interests, complex decision chain, lack of time, lack of internal control Identifying the inefficiencies, implementing the interventions, Lack of awareness or ignorance

Internal

Government/politics Technology services suppliers Designers/manufacturers Energy suppliers Capital suppliers Economic Organizational behavior

Competences Awareness

Source: Reproduced from Alhajji M, Demirel Y. Energy intensity and environmental impact metrics of the back-end separation of ethylene plant by thermodynamic analysis. Int J Energy Environ Eng 2016;7:45–59.

3. 4. 5. 6.

Mandatory submetering requirement for significant energy consuming equipment. Promoting the need for energy managers within large energy intensive companies. Facilitating development of insurance products for energy savings guarantee. Promoting and facilitating further potential for resource sharing among industrial companies.

5.2.5.2

Agricultural Sector

According to the ACEEE [43], an extremely conservative estimate of potential energy savings in the agricultural sector is $1 billion per year. The largest savings are available in the motor system (especially irrigation pumping), onsite transportation, and lighting energy. These potential savings add to 10% of total energy use for the agricultural sector nationwide [48].

5.2.5.2.1

Farm vehicles

The biggest opportunities for energy savings from farm vehicles can be found in tillage systems and tractor fuel efficiency. Proper tire inflation, regular vehicle maintenance, and reduced idling can increase tractor fuel efficiency as well as extend the life of the tractor. Farmers can also use overlap reduction systems such as auto-steer, obstacle isolation, and proper equipment sizing to gain significant reductions in fuel use and equipment wear.

5.2.5.2.2

Dairy operations

The dairies rely heavily on electrical energy for milking (vacuum pumps), cooling and storing milk, heating water, and lighting. When energy costs are high and dairy prices are flat, energy costs must be reduced with ECMs that include energy-efficient lighting, ventilation, milk-cooling, water-heating, and vacuum pump motors (e.g., VSDs).

5.2.5.2.3

Fertilizer use

About a third of all energy used in US agriculture goes to commercial fertilizer and pesticide production. Successful strategies include the use of cover crops and manures, nitrogen-fixing crops in rotations, composting, and integrated pest management (IPM). In addition, precision farming can reduce overlap of fertilizer applications.

5.2.5.2.4

Greenhouse heating with long-term solar energy storage

Typical annual greenhouse energy usage is 75% for heating, 15% for electricity, and 10% for vehicles, and some possible ECMs include extremely efficient heating, cooling, and watering systems [43,48]:

• • • • •

Reduce air leaks by using door closers, weather stripping (doors, vents, fan openings). Double covering on sidewalls and end walls. Poly with an infrared inhibitor on the inner layer can give 15% energy savings. Thermal blankets can achieve 20–50% energy savings. Foundation and sidewall insulation can result in considerable energy savings.

A seasonal solar energy storage system retains heat deposited during the hot summer months for use during colder winter weather for heating. The heat is typically captured using solar collectors [49]. Fig. 8 shows the schematics of the seasonal solar energy storage systems by paraffin for greenhouse heating with a data acquisition and control system [50–52]. The three common processes in a seasonal solar energy storage for greenhouse heating are (1) charging is for capturing solar energy by solar air heaters and feeding the warm air to the latent heat storage unit through the coils embedded within the heat storage tank, (2) storing the

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Energy Conservation

Air in

Flat plate solar air collectors

Charging: heat storage

Data acquisition unit

Solar energy Heat loss

Heat storage unit

Discharging: heat recovery

Air

Solar air heaters

Heat loss Heat storage

1

2

Charge process

Greenhouse 3

Discharge process

Greenhouse (A)

Solar energy Heat loss

(B)

Fig. 8 (A) Seasonal heat storage system using paraffin as a phase change material (PCM) for heating a greenhouse (Reproduced from U.S. DOE. Energy efficiency and renewable energy (EERE). Available from: http://energy.gov/eere/; 2016 [accessed 15.07.16]); (B) charging and discharging operations within the three units of Unit 1: solar air heaters, Unit 2: heat storage, and Unit 3: greenhouse (Reproduced from Demirel Y, Ozturk HH. Thermoeconomics of seasonal heat storage system. Int J Energy Res 2006;30:1001–2). Table 18 Comparison of typical storage densities of various materials for energy storage in the forms of sensible and latent heats 1

Temperature, oC

Method/material

kJ kg

Sensible heat Granite Water

17 84

DT¼20 DT¼20

Latent heat of melting Water Lauric acid Capric acid Butyl stearate Paraffin Paraffin C18 Salthydrate Salt

330 178 153 140 200 244 200 300–700

0 42–44 32 19 5–130 28 5–130 300–800

Source: Reproduced from Kenisarin M, Mahkamov, K, Solar energy storage using phase change materials. Renewable Sustain Energy Rev 2007;11:1913–65; Mehling E, Cabeza L.F. Phase changing materials and their basic properties. In: Paksoy HO, editor. Thermal energy storage for sustainable energy consumption, NATO science series, Dordrecht: Springer; 2007.

captured solar energy by sensible and latent heats using a phase change material (PCM) in a well-insulated heat storage tank, and (3) discharging the stored heat by the cold air flowing through the heat storage tank and delivering the warm air to the greenhouse directly when necessary when the temperature in the greenhouse drops below a set value. Some of the major benefits of heating agricultural buildings by the stored energy are:

• • • •

Reduced energy consumptions, carbon footprint, and pollutants. Reduced initial equipment and maintenance costs. Increased flexibility of operation, efficiency, and effectiveness of equipment utilization. Adjust the temperature and the amount of energy if necessary.

Solar collector systems that use air as the transfer medium [49] are being recommended for space heating/cooling because of direct heat transfer and less potential for damage from a leak or frozen water. Total heat stored by a solid-to-liquid phase changing material between initial and final temperatures would be estimated by qstored ¼ Solid sensible heat þ Latent heat þ Liquid sensible heat qstored ¼ mCps; av ðTm

Ti Þ þ mDHm þ mCpl;av ðTf

Tm Þ

ðTf 4Tm 4Ti Þ

ð31Þ ð32Þ

where m is the mass of phase changing material, Cps,av and Cpl,av are the average heat capacities for solid and liquid phases, respectively, Tm is the temperature of melting, Ti and Tf are the initial and final temperatures, respectively, and DHm is the heat of melting. Various phase changing materials are available in any required temperature range from 5 up to 190oC storing 5 to 14 times more heat per unit volume than conventional storage materials such as water [53,54]. Table 18 presents a short list of some

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Energy Conservation

common materials for heat storage. The most commonly used PCMs are salt hydrates, fatty acids, esters, and various paraffins (such as octadecane). Example 8 illustrates the latent heat storage calculations. Example: a PCM of 600 kg octadecane is heated from 20 to 30oC by a solar air collector energy system, which supplies 15 kW. Assume that the octadecane is fully melted. Estimate the minimum size of the storage unit and the time necessary for the charging process. Solution: assume that there is no heat loss from the thermal energy storage system. Tsc ¼ 201C;

Tsh ¼ 301C;

Ts ¼ 281C;

ms ¼ 600:0 kg;

q_ net ¼ 15 kW;

q_ loss ¼ 0 kW

Use data (Demirel, 16) Phase change material (PCM)

Tm oC

∆Hm kJ kg 1

Cpl,av kJ kg 1 K 1

Cps,av kJ kg 1 K 1

kl W m K 1

Octadecane CH3(CH2)16CH3

28

243

2.2

1.8

0.15

 Total heat stored : qs ¼ ms Cps;av ðT s

1

ks W m K 1

1

0.42

T sc Þ þ DHm þ Cpl;av ðT sh

rl kg m

775

3

rs kg m

3

B900

 TsÞ

qs ¼ 600 kg½ð1:8 kJ=kg KÞð28220Þ1C þ 243 kJ=kg þ ð2:2 kJ=kg KÞð30228Þ1CŠ ¼ 157;080 kJ   Vtank ¼ m=rs ¼ 600 kg= 775 kg=m3 ¼ 0:8 m3

Energy balance: energy supplied ¼ energy stored þ energy lost Energy lost: 0 kW

Energy supplied ¼ ðq_ net ÞðDt Þ ¼ qs ¼ 157;080 kJ-Dt ¼ 10; 472 s ¼ 2:91 hr Contributions of sensible heats: qs ¼ 600 kg½ð1:8 kJ=kg KÞð28220Þ1C þ ð2:2 kJ=kg KÞð30228Þ1CŠ=15; 708 kJ qs ¼ ð8640 þ 2640Þ kJ=157;080 kJ ¼ 0:055 þ 0:017 Contributions of sensible heats are 5.5% for solid state and 1.7% for liquid state; therefore the main contribution toward heat storage comes from latent heat of octadecane. The charging process needs 2.92 h to supply the heat of 157,080 kJ required.

5.2.5.2.5

Energy self-assessment tools

The energy self-assessment tools include both energy conservation and renewable energy tools to help farmers identify ways to reduce their energy costs. The energy conservation modules determine if energy conservation equipment is being used and then estimate the current energy usage. The tools calculate the estimated energy and cost savings for the use of high efficiency equipment and energy conserving practices based on models and help to determine which equipment or practices are worth pursuing to conserve energy [55]. The renewable energy tools can estimate the energy production from solar photovoltaic panels, solar hot water panels, wind turbines, biogas from anaerobic digesters, and biomass (mainly second generation). Before investing in renewable energy production, it is usually more cost-effective to invest in energy conserving equipment and processes to reduce the energy demands and the investment cost of renewable energy technologies as much as possible. For example, if guidance systems were used on 10% of the planted acres in the United States, fuel use would be cut by 16 million gallons, herbicide use by 2 million quarts, and insecticide use by 4 million pounds per year [43,48]. The USDA NRCS’s Energy Estimator offers a variety of energy saving suggestions including nitrogen, tillage, irrigation, and animal housing [55]:

• • • •

Grain drying tool provides energy cost savings that a producer might expect from selecting specific in-bin or high-temperature drying systems based on user input. Prescribed grazing systems offer an effective way to reduce energy use, decrease costs, and improve animal health and productivity. Such systems improve the health and vigor of plants, enhance the quality and quantity of water, and reduce accelerated soil erosion and improve soil condition on the land. IPM techniques can reduce energy use and environmental risk while maintaining the quality of their agricultural products. IPM focuses on long-term prevention of pests through use of resistant varieties, biological control, habitat manipulation, and changing of cultural practices. Nitrogen tool enables farmers to calculate the potential cost savings of nitrogen product use on a farm based on user input. NRCS’s agronomists developed this model to integrate general technical information on nitrogen use with farm-specific information on fertilizer types, costs, timing, and placement. Nutrient management is a conservation practice that involves proper timing and placement of the right amounts of nutrients and soil amendments for adequate soil fertility and to minimize potential environmental degradation.

Energy Conservation

•

Irrigation systems are, usually, not as efficient as they should be; on average, about 25% of the electrical energy used for irrigation could be wasted due to poor pump and motor efficiency. Properly designed irrigation systems promote correct soil moisture levels and conserve energy and water: ○ ○ ○ ○

•

•

71

efficient irrigation pumps with variable speed pump motors and proper pump-sizing. frequent management/maintenance of irrigation systems. use efficient irrigation system, for example, from wheel lines to pivot or linear sprinkler systems. farmers can estimate energy savings associated with pumping water for irrigation by integrating general technical information farm-specific crops, energy prices, and pumping requirement.

Tillage reduction is a conservation practice that leaves the crop residue undisturbed from harvest through planting that leads to increased efficiency of irrigation and control of erosion. The energy estimator for tillage tool estimates diesel fuel use and costs in the production of key crops in your area and compares potential energy savings between conventional tillage and alternative tillage systems. NRCS agronomists have identified these crops and estimated the fuel use associated with common tillage systems. Animal housing tool estimates potential energy savings associated with swine, poultry or dairy cows housing operations on a farm. Confined animal operations require a great deal of energy for lighting, heating, ventilation, cooling, and pumping. High-efficiency motors (operating at least 2500 h per year) can reduce energy consumption by 3–8%. These tools evaluate major energy costs and provide the farmers with general recommendations [48,55]:

• • • • •

Producers with animal feeding operations can conserve energy by regularly maintaining their ventilation, heating, and using more energy-efficient equipment. Using low pressure irrigation systems could cut energy costs. Doubling the amount of no-till acreage (from 62 to 124 million acres) could save farmers and ranchers an additional 217 million gallons of diesel per year. Doubling the application of manure-based nitrogen to replace fertilizer produced from natural gas could save considerable energy. Reducing application overlap of cropland could save in fertilizer and pesticide costs.

5.2.5.3

Transportation Sector

World delivered energy consumption in the transportation sector increases at an annual average rate of 1.4%. Worldwide, liquid fuels remain the dominant source of transportation energy consumption, although their share of total transportation energy declines [22]. The transportation sector has low energy efficiency and very high heat loss, consuming approximately 28% of all end-use energy in the United States. While significant advances have been made recently to improve the overall efficiency of the sector, particularly with regards to fuel economy, the opportunity for further fuel savings still exists. Technical improvements in vehicles and reasonable government policies that encourage vehicle efficiency could substantially reduce energy consumption. Strategies such as pay-as-you-drive insurance and incentives to encourage compact vehicles may help achieve maximum fuel savings from transportation [56].

5.2.5.3.1

Alternative fuel vehicles

There are a variety of alternative and advanced technology vehicles such as flexible fuel vehicles, fuel cell vehicles, hybrid and plugin electric vehicles, natural gas vehicles, and propane vehicles. These vehicles can use batteries, internal combustion engines (ICEs), electric engines, and fuel cells [57].

• •

•

•

Hybrids provide the consumers with excellent fuel economy, run on gasoline, and drive just like regular cars. The share of hybrids may rise in parallel to the rising prices of energy; however, some hybrids cost much more than similar conventional cars, and gas mileage may be lower than that promised. Plug-in hybrids can be solutions for short trips where recharging infrastructure is available. Gas engines can extend range for long trips, cheaper cost per mile, and no vehicle emissions when running in electric heating and cooling sector mode. However, expensive batteries plus a gas engine drive up prices, daytime recharging could strain the electric grid, and they need to be plugged in to deliver any benefit. Gas-mileage benefits are highly dependent on driving habits and frequently overstated [58]. Battery electric vehicles provide customers with quiet running, instant torque from electric motor, no emissions from the car, and low cost per mile. Electricity needed can be partially or wholly derived from renewable sources. However, long recharging times, limited range, and expensive batteries are some of the disadvantages. Also much of the electricity production uses coal, which is not a clean-burning source. High-voltage home chargers can be expensive and public chargers need widespread electric infrastructure. Diesel vehicles can offer 30% better fuel economy than an equivalent gasoline vehicle and can run on a blend of renewable biodiesel fuel. With effort and investment, older diesel engines can be converted to run on biofuels. However, they have more engine noise and vibration.

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Energy Conservation

5.2.5.3.2

Alternative fuels

To power alternative fuel vehicles there are a number of fuels available, which include biodiesel, electricity, ethanol, hydrogen, natural gas, methanol, and propane.

• •

• •

•

Biodiesel leads to a promising blend, which is renewable and fairly widely available. On the other hand, quality of biodiesel varies widely and biodiesel costs more than petroleum diesel [59]. Bioethanol provides the consumers with low emissions, high octane, and can potentially be produced from waste materials. Existing cars can use 10% blends (E10), and more than 8 million cars already on the road can use 85% blends (E85). Therefore, bioethanol helps reduce demand for foreign oil. However, bioethanol provides the customers with 25% lower fuel economy on E85 than gasoline. Less than 1 percent of US gas stations carry E85 [1,14]. Expanding the use of bioethanol in higher percentage blends would lead to lesser energy content per gallon and hence purchasing decisions based on energy content or cheapest fuel. Compressed natural gas (CNG) costs much less than gasoline, burns much cleaner, and provides comparable power. However, huge gas storage tanks reduce trunk space and carry the equivalent of only a few gallons of gasoline. CNG provides limited range, and there are few places for consumers to refuel; refueling is also relatively slow [14,57]. Hydrogen fuel cells emit water vapor only with fuel economy equivalent to about twice that of gasoline vehicles. Hydrogen can be produced from renewable energy but currently hydrogen fuel is made from natural gas in a process that creates high levels of GHG emissions. Besides, the fuel cells are expensive and acceptable range requires extremely high pressure. The transport and storage of hydrogen are very expensive [14,60]. Methanol from coal-based syngas process has the highest emission of GHG, which is around 2.8–3.8 kg CO2/kg methanol. Typical energy efficiency for the coal-based methanol is in the range of 48–61%. Methanol synthesis from water, renewable electricity, and CO2 may lead to chemical storage of renewable energy, carbon recycle, fixation of carbon in chemical feedstock, as well as extended market potential for electrolysis. Methanol can be used as a fuel within the existing fuel storage and distribution network [61].

5.2.5.3.3

Vehicles technology and energy efficiency

Energy prices have a significant impact on the introduction and pace of the development of energy efficient vehicle technologies. Plug-in hybrids and battery electrics are the latest technologies along with the continuous energy efficiency improvements with direct injection, turbocharging, and variable valve timing, in the engine technology. Interest in natural gas vehicles is increasing due to the availability of domestic supplies of natural gas, supporting environmental and economic goals. Today “clean diesels” are produced, with superior fuel economy. Hybrid-electrics are now available in most market segments. ACEEE [43] assesses the potential for new vehicle efficiency technologies to meet environmental and economic goals. The high cost of advanced technology and fuel-efficient vehicles is a key barrier to their widespread use. Financial incentives, such as tax credits, have proven to be one of the more effective ways to encourage consumers to purchase these energy efficient vehicles [56]. ICEs emit varying amounts of water vapor, CO2, nitrogen, oxygen as well as pollutants such as carbon monoxide, nitrogen oxides, unburned hydrocarbons, volatile organic compounds, and particulate matter. Certain vehicle emissions can lead to the formation of ground-level ozone, which reduces air quality and causes respiratory problems. Freight trucks are responsible for almost 20% of all transportation-related fuel consumption and GHG emissions in the United States [43].

5.2.5.3.4

Identifying future transport pathways

A wide set of data is necessary to have accurate and energy efficient transport pathways (Table 19). Such data consist of annual transport fuel use from national energy balances, transport activity statistics, and vehicle registration data with detailed characteristics. Cross checking of vehicle fuel use with energy balances is possible with vehicle fuel efficiency, which is a key variable. First fuel use estimates are possible with little investment and the better the baseline, the easier to find key parameters to increase transport efficiency and to develop more effective policy [56,62]: Table 19

Efficient transport sector concepts

Avoid/reduce Reduce/avoid unnecessary travel

• • • •

Integrate transport and land usage Develop smart logistic concepts Parking policy Urban design

Select Environmentally friendly models

• • • • • •

Transport demand management Mode shift to motorized transport Mode shift to public transport Alternative vehicles: plug-in hybrids, electric vehicles, fuel cell vehicles Alternative fuels/biofuels Better efficiency

Improve Energy efficiency of transport models

• • • • • • • • •

Vehicle technology Fuel efficiency standards Low friction lubricants Optimal tire pressure Low rolling resistance tires Speed limits Eco driving Awareness Fuel and carbon taxation tax

Source: Reproduced from Körner A. Energy technology perspectives 2012. Pathways to a clean energy system. Transport sector: trends, indicators energy efficiency measures. International Energy Agency, Paris: OECD/IEA; 2012.

Energy Conservation

• • • • •

73

Identify policies to achieve a sustainable transport system by collecting and interpreting transport indicators. The drivers of transport demand need to be understood. The sustainability target needs to be defined. A set of possible strategies for future development needs to be identified. The different strategies need to be evaluated, for example, with the help of scenario modeling.

5.2.5.3.5

Mobility model

The mobility model is a spreadsheet model of global transport suitable for handling regional and global issues (Fig. 9). It displays energy use, emissions, safety, materials use, analysis of a multiple set of scenarios, and projections to 2050. Based on hypotheses on economic growth and population growth, fuel economy, costs, travel demand, vehicle, and fuel market shares future projection can be made. The model needs a large amount of data on technologies and fuel pathways, full evaluation of the life cycle GHG emissions, cost estimates for new light duty vehicles, estimates for fuels costs and taxes, and fuel distribution infrastructure, and a section on material requirements for vehicle manufacturing. Energy use is associated with transport activity and transport structure together with energy and carbon intensity data [62].

5.2.5.4

Residential Sector

In the residential sector, including homes and public buildings, world delivered energy consumption grows by an average of 1.4% per year [22]. American homes use almost 25% of the energy consumed in the United States; about 80% of that energy is used in single-family homes. Housing stock offers considerable worldwide opportunities for energy conservation. The value of energy efficient construction standards is universally recognized as the most cost-effective way to help consumers conserve energy, make housing more affordable, and reduce air pollution [43]. However, capturing this savings potential within the existing home improvement market may create some challenges and many efficiency gains are being offset by increases in the number of electronics and appliances in the average home [43]. Building modeling and simulation is a growing discipline, which can be used to estimate a building’s projected energy and water use, as well as building performance, during the design phase of construction. An energy audit or energy assessment is a thorough accounting of the energy use of a building and is a powerful way to improve the energy efficiency of a building [43]. Generally, new construction building has many more opportunities for the integration of energy efficiency measures than existing buildings.

5.2.5.4.1

Energy conservation in home heating and cooling

Heating and cooling at home accounts for around 55% of the utility bill (Fig. 10) [15–17]. Water heating typically accounts for about 12% of the utility bill. Replacing water heaters older than 7 years may reduce the cost of energy. The potential energy savings from reducing drafts in a home may range from 5 to 30% per year. Heat loss through the ceiling and walls in a home could be very large if the insulation levels are less than the recommended minimum for a zone. Use of natural gas has the largest source of fuel for heating houses in the United States. In colder climates, windows that are gas filled with low emissivity coatings on the glass reduce heat loss. In warmer climates, windows with selective coatings may reduce heat gain.

Transport sector energy usage

Data analysis

Vehicle stock by mode and technology Identify system parameters with high impact at low cost

Modelling

Fuel economy

Utilize these parameters for modeling

Modelling

Setting fuel economy standards Fig. 9 An approach to make the transport sector more economical and sustainable. Reproduced from Körner A. Energy technology perspectives 2012. Pathways to a clean energy system. Transport sector: trends, indicators energy efficiency measures. International Energy Agency, Paris: OECD/IEA; 2012.

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Energy Conservation

Refrigeration 8% Appliances 9% Computers and electronics 9% Lighting 11%

No heating system 1%

Other 8% Space heating 31%

Water heating 12%

Other 9%

Natural gas 53%

Space cooling 12%

(A)

Fuel oil 7%

Electricity 30%

(B)

Fig. 10 (A) Home usage of energy: Heating is the largest part of the energy cost. Heating and cooling consume more energy than any other system at home. Typically, 50% of the utility bill goes for heating and cooling. (B) Household heating by various sources; use of natural gas has the largest source of fuel (450%) for heating houses; electricity has also considerable source of energy (430%). Reproduced from American Society of Heating Refrigerating and Air-Conditioning Engineers Inc., Method of testing for annual fuel utilization efficiency of residential central furnaces and boilers report No. BSR/ASHRAE Standard 103-1993R. Atlanta, GA. First Public Review; 2003; Energy Saver. Available from: www. energysavers.gov/your_home/space_heating_cooling/index.cfm/mytopic=12530; 2016 [accessed 15.06.16]; Lekov AB, Franco VH, Meyers S, et al., Electricity and natural gas efficiency improvements for residential gas furnaces in the U.S. LBNL-59745. Available from: http://eetd.lbl.gov/ publications/electricity-and-natural-gas-efficiency-improvements-residential-gas-furnaces-us; 2006 [accessed 06.07.16].

Building owners rarely have access to the information they need to understand the energy efficiency of a given building and opportunities for improvement. This information can motivate owners to upgrade their building envelope. Windows include typical single-, double-, or triple-paned, as well as commercial building glazing. Windows are mainly less efficient than walls, and are also a common site for air infiltration. Heating, ventilation and air conditioning (HVAC) systems are comprised of the heating, air conditioning, and ventilation systems in a residential or commercial building and are subject to various ECMs [1,43,63]: Also the followings ECMs can reduce the cost [1,15–17]:

• • •

Clean or replace filters on furnaces once a month or as needed. Clean baseboard heaters and radiators and make sure that they are not blocked. Select energy-efficient products when you buy new heating and cooling equipment.

5.2.5.4.2

Home heating by fossil fuels

Residential furnaces have a heat input rate of less than 225,000 Btu h 1 (66 kW) and residential boilers have a heat input rate of less than 300,000 Btu h 1 (88 kW). A condensing furnace condenses the water vapor produced in the combustion process and uses the heat from this condensation. Although condensing units cost more than noncondensing units, the condensing unit can reduce the consumption of fuel and the cost over the 15- to 20-year life of the unit. Old furnaces and boilers can be retrofitted to increase their efficiency, such as by installing programmable thermostats, upgrading ductwork in forced-air systems, and adding zone control for hot-water systems. Furnaces and boilers can use heating oil blended with biodiesel, which produces less pollution than pure heating oil [1,15].

5.2.5.4.3

Home heating by electricity

An all-electric furnace or boiler converts nearly 100% of the electrical energy to heat. However, most electricity is produced from oil, gas, or coal by converting only about 30% of the fuel's energy into electricity. Because of production and transmission losses, electric heat may be more expensive than heat produced using combustion appliances, such as natural gas, propane, and oil furnaces. It is also possible to use heat storage systems to avoid heating during times of peak power demand [51,52]. A built-in thermostat prevents overheating and may shut the furnace off if the blower fails or if a dirty filter blocks the airflow. Heat losses of the duct system or piping can be as much as 35% of the energy for output of the furnace. Table 20 shows some typical values of AFUE for furnace and boiler using various fossil fuels and electricity [15–17]. The annual savings from replacement of a heating system with a more efficient one may be estimated by using Table 21 assuming that both systems have the same heat output. AFUE is calculated using ASHRAE Standard 103 [64]. A furnace with a thermal efficiency of 78% may yield an AFUE of only 64% under the standards’ test conditions. Heat pumps, in most climates, can cut electricity use by 50% when compared with electric resistance heating. Likewise, when a heat pump operates near its most inefficient outside temperature, typically 01F, the heat pump will perform close to the same as a resistance heater [63].

5.2.5.4.4

Home heating by solar systems

Heating homes with active and passive solar energy systems can significantly reduce the fossil fuel consumptions, air pollution, and GHG emission when they are used for most of the year [5,6,65]. The economics of an active space heating system improve if it

Energy Conservation

75

Table 20 Some typical values of annual fuel utilization efficiency (AFUE) for furnace and boilers Fuel

Furnace/boiler

AFUE, %

Fuel oil heating

Retention head burner Mid-efficiency Central or baseboard Conventional Mid-efficiency Condensing Conventional Mid-efficiency Condensing Conventional Advanced State-of-the-art

70–78 83–89 100 55–65 78–84 90–97 55–65 79–85 88–95 45–55 55–65 75–90

Electric heating Natural gas

Propane

Firewood

Source: Reproduced from American Society of Heating Refrigerating and Air-Conditioning Engineers Inc., Method of testing for annual fuel utilization efficiency of residential central furnaces and boilers report No. BSR/ASHRAE Standard 103-1993R Atlanta, GA. First Public Review; 2003; Energy Saver. Available from: www.energysavers.gov/your_home/space_heating_cooling/index. cfm/mytopic=12530; 2016 [accessed 15.06.16]; Lekov AB, Franco VH, Meyers S, et al., Electricity and natural gas efficiency improvements for residential gas furnaces in the U.S. LBNL-59745. Available from: http://eetd.lbl.gov/publications/electricity-and-natural-gas-efficiency-improvements-residential-gas-furnaces-us; 2006 [accessed 06.07.2016]; and ASHRAE. Standard Project Committee 103. Available from: http://spc103.ashraepcs.org/; 2016 [accessed 06.07.16].

Table 21 Assuming the same heat output, estimated savings for every $100 of fuel costs by increasing an existing heating equipment efficiency Existing annual fuel utilization efficiency (AFUE)

55% 60% 65% 70% 75% 80% 85%

New and upgraded system AFUE 60%

65%

70%

75%

80%

85%

90%

95%

$8.3 – – – – – –

$15.4 $7.7 – – – – –

$21.4 $14.3 $7.1 – – – –

$26.7 $20.0 $13.3 $6.7 – – –

$31.2 $25.0 $18.8 $12.5 $6.5 – –

$35.3 $29.4 $23.5 $17.6 $11.8 $5.9 –

$38.9 $33.3 $27.8 $22.2 $16.7 $11.1 $5.6

$42.1 $37.8 $31.6 $26.3 $21.1 $15.8 $10.5

Source: Reproduced from Demirel Y. Energy: production, conversion, storage, conservation, and coupling. 2nd ed. London: Springer; 2016 and ASHRAE. Standard Project Committee 103. Available from: http://spc103.ashraepcs.org/; 2016 [accessed 06.07.16].

also heats domestic water. Active solar heating systems use either water or air heated in the solar collectors. Liquid systems are more often used when storage is included, and are well suited for boilers with hot water radiators and heat pumps. In passive solar building design, windows, walls, and floors are made to collect, store, and distribute solar energy in the form of heat in the winter and reject solar heat in the summer. A passive solar building takes advantage of the local climate. Elements to be considered include window placement, thermal insulation, thermal mass, and shading [65].

5.2.5.4.5

Home cooling

SEER rating more accurately reflects overall system efficiency on a seasonal basis and EER reflects the system’s energy efficiency at peak day operations [5,6]. Typical EER for residential central cooling units¼ 0.875  SEER. A SEER of 13 is approximately equivalent to a COP of 3.43, which means that 3.43 units of heat energy are removed from indoors per unit of work energy used to run the heat pump. Air conditioner sizes are often given as “tons” of cooling where 1 t of cooling is equal to 12,000 Btu h 1 (3.5 kW). This is approximately the power required to melt one ton of ice in 24 h. The COP of an air conditioner using the Carnot cycle is: COPCarnot ¼

TC TH

TC

ð33Þ

where TC and TH are the indoor and outdoor temperatures in K or R, respectively. The EER is calculated by multiplying the COP by 3.413, which is the conversion factor from Btu h 1 to W: EERCarnot ¼ 3:413ðCOPCarnot Þ

ð34Þ

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Energy Conservation

For an outdoor temperature of 311K and an indoor temperature of 308K, the above equation gives a COP of 103, or an EER of 350. This is about 10 times as efficient as a typical home air conditioner available today. The maximum value of EER decreases as the difference between the inside and outside air temperature increases. For example: TH ¼ 491C ¼ 322.15K, and TC ¼27oC ¼ 300.15K COPCarnot ¼ 300.15K/(322.15–300.15)K ¼13.6 or EER¼(3.413) (13.6) ¼ 46.4. The maximum SEER can be calculated by averaging the maximum values of EER over the range of expected temperatures for the season. Central air conditioners should have a SEER of at least 14. Substantial energy savings from more efficient systems are possible. For example, by upgrading from SEER 9 to SEER 13: (1 9/13)¼0.30 means that the power consumption is reduced by 30%. Residential air conditioning units may be available with SEER ratings up to 26. Possible annual saving in cooling by using a unit operating at a higher SEER rating can be significant [1,64].

5.2.5.4.6

Commercial buildings

Commercial buildings, such as office and retail buildings, educational and health-care buildings, and lodging, account for 19% of the energy consumed in the United States. More than half the energy used by commercial buildings goes toward heating and lighting [22]. Retail and wholesale trade buildings are the largest consumers of energy among nonresidential buildings accounting for 28% of total energy consumption in the nonresidential building sector. Office buildings are the second largest consumers (23%) of energy among nonresidential buildings [23]. The majority of energy consumption and energy efficiency opportunities occur in heating and lighting. It is assumed that absolute energy consumption will also decline by 21% by 2020 from 2010 levels due to both energy efficiency improvements and declining retail space [23].

5.2.5.4.7

Accommodation and food service activities

Accommodation and food service activities accounted for 11% of total energy consumption in the nonresidential building sector in 2012, amounting to approximately 10.5 MTOE [23]. There is a high amount of energy waste within the accommodation sector, attributed to guest behavior, which presents significant further opportunity for energy reduction potential. Based on industry benchmarks, a reduction in energy consumption by 10–15% should be achievable using available technology. Evidence has shown that equipment used in commercial kitchens is only 50% efficient. Thus more energy-efficient equipment could result in significant energy savings [23].

5.2.5.4.8

Information and communications

Information and communications equipment is estimated to consume approximately 14.7 MTOE of energy in 2012 and will see a growth of 16% up to 2018 from 2012 levels (i.e., 2.5% per year), due to the growth in enterprise cloud computing, content-heavy applications, and machine-to-machine connectivity. There are also growing global trend toward green data centers, which sees 77

LIGHTS 77

83

PF-WATER

CU-WATER

77

83

PREFLASH NAPHTHA QC=–17246 QR=0 QF=58013

93

204

HNAPHTHA

CU-STM1 CRUDE

MIXCRUDE

190

QC=−28894 QR=0 QF=62662

204

66

KEROSENE

OFF-GAS 204

PF-STEAM

273

186 CU-STM2

CDU-FEED

229 DIESEL

LVGO VDU

204 Temperature (C)

327 204

Q

QC=0 QR=0 QF=28919

358

CU-STM3

Duty (kW)

348

HVGO

AGO CU-STEAM

435

RED-CRD 204

RESIDUE VDU-STM

Fig. 11 Base case process flow diagram. The temperature is in 1C and the values of heats (Q) are in MW. CU-STEAM: crude unit steam; HVGO, heavy vacuum gas oil; LVGO, light vacuum gas oil; PF-STEAM: preflash steam; VDU-STM: vacuum distillation unit steam. Reproduced from Alhajji M, Demirel Y. Energy and environmental sustainability assessment of crude oil refinery by thermodynamic analysis. Int J Energy Res 2015;39:1925–41.

77

Energy Conservation

energy performance of the sector steadily improving over time. The average power usage effectiveness has improved from 1.89 in 2011 to 1.7 In 2014, reflecting the reality that the biggest infrastructure efficiency gains have already happened, and further improvements will require significant investment and effort, with increasingly diminishing returns [23].

5.2.5.4.9

Behavior and human dimensions of energy conservation

There are large variations of the energy efficiencies of various domestic appliance models. Salespeople have the belief that customers are not interested in energy efficiency and perceive that it is just a technical detail. However, many manufacturers E16

LIGHTS

DIESEL

OFGAS

E10

PREFLASH KEROSEN

S12

HNAPHTHA

PF-WATER

CU-WAT

E9 E8 LVGO

E14

CRUDE

WATER

E15

NAPHTHA

S13 E11

S8

S25

AGO S7

E12

HVGO

CU-STM1 S22

E13 CU-STM2

E1

E2

E3

E4 S15

S14 E5

PF-STEAM

VDU

CU-STM3

CDU-FEED E6 S19

E7

CU-STEAM

R-C

S18 S17

RC

S23

S16

VDU-STM S24

MCRUDE RESIDU

Fig. 12 Process flow diagram after using newly installed heat exchangers in order to match the available and required heats. HVGO, heavy vacuum gas oil; LVGO, light vacuum gas oil; PF-STEAM: preflash steam; VDU-STM: vacuum distillation unit steam. Reproduced from Alhajji M, Demirel Y. Energy and environmental sustainability assessment of crude oil refinery by thermodynamic analysis. Int J Energy Res 2015;39:1925–41.

Composite curves 450 400 350

Temperature (C)

300 250 200 150 100 50.0

1.6e+005

1.4e+005

1.2e+005

1.0e+005

8.0e+004

6.0e+004

4.0e+004

2.0e+004

0.00

0.000

Enthalpy (kW) Fig. 13 Composite curve diagram for the process where the solid line represents the hot composite curve and the dashed line represent the cold composite curve. Reproduced from Alhajji M, Demirel Y. Energy and environmental sustainability assessment of crude oil refinery by thermodynamic analysis. Int J Energy Res 2015;39:1925–41.

78

Energy Conservation

display energy efficiencies (i.e., Energy Star labels) and savings through the energy conserved. Targeted incentives and rewards, such as exchanging efficient appliances with inefficient ones, increase participation and commitment to energy efficiency actions [43]. Behavior and the human dimensions of energy conserving is a growing area of interest to utilities, businesses, and governments. Social science can shape the ways that customers use energy-derived goods and services. Technology and program designs should directly engage energy users in new decisions and actions that conserve energy by giving energy users greater control and real-time information about their energy use.

5.2.5.5

Case Studies for Energy Conservation Measures

The following case studies are for energy conservation opportunities in the industrial sector, which uses distillation columns for separation of multicomponent mixtures. The CTT is used to prepare the CGCC, and exergy loss profiles at PREFLASH column grand composite curve (Stage-H) 10 Ideal profile Actual profile

9 8

Stage

7 6 5 4 3

18,000

16,800

15,600

14,400

13,200

12,000

10,800

9600

8400

7200

6000

4800

3600

2400

1200

1

0

2

Enthalpy deficit kW

(A)

PREFLASH exergy loss profile (stage-exergy loss) 10 Stage 9 8

Stage

7 6 5 4 3

(B)

1800

1700

1600

1500

1400

1300

1200

1100

1000

900

800

700

600

500

400

300

200

0

1

100

2

Exergy loss kW

Fig. 14 Preflash operation (A) stage-H column grand composite curve (CGCC) and (B) exergy loss profiles. Reproduced from Alhajji M, Demirel Y. Energy and environmental sustainability assessment of crude oil refinery by thermodynamic analysis. Int J Energy Res 2015;39:1925–41.

Energy Conservation

79

each stage are used to determine the ECMs. Besides, for the recovery of heat, composite curves and a HENS are used in the refinery operation.

5.2.5.5.1

ECMs in a crude oil refinery by thermodynamic analysis

This study presents the assessment of energy and environmental sustainability metrics for the three distillation columns used in a crude oil refinery (Fig. 11) by the CTT and the energy analyzer. The main objective of this study is to explore the scope of reducing the thermal energy consumption and GHG emissions for a more sustainable refinery operation [66,67]. 5.2.5.5.1.1 Process description Mixed crude of light and heavy enters the preflash column to produce 12,670.9 barrel (bbl.) h 1 lights and 756.14 bbl h 1 naphtha in the distillate. Preflash bottom produces a mixture of 4102.35 brl h 1 that is fed to the main crude column at 2291C. The crude column produces 387.44 bbl h 1 of heavy naphtha, 584.38 bbl h 1 of kerosene, 716.06 bbl h 1 of diesel, and 470.59 bbl h 1 of automotive gas oil (AGO); the bottom produces 2070.34 bbl h 1 mixture that enters the vacuum distillation unit (VDU) at 3581C. The VDU produces 475.334 bbl h 1 of light vacuum gas oil (LVGO), 905.39 bbl h 1 of heavy vacuum gas oil (HVGO), and 629.509 bbl h 1 of residue [67]. 5.2.5.5.1.2 Composite curve and heat exchanger network system For the considered refinery a heat integration is proposed by matching the available and required heat by the hot and cold streams within with DTmin ¼101C. Fig. 12 shows this HENS, while Fig. 13 shows the hot and cold composite curves. The thermal analysis capability of the CTT is used to reduce the column reboiler and condenser duties and stage exergy losses and consequently the GHG emissions. The carbon tracking with a selected fuel source of crude oil is used to estimate the CO2e emissions due to the utilities for all the columns. The modified case operations after the retrofits are compared with the base case operations using the sustainability metrics to analyze and assess the impacts of retrofits [37]. 5.2.5.5.1.3 Column grand composite curves and exergy losses 5.2.5.5.1.3.1 Preflash column Fig. 14 shows the (stage-H) CGCC, and exergy loss profiles for the preface column performance; the current operation is close to optimum for most of the stages, except the sharp enthalpy change in stage 1 (the condenser side). This requires either lowering the feed temperature or moving the feed stage toward the reboiler. However, changing the furnace temperature has caused reduction in the naphtha production rate. Moreover, because of Petrobras configuration with the built-in furnace, feed location modification is not recommended. Fig. 14(A) shows that there is no distance between the pinch point and ordinate and hence there is no need for the RR modification. Similarly, due to the closeness of the actual and ideal operations in most of the column, the side heating or cooling modifications are not needed. Fig. 14(B) shows higher exergy losses in stage 1 (condenser stage) and at the bottom where steam is injected [67]. By using the HENS shown in Fig. 12, the available hot streams from the crude and VDU are used to gradually heat the mixed crude feed from 25 to 971C. The installed four heat exchangers have recovered 23.12 MW from the product streams. Table 22 shows how the modifications affect energy and environmental impact metrics, which are obtained by normalizing the indicators per unit mass of product. Reductions are the furnace duty by about 2%, heating duty for the feed by about 146 MJ mt 1, and GHG emission by 0.031 kg of CO2e kg 1 feed [67]. 5.2.5.5.1.3.2 Crude column Fig. 15 shows the stage-H CGCC, and exergy loss profiles for the base case operations of the crude column displaying sharp enthalpy changes toward the condenser side through the stages between 1 and 13. The feed stage is moved to be at stage 23 instead Table 22

Sustainability metrics for preflash column with the process heat integration

Sustainability metrics

Preflash column

Energy intensity (MJ mt 1) Cold utility/distillate Hot utility/feed Total process heat integration (heatx1-4)/feed Total exergy loss/product Cost ($ mt 1) Cold utility cost/distillate Hot utility cost/feed CO2e fee (hot utility)/feed Environmental impact: CO2e/product (mt h 1) per (mt h 1) Hot utility (furnace þ PF-STEAM)/feed Total process heat integration (heatx1–4)/feed

Base 691.86 377.41 18.26

Modified 691.86 370.14 146.01 18.26

Change, %

1.65 1.58 0.35

1.65 1.55 0.34

1.95 1.95

0.0348

0.0342 0.0310

1.95 100.0

1.95 100.0

Source: Reproduced from Alhajji M, Demirel Y. Energy and environmental sustainability assessment of crude oil refinery by thermodynamic analysis. Int J Energy Res 2015;39:1925–41.

80

Energy Conservation

(A)

31,200

28,600

26,000

23,400

20,800

18,200

15,600

13,000

10,400

7800

2600

5200

Ideal profile Actual profile

0

Stage

CRUDE column grand composite curve (stage-H) 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

Enthalpy deficit kW

(B)

2400

2250

2100

1950

1800

1650

1500

1350

1200

1050

900

750

600

450

300

150

Stage

0

Stage

CRUDE exergy loss profile (stage-exergy loss) 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

Exergy loss kW

Fig. 15 Base case operation for crude unite with TF ¼1291C; NF ¼22; PA-1 Draw at stage 8 return to stage 6, PA-2 Draw at stage 14 return to stage 13; TF: feed temperature, NF: feed stage, and PA: pumparound. (A) Column grand composite curve (CGCC) (stage-H) and (B) exergy loss profiles. Reproduced from Alhajji M, Demirel Y. Energy and environmental sustainability assessment of crude oil refinery by thermodynamic analysis. Int J Energy Res 2015;39:1925–41.

of stage 22. Furthermore, the stage-H CGCC displays sharp enthalpy changes on stages 6, 8, 13, and 14 where the pumparounds are installed. Therefore, pumparound stages are moved down the column. Fig. 15(B) shows the exergy loss is higher in the condenser and reboiler mainly due to steam injection at the bottom and the working configurations of the crude unit. The exergy losses around the pumparound stages suggest modifying the pumparound draw and return stages [67]. The available hot streams from the crude and vacuum distillation units are used to gradually heat the feed from 228 to 2631C by using the HENS and the duty in the furnace is reduced by 12.11 MW. Newly installed two heat exchangers have recovered

Energy Conservation

81

Table 23 Sustainability metrics for crude with the process heat integration and feed conditioning: TF ¼2281C-2631C; NF ¼ 22-23; PA-1 Draw at stage 8 return to stage 6-Draw at stage 12 return to stage 10, PA-2 Draw at stage 14 return to stage 13-Draw at stage 17 return to stage 16 Sustainability metrics

Crude column 1

Energy intensity, MJ mt Cold utility/distillate Hot utility/feed Total process heat integration (heat-x: 5-6) feed Total exergy loss/product Cost, $ mt 1 Cold utility cost/distillate Hot utility cost/feed CO2e fee (hot utility)/feed Environmental impact, kg h 1 per kg h 1 Hot utility (furnace þ CU-STEAM)/feed Total reduced CO2e due to process heat integration/feed

Base 2430.06 469.51 70.54

Modified 2439.06 378.92 93.62 65.69

Change, % þ 0.37 19.29 100 6.87

5.84 2.02 0.45

5.84 1.63 0.38

19.03 19.35

0.046

0.037 0.0064

18.10 100

Source: Reproduced from Alhajji M, Demirel Y. Energy and environmental sustainability assessment of crude oil refinery by thermodynamic analysis. Int J Energy Res 2015;39:1925–41.

12.49 MW from the product streams in order to heat the feed stream. Table 23 shows how these modifications affect energy and environmental impact metrics. The reductions are the furnace duty by about 19%, heating duty for the feed by about 93.62 MJ mt 1, and the GHG emissions by 0.0064 kg of CO2e kg 1 feed [67]. 5.2.5.5.1.3.3 Vacuum distillation unit Fig. 16 shows the stage-H CGCC and exergy loss profiles for the VDU. The stage-H CGCC displays sharp enthalpy change in stages where the pump around is installed. Fig. 16(B) shows that the exergy loss is higher at the bottom mainly due to direct steam injection. There is only one available hot stream from the VDU, which is used to increase the feed (RED-CRD) temperature from 358 to 3821C. The reduction in furnace duty is 5.02 MW. The installed heat exchangers have recovered 4.69 MW from the product streams in order to heat the feed stream. Table 24 shows considerable reductions in energy and environmental impact metrics. The reduction in the furnace duty is about 14%, the total reduction in heating duty for the feed is about 0.067 MJ mt 1, and the reduction of GHG emission is about 0.0571 kg of CO2e kg 1 feed [67]. 5.2.5.5.1.3.4 Economic evaluation Tables 25 and 26 show the estimated thermodynamic efficiency and the energy savings for operation hours of 8520 h year 1. The saved energy equivalent electricity is around $403,714 year 1 by the retrofits suggested by the CTT. After applying the HENS for process heat integration, 24.28 MW of energy is recovered in the preflash column with a total reduction of 0.0316 kg of CO2 kg 1 feed, while 24.60 MW of energy is recovered in the crude column with a total reduction of 0.0154 kg of CO2 kg 1 feed, and 9.71 MW of energy is recovered in the VDU with a total reduction of 0.064 kg of CO2 kg 1 feed. Furthermore, higher thermodynamic efficiencies are obtained for all three columns; the total energy savings due to these modifications are about $38 million year 1 after a one-time fixed capital cost (FCC) of about $3.4 million (with US$2014). These results illustrate that it may be possible to achieve a more sustainable refinery process by simple retrofits determined by the thermodynamic analysis (TA) and energy analyzer [67].

5.2.5.5.2

Energy conservation measures in the back-end separation of an ethylene plant

The back-end separation of an ethylene plant consists of three interacting distillation columns. The objective is to explore the scope of reducing the energy for utilities and GHG emissions. The high purity recovery and low relative volatility require toll distillation columns with very high installation and operating costs in ethylene plants. Therefore, the olefin/paraffin separation process of ethylene, propylene, and other high volume olefin petrochemicals is highly energy-intensive. Cryogenic distillation is the commercially viable separation; however, it consumes over 20 GJ of energy for every ton of ethylene produced [69]. 5.2.5.5.2.1 Process description Ethylene is produced by steam cracking in which light hydrocarbons are heated to 750–9501C, inducing many reactions. Ethylene is separated from the resulting complex mixture by repeated compression and distillation processes. Fig. 17 shows a conventional ethylene plant where Stream 12 has a flow of 20.39 kg s 1, at 161C and 39 bar, and consists of 5.83 kg s 1 of ethane, 10.98 kg s 1 of ethylene, 1.96 kg s 1 of hydrogen, 1.12 kg s 1 of methane, 0.003 kg s 1 of acetylene, 0.342 kg s 1 of propylene, 0.111 kg s 1 of propane, 0.012 kg s 1 of butadiene, 0.007 kg s 1 of butene, 0.011 kg s 1 of butane, and 0.003 kg s 1 of benzene. The feed enters a splitter S2. The separated streams pass through reactors and flash separators till they reach the separation section containing the three RadFrac columns. The streams pass through the columns to produce ethylene as the distillate from column 3 and ethane as the bottom product, which is recycled to C2REC reactor. Propylene is the bottom stream of column 2 [69].

82

Energy Conservation

VDU column grand composite curve (stage-H) 6 Ideal profile Actual profile

5

Stage

4

3

18,000

17,000

16,000

15,000

14,000

13,000

12,000

11,000

10,000

9000

8000

7000

6000

5000

4000

3000

2000

1000

1

0

2

Enthalpy deficit kW

(A)

VDU exergy loss profile (stage-exergy loss) 6 Stage 5

Stage

4

3

2

(B)

5500

5000

4500

4000

3500

3000

2500

2000

1500

1000

500

0

1

Exergy loss kW

Fig. 16 Base case operation for vacuum distillation unit (VDU) with TF ¼3421C, TFr ¼ 4511C; TF: feed temperature, and TFr: furnace temperature. (A) Column grand composite curve (CGCC) (stage-H) and (B) exergy loss profiles.

Column 1 has three feeds and the overhead contains the hydrogen and methane that are recycled, while the bottom flow contains the mixture of ethane, ethylene, propylene, butadiene, butane, butane, and benzene, which are separated in column 2. Ethane and ethylene in the presence of hydrogen goes to the overhead and finally becomes the feed to column 3 where the ethylene is the overhead product, while the ethane in the bottom is recycled [69]. Fig. 18 displays the back-end separation section of the ethylene plant considered in this study. 5.2.5.5.2.2 Column grand composite curves and exergy loss profiles CGCCs and exergy loss profiles are employed for the analyses of the three columns to determine the possible ECMs. 5.2.5.5.2.2.1 Column 1 Fig. 19 shows the stage exergy loss profiles for the base case and modified case for column 1 after the retrofits (the RR, feed plate location, and heating feed 1 of column 1) to reduce the exergy losses particularly at the feed stages, the reboiler, and the condenser.

Energy Conservation

83

Table 24 Sustainability metrics for the vacuum distillation unit (VDU) with the process heat integration and feed conditioning: TF ¼ 3581C-3821C Sustainability metrics

VDU

Energy intensity, MJ mt 1 Hot utility (furnace þ PF-STEAM)/feed Total process heat integration (heat-x: 7)/feed Total exergy loss/product Cost, $ mt 1 Hot utility cost/feed CO2e fee (hot utility)/feed Environmental impact, mt h 1 per mt h 1 Hot utility (furnace þ CU-STEAM)/product Reduced CO2e due to process heat integration/product

Base 0.51 0.11

Modified 0.44 0.067 0.11

Change, % 13.9 100.0 þ 0.21

2.02 0.47

1.70 0.41

15.4 14.1

0.047

0.040 0.057

14.1 100.0

Source: Reproduced from Alhajji M, Demirel Y. Energy and environmental sustainability assessment of crude oil refinery by thermodynamic analysis. Int J Energy Res 2015;39:1925–41.

Table 25

Estimated efficiencies and exergy savings for the three columns in the refinery

Base case Unit Preflash Crude VDU Total

Modified case Exmin kW 14,836.6 9,749.9 3,813.3

Exlossa kW

Z%

3,385.4 9,414.7 8,051.7

81.4 50.8 31.1

Exmin kW 18,229.5 41,015.9 6,210.5

Exloss kW

Z%

3,385.5 8,768.0 8,085.9

84.3 82.4 43.4

Saved Exloss kW 0.8 646.7 34.4

Change Exloss % 0.005 6.8 0.3

Electricity Savingb $ year-1 554.6 427,009.4 22,740.7 403,714.0

a

Exloss: Total column exergy loss from the converged simulation by Aspen Plus with the BK-10 method [21]. Electricity equivalent of energy saving is based on a unit cost of electricity of $0.0775 kWh 1. Source: Reproduced from Alhajji M, Demirel Y. Energy and environmental sustainability assessment of crude oil refinery by thermodynamic analysis. Int J Energy Res 2015;39:1925–41. b

Table 26

Estimated utility savings for the three columns after heat exchanger network system (HENS)

Unit

Base duty, kW

Modified duty, kW

Saved duty, kW

Preflash furnace Crude furnace Vacuum distillation unit (VDU) furnace Heatx-1 Heatx-2 Heatx-3 Heatx-4 Heatx-5 Heatx-6 Heatx-7 Total

58,012.6 62,661.6 28,918.5

56,861.1 50,572.6 23,899.4

1,151.6 12,089.0 5,019.2

1,486.2 5,513.2 5,707.8 10,411.3 3,578.8 8,916.2 4,692.8

1,486.2 5,513.2 5,707.8 10,411.3 3,578.8 8,916.2 4,692.8

Change duty, %

FCCa, $ retrofit

1.9 19.3 17.3 100 100 100 100 100 100 100

Electricity savingb, $ year 1 760,388.2 7,982,386.5 3,314,171.1

517,000 432,000 520,000 617,000 263,000 512,000 535,000 3,396,000

981,331.2 3,640,379.1 3,768,853.7 6,874,581.4 2,363,127.8 5,887,386.6 3,098,655.8 38,671,261

a

FCC: Fixed capital cost with the chemical engineering plant cost index (CEPCI) of 580.2 for September 2014 [68]. Electricity equivalent of energy saving is based on a unit cost of electricity of $0.0775 kWh 1. Source: Reproduced from Alhajji M, Demirel Y. Energy and environmental sustainability assessment of crude oil refinery by thermodynamic analysis. Int J Energy Res 2015;39:1925–41. b

Table 27 compares the sustainability metrics for both the base and modified cases. The modifications applied are the RR, feed plate location, and heating feed 1 of column 1. As seen, the modifications reduced the exergy losses by around 92% after the modifications leading to efficient usage of available energy and more thermodynamically optimum operation. 5.2.5.5.2.2.2 Column 2 Column 2 uses the bottom flow of column 1. Table 28 compares the sustainability metrics for the base case and modified case operations for column 2. As seen, the duties and cost of energy are decreased in the reboiler side, while the condenser duty is

84

Energy Conservation

EXP

H7

To methane recovery process

Separation section

25

28

8

14 S2 6

12 10

15

H4 HX2 Ethane

F1

5

S1

7

H1

HX1

19

Reactor

23

F2

H6

20

NF=15 NF=25

N=50

H3 Column1 36

N= 60

NF=35 Column3

31

NF=10

17 13

3

1

F3

16

9

2

32 33

HX4 18

34

24

21

11 H5

H2

Hydrogen

22

HX3

4

Ethylene

M1

27 26

Ethane N= 50

29 NF=28

30

35 Propylene

Column2

Gas from furnace VLV

Fig. 17 Process flow diagram of ethylene plant with back-end separation. Reproduced from Alhajji M, Demirel Y. Energy intensity and environmental impact metrics of the back-end separation of ethylene plant by thermodynamic analysis. Int J Energy Environ Eng 2016;7:45–59.

M=1.36 T=–37.78 p=21.1 F3=3.74 T=–129 p=37

F=80.41 T=–36 p=17

Hydrogen F=2.99 T=–99.6 p=35.15

NF=35 N=60 Reactor

F2=59.82 T=–98 p=37 F1=97.3 T=–37 p=37.3

NF=10 NF=15 NF=25

F=137.23 T=–24.6 Column3 p=17.6

N=50 NF=28

F1=56.82 T=−15 p=17

N=50

Column1 F=157.86 T=5.5 p=35.15

Column2

F=20.63 T=74.4 p=24

Fig. 18 Section of ethylene plant back-end separation; N: number of total stages; NF: feed plate location; F: mass flow, mt h 1, M: mole flow rate kmol h 1, T: temperature, 1C, P: pressure, bar. Reproduced from Alhajji M, Demirel Y. Energy intensity and environmental impact metrics of the back-end separation of ethylene plant by thermodynamic analysis. Int J Energy Environ Eng 2016;7:45–59.

increased due to heating the feed. In a similar trend, the emissions of CO2 decreased around 31.6% in the reboiler, while increased around 6.6% in the condenser. This indicates the trade-off taking place during the modifications. The reduced exergy losses lead to a more thermodynamically optimum operation. 5.2.5.5.2.2.3 Column 3 Column 3 uses the distillate rate of column 2 as the feed and produces ethane at the bottom and ethylene at the distillate. From the CGCC the RR modification is implemented. Table 29 displays the sustainability metrics for column 3. The total exergy losses and total CO2 emissions are reduced around 17.4% and 20%, respectively, for column 3 [69]. Table 30 shows the estimated thermodynamic efficiency and the energy savings based on electricity, which is around $2 million against the FCC of around $624,600 (2014 US$). This considerable energy saving, especially from reduction in exergy losses, also leads to around 11% reduction in GHG emissions; the total reductions in the cold utility is around 5.1%, while the total reductions in the hot utility is around 4.5%. The results illustrate that it may be possible to achieve an improved and more sustainable distillation operation by simple retrofits determined by TA. The results show that the total reductions in exergy loss and the total hot and cold utility are around 44% and 10%, respectively; the total reductions in GHG emissions are around 14%. These improvements lead to considerable reductions in the operating costs [69].

Energy Conservation

85

50 EX=0.043 at stage 50

Stage

45 40 35

Stage

30 25

EX=0.455 at stage 25

20 15 EX=0.587 at stage 15 10

EX=0.056 at stage 10

5 EX=0.028 at stage 1 0 0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.55

0.60

Exergy loss MW

(A)

55 EX=0.003 at stage 55

Stage

50 45 40

Stage

35 30 25 EX=0.031 at stage 25 20 15 EX=0.049 at stage 15

EX=0.005 at stage 11

10 5 0 0.000

EX=0.002 at stage 1 0.005

(B)

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0.045

0.050

Exergy loss MW

Fig. 19 Exergy loss profiles for (A) base case and (B) modified case operation for Column 1 with: N ¼50-55; NF1¼25, NF2 ¼15, NF3¼11; RR¼ 0.65-0.328; TF1¼ 371C- 301C; N: number of total stages; RR: reflux ratio, NF: feed stage. Reproduced from Alhajji M, Demirel Y. Energy intensity and environmental impact metrics of the back-end separation of ethylene plant by thermodynamic analysis. Int J Energy Environ Eng 2016;7:45–59.

5.2.6

Results and Discussion

Adequate and affordable energy supplies have been the key to economic development and the transition from subsistence agricultural economies to modern industrial and service-oriented societies. Energy is the central driver toward improved social and economic well-being, and indispensable to most industrial and commercial wealth generation. Many factors including harnessing sustainable energy sources, utilizing sustainable energy carriers, increasing efficiency, reducing environmental impact, and improving socioeconomic acceptability may lead toward energy sustainability. The latter factor includes community involvement and social acceptability, economic affordability and equity, lifestyles, land use, and esthetics. Energy conservation is of great importance to the overall sustainability given the pervasiveness of energy use, its importance in economic development and living standards, and its impact on the environment [1–3,6,31].

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Table 27 Sustainability metrics for column 1 with the modification: N ¼50-55; NF1¼25, NF2¼15, NF3 ¼11; RR¼0.65-0.328; TF1¼ 371C- 301C 1

Base

Modified

Change%

Condenser duty Reboiler duty Feed conditioning

356.92 212.71 0

345.67 199.99 20.28

3.17 5.98

Cost, $ kg 1 Condenser duty cost Reboiler duty cost Duty in feed 1 conditioning cost Total exergy loss

0.015 0.0007 0 70.98

0.014 0.0006 0.00004 5.56

3.12 5.97

0.0191 0.011 0.001

3.53 8.33

Energy intensity metrics, kJ kg

Environmental impact metrics, kg h Condenser CO2 emissiona Reboiler CO2 emissiona Feed conditioning CO2 emissiona

1

CO2 per kg h 0.0198 0.012 0

1

92.15

product

a

Emission based on US-EPA-Rule-E9-5711 and natural gas [21]. Source: Reproduced from Alhajji M, Demirel Y. Energy intensity and environmental impact metrics of the back-end separation of ethylene plant by thermodynamic analysis. Int J Energy Environ Eng 2016;7:45–59.

Table 28

Sustainability metrics for column 2 with the modifications: N ¼50-55; NF¼33; R ¼0.65-0.53; TF¼ 51C-91C 1

Base

Modified

Change %

Condenser duty Reboiler duty Feed conditioning

167.49 2,837.58 0

179.41 1,941.14 127.51

þ 6.63 31.60

Cost, $ kg 1 Condenser duty cost Reboiler duty cost Duty in feed 1conditioning cost Total exergy loss

0.0019 0.0054 0 166.86

0.0021 0.0038 0.0002 104.94

þ 6.63 31.60

product 0.009 0.16 0

0.01 0.11 0.007

þ 10.00 31.25

Energy intensity metrics, kJ kg

Environmental impact metrics, kg h Condenser CO2 emissiona Reboiler CO2 emissiona Feed conditioning CO2 emissiona

1

CO2 per kg h

1

37.10

a

Emission based on US-EPA-Rule-E9-5711 and natural gas [21]. Source: Reproduced from Alhajji M, Demirel Y. Energy intensity and environmental impact metrics of the back-end separation of ethylene plant by thermodynamic analysis. Int J Energy Environ Eng 2016;7:45–59.

Table 29

Sustainability metrics for column 3 with modifications: N ¼66; NF¼35; RR ¼4.49

Energy intensity metrics, kJ kg

1

Base

Modified

Condenser duty Reboiler duty Total exergy loss

1692.98 2,039.87 75.29

1570.57 1,845.17 62.18

7.23 9.54 17.40

Cost, $ kg 1 Condenser duty cost Reboiler duty cost

0.0286 0.0065

0.0266 0.0058

7.22 9.58

0.08 0.10

11.11 9.09

Environmental impact metrics, kg h Condenser CO2 emissiona Reboiler CO2 emissiona

1

CO2 per kg h 0.09 0.11

1

Change, %

product

a Emission based on US-EPA-Rule-E9-5711, natural gas [21]. Source: Reproduced from Alhajji M, Demirel Y. Energy intensity and environmental impact metrics of the back-end separation of ethylene plant by thermodynamic analysis. Int J Energy Environ Eng 2016;7:45–59.

Energy Conservation

Table 30

Estimated efficiencies and energy savings for the three columns

Base case System Column 1 Column 2 Column 3 Total

87

Modified case Exmin MW

Exloss MW

Z%

2.63 1.72 0.77

1.85 3.73 1.68

62.4 31.5 31.4

Exmin MW

Exloss MW

Z%

2.48 1.69 0.97

0.12 2.34 1.39

95.2 41.9 41.0

Saved Exloss MW

Change Exloss %

FCCa, $ Retrofits$

Electricity Savingb $ year

1.46 1.38 0.29

92.2 37.1 17.1

100,600 186,000 338,000 624,600

964,038 911,214 191,487 2,066,739

1

a

FCC: Fixed capital cost. Electricity equivalent of energy saving is based on a unit cost of electricity of $0.0775 kW 1 h 1 Exloss: Total column exergy loss from the converged simulation by Aspen Plus with the SRK method [21,69]. Source: Reproduced from Alhajji M, Demirel Y. Energy intensity and environmental impact metrics of the back-end separation of ethylene plant by thermodynamic analysis. Int J Energy Environ Eng 2016;7:45–59. b

Sustainable development has been defined as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” Adequate and affordable energy supplies have been the key to sustainable development and the transition from agricultural economies to modern industrial and service-oriented societies. Energy is central to improved social and economic well-being, and is indispensable to most industrial and commercial wealth generation. When choosing energy resources and associated technologies for the production, delivery, and use of energy services, it is essential to take into account the sustainability [1–3]. Many factors including harnessing sustainable energy sources, utilizing sustainable energy carriers, increasing efficiency, reducing environmental impact, and improving socioeconomic acceptability may lead toward energy sustainability. Increasing efficiency also has sustainability implications as it makes the resources available for a longer time [10–12]. The latter factor includes community involvement and social acceptability, economic affordability and equity, and lifestyles. Energy sustainability is of great importance to overall sustainability because of its importance in economic development and living standards as well as its impact on the environment [70,71]. The French physicist Gouy in 1889 and the Swiss engineer Stodola in 1905 stated that the lost available work or dissipated energy is directly proportional to the level of irreversibility in real processes. The irreversibility is directly proportional to the rate of entropy production and hence to the level of thermal energy dissipation. So the design question that followed the Gouy–Stodola theorem is how to design a thermal system to produce the least possible entropy production? Since the least entropy production in a system leads to less energy, dissipation energy will be conserved. Energy has several forms, each endowed with a different quality that is a measure of its capacity to cause change. Exergy is the maximum amount of work theoretically available by bringing a resource into equilibrium with its surroundings through a reversible process. Since exergy is a measure of the departure of the state of the system from that of the environment, irreversibility is closely related to efficiency; systems containing highly irreversible processes will have a low thermal energy efficiency. TA mainly consists of three parts. The first part mainly assesses the thermodynamic performance of the current operation. The second part identifies the retrofits to reduce the energy dissipation. The third part involves the assessment of the thermodynamics and economic effectiveness of the retrofits. Assessing the level of irreversibility in energy intensive processes, and suggesting possible ways of reducing the irreversibility, can be carried out with various methodologies including second law analysis, exergy analysis, pinch analysis, equipartition principle, and exergoeconomics. Economic analysis and sustainability assessment can evaluate the impact of retrofits. Exergy analysis and pinch analysis are the two most popular methodologies applied widely for energy intensive systems including power production, refrigeration, waste heat utilization, and distillation column systems. Exergoeconomics and extended exergy analyses tie the TA, particularly exergy analysis, to economy, environment, and sustainability, which has three overlapping elements of economy, environment, and society. The energy type and usage may affect all three elements. TA is also seen as indicating natural limits on the conservation of energy and the attainment of sustainability. Exergoeconomics converts monetary expenses into equivalent exergy fluxes [31] and the optimization of energy usage for a process is based on these exergy fluxes. Normally, in conducting an exergoeconomic balance, a system of simultaneous equations with a higher number of unknowns than equations is obtained, thus some additional equations and assumptions may be required [70–73]. Such an extended representation of exergy flow diagrams constitutes a substantial generalization of cumulative exergy consumption procedure, and may provide a coherent and consistent framework for including nonenergetic quantities like capital cost, labor cost, and environmental impact into an engineering optimization procedure. It is widely acknowledged that exergy has a significant role to play in evaluating energy conservation and the environmental impact. However, there is a possibility of overstating the consequences of the laws of thermodynamics as exergy analysis tries to relate the thermodynamic imperfections to the best possible ECMs [31]. Although increasing efficiency generally requires greater use of materials, labor, and advanced technologies, the additional cost may be justified by the resulting benefits. Three relationships between exergy and environmental impact are the loss of order, resource degradation, and waste exergy emissions. The loss of order is a form of environmental damage and the exergy of an ordered system is greater. The degradation of resources found in nature may be a form of environmental damage. The exergy associated with waste emissions can be as a potential for environmental damage as they have a potential to cause change. Many of

88

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these side effects of energy production and consumption may lead to resource uncertainties and potential environmental hazards on a local, regional, and global scale. Examples include:

• • • • •

Depletion of oil and natural gas resources. Generation of smog from urban road transport. Formation of acid rain via pollutant emissions primarily from fossil fuel power stations. Difficulty of long-term safe storage of radioactive wastes from nuclear power plants. Possibility of the enhanced greenhouse effect from combustion-generated pollutants.

5.2.7

Future Directions

TA can assess the unnecessary energy dissipation because of irreversibility in an energy intensive system, such as the industrial and transportation sectors, and identify the retrofits to reduce energy dissipation and hence increase the usage of available energy. TA methodologies should be used as novel energy conserving measures for existing systems as well as the new designs. Sustainability has environmental, economic, and social dimensions, and requires the responsible use of energy resources and reduction in CO2 emission as well as toxic chemical emissions. The three intersecting dimensions illustrate the 3D sustainability metrics that include nonrenewable material and energy use, and toxic and pollutant emissions per unit product. Nonrenewable energy usage affects the environment adversely through the emission of GHGs. Therefore, a comparative assessment with the sustainability metrics may prove useful in identifying the scope for retrofits for possible ECMs and emission of GHGs for the energy intensive processes and systems. TA helps estimating and improving the energy and environmental sustainability metrics, and hence can lead to more sustainable thermal processes [31,74].

5.2.8

Closing Remarks

Depletions of mostly limited natural resources and environmental degradations on the regional and global scales may result from energy production and consumption. Therefore, the energy sector plays an important role in balanced interactions among environment, economics, and society toward sustainable development. Energy conservation may be one of the key measures of the energy sector toward a low carbon economy and society, since the energy conservation saves money, reduces harmful emissions, creates economic activity, and brings up technological innovations toward improved and affordable living conditions. TA can provide the ECMs toward a more sustainable energy sector. TA targets reducing irreversibility that causes unnecessary heat dissipation due to mismatches between the operating conditions and design parameters. However, TA mainly leads to thermodynamic optimum, which may not always lead to monetary advantage and is a methodology based on trade-offs among the complex competing factors.

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20]

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[21] Aspen Technology. Available from: http://www.aspentech.com/products/aspen-plus.aspx; 2016 [accessed 06.07.16]. [22] International Energy Outlook (IEO) 2016. U.S. Energy Information Administration (EIA)-DOE/EIA-0484. Available from: http://www.eia.gov/outlooks/ieo/pdf/0484(2016).pdf; 2016 [accessed 06.07.16]. [23] Annual Energy Outlook with Projections to 2050 U.S. Energy Information Administration (EIA)-AEO. Available from: http://www.eia.gov/outlooks/aeo/pdf/0383(2017).pdf; 2017 [accessed 09.07.16]. [24] The International Renewable Energy Agency (IRENA). Renewable energy in the water energy & food nexus. Available from: http://www.irena.org/documentdownloads/ publications/irena_water_energy_food_nexus_2015.pdf; 2015 [accessed 10.07.15]. [25] S. Nolen. Leveraging energy management for water conservation, Industrial Energy Technology Conference. New Orleans, LA; 2015. [26] Stefanovic IL. Ethics, sustainability, and water management: A Canadian case study. In: Filho WL, Sumer V, editors. Sustainable water use and management. Dordrecht: Springer; 2015. [27] Moldavska ZA, Welo T. On the applicability of sustainability assessment tools in manufacturing. Proc CIRP 2015;29:621–6. [28] Matzen M, Demirel Y. Methanol and dimethyl ether from renewable hydrogen and carbon dioxide: alternative fuels production and life-cycle assessment. J Cleaner Prod 2016;139:1068–77. [29] Wall G. Conditions and tools in the design of energy conversion and management systems of a sustainable society. Energy Convers Manag 2002;43:1235–48. [30] Demirel Y. Nonequilibrium thermodynamics transport and rate processes in physical, chemical and biological systems. 3rd ed. Amsterdam: Elsevier; 2014. [31] Demirel Y. Thermodynamic analysis. Arab J Sci Eng 2013;38:221–49. [32] White DC. Optimize energy use in distillation. Chem Eng Prog 2012;108:35–41. [33] Demirel Y. Thermodynamic analysis of separation systems. Sep Sci Technol 2004;39:3897–942. [34] Dhole VR, Linnhoff B. Distillation column targets. Comp Chem Eng 1993;17:549–60. [35] Bandyopadhyay S, Malik RK, Shenoy UV. Temperature-enthalpy curve for energy targeting of distillation columns. Comput Chem Eng 1998;22:1733–44. [36] Demirel Y. Sustainable operations for distillation columns. Chem Eng Process Technol 2013;1005:1–15. [37] Demirel Y. Retrofit of distillation columns by thermodynamic analysis. Sep Sci Technol 2006;41:791–817. [38] Pinto FS, Zemp R, Jobson M, Smith R. Thermodynamic optimization of distillation columns. Chem Eng Sci 2011;66:2920–34. [39] Demirel Y. Exergy analysis of distillation columns. Int J Exergy 2006;3:345–61. [40] Chan Y, Kantamaneni R. Study on energy efficiency and energy saving potential in industry from possible policy mechanisms contract No. ENER/C3/2012-439/ S12.666002. Available from: https://ec.europa.eu/energy/; 2015. [41] Abdelaziz EA, Saidur R, Mekhilef S. A review on energy saving strategies in industrial sector. Renew Sustain Energy Rev 2011;15:150–68. [42] Rietbergen MG, Blok K. Setting SMART targets for industrial energy use and industrial energy efficiency. Energy Policy 2010;38:4339–54. [43] American Council for an Energy Efficient Economy (ACEEE). Available from: http://aceee.org/sector/industrial; 2016 [accessed 07.02.16). [44] Tsatsaronis G. Recent developments in exergy analysis and exergoeconomics. Int J Exergy 2008;5:489–99. [45] Çengel YA, Boles MA. Thermodynamics: an engineering approach. 8th ed. New York, NY: McGraw-Hill; 2014. [46] Alliance to Save Energy ASE. Available from: https://www.ase.org; 2012 [02.03.17]. [47] U.S. DOE. Energy efficiency standard (EES). Available from: https://ees.lbl.gov; 2016 [accessed 15.10.16]. [48] WSDA. Available from: http://agr.wa.gov/Bioenergy/energyefficiency.aspx; 2012 [accessed 15.02.17]. [49] Demirel Y, Kunç S. Thermal performance study on a solar air heater with packed flow passage. Energy Convers Manag 1987;27:317–25. [50] Demirel Y, Ozturk HH. Thermoeconomics of seasonal heat storage system. Int J Energy Res 2006;30:1001–12. [51] Demirel Y, Paksoy HO. Thermal analysis of heat storage materials. Thermochim Acta 1993;213:211–21. [52] Ozturk HH, Demirel Y. Exergy-based performance analysis of packed bed solar air heaters. Int J Energy Res 2004;28:423–32. [53] Kenisarin M, Mahkamov K. Solar energy storage using phase change materials. Renew Sustain Energy Rev 2007;11:1913–65. [54] Mehling E, Cabeza LF. Phase changing materials and their basic properties. In: Paksoy HO, editor. Thermal energy storage for sustainable energy consumption, NATO science series. Dordrecht: Springer; 2007. [55] USDA National Resources Conservation Services (NRCS). Available from: https://www.nrcs.usda.gov/wps/portal/nrcs/main/national/energy; 2016 [accessed 07.02.16]. [56] Bureau of Transportation Statistics. National transportation statistics, Transportation energy data book (BTS): https://www.bts.gov/; 2016 [accessed 07.02. 2016]. [57] National Academy of Sciences. Real Prospects for energy efficiency in the United States. doi: 10.17226/12621 Washington, DC: The National Academies Press; 2010. [58] Markel A. Plug-in hybrid-electric vehicle analysis. Golden, CO: NREL; 2007. [59] Nguyen N, Demirel Y. Retrofit of distillation columns in biodiesel production plants. Energy 2010;35:1625–32. [60] Consumer Reports. Available from: http://www.consumerreports.org/; 2016 [accessed 07.02.16]. [61] Matzen M, Alhajji M, Demirel Y. Chemical storage of wind energy by renewable methanol production: Feasibility analysis using a multi-criteria decision matrix. Energy 2015;93:343–53. [62] A. Körner, Energy technology perspectives 2012. Pathways to a clean energy system. Transport sector: trends, indicators energy efficiency measures. International Energy Agency, Paris: OECD/IEA; 2012. [63] Herold KE. Waste heat recovery applications: absorption heat pumps. In: Anwar S, editor. Encyclopedia of energy engineering and technology, . 2nd ed. Boca Raton, FL: CRC Press; 2014. [64] ASHRAE. Standard Project Committee 103. Available from: http://spc103.ashraepcs.org/; 2016 [accessed 06.07.16]. [65] Guan L, Chen G. Solar energy: building operations use. In: Anwar S, editor. Encyclopedia of energy engineering and technology, . 2nd ed. Boca Raton, FL: CRC Press; 2014. [66] Al-Muslim H, Dinçer I. Thermodynamic analysis of crude oil distillation systems. Int J Energy Res 2005;29:637–55. [67] Alhajji M, Demirel Y. Energy and environmental sustainability assessment of crude oil refinery by thermodynamic analysis. Int J Energy Res 2015;39:1925–41. [68] Chemical Engineering. Available from: http://www.chemengonline.com; 2014 [accessed 11.07.16]. [69] Alhajji M, Demirel Y. Energy intensity and environmental impact metrics of the back-end separation of ethylene plant by thermodynamic analysis. Int J Energy Environ Eng 2016;7:45–59. [70] Sciubba E. Exergo-economics: thermodynamic foundation for a more rational resource use. Int J Energy Res 2005;29:613–36. [71] Valero A, Serra L, Uche J. Fundamentals of exergy cost accounting and thermoeconomics. Part II: applications. J Energy Resour Technol 2006;128:9–15. [72] Kelly S, Tsatsaronis G, Morosuk T. Advanced exergetic analysis: approaches for splitting the exergy destruction into endogenous and exogenous parts. Energy 2009;34:384–91. [73] Petrakopoulou F, Tsatsaronis G, Morosuk T, Carassai A. Advanced exergoeconomic analysis applied to a complex energy conversion system. J Eng Gas Turbines Power 2012;134:031801–8. [74] Hammond GP. Engineering sustainability: thermodynamics, energy systems, and the environment. Int J Energy Res 2004;28:613–39.

Relevant Websites https://www.energystar.gov/buildings/facility-owners-and-managers/industrial-plants Energy Star.

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http://www.fao.org/energy/water-food-energy-nexus/en/ Food and Agriculture Organization of the United Nations. http://www.neep.org/initiatives/high-efficiency-products/industrial-energy-efficiency Northeast Energy Efficiency Partnerships. https://energy.gov/eere/amo/advanced-manufacturing-office U.S. Department of Energy – Advance Manufacturing Office. https://energy.gov/eere/amo/industrial-assessment-centers-iacs U.S. Department of Energy – Industrial Assessment Centers. https://energy.gov/eere/amo/downloads/industrial-energy-efficiency-designing-effective-state-programs-industrial-sector U.S. Department of Energy – Industrial Energy Efficiency. https://energy.gov/eere/amo/downloads/save-energy-now-assessment-helps-expand-energy-management-program-shaw-industries U.S. Department of Energy – Save Energy Now. https://www.water-energy-food.org/start/ Water-Energy-Food Nexus.

5.3 Waste Energy Management Yildiz Kalincı, Dokuz Eylul University, Izmir, Turkey Ibrahim Dincer, University of Ontario Institute of Technology, Oshawa, ON, Canada r 2018 Elsevier Inc. All rights reserved.

92 93 93 94 96 98 98 98 98 98 99 99 99 100 101 102 102 106 106 113 114 116 117 117 118 122 123 123 124 125 130 130 130 132 133

5.3.1 Introduction 5.3.2 Classification 5.3.2.1 Waste by Producer 5.3.2.1.1 Municipal waste 5.3.2.1.2 Industrial waste 5.3.2.1.3 Hazardous waste 5.3.2.2 Waste by Chemical Composition 5.3.2.2.1 Organic compounds 5.3.2.2.2 Inorganic compounds 5.3.2.2.3 Microbiological wastes 5.3.3 Legislative Trends 5.3.4 Systems 5.3.4.1 Recycling 5.3.4.2 Refuse Derived Fuel 5.3.4.3 Landfill 5.3.4.4 Thermal Methods 5.3.4.4.1 Incineration 5.3.4.4.2 Pyrolysis 5.3.4.4.3 Gasification 5.3.4.5 Biological Methods 5.3.5 Illustrative Examples 5.3.6 Case Study of WtE 5.3.6.1 System Description 5.3.6.2 Analysis 5.3.6.2.1 Exergy analysis 5.3.6.2.2 Specific exergy cost (SPECO) analysis 5.3.6.3 Results and Discussion 5.3.6.3.1 Exergy analysis 5.3.6.3.2 SPECO analysis 5.3.7 Future Directions 5.3.8 Conclusions Acknowledgments References Further Reading Relevant Websites

Nomenclature c C CRF C_ CA_ D e

_ Ex F A_ h HHV i

Unit cost ($/GJ) Cost ($) Capital recovery factor Cost rate ($/h) Annual capital cost ($/year) Fuel cost ($/year) Standard chemical exergy of a pure chemical compound i, difference inflation rates, kJ kmol 1, % Exergy rate (kW) Annual fuel cost ($/year) Specific enthalpy (kJ/kg) Higher heating value (MJ/kg or MJ (N/m3)) Interest (%)

Comprehensive Energy Systems, Volume 5

I_ LHV _ m n P Pe PEC PWF _ PW _ Q r R s S_ T

doi:10.1016/B978-0-12-809597-3.00510-1

Irreversibility (exergy destruction) rate (MW) Lower heating value (MJ/kg or MJ (N/m3)) Mass flow rate (kg/s) Year Pressure (bar) Total fuel cost for n years ($) Purchased equipment cost ($) Present worth factor (–) Present worth ($) Heat transfer rate (thermal energy rate) (MW) Inflation (%) Universal gas constant (kJ/kmol/K) Entropy (kJ/kg/K) Salvage value ($/h) Temperature (1C or K)

91

92

Waste Energy Management

Z_

Sum of capital investment and operating and maintenance cost rate ($/h)

Greek letters b Quality factor (–)

e t

Exergetic or second law efficiency (%) Annual number of hours of system operation

Subscripts ASU c CT F HPT I,k in

out P ph q T w n 0

Outlet Product Physical Heat Total Work Nominal Reference index

Superscripts . Over dot quantity per unit time CI Capital investment

OM T

Operating & maintenance Total

Abbreviations APP Advanced plasma power ASEAN Association of Southeast Asian Nations ASU Air separate unit C40 The cities climate Leadership Group CEN European Committee for Standardization CFBG Circulating fluidized bed gasifier CHP Combined heat and power CT Condensing turbine EEA European Environment Agency ESDA Exploratory spatial data analysis EU European Union HPT High pressure turbine HTS High temperature shift HX Heat exchanger ISWA International Solid Waste Association LCA Life cycle analysis LTS Low temperature shift

MSW MTS OM PSA RDF SDS SPECO SRF SuMMa TDF TTGV

Municipal solid waste Medium temperature shift Operating & maintenance Pressure swing adsorption Refuse derived fuel Sustainable development strategy Specific exergy cost Solid recovered fuel Sustainable materials management Tire derived fuel Technology Development Foundation of Turkey United Nations Environment Programme Upgraded waste cooking oil Waste of electrical and electronic equipment Waste plastic disposal Waste to energy

_ W y

5.3.1

Work rate or power (MW) Molar fraction of component i in the gas phase (%)

Air separate unit Chemical Condensing turbine Fuel High pressure turbine Per component Inlet

UNEP UWCO WEEE WPD WtE

Introduction

Waste management is attracting more attention day by day because the problem directly affects human health and environment. Also, social environmental activists and community awareness build pressure on governments to implement laws for environmental protection. On the other hand, social and industrial life has to go on producing waste. The problem is how the waste can be converted to energy or destroyed. Today there are some technological solutions, mainly thermal and biological solutions. The processes can produce fuel pellets, gas, or compost according to the technology and their energy content. When the history of waste is studied, it can be seen that the first recorded regulations about municipal waste management were applied by the Minoan civilization (in Crete, 3000–1000 BC). They used large covered pits for solid wastes. The basic landfill method is still used today. The first garbage collection service was used by the Roman Empire. After the Roman Empire, the subject of waste was ignored in Europe until epidemics such as the Black Death, which struck England in 1347. The waste problems went on in the 19th century when people could dump their wastes on the streets or other convenient places as the order of day, or openly burn garbage. A report that explained the link between diseases and a filthy environment was published in England in 1842 [1]. In addition to this, garbage collection services had been provided in all large cities of the United States by 1930. Although the

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mentioned methods went on to be used, an innovative waste management method, called reduction, came out at the turn of the 20th century. In the beginning, the British and Germans led the incineration method, which had two advantages: reducing the volume of the waste and producing energy. Then the method was used commonly in the United States. The other important development was the chemical age, which exponentially grew in parallel with the petrochemical industry after World War II. So, people were exposed to new synthetic wastes such as nylon, rayon, polystyrene, polyethylene, etc. Parallel to the chemical age, new health problems arose, and people were excessively exposed to toxic substances, causing adverse health effects; these include respiratory diseases, cancers, and preterm deliveries [1–3]. Environmental problems, such as the greenhouse effect, ozone depletion, ground water depletion and pollution, deforestation, and desertification, were taken into consideration by governments in the 1970s when the problems became life threatening. The independent World Commission on Environment and Development investigated the impact of development to the environment and published a report called “Our Common Future” in 1987. In the report, they used the term “sustainable development” for the first time [1]. According to the United Nations Department of Economic and Social Affairs: Population Division, the world population is projected to reach the 9.7 billion mark in 2050 and 11.2 billion by 2100, representing a 53% increase from today [4]. The increasing population, and hence the increasing wastes, indicate that these wastes will go on to create serious problems to human life. For a long time, numerous scientists and researchers have explored the solutions to these problems. Some of the current examples are given below. Agovino et al. [5] analyzed waste managements of 103 Italian provinces applying cluster analysis and exploratory spatial data analysis (ESDA) techniques for the year 2011. They highlighted that there was an inverse relationship between separate waste collection and disposal in landfills. Also, Moh and Manaf [6] conducted a study based on solid waste management in Malaysia. While the landfill method has been commonly used until today, Malaysia has implemented SWCorp Strategic Plan 2014–2020, which especially focuses on source separation and recycling. So, Malaysia has targeted a recycling rate of 22% by 2020, eventually aiming to be a zero waste nation. From the energy perspective, numerous researchers have focused on producing energy from waste via thermal methods. Couto et al. [7] examined a gasification process of municipal solid waste (MSW) in Portugal from the energy and exergy points of view. Optimal operating point was determined to be at 9001C and an equivalent ratio of 0.25 was obtained. According to their study, 17.74 MW syngas was produced from the MSW consisting of 22.67 MW energy. Also, You et al. [8] compared the cogasification of sewage sludge and food wastes and used cost–benefit analysis of gasification and incinerationbased waste treatment schemes in Singapore. They found that the gasification-based schemes were financially superior to the incineration-based scheme based on the data of net present value. Santagata et al. [9] presented an environmental assessment of electricity production from slaughterhouse residues linking urban, industrial, and waste management systems. Slaughterhouse waste represents an important potential source of renewable energy: on average, 40%–50% of a live animal is waste, with a potential energy content closest to diesel fuel. They investigated a real plant for 1 MWh electric production from animal fat using life cycle analysis (LCA). Also, Cucchiella et al. [10] investigated the sustainability of a WtE plant as an alternative to landfill. They used two specific indicators, the reduction of the emissions of equivalent carbon dioxide and financial net present value, in their analyses. They reported that using a WtE plant as an alternative to landfill supplied a profit of €25.4 per kiloton of treated waste. Also, 370 kgCO2eq per ton of treated waste was avoided. Finally, it can be stated that the problems with waste will continue in the future due to the fact that every technological development produces waste. Thus, people have to implement new systems to convert waste to energy (WtE) to protect the environment. Therefore, this study aims to contribute a more detailed understanding of waste management systems. In accordance with the purpose, the paper presents (1) types of waste, (2) legislative trends, (3) technologies, (4) some example processes, (5) a case study of WtE system containing exergy and cost analyses, and (6) future directions of waste with zero waste, circular economy, and industrial symbiosis concepts.

5.3.2

Classification

Waste can be defined as materials that have completed their lifespan. Sometimes, it can be defined as unwanted and rejected materials, too. According to the United Nations Environment Programme (UNEP) [11], waste can be viewed as the combination of four wrongs – a wrong substance, in a wrong quality, in a wrong place at a wrong time. Also, it can be said that waste is unwanted outputs of human activity such as gases, liquids, and solids that are emitted to the three environmental receiving media of air, water, and land. The wastes can be categorized by producer or chemical type as shown in Fig. 1. In terms of producers, the wastes can be divided into three subsections, i.e., municipal, industrial, and hazardous, while their chemical compositions are investigated in terms of organic, inorganic, and microbiological.

5.3.2.1

Waste by Producer

The wastes come from different places such as homes, schools, restaurants, office buildings, factories, industrial areas, hospitals, etc. Hence, their contents can be very different. For example, while a house can produce food waste, plastics, paper, or yard waste, a school produces mostly paper and a hospital produces biological waste.

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Waste classification

Waste by producer

Municipal

Waste by chemical composition Organic compounds

Industrial Inorganic compounds Hazardous Microbiological waste Fig. 1 Waste classification.

Table 1

Amount of generation of MSW in ASEAN countries

Countries

Per capita MSW generation (kg/capita/day)

Annual MSW generation (t)

Brunei Darussalam Cambodia Indonesia Lao PDR Malaysia Myanmar Philippines Singapore Thailand Viet Nam

1.4 0.55 0.7 0.69 1.17 0.53 0.69 3.763 1.05 0.84

210,480 1,089,429 64,000,000 77,380 12,840,000 841,508 14,660,000 7,514,500 26,770,000 22,020,000

Source: Data from United Nations Environment Programme (UNEP). Waste management in Asean countries, summary report. Available from: http://www.unep.org; 2017 [accessed 15.07.17].

In summary, in terms of the producer, the wastes can be divided into three subsections, i.e., municipal waste, industrial waste and hazardous waste. The following sections investigate these in more detail.

5.3.2.1.1

Municipal waste

It can be said that nearly all cities around the world have waste problems due to increasing population and life standards. Wastes can be produced in any place, such as houses, schools, restaurants, hospitals, office buildings, parks, etc. Thus, municipal wastes consist of a lot of different components such as paper, glass, organic materials, plastics, textiles, tires, etc. Thereby, it is difficult to collect, recycle, or convert the municipal wastes to energy. A report from UNEP [12] presented some data related to waste management in Association of Southeast Asian Nations (ASEAN) countries. The organization comprises 10 Southeast Asian states that have a combined population of 625 million, covering 8.8% of the world’s population. According to the report, per capita MSW generation is 1.14 kg/per capita/day. The order of the MSW generation is as follows: Indonesia produces 64 million tonnes/year as the highest, second is Thailand (26.77 million tonnes/year), followed by Viet Nam (22 million tonnes/year), Philippines (14.66 million tonnes/year), Malaysia (12.84 million tonnes/year), Singapore (7.5 million tonnes/year), Myanmar (0.84 million tonnes/year), and Lao PDR, which has the lowest quantity at 0.07 million tonnes/year, as given more detail in Table 1. When the composition of the MSW is analyzed from Table 2, food/organic waste makes up the most important part, followed by plastic waste. But the situation is quite different for Singapore. Its MSW is mainly composed of organic waste (10.5%), paper (16.5%), plastic (11.6%), and metal (20.8%). When the European scenario is considered, the European Environment Agency (EEA) [13] defines that MSW covers about 10% of total waste generated in Europe. They presented a report covering EU-28 Member States, Iceland, Norway, Switzerland, and Turkey with some information from the Western Balkan countries, although there were some differences about the definition of MSW among the countries. Fig. 2 shows MSW generation per capita in these countries; according to the figure, the highest waste is generated in Denmark, Switzerland, and Germany, while lowest waste comes from Romania, Poland, and Serbia. It is clear that the wealthier countries produce more waste per capita. Also, the tourism sector triggers an increase in waste as can be seen in Cyprus and Malta.

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Table 2

Composition of MSW in ASEAN countries

MSW Composition (%) Brunei Darussalam Cambodia Indonesia Lao PDR Malaysia Myanmar Philippines Singapore Thailand Viet Nam

Food/organic waste

Paper

Plastic

36 60 60 64 45 73 52 10.5 64 55

18 9 9 7 8.2 2.24 8.7 16.5 8 5

16 15 14 12 13.2 17.75 10.55 11.6 17.62 10

Metal 4 4.3 1

4.22 20.8 2 5

Glass 3 3 1.7 7 3.3 0.45 2.34 1.1 3 3

Textile

Rubber

1 3.5 5

1 5.5 3

1.14 1.61 2.1 1.4

1 4

Source: Data from United Nations Environment Programme (UNEP). Waste management in Asean countries, summary report. Available from: http://www.unep.org; 2017 [accessed 15.07.17].

Denmark Switzerland Germany Cyprus Luxembourg Malta Austria Iceland Netherlands France Czech Republic Italy United Kingdom Finland Portugal Bulgaria Sweden Belgium Spain Lithuania Slovenia Norway Tuirkey Croatia Hungary Former Yugoslav Republic of Macedonia Estonia Bosnia and Herzegovina Latvia Slovakia Serbia Poland Romania Ireland Greece

2004 2014

Municipal waste generated in European countries (kg/capita) Fig. 2 Municipal waste generated per person in European countries (2004 and 2014). Adapted from European Environment agencity (EEA), Municipal waste management across European countries. Available from: https://www.eea.europa.eu/publications/municipal-waste-managementacross-european-countries; 2016 [accessed 22.07.17].

In addition to these, Fig. 3 gives the MSW recycling in European countries for the years 2004 and 2014. The figure shows that Europe has been applying a successful environmental policy. The countries achieved an average total recycling rate of 33% in 2014, though it was 23% in 2004. Especially, Germany, Austria, Belgium, Switzerland, the Netherlands, and Sweden managed to recycle nearly half of their MSW. The highest acceleration was seen in Lithuania, Poland, Italy, the United Kingdom, and the Czech Republic with a rate of 20%–29%. In 2015, the European Commission set new targets for MSW, aiming to increase the recycling and preparing for reuse of the MSW to 60% by 2025 and 65% by 2030.

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Germany Austria Belgium Switzerland Netherlands Sweden Luxembourg United Kingdom Denmark Norway Italy France Ireland Slovenia Czech Republic Spain Finland Poland Hungary Estonia Iceland Portugal Lithuania Bulgaria Latvia Greece Cyprus Croatia Romania Malta Slovakia Serbia Turkey Bosnia and Herzegovina

2004 2014

40 0 60 80 20 Municipal waste recycling percentage in European countries (%) Fig. 3 Municipal waste recycling in European countries (2004 and 2014). Adapted from European Environment agencity (EEA), Municipal waste management across European countries. Available from: https://www.eea.europa.eu/publications/municipal-waste-management-across-europeancountries; 2016 [accessed 22.07.17].

A general global outlook of MSW is investigated in detail in a 2015 UNEP report [11]. The report investigates the MSW production and its composition according to income levels of countries as shown in Fig. 4. It can be clearly seen from the figure that the major difference lies in the organic material percent. The MSW consists of 46%–53% organic material in low and middleincome countries despite the fact that the rate reduces to 34% in high income level countries. Also, it appears that the paper waste is in second order. Its percentage changes from 6% to 24% from low income to high income countries, respectively. The annual per capita consumption of paper worldwide is described as 240 kg in North America, 140 kg in Europe, 40 kg in Asia, and 4 kg in Africa. Another important waste is plastics; its percentage changes in a narrow range from 7% to 12%. In addition, the percentages of glass and metal wastes are observed to have a parallel increase with the income level.

5.3.2.1.2

Industrial waste

Basically, industry converts raw materials (renewable or nonrenewable) to valuable products. Industrial wastes are produced during the process. When analyzed from the waste point of view, some sectors come forward such as the mining, oil, gas, and agriculture sectors. The wastes dumped into the environment in an uncontrolled manner, which has caused major damage in the past. Hence, today industrial manufacturing has to change its direction from waste management to waste utilization to move toward sustainable production. Scarcity of resources leads to a lot of problems in industrial areas such as increasing cost of products and compatibility. Only, the problem caused a lot of conflicts in the past as briefly shown in Table 3. It can be observed that it goes on today as well, but in different forms. Thus, industrial wastes are as worthy as resources in the present world. Effects of some main industries can be investigated in more detail as examples. Firstly, the cement industry is adopted as an essential industry needing sustainable development in countries. The main contamination source of the industry is solid waste called cement bypass dust, which is collected from the bottom of dust filters. When the cement process line is analyzed, limestone

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Other 18% Organic material 53%

Other 28% Textiles 3% Metals 3% Glass 3% Textiles 2% Metals 2% Glass 2% Plastics 7%

Plastics 9%

Paper 11%

Paper 6%

Lower-middle

Low Income Upper-middle Other 11% Textiles 3%

Organic material 46%

High

Other 19%

Metals 4%

Organic material 34%

Textiles 1% Glass 5%

Metals 5%

Plastics 12%

Glass 6%

Plastics 11% Paper 19% Paper 24% Fig. 4 Variation in MSW composition according to income levels of countries. Data from United Nations Environment Programme (UNEP). Global Waste Management Outlook (GWMO). Available from: http://www.unep.org; 2015 [accessed 15.07.17].

Table 3

Wars connected with raw materials

Country

Raw material

Period of conflict

Peru Angola Sierra Leone Liberia Afghanistan Papua New Guinea – Bougainvillea

Coca Diamonds, oil Diamonds Diamonds, wood, palm oil, iron, cacao, coffee, marihuana, rubber Opium, jewelry Copper, gold

1980–1995 1975–2002 1991–2000 1986–1996 1978–2001 1988–2001

Source: Adapted from United Nations Environment Programme (UNEP). PRE-SME – Promoting Resource Efficiency in Small & Medium Sized Enterprises Industrial training handbook. Available from: http://www.stenum.at/media/documents/UNEP%20PRE%20SME%20Industrial%20training%20handbookpdf; 2010 [accessed 22.07.17].

and clay are the main raw materials. Also, other additives can be used according to the desired cement properties. Firstly, the limestone is crushed into small rocks and then ground. Next, the mix enters the rotating kiln to be heated to approximately 14501C to produce the clinker, which is then ground to form the fine powder of cement. The cement bypass dust is collected below stacks by means of filters. The cement bypass dust is considered as one of the most dangerous industrial pollutants due to some of its characteristics such as particle size of 1–10 mm and high alkalinity, with a pH level of 11.5. Also, the stack emissions of the manufacturing process consist of dust, sulfur oxide, organic compounds, nitrogen oxides, carbon oxides, chlorine compounds, fluorine and its compounds, etc. To control air pollution, bag filters, cyclone separators, and electrostatic precipitators can be used. It is estimated that a single process line in any cement factory produces a minimum of 300 t of bypass dust per day. It is known that a cement factory consist of three or more production lines, so the cement bypass dust produced is nearly 1000 t/day, or 0.35 million ton/year. Finally, the cement bypass dust causes serious health problems, and its alkalinity (pH ¼ 11.5) can create corrosive effects on machines and buildings. But, the cement bypass dust can be used as a raw material in other industrial sectors. For instance, onsite recycling in the cement production process, production of tiles/bricks/interlocks blended cements, production of

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safe organic compost (soil conditioner) by stabilizing municipal waste water sludge, and production of glass and ceramic glass. Also, more detail about the cement industry and the cement bypass dust can be found in Ref. [14]. Another important industrial sector is the steel industry. Indeed, a lot of other sectors could not have been established without this sector. There are three ways to obtain finished steel products: (1) integrated steel production, (2) secondary processing, and (3) direct reduction. In the integrated steel production process, coal is transformed to coke in coke ovens, while iron ore is sintered and pelletized. The ore is reduced in a blast furnace, where a hot metal consisting of 4% carbon and smaller rates of other alloying elements is obtained. Then the hot metal is converted to steel using the basic oxygen furnace and is cast to obtain semifinished products such as blooms, bars, and slabs. The secondary process starts melting steel scraps in an electric arc furnace. The product may be treated in a ladle furnace and then cast and finished in a rolling operation. The direct reduction method starts converting high grade iron core pellets to sponge pellets using natural gas. The sponge pellets are fed into an electric arc furnace. Then the steel is continuously cast and rolled into its final shape. The main waste of the sector is slag that amounts to 20%–40% of hot metal production. The slag can be used as a raw material in other sectors, for instance, virtually all of the slag produced is utilized either in the cement sector or as road filling in Western Europe and in Japan [14]. Furthermore, drill cuttings, petroleum sector is the main sector in the world that is related to energy markets and grows every day. Drilling activities, which can be onshore or offshore, are essential activities in the oil and gas production sector. During the drilling of a well several chemicals such as oil-based, water-based, and synthetic-based mud are used. Cuttings coming out from the ground are contaminated by the chemicals, mud, and oil. The wastes of the drilling operation are usually drill cuttings, solids, hydraulic fluids, used oil, rig wash, spilled fuel, spent and unused solvents, paints, scrap metal, solid wastes, and garbage. Also, the mud consists of soda ash, calcium carbonate, caustic soda (NaOH), magnesium hydroxide, acids, bactericides, and shale control inhibitors with other chemicals. To supply cleaner production, it is proposed that the mud can be used in a cement kiln as an alternative fuel [14].

5.3.2.1.3

Hazardous waste

Hazardous wastes are defined according to different jurisdictions but if a waste is toxic or hazardous to humans or the environment, it can be considered to be in this category. Subparameters that are considered to categorize a substance as hazardous include flammability, corrosiveness, reactivity, and toxicity. These types of wastes must be sent to specially licensed hazardous facilities or special hazardous waste cells located at regulated landfills. The hazardous wastes can be organic sludge, solvents or organic solutions, oil and greases, oil/water mixtures, organic and oily residues, heavy metal solutions, miscellaneous chemicals, paint, aqueous solutions with organics, sludge and inorganic residuals, pesticides, herbicides, etc. In addition, radioactive wastes are also included in this category. They come mainly from nuclear power plants, medical procedures, or specialized industrial processes. Certainly, they can generally be considered as a fraction of industrial waste [1,15].

5.3.2.2

Waste by Chemical Composition

Another way to categorize the types of waste is by their chemical composition. This is a more rigorous categorization. It can be classified into three main groups: organic, inorganic, and microbiological materials.

5.3.2.2.1

Organic compounds

Organic chemicals comprise mainly carbon compounds that form between carbon, hydrogen, oxygen, and nitrogen. Although, living organisms form organic compounds naturally, they can also be made synthetically. The organic compounds have some determinant attributes such as gravity (an organic compound sinks or floats in water), solubility (it can be dissolved in water), volatility (it can be evaporated in air), adsorption (a compound can stick to a surface, e.g., soil), and degradation (it can be decompose to simpler molecules) [1]. Although the organic wastes have been directed to landfills up till today, they cause some environmental and health hazards. For instance, leachate from organic materials in the area can cause ground water contamination and soil contamination. Instead of landfill, the organic compounds can be used to produce biogas via other methods [16].

5.3.2.2.2

Inorganic compounds

The chemical compounds that are not organic compounds are classified as inorganic. The group can be divided into three subsets: (1) major inorganic elements and their compounds (silicates, sulfates, cyanides, or iron), (2) trace metals (aluminum, cadmium, copper, mercury, nickel, selenium, zinc, and their compounds), and (3) arsenic and its compounds [1].

5.3.2.2.3

Microbiological wastes

This consists of biomedical wastes that contain materials toxic to living cells. It has two subsets: bacteria and viruses. Bacteria are single-celled microorganisms lacking chlorophyll. Though some of them are useful for the human body, some toxin producing bacteria can cause diseases such as diphtheria, tetanus, and botulism. Viruses are much smaller than bacteria and need to grow in an animal, plant or bacterial cell. They can cause some infectious diseases such as rabies, measles, or the common cold. The wastes can include human tissues, organs, body parts, animal parts, and nonanatomic wastes such as cultures and blood [1].

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Legislative Trends

Legal obligations for waste management vary from country to country. For instance, the European Union (EU) issued the Landfill Directive 1999/31/EC in 1999 to divert waste from landfills and forced its member states especially for biodegradable municipal waste. Although a landfill tax was introduced in 1996, the European Landfill Directive became a law in 2003 [5]. Indeed, the European Council adopted the first EU Sustainable Development Strategy (SDS) in 2001, following the commitments taken during the Earth Summit in Rio de Janeiro (Brazil) in 1992 [17]. The aim of SDS is to support quality of life and environmental actions like the 7th Environmental Action Programme. Also, one of the main recommendations of the 2003/30/EC EU Directive is a minimal blending share of biomass/renewable fuels into transportation fuel needs. The EU had targeted an enhancement in the biofuel share (energy content) by 2010 to increase 5.75% and by 2020 to achieve 10% [18]. In addition to this, the member countries of the EU are required to implement waste management systems with the following order of priority: prevention of waste generation; enhancing its reuse, recycling, and recovery; and finally disposal (Directive on Waste, 2008/98/EC of the European Parliament and Council of 19th November 2008) [19]. Other countries implemented some regulations similar to the EU. Japan established some legislation such as the Waste Management and Public Cleansing Law, Home Appliance Recycling Law, and Small Appliance Recycling. The Chinese government has enacted the Environmental Protection Law of the People's Republic of China, Law of the People's Republic of China on Circular Economy Promotion, and Law of the People's Republic of China on Cleaner Production Promotion [20]. In addition, Australia passed the National Waste Policy in 2009 [21]. On the other hand, laws change day by day according to waste type. For instance, today waste of electrical and electronic equipment (WEEE or e-waste) is one of the most important problems as hazardous waste, when there is no past. The total e-waste production was estimated as 41.8 million tonnes in 2014 and expected to rise to 50 million tonnes by 2018. According to this, its annual growth rate is foreseen as 3%–5%, which is about three times higher than other waste streams [22]. The EU has established a directive on WEEE or e-waste Directive since 2002 to prevent the usage of hazardous substances in electrical and electronic equipment. Also, many countries have set up similar e-waste legislation. Although, to control transboundary movements and disposal of hazardous wastes, the Basel Convention was designed in 1992 under UNEP; however, some developed countries such as the United States did not ratify the convention, and continued to export their e-wastes to developing countries such as China and India [20,22]. Today the EU, Japan, and South Korea have implemented very sophisticated regulations to control e-wastes. Although some developing countries such as India and Brazil have promulgated their e-waste regulations, many countries do not have any regulations yet [20]. Another important waste is radioactive waste. Nuclear facilities have been used in different areas such as medicine, research, military, and electric power production for more than 50 years. There is an increasing need to supply long-term safety for radioactive waste management involving requirements for acceptance for storage and final disposal of radioactive waste and metrological requirements for radioactive waste characterization. The International Atomic Energy Agency and the European Commission is trying to improve directives related to these issues in Europe [23]. Although the waste management legislation is mainly prepared by government agencies, identifying factors influencing willingness of individuals to accept the waste management programs is also important. Triguero et al. [24] presented an inclusive study related to these issues. Nearly 24,000 individuals from 28 European countries were analyzed for three different options of waste management: government-based, consumer-based, and producer-based. They found out that there were a lot of parameters affecting the choice of waste management such as gender, education level, occupational status, household size, and living area.

5.3.4

Systems

Wastes can be hazardous or nonhazardous, toxic or nontoxic, with complex structure or simple component material. Although developed and industrialized countries produce toxic, nonorganic, and nonbiodegradable waste, developing countries usually produce organic matter and ashes. There are some technological methods to reduce or convert waste to energy. But their energy contents are also important to consider a WtE method; for instance, calorific values of some wastes are given in Table 4. These methods are explained in more detail in the following sections.

5.3.4.1

Recycling

Recycling denotes mainly blue-box or street-side programs that include paper, cardboard, metal, aluminum, and plastics. To achieve an effective recycling program, legislative guidance is necessary because it is shown that people do not want the recycling programs to be voluntary. The recycling programs contain some applications such as reusing soft-drink, beer, wine bottles, etc.; using of recycling material in manufacturing new products; giving up excessive packing of products, etc. So, the method has both environmental and economical advantages. Also, public awareness is important; many people do not know that recycling one glass bottle saves the energy equivalent to burning a 100-Watt light bulb for 4 h, or that recycling 54 kg of paper saves one tree [1]. The recycling systems supply four main advantages: (1) protection of natural resources, (2) less energy required for recycling than new production, (3) reducing the amount of waste, and (4) economic contribution.

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Table 4

Approximate net calorific values for some wastes

Waste

Calorific value (MJ/kg)

Paper Organic material Plastics Textile Metals Glass Wood

16 4 35 19 0 0 15

Source: Data from Ref. World Energy Resources Waste to Energy-2016 rapor. Available from: https://www.worldenergy.org/wp-content/uploads/2017/03/ WEResources_Waste_to_Energy_2016.pdf; 2017 [accessed 10.06.17].

Table 5

EN 15359 classes classification

Classification property

Net calorific value (MJ/kg) CI (%) Hg (mg/MJ)

Statistics measure

Average Average Median

EN 15359 classes 1

2

3

4

5

Z25 r0.2 r0.02

Z20 r0.6 r0.03

Z15 r1.0 r0.08

Z10 r1.5 r0.15

Z3 r3.0 r0.50

Source: Data from Gallardo A, Carlos M, Bovea MD, Colomer FJ, Albarrán F. Analysis of refuse-derived fuel from the municipal solid waste reject fraction and its compliance with quality standards, J Clean Prod 2014;83:118–25.

When the history of recycling is observed, it is seen that people have done it for thousands of years. In 1600 B.C. religious rules played an important role in the behavior of people. For instance, according to Jewish code of sanitary laws, people are responsible for their own garbage. In 1031, Japan began to use waste paper. Another important date is 1776, when America declared its independence from England and rebels had to use recycled materials in the war. Curbside recycling starts in Baltimore, Maryland in 1874. The first aluminum can recycling plants began operating in Chicago and Cleveland in 1904. Especially, during World War II, thousands of tons of recycled materials were used. The first bottle bill was introduced in Oregon in 1972 [25]. After the 1970s, a lot of governments arranged their laws to increase the recycling rates in their countries.

5.3.4.2

Refuse Derived Fuel

Today, many industrial sectors are challenged with intensive energy usage and environmental problems. To overcome the problems, they have utilized alternative fuels such as refuse derived fuel (RDF). RDF is a recovered fuel from wastes, which can be used instead of fossil fuels. A more limited description can be seen from the literature as solid recovered fuel (SRF), which means that the fuel is produced from sorted or mixed wastes such as municipal wastes, commercial wastes, or production wastes [26]. In Europe, the European Committee for Standardization (CEN) published the standard reference EN 15359 (2011) that establishes standards and technical specifications for SRF for European markets. According to the standard, SRF is explained as a combustible fuel taken from nonhazardous waste, while RDF can be made from any type of waste, hazardous or nonhazardous [27]. In addition to this, Table 5 gives a classification of SRF to be commercialized. The RDF can be used in different applications such as monoincineration or coincineration systems. So, RDF must supply the following general quality requirements: (1) well defined calorific value, (2) low chlorine content, (3) quality controlled composition (few impurities), (4) defined grain size, (5) defined bulk density, (6) availability of sufficient quantities [26]. Reza et al. [28] conducted a study about environmental and economic aspects of production and utilization of RDF as an alternative fuel in cement plants. They presented a LCA for coprocessing in two cement kilns in Metro Vancouver. They defined that RDF usage in cement kilns has some advantages from the environmental and economical point of views. Rahman et al. [29] conducted a study about alternative fuels using Aspen Plus based simulation. They investigated three fuels, namely tire derived fuel (TDF), meat and bone meal, and RDF for the cement industry. Their results indicated that when tires were used for about 25% thermal energy requirement, energy efficiency increased by 3% and a 2.5% reduction of CO2 could be achieved. Also, a diesel engine was compared with three upgraded waste source fuels, namely, TDF, waste plastic disposal fuel (WPD), and upgraded waste cooking oil (UWCO) by Adam et al. [30]. As compared to diesel, UWCO gave 14% higher power and 13.8% higher torque. WPD showed the lowest NOx, while TDF emitted the highest emissions (CO, CO2, NO, and NOx). A recent study was conducted by Valiullin et al. [31] about combustion of the waste-derived fuel compositions metallized by aluminum powder. They investigated the effects of adding aluminum powder (up to 5 wt%) into waste-derived coal–water slurry with waste petrochemicals. They

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indicated that combustion of the metallized waste fuel was the most efficient at a metallization degree of 3 wt% and oxidant flow temperature around 975K. Also, according to the experimental results, the ignition delay time was 5–7% shorter.

5.3.4.3

Landfill

This method is the oldest method. It can have different configurations such as open dumping, sanitary landfill (without gas capture), or modern landfill (with gas collection to produce energy). Today, developing countries still employ this method as the dominant method, while developed countries have endeavored to reduce it. This is because the modern landfill has some disadvantages such as leachate production to soil or ground water, rodent infestation, as well as land space requirement. For instance, approximately 28,500 t of MSW needs 1 ha of land according to Zhang et al. [32]. Fig. 5 shows a landfill cross section comprising of its main layers. Today, the main features of a sanitary landfill system are floor impermeability, leachate water management, top cover, and gas management. It needs an impermeable layer to protect the ground water and soil from the leachate. The layer has to have some physical, chemical, mechanical, or hydraulic specifications. If the geological layer does not supply the required features naturally, artificial layers can be used. Components of the leachate water relate to physical, chemical, or biological processes of organic or inorganic wastes. To protect the ground water or soil, it is not enough to use the impermeable layer – a water collection and drainage system is required. There are drainage pipes in the drainage layer, so the leachate water can be sent to a collection pool. Rain water is another problem in the landfill area increasing the leachate water. The problem can be solved by good rain water management. Landfill gas is the main product in the system. After aerobic organisms consume all oxygen in the waste, anaerobic bacteria produce a gas comprising mainly of CH4 and CO2. The gas is collected by a gas collection system under the top cover as well as horizontal/vertical gas wells in waste. The gas formation rate mainly relates to the biological disposal rate of the waste [33]. There are a lot of landfill examples around the world. An important example is the Bordo Poniente Landfill in Mexico City. When it opened in 1985, it was the sole sanitary landfill for Mexico City. Along with the population growth of the city, Bordo Poniente had grown to 370 ha with over 70 million tonnes of garbage piled 17 m deep in the 25 years after its launch. So, it had become one of the world’s largest rubbish dumps. To solve the problem, the Mexico City Government and the Federal Government signed an agreement on November 22, 2010. According to the agreement, Mexico City would close the landfill with an engineering project, working together with the Cities Climate Leadership Group (C40) and the Clinton Climate Initiative. They launched a comprehensive program before the Bordo Poniente landfill was shut down. The aim of the program was to increase

Electricity generated at methane facility

Leachate pond

Working face of cell Filled garbage layers

Methane facility Methane wells and collection pipes

Leachate collection pipes

Soil layer Pea gravel Geotextile mat Compacted clay

Groundwater

Polyethylene liner

Illustration courtesy of Rich Bishop

Fig. 5 Landfill cross section, mainly. Landfill cross section. Available from: http://www.keyword-suggestions.com/bGFuZGZpbGwgY3Jvc3Mgc2Vjd Glvbg/; 2017 [accessed 14.04.17].

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recycling, to produce compost for gardens and parks, and to reduce the amount of daily waste so to reduce the need for landfill. The dump was closed in late 2011 to convert it into a more modern facility using waste management programs. Mexico City has 12,500 metric tonnes of MSW per day and 5500 t of the amount goes to landfill, 1023 t waste is converted into energy, and 507 t of it diverts into compost. Also, approximately 750 t goes to a material recovery facility for recycling. Another important event in the reduction of waste going to the landfill is another agreement between the Mexico City Government and CEMEX, which is one of the world’s largest building materials suppliers and cement producers. According to the agreement, the Mexico City Government delivers up to 3000 t of inorganic solid waste per day for use in their kilns as fuel [34]. Also, a concession for methane plant was granted in 2012. But the project was delayed due to various problems. Today, the problems have been overcome and the plant has been planned to power 517,000 streetlights and 1700 public buildings [35]. The next example is the Sudokwon Landfill, which is the largest dumping site in South Korea. Its cumulative waste amount was 97,074 kilo tonnes between 1992 and 2005. Also, its annual average waste is 6934 kilo tonnes. Sudokwon Landfill Site Management Corporation decided to install a landfill gas power plant with capacity 50 MW in 2000. There were three main aims of the project: (1) destruction of methane in a more efficient manner, (2) reduction of greenhouse gases, and (3) construction of a cleaner generation system. The landfill consists of four sites, and its central incineration station has six units. The first four units were installed in 1996 and the other two units in 1998. Then a 6.5-MW power plant and a 3.38-MW power plant were installed in 2001 in 2003 respectively. The 50-MW power plant was brought into operation in 2006. The boiler for this plant was supplied and installed by Doosan Heavy Industry and the steam turbine was provided by MHI (Mitsubishi Heavy Industry). The boiler uses methane as a fuel to produce hot flue gases, which produce steam for the steam turbine. In normal operation, the landfill gas is mainly used for the 50-MW power plant; in case of emergency it can also be used by a small generating system (6.5 MW þ 3.38 MW). Nowadays, the plant site has been designated for various ecotourism activities [36,37]. There are two good examples of landfill facilities in Istanbul, Turkey: The Odayeri-Eyup landfill and the Komırcuoda-Sile landfill as shown in Fig. 6. Project details of the two landfills can be taken from Ortadogu Energy [38]. According to their data, the Odayeri landfill, comprising an area of 50 ha, received a total waste of 40 million tonnes between 1995 and 2012. The average and maximum waste heights are 30 and 90 m. The gas produced has been collected in a total of 239 wells with an average depth of 30 m. Also, 125-km-long pipelines were used for water, compressed air, and gas products in the project. Nearly 60–70 cm of clay and 50 cm of soil were used for top coating. In addition to these, there are two stacks of 2500 m3/h capacity to flare with each. Total capacity of the plant is reported as 33.807 MW (with 20 piece gas engines). Komurcuoda landfill is smaller than the Odayeri plant. It comprises an area of 40 ha and 25 million tonnes of waste, which was collected in the period 1995–2012. Average waste height and well depth are 19 m and 20 m, respectively. There are in total 152 wells. One stack is used with 2500 m3/h capacity. As a result, the plant capacity is 14.15 MW (10 pieces gas engines).

5.3.4.4

Thermal Methods

Thermal methods consist of incineration, pyrolysis, and gasification, which are promising technologies to reduce waste volume as well as to produce energy.

5.3.4.4.1

Incineration

Incineration technology has been well known and commonly used for years. Wastes coming to mass burn facilities after the recycling process are used for two main purposes, which are to reduce the volume of waste and to produce energy. Generally, waste is fed into large burners to produce heat energy/steam, and then the steam is utilized to produce electrical energy. The remaining noncombustible material is extracted as ash. The method has some disadvantages such as high investment and operation costs and byproducts that are harmful to environmental and public health. There are different incinerator examples around the world. One of them is Lausanne, Switzerland, which is considered as a pioneer in thermal waste treatment. It was designed and built by Hitachi Zosen Inova and began to operate in 1958, thus, it is the world’s oldest incinerator still in service. The plant was replaced by a larger and a more efficient new facility in 2006. The new plant had to meet demands of Tridel SA that included low emissions, maximum energy output, economical operation, and high availability. In accordance with these, the process flow diagram of the designed system can be seen in Fig. 7. There are two combustion chambers, each equipped with a Hitachi Zosen Inova grates. Firstly, a consistent and optimum burnout of the waste at 10001C is conducted followed by a guaranteed maximum flue gas burnout. Further downstream, electrostatic precipitators and external economizer optimize the energy recovery. In the project, Hitachi Zosen Inova designed a system to use rain water as a utility in wet scrubber as a different innovation. The plant capacity is 160,000 t/a. It includes two trails, which make the per train capacity as 10 t/h (nom) and 12.5 t/h (max). Calorific value of waste coming from MSW or commercial wastes (special waste fractions from hospital waste, sewage sludge) is defined as 14.4 MJ/kg (nom), 7.2–18 MJ/kg (min/max). Steam quantity per trail is 48.3 t/h at 4001C and 50 bar. Also, flue gas produced per train is 63,000 m3/h (at standard conditions) and 1601C after external economizer. The plant capacity is 20 MW electrical output and 50 MW heat. Also, it provides a recovery of 180 t/a zinc and 1.7 t/a mercury. Finally, the total investment cost of the new plant is stated as CHF 360 million [39]. Another example can be given from Vienna, the Spittelau waste incineration plant in Austria. As shown in Fig. 8, the plant means waste, energy, and art for the Viennese. The plant was built between 1969 and 1971, but an important part of it was lost due to a fire in 1987. Then, in 1992, the new Spittelau was rebuilt at the same site with new standards to protect the environment and was completely revamped by 2015 to optimize energy efficiency. The plant processes around 250,000 t of household waste per

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(A)

(B) Fig. 6 Plant pictures of landfills: (A) Odayeri and (B) Komurcuoda. Ortadog˘u. Energy. Available from: http://ortadoguenerji.com.tr/projelerimiz-vesantrallerimiz/proje-ve-lisans-haritasi/; 2017 [22.08.2017].

year. The waste is stored in a waste hopper about 7000 m3 in size. It is then sent to two waste furnaces for incineration. The hot flue gas is used to produce steam via a heat exchanger. The steam is used to produce electricity and district heating. The annual plant capacity is reported as 40,000 MWh of electricity, 470,000 MWh of district heating, 6000 t of scrap iron and 60,000 t of clinker, ash, and filter cake. The heat produced at Spittelau is enough to heat more than 60,000 houses in Vienna. Also, the plant has a number of state-of-the art technologies to clean the produced flue gas. The purified flue gas is exhausted from the chimney at a height of 126 m [40]. The next example is from Poland, where most of the electricity production is based on coal thermal plants. The Polaniec plant was built in the late 1970s with eight 200-MW coal-fired units. After that, the capacity was upgraded to 225 MW by 1995 and the total capacity became 1.8 GW, as shown in Fig. 9. Then, Poland has committed to change its energy strategies according to the

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9

24

3

15

1

16

17 18

20

25 19 13 6

5 2

26

14

28

11 29 8

7

Waste receiving and storage

Grate combustion and boiler

1. Tipping hall

4. Feed hopper

2. Waste pit

5. Ram feeder

3. Waste crane

10

10. Primary air fan

13. Recirculation fan

8. Bottom ash conveyor belt 14. Secondary air injection 9. Primary air intake 15. Four-pass boiler

21

22

27

11. Primary air distribution 6. Hitachi Zosen Inova grate 12. Secondary air fan 7. Bottom ash conveyor

23

12

Flue gas treatment

Residue treatment

16. Electrostatic precipitator

25. Ash removal

17. External economizer 26. Acid fly ash washing 18. Gas/gas heat exchanger 1

27. Collection tank scrubber blow down

19. Wet scrubber

28. Lime milk preparation

20. Gas/gas heat exchanger 2 21. SCR catalyst 22. Induced draft fan 23. Silencer 24. Stack

29. Zinc filter cake storage

Fig. 7 Process flow diagram of Lausanne, Switzerland energy from waste plant. Adapted from Hitachi Zosen Inova, Lausanne Switzerland Energyfrom-Waste Plant. Available from: http://www.hz-inova.com/cms/wp-content/uploads/2014/11/hzi_ref_lausanne-en.pdf; 2017 [accessed 30.08.2017].

Fig. 8 Spittelau waste incineration plant. Adapted from Spittelau waste incineration plant. Available from: https://www.wienenergie.at/eportal3/ep/ channelView.do/channelId/-51715; 2017 [accessed 30.08.2017].

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Fig. 9 Polaniec biomass power plant. Adapted from Polaniec biomass power plant. Available from: http://www.renewable-technology.com/ projects/polaniec-biomass-power-plant/; 2017 [accessed 30.08.2017].

Fig. 10 Tuas south incineration plant. Adapted from Tuas south incineration plant. Available from: www.nea.gov.sg/docs/tsip-brochure; 2017 [accessed 31.08.2017].

European Union criteria like other member countries. According to this, GDF Suez, which owns and operates the plant, constructed a biomass power plant at the same site and started commercial operation in November 2012. The biomass plant is considered as the world’s biggest 100% biomass fueled power plant, which uses a mixture of 80% wood chips and 20% agricultural byproducts. The plant uses Foster Wheeler’s advanced bio circulating fluidized boiler-CFB technology to burn efficiently and still meet the tight environmental regulations. The capacity of the plant is 205 MWe and 447 MWth by means of steam produced which is 158.3/135.1 kg/s, 535/5351C and 127.5/19.5 bar (a) [41,42]. Another plant is the Tuas South incineration plant, which is the fourth and largest refuse incineration plant in Singapore as shown in Fig. 10. The plant was completed in June 2000 with a cost of $890 million. The plant was designed with the state-of-theart technology for 3000 t capacity of refuse per day. Incineration achieves about 90% reduction in volume. The Tuas South incineration plant with other incineration plants and Semakau landfill meet the refuse disposal needs of Singapore. In the plant, heat from combustion is used to produce steam in boilers. Steam generation per boiler is 105 t/h, 35 barg at 3701C. The steam

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Fig. 11 Långmossebergen WtE power plant. Adapted from Långmossebergen state of the art waste-to-energy plant. Available from: http://swindo.com/langmossebergen-state-of-the-art-waste-to-energy-plant/; 2017 [accessed 15.08.2017].

drives two steam turbines to produce electricity. Power generation capacity is 80 MW. The plant consumes 20% of the electricity produced and the remaining 80% is sold. The ash and slag from the plant are transported via vibration conveyors to the ash pits, while ferrous scrap metal is picked up by electromagnetic separators and sent to the scrap pits. Then, the scrap metals are sent to a local steel mill for recycling [43]. The last example is Vantaa Energy, which is one of Finland’s largest city energy companies. The company belongs to the city of Vantaa (60%) and the city of Helsinki (40). It produces and sells electricity and district heat. Electricity generation of the company is based on two of its own combined heat and power (CHP) generation plants, the Martinlaakso power plant and the Långmossebergen WtE power plant. The Långmossebergen WtE power plant capacity is 340,000 t/year; energy entered is taking its average heating value as 10.5 MJ/kg (116.6 MW) waste plus natural gas of 91.8 MW. Electric and heat productions from the plant are 80.5 MW and 119.3 MW, respectively. Commercial operation of the plant started in 2014 and a picture of Långmossebergen WtE power plant is shown in Fig. 11. Its technology consists of a grade fired combined WtE, plus natural gas and gas turbine process. Steam produced is 90 bar 4001C and 88 bar 5151C. Also, bottom and fly ash are to be evaluated in terms of removal of magnetic metal (electromagnetic), removal of other metal (like aluminum) with eddy current technic, fly ash mixing water and cement additives or deposition to hazardous landfill area [44,45].

5.3.4.4.2

Pyrolysis

Pyrolysis is a conversion of material to liquid, solid, and gaseous fractions by heating in the absence of air. In addition to the gaseous products, pyrolysis can produce a liquid product called bio-oil, which is so important to develop various energy fuels and chemicals. Pyrolysis may be defined as an incomplete thermal degradation of carbonaceous materials to char, condensable liquids (tar, oils, or bio-oils), and noncondensable gases in the absence of air or oxygen. In addition to this, fast pyrolysis is a thermal or thermocatalytic conversion process, which can be characterized by rapid heating rates, quick quenching, and exclusion of oxygen from the reaction zone. It yields valuable chemical intermediates as well as synthesis gas. It is commonly accepted that the pyrolysis process occurs in two steps: (1) formation of a solid char residue at 200–4001C and (2) transformation of the char residue to another product above 4001C. Pyrolysis at temperatures between 500 and 5501C is used when oils are the desired product, while temperatures of 7001C or higher are used to produce primarily high quality syngas [46,47].

5.3.4.4.3

Gasification

When compared to incineration, gasification is generally not only more efficient but also brings much less environmental concern because the oxygen-deficient environment in a gasifier does not favor the formation of those environmental pollutants produced in an incinerator. Moreover, the gasification technology is well suited for the decentralized application, which offers significant flexibility to waste treatment. Thanks to least technological developments, the gasification technology is not limited to solid feedstock, and liquid or even gas fuels can be used to produce more valuable gases. Gasification technology has a long history. The first industrial application was for lighting in 1792; after that, a lot of gasification processes were built around the world. By the late 1920s, more than 1200 gasification plants had been constructed in the United States [48]. After the Carl von Linde commercialized the cryogenic separation of air during the 1920s, gasification processes gained acceleration using an oxygen gasifier to produce syngas and hydrogen. Also, new gasification processes were improved at this time, i.e., the Winkler fluid-bed process (1926), the Lurgi moving-bed pressurized gasification process (1931), and the Koppers–Totzek entrained-flow process (1940s). After World War II, discovery that natural gas had a high heating value

Waste Energy Management

1788 Robert Gardner: First gasification patent

1920 Carl von Linde: Cryogenic seperation of air, fully continuous gasification process

1659 Thomas Shirley: Discovered gas from coal mine

1801 Fourcroy: Water gas shift reaction

1739 Dean Clayton: Distilled coal in a closed vessel

1931 Lurgi: Pressurized moving-bed process

1926 Winkler: Fluidized bed gasifier

1861 Siemens gasifier: First successful unit

107

1997 First commercial gasification plant in U.S.

1974 Arab oil embargo: Renewed gasification interest

1945 − 1974 Post war ‘Oil glut’

2001 Advanced gasification biomass renewable energy projects

1792 Murdoc: First use of coal gas for interior lighting Fig. 12 Historical milestones and landmarks in gasification technology. Modified from Basu P. Biomass gasification and pyrolysis. Amsterdam: Elsevier; 2010.

reduced interest in gasification processes. But in the early 1970s, the first oil crisis with potential shortage of natural gas revived the gasification technologies [49]. Some important milestones of the gasification technology are shown in Fig. 12. Gasification is the conversion of fuel or wastes into a combustible gas mixture via the partial oxidation at high temperatures, typically varying from 800 to 9001C [46]. Also, the syngas content can change depending on the reactor temperature, gasification material, residence time of material in the reactor, supplied gas type, supplied gas rate, gasification technique, reactor type, etc. Air, oxygen, steam, CO2, or a mixture of them are widely used as gasifying agents. Reaction conditions with heating values are as follows: (1) Oxygen gasification: It yields a better quality gas with heating value of 10–15 MJ N/m3. In this process, the temperatures between 10001C and 14001C are achieved. O2 supply may bring a simultaneous problem of cost and safety. (2) Air gasification: It is the most widely used technology because it is cheap, a single product is formed at high efficiency, and it does not requiring oxygen. A low heating value gas is produced containing up to 60% N2 having a typical heating value of 4–6 MJ N/m3 with byproducts such as water, CO2, hydrocarbons, tar, and nitrogen gas. The reactor temperatures are between 9001C and 11001C. (3) Steam gasification: steam gasification converts carbonaceous material to permanent gases (H2, CO, CO2, CH4, and light hydrocarbons), char and tar. This method has some problems such as corrosion and poisoning of catalysts, but minimizes tar components [46]. According to the type of gasifiers, they are mainly divided to three groups: fixed/moving bed, fluidized bed, and entrained bed. Also, each group can be separated into subgroups such as downdraft, updraft, and crossdraft for moving bed, bubbling, circulating for fluidized bed, etc. [50]. In addition to these, the application ranges of each gasifiers are different. For the moving bed while the updraft and downdraft types are used for small units (10 kWth–10 MWth), the crossdraft gasifiers are the smallest capacities. The fluidized bed types are convenient for intermediate capacities (5–100 MWth). The biggest size belongs to entrained bed gasifiers (450 MWth) [50]. In the fixed/moving bed gasifier, fuel is supported on a grate. The major attraction of this type is that they can be built inexpensively at small sizes therefore the types of gasifier are widely used around the world. Its disadvantage is that it is difficult to supply a uniform distribution of fuel, temperature, and gas composition [49,50]. The updraft gasifiers are the oldest ones. While fuel moves down, a gasification agent (air, oxygen, or steam) goes upward. Reaction sequence of fuel is drying, pyrolysis, gasification, and oxidation as shown in Fig. 13. The updraft gasifiers can be used for high ash (up to 25%), high moisture (up to 60%), and low volatile fuels such as biomass and charcoal. The gasifiers are more convenient for direct firing, like small cooking stoves, due to the fact that they have extremely high tar content [50–52]. In a downdraft gasifier, fuel and syngas move downward, air is supplied to the gasifier from a certain height below the top and syngas leaves passing a bed of hot ash. During this transition, tar components in syngas can crack. So, the downdraft gasifiers have the lowest tar content (0.015–3 g N/m3). It is suggested that moisture in fuel must not exceed 25%; also it gives its best performance with pelletized fuel instead of fine light biomass. These advantages supply their usage in internal combustion engines. Also they can be separated as throatless and throated-type as shown in Fig. 14A and B. As drawbacks, grate blocking, channeling, and bridging problems can be listed [50,53].

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Biomass

Gas+fly ash Drying Devolatilisation Reduction Combustion Air+steam

Air+steam Bottom ash

Fig. 13 Schematic of an updraft gasifier. Adapted from Huang S, Wu S, Wu Y, Gao J. Structure characteristics and gasification activity of residual carbon from updraft fixed-bed biomass gasification ash. Energy Convers Manag 2017;136:108–18.

Biomass

Air Gasifier

Syngas Cyclone

Drying zone Pyrolysis zone

Air Air

Combustion zone

Air

Char

Combustion zone Rice husk

Thermocouple

Reduction zone

Grate Ash removal pit

(A)

To syngas cleaning unit

Syngas

Ash

Ash (B)

Fig. 14 Schematics of downdraft gasifier (A) throated type and (B) throatless type. Adapted from Susastriawan AAP, Saptoadi H. Purnomo, Small-scale downdraft gasifiers for biomass gasification: a review. Renew Sustain Energy Rev 2017;76:989–1003.

In a crossdraft gasifier, fuel is supplied from the top, air is injected through a nozzle from the side, and syngas is taken opposite side as shown in Fig. 15. The gasifiers are usually used for gasification of charcoal with low ash content. These type gasifiers are generally used for small scale biomass units. Their advantages are their startup time (5–10 min) and low tar production (0.01–0.1 g N/m3). Also, they require a simple gas cleaning system [50,51]. Fluidized bed gasifiers are generally used at intermediate size units. Also, they have various advantages, such as easy scale-up, flexibility feedstock type and size, uniform temperature distribution, and high carbon conversion efficiency. So they are suitable for biomass gasification. The fluidized beds supply perfect mixing and uniform temperature to prevent fuel agglomeration. Also, the fluidized bed consists of granular solids. Unlike the fixed bed gasifier, zones such as drying, pyrolysis, oxidation, and reduction cannot be distinguished. Schematic of a fluidized bed is as given in Fig. 16. There are mainly two types of fluidized bed gasifiers, i.e., bubbling and circulating. The bubbling fluidized bed gasifier developed by Fritz Winkler in 1921 may be the oldest fluidized bed gasification, used for gasification of coal for many years. Then, they became a popular option for biomass gasification. The gasifiers can be operated at atmospheric or higher pressures. A CFB gasifier consists of a gasifier reactor, a cyclone and a solid recycle device. The fluidized velocity of CFB (3.5–5.5 m/s) is higher than a bubbling fluidized bed gasifier (0.5–1 m/s). Solid particles can migrate out of the CFB reactor; they are captured and returned again the reactor. According to fuels and operation conditions, the gasification reactor operates between 800 and 10001C [49,50].

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Biomass

Drying Gasification Pyrolysis Combustion AIr

Gas

Ash Fig. 15 Schematic of a crossdraft gasifier. Adapted from Basu P. Biomass gasification and pyrolysis. Amsterdam: Elsevier; 2010.

Hot production gas Gasification reactor

Cyclone

Biomass

Feed hopper Fluidized bed

Grid

Feeding screw Air Ash removal screw

Ash Fig. 16 Schematic of a fluid-bed gasifier. Adapted from Andritz. Bubbling fluidized bed gasification. Available from: https://www.andritz.com/ products-and-services/pf-detail.htm?productid¼14969; 2017 [accessed 15.06.17].

Entrained flow gasifiers are usually convenient for large-scale gasification using high rank coal, petroleum coke, and refinery residues as feedstock. Fuels consisting of high moisture like lignite, biomass cannot be attractive for the systems. But using biomass in the gasifiers can be arguable due to the fact that the gasifiers can destroy tar problems of biomass gasification, easily. This is because the gasification temperature of the entrained flow gasifier exceeds 10001C. So the syngas produced is nearly tar-free and has very low methane content [50]. Even though biomass gasification with entrained flow gasifier is usually not appropriate, there is a successful example known as the Choren process [49] as shown in Fig. 17. Finally, basic features of the three main gasification systems are summarized in Table 6. Also, plasma gasification is known to be a promising technology among gasification technologies. Plasma, which consists of free electrons, ions, and neutral particles, is defined as the fourth state of matter. Although the presence of electrons and charged particles is the reason why the plasma is considered entirely neutral. To become and sustain plasma energy needs from electric, thermal or ultraviolet light etc. Plasmas are classified into two main groups: high temperature plasma and fusion plasmas, in which all species (electrons, ions, and neutral species) are in a thermodynamic equilibrium state and the low temperature plasmas or gas discharges. In the low temperature plasmas, a further distinction can be made between the thermal plasmas in which a quasi-equilibrium state occurs (high electron density and 2  103oTplasmao3  1041C) and the cold plasmas where a nonequilibrium state takes place. Among all the plasma processes, the thermal plasmas are the most suitable for waste materials treatment, because the organic compounds,

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Carbo-V gasifier

Low temperature gasifier (NTV)

Oxygen

Bimass

Pyrolysis gas

Raw gas Steam

Gas scrubber Syngas BFW

Oxygen

Deduster Mill Char Residual Vitrified slag

char/ash Waste water

Fig. 17 Choren process for biomass. Adapted from Higman C, van der Burgt M. Gasification, second ed. Houston, TX: Gulf Professional Publishing; 2008.

Table 6

Comparison of some commercial gasifiers

Parameters

Fixed/moving bed

Fluidized bed

Entrained bed

Feed size Tolerance for fines Tolerance for coarse Exit gas temperature Feed stock tolerance Oxidant requirements Reaction zone temperature Steam requirement Nature of ash produced Cold-gas efficiency Application Problem areas

6–50 mm Limited Very good 425–6501C Low-rank coal Low 10901C High Dry 80% Small capacities Tar production and utilization of fines

6–10 mm Good Good 900–10501C Low-rank coal and excellent for biomass Moderate 800–10001C Moderate Dry 89% Medium-size units Carbon conversion

o100 mm Excellent Poor 1250–16001C Any coal including caking but unsuitable for biomass High 19901C Low Slagging 80% Large capacities Raw-gas cooling

Source: Data from Higman C, van der Burgt M. Gasification, second ed. Houston, TX: Gulf Professional Publishing; 2008. Basu P. Biomass gasification and pyrolysis. Amsterdam: Elsevier; 2010.

under high temperature conditions, are decomposed into their constituent elements and the inorganic materials (glass, metals, silicates, heavy metals) are melted and converted into a dense, inert, nonleachable vitrified slag [54,55]. The plasma gasification has some advantages such as absence of tar/ash issues and smaller installation size, and it treats a wide range of heterogeneous and low calorific value materials including various hazardous wastes, such as medical waste and low-level radioactive wastes. Also, by using this method, pollutant emissions could be reduced to almost zero and valorization of all the components of the wastes could be achieved. The systems are adopted as multigenerational systems producing electricity, glassy slag, and foam glass. Despite these advantages, the technology is still under debate due to the power required. The main streams of a plasma gasifier are shown in Fig. 18. There are some good examples of waste gasification around the world. One of them was the EcoValley plasma gasification facility locating in Utashinai, Japan on the island of Hokkaido. It can be said that the plant was an important milestone for plasma technology. The plant had fully operated from 2003 to 2013. The plant design capacity was up to 220 t per day of MSW or up to 165 t per day consisting of 50/50 mixture of auto shredder residue and MSW. Its technology was improved by collaboration with Westinghouse Plasma Corp. and Hitachi Metals. When the process is examined, the waste materials enter at the top of the vessel and are converted to syngas (e.g., CO, H2 and CH4), which exits at the top of the gasifier. Inorganic materials are melted and exit the bottom of the gasifier as slag, which can be used in various forms such as vitreous and granules. The syngas goes to an afterburner and is combusted in there. The hot gas leaving the after burner travels to a heat recovery steam generator to produce steam and the steam is used to produce power using steam generators. The main equipment is a plasma torch system in there. The Westinghouse Plasma Corporation plasma torches used at EcoValley were

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Syngas outlet

Waster inlet Air or oxygen Plasma torches

Slag and recovered metals

Fig. 18 Schematic of a plasma gasifier. Adapted from Westinghouse, Waste to energy. Available from: http://westinghouse.com/story-waste-toenergy/; 2017 [accessed 15.06.17].

high temperature heating devices that were capable of superheating the process gas (air) to temperature in excess of 55001C. Four plasma torches were located around the perimeter near the bottom of the reactor. Power generated and exported was 7.9 MW (design) and 4.3 MW (design), respectively. There were three main issues in the plant, i.e., the bottom diameter of the reactor was too large, and there was an incorrect refractory configuration with particulate carryover [56,57]. Process flow diagram of the plant is as given in Fig. 19. Another example is the Lahti Energy’s Kymijärvi II power plant, which is the first gasification power plant in the world using SRF to produce electricity and district heat, as shown in Fig. 20. Lahti was selected due to its geographical location, as it is close to good transport routes and there is plenty of recycle waste to produce SRF in the south of Finland. The original budget of the project was EUR 157 million and then it increased to EUR 160.5 million due to additional purchases. Kymijärvi II started commercially on May 21, 2012. The fuel of the plant consists of highly combustible waste that cannot be recycled, known as energy waste; for example, unclean plastic, paper, cardboard and wood from household, industry, shops, and construction sites. Firstly, the domestic energy wastes come to the waste reception station of Päijät-Hämeen Jätehuolto where it is shredded into strips of about 2–4 cm and prepared into SRF. Then the SRF is transported to the Kymijärvi II fuel reception center. The plant uses an atmospheric pressure CFBG with height of 25 m and an outer diameter of 5 m. The gasifier is started up with natural gas. Also, the gasifier bed consists of sand and lime and it fluidizes when air is blown under the gasifier. When the SRF is fed from the silo to the gasifier reactor, fluidized sand at 850–9001C surrounds the fuel pieces and gasification takes place. Although the main components of produced gas vary via operation conditions, the main components of the gas are N2 from air, CO, CH4, H2, CO2, and H2O. While the sand, lime, and ash mixed are recovered, the product gas goes to gas cooler. During the gasification of the SRF, some impurities that can create a corrosive effect on the boiler are transferred into the gas. So the gas is cooled from 9001C to about 4001C. By this process, some impurities in the product gas can be converted to solid particles such as alkali chlorides. Then the solid particles are filtered out. But the hot gas must not be cooled so much, because tar components in the gas can condensate in lower temperatures. During the cooling process, waste heat taken is used to preheat feed water of the boiler. Then, the cooled gas travels to mechanical hot filtering. All of the process procedure has three kinds of ash: bottom ash, filter ash, and fly ash. Bottom ash components are fuel and bed materials, while filter ash is formed in the filter units via carbon and impurities. Ash content of the SRF in Kymijärvi II is reported as about 10%. The boiler used is a natural-circulation steam boiler that can use both natural gas and product gas. Produced steam from the boiler is at 5401C and 121 bar. The superheated steam drives the steam turbine to produce electricity. Also the steam leaving from the steam turbine has still an important energy capacity so it is directed to district heat exchangers. Finally, the capacity of Kymijärvi II is 300 GWh of electricity, which covers the annual electricity need of 75,000 apartments and 600 GWh of district heat, or the annual heating need of 30,000 detached one-family houses [58]. As another example, the Vaskiluodon Voima Oy plant in Finland is known as the first biomass gasification plant in the world with such a large scale replacement of fossil fuels. Indeed, the plant has operated since 1982 as a thermal plant with a 560 MW thermal capacity. It produces 230 MWe and 170 MW district heating. Coal consumption is 400,000–500,000 t/a. The aim of the Vaskiluodon Voima Oy gasification project is to enable to replace a large share of coal with biomass; a contract was signed in 2011 and the gasification plant started in 2012 with total project cost of o40 M€. So, Vaskiluodon Voima Oy comes from two separate

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Exhaust stack Bag house systems for dust filtration

Heat recovery steam boilers

Syn-gas after burner Plasma gasification reactor

Induced draft fans

Crane

Slag granulation Steam turbine Gas clean-up system

Waste pit

Slag storage pit

Metal separator

Fig. 19 EcoValley plasma gasification facility in Utashinai process flow diagram. Adapted from Osada S. Early Evolution of the Westinghouse Plasma Gasifier – Lessons Learned from Eco Valley, Japan, Alter NRG Open House 2015. Available from: http://apageinc.com/wp-content/uploads/ 2015/07/Osada-June-Presentation-2015.pdf; 2015 [accessed 29.06.17].

Fig. 20 Kymijärvi II gasification power plant. Adapted from Lahti Energia. Available from: https://www.lahtigasification.com/power-plant/powerplant-technology; 2017 [accessed 25.07.17].

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Product gas Instrumentation, electrification and automation Dried biomass Wet biomass

Biomass receiving and pre-handling

Large-scale belt dryer

CFE gasifier 140 MW fuel

Existing PC boiler modifications and new burners

Fig. 21 Vaskiluodon Voima biomass gasification plant. Adapted from The biomass gasification plant to Vaskiluodon Voima in Vaasa 2017. Available from: http://www.zelenaenergija.org/blobs/eb16f372-fa48-42fe-b621-e9cf0b5fdc7a.pdf; 2017 [accessed 05.07.17].

power plants. One is located on the island of Vaskiluoto in Vaasa and the other by Lake Kyrkösjärvi in Seinäjoki. The gasification biomass plant is designed with 140 MWfuel capacity and up to 40% replacement of coal. These two power plants supply about 2% of the whole country’s electricity demand per year. Also they meet the heating demands of more than 60% of Vaasa and 90% of Seinäjoki regions. Domestic fuels are supplied within a radius of about 100 km from the plant. Also the Vaskiluodon Voima creates 70,000 t per year of ash, which as a byproduct is excellent for earthworks [59,60]. General components of the plant are shown in Fig. 21. As a new project, construction of a WtE gasification plant in the West Midlands known as the SynTech Energy Centre has been started and backed by the UK’s Energy Technology Institute (ETI), which is a public/private partnership between energy and engineering firms. ETI will invest d5 m in the project with a matching investment from Denver-based SynTech Bioenergy LLC. The plant will consist of a waste gasification system that will have high efficiencies and will deliver chemicals or fuels such as green aviation fuel. The gasification technology is being provided by the US company Frontline Bioenergy, in which SynTech US is a major stakeholder, and will be built in the UK. FluiMax technology by Frontline Bioenergy will vent nothing to the atmosphere except clean exhaust from the engine generator set. The emissions are expected to meet EU and US air quality standards. The plant is considered as more compact than many other WtE designs. The plant capacity will be 1.5 MW using approximately 40 t per day of post recycled RDF. It is expected to supply enough power for 2500 homes and heat for over 1000 homes. Also, the plant will incorporate a unique test facility that will allow the testing of new engines, turbines, and upgrading processes [61,62].

5.3.4.5

Biological Methods

Biological conversions of MSWs use some microorganisms to convert complex molecules to basic molecules. The most common processes of the microbial conversion aim to produce liquid fuel (ethanol) or gaseous fuels (methane and hydrogen). But composition of the waste has significant influence on the yield. Thus, food and vegetable wastes are preferred, especially, because they are easily degradable. Ethanol is considered as one of the most important liquid biofuels and has been used as a vehicle fuel since 1896 when Henry Ford designed his first car, which ran on pure ethanol [63]. Today, ethanol can be used in vehicle blending with gasoline at different ratios from 5% (E5) to 100% (E100). Up to E10, ethanol can be directly used in conventional cars without modification. Other advantages of ethanol are the enhancement of the octane number and oxygen content of fuel as well as reduction of some emissions such as carbon monoxide, sulfur oxides, volatile organic compounds, fine articulate matter, benzene, and hydrocarbons [64]. Another important gas, methane, can be produced by digesting of organic materials in the absence of oxygen. The produced gas is composed mainly of methane and CO2 as well as other compounds such as sulfur compounds, ammonia, alcohols, carbonyl compounds, etc. The anaerobic digestion process is a complex procedure involving multiple stages, using different consortia of microorganisms. Firstly, the complex molecules are converted to monomers by hydrolysis, followed by the steps of acidogenesis, acetogenesis, and methanogenesis [65]. In addition to these, hydrogen is a promising fuel. Instead of greenhouse gases, H2O is produced during the combustion process of hydrogen. Also, hydrogen can be directly used in a fuel cell equipment to produce electricity. Biological production of H2 can be classified as fermentation (photo fermentation or dark fermentation) or photosynthetic [66].

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5.3.5

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Illustrative Examples

In previous sections, several WtE plant examples related to the technology were mentioned. According to technological developments, the technologies can be used as combined systems as well as single technology. The chapter presents additional examples of investigations and applications for WtE . Melaré et al. [67] presented a detailed literature review focused on solid waste management. They investigated mainly technologies and decision support systems. Distribution of research on the subject in the world according to the study is shown in Fig. 22. It can be seen from the figure that China, Malaysia, and Canada are leading in this research area. Another study was conducted for municipal waste in Nepal (with a population of about 3 million people) by Ripa et al. [19]. Firstly, they investigated mass flow showing MSW management in Naples as given in Fig. 23. Then, they improved alternative scenarios using SimaPro software version 8.0.5.13 for life cycle, Ecoinvent v3.1 (2015) database to obtain the environmental loads, and the ReCiPe Midpoint (H) v.1.12 for impact assessment. Their scenarios mainly aim to convert the MSW to electricity or recycling. In another example, a new integrated gasification system was studied, which consisted of a fluid bed gasification and plasma converter by Morrin et al. [68]. The technology was developed by Advanced Plasma Power (APP) to transform solid waste into energy at a commercial scale. The system comprises six subdivisions, which are fuel preparation, fluid bed gasifier, plasma converter, heat recovery, gas cleaning, and power generation. In the process, 16–21 MWe electricity is produced from 90 kt/year RDF as well as vitrified slag, as shown in Fig. 24. Also, Canada came forward to convert MSW via different methods, which are mainly incineration, gasification, and plasma gasification. In general, MSW composition on a wet weight basis mainly consists of a large organic fraction (40%–60%), ash and fine earth (30%–40%), paper (3%–6%) and plastic, glass, and metals (\1%) [69]. Accordingly, some WtE facilities in Canada are listed in Table 7. Among them, Plasco Trail Road commercial demonstration facility was designed to convert 36,500 t of MSW per year into energy as shown in Fig. 25. Another study, about illegal landfilling in England, known as the dustbin of Europe, was conducted by Liu et al. [70]. Because every year 57 million tons of rubbish with industrial waste are being disposed of in landfill sites, it is foreseen that landfill sites will run out in 2018. There are two types of landfill costs, the landfill tax and landfill gate fee, in England. While the landfill tax includes in the council tax bill for household trash, the landfill gate fee must be paid for business waste to enter licensed landfill sites with a registered waste carrier [70]. Although there are legislative obligations about using landfills, illegal landfilling is one of the most important environmental problems. Household waste generation and its management are given in Fig. 26.

The world by number of studies High

Low No data Fig. 22 Distribution of the number of studies on solid waste management by country. Reproduced from Melaré AVS, González SM, Faceli K, Casadei V. Technologies and decision support systems to aid solid-waste management: a systematic review Waste Manag 2017;59:567–84.

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Fig. 23 Mass balance flow charts showing MSW management in Naples in 2012. Modified from Ripa M, Fiorentino G, Vacca V, Ulgiati S. The relevance of site-specific data in Life Cycle Assessment (LCA). The case of the municipal solid waste management in the metropolitan city of Naples (Italy). J Clean Prod 2017;142:445–60.

In addition to these, Hognert and Nilsso [71] studied the small-scale production of hydrogen, with the coproduction of electricity and district heat, by means of the gasification of MSW. They defined that the allocation of energy of the products obtained in the process is 29% hydrogen, 26% electricity, and 45% district heat as given in Fig. 27. A valuable complex application from Turkey is IZAYDAS. IZAYDAS was founded by Izmit Metropolitan Municipality in May 1996 to operate for the Izmit integrated environment project. IZAYDAS consists of a clinical and hazardous waste incineration plant and energy generation unit, management of regular landfill sites, excavation waste landfills, waste reception vessel, medical waste sterilization plant, biogas facility, and licensed waste transportation services. The clinical and hazardous waste incineration and energy generation facility has 5400 kg/h (solid waste 3800 kg/h, liquid waste 1600 kg/h) incineration capacity. The process uses combustible plastic waste, used oils, pharmaceutical and cosmetic wastes, petrochemical waste, PVC, solvent, dye waste, glues, adhesives, treatment sludge, other type of hazardous wastes and clinical wastes. The main sections of the facility are incineration, steam and power generation system, waste gas treatment and emission measurement system, waste water treatment system, and ash and clinker collection systems. The turbine generator system produces a maximum of 5.2 MWe , which meets the demand of the plant, and the excess is sold to the national grid. Also, there is a biogas plant with 30 t/day capacity consisting of grass, fruit and vegetable market wastes, and poultry and cattle manure. The plant produces 350 kWe and 350 kWth, organic solid and liquid fertilizer [72]. The process is pictured in Fig. 28. A success story belongs to Güssing, which is a small town located in eastern Austria. In 2002, a gasification plant changed Güssing’s destiny, rescuing it from dereliction and making it into an enviable town. The plant has not only supplied energy security but also seriously contributed to reducing CO2 emissions. So, Güssing became the first community in the European Union to cut carbon emissions by more than 90% [73]. A picture of the facility is given in Fig. 29. The biomass based CHP plant of Güssing has 2 MWe and 4.5 MWth (district heating) capacity with an overall efficiency of 80%. An internally fast CFB gasification system, called a dual fluidized bed gasification technology, was used in the plant. In it, combustion and gasification reactors are separated and heat of gasification is supplied from the combustion reactor. Using steam as a gasification agent in the gasification reactor gives low tar content syngas consisting of H2 (35%–45%), CO (20%–30%), CO2 (15%–25%), CH4 (8%–12%), and N2 (3%–5%). The syngas is used in a gas engine to produce power [73].

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RDF 90 kt y−1

Moisture: 10−17% Ash:10−20% Calorific value: 12−16 MJ kg−1

Fluid bed gasifier 850 °C

O2

Solid residue

Crude syngas

Steam

Power

Vitrified slag

Plasma converter 1200 °C

Enhanced syngas

Calorific value 10−14 MJ N/m3

Power 16−21 MWe Fig. 24 APP commercial process. Adapted from Morrin S, Lettieri P, Chapman C, Taylor R. Fluid bed gasification – plasma converter process generating energy from solid waste: experimental assessment of sulphur species. Waste Manag 2014;34:28–35.

Table 7

Facility information in Canada

Company

Facility

Location

Technology

Product

Start date

Covanta Energy Corp. U-PAK Group of Companies Plasco Energy Group Inc. Nexterra Systems Corp. Enerkem

Metro Vancouver WTEF Emerald Energy from Waste Inc. Plasco Trail Road Demonstration Facilitya UBS Bioenergy Research & Demonstration Facilitya Enerkem Alberta Biofuels LP

Burnaby, BC Brampton, ON Ottawa, ON Vancouver, BC Edmonton, AB

Incineration Incineration Plasma gasification Gasification Gasification

Steam None Syngas Steam Biomethanol

1988 1992 2008 2012 2014

a

Commercial demonstration facility. Source: Adapted from Shareefdeen Z, Elkamel A, Tse S. Review of current technologies used in municipal solid waste-to-energy facilities in Canada. Clean Technol Environ 2015;17:1837–46.

A showcase WtE project, Reppie, as shown in Fig. 30, was begun in September 2014 in Addis Ababa, Ethiopia, and is scheduled to start in 2017. The plant capacity is 1400 t/day of municipal waste to produce 185 GWh of electricity annually, which will be exported to the national grid. In the project Martin’s SITY2000 Grates (2  Separate lines) are used to combust the waste. Also it is prospected that the waste will have 5.5–9.5 MJ/kg calorific values. Energy recovery from the facility will be supplied by steam generators (2  25 MWe). According to the project, over 80% of waste is eliminated; also the bottom ash of the plant is considered as a building material for the local construction industry [74]. Also, China has initiated to build the largest WtE facility in the world in megacity Shenzhen with a capacity of 168 MW. China has suffered from municipal or industrial waste for many years due to its density of population and fast industrial development. Shenzhen Energy Group, the local municipality, and developers in Guangdong have decided to build a magnificent WtE plant with 267,000 m2 [75]. The main equipment, including the DynaGrate combustion grate system, hydraulics, burners, and other boiler components, will be supplied by B&W Vølund. The facility is planned to begin commercial operation in mid-2019. It will use up to 5600 t/day municipal waste to produce 168 MW energy. Also, the roof of the plant will be covered with 44,000 m2 solar panels. In addition to these, the plant will contain a visitor center, an observation platform, and a surrounding park [75] as shown in Fig. 31.

5.3.6

Case Study of WtE

Until now, some processes and application plants were defined without analyses. Here, an example system of WtE is investigated from the thermodynamic point of view. Firstly, inputs and outputs of the system are described in detail. Next, exergy analysis is applied for all states, so exergy flows can be seen in the system. Finally, similar to the exergy flows, economic flows are calculated. Thereby, all of equipment can be investigated from an economic point of view as well as defining the cost of the products.

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Municipal solid waste

Front end seperation

Inert materials, metals

Solid residue

Conversion chamber

Energy

Carbon recovery vessel

Crude syngas

Aggregates

Refinement chamber

Non-hazardous waste

Refined syngas

Steam

Gas cleanning and cooling

Water

PlascoSyngas

Steam turbine

Engines

Heat

Electrical energy Fig. 25 Plasco waste-to-energy process. Adapted from Shareefdeen Z, Elkamel A, Tse S. Review of current technologies used in municipal solid waste-to-energy facilities in Canada. Clean Technol Environ 2015;17:1837–46.

5.3.6.1

System Description

In this section, a CFBG is investigated through the conversion process of biomass to energy. Operation conditions of the system are taken from Ref. [76], then the system is analyzed from the exergy and economic points of view. The CFBG system was compared with other gasification systems in previous papers of the authors; more details can be found in Ref. [77]. The process includes a steam/oxygen blown gasifier and due to the process size range a pressurized (30 bar) CFBG is used. The wood biomass needed for the gasification is milled into smaller particles (5 mm) before usage. The wood is then dried in a rotary dryer at 1201C, and oxygen is taken from an air separator unit (ASU) equipment. Syngas, which is produced at 30 bar and 8501C, passes through a tar cracker for both thermal tar decomposition as well as a catalytic tar decomposition and follows the sulfur removal and water–gas shift reactors. After the water–gas shift, the gas is quenched to near ambient temperature whereby water is condensed and removed. The gaseous components are led to a pressure swing adsorption (PSA) that separates the gaseous species into hydrogen and others, using a separation calculation block. The PSA is assumed to give a high enough purity, 99.99%, at a high range of hydrogen recovery of around 70%. The waste heat taken from heat exchangers in the system is used to generate steam. The steam is generated at 50 bar and first expanded down to 30 bar through a primary turbine for power generation. The steam flow is then split and a part of it goes to the gasifier and the rest is condensed through a secondary turbine for additional power generation. A flow diagram of the integrated process is depicted in Fig. 32.

5.3.6.2

Analysis

The analysis consists of two steps, namely exergy and exergoeconomic analysis. Also, the exergy analysis includes exergy rates of all steams. The main assumptions made in the analysis are as follows:

• •

The system operates at steady state. Kinetic and potential energy effects are ignored.

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Household waste generation in England 22,366 (million tons) 2014 Residual waste

Waste received by recycling authority 10,021

Dry 5799 25%

Kerbside waste 10,481 46%

Organic 4222 19%

Bulky 228 1%

CA residual 1620 7%

Household waste management 22,366 (million tons) 2014 Recycling 10,021 (44.8%)

Disposal 9791 (43.4%) Recovery 2264 10%

Dry 5799 25%

Landfill 5952 26.6%

Food 292 1%

Other organic 4222 19%

Incineration 3749 16.8%

Fig. 26 England local authority collected household waste. Adapted from Liu Y, Kong F, Gonzalez EDRS. Dumping, waste management and ecological security: evidence from England. J Clean Prod 2017;167:1425–37. Defra UK Statistics on Waste. Available from: https://www.gov.uk/ government/uploads/system/uploads/attachment_data/file/547427/UK_Statistics_on_Waste_statistical_notice_25_08_16_update__2_.pdf; 2016 [accessed 16.08.16].

• • • •

Ideal gas principles are applied for the gases. The syngas produced by the gasifier is at chemical equilibrium. Heat losses from the components are neglected. The reference environment temperature T0 is 251C and pressure P0 is 101.325 kPa.

5.3.6.2.1 Exergy analysis

 _ c and physical exergy rate Ex _ ph , and total exergy of a The exergy rate of a material stream may include chemical exergy rate Ex material stream is as follows: _ ph _ ¼ Ex _ c þ Ex Ex

ð1Þ

The physical exergy is resulted from the difference in temperature and pressure between operation and reference environmental conditions. The physical exergy rate of a pure compound in a mixture can be easily calculated using enthalpy and entropy data for the given system: _ ph ¼ m _ ½ðh Ex

h0 Þ

T0 ðs

s 0 ފ

ð2Þ

where h and s are enthalpy and entropy of a system at given temperature and pressure, and h0 and s0 are the values of these functions at the environmental temperature and pressure, respectively. The physical exergy rate of gas mixture is derived from the conventional linear mixing rule: X _ ph ¼ _ i;ph ð3Þ yi Ex Ex i

and the chemical exergy rate of gas mixture is given by _ c¼ Ex

X X yi e0;i þ RT0 yi lnyi i

ð4Þ

i

where e0,i is the standard chemical exergy of a pure chemical compound i and is summarized in Table 8 for different compounds. The chemical exergy of biomass is hard to define and therefore, the statistical correlations of Szargut and Styrylska [78] may be used as follows: e0;biomass ¼ bLHV biomass

ð5Þ

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Waste

Dryer

Gasification reactor

Heat

Heat to district heat

Heat plant

Bio fuel

Steam cycle

119

Electricity from steam turbine

Dry air PSA Water

Steam generator

H2

Gas turbine

CO2

Air

Electricity Flue gas

Electricity

Mass flow

Electricity from gas turbine

Heat flow

Fig. 27 Schematic diagram of the system including the unit operations, processes, and the mass and energy flows. Adapted from Hognert J, Nilsson L. The small-scale production of hydrogen, with the co-production of electricity and district heat, by means of the gasification of municipal solid waste. Appl Thermal Eng 2016;106:174–79.

Fig. 28 Process picture of IZAYDAS. IZAYDAS. Available from: https://www.izaydas.com.tr/flv/katalog2013tr.pdf; 2017 [accessed 16.06.17].

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Fig. 29 Picture of biomass gasification plant in Güssing, Austria. Biomass gasification, Güssing. Available from: http://biomasspower.gov.in/ document/flipbook-pdf-document/Biomass%20gasification%20based%20combined%20heat%20and%20power%20plant%20at%20G%C3%BCssing, %20Austria.pdf; 2017 [accessed 16.06.17].

Fig. 30 Reppie waste to energy plant. Reppie waste to energy plant. Available from: http://africawte.com/; 2017 [accessed 17.06.17].

where LHV is the lower heating value of biomass, and b is quality factor; b¼

1:0412 þ 0:2160ðH=CÞ

0:2499ðO=CÞ½1 þ 0:7884ðH=Cފ þ 0:0450ðN=CÞ 1 0:3035ðO=CÞ

for

O r2:67 C

ð6Þ

where O, C, H, and N are the weight fractions of oxygen, carbon, hydrogen, and nitrogen, respectively, in the biomass. To calculate HHV and LHV correlations can be used from Refs. [79,80], respectively, as follows: HHV ¼ 0:3491C þ 1:1783H þ 0:1005S

0:1034O

0:0151N

0:0211ASH

ð7Þ

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Fig. 31 Shenzhen East Waste-to-Energy Plant, China. Shenzhen East Waste-to-Energy Plant Shenzhen/China. Available from: http://www.shl.dk/ shenzhen-east-waste-to-energy-plant/; 2017 [accessed 17.06.17].

28 4

6

27

25 14

12

10

8

16

17 Hydrogen

15

18

1. Oxygen HX1

2. Biomass

CFBG

Tar craker

9

HX2

De-sulpherization

HX5

HX4

HX3 7

3. Steam

13

11 MTS

HTS

LTS

PSA 24

5. Oxygen

23

26

Water PSA Off-gas

29 Dryer 22

31

21

20

19. Air

30

32

Off-gas combustion Wet biomass

CT

HX6

Gas line Steam line

HPT

33

Fig. 32 Block diagram for hydrogen production from the CFBG. Modified from Hulteberg PC, Karlsson HT. A study of combined biomass gasification and electrolysis for hydrogen production. Int J Hydrogen Energy 2009;34:772–82.

and LHV ¼ HHV ð1

H2 OÞ

2440ðH2 O þ 9HÞ

ð8Þ

where H2O is the moisture in fuel as weight fraction. Although the magnitude of the physical exergy of biomass is small, it is calculated in this simulation after the drying process. There are heat transfers in the system components. In this regard, the thermal exergy rate needs to be

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Table 8

Standard chemical exergy for different components

Component

Standard chemical exergy (kJ/kmol)

H2 CO CO2 N2 CH4 H2O

235,249 269,412 14,176 639 824,348 8,636

(g)

Source: Data taken from Bejan A, Tsatsaronis G, Moran M. Thermal design and optimization. Toronto, Canada: John Wiley & Sons Inc; 1996.

defined as  T0 _ ∂Q T

ð9Þ

X X _ out þ I_ _ in ¼ Ex Ex

ð10Þ

_ q¼ Ex

Z 

1

_ is heat rate. where Q The exergy balance equation becomes out

in

_ out are the exergy flow rates of input and output material streams, respectively, while I_ is the internal exergy loss _ in and Ex where Ex rate due to irreversibility. Finally, the exergy efficiency becomes e¼

5.3.6.2.2

_ out Ex _ in Ex

ð11Þ

Specific exergy cost (SPECO) analysis

The SPECO method is applied to thermal systems to investigate them from an economic point of view. Tsatsaronis and Moran [81] used the SPECO method to exergoeconomically improve the power plant components. Bejan et al. [82] explained the method in more detail. A cost accounting in a company is concerned primarily with (1) determining the actual cost of products or services, (2) providing a rational basis for pricing goods or services, (3) providing a means for allocating and controlling expenditures, and (4) providing information on which operating decisions may be based and evaluated. In a conventional economic analysis, a cost balance is usually formulated for the overall system operating at steady state: C_ P;T ¼ C_ F;T þ Z_

ð12Þ

where, C_ is the cost rate and Z_ means sum of the capital investment and operating and maintenance costs in this study. For a system operating at steady state there may be a number of entering and exiting material streams as well as both heat and work interactions with the surroundings. Since exergy measures the true thermodynamic value of such effects and cost should only be assigned to commodities of value, it is meaningful to use exergy as a basis for assigning costs in thermal systems. Indeed, thermoeconomics rests on the notion that exergy is the only rational basis for assigning costs to the interactions that a thermal system experiences with its surroundings and to the sources of inefficiencies with it. We refer to this approach as exergy costing. In exergy costing, a cost is associated with each exergy stream. Thus, for entering and exiting streams of matter with associated _ in and Ex _ out , power W _ and the exergy transfer rate associated with heat transfer Ex _ q , they can be written, rates of exergy transfer Ex respectively, _ in C_ in ¼ cin Ex

ð13Þ

_ out C_ out ¼ cout Ex

ð14Þ

_ C_ w ¼ cw W

ð15Þ

_ q C_ q ¼ cq Ex

ð16Þ

where c and subscript w state unit cost and work. One can write the following: X X C_ in;k þ C_ q;k þ Z_ C_ out;k þ C_ w;k ¼ OM CI T Z_ k ¼ Z_ k þ Z_ k

ð17Þ ð18Þ

Waste Energy Management
 _ is given as follows [83]: The present worth of the investigated system P W _ system ¼ C_ system PW

S_ system PWFði; nÞ

123

ð19Þ

The present value factor (PWF):


 The salvage value S_ is
ignored in this study.  _ : The annual capital cost CA

PWF ¼ 1=ð1 þ iÞn

ð20Þ

_ system CRF CA_ system ¼ PW

ð21Þ

CRF ¼ ½ið1 þ iÞn Š=½ð1 þ iÞn

1Š

ð22Þ

1 þ i ¼ ð1 þ in Þ=ð1 þ r Þ

ð23Þ

where in, r, i, and n mean nominal interest, inflation, real interest rates,  CI and life time of processes as year, respectively. The hourly levelized capital investment cost of kth component Z_ k : PECk CI CI Z_ k ¼ Z_ system P system PECk

ð24Þ

The  OM  hourly operating and maintenance cost of kth component is obtained from the annual operating and maintenance costs : Z_ k

PECk OM OM Z_ k ¼ Z_ system P system PECk

ð25Þ

In this study we consider cost of fuel (biomass, electric, and water) separately from operating and maintenance cost because the inflation rate of energy is bigger than the general inflation rate [84]. e ¼ rfuel

ð26Þ

r

If the present cost of fuel needed yearly is denoted by D, total fuel cost for n years (Pe) can be calculated as follows: Pe ¼ Df½ð1 þ eÞ=ð1 þ iފn

1g=ðe

iÞ

ð27Þ

F A_ ¼ PeCRF

ð28Þ

_ C_ fuel ¼ F A=t

ð29Þ

where, F A_ is annual fuel cost.

5.3.6.3

Results and Discussion

CFBG is preferred at bigger powers as MW. In the study, the systems firstly were analyzed exergetically, then cost of all streams is calculated using the SPECO method.

5.3.6.3.1

Exergy analysis

Exergy is defined as the maximum work that can be produced by a stream or system in a specified environment; exergy is a quantitative measure of the ‘‘quality’’ or ‘‘usefulness’’ of an amount of energy. All of the system states are examined from the exergy point of view. Biomass is pine wood and has a chemical exergy under the environmental condition; chemical exergy rate has been calculated as 174.872 MW. Also, the biomass is given to gasifier at 1201C, so it has a physical exergy rate as 0.197 MW, which is very small according to the chemical exergy rate. Exergy rate of steam, which is produced from high pressure turbine at 4411C and 30 bar, has 3.563 MW exergy rate. Also, the gasifier is fed with oxygen, which is produced with ASU component, also the oxygen is pressurized to 30 bar. Its exergy rate becomes 0.966 MW, respectively. After gasifiers, syngas exergy rate is 133.046 MW. The CO-shift reactors aim to increase H2 content in syngas. Before the PSA equipment, hydrogen content is 54.2% (v/v). The shifted gas stream must contain at least 70 mol % of hydrogen before it can be economically purified in the PSA unit. After the PSA unit, the hydrogen production value is 0.521 kg/s with 63.468 MW exergy rate. In addition to this, exergy rates of all other streams are given in Table 9. Also, all of the waste heat is assessed to produce the pressurized steam. In the case, the power is produced as 1.66 MWe and 12.04 MWe via high pressure turbine and condensing turbine. In the system, ASU and auxiliary equipment consume electric powers of 9.73 MWe and 2.8 MWe, respectively. Thus, the system can

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Table 9

Exergy flow rates and cost flow rates associated with each stream

State

Stream

m_ (kg/s)

T (K)

P (bar)

E_ x (MW)

C_ ($/h)

c ($/GJ)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

Oxygen Biomass Steam Syngas Oxygen Syngas Syngas Syngas Syngas Syngas Syngas Syngas Syngas Syngas Syngas Cond. H2O H2 Off-gas Air Off-gas Off-gas Off-gas Feed water Water Water Water Water Water Water Water Water Water Water W_ HPT W_ CT W_ ASU

2.504 8.500 2.908 13.911 2.448 16.359 16.359 16.294 16.294 16.294 16.294 16.294 16.294 16.294 14.325 1.969 0.521 13.804 32.304 46.108 46.108 48.709 16.731 16.731 16.731 16.731 16.731 16.731 16.731 16.731 16.731 13.823 13.823

298.15 393.15 714.20 1123.20 363.00 1573.20 623.15 623.15 619.85 773.15 529.70 598.15 457.10 488.20 298.15 298.15 298.15 298.15 309.600 873.2 773.15 557.7 298.15 300 435.9 478.3 537.1 537.1 617 768.15 714.15 714.15 318.96

30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 1 30 1 1 1 1 1 1 50 50 50 50 50 50 50 30 30 0.1

0.966 174.872 3.563 133.046 0.958 130.064 109.527 107.94 107.88 106.603 102.681 101.841 100.45 100.018 97.72 0.00492 63.468 31.62 0.017 16.26 12.74 7.72 0 0.0956 1.84 2.91 6.29 6.35 19.1 22.616 20.49 16.93 4.89 1.66 12.04 9.73

1079.02 2118.87 394.04 4675.43 1070.40 5879.09 4950.79 5160.98 5158.11 5191.42 5000.42 5033.73 4964.98 4998.29 4883.45 0 3875.36 1934.78 9.49 1945.08 1524.00 923.49 540.47 545 663.49 734.89 928.21 931.40 1948.61 2514.22 2277.87 1882.10 543.62 439.02 1510.14 2149.44

310.28 3.37 30.72 9.76 310.37 12.56 12.56 13.28 13.28 13.53 13.53 13.73 13.73 13.88 13.88 0 16.96 17.00 155.07 33.23 33.23 33.23 * 1583.57 100.16 70.15 40.99 40.74 28.34 30.88 30.88 30.88 30.88 73.46 34.84 61.36

produce 1.17 MWe to sell to the grid. In conclusion, the exergy efficiencies are obtained to be 74.16% and 36.56% for the gasifier and the overall system respectively.

5.3.6.3.2

SPECO analysis

The SPECO method is applied to thermal systems to investigate them from an economic point of view. Similar to the exergy analysis, cost flow rates of all streams are calculated via this method. So, problematic points and equipment are defined more easily. In this method, there are two main costs, which are capital investment and OM. Energy, biomass, and water costs contain the main section of OM. In this study, we investigate the costs separately from OM, due to their higher inflation rates. Before calculating these, some parameters should be determined such as inflation rate, nominal interest and process life time. Process life time is 15 years, while the inflation rate and energy inflation rate are taken from Central Bank of the Republic of Turkey as 8.91% and 19.25%, respectively (for 5 October 2010). Also, nominal interest is taken as 15.48%. According to these data, the real interest rate can be calculated as 6%, while the CRF is defined as 0.103. Also, the unit energy, water and biomass costs are taken as 31.38 $/GJ, 4.59 $/t and 35.42 $/t, respectively [77]. Capital investment and operating/maintaining costs for the process are given in Table 10. As can be seen in the table, the total cost of the investment and OM is 3849.03 $/h. Another important consumption is the electrical energy rate for the system. The required electrical energy is 12.53 MWe, which corresponds to ASU and auxiliary equipment consumptions. Due to the fact that the system produces power by the steam turbines at the same time, net power to sell to the grid is defined as 1.17 MWe. The systems give us two main products, H2 and electric power. When electric gain is subtracted from the total cost rates, hydrogen unit costs are calculated as 3.33 $/kg.

Waste Energy Management

Table 10

Capital investment and OM cost rates

Capital investment

PEC ($)

PEC/PECt (%)

Biomass þ pretreatment ASU þ O2 Comp Gasifier þ cyclone Tar cracking Desulfurization (filters. scrubbers. guard beds) Shift reactors-total (hts.mts.lts) PSA HX1 HX2 HX3 HX4 HX5 HX6-steam boiler Combustion chamber Water pump Air fan Steam turbine Expansion turbine Total

20,592,700 42,720,000 39,315,000 7367,000 11,620,000 5,523,700 51,230,000 4,915,000 17,900 128,000 146,400 201,800 7,990,000 45,000 250,600 27,600 11,204,000 9,490,000 212,784,700

9.68 20.08 18.48 3.46 5.46 2.60 24.08 2.31 0.01 0.06 0.07 0.09 3.75 0.02 0.12 0.01 5.27 4.46 100.00

Operating cost Ash deposition Personnel Management License and catalyst Personnel overhead Administration Total

C_ ($/year) 339,900.28 1,171,800.00 175,770.00 6,051,211.33 908,145.00 227,036.25 8,873,862.86

5.3.7

125

CI Z_ ($/h)

OM Z_ ($/h)

Z_ k ($/h)

265.15 550.06 506.22 94.86 149.62 71.12 659.63 63.29 0.23 1.65 1.89 2.60 102.88 0.58 3.23 0.36 144.26 122.19 2739.79

107.35 222.70 204.95 38.40 60.57 28.79 267.06 25.62 0.09 0.67 0.76 1.05 41.65 0.23 1.31 0.14 58.41 49.47 1109.23

372.50 772.75 711.16 133.26 210.19 99.92 926.69 88.91 0.32 2.32 2.65 3.65 144.53 0.81 4.53 0.50 202.67 171.66 3849.03

Future Directions

Waste issues have gone parallel with human history. In the early times, people were responsible for their wastes according to their culture or religious rules. However, later people settled and established big cities without waste infrastructure. The wastes were discharged on the streets causing disease epidemics and leading to thousands of deaths. Hence, people began to take the rules to manage their wastes more seriously. The first methods were open dumping outside of cities and then landfilling, which are commonly used today. Although today modern landfill facilities produce energy at the same time, the old method has various environmental problems such as big space requirements, leakage to soil or water, etc. After industrial improvement, combustion of wastes was found appropriate due to reduction in volume of wastes and additional energy production. The method has commonly been used in industrial sectors today too, although it has a lot of environmental problems such as CO2 emission. After the classic methods, the gasification method became another option, especially due to increasing environmental awareness. Although the gasification method was used only for coal gasification at the beginning, it started to be used for wastes, too. The syngas produced can be used for multiple purposes such as fuel or chemical feedstock, power, and heat. There are different gasification methods as mentioned before. Among them plasma technology has been used in different industrial areas for many years but it has been recently used with solid wastes as well. The method is the most advantageous in terms of energy recovery and byproduct production, though it has high capital and operational costs. To prefer these methods, there are some parameters such as the type and composition of waste, the desired final energy form, operation conditions, etc. When predicting future trends, the World Bank foresees that global waste generation will be over 6 million tonnes per day by 2025 [85]. Also, it is estimated that global waste generation will reach 11 million tonnes per day by 2100 [86]. The authors used three scenarios: SSP1 (7 billion population, 90% urbanization, development goals achieved, reduction of fossil-fuel consumption, and environmentally conscious populations), SSP2 (business-as-usual forecast, with an estimated population of 9.5 billion and 80% urbanization), and SSP3 (13.5 billion population with 70% urbanızation, moderate wealth) and their results are shown in Fig. 33. Pike Research [87] presents optimistic forecasts about states of waste methods for 2010–2022 as shown in Fig. 34. According to the figure, landfilling and open dumping remain as the world’s preferred method, increasing the size. Recovery/composting and methods of energy from waste have a slight increase. According to a WtE state-of-the-art-report in 2013 revision made by International Solid Waste Association (ISWA) [88], there were 455 WtE plants in 18 European countries as well as 86 plants in the United States. The report was prepared for more than 15 t

Waste Energy Management

Waste generation (millions of tonnes per day)

126

12 SSP1

SSP2

SSP3

8

4

0 1900

1950

2000

2050

2100

Fig. 33 Past and projected global waste generation. Adapted from Hoornweg D, Bhada-Tata P, Kennedy C. Waste production must peak this century. Nature 2013;502:615–17.

3.5 Recovery/composting 3.0

Landfilling/open dumping WtE (thermal and biological)

Billion tons

2.5 2.0 1.5 1.0 0.5 − 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Fig. 34 MSW management by disposal method, World Markets: 2010–2022. Adapted from Pike Research, Executive summary: Waste-to-Energy Technology Markets. Available from: http://www.navigantresearch.com/wp-content/uploads/2012/03/WTE-12-Executive-Summary.pdf; 2012 [accessed 17.06.17].

per day capacities of plants as shown in Fig. 35. As can be seen from the figure, the most plants belong to France, the United States, and Germany, respectively. The WtE plants help to achieve the EU’s aim to supply 20% of total energy consumption from renewable energy sources by 2020. About 50% of the energy produced by WtE plants come from biodegradable biomass. About 50 TWh of renewable energy was supplied from WtE plants in 2010, in Europe. It is foreseen that the value will reach at least 67 TWh and potentially 98 TWh by 2020. Total energy produced (renewable þ carbon components) from WtE plants would increase to 198 TWh by 2020, which is enough to supply demand of electricity (for 45 million inhabitants) and heat (for 24 million inhabitants) [89]. When nature is observed, it can be seen easily that nature does not produce waste. Remaining material from an ecosystem is used by another system as a source and the model is known as zero waste. Today, people who design the future of waste focus on the zero waste or circular economy concepts. According to the zero waste concept, the goal is to increase resource recovery, and so protect natural resources by ending incineration, dumping, and landfilling. Until now, a linear economic model, which means “make–use–dispose,” has been used; in this model it is assumed that resources are abundant, available, and cheap. But in the real world, the opposite is observed, and resources are scarce and are getting more expensive day by day. Valuable materials are leaking from the economy according to the linear system. Hence, various countries are trying to move toward a more circular economy model of “make–use–return,” which aims to utilize a product again and again in different subcycles, keeping the resources in the economy and eliminating waste, as shown in Fig. 36. In Europe, the European 2020 strategy covers the transition. It is estimated that the resource efficiency improvements could reduce material inputs needed by 17–24% by 2030 and overall saving potential due to better usage of resources could be €630 billion per year for European industry. Also, innovative financial instruments are developing to reduce risk of investors such as the Natural Capital Financing Facility of the Commission and the European Investment Bank. Also, The Sustainable Process Industry through Resource and Energy Efficiency and the Bio-Based Industries Joint Technology Initiative supply the circular economy as well as public–private partnerships [90].

Waste Energy Management

127

140

Number of plants (−)

120 100 80 60 40

United Kingdom

Switzerland

Spain

Sweden

Portugal

Norway

Netherlands

Italy

Ireland

Hungary

Germany

France

Finland

Denmark

Czech Republic

Austria

Belgium

0

United...

20

Fig. 35 Number of plants per country. Data from International Solid Waste Association (ISWA) Waste-to-Energy State-of-the-Art-Report, Statistics 6th Edition; (Revision: November 2013-b). Available from: https://www.iswa.org/index.php?eID¼tx_iswaknowledgebase_download...3119; 2012 [accessed 20.06.17].

Des

ign

Residua

Re

cyc

ction Produ turing ufac reman

ling

Raw materials

l waste Circular economy

D

is

tri bu t

io

n

ion

lect

Col

Cons use, r umption euse, repair

Fig. 36 Circular economy. Adapted from Newinnonet. Available from: http://www.newinnonet.eu/?artid ¼11; 2017 [accessed 08.09.17].

Also, Canada spent approximately $3.2 billion to manage 34 million tonnes of waste every year. In Canada, the National Zero Waste Council, founded by Metro Vancouver in collaboration with the Federation of Canadian Municipalities in 2013, has been trying to bring together governments, businesses, and nongovernment organizations to prevent waste at an advance level. The council has united five of the largest metropolitan regions, Metro Vancouver, Toronto, Montreal, Halifax, and Edmonton with their main businesses, government leaders, academia, and nonprofit organizations. The Council presents five circular business models, i.e., circular supplies, resource recovery, product life extension, sharing platforms and product as a service. The mission of circular supplies is to provide renewable energy, bio-based or fully recyclable input material to replace single-lifecycle inputs. Resource recovery takes energy or resources from disposed products. Product life extension means using of products or components for as many years as is possible through repairing, upgrading, or reselling. The sharing platforms allow collaboration among product users to facilitate sharing of overcapacity or underutilization. The product as a service business model changes the traditional model of “buy and own” to “lease or pay-for-use” [91].

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There are some cities around the world that define their aims related to the future of their wastes. The cities have initiated sophisticated projects to use their wastes as resources. For instance, San Francisco is considered as an ambitious city to be a leader in waste management with its zero waste goal. The city has been applying three methods, which are enacting strong waste reduction legislation, partnering with a like-minded waste management company to make innovative waste programs, and creating a new culture about recycling and composting. San Francisco began the journey in 1989 with enactment of a state law, the Integrated Waste Management Act. According to the law, cities and counties have to divert 25% of MSW by 1995 and 50% by 2000 and the city has achieved the goals. Following that, in 2002, the city established a new goal of zero waste by 2020. New special legal arrangements have been done since then such as the Construction and Demolition Debris Recovery Ordinance of 2006 and the Food Service Waste Reduction Ordinance of 2007. Also, legal arrangements in 2009 and in 2012 mandated recycling and composting for all residents and businesses. The laws worked to change minds, habits, and culture of people about waste. The city turned its millionth ton of organic waste into compost in 2012 [92]. San Francisco thinks that over half of the trash sent still to landfill bins can be recycled in blue bins or composted in green bins. If all materials are sent to the correct bins, San Francisco’s diversion rate can increase from 80% to 90%, so San Francisco believes that zero waste is possible [93]. The highest waste diversion rate in Europe belongs to Flanders in Belgium; almost three-fourths of residential waste produced in the region is reused, recycled, or composted. In Belgium, every region has to solve its own environmental issues and can implement policies independently. In Flanders, the Public Waste Agency (OVAM) is responsible for developing waste management and monitoring legislation and policies and soil remediation. Waste management policies in Flanders go back to 1981 when the first waste decree was approved and since then every four to five years, new plans have been developed [92]. Flanders has become the global leader in the field of waste treatment with their extensive system of waste collection, sorting, and recycling. After the experience, Flanders has realized the importance of sustainable material management. Due to increasing population, many materials are becoming scarcer and more expensive. Also, similar to Europe, Flanders owns hardly any material sources and generally depends on imports. Their solution is sustainable materials management in a green circular economy. To achieve the goal, the OVAM launched the public–private Flanders' Materials Program in 2012. The program consists of three pillars: Plan C, the Policy Research Centre Sustainable Materials Management (SuMMa), and Agenda 2020, which reinforce each other. The role of Plan C is to break through in sustainable material management. SuMMa brings together researchers to investigate preconditions of the transition to a material-efficient circular economy from the economic, policy, and social points of view. Agenda 2020 focuses on the implementation of 45 concrete projects [94].

Energy

Water

Materials

1. Steam 2. District heating 3. Power to grid 4. Warm condensate 5. District heating

6. Waste water 7. Cleaned waste water 8. Surface water 9. Technical water 10. Used cooling water 11. Deionized water 12. Sea water 13. Drain water 14. Tender water 15. Process water 16. Cleaned surface water

17. Waste 18. Gypsum 19. Fly ash 20. Sulphur 21. Slurry 22. Bioethanol 23. Sand 24. Sludge 25. C5/C6 sugars 26. Lignin 27. NovoGro 30 28. Ethanol waste 29. Biomass

Lake tisse

Kara/Noveren

Katundborg utility

DONG Novo nordisk and novozymes land owner’s association

Gyproc

Statoil

Novo nordisk

Katundborg municipality alge plant

Water reservoir

Novozymes

Inbicon

Novozymes wastewater and biogas

Fig. 37 Kalundborg Symbiosis (for 2015). Adapted from Kalundborg Symbiosis. Available from: http://www.symbiosis.dk/en/evolution; 2017 [accessed 09.09.17].

Waste Energy Management

Fruit pulb

Feed production

Fruit juice production

Cement production

Waste heat Forest waste Lime production

Waste heat

Feed

Animal waste Biogas production

Forestry

Fertilizer production

Iron fertilizer

Iron and steel production

Slag

Soda industry

Waste oils

Construction transportation highways Waste accumulators and tires Recycling sector

Livestock

Fertilizer

Cotton seed Soda process waste

129

Corn waste Fertilizer

Agriculture

Improved soil Cotton seed production

Linter waste

Environmental technologies: bioremediation Soil contaminated with oil

Waste oils and bilge

Oil pipeline management

Fig. 38 Application network of industrial symbiosis in the Iskenderun Bay region. Modified from Industrial Symbiosis in Iskenderun Gulf. Available from: http://www.endustriyelsimbiyoz.org/wp-content/uploads/2014/09/%C4%B0skenderun-K%C3%B6rfezinde-End%C3%BCstriyelSimbiyoz-Sonu%C3%A7-Bro%C5%9F%C3%BCr%C3%BC.pdf; 2017 [accessed 10.09.17].

Also, a circular economy village was inaugurated in Riihimäki, Finland in 2016. The village is a refinery complex developed by Ekokem. In the village municipal waste is processed through the eco refinery, an automated sorting plant, the plastic refinery and the bio refinery. The eco refinery at full capacity receives almost 100,000 t of municipal waste annually and separates bio waste (about 30% of the waste), plastic (4%), metal (3%), and recovered fuel for industrial use (50%), while the remainder of it is rejected. Bio waste is converted to biogas and fertilizers, while plastic and metal are turned into recycled raw material for industry. The rejected part is sent to WtE plants to generate electricity and district heating in Riihimäki [95]. Another important concept to achieve the circular economy in the industry is symbiosis, which means a business-to-business network that supplies collaboration of resource usage; thereby businesses can have some economic advantages as well as creating social and environmental benefits. Its principle is simple: instead of throwing away or destroying the surplus resources in a process, they can be used for another process or processes as a new input or resources. It is not necessary to only be the material; it can be energy, waste water, transportation, asset utilization, and even expertise [96]. Related to this, a good example can be found in Denmark. Kalundborg Symbiosis is the world’s first large-scale industrial symbiosis. The Kalundborg Symbiosis came into existence owing to private conversations of a few enterprise managers from the region in the 1960s and 1970s. Since then, the industrial symbiosis has developed due to its economic, environmental, and cultural benefits. The Kalundborg Symbiosis began in 1961 when Statoil (then Esso) needed water for their refinery and first conduits pipes were installed from Lake Tissø to Statoil. Then Statoil signed an agreement with Gyproc, a gypsum production enterprise, in 1972. According to the agreement, Statoil supplied their excess gas to Gyproc to be used for drying of produced plasterboard. The next year, Dong Energy (then the Asnæs Plant) was connected to the Statoil water pipe. Over the years, a lot of businesses have linked with Kalundborg Symbiosis. The first time the term “industrial symbiosis” was used in 1989, to describe this collaboration. As shown in Fig. 37, a lot of businesses share their resources with each other so they produce more compatible and green products, sending minimum waste to the environment [97]. Another example can be taken from Turkey’s Iskenderun Bay industrial symbiosis, which is the first industrial symbiosis application in the country. The project was launched by Baku-Tiflis-Ceyhan Oil Line (BTC) Co. and an agreement was made with the Technology Development Foundation of Turkey (TTGV) in 2010. While BTC funded the project, TTGV became propulsive. Also the project was applied in collaboration with International Synergies Ltd and Middle East Technical University. The aim of the project is to emerge all of symbiosis potential of businesses in Iskenderun Bay region as well as investigate new products from an academic point of view. The project was completed in the period of 2011–2014. In this period, some pilot projects were developed

130

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including i) animal food production from fruit pulp, ii) energy production from agricultural and animal wastes, iii) production of bioremediation product from cotton seed waste, iv) electric production from waste oil, v) production of granules from end-of-life tires, vi) recovery of lead from scrap batteries, vii) usage of slag produced from iron-steel production in road construction, viii) usage of soda processing wastes in cement industry as an additive, ix) evaluation of various wastes as fuel in the lime kiln, and x) usage slag and oxide layer produced from the iron-steel industry in fertilizer production. The application network is shown in Fig. 38. Potential gains from the project are explained mainly as evaluated waste (330,000 t/year), total savings/energy produced (34,000,000 kWh/year), CO2 reduction (37,000 t/year), savings of water (6,500 m3/year), new product (10), capital cost ($7,000,000), and annual net earnings ($6,400,000) [98].

5.3.8

Conclusions

Wastes from residencies or industrial processes will be a problem for human health and the environment in the near future. Hence, developing of conversion technologies is important. In this study, the different types of waste are classified, firstly, and then legislative trends of countries about the matter are investigated. Main solutions consist of thermochemical technologies to eliminate the wastes as well as reduction, recycling, reuse, and landfill. Also, in the study, a case study is presented to convert waste to energy such as electricity and hydrogen using exergy and SPECO methods. Then, some important WtE applications existing around the world are presented. Finally, the future directions of waste are discussed. Significant considerations are presented as follows:

• • •

•

•

Defining type of waste is the first step of waste management. Today, developed countries tend to reduce production of waste at the household or industrial level. Then recycling and reuse become the second and third steps to protect resources. New wastes come up according to technological developments day by day. This requires updating of existing legal arrangements or making new laws. Today, numerous developing countries do not have rigorous laws about the matter. There are some methods, e.g., landfill, thermal, and biological processes, to convert the waste to energy. Landfill rate is desired to be reduced due to its negative effects on the environment. Especially, thermal systems come forward to gain energy from the waste. While incineration yields greenhouse gases as well as ash, gasification systems can be considered to reduce the greenhouse gases due to partial oxidation. Also, the systems can be used for large scale plants up to MWs. In addition to these, plasma gasification has some advantages such as using heterogeneous wastes and taking multiple generations (e.g., electricity, glassy slag, foam glass). A case study explains the exergy and exergoeconomic concepts in detail and conducts an analysis of a system that produces 1.17 MWe and 0.521 kg/s hydrogen from 8.5 kg/s biomass waste. From the economic perspective, the hydrogen cost is defined as 3.33 $/kg. Increasing the number of systems means that the cost of their byproducts must be comparable with free market prices. Also, some developed countries with high environmental awareness are trying to form their future from today. To protect their resources, they aim to achieve zero waste, focusing on recycling, reuse, and compost methods. Also, the countries divert from the linear economic model to the circular economic model, improving a lot of national projects. Also, they try to implement big industrial symbiosis projects.

Finally, people assume that they are strong and can do everything as individuals due to their self-confidence. But they forget that everything affects everything and everyone else in this world. For instance, when a person throws away trash, a resource is destroyed and the environment is contaminated. People should take nature as an example; it does not produce waste and everything interacts with everything else. To form the future from today, people need national or international symbiosis projects.

Acknowledgments The authors gratefully acknowledge the support provided by Dokuz Eylul University, Yildiz Technical University, and University of Ontario Institute of Technology as well as the Natural Sciences and Engineering Research Council of Canada.

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Further Reading Bargigli S, Ulgiati S. Life cycle assessment of urban waste management: energy performances and environmental impacts. the case of Rome, Italy. United States: Elsevier; 2008. Freeman HM. Standard handbook of hazardous waste treatment and disposal. New York, NY: McGraw-Hill Book Company; 1989. Greenberg M. Nuclear waste management, nuclear power, and energy choices. London: Springer; 2012.

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Hammer MJ. Water and wastewater technology. second ed. United States: John Wiley and Sons Inc.; 1986. New York, NY. Junior C, Jänsch D, Dingel O. (2016;2017). Energy and thermal management, air conditioning, waste heat recovery: 1st ETA conference, December 1-2, Berlin, Germany. Cham: Springer; 2016 doi:10.1007/978-3-319-47196-9. Patterson JW. Industrial wastewater treatment technology. Second ed. Stoneham, MA: Butterworth Publishers; 1985. Waldron K. Handbook of waste management and co-product recovery in food processing. Boca Raton, FL/Cambridge: CRC Press; 2007. Wang J, Yin Y. Biohydrogen production from organic wastes. Singapore: Springer; 2017. Waste management renewable energy 2017; Costa Mesa: Experian Information Solutions, Inc. Young GC. Municipal solid waste to energy conversion processes: economic, technical, and renewable comparisons. Hoboken, NJ: John Wiley; 2010.

Relevant Websites https://www.studentenergy.org/topics/waste-to-energy Waste to Energy. https://www.eia.gov/energyexplained/index.cfm?page=biomass_waste_to_energy Independent Statistics & Analysis – US Energy Information Administration. http://www.metrovancouver.org/services/solid-waste/about/wte/pages/index.aspx Waste to Energy. http://www.deltawayenergy.com/wte-tools/wte-anatomy/ DELTAWAY. http://ecosolutions.com/waste-to-energy/ Eco Waste Solutions. http://www.wm.com/sustainability/renewable-energy.jsp WM Waste Management. https://waste-management-world.com/waste-to-energy WMW – Waste Management World. http://www.conserve-energy-future.com/waste-to-energy.php WMW – Waste to Energy. http://www.gasification-syngas.org/applications/waste-to-energy-gasification/ GSTC – Gasification & Syngas. http://www.eneco.ca/waste-management.html EnEco – Waste Management.

5.4 Energy Reliability and Management Alessia Arteconi, ECampus University, Novedrate, Italy Kenneth Bruninx, KU Leuven, Leuven, Belgium; EnergyVille, Genk, Belgium; and VITO, Mol, Belgium r 2018 Elsevier Inc. All rights reserved.

5.4.1 Introduction 5.4.2 Fundamentals 5.4.2.1 Reliability Definition 5.4.2.2 Reliability Assessment 5.4.2.2.1 Generation (HL1) 5.4.2.2.2 Transmission (HL2) 5.4.2.2.3 Distribution (HL3) 5.4.2.2.4 Calculation methods 5.4.2.2.5 Economic implications 5.4.2.2.6 New metrics 5.4.2.3 Energy Management and Reliability 5.4.2.3.1 Backup and energy storage systems 5.4.2.3.2 Demand side management 5.4.3 Application: Demand Response and Reliability 5.4.3.1 Demand Response Definition 5.4.3.2 Benefits and Challenges 5.4.3.3 Demand Response Loads and Simulation Tools 5.4.3.4 Demand Response and Reliability: State of the Art 5.4.4 Analysis and Assessment 5.4.4.1 Integrated Model 5.4.5 Case Studies 5.4.5.1 Case Study A 5.4.5.2 Case Study B 5.4.5.3 Case Study C 5.4.5.4 Case Study D 5.4.6 Results and Discussion 5.4.6.1 Energy Management and Demand Flexibility 5.4.6.2 Demand Response Related to Adequacy: Peak Shaving 5.4.6.3 Demand Response Related to Security: Reserve Provision 5.4.6.4 Methodological Improvements: Demand Response Limited Controllability 5.4.6.5 Comparison With Other Approaches for Increasing Reliability 5.4.6.5.1 Demand response versus electric energy storage system 5.4.6.5.2 Energy storage 5.4.6.5.3 Distributed generation 5.4.6.5.4 Electric vehicles 5.4.6.5.5 Incentives 5.4.7 Future Directions 5.4.8 Closing Remarks Acknowledgment References Further Reading Relevant Website

4 5 5 6 6 7 7 9 9 9 10 10 12 13 13 13 14 14 17 17 18 19 19 20 20 20 20 22 24 27 28 28 28 28 29 29 29 29 30 30 32 32

Nomenclature A,B ACH ACHP ADR

134

state space matrix air change per hour air coupled heat pump active demand response

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135

AR Arb ASAI ASIDI ASIFI CAES CAIDI CAIFI CCDF CCGT CEMIn CEMSMIn

(kW)

CENI ci(ri) CN CNk4n CO2Ti,j COP CPP CTAIDI curj D dfix j

(pu) ($) (pu) (pu) $ (pu) ($/kWh) (h) (pu) (h/occurrence) (kW)

alternative resource arbitrage average service reliability index average system interruption duration index average system interruption frequency index compressed air energy storage customer’s average interruption duration index customer’s average interruption frequency index composite customer damage function combined cycle gas turbine customer experiencing multiple interruptions customer experiencing multiple sustained interruptions and momentary interruptions events customers experiencing none interruptions cost for the outage duration ri total number of customers who experienced a sustained interruption total number of customers who experienced more than n sustained interruptions emissions costs coefficient of performance critical peak pricing customer’s total average interruption duration index curtailment duration fixed electricity demand profile

dH;fix j

(kW)

fixed electricity demand from electric heating systems

dH;var j ^dH;var j

(kW)

variable electricity demand from electric heating systems

DG DHW DR DRR DSM ECOST EDLC EENS EER EES EEUI EFLC eh EID EIF EPE ESS EV ESWE EWES F FBES FCi,j FC-HES FES fh FOR PP gi;j gjRES

(pu) (h) (pu) (h) (pu)

(pu) (pu)

variable electricity demand from electric heating systems as stochastic variable

occurrence (pu) (kW)

distributed generation domestic hot water demand response demand recovery ratio demand side management expected interruption costs expected duration of load curtailment expected energy not served expected energy retrieved electric energy storage expected energy unserved per interruption expected frequency of load curtailment difference between load and generation capacity average interruption duration of deferred loads average interruption rate of responsive loads expected energy of deferred loads energy storage system electric vehicles expected surplus wind energy expected wind energy supplied frequency flow batteries energy storage fuel costs fuel cells–hydrogen energy storage flywheel energy storage frequency of load greater than generation capacity forced outage rate power plant output

(kW)

renewable power plant output

(pu) ($/year) (h/year) (kWh/year) (kWh/year) (kWh/occurrence) (occurrence/year) (kW) (h/occurrence) (occurrence/year) (kWh/year)

(kWh) (kWh) (occurrence/year) $

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h HL hor i IALSD IEAR IEED IEEI ILOLP ILSE ISO j Li LOEE LOLD LOLE LOLF LOLP Lt MAIFI MAIFIe mi MO nb Ni NSR Nt OCGT pDR PjHP PjAUX PCM PHES pp PV qDHW j qIj qSj r Rc RCi,j REDR Ref Reg REP RES RTP RTS RUS s SAIDI SAIFI SCi,j SR TES th

(pu) (kW)

hour hierarchical level optimization horizon number of interruption states island average load shedding duration interrupted energy assessment rate island expected energy deficiency island expected energy interrupted island loss of load probability island load shedding expectation independent system operator hourly time step kVA load interrupted for each event i loss of energy expectation loss of load duration loss of load expectation loss of load frequency loss of load probability total kVA load served momentary average interruption frequency index momentary average interruption event frequency index load curtailed merit order number of buildings number of interrupted customers for each event i non-spinning reserve total number of customers open cycle gas turbine DR participation rate heat pump power

(kW)

auxiliary heater power

(kW)

phase change material pumped hydro energy storage percentage point photovoltaic DHW demand

(h) (h) (h) ($/kWh) (kWh/occurrence) (kWh/occurrence) (pu) (kWh/occurrence) (h) kVA (kWh/year) (h/occurrence) (hours/year) (occurrence/year) (occurrence/year) kVA (pu) (pu) (kW)

(pu) (pu)

(kW)

internal heat gains

(kW)

solar heat gains

(h/occurrence) (pu) $ (pu)

mean duration under outage condition or restoration time relative operational costs ramping costs renewable energy dispatch ratio reference regulation renewable energy penetration renewable energy sources real time pricing reliability test system rural utility service component’s mean duration in service system average interruption duration index system average interruption frequency index start-up costs spinning reserve thermal energy storage time when load is greater generation capacity

(pu) ($/kWh) (h) (h/occurrence) (h) (pu) $

(h)

Energy Reliability and Management

temperature vector comfort constraint minimum temperature

Tj Tjmin

(1C) (1C)

Tjmax

(1C)

comfort constraint maximum temperature

(1C) (1C) ($) ($/kWh) (h/year)

ambient air temperature ground temperature total operative costs time of use unavailability unit commitment unit cost reliability benefit wind source wind utilization factor nonproportional component of the stochastic variable probability that the UC schedule is inadequate to meet the load failure rate standard deviation of the nonproportional component, δNP

Te,j Tg,j TOC TOU U UC UCRB WS WUF δNP ɛ l sNP

5.4.1

(pu) (pu) MW (occurrence/year)

137

Introduction

Nowadays the electric power sector is experiencing an increasing production of electricity by means of renewable energy sources (RES) and also distributed generation (DG) is assuming an important role, competing with conventional baseload plants placed in a few production sites. In 2014, 13.8% of the world total primary energy demand was met with energy produced from distributed renewable sources [1]. These aspects affect the operation of the power system and especially its reliability [2]. Indeed, DG creates challenges for the management of the network, for example, with respect to local congestion and voltage problems, as the network is generally operated in different conditions from those specified in the network design phase. Intermittent RES may introduce uncertainty in the available production capacity and require backup power and energy storage systems (ESS) to ensure a reliable power supply. Furthermore, the growing world energy demand [3] reduces the capacity margins. Such variability and uncertainty on the supply side may reduce the adequacy and security of the power system. Osborne and Kawann [4] propose possible ways to improve the reliability of the electricity systems addressing the supply or the demand side. On the supply side, it is possible to intervene at the generation or at the transmission level. They state that siting new generation capacity has a central role, while it is ever more difficult to find the right place close to the growing loads [4]. For DG and RES, instead, new standardized protocols for interconnection are of paramount importance to support the advent of these technologies. At the transmission level it is necessary to (1) improve grid utilization by means of network management and load forecasting; (2) improve resource sharing with interconnected utilities; (3) develop planning instruments, for example, monitoring and control systems, regulatory frameworks, make information about reliability publicly available; and (4) organize outage management by means of a maintenance schedule or introducing penalties to fine failures. On the demand side, energy management (demand side management, DSM) strategies can help to match demand and supply, boosting the reliability of power systems [4]. Among DSM techniques, demand response (DR) may offer a valuable contribution to the reliability of a power system. DR programs perform load shifting by means of variable electricity prices or incentives that induce the final user to modify his demand load shape on the basis of the requests from, for example, the power system operator. DR is recognized as an instrument providing peak shaving, arbitrage, and regulation services in a power system [5]. Indeed, DR allows load to follow RES based generation, limiting the variability of the residual load (i.e., the load corrected for the available RES based generation) of the power system. As a consequence, DR can help to improve both the power system adequacy and security. All controllable electrical loads can be addressed by DR programs, however, the residential sector has a central role, given its big share in the overall energy consumption. It accounts for about 40% of the total energy consumption both in Europe and in the US [6,7]. Heating and cooling in buildings and industry represent half of the EU’s energy consumption [8]. Moreover, the electricity demand for providing heating and cooling in buildings is foreseen to increase in the near future, given the ongoing electrification of this sector, as demonstrated by the growth of the heat pump market (about 7 million units in the EU in 2013) [9]. Furthermore, it is worth mentioning that recently, in a broader perspective, the importance of the reliability concept is reinforced by the advent of integrated energy systems, consisting in multicarrier energy systems characterized by a mutual dependence among the infrastructures of the different systems. Reliability is a key point to take into account when assessing the interdependence among different forms of energy [10]. The redundancy due to the interconnection of the systems has generally a positive impact on reliability [11]. In particular, it is important to consider the dynamic behavior of thermal loads in these systems, because their flexibility allows smaller size of the components involved in the integrated system [12]. Once

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more, thermal and electrical flexibility appear strictly connected and this reinforces the link between DR and electric thermal loads, as stated above. The appeal of such kind of applications is also demonstrated by existing utilizations of this principle, as in the case of smart thermostats regulating the peak hour consumption in order to optimize comfort and energy savings [13]. Given the significance of reliability evaluations specifically for a power system, in this chapter, an innovative aspect of this concept is analyzed. Namely, the role of demand side energy management and particularly of DR to improve the reliability of a power system is investigated. In general, both reliability and DR have their own fields of application and objectives. In this piece of work the focus is given especially to the interaction of these two concepts: how can DR affect the reliability of a power system? A qualitative discussion is provided, supported by the comprehensive analysis of case studies. The remainder of this chapter is organized as follows: Section 5.4.2 illustrates the fundamentals about reliability and energy management. Firstly, definitions of reliability and reliability assessment methods are provided. Secondly, the relationship between reliability and energy management (both on supply and demand side) is pointed out through a thorough review of the state of the art in the field. Section 5.4.3 introduces the definition and theory about DR. In addition, some critical findings available in the literature about the interdependence between DR and reliability are provided. In Section 5.4.4 the model used to support the analysis is illustrated, while the details about the considered case studies, in which DR with electric heating systems is leveraged to provide arbitrage, peak shaving, and regulation services, are described in Section 5.4.5. A qualitative discussion of the results is given in Section 5.4.6, where also a comparison with results from other methodologies is included. Future research opportunities are highlighted in Section 5.4.7 and finally closing remarks are formulated in Section 5.4.8.

5.4.2 5.4.2.1

Fundamentals Reliability Definition

The reliability of a power system is defined as “the degree of performance of the elements of the bulk electric system that results in electricity being delivered to customers within accepted standards and in the amount desired” [14]. In other words, reliability is a measure of the ability to deliver electricity to all customers at any time. Reliability of a power system is composed of two basic aspects: adequacy and security [15]. More specifically, according to the North American Electric Reliability Council definition [14], adequacy is “the ability of the electric system to supply the aggregate electrical demand and energy requirements of the customers at all times, taking into account scheduled and reasonably expected unscheduled outages of system elements,” whereas security is “the ability of the electric system to withstand sudden disturbances such as electric short circuits or unanticipated loss of system elements.” Therefore, adequacy takes into account the availability of all the necessary facilities/ resources to generate the demanded electricity and to deliver it to the customers. It does not consider the effect of unexpected disturbances that can affect the elements of the electric system, which are, instead, related to the concept of security. Generally, adequacy is particularly relevant for long-term planning and investments, while security typically refers to short term operation, as exemplified in Fig. 1 [16]. Reliability studies are conducted accordingly to the relative functional zone of the power system: generation, transmission, and distribution [15]. Precisely, power system reliability studies can be classified as specific, if they deal with a specific part of the power system, or as integrated, if they take into account the relationships among different subsystems [16].

Adequacy

Expansion planning

Security Operational planning Operations

Second

Minute

Hour

Day

Week

Year

Time Fig. 1 Reliability analysis time frame. Inspired by Schilling MT, Do Coutto Filho MB, Leite da Silva AM, Billinton R, Allan RN. An integrated approach to power system reliability assessment. Electr Power Energy Syst 1995;17:381–90.

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HL0 - Energy

HL1 - Generation

HL2 - Transmission

HL3 - Distribution Fig. 2 Hierarchical levels of reliability analysis. Inspired by Schilling MT, Do Coutto Filho MB, Leite da Silva AM, Billinton R, Allan RN. An integrated approach to power system reliability assessment. Electr Power Energy Syst 1995;17:381–90.

A reliability assessment can be classified according to “hierarchical levels” at which the analysis is targeted (Fig. 2) [16]:

• • • •

HL0 – Energy: at this stage the subject of the study is just a preliminary balancing of energy demand and available electricity generation capacity of the entire electric power system (for this reason a specific metric related to HL0 does not exist, as described below). HL1 – Generation: it is a generating capacity reliability evaluation, aimed at assessing the adequacy of the generation system with respect to the total load requirement. HL2 – Transmission: it is a bulk transmission system evaluation that indicates the ability of the system to deliver the energy demanded to the transmission load points. HL3 – Distribution: the range of the analysis is widened to incorporate also the distribution system and the ability of the system to serve final users with the required energy is considered.

5.4.2.2

Reliability Assessment

Reliability is quantified by means of several indices, which vary according to the above mentioned hierarchical levels of the analysis.

5.4.2.2.1

Generation (HL1)

At the first level HL1, the evaluation concerns only the electricity generation facilities. Combined, these units must represent sufficient capacity to cover the electricity demand at each moment of the year and to allow scheduled maintenance. Both specific generation elements and system indicators are used:

•

Frequency (F): is the frequency that an element goes from the service state to the outage state

•

where s is a component’s mean duration in service, r is the mean duration of the outage condition, and l is its failure rate [17]. Duration (D): is the average duration that an element is in its outage condition [17]

F ¼ s
l=ðs þ r Þ

D ¼ r=ðs
lÞ

•

ð2Þ

Unavailability (U) or forced outage rate (FOR): is the probability that an element is in the outage condition of an element [17] FOR ¼ F
D ¼ r=ðs þ r Þ

•

ð1Þ

ð3Þ

Loss of load expectation (LOLE): represents the number of hours per annum in which it is expected that supply will not meet demand [18]. It depends on all those aspects that can affect the balance between supply and demand (e.g., a consistent number of power plants not working on a given occasion, etc.) LOLE ¼

8X 760

th

h¼1

where th is 1 when load is greater than the available electricity generation capacity and zero otherwise [19].

ð4Þ

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Loss of energy expectation (LOEE) (some authors also call it expected energy not supplied (EENS), see Section 5.4.2.2.2) measures the expected unsupplied energy when the demand exceeds the available electricity generation capacity. It gives an idea of the severity of shortages but not of their frequency and duration, represented, instead, respectively, by loss of load frequency (LOLF) and loss of load duration (LOLD) defined below. LOEE is influenced by static conditions, such as power plant size and type, availability, maintenance requirements, load profile and its uncertainty. Neither the LOLE nor the LOEE normally include operational considerations (e.g., spinning reserve requirements, dynamic and transient system disturbances, etc.), therefore they cannot be considered as absolute measures of power system reliability [20]. LOEE is expressed as

LOEE ¼

8760 X

eh

ð5Þ

h¼1

where eh is the difference between load and the available electricity generation capacity when the load is greater than the electricity generation capacity, or zero otherwise [19].

•

LOLF: is the number of times per annum that the demand cannot be satisfied with the available electricity generation capacity: LOLF ¼

8760 X

fh

ð6Þ

h¼1

•

where fh is 1 when load is greater than generation capacity at time h and zero at time h 1, otherwise it is zero [19]. The loss of load probability (LOLP) is defined similarly. LOLD: is the average duration of an event in which the demand exceeds the available electricity generation capacity [18]: LOLD ¼

5.4.2.2.2

LOLE LOLF

ð7Þ

Transmission (HL2)

At the HL2 stage, the framework used in the HL1 evaluations is extended to include the transmission system. Two sets of indices exist: the load point indices and the overall system indices. They are complementary and not alternatives. The load point indices show the effect on individual bus bars and provide inputs to the next hierarchical level. The system indices, instead, give an assessment of the overall adequacy. Among them, conceptually similar to their corresponding indices in HL1, there are: EENS, expected frequency of load curtailment (EFLC), expected duration of load curtailment (EDLC) [18]. Even if at this level more realism is included by considering the bulk transmission system, the evaluations typically do not take into account the power system dynamics [21]. The redundancy of individual links in transmission networks is critical because the effects of transmission disturbances may be much more widespread than the effects of distribution disturbances. Furthermore, an extensive transmission network can enhance competition in wholesale electricity markets by enabling consumers, retailers, and generators to access distant, but cost-effective sources of electricity generation, lowering the overall cost of electricity generation [4].

5.4.2.2.3

Distribution (HL3)

The third hierarchical level, HL3, includes the reliability assessment of the overall system (generation, transmission, and distribution, terminating at the customer’s individual load points). However, due to the complexity of the problem, at this stage the distribution system is generally investigated as a separate entity using the HL2 load points indices as input values. Indeed, the distribution system is that part of the electric power system that connects the bulk transmission system load points to the customers. Such connections are often radial in nature and susceptible to outages due to a single event. It has been found that 80% of all interruptions at the consumer’s level occur due to failures in the distribution system [22]. From the consumer’s perspective, transmission and distribution related outages are most important to real-time reliability (system security). Generation and other system component outages are typically most significant to system planners, because they tend to affect the reliability of the electricity system as a whole [4]. However, proper operational planning (e.g., maintaining sufficient regulation services) allows fully mitigating the effects of outages at the supply side, limiting their impact on end consumers. The primary indicators of the load points of distribution system are (similarly to the indices for generation facilities) [22]:

•

Failure rate, l: is the failure frequency of load point l¼

•

•

X

li

where i is the number of interruption states. Outage time, r: is the average load point duration of a failure P li
ri r¼ P li

ð8Þ

ð9Þ

Annual outage time, U: is the annual unavailability of the load point U¼l
r

ð10Þ

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The above mentioned factors are important for the analysis of a particular load point, but they do not give any information about the overall distribution system. They are useful to calculate other system indices for reliability of distribution network in electric power systems, as defined by the IEEE guide [23]. • System average interruption frequency index (SAIFI): it provides information about the average frequency of sustained interruptions per customer over a predefined area. P Ni ð11Þ SAIFI ¼ NT

•

System average interruption duration index (SAIDI): it provides information about the average time the customers are interrupted. P ri Ni SAIDI ¼ P ð12Þ NT

•

Customer average interruption duration index (CAIDI): it represents the average time required to restore service to the average customer per sustained interruption. P SAIDI ri Ni ð13Þ ¼ CAIDI ¼ P Ni SAIFI

•

Customer’s total average interruption duration index (CTAIDI): it represents the total average time in the reporting period the customers who experienced an interruption were without power (customers that experienced multiple interruptions are counted only once). P ri Ni CTAIDI ¼ ð14Þ CN

•

Customer’s average interruption frequency index (CAIFI): it gives the average frequency of sustained interruptions for those customers experiencing sustained interruptions (customers are counted once regardless of the number of times interrupted). P Ni ð15Þ CAIFI ¼ CN

•

Average service availability index (ASAI): it represents the fraction of time (often in percentage) that a customer has power provided during the defined reporting period. P NT
h ri Ni ASAI ¼ ð16Þ NT
h

•

Average system interruption frequency index (ASIFI): it gives information on the system average frequency of interruption, but it is based on load rather than number of customers. P Li ð17Þ ASIFI ¼ LT

•

Average system interruption duration index (ASIDI): it provides information on system average duration of interruptions. P ri Li ð18Þ ASIDI ¼ LT

•

Customers experiencing multiple interruptions (CEMIn): it is designed to track the number of sustained interruptions n of a specific customer. CEMIn ¼

•

CNk4n NT

ð19Þ

Rural Utility Service (RUS): it is used to determine the average outage hours for customers in rural areas. These customers may experience longer recovery periods from disturbances than other customers do because of the lower density of loads along rural feeders.

RUS ¼

P

ri CN

ð20Þ

The parameters used in the above definitions are as follows: Ni is the number of interrupted customers for each interruption event during reporting period; NT is the total number of customers served for the area being indexed; ri is the restoration time for each interruption event; CN is the total number of customers who have experienced a sustained interruption during the reporting period; h is the number of hours per year; Li is connected kVA load interrupted for each interruption event and LT is total connected kVA load served; and CNk4n is the total number of customers who have experienced more than n sustained interruptions during the reporting period.

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Similarly, for momentary interruptions (lasting less than 5 min) other indices are defined [23], namely: momentary average interruption frequency index (MAIFI), momentary average interruption event frequency index (MAIFIE), and customers experiencing multiple sustained interruptions and momentary interruption events (CEMSMIn).

5.4.2.2.4

Calculation methods

The methods to calculate the reliability indices are classified in analytical methods and simulation techniques. Analytical techniques represent the system by means of a mathematical, probabilistic model to calculate the aforementioned indices. Commonly used analytical techniques are, among others, fault tree analysis, failure mode and effect analysis, minimal path methods, minimal cut methods, and fault traversal algorithm [24]. Analytical models have been widely used, but their applicability is limited for complex systems. Simulation methods instead estimate the reliability indices by simulating a stochastic behavior of the actual process. They include Monte Carlo simulation, artificial neural networks, and nonexponential distribution methods [24]. Simulation methods may require large amounts of computing time; on the other hand they can include any system effect and allow estimating probability density functions that describe, for example, the probability of load shedding. Each technique has its merits and applications and sometimes there are methods that combine both approaches (i.e., analytical and simulations techniques). Moreover, due to the uncertainties in data required to support the studies (load, failure rates, restoration time, etc.), absolute reliability metrics are typically not attainable [15]. It is of paramount importance that the data collected for performing such analysis are sufficiently comprehensive, but at the same time restrictive enough to exclude irrelevant events [21]. The IEEE reliability test system (IEEE-RTS) has been developed to satisfy the need for a standardized data base to test and compare results from different power system reliability evaluation methodologies [25].

5.4.2.2.5

Economic implications

The concept of reliability is strongly related to the economics of a power system. Generally speaking, increased reliability normally requires higher investment costs, while maintenance and damage costs decrease when reliability is improved. Customers’ satisfaction is therefore augmented and as a consequence energy demand, then specific system costs (per energy unit) decrease [24]. In order to set the optimal level of reliability, a benefit–cost analysis is necessary, where the adequacy costs are compared to the value associated with adequacy (adequacy “worth”: the benefit derived by the utility, customers, and society). Such assessment requires the determination of worth from the customers’ perspective. These benefits are difficult to quantify; generally a common approach is an indirect evaluation of the costs associated with supply interruptions. These costs depend both on customer type and interruption characteristics. The main methodology used to assess direct short term outage costs is the customer’s survey method [26]. These surveys are designed to quantify the monetary losses that would be sustained under certain specific scenarios of interruption and the willingness to pay in order to avoid them. Data collected are used to estimate composite customer damage functions (CCDF), which represent the overall average cost of interruptions as a function of the interruption duration in a given service area. Alternatively, probability distribution methods can be used to model the outage costs [26]. Reliability worth index has been introduced, interrupted energy assessment rate (IEAR), which links the EENS and outage costs: IEAR ¼

ECOST EENS

ð21Þ

expressed in $/kWh. ECOST represents the expected interruption costs due to all possible load curtailment outage events: X ECOST ¼ ci ðri Þli mi ð22Þ i

where mi is load curtailed due to capacity shortfall, li is the frequency of outage event i, ri is duration of outage event i, ci(ri) is cost of outage duration ri expressed by the CCDF function [26]. In conclusion, the IEAR metric provides a monetary evaluation of the energy deficiency from the point of view of the customer.

5.4.2.2.6

New metrics

Considering the recent changes in power systems worldwide, in particular the inclusion of more renewable sources in the generation mix, the advent of DG, and the development of microgrids, new metrics have been suggested to characterize the reliability of power systems under these conditions. For example, Karki and Billinton [27] introduced indicators to assess the effect of wind power penetration in existing power systems. Even if these indicators do not represent directly the reliability, they can be related with it because they characterize a renewable source penetration. They are the expected wind energy supplied (EWES) and the expected surplus wind energy (ESWE). EWES measures the conventional fuel energy offset by wind application and can be used to assess fuel cost and emission penalty cost savings. ESWE, instead, is defined as the amount of wind energy available but not utilized. The ratio between EWES to the total energy produced by wind turbines is called the wind utilization factor (WUF). Whereas, Wang et al. [28] defined new metrics to assess the reliability of microgrids, containing RES based generation, in distribution networks. They include operational indices related with reliability in island mode, indices reflecting the DG and load characteristics, economic indices, and customer based reliability indices. Some of the proposed indices (evaluating directly or indirectly reliability issues) are listed below [28]:

•

Island loss of load probability (ILOLP): the fraction of time that load demand is not satisfied during microgrid island mode.

Energy Reliability and Management

• • • • • • • • •

Island expected energy deficiency (IEED): average energy deficiency during island mode due to hours when island load exceeds total available island power generation capacity. Island expected energy interrupted (IEEI): expected load energy interrupted during island mode of a microgrid due to a deficiency of available generation capacity. Island load shedding expectation (ILSE): the average kW load that is shed during each interruption in island mode. Island average load shedding duration (IALSD): the average load interruption duration in island mode. Renewable energy penetration (REP): the percentage of demand covered by renewable energy in a microgrid in 1 year. Renewable energy dispatch ratio (REDR): the maximum ratio of renewable energy generation output over the total dispatchable power generation in order to maintain the stability of a microgrid. Unit cost reliability benefit (UCRB): the ratio of the reliability benefit to the generation cost of a microgrid. Customers experiencing no interruption (CENI): the percentage of microgrid CENI at all, normally on an annual basis. Other indices are: Expected energy retrieved (EER) [29]: it is designed to quantify flexible reliability. EER ¼ AR r l

•

ð23Þ

where AR is the amount of available alternative capacity resource in a load point for a given failed element. Expected energy unserved per interruption (EEUI) [30]: EEUI ¼ EENS=LOLF

•

143

ð24Þ

For the interruption of responsive loads in DR programs Safdarian et al. [31] introduced average interruption rate of responsive loads (EIF), average interruption duration of deferred loads (EID), and expected energy of deferred loads (EPE).

5.4.2.3

Energy Management and Reliability

Among the ways to improve reliability, those that can mitigate the potential electricity system resource deficiencies have an important role. They belong to the wider category of energy management methods. Energy management “is the proactive, organized and systematic coordination of procurement, conversion, distribution and use of energy to meet the requirements, taking into account environmental and economic objectives” [32]. The energy management methodologies may be useful to guarantee the power system reliability and can be realized by employing the following different approaches that affect the supply side or the demand side (Fig. 3). They can be categorized, on the supply side, as backup and ESS, and as DSM strategies on the demand side. They are discussed below.

5.4.2.3.1

Backup and energy storage systems

Backup and storage systems are considered energy management technologies available on the supply side of a power system. The increasing penetration of RES (i.e., wind and solar) in the generation mix poses the challenge of continuity of supply, because of the typical intermittent behavior of such sources. Then, renewable sources generally require an energy backup system [33]. A backup system can be a conventional generator, usually fast ramping, that steps in during an outage of another conventional generator or during unexpected availability of RES based generation [33]. Similarly, storage devices can be introduced to store the surplus electric energy during periods of low demand and/or high RES-based generation, which has to be released at a later time when the demand exceeds the available generation capacity. Energy management Backup systems Supply side management Energy storage

Energy storage Demand side management Demand response Fig. 3 Energy management technologies addressed to the supply and demand side of a power system.

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Energy Reliability and Management

Electricity storage technologies can be divided into chemical, mechanical, and thermal [34]. They can also be classified on the basis of their purpose: energy management (system adequacy) or power quality (system security) [35]. A brief description of the most relevant ones is provided below [34]. Energy management storage technologies:

•

•

• • •

• •

Pumped hydro energy storage (PHES): these systems use the potential energy of water, stored in two reservoirs at different height. Electric power is used to pump the water from the lower reservoir to the upper reservoir during off-peak hours. Vice versa, the water flows from the upper reservoir to the lower reservoir and passes through hydraulic turbines to produce electricity during peak times. Pumped hydroelectric systems have a good conversion efficiency, but the storage capacity is limited by the geographical constraints (elevation and available land). Thermal energy storage (TES): TES systems can be classified into sensible, latent, and thermochemical heat storage. In sensible TES (e.g., thermally stratified water tanks), energy is stored thanks to the variation of the temperature of the storage medium. In latent TES, during the energy storage process, phase change of the storage medium occurs, thus it is named phase change material (PCM). PCMs have the advantage of requiring smaller storage volumes, because generally latent heat is much higher than sensible heat for a substance. Moreover, the phase change occurs at nearly constant temperatures and this guarantees limited temperature variations during operation. In thermochemical TES heat can be stored also through the use of reversible chemical reactions: heat is first used to induce an endothermic chemical reaction and then it is recovered by reversing the reaction. High energy densities and long storage duration are the main features of this process. Compressed air energy storage (CAES): it uses electric power during off-peak hours (storage hours) in order to compress air and store it under pressure. During peak hours (retrieval hours), the pressurized air is expanded in an expansion turbine driving a generator for power production. They can be large scale (e.g., underground reservoir) or small scale (e.g., cylinders) facilities. Large scale batteries or flow batteries (FBES): flow batteries are composed of two chemical compounds in liquid state, separated by a membrane. This system converts chemical energy in electricity. The electrolyte is stored in external tanks and pumped into the cell when the electricity needs to be produced. Fuel cells–hydrogen energy storage (FC–HES): off-peak electricity is used to produce hydrogen through the electrolysis of water. Hydrogen is stored to be used later in fuel cells where a reaction between hydrogen and oxygen from the air occurs to generate electricity during peak hours. ESS, deployed in the context of power quality management: Superconducting magnetic energy storage: energy is stored by means of a magnetic field created by a current that flows in a superconducting coil maintained at very low temperature. Flywheel energy storage (FES): a flywheel is a rotating mechanical device that is used to store rotational energy.

Several studies deal with the role of ESS to improve the reliability in a power system. These studies mainly address three topics: (1) network configuration and overall power system management, (2) photovoltaic (PV) solar power integration, and (3) wind power integration in the power system. 1. Network configuration and overall power system management: Saboori et al. [35] propose an optimization model to determine the location and size of ESS for reliability improvement in radial electrical distribution networks. It is shown how ESS reduce the EENS and consequently the operational costs of the system. Xu and Singh [36] present an energy storage operation strategy for a load aggregator to improve bulk power system reliability by minimizing its energy purchasing cost in a wholesale electricity market. Kahrobaee and Asgarpoor [37] analyze the role of a standby electricity storage system placed at specific load points in the network and its effect on the total reliability costs. The calculated indices in this study show the reliability improvement at both load point and system level. Arifujjaman [38] assesses the power losses (of related electrical devices), efficiency, reliability, and cost of a grid-connected energy storage system (composed of a battery and a power conversion system) for frequency regulation. This paper takes into account the conduction and switching losses of the semiconductor devices. For the case analyzed, the mean time between failures of the electric energy storage (EES) is 8 years and reliability remains at 73% after a year (considering a reliability assessing method specific for ESS). Dong et al. [39] present a storage and reserve sizing problem (reserve sizing is cooptimized with storage sizing to minimize the total cost of the considered microgrid) for microgrids with a high penetration of RES based generation, considering reliability indices (e.g., LOLP) within the model. 2. PV solar power integration: Koh et al. [40] evaluate the impact of photovoltaic systems on power system reliability at hierarchical level 1 (HL1) on the IEEE-RTS. PV panels are considered coupled with energy storage in order to improve system reliability otherwise compromised by the variability of the PV power output. The potential of PV coupled with ESS to benefit system adequacy and reduce energy cost is demonstrated. Although in this field generally battery storage systems are considered, Aihara et al. [41] instead propose a pumped storage power plant to mitigate the impact of PV on power system reliability. Pumped storage systems are not operated differently during daytime and nighttime as usually happens, but the operating patterns are modified in order to absorb the excess power produced by the PV systems, augmenting the power supply reliability during peak demand periods. 3. Wind power integration: with respect to wind power integration, Hu et al. [42] analyze the role of ESS for mitigating the potential risk related to wind power fluctuations. It is shown the importance of energy storage capacity and operational strategies during the evaluation process. Different system configurations (energy storage capacity and operating constraints, wind power dispatch restrictions, wind energy penetration level, and wind farm location) are considered to assess the impact of

Energy Reliability and Management

Table 1

145

Summary of the main findings of the papers about the interaction between demand side management (DSM) and reliability

Main findings

References

Demand side management (DSM) reduces the optimal planning reserve margin Total societal cost of electricity is reduced by means of DSM DSM is beneficial for composite generation and transmission system reliability Peak clipping strategies introduce more reliability improvements than other strategies, but they are extreme actions Load shifting introduces reliability improvements similar to peak clipping strategies, but it is more easily implemented Valley filling strategies have little impact on reliability because the load involved is generally low Demand side load curtailment helps contingency management in restructured power systems and reduces operational costs (if interruptible loads are placed in optimal locations) Load forecast uncertainty negatively influences reliability, but DSM can counteract this effect Redundancy in the design of communication devices in a smart grid with DSM minimizes the cost of system failure Web based reliability information systems are helpful for implementing DSM

[56] [56] [53] [53,54] [52,54] [53,54] [55,57] [52,57] [59] [58]

energy storage on power system reliability. Moreover, Thapa and Karki [43] indicate that the energy storage potential to offset the uncertainty on wind power forecasts is limited by the rated capacity and the discharge time of the storage system. It is also shown that the storage contributes to reduce wind power curtailment. Abdullah et al. [44] instead propose an effective power dispatch control strategy of wind farms with integrated battery storage systems to improve the supply reliability by means of a novel scheduling algorithm using stochastic programming, taking into account the uncertainty on wind power and load forecasts. Qin et al. [45] discuss a reliability oriented energy storage sizing approach for wind power dominated systems, where power ratings, energy storage capacity, investment cost, and control strategy of the energy storage are all taken into account. Bhuiyan and Yazdani [46] also present a reliability assessment and components rating methodology considering a wind power system with integrated battery energy storage. They show that the battery capacity plays the most crucial role. Moreover, they suggest that the battery maximum discharging power has to be chosen slightly higher than the expected maximum load peak power, while a considerably larger value can be assigned to the maximum charging power in order to ensure rapid energy storage and higher reliability.

5.4.2.3.2

Demand side management

As explained previously, DSM is an energy management tool on the demand side. DSM is defined as “the planning, implementation, and monitoring of those utility activities designed to influence the customer’s use of electricity in ways that will produce desired changes in the utility’s load shape, i.e., changes in the pattern and magnitude of a utility’s load” [47]. All those strategies proposed to impact the customers’ use of energy are considered DSM and can be leveraged to reduce customer’s demand at peak times (namely peak clipping or peak shaving), reduce energy consumption seasonally or yearly (energy conservation), change the timing of end-use consumption (load shifting) from high cost periods to low cost periods, and increase consumption during off-peak periods (valley filling) [48]. On the basis of its definition it is evident that DSM may be of paramount importance in increasing the reliability of a power system: it allows to augment the customers’ satisfaction by adapting their demand to the available production. Although it has a wide definition, DSM mainly results in the implementation of three types of strategies: (1) energy efficient end-use devices; (2) energy storage; and (3) DR [49]. Among the ESS, it is worth mentioning the role of TES installed on the demand side of the power system. TES, indeed, is identified as a means for reducing peak electrical demand and high costs for electricity in peak-hours, because it can help offset the mismatch of availability of renewable electricity and demand for electricity when coupled to heating and cooling systems [50]. Whereas, DR is intended to achieve changes in customers’ electrical usage in response to, for example, changes in the price of electricity over time [51]. Further details about DR are provided in the following section. Many authors have investigated the influence of DSM strategies on power system reliability. The main findings of these works are summarized in Table 1. Huang and Billinton [52], for example, analyze the impact of implementing DSM in terms of reliability benefits by applying load shifting procedures and considering load forecast uncertainty by means of Monte Carlo simulations. They prove that DSM increases the reliability and stability of the system over time. Moreover, the application of DSM tends to counteract the effects of load forecast uncertainty. Zhou et al. [53] estimate the impact of DSM resources on composite generation and transmission system reliability by means of Monte Carlo simulations. The DSM strategies considered are peak clipping, valley filling, strategic conservation, load shifting, and strategic load growth. Results show that DSM actions, especially peak clipping, give an important contribution to composite system reliability improvement and the reliability indices are dependent on the network topology. Huang et al. [54], too, test different DSM strategies and assess their impact on the adequacy of the bulk power system. Huang et al. [54] conclude that peak clipping has a major effect on the reliability indices but may not be practical as it reduces the energy supplied to customers. Load shifting results in a similar improvement in reliability but it is a more practical solution. Valley filling allows a system to provide more energy to customers without affecting the reliability. DG sources (e.g., wind power) provide additional generation capacity but at the same time can increase the uncertainty on the residual load profile due to their limited predictability.

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Goel et al. [55], instead, propose a reliability assessment of restructured power systems with hybrid market models considering demand side load curtailment. The calculated reliability indices provide the expected demand curtailed for a particular customer who can be made aware of the relevance of his participation. Thus, the developed method is a possible tool for the ISO to implement the participation of customers in reliability management. Billinton and Lakhanpal [56] evaluate the impact of DSM on the reliability cost and reliability worth. The paper illustrates how the optimal planning reserve margin can vary with the introduction of DSM. Indeed, less operational reserves are needed in presence of DSM. Moreover, the total societal cost of electricity generation is reduced by the introduction of demand side activities. Yousefi Ramandi et al. [57] show that using interruptible loads, together with spinning reserves offered by conventional generation units, improves the reliability of a power system including wind farms, when the interruptible loads are placed in optimal locations, and reduces the expected system cost. A wind dominated system is also considered by Choi et al. [58]. Choi et al. demonstrate the role of a web-based monitoring system as an instrument to observe power system reliability. Niyato et al. [59] study a reliability assessment of wireless communications system in a smart grid environment to support DSM. The availability performance (i.e., the probability that the wireless connectivity between a smart meter and the meter data management system is available) is used to calculate the cost of failure, and redundancy design approaches are provided.

5.4.3

Application: Demand Response and Reliability

In this piece of work, the role of a specific DSM methodology, namely DR, is investigated in order to put into evidence its peculiar contribution toward the power system reliability.

5.4.3.1

Demand Response Definition

DR can be distinguished as active and passive DR. Active demand response (ADR) is defined as “changes in electric usage implemented directly or indirectly by end-use customers/prosumers from their current/normal consumption/injection patterns in response to certain signals” [60]. In contrast, passive DR is related to changes in the normal consumption/injection patterns without interacting with the consumers (e.g., rolling blackouts). Hereafter ADR will be considered and referred to simply as DR. DR can be performed by means of price based programs and incentive based programs. A price based program induces a change in customers’ load pattern by acting on time varying electricity rate. Different tariff structures exist [61]:

• • •

Time-of-use (TOU): different tariffs are applied during different periods of time in a day. TOU tariffs ideally reflect the average cost of generating and delivering electric energy during the corresponding periods of time. Real-time pricing (RTP) or dynamic pricing: the retail price for electricity typically varies, for example, hourly on the basis of the wholesale price of electricity. Customers are generally notified in advance of the dynamic rate (on a day-ahead or hour-ahead basis). Critical peak pricing (CPP): it is a combination of the TOU and RTP tariffs. CPP is composed of TOU rates in normal times, while a peak price is used when specific critical conditions occur (e.g., when system reliability is compromised or supply prices are very high).

An incentive based program, also called reliability based DR, operates load reduction by means of monetary incentives (e.g., a discounted, but fixed electricity tariff or an annual payment to the consumer) to the customers. Typically a change in the normal energy use is requested when reliability conditions are threatened or when market prices are well above average values. If the customer enrolled in such a program fails to respond, a penalty can be applied. Possible incentive based programs are [61]:

• • • • • •

Direct load control: the utility remotely controls customers’ electrical equipment (e.g., air conditioner, water heater) and, on short notice, it can switch them on or off on the basis of its needs. Interruptible/curtailable service: customers have a reduced rate if they agree to lower their demand to a certain level (curtailable rate) or even to zero (interruptible rate) during system contingencies. Typically this program is addressed to industrial or commercial customers. Demand bidding/buyback program: customers voluntarily submit load reduction bids to lower their load when the utility communicates the possibility to take part to a DR action, generally on a day-ahead basis. Emergency DR programs: customers receive incentives to reduce their loads during emergency events. Economic DR programs: customers are invited to reduce their load when the electricity price rises too high in spot events. Ancillary services DR program: customers receive incentives in exchange of load curtailment and/or fast downward ramping their demand. In this case the load modification is used as operating reserve or frequency regulation service.

5.4.3.2

Benefits and Challenges

O’Connell et al. [62] examine the main benefits and challenges for DR program introduction. The operating advantages, widely recognized in the literature, are mainly related to the DR program’s ability to increase the flexibility and overall reliability of the entire system [62]. Indeed, by altering the energy demand over time, DR allows matching the demand with the available electricity production. This is particularly useful in a power system including limited predictable and intermittent electricity generation from

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renewable resources, for example, wind and solar power. Moreover, customers taking part in a DR program can provide fast response to the requests received from, for example, the grid operator. DR may reduce the need for interconnections with neighboring power systems. From a system planning point of view, DR may reduce the necessity for new generation capacity. The geographical diversity of the customers may help to solve congestion problems and reduce the need for network upgrades. Eventually, from an economic point of view, DR may lower the overall costs of a power system, even if the benefit for a singular customer can be very limited, especially when a wide participation in the DR program is foreseen [63]. For O’Connell et al. [62], the main challenges, instead, for the effective uptake of DR are related to the lack of proper market mechanisms for its realization and also to the difficulty in controlling and interacting with the customers involved. At present, DR is primarily employed for the provision of emergency contingency support and ancillary services, with limited participation in the day-ahead market. Moreover the actual tariff structures are not always very clear, which may make it difficult to make the customer aware of the advantage of taking part in a DR program. With the introduction of DR, the customer becomes an active player in the energy market and shares the responsibility for maintaining system security. Customers could be exposed to extremely high or fluctuating prices if no protecting mechanisms are put in place. Furthermore, it is difficult to control and predict the final users’ behavior: they could always decide not to participate into the program, causing reliability issues in the overall system [62]. Summarizing, the main efforts to push the uptake of DR programs have to be aimed at removing the regulatory, market, and technology (e.g., smart metering, communication networks, and automated control technologies) barriers and at improving customer knowledge and acceptance of DR [61].

5.4.3.3

Demand Response Loads and Simulation Tools

DR programs can be addressed to big commercial and industrial customers, but also to residential customers. In this context it can act on deferrable loads (e.g., dishwashers, laundry machines, etc.) or thermostatically controlled loads (heat pumps, refrigerators, and air conditioners). The last category is particularly relevant taking into account the big share of the total energy demand that comes from buildings and in particular from their cooling/heating needs [50]. The built environment offers an important possible field of application for DR and it is also practically easily implementable: load shifting of heating/cooling devices can be attained via intelligent control strategies, without loss of thermal comfort for the user, thanks to the inherent thermal storage that a building can offer [63]. In order to assess the real contribution that a DR program can provide to a power system, it is of paramount importance to use proper simulation models. They can be classified into models focused on the supply side (e.g., price-elasticity models, virtual generator models), models focused on the demand side (using a fixed price profile), and integrated models. Only integrated models allow to represent in a detailed way both the demand and supply side and to take into account their real interaction. In fact, when customers react to a price signal by shifting their demand, as a consequence they affect the price signal, reflecting the new energy demand–supply balance. Neglecting such interaction could lead to erroneous conclusions about the potential financial gains associated with DR [64].

5.4.3.4

Demand Response and Reliability: State of the Art

In this section the state of the art on the relationship between reliability and DR is thoroughly analyzed. DR can be used within the context of a power system to reduce the required capacity, ancillary services, or emergency reserves [65]. Indeed, it allows load shifting, thus reducing energy consumption during peak periods, thereby postponing new generation capacity investments and/or operational reserve requirements. For this reason DR contributes to improvements of the adequacy of a power system and is defined in Ref. [62] as nonemergency DR, contributing to the capacity margin (see Fig. 4 for the relationship between peak load, installed capacity, and capacity margin [66]). Furthermore, DR customers can be asked to curtail or shift their deferrable loads in case of emergency (namely emergency DR [65]). In this sense DR can also improve the power system security, as demonstrated by Mohagheghi et al. [67]. Mohagheghi et al. [67] discuss qualitatively the DR potential to reduce the overall system demand during peak times, providing a safety margin to the power system in case it is exposed to faults and disturbances. Moreover, they show that DR can improve the dynamic performance of the system when it allows emergency load reduction. Considering both DR attributes (allowing peak shaving and emergency reserves), its role in improving system reliability affects not only the availability of service, but also system security. This article concludes that in general power system reliability benefits from applying DR. However, a successful implementation of DR requires the usage of smart devices (sensors, meters, and actuators), which pose additional reliability considerations associated with their operation. In order to properly evaluate DR programs, especially in case they act as emergency (or operational) reserves, it is important to define the overall duration and the development over time of a DR event (Fig. 5): DR customers need an advance notification (a few hours or the day ahead) to decide whether they will participate in the action requested, after which it takes a certain amount of time (ramp period) until the consumer’s demand is reduced to the requested levels. After the ramp period there is the deployment of the DR resource. A transition period (recovery period) may be necessary before resuming normal operation. Ramping times are strictly related to the type of customer and they can range from a few seconds to a few minutes [67]. Below, the available papers in the scientific literature are grouped on the basis of the following classification: (1) DR related to adequacy purposes and (2) DR related to security purposes. Considering the intimate relationship between the two different

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Installed capacity Marginal capacity

Load

Peak load

Time Fig. 4 Relationship among peak load, capacity, and capacity margin. Inspired by Billinton R, Allan RN. Reliability evaluation of power systems. Boston: Pitman; 1984.

Demand response event Advance notice

DR deployment Ramp period Reduction deadline

Response period

Release/Recall

Recovery period Normal operation

Fig. 5 Time development of a demand response (DR) event. Inspired by Lee S-S, Lee H-C, Yoo T-H, et al. Demand response operation rules based on reliability for South Korean power system. In: IEEE Power and Energy Society General Meeting; 2011.

aspects of reliability (i.e., adequacy and security), such categories are not always strictly separated and some studies can cover both purposes. Main findings of the available works in the scientific literature are also collected in Table 2. 1. DR related to adequacy: The role of customers in a DR program is of course of paramount importance. Regarding this issue, Kwag and Kim [68] evaluate the effects of DR programs taking into account the customers’ behavior and particularly what happens when the DR customers fail to adhere to the scheduled demand reduction. Such a situation could potentially cause new reliability problems. In detail, the impact of customers’ behaviors on the DR resource availability is represented in the same form as conventional generation units, which can be available or unavailable. It is highlighted that an improvement of

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Table 2 Summary of the main findings of the papers about the interaction between demand response (DR) and reliability. Reliability category: (1) DR related to adequacy; (2) DR related to security Category

Main findings

Reference

1

Interruptible loads at specific buses reduce considerably the total societal cost of electricity Direct load control improves system reliability Design guidelines of a real demand response (DR) program are provided DR has considerable impact on reliability, also with a limited customers’ participation and it is better to improve the exploitation of available DR programs without increasing the amount of further initiatives DR programs, aimed at high energy demand reduction, generally do not increase system reliability DR causes inevitable energy payback (due to load restoration) that produces lower but longer peak loads and such effect can negatively affect system reliability DR supports both availability of service by reducing peak demand and security by providing a safety margin, for example, DR helps to manage the occurrence of contingencies in generations units DR allows network operators to modify the system load profile, instead of shedding loads, thus improving service reliability DR can provide flexible reliability: high priority customers can be supplied also during contingency states DR programs counteract the negative effect of volatility of renewable energy sources on power system reliability DR based reliability constrained model can be used to optimize the participation of energy service providers in the electricity market

[69] [70] [65] [68,71]

2

[72] [30] [67,73,75] [31] [29,77] [74] [76]

the participation in existing DR programs has a much stronger positive effect on the reliability of the power system than increasing the amount of DR initiatives. DR strategies can have a positive effect, if well designed, on the power system both using incentive based programs and price based programs, as demonstrated by several studies in the scientific literature. FotuhiFiruzabad and Billinton [69] present a hybrid probabilistic/deterministic technique for system reliability evaluation considering interruptible loads as a load management alternative (an interruptible load contract allows the utility to reduce the demand when requested by the utility and provides an incentive to the customers involved). The study results indicate that the interruptible load initiative has a great cost savings potential in terms of reducing the total societal cost of electricity. Azami and Fard [70] evaluate the effects of direct load control DR program on reliability indices and show that DR programs can improve the system and nodal reliability. Instead, Samadi et al. [71] analyze the effects of price-based (TOU) DR programs on the modification of the load demand curve and reliability. They show that DR programs reduce the peak demand, improve the load factor (ratio between actual energy consumption and maximum generation capacity), and reduce the expected energy not served. However, even if most of the DR programs improve the reliability indices, inappropriate designs of DR programs can lead to the undesirable result of reducing system reliability. Indeed, it is relevant to consider the energy demand modification after DR events, i.e., load restoration. In line with these concerns, Zhou et al. [30] investigate the reliability implications of deploying DR and EES systems at the system level, i.e., the consequences on the displacement of generation capacity. Results show that DR and electric energy storage (EES) can reduce the frequency and cumulative duration of interruptions, but that these interruptions – when they occur – may become more severe. It is thus of paramount importance to take into account the side effects of load restoration after voluntary load shifting. Similarly, Nikzad and Mozafari [72] quantify the impact of DR programs on the reliability of restructured power systems. An optimization model is used to determine load curtailment and generation redispatch for each contingency state and includes incentive and penalty mechanisms together with different customers’ behaviors. Reliability indices for load points, generation companies, transmission network, and the whole system are calculated for the Iranian power system. Results show that programs for intensive energy reduction (one of the goals of the Iranian system operator in designing DR programs) do not always guarantee an increased system reliability. 2. DR related to security: Aghaei et al. [73] study the influence of emergency DR programs in improving reliability in case of failure of generation units. They demonstrate that emergency DR programs may have significant influence, even at a low DR penetration: emergency DR can both prevent price spikes in specific hours, and acts as additional reserve capacity to increase the overall reliability of the system during critical hours. Moshari et al. [74], instead, consider the effects of DR programs on short term reliability of wind dominated power systems. In the short term reliability assessment, the failure probabilities depend on time of occurrence and on the initial states of system components. Moreover, it is important to take into account that consumers need a certain time to successfully manage their load, thus DR programs should be announced beforehand. This work proposes a model that represents DR uncertainty and shows how the lead time of remedial resources affects the short term reliability assessment of the power system. Gaspar and Gomes [75] investigate the role of controllable demand for guaranteeing adequate reliability in short term operation of power systems. An evolution strategy to design adequate control actions is developed by the authors. It is applied over end-use loads and its impact is assessed by means of the reliability indices. Results confirm that controllable loads can be an effective alternative to reserve generation capacity. The influence of DR on the distribution network is studied by Mahboubi-Moghaddam et al. [76]. They propose a decision model helpful for energy service providers. It includes DR programs and considers their effect on network reliability (assessed by

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means of a failure-mode-and-effect analysis approach). The simulation results demonstrate the significant benefits of DR on the performance of such energy service providers who can, thanks to the proposed decision model, effectively take into account, in a robust manner, the DR potential and price uncertainty. A beneficial effect of increasing the customers’ adherence to the DR program is discussed by Safdarian et al. [31]. They assess the potential impact of DR on service reliability in a residential distribution network. The analysis is based on metered hourly consumption profiles and survey data of hundreds of customers from a Finnish city. The obtained results show that great reliability improvements can be achieved by enabling DR without customers’ discomfort when a sufficiently large number of customers is participating in the program. Syrri and Mancarella [77], instead, define a particular application of DR, so called postfault DR. It relies on DR customers accepting contracts for differentiated reliability, driven by the willingness to accept lower service quality for economic benefits. Postfault DR is used along with network automation with the aim to provide congestion management. It is demonstrated that with DR the network could be stressed up to its maximum limits without compromising the reliability comfort of non-DR customers and without proceeding to network reinforcements, with a small increase in the unreliability costs. Eventually, Baboli et al. [29] introduce the concept of flexible reliability, meaning that, while a minimum reliability level is provided for all customers in the distribution network, there are high priority customers that are supplied also in contingency states (paying the corresponding cost). Demand side resources, including DR, are considered as instruments to operate a microgrid with such flexible reliability targets. A new index, EER, is used to quantify the effectiveness of flexible reliability approach and the results illustrate the positive impact of demand side resources. On the basis of the state of the art review here presented, it is evident that there are a lot of implications associated with the introduction of DR programs in a power system, affecting the electricity generation system, the transmission and distribution network, and the demand side. In the following sections some of the main effects associated with the implementation of DR programs will be further explored by means of a case study, so as to make the relationship between DR and reliability even clearer to the reader.

5.4.4

Analysis and Assessment

As demonstrated by the literature review in the previous section, the influence of DR on power system reliability may be considerable. In this section, we will further illustrate this intimate relationship qualitatively by means of case studies. We employ a so-called integrated operational model, which represents both the supply side and demand side. The model is referred to as integrated, because it takes into account the interactions between the two different parts of the power system. Indeed, this interaction of electricity generation and demand cannot be neglected if one aims to account effectively for their mutual influence on the electricity price. In this model the transmission and distribution grid is not considered. In our case studies, it is assumed that DR programs are focused on residential electric heating systems (heat pumps and auxiliary electric resistances) coupled with passive (building envelope) and active (domestic hot water (DHW), storage tank) TES. The DR strategy considered is direct load control: the utility can intervene to shift the customers’ electricity demand in order to optimize the overall system, i.e., to minimize the operational costs associated with meeting the demand for electricity and thermal comfort (see below). The focus is on electric heating systems because they represent a relevant share of the total domestic electricity demand. Furthermore, the electricity demand from electric heating systems is foreseen to increase in the near future due to the increasing penetration of such systems, triggered by their good efficiency and their potential to accommodate the growing production of electricity from RES [9]. The main advantage of thermostatically controllable loads as DR resources over the other deferrable loads (e.g., dishwashers, driers, fridges, etc.) is related to buildings’ inherent thermal inertia, which allows altering the electricity usage profile of the heating systems without tampering with the indoor thermal comfort. However, the assessment of this kind of DR program is a bit more complicated, because it requires taking into account the thermal behavior of the building active and passive thermal mass in a time-dependent analysis. The description of the loads that can be controlled is of paramount importance, because it affects the operational characteristics of DR and the load restoration after the DR event.

5.4.4.1

Integrated Model

The model used belongs to the group of integrated models. Such models represent both the dynamic behavior of the supply and demand side. Main advantages of this approach are (1) the electricity demand (from electric heating systems here) is close to reality, thanks to the detailed representation of the system; (2) the influence of the demand on the electricity price, and vice versa, is taken into account; and (3) it is possible to guarantee at the same time both the internal thermal comfort and the DR balancing services to the power system. On the other hand, solving these models often requires a significant computational effort, thus some simplification can be introduced, as better explained below. In the most general formulation of the model, expressed as a mixed integer linear programming model, the supply side is represented by a unit commitment (UC) and economic dispatch (ED) model, which minimizes the overall operational costs composed of start-up costs (SC), fuel costs (FC), ramping costs (RC), and emission costs (CO2T) (Eq. (25)): min

hor X ij

  XX PP ¼ SC þ FCi;j þ RCi;j þ CO2 Ti;j cost gi;j i

j

i;j

ð25Þ

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  for every power plant i at every hourly time step j over the optimization horizon, hor. The output of the power plants gi;j PP is assessed taking into account some technical constraints, such as the minimum and maximum operating point of each power plant, ramping constraints, minimum on- and off-times (Eq. (26)):   PP ¼0 ð26Þ f gi;j Then, the electricity produced has to equal the overall electricity demand, as shown in Eq. (27):   X  1 pDR
dH;fix þ pDR
dH;var gijPP þ cur j
gjRES dfix ¼ j þ nb
j j

ð27Þ

j

where gjRES represents the available RES based generation. This electricity generation can be curtailed (curj) and the curtailment is assumed to be free (Eq. (28)): ð28Þ

0rcurj r1

In order to reduce the computational effort required to solve this problem, another formulation of the supply side is also considered in the case studies presented in the next section. It employs a so-called merit order (MO) model, which consists of a mere ranking of the different power plants  in an ascending order of average operational costs. The MO model can calculate the PP considering the minimum and maximum operating point of each power plant, but hourly output of each power plant gi;j neglecting ramping constraints, minimum on- and off-times and SC (this affects the formulation of Eq. (26) and Eq. (27), modified accordingly). The validity of such a simplified approach in the context of DR, rather than a complete UC and ED model, has been demonstrated by Patteeuw et al. [64].   Regarding the electricity demand, instead, it is composed of a fixed electricity demand profile dfix plus the electricity demand j of the electric heating systems fora certain number of representative buildings (nb). The demand from the electric heating systems    H;fix can be adherent to a DR scheme dH;var or can be fixed to a predefined profile d . The share of flexible (pDR) and inflexible j j DR DR (1–p ) demand from electric heating systems is depicted by the parameter p , i.e., the DR participation rate. The DR adherent demand is evaluated on the basis of a physical demand side model, representing the thermal behavior of the dwellings with the electric heating systems and thermal storage systems. This demand side model is described by Eqs. (29)–(31): ¼ PjHP þ PjAUX dH;var j

ð29Þ

h i X Tjþ1 ¼ A
Tj þ B
PjHP ; PjAUX ; qDHW ; Te;j ; Tg;j ; qSj ; qIj j

ð30Þ

j

Tjmin rTj rTjmax

ð31Þ 

dH;var j



The demand from the electric heating systems (Eq. (29)) that participate to a DR scheme is due to the heat pump    PjHP or the backup auxiliary heater PjAUX . They both heat the building and the DHW tank, which are represented by a state space model (Eq. (30)) with state space matrices A and B. The vector with the states (Tj) contains the temperatures of the building and DHW tank.The temperature has to be within the comfort bounds, Tjmin and Tjmax (Eq. (31)). The  disturbances included are 



DHW demand qDHW , ambient air temperature (Te,j), ground temperature (Tg,j), solar heat gains qSj , and internal heat gains j   qI . The same demand side model is used to assess the electricity demand of the heating systems not adherent to a DR scheme  j  : it is the minimum energy used to comply with the thermal demand necessary for maintaining the comfort. In such case dH;fix j any interaction with the supply side model is neglected (pDR ¼ 0%). An in-depth description of the model is provided in [64]. The described model is used to assess the impact of introducing DR programs on the power system, quantified in terms of overall operational costs, RES based generation utilization, peak shaving potential, and expected load shedding volumes. In Section 5.4.5, the four case studies are presented. The results are shown in Section 5.4.6 and deal with three applications: 1. Energy management, i.e., load shifting and demand flexibility that leads to a more efficient scheduling of the electricity generation system and a higher utilization of RES based generation, resulting in operational costs reduction; 2. DR related to adequacy: DR programs may entail a significant peak shaving potential, improving the power system adequacy; 3. DR related to security: DR adherent load may offer cost effective regulation services, resulting in high reliability levels (security levels) at a lower expected operational cost. The first application allows illustrating how the model works and what kind of results one can expect by introducing DR programs, facilitating the understanding of the relationship between DR and reliability. This intimate relationship is discussed by means of the second and third application, focusing on system adequacy and security, respectively.

5.4.5

Case Studies

Four different case studies are considered. In all of them the power system is inspired by the Belgian power system, but may consider different reference scenarios, thus different installed capacities, generation mix, and building stocks are assumed. In this

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Table 3

Overview on the four case studies used in the analysis

Case study

Supply side representation

A

• • •

MO model RES 30% (50% wind and 50% PV) Only gas fired power plants

• •

B

• • • • • •

UC&ED model RES 20% (50% wind and 50% PV) Mixed fossil fueled power plants UC&ED model RES 40% (30% wind & 10% PV) Only gas fired power plants

•

• • •

UC&ED model RES 50% (wind) Mixed fossil fueled power plants þ PHES

C

D

Demand side representation

• • • • •

Reference scenario

Objective

Good insulated buildings Floor heating

2030

• •

Buildings from the period 2005–10 Radiators Different building typologies Radiators and floor heating Good insulated buildings Floor heating

2013

•

2030

•

DR peak shaving potential and system adequacy

2013

•

DR short term behavior and system security DR limited controllability

•

DR energy management DR peak shaving potential and system adequacy DR energy management

Abbreviations: DR, demand response; ED, economic dispatch; MO, merit order; PHES, pumped hydro energy storage; PV, photovoltaic; RES, renewable energy sources; UC, unit commitment.

way a broad representation of possible scenarios is possible and a wider set of results could be obtained. In the following the details of each case study are described. The main features of each case study and the objective in its analysis are summarized in Table 3.

5.4.5.1

Case Study A

This case study is the same as in Arteconi et al. [63] and has been simulated by means of the MO supply model (see Section 5.4.4.1). The electricity generation system represents a future scenario in 2030 and it is composed only of gas fired power plants as fossil based generation, with a total installed capacity of 11,200 MW combined cycle gas turbines (CCGT) and 5800 MW open cycle gas turbines (OCGT). It has been assumed that RES based electricity generation is capable of covering  30%  of the and the electricity demand and consists of 50% solar and 50% wind energy. Both the fixed electricity demand profile dfix j   RES are taken from the Belgian transmission grid operator [78]. electricity generation from RES gj Regarding the demand side, the nb is assumed to be about one million, which is the expected number of detached buildings for Belgium in 2030 [79]. An average building, taken from the TABULA project [80], is assumed as reference with a total surface area of 270 m2, an average U-value of 0.3 Wm 2K 1, and a ventilation rate of 0.4 air changes per hour (ACH). The state space building model is based on Reynders et al. [81]. Stochastic profiles to represent the user behavior, namely the temperature set points and DHW demand, are employed, as suggested by [82]. The lower bounds for the indoor temperature set points are 20 and 181C for the day zone and night zone, respectively, while the upper bounds are 21C higher than lower bounds [83]. The DHW storage tanks are either 200 or 300 L, depending on the maximum daily hot water demand. DHW is supplied at 501C, while the upper bound for the DHW storage tank is 601C. Measurements in Uccle (Brussels, Belgium) for the weather data for 2013 are used. The heating system consists of an air coupled heat pump (ACHP), which supplies heat both to the floor heating system and to the storage tank for DHW. The heat pump is sized to meet 80% of the peak heat demand. A backup electric resistance heater is also included. The coefficient of performance (COP) of the heat pump is determined according to Bettgenhauser et al. [84]. The nominal supply water temperature of the floor heating is 351C. Based on this, the COP is predetermined and assumed to be constant throughout each optimization period [85]. A variable share of flexible demand from electric heating systems (pDR) is considered, in order to assess its influence on the operation of the overall power system. The DR penetration can vary between 5 and 100% (for more details about the case study see Ref. [63]). This case study is used in the following to illustrate the energy management potential of DR (Section 5.4.6.1). Furthermore it is considered to assess the impact of DR on power system adequacy, specifically on peak shaving potential (Section 5.4.6.2).

5.4.5.2

Case Study B

This case study is the same as in Patteeuw et al. [64]. Here the more general formulation of the model, with UC and ED representation, for the supply side is employed. In this case the reference is the present Belgian scenario. Differences with Case Study A are listed below.

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The electricity generation is composed of 1 nuclear power plant (1200 MW), 5 coal-fired steam power plants (4000 MW), 10 gas-fired combined cycle power plants (CCGT, 4000 MW), and 10 OCGT and oil-fired power plants (OCGT, 1000 MW). RESbased electrical energy accounts for 20% of the generated electrical energy over the year. Buildings characteristics refer to a typical Belgian building built between 2005 and 2010. The building considered has a floor surface of 270 m2. Infiltration and ventilation combined cause 1.5 air changes per hour. The exterior walls, roof, and windows, respectively, have a U-value of 0.4, 0.5, and 1.4 W m 2 K 1. The average window surface in each cardinal direction is of about 10 m2. The electric heating system is an air source heat pump coupled in this case with radiators as emission systems. This case study is again applied in Section 5.4.6.1 to illustrate the load shifting potential of DR. Such results, referred to the present scenario, are compared with those obtained for Case Study A, referred instead to a future scenario.

5.4.5.3

Case Study C

In this case study, the electricity generation side is represented by a so-called MO model, as discussed in [40]. The reference year for the case study is 2030. A RES share of 40% (on an annual electric energy consumption basis) is considered, divided into 30% wind energy and 10% solar photovoltaic energy. In terms of conventional generation capacity, only gas-fired power plants are considered, as in Case Study A. The peculiarity of this case study is the detailed representation of the demand side, where 36 different building types are considered, based on results of the TABULA project [80], as discussed in Protopapadaki et al. [79]. These building archetypes represent the Belgian residential building stock. Three different single family buildings (detached, semidetached, and terraced houses) are taken into account, belonging to six age classes (i.e., before 1945, 1945–70, 1971–90, 1991–2005, 2006–12, after 2012), undergone to two possible renovation levels (mild or thorough). The electric heating system is represented by heat pumps coupled with floor heating systems or radiators. The penetration of this technology is assumed to be equal to 250,000 units by 2030 for each building topology, which are studied individually. A more detailed description of this case study can be found in [86]. Case Study C is used in Section 5.4.6.2 to show the effect of different building types and their corresponding heating system on peak power reduction, thus on power system adequacy.

5.4.5.4

Case Study D

The fourth case study is defined by Bruninx [5,87]. For this case study, the integrated model has been extended to include the procurement and activation of reserves, offered by the DR resource, and a possible limited controllability of the DR resource. The generation system is represented by a detailed, state of the art UC model with endogenous, probabilistic reserve sizing and activation [88]. The power system is inspired on the Belgian power system in the year 2013, complemented with eight additional 450 MW CCGTs to cover the additional electrical heating demand. In detail, the electricity generation is composed of nuclear power plants (5925 MW), coal-fired steam power plants (760 MW), combined cycle power plants (CCGT, 9575 MW) and small peaking units, such as OCGT and oil-fired power plants (1260 MW). One PHES system with a maximum storage capacity of 3924 MWh, a round trip efficiency of 75%, and a capacity of 1308 MW is included in the power system. A 50% wind energy penetration (relative to the annual energy demand) is assumed. The demand side is represented in the same way as in Case Study A, assuming that approximately 1 million households have a DR-adherent heating system (pDR ¼ 100%). Case study D is used in the evaluation of DR role on security of a power system (Section 5.4.6.3).

5.4.6

Results and Discussion

To allow framing the impact of DR on the reliability of the power system, Section 5.4.6.1 briefly discusses the operational impact of such DR schemes. Section 5.4.6.2 deals with DR based peak shifting as a method to increase the adequacy of a system. Short term reliability (security) and DR based reserves is the topic at hand in Section 5.4.6.3.

5.4.6.1

Energy Management and Demand Flexibility

In Fig. 6 the output of the electricity production plants obtained with the simulation model for the above mentioned Case Study A is visualized. In this case study, the electricity generation mix contains only gas fired power plants (CCGT and OCGT) and RES based generation, representing a possible future electricity generation mix. The CCGT plants are more efficient; their efficiency can vary between 50 and 60% [63], thus they are used to covering the baseload residual demand, i.e., the electricity demand from which the electricity generation from RES is subtracted. For the peak residual demand, instead, the OCGT plants are used, whose efficiency ranges between 30 and 40% [63]. Fig. 6 shows the effect of DR programs on the power output during 48 typical winter operating hours and highlights what happens when the DR participation increases (Fig. 6(A) pDR ¼ 25% vs. Fig. 6(B) pDR ¼ 100%). It is evident that the load shifting due to the introduced DR flexibility produces valley filling over time and, consequently, reduces RES curtailment and OCGT use for peak residual demand. This increases overall system efficiency. Fig. 7 contains, instead, results obtained with a similar simulation model (using a UC, and ED model to represent the supply side) for Case Study B [63], which show even more clearly the impact of load shifting on the use of different electricity generation

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Fig. 6 Case Study A. Output of the electricity generation system (open cycle gas turbines (OCGT), combined cycle gas turbines (CCGT), and renewable energy sources (RES)) for two shares of demand response participation rate: (A) 25% and (B) 100%. Inspired by Arteconi A, Patteeuw D, Bruninx K, et al. Active demand response with electric heating systems: impact of market penetration. App Energy 2016;177:636–48.

Nuclear

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Fig. 7 Case Study B. Output of the electricity generation system (open cycle gas turbines (OCGT), combined cycle gas turbines (CCGT), and renewable energy sources (RES), coal, oil, nuclear) for two shares of DR participation rate: (A) 0% and (B) 100%. Inspired by Patteeuw D, Bruninx K. Arteconi A, et al. Integrated modeling of active demand response with electric heating systems coupled to thermal energy storage systems. App Energy 2015;151:306–19.

facilities. Indeed, the electricity generation system consists of a wider variety of technologies (nuclear power plant, coal fired steam power plants, CCGT, OCGT, and RES). The nuclear power plants, followed by gas fired CCGT plants cover the baseload residual demand, and gas fired OCGT plants are used as peaking units. Going from the normal operation of the power system without DR (Fig. 7(A)) to the power system including DR programs with a 100% participation of the potential flexible electrical demand (Fig. 7 (B)), valley filling during nighttime and load shifting during peak hours occur. The OCGT plants that cover the peak residual demand are no longer switched on during the considered simulated period. As a result of such behavior, the cost of electricity generation is reduced, because more efficient power plants work for longer time periods. Consequently, these units set the electricity price during more time steps, which results in a lower electricity price (on average). In Fig. 8 the trend of the power system relative operational costs, Rc, is shown. Rc is defined as the ratio between the total operation costs (TOC) with DR and the total operational cost in case of no DR participation [63]. Fig. 8 shows a reduction of the relative operational costs up to 2% of the TOC. It is a small percentage, but the absolute value can be very big. Note that these numbers are highly case study dependent (see Section 5.4.6.3). However, it has been shown by Arteconi et al. [63] that the economic benefit for a single user can be limited, thus a detailed cost-benefit analysis from customers’ point of view (considering investment costs for smart thermostats, reduced energy bills, indoor thermal comfort experienced) is necessary when a DR strategy is designed. The costs trend in Fig. 8 reflects the increasing flexibility induced by a growing adherence of the considered final users to DR programs. This flexibility allows for (1) a more efficient operation of the available electricity generation capacity (as explained above) and (2) a higher utilization rate of the available RES based generation. Fig. 9 shows the RES curtailment as a function of the DR penetration rate: it reduces from 3% to about 1% going from a 5% DR penetration rate to 100% penetration rate [63]. As described in Section 5.4.2.2, the REDR indicator, pertinent in this evaluation, would increase accordingly to the DR participation. Considering a higher RES share in the generation mix, the flexibility of the demand also gains more relevance (see Section 5.4.6.3).

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Relative operational cost Rc (%)

100

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98.5 5

25

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DR penetration rate (%) Fig. 8 Case Study A. Relative operational costs by varying the demand response (DR) participation rate. Inspired by Arteconi A, Patteeuw D, Bruninx K, et al. Active demand response with electric heating systems: impact of market penetration. App Energy 2016;177;636–48.

4

Curtailment (%)

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2

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0 5

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DR penetration rate (%) Fig. 9 Case Study A. Renewable energy sources curtailment by varying demand response (DR) participation rate. Inspired by Arteconi A, Patteeuw D, Bruninx K, et al. Active demand response with electric heating systems: impact of market penetration. App Energy 2016;177:636–48.

Fig. 10 shows the DRR ratio versus the DR penetration rate for different RES shares. The DRR ratio is defined as the ratio between the observed electric energy use by the flexible electric heating systems and the minimum electric energy use of those heating systems (pDR ¼ 0%) [63]. DRR is always greater than or equal to 1 because when there is a DR program in place, the electric demand is shifted (e.g., a preheating of the building or DHW tank is requested) and load shifting leads to higher temperatures in the building and in the DHW storage tank. Consequently, additional thermal losses and an increase in overall energy use occur. Higher values of DRR indicate that the flexibility offered by the buildings involved in the DR program is bigger on a per-building basis. Fig. 10 highlights that for higher RES share, more flexibility is necessary. This confirms once again that a power system with more intermittent electricity production benefits more from a flexible demand that can contribute to strengthen the reliability of the overall system.

5.4.6.2

Demand Response Related to Adequacy: Peak Shaving

As already discussed in the previous section, the electric heating system in buildings can be leveraged to provide power system flexibility that allows shifting the energy demand from peak hours to off-peak hours. Such scheme may be used to improve the adequacy of a power system by peak shifting or peak clipping. Fig. 11 shows the peak shaving produced by DR in the considered Case Study A and links the peak shaving capability to the DR penetration rate among participants. The peak shaving potential has been quantified during the coldest winter week, when the power system is stressed the most and the electric power demand, especially for electric heating purposes, is the highest. It amounts at most to 2 GW for this case study [63], which is equal to about 12% of the total installed capacity. Similarly, the peak shaving potential increases with a higher participation in the DR programs. Actually, the peak residual demand decreases strongly if the DR penetration rate is lower than 50% and flattens when the DR penetration grows beyond this threshold. This effect has been referred to as a saturation effect. It illustrates that the power system

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RES 0%

RES 30%

RES 50%

1.1

DRR (−)

1.08 1.06 1.04 1.02 1 5

25

50

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DR penetration rate (%) Fig. 10 Case study A. Demand recovery ratio (DRR) by varying the demand response (DR) participation rate for different renewable energy source (RES) shares. Inspired by Arteconi A, Patteeuw D, Bruninx K, et al. Active demand response with electric heating systems: impact of market penetration. App Energy 2016;177:636–48.

Peak demand (GW)

17

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14 5

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DR penetration rate (%) Fig. 11 Case Study A. Peak residual demand by varying the demand response (DR) penetration rate. Inspired by Arteconi A, Patteeuw D, Bruninx K, et al. Active demand response with electric heating systems: impact of market penetration. App Energy 2016;177:636–48.

needs and uses the flexibility provided by the customers (in this case by electric heating systems in buildings), but after a certain limit the additional flexibility available is less useful and cannot be fully exploited due to the limited load shifting potential of the DR resource. This means that when more participants are involved in the DR program, a lower effort per participant is requested (i.e., lower load shifting), but at the same time also the benefits perceived by each customer are reduced [63]. The reduction of peak demand implies a reduction in the use of peaking generation units (i.e., OCGT in Case Study A). This affects also the electricity price, which decreases accordingly. In Fig. 12 the duration curve of the electricity price for the scenarios with DR¼0% and DR¼ 100% is represented. The effect produced on price by peak shaving is highlighted in the figure: the peak price duration time is diminished by about 2000 h. Eventually, it is important to point out the influence of the building type and heating system configuration (i.e., distribution system) involved in the DR program on the peak power demand. This effect is illustrated by means of Case Study C and is shown in Fig. 13. When electric heating systems are introduced in buildings to replace traditional heating system (e.g., boilers), the overall electricity demand for a building increases. Due to possible concurrence of such building electricity demand with the fixed electricity demand (dfix), even the peak power demand of the overall system increases. In Fig. 13, it is demonstrated that the additional peak power per building is limited when those buildings participate in DR. This confirms once again the peak shaving potential of heat pumps adhering to DR programs, thus their effect on power system adequacy. Fig. 13 highlights also that the peak shaving ability is higher for HPs with lower nominal electric power demand, because, considering Case Study C, they are installed in buildings with better thermal insulation and floor heating systems, allowing lower thermal losses and longer load shifting due to their larger thermal inertia. In the scientific literature, other studies analyzed the peak shaving potential of different DR programs. In Ref. [72], for example, the Iranian power system is investigated and it is shown that DR can reduce the peak demand in a range between 6 and 10%,

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0% DR 100% DR

Electricity price ( /MWh)

150

Peak shaving

8000

Time (h)

Peak power per building (kW)

Fig. 12 Case Study A. Electricity price duration curve for the case with demand response (DR) participation rate at 0 and 100%. The shifting between the two curves is due to the peak shaving effect. Inspired by Arteconi A, Patteeuw D, Bruninx K, et al. Active demand response with electric heating systems: impact of market penetration. App Energy 2016;177:636–48.

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HP nominal power (kW) Fig. 13 Case Study C. Additional peak power demand per building produced by the replacement of conventional heating systems with electric heating systems (HPs) by varying the heat pump nominal power demand for the case without demand response (DR) (0%) and with DR (100%). Inspired by Patteeuw D, Reynders G, Bruninx K, et al. CO2-abatement cost of residential heat pumps with active demand response: demand- and supply-side effects. App Energy 2015;156:490–501.

considering an installed peak power of about 33 GW. The DR peak shaving potential is of paramount importance for improving the power system adequacy. DR guarantees the necessary flexibility of the demand side during periods of peak demand or system distress, reducing the required investments in new dispatchable power plant capacity without increasing the risk of load shedding. However, the DR program has to be properly designed. Indeed Samadi et al. [71] showed that erroneous formulations of DR tariff structures could give rise to an increase in the peak demand and this affects the EENS negatively, thus the reliability of the power system is decreased. When, instead, a peak reduction is achieved, the benefit in terms of reduction of electric energy not served is evident. In their case study with an initial peak load of 2850 MW, a 5% reduction of peak, due to a TOU tariff based DR, leads to a 25% reduction of EENS.

5.4.6.3

Demand Response Related to Security: Reserve Provision

For the discussion of the possible benefits associated with DR based regulation services or reserves (i.e., operational flexibility), a set of results from Bruninx et al. [5,87] is reproduced below. The purpose of this analysis is to show how the different available flexibility providers (DR, spinning reserves, nonspinning reserves, and energy storage) interact. The developed model allows studying the operational costs that a system operator incurs to meet the demand for electricity, while maintaining power system security. The focus is on the interaction between operational costs, reserve provision (security), and DR resources. Indeed, in realtime operation it can be cost-efficient to exploit the flexible demand in order to mitigate the impact of a contingency or the unpredicted behavior of RES based generation. The operational model specifically allows scheduling DR resources, in this particular case study electric heating systems, as reserves, while simultaneously guaranteeing the thermal comfort of the owners of the DR resource. Case Study D has been considered, using a state of the art UC model for the representation of the supply side, considering reserve constraints to account for the limited predictability of RES based generation. The reliability of the obtained electricity

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Average SR −6%

E(TOC)(%)

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−7%

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Fig. 14 Case Study D. Expected total operational costs for 3 unit commitment (UC) strategies (SR, SR&NSR, SR, NSR&PHES) considering 3 different demand response (DR) settings (Ref. ¼No DR; Arb.¼arbitrage services; Reg.¼arbitrage & regulation services). The analysis is performed for an average week in heating season and for a week outside heating season Inspired by Bruninx K. Improved modeling of unit commitment decisions under uncertainty [Ph.D. thesis]. KU Leuven; 2016.

generation schedules with respect to this uncertain RES based generation is tested in Monte Carlo ED simulations, leveraging the flexibility at the demand side and the supply side. In this analysis of the system value of DR based arbitrage and regulation services, three UC strategies are considered. In the SR case only spinning reserves (i.e., online reserves) may be scheduled. In the SR and NSR case additionally nonspinning reserves (i.e., offline or standing reserves) are available to meet the reserve requirements. In the case of SR, NSR and ES, spinning, nonspinning, and ES based reserves are available. For each of these UC strategies, the expected total operational cost (E(TOC)), the expected wind utilization factor (E(WUF)), the resulting total demand (E(Load)), the share of electrical energy generated from nonrenewable resources (1-E(WS)), and EENS in three DR settings are calculated. The DR settings are the following: 1. In the reference case (Ref.), the DR capable load is not responsive. The electricity demand of the electric heating systems is fixed to a minimum energy use profile (see Section 5.4.4.1); 2. The DR capable heating systems are only used for arbitrage purposes (Arb.), i.e., load shifting aimed at reducing operational costs, under forecast conditions; 3. Both arbitrage and regulation services may be procured from the DR load (Reg.), i.e., also reserve provision for security purposes is considered. Results are presented for (1) a week outside the heating season and (2) an average week (based on simulations of 4 weeks, properly selected to represent the whole year) [5,87]. Significant cost savings are to be expected from DR based arbitrage and regulation services (Fig. 14). On average, the operational cost decreases by 6 percentage points (pp) when considering DR based arbitrage (Arb., Fig. 14). An additional one percentage point decrease can be realized when the DR adherent loads are also allowed to provide regulation services (Reg., Fig. 14). The reliability of the resulting UC schedules is unaffected: the EENS is at most 0.0004% of the total load and does not vary significantly across the considered DR cases. Remarkably, the value of DR based arbitrage and regulation services remains unaffected when other flexibility providers, here nonspinning reserves and ES based reserves, are available to meet the reserve requirements. Indeed the presence of these flexibility providers, in particular nonspinning reserves, does decrease the operational cost (on average 4 pp), but does not affect the value of DR based arbitrage and regulation services. Outside the heating season, the demand of the electric heating systems, thus the available DR flexibility, is significantly lower. The operational cost decrease resulting from DR based arbitrage and regulation is limited (max. 2 pp). Allowing nonspinning reserves and PHES based reserves results in an expected operational cost decrease of 11% outside the heating season. The main driver of these cost reductions is an increased utilization of the available wind power (Fig. 15) and a more efficient scheduling and dispatching of the conventional power plants. On average, the WUF increases from 74.9–77.5% (Ref.) to 82.1–84.2% (Arb.) to 83.4–85.3% (Reg.). This increase of WUF is the result of (1) shifting demand to periods of excess wind power generation and (2) increasing the DR adherent demand to increase the indoor temperature in order to allow the DR adherent heating systems to provide upward reserves. This increase in indoor temperature (under forecast conditions) allows activating DR based reserves without tampering with the thermal comfort of the homeowner providing this flexibility. This does, however, increase the total demand as a result of increased thermal losses and a higher average indoor temperature (Fig. 16). The average increase in total demand amounts to 2.5% (ES, Arb.) and to 3.2% (SR, Arb., Reg.). The availability of nonspinning and ES based reserves limits the increase in demand, as less excess wind power is available to be absorbed by the DR adherent heating systems (Fig. 15). On the contrary, the consideration of DR based reserves typically increases the total demand due to the higher indoor temperatures required to provide (upward) reserves. As a result, the share of nonrenewable energy sources in the fuel mix (Fig. 17)

Energy Reliability and Management

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Fig. 15 Case Study D. Wind utilization factor (expresses as a percentage of the available wind energy) for 3 unit commitment (UC) strategies (SR, SR&NSR, SR, NSR&PHES) considering 3 different demand response (DR) settings (Ref.¼No DR; Arb.¼arbitrage services; Reg.¼arbitrage & regulation services). The analysis is performed for an average week in heating season and for a week outside heating season. Inspired by Bruninx K. Improved modeling of unit commitment decisions under uncertainty [Ph.D. thesis]. KU Leuven; 2016.

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Fig. 16 Case Study D. Total demand for electrical energy for 3 unit commitment (UC) strategies (SR, SR&NSR, SR, NSR&PHES) considering 3 different demand response (DR) settings (Ref. ¼No DR; Arb.¼arbitrage services; Reg.¼arbitrage & regulation services). The analysis is performed for an average week in heating season and for a week outside heating season. Inspired by Bruninx K. Improved modeling of unit commitment decisions under uncertainty [Ph.D. thesis]. KU Leuven; 2016.

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Fig. 17 Case Study D. The share of the demand covered by nonrenewable sources for 3 unit commitment (UC) strategies (SR, SR&NSR, SR, NSR&PHES) considering 3 different demand response (DR) settings (Ref. ¼No DR; Arb.¼ arbitrage services; Reg.¼arbitrage & regulation services). The analysis is performed for an average week in heating season and for a week outside heating season. Inspired by Bruninx K. Improved modeling of unit commitment decisions under uncertainty [Ph.D. thesis]. KU Leuven; 2016.

does not decrease as fast as the WUF increases. On average, 67.7–66.2% of the demand would be satisfied with electricity generated from non-RES in the absence of DR. This drops to 65.6–64.4% and 65.1–63.9% when considering DR based arbitrage and regulation, respectively. In conclusion, DR allows maintaining system reliability at a reduced operational cost, even at high RES penetration rates. If thermal discomfort is not allowed, these operational cost savings can be almost fully attained by leveraging DR loads to perform arbitrage. This will allow other resources to cost effectively fulfill the reserve requirements, which may limit the additional value in

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DR based reserves. Allowing thermal discomfort may increase the attainable operational cost savings via DR based reserves [5,87]. However, the operational cost savings may not justify the impact on thermal comfort for end consumers [5,87]. Other authors evaluated the effect of DR programs on short-term reliability. In particular, in Ref. [74] the contribution of DR together with wind power plants to the system reliability is also quantified. As maximum these authors allow an incremental peak load carrying capability (defined as the increase in peak load to reach the same level of risk as before their introduction) of about 10 MW, assessed by means of loss of expected energy for the case of peak demand 185 MW and wind power installed 20 MW. In Ref. [31], instead, the reliability improvement on the distribution network is analyzed, considering the reserve provision by DR programs, allowing to shift the demand after the service, interrupted by a contingency, is restored. Part of a real Finnish network with 61 distribution substations is considered. On the basis of the contingency, the energy not served can be diminished at different levels with a maximum up to 90%.

5.4.6.4

Methodological Improvements: Demand Response Limited Controllability

The relevance of DR with electric heating systems to improve the reliability of a power system has been demonstrated by means of the case studies described in the previous sections. The integrated model considered contains some simplifying assumptions that do not affect the general results obtained. However, such simplifications could prevent to take into account possible particular issues related to the introduction of DR programs. For example, the effect of a possible imperfect controllability of DR resources is an important aspect to evaluate, because not all the components of the power system are equally controllable. Bruninx et al. [87] use chance constrained programming to account for the possible variability in the response of DR loads. In this approach the deterministic variable that represents the energy demand from flexible electric heating systems (see Eq. (29)) becomes a stochastic variable. Bruninx et al. assume this stochastic variable can be modeled as a disturbance on the original DR demand. This disturbance is assumed to consist of a non-proportional disturbance (δNP) that follows a Normal distribution (Eq. (32)): ^dH;var ¼ dH;var þ δNP j j

ð32Þ

The scheduled generation capacity does not coincide exactly with the expected demand, but has to exceed the demand with a certain mark-up, so that the obtained schedule allows meeting each real time realization of the demand with a given probability (1–ɛ), which is an indicator of the risk attitude of the system operator. Bruninx et al. [87] used Case Study D to test their expanded model to investigate the influence of limited controllability of DR loads on the expected operational cost, RES utilization and reliability. In their case study, they assume that DR is only used for arbitrage and DR controllability is represented by means of a non-proportional term (δNP) with a zero mean and three possible standard deviations (sNP): 50, 100, and 250 MW. Results show that if the variability of DR adherent loads is limited, the possible cost savings produced by DR in case of perfect controllability do not decrease significantly. Instead, if the system operator is risk adverse (ɛ-40) and the expected variability in the DR adherent load is high, the operational costs of the power system may be increased by the introduction of DR. This is due to the necessity of more scheduled capacity, which leads to a less efficient dispatch of the scheduled units and a reduced utilization of RES based generation. If a system operator, however, approaches risk-neutral behavior (ɛ-40.5), the obtained schedule may not allow meeting the load in all RES based generation and DR scenarios. This is illustrated in Fig. 18, which shows the electric energy not served as function of the risk attitude of the system operator (1–ɛ): when the risk attitude is bigger, then the possibility of not satisfying the energy demand is higher and the EENS increases.

E(EENS) (MWh)

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Fig. 18 EENS in case of limited controllability of demand response for different risk policies of the system operator (1–ɛ). The analysis refers to an average week in heating season (the same as specified in Section 5.4.6.3). Three different normal distribution for the nonproportional (δNP) component of the electric heating systems energy demand stochastic variable are considered with zero as mean value and standard deviation (sNP) equal to 50, 100, and 250 MW. Inspired by Bruninx K, Dvorkin Y, Delarue E, D'haeseleer W, Kirschen DS. The value of demand response controllability, KU Leuven Energy Institute Working Paper WP EN2017-03. Available from: http://www.mech.kuleuven.be/en/tme/research/ energy_environment/Pdf/wp-en2017-3.pdf; 2017.

Energy Reliability and Management 5.4.6.5

161

Comparison With Other Approaches for Increasing Reliability

In this section the capability of improving power system reliability by other methods rather than DR is illustrated. As outlined previously, there are different techniques to be used on the supply side or on the demand side to put energy management strategies into action (see Section 5.4.2.3). In the following, their potential on power system reliability is quantified by means of case studies presented in the scientific literature. In detail: 1. DR versus EES systems: the effect of DR on power system reliability is compared with the effect produced by EES systems installed on the supply side, in order to highlight their differences in operation; 2. Energy storage: the potential of improving reliability by EES systems and their optimal planning is further investigated when they are situated on the distribution network or on the generation level; 3. Distributed generation: the role of DG units for reserve provision is considered; 4. Electric vehicles (EV): EV as instruments for increasing the operational flexibility, thus augmenting reliability, of a power system are also investigated; and 5. Incentives: the influence of incentive policies to boost the best strategies to optimize power system reliability is reported.

5.4.6.5.1

Demand response versus electric energy storage system

Zhou et al. [30] compare the contribution of DR and EES to the adequacy of supply. They consider different kinds of controllable loads, represented with existing load profiles without including any physical description. However, they take into consideration the load restoration issue (i.e., payback) through predefined payback coefficients. Moreover, different DR scenarios with four different payback effects are considered (no payback, unconstrained payback with load fully restored, constrained payback with load half restored, constrained payback with load fully restored). Instead, the EES is represented without any reference to a specific type of storage, but using its operating parameters (energy capacity, power rating, efficiency). The model optimizes the power system in order to minimize the peak load. The adequacy of the supply is evaluated by means of the indicators LOLE, EENS, LOLF, and LOLD. Results show that both DR and EES contribute positively to the system reliability but in a different manner. Regarding DR, with the same payback setting, LOLE, EENS, and LOLF decrease with the increase of customer’s flexibility (a certain saturation effect is also evident after a certain flexibility level). For different scenarios, when the payback is less constrained the effect on adequacy is stronger, because there is a reduced risk of creating another peak load. LOLD, instead, increases with DR. This is due to the stretch of the peak load: if a shortfall event occurs during this period, its drawbacks could be even more severe. As far as the EES is concerned, the use of energy storage causes a decrease of LOLE, EENS, and LOLF. The main parameter to be taken into account is the power rating, which poses the limit to the maximum energy capacity necessary: beyond a certain limit a saturation effect occurs, because storage with a given power rating cannot use more than a given energy capacity when it shifts peak energy demand to off-peak hours. Or it can happen that the given power rating is not enough to charge the ESS fully during off-peak periods. Also the increased storage efficiency produces benefits on the adequacy up to the limit that equals the peak reduction to the storage power rating (as explained above, the peak reduction cannot be bigger than the power rating). In both cases it is not possible to displace as much generation as the peak reduction provided by DR or EES, if the original level of adequacy needs to be maintained. The results presented in this paper show that the ability to perform peak shaving by means of DR is more effective than that provided by EES in terms of MW curtailed (about 1000 vs. 500 MW for the case study considered [30]), but results are case sensitive and a generalization is difficult.

5.4.6.5.2

Energy storage

Sabori et al. [35] consider the use of ESS to augment the reliability of a distribution network (HL3). In this work, the optimal size and place for EES in a radial electrical distribution network that allow the minimization of the electric energy not served are studied. The methodology proposed is applied to a case study, composed of an 11-kV and 30-bus radial distribution network with different-capacity EES to be installed on 15 buses. Results show that EENS can be reduced with respect to the case without EES of 33%, while the total operation costs decrease by about 10%. Bruninx et al. [89] develop a set of constraints to allow optimal EES based reserve scheduling in deterministic and improved interval UC models. In a case study, inspired on the Belgian power system assuming a high RES penetration, they show that EES based reserves lead to significant operational cost savings without reducing the reliability of the obtained UC schedules.

5.4.6.5.3

Distributed generation

Sabpayakom and Sirisumrannukul [90] investigate the role of very small power units (o10 MW) to improve power network reliability. In the case of radial and single circuit distribution networks, the risk of not being able to serve some users when contingencies occur is relevant. DG can be used as an operational reserve to provide electricity to the disconnected segments of the grid during contingencies, thus the network becomes an active element able also to improve the reliability of the power system. The small power units can reduce the outage duration when an islanding operation of the disconnected network is possible and the unit is within the islanded area. A case study is defined by the authors to show the effects of DG on reliability: it is inspired to an urban 24-kV distribution system in Thailand with 34 customer load points and a DG of 2 MW is connected to the middle of a branch. Results show the role of small DG under different point of views: (1) capacity: the network reliability indices (SAIDI, EENS and outage duration time) are improved; (2) size: the reliability benefits more when the size of the power unit increases, but it is

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necessary not to exceed the demand otherwise reverse power flows toward the transmission grid occur and it could cause additional problems; (3) connection point: the position of the DG has to allow picking up some loads, preferably it has to be close to the end of the feeder; (4) number of units: generally increasing the number of DG improves the system reliability, but it depends also on the interaction between the supply and demand in the isolated areas.

5.4.6.5.4

Electric vehicles

Bozic and Patos [91] analyze the impact of EV on power system reliability. This is an issue frequently discussed in the scientific literature and several studies highlighted the potential role of EV as ancillary service or frequency regulation provider, thanks to their inherent electric battery storage system. In [91] it is investigated in particular to what extent such EVs can contribute to the power system reliability and the influence of the selected charging/discharging strategy on this contribution. Such strategy is determined by means of an optimization problem that minimizes the reliability indices LOLE and EENS. It is shown that the introduction of EV brings a benefit to the reliability of the system up to a certain number of vehicles, afterward the reliability indices increase again. In the case study considered by the authors, 68,750 vehicles as maximum are taken into account with a battery capacity of 25 kWh each and a charging and discharging efficiency of 90 and 93%, respectively. Full charge and discharge phases take 2 h and the discharging conversion factor is 6 km/kWh. Results highlight that 31,950 vehicles optimize the reliability of the power system, but the users have to be rewarded by an incentive because this configuration does not minimize the transportation costs. Thus, in order to have reserve provision by EV, it is necessary to have a compensation on average of 1.00 cEUR or 1.85 cEUR per 100 km per each EV if, respectively, LOLE or EENS need to be reduced by 1%.

5.4.6.5.5

Incentives

Ibanez-Lopeza et al. [92] consider the effect of different incentive schemes on the power system reliability. They use a so-called system dynamics model to assess the technical, economic, and environmental impact of renewable energy incentives and capacity payment policies. The analysis is referred to the Spanish power system, where the need for adequate reserve margins that guarantee reliability is highlighted. Main results obtained are here summarized: (1) capacity payments: increased capacity payments for base load technologies allow an increased system reliability, with limited increase in CO2 emissions, but cause also a consistent growth of the total costs; (2) alternative energy incentives: more renewable energy in the generation mix significantly reduces CO2 emissions, however, it is not able to secure more reliability. Even in this case costs are bigger than in the base case scenario without incentives, because the increased wind power capacity considered by these authors reduces the capacity margins and produces wind price spikes, negatively affecting the overall system costs. This confirms what other authors stated about the necessity of ESS on the supply side to favor the renewable energy integration in the generation mix in order to maintain proper reliability levels [42].

5.4.7

Future Directions

In this work the relationship between reliability of a power system and DR programs has been discussed and demonstrated. While it is evident that such relationship exists and DR can be beneficial for the system adequacy and security, it is not easy to quantify the DR impact exactly. In the scientific literature, different studies are available, but all of them have different assumptions and, necessarily, contain some simplifications that make difficult to take into account all possible influencing variables and phenomena. Indeed, while some papers highlight only positive effects on the reliability due to the introduction of DR, other works point out possible drawbacks, such as those related to the restoration of the load after a DR event that could cause even worse conditions on the system. Furthermore, it is very difficult to find modeling tools that represent the generation side, the network, and the demand (including different kinds of loads with different behaviors) in a detailed way. This aspect limits the ability to quantify thoroughly the influence of DR on reliability. As far as the model presented in this piece of work is concerned, it has as its strength the ability to represent both the supply side and the demand side with a good level of detail in an integrated way that allows taking into account their interactions. Nevertheless, it would be necessary to include also the transmission and distribution network in the model, thus to assess both the reliability at all hierarchical levels by means of proper reliability indices. The effect of uncertainty on the production side (RES) and on the demand side (users’ behavior, etc.) should be further investigated and included in the model by means of probabilistic simulations, as already outlined in Section 5.4.6.4. Other DR loads could also be added (e.g., domestic appliances, EV, etc.). Only in this way it will be possible to provide a comprehensive evaluation of the actual relationship between reliability and DR.

5.4.8

Closing Remarks

Key points of this contribution are listed below:

• •

A strong correlation between reliability and energy management to increase system adequacy and security has been demonstrated. DR is a good instrument, within demand side energy management strategies, to increase power system reliability.

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• • •

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DR is beneficial for reliability thanks to its ability to shift loads. Indeed, it allows reducing RES curtailment and using in a more efficient and effective way the existing generation capacity. DR increases the power system adequacy by means of peak shaving. This implies a reduced need for new generation capacity and decreased risk of load shedding. DR increases the power system security by means of its arbitrage and regulation services potential, which provides a flexible reserve for short term operation.

Acknowledgment The authors wish to thank Dieter Patteeuw, Lieve Helsen, Erik Delarue, and William D’haeseleer for their helpful support during the preparation of this work.

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Further Reading Billinton R, Allan RN. Reliability evaluation of power systems. Boston: Pitman; 1984. Billinton R, Allan RN. Reliability evaluation of engineering systems. Concepts and techniques. New York, NY: Springer; 1992. Gellings CW. The smart grid. Enabling energy efficiency and demand response. Lilburn, GA: The Fairmont Press; 2009. Haider HT, See OH, Elmenreich W. A review of residential demand response of smart grid. Renew Sustain Energy Rev 2016;59:166–78. Kirby BJ. Demand response for power system reliability: FAQ, Oak Ridge National Laboratory. Available from: http://www.consultkirby.com/files/TM-2006-565-DemandResponse-For-Power-System-Reliability-FAQ.pdf; 2006. Luickx P. The backup of wind power analysis of the parameters influencing the wind power integration in electricity generation systems [Ph.D. thesis]. Available from https:// lirias.kuleuven.be/bitstream/123456789/237544/1/Patrick þ Luickx- þ PhD.pdf; 2009. Mercados AF, Bridge E. Identification of appropriate generation and system adequacy standards for the internal electricity market. In: Final report by REF-E prepared for European Commission; 2016. Shariatzadeh F, Mandal P, Srivastava AK. Demand response for sustainable energy systems: a review, application and implementation strategy. Renew Sustain Energy Rev 2015;45:343–50. Siano P. Demand response and smart grids – A survey. Renew Sustain Energy Rev 2014;30:461–78.

Relevant Website http://iiesi.org/resources.html The International Institute of Energy Systems Integration.

5.5 Exergy Management Ibrahim Dincer, Marc A Rosen, and Maan Al-Zareer, University of Ontario Institute of Technology, Oshawa, ON, Canada r 2018 Elsevier Inc. All rights reserved.

5.5.1 Introduction 5.5.2 Exergy and Environmental Problems 5.5.2.1 Environmental Concerns 5.5.2.1.1 Global climate change 5.5.2.1.2 Stratospheric ozone depletion 5.5.2.1.3 Acid precipitation 5.5.2.2 Potential Solutions to Environmental Problems 5.5.2.3 Energy and Environmental Impact 5.5.2.4 Thermodynamics and the Environment 5.5.3 Energy Management 5.5.4 Exergy Management 5.5.5 Exergy and Sustainable Development 5.5.5.1 Industrial Ecology and Resource Conservation 5.5.5.2 Energy and Sustainability 5.5.5.3 Exergy and Sustainability 5.5.5.4 Renewable Energy and Sustainable Development 5.5.5.4.1 Tools for environmental impact and sustainability 5.5.5.4.2 Ecologically and economically conscious process engineering 5.5.5.5 Exergy as a Sustainability Quantifier 5.5.6 Illustrative Example 5.5.6.1 Implications Regarding Exergy and Energy 5.5.6.2 Implications Regarding Exergy and the Environment 5.5.6.3 Implications Regarding Exergy and Sustainable Development 5.5.7 Applications of Exergy in Industry 5.5.7.1 Advantages of Exergy Methods 5.5.7.1.1 Understanding thermodynamic efficiencies and losses through exergy 5.5.7.1.2 Efficiency 5.5.7.1.3 Loss 5.5.7.1.4 Discussion 5.5.7.1.5 Understanding energy conservation through exergy 5.5.7.2 Disadvantages of Exergy 5.5.7.3 Possible Measures to Increase Applications of Exergy in Industry 5.5.8 Exergy and Industrial Ecology 5.5.8.1 Industrial Ecology 5.5.8.2 Linkage Between Exergy and Industrial Ecology 5.5.8.2.1 Depletion number 5.5.8.2.2 Integrated systems 5.5.8.2.3 Gas turbine combined cycle with hydrogen generation 5.5.8.2.4 Exergy analysis of gas turbine combined cycle with hydrogen generation 5.5.9 Results 5.5.10 Exergy in Policy Development and Education 5.5.10.1 Exergy Methods for Analysis and Design 5.5.10.2 The Role and Place for Exergy in Energy-Related Education and Awareness Policies 5.5.10.2.1 Public understanding and awareness of energy 5.5.10.2.2 Public understanding and awareness of exergy 5.5.10.2.3 Extending the public’s need to understand and be aware of exergy to government and the media 5.5.10.3 The Role and Place for Exergy in Education Policies 5.5.11 Future Directions 5.5.12 Closing Remarks References Further Reading Relevant Websites

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Introduction

The relationship between energy and economics was a prime concern in the 1970s, but the linkage between energy and the environment received little attention. As environmental concerns like acid rain, ozone depletion, and global climate change came to the fore in the 1980s, the link between energy utilization and the environment became increasingly recognized. Since then, this interest has increased and it has become evident that the energy processes that often benefit people impact the environment. Concern has also grown over the unsustainable nature of human activities, prompting initiatives for achieving sustainable development. The relation between sustainable development and resource use is important to society. Attaining sustainable development requires that sustainable energy resources be used in an efficient manner. As exergy methods are useful for improving efficiency, they are thus related to sustainable development. The exergy of a substance is a measure of its potential to cause change, suggesting exergy may provide a basis for an effective measure of environmental impact potential. These topics are interconnected. For instance, environmental emissions can be reduced by increasing efficiency or energy conservation, which helps preserve resources. Increasing efficiency lengthens the lives of reserves, but usually necessitates increased use of materials and labor. Depending on the situation and the stakeholders, this can lead to increased security due to less dependence on energy resources, reduced environmental impact, and enhanced social stability. Thus, exergy can be seen as a confluence of energy, environment, and sustainable development (see Fig. 1). Research into the linkages between the exergy and the environment has increased in the last few decades [1].

Energy

Exergy

Environment

Sustainable development Fig. 1 Interdisciplinary triangle covered by the field of exergy analysis.

In this chapter, the ideas presented in our earlier works are extensively treated to fit the content of this volume. The objective is to present a unified exergy-based structure that provides useful insights and direction to those involved in exergy management, in part by understanding the use of exergy methods in environmental stewardship and sustainable development. In practice, an understanding of exergy and its ties to efficiency, environmental impact, and sustainability is important for those working on energy and the environment. Since energy policies, sustainability issues, and environmental concerns (from local to global), policy makers can benefit from an appreciation of exergy and its implications.

5.5.2

Exergy and Environmental Problems

In this section, we consider the environmental concerns, which span a continuously growing range of pollutants, hazards, and ecosystem degradation factors that affect areas ranging from local through regional to global. After introducing the environmental concerns the section introduces some potential solutions to current environmental problems, including pollutant emissions, that have recently evolved. Finally, the section is concluded through a discussion of the relationship between energy and environment impact followed by thermodynamics and the environment.

5.5.2.1

Environmental Concerns

Environmental concerns span a continuously growing range of pollutants, hazards, and ecosystem degradation factors. Some of these concerns arise from effects on human health, while others stem from actual or perceived environmental risks such as possible accidental hazardous releases. Many environmental issues are caused by or relate to the production, transformation, and use of energy. Significant environmental concerns affected by energy include major environmental accidents, water and maritime pollution, land use and siting impact, radiation and radioactivity, solid waste disposal, hazardous air pollutants, ambient air quality, acid deposition, stratospheric ozone depletion, and global climate change. Environmental-impact control, through clean energy sources and efficient energy technologies, has received increasing attention over the last couple of decades. Environmental concerns are often complex. The ability to identify and quantify sources, causes, and effects of harmful substances has advanced. In the past, most environmental control focused on conventional pollutants (e.g., SOx, NOx, CO, and particulates). Subsequently, environmental control efforts were extended to hazardous air pollutants and globally significant pollutants such as CO2. Industrial processes and systems often lead to environmental problems. For instance, major increases in recent decades in the transport of industrial goods and people by car have led to increased concerns about the effects and sources

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of NOx and volatile organic compound (VOC) emissions. Industrial devices also affect the aesthetics and ecology of the planet, endangering many biological systems and reducing ecological diversity. Guiding the development of industry using exergy as a tool may help reduce energy consumption and environmental degradation. Details of the impacts on the environment and humans by various pollutants may be found in Ref. [1]. Some gaseous pollutants are listed in Table 1, along with their environmental impacts. Some of the most significant environmental concerns, which are listed in Table 1, are covered in the rest of this section.

5.5.2.1.1

Global climate change

Global climate change, including global warming, refers to the warming contribution of the Earth of increased atmospheric concentration of CO2 and other greenhouse gases. In Table 2, the contributions of various greenhouse gases to the processes involved in global climate change are listed. CO2 emissions account for about 50% of the anthropogenic greenhouse effect. Other gases such as CH4, chlorofluorocarbons (CFCs), halons, N2O, ground ozone, and peroxyacetylnitrate, produced by industrial and domestic activities, also contribute (Fig. 2). Global climate change is associated with increasing atmospheric concentrations of greenhouse gases, which trap heat radiated from the Earth’s surface, thereby increasing the surface temperature of the Earth. The Earth’s surface temperature has increased about 0.61C over the last century, and as a consequence the sea level has risen by perhaps 20 cm. The role of various greenhouse gases is summarized in Ref. [2]. Humanity contributes to the increase in atmospheric concentrations of greenhouse gases. CO2 releases from fossil fuel combustion, methane emissions from human activity, chlorofluorocarbon releases and deforestation all contribute to the greenhouse effect. Most scientists and researchers agree that emissions of greenhouse gases have led to global warming and that if atmospheric concentrations of greenhouse gases continue to increase, as present trends in fossil fuel consumption suggest, the Earth’s temperature may increase this century by 2–41C. If this prediction is realized, the sea level could rise 30–60 cm by 2100, leading to flooding of coastal settlements, displacement of fertile zones for agriculture and food production toward higher latitudes, reduced fresh water for irrigation and other uses, and other consequences that could jeopardize populations. Table 1

Impacts on the environment of selected gaseous pollutants

Pollutant

Source

Contribution

Carbon monoxide Carbon dioxide Nitrogen oxides

Incomplete combustion of fuels Burning of fossil fuels and by natural processes Combustion in road transportation

Sulfur dioxide

Burning of fossil fuels

Volatile organic compounds Ground level ozone Persistent organic pollutants

Road transportation as well as solvents Forming reactions Volatile chemicals released into atmosphere from industrial use Directly emitted and reactions in the atmosphere Burning of fossil fuels

Reacts with atmospheric gases to produce ozone Anthropogenic global warming Global warming, smog, and gets involved in ground level ozone forming reactions Smog, acid rain, and causes wheezing and breathing problems Smog, and ground level ozone forming reactions Smog Health effects on wildlife and humans

Particulate matter Heavy metals

Causes haze and lung problems Human health problems

Source: Speight JG. Environmental technology handbook. Washington, DC: Taylor & Francis; 1996.

Table 2

Contributions of selected substances to global climate change

Gas

Pre-1750 trospospheric concentration

2016 Tropospheric concentration

GWP (100-year time horizon)

Increased radiative forcing (W/m2)

Carbon dioxide (CO2) Nitrous oxide (N2O) Methane (CH4) Tropospheric ozone (O3) CFC-11 CFC-12 CFC-113 HCFC-22 HCFC-141b HCFC-142b HCFC-134a Carbon tetrachloride

280 270 722 237 0 0 0 0 0 0 0 0

399.5 ppm 328 ppb 1834 ppb 337 ppb 232 ppt 516 ppt 72 ppt 233 ppt 24 ppt 22 ppt 84 ppt 82 ppt

1 265 28 – 4660 10,200 5820 1760 782 1980 1300 1730

1.94 0.20 0.50 0.40 0.06 0.166 0.022 ,0.049 0.0039 0.0041 0.0134 0.0140

ppm ppb ppb ppb

Abbreviations: CFC, chlorofluorocarbon; HCFC, hydrochlorofluorocarbon. Source: Carbon dioxide information analysis center. Recent greenhouse gas concentration. Available from: http://cdiac.ess-dive.lbl.gov/pns/current_ghg.html; 2016.

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Radiation from the sun

Increases in the concentrations of the greenhouse gases traps the heat, which results in increasing the earth surface temperature Reflected back due to the greenhouse effect

NOx

CO2

CFCs Reflected from earth surface

Absorbed

Radiation from earth (heat) Reflected from earth surface

CO2 Absorbed

Fig. 2 Processes involved in the greenhouse effect.

Most efforts to control global climate change must consider the costs of reducing carbon emissions. Achieving a balance between economic development and emissions abatement requires policies aimed at improving the efficiency of energy use, encouraging energy conservation and renewable energy use, facilitating fuel switching (particularly to hydrogen), and increasing access to advanced technologies.

5.5.2.1.2

Stratospheric ozone depletion

Ozone in the stratosphere (at altitudes of 12–25 km) absorbs ultraviolet (UV) radiation (wavelengths 240–320 nm) and infrared radiation. The regional depletion of the stratospheric ozone layer has been shown to be due to emissions of CFCs, halons (chlorinated and brominated organic compounds), and nitrogen oxides (NOx) (Fig. 3). These emissions can lead to increased levels of damaging UV radiation reaching the ground, causing increased rates of skin cancer, eye damage, and other harm to biological species. Researchers have studied the chemical and physical phenomena associated with ozone depletion, and mapped of ozone losses in the stratosphere. Many activities lead to stratospheric ozone depletion; for example, CFCs, which are used in air conditioning and refrigerating equipment as refrigerants and in foam insulation as blowing agents, and NOx emissions from fossil fuel and biomass combustion, natural denitrification, nitrogen fertilizers, and aircraft. In 1987 an international landmark protocol was signed in Montreal to reduce the production of CFCs and halons, and commitments for further reductions and eventually banning were undertaken subsequently (e.g., the 1990 London Conference). Alternative technologies that do not use CFCs have increased substantially and may allow for a total ban of CFCs. More time will be needed in developing countries, some of which have invested heavily in CFC-related technologies.

5.5.2.1.3

Acid precipitation

Acid rain (acid precipitation) is the result of emissions from combustion of fossil fuels from stationary devices, such as smelters for nonferrous ores and industrial boilers, and transportation vehicles. The emissions are transported through the atmosphere and deposited on the Earth via precipitation. The acid precipitation from one country may fall on other countries, where it exhibits its damaging effects on the ecology of water systems and forests, infrastructure, and historical and cultural artifacts. The impacts of

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Photodissociation O3 + h → O2 + O Cl + O3 → ClO + O2 Ozone depletion NO + O3 → NO2 + O2 reaction Cosmic radiation

CFCs HCl

NOx HCl

CFCs

• Volcanic activities

• Refrigeration • Aerosol sprays • Polymer foams

• Aircrafts Stratosphere

• Combustion processes • Natural denitrification • Nuclear explosions • Nitrogen fertilisers

NOx

Fig. 3 Processes involved in stratospheric ozone depletion.

acid precipitation include acidification of lakes, streams, and ground waters, and the consequent damage to forests, crops, plants, aquatic life, and materials (e.g., buildings, metal structures, and fabrics). Acid precipitation also alters of the physical and optical properties of clouds due to the influence of sulfate aerosols. Acid rain is mainly attributable to emissions of SO2 and NOx, which react in the atmosphere with water and oxygen to form such substances as sulfuric and nitric acids, respectively (Fig. 4). These acids are sometimes deposited on ecosystems that are vulnerable to excessive acidity. The control of acid precipitation requires control of SO2 and NOx emissions. These pollutants cause local concerns related to health and contribute to the regional and transboundary problem of acid precipitation. Other contributing substances include VOCs, chlorides, ozone, and trace metals, which may participate in chemical transformations in the atmosphere. Many energy-related activities lead to acid precipitation; for example, electric power generation, residential heating, and industrial energy use account for about 80% of SO2 emissions. Sour gas treatment releases H2S that reacts to form SO2 when exposed to air. Most NOx emissions are from fossil fuel combustion in stationary devices and road transport. VOCs from various sources contribute to acid precipitation. The largest contributors to acid precipitation are the United States, China, and the countries from the former Soviet Union.

5.5.2.2

Potential Solutions to Environmental Problems

Some of the potential options for addressing environmental problems include recycling, process change and sectoral modification, acceleration of forestation, application of carbon and/or fuel taxes, materials substitution, promoting public transport, changing lifestyles, increasing public awareness of energy-related environmental problems, increased education and training, and policy integration. More specifically, potential options for addressing energy-related environmental problems include use of renewable and advanced energy technologies, energy conservation and increased efficiency, application and use of alternative energy forms and sources for transport, energy-source switching from fossil fuels to environmentally benign energy forms, use of clean coal technologies, monitoring and evaluation of energy indicators, and use of energy storage, cogeneration, trigeneration, and district heating and cooling. Among these options, some of the most important are the use of renewable and advanced energy technologies. An important step in moving toward the implementation of such technologies is to identify and remove barriers. Several barriers to renewable and advanced energy technologies include technical and financial constraints, limited information and knowledge of options, uncoordinated and/or ambiguous national aims related to energy and the environment, uncertainties in government regulations

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171

Photochemical reactions Oxidation (Acid) SO2+H2O = H2SO4

(Acid) NOx+H2O = HNO3

Atmospheric moisture Dissolution SO2

Winds carrying ash, dust, CO2, SO2, NOx .etc

2H+ + SO42−

H+ + NO3−

NOx

Wet deposition

Dry deposition

Combustion of fossil fuels 1−2 km

More than 100 km Fig. 4 Processes involved in the formation and transport of acid precipitation.

and standards, and mismanagement of human resources. Furthermore, the following are often lacking: facilities, expertise within industry and research organizations, organizational structures, differentiated electrical rates to encourage off-peak electricity use, societal acceptability of consumer demand for new renewable and advanced energy technologies, and infrastructure for recycling, recovery, and reuse of materials and products. Establishing useful methods for promoting renewable and advanced energy technologies requires analysis and clarification about how to combine environmental objectives, social and economic systems, government policies, and technical development.

5.5.2.3

Energy and Environmental Impact

Energy resources are required to supply the basic human needs of food, water, and shelter, and to improve the quality of life. The United Nations indicates that the energy sector must be addressed in any broad atmosphere-protection strategy, through programs in two major areas: increasing energy efficiency and shifting to environmentally sound energy systems. The major areas investigated promote (1) the energy transition; (2) increased energy efficiency and, consequently, increased exergy efficiency (EE); (3) renewable energy sources; and (4) sustainable transportation systems. It was reported that (1) a major energy efficiency program would provide an important means of reducing CO2 emissions, and (2) the activities should be accompanied by measures to reduce the fossil fuel component of the energy mix and to develop alternative energy sources. These ideas have been reflected in many recent studies concentrating on the provision of energy services with the lowest reasonable environmental impact and cost and the highest reasonable energy security. Waste heat emissions to the environment are also of concern, as irresponsible management of waste heat can significantly increase the temperature of portions of the environment, resulting in thermal pollution. If not carefully controlled so that local temperature increases are kept within safe and desirable levels, thermal pollution can disrupt marine life and ecological balances in lakes and rivers. Measures to increase energy efficiency can reduce environmental impact by reducing energy losses. Within the scope of exergy methods, as discussed in the next section, such activities lead to increased EE and reduced exergy losses (both waste exergy emissions and internal exergy consumptions). In practice, potential efficiency improvements can be identified by means of

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modeling and computer simulation. Increased efficiency can help achieve energy security in an environmentally acceptable way by reducing the emissions that might otherwise occur. Increased efficiency also reduces the requirement for new facilities for the production, transportation, transformation, and distribution of the various energy forms, and the associated environmental impact of these additional facilities. To control pollution, efficiency-improvement actions often need to be supported by pollution amelioration technologies or fuel substitution. The most significant measures for environmental protection are usually those undertaken at the regional or national levels, rather than by individual projects.

5.5.2.4

Thermodynamics and the Environment

People are sometimes concerned by the implications of the laws of thermodynamics on the environment. Within the limits imposed by these laws, all real processes must have some impact on the environment, and the second law is instrumental in providing insights into environmental impact (e.g., Ref. [3]), Thus exergy, which is based on the second law, has an important role to play. Exergy may provide the most appropriate link between the second law and environmental impact in part because it is a measure of the departure of the state of a system from that of the environment [3]. The magnitude of the exergy of a system depends on the states of both the system and the environment. This departure is zero only when the system is in equilibrium with its environment. An understanding of the relations between exergy and the environment may reveal the underlying fundamental patterns and forces affecting changes in the environment, and help address environmental damage. Three relationships between exergy and environmental impact [3] are discussed below:

•

•

•

Order destruction and chaos creation: the destruction of order, or the creation of chaos, is a form of environmental damage. Entropy is fundamentally a measure of chaos, and exergy of order. A system of high entropy is more chaotic or disordered than one of low entropy, and relative to the same environment, the exergy of an ordered system is greater than that of a chaotic one. That people are bothered by a chaotically polluted landscape suggests that, on a more abstract level, ideas relating exergy and order in the environment may involve human values [4] and that human values may in part be based on exergy and order. Resource degradation: the degradation of resources found in nature is a form of environmental damage. Ref. [5] defines a resource as a material, found in nature or created artificially, which is in a state of disequilibrium with the environment, and notes that resources have exergy as a consequence of this disequilibrium. Two main characteristics of resources are valued: composition (e.g., in metal ores) and reactivity (e.g., in fuels). Waste exergy emissions: the exergy associated with waste emissions can be viewed as a potential for environmental damage in that the exergy of the wastes, as a consequence of not being in stable equilibrium with the environment, represents a potential to cause change. When emitted to the environment, this exergy represents a potential to change the environment. Usually, emitted exergy causes a change that is damaging to the environment, such as the deaths of fish and plants in some lakes due to the release of specific substances in stack gases as they react and come to equilibrium with the environment. Unconstrained exergy (a potential to cause change in the environment)

Emissions of exergy to the environment

Constrained exergy (a potential to cause change)

Fig. 5 Comparison of constrained and unconstrained exergy illustrating that exergy constrained in a system represents a resource, while exergy emitted to the environment becomes unconstrained and represents a driving potential for environmental damage.

Although the previous two points indicate simultaneously that exergy in the environment in the form of resources is of value, while exergy in the environment in the form of emissions is harmful due to its potential to cause environmental damage, confusion can be avoided by considering whether or not the exergy is constrained (see Fig. 5). Most resources found in the environment are constrained and by virtue of their exergy are of value, while unconstrained emissions of exergy are free to impact in an uncontrolled manner on the environment. The decrease in the environmental impact of a process, in terms of several measures, as the process EE increases is illustrated approximately in Fig. 6.

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173

cy ffic ien ye

Resource degradation

ex erg

se rea ec sd ion iss em nd na tio uc str De

Order destruction and chaos creation

in t

he

Waste exergy emissions

Inc

rea

se

Environmental impact

Fig. 6 Qualitative illustration of the relation between the exergy efficiency of a process and the associated environmental impact in terms of order destruction and chaos creation, or resource degradation, or waste exergy emissions.

5.5.3

Energy Management

To many, the meaning of energy management is usually expressed in terms of energy conservation, usually related to solving problems regarding energy resources or technologies. But energy conservation can refer to many actions that can be taken by engineers. For example, energy conservation can imply increasing efficiencies of devices and processes so they use fewer energy resources to provide the same levels of services or products, thereby preserving energy resources. Increasing efficiency can be accomplished either by incremental improvements to existing devices or processes, or by major design alterations. Energy conservation can also imply reducing energy requirements by reconsidering what the energy is being used for, in hopes of finding ways to satisfy the overall objective(s), while using fewer energy resources. In the electrical sector of an economy, this concept involves reducing electrical energy demands of users and is sometimes referred to as “demand side management.” This can sometimes involve changing lifestyles so that we need and use less energy resources (e.g., substituting the use of more mass transit and bicycles for automobile use). In the extreme, some suggest we “return to the past” and radically curtail our use of energy resources by retreating from the highly energy-intensive lives adopted over the last few centuries. These ideas are usually equated to accepting lower standards of living. Energy conservation can further imply substituting alternative energy resources and forms for ones we deem precious and wish to preserve. This interpretation of energy conservation can, for example, involve switching heating systems from natural gas to a renewable energy resource like solar energy.

5.5.4

Exergy Management

To understand the meaning of energy management, we first have to define exergy and explain how it is different from energy. Exergy is based on the first and second laws of thermodynamics, and accounts for the quantity, as well as quality of energy. It is the second law that defines an ideal or perfect process or device as one that is reversible. This idea can be clearly grasped because energy is conserved in any system, ideal or otherwise, while exergy is conserved only for an ideal or perfect process or device. Exergy is not conserved for real processes or devices. De Nevers and Seader [6] put it another way: “Energy is conserved in all of our most wasteful uses of fuels and electricity.” Thus, if one aims for thermodynamic perfection, exergy conservation is a logical and meaningful target that is fully consistent with the objective. Energy conservation is not and, in fact, is meaningless in this regard. We of course can never in reality achieve the ideality associated with exergy conservation, but knowing of its hypothetical existence certainly provides a clear upper limit for conservation efforts. Of course, we never aim for thermodynamic perfection in the real world. Too many other factors come into play, like economics, convenience, reliability, safety, etc. Thus decision making about how far we take efforts to shift the actual level of performance nearer to the ideal, i.e., to conserve exergy, involves complex trade-offs among competing factors. What is critical is that, although other factors temper conservation goals, it is exergy or commodities and resources that have high exergy contents we seek to preserve when we speak of energy conservation. Exergy is what we value because it, not energy, consistently represents the potential to drive processes and devices that deliver services or products. In fact, it seems that exergy conservation is what lay people mean when they say energy conservation. This is important because we need to be clear about what we say and mean. If we confuse ourselves by using energy conservation not just to describe a basic scientific conservation principle, but also to describe efforts to solve energy-related problems, we cannot effectively address those problems. Fig. 7 shows the connection between energy and exergy management, and how the application of the concept of energy management is insufficient and does not ensure consideration of quality of energy as well as quantity.

5.5.5

Exergy and Sustainable Development

Numerous factors contribute to sustainable development, including the need to satisfy the needs and aspirations of society, be environmentally and ecologically benign, and have sufficient resources (natural and human). Thus, sustainable development

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Exergy Management

Exergy is managed by the second laws of thermodynamics

Energy is managed by the first law of thermodynamics

Fig. 7 Comparison of energy and exergy management domains.

requires a supply of energy resources that is sustainably available at reasonable cost and causes no or minimal negative societal impacts. Clearly, energy resources such as fossil fuels are finite and thus lack the characteristics needed for sustainability, while others such as renewable energy sources are sustainable over the relatively long term. Environmental concerns are also a major factor in sustainable development, as activities that degrade the environment are not sustainable. Environmental limitations on sustainable development from emissions can be in part addressed through increased efficiency. Activities that have no or little negative impact on the environment are more likely to contribute to sustainable development. The term sustainable development was popularized in the 1987 report of the World Commission on Environment and Development (the Brundtland Commission), and given a global mission status by the UN Conference on Environment and Development in Rio de Janeiro in 1992. The Brundtland Commission defined sustainable development as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” The Commission noted that its definition contains two key concepts: needs, meaning “in particular the essential needs of the world’s poor,” and limitations, meaning “limitations imposed by the state of technology and social organization on the environment’s ability to meet present and future needs” [7]. The Brundtland Commission’s definition was also about equity among people across the planet and among generations. Sustainable development for the Brundtland Commission includes environmental, social, and economic factors, but prioritized social and economic problems. An alternate definition of global sustainable development is presented in the Encyclopedia of Life Support Systems [8] as “the wise use of resources through critical attention to policy, social, economic, technological, and ecological management of natural and human engineered capital so as to promote innovations that assure a higher degree of human needs fulfillment, or life support, across all regions of the world, while at the same time ensuring intergenerational equity.” A growing need exists for more efficient and sustainable processes. As the world increasingly strives for a more sustainable society, it must overcome such problems as lack of and inequitable distribution of wealth, insufficient food production and energy supply, and increasing environmental impact. Sustainability has been called a means for reconciling economic and ecological necessities. Features that make sustainability useful for strategic planning include its long-term view and its ability to accommodate changing conditions. Some key requirements for sustainable development are societal, economic, environmental, and technological in nature, as outlined in Fig. 8. The kinds of technoeconomic changes that may be necessary for sustainability usually include sharp reductions in the use of fossil fuels to mitigate the risks of global climate change. Alternatives to using fossil fuels include use of nuclear power, photovoltaics, wind power, biomass cultivation, and hydroelectric generation. The ecological criterion for sustainability acknowledges that some functions of the natural environment likely cannot be replaced within any realistic timeframe by human technology. Some examples include the need for arable land, water, and a benign climate for agriculture; the role of reducing bacteria in recycling nutrient elements in the biosphere; and the protection provided by the stratospheric ozone layer. The ecological criterion for long-term sustainability implicitly allows for some technological intervention. Otherwise, problems such as climate change, widespread desertification, deforestation of the tropics, accumulation of toxic heavy metals and nonbiodegradable halogenated organics in soils and sediments, and reductions in biodiversity are probable. The report of the Brundtland Commission also stimulated debate about the physical or ecological limits to economic growth. From this perspective, sustainability can be defined in terms of carrying capacity of the ecosystem, and described with input–output models of energy and resource consumption. Sustainability then becomes an economic state where the demands placed on the environment by people and commerce can be met without reducing the capacity of the environment to provide for future generations. Sustainability-related limits on society’s material and energy throughputs might require that rates of use of renewable resources should not exceed their rates of regeneration, rates of use of nonrenewable resources should not exceed the rates at which renewable substitutes are developed, and rates of pollutant emissions should not exceed the corresponding assimilative capacity of the environment [7]. Sustainability can also be considered in terms of geographic scope. Some activities may be global in extent, for example, climate change or depletion of the stratospheric ozone layer, while others affect geographic regions, for example, acidifying gases that kill

Exergy Management

Societal sustainability • Meet societal needs • Facilitate societal aspirations • Satisfy societal standards, culturally, ethically, etc • Ensure awareness and education

175

Economic sustainability • Supply affordable resources • Provide affordable technologies and services • Facilitate attainment of a good standard of living

Sustainable development

Environmental sustainability • Maintain a healthy, aesthetically pleasing and utilizable environment • Keep environmental impacts as low as reasonably possible • Remediate environmental damage as appropriate

Technological sustainability • Supply necessary resources • Accommodate green and environmentally friendly technologies • Proivide well educated and skilled work force • Utilize life cycle assessment • Utilize industrial ecology

Fig. 8 Some key requirements of sustainable development.

vegetation and cause famine in a region. Overall, sustainability is more a global than a local concern since life is threatened if environmental impact exceeds the carrying capacity of the planet, but if this occurs in one area, then that area may become uninhabitable but life can most likely continue elsewhere. There are also social and economic aspects of sustainability. Some may consider a way of life not worth sustaining under certain circumstances, such as extreme oppression or deprivation. Nonetheless, if ecosystems are irreparably altered by human activity, then subsequent human existence may become infeasible. The heterogeneity of the environmental, social, and economic aspects of sustainability should also be recognized. Environmental and social considerations often refer to ends, the former having perhaps more to do with the welfare of future generations and the latter with the welfare of present people. Rather than an end, economic considerations can perhaps more helpfully be seen as a means to the various ends implied by environmental and social sustainability.

5.5.5.1

Industrial Ecology and Resource Conservation

In the field of industrial ecology, processes like waste cascading, resource cycling, increasing EE, and renewable exergy use can delink consumption from depletion in evolving biological ecosystems and can be used as resource-conservation strategies for delinking consumption from depletion in immature industrial systems [9]. Connelly and Koshland [10] demonstrate that the relation between these strategies and propose an exergy-based definition for ecosystem evolution. They discuss the four conservation strategies in the context of a simple, hypothetical industrial ecosystem consisting initially of two solvent consumption processes and the chain of industrial processes required to deliver solvent to these two processes. One solvent consuming process is assumed to require lower purity feedstock than the other. All solvent feedstocks are derived from nonrenewable, fossil sources, and all solvent leaving the two consumptive processes is emitted to the atmosphere. This is a linear process that takes in useful energy and materials and releases waste energy and material.

•

Waste cascading: waste cascading may be described in thermodynamic terms as using outputs from one or more consumptive processes as inputs to other consumptive processes requiring equal or lower exergy. Waste cascading reduces resource consumption in two ways: by reducing the rate of exergy loss caused by the dissipation of potentially usable wastes in the environment, and by reducing the need to refine virgin resources. In our hypothetical industrial ecosystem, cascading allows used (i.e., partially consumed) solvent from the first process to be used in the second solvent consumption process, eliminating solvent emissions from the first process and the need to refine and supply pure solvent to the second process. The solvent consumption rate in the two processes is unchanged, but the rate of resource depletion associated with these processes is reduced. Although waste cascading reduces demand for other resources and hence is an important resource conservation strategy, cascading does not return to a waste the exergy that was removed from it during its use. Thus, cascading cannot form a resource cycle. Losses associated with the upgrade and supply of solvent to the top of the cascade, the consumption of resources in the two processes constituting the cascade, and dissipation of waste solvent released from the bottom of the cascade cannot be avoided. Cascading can thus reduce the linkage between consumption and depletion, but it cannot fully delink the two.

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176

•

•

Resource cycling: to reduce emissions from the bottom of a waste cascade (or at the outlet of a single consumptive process) and return this bottom waste to the top of a resource cascade, the exergy removed from a resource during consumption must be returned to it. This process of exergy loss through consumption followed by exergy return through transfer is the basis of resource cycling. Adding a solvent recycling process and its associated chain of industrial processes to the hypothetical system reduces depletion both by eliminating exergy loss from the dissipation of released solvents, and by substituting a postconsumption upgrade path for a virgin resource upgrade path. An activated carbon solvent separation system, for example, will generally be far less exergy intensive than the fossil-based manufacture of virgin solvent. Cycling cannot, however, eliminate depletion. In accordance with the second law, all exergy transfers in real (irreversible) processes must be accompanied by exergy loss (i.e., total exergy must always decrease). Hence, in any real cycling process, the overall resource depletion rate will exceed the rate of exergy loss in the consumptive process whose wastes are being cycled. In the above example, the two solvent consumption processes and the exergy removed from nonrenewed resources for the purpose of upgrading the solvent would contribute to resource depletion in the case of complete solvent cycling. Increasing EE: one way to reduce the resource depletion associated with cycling is to reduce the losses that accompany the transfer of exergy to consumed resources by increasing the efficiency of exergy transfer between resources (i.e., increasing the fraction of exergy removed from one resource that is transferred to another). EE may be defined as Exergy efficiency ¼ Exergy output=Exergy input where Exergy loss ¼ Exergy input2Exergy output

•

Compared to energy efficiency, EE may be thought of as a more meaningful measure of efficiency that accounts for quantity and quality aspects of energy flows. Unlike energy efficiency, EE provides an absolute measure of efficiency that accounts for first- and second-law limitations. In the current example, increasing EE in the case of complete cycling would involve increasing the efficiency of the solvent upgrade process. Although technological and economic limitations to efficiency gains prevent EE from approaching unity, many industrial processes today operate at very low efficiencies, and it is widely recognized that large margins for efficiency improvement often remain. However, even if EE could be brought to 100%, the resource depletion associated with solvent consumption and upgrade in the example would still not be eliminated. Recycling with a 100% exergy efficient upgrade process would result in a depletion rate equal to the consumption rate of the two solvent consumption processes. To fully delink consumption from depletion, it is necessary to use resources that supply exergy without being depleted. Renewable exergy use: to fully delink consumption from depletion, the exergy used to upgrade consumption products must be derived from renewable exergy sources (i.e., sources such as electricity generated directly or indirectly from solar radiation or sources such as sustainably harvested biomass feedstocks). In the solvent cycling example, using a sustainably harvested biomass fuel as the exergy source for the solvent upgrade process could in theory create a solvent cycling system in which a closed solvent cycle is driven entirely by renewable exergy inputs. In this situation, the depletion rate becomes independent of the EE of the solvent upgrade process.

5.5.5.2

Energy and Sustainability

The relation between sustainable development and the use of resources, particularly energy resources, is important [11]. A supply of energy resources is generally agreed to be a necessary, but not sufficient, requirement for development within a society. Societies, such as countries or regions, that undergo significant industrial and economic development almost always have access to a supply of energy resources. For sustainable development, there are further conditions. Principally, such societies must have access to and utilize energy resources that are sustainable in a broad sense, i.e., that are obtainable in a secure and reliable manner; safely utilizable to satisfy the energy services for which they are intended with minimal negative environmental, health and societal impacts; and usable at reasonable costs. An important implication of the above statements is that sustainable development requires not just that sustainable energy resources be used, but that the resources be used efficiently. Exergy methods are essential in evaluating and improving efficiency. Through efficient utilization, society maximizes the benefits it derives from its resources, while minimizing the negative impacts (such as environmental damage) associated with their use. This implication acknowledges that most energy resources are to some degree finite, so that greater efficiency in utilization allows such resources to contribute to development over a longer period of time, i.e., to make development more sustainable. Much of the environmental impact in a society is associated with energy-resource utilization. Ideally, only energy resources are used that cause no environmental impact. Such a condition can be attained or nearly attained by using energy resources in ways that cause little or no wastes to be emitted into the environment, and/or that produce only waste emissions that have no or minimal negative impact on the environment. This latter condition is usually met when relatively inert emissions that do not react in the environment are released, or when the waste emissions are in or nearly in equilibrium (thermal, mechanical, and chemical) with the environment, i.e., when the waste exergy emissions are minimal. In reality, however, all resource use leads to some degree of environmental impact. A direct relation exists between EE (and sometimes energy efficiency) and environmental impact, in that through increased efficiency, a fixed level of services can be satisfied with fewer energy resources and, in most instances, reduced levels of related waste emissions. Thus, the limitations imposed on sustainable development by environmental emissions and their negative impacts can be in part overcome through increased efficiency.

Exergy Management 5.5.5.3

177

Exergy and Sustainability

Exergy as the confluence of energy, environment and sustainable development, as shown in Fig. 1, highlights its interdisciplinary character and central focus. Exergy methods can be used to improve sustainability. Cornelissen [12] points out that one important element in obtaining sustainable development is the use of exergy analysis. By noting that energy can never be “lost,” while exergy can, Cornelissen suggests that exergy losses, particularly due to the use of nonrenewable energy forms, should be minimized to obtain sustainable development. Further, the study shows that environmental effects associated with emissions and resource depletion can be expressed in terms of one exergy-based indicator, founded on physical principles. Sustainable development also includes economic viability. Thus, the methods relating exergy and economics also reinforce the link between exergy and sustainable development. The objectives of most existing analysis techniques integrating exergy and economics include the determination of (1) the appropriate allocation of economic resources so as to optimize the design and operation of a system, and/or (2) the economic feasibility and profitability of a system. Exergy-based economic analysis methods are referred to by such names as thermoeconomics, second-law costing, cost accounting, and exergoeconomics. Fig. 9 illustrates the relation between exergy and sustainability and environmental impact. There, sustainability is seen to increase and environmental impact to decrease as the process EE increases. As EE approaches 100%, environmental impact approaches zero since exergy is only converted without loss, while sustainability approaches infinity because the process approaches reversibility. As EE approaches 0%, sustainability approaches zero because exergy-containing resources are used but nothing is accomplished, while environmental impact approaches infinity because an ever-increasing quantity of resources must be used and a correspondingly increasing amount of exergy-containing wastes are emitted. Some important contributions that can be derived from exergy methods to increase the sustainability of nonsustainable development are presented in Fig. 10. Development typical of most modern processes, which are generally nonsustainable, is shown at the bottom of the figure. A future in which development is sustainable is shown at the top of the figure, while some key exergy-based contributions toward making development more sustainable are shown, and include increased EE, reduction of exergy-based environmental degradation, and use of sustainable exergy resources. Fig. 11 outlines a typical industrial process, and throughputs of materials and energy. Cleaner production of materials, goods, and services is one of the tools for sustainable development. Such production entails the efficient use of resources and the corresponding production of only small amounts of waste. Clean production often also involves the use of renewable resources. This does not mean that cleaner production is necessarily contradictory to the economic approach of minimizing costs and maximizing profits. Life cycle assessment (LCA) aims to improve or to optimize processes so that they consume fewer resources and produce fewer emissions and wastes. Common routes for achieving this often include end-of-pipe treatment such as waste water treatment plants, filters, and scrubbers. Often, however, expensive end-of-pipe treatment solutions are unavoidable. Exergy analysis appears to be a significant tool for improving processes by changing their characteristics, rather than simply via end-of-pipe fixes. As a basic example, consider the conversion of mechanical work to heat ideally, i.e., with 100% efficiency. Heat has a lower exergy, or quality, than work. Therefore, heat cannot be converted to work with a 100% energy efficiency. But, the conversion can be in theory achieved with a 100% EE. Thus exergy analysis helps identify the upper limit for efficiency improvements. Some examples of the difference between energy and exergy are shown in Table 3. Hot water and steam with the same enthalpy have different exergy, the value for steam being higher than for hot water. Fuels like natural gas and gasoline have exergetic values comparable to their net heating values. Work and electricity have the same exergy and energy. Exergy is calculated in Table 3 as the product of energy and a quality factor.

80

y eff Exerg

y icienc

100

60 40 20 0 0 20 En 40 vir on me 60 nta l im 80 pa ct

20 40

ity

60 80 100

100

bil

a ain

st

Su

Fig. 9 Qualitative illustration of the relation between the environmental impact, sustainability, and exergy efficiency (EE) of a process.

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Sustainable development

Use of sustainable exergy resources

Reduction of exergy-related environmental degradation

Increased exergy efficiency

Non-sustainable development Fig. 10 Some key contributions of exergy methods to increasing the sustainability of nonsustainable systems and processes.

Products

Useful energy

Resources

Industrial process

Wastes

Material

Waste energy

Emissions

Fig. 11 An industrial process.

Table 3 Relative energy and exergy values of various energy forms, considering 1000 J of each energy form Source

Exergy (J)

(Exergy/energy) ratioa

Water at 801C Steam at 1201C Natural gas Electricity/work

160 240 990 1000

0.16 0.24 0.99 1.00

a

For heat, the (exergy/energy) ratio is the exergetic temperature factor t ¼ (1–T0/Ts), where T0 is the absolute temperature of the environment and Ts is the absolute temperature of the stream. Calculations can be often simplified as Exergy ¼ Energy  (Exergy/Energy) ratio. Source: van Schijndel PPAJ, Den Boer J, Janssen FJJG, Mrema GD, Mwaba MG. Exergy analysis as a tool for energy efficiency improvements in the Tanzanian and Zambian industries. In: International conference on engineering for sustainable development, July 27–29. University of Dar Es Salaam, Tanzania; 1998.

5.5.5.4

Renewable Energy and Sustainable Development

Renewable energy resources are often sustainable. Most energy supplies on Earth derive from the sun, which continually warms us and supports plant growth via photosynthesis. Solar energy heats the land and sea differentially and so causes winds and consequently waves. Solar energy also drives evaporation, which leads to rain and in turn hydropower. Tides are the result of the gravitational pull of the moon and sun and geothermal heat is the result of radioactive decay within the Earth. Nonetheless, energy has challenges [1]:

•

Growing energy demand: the annual population growth rate is currently around 2% worldwide and higher in many countries. By 2050, world population is expected to double and economic development is expected to continue, improving standards of

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•

• •

179

living in many countries. Consequently, global demand for energy services is expected to increase by up to 10 times by 2050 and primary-energy demand by 1.5–3 times. Excessive dependence on specific energy forms: society is extremely dependent on access to specific types of energy currencies. The effect of the multiday blackout of 2003 in Ontario and several northeastern US states illustrated the dependency on electricity supply, as access was lost or curtailed to computers, elevators, air conditioners, lights, and health care. Energy-related environmental impacts: continued degradation of the environment by people, most agree, will have a negative impact on the future, and energy processes lead to many environmental problems, including global climate change, acid precipitation, stratospheric ozone depletion, emissions of a wide range of pollutants including radioactive and toxic substances, and loss of forests and arable land. The dominance of nonsustainable and nonrenewable energy resources: limited use is made today of renewable energy resources and corresponding technologies, even though such resources and technologies provides a potential solution to current and future energy-resource shortages. By considering engineering practicality, reliability, applicability, economics, and public acceptability, appropriate uses for sustainable and renewable energy resources can be found. Of course, financial and other resources should not always be dedicated to renewable energy resources, as excessively extravagant or impractical plans are often best avoided. Energy pricing that does not reflect actual costs: many energy-resource prices have increased over the last couple of decades, in part to account for environmental costs, yet many suggest that energy prices still do not reflect actual societal costs. Global disparity in energy use: wealthy industrialized economies that contain 25% of the world’s population use 75% of the world’s energy supply.

These and other energy-related issues need to be resolved if humanity and society are to develop sustainably in the future. Renewable energy resources appear to provide one component of an effective sustainable solution, and can contribute over the long term to achieving sustainable solutions to today’s energy problems. The attributes of renewable energy technologies (e.g., modularity, flexibility, low operating costs) differ considerably from those for traditional, fossil fuel-based energy technologies (e.g., large capital investments, long implementation lead times, operating cost uncertainties regarding future fuel costs). Renewable energy technologies can provide cost-effective and environmentally beneficial alternatives to conventional energy systems. Some of their benefits include being relatively independent of the cost of oil and other fossil fuels, which are projected to rise significantly over time; relatively straightforward implementation; and reduced environmental degradation. Renewable energy technologies are often particularly advantageous in developing countries. The market demand for renewable energy technologies in developing nations will likely grow as they seek a better standard of living. Some technical and economic challenges of renewable energy resources are that they often are diffuse, not fully accessible, intermittent, and regionally varying. The overall benefits of renewable energy technologies are often not well understood. For renewable energy technologies to be assessed comprehensively, all of their benefits must be considered. For example, many renewable energy technologies can provide, with short lead times, small incremental capacity additions to existing energy systems. Such power generation units usually provide more flexibility in incremental supply than large devices like nuclear power stations. Renewable energy has an important role to play in meeting future energy needs in both rural and urban areas [13]. The development and utilization or renewable energy should be given a high priority, especially in the light of increased awareness of the adverse environmental impacts of fossil-based generation. The need for sustainable energy development is increasing rapidly in the world. Renewable energy technologies favor system decentralization and local solutions that are somewhat independent of the national network, thus enhancing the flexibility of the system and providing economic benefits to small isolated populations. Also, the small scale of the equipment often reduces the time required from initial design to operation, providing greater adaptability in responding to unpredictable growth and/or changes in energy demand. To seize the opportunities, countries should establish renewable energy markets and gradually develop experience with renewable technologies. The barriers and constraints to the diffusion of renewables should be removed. The legal, administrative and financing infrastructure should be established to facilitate planning and application of renewable energy projects. Government could pay a useful role in promoting renewable energy technologies by initiating surveys and studies to establish their potential in both urban and rural areas. Fig. 12 shows the major considerations for developing renewable energy technologies. As existing energy utilities often play a key role in determining the adoption and contribution of renewable energy technologies, the utility structure and the strategy for integrating renewables should be reviewed and studied. Utility regulations should be framed to reflect the varying costs over the networks, increase competitiveness, and facilitate access of independent renewable energy production. A major challenge for renewables is to get them into a reliable market at a price that is competitive with energy derived from fossil fuels, without disrupting local economies. Since the use of renewable energy often involves awareness of perceived needs and sometimes a change of lifestyle and design, it is essential to develop effective information exchange, education, and training programs. Knowledge of renewable energy technologies should be strengthened by establishing education and training programs. Energy research and development and demonstration projects should be encouraged to improve information and raise public awareness. The technology transfer and development process should be institutionalized through international exchanges and networking. To overcome obstacles in initial implementation, programs should be designed to stimulate a renewable energy market so that options can be exploited by industries as soon as they become costeffective. Financial incentives should be provided to reduce up-front investment commitments and to encourage design innovation.

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Increased sustainability

Increased utilization of renewable energy

Social impact

Public awareness

Environmental impact

Environmental impact reduction

Social benefits

Technical aspects

Economic factors

Commercialization

Avialability

Investments

Research and development

Grid connection

Generation costs

Innovation

Technology level and use

Externalities

Incentives

Training

Fig. 12 Major considerations involved in the development of renewable energy technologies for sustainable development.

Table 4

Selected methods and tools for environmental assessment and improvement

Risk tools

Environmental tools

Thermodynamic tools

Sustainability tools

Risk assessment Monte Carlo risk analysis

Environmental performance indicators Environmental impact assessment Ecological footprints

Exergy analysis Material flux analysis

Life cycle assessment Sustainable process index Industrial ecology

5.5.5.4.1

Tools for environmental impact and sustainability

An energy system is normally designed to work under various conditions to meet different expectations (e.g., load, environment, and social expectations). Table 4 lists some available environmental tools (e.g., Ref. [14]):

•

•

• •

LCA: is an analytical tool used to assess the environmental burden of products at the various stages in a product’s life cycle. In other words, LCA examines such products “from cradle-to-grave.” The term “product” is used in this context to mean both physical goods as well as services. LCA can be applied to help design an energy system and its subsystems to meet sustainability criteria through every stage of the life cycle. LCA, as an environmental accounting tool, is very important. Environmental impact assessment (EIA): is an environmental tool used in assessing the potential environmental impact of a proposed activity. The derived information can assist in making a decision on whether or not the proposed activity will pose any adverse environmental impacts. The EIA process assesses the level of impacts and provides recommendations to minimize such impacts on the environment. Ecological footprint analysis: is an accounting tool enabling the estimation of resource consumption and waste assimilation requirements of a defined human population or economy in terms of corresponding productive land use. Sustainable process index (SPI): is a measure of the sustainability of a process producing goods. The unit of measure is m2 of land. It is calculated from the total land area required to supply raw materials, process energy (solar derived), provide infrastructure and production facilities, and dispose of wastes.

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181

Material flux analysis (MFA): is a materials accounting tool that can be used to track the movement of elements of concern through a specified system boundary. The tool can be adapted further to perform a comparative study of alternatives for achieving environmentally sound options. Risk assessment: can estimate the likelihood of potential impacts and the degree of uncertainty in both the impact and the likelihood it will occur. Once management has been informed about the level of risk involved in an activity, the decision of whether such a risk is acceptable can be subsequently made. Exergy analysis: as discussed throughout this book, exergy is the quality of a flow of energy or matter that represents the useful part of the energy or matter. The conversion of energy in a process usually is driven by the consumption of energy quality. It is found that using the exergy concept to estimate the consumption of physical resources can improve the quality of the data necessary for LCA.

5.5.5.4.2

Ecologically and economically conscious process engineering

Numerous efforts have been made to develop and promote ecologically and economically sustainable engineering. Industrial and ecological systems are treated as networks of energy flows in these methods. Ecosystems convert sunlight to natural resources, while industrial systems convert natural resources to economic goods and services. Thus, all products and services can be considered as transformed and stored forms of solar energy. An energy flow chart for a typical industrial system that includes ecological and economic inputs is shown in Fig. 13. LCA focuses mainly on the waste streams, and their impact, while systems ecology ignores wastes and their impacts. The thermodynamic approach to LCA and design accounts for economic and ecological inputs and services, and the impact of emissions. This approach is related to exergy. Exergy analysis is popular for improving the thermodynamic efficiency of industrial processes. However, it ignores ecological inputs and the impact of emissions. These shortcomings of exergy analysis have been overcome by combining it with life cycle impact assessment and energy analysis. Energy analysis is a popular approach for analyzing and modeling ecosystems. The resulting approach bridges systems ecology with systems engineering. Applications of this approach to LCA and process design are being developed.

5.5.5.5

Exergy as a Sustainability Quantifier

Exergy has qualities that make it suitable as a common quantifier of process sustainability:

• • •

Exergy is an extensive property whose value is uniquely determined by the parameters of both the system and the reference environment. If a flow undergoes any combination of work, heat and chemical interactions with other systems, the change in its exergy expresses not only the quantity of the energetic exchanges but also the quality. Provided a chemical reference state is selected that is reflective of the actual typical chemical environment on Earth, the chemical portion of the exergy of a substance can be evaluated. The exergy of a substance such as a mineral ore or of a fossil fuel is known when the composition and the thermodynamic conditions of the substance and the environment at the extraction site are known. The chemical exergy of a substance is zero when it is in equilibrium with the environment, and increases as its state deviates from the environment state. For a mineral, for example, the exergy of the raw ore is either

Renewable resources

Non-renewable resources

Industrial processes Wastes Waste treatment

Environment Fig. 13 Flow diagram for an industrial process that includes resource and economic inputs.

Economic resources

Products and services

182

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Environmental sustainability

Social sustainability

Sustainable development

Energy sustainability

Resource sustainability

Economic sustainability

Fig. 14 Factors impacting sustainable development and their interdependences.

• •

zero (if the ore is of the same composition as the environmental material) or higher if the ore is somewhat concentrated or purified. The value of a product of a process, expressed in terms of “resource use consumption,” may be obtained by adding to the exergy of the original inputs all the contributions due to the different streams that were used in the process. If a process effluent stream is required to have no impact on the environment, the stream must be brought to a state of thermodynamic equilibrium with the reference state before being discharged into the environment. The minimum amount of work required to perform this task is by definition the exergy of the stream. For this reason, many suggest that the exergy of an effluent is a correct measure of its potential environmental cost.

Some researchers (e.g., Ref. [15]), proposed that an “invested exergy” value be attached to a process product, defined as the sum of the cumulative exergy content of the product and of the “recycling exergy” necessary to allow the process to have zero impact on the environment. They further suggest the following, for any process:

• •

A proper portion of the invested exergy plus the exergy of a stream under consideration can be assigned to the stream, thereby allowing the process to be “charged” with the physical and invested exergy of its effluents. If a feasible formulation exists to convert the remaining “nonenergetic externalities” (labor and capital) into exergetic terms, their equivalent input in any process could be added to the exergy and invested exergy of each stream. The exergy flow equivalent to labor can perhaps be estimated by assigning a resource value to the work hour, computed as the ratio of the yearly total exergetic input in a society or region to the total number of work hours generated in the same period of time. Similarly, the exergy flow equivalent to a capital flow can perhaps be estimated by assigning a resource value to the monetary unit, computed as the ratio between the yearly total exergetic input in a society or region and the total monetary circulation (perhaps in terms of gross domestic product, or total retail sales, or a different financial measure) in the same period of time.

In summary, we consider sustainable development here to involve four key factors (see Fig. 14): environmental, economic, social, and resource/energy. The connections in Fig. 14 illustrate that these factors are interrelated.

5.5.6

Illustrative Example

The ideas discussed in this chapter are demonstrated for the process of electricity generation using a coal-fired steam power plant. The plant considered is the Nanticoke generating station. Individual units of the station each have net electrical outputs of approximately 500 MW. A single unit as the one shown in Fig. 15 consists of four main sections [3]:

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183

Power production

B

C

D

D

A

E

Condensation

Steam generation

F I

K

H

G

J

Preheating A: Steam generator and reheater B: High-pressure turbine C: Intermediate-pressure turbine D: Low-pressure turbine E: Generator and transformer F: Condenser

G: Hot well pump H: Low-pressure heat exchangers I: Open deaerating heat exchanger J: Boiler feed pump K: High-pressure heat exchangers

Fig. 15 Breakdown of a unit in the coal-fired electrical generating station into four main sections. The external inputs are coal and air, and the output is stack gas and solid waste for unit A. The external outputs for unit E are electricity and waste heat. Electricity is input to units G and J, and cooling water enters and exits unit F.

Electricity 511

Air 0

CW 0

CW 11

Ash 0 Stack gas 62 Coal 1427

Electricity 511 Coal 1368

System

System Stack gas 74

Electricity 511 CW 746 CW 0

CW 11

(A)

(B)

Ash 0

Air 0

Fig. 16 Overall energy and exergy balances for the coal-fired electrical generating station. The rectangle in the center of each diagram represents the station. Widths of flow lines are proportional to the relative magnitudes of the represented quantities. CW denotes cooling water. (A) Exergy balance showing flow rates (positive values) and consumption rate (negative value, denoted by hatched region) of exergy (in MW). (B) Energy balance showing flow rates of energy (in MW).

1. Steam generators: pulverized-coal-fired natural circulation steam generators produce primary and reheat steam. Regenerative air preheaters are used and flue gas exits through chimneys. 2. Turbine generators and transformers: primary steam from the steam generators passes through turbine generators, which are connected to a transformer. Steam exhausted from the high-pressure cylinder is reheated and extraction steam from several points on the turbines preheats feed water. 3. Condensers: cooling water condenses the steam exhausted from the turbines. 4. Preheaters and pumps: the temperature and pressure of the condensate are increased.

5.5.6.1

Implications Regarding Exergy and Energy

Overall balances of exergy and energy for the station are illustrated in Fig. 16, where the rectangle in the center of each diagram represents the station. The main findings follow:

• •

For the overall plant, the energy efficiency, defined as the ratio of net electrical energy output to coal energy input, is found to be about 37%, and the corresponding EE 36%. In the steam generators, the energy and exergy efficiencies are evaluated, considering the increase in energy or exergy of the water as the product. The steam generators appear significantly more efficient on an energy basis (95%) than on an exergy

184

• •

Exergy Management

basis (50%). Physically, this discrepancy implies that, although most of the input energy is transferred to the preheated water, the energy is degraded as it is transferred. Most of the exergy losses in the steam generators are associated with internal consumptions (mainly due to combustion and heat transfer). In the condensers, a large quantity of energy enters (about 775 MW for each unit), of which close to 100% is rejected; and a small quantity of exergy enters (about 54 MW for each unit), of which about 25% is rejected and 75% internally consumed. In other plant devices, energy losses are found to be small (about 10 MW total), and exergy losses are found to be moderately small (about 150 MW total). The exergy losses are almost completely associated with internal consumptions.

5.5.6.2

Implications Regarding Exergy and the Environment

In this example of a conventional coal-fired electrical generating station, each of the relationships between exergy and environmental impact described in Section 5.5.2.4 is demonstrated:

•

•

•

Waste exergy is rejected from the plant with waste stack gas, solid combustor wastes, and the waste heat released to the atmosphere and the lake from which condenser cooling water is obtained. The exergy of these emissions represents a potential to impact on the environment. Societal concern already exists regarding emissions of harmful chemical constituents in stack gases and thermal pollution in local water bodies of water, but the exergy-based insights into environmental-impact potential of these phenomena are not yet well understood or recognized. Coal, a finite resource, is degraded as it drives the electricity generation process. Although a degree of resource degradation cannot be avoided for any real process, increased EE can reduce the amount of degradation, for the same services or products. In the extreme, if the process in the example were made thermodynamically ideal by increasing the EE from 37% to 100%, coal use and the related emissions would each decrease by over 60%. Order destruction occurs during the exergy consuming conversion of coal to less ordered stack gases and solid wastes, and chaos creation occurs as wastes are emitted to the environment, allowing the products of combustion to move and interact without constraints throughout the environment.

5.5.6.3

Implications Regarding Exergy and Sustainable Development

The exergy-related implications discussed in this section assist in achieving sustainable development by providing insights into efficiency improvement and environmental-impact reduction. These insights, combined with economics and other factors, can assist in improving the sustainability of (1) the electricity-generation process considered and (2) the broader provision of electricity and electrical-related services in regions.

5.5.7

Applications of Exergy in Industry

Many researchers and practicing engineers refer to exergy methods as powerful tools for analyzing, assessing, designing, improving, and optimizing systems and processes. It is not surprising, therefore, that exergy methods are used in some industries. Others have also noticed an increase in exergy use by industry. For instance, Tadeusz Kotas wrote in the preface to his 1995 book on exergy [16], “ever since the … early 70s, there has been a steady growth in the interest in exergy analysis … This increase manifests itself in … the more widespread use of exergy analysis in industry.” Also, Bejan [17] recently wrote, “As the new century begins, we are witnessing revolutionary changes in the way thermodynamics is … practiced. The methods of exergy analysis … are the most visible and established forms of this change.” A few examples can help illustrate this point. Some electrical generation companies utilize exergy methods to design better stations, and to improve efficiency and avoid performance deterioration in existing stations. Also, some cogeneration (or combined heat and power) facilities use exergy methods both to improve efficiency and to resolve economic costing and pricing issues. Given that exergy analysis is first and foremost a technical tool for guiding efficiency-improvement efforts in engineering and related fields, one would expect industry to wholeheartedly embrace the use of exergy. However, exergy analysis is used only sparingly by many industries, and not at all by others. Clearly, therefore, exergy is not generally and widely accepted by industry at present. There have been several initiatives to improve this situation. One of the key successes has been the formation in 2006 of a new group entitled “Exergy Analysis for Sustainable Buildings,” within the American Society for Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). That group has the following mission and strategic goals: Mission: Exergy Analysis for Sustainable Buildings is concerned with all exergy aspects of energy and power utilization of systems and equipment for comfort and service, assessment of their impact on the environment, and development of analysis techniques, methodologies, and solutions for environmentally safer, sustainable low-exergy buildings. Strategic goals:

•

Make “Exergy Analysis” a primary tool for design, analysis, and performance improvement of building HVAC systems for better environment and sustainability.

Exergy Management

• • • • •

• • • • • •

185

Develop simple to understand, easy to apply, yet very effective and comprehensive analysis packages. Develop exergy as a common eco-engineering metric. Develop new exergy policies and strategies to complement energy policies and strategies for better global sustainability and future. Develop robust, seamless, and easy to understand definitions and simplified equations, charts, tables etc. for green buildings that are easy to understand by every discipline involved (architects, builders, decision makers, etc.). Develop and maintain products and services to meet the needs of ASHRAE members and the engineering community at large; develop guides; standards, handbook chapters; organize professional development courses (PDCs), e-learning course material; maintain a very strong website; organize symposia, forums, seminars, publish technical bulletins; cooperate with other organizations. Create a culture of exergetic innovation, resilience, and flexibility within ASHRAE that recognizes and responds to technological and ecological needs of the HVAC and building industries. Develop exergy-related design and evaluation parameters, algorithms to be used in various certification and evaluation codes like LEED. Develop equipment rating system similar to EER (e.g., EE, exergetic improvement ratio (EIR)). Close coordination and cooperation with other ASHRAE Technical Committees, groups, and other national and international associations: identify institutions within and outside ASHRAE and select the Group Liaisons to these institutions. Develop a common and interdisciplinary Exergy Definitions and Nomenclature Library. Identify different exergy analysis parameters like embodied, operating, and other parameters in Exergy Analysis assigned to this group, like the energy/exergy required to produce and assemble the materials the building is made of (upstream) and exergy destruction (downstream) and environmental impact. Optimize the use of energy and exergy analysis for the next generation designs and optimize volunteers’ time.

This activity provides a strong indication that industries and engineers are now keener than ever to improve their performance by utilizing exergy methods. But industry’s relatively limited use of exergy methods at present leads to several pertinent questions:

• • • •

Why are exergy methods not more widely used by industry? What can be done to increase industry’s use or even acceptance of exergy? Is industry’s minimal use of exergy appropriate? Should steps be taken to make exergy methods more widely used by industry?

In addition, questions arise due to the observation that the use of exergy methods appears to vary geographically. For instance, more companies in Europe than in North America seem to utilize exergy methods to enhance and maintain plant performance. Exergy methods can help in optimization activities. Berg [18] noted this advantage when examining the different degrees of use of exergy in industry. He wrote, “In some industries, particularly electric utilities, use of second law analysis in various forms has been a long standing practice in design. In other industries, the more direct techniques of second law analysis were not widely used; other less direct and less exacting techniques were used instead. Even though the approach to optimization in the latter cases was slower, and ultimately less perfect, the approach was nevertheless made.” Consequently, many design applications of exergy analysis have occurred that aim to evaluate, compare, improve, or optimize energy systems.

5.5.7.1

Advantages of Exergy Methods

Some of the main advantages of exergy methods are a better perspective on efficiency and losses and to enhance the understanding of energy conservation. Each of these advantages are explained and presented in the following sections.

5.5.7.1.1

Understanding thermodynamic efficiencies and losses through exergy

Decisions regarding resource utilization and technical design have traditionally been based on conventional parameters like performance, economics, and health and safety. In recent decades, new concerns like environmental damage and scarcity of resources have increased the considerations in decision making. But always, efficiencies and losses have been important. We hear references to energy efficiencies and energy losses all the time, whether we are dealing with companies, government, the public, or others. People have developed a sense of comfort when dealing with such terms as energy efficiencies and losses, perhaps through repetitious use and exposure. Yet numerous problems are associated with the meaning of energy efficiencies and losses. For instance, efficiencies based on energy can often be nonintuitive or even misleading, in part because energy efficiencies do not necessarily provide a measure of how nearly a process approaches ideality. Also, losses of energy can be large in quantity, when they are in fact not that significant thermodynamically due to the low quality or usefulness of the energy that is lost. Exergy efficiencies do provide measures of approach to ideality, and exergy losses do provide measures of the deviation from ideality. This situation, where confusion and lack of clarity exists regarding measures as important as efficiency and loss, is problematic. In general, such clarity can be achieved through the use of efficiency and loss measures that are based on exergy. Consequently, several actions are needed:

• •

Be clear about what is meant when we discuss thermodynamic efficiencies and losses. Ensure that the measures we use for efficiencies and losses are meaningful.

186

• •

Exergy Management

Utilize efficiency and loss measures based on exergy as much as possible. Where energy-based measures are used, indicate clearly the meaning and proper interpretation of the values, as well as any limitations associated with them.

5.5.7.1.2

Efficiency

To understand what we mean or intend to mean when we cite an efficiency, it is helpful to consider definitions. Efficiency is defined in one dictionary as “the ability to produce a desired effect without waste of, or with minimum use of energy, time, resources, etc.” Efficiency is used by people to mean the effectiveness with which something is used to produce something else, or the degree to which the ideal is approached in performing a task. For engineering systems, nondimensional ratios of quantities are typically used to determine efficiencies. For engineering systems whose primary purpose is the transformation of energy, ratios of energy are conventionally used to determine efficiencies. A process has maximum efficiency according to such energy-based measures if energy input equals product energy output (i.e., if no “energy losses” occur). However, efficiencies determined using energy are misleading because in general they are not measures of “an approach to an ideal.” To determine more meaningful efficiencies, a quantity is required for which ratios can be established that do provide a measure of an approach to an ideal. The second law of thermodynamics must be involved in obtaining a measure of an approach to an ideal. This law states that maximum efficiency is attained (i.e., ideality is achieved) for a reversible process. However, the second law must be quantified before efficiencies can be defined. Some approaches follow:

•

•

• •

The “increase of entropy principle” quantifies the second law, stating that the entropy creation due to irreversibilities is zero for ideal processes and positive for real ones. From the viewpoint of entropy, maximum efficiency is attained for a process in which the entropy creation due to irreversibilities is zero. The magnitude of the entropy creation due to irreversibilities is a measure of the nonideality or irreversibility of a process. In general, however, ratios of entropy do not provide a measure of an approach to an ideal. A quantity that has been discussed in the context of meaningful measures of efficiency is negentropy (e.g., see Chapter 21 of the report [4] for a major study carried out by the International Institute of Applied Systems Analysis). Negentropy is defined such that the negentropy consumption due to irreversibilities equals the entropy creation due to irreversibilities. From the viewpoint of negentropy, maximum efficiency is attained for a process in which negentropy is conserved. Negentropy is consumed for nonideal processes. Furthermore, negentropy is a measure of order. Consumptions of negentropy are therefore equivalent to degradations of order. Since the abstract property of order is what is valued and useful, it is logical to attempt to use negentropy in developing efficiencies. However, general efficiencies cannot be determined based on negentropy because its absolute magnitude is not defined. Order and negentropy can be further quantified through the ability to perform work. Then, maximum efficiency is attainable only if, at the completion of the process, the sum of all energy involved has an ability to do work equal to the sum before the process occurred. Such measures are based on both the first and second laws. Exergy is defined as the maximum work that can be produced by a stream or system in a specified environment. Exergy is a quantitative measure of the “quality” or “usefulness” of an amount of energy. From the viewpoint of exergy, maximum efficiency is attained for a process in which exergy is conserved. Efficiencies determined using ratios of exergy do provide a measure of an approach to an ideal. Also, exergy efficiencies quantify the potential for improvement.

Other researchers have also indicated support for the use of exergy efficiencies. For example, Gaggioli [19] refers to exergy efficiencies as “real” or “true” efficiencies, while referring to energy efficiencies as “approximations to real” efficiencies. Exergy efficiencies are often more intuitively rational than energy efficiencies, because efficiencies between 0% and 100% are always obtained. Measures of merit that can be greater than 100%, such as coefficient of performance, normally are between 0% and 100% when exergy is considered. Of course, other exergy-based measures of efficiency than the one described earlier can be defined. The different definitions simply answer different questions. For instance, Kotas [16] prefers the use of the rational efficiency, citing features that render it “particularly suitable as a criterion of the degree of thermodynamic perfection of a process.” The key point: efficiencies based on exergy are normally meaningful and useful. Sometimes confusion can arise when using exergy efficiencies, in part because several exist. For instance, different values can be obtained when evaluating a turbine using different exergy efficiencies (and using entropy-related efficiencies like isentropic efficiency, which are indirectly related to exergy efficiencies). These must be well understood before they are used. What is important though is that, unlike energy efficiencies, exergy-based efficiencies are reasonable in that they provide measures of approach to ideality.

5.5.7.1.3

Loss

Losses occur when the efficiency of a device or process deviates from the efficiency that would occur if the device or process were ideal. The value of a loss is a measure of this deviation from ideality. Energy losses are not necessarily indicative of a deviation from ideality. For instance, some processes lose heat to the surroundings, but if this heat is emitted at the temperature of the surroundings the loss does not lead to an irreversibility. Conversely,

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some processes have no energy losses, such as the combustion of fuel in air in an isolated vessel, yet the process is highly irreversible and therefore nonideal. Exergy losses, on the other hand, do provide quantitative measures of deviations from ideality. In addition, exergy losses allow the location, type, and cause of a loss, or inefficiency, to be clearly identified. This information is critical for efforts to increase EE. An additional insight obtained through exergy losses relates to the fact that they can be divided into two types: the losses associated with waste exergy effluents, and the losses associated with internal irreversibilities in a system or process (i.e., exergy consumptions). Examples: Many examples can be used to illustrate how use of exergy clarifies measures of thermodynamic efficiency and loss. Only a few are presented here. However, more clear illustrations are needed, both to educate and convince people of the benefits of exergy methods. Consider a Carnot heat engine operating between a heat source at a temperature of 600K and a heat sink at 300K as shown in Fig. 17. The energy efficiency of this device is 50% (i.e., 1 – (300/600) ¼ 0.5). Yet a Carnot engine is ideal. Clearly, the energy efficiency is misleading as it indicates that a significant margin for improvement exists when in fact there is none. The EE of this device is 100%, properly indicating its ideal nature in a straightforward and clear manner. Consider next an electrical resistance space heater. Almost all of the electricity that enters the unit is dissipated to heat within the space. Thus the energy efficiency is nearly 100% and there are almost no energy losses. Yet the EE of such a device is typically less than 10%, indicating that the same space heating can in theory be achieved using one-tenth of the electricity. In reality, some of these maximum savings in electricity use can be attained using a heat pump. The use of even a relatively inefficient heat pump can reduce the electricity used to achieve the same space heating by one-third. Clearly the use of energy efficiencies and losses is quite misleading for electrical heating. Finally, consider a buried thermal energy storage tank. A hot medium flows through a heat exchanger within the storage and heat is transferred into the storage. After a period of time, a cold fluid is run through the heat exchanger and heat is transferred from the storage into the cold fluid. The amount of heat thus recovered depends on how much heat has escaped from the storage into the surrounding soil, and how long the recovery fluid is passed through the heat exchanger. But a problem arises in evaluating the energy efficiency of this storage because the energy efficiency can be increased simply by lengthening the time that the recovery fluid is circulated. What is neglected here is the fact that the temperature at which the heat is recovered is continually decreasing toward the ambient soil temperature as the fluid circulates. Thus although the energy recovered increases as the recovery fluid continues to circulate, the exergy recovered hardly increases at all after a certain time, reflecting the fact that recovering heat at nearenvironmental temperatures does not make a storage more efficient thermodynamically.

5.5.7.1.4

Discussion

The points raised here are practical, since efforts to improve efficiency are guided by what we perceive to be efficiencies and losses. If that is wrong, then all efforts may be in vain. When we allow ourselves to be guided by energy efficiencies and losses, in particular, we may be striving for the wrong goal or even be trying to achieve the unachievable. The ramifications of such errors can vary. They can be relatively small when a company engineer wastes time trying to improve the efficiency of an already nearly ideal device. But the ramifications can also be very large in many situations, such as when a company or government invests millions of dollars on research and development to improve efficiencies of technologies that are perhaps not as in need of improvement as others that deviate excessively from ideality. Consequently, exergy-based efficiencies are required to address energy problems effectively and to prioritize efficiency-improvement efforts appropriately.

TH = 600K QH

HE Wout QL TL = 300K

Fig. 17 Heat engine (HE) producing power by taking heat rate from a heat source at a temperature of 600K and rejecting the remaining heat rate to a heat sink at a temperature of 300K.

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5.5.7.1.5

Understanding energy conservation through exergy

Energy conservation, although widely used, is an odd term. It is prone to be confusing and is often misleading. Energy conservation is nothing more than a statement of the principle of conservation of energy, which is embodied in the first law of thermodynamics. Yet the term energy conservation normally means something much different when it is used by lay people as well as many technical people. Exergy can help us understand this dual set of views about energy conservation in a rational and meaningful way. Further, exergy can help clarify this confusion by preserving the appropriate use of the term energy conservation as a statement of a scientific principle, while giving proper understanding to the meaning implied by most people when they discuss energy conservation. In fact, it can be argued that the meaning in the latter case is better expressed through the term exergy conservation. To nonthermodynamicists, many meanings are expressed by the term energy conservation, meanings usually related to solving problems regarding energy resources or technologies. For example, energy conservation can mean:

• • •

•

Increasing efficiencies of devices and processes so they use fewer energy resources to provide the same levels of services or products, thereby preserving the energy resources. Increasing efficiency can be accomplished either by incremental improvements to existing devices or processes, or by major design alterations. Reducing energy requirements by reconsidering what the energy is being used for, in hopes of finding ways to satisfy the overall objective(s), while using fewer energy resources. In the electrical sector of an economy, this concept involves reducing electrical energy demands of users and is sometimes referred to as “demand side management.” Changing lifestyles so that we need and use fewer energy resources (e.g., substituting use of more mass transit and bicycles for automobile use). In the extreme, some suggest we “return to the past” and radically curtail our use of energy resources by retreating from the highly energy intensive lives adopted over the last few centuries. These ideas are usually equated to accepting lower standards of living. Substituting alternate energy resources and forms for ones we deem precious and wish to preserve. This interpretation of energy conservation may, for example, involve switching heating systems from natural gas to a renewable energy resource like solar energy.

As noted earlier, those familiar with thermodynamics regard the term energy conservation simply as a statement of a scientific principle or law. So how do we reconcile these two radically different interpretations and understandings of energy conservation? How can energy conservation be the essence of a scientific principle or law, while it simultaneously reflects a wide range of objectives for solving energy-related problems? Exergy is the key to providing simple, meaningful, and practical answers to the above questions. As noted earlier, exergy is based on the first and second laws of thermodynamics, and the second law that defines an ideal (reversible) process or device. Exergy is not conserved for real processes or devices, so exergy conservation is a logical objective if one seeks thermodynamic perfection, while energy conservation is not. The ideality of exergy conservation can never be attained in reality, but conservation efforts are made more effective by understanding the hypothetical upper limit it provides. These ideas are consistent with statements of [20] who wrote “…energy analysis generally fails to identify waste or the effective use of fuels and resources. For instance, the first law does not recognize any waste in an adiabatic throttling process – one of the worst processes from the thermodynamic viewpoint.” They go on to state “exergy analysis … calculates the useful energy associated with a thermodynamic systems … [and] identifies and evaluates the inefficiencies of an exergy system.” Of course, we never aim for thermodynamic perfection in the real world. Too many other factors come into play, like economics, convenience, reliability, safety, etc. Thus decision making about how far we take efforts to shift the actual level of performance nearer to the ideal, i.e., to conserve exergy, involves complex trade-offs among competing factors. What is critical is that, although other factors temper conservation goals, it is exergy – or commodities and resources that have high exergy contents – that we seek to preserve when we speak of energy conservation. Exergy is what we value because it, not energy, consistently represents the potential to drive processes and devices that deliver services or products. In fact, it seems that exergy conservation is what lay people mean when they say energy conservation. This is important because we need to be clear about what we say and mean. If we confuse ourselves by using energy conservation not just to describe a basic scientific conservation principle, but also to describe efforts to solve energy-related problems, we cannot effectively address those problems. By accepting that it is exergy conservation to which we aspire, in concert with other objectives, we can effectively address important energy-related problems in society like security of supplies of useful energy resources, and resolving shortages of useful energy resources. In addition, this understanding of exergy conservation provides us with the underpinning needed to develop useful and meaningful measures of efficiency. Examples: Others have in the past also noted the misleading aspects of energy conservation, while recognizing the need to focus on exergy instead. Some interesting examples follow below:

• •

After the first “energy crisis” in the early 1970s, Keenan et al. [21] wrote “… energy, rather than being consumed in any process, is always conserved. When opportunities for fuel conservation are to be assessed, it becomes necessary to use a measure other than energy.” They went on to discuss what is now commonly called exergy as the preferred measure. Around the same time, Berg [22], then an engineer with the U.S. Federal Power Commission, wrote “National efforts to conserve energy resources could be much enhanced by the adoption of [exergy] to measure the effectiveness of energy

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utilization.” He also noted that “the first law of thermodynamics guarantees that energy can be neither created nor destroyed; thus it would hardly seem necessary to have a national policy addressed to its conservation.” Clarity of thought and soundness of understanding are needed to address issues successfully. This idea applies to energy conservation as well as to other issues. The clarity and understanding that often seems to be present when energy conservation is discussed ought to be resolved. Exergy provides the means to this resolution, which is needed if energy-related issues are to be addressed effectively.

5.5.7.2

Disadvantages of Exergy

Those in industry who choose not to utilize exergy often do so for several reasons, some of which are described below:

• • • •

Exergy methods are considered too cumbersome or complex by some users. For example, the need to choose a reference environment in exergy analysis is considered by some to render the technique too challenging. The results of exergy analyses are regarded by some as difficult to interpret, understand, and utilize. Many potential users are simply unfamiliar with exergy, being educated about energy and therefore more comfortable with it. Perhaps most importantly, some practicing engineers have simply not found exergy methods to lead to tangible, direct results.

It is important to consider these reasons carefully, as they may provide insights regarding what actions are needed to improve the situation. Certainly, there are varied opinions on the subject of industry’s use of exergy. Many views related to exergy and industry were expressed at a 1996 panel session on The Second Law in Engineering Education, at the American Society of Mechanical Engineers’ International Mechanical Engineering Congress and Exposition [23]. The panelists were from academia and industry. Some panelists explained the need for education in exergy methods for the benefit of industry, while some felt otherwise, questioning even the level to which the second law of thermodynamics should be taught. This point is relevant because industry feels the impacts of thermodynamics education.

5.5.7.3

Possible Measures to Increase Applications of Exergy in Industry

Exergy methods are useful and can be extremely beneficial to industry and others. The concerns about exergy are in reality barriers that can be overcome to increase industry’s adoption of exergy. The use of exergy can benefit not only industry, but also society (e.g., through a cleaner environment). These benefits are too great to be passed by. Although industry’s grounds for often not using exergy are not well founded, it is true that perception is often reality. So, if industry is to adopt or be convinced to adopt exergy methods on a more widespread basis, several actions by exergy proponents are needed. Some examples of actions that are necessary or would be beneficial follow. This list is by no means intended to be exhaustive, as many other suggestions can be made.

• • •

Practitioners must be educated about exergy methods and their applications, through college and university programs, continuing education courses and on-the-job training. Concerted efforts must be made to point out clearly and unambiguously to industry the benefits of using exergy methods. These efforts should be supplemented by case studies and “demonstration projects” where exergy has been applied beneficially, and promotion activities. In particular, we need clear and understandable success stories about exergy applications.

The launching of International Journal of Exergy, which evolved from Exergy – An International Journal, will almost certainly help increase industry’s acceptance and use of exergy. By providing a focal point for reports of exergy research and applications, this journal provides an excellent conduit through which advances in exergy methods and their uses can be clearly conveyed to potential users in industry. Of course, it is also important that articles on new applications of exergy appear in journals focused on applications (e.g., journals on energy technologies and resources, like nuclear, solar, and hydrogen energy). But the focused outlet provided by the International Journal of Exergy is essential, especially for research on the intricacies of exergy methods. This need became most clear to me on learning that some articles on exergy methods were being rejected by applications-oriented journals, almost solely because the articles were deemed outside the scope of the journals.

5.5.8

Exergy and Industrial Ecology

Industrial ecology is an approach to designing industrial systems that promotes systems that are less damaging to the environment. The approach seeks a reasonable balance between industrial profit and environmental stewardship and thereby can contribute to sustainable development. Industrial ecology methods can beneficially incorporate exergy to provide more powerful tools. Exergy analysis pinpoints significant process and device exergy losses, or nonrecoverable losses of fuel exergy. It is generally accepted that an increase in efficiency of fossil fuel utilization makes industrial technologies more ecologically benign and safe. Therefore, exergy methods can help in rational modification of contemporary technologies.

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Exergy Management

Szargut [24] cites the following example. In a combined power plant equipped with a coal boiler and gas turbine, the heat transfer exergy losses in the heat recovery boiler of the gas turbine can be reduced by shifting the steam superheater from the coal boiler to the heat recovery boiler of the gas turbine. In another example, from the chemical industry, energy and exergy analyses of a traditional one-stage crude oil distillation unit and a newly proposed two-stage unit are conducted to investigate the efficiencies and exergy losses [25]. The results are compared for both one- and two-stage distillation units. The proposed two-stage distillation unit exhibits a 43.8% decrease in overall exergy losses and 125% increase in the overall EE, leading to the recommendation to perform distillation in two stages rather than one to reduce the heat duty of the heating furnace and thus reduce irreversible losses.

5.5.8.1

Industrial Ecology

Industrial ecology is concerned with shifting industrial processes from linear (open loop) systems, in which resource and capital investments move through the system to become wastes, to closed loop systems where wastes become inputs for new processes [26]. Industrial ecology was popularized by Frosh and Gallopoulos [27] who asked why industrial systems do not behave like an ecosystem, where wastes of one species are a resource to another species. Why should not the outputs of one industry be the inputs of another, thereby reducing the use of raw materials and pollution, and saving on waste treatment? Lowe and Evans [28] note that industrial ecology suggests using the design of ecosystems to guide the redesign of industrial systems to achieve a better balance between industrial performance and ecological constraints and consequently to determine a path to sustainable development. According to this conception, modern industrial technologies should be designed like ecosystems where (1) input mass and energy flows are minimized and (2) energy supply is provided by renewable energy sources. Minimization of the fossil fuel energy consumption in industrial processes implies eliminating output waste energy flows or the emission of wastes that are in equilibrium with the conditions (pressure, temperature, composition) of the environment. Applying these principles to industrial processes, like power generation and transportation, leads to several interesting observations. The technical ability to transform renewable energy to electricity for industrial and other needs is developed, but the relevant technologies involve significant consumptions of resources such as construction materials per unit of output generated and are often less attractive economically and sometimes less attractive environmentally than traditional fossil fuel plants.

5.5.8.2

Linkage Between Exergy and Industrial Ecology

Graedel [29] writes, “The term industrial ecology was conceived to suggest that industrial activity can be thought of and approached in much the same way as a biological ecosystem and that in its ideal form it would strive toward integration of activities and cyclization of resources, as do natural ecosystems.” He goes on to note that little has been done to explore the usefulness of the analogy. The use of exergy in conjunction with industrial ecology can provide a useful tool that permits practical applications [9]. Waste exergy emissions and exergy destructions, unlike energy losses, can account for the environmental impacts of energy utilization [30]. Szargut et al. [31] suggest that the cumulative consumption of nonrenewable exergy provides a measure of the depletion of nonrenewable natural resources. _ D due to reducing the irreversibility of the Reducing entropy generation leads to a decline in exergy destruction (losses) Ex processes constituting an industrial system. According to the Gouy–Stodola formula: _ D ¼ T0 S_ gen Ex

ð1Þ

where T0 is the reference environment temperature (often fixed at 298K or the local temperature) and S_ gen is the entropy generation rate in a process or device.

5.5.8.2.1

Depletion number

Connelly and Koshland [10] suggest that the efficiency of fossil fuel consumption be characterized by a depletion number Dp: _ D Ex ð2Þ _ in Ex _ in (in this chapter only _ D and total exergy consumption rate Ex which represents the relation between the exergy destruction rate Ex direct exergies are considered). In line with the definition of EE, if there are no waste exergy emissions the EE c is expressible as follows: Dp ¼

c¼1

Dp

ð3Þ

The EE is always a measure of how nearly a process approaches the ideal version of that process.

5.5.8.2.2

Integrated systems

The efficiency of integrated or combined technologies (e.g., cogeneration) can be evaluated and compared by examining the depletion numbers Dp for the separate and combined technologies (see Fig. 18).

Exergy Management

Input fossil fuel exergy rate

Exergy rate of product 1 Technology 1

Exin(1)

191

(1)

Exp1

Depletion number Dp(1) Exergy rate of product 2

Input fossil fuel exergy rate Technology 2

Exin(2)

(2)

Exp2

Depletion number Dp(2) Combined technology

Input fossil fuel exergy rate

Technology 1

Exergy rates of product 1 and product 2 comb

Exp1

Exincomb

comb

Exp2

Technology 2

Depletion number Dp(comb) Fig. 18 Input and output exergy rates for separate and combined technologies to produce two products.

The consumption of nonrenewable energy resources corresponds to lower depletion numbers (see Eq. (2)). Consequently, the ðcombÞ should be lower than the weighted sum of the depletion depletion number for an advanced combined technology Dp ðsepÞ ðsepÞ numbers Dp is expressible as follows: for the separate technologies. For the system in Fig. 18, Dp DðsepÞ ¼ p

_ comb _ comb Ex Ex p1 p2 ð1Þ D þ Dð2Þ p p _ comb _ comb _ comb _ comb þ Ex þ Ex Ex Ex p1 p2 p1 p2

ð4Þ

ð2Þ ð1Þ _ comb and Ex _ comb are the rates of output exergy where Dp and Dp are depletion numbers for two separate technologies and Ex p1 p2 flows for products 1 and 2, respectively.

Illustrative Example: The principles discussed in this chapter are demonstrated for a combined gas turbine cycle with a hydrogen generation unit [32]. This design includes two important technologies: a solid oxide fuel cell (SOFC) with internal natural gas reforming and a membrane reactor (MR), and their combination with a hydrogen generation unit. A common feature of SOFCs and MRs is their utilization of high-temperature oxygen ion-conductive membranes. Such membranes are conductive to negatively charged ions of oxygen and permit the separation of oxygen from air. This property accounts for their application as an electrolyte in SOFCs, where the chemical exergy of methane, through an intermediate stage involving its conversion to hydrogen and carbon monoxide and electrochemical oxidation with oxygen, is transformed into electrical work. In a MR, the membrane conducts both oxygen ions and electrons in opposite directions; such membranes are consequently often called mixed conducting membranes. In the present case, electrical work is not generated, but oxygen is separated from air and fuel combustion proceeds in an atmosphere of oxygen. Oxygen ion-conductive membranes are made of ceramic materials (usually zirconia oxides) and have good performance characteristics at temperatures higher than 7001C. An SOFC stack is often introduced into traditional power generation cycles, where it operates at temperatures of 800–11001C. A MR is being developed for operation up to 12501C, as a substitute for combustion chambers in advanced zero-emission power plants. New materials for the anodes of SOFCs contain a catalyst for the methane reforming process, allowing methane conversion into a mixture of hydrogen and carbon monoxide directly on the surface of the anode [33]. SOFCs thereby become more flexible, compact, and effective, and avoid the need for preliminary reforming of methane.

5.5.8.2.3

Gas turbine combined cycle with hydrogen generation

A combined gas turbine cycle with a hydrogen generation unit is presented in Fig. 19. The initial stream of natural gas, after heating in device 14 (in order to achieve after compression the temperature of combustion products) and compression in device 15, is divided into two flows. The first is mixed with combustion products (carbon dioxide and steam) and directed to the anodes of the SOFC stack (device 4), where two processes occur simultaneously: conversion of methane into a mixture of carbon monoxide and hydrogen on the surface of the anodes and electrochemical oxidation of the resultant mixture with oxygen. The oxygen reduction is accompanied by electricity generation in the SOFCs. The gaseous mixture from the anodes (conversion and combustion products) is cooled in a heat exchanger (device 10), compressed in device 11, and directed to the MR (device 1), where the remainder of the conversion products combust in oxygen, and then expand in a turbine (device 2).

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1 3 a 11

13

2

E5

Electricity

12 Air (P0 = 1 atm, T0=298K)

−

+

4

15 c

b

10

a 5

E4

6

E1

d

7 Exhaust gases (P0 =1 atm, T0 = 298K)

9

14

Natural gas (methane) (P0 = 1 atm, T0 = 298K)

E2

Syngas to shift reactor for hydrogen production (P0 = 1 atm, T = 673K)

8 E3

Fig. 19 An application of a solid oxide fuel cell (SOFC) and membrane reactor (MR) in a combined gas turbine cycle with a hydrogen generation unit. Numbers indicate devices according to the following legend: 1 – MR; 2, 3, 6, 8 – turbines; 11, 13, 15 – compressors; 4 – SOFC stack; 5 – methane converter; 7, 9, 10, 12, 14 – heat exchangers; a – oxygen ion-conductive membranes; b, c – anode and cathode of SOFC stack, respectively.

The combustion products are then divided into two flows. The first is mixed with the initial flow of methane and directed to the SOFC stack, while the other is mixed with the second flow of methane and enters the catalytic methane converter (device 5). After methane conversion to hydrogen and carbon monoxide in device 5, the gaseous mixture is expanded in a turbine (device 8), cooled in a heat exchanger (device 9), and directed to the shift reactor, where the remainder of the carbon monoxide and steam is converted to hydrogen. Air is heated in device 12, compressed in device 13, directed to the MR (device 1), where some quantity of oxygen is transferred through the oxygen ion-conductive membrane and combusted with fuel. The air heating in device 12 is required in order to achieve after compression the temperature of the fuel flow, which is directed, like air, to the MR. The temperature of air reaches its maximum at the MR (device 1) outlet, at which point it is expanded in the turbine (device 3) and directed to the cathodes of the SOFCs (device 4). In the SOFCs, the oxygen concentration in the air decreases, and the air is heated and enters the space between pipes in the catalytic converter (device 5). In device 5, heat is transferred from the air to the reaction mixture in the pipes. The mixture is then expanded in the turbine (device 6), and cooled in the heat exchanger (device 7). The power generation design combines a traditional gas turbine cycle – which consists of compressors (devices 11 and 13), a combustion chamber (which is represented by the MR, device 1), and turbines (devices 2 and 3) – with the SOFC stack (device 4) and the methane converter (device 5). Heat exchangers are conditionally divided into the heat releasing (devices 7, 9 and 10) and heat receiving (devices 12 and 14) types. Mechanical work is produced in the turbines and consumed in the compressors. The work is transformed into electrical energy, which is also directly generated in the SOFC stack. The endothermic process of methane conversion to hydrogen (via a synthesis gas) in device 5 is implemented into the power generation cycle.

5.5.8.2.4

Exergy analysis of gas turbine combined cycle with hydrogen generation

The general assumptions applied in the exergy analysis of the proposed design follow: (1) gases are modeled as ideal; (2) energy losses due to mechanical friction are negligible; (3) thermodynamic and chemical equilibria are achieved at the outlet of the SOFC stack and methane converter; and (4) all combustible components are combusted completely in the MR. The general

Exergy Management

Table 5

193

General parameter values for the combined power generation cycle in Fig. 19

Parameter

Value

Isentropic efficiency of turbines Zt Isentropic efficiency of compressors Zcmp Operational circuit voltage of the solid oxide fuel cell (SOFC) stack (V) Maximum pressure in the gas turbine cycle pmax (atm) Minimum pressure in the gas turbine cycle pmin (atm) Maximum temperature in the cycle (at the membrane reactor (MR) outlet) Tmax (K) Temperature of fuel at the inlet of the SOFC stack Ts (K) Temperature of fuel and air at the outlet of the SOFC stack Ts (K) Ratio of methane combusted in the power generation cycle to the methane converted Molar ratio of combustion products after the MR to methane combusted in the power generation cycle Ratio of amounts of combustion products directed to SOFC and methane converter Standard temperature T0 (K) Standard pressure p0 (atm) Air composition (on volume basis)

0.93 0.85 0.85 10 1 1573 1273 1273 1.0:0.7 6 1:1 298 1 21% O2, 79% N2

parameters used in the combined power generation cycle are listed in Table 5. Values for the parameters, Zt, Zcmp, Pmax, Pmin, and Tmax, are often cited. An exergy balance of a system permits evaluation of the efficiency with which input energy flows are utilized. For the power generation scheme presented in Fig. 19 the exergy balance can be expressed as X _ ¼ Ex _ in Ex _ out ¼ SW _ i þ DEx _ Tþ _ Di DEx Ex ð5Þ _ is the rate of exergy change in the system, Ex _ in is the sum of the exergy rates of the input flows of methane and air, Ex _ out is here, DEx the sum of the exergy rates of the output flows of conversion products (synthesis gas) directed to a shift converter and exhaust _ is the sum of powers generated in the turbines and in SOFCs and consumed in the compressors (with a negative sign), gases, SW _ DExT is the sum of thermal exergy rates released in heat exchangers 7, 9, and 10 and consumed in 4 and 12 (with a negative sign), P _ D is the sum of the exergy loss rates in the devices of the system. and Ex

5.5.9

Results

The analysis results are presented in Tables 6–8. Table 7 presents the mechanical and electrical work generated in the turbines and SOFC stack, the mechanical work consumed in the compressors (with a negative sign), and the exergy losses accompanying these processes. Table 8 presents the exergy losses in the MR and methane converter. Table 8 also lists the exergy losses exDtr accompanying the heat transfer from hot to cool flows, and the excess of thermal exergy DexT, which can be converted to mechanical work in a bottoming steam-water (Rankine) cycle (not shown in Fig. 19) with an EE cR of about 60%, so that WR ¼ ZR Dex T and ex DR ¼ DexT

WR

ð6Þ

After substituting WR and ex DR into Eq. (5) instead of DexT, the exergy change Dex ¼684.8 kJ/mol in the system is distributed only between work W ¼ 516.8 kJ/mol and the exergy losses (destruction) exD ¼168.0 kJ/mol. Since data are calculated per mole of methane combusted to generate electricity and 0.7 mol of methane converted to hydrogen, and ðcombÞ becomes the value of standard exergy of methane ex0CH4 ¼ 831:7 kJ=mol [34], the depletion number of the combined system Dp P 168:0 ex Di ¼ ¼ 0:12 ð7Þ DpðcombÞ ¼ 1:7ex0CH4 1414:0 The combined system yields two products: electricity and synthesis gas (a mixture of carbon monoxide and hydrogen). The exergy of electrical work is equal to its energy and standard exergies of carbon monoxide and hydrogen are ex oH2 ¼ 236:1 kJ=mol and ex oCO ¼ 275:1 kJ=mol [34]. Then the exergy of the synthesis gas directed to the shift reactor to produce hydrogen (see Fig. 18) is ex oSG ¼ 656:1 kJ=mol (for one mole of methane combusted and 0.7 mol of methane converted). The EE of a combined gas turbine steam power cycle where only electrical work is generated is taken to be c(1) ¼ 0.54 and the EE of methane conversion to synthesis ð1Þ ð2Þ gas is c(2) ¼0.84. With Eq. (3), the depletion numbers are calculated Dp ¼ 0:46 and Dp ¼ 0:16. Substitution of these values into ðsepÞ (Eq. (4)) yields the following: the expression for Dp ¼ DðsepÞ p

W Ex 0SG Dð2Þ ¼ 0:29 Dð1Þ þ W þ Ex 0SG p W þ Ex 0SG p ðsepÞ

ð8Þ

The depletion number for the separate technologies Dp is seen to be more than two times greater than that for the combined ðcombÞ . The implication is that the combined technology is more environmentally benign (and behaves more like an system Dp ecosystem) than the separate devices, and requires combustion of less natural gas.

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Table 6

Device number in Fig. 19

exD (kJ/mol)

W (kJ/mol)

2 3 4 6 8 11 13 15 Total

89.7 207.1 497.4 85.0 35.6 89.8 324.4 18.8 481.8

1.6 4.1 29.4 2.3 0.2 4.2 22.3 0.7 64.8

a

Data are given per mole of methane combusted in the power generation cycle.

Table 7

Exergy losses in the membrane reactor (MR) and methane convertera

Device number/name in Fig. 19

exD (kJ/mol)

1 5 Methane mixing Total

27.6 15.9 10.0 53.5

a

Data are given per mole of methane combusted in the power generation cycle, which corresponds to 0.7 mol of methane converted in methane converter 5.

Table 8

Released thermal exergy DexT and its utilization in the Rankine bottoming cyclea

(DexT) (kJ/mol)

exDt R (kJ/mol)

WR (kJ/mol)

exDR (kJ/mol)

58.4

26.3

35.0

23.4

a

Data are given per mole of methane combusted in the power generation cycle. ð1Þ

ðsepÞ

ðcombÞ

with The limiting value of Dp for the separate electricity generation process can be obtained by equalizing Dp ¼ Dp ð1Þ ð2Þ the given value of Dp . In this case, the limiting value is found to be Dp ¼ 0:068, which corresponds to an exergy efficiency of electricity generation c(1) ¼ 0.93 (Eq. (3)). This value is unrealistic, as it exceeds even the highest SOFC efficiency obtained in laboratory experiments. Thus, this magnitude of efficiency can be attained only through an integrated process like cogeneration. The conducted analysis confirms that integrated energy systems, developed via an appropriate combination of technologies, represent an important opportunity for increasing the utilization efficiency of natural resources and thereby achieving the aims of industrial ecology.

5.5.10

Exergy in Policy Development and Education

It is important for the public to have a basic understanding, appreciation, and awareness of many technical issues. Such understanding and awareness fosters healthy public debate about problems and possible solutions, often helps guide how public funds are spent and facilitates policy development. Energy issues are no exception. Yet, the public’s understanding of energy issues is often confused. In large part, this situation is attributable to the public having next to no understanding of exergy. It is easier for the public to be educated about and aware of exergy if students are adequately educated about exergy in appropriate venues (university and college thermodynamics courses, primary and secondary schools, etc.). Consequently, this section deals with education and awareness of exergy, first by focusing on the public and then by dwelling on the education of thermodynamicists as well as other technical people. Exergy can play a key role in developing appropriate and beneficial energy-related policies relating to education and awareness. Two main areas where exergy can have an impact on policies are discussed in this chapter: public education and awareness and student education. The former is more general, but is supported by the latter. Regarding public education and awareness about exergy, it appears that the public is often confused when it discusses energy, and needs to be better educated about exergy if energy issues and problems are to be addressed appropriately. Regarding the education of students about exergy, it appears that the coverage of exergy in thermodynamics education is often insufficient and inappropriate. Better coverage of exergy is needed to

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improve thermodynamics education and to make it more interesting to students, and a basic level of “exergy literacy” is needed among engineers and scientists, particularly those involved in decision making, respectively.

5.5.10.1

Exergy Methods for Analysis and Design

Some features of exergy that are particularly pertinent to this chapter follow:

• • •

When energy quality decreases, exergy is destroyed. Exergy is the “part” of energy that is useful to society and has economic value and is thus worth managing carefully. A system has no exergy when it is in complete equilibrium with its environment. Then, no differences appear between the system and the environment in temperature, pressure, or constituent concentrations. The exergy of a system increases as the deviation of its state from that of the environment increases. For instance, hot water has a higher exergy content in winter than on a hot summer day, while a block of ice contains little exergy in winter but a significant quantity in summer. Two simple examples are illustrate well the attributes of exergy:

•

•

Consider an adiabatic system containing fuel and air at ambient conditions. The fuel and air react to form a mixture of hot combustion gases. During the combustion process, the energy in the system remains fixed because it is adiabatic. But the exergy content declines as combustion proceeds due to the irreversibilities associated with the conversion of the high-quality energy of fuel to the lower quality energy of combustion gases. The different behaviors of energy and exergy during this process are illustrated qualitatively in Fig. 20. A mineral deposit “contrasts” with the environment of Earth, and is thus a carrier of exergy. This contrast increases with the concentration of the mineral, as shown in Fig. 21. When the mineral is mined, its exergy content is low or zero (depending on the concentration of the mineral in the environmental deposit), while the exergy content increases if its concentration is enriched. A poorer mineral deposit contains less exergy than a concentrated one. Conversely, when a concentrated mineral is dispersed in the environment, its exergy content decreases.

As pointed out throughout this book, exergy analysis, a methodology for the analysis, design, and improvement of energy and other systems, is useful for improving the efficiency of energy-resource use. Exergy has many other implications on and links with other disciplines, as discussed in detail in previous chapters. A link exists between exergy and environmental impact and sustainability. Energy production, transformation, transport, and use impact on the Earth’s environment. The exergy of a quantity of energy or a substance can be viewed a measure of its usefulness, quality, or potential to cause change. Exergy appears to be an effective measure of the potential of a substance to impact the environment. This link between exergy and environmental impact is particularly significant since energy and environment policies are likely to play an increasingly prominent role in the future in a broad range of local, regional, and global environmental concerns. The tie between exergy and the environment has implications regarding environmental impact and has been investigated previously by several researchers, including the authors. Exergy is a useful concept in economics. In macroeconomics, exergy offers a way to reduce resource depletion and environmental destruction, by such means as exergy taxes or rebates. In microeconomics, exergy has been combined beneficially with

Energy

Exergy

100

Energy or exergy (relative units)

80 60 40 20 0 Fuel and air before combustion

Fuel and hot gases after partial adiabatic combustion

Fig. 20 Qualitative comparison of energy and exergy during combustion.

Hot gases after complete adiabatic combustion

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Exergy (relative units)

196

0

0.2

0.4 0.6 0.8 Concentration of mineral (%)

1

Fig. 21 Qualitative variation of the exergy of a mineral with concentration.

cost–benefit analysis to improve designs. By minimizing life cycle cost, we find the “best” system given prevailing economic conditions and, by minimizing exergy losses, we also minimize environmental effects. Finally, exergy has been proposed as an important consideration in policy making related to energy. The present chapter expands this area, by focusing specifically on education and awareness.

5.5.10.2

The Role and Place for Exergy in Energy-Related Education and Awareness Policies

Before considering understanding and awareness by the public of exergy, and its role and place in energy-related education and awareness policies, it is informative to consider the public’s understanding and awareness of the more conventional quantity energy.

5.5.10.2.1

Public understanding and awareness of energy

The typical lay person hears of energy and energy issues daily, and is generally comfortable with receiving that energy-related information and feels that he/she follows it. He or she even understands it, or at least thinks he/she does. This sense of comfort and understanding exists despite all of the problems associated with energy. For instance, consider the following:

•

•

Efficiencies based on energy can often be nonmeaningful or even misleading, because energy efficiency is not a consistent measure of how nearly a process approaches ideality. For instance, the energy efficiency of electric space heating is high (nearly 100%) even though the process is far from ideal. The fact that the same space heat can be delivered by an electric heat pump using much less electricity than the electric space heater corroborates this observation. Losses of energy can be large in quantity, when they are in fact not that significant thermodynamically due to the low quality or usefulness of the energy that is lost. For example, the waste heat exiting a power plant via cooling water has a lot of energy, but little exergy (because its state is near to that of the environment).

5.5.10.2.2

Public understanding and awareness of exergy

An understanding of exergy, similar to that which exists for energy, is almost entirely nonexistent in lay members of the public. This lack of understanding exists despite the fact that exergy overcomes many of the deficiencies described above of energy methods. Worse still, the public is often confused when it refers to energy. To those who deal with exergy, it often seems that members of the public actually mean exergy when they say energy. For example, two respected exergy researchers, Wepfer and Gaggioli [35], begin an article with “Exergy … is synonymous with what the layman calls “energy.” It is exergy, not energy, that is the resource of value, and it is this commodity, that “fuels” processes, which the layman is willing to pay for.” These points illustrate why it is essential that the public develop or be helped to develop a basic understanding of exergy. The level of understanding needed by the public about exergy should at least be comparable to that for energy. To help illustrate the above contentions, some examples follow below of the problems associated with a lack of knowledge of exergy by the public:

•

•

One example of the confusion exhibited by the public when speaking of energy is the well-used term energy conservation. When members of the public say energy conservation, they are usually referring to an objective of efforts to solve energy problems. Yet the term energy conservation is meaningless in that regard, in that it simply states the first law of thermodynamics. Exergy, however, is not conserved and it appears that what the public is really interested in conserving is exergy, the potential to drive processes and systems that deliver services or products. Another example of confusion in the public surrounds the drive for increased energy efficiency. Energy efficiencies do not necessarily provide a measure of how nearly a process approaches ideality, yet that is what the public means by energy

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•

•

197

efficiency. Exergy efficiencies do provide measures of approach to ideality, and so it appears that the public means increased exergy efficiency when discussing increased energy efficiency. A third example of the problems that can develop when the public does not have a knowledge of exergy, but retains only a confused understanding of energy, relates to the energy crisis. For instance, during the energy crisis of the 1970s, oil scarcities existed due to reductions in oil production. Most of the energy that was available to the public before the crisis was available during it. For instance, huge amounts of solar energy continued to stream into the Earth every day. Waste thermal energy was continually emitted from facilities and buildings. The commodity for which there was a crisis, therefore, appeared to be exergy, not energy. That is, energy forms capable of delivering a wide range of energy services (like oil), which have high exergies, were in short supply. Of course, there were also other issues related to the energy crisis, particularly the shortage of reasonably inexpensive and widely available resources. But, the key point here is that the crisis was about exergy, not energy, yet the public referred to the situation as an energy crisis. A fourth example of public confusion about energy relates to the oft-pronounced need for energy security. If it were simply energy for which we desire a secure supply, there would be no real problem. We have energy in abundance available in our environment, and even when we use energy we still have equivalent quantities of energy left over because our use is really only energy conversion or transformation. However, we are not concerned about ensuring secure supplies of energy, but rather of only those resources that are useful to us, that can be used to provide a wide range of energy services, that can satisfy all our energy-related needs and desires. That is, we are concerned with having secure supplies of exergy, or what might be called exergy security.

The lack of clarity regarding the points raised in these four examples has been discussed in more detail previously, focusing on scientists, engineers, and other technical readers. This discussion, however, is intended to raise these points in a different context, and emphasize that this lack of clarity extends to the public, where the problems caused are different, but perhaps just as or more important.

5.5.10.2.3

Extending the public’s need to understand and be aware of exergy to government and the media

By extension of the above arguments, government officials require a rudimentary understanding of exergy to improve or least complement their understanding of energy issues. This understanding can help guide the development of rational energy policies. Government, being another type of reflection of the public, will be far less prone to use exergy methods, even when they can be beneficial, if it feels that the public does not understand exergy even in the simplest way and therefore will not appreciate government efforts. The importance of such government involvement should not be understated and has been investigated by researchers. Similarly, members of the media including the press, television, and radio need to be informed, at least at a basic level, about exergy and its roles. In a sense, the media are a reflection of the public. If the media have an appreciation of exergy, they can help ensure lay members of the public have an understanding about exergy. Educating via television, in particular, can be an especially powerful method for increasing public awareness about exergy. However, for the press and media to run exergy-related articles, it requires that the public have a rudimentary understanding of and interest in exergy matters. Otherwise, the press and media tend to neglect exergy-related topics for fear of boring or confusing the public. A first step to resolving the reluctance of the press to write about exergy is education. The next section of this article focuses on educating students about exergy, which is one manner of directly and indirectly educating the public, in the long run, about exergy.

5.5.10.3

The Role and Place for Exergy in Education Policies

Thermodynamics education is often thought of as a mature discipline, yet it remains the subject of continual debate. The emergence of exergy methods as important elements and tools of thermodynamics has provided additional subject matter for dialogue, especially regarding the role and place for exergy in curricula. The impact of exergy on the teaching of thermodynamics has been and continues to be significant. Developments in this area abound. For instance, Bejan [17] noted in an editorial in the inaugural issue of Exergy – An International Journal, “As the new century begins, we are witnessing revolutionary changes in the way thermodynamics is taught.” Further on, he observed that “the methods of exergy analysis … are the most visible and established forms of this change.” One point of contention is whether present coverage of exergy in thermodynamics education is sufficient and appropriate. Views on this issue are often not in agreement. Exergy, where it forms part of the curriculum, is normally taught at the college and university levels. However, many feel that it should be covered in primary and/or secondary education levels. That point is also disputed. Most of the remainder of this section focuses on the college and university levels, since exergy is normally taught at the postsecondary level. In some ways and at some schools, present coverage of exergy is sufficient. Some evidence to support this claim follows:

•

Several articles have appeared in the engineering and education literature on teaching exergy analysis. For instance, Cengel [36] proposed “a “physical” or “intuitive” approach … as an alternative to the current “formula based” approach to learning thermodynamics” and incorporated exergy into the approach. Dunbar and Lior [37] recommended an exergy-based approach

198

•

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to teaching energy systems. They noted that the approach highlights “important conclusions from exergy analysis, not obtainable from the conventional energy analysis.” In addition, they felt that “the approach evoked the intellectual curiosity of students and increased their interest in the course.” Most texts on thermodynamics have over the last few decades incorporated sections or chapters on exergy methods. Even in 1988, while commenting on the increased attention being paid to exergy analysis, Bejan [17] pointed out that “every new undergraduate engineering thermodynamics textbook has at least one chapter on the subject.”

Such materials have made it easier to expand the coverage of exergy in thermodynamics courses. Yet in general room exists for improvement in the area of exergy coverage in thermodynamics education, and efforts should be made to achieve these improvements. Three points related to improving thermodynamics education through better coverage of exergy are addressed in the following three subsections.

•

•

•

Need for exergy literacy in scientists and engineers: we need to ensure that our education systems provide all students who study thermodynamics with a good grounding in exergy. For exergy methods to become more widely used and beneficially exploited, those who study and work in technical fields particularly where thermodynamics is applied should have a basic understanding of exergy. In addition, technical managers and decision makers require at least an appreciation of what exergy is and how it is used, if they are to make proper decisions on matters where exergy is, or should be, considered. Understanding the second law through exergy: the second law of thermodynamics often makes students of thermodynamics fearful. Introducing the concept of entropy usually only increases their trepidation. Even students who pass courses on thermodynamics and ultimately graduate often retain fears of the second law and entropy and feel they do not really understand these topics. Ahrendts [38], for example, begins one of his articles on exergy methods with “Thermodynamics is not a very popular science, because the concepts in thermodynamics do not conform to the unsophisticated human experience.” Focusing on the second law, he continues “Traditional formulations of the second law prevent a simple understanding of energy conversions, because the application of the entropy concept to those processes is often looked upon as a miracle.” Others also have agreed with these concerns and developed different approaches to teaching the second law. One example is a thermodynamics text by Dixon [39], the preface of which states “entropy is [not] the most significant or useful aspect of the Second Law” and “the Second Law has to do with the concept of degradation of energy; that is, with loss of useful work potential.” Dixon introduces the second law through the concept of degradation of energy, claiming “degradation … because it is a work term, is an easily grasped concept.” Exergy’s place in a curriculum: a challenging issue is where and to what degree exergy should be covered in a curriculum. In engineering programs, for example, exergy is sometimes covered lightly in thermodynamics courses at the undergraduate level. Sometimes exergy is covered separately, as either a core or an elective undergraduate course, while in some schools exergy is only covered at the graduate level. In the latter case, the rationale often provided is that students need a firm grounding in traditional thermodynamics before they deal with exergy. Those who support including exergy as a part of the undergraduate curriculum, on the other hand, claim this approach is necessary because exergy forms a critical and important part of basic thermodynamics. Further support for this argument is added by earlier statements in this article about exergy providing a preferable approach to dealing with and teaching the second law.

Exergy methods can also be incorporated into courses that apply to thermodynamics. In Thermal Design and Optimization, for instance, Bejan et al. [40] feature a substantial amount of material on exergy and related methods. They explain in the preface that they include exergy in the text “because an increasing number of engineers and engineering managers worldwide agree that it has considerable merit and are advocating its use.” They state further that their aim in featuring exergy and related methods is “to contribute to the education of the next generation of thermal system designers and to the background of currently active designers who feel the need for more effective design methods.”

5.5.11

Future Directions

Some key points, which will likely be useful to scientists and engineers as well as decision and policy makers, can be drawn from this chapter:

•

• •

Moving toward sustainable development requires that environmental problems be resolved. These problems cover a range of pollutants, hazards, and types of ecosystem degradation, and extend over various geographic areas. Some of the most significant environmental problems are acid precipitation, stratospheric ozone depletion, and global climate change, with the latter being potentially the most significant. Sustainable development requires a sustainable supply of energy resources that, in the long term, is sustainably available at reasonable cost and can be utilized for all required tasks without causing negative societal impacts. Energy sources such as sunlight, wind, and falling water are generally considered renewable and therefore sustainable over the relatively longer term. Assessments of the sustainability of processes and systems, and efforts to improve sustainability, should be based in part upon thermodynamic principles, and especially the insights revealed through exergy analysis.

Exergy Management

Integrated exergoenvironmental management

199

Integrated exergoenvironmental and exergoeconomic management

Exergy management

Energy management

Fig. 22 The development track of systems management.

• •

For societies to attain or try to attain sustainable development, effort should be devoted to developing renewable energy resources and technologies. Renewable energy technologies can provide environmentally responsible and sustainable alternatives to conventional energy systems, as well as more flexibility and decentralization. To realize the energy, exergy, economic, and environmental benefits of renewable energy sources, an integrated set of activities should be conducted including research and development, technology assessment, standards development, and technology transfer. These can be aimed at improving efficiency, facilitating the substitution of renewable energy and other environmentally benign energy currencies for more harmful ones, and improving the performance characteristics of renewable energy technologies.

In support of the need for public understanding and awareness of exergy, it should take on a prominent place in thermodynamics courses. Beyond elucidating the concepts of the second law and entropy, such an approach can help ensure a rudimentary understanding of exergy in all technical personnel. An approach based on exergy could make the second law more interesting, appealing, and practical, as well as less daunting and confusing. Then, it may be easier to improve general understanding of exergy in the scientific and engineering communities, as well as the general public, by ensuring that a basic level of “exergy literacy” exists among engineers and scientists, particularly those involved in decision making. Education policies that support inclusion of exergy in relevant curricula, at all appropriate education levels, should be considered. Fig. 22 shows the future development of the systems management methodology where energy management must be upgraded to exergy management. Exergy management presents a relationship between exergy and energy, where the quality of the energy must be considered so that the focus on decreasing the losses will be redirected to more valuable sources.

5.5.12

Closing Remarks

This chapter discusses exergy management, via the relations between exergy and energy, environmental impact, and sustainable development. Relations between exergy and environmental impact are extensively discussed, and the potential usefulness of exergy analysis in addressing and solving energy-related sustainable development and environmental problems is shown to be substantial. In addition, thermodynamic principles, particularly the concepts encompassing exergy, are shown to have a significant role to play in evaluating energy and environmental technologies. Through measures such as those outlined here and others, the broad potential of exergy can come to be fully realized by industry in the future. For the direct benefit of industry in particular, and society in general, it is critical that the potential benefits of exergy be exploited. Industrial ecology is an approach that suggests designing industrial systems like ecosystems, where the wastes of one species are often the resource of another. Important ways of implementing industrial ecology include the appropriate combination of separate technologies in order to match the waste outputs of one with the inputs of the other, and the introduction of processes that reduce nonrenewable energy consumption. Exergy analysis can help in designing industrial systems that follow the principles of industrial ecology, and in the evaluation of the efficiencies and losses for such activities. One such evaluation measure is the depletion number, which relates the exergy destruction and exergy input for a system. Key elements of this chapter are awareness, understanding, and education as they relate to exergy and its role in policy making. An understanding and awareness of exergy requires education. The types of education that are appropriate for technical persons

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such as engineers and scientists are shown to be different from those that are appropriate for nontechnical persons such as members of the public, government, or media. The arguments presented in this chapter demonstrate that the public is often confused when discussing energy, and a need exists to improve public understanding and awareness of exergy. Such understanding and awareness is essential if we are to better address the energy issues and problems of today and tomorrow. Thus, exergy can play a key role in developing appropriate and beneficial energy-related policies, but exploiting the potential of exergy requires appropriate support for public education and awareness about exergy.

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40]

Dincer I, Rosen M. Exergy: energy, environment and sustainable development. 2nd ed. Oxford: Elsevier Science; 2013. CDIAC. Carbon dioxide information analysis center. Recent greenhouse gas concentration. Available from: http://cdiac.ess-dive.lbl.gov/pns/current_ghg.html; 2016. Rosen MA, Dincer I. On exergy and environmental impact. Int J Energy Res 1997;21:643–54. Hafele W. Energy in a finite world: a global systems analysis. Toronto: Ballinger; 1981. Kestin J. Availability: the concept and associated terminology. Energy 1980;5:679–92. De Nevers N, Seader JD. Lost work: a measure of thermodynamic efficiency. Energy 1980;5:757–70. OECD, 1996. Pollution prevention and control: environmental criteria for sustainable transport. Organisation for economic co-operation and development Report No: 96–136. EOLSS. Encyclopedia of life support systems: conceptual framework. Oxford: EOLSS Publishers; 1998. Connelly L, Koshland CP. Exergy and industrial ecology. Part 1: an exergy-based definition of consumption and a thermodynamic interpretation of ecosystem evolution. Exergy: Int J 2001;3:146–65. Connelly L, Koshland CP. Exergy and industrial ecology. Part 2: a non-dimensional analysis of means to reduce resource depletion. Exergy: Int J 2001;1:234–55. Goldemberg J, Johansson TB, Reddy AKN, Williams RH. Energy for a sustainable world. New York, NY: Wiley; 1988. Cornelissen RL. Thermodynamics and sustainable development [PhD thesis]. Enschede: University of Twente; 1997. Hui SCM. From renewable energy to sustainability: the challenge for Hong Kong. Hong Kong: Hong Kong Institution of Engineers; 1997. p. 351–8. Lundin M, Bengtsson AM, Molander S. Life cycle assessment of wastewater systems: influence of system boundaries and scale on calculated environmental loads. Environ Sci Technol 2000;34:180–6. Sciubba E. Exergy as a direct measure of environmental impact. In: Proc. of the ASME advanced energy systems division, vol. 39. American Society of Mechanical Engineers; 1999. p. 573–81. Kotas TJ. The exergy method of thermal plant analysis. Malabar, FL: Krieger; 1995. Bejan A. New century, new methods. Exergy, Int J 2001;1:2. Berg CA. Process integration and the second law of thermodynamics: future possibilities. Energy 1980;5:733–42. Gaggioli RA, Petit PJ. Use the second law first. Chemtech 1977;7:496–506. Tsatsaronis G, Valero A. Thermodynamics meets economics. Mech Eng 1989;8:84–6. Keenan JH, Gyftopoulos EP, Hatsopoulos GN. The fuel shortage and thermodynamics: the entropy crisis. In: Macrakis MS, editor. Energy: demand, conservation, and institutional problems. Cambridge, MA: MIT Press; 1973. p. 455–66. Berg CA. A technical basis for energy conservation. Mech Eng 1974;96:30–42. El-Sayed YM, Gaggioli RA, Ringhausen DP, et al. A second law in engineering education. In: Ramalingam ML, Lage JL, Mei VC, Chapman JN, editors. Proc. ASME advanced energy systems division, AES-vol. 37. New York, NY: American Society of Mechanical Engineers, 1997. p. 77–85. Szargut J. Exergy analysis. Mag Polish Acad Sci 2005;7:31–3. Husain A, Dincer I, Zubair SM. Exergy analysis of single- and two-stage crude oil distillation units. J Energy Resources Technol 2003;125:199–207. Graedel TE, Allenby BR. Industrial ecology. Englewood Cliffs, NJ: Prentice-Hall; 1995. Frosh D, Gallopoulos N. Strategies for manufacturing. Sci Am 1989;261:94–102. Lowe EA, Evans LK. Industrial ecology and industrial ecosystems. J Clean Prod 1995;3:47–53. Graedel TE. On the concept of industrial ecology. Annu Rev Energy Environ 1996;21:69–98. Dincer I, Rosen MA. Thermodynamic aspects of renewables and sustainable development. Renew Sustain Energy Rev 2005;9:169–89. Szargut J, Ziebik A, Stanek W. Depletion of the non-renewable natural resources as a measure of the ecological cost. Energy Convers Manage 2002;43:1149–63. Granovskii M, Dincer I, Rosen MA. Life cycle assessment of hydrogen fuel cell and gasoline vehicles. Int J Hydrogen Energy 2006;31:337–52. Weber A, Sauer B, Muller A, Herbstritt D, Ivers-Tiffee E. Oxydation of H2, CO and methane in SOFCs with Ni/YSZ-cermet anodes. Solid State Ionics 2002;152-153:543–50. Szargut J, Morris DR, Steward FR. Exergy analysis of thermal, chemical, and metallurgical processes. New York, NY: Hemisphere; 1988. Wepfer WJ, Gaggioli RA. Reference datums for available energy. In: Thermodynamics: second law analysis. ACS symposium series 122. Washington, DC: American Chemical Society; 1980. p. 77–92. Cengel YA. An intuitive and unified approach to teaching thermodynamics. In: Proc. ASME advanced energy systems division AES-vol. 36. New York, NY: American Society of Mechanical Engineers; 1996. p. 251–60. Dunbar WR, Lior N. Teaching power cycles by both first- and second-law analysis of their evolution. In: Boehm RF, editor. Thermodynamics and the design, analysis and improvement of energy systems. 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Further Reading Dincer I. Heat transfer in food cooling applications. London: Taylor & Francis; 1997. Dincer I. Refrigeration systems and applications. 3rd ed. London: Wiley; 2017. Dincer I, Joshi AS. Solar based hydrogen production systems. New York, NY: Springer-Verlag; 2013. Dincer I, Rosen M. Thermal energy storage systems and applications. West Sussex: Wiley; 2011. Dincer I, Zamfirescu C, Dinçer ˙I, Zamfirescu C, Dincer I, Zamfirescu C. Sustainable energy systems and applications, vol. 6. Berlin: Springer; 2011. Kanog˘lu M, Çengel YA, Dinçer ˙I, Kanoglu M, Cengel Y, Dincer I. Efficiency evaluation of energy systems. Berlin: Springer; 2012. Markovich, S.J. Autonomous living in the ouroboros house. In: Cornelissen RL, editor. Solar energy handbook. Popular Science; 1978. p. 46–8. Naterer GF, Dincer I, Zamfirescu C. Hydrogen production from nuclear energy. Berlin: Springer; 2013.

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Relevant Websites http://cdiac.ess-dive.lbl.gov/pns/current_ghg.html CDIAC. https://earth.nullschool.net/ EarthWindMap. https://earthobservatory.nasa.gov/ Earth Observatory. https://www.epa.gov/climate-indicators/climate-change-indicators-atmospheric-concentrations-greenhouse-gases EPA.

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5.6 Energy Management Softwares and Tools Khizir Mahmud, Macquarie University, Sydney, NSW, Australia Danny Soetanto, University of Wollongong, Wollongong, NSW, Australia Graham E Town, Macquarie University, Sydney, NSW, Australia r 2018 Elsevier Inc. All rights reserved.

5.6.1 5.6.2 5.6.2.1 5.6.2.2 5.6.2.3 5.6.2.4 5.6.2.5 5.6.2.6 5.6.2.7 5.6.2.8 5.6.2.9 5.6.2.10 5.6.2.11 5.6.2.12 5.6.2.13 5.6.2.14 5.6.2.14.1 5.6.2.14.2 5.6.2.14.3 5.6.2.14.4 5.6.2.14.5 5.6.2.14.6 5.6.2.14.7 5.6.2.14.8 5.6.2.14.9 5.6.3 5.6.3.1 5.6.3.2 5.6.3.3 5.6.3.4 5.6.3.5 5.6.3.6 5.6.3.7 5.6.3.8 5.6.3.9 5.6.3.10 5.6.3.11 5.6.3.12 5.6.3.13 5.6.3.14 5.6.3.15 5.6.3.16 5.6.3.17 5.6.3.18 5.6.3.19 5.6.3.20 5.6.3.21 5.6.3.22 5.6.3.23 5.6.3.24 5.6.3.25

202

Introduction List of Simulation Tools Power and Energy Systems Modeling and Analysis Tools Power Flow and Short-Circuit Analysis Tools Power Generation, Transmission, and Distribution Systems Modeling and Analysis Power Quality Analysis Tools Switchgear and Protection Systems Analysis Tools Energy and Power Systems Security Analysis Tools Communication and Information Transfer Analysis Tool Energy Generation and Demand Forecasting Energy Management Tools Energy-Economy and Energy-Market Modeling and Analysis Tools Energy Systems Planning, Policy and Decision-Making Tools Energy Systems Optimization Tools Customer’s End Energy Systems Modeling and Analysis Tools Distributed Renewable Energy Resources and Integration Tools List of tools to analyze and integrate hydroenergy into the grid List of tools to analyze and integrate PV power generation into the grid List of tools to analyze and integrate wind-power generation into the grid List of tools to analyze battery management and its integration to the grid List of tools to analyze and integrate EVs into the grid List of tools to analyze and integrate hydrogen energy into the grid List of tools to analyze and integrate fuel-cell power generation into the grid List of tools to analyze and integrate biomass power generation into the grid List of tools to analyze and integrate geothermal power generation into the grid List of Simulation Tools ADMS AMES Cepel Toolkit D-GEN Pro DOE-2, VisualDOE, eQUEST EA-PSM EFEN e-ISOFForecast, e-PowerForecast, e-AccuWind, e-SolarForecast, e-LoadForecast, e-DR EZ Sim GAMS GenOpt ICARUS INFORSE IPM LEAP LoadSEER, GridStore, DSMore, DRPricer, XactFit, SmartSPOTTER METEODYN Toolkit Modelica Mosaik NeSSi OMNet++ PADEE PLEXOS PSAPAC, DYNRED, LOADSYN, IPFLOW, TLIM, DIRECT, LTSP, VSTAB, ETMSP, SSSP PSCAD/EMTDC

Comprehensive Energy Systems, Volume 5

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doi:10.1016/B978-0-12-809597-3.00518-6

Energy Management Softwares and Tools 5.6.3.26 PSS NETOMAC, PSS SINCAL, PSS PDMS 5.6.3.27 RETScreen 5.6.3.28 Smart Grid Co-Simulator 5.6.3.29 SmartGridToolbox 5.6.3.30 TOP-Energy (eSim, eVarient, eValuate, eSensitivity) 5.6.3.31 Transient Security Assessment Tool 5.6.3.32 YALMIP 5.6.4 Case Studies Acknowledgment References Further Reading

5.6.1

203 244 244 244 244 245 245 245 245 248 248 257

Introduction

The complexity of modern electrical energy systems is increasing due to the increasing use of telecommunications for remote monitoring, control, and coordination of increasing numbers of distributed and intermittent energy sources, storage, and noncritical loads in so-called “smart” grids. Consequently, the analysis and design of modern electrical energy systems require the use of software tools to minimize costs by using model-based design to reduce the chances of costly mistakes and accelerate the design process. A large number of commercial and open-source software tools are available for a wide range of applications, including power flow and quality analysis, stability and protection analysis, generation and demand forecasting, energy storage management, system planning and optimization, market analysis, etc. Furthermore, these tools are often tailored to model power systems on a specific temporal scale, for example, from milliseconds to days, and/or on a specific physical scale, for example, from large-scale generation and distribution systems to micro-grids. Modeling more than one function across multiple physical scales can become extremely complicated, and will likely require a careful choice of tools to ensure that subtle but important interdependencies are not missed. The aim of this review is to assist those involved in the analysis and/or design of modern and complex electrical energy systems to identify the best and most appropriate software tools for their purpose. To achieve this aim, each tool is categorized by reference to the CEN-CENELEC-ETSI Smart Grid Reference Architecture [1] (Fig. 1), which has three primary dimensions of classification: layer (defining distinct interoperable roles), domain (within the electrical energy conversion chain), and zone (within the power system management hierarchy). To find the relevant software tools, it is suggested that the reader start by identifying the most relevant combinations of domain and zone, or sectors within the Smart Grid Plane in Figs. 1 and 2; [1]. Brief descriptions of the software tools are grouped by application in Fig. 3 and Tables 1–22 in Section 5.6.2 of the text. Selected tools with wide application are described in more detail in Section 5.6.3 of the text.

5.6.2

List of Simulation Tools

The simulation tools have been categorized based on the most common functionalities of power and energy systems. A detailed list of the appropriate tools at different levels of power systems is provided in Figs. 1–3. The list of tools in Figs. 1 and 2 is based on the CEN-CENELEC-ETSI Smart Grid Reference Architecture [1], and the list of tools in Fig. 3 is based on the traditional structure of power systems. The availability of the tools has been divided into four categories: free to use, open source, commercial, and limited. Opensource tools are free for everyone to use and develop. Most of the commercial tools have a demo version to use for basic simulation within a limited time. The limited tools are only available to a specific group of people, or for specific project purposes.

5.6.2.1

Power and Energy Systems Modeling and Analysis Tools

Power systems/energy systems infrastructure, generation, distribution, and consumption will go through a major change in future. Power generation will be disaggregated and more distributed renewable energy resources (PV, wind, hydro, EV, geothermal, biomass, battery) will be connected from distributed locations. Simulation tools to analyze and design such renewable energy resources are required. There are many simulation tools available in the market that can be used to deal with the design, modeling, and analysis of electrical power systems, for example, distributed renewable energy systems management, and they are listed in Table 1. The typical applications include power and energy systems modeling, analysis, optimization, management, automation, and distributed renewable energy sources integration. Five commonly used tools in this area are reviewed below.

204

Energy Management Softwares and Tools

Fig. 1 List of tools in different layers of the CEN-CENELEC-ETSI Smart Grid Reference Architecture.

Energy Management Softwares and Tools

Fig. 2 List of tools in different zones and domains of the CEN-CENELEC-ETSI Smart Grid Reference Architecture depicted in Fig. 1.

205

206

Energy Management Softwares and Tools

Fig. 3 List of simulation tools based on the traditional architecture of electrical energy systems.

a) DigSilent, commonly known as PowerFactory (www.digsilent.de), is powerful power system simulation software that can be used for various power systems studies [4,22,33,71–75]. It can be used to carry out the following: • Load flow analysis • Probabilistic load flow analysis

Table 1

List of computer tools to model and analyze power and energy systems

Tool

Description/source

Availability

Typical application

AEOLIUS [2,3]

Universität Karlsruhe (www.iip.kit.edu/65.php)

Limited

AGORA [4–6] BALMOREL [7–10]

EleQuant (www.elequant.com/products/agora) Hans Ravn, RAM-lose, Balmorel (http://eabalmorel. dk/) Benchmarking and Energy Saving Tool (BEST), Lawrence Berkeley National Laboratory (LBNL) (http://en.openei.org/wiki/ Benchmarking_and_Energy_Saving_Tool) CESI (www.cesi.it) CYME International (cyme.com) 3DS (www.3ds.com) NexGEN (www.nexgenconsultancy.com) EDIF (www.edifgroup.com) ETAP (www.etap.com)

Commercial Free to use

Energy systems modeling and analysis based on the integration of intermittent energy sources such as PV and wind power [2,3] Design, monitor, and analyze power systems [4–6] Modeling and analysis of energy systems [7–10]

Free to use

Energy systems analysis tool [11]

Commercial Commercial Commercial Commercial Commercial Commercial

BEST [11]

CESI Software (SPIRA, SICRE, CRESO, PROMED) [12] CYME Tool Kit [4,13,14] Dymola [4,15–20] EL-Psoft/ENMS/Elint-TMS [4] ERACS [21] ETAP toolkit [4,22–30]

Cloud-computing based smart-grid simulation [32]

Free to use

GridSim [4,43–45]

Open source (www.cloudbus.org/gridsim)

Free to use

GridSpice [4,46–48]

Open source (https://code.google.com/p/gridspice/)

Free to use

Hybrid2 [4,49–51]

Limited

Modelica Toolkit [4,51,56–64]

RERL, University of Massachusetts, USA (www.nrel. gov/docs/legosti/old/21272.pdf) Universidad de Zaragoza (www.unizar.es/rdufo/ grhyso.htm) Integrating Distributed Resources into Optimal Portfolios (IDROP), Integral Analytics (www. integralanalytics.com) InterPSS community (www.interpss.org) IPSA Power (www.ipsa-power.com) PRDC (www.prdcinfotech.com/business/softwareproducts/mipower) Modelica (www.modelica.org)

Power systems and distributed energy systems simulation [4,33–42] Design, manage, and schedule large-scale distributed systems [4,43–45] Modeling, analysis, and optimization of a smart grid [4,46–48] Simulate hybrid power systems [4,49–51]

Mosaik [65–69] ObjectStab [4,51,56–64]

Open source (https://mosaik.offis.de) Modelica (www.modelica.org/library/ObjectStab)

GridControl [32]

iGRHYSO [4,51] IDROP [52,53]

InterPSS [4,54] IPSA [4] MiPower [4,55]

Commercial (Spanish) Commercial

Grid connected renewable energy optimization, distributed generation, storage, and EV systems analysis [4,52] Distributed energy sources modeling and simulation [52,53]

Free to use Commercial Commercial

Cloud-based power system simulation tool [4,54] Power systems design and analysis [4] Power system analysis and simulation [4,55]

Free to use

Object-oriented multidomain complex system design, specially energy systems, power systems [4,51,56–64] Large-scale smart-grid scenario simulator [65–69] Simulate power systems [4,51,56–64] (Continued )

Open source Free to use

207

Limited

EUROSTAG [4,31]

Energy Management Softwares and Tools

Commercial

GridLAB-D [4,33–42]

Tractebel Engineering GDF SUEZ and RTE (www. eurostag.be) Cornell University, Washington State University (www.cs.cornell.edu/projects/gridcontrol/) Open source (www.griedlabd.org)

Power systems design and analysis tool [12] Power systems simulation tool [4,13,14] Model and analyze energy and environment [4,15–20] Design, monitor, control electrical power systems [4] Power systems analysis [21] Electrical power systems design, analysis, optimization, management [4,22–30] Design and analyze power systems [4,31]

208

Table 1

Continued Description/source

Availability

Typical application

Paladin suite [4] POM Applications Suite [4] Power Engineering EE Helper [70] PowerFactory [4,22,33,71–75]

Power Analytics (www.poweranalytics.com) V&R Energy Systems (www.vrenergy.com) Sumatron Inc. (www.sumatron.com) DigSILENT GmbH (www.digsilent.de)

Commercial Commercial Commercial Commercial

Power*Tools [76]

SKM Systems Analysis (www.skm.com)

PowerWorld Simulator [4,22,78–80] PSAT [4,77,81–83] PSCAD/EMTDC [84–87]

PowerWorld Corporation (www.powerworld.com) Open source (http://faraday1.ucd.ie/psat.html) Manitoba HVDC Research Centre (www.hvdc.ca/ pscad) Siemens (www.siemens.com)

Commercial (few versions are free) Commercial Free to use Free to use

Model and analyze power systems [4] Power system research tool [4] Power engineering systems calculation tool [70] Model, analyze, and simulate power quality, control, and other power systems applications [4,22,33,71–75] Analyze power systems [77]

PSS/E, PSS NETOMAC, PSS SINCAL [4,22,88–95]

Commercial

Simulate power systems [4,22,78–80] Power systems analysis toolkit Design, analyze, and control power systems [84–87] PSS/E, PSS NETOMAC: Simulate power systems, dynamics, transient, load flow analysis, and optimization PSS SINCAL: High, medium, and low voltage grid designing and analysis; unbalanced systems modeling and analysis, and optimization; power flow, volt/var optimization; data processing from SCADA, GIS, and meter; dynamic and reliability analysis [4,22,88–95] Design, analyze and control power systems [4]

Free to use

RAPSim [51,96,97]

K. W. Cheung, J. Chow, G. Rogers (www.eps.ee.kth. se/personal/vanfretti/pst/Power_System_Toolbox_Webpage/PST.html) Open source (https://sourceforge.net/projects/rapsim)

SE toolkit (GAP/NAP/DAP/LAP/REBAN) [4] Simpow [4,33,98] Simscape Power Systems [22,99–103]

Systems Europe (www.systemseurope.be) Solvina Energy Excellence (www.solvina.se/simpow) MathWorks (www.mathworks.com)

Free to use Commercial Commercial

SimSci Toolkit [4]

Commercial Free to use Open source

Simulate energy-related physical systems [4,104] Smart-grid and future-grid simulation, optimization

Smart Grid Analytics [106]

Schneider Electric (www.software.schneider-electric. com) ITI Simulation Solution (www.simulationx.com) National ICT Australia (NICTA), ANU, Actew-AGL (http://nicta.github.io/SmartGridToolbox/) Itron Inc. (www.itron.com)

Simulate micro-grid model, grid control including all renewable resources and storage [51,96,97] Analyze power systems [4] Simulate power systems [4,33,98] Power systems simulation including distributed renewable energy sources [22,99–103] Analyze power systems [4]

Commercial

VCCS/CVA/PLSC/PLSC/PVR/POLC/BCS/PVSA [4] Visual PSA/Visual DSA/Visual CON/Visual EMF [4] Xendee Tool [4,107]

Intellicon (intellicon.biz) PSI (www.visualpes.com) Xendee (https://www.xendee.com)

Limited Commercial Commercial

Smart-grid reliability and efficiency analysis, operational improvement, distributed generation systems analysis, customer engagement analysis [106] Analyze power systems [4] Simulate power systems [4] Simulate smart-grid and microgrid applications [4,107]

PST/MatNetFlow/MatNetEig [4]

SimulationX [4,104] SmartGridToolbox [105]

Free to use

Energy Management Softwares and Tools

Tool

Energy Management Softwares and Tools

• • • • • • • •

209

Unbalanced power flow analysis Harmonic load flow analysis Optimal power flow analysis Assessing renewable energy sources integration Modeling automatic generation control (AGC) Transmission systems modeling under any unsymmetrical conditions Power systems stability analysis Active and reactive power control

Refs [4,22,33,71–75] provide comprehensive explanations of how to use PowerFactory for different types of power systems studies, and provide details on how to model, analyze, and simulate power systems with distributed resources. PowerFactory has several licenses for research and commercial use starting from a student license, which is free with a valid student ID number, ranging to pay education, research, and commercial licenses. It is used widely in both industry and research organizations [75]. b) ETAP Toolkit has a wide range of power-systems simulation modules that cover almost all applications of power generation, transmission, distribution, automation, monitoring, protection, and power quality analysis [4,22–30]. It can be used to carry out the following: • Power flow, short circuit, stability, and harmonics analysis • Distributed energy resources integration and management • Intelligent substation management • Intelligent load-shedding, intelligent load allocation, and switching optimization • Protection, and automation systems modeling ETAP toolkits have a user-friendly graphical user interface, and can exchange data with external data sources, which provides flexibility to simulate diversified applications [4,22–30]. The ETAP GIS Map module can design and map electrical one-line diagram according to the geographical location. It is a commercial tool [4,22–30], however a free demo version is available with limited capability [4,22–30]. c) PSS/E is powerful software to simulate various power systems applications [4,22,88–95]. It can be used to carry out the following: • Optimal power flow analysis • Load flow analysis • Balanced or unbalanced fault analysis • Short-circuit analysis • Small-signal stability analysis • Harmonics analysis PSS/E is maintained by Siemens [4,22,88–93]. It supports a python-style scripting language to create flexible user-defined scripts [4,22,88–95]. d) GridLAB-D [4,33–42] is an open-source tool, developed by the Pacific Northwest National Laboratory (PNNL), United States [4,33–42]. It can integrate distributed renewable energy resources such as photovoltaic (PV), wind generator, battery storage, and electric vehicles (EVs) [4,33–42]. It can analyze the impact of single or aggregated EVs on the grid. It can be used to carry out the following: • Single-phase, three-phase balanced/unbalanced load flow analysis • Transient behavior analysis • Voltage fluctuations and fault analysis • Energy market and demand response analysis • Residential power demand and consumer energy consumption behavior analysis GridLAB-D allows the user to import external data to simulate realistic systems, for example solar insolation data to PV, and temperature data to weather-dependent loads [4,33–42]. Currently, GridLAB-D is used by a variety of research institutions and power grid companies to analyze various storages (EV, battery), and distributed renewable energy sources [4,33–42]. e) Simscape Power Systems, previously known as SimPowerSystems, is an add-on to Simulink (graphical programming in MATLAB by MathWorks) for simulating various functions of electrical power systems [22,99–103]. It provides various analysis tools and an electrical component library to model the control systems and to analyze the power flow and harmonics of electrical systems [22,99–103]. The component library includes electrical machines and drives, renewable energy sources, flexible AC transmission systems (FACTS), etc. [22,99–103]. Simscape Power Systems provides a facility to integrate thermal, hydraulic, mechanical, and other user-defined physical systems by using components from the Simscape family of products [22,99–103]. It supports C code generation to export the simulation model to other simulation environments such as hardware-in-the-loop (HIL) systems [22,99–103].

List of computer tools to analyze power flow and short circuits Description/source

Availability

Typical application

ALF [108]

Argonne Load Flow Model (ALF) [108], Center for Energy, Environmental, and Economic Systems Analysis (CEEESA) [108], ANL (http://ceeesa.es.anl.gov/projects/ PowerAnalysisTools.html) Eletrobras Cepel, Electrical Energy Research Center (www.cepel.br)

Limited

Real and reactive power-flow analysis, and power-system response analysis when components are changed [108]

Limited

Limited

Fault simulation, distribution network analysis, optimal power flow analysis, transient analysis, reliability analysis, control systems analysis, load forecasting, energy systems modeling [109–121] Power-flow computational tool [122]

Free to use

Short-circuit and power-flow analysis [123]

Commercial Open source

Short-circuit and load-flow analysis [124] DC optimal-power-flow analysis tool [107,125,126]

Limited

Static and dynamic load, power flow, small-signal, and stability analysis [127,128]

Open source

Small-signal, stability, and optimal-power-flow analysis [107]

Free to use Open source

Analyze DC/AC power distribution, load flow and geographical load flow [4] Power-flow analysis [130]

Free to use

Analyze power flow of power systems [4,107,131]

Open source

AC-DC power flow, steady-state analysis [107,132–135]

Open source

Optimal power flow and power-systems dynamics analysis [4,107,136] Optimal power-flow simulation [4,107,137–139] Load flow, transient stability, fault analysis [140] Power distribution systems simulation, power-flow analysis, power-systems control analysis [22] Economic dispatch of power, power flow, and fault analysis [141] Optimal power flow for both AC and DC systems, and transient stability analysis [142,143] Web-based simulator; simulates power flow, fault, and stability [4] Power flow, voltage stability [4,82,107,144]

ANAFAS, ANAREDE, ANATEM, Encad, FLUPOT, FormCepel, HarmZs, Iself, NH2, PacDyn, PlotCepel [109–121] ARTERE [122] CAPSA [123] CERBERUS [124] DCOPFJ [107,125,126] DIRECT, DYNRED, IPFLOW, LOADSYN [127–129] Dome [107] Fendi [4] GridCal [130] IPSYS [4,107,131] MATACDC [107,132–135] MatDyn [4,107,136] MATPOWER [4,107,137–139] Power Designer [140] PowerFlow [22]

University of Liège (www.montefiore.ulg.ac.be/Bvct/ software.html) Computer-Aided Power System Analysis, Dr. George Kusic (www.powersysconsultants.us) Adapted Solutions [124] (www.adapted-solutions.com) Junjie Sun and Leigh Tesfatsion (www.2.econ.iastate.edu/ tesfatsi/DCOPFJHome.htm) Load synthesis program (LOADSYN), Interactive power flow (IPFLOW) (www.eee.hku.hk/Bcees/software/ psapac.htm) Federico Milano, University College Dublin, Ireland [107] (http://faraday1.ucd.ie/dome.html) Open source (www.martinole.org/Fendi) Power systems solver (https://github.com/SanPen/ GridCal) Dr. Jovan Ilic (www.ece.cmu.edu/Bnsf-education/ software.html) Jef Beerten, University of Leuven (www.esat.kuleuven.be/ electa/teaching/matacdc) KU Leuven (www.esat.kuleuven.be/electa/teaching/ matdyn) Cornell University (www.pserc.cornell.edu/matpower) Lotfi Baghli (www.baghli.com) EasyPower LLC (www.easypower.com)

Free to use Free to use Commercial Limited

PYPOWER/PYPOWER-Dynamics [142,143]

Power System Analysis Program (PSAP), Bert Allan McDowell (www.eng.auburn.edu/Bgross/readme.htm) Open source (https://github.com/rwl/PYPOWER)

RPowerLABS [4]

R Language (www.rpowerlabs.org)

UWPFLOW [4,82,107,144]

University of Waterloo [4,82,107,144] (https://ece. uwaterloo.ca/Bccanizar/software/pflow.htm) Advanced grounding concepts (www.ap-concepts.com)

Free to use (academics) Open source

PSAP [141]

WinIGS [145,146]

Open source

Commercial

Grounding design and analysis, fault analysis, lightning on power systems analysis [145,146]

Energy Management Softwares and Tools

Tool

210

Table 2

Energy Management Softwares and Tools 5.6.2.2

211

Power Flow and Short-Circuit Analysis Tools

Short-circuit and load-flow analysis tools are listed in Table 2. The most common applications include real and reactive power-flow analysis, static and dynamic load analysis, short-circuit and fault analysis.

5.6.2.3

Power Generation, Transmission, and Distribution Systems Modeling and Analysis

Tools that deal with conventional power generation, transmission, and distribution systems are listed in Table 3. The common applications include designing and analyzing power generation and its optimal expansion; designing and analyzing AC/DC high, medium, and low voltage power transmission and distribution systems; and complex power network analysis.

5.6.2.4

Power Quality Analysis Tools

Tools to analyze the power quality of power systems are listed in Table 4. The common applications of these tools include harmonics, transient stability, voltage stability, electromagnetics, and systems reliability analysis.

5.6.2.5

Switchgear and Protection Systems Analysis Tools

Tools that deal with the switchgear and protection systems of power systems are listed in Table 5. Typical applications of these tools include grid protection systems modeling and analysis, and protective devices coordination and management.

5.6.2.6

Energy and Power Systems Security Analysis Tools

A number of smart-grid, or Internet of Energy (IoE), security-systems analysis tools are listed in Table 6. Common applications of these tools include power-systems security infrastructure design, security systems analysis, security assessment, security management, security services, and security policy analysis.

5.6.2.7

Communication and Information Transfer Analysis Tool

Future power systems will be more automated and disaggregated by connecting distributed energy resources. The coordination between power generators and demand needs information transfer through a communication infrastructure [257]. A list of smartgrid communication infrastructure and information-transfer systems analysis tools is in Table 7. The applications of these tools include power systems and communication systems interface modeling, communication infrastructure design and analysis, communication network management, analysis of information transfer between distributed energy resources, analysis of the communication between automation devices, and simulation of the communication between several smart grids.

5.6.2.8

Energy Generation and Demand Forecasting

Future power systems will include intermittent renewable energy resources from distributed locations. For better energy management, it is necessary to balance generation and demand, which needs a prediction of power generation from intermittent sources and the demands of consumers. Table 8 gives a list of computer tools to forecast customers’ load demand and energy generation. The typical applications of these tools include the forecasting of the short-term and long-term load demand and energy generation, hourly building load forecast, wind-power generation forecast, PV-power generation forecast, energy price forecast, distributed energy generation forecast, global energy scenario forecast, and international energy price forecast.

5.6.2.9

Energy Management Tools

Tools related to energy management of power and energy systems are listed in Table 9. Typical applications of energy management tools include distributed energy resources management, power distribution systems management, energy generation and demand management, energy pricing and scheduling management, renewable-energy generation prediction and management, distributedload data collection and management.

5.6.2.10

Energy-Economy and Energy-Market Modeling and Analysis Tools

A list of energy-economy and energy-market modeling and analysis tools is in Table 10. Common applications of these tools include electricity market modeling and analysis, energy-project economy analysis, distributed energy resources economy analysis, renewable energy generation economy analysis, domestic energy usage economy analysis, economic energy transactions, and economic energy generation and consumption optimization.

5.6.2.11

Energy Systems Planning, Policy and Decision-Making Tools

Common simulation tools to plan energy systems, make policy, and take decisions for sustainable energy security and energy infrastructure are listed in Table 11. Common applications of these tools include socioeconomic energy scenario analysis;

Table 3

List of computer tools to model and analyze power generation, transmission, and distribution systems Availability

Application

Apros [147] ATP [4,22,148,149]

Fortum, VTT (www.apros.fi) ATP research group (www.emtp.org)

Commercial Commercial

CASPOC [4,150,151]

Integrated Engineering software (www.integratedsoft.com/ products/caspoc) Engineering Computation (www.engineeringcomputation. com) Power System EDSA (www.edsa.com)

Commercial

Nuclear and thermal power-plant simulation Complex power network analysis, control systems of arbitrary networks, transient analysis [4,22,148,149] Mechatronics systems, power generation, and distribution systems analysis [4,150,151] Analyze power distribution systems [4,107,152,153]

Cymdist [4,107,152,153] EDSA Paladin Toolkit [4,33,107,154]

Commercial Commercial

GridPACK [4,155–157] OpenDSS [4,33,107,158–163] PADEE [164]

Battelle (www.gridpack.org) Open source (www.electricdss.sourceforge.net) MATMOR, Power Distribution Analysis Software (http:// padeepro.com/padeeing.html)

Free to use Free to use Commercial

PSLF [4,22,165,166] ReticMaster/PowerOffice [167,168] SimSEE [169]

GE Energy (www.geenergyconsulting.com) Inspired Interface (www.reticmaster.com) Simulation of Systems of Electrical Energy (https:// sourceforge.net/projects/simsee/?source=directory) Danish TSO Energinet.dk (www.energinet.dk/)

Commercial Commercial Open source

SIVAEL [170–172] Synergi Electric [173] VOLTTRON [174–179] WASP [180,181]

3KEYMASTER [182–184]

DNV-GL (www.dnvgl.com) PNNL (www.gridoptics.pnnl.gov/VOLTTRON) WASP (Wien Automatic System Planning Package), IAEA (International Atomic Energy Agency) [180,181], ANL (www.iaea.org) WSC (www.ws-corp.com/wsc08/wsc14/)

Free to use Commercial Free to use Free to use (for members) Commercial

Power system design and simulation tool, AC/DC distribution systems analysis [4,33,107,154] Design and analyze power systems and electric grid [4,155–157] Power distribution systems simulation tool [4,33,107,158–163] Design, analyze, and plan medium and low scale distribution networks, protection systems, demand forecast, energy loss analysis, budget analysis Analyze power distribution and grid [4,22,165,166] Power network analysis [167,168] Simulate hydrothermal power systems and optimal dispatch Power systems simulation with thermal and wind power generation [170–172] Design and analyze power distribution systems [173] Control power distribution systems [174–179] Power generation systems analysis, optimal power-plant expansion analysis, biomass and nuclear power generation analysis [180,181] Renewable energy and grid systems, power generation systems by hydro, nuclear, fossil-fuel analysis [182–184]

Substation design and analysis tools:

• • • • •

Bentley Substation [185] (Commercial) (www.bentley.com), used for substation physical layout, protection and control systems, material estimation design. AUTODESK Substation Design [186] (Commercial) (www.substationdesignsuite.com), used for physical layout, control, and protection systems design. Primtech [187] (Commercial) (www.primtech.com), used for physical layout, control, and protection systems design and analysis. Substation Explorer [188] (Commercial) (www.abb.com), used for substation 3D layout and finance calculation. CYMGRD [189–194] (Commercial) (www.cyme.com), substation grounding design and analysis to optimize the total substation.

Transmission-distribution and underground cable analysis tools:

• • • •

CYMCAP [195–198] (Commercial) (www.cyme.com), used to analyze cable ampacity, temperature rise, optimal cable size, magnetic field, etc. QuickCable [199] (Commercial) (www.powercad.com.au), used to design cable size and analyze cable capacity. ELEK Cable [200] (Commercial) (www.elek.com), used to analyze cable sizing, capacity, standards, reliability, and protection. PowerPac [201] (Commercial) (www.spearhead.com.au), used to analyze cable size, voltage drop, earth fault, and demand calculation.

Energy Management Softwares and Tools

Description/source

212

Tool

Table 4

List of computer tools to analyze power quality Description/source

Availability

Application

CDEGS [107,202–205]

Commercial

DINIS [4,107]

Safe Engineering Services & Technology Ltd. (www.sestech.com) FUJITSU (www.dinis.com)

Commercial

DSATools [4,22,107,206,207]

Powertech Labs Inc (www.dsatools.com)

Commercial

EMTP-RV [4,208–211]

POWERSYS (www.emtp-software.com)

Commercial

LTSP [127–129]

Long term stability program (LTSP) (www. eee.hku.hk/Bcees/software/psapac.htm) Microtran power systems analysis corporation [22,212], University of British Columbia (www.microtran.com) NEPLAN (www.neplan.ch)

Limited

Power distribution systems electromagnetics, transients, and protection systems design and analysis [107,202–205] Load management, transient stability, protection coordination, lowvoltage load allocation analysis, reliability analysis and enhancement [4,107] Power system modeling, planning, and analysis toolkit [4,22,107,206,207] Large power network simulation, harmonics analysis, load-flow analysis [4,208–211] Long-term stability of large networks, and voltage stability analysis

Commercial

Short-circuit analysis, harmonics analysis [22,212,213]

Commercial

Commercial

Power system, renewable energy and smart grid, transient stability, optimal power flow, reliability analysis [4,22,73,107,214,215] Transient stability, short circuit, load flow, reliability, harmonics, and protection [216] Load flow, short circuits, harmonics, power loss in line and transformer analysis [217–219] Harmonics and power quality analysis [220]

Free to use

Power quality analysis specially for teaching [221]

Commercial

Energy transmission and distribution systems power quality and energy efficiency analysis [4,222–226] Harmonics, power flow, short circuits, voltage stability analysis [4,22,227] Power flow, static and dynamic load, long-term stability of a large network, voltage stability, and small-signal analysis [127–129]

Microtran [22,212,213]

NEPLAN | Electricity [4,22,73,107,214,215] PASHA/POUYA [216] PCFLO, PCFLOH [217–219] PQS [220] PQ Teaching Toy [221] PQView, PQWeb, PQSoft, SuperHarm, TOP [4,222–226] PSAF [4,22,227] PSASP, VSTAB, ETMSP, SSSP [127–129]

SOLV [228] TEFTS [107,229]

INTELECTRICOM, Tom Industrial Consultant (www.tomcad.com) The University of Texas (http://users.ece. utexas.edu/Bgrady/PCFLO.html) SchaffnerPQS – Power Quality Simulator (https://pqs.schaffner.com) Power Standards Lab (www.powerstandards. com) Electrotek Concepts (electrotek.com ) Engineering Computation (www. engineeringcomputation.com) Power system analysis software package (PSASP), voltage stability (VSTAB), extended transient midterm stability program (ETMSP), small-signal stability program (SSSP) (www.eee.hku.hk/Bcees/ software/psapac.htm) Mirus International Inc. (www. mirusinternational.com/solv.php) University of Waterloo, Canada (https://ece. uwaterloo.ca/Bccanizar/software/tefts.htm)

Free to use Limited

Commercial Limited

Free to use

Harmonics and power quality analysis [228]

Free to use

Power systems transient stability, dynamics modeling of AC-HVDC systems [107,229]

Energy Management Softwares and Tools

Tool

213

214

List of computer tools to model and analyze switchgear and protection systems

Tool

Description/source

Availability

Application

ASPEN Toolkit [4,22,107]

ASPEN (www.aspeninc.com)

Commercial

CAPE [4,22,107] EA-PSM [230]

Electrocon (www.electrocon.com) Energy advice [230] (www.energyadvice.lt/en/ea-psm/)

Commercial Commercial

EasyPower [4,22,107,231]

EasyPower LLC (www.easypower.com)

Commercial

Elplek [232] OpenETran [107,233,234]

Ilkka Leikkonen (http://pp.kpnet.fi/ijl/) Electric power system transient simulator (https://github.com/epri-dev/ OpenETran) Protection device management systems (PDMS), SIEMENS (www.siemens. com) Fractal (www.fractal.hr)

Free to use Open source

Commercial

PowerCad-5, PowerCalc-H, PowerCalc, QuickCable [235]

PowerCad Software Pty. Ltd. (www.powercad.com.au)

Commercial

SPARD Power [4]

Energy computer system (SPARD) (www.energyco.com)

Commercial

Transmission 2000 [4,22]

Commonwealth Association Inc. (www.cai-engr.com)

Commercial

Short-circuit analysis, relay coordination, protection systems, power-flow analysis, harmonics and reliability analysis, balance/unbalance systems, overhead transmission lines and underground cables constraints analysis [4,22,107] Grid protection systems analysis [4,22,107] Load flow, short circuits, harmonics, protection and automation systems analysis [230] Protective device coordination, power systems monitoring, control analysis, power flow, stability, harmonics analysis, power-systems risk analysis [4,22,107,231] Short circuits, load flow, protection systems analysis [232] Analyze protection systems (surge arresters, insulation, grounding, lightning protection) [107,233,234] Power systems protection devices management tool [4,22,88–95] Distribution networks, power flow, short circuits, protection systems design [4] Harmonics analysis, stability analysis, protection systems, power-factor correction, power distribution systems modeling [235] Load flow, short circuits, and harmonic analysis; protection systems optimization tool [4] Analyze the utilities of transmission systems, line constraints, protection systems, transient stability, short circuits, economic dispatch [4,22]

PSS PDMS [4,22,88–95] PowerCAD, WINDis [4]

Commercial

Energy Management Softwares and Tools

Table 5

Table 6

List of computer tools to analyze power systems (smart grid, or Internet of Energy, IoE ) security Description/source

Availability

Application

Gridtoolkit [236] GridBox [237] GridSite [238] GridTrust [239,240] Monarch [241] NeSSi [242–246]

Anto (https://sourceforge.net/projects/gridtoolkit/) Open Source (https://sourceforge.net/projects/gridbox/) Open source (https://sourceforge.net/projects/gridsite/) Open source (https://sourceforge.net/projects/gridtrust/?source=directory) Open Systems International, Inc. (www.osii.com) Network security simulator (NeSSi), DAI-Labor, Deutsche Telekom Laboratories (www.nessi2.de) Ximdex (https://sourceforge.net/projects/osgridmanager/) Penetration Testing Toolkit (PT2) (https://github.com/epri-dev/PT2/releases) Transient security assessment tool (TSAT) [22,249–256], Powertech (www.powertechlabs.com)

Open source Open source Open source Open source Commercial Open source

Power-grid security analysis [236] Grid security infrastructure design and analysis [237] Grid security service, policy analysis [238] Analyze next-generation grid security [239,240] Multilevel security modeling of smart-grid automation [241] Network security analysis tool [242–246]

Open source Open source Commercial

Power grid management and security analysis [247] Power systems security assessment tool [248] Security assessment based on relay margins, transient, frequency, and voltage stability [22,249–256]

OSGridManager [247] PT2 [248] TSAT [22,249–256]

Energy Management Softwares and Tools

Tool

215

216

List of computer tools to model and analyze power systems communication and information transfer

Tool

Description/source

Availability

Application

Dexter [258]

Eduardo Rodriguez (https://sourceforge.net/projects/ commexpert/) Cornell University (www.cs.cornell.edu/hopkik/epochs. htm) GridMaven Network Manager (GNM), GridMaven (www. gridmaven.com)

Open source

Simulate the interface between power-systems automation and communication systems [258] A middleware platform between power and communication systems to simulate the combined environment [259–261] Performance monitoring and fault management in the grid communication network, specially used by utility network operators [262] Smart-grid data communication tool [263–267] Smart-grid communication network management [268] Discrete-event network simulation, communication application of smart grid [269–273] Simulate smart-grid communication [274–280] Smart-grid communication simulation [281,282] Model the communication between distributed energy resources [283] Smart-grid communication simulation [284–287] Prototype power-systems control, automation, and protection with required communications [288] Smart-grid communication, IT infrastructure and energy management [289]

EPOCHS [259–261] GridMaven Network Manager [262]

Free to use Commercial

GridStat [263–267] IntelliTeam CNMS [268] NS2, NS3 [269–273]

Washington State University (http://gridstat.net) S&C Electric Company (http://sandc.com) NS (network simulator), ns-3 project (www.nsnam.org)

Commercial Commercial Free to use

OMNet þ þ [274–280] OMNEST [281,282] OpenIEC61850 [283]

OpenSim Ltd (https://omnetpp.org) Simulcraft (https://omnest.com) Stefan Feuerhahn (www.openmuc.org/iec-61850/)

Free to use Commercial Open source

OPNET Modeler [284–287] Rapid61850 [288]

OPNET (www.opnet.com) Rapid-prototyping protection and control schemes with IEC 61850 (https://github.com/stevenblair/rapid61850) SAS (www.sas.com/en_au/industry/retail/demandforecasting.html)

Commercial Open source

SAS Grid Manager, SAS Data Management [289] ScorePlus [290]

ScorePlus: Smart Grid Experiment (https://sourceforge. net/projects/scorepluset/)

SG-CAT [291]

Smart Grid Communications Assessment Tool (SG-CAT), Siemens (www.siemens.com) Open source (https://sourceforge.net/projects/ smartgridcosimu/) Open source (http://web.ornl.gov/B1qn/thyme/docs)

Smart Grid Co-Simulator [292] THYME [4,107] UnitySuite [293] VPNET [4,294–296]

Trilliant Holdings (http://trilliantinc.com) RWTH Aachen University (www.acs.eonerc.rwth-aachen. de)

Commercial (Free under university edition) Open source

Commercial Open source Free to use Commercial Limited

Simulate the smart-grid environment including communication systems, micro renewable generators and physical devices [290] Smart-grid communication simulation [291] Simulate the smart grid practically by interfacing power device models and communication systems [292] Power-system control, communications and electromechanical dynamics analysis tool [4,107] Manage multiple smart-grid communications [293] Simulate interactions between advanced power systems and digital communication networks [4,294–296]

Energy Management Softwares and Tools

Table 7

Table 8

List of computer tools to forecast energy generation and demand Availability

Application

AGORA Load Forecast [297] Aiolos [298] AleaSoft Toolkit [299] ANNSTLF [300–302]

EleQuant (www.elequant.com) Vitec (http://aiolosforecaststudio.com) Alea Business Software S.L. (www.aleasoft.com) Electric Power Research Institute (EPRI) [300–302] (www. epri.com) Autoregressive integrated moving average (ARIMA) model, autoregressive and moving average with exogenous variables (ARMAX) model, seasonal autoregressive integrated moving average (SARIMA) model, Holt–Winters (HW) model, Seasonal Holt–Winters (SHW) model, backpropagation neural network (BPNN) model Singular Spectrum Analysis (SSA), Caterpillar (www. gistatgroup.com) Enerdata (www.enerdata.net) Innovation Energie Developpement (www.ied-sa.fr) Pattern Recognition Technologies (www.prt-inc.com)

Commercial Commercial Commercial Commercial

Short-term load forecast [297] Electricity production and demand forecasting [298] Energy generation and demand forecasting [299] Hourly load forecast tool [300–302]

Free to use

Traditional models for load forecasting [303–312]

Commercial

Short-term load forecasting [311]

Commercial Commercial Commercial

Energy demand forecast simulation tool by country [309] Energy demand forecast [310] Forecasting of power generation, energy demand, energy price, windpower generation, and solar-power generation [311]

Enerdata (www.enerdata.net) Fortech Energy Inc. (www.fortechenergyinc.com) Itron Inc. (www.itron.com)

Commercial Commercial Commercial Commercial Limited

Electrical load forecast [314]

Commercial Limited

Load forecasting tool [315] Energy-production and energy-using technology analysis in industry forecasting [108]

Commercial

Forecasting of electrical load, solar, and wind power generation [316]

Limited

Long-term energy and electricity demand forecasting [108]

MEDPRO, MEDEE [309]

GMDH LLC (www.gmdhshell.com/electricity-loadforecasting-software) Universiti Teknologi Malaysia (UTM) (http:// smartdigitalcommunity.utm.my/electricalsoftware/) IBM (www.ibm.com/analytics/us/en/technology/spss/) Long-term Industrial Energy Forecasting (LIEF), CEEESA, ANL (http://ceeesa.es.anl.gov/projects/ EnergyAnalysisTools.html) Load Forecasting (LOADFOR), Solar Power Forecasting (SOLARFOR), Wind Power Prediction Tool (WPPT), EMD International A/S (www.emd.dk/forecasting-solutions/wppt/ ) Model for Analysis of Energy Demand (MAED) [108], IAEA, CEEESA, ANL (http://ceeesa.es.anl.gov/projects/ PowerAnalysisTools.html) Enerdata (www.enerdata.net)

Global energy demand, price forecast [309] Short- and long-term power generation and demand forecast [312] Short-term, long-term energy demand forecasting and management [106] Power-systems load forecasting tool [313]

Commercial

NELF-LT, SDLF [311] Nostradamus [188] OpenSTLF [241]

Pattern Recognition Technologies (www.prt-inc.com) ABB (www.abb.com) Open Systems International, Inc. (www.osii.com/)

Commercial Commercial Commercial

Long-term load curve, energy demand, and greenhouse-gas emission forecasting tool [309] Load forecast tool [311] Energy demand and price forecasting tool [188] Short-term load forecasting tool [241] (Continued )

ARIMA, ARIMAX, SARIMA, HW, SHW, BPNN [303–307]

CaterpillarSSA [308] Country Energy Demand Forecasts [309] Demand Analyst [310] e-LoadForecast, e-ISOFForecast, ePowerForecast, e-AccuWind, eSolarForecast [311] EnerFuture [309] ForCast [312] Forecast Manager, MetrixIDR, MetrixLT, MetrixND [106] GMDH Shell [313] H þ [314] IBM SPSS [315] LIEF [108]

LOADFOR, SOLARFOR, WPPT [316]

MAED [108]

217

Description/source

Energy Management Softwares and Tools

Tool

218

Table 8

Continued Description/source

Availability

Application

POLES [309]

Enerdata (www.enerdata.net)

Commercial

PSMS [4,22–30] SAS Demand Forecasting [289]

ETAP (www.etap.com) SAS (www.sas.com/en_au/industry/retail/demandforecasting.html)

STLF [317]

Short-term load forecasting (STLF), Advanced Control Systems Inc. (http://acspower.com) DNV-GL (www.dnvgl.com) Tiberius Data Mining (www.tiberius.biz/)

Commercial Commercial (Free under university edition) Commercial

Long-term energy scenario, international energy market; nationalregional energy balancing and emission analysis tool [309] Electrical load forecasting tool [4,22–30] Power demand forecasting [289]

Synergi Forecaster [173] Tiberius [318]

Commercial Commercial

Short-term load forecasting tool [317] Energy demand forecasting [173] Electrical load forecasting tool [318]

Energy Management Softwares and Tools

Tool

Table 9

List of energy management tools Availability

Application

ADMS [319]

Schneider Electric DMS NS LLC (www.schneider-electric-dms.com)

Commercial

AEMPFAST, SUREFAST [320]

Optimal, Otii (www.otii.com) (www.updatefrom.com/optimal/0708/focuson. html)

Commercial

elec calc [321] ENPEP-BALANCE [181]

Commercial Free to use

DEXCell Energy Manager [323]

Trace Software International (www.trace-software.com) Energy and Power Evaluation Program (ENPEP) [181], ANL, (http://en. openei.org/wiki/Energy_and_Power_Evaluation_Program) Energy transaction integration manager (ETIM), SoftSmith (www.softsmiths. com/products.htm) Australian Bureau of Agricultural and Resource Economics (ABARE) (www. energyplan.eu/othertools/national/e4cast/) DEXCell Energy Manager (www.dexmatech.com)

Energy management, power distribution systems management, power control systems, data acquisition and distributed energy management, demand response management, outage management [319] Analyze, optimize, and manage power-systems generation, transmission, and distribution; demand-side management of distributed resources [320] Manage the lifecycle of an electrical installation [321] Energy systems demand and supply analysis [181]

energyTRADE [316]

EMD International A/S (www.emd.dk/energytrade/)

Commercial

ESCOWare [324]

ESCOAdvisors (www.escoware.com)

Commercial

Grid 360/iEnergy [4]

Nexant (www.nexant.com)

Commercial

GTMax [4,181]

GTMax (Generation and Transmission Maximization Model), ANL (www.anl. gov/energy-systems) Instituto Superior Técnico, University of Zagreb (http://h2res.fsb.hr/)

Commercial

INFORSE (International Network for Sustainable Energy), International Sustainable Energy Network (www.inforse.org/europe) INSEL (www.insel.eu)

Limited

Limited

MASGrip [4]

Energy Economics Group (EEG), Vienna University of Technology (www. invert.at) P. Oliveira, T. Pinto, H. Morais, Z. Vale

MGmanager [310]

Innovation Energie Developpement (www.ied-sa.fr)

Commercial

Modelica Buildings Library [4,326]

Open source (http://simulationresearch.lbl.gov/modelica/)

Open source

TOP-Energy (eSim, eVarient, eValuate, eSensitivity) [327,328]

TOP-Energy (www.top-energy.de/en/)

Commercial

ETIM [322] E4Cast [181]

H2RES [181] INFORSE [181] INSEL [51,107] Invert/EE-Lab [325]

Commercial Limited Commercial

Limited

Commercial

Limited

Power generation, transmission, electricity retail management [322] Long-term energy production, consumption, and trade analysis [181] Energy management (mainly on buildings with renewables) and economy analysis [323] Daily energy generation, planning, optimization, and management [316] Energy systems analysis, energy pricing, demand forecasting, energy scheduling, energy management [324] Grid analysis, distributed energy resources, energy management, optimal power flow, optimize grid operation [4] Electricity generation and economic trade management [4,181] Distributed renewable energy sources integration, modeling, and analysis [181] Analyze energy balancing between energy market, economy, and environment [181] Design, monitor, and analyze complex energy systems [51,107] Energy systems including renewables modeling and analysis [325] Simulate smart-grid management, EV and distributed power generation [4] Energy production, distribution systems, and customer management [310] Building energy management, control, HVAC systems, load prediction and demand response, ac-dc balanced/ unbalanced systems [4,326] Modeling and analysis of complex energy systems [327,328] (Continued )

219

Description/source

Energy Management Softwares and Tools

Tool

220

Table 9

Continued Description/source

Availability

Application

DRQAT [329]

Demand Response Quick Assessment Tool (DRQAT), LBL (http://drrc.lbl.gov/ tools/demand-response-quick-assessment-tool-drqat) Catapult Software (www.catapultsoftware.com/products/hmi-scada-2.html)

Limited

Domestic energy demand management to save energy, predict energy [329] Industrial power-systems data collection, automation, monitoring, and energy management [330] Energy supply systems decision management [181]

iFIX, CIMPLICITY, iPower, PowerLink Connect, envisage, onDemand [330] PERSEUS [181]

Commercial Limited

PowerLogic SCADA [331]

PERSEUS (Programme-package for Emission Reduction Strategies in Energy Use and Supply-Certificate Trading) [181], Universität Karlsruhe (www.iip. kit.edu/1605.php) ION Enterprise (www.powerlogic.com)

RAMSES [181]

University of Liège (www.montefiore.ulg.ac.be/Bvct/software.html)

Limited

REFlex [50]

NREL (http://en.openei.org/wiki/REFlex)

Limited

SEPIA [300–302]

EPRI, US (www.epri.com)

Free to use

SimREN [181]

Limited

U-PLAN [127–129]

SimREN (Simulation of Renewable Energy Networks), Institute for Sustainable Solutions and Innovations (iSUSI) (www.isusi.de) NSERC, Canada (https://uwaterloo.ca/power-energy-systems-group/ downloads/smart-residential-load-simulator-srls) TARA (Transmission Adequacy & Reliability Assessment), PowerGEM (www. power-gem.com/TARA.html) LCG Consulting (www.eee.hku.hk/Bcees/software/u-plan.htm)

Limited

windOPS [316]

EMD International A/S (www.emd.dk/windops/)

Commercial

SRLS [4] TARA [332]

Commercial

Free to use Commercial

Control and manage electrical power with necessary communication [331] Energy systems, generation, and cost analysis; plant management [181] Renewable energy generation, energy demand response analysis [50] Simulate electric power transmission, communications, load/ market management [300–302] Renewable energy, distributed energy systems, energy demand, energy management modeling and analysis [181] Simulate smart-grid energy management systems including renewable energy [4] Steady-state power flow, reliability, and energy management analysis [332] Demand-side management, cost-reliability analysis, least-cost planning, optimal resource planning, financial planning and revenue evaluation [127–129] Wind-farm power generation and management tool [316]

Energy Management Softwares and Tools

Tool

Table 10

List of computer tools to analyze energy market and energy economy

Tool

Description/source

Availability

Application

AMES [107]

AMES [107] (www.2.econ.iastate.edu/tesfatsi/ AMESMarketHome.htm) AAU, Denmark (energyinteractive.net) Cost of Renewable Energy Spreadsheet Tool (CREST), NREL (https://financere.nrel.gov/finance/content/crestcost-energy-models) LBNL (www.bnl.gov/SET/DER-CAM.php)

Free to use

Electricity market modeling, analysis, and management [107] Technoeconomic energy-project analysis tool [4,181] Economy of renewable energy analysis [50]

Demand-Side Management Option Risk Evaluator (DSMore), Integral Analytics (www.integralanalytics. com) Energy asset decision support system (EADSS), Market clearing engine (MCE), ICF International (www.icfi.com/ insights/products-and-tools/eadss) EMCAS (Electricity Market Complex Adaptive System) [4,33,181], ANL, US (www.anl.gov) EFI's Multi-area Power-market Simulator (EMPS), SINTEF (Stiftelsen for industriell og teknisk forskning) (www. sintef.no/en/) NREL (http://en.openei.org/wiki/RETFinance)

Commercial

COMPOSE [4,181] CREST [50]

DER-CAM [4,33] DSMore, DRPricer, XactFit, LoadSEER, SmartSPOTTER [53] EADSS, MCE [333]

EMCAS [4,33,181] EMPS [334]

RETFinance [50] D-GEN Pro [335]

EZ Sim [337] HOMER [4,181] MARKAL/TIMES [4,181] MASCEM [4] Metrix [338] NEMS [4,181] Nexant SCOPE [4] PLEXOS [4,33] PROBE [339]

Commercial

Limited Limited

Free to use Commercial

Commercial Free to use

National Renewable Energy Laboratory, Homer Energy, U. S. (www.homerenergy.com) International Energy Agency (www.etsap.org) Z. Vale, T. Pinto, I. Praça, H. Morais ([email protected]) Abraxas Energy (www.optegy.com/sware.html)

Commercial Limited Commercial

National Energy Modeling System (NEMS), US Energy Information Administration (www.eia.doe.gov) NEXANT (www.nexant.com)

Free to use (partial) Commercial

Energy Exemplar (www.energyexemplar.com) PROBE (Portfolio Ownership & Bid Evaluation), PowerGEM (www.power-gem.com/PROBE.html)

Commercial Commercial

Free to use

Simulate distributed energy resources economic models [4,33] Energy systems financial analysis, smart-grid analysis, demand-side management, demand-response analysis [53] Details energy market and operational rules modeling, analysis, and optimization tools [333] Electricity sector technoeconomic model analysis [4,33,181] Forecasting and planning the electricity market, hydrothermal power systems optimization [334] Analyze the cost of different renewable energy generation [50] Distributed power generation economy and feasibility analysis [335] Identify domestic energy reduction and cost for long-term planning [336] Analyze domestic energy patterns, compare with the ideal and save energy, billing of energy [337] Microgrid design and economy analysis [4,181] Energy-economy analysis tool [4,181] Simulate power systems and electricity market [4] Domestic-level energy pricing, energy saving, planning of alternative energy systems [338] Energy-economic systems modeling and analysis [4,181] Analyze and optimize power systems, electric market analysis [4] Integrated energy systems modeling [4,33] Electricity market modeling and decision support tool [339]

Limited (Continued )

221

Power Market Simulator [108]

Limited

Energy Management Softwares and Tools

Energy Profile Tool [336]

Gas Technology Institute, InterEnergy Software, Des Plaines, IL (http://sales.gastechnology.org/020160. html) EnerSys Analytics Inc., Xmodus Software Inc. (www. energyprofiletool.com/subscription/default.asp) Advanced buildings (www.advancedbuildings.net)

Free to use Limited

222

Table 10

Continued

Tool

Description/source

Availability

PowerWeb [340] PRIMES [4,181] Pylon [4] SMN [108]

Cornell University (http://pserc.wisc.edu/resources/ software_tools.aspx) National Technical University of Athens (www.e3mlab. ntua.gr) Richard Lincoln (www.pypi.python.org/pypi/Pylon) SMN (Spot market network model) [108], CEEESA, ANL (http://ceeesa.es.anl.gov/projects/PowerAnalysisTools. html)

Limited Limited Free to use Limited

Calculate market clearing prices for all generation units, profit/loss analysis for energy generators participating in the market [108] Internet-based tool to analyze energy and power market; design and test power market auction [340] Analyze energy supply and demand in market scenarios [4,181] Simulate power system, energy markets [4] Model economic energy transactions between utility companies and cost minimization [108]

Energy Management Softwares and Tools

CEEESA, ANL [108] (http://ceeesa.es.anl.gov/projects/ PowerAnalysisTools.html)

Application

Table 11

List of computer tools for energy planning, decision making, and policy analysis

Tool

Description/source

Availability

Application

ADAPT [170]

Danish TSO Energinet.dk (www.energinet.dk/DA/El/Udvikling-af-elsystemet/ Analysemodeller/Sider/ADAPT.aspx) CEEESA, ANL (http://ceeesa.es.anl.gov/projects/PowerAnalysisTools.html)

Free to use

Socioeconomic scenario assessment for EV, PV, wind power systems Evaluate power generation, cost, reliability, transmission systems, and expansion alternatives for utilities [108] Analyze the impact of energy policies on expenditure, minor energy consumption, and economic welfare [108] Design, monitor, plan, schedule power systems [4] Model new energy technologies and analyze energy solutions in the market [181] Multiple energy access policy assessment tool [341] Energy planning including electricity [4,33,107,181] Energy policy simulation tool [342] Regional energy systems decision making based on the optimal energy needs and the available technology [343] Energy, economy, and climate analysis and policy making [342]

APEX, PACE, DECADES [108] DIAM [108] DPLAN [4] EMINENT [181] ENACT [341] EnergyPLAN [4,33,107,181] Energy Policy Simulator [342] ETEM [343] FeliX, Vensim [342]

ICARUS [108] IDEA, DAM, PASS, ARAM, STATS [108] IPM [333]

Distributive Impacts Assessment Model (DIAM), CEEESA, ANL (http://ceeesa. es.anl.gov/projects/EnergyAnalysisTools.html) Fractal (www.fractal.hr) EMINENT project, TNO, Netherland (www.energyplan.eu/othertools/national/ eminent/) IIASA (www.iiasa.ac.at/web-apps/ene/ENACT/AccessTool.html) Energy Plan, AAU, Denmark (www.energyplan.eu) Ventana Systems (http://vensim.com/energy-policy-simulator/) ORDECSYS (http://apps.ordecsys.com/etem) International Institute for Applied Systems Analysis, FeliX (Functional Enviroeconomic Linkages Integrated neXus) (www.felixmodel.com) (http:// vensim.com/) ICARUS (Investigation of Cost and Reliability in Utility Systems) [108], CEEESA, ANL (http://ceeesa.es.anl.gov/projects/PowerAnalysisTools.html) CEEESA, ANL (http://ceeesa.es.anl.gov/projects/PowerAnalysisTools.html)

Limited Limited Commercial Limited Free to use Free to use Open source Free to use Commercial

Limited

Energy systems planning tool [108]

Limited

Limited Free to use Limited

Regional electricity-dispatch simulation tool [4,181] Energy systems policy modeling and analysis [344]

PSS ODMS [4,22,88–95]

Long-range energy alternatives planning (LEAP), Stockholm Environment Institute (www.energycommunity.org) ORNL, US (www.ornl.gov) Policy Analysis Modeling System (PAMS), LBL (http://en.openei.org/wiki/ Policy_Analysis_Modeling_System) Siemens (www.siemens.com)

Commercial

MACCs [309] MCA [341]

Marginal Abatement Cost Curves (MACCs), Enerdata (www.enerdata.net) IIASA (www.iiasa.ac.at/web-apps/ene/GeaMCA/McaTool.html)

Commercial Free to use

MELP, METPE, PREVMERCADO [109–121]

Eletrobras Cepel (www.cepel.br)

Limited

MesapPlaNet [4,181]

Mesap (Modular Energy System Analysis and Planning Environment) and PlaNet (Planning Network), University of Stuttgart, Mesap Planning Network (www.seven2one.de/de/technologie/mesap.html) International Institute for Applied Systems Analysis (IIASA) (http:// webarchive.iiasa.ac.at/Research/ENE/model/message.html)

Commercial

Power-transmission systems planning, operation, and maintenance [4,22,88–95] Energy, economy, and emission planning tool [309] Analyze major energy challenges in planning and decision making [341] Long-term power generation planning and optimization, future market planning, energy demand, and market prediction [109–121] Energy demand, supply, and cost analysis and its impact on local, regional, and global energy systems [4,181]

LEAP [181] ORCED [4,181] PAMS [344]

MESSAGE [181]

Free to use (for academics)

Medium- and long-term energy systems planning and optimization [181] (Continued )

223

Commercial

Energy Management Softwares and Tools

Integrated Planning Model (IPM), ICF International (www.icfi.com/insights/ products-and-tools/ipm)

Energy, environment, technology, impact-related decision analysis tool [108] Integration of wholesale power considering system reliability and environmental constraints; plan transmission systems and capacity expansion, and fuel choice [333] Energy production, consumption, and planning tool [181]

224

Table 11

Continued Description/source

Availability

Application

MiniCAM [181]

Pacific Northwest National Laboratory (www.pnl.gov)

Free to use (upon request)

PLANTAC, SAPRE [109–121]

Eletrobras Cepel (www.cepel.br)

Limited

STREAM [181] UniSyD3.0 [4,181] Windmil [4] 4see [4,181]

Danish company Ea Energy Analyses (www.streammodel.org) UniSyD (www.unitec.ac.nz) MILSOFT Utility Solutions (www.milsoft.com) Dr. Simon Roberts, ARUP Foresights (www.energyplan.eu/othertools/ national/4see/)

Free to use Free to use Commercial Limited

Simulate long-term large-scale changes in energy and agricultural systems on both regional and global scales [181] Power transmission systems planning considering economic reliability, power-systems project planning [109–121] Energy scenario modeling and analysis [181] Analyze regional energy scenarios [4,181] Plan and analyze power distribution systems [4] Socioeconomic trend analysis to test future energy scenario analysis [4,181]

Energy Management Softwares and Tools

Tool

Energy Management Softwares and Tools

225

analyzing the impact of energy policy on the economy; new energy technology modeling and analysis considering its economic, social, and environmental impact; multiple energy access assessment; regional and global energy policy planning based on demand generation and reservation; and future energy trend assessment according to socioeconomic structure.

5.6.2.12

Energy Systems Optimization Tools

Common simulation tools and solvers to optimize the design, modeling and computation of power and energy systems are listed in Table 12. The common applications of these tools include smart-grid operation optimization, power generation, transmission and distribution systems optimization, distributed energy generation systems optimization, distributed control systems optimization, economic energy dispatch optimization, energy scheduling optimization, energy market optimization, energy demandgeneration optimization based on generation-consumption forecasts, and energy management optimization.

5.6.2.13

Customer’s End Energy Systems Modeling and Analysis Tools

Common simulation tools to model and analyze end energy systems of customers (e.g., building, industry, school, university) are listed in Table 13. Typical applications of these tools include building and industrial energy simulation; building energy consumption prediction and management; heating, ventilation, and air-conditioning (HVAC) systems analysis; building and industrial energy usage optimization; grid-connected building energy systems modeling with renewables; building energy systems economy and performance analysis; and building energy consumption impacts on grid analysis.

5.6.2.14

Distributed Renewable Energy Resources and Integration Tools

In this section, distributed renewable energy resources modeling, control, management, and their integration to the grid have been analyzed. The future smart grid and the IoE are expected to be automated, including various distributed renewable energy resources such as hydroenergy, PV energy, wind energy, hydrogen energy, fuel-cell energy, biomass energy, and geothermal energy [257]. The continuous reduction of battery prices and improvement of electric-vehicle technology will increase the usage of batteries in the IoE, either in stationary or mobile mode. Therefore, considering the future energy structure, EVs and battery management tools are discussed in this section.

5.6.2.14.1

List of tools to analyze and integrate hydroenergy into the grid

Common simulation tools to model and analyze hydroenergy integration to the grid are listed in Table 14. The common applications of these tools include grid-connected hydroenergy modeling, analysis, optimization, hydroenergy management, hydropower generation forecasting, hydropower economy, and environmental impact analysis.

5.6.2.14.2

List of tools to analyze and integrate PV power generation into the grid

Common simulation tools to simulate grid-connected and stand-alone PV power generators are listed in Table 15. Typical applications of these tools include small-scale and large-scale real-time PV power generation analysis, PV power impact to grid analysis, PV power economy analysis, PV performance analysis, and solar thermal systems analysis. The simulation tools dealing with real-time PV power generation need real-time solar radiation data. Therefore, a list of solar radiation databases has been included in Table 15 so that the data can be fed to the simulation tools to get actual PV power generation.

5.6.2.14.3

List of tools to analyze and integrate wind-power generation into the grid

Tools that can simulate wind-power generation and its integration to the grid are listed in Table 16. Common applications of these tools include the modeling, analysis, forecasting, optimization, and management of small- and large-scale wind-power generation.

5.6.2.14.4

List of tools to analyze battery management and its integration to the grid

Tools that can simulate battery management and its integration to the grid are listed in Table 17. Common applications of these tools include battery modeling, analysis, management, safety and reliability analysis, domestic and grid-scale battery performance analysis, battery life-cycle analysis, and battery energy finance analysis.

5.6.2.14.5

List of tools to analyze and integrate EVs into the grid

Tools that deal with electric-vehicle (EV) integration to the grid and bidirectional energy transfer through vehicle-to-grid (V2G) are listed in Table 18. A detailed list of the tools can be found in [4,33]. Common applications of these tools include EV integration to the grid, EV charge scheduling, EV management, V2G control, EV impact on grid analysis, and V2G economy analysis.

5.6.2.14.6

List of tools to analyze and integrate hydrogen energy into the grid

Hydrogen energy usage for different levels and devices will increase in future. Tools that can simulate hydrogen energy conversion and its integration to the grid, hydrogen energy management, and hydrogen energy economy, and simulation tools that deal with hydrogen energy are listed in Table 19.

226

Table 12

List of computer tools to optimize energy and power systems Description/source

Availability

Application

AIMMS [345]

AIMMS (Advanced Interactive Multidimensional Modeling System), AIMMS B.V. (www.aimms.com) AMPL Optimization Inc. (http://ampl. com/) IBM (www.ibm.com)

Commercial (free for academics)

Complex energy systems and smart-grid optimization [345]

Commercial

Convex Programming (http://cvxr.com/ cvx/) (http://cvxopt.org/) DESOD (Distributed Energy System Optimal Design), University of Genoa ORDECSYS (http://apps.ordecsys.com/ det2sto) Eletrobras Cepel (www.cepel.br) EMD International A/S (www.emd.dk/ energypro/) Power & Water Systems Consultants Ltd. (PWSC) (www.pwsc.co.uk/moreepsim. php) Energy & Climate Research Centre, Jülich (www.fz-juelich.de/portal/EN/Research/ _node.html) General Algebraic Modeling System (GAMS), World Bank (www.gams.com) University of California, LBL, DOE (http:// simulationresearch.lbl.gov/GO/index. html) Minpower [107] (http://adamgreenhall. github.io/minpower) Dag Henning, Optensys Energianalys AB (www.optensys.se/index-filer/Page677. htm) Mosek ApS (www.mosek.com) Gassmann, Horand I., MSLiP (www. swmath.org/software/1410) KTH Royal Institute of Technology (www. osemosys.org) Eletrobras Cepel (www.cepel.br) Ferris, Michael C.; Munson, Todd S. (http://pages.cs.wisc.edu/Bferris/path. html)

Free to use Limited

Optimization tool with different solvers to analyze various optimization problems in energy and communication systems [346] Mathematical programming solver for mixed integer programming, linear programming (LP), and quadratic programming, which can be used in energy and power systems optimization [347] Solve convex complex optimization problems; distributed control of power systems optimization [348] Distributed energy systems optimal design and optimization [349]

Free to use

System optimization, transforms a stochastic model into deterministic equivalent [350]

Limited Commercial

Electrical power distribution systems designing and optimization [109–121] Energy systems, energy economy analysis and optimization [316]

Commercial

Power systems expansion optimization tool [351]

Commercial

Energy system cost optimization tool [4,181]

Commercial

Power systems and market optimization tool [352,353]

Free to use

Cost function optimization tool [354]

Open source

Optimal power flow and economic dispatch optimization [107]

Limited

Energy systems modeling and optimization [355,356]

Commercial Free to use Open source

Large-scale power and energy systems mathematical optimization [357] Stochastic LP problem optimization; power generation and hybrid power systems optimization [358] Energy systems optimization tool [359]

Limited Free to use

Power generation and demand management and optimization [109–121] Clean-energy deployment optimization, energy network optimization [353]

AMPL tool [346] CPLEX [347]

CVX, CVXOPT [348] DESOD [349] DET2STO [350] ELEKTA [109–121] energyPRO [316] EPSIM [351]

IKARUS [4,181]

GAMS [352,353] GenOpt [354]

Minpower [107] MODEST [355,356]

MOSEK [357] MSLiP [358] OSeMOSYS [359] OTSI/SSI [109–121] PATH Solver [353]

Commercial

Energy Management Softwares and Tools

Tool

ProdRisk [334] Saber [4] SNOPT [360] TEMOA [361]

URBS [362]

W-ECoMP [363] YALMIP [364]

SINTEF (www.sintef.no/en/software/ prodrisk/) Synopsys (www.synopsys.com) SQP algorithm-based optimization tool (www.swmath.org/software/2300) Tools for Energy Model Optimization and Assessment (Temoa) (www. temoaproject.org) URBS (Urban Research ToolBox Energy System) (https://github.com/tum-ens/ urbs) Wecomp, GmbH (www.wecomp.com/ Startseite) Johan Löfberg, MATLAB dependent tool, (www.mathworks.com/matlabcentral/ newsreader/view_thread/59636)

Commercial

Hydrothermal power systems modeling and optimization [334]

Commercial Limited

Modeling/analysis physical systems, electric-power generation/conversion/distribution and optimization [4] Large-scale constrained optimization tool, renewable energy management optimization [360]

Free to use

Energy systems modeling and optimization [361]

Open source

Distributed energy systems optimization tool [362]

Commercial

Energy systems design and management optimization [363]

Free to use

A MATLAB toolbox for prototyping optimization problems such as AC power flow, decentralized control of a river power plant, grid-connected storage systems optimization [364]

Energy Management Softwares and Tools 227

228

Table 13

List of computer tools to model and analyze the customer’s end of energy and power systems Description/source

Availability

Application

AECOsim [326] AkWarm [365] ApacheHVAC, ApacheSim [328,366]

Bentley Systems (www.bentley.com) Alaska Housing Finance Corporation (www.ahfc.us) Integrated Environmental Solutions (IES) (www.iesve.com)

Commercial Free to use Free to use

BCHP Screening Tool [335]

Oak Ridge National Laboratory, US (http://eber.ed.ornl.gov/ bchpsc/) Building Energy Consumption Estimation (http://members. ozemail.com.au/Bacadsbsg/) Building Energy Optimization (Beopt), NREL (http://en.openei. org/wiki/BEopt) BEST (building energy simulation tool), Japan Sustainable Building Consortium (www.ibec.or.jp/best/english/about/ program.html) Buildings Industry Transportation Electricity Scenarios (BITES), NREL (https://bites.nrel.gov/index.php) Danish Building Research Institute (http://sbi.dk/en/bsim)

Free to use

Free to use

Building energy performance simulation [326] Home energy systems and energy ratings analysis [365] Building dynamic thermal systems, solar surface, energy systems analysis [328,366] Commercial and institutional building energy systems modeling and analysis [335] Domestic energy systems, energy consumption, and demand analysis [365] Residential building energy optimization [50]

Commercial

Domestic energy systems analysis [365]

Limited

Building energy software [328] (www. buildingenergysoftwaretools.com) InterEnergy Software (http://sales.gastechnology.org/ 040024.html)

Commercial

Impact of building energy efficiency on renewable energy generation and carbon emission [50] Building energy consumption, and PV-integrated electrical systems modeling and analysis [328,365,367] Whole building energy systems modeling and simulation, renewable energy systems [328] Building energy systems and economy analysis, on-site power generation, thermal storage, compare energy options [335,367] Annual building energy usage and analysis [365,367] Model, analyze, and manage domestic energy systems [365]

BEAVER [365] Beopt [50] BEST [365]

BITES [50] Bsim [328,365,367] BuildingAdvice [328] Building Energy Analyzer PRO [335,367]

Commercial

Commercial

Commercial

BV2 [365,367] Cepenergy Management Software for Buildings [365] CHP Capacity Optimizer [365,368]

BV2 (www.bv2.se/9eng/) Artequim [365] (www.artequim.com/)

Free to use Commercial

Cooling, heating, and power (CHP) capacity optimizer, ORNL (www.buildingenergysoftwaretools.com)

Free to use

COMFIE [369]

Izuba energy (www.izuba.fr/logiciel/pleiadescomfie)

Commercial

CoolSim [50]

NREL (www.nrel.gov/transportation/vtm_models_tools. html#coolsim) Czech Technical University, National Calculation Tool II (http://nkn.fsv.cvut.cz/) Estia (www.estia.ch)

Free to use

Czech National Calculation Tool [328,365] DIAL þ Lighting, EPIQR þ [370] DIALux [365] DOE-2, VisualDOE [50,328,366,367] eQUEST [50,328,367] CAN-QUEST [371]

DIAL GmbH (www.dial.de/en/dialux/) Department of Energy, US (http://doe2.com/DOE2/index. html) eQUEST (Quick Energy Simulation Tool), Department of Energy, US (www.doe2.com/equest/)

Limited Free to use (for noncommercial) Free to use Free to use Free to use Limited

Evaluate on-site cogeneration systems, distributed-generation systems analysis, generation capacity optimization [365,368] Residential energy systems, solar energy, energy performance analysis [369] Building energy modeling and analysis tool [50] Residential energy demand and energy performance calculation [328,365] Energy demand analysis and optimization [370] Design, analyze, and visualize whole-building lighting [365] Building energy demand forecasting, management, and cost analysis [50,328,366,367] Building energy simulation tool [50,328,367]

Energy Management Softwares and Tools

Tool

Derob-LTH [365,367]

Natural resource odell (www.nrcan.gc.ca/energy/efficiency/ buildings/eenb/16600) University of Texas, Lund Institute of Technology (www.ebd. lth.se/english/research/software/derob-lth/)

Commercial

DesignBuilder Software Ltd (www.designbuilderusa.com)

Commercial

EE4 [365]

Free to use

EFEN [372] EnerCAD [365]

Natural Resource Canada (www.nrcan.gc.ca/energy/softwaretools/7453) Carli, Inc. (www.fenestration.com/efen.php) EnerCAD (www.enercad.ch)

Commercial Commercial

EnergyElephant [373] EnergyGauge [365]

EnergyElephant (https://energyelephant.com) EnergyGauge (www.energygauge.com/)

Free to use Commercial

ENERPASS [365]

ENERPASS (www.buildup.eu/en/learn/tools/enerpass)

Commercial

EnergyPlus [50,328,366] Energy Scheming [365]

DOE, Building technologies office (https://energyplus.net/) G. Z. Brown, Tomoko Sekiguchi, and Jeffrey Kline, University of Oregon (http://web.utk.edu/Barchinfo/EcoDesign/ escurriculum/PEDAGOGY/ESReview/ESReviewJAE.html) NREL, DOE (www.nrel.gov/buildings/energy_analysis.html) Texas A&M University & Degelman Engineering Group, Inc. (http://pages.suddenlink.net/enerwin/) ESRU, University of Strathclyde (www.esru.strath.ac.uk/ Programs/ESP-r_overview.htm) Lawrence Berkeley National Laboratory (LBNL), DOE (www. elitesoft.com/web/hvacr/elite_ezdoe_info.html) HAP (hourly analysis program), CARRIER [365,366] (www. carrier.com)

Open source Commercial

Free to use

LESOSAI [365]

Home energy efficient design (HEED) (www.energy-designtools.aud.ucla.edu/heed/) LBNL (www.homeenergysaver.lbl.gov/consumer/) Natural Resource Canada (http://www.nrcan.gc.ca/energy/ software-tools/7451) CanmetENERGY (http://www.nrcan.gc.ca/energy/softwaretools/7445) Lesosai (www.lesosai.com)

MarketManager [338]

Abraxas Energy (www.optegy.com/sware.html)

Commercial

MC4Suite 2009 [365]

Mc4Software (www.mc4software.com)

Commercial

Energy-10 [50] ENER-WIN [328,365] ESP-r [328,365] EZDOE [365,366] HAP [365,367]

HEED [328,365,367] Home Energy Saver [365] HOT2EC [365] HOT2XP [374]

Free to use Commercial Open source Commercial

Domestic energy analysis tool [372] Building energy efficiency analysis, preliminary design optimization, designing large-scale buildings such as universities, schools [365] Domestic energy billing and pricing comparison [373] Building energy modeling, simulation, and economy analysis [365] Small, residential, and commercial building energy systems analysis [365] Whole-building energy simulation tool [50,328,366] Building architecture with energy systems design and analysis [365] Building energy systems analysis [50] Hourly building energy simulation, cost analysis, greenhouse gas calculation [328,365] Building energy management software [328,365]

Free to use

Building energy consumption analysis tool [374]

Commercial

Domestic energy systems analysis, domestic load calculation [365] Building energy systems modeling, planning, and analysis [338] Commercial and residential building energy systems modeling, HVAC systems, solar systems modeling and analysis [365] (Continued )

Commercial

229

Free to use Free to use

Commercial building energy systems simulation tool [365,366] Domestic energy systems modeling and analysis; energy consumption and energy cost analysis and comparison [365,367] Domestic energy systems modeling and analysis tool [328,365,367] Internet-based domestic energy simulation tool [365] Building energy consumption analysis tool [365]

Energy Management Softwares and Tools

DesignBuilder [328,365]

Building energy modeling and analysis tool (specialized for Canada) [371] Energy balance of buildings, solar energy, peak load and energy demand analysis, energy performance analysis [365,367] Building energy analysis, building integrated renewables, energy optimization, and cost analysis [328,365] Building energy analysis tool [365]

230

Table 13

Continued Description/source

Availability

Application

ME3A [375]

Commercial

Building energy systems simulation [375]

MICROPAS [365] Modelica Buildings Library [4,51,56–64,326]

ME3A (Mejora de la Eficiencia Energética de Edificios en Andalucía) (http://me3ahome.esy.es/) ENERCOMP, Inc. (http://micropas.com) University of California, LBNL, DOE (https:// simulationresearch.lbl.gov/modelica/)

Commercial Open source

ParaSol [365]

ParaSol (www.ebd.lth.se/english/research/software/parasol/)

Free to use

PHPP [328,365]

Limited

PowerCalcPaK [376]

Passive House Institute (http://passivehouse.com/04_phpp/ 04_phpp.htm#PH9) Electric Power Calc Holdings, Inc. (http://powercalc.us)

Commercial

REM/Rate, REM/Design [328,365]

REM/Rate [328,365] (www.remrate.com)

Commercial

Right-Energy [377]

Wrightsoft [377] (www.wrightsoft.com)

Commercial

RIUSKA [328,365]

Granlund (www.granlund.fi/en/software/riuska/)

Commercial

SEMERGY [365,378]

XYLEM Technologies (www.xylem-technologies.com)

Commercial

SIMBAD [328,365]

Commercial

System Analyzer [365]

CSTB – ESE/AGE [328,365] (www.simbad-cstb.fr/simbad. html) LBNL, DOE, Simulation Problem Analysis and Research Kernel (SPARK) (http://gundog.lbl.gov/VS/spark.html) Centre for Building and Thermal Systems, NREL (www.nrel. gov/buildings/sunrel.html) Trane, Ingersoll Rand (www.trane.com)

Domestic energy systems modeling and analysis [365] Domestic energy systems dynamics simulation, control, modeling, electrical balanced/unbalanced systems [4,51,56–64,326] Analyze a comparison of peak load and energy demand of heating/cooling of houses [365] Domestic energy balancing, performance, and passive house modeling [328,365] Residential entire power distribution circuit design, power grid to home power distribution design [376] Modeling and analysis of residential energy systems, and home solar systems analysis [328,365] Domestic energy systems modeling and analysis, load analysis and HVAC selection [377] Domestic energy consumption, systems modeling, and simulation [328,365] Building energy analysis, efficiency evaluation, attached solar and PV systems analysis [365,378] Building energy systems analysis, integrated control systems, transient analysis [328,365] Complex energy systems, domestic energy systems, energy performance analysis [50,367] Building energy simulation tool [50,367]

TAS [328,365]

Environmental Design Solutions Limited (www.edsl.net)

Commercial

TRACE 700 [365,367]

Trane, Ingersoll Rand (www.trane.com)

Commercial

TRNSYS [181,328,366]

Transient system simulation tool (TRNSYS) (www.trnsys. com) StruSoft [379] (www.strusoft.com/products/vip-energy)

Commercial

SPARK, VisualSPARK [50,367] SUNREL [50,367]

VIP-Energy [379]

Free to use Free to use Commercial

Commercial

Building energy analysis, system evaluation with different equipment, load calculations [365] Building thermal and energy simulation, building thermal dynamics simulation [328,365] Energy systems utilization and scheduling, Compare energy systems and economy of buildings, HVAC systems optimization [365,367] Building energy simulation, energy systems simulation, renewable energy simulation [181,328,366] Energy performance analysis of residential and commercial buildings [379]

Energy Management Softwares and Tools

Tool

Table 14

List of computer tools to model and analyze hydro energy generation and its integration to the grid

Tool

Description/source

Availability

Application

ALLOCATE [380]

ALLOCATE (www.hydroallocate.com)

Commercial

APS, PC-VALORAGUA [108]

Argonne Peak Shave Model (APS) [108], PC-VALORAGUA (Value of Water) Model [108], CEEESA, ANL (http://ceeesa. es.anl.gov/projects/PowerAnalysisTools.html) CddHoward Consulting Ltd (www.cddhoward.com/index. html) EOPS (One-area Power-market Simulator), SINTEF (www. sintef.no/en/software/eops-one-area-power-market-simulator/) GoldSim Technology Group (www.goldsim.com/Web/ Products/GoldSimPro/) Power Vision Engineering (www.powervision-eng.ch/ HydroClone/MainFeaturesHydroClone.html) HydroSys (www.hydrohelp.ca/eng/home.htm) Modelica-based hydro power library (www.modelon.com)

Limited

Grid-connected hydropower-station efficiency optimization tool [380] Simulates hourly electricity generation from hydroelectric plants and determines optimal electricity generation, and analyzes power production cost [108] Hydroelectric power optimization tool [381]

CddHoward – Hydroelectric [381] EOPS, Samnett, Samlast [334] GoldSim [382] Hydro-Clone [383] HYDROHELP [384] Hydro Power Library [4,51,56–64,326] HydroSCOPE [385] IMP [384]

OptiPower [387] SHOP, SHARM [334]

SIMAHPP [388]

Power Vision Engineering (www.powervision-eng.ch/ OptiPower/MainFeatures.html) SHOP (short-term hydro operation planning), SHARM (shortterm hydropower application with risk modeling), SINTEF (www.sintef.no/en/) SIMAHPP 4, SIMAHPP 5 (www.hydroxpert.com)

SIMPACTS HydroPacts [108] SIMSEN [389]

CEEESA, ANL (http://ceeesa.es.anl.gov/projects/ EnergyAnalysisTools.html) École Polytechnique Fédérale de Lausanne (EPFL) [389] (http://simsen.epfl.ch/)

TURBNPRO [390]

Hydro Info Systems (www.turbnpro.com)

Commercial

Medium- and long-term hydropower systems scheduling and expansion planning [334]

Commercial (free for academics) Commercial

Hydropower systems project design and analysis tool [382]

Free to use Commercial Limited

Hydropower systems simulation tool [383] Hydroelectricity design tool [384] Hydropower plant modeling and control analysis [4,51,56–64,326] Hydropower systems simulation tool [385]

Free to use

Small-scale hydroelectric power sites modeling and analysis [384]

Commercial

Hydropower systems modeling and optimization, real-time operation [386] Hydropower modeling, power generation forecast, and system optimization [387] Hydropower systems modeling and optimization [334]

Commercial Commercial

Commercial Limited Commercial

Commercial

Hydropower project and small hydropower systems simulation [388] Simulates the impact of hydropower plant on the environment and people, analyzes the potential economic damage [108] Power distribution networks, hydropower systems, subsynchronous resonance (SSR), fault, and transient stability analysis [389] Hydropower systems design tool [390]

Energy Management Softwares and Tools

MAXHYDRO [386]

Sandia Corporation, DOE (http://energy.sandia.gov/energy/ renewable-energy/water-power/hydropower-optimization/) Integrated Method for Power Analysis (IMP), CANMET–Natural Resources Canada (www.small-hydro. com) MAXHYDRO (www.maxhydro.com)

Commercial

231

List of computer tools to model and analyze photovoltaic (PV) power generation and its integration to grid Description/source

Availability

Application

APOS photovoltaic StatLab [391,392] Archelios [321] BlueSol [392] CalSol [367] DDS-CAD PV [391] easy-pv [393] JEDI PV [50] METEODYN PV [394]

RWTH Aachen University, TÜV Rheinland Group (www. pvstatlab.rwth-aachen.de/index.php/apos.html) Trace Software International (www.trace-software.com) CadWare (www.bluesolpv.com) Ines (http://ines.solaire.Freetouse.fr/) Data Design Systems (www.dds-cad.net/products/dds-cad-pv/) Midsummer Energy Ltd (http://easy-pv.co.uk/) NREL (https://jedi.nrel.gov/index.php) Meteodyn (www.meteodyn.com)

Limited

PV data analysis and quality control [391,392]

Pvcad [328] Pvcad (www.iset.uni-kassel.de/pvcad) PV F-CHART [391,392] F-Chart Software (www.fchart.com/pvfchart/) PVGis [367] PV online calculator (http://photovoltaic-software.com/pvgis. php) PV*SOL [391,392,395] Valentin Software GmbH (www.valentin-software.com/en) PVSYST [391,395]

PVsyst SA (www.pvsyst.com)

PVWatts [50]

NREL (http://pvwatts.nrel.gov/)

Polysun [391] SAM [50,391,392] SolarDesignTool [391] Solar Pro [391,395]

Vela Solaris (www.velasolaris.com) System advisor model (SAM), NREL (https://sam.nrel.gov) Verdiseno (http://get.solardesigntool.com) Laplace Systems Co., Ltd. (www.lapsys.co.jp)

Commercial Commercial Free to use Commercial Free to use Free to use Commercial

PV systems design and analysis, PV project economy analysis [321] Country-based photovoltaic (PV) systems design [392] Online simulation tool to design PV systems [367] PV systems planning, installation design, PV project design [391] PV systems design [393] Analyze the economic impact of constructing PV plant in a locality [50] Solar resource and production assessment, PV panel layout design and power generation optimization, designing large-scale solar power plants [394] Free to use Grid-connected PV systems [328] Commercial Comprehensive PV systems design and analysis [391,392] Free to use PV energy calculation online [367]

Commercial Small-scale off-grid to large-scale grid-connected PV systems design and analysis, finance analysis, PV attached battery systems modeling and analysis [391,392,395] Commercial Grid-connected, stand-alone, DC grid-connected complete PV systems designing and analysis tool [391,395] Free to use Grid-connected PV energy production, cost, and performance analysis throughout the year around the world [50] Commercial Solar systems, geothermal systems design and analysis tool [391] Free to use Renewable energy and storage modeling and analysis tool [50,391,392] Commercial Online PV systems design tool [391] Commercial Simulate sophisticated PV energy systems [391,395]

Solar data to analyze a particular area by NREL: OpenPV, NSRDB, MapSearch, OpenEI, GIS Solar Data, Swera, BinMaker PRO [50] Solar radiation mapping and analysis tool: Focus Solar (Commercial), Geomodel, SolarGIS (Commercial), WhiteBox Technologies (Commercial), 3TIER (Commercial), Photovoltaic Geographical Information System (free to use), Urbasun (Commercial), Horizon (Commercial), Amethyst ShadowFX (Commercial), SUNEARTHTOOLS–Sun position (free to use) Mobile Apps: Easysolar (Commercial), Solar Shading (Commercial) Solar thermal systems analysis software [396]: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

CEC-FChart (www.energy.ca.gov/title24/swh_calculator/) (free to use) CombiSun (www.elle-kilde.dk/altener-combi/dwload.html) (free to use) DSPACE (http://dspace.mit.edu/handle/1721.1/58087) (free to use) GetSolar (www.ahornsolar.de/getsolar-pb.php) (commercial) KALKENER (http://kalkener.com/en/) (free to use) OVENTROP (http://oventrop.solar-software.de/system/lang/eng) (free to use) ScanTheSun (http://scanthesun.com/scanthesun.php) (free to use) SIMSOL (http://enr.cstb.fr/webzine/preview.asp?id_une=222) (free to use) SOLO – TECSOL (www.tecsol.fr/st_uk/default-uk.htm) (free to use) TRANSOL (http://aiguasol.coop/en/transol-solar-thermal-energy-software/) (commercial) T*SOL, T*SOL Pro (www.valentin.de/en/products/solar-thermal/14/tsol-pro) (commercial) [391,392,395]

Energy Management Softwares and Tools

Tool

232

Table 15

12. VIESSMAN (http://viessmann.solar-software.de/index.php?lang=en) (free to use) Solar radiation databases to feed into software to get real-time PV power generation [397]: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.

Australian Bureau of Meteorology (www.bom.gov.au/australia/satellite/) Canada Natural Resources (https://glfc.cfsnet.nfis.org/mapserver/pv/index.php) HelioClim-1 (www.soda-is.com/radiation/index.html) INFOCLIMAT (www.infoclimat.fr/climatologie/) Meteonorm (www.meteotest.ch/en/footernavi/solar_energy/meteonorm/) NASA SSE (https://eosweb.larc.nasa.gov/sse/) NCEP/NCAR (www.ncdc.noaa.gov/) NREL/USA (https://maps.nrel.gov/re-atlas) [50] OpenSolarDB (www.opensolardb.org/db/extractcopypaste) S@tel-light (www.satel-light.com/core.htm) SoDa HélioClim3 (www.soda-is.com/) Solar and Wind Energy Resource Assessment (SWERA) (http://maps.nrel.gov/SWERA) SOLEMI (www.dlr.de/tt/en/desktopdefault.aspx/tabid-2885/4422_read-6581/) SRRI (www.nrel.gov/rredc/solar_data.html) [50] Worldclimate (www.worldclimate.com) World Radiation Data Centre (http://wrdc-mgo.nrel.gov/) [50]

Energy Management Softwares and Tools 233

234

List of computer tools to model and analyze wind-power generation and its integration to the grid

Tool

Description/source

Availability

Application

AdWiMo [398] Breeze Production, Breeze Development [399] RT WINDMAP, METEODYN FORECAST, METEODYN WT, URBAWIND [394] Openwind [400] RT-MBDyn [401] SimWindFarm [402] WAsP [400] WndScreen3 [403]

MSC Software (www.mscsoftware.com) Greenbyte AB (www.breezesystem.com)

Commercial Commercial

Wind turbine modeling and analysis [398] Wind resource monitoring and wind-farm management tool [399]

Meteodyn (www.meteodyn.com)

Commercial

AWS Truepower (www.awstruepower.com) MultiBody Dynamics (MBDyn) (www.mbdyn.org) Aeolus FP7 (www.ict-aeolus.eu/SimWindFarm/) DTU Wind Energy (www.wasp.dk) UMassAmherst (www.umass.edu/windenergy/research/ topics/tools/software/wndscreen3) WindSim (www.windsim.com) Integral Analytics (www.integralanalytics.com) Wind Turbine Estimator (https://sourceforge.net/projects/ windturbineestimatorlite/) WILMAR (Wind Power Integration in Liberalized Electricity Markets), WILMAR project, EU (www.wilmar.risoe.dk) EMD International A/S (www.emd.dk/windpro)

Commercial Free to use Free to use Commercial Free to use (conditional) Commercial Commercial Open source

WINDMAP monitors real-time wind data, FORECAST calculates, forecasts, and optimizes wind power production, WT assesses wind resources and designs the most profitable wind farms [394] Wind-power generation project design and optimization [400] Wind-power systems control and analysis [401] Wind-farm modeling and analysis [402] Grid-connected wind energy systems modeling and analysis [400] Wind-diesel power systems design and analysis [403]

WindSim [404] WindStore [53] Wind Turbine Estimator LITE [405] WILMAR Planning Tool [181] windPRO [316]

Limited Commercial

Wind-farm simulation and optimization tool [404] Grid-connected wind power simulation tool [53] Analyze power generated by wind turbine [405] Analyze the optimal operation of power systems, forecasting wind power and load [181] Single and large wind-power farm modeling and simulation tool [316]

Energy Management Softwares and Tools

Table 16

Table 17

List of computer tools to model and analyze battery storage and its integration to the grid

Tool

Description/source

Availability

Application

AutoLion [406]

AutoLion-1D, AutoLion-3D, AutoLion-ST, EC Power (http://ecpowergroup.com/) Battery Design LLC (www.cd-adapco.com) Battery Lifetime Analysis and Simulation Tool (BLAST), NREL (www.nrel.gov/transportation/energystorage/ blast.html) Battery Ownership Model (BOM), NREL (www.nrel.gov/ transportation/energystorage/ownership.html) CAEBAT (Computer Aided Engineering for Batteries) Virtual Integrated Battery Environment (VIBE) (http:// batterysim.org) COMSOL (www.comsol.com)

Commercial Commercial Limited

Battery modeling, analysis, management, safety and reliability analysis [406] Battery cell design, battery performance analysis [407] Battery performance analysis tool [50]

Limited

Analyze financial aspects of using batteries in different applications [50]

Open source

Battery systems modeling and analysis [408]

Commercial

Battery modeling and analysis [328]

Commercial

Battery systems and management analysis [409]

Battery Design Studio [407] BLAST [50]

BOM [50] CAEBAT VIBE [408]

COMSOL Multiphysics- Batteries & Fuel Cells Module [328] bqStudio [409] B2U [50]

MapleSim Battery Library [411] Toolbox peichersysteme (Energy Storage Toolbox) [412] TrueData X-HVE [412]

Free to use

Batteries second-use analysis [50]

Commercial Commercial Free to use (conditional) Commercial Limited

Battery energy storage systems modeling and analysis [4,22–30] Grid-scale battery technology simulation [53] Simulate power producing systems which incorporate batteries [410] Battery systems modeling, analysis, performance evaluation [411] Battery storage systems analysis [412]

Impressum (www.fuelcon.com)

Commercial

Simulates the real behavior of battery modules in vehicles and grid [412]

Energy Management Softwares and Tools

ETAP Battery Sizing module [4,22–30] GridStore [53] KiBaM [410]

Battery management studio (bqStudio), Texas Instrumentation (www.ti.com/tool/bqstudio) Battery second use (B2U), NREL (www.nrel.gov/ transportation/energystorage/use.html) ETAP (http://etap.com/dc-systems/dc-battery-sizing.htm) Integral Analytics (www.integralanalytics.com) KiBaM (Kinetic Battery Model) (www.umass.edu/ windenergy/research/topics/tools/software/kibam) Maplesoft (www.maplesoft.com) dSPACE, RWTH Aachen University (www.dspace.com)

235

236

List of computer tools to model and analyze electric-vehicle integration to grid

Tool

Description/source

Availability

Application

ADVISOR [4,33,413]

Advanced Vehicle Simulator (ADVISOR), NREL (https:// sourceforge.net/projects/adv-vehicle-sim/) ANSYS Inc. (www.ansys.com)

Open Source

Hybrid electric vehicle (EV) power train, fuel economy, and emission analysis [4,33,413] Simulate multidomain systems including electrical power systems, EV, and energy market [4,33] Calculates total energy consumption from renewable and nonrenewable energy sources with corresponding gas emission [4]

ANSYS Simplorer [4,33] GREET 2014, IMPACTT [4]

HYPERSIM, ePOWERgrid, eMEGAsim, ePHASORsim [4] PHEV-CIM/PEV-CIM [4] SINDA/FLUINT [4] SOMES [4,181] V2G-Sim [4,33]

GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) [4], Integrated Market Penetration and Anticipated Cost of Transportation Technologies (IMPACTT) [4], ANL (greet.es.anl.gov) OPAL-RT Technologies (www.opal-rt.com) Natural Resources Canada (NRCan) (www.nrcan.gc.ca/ energy/software-tools/7441) C&R tech (crtech.com) Utrecht University, Netherlands (www.web.co.bw/sib/ somes_3_2_description.pdf) Lawrence Berkeley National Laboratory (v2gsim.lbl.gov)

Commercial Free to use

Commercial Free to use Commercial Commercial Restricted

Power systems, V2G technology, HVAC, HVDC, FACTS and power system real-time digital simulator [4] Analyze impact of electric vehicles (EVs) on grid [4] Design and analyze power generation, alternative energy, and automobile systems [4] Renewable energy system including PV, wind, generator, grid, battery storage, EV [4,181] Simulate vehicle-to-grid (V2G) systems [4,33]

Energy Management Softwares and Tools

Table 18

Energy Management Softwares and Tools

237

List of computer tools to model and analyze hydrogen energy integration to the grid

Table 19 Tool

Description/source

Availability

Application

SERA [50]

Scenario Evaluation, Regionalization & Analysis (SERA), NREL (www.nrel.gov/hydrogen/energy_analysis.html) Institute for Energy Technology (www.hydrogems.no)

Limited

Hydrogen analysis (H2A), NREL (www.hydrogen.energy. gov/h2a_analysis.html) Hydrogen Financial Analysis Scenario Tool (H2FAST), NREL (www.nrel.gov/hydrogen/h2fast/)

Limited

Optimal production, delivery, and management of hydrogen energy [50] Hydrogen energy systems modeling and integration to energy systems analysis [414] Analyze hydrogen production from renewable sources, analyze the economic point of its delivery [50] Financial analysis of hydrogen fueling stations [50]

HYDROGEMS [414] H2A [50] H2FAST [50]

Limited

Free to use

List of computer tools to model and analyze fuel-cell power integration to the grid

Table 20 Tool

Description/source

Availability

Application

ANSYS CFD [415]

ANSYS Inc. (www.ansys.com)

Commercial

FCPower [50]

FCPower Model, NREL (www.hydrogen. energy.gov/fc_power_analysis.html) MSC Software (www.mscsoftware.com/ product/easy5) Modelica-based fuel-cell library (www. modelon.com)

Limited

Fuel-cell energy systems analysis [415] Fuel-cell energy analysis [50]

Easy5 [398] Fuel Cell Library [4,51,56–64,326]

Commercial Commercial

Stationary or mobile fuel-cell design and analysis [398] Fuel-cell modeling, control, and analysis [4,51,56–64,326]

List of computer tools to model and analyze biomass energy integration to the grid

Table 21 Tool

Description/source

Availability

Application

BIOBIL, DATEVAL, BAUM/ DESIGN [416] BSM [50]

BIOENERGIESYSTEME GmbH (www. bios-bioenergy.at/en/) Biomass Scenario Model (BSM), Dept. of Energy office of the Biomass program, NREL (http://en.openei. org/wiki/Biomass_Scenario_Model) The University of Zagreb (http://h2res. fsb.hr) SimTech (www.simtechnology.com/ CMS/index.php/ipsepro)

Commercial

Biomass system design, analysis, power generation process [416] Biomass scenario, harvest, transport, collection, conversion, distribution, consumption analysis [50]

H2RES [181] IPSE pro [417]

5.6.2.14.7

Free to use

Limited Commercial

Biomass energy managing, planning, and analysis tool [181] Biomass energy generation modeling and analysis [417]

List of tools to analyze and integrate fuel-cell power generation into the grid

The usage of fuel cells is increasing to meet energy demand at home and in vehicles (fuel-cell vehicles). Fuel cells will also be integrated in the IoE along with other distributed energy resources. Therefore, simulation tools that deal with fuel-cell energy systems modeling, analysis, and management are listed in Table 20.

5.6.2.14.8

List of tools to analyze and integrate biomass power generation into the grid

Simulation tools that deal with biomass energy systems modeling, analysis, and management are listed in Table 21.

5.6.2.14.9

List of tools to analyze and integrate geothermal power generation into the grid

Computer tools that can simulate geothermal energy systems modeling, analysis, and integration to the grid are listed in Table 22.

5.6.3

List of Simulation Tools

In this section, some selected simulation tools listed in Tables 1 to 22 are described in brief. Each description contains the main features of the tool, common applications of the tool, and its modeling methodology.

238

List of computer tools to model and analyze geothermal energy integration to the grid

Tool

Description/source

Availability

Application

COMSOL Multiphysics [418] CYRIC geothermal model [419] FALCON [420]

COMSOL (www.comsol.com)

Commercial

Geothermal energy systems modeling and analysis [418]

CyRIC (www.cyric.eu/solutions/energy-systems-simulator.html)

Limited

Geothermal energy systems modeling and analysis [419]

Fracturing And Liquid CONvection (FALCON), Idaho National Laboratory (https://github.com/idaholab/falcon) DHI Technologies [421] (www.mikepoweredbydhi.com) Precision Geothermal, LLC (www.precisiongeothermal.com) GmbH (www.geologik.com/sf) Intrepid Geophysics (www.intrepid-geophysics.com) Valentin Software GmbH (www.valentin-software.com/en)

Open source

Model and analyze geothermal energy systems [420]

Commercial Commercial Commercial Commercial Commercial

Geothermal energy systems modeling and optimization [421] Geothermal energy systems data analysis [422] Geothermal energy systems analysis [423] Geothermal power systems modeling tool [424] Design and planning of heat-pump and geothermal power systems [391,392,395] Geothermal systems modeling and analysis [425]

FEFLOW [421] GeoCube [422] GeoLogik [423] GeoModeller [424] GeoT*SOL [391,392,395] HyGCHP [425] RETScreen [181] SHEMAT [426] SVHeat [427]

HyGCHP (hybrid ground-coupled heat pumps), Seventhwave (www. seventhwave.org/hygchp) CanmetENERGY, Natural Resources Canada (www.nrcan.gc.ca/energy/ software-tools/7465) SHEMAT (Simulator for heat and mass transport) (www.tuhh.de/rzt/ tuinfo/software/sim/shemat.html) SoilVision Systems Ltd (www.svheat.com)

Free to use Free to use Commercial

Renewable energy, clean energy, geothermal energy modeling and analysis software [181] Geothermal energy systems analysis [426]

Commercial

Geothermal systems modeling and analysis tool [427]

Energy Management Softwares and Tools

Table 22

Energy Management Softwares and Tools 5.6.3.1

239

ADMS

Advanced Distribution Management Systems (ADMS) is a power-systems management tool [319]. It is a commercial tool developed by Schneider Electric [319]. The tool can simulate energy management systems, data acquisition and control systems (especially supervisory control), demand response management, outage management, and power distribution management [319]. The tool provides a platform to reduce administrative and systems cost, and utility maintenance cost. The tool can model and analyze peak-load management, peak shaving, distributed generators, and distributed storage systems [319]. It provides mathematical models of power networks and real-time monitoring and control strategies [319]. It analyzes various power quality problems such as fault analysis, voltage degradation, short circuits, power distribution loss, systems reliability, and improves quality and performance [319].

5.6.3.2

AMES

Agent-Based Modeling of Electricity Systems (AMES) is an electricity-market simulation tool [107]. It is an open-source tool and free to use [107]. The tool can manage central management by an independent market operator [107]. It consists of a two-settlement system that can simulate market operation a day ahead to ensure balancing between demand and supply [107]. It can simulate locational marginal pricing (LMP) to manage grid congestion, i.e., power pricing based on the location and timing of the power injection and power consumption [107]. It can also model and analyze the whole energy market. Wholesale-market simulation can be done over a user-specific AC transmission grid from day 1 to a user-specified maximum day [107]. It can analyze energy management efficiency based on the wholesale market and the transmission-distribution line constraints. It has a user-friendly graphical user interface that allows users to modify the system visually [107].

5.6.3.3

Cepel Toolkit

The Electrical Energy Research Center (Cepel), the largest electrical energy research institution in South America, has a number of simulation tools to model, analyze, plan, optimize, and manage energy and power systems [109–121]. Cepel has developed a wide range of tools such as ANAFAS, ANAREDE, ANATEM, Encad, FLUPOT, FormCepel, HarmZs, Iself, NH2, PacDyn, PlotCepel, MELP, METPE, PREVMERCADO, PLANTAC, SAPRE, ELEKTA, and OTSI/SSI [109–121]. The ANAFAS, ANAREDE, ANATEM, Encad, FLUPOT, FormCepel, HarmZs, Iself, NH2, PacDyn, PlotCepel tools are used for power transmission/distribution network analysis, faults, transient analysis, optimal power flow, reliability analysis, power system control analysis, load forecasting, and energy system modeling [109–121]. For long-term power systems and power market planning, demand-generation and price prediction, and systems optimization, the MELP, METPE, PREVMERCADO tools are used [109–121]. The PLANTAC and SAPRE tools are used for power systems, power transmission, and power project planning and analysis [109–121]. The ELEKTA tool is used for electrical power distribution systems planning and optimization. The OTSI/SSI tool is used for power demand management and optimization [109–121].

5.6.3.4

D-GEN Pro

D-GEN Pro is a distributed power-generation feasibility and economy analysis tool [335]. It is a commercial tool that provides a quick solution to evaluate cost-effective distributed power generation [335]. The tool can analyze hourly load profile data and analyze the performance of the power generation. It produces billing information and provides easy-to-understand energy marketing graphs and reports [335]. The tool can use the climate from over 700 locations [335]. It can model and analyze various onsite power-generation capabilities such as automatic generator deployment, part-load efficiency, weekend operation, etc. [335]. The tool has a user-friendly graphical user interface and is compatible with 64-bit machines [335]. It can create an easy-to-read energy profile graph for customers [335]. It can include economic assumptions before adopting any user-defined power generation system [335].

5.6.3.5

DOE-2, VisualDOE, eQUEST

DOE-2, eQUEST, VisualDOE are building energy systems simulation tools [50,328,366,367]. DOE-2 and eQUEST are free to use, and VisualDOE is limited [50,328,366,367]. DOE-2 is a building energy-systems analysis tool that can predict the energy usage and cost of all types of buildings [50,328,366,367]. It considers the building layout, conditional systems (HVAC, lighting, etc.), operating schedules, energy rates provided by the utilities, and weather data in hourly building energy simulation and associated costs [50,328,366,367]. The eQUEST (quick energy simulation) tool provides users with a comparative analysis of the building design and the energy technology used on it [50,328,366,367]. It can model and analyze existing and newly adopted building energy systems and building design performance [50,328,366,367]. It is compatible with DOE-2. VisualDOE evaluates the energy savings of building design options. It uses the DOE-2 tool to calculate the hourly energy usage, peak load demand, and energy profile analysis. VisualDOE is compatible with DOE-2 and other advanced calculation engines like EnergyPlus [50,328,366,367].

240

Energy Management Softwares and Tools

5.6.3.6

EA-PSM

EA-PSM is used to model and analyze transmission and distribution networks [230]. It is a commercial tool (free demo version) and maintained by Energy Advice [230]. It is an excellent tool for utilities and electrical project developers to model and analyze grid and power equipment operation [230]. It can integrate various grid components such as synchronous generators, distributed renewable energy resources, asynchronous motors, transformers, static loads etc. It has various power-systems analysis modules such as load flow, short circuit, arc flash calculations, harmonics, load flow, protection, and automation [230]. It can model and analyze energy consumption systems and distribution costs. It analyzes the system reliability by coordinating various power components. The tool can reduce energy consumption by coordinating generation and consumption devices [230].

5.6.3.7

EFEN

EFEN is used to model and analyze the energy systems of commercial buildings [372]. It is a commercial tool (free demo), developed by Carli Inc. [372]. EFEN is an advanced simulation tool for analyzing next-generation building energy systems [372]. It uses an intelligent building generator and an EnergyPlus simulation engine [372]. It easily makes a comparative energy analysis between different options. It can provide decisions for designing a building on an energy perspective by predicting energy demand [372]. It can analyze the impact of the building design on the energy usage and finance [372]. It can calculate the sizes of HVAC systems, energy and cost savings, first-year equipment cost savings, and the building energy usage based on the weather [372]. It simulates the annual energy usage and the average and peak load conditions. It can model daylight control to save building energy [372].

5.6.3.8

e-ISOFForecast, e-PowerForecast, e-AccuWind, e-SolarForecast, e-LoadForecast, e-DR

e-LoadForecast, e-ISOFForecast, e-PowerForecast, e-AccuWind, and e-SolarForecast are commercial tools for power generation and demand forecasting. e-POwerForecast and e-ISOFForecast provide a real-time hourly electric load and energy price forecasting tool [311]. They can forecast the power generation and market up to 15 days ahead. They have intelligent systems for predictions that can take weather data input for forecasting analysis [311]. e-AccuWind and e-SolarForecast predict the hourly and subhourly wind power and PV power generation respectively. e-LoadForecast predicts customer energy demand, which helps in energy generation and energy management [311]. The E-DR tool is used for demand-side management and to optimize the energy cost [311]. These tools have a user-friendly graphical user interface and the forecast data is available in different user-compatible formats [311]. These tools are developed by PRT. They also developed some other forecasting tools such as artificial neural network shortterm load forecaster (ANNSTLF) (used by EPRI), similar day load forecaster (SDLF), and neural electric load forecaster (NELF) [311].

5.6.3.9

EZ Sim

EZ Sim is used to analyze the energy usage of commercial buildings and the impact of retrocommissioning measures [337]. It is a noncommercial tool and free to use [337]. It can diagnose and analyze energy patterns and energy consumption trends [337]. It can analyze strategies to save energy usage by estimating the end-users’ energy consumption within the facility [337]. It uses building information (energy equipment, HVAC, building design), daily average temperature, and monthly actual energy bills to calculate the pattern of energy usage [337]. It then compares and analyzes the predicted electricity bill and fuel usage with the actual electricity bills and fuel usage to find the reasons for difference [337].

5.6.3.10

GAMS

General Algebraic Modeling System (GAMS) is used to model and analyze mixed-integer, linear, and nonlinear optimization problems [352,353]. It is useful to analyze large and complex systems [352,353]. The GAMS tool is widely used to solve various power and energy systems complex optimization problems [352,353]. It is used to develop power-systems mathematical models with concise algebraic statements [352,353]. The components (data, variables, models, sets, outputs) of any mathematical model is coded into GAMS to solve optimization problems [352,353]. It provides various solvers such as LP, nonlinear programming (NLP), nonlinear mixed-integer programming (MINLP), and linear mixedinteger programming (MILP) [352,353]. Some power-quality problems such as optimal power flow and optimal load flow can be solved by using these GAMS solvers [352,353]. In addition, fuel-supply optimization, production costing optimization, load management optimization, integrated transmission-systems planning, and optimally distributed energy systems modeling can also be done using GAMS [352,353].

5.6.3.11

GenOpt

GenOpt is an optimization tool to minimize the cost function and for various optimization problems [354]. It can minimize various power-systems, smart-grid, and energy-systems cost functions generated from other simulation tools such as DOE-2,

Energy Management Softwares and Tools

241

Dymola, IDA-ICE, TRNSYS, and EnergyPlus [354]. It is compatible with any simulation tools that read and write text files and the variables can be continuous, discrete, or mixed [354]. GenOpt has been developed to optimize cost functions where it is computationally expensive and the derivatives do not exist. It has a global one-dimensional and multidimensional optimization algorithm library and algorithms for parametric runs [354]. In addition, new optimization algorithms can be added to the GenOpt library without knowing the structural details. However, it cannot compute quadratic problems, LP problems, and optimization problems where the cost function's gradient is available [354]. GenOpt is free to use [354]. It is written in Java, which makes it platform-independent and offers a general interface to other programs [354]. It can run parallel simulations in different CPUs to minimize the computational time.

5.6.3.12

ICARUS

Investigation of Cost and Reliability in Utility Systems (ICARUS) is used to plan energy systems [108]. ICARUS is maintained by the Center for Energy, Environmental, and Economic Systems Analysis (CEEESA), Argonne National Laboratory (ANL), US [108]. The tool can assess the reliability and performance of various electricity generation systems and expansion plans for a largescale energy systems planning [108]. It calculates the expected energy generation for large-scale energy systems, costs, types of energy, and maintenance schedules [108]. It analyzes the necessary reserve capacity for reliable energy systems, and the loss-of-load probability [108]. Its simplified probabilistic simulation reduces the heavy computational requirements to analyze energy systems planning [108].

5.6.3.13

INFORSE

International Network for Sustainable Energy (INFORSE) provides a national energy systems modeling and energy balancing tool [181]. This tool is limited and only distributed to nongovernment organizations [181]. The tool takes the input through a spreadsheet to model energy systems. In its modeling it includes energy demand, energy generation, energy policy, and energy trends [181]. It considers renewable energy, thermal, hydrogen, transportation energy systems in its modeling [181]. However, it does not consider tidal power, pumped-heat electrical storage, battery storage, EVs, and V2G technology [181]. The analysis of this tool provides decisions on energy services, energy efficiency, renewable energy usage, energy trends, and energy policy [181]. The tool also can provide the energy cost and corresponding CO2 emission. It can simulate 100 years ahead. The tool has been used in different countries (Denmark, Bulgaria, Latvia, Belarus, Romania, Russia, and Ukraine) to forecast the utilization of renewable energy up to 2050 [181].

5.6.3.14

IPM

Integrated Planning Model (IPM) is used to simulate power market and environmental issues [333]. It can simulate and optimize wholesale power integration, power systems reliability considering fuel choice, transmission systems and capacity expansion, key operational elements of generators, and environmental constraints [333]. The tool can validate power generation and transmission assets. It can forecast the regional energy and energy capacity price [333]. It analyzes the impact of alternative environmental regulatory standards on energy systems [333]. It can provide a decision as to when a power plant should stop operation considering environment, cost, efficiency, and power generation. It can also analyze the combined heat and power (CHP) system [333]. The tool determines the least cost to meet energy generation and capacity requirements under specific constraints, air pollution regulations, and plan-specific operational constraints [333]. The tool has been used for their research by various organizations such as the US Environmental Protection Agency (EPA) and the Federal Energy Regulatory Commission (FERC) [333].

5.6.3.15

LEAP

Long-Range Energy Alternatives Planning (LEAP) is used to analyze national energy systems [181]. LEAP was developed by the Stockholm Environment Institute [181]. It is free for qualified users in developing countries. Currently, it has over 5000 users in 169 countries including academics, NGOs, energy utilities, consulting companies, and government agencies [181]. The tool requires only 3–4 days training for the basic simulation [181]. It can be used to analyze energy production, consumption, and resource extraction [181]. It can simulate the methodologies and policies of electricity generation and expansion planning [181]. It supports a number of modeling methodologies such as enduse macroeconomics modeling [181]. LEAP simulates using a time step of up to 50 years [181]. LEAP cannot simulate energy systems modeling optimization. It has been used in different applications such as energy demand and GHG emissions reduction within road transport in China, and sustainable penetration of renewable energy in the Greek islands [181].

5.6.3.16

LoadSEER, GridStore, DSMore, DRPricer, XactFit, SmartSPOTTER

These are electricity-systems modeling and analysis tools [53]. They are commercial tools, maintained by Integral Analytics Inc. [53].

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LoadSEER can design and simulate load forecasting, demand-side management, integrated distributed resource planning, and risk analysis [53]. The tool is compatible with, and can import data from, other power-systems and power-flow analysis tools like CYME and SynerGEE [53]. GridStore is a grid-scale battery technology modeling and analysis tool [53]. It can model and analyze a grid-connected battery system’s day-to-day performance, market-based risk, operational cost, and total revenue [53]. It can analyze how grid-scale energy storage supports the energy market, and energy production. Demand Side Management Option Risk Evaluator (DSMore) is used to simulate the costs, benefits, and risks of demand-side management [53]. It also includes energy efficiency calculations and smart-grid programs and services in the simulation process. The Demand Response Pricer (DRPricer) tool provides a clear picture of the potential revenue impact on load management and finds the system reliability risk [53]. It can also simulate a customer’s energy consumption response and its impact on the power system [53]. The XactFit tool models and analyzes cost-effective energy billing systems. SmartSPOTTER can simulate energy price, consumption according to location, and provide energy savings strategies [53].

5.6.3.17

METEODYN Toolkit

METEODYN has developed a wide range of simulation tools to model, analyze, manage, and optimize wind and PV energy systems [394]. The tools are METEODYN WINDMAP, METEODYN FORECAST, METEODYN WT, URBAWIND, and METEODYN PV [394]. These tools are commercial and a demo version is free to use [394]. The METEODYN WT tool is used to assess wind resources, predict annual wind energy, wind generation site modeling, and analysis [394]. It is also used to model, analyze, and optimize wind energy generation. The METEODYN FORECAST tool is used to predict wind energy generation and optimize small-scale and large-scale wind farms [394]. The METEODYN PV tool is used to assess solar resources and associated power generation. It models, analyzes, and optimizes small and large-scale PV power generation systems [394].

5.6.3.18

Modelica

Modelica is an object-oriented multidomain tool to model complex systems, for example, electrical power systems and control systems [4,51,56–64]. The free Modelica standard library contains 1360 components, and performs over 1280 functions in various domains [4,51,56–64]. It simulates various power and energy systems applications through its various commercial and free library functions [4,51,56–64]. It is an open-source platform and any user can contribute to add functions in this tool. The PowerSystems library of Modelica is used to simulate electrical power systems in steady-state and transient modes [4,51,56–64]. The Electric Power library is used to simulate power systems components. Its Modelica_EnergyStorages library is used to simulate complex electrical energy storage systems with associated loads, charging/discharging control, and battery management systems [4,51,56–64]. The Modelica Building Library can model and analyze HVAC systems, building energy systems, and building electrical systems [4,51,56–64]. It has a FuzzyControl library to implement fuzzy-logic control in various applications of power and energy systems [4,51,56–64]. It can also integrate and simulate renewable energy resources in power systems. It can optimize electrical and energy systems through its Optimization Library [4,51,56–64].

5.6.3.19

Mosaik

Mosaik is a cosimulation framework to simulate the smart grid at a large scale by using its simple API [65–69]. Its API allows it to integrate a large number of components, simulators, and control strategies. It is a user-friendly API that can easily interface to other simulators regardless of their programming language [65–69]. Mosaik provides a testbed by connecting thousands of distributed process simulators in a smart grid under various control strategies such as centralized control, multiagent systems. Some research [65–69] has been done to simulate the whole smart grid by connecting Mosaik and other simulation tools (OMNeT++, RT-LAB, IPSYS) to simulate power systems, communication systems, and automation together. Mosaik is an open-source tool and free to use, written in Python [65–69].

5.6.3.20

NeSSi

Network Security Simulator (NeSSi) is a power-network security analysis tool [242–246]. It has the ability to generate profilebased automation attacks and traffic analysis. Its support for detection algorithm plugins helps to evaluate power system security [242–246]. This tool can configure the security-related settings and evaluate the vulnerability of the security frameworks of power systems. Its advanced algorithm can easily model, detect, and analyze malicious activity on the system and any attempts to degrade the service of the system [242–246]. NeSSi is an open-source tool developed at DAI-Labor sponsored by Deutsche Telekom Laboratories [242–246]. It comprises three distinct components: simulation backend, graphical user interface, and result database. Each of these components may be run on separate machines, which makes it a user-friendly tool for researchers to exchange data [242–246].

Energy Management Softwares and Tools 5.6.3.21

243

OMNet++

Object Modular Network Testbed in C++ (OMNet++) is a modular network simulation tool [274–280]. It is an extensible, component-based modular C++ library primarily used for making various network simulators such as wireless communication networks, queueing networks, on-chip networks, etc. [274–280]. It has a generic architecture and can be used to solve various problems such as protocol modeling, modeling wired and wireless communication networks, modeling queuing networks, analyzing the performance of complex software and hardware systems, modeling and simulation of any discrete events, and can exchange messages [274–280]. Although OMNet++ uses a C++ platform, user-defined modules can be added in other programming languages like Java and C#, and used in it [274–280]. It provides a graphical runtime environment, Eclipse-based IDE, and a host of other tools [274–280]. OMNeT++ is a widely used tool among the scientific and industrial community. It is open-source and free to use [274–280].

5.6.3.22

PADEE

PADEE is a design, analysis, and planning tool for low and high-voltage power distribution networks [164]. PADEE uses an AUTOCAD front end to analyze electrical power distribution networks and perform load-flow calculations [164]. It also uses GIS (geographical information systems) to design and map distribution networks. Its SGMAP module automatically links with Google Maps and copies Google Images to Autocad Map [164]. PADEE simulates various power systems applications through its various modules such as intelligent maps program (IMP); customers program (CP); distribution network maintenance program (DNMP); primary network analysis program (PNAP); secondary network analysis program (SNAP); transformer load management program (TLMP); demand forecast for short, medium, and long term program (DFP); energy loss program (ELP); equipment information and photo program (EIPP); budget calculation module (COMWIN); overcurrent protection coordination program (OPCP); and interrupts and operations program (IOP) [164]. All these modules simulate almost all applications of power systems, including power quality analysis, power flow analysis, energy and power management through customer engagement, demand forecast, protection systems, power systems economy analysis, etc. [164]. PADEE can export/import data from other third-party tools to facilitate the simulation process. Currently, a large number of institutions and utilities are using PADEE software to design and model power systems [164].

5.6.3.23

PLEXOS

PLEXOS Integrated Energy Model (PLEXOS) is a simulation tool to model and analyze the integrated energy market [4,33]. It is a widely used commercial tool (free demo), developed by Energy Exemplar [4,33]. A significant number of academics, researchers, research institutions, utilities, and power-generation companies use this tool [4,33]. It has various optimization technology partners such as IBM CPLEX Optimizer, Dash Optimization, MOSEK, and Zuse Institution Berlin [4,33]. PLEXOS is used to plan capacity expansion of power plants, model and analyze the energy production cost, forecast the electricity market, plan other energy systems (gas) infrastructure, design and analyze energy market design, analyze power transmission constraints, optimize energy systems, plan smart-grid systems, model and analyze renewable energy to grid, analyze and manage energy systems risk, and manage hydroenergy resources [4,33]. It can also model and analyze optimal energy generation, energy dispatch, and energy pricing. It can provide a comparative modeling of renewable power generation and smartgrid technologies [4,33]. It can also simulate single to a large number of power generators and optimize their operation [4,33]. It has a user-friendly graphical user interface. It can simulate energy generation and energy systems from one minute up to 10 years [4,33].

5.6.3.24

PSAPAC, DYNRED, LOADSYN, IPFLOW, TLIM, DIRECT, LTSP, VSTAB, ETMSP, SSSP

PSAPAC (Power System Analysis Package) comprises several power-systems static and dynamic analysis tools such as DYNRED, LOADSYN, IPFLOW, TLIM, DIRECT, LTSP, VSTAB, ETMSP, and SSSP [127–129]. PSAPAC is maintained by EPRI and written in FORTRAN [127–129]. The PSAPAC packages include static analysis programs (transmission limit and power flow programs), dynamics analysis programs (small-signal stability, transient stability, voltage stability), and data analysis programs (load synthesis, dynamic reduction program) [127–129]. Dynamic Reduction Program (DYNRED) simulates aggregated machines and networks to reduce them to an equivalent network [127–129]. LOADSYN (Load Synthesis Program) models and simulates static and dynamic networks and loads [127–129]. The IPFLOW (Interactive Power Flow) program supports steady-state analysis, voltage and power-flow analysis, and generation dispatch analysis. The Transfer Limit (TLIM) program calculates real power flow, load and generation dispatch analysis. The DIRECT tool is used to analyze the stability of power systems. Long-Term Stability Program (LTSP) is used to simulate the long-term dynamics of large power systems. Voltage Stability (VSTAB) is used to analyze the voltage stability of a large complex power system [127–129]. Extended Transient Midterm Stability Program (ETMSP) performs transient and stability analysis of a large power system [127–129]. Small-Signal Stability Program (SSSP) is used to analyze the small signals of power systems [127–129].

244 5.6.3.25

Energy Management Softwares and Tools PSCAD/EMTDC

Power-Systems Computer-Aided Design (PSCAD) and Electromagnetic Transients Including DC (EMTDC) are power-system functionality simulation tools. PSCAD/EMTDC is developed by Manitoba HVDC Research Centre and a free version is available to use [84–87]. It can analyze the power quality issues of power systems such as transients, faults, short circuits, transformer saturation, voltage flicker due to a variable load, and voltage dips [84–87]. It can also simulate the power quality improvement devices (e.g., FACTS). It can simulate distributed renewable energy sources such as wind power and PV. It has a battery library to simulate the energy storage systems of a smart grid [84–87]. In addition, the generators, motors, and power electronics modules can simulate the associated applications.

5.6.3.26

PSS NETOMAC, PSS SINCAL, PSS PDMS

These are power-systems simulation tools maintained by Siemens. PSS NETOMAC serves to access and manage any kind of information on dynamic performance analysis of power systems. It is also used analyze short-circuit currents and steady-state load flow, and to optimize the systems [4,22,88–95]. Siemens Network Calculator (SINCAL) is used for power-systems planning, designing, and operation [4,22,88–95]. It can plan both short-term and long-term to simulate the future scenario and to avoid costly design errors and poor investments. It can simulate short circuits, load flow (balanced and unbalanced), dynamics, harmonics, systems reliability, and protection coordination (with PSS PDMS) [4,22,88–95]. PSS Protection Device Management Systems (PDMS) is used to manage protection devices. It can manage data stored centrally and exchange it with other power-systems software such as PSS SINCAL [4,22,88–95].

5.6.3.27

RETScreen

RETScreen is a clean-energy management tool, specialized to analyze renewable energy efficiency, and energy project feasibility. It can investigate the viability of the energy project, and investigate the energy performance [181]. It was developed by Natural Resources Canada in 1996 [181]. It is free to use and available in multiple languages. A wide range of researchers, academics, and organizations use this tool [181]. This tool has been used to develop wind farms in Algeria, PV power generation project feasibility assessment in Egypt, and solar heating systems in Lebanon [181]. RETScreen helps decision makers to model, analyze, and optimize the technical and financial feasibility of clean-energy systems projects [181]. It assesses the performance of clean-energy systems, which helps to produce additional energy production and save energy wastage [181]. It can analyze energy production, associated costs, and emissions. It can include various products, projects, renewable energy resources, climate databases, and hydrology in its simulation process [181]. It can analyze energy systems in monthly or yearly time steps for up to 50 years. It uses five steps for every energy-system analysis: energy systems model (renewable resources integration, loads, project type, methodology, annual energy production, energy savings, etc.), cost analysis (initial, periodic, and annual costs), greenhouse-gas emission analysis (determine the annual emission reduction through the proposed energy system), financial summary (avoided costs, inflation, discount, tax, debt, emission reduction cost), and sensitivity and risk analysis (identify the uncertainty parameters for the project viability) [181].

5.6.3.28

Smart Grid Co-Simulator

Smart Grid Co-Simulator is a tool to simulate practical scenarios of the smart grid [292]. It is an open-source tool and free to use [292]. It is used to co-simulate power systems and communication systems and their interface to investigate the total smart-grid environment [292]. It can interface with OpenDSS to simulate smart-grid power systems and with OMNet++ to simulate smartgrid communication systems [292]. The tool can investigate the behavior of the smart grid by synchronizing both power systems and communication systems responses together [292]. The tool has been tested to simulate the smart grid where various distributed renewable energy sources have been integrated to the existing power system [292].

5.6.3.29

SmartGridToolbox

SmartGridToolbox is a discrete event-based simulation tool to model, analyze, and optimize the smart grid [105]. It was developed by Optimization Research Group (Energy Systems subgroup), National ICT Australia (NICTA) with the help of Actew-AGL and the Australian National University (ANU) [105]. It is an open-source tool and free to use [105]. The tool can simulate present and future smart grids with necessary optimization. It can model the smart-grid load by connecting distributed renewable energy resources, EVs, battery components, and weather-dependent loads. It can simulate various power-quality parameter analyses with their necessary optimization [105]. It can analyze islanded networks or network isolation due to faults and carry out simulations of the rest of the network to find the system reliability [105].

Energy Management Softwares and Tools 5.6.3.30

245

TOP-Energy (eSim, eVarient, eValuate, eSensitivity)

TOP-Energy is an energy system modeling and optimization toolkit [327,328]. It is a commercial tool. It has various modules to model, analyze, and optimize energy systems, such as eSim, eVarient, eValuate, and eSensitivity [327,328]. Module eSim can simulate, create, and visualize complex energy systems. It provides the flexibility to use prefabricated models, or a user’s custom model having load profile and boundary conditions, to integrate in the system [327,328]. The eVarient module is used to analyze and summarize the simulation results. The eValuate module performs a comparative assessment of energy systems, environment, emission, and energy demand [327,328]. The eSensitivity module is used to investigate energy and power dependence based on input or boundary-condition changes [327,328]. For example, energy cost can be varied and hence the dependence on power systems can be changed, which might affect renewable generation or other cogeneration units [327,328].

5.6.3.31

Transient Security Assessment Tool

Transient Security Assessment Tool (TSAT) is a time-domain simulation tool to analyze electrical power systems [22,249–256]. It can assess the dynamic behavior of complex power systems [22,249–256]. It analyzes the security limits of any specified transfer conditions and contingencies of power systems. It is used to study the voltage and frequency stability, to design the protection systems [249–256]. It can tune the control and protection parameters [22,249–256]. It can also perform transmission systems design and operation studies by assessing stability limits [22,249–256]. The TSAT library allows user-defined models to simulate transient stability, voltage/frequency, and relay margins, to search automatic security limits [22,249–256]. It has a user-friendly graphical user interface and is compatible with other DSA tools. It is a commercial tool, maintained by Powertech [22,249–256].

5.6.3.32

YALMIP

YALMIP is a free tool to prototype optimization problems [364]. It is entirely based on MATLAB code, which gives an easy interface with MATLAB functions [364]. The objective functions and constraints of any optimization problem are defined by using MATLAB functions. It can categorize optimization problems and select a solver automatically [364]. If any suitable solvers are not available, then it tries to convert the problem to solve it. It supports a wide range of solvers such as MOSEK, CDD, MPT, OOQP, CSDP, SDPA SDPT3, PENBMI, MAXDET, CPLEX, SEDUMI, LMILAB, LINPROG, DSDP, PENSDP, GLPK, QUADPROG, KYPD, and NAG [364]. YALMIP can be used for a wide range of optimization programming such as quadratic programming, semidefinite programming (SDP), mixed-integer programming, LP, geometric programming, multiparametric programming, second-order cone programming, and nonconvex semidefinite programming [364]. By using these optimization programs and solvers YALMIP can solve various power systems, smart-grid, and energy systems optimization problems [364].

5.6.4

Case Studies

In this section a case study illustrating the use of two commonly used tools for energy management is described. The tools are GridLAB-D and Simscape Power Systems (previously known as SimPowerSystems). The tools were used to calculate the impact of domestic energy demand management on the electricity distribution network. A schematic of the residential energy demand management system is illustrated in Fig. 4. The residential energy management system coordinates PV, battery storage systems (BESSs), and V2G-enabled EVs to reduce the peak load demand [35,37,428]. A controller reads the grid load conditions, battery and EV SOC conditions, EV availability, and PV power generation and provides a decision based on a chosen algorithm [35,37,428]. The domestic energy management system illustrated in Fig. 4 [35,37,428] was modeled using GridLAB-D and Simscape Power Systems as described in the following subsections. Home

Grid

Meter lug

Gp

V2

ESS

B

Controller EV Fig. 4 A schematic of the residential energy demand management system.

PV

246

Energy Management Softwares and Tools

Fig. 5 Modeling of the residential energy management system using GridLAB-D simulation tool.

4.1

GridLab-D [4,33–42] is an open source tool, developed by the Pacific Northwest National Laboratory (PNNL), US [4,33–42]. The tool is suitable to model and analyze the energy market, power system transient behavior, balanced/ unbalanced load flow, power quality, and renewable energy integration to the grid. The tool can also deal with residential energy management, i.e., power demand management strategies [4,33–42]. GridLAB-D models the behavior of various power system objects over time and its agent-based simulator performs the time-series simulation. It has an advanced clocking system that allows objects to show their dynamic behavior over time [4,33–42]. GridLAB-D uses various modules to simulate power-systems applications, i.e., the climate module, market module, powerflow module, residential module, and reliability module. The climate module can read the typical meteorological year (TMY) file to simulate the weather conditions in any specific area, and hence simulate the behavior of weather-dependent loads and sources at a particular date and place such as PV, heating systems, cooling systems [4,33–42]. The market module provides modeling and simulation of the wholesale energy market and its control. The powerflow module supports power distribution system modeling and analysis. The residential module supports the modeling and analysis of single- and multiple-house energy demand, individual appliance behavior, and their controlling. The reliability module supports power-systems reliability analysis [4,33–42]. Under every module there are several classes and objects to simulate the static and dynamic behavior of power systems. The

Energy Management Softwares and Tools

IPV_max*

IPVout

IPVmax

PV VDC1*

Battery

Battery

IConvPVout

VDC1

IBat_maxD*

IBattmaxD

VDC2*

IConvPVout

VDC2

IBatt_maxC*

IConvBattout

IBattmaxC

IConvBattout VBatt*

Ipv

Iinv

VBatt

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LoadON

Out

Domestic load management Partial shading

247

Load profile

S(KVA)

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+ –i

+ –i

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Iinv

1 PF

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IConvBattout

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A +

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m

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+M M –M Mesures 2

3-phase AC load

VDC2 DC

IBattmaxD

+ –i DC

DC/DC boost converter

– Battery1

VBatt DC Discrete, Ts = 0.0001 s.

z

AC DC/AC converter

Battery

LoadON

DC

Ipv

V2G capable EV 85 kWh

IBattmaxC

DC DC/DC buck converter

Fig. 6 Modeling of the residential energy management system using Simscape Power Systems. Hedegaard K. Wind power integration with heat pumps, heat storages, and electric vehicles. Risø: DTU Management Engineering; 2013. Mahmud K, Morsalin S, Kafle YR, Town GE. Improved peak shaving in grid-connected domestic power systems combining photovoltaic generation, battery storage, and V2G-capable electric vehicle. In: 2016 IEEE international conference on power system technology (POWERCON), September 28–October 1, 2016; 2016. Morsalin S, Mahmud K, Town G E. Electric vehicle charge scheduling using an artificial neural network. In: 2016 IEEE innovative smart grid technologies-Asia (ISGT Asia), November 28–December 1, 2016; 2016. Mahmud K, Town G E, Hossain MJ. Mitigating the impact of rapid changes in photovoltaic power generation on network voltage. In: 2017 IEEE power and energy conference at Illinois (PECI), February 23–24, 2016; 2016. Gran RJ. Numerical computing with simulink, vol. 1: creating simulations Philadelphia, PA: SIAM; 2007. Zamboni L. Getting started with simulink. Birmingham: Packt Publishing Ltd; 2013. Karris ST. Introduction to Simulink with engineering applications. Fremont, CA: Orchard Publications; 2006. Keyhani A, Marwali MN, Dai M. Integration of green and renewable energy in electric power systems. New York, NY: John Wiley & Sons; 2009. Mahmud K, Tao L. Power factor correction by PFC boost topology using average current control method. In: 2013 IEEE global high tech congress on electronics, November 17–19, 2013; 2013.

4.2

classes are player, shaper, recorder, collector etc. The player can read data from CSV (comma-separated value) files, and update a single object variable at any particular time. The recorder records data to a stream. Generally, GridLAB-D takes input from two types of file format, XML (extensible markup language), and GLM (GridLAB-D model) [4,33–42]. The residential energy demand management system in Fig. 4 has been modeled using GridLAB-D as in Fig. 5. Various energy devices (PV, EV, and battery), converters, domestic appliances, distribution line, and meter are considered as the objects in GridLAB-D. The objects work under various modules such as power flow, residential, climate, generator, etc. The time and the time zone of the simulation have been set using the “climate object” and reading the TMY of any specific area. Simscape Power Systems, previously known as SimPowerSystems, is an add-on to Simulink (graphical programming in MATLAB by MathWorks) for simulating various functions of electrical power systems [22,99–103]. It provides various analysis tools and an electrical component library to model and analyze electrical machines and drives, power distribution systems, protection systems, power quality, etc. It also helps to develop control systems at various levels of power systems. It can integrate various physical systems into a user-defined model using components from the Simscape family of products [22,99–103], Simscape Driveline, Simscape Electronics, Simscape Fluids, Simscape Multibody, and Simscape Power Systems [22,99–103]. The capabilities of the Simscape Power Systems can be expressed as follows [22,99–103]: • Power generation: synchronous and asynchronous machines, wind turbines, PV arrays, EVs. • Power transmission: microgrid, transmission lines, bus, flexible alternating-current transmission system (FACTS), unified power-flow controller (UPFC), static VAR compensator (SVC), transformers (wye-delta, delta-delta, zigzag-deltawye), etc. • Power consumption: electric drives and controller, power converters (inverter, chopper), power converter controller, motor etc. • Control: voltage control, frequency control, etc. • Power quality: load-flow analysis, harmonics analysis.

248

Energy Management Softwares and Tools

10000 Normal load condition Load condition with controlled PV, EV, and 6 kWh battery

Load (W)

8000 6000 4000 2000 0 0

100

200

300

400

500

Time (10 minutes interval) Fig. 7 Grid load with and without the energy management controller, as modeled using GridLAB-D. Mahmud K, Morsalin S, Kafle YR, Town GE. Improved peak shaving in grid-connected domestic power systems combining photovoltaic generation, battery storage, and V2G-capable electric vehicle. In: 2016 IEEE international conference on power system technology (POWERCON), September 28–October 1, 2016; 2016. Mahmud K, Morsalin S, Hossain MJ, Town GE. Domestic peak-load management including vehicle-to-grid and battery storage using an artificial neural network. In: 18th annual international conference on industrial technology (ICIT). IEEE; 2017.

A Simscape Power System model of the residential energy demand management system (Fig. 4) is shown in Fig. 6. [10,22,99–103]. The domestic load conditions without and with the energy management controller modeled using GridLAB-D are illustrated in Fig. 7. All the available energy sources and domestic appliances such as EV, PV, battery storage, heating systems, cooling systems, lighting systems, cooktop, and washing machine were connected to an AC bus, as illustrated in Fig. 5. The 24- kWh EV, 2.5-kW PV, and 6-kWh battery storage were used to shave the domestic peak load. The EV V2G energy transfer is activated only if its state-ofcharge (SOC) goes above 85% of its capacity, i.e., the energy management system uses only the top 15% capacity of the EV. The SOC of the static battery never goes below 40%, or above 95%, and charges are either directly from PV (through DC–DC converter as in Fig. 5), or the grid. The resulting grid load curve over four days, shown in Fig. 7, illustrates the effectiveness of the peakshaving controller design and implementation.

Acknowledgment We would like to thank Dr. Keith Imrie for his valuable suggestions.

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Further Reading TrueData X-HVE Hochvolt-Emulatoren für Antriebsstrangkomponenten. Germany: FuelCon AG; 2015. Motapon SN, Dessaint LA, Haddad KA. 2014. A comparative study of energy management schemes for a fuel-cell hybrid emergency power system of more-electric aircraft. IEEE Trans Ind Electron 2014;61(3):1320–34.

5.7 Energy Quality Management Hai Lu, Yunnan Power Grid, Kunming, China and KTH Royal Institute of Technology, Stockholm, Sweden Jianyong Chen, Guangdong University of Technology, Guangzhou, China Li Guo, Tianjin University, Tianjin, China r 2018 Elsevier Inc. All rights reserved.

5.7.1 5.7.1.1 5.7.1.2 5.7.2 5.7.2.1 5.7.2.2 5.7.2.3 5.7.2.4 5.7.2.4.1 5.7.2.4.1.1 5.7.2.4.1.2 5.7.2.4.1.3 5.7.2.4.1.4 5.7.2.4.1.5 5.7.2.4.1.6 5.7.2.4.1.7 5.7.2.4.2 5.7.2.4.2.1 5.7.2.4.2.2 5.7.2.4.2.2.1 5.7.2.4.2.2.2 5.7.2.4.2.2.3 5.7.2.4.2.3 5.7.2.4.2.3.1 5.7.2.4.2.3.2 5.7.3 5.7.3.1 5.7.3.2 5.7.3.2.1 5.7.3.2.2 5.7.3.2.3 5.7.3.3 5.7.3.3.1 5.7.3.3.2 5.7.3.3.2.1 5.7.3.3.2.2 5.7.3.4 5.7.4 5.7.4.1 5.7.4.2 5.7.4.3 5.7.4.4 5.7.4.4.1 5.7.4.4.2 5.7.4.4.3 5.7.4.5 5.7.5 5.7.5.1 5.7.5.2 5.7.5.2.1 5.7.5.2.2

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Definition of Energy Quality Energy Quality Background Mathematics Model for Energy Quality Development of Energy Quality Management Definition of Energy Quality Management Energy Quality Management in Built Environment Energy Quality Management for Energy Conversion System Development of Energy Quality Management Tools Brief information on existing tools EnergyPLAN Compare options for sustainable energy E4cast MESSAGE RETScreen GenOpt District energy system design and optimization Energy quality management tool evolution Necessity for energy quality management tool evolution Algorithm for energy quality management tool Genetic algorithm Harmony search algorithm Particle swarm optimization Elements for establishment of energy quality management tool Decision variables (example) Optimization objective and constraint Energy Quality Management Case Study 1 Basic Information for Case Study 1 Optimization Results Representative winter day Representative midseason day Representative summer day Parametric Analysis Environment impact parameter Energy performance parameter Efficiency increase of microturbine technology Efficiency increase of photovoltaic technology Discussion Energy Quality Management Case Study 2 Basic Information for Case Study 2 Energy Quality Management Initiation Result of Energy Quality Management Parametric Study for Energy Quality Management Investment parameters Energy policy parameter Energy performance parameter Discussion Energy Quality Management Case Study 3 Basic Information for Case Study 3 Energy Quality Management Initiation Optimization objectives Decision variables

Comprehensive Energy Systems, Volume 5

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doi:10.1016/B978-0-12-809597-3.00521-6

Energy Quality Management 5.7.5.3 Optimization Results 5.7.5.4 Comparative Study 5.7.5.5 Parametric Analysis of Energy Storage System 5.7.6 Energy Quality Management Case Study 4 5.7.6.1 System and Assumption for Case Study 4 5.7.6.2 System Modeling 5.7.6.2.1 Thermodynamics analysis 5.7.6.2.2 Economics analysis 5.7.6.2.3 Thermoeconomic analysis 5.7.6.2.4 Energy quality management process 5.7.6.3 Energy Quality Management Results 5.7.6.4 Discussion 5.7.7 Energy Quality Management Case Study 5 5.7.7.1 System for Case Study 5 5.7.7.2 Results 5.7.7.2.1 Comparison for the new ejector refrigeration system and conventional ejector refrigeration system performance 5.7.7.2.2 Effect of working conditions on the new ejector refrigeration system performance 5.7.7.2.3 Effect of the composition on the new ejector refrigeration system performance 5.7.7.3 Energy Quality Management of the New Ejector Refrigeration System 5.7.7.4 Discussion 5.7.8 Advanced Energy Quality Management 5.7.8.1 Theory 5.7.8.2 Case Study for Ejector Refrigeration System 5.7.8.2.1 Conventional energy quality management 5.7.8.2.1.1 Conventional energy quality management for the ejector 5.7.8.2.1.2 Conventional energy quality management for the refrigeration 5.7.8.2.1.3 Conventional results 5.7.8.2.2 Advanced energy quality management 5.7.8.2.3 Sensitivity analysis by advanced energy quality management 5.7.8.3 Discussions 5.7.9 Conclusions References Further Reading Relevant Websites

Nomenclature E _ Ex EN E_ D;k EX E_ D;k _EUN D;k AV E_ D;k UN;EN E_ UN;EX E_ D;k

Energy (MJ) Exergy (MJ) Endogenous exergy destruction (MJ) Exogenous exergy destruction (MJ) Unavoidable exergy destruction (MJ) Avoidable exergy destruction (MJ) Unavoidable endogenous exergy destruction (MJ) Unavoidable exogenous exergy destruction (MJ)

Subscripts CC DHW EL ele

Cooling capacity demand Domestic hot water demand Electricity demand Electricity

D;k

Abbreviations AC Air conditioner

259 290 290 291 294 294 294 294 295 295 296 296 299 299 299 301 301 302 303 304 304 305 305 306 307 307 308 308 309 310 312 312 313 314 314

AV;EN E_ D;k AV;EX E_ D;k FQ i P Q T T0 o

Avoidable endogenous exergy destruction (MJ) Avoidable exogenous exergy destruction (MJ) Carnot factor Inflation rate Power (kW) Energy demand (MJ) Thermal source temperature (K) Constant ambient temperature (K) Weight of benefit

H heat in ke out

Heat load demand Thermal energy Input Kinetic energy Output

BCHP BGB

Biofuel micro-turbine power and heat Biogas boiler

260

CC CD DHW EE EH EL FN FST GA H HP ID LCC

5.7.1

Energy Quality Management

Cooling capacity Commercial district Domestic hot water Exergy efficiency Electricity heater Electricity Linear Fresnel concentrating solar power generation Linear Fresnel concentrating solar thermal energy Genetic algorithm Heat load Air source heat pump Industrial district Life cycle cost

LCCO2 OD PE PST PT PV PVT RD SAC SH SPS STH WHU WT

Life cycle CO2 equivalent Official district Public electricity grid Parabolic trough solar thermal energy Parabolic trough solar power generation Solar photovoltaic Solar photovoltaic/thermal Residential district Solar absorption cooling Space heating Solar power subsidy Solar thermal heater Waste heat utilization Wind turbine

Definition of Energy Quality

Energy quality could be expressed as “exergy,” which describes the useful work of a specific amount or flow of energy.

5.7.1.1

Energy Quality Background

Nowadays, how to use energy effectively is becoming a core target for energy analysis processes. Energy systems must be analyzed in terms of quantity and quality. For a long period, the first law of thermodynamics was used only for quantitative evaluations of energy systems; thus energy efficiency was the only indicator to assess the energy performance of any energy supply system. The quality of energy, or its so-called exergy, had been long neglected though it has received more attention in recent decades. Exergy is the measure of the maximum useful work that can be done by a system interacting with a reference environment at a constant pressure P0 and a constant ambient temperature T0. The reference environment is characterized by thermodynamic equilibrium. Additionally, the reference environment’s intensive properties do not change as a result of energy interactions. Exergy derives from the first and second laws of thermodynamics. Five key points are reported to highlight the importance of exergy and its essential utilization in numerous ways: (1) exergy is a primary tool in best addressing the impact of energy resource utilization on the environment; (2) exergy is an effective method that uses the conservation of mass and energy principles together with the second law of thermodynamics to design and analyze energy systems; (3) exergy is a suitable technique for furthering the goal of more efficient energy resource use because it allows the locations, types, and true magnitudes of wastes and losses to be determined; (4) exergy is an efficient technique for revealing whether or not and to what extent more efficient energy systems can be designed by reducing the inefficiencies in existing systems; (5) exergy is a key component in obtaining a sustainable development [1]. Exergy has been proven to be one of the most important unambiguous thermodynamic tools to evaluate the energy performance of energy systems [2–4]. Exergy analysis was first applied to different energy supply systems, such as photovoltaic (PV) systems, microturbine combined heat and power (CHP), geothermal, and bioenergy systems [5–7]. In these studies, the objective was to increase the exergy output with certain energy input. In addition to utilization for energy systems, other studies have addressed the systematic applications of the exergy analysis in buildings [8–11]. The targets of exergy analysis were completely distinct when applied in the built environment compared to when utilized for power generation. In buildings, the exergy output was settled by certain energy demands and the ambient environment. Therefore the exergy analysis in buildings focused on the exergy input, which needs to decrease [9].

5.7.1.2

Mathematics Model for Energy Quality

The energy quality efficiency (exergy efficiency (EE)) of an energy system is expressed as below: _ in _ out =Ex ðjÞ ¼ Ex

ð1Þ

_ in are total exergy output and total exergy input for the whole energy supply and service system, respectively, _ out and Ex where Ex and (j) is the EE of the energy system. The same amount of energy contained in electricity (EL) and in hot water at 451C has totally distinct exergy values. Hence, exergy calculation processes are different for different types of energy. Eqs. (2–5) address the exergy calculation process of various main types of energy. For heat, exergy can be calculated as below: _ heat ¼ Eheat  FQ Ex

ð2Þ

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Eheat is the heat amount, and FQ is the Carnot factor, which can be expressed as below: FQ ¼ 1

T=T0

ð3Þ

T is the temperature of the thermal source and T0 is a constant ambient temperature. For EL, exergy can be calculated as below: _ electricity ¼ Eelectricity Ex

ð4Þ

Eelectricity is the amount of EL. If the energy is generated by kinetic energy, such as wind energy, exergy can be expressed as below [4]: _ ke ¼ 0:91  Eke Ex

ð5Þ

Eke is the amount of kinetic energy.

5.7.2 5.7.2.1

Development of Energy Quality Management Definition of Energy Quality Management

Energy quality management (EQM) was firstly defined as the management of the whole energy chain from energy generation to end-use and covers a wide range of issues from energy demand to energy supply systems [12]. Then, this definition of EQM was updated and could be reproposed as below [12]: EQM represents a technique that aims at optimally utilizing the exergy content of various renewable energy sources, identify inefficiencies in energy systems, and therefore reduce the primary energy consumption.

The concept of EQM has been widely used in the context of many areas, such as energy conversion systems and built environment. Currently, EQM is developed toward advanced EQM, which will be addressed in Section 5.7.2.4.

5.7.2.2

Energy Quality Management in Built Environment

Trias Energetica is a simple and logical concept that achieves energy savings, reduces dependence on fossil fuels, and saves the environment [12]. Three steps are included in the Trias Energetica pyramid: reducing energy demand, applying renewable energy, and efficiently supplying the remaining energy with fossil fuels [13]. Coupled with the Trias Energetica concept, EQM in built environment could be expressed as below: 1. Applying distributed renewable energy sources to satisfy all energy demands. 2. Utilizing energy technology in a sustainable way. 3. Reducing energy demands. According to the conception above, the tasks of EQM include energy supply system optimization and energy demand prediction (shown in Fig. 1). Energy supply system optimization designs an appropriate energy supply system that matches actual energy demand. The optimal system connects with the first two aspects of EQM: applying renewable energy and making energy technologies sustainable. The connection means that the optimum system should be based on renewable energy sources and fulfills various sustainable requirements like high energy performance, low environmental impacts, and acceptable system reliability. This task requires exploring the optimal solution from a large number of design solutions (combinations of energy sources, energy technologies, and operations). Energy demand prediction develops an energy demand model capable of reducing unnecessary energy consumption and providing accurate inputs for energy supply system optimization. This model should be based on a thorough understanding that prioritizes the design criteria affecting energy demand and generally distinguishes the more important criteria from less important ones. The results could be a reliable decision base for future energy demand models. Folk Björk began to introduce the concept of EQM into built environment. The presentation puts forth EQM as the stepwise process of taking care of the quality of energy better. Here, EQM for buildings was initiated as a toolbox that was particularly useful to control the primary energy use in the built environment [14]. Several methods, which include reducing the heating and cooling demand, making use of passive building techniques, exploiting local renewable sources and utilizing efficiently nonrenewable energy, are mentioned in the toolbox. It is concluded that energy quality (exergy) is a vital aspect for low-energy architecture and reducing unnecessary CO2 emissions. An EQM model called the rational energy management model (REMM) was demonstrated as its first version. Then, Kilkis¸ and Molinari, who come from the same group as Folk Björk, coupled energy quality with building design. REMM began to be utilized as a necessary tool for individual buildings. As indicated in Ref. [8] by Kilkis¸, EQM was proven to be able to mitigate the problems related to energy use in the built environment through a reduced and more efficient use of energy. REMM was applied to analyze the pathways in which it is possible to lead the built environment into addressing structural overshoots in its exergy supply to curb CO2 emissions. EQM is regarded as an effective tool to abate environmental problems for buildings. Molinari [9] applied EQM for the definition of the efficient energy use in the built environment. Heat pumps (HPs) are used as an example. The analysis of a multistep HP to supply energy at two temperature levels, for space heating (SH) and domestic hot water

262

Energy Quality Management

Energy quality management for new BCDs

Energy demand modeling

Energy supply system optimization

Existed energy demand analysis

Existed optimization tools comparison and analyis

Identification of design criteria affecting energy demand

Optimization algorithms selection Exergy efficiency

Priorities evaluation of design criteria

Multi objective optimization for energy supply system

Environmental impact

System reliability Two BCDs case studies Fig. 1 Energy quality management (EQM) in built environment. BCDs, building clusters and districts.

(DHW) production, exemplified that a more efficient use of energy could be achieved by controlling the exergy loss. Also, builders should pay attention to cut down the unnecessary exergy input. Buildings with decreasing need of exergy input could contribute to improving the efficiency of systems and encouraging the use of low quality energy (i.e., waste heat and energy from lowtemperature renewable sources). Meanwhile, the International Energy Agency (IEA)’s Annex 49 strengthened the position of EQM to the built environment and extended the focus to the community level [15]. Following Annex 49, Lu [12] worked to extend the scope of EQM from individual building to district and developed energy optimization approach for building clusters. Following the definition of EQM, efficient energy systems for buildings or districts are required to reduce the useful energy loss, which could be expressed to reduce exergy loss. Exergy loss is often occurred when the high-exergy energy supply technologies are used for satisfying low-exergy energy demand, i.e., EL is utilized to provide domestic thermal energy. Such EL-driven thermal energy system has relative high energy efficiency but contributes to high-exergy loss. This phenomenon is called energy quality mismatch, which needs to be avoided in energy system design, especially the thermal energy conversion system. Later, Lu [16] applied EQM to a potential net-zero exergy district (NZEXD) in Hangzhou, China. NZEXD is a district whose energy relations are linked with distributed energy sources (solar, wind, biomass, etc.) and has a sum of net-zero exergy transfer across the district boundary. Here, the energy demands of NZEXD are presumed to be completely satisfied by the distributed energy system (DES). In this district, energy demands are mainly fulfilled by centralized energy grid currently. The EL demands are completely covered by centralized EL grid as the heat demand is taken charge by centralized gas grid coupled with solar water tank. Following the requirements of NZEXD, transition process from the existing energy system to DES needs to be completed in 2030. Based on the EQM, the transition path should be divided into several parts. As a single part, it is challenging to find out the suitable hybrid energy systems comprised in the path. An approach coupled with EQM is applied. Recently, Lu began to apply energy quality tools for offering a promising solar energy pattern for building clusters at the community level. The work presents a comparative study about solar energy utilization patterns for different types of districts located in Kunming, China. Four types of districts are considered: residential district (RD), official district (OD), commercial district (CD), and industrial district (ID). For each district, it is necessary to explore the optimum energy system that contains solar technologies in order to identify the suitable solar energy utilization pattern. According to the requirement of energy quality

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263

(exergy), an optimum energy system should contribute to minimizing the CO2 emissions by maximizing the EE. Therefore, a multiobjective energy quality design tool based on genetic algorithm (GA) is proposed for such exploration. Through the energy quality scope, solar energy has been regarded as a main participant to the energy supply in ID but hardly takes part in the energy generation for RD in Kunming. Also, solar power generation technologies, especially solar PV technology, have limited contribution to energy supply in these districts. Currently, the mainstream of using solar energy is to produce low-temperature thermal energy. Accordingly, solar thermal heater (STH) is the most commonly used solar technology in Kunming, Yunnan, China, via the energy quality analysis.

5.7.2.3

Energy Quality Management for Energy Conversion System

Since the concept of energy quality was introduced to energy conversion technologies, it is significant and meaningful to manage energy systems in terms of energy quality (exergy). High energy efficiency is not absolutely equal to high EE. There are exergy mismatches between energy conversion systems and energy demands if high-exergy supply technologies are used for providing low-exergy demand. For instance, if an electrical heater is applied for SH generation, the energy efficiency could get a fantastic score but the EE may not reach to an acceptable level. So, it is a critical issue to avoid exergy mismatch during energy supply/service system. Moreover, the first law of thermodynamics, expressing conservation of energy, is one of the most fundamental relationships in energy analysis. But it does not always provide a full assessment. A typical example for this is the process through a throttling valve (TV), which is normally assumed to be isenthalpic and no energy loss is found. However, it is recognized that the lower pressure after the throttling process reduces the usable work potential. As a result, the “energy quantity” is the same, while the energy quality (exergy) is reduced during this process. The use of renewable energy systems for real life applications has been increasing for the last few decades due to the growing concern about global warming and environmental pollution. Renewable energy sources are clean and freely available in nature, however, their efficient utilization is still a cause of concern among the scientific and business communities. EE could be noticed in every energy field, such as PV system, microturbine CHP, geothermal, and bioenergy systems [3,17–19]. It is suggested that exergy analysis should be used for PV system evaluations and assessments, so as to allow for more realistic modeling, evaluation, and planning for PV systems. It is found that the fill factor plays an important role in order to know the behavior of the EE of the PV systems and also gives an idea of possible improvement of the same. The higher the fill factor, the better would be the EE [20]. Also, Ozenger reviewed the studies conducted on the energy and exergy analysis of solar-assisted HP (SAHP) systems in Turkey and around the world [21]. As a result, performing an exergy analysis of SAHP systems might aim at better achieving process efficiencies and losses. In other words, the analysis provides a SAHP owner/user with a better, quantitative grasp of the inefficiencies and their relative magnitudes. Furthermore, such exergy analysis could draw an engineer’s attention toward the components that are essential to improve the efficiency of the SAHP system. Panapoulos [22] took exergy analysis on the integration of a near atmospheric solid oxide fuel cell (SOFC) with a novel all-thermal biomass steam gasification process into a CHP system of less than MWe range. DiPippo [23] gave a very detailed description of the application of exergy analysis in geothermal power systems. The EE of the components, such as turbine, heat exchanger, flash vessel, compressor, pump (PU), are interpreted. These publications are meaningful to get inspiration for establishing an EE model for other types of energy conversion systems. Besides this work, exergy analysis was also applied for the energy conversion systems at the residential level, such as Refs. [24,25].

5.7.2.4 5.7.2.4.1

Development of Energy Quality Management Tools Brief information on existing tools

For EQM, there are a number of targets to be considered; also, determining the optimal alternative combination is quite difficult and time-consuming. With awareness of this problem, some commercial tools have worked for energy supply system optimization with different objectives. 5.7.2.4.1.1 EnergyPLAN EnergyPLAN was developed and introduced as a renewable energy system analysis tool in 1999. The latest version is EnergyPLAN 9.0, which was updated in February 2011. The previous version had been applied in many activities, including expert committee work for the Danish authorities [26]. EnergyPLAN optimizes the operation of a given system compared to other tools that only optimize initial investments in the system. This tool was applied in different kinds of energy systems in Estonia, Denmark, Germany, Ireland, Poland, Spain, and the United Kingdom. The energy systems included a small-scale CHP system [27], as well as the integration of wind power, wave power, and PV into the EL supply [28], the effect of energy storage [29], the management of surplus EL production from renewable energy [30], and the use of waste for energy purposes. 5.7.2.4.1.2 Compare options for sustainable energy Compare Options for Sustainable Energy (COMPOSE) is a technoeconomic energy project assessment tool developed by Aalborg University in Denmark in 2008 [31]. This tool assesses which energy projects match the case energy demand. The objective is to offer realistic cost evaluations for energy options.

264

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This tool has been used with wind power in the West Danish energy system [31]. It has also been used to analyze the benefits of energy storage and relocation options (such as the integration of HPs with CHP plants) [29]. 5.7.2.4.1.3 E4cast E4cast is a partial-equilibrium tool for the Australian energy system that is used by the Australian Bureau of Agricultural and Resource Economics. Typically, E4cast is used to simulate future energy demand and identify how this demand can be met. Total economic cost and energy consumption are the assessment objectives for different energy scenarios. Up to now, E4cast has been used to predict future scenarios within the Australian energy system [32]. The main benefit of this tool is to predict future energy scenarios from a relatively wide view, including all possible costs (such as mining, manufacture, and transport) and all promising energy sources (such as crude oil, brown coal, and renewable energy). The main drawback of this tool is that has been limited to Australia and could not be applied in other countries until now. 5.7.2.4.1.4 MESSAGE MESSAGE stand for Model for Energy Supply Strategy Alternatives and Their General Environment Impact. It was developed in the 1980s by the International Institute for Applied System Analysis in Austria [33]. This system engineering optimization tool is used for the planning of medium- to long-term energy systems. This tool can provide cost-effective and environmentally friendly energy scenarios for national and global regions. The objectives are to minimize economic costs and greenhouse gas emissions. The tool has been used to develop energy scenarios in the Baltic states [34] and design a sustainable energy supply system for Cuba [35]. 5.7.2.4.1.5 RETScreen The RETScreen, the “Clean Energy Project Analysis Software,” is an investment optimization tool for energy projects developed by Natural Resources Canada in 1996 [36,37]. This tool offers a comparative study between a basic case, which is typically the conventional technology, and a proposed case, which are the sustainable energy technologies. Normally, the comparison includes all costs and numerous economic indicators. This tool is widely used in the world with over 200,000 downloads as of the date of this thesis. RETScreen has been used to analyze the feasibility of solar water heating in Lebanon [38], the feasibility of wind farm developments in Algeria [39], and the potential of PV systems in Egypt [40]. 5.7.2.4.1.6 GenOpt GenOpt is a commercial optimization tool that was developed by the simulation research group at Lawrence Berkeley National Laboratory. The software is a generic optimization program that can be used for system optimization. GenOpt is an optimization program that minimizes cost functions that are evaluated by external simulation programs, such as EnergyPlus, TRNSYS, SPARK, IDA ICE, and DOE-2. GenOpt was developed for optimization problems where the cost function is computationally expensive and its derivatives are not available or may not even exist [41]. According to previous literature [42–44], GenOpt had been implemented in the optimization of buildings and HVAC systems. In these studies, GenOpt was combined with EnergyPlus and IDA Indoor Climate and Energy 3.0 program (IDA ICE 3.0). The main advantage of this tool is that it can input from an external simulation program; thus the tool can easily combine with other energy demand tools. GenOpt also covers almost all optimization algorithms, and users can choose suitable algorithms according to the user manual. The biggest drawback of the tool is that GenOpt can only be applied for single-objective optimization. 5.7.2.4.1.7 District energy system design and optimization The district energy system design and optimization (DESDOP) tool was developed by Imperial College in the United Kingdom [45]. This tool combines the consideration of all different energy services with a perspective situated at the district level. The objective function of this tool, defining the optimal mix of technologies, can be expressed in terms of costs or emissions. The process of this tool could be easily described as the input and output. The inputs should be a small city, with its layout, its available renewable energies, its buildings and their related consumption profiles, as well as which combination of energy conversion technologies (and therefore energy sources) will be best suited to meet the energy services, how these technologies will be combined, where in the district these technologies should be located (centralized or distributed), and how the layout of the energy distribution network should be arranged (provided a distribution network is required). The results (or outputs) should include an appropriate mix of technologies, together with the (potential) distribution network [45]. The main drawback of this optimization tool is that it works for only a single objective (cost or environmental value) each time. According to the analysis above, the findings gathered could be performed in Table 1. The table includes basic information for each tool and the gaps the present research intends to fill.

5.7.2.4.2

Energy quality management tool evolution

5.7.2.4.2.1 Necessity for energy quality management tool evolution Through Table 2, it is found that the objectives of these tools are predefined and the most interesting objective of these tools is to minimize economic costs. It is difficult to insert new optimization objectives into these tools and change any objectives of these tools. Energy system optimization should pay attention to many elements, such as energy performance, economic cost and

Table 1

Existing tools for energy system optimization

Model

Country

Objective

Limitation

EnergyPLAN

Denmark

Minimum initial investment

RETScreen Compare Options for Sustainable Energy (COMPOSE) GenOpt District Energy System Design and Optimization (DESDOP)

Canada Denmark

Minimum economic cost Minimum realistic cost

Only initial cost is considered during the optimization process. Economic cost assessment should be calculated during life cycle time Economic cost is the only objective for the energy system optimization. Other objectives need to be elaborated

United States United Kingdom

E4cast

Australia

Model for Energy Supply Strategy Alternatives and Their General Environment Impact (MESSAGE)

Austria

Minimum economic cost Minimum economic cost Or Minimum environmental impacts Minimum economic cost Maximum energy efficiency Minimum economic cost Minimum environmental impacts

• •

The layout of district that needs the optimization is required as inputs Although two objectives are included in the tool, only one objective is considered for each optimization process

This tool cannot be used in other locations except for Australia

• •

Energy performance indicators are not included as the optimization objectives There is no possibility to insert any new objectives into the tool

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266

Comparison of existing genetic algorithm (GA) optimizations

References

Optimization subject

Optimization objective

References

Optimization subject

Optimization objective

[46]

Heating, ventilating, and airconditioning (HVAC)

Minimum CO2 emission

[47]

Green building

[48]

HVAC

Minimum equipment size

[49]

Building energy system

[50]

HVAC

Minimum equipment size

[51]

[52]

Building energy system

[53]

[54]

Building cogeneration

Minimum primary energy consumption Minimum primary energy consumption

Nearly-zero-energy building and its energy system Multigeneration system

[55]

PV–wind–battery system

[56]

Hybrid energy system

Minimum economic cost

[57]

PV–wind–battery system

[58]

Desalination plant

Minimum economic cost

[59]

Districts and its energy system

Minimum life cycle environmental impacts Minimum life cycle economic cost Minimum economic cost Minimum environmental impacts Minimum economic cost Minimum environmental impacts Minimum economic cost Maximum exergy performance Minimum annualized cost Keep acceptable loss of power supply probability (LPSP) Minimum annualized cost Minimum acceptable LPSP Minimum environmental impacts Maximum exergy performance

[60]

Building

Minimum primary energy consumption Minimum equipment size

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Table 2

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267

environmental impact. An optimal energy system should be a trade-off between these objectives. Therefore the optimization tools shown above are unable to search for such trade-offs. Accordingly, a new multiobjective approach must be developed. This approach needs to provide different energy system scenarios with different objectives and have expansibility for inserting new optimization objectives in future. To develop such optimal design approaches, a suitable algorithm must be chosen that can explore the optimal solution from numerous design solutions with multiple objectives. 5.7.2.4.2.2 Algorithm for energy quality management tool Regarding the EQM issue, further investigation was conducted by employing the multicriteria decision-making (MCDM) tools. Therefore the suitable algorithm can be chosen from the various MCDM algorithms shown below. 5.7.2.4.2.2.1 Genetic algorithm GA was introduced by Holland and has been applied to a diverse range of scientific and economic problems [61]. This algorithm is an efficient method to optimize the sizing of hybrid systems, especially in complex systems, where a large number of parameters have to be considered [46,62]. It has roughness in finding an optimal solution and can provide a near optimal solution in a short simulation time. This algorithm is stochastically navigated using a single-minded search method that mimics the metaphor of natural biological evolution. Beginning with randomly determined start solutions, GA finds increasingly better solutions. The system always converges quickly to reach an optimum. GA is a powerful general purpose stochastic optimization method that was inspired by the Darwinian evolution of a population subject to evolution operators in a selective environment where the fittest survives [63]. The basic evolution operators of GA are: ● Selection: the quality of each individual is presented through its fitness function used by the operation of selection. ● Crossover: crossover combines the genes of two parent individuals to produce a completely unique individual. ● Mutation: mutation changes the structure of genes of each individual separately. GA combines the artificial survival of the fittest with genetic operators abstracted from nature to form a robust mechanism that is suitable for a variety of optimization problems. In mathematical terms, the goal of GA is to minimize an objective function O (Sk) (or minimize a negative objective function O(Sk) as maximization problems), where Sk is the search candidate (optimal solution), which is the kth individual in the population S (where the population is the set of possible solutions). The individuals of the population are expressed in a binary string form, and the GA then manipulates these strings by using genetic operators (selection, crossover, and mutation) to obtain improved solutions (where the fittest individuals survive) until the optimal solution is obtained [64]. GA has bloomed in recent decades. Several publications [48,50,63] adopted the GA method to solve heating, ventilating, and air-conditioning (HVAC) control problems in order to optimize performance and primarily to save power and curb CO2. The most promising energy machinery combination must be chosen for new energy supply systems; thus, GA could be used in the design process. A GA optimal design method used for building energy systems was developed by researchers, such as Ooka [52] and Kayo [54] at the University of Tokyo. The only objective is minimization of primary energy consumption, which does not include startup energy, only the operational requirements. Refs. [56,58] used a single-objective GA method, which focused on economic cost minimization, as a tool for a hybrid renewable energy system (consisting of fuel cells, thermal storage, and HPs) and desalination plant design, respectively. To assess and design a system by a single objective is inefficient. Therefore multiobjective assessment and optimization combined with GA are necessary for the design process. Hamdy employed a multiobjective GA (MOGA) combined with IDA ICE software to determine the minimum primary energy use and the minimum equipment size of building energy systems [60]. Wang in Canada used a novel MOGA, combined with life cycle analysis, in a green building design process. The life cycle analysis methodology was employed to evaluate both economic and environmental criteria [47]. Ref. [49] minimized the environmental impacts (CO2 emission) and economic costs in dwelling buildings by using a three-phase multiobjective optimization approach (PR_GA_RF). The approach aimed to reduce the random behavior of GA by using a good initial population from the preparation phase (PR). The study focused on the influence of energy sources, ventilation heat recovery systems, and building envelopes. The optimal solution was not only dependent on the heating and cooling energy source but also on the performance of HVAC systems and building shading. The influence of the supply heating and cooling system type (energy source and HVAC system) is more significant than other variables. Hamdy et al. [51] utilized GA as the main methodology to search for economic and environmental solutions toward nearly-zero-energy building. It focused on exploring the possible combination of energy saving measures (envelopes, heat recovery system) and energy supply systems (PV and wind turbine (WT)) [51]. Ahmadi et al. [53] implemented a MOGA optimization approach to present a comprehensive study of a multigeneration system, based on a heat recovery unit, an organic Rankine cycle, an ejector (EJ) refrigeration cycle, and a domestic water heater, for residential applications. The optimization objectives utilized were the total cost rate of the system and the system EE. A parametric study was included to analyze the effects of varying design parameters and operation conditions [53]. Recently, some researchers have begun to pay attention to energy system reliability, thus system reliability indicators were coupled with multiobjective energy system optimization and considered as one of the optimization objectives in Refs. [55,57,59].

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Though GA had been proved as a promising tool for building and energy optimization, the existing GA optimization approaches are hardly applied for building clusters and districts’ (BCDs) EQM for two reasons. The first one is that most of these approaches were based on individual buildings, thus the optimization variables were concentrated as details of buildings, i.e., building envelopes and PV roofs. Optimization of BCDs should have its specific variables. The other reason is the limitation of optimization objectives. Economic cost was the most popular optimization objective in the previous works. In fact, other objectives, such as energy system reliability and exergy performance, need to be treated as important as economic cost and connect with community level. 5.7.2.4.2.2.2 Harmony search algorithm Harmony search (HS) algorithm is a newly developed optimization method originally developed for discrete variable problems [65]. The algorithm was later introduced to continuous-variable problems by K.S. Lee [66]. This algorithm is simple in concept as it involves only a few parameters and can be easily implemented. Thus HS has been successful in a wide variety of real-world applications. This novel algorithm poses a few advantages: (1) HS imposes fewer mathematical requirements and does not require initial value settings of the design variables; and (2) HS generates a new solution vector after considering all of the existing vectors. Besides the advantages shown above, the disadvantages cannot be ignored. The HS algorithm performs well for global searching; therefore, HS may take a relatively long time to converge to a local optimum. Recently, this algorithm has been used for multiobjective building energy optimization processes [67]. The optimization addresses life cycle CO2 emissions and energy costs. The results were expressed as Pareto-optimal solutions and helped designers better understand the trade-off between cost and environmental value. 5.7.2.4.2.2.3 Particle swarm optimization Particle swarm optimization (PSO) is a population-based optimization technique inspired by the motion of bird flocks and schooling fish. PSO shares many similarities with evolutionary computation techniques. The system is initialized with a population of random solutions, and the search for the optimal solution is performed by updating generations. Unlike GA, PSO has no evolution operators, such as crossover and mutation. In PSO, the potential solutions, called particles, move in the problem space by following the current optimum particles. PSO is computationally more efficient in terms of both speed and memory requirements. The undeniable drawbacks of PSO include that it is comparatively less practical and less accurate than GA. Due to PSO’s drawbacks and profits, more researchers began to use a combined PSO-GA algorithm for optimization. For instance, the PSO-GA algorithm was applied for HVAC system optimization. System performance is evaluated in terms of user comfort, energy use, and financial costs. The multiobjective optimization can achieve substantial energy savings, while granting good load profile tracking with respect to standard approaches [68]. Another research used the PSO-GA algorithm for distributed generation (DG). This methodology optimizes location and sizing of DG on distribution systems. The objective is to minimize network power losses, improve voltage regulation, and improve voltage stability [69]. 5.7.2.4.2.3 Elements for establishment of energy quality management tool 5.7.2.4.2.3.1 Decision variables (example) Decision variables for the initial are classified into two categories: discrete variables (xi) and continuous variables (yi). For this part, an example is used to explain the decision variable initiation. Discrete variables (xi) indicate which energy conversion and heat recovery methods will be chosen to the energy system scenario that meets the requirements of EQM. Hybrid energy systems should avoid being complicated due to practical reasons; therefore, the maximum number of EL supply technologies included in one solution is limited to three. Based on the quantitative constraint, the EL supply technologies (decision variable vector X1) include the options from x1 to x3. Correspondingly, the SH options (X2) refer to the decision variables x4–x5. The options of DHW production (X3) have two variables (x6–x7) and cooling capacity (CC) supply options (X4) are decision variables x8–x9. The decision variable vectors are expressed in Eq. (6): X 1 -x 1 ; x2 ; x3 ; X 2 -x4 ; x5 ; X3 -x6 ; x7 ; X 4 -x8 ; x9 ; Xi A ð1; 2; …; nÞ ðir4Þ

ð6Þ

where, n means the number of potential energy supply options. The last three variables (x10–x12) indicate whether waste heat produced by the EL generation process is used for thermal energy (SH, DHW, and CC) supply or not (X5). The decision variable vector is: X 5 -x10 ; x11; x12 ; X 5 A ð0; 1Þ

ð7Þ

In Eq. (7), the binary number “0” means that waste heat produced by EL generation process is not utilized for other energy supply and “1” means that waste heat is used for other energy supply. Continuous variables (y1–y9), variable vectors (Y1–Y4) are to optimize the equipment sizes in the energy supply technology candidates (X1–X4). The equipment sizes for x1–x9 can be calculated by multiplying energy demand peak load with corresponding variables y1–y9. The total capacity of the hybrid energy system needs to match the energy demand peak load and the energy demands in all

Energy Quality Management

conditions should be fulfilled completely. The corresponding variable constraints are in Eqs. (8)–(11): 3 3 Z ti X X yi  1; PELðiÞ ðti Þdðti Þ  QEL Y 1 -y1 ; y2 ; y3 ; i¼1

Y 2 -y4 ; y5 ;

5 X

i¼4

i¼4

Y 3 -y6 ; y7 ;

7 X

yi  1;

i¼6

Y4 -y8 ; y9 ;

5 Z X

7 Z X

i¼6

9 X

i¼8

yi  1;

ð8Þ

0

i¼1

yi  1;

269

ti

PSHðiÞ ðti Þdðti Þ  QSH

ð9Þ

PDHWðiÞ ðti Þdðti Þ  QDHW

ð10Þ

0

ti

0

9 Z X

i¼8

ti

PCCðiÞ ðti Þdðti Þ  QCC

ð11Þ

0

where, PELðiÞ ðti Þ, PSHðiÞ ðti Þ, PDHWðiÞ ðti Þ, and PCCðiÞ ðti Þ represent the supply profile of each EL, SH, DHW, and CC supply technology, ti is operation time of each energy supply technology. QEL , QSH , QDHW , and QCC are the total amounts of the EL, SH, DHW, and CC demand, respectively. The last three variables (y10–y12) describe ratios of waste heat utilization (WHU) (decision variable vector Y5), indicating the percentage of WHU for thermal energy production (SH, DHW, and CC). The sum of WHU for thermal energy could not exceed the total amount of waste heat. The decision variable vector is expressed as: Y 5 -y10 ; y11 ; y12 ;

12 X

yi r1

ð12Þ

i ¼ 10

5.7.2.4.2.3.2 Optimization objective and constraint After defining decision variables, the next step is to identify the optimization objectives and constraints. The following sections addressed the promising options for objectives and constraints. 1. Energy performance objective: EE: For the chapter, all contents are related to EQM; so selection of energy performance indicators should relate to energy quality. As mentioned, energy quality can be expressed as exergy. Therefore “EE” is applied as the energy performance indicator. The objective function is to maximize the EE of the whole energy system, which is defined as: Max f ðx; yÞ x ¼ ½x1 ; x2 ; …xm Š y ¼ ½y1 ; y2 ; …yn Š

ð13Þ

where f is the EE of the energy supply system, x is the combination of discrete decision variables ðx1 ; x2 ; …xm Þ, m is the number of the discrete decision variables, y is the combination of continuous decision variables ðy1 ; y2 ; …yn Þ, and n is the number of continuous decision variables. Discrete decision variables ðx1 ; x2 ; …xm Þ and continuous decision variables ðy1 ; y2 ; …yn Þ are mentioned and explained by users. Objective values (f) change with different combinations of decision variables (discrete variables (xi) and continuous variables (yi)). According to definition of energy quality, the EE of holistic energy systems can be calculated as: _ out EE ¼ Ex

_

total =Ex in total

ð14Þ

_ in total are total exergy output and total exergy input for the whole energy system, respectively. For _ out total and Ex where Ex different types of energy sources and energy outputs, the exergy values can be calculated with Eqs. (2)–(5). 2. Environmental impact objective: life cycle global warming potential (GWP): The objective function to “minimize the total life cycle GWP” is used to present environmental impact indicator. GWP could be calculated in CO2 equivalents. Thus, the objective is to “minimize the life cycle CO2 equivalent (LCCO2).” LCCO2 is the LCCO2 for one unit (per kWh) energy generated by the whole energy supply system. The objective function is defined as: Min f ðx; yÞ x ¼ ½x1 ; x2 ; …xm Š y ¼ ½y1 ; y2 ; …yn Š

ð15Þ

where f is the LCCO2 of the energy supply system, x is the combination of discrete decision variables ðx1 ; x2 ; …xm Þ, m is the number of the discrete decision variables, y is the combination of continuous decision variables ðy1 ; y2 ; …yn Þ, and n is the number of continuous decision variables. Discrete decision variables ðx1 ; x2 ; …xm Þ and continuous decision variables ðy1 ; y2 ; …yn Þ are defined by users. Objective values (f) are specific for different combinations of decision variables (discrete variables (xi) and continuous variables (yi)). The LCCO2 of this function can be calculated as below from a “cradle-cradle” scope. LCCO2 ¼ CO2 ðCÞþCO2 ðOÞþCO2 ðDÞ

ð16Þ

where CO2(C), CO2(O), and CO2(D) stand for CO2 equivalents during the construction process, operation process, and demolition process, respectively. CO2(C) is calculated by the following equation:

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Table 3

Samples of CO2 equivalents for materials

Material

CO2 equivalent (kg CO2 /kg)

Copper Steel Acrylonitrile-butadiene-styrene (ABS) Concrete

2.8 0.9 3.3 0.3

Source: Reproduced from Wuppertal Institute. Material input per service unit mannual. Wuppertal: Wuppertal Institute.

Table 4

Samples of CO2 equivalents for energy sources

Energy source

CO2 equivalent (kg CO2 /MJ)

Biomass Natural gas Wood Coal

0.10435 0.0601 0.0953 0.0962

Source: Reproduced from Davis K. Greenhouse gas emission factor review: Final technical memorandum. Irvine, CA, Austin, TX: Edison Mission Energy; 2003.

CO2 ðCÞ ¼ CO2 ðCÞm þ CO2 ðCÞe þCO2 ðCÞt

ð17Þ

where CO2(C)m is the CO2 equivalent of construction materials production; CO2(C)e is the CO2 equivalent accompanied with construction energy use; and CO2(C)t is the CO2 equivalent during material transportation. Material and energy are related to natural resources; thus, the main task is to find a set of reliable conversion factors between CO2 equivalents and natural sources. For this reason, the material input per service unit (MIPS) method is selected for converting materials consumptions to CO2 equivalents. Each material is analyzed and calculated from the life cycle scope. Table 3 lists a portion of the CO2 equivalents for different materials. In addition to material consumption, CO2 equivalents for different energy sources are converted by the greenhouse gas factor (GGF) method. GGF indicates the amount of CO2 equivalents in producing one MJ of energy by some energy source. Table 4 lists a portion of CO2 equivalents for energy sources.CO2(O) consists of three parts as well: CO2 equivalent by maintenance material (CO2(O)mm), CO2 equivalent by energy source use (CO2(O)e), and CO2 equivalent by energy source transportation (CO2(O)et). The equation is shown as: CO2 ðOÞ ¼ CO2 ðOÞmm þCO2 ðOÞe þCO2 ðOÞet

ð18Þ

The MIPS method converts material consumptions to CO2 equivalents; hence, CO2 equivalents by maintenance materials can be calculated by the MIPS method. CO2 equivalents by energy source use are converted by the GGF method. CO2(D) consists of two elements: CO2 equivalent by energy consumption for the demolition process (CO2(D)e), and CO2 equivalent savings from material recycling (CO2(D)mr). CO2(D)CO2 ðDÞ can be calculated as: CO2 ðDÞ ¼ CO2 ðDÞe þCO2 ðDÞmr

ð19Þ

As mentioned above, the MIPS and GGF methods are applied for converting materials and energy sources to CO2 equivalents, respectively. The system boundary of this LCCO2 model in this chapter is shown in Fig. 2. 3. Economic cost objective: life cycle cost (LCC): The objective function is to minimize LCC of the entire system, covering the cumulative cost throughout its life cycle from the installation to recycling. The objective function is defined as: Min f ðx; yÞ x ¼ ½x1 ; x2 ; …xm Šy ¼ ½y1 ; y2 ; …yn Š

ð20Þ

where f is the LCC of the energy supply system, x is the combination of discrete decision variables ðx1 ; x2 ; …xm Þ, m is the number of the discrete decision variables, y is the combination of continuous decision variables ðy1 ; y2 ; …yn Þ, and n is the number of continuous decision variables. Discrete decision variables ðx1 ; x2 ; …xm Þ and continuous decision variables ðy1 ; y2 ; …yn Þ are defined by users. Objective values (f) are specific for different combinations of decision variables (discrete variables (xi) and continuous variables (yi)). In the model, LCC incorporates five parts: component cost (C0), installation cost (Cint), replacement cost (Crep), maintenance cost (Cman) and recycling cost (Crecy), and the objective function f2 is their sum: LCC ¼ C0 þ Cint þ Crep þ Cman þ Crecy

ð21Þ

Energy Quality Management

Cradle

Manufacture

Cradle

Time

Operation

271

Disposal

Recycling material CO2 equivalents by: CO2 equivalents by: CO2 equivalents by: 1. Maintenance material 1. Construction material 1. Energy for demolish process 2. Energy used for construction 2. Energy generating process 2. Material recycling 3. Energy source transportation 3. Material transportation

Fig. 2 System boundary of life cycle CO2 equivalent (LCCO2) model.

The replacement costs are calculated knowing present worth for all components as Eq. (22). X C0;i Crep ¼ Pr 

ð22Þ

where, C0,i is the replacement cost related to each component in the entire energy system and Pr is the present worth factor for an item that will be purchased n years later [70]. The present worth factor for the single payment including inflation is calculated from:   1þi n ¼ xn ð23Þ Pr ¼ 1þd where, i represents the inflation rate and d is the discount rate. Cman could be calculated using cumulative present worth factor as Eq. (24): X Cman ¼ ðPW man Þ  xn ð24Þ

where, PWman is present worth of maintenance cost and x is defined in Eq. (23). 4. Constraints: system reliability:

In addition to optimization objectives, such as energy performance, environmental burden and economics cost, the reliability of the energy system must be noticed to be as constraints. Unlike the traditional fossil fuel energy systems, renewable energy systems, especially solar and wind energy systems, are influenced by energy sources because of the intermittent solar radiation and wind speed characteristics. Accordingly, energy system reliability analysis is an important step. A reliable energy system means that a system has sufficient power to feed the load demand during a certain period. Therefore system reliability is additionally included in the objective functions by means of one of the following parameters: ● Loss of load probability (LOLP): power failure time period divided by a given period of time (generally 1 year). ● Loss of power supply probability (LPSP): probability that an insufficient power supply will result when the hybrid renewable energy system is unable to satisfy the load demand. ● Unmet load (UL): nonserved load divided by the total load of a period (generally 1 year). LPSP is widely applied in energy system optimization, especially the renewable energy systems, including solar and wind technologies [55,57,71,72]. Hence, LPSP is an indicator to represent system reliability in this thesis. LPSP is defined as the probability that an insufficient energy supply results when the hybrid energy supply system is unable to satisfy the load demand. An LPSP value of 0 means the load demand will be always satisfied and the LPSP value of 1 means that the load demand will never be satisfied. The constraint objective function is to keep the LPSP value of holistic energy supply system from exceeding an acceptable desired value. The objective is defined as: fC ðx; yÞrC x ¼ ½x1 ; x2 ; …xm Š y ¼ ½y1 ; y2 ; …yn Š

ð25Þ

where fC is the LPSP value of the energy supply system, x is the combination of discrete decision variables ðx1 ; x2 ; …xm Þ, m is the number of the discrete decision variables, y is the combination of continuous decision variables ðy1 ; y2 ; …yn Þ, n is the number of continuous decision variables, and C is the desired LPSP value that is predefined by the user as a constraint. For developing new sustainable energy systems, the desired LPSP value should be no more than 5 to 10% [55,57]. Discrete decision variables ðx1 ; x2 ; …xm Þ and continuous decision variables ðy1 ; y2 ; …yn Þ are defined by users. Each f3 value should have its specific combination of decision variables (discrete variables (xi) and continuous variables (yi)). It is calculated from Eqs. (26) and (27) :   PTime Time Psupply ðt ÞoPdemand ðt Þ ð26Þ LPSP ¼ t ¼ 0 Time

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Table 5

Energy demands and specific loads for the fictitious development area located in Trondheim on three representative days

Type of energy demand

Winter days

Midseason days

Summer days

Specific load (MW)

Energy consumption (MJ/day)

Specific load (MW)

Energy consumption (MJ/day)

Specific load (MW)

Energy consumption (MJ/day)

Electricity (EL) Space heating (SH) Domestic hot water (DHW) Cooling capacity (CC)

1.73 2.94 1.06

102,413 141,166 40,370

1.68 0.58 0.98

101,246 19,994 40,018

1.64 0 0.96

100,954 0 39,985

0.06

3,347

0.06

3,347

0.29

9,819

Energy demand load (MW)

3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time EL load (winter) CC load (winter) DHW load (mid-season) DHW load (summer)

SH load (winter) EL load (mid-season) CC load (mid-season) CC load (summer)

DHW load (winter) SH load (mid-season) EL load (summer)

Fig. 3 Energy load profiles for area located in Trondheim on representative days. CC, cooling capacity; DHW, domestic hot water; EL, electricity; SH, space heating.

where, Time is number of hours which require energy demand, Psupply ðt Þ and Pdemand ðt Þ are power of energy supply and energy demand. For hybrid energy system, Psupply ðt Þ can be expressed as: Psupply ðt Þ ¼

n X

PsupplyðiÞ ðt Þ þ Pstor ðt Þ

ð27Þ

i¼0

where, PsupplyðiÞ ðt Þ is the power of each energy conversion system, Pstor ðt Þ is the power of storage system.

5.7.3 5.7.3.1

Energy Quality Management Case Study 1 Basic Information for Case Study 1

This case study defines the system reliability as a constraint function. EQM is applied to guide users to make optimization for an energy system under the predefined constraint. This case study makes use of a BCD located in Trondheim, Norway. The volume of the BCD includes 50 houses (average area 150 m2), 200 apartments (average area 70 m2), seven offices (average area 6000 m2), five hospitals (average area 3000 m2), and two restaurants (average area 7000 m2). Basic information of this district (energy demand, peak load, load profiles, and climatic conditions) could be found in Table 5 and Fig. 3. An August day represents summer energy demand and load profiles, an April day represents midseason energy demand and load profiles, and a January day represents winter energy demand and load profiles. Fig. 4 depicts the possible routes of energy flows from each energy resource via energy conversion systems to each type of consumption.

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273

Waste heat utilization

Waste heat

BCHP

SH/DHW/CC

TC

SH DHW

BGB

EL

SWT

CC PV SH/DHW/CC

SAC

WT Ground thermal

HP Fig. 4 Overview of energy system. BCHP, biofuel micro-turbine power and heat; BGB, biogas boiler; CC, cooling capacity; DHW, domestic hot water; EL, electricity; HP, heat pump; PV; solar photovoltaic; SAC; solar absorption cooling; SH, space heating; SWT, solar water heater; TC, solar thermal collector combined heat and power system; WT, wind turbine.

5.7.3.2

Optimization Results

Optimization results appeared as Pareto fronts and they are shown in Figs. 5–7. In brief, a Pareto front indicates the increase in EE the decision-maker has to be ready to accept a corresponding increase in life cycle GWP, when the LPSP is at the acceptable level. The solid shapes represent the optimal solutions that are the most appropriate sets of decision variables for the case with certain combination of objectives and that can transfer to optimal scenarios. The maximum allowable LPSP values 0, 1, and 5% were used in the computational analysis. Three groups of optimization results, which correspond to these three values, are presented and compared in Tables 6–8. Here, it is highlighted that all the optimization objectives have equal importance. Weight combination for the case study is 0.5/0.5.

5.7.3.2.1

Representative winter day

Optimal solutions in Fig. 5 could be transferred as energy system scenarios shown in Table 6. According to Table 6, EL is primarily supplied by the BCHP system. The ratio of EL produced by the BCHP system grows from 50.15 to over 57% with increase of LPSP value (from 0 to 5%). The remaining EL is generated by WT system. For SH generation, waste heat and HP system are applied when LPSP value is 0. If LPSP value increases to 1 or 5%, SH is fully provided by waste heat. DHW is primarily satisfied by waste heat and another part is from the HP system for all LPSP values. The ratio of DHW produced by waste heat goes up (from 85.74 to 92.28%) with growth of LPSP value. CC is fulfilled by HP system for all maximum allowable LPSP values. The fundamental components of the optimal scenarios are also shown in Table 6.

Energy Quality Management

EE (%)

274

75 74 73 72 71 70 69 68 67 66 65 50

Pareto solution (LPSP=0) Optimal solution (LPSP=0) Pareto solution (LPSP=1%) Optimal solution (LPSP=1%) Pareto solution (LPSP=5%) Optimal solution (LPSP=5%)

55

60 LCCO2 (g/kWh)

65

70

EE (%)

Fig. 5 Pareto front for the representative winter day profile. EE, exergy efficiency; LCCO2, life cycle CO2 equivalent; LPSP, loss of power supply probability.

83 81 79 77 75 73 71 69 67 65

Pareto solution (LPSP=0) Optimal solution (LPSP=0) Pareto solution (LPSP=1%) Optimal solution (LPSP=1%) Pareto solution (LPSP=5%) Optimal solution (LPSP=5%)

50

55

60

65 70 LCCO2 (g/kWh)

75

80

85

Fig. 6 Pareto front for the representative midseason day profile. EE, exergy efficiency; LCCO2, life cycle CO2 equivalent; LPSP, loss of power supply probability.

85 Pareto solution (LPSP=0)

EE (%)

80

Optimal solution (LPSP=0)

75

Pareto solution (LPSP=1%)

70

Optimal solution (LPSP=1%)

65

Pareto solution (LPSP=5%)

60 40

Optimal solution (LPSP=5%)

45

50

55 60 LCCO2 (g/kWh)

65

70

Fig. 7 Pareto front for the representative summer day profile. EE, exergy efficiency; LCCO2, life cycle CO2 equivalent; LPSP, loss of power supply probability.

5.7.3.2.2

Representative midseason day

Optimal solutions in Fig. 6 could be transferred as energy system scenarios shown in Table 7. Table 7 shows that the large portion of EL is provided by WT system. The ratio of EL supplied by WT system keeps increasing trend (from 82.08 to 92.14%) with rise of LPSP value. BCHP system takes charge for the remaining minor part of EL. SH is mainly generated by waste heat. As LPSP value raises from 0 to 1%, a decline occurs for SH satisfied by waste heat, from 84 to 56%. The remaining SH is offered by the HP system. At the LPSP value of 5%, SH is fully provided by waste heat. DHW is fulfilled by combination of waste heat and HP system at the LPSP values of 0 and 1%. As LPSP value climbs to 5%, the HP system is used to

Table 6

Optimal energy system scenarios and configurations on representative winter day SH supply

DHW supply

TEE (%)

0

65.73 59 50.15 49.85 91.42 (78.16) 8.58 Scenario: 0.87 MW BCHP, 0.9 MW WT, 0.25 MW HP for SH, 0.15 MW HP for DHW, and 0.06 MW HP for CC 66.71 57.8 55.76 44.24 100 (77.7) N/A Scenario: 0.97 MW BCHP, 0.8 MW WT, 0.09 MW HP for DHW, and 0.06 MW HP for CC 72.01 54.6 57.15 42.85 100 (78.05) N/A Scenario: 0.99 MW BCHP, 0.78 MW WT, 0.08 MW HP for DHW, and 0.06 MW HP for CC

1% 5%

LCCO2 (g /kWh)

EL supply

Allowable LPSP

Percentage of BCHP production (%)

Percentage of WT production (%)

Percentage of waste heat (%)/ percentage of all waste heat (%)

Percentage of HP (%)

CC supply

Percentage of waste heat (%)/ percentage of all waste heat (%)

Percentage of HP (%)

Percentage of HP (%)

85.74 (21.84)

14.26

100

91.32 (22.3)

8.68

100

92.28 (21.95)

7.72

100

Abbreviations: BCHP, biofuel microturbine power and heat; CC, cooling capacity; DHW, domestic hot water; EL, electricity; HP, heat pump; LCCO2, life cycle CO2 equivalent; LPSP, loss of power supply probability; TEE, total exergy efficiency; WT, wind turbine.

Energy Quality Management 275

276 Energy Quality Management

Table 7

Optimal energy system scenarios and configurations on representative midseason day

Allowable LPSP

TEE (%)

0

68.28 54.1 17.92 82.08 84.09 (28.83) 15.91 91.13 (65.14) Scenario: 0.31 MW BCHP, 1.45 MW WT, 0.08 MW HP for SH, and 0.09 MW HP for DHW 70.6 56.6 14.25 85.75 56.09 (29.07) 43.91 88.26 (60.58) Scenario: 0.25 MW BCHP, 1.5 MW WT, 0.22 MW HP for SH, 0.12 MW HP for DHW, and 0.06 MW HP for CC 78.38 58.5 5.88 94.12 100(100) N/A N/A Scenario: 0.1 MW BCHP, 1.65 MW WT, 0.98 MW HP for DHW, and 0.06 MW HP for CC

1% 5%

LCCO2 (g /kWh)

EL supply Percentage of BCHP production (%)

SH supply Percentage of WT production (%)

Percentage of waste heat (%)/percentage of all waste heat (%)

DHW supply Percentage of HP (%)

Percentage of waste heat (%)/percentage of all waste heat (%)

CC supply Percentage of HP (%) 8.87 11.74 100

Percentage of waste heat (%)/percentage of all waste heat (%)

Percentage of HP (%)

100 (6.03)

N/A

N/A

100

N/A

100

Abbreviations: BCHP, biofuel microturbine power and heat; CC, cooling capacity; DHW, domestic hot water; EL, electricity; HP, heat pump; LCCO2, life cycle CO2 equivalent; LPSP, loss of power supply probability; TEE, total exergy efficiency; WT, wind turbine.

Table 8

Optimal energy system scenarios and configurations on representative summer day DHW supply

TEE (%)

0

68.23 51.6 16.15 83.85 100(79.48) Scenario: 0.28 MW BCHP, 1.45 MW WT, and 0.01 MW HP for CC 70.24 49.2 16.4 83.6 100(79.01) Scenario: 0.29 MW BCHP, and 1.44 MW WT 77.76 49.6 9.27 90.73 72.29(100) Scenario: 0.16 MW BCHP, 1.57 MW WT, 0.27 MW HP for DHW, and 0.98 MW HP for CC

1% 5%

LCCO2 (g/kWh)

EL supply

Allowable LPSP

Percentage of BCHP production (%)

Percentage of WT production (%)

Percentage of waste heat (%)/percentage of all waste heat (%)

CC supply Percentage of HP (%)

Percentage of waste heat (%)/percentage of all waste heat (%)

Percentage of HP (%)

N/A

96.35(20.52)

3.65

N/A

100(20.99)

N/A

27.71

N/A

100

Abbreviations: BCHP, biofuel microturbine power and heat; CC, cooling capacity; DHW, domestic hot water; EL, electricity; HP, heat pump; LCCO2, life cycle CO2 equivalent; LPSP, loss of power supply probability; TEE, total exergy efficiency; WT, wind turbine.

Energy Quality Management 277

Energy Quality Management

40

15 Electricity from PE system Thermal energy from HP system

Ratio of electricity (%)

12

Thermal energy from EDTE system

35 30 25

9

20 6

15 10

3

Ratio of thermal energy (%)

278

5 0

0 0

5 10 15 20 25 Ratio of CO2 equivalent reduction (%)

Fig. 8 Parametric analyzes of environmental parameters. EDTE, electric drive thermal energy; HP, heat pump; PE, public electricity.

match all DHW demand. CC is fully covered by waste heat when LPSP value is 0. At LPSP values 1 or 5%, all CC is satisfied by HP system. The fundamental components of the optimal scenarios are also shown in Table 7.

5.7.3.2.3

Representative summer day

Optimal solutions in Fig. 7 could be transferred as energy system scenarios shown in Table 8. As shown in Table 8, the WT system is utilized to generate the major part of EL. The ratio of EL produced by the WT system is nearly 84% when the LPSP value is either 0 or 1%. If LPSP value rises to 5%, more than 90% EL is produced by the WT system. The rest is produced by the BCHP system. DHW is completely provided by waste heat when LPSP values are 0 and 1%. As LPSP value jumps to 5%, DHW is covered by waste heat and HP system (72.29/27.71%). All CC is satisfied by waste heat when LPSP values are 0 and 1%. After LPSP value increases to 5%, the HP system is supposed to be the promising solution. The fundamental components of the optimal scenarios are also shown in Table 8.

5.7.3.3

Parametric Analysis

It is noticed that the optimization objectives are conflicting, i.e., the good performance of a candidate system in EE can compensate for its poor performance with LCCO2. Therefore it is meaningful to consider questions like how the basic energy system will change with the CO2 emission reduction of one energy technology with good exergy performance. To answer such questions, a parametric study is necessary to investigate. The parametric analysis shows the effects of varying parameters on the energy system selections for the case. Three types of parameters are considered in Sections 5.7.3.3.1–5.7.3.3.2.

5.7.3.3.1

Environment impact parameter

The environmental impacts of certain energy technologies are assumed to be decreasing at a certain rate predefined as a constraint (5–25%). The chosen technology is centralized power generation. The analyzed results related to these environmental parameters are shown in Fig. 8. Fig. 8 presents the system variations along with the CO2 equivalent reduction of centralized power generation. Coal is the main source for the existing centralized power generation in this district. To establish the green grid for this district, an increasing number of clean centralized power generation systems are planned. This development will lead to less CO2 equivalent of centralized power generation. With control of CO2 equivalent, two types of changes appear in Fig. 8. The first one is that the proportion of EL from PE appears as an upward trend; the other one is that more thermal energy would be converted by the electric drive thermal energy (EDTE) systems, which consist of an air conditioner (AC), EL thermal heater, and HP. As shown in Fig. 8, a sharp increase (0–9.9%) occurs on the ratio of EL from the PE system since the CO2 equivalent of centralized power generation falls to 90% of the current level. Then, the ratio keeps stable for a while and continues to climb with the constant decrease of CO2 equivalent. The final share of annual EL from the PE system could reach to 12.3%. On the other hand, the ascent stage of annual thermal energy converted by EDTE systems also starts when the CO2 equivalent reduction ratio is set as 10%. Since this point, the share of thermal energy from EDTE systems keeps a rapid rise from 14.7 to 29.1%. It should be noticed that there is little difference between the lines “thermal energy from EDTE systems” and “thermal energy from HP system.” That means a major proportion of thermal energy from EDTE systems is taken by the HP system. The increasing amount of thermal energy from EDTE systems is caused by the popularity of HP systems. Therefore HP systems have potential to gain importance with the development of environmentally friendly centralized power technology.

Energy Quality Management 5.7.3.3.2

279

Energy performance parameter

Efficiencies of two types of energy conversion technologies are assumed to keep rising at a certain rate predefined as a constraint (5–25% of current level). The selected ones are microturbine technology and PV technology. The analyzed results related to these parameters are shown below. 5.7.3.3.2.1 Efficiency increase of microturbine technology The efficiency change of microturbine technology might have impacts on the development trends of the microturbine-based energy (MTBE) systems, which is BCHP system. According to the analysis, it is concluded that the total amount of annual energy from BCHP systems presents a stepwise upward trend with the efficiency increase of microturbine technology; however, the trends of EL and heat from BCHP systems are not quite similar. Since the efficiency of microturbine starts to be improved, the amount of EL converted from the BCHP system begins to rise urgently. When the ratio of efficiency increase reaches to 25%, the amount of EL converted from the BCHP system accounts for 28.2% of annual energy generation. On the other hand, the ratio of annual heat from the BCHP system appears as a descent in fluctuations. When the efficiency starts to be improved, there is a minor increase in the thermal energy production of the BCHP system. When the ratio of efficiency increase reaches to 25%, only 55.2% of annual thermal energy is provided by the BCHP system. 5.7.3.3.2.2 Efficiency increase of photovoltaic technology The main reason preventing solar energy power systems from being used for this district is their relative poor energy performance. This part aims to present the trends of solar power technologies varied with the efficiency increase of PV technology. Since the efficiency of PV technology begins to be improved, a PV system is introduced for energy supply. The share of energy from the PV system is only 1.6%. Then, it is noticed that the proportion of annual energy from the PV system skyrockets from 2.3 to 18.0% when the efficiency increase changes from 10 to 20%. The reason for this phenomenon is that over 56% more energy (from 7.1 to 63.2%) is provided by the PV system in summer. Finally, the ratio of energy demands covered by the PV system reaches to approximately 19.1%.

5.7.3.4

Discussion

In this part, EQM for the design and evaluation of energy systems of district has been introduced. The optimization scheme minimizes the GWP and maximizes the exergy performance as well as keeps system reliability at an acceptable level. According to the case study, the results demonstrate that the optimization approach is capable of introducing some more constraints by following some specific user requirements. Nowadays, energy system design for the realistic case needs to concern a number of objectives, which cover many aspects, such as environment, economics, efficiency, reliability, and comfort. Such expansibility guarantees that the optimization approach could be utilized in a realistic case design.

5.7.4

Energy Quality Management Case Study 2

In this case, EQM aims at selecting the most appropriate solar energy solutions for different district typologies.

5.7.4.1

Basic Information for Case Study 2

Four types of districts, the OD, RD, ID, and CD, are considered in the study. All these districts are located in Kunming, Yunnan Province, China (1021100 E, 241230 N). There are four types of energy demands for these districts: EL, heat load (H), DHW, and CC. It should be noted that heat load for OD and RD are mainly applied as SH, thus H is replaced by SH for OD and RD. The detailed information about energy demands in these four types of districts is shown in Fig. 9. It is noted that prices of EL for these four districts are unlike. Price of EL in OD and CD is 20% higher than that in RD. Solar power technologies, such as solar PV systems, have more potential to be applied for EL production in the district, which has to use expensive EL.

5.7.4.2

Energy Quality Management Initiation

For the case study, the four types of solar energy power generation technologies are solar PV system, solar PV/thermal (PVT) system, parabolic trough (PT) solar power generation system, and linear Fresnel concentrating solar power generation (FN) system. Besides them, four types of solar thermal energy system are solar thermal collector heater (STH), parabolic trough solar thermal energy (PST) system, linear Fresnel concentrating solar thermal energy (FST) system and solar absorption cooling (SAC) system. It should be noted that the annual energy conversion time of the PV and PVT systems is 1450 h (3, 4, and 5 h for winter, midseason, and summer day, respectively) and that for PT and FN systems is approximately 1600 h (3, 4.5, and 6 h for winter, midseason, and summer day, respectively). Other solar thermal energy systems might operate for 1950 h per year (4, 5, and 7 h for winter, midseason, and summer day, respectively). Annual energy conversion time of wind power technology is set as 1500 h. To promote the system reliability, the bioenergy conversion technologies are assumed to operate for 7000 h per year.

280

Energy Quality Management

SH(winter) DHW(winter)

0.60

0.70

EL(winter)

CC(winter)

0.50 0.40 0.30 0.20 0.10 0.00

0.60 0.50 0.40 0.30 0.20 0.10

DHW(winter)

0.40 0.30 0.20 0.10 0.00

0.50 0.40 0.30 0.20 0.10

1 3 5 7 9 11 13 15 17 19 21 23 EL(summer) DHW(summer) CC(summer)

0.30 0.20 0.10

0.20

EL(mid-season) H(mid-season) DHW(mid-season) CC(mid-season)

0.70 0.60 0.50 0.40 0.30 0.20 0.10

0.00

0.00 Time

Time

Energy demand for winter

1 3 5 7 9 11 13 15 17 19 21 23 CC(summer)

0.60 0.50 0.40 0.30 0.20 0.10

Time 0.80

Energy demand for mid-season EL(mid-season) H(mid-season) DHW(mid-season) CC(mid-season)

Energy demand for summer

0.70

EL(summer)

0.60

H(summer)

0.50

CC(summer)

DHW(summer)

0.40 0.30 0.20 0.10 0.00 1 3 5 7 9 11 13 15 17 19 21 23

1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00

DHW(summer)

0.70

1 3 5 7 9 11 13 15 17 19 21 23

Energy demand (MW)

EL(winter) DHW(winter) H(winter) CC(winter)

EL(summer) H(summer)

0.00 1 3 5 7 9 11 13 15 17 19 21 23

0.10

Energy demand for summer

0.80

1 3 5 7 9 11 13 15 17 19 21 23

0.30

Energy demand for mid-season

Energy demand (MW)

0.40

0.80

Energy demand (MW)

0.50

0.90 Energy demand (MW)

EL(winter) H(winter) DHW(winter) CC(winter)

Time 0.90

1 3 5 7 9 11 13 15 17 19 21 23

Energy demand (MW)

0.40

Energy demand for summer

Time

0.60

1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00

0.50

1 3 5 7 9 11 13 15 17 19 21 23

1 3 5 7 9 11 13 15 17 19 21 23 0.70

0.10

0.00

1 3 5 7 9 11 13 15 17 19 21 23

Energy demand (MW)

Energy demand for winter

(C)

(D)

EL(mid-season) SH(mid-season) DHW(mid-season) CC(mid-season)

Time

0.80

0.20

Time

0.00

(B)

0.30

0.60

Energy demand for mid-season

Energy demand (MW)

0.50

Energy demand (MW)

SH(winter)

DHW(summer)

0.40

1 3 5 7 9 11 13 15 17 19 21 23

1 3 5 7 9 11 13 15 17 19 21 23 Energy demand (MW)

EL(winter)

0.60

EL(summer)

CC(summer)

0.50

Time 0.60

Energy demand for winter

0.70

Energy demand for summer

0.60

0.00

Time

0.90

0.70

0.00

(A) 0.80

EL(mid-season) SH(mid-season) DHW(mid-season) CC(mid-season)

Energy demand for mid-season

Energy demand (MW)

Energy demand for winter

Energy demand (MW)

Energy demand (MW)

0.80 0.70

Time

Time

Time

Fig. 9 Energy demand profiles for different district typologies. (A) Energy demand for OD. (B) Energy demand for RD. (C) Energy demand for ID. (D) Energy demand for CD. CC, cooling capacity; CD, commercial district; DHW, domestic hot water; EL, electricity; H, heat load; ID, industrial district; OD; official district; RD, residential district; SH, space heating. All energy demands are measured by Yunnan Power Grid and Zhejiang University during 2014.

Additionally, there are also some other energy conversion alternatives: biofuel microturbine power and heat (BCHP) system, small-scale WT, electrical AC, air source HP, biogas boiler (BGB), electrical thermal heater (ETH), as well as public centralized electricity grid (PE). All these energy conversion options have potential to be used for satisfying EL demand and thermal energy demands, such as heat load (H), DHW, and cooling (CC). Waste heat from the EL generation process is proposed to contribute to providing thermal energy.

Energy Quality Management

281

Energy quality management (EQM) subjects (optimization variables) instantiation

Table 9 Variables

y

Variable type

Range of value

Step

Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio Ratio

y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 y12 y13 y14 y15 y16 y17 y18 y19 y20 y21 y22 y23 y24

Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous

[0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%] [0,100%]

0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1% 0.1%

of of of of of of of of of of of of of of of of of of of of of of of of

BCHP WT PV PVT PT FN PE HP for H (SH in OD and RD) STH for H (SH in OD and RD) PST FST for H (SH in OD and RD) air conditioner (AC) for H (SH in OD and RD) ETH for H (SH in OD and RD) HP for DHW STH for DHW FST for DHW BGB ETH for DHW SAC HP for CC AC for CC WHU for H supply WHU for DHW supply WHU for CC supply

Abbreviations: BCHP, biofuel microturbine power and heat; BGB, biogas boiler; CC, cooling capacity; CD, commercial district; DHW, domestic hot water; ETH, electrical thermal heater; FN, linear Fresnel concentrating solar power generation; FST, linear Fresnel concentrating solar thermal energy; H, heat load; HP, heat pump; OD; official district; PE, public electricity; PST, parabolic trough solar thermal energy; PT, parabolic trough; PV, photovoltaic; PVT, solar PV/thermal; RD, residential district; SAC, solar absorption cooling; SH, space heating; STH, solar thermal heater; WHU, waste heat utilization; WT, wind turbine.

Based on these energy conversion systems, 24 EQM subjects (optimization variables) are listed in Table 9. Besides, the EQM targets are maximizing the EE of the entire energy system and minimizing the LCC and LCCO2 for providing one unit (kWh) of energy at the boundary of the demand point. The general form of the optimization problem can be expressed as Eq. (28): Max f1 ðyÞ; Min f2 ðyÞ; f3 ðyÞ; ¼ ½y1 ; y2 ; …; yn Š

ð28Þ

where, f1, f2, and f3 are the EE, LCC, and LCCO2 of the energy system, respectively. y is the vector of EQM subjects ðy1 ; y2 ; …; yn Þ. For the case, all of these management targets should be compressed as a fitness function in order to make the management process more time-effective. The fitness function introduced in the case aims at maximizing the benefits of the hybrid energy system that contains solar energy, shown as Eq. (31):       X f1 ðyÞ f1 ðOÞ f2 ðOÞ f2 ðyÞ fi ðOÞ fi ðyÞ Max U ðyÞ ¼ o1 þ o2 þ ⋯ þ oi x ¼ ½x1 ; x2 ; …; xm Š; oi ¼ 1 ð29Þ f1 ðOÞ f2 ðOÞ fi ðOÞ where, o1 is the weight of benefit of fi. fi(O) is the fi value of the reference energy system. Number “i” means the number of management target. Here, number “i” is equal to 3, thus the weights are assumed as equal importance ðo1 =o2 =o3 ¼ 0:33=0:34=0:33Þ. Fitness function will be calculated as the weighted sum of benefits of f1, f2, and f3, normalized by using the reference EE, LCC, and LCCO2 of the existing energy system.

5.7.4.3

Result of Energy Quality Management

According to the EQM process, the details of the optimal solar energy utilization patterns is expressed in Table 10 and Fig. 10. “Ratio” in Table 10 means the share of annual energy consumption fulfilled by one specific energy conversion system. The ratios of annual energy demands matched by solar energy technologies are 5.9, 3.7, 21.4, and 7.9% for OD, RD, ID, and CD, respectively. Solar energy technologies applied in these four districts are rather distinct. Some meaningful information about the differences is listed as below: 1. Overall, solar energy systems are only the potential energy conversion alternatives for these four districts. Currently, the main energy supply methods are BCHP and HP system. Over 50% of annual energy demand is satisfied by BCHP system. Solar energy technologies are hardly regarded as the rival to BCHP system for energy generation till now. Ratio of annual energy from

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Energy Quality Management

Table 10

Optimal solar energy utilization patterns for different district typologies

District typology Energy conversion system

BCHP WT FN PVT PE STH FST HP SAC

OD

RD

ID

CD

Size (kW)

Ratio (%)

Size (kW)

Ratio (%)

Size (kW)

Ratio (%)

Size (kW)

Ratio (%)

514 79 27 – – 187 – 447 –

62.6 1.3 1.7 – 15.6 4.2 – 14.6 –

553 85 – 5 – 102 – 706 –

66.5 1.6 – 0.5 4.4 3.1 – 23.9 –

525 150 95 – – 476 – 660 320

50.1 2.8 10.2

825 190 45 – – 146 87 1040 –

59.1 4.3 2.2 – 5.2 5.7 2.8 20.7–

10.2 8.0 – 15.5 3.2

Note: Waste heat from electricity generation process is applied to match thermal energy demands. Ratio of energy converted by waste heat had been included in the ratios of BCHP, PVT, and FN system. Abbreviations: BCHP, biofuel microturbine power and heat; CD, commercial district; FN, linear Fresnel concentrating solar power generation; FST, linear Fresnel concentrating solar thermal energy; HP, heat pump; ID, industrial district; OD; official district; PE, public electricity; PVT, solar PV/thermal; RD, residential district; SAC, solar absorption cooling; STH, solar thermal heater; WT, wind turbine.

solar energy conversion systems is lower than 8% in OD, RD, and CD. Only for ID, there are over 20% of energy demands covered by solar energy technologies. It is summarized from Figs. 1–3 that the major part of solar energy is applied to match thermal energy demands. 2. Solar PV technologies, which include PV and PVT systems, are barely implemented in RD. The equipment size of the PVT system in RD is only 5 kW. Besides RD, the other districts prefer to use a concentrated solar power system, such as an FN system. Over 10% of annual energy demand is covered by an FN system in ID, but the percentage of annual energy provided by the FN system is merely 1.7 and 2.2% for OD and CD, respectively. 3. The majority of solar energy is presently used by a STH system for these districts. The explanation is that a number of lowtemperature thermal energy demands (i.e., SH and DHW) are required in these districts. STH technology is regarded as the most cost-effective and mature solar technology to provide low-temperature energy [6]. It is noted that the ratios of STH system utilization in the whole solar energy utilization are less than 38% for ID and CD. The reason for this phenomenon is that thermal energy demands in these two districts include not only low-temperature but also mid- or high-temperature heat load, STH technology is hardly capable to provide thermal energy in such temperature range. Other types of solar thermal energy systems, such as PST and FST, which could provide mid- or high-temperature thermal energy, are needed.

5.7.4.4

Parametric Study for Energy Quality Management

As known, STH technology is widely implemented in all these districts because it is cost-effective. Therefore it is interesting to consider questions like how the solar energy utilization patterns will change with reducing investments of other solar energy technologies for these district typologies. To answer such questions, a parametric study is necessary to investigate. The parametric analysis shows the effects of varying parameters on the solar energy utilization patterns for these districts located in Kunming. Three types of parameters are considered in Sections 5.7.4.4.1–5.7.4.4.2.

5.7.4.4.1

Investment parameters

In this part, investments of solar energy conversion components are assumed to be decreasing at a certain rate predefined as a constraint (5–25%). The analyzed results related to these four district typologies are shown in Fig. 11. As shown in Fig. 11, the trends of solar energy utilization patterns for these four districts are explained below: 1. Fig. 11(A) presents how the share of energy provided by solar energy system changes in OD. Initially, the ratio of energy from solar systems keeps a stepwise increase from 5.9 to 13.9% since the investment reduction ratio changes from 0 to 10%. A sudden jump happens when the investment falls to 85% of current level. At that point, over 27% of energy is generated by solar energy conversion systems. Extensive application of FN system is the main contributor to this phenomenon. With decreasing prices of solar energy components, the FN system begins to play a relatively important role for fulfilling the energy demands of OD. Percentage of energy demands covered by FN system climbs from 3.1 to 14.5%. After this critical point, the FN system keeps a stable rise till 19.2% of energy demands are provided by it. The final utilization ratio of solar energy is 31.8%. There is only a 4.4% increment when the reduction ratio improves from 15 to 25%. Accordingly, if investments of solar energy components decrease, solar energy technologies, especially FN technology, will be in intensive development in OD until the

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Energy Quality Management

38.5

9.5

3.7

21.4

Time

FN 47.7%

100 90 80 70 60 50 40 30 20 10 0

Other energy Solar energy

90.7

95

88.8

92.1

FST 35.4%

STH 36.7%

9.3

r in

te

ea

W

ly

(D)

r

te r

Time

r

12.8

STH 85.2%

ye a

10

in SAC 14.9%

Al

r te in

3.7

20

W 78.6

87.2

STH 37.4%

W

0.5

PVT 14.8%

30

r

61.5

19.6

96.3

(B)

Other energy Solar energy

80.4

90.5

0

Time

100 90 80 70 60 50 40 30 20 10 0

96.3

Al l

5.9

99.5

40

ye a

te r in W

1.4

Al l

3.9

18.2

se Mid as o Su n m m er

10

STH 70.5%

50

5

11.2

7.9

FN 27.9%

r

20

60

ea

30

(A)

Rotion of energy demand (%)

FN 29.5%

40

0

(C)

94.1

98.6

Rotion of energy demand (%)

96.1

50

70

ly

81.8

se Mid as Su on m m er

60

80

Al

70

Other energy Solar energy

90

se Mid as o Su n m m er

80

100 Rotion of energy demand (%)

Other energy Solar energy

90

se Mid as o Su n m m er

Rotion of energy demand (%)

100

Time

Fig. 10 Solar utilization patterns for different district typologies. (A) Pattern of official district (OD). (B) Pattern of residential district (RD). (C) Pattern of industrial district (ID). (D) Pattern of commercial district (CD). FN, linear Fresnel concentrating solar power generation; FST, linear Fresnel concentrating solar thermal energy; SAC, solar absorption cooling; STH, solar thermal heater; PVT, solar PV/thermal.

reduction ratio reaches to 15%. At this point, investment decrease has limited effects on the solar energy utilization status for OD. 2. Fig. 11(B) shows how the share of energy demands covered by solar energy changes in RD. The image of solar energy technologies could hardly be improved by the reducing prices of solar energy components. Ratio of energy generated by solar energy technologies is in a gradual ascent from 3.9 to 9.9% until the investments of solar energy components fall to only 75% of current level. There is only a tiny jump when the investment reduction ratio varies from 15 to 20%. As presented in the figure, increasing STH system utilization is the main contributor to improving the solar energy status in this district. Unfortunately, decreasing cost of solar energy systems is unable to affect the utilization of PV technology. The ratio of energy from the PVT system cannot exceed 1% for the RD. 3. For ID, although over 20% of energy is converted from solar energy at present, it is seen from Fig. 11(C) that solar energy utilization status will still be promoted constantly (from 21.4 to 38.9%) by reducing the investments of solar energy components. Since the solar energy component prices continue to drift lower, the ratio of energy generated from the FN system keeps a sustainable growth to 25.3%. When the decreasing ratio reaches to 15%, solar PV technology begins to participate in the energy supply. One percent of energy demands are fulfilled by the PVT system and this share will climb to 5.0% eventually. FN and PVT systems have the capability to offer power and thermal energy to districts simultaneously, thus more and more thermal energy demands are satisfied by the increasing number of such solar power systems. Namely, promotion of solar power generation technologies (i.e., FN and PVT) might cause the recession about solar thermal energy systems (i.e., STH and SAC) for this ID. The share of energy from solar thermal energy conversion systems falls from 11.2 to 8.6%. 4. Fig. 11(D) presents how the solar energy technologies trend with decreasing prices of solar energy components in CD. Solar energy is gaining increasing importance in the energy supply for this district. Ratio of energy converted from solar energy shows a gradual growth from 7.9 to 17.0%. There are two main contributors to this increment. The first one is the promotion of FN

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Energy Quality Management

30%

FN

STH

SAC

0.9

0.3 0.2

25%

11.1 12.7

20% 15%

0.3 0.1

10% 5% 0%

11.7

8.9

10.5

4.2 1.7

2.1

3.1

0%

5%

10%

18.6

19.2

14.5

15%

20%

35%

FN

PVT

STH

SAC

30%

0.9

25%

10.1

20%

3.2

15%

8.0

10%

18.7

8.6 8.2

0.4 9.4

21.5

8.2 1.0

23.3

5.0

4.2

23.5

25.3

10.2

Ratio of energy from solar conversion technology (%)

Ratio of energy from solar conversion technology

STH

8.9

8.4 5.3

6.0

5.9

3.1 0.6

0.6

0.7

0.9

0.9

1.0

0%

5%

10%

15%

20%

25%

Ratio of solar component investment reduction (%) 18%

40%

0%

16%

FN

PVT

14%

STH

SAC 3.3

10%

3.2

8%

0.9

6%

2.9

4%

2.8 2.2

1.8

1.2

0.9

1.1

5.0

5.3 5.4

4.5

0.8

0.3

4.2

3.5

3.1

3.3 3.5

12%

2%

FST

4.4

5.1

10%

15%

5.9

6.3

20%

25%

0% 0%

(C)

PVT

(B)

45%

5%

10% 9% 8% 7% 6% 5% 4% 3% 2% 1% 0%

25%

Ratio of solar component investment reduction (%)

(A)

Ratio of energy from solar conversion technology

Ratio of energy from solar conversion technology

35%

5%

10%

15%

20%

25%

Ratio of solar component investment reduction (%)

0% (D)

5%

Ratio of solar component investment reduction (%)

Fig. 11 Parametric analyses of investment parameter for different district typologies. (A) Solar energy utilization patterns for official district (OD). (B) Solar energy utilization patterns for residential district (RD). (C) Solar energy utilization patterns for industrial district (ID). (D) Solar energy utilization patterns for commercial district (CD). FN, linear Fresnel concentrating solar power generation; FST, linear Fresnel concentrating solar thermal energy; SAC, solar absorption cooling; STH, solar thermal heater; PVT, solar PV/thermal.

and FST systems. Shares of energy demands fulfilled by FN and FST systems rise from 2.2 and 2.8% to 6.3 and 5.0%, respectively. Another reason is that PVT and SAC systems begin to be considered in this district. When the investments reduce to 95% of current level, 3.2% of energy demands are satisfied by the SAC system. This ratio keeps stable with the reducing investments. Additionally, the PVT system is responsible for approximately 0.3% of energy demand when the decreasing ratio reaches to 20%. With the constant drop of solar energy component prices, the ratio of energy from the PVT system stands on the value of 0.8%.

5.7.4.4.2

Energy policy parameter

As mentioned above, economic issues are regarded as one of the biggest obstacles for solar energy development. Decreasing investments of solar energy components might be a potential alternative to solve this obstacle. If the technology development could not reduce the investments of solar technology components, it is important to find another way for dealing with the economic issues. Solar energy subsidy provided by government is considered as a promising solution. In this part, the energy policy parameter solar power subsidy (SPS) is assumed to be predefined as constraints, which are subsidies of $0.02, 0.04, 0.06, 0.08, and 0.1 for one unit (per kWh) of power generated by solar power generation systems. The analyzed results related to these four district typologies are shown in Fig. 12. Through Fig. 12, it is found that solar energy utilization patterns for these four districts will change significantly with the varying SPS. The details are listed below: 1. Fig. 12(A) illustrated that the subsidized solar price might contribute to encouraging the promotion of solar energy utilization in OD. When SPS is assumed as $0.02, there more than 10% growth on the ratio of energy converted from solar energy. As SPS increases to $0.10, the final ratio will reach to 29.6% constantly. As SPS rises, ratio of energy from solar power generation system raises from 1.7 to 12.3% gradually. To be clear, FN system is not the only solar power technology for this OD. The ratio of energy from the FN system appears as an ascent in fluctuations. When SPS starts to be improved, there is an acceptable increase in the utilization of the FN system. Thereafter, an increasing proportion of energy from the FN system is

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Energy Quality Management

14%

30%

FN

PVT

STH

SAC

25% 17.3

20% 19.1

0.2

15%

19.3

19.8

12.1

10%

3.7 2.5

5% 0%

4.2 1.7

4.3

0.00

0.02

1.2 3.1

0.04

1.4 3.0

0.06

6.1

0.08

8.6

0.10

Solar power subsidy ($/kWh)

(A)

Ratio of energy from solar conversion technology

Ratio of energy from solar conversion technology

35%

STH

PVT SAC 4.1

30% 25%

9.1

20%

3.2

15%

8.0

10%

20.3

3.9 9.4 1.8

23.3

2.9 9.5 3.2

24.0

1.8 7.4 6.1

24.6

0.6 6.7 7.7

25.1

10.2

Ratio of energy from solar conversion technology

Ratio of energy from solar conversion technology

STH

10% 8%

8.1 8.1

6%

6.3 5.5

4% 2%

3.1

0%

0.6

3.3 1.2

1.4

0.2 2.0

0.02

0.04

0.06

0.8

1.5

2.2

2.3

0.08

0.10

Solar power subsidy ($/kWh)

(B)

18%

0.00

0.02

0.04

0.06

0.08

Solar power subsidy ($/kWh)

0.10

FN

PVT

STH

FST 6.8

15% 4.8

9% 6% 3%

3.7 2.0 2.6 4.2 1.7

0.00 (D)

7.7

6.1

12%

0%

0% (C)

FN

21% FN

35%

5%

PVT

0.00

45% 40%

12%

4.2

0.02

2.1 0.5 5.7

0.04

2.4

1.6 2.2

2.8 0.7

1.6

7.6

8.1

8.5

0.06

0.08

0.10

Solar power subsidy ($/kWh)

Fig. 12 Parametric analyses of energy policy parameter for different district typologies. (A) Solar energy utilization patterns for official district (OD). (B) Solar energy utilization patterns for residential district (RD). (C) Solar energy utilization patterns for industrial district (ID). (D) Solar energy utilization patterns for commercial district (CD). FN, linear Fresnel concentrating solar power generation; FST, linear Fresnel concentrating solar thermal energy; SAC, solar absorption cooling; STH, solar thermal heater; PVT, solar PV/thermal.

replaced by other types of energy systems. When SPS reaches to $0.04, only 3.0% of annual energy is provided by the FN system. Eventually, the ratio rebounds to the peak value, which is equal to 8.6%. It is noted that the PVT system begins to be applied for participating in the energy supply when SPS meets its critical point ($0.04). The ratio of energy demand covered by the PVT system climbs from 1.2 to 3.7%. 2. According to Fig. 12(B), it is found that solar power generation technologies play more and more important roles in the energy supply for RD as SPS rises. The overall ratio of energy generated from solar power systems varies from 0.6 to 3.8%. So, the PVT system is regarded as the main supply technology. Ratio of energy from the PVT system keeps growing, from 0.6 to 2.3%. It should be noted that the growth rate is slowing down since SPS is fixed as $0.06. In the meantime, the FN system begins to be responsible for a portion of energy demand in addition to the PVT system. The initial value is 0.2%, and then the value jumps twice and reaches to 1.5% eventually. Additionally, solar thermal energy systems, such as STH, maintain a steady growth, from 3.1 to 8.1%. 3. Fig. 12(C) demonstrates that increasing SPS has benefits for the promotion of solar energy utilization in ID. The justifiable amount of SPS for ID should be kept around $0.04. As shown in the figure, the utilization ratio of solar energy approaches its saturation point, which is approximately 40.0%, when SPS is fixed as $0.04. At this point, there is little growth in the ratio of energy provided by solar energy systems. During the solar energy promotion process, development trends of different solar systems appear in their own ways. For the FN system, a jump from 10.2 to 20.3% occurs as SPS rises to $0.02, and then the ratio shows a sluggish growth till the final value of 25.1%. The PVT system starts to be applied for satisfying the energy demand when SPS achieves $0.04. Utilization ratio of PVT system improves from 1.8 to 7.7% sharply. In contrast to these solar power generation systems, the trend of solar thermal systems (STH and SAC) increases at first but later decreases. The final ratio is 7.3%. The increasing amount of thermal energy demand covered by the PVT and FN systems is the major contributor to such decrease. 4. Fig. 12(D) illustrates that there is a significant ascent (from 7.9 to 20%) on the overall utilization ratio of solar energy for CD with the increasing SPS. The FST system and solar power generation systems play the major roles in this rising ratio. When SPS reaches to $0.10, the FST system needs to take charge for approximately 7.7% of energy demand, which is nearly four times that

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Energy Quality Management

30%

30%

FN

PVT

STH

SAC

0.9 0.3 0.2

25%

16.8

0.3

20%

0.6

15%

16.8 16.4

16.1 16.0

10%

3.3

5% 0%

4.2 1.7

3.0

0.4 5.1

0%

5%

10%

6.6

6.8

3.5

7.3

6.9

15%

20%

FN

PVT

STH

SAC

2.4 2.8 3.3

30%

3.5

7.9 8.0

25%

8.5

7.3

1.5 6.4 5.7

3.2

1.4

3.2 8.0

10%

18.7

21.9

23.5

25.7

27.3

10.2

0%

17.5 16.9 15.6

10%

16.3

12.2

0.7

5% 3.1 0.6

1.9

2.9

4.1

0%

5%

10%

15%

2.3

6.6

7.4

20%

25%

Ratio of solar energy efficiency increase

20%

5%

10%

15%

20%

25%

Ratio of solar energy efficiency increase

FN

PVT

STH

SAC

15% 1.7 2.7

10% 5%

2.1 2.4 4.2

2.6

1.1

1.7

4.2

4.2

2.6

4.2

2.8

0.4 4.0

0.7 4.6

5.9

7.4

5%

10%

15%

20%

2.2

3.5 0.3 4.3 3.8

1.4

2.9

0% (D)

FST

3.2

0% 0%

(C)

STH

15%

(B)

Ratio of energy from solar conversion technology

Ratio of energy from solar conversion technology

35%

5%

FN

25%

40%

15%

PVT

20%

25%

45%

20%

25%

0%

Ratio of solar energy efficiency increase

(A)

Ratio of energy from solar conversion technology

Ratio of energy from solar conversion technology

35%

9.8

25%

Ratio of solar energy efficiency increase

Fig. 13 Parametric analyses of energy performance parameter for different district typologies. (A) Solar energy utilization patterns for official district (OD). (B) Solar energy utilization patterns for residential district (RD). (C) Solar energy utilization patterns for industrial district (ID). (D) Solar energy utilization patterns for commercial district (CD). FN, linear Fresnel concentrating solar power generation; FST, linear Fresnel concentrating solar thermal energy; SAC, solar absorption cooling; STH, solar thermal heater; PVT, solar PV/thermal.

at the present time. Opposite to the FST system, the ratio of energy provided by the STH system shows a downward trend from 4.2 to only 1.6%. There are two types of solar power generation technologies in the figure. Currently, the FN system is only responsible for fulfilling 1.7% of energy demand; however, the ratio could achieve over 8.5% eventually. As SPS rises to $0.04, the PVT system becomes an energy supply option for this district. Ratio of energy produced by the PVT system keeps a consecutive growth from 0.5 to 2.2%.

5.7.4.4.3

Energy performance parameter

Besides economic issues, comparatively poor energy performance of solar technologies, especially solar power generation systems, is another obstacle for promoting use of solar energy. In this part, conversion efficiencies of solar energy technologies are assumed to keep rising at a certain rate predefined as a constraint (5–25% of current level). The analyzed results related to these four district typologies are shown in Fig. 13. According to Fig. 13, the detailed information about how the solar energy utilization patterns for different districts change is presented as below: 1. Fig. 13(A) shows that solar energy utilization pattern for OD is sensitive to the conversion efficiency improvement of solar energy conversion systems. When the increase ratio is initiated at 5%, the ratio of energy from solar energy (19.6%) is more than three times that with current energy efficiency level (5.9%). The main contributor is the promotion of solar thermal systems consisting of a STH and SAC system (from 4.2 to 16.6%). As the energy efficiency continues to rise, the share of energy converted from solar energy also remains a rapid growth (from 16.6 to 31.8%). The main reason to explain such rapid growth is the promotion of solar power technologies. The ratio of energy demand fulfilled by solar power systems rises from 3.0 to 14.1%. Therein, the trend of FN system development appears as a constant ascent, from 3.0 to 7.3%. Meanwhile, there is a sudden jump of the ratio of energy supplied by PVT system, from 0.4 to 3.3%, when the increase ratio changes from 10 to 15%. The final ratio of energy from the PVT system is 6.8%, which is almost similar to that from the FN system. 2. As seen in Fig. 13(B), utilization ratio of solar energy in RD is extremely sensitive to the growth of solar energy conversion efficiency. When the enhancement of efficiency is 5%, the percentage of energy demand satisfied by solar energy skyrockets

Energy Quality Management

287

from 3.1 to 14.1%. After that, the share keeps a comparatively rapid ascent to 27.2%. Therein, the STH system is the dominant contributor. Utilization ratio of STH system appears as a sharp increase from 3.1 to 17.5%. Besides the STH system, promotion of the PVT system is another reason for this increase. The proportion of energy from solar energy goes up from only 0.6 to 7.4% steadily. Compared with other parameters mentioned in Sections 5.7.5.1 and 5.7.5.2, conversion efficiency improvement of solar energy has more meaningful effect on accelerating development of the PVT system. Additionally, the FN system is regarded as a promising supplement for the energy supply as the efficiency increase ratio is 20%. There will be 2.3% of energy demands fulfilled by the FN system. 3. According to Fig. 13(C), solar energy conversion efficiency increase has more significant effect on the utilization ratio of solar power systems for ID compared to those with other varying parameters. When the efficiency starts to be improved, there is a jump of the utilization of FN system, from 10.2 to 18.7%. Thereafter, the ratio remains on an increasing trend till the final ratio of 27.3%. Meanwhile, the PVT system begins to be applied for this district when the increase ratio of efficiency reaches to 15%. Ratio of energy provided by the PVT system initiates at only 1.4%; then this ratio shows a burgeoning. The final value of this ratio is three times more than the initial one. However, the solar thermal system, which contains an STH and SAC system, is becoming less important in ID with the rising efficiency. After a minor increase from 11.2 to 12.0%, the proportion of energy from the solar thermal system falls to 7.9% eventually. All in all, a growing amount of energy demand is covered by solar energy and the final share stays at 40.9%. 4. Through Fig. 13(D), it is found that the general trend of solar energy utilization in CD is upward with the improving solar energy conversion efficiency. In accordance with the general trend, a remarkable ascent, from 2.2 to 9.8%, happens to the utilization ratio of the FN system. In particular, the PVT system might become a promising energy supply option for the district when the efficiency starts to be improved. The final proportion of energy from the PVT system is 3.8%. Besides solar power systems, three types of solar thermal energy systems are also considered. The general trend of the FST system is expressed as an increment from 2.8 to 4.2%. However, such ascent only happens when the efficiency increase ratio changes to 5%, then the utilization ratio of FST keeps stable at around 4.3%. The proportion of energy generated by SAC appears as an increase in fluctuations. The value achieves 3.5% eventually. In contrast to these solar systems, the general trend of STH system is on a decline. Only 0.3% of energy is provided by the STH system when the efficiency increase ratio is 25%. The final ratio of energy converted by solar energy could achieve 21.7%.

5.7.4.5

Discussion

For the case study, the suitable solar energy utilization patterns of different district typologies, i.e., official, residential, industrial, and CD, are investigated and compared. These four types of districts are located in Kunming, China. In order to identify the suitable patterns, a multiobjective approach is proposed to select the optimum energy systems including solar energy for these districts. The optimization scheme minimizes the GWP and economic cost during the life cycle and maximizes the exergy performance. Although a number of parameters have been investigated here, a study about the synergistic effects of these parameters is absent. A deep study working to solve these limitations is required.

5.7.5

Energy Quality Management Case Study 3

5.7.5.1

Basic Information for Case Study 3

The MV stand-alone microgrid (MG) system of the aboriginal community in Canada is used for the case study, and the topology is as shown in Fig. 18. The system includes two radial segments, and three 1.1 MW diesel generators (GEs) that are in urgent need of replacement, which are connected to the 12.5-kV bus via a step-up transformer. Because it is isolated from the public power grid, the EL supply of the community is forced to depend mainly on a diesel GE. The unit quantity of EL is priced as high as 1.60 C$/kWh in 2011, which is much higher than the average in Canada. Alternatively, the local wind resource is so abundant that it is a feasible solution to transform the original grid into a SAMG system, including diesel GE, WT, and energy storage system (ESS) (Fig. 14). The annual duration load curve is as shown in Fig. 15, growing 8% a year. The physical and economic parameters of the diesel GE and ESS are listed in Table 11, and the parameters of WT can be found in Table 11. Compared with the high cost of generating units, the compensating capacitor is cheap enough to be disregarded. The simulation time step is set to be 1 h and the life cycle of the project is 10 years. The load reserve factor and the WT power reserve factor both are 0.05. The allowed maximum ratio of the EL shortage is 2%. The discount rate is 6.72%. The cost coefficient of the active power loss refers to 0.45 C$/kWh. The optimization range of the contract price is between 0.1 and 0.4 C$/kWh. The nodes #1–6 in Fig. 18 are the optional sites for WT and ESS.

5.7.5.2 5.7.5.2.1

Energy Quality Management Initiation Optimization objectives

The SAMG system planning is undertaken by the distribution company (DisCo) and distributed generation owner (DGO) together. In the current market environment, the aim of DisCo is to pursue the minimization of total payment for satisfying load growth, while guaranteeing the system’s stable operation. As for DGO, the behaviors are utterly out of economic motivation, the goal of which is to realize profit maximization. Minimizing the generating cost of DisCo and the maximizing of internal rate of return (IRR) of DGO are taken as the optimal objectives simultaneously.

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Energy Quality Management

Diesel generators 0.4 kV/12.5 kV Segment0 1

Segment1

Segment2

4

2

5

3

6

Fig. 14 Structure diagram of the power supply system of the community.

The generating cost in SAMG is expensive compared with grid-connected regions, and DisCo has to rely mainly on government subsidies to keep a no-loss level. Considering that the sale price paid by customers is set by the government, which is basically the same as that in grid-connected regions, lowering the generating cost is the common aspiration of DisCo and the government. Based on this, the cost-minimizing model of DisCo rather than the benefit model was formulated, expressed as   f1 :min Cg ð30Þ

Cg is the present value of generating cost paid by DisCo, including the initial investment in construction period, the generating cost in the life cycle, and the salvage cost of all equipment invested by DisCo in the final year. Present value means the conversion value of annual cost in the present year, which can reflect the project investment more reasonably, and the following equation provides a model expressing the present value criterion by employing the coefficient. Cg ¼ Cgi þ

T X

Ca;t  ðat Þt

BgT  ðat ÞT

ð31Þ

t¼1

at ¼ 1=ð1 þ iÞt

ð32Þ

where Cgi represents the investment cost in construction period; Ca,t represents the total generating cost in the tth year; BgT refers to the salvage value of investment of DisCo, which is nonzero value only in the final year; T is the life cycle of the project; at is the discount rate in tth year; i is the interest rate. The total generating cost Ca,t consists of four parts. The first part is the operation and maintenance cost, including the replacement cost and fuel expense of diesel GE and the maintenance cost of the system. Since DisCo is responsible for the system’s

Energy Quality Management

289

3.5

3

Load (MW)

2.5

2

1.5

1

0.5 0

1000

2000

3000

4000

5000

6000

7000

8000

9000

T (hour) Fig. 15 Annual duration load curve.

Table 11

Physical and economic parameters

Device

Parameter

Value

Diesel generator (GE)

Rated capacity (kVA) Intercept coefficient(L/kWh) slope (l/kwh) Minimum load rate (%) Minimum operation time (h) Fuel cost ($/L) CO2 (g/L) CO (g/L) Life (h) Investment cost (C$) O&M cost (C$/h) Maximum state of charge (SOC) Minimum SOC Maximum discharge rate Maximum charge rate Investment cost (C$/kWh) Life (year) Inversion efficiency Rectification efficiency Investment cost (C$) Life (year)

1,100 0.08415 0.246 25 3 1.49 2,487 6.5 120,000 1,094,310 75 0.9 0.1 0.4 0.25 200 10 0.95 0.95 160 10

Battery

Power conditioning system (PCS)

stable and economical operation, the power loss cost of the system should be included in DisCo’s generating cost, which is the second part of Ca,t. The usage of diesel generates enormous quantities of pollution, like CO2, CO, NOx, SO2, and hydrocarbon, among which the carbon dioxide emissions have the largest percentage. Under the latest update to the Canada federal climate change plan, the price in all cases would start at per ton of carbon and rise in steps to $65 by 2018, so the third part of Ca,t is the carbon cost. In addition, the integration of DG is via private line to the grid, and Cg also covers the power purchasing cost paid to DGO. Based on the above analysis, Cg can be expressed as Ca;t ¼ CO& M;t þ CLOSS;t þ CEMISS;t þ CPURCH;t

ð33Þ

where, CO& M;t ¼ Cgr;t þ Cgm;t þ

TH X

th ¼ 1

Cgf ;t;th

ð34Þ

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Energy Quality Management

CLOSS;t ¼

TH X

ð35Þ

Ploss;t;th l

th ¼ 1

CEMISS;t ¼

M X

Et b

ð36Þ

Pt;th lc

ð37Þ

m¼1

CPURCH;t ¼

TH X

th ¼ 1

where, CO&M,t is the operation and maintenance cost; CLOSS,t is the power loss cost; CEMISS,t is the carbon cost; CPURCH,t is the power purchasing cost paid to DGO; Cgr,t and Cgm,t refer to the replacement and maintenance cost, respectively; Cgf,t,th is the fuel cost at time th in the tth year; Ploss,t,th represents the active power loss at time th in the tth year, and l is the cost coefficient; Et is the total amount of pollutant emission in the tth year, and b is the penalty cost of emission; Pt,th is the output power of DG, and lc is the contract price paid to DGO; TH is the amount of simulation steps. The goal of DGO can be expressed as f2 :max ðIRR d Þ

ð38Þ

IRR is defined as the discount rate that equates the present value of the project’s future net cash flows with the project's initial cash outlay, which is the reflection of the project’s profit capacity, and can be given by Cd þ

T X

Sd;t  ðIRR d Þt ¼ 0

ð39Þ

t¼1

where, Cd is the initial cash outlay of DGO, including the investment cost of DG and ESS; Sd,t is the net cash flows in the tth year, as Sd;t ¼

TH X

PDG;t;th lc

Cdr;t

Cdm;t

BdT

ð40Þ

th ¼ 1

where, Cdr,t and Cdm,t refer to the replacement and maintenance cost of DG and ESS, respectively; BdT refers to the salvage value of investment of DGO, which is nonzero value only in the final year.

5.7.5.2.2

Decision variables

The number of diesel GEs, the number of WTs, the capacity of batteries and PCS, the site of WT and ESS, as well as the contract price between DGO and DisCo are taken as the optimal variables in this chapter, which can be described as: X ¼ ½Ndiesel ; NWT ; Cbat ; Ccon ; LWT & ESS ; lc Š

ð41Þ

where, Ndiesel, NWT are the number of diesel GEs and WTs; Cbat and Ccon refer to the capacity of batteries and PCS; LWT&ESS is the site of WT, the same as ESS; lc is contract price paid to DGO by DisCo.

5.7.5.3

Optimization Results

The Pareto-optimal front with the proposed method is shown in Fig. 16. Numerical results in which IRR of DGO is between 10 and 30% are provided in Table 12. It can be observed that the interests of DGO and DisCo conflict, and the capacity of WT and ESS correlate to several factors. Note that although the high-capacity WT can increase IRR of DGO along as the contract price increases, greater cost in EL purchase needs to be paid by DisCo. Thus in the Pareto-optimal set, the capacity of WT declines when the contract price is rising, which can maximize the comprehensive benefits. From the table, IRR of DGO increases with the decrease of WT capacity, since on one hand, the loss caused by power curtailment of WT is less than the benefits caused by high contract price, while on the other hand, the optimal capacity of ESS is related to WT capacity, and the small capacity lowers the investment. For DisCo, the generating cost rises with the decrease of WT capacity, since the generating energy of the diesel GE increases, leading to greater generating cost of diesel, and meanwhile expense contract price needs to be paid to DGO. The optimal site of WT and ESS is node #5, which is close to the end of the feeder, and thus can minimize the power loss along the feeder. The final planning scheme needs negotiations between both parties and effective references can be provided by the method of this chapter in the practical planning of the SAMG system.

5.7.5.4

Comparative Study

A comparative study and analysis is carried out between the results attained by proposed planning model and the traditional plan with total reliance on diesel GE, as shown in Table 13. Three different planning schemes in the Pareto-optimal front are selected, and the serial numbers are 6, 11, and 18, respectively. The traditional plan is obtained by solving the single-objective optimal programming model with the goal of minimizing the generating cost of DisCo. Note that the cost in Table 13 all means to the present value. Table 13 shows that the number of diesel GEs can be reduced by transforming the original system into a hybrid SAMG system. Compared with the traditional planning result, the costs of diesel GEs and carbon emission decrease by 29.67 and 31.30%,

Energy Quality Management

291

0.5

IRR of DGO

0.4

0.3

0.2

0.1

0 110

115

120

125

130

135

140

Generating cost of DisCo (million C$) Fig. 16 Pareto-optimal front. DGO, distributed generation owner; DisCo, Distribution Company.

Planning schemes in which internal rate of return (IRR) of distributed generation owner (DGO) is between 10 and 30%

Table 12 Plan number

Battery capacity Power conditioning system (PCS) Diesel generator Wind turbine (WT) WT capacity (KW) number location (kWh) number

Contract price (C$)

Goal1 (million C$)

Goal2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

10 10 8 9 10 9 9 8 7 7 8 8 7 6 6 6 6 6

0.19 0.20 0.19 0.21 0.23 0.22 0.23 0.23 0.22 0.23 0.24 0.26 0.26 0.25 0.26 0.27 0.28 0.29

118.381 119.433 12.0.825 121.393 121.967 122.087 122.973 124.001 124.424 125.137 124.802 126.313 127.329 127.897 128.562 129.281 129.956 130.643

0.0996 0.1188 0.1316 0.1412 0.1482 0.1513 0.1620 0.1794 0.1844 0.1963 0.1930 0.2113 0.2311 0.2442 0.2551 0.2727 0.2851 0.2982

5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5

5100 3600 3000 2700 2800 3200 2900 2500 2600 2700 2200 2800 2800 2300 2400 1800 1800 1800

900 900 800 600 800 900 700 700 700 800 900 700 800 700 600 500 500 700

5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5

respectively, in plan 6, and thus a 16.08% reduction of the generating cost of DisCo can be realized. At the same time, IRR of 15.13% can be gained by DG. In plan 18, with less WT and higher contract price, a decent IRR of 29.82% can be gained by DGO, and the generating cost of DisCo still can decline by 10.20% compared with the traditional plan. Therefore, a win–win situation would occur by transforming the original system into a hybrid isolated system including diesel GE, WT, and ESS, increasing significantly the comprehensive benefits.

5.7.5.5

Parametric Analysis of Energy Storage System

To study the effect of ESS on each interest subject, we will consider different modes of investment and operation: (1) allocating ESS in system by DGO, and adopting the proposed dispatch control method including the voltage regulating strategy; and (2) not allocating ESS and regulating the voltage by constraining the power of WT. The results are shown in Table 14. Table 14 shows significant reduction in power curtailment of WT when allocating ESS in the system, which is up to 80.53%. The addition of the utilization rate of wind power from 63.43 to 68.02% also indicates the effect of ESS. The allocation of ESS, on one

292

Table 13

Energy Quality Management

Comparison with the traditional plan

Index

Plan 6

Plan 11

Plan 18

Traditional plan

Number of diesel generators (GEs) Number of wind turbines (WTs) Capacity of battery (kWh) Capacity of power conditioning system (PCS) (kWh) Contract price (C$) Annual power loss (MWh) Cost of diesel generators (GEs) (million C$) Diesel GEs cost per unit electricity (EL) (C$) Cost of carbon emission (million C$) Purchasing cost of Distribution Company (DisCo) (million C$) Generating cost of DisCo (million C$) Distributed generations (DG’s) generating cost (million C$) DG’s cost per unit electricity (EL) (C$) Internal rate of return (IRR) of distributed generation owner (DGO) (%)

5 9 3200 900 0.22 2114.5 96.46 0.3855 0.0090 18.09 122.08 11.77 0.101 15.13

5 8 2200 900 0.24 2116.9 98.60 0.3843 0.0093 18.66 124.80 10.20 0.093 19.30

5 6 1800 700 0.29 2101.6 103.48 0.3826 0.0098 19.67 130.64 7.77 0.081 29.82

7 0 0 0 / 2333.5 137.16 0.3730 0.0131 0 145.48 0 0 0

Table 14

Comparison with the wind–diesel plan

Index

Without energy storage system (ESS)

With ESS

Number of diesel generators (GEs) Number of wind turbines (WTs) Capacity of battery (kWh) Capacity of power conditioning system (PCS) (kWh) Contract price (C$) Cost of diesel GEs (million C$) Cost of carbon emission (million C$) Annual pollutant emission (ton) Purchasing cost of Distribution Company (DisCo) (million C$) Generating cost of DisCo (million C$) Cost of ESS (million C$) Internal rate of return (IRR) of distributed generation owner (DGO) (%) On-grid WT power (MWh) Utilization rate of WT (%) Annual discharging energy of ESS (MWh) Reactive power of PCS (MVar) Cutting-down energy of WT for voltage regulation (MWh)

5 10 0 0 0.23 96.85 0.0092 18,833 18.47 122.691 0 15.57 11,286 63.43 0 0 242.9

5 10 3,500 1,000 0.23 94.61 0.0089 18,293 19.81 121.967 1.5913 14.82 12,103 68.02 437.86 514.6 47.3

hand, enhances the capacity of WT power absorption in system, while on the other hand, when the voltage is over the upper limit, the absorption of distributed power by ESS and reactive power adjustment capacity though PCS can be used to reduce the cut power of WT. Additionally, the generated output power of the diesel GE decreases with WT power increasing, and so does the fuel consumption and the pollutant emission; accordingly the generating cost of DisCo reduces. But a noteworthy problem is that the ESS cost is up to 1.59 million C$, outweighing the benefits caused by ESS, resulting in the IRR of DGO decreasing from 15.57 to 14.82%. The investment mechanism of ESS will be analyzed later. In these two kinds of operating modes, the variation curve of the maximum voltage in the system, active and reactive power of ESS before regulation (BR) and after regulation (AR) of the voltage, and the wasted power in system within 24 h choosing from the year is shown as (A), (B), and (C) in Fig. 17. As it is seen from the figure, the overvoltage problem exists at the 2nd to 8th, 20th, and 22nd to 23rd hours, and the system allocating ESS avoids effectively the power curtailment of WT by increasing the charging power of ESS at the 2nd and 4–7th hours, and the reactive power of PCS is utilized to regulate the voltage because of the maximum limit of charging power at the 3rd, 8th, 20th, 22nd to 23rd hours. Through the regulation process proposed in this chapter, a reduction of the wasted power of WT is obtained, and the utilization rate of wind power is increased effectively. The expense cost of ESS leads to a loss of DGO, but the future economic assessment may be different as ESS gets cheaper and more prevalent. Besides, considering that allocating ESS can reduce the generating cost of DisCo, the win–win situation may be achieved with certain subsidies for the investment of ESS provided by DisCo. The investment analysis for different values of percentage of investment subsidy from DisCo in overall cost of ESS and the decline proportion of ESS cost, represented by a and b, is provided in Table 15. The first item within parentheses is the generating cost of DisCo (million C$), and the second item is IRR of DGO (%) in Table 15.

Energy Quality Management

293

Voltage (p.u)

1.08 1.06 1.04 Voltage (BR) Voltage witt ESS (AR) Voltage without ESS (AR)

1.02 1 1

5

10

ESS power (KW/KVar)

15

20

24

Time (h)

(A)

1000 500 0 ESS active power (BR) ESS active power (AR) ESS reactive power (AR)

–500 –1000 1

5

10

15

20

24

Time (h)

(B)

Waste power (MW)

4 3 2 1 With ESS Without ESS

0 1

5

10

15

20

24

Time (h)

(C)

Fig. 17 Variation curves of the voltage, energy storage system (ESS) power, and the wasted power. (A) The variation of voltage before regulation (BR) and after regulation (AR) in 24 h; (B) the variation of ESS power BR and AR in 24 h; and (C) the variation of system waste power BR and AR in 24 h.

Table 15

Investment analysis of energy storage system (ESS)

a

0

20%

40%

b 1 90% 80% 70%

(121.97, (121.97, (121.97, (121.97,

14.82) 15.05) 15.28) 15.51)

(122.29, (122.25, (122.22, (122.19,

15.28) 15.46) 15.65) 15.84)

(122.60, (122.54, (122.47, (122.41,

15.74) 15.89) 16.03) 16.17)

As it is seen from Table 15, in the ESS cost invariable situation, IRR of DGO tends to increase with the increase of a. Considering that a increases from 0 to 0.4, IRR of DGO increases from 14.82 to 15.74%, and rises by 0.17% associated to a¼ 40%, compared with the operating mode without ESS. Meanwhile the generating cost of DisCo can still reduce from 122.691 C$ to 122.60 C$, which indicates that the win–win situation is produced for both parties. As the ESS cost decreases over the next several years, ESS brings in higher revenues for DGO, and the generating cost of DisCo decreases simultaneously. Considering a ¼40%, and the investment cost of ESS falling to 70%, IRR of DGO can increase by 0.6%, and a reduction of generating cost from 122.691 C$ to 122.41 C$ can be gained by DisCo. It can be observed that if DGO and DisCo share the ESS cost, the allocating of ESS cannot only raise the utilization rate of renewable resources and reduce the pollutant emission, but also improve economic performance of both parties. What is more, as the investment cost of ESS decreases in the future, the economic returns of DGO and DisCo would further enhance.

294

Energy Quality Management

5.7.6

Energy Quality Management Case Study 4

5.7.6.1

System and Assumption for Case Study 4

Fig. 18 schematically shows an ERS with its T–s diagram. The system consists of a GE, a condenser (CO), an evaporator (EV), an EJ, a PU, and a TV as well as three brine side paths. The heat load (QGE) from the renewable energy or waste heat is delivered to the GE for vaporization. High-pressure vapor out from the GE, i.e., the primary flow, enters into the EJ nozzle and draws low pressure vapor from the EV, called the secondary flow. The two flows undergo mixing and pressure recovery in the EJ, and then the mixed flow feeds to the CO, where condensation takes place by rejecting heat (QCO) to the heat sink. Liquid from the CO is divided into two parts. One partial liquid goes through the TV to the EV, and produces a refrigerating effect (QEV). The rest of the liquid is pumped back to the GE via the PU, and finishes a complete cycle. The EJ is the compression agent and fulfills the function of a compressor. For simplicity, the following thermodynamic assumptions are made:

• • • • • •

The system is in steady state. Heat and pressure losses in heat exchangers and pipelines are neglected. R600a is selected as the working fluid, and it is saturated at the exits of the GE, CO, and EV. Moreover, the system refrigerating capacity is fixed at QEV ¼ 100 kW. The EJ is adiabatic, and has three efficiencies in the nozzle (Zn), mixing process (Zm), and diffuser (Zd), which are assumed at Zn ¼ 0.95, Zm ¼ Zd ¼0.9. The PU has a constant isentropic efficiency ZPU ¼ 0.5. The GE secondary side is supplied with saturated steam at 1051C, which is condensed. The condensate leaves the GE as saturated liquid, i.e., T7 ¼T8 ¼ 1051C. The water at the inlet and outlet temperatures of the CO and EV are kept at T9 ¼201C, T10 ¼ 311C, T11 ¼ 131C, and T12 ¼ 201C. The kinetic and potential energy changes of the working fluid for an individual component are negligible. The reference state is the same as the environment state at temperature (T0) of 251C and pressure (P0) of 101.33 kPa.

5.7.6.2 5.7.6.2.1

System Modeling Thermodynamics analysis

Using the assumptions stated above, energy conservation for each component in the ERS can be written as: X X _ out hout W þ Q ¼ 0 _ in hin m m

ð42Þ

The coefficient of performance (COP) of the ERS can be written as: COP ¼

_ EV
ðh6 m QEV ¼ _ GE
ðh1 QGE þ WPU m

ðh6 h5 Þ ¼m
h3 Þ ðh1

h5 Þ h3 Þ

ð43Þ

_ EV) coming from the where m is the EJ entrainment ratio, defined as the ratio between the mass flow ratios of the secondary flow (m _ GE) supplied by the GE. EV and the primary flow (m Heat source 7

QGE

8 T

4

1 Generator

8(105°C)

7(105°C)

PGE

ΔTGE 13

Pump

Heat sink 9

WPU

QCO

3 Condenser

4

10 Ejector

PCO

2

PU

2

CO 3

ΔTCO

TV 9(20°C)

Throttling valve

12(20°C)

10(31°C) PEV

11(13°C) ΔTEV

5

6

5

Evaporator

11 (A)

1

GE

QEV Cooling effect

12

EV

6

S (B)

Fig. 18 (A) Schematic of an ejector refrigeration system (ERS) and (B) its T–s diagram.

Energy Quality Management

295

The exergy in ERS is only physical exergy, which consists of the thermal exergy eT and mechanical exergy eM, it can be calculated as: _
½ðh _ ¼m _
ðeT þ eM Þ ¼ m E_ ¼ me

h0 Þ

T0 ðs

s0 ފ

ð44Þ

By introducing the concept of “fuel-product” into the exergy analysis, the exergy balance of the kth component in the system and its EE are expressed as: E_ F;k ¼ E_ P;k þ E_ D;k

ð45Þ

ek ¼ E_ P;k =E_ F;k

ð46Þ

where the E_ F,k, E_ P,k, and E_ D,k are the exergy of fuel, the exergy of the product, and the exergy destruction, respectively. It should be pointed out that the concept of the exergy of fuel employed here is in a general sense and not necessarily restricted to being an actual fuel, such as coal, natural gas, or oil.

5.7.6.2.2

Economics analysis

The principal costs of the ERS are the capital investment cost and operating and maintenance cost. The economic analysis carried out in this study is a simplification of the total revenue requirement approach [73]. The total capital investment (TCI), including purchased equipment costs (PECs) and additional expenditure, for example, instrumentation, controls, electrical equipment, etc., is estimated as a percentage of the sum of all the PECs. The percentage is taken as g ¼ 4.75 in Eq. (47), following Wang [74]. X TCI ¼ g PECk ð47Þ

To covert cost escalation to a financially equivalent constant quantity over a specified time, the concept of levelized costs is generally applied. The levelized carrying cost (CCL) and the levelized operating and maintenance cost (OMCL) are given as Ref. [74]: CCL ¼ TCI
CRF CRF ¼

ið1 þ iÞn ð1 þ iÞn 1

OMCL ¼ ðjÞ
TCI
CELF     1þr 1þr n CELF ¼
1
CRF i r 1þi

ð48Þ ð49Þ ð50Þ ð51Þ

where CRF is the capital recovery factor that identifies the annuities as a series of equal-amount monetary transactions of the known present value at equal periods over the economic life. The constant escalation levelization factor (CELF) is used to express the relationship between the value of the expenditure at the beginning of the first year and an equivalent annuity. The symbols i, n, and r are the annual effective interest rate (10%), the ERS economic life (20 years) and the nominal escalation ratio (3%), respectively. (j) (0.06) represents a constant factor for calculating the OMCL [74]. The PECs of all components should be expressed as functions of key characteristic variables, called cost functions. The expression of cost functions is one of the most difficult parts and may cause the largest uncertainties in mathematical optimization problems. Due to lack of appropriate cost functions for estimating EJ purchase equipment cost PECEJ, it is simply assumed to be fixed at $2000 and the EJ is able to meet all work conditions required. The cost functions of PECs for the remaining components in ERS are used here as [75]: log10 PECHE ¼ 3:2138 þ 0:2688log10 AHE þ 0:07961ðlog10 AHE Þ2

ð52Þ

log10 PECPU ¼ 3:3892 þ 0:0536log10 WPU þ 0:1538ðlog10 WPU Þ2

ð53Þ

PECTV ¼ δ
E_ P;TV

ð54Þ

where the PECs of three heat exchangers (the GE, CO, and EV) are based on the heat transfer areas AHE. The PEC of the TV (Eq. 54) is based on the exergy of product, and δ is a constant with a value of 37 $/kW.

5.7.6.2.3

Thermoeconomic analysis

Integrating the exergy analysis with economic principles, thermoeconomic analysis aims to reveal the cost formation process and calculate the cost per exergy unit of the streams of the system. This analysis is performed by applying the cost balance equation on each component of the system. X X C_ k;in þ Z_ k ¼ C_ k;out ð55Þ Z_k ¼

CCL þ OMCL PECk
P t
o PECk

ð56Þ

_ c, t, and o are the cost rate of exergy, cost per unit of exergy, the annual number of operating hours _ The symbols C, where C_ ¼ c E. (6000 h), and annual capacity factor (0.8) [74], respectively. The term Z_ k is the cost rate related to the capital investment and

296

Energy Quality Management

Equations for analyzing the ejector refrigeration system (ERS)

Table 16 Component

Energy analysis

Generator (GE) Condenser (CO) Evaporator (EV) Ejector (EJ) Pump (PU) TV Overall

QGE ¼ m_ 1 (h1–h4) QCO ¼ m_ 2 (h2–h3) QEV ¼ m_ 6 (h6–h5) – WPU ¼ m_ 1 (h4–h3) h3 ¼h5 m_ 5 ¼ m_ 6 ¼QEV/(h6–h5) m_ 1 ¼ m_ 4 ¼ m_ 6/m m_ 2 ¼ m_ 3 ¼ m_ 1 þ m_ 6

Exergy analysis

Thermoeconomic analysis

E_ F,k

E_ P,k

Cost balance

E_ F,GE ¼ m_ 8 (e8–e7) E_ F,CO ¼ m_ 2 (e2–e3) E_ F,EV ¼ m_ 6 (e6–e5) E_ F,EJ ¼ m_ 1 (e1–e2) E_ F,PU ¼WPU E_ F,TV ¼ m_ 5 (e3M–e5M þ e3T) E_ F,tot ¼ E_ F,GE þ E_ F,PU E_ L,tot ¼ E_ P,CO etot ¼ E_ P,tot/E_ F,tot

E_ P,GE ¼ m_ 1 (e1–e4) E_ P,CO ¼ m_ 9 (e10–e9) E_ P,EV ¼ m_ 12 (e12–e11) E_ P,EJ ¼ m_ 6 (e2–e6) E_ P,PU ¼ m_ 4 (e4–e3) E_ P,TV ¼ m_ 5 e5T E_ P,tot ¼ E_ P,EV

_ 8e8e8 þ m_ 4e4e4 þ Z_ GE ¼ m_ 1e1e1 þ m_ 7e7e7 m m_ 2e2e4 þ m_ 9e9e9 þ Z_ CO ¼ m_ 3e3e3 þ m_ 10e10e10 m_ 5e5e5 þ m_ 12e12e12 þ Z_ EV ¼ m_ 6e6e6 þ m_ 11e11e11 m_ 1e1e1 þ m_ 6e6e6 þ Z_ EJ ¼ m_ 2e2e2 C_ 13 þ m_ 4e3e3 þ Z_ PU ¼ m_ 4e4e4 m_ 5e3e3 þ Z_ TV ¼ m_ 5e5e5 Auxiliary equations c5 ¼c6 c7 ¼c8 c2 ¼c3

operating and maintenance expense. Table 16 summarizes all the equations used, and the subscripts are corresponding to those of Fig. 18. The thermoeconomic variables, the average unit cost of fuel cF,k, average unit cost of product cP,k, cost rate of exergy destruction C_ D,k, and thermoeconomic factor fk in the kth component are introduced to evaluate thermoeconomic performance of the ERS components. They can be expressed as [73]:

5.7.6.2.4

cF;k ¼ C_ F;k =E_ F;k

ð57Þ

cP;k ¼ C_ P;k =E_ P;k

ð58Þ

C_ D;k ¼ cF;k
E_ D;k

ð59Þ

fk ¼ Z_ k =ðZ_ k þ C_ D;k Þ

ð60Þ

Energy quality management process

The EQM usually involves the elements of objective functions, decision variables, and constraints. In the chapter, the objective function (OBF) is defined as the sum of the cost rate of the steam C_ steam, the water C_ water, and the EL for PU C_ electricity as well as the cost rate Z_ k. X Z_ k ð61Þ OBF ¼ C_ steam þ C_ water þ C_ electricity þ Two types of variables are categorized in the system design and optimization. They are decision variables that can be varied in optimization studies, and parameters that remain constant. The decision variables in this chapter are selected as the pinch point temperatures in the GE DTGE, the CO DTCO, and the EV DTEV, as shown in Fig. 18. In spite of the remarkable effects of the EJ efficiencies (Zn, Zm, and Zd) on the COP of an ERS, these efficiencies are used as parameters since there is no appropriate cost function for PECEJ. Additionally, energy consumption of the PU is insignificant compared to the heat loads in three heat exchangers, thus the PU efficiency ZPU is also treated as a parameter. Although the decision variables are varied, they are normally subjected to sets of constraints due to the physical limitations. Such constraints of the pinch point temperatures in the three heat exchangers (DTGE, DTCO, and DTEV) are given in the same wide range: 31CrDTr201C

ð62Þ

The iterative technique [73] is chosen in the present study to minimize the OBF under the stated constraints. The calculating program is written with Fortran language and refrigerants’ thermodynamic properties are taken from the NIST database and subroutines [74].

5.7.6.3

Energy Quality Management Results

In the preliminary design of the ERS, the decision variables are equally set at ΔTGE ¼ΔTCO ¼ ΔTEV ¼ 51C, with other parameters given in the assumptions, which is referred as the base case. This base case is imposed upon by two economic scenarios: (I) at base case I, which is abbreviated as B-I, the prices of steam as heat source to the GE, water for the CO and the EV, and EL for the PU are charged at 6.614 $/ton, 0.0368 $/ton, and 0.2 $/kW/h, respectively; (II) when steam and water are considered as free, and only EL is charged at 0.2 $/kW/h, it is named as base case II (B-II). Clearly, the only difference between B-I and B-II is the price of the steam and water.

Energy Quality Management

Table 17

Thermodynamic properties and exergy costing for base cases

Locations

Exergy carrier

Thermodynamic properties

Exergy costing B-I

1 2 3 4 5 6 0 7 8 9 10 11 12 0

R600a R600a R600a R600a R600a R600a R600a steam steam water water water water water

297

B-II

m_ (kg/s)

T (1C)

P (kPa)

H (kJ/kg)

s (kJ/kg/K)

E_ (kJ/kg)

c ($/GJ)

C_ ($/h)

c ($/GJ)

C_ ($/h)

0.64 0.99 0.99 0.64 0.35 0.35 – 0.11 0.11 7.48 7.48 3.41 3.41 –

100.00 50.34 35.08 37.07 8.00 8.00 25.00 105.00 105.00 20.00 31.00 13.00 20.00 25.00

1985.69 465.52 465.52 1985.69 206.24 206.24 101.33 120.90 120.90 101.33 101.33 101.33 101.33 101.33

677.66 631.06 284.52 290.17 284.52 565.76 598.92 440.27 2683.39 84.01 130.00 54.70 84.01 104.92

2.38 2.41 1.29 1.30 1.30 2.30 2.51 1.36 7.30 0.30 0.45 0.20 0.30 0.37

118.66 62.67 50.66 53.59 46.99 29.98 0 38.35 512.90 0.18 0.25 1.04 0.18 0

51.09 78.41 78.41 84.54 85.29 85.29 – 12.90 12.90 207.49 893.34 241.58 207.49 –

13.90 17.56 14.20 10.39 5.13 3.27 – 0.20 2.62 0.99 5.99 3.08 0.45 –

26.58 41.88 41.88 50.01 45.90 45.90 – 0 0 0 511.83 138.99 0 –

7.24 9.38 7.58 6.15 2.76 1.76 – 0 0 0 3.43 1.77 0 –

Table 17 presents the data of the ERS at the base case, with these numbered locations corresponding to Fig. 18. Since B-I and BII are working at the thermodynamic condition, their thermodynamic properties are the same. When B-I and B-II are imposed to the economic conditions, the costs are allocated to each exergy stream, called exergy costing. It is obvious that different economic conditions lead to large differences in the exergy costing. Therefore it is of great interest to study the EQM characteristics through the optimization process. The optimizations of the base cases B-I and B-II are carried out individually to minimize the OBF by charging the decision variables, and their optimized cases are referred to as OPT-I and OPT-II, respectively. Table 18 lists the obtained EQM variables as well as the EE of the base cases and the optimized results. Since the cost functions of the components in the ERS only depend on thermodynamic conditions, the cost rate Z_ k is independent on the economic conditions, leading to these equal values of Z_ k in B-I and B-II. Their thermodynamic performance, such as EJ entrainment ratio m, system COP, and EE e, is also the same. Moreover, the OBF of B-II is much smaller than that in B-I due to the fact that the steam and water is free in scenario II. In other words, the costs of the steam and water play very important roles in the B-I. Obviously, the ERS is much more economical when using the free heat source, like the waste heat, and free fluids for brine side. It is seen from Table 18 that the OBF of the optimized case OPT-I is reduced by 8.1% from base case B-I of 9.27–8.52 $/h, the thermodynamic performance of OPT-I is also improved compared to that of B-I. The OBF of OPT-II is reduced by 7.5% relative to base case B-II, but the thermodynamic performance of OPT-II is a little lower than B-II. This can be explained as follows: with the assistance of Eq. (20), optimization of B-I is to minimize OBF by a trade-off among its four parts, i.e., C_ steam, C_ water, C_ electricity, and Z_ k. For instance, an increase in ΔTGE means, on one hand, a lower TGE and PGE, resulting in a smaller COP. As the system refrigerating capacity QEV is constant, a larger GE heat load QGE is obtained according to Eq. (43) and a larger CO heat load QCO can be also expected, leading to higher C_ steam and C_ water. On the other hand, a small pressure difference (PGE–PCO) leads to low EL consumption by the PU, therefore, a smaller C_ electricity is obtainable. Moreover, an increase in ΔTGE generally causes a smaller heat transfer area AGE in the GE, resulting in a lower PECGE and a smaller Z_ k. In a word, minimizing OBF of B-I by changing ΔTGE is a compromise between C_ steam þ C_ water and C_ electricity þ Z_ k. With respect to B-II, the target is only to minimize the C_ electricity þ Z_ k since the steam and water are free. The main attention should be paid to the Z_ k because of its dominant contribution to the OBF, as P shown in Fig. 19. To minimize the Z_ k, it has to sacrifice the system thermodynamic performance. The EQM factor fk in Eq. (60) identifies the major cost source (capital investment or cost of exergy destruction). In the four cases shown in Table 18, the TV has the lowest value of fTV, followed by the EJ fEJ. It is suggested that cost savings might be achieved by improving the efficiencies of TV and EJ, and the EJ should be focused on. The highest fGE in the GE at B-II implies that a decrease in the investment cost of the GE is needed at the expense of its exergetic efficiency, which is confirmed by eGE from 79.4% in B-II decreased to 69.9% in OPT-II. Fig. 19(A) shows that the costs of steam and water are very important parts in OBF; their sum, C_ steam þ C_ water, contributes 43.9 and 40.2% to OBF in B-I and OPT-I, respectively. When the steam and water are considered as free in scenario II, the cost predominantly comes from Z_ k. Moreover, the EL cost is not significant due to the small amount of EL consumed by the PU. Fig. 19 (B) indicates that the PECs of the ERS components need to slightly increase to achieve better thermodynamic performances and a lower OBF for scenario I. On the other hand, optimization of OBF in B-II is obtained by reducing the PECs, but compromising the system thermodynamic performance.

298

Comparison between base cases and the optimized results Component

B-I cF,k ($/GJ)

S C E N A R I O I

Generator (GE) Condenser (CO) Evaporator (EV) Ejector (EJ) Pump (PU) Pump (PU) SUM Overall

S C E N A R I O II

GE CO EV EJ PU TV SUM Overall

OPT-I cF,k ($/GJ)

C_ D,k ($/h)

12.90 23.54 0.50 78.41 2590.71 3.21 85.29 248.60 0.95 51.09 165.98 4.42 55.56 190.47 0.35 184.46 224.36 0.87 – – 10.30 ΔTGE ¼ΔTCO ¼ΔTEV ¼51C m¼0.558, COP¼0.399, e¼ 5.27% Objective function (OBF)¼9.27 $/h B-II 0 7.29 0 41.88 1778.51 1.72 45.90 167.59 0.51 26.58 90.75 2.30 55.56 190.47 0.35 98.52 120.75 0.46 – – 5.34 ΔTGE ¼ΔTCO ¼ΔTEV ¼51C m¼0.558, COP¼0.399, e¼ 5.27% OBF¼5.21 $/h

Z_ k ($/h)

fk (%)

k (%)

cF,k ($/GJ)

C_ D,k ($/h)

Z_ k ($/h)

fk (%)

k (%)

1.09 1.64 0.77 0.38 0.56 0.04 4.49

68.5 33.7 44.8 8.0 61.8 4.9 –

79.4 4.5 48.6 32.6 51.9 82.9 –

12.90 23.78 0.45 85.48 2624.13 2.42 91.05 256.35 0.77 54.83 177.07 3.85 55.56 211.01 0.27 230.75 272.38 0.79 – – 8.55 ΔTGE ¼7.21C, ΔTCO ¼ΔTEV ¼31C m ¼0.678, COP¼0.494, e¼6.53% OBF ¼8.52 $/h

0.83 1.78 0.98 0.38 0.53 0.04 4.54

65.2 42.3 55.8 9.0 66.4 4.7 –

77.3 5.5 55.5 33.0 51.6 85.3 –

1.09 1.64 0.77 0.38 0.56 0.04 4.49

100.0 48.8 60.1 14.3 61.8 8.9 –

79.4 4.5 48.6 32.6 51.9 82.9 –

0.63 1.76 0.85 0.38 0.54 0.04 4.21

100.0 55.0 71.4 17.4 64.8 11.0 –

69.9 4.5 51.5 31.2 51.8 83.1 –

cF,k ($/GJ)

OPT-II 0 4.08 0 31.29 1494.08 1.44 34.27 146.91 0.34 20.52 75.42 1.81 55.56 202.63 0.29 77.95 95.80 0.34 – – 4.22 ΔTGE ¼191C, ΔTCO ¼4.91C, ΔTEV ¼ 4.11C m ¼0.455, COP¼0.338, e¼4.53% OBF ¼4.82 $/h

Energy Quality Management

Table 18

Energy Quality Management

B-I

OPT-I 53.32%

48.39% 7.77%

25,000

23,446 23,738 1%

6.5%

20,000

0.86%

12.49%

11.68%

8.53%

8.43%

17.27%

21.51%

36.45%

39.16%

299

23,446 21,993

1%

1.01% 12.84%

12.49% 8.53%

9.09%

15.57%

PECk ($)

15.29% 24.89%

28.27%

OPT-II

B-II 86.17%

87.38%

15,000

10,000

17.27% 20.22%

36.45%

TV PU EJ EV CO GE

41.76%

5000 24.26%

12.62%

13.83% Csteam

Cwater

Celectricity

24.26% 15.08%

0 B-I

ΣZk

(A)

18.36%

OPT-I

B-II

OPT-II

(B)

Fig. 19 (A) Distributions of objective function (OBF) and (B) the purchase equipment cost (PEC). CO, condenser; EJ, ejector; EV, evaporator; GE, generator; PU, pumb, TV, throttling valve.

5.7.6.4

Discussion

For the case study, an optimization of an ERS is presented by using the EQM concepts. It aims to minimize the objective function defined as the sum of all the costs by changing the three decision variables under two different economic scenarios, which are very crucial and result in quite different optimum results. Scenario I considers all the related costs, and it is found that the costs of the steam and water significantly contribute to the objective function. Scenario II excludes the cost of the steam and water by using the free brine; it turns out that the cost related to capital investment and operation and maintenance expense dominates the objective function. The ERS is much more economically applicable when using the free brine side fluids. The thermodynamic analysis on the ERS has been conducted rigorously and accurately, and the EQM evaluations can be discussed, especially the economic modeling, since the parameters can suffer from large variations from period to period and place to place. It is recommended to further perform a sensitivity analysis to get a deep insight of its EQM characteristics and the optimum performance.

5.7.7 5.7.7.1

Energy Quality Management Case Study 5 System for Case Study 5

Fig. 20 schematically shows a basic CERS. It mainly consists of a GE, an EV, a CO, an expansion device, an EJ, and a circulating PU. Low-grade thermal energy is delivered to the GE for vaporization. The high pressure vapor from the GE, i.e., the primary flow, enters into the EJ nozzle and draws the low pressure vapor from the EV, i.e., the secondary flow. The two flows undergo mixing and pressure recovery in the EJ. The mixed flow is then fed into the CO for condensation. The liquid from the CO is divided into two parts. One goes through the expansion device to the EV to produce the refrigerating effect. The rest of the liquid is pumped back to the GE by the circulation PU, and finishes a circle. The proposed NERS is shown in Fig. 21. Compared to the CERS in Fig. 20, this new system further employs a gas–liquid separator and one more CO (CO 2). Thus the working process of this NERS is a little different: the working fluid from the EJ undergoes partial condensation in CO 1, and then flows to the separator. After that, the remaining gas mixture is fed into CO 2 and is completely condensed. The liquid flow out from the CO 2 is then pumped back to the GE, while the liquid mixture from the separator goes to the EV through the expansion device. The rest of the processes are the same as the CERS. Because of the use of the separator and zeotropic mixtures, the gas mixture and liquid mixture have different mass fractions. The P–h diagram for the NERS working processes is graphically described in Fig. 22, in which numbers correspond to those in Fig. 21. The EJ is a key component in the ERS operation. There are two types of EJs used in refrigeration technology. Generally, the EJ known as the constant-pressure mixing EJ has better performance than the constant-area mixing EJ and was thus widely used. Therefore, the constant-pressure mixing EJ is adopted in the present study. The one-dimensional EJ model is applied to this situation as was described in the literature [76]. The EJ entrainment ratio depending on operating conditions and refrigerant properties is expressed as:

m¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Zn Zm Zd ðhpf ;n1 hpf ;n2;s Þ=ðhmf ;d;s hmf ;m Þ

1

ð63Þ

300

Energy Quality Management

Qg Generator

Pump Qc

Ejector

Condenser

Expansion device

Qe

Evaporator Fig. 20 A conventional ejector refrigeration system (CERS).

Qc2 4

Qg

Condenser 2

5

6

1

Generator Pump

Qc1

Gas−liquid separator

3

Condenser 1

Ejector 2

7 Expansion device

Qe Evaporator 9

8 Fig. 21 A new ejector refrigeration system (NERS).

Mass fraction

P 6 5

Pg

1 4

Pc

P h 3

2 Pc

P

h 7 8

Pe 9

h Fig. 22 P–h diagram of the new ejector (EJ) refrigeration system.

Energy Quality Management

301

where Zn,Zm, Zd are the efficiencies taking all kinds of losses into consideration for the EJ nozzle, mixing chamber, and diffuser, respectively; hpf,n1, hpf,n2,s, hmf,d,s, and hmf,m are the enthalpy parameters of primary fluid, secondary fluid, and mixed fluid in the EJ [76]. Conducting an energy balance based on the first law of thermodynamics for each component, using state point numbers specified in Figs. 21 and 22, the COP is written as: COP ¼ Qe =ðQg þ Wpump Þ ¼ me ðh9

h8 Þ=mg ðh1

h5 Þ

ð64Þ

EQM analysis based on the second law of thermodynamics is preferred as a tool to analyze losses in complicated systems, such as power plants or refrigeration systems. Irreversibilities in each component of the NERS are given as follows.   ð65Þ Generator exergy loss: Ig ¼ Tref mg ðs1 s6 Þ Qg =Tg Condenser 1 exergy loss: Ic1 ¼ Tref ðmc ðs3

s2 Þ þ Qc1 =Tref Þ

ð66Þ

 Condenser 2 exergy loss: Ic2 ¼ Tref mg ðs5

s4 Þ þ Qc2 =Tref



ð67Þ

Evaporator exergy loss: Ie ¼ Tref ðme ðs9 Ejector exergy loss: Iej ¼ Tref ðmc s2 Pump exergy loss: Ipump ¼ Wpump þ mg ððh6

s8 Þ

Qe =Troom Þ

ð68Þ

me s9 Þ

mg s1 h5 Þ

Tref ðs6

Expansion device exergy loss: Iexp ¼ Tref me ðs8

ð69Þ s5 ÞÞ

ð70Þ

s7 Þ

ð70Þ

Total irreversibility: Itotal ¼ Ig þ Ic1 þ Ic2 þ Ie þ Iej þ Ipump þ Iexp

ð71Þ

In order to evaluate the system performance, a computational procedure is needed to calculate the COP and irreversibility of each component. The calculating program is written with Fortran language and properties for refrigerants are taken from the NIST reference database REFPROP.

5.7.7.2

Results

The simulation is based on the NERS with reference temperatures of Tg ¼751C, Tc ¼351C, and Te ¼ 101C, and the heat source and heat sink have constant 41C temperature difference with the temperatures of Tg, Tc, and Te. EJ isentropic efficiencies are assumed as Zn ¼ 0.9, Zm ¼0.95, and Zd ¼0.9. Considering the important function of the circulation PU in the new system, the PU efficiency has also been taken into account in the simulation by assuming the isentropic efficiency of 0.75 (Zpump ¼ 0.75). Those efficiencies are fixed as constants in all cases and do not vary with the working conditions. Six binary zeotropic mixtures, R32/R134a, R32/R152a, R420A, R152a/R142b, R290/R600a, and R600a/R600, are used to study the characteristics of this NERS. Some basic properties of these refrigerants as the mass fraction assumed are presented in Table 19.

5.7.7.2.1

Comparison for the new ejector refrigeration system and conventional ejector refrigeration system performance

All six zeotropic mixtures with the compositions shown in Table 19 are used for comparative study. Fig. 4 shows the performance comparison between the NERS and the CERS, which is based on the reference temperatures and efficiencies. For the CERS, the mildly zeotropic mixtures (R134a/R142b, R152a/R142b, and R600a/R600) have higher COPs than the strong zeotropic mixtures (R32/R134a, R32/R152a, and R290/R600a). From Table 19 and Fig. 23, R600a/R600 with a temperature Table 19

Basic properties of the selected zeotropic working fluids

Working fluids

1

2

3

4

5

6

Components Mass fraction Molar mass (kg/kmol) NBP (1C) Critical temperature (1C) Critical pressure (kPa) Temperature glide (1C) LFL (%) ODP GWP 100 year

R32/R134a 0.3/0.7 79.19 41.7 91.6 4857.6 7.2 wff 0 1200

R32/R152a 0.5/0.5 58.2 43.6 96 5402 8.1

R134a/R142b 0.88/0.12 101.85 24.9 104.8 4087.5 0.8 none 0.008 1500

R152a/R142b 0.5/0.5 79.71 20.1 121.6 4360.5 2

R290/R600a 0.5/0.5 50.15 32.5 116.7 4128.6 8.6 2 0 B20

R600a/R600 0.5/0.5 58.12 6.6 143.5 3719.2 1.1 1.6 0 B20

Abbreviations: GWP, global warming potential; LFL, lower flammability limit (% by volume in air); NBP, normal boiling point; ODP, ozone depletion potential; wff, the worst case of fractionation may become flammable.

302

Energy Quality Management

0.25 NERS CERS 0.20

COP

0.15 0.10 0.05 0.00 1

2

3

4

5

6

Working fluids Fig. 23 Coefficient of performances (COPs) of new ejector refrigeration system (NERS) and conventional ejector refrigeration system (CERS).

0.40

Tc =35°C, Te =10°C R32/R134a R152a/R142b R600a/R600

0.36

COP

0.32 0.28 0.24 0.20 0.16 0.12 60

70

80

90

100 Tg

110

120

130

Fig. 24 Variation of coefficient of performance (COP) with the generator (GE) temperature Tg.

glide of 1.11C has the highest COP, and the R290/R600a with a temperature glide of 8.61C gives the lowest COP. However, performance of the NERS is superior to the CERS for all the working fluids and COPs are 1.5–35.4% higher. In addition, the NERS working with the strong zeotropic mixtures have much more improved COP, compared to the mildly zeotropic mixtures.

5.7.7.2.2

Effect of working conditions on the new ejector refrigeration system performance

In the Figs. 24–26, results are obtained on the basis of reference temperatures Tg ¼ 751C, Tc ¼351C, and Te ¼ 101C, with the fixed temperature difference to the heat source/sink and EJ isentropic efficiencies stated above. When one of those temperatures is varied, the other two are held constant. Three zeotropic mixtures, R32/R134a with a mass fraction of 0.3/0.7, R152a/R142b with 0.5/0.5, and R600a/R600 with 0.5/0.5, are selected in the analysis of working characteristics of this NERS. Performance of the new system increases for all working fluids when the GE temperature increases, as shown in Fig. 24, which is the same as for the CERS working with pure refrigerant. Clearly, R32/R134a has a relatively low performance at limited heat source temperatures. Since R600a/R600 and R152a/R142b have higher critical temperatures, they have a wider range of Tg and they are suitable for high temperature sources. The GE temperature Tg for R600a/R600 can reach to 1301C. Figs. 25 and 26 show the effects of the CO temperature Tc and EV temperature Te on the NRES COP, respectively. As expected, the performance of the new system decreases as Tc increases; in contrast, increasing Te increases the COP dramatically. COPs of R152a/R142b and R600a/R600 are not significantly different from each other in all the working range, but R600a/R600 yields slightly higher performance. Changing Tc or Te affects the COP of the system more than the shift of Tg. One Kelvin change of Tc or Te changes the COP more than 6%, while one Kelvin change of Tg only changes the COP by approximately 0.8%. In general, Tc depends on the ambient conditions. Therefore, this clearly illustrates the importance of

Energy Quality Management

303

Tg =75°C, Te =10°C

0.28

R32/R134a R152a/R142b R600a/R600

0.26 0.24 COP

0.22 0.20 0.18 0.16 0.14 0.12 32.5

35.0

37.5

40.0

Tc Fig. 25 Variation of coefficient of performance (COP) with the condenser (CO) temperature Tc.

0.36

Tg = 75°C, Tc = 35°C R32/R134a R152a/R142b R600a/R600

COP

0.32 0.28 0.24 0.20 0.16 6

8

10

12

14

16

18

Te Fig. 26 Variation of coefficient of performance (COP) with the evaporator (EV) temperature Te.

ambient conditions, i.e., the coupling to a good heat sink with a low-temperature difference and an efficient cooling supply system.

5.7.7.2.3

Effect of the composition on the new ejector refrigeration system performance

It is well known that zeotropic mixtures have the advantage of temperature glide, which might be beneficial to the system operation. In the systems using zeotropic mixtures, the pressure–temperature relationship of the mixture greatly depends on the composition. At a given temperature, the pressure changes as the composition of the mixture varies, which affects the system performance. So the composition of the zeotropic mixtures is an important variable in the system. R32/R134a, R152a/R142b, and R600a/R600 are selected in this new system to study the effect of the composition. Variations of the COP in the NERS with increasing mass fraction of the component with the lower boiling point are displayed in Fig. 27, and are based on the reference conditions given above. The temperature changes of the heat source/sink are assumed to match up to the temperature glide of the mixtures. The heat source/sink have constant 41C temperature difference with the mixtures. For the pure refrigerants, as a result, the log mean temperature difference (LMTD) is 41C. COPs for the pure refrigerant are obtained when working at the same conditions of the heat source/sink with the mixtures. As known, the CERS working with mildly zeotropic mixtures or almost azeotropic mixtures has higher COPs than pure refrigerants. For the NERS, this is not found in the present study. The COP of the pure refrigerants could have higher values than some compositions. However, it is still reasonable to motivate the use of the mixtures since the heat source/sink can be better utilized as the temperatures match. Obviously, the R32/R134a has the largest variation of COP, which may be a result of the large temperature glide and the molecular mass difference. R600a/R600 has a relatively steady performance due to its zero molecular mass difference and very small temperature glide. With the small mass fraction of low boiling point component, R600a/R600 gives the best performance. R32/R134a shows the highest COPs compared to the other two candidates when the mass fraction is up to 0.8.

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Energy Quality Management

0.26

Tg=75°C, Tc=35°C, Te=10°C R32/R134a R152a/R142b R600a/R600

COP

0.24

0.22

0.20

0.18 0.0

0.2

0.4 0.6 Mass fraction

0.8

1.0

Fig. 27 Variation of coefficient of performance (COP) with the mass fraction of low boiling point component.

R32/R134a(0.3/0.7)

R152a/R142b(0.5/0.5)

2%

R600a/R600(0.5/0.5) 2%

2% 2% 5%

16%

6%

54%

15%

Generator

Condenser1

12%

4%

12%

9% 7%

58%

Condenser2

8%

Evaporator

Pump

58%

Ejector

7% 10% 6% 5%

Expansion device

Fig. 28 The irreversibilities distribution.

5.7.7.3

Energy Quality Management of the New Ejector Refrigeration System

Steady-state analysis has demonstrated the working characteristics of the new system at different operating conditions and mass fractions in terms of the COP. However, the process, including losses, is hardly identified by the steady-state analysis. The energy quality analysis based on the second law of thermodynamics can evaluate the amounts and locations of irreversibilities within the components of the energy systems. Irreversibilities in each component are calculated according to Eqs. (65)–(71). R32/R134a with mass fraction 0.3/0.7, R152a/R142b with 0.5/0.5, and R600a/R600 with 0.5/0.5 are still selected under the reference temperature conditions and efficiencies mentioned previously. In addition, the ambient air temperature is assumed at 301C, which also is used as the reference temperature for the analysis. The room temperature is 201C. The percentage of exergy loss within each component of the new system is shown in Fig. 28. It can easily be seen that, for the NERS, the largest loss occurs in the EJ, followed by the GE. The exergy loss in the EJ accounts for more than half of the total loss in this new system. This indicates more attention should be paid when designing an EJ for this new system. The exergy loss in the EJ is due to the friction losses of the flow inside the EJ, the nonideal process, and the corresponding irreversibilities. It should be noted that R600a/R600 has high flammability and this fact must not be taken lightly when designing a system for this refrigerant. However, the effects of the composition of R600a/R600 on its flammability can be neglected since R600a and R600 have very closed values of LFL. As is well known, higher temperature leads to lower LFL and higher upper flammability limit (UFL), causing wider flammability range. One alternative to reduce the risks of flammability is to decrease the amount of refrigerant in the system by use of indirect systems, compact heat exchangers, and compact designs [77].

5.7.7.4

Discussion

For the case study, a NERS working with zeotropic mixtures is proposed and its performance in terms of the COP is discussed theoretically in this chapter. From the simulation results, the new system gets an advantage over the CERS under the same working conditions. As expected, performance of the new system increases when the GE temperature or EV temperature increases, and the increasing CO temperature decreases the COP. For the case of R32/R134a, the mass fraction has significant impact on its performance, while R600a/R600 gives a steady performance. The second law of thermodynamics indicated that the largest loss

Energy Quality Management

305

takes place in the EJ, the loss of which is more than 50% of the total loss, thus the EJ needs good design and very careful treatment. Combining the advantages of utilizing low-grade thermal energy and temperature glide, this new system shows its promise in using zeotropic mixtures for the ERS.

5.7.8 5.7.8.1

Advanced Energy Quality Management Theory

The energy quality (exergy) analysis introduced above, termed as conventional exergy analysis, has shown its usefulness to identify the location, magnitude, and sources of exergy destruction (loss). However, it cannot assess the mutual interdependencies among the system components. A recently developed technique, advanced exergy analysis, makes this possible by splitting the exergy destruction in each component into endogenous and exogenous parts. An additional splitting of the exergy destruction into avoidable and unavoidable parts provides a realistic evaluation of the potential for improvement. In addition, a combination of the two splitting approaches is also helpful and further improves the understanding of thermodynamic characteristics in the system [78–80]. The advanced exergy analysis provides additional and useful information that cannot be obtained through the conventional exergy analysis. The split parts of exergy destruction within the kth component are explained as:

• • • •

EN Endogenous exergy destruction E_ D;k : relates only to the kth component’s own irreversibility and is irrelevant to the irreversibilities in the remaining system components. It is obtained when all other components operate ideally and the kth component under consideration operates with the real efficiency. EX Exogenous exergy destruction E_ D;k : this is the remaining part of the exergy destruction in the kth component found by excluding the endogenous part. It is the exergy destruction imposed on the kth component but caused by irreversibilities in the remaining system components. UN Unavoidable exergy destruction E_ D;k : cannot be eliminated even if the best available technology would be applied, and always exists as long as the kth component is being used. Irreversibilities are due to technical limitations. AV Avoidable exergy destruction E_ D;k : this is the difference between the exergy destruction and the unavoidable part in the kth component, and is recoverable. This part represents the real potential for improving the system component, thus it should be paid more attention to. EN EX Splitting the exergy destruction in the kth component E_ D;k into endogenous E_ D;k and exogenous E_ D;k parts shows the interactions among different components in the system. This is very useful to decide whether the improvement should be focused on the kth component being considered or on the remaining system components. The exergy destruction in the kth component E_ D;k can be rewritten as: EN EX E_ D;k ¼ E_ D;k þ E_ D;k

ð72Þ

EN

Generally, the endogenous exergy destruction E_ D;k can be decreased through improving the kth component itself, which also results in the decrease in the exogenous exergy destruction within the remaining system components. As a consequence, the EX exogenous exergy destruction in the kth component, i.e., E_ D;k , is also reduced through the improvement of the remaining system components caused by improvement of the kth component. In other words, an improvement in the kth component not only leads to a reduction of the exergy destruction in the kth component itself, but also promotes a decrease in the exergy destruction within other components. AV UN Splitting the exergy destruction in the kth component E_ D;k into unavoidable E_ D;k and avoidable E_ D;k parts provides a realistic measure of the potential of improvement for the component being considered, which is given as: UN AV E_ D;k ¼ E_ D;k þ E_ D;k

AV

ð73Þ

The avoidable exergy destruction, E_ D;k , can be avoided with structural modifications, and efficiency improvements of individual UN components. The unavoidable exergy destruction E_ D;k is determined by appropriately selecting the most important thermodynamic parameters of each component that represent only the unavoidable exergy destruction. These two approaches of splitting the exergy destruction can be combined to produce new terms of interest and provide more detailed information. The combined parts of exergy destruction in the kth component are presented as [78–80]: UN;EN E_ D;k : cannot be reduced due to technical limitations in the kth component. • Unavoidable endogenous exergy destruction UN;EX • Unavoidable exogenous exergy destruction E_ D;k : cannot be reduced due to technical limitations in the remaining components of the overall system. AV;EN • Avoidable endogenous exergy destruction E_AV;EX D;k : can be reduced through improving the efficiency of the kth component. • Avoidable exogenous exergy destruction E_ D;k : can be reduced by a structural improvement of the overall system or by improving the efficiency of the remaining system components.

306

Energy Quality Management

EN

=

ED,k

UN,EN

ED,k

+

=

EX

+

ED,k

=

UN,EX

+

UN

+

ED,k

ED,k

ED,k

AV,EN

ED,k

+

AV,EX

ED,k

AV

ED,k

Fig. 29 Splitting the exergy destruction within the kth component.

Heat source input 8

7 QGE

T

4

1 Generator

8 7

Heat sink

Pump

10

9

WPU 3

Condenser

1

Generator

QCO

4

Ejector

2

PCO e4

Condenser

ei

3 Throttling valve

2 10 P EV

9

5

PGE

12

6

11

Evaporator

6 5

Evaporator

e1 e3 e2

QEV 11 (A)

12 Cooling effect

S (B)

Fig. 30 Schematic of (A) the ejector (EJ) refrigeration system and (B) its T–s diagram.

Therefore the exergy destruction in the kth component E_ D;k is alternatively written as: UN;EN UN;EX AV;EN AV;EX E_ D;k ¼ E_ D;k þ E_ D;k þ E_ D;k þ E_ D;k

ð74Þ AV;EN

It is emphasized that the efforts to improve the system component should be focused on the avoidable endogenous E_ D;k AV;EX the avoidable exogenous parts E_ D;k . The options of splitting the exergy destruction in the kth component E_ D;k are shown in Fig. 29.

5.7.8.2

and

Case Study for Ejector Refrigeration System

An ERS consists of three heat exchangers (a GE, a CO, and an EV), an EJ, a PU, and a TV, as well as three brine side fluid paths (green lines), as shown in Fig. 30(A). The primary flow refrigerant enters the GE in liquid state at high pressure. The heat (QGE) is supplied to the primary flow in the GE. The refrigerant leaves the GE in vapor state and enters the EJ nozzle. The primary flow expands through the nozzle and exits at low pressure. This low pressure creates suction for the low pressure vapor from the EV, termed the secondary flow. The two flows then undergo mixing and introduce shock trains in the mixing chamber, followed by pressure recovery in the diffuser of the EJ. The mixed flow is fed into the CO, where condensation takes place by rejecting heat to the heat sink (QCO). The liquid from the CO is divided into two parts. One part goes through the TV, and then enters the EV to produce a refrigerating effect (QEV). The remaining liquid is pumped back to the GE via the circulation PU, and completes the cycle. Brine side fluids deliver heat at high temperature to the GE, remove heat load from the CO, and carry fluid for cooling from the EV. Fig. 30(B) shows the T–s diagram of all the processes in the ERS, including the real processes (blue lines) and the ideal processes (red lines) in the EJ. R245fa is selected as the refrigerant. The system refrigerating capacity is fixed at QEV ¼ 10 kW, i.e., the system exergy of the product is constant. The system uses liquid water as the brine side fluids. The inlet and the outlet temperatures of water are kept at: T7 ¼1001C, T8 ¼ 1051C, T9 ¼ 271C, T10 ¼ 321C, T11 ¼ 101C, and T12 ¼151C. The reference state is an ambient temperature (T0) of

Energy Quality Management

Table 20

307

Parameters used in different conditions

Component

Parameter

Real

Ideal

Unavoidable

Generator (GE) Condenser (CO) Evaporator (EV) Ejector (EJ)

ΔTGE ΔTCO ΔTEV Zn Zm Zd ZPU –

7.161C 3.011C 2.001C 0.90 0.90 0.90 0.75 Isenthalpic

0 0 0 E_ D;EJ ¼ 0

0.501C 0.501C 0.501C 0.98 0.95 0.98 0.95 Isenthalpic

Pump (PU) Throttling valve (TV)

1 Isentropic

Ejector variables: External parameters: Tg,-Tc,-Te,-ΔTg,-ΔTe Performance:

Ejector geometry: Ar(A3/A1)

COP; , -ED,EJ Internal parameters: n,-m,-d Design process

Performance evaluation

Fig. 31 Interactions of various ejector (EJ) parameters.

251C and pressure (P0) of 101.32 kPa. The working parameters of each component at real, ideal, and unavoidable conditions are listed in Table 20.

5.7.8.2.1

Conventional energy quality management

5.7.8.2.1.1 Conventional energy quality management  for the ejector     For the EJ, the exergy destruction in the nozzle E_ D;n , mixing chamber E_ D;m , and diffuser E_ D;d are given as below, with the nomenclature corresponding to Fig. 22:   _ c
sc;i m _ g
sg;o m _ e
se;o ð75Þ E_ D;EJ ¼ Tref
m _ g
ðs2 E_ D;n ¼ Tref
m

sg;o Þ

ð76Þ

_ c
ðsc;i E_ D;d ¼ Tref
m

s4 Þ

ð77Þ

E_ D;d

ð78Þ

E_ D;m ¼ E_ D;EJ

E_ D;n

The exergy destruction associated with the normal shock is calculated as: _ c
ðs5 E_ D;sh ¼ Tref
m

s4 Þ

ð79Þ

It should be noted that the exergy destruction of the shock process E_ D;sh is integrated into the exergy destruction of the diffuser E_ D;d . In other words, the exergy destruction in the diffuser is due to the shock, and frictional losses, flow separation, etc. The exergy destruction E_ D has been synonymously referred to as irreversibility, symbolized by I (Fig. 31). The EJ performance and the exergy destruction are influenced by many parameters, for example, the EJ geometries, the operating conditions, and the selected refrigerant. Moreover, these parameters are closely related and interacting, as shown in Fig. 22. To design an EJ, the operating temperatures have to be defined based on the applied heat source, heat sink, and cooling purpose, and the EJ efficiencies have to be carefully determined since they influence the calculated EJ area ratio. To evaluate the EJ performance, the EJ geometrical features determine the optimum operating conditions, and influence the EJ efficiencies, which also depend on the operating conditions. All external, internal, and geometrical parameters eventually impact the EJ performance and the exergy destructions as well as the distribution of the exergy destructions in the EJ.

308

Energy Quality Management

It should be noted that the mixing efficiency Zm is a kinetic energy ratio. Moreover, the process in the mixing chamber is rather complicated, involving supersonic flow, strong flow interactions, turbulent mixing, etc. Thus the mixing efficiency might not represent all the exergy destruction of the mixing process. It was defined in this manner for the sake of simplifying the EJ modeling. 5.7.8.2.1.2 Conventional energy quality management for the refrigeration Regarding the exergy analysis for the entire ERS, the concept “fuel-product” is introduced. The exergy balance at steady-state conditions can be written as the following by using proper definitions of the exergy of fuel E_ F;k and the exergy of product E_ P;k : E_ F;k ¼ E_ P;k þ E_ D;k

ð80Þ

where the subscript k indicates the kth component in the energy conversion system. The exergy of product E_ P;k is the desired result (expressed in exergy terms) achieved by the kth component being considered, and the exergy of fuel E_ F;k represents the exergy resources consumed in the kth component to generate the exergy of the product. It should be noted that the E_ F;k is not necessarily restricted to being an actual fuel, such as coal, natural gas, or oil, and the E_ P;k is not specified as the power or cooling effect, either. The concept “fuel-product” is in a general sense of the exergy resource and desired results.   The exergy loss is the transferred exergy to the environment that is not further being used in any systems. The exergy loss E_ L appears only at the level of the total system, for which the exergy balance becomes: X E_ D;k þ E_ L;tot E_ F;tot ¼ E_ P;tot þ

ð81Þ

Two indexes are used to evaluate the component and the overall system from the exergetic point of view. One is the EE e, defined as the ratio between the exergy of product and the exergy of fuel; the other index is the exergy destruction ratio y, defined as the exergy destruction within the kth component or the overall system divided by the total exergy of fuel for the overall system. They are written as: ek ¼ E_ P;k =E_ F;k

ð82Þ

etot ¼ E_ P;tot =E_ F;tot

ð83Þ

yk ¼ E_ D;k =E_ F;tot

ð84Þ

ytot ¼ E_ D;tot =E_ F;tot

ð85Þ

The kth component in the ERS is the GE, CO, EV, EJ, PU, or TV. 5.7.8.2.1.3 Conventional results The results taken by the conventional EQM are shown in Table 21. Table 21 shows the main results obtained from the conventional exergy analysis of the ERS. It is suggested that the EJ has the  ¼53.6%). Moreover, the EJ has the largest exergy destruction, which is more than half of the overall system exergy destruction (yEJ lowest exergetic efficiency (EJ ¼ 29.2%), the remaining components have relatively high exergetic efficiencies. Thus the major  ¼24.7%, attention should be paid to the improvement of the EJ. The second largest exergy destruction occurs in the GE with yGE  ¼ 15.9%), and then by the EV, TV, and PU. In addition, the exergy destruction within the EV, TV, and PU followed by the CO (yCO are very small; together, they contribute only 5.8% to the overall system exergy destruction. From the perspective of the overall system, the overall exergy of fuel comes from the heat supplied to the GE by the high temperature water and the work to the PU, and the overall exergy of product is the refrigerating effect in the EV. The overall exergy destruction consists of the exergy destruction within each component, while the system exergy loss is from the CO since it is not further used. The overall system has a low exergetic efficiency of 6.9%, and as a consequence, most of the overall exergy of fuel is destroyed as indicated by ytot ¼83.7%. This could explain the limited usage of ERSs. Table 21

Conventional results for ejector (EJ) refrigeration system (ERS)

Component

Generator (GE) Condenser (CO) Evaporator (EV) Ejector (EJ) Pump (PU) Throttling valve (TV) Overall system

E_ F;k

E_ P;k

E_ D;k

E_ L

ek

yk

yk

(kW)

(kW)

(kW)

(kW)

%

%

%

6.180 1.429 0.604 3.993 0.123 0.742 6.303

4.879 0.591 0.437 1.166 0.093 0.631 0.437

1.301 0.838 0.167 2.827 0.030 0.111 5.274

– – – – – – 0.591

78.9 – 72.4 29.2 75.6 85.0 6.9

20.6 13.3 2.6 44.9 0.5 1.8 83.7

24.7 15.9 3.1 53.6 0.6 2.1 100.0

Note: yk is expressed by its exergy destruction divided by the total exergy of fuel of the overall system and yk* is the percentage of exergy destruction within the k-th component in the total exergy destruction.

309

Energy Quality Management

T

T

EN

ED,EJ

8

7 Generator

ΔTGE

1i 1

1UN 7 4UN

4i 4 Condenser

3 9

2i

3i

5

Evaporator

UN

12

ΔTEV

S

5

2UN 10 12

11 5UN

2

Condenser

9

UN ΔTEV

6

(A)

3 3UN

ΔTCO

10

1

Generator 4

ΔTCO

2 2****

6i 11 5i

8

UN ΔTGE

6UN Evaporator

6

S

(B)

Fig. 32 (A) Hybrid cycle for the endogenous exergy destruction within the ejector (EJ); and (B) cycle for the unavoidable exergy destruction.

Table 22

The advanced exergy analysis results

Component

Splitting the exergy destruction

Generator (GE) Condenser (CO) Evaporator (EV) Ejector (EJ) Pump (PU) Throttling valve (TV) Overall system (tot)

5.7.8.2.2

E_ D;k (kW)

EN E_ D;k (kW)

EX E_ D;k (kW)

UN E_ D;k (kW)

AV E_ D;k (kW)

1.301 24.7% 0.838 15.9% 0.167 3.2% 2.827 53.6% 0.03 0.6% 0.111 2.1% 5.274 100.0%

0.27 (20.8%) 0.291 (34.7%) 0.167 (100.0%) 2.013 (71.2%) 0.006 (20.0%) 0.077 (69.4%) 2.819 (53.5%)

1.031 (79.2%) 0.547 (65.3%) 0 (0.0%) 0.814 (28.8%) 0.024 (80.0%) 0.034 (30.6%) 2.455 (46.6%)

1.043 (80.2%) 0.475 (56.7%) 0.110 (65.9%) 1.705 (60.3%) 0.005 (16.6%) 0.089 (79.9%) 3.427 (65.0%)

0.258 (19.8%) 0.363 (43.3%) 0.057 (34.1%) 1.122 (39.7%) 0.025 (83.4%) 0.022 (20.1%) 1.847 (35.0%)

Advanced energy quality management

Based on the conventional EQM, the advanced exergy analysis aims to split the exergy destruction with the system components into endogenous/exogenous parts or unavoidable/avoidable parts. The endogenous exergy destruction within the EJ is shown as 3i–4i–1i and 3i–5i–6i-2****–3i in Fig. 32(A). A cycle with only unavoidable conditions in each component needs to be created, which is shown as 3UN–4UN–1UN and 3UN–5UN–6UN-2UN–3UN in Fig. 32(B). The results from the advanced exergy analysis are given in Table 22. From the main results obtained from the conventional exergy analysis (E_ D,k), it is suggested that the EJ has the largest exergy destruction, which is more than half of the overall system exergy destruction (53.6%). EN EX Splitting exergy destruction within the kth component into endogenous and exogenous parts, E_ D;k and E_ D;k show that: 1. The exergy destruction within the EV is 100% endogenous because the EV is to maintain the fixed refrigerating capacity and overall exergy of product. This behavior is also found in other refrigeration systems. EN 2. The exergy destruction within the EJ is mainly from itself due to the dominant endogenous parts E_ D;EJ (71.2%) over the EN EX exogenous parts E_ D;EJ (28.8%). The TV has similar behavior, but the E_ D;TV is really small. Therefore, it is more effective to improve the EJ internal efficiencies. EN EX 3. The GE, the CO, and the PU have much smaller values of E_ D;k than that of E_ D;k . For each one of these three components, the exergy destruction is mainly caused by the other remaining components; in other words, the exergy destruction can be largely reduced by the improvements of the other components. EN EX 4. For the overall system, the endogenous exergy destruction E_ D;tot is comparable with the exogenous part E_ D;tot . It shows closed mutual interdependencies among system components.

310

Energy Quality Management

UN AV Splitting the exergy destruction within the kth component into unavoidable E_ D;k and avoidable E_ D;k parts is helpful to know the realistic potential of improvement for each system component. It shows that (1) all the components with the exception of the PU have larger unavoidable exergy destruction than the avoidable part; (2) the EJ has the largest values of avoidable exergy destruction AV E_ D;EJ of 1.122 kW, followed by the CO and then the GE; and (3) at the studied condition, 35% of the overall exergy destruction can be avoided. It is concluded that the application of advanced exergy analysis to the ERS provides detailed and very useful information, and identifies the directions for system improvement. It can be viewed as a valuable supplement to the conventional exergy analysis.

5.7.8.2.3

Sensitivity analysis by advanced energy quality management

In order to investigate characteristics of the exergy destruction, it is of great importance to perform a sensitivity study with each parameter varying alone for a set of fixed values of the others. Since the TV and the PU have the smallest exergy destructions, they are excluded in the present analysis. Therefore the temperature differences in the GE DTGE , CO DTCO , and EV DTEV , as well as EJ efficiencies (Zn , Zm , and Zd ) are selected as variables and studied individually. It should be noted that the EJ efficiencies do not necessarily have to have identical values. However, in this discussion, they are set to have the same value, and generalized as a symbol ZEJ , i.e., ZEJ ¼ Zn ¼ Zm ¼ Zd . Figs. 33–35 show the effects of these parameters on the exergy destructions, respectively. Fig. 33 shows the dependency of the studied parameters on the GE temperature difference DTGE as other operating parameters are kept constant at the same values as for a real process in Table 23. It is seen that an increase in DTGE leads to the increasing GE exergy destruction E_ D;GE and overall system exergy destruction E_ D;tot . The decreasing COP is due to a lower GE temperature that comes from the enlarged DTGE at the condition of fixed brine side fluid temperatures. A change of one Celsius degree in DTGE changes E_ D;GE and E_ D;tot by 0.06 and 0.078 kW, respectively. More interesting features can be found in Fig. 33(B), which shows that EN an increase in DTGE gives a slight rise of the endogenous exergy destruction E_ D;GE , and leads to a relatively large increase in the EX EX EN exogenous exergy destruction E_ D;GE . Moreover, the E_ D;GE is much larger than E_ D;GE . This means the E_ D;GE can be reduced mainly by AV improving the other remaining system components. Moreover, the large increase in the avoidable exergy destruction E_ D;GE is 8

0.5 EN

ED,GE

AV,EN

ED,GE

0.4 0.3

0.6

0.2

2

0.2

0.3

0.1

2

4

6

2

8

ΔTGE (°C)

(A)

0.0

0.0

0.1

0

(kW)

0.9

EN

0.3

EX

ED,GE

AV,EN

4

1.2

ΔTCO = 3.01°C, ΔTEV = 2°C, EJ = 0.90

AV

0.4

EX

0.5

AV

ED,GE

ED,GE , ED,GE

ED,tot

6

ΔTCO =3.01°C, ΔTEV = 2°C, EJ = 0.90

ED,GE , ED,GE (kW)

COP

COP

ED,GE , ED,tot (kW)

1.5

0.6 ED,GE

4

6

8

ΔTGE (°C)

(B)

Fig. 33 Effects of the temperature difference in the generator (GE) on (A) coefficient of performance (COP) and exergy destructions and (B) the split exergy destructions.

0.2

EX

2

EN

0.3

6

4 ΔTCO (°C)

1.2

0.9

0.9

0.6

0.6

0.3

0.3

0.0

0.1 2

1.2

0.0 2

8 (B)

AV,EN

4

EX

ED,CO

1.8

AV ED,CO ΔTGE = 7.16°C, ΔTEV = 2°C, 1.5 AV,EN EJ = 0.90 ED,CO

AV

0.4

ED,CO , ED,CO (kW)

1.5

0.5

6

0 (A)

EN

ED,CO

ED,CO , ED,CO (kW)

ED,tot

COP ΔTGE=7.16°C, ΔTEV=2°C, EJ=0.90

COP

ED,CO, ED,tot (kW)

ED,CO

8

1.8

0.6

10

4

6

8

ΔTCO (°C)

Fig. 34 Effects of the temperature difference in the condenser (CO) on (A) coefficient of performance (COP) and exergy destructions and (B) the split exergy destructions.

Energy Quality Management

ED,tot

EJ = 0.90

0.4

0.5

0.3

2

0.2

0

0.1

EN

4

6

4

0.3

0.3

0.2

0.2

0.1

0.1

0.0

0.0

8

ΔTEV (°C)

(A)

D,EV

2 (B)

4

6

AV,EN

0.4

COP

6

EX

ED,EV

AV

ED,EV ΔT = 7.16°C, ΔT = 3.01°C, CO GE AV,EN EJ = 0.90 0.4 E

AV

COP

2

0.5 EN

ED,EV

EX

ΔTGE = 7.16°C, ΔTCO = 3.01°C,

ED,EV , ED,EV (kW)

ED,EV

tED,EV , ED,EV (kW)

8 ED,EV, ED,tot (kW)

0.5

0.6

10

311

8

ΔTEV (°C)

Fig. 35 Effects of the temperature difference in the evaporator (EV) on (A) coefficient of performance (COP) and exergy destructions and (B) the split exergy destructions.

Table 23

Parameters used for the real cycle, ideal cycle, and the cycle for the unavoidable exergy destruction

Component

Parameter

Real

Ideal

Unavoidable

Generator (GE) Condenser (CO) Evaporator (EV) Ejector (EJ)

ΔTGE ΔTCO ΔTEV Zn Zm Zd ZPU –

7.161C 3.011C 2.001C 0.90 0.90 0.90 0.75 Isenthalpic

0 0 0 E_ D;EJ ¼ 0

0.501C 0.501C 0.501C 0.98 0.95 0.98 0.95 Isenthalpic

Pump (PU) Throttling valve (TV)

1 Isentropic

mainly due to the fact that a larger DTGE causes larger deviation from unavoidable conditions in Table 23, giving a much higher AV;EN potential for improvement. The avoidable endogenous part of the exergy destruction E_ D;GE is rather small in the studied range. In a word, a smaller DTGE benefits the system performance in terms of reducing the exergy destruction. An increase in the CO temperature difference DTCO gives a rise in the CO exergy destruction E_ D;CO and the overall exergy destruction E_ D;tot with sensitivity of 0.228 and 0.561 kW per Celsius degree, respectively, as shown in Fig. 34(A). The decreasing AV;EN AV EX EN COP is due to a higher CO temperature. Tendencies of the split parts of exergy destruction E_ D;CO , E_ D;CO , E_ D;CO , and E_ D;CO observed EX EN for DTCO are similar to those of DTGE , but with larger gradients. Moreover, it also has E_ D;CO 4E_ D;CO , and the larger DTCO is, the EX EN larger the difference between E_ and E_ becomes (see Fig. 34(B)). Therefore a reduction of E_ D;CO is more significant through D;CO

D;CO

the improvements of the other remaining components. Fig. 35 illustrates the variation of the COP and exergy destructions by changing the EV temperature difference DTEV . The DTEV has a small impact on its own exergy destruction E_ D;EV , but leads to a large change of the overall exergy destruction E_ D;tot . An increase of one Celsius degree in DTEV increases E_ D;EV and E_ D;tot by 0.038 kW and 0.5 kW, respectively. However, the magnitude of the E_ D;EV is quite small, as shown in Fig. 35(A), leading to a relatively small potential of improvement. The COP decreases with increasing of DTEV , which is a result from the decrease in EV temperature. Since the E_ D;EV entirely comes from the endogenous part AV;EN AV EX EN E_ D;EV , its exogenous part E_ D;EV is zero, and the avoidable part is the same as the avoidable endogenous part, i.e., E_ D;EV ¼ E_ D;EV (see the overlapped lines in Fig. 35(B)). The EJ has the largest exergy destruction and should be paid more attention to. The EJ efficiencies play very important roles in determining the EJ entrainment ratio m and the system COP. They are also closely related to the irreversibilities occurring in the EJ sections. Fig. 36 gives a detailed view of the effects of the EJ efficiency ZEJ not only on the exergy destruction in the EJ itself, but also in the other remaining components. A larger value of ZEJ is always good for the EJ itself by reducing its exergy destruction E_ D;EJ , and also benefits the system performance in terms of decreasing the overall exergy destruction E_ D;tot and enhancing the system COP EN EX AV AV;EN (see Fig. 36(A)). As shown in Fig. 27(B), the exergy destruction parts E_ D;EJ , E_ D;EJ , E_ D;EJ , and E_ D;EJ decrease with increasing of ZEJ . EN EX is always larger than E_ in the studied conditions, which suggests that improvements of the EJ itself should Moreover, the E_ D;EJ

D;EJ

be more effective than improving the remaining components that may improve the EJ performance. The potential improvement, AV;EN indicated by the E_ D;EJ , is more obvious when ZEJ is low. Fig. 36(C) shows how the exogenous exergy destruction within other EX EX components is impacted by ZEJ . To improve the clarity of the figure, the E_ D;PU and E_ D;TV are plotted at the right vertical axis since they are quite small. It is found that an increase in ZEJ also leads to the decrease of the exogenous exergy destruction within the GE EX EX EX E_ D;GE , the CO E_ D;CO , and the PU E_ D;PU . This can be derived from the decreased mass flow rate passing through these three

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Energy Quality Management

0.6

10 ED,tot

8

4

ΔTEV = 2°C

0.5

0.4

4

0.3

2

0.2

3

0.1

0 0.88

0.89

(A)

0.90 0.91 EJ

0.92

0.93

EN ED,EJ

AV ED,EJ

ΔTGE = 7.16°C, ΔTCO = 3.01°C,

EX ED,EJ

AV,EN ED,EJ

ΔTEV = 2°C

2

2

1

1

0

0 0.87

0.94

0.88

0.89

(B)

2.0

0.90 0.91 EJ

0.92

0.93

0.94

0.20 EX E D,k

ΔTGE = 7.16°C, ΔTCO = 3.01°C, ΔTEV = 2°C

1.5

GE

CO

EV

PU

0.15 TV

1.0

0.10

0.5

0.05

0.0

0.00 0.87

(C)

0.88

0.89

0.90 0.91 EJ

0.92

0.93

EX EX EX ED,EV , ED,TV (kW) , ED,PU

EX (kW) EX ED,GE , ED,CO

3

AV AV,EN (kW) ED,EJ , ED,EJ ,

6

0.87

4

ΔTGE = 7.16°C, ΔTCO = 3.01°C,

COP

ED,EJ , ED,tot (kW)

COP

EN EX ED,EJ , ED,EJ (kW)

ED,EJ

0.94

Fig. 36 Effects of the ejector (EJ) efficiencies. EN components, which therefore reduces the exergy destruction E_ D;k , and their constant endogenous exergy destruction E_ D;k due to the unchanged temperature differences in the GE and CO and the fixed PU efficiency. To be more specific, as ZEJ increases, the EJ _ GE reduces due to the constant mass flow rate of the entrainment ratio m increases. The mass flow rate of the primary flow m _ EV that is governed by the fixed conditions, including temperatures and capacities, in the EV. Moreover, the secondary flow m EX EX conditions in the TV are also fixed. These result in the unchanged exogenous exergy destructions in the EV E_ D;EV and TV E_ D;TV in Fig. 36(C).

5.7.8.3

Discussions

Conventional and advanced EQM are employed to an ERS at a given operating condition. It is obtained from conventional EQM that the highest exergy destruction occurs in the EJ, accounting for 53.6% of the total system exergy destruction. By introducing the advanced EQM, splitting the exergy destruction into endogenous and exogenous parts reveals the more realistic potential of improvements and shows that 35% of the overall exergy destruction for the ERS can be avoided. The application of advanced EQM to the ERS provides detailed and very useful information, and identifies the directions for system improvement. It can be viewed as a valuable supplement to the conventional exergy analysis.

5.7.9

Conclusions

According the deep analysis about EQM, it is confirmed that EQM could be applied for designing the optimal energy system in the built environment and providing an acceptable scheme for energy conversion planning. EQM is proven to be able to reduce computational effort significantly. Also, it can provide different energy system scenarios along with a variation of optimization objective combinations. Accordingly, the approach can be adapted to satisfy specific objective combinations. It should be highlighted that, for the same optimization objectives, optimization results might change with different weight combinations. Hence, optimal scenarios must be related to their specific corresponding weight combinations, which could be predefined by different types of users.

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It is noted that advanced EQM has been introduced by researchers. As a novel approach for EQM, the advanced EQM for an ERS might avoid 35% of the overall exergy destruction in the whole system. More applications about the advanced EQM would be discussed in future.

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[54] Kayo G, Ooka R. Building energy system optimizations with utilization of waste heat from cogenerations by means of genetic algorithm. Energy Build 2010;42:985–91. [55] Yang H, Zhou W, Liu L. Optimal sizing method for stand-alone hybrid solar-wind system with LPSP technology by using genetic algorithm. Solar Energy 2008;82:354–67. [56] Komamura K, Ooka R. Optimal design method for building energy systems using genetic algorithms. Build Environ 2009;44:1538–44. [57] Ould Bilal B, Sambou V, Ndiaye PA, et al. Optimal design of a hybrid solar-wind-battery system using the minimization of the annualized cost system and the minimization of the loss of power supply probalility (LPSP). Renew Energy 2010;35:2388–90. [58] Bourouni K, M’Barek TB, Al Taee A. Design and optimization of desalination reverse osmosis plants driven by renewable energies using genetic algorithms. Renew Energy 2011;36:936–50. [59] Lu H, Alanne K, Martinac I. Energy quality management for building clusters and districts (BCDs) through multi-objective optimization. Energy Convers Manag 2014;79:525–33. [60] Hamdy M, Hasan A, Siren K. Impact of adaptive thermal comfort criteria on building energy use and cooling equipment size using a multi-objective optimization scheme. Energy Build 2011;43:2055–67. [61] Holland JH. Adaptation in natural and artificial systems. Ann Arbor, MI: The University of Michigan Press; 1975. [62] Stanislav HZ. Systems and control. New York, NY: Oxford University Press; 2003. [63] Congradac V, Kulic F. HVAC system optimization with CO2 concentration control using genetic algorithms. Energy Build 2009;41:571–7. [64] Randy LR, Haupt SE. Practical genetic algorithms. Hoboken, NJ: John Wiley & Sons Inc.; 2004. [65] Geem ZW, Kim JH, Loganathan G. A new heuristic optimization algorithm: harmony search. Simulation 2001;76:60–8. [66] Lee KS, Geem ZW. A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Method Appl Method 2005;194:3902–33. [67] Fesanghary M, Asadi S, Zong WG. Design of low-emission and energy-efficient residential buildings using a multi-objective optimization algorithm. Building Environ 2012;49:245–50. [68] Beghi A, Cecchinato L, Cosi G, et al. A PSO-based algorithm for optimal multiple chiller systems operation. Appl Therm Eng 2012;32:31–40. [69] Moradi MH, Abedini M. A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. Electr Power Energy Syst 2012;34:66–74. [70] Abbes D, Martinez A, Champonois G. Life cycle cost, embodied energy and loss of power supply probability for the optimal design of hybrid power systems. Math Comput Simul 2014;98:46–62. [71] Diaf S, Notton G, Belharnel M, et al. Design and techno-economical optimization for hybrid PV/wind system under various meteorological conditions. Appl Energy 2008;85:968–87. [72] Shen WX. Optimally sizing of solar array and battery in standalone photovoltaic system in Malaysia. Renew Energy 2009;48:348–52. [73] Bejan A, Tsatsaronis G, Moran M. Thermal design and optimization. New York, NY: Wiley; 1996. [74] Wang L, Yang Y, Dong C, et al. Multi-objective optimization of coal-fired power plants using differential evolution. Appl Energy 2014;115:254–64. [75] Turton R, Bailie RC, Whiting WB, et al. Analysis, synthesis, and sesign of chemical processes. 4th ed. Upper Saddle River, NJ: Prentice Hall; 2012. [76] Yu JL, Chen JL, Li YZ. Theoretical study on an innovative ejector enhanced Joule–Thomson cycle. Int J Energy Res 2010;34(1):46–53. [77] Palm B. Hydrocarbons as refrigerants in small heat pump and refrigeration systems – a review. Int J Refrig 2008;31(4):552–63. [78] Morosuk T, Tsatsaronis G. A new approach to the exergy analysis of absorption refrigeration machines. Energy 2008;33(6):890–907. [79] Morosuk T, Tsatsaronis G. Advanced exergetic evaluation of refrigeration machines using different working fluids. Energy 2009;34(12):2248–58. [80] Chen J, Havtun H, Palm B. Conventional and advanced exergy analysis of an ejector refrigeration system. Appl Energy 2015;144:139–51.

Further Reading Lu H. Energy quality management for building clusters and districts using multi-objective optimization approach [Ph.D. thesis]. Stockholm: Royal Institute of Technology; 2016.

Relevant Websites www.energyplan.eu/ EnergyPLAN. https://simulationresearch.lbl.gov/GO/ GenOpt. www.homerenergy.com/ HOMER Energy. www.kth.se KTH Royal Institute of Technology.

5.8 Sustainable Energy Management Tahir Abdul Hussain Ratlamwala, National University of Sciences and Technology, Islamabad, Pakistan Ibrahim Dincer, University of Ontario Institute of Technology, Oshawa, ON, Canada r 2018 Elsevier Inc. All rights reserved.

5.8.1 5.8.2 5.8.2.1 5.8.2.2 5.8.2.3 5.8.2.3.1 5.8.2.3.2 5.8.2.3.3 5.8.2.3.4 5.8.2.4 5.8.2.4.1 5.8.2.4.1.1 5.8.2.4.1.2 5.8.2.4.1.3 5.8.2.4.1.4 5.8.2.4.1.5 5.8.2.4.1.5.1 5.8.2.4.1.5.2 5.8.2.4.2 5.8.2.4.2.1 5.8.2.4.2.2 5.8.2.4.2.3 5.8.2.4.2.3.1 5.8.2.4.2.3.2 5.8.2.4.2.3.3 5.8.2.4.3 5.8.2.4.3.1 5.8.2.4.3.2 5.8.2.4.4 5.8.2.4.4.1 5.8.2.4.4.2 5.8.2.4.4.2.1 5.8.2.4.4.2.2 5.8.2.4.4.2.3 5.8.2.4.4.3 5.8.3 5.8.3.1 5.8.3.2 5.8.3.2.1 5.8.3.2.2 5.8.3.3 5.8.4 5.8.4.1 5.8.4.2 5.8.4.3 5.8.5 5.8.5.1 5.8.5.2 5.8.5.2.1 5.8.5.2.1.1 5.8.5.2.1.2 5.8.5.2.1.2.1

Introduction Energy Importance of Energy Classification of Energy Sustainable Sources of Energy Solar energy Wind energy Hydro energy Tidal energy Applications of Energy Solar energy Agriculture Solar lighting Water heating Water treatment Electricity generation Photovoltaic Concentrated solar power Application of wind energy Propel and agriculture Water pumping Electricity generation Difference between onshore and offshore wind farm technologies Onshore farms Offshore farms Applications of hydro energy Hydroelectricity Types of hydropower plants Applications of tidal energy Tidal electricity Barrages Flood generation Ebb generation Two-way formation Tidal fence The Environment Environmental Impact Major Environmental Problems Global warming Acid precipitation Possible Solutions Sustainability Sustainable Development Brundtland Definition American Reinvestment and Recovery Act Energy Management Importance Practices Key step practice Commitment of top management Understanding the issue Grasp the current energy use

Comprehensive Energy Systems, Volume 5

doi:10.1016/B978-0-12-809597-3.00522-8

317 317 317 318 318 319 320 320 321 321 322 322 322 322 322 322 322 323 323 323 323 324 324 324 324 325 325 326 327 327 327 328 328 328 328 329 329 329 330 330 330 331 332 332 333 333 333 333 333 334 334 334

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5.8.5.2.1.2.2 Identify management strength and weakness 5.8.5.2.1.2.3 Analyze stakeholder’s needs 5.8.5.2.1.2.4 Anticipate barriers to implement 5.8.5.2.1.2.5 Estimate the future trend 5.8.5.2.1.3 Plan and organize 5.8.5.2.1.3.1 Develop a policy 5.8.5.2.1.3.2 Make out a plan/program 5.8.5.2.1.4 Implementation 5.8.5.2.1.5 Controlling and monitoring performance 5.8.5.2.1.6 Management review 5.8.5.2.1.7 Implementation of key step approach 5.8.5.2.1.7.1 Energy audit 5.8.5.2.1.7.2 Advanced monitoring and metering solutions 5.8.5.2.2 Six Sigma practice 5.8.5.2.2.1 Implementation of Six Sigma approach (DMIAC methodology) 5.8.5.2.2.1.1 Detect 5.8.5.2.2.1.2 Measure 5.8.5.2.2.1.3 Analyze 5.8.5.2.2.1.4 Improve 5.8.5.2.2.1.5 Control 5.8.6 Smart Energy Management 5.8.6.1 Better Efficiency 5.8.6.2 Better Cost-Effectiveness 5.8.6.3 Better Resource Management 5.8.6.4 Better Design 5.8.6.5 Better Environment 5.8.6.6 Better Sustainability and Performance 5.8.7 Case Studies 5.8.7.1 Borehole Thermal Energy Storage at University of Ontario Institute of Technology 5.8.7.1.1 System description 5.8.7.1.2 Analysis 5.8.7.1.3 Results and discussion 5.8.7.2 Solar Energy Management and Integration at Boston College 5.8.7.2.1 Financial analysis 5.8.7.3 Case Study: Optimization and Management of Low Head Hydropower Plant 5.8.8 Future Directions 5.8.9 Concluding Remarks References Further Readings Relevant Websites

h˚ _ m M N _ Q T

Specific enthalpy at reference state (kJ/kmol) Mass flow rate (kg/s) Atomic mass Number of moles Rate of heat (kW) Temperature (K)

Subscripts B Boiler BHE Borehole energy HP Heat pump

HW P R SH

Hot water Product, pump Reactant Space heating

Abbreviations ARRA American Reinvestment and Recovery Act

BC BRBD

Boston College Babanwala River Bedian Dipalpur

Nomenclature Cp ex _ Ex e h˚f

Specific heat capacity (kJ/kg K) Specific exergy (kJ/kg) Rate of exergy (kW) Exergy efficiency Specific enthalpy of formation (kJ/kmol)

334 334 334 334 335 335 335 335 335 335 335 335 335 336 337 337 337 337 337 337 338 338 338 338 339 339 339 340 340 340 340 341 344 344 346 347 349 349 349 350

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CSP DMAIC DMADV GHG HHV IEA LCC MDGs OS PACT PCT PPA

5.8.1

Concentrated solar power Detect, measure, analyze, improve, and control Define, measure, analyze, design and verify Greenhouse gasses Higher heating value International energy agency Lower Chenab Canal United Nations Developments Goals Operating system Program administrator cost test Participant cost test Power purchase agreement

PPM PV PW RIM ROI SCT SODIS TRC UCC UNFCC WHO

317

Parts per million Photovoltaic Petawatt Ratepayer impact measure test Return on investment Societal cost test Solar water disinfection Total resource cost test Upper Chenab canal United Nations Framework Convention on Climate Change World Health Organization

Introduction

Considering the problems that one society has to face due to climatic and environmental changes from past few decades, the need of preponderant study on sustainable energy systems has arisen. Sustainable energy systems are a diverse topic which includes the hands on knowledge of different energy transformations techniques, heating processes, cooling processes followed up by different kinds of molecular and chemical changes. This single topic comprises of some main and distinct features of many other complex engineering fields such as: fluid mechanics, heat transfer, and thermodynamics. All of these fields need to be encountered while designing a sustainable energy-based application. There is an intimate connection between energy, the environment, and sustainable development. A society seeking sustainable development ideally must utilize only energy resources which cause no environmental impact (e.g., which release no emissions to the environment). However, since all energy resources lead to some environmental impact, it is reasonable to suggest that some (not all) of the concerns regarding the limitations imposed on sustainable development by environmental emissions and their negative impacts can be in part overcome through increased energy efficiency. Clearly, a strong relation exists between energy efficiency and environmental impact since, for the same services or products, less resource utilization and pollution is normally associated with increased energy efficiency. While environmental issues in general have been influencing developments in the energy sector for some time, climate change poses an altogether different kind of challenge. Problems such as acid precipitation could be dealt with in part by administrative measures, such as vehicle exhaust standards or emission limits for power stations, which affected comparatively small numbers of economic actors. Technical fixes with a relatively limited scope, such as fitting flue gas cleaning equipment or catalytic converters, could contain the problem. However, emissions of greenhouse gases (GHG) are so dispersed that it is not possible to take this local and relatively small approach in dealing with climate change. The nature of the problem demands a more comprehensive energy policy response which influences the actions of energy consumers and producers in all countries. To analyze the basis of any sustainable energy system, one should possess the basic knowledge and concepts of all the energy conversions types, there need and their significance based on the criteria to evaluate them on the basis of those basic principles and governing equations of thermodynamics, fluid mechanics, heat transfer, and chemical changes which are universal. SO, in this chapter we will study all types of sustainable energy systems that are available commercially, their sources backed up with their efficiencies, needs, and significance.

5.8.2

Energy

A number of factors are considered to be important in determining the future level of a country’s energy consumption and production, including population growth, economic performance, consumer tastes, technological developments, government policies concerning the energy sector, and developments on world energy markets. Energy conservation is vital for sustainable development and should be implemented by all possible means, despite the fact that it has its own limitations. This is not only required for us but also for the next generation as well. Energy conservation is of great importance in terms of sectoral energy utilization.

5.8.2.1

Importance of Energy

To bring the people above the average poverty line is really a crucial thing to do as per United Nations Development goals (MDGs), as per the study of, the main factor through which it can be done is by providing them with sufficient amount of energy. Every member nation country is putting their efforts in order to develop new efficient energy technologies which would be costeffective at the same time. But at the same time, for the under developed nation access and importance of modern and efficient energy yet haven’t received sufficient attention. The principal aims of most of the MDG clause is to come up with such technologies that could address the access of reliable and sustainable energy, without taking in account the source of energy. International

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World total population 7.6 Billion

Living without power 1.5 Billion

Earning less than $60/month 7.6 Billion Fig. 1 The scale of global energy poverty.

Energy Agency (IEA) recently have developed an index in which they are measuring and then rating countries on the basis its transition in energy sector to as well as the rate of maturity of its end use. Thus, for the development of any society, competitive and diverse energy development need to be developed for the growth of domestic hubs like industries. Mainly there are two basic forms of energy on the surface of earth: potential energy and kinetic energy. Potential energy is often referred to as the type of energy which stored in a body by virtue of its position with respect to other. This form of energy has the capacity to change the state of the objects linked to it. The basic types of potential energy is gravitational energy, chemical energy, nuclear energy, and elastic energy. The other form of energy is kinetic energy which is referred to as the type of energy of mass in motion. Energy which any body of mass can possess by virtue of its motion. The basic examples of kinetic energy are thermal energy, electrical energy and sound energy. This chapter focusses mainly on two types of energy developments which is necessary for the safe and secured advancement of the modern society. That two kind of energies are: electricity (light) and heat energy.

5.8.2.2

Classification of Energy

The fact of global energy limitation is difficult for most of the people to understand. As Fig. 1 shows, almost quarter of the world’s total population lacks the access of electricity so that they could live an improved quality of life [1]. As nowadays, electricity is considered to be the foundation to nearly all aspects of life to have a standard of living which is directly linked to the socioeconomic development. Currently, electricity is essentially required for every aspect of human life, whether it is communication, transportation, construction, entertainment, food, etc. All of them are walled by the application of it. But, there are also some disadvantages concerning to this thing which we have discussed previously in quite detail. For instance, most of the energy which is produced nowadays have been generated by the burning of fossil fuels. Climate change concerns coupled with high fossil fuel prices are driving increase in sustainable energy legislation, incentives and commercialization.

5.8.2.3

Sustainable Sources of Energy

This fact cannot be denied that presently, oil is the major source for the generation of this all important form of vitality. The vast impact which oil has put on human life, cannot be replaced by any other sources, currently. Oil in its various forms like diesel, kerosene and other, together has a unique and distinct characteristic. These include: ease of transportation and storage, relative safety and great versatility at end use. Sustainable energy sources are considered to be a substitution of oil when all of these aspects are considered. None appears to completely equal oil. But oil, like other fossil fuels, is a finite source that it has to eliminate from the surface of earth at some point of time; moreover, as the reserves of oil has been continuously depleting, the cost to recover what remains will be beyond the value of oil. Also, a time will be reached when the energy needed to recover the oil will be equal or exceed the value which can be generated from it and that will be the break-even, or net energy loss situation. Realizing the above phenomenon in its practical terms, researchers are giving importance to find out all the renewable/ sustainable energy sources that can replace oil. As sustainable energy means the form of energy that can be extracted through resources without putting them into danger of getting depleted or vanished; moreover, which also does not put an unpleasant effect on our environment. Sustainable energy resources are the best alternate to fossil fuel through which we can not only fulfill our current energy needs, but also it cannot be vanished from the surface of earth as they are renewable (i.e., they can be utilized again and again). These are the sole reasons why currently, every society urges on the study of different methods through which these resources can be utilized successfully. In Table 1 all the available sources of sustainable energy are enlisted, we here briefly discuss all of the available sources that extract energy, i.e., renewable or nonrenewable, so that better conclusion can be drawn on the basis of their limitation, significance, and importance.

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319

Energy sources

Renewable sources

Nonrenewable sources

Hydroelectric power Solar energy Wind energy Tidal energy

Oil Natural gas Coal Gas hydrates

Fig. 2 Photovoltaic (PV) energy conversion system.

5.8.2.3.1

Solar energy

Solar energy is the form of energy which human beings are harnessing since centuries. The energy which earth receives in the form of solar rays from sun can be harnessed by using range of growing technologies. Solar radiation also gives birth to most of the secondary sustainable resources like wind and wave energy but currently only a minuscule fraction of the total radiation we are receiving has been utilized. Solar conversion technologies provide electrical generation by means of heat engines or photovoltaic (PV). The application of solar energy includes: space heating and chilling through solver conversion systems, water through distillation, solar lighting, hot water, heat energy for cooking, and high temperature for industrial process purposes. Fig. 2 shows PV cells used to convert solar energy into electrical energy, these are active solar technology. Solar energy generation has been widely characterized into two broad spectrums: active solar and passive solar depending on the criteria following energy development, conversion and their distribution. Active solar energy harnessing techniques include the use of PV panels, mechanical equipment’s and solar thermal collector to collect heat from sun and convert it to generate useful outputs. Passive solar technique includes the designing of the building in such a way that it would face sun directly, selecting material with good thermal mass and light diverging properties, and spaces that designed for the purpose to naturally circulate air. Our earth receives 174 peta-watts (PW) of solar radiation from sun daily. Out of which, approximately 30% of the radiation is reflected back and remaining has been absorbed by its environment like ocean, cloud, and other thermal masses. This increases the temperature of these thermal masses. Therefore, warm air containing evaporated water particle rises from the surface of the ocean, causes atmospheric circulation, and convection. As the hot air rises upwards, when it attains enough altitude where temperature is low, vapors condenses itself and again returned back to the surface of earth in the form of rain completing the water cycle. This latent heat of water condensation amplifies the convection thus giving birth to winds and cyclones. Heat absorbed by the ocean and land keeps the average temperature of our atmosphere to 14oC which is necessary to sustain human life. Plants convert solar energy to chemical energy which produces food and wood. Table 2 shows yearly irradiation received by earth from sun in form of various sources.

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Table 2

Yearly solar energy received and human consumption

Sources

Energy consumption (EJ)

Solar Wind Biomass Primary energy use (2005) Electricity (2005)

3,860,000 2250 3000 487 56.7

800 Frequency Energy

700 600

MWh

Hours

500 400 300 200 100 0 0

5

10

15

20

25

Wind speed (m/s) Fig. 3 Lee ranch wind speed frequency. Reproduced from Wind-power-program. Wind statistics and the weibull distribution. Available from: Wind-power-program.com; 2013.

The total solar energy absorbed by the earth atmosphere is 3,860,000 EJ per year. According to the research in 2002, it was more energy in 1 h than the world consumes in 1 year. Photosynthesis consumed approximately 3000 EJ in 1 year. This clarify the fact that the amount of energy the earth receives from sun is so vast that it is assumed that we are receiving twice the energy per year from sun than it can be produced from total nonrenewable sources from earth.

5.8.2.3.2

Wind energy

It is also one of the most used resources for the generation of sustainable energy. This concept comprises of harnessing energy from wind and utilizing it for the generation of another useful form of energy, i.e., electricity. For centuries wind energy has been successfully utilized to convert wind energy into mechanical energy for work-done but; nowadays, same mechanical energy is used to generate electricity by connecting it to inductance generator. Wind farms are typically connected to the local transmission network with mechanical turbine to generate electricity and supply it to isolated areas. As it is renewable, plentiful, widely distributed, and clean, many environmentalists recommend it as an alternative to fossil fuel. Distribution of wind speed was studied at Lee Ranch facility at Colorado. In Fig. 3, their finding was represented in a form of histogram. The curve formed is known as Rayleigh model for distribution of same average wind speed. Through this experiment it was claimed that total available wind energy on the surface of earth is 15.4 gigawatts-per hours. Due to the fact that earth receives heat unevenly, i.e., more heat has been received by equator than pole. This temperature difference in heating and cooling drives a global atmospheric convection system reaching from the atmosphere to sun surface and act as a virtual ceiling. Eventually, most of the wind energy is converted by means of friction through diffuse heat throughout the earth’s surface and atmosphere.

5.8.2.3.3

Hydro energy

Hydro energy is the form of energy which could be harnessed through movement of water to power machinery or create electricity. As it is known that water constantly move under a global cycle of evaporation that is it evaporates from rivers, oceans, and seas to the sky in form of vapors, forms cloud, precipitates to rain or snow and then again flow back to the surface of earth as shown in Fig. 4. The energy of this whole process is driven by sun which can be accumulated to produce electricity of other mechanical task efficiently. Due to the fact that water cycle is an endless process which continuously repeats itself, hydro energy is considered to be a sustainable energy source. When flowing water is utilized to form all important electricity then the process is called hydroelectric power generation. Nowadays, several facilities have been installed to harness kinetic energy from the flowing water to downward stream and convert

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Condensation

Evaporation Precipitation

Collection

Fig. 4 The water cycle.

it into electricity. Several types of turbines and generators are used to convert energy which is then fed to the national grid so that it can be consumed at homes, offices, and other public places. In United States, through this form of sustainable energy they produce enough energy to lighten up nearly 28 million households with electrical energy, which is equivalent of using 500 million barrel of oil. The mission state of US department of energy’s hydropower program is to determine the potential, conduct, and coordinate research and development with several other industries and agencies to improve the efficiency, technical aspects, and minimize the environmental effects [2]. Through hydro energy, electricity can be generated by two ways: one is “run of river” scheme and other is significant water storage. Both of these methods require dams. Dam performs two significant tasks for hydroelectric power plant, first task is that it stores enough amount of water which can be utilized when it does not rain and other one is it allows water to fall from the gates of dams called penstock on turbines. The more the water falls the faster turbine will rotate and generate greater energy. The process of extracting energy from water does not produce GHG, keeping aside those which is produced in the construction phase of project.

5.8.2.3.4

Tidal energy

In this form of energy, tides of ocean or sea are used to generate electricity. Tides move a large quantity of water twice a day, and by utilizing it we can generate all important electricity. In spite of the fact that tide could provide sufficient amount of energy which can be utilized in our daily routine, the process of harnessing it is not that simple. Tide is generated in ocean due to the incline and decline in the level of water relative to coastline. This is originated by the relative motion of earth around sun and moon around earth. The gravitational pull of sun and moon, along with the revolution of earth results in tide. Studies suggest that moon exerts comparatively large gravitational energy to earth as compared to sun due to the fact that it is much nearer to earth, though it is smaller in mass. This gravitational force causes the ocean to bulge around the axis toward the moon producing tides. Hence, tides are produced by the rotation of earth beneath this lump, resulting periodic rise, and fall of ocean levels. Similarly, gravitational attraction of sun also effects tides, but to lesser extent. However, when the sun, moon, and earth are positioned in a straight line, i.e., in full moon, the gravitational force combined resulting in large tides. Whereas, in half moon condition, they are positioned at right results in lower tides. To build a tidal power station, the selection of correct location plays a vital role for its successful working. To generate enough amount of power that can be put in use, deviation of at least 5 m is required between high and low tides. Geographically, only 40 sites around the world are considered to qualify certain parameters of tidal range. The higher the tide the more will be the amount of electricity generated. Similarly, this is inversely proportional to the cost of electricity. Exploiting sites with such parameter are also very economical as study suggests that almost 3000 GW of energy are available from these tides worldwide. However, only 2%, i.e., around 60 GW, has been potentially generated consuming tidal energy. Few years ago, the only method to harness energy from tides is the construction of barrages at suitable location, but nowadays there are other options too. This source of harnessing energy is clean and green and does not involve any usage of fossil fuel. However, some environmental concerns still exist which mainly has to deal with slit formation at the shore (due to blocking tides to reach the shore and washing away slit), harming marine life who dwell near the basin. In terms of reliability, tidal energy project is considered to be more predictable source of energy as compared to wind or solar since its occurrence are almost predictable.

5.8.2.4

Applications of Energy

This section of the report discusses in detail individual applications of the above discussed sustainable energy sources.

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5.8.2.4.1

Solar energy

Solar energy refers primarily to the use of it for practical ends. All the sustainable energy resources mentioned previously fundamentally derives their energies from the sun. As previously discussed that solar energy generation have been widely characterized into two broad spectrums: active solar and passive solar depending on the criteria to generate, convert, and their distribution. Active solar conversion techniques include the use of PV panels, mechanical equipments and solar thermal collector to collect heat from sun and convert it to generate useful outputs. Passive solar technique includes the orienting a building toward sun, selecting material with good thermal mass and light diverging properties, and spaces that designed for the purpose to naturally circulate air. Active solar technologies are considered as supply side technologies due to its application of increase in supply. Whereas, passive technologies are considered as demand side technologies as it has the application to reduce the need of alternate resources. 5.8.2.4.1.1 Agriculture Enormous evolution has occurred in field of agriculture once different methods of harnessing energy has been introduced by mankind. Agriculture sector always involves the optimized use of solar energy for its productivity. In past times, short growing seasons of little ice age, French and English farmers; deployed a fruit walls to maximize the collection of all important solar energy. Those walls acted as a thermal mass to capture heat from sun and keeping plants warmer; moreover, in 1699 a system was suggested which can track the sun to take the maximum advantage from the sun energy. A part from it, nowadays solar energy also includes pumping water, drying crops, etc. Furthermore, concept of greenhouses also introduced which converts solar energy into heat and allows the product of special crops round the year. 5.8.2.4.1.2 Solar lighting These systems collect and distribute sunlight to provide interior illumination. This passive technology directly used to minimize the consumption of energy and also reducing the need for air-conditioning. This system comprises of careful interior designing, placement and selection of windows types and sizes; collectively exterior shading devices can also be considered. Hybrid solar lighting is an active method used to provide interior illumination. These systems collect sunlight using mirrors and transmit it inside the building using optical fibers. For a small application, HSL systems can provide up to 50% of the total required lighting. Solar street lightning is another phenomenon which is extensively used as its stores energy in daytime and utilizes it at night. 5.8.2.4.1.3 Water heating Hot water solar systems have been extensively used nowadays. It utilizes heat energy from the sun to rise the temperature of water. Most of these systems are deployed in the area of low geographical latitude (below 401C) where 60–70% of the hot water can be provided using these heating systems. The common types of solar thermal collectors are evacuated tubes, flat plate collectors, etc. As of now the total energy which could be extracted from these systems are 154 Gigawatts, among which China is the leader by harnessing 70 Gigawatts out of world’s total extraction. 5.8.2.4.1.4 Water treatment Another useful application of solar energy is that it can be used to convert the saline water into brackish and drinkable water as shown in Fig. 5. Solar water disinfection (SODIS) involves the exposure of water filled plastic bottle to sunlight for different period of time depending upon the climate of the region. It takes minimum of 6 h in high temperature areas and of 2 days in over-casted areas. SODIS has been verified and recommended by world health organization (WHO) as effective method for home-based water treatment. In most part of world, solar energy has been utilized in small sewerage systems for the water stabilization without using any chemicals or electricity. 5.8.2.4.1.5 Electricity generation Nowadays, solar energy can easily be converted into electrical energy after a successful study, research and development of technologies like PV and concentrating solar power troughs and dishes (CSP). PV is mainly used to power small scale and medium sized application like powering a calculator using a single PV cell to a single story house by using an arrays of it. On the other hand, CSP technologies are mainly used in large scale production of electricity. As a discontinuous power source, solar energy needs a backup supply which can be utilized after dusk. Mainly, different kinds of lead acetate batteries are utilized for these purposes. 5.8.2.4.1.5.1 Photovoltaic It is a device that converts solar energy into useful electrical energy by using a photoelectric effect. Although early PV cells converted less than 1% of the total incident light in electrical energy, this fact also could not hold on researchers like Ernest Werner and James Clerk Maxwell to study it more often and accepts its importance. The ancient significant application of these cells are vanguard I on which these cells were installed as a source to give power backup. After a successful observation, these cells were deployed in many of the American and Russian satellites.

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Used water

Treated water

Membrane distillation Fig. 5 Small scale solar sewerage treatment plant.

Fig. 6 Demister water pumping windmill.

After 1973 oil crisis, this subject again grabbed attention of all think tanks of that time and enormous increase in production and development of PV has been noticed. This minimizes the higher retail cost of PV cells from 100 USD/watts to 7 USD/watts in 1985. These factors play a vital role in moderating its growth for approximately 18% per year from 1984 to 1996. 5.8.2.4.1.5.2 Concentrated solar power This method of harnessing energy from sunlight was firstly used by Archimedes. He used polished sheet to direct rays from the sun to attack Roman fleet. CSP systems use dishes or trough made up from a reflecting surface. The system focusses large area of sunlight onto a small point of concentration which is then used as a heat source for any other conventional plant or system, reducing the amount energy required to operate it. A wide range of CSP technologies has been developed which has their own significant applications. The most developed are solar trough, parabolic dishes and solar tower. In all of these systems, conductive working fluid is heated by absorbing energy through a focal point and then used for a power generation or energy storage.

5.8.2.4.2

Application of wind energy

5.8.2.4.2.1 Propel and agriculture In ancient time, wind energy has been extensively used as propel in sailboats and sailing ships. Many of the architects have also designed wind-driven ventilation systems in their buildings. Babylonian emperor Hammurabi has designed a project to utilize wind energy for agriculture in late 17th century BC. 5.8.2.4.2.2 Water pumping Life of many people, animal and crops depend upon the supply of fresh, cost-effective and reliable supply of clean water. Mechanical wind pumping machines has been used to pump water from the wells from centuries as shown in Fig. 6. This technology has been vastly used since then due to the reason of possessing properties like: simple mechanism, maintenance requirement is modest and the replacement of parts are not difficult to obtain.

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Rotor blade

Gear box Nacelle

Generator Power to light house Power cables Tower

Fig. 7 Wind turbine mechanism.

5.8.2.4.2.3 Electricity generation One of the great applications of wind energy is electricity generation keeping the fact in mind that wind power has no fuel cost. As wind power is the form of kinetic energy, therefore, there are numbers of criteria through which electricity can be generated as shown in Fig. 7. The most commonly used method of harnessing energy from wind for energy production is passing wind through induction generator called as wind turbines. Wind turbines works on a simple principle, when kinetic energy of winds strikes the propeller (blades) it start rotating the propeller in the direction opposite to the wind as per Newton’s third law of motion. Propeller is connected to generators rotor. Rotor is connected to the main shaft, which spins a generator to create electricity. Through this process wind energy is utilized to create electricity, it converts the kinetic energy from wind into mechanical power which is further taken forward to create all important electricity. Huge landmarks have been achieved in terms of development in the following field, in 2014, facilities generating more than 50 GW of electricity has been setup globally which increased to 60 GW in 2015 with 22% of annual market growth. Such market growth and extensive use of wind energy arises due to the fact that it consumes no fuel. The price of wind energy is therefore much stable than that of fossil fuels [3]. It was also estimated that marginally cost of wind energy after the system is functional is less than 1 cent per kWh [4]. Without a doubt wind energy is feasible, sustainable, and profitable source of electricity generation and this technology is experiencing a rapid growth. But beneath the umbrella of wind harnessing energy process, two categories lies: wind farm technology and type of wind turbines used. Both of these types have their own significance and importance and if rightly used then in proper condition with proper turbine one can generate more amount of electricity efficiently. 5.8.2.4.2.3.1 Difference between onshore and offshore wind farm technologies The major difference between the usages of both technologies is its environmental impact. Both onshore and offshore wind turbines come from the concept of ease in manufacturing and later recycling. Wind turbines are an ecofriendly technology due to the fact that it does not emit GHG in our environment. Below is a brief look at the environmental cost of onshore versus offshore wind power. 5.8.2.4.2.3.2 Onshore farms Onshore wind electricity has an impact on the surroundings particularly in form of noise pollution, visual air pollution and harm to birds as shown in Fig. 8. However, there are also many cost advantages to onshore wind electricity that with the aid of extension impact the environment. Onshore wind frequently has the benefit of being close to existing electrical grids, lowering the environmental effects associated with building new electrical grids. 5.8.2.4.2.3.3 Offshore farms Offshore wind power plants are mostly designed to build in the middle of the sea at higher altitude so that highly diverse blowing sea winds can be utilized to extract energy as displayed in Fig. 9. This system is placed on hard concrete structure that extend to the bottom of the sea and gives it immense strength to standby in floods. However, as these farms move further out from the shore, the effect they have on humans is lowered, as is the effect on many living sea organisms as the floating wind turbines do not harm the sea, but this increases the initial cost of material needed to build heavy platforms for floating wind turbines that can withstand conditions at deep sea levels are eventually canceled out through the added efficiency that comes with placing turbines further offshore.

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Fig. 8 Onshore wind farm.

Fig. 9 Offshore wind farm.

5.8.2.4.3

Applications of hydro energy

The energy of falling water has been extracted to produce other forms of energies or to perform desired work for more than 2000 years. In one of the earliest inventions of system utilizing hydro energy as fuel lies a trip hammer powered by vertical-set water wheel to pound and hull grain, digging and early paper making in China during the Han dynasty in early 202 BC. The Greeks also used water wheel for grinding wheat into flour. Besides this, the power of water was also being utilized in cutting woods and powering textile mills and manufacturing plants. Richard Arkwright in 1771 set up a cromford wheel in England’s Derwent valley to spin cotton and therefore developed world’s first manufacturing plant fueled by hydro energy solely. He was so convinced with his driving force that 6 years later he started using steam engine to pump water in his mill pond rather than pumping it directly using machinery to save energy. Technology of harnessing energy from water and utilizing it to form electricity have been existed for more than a century. The evolution in this field began in mid-1700s when French hydraulic and military engineer, Bernard Forest wrote his four volume book “Architecture Hydraulique” which becomes a basic foundation for modern hydropower turbine design. During 1700s and 1800s, development and research of water turbine was at its peak. In 1880, a brushed dynamo driven turbine was used to supply electricity to theater in grand rapid Michigan; and in 1881, same kind of turbine was installed at Niagara Falls, New York for street lighting. 5.8.2.4.3.1 Hydroelectricity Hydropower has been utilized for creating power as far back as the end of the 19th century, and is currently utilized as a part of 159 nations around the globe. Today, it is the most broadly utilized renewable resource for creating power around the world. At

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around 3490 TWh, hydropower represented 16% of the world's power generation in 2011. Given rising interest, the proportion of hydropower around the globe is likely to be expanded further in 2020, and will add up to around 4500 TWh. Into the 21st century, hydropower keeps on catalyzing development around the globe. For instance, it has assumed a key part in changing Brazil into the seventh biggest nation by GDP in 2012; not minimum through a time of exceptionally quick monetary development somewhere around 2000 and 2010, which saw its expansion in GDP esteem just outpaced by the United States and China. This was just conceivable with the enormous increments in power yield that have been conveyed by its interest in hydropower. In 2010, Brazil delivered 349,000 GWh of power, and by 2011 this had expanded by 40% to 489,000 GWh. Amazingly, only 2% of this vitality originated from imports, and around 80% from hydropower. The outcome is an extremely cutting edge fleet of substantial hydropower stations – of which no less than 24 are appraised at 500 MW or above. Brazil has benefited as much as possible from its rich hydrological asset to change itself into a pioneer on the world stage, minimizes expenses and keep up its vitality independence from whatever remains of the world. This is only one case of the huge boost to economic development that hydropower can give; as we look toward the future the innovation has a tremendous part to play in conveying development and success to the developing world. For the efficient use of this all important source of energy to factors need to be considered on the basis of magnitude of power required and geographic location. Those two factors are: types of hydropower plant and type of turbine used. 5.8.2.4.3.2 Types of hydropower plants Hydropower is an adaptable, flexible innovation that at its least can control a solitary home, and at its biggest can supply industry and people in general with renewable power on a national and even on local scale. As far as today's world’s capacity, hydro represents eight of the world's 10 greatest power stations. This wide spectrum is further subdivided in to four broad categories: Diversion or Run of river hydro plant: facility that channels streaming water from a waterway through a trench or penstock to turn a turbine. Ordinarily, a run of river venture will have practically zero storing capacity. This facility gives a persistent supply of power (base load), with some flexibility of operation for every day vacillations sought after through water stream that is controlled by it. Impoundment: this is the most commonly used type of hydropower generation. In this type of plant, it uses a dam as a reservoir to store sufficient amount of water. It then allows water from reservoir to flow through turbine, rotating it, which in turn activate the generator to produce electricity. Pumped storage: gives crest stack supply, tackling water which is cycled between a lower and upper storages by pumps which utilize surplus vitality from the system on occasion of low energy requirement. At the point when power requirement is high, water is discharged back to the lower repository through turbines to create power. Types of turbines – Turbines are broadly characterized into two main types: impulsive turbines and reactive turbines. The type of turbines are selected depending on the number of factors such as: the height of standing water often referred to as “head”, flow of water, volume of water, efficiency and cost. These two types of turbines are discussed in detail including their subtypes and applications. 1. Impulsive turbine: in an impulsive turbine, a speedy moving fluid is given up through a tight gush at the turbine front lines to make them pivot. The sharp edges of turbine are of the state of hollow shaped bucket so they get the fluid and direct it off at a point or at times even back the path from where it came (in light of the fact that that gives the best trade of imperativeness from the fluid to the turbine). In an impulsive turbine, the fluid is constrained to hit the turbine at quick. For example, consider a wheel to turn around by kicking a soccer ball onto its paddle. One’s requirement should be that, ball should hit hard and bounce back in order to rotate a wheel with good torque. So, these constant energy impulses are the key to your wheel spinning. As the law of conservation of energy tells us that the energy each time the paddles gain, will be approximately equal to the energy loss by soccer ball which results that ball will be bounced back with lesser energy. Similarly, Newton’s law of motion tells us that the moment gained by the paddle when it strikes will be equal to the moment lost by ball after striking back. Water turbine is often made on impulse turbine phenomenon as they are easy to design, maintain and are cheap in comparison to that of reaction turbine. Impulsive turbine contains the following types: a. Pelton wheel: this type of turbine consists of a cup-shaped blades arranged along a circumference of central hub. Pelton’s efficiency is enhanced by dividing the blades in the center to prevent dead area. The lips of the blades are slightly relieved being cut back so that a smooth transition of water flow could make possible. Water is infused onto the blades which are orchestrated at positions around the runner. The spouts convert the potential energy of the water to high kinetic velocity and as the water strikes the mugs, cause an impulse, and causes the turbine to rotate. b. Crossflow: this type of turbine consists of number of trough shaped blades arranged radially along the cylindrical runner; they are tapered to a fine point in water inlet and outlet edges promoting a fine and efficient flow of water. The crossflow turbine has two free spouts which are set at a point of 45oC anticipating the water flow from the optimum angle, at the end of the day changing over the potential energy of the water to active vitality. c. Turgo water turbine: a turgo wheel turbine is the modified version of Pelton wheel, made exclusively by Gilkes, England. Turgo turbine generally resembles to that of fan blades that is close on outer edges. The water stream strikes the wheel from one side and exits from other.

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2. Reaction turbine: in a reaction turbine, the edges designed to handle much bigger volume of liquid and rotate as the liquid streams past them. A reaction turbine does not alter the course of the liquid stream as radically as an impulse turbine. It basically turns as the liquid pushes through and past its cutting edges. Wind turbines are the most well-known example of reaction turbines. For reaction turbine consider an example that you are lying on ground and a stream of water touches your hands transferring its energy to your hand and made them move in the direction of it. In reaction turbine, we want water to touch blades smoothly for a longer period of time and transfer as much energy to its blades as possible. a. Propeller: this type of turbine generally consists of three or more propeller shaped blades that remains in contact of water constantly. Since these turbines can achieve very high rotational speeds, propeller turbines are greatly utilized under low head. Therefore, this type of water turbine is suitable for run of river power plants. b. Francis turbine: the most commonly utilized turbine as a part of Hydro-Québec's energy systems. Water strikes the edge of the runner, pushes the cutting edges and afterward streams toward the hub of the turbine. It escapes through the draft tube situated under the turbine. It was named after James Bicheno Francis (1815–92), the American designer who designed the device in 1849. c. Kaplan turbine: Austrian designer Viktor Kaplan (1876–1934) designed this turbine. It is like the propeller turbine, with the exception that its edges are movable; their position can be set by accessible stream. Due to this factor Kaplan turbine has been greatly utilized under run of river plants producing stations where the waterway stream differs extensively. d. Kinetic turbine: kinetic turbines, also called free-stream turbines, produce power from the kinetic energy exhibit in streaming water as opposed to the potential energy from the head. The system may work in waterways, man-made channels, tidal waters, or sea streams. Kinetic turbine uses the water stream's characteristic pathway. They do not require the redirection of water through artificial channels, riverbeds, or funnels, despite the fact that they may have applications in such courses. Kinetic systems do not require huge common works; in any case, they can utilize existing structures, for example, extensions, tailraces, and channels.

5.8.2.4.4

Applications of tidal energy

Tidal energy is one of the many types of renewable energy like solar, wind and geothermal energy. It is harnessed from the development of waves or tides because of the gravitational attraction of the Earth and the Moon. Tidal energy is a type of gravitational energy which can be utilized to do work or be changed over in different types of energy. Tidal energy is still a juvenile innovation with progressions in tidal energy not as fast as in the field of several other types. While exclusive and way breaking methodologies are being produced to outfit the uninhibitedly accessible renewable wave and tidal energy, the full business improvement is still some way away. On the other hand, Tidal Barrages is a developed innovation, however its advancement too has been moderate on account of high venture and long building time. Tidal energy has been utilized for several years. Just like Wind Mills, it was first utilized for the mechanical pulverizing of grains in Grain Mills. The development of turbines using tidal energy was utilized as a part of the smash Grains. However, with the approaches of fossil fuels, this use of tidal energy has turned out to be very low. Tidal energy can likewise be utilized as a source to store energy. Like a significant number of the hydroelectric dams which can be utilized as a vast energy storage, tidal barrages with their repositories can be altered to store energy. Though this has not been attempted out, with reasonable changes tidal energy can be utilized; however, expenses may end up being high. Tidal barrages can counteract damage to the coast amid high storms furthermore give a simple transport strategy between the two arms of a Bay or an estuary on which it is manufactured. 5.8.2.4.4.1 Tidal electricity The process of harnessing energy from tides are in practice for about 100 years. Anciently, tide mills were used for this process. It works on a principle that when tide comes in, the water comes in through passage into the storage pond and when it is returned, the water flows back into ocean using water wheel. The significant difference between tide mill and modern tidal power plant is its capacity to generate electricity and storage. Nowadays, tidal power plants are also known as barrage build across bay allowing water to flow in and flow out through a series of sluice. At high tide, the sluice of barrage is closed, creating a head of water on the ebb side. Simultaneously releasing water in series of turbines results in generation of electricity. Modern technologies of harnessing kinetic energy from tides revolve around two major concepts: tidal range and tidal stream. Tidal range utilizes the head pressure built when the sluice is closed. When passages are opened, it allows the water to flow through turbine results in generation of electricity. Whereas, in tidal stream it captures the kinetic energy of tidal current similar to wind turbine. In this case most of the energy escapes through sides but in spite of it even small facility can generate large amount of energy. Devices designed to extract the all-important energy from this source comes in different forms and their applications also differ on the basis of geographical location and power needs. But, indeed they all are extracting either kinetic energy or potential energy from tides to generate electricity. Nowadays, out of various devices three devices are considered to be most efficient in terms of tidal energy extraction: barrages and tidal fences. Barrages work on the principle of tidal range which was discussed in quite detail in our previous section whereas tidal fence and tidal turbines works on the principle of tidal stream. 5.8.2.4.4.2 Barrages The tidal barrage or tidal power plant, as it is likewise known, is a type of "marine renewable vitality" system that consist of long dividers, dams, floodgate entryways or tidal locks to catch and store the potential vitality of the sea. A tidal barrage is a sort of tidal

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power technology conspire that includes the development of a genuinely low walled dam, known as a “tidal barrage.” The base of the barrage is constructed at the depth of sea with the gates just above the water level. Beneath the surface of water there are number of passages which controls the flow of water through turbine to generate electricity. These passages are known as sluice gates. The water which moves in and out through sluice gates possess enormous amount of kinetic energy which is extracted through this device as much as it can and then converted into electricity. This method of energy extraction is very similar to hydroelectric power generation; the only difference is that in tidal barrage the direction of flow of water is two rather than one. On incoming tides, i.e., high tides the water fills the reservoir with water while outgoing ebbing tide, it flows in opposite direction and empties it. As tide is the vertical movement of water due to exerted gravitational force due to sun and moon, this method exploits this natural phenomenon and generates the most important form of energy, i.e., electricity. The consequence of funneling large share of this water is that the stature of the ocean level once inside these tunnels can increase vertically many meters each day as it is being pushed forward by the approaching ocean water behind it. This expansion in the ocean level can make a tidal scope of more than 10 m in stature in a few estuaries and areas which can be utilized to create power. A tidal barrage technique uses the head difference of high and low tides to generate energy. There are three different formations of barrages which can be utilized with each having its own significance and importance to harness energy: flood generation, Ebb generation and two-way generation. 5.8.2.4.4.2.1 Flood generation This formation utilizes energy of incoming high tides as it moves toward the land. The tidal basin is emptied using a sluice gates located on barrage and hence the lower tide gets effectively empty. As the tides flow back the sluice gates are closed creating a head difference on either side of barrage. The barrage reservoir is filled up passing through turbine tunnel. This flow of water spins the turbine and generates electricity. This formation of barrage is a one-way system, i.e., one can only utilize the energy flowing through high tide to tidal basin which makes it restricted to about 6 h per tidal cycle. 5.8.2.4.4.2.2 Ebb generation A tidal barrage Ebb generation utilizes the vitality of an active or falling tide, alluded to as the “ebb tide,” as it returns back to the ocean making it the inverse of the past surge tidal barrage formation. At low tide, all the floodgate and sluice gates along the barrage are completely opened permitting the basin to fill gradually at a rate controlled by the approaching high tide. At the point when the sea or ocean level encourages the basin achieves its most astounding point at high tide, everyone of the floodgates and sluice are then shut entangling the water inside tidal basin. This repository of water may keep on filling up because of inland waterways and streams associated with it from the land. 5.8.2.4.4.2.3 Two-way formation Preciously we have seen “Flood formation” and “Ebb tide” formation of barrages which only utilizes the one-directional flow of water. But in order to increase power generation and efficiency of system, a new formation has been introduced which enables harnessing energy from tides in both directions. Two-way tidal barrage utilizes a part of both high and low tide in order to rotate turbine and generate electricity. This formation requires more accurate and precise controlling of sluice gates. Sluice gates should remain closed until the heads of either side is sufficient enough. This will enable water in a reservoir to move back and forth hence moving a generator in both directions producing electricity. But in any case, this two-way barrage is as a rule less proficient than one-way barrage or ebb barrage as the required head is lesser which decreases the period over which typical one-way barrage may have generally worked. Likewise, bidirectional tidal turbine generators are developed to work in both directions which make them generally costlier and less productive than unidirectional tidal generators.

5.8.2.4.4.3 Tidal fence A tidal fence is another type of tidal stream innovation, which uses quick streaming submerged sea ebbs and flows for energy conversion. From multiple points of view, a tidal fence construction is a cross between a tidal barrage and a tidal turbine stream system. Unlike submerged tidal turbines which exclusively rotates around its own vertical axis, tidal fence is made out of individual vertical-axis turbines that are mounted together inside a solitary fence like structure. The motivation behind a tidal fence, otherwise called a “caisson,” is to harness maximum available kinetic energy of stream. These tidal fence acts like submerged tidal barrages over a channel or estuary, with the tidal streams being compelled to flow across the turbine edges, making them pivot, which thus controls generators and generate electricity. When we compare tidal barrage and tidal fence, tidal fence does not hinder the stream of water permitting the water to ceaselessly back and forth movement through it, making them less expensive to install than a strong cemented tidal barrage. As per its name, a “tidal fence” is a wall-like structure made out of concrete or steel. Tidal fence is utilized as a part of quick streaming territories, for example, the channels between two land masses where it guides the ocean water to the turbines when it goes through the fence. As their structure is open, tidal wall have less effect on nature than a strong tidal barrage or dam; however, they can even effect the movement of fish and other substantial marine creatures. To overcome this issue, wide openings between

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the caisson wall and pivoting turbines permits fish to swim through, unlike tidal barrage which prevents these creatures to swim in by closing passages. From many aspects tidal turbines are same to that of wind turbines. These turbine generators are also called as “stream generators” or “marine ebb and flow turbines,” they are placed on the sea floor, the stream flows through turbine sharp edges driving a generator creating electricity. In fact, some locations where tidal stream facilities are installed, resembles to that of wind farm in which array of turbines moves simultaneously with flow to generate electricity. The generated energy then supplied to the local grids through long, especially fabricated, submarine wire. These offshore tidal turbines can be either partially or fully submerged beneath the surface of the water, with partially submerged turbines being easier and less costly for maintenance.

5.8.3

The Environment

While environmental issues in general have been influencing developments in the energy sector for some time, climate change poses an altogether different kind of challenge. Problems such as acid precipitation could be dealt with in part by administrative measures, such as vehicle exhaust standards or emission limits for power stations, which affected comparatively small numbers of economic actors. Technical fixes with a relatively limited scope, such as fitting flue gas cleaning equipment or catalytic converters, could contain the problem. However, emissions of GHG are so dispersed that it is not possible to take this local and relatively small approach in dealing with climate change. The nature of the problem demands a more comprehensive energy policy response which influences the actions of energy consumers and producers in all countries. Recently, some potential solutions have evolved regarding the possible problems associated with CO2 emissions, including: energy conservation through improved energy efficiency, a reduction in the usage of fossil fuels and an increase in the supply of environmentally benign energy forms which is leading to the use of renewable energy sources and technologies, acceleration of forestation to absorb CO2, and reduced energy usage by changing life styles and increasing public awareness. Consequently, it is important to point out that a significant characteristics of each of the examples of regional and global pollution problems mentioned above is that the nature and magnitudes of the damages caused are subject to major uncertainties. For example, in the acid rain, uncertainties relate mainly to the magnitudes of the physical and biological effects, rather than to be nature of the effects themselves, and to the transformation of these physical and biological impacts into economic damages. For ozone depletion, uncertainties are far more comprehensive because the phenomenon of ozone depletion is not well understood and research conducted on ozone depletion bases mostly model-oriented studies. In addition, there are various uncertainties in terms of the processes and consequences of global climate change such as the regional impacts of the various manifestations of the process and the timing of the changes and their consequences.

5.8.3.1

Environmental Impact

As discussed earlier, there is a direct relationship between the energy demand and environment. For a continuous development; every society must ideally take steps to minimize the impact cause on environment by harmful ways of energy development in order to fulfill their own needs. However, the major drawback with it is that, every energy resource available lead to some kind of environmental impact. In this case, it is reasonable to suggest some resources over others which gives enough efficiency while minimizing environmental impact, i.e., to get the same product by utilizing less resources and generating minimum pollution higher efficiency can be achieved. Sustainable energy management is also concerned with the environmental problems of the nation. Environmental concerns mainly have to deal with the emission of carbon foot prints and other GHG in earth’s atmosphere. These problems are stated as global warming or climate change. These factors are not only rising the earth’s annual average temperature but also are considered as the major reason of ozone depletion. Sustainable energy management utilized specially for minimizing the use of fossil fuel is the major job among various countermeasures of this problem. For the solution of the said issue, there have been numerous worldwide or universal participation activities. One of those is intergovernmental panel on climate change, which began in November 1988. It has three working groups and one task force. There are also numerous steps which have been taken after the formation of United Nations Framework Convention on Climate Change (UNFCC), in which various nations cooperates for the effort of reducing GHG emission.

5.8.3.2

Major Environmental Problems

Environmental problems associated with energy use span a spectrum of pollutant emissions, hazards, and accidents, as well as the degradation of environmental quality and natural ecosystems. Over the past few decades, energy-related environmental concerns have expanded from primarily local or regional issues, to the international and global nature of major energy-related environmental problems. Particularly in developing or newly industrialized countries, where energy consumption growth rates are typically extremely high and where environmental management has not yet been fully incorporated into the infrastructure, environmental problems are becoming apparent or already exist. Nevertheless, industrialized countries are at present mainly

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Table 3

Past 16 years average temperature record of NASA

Rank

Year

Anomaly (1C)

1 2 3 4 5 6 6 8 9 9 9 12 13 13 15 15

2015 2014 2010 2013 2005 1998 2009 2012 2003 2006 2007 2002 2004 2011 2001 2008

0.90 0.74 0.70 0.66 0.65 0.63 0.63 0.62 0.61 0.61 0.61 0.60 0.57 0.57 0.54 0.54

responsible for air pollution, ozone depletion, and carbon emissions because of the small contribution of the developing countries.

5.8.3.2.1

Global warming

In the past, wood and waste products were used as the major resources for energy development. In other words, biomass was the only way to develop energy. When more research and development has been conducted, then fossil fuels like coal, oil, and natural gas were also find out to be one of the resources from which energy can be extracted. This finding provided a boom to mankind as they were widely available and is can be extracted easily. When mankind started an extensive use of fossil fuel to meet their increasing daily requirement, this led them to cause a degradation in environment as coal and oil was found out to be two major factors that emits large amount of carbon dioxide (CO2) in air thus giving birth to a globally concerned phenomenon “Global warming.” Global warming subjects to a pattern that how world’s temperature has been changed for past decades or more. It is used to describe the fact that the average temperature of the earth’s atmosphere and oceans seems to be gradually increasing as shown in Table 3; experts analyzed that trend is accelerating with every interval year as 16 hottest years of NASA’s 134 year record has occurred since 2000 [1]. Earth receives all the heat from sun through a process of radiation. A medium is needed so that enough heat could be transmitted to earth necessary to sustain life. GHG plays a vital role in trapping enough amount of heat through the process known as greenhouse effect. Without greenhouse effect it is considered that earth would be 331C cooler than it is today [5]. In recent centuries, substantial increase in GHG has been noticed as a result of excessive use of fossil fuels for energy development purposes. The rise of GHG is the prime cause of global warming over the last century. Since the industrial revolution in 1750, one of the major factors involving in the rise of GHG is the emission of carbon dioxide (CO2) and methane (CH4) in earth atmosphere. The concentration of CO2 has increased by approximately 44% from 275 parts per million (ppm) in 1960 to 410 ppm in 2015. It can be clearly stated that due to the increase in substantial harmful affect to our environment and risk of getting expired; researchers had to look for some sustainable, alternative, and renewable resources to extract energy. Our current requirement is to look for a source that are widely available, causes no harm to our environment and are replenishable.

5.8.3.2.2

Acid precipitation

Reaction of emission gases such as sulfur oxides and nitrogen oxides with water leads to creation of acids such as sulfuric and nitric acids which then fall back to the earth surface in the form of acid rain as showcased in Fig. 10. The main cause of the acid rain is the burning of fossil fuels which result in the exhaust of harmful gases such as sulfur oxides and nitrogen oxides. The major problem associated with the acid rain is that more often than not, it does not fall in the country where harmful gasses are emitted, rather in a near buy country. Some of the damages caused by acid rains are (1) acidification of water in lakes, upstream and ground, (2) corrosion, (3) negative impact on marine life, (4) destruction of agriculture lands and forests, (5) building and structure maintenance, and (6) harmful effects on the composition of natural clouds.

5.8.3.3

Possible Solutions

The environmental issues caused by the use of energy resources can possible countered by taking following major steps:

•

Pre-cleaning of fossil fuels to remove harmful chemical from it through chemical processes.

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Wind carrying harmful gasses Photochemical based oxidation reaction

Exhaust of harmful gasses from the fossil fuel usage

Formation of sulfuric acid and hydrogen nitrite

Dissolution of atmospheric moisture Rain clouds

Formation of hydrogen, sulfur oxide and nitrogen oxide

Acid rain

Fig. 10 Acid rain formulation process. Reproduced from Dincer I, Zamfirescu C. Sustainable energy systems and applications. New York, NY: Springer; 2012.

• • • • • • •

Burning coal in a comparatively environmentally friendly manner with the help of advanced technology such as fluidized bed. Using renewable energy sources to offset part of the energy being generated by using fossil fuels. Moving forward with the concept of hydrogen fuel based economy. Applying sustainable energy measures for better utilization of resources. Making use of energy storage techniques to offset the peak energy demands by utilizing produced energy from the off peak hours. Introduction of more efficient transport technologies. Providing proper infrastructure and facilities for public transport in order to promote its vast usage.

5.8.4

Sustainability

By sustainable energy it is meant to extract energy from such sources which are present in bulk quantity and by utilizing them for our purpose they won’t get depleted. It can also be defined as the development that is sufficient to meet our current need, without compromising the ability of future generations to meet their own. One of the reasons for which development of sustainable energy systems recommended throughout the world is its characteristics of not to harm or affect the environment and most importantly the ozone; moreover, it is available free of cost throughout the day-cycle. All renewable forms of energy like geothermal, solar, tidal, biomass, and wind are known as sustainable as they are not only stable but also available naturally in bulk throughout the day and can easily be converted and stored after the successful study and inventions of different modules like lead acid battery,

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chillers, methane controlled chambers, turbines, etc. In this chapter, we will independently study all of the sources of sustainable energy and their individual applications as well as significance. The phenomenon which gave birth to this topic is the deep consideration of the facts that sun will continuously shed its rays on earth till its (earth’s) existence. Heat caused by the sun will continuously produce winds as well as earth will continue to receive heat energy from it which can be utilized or stored until or unless something miss fortunate happens. The circular movement of earth, sun, and moon around its fixed axis will keep producing those tides. The cycle of evaporation that causes water to fall down on earth’s surface in form of rain or ice get mixed with the flow of streams or rivers and merge in the oceans can be utilized to produce energy through hydropower. All of this continuous supply of precious renewable and sustainable energy needs to be utilized for which different aspects of engineering and management has to be studied in order to consume them successfully. In spite of having such a tremendous gift from nature in an immense amount at a time globally, fossil fuel is used as a core source of energy development to overcome society’s daily energy needs. All of the sustainable sources discussed previously can be utilized to produce same amount of energy alternatively and stop use of fossil fuels. This alternatively produced energy will be more replenish and also enable us to stop emission of certain GHG which causes a certain damage to our environment. One more drawback of fossil fuel is that it is not stable and at some time it has to vanish as we do not have unlimited reserves of it. Fossil fuels can never be considered as a sustainable energy source due to its limited availability, which not only causes enormous pollution, but is also not available everywhere on earth surface. It normally includes the use of coal, natural gas, and oil for the production of energy. Globally several steps have been considered to reduce our dependency on fossil fuels. As of now, around 20% of world’s energy comes from renewable and sustainable energy sources.

5.8.4.1

Sustainable Development

Due to enormous increase in population, the demand of energy is also growing day by day. As energy is considered to be the major and significant factor for the economic development and advancement of any society, it makes the reserves of the resources from which it can be extracted to be more significant. For maintaining a balance in energy distribution and development, two elements need to be considered: energy demand and energy supply. In order to attain such balance there are number of factors of which importance need to be realized by every society/country, so that the cart of economy can move smoothly. Those factors are: controlled population growth, enhanced economic performance, Hi-tech scientific developments, and government policies. As previously mentioned, there is a direct relationship between the energy demand and environment. For a continuous development, every society must ideally take steps to minimize the impact caused on environment by harmful ways of energy development in order to fulfill their own needs. However, the major drawback with it is that, every energy resource available lead to some kind of environmental impact. In this case, it is reasonable to suggest some resources over others which gives enough efficiency while minimizing environmental impact, i.e., to get the same product by utilizing less resources and generating minimum pollution, higher efficiency can be achieved. To survive in today’s market, the demand of organizational efficiency in factors like energy, investments, and workforce is increasing day by day. Nowadays, energy is considered to be a most vital factor for the reduction of operating margin. However, firms need to evaluate the effect of rapid price fluctuation on its operations in order to sustain in volatile market. The key step to control this problem is to smartly reduce the cost relating to energy sector and utilize it somewhere else. In this highly competitive market, organizations should reduce all the extra cost in their operations, in order to sustain longer. Energy is one of the factors, which consumes a large proportion of organizational budget. However, recently a large decline in energy cost has been noticed; but cost of energy will remain volatile. Therefore, sustainable energy generation along with sustainable energy management practices must be realigned with normal industrial operations in order to extract as much from it as we can. For example, United States is strongly emphasizing on energy independence and efficiency, for this purpose a bill “American Reinvestment and Recovery Act (ARRA)” has been passed by Congress. The main features of this bill are discussed below.

5.8.4.2

Brundtland Definition

In 1987, the world commission on environment and development also famously known as Brundtland commission published its first report using the word sustainable development which became highly popular and helped in redirecting the focus of international communities on economical, environment, and social developments for better sustainability of future. This report defined sustainable development as “development which meets the needs of current generations without compromising the ability of future generations to meet their own needs.” The main focus of this definition is to focus on environmental, economic and social issues and integrated all of them together for a better future, especially for the ones living low standard life. The report suggested that all the developments happening have to look into the cost of the development for future generations to come. The Brundtland Commission is named after ex-prime minister of Norway, Gro Harlem Brundtland, who explained the concept to a large audience in a United Nation conference held in 1992 at Rio de Janerio. Mr. Brundtland succeeded in getting the proposed document approved by many of the world leaders to make sure that sustainable developments become a key objective of all the future developments to take place. Despite having some ambiguity in the document, the Brundtland concept has continued to flourish. By the time of United Nations summit to be held in Johannesburg in 2002, the idea of sustainability had started to take a back seat but with pursuance of influential world leader it remained alive. However, modifications were made to the first

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suggested working paper to make it more focused on few things in order to make it practical. A decision was made to focus on three different items for two-year cycle so that fast and focused actions are taken by the communities around the globe. The three area of energy, water, and health, are not new, of course, but the sustainable development umbrella might be new for some of the actors. Although one can say that even these three areas are far diverse, but other commissions are being formed to drive policies for individual area for better implementation. Many years fast forward, decades of experience in the area of sustainable development has generated both some success stories and challenges.

5.8.4.3

American Reinvestment and Recovery Act

This Act is also commonly known as “The Stimulus.” This Act was enforced by 111th congress on February 2009 and later signed into law by President Mr. Barack Obama on February 17, 2009. The primary objective of ARRA was to create vacancies on immediate basis. The secondary objective was to directly invest in infrastructure, health, education and most importantly renewable energy and its management schemes. The initial allocated budget for ARRA was $787 billion, which was later revised to $832 billion during 2009 to 2019 [6]. Total budget allocated for investment in energy sector was $27.2 billion, mainly in renewable vitality. However, this budget was further distributed in following manner to utilize it more efficiently: $6.3 billion to state and local governments; allowing them to invest in energy efficiency projects, $4.5 billion to federal buildings to increase their energy efficiency, $6 as independents renewable power generation loans at easy markup; allowing small firms to generate their own electricity and $11 for the modernization and maintenance of U.S. electrical power grid. Beside material cost, energy cost is a major pressure factor for several organizations and manufacturers. A sound and easy to implement business strategy can yield more production stability by reducing cost. In order to work in profit and with efficiency, modern operations largely depend on the low cost of energy it consumes. Energy conservation and independence are also considered as major strategies for creating a competitive advantage in business. Realizing the fact that energy management could play a vital role in addressing social, economic and environmental concerns, organization are readily adopting these practices to minimize the risks. Overall, energy efficiency and management practices are among the most important option to increase the profit of organization as well as to reduce their dependencies on highly volatile fossil fuel prices.

5.8.5

Energy Management

5.8.5.1

Importance

Energy management is a fusion of technology and management to increase the efficiency of production and enhance the results of output energy performance. It is necessary that management is related to renewable energy so that proper integration of energy systems can be achieved. It is important to control the budget and cost of energy consumption within the required regulations so that a company can have proactive growth for future investments. Energy needs to be utilized in a productive manner so that there is an increase in the chance of district energy systems to be implemented with effective cost budget. Management in energy sector has been a real problem for many developed economies due to huge losses in heating and cooling systems. To maintain energy efficiency in a system it is vital to manage renewable energy sources with comparatively low heat loss. This will certainly bring down the cost per unit price of energy and lessen the burden of GHG emissions as well. District energy systems once implemented through proper industrial and domestic channel, it improves the productivity and reduces the vulnerability of high waste heat energy. A few organizations are working for development of district energy systems so that high-tech technology investment could be a source of savings for the government in long term. Revenue generated with district energy systems is basically utilized in maintenance and upgradation of the systems involved in recycling heat waste energy.

5.8.5.2

Practices

Several energy management practices are used worldwide to implement better energy management in the community. In this section, we discuss two main practices, namely (1) key step and (2) six sigma.

5.8.5.2.1

Key step practice

These days, corporates decision taking and action planning are decided on the basis of strategic approach to make the action or decision sustain longer, successfully. Otherwise, the action or plan can never be successful enough under the rapidly changing circumstances and soon corporates will find themselves in an uncertain situation of fighting for its existence. Key steps for successful strategic approach has been discussed in quite detail in this section, so that user could grasp its essence and take steps immediately without any further dely. It consists of following key steps: (1) Commitment of top management (2) Understanding the issues like:

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(3)

(4) (5) (6)

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(a) Grasp current energy use (b) Identify management strength and weakness (c) Analyze stakeholder needs (d) Anticipate barriers to implement (e) Estimate the future trend Plan and organize, including (a) Develop a policy (b) Make out a plan/program Implementation Controlling and monitoring performance Management review.

5.8.5.2.1.1 Commitment of top management It is the most essential for the accomplishment of energy conservation exercises inside organizations or industrial facilities to have clear and authority duty of top administration – either the corporate top (senior) administration or manufacturing plant chiefs. The top (senior) administration should express responsibility toward the energy management (or vitality conservation) and carry on along this line, for example, they should take a part in energy conservation activities themselves and encourage their staff as well.

5.8.5.2.1.2 Understanding the issue Before attempting to make out any future projects or activity arranges, it is fundamental for the organization or production line administration to comprehend the present circumstance in a legitimate and precise way. This incorporates the status of their own operation as well as other significant data, for example, contenders' operation, conditions around the organization and their pattern in future, positioning the organization itself in the neighborhood and in worldwide markets, and so on. The key steps for this purpose are: 5.8.5.2.1.2.1 Grasp the current energy use The information regarding current consumption of energy should be gathered through measurements, estimations, or calculations of every individual unit under the premises of organization, with the classification on the basis of type of energy. The data should be collected regularly and arranged in daily, weekly, monthly, or yearly manner depending upon the requirement and precision set by its stakeholders. Then the data should be analyzed and a relation should be obtained between different operational modes and production scales. This data can also be utilized in the prediction of future trends. 5.8.5.2.1.2.2 Identify management strength and weakness After the data collection, it should be compared with the pioneers or benchmarks in the industry. If such reference data is not easily available, then their historical data can be compared with the present data of their competitor so that right steps could be taken to get an edge over their competitor. Along with it, the strength and weaknesses of the company should also be evaluated considering the competitor situation in local and global market. 5.8.5.2.1.2.3 Analyze stakeholder’s needs In an organization, stakeholders are basically top level senior managers, directors, staff/engineers, and workers/operators. The need and expectation of these stakeholders must be taken into account so that everyone could adopt the changes caused by SEM easily and large benefits can be extracted out of it. 5.8.5.2.1.2.4 Anticipate barriers to implement Designing an easy to implement and practically possible program also need consideration of expected barriers that could come along in its way of creating an organization that follows all the steps of SEM and contributes toward its social, economic, and environmental amenability. Some possible barriers could be:

• • • •

Insufficient support of top management Inadequate level of understanding and willingness of cooperation between multiple managers of same organization Untrained workforce Insufficient budget allocation for SEM implementation activities.

5.8.5.2.1.2.5 Estimate the future trend The future trend of energy demand could be estimated by using the historical data of the organization. This estimation enables the organization to increase or decrease its power generation capabilities depending on rapidly changing circumstances of global market. It also provides a check and balance between the energy consumed and production of the organization for the particular period of time.

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5.8.5.2.1.3 Plan and organize Based on the analysis of previously collected data and understanding the position of company in local and global market and also identifying the strength and weakness of organization, the following step should be taken in order to design a relevant and good strategic plan to get a maximum out of this effort. 5.8.5.2.1.3.1 Develop a policy It is fancied that the top (senior) administration announce the “Energy Policy Statement.” This is exceptionally viable to let individuals inside and outside the organization unmistakably knows the administration's dedication to energy management (or energy protection). The configuration of the energy strategy statement is different; however, it generally incorporates the objective or goal of the organization and the more concrete focuses in the field of energy management (or energy conservation). 5.8.5.2.1.3.2 Make out a plan/program Any plan under consideration should be easy to implement, practical, and attainable. It should also take into an account, all the resources and related elements of the company which can be classified into measurable or quantifiable. It should also include the awareness campaign relating to SEM, motivation techniques, training, and so on. 5.8.5.2.1.4 Implementation The accepted plan should be enforced within an organization and all the organizational resources should be consumed in order to ensure smooth implementation of the plan. The responsible person or committee shall continue to work for the promotion of activities and training of workforce which is essential for the plan to survive. 5.8.5.2.1.5 Controlling and monitoring performance After the implementation, all the processes should be closely monitored in order for it to work smoothly. If any problem arises, or any variance between estimated and observed value noted, then necessary steps should be taken in order to overcome and stabilize it. 5.8.5.2.1.6 Management review After the plan or program has been completed, a report mentioning all the events, success and failures faced during its implementation should be submitted to top management. In it all the results should be analyzed in quite detail for any good and bad points with possible recommendations. This report shall be utilized as a feedback for subsequent program. Thus all activities could be repeated to form a cyclic movement. 5.8.5.2.1.7 Implementation of key step approach In order to implement sustainable energy management program effectively, key factors approach which is discussed in quite detail in our previous section should be utilized. The major step toward the implementation of SEM program in any organization is the energy audit. Energy audit enables the organization to identify the problems or factors which could become a hurdle in the way of its implementation. Energy audit can be conducted by hiring an expert consultancy agency or by utilizing internal technical and trained staff. 5.8.5.2.1.7.1 Energy audit There are number of stages in energy audit process, each having its own importance. The process includes: collection and analysis of data, site investigations, cost and benefit analysis, preparation of concise report, creating an action plan for the project implementation, and monitoring and controlling. Energy audits acts as a foundation of developing an SEM program that will give an edge to the organization while creating more efficient operations. It also enables the analysis on where the most effective use of limited capital should be employed to achieve energy goals. Through energy audit, specific type of system can be monitored throughout the operation, which can be optimized, modified or replaced based on the requirement. It also helps to identify the operations which yield greatest Rate of Investment (ROI), so that it could be modified and kept up to date in order compete with the changing circumstances of global market. Monitoring systems energy consumption throughout the day and night cycle, and correlating it with the production delivers an important information regarding that systems efficiency. With newly introduced wireless energy monitoring technologies, this equipment’s can be installed in a very cost-effective manner on existing system. Data collection with every 15 min interval will be sufficient enough to estimate the efficiency of the system. The process cycle governing Energy Audit is shown is Fig. 11. 5.8.5.2.1.7.2 Advanced monitoring and metering solutions As discussed previously that to conduct a successful energy audit, monitoring devices needs to installed within premises in order to prepare a successful plan of action and correct estimation of future trends. These frameworks offer both modes, check of the utilities overwhelmed by a far reaching report, including droops and surges, and the capacity to power factor, harmonically disturbed waves, and different parameters consistently. These solutions are adequate for obtaining metrics without any high capital investment or changing of existing system flows. It provides an important statistics of the real time process on the basis of which decision can be taken for corrective actions. Whether it is effectively measuring a capacitor bank to enhance control

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Sustainable Energy Management

SEM system Parameters

Alerts Data collection unit Unit consumed

Production

Fig. 11 Energy efficient operation.

Detect

Measure

Analyze

Improve

Control

Fig. 12 Detect, measure, analyze, improve and control (DMIAC) methodology (Six Sigma steps).

elements, performing load shedding, or deciding squandered vitality utilization, advance metering offers many preferences basically from gathering precise information from dissimilar sources. Hence, advanced metering is an approach to successful implementation of SEM on a distributed architecture and topology that will grow according to the requirement of organization. It will act as an essential strategic tool for optimization and evaluation of already installed process, operation, or a system.

5.8.5.2.2

Six Sigma practice

Concerns regarding the importance of conservation and effective utilization of energy are increasing day by day. As the evidence of the above statement it can be given that nowadays people are in a habit of switch off all the necessary equipment’s, when not in used so as to use energy effectively. Bulk amount of energy generated by any nation has been consumed by the production or manufacturing industries to increase the country’s GNP; therefore, certain approaches are required so that effective energy can be utilized by these sectors so as to increase their efficiency. The implementation of such systematic approach not only makes the nation industrialize and modern but is also effect the lifestyle of each individual of a society in a better perspective. Cost associated with energy consumption is no longer considered as a minor component of total production expenditure. Inspite of its greater importance and influence, there are certain facilities which don’t take its advantage by properly managing it and minimize its effect on expenditure sheet, which directly minimize the production costs. Facilities without proper power managing systems and determined energy managing approaches, don’t have proper understanding regarding their energy usage and production ratio; such facilities cannot consume their resources to its fullest being efficient at the same time. While optimizing power monitoring investments, it is necessary to identify both intended application and prioritize energy consuming units within a facility. Sustainable energy management is an effective tool which gives a certain edge to any facility over other (i.e., its competitor) by implementing it in terms of effective savings. These savings increase the GNP of overall nation, i.e., if the manufacturer invests less on a product, they will further sell product in lesser amount to the end user. It also increases the purchasing power of the individual of any society; hence, larger the trade yields better GNP of nation. In addition to these advantages, SEM also minimizes the air pollution which we generate by burning fossil fuels. We cannot visualize it as we do when we start a car but whenever we switch on a light, we generate some amount of pollution in power plant, which is then released in air in terms of carbon footprint and is a reason of global warming. The necessity of an hour is to utilize this all important form of energy is such a manner that it gives us advantage economically as well as ecologically. The Six-Sigma is a proven approach in terms of quality management and implementation, as an extension to same principles this approach has been tested over the phenomenon of energy efficiency/conservation in number of facilities and their result came out to be unique and attractive as shown in Fig. 12. Six Sigma at numerous associations just means a measure of value that takes a stab at close flawlessness. Six Sigma is restrained, information driven approach and system for taking out deformities in any procedure – from assembling to value-based and from item to benefit. The core objective of Six Sigma methodology is to develop a measurement-based strategy that primarily focuses in reducing the process variations and improve process outcomes. This objective is attained by implementing two Six Sigma sub methodologies

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337

namely: detect, measure, analyze, improve and control (DMAIC) and define, measure, analyze, design and verify (DMADV) in a facility. DMAIC mainly focuses on the improvement of existing processes falling below expected values; whereas, DMADV is methodology used to design new processes and system considering specific requirements. Energy conservation plan mainly developed keeping DMAIC process in consideration as it mainly used to implement on the existing processes to enhance efficiency. 5.8.5.2.2.1 Implementation of Six Sigma approach (DMIAC methodology) 5.8.5.2.2.1.1 Detect The phenomenon of energy saving is considered to be more where its consumption is higher. Therefore, the key is to attack the larger energy consumer rather than implementing it and worrying about the minor one. From this point of view, while designing a plan for energy management first target larger energy consumer within a facility, i.e., heating systems, cooling systems, lightning etc. Those points also need to be detected in a process where energy has been wasted or exhausted for effective and long lasting saving. In order to detect/define such points and elements in a process, traditional approach of installing metering gives snapshot data of energy consumption which is not sufficient enough, for effective monitoring real time data logging devices need to be installed. There are certain rules to install power monitoring devices which are given below: (1) Advanced monitoring systems need to be installed with main electrical switchgear whereas less sophisticated metering devices should be deployed to each of the identified bulk energy consumer. The advantage of installing advance monitoring system with main grid is that it will not only monitor the electrical parameters of the facility but also the power quality or power factor it is receiving. This approach enables its user to monitor basic electrical parameter and on the same time grasping the firsthand knowledge of the quality of power facility is receiving through electric utility. (2) As discussed, continuous monitoring of large loads allows to identify and predict accurate energy savings; therefore, the more the monitoring points will be, the better electrical model can be generated for statistical predictions. 5.8.5.2.2.1.2 Measure After identifying/detecting which load to measure, accurate measurement devices need to be installed in order to do quantitative analysis. Properly installed and verified measuring system could be a valuable asset for any organization. Annual energy consumption and production are the major concerns of an organization. An electrical measuring system could contain one or discrete points which are interconnected on a single station so as to enable a single user to monitor all the happening of the at a single point. An efficient measuring system contains three major components: metering devices to measure data, application software to manage, accumulate, display data and matched communication module in order to link metering devices with application software. This measuring system should be robust enough in order to work and gather real-time data 24/7. This continuous extraction of important information mostly with the frequency of every 15 min enables the user in correct decision making. Also, this will give accurate information regarding how much energy is consumed, in which part of the day the consumption is greater and what unit/ load consumes larger energy. This knowledge plays a vital role in reducing the energy consumption and increase the efficiency of the process. 5.8.5.2.2.1.3 Analyze Two types of analysis is mostly done in order to come up with an accurate energy management plan which is of energy consumption and quality. All the gathered data is then analyzed with respect to these two segments and parameter of interest are current and voltage consumption during the startup of load, power factor, and energy consumption. These observed parameters then can be compared with the actual in order to identify deviation of each load. These analysis helps the production engineer with energy consumption pattern for planning shift activities such as production rates reducing production breakdowns, maintenance engineer to check that whether the equipment is due for maintenance or not and planners to plan appropriate sizing of facility. 5.8.5.2.2.1.4 Improve This analysis is then used in creating an appropriate energy management strategy for optimum and efficient plant operation, this includes: (1) Enabling the organization to predict the energy consumption pattern in manufacturing and production facilities with respect to any season, part of the day or year. (2) Standardize the energy consumption patterns for different points, loads or facilities within the plant. (3) Enable to shift the operations in the off-peak times, this is mostly suitable for the countries in which load shedding is commonly done. (4) Prediction of possible energy interruption during the operation which could affect the process a great deal. (5) Automatically improves power factor by adding a capacitor banks if correct prediction of its arrival can be made. 5.8.5.2.2.1.5 Control After taking steps to improve the power efficiency of the system, certain controls are needed to make it long lasting. The remarkable work in this field enable the development of devices like adjustable speed control motor drives and shunt capacitors for power factor correction and reduce losses.

338

5.8.6

Sustainable Energy Management

Smart Energy Management

It is important to introduce sustainable energy systems that can address local and global energy issues without causing any negative impacts on the environment, resources, economy, and energy security specifically, and on society at large, and without hampering future generations. For this purpose, Dincer’s six targets are introduced by Ibrahim Dincer as key criteria to achieve better sustainability and address local and global issues as follows: better efficiency, better cost-effectiveness, better resources use, better environment, better design and analysis, and better sustainability. Each of these six targets are discussed in detail below.

5.8.6.1

Better Efficiency

Development in energy sector is the key to guarantee reliable, competitive, safe, and sustainable life in future. This parameter is most important for every country to progress as through this they could address there several deficiencies like: security, environmental concerns, and economic challenges. The efficiency of any system could be measured by the amount of raw material it uses to develop a certain amount of output. So, beside input material cost, developed energy cost is a major pressure factor for several organizations and manufacturers. A sound and easy to implement business strategy can yield more production stability by reducing cost. In order to work in profit and with efficiency, modern operations largely depend on the low cost of energy it consumes. Energy conservation and independence are also considered as major strategies for creating a competitive advantage in business. Realizing the fact, that energy management could play a vital role in addressing social, economic and environmental concerns, organization are readily adopting these practices to minimize the risks. Overall, energy efficiency and management practices are among the most important options to increase the profit of organization as well as to reduce their dependencies on highly volatile fossil fuel prices. The best way to understand this fact is by going through some examples like: in smarter home, the conventional windows has now been replaced with energy efficient one, these windows prevents heat from escaping in the winter and from entering in summers using several layers of insulation material within it. By installing it you can easily save energy by turning off your heaters in winter and air conditioners in summers. While at the same time doing so will not affect your comfort in any ways. In a similar way, when you replace your old air conditioner with new DC inverter technology or your old big tower PC with latest generation PC and other equipment’s with several energy efficient models, this will not only save your money but also minimize the GHG emission into the atmosphere. Most of the times people get confuse in between two phenomena which is energy efficiency and energy conservation. Energy conservation is basically a way to save energy while compromising your comfort as well. For example, turning of the light of room is energy conservation. But replacing, old florescent lamp with modern energy savors in energy efficiency. But however, both of these phenomenon can reduce greenhouse emission.

5.8.6.2

Better Cost-Effectiveness

By implementing energy efficiency methods in homes, commercial buildings, government buildings, schools, and industries is the most cost-effective and constructive way to address the highly volatile prices, environmental concerns, and global climate change that arises due to immense use of fossil fuel because as per the studies suggests, these sectors consume for about 70% of the total developed energy and natural gas of United States. By considering the following solution, this could help United States in saving approximately 50% or more of the expected electricity consumption in coming years, resulting, billions of dollars saved in terms of energy bills along with also minimizing the significant amount of GHG. Several methods have been developed in order to measure the cost-effectiveness of the system depending upon various parameters and scenarios. These tests are: participant cost test (PCT), program administrator cost test (PACT), total resource cost test (TRC), ratepayer impact measure test (RIM), and societal cost test (SCT). All of these tests have been summarized in Section 5.8.6.3.

5.8.6.3

Better Resource Management

Resource management deals with a concept of achieving more while utilizing lesser. It is basically a criterion to manage the human usage of resources in such a way that while extracting benefits from it for current generation, we also might not forget the future ones. This grabs the attention of many researchers in a debate of sustainable development. One of the findings yields that certain resources are becoming extremely rare, so for us; in order to conserve it for future, have to utilize them cautiously and substitute these rare material with easily available or renewable one. The argument of concern is here that, to improve the productivity it is essential to minimize the impact on natural heritage to assimilate waste materials and energy [7]. Another researcher graham [8] states that, the civil industry is one of the major consumer of natural resources, therefore; number of initiative has to be pursued In order to create an ecological supportive buildings are focusing on increasing the efficiency of resource use. Such things can be carried out by designing of solar passive design which aim to reduce the consumption of nonrenewable resources. Methodology that helps minimizing the material and construction wastage during building phase will also provide number of opportunities for recycle and reuse of material in order to implement better resource management. Resource conservation deals in variety conservation strategies which is showcased in Table 4 and Fig. 13, discussing all the parameters required to implement it.

Sustainable Energy Management

Table 4

339

Cost-effectiveness measuring tests

Test

Acronym

Key question answered

Summary approach

Participant cost test

PCT

Program administrator cost test Total resource cost test

PACT

Will the participant get any benefit from the measured life? What will be the effect on utility bills? Will the total cost of energy I the utility territory decrease? Is there any chances of the utility rates to increase? Is the utility, state, or a nation is better off as a whole?

Detailed comparison of all the cost and benefits of the customer installing the measure Detailed comparison of program administrator approved cost and supply side resource cost Comparison of program administrator and customer costs to utility resource saving Comparison of administrator costs and utility bill reductions to supply side costs Comparison of society’s cost of energy savings and non-cash costs and benefit

TRC

Ratepayer impact measure test Societal cost test

RIM SCT

Resource conservation

Energy conservation

Material conservation

Water conservation

Land conservation

Fig. 13 Resource conservation.

5.8.6.4

Better Design

Buildings, whether it’s of commercial or residential use, are of great important to human society. These are also an important sector where energy improvements around the world should be implemented as these are the major consumer of it. The building sector consumes energy in a measure to provide comfortless to its user in terms of lighting, heating, ventilation, cooling, automation, etc. all of these consumes energy. Typically, the rate of energy consumed or utilized are found by dividing energy with the floor area of the building yielding in specific energy consumption rates. Thus, a better design is a term used to adapt a balanced approach to energy efficiency in structures than simply trying to minimize the energy consumption. These better designing often includes passive measure that inherently reduces the use of energy, such as better insulation or by minimizing the use of artificial lights in indoor conditions and allowing more and more natural light to take its place. A building’s location also plays a vital role in regulating its temperature and illumination such as landscaping, trees and hills can be utilized to provide shade and block winds. Additionally, tight building design, such as installing energy sufficient windows, thermal insulation on walls, well-sealed doors, can reduce the heat loss by 20–25% [9]. A study suggests that darker roofs can become 39oC hotter than most of the reflective white painted roofs [10].

5.8.6.5

Better Environment

The growth of human society is directly having to deal with the relationship between the humans and their natural, social and built environments. This factor is also termed as human ecology, which broadens the concept of sustainable development by including the much needed domain of human health. Essential human needs, for example, the accessibility to pure air, water, nourishment and sanctuary are additionally the biological establishments for development in sustainable way; [11] tending to general wellbeing hazard through interests in biological community administrations can be an intense and transformative drive for supportable advancement which, in this sense, reaches out to all species. Ecological manageability concerns the indigenous habitat and how it bears and stays assorted and beneficial. Since regular assets are received from the earth, the condition of air, water, and the atmosphere are of specific concern. Environmental sustainability obliges society to outline exercises to address human issues while saving the life supportive networks of the planet. This, for instance, involves utilizing water economically, using sustainable power source, and concerned with sustainable material supplies.

5.8.6.6

Better Sustainability and Performance

Energy sustainability is turning into a worldwide need, given the inescapable utilization of energy assets all around, the effects on nature of energy producing methods and on to nearby local and worldwide domains, and the expanding globalization of the world's economy. Energy is specifically connected to the more extensive idea of sustainability and influences the greater part of

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human advancement. That is especially apparent since energy assets drive much if not the greater part of the world's monetary movement, in essentially all financial segments, for example, industry, transportation, private, and business. Likewise, energy assets, regardless of whether carbon-based or inexhaustible, are acquired from the earth, and squanders from energy forms (production, transport, stockpiling) are often discharged to nature.

5.8.7

Case Studies

5.8.7.1 5.8.7.1.1

Borehole Thermal Energy Storage at University of Ontario Institute of Technology System description

The borehole thermal energy storage system assessed in this section is installed on at University of Ontario Institute of Technology as studied by Ref. [12]. A schematic representation of this system for heating season is shown in Fig. 14. During summer, the fluid circulating through tubing extended into the wells, collects heat from the buildings and carries it to the ground. In winter, the system reverses to take heat from the ground and transmits it into the buildings. For a longer operation, the heat load is balanced using cooling towers in summer season. The borehole thermal energy storage system coupled with ground source heat pump facility supplies heat for the whole campus buildings, utilizing the energy obtained from the ground. Heat pumps are used to pump energy from the borehole thermal energy storage system into the buildings. The total heating load of the campus buildings is about 6800 kW. The amount of energy pumped by the heat pumps is about 40% of the overall heating demand of the buildings. The rest of the heating demand is supplied by natural gas boilers as seen in Fig. 14. The refrigerant R407C is used in the ground source heat pump system and the total heat pump capacity is 2770 kW. In order to meet the energy demand of campus buildings, it was determined using the thermal conductivity test results that a field of 370 boreholes, each 200 m in depth, would be required. The borehole thermal energy storage system is embedded under the ground which consist of polyethylene pipes and filled with 15% glycol solution that circulates through the underground pipe network. Inlet and outlet temperatures of the solution are 5.61C and 9.31C, respectively. The inlet and exit temperatures of the solution to/from the fan-coils in buildings are 521C and 41.31C, respectively. When the heating load is bigger than the heat pumps’ capacity, natural gas boilers take place and support the heat pumps by heating the secondary fluid.

5.8.7.1.2

Analysis

For combustion process in the boilers, the energy is written as:  P  NP h˚f þ h h˚ R hP hR ¼

P

 NR h˚f þ h

h˚



P

ð1Þ

M

where h˚f is specific enthalpy of formation, h˚ is specific enthalpy at reference state and h is specific enthalpy in kJ/kmol. NR stands for number of moles, M stands for atomic mass and subscripts R and P represents reactants and products, respectively. The rate of heat extracted from the ground by boreholes is determined by   _ BHE ¼ m _ BW Cp;BW TBW;out TBW;in Q ð2Þ The space heating loads of campus buildings are calculated by  _ FC ¼ m _ HW Cp;HW THW;out Q

THW;in



ð3Þ

The coefficient of performance of the heat pump system is written as COPHP ¼

_C Q _P _ CþW W

ð4Þ

The chemical reaction occurring in the combustion chamber for one mole of methane is given by: CH4 þ 2:5ðO2 þ 3:76N2 Þ-CO2 þ 2H2 O þ 0:5O2 þ 9:4N2

ð5Þ

During the combustion process, the heat transferred to the water from boiler is calculated by   P  ˚ P  ˚ NR hf þ h h˚ NP hf þ h h˚ R P _B¼m _ CH4 _ CH4 ðhR hP Þ ¼ m Q MCH4

ð6Þ

The energy efficiency of the overall boiler is written as ZB ¼

_B Q HHV

The chemical exergy term for a combustion process can be written as  P  ˚ P  ˚ NR hf þ h NP hf þ h h˚ T0 s R exch ¼ M where s is specific entropy in kJ/kmolK.

ð7Þ

h˚

T0 s



P

ð8Þ

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Sustainable Energy Management

A1

17

16

A2

18

20

A3

21

19

23

A4

24

22

26

25

A5

27

29

A6

30

28

A7

32

33

31

10

3

38

37

39

41

A10

42

40

13

2

7

44

45

43

1

8

4

14 6 Heat pumps 2 7×198 kW

Heat pumps 1 7×198 kW

Boilers 4×1030 kW

A9

15

12

11

36

34

46

9

35

A8

5

51

48

50

47 52

49

Borehole heat exchangers 370×200 m

Fig. 14 Schematic of borehole thermal energy system installed at UOIT. Reproduced from Kizilkan O, Dincer I. Borehole thermal energy storage system for heating applications: thermodynamic performance assessment. Energy Conser Manag 2015;90:53–61.

For determining the exergetic efficiency, numerous ways of formulation can be found for various energy systems in literature. Here, in a similar way the exergy efficiency is defined as the ratio of total exergy output to total exergy input: esys ¼

5.8.7.1.3

_ output Ex _ input Ex

ð9Þ

Results and discussion

The calculated properties for borehole thermal energy system are presented in Table 5, according to reference points illustrated in Fig. 14. For comparison purposes, the calculated results of exergy destruction rates, relative irreversibility and exergy efficiencies of system components and overall system are given in Table 6. The heating coefficient of performance of the heat pump unit is found to be 2.65. The overall energy efficiency of the boilers is determined to be 83.2%. This means that 16.8% of heat is lost through boiler walls and by flue gases. From the results of exergy analysis, it can be seen that the major exergy destruction rate occurs in boilers. Thus, exergy destruction of the boiler is equal to 1041.4 kW with the efficiency of 35.83%. It may be stated that exergy destruction is high due to the fact that the boiler is not fully adiabatic. The other major exergy destruction occurs in fan-coils, followed by compressors, evaporators, and expansion valves. In addition, the exergy efficiency of the overall heating system is calculated to be 41.35%. Ambient state plays an important role in determining the performance of a system specially in terms of exergy. Consequently, the result of exergy analysis generally is sensitive to variations in these properties. Before exergy analysis can be applied with confidence to engineering systems, the significance of the sensitivities of exergy analysis result to reasonable variations in deadstate properties must be assessed. Fig. 15 shows exergy destruction rate and exergetic efficiency of the overall system versus reference environment temperature for the system. One can easily say that as the reference environment temperature increases, the exergy efficiency of the system decreases and exergy destruction increases significantly. Fig. 16 illustrates the variation of exergy destruction results of the heat pump system with entering glycol-water temperature. The entering glycol-water temperature to the heat pump system (i.e., evaporator) will be lower than the ambient temperature for winter conditions. The entering glycol-water temperature is perhaps the single most representative parameter of the systems. Fig. 17 shows the variation of exergy destruction

342

Table 5

Sustainable Energy Management

Calculated thermos-physical property table of the studied system

Number

Fluid

Phase

Temperature (1C)

Mass flow rate (kg/s)

Enthalpy (kJ/kg)

Entropy (kJ/kg K)

Specific exergy (kJ/kg)

Exergy (kW)

0 00 0000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52

Water (15%)a Water (30%)b R407C R407C R407C R407C R407C R407C R407C R407C R407C Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (30%) Water (15%) Water (15%) Water (15%) Water (15%) Water (15%) Water (15%)

Dead state Dead state Dead state Superheated vapor Superheated vapor Subcooled liquid Wet vapor Superheated vapor Superheated vapor Subcooled liquid Wet vapor Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid Liquid

1.5 1.5 1.5 1 77.24 45 4 1 77.24 45 4 41.3 52 41.3 52 41.3 52 52 52 52.01 41.3 52 52.02 41.3 52 52.03 41.3 52 52.02 41.3 52 52.02 41.3 52 52.03 41.3 52 52.02 41.3 52 52.01 41.3 52 52.02 41.3 52 52.02 41.3 41.3 9.3 5.6 5.601 9.3 5.6 5.601

– – – 7.234 7.234 7.234 7.234 7.234 7.234 7.234 7.234 101.5 101.5 34.15 34.15 34.15 34.15 169.8 22.2 22.2 22.2 24.81 24.81 24.81 24.09 24.09 24.09 22.2 22.2 22.2 22.2 22.2 22.2 7.764 7.764 7.764 6.353 6.353 6.353 18.87 18.87 18.87 10.44 10.44 10.44 10.88 10.88 10.88 169.8 70.67 70.67 70.67 70.67 70.67 70.67

27.54 58.6 273.3 268.3 316.8 125.2 125.2 268.3 316.8 125.2 125.2 206.7 247.3 206.7 247.3 206.7 247.3 247.3 247.3 247.4 206.7 247.3 247.4 206.7 247.3 247.4 206.7 247.3 247.4 206.7 247.3 247.4 206.7 247.3 247.4 206.7 247.3 247.4 206.7 247.3 247.4 206.7 247.3 247.4 206.7 247.3 247.4 206.7 206.7 58.42 43.76 43.76 58.42 43.76 43.76

0.1012 0.2195 1.181 1.034 1.055 0.4576 0.4895 1.034 1.055 0.4576 0.4895 0.723 0.8499 0.723 0.8499 0.723 0.8499 0.8499 0.8499 0.8501 0.723 0.8499 0.8502 0.723 0.8499 0.8503 0.723 0.8499 0.8502 0.723 0.8499 0.8502 0.723 0.8499 0.8503 0.723 0.8499 0.8501 0.723 0.8499 0.8501 0.723 0.8499 0.8501 0.723 0.8499 0.8502 0.723 0.723 0.212 0.1598 0.1598 0.212 0.1598 0.1598

– – – 34.65 77.36 49.83 41.06 34.65 77.36 49.83 41.06 9.845 15.57 9.845 15.57 9.845 15.57 15.57 15.57 15.58 9.845 15.57 15.59 9.845 15.57 15.59 9.845 15.57 15.59 9.845 15.57 15.59 9.845 15.57 15.59 9.845 15.57 15.59 9.845 15.57 15.58 9.845 15.57 15.58 9.845 15.57 15.59 9.845 9.845 0.43 0.1201 0.1202 0.43 0.1201 0.1202

– – – 250.6 559.6 360.5 297 250.6 559.6 360.5 297 999.4 1581 336.2 531.8 336.2 531.8 2644 345.7 345.9 218.6 386.4 386.7 244.3 375.1 375.6 237.1 345.7 346 218.6 345.7 346 218.6 120.9 121.1 76.44 98.94 99.01 62.54 293.9 294.1 185.8 162.6 162.7 102.8 169.4 169.6 107.1 1672 30.39 8.491 8.493 30.39 8.491 8.493

a

15% glycol-water solution. 30% glycol-water solution. Source: Reproduced from Kizilkan O, Dincer I. Borehole thermal energy storage system for heating applications: thermodynamic performance assessment. Energy Conser Manag 2015;90:53–61. b

Sustainable Energy Management

Table 6

343

Exergetic assessment of the system

Component

Exergy destruction rate (kW)

Exergy efficiency (%)

Relative irreversibility (%)

Boilers Compressors Condensers Expansion valves Evaporators Fan-coils BHE Pumps Overall

1041.4 425.6 7.016 126.9 136.6 582.1 53.2 11.4 2384.2

35.83 59.22 98.24 82.4 47.19 40.15 45.14 13.23 41.35

43.68 17.85 0.29 5.32 5.73 24.42 2.23 0.48 –

2420

0.52

2400

0.48

2380

0.44

2360

0.4

sys

Exdest,sys (kW)

Source: Reproduced from Kizilkan O, Dincer I. Borehole thermal energy storage system for heating applications: thermodynamic performance assessment. Energy Conser Manag 2015;90:53–61.

sys 2340

0.36

Exdest,sys

2320 –6

0.32 –4

–2

0

2

4

6

T0 (°C) Fig. 15 Effect of ambient temperature on exergy destruction rate and exergy efficiency. Reproduced from Kizilkan O, Dincer I. Borehole thermal energy storage system for heating applications: thermodynamic performance assessment. Energy Conser Manag 2015;90:53–61.

0.394

2550

0.392 sys 0.39

2480 0.388

sys

Exdest,sys (kW)

2515

2445 0.386 2410

0.384

Exdest,sys

2375 6

7

8

9

0.382 10

TBW,in (°C) Fig. 16 Effect of glycol-water temperature on exergy destruction rate and exergy efficiency. Reproduced from Kizilkan O, Dincer I. Borehole thermal energy storage system for heating applications: thermodynamic performance assessment. Energy Conser Manag 2015;90:53–61.

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0.41

3100

0.4

2700 0.39 sys

Exdest,sys (kW)

2900

2500 0.38 2300

sys

1900 100

0.37

Exdest,sys

2100

150

200

250 Tprod (°C)

300

0.36 350

Fig. 17 Effect of condenser temperature on exergy destruction rate and exergy efficiency. Reproduced from Kizilkan O, Dincer I. Borehole thermal energy storage system for heating applications: thermodynamic performance assessment. Energy Conser Manag 2015;90:53–61.

and exergetic efficiency with product temperature in boilers. It is clear from the figure that with the increase in temperature, exergy destruction rate increases, and exergetic efficiency decreases.

5.8.7.2

Solar Energy Management and Integration at Boston College

Boston College (BC) is located at chestnut Hill, MA, 02467, United States. Annie Meyer, Farhin Zaman, and Elizabeth Norton are students of Boston University. They proposed a project of “Campus Sustainability Management” in order to utilize concentrated energy from sun using PV, so that maximum energy savings could be done as to make their proposed project an iconic to the university. According to them, their college would be a good place to start as if their findings came positive, College administration would add some more of these systems. Their prime objective was to create a realistic plan, following which this proven technology could be deployed successfully at the campus and they could harness maximum energy from it. BC would be an idealistic place to prove this study as of having more than 9000 undergraduate students, two soccer stadiums, three major dining locations, and over 20 dormitories. Keeping these facilities in mind it would be correctly assumed that BC consumes huge energy to keep these things running. In their project they have picked up four different locations at the campus and analyzed that how PV system responses to each of it. To identify best locations out of several, project team decided to meet John Macdonald (BC energy supervisor) and Bob peon (BC sustainability director). After meeting them collectively they have set the criteria to pick up the locations having following characteristic: (1) esthetics, (2) BC ten-year plan, and (3) annual energy use. At initial phase, the project was to implement solar panels on commonwealth ave parking garage, but this idea was ruled out due to gothic architectural esthetics. BC administration was most dedicated to their esthetics in regards to the campus appearance. Therefore, all the buildings including gothic architecture were ruled out form the plan. Among these buildings were Devlin, Lyons, Gasson, Fulton, and Stokes Hall. Next step was to review 10 year construction plan of BC to get the firsthand knowledge of the buildings that will be knocked down in near future for maintenance purposes. Our team decided to use such buildings in order to fully monitor the outcomes of project during its lifecycle. These buildings include Edmond’s Hall and Carney. Remaining buildings were reviewed depending on it’s, throughout the year energy consumption. Those buildings were also ruled out from the list which is not constantly used in summers as solar panels are most efficient for the places were energy is consumed all the time. One more hurdle in a way of this project was the fact that all campus runs on same electric meter. Therefore, it will be difficult to track this another form of energy connected in the BC grid. The energy offset from the panels would come out of the campus total energy use rather than out of just the energy use of the building it is installed on. Therefore, the team decided that as solar energy system is to be installed for the first time in their college, so, it will be best to contaminate this system on a single building. This will enable the administration to monitor it throughout with ease and make possible decisions in future. This process of elimination has narrowed down their search and they selected Cardigan alumni center, 129 Lake Street, the beacon street garage and St. Clément’s Hall. All of these spots have a flat roofs enabling the successful installation of solar panels in proper angle and direction to harness maximum energy.

5.8.7.2.1

Financial analysis

Now, the need of financial analysis is arisen by accepting the fact of estimating the initial cost required to install appropriate size of system. This was made possible by the knowledge provided by John Macdonald. The approach was to find the total systems energy production (kilowatt per hour – kWh) that would be able to fit the size of roof and the cost of purchasing that system. Firstly, they found out total area of the roof and then divided it with the size of standard solar panel which is 19.5 sq-ft, this enable them to

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find the quantity of panels which could be fitted there. Afterwards, they multiply each standard panel with their standard rating of 250 Watts per hour to find out total energy that could be generating utilizing that area. In order to get most accurate value, they have utilized three sources and then took the average of all three of them to get the best and nullify errors if present. First source was their own manual calculations, they used google map to find the approximate area of each roof in order to estimate the size of system that could be installed. They divided the total area by area of standard sized panel in order to obtain the number of panels that could be deployed there. As each standard sized panel produces 250 Watts per hour, they multiplied with the quantity of panels to estimate system size in kilowatt hour. To estimate the cost of system, from market survey they came to know that these systems would cost up to $2.50 per watt hour. So, in order to calculate the cost of panels they multiplied this per watt hour cost with the total estimated system size in kilowatt hour. As their second source, they have utilized the calculator of PV Watts’s website. Utilizing their program, they again mapped out the rooftop zone of every building, and were given the system measure in kilowatt hour. Then ascertained the cost utilizing the given system measure, utilizing the technique specified already. As the last source, they again used a website calculator energysage. On their program they again selected the building top roof position to get the estimated system that could be deployed and power produced through it. One thing which should keep in account after utilizing these three resources, which is that, these sources could possess some errors, for example, that cardigan building have some skylights that should not be the part of panel area but software includes it. As discussed previously, all of these three sources could possess some errors. Therefore, to minimize their effect average is taken of all three values in order to provide the BC administration with the best possible estimate. From these factors they have summed up their findings below in Table 7. The financial cost or saving would be a topic of interest for any institution like BC to whether accept or reject the proposal. For this analysis the tool which they have utilized are proforma financial statements. In this case, Matt Mourino from First wind (i.e., a company that deals in solar power plant installation) was kind enough to provide them their companies Performa in order to calculate the yearly savings and compare it with both private ownership and third party ownership in order to deduce most financially sound conclusion. In this case this Performa model is used to outline the thrid party ownership of the system through power purchase agreement (PPA). In PPA, BC would have to pay a discounted rate per kilowatt hour for the energy produced by system. First wind gave them an offer that they would charge 0.12$ per kWh for the energy produced by their system. If we talk about Alumni center, then it would have and annual electrical consumption as calculated in Table 8. To calculate the amount of energy produced by system, they took 15% of the total energy consumption for this particular location which came out to be 79,280.7 kWh. So, for this energy, BC will pay lesser amount annually. Through this Alumni center could save up to "$10,782.18 on its usual bill. Alumni center’s current bill as calculated in Table 7 came out to be $71,881.17 but by including this new system the annual bill will drop on to $61,098.99. If this value is carried out up to the life of these solar panel, then this single building could save up to $25,369.82. However, if BC administration decided to purchase solar panels in private ownership then they have to bear the high initial cost but that will give a long lasting relief. In the same example from Table 8 of Cadigan Center, they utilized their previous calculation of the solar system size (152.7 kWh) that BC could install on the rooftop of the building and calculated potential annual production that a 152.7 kWh solar system could generate throughout the year, which is 1,231,372.8 kWh of energy. However, based on the result of EnergySage, a solar system on top of Cadigan could only produce 10% of the total expected potential energy. Using that information, it is predicted that Cadigan Center would only be able to use 123,137.82 kWh of energy from the panels, but would still save $16,746. 67 on their current utility bill. Over the course of 20 years, this would amount to $334,933.40 in savings. However, this number is not entirely accurate since BC would first have to break even on the investment of the solar system, which would cost around $381,750.00, as shown in Table 8. The time for BC to break even on the investment could take several years and is based on calculations that they did not explore it as it is not in the scope of this project. Based on this calculation, estimations, and findings it is concluded that most optimal location for the installation of solar panels will be St. Clements hall. Even though these panels will only produce 10% of the total energy capable of producing, still it is capable of reducing total annual bill a great deal, means more saving for that particular time span; Moreover, third-party ownership will be more suitable for the commercial location like BC as it means immediate saving rather than being initial deficit. By installing solar power generation system on St. Clements hall, BC could save annually up to $10,000 and due to the fact that BC will not own it, therefore, its maintenance will be the responsibility of collaborating firm as per PPA.

Table 7

Annual electrical bill and consumption for the selected location

Location

Annual electrical consumption (kWh)

Annual bill

Alumni Center 129 Lake Street Beacon St. Garage St. Clements College

528,538 261,544 1,576,800 4,091,784

$71,881.17 $35,569.98 $214,444.80 $556,482.62

Source: Reproduced from Meyer A. A case study: solar panels at Boston College: Chestnut Hill, MA: Boston College; 2014.

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Estimated pro forma for Cardigan Alumni Center

Cardigan Alumni Center BC demand Building load Electricity unit rate for the year Annual electric bill

528.538 $0.136 $71,881.7

Annual MWh

Third party economics Proposed solar system size Solar system offset Net solar system production Gross solar system production Proposed discounted unit rate as per PPA Solar PPA billing Annual electric bill New estimated bill New gross annual billing Annual saving Term of contract Total saving in span

1 15% 79.28 79280.70 $0.12 $9,513.6 $71,881.7 $61,098.00 $70612.68 $1268.49 20 years $25,369.28

MW

Private ownership Solar project size through calculation Potential energy production Estimated production Available energy to be utilized Annual electric bill New estimated bill Total annual savings System term Total saving in span

152.7 123,1372.8 10% 123137.28 $71,881.7 $55,134.50 $16,746 20 years $334,933

Initial cost

$381,750

Multiplying demand with unit rate

MW kWh Multiplying gross system production with discounted PPA rate

kWh kWh Based on energyserge predictions kWh (10% of potential energy produced)

Source: Reproduced from Meyer A. A case study: solar panels at Boston College: Chestnut Hill, MA: Boston College; 2014.

5.8.7.3

Case Study: Optimization and Management of Low Head Hydropower Plant

As Pakistan is going through severe power shortfall for last few decades, correct steps need to be taken to control this situation so that it could not get further worsen. For this purpose, several small hydropower projects can play a vital role. For any plant to be operational efficiently, some factors need to be optimized like efficiencies of components, energy produced, and financial/ economic parameters. In this case study, optimal sizing of Upper Chenab Canal (UCC) at bambanwala is presented [13]. UCC has been located on Chenab River. Initially, canal was designed to discharge 340m3/s of water in 1915 which was then further improved to 470 m3/s in 2006. This project is divided into two parts, UCC (Upper) and UCC (Lower), to link it with River Ravi. The babanwala river bedian dipalpur (BRBD) canal takes off from the left and link UCC cross regulator through babanwala regulator designed to discharge 206 m3/s. Whereas, Nokhar canal regulator is on the right side which is designed to discharge 20.5 m3/s as shown in Fig. 18. Optimization techniques have been used to determine size of plant. For this purpose, 10 samples of flow rate from daily routine have been picked up for 20 years. This data helped to estimate the average flow rate of the streams so that right number of units can be installed. From collected data, the mean came out to be 170 m3/s. Based on the calculated value two or three horizontal shaft double regulated pit turbine are considered to be sufficient enough to generate electricity from available flow. Due to increase in number of unit, substantially increases the project overhead cost. In light of the foregoing study, 2 units are considered suitable for the power house. So, the design discharge for two turbines came out to be 85 m3/s. In Table 9 a brief comparison between 2 and 3 units are given. To find out the turbine size, below mentioned date is used.

• • •

Required discharge: 170 m3/s Head: 2.39 m Site elevation: 229 m The solution and information obtained after analysis were as below:

• •

Type: horizontal pit Subtype: Kaplan

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Chenab river

UCC upper

al

BRBD link canal

ch can

r bran

Nokha

UCC lower

Ravi river Fig. 18 Location of plant. BRBD, Babanwala River Bedian Dipalpur; UCC, Upper Chenab canal. Reproduced from Yousuf I, Ghumman AR, Hashmi, HN. Optimally sizing small hydropower project under future projected flows. J Civil Eng 2017;21:1964–78.

Table 9

Hydropower design parameters

Number of units 3

Required discharge (m /s) Head (m) Discharge per turbine (m3/s)

2

3

170 2.39 85

170 2.39 56.66

Source: Reproduced from Yousuf I, Ghumman AR, Hashmi, HN. Optimally sizing small hydropower project under future projected flows. J Civil Eng 2017;21:1964–78.

• • • • • • • •

Draft type: straight Spout opening: 3988 mm Speed can attain: 83.3 rpm No of blades: 3 Hub dia: 1608 mm Rated output: 1.788 MW Efficiency: 89.7% Installed capacity: 3.58 MW

Thus, by these steps it is concluded that 3.58-MW hydropower plant utilizing Kaplan Pit turbine with 2 units of equivalent size is essentially and monetarily attainable to fulfill desired requirement.

5.8.8

Future Directions

Due to the vast demand and rapid development in wireless communication and embedded systems, wireless sensor networks (WSNs) is grabbing attention of its users worldwide. The significant feature of WSNs is its large-scale application, low capital requirement, compact size and low power consumption. The main component of a typical WSN network is a sensor node which further comprises of several equipments: power supply, processing unit, sensing module and a signal modulator/transceiver. By combining these essential components in a miniature, portable device; these sensor nodes acts as multifunctional device and can be utilized in variety of applications such as border monitoring, environment monitoring, health care, home building automation, etc. WSN is a diverse technology which can be easily scalable, comparatively low cost, and relatively small dimensions. There are numerous studies currently going on to deploy specific sensor protocol and advanced algorithms which could enable it to recognize things and make decision automatically, this will make the whole sensor network low maintenance and more robust against any failures cause by any of the nodes malfunction, its mobility or energy dissipation being adaptive at same time.

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Moreover, they can also be deployed in some harsh environmental condition with self-organized and adaptive network so that assigned task could be carry out efficiently without human interference. But, inspite of being such an enormous technology and having potential to support number of more advanced applications such as real-time monitoring, its growth is limited due to some inherent disadvantages of WSN. These disadvantages include, limited processing speed and low data transfer rates among the peripherals. These disadvantages cannot guarantee the efficient and appropriate performance of sensor nodes especially when it comes to real-time monitoring. However, further and potential applications are limited due to inherent WSN disadvantages. Short communication range also causes loads of energy waste making the system inefficient. For this reason, a multi-hop network is always required for data transportation between the source node and sink node. This gives a rise to severe energy constraints while implementing WSNs, the improvement of data processing ability of the system by utilizing powerful processors cannot be done due to the reason that energy will again get depleted really soon, restricting the device to complete its task. Limited energy accessibility also yields that the system would not be able to maintain its hop-network for longer period of time. Whereas, these devices are expected to be utilized heavily for data monitoring and transmitting purposes where frequent changing of the batteries are not possible. For this reason, the need of an efficient WSN emerges which can work under these conditions efficiently, by minimizing the energy utilization while at same time not compromising the processing ability and performance of network. The study and research in this emerging field, promises better and good quality of life for its user in number of aspects and applications, mainly in our daily routing activities. However, the constraint of energy limitation is the big hurdle in the way of its development and significantly restricts their functionality. Therefore, this has made the energy utilization and conservation in an efficient way; one of the most important topics to work on. For this reason, analysis of energy consumption, conservation, and utilization is really a critical topic in the designing and implementation of an efficient WSN. In past years, number of studies has been conducted to efficiently conserve energy for a longer period of time in WSNs. These studies mainly splits on three broad spectrums: (1) simulation/emulation-based approach for energy efficiency, (2) Hardwarebased approach, and (3) optimization-based strategy. These strategies were designed in order to explore this field of energy management and find out the ways to make the process energy efficient. Firstly, we will discuss the simulation-based approach toward energy management which is more useful and implemented due to the facts that the simulation gives more realistic and simplified model for the conservation of energy rather than the hard mathematical models-based upon certain hypothesis and assumptions. Simulations-based approach is relatively a slow process and contain numerous steps needed to be implemented correctly but at a same time it offers well tradeoff between the accuracy and efficient management. There are number of tools available easily on the internet which can be used for its simulation like Prowler, OMNet þ þ , and NS-w of which scripts are mainly written in C þ þ and MATLAB, implementing these software provides a good verification of the concept when it is in early stage. But at the same time, coarse energy can be evaluated using these tools due to the lack of low level model availability. These drawbacks have been wiped-out by the utilization of emulation tools such like ATEMU and TOSSIN. These tools can compensate it but at the same time on cost of efficiency because they are basically limited to specific Operating Systems (OS) such as TinyOS is used with TOSSIN in order to evaluate sensor nodes, while ATEMU utilizes AVR microcontroller based node platform. Whereas, for hardware-based method, numerous research has already been focused on efficient and long lasting energy consumption in real world sensor nodes. Among these researches, a detailed study has been presented in by the MICAz mote [14], number of benchmarks are used for energy estimation/calculation, charging effect of battery, and lifetime of batteries. If we discuss the optimization-based approaches, then number of study has been conducted for the optimization of both hardware and software. From the hardware perspective, better energy ratings can be reached by optimizing the power consumption of all the linked hardware components. While, from software perspective, optimization can be done by the successful development of new protocols, the adoption and implementation of efficient energy consumption methods and through configuration of existence set of protocol and make them work your way. Effects of several clean energy solutions application on sustainable development were studied by Dincer and Canan [15]. Based on their study they concluded that the overall performance rankings from highest to lowest are as follows: nuclear (7.06/10), wind (6.57/10), geothermal (6.49/10), large-scale hydro (6.44/10), small-scale hydro (5.40/10), biomass (4.17/10), solar CSP (3.14/ 10), ocean (2.66/10), and solar PV (2.30/10) on the basis of annual generation, capacity factor, mitigation potential, energy requirements, GHG emissions, and production costs of power generation systems. The below mentioned rankings change to (from highest to lowest): geothermal (7.23/10), wind (6.93/10), hydro (run of river, 6.68/10), ocean (5.65/10), solar (4.85/10), hydro (4.54/10), nuclear (4.02/10), and biomass (3.72/10) on the basis of non-air pollution environmental impact criteria (land use, water consumption/discharge, solid waste, and biodiversity). In regard to hydrogen production, energy sources are ranked based on energy and exergy efficiencies, global warming and air pollutants, and production cost. Performance rankings of the sources from highest to lowest are found to be as follows: nuclear and hydropower (6.5/10), geothermal and ocean (5.7/10), wind (5.3/ 10), solar (4.5/10), and biomass (3.6/10). The study concluded that renewable energy-based integrated systems for multigeneration purposes are useful as they help in lowering harmful gas emissions, lowering the carbon footprint and providing better sustainability.

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349

Concluding Remarks

By sustainable energy it is meant to extract energy from such sources which are present in bulk quantity and by utilizing them for our purpose they won’t get depleted. It can also be defined as the development that is sufficient to meet our current need, without compromising the ability of future generations to meet their own. One of the reasons for which development of sustainable energy systems recommended throughout the world is its characteristics of not to harm or affect the environment and most importantly the ozone; moreover, it is available free of cost throughout the day-cycle. All renewable forms of energy, like geothermal, solar, tidal, biomass and wind, are known as sustainable as they are not only stable but also available naturally in bulk throughout the day and can easily be converted and stored after the successful study and inventions of different modules like lead acid battery, chillers, methane controlled chambers, turbines, etc. In this chapter, we will independently study all of the sources of sustainable energy and their individual applications as well as significance. Beside material cost, energy cost is a major pressure factor for several organizations and manufacturers. A sound and easy to implement business strategy can yield more production stability by reducing cost. In order to work in profit and with efficiency, modern operations largely depend on the low cost of energy it consumes. Energy conservation and independence are also considered as major strategies for creating a competitive advantage in business. Realizing the fact, that energy management could play a vital role in addressing social, economic and environmental concerns, organization are readily adopting these practices to minimize the risks. Overall, energy efficiency and management practices are among the most important option to increase the profit of organization as well as to reduce their dependencies on highly volatile fossil fuel prices. Cost and environmental factors are two major concerns of the modern society in terms of sustainable energy. In this chapter, various approaches have been discussed for the successful implementation of sustainable energy program within the organization, town, state, or country.

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]

Anderson C. Importance of electricity – how it changed People’s lives; 2009. US Department of energy. Hydropower vision; 2008. National Grid US. Transmission and wind energy: capturing the prevailing winds for the benefit of customers; 2006. Patel MR. Wind and solar power systems – design, analysis and operation 2006;2:303. N. C. f. E. Information. Global analysis. Silver Spring: NOAA; 2015. Congressional Budget Report, 2012. Halliday S. Sustainable construction. London: Butterworth Heinemann; 2008. Graham P. Building ecology – first principles for a sustainable built environment. Hoboken, NJ: John Wiley and Sons; 2002. Scerri A, James P. Accounting for sustainability: combining qualitative and quantitative research in developing ‘indicators’ of sustainability. Int J Soc Res Methodol 2010;13(1):41–53. James P, Magee L, Scerri A, Steger MB. Urban sustainability in theory and practice: circles of sustainability. London: Routledge; 2015. White F, Stallones L, Last J. Global public health: ecological foundations. Oxford: Oxford University Press; 2013. Kizilkan O, Dincer I. Borehole thermal energy storage system for heating applications: thermodynamic performance assessment. Energy Conser Manag 2015;90:53–61. Yousuf I, Ghumman AR, Hashmi HN. Optimally sizing small hydropower project under future projected flows. J Civil Eng 2017;21:1964–78. CrossbowTechnology Inc. MicaZ datasheet. Available from: http://www.openautomation.net/uploadsproductos/micaz_datasheet.pdf. Dincer I, Acar C. A review on clean energy solutions for better sustainability. Int J Energy Res 2015;39:585–606.

Further Readings Abbas SZ, Rafatullah M, Ismail N, Syakir MI. A review on sediment microbial fuel cells as a new source of sustainable energy and heavy metal remediation: mechanisms and future prospective. Int J Energy Res 2017;41(9):1242–64. Akber MZ, Thaheem MJ, Arshad H. Life cycle sustainability assessment of electricity generation in Pakistan: policy regime for a sustainable energy mix. Energy Policy 2017;111:111–26. Baumgartner RJ, Rauter R. Strategic perspectives of corporate sustainability management to develop a sustainable organization. J Clean Prod 2017;140(1):81–92. Chen CF, Xu X, Arpan L. Between the technology acceptance model and sustainable energy technology acceptance model: investigating smart meter acceptance in the United States. Energy Res Soc Sci 2017;25:93–104. Domingues AR, Lozano R, Ceulemans K, Ramos TB. Sustainability reporting in public sector organizations: exploring the relation between the reporting process and organizational change management for sustainability. J Environ Manag 2017;192:292–301. Geetha A, Subramani C. A comprehensive review on energy management strategies of hybrid energy storage system for electric vehicles. Int J Energy Res 2017;41 (13):1817–34. Hong SeG, Hyun HL, You W. Core design options of an ultra-long-cycle sodium cooled reactor with effective use of PWR spent fuel for sustainable energy supply. Int J Energy Res 2017;41(6):854–66. Nguyen HP, Matsuura Y. Designing a sustainability framework for the initiation and management of coordination in an energy exchange. J Clean Prod 2017;162:s26–34. Oudes D, Stremke S. Spatial transition analysis: spatially explicit and evidence-based targets for sustainable energy transition at the local and regional scale. Landsc Urban Plan 2018;169:1–11. Pearce AR. Sustainable urban facilities management. Encycl Sustain Technol 2017;351–63. Roskilly AP, Yan J. Sustainable thermal energy management. Appl Energy 2017;186(3):249–50. Saavedra MRM, Fontes CHDO. Sustainable and renewable energy supply chain: a system dynamics overview. Renew Sustain Energy Rev 2018;82(1):247–59. Tommasi T, Lombardelli G. Energy sustainability of microbial fuel cell (MFC): a case study. J Power Sources 2017;356:438–47.

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Vivas FJ, Hera A De Las, Segura F, Andujar JM. A review of energy management strategies for renewable hybrid energy systems with hydrogen backup. Renew Sustain Energy Rev 2018;81(1):669–81. Wang M, Fu X, Zhang W, Lian C, Chen D. Preliminary sustainable and economic analysis of nuclear fuel cycle with subcritical system. Int J Energy Res 2017; doi:10.1002/ er.3898.

Relevant Websites http://www.aseanenergy.org/engagements/asean-eu/aemas/ Asean Centre for Energy. http://cordis.europa.eu/project/rcn/85679_en.html European Commission. http://www.cleanenergyministerial.org/Portals/2/pdfs/GSEP_knowledge_skills_EnMS_implementation.pdf Global Superior Energy Performance Partnership Energy Management Working Group. https://www.gov.uk/government/publications/sustainable-energy-management-and-the-built-environment GOV.UK. http://www.nextcontrols.com/energy-management-solutions/energy-management-systems Next. http://blog.schneider-electric.com/building-management/2015/02/26/better-sustainability-bems-building-energy-management-system/ Schneider Electric. http://www.sems-project.eu/ SEMS. https://sftool.gov/learn/about/480/energy-management-systems-enms SF Tool. http://nep.benfranklin.org/sustainable-energy-management-systems/ Sustainable Energy Management Systems. http://www.wwise.co.za/energy-management-systems/ WWISE.

5.9 Optimization in Energy Management Pouria Ahmadi, University of Illinois at Urbana-Champaign (UIUC), Urbana, IL, United States Ibrahim Dincer, University of Ontario Institute of Technology, Oshawa, ON, Canada r 2018 Elsevier Inc. All rights reserved.

5.9.1 Introduction 5.9.1.1 Energy Management Aspects 5.9.2 Need for Optimization 5.9.3 Optimization 5.9.3.1 System Boundaries 5.9.3.1.1 Objective functions and system criteria 5.9.3.1.2 Decision variables 5.9.3.1.3 Constraints 5.9.4 Optimization Methods 5.9.4.1 Classical Optimization 5.9.4.2 Numerical Optimization Methods 5.9.4.3 Genetic Algorithm 5.9.4.4 Artificial Neural Network 5.9.4.5 Fuzzy Logic 5.9.5 Multiobjective Optimization 5.9.6 Case Studies for Energy Management Optimization 5.9.6.1 Energy Management Optimization in Steam Power Plant 5.9.6.1.1 Steam power plants 5.9.6.1.2 Modeling and analysis 5.9.6.1.3 Objective functions, design parameters, and constraints 5.9.6.2 Energy Management Optimization for Energy Storage Tank 5.9.6.2.1 Thermodynamic modeling 5.9.6.2.2 Objective function, design parameters and constraints 5.9.6.2.3 Optimization results 5.9.6.3 Hybrid Wind–Photovoltaic Battery System 5.9.6.3.1 Modeling 5.9.6.3.2 Photovoltaic panel 5.9.6.3.3 Wind turbine 5.9.6.3.4 Battery 5.9.6.3.5 Objective function, design parameters, and constraints 5.9.6.3.6 Real parameter genetic algorithm 5.9.6.3.7 Case study 5.9.6.3.8 Results and discussion 5.9.6.4 Heat Exchanger Optimization for Thermal Management of Electric Vehicles 5.9.6.4.1 Thermal modeling of heat exchanger 5.9.6.4.2 Objective functions, design parameters, and constraints 5.9.6.4.3 Effective properties of the phase change materials and nanotubes 5.9.6.4.4 Model description 5.9.6.4.5 Optimization using genetic algorithm 5.9.6.4.5.1 Sensitivity analysis 5.9.7 Future Directions 5.9.8 Closing Remarks References Further Reading Relevant Websites

Nomenclature C Cp C_ D

Cost per unit exergy ($/MJ) Specific heat at constant pressure (kJ/kg K) Cost of exergy destruction ($/h)

Comprehensive Energy Systems, Volume 5

cf ex _ Ex _ D Ex

doi:10.1016/B978-0-12-809597-3.00523-X

352 354 355 356 356 356 356 357 357 357 357 358 358 358 358 359 359 360 362 363 365 366 366 367 367 367 367 369 370 370 370 371 371 372 374 378 379 380 380 380 384 385 386 387 387

Cost of fuel per unit of energy ($/MJ) Specific exergy (kJ/kg) Exergy flow rate (kW) Exergy destruction rate (kW)

351

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Optimization in Energy Management

h LHV _ m P Q R s T

Specific enthalpy (kJ/kg) Lower heating value (kJ/kg) Mass flow rate (kg/s) Pressure (bar) Heat (kJ) Gas constant (kJ/kg K) Specific entropy (kJ/kg K) Temperature (1C or K)

W _ W x Z_ Zk

Adiabatic temperature in combustion chamber primary zone (K) Work (kJ) Work rate (kW) Molar fraction Capital cost rate ($/h) Component purchase cost ($)

Greek symbols g Specific heat ratio e CO2 emission per unit net electricity output (kgCO2 =MWh) Z Energy efficiency ZAC Air compressor isentropic efficiency

ZDB ZGT ξ j C

Duct burner efficiency Gas turbine isentropic efficiency Chemical exergy/energy ratio Maintenance factor Exergy efficiency

Subscripts A AC Act Amb Bot CC CCPP Cond COP CRF D DB e env Eva F FP g Gen GT GTIT HRSG

i in is k L mix OF Opt Pc Ph Pm PP Q r ref SF ST T Top Tot w x

Interest rate Inlet condition Isentropic Component Loss Mixture Objective function Optimum Probability Physical Mutation Pinch point Heat transfer Pressure ratio Reference Supplementary firing Steam turbine Temperature Topping cycle Total Work Concentration

0


Reference environment condition Rate

Air Air compressor Actual Ambient Bottoming cycle Combustion chamber Combined cycle power plant Condenser Coefficient of performance Capital recovery factor Destruction Duct burner Exit condition Environment Evaporator Fuel Feed pump Combustion gases Generator Gas turbine Gas turbine inlet temperature Heat recovery steam generator

Superscripts Ch Chemical

5.9.1

TPZ

Introduction

Energy drives processes and is essential to life. Energy exists in several forms, for example, light, heat, and electricity. Concerns exist regarding limitations on easily accessible supplies of energy resources and the contribution of energy processes to global warming as well as other environmental concerns, such as air pollution, acid precipitation, ozone depletion, forest destruction, and radioactive emissions [1]. There are various alternative energy options to fossil fuels, including solar, geothermal, hydropower, wind, and nuclear energy. The use of many of the available natural energy resources is limited due to their reliability, quality, and energy density. Nuclear energy has the potential to contribute a significant share of large-scale energy supply without contributing to climate change. Advanced technologies, aimed at mitigating global warming, are being proposed and tested in many countries. Any organization that needs good management for long-term success and efficient operation should dwell on energy management practices and their implementations. However, the management of energy is often neglected, even though there is

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Initial review

Management commitments

Energy policy

Energy strategies Organization Compliance Investments Procurement Energy information Opportunities identifications Communications Action plans

Management review Fig. 1 Different elements for energy management.

considerable potential to save energy and reduce costs. At the same time, there is also increasing pressure from rising energy prices, climate change legislation and the need to be seen to be environmentally responsible by customers and stakeholders. In simple words, energy management is the systematic use of management and technology to improve the energy performance of an organization. It is worth noting that energy management for a small office is at a very different level to that for a complex industrial company. Nevertheless, the fundamental principles are much the same. Energy management includes planning and operation of energy production and energy consumption units. Objectives are resource conservation, climate protection, and cost savings, while the users have permanent access to the energy they need. It is connected closely to environmental management, production management, logistics, and other established business functions. With the advancement of civilization and evolution of technology, energy demand has become a basic issue for the development of a society today. The usual ways to address this demand today are based mostly on resources, such as fossil or nuclear fuels, which have a negative impact on the environment, either contributing with greenhouse gases (GHGs), or by production of radioactive or inert solid waste. For this reason, every day the need to migrate to more environmentally responsible energy production models becomes more evident. Together with the above, due to the high-energy requirement, it is necessary to look for generation models to ensure maximum system performance, minimizing the use of resources, cost and thus the environmental impact. In recent decades, the use of distributed generation has emerged as a viable and safe solution to increase electrical system performance, reducing the distance between generation and demand. One of the major parts of energy management is facility management as a huge proportion (i.e., average 25%) of complete operating costs is energy costs. Facility management is a profession that encompasses multiple disciplines to ensure functionality of the built environment by integrating people, place, processes, and technology. The central task of energy management is to reduce costs for the provision of energy in buildings and facilities without compromising work processes. Especially the availability and service life of the equipment and the ease of use should remain the same. Fig. 1 shows the main elements for energy managements. As it is shown in this figure, putting an energy management system in place takes time and it will continue to develop as energy performance improves and attention moves to different issues. As the population grows the need for more energy is getting more attention. More energy will produce more emission and will encounter the environment. Thus, energy management can improve the efficiency of the system, which results in reducing the energy losses. When we talk about energy management we need to consider the following items:

• • • • •

Strategic: define the long-term strategic problems and goals, and how they are going to be addressed. Management: identify who is responsible for your energy strategy and define the roles. Resources: identify the internal and external resources required. Financials: identify the budget and financial objectives, investment criteria, and life-cycle costing. Reporting and monitoring: specify which information will be monitored, and the reporting mechanisms and timeframes.

Optimization is a significant tool in engineering for determining the best, or optimal, value for the decision variable(s) of a system. For various reasons, it is important to optimize processes so that a chosen quantity, known as the objective function, is maximized or minimized. For example, the output, profit, productivity, product quality, etc., may be maximized, or the cost per item, investment, energy input, etc., may be minimized. Energy management is the systematic use of management and technology to improve the energy performance of an organization. To be fully effective it needs to be integrated, proactive, and incorporate

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energy procurement, energy efficiency, and renewable energy. Like all management disciplines, energy management should be applied in a manner appropriate to the nature and scale of the organization. Energy management for a small office-based organization will be at a very different level to that for a complex industrial company with a multimillion pound energy bill. This particular chapter focuses on the use of optimization tools in energy management practices for energy processes, systems, and applications in small- and large-scale applications. It also discusses the methods for optimization to consider for the objective functions in single and/or multi forms. It considers various example systems and applications being optimized for better efficiency, better cost effectiveness, better environment, and hence better sustainability.

5.9.1.1

Energy Management Aspects

An energy management aspect (EMA) is a systematic process for continually improving energy performance, which can result in energy savings. The principle of an EMA is to engage and encourage staff at all levels of an organization to manage energy use on an ongoing basis. An energy management strategy (EMS) achieves this for companies in the iron and steel sector by establishing a framework for industrial facilities to manage their ongoing energy use and identify opportunities to adopt energy-saving technologies, including those opportunities that do not necessarily require capital investment. An EMS helps ensure that energy efficiency improvements do not just happen on a one-time basis, but rather are continuously identified and implemented in a process of constant improvement. Experience has shown that even optimized systems lose their initial efficiency gain over time due to personnel and production changes if energy efficiency is not integrated into management practices. An EMA allows companies to systematically track, analyze, and plan their energy use, thereby enabling greater control of energy performance, as well as operational performance. Depending on the operating practices of an iron and steel producing facility, there is often considerable potential for energy savings from making operational changes alone, rather than the typically capitalintensive technology changes. Where operating and maintenance practices are close to best practice, an EMS is still a strategic tool that can better inform investment decisions in best-available technologies (BATs). Some examples of key operational benefits that can reduce the energy intensity, and therefore the cost per ton of product for companies within the iron and steel sector following the implementation of an EMS, are as follows:

• • • • • • •

optimization of the purchase and consumption of all fuel types and energy inputs; helping plant operators in the iron and steel industry to monitor and optimize their energy flows; enabling detection of avoidable energy losses; effective use of waste heat and gas recycling; early detection of leaks (e.g., air and heat); generating consumption forecasts and minimizing peak loads; and improving monitoring of surplus heat, electricity and gas streams to help generate value through export to grids and local users.

One of the energy management strategies is in buildings as a good example where energy management play a crucial role. The numbers of residential buildings are increasing and the need for more energy is growing. Such measures are aiming to either generate energy onsite or manage the demand via energy efficient designs, upgrades, and demand response (DR) policies; often a combination is present. These energy systems are subject to high degrees of optimization in terms of financial savings as the value of energy is variable and affected by a range of factors, such as the time of day, season and the energy source. Whenever more than one energy source is used to supply a certain load, the need for an efficient EMS arises. This strategy guides the flow of energy through the supply system. This need is not only essential for a standalone hybrid system but also for hybrid renewable energy systems that are connected to the main grid. There are several studies where energy management has been studies. For instance, Lazos et al. [2] studied the optimization of energy management in commercial buildings with weather forecasting inputs. The results showed that weather variables are significant components of the evolution of building energy systems and minimizing the uncertainty in predicting their evolution can lead to significant savings, usually in the range of 15%–30% compared to a deterministic and non-weather sensitive control approach. In most of the energy management problems, we look to manage energy consumption, energy losses, and the cost. When we manage the consumption, the energy saving is achieved and subsequently the cost will be saved. Exergy as a potential tool can also help us to identify the location and magnitude of the losses within the system and come up with solutions for improvement and energy managements [3]. There are several studies where exergy is applied for better design and better performance assessment of the system. For example, Ahmadi et al. [4] investigated the cost and entropy generation minimization of a cross-flow plate fin heat exchanger using a multiobjective genetic algorithm (GA). They considered the fin pitch, fin height, fin offset length, cold stream flow length, no-flow length, and hot stream flow length as six decision variables. Fast and elitist nondominated sorting genetic algorithm (i.e., NSGA-II) was applied to minimize the entropy generation units and the total annual cost (TAC) (sum of initial investment and operating and maintenance costs) simultaneously. They also studied the exergetic optimization of shell-and-tube heat exchangers using NSGA-II [5]. Thence, these studies indicate that optimization is widely used for optimal design and size of various types of heat exchangers. Vivas et al. [6] studied a review of energy management strategies for renewable hybrid energy systems with hydrogen backup. They showed that the choice of a correct EMS should guarantee an optimum performance of the whole hybrid renewable system; therefore, it is necessary to know the most important criteria in order to define a management strategy that ensures the best solution from a technical and economic point of view. They presented a critical review and analysis of different energy management

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strategies for hybrid renewable systems based on hydrogen backup. Kamal et al. [7] investigated the energy management and control of grid-connected wind/fuel cell/battery hybrid renewable energy system. This paper concludes with a classical based energy management and power control of a hybrid renewable energy system, which is composed of renewable energy source (wind turbine (WT)), hydrogen energy (solid oxide fuel cell (SOFC)), and battery. The proposed system removes the deficiency of a single power source and provides a better hybrid system that meets the load demand for 24 h without any interruption.

5.9.2

Need for Optimization

Optimization is a significant tool in engineering for determining the best, or optimal, value for the decision variable(s) of a system. For various reasons, it is important to optimize processes so that a chosen quantity, known as the objective function, is maximized or minimized. For example, the output, profit, productivity, product quality, etc., may be maximized, or the cost per item, investment, energy input, etc., may be minimized. The success and growth of industries today is strongly based on their ability to optimize systems and processes, as well as their designs. With the advent in recent years of new materials, such as composites and ceramics, and new manufacturing processes, several traditional industries (e.g., steel) have faced significant challenges and, in some cases, diminished in size, while many new fields have emerged. It is important to exploit new techniques for product improvement and cost reduction in traditional and new industries. Even in expanding areas, such as consumer electronics, the prosperity of a company is closely connected to its ability to apply optimization to new and existing process and system designs. Consequently, engineering design, which has always been important, has become increasingly coupled with optimization. Energy engineering is a field where optimization plays a particularly important role. Engineers involved in thermal engineering, for instance, are required to answer such questions as

• • •

What processes or equipment should be selected for a system, and how should the parts be arranged for the best outcome? What are the best characteristics for the components (e.g., size, capacity, impact, cost)? What are the best process parameters (e.g., temperature, pressure, flow rate, and composition) of each stream interacting with the system?

In order to answer such questions, engineers are required to formulate an appropriate optimization problem. Proper formulation is usually the most important and sometimes the most difficult step in optimization. In order to formulate an optimization problem, there are numerous elements that need to be defined, including system boundaries, optimization criteria, decision variables, and objective functions. In order to have an optimized system, which can reduce the cost and environmental impacts and at the same time increase the efficiency of the system, optimization is useful. Optimization has a wide range of applications in energy systems ranging from simple energy systems to more sophisticated systems, such as advanced power plants and integrated energy systems. As an example, the liquid entering a distillation column is heated to enable the distillation process to occur. Or a process liquid is cooled so that it can be properly stored. Such heating and cooling processes are often done by transferring heat from one fluid to another in devices called heat exchangers. Optimization is one of the key elements in management, design, analyses, and performance assessment of power plants. As it is obvious, any improvements in a power plant, which can lead to an increase in the efficiency, can reduce emissions and reduce the cost, which eventually leads to energy saving and better energy managements. This is where energy management strategies of such systems have been of the great importance during the last several decades. Gas turbine power plant is one of the examples. Ameri and Ahmadi [8] performed the exergy optimization of the supplementary firing (SF) in heat recovery steam generator (HRSG) in a combined cycle power plant (CCPP). Their results showed that if a duct burner (DB) is added to a HRSG, the first and second law efficiencies are reduced. Nevertheless, the results show that the CCPP output power increases when the DB is used. Using the optimization procedure with respect to thermodynamics laws, as well as thermoeconomics is essential. In fact, objectives in this regard involved in the design optimization process are as follows [9,10]: thermodynamic (e.g., maximum efficiency, minimum fuel consumption, minimum irreversibility, and so on), economic (e.g., minimum cost per unit of time, maximum profit per unit of production), and environmental (e.g., limited emissions, minimum environmental impacts). Some researchers have carried out the optimization in power plants and CHP systems. They usually used evolutionary algorithm in their studies. Sahoo [11] carried out the exergoeconomic analysis and optimization of a cogeneration system using evolutionary programming. The researcher considered a cogeneration system, which produced 50 MW of electricity and 15 kg/s of saturated steam at 2.5 bar. As mentioned, the unit was optimized using exergoeconomic principles and evolutionary programming. The results showed that, for the optimum case in the exergoeconomic analysis, the cost of electricity and production cost were 9.9% lower in comparison with the base case. Sayyaadic [12] performed the exergoeconomic optimization for a 1000 MW light water reactor power generation system using a GA. In this study, the researcher considered 10 decision variables. Moreover, it was shown that by optimization techniques considered in his research (although fuel cost of optimized system was increased in comparison to the base case plant), the shortcoming of optimized system was compensated by larger monetary saving on other economic sectors. Sanaye et al. [13] analyzed the optimal design of a CHP plant in Iran. Even though they used a single-objective function representing the total cost of the plant in terms of dollar per second, results showed that by increasing the fuel cost the numerical values of decision variables using GA in the thermoeconomically optimal design tend to those of the thermodynamically optimal design. On the other hand, there are other studies in the literature carried out by considering the environmental aspects of thermal systems. Mozafari et al. [14] performed the optimization of a micro-gas turbine by exergy, economic, and environmental approaches. They performed

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their analysis for various fuels. The results showed that optimization results were little affected by the type of fuels considered, and trends of variations of second law efficiency and cost rate of owning and operating the whole system were independent of the fuels. Ahmadi and Dincer applied a different optimization algorithm for various types of power plants [10,15–19]. Another important energy system is refrigeration systems. Refrigeration plays an important role in daily life and is used for an extensive range of applications, including cooling of foodstuffs, homes, and electronic devices. Although mainly considered a discipline within mechanical engineering, refrigeration is somewhat multidisciplinary drawing in other disciplines, such as chemical engineering, process engineering, food engineering, heating, ventilation & air conditioning (HVAC), cryogenics, and others. Thermodynamics is at the core of refrigeration, and optimization is an important tool for finding the best refrigeration system for a given application. Anvari-Moghaddam et al. [20] studies a multiagent-based energy management solution for integrated buildings and microgrid system. They consider a multiagent-based energy management system (EMS) for monitoring and optimal control of an integrated homes/buildings and microgrid system with various renewable energy resources (RESs) and controllable loads. To verify the effectiveness and applicability of the proposed multiagent-based EMS, several case studies were carried out the results were discussed. The results showed that through an agent-based control of storage options it is possible to mitigate the effects of uncertain parameters in the environment and guarantee the secure and optimal operation of the system. Ouachani et al. [21] developed a renewable energy management algorithm for a water pumping system. The hybrid system combines a WT with photovoltaic panels (PV) as the sources of energy, storing energy in a battery bank. The algorithm aims to ensure the system autonomy, a continuous load supply and a safe operation for the battery bank, with the load considered the water pumping in a rural area. The results show that in fact, the fuzzy logic allows the state-of-charge (SOC) of the battery bank to be higher. Moreover, with the fuzzy logic, the wind power is used in total. Ahmadi et al. [50] studied the optimization of a novel multigeneration energy system. The system composed of an ocean thermal energy conversion system and equipped with flat plate and photovoltaic/thermal solar collectors, a reverse osmosis desalination unit to produce fresh water, a single effect absorption chiller and a polymer electrolyte membrane electrolyzer. This system has the potential to produce electricity, heating, cooling, fresh water, and hydrogen. The used multiobjective optimization to optimize exergy efficiency and total cost rate of the system.

5.9.3

Optimization

From a mathematical point of view, optimization is the process of maximizing or minimizing a function subject to several constraints, for a number of variables, for each of which a range exists [22]. Put more simply and practically, optimization involves finding the best possible configuration for a given problem subject to reasonable constraints. When an optimization problem involves only one objective function, the task of finding the optimum solution is called singleobjective optimization. Single-objective optimization thus considers the solution to the problem with respect to just one criterion. Single-objective optimization has been applied for decades, for a wide range of applications. The need to consider more than one objective function and the importance of doing so led to the advent of multiobjective optimization. In management disciplines, such optimization problems are commonly known as multiple criterion decision making. Most real world optimization problems inherently involve multiple objective functions. The principles and intent of optimization cannot be reasonably applied with only one objective function when other objectives are also important. Some important optimization concepts and terms are described and defined in the next four subsections.

5.9.3.1

System Boundaries

The first step in any optimization problem is to define the system boundaries. All subsystems that affect system performance should be included. When the system is overly complex, it is often desirable to divide it into smaller subsystems. In this case, it is often reasonable to carry out optimization on each subsystem independently, i.e., suboptimization of the subsystems is performed.

5.9.3.1.1

Objective functions and system criteria

The next step in an optimization problem is to define the system criteria, which are sometimes called objective functions. An objective function is based on the desire or purpose of the decision maker, and it can be either maximized or minimized. Optimization criteria can vary widely. For instance, optimization criteria can be based on economic aims (e.g., total capital investment, total annual levelized costs, cost of exergy destruction, cost of environmental impact), efficiency aims (e.g., energy, exergy, other), other technological goals (production rate, production time, total weight), environmental impact objectives (reduced pollutant emissions), and other objectives. Note that we can consider more than one objective function when solving for the optimal solution for an optimization problem, via multiobjective optimization.

5.9.3.1.2

Decision variables

Another essential step in formulating an optimization problem is the selection of independent decision variables that adequately characterize the possible design options. To select decision variables, it is important to (1) include all important variables that can affect the performance and cost effectiveness of the system, (2) not include variables of minor importance, and (3) distinguish among

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independent variables whose values are amenable to change. In a given optimization problem, only decision variables are changing. Variables whose values are calculated from the independent variables using mathematical models are dependent variables.

5.9.3.1.3

Constraints

The constraints in a given design problem arise due to limitations on the ranges of the physical variables, basic conservation principles that must be satisfied, and other restrictions. Restrictions on variables may arise due to limitations on the space, equipment, and materials that are employed. That is, we may restrict, the physical dimensions of a system, the temperatures (high and/or low) that components can attain, maximum allowable pressure, material flow rate and force generated, etc. Also, minimum values of the temperature may be specified for thermoforming of a plastic and for ignition to occur in an engine. Thus, both minimum and maximum values of design variables may be involved in constraints. Many constraints in thermal systems arise because of conservation laws, particularly those related to mass, momentum, and energy. For instance, under steady-state conditions, mass inflow to a system must equal mass outflow. This condition gives rise to an equation that must be satisfied by the relevant design variables, thus restricting the values that may be employed in the search for an optimum. Similarly, energy balance considerations are important in thermal systems and may limit the range of temperatures, heat fluxes, dimensions, etc., that may be used. Several such constraints are often satisfied during modeling and simulation because the governing equations are based on conservation principles. In this way, the objective function being optimized already considers these constraints. In such cases, only the additional limitations that define the boundaries of the design domain remain to be considered.

5.9.4

Optimization Methods

There are several optimization methods; each has their benefits and constraints for various applications. In the following sections, various optimizations are discussed:

5.9.4.1

Classical Optimization

Classical optimization techniques are useful for finding the optimum solution or unconstrained maximum or minimum of continuous and differentiable functions. Some specifications for classical optimization can be selected based on this understanding, as described below:

• • • •

These are analytical methods that make use of differential calculus in locating the optimum solution. Classical methods have limited scope in practical applications as some involve objective functions that are not continuous and/ or differentiable. These methods assume that the function is differentiable twice with respect to the design variables and that the derivatives are continuous. Three main types of problems can be handled by classical optimization techniques: ○ Single variable functions. ○ Multivariable functions with no constraints. ○ Multivariable functions with both equality and inequality constraints. In problems with equality constraints the Lagrange multiplier method can be used. If the problem has inequality constraints, the Kuhn–Tucker conditions can be used to identify the optimum solution.

5.9.4.2

Numerical Optimization Methods

Numerical optimization methods have been used for several years for various applications. Several major categories of this optimization technique exist as:

•

• • • •

Linear programming: applies to the case in which an objective function f is linear and the set A, where A is the design variable space, is specified using only linear equalities and inequalities. This method is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships. Linear programming is a special case of mathematical programming (mathematical optimization). Integer programming: applies to linear programs in which some or all variables are constrained to take on integer values. This method in which some or all of the variables are restricted to be integers. In many settings the term refers to integer linear programming (ILP), in which the objective function and the constraints (other than the integer constraints) are linear. Quadratic programming: allows the objective function to have quadratic terms, while the set A must be specified with linear equalities and inequalities. Nonlinear programming: applies to the general case in which the objective function or the constraints or both contain nonlinear parts. It is the subfield of mathematical optimization that deals with problems that are not linear. Stochastic programming: applies to the case in which some of the constraints depend on random variables. It is a framework for modeling optimization problems that involve uncertainty. Whereas deterministic optimization problems are formulated with known parameters, real world problems almost invariably include some unknown parameters.

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Dynamic programming: applies to the case in which the optimization strategy is based on dividing the problem into smaller subproblems. It is a method for solving a complex problem by breaking it down into a collection of simpler subproblems, solving each of those subproblems just once, and storing their solutions. Combinatorial optimization: concerns problems where the set of feasible solutions is discrete or can be reduced to a discrete one. Evolutionary algorithm: involves numerical methods based on random search. An evolutionary algorithm utilizes techniques inspired by biological evaluation reproduction, mutation, recombination and selection. The candidate solutions to the optimization problem play the role of individuals in a population, and a fitness function determines the environment within which the solutions “live.” Evolutionary algorithm methods include GAs, artificial neural networks (ANNs), and fuzzy logic. These approaches are discussed further below. Each of the approaches is available in toolboxes developed by Math Works and can thus be used straightforwardly with MATLAB software. Following are the famous evolutionary algorithms, which are listed below.

5.9.4.3

Genetic Algorithm

A GA is a search method used for obtaining an optimal solution. The method is based on evolutionary techniques that are similar to processes in evolutionary biology, including inheritance, learning, selection, and mutation. The process starts with a population of candidate solutions called individuals, and progresses through generations, with the fitness of each individual being evaluated. Fitness is defined based on the objective function. Then multiple individuals are selected from the current generation based on fitness and modified to form a new population. This new population is used in the next iteration and the algorithm progresses toward the desired optimal point. In a GA, a population of candidate solutions – called individuals, creatures, or phenotypes – to an optimization problem evolves toward better solutions. Each candidate solution has a set of properties that can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals, and is an iterative process, with the population in each iteration called a generation. In each generation, the fitness of each individual in the population is evaluated; the fitness is usually the value of the objective function in the optimization problem to be solved. The more fit individuals are stochastically selected from the current population, and each individual’s genome is modified to form a new generation. The new generation of candidate solutions is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. Some advantages of using GAs for optimization follow:

• • • • • •

GAs can solve any optimization problem that can be described with the chromosome encoding. GAs can provide multiple solutions for problems. Since the GA execution technique is not dependent on the error surface, they can be utilized to solve multidimensional, nondifferential, noncontinuous, and even nonparametrical problems. Structural GAs provide the possibility of solving solution structures and solution parameter problems simultaneously. GAs are easy to understand and require little knowledge of mathematics. GAs are easily transferred to existing simulations and models.

5.9.4.4

Artificial Neural Network

ANNs are interconnected groups of processing elements, called artificial neurons, similar to those in the central nervous system of the body. The approach is thus analogous to some elements of neuroscience. The characteristics of the processing elements and their interconnections determine the processing of information and the modeling of simple and complex processes. Functions are performed in parallel and the networks have both nonadaptive and adaptive elements, which change with the inputs and outputs and the problem. The ANN approach leads to nonlinear, distributed, parallel, local processing and adaptive representations of systems.

5.9.4.5

Fuzzy Logic

Fuzzy logic allows us to deal with inherently imprecise concepts, such as cold, warm, very, and slight, and is useful in a wide variety of thermal systems where approximate, rather than precise, reasoning is needed. Fuzzy logic can be used for the control of systems and in problems where a sharp cutoff between two conditions does not exist.

5.9.5

Multiobjective Optimization

Optimal conditions are generally strongly dependent on the chosen objective function. However, several aspects of performance are often important in practical applications. In thermal and energy systems design, efficiency (energy and/or exergy), production rate, output, quality, and heat transfer rate are common quantities that are to be maximized, while cost, input, environmental impact, and pressure are quantities to be minimized. Any of these can be chosen as the objective function for a problem, but it is

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OF1

359

OF1 Pareto front

1 a

2

4 b 5

d

e

3 c f

OF2

(A)

OF2

(B)

Fig. 2 Multiobjective optimization with two objective functions OF1 and OF2 that are to be minimized, showing (A) dominant designs and (B) the Pareto frontier.

usually more meaningful and useful to consider more than one objective function. Users of simple optimization are able to determine the minimum and maximum of a single variable function, and can utilize first and second derivative techniques to find the optimal value of a given function. At the advanced level, users of optimization are able to find an optimum value of multivariable functions. In addition, they are can solve multivariable optimization problems with constraints. Constraint optimization is an important subject in practice science since most real world problems contain constraints. Multiobjective optimization has been extensively used and studied. There exist many algorithms and application case studies involving multiobjective optimization. One of the common approaches for dealing with multiple objective functions is to combine them into a single-objective function that is to be minimized or maximized. For example, in the design of heat exchangers and cooling systems for electronic equipment, it is desirable to maximize the heat transfer rate. However, this often comes at the cost of increased fluid flow rates and corresponding frictional pressure losses. A multiobjective optimization problem has objective functions that are either minimized or maximized. As with single-objective optimization, multiobjective optimization involves several constraints that any feasible solution including the optimal solution must satisfy. A multiobjective optimization problem can be formulated as: Minimize=maximize fn ðxÞ Subject to

n ¼ 1; 2; …N

gj ðxÞ40 j ¼ 1; 2; …J hk ðxÞ ¼ 0 k ¼ 1; 2; …K ðLÞ

ðUÞ

xi rxi rxi

ð1Þ

i ¼ 1; 2; …n

A solution of this problem is x, which is a vector of n decision variable or design parameters. The last set of constraints here is called variable bounds, which restrict the searching bound. Any solution of the decision variables should lie within a lower bound ðLÞ ðUÞ (xi ) and upper bound (xi ). To illustrate, we consider two objective functions, OF1 and OF2. We assume that these are to be minimized (although maximization can be similarly handled since it is equivalent to minimization of the negative of the function). Fig. 1(A) shows values for the two objective functions at five design points. Design 2 is clearly seen in Fig. 1 to be preferable to design 4 because both objective functions are smaller for design 2 compared to design 4. Similarly, design 3 is preferable to design 5. However, designs 1, 2, and 3 are not preferable, or dominated, by any other designs. The set of nondominated designs is introduced as the Pareto frontier, representing the best collection of design points. This is shown in Fig. 2(B). Note that any point on the Pareto frontier can be considered as an optimal design condition. The selection of a specific design from the set of points constituting the Pareto frontier is at the discretion of the decision maker, typically an engineer or designer.

5.9.6

Case Studies for Energy Management Optimization

In this part, we will try to address energy management for several case studies and how optimization will result in energy management and cost saving. It is obvious that optimization play a significant role in this case thus the selection of objective functions and their constraints will be fully explained and the results will be discussed.

5.9.6.1

Energy Management Optimization in Steam Power Plant

Electrical power plants are one of the most important technologies in modern society as electricity plays a significant role in most aspects of our lives, from charging cell phones to powering our homes and businesses. Although new technologies for power generation are being introduced, conventional thermal power plants, such as steam, gas, and combined cycle plants, remain the core of global electricity generation. Power generation cycles are typically complex and composed of a wide range of devices, such

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as heat exchangers, boilers, heaters, pumps, cooling systems and valves. As population grows, the need for energy and in particular electricity will likely become increasingly important [23]. Problems with energy supply and use are related not only to global warming, but also to such other environmental concerns as air pollution (e.g., emissions of such pollutants as SO2 and NOx), acid precipitation, ozone depletion, forest destruction, and emission of radioactive substances. These issues must be accounted for if humanity is to achieve a sustainable energy future with little environmental impact. Electricity generation accounts for approximately 25% of total worldwide GHG emissions [24]. Therefore, more efficient and cost-effective power generation systems with lower GHG emissions are being considered or installed worldwide to support efforts to reduce GHG emissions. For such complex systems, optimization methods can assist through attaining more beneficial system designs, in terms of efficiency and sustainability. The long-term trends of rising prices of energy and decreasing fossil fuel resources make the optimum application and management of energy and energy resources of great importance. In most countries, numerous conventional power plants driven by fossil fuels like oil, coal, and natural gas or by other energy resources like uranium are in service today. During the past decade, many power generation companies have developed and introduced a range of process improvements to steam power plants, especially measures that improve plant efficiencies and/or reduce environmental impacts [3]. Among conventional power plants, steam power plants, gas turbine power plants, and CCPP are the most common. They exhibit relatively low costs and good flexibility, although their efficiencies and emissions vary by plant. As a consequence, it is important to model system and determine the optimal design parameters for each, and this constitutes the focus of this chapter. A simple steam power plant uses water as the working fluid exploits fossil fuels or nuclear energy in a boiler to produce high temperature and high pressure steam. The steam expands in a steam turbine (ST), causing it to rotate and generate electrical power. Saturated vapor exits the ST and enters the condenser. The liquid condensate enters a pump and preheaters, after which it is at the desired boiler inlet conditions. A simple gas turbine power plant consists of an air compressor (AC), a combustion chamber and a gas turbine. In gas turbine power plants, the working fluid is air and the fuel usually is natural gas. Since much energy is wasted in simple gas turbine power plants, a heat exchanger called a HRSG is often added to the gas turbine power plant to facilitate utilization of the waste energy and generation of additional electrical power in a bottoming cycle. Such an integrated system is called CCPP, and usually exhibits a higher thermal efficiency than simple steam and gas turbine power plants. It is obvious that any energy optimization techniques that can result in reducing the losses in power plants will result in energy saving and generating of more electricity. As it was already explained, the main objectives of energy management are to decrease industrial energy use and reduce GHG emissions through the implementation of an optimization.

5.9.6.1.1

Steam power plants

Steam power plants are one of the common systems for electrical power generation. Real plants are quite complex and can generate up to 1000 MW of electricity in units with large STs [24]. One of the main technologies for electricity generation, especially in countries where fossil fuels like coal or natural gas or oil are abundant, steam power plants can use various fuels. Since steam power plants are responsible for most of the electricity generation in the world, a small increase in thermal efficiency can lead to large fuel savings and GHG emission reductions. A simple, idealized Rankine cycle for a steam power plant is shown in Fig. 3. The idealized Rankine cycle consists of the following main processes:

• • • •

isentropic compression in a pump combustion of fuel in a boiler and heat addition at constant pressure isentropic expansion in a turbine heat rejection in a condenser at constant pressure

The water enters the pump of the Rankine cycle in Fig. 3 at point 1 as a saturated liquid and is compressed to the boiler pressure. The temperature increases during isentropic compression and there is a slight decrease in specific volume. High pressure water enters the boiler at state point 2 where heat is added, usually by combusting fuel in air. The boiler is essentially a large heat exchanger composed of several heating elements, for example, economizers, evaporators, and superheaters. The combustion reaction takes place in the boiler if it is fossil fuel-driven and heat is transferred to the water to produce steam at high temperature. In the idealized system, no pressure losses are assumed across the boiler. The high temperature and high pressure steam from the boiler enters the ST where it expands isentropically and produces work by rotating a shaft connected to a generator. Both temperature and pressure decline to the values at point 4 during this process. The steam is then changed back to a liquid in the condenser, where heat qout is rejected to a cooling medium, using a lake, a river, or a cooling tower. Steam exits the condenser as a saturated liquid and enters the pump where its pressure increases to the desired point 2. The efficiency of this simple steam power plant is defined as: Z¼

wturbine wpump qin

ð2Þ

In advanced real steam power plants, additional components are added to the simple cycle in Fig. 3 to increase the thermal efficiency and provide economic and other benefits. One method involves increasing the temperature entering the boiler using

Optimization in Energy Management

361

qin 2

Boiler 3 Wturbine

Wpump

Turbine

Pump 1

4 qout

Condenser

Fig. 3 A simple idealized Rankine cycle.

14

LPT

Gen.

15

10

IPT

LPT

25

Air

13

18

20

21

HPT

Fuel

11

23

Cond.

Exhaust

12 St. Gen.

16

1

CFWH #1 2

CFWH #2 3

OFWH

CFWH #3 4

24

22

CFWH #5 8

5

CP 26

CFWH #4

7 6

9

17 19

BFP

Fig. 4 Schematic of real steam power plant. CFWH, closed feedwater heater; HPT, high pressure turbine; LPT, low pressure turbine; OFWH, open feedwater heater.

feedwater heaters. Another effective method is to decrease the average temperature at which heat is rejected from the working fluid in the condenser. A process flow diagram of a real steam power plant is shown in Fig. 4. This steam power plant in Fig. 4 is located 25 km from the city of Qazvin in Iran, and is a typical plant. The steam power plant uses natural gas as the main fuel and diesel oil as the secondary fuel. The power plant can produce up to 250 MW of electrical power at full load conditions. The main components of the steam power plant are a three stage ST (i.e., a turbine with high pressure, intermediate pressure, and low pressure stages), a steam generator, a drum boiler, feedwater heaters, and a condenser. The natural gas fuel is a mixture of 98.57% methane (CH4), 0.63% ethane (C2H6), 0.1% propane (C3H8), 0.05% butane (C4H10), 0.04% pentane (C5H12), 0.6% nitrogen (N2), and 0.01% carbon dioxide (CO2), where the values represent volume fractions [25]. Data on the operation of this power plant are listed in Table 1.

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Optimization in Energy Management

5.9.6.1.2

Modeling and analysis

For each component, rate balances of mass, energy, exergy, and cost can be applied in order to determine such quantities as work, heat, exergy flow, thermodynamic properties, and energy and exergy efficiencies. Several rate balances based on energy and exergy, for the system component are given below. For a steady-state process, mass, energy, and exergy rate balance equations can be written as follows: X X _ in ¼ _ out m m ð3Þ _ Q

_ Q Ex

_ ¼ W

E_ W ¼

X

X

X

E_ out

_ e Ex

X

E_ in

ð4Þ

_ i þ Ex _ D Ex

ð5Þ

Since the differences in elevation and velocity in a steam power plant are usually not significant, potential and kinetic energy can be assumed negligible. With this assumption, Eq. (4) becomes: X X _ W _ ¼ _ e he _ i hi m m Q ð6Þ

_ Q and E_ W are the exergy rates associated with heat transfer across the system boundary and work produced, In Eq. (5), Ex respectively. The terms can be expressed as follows:  X To _ _ Q¼ 1 Q ð7Þ Ex Tb _ ¼W _ Ex

ð8Þ

_ can be expressed as Neglecting potential and kinetic exergy, the total exergy rate Ex _ ¼ mðex _ Ex ph þ ex ch Þ

ð9Þ

where the specific physical exergy is: exph ¼ ðh

ho Þ

To ðs

so Þ

ð10Þ

The specific chemical exergy of the fuel for the steam power plant can be written as: ex fuel ¼ ξfuel  LHV

ð11Þ

here, LHV represents the lower heating value of the fuel, which is natural gas for this power plant (see Table 1). For gaseous fuels with a general chemical formula of CxHy, a chemical to heating value ratio (ξfuel) can be determined from the following empirical relation as [26]: ξfuel ¼ 1:033 þ 0:0169

y x

0:0698 x

ð12Þ

The exergy rate balance equations and exergy efficiency expressions for the main components of the steam power plant in Fig. 4 are listed in Table 2. The energy rate balance equations, which are based on the first law of thermodynamics, can be used to Table 1

Table 2

Operating conditions of a steam power plant

Parameter

Value

Fuel mass flow rate (kg/s) Plant gross electrical power (MW) Stack flue gas temperature (1C) Lower heating value of fuel (kJ/kg)

13.89 263 115 49433.96

Equations for exergy destruction rate and exergy efficiency for the power plant components

Component

Exergy destruction rate

Boiler

_ fuel þEx _ i E x_ D 0 boiler ¼ Ex _ExD 0 turbine ¼ Ex _ i Ex _ e

Turbine Pump

_ i E x_ D 0 pump ¼ Ex

Feedwater heater

_ i E x_ D 0 fwh ¼ Ex

Condenser

Exergy efficiency _ e Ex W_ turbine

_ e þ W_ pump Ex

_ e Ex P E x_ D 0 condenser ¼ k E x_ i

E x_ e

_ i Zboiler ¼ ðEx Zturbine ¼ 1





_ e Þ=Ex _ fuel Ex _Exturbine =Ex _ i

_ pump =W _ pump Ex   _ fwh =Ex _ i Zfwh ¼ 1 Ex P _ P _ Zcondenser ¼ k Exe = k Exi Zpump ¼ 1

_ e Ex 



Optimization in Energy Management

Table 3

Thermodynamic quantities for the steam power plant in Fig. 4

Point

P (bar)

T (1C)

m_ (kg/s)

h (kJ/kg)

s (kJ/kg K)

ex (kJ/kg)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

0.19 0.22 0.21 0.21 0.21 0.21 168.90 168.90 168.90 137.20 36.60 36.60 33.60 7.37 0.29 36.60 18.53 2.69 8.57 2.69 1.43 1.43 0.62 0.56 61.27 0.27

59.2 61.2 82.6 105.7 125.3 164.6 167.7 203.6 243.1 538 349.1 349.1 538 318.3 68.87 349.1 208.6 318.3 173.3 318.3 210.1 111.3 149.1 88.2 86.7 66.8

173.8 173.8 173.8 173.8 173.8 215 215 215 215 215 211.4 192.7 192.7 171.1 154.4 17.46 17.45 11.81 29.36 11.81 5.948 5.948 6.802 12.75 6.127 18.88

247.8 256.2 345.9 443.3 526.2 695.8 718.4 874.9 1054 3430 3098 3098 3539 3096 2484 3098 894.2 3097 733.8 3097 2888 466.8 2772 369.4 2637 279.6

0.8211 0.8462 1.105 1.363 1.581 1.985 2.000 2.341 2.701 6.535 6.628 6.628 7.286 7.338 7.361 6.628 2.417 7.341 2.074 7.341 7.413 1.433 7.468 1.172 7.478 0.9157

7.543 8.43 20.99 41.51 59.26 108.7 126.7 181.5 252.9 1487 1126 1126 1371 912.8 293.9 1126 177.9 913.2 119.8 913.2 681.8 44.18 550.4 24.59 411.8 11.14

Table 4

363

Comparison of simulated and actual data for the steam power plant

Component

Simulated value

Actual value

Difference (%)

High pressure turbine produced power (MW) Intermediate pressure turbine produced power (MW) Low pressure turbine produced power (MW) Condensate pump consumed power (MW) Boiler feed pump consumed power (MW) Mass flow rate of fuel (kg/s)

80.6 83.4 99.9 0.19 4.85 13.86

79,983 83,114 100,878 208.5 4860 13.89

0.87 0.37 0.88 8.9 0.04 0.21

determine energy related properties. Thermodynamic properties and quantities for the steam power plant in Fig. 4 are listed in Table 3. A simulation code is developed and used in the analyses. In order to verify the simulation code, the results are compared with actual data provided for the power plant in Fig. 4. The comparison results are listed in Table 4. The simulation results are found to be in good agreement with actual data, with most differences due to the assumptions considered in the modeling and the steadystate approximation. The exergy analysis results are presented and the results show that the highest exergy destruction rate is exhibited by the boiler. The boiler irreversibility is associated with two important processes: the irreversibility that arises from the combustion reaction and the irreversibility that arises during heat transfer to the working fluid. In the process where combustion occurs there is a large entropy generation rate, which corresponds to a high exergy destruction rate. In addition, there are two separate streams passing through the boiler, combustion gases at high temperature and cold water; the large temperature difference between these streams also contributes to a high entropy generation rate and exergy destruction rate. The results help identify possible improvements for this device, like increasing the water temperature entering the boiler, preheating the combustion air, and replacing the water tubes in the boiler with ones having better heat transfer characteristics.

5.9.6.1.3

Objective functions, design parameters, and constraints

To apply a multiobjective optimization, we first define objective functions and establish proper constraints. We also need to consider the major design parameters for the system. In a steam power plant, several design parameters are possible, but we consider only the major ones here. For the given steam power plant, exergy efficiency and total cost rate are taken to be the

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Optimization in Energy Management

objective functions. They are defined, respectively, as follows: c¼

_ net W _ fuel LHV ξm

ð13Þ

aFZin þ C_ f ð14Þ t  3600 here, the maintenance factor is f ¼1.06, and Zin is the total purchase equipment cost (in US dollars), listed in Table 5. Also, a is the annual cost coefficient, evaluated as Z_ ¼

a¼

i ð1 þ iÞ

1

ð15Þ

n

here, i and n are the interest rate and depreciation time, respectively [15]. In Eq. (14), C_ f is the fuel cost rate, which is calculated as _ f LHV C_ f ¼ cf m

ð16Þ

The design parameters are selected for consideration when maximizing the exergy efficiency and minimizing the total cost rate are ST inlet pressure, ST inlet temperature, extraction pressures for the turbines, isentropic efficiencies for the turbines and pumps, reheat pressure, and condenser pressure. The constraints considered for the optimization study are listed in Table 6. The steam cycle optimum design parameters are obtained for the operating power plant described in the previous section. As already explained, the exergy efficiency and total cost rate are the objective functions. The number of iterations for finding the global extremum in the overall searching domain is about 3  1033. The system is optimized for a depreciation time n¼ 20 years, Table 5

Cost functions in terms of thermodynamic parameters for the steam power plant components

System component

Capital or investment cost functions

Boiler

ZBoiler ¼ a1 ðm_ boiler Þ 2 Fp FT FZ FSH=RSH      Fp ¼ exp Pe a3P e ; FT ¼ 1 þ a5 exp Te a6T e ; FZ ¼ 1 þ 11

a

_

RSH FSH=RSH ¼ 1 þ Te TTe iSH þ mm_ boiler
TeRSHTeRSHTiRSH

Z1 Z1

a4

T e ¼ 5931C; P e ¼ 28 bar; Z1 ¼ 0:9; a1 ¼ 208582 $=kgs a2 ¼ 0:8; a3 ¼ 150 bar; a4 ¼ 7; a5 ¼ 5; a6 ¼ 10:421C a2

Deaerator

ZDearator ¼ a1 ðm_ water Þ

a1 ¼ 145315 $=kW0:7

a2 ¼ 0:7

    3   0:7 a 866K  1 þ 5:exp T10:42K 1 þ 10:05 ZST ¼ a51 :PST Z

Steam turbine (ST)

ST

a51 ¼ 3880:5 $=kW0:7 _

Condenser

cond ZCOND ¼ a61 : kQ:DT þ a62 :m_ CW þ 70:5:Q_ cond  in



 0:6936:Ln T CW

a61 ¼ 280:74 $=m 2 ; a62 ¼ 746 $=ðkg:sÞ; k ¼ 2200 W =ðm 2 :KÞ   0:71 1 þ 1 0:2 ; a71 ¼ 705:48 $=ðkg:sÞ ZPUMP ¼ a71 :PPUMP Z

Pump

  Tb þ 2:1898

PUMP

Source: Reproduced from Ameri M, Ahmadi P, Hamidi A. Energy, exergy and exergoeconomic analysis of a steam power plant: a case study. Int J Energy Res 2009;33(5):499–512.

Table 6

Design parameters for the steam power plant and their ranges

Design parameter

Lower bound

Upper bound

T10 (1C) P10 (MPa) P16 (MPa) P18 (MPa) P20 (MPa) P21 (MPa) P23 (MPa) P25 (MPa) P1 (MPa) Zt ZP

500 8 2 1 0.4 0.3 0.1 0.05 0.005 0.7 0.5

550 17 4 2 1 0.4 0.3 0.1 0.5 0.9 0.8

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Total cost rate ($/s)

1.3

365

C

1.25

B 1.20 Ideal point A 1.15 0.37 0.375 0.38 0.385 0.39 0.395 0.4 0.405 0.41 Exergy efficiency

Fig. 5 Pareto frontier, identifying best trade off values for the objective functions.

Table 7

Values of design parameters for optimum selected points A–C in Pareto optimum front

Design parameter

A

B

C

T10 (1C) P10 (MPa) P16 (MPa) P18 (MPa) P20 (MPa) P21 (MPa) P23 (MPa) P25 (MPa) P1 (MPa) Zt ZP

545 9 2.07 1 0.5 0.33 0.28 0.05 0.007 0.88 0.69

540 9 2.08 1.08 0.52 0.34 0.29 0.05 0.007 0.87 0.66

550 16.71 2.2 1.05 0.55 0.35 0.3 0.0521 0.007 0.9 0.65

and an interest rate i¼ 0.11. The developed GA code is applied for optimization for 600 generations using a search population size of M¼ 100 individuals, a crossover probability of pc ¼0.9, a gene mutation probability of pm ¼ 0.03 and a controlled elitism value c ¼ 0.55. The results for the multiobjective optimization of the steam power plant are shown in Fig. 5. The Pareto frontier solution is shown for this system with objective functions as shown in Eqs. (13) and (14) in multiobjective optimization. It can be seen in this figure that the total cost rate of products increases moderately as the total exergy efficiency of the cycle increases to about 40%. As shown in Fig. 5, the maximum exergy efficiency exists at design point C (40.05%), while the total cost rate of products is the greatest at this point (1.28 $/h). But, the minimum value for the total cost rate of product occurs at design point A which is about 1.15 $/h. Design point A is the optimal situation when total cost rate of product is the sole objective function, while design point C is the optimum point when exergy efficiency is the sole objective function. In multiobjective optimization, a process of decision making for selection of the final optimal solution from the available solutions is required. The process of decision making is usually performed with the aid of a hypothetical point in Fig. 5 (the ideal point), at which both objectives have their optimal values independent of the other objectives. The optimal design parameters for points on the Pareto curve are listed in Table 7. The optimization results for this power plant demonstrate that if the design parameter point C in Fig. 5 is selected the exergy efficiency of the plant increases for approximately 4% which is a significant improvement.

5.9.6.2

Energy Management Optimization for Energy Storage Tank

By increasing the interests in renewable energy technologies, the need for energy storage has increases during the last decades. In addition, the efficiency of thermal, cooling, and power systems can be improved by decoupling the production of electricity, cooling, and heating by energy storage tank (EST) systems where energy that is not needed during the production period can be stored for later use. For example, most of the design rules of solar plants are based on steady-state models, whereas solar irradiance, consumption, and thermal accumulation are inherently transient processes [27]. In this section, thermodynamic modeling of EST is conducted; and the equipment size is optimized by minimizing the TAC. It is obvious that energy management optimization will result in total cost saving. In order to do this, 24 design parameters

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Optimization in Energy Management

200 Typical required load Some possible Chiller load

180 160

Partial storage

Full storage

Load (kW)

140 120 100 80 60 40 20 0 0

2

4

6

8

10

12 14 Time (h)

16

18

20

22

24

Fig. 6 Typical required cooling load and some possible chiller loads.

including the operational strategy of chiller in each hour during a sample day are selected. Since the number of design parameters is high, evolutionary based algorithms that are capable of handling high number design parameters optimization are used. Particle swarm optimization (PSO) and GA are applied to determine the optimal design parameters satisfying several practical constraints.

5.9.6.2.1

Thermodynamic modeling

Typical required cooling loads as well as chiller supplied load for a typical day are shown in Fig. 6. Here f(t) and g(t), respectively, represent the required cooling and chiller loads. During the times between t0–t1 and t2–tend the chiller loads are higher than the required cooling load demands and excess load is stored in the tank. On the other hand in the t1 to t2 period, the required cooling load is higher than the chiller load and some part of energy is released from tank (discharging case). The total charging energy in the storage tank during a day is: Z t ¼ t 1  Z t ¼ tend ðgðtÞ f ðtÞÞdt þ ðgðtÞ f ðtÞÞdt ð17Þ Qcharg ¼ Zstor t ¼ t0

t ¼ t2

where Zstor is storage tank efficiency. The total discharging energy in the stored tank during a day is also estimated as: Z t ¼ t 2  ðf ðtÞ gðtÞÞdt Qdis ¼ Zstor

ð18Þ

t ¼ t1

In this case, the required capacity for the chiller is estimated as: ¼ tend Qchil ¼ max ðgðtÞÞjtt ¼ t0

In addition, the required capacity for the storage tank is estimated as: Z t ¼ t1 Z t ¼ tend Qstor ¼ max ðgðtÞ ðgðtÞ f ðtÞÞdt; t ¼ t2

t ¼ t0

ð19Þ

f ðtÞÞdt



ð20Þ

here, three different operational strategies including partial storage (PS), full storage (FS), and variable storage (VS) as shown in Fig. 6 are studied. In the PS strategy, the constant cooling load is supplied by the chiller during all hours in a day. This strategy has the lowest flexibility for the best matching between the required and chiller cooling load. As a result, it is predictable that PS has the lowest advantage compared with other cases. Such as PS strategy, constant chiller cooling load is produced in the FS strategy but no chiller cooling load is provided for the hours in which the required load is bigger than the chiller nominal capacity. In fact, the cooling load is supplied by the storage tank in these hours.

5.9.6.2.2

Objective function, design parameters and constraints

For the optimization, TAC is considered as the sole objective function and optimization technique is applied. The cost objective function can be expressed as: Ctotal ¼ a:j:Cin þ Cop

ð21Þ

Optimization in Energy Management

367

In this equation, Cin is the equipment purchase cost including chiller and storage tank in US dollars, which is estimated as [28]: Cin;chil;el ¼ 482ðQchil Þ0:93

159:7ðQchil Þ

ð22Þ

Cin;chil;ab ¼ 540ðQchil Þ0:8713

ð23Þ

Cin;stor ¼ 33ðQstor Þ

ð24Þ

here, Cin,chil,el and Cin,chil,ab are investment cost of the electrical and absorption chiller, respectively. Furthermore j is a maintenance factor and a is the annual cost coefficient defined as: a¼

i ð1 þ iÞ

1

n

ð25Þ

where i and n are the interest rate and depreciation time, respectively. In addition, Cop is the yearly operational cost for energy (electricity for electrical chiller and fuel for absorption chiller) required for the chiller, which is computed for electrical and absorption chillers as below: " # tX ¼ tend ðgðtÞ  ξel ðtÞÞ Cop;el ¼ t ð26Þ ðtend t0 ÞCOP t ¼ t0 Cop;el ¼

"

tX ¼ tend t ¼ t0

# gðtÞ  ξf 3600 t ðtend t0 ÞCOP  LHV 

ð27Þ

In these equations ξf and ξel(t) are the fuel cost and electricity unit cost for each hour during a day and t is the annual number of operating hours. COP and LHV are the chiller coefficient of performance (COP) and fuel LHV, respectively. In order to minimize the TAC, 24 design parameters including the operational strategy of the chiller at each hour during a sample day are selected. Two more common strategies named partial and FS are also included in the possible optimum design [29]. Basically, the total charging energy in the tank should be equal or higher than the discharging energy to provide the total required loads. As a result the following constraint should be considered in the optimization process: Z t ¼ tend Qcharg  Qdis ) ðf ðtÞ gðtÞÞdt  0 ð28Þ t ¼ t0

5.9.6.2.3

Optimization results

As it was mentioned, TAC is our objective function. Thus, optimizations are performed separately for three different strategies including PS, FS, and VS. In addition, the above procedure is performed for both electrical and absorption chillers. Design parameters are the operational chiller strategy during a day, which is specified by the g(t) function. To estimate the optimum g(t) function, the value of cooling supplied by chiller in each hour is selected as a design parameters, which leads to the 24 design parameters for a typical load demand during a day. The lower and upper bound of the design parameters (decision variables) are selected as 0 and maximum required cooling load, respectively. The PSO is performed for 1000 iterations, using 100 particles, an inertia weight of w¼ 0.75, a self-confidence factor of c1 ¼ 1.5 and a swarm confidence factor c2 ¼ 1.6 for each of the strategies including PS, FS, and VS and for both electrical and absorption chillers. Their results are presented in Table 1 and Figs. 7 and 8. The optimum TAC obtained are 11,777, 15,746, and 13,662 $/y, respectively, for the VS, PS, and FS strategies in the case of the electrical chiller. Actually a 25.21% and 13.80% improvement in TAC is observed in the VS strategy compared with PS and FS strategies, respectively. Furthermore, the optimum TAC are obtained 12,473, 16,299, and 13,559 $/y, respectively, for VS, PS, and FS strategies in the case of the absorption chiller. Actually a 23.47% and 8.01% improvement in TAC is observed in the VS strategy compared with PS and FS strategies, respectively (Table 8).

5.9.6.3

Hybrid Wind–Photovoltaic Battery System

Hybrid energy systems are a kind of integrated energy system that can use multiple energy sources to produce useful output such as electricity, heating, cooling, hydrogen, etc. Here a wind–PV battery hybrid system is considered and energy management and optimization are conducted and results are presented.

5.9.6.3.1

Modeling

In this section, we consider a hybrid solar–wind system with battery storage, as shown in Fig. 9. It is mainly comprised of solar PV arrays, wind fields, a battery bank, as well as an inverter to convert the direct electrical current to alternating current. Modeling of the components is described in the following subsections.

5.9.6.3.2

Photovoltaic panel

The total solar radiation incident on a tilted panel is a function of the direct beam and diffuse radiation. The latter comes from all areas of the sky except the solar position, and includes circumsolar diffuse radiation, diffuse radiation from the horizon, reflected

368

Optimization in Energy Management

250 Demand load VS strategy PS strategy FS strategy

Load (kW)

200

150

100

50

0

0

2

4

6

8

10

12 14 Time (h)

16

18

20

22

24

Fig. 7 Optimum results of operating electrical chiller in different operational strategies. FS, full storage; PS, partial storage; VS, variable storage.

250 Demand load VS strategy PS strategy FS strategy

Load (kW)

200

150

100

50

0 0

2

4

6

8

10

12 14 Time (h)

16

18

20

22

24 25

Fig. 8 Optimum results of operating absorption chiller in different operational strategies. FS, full storage; PS, partial storage; VS, variable storage.

Table 8

Optimum total annual cost (TAC) for electrical and absorption chillers in various strategies

Electrical chiller

Absorption chiller

Variable storage (VS)

Partial storage (PS)

Full storage (FS)

VS

PS

FS

11,777

15,746

13,662

12,473

16,299

13,559

radiation from the surroundings. The total solar radiation incident on a tilted surface can be written as [30]:    I_ b Rb þ I_ d 1 I_ t ¼ I_ b þ I_ d I_ h

I_ b I_ h



    qffiffiffiffiffiffiffiffiffiffiffi 1 þ cos b b 1 1 þ I_ b =I_ h sin3 þ I_ h rg 2 2

cos b 2



ð29Þ

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369

PV array

Wind turbine

Charge wind controller

PV charge controller

DC−AC Inverter

120/240 VAC load

Battery bank Fig. 9 Schematic diagram of hybrid solar–wind system with a battery storage system. PV, photovoltaic.

here, I_ b , I_ d , I_ h , Rb, b, and rg denote direct beam radiation, diffuse radiation, sum of direct and diffuse beam, the ratio of total radiation incident on the titled surface to that incident on a horizontal surface, surface tilt angle, and reflectance from the surroundings. Details of computing direct beam and diffuse radiation are described elsewhere [31]. The net output power of a PV panel (Ppv) with an open circuit voltage Voc_real and short circuit current Isc_real under real operating conditions can be written as follows [32]: Ppv ¼ ff ðVoc_real  Isc_real Þ

ð30Þ

ff ¼ Pmax =ðVoc Isc Þ

ð31Þ

where ff is the fill factor, given by: here, Pmax denotes the maximum output power of a PV collector, Voc is the open circuit voltage of the PV collector in the laboratory condition and Isc is the PV short circuit current under laboratory conditions. Note that values of Pmax, Voc, and Isc are typically obtained from PV module manufacturers. The quantities Voc_real and Isc_real can be expressed as follows [32]: Voc_real ¼ Voc þ fVoc_T  Tc  Isc_real ¼ ½ISC þ fIsc_T ðTc Tstd ފ I_ t =I_ std I_ t

ð32Þ ð33Þ

where fVoc_T and fIsc_T are temperature correction coefficients of current and voltage, respectively. Also, Tstd (in 1C) and I_ std (in W/ m2) are the temperature and solar radiation at standard conditions, respectively, and Tc (1C) is the surface temperature of the PV panel. The parameters fVoc_T, fIsc_T, Tstd, and Gstd depend on the type of module used and are usually obtained from PV module manufacturers.

5.9.6.3.3

Wind turbine

WT manufacturers usually provide turbine power curves at different wind speeds. If the power curve of the turbine is not available, the following can be used to estimate the power output of a WT [33]:  0 if VoVc    n  Vcn P V if Vc oVoVr Ptur ¼  er ð34Þ Vrm Vcn    Per if Vr oVoVf

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Optimization in Energy Management

where Ier is the rated electrical power, V (in m/s) is the wind speed, Vc is the cut-in wind speed; Vr is the rated wind speed, and Vf is the cut-out wind speed. Setting the exponents m and n to 2 and 3 is often sufficiently accurate for analysis of wind power systems [33].

5.9.6.3.4

Battery

Due to the intermittency of solar collectors and WTs, the battery capacity constantly changes in PV/WT/battery-based hybrid system. In such a system, the SOC of the battery is evaluated under two possible states. When the total power output of the PV panels and WTs exceeds the demand load, the battery bank is in a charging situation. Quantities involved in battery charging can be determined with the following expression [34]:  PBat ðtÞ ¼ PBat ðt 1Þ  ð1 sÞ þ PPV ðtÞZInv þ PWT ðtÞ  Z2Inv Pdmn ðtÞ=ZInv  ZBat ð35Þ

where PBat(t) and PBat(t 1) are the charge quantities of the battery bank at times t and t 1, respectively. In addition, s denotes the hourly self-discharge rate, ZInv the inverter efficiency, Pdmn the demand, and ZBat the battery charge efficiency. When the total output of the PV panels and WTs is lower than the demand load, the battery bank is in a discharging situation. For simplicity, the discharge efficiency of battery bank is assumed here to be 1. As a result, the discharge quantity of the battery bank at time t can be expressed as [34]:  PBat ðtÞ ¼ PBat ðt 1Þ  ð1 sÞ PPV ðtÞZInv PWT ðtÞ  Z2Inv þ Pdmn ðtÞ=ZInv =ZInv ð36Þ

5.9.6.3.5

Objective function, design parameters, and constraints

In this section, the TAC is considered as the objective function. TAC includes the capital cost of equipment including WTs, PV panels, batteries, and the inverter as well as the maintenance cost, and can be written as: TAC ¼ aCin þ Cm

ð37Þ

Cin ¼ n1 CPV þ n2 CWT þ n3 CBat þ n4 Cinv

ð38Þ

Cm ¼ n1 CPV;m þ n2 CWT;m

ð39Þ

where

here, n1, n2, n3, and n4 are the numbers of PV panels, WTs, batteries and inverters, respectively, while CPV, CWT, CBat, and Cinv are unit cost of the PV panels, the WTs, the batteries, and the inverters, respectively. Also, CPV,m and CWT,m are the unit costs of maintenance for the PV panels and WTs, respectively, and a denotes the annual cost coefficient, defined as [35]: a¼

1

i ð1 þ iÞ

y

ð40Þ

where i and y are the interest rate and the depreciation time, respectively. Some equipment in the PV/WT/battery system needs to be replaced several times during the project lifetime. Here, the battery lifetime is considered to be 5 years. Using the single payment present worth factor the present worth of battery CBat can be expressed as follows: ! 1 1 1 CBat ¼ PBat 1 þ þ þ ð41Þ ð1 þ iÞ5 ð1 þ iÞ10 ð1 þ iÞ15 where PBat is the price of the battery. Also, the lifetime of the inverter is considered here to be 10 years, so the present worth of inverter CInv can be expressed using the single payment present worth factor as follows: ! 1 CInv ¼ PInv 1 þ ð42Þ ð1 þ iÞ10 where PInv is the inverter price. The numbers of PV panels, WTs, and inverters are considered as design parameters. For the PV/WT/battery system, at any time, the charge quantity of the battery bank should be selected in the range of PBat,min to PBat,max. The maximum charge quantity of the battery bank takes on the value of the nominal capacity of the battery bank and the minimum charge quantity of the battery bank is obtained by maximum depth of discharge (DOD), which can be calculated as: PBat ¼ ð1

DODÞSBat

ð43Þ

here SBat is the nominal capacity of the battery bank (in Wh).

5.9.6.3.6

Real parameter genetic algorithm

Binary coded GAs are used in many engineering optimization problems. But, application of the binary GA in continuous search space has two difficulties. The first one is the Hamming cliffs related to the certain strings in which a transition to a neighboring solution needs the variation of many bits [36]. The second problem is the inability to have any arbitrary precision in the optimal solution. To overcome these difficulties, the real parameter genetic algorithm (RPGA) is used. The main difference between binary and real parameter GAs is in the crossover and mutation operators. In fact, the decoding operator in binary coding is eliminated in

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371

RPGA and the optimization problem is a step easier compared with the binary coded GAs. Since the selection operator works with the fitness value, any selection operator used with binary coded GAs can also be used in real parameter GAs. The steps in the RPGA, which is used for optimization in this analysis of the system, are as follows: 1. The initial population with M chromosomes is randomly generated using lower and upper bounds of the design parameters, xmin and xmax, as follows: xt0 ¼ xmin þ randðxmax

xmin Þ

ð44Þ

where rand is a uniformly distributed random function. 2. Each chromosome is exported to the thermoeconomic modeling section and returned back with a value of objective function (TAC). 3. The selection operator is performed to choose the better chromosomes in the GA method [36]. 4. The crossover operator is performed using the following relations [36]: n o ð1;tþ1Þ ð1;tÞ ð2;tÞ ð45Þ ¼ 0:5 ð1 þ bi Þxi þ ð1 bi Þxi xi ð2;tþ1Þ

xi here, bi is: δ¼

(

n ¼ 0:5 ð1

ð2 randÞ1=ð1þam Þ 1

½2ð1

ð1;tÞ

bi Þxi

ð2;tÞ

þ ð1 þ bi Þxi

1

randފ1=ð1þam Þ

o

if

rando0:5

if

rand  0:5

where ac is crossover constant parameter. 5. Then, mutation operator is performed on the population as follows [36]:  ð1;tþ1Þ ð1;tþ1Þ ¼ xi þ xmax xmin δ xi i i

ð46Þ

ð47Þ

ð48Þ

where δ is:

DTlm ¼

ðTh;i Tc Þ ðTh;o lnððTh;i Tc Þ=ðTh;o

Tc Þ Tc ÞÞ

ð49Þ

in which, ac is mutation constant parameter. 6. This procedure is repeated from step 2 until the convergence criterion is met.

5.9.6.3.7

Case study

The PV/WT/battery system optimization procedure is applied for a residential area located in three provinces in Iran. These include Tabriz, Tehran, and Zahedan, representing, respectively cold, moderate, and hot climates. The values of equipment lifetime (k) and interest rate (i) are considered to be 15 years and 15%, respectively. In addition, a WT with a nominal capacity of 9.8 kW, a battery with nominal capacity of 200 Ah and PV panels with a nominal capacity of 240 W are used. The constants of investment cost in Eq. (37) are considered to be n ¼ (3200, 614, 130, 40) based on equipment available in the marketplace. Hourly and daily variations in electrical demand load for the studied case are shown in Fig. 10.

5.9.6.3.8

Results and discussion

The hourly variation of solar radiation, wind velocity and ambient temperature for the three studied climates – including cold (Tabriz), moderate (Tehran), and hot (Zahedan) – are depicted in Fig. 11. Then, the RPGA is applied for 200 iterations, using 100 chromosomes, a mutation factor of am ¼ 2 and a crossover factor ac ¼ 2. Note that the RPGA is run three times using a core i7-3200 GHz processor for each case and the best results are presented here. To ensure a reasonable result, the hourly analysis during a year is performed. As a result of there being 8760 h during a year, each optimization process needs about 5 h to be completed. To accelerate the optimization process, six separate optimization programs are run simultaneously on the mentioned processor. The optimum results for the TAC and corresponding values of the design parameters for each studied case are listed in Table 9. Zahedan with its hot climate has a significantly lower TAC compared with the other studied cases. Tabriz has the next lowest TAC, followed by Tehran. The annual average ambient temperature, incident solar radiation, and wind speed are listed in Table 10. Zahedan is observed to have the highest average of incident solar radiation and wind velocity. Although the higher temperature decreases the PV efficiency for the case of Zahedan compared with other studied cases, the higher potential solar and wind power more than compensate for this reduction. Due to the low wind velocity and low incident solar radiation in Tehran, the highest TAC is obtained in this case. To attain good insight into the hourly variation of power supply by PV and wind, demand power and battery charging and discharging, variations of these parameters at the optimum condition for the three studied cases during 6 selected months are

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Optimization in Energy Management

4.5 Hourly Daily

4.0

Demand load (kW)

3.5 3.0 2.5 2.0 1.5 1.0 0.5

0

1000

2000

3000

4000 5000 Time (h)

6000

7000

8000

9000

Fig. 10 Variation in electrical demand load for studied case.

shown in Figs. 12–14. Note that the variation is shown for the middle week of each month. The following points can be are illustrated in Figs. 12–14: In summary following conclusions can be obtained from this hybrid energy system.

• • •

• • •

Discharging is high in the cold months of a year, such as January and November. The insolation angle on PV panels during these months is high and as a result lower radiation levels are received by the panels. As a result, stored electricity is released. The minimum number of batteries required is determined by the months in which lower levels of solar radiation are available. Because of the delay between periods of PV power generation and peak demand, excess electricity is generated and stored during periods with the maximum incident solar radiation. The best matching between PV collectors and WTs occurs when the maximum solar radiation is received at different times than when the maximum of wind speeds occur. For this situation the lowest amount of energy must be stored and, as a result, a lower capacity storage system is needed, which reduces the TAC. For the three studied regions, Zahedan exhibits the best matching between solar radiation and wind speed and thus has a much lower TAC compared with Tehran and Tabriz. Regular fluctuations in battery charging and discharging are observed for months with high levels of solar radiation, such as May and September due to the dominant share of PV power output and roughly regular variations of solar radiation during these months. Due to the low level of incident solar radiation in Tabriz in January, more WTs should be employed to compensate for the reduction of solar-based electrical power. Alternatively, other means could be employed to provide the lacking electrical power for these months, to decrease the number of WTs. A significant portion of the electricity generated by this plant is in general wasted due to the large variations between electrical supply and load demand during the year.

5.9.6.4

Heat Exchanger Optimization for Thermal Management of Electric Vehicles

The change in recent global average temperature cannot be justified only as a specific phenomenon of nature. Designs and modifications in industrial processes can leave an impact on the environment and lead to related issues [37]. This shows the deterministic effect of energy systems in the development of human society and its shift to sustainability. The main difference between electric powered and internal combustion engine (ICE) cars, from the energy point of view, is the source of energy. While the gas is a fossil-based energy source used in ICE mode (apart from new developments like cold flame combustion for conventional vehicles), the exploited electricity that is stored in the battery packs in electric vehicle (EV) or hybrid electric vehicles (HEVs) can be produced either from power plants with fossil fuels or from renewable energy sources. This is a very critical point in development process of these cars though in a controversial case. If the trend is going to use more renewable energies to produce electricity, environmental impacts of EVs and HEVs will be reduced. In addition, the conventional vehicles using these fossil fuels cause excessive atmospheric concentrations of GHGs, where the transportation sector is the largest contributor in the United States with over a quarter of the total GHG emissions [38]. Due to the limited time spent in vehicles compared to buildings, along with the competing energy requirements between the cabin and the battery, the thermal management systems (TMS) must be capable of conditioning the air in the passenger cabin

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Tabriz

V (m/s)

15 10 5 0

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0

1000

2000

3000

4000 5000 Time (h)

6000

7000

8000

9000

R (kW/m2)

1.5 1.0 0.5 0

T (°C)

40 20 0 −20

Tehran

V (m/s)

15 10 5 0

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0

1000

2000

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4000 5000 Time (h)

6000

7000

8000

9000

R (kW/m2)

1.5 1.0 0.5 0

T (°C)

50 0 −50

Zahedan

V (m/s)

15 10 5 0

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0

1000

2000

3000

4000 5000 Time (h)

6000

7000

8000

9000

R (kW/m2)

1.5 1.0 0.5 0

T (°C)

50 0 −50

Fig. 11 Hourly variation of solar radiation, wind velocity, and ambient temperature for the three studied cities.

373

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Optimization in Energy Management

Table 9

Values of optimum design parameters and objective function for three studied cases

Region

Number of photovoltaic (PV) units

Number of wind turbine (WT) units

Number of battery units

Total annual cost (TAC) ($/y)

Tabriz Tehran Zahedan

52 122 45

10 1 2

30 23 27

10,145 10,516 6,186

Table 10

Annual average of selected parameters for the studied cases

Region

Ambient temperature (1C)

Incident solar radiation (kW/m2)

Wind speed (m/s)

Tabriz Tehran Zahedan

12 17 18

0.20 0.20 0.22

3.27 2.45 3.33

quickly and quietly, while keeping the vehicle components operating under ideal operating temperature ranges (especially the electric battery) to prolong their lifetime, increase the fuel efficiency and all electric range. TMS utilize the available and waste heat sources and dissipation opportunities in order to minimize the heat energy loss and increase the efficiency. It requires rather reasonable balance among the resources for different operating conditions. The core of the future improvement of these HEVs will be definitely focused on better using of heat source and sink in a proper way, to decrease the amount of net energy extracted from the high voltage battery pack. This will lead to longer life of the battery pack and extended range drive for the HEV/EVs. Thermal management system of the HEVs has been improved by introducing the phase change materials (PCM) as passive cooling (heating) system. Based on the literature review the shell-and-tube heat exchanger has better effectiveness when the PCM fills the shell side. In this section, two tube configurations have been considered, including straight and helical tube heat exchanger. In addition the role of extended surfaces is also taken into account. Based on the constraints, such as limited volume and length of the heat exchanger, the optimization has been carried out. These design aspects are mostly caused by the fact that the heat exchanger should be placed in the vehicle’s available space.

5.9.6.4.1

Thermal modeling of heat exchanger

Logarithmic mean temperature difference (LMTD) method is applied here for predicting the heat exchanger performance. The rate of heat transfer is estimated as [39]: _ p DTÞh ¼ ðmc _ p DTÞc Q ¼ UAtot DTlm ¼ ðmc

ð50Þ

DTlm is the logarithmic mean temperature calculated from: DTlm ¼

ðTh;i Tc Þ ðTh;o  ln ðTh;i Tc Þ=ðTh;o

Tc Þ Tc Þ

ð51Þ

where, h and c are subscripts of hot and cold stream, respectively. U is the overall heat transfer coefficient and Atot is total heat transfer surface area computed from [39]: U¼

1 Ao 1 Ai hi

þ

1 Zo ho

þ

Rf ;o Zo

þ AAoi Rf ;i þ Ao Rw

Atot ¼ Ab þ Nf  sf

ð52Þ ð53Þ

Ab and Ai are outside base and internal heat transfer surface area defined as: Ab ¼ pdo Nf ðsf

tÞ

Ai ¼ pdi Nf sf

ð54Þ ð55Þ

where di, do, t, Nf, and sf are inside and outside tube diameters, fin thickness, number of fins, and distance between the fins. Moreover, Zo in Eq. (52) is overall surface efficiency defined as follows and Rw is wall thermal resistance [39]: Zo ¼ 1

Nf Af ð1 Atot

Zf Þ

ð56Þ

and Zf is efficiency of a single fin. It is worth mentioning that the fin’s efficiency is 1 when there are no fins. Considering the circular fin for the external surface, the fin efficiency is defined as: Zf ¼

C2 ½K1 ðmr1 ÞI1 ðmrc2 Þ I1 ðmr1 ÞK1 ðmrc2 ފ ½I0 ðmrr1 ÞK1 ðmrc2 Þ þ K0 ðmr1 ÞI1 ðmrc2 ފ

ð57Þ

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January

20

16

18 Power output or load (kW)

Power output or load (kW)

18

14 12 10 8 6 4 2 0

20

40

60

80 100 Hour of week

120

140

May

12 10 8 6 4

20

40

60

Demand load PV Wind SOC

10 8 6 4 2

16

20

40

60

80 100 Hour of week

120

140

140

160

120

140

160

10 8 6 4

20

40

60

80 100 Hour of week November

20 Demand load PV Wind SOC

18 Power output or load (kW)

Power output or load (kW)

120

12

0

160

14 12 10 8 6 4 2 0

160

14

September

16

140

2

20 18

120

Demand load PV Wind SOC

18

12

80 100 Hour of week July

20

14

0

14

0

160

Power output or load (kW)

Power output or load (kW)

16

16

Demand load PV Wind SOC

2

20 18

March

20

Demand load PV Wind SOC

375

16

Demand load PV Wind SOC

14 12 10 8 6 4 2

20

40

60

80 100 Hour of week

120

140

160

0

20

40

60

80 100 Hour of week

Fig. 12 Hourly variation over 1 week of PV power output, wind power output, demand load, and battery state-of-charge (SOC) for several months in Tabriz. PV, photovoltaic.

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376

January

March

30 Demand load PV Wind SOC

20 15 10 5 0

Demand load PV Wind SOC

25 Power output or load (kW)

25 Power output or load (kW)

30

20 15 10 5

20

40

60

80 100 Hour of week

120

140

0

160

20

40

60

80 100 Hour of week

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140

160

120

140

160

60 80 100 120 Hours of week

140

160

July

May 30

30 Demand load PV Wind SOC

25 Power output or load (kW)

Power output or load (kW)

25 20 15 10

20 15 10 5

5 0

Demand load PV Wind SOC

20

40

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80 100 Hour of week

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140

0

160

20

40

60

80 100 Hour of week

September

November 30

Demand load PV Wind SOC

25

Power output or load (kW)

Power output or load (kW)

30

20 15 10 5 0

25

Demand load PV Wind SOC

20 15 10 5

20

40

60

80 100 Hours of week

120

140

160

0

20

40

Fig. 13 Hourly variation over 1 week of PV power output, wind power output, demand load, and battery state-of-charge (SOC) for several months in Tehran. PV, photovoltaic.

Optimization in Energy Management

January

March

14

14 Demand load PV Wind SOC

Demand load PV Wind SOC

12 Power output or load (kW)

Power output or load (kW)

12 10 8 6 4 2 0

10 8 6 4 2

20

40

60

80 100 Hour of week

120

140

0

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20

40

Demand load PV Wind SOC

Power output or load (kW)

Power output or load (kW)

120

140

160

120

140

160

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140

160

Demand load PV Wind SOC

12

10 8 6 4 2

10 8 6 4 2

20

40

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80 100 Hour of week

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140

0

160

20

40

September

60 80 100 Hours of week November

14

14 Demand load PV Wind SOC

Demand load PV Wind SOC

12 Power output or load (kW)

12 Power output or load (kW)

80 100 Hour of week

14

12

10 8 6 4

10 8 6 4 2

2 0

60

July

May 14

0

377

20

40

60

80 100 Hours of week

120

140

160

0

20

40

60 80 100 Hours of week

Fig. 14 Hourly variation over 1 week of PV power output, wind power output, demand load, and battery state-of-charge (SOC) for several months in Zahedan. PV, photovoltaic.

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where I and K are modified Bessel function of first and second kind. In addition, C2 and m are as follows: C2 ¼

2r 2 r12 Þ mðr2c

ð58Þ

sffiffiffiffiffiffiffiffi 2ho m¼ kw t

ð59Þ

In addition hi in Eq. (60) is convection heat transfer coefficient in tube side estimated as follows [40]: ! kf 0:0677:ðRe:Pr:di =LÞ1:33 hi ¼ : 3:657 þ for Rer2300 di 1 þ 0:1:PrðRe:di =LÞ0:3 8 9 > f > kf < 2  ðRe 1000Þ:Pr = qffiffi hi ¼
for 2300oRer10; 000 di > :1 þ 12:7: f ðPr 0:67 1Þ> ;

ð60Þ

ð61Þ

2

f ¼ ð1:58logðReÞ

8 > kf < hi ¼ : di > :1:07 þ 900 Re

f 2

3:28Þ

2

9 > =

 Re:Pr

0:63 1þ10Pr

qffiffi þ 12:7: 2f ðPr 0:67

f ¼ 0:00128 þ 0:1143 ðReÞ

for

Re410; 000

ð62Þ

> 1Þ;

0:311

f is the friction factor and Re is Reynolds number defined as: _ Re ¼ 4m=ðpd i mNÞ

ð63Þ

where N is the number of tubes. Furthermore, the pressure drop was also estimated from: _ 2 =ðrp2 d2i Þ DP ¼ 4N  f  L  m

ð64Þ

where f is the friction factor in the tube side. Furthermore, the outside convection heat transfer coefficient (ho) is estimated as follows [41]: Nu  kf ð65Þ Drm where kf, Drm, and Nu are PCM conductivity, thickness of heat storage material, and Nusselt number estimated as follows [41]:   Drm Ra 0:25 for Ra  1000 and Drm r0:006 ð66Þ Nu ¼ 0:28 L ho ¼

Nu ¼ 1

for

Nu ¼ 0:133ðRaÞ0:326



Rao1000 and Drm r0:006 0:0686 Drm for Drm 40:006 L

ð67Þ ð68Þ

where L is the length of tubes and Ra is the Rayleigh number defined as a function of Grushof and Prandtl numbers as follows: Ra ¼ Gr  Pr Gr ¼

gbðTh;i

Tc ÞDrm 3 u2

ð69Þ ð70Þ

The shell diameter was estimated from [42]: Ds ¼ 0:637pt

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðpNt ÞCL=CTP

ð71Þ

where pt is tube pitch and CL is tube layout constant that has a unit value for 45 and 90 degrees tube arrangement and 0.87 for 30 and 60 degrees tube arrangement. Also CTP is tube count constant, which is 0.93, 0.9, 0.85 for single pass, two passes, and three passes of tubes, respectively [42].

5.9.6.4.2

Objective functions, design parameters, and constraints

The main criteria in the heat exchanger are the occupied space. The length of the heat exchanger has been defined as the objective function and designed parameters are considered as:

• • •

number of tubes tube inside and outside diameter shell diameter

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379

When the phase change material is used as storage media, the length of the heat storage system exceeds the limits. This is because of the main drawback of PCM, which is low thermal conductivity. The predicted length for this case will be presented in the following sections. To overcome this problem, the nanoparticles have been introduced to increase the thermal conductivity and rate of heat transfer in the PCM. This will lead to a more compact storage system, which satisfies the objective function. Carbon nanotubes and graphene nanoplatelets have been added to PCM as described in the experimental section.

5.9.6.4.3

Effective properties of the phase change materials and nanotubes

By mixing the nanotubes in the PCM, the effective properties will be changed. The predicted thermal conductivity of the mixture has been introduced in the designing parameters of the heat exchanger. As the formulas show, effective thermal conductivity mainly depends on the direction of the nanotubes. If they are placed in series configuration, the effective thermal conductivity increases tremendously. On the other hand, the effective thermal conductivity will not increase largely when the nanotubes are placed in the parallel arrangement. Fig. 15 shows the variations of effective thermal conductivity for the parallel arrangement of the particles of carbon. This can be considered as the worst scenario (Fig. 16). The label pointing to the zero concentration corresponds to the following effective thermal conductivity as [43]: keff ¼ 5:067  10 kcnt;parallel

5

Therefore: keff ;parallel ¼ 5:067  10

5

 3000 ¼ 0:152 W=m K

The obtained value is identical to thermal conductivity of pure. The best scenario corresponds to the case where the carbon nanotubes are set in series with the temperature gradient’s direction. On the other hand, the highest thermal conductivity is expected for series configuration. For example, in 90% concentration for PCM, which is equal to 10% concentration of CNTs, we will have:

Thermal conductivity (W/m K)

0.21

0.20

0.19

0.18

0.17

0.16

0.15

0

5

10 15 CNT concentration (%)

20

25

Fig. 15 Effect of carbon nanotube (CNT) concentration on the thermal conductivity of the mixture in parallel configuration.

0.35 0.30

keff/kcnt

0.25 0.20 0.15 0.10 0.05 0

0

5

10 15 CNT concentration (%)

20

25

Fig. 16 Effective thermal conductivity of the phase change materials (PCM) and nanoparticles in series arrangement. CNT, carbon nanotube.

380

Optimization in Energy Management

c¼

Vpcm Vpcm ¼ Vtot Vpcm þ Vcnt

keff ðparallelÞ ¼



c kpcm

þ

1 c kcnt

keff ðseriesÞ ¼ ckpcm þ ð1

ð72Þ



1

ð73Þ ð74Þ

cÞkcnt

keff ¼ kcnt  0:1 ¼ 3000  0:1 ¼ 300 W=mK

5.9.6.4.4

Model description

The main goal of this section is describing the operating condition of the heat exchanger. The hot water with the minimum mass flow rate of 0.02 kg/s enters in the tube side as hot stream. The PCM is put in the shell side to absorb the minimum 300 W heat generated by the battery. The melting point of PCM is assumed to be 28.51C. Coolant is 50:50 water-ethylene glycol, which leaves the tubes at 29.51C. Two types of tubes including the straight tube and helical tubes are studied. Moreover, both finned and unfinned tube structures are considered in the straight tube.

5.9.6.4.5

Optimization using genetic algorithm

In this section the length of heat exchanger is considered as objective function. To minimize this objective, five design parameters including number of tubes, index of each tube, shell (tank) diameter, CNT concentration, and CNT series probability were selected. Design parameters and their range of variation are shown in Table 11. Due to the specific space limitations in vehicle applications, the maximum allowable tank diameter to be selected is considered as 0.3 m. Moreover, the maximum allowable CNT concentration and series probability are chosen to be 10% and 0.2, respectively. At 9% the mixture will look semisolid. Concentrations more than 10% are rarely reported in literature. Tube schedules, outside diameter, tube thickness, and tube fin length are listed in the Table 12. Analyses for the tubes with/without fans have been carried out in this section. The GA optimization was performed for 150 generations, using a search population size of M ¼ 100 individuals, crossover probability of pc ¼ 0.9, gene mutation probability of pm ¼ 0.035 for both cases (with and without finned tube). The results for optimum length versus generation for both cases are shown in Fig. 17. The optimum values for the heat exchanger length are 32.95 and 41.09 cm, respectively, for the case of without and with finned tubes. As a result, the application of the fin for tubes is not recommended in this case. The optimal design parameters for each case are listed in Table 13. It is worth mentioning that the tube with schedule number less than 5/16 is not available in the market. As a result, the tubes with schedule number smaller than 5/16 are omitted in the optimization process. 5.9.6.4.5.1 Sensitivity analysis The optimum value of effective thermal conductivity was obtained as 34 W/m K for the case of without finned tube. The variation on length of heat exchanger versus effective thermal conductivity of the PCM is shown in the Fig. 18. It is observed that by increasing of the effective thermal conductivity, the heat exchanger length decreases. Actually by increase of PCM thermal conductivity, the overall heat transfer coefficient increases and as a result the required heat transfer surface area decreases for specific heat duty. Consequently, by decreasing the heat transfer surface area, the length of tube and heat exchanger decrease. Table 11

Design parameters and their range of variation Lower bound Upper bound

Number of tubes 1 Index of tube 1 Tank diameter (m) 0 Carbon nanotube (CNT) concentration (%) 0 CNT series probability (%) 0

Table 12

200 5 0.3 10 20

Tube specification for the optimization

Tube schedule number

3/16

1/4

5/16

3/8

1/2

Tube outside diameter (mm) Tube thickness (mm) Tube fin length (mm)

6.096 0.752 5.08

8.128 0.762 5.588

9.8552 0.889 6.096

11.4554 0.889 6.604

16.002 1.5748 8.89

Source: Data from Park D, Markus M. Analysis of a changing hydrologic flood regime using the Variable Infiltration Capacity model. J Hydrol 2014;515:267–80.

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44 Heat exchanger length (cm)

With finned tube Without finned tube

42 40 38 36 34 32

0

50

100

150

Generation

Fig. 17 Convergence of objective function vs. number of generation for both cases.

Table 13

Comparing of the optimum results in two cases including with and without finned tubes

Type

Tube type

Length of H  X (cm)

Tank diameter (cm)

Number of tubes

Carbon nanotube (CNT) concentration (%)

CNT series probability

Finned No fin

5/16 5/16

41.09 32.95

33.97 27.56

47 62

10 8.6

20 19.7

Length of heat exchanger (cm)

41 40 39 38 37 36 35 34 33 32

0

5

10 15 20 25 30 Effective thermal conductivity (W/m K)

35

Fig. 18 Variation on length of heat exchanger vs. effective thermal conductivity of the phase change materials (PCM).

The variation of heat exchanger length and shell diameter in terms of the variation of standard tube (with specific inner and outer diameter) are depicted in Table 14. It is concluded that by increasing the tube diameter, both heat exchanger length and shell diameter increase. As a result, the minimum available tube diameter in the market is suitable in this case. Actually, by increase of tube diameter, the Reynolds number decreases and as a result the inner convection heat transfer coefficient and overall heat transfer coefficient decrease. By decreasing the overall heat transfer coefficient, the total heat transfer surface area (length of tubes) should increase [44]. GA is running for different copper tubes. Based on the results, the available tubes in the market are considered as designing output. Figs. 19 and 20 illustrate these variations for the tubes, respectively. By increasing the tube index, the tube inside and outside dimensions are increased too. It is found that, by increase in the tube diameter, both L/di and D/do decreases. The following Fig. 21 shows the variations of L/di and D/do versus the variation of tube index. It is deduced that the rate of increment in the tube inside and outside diameter is higher than the rate of increment in the tube length and shell diameter. The variation of optimum value of tube length versus tube inner diameter for various heat loads y is shown in Fig. 21. As it can be seen, the optimum tube length increases by increasing the tube inner diameter with a constant slope. Furthermore, the optimum value of tube length increases by an increase in the rate of heat transfer flow.

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Table 14

The variation of heat exchanger length and shell diameter vs. tube diameter Index

di

do

L

D

1/16 1/8 3/16 1/4 5/16 3/8 7/16 1/2

1 2 3 4 5 6 7 8

1.14 1.65 3.23 4.83 6.30 7.90 9.49 11.13

1.59 3.18 4.75 6.35 7.93 9.53 11.11 12.70

16.4 16.72 20.8 24.7 28.41 32.1 35.33 39.33

13.7 13.93 17.53 20.74 23.79 26.77 29.51 32.64

Optimum diameter of the tank (cm)

Tube

35 32.64 30

29.51 26.77

25

23.79 20.74

20 17.53 15 10 0.00

13.7

2.00

13.93

4.00

6.00

8.00

10.00

12.00

14.00

Outside diameter of the tubes without fin (mm) Fig. 19 Variation of optimum shell (tank) diameter vs. tube outside diameter in the case of without fin.

Optimum length of the tube (cm)

40 39.33 35 35.33 30

32.1 28.41

25 24.7 20 15 10 0.00

20.8 16.416.72 2.00

4.00 6.00 8.00 Inside diameter of tube (mm)

10.00

12.00

Fig. 20 Variation of optimum tube length vs. tube inside diameter in the case of without fin.

The variation of tube length versus tube inner diameter at various rate of heat transfer is shown in Fig. 22 and the variation of L/d at different Reynolds number and rate of heat transfer is shown in Fig. 23. It can be seen that the higher value of heat transfer needs the higher value of Reynolds number and L/d. Variation of heat exchanger length versus CNT series probability and CNT concentration in optimum point are shown in Figs. 24 and 25. It should be mentioned that the values of heat exchanger length that cannot satisfy the problem constraints are not illustrated in these figures. It also can be seen that the heat exchanger length decreases by increasing of both CNT probability and concentration. The maximum length is taken at the minimum possible CNT probability and concentration. Furthermore, in addition at the zero CNT probability and concentration (pure PCM) there is no optimum design to satisfy the constraint. Optimum heat exchanger length has been illustrated as a function of CNT concentration and series configuration probability. Contours reveal the regions that cannot satisfy the constraints. The bottom left corner, corresponds to the pure PCM, which will give the lengths that fail to meet the requirements and to satisfy the constraint.

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16

10

14

9 8

12

7

10

6

8

5

6

4

D/do

L/di

383

3

4

2

2

1 0

0 0

2

4 Tube index

6

8

Optimum length of the tube (cm)

Fig. 21 Variation of L/di and D/do vs. the tube index.

50 40 30 20 10 0 0

2

4

6

8

10

Inner diameter of tube (mm) Q=300 W

Q=350 W

Q=400 W

Q=500 W

Q=550 W

Q=600 W

Q=450 W

Fig. 22 Variation of optimum value of tube length vs. tube inner diameter for various rate of heat transfer.

d2 250

L/d

150

d3

100 d4

50 d5

0 8000

d6 0.7

6000

0.6 4000 Re

0.6

2000

0.3 0.2

0.4 Q (kW)

Fig. 23 Dependency of Re, rate of heat transfer and L/d for various tube diameter.

The optimum length has been obtained for different values of heat generation of Q and various mass flow rates, which gives different Reynolds number. Once all the available tubes, starting from 1/1600 diameter up to 100 diameter, are investigated with respect to the variable mass flow rates and heat transfer to be handled in through the heat exchanger, the following relationship fits the set of diagrams with the least error [45].

Optimization in Energy Management

Heat exchanger length (cm)

384

60 55 50 45 40 10

35

8

30

25

6 20

15

4 10

2

5

0

0 CNT concentration (%)

CNT series probability (%)

Fig. 24 Variation of heat exchanger length vs. carbon nanotube (CNT) series probability and CNT concentration in optimum point.

30 60

CNT series probability (%)

25 55 20 50 Heat exchanger length (cm)

15

45 10 40 5 35 0

0

1

2

3

4 5 6 CNT concentration (%)

7

8

9

10

Fig. 25 Contour of heat exchanger length vs. carbon nanotube (CNT) series probability and CNT concentration in optimum point.

   LD Q Re 1 þ ¼ 471:55 þ Re 1 þ 381:28  d2 L 893

ð75Þ

“L” and “D” are optimum length and diameter of the tank. L is latent heat of fusion for the phase change material.

5.9.7

Future Directions

Energy use is directly linked to well-being and prosperity across the world. Meeting the growing demand for energy in a safe and environmentally responsible manner is an important challenge. A key driver of energy demand is the human desire to sustain and improve our lives, our families and our communities. There are around 7 billion people on Earth, and population growth will likely to increase energy demand, which depends on the adequacy of energy resources. In addition, increasing population and economic development in many countries have serious implications for the environment, because energy generation processes (e.g., generation of electricity, heating, cooling, and shaft work for transportation and other applications) emit pollutants, many of

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which are harmful to ecosystems. Burning fossil fuels results in the release of large amounts of GHGs, particularly carbon dioxide. In the analysis and design of energy systems, the techniques often used combine different disciplines of (mainly thermodynamics) and economic disciplines (mainly cost accounting) to achieve optimum designs. Energy engineering is a field where optimization plays a particularly important role. Engineers involved in thermal engineering, for instance, are required to answer such questions as

• • •

What processes or equipment should be selected for a system, and how should the parts be arranged for the best outcome? What are the best characteristics for the components (e.g., size, capacity, cost)? What are the best process parameters (e.g., temperature, pressure, flow rate, and composition) of each stream interacting with the system?

In order to answer such questions, engineers are required to formulate an appropriate optimization problem. Proper formulation is usually the most important and sometimes the most difficult step in optimization. In order to formulate an optimization problem, there are numerous elements that need to be defined, including system boundaries, optimization criteria, decision variables, and objective functions. In order to have an optimized system, which can reduce the cost and environmental impact, and at a same time, increase the efficiency of the system, optimization is useful. Energy management is the systematic use of management and technology to improve the energy performance of an organization. To be fully effective it needs to be integrated, proactive, and incorporate energy procurement, energy efficiency, and renewable energy. Like all management disciplines, energy management should be applied in a manner appropriate to the nature and scale of the organization. Energy management for a small office-based organization will be at a very different level to that for a complex industrial company with a multimillion pound energy bill. In this chapter, the importance of energy management was discussed and the need for optimization of energy management was addressed for better performance of the system. Various optimization techniques were explained with their benefits. In addition, the application of optimization is better known when economics is highly affected by the decision makers; this is where the connection between system performance and economy is necessary. Although we tried to cover several case studies, there are still several published papers that studied the application of energy management specially for integrated energy systems. Isolated configurations that use solar panels and WTs as hybrid renewable generation, and batteries and fuel cells as hybrid storage systems are studied in Refs. [46–49]. In these configurations, hybrid generation and storage systems are used to solve the disadvantages of each technology taking advantage of the other ones. Hydrogen generation by electrolyzer is not considered in these applications. To develop an EMS in a hybrid renewable energy system it is necessary to take into account technical and economic criteria. These parameters will help to design a correct energy control, increasing the system performance. These studies for various energy systems suggest that the application of energy management optimization is not limited to specific systems, and it can be applied to any systems involving processes where cost and performance assessment is necessary. It is necessary to consider exergy, economics, and sustainability for designing any new energy systems to see if they are economically viable and they can be cost competitive with other existing energy systems. Since sustainable development has attracted ample attention during the last few decades and there are several emerging new energy systems down the road, the need for energy optimization will be significant.

5.9.8

Closing Remarks

In this chapter, basic principles of energy management optimization were explained for energy systems and the connection between energy saving, cost saving, and efficiency improvements were addressed. The results show that energy management optimization methods provide quite useful data in the way of improving the system to be cost-effective. Some practical case studies were investigated where energy management optimization was applied to maximize useful output, energy saving, and minimize the total cost rate. It was concluded that energy management can be a potential candidate for better improvement of energy systems as they will assist us to determine the losses in the systems and try to find the solution to reduce the losses. It is obvious that any reduction in the losses within the system can result in cost saving. A good example for this is in residential buildings where a lot of energy is being lost. An optimized energy management option will help us to save energy, which eventually leads to reduce both GHG emissions and cost. The case studies here were steam power plant, storage tank, renewable based hybrid energy systems, and battery for EVs. The energy management optimization of the steam power plant indicated that determining the optimal design parameters for this plant will result in an increase in output power and reduces the total cost of the power plant. The results of the storage tank showed that applying cold and heat storage facilities reduced the equipment sizes and leads to more benefit in comparison with the case with none. It was investigated that the optimum total cost had been improved by 9.48%, 5.19%, and 2.23% with applying TES þ CES tanks compared with the none, thermal energy storage (TES), and cold energy storage (CES) cases, respectively. The hybrid energy system results showed that discharging is high in the cold months of the year such as January and November. The insolation angle on PV panels during these months is high and as a result lower radiation levels are received by the panels. As a result, stored electricity is released. The minimum number of batteries required is determined by the months in which lower levels of solar radiation are available. Because of the delay between periods of PV power generation and peak demand, excess electricity is

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generated and stored during periods with the maximum incident solar radiation. The energy management for battery pack of the EV was another practical example. As the battery performance will drop during its operation, the need for thermal management of battery pack is significant. Although water has been used for cooling the battery packs, they require energy from the vehicle to cool down the water in order to maintain the desired temperature. This case study was designing a passive system to cool down the battery temperature using PCM. A heat exchanger was designed and optimized and PCM was used for cooling media. The results showed that using PCM in EVs will results in a good energy management and improving the battery performance.

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Further Reading Babayo AA, Anisi MH, Ali I. A review on energy management schemes in energy harvesting wireless sensor networks. Renew Sustain Energy Rev 2017;76:1176–84. Baniassadi A, Sajadi B, Amidpour M, Noori N. Economic optimization of PCM and insulation layer thickness in residential buildings. Sustain Energy Technol Assess 2016;14:92–9. Barklund E, Pogaku N, Prodanovic M, Hernandez-Aramburo C, Green TC. Energy management in autonomous microgrid using stability-constrained droop control of inverters. IEEE Trans Power Electron 2008;23(5):2346–52. Chaabene M, Ammar MB, Elhajjaji A. Fuzzy approach for optimal energy-management of a domestic photovoltaic panel. Appl Energy 2007;84(10):992–1001. Chen Y-K, Wu Y-C, Song C-C, Chen Y-S. Design and implementation of energy management system with fuzzy control for DC microgrid systems. IEEE Trans Power Electron 2013;28(4):1563–70. Fernando Y, Hor WL. Impacts of energy management practices on energy efficiency and carbon emissions reduction: a survey of malaysian manufacturing firms. Resour Conservat Recycl 2017;126:62–73. Hajabdollahi H. Evaluation of cooling and thermal energy storage tanks in optimization of multi-generation system. J Energy Storage 2015;4:1–13. Hajabdollahi H. Investigating the effects of load demands on selection of optimum CCHP-ORC plant. Appl Therm Eng 2015;87:547–58. Hajabdollahi H, Hajabdollahi Z, Hajabdollahi F. Soft computing based optimization of cogeneration plant with different load demands. Heat Transfer – Asian Res 2016;45 (6):556–77. Hanafizadeh P, Siahkalroudi MM, Ahmadi P. Experimental and numerical investigation of optimum design of semi industrial heat recovery steam generator inlet duct. Appl Therm Eng 2016;104:375–85. Mamaghani AH, Najafi B, Casalegno A, Rinaldi F. Long-term economic analysis and optimization of an HT-PEM fuel cell based micro combined heat and power plant. Appl Therm Eng 2016;99:1201–11. May G, Stahl B, Taisch M, Kiritsis D. Energy management in manufacturing: from literature review to a conceptual framework. J Cleaner Product 2017;167:1464–89. Moreno J, Ortúzar ME, Dixon JW. Energy-management system for a hybrid electric vehicle, using ultracapacitors and neural networks. IEEE Trans Ind Electron 2006;53 (2):614–23. Piacentino A, Gallea R, Cardona F, Lo Brano V, Ciulla G, Catrini P. Optimization of trigeneration systems by mathematical programming: influence of plant scheme and boundary conditions. Energy Convers Manage 2015;104:100–14. Roldán-Blay C, Escrivá-Escrivá G, Roldán-Porta C, Álvarez-Bel M. An optimization algorithm for distributed energy resources management in micro-scale energy hubs. Energy 2017;132:126–35. Salah CB, Chaabene M, Ammar MB. Multi-criteria fuzzy algorithm for energy management of a domestic photovoltaic panel. Renew Energy 2008;33(5):993–1001. Wang J-J, Jing Y-Y, Zhang C-F. Optimization of capacity and operation for CCHP system by genetic algorithm. Appl Energy 2010;87(4):1325–35. Zare V, Mahmoudi SMS, Yari M, Amidpour M. Thermoeconomic analysis and optimization of an ammonia–water power/cooling cogeneration cycle. Energy 2012;47(1):271–83.

Relevant Websites http://www.eprg.group.cam.ac.uk/wp-content/uploads/2008/12/wallace.pdf ARM, The Architecture for the Digital World, Intelligent Energy Management in Buildings. https://www.ashrae.org/ ASHRAE, Shaping Tomorrow’s Built Environment Today. https://www.asme.org/ ASME, The American Society of Mechanical Engineers. http://www.eiolca.net/ Carnegie Mellon University. http://www.combustion-institute.ca/ Combustion Institute – Canadian Section. https://www.eia.gov/state/ EIA, U.S. Energy Information Administration. https://www.journals.elsevier.com/energy-conversion-and-management Elsevier, Energy Conversion and Management. https://energy.gov/science-innovation/clean-energy ENERGY.GOV. https://www.epa.gov/ EPA, United States Environmental Protection Agency. http://www.fchart.com/ees/ F-Chart Software.

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http://www.fchea.org/ FCHEA, Fuel Cell & Hydrogen Energy Association. http://www.fleetlca.com/ fleetLCA, The Fleet Sustainability Tool. http://www.hgeosoft.com/ HGS, Software and Consulting. http://www.iahe.org/ International Association for Hydrogen Energy. http://districtenergy.org/ International District Energy Association. http://www.lcacalculator.com/ LCA Calculator. https://www.mathworks.com/ Mathworks. http://www.seia.org/about/solar-energy SEIA, Solar Energy Industries Association. http://www.sustainabilityma.org/ Sustainability Management Association. http://www.tandfonline.com/toc/ljge20/current Taylor & Francis Online, International Journal of Green Energy. http://www.exergoecology.com/ The Exergoecology Portal. http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-114X Wiley Online Library, International Journal of Energy Research. http://www.wsset.org/ WSSET, World Society of Sustainable Energy Technologies.

5.10 Wireless Technologies in Energy Management Imran Amin and Atif Saeed, SZABIST, Karachi, Pakistan r 2018 Elsevier Inc. All rights reserved.

5.10.1 5.10.2 5.10.2.1 5.10.2.1.1 5.10.2.2 5.10.3 5.10.3.1 5.10.3.2 5.10.3.2.1 5.10.3.2.2 5.10.4 5.10.4.1 5.10.4.1.1 5.10.4.2 5.10.4.3 5.10.4.3.1 5.10.4.3.1.1 5.10.4.3.1.2 5.10.4.3.1.2.1 5.10.4.3.1.2.2 5.10.4.3.1.2.3 5.10.4.3.1.2.4 5.10.4.3.1.2.5 5.10.4.3.1.3 5.10.4.3.1.3.1 5.10.4.3.1.3.2 5.10.4.3.1.4 5.10.4.3.1.5 5.10.4.3.1.6 5.10.4.3.1.7 5.10.4.3.1.7.1 5.10.4.3.1.7.2 5.10.4.3.2 5.10.4.3.2.1 5.10.4.3.2.1.1 5.10.4.3.2.1.2 5.10.4.3.2.1.3 5.10.4.3.2.1.4 5.10.4.3.2.1.5 5.10.4.3.3 5.10.4.3.3.1 5.10.4.3.3.2 5.10.4.3.3.2.1 5.10.4.3.3.2.2 5.10.4.3.3.2.3 5.10.4.3.3.2.4 5.10.4.3.3.2.5 5.10.4.3.3.3 5.10.4.3.3.3.1 5.10.4.3.3.3.2 5.10.4.3.3.4 5.10.4.3.4 5.10.4.3.4.1

Introduction Wireless System Formation and Architecture Logical System Architecture The open system interconnect network model Physical System Architecture Wireless Communication The Radio Frequency Spectrum The Infrared Spectrum Application Link Wireless Energy Management Techniques American Reinvestment and Recovery Act Energy efficiency Economic Factors of Energy Management Environmental Factors of Energy Management Key step approach to energy management Commitment of top management Understanding the issue Grasp the current energy use Identify management strength and weakness Analyze stakeholders needs Anticipate barriers to implement Estimate the future trend Plan and organize Develop a policy Make out a plan/program Implementation Controlling and monitoring performance Management review Implementation of SEM (key step approach) Energy audit Advanced monitoring and metering solutions Six-sigma approach to energy management Implementation of six sigma approach (detect, measure, analyze, improve and control methodology) Detect Measure Analyze Improve Control EMrise: wireless energy management platform for wireless sensor network EMrise wireless sensor network energy management program (design and implementation) EMrise-Simulation System Elaborate Lib based on registers Multi-abstraction energy evaluation Energy calibration Transition state energy Performance metrics EMrise-Measurement System Multichannel energy measure device Energy data management software EMrise-OpS Electric vehicle energy management system based on vehicular ad hoc networks Segment leader selection

Comprehensive Energy Systems, Volume 5

doi:10.1016/B978-0-12-809597-3.00524-1

390 391 391 391 392 393 393 395 395 395 396 396 396 396 397 397 397 397 397 398 398 398 398 398 398 398 398 398 398 398 399 399 399 400 400 400 400 401 401 401 402 402 403 403 403 403 403 403 403 404 404 404 405

389

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5.10.4.3.4.2 Time slot reservations 5.10.5 Types of Wireless Networks 5.10.5.1 Wireless Personal Area Networking 5.10.5.1.1 802.15.1 (Bluetooth) 5.10.5.1.1.1 History of development and characteristic 5.10.5.1.1.2 Working of bluetooth 5.10.5.1.1.3 Applications 5.10.5.1.1.3.1 Healthcare 5.10.5.1.1.3.2 Office products 5.10.5.1.1.4 Case study: smart home, energy management solution based on bluetooth technology 5.10.5.1.1.4.1 The proposed architecture 5.10.5.1.1.4.2 Wireless architecture 5.10.5.1.1.4.3 Performance evaluation 5.10.5.1.2 802.15.4 (ZigBee) 5.10.5.1.2.1 History of development and characteristic 5.10.5.1.2.2 Working of ZigBee 5.10.5.1.2.3 Applications 5.10.5.1.2.3.1 Building automation 5.10.5.1.2.3.2 Healthcare 5.10.5.1.2.3.3 Smart energy efficiency 5.10.5.1.2.4 Case study: Zigbee-based efficient home energy management system 5.10.5.1.2.4.1 System description 5.10.5.2 Wireless Local Area Networking 5.10.5.2.1 Wi-Fi 5.10.5.2.1.1 History of development and characteristic 5.10.5.2.1.2 Working of Wi-Fi 5.10.5.2.1.3 Case study: energy management via smart electrical socket design 5.10.6 Future Directions 5.10.7 Concluding Remarks References Further Reading Relevant Websites

5.10.1

405 405 406 406 406 407 408 408 408 409 409 410 411 411 411 412 414 414 414 414 414 414 416 416 416 417 417 420 420 421 422 422

Introduction

Nowadays, wireless is one of the top growth areas in the field of telecommunication. Wireless is the term used to describe the communication between different peripherals through the medium of electromagnetic waves. In order to communicate, we, humans share ideas, experiences, and knowledge. This communication is usually done using a medium of speaking, writing, gestures, sign languages, and broadcasting. Moreover, it can be done being interactive, formal, in-formal, verbal, or nonverbal. Similarly, in order to make different electronic component communicate with each other and with WEB without using hardwired cables different wireless technologies have been developed with the passage of time, so as to enable us making an effective communication between different hardware products for the Internet of Things (IoT) and Machine to Machine communication (M2M). The evidence of usage of wireless technologies can be found back to University of Hawaii’s ALOHANET research framework in 1970s. Also, the key event that led this technology to grow faster was the endorsement of Institute of Electrical and Electronics Engineer (IEEE) 802.11 standard, in the start of 21st century and the development of interoperability certification by Wi-Fi alliance. From 1970 to 1990, the immense demand of this essential growing technologies could only be met through the usage of narrow range expensive hardware. After the successful implementation of 802.11 standard, this acts as a major milestone in the development of modern wireless networking, and an initiating point to strong and recognizable brand – Wi-Fi. This grabs the attention of researchers and network developers to contribute as much as they can in this field of wireless technologies. Various Wi-Fi variants is also proposed after successfully implementing standard 802.11, this advancement was among the various important headlines of last decade, these variants followed the similar timeline with the first IrDA specifications, published in 1994. During the same year, Ericson has empowered its local R&D wing and they started the research on wireless connectivity between mobile phones and other useful accessories, this was the foundation of the idea of “Bluetooth,” given by the IEEE 802.15.1 working group in late 1999. During the period of rapid advancement in this field, number of other wireless networking technologies have been introduced differing on behalf of data rate, operating range and power consumption as shown in Fig. 1. Different wireless variants works over magnitude of four orders, in terms of data transfer rates (i.e., Zigbee at 20 Kbps to wireless USB at 500 Mbps), and over the magnitude of six orders in terms of range (i.e., NFC having 5 cm to Wi-Fi having 50 Km).

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100−1000

PHY data layer

WUSB (optional) WUSB (mandatory)

802.11n

802.16

10−100

IRDA

802.11a

802.11g

802.16d Wimax

1−10

NFC

Bluetooth Class 1

Bluetooth Class 2

WCDMA 3G Bluetooth Class 3

0.1−1

IRDA SIR 0.1−1

391

ZIGBEE 1−10

10−100

100−1000

Range Fig. 1 Emerging wireless technologies.

In order to continue development in this field, many research institution and research scientists have contributed. This contribution yields a remarkable technologies like: from Frequency Hopping Spread Spectrum to Low Density Parity Codes. This breakthrough in the field of high efficiency data transmission enables us to take steps toward gigabit wireless networks. This fascinating advancement in the field of wireless technologies is the sole motivation behind this chapter, which gives its reader an understanding of all fundamental concepts and appreciate the diversity of this matured field, while avoiding getting down to the technicalities required by system engineer.

5.10.2

Wireless System Formation and Architecture

The basic structure of wireless networks depends on various standards and protocols that enable different devices or nodes to communicate with each other. These structures also controls the routing or flow of data between different peripherals. As in some cases logical operations/architecture rely on the physical architecture for its complete operation, but also parallel to these, both of these factors also have independencies to greater extent. This can be classified on the basis of applications. Both of these factors will be discussed in quite a great detail.

5.10.2.1 5.10.2.1.1

Logical System Architecture The open system interconnect network model

The open system interconnect (OSI) model has been developed by International Standard Organization (ISO) so as to act as a guideline or working model for the development of standards in order to connect different computing peripherals. The OSI is basically a systematic approach to develop such standards and is not a standard itself. The OSI model breakdown entire networking systems into device to device connection, or more accurately can be said as application to application connection into seven layers of logical operations. In Table 1, all of this layers are characterized with respect to operations performed. The procedure begins with the sender writing a message into a PC email application as shown in Fig. 2. At the point when the client chooses “Send,” the working framework consolidates the message with an arrangement of Application (Layer 7) guidelines that will in the long run be perused and actioned by the relating working framework and application on the accepting PC. The message in addition to Layer 7 directions is then passed to the part of sender’s working framework that arrangements with Layer 6 presentation tasks. These incorporate the interpretation of information between application layer designs and additionally a few sorts of security, for example, secure socket layer (SSL) encryption. This procedure proceeds down through the progressive programming layers, with the message assembling extra guidelines or control components at every level. At Layer 4, the email is broken into small discrete packets of information each carrying its identity, i.e., source, destination and IP, and transfers data wirelessly through network layer, i.e., Layer 3. Information Link layer the IP address is “settled” to decide the physical address of the principal gadget that the sending PC needs to transmit edges to – the purported MAC or media access control address. In this illustration, this gadget might be a system switch that the sending PC is associated with or the default gateway to the Internet from the sending PC’s LAN. At the physical layer, additionally called the PHY layer, the information bundles are encoded and regulated onto the transporter medium – a bent wire combine on account of a wired system, or electromagnetic radiation on account of a wireless systems – and transmitted to the gadget with the media access control (MAC) address settled at Layer 2. This transmission of message is achieved along the internet after number of device to device traveling involving PHY and data link layers of each routed or connected device in the chain. After reaching the receiving PC, the PHY layer will demodulate and decipher the voltages and frequencies distinguished from the transmission medium, and pass the got information stream up to the Data Link layer. Here the MAC and LLC components, for example, a message trustworthiness check, will be separated from the information stream and executed, and the message in addition to guidelines left behind the convention stack. At Layer 4, a convention, for example, transport control protocol (TCP), will guarantee that all information outlines making up the message

392

Table 1

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OSI Model (seven layers characterization)

Layers

Description

Standards and protocols

Layer 7 – application layer Layer 6 – presentation layer

Benchmarks to characterize the arrangement of administrations to applications Norms to control the interpretation of approaching and active information from one form to another Norms to deal with the correspondence between the presentation layer of the sending and accepting PCs. This correspondence is accomplished by building up, overseeing and ending “sessions” Norms to ensure reliable completion and secured data transfer Norms to define and manage the standard between different network connections Norms to specify the way in which data is been transmitted and received efficiently Norms to control the transmission of various data streams with different levels of computer programming, voltages, signal duration and frequencies

HTTP, FTP SSL

Layer 5 – session layer

Layer 4 – transport layer Layer 3 – network layer Layer 2 – data layer Layer 1 – physical layer

ASAP, SMB

TCP, UDP IPv4, IPv6 ARP Ethernet, Wi-Fi, Bluetooth Wi-MAX Bluetooth

Abbreviations: ASAP, Aggregate Server Access Protocol; FTP, File Transfer Protocol; HTTP, Hypertext Transfer Protocol; SBM, Server Message Block; SSL, secure sockets layer; TCP, Transmission Control Protocol; UDP, User Datagram Protocol.

Message sent from email (layer 7 application layer)

Message is broken into several elements (layer 6 and 5 presentation and session layer)

It is further broken into packets headed by transmission layer (layer 4 transport layer)

Data frame created from data packet (layer 3 network layer)

Data frame encrypted, frame control header added (layer 2 data link layer)

Access gained to physical address (layer 1 physical layer) Fig. 2 The basic operational open system interconnect (OSI) model.

have been gotten and will give mistake recuperation if any edges have disappeared. At long last the email application will get the decoded ASCII characters that make up the first transmitted message.

5.10.2.2

Physical System Architecture

The topology of a wired system alludes to the physical arrangement of connections between organized gadgets or hubs, where every hub might be a PC, an end-client gadget, for example, a printer or scanner, or some other bit of system equipment, a center

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393

Fig. 3 Point to point, bus and ring connections.

point, switch, or router. The basis of different hardwired connection topologies are constructed on the basis point to point hardwired connection between different peripherals. Globally, to kind of hardwired topologies are used extensively: bus and ring as shown in Fig. 3. For the ring topology, there are two conceivable variations relying upon regardless of whether the connections between the hub are simplex (one-way) or duplex (two-way). In the simplex case, each between hub interfaces has a transmitter toward one side and a beneficiary at the other, and messages circle in one bearing around the ring, while in the duplex case every connection has both transmitter and recipient (an alleged handset) at every end, and messages can flow in either heading. Bus and ring topologies are helpless to single-point fatigues, where a solitary broken connection can disconnect segments of a transport system or stop all movement on account of a ring. The progression that opens up new potential outcomes is the presentation of specific system equipment hubs, intended to control the stream of information between other organized gadgets. The most straightforward of these is the networked center, which is the central point for LAN cabling in star and tree topologies. A dynamic hub, otherwise called a repeater, is an assortment of detached center point that additionally intensifies the information strength to enhance sigma; quality over long system associations (Fig. 4).

5.10.3

Wireless Communication

The OSI network model illustrates how data and protocol messages from the application level cascade down through the logical layers and result in a series of data frames to be transmitted across the physical network medium. In a wireless network that physical layer is provided by radio frequency (RF) or infrared (Ir) communications. Starting with the RF spectrum, the regulation of spectrum use is briefly described and spread spectrum techniques are then introduced. This is a key technology that enables high data link reliability by making RF communications less susceptible to interference. Multiple access methods that enable many users to simultaneously use the same communication channel are then discussed. Signal coding and modulation is the step that encodes the data stream onto the RF carrier or pulse train, and a range of coding and modulation techniques applied in wireless networking will be covered, from the simplest to some of the most complex. The various elements that impact on RF signal propagation will be described, enabling a calculation of the link budget – the balance of power available to overcome system and propagation losses to bring the transmitted signal to the receiver at a sufficient power level for reliable, low error rate reception. All of these factors are discussed in quite detail below:

5.10.3.1

The Radio Frequency Spectrum

The radio frequency, or RF, communication at the heart of most wireless networking operates on the same basic principles as everyday radio and TV signals. The RF section of the electromagnetic spectrum lies between the frequencies of 9 kHz and 300 GHz,

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Fig. 4 Star topologies.

Fixed

Mobile

Space research

Space research

Space operation

Earth exploration

Fixed

Mobile

Amateur Broadcasting satellite Fixed

Satellite

Radiodetermination satellite

Mobile satellite

Broadcasting satellite

Fixed

Radio astronomy

Space research

Earth exploration

Fig. 5 Radio frequency (RF) spectrum distribution.

and different bands in the spectrum are used to deliver different services. Recalling that the wavelength and frequency of electromagnetic radiation are related via the speed of light, so that wavelength (l)¼speed of light (c)/frequency (f), or wavelength in meters¼ 300/frequency in MHz Beyond the extremely high frequency (EHF) limit of the RF spectrum lies the infrared region, with wavelengths in the tens of micrometer range and frequencies in the region of 30 THz (30,000 GHz). Virtually every hertz of the RF spectrum is allocated for one use or another (Fig. 5), ranging from radio astronomy to forestry conservation, and some RF bands have been designated for unlicensed transmissions. The RF bands which are used for most wireless networking are the unlicensed ISM or instrument, scientific and medical bands, of which the three most important lie at 915 MHz (868 MHz in Europe), 2.4 GHz, and 5.8 GHz. The use of the RF spectrum, in terms of the frequency bands that can be used for different licensed and unlicensed services, and the allowable transmission power levels for different signal formats, are controlled by regulatory authorities in individual countries or regions as shown in Table 2.

Wireless Technologies in Energy Management

Table 2

395

Radio frequency (RF) spectrum

Country/region

Regulatory body

United States Canada Europe Japan

Federal Communications Commission (FCC) Industry Canada (IC) European Telecommunications Standards Institute (ETSI) Association of Radio Industries and Businesses (ARIB)

R

T

T

R Fig. 6 Conventional wireless configuration.

Although there is an increasing trend toward harmonization of spectrum regulation across countries and regions, driven by the International Telecommunications Union’s World Radio Communication Conference, there are significant differences in spectrum allocation and other conditions such as allowable transmitter power levels which have an impact on wireless networking hardware design and interoperability.

5.10.3.2

The Infrared Spectrum

IR communication includes the utilization of various valence space of moving light near the frequency spectrum of infrared band for transmission of important information [1–3] as shown in Fig. 6. This communication can be made by linking one peripheral portable device with other through a tethered device known as access point or base station which acts as an information hub for the system. Some of the portable devices includes computers, laptop telephones, etc., all of them are networked together. Wireless IR communication can be easily characterized on the basis of two broad spectrum, i.e., the application for which it has to be used and link type.

5.10.3.2.1

Application

IR communication has broad pool of applications. So of its commercial applications are discussed below:

• • •

IR communication has been extensively used in a short range wireless connectivity for essential information exchange and transmission, such as file sharing, business cards, schedules, etc. One of the basic applications of IR communication in this domain is IrDA system. It can also be used to spread the connectivity within the building. This thing can be done by either making it an extension to existing LANs so that mobility can be achieved, in addition to it, where there is no LAN, “ad-hoc” network can be created using it. It can also be used to give wireless input to existing devices, such as wireless mouse, electronic remote car keys, and game controller.

5.10.3.2.2

Link

IR communication can also be characterized on the basis of link type as discussed, by link type it is meant that in which arrangement or manner the transmitter and receiver are connected with each other to share information. Fig. 7 shows the two most common configurations of link type used in IR communication: point to point system and diffuse system. Point to point configuration is the essential technique used for the connection of transmitter and receiver, to create this link, transmitter and receiver must be assembled in a way that they point exactly on one another to establish a link. There should be no obstruction in between the line of sight (LOS) path of transmitter and receiver and most of the light transmitted by the transmitter should reach receiver in order to communicate efficiently. Therefore, point to point configuration is also known as LOS. This links can be used to obtain data in sessions, i.e., by connecting them temporarily and can also be used as permanent by utilizing the base station unit in LAN replacement application. In diffuse system configuration, there is no necessity of receiver and transmitter to point in the same vicinity, it works on the principles of reflecting or bouncing of light propagation through reflecting surfaces, wall ceilings, furniture, etc. Therefore; diffuse system is also known as nondirected. In this configuration the transmitter transmits data in a wide beam and receiver also has a wide field of view. This also omits the requirement of LOS path as diffuse system does not require for its peripheral to point toward each other.

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TV

IR remote

Point to point

TV

Diffuse

IR remote

Fig. 7 Linking configurations.

IR communication system provides a useful and significant replacement to RF-based system, particularly for the system which require low cost, low/moderate data rates, lesser weights, and shorter ranges. However; range can also be dramatically increased if LOS can be insured. The devices comprises of short range IR wireless communication, considered to have a tremendous market growth in coming decades, and this technology can compete in number of arenas. This communication techniques have already proven their effectiveness and efficiency in short range wireless communication.

5.10.4

Wireless Energy Management Techniques

To survive in today’s market, the demand of organizational efficiency in factors like energy, investments, and workforce is increasing day by day. Nowadays, energy is considered to be a most vital factor for the reduction of operating margin. However, firms need to evaluate the effect of rapid price fluctuation on its operations in order to sustain in volatile market. The key step to control this problem is to smartly reduce the cost relating to energy sector and utilize it somewhere else. In this highly competitive market, organizations should reduce all the extra cost in their operations, in order to sustain longer. Energy is one of the factors, which consumes a large proportion of organizational budget. However, recently a large decline in energy cost has been noticed; but cost of energy will remain volatile. Therefore, sustainable energy generation along with sustainable energy management practices must be realigned with normal industrial operations in order to extract as much from it as we can. For example, United States is strongly emphasizing on energy independence and efficiency, for this purpose a bill “American Reinvestment and Recovery Act” has been passed by Congress. The main features of this bill are discussed below.

5.10.4.1

American Reinvestment and Recovery Act

This act is also commonly known as “The Stimulus.” This act was enforced by 111th congress on February 2009 and later signed into law by President Mr. Barack Obama on February 17, 2009. The primary objective of ARRA was to create vacancies on immediate basis. The secondary objective was to directly invest in infrastructure, health, education and most importantly renewable energy and its management schemes. The initial allocated budget for ARRA was $787 billion, which was later revised to $832 billion during 2009 to 2019 [4]. Total budget allocated for investment in energy sector was $27.2 billion, mainly in renewable Vitality. However, this budget was further distributed in following manner to utilize it more efficiently: $6.3 billion to state and local governments; allowing them to invest in energy efficiency projects, $4.5 billion to federal buildings to increase their energy efficiency, $6 as independents renewable power generation loans at easy markup; allowing small firms to generate their own electricity and $11 for the modernization and maintenance of US electrical power grid.

5.10.4.1.1

Energy efficiency

Beside material cost, energy cost is a major pressure factor for several organizations and manufacturers. A sound and easy to implement business strategy can yield more production stability by reducing cost. In order to work in profit and with efficiency, modern operations largely depend on the low cost of energy it consumes. Energy conservation and independence are also considered as major strategies for creating a competitive advantage in business. Realizing the fact, that energy management could play a vital role in addressing social, economic and environmental concerns, organization are readily adopting these practices to minimize the risks. Overall, energy efficiency and management practices are among the most important option to increase the profit of organization as well as to reduce their dependencies on highly volatile fossil fuel prices.

5.10.4.2

Economic Factors of Energy Management

Energy management and saving is really an important at any all levels of human interference, whether it’s an organization, a nation, a small scale institute, or an individual. This practice reduces the energy cost and increases the overall profitability of organization.

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397

For example, Thailand started to take steps for energy conservation and efficiency after the first oil crises in (1973). In this regard, “Energy Conservation Act” was put into action in 1992. Moreover, the announcement of “National Energy Conservation Strategic Plan” (2002–2011) and “Five year Conservation Plan” (2002–2006) took place. The nation-wide joined effort toward energy efficiency plays a vital role in reducing their dependency on costly energy resources, i.e., crude oil. Private organizations are widely affected by energy cost; this not only directly affects their profitability but also affects their viability to sustain in global market. The higher the cost of consumed energy, the higher will be the cost of product in world market, which can never be a good sign for national trade.

5.10.4.3

Environmental Factors of Energy Management

EM is also concerned with the environmental problems of the nation. Environmental concerns mainly have to deal with the emission of carbon foot prints and other greenhouse gasses (GHG) in Earth’s atmosphere. These problems are stated as global warming or climate change. These factors are not only rising the Earth’s annual average temperature but are also considered as the major reason of ozone depletion. Scanning electron microscope (SEM), especially minimizing use of fossil fuel is the major among various countermeasures of this problem. For the solution of the said issue, there have been numerous worldwide or universal participation activities. One of those is intergovernmental panel on climate change (IPCC), which began in November 1988. It has three working groups and one task force. One of six directors of the group originates from Thailand. There are also numerous steps have been taken after the formation of United Nations Framework Convention on Climate Change (UNFCC), in which various nations cooperates for the effort of reducing GHG emission.

5.10.4.3.1

Key step approach to energy management

These days, corporates decision taking and action planning are decided on the basis of strategic approach to make the action or decision sustain longer; successfully. Otherwise, the action or plan can never be successful enough under the rapidly changing circumstances and soon corporate will find itself in an uncertain situation of fighting for its existence. Key steps for successful strategic approach has been discussed in quite detail in this section, so that user could grasp its essence and take steps immediately without any further dely. It consists of following key steps: 1. Commitment of top management 2. Understanding the issues like a. Grasp current energy use b. Identify management strength and weakness c. Analyze stakeholder needs d. Anticipate barriers to implement e. Estimate the future trend 3. Plan and organize, including a. Develop a policy b. Make out a plan/program 4. Implementation 5. Controlling and monitoring performance 6. Management review 5.10.4.3.1.1 Commitment of top management It is the most essential for the accomplishment of Energy Conservation exercises inside organizations or industrial facilities to have clear and authority duty of top administration – either the corporate top (senior) administration or manufacturing plant chiefs. The top (senior) administration should express responsibility toward the Energy Management (or Vitality Conservation) and carry on along this line – for example, they should take a part in energy conservation activities themselves and encourage their staff as well. 5.10.4.3.1.2 Understanding the issue Before attempting to make out any future projects or activity arranges, it is fundamental for the organization or production line administration to comprehend the present circumstance in a legitimate and precise way. This incorporates the status of their own operation as well as other significant data, for example, contenders’ operation, conditions around the organization and their pattern in future, positioning the organization itself in the neighborhood and in worldwide markets, and so on. The key steps for this purpose are: 5.10.4.3.1.2.1 Grasp the current energy use The information regarding current consumption of energy should be gathered through measurements, estimations, or calculations of every individual unit under the premises of organization, with the classification on the basis of type of energy. The data should be collected regularly and arranged in daily, weekly, monthly, or yearly manner depending upon the requirement and precision set by its stakeholders. Then the data should be analyzed and a relation should be obtained between different operational modes and production scales. This data can also be utilized in the prediction of future trends.

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5.10.4.3.1.2.2 Identify management strength and weakness After the data collection, it should be compared with the pioneers or benchmarks in the industry. If such reference data is not easily available, then there historical data can be compared with the present data of their competitor so that right steps could be taken to get an edge over their competitor. Along with it, the strength and weaknesses of the company should also be evaluated considering the competitor situation in local and global market. 5.10.4.3.1.2.3 Analyze stakeholders needs In an organization, stakeholders are basically top level Senior Managers, Directors, Staff/Engineers, and Workers/Operators. The need and expectation of these stakeholders must be taken into account so that everyone could adopt the changes caused by SEM easily and large benefits can be extracted out of it. 5.10.4.3.1.2.4 Anticipate barriers to implement Design of an easy to implement and practically possible program. It also need consideration of expected barriers that could come along in its way of creating an organization that follows all the steps of SEM and contributes toward its social, economic, and environmental amenability. Some possible barriers could be:

• • • •

Insufficient support of top management Inadequate level of understanding and willingness of cooperation between multiple managers of same organization Untrained workforce Insufficient budget allocation for SEM implementation activities

5.10.4.3.1.2.5 Estimate the future trend The future trend of energy demand could be estimated by using the historical data of the organization. This estimation enables the organization to increase or decrease in its power generation capabilities depending on rapidly changing circumstances of global market. It also provides a check and balance between the energy consumed and production of the organization for the particular period of time. 5.10.4.3.1.3 Plan and organize Based on the analysis of previously collected data and understanding the position of company in local and global market and also identifying the strength and weakness of organization, the following step should be taken in order to design a relevant and good strategic plan to get a maximum out of this effort. 5.10.4.3.1.3.1 Develop a policy It is fancied that the top (senior) administration announce the “Energy Policy Statement.” This is exceptionally viable to let individuals inside and outside the organization unmistakably knows the administration’s dedication to Energy Management (or Energy Protection). The configuration of the Energy strategy statement is different, however it generally incorporates the objective or goal of the organization and the more concrete focuses in the field of Energy Management (or Energy Conservation). 5.10.4.3.1.3.2 Make out a plan/program Any plan under consideration should be easy to implement, practical and attainable. It should also take into an account, all the resources and related elements of the company which can be classified into measurable or quantifiable. It should also include the awareness campaign relating to SEM, motivation techniques, training and so on. 5.10.4.3.1.4 Implementation The accepted plan should be enforced within an organization and all the organizational resources should be consumed in order to ensure smooth implementation of the plan. The responsible person or committee shall continue to work for the promotion of activities and training of workforce which is essential for the plan to survive. 5.10.4.3.1.5 Controlling and monitoring performance After the implementation, all the processes should be closely monitored in order for it to work smoothly. If any problem arise, or any variance between estimated and observed value noted; then necessary steps should be taken in order to overcome and stabilize it. 5.10.4.3.1.6 Management review After the plan or program has been completed, a report mentioning all the events, success and failures faced during its implementation should be submitted to top management. In it all the results should be analyzed in quite detail for any good and bad points with possible recommendations. This report shall be utilized as a feedback for subsequent program. Thus all activities could be repeated to form a cyclic movement. 5.10.4.3.1.7 Implementation of SEM (key step approach) In order to implement EM program effectively, key factors approach which is discussed in quite detail in our previous section should be utilized. The major step toward the implementation of EM program in any organization is the energy audit. Energy audit

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SEM system Parameters

Alerts Data collection unit Unit consumed

Production

Fig. 8 Energy efficient operation. Reproduced from Shah H, Spada S. Siemens ARC-White papers, Seimens Energy Efficiency; 2009.

enables the organization to identify the problems or factors which could become a hurdle in the way of its implementation. energy audit can be conducted by hiring an expert consultancy agency or by utilizing internal technical and trained staff. 5.10.4.3.1.7.1 Energy audit There are number of stages in energy audit process, each having its own importance. The process includes: collection and analysis of data, site investigations, cost and benefit analysis, preparation of concise report, creating an action plan for the project implementation, and monitoring and controlling. Energy audits act as a foundation of developing an EM program that will give an edge to the organization while creating more efficient operations. It also enables the analysis on where the most effective use of limited capital should be employed to achieve energy goals. Through energy audit, specific type of system can be monitored throughout the operation, which can be optimized, modified or replaced based on the requirement. It also helps to identify the operations which yield greatest rate of investment (ROI), so that it could be modified and kept up to date in order to compete with the changing circumstances of global market. Monitoring systems energy consumption throughout the day and night cycle, and correlating it with the production delivers an important information regarding that systems efficiency. With newly introduced wireless energy monitoring technologies, this equipment’s can be installed in a very cost-effective manner on existing system. Data collection with every 15 min interval will be sufficient enough to estimate the efficiency of the system. The process cycle governing energy audit is shown in Fig. 8. 5.10.4.3.1.7.2 Advanced monitoring and metering solutions As discussed previously that to conduct a successful energy audit, monitoring devices needs to installed within premises in order to prepare a successful plan of action and correct estimation of future trends. These frameworks offer both modes, check of the utilities overwhelmed by a far reaching report, including droops and surges, and the capacity to power factor, harmonically disturbed waves and different parameters consistently. These solutions are adequate for obtaining metrics without any high capital investment or changing of existing system flows. It provides an important statistics of the real time process on the basis of which decision can be taken for corrective actions. Whether it is effectively measuring a capacitor bank to enhance control elements, performing load shedding, or deciding squandered vitality utilization, advance metering offers many preferences basically from gathering precise information from dissimilar sources. Hence, advanced metering is an approach to successful implementation of EM on a distributed architecture and topology that will grow according to the requirement of organization. It will act as an essential strategic tool for optimization and evaluation of already installed process, operation or a system.

5.10.4.3.2

Six-sigma approach to energy management

Concerns regarding the importance of conservation and effective utilization of energy are increasing day by day. As the evidence of the above statement it can be given that nowadays people are in a habit of switch off all the necessary equipments, when not in use so as to use energy effectively. Bulk amount of energy generated by any nation has been consumed by the production or manufacturing industries to increase the countries GNP; therefore, certain approaches are required so that effective energy can be utilized by these sectors so as to increase their efficiency. The implementation of such systematic approach not only makes the nation industrialize and modern but also affects the lifestyle of each individual of a society in a better perspective. Cost associated with energy consumption is no longer considered as a minor component of total production expenditure. Inspite of its greater importance and influence, there are certain facilities which don’t take its advantage by properly managing it and minimize its effect on expenditure sheet; which directly minimize the production costs. Facilities without proper power managing systems and determined energy managing approaches, don’t have proper understanding regarding their energy usage and production ratio; such facilities cannot consume their resources to its fullest being efficient at the same time. While optimizing power monitoring investments, it is necessary to identify both intended application and prioritize energy consuming units within a facility. Sustainable energy management is an effective tool which gives a certain edge to any facility over other (i.e., its competitor) by implementing it in terms of effective savings. These savings increases the GNP of overall nation, i.e., if the manufacturer invests less

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Detect

Measure

Analyze

Improve

Control

Fig. 9 Detect, measure, analyze, improve and control (DMIAC) methodology (Six Sigma steps).

on a product, they will further sell product in lesser amount to the end user. It also increases the purchasing power of the individual of any society; hence, larger the trade yields better GNP of nation. In addition to these advantages, EM also minimizes the air pollution which we generate by burning fossil fuels. We cannot visualize it as we do when we starts a car but whenever we switch on a light, we generates some amount of pollution in power plant, which is then released in air in terms of carbon footprint and is a reason of global warming. The necessity of an hour is to utilize this all important form of energy in such a manner that it gives us advantage economically as well as ecologically. The Six-Sigma is a proven approach in terms of quality management and implementation, as an extension to same principles this approach has been tested over the phenomenon of energy efficiency/conservation in number of facilities and there result came out to be unique and attractive. Six Sigma at numerous associations just means a measure of value that takes a stab at close flawlessness. Six Sigma is restrained, information driven approach and system for taking out deformities in any procedure – from assembling to value-based and from item to benefit. The core objective of Six Sigma methodology is to develop a measurement-based strategy that primarily focuses in reducing the process variations and improve process outcomes. This objective is attained by implementing two Six Sigma sub methodologies namely: detect, measure, analyze, improve and control (DMAIC) and define, measure, analyze, design and verify (DMADV) in a facility. DMAIC mainly focuses on the improvement of existing processes falling below expected values; whereas, DMADV is methodology used to design new processes and system considering specific requirements. Energy conservation plan mainly developed keeping DMAIC process in consideration as it mainly used to implement on the existing processes to enhance efficiency (Fig. 9). 5.10.4.3.2.1 Implementation of six sigma approach (detect, measure, analyze, improve and control methodology) 5.10.4.3.2.1.1 Detect The phenomenon of energy saving is considered to be more where its consumption is higher. Therefore, the key is to attack the larger energy consumer rather than implementing it and worrying about the minor one. From this point of view, while designing a plan for energy management first target larger energy consumer within a facility, i.e., heating systems, cooling systems, lightning, etc. Those points also need to be detected in a process where energy has been wasted or exhausted for effective and long lasting saving. In order to detect/define such points and elements in a process, traditional approach of installing metering gives snapshot data of energy consumption which is not sufficient enough, for effective monitoring real time data logging devices need to be installed. There are certain rules to install power monitoring devices which are given below: 1. Advanced monitoring systems need to be installed with main electrical switchgear whereas less sophisticated metering devices should be deployed to each of the identified bulk energy consumer. The advantage of installing advance monitoring system with main grid is that it will not only monitor the electrical parameters of the facility but also the power quality or power factor it is receiving. This approach enables its user to monitor basic electrical parameter and on the same time grasping the firsthand knowledge of the quality of power facility is receiving through electric utility. 2. As discussed, continuous monitoring of large loads allows to identify and predict accurate energy savings; therefore, the more the monitoring points will be, the better electrical model can be generated for statistical predictions. 5.10.4.3.2.1.2 Measure After identifying/detecting which load to measure, accurate measurement devices need to be installed in order to do quantitative analysis. Properly installed and verified measuring system could be a valuable asset for any organization. Annual energy consumption and production are the major concerns of an organization. An electrical measuring system could contain one or discrete points which are interconnected on a single station so as to enable a single user to monitor all the happening of the at a single point. An efficient measuring system contains three major components: metering devices to measure data, application software to manage, accumulate, display data and matched communication module in order to link metering devices with application software. This measuring system should be robust enough in order to work and gather real-time data 24/7. This continuous extraction of important information mostly with the frequency of every 15 min enables the user in correct decision making. Also, this will give accurate information regarding how much energy is consumed, in which part of the day the consumption is greater and what unit/load consumes larger energy. This knowledge plays a vital role in reducing the energy consumption and increase the efficiency of the process. 5.10.4.3.2.1.3 Analyze Two type of analysis is mostly done in order to come-up with an accurate energy management plan which is of energy consumption and quality. All the gathered data is then analyzed with respect to these two segments and parameter of interest are current and voltage consumption during the startup of load, power factor, and energy consumption. These observed parameters then can be compared with the actual in order to identify deviation of each load. These analysis helps the production engineer

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with energy consumption pattern for planning shift activities such as production rates reducing production break-downs, maintenance engineer to check that whether the equipment is due for maintenance or not and planners to plan appropriate sizing of facility. 5.10.4.3.2.1.4 Improve This analysis is then used in creating an appropriate energy management strategies for optimum and efficient plant operation, this includes: 1. Enabling the organization to predict the energy consumption pattern in manufacturing and production facilities with respect to any season, part of the day or year. 2. Standardize the energy consumption patterns for different points, loads or facilities within the plant. 3. Enable to shift the operations in the off-peak times, this is mostly suitable for the countries in which load shedding is commonly done. 4. Prediction of possible energy interruption during the operation which could affect the process a great deal. 5. Automatically improves power factor by adding a capacitor banks if correct prediction of its arrival can be made. 5.10.4.3.2.1.5 Control After taking steps to improve the power efficiency of the system, certain controls are needed to make it long lasting. The remarkable work in this field enable the development of devices like adjustable speed control motor drives and shunt capacitors for power factor correction and reduce losses.

5.10.4.3.3

EMrise: wireless energy management platform for wireless sensor network

Due to the vast demand and rapid development in wireless communication and embedded systems, wireless sensor networks (WSNs) is grabbing attention of its users worldwide. The significant feature of WSNs is its large scale application, low capital requirement, compact size, and low power consumption. The main component of a typical WSN network is a sensor node which further comprises of several equipment’s: power supply, processing unit, sensing module, and a signal modulator/transceiver. By combining these essential components in a miniature, portable device; these sensor nodes acts as multifunctional device and can be utilized in variety of applications such as border monitoring [5], environment monitoring [6], health care [7], home building automation, etc. WSN is a diverse technology which can be easily scalable, comparatively low cost and relatively small dimensions. There are numerous studies currently going on to deploy specific sensor protocol and advanced algorithms which could enable it to recognize things and make decision automatically, this will make the whole sensor network low maintenance and more robust against any failures cause by any of the nodes malfunction, its mobility or energy dissipation being adaptive at same time. Moreover, they can also be deployed in some harsh environmental condition with self-organized and adaptive network so that assigned task could be carry out efficiently without human interference. But, inspite of being such an enormous technology and having potential to support number of more advanced applications such as real-time monitoring, its growth is limited due to some inherent disadvantages of WSN. These disadvantages includes, limited processing speed and low data transfer rates among the peripherals. These disadvantages cannot guarantee the efficient and appropriate performance of sensor nodes especially when it comes to real-time monitoring. However, further and potential applications are limited due to inherent WSN, disadvantages. Short communication range also causes loads of energy waste making the system inefficient. For this reason, a multi-hop network is always required for data transportation between the source node and sink node. This gives a rise to severe energy constraints while implementing WSNs, the improvement of data processing ability of the system by utilizing powerful processors cannot be done due to the reason that energy will again get depleted really soon, restricting the device to complete its task. Limited energy accessibility also yields that the system would not be able to maintain its hop-network for longer period of time. Whereas, these devices are expected to be utilized heavily for data monitoring and transmitting purposes where frequent changing of the batteries are not possible. For this reason, the need of an efficient WSN emerges which can work under these conditions efficiently, by minimizing the energy utilization while at same time not compromising the processing ability and performance of network. The study and research in this emerging field, promises better and good quality of life for its user in number of aspects and applications, mainly in our daily routing activities. However, the constraint of energy limitation is the big hurdle in the way of its development and significantly restricts their functionality. Therefore; this has made the energy utilization and conservation in an efficient way; one of the most important topic to work on. For this reason, analysis of energy consumption, conservation and utilization is really a critical topic in the designing and implementation of an efficient WSN. In past years, number of studies has been conducted to efficiently conserve energy for a longer period of time in WSN’s. These studies mainly splits on three broad spectrums: (1) simulation/emulation-based approach for energy efficiency, (2) hardware-based approach, and (3) optimization-based strategy. These strategies were designed in order to explore this field of energy management and find out the ways to make the process energy efficient. Firstly, we will discuss the simulation-based approach toward energy management which is more useful and implemented due to the facts that the simulation gives more realistic and simplified model for the conservation of energy rather than the hard mathematical models-based upon certain hypothesis and assumptions. Simulations-based approach is relatively a slow process

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and contain numerous steps needed to be implemented correctly but at a same time it offers well tradeoff between the accuracy and efficient management. There are number of tools available easily on the internet which can be used for its simulation like Prowler [8], OMNet þ þ [9], and NS-w of which scripts are mainly written in C þ þ and MATLAB, implementing these software provides a good verification of the concept when it is in early stage. But at the same time, coarse energy can be evaluated using these tools due to the lack of low level model availability. These drawback has been wiped-out by the utilization of emulation tools such like ATEMU and TOSSIN. These tools can compensate it but at the same time on cost of efficiency because they are basically limited to specific operating systems (OS) such as TinyOS is used with TOSSIN in order to evaluate sensor nodes, while ATEMU utilizes AVR microcontroller based node platform. Whereas, for hardware-based method, numerous research has already been focused on efficient and long lasting energy consumption in real world sensor nodes. Among these researches, a detailed study has been presented in Ref. [10] by the MICAz mote [11], number of benchmarks are used for energy estimation/calculation, charging effect of battery, and lifetime of batteries. If we discuss the optimization-based approaches, then number of study has been conducted for the optimization of both hardware and software. From the hardware perspective, better energy ratings can be reached by optimizing the power consumption of all the linked hardware components [12,13]. While, from software perspective, optimization can be done by the successful development of new protocols, the adoption and implementation of efficient energy consumption methods and through configuration of existence set of protocol and make them work your way [14,15]. 5.10.4.3.3.1 EMrise wireless sensor network energy management program (design and implementation) All the limitation and suggestion discussed upon, an efficient energy management system-based upon WSN is proposed briefly in this section. As discussed that the energy consumption can be evaluated by three means, this system offers three different modules for each of the proposed evaluation method. Firstly, for simulation/emulation-based analysis Emrise-Simulation System (EMriseSS) has been developed on system-C based environment of which various features will be discussed, for hardware energy management EMrise-Measurement System (EMrise-MS) is proposed which deals in hardware-based energy management platform for the extraction, gathering of the realistic data from several nodes and at the end for optimized evaluation, EMrise-ops; a genetic algorithm (GA)-based optimization framework is suggested to enable the optimized energy management on WSNs. 5.10.4.3.3.2 EMrise-Simulation System This module of the management program comprises of SystemC supported components such as ports, channel, interfaces, and several connection as shown in Fig. 10. These components also corresponds to basic hardware modules in order to complete efficient networking simulation, this modules include microcontroller, transceiver, sensors, battery for power, and several other peripherals. Each component of EMrise-SS is modeled to act as a module for SystemC; enabling efficient transmission. In this system, the sensor is modeled to sense the surrounding and transmit data periodically to microcontroller. Microcontroller acts a hub of information where all the data collected from sensor are received for further analysis, as sensors sends analog signals; microcontroller unit (MCU) converts that analog signals into digital signals to allow them pass to transceiver as per scenario and application requirement. This component (transceiver) or radio module is responsible for the transmission and receiving of data among various nodes wirelessly and also clear channel assessment (CCA). The energy module works as a monitor, which continuously monitors the power consumption of several components at several specified abstraction level; set by the designer as per their specification and requirement. In timer module, several timer can be defined as per synchronous movement of data, it can be used as sample timer, transmission timer, or any other application based on conditions. Lastly, the most important module of the system, i.e., network module is used to handle network topology and various transmission collision between data sending and receiving of various peripherals. Energy module is the most significant component of entire model, as this plays a significant role in measuring, calculating and estimating the energy usage; therefore, it needs to be well designed and optimized, so that it could be adjusted to extract accurate, realistic and easy to get energy consumption parameters. The energy model composed of various inherent components of which detailed description and analysis is given below. SCNSL

Timer

Sensor

MCU

Radio

Energy

Port

Interface

Component

Connection

Fig. 10 EMrise-Simulation System (EMrise-SS) framework. MCU, microcontroller unit; SCNSL, SystemC Network Simulation Library. Reproduced from Nanhao Zhu AVV. Energy management in wireless cellular and Ad-hoc networks. Switzerland: Springer; 2016.

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5.10.4.3.3.2.1 Elaborate Lib based on registers An elaborate lib has been induced in energy module so as to monitor energy consumption of overall unit as well as at each modes/ hardware. This Lib works on the basis of register. In EMrise-SS energy module, different modes and hardware components are mapped together using specific registers. These components act as a mirror and every updated value is synchronous with their dedicated registers. This functionality enables the researchers to get great benefits from this design method, due to the fact that the tracing of the energy value is always constant and does not depend on the component configuration. This made the whole simulation process flexible enough to adjust in between scenarios where hardware parameters need to be changed again and again. This is the reason that this Lib made whole energy model to work without any interruptions as no new configurations are required for every changed scenario. 5.10.4.3.3.2.2 Multi-abstraction energy evaluation Using its energy model, EMrise-SS is able to trace all the energy consumption at different abstraction level of modes. For example, if the abstract level is set on high, the change in state of operation of every component is linked to it enabling to identify the current consumption of each model and then calculate its rating using a simple formula (P ¼ V*I). 5.10.4.3.3.2.3 Energy calibration For the sake of realistic and accurate measurement of data, this module incorporates calibrated energy values for better evaluation. Since; power rating parameters are not easy to extract from product, this modules incorporates various electronic chips which need the formation of network among several motes for their successful operation. This makes the calibration process essential within energy module. 5.10.4.3.3.2.4 Transition state energy One of the drawbacks of calibration device is that; it neglects the transition state of the devices. Whereas, energy module of EMriseSS incorporates features to consider such transition states of every components. This is essential because while transition, extra cost is added to overall energy consumption of the device which can never be ignore for its accurate monitoring. So, the total energy consumption in EMrise-SS is calculated by adding the power consumption of each peripheral in every operational state for its accurate measurement. 5.10.4.3.3.2.5 Performance metrics This energy management device is compatible to calculate and present its reading in number of performance metrics such as mW, mJ, etc. It can also present the battery usage as well as their lifetime performance in minutes, days, hour, sec, etc. 5.10.4.3.3.3 EMrise-Measurement System This segment of entire program is the hardwired energy measurement and management platform that compose of multilevel devices at different motes to perform the task as shown in Fig. 11. The platform composed of multichannel energy measure device (MEMD) as well as energy data management software (EDMSP) for its successful measurement. MEMD enables the system to carry on simultaneous measurements at different sensor nodes of different peripherals at a time; whereas, EMDSP enable the user to access this all important information using an easy to use graphical user interface (GUI) and save energy by taking necessary steps where required. Each of this components is explained below. 5.10.4.3.3.3.1 Multichannel energy measure device This device is implemented in the system due to its feature that it does not leave any bad impact or side-effect to the sensor node of hardware or software while energy measurements. This enables its user to get rid of expensive measurement instruments such as

MCU

ADC

Amplifier

Power supply

Test-bed node

Fig. 11 EMrise-Measurement System (EMrise-MS) platform. ADC, analog-to-digital converters; MCU, microcontroller unit. Reproduced from Nanhao Zhu AVV. Energy management in wireless cellular and Ad-hoc networks. Switzerland: Springer; 2016.

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oscilloscope and various acquisition cards; alternatively save cost. One of the essential components of MEMD is a shunt resistor of known value; placed in between power module and sensor node. This shunt resistor Rs has its own value as its enable the system to measure energy consumption at each and every node using voltage drop Vs across it. The Vs is then amplified, so that it can be used to measure energy consumption. The amplified voltage is fed into ADC, so that this signal could be sampled out and these small samples could be saved into the buffer of PIC18. These packets are saved continuously in a buffer and at the same time sent out, in an unbuffered sampling mode. For further simplification of the process, commercial com port module is used for the effective data transmission between PIC18 and data node. SerialUART signals generated by the MCU is able to be sent over UB232R to data terminal for later evaluation and analysis. But however; due to this large data packets communication; memory problems could also take place if experimentation is done over a long period of time to take more and more samples in order to increase the efficiency of the process. 5.10.4.3.3.3.2 Energy data management software This component of Emrise-MS module is implemented using CSerialPort class which is developed by the collaborative efforts of Windows API and MFC. This component enable user to work and access different working power meters using a simple GUI. These parameters could be a specified port number, data bit, stop bit and baud rate as well. Once the port receives the data coming from various nodes it is recorded and seen on GUI. As, this module can represent the data in various formats such as hex, decimal, voltage value and current value; it can also represent data graphically. 5.10.4.3.3.4 EMrise-OpS EMrise-OpS is integrated with iMASKO framework due to its versatility and intelligent features of GAs, this section of system has the ability to find an effective solution automatically to best fit the data and enable its user to predict values and system behavior in future. A MATLAB based GUI is designed to link both sections, i.e., EMrise-OpS and EMrise-SS. This is essential as this enables the user to visualize the process in an effective manner before optimizing it. The parameter can be set using the GUI interface. EMrise-OpS, composed of various types of fitness function depending upon the curves. This enable this system to use the data of NS-2, OMNeT þ þ and prowler for further evaluation and results optimization. EMrise is an effective platform to monitor and allow effective and efficient use of all important energy wirelessly. As, it has a feature of SystemC support, this enable this platform to support numerous energy consumption and evaluation techniques mainly for heterogeneous sensor network, with a hardwired measurement platform that is also cost-effective. Various simulation and emulation can also be conducted for the realistic prediction of energy consumption, including GA-based optimization framework to extract information and fit it in real-time. There are number of ongoing researches to further enhance the accuracy of simulation and communication protocols so that more realistic energy evaluation could be conducted. Redesigning of hardware platform into smaller one has also been conducted in order to make the overall system portable.

5.10.4.3.4

Electric vehicle energy management system based on vehicular ad hoc networks

The concept of electric vehicles (EV) was first given in mid of 19th century and since then it was considered that, this technology would dominate the transportation industry in near future. Utilizing this technology would not only help us in reduction of fossil fuel dependency but also enable us to stop the emission of CO2 and other harmful greenhouse gases, this yields the higher efficiency of vehicle engine by utilizing sustainable sources [16]. This will not only increase the purchasing power of citizen but also consumption on conventional energy resources can be reduced to greater extent and help this society to tackle any future energy crises. Various studies have been conducted in order to compare the efficiency of electrical vehicles with conventional diesel engine and the result demonstrates that EV are more efficient in terms of fuel efficiency [17]. But however, every good findings have some limitations associated with it, similarly one issue that could arise if this technology started to get utilized worldwide is that, it is necessary to design its charging/energy management system so that it could be refueled remotely. Due to the reason that EV could take longer time in recharging itself if we compare it to our conventional cars which take some minutes to get refilled; therefore, an area-based charging infrared management system is required with an information system that could easily interact with mobile EV and charging stations. In near future, charging fleet provider might provide energy to these EV of which price depends upon the factors of time and location. These vehicles could also be utilized as a vessel to store electrical energy for use when peak/average exceeds the sett loads. The main concern before implementing these technology is to design an electrical vehicle information system (EVIS). EVIS will act as a platform which enable to transmit useful information such as, nearest refueling station location, a low cost station, less time consuming station, etc. The working structure of EVIS is shown in Fig. 12. EVIS will communicate with the EV fleet and the spatially distributed control servers. For this to happen, EVIS should be able to exchange important information from vehicle to infrastructure (V2I) and infrastructure to vehicle (I2V), these transmissions can be done on uplink and downlink, respectively. In a vehicular ad hoc networks (VANET)-based system, infrastructure nodes (IN), are placed alongside roads at some critical points in a form of transmission towers as shown in figure. These IN works as relay, any moving vehicle who tends to seek any information regarding any of the offered facilities could send a request to information server. If this IN is located outside the range of vehicle, then multi-hop wireless transmission networks for the information to reach IN from vehicle. Utilizing a stable backbone network, IN then transmits this information to the center information server (CIS) which after processing it replies back to vehicle via IN.

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Central information server Backbone network

Backbone network

Service response message Service request message Location register message

Fig. 12 Electrical vehicle information system (EVIS) infrastructure. Reproduced from Nanhao Zhu AVV. Energy management in wireless cellular and Ad-hoc networks. Switzerland: Springer; 2016.

The successful implementation of EVIS may face two difficulties; firstly, due to involvement of multi-hop transmission networks, VANET could have suffered from broadcast storm and the hidden nodes data transmission collision [18]. In this circumstances, larger packet losses may occur which could harm the overall efficiency of the system. Secondly, in a mean time of traveling, vehicle could have moved closer to other IN, therefore keeping track of vehicle mobility is essential for its efficient work, in order to overcome this an algorithm need to be designed which can efficiently communicate using shortest path. 5.10.4.3.4.1 Segment leader selection In order to improve the efficiency of multi-hop message transmission, this system proposed to divide every road segments into fixed size and to allocate segment leader as a potential relay node. The segment leader will act as an information hub which can only transmit the service messages. By doing so, the number of transmission per unit time is reduced and broadcast storm is prevented. By utilizing the periodic messages, the vehicle with longest remaining time in a given segment road acts as an initial segment leader. It works in a sequence that before the segment leader about to leave a segment, it appoints next segment leader, i.e., the next vehicle with longest remaining time [19]. 5.10.4.3.4.2 Time slot reservations As discussed above, in a particular interval of time there would be several vehicle that would be communicating with IN simultaneously due to which threat of possible node connection is expected. This possible node collision effects the quality of data transmission and communication. To get rid of such collision, time slot reservation mechanism is introduced of which process flow is shown in figure. In this network architecture, each time slot is divided into two parts Ts and Tr, where Ts is reserved for messages transmission and receiving; whereas, Tr is the location register message. These two slots are further divided into numerous multi-hop time slots as shown in figure. As it is observed that size of request message and location message are different so as the time slots Ts and Tr. The aim of VANET-based EV information system is to conserve and save energy by minimizing the dependency of modern society on fossil fuel. There are numerous challenges for implementing EVIS as such services require the immediate sharing of information through exchange of messages between EV and the central information server through information node. For the efficient transmission of multi-hop messages, time slotted technique which enable the system to get rid of broadcasting storm as well and possible node collision. Moreover, to select a shortest path for data transmission, a location registering and signaling mechanism is used to help find the nearest IN to enhance the efficiency of overall system.

5.10.5

Types of Wireless Networks

Originally, wireless networks were designed to transmit the voice traffic and were not considered suitable for data traffic. For example, delay of almost 100 ms is required for voice traffic so as to avoid unwanted echoing effects. For instance, the feature of

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packetized speech enables us to tolerate some packet loss and bit error rates (BER) of 0.001. This could result as slight quality loss but no major difference is observed. Data transmission is a broad field having variety of different aspects right from the shorter span of an email to larger span of telephone calls. Wireless networks are mainly classified into two broad spectrum each having its own significance and importance namely: wireless personal area networking (WPAN) and wireless local area networking (WLAN). Both of these types are discussed in detail on the basis of its applications, advantages, and restrictions.

5.10.5.1

Wireless Personal Area Networking

As this world is getting more and more surrounded by the modern electronics equipments, the need of binding them together through a wireless and manageable network is also increased. For this reason researchers have studied a lot on WPAN, as it can be operated around a personal operating space (POS), for this reason it is expected to play an essential part in fourth generation communication (4G) by enabling short-range wireless connectivity [20–22]. It is also observed that WPAN have some similarities with fixed WSN and [23] also some distinct differences. For example, the WPAN networks are specified on the basis of their small nodes and specific group mobility pattern at uniform speed; whereas, WSN consist of large number of nods with limited mobility. Presently, WPAN is characterized into two wide standards on the basis of their range of wireless communication and transfer rate: IEEE 802.15.13 and IEEE 802.15.14. IEEE Standard 802.15.13 has been developed for high-rate WPA networks (HR-WPAN), it defines the protocols and their primitives so that it could transmit data at higher rates over short range; whereas, 802.15.14 is a low-rate WPA networks (LR-WPAN), which defines the protocols and their primitives so that it could transmit data at lower rates over short range transmission channels. A WPAN network has the specification of light power consumptions, low cost, ease of commissioning, secured data transfer and well defined protocol structure and chain. This network comprises of several nodes to transmit and receive data packets over a wireless channel. One of these nodes are required to act as a hub of information oftenly known as coordinator. The function of this network coordinator is to allocate collision free time slots when requested by any of the network node. Addition to that it also controls the connection and disconnection of several nodes within a network simultaneously. The WPAN networks are only designed for medium access control layer and are defined to use for the application of industry, scientific, and medical (ISM) bands. Soon after the standardization of WPAN standard and acceptance of its importance and significance by the industries, this topic grabs the attention of several researchers in order to provide a comprehensive introduction to the protocol stack, evolution to the draft and design requirement [24]. Different types of wireless technologies are discussed in the next article to give an overview of its structure and compatibility.

5.10.5.1.1

802.15.1 (Bluetooth)

5.10.5.1.1.1 History of development and characteristic We as a whole have encountered the burden that emerges when we begin associating peripherals to a PC, or when we interface other electronic gadgets, with a considerable measure of links that gets to be distinctly hard to control. At that point we begin to think how simple it would be if everyone of these associations were done utilizing an alternate route from the physical links, similar to infrareds, radio, or microwaves. The initiative to link mobile phones with other accessories using radio waves was first took by Ericson mobiles in 1994, as various other firms noticed the potential in this newly introduced technology; Ericson, IBM, Nokia, and Toshiba launched a combined Bluetooth Special Interest Group (SIG) so that this technology can be studied in more detail and its concept can be broaden by wirelessly linking PCs with other devices. Several organizations of software engineering and broadcast communications expected to build up an opened, ease interface to make simpler the correspondence between gadgets without utilizing links. This is the source of the innovation which key name is “Bluetooth.” This is a reality these days, however now another issue emerges and is that there are a ton of principles and innovations, inconsistent between them. What we require now is an all-inclusive, substantial gadget for the association of a wide range of fringe, and that works straightforwardly for the user. Soon after the IEEE formed a group to develop wireless PAN standards in 1999, Bluetooth SIG group was the only respondent of WG15’s Call for Responses; hence, Bluetooth and IEEE 802.15.1 since then became synonyms. After introducing wireless headsets, which utilizes Bluetooth for its wireless transmission and receiving in 2000, it is noted that the cost and power consumption is largely reduced. Since then, Bluetooth became an add-on and essential feature of almost all mobile phones. After the success of this idea, Bluetooth 1.1 was introduced which works on 2.4 GHz ISM band at a PHY layer have data transfer rate of 1 Mbps. After sometime, Bluetooth 2.0 was introduced in November 2004 which supports similar to that band to that of Bluetooth 1.1 but data transfer rate was increased to 1 Mbps to 2 or 3 Mbps. This technology supports the wide variety of devices, from ordinary looking headsets to PDA synchronization. Different parts of Bluetooth calls up for different parts of its protocol stack. Bluetooth is a standard utilized as a part of connections of radio of short extension, bound to supplant wired associations between electronic gadgets like cell phones, personal digital assistants (PDA), PCs, and numerous different gadgets. Bluetooth innovation can be utilized at home, in the workplace, in the auto, and so forth. This innovation permits the users, a quick associations of voice and data between a few gadgets progressively. The method for transmission utilized guarantees security against obstructions and wellbeing in the sending of data. Between the essential attributes, must be named the robustness, ease of usage, low consumption of power, and cost-effective. The Bluetooth is a little microchip that works in a band of accessible recurrence all through the world.

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Bluetooth protocol stacks is further divided into numerous profiles, each having its own significance for the particular usage model as shown in Fig. 13. Profile provides the basis of exchanging important information between Bluetooth and number of different usage models as shown in Fig. 13. The most important feature of Bluetooth, which is, its low power consumption and low component cost is achieved by limiting transmission range at moderate data rates. In very less time this technology has proved its effectiveness in various PAN tasks, such as short range ad-hoc networking or telephonic connections, has yields its importance in various available PAN options (Table 3). 5.10.5.1.1.2 Working of bluetooth Bluetooth technology allows the modern world to connect, transmit, and receive variety of data without any wired connection. The concept of Bluetooth was first given by Swedish engineers working for Ericson, and till now this technology is not owned any single firm rather by a group of companies named as SIG. With the advancement of modern society, number of devices have been developed and is been used extensively consuming this technology in order to work wirelessly. Such devices include speakers, telephones, and computers, etc. So, the question arises that how this fantastic technology works and links two different peripherals, wirelessly? Well, Bluetooth technology requires both a hardware component and software component for its complete description. To send and receive signals, devices must be equipped with a small microchip having antenna on it. This electronic chips work as a hub of transmitting various information to different devices connected to it. Moreover, the device should also have a software in order to decode transmitted information. This software oftenly differs with respect the data rates and bands. When two different peripherals are connected using Bluetooth, the automatically creates a personal area network (PAN) in between them so as to communicate with one another wirelessly and automatically without human interference. In order to communicate wirelessly, the Bluetooth exchanges data with other peripheral devices utilizing a low power, short range RF band of 24.02–24.85 GHz allocated for industrial, scientific, and medical (ISM) applications. Since this technology uses low power signals to transmit, it gives it an advantage to consume low battery but at the same time it also restricts the data communication range for up-to 30 feet. There are certain power classes of Bluetooth shown in Table 4 which gives an extended data communication range but at the same time increases power consumption of the device in order to use comparatively high power signals.

Protocols

Applications

RFCOMM Profile A

Profile B

OBEX

Profile C

Profile D

Profile E

LMP

L2CAP

SDP Fig. 13 Various profiles of bluetooth technology.

Table 3

Description of various Bluetooth profiles

Profiles

Description

Personal area networking (PAN) Synchronization profile

Used to enable general internet protocols (IP) over an ad-hoc piconet Used to enable the transfer of several personal items such as calendar and address book between several peripherals Used to enable printing wirelessly by making an ad-hoc network Used to manage, transfer or delete file wirelessly on a server Used to enable audio transfer among several peripherals wirelessly Used to enable a dialing feature between several personal digital assistants (PDA) and other remote network

Printing profile File transfer profile Headset profile Telephonic profile

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Table 4

Power classes of Bluetooth technology

Power classes

Range (Ft)

100 mW 2.5 mW 1 mW

Up-to 300 30 0.3–3

Despite of this weakness, it also has an advantage that Bluetooth possessing devices don’t require the LOS with each other in order to transfer data. The radio waves have the ability to travel along the wall and different hurdles without being deflected back to origin. Also, their low amplitude reduces the chances to great deal of it to be detected by other RF devices such as baby monitors, door opener, etc. Besides it, Bluetooth technology also possess some advanced techniques in order to avoid interference. One of the widely used technology for this purpose is spread spectrum frequency. This technique allows the device to hop and transmit among any one of the 79 different available frequency. The frequency will be randomly chosen while changing frequencies for up to 1600 per second. This also enable to make a network connection between eight different devices at a time installed within the radius of 30 ft for low power signals. 5.10.5.1.1.3 Applications This technology has the tremendous potential of connecting, moving and synchronizing different devices within a localized hub. Potential of further development in this technology is all important, due to the fact that interacting with things which is near to us far more important than that which is far away. This is the reason why Bluetooth has bring a huge revolution in the field of wireless market. The technology which only bears some theoretical aspects few years ago is now one of the most extensive technologies used by almost every cellular brand. This technology has changed the medium of data transfer which was used previously that is hardwired to that of done using radio waves. In this section the application of this technology with respect to different field has been discussed in quite a detail along with the future aspects. Some of the application in which it is widely used are given below. 5.10.5.1.1.3.1 Healthcare Previously, this technology has been refrained by various hospital and medical facility providers from installing it in life sensitive intensive care unit (ICU), but now more and more hospitals are updating there equipment’s in order to monitor and diagnose their patients wirelessly. Vice President of Qualcomm, a leading healthcare product developers states that “those bans are falling away because health care professionals recognize the value of anytime, anywhere communications and because fears of electronic interference have subsided [25].” Some of the widely used healthcare devices utilizing this technology are enlisted below.

•

•

Cardionet, a healthcare equipment designing firm of San Diego utilized this technology to monitor and diagnose outpatient’s heart. The device comprises of various ECG sensors which continuously monitors any notable changes of patient’s hearth and then wirelessly communicate the report to hospital via Bluetooth. Using any local cellular service, the device automatically detects any unwanted rhythm of human heart and transmit the data to cardionNet server [26]. Texas-based firm, Tiba Medical, have recently developed a wireless oximeter to measure the level of oxygen in human blood. The device enable the chronically ill patients to send their readings on their mobile, laptops and ultimately to their physicians so that necessary actions could be taken [27].

5.10.5.1.1.3.2 Office products In an office environment, there are various devices that is time consuming if work is done manually. Some of this devices are printers, surveillance devices, etc. By the development of this technology, it automated ordinary looking environment right from the safety to print. Some of the devices utilizing this technology are enlisted below.

• • •

Ericson has designed a new universal Bluetooth controller 2.0, which allows the user to alter the existing behavior as well as add support for new application. This device also supports Java and VB scripts, user can easily code and link different buttons wirelessly from their mobile. Another application is extensively used nowadays which yields the efficient way to call on a number not saved on mobile. Bluetooth PC-dialer is both, a hardware module and an outlook plugin through which easily call can be made to email sender. LockitNow 1.2 is an app through which remotely one can lock its desktop pc when you are away using wireless communication and automatically unlocks when user returns.

As we now have a reasonable thought of Bluetooth items in the present market and what this innovation has to offer, it clears up the photo for why Bluetooth has spiked its encouraging as of late. Not generally has Bluetooth been this way, before when Bluetooth appeared in the 1990s it began off with enormous desires, however those desires inevitably dropped. From that point forward, SIG has held up the innovation with its details to keep itself alive. At first, Bluetooth was for the most part utilized as a part of portable handsets as it were. In mid-2004, the cell showcase turned out to be extremely noticeable, therefore of which an ever increasing number of cutting edge cell phones appeared. This insurgency in the cell phone industry acquired Bluetooth back

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the market. Before long Bluetooth’s abilities were acknowledged more than just in a phone. In the wake of having a firm nearness in headsets and sans hands gadgets by 2005, Bluetooth stretched out itself to different restorative fields, office items and family items as specified previously. 5.10.5.1.1.4 Case study: smart home, energy management solution based on bluetooth technology With the advancement of human society in the field of wireless communication, i.e., data transmission and receiving across the ends wirelessly; various research has been conducted for the implementation of home automation (HA) concept using wireless communication [28]. By term smart home, we can visualize a normal dwelling equipped with modern technologies, sensors and actuators, which collect data as per residents’ activities and communicate to several peripherals. It refers to an automatic control of various in-house as well as outhouse activities; typically, HA provides one end or centralized control of various electrical appliances, i.e., air conditioner, lighting, security system and even home theater, without human interference. By introducing this technology in our homes, one can extract excellent level of comforts as well as energy savings. Due to modernized of human society, the need to deploy more and more electronics equipment in households is increased for the comfort of its consumer. The increase in the usage of modern equipment also gives rise to its power consumption. Inspite of the fact that recent development in integrated circuit (IC) had enhanced the power efficiency of overall system but the energy crises mostly in under developed countries need more efficient ways to consume the available energy. There were several methods proposed for this purpose among which the integration of power line communication was also one [29]. This proposed system was composed of one controller to manage the communication and a network adapter connected with every home appliance. In [30], one more research based on PLC power monitoring was proposed; combining home energy management system (HEMS) with internet. It composed of a smart meter which gathers power consumption data and put it on web through residential gateway. In one more research, remote monitoring and controlling power socket was developed for the atomized power consumption monitoring of home devices [31]. These power sockets not only enable its user to remotely monitor the real-time data, but also take necessary steps through online portal. Consumer plays a vital role in energy savings [32], reduction in energy consumption is only possible by providing them with consumption profile of appliances and helping them to change their behavior accordingly, this approach of energy management is known as demand side energy management (DSM). This approach is based on two directional communication that is enabled by utilizing the smart grid in order to smoothen the load curve of traditional grids; as a result, this programs works by affecting the behavior of consumer regarding electricity consumption by matching present values with demand and controlling it through optimizing electrical appliances at consumer end. For example, a more practical usage of this approach is the shifting of appliances from peak times to off peak times based on Time-of-Use (TOU) [33] pricing. Consumer based on the tariff provided by their electricity provider can choose the best possible time to toggle different home appliances in order to improve energy utilization; moreover, the cost. But, embedding several electrical devices in single household such that they could communicate effectively; is in itself, is an open challenge due to the fact that there is no standardize communication protocol between them [34]. With the development in the field of wireless networks, this technology has become a part of our daily life. Smart homes are utilizing these wireless network technology to enhance the comfort level of its user [35]. In order to inject intelligence in our conventional living standards, several wireless technologies have been proposed such as Bluetooth, ZigBee and Wi-Fi and literature has been presented, out of which the researchers of Ref. [36] introduced a working prototype that could visually define the behavior of wireless network in home. The prototype also includes relevant features such as a sensor and actuator network based on IoT and programming interface in order to set the threshold levels. Another management system based on WN characteristic was proposed in Ref. [37]. This system allows the consumer to monitor and manage both, the daily energy consumption of home and climate characteristics in order to reduce the energy consumption, this proposed system was versatile enough to show good modularity being simple at same time with low power consumption. In Ref. [38], the authors proposed the fuzzy logic-based approach using WN and smart grid, so as to reduce the load demand. Simulation showed that their proposed approach reduced the load demand without compromising the thermal comfort based on the data received from sensors and other peripherals. 5.10.5.1.1.4.1 The proposed architecture As discussed above, several literature proposes different approaches to utilize this technology in order to get a smart home; in fact, the use of advanced information technologies (ICT), efficient energy harvesting methods and innovative/efficient metering solutions has been widely used to accomplish this task. The flow diagram of proposed system is shown in Fig. 14, the system consist of several energy sources, electrical appliances, storage devices and a control unit which will act as an information hub and bridge in between several devices. As, lots of work has been done on renewable energy which enable us to this of a home generating its own energy through wind turbine/solar panel and stores this useful energy in batteries so as to use it in a time when these sources are not available. As, the renewable power generating methodology is not the main aim of this study but it is essential to highlight it as the essential feature should be considered to install in smart home. The main aim of this study is to investigate the energy management unit (EMU) and storage devices, charging station is the key component of EMU, it is a kind of circuit that converts the direct current generated from renewable sources into alternating current so as to power different home appliances. In addition to it, this domestic energy solution also aims at reducing home electricity consumption charges by shifting the usage of various appliances from peak demand hours to off peak ones. We use several electrical appliances everyday depending upon our need and day of the week, for example, some people uses washing machines to wash clothes on a particular day of the

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Smart meter unit

External energy production unit

Storage

Home start block

Fig. 14 Proposed concept. Reproduced from Yardi VS. Design of smart home energy management system. Int J Innov Res Comput Commun Eng 2015;3: 3.

week, there are notable differences in the usage time of appliances everyday, another example of it is that an air conditioner can be operated for 15 min every half an hour which could save energy without compromising the comfort, similarly a refrigerator can work for every 25 min in 50 min cycle without affecting the quality of food stored within it. This domestic system involves an effective communication among smart appliances, a central EMU and wireless network nodes. EMU acts as a decision making unit and hub on information which based on a fuzzy logic-based algorithm. This system enables the consumer to turn on any device of use at any point of time without having a knowledge that it could also be a peak hour as per their tariff. The fuzzy-based algorithm within EMU will suggest its consumer that what will be more suitable time to turn on that particular device in order to conserve all important energy. Moreover, when the device is turned on it will check for the stored available energy in battery necessary for it to operate, if the stored energy is lesser then it would turn of any other unnecessary appliance in order to conserve energy and use it for consumer’s purpose. It will also communicate with the smart meter so that the system could be updated with any of the price change associated in that time slot. This approach is not limited to the devices that is used on periodic basis such as refrigerators, washers, air conditioners, etc. Infact, the system is valid for all kind of electronic appliances. Consider an example of a consumer that has the need of drying his hair using a hair dryer after the shower, now when he plugin and turn the appliance on, the consumer can reject any scheduling proposal from EMU as he/she is in need of using that dryer to get his hair dry and cannot wait for a time of low peak hours. But, if he wants to use a vacuum cleaner or an iron for which consumer can agrees for it to postpone and use these machines for cleaning purposes as per the schedule suggested by the EMU. This developed mechanism of acceptance or rejection of schedule can be managed by smart plugs. 5.10.5.1.1.4.2 Wireless architecture Bluetooth low energy (BLE) module works within the frequency range of 2.4 GHz, i.e., ISM band having at most 40 channels 2 MHz apart from each other. The device is capable of transmitting data at the rate of 1 Mbit/s. BLE utilizes an adaptive frequency hopping; therefore, has a slower transmission rate as compare to Bluetooth classic. In a piconet configuration of Bluetooth classic, each of the master device could control up-to 7 slaves connection, but in BLE there is no such limitations as it has been proven theoretically that hundreds of slaves can be connected to a single master node. Due to the need of smaller transmission distances between electrical peripherals and EMU with limited data transmission BLE is preferred. In addition to it, the optimization of the range of radio waves as per the application is also a major factor behind its selection. Majority of the devices available out there in market are capable of data transmission within 30 ft. of range, but there is not certain limit imposed as range can be altered, as per application requirement. This proposed system accompanied with a network architecture is composed of numerous wireless cells, which is managed by BLE master device and act as an EMU integrated with smart meter module. The wireless network is composed of several field devices, i.e., BLE connected to several other appliances in order to complete associated tasks utilizing smart plug. In the proposed network configuration, there are several WC’s that independently works as WSN’s as they have the capability to continuously monitor the surrounding without human interference and in also low power. The master nodes are capable of receiving and sending data to FD’s placed within the loop of WC. Apart from it, it also allows its consumer to send commands to FD nodes. This allows to continuously monitor data received by sensor nodes and could transmit any possible commands through it. This kind of wireless networks can be accompanied in variety of scenario; the list of examples of the system in which it can be embedded are given below:

• • • •

Energy saving: central chilling system, HVAC, atomized shades and many other FD. The common thing in all of this devices are that they collect information/data that is our monitoring parameters such as light, temperature, humidity, pressure, etc. and can control it to certain threshold so that waste of energy could be avoided. Metering: it can be utilized to observed the peak energy consumption hours of household devices and send an alert to its consumer so that load can be reduced. Lighting control: unnecessary usage of lights can be avoided by atomizing it with several other sensor that could sensor the movement of its consumer and can only open light of the area or particular room that is in use at a time keeping remaining off. Safety precautions: several safety sensor like smoke detectors, motion sensors, etc. can be embedded in it so as to make overall system work as per safety requirement of premises and could trigger appropriate actions of commands if needed.

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5.10.5.1.1.4.3 Performance evaluation Performance evaluation is really essential for the long term and effective use of any system. It yields the credibility of the system along with, it also enable its consumer that to what extent he has to rely on a particular system. For the performance evaluation of the proposed system, numerous tests were conducted on Network Simulator Version II (NS-2). These evaluations were based on a comparison with several similar looking energy management approaches proposed by different researchers. These approaches includes Erol-Kantarci et al. [39] and Mahmood et al. [40], in both of these literature they have formulated and discussed efficient HEMS based on wireless networks. The main difference between both of these systems and this system is that, in Refs. [39,40] researchers presented an algorithm in which they didn’t take into an account the feedback of consumer. In this case study, the evaluation has been carried out in a similar way conducted by Erol-Kantarci et al. and Mahmood et al. so that all of these systems could be compared directly with one another. Four loads is taken into account, i.e., washer, dryer, dishwasher, and a coffeemaker, the duration for which it is used is consumer specific; whereas, the power which it consumes per cycle given in Ref. [41] is used. For the similarity purpose, the power rating of these appliances are considered to be 0.4 KWh for coffeemaker, 0.89 KWh for washer, 1.19 KWh for dishwasher, and 2.46 KWh for dryer; whereas, there cyclic times are taken as 10, 30, 60, and 90 min, respectively. Moreover; it is also essential to note down the peak hours of the day for proper evaluation purposes and is core consumer related. In this study, the peak hours have been taken from 8:00 a.m. to 2 p.m., the energy consumption behavior is plotted using a discrete random distribution, i.e., Poisson random distribution. The toggling of devices between on and off is considered as a Poisson distribution. This simulation has been for 7 months so as to get the maximum data and the average value has been plotted in Fig. 15. The curve plotted in figure shows the average electricity consumption measured in a single day. The value which has been extracted for 7 months has been summarized as an average representation of a single day so as to describe the general behavior of the system. From the curve it is clear that proposed approaches of Erol-Kantarci et al. and Mahmood et al. is efficiently shifting the load from peak hours to off peak hours. But however; it is clear from the plot that this proposed system obtains the best performance and consumer lesser amount of energy as compare to both of them. After evaluation and comparison of performance, the most important factor to compare is the economic benefits from the proposed system. But, however, to monitor cost reduction it is essential to follow a tariff of electrical charges of particular country as the tariff prices changes across the world. For this study, the considered tariff is [42] proposed by Italian Enel, it is a multinational extruder and distributor of oil and gas. The proposed tariff costs around 0.0365 Euros/KWh for off peak timing and 0.1725 Euros/KWh for peak timing, i.e., from 8:00 a.m. to 7:00 p.m. The energy consumption cost has been evaluated for both, when there is no energy management applied and when the approaches of Erol-Kantarci et al. and Mahmood et al. along with proposed system is applied for better representation of results. The result of 7 months electricity bill has been represented in Fig. 16 which yields that in term of cost reduction also, the proposed system works in a better way as compare to remaining approaches.

5.10.5.1.2

802.15.4 (ZigBee)

5.10.5.1.2.1 History of development and characteristic This wireless technology is widely renowned due its low power consumption, low cost, and wireless mesh networking standard. This low cost feature allows this technology to be widely deployed in various smart monitoring and control applications; whereas, due to its low power consumption it is capable of working more utilizing small batteries only and mesh networking allows the reliable data transmission among various nodes. 5.0 Without HEM Erol−Kantarcl et al. Mahmood et al. Proposed solution

Electricity consumption (kWh)

4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0

2

4

6

8

10

12 14 Hours

16

18

20

22

24

Fig. 15 24 h Load consumption cycle. Reproduced from Nanhao Zhu AVV. Energy management in wireless cellular and Ad-hoc networks. Switzerland: Springer; 2016.

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400 350

Without HEM Erol−Kantarcl et al. Mahmood et al. Proposed solution

Energy cost (C)

300 250 200 150 100 50 0 10

30

50

70

90

110 Days

130

150

170

190

210

Fig. 16 Cost evaluation. Reproduced from Nanhao Zhu AVV. Energy management in wireless cellular and Ad-hoc networks. Switzerland: Springer; 2016.

ZigBee type networks started to grab attention in 1998 when both Wi-Fi and Bluetooth was found out to be “limited” for some applications. This was the first time when researchers started to study and find for the ad-hoc network that should be suitable for digital radio data transmission. The standardization if IEEE 802.15.4 was finalized in May 2003 and formally documented in in publication of 802.15.4-2006. Philip Semiconductors was the first stakeholder to cease the investment in the summers of 2003 and remains a promoter member of ZigBee alliance board of directors. In October 2004, the ZigBee alliance announced that the members companies to contribute in the expansion of this useful technology is increased to more than 100 in 22 countries. By April 2005 it reaches to 150 companies and continues to expand by December 2005 making a pool of more than 200 companies ready to invest. This alliance announced ZigBee specification 1.0 on 14 December 2004 known as ZigBee specification 2004; whereas, specification 2.0 is announced on 2006 and finally the advanced ZigBee specification was finalized and presented by 2007 known as ZigBee PRO. ZigBee is released with two stacks representation, stack profile 1 is for domestic and light commercial use whereas stack profile 2 contains some additional features such as multi-casting, advanced security with symmetric key-key exchange (SKKE), smaller footprint of ram and flash to provide complex mesh networking and compatibility to work with all ZigBee applications. The ZigBee 1.0 specification was again altered back in December 2004 and made available to all the members of alliance, currently the ZigBee 2007 specification was posted on October 2007 and the first most ZigBee application profile, i.e., HA, was announced on October 2, 2004. This technology offers wide variety of smarter and greener connectivity between different devices so that they can work together effectively and efficiently with minimum human interference to control your surroundings. After 10 years of its research and millions of experimentation, this technology has proved itself to be the most reliable portal to smartly connect different devices at your workplace, your home or anywhere you play. This innovative standards are designed to let manufacturers enable there customer to make their own IoT and M2M as per their own requirements to smartly gain control of and even improve their everyday activities. This technology helps its user to cost effectively add effective new feature to improve their safety, convenience, efficiency, and security of products of our daily use. This technology enables its user to effectively utilize their top most resources that is money and energy by gaining a control of their domestic premises. Moreover, improve and monitor their health and fitness. Without this technology it would never be so easy to effectively synchronize all gadgets in a smarter and greener way [43]. 5.10.5.1.2.2 Working of ZigBee In this modern era, there are several standards that are available for high data rate transfer, but none of them seems to be compatible for sensors and control devices communication standards. This communication not only required high data rates but also low tenancy and power consumption at lower bandwidths. Due to the fact that all of these specifications are present in this ZigBee technology, it is making this “a best fit” for several embedded applications, industrial control, HA, and so on. This technology is mainly used for sensor and automation control network on IEEE 802.15.4 standards for WPAN. The type of communication standards define the access of MAC layer to incorporates many devices at low data rates. This technology can be operated at 868 MHz, 902-928 MHz, and 2.4 GHz frequencies depending upon the requirement of application; whereas, the data rate of 250 Kbps is best suited for two way communication between several sensors nodes and controllers. ZigBee is widely used to control several devices within the range of 10–100 m. The communication system is cost-effective and simple to use that any other short range wireless technology as Bluetooth and Wi-Fi (Fig. 17). System structure of ZigBee technology consists of three main components: ZigBee coordinator, Router, and end device. Every ZigBee network has to consist one coordinator which acts as a bridge of network. The coordinator acts as a hub of receiving and

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Fig. 17 ZigBee PRO modem. Reproduced from Agarwal T. ZigBee wireless technology architecture and applications, elprocus.

Wired connection

ZigBee network

Wireless connection

Existing network

ZigBee coordinator (ZC) ZigBee router (ZR) ZigBee end device (ZED)

Fig. 18 ZigBee architecture. Reproduced from Agarwal T. ZigBee wireless technology architecture and applications, elprocus.

Table 5

Specification of physical layer of ZigBee

Frequency

Band

Coverage

Data rates

Number of channels

2.4 GHz 868 MHz 915 MHz

Industry, scientific and medical (ISM)

Worldwide Europe America

250 Kbps 20 Kbps 40 Kbps

11–26 0 1–10

ISM

storing important information during a process of transmitting data operations. ZigBee router acts as an intermediate between the hub of information and end devices which permits the traffic or commands to move through them to the end device as shown in Fig. 18. End devices have limited access of communication with their parents nodes such that to save useful power, energy or battery itself. The pattern in which these three components are connected with each other’s depends on star, tree, and mesh networks. ZigBee protocol architecture consists of different layers as per IEEE 802.15.4 standards. Each layers has its own characteristic and working, which is explained in detail below (Table 5). 1. Physical layer: this layer performs the task of modulation as well as demodulation of various transmitted and received signals, respectively. Various frequency, data rates, and channel are used with this layers depending upon the locality. 2. MAC Layer: the function of this layer is to enable reliable data transfer communication by accessing different networks with the carrier sense multiple access collision avoidance (CSMA).

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3. Network Layer: this layer accompanies for all network related operations such as connection between router and different end devices, disconnection to network, routing and various device configuration. 4. Application support sublayer: this layer is responsible interfere ZigBee devices with different object application device in order to communicate through network layer. It is responsible of matching to peripherals on the basis of their services, application, and needs. 5. Application framework: it gives two sorts of information administrations as key esteem combine and nonspecific message administrations. Nonspecific message is a developer characterized structure, while the key esteem match is utilized for getting properties inside the application objects. ZDO gives an interface between application items and APS layer in ZigBee gadgets. It is in charge of distinguishing, starting and restricting different gadgets to the system. ZigBee data transmission technology mainly works under two modes: non-Beacon mode and Beacon mode. In first mode, i.e., beacon mode, coordinators and routers continuously monitor any changes of flowing data; therefore, more power is consumed. The routers and coordinators cannot sleep in this mode as at anytime node could receive any signal to communicate and respond. Nonetheless, it requires more power supply and its general power utilization is low on the grounds that the greater part of the gadgets are in a latent state for over long stretches in the system. Contrary, in beacon mode the router and coordinators enters the sleeping mode when there is no data transmission. There is a cyclic process using relays which counters, which periodically switch on and off routers to transmit data to multiple nodes within a network. These networks are work for available time slots which implies, they work when the correspondence required outcomes in lower duty cycles and longer battery use. 5.10.5.1.2.3 Applications 5.10.5.1.2.3.1 Building automation ZigBee technology-based building automation system offers wide variety interoperable products which offers the secured and reliable monitoring of commercial and domestic building systems. It various users, such as tenants, owners, and operators can take advantage from its increased energy savings, low and effective lifecycle cost due to its green and easy to install wireless network. By utilizing ZigBee Building Automation items in your building, you can contribute toward fulfilling credits in the classifications of sustainable sites, energy and atmosphere, indoor environmental quality. This system help us get rid of those hardwired cable lying on ground of sites to monitor and manage. This technology allows speedy system configuration to accommodate it in variety of different situations at a same time reducing remodeling cost. 5.10.5.1.2.3.2 Healthcare ZigBee technology, enables uninterruptable monitoring and efficient noncritical health management normally targeted to certain chronic diseases, increasing aging dependencies, general health, proper wellness, and body fitness. This technology, promotes aging independence in a smart and easy way. Number of gadgets are developed under this technology that even offer an innovative connection with professionals like doctors and nurses, allowing them to monitor your health wirelessly when you are at home, work or any other place. 5.10.5.1.2.3.3 Smart energy efficiency A wide variety of gadgets has been developed under the umbrella of this technology that offers efficient monitoring, control, communicate, and automate the efficient use of energy and water. There are several products that help giving its consumers “greener homes” by giving them a full control needed easily to reduce their power consumption and ultimately save money. 5.10.5.1.2.4 Case study: Zigbee-based efficient home energy management system The use of energy management systems has changed the perspective of human living completely. They are used to monitor, measure, and control the electricity consumption of an individual ore commercial entities. The HEMS can be used as a central hub to control all the equipments within a premises such as lights, TV, air conditioners, etc. In commercial locations such as mall, restaurant HEMS is extensively used to control light at multiple locations from a single platform. HEMS system provides the facility of local metering, sub metering, and monitoring functions. It enables the system to gather data from various locations and take action if needed accordingly or it can be made autonomous enough to take decisions on its own as per the designed and fed algorithm. The HEMS system proposed in this study offers the scheduling, planning, and monitoring of energy related operation. The main motive behind this research is resource conservation, cost saving, and electrical appliance protection to make them efficient. The management system is designed to effectively use the energy generated through renewable sources and make system more compliant to efficiency. The HEMS has to consider both, the energy generation through renewable sources and energy management. The ZigBee-based energy management module is proposed to monitor the energy consumption of home and server. The energy management and renewable energy generation are simultaneously though to have a solution to this problem and save the increasing cost of energy. 5.10.5.1.2.4.1 System description The system architecture is shown in Fig. 19, the system generates the energy using renewable resources and minimizes the consumption using management system. All of the wireless communications were carried out through ZigBee module. The energy

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Fig. 19 Hardware description. MCU, microcontroller unit. Sasi K, Pavitra V, Banu R, Supriya G. Smart home energy management system including renewable energy based on Zigbee and ARM9 microcontroller. Asian J Appl Sci Technol 2017;1(2):240–4.

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consumption of home appliances is monitored and displayed on user mobile to get the firsthand knowledge regarding the current energy use. All the generated electricity through solar panel, wind and hydroelectric turbine is stored in battery bank, battery is controlled using charging controlled module. This module regulates the charging current and voltage in order to avoid over charging phenomenon which damages the battery. Furthermore, ARM 9 controller is used to control relay module which alternatively controls each and every appliances. The user can access the defined HA webpage. On webpage through list of appliances connected, he/she can select the desired appliance and send commands or instructions to server via ZigBee. The home server can be accessed anytime using modem. The home server works a bridge to connect user interface and hardware interface. It communicated with user and MCU wirelessly through ZigBee protocol. It sends commands to MCU through ZigBee transceiver. Afterwards, the MCU get specific instructions from user and it implement commands accordingly. MCU controls the appliances using relay module which individually can turn on or off any device, making it a complete HA system working wirelessly. The software architecture of the system is shown in Fig. 20. The energy management and control unit (EMCU) is the key component of the system. This module is composed of measurement, metering, and communication block. The measurement block is used to measure the power consumption and power factor of the individual appliances. The sample of voltage and current is taken at regular interval of time which is then processed further in MCU. Through this readings, power and power factor is calculated on real time. The flow diagram of EMCU is shown in Fig. 21. The communication block composed of ZigBee, ZigBee is a standard IEEE protocol 802.15.4. The transmitting and receiving range of ZigBee is around 100 m. The reason of choosing ZigBee over other protocols is due to its high range comparatively double than that of Bluetooth. The communication block is to use to transmit data between energy management and server. The proposed system could be greatly used in order to save energy and cost at home or office. Another main application of this system is that, it could be greatly beneficial to senior citizen as it will enable them to control their home at their fingers tips. A part from it, as this system utilizes energy generated from renewable sources, it makes this system more efficient. Selecting ZigBee as a

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Fig. 21 Energy management and control unit (EMCU) architecture. MAC, media access control; MCU, microcontroller unit. Sasi K, Pavitra V, Banu R, Supriya G. Smart home energy management system including renewable energy based on Zigbee and ARM9 microcontroller. Asian J Appl Sci Technol 2017;1(2):240–4.

source of data transmission enables the communication with greater range, hence, this system can be implemented in home, small scale industries, commercial shops, etc., effectively.

5.10.5.2

Wireless Local Area Networking

Advancement in computer technology has been extensively noted around the past decade. Many of these arouse from the need of internet as nowadays it is essential to link various computers to establish a secured online connection. As, the primary industries started to move from hardwired to wireless, since, WLAN networks are considered to be the most important and popular working environment. Companies and individuals are interconnected with each other’s wirelessly through it and can entertain/share much more essential information wirelessly as they could dispose at their own. Previously, all data networks and peripherals were hardwired together in a fixed location. The development of this technology has enabled them to not only simplify complex hardwired networks but also utilize it efficiently. As per cisco report in 2000, WLAN exactly do what its name implies, like an ordinary LAN it peers and connects different machines together but the difference is that, LAN makes an hardwired connection; whereas, WLAN make similar connection wirelessly. It can also be defined as interlinked information handling and sharing network, typically a packet communication network, limited in geographic scope. This enable its user to vary around at different location while at a same time remain connected to the network. Mobile phones would be the most suitable example to understand this phenomenon as it gives its people an advantage to call from anywhere freely. There are various characteristic of this WLAN technology which gave a rise to its extensive study and research. Some of the characteristic are: mobility, ease of installation, and portability.

5.10.5.2.1

Wi-Fi

Wi-Fi is a short range wireless technology that has inspired the mankind since the day it was released. Nowadays it has become an essential need, required for the progress of this society. Almost every year, it is estimated that tens of millions of Wi-Fi devices sold which yields its importance for the advancement of human society. Statistics suggests that this numbers could cross 100 m users in coming years, it has become the major needs of home, offices, airport, hotels, so as to access the internet within their premises wirelessly. But this was not the case, always; as yet merely 8 years ago, Wi-Fi was a niche technology. 5.10.5.2.1.1 History of development and characteristic This renowned technology would have never existed without the decision taken by Federal Communications Commission (FCC) in 1985, allowing several wireless spectrum bands to get opened and used without seeking of permission or license from government. This was the remarkable move taken by government as except radio channels, there were small amount of unlicensed band available. According to FCC spokespersons, Michael Marcus, they took the three chunks of ISM, and made them available for the communications entrepreneurs.

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These bands can be operated on the frequency of 900 MHz, 2.4 GHz and 5.8 GHz, that were already allocated for radiofrequency signal transmission other than communication such as microwave ovens that utilizes radio waves to heat food. The FCC allowed these bands to be used for communication as well with a condition that effect or guide efficiency of other equipment’s using the same bands. They could do this thing by using a spread spectrum which was previously used by military that could spread a particular radio signal over a wide range of frequencies on contrast of adapting the same or single frequency to transmit it. This make the signal both, i.e., difficult to intercept and less effected due to several interference. In initial stages, Proxim and Symbol, two of the most renowned wireless vendors developed their own kind of equipment’s that could work on unlicensed bands effectively. This enable them not to interfere or effect other equipment’s developed by some other vendors and allow smooth transmission of signals. This gives birth to standardize this technology so that particular vendor could use and get benefit from this technology following a set of standards that is globally recognized. NCR Corporation in 1988, wanted to utilize this unlicensed spectrum sent their spokespersons, Mr. Victor Hayes and Bruce Tuck to IEEE in order to initiate and standardize its usage. IEEE formed a committee called 802.3 that had defined the Ethernet standards previously. Mr. Hayes became chairman of committee and soon 802.11 was set up and open for negotiations. The committee composed of different vendors took a long time in order to come up with a draft which was accepted by 75% of its members and hence, in 1977 committee agreed on basic specifications of the standard. The standard allowed the data transmission for up-to the rate of 2 megabytes per second using either of the two technologies; frequency hopping or direct sequence transmission. This new standard was finally published in 1977 and companies immediately started working on it and on equipment that could extract this feature. Afterwards, two new variants were also introduced; namely, 802.11a (that operates in 2.4 GHz band) and 802.11b (that operates in 5.8 GHz band), in 1999 and 2000, respectively. 5.10.5.2.1.2 Working of Wi-Fi This technology utilizes radio waves to provide network connectivity wirelessly. This connection could be established using a wireless adaptor which creates hotspot, i.e., areas under the reach of wireless router and allow its user to access services on internet. After successful configuration, Wi-Fi provides wireless connectivity to its peered devices by transmitting data in between the frequency of 2.4–5 GHz; based on the amount of data on the network as shown in Fig. 22. The key source of successful Wi-Fi networking is the radio waves. This waves are transmitted from transmitter and received by receivers that are normally equipped in our cell phones or laptops configured with Wi-Fi cards. This transmission works within a limit of Wi-Fi network that normally is 300–500 feet. The Wi-Fi cards decode the signals received at the receiver end and thus network is connected. This transmission and receiving of data at antenna and router creates access points as shown in Fig. 23. Antennas has the larger range of 300–500 feet which is mostly used in public places where larger number of users are available; whereas, for domestic and in house use, router with the range of 100–150 feet is used. 5.10.5.2.1.3 Case study: energy management via smart electrical socket design With the rapid development in the field of wireless electrical transmission, life of an individual becomes more comfortable and convenient. As we walk further on the road to industrialization and modernization, the need and consumption of all important electrical energy increases, and at time it is increasing with rapid rates. On one hand several researches and studies has been carried out to answer the question; How to make this growth sustainable? And on the other hand the question arises that what are the Laptop

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Electricity flows into transmitter antenna making electrons vibrate, producing radio waves.

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Fig. 23 Radio waves transmission.

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Fig. 24 Smart socket hardware architecture.

ways to consume more and more natural resources for electricity generation and decrease our dependency on fossil fuel which is harmful for our environment [44]. Sustainability is the area of more interest as it enables us to find out ways to change our existing systems in such a way that the become energy efficient and consumes less energy as compare to before, inspite of installing a complete new one because it causes a lot of capital investment. Under the umbrella of IoT, condition monitoring of systems and sharing of its data with several other peripherals and server wirelessly has become more practically liable. This case study discusses the designing similar kind of electrical socket that extract data of any device connected to it and share it wirelessly so that necessary action could be taken if the device is put on for several time or it is consuming a lot of energy. Smart electrical socket in the necessity of every household, it can monitor and control the electrical consumption of any device and share it wirelessly on server using Wi-Fi wireless technology [45]. Smart socket used nowadays are mainly used for the purpose of security and protections, this case study proposes just and addition to its normal feature to give it more edge. The power monitoring and data transmission will enable the user to make necessary decision in order to lower power consumption. The requirement of the design is to install the Wi-Fi module within electrical socket [46], it gathers data at regular interval and wirelessly transmits it on website or user’s mobile phone through app. Through this user can actually control his/her equipment’s wirelessly from anywhere; even if he’s not at home/office. This intelligent controlling could save a lot of energy because in our normal life’s we do so many blunders which causes us with high electricity bills and untuneful wastage of precious energy, for example; usually in hurry to reach office, we normally leave some of our lights open, through this we can put it off after getting there. Moreover, when we reach home after being out in a hot sunny weather, we switch on our air conditioners of 18 degrees so that our room get cooler ASAP; however, if we turn it on 20 min before reaching home at 28 degrees, we could avoid the peek current which is the main cause of our high electricity bills. The system design of the smart socket is shown in Fig. 24. This complete hardware has been controlled by a user defined software burnt within the processor. The purpose of this defined function is to assure that server receives all the serial data transmitted by the port, and after processing it according to the predefined inventory list and threshold values take intelligent decisions such as breaking connection, etc. In addition to it, client application launches three threads, i.e., receiving data threads, bit processing thread, and connection making thread. They cycle of the process is, client application sends a connection request to the server to establish a connection. After the connection is established, client requests the server to accepts data, the server processes the request and start sending data bits; afterwards, clients starts the corresponding thread for processing. The complete flow of the data transmission is given in Fig. 25. The systems make it really easier for an individual to control over the excessive usage of electricity within his premises and adding a part in the global cause of saving electricity. The website was designed to display the reading of electrical consumption in graphical way as shown in Figs. 26 and 27, so that the nontechnical user could also interpret it easily and take decisions

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Fig. 25 Smart socket software architecture.

Fig. 26 Graphical user interface (GUI). Reproduced from Yichao Jin LSRW. The design of the intelligent electrical outlet in internet environment. Res Dev Comput Mach 2010;47:321–6.

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Fig. 27 Smart socket graphical output. Reproduced from Jiang Feng, Dai Jian, Wu Fei, Zou Yan, Design of socket based on intelligent control and energy management. Int J Adv Comput Sci Appl 2016;6(10):50–6.

accordingly. All of the communication, data and commands transmission and controlling has been done using Wi-Fi module, the reason of selecting this protocol over others that in household it more easily available and almost everyone has the access of this all important technology. Wireless communications have enabled us to comprehend various aspect of lives with relatively easily approach. If we continue to move forward in development of this with same phase, than we could easily change the perspective of seeing world.

5.10.6

Future Directions

Since more and more devices are being connected to the internet using wireless technologies, one can anticipate the future of high speed wireless connectivity using Wi-Fi and other technologies. There will be smart and energy efficient wireless sensors in the very near future. Good signal to noise ratio devices will make the data transmission rates efficient. Wireless electricity transmission will be another area which will grow and more devices are likely to come in this area.

5.10.7

Concluding Remarks

Nowadays, wireless is one of the top growth areas in the field of telecommunication. Wireless is the term used to describe the communication between different peripherals through the medium of electromagnetic waves. In order to communicate; we, humans share ideas, experiences, and knowledge. This communication is usually done using a medium of speaking, writing, gestures, sign languages, and broadcasting. Moreover, it can be done being interactive, formal, in-formal, verbal, or nonverbal. Similarly, in order to make different electronic component communicate with each other and with WEB without using hardwired cables different wireless technologies have been developed with the passage of time, so as to enable us making an effective communication between different hardware products for the IoT and M2M. The evidence of usage of wireless technologies can be found back to University of Hawaii’s ALOHANET research framework in 1970s. Also, the key event that led this technology to grow faster was the endorsement of IEEE 802.11 standard, in the start of 21st century and the development of interoperability certification by Wi-Fi alliance. From 1970 to 1990, the immense demand of this essential growing technologies could only be met through the usage of narrow range expensive hardware. After the successful implementation of 802.11 standard, this acts as a major milestone in the development of modern wireless networking, and an initiating point to strong and recognizable brand – Wi-Fi. This grabs the attention of researchers and network developers to contribute as much as they can in this field of wireless technologies. This fascinating advancement in the field of wireless

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technologies is the sole motivation behind this chapter, which gives its reader an understanding of all fundamental concepts and appreciate the diversity of this matured field, while avoiding getting down to the technicalities required by system engineer. To survive in today’s market, the demand of organizational efficiency in factors like energy, investments, and workforce is increasing day by day. Nowadays, energy is considered to be a most vital factor for the reduction of operating margin. However, firms need to evaluate the effect of rapid price fluctuation on its operations in order to sustain in volatile market. The key step to control this problem is to smartly reduce the cost relating to energy sector and utilize it somewhere else. In this highly competitive market, organizations should reduce all the extra cost in their operations, in order to sustain longer. Energy is one of the factors, which consumes a large proportion of organizational budget. However, recently a large decline in energy cost has been noticed; but cost of energy will remain volatile. Therefore, sustainable energy generation along with sustainable energy management practices must be realigned with normal industrial operations in order to extract as much from it as we can.

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Further Reading Abedin SF, Alam MGR, Haw R, Hong CS. A system model for energy efficient green-IoT network. In: International conference on information networking; 2015. p. 177–82. Balandin S, Andreev S, Koucheryavy Y. Internet of things, smart spaces, and next generation networks and systems; 2014 . Chilamkurt N, Zeadally S, Chaouchi H. Next generation wireless technologies: 4G and bayond. London: Springer; 2013. Cho K, Park W, Hong M, et al. Analysis of latency performance of bluetooth low energy (BLE) networks. Sensors (Switzerland) 2015;15(1):59–78. Garroppo RG, Nencioni G, Procissi G, Tavanti L. The impact of the access point power model on the energy-efficient management of infrastructured wireless LANs. Comput Netw 2016;94:99–111. Jan MA, Energy-efficient routing and secure communication in wireless sensor networks; 2016. Lai CF, Lai YX, Yang LT, Chao HC. Integration of IoT energy management system with appliance and activity recognition. In: Proceedings – 2012 IEEE internationl conference on green computing and communications, GreenCom 2012, conference on internet of things, iThings 2012 and conference on cyber, physical and social computing, CPSCom 2012; 2012. p. 66–71. Pughat A, Sharma V. Performance analysis of an improved dynamic power management model in wireless sensor node. Digit Commun Netw 2016;3(1):19–29. Sakrani H, Tariq Butt T, Hassan M, Hameed S, Amin I. Implementation of load shedding apparatus for energy management in Pakistan, vol. 281, CCIS; 2012. Saquib SMT, Hameed S, Usman Ali SM, Jafri R, Amin I. Wireless control of miniaturized mobile vehicle for indoor surveillance. IOP Conf Ser Mater Sci Eng 2013;51 (1):12025.

Relevant Websites http://www.wsn.agh.edu.pl AGH. https://www.bluetooth.com/ Bluetooth. https://www.cisco.com/c/en/us/training-events/training-certifications/training-catalog/wireless.html Cisco. http://wordinfo.info/unit/4003/s:technology English-Word Information. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6668 IEEE Xplore. https://www.link-labs.com/blog/types-of-wireless-technology Link Labs. http://eu.mouser.com/applications/rf-wireless-technology/ Mouser. http://thefutureofthings.com/?s=wireless tfot. http://www.teslasociety.com/tesla_tower.htm Tesla. http://www.wems.co.uk/ WEMS.

5.11 Smart Energy Management Kaile Zhou and Shanlin Yang, Hefei University of Technology, Hefei, China r 2018 Elsevier Inc. All rights reserved.

5.11.1 5.11.2 5.11.2.1 5.11.2.1.1 5.11.2.1.2 5.11.2.1.3 5.11.2.1.4 5.11.2.1.5 5.11.2.1.6 5.11.2.1.7 5.11.2.2 5.11.2.2.1 5.11.2.2.2 5.11.2.2.3 5.11.2.2.4 5.11.2.3 5.11.2.3.1 5.11.2.3.2 5.11.2.3.3 5.11.3 5.11.3.1 5.11.3.2 5.11.3.2.1 5.11.3.2.2 5.11.3.2.3 5.11.3.2.4 5.11.4 5.11.4.1 5.11.4.1.1 5.11.4.1.2 5.11.4.1.3 5.11.4.1.4 5.11.4.1.5 5.11.4.2 5.11.5 5.11.5.1 5.11.5.1.1 5.11.5.1.2 5.11.5.1.3 5.11.5.1.4 5.11.5.1.5 5.11.5.2 5.11.5.2.1 5.11.5.2.2 5.11.5.2.3 5.11.5.2.3.1 5.11.5.2.3.2 5.11.5.2.3.3 5.11.5.2.3.4 5.11.5.3 5.11.5.3.1 5.11.5.3.2 5.11.5.3.3

Introduction Background Related Concepts Prosumer Aggregator Virtual power plant Microgrid Smart grid Energy Internet Energy big data Evolution of Energy Systems Primitive energy system Industrialized energy system Distributed energy system Smart energy system Dimensions of Smart Energy Management Energy product dimension Participating object dimension Management science dimension Smart Energy Systems Overall Structure Key Technologies Energy production technologies Internet of Things technologies Big data analytics technologies Security and privacy protection technologies Energy Big Data Analytics Energy Big Data User description data Household data Energy system data User behavior data Relevant system data Demand Side Management Case Studies Ubiquitous Energy Internet Concept of Ubiquitous Energy Internet Demonstrative projects of Ubiquitous Energy Internet Composition of Ubiquitous Energy Internet Architecture of Ubiquitous Energy Internet System design of Ubiquitous Energy Internet Smart Energy Management in Smart Buildings Energy consumption in building Smart building Energy management of smart buildings Integration of smart buildings with smart grid The framework for energy management of smart buildings Balance model for energy management of smart buildings The specific technologies for energy management of smart buildings Smart Energy Management in Manufacturing Industry Energy consumption in manufacturing industry Smart manufacturing Energy management of smart manufacturing

Comprehensive Energy Systems, Volume 5

doi:10.1016/B978-0-12-809597-3.00525-3

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5.11.5.3.3.1 Energy management philosophy in smart manufacturing 5.11.5.3.3.2 Energy management technologies of smart manufacturing 5.11.5.3.3.2.1 Internet of Things 5.11.5.3.3.2.2 Cloud computing 5.11.5.3.3.2.3 Big data analytics 5.11.5.3.3.2.4 Mobile intelligence 5.11.5.3.3.3 Smart energy management system in the manufacturing industry 5.11.5.3.3.3.1 The roles of energy management system in the manufacturing industry 5.11.5.3.3.3.1.1 Energy data acquisition, storage, and management 5.11.5.3.3.3.1.2 Reducing operating costs and improving productivity 5.11.5.3.3.3.1.3 Accelerating system fault handling 5.11.5.3.3.3.1.4 Energy saving and environment protection 5.11.5.3.3.3.2 Overall architecture of manufacturing smart energy management systems 5.11.5.4 Smart Energy Management in the Transportation Sector 5.11.5.4.1 Energy consumption in the transportation sector 5.11.5.4.2 Smart transportation 5.11.5.4.3 Energy management of smart transportation 5.11.5.4.3.1 Management principles 5.11.5.4.3.2 Application scenarios 5.11.5.4.3.2.1 Management of electric vehicles 5.11.5.4.3.2.2 Management of smart vehicles 5.11.5.4.3.2.3 Intelligent parking management 5.11.5.4.3.3 Key technologies 5.11.5.4.3.3.1 Big data technologies of smart transportation 5.11.5.4.3.3.2 Cloud technologies of smart transportation 5.11.5.4.3.3.3 Internet of Things technologies of smart transportation 5.11.6 Research Paradigms 5.11.6.1 Energy Informatics 5.11.6.2 Social Informatics 5.11.6.3 Energy Social Science 5.11.6.4 Energy Social Informatics 5.11.7 Future Directions 5.11.8 Closing Remarks Acknowledgments References Relevant Websites

5.11.1

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Introduction

With the rapid development of the economy and society, the rapid growth of world population, the increasing seriousness of resource-related and environmental problems, and people’s increasing demand for energy service quality, the defects and shortcomings of traditional energy systems are become more and more apparent. In terms of energy production, the large-scale production of coal-fired energy has led to serious environmental pollution. It is estimated that the carbon emissions from electric power production had exceeded 7700 million tons, which accounted for nearly 37.5% of the total carbon emissions each year [1]. Also, to meet the increasing demand of energy consumption, the investments of substantial expanded energy production infrastructure are growing fast [2]. From the energy consumption perspective, the world’s total primary energy consumption has surged significantly in the past few decades [3]. In terms of energy consumption structure, renewable energy still only accounts for a small proportion [4]. In addition, with the development of the Internet of Things (IoT) and smart city related technologies, more and more smart home appliances are being installed in households [5]. As a result, residential electricity consumption is becoming more flexible and load fluctuation becomes more obvious. The imbalance of energy supply and demand often brings serious threats to the security and reliability of energy systems [6]. Moreover, for an industry with monopoly characteristics, the energy sector lacks the motivation to provide innovative energy products and services in many countries. Consumers’ energy service quality cannot be improved continuously. Therefore, people’s demand for building SESs is becoming increasingly urgent. Recently, some new development concepts for energy systems have been proposed and implemented, such as microgrid (MG) [7], smart grid (SG) [8], and Energy Internet (EI) [9], which are discussed in more detail in Section 5.11.2.1. Smart energy systems (SESs) are the new forms of energy systems, which have been developed to deal with the many challenges of traditional energy systems and to better satisfy people’s growing demand for high quality and personalized energy services [10]. SESs can be seen as

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the fusion of advanced information technologies, massive penetration of clean energy, innovative business models and service models, and energy production infrastructure. The information and communication technologies (ICTs), particularly the IoT, cloud computing, and big data analytics and other emerging information technologies, are increasingly penetrating into many industries and people’s daily life, thus changing the landscape of many industries and forming the “Internet þ industry” or “smart industry.” In recent years, the Internet and big data are gradually changing the operation modes and business models of enterprises as well as the behavioral patterns of individuals [11]. For the energy industry, the whole process of energy production, transmission, distribution, and consumption is being digitalized and reconstructed. The behavioral patterns and demand levels of energy consumers are also changing. In addition, the wide access and deep penetration of clean energy sources and storage devices (for instance, wind power, solar power, storage units, and electric vehicles (EVs)) bring more opportunities for energy efficiency improvement and energy service improvement. The plug and play (PNP) of clean energy in SESs also has great significance for achieving energy conservation and emission reduction. Business model and service model innovation is also an essential part of SESs, due to the fact one of the most important objectives of smart energy is to better meet the personalized energy service requirements of consumers. The increasingly available energy big data (EBI) has brought a new effective way for business and service model innovation. Through EBI analytics, group and individual energy consumption behavior patterns can be discovered, such that differentiated and personalized marketing strategies can be developed. Finally, energy production infrastructure is also indispensable for constructing SESs, similar to the traditional energy systems. Therefore, we can present a definition of smart energy management. Smart energy management refers to the combination of a series of management objectives, strategies, concepts, tasks, models, processes, mechanisms, and measures based on big data analytics and advanced ICTs, thus supporting the establishment and optimal operation of smart energy systems.

The remainder of this chapter is organized as follows. In Section 5.11.2, it introduces some background of smart energy management. The related concepts of prosumer, aggregator, virtual power plant (VPP), MG, SG, EI, and EBD are first briefly introduced. The four stages of energy system evolution and the three dimensions of smart energy management are also presented and discussed. The evolution of energy systems can be divided into four stages, namely primitive energy systems (PESs), industrialized energy systems (IESs), distributed energy systems (DESs), and SESs. Smart energy management can also be studied from three dimensions, namely the energy product dimension, the participating object dimension, and the management science dimension. Then, the overall structure and key technologies of SESs are provided in Section 5.11.3. Section 5.11.4 introduces the sources of EBD and its application in demand side management (DSM). Section 5.11.5 provides some case studies about smart energy management, including China’s Ubiquitous Energy Internet, as well as smart energy management in smart buildings, the manufacturing industry, and the transportation sector. These cases can help readers to enhance their understanding of SESs and smart energy management. The research paradigms of smart energy management are discussed in Section 5.11.6. It is believed that energy science, information science, and social science cross each other, thus forming some new interdisciplinary research fields. Finally, the future directions of smart energy management are pointed out in Section 5.11.7. They can be summarized in five aspects, namely strategy, data, behavioral, security, and regulatory issues of smart energy management.

5.11.2

Background

5.11.2.1 5.11.2.1.1

Related Concepts Prosumer

When it was first proposed, the concept of the prosumer referred to someone who blurs the distinction between a consumer and a producer [12]. Now the meaning of prosumer has been extended to many different fields. For the energy industry, the high penetration of distributed energy resources (DERs) make the consumers in the energy sector not just consumers of energy, but also independent energy producers [13]. In SESs, the energy consumers are no longer passive users. That is to say, traditional energy consumers have evolved into a new identity with producer and consumer properties at the same time (i.e., the prosumer). In some countries, the wind or solar power generation based on crowd-financing and financing leases have become a new business model for SESs [14]. These new prosumers are increasingly involved in the energy production or investment activities.

5.11.2.1.2

Aggregator

The aggregator is an emerging key stakeholder in the energy market of smart energy systems. The aggregator is an actor in energy systems, and can be defined as an agent who “offers services to aggregate energy production from different sources (generators) and acts toward the grid as one entity, including local aggregation of demand (demand response management) and supply (generation management)” [15]. Due to the regulatory characteristics of the energy market, small consumers are not allowed to participate in the wholesale electricity market directly. In the competitive electricity market, the aggregator can provide its small consumers with a wide range of innovative energy services, including consumer engagement, bill management, energy efficiency, distributed energy management [16]. The aggregator combines the small consumers into a single purchasing unit to negotiate with the retailers. The aggregator also negotiates demand response with the retailer, and electric power suppliers [17].

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5.11.2.1.3

Virtual power plant

This concept is traditionally used to represent VPPs but lately also refers to virtual power players. In the smart energy management environment, VPP has become a new operation and management concept. VPP refers to the aggregation of many different DERs, such as wind turbines, solar panels, storage units, EVs, and demand response resources by VPP engineering. Through integration and aggregation, it can make up the instability deficiencies of many DERs [18]. Based on the new concept of VPP, each of the DESs with DERs can be regarded as a controllable aggregated group to offer many services to the operators and make contracts in wholesale market [19]. The VPP is also one of the players in SESs, and one of the tasks of the VPP is to manage and optimize the operation of the aggregated DERs [20].

5.11.2.1.4

Microgrid

MG can be seen as a small-scale power system that can achieves electricity provision by some DERs, such as distributed generators and storage devices [21]. In the SESs, MG is an important complement of the large power grid. MG is composed of renewable power sources (e.g., wind power and solar power), small-scale generating units (e.g., microturbines and diesel generators) and some storage devices (e.g., fuel cells). The units usually distributed in areas near the low-voltage load. Thus, MG has some unique advantages, including various generation modes, reliable system operation, flexible power supply, and low emissions.

5.11.2.1.5

Smart grid

The intelligent development of electric power systems is an important sign of SESs. Since it was first proposed at the beginning of the 21st century, SG has become an important concept in SESs. With the increasing penetration of ICTs into the power sector, energy flow and information flow are integrated in SG [22–24]. The electricity big data, including smart meter data, asset management, and related business data are collected in near real-time by advanced metering infrastructure (AMI) of SG. Therefore, based on electricity big data analytics, SG can achieve stable, reliable, optimal, and intelligent operation. SG is the initial stage and basic form of SESs.

5.11.2.1.6

Energy Internet

The initial concept of EI was first proposed after the serious US and Canada blackout accident [25]. Inspired by the PNP and selfhealing characteristics of the Internet, the traditional power grid can be digitalized such that it becomes intelligent, flexible, responsive, efficient, self-healing, and personalized. Therefore, EI can support the access of many different DERs in flexible and efficient ways. EI was first comprehensively introduced and systematically discussed by Jeremy Rifkin in 2011 [26]. As the book suggested, he held that EI is a new concept of energy utilization system that utilizes the concept and technology of the Internet to integrate various DERs, including renewable energy resources, distributed power generation, EVs, and other storage units. EI has become an important concept and a major future development trend of smart energy management [9].

5.11.2.1.7

Energy big data

In SESs, with the increasing digitalization of the energy sector, a large amount of data about energy production, consumption, and system operation can be collected, integrated, processed, and analyzed. EBD has become a basic infrastructure for energy system operation and an essential resource for energy marketing and service provision. Based on EBD analytics, more and more innovative and personalized energy products and services can be stimulated and developed. The multisource and heterogeneous EBD are mainly composed of two parts, namely the internal data including energy use data, asset management data, and customer service data, and the external data including weather data, geographic information system (GIS) data, EV data, and social media data [10].

5.11.2.2

Evolution of Energy Systems

Since it was invented during the second industrial revolution period, electricity has become an important secondary energy, supporting the rapid development of economy and society and satisfying the needs of people’s daily life. However, the energy system architecture, energy production methods and structure, energy consumption pattern, and energy management concept and techniques have been in continuous evolution. The evolution of energy systems can be divided into four stages, namely PESs, IESs, DESs and SESs, as shown in Fig. 1.

5.11.2.2.1

Primitive energy system

PES is the original energy system in the early stage of its invention. In this stage, the energy production related technologies, utilization, and management levels were still very low. The electric power system was still in its infancy. The small scale generators with relatively low technological levels were adopted to meet people’s demand for energy. During this period, electricity production and consumption were self-sufficient to a great extent.

5.11.2.2.2

Industrialized energy system

In the period of large-scale industrialized production, great progress has been made in energy production and utilization. Largescale and industrialized thermal power became the major way of electric power production. Large-scale centralized energy generation, as an independent industry sector, had improved the efficiency of energy provision greatly. Energy efficiency and energy utilization costs had been greatly improved through this kind of centralized production, long-distance grid transmission,

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Smart and connected system Global optimization Smart energy system (SES)

Personalized consumption Big data driven management Service-oriented applications

Distributed production Distributed energy system (DES)

Self-sufficiency Diversified modes of production Flexible consumption Clean and sustainable

Advanced technology Centralized large scale production Industrialized energy system (IES)

High efficiency Long distant transmission High pollution and emissions

Simple production mode Primitive energy system (PES)

Backward technology Low efficiency Lacking of scale

Fig. 1 Evolution of energy systems.

and reliable operation of the power system. However, the large-scale coal-fired power generation method also produced a lot of greenhouse gas emissions and resulted in serious environmental problems [27].

5.11.2.2.3

Distributed energy system

With the rapid development of the economy and society, large-scale industrialized energy production has caused serious resource and environmental challenges, and the need for building a clean and sustainable energy system is becoming more and more urgent [28–30]. The rapid development of clean energy and low-carbon technologies made it possible to develop DESs with low emissions and personalized energy provision, such as MG and distributed power generation. In the future, people’s dependence on fossil energy and large-scale production will gradually decline, while the demand for flexible distributed energy provision and a self-sufficient energy ecosystem will increase [31]. However, it must be acknowledged that DESs cannot and will not completely replace the IESs. DESs are just an important supplement of traditional centralized energy systems. DES plays a great role in ensuring the energy supply/demand balance, improving energy efficiency, and promoting sustainable development.

5.11.2.2.4

Smart energy system

The increasing penetration of ICTs has reshaped the landscape of traditional energy industry. In recent years, many emerging information technologies, including IoT related technologies, cloud computing, big data, and the Internet, have constantly penetrated into the energy system, such that the energy systems are digitalized, thus forming the SES [10]. In the SG environment, EBD throughout the energy system service chain has become a strategic manmade resource for all involved in the energy system. In a SES, advanced metering infrastructure and other sensing technologies can collect large amounts of EBD. Then based on EBD analytics, knowledge discovered about user behavior, demand trends, or asset status can support the energy business process and

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service model innovations. In future smart and connected energy systems, the key competitive advantages of energy product and service providers will be new energy products and services, as well as new business models and marketing strategies based on data value innovation, user value innovation, and efficiency value innovation [32]. In the future, the goal of energy system development and evolution is to form a new energy production, consumption, and service system that is flexible, efficient, personalized, reliable, diversified, smart, and sustainable. This kind of energy system can provide clean and sustainable power for people’s life quality improvement and societal development. As we discussed about the energy system evolution, the PES and IES are typical conventional energy management systems, while the DES and SES are important smart energy management systems. The comparison between conventional and smart energy management systems is summarized in Table 1.

5.11.2.3

Dimensions of Smart Energy Management

For SESs, smart energy management is a multidimensional problem. From a different dimension, there are many different management tasks and objectives. We believe that there are at least three dimensions for smart energy management, namely energy production dimension, participating object dimension, and management science dimension, as shown in Fig. 2.

5.11.2.3.1

Energy product dimension

From the perspective of the energy production process (e.g., power generation, transmission, and consumption), there are different management objectives. For the electric power system, the overall goal is to achieve the whole process of smart energy management, including smart generation management, smart transmission management, smart distribution management, and smart consumption management. For example, in order to achieve smart generation management, the dynamic modeling and real-time monitoring of power generators as well as the intelligent load dispatch are the important components. For smart consumption management, DSM (e.g., demand response, voltage management, and outage management) plays an important role. Dynamic Table 1

Comparison between conventional and smart energy systems (SESs)

Product type Production mode Consumption and service Pollution emissions Main tasks Management techniques

Conventional energy management systems

Smart energy management systems

Energy Relatively single, lacking flexibility, centralized Passive consumption, fixed pricing, lacking of service High Energy production and management technologies innovation Traditional optimization, evaluation, forecasting, and decision-making techniques

Energy and energy-based services Diversified, flexible, distributed Real-time interaction, dynamic pricing, demand response, data-based services Low Energy service and business model innovation Emerging management techniques, including big data analytics, artificial intelligence, and deep-leaning method

Management science dimension Forecasting

Evaluation

Decision-making

Optimization

Smart household energy management

Generation management

Smart building energy management Smart city energy management

Participating object dimension Fig. 2 Different dimensions of smart energy management.

Transmission management

Distribution management

Consumption management

Energy product dimension

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pricing is a key technique to achieve smart consumption management. The day-ahead price is determined every hour during the day before the electric energy is delivered to users. In contrast to the day-ahead prices, real-time prices are calculated depending on current demand every 5 min. Driven by the dynamic pricing mechanism, a grid level optimization could be achieved through the modification of instantaneous load demand, consuming time, or total electricity consumption [10].

5.11.2.3.2

Participating object dimension

From the perspective of participating objectives, smart energy management includes smart home energy management, smart building energy management, smart community energy management, smart city energy management, etc. For example, smart homes have become a hot topic of research and application, due to the high penetration of emerging information technologies in the household. In the smart home, the household facilities are integrated and connected using intelligent sensing technology, network communication technology, automatic control technology, integrated wiring technology, and visualization technology, such that the household becomes more convenient, comfortable, safe, and energy efficient. Household energy efficiency management based on intelligent switch, gateway, appliances, and mobile apps is an important component of smart home management. In addition to smart home energy management, smart building energy management, smart community energy management, smart city energy management are also major types of smart energy management, in which the participating objects are building, community, and city respectively.

5.11.2.3.3

Management science dimension

For management science, optimization, decision-making, evaluation, and forecasting are the four major functions of management. From this dimension, each of these four management functions corresponds to some specific smart energy management tasks. In management science, optimization refers to the process of achieving best results through effective allocation of all kinds of resources inside and outside an organization under certain circumstances. Optimization methods play an important role in smart energy management. For example, on the power supply side, the load dispatch and unit commitment of traditional thermal power and distributed generation are typical optimization problems. Similarly, on the demand side, the optimal scheduling of household appliances under dynamic pricing and incentive-based DR programs is also an important task of optimization. The joint scheduling of both supply and demand sides is an emerging and more complex optimization problem [33]. Decision-making is the core of management. Therefore, the essence of smart energy management is smart decision-making. In SESs, there are many decision-making problems for all of the providers, operators, consumers, and regulators. For instance, in DSM, the DR service providers have to make decisions on how to dispatch different appliances. Home appliances can be divided into three categories, namely nonshiftable appliances (e.g., television and refrigerator), controllable appliances (e.g., HVAC and lightening), and shiftable appliances (e.g., dishwashers and washing machines). The demand for nonshiftable appliances needs to be supplied continuously to sustain the comfort level of the consumers. The electricity demand of controllable appliances can be modified. While the energy demand of shiftable appliances can be totally shifted from peak to off-peak hours. Evaluation is also an important content of management. From the perspective of management science, evaluations in SESs include both the evaluation on decisions that have been implemented and the evaluation on future unknown risks. Based on the time period when evaluation is conducted, evaluation can also be divided into three categories, namely ex ante evaluation, evaluation in the process, and ex post evaluation. Evaluation results provide important support for the optimization, decision-making, and forecasting of SESs. For example, in smart asset management, the operational status and fault condition should be evaluated in near real time. Forecasting, as a key management function, is an indispensable tool for supporting the smart energy management. There are many forecasting tasks and objectives in smart energy management. For example, load forecasting is an important component of a power system’s economic dispatch, and a key module in SESs [34]. Load forecasting means the determination of load data based on the system operation characteristics, natural conditions, and social effects, using a variety of forecasting methods. According to the different purposes, load forecasting can be generally divided into ultrashort-term load forecasting, short-term load forecasting, midterm load forecasting, and long-term load forecasting.

5.11.3 5.11.3.1

Smart Energy Systems Overall Structure

In the future, the energy system will evolve into a smart and connected energy system, which can provide more personalized and efficient energy products and services, by means of big data analytics. The architecture of SESs is shown in Fig. 3. As shown in Fig. 3, the SES is composed of three internal modules and three external modules. The three internal modules are the infrastructure module, the communication module, and the big data applications module. The three external modules include the security and privacy module, the connection module, and the external big data sources module. Three internal modules, which support the operation and management of SES from bottom to top, are the key component of the SES. The infrastructure module provides necessary hardware and software to support the basic functions of smart energy management, such as energy production, data collection, user interaction and engagement, and DSM. The communication module provides network communications by the protocols that enable communication between the physical infrastructure and big data applications. The big data applications module is the greatest difference between a smart energy management system and a traditional energy management system. It can provide the various smart energy services and applications based on

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External big data sources Weather big data, transportation big data, social media big data, GIS big data ...

Big data applications Energy efficiency services Consumer engagement, personalized recommendation, bill management, customer support Demand response Peak power reduction, real-time interaction, customer segmentation and targeting, load forecasting and notification Smart home Multiple devices communications, remote control, thermostat management, efficiency improvement Asset management Remote fault diagnosis, real-time asset status monitoring, substation automation, revenue protection

Security and privacy protection

Communication Network communication Protocols that enable communication between the physical infrastructure and the big data applications

Connection with other business systems

Infrastructure Software infrastructure Operation systems, management information systems, soft applications, user interaction interface Hardware infrastructure Energy production units, embedded sensors, transmission networks, smart meters

Fig. 3 Architecture of smart energy systems (SESs).

big data analytics. These services and applications include asset management (remote fault diagnosis, real-time asset status monitoring, substation automation, and revenue protection), smart home (multiple devices communications, remote control, thermostat management, and efficiency improvement), demand response (peak power reduction, real-time interaction, customer

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segmentation and targeting, and load forecasting and notification), and energy efficiency services (consumer engagement, personalized recommendation, bill management, and customer support). Besides the three internal modules, there are also three external modules in the architecture of SESs. These three external modules provide external supports for the achievement of smart energy management. First, due to the high penetration of emerging information technologies and data science technologies in energy systems, the SES is more vulnerable to attacks. Also, the privacy protection problem is becoming more severe. Therefore, the security and privacy module provides effective protection mechanisms for all of the infrastructure, communication, and big data application modules. Second, compared with the traditional relatively closed energy system, the SES is more open. To provide more personalized and varied energy products and services through smart energy management, its key modules are increasingly integrated with other business systems, including the transportation system, the building system, and GIS. The energy system and these external systems are mutually connected by the two-way data flow. Third, the achievement of top-layer big data applications needs the support of both the EBD and the external big data resources. The external big data sources module provides the necessary big data resources to support the smart energy management applications. These big data sources include the weather big data, the transportation big data, the social media big data, and the GIS big data.

5.11.3.2

Key Technologies

In the SES, energy flow, data flow, business flow, and capital flow coexist throughout the whole structure. The coordinated operation and integration of these flows promote the achievement of smart energy management. The operation of a SES needs the support of the following key technologies.

5.11.3.2.1

Energy production technologies

Energy production technology is still the underlying fundamental technology of a SES. Generalized energy production technologies include energy production technology, energy transmission technology, energy distribution technology, and energy consumption management technology. Particularly, in SES, renewable energy generation, energy storage, and EVs related technologies are also important energy production technologies.

5.11.3.2.2

Internet of Things technologies

IoT related technologies, including smart sensing, real-time communication, and positioning technology, are also key technologies of SESs. The provision of smart energy products and services depends on the high digitalization of the energy system and big data analytics. IoT technologies are the foundation of energy system digitalization and EBD collection.

5.11.3.2.3

Big data analytics technologies

EBD analytics is the key technology of providing personalized, flexible, efficient, and smart energy services for all of the producers, operators, and consumers. Big data analytics technologies in SESs include big data storage technology, big data analysis technology, and big data visualization technology. In SESs, the storage of multisource, heterogeneous, high-dimensional, dynamic, and massive EBD is the primary task of EBD analytics. Data analysis, including data fusion and integration, deep learning based feature selection, and data quality modeling, is the critical path to achieve big data driven smart energy management. Finally, data and knowledge visualization and application is the ultimate goal of EBD analytics.

5.11.3.2.4

Security and privacy protection technologies

Energy systems are a traditional vulnerable industrial system. Due to the digitalization of energy systems and their business processes, and the more interaction with other business systems, SESs are facing more serious security and privacy problems. These security issues include virus attack, exploit attack, false data injection, eavesdropping, denial of service, etc. In addition, privacy protection of individuals and organizations in SESs is also becoming more severe, since privacy-related data and information are easier to obtain in the digitalized energy system. Therefore, the operation of SESs and the achievement of smart energy management objectives need the support of advanced security and privacy protection technologies.

5.11.4

Energy Big Data Analytics

5.11.4.1

Energy Big Data

EBD is not just the internal operation and consumption data of the energy system. In the environment of smart energy management, EBD has a more extensive connotation. EBD mainly include five categories, namely user description data, household data, energy system data, user behavioral data, and relevant system data (Fig. 4). All these multisource data are integrated to form EBD.

5.11.4.1.1

User description data

User description data is the descriptive data of energy consumers. User demographic data, including the population structure, ages, education level, marital status, occupation, and income, is an important kind of user description data. For residential users, the residential characteristics data (such as housing area, building structure, and geographical location) is also a source of user description data.

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User description data Demographic data: population structure, ages, education levels, marital status, occupation, income, consumption level, ... Residential characteristic data: housing area, building structure, geographical location...

Household data

Relevant system data Weather data: temperature, wind speed, light intensity, ...

Household energy use data: smart metering data (15 min, 1 hour, daily, monthly), ...

GIS data: geographical location data, ...

Smart home data: smart switch data, household appliances data, ...

Energy big data

Energy system data

Transportation data: transportation flow, electric vehicle data, ...

User behavioral data

Energy production data: energy production planning, production structure, production scheduling data, ...

Marketing system data: online payment, consumer engagement, pricing data, ... Social media data: user complaints,energy saving experience sharing, peer comparison, ...

Asset management data: energy generation unit data, energy transmission system data, ... Fig. 4 Energy big data (EBD).

Table 2

The amount of data collected by 1 million metering devices in a year

Collection frequency

1/day

1/hour

1/30 min

1/15 min

Records (billion) Volume of data (Tb)

0.37 1.82

8.75 730

17.52 1460

35.04 2920

Source: Lavastorm. Big data, analytics, and energy consumption. Available from: http://www.lavastorm.com/blog/2012/04/09/big-data-analytics-and-energy-consumption/; 2012.

5.11.4.1.2

Household data

Household data is the related big data within a household. These kinds of data include not only the household energy use data collected by the smart meters, but also the smart home data obtained from smart switches and smart appliances. The energy consumption data collected from smart meters and other intelligent sensing and measuring terminals is the core component of EBD. The energy consumption data are massive, dynamic, and complex. Considering different collection frequency, the amount of data collected by 1 million metering devices in a year is shown in Table 2.

5.11.4.1.3

Energy system data

Energy system data is the internal data of an energy system. Energy production data (e.g., production planning, structure, and scheduling) is the major energy system data. In addition, due to the fact that the energy industry is an asset-intensive industry, the operational status data of the energy infrastructure (including generation units, transmission network, and smart sensors) is also an important kind of energy system data.

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433

User behavior data

Analyzing and understanding user behavior is important for developing personalized smart energy services. Therefore, user behavior data is an indispensable part of EBD. Marketing system data is one type of user behavior data, which mainly includes online payment data, consumer engagement data, and pricing data. In addition, consumers in SESs are becoming more and more social and mobile. Their complaints and experience sharing data on social media is also a big data source to characterize user behavior.

5.11.4.1.5

Relevant system data

SESs are more open and interactive compared with traditional energy systems. Therefore, the interaction data of SESs with other business systems is critical for smart energy management. For instance, weather data is a key data resource for the smart monitoring and forecasting of renewable power generation. GIS data is conducive to the provision of region-specific smart energy services. Transportation data, including transportation flow data and EV data, is of great significance for the decision-making of a smart and connected energy system. EBD also has the 4V characteristics of big data, namely volume, velocity, variety, and value.

5.11.4.2

Demand Side Management

Currently, energy demand pressure is still great worldwide, which always leads to the imbalance between supply and demand and a series of subsequent serious problems. In addition, people’s demand for a more efficient, flexible, clean, and SES is also increasing. In such a situation, the technological and management measures implemented in the supply side traditionally are not sufficient for solving the many challenges faced by the energy system. The supply side management (SSM) measures, including the installation of new power generation units and the economic load dispatch [35], are not so flexible with high costs. However, from the demand side, changing consumers’ energy use behavior through economic or incentive measures is more efficient. The smart energy management objectives can be achieved by DSM more flexibly and with lower costs. The original concept of DSM was first proposed by Gellings in 1985 [36]. At the beginning, DSM referred to load management, which was defined as “those [activities] which involve actions on the demand (i.e. customer) side of the electric meter, either directly or indirectly stimulated by the utility. These activities include those commonly called load management, strategic conservation, electrification, strategic growth or deliberately increased market share” [37,38]. Generally, the objective of DSM is to alter users’ electricity consumption behavior (i.e., the load shapes) in six broad ways, namely peak clipping, valley filling, load shifting, strategic conservation, strategic load growth, and flexible load shape. Readers can refer to Refs. [36,37,39] for more information about the definition and tasks of each type of DSM objective. Demand response (DR) is a specific application of DSM. DR was first defined by the US Department of Energy (DOE) as “changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized” [40]. Therefore, DR means the change and adjustment of consumers’ energy use behavior by a set of actions when unit outage, unpredictable change in demand or renewable generation, or some other contingencies occur that threaten the supply/demand balance. DR programs can enhance the reliability and stability of an energy system, improve the energy market efficiency, and reduce the total system operation costs during peak hours by price signals or incentive mechanisms. The changes of user behavior include the changing of instantaneous load demand and total energy consumption level through load curtailment strategies, as well as shifting of load demand to a different time period. The transition of load demand from main grid to standby distributed power generation is also a result of DR programs. DR is an important kind of DER, which includes all planned electricity consumption pattern modifications by end-use customers in response to changes in the price signal or incentive payments. It is a valuable resource for the efficient and secure energy system operation. DR programs can be generally divided into two categories, namely the price-based DR programs and the incentive-based DR programs. Price-based DR programs, a.k.a. nondispatchable DR, are driven by dynamic pricing. In price-based DR, different pricing mechanisms are used to stimulate the change of some of users’ electricity consumption behavior. The response of consumers to this kind of DR program is completely voluntary and nondispatchable, due to the fact that it is unknown whether consumers will make a response, and the amount and timing of response are also unknown. Therefore, this kind of DR program is relatively flexible, and its successful implementation depends on consumers’ self-consciousness, attitudes, and habits. Price-based DR programs mainly include real-time pricing (RTP), time of use (TOU), critical-peak pricing (CPP), peak time rebate (PTR), and inclining block rate (IBR). Incentive-based DR is a kind of dispatchable DR resource. It refers to the planned changes of some electricity consumption behavior of consumers based on their prior agreement with power companies. In incentive-based DR, the participated users will get some awards while the users that registered but did not respond will be punished. The incentive-based DR programs mainly include direct load control (DLC), emergency demand response (EDR), capacity market programs (CMPs), interruptible/curtailable load (ICL), ancillary services market (ASM) programs. The definitions of some well-known DR programs are summarized in Table 3. To further understand the form and mechanism of different price-based DR programs, the schematic diagrams of three pricebased DR programs (i.e., RTP, TOU, and CPP) are shown in Fig. 5. DSM is an important way to achieve smart energy management from the energy demand side. EBD analytics and behavioral science play important roles in fully realizing the potential of DSM in SESs. The core concept of DSM is to change some behavior

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Table 3

DR programs and definitions

DR category

Definition

Programs

Program definition

Price-based DR

Consumer consumption patterns are influenced and changed by different electricity pricing mechanisms [41,42]

Real time pricing (RTP) Time of use (TOU) Critical peak pricing (CPP)

Incentive-based DR

Planned changes in electricity consumption that the customer agrees to make in response to the requests from utilities or service providers [43–45]

Direct load control (DLC)

Rates that vary continually in response to the wholesale market prices. Rates with fixed price blocks that differ by time period. Rates that include a prespecified, extra-high rate triggered by the utility and in effect for a limited time period. Customers receive incentive payments from the utility for allowing a degree of control over some appliances. Customers receive incentive payments for load reductions when necessary to ensure reliability. Customers receive incentive payments for providing load reductions as substitutes for system capacity. Customers receive a discounted rate for agreeing to reduce load on request. Customers receive incentive payments from a grid operator for committing to curtail load when needed to support operation of the power grid.

Emergency demand response (EDR) Capacity market programs (CMPs) Interruptible/curtailable load (ICL) Ancillary services market (ASM)

Source: Siano P. Demand response and smart grids – a survey, Renew Sustainable Energy Rev 2014;30:461–78. Motegi N, Piette MA, Watson DS, Kiliccote S, Xu P. Introduction to commercial building control strategies and techniques for demand response. Berkeley, CA: Lawrence Berkeley National Laboratory; 2007.

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Price ($/kWh)

Price ($/kWh)

Price ($/kWh)

Event 1

Time (h) (A)

Event 2

Time (h) (B)

RTP

435

Time (h) (C)

TOU

CPP

Fig. 5 Schematic diagrams of three price-based demand response (DR) programs. RTP, real time pricing; TOU, time of use; CPP, critical-peak pricing.

Intrapersonal factors (habit, attitude, values,...)

Energy consumption behavior

Intrapersonal factors (habit, attitude, values,...)

Interpersonal factors (norms, social comparison,...)

Fig. 6 Influencing factors of energy consumption behavior.

of consumers such that it enhances the reliability of energy system operation. Therefore, identifying consumer groups with similar energy consumption patterns, analyzing the behavioral characteristics of different consumers, understanding the influence factors of different consumers’ energy consumption behavior, and developing targeted intervention strategies are important supports for smart energy management. Big data analysis techniques can be used for consumer segmentation and characterizing different energy consumers. Then, identifying and analyzing the affecting factors of different consumers’ energy demand is a prerequisite of DSM and further smart energy management. Traditional models of energy demand showed that energy prices and the income level of consumers are the significant factors. In the extended energy demand models, some other noneconomic and exogenous factors have been included [46]. The factors that influence household energy consumption can be divided into three categories, namely intrapersonal factors, interpersonal factors, and external factors [32], as shown in Fig. 6. Electricity consumption pattern mining, a.k.a. load profiling, is an important way to identify the electricity consumption patterns of different consumers, which is important for implementing targeted DSM strategies according to the electricity consumption behavior [47]. Clustering is a necessary technique for electricity consumption pattern mining. For example, fuzzy cmeans (FCM) clustering [48–50], a popular fuzzy clustering method, has been successfully used in electricity consumption pattern mining. The objective function of FCM is expressed as Jm ðU; VÞ ¼

n c X X

2 mm ij dij

ð1Þ

i¼1j¼1

where U is the membership, V is the cluster center matrix, n is the total number of consumers (data objects) in the data set, c is the number of groups, m is the fuzziness parameter, vi is the center of group i, mij denotes the membership degree of the jth data object xj to cluster vi, d2ij is the Euclidean distance of the data object xj to the ith cluster center vi, and dij ¼ ||xj-vi||. The meanings of the symbols in Eqs. (2) to (8) are the same as those in Eq. (1). The iterative procedure updates the membership degree mij and the cluster centers vi by mij ¼ 1=

c X

2

ðdij =dkj Þm

1

ð2Þ

k¼1

vi ¼

n X j¼1

mm ij xj =

n X j¼1

mm ij

ð3Þ

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where mij satisfies mij A ½0; 1Š;

c X

mij ¼ 1; 8j ¼ 1; …; n; 0o

i¼1

n X

mij on; 8i ¼ 1; …; c

ð4Þ

j¼1

Since clustering is an unsupervised learning process, cluster validity index (CVI) is always used in determining an appropriate number of groups. Some CVIs for fuzzy clustering are defined as follows: n c P P

XB ¼

i¼1j¼1

i¼1j¼1

n c P P

VT ¼

i¼1j¼1

m2ij d2ij þ 1c

c P

jjvi

vjj2

i¼1

minia j jjvi m2ij d2ij þ cðc1 1Þ

ð5Þ

vj jj2

n  minia j jjvi

n c P P

VK ¼

m2ij d2ij

c P

ð6Þ

vj jj2 c P

jjvi

vk jj2

i ¼ 1 k ¼ 1;ka i minia k jjvi vk jj2 þ 1=c

ð7Þ

The optimal cluster number c* is found at the minimum value point of the XB, VK, or VT index. Here we provide an illustrative example of electricity consumption pattern mining using the above methods. The input data is the daily electricity consumption of 800 residential users in a city in China in December 2014. The original electricity consumption profiles and the clustering results are shown in Figs. 7 and 8, respectively.

5.11.5

Case Studies

Currently, there have been some initiatives and engineering practices on smart energy management worldwide. Some typical cases are summarized in Table 4. In this section, we provide some case studies of smart energy management, including China’s Ubiquitous Energy Internet, as well as smart energy management in smart buildings, the manufacturing industry, and the transportation sector.

5.11.5.1 5.11.5.1.1

Ubiquitous Energy Internet Concept of Ubiquitous Energy Internet

Ubiquitous Energy Internet is a typical case of smart energy management. The concept of the Ubiquitous Energy Internet was first proposed by the ENN Group in China in 2010 [51]. Ubiquitous Energy Internet is a smart energy network system that efficiently combines the energy network, IoT, and Internet by advanced energy and information technologies. Ubiquitous Energy Internet is a kind of flexible and multilateral Internet Energy, in which energy producers and consumers share symmetrical information and participate equally.

Electricity consumption (kWh)

120 100 80 60 40 20 0

5

10

15 20 Date (December, 2014)

Fig. 7 Original electricity consumption profiles of 800 residential users.

25

30

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100

100

100

100

50

50

50

50

0

0 10

20

30

0 10

20

30

0 10

20

30

100

100

100

100

50

50

50

50

0

10

20

30

0

10

20

30

0

437

10

20

30

0

10

20

30

10

20

30

Fig. 8 Electricity consumption patterns grouping results of 800 residential users.

Table 4

Some cases of smart energy management worldwide

Country

Smart energy management projects

Description

US Germany

Renewable Electric Energy Delivery and Management Systems (FREEDM), 2008 E-Energy project, 2008

Japan

Digital Grid, 2011

China

Ubiquitous Energy Internet, 2010

To build the EI based on open standards. The EI is composed of PNP interface, energy router and FREEDM operating system. To establish the Internet of Energy, and six demonstration projects have been implemented. To reduce the cascading failures in some large areas and to realize the high penetration of renewable energy. To achieve real-time collaboration of energy input and output cross time and areas, by coupling the energy and information through the whole process of energy production and consumption. To establish a globally interconnected strong and SG, utilizing the ultra-high voltage (UHV) power grid as backbone and promoting the transmission of clean energy.

Global Energy Interconnection (GEI), 2015

Based on the structure of the Ubiquitous Energy Internet, a modern smart energy management system is one in which renewable energy is preferred, fossil energy is a supplement, distributed energy is the main body and centralized energy is the auxiliary, and supply and demand are interactive. Through the transformation of energy production and utilization ways, a new networked energy ecosystem can be formed, thus promoting safe, reliable, economical, and clean energy consumption, and realizing the maximization of customer value. Ubiquitous Energy Internet is a new energy management system, which achieves the lifecycle optimal operation and energy efficiency improvement. Energy production and consumption are transformed from isolated, closed, and linear simple applications to recycled, collaborative, and smart applications with multiple energy sources.

5.11.5.1.2

Demonstrative projects of Ubiquitous Energy Internet

Some demonstrative projects of Ubiquitous Energy Internet have been implemented in China, including the Ubiquitous Energy planning project in Zhaoqing, New District of Guangdong; the energy planning project of Sino German Eco Park; and the energy planning project of Shenyang Dingxianghu New Town. Here, we introduce the energy planning project of Sino German Eco Park in more detail. Sino German Eco Park is located in the north of the Qingdao Economic and Technological Development Zone, Shandong Province, China. Its planning area is 11.6 square kilometers. In July 2010, the Chinese Ministry of Commerce and the German Economic and Technical Department signed an agreement to determine cooperation in the establishment of the Sino German Eco Park in Qingdao Economic and Technological Development Zone, China. The energy system of the Sino German Eco Park is based on the Ubiquitous Energy Internet. In the early stages of the project, it has achieved 15  104 tce of energy conservation. The energy-saving rate reached 50.7%, the carbon emission reduction rate reached 64.6%, the clean energy utilization rate reached 80.4%, and the renewable energy utilization rate reached 20.6% [52]. The implementation of Ubiquitous Energy Internet in the Sino German Eco Park greatly promoted integration and fusion of energy, resources, and information, which plays an important leading and exemplary role.

5.11.5.1.3

Composition of Ubiquitous Energy Internet

Ubiquitous Energy Machine, ubiquitous energy station, Ubiquitous Energy Efficiency Service Platform, and Ubiquitous Energy Cloud Platform are the four key components of the Ubiquitous Energy Internet system.

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(a) Ubiquitous Energy Machine. Ubiquitous Energy Machine is the machine to achieve energy production, transformation, storage, and other functions of various forms of energy in the Ubiquitous Energy Internet system. It can support the multisource input of different types of fossil energy, renewable energy, and environmental potential energy. Ubiquitous Energy Machine can also complete the output of multigrade energy, such as gas, electricity, cold, and heat. (b) Ubiquitous energy station. Ubiquitous energy station is a kind of distributed energy near the user side, which transforms various gases into cold, heat, electricity, and other forms of energy, thus meeting the different demands of end users. In the ubiquitous energy station, the energy conversion and control equipment can transform the natural gas into other forms of energy efficiently and fully utilize the waste heat. In addition, by means of storage and peak shaving technologies, it can reduce or even eliminate the problem of peak/valley difference, thus reducing the impact on the main power grid, heat network, and gas network. Compared with traditional coal-fired power generation, the overall energy utilization efficiency of ubiquitous energy station is relatively high. It plays an important role in enhancing system reliability and promoting energy conservation and emission reduction, as well as ensuring energy security. (c) Ubiquitous Energy Efficiency Service Platform. Ubiquitous Energy Efficiency Service Platform is a smart energy efficiency operational management platform that has the functions of planning, organizing, scheduling, optimization, decision-making, control, monitoring, and evaluation in the four processes of energy production, storage, utilization, and reuse. In addition, due to the high penetration of DERs in Ubiquitous Energy Internet, Ubiquitous Energy Efficiency Service Platform can also provide intelligent planning for mixed energy resources. This platform can better satisfy the needs of complex energy systems in system design and structure optimization. Generally, the Ubiquitous Energy Efficiency Service Platform mainly includes a real-time optimization system, virtual operational system, technical and economic analysis system, etc. (d) Ubiquitous Energy Cloud Platform. The main function of Ubiquitous Energy Cloud Platform is to achieve the value exchange of Ubiquitous Energy Internet. Based on cloud computing and big data analytics, it can provide ubiquitous energy trading, system operational management, ubiquitous energy scheduling, and data-based services. The ultimate goal of Ubiquitous Energy Cloud Platform is to achieve the maximization of the value of energy and resources.

5.11.5.1.4

Architecture of Ubiquitous Energy Internet

By means of ubiquitous energy station, Ubiquitous Energy Internet combines the four stages of production, storage, utilization, and reuse. In Ubiquitous Energy Internet, the Ubiquitous Energy Microgrid with some sububiquitous energy station as centers and the regional Ubiquitous Energy Internet with some regional ubiquitous energy station as centers are established. The Structure of Ubiquitous Energy Internet can be divided into three layers, namely the infrastructure layer, the sensing and control layer, and the smart connected layer [53]. (a) Infrastructure layer. The regional ubiquitous energy station, sububiquitous energy station, CNG storage station, LNG receiving station, biomass energy, and wind power station are scheduled by the Ubiquitous Energy operation center within a region. The smart closed loop network with energy production, storage, utilization, and reuse can be formed. State grid, gas gate station, wind power generator, and rooftop solar panels are combined to form the energy production process. The energy storage units of the Ubiquitous Energy Internet include CNG storage station, LNG receiving station, and charging station. The regional ubiquitous energy station provides various forms of energy like electric power, steam, and heat to the sububiquitous energy station. The sububiquitous energy stations constitute the application of regional ubiquitous energy stations. The waste heat utilization of bio gas and surrounding thermal power plant is the reuse process of regional ubiquitous energy stations. Therefore, regional ubiquitous energy stations are the production process of the sububiquitous energy stations. The energy storage process of each sububiquitous energy station is composed of the electricity, heat, and cold storage systems. Through the power grid, gas network, and gas network within the region, the ubiquitous energy MG based on sububiquitous energy station is the application process. The utilization of waste energy is the reuse process of the ubiquitous energy station. (b) Sensing and control layer. The sensing and control layer of the Ubiquitous Energy Internet is composed of the ubiquitous energy station, CNG storage station, LNG receiving station, bio gas, and wind power generator within the region, as well as the real-time controller, smart meter, field actuator, and other control points. In this layer, the operational data of the dispatching gateway, optimization gateway, and each ubiquitous energy station are collected in real-time. Therefore, the real-time control of regional energy provision, the dynamic balance of regional energy production/consumption, and the improvement of overall energy efficiency can be achieved. (c) Smart connected layer. Through the wireless private network, public network channels and the optical fiber network, the operational centers within the region, the gas gate stations outside the region, the state grid, the power generation plants, and bio gas are connected to form an EI. This EI can realize the integrated organization, scheduling, and optimization of energy within the region. The ultimate goal is to achieve the whole system intelligence.

5.11.5.1.5

System design of Ubiquitous Energy Internet

The design concepts of Ubiquitous Energy Internet are grade consistency, cascade utilization, peak shaving and valley filling, and complementary load shifting. In the Ubiquitous Energy Internet system, more clean energy and renewable energy should be fully utilized, gradient utilization of fossil energy and recycling utilization of renewable energy should be achieved, centralized and distributed energy production methods should be combined, and the independent system and mixing system should be coupled. As a new combination type of energy provision system, the Ubiquitous Energy Internet system combines many new energy

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technologies, and reasonably allocates all kinds of system resources. From the perspective of four stages of energy utilization lifecycle, Ubiquitous Energy Internet can improve the overall energy utilization efficiency, reduce the emission of pollutants, promote the safe operation of energy systems, and enhance the intelligent level of energy systems. The system design of the Ubiquitous Energy Internet should consider the four stages of energy production and utilization, namely production, storage, utilization, and reuse. The design of the whole energy provision system can meet the regional requirements of cold, heat, gas, hot water, and part of the electricity demand. In the Ubiquitous Energy Internet system, the four stages of energy production, storage, utilization, and reuse are combined to form a closed loop energy system. The optimization and schedule of the four stages is achieved by the ubiquitous energy operation center based on the ubiquitous energy efficiency platform. (a) Energy production system. In the energy supply side, the ubiquitous energy stations integrate and comprehensively utilize the municipal electric power, gas, water, and heat near the region. Through the ubiquitous energy stations, the energy provided near the regions can be allocated efficiently. Then the electric power, gas, heat are transmitted to each sububiquitous energy station through the Regional Ubiquitous Energy. In each of the sububiquitous energy stations, energy is provided on demand according to the energy consumption characteristics of different end users. (b) Energy storage system. The L/CNG stations are constructed in the region, and the heat/cold/electric power storage devices are built in each sububiquitous energy station. The energy storage system can better promote peak clipping and valley filling and better satisfy the energy demand during peak periods. Therefore, the amount and costs of energy consumption can be reduced significantly. In addition, the energy transmission among energy storage systems can be optimized. The regional ubiquitous energy stations and sububiquitous energy stations are connected by the pipe network system, such that the energy among these stations can be mutually adjusted and the safety of energy utilization within each sububiquitous energy station can be ensured. (c) Energy utilization system. In the energy demand side, the temporal and spatial complementary energy use mode is adopted. During the daytime, energy that households do not need is transferred to offices, commercial buildings, and industrial areas. At night, it is just the opposite. Also, the radiant floor heating supply mode is adopted within the building. It not only reduces the temperature of the heating medium, but also improves the comfort of end users. The end use can be monitored throughout the day, and the collected data are simulated and analyzed. Therefore, the production and demand side load can be optimized. (d) Energy reuse system. In the energy recycling side, some waste energy of the regional ubiquitous energy station and sububiquitous energy station is recovered. By setting the circulating water heat pump, the quality of waste energy is improved and the waste energy is finally supplied to the end users. In addition, the garbage and waste water are classified and processed, and then they can be used for the production of biomass gas. Based on the Ubiquitous Energy technology, the design of a regional ubiquitous energy system fully considers the conditions of energy, resources, and environment with the region. Based on the dynamic matching and balancing of supply and demand of energy and resources, the global energy, resources, and information are coupled together, thus achieving energy conservation and emission reduction and sustainable development within the region.

5.11.5.2 5.11.5.2.1

Smart Energy Management in Smart Buildings Energy consumption in building

The growing population and developing economy have made the construction business prosper [54]. Nowadays, most daily activities are taken indoors, and a comfortable environment with a comprehensive use function requires a substantial amount of energy support [55]. A large proportion of the world’s total energy consumption, nearly 40%, is consumed by buildings. Half of this consumption is used by temperature controllers, e.g., air conditioners, heating, and refrigerators [56,57]. The different practical purposes of buildings lead to different magnitudes in energy demand, different consumption characteristics, and different load shifting ability. According to the practical purpose of the buildings, their energy consumption can be divided into industrial, commercial, residential, and public building energy consumption [58]. The energy consumption of industrial buildings has a simple mode, which mainly consists of electricity and other energy consumption for machinery and factory operation. Known as public buildings, nonresidential buildings are one main part of urban buildings, including government buildings, hospital buildings, school buildings, and commercial buildings [59]. The average energy consumption of public buildings is over two times higher than that of residential buildings in China [60]. And in public buildings, the frequency of high energy consumption of hospital buildings is significantly more than that of government or school buildings [59]. Previous research [61–65] has pointed out that the energy-saving potential mainly exists in two aspects, namely lighting and air-conditioning energy consumption. Stable energy supply is the primary function in commercial buildings, and guarantees users’ working conditions and efficiency. Another major function of commercial buildings is to create a good indoor environment for occupants, which includes suitable thermal, humidity, and air quality. Generally, large office complexes for commercial use have a large magnitude of daily energy demand and centralized energy consumption time period [66]. Energy storage and the ancillary services provided by SES for buildings are conductive to avoid high end prices and alleviate the increasing energy cost [67]. Residential buildings have less consumption than commercial and industrial buildings, and they usually cannot interact with energy generation-side and transmission-side directly. Similarly, different from the commercial buildings, occupants of residential

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buildings have more energy-saving consciousness due to the direct and timely cost feedback. The relatively low load level makes the residential users have little negotiation power with public utilities [68]. However, the lower energy consumption of residential buildings also made the electricity market incentives translate into specific control signals for them more easily.

5.11.5.2.2

Smart building

Smart building is a type of building with reasonable investment, efficient energy management, and comfortable and convenient environment, designed by considering the optimized relationship among structure, system, service, and management [69]. As denoted by the term “smart” building, it has intelligent control systems and smart and interconnected devices beyond the traditional building structure and function. The modernized sensor-embedded residence with various integrated systems was thought to be the basis of smart buildings in initial research [70]. The system communication between external and internal was operated remotely and efficiently [71]. The IoT, which first appeared in 1999 [72], is one of the major technologies of smart buildings. It is supported by web-enabled hardware, automation devices, and sensor networks. IoT technologies can be used to connect the smart applications and devices in buildings with wired or wireless technologies [73,74]. IoT even provides a practical way for future prefabricated construction of smart building, accompanied by the building information modeling [75]. As lighting, heating, ventilation, and air-conditioning are becoming the main energy consumption of buildings, the control measures of automatic lighting systems and controlling ventilation have improved the energy performance of buildings [76,77]. Hybrid electrical energy storage (HEES) systems [78] will be widely used in smart buildings that are equipped with some renewable sources of power generation such as solar panels mounted on the rooftop [79]. HEES systems enable shifting peak electricity consumption by converting into electrical energy and delivering to electrical systems for each electrical energy storage (EES) element, to meet energy efficiency and comfort requirements [80]. The existing EES elements, such as batteries, have not achieved optimum performance with low cost/weight per unit capacity and long cycle life [81]. Smart building also provides a better air ventilation system to improve the environmental quality. The temperature, humidity, and ventilation rates are controlled by the intelligent devices. And smart building can be seen as a thermal storage medium by utilizing building mass [66]. It helps the inside ventilation with heating, ventilation, and air conditioning (HVAC). Buildings with thermal storage will also benefit to peak shifting and response to the energy price [82]. Many studies have shown that electricity peak demand will be reduced by smart building thermal storage devices [83–85]. And the outside air temperature can be used to predict and control heating load of smart buildings [86]. Heat transfer and thermal storage processes for buildings require the consideration of solar radiation, conduction through walls, outdoor convection, etc. There are different ways in smart buildings to account for the solar radiation and conduction through walls or windows [87]. Thus, not only smart devices, but also some smart equipment, i.e., thermotropic windows [88], also provide reversible transmission behavior in response to improving smart building thermal storage. Although advanced energy technologies and energy efficiency are emphases in current research, the occupants and their sense of comfort are still the most important parts of smart building [89]. It should be the main purpose to utilize smart building. Therefore, the preferences of the inhabitants and occupants are taken into consideration for designing of building automation, balancing occupancy comfort with optimal energy efficiency [90,91]. The occupants’ comfort is a wide concept, including thermal comfort, visual comfort, indoor air quality, and application operation comfort [92].

5.11.5.2.3

Energy management of smart buildings

Energy consumption management is an important development direction of modern urbanization [58]. Traditional fossil fuel burning has resulted in public concern about carbon emissions and environmental deterioration. Therefore, the renewable energy generation has attracted more and more attention in recent years, which brings new challenges to the energy management of smart buildings. Electricity consumption management of smart buildings is conducive to the stability and efficiency of power grid operation by its own role as a prosumer in response to current conditions [54]. 5.11.5.2.3.1 Integration of smart buildings with smart grid Sustainable and renewable energy sources will be increasingly used in smart buildings to reduce greenhouse gas emissions. Sustainable energy supply from distributed generation will be a focus of energy management in smart buildings. The coordination of building operations and its interaction with the SG will also play an important role [93]. The integration of smart buildings with a SG helps to manage the expanded generation of renewable energy in the grid. Smart buildings and SG have some common properties, for instance, both of them are independent and unique control systems, and their operations are based on their specific information systems while oversimplifying interaction from the other [94]. The common properties support the interaction and integration of smart buildings and the SG. Connected with smart buildings, remote controllers and devices respond to the SG by using the operation data they provided, and adjust the electricity consumption to shift the peak loads [95]. The adjustment always aims to achieve the objective that the load can be delayed or suspended and the demand can be met in more suitable time according to the implemented electricity tariff [96]. The fundamental research directions for smart building to integrate with SG are the control capability and data exchange [54]. And the energy management is thought to have more automatic control instead of manual programs [97,98] Automated demand response (ADR) [99] can support the energy management to improve system flexibility and reduce device costs. Then the saved cost can be invested to the generation side and transmission and distribution side to improve energy efficiency.

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5.11.5.2.3.2 The framework for energy management of smart buildings The energy use optimization of smart building is apparently important. The optimization will accord with the occupants’ behavior, the building’s using condition, and operational policies to provide suggestions for reducing building energy consumption [100]. For instance, researchers found that even at peak consumption times, the average occupancy of commercial buildings is less than a third of the building’s design occupancy by observing actual buildings [101]. It suggested the potential reduction of building energy consumption. The occupant’s comfort is found to have larger range [102], which also means that the reduction of building energy consumption can be more flexible and adaptive to the preset points in HVAC and lighting systems [103]. Building energy management systems (BEMSs) are designed to calculate the demand of a building and meet it by controlling the connected power plants [58]. BEMS can promote the communication between people and machine. The role of the BECM is quite significant as it contributes the efficient operation of smart buildings at reduced time and costs both in financial and energy utilization aspects, as well as the provision of a safe and comfortable living/working environment [104,105]. The reduction of building energy consumption and the integration of smart building with the SG need the sophisticated designs and materials, advanced HVAC systems, as well as smart appliances [93]. And it cannot be a trivial task to fix the user comfort with them by adding users’ input and feedback services in smart building operation [106]. Multiagent systems (MAS) provide a complex system of multiagent with new additional services considering the dynamics, response, and behavior of buildings and their users [107]. The control approach based on MAS is a popular energy management method of distributed generation, which is widely studied in the control and operation of smart buildings. It has a wide range of applications in power systems, e.g., condition monitoring, system restoration, market simulation, network control, and automation [93]. The MAS always consists of three hierarchical management levels, i.e., BEMS agent, zone agent and room agent, accompanied by the comfort agent. According to the building structure, the comfort agent controls the comfort level for the whole building, and each agent is responsible for a certain range of its duty [94]. The advantages of MAS are that it not only promotes energy savings and comfort optimization, but also provides voltage grid support [108]. The MAS has both learning and adaptive capabilities to control smart buildings, and the developed system provides users with improved comfort, as they need to make limited control decisions. 5.11.5.2.3.3 Balance model for energy management of smart buildings To solve the problem of the high energy consumption level of buildings, energy efficiency improvement has received much attention. Therefore, the concept of “nearly zero energy buildings” was proposed by the EU Directive on Energy Performance on Buildings (EPBD) to reduce the energy consumption, and it will be implemented by the end of 2020 [109]. For the United States, the strategic goal for marketable zero energy homes will be achieved by the same year and commercial zero energy buildings will be possible by 2025 [110]. There have been some research efforts that focused on the energy standards for smart buildings to specify the concept of zero energy buildings [111]. The factors that should be taken into consideration include carbon emission level, thermal needs, and energy (i.e., electricity, natural gas, oil, biofuel, etc.) demand [112–115]. The widespread integration of renewable energy with distributed generation in power grids raises the development problems of zero energy buildings to a high level. The zero energy balance (ZEB) [111] was introduced to help solving the problem as: jweighted demandj ¼1 jweighted supplyj

ð8Þ

As shown in Fig. 9, a weighting system converts the physical units into other metrics, to determine which energy uses (e.g., heating, cooling, ventilation, hot water, lighting, and appliances) are included in the balance process. And political preferences are also weighting factors with purely scientific or engineering considerations. The ZEB method is an important aspect in smart building management, which quantized energy consumption and energy efficiency criteria [116]. 5.11.5.2.3.4 The specific technologies for energy management of smart buildings Smart building is a technological complexity construct with many components and subsystems, i.e., sensor network [117], communication infrastructure [118], heating/ventilation system, BEMS, audiovisual system [119], security system, telemedical system [120,121], indoor air quality and environmental conditions monitoring system [122], health conditions of inhabitants, etc. [123]. The management of smart building is thought to be a complex combinatorial-optimization problem. The following several methods will be introduced to help deal with the complex interaction of structures and collectively contribute to innovation. Digital data streams (DDSs) [124] can be a solution for building electricity equipment management, such as temperature controllers and lighting equipment. Each DDS can be used to describe an electricity equipment state and the sum of DDSs will make smart energy management easier. In addition, not only smart meters but also smart devices can be remotely operated and monitored in smart buildings. These new technologies promote increased intelligence and efficiency in the generation, transmission, and distribution of power. Energy model predictive control (EMPC) is a control algorithm of building data management. It focuses on the data collection, analysis sharing, and analytical tools for the coordination control on DERs [125]. EMPC proves capable of handling a large number of energy management problems. Early warning application (EWA) for real-time detection of energy consumption anomalies can be used as an important tool for smart energy management in buildings. Based on the collected data from smart meters and various sensors of different

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Energy grid

Delivered energy

Industrial buildings Load

Commercial buildings

Residential buildings Exported energy

Generation

Electricity Distict heating/cooling Naturalgas Biomass Other fuels

On-site renewable

Weighting system (kWh, CO2, etc.)

Weighted demand

Weighted supply

Fig. 9 Zero energy balance in smart building. Sartori I, Napolitano A, Voss K. Net zero energy buildings: a consistent definition framework. Energy Build 2012;48:220–32.

stakeholders, it provides a real-time visualization of anomalous consumption by monitoring and detecting anomalous power consumption [126]. The occupants’ behavior also greatly affects energy consumption management; as shown in the existing studies, it can raise the consumption one-third higher [77] or reduce by 20–50% [127]. Therefore, providing the knowledge about energy-related information to occupants is effective. Also, the future work on energy management of buildings will focus on large-scale practical applications and the informational exchange requirements of complex smart devices. The development of smart buildings will not only promote the utilization of renewable energy, but also provide building energy efficiency with broader space and larger chances of development.

5.11.5.3 5.11.5.3.1

Smart Energy Management in Manufacturing Industry Energy consumption in manufacturing industry

Energy is essential for human survival and development, and it is crucial to the national economy and security [128,129]. From the perspective of the world, energy provides an important foundation for the rapid development of the world economy, in which the proportion of fossil energy consumption accounts for the vast majority of total energy consumption [130,131]. However, with the increasing depletion of fossil energy, environmental pollution, and climate change, energy is becoming a bottleneck, restricting social and economic development [132,133]. At present, many countries in the world are actively developing energy conservation strategies. Improving energy efficiency and reducing energy intensity are regarded as the key to energy sustainable development strategy. Manufacturing industry directly reflects the level of productivity of a country, which is an important factor to distinguish between developing and developed countries. The manufacturing industry in the developed countries occupies an important share of the national economy. However, the manufacturing industry brings a lot of energy consumption and environmental pollution. The manufacturing sector is responsible for about 33% of the primary energy use and 38% of the CO2 emissions globally [134,135]. German industry consumes around 46% of the country’s overall energy. And China’s manufacturing industry utilizes around 50% of the entire electricity generated every year, and produces at least 26% of the total CO2 emissions. Thus, the increasing energy costs and current trend of sustainability require manufacturing enterprises to reduce energy consumption for both cost saving and environmental friendliness [136,137].

5.11.5.3.2

Smart manufacturing

The technological revolution initiated in the 18th century in England was a great revolution in the history of technological development, which pioneered the era of machine tools instead of hand tools. It was not only a technical reform, but also a

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profound social change. This revolution was the beginning of the birth of industrial machines, and the steam engine as a power machine was widely used as a symbol. This technological revolution and its transformation of the relevant social relations is called the first industrial revolution. On the one hand, this revolution had greatly raised the level of social productive forces and promoted great changes in the mode of production. On the other hand, it created a new social demand and endless new products, new machines, new industrial sectors, and new production technology to promote the development of human society. The second industrial revolution began in the 1860s and 1870s, and its main symbol was the widespread use of electric power and the invention of internal combustion engines. This revolution has realized the exchange of electric power and mechanical energy, and the electric power industry and the electric appliance manufacturing industry developed rapidly. This was a great change in the history of humankind, which promoted the development of social productivity, and improved the material life of human beings. It also promoted the formation and consolidation of the new relations of production, and changed the relationship between countries. The third scientific and technological revolution began in the 1940s and 1950s, and the main symbol was the invention and application of atomic energy, electronic computers, space technology, and biotechnology. This revolution involved many fields, such as atomic energy, computer, microelectronics, aerospace technology, molecular biology, and genetic engineering. Meanwhile, it produced a large number of new industries. One of the most epoch-making significance was the rapid development and wide application of electronic computers, which opened up the information age. The fourth industrial revolution is a new technological revolution represented by the Internet industry, industrial intelligence, and industrial integration. Smart manufacturing is an important part of the fourth industrial revolution. Smart manufacturing is the human–machine integration intelligent system composed of intelligent machines and human experts, which can be embedded in intelligent activities in the manufacturing process. Smart manufacturing aims at using smart technologies to improve efficiency on agility, asset utilization, and sustainability [138]. These smart manufacturing technologies include sensor networks, process analysis, information technology (IT), and production management and control software, etc. At present, smart manufacturing technologies can be used in engineering design, process design, production scheduling, and fault diagnosis, etc. Smart manufacturing has been deemed by many manufacturing experts as the next frontier of manufacturing that will revolutionize future manufacturing [139–141]. Smart manufacturing can greatly improve labor productivity, and reduce labor in the proportion of total industrial investment. The advanced experiences of developed countries show that the development of industrial robots, flexible manufacturing systems, and other modern equipment manufacturing can control the commanding heights of new industries and provide sufficient possibilities for the reconstruction of the manufacturing industry and the real economy [142,143]. Competition in the global industry is becoming more and more intense. In 2009, the United States launched a “reindustrialization” plan in order to develop the advanced manufacturing industry, realize the intellectualization of the manufacturing industry, and keep its high, global controller position in the manufacturing value chain. Germany’s famous “Industrial 4.0” program was launched in 2013; this was a new manufacturing upgrade program. Industrial 4.0 aims to build a flexible production model of digital and personalized products and services, with real-time interactions between products, people, and devices during the production process [144,145]. It affects not only German industry, but also the development of world industry. China launched “Made in China 2025” in 2015, which aimed to transform China from a big nation in manufacturing to a powerful one. It pointed out that digitization, networking, and intellectualization of manufacturing industries are the core technologies of a new industrial revolution. Policies of the world’s major countries dealing with intelligent manufacturing are shown in Table 5.

Table 5

Policies of the world’s major countries dealing with smart manufacturing

Policy

Country

Time

Purpose of policy

Innovation 25 strategy

Japan

2006

Reindustrialization

USA

2009

Industrial 4.0

Germany

2013

High value manufacturing

Britain

2014

Make in India Campaign

India

2014

Made in China 2025

China

2015

The core idea of the plan is the “intelligent manufacturing system,” which aims to promote the sustained growth of the Japanese economy and cope with the global era of high competition. Developing the advanced manufacturing industry to realize the intellectualization of the manufacturing industry and keep the high position and the global controller position in the manufacturing value chain. Building a flexible production model of digital and personalized products and services, with real-time interactions between products, people, and devices during the production process, in turn, to deal with the smart manufacturing–led fourth industrial revolution. Through the use of intelligent technology and expertise to bring sustained growth and high economic value of potential products and related services, in turn, to achieve the goal of reviving the British manufacturing industry. Through the extensive application of intelligent manufacturing technology to build a new global manufacturing center in India. Accelerating the upgrading of manufacturing and transforming China from a big nation in manufacturing to a powerful one.

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5.11.5.3.3

Energy management of smart manufacturing

5.11.5.3.3.1 Energy management philosophy in smart manufacturing With the development of the manufacturing industry, energy and environmental problems have become more and more serious. Green manufacturing is the inexorable selection of achieving sustainable development [146,147]. It is of great significance to realize the transformation from the extensive consumption of resources and the emission of pollutants to the resource-saving and environment-friendly manufacturing [148–150]. Thus, manufacturing companies should accelerate the development of advanced energy-saving environmental protection technology, processes, and equipment. And manufacturing companies should accelerate the transformation and upgrading of the manufacturing industry and improve the efficiency of the use of resources. Meanwhile, manufacturing companies should strengthen the green management of the whole life cycle of the product and strive to build an efficient, clean, low-carbon, recycling green manufacturing system [151]. The manufacturing industry has entered the era of big data, but most of the manufacturing companies cannot make good use of manufacturing data to adjust the production strategy and improve production system efficiency [152,153]. How to use the manufacturing data effectively is of great significance to increase the competitiveness of manufacturing companies. The rapid development of IoT, cloud computing, and big data analytics provides a new way to solve this problem in energy management of the manufacturing industry [154]. Thus, energy management of smart manufacturing based on information technologies is an important developing direction. 5.11.5.3.3.2 Energy management technologies of smart manufacturing 5.11.5.3.3.2.1 Internet of Things The IoT is a novel network that works through information sensing devices to connect any items with the Internet according to the agreed protocol [155]. These information sensing devices include two-dimensional code reading devices, radio frequency identification (RFID) devices, infrared sensors, GPS and laser scanners, etc. This network can achieve intelligent identification, positioning, tracking, monitoring, and management through exchange information and communication [156,157]. IoT in the manufacturing industry has a wide range of applications, mainly in the product intelligence, production process monitoring and management, intelligent logistics, etc. For example, in the production process of the manufacturing sector, manufacturing companies can collect the production line information, quality information, production information, and abnormalities by IoT technology. Thus, manufacturing companies can monitor the production process to adjust the production plan, strengthen the quality management, and achieve production process visualization, which gradually achieves flexible production and fine management [158]. The manufacturing industry can realize the ubiquitous sensing of the manufacturing process by key technologies of the IoT, then they can improve product quality by implementing optimization control. The IoT can greatly improve the level of intelligent production and product accuracy through the intelligent control of the production process, production line detection, real-time parameter acquisition, production equipment and product monitoring and management, material consumption monitoring, etc. Thus, the integration of manufacturing and IT promotes the transformation from traditional manufacturing to smart manufacturing. In addition, the IoT technology can promote the development of electronic information products, software industry, information service industry, and many other industries. 5.11.5.3.3.2.2 Cloud computing Cloud computing is a new computing model with the rapid development of computing, storage, and communication technology, which can provide users with configurable and shared resources [159]. Cloud computing providers build up one or more large data centers by connecting a large number of nodes and network devices. Then they provide different services based on data centers, such as infrastructure services, platform services, storage services, and software services. There are many companies trying to solve the problem of enterprise data and resource concentration through cloud computing. Cloud computing can be widely used in the manufacturing industry. It can connect the manufacturing information system, supply chain information service system, logistics information service system, and manufacturing storage and transmission system through the network in the cloud platform [160,161]. It is possible to realize the digital manufacturing information system based on cloud computing platforms, that is, through the cloud computing technology it is possible to transform the existing manufacturing information system, so that each enterprise manager can be connected to the manufacturing information system and query the company’s production and sales information [162,163]. Cloud computing technology has shown a bright future for the development of the manufacturing information industry. Cloud computing in the manufacturing sector will be applied to the following areas: product design, production, and sales. Before product design, one can obtain product requirements and the best design through the cloud computing platform. And manufacturing can obtain the latest price information of raw materials and the most appropriate production mode by cloud computing platform before production. In the sales phase, manufacturing companies can publish the latest product information and instructions through the cloud computing platform. In addition, cloud computing technology can be used to construct a service hardware platform whose storage capacity, computing performance, and external connection capacity can be almost unlimited [164,165]. Based on this, a suitable manufacturing information system can be developed for the majority of manufacturing enterprises to solve current manufacturing enterprises’ self-built system issues.

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5.11.5.3.3.2.3 Big data analytics Big data refers to the massive data resources that have the 4V characteristics, namely volume, velocity, variety, and value [166,167]. Manufacturing data also has these 4V characteristics, which are described as: 1. Volume: Volume is used to describe the large amount of data generated in the manufacturing sector, including a variety of dynamically collected data in different forms and historical data accumulated for a long time. 2. Velocity: Velocity describes how fast data are produced and captured in the manufacturing sector. Relevant information is exchanged at a high speed during interactions between people, machines, and processes. Manufacturing has a high demand for the speed of data processing due to data’s features. 3. Variety: The types of manufacturing data are complex. The data includes not only product data and operational data, but also a large number of energy consumption data. Identifying and transforming the collected data into different formats allows manufacturers to utilize and analyze them more efficiently. 4. Value: The knowledge mined in manufacturing data is of great value in supporting power system management decisions. Reasonable use of data is conducive to improving production efficiency, reducing energy consumption and environmental pollution. Meanwhile, it contributes to providing personalized product service and promotes the coordination of supply and marketing. Big data analytics refers to obtaining valuable information from various types of data. The main purpose of the development of big data is to accelerate the transformation of traditional manufacturing to smart manufacturing through the integration of industrialization and IT, manufacturing, and service industries [168,169]. Currently, most of the manufacturing companies do not make good use of manufacturing data to adjust the production strategy and improve production system efficiency. How to use the manufacturing data effectively is an important problem for manufacturing companies. 5.11.5.3.3.2.4 Mobile intelligence With the development of IT, the traditional workshop management model has been unable to adapt to the rapid development of modern manufacturing. The emergence of networked manufacturing makes the workshop management begin to develop to the network. On the other hand, along with the emergence of mobile 3G and 4G networks, intelligent mobile terminals are becoming more and more popular [170]. Management personnel hope to deal with the related management work at any time and any place by using intelligent mobile terminals. The application of intelligent mobile devices in the field of manufacturing is not only the diversity of data processing equipment and the migration of computing platforms, but also changing the business model and production mode of the manufacturing industry. The upstream and downstream enterprises of the supply chain, and different departments and teams within the enterprise can use mobile devices and industrial APP to work together anytime and anywhere to form a new working mode and process. At the same time, in the procurement, marketing, and customer service stage, the application of mobile intelligence and terminal equipment will increase the degree of social interaction. Upstream and downstream enterprises, as well as end users, will become part of the manufacturing community. Therefore, the business management model needs to make a huge adjustment. The same as cloud computing, IoT, and big data analytics, the widespread use of mobile devices and industrial APPs in the industrial sector is also an important foundation for revolutionary technological innovation in the manufacturing industry.

5.11.5.3.3.3 Smart energy management system in the manufacturing industry 5.11.5.3.3.3.1 The roles of energy management system in the manufacturing industry 5.11.5.3.3.3.1.1 Energy data acquisition, storage, and management The energy management system analyzes and processes energy data so that professional energy management personnel can grasp the state of the system in real time and ensure that the system is running in the best condition through reasonable adjustment. 5.11.5.3.3.3.1.2 Reducing operating costs and improving productivity The main purpose of a BEMS is to realize the optimization of energy monitoring and energy management based on information analysis. Thus, energy management systems can reduce operating costs and improve productivity. Meanwhile, energy management systems should meet the automation requirements of energy equipment and operation management, so that energy management systems can reduce the input of human resources. 5.11.5.3.3.3.1.3 Accelerating system fault handling An energy management system can quickly understand the operation of the system and the impact of the degree of failure from a global perspective. Thus, it can take timely measures to limit the further expansion of the scope of the fault, and restore the normal operation of the system effectively. 5.11.5.3.3.3.1.4 Energy saving and environment protection The energy management system will improve energy balance by optimizing energy management methods and techniques, and it can understand in real time the energy demand and consumption situation of manufacturing companies. Thus, the energy management system can effectively reduce energy consumption and pollutant emissions of manufacturing companies.

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Application and display layer

Display device

Device monitor

Energy consumption analysis

Optimization decision

Terminal device PC/Mobile Processing layer Cloud computing

Big data analytics

Network communications Perceptual layer Temperature sensors

Vibration sensors

Pressure sensors

...

Fig.10 Architecture of the manufacturing smart energy management system.

5.11.5.3.3.3.2 Overall architecture of manufacturing smart energy management systems The overall architecture of the manufacturing smart energy management system is shown in Fig. 10. The system consists of three layers: perceptual layer, processing layer, and application and display layer. The perceptual layer is used for information sensing and acquisition based on the IoT technology. Information sensing and acquisition devices include temperature sensors, vibration sensors, pressure sensors, cameras and infrared sensors, etc. The processing layer is used for energy consumption data processing and analysis based on cloud computing and big data analytics technology. The application and display layer is the application and display of data processing results, which provides a solution for the energy management of the manufacturing industry. IoT, cloud computing, and big data analytics are organically integrated by the manufacturing energy management system. This system acquires a variety of energy consumption data, including intelligent sensor-aware data. Then the energy management system analyzes and processes massive energy consumption data, and provides the instructive energy efficiency control strategy. So it achieve the energy consumption monitoring and energy efficiency dynamic optimization control of various energy consuming equipment in the production process. And this system can be monitored and controlled anytime and anywhere through the intelligent mobile terminal. Building an effective manufacturing energy management system can improve the operation management and safety management level of the energy systems of manufacturing companies. Meanwhile, it can improve the evaluation system of energy production and use, labor productivity, and environmental quality, and reduce energy consumption. Thus, building an effective manufacturing energy management system is of great significance to improve the market competitiveness of manufacturing companies.

5.11.5.4 5.11.5.4.1

Smart Energy Management in the Transportation Sector Energy consumption in the transportation sector

Transportation plays a vital role in the development of national economy, and it is also known as an energy consuming industry. Both passenger and freight transport drive a continuous increase in national energy consumption. It is suggested that the traffic energy consumption of developing countries is growing faster than that of developed countries [171]. As the International Energy Agency (IEA) forecasted in 2007, the fuel consumption of China’s transportation sector in 2030 will be four times that of 2005. For China, as economic growth and urbanization speed up, the demand for energy in the transportation sector is increasing. City transportation has developed rapidly and has become one of the most important drivers of the increase in energy demand [172]. Besides railway transport, highway transport, waterway transport, air transport, and pipeline transport are considered the five basic ways of national transportation. For most countries, 80% of the energy consumption in transportation sector is contributed by highway transportation [173]. And for highway transportation, infrastructure construction, road conditions, vehicle performance, driver behavior, and external factors are the five main influence factors of energy consumption. (a) Infrastructure construction. Infrastructure is the fundamental part of a transportation system, on which the service platform can be built. Energy consumption accompanies the construction process of infrastructure, such as highway construction, parking lot construction, sensor deployment, etc.

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(b) Road conditions. Is it a smooth road or a bumpy path? Is the road unobstructed to drive on, or there is a traffic jam? Poor road conditions will reduce transport efficiency, thus resulting in more energy consumption. According to Ref. [57], there are 140,000 L of gasoline consumed for every 1 million cars for every 10 min worth of running their engines. Road congestion during rush hour turns cities into huge “parking lots,” in which the engine of every car never stops running. Thus, energy consumption is continually increasing. (c) Vehicle performance. The fuel consumption of a vehicle mainly depends on the following parameters: vehicle displacement, curb weight, full weight, and some actual engine work parameters. In general, a vehicle consumes more fuel if it has large displacement. (d) Driver behavior. Drivers’ habits in terms of speed control and choice of routes are the two main reasons for differentiated fuel consumption. In addition, drivers’ improper behavior brings about traffic accidents. At busy intersections, traffic jams will be triggered, thus increasing energy consumption. (e) External factors. Represented by extreme weather, the external factor is most likely to trigger large-scale blackout [174,175]. Therefore, such external factors will bring great difficulties in transportation security, infrastructure maintenance, and the dispatch of energy. Under this circumstance, energy utilization in the transportation sector is also affected.

5.11.5.4.2

Smart transportation

In recent decades, traditional energy has become much scarcer on a worldwide scale [176–178]. Meanwhile, the biological environment has been damaged in the process of energy exploitation and utilization. In large and medium-sized cities, the emissions of motor vehicles have proved to be a major reason for urban pollution. Plus traffic congestion is becoming increasingly common around the whole country. Approaches and theories on energy efficiency improvement and energy structure adjustment are generally developed in correspondence with energy savings, environmental protection and ensuring transportation security. As early as the 1960s, the concept of intelligent transportation system (ITS) [179,180] was proposed. Based on advanced IT, communication technology, artificial intelligence (AI), sensor technology, cybernetics and systems theory, ITS is considered as a comprehensive transportation system with connections among vehicles and roads. Its main feature is to achieve timely, accurate, and efficient transportation by the sharing of traffic information obtained from various channels. The development of ITS has changed and has continuously optimized the structure of energy consumption. Also, it has promoted energy efficiency. The ITS in Stockholm reduced city traffic by 22% and cut down 12–40% of emissions. Meanwhile, 40,000 people use public transport per day [74]. In today’s big data environment [181], with the rapid development of the IoT [182,183], cloud computing [184], and the emerging information technologies [185–187], more advanced technologies and management theories have been deeply applied in the field of transportation. Intelligent transportation has been further improved and developed, which has accelerated the generation of smart transportation. At the end of 2008, IBM put forward the concept of the smart planet [74]. Then in 2010, the idea of the smart city came up. Because smart transportation would be an important part of the smart city, specialized solutions on smart city transportation were designed at that time. Smart transportation systems (STS) are considered as a more comprehensive transportation system with enhanced connections among vehicles, roads, and drivers. We believe it works towards real-time, dynamic, and multisource information and responds to emergencies in a timely manner. Besides, in the era of smart transportation, we can get a deeper understanding of the traffic problems and find more scientific solutions. Many problems existing in the former ITSs will be properly dealt with. More efficient, convenient, environmentally friendly. and safer transportation can be within our reach. Also the transformation and upgrading of related industries can be achieved. The particularities of smart transportation at the time of big data are mainly reflected in the following three aspects: all-sided cooperation mechanism, increasingly comprehensive integration, and real-time and dynamic response. (a) All-sided cooperation mechanism. The all-sided cooperation happened in the entire transportation system among transportation businesses, transportation facilities, various enterprises and persons. It is involved in many aspects, work procedures, and departments. Considering that pervasive connectivity is achieved because of IoT, all-sided coordination will definitely become a fundamental requirement for the implementation of smart transportation. Through all-sided coordination, optimal allocation of resources can be realized. (b) Increasingly comprehensive integration. Data integration occurs among the internal data of a single platform. Also, it happens among various sensors of different platforms. The initial transportation system cannot be replaced by a smart one immediately. Therefore, the establishment of a standardized management platform in the transition stage is necessary, which brings about increasingly comprehensive integration. (c) Real-time and dynamic response. Based on big data technologies, general laws of transportation can be obtained by analyzing and mining historical data. More importantly, real-time analysis is conducted on sensor data, which provides users with realtime updates of road conditions, reasonable route planning and more proper parking solutions, etc. Under this circumstance, bad traffic situations will be warned of in a timely manner, and many traffic risks can be reduced. Decision-making changes from experienced to data-based and becomes more scientific.

5.11.5.4.3

Energy management of smart transportation

5.11.5.4.3.1 Management principles In the energy management of smart transportation, there are three major principles that should be followed.

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(a) Demand oriented. Give full consideration of the demands of the public (such as the diversity of transport modes, safety and efficiency of the traffic, etc.). Then, build up an efficient and people-oriented management mechanism to achieve more rational and safer utilization of energy. (b) Overall coordinating. The construction of smart transportation involves many departments and enterprises. It involves multiple subjects. Therefore, the coordination among various government departments and different enterprises should be considered overall. Besides, different platforms, information systems, and various databases should also work cooperatively. Information should be shared among them to support effective management. (c) Scientific planning. Short-term, medium-term, and long-term implementation plans should be scientifically made and properly coordinated to avoid redundant construction. 5.11.5.4.3.2 Application scenarios 5.11.5.4.3.2.1 Management of electric vehicles The development of new energy vehicles leads the revolution in and innovations of the transportation sector. It is an important measure to reduce vehicle fuel consumption, decrease exhaust emissions, and improve the quality of the environment [188]. New energy vehicles include fuel cell vehicles, hybrid power automobiles, hydrogen-powered cars and solar cars, etc. Their emissions are relatively low. In China, EV technologies have made leaps and bounds over the last decade and government has introduced a large amount of incentives to encourage the sales of these new energy cars [189,190]. A famous company in China, Chery, is committed to the development of new energy vehicles. In Table 6, two EVs successfully developed by this company are introduced. In the urban traffic, EVs need to be managed properly when they are plugged in to the power grid [191]. EVs charged by the grid are not likely to bring lower carbon emissions than conventional fuel vehicles [192]. And it is difficult to reduce EVs’ dependence on traditional fossil fuels. Under this circumstance, we should vigorously develop power generation systems of renewable energy and increase their utilization [193]. Meanwhile, we should realize the local accommodation of renewable energy through the MG [194]. Finally, we should reduce the indirect carbon emissions of EVs [195]. 5.11.5.4.3.2.2 Management of smart vehicles Based on environmental perception, traffic monitoring, and vehicle navigation, the intelligent vehicles can achieve autonomous driving. As early as 1998, a German company integrated radar, computer vision, laser scanners, and other sensors into one system. The company obtained sufficient information for vehicles’ safe and stable operation because of making full use of the complementary data and redundant information [196]. For intelligent vehicles, when the driver is struck with panic and cannot react to emergencies immediately, the control of the vehicle will be automatically taken over, thus bringing it back to a safer state. Besides, when traffic is heavy, the frequent starting and stopping of the vehicle makes the driver always in a state of tension. In this situation, automatic driving technology can remove the driver’s tension, better control the speed, and also achieve safer driving. Nowadays, driver assistant systems are generally achieved to provide reasonable driving strategies (such as safe distance information, whether the overtaking and lane changing actions can be taken). Even smart cars that need no drivers have been successfully developed. The driving speed can be automatically decided and adjusted according to the parameters of car design, weather condition, speed limit, and other factors. The completely autonomous car can realize reasonable path planning and emergency decision-making by applying artificial intelligence technologies. Once the starting point and destination are determined, the optimal route will be chosen from the shortest path, the least time-consuming route, the least toll-consuming route, etc., according to a person’s preference. This car can also adjust the driving route dynamically according to real-time conditions. As one of the main means of transportation for urban residents, buses have advantages in transportation efficiency and energy utilization. In order to deal with problems of urban traffic and meet the needs of the citizens, the development of public transport has become a common solution. Based on intelligent identification, network communication, GIS technologies and GPS technologies, advanced public transportation systems can achieve intelligent planning and scheduling, and this can also improve the utilization rate of public transport. Many cities in China, like Beijing and Suzhou, have gradually put advanced public Table 6

Two types of Chery’s electric vehicles (EVs)

Parameters of EV

Chery’s QQ3EV

Drive mode Max speed (km/h) Max gradeability (%) Acceleration time of 0–50 KM/h (s) Driving distance (km) Motor type Max power (kW) Max torque (N m) Battery type Charge time (h)

Front drive / 15–20 / 120 (uniformly 30km/h) PMSM 12 72 Lead-acid gel battery 8–10 (voltage of 220 V)

Chery’s eQ

100 Z25 5.9 Z170 (uniformly 60 km/h) 41.8 150 Lithium ion battery

Source: C.N. ENERGY. Parameter table of Chery’s QQ3EV. Available from: http://www.cherynewenergy.com/profile.php?sid=1; 2014. C.N. ENERGY. Parameter table of Chery’s eQ. Available from: http://www.cherynewenergy.com/profile.php 2014.

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transportation systems into operation, giving residents access to great convenience. Also the idea of low-carbon development and environmental protection is put into practice. 5.11.5.4.3.2.3 Intelligent parking management In a small business district in Los Angeles, the added driving mileage of all the cars looking for parking spaces is about 38 trips around the world. This will consume 47,000 gallons of gasoline and cause 730 t of carbon dioxide emission [74]. With the rapid increase in the number of cars, parking and related management issues are becoming more and more important in the transportation sector. The Parking Guidance and Information System (PGIS) was produced towards the difficult situation of finding available parking spaces. It first appeared in the German city of aachen, and then was widely used in Europe and Japan. It provides drivers with real-time, accurate, and sufficient parking information and guides drivers to reach the target parking lot through the proper path. It is helpful to reduce the traffic volume along with searching and improving the utilization rate of parking lots. Now many countries around the world have developed intelligent parking systems. Parking management in China is stepping into the intelligent era. In 2014, Ali cloud first marched into the area of intelligent parking [197]. In Hangzhou, an intelligent charging system for car parking was built up. More than 20,000 parking spaces were covered in this project. Vehicles’ entering and leaving can be observed through intelligent methods. The prompt messages are sent to administrators of the parking lot. This has quite improved the cycle efficiency of parking spaces. 5.11.5.4.3.3 Key technologies 5.11.5.4.3.3.1 Big data technologies of smart transportation Big data is generated at high velocity and high volume, also with high variety. They can bring about revolutions and innovations in many industries [198]. Big data analysis is now widely applied in the fields of astronomy [199], agriculture [200], medical science [201,202], computer science [203,204], social and behavioral science [205,206] and public transportation [207,208]. They are of increasingly importance throughout the world. Smart transportation data possess the same properties as big data. They are obtained from various sources with high volume and high variety and increasingly with high velocity. In a big data environment, traditional management styles of transportation are dramatically changed. Timely, efficient, and accurate traffic data are easy to access. Some major urgent problems of different cities that need to be solved, such as traffic congestion, environment pollution, and traffic accidents, can be more properly dealt with. As we know, a traditional way to deal with traffic congestion is to increase manpower investment or expand the construction of infrastructure. For example, build more parking lots and widen the roads – which would cost a lot of money. Based on the method of big data, rules can be mined and accurate predictions can be made. Drivers may know little about the possibility of traffic jams. Big data helps users to get that in advance. Before the driver starts his journey, big data management systems will provide the best possible route without traffic congestion, based on weather factors, real-time conditions, etc. Under this circumstance, for common big data processing methods, there are bloom filters [209], hashing [210], indexing [211], and parallel computing [212] technologies. Besides, for big data analysis methods, there are neural networks [213], deep belief nets [214], clustering [215], extreme learning machines [216], deep learning [217] etc. Other big data technologies that have been successfully developed include Hadoop, Tableau, and Storm. These will scientifically support big data decision-making. 5.11.5.4.3.3.2 Cloud technologies of smart transportation Services provided by cloud computing can be divided into the following three types: infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) [218]. IaaS means that consumers can obtain services of computer infrastructures, such as hardware servers, through the Internet. PaaS indicates that the development platform of software will be as a service. As for SaaS, the users do not need to buy the software, but to hire it to manage the business activities. Because of cloud technologies, the information resources, business resources, and the networking system of the transportation sector are deeply integrated. Entities such as government departments, enterprises, and individuals can conveniently obtain their required services. Specifically, required services can be the information services of public travel, logistics services, etc. The cloud of transportation mainly focuses on storage technologies of large-scale data, I/O rate, and information security. Data for cloud computing are stored in a distributed and redundant way (i.e., multiple copies are kept) to ensure the reliability of the data [219,220]. Besides, cloud computing is dealing with and analyzing a large number of traffic data. And it provides users with various services. Therefore, reliable and efficient data processing technologies are absolutely necessary. What’s more, due to the high concentration of users and information resources, the risks of cloud services are much higher than traditional methods. The safety of traffic information is directly related to the running of the whole traffic network. So it is very important to focus on security technologies such as hybrid cloud and firewalls. 5.11.5.4.3.3.3 Internet of Things technologies of smart transportation Applying IoT technologies in the transportation sector will realize real-time collection, transmission, and processing of the traffic data. And a comprehensive, accurate, efficient, convenient, safe, and environmentally friendly system of transportation management will be established [221,222]. In 2003, vehicle infrastructure integration (VII) [223] was first proposed in the United States. VII integrates vehicles and road facilities based on ICT. Accidents at intersections were effectively reduced, and services on real-time traffic information and service charges were provided. In 2009, the US Department of Transportation changed the name of VII to IntelliDrive and conducted more studies. Besides, the European Union developed the COOPERS system in 2006.

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Because of an uninterrupted two-way communication between vehicles and highway infrastructure, it improved the safety of traffic [224,225]. The core technology of IoT in smart transportation is VII. The IntelliDrive projects of the United States and the COOPERS system of Europe are both based on the VII technology to solve traffic problems. VII coordinated and integrated pedestrians, vehicles, and roads through networking integration technology. Thus, the early warning of accidents can be achieved to improve traffic safety.

5.11.6

Research Paradigms

The theoretical methods in one area cannot solve the smart energy management tasks and meet the requirements of smart energy management objectives. Currently, energy is not just an independent science field. Energy science is more and more integrated with other subjects, such as social science and information science. Therefore, some new cross-research fields are formed. The interdisciplinary research fields and methods of smart energy management are shown in Fig. 11.

5.11.6.1

Energy Informatics

The advanced ICTs, including smart sensing, wireless communication, cloud computing, big data analytics, and mobile intelligence, are increasingly integrated with the whole energy sector production, operation and management process [226]. In SESs, the large amount of EBD collected is a kind of strategic resource for supporting smart energy management. For example, the individuals’ electricity use data collected by smart meters in the SG in near real-time can effectively support the implementation of DSM and DR programs. In the field of energy, resources, and environment, ICTs are playing increasingly significant roles [227–229]. As a result, energy science and information science are gradually integrated. The fusion of energy science and information science promotes the formation of energy informatics, which is an interdisciplinary field using big data, knowledge information, and ICTs to reduce energy consumption and improve energy efficiency. Watson et al. [230] suggested that the aims of energy informatics include increasing the efficiency of energy systems and optimizing the energy distribution and utilization networks by EBD analytics and system reconstruction. The key concept of their model was “Energy þ InformationoEnergy.”

5.11.6.2

Social Informatics

Social informatics, which is also known as computational social science, is an interdisciplinary field of social and information science [231]. It refers to an interdisciplinary research field “focusing on the relationships between information and communications technologies (ICTs) and the larger social context in which these ICTs exist” [232]. The study objective of social informatics research focuses on the social and behavioral problems based on data and information. In the big data environment, data resources bring about many new opportunities and challenges at the same time for social science research. Through big data analysis, many hidden behavioral patterns of individuals and groups can be discovered [233]. The research methodology and paradigm of social informatics is an important support of smart energy management.

5.11.6.3

Energy Social Science

Discovering and understanding the consumers’ energy consumption behavior is an important way to improve energy efficiency and promote energy conservation [234]. The research focus of energy social science is to develop behavioral or psychological models to analyze users’ energy consumption behaviors and to develop effective ways to improve energy efficiency and environmental sustainability objectives. There have been some research efforts that have demonstrated the significance of exploring energy consumption in social science context. These studies suggest that though promoting changes in individual behavior is important, social level analysis provides a broader framework for understanding energy use. There have been some social science models, including the cultural model of household energy consumption [235] and the value–belief–norm theory [236], that have A. Energy science B. Information science

B

C. Social science E

D

D. Energy informatics

G C

F

A

E. Social informatics (computational social science) F. Energy social science G. Energy social informatics

Fig. 11 Interdisciplinary research fields and methods of smart energy management.

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been developed and applied in smart energy management. The great significance of looking at demand response from a social science perspective has attracted more and more attention [237]. There are mainly two research directions on energy consumption behavior based on energy social science, namely the behavioral and psychological factors influencing people’s energy use behavior and the intervention strategies aiming to change energy consumption behavior [32]. (1) Factors determining people’s energy use behavior from the behavioral and psychological perspective. Generally, individual energy consumption behavior is influenced by many different factors. Both objective factors that do not depend on the subjective sense of individuals and subjective ones related to individuals’ intention and awareness are included. Specifically, for example, housing characteristics, household structure, income levels, energy prices, and climatic conditions are common objective factors. The effects of different factors on energy consumption behavior are important research areas of energy social science. (2) Intervention strategies aiming to change some people’s energy consumption behavior and promote energy conservation, including goal-setting, feedback, information, and prompts. The intervention strategies can be generally divided into three types, namely structural, antecedence, and consequence strategies. For further details about each type of intervention strategy, readers are referred to Ref. [238].

5.11.6.4

Energy Social Informatics

Energy social informatics (ESI) is a new interdisciplinary field in smart energy management, which integrates energy science, social science, and information science. The research objective of ESI is to improve energy efficiency and environmental sustainability of SESs by advanced information technologies and behavioral/psychological models.

5.11.7

Future Directions

SESs are still in their initial development stage. There are still many challenges for smart energy management. The future research directions of smart energy management mainly include five aspects, namely strategy issues in smart energy management, data issues in smart energy management, behavioral issues in smart energy management, security issues in smart energy management, and regulatory issues in smart energy management [9]. (1) Strategy issues in smart energy management. For governments, overall planning and top-level design are very important for the development of SESs for a country. The system structure design of SESs should on the basis of the specific situations of a country or an area. Energy producers, operators, and service providers should rethink their positions in smart energy ecosystems and reconstruct their strategies and business processes to better satisfy the needs of energy prosumers. (2) Data issues in smart energy management. SESs are digitalized. In the system, EBD plays an important role in supporting strategy development and decision-making of all involved. To fully play the potential of EBD, the data issue is also a future research direction. The specific research problems include data quality evaluation and modeling, data processing and mining, security and privacy protection, data auditing, data sharing, and data trading. (3) Behavioral issues in smart energy management. Energy consumers in SESs are also digitalized. In SESs, consumers are becoming more mobile, social, and interconnected. Similar to Internet users, energy users combine energy consumption with their personal values. Therefore, though it is still a key factor, price is not just the only factor that affects energy consumption behavior. Internal factors like habit, values and attitudes, and interpersonal factors like norms and social comparison are playing more and more important roles in energy efficiency improvement based on behavioral analysis. (4) Security issues in smart energy management. Conventionally, energy systems are vulnerable industrial systems. In a smart energy environment, the interconnectivity of systems, sensors, software, data, and users further increase the risks, which are more difficult to find. The security issues of SESs include virus attack, false data injection, exploit attack, denial of service, eavesdropping, etc. Additionally, privacy protection is of great importance for smart energy systems, since users’ privacy information is easier to obtain in SESs. The security and privacy protection of SESs should be of concern in the whole process of big data analytics and system operation. (5) Regulatory issues in smart energy management. Regulation including developing strict policies and rules is important for smart energy management. However, to restore the commodity property of energy and promote the formation of an open energy market, governments should also relax some regulation. The regulation of SESs can be improved by emerging ICTs. In smart energy management, IT or big data-based regulation will gradually replace human supervision.

5.11.8

Closing Remarks

People’s demand for building SESs is becoming more and more urgent. To deal with the many deficiencies of traditional energy systems and better meet the personalized requirements of people’s energy demand, SESs are a newly developed form of energy system, in which there are many smart energy management tasks. In this chapter, some related concepts of smart energy

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management, including prosumer, aggregator, VPP, MG, SG, EI, and EBD, are first introduced. The four stages of energy system evolution (PES, IES, DES, and SES) and the three dimensions of smart energy management (energy product dimension, participating object dimension, and management science dimension) are also proposed. Then we propose the overall structure and key technologies of SESs. The sources of EBD and application in DSM are also discussed. We also present some case studies of smart energy management, including the China’s Ubiquitous Energy Internet, as well as smart energy management in smart buildings, the manufacturing industry, and the transportation sector. Research paradigms and future directions are finally presented. It should be noted that smart energy management is still in its infancy. The specific roadmap for future smart energy might be still unclear, but the business values and social benefits of smart energy management are becoming increasingly apparent.

Acknowledgments We would like to thank Li Sun, Xinhui Lu and Chen Wang for their contributions to the case studies. This work is supported by the National Natural Science Foundation of China (No. 71501056), China Postdoctoral Science Foundation (No. 2017M612072), Anhui Provincial Natural Science Foundation Program (No. 1608085QG165), the Fundamental Research Funds for the Central Universities (No. JZ2016HGTB0728), Anhui Provincial Philosophy and Social Science Planning Project (No. AHSKQ2015D42), and the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No. 71521001).

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Relevant Websites http://www.bitstoenergy.ch/ Bits to Energy Lab, ETH Zurich & University of St. Gallen. https://energy.gov/data/green-button Green Button, US Department of Energy. http://www.greentechmedia.com/ Green Technology, Greentech Media. http://www.sdu.dk/en/Om_SDU/Institutter_centre/CentreForEnergyInformatics SDU Center for Energy Informatics, The University of Southern Denmark. http://www.smartenergy.com/ SmartEnergy. http://www.smartenergygb.org/ Smart Energy GB. http://smartgrid.ucla.edu/ UCLA Smart Grid Energy Research Center, University of California, Los Angeles. http://www.eia.gov/ U.S. Energy Information Administration.

5.12 Energy Management in Smart Cities Paris A Fokaides, Frederick University, Nicosia, Cyprus and Kaunas University of Technology, Kaunas, Lithuania Rasa Apanaviciene and Egle Klumbyte, Kaunas University of Technology, Kaunas, Lithuania r 2018 Elsevier Inc. All rights reserved.

5.12.1 Introduction 5.12.2 Smart Cities Initiatives and Models 5.12.2.1 European Innovation Partnership on Smart Cities and Communities 5.12.2.2 TU Wien Smart City Model 5.12.2.3 IEEE Smart Cities Initiative 5.12.2.4 Smart Cities Council 5.12.2.5 Horizon 2020 – Smart and Sustainable Cities 5.12.3 Smart Cities Technical Standards 5.12.3.1 ISO/IEC Joint Technical Committee Study Group on Smart Cities 5.12.4 Recent Advancements in Smart Cities Energy Management 5.12.4.1 Main Trends in Smart Cities Energy Management 5.12.4.2 Assessing the Intelligence of Smart Cities 5.12.4.3 Internet of Things and Big Data 5.12.4.4 The Role of Zero Energy Buildings Into Achieving Smart Cities 5.12.4.5 Life Cycle Analysis of Smart Cities 5.12.5 Smart Energy Cities and Smart Energy Regions 5.12.5.1 Cost Action on Smart Energy Regions 5.12.5.2 Integration Potentials of Insular Energy Systems to Smart Energy Regions 5.12.6 Barriers for the Development of Smart Energy Cities 5.12.7 Case Studies – Lighthouse Projects 5.12.7.1 Smart INitiative of Cities Fully cOmmitted to iNvest In Advanced Large-Scaled Energy Solutions 5.12.7.1.1 City of Innsbruck 5.12.7.1.2 City of Bolzano 5.12.7.2 City-zen, a Balanced Approach to the City of the Future 5.12.7.2.1 City of Amsterdam References Relevant Websites

5.12.1

457 458 458 458 459 459 460 460 461 463 463 464 465 466 466 467 468 468 469 470 470 470 471 471 471 471 472

Introduction

A smart city is defined as an effective integration of physical, digital, and human systems in the built environment to deliver a sustainable, prosperous, and inclusive future for its citizens [1]. A smart city brings together technology, government and society to enable a smart economy, smart mobility, a smart environment, smart people, smart living, and smart governance. A smart city includes smart buildings, smart living, smart transportation, smart energy, smart communications, smart networks, and environmental awareness. A smart city is promoted to use urban informatics and technology to improve the efficiency of its services. The intelligence of a city is largely determined by the way it manages its natural resources. These include, among others, its energy resources, the management of which is of particular interest, for reasons that have been discussed in many cases in the recent past. Energy resources are not inexhaustible. Concerning fossil energy resources the best predictions suggest that crude oil will be exploitable until 2070. Also, the utilization of fossil energy resources with the use of combustion leads to adverse environmental effects, which are summarized in the greenhouse effect and climate change. Energy security issues require the use of endogenous resources, which are translated into renewable energy sources, as well as to energy savings and intelligent energy management. Based on the above, a prerequisite for smart cities is the intelligent energy management. Given the fact that smart cities are generally relying on technology and on the way their infrastructure is being used, energy management in an intelligent city is expected to be directly linked to the way a city manages its energy infrastructure. In order to accomplish ideal energy management in a very multifaceted system like an intelligent city, not only do most of its energy elements need to be recognized and considered, but the implicit links among them also have to be identified. Additionally, thorough modeling is required to validate and advance current and novel systems. Although numerous models are available and employed in the urban context since many years, these are usually tailored with specific objectives to be used for the management of individual systems. This aspect introduces a significant limitation in the management of systems for smart cities, as it neglects their interaction and does not account for their combined operation. Holistic and comprehensive approaches in the field of energy management of smart cities combined with the concept of Internet of Things (IoT) are anticipated to prevail in the following years.

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The purpose of this chapter is to integrate the reader into the key points that are anticipated to govern energy management in future smart cities. The chapter is divided into six sections. Following the introductory section, the second section introduces the intelligent city and its main parameters. There is a discussion on the different standards and initiatives for smart cities, with particular reference to their content. In the third section, a detailed reference to technical standards and standardization initiatives related to smart cities is presented. In the fourth module, the recent advancement in the field of energy management in smart cities is presented. Emphasis is given on the metrics which are used to assess the intelligence of smart cities, the IoTs and the handling of big data, as well as on the role of zero energy buildings (ZEBs) and life cycle assessment (LCA) in the smart cities vision. In the fifth section, a discussion concerning smart energy regions and energy management aspect is presented. In this section special concern is given to the integration potentials of insular energy systems into smart energy regions. Finally, the last module focuses on the obstacles observed in the adoption of the smart cities concept.

5.12.2

Smart Cities Initiatives and Models

Several initiatives and models have been recently developed either to support the advancement of the smart city business sector, or to support cities to adopt intelligent practices and become smart. In this section the following models and initiatives are described:

• • • • •

European Innovation Partnerships (EIPs) on Smart Cities and Communities (SCC) TU Wien Smart City Model IEEE Smart Cities Initiative Smart Cities Council (SCC) The Horizon 2020 Smart and Sustainable Cities cross-cutting call.

5.12.2.1

European Innovation Partnership on Smart Cities and Communities

EIPs, launched in 2015, presents the EU agenda on research and innovation. EIPs focus on societal benefits and a rapid modernization of the associated sectors and markets. They act across the whole research and innovation chain, bringing together all relevant actors and stakeholders at EU, national and regional levels. The purpose of EIP is to step up research and development efforts, to coordinate investments in demonstration and pilots, and to develop fast-track any necessary regulation and standards. The EIPs on Smart Cities and Communities (EIP-SCC) brings together cities, industry and citizens to improve urban life through more sustainable integrated solutions [2]. The EIP-SCC combines Information and Communication Technologies (ICT), energy management and transport management to come up with innovative solutions to the major environmental, societal and health challenges facing European cities today. With the aim to contribute to the EU’s 20/20/20 climate action goals [3], EIP-SCC looks to reduce high energy consumption, green-house-gas emissions, bad air quality and congestion of roads. The ultimate goal of EIP-SCC is to establish strategic partnerships between industry and European cities to develop the urban systems and infrastructures of tomorrow. The EIP-SCC consists of a high level group and a stakeholders platform.

• •

In the high level group there are representatives from industry, research, and cities. The high level group is responsible for the Strategic Implementation Plan (SIP), which helps define how concepts promoting Smart Cities are put into practice. It also looks at how the European Commission can support these measures in the Research Framework Program – Horizon 2020. The Smart Cities Stakeholder Platform is the collaborative, networking and knowledge sharing tool of SCC. It collects and analyses input from all stakeholders in order to give advice to the High Level Group to feed into the SIP and to provide detailed feedback to stakeholders who can use it to create their own activities and projects.

Lighthouse projects describe the funding of large scale projects, which come from policy recommendations in the SIP and can be funded through a number of different channels, including Horizon 2020 and structural funds. They are specifically designed to raise awareness of the Partnership and give it increased visibility.

5.12.2.2

TU Wien Smart City Model

A well-established model to evaluate the intelligence of a city is the one developed by the Technical University of Wien, Austria entitled European Smart Cities [4]. This model was delivered by the FP7-project entitled Planning for Energy Efficient Cities (PLEEC), funded by the European Union [5]. The European Smart Cities model provides an integrative approach to describe and rank European cities with population from 100,000 to 500,000 inhabitants, considered as medium-sized cities with development perspectives. This smart cities model is considered as a tool for effective learning processes regarding urban innovations in specific fields of urban development. The smart city model is built on a combination of endowments and activities of self-decisive and independent citizens, including the following six features: economy, mobility, environment, governance, living, and people. The model exploits the z transformation rational to standardize the performance of the considered cities in the above characteristics, transforming in this manner all indicator values into standardized values with an average 0 and a standard deviation 1. The Smart City model key fields and the evaluation criteria per field are given in Table 1. As of mid-2017 four versions of the smart city model were released.

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Table 1

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Smart city model key fields and evaluation criteria per field

Smart city key fields

Evaluation criteria

Economy

• • • • • • • • • • • • • • • • • • • • • • • • • • •

Mobility

Environment

Governance

Living

People

Innovative spirit Entrepreneurship City image Productivity Labor market International integration Local transport system (Inter-)national accessibility ICT-infrastructure Sustainability of the transport system Air quality (no pollution) Ecological awareness Sustainable resource management Education Lifelong learning Ethnic plurality Open-mindedness Cultural and leisure facilities Health conditions Individual security Housing quality Education facilities Touristic attractiveness Social cohesion Political awareness Public and social services Efficient and transparent administration

Source: Reproduced from European Smart Cities. Available from: http://www.smart-cities.eu; 2017 [accessed 04.08.17].

5.12.2.3

IEEE Smart Cities Initiative

The Institute of Electrical and Electronics Engineers (IEEE) announced in 2013 its initiative entitled IEEE Smart City [6]. Having identified those technologies which are associated with smart cities, IEEE saw an opportunity to assist municipalities in managing their transition to intelligent urbanization. A fundamental undertaking of the IEEE Smart Cities initiative, the IEEE Core Smart Cities program, introduced in 2013, recognized cities that were establishing and investing both human and financial capital into smart city plans. These Core Smart Cities were chosen through a process assessing criteria including focus, commitment, diversity, roadmap, presence of a strong IEEE Chapter. The selected cities received investment in funding and strategic and practical advice from IEEE to conduct activities and further the well-being of their citizens in a sustainable environment. Current IEEE Core Smart Cities include Casablanca, Morocco; Guadalajara, Mexico; Kansas City, Missouri, United States; Trento, Italy; and Wuxi, China. Following the Core Smart Cities initiative, the IEEE announced the Affiliated Smart Cities program, which allowed more cities to participate in and enjoy benefits of the IEEE Smart Cities program and network. The Affiliated Smart Cities program provides education, insights, and good practices, raising awareness of the many different approaches to become “smart.” Under the context of the IEEE Smart Cities initiative, numerous conferences, and training activities have been held by IEEE the past years.

5.12.2.4

Smart Cities Council

The SCC is a for-profit, partner-led association for the advancement of the smart city business sector [7]. SCC includes lead partners, who provide financial support and guide specific actions to help cities worldwide and in particular regions via membership on the Steering Committee. The council also includes associate partners and advisors, who have full access to all events, publications and research and represent their constituents' interests and contribute suggestions for targets and priorities. SCC web site is one the Internet's largest sources of free smart city tools, resources and case studies. Its Readiness Guide constitutes a well-established reference concerning the development of smart cities. SCC Readiness Guide was assembled with

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input from many of the world’s leading smart city practitioners, as well as members and advisors of the SCC. Readiness Guide goals of SCC Readiness Guide are as follows:

• •

provide a “vision” of a smart city, to help understand how technology will transform the cities of tomorrow; guide the development of roadmaps to enable the transition of a city to a smart one. It suggests the aspired goals, the features and functions to be specified and the best practices that will gain the maximum benefits for the minimum cost, at reduced risk.

The Readiness Guide is intended for mayors, city managers, city planners and their staffs and it includes sections for the built environment, energy, telecommunications, transportation, water and wastewater, waste management, health and human services, public safety, digital services, and smart finance.

5.12.2.5

Horizon 2020 – Smart and Sustainable Cities

Horizon 2020 (H2020) is the biggest EU Research and Innovation program ever with nearly €80 billion of funding available over a time period of 7 years (2014 to 2020) – in addition to the private investment that is expected to be attracted by the program. H2020 was developed promising more advances and world-firsts by commercializing great ideas [8]. By coupling research and innovation, H2020 targets to help attain this target. Emphasis is placed on scientific excellence, industrial leadership, and tackling societal challenges. In terms of the H2020 initiative, a cross-cutting call entitled Smart and Sustainable Cities was announced in 2014 [9]. Key challenges for Smart and Sustainable Cities are to deliver solutions to significantly stimulate cities' overall energy and resource efficiency through activities addressing

• • • • •

the building stock energy systems mobility climate change water and air quality.

The actions under the H2020 Smart and Sustainable Cities call, are anticipated to bring profound economic, social and environmental impacts, resulting in a better quality of life, competitiveness, jobs and growth. This H2020 cross-cutting focus area has the aim of bringing together cities, industry and citizens to demonstrate solutions and business models that can be scaled up and replicated, and that lead to measurable benefits in energy and resource efficiency, new markets and new jobs. The scope will include the creation of urban spaces powered by

• • • • •

secure, affordable, and clean energy smart electro-mobility smart tools and services innovative nature-based solutions showcasing economic viability. The ongoing cross-cutting call on Smart and Sustainable Cities comprises of four distinct topics, described as follows:

SCC-1-2016-2017: SCC lighthouse projects: Smart Cities and Communities (SSC1) focusses on demonstrating sustainable, costeffective and replicable district-scale solutions at the intersection of energy and transport enabled by ICT. Solutions discussed under this topic should integrate smart homes and buildings, energy efficiency measures, very high shares of renewables, smart energy grids, energy storage, electric vehicles, and smart charging infrastructures. The 2020 goal is to have a significant number of new lighthouse cities of all sizes all over Europe, in a very large number of Member States with various, climatic, and economical positions. SCC-02-2016-2017: demonstrating innovative nature-based solutions in cities: projects under this topic should adopt a “frontrunner” and “follower” cities approach, to facilitate the rapid exploitation, replication and up-scaling of smart cities solutions and via large-scale demonstrations. SCC-03-2016: new governance, business, financing models and economic impact assessment tools for sustainable cities with nature-based solutions (urban renaturing): activities under this topic are expected to map and examine current experiences and practices and endorse innovative business models, funding mechanisms and public arrangements to improve socially acceptable urban “renaturing” planning through participatory, multi-stakeholder and transdisciplinary way, involving also local communities. SCC-04-2016: sustainable urbanization: proposals under this topic should pool the essential monetary resources from the contributing national research programmes with a view to applying a joint call for proposals resulting in grants to third parties with EU co-funding in the area of sustainable growth and urbanization.

5.12.3

Smart Cities Technical Standards

Technical standards are an established norm or requirement in regard to technical systems. They are usually formal documents that establish a uniform engineering or technical methods and practices. Technical standards are anticipated to dominate the

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Strategic

ISO advisory group on smart cities

Process

Technical

ISO/IEC JTC1 Study group on smart cities

ISO/TC 268 Sustainable development in communities

CEN CENELEC ETSI Coordination group on smart and sustainable, cities, and communities

ISO/TC 268/SC 1 smart community infrastructure

ITU-T focus group on smart, sustainable cities

461

IEC systems evaluation group on smart cities

Fig. 1 Placing major worldwide standards activities. Reproduced from PD 8100: 2015. Smart cities overview – guide. London: BSI Standards Publication.

realization of future smart cities, a fact that has been well interpreted by standardization organizations. To this end it comes as no surprise that in the recent years numerous standards were developed in the field of smart cities development, metrics and management. Standardization organizations that have already issued standards on smart cities include

• • • • •

the International Organization for Standards (ISO), which is the main global body of national standards bodies, the European Committee for Standardization (CEN), the European Committee for Electrotechnical Standardization (CENELEC), the International Electrotechnical Commission (IEC), which drafts and publishes International Standards for electrical and electronic related technologies, the International Telecommunication Union (ITU-T).

British Standards Institution (BSI) has published a Smart cities overview guide [10], in which the role of standards and guidance documents for the implementation of the smart cities is interpreted. According to this guide, three levels of standards relating to smart cities exist, namely the strategic level (Level 1), the process level (Level 2) and the technical level (Level 3), each of which can play an important role in ensuring that the smart city is built on firm foundations.

• • •

Strategic-level standards are of most relevance to city leadership and process-level standards to people in management posts (Level 1). They include guidance in identifying priorities, how to develop a roadmap for implementation and how to effectively monitor and evaluate progress along the roadmap. Process-level standards (Level 2) cover good practice in procuring and managing cross-organizational and cross-sectorial smart city projects, including guidance on putting together appropriate financing packages. Technical specifications (Level 3) cover the practical requirements for smart city products and services to ensure that they achieve the results needed.

In Fig. 1 the placing of the major worldwide standards activities into the different levels (strategic, process, and technical) is depicted [10]. The purpose of standardization in the field of smart community infrastructures is to promote the international trade of community infrastructure products and services and disseminate information about leading-edge technologies to improve sustainability in communities by establishing harmonized product standards to evaluate their technical performances contributing to sustainability of communities. The users and associated benefits of these metrics are illustrated in Fig. 2. The focus group on Smart Sustainable Cities of the ITU-T has also published the key performance indicators (KPIs) definitions for smart and sustainable cities [11]. This standard focuses on the impact of the integration of ICTs into existing urban services, with an emphasis on improving the energy efficiency, operation and transparency of the urban infrastructure, resilience of road networks, wastewater management, and security.

5.12.3.1

ISO/IEC Joint Technical Committee Study Group on Smart Cities

International standardization in the field of information technology includes the specification, design and development of systems and tools dealing with the capture, representation, processing, security, transfer, interchange, presentation, management, organization, storage, and retrieval of information. The Joint Technical Committee (JTC) 1 is the standards development environment where experts come together to develop worldwide ICT standards for business and consumer applications [12]. Working Group

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Benefits:

Owners and operators

− Easier planning: − Easier infrastructure procurement; − Easier purchase decision; − Easier management of multiple providers

Countries, nations, governments, investors, developers, etc.

Facilitation of interaction

Providers Vendors, consultants, etc

Standardized metrics Community infrastructures as integrable and scalable products Benefits: − Better understanding of owner needs; − More efficient and effective global sales; − More efficient and effective R&D

Fig. 2 Users of the metrics and associated benefits. Reproduced from ISO/TR 37150. Smart community infrastructures – review of existing activities relevant to metrics; 2014.

(WG) 11 of JTC 1, entitled Smart Cities, serves as the focus of and proponent for JTC 1's Smart Cities standardization program. The tasks of WG 11 include:

• • • • •

the development of foundational standards for the use of ICT in Smart Cities the development of a set of ICT related indicators for Smart Cities the identification of JTC 1 subgroups that are developing standards and related material that contribute to Smart Cities, and where appropriate, investigate ongoing and potential new work that contributes to Smart Cities the development and maintenance of liaisons with all relevant JTC 1 subgroups the establishment of strong relationship with Smart Cities activities in ISO and IEC.

The initiatives of WG11 of JTC1 are anticipated to be the locomotive for the realization of the required environment under which the appropriate metrics and management practices for smart cities will develop. Following standards and methodologies were developed as of mid-2017 in the field of smart cities standardization: 1) ISO 37120:2014 [13] defines and establishes methodologies for a set of indicators to steer and measure the performance of city services and quality of life. It follows the principles set out and can be used in conjunction with ISO 37101:2016 [15]. This standard is applicable to any city, municipality, or local government that undertakes to measure its performance in a comparable and verifiable manner, irrespective of size, and location. 2) ISO/TR 37150:2014 [14], smart community infrastructures – review of existing activities relevant to metrics, provides a review of existing activities relevant to metrics for smart community infrastructures. In ISO/TR 37150:2014, the concept of smartness is addressed in terms of performance relevant to technologically implementable solutions, in accordance with sustainable development and resilience of communities, as defined in ISO/TC 268. ISO/TR 37150:2014 addresses community infrastructures such as energy, water, transportation, waste and information and communications technology (ICT). It focuses on the technical aspects of existing activities which have been published, implemented, or discussed. 3) ISO 37101:2016 [15], entitled sustainable development & resilience of communities, establishes requirements for a management system for sustainable development in communities, including cities, using a holistic approach, with a view to ensuring consistency with the sustainable development policy of communities. The intended outcomes of a management system for sustainable development in communities include: a) managing sustainability and fostering smartness and resilience in communities, while taking into account the territorial boundaries to which it applies b) improving the contribution of communities to sustainable development outcomes c) assessing the performance of communities in progressing toward sustainable development outcomes and the level of smartness and of resilience that they have achieved. ISO 37101:2016 is intended to help communities become more resilient, smart and sustainable, through the implementation of strategies, programmes, projects, plans and services, and demonstrate and communicate their achievements. ISO 37101:2016 is applicable to communities of all sizes, structures and types, in developed or developing countries, at local, regional or national levels, and in defined urban or rural areas, at their respective level of responsibility. d) ISO 37120 sustainable development of communities – indicators for city services and quality of life [16]. This standard identifies 100 indicators that cities should track to allow them to benchmark progress. According to this standard, there are 17 areas, 46 core and 54 supporting indicators that cities either “shall” (core) or “should” (supporting) track and report. The World Council on City Data (WCCD) has been set up by cities to benchmark cities and has certified 17 global cities.

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e) ISO/TR 37121:2016 [17] provides an inventory of existing guidelines and approaches on sustainable development and resilience in cities. This standard focuses on resilience understood as the ability of a city, system, community, local government, or society exposed to hazards to resist, absorb, accommodate to and recover from the effects of a hazard in a timely and efficient manner, including through the preservation and restoration of its essential basic structures and functions. Resilience indicators are intended to assess the extent to which cities are helping residents, businesses, institutions, and infrastructure resist, absorb, accommodate to and recover from the effects of hazards in a timely and efficient manner. f) ISO/TS 37151:2015, smart community infrastructure metrics – general principles & requirements [18], gives principles and specifies requirements for the definition, identification, optimization, and harmonization of community infrastructure performance metrics, and gives recommendations for analysis, including smartness, interoperability, synergy, resilience, safety, and security of community infrastructures. Community infrastructures include, but are not limited to, energy, water, transportation, waste, and ICT. The principles and requirements of this technical specification are applicable to communities of any size sharing geographic areas that are planning, commissioning, managing, and assessing all or any element of its community infrastructures.

5.12.4

Recent Advancements in Smart Cities Energy Management

The scientific literature presents a large number of works, which are related to best practices in the field of energy management in smart cities. This paragraph presents recent developments and trends in this field.

5.12.4.1

Main Trends in Smart Cities Energy Management

The methods and practices smart cities will use to manage their energy resources is one of the challenges of their success. With this statement, the question arises as to what energy management is. The answer to this question is not one-dimensional, and it depends on the level of analysis. At strategic state level, energy management includes the planning and operation of energy production and energy consumption units. This definition implies that a state perceives energy management as a function in which production and energy demand should be synchronized. That is, the energy storage, energy import, and export storage mechanisms should be in place to ensure that society always meets its energy needs. The above issues are usually regulated by state actors involved in energy transfer and management, such as energy regulators and transmission system operators. In the case of an organization, energy management is a systematic and continuous effort to improve energy efficiency within an organization. It can take many forms and involves all types of interactions with energy, from procurement and purchasing strategies to technological improvements and behavioral changes. With this definition, the question arises as to what energy efficiency is. Energy efficiency is the use of the minimum amount of energy while maintaining the desired level of economic activity or service. In other words, energy efficiency is the amount of useful output achieved per unit of energy input. Improving energy efficiency means either achieving more of the same input or achieving the same output with less energy. The above definitions are derived from the ISO 50001 Energy Management System [19], which governs energy management in companies and organizations, and is an established method by which critical business management and company management parameters can be defined. The question that arises concerns the definition of the energy management in the case of smart cities. The answer is that this is somewhere between the two definitions described. The standards discussed in Section 5.12.3, as well as the schemes used to designate smart cities, which have been analyzed in Section 5.12.2, contain important aspects that can regulate issues related to energy management. The fact is, however, that for the evaluation of energy management mechanisms in smart cities, different methodologies have been developed in recent years, many of which are scattered in scientific articles. The study of Calvillo et al. [20] delivered an extensive review of the prevailing methods in the field of energy management in smart cities. The authors reviewed and compared all energy-intervention fields of a smart city and their links, and they presented different existing energy models and simulation tools. This study revealed some clear trends can in all intervention areas. It was concluded that

• • •

energy-efficient facilities will be making their way into future intelligent cities with better appliances, control systems, and demand-response schemes the microgrid and smart-grid paradigms will eventually become the standard in the transport sector parking assistants, travel planners, and other similar system will be applied. Electric vehicles are also anticipated to be an integral component of smart cities.

Another aspect which is highlighted in the study of Calvillo et al. [20] is the need for the development of holistic and comprehensive smart energy management tools that would consider the management of all energy elements of the smart city, integrated into single computational models. These models should be applicable to any kind of city and adaptable to novel technologies and applications and they should rank the different energy operations of the smart city using a scale of importance, based on specific parameters and constraints. The significance of the accurate prediction of the climatic conditions and the behavior of natural resources (irradiation, wind velocity, weather conditions) is imperative for the intelligent energy management in the urban context. Addressing all the issues discussed in allows for the creation of a complete and adequate smart-city energy

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model, one that will assist decision makers in both government and industry to develop, simulate, and implement the best systems at minimum cost, fostering smarter, and more-efficient cities.

5.12.4.2

Assessing the Intelligence of Smart Cities

The term smart cities contain many elements of complexity, while at the same time it represents the backbone of a revolution that involves modern cities. The analysis of the technical literature presents several elements which confirm this complexity. First of all, there is a different understanding of how the term "smart city" is interpreted. Although technical standards give the answer to this question, there is no common understanding in the bibliography, and in many cases the term is interpreted differently. What is highlighted in the literature is the fact that the term smart is generally associated with new technologies, without taking into account the cost of adopting them. There are, however, those cases where the evaluation of the city's intelligence is done through a more integrated approach, which takes into account the cost of the proposed solutions, resulting to the concept of a smart and sustainable city. The question that arises is whether the term sustainable also includes the definition of smart, and whether it can by itself cover the required urban intelligence. For this purpose, several quantitative models have been developed, with the use of which decision-making can be made in relation to the development of smart cities. Girardi and Temporelli [21] introduced a novel concept for assessing smart energy cities, called Smartainability (from smart and sustainability). Smartainability is aimed to estimate with qualitative and quantitative indicators, the actual contribution of technologies for smart solutions to the increase of the energy efficiency and the environmental sustainability in a city. The application of Smartainability allows the estimation of the impact of smart solutions on the improvement of the smartness of a city, compared to traditional assets for specific performance indicators. In Ref. [21] Smartainability was applied to assess the energy solutions deployed in Expo Milano 2015 digital smart city, giving some quantitative results by means of tons of CO2, SO2, and NOX equivalent saved, as well as the saved energy. The employed case study proved that Smartainability can be adopted by decision makers to provide justified answers with regard to the benefits arising by deployed smart solutions. The concept is tailored for decision-makers because the benefits are expressed with quantitative indicators, the indicators are estimated before technologies or solutions implementation and the benefits are connected to technologies or solutions deployment. Compared with other proposed smart city assessment methodologies, Smartainability has the ability to connect benefits to functionalities and to enable assets. Papastamatiou et al. [22] presented a novel and effective framework for assessment and optimization of the energy use in buildings, tailored for public authorities and smart municipalities, in which the reduction of the CO2 emissions and the energy cost minimization is targeted. The added value of the proposed methodology lies on the combination of energy efficiency and energy management using multidisciplinary data sources. The model is based on two pillars, the assessment and the optimization pillar. The assessment pillar reveals the underperforming sectors and the potentials of the city whereas the optimization pillar presents the roof of improvement of the energy performance of the city through targeted energy plans. The character of this model is post-constructive, as it offers the ability to evaluate the progress of the city’s energy efficiency through the ex-post status of the city after the implementation of selected actions. The targeted energy plans of the “Optimization” pillar derive from the decision support system for Energy Management component, which offers short-term actions in a weekly basis, and from the decision support system for Energy Efficiency measures component, which offers long-term actions in a yearly basis. The framework is applicable to different types of buildings, including municipal and office buildings, entertainment or sports facilities, etc. Models for assessing the smartness of a city have also been developed based on the context of the smart cities technical standards, discussed in Section 5.12.3. Dall’O et al. [23] proposed a methodology which was based on the published standards of ISO/IEC on smart cities, as well as on the Covenant of Majors [24], promoted by the European Union. In this study a method for assessing the smartness of a city through a set of indicators that are applicable to small and medium-size cities and communities is described. The interest of this study in small and medium-size cities is justified by the fact that most of the studies on the European Innovation Platform of SCC focus on large cities. This rational is also observed in the TU Wien Smart City model, in which small to medium size cities are considered. The proposed model of this study was applied on an experimental basis in three municipalities located in Northern Italy, with comparable number of inhabitants but different socio-economic features. The flexibility of the approach proposed, which focuses on the important role of the Technical Committee on the choice of indicators and the definition of weights to apply to the indicator and evaluation areas, is the most important feature of this methodology. The experience of the European project Covenant of Majors, adopted by small and medium-size cities, showed the interest and the will to promote local governance. The inclusion of indicators in this methodology already present in the Sustainable Energy Action Plans proved to be useful in monitoring these indicators. This study highlighted the necessity for synthetic indices that could be updated on the basis of real-time data, as the indicators of the Covenant of Majors, used in this study, did not allow providing realtime information on city smart evolution, and were derived on an annual update. An issue often discussed concerns the European building stock and the barriers to its transition into smart buildings. Although numerous building services technologies and building envelope renovation practices are currently available, their smartness has still not been classified. In the study of Kylili et al. [25] the available literature that employed the KPIs approach in buildings renovation for the measurement of the sustainability of the built environment was reviewed. Eight generic KPI categories have been identified, related to economic, environmental, social, technological, time, quality, disputes, and project administration aspects, and also a large range of sub-categories for each. The analysis undertaken in this work has established the effectiveness of KPIs in assessing the sustainability and the level of intelligence of building renovation projects, and also identified the most

Energy Management in Smart Cities

Key performance indicators (KPIs) for the assessment of buildings

Economic

Direct costs Indirect costs

Environmental

Generic Atmosphere Land use Water resources Ecology Noise Visual impact Indoor quality Energy Reuse/recycle Waste management Public health

Social

Cultural heritage Public access Public perception Functionality Occupational safety

Technological

Innovation Intelligence Maintenance

Time

Planning Unexpected

Quality Disputes Project administration

465

Generic Materials Labour Generic site disputes Contract procurement

Fig. 3 Categories and sub-categories of key performance indicators (KPIs) on the performance of smart buildings. Reproduced from Kylili A, Fokaides PA, Jimenez PAL. Key performance indicators (KPIs) approach in buildings renovation for the sustainability of the built environment: a review. Renew Sustain Energy Rev 2016;56:906–15.

commonly employed KPIs in past studies. The categories and sub-categories of KPIs on the performance of smart buildings are summarized in Fig. 3. Although the research community agrees on the generic KPIs categories, there is still not a common consensus on the definition of sub-categories. In view of that, in this study specific gaps have been identified in employing the KPIs approach in buildings renovation – the lack of a sustainable building standardized basis, which is established on a set of relevant well-defined performance indicators, for national and international building policies. The next generation of building assessment methodologies are anticipated from the implementation of research initiatives and projects that are currently being carried out in the field of buildings energy renovation. The research community is concentrating on the development of a sustainable environmental index of buildings, which is based on performance indicators such as the building materials, location, energy usage, construction process, and waste management. Additionally, since the wider range of performance issues cannot avoid using qualitative metrics, the transition to a good combination and the simultaneous processing of linguistic and numerical variables, or quantitative and qualitative criteria, will ensure a comprehensive and effective evaluation. The subsequent step is expected to be the development and establishment of a sustainable building standardized basis for national and international building policies.

5.12.4.3

Internet of Things and Big Data

IoT is anticipated to be used in the near future as the network to connect different and divert objects in such a way that all services and contents will be available around societies for current and upcoming applications. The IoT structures are expected to enable a new approach in accomplishing energy related tasks, enabling in this manner a new “modus vivendi” for smart cities. In the future, there will be numerous IoT applications for the intelligent built environment, including the intelligent energy control for buildings, as well as the bidirectional energy chain, which will be based on the distributed generation concept. An IoT system will be in place to link different and heterogeneous devices through the internet, a fact which shows the necessity for flexible layered architecture of IoT models for smart cities. In the study of Khajenasiri et al. [26] an overview of IoT software platforms and enabling technologies, as well as some of the IoT challenges coming from IoT software and hardware immaturity are described. In this study, an IoT architecture model, aimed

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for a smart city, is described. In this model, things, people and cloud services are combined to facilitate specific application tasks. The architecture’s key components of the proposed model are described in this study, with the application of smart cities in mind. The study concludes that the main promises of an efficient IoT system will be realizable when a secure, reliable and user-friendly IoT system will be established, which at the same time will be able to offer daily comfort and convenience to users. Data management in smart cities will include big data collection, delivered from different and heterogeneous data sources. This data will provide the opportunity to assess different aspects of smart cities on different scales, including both the local view as well as comprehensive level. The integration of data is anticipated to accelerate the achievement of future smart cities, supporting in this way the involved stakeholders to establish a critical mass of novel energy services for the urban environment and beyond. Zuccalà and Verga [27] reviewed the recent advances in technology contributing to the creation of smart cities, focusing on the aspects of heterogeneous data collection and their integration. A city-scale solution, enabled by ICT, which was built in Milan in terms of the “Sharing Cities” Horizon 2020 project [28] is presented in this study. The authors discuss on an urban sharing ecosystem, synergically integrating the E015 digital ecosystem and state-of-the-art ICT technologies and trends to support and foster data sharing and consumption between different city stakeholders according to a competitive model. The proposed approach enables innovative solutions for citizen engagement and awareness, data management, service federation and interoperability, as well as scientific evaluation and monitoring of the smart city itself.

5.12.4.4

The Role of Zero Energy Buildings Into Achieving Smart Cities

ZEBs are described as buildings that have zero carbon emissions on an annual basis. In practice, this is achievable by reducing the energy demand of the building and by exploiting renewable energy sources using appropriate technologies to satisfy the reduced energy requirements. The ZEB principle is anticipated to contribute significantly toward the achievement of the future smart cities, envisioned by the European Union and promoted through its regulatory framework. According to the recast of the Directive on the energy performance of buildings (Directive 2010/31/EU) [29], all new buildings ought to be nearly zero-energy from 2020, while the new public buildings should set the example by complying with this requirement 2 years in advance. In addition, the European Commission encourages the Member States to develop policies, financial measures and other instruments for the promotion of the cost-effective transformation of all existing buildings into nearly ZEBs. Moreover, according to the European Strategic Energy Technology Plan (SET-Plan), at least half of the existing buildings in 25 demonstration cities are required to be transformed into nearly ZEBs by 2020 [2]. The study entitled “European Smart Cities: The Role of ZEBs” [30] demonstrated the potential contribution of the ZEB principle toward achieving smart cities in Europe. The main conclusions of this study are as follows:

• •

• •

ZEBs will contribute significantly to smart cities on the energy efficiency, energy conservation, and renewable energy generation aspects. The review of past scientific works indicated that buildings can make the transition to ZEBs by reducing their energy consumption by at least two-thirds compared to their current energy consumption. There are significant controversial points regarding the sustainability of ZEBs, including LCA, rebound effect and social, as well as climate change considerations, all of which require further efforts for their research and development. The majority of studies take into consideration only the energy during the use phase of the building, while the embodied energy of the construction materials has been hardly given attention in literature. Further investigation is also encouraged by the social attitude of the residents of buildings toward the environment and sustainable development, so that outcomes such as the rebound effects are avoided. ZEBs will also need to be further developed and upgraded to satisfy the demands of the future smart cities. The key objective of the future improved ZEBs is their active interaction with the urban energy grids by employing automated system and parameter design, learning algorithms, and fine grained sensor networks. The European community should consider the further development of the current ZEB concept, past the energy consumption of the use phase of the buildings and the energy efficiency criteria. Innovative methodologies should be adopted that promote a holistic approach, incorporating a combination of technologies and solutions of real time energy management, life-cycle and social considerations, and progressive economic feasibility considerations. The adoption of such methodologies will not only contribute more effectively but also more rapidly than anticipated to the achievement of the SET-Plan’s target for the transition of the European cities into smart ones. The ZEB aspects considered as an integral part of smart cities are depicted in Fig. 4.

5.12.4.5

Life Cycle Analysis of Smart Cities

In its policies and legislation, the EC established life cycle analysis as the best currently available framework for evaluating the performance of buildings and assets of the built environment. The industry also considers LCA as the most effective means in demonstrating green product quality. The energy, environmental, and economic performance of any building incorporates the benefits and deterioration arising from the extraction of the raw materials, the manufacturing of the product or the construction of the building, the operation and maintenance phase, and the selected waste management route. Such a holistic approach provides contractors, final users and all relevant stakeholders, reliable evidence and valid data, upon which the best decisions can be made.

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ZEB aspects as integral part of smart cities

Environmental design and building practices − Buildings environmental design − Contemporary and innovative Building materials − Best design practices (e.g., avoidance of thermal bridging)

Labeling of buildings electromechanical equipment − Eco labeling for air conditioners, boilers, dishwashers fans, lamps, refrigerating appliances, television, washer-driers, washing machines, water pumps. − Minimum requirements for heating systems, hot water systems, air-conditioning systems and large ventilation systems

Renewable energy technologies − On-site renewables − Solar PV − Solar thermal − Biomass boilers − Off-site renewables − Wind turbines − Hydro turbines − Geothermal power generation

Intelligent energy management

Fig. 4 Zero energy buildings (ZEB) aspects as integral parts of smart cities. Reproduced from Kylili A, Fokaides PA. European smart cities: The the role of zero energy buildings. Sustain Cities Soc 2015;15:86–95.

Standardization of the methodology has also been established for having common grounds for measuring and communicating the life cycle environmental performance of products or systems (ISO 14040:2006) [31]. Krozer [32] discussed the possibilities of fostering smart cities, based on the fact that concentration of knowledge workers in cities generates innovations, entailing economic development. The issue presented in this study concerns the construction of new office and residential housing in suburbs, which is often more attractive to project developers than upgradation of the existing one in cities. This fact undermines policies aiming the smart cities, because the dislocation of offices and residential areas causes imbalances on the real estate markets, which dilute knowledge work and enlarge commuting. Next to the direct social costs of commuting, there are losses in productivity of distribution and welfare losses in the urban communities and in nature. These losses are unpaid external effects of the real estate markets. The study concluded that about 74% energy and carbon dioxide emissions can be reduced through the local office systems because they use the office space efficiently and do not need fuels for commuting. Kylili and Fokaides [33] reviewed the most relevant existing European policies and legislation for the built environment, emphasizing on the sustainability of the construction sector in a framework of holistic life cycle considerations. In this work it is demonstrated that the promotion of a holistic life cycle approaches in the assessment of the sustainability of construction products and buildings is effective in identifying opportunities for energy and cost savings, for utilizing the natural resources more efficiently and for achieving reductions in the waste generated throughout the life cycle of a smart buildings. Within this context, the research-relevant dominant trends in the field of holistic approaches for the assessment of the environmental performance of construction materials and smart buildings were defined. Through this analysis, the policy-makers and scientific community are encouraged to move to a new era in building assessment for realizing a truly green sustainable construction market. According to this study, this will arise through

• • •

the use of alternative, recycled, natural, and unconventional construction materials and thermal insulation materials and the use of prefabricated building elements in construction, the integration of LCA with BIM for achieving building sustainability at an early design stage, and the employment of multi-objective optimization methodologies for building design decision-making.

Furthermore, considering the influential impact of globalization and government intervention in the greening of the building sector, in this study the need for country-specific data in the sustainability assessment of smart constructions is raised.

5.12.5

Smart Energy Cities and Smart Energy Regions

Up to this point, this chapter discusses on the principle of smart cities. A concept that has been though widely employed in the past years in the literature concerns smart energy regions, which apparently refers to a bigger scale several initiatives were undertaken in this field, the main of which are discussed in this section.

468 5.12.5.1

Energy Management in Smart Cities Cost Action on Smart Energy Regions

Cost actions are a flexible, fast, effective, and efficient networking instrument for researchers, engineers, and scholars to cooperate and coordinate nationally funded research activities. Cost actions allow European researchers to jointly develop their own ideas in any science and technology field [34]. In February 2012 the cost action “Smart Energy Regions” was initiated [35]. The smart energy regions cost action involved 27 countries with specialists from disciplines including architects, engineers, planners, and scientists covering a broad range of Regions across Europe. The Action took a regional perspective on energy and the low carbon agenda, including the drivers that are being used to promote and encourage low carbon regions and also the barriers that are blocking progress. The Action “Smart Energy Regions” showcased the benefits of low-carbon innovation in the sectors of policy, planning, design, and technology. Addressing technological issues as well as societal and economical needs, guidance has been provided for the large-scale transition to a low-carbon built environment. Considering local context and difference across European countries, drivers and barriers have been identified through a series of case studies. A framework of solutions has been developed as the main outcome of the action, and disseminated to national and regional governments, governmental organizations, homeowners and social landlords, private businesses, professionals of the building sector such as planners, designers and developers and academics. Due to the fact that the action was initiated prior to the publication of the smart cities standards series, the term “smart” was interpreted as a system, such as a smart city, a smart grid or a smart meter, where ICT plays a central role in governing the functions of the systems, maximizing its performance and minimizing the consumption of resources. It can be deduced from the definition of smart in this action that although sustainability aspects were considered as well, emphasis was placed on the continuous process of data collection and feedback which allows the system to regulate itself in real time. The action also emphasized on the importance of the social factors on the realization of smart energy regions. The potential of smart energy regions are considered to be capable to improve social awareness and participation. This fact implies that technical disciplines need to be paired with social sciences when studying the matter, and also that cultural advancement are needed ad well as technological ones. Another aspect of the smart energy regions cost action which was also highly addressed in its activities was the role of renewable energy technologies. Smart energy regions were envisioned as regions where reduction of consumption and efficient management on the demand side would enable the minimal requirement for resources and energy to be met by renewable sources. A distinctive feature of a smart energy region would be its energy grid, which would need to manage and balance the demand and supply of energy from individual renewable energy producers. The smart energy region action delivered three books, entitled

• • •

Smart energy regions (Working Group 1 output) [36] Smart energy regions – skills, knowledge, training, and supply chains (Working Group 2 output) [37] Smart energy regions – cost and value (Working Group 3 output) [38].

The focus of the smart energy regions handbook [36] was to demonstrate how different policies are being implemented in the European countries participating in this Action, that are helping to progress the low-carbon agenda, and to illustrate how industry and broader stakeholder groups are involved in the process. The analysis of the regions included within this handbook together with the specific case studies will help to provide an understanding of how low carbon technologies can be made appropriate and transferable within and between regions. A task from the smart energy regions cost action has been to investigate good practice that have been used to enhance skills, knowledge, training, and supply chains associated with energy and low carbon agenda at a Regional scale. Seventy people from 27 countries have contributed to this investigation including researchers not directly involved in the action. Good practices concerning skills, knowledge, and training as well as the supply chains in smart energy regions were summarized in the Handbook smart energy regions – skills, knowledge, training, and supply chains [37]. Workgroup 3 focused explicitly on demonstrating the link between cost and value of such retrofitting, applicable to both new and old buildings, as well as infrastructure and energy systems, at regional and national levels. Chapters in the Handbook smart energy regions – cost and value [38] collated papers from different member states covering aspects related to cost and value, covered under four principal chapter headings, namely environmental design, sustainable retrofitting, energy systems and technologies as well as smart energy regions, touching on strategies for a top-down versus a bottom-up approach.

5.12.5.2

Integration Potentials of Insular Energy Systems to Smart Energy Regions

The distinctiveness of insular energy systems, defined by the country’s inability to interconnect with other energy generators and/or consumers through a wider transmission grid outside its national borders is what drives their need to develop into smart [39]. This interconnection inability could be attributed to the smallness of the system, its remoteness, or due to political aspects. The great dependency of insular systems on imported conventional fossil fuels typically makes their power generation costly and insecure. Furthermore, the potential of these energy systems for renewable energy generation only extends up to the point allowed by the locally-available environmental conditions. In the study of Fokaides et al. [40] the focus was on the insular energy systems, and their potential to develop into smart energy regions. Due to the unique characteristics of insular energy systems, a special approach was required toward defining the dominant parameters that can drive this development. In terms of this work the main features of the smart energy regions, as well as of the insular energy systems were defined. An overview of the insular energy systems worldwide was performed and the systems were classified based on specific criteria, resulting to three categories.

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The first category included islands having installed generation capacity of up to 100 MW. The countries in this category have limited energy demand and in many cases large distance from the mainland. The small energy requirements aggrandize the impact of establishing renewable energy power stations as the small economy of these regions is vulnerable to large scale investments. The second category included islands with installed electricity capacity of over 100 MW and up to 15 GW. Most of the islands in this category exploited in a small or large extend renewable energy technologies due to their larger consumption demand, and also due to their higher GDPs. The third category of insular energy systems included the regions that are situated in the mainland but are isolated due to lack of electric grid connection. Most of the countries of this category are situated in Africa and their GDP is lower than $5000.

This work also examined the necessity and the ability of a number of energy insular countries to develop into intelligent ones. Two factors, namely the ability and the necessity index were defined and calculated for all insular energy systems. Each of the indices has resulted as the weighted sum of a variety of parameters, namely the cost of energy, the dependency on imported energy sources, GHG emissions, political obligations, GDP, growth rate and level of RET utilization. The main outcome of this analysis was that the development of insular energy systems into smart ones will be mainly driven by technological and environmental motivations including the reduction of GHG emissions, the introduction of political obligation toward promoting environmental friendly policies, and the increase of RES utilization for the diversification of their energy mix.

5.12.6

Barriers for the Development of Smart Energy Cities

It is anticipated that the realization of smart cities will be limited by specific barriers of technical, financial, environmental and social nature, the prediction of which in advance and their avoidance will be decisive to avoid unexpected losses of project resources. The development of methodologies and practices for ranking the importance of barriers affecting the implementation of smart energy cities, will assist project coordinators and policy makers to better understand, predict and prioritize implementation barriers facing them and to develop proper action and policy interventions to ensure successful implementation of smart energy cities projects. Mosannenzade et al. [41] identified the barriers to the implementation of smart energy cities projects in Europe and proposed a multidimensional approach for barrier prioritization applicable by project coordinators and policy makers. A total of 35 barriers to the implementation of smart energy cities projects were identified through an empirical approach, gathering information on 43 CONCERTO communities. CONCERTO [42] is a European Commission initiative within the European Research Framework Program which aims to demonstrate that the optimization of the building sector of whole communities is more efficient and cheaper than optimization of each building individually. According to this analysis, the barriers for the implementation of a smart energy city can be categorized into the following nine groups:

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policy administrative legal financial market environmental technical social information and awareness.

The multidimensional model suggested by this study to prioritize the barriers for the development of smart energy cities, considered the frequency, the causal relationships, the scale the origin and the level of impact of the different barriers. Rather than considering each of these aspects independently, their simultaneous analysis delivered a more effective prioritization of their impact. The concept of “criticality”, employed in risk-assessment, for evaluating the importance of a barrier, which is a function of its frequency and impact, was used to rank the importance of each barrier. This study investigated and applied interaction among barriers instead of treating barriers in an isolated and piecemeal way. Inevitability, a new indicator for the level of action required for tackling a barrier, was introduced in this study. This indicator shows if a barrier is more likely to be influenced at the project level, or policy level, or both. The proposed methodology for barrier prioritization is applicable to other types of barriers, such as barriers to energy efficiency and technology diffusion. Fig. 5 shows the causal relationships among barriers according to Ref. [41]. The implementation and uptake of the smart energy cities concept also depends on key local actors such as investors and developers and local authorities. In Ref. [43] the main barriers for the implementation of smart energy cities projects were defined to be the commitment of local administrations, the choice of accompanying activities such as dissemination of information, the use of appropriate communication tools, the awareness raising and the active involvement of relevant decision makers, user groups and market actors.

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Perception of interventions as complicated and expensive, with negative impacts Lack of awareness among authorities Insufficient information on the part of potential users 34

Low acceptance of new projects and technologies Lack of values and interest

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Fig. 5 Causal relationships among barriers. Each barrier is shown as a filled circle, and the relationships are shown as arrows. The arrow direction shows the direction of the causal relationship. Reproduced from Mosannenzadeh F, Di Nucci MR, Vettorato D. Identifying and prioritizing barriers to implementation of smart energy city projects in Europe: an empirical approach. Energy Policy 2017;105:191–201.

5.12.7

Case Studies – Lighthouse Projects

The initiation of the H2020 call (described in Section 5.12.2.5), in compliance with the EIPs on SCC (described in Section 5.12.2.1) resulted in some lighthouse projects in the European continent, which can be considered as case studies recently implemented in the field of smart cities. In this section three such lighthouse projects are presented, included in two EU H2020 funded projects, Smart INitiative of Cities Fully cOmmitted to iNvest In Advanced Large-Scaled Energy Solutions (SINFONIA) and CityZEN.

5.12.7.1

Smart INitiative of Cities Fully cOmmitted to iNvest In Advanced Large-Scaled Energy Solutions

The SINFONIA project is a five-year initiative to deploy large-scale, integrated and scalable energy solutions in mid-sized European cities. At the heart of the initiative is a unique collaboration among the cities of Bolzano and Innsbruck, working hand in hand to attain 40%–50% primary energy savings and rise the portion of renewables by 20% in the two pioneer districts. In terms of the project, an integrated set of measures linking the retrofitting of more than 100,000 m² of living space, optimization of the electricity grid, and answers for district heating and cooling. A large part of the SINFONIA initiative, is therefore dedicated to the transferability and scalability of the solutions applied in the two pioneer districts. To achieve this, SINFONIA will describe a limited set of district typologies and consistent restoration models, enabling cities to easily evaluate their needs and resourcefully define their long-term restoration strategies. To further guarantee their scalability and transferability, these models and typologies will be tested and validated with all stakeholders involved – public and private, from citizen to energy regulators.

5.12.7.1.1

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City of Innsbruck

Innsbruck, located in Austria, with 120,000 inhabitants, is participating in SINFONIA project with its eastern district. The aim of the city is through the demonstration of large scale implementation of energy efficient measures, to achieve an average of 40%–50% primary energy savings in the demo sites and to raise at least by 30% the stake of renewables in the district’s energy mix. Within the SINFONIA project, 66,000 m² of residential and public buildings, built in the time period from the 1930s to the 1980s, will be retrofitted to drastically with the aim to improve their indoor quality and energy performance, and decrease their final energy demand by up to 80%. The measures to achieve this consumption decrease include the improvement of the building envelope through insulation, and improvement of the thermal bridges losses, the installation of high efficiency ventilation systems with heat recovery, and the integration of renewable energy technologies into the building shell, mainly PVs, solar thermals and heat pumps. Another measure foreseen in Innsbruck toward achieving higher smartness is the extension of city’s district heating network will be extended and optimized to increase the use of renewable energy sources by 95% and reduce the use of fossil fuel by 22%. To achieve that, measures that include the deployment of a low temperature grid, the recovery and the exploitation of heat and cold from local industries, the integration of solar thermal plants as well as biomass gasification is anticipated.

Energy Management in Smart Cities

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Measures also include smart grids and smart home applications, which will combine demand and supply side measures to reduce the overall electricity demand by 3%.

5.12.7.1.2

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City of Bolzano

The city of Bolzano in Italy (100,000 habitants) has developed since 2005 an aspiring investment plan for large-scale urban renovations in cooperation with both public and private investors. The south-west district of Bolzano is participating in SINFONIA project with the aim to accomplish part of this work, and targets to accomplish 40% to 50% primary energy savings as well as increase by 20% of renewables share. The measures include the retrofitting of 37,000 m² of social housing built between the 1950s and the 1970s, in order to achieve high energy performance cost effectively and increase the levels of indoor thermal comfort with minimal impact on tenants. Particularly building envelopes will be thermally insulated, renewable energy technologies will be integrated into buildings and solar-based technologies focusing on power (photovoltaics) domestic hot water (solar thermal panels) will be installed. Within the Sinfonia project, the district heating and cooling network of the city of Bolzano will also be extended, and optimized, in order to reduce both the CO2 emissions. The measures to be implemented include the real time monitoring and forecasting of peak loads and energy demand, hybrid hydrogen/methane backup systems as well as the recovery of wasted energy from local industrial park. Other measures in terms of the Sinfonia project in the field of power and transportation include recharge points for vehicles and bicycles, intelligent upgrading of the public lighting as well as the implementation of an Urban Service-Oriented Sensible Grid (USOS-grid) system.

5.12.7.2

City-zen, a Balanced Approach to the City of the Future

City-zen project, funded under the H2020 program, focuses on the development of the cities of the future. The major goals of Cityzen project include the realization of smart cities as well as more effective collaboration models. The project also aims to connect smart cities with the industry, and have them develop specific technologies for the benefit of smart cities and to showcase to society ambitious pilot projects. City-zen project’s expected impacts include the realization of 22 innovations in Grenoble and Amsterdam, the achievement of savings of 59,000 t per year CO2, the renovation of 90,000 m² of residential buildings and the connection of 10,000 dwellings with a Smart Grid.

5.12.7.2.1

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City of Amsterdam

Amsterdam, the capital of the Netherlands, has 825,000 residents, with origins from 180 different countries. Amsterdam’s residents own over 600,000 bicycles and the wider Amsterdam Metropolitan Area has 2.1 million inhabitants. The city continues to encourage green research, development and investment in sustainable initiatives. Amsterdam enjoys a rapid uptake of electric transportation. A growing number of companies are developing sustainable products that influence global business. Amsterdam is exceptionally well connected, both physically and digitally. The City-zen project is one of the major projects in which the city is working with its partners to scale up innovative energy solutions and open networks. Within the City-Zen project, 700–900 dwellings will be renovated in Amsterdam and with the target to achieve CO2 reduction of 3000 t per year. The existing building stock will be improved to ensure affordable total living costs (rent and energy) for tenants now and in the future, while in the same time establish better comfort in the dwellings. To reach these goals, the key is to empower tenants and home-owners to save energy and to involve them in to the codesigning with other stakeholders innovative approaches for energy efficient retrofitting. A lab-home will be developed in which new and innovative solutions will be showcased. This Lab Home will demonstrate a hybrid heat pump, and many more innovations by SME’s and start-ups. It is also be the place to find out how to involve people toward a more sustainable lifestyle. Amsterdam will also smartify its electricity grid with computer and sensor technology at key nodes. The smart grid of the city will provide an improved, low-priced option to enable the energy transition. Also the existing network power and the network structure will be improved in key areas. Slumbering power outages will be visible in the smart grid and it will be able to be prevented. Also 10,000 dwellings will be connected to a smart grid, and the residents will achieve better control over their energy use. The new system will allow for instance to citizens to market the produced energy by onsite renewable energy stations to end-users. The CO2 performance of the heating and cooling supply of the city of Amsterdam will be improved at acceptable costs for the citizens. The objective for CO2 reduction is set at 4500 t per year. To achieve this, the city’s waste-to-energy power plant will be enhanced with new heat sources which include innovative solar thermal collectors. Also existing multifamily buildings will be connected to the heat grid. Also off grid, heat from sewer system will be added to an underground storage.

References [1] BSI Standards Publication. PAS 180, smart cities – vocabulary.

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[2] The european innovation partnership on smart cities and communities. Available from: http://ec.europa.eu/eip/smartcities/index_en.htm; 2017 [accessed 05.08.17]. [3] European Parliament and European Council. Directive 2009/28/EC on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC; 2009. [4] European Smart Cities. Available from: http://www.smart-cities.eu; 2017 [accessed 04.08.17]. [5] Planning for Energy Efficient Cities. Available from: http://cordis.europa.eu/project/rcn/186984_en.html; 2017 [accessed 04.08.17]. [6] IEEE Smart Cities. Available from: http://smartcities.ieee.org; 2017 [accessed 04.08.17]. [7] Smart Cities Council. Available from: http://smartcitiescouncil.com/; 2017 [accessed 04.08.17]. [8] European Commission. What is Horizon 2020. Available from: https://ec.europa.eu/programmes/horizon2020/en/what-horizon-2020; 2017 [accessed 12.08.17]. [9] European Commission (2017). Horizon 2020, smart cities and communities. Available from: https://ec.europa.eu/inea/en/horizon-2020/smart-cities-communities; 2017 [accessed 12.08.17]. [10] PD 8100. Smart cities overview – guide. London: BSI Standards Publication; 2015. [11] ITU-T, FG-SSC. Key performance indicators definitions for smart sustainable cities. Focus Group Technical Report 02/2015; 2015. [12] International Standardization Organization. Available from: https://www.iso.org/committee/45020.html; 2017 [accessed 05.08.17]. [13] ISO 37120. Sustainable development of communities – indicators for city services and quality of life; 2014. [14] ISO/TR 37150. Smart community infrastructures – review of existing activities relevant to metrics; 2014. [15] ISO 37101. Sustainable development in communities – management system for sustainable development – requirements with guidance for use; 2016. [16] ISO 37120. Sustainable development of communities – indicators for city services and quality of life; 2014. [17] ISO/TR 37121. Sustainable development in communities – inventory of existing guidelines and approaches on sustainable development and resilience in cities; 2017. [18] ISO/TS 37151. Smart community infrastructures – principles and requirements for performance metrics; 2015. [19] ISO 50001. Energy management system; 2011. [20] Calvillo CF, Sánchez-Miralles A, Villar J. Energy management and planning in smart cities. Renew Sustain Energy Rev 2016;55:273–87. [21] Girardi P, Temporelli A. Smartainability: a methodology for assessing the sustainability of the smart city. Energy Procedia 2017;111:810–6. [22] Papastamatiou I, Marinakis V, Doukas H, Psarras J. A decision support framework for smart cities energy assessment and optimization. Energy Procedia 2017;111:800–9. [23] Dall’O G, Bruni E, Panza A, Sarto L, Kayathian F. Evaluation of cities’ smartness by means of indicators for small and medium cities and communities: a methodology for Northern Italy. Sustain Cities Soc 2017;34:193–202. [24] Covenant of Mayors. Available from: http://www.covenantofmayors.eu/index_en.html; 2017 [accessed 11.08.17]. [25] Kylili A, Fokaides PA, Jimenez PAL. Key performance indicators (KPIs) approach in buildings renovation for the sustainability of the built environment: a review. Renew Sustain Energy Rev 2016;56:906–15. [26] Khajenasiri I, Estebsari A, Verhelst M, Gielen G. A review on internet of things solutions for intelligent energy control in buildings for smart city applications. Energy Procedia 2017;111:770–9. [27] Zuccalà M, Verga ES. Enabling energy smart cities through urban sharing ecosystems. Energy Procedia 2017;111:826–35. [28] Sharing Cities Research Project. Grant Agreement N 691895. Available from: http://www.sharingcities.eu/; 2017 [accessed 11.08.17]. [29] European Parliament and European Council. Directive 2010/31 on the energy performance of buildings (recast); 2010. [30] Kylili A, Fokaides PA. European smart cities: the role of zero energy buildings. Sustain Cities Soc 2015;15:86–95. [31] ISO 14040. Environmental management – life cycle assessment – principles and framework; 2006. [32] Krozer Y. Innovative offices for smarter cities, including energy use and energy-related carbon dioxide emissions. Energy, Sustain Soc 2017;7(1):6. [33] Kylili A, Fokaides PA. Policy trends for the sustainability assessment of construction materials. Sustain Cities Soc 2017;37:280–8. [34] Cost Action. Available from: http://www.cost.eu/COST_Actions; 2017 [accessed 11.08.17]. [35] Smart Energy Regions. Smart energy regions – cost action TU1104. Available from: http://smart-er.eu/; 2017 [accessed 11.08.17]. [36] Jones P, Lang W, Patterson J, Geyer P. Smart energy regions. Cardiff: The Welsh School of Architecture, Cardiff University; 2014. ISBN-978-1-899895-14-. [37] Roset Calaada J, Kaltenegger I, Patterson J, Varriale. F. Smart energy regions. skills, knowledge, training and supply chains. Cardiff: The Welsh School of Architecture, Cardiff University; 2016. ISBN 978-1-899895-21-2. [38] Jones P, Buhagiar V, Amparo López-Jiménez P, Djukic A. Smart energy regions, cost and value. Cardiff: The Welsh School of Architecture, Cardiff University; 2016. ISBN - 978-1-899895-22-9. [39] Fokaides PA, Kylili A. Towards grid parity in insular energy systems: the case of photovoltaics (PV) in Cyprus. Energy Policy 2014;65:223–8. [40] Fokaides PA, Kylili A, Pyrgou A, Koroneos CJ. Integration potentials of insular energy systems to smart energy regions. Energy Technol Policy 2014;1(1):70–83. [41] Mosannenzadeh F, Di Nucci MR, Vettorato D. Identifying and prioritizing barriers to implementation of smart energy city projects in Europe: an empirical approach. Energy Policy 2017;105:191–201. [42] CONCERTO. Research Project. Grant Agreement N. Available from: https://www.concertoplus.eu/; 2017 [accessed 11.08.17]. [43] Di Nucci MR, Pol O. Nachhaltiger Stadtumbau und Klimaschutz in der CONCERTO-Initiative. Energiewirtschaftliche Tagesfragen 2009;59(1):44.

Relevant Websites https://www.concertoplus.eu/ CONCERTO. http://www.covenantofmayors.eu/index_en.html Covenant of Mayors. https://ec.europa.eu/inea/en/horizon-2020/smart-cities-communities European Commission – Horizon 2020, Smart Cities and Communities. http://cordis.europa.eu/project/rcn/186984_en.html European Commission – Planning for Energy Efficient Cities (PLEEC). http://ec.europa.eu/eip/smartcities/index_en.htm European Commission – The European Innovation Partnership on Smart Cities and Communities. https://ec.europa.eu/programmes/horizon2020/en/what-horizon-2020 European Commission – What is Horizon 2020. http://www.smart-cities.eu European Smart Cities. http://smartcities.ieee.org IEEE Smart Cities. https://www.iso.org/committee/45020.html International Organization for Standardization.

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http://www.sharingcities.eu/ Sharing Cities Research Project. Grant Agreement No. 691895. http://smartcitiescouncil.com/ Smart Cities Council. http://smart-er.eu/ Smart Energy Regions – COST Action TU1104.

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5.13 Smart Grid Energy Management Konark Sharma, National Institute of Technology Kurukshetra, Kurukshetra, Haryana, India and BRCM College of Engineering and Technology Bahal, Bhiwani, Haryana, India Lalit Mohan Saini, National Institute of Technology Kurukshetra, Kurukshetra, Haryana, India r 2018 Elsevier Inc. All rights reserved.

5.13.1 5.13.2 5.13.2.1 5.13.2.1.1 5.13.2.1.2 5.13.2.2 5.13.2.2.1 5.13.2.2.2 5.13.2.2.3 5.13.3 5.13.3.1 5.13.3.2 5.13.3.2.1 5.13.3.2.2 5.13.3.2.3 5.13.3.2.4 5.13.4

Introduction Residential and Commercial Buildings Energy Management Analysis Residential Buildings Energy Management Analysis Stochastic model analysis for residential building energy management State estimation based on the model Commercial Buildings Energy Management Analysis Commercial building heating, ventilation, and air-conditioning model Residential building heating, ventilation, and air-conditioning model Energy storage system model AC, DC, and AC/DC (Hybrid)-Based Microgrids Energy Management Analysis Classification of Microgrids Mathematical Model of Wind Generation, Photovoltaic, Fuel-Cells-Based Microgrid Doubly-fed induction generator model for unbalanced grid voltage conditions Nonlinear model of bidirectional power converter Model of photovoltaic system and DC/DC boost converter Modeling of FC stack, DC/DC converter and three-phase three-wire voltage source-converter Energy Storage and Electric Vehicles (i.e., Hybrid Electric Vehicles, Plug-in-Electric Vehicles, Fuel Cell Electric Vehicles and Electric Vehicle Gasoline Powered Vehicles Energy Management Analysis) 5.13.4.1 Classification of Electric Vehicles 5.13.4.2 Electric Vehicle Modeling Analysis 5.13.4.2.1 Battery balancing modeling 5.13.4.2.2 Active balancing modeling 5.13.4.2.3 Capacitive-based balancing 5.13.4.2.4 Inductive/transformer-based and mixed balancing 5.13.5 Wind and Solar Energy-Based Energy Management Analysis 5.13.6 Results and Discussion 5.13.7 Future Directions 5.13.8 Closing Remarks Acknowledgments References Further Reading Relevant Websites

5.13.1

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Introduction

Energy is the backbone of today’s modern life. With the increasingly limited availability of traditional energy resources, for example, crude oil, coal and nuclear, together with environmental concerns, there is increased worldwide environmental awareness that energy needs to be used/implemented more efficiently and generated in line with thinking on its sustainability [1]. Ready accessing to use “clean-green” energy and cut down greenhouse gas (GHGs) emissions is must if we want to maintain our current way of daily life without compromising our well-being or the carrying capacity of the planet. Historically energy developments, as shown in Fig. 1, at various increasing network edges are being selected and followed suitable future directions. China, one of the world’s largest energy consuming countries, as well as one of the biggest GHGs emitter, due to its speedy economic growth, urbanization, and industrialization. In 2014, China also promised in a joint announcement with the United States, to stabilize carbon emissions by the year 2030, while at least 20% of its power will come from sources other than fossil energy by the year 2030 [2]. The United States is the second largest GHGs emitter in the world, which has recently pledged a reduction of net GHGs emissions up to 28% below its 2005 level by the year 2025 [3]. India presently using the large share of fossils in its energy supply chain (over 80% share of coal-based power in the grid) and is the fourth largest GHG emitter globally [4]. It has envisioned 15% of electricity contribution from renewable energy resources (RERs) sources by the year 2020

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Fig. 1 The evolution of worldwide energy for next 25 years. Reproduced from Duke energy. Available from: https://www.duke-energy.com/pdfs/ infographic_energyofthefuture.pdf; 2017 [accessed 23.08.17].

as against the present share of 6%, advocated under the national action plan on climate change (NAPCC). During the year 2022, the electricity requirement is estimated to increase to 1900 billion units (BU) from the present levels (nearly 1100 BU). Hence, Green power over 300 BU would be required as against the fivefold increase of present levels (60 BU). This would necessitate the significant use of RE capacity in decentralized formats to enable energy access to the unelectrified/underelectrified rural masses. European Union (EU) members already pledged to satisfy at least 20% of total electrical and thermal energy consumptions using renewable resources by the year 2020 [5]. Concurrently, several European countries, i.e., Germany, Sweden, Denmark, and Finland strived to achieve the high level of DG system penetration into their existing electrical power systems, with various supporting policies and financial schemes implementation. As per the classical IPAT model and adopted the extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, we can determine the main driving factors for energy-related carbon emissions in a particular territory or a city [2]. Developing country like Bangladesh having a gross domestic product (GDP) per capita income of USD $1700 in the year 2010 and an average annual GDP growth (up to 6%), has already finalized the RERs policy and has enacted the sustainable and renewable energy development authority (SREDA) act of the year 2012 to mitigate the effects of both climate change and environmental degradation in country [6]. Presently, “smart or future energy” has become a much-publicized buzzword among worldwide nations, industries, and universities, which can describe anything from global energy production to efficient use of that energy. The world of energy is changing drastically and there is a need to reduce energy-related CO2 emissions to slow down global warming. Therefore, worldwide nations are now reducing their dependency on imported fossil fuels by stimulating energy savings and the use of sustainable sources, such as solar and wind energy, but those all are not permanently available. During Conference of the Parties (COP)21 in Paris, it has been confirmed that technological innovations, such as smart energy systems will have to play a challenging role in climate change adaptation [7]. Smart energy system may be software and hardware or dedicated application, which gives favorable promises for end-to-end communications, from the level of household electric appliances, monitoring/ controlling applications to distributed generation (DG) level-based management/control applications with existing network and back to the sources of energy, as shown in Fig. 2. Thanks to latest sensors/controls and power-line communications (PLC) standards/technologies, which has already embedded into the smart energy systems, and are playing a vital role in making our energy practices/requirements “smart.” Different countries and utility companies are working toward the establishment of better communication standards/technologies and control over

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Thermal power-plant

Hydro power-plant

Smart energy system

Smart energy system

Solar power-plant

Smart energy system

Wind power-plant

Smart energy system

Transmission grid High-voltage (HV) lines Medium-voltage (MV) lines Substation

Low-voltage (LV) lines

Substations

Smart energy system

Smart energy system

Small scale industries

Large scale industries

Smart energy system

Smart energy system

Smart energy system

Commercial complexes

Households

Institutions/ universities

Smart energy system

Hospitals

Fig. 2 Smart grid (SG) power system architecture using smart energy system.

their electricity resources, manage peak demand, operate more efficiently, and accommodate massive amounts of multiple distributed energy resources (DERs). The electrification of our present energy use adds challenges to our future energy supply system faces and electrifying the future is about to unlock the third generation of wind power systems and living the future today with smart grids (SGs) [8]. SG, generally referred to as the next-generation power grids, is considered as a revolutionary and evolutionary regime of existing power grids, as shown in Fig. 3. PLC with their recent advancements in technological regulations, standardizations, and certifications as shown in Fig. 4, have proven to be susceptible to the next-generation power transmission/distribution systems with end-to-end communication capability, considered as a revolutionary and evolutionary regime of existing smart power grids [9]. Among the various PLC standards, PoweRline Intelligent Metering Evolution (PRIME) and G3-PLC, both technologies are offered/supported by orthogonal frequency division multiplexing (OFDM) technology, and have a particular visibility due to their large number of participating manufacturers and certified products for advanced metering infrastructure (AMI) and grid control/monitoring applications [10]. During the year 2009, PRIME technology was developed to define a future-proofed PLC-based infrastructure to support large scale smart metering infrastructure (SMI) and other SG-based applications [11]. The PRIME Alliance announced that PRIME certified products, their interoperability among equipment/systems from different manufacturers have been achieved and deployments of over 10 million PRIME meters by utilities and solution providers over 15 countries, across Europe with recent expansion into Brazil and Australia. During the year 2011, G3-PLC technology was promoted by the G3-PLC Alliance by 12 founding members to standardize and promote on a worldwide scale. Presently G3-PLC Alliance has more than 70 member companies, including those from Japan, Singapore, China, India, and Taiwan [12]. In the year 2015, ADD-GRUP in collaboration with Sadales tikls AS investigated G3-PLC technology-based pilot project in Jelgava’s LV grid, as shown in Fig. 5. The main challenge was to run G3-PLC technology on the same meters that had already been installed in the same grid and were working on PRIME (version 1.3.6) technology. The experiment took place in four stages and found that G3-PLC performs better in noisy conditions. The superior speed of PRIME (version 1.3.6) technology is good for quick transmission of alarms, disconnection/reconnection, and broadcast messages for configuration/firmware update, and also recommended for clean networks. PRIME (version 1.4) technology has a robust mode and is supposed to demonstrate a similar resilience to noises as G3-PLC technology. Today nearly 1 million G3-PLC products (i.e., smart metering, home energy management, street lighting,

Photovoltaic (solar) power/wind power /battery PCS

DMS (Distribution network stabilization solution)

AMI, MDM system

Distribution network stabilization solution (DMS) Business operations

Electric rate systems

Flow of information

Power distribution equipment management systems Power company headquarters and branches

Central load dispatching center

Electricity trade solutions Power generation

HEMS, Demand side management

Hydro power, pumped strorage hydro

Power company sales offices

Cell center system

Energy management systems (EMS) FEMS

Power transmission and distribution

Consumers CEMS

Power plant supervisory control systems Thermal power

Power protection control systems

Ultra high-voltage substation

Nuclear power Photovoltaic (solar) power generation

Voltage stabilization control Power conditioners for photovoltaic (solar) and wind power generation

Energy storage batteries Wind power generation

Energy storage battery systems for renewable

HVDC (High-voltage direct current)

Primary substation

SVC for power distribution

Distribution substation

Reactive power compensation device (STATCOM) DC converter station

Energy storage batteries

HEMS PV

AMI Energy storage batteries

AC−DC conversion equipment for power networks

D-STATCOM

EV charger

Advanced metering infrastructure (AMI) systems

High-voltage DC transmission

STATCOM (static synchronous compensator)

D-STATCOM (SVC for power distribution)

EV charging management system

Fig. 3 Smart grid (SG) architecture. AMI, advanced metering infrastructure; BEMS, building and energy management system; CEMS, community energy management system; DMS, distribution management system; EV, electric vehicle; FEMS, factory energy management system; HEMS, home energy management system; MDM, meter data management; PCS, power conditioning system; PHEV, plug-in hybrid electric vehicle; PV, photo-voltaic; SVC: static Var compensator. Reproduced from Hitachi. Available from: http://www.hitachi.com/businesses/infrastructure/product_solution/energy/smartgrid/; 2017 [accessed 23.08.17].

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Energy storage battery systems

Flow of electricity

BEMS

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478

PLC

UNB-PLC

KONNEX EN50065-1

X-10 95−125 kHz

95−140 95 140 kHz 125−140 kHz 1.2−2.4 1.2 2.4Kbps kbps 1.2-2.4

AM

1−10 MHz 2 Mbps

IEC 61334 3−95 kHz 2.4 Kbps S-FSK

ISO/IEC14908-3 86−131 kHz 3.6−5.4 kbps FSK, S-FSK

MFSK

HomePlug Green PHY (GP)

HomePlug AV PHY

1.8−30 MHz 3.8−9.8 Mbps

1.8−30 MHz 4−200 MbPS BPSK, QPSK, 16-QAM, 64QAM, 256-QAM, 1024-QAM

QPSK

BB-PLC

QB-PLC

NB-PLC

0.3−3kHz and 30−300 Hz 100−120 Bps

G3-PLC

PRIME

3−490 kHz 5.6−46 Kbps

3−95 kHz 130 Kbps

DBPSK, DQPSK, D8PSK, BPSK, QPSK, 8PSK, 16-QAM

DBPSK, DQPSK, D8PSK

IEEE P1901.2−2013

ITU G.hnem

9−500 kHz 500 Kbps

Up to 500 kHz 1 Mbps

DBPSK, DQPSK, D8PSK, BPSK, QPSK, 8PSK, 16-QAM

BPSK, QPSK, 16-QAM

HD-PLC

IEEE P1901−2010

G.hn (G.9960)

HomePlug AV2

2−28 MHz 240 MbPS

Below 100 MHz Up to 500 Mbps

Below 100 MHz up to 1 Gbps 4096-QAM (12-bit QAM)

2−86 MHz up to 1.8 Gbps (2 streams)

Wavelet OFDM

OFDM, Wavelet OFDM

4096-QAM

Fig. 4 Classification of power-line communications (PLC) standards. Reproduced from Sharma K, Saini LM. Power-line communications for smart grid: progress, challenges, opportunities and status. Renew Sustain Energy Rev 2017;67:704–51.

Fig. 5 G3-power-line communications (PLC) technology-based pilot project in Jelgava’s low voltage (LV) grid. Reproduced from ADD group. Available from: http://addgrup.com/files/CASE_STUDY._Transition_from_PRIME_to_G3-PLC_on_the_same_hardware_now_real.pdf; 2017 [accessed 23.08.17].

renewable energy, and electronic vehicle charging spots) are available in worldwide SG applications and aims to have 10 million by the year 2017, and 100 million by the year 2020. G3-PLC technology with the experimental investigation for BANs applications [13] and industrial area networks (IANs) applications [14] as shown in Fig. 6 has recently emerged as the future leading PLC technology for energy-efficient communication on Indian power-lines.

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479

R DC adaptor line G3-PLC modem A

Y B RS232 cable

Distribution-transformer A

Distribution-transformer B

Laptop A DC line

ACSR concuctor

55 meters RG58 cable

5V power supply G3-PLC RS232 cable modem B

Laptop B Laptop A

Laptop B

DC line

(A)

1 kilometer AC line/DC line

G3-PLC modem B

G3-PLC modem A

(B)

RS-232

RS-232

Fig. 6 G3-power-line communications (PLC) performance analysis in Indian context: (A) G3-PLC test for building area networks (BANs) applications in National Institute of Technology Kurukshetra, India (Reproduced from Sharma, K, Saini LM. Reliability and survivability analysis of narrowband power-line communication links in low-voltage building network applications. J Cent Power Res Inst 2015;11:505–16). (B) G3-PLC test over distribution transformers for industrial area networks (IANs) applications in Panipat, India (Reproduced from Sharma K, Saini LM. Performance evaluation of G3-PLC over distribution transformers in Indian context. J Cent Power Res Inst 2016;12:463–72).

In the year 2010, the IEEE 1905.1 standard was sponsored by the Power Line Communication Standards Committee (PLCSC), the IEEE 1905.1 working group to begin development of convergence digital home network specifications [15]. The developed “hybrid networking” infrastructure (i.e., Ethernet, wireless local area network (LAN), and PLCs) with IEEE 1905.1 abstraction layer as shown in Fig. 7, which enables the home network infrastructure devices, and allows the use of parallel links to transmit flows/ reroute traffic in case of lines/links degradation or failure. Different countries like China [16], Australia [17], Spain [18], and Brazil [19] are going toward the massive entry of SG deployments in the next few years. SGs are attracting growing attention owing to their inherent capacity to realize sustainable energy management systems (EMS) by using intelligent grids for future prospective. In early days the unidirectional flow of electricity with conventional electricity distribution networks was operated/directed from the power plants (i.e., thermal, hydro, solar, and the wind) toward industrial sites, commercial buildings/apartments, and houses. Power consumption information was cumulative and was attained by manual meter readings. SG with the help of information and communication technologies (ICTs) renovates the conventional grid. According to Ref. [20], by the year 2050, it is also projected that 7 out of 10 people across the world will live in urban areas. Urban dwellers in these areas have several advantages for access to electricity and they are more likely to have access to low-cost energy from utility-scale power plants and the distribution infrastructures. However, the generation capacity is often insufficient to serve all the connected loads and utilities have trouble collecting payments for all the energy they provide. Present SMs, embodied with latest metrological and communication interfaces offer solutions to both of these problems [21]. SMs empower end-users/customers to monitor/control the condition of electricity, water, gas and run time clocks (i.e., heating, ventilation, and air-conditioning applications) consumption and generation costs, balancing the “demand” and “supply.” SMs can recover billions of dollars of lost revenues and reduce the burden from the paying customers globally. Power restoration schemes incorporated into distribution automation (DA) system and AMI quickly help to restore the power during emergency situations like blackouts [22]. In India, the idea of smart cities (SCs) is at evolving stage and various domains of SGs are infused with professional interventions to adopt and will rollout international standard process and products. Electricity theft in India is a big loss of up to a third of its total power generation [23]. Therefore, the accurate measurement of electricity consumption is a must for Indian SG applications. Worldwide adopted/certified by various standard bodies like The International Electrotechnical Commission (IEC), The Institute of Electrical and Electronics Engineers (IEEE), National Institute of Standards and Technology (NIST), European Committee for Electrotechnical Standardization (CENELEC), The American National Standards Institute (ANSI), and The European Directive on Measuring Instruments (MID) are engaged in standardization, characterization, reliability and safety aspects of the actual electrical energy measurement for various SGs activities [24]. To simplify and standardize the AMI networks across India, the ministry of power (MoP), Government of India requested the Bureau of Indian Standards (BIS) to develop a national standard for SMs [25]. The BIS and India Smart Grid Task Force (ISGTF) worked together and released two SMs standards in August 2015 and March 2016 [26]. Recently, India Smart Grid Forum (ISGF) also issued necessary recommendations and guidelines to install 50 million SMs across India by the year

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HLE management Communication interface Vendor specific CMDUs / TLVs

Abstraction layer management entity (ALME) (e.g., local path selection)

Adjustments (Improvements, test data for link metric calculation, etc.)

User space

Link metrics

Topology discovery

Security management

AP autoconfiguration

ALME-SAP primitives

1905.1 Abstraction layer

1905.1 Bridge (forwarding entity)

Kernel space

Wi-Fi HAL

Eth HAL

PLC HAL

Wi-Fi device driver

Eth device driver

PLC device driver

Fig. 7 The implementation of the 1905.1 abstraction layer. PLC, power-line communication. Reproduced from Mengi A, Kortebi A, Lucht, H. et al., IEEE 1905. 1 hybrid home networking standard and its implementation with PLC, Wi-Fi and ethernet technologies. Power Line Commun Appl (ISPLC), pp.162–166; 2016. 10.1109/ISPLC.2016.7476274.

2020 [25]. As per ISGTF, the block diagram of Indian smart meter standard is shown in Fig. 8, which is the combination of BISbased Indian energy meter standards (including all amendments) and various IEEE standards [27]. The proposed sample shifting (SS) technique helps SM developers to design a true single-chip SM solution with the highest performance and standard accuracy for Indian SG applications [28]. In this chapter as per IS 13779-1999 standard (Up to amendment 4 clause 11.1), we implemented the proposed SS technique on 71M6543F [29], which is a system-on-chip (SoC)-based SM solution for three-phase power measurement. The block diagram of the proposed three-phase smart energy meter (class 1 type) measurement set-up is shown in Fig. 9. The proposed three-phase smart energy meter (class 1 type) was experimentally tested on a real test bench in National Accreditation Board for Testing and Calibration Laboratories (NABL) accredited meter testing laboratory, Dhulkote, Ambala City, India. We performed the satisfactory completion of following four tests:

• •

•

•

Test of no load condition (up to amendment 4 clause 11.1 of IS 13779:1999): during this test current circuit was open and a voltage of 115% of rated voltage was applied to the voltage circuit. The test output of the proposed three-phase smart energy meter (class 1 type) does not produce more than one output pulse/count. Test of starting condition (up to amendment 4 clause 11.1 of IS 13779:1999): the proposed three-phase smart energy meter (class 1 type) was fully functional within 5 s when the rated voltage was applied to its terminals. The proposed three-phase smart energy meter (class 1 type) started and continued to register the energy at 0.4% of Ib, at unity power factor (UPF) with the standard reference voltage, Vref. Test of repeatability (up to amendment 4 clause 11.1 of IS 13779:1999): this test was performed to verify the short-term stability of the proposed three-phase smart energy meter (class 1 type) with respect to time. The test was carried out at 5% of basic current, Ib at UPF load under the reference test conditions. Twelve error samples were taken at time intervals of 5 min. Identical test conditions were maintained throughout the test. Permissible limits for class 1 is 5% Ib, at UPF load: 70.5 (see Table 1). Register test: active energy (up to amendment 4 clause 11.1 of IS 13779:1999): this test verified the relationship between the test output and the indicated display results, which was complied with the standard specifications. The standard requirement was also verified at maxim current, Imax with UPF (see Table 2).

Puducherry SG pilot project [30], as shown in Fig. 10, which is one of the 14 pilot projects across India have been deployed with latest SGEM technologies such as AMI, power quality management (PQM), outage management system (OMS), peak load management (PLM), renewable energy integration, and electric vehicles (EVs), etc. The investment of this pilot project is USD $6,981,784.02, where 40,000 customers (i.e., 60% single-phase and 40% three-phase connections) embodied with tamper proof SMs, are resulting in 20% increase in revenue for the utility.

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IS 13779:1999

IS 15884:2010

IS 15959:2011

IEEE 802.15.4

IEEE 1901-2010

IEEE 1901.2-2013

Alternating

Alternating

Data exchange

Standard for

Standard for

Standard for

current static

current direct

for electriciy

local and

broadband

low-frequency

watthour

connected

metering

metropolitan

over power

narrow band

meters

static

reading, tariff

area

line networks:

power line

(class 1 and 2)

payment

and load

networks

medium access

communications

meters for

control -

control and

for smart grid

active

Indian

physical layer

applications

energy

companion

specifcations

(class 1 and 2)

specification

481

Alternating current static watthour smart meters, direct connected (class 1 and 2)

Fig. 8 Block diagram of Indian smart meter standard. Reproduced from Desi Smart Grid. AC static direct connected watthour smart meters class 1 and 2 ─ specifications. Available from: http://www.desismartgrid.com/wp-content/uploads/2015/05/ET-136823_11022015.pdf; 2017 [accessed 23.08.17].

SM with a smart switch gear [20], is also helpful to mitigate problems of insufficient generation by enabling more sophisticated load management techniques (i.e., demand response and real-time dynamic pricing). Recently proposed solutions such as multiparty computation (MPC) protocol [31], and SMs using SG data-driven methodology [32], not only provide data privacy but also protect SMs from false data injection problems (i.e., data integrity). The Smart Watts project [33], which is part of the German federal government’s E-Energy funding program, opened new routes to greater energy utility and efficiency for public utilities, system/device manufacturers, service providers, and other energy customers. The project work produced a smart meter gateway that receives, process, and distributes additional information – such as dynamic energy prices and current consumption values to smart homes/apartments, delivering the basis for smart, demand-based energy management and works as an interface to the “Internet of Energy.” Bidirectional power and information connections [34], from generation to customers plays an important role in SG such as smart metering data, renewable energy data, customer data, energy storage data, and monitor/control data as shown in Fig. 11. The data, if efficiently collected and successfully processed, could provide much insight to SG stakeholders: On the operational side, equipment failures can be intelligently managed, and thus overall power system reliability and market flexibility can be significantly improved. On the customer side, both the user experience and billing system can be enhanced. The complexity of data processing always increases dramatically with the increasing amount of SMs and growing frequent data readings. The proposed distributed communication (DCA) architecture as shown in Fig. 12, which helps to reduce the burden by breaking the large data loads into several streams and process the SM data locally [35]. Based on SMs deployment in Sweden, Finland, Denmark, Germany and the Netherlands, it has been observed that still it is highly influenced by governments, policy makers and stake holder’s interventions [36]. And there is need to address regulatory, financial, and social acceptance to facilitate global SMs diffusion.

5.13.2 5.13.2.1

Residential and Commercial Buildings Energy Management Analysis Residential Buildings Energy Management Analysis

Today, the global energy conservation is in a critical situation and experts from academic and professional fields, are also trying to find smart energy management solutions/technologies, RERs and useful strategies to minimize CO2 emissions. Active buildings, which

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Fig. 9 Block diagram of the proposed three-phase smart energy meter (class 1 type) measurement set-up on a real test bench in National Accreditation Board for Testing and Calibration Laboratories (NABL) accredited meter testing laboratory in Dhulkote, Ambala City, India.

Table 1

Test of repeatability at power factor, PF ¼1

S. No.

Current, I3 (%)

Tolerance

Test mode, þ P (Active energy import)

Meter under test (MUT) error %

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

5 5 5 5 5 5 100 100 100 100 100 100

Up Up Up Up Up Up Up Up Up Up Up Up

✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

þ 0.34 þ 0.34 þ 0.34 þ 0.34 þ 0.35 þ 0.35 þ 0.21 þ 0.28 þ 0.21 þ 0.28 þ 0.26 þ 0.26

to to to to to to to to to to to to

þ 1.5% þ 1.5% þ 1.5% þ 1.5% þ 1.5% þ 1.5% þ 1.0% þ 1.0% þ 1.0% þ 1.0% þ 1.0% þ 1.0%

refer to buildings that are embodied with smart energy management features, are contributing to the various SG developments/ operations [37]. The Swedish Governmental Agency for Innovation Systems (Vinnova) and the Swedish Energy Agency, along with different private and public actors, funded the “Active building in the sustainable city” pilot project that involved the development of buildings with SG features in Stockholm Royal Seaport (Vinnova 2015). The global home energy management system (HEMS) market is expected to grow from USD $864.2 Million in year the 2015 to USD $3.15 Billion by the year 2022, at a compound annual growth rate (CAGR) of 18.36% between the year 2016 and the year 2022 [38]. The HEMS market is expected to grow substantially because of factors such as increasing real-time energy conservation approach, the convenience of cloud computing and data analytics, and increased device interconnectivity. During the year 2017, smart home/house hardware and services will reach USD $83 billion through entertainment, automation, healthcare, and connected devices [39]. Of these, home automation and smart appliances will be the two fastest growing segments over the next 5 years driving by established providers including Samsung, Bosch, and GE Appliances. During the next 5 years, Juniper Research believes Alphabet, Amazon, Apple, and Samsung will continue to grow as the leaders in the smart home because of their strength in cloud services, and incumbent device bases. Of these four, Amazon will continue to be the

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Table 2

483

Register test: active energy

S. No.

Current, I3 (%)

Tolerance

Power factor

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

5 10 20 100 100 10 20 100 100 10 20 100 100

71.5% 71.0% 71.0% 71.0% 71.0% 71.5% 71.0% 71.0% 71.0% 71.5% 71.0% 71.0% 71.0%

1.0 1.0 1.0 1.0 1.0 0.5 0.5 0.5 0.5 0.8 0.8 0.8 0.8

Reactive power factor

0.0 0.0 0.0 0.0 0.0 0.9 0.9 0.9 0.9 0.6 0.6 0.6 0.6

Test mode, þ P (active energy import)

Meter under test (MUT) error %

✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓

þ 0.99 þ 0.19 þ 0.17 þ 0.11 þ 0.15 þ 0.41 þ 0.39 þ 0.11 þ 0.02 þ 0.23 þ 0.19 þ 0.21 þ 0.31

Fig. 10 Smart grid (SG) pilot project in Puducherry, India. Reproduced from Kappagantu R, Daniel SA, Suresh NS. Techno-economic analysis of smart grid pilot project-Puducherry. Resour-Effic Technol 2016;2:185–98.

Power flow

Power flow (e.g., Generation capacities)

(e.g., Alteration potentials)

Transmission and distribution Suppliers

Consumers Communication

Information flow (e.g., Dynamic prices)

Information flow (e.g., Power demands)

Fig. 11 Schematic of interaction between commercial buildings and the smart grid (SG). Reproduced from Xue X, Wang S, Sun Y, Xiao F. An interactive building power demand management strategy for facilitating smart grid optimization. Appl Energy 2014;116:297–310.

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GIS

AMS

CIS

DMS

OMS

MDMS Central operation center

Distributed operation center

Data concentrator Smart meter

Distributed operation center

Data concentrator

Data concentrator Smart meter

Smart meter

Data concentrator Smart meter

Fig. 12 Illustration of a distributed communication architecture (DCA) for supporting advanced metering infrastructure (AMI) in smart grid (SG). Reproduced from Jiang J, Qian Y. Distributed communication architecture for smart grid applications. IEEE Commun Mag 2016;54:60–7.

overall leader because of its cloud services through Amazon Alexa and its ability to merge its e-commerce business into its products. Amazon has managed to maximize its value proposition for Alexa by partnering with a large range of complementary players in the market, whilst utilizing its own cloud platform to set Echo and Alexa apart from its competitors in terms of functionality. Presently energy management inside smart distribution networks (SDNs) is becoming a very important and challenging issue [40]. The SDN concept is most reliable and effective way to reach high DG penetration levels without putting in risk the network stability and also increasing the power quality. A novel way to embed DG within SDNs is the so-called nearly-zero energy buildings the (nZEB). The nZEB is a building in which the net imported energy from the electric grid should be “nearly” zero within an interval of a year. The penetration of these kinds of buildings in the European stage will not be negligible since the EU Directive 2010/31/UE established that all new buildings must be the nZEB after the year 2020. The proposed SDN with a typical distribution network topology is shown in Fig. 13, where a building or an aggregation of buildings is connected. The nZEB can be equipped with dispatchable, non-dispatchable renewable generation and energy storage systems (ESS). All devices can be connected by means of three-leg or four-leg converters. A cascaded optimization coordination procedure among all generators and ESS of the building has been implemented and tested at the LEMUR microgrid (MG) in the University of Oviedo as shown in Fig. 14. This MG is a highly configurable and flexible for testing different types of AC, DC, and also Hybrid distribution topologies. It has two 400 V AC four-wire feeders used in this case for validating the proposed approach. For emulating the 76 homes/apartments loads, the input data obtained from the SMs, which were installed by a local distributor network operator (DNO). The input dataset includes the active/reactive powers recorded during 1 month at each hour. In order to map the real data to the proposed structure, the input data is randomly grouped into the 2 internal nodes of each feeder representing the buildings. SGEM framework for worldwide applications from residential customers to DGs level-based management/control applications has become a significant research and development field, as a result of the advances in the electrical power grid technologies. More importantly, with the integration of advanced ICTs, SGs are expected to greatly enhance efficiency and reliability of future power systems with RERs, as well as distributed intelligence and demand response applications. Demand side management (DSM) of energy systems becomes increasingly popular, because of its great potential in improving energy efficiency in home automation. Home automation as a mechanism removes much human interaction as technically possible and desirable in various domestic processes and replaces them with programmed electronic devices/systems. Ultimately it was aimed to heighten the quality of life with the automation of household activities monitor/control over the Internet or telephone and used to control heating, ventilation, and air-conditioning (HVAC) as well as fire safety and security applications [41]. Presently with latest technological advancements and standardizations, home automation, as shown in Fig. 15, which includes various features for security, surveillance, lighting, energy management, access control, entertainment-appliances, interfaces and software to reduce energy consumption, cost expenditure as well as increased convenience.

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485

nZEB B

B: Building

SDN

A: Aggregated

D

S

A

R

L

D: Dispatchable S: Storage R: Renewable L: Load

Fig. 13 Nearly-zero energy building (nZEB) representation embedded in a smart distribution network (SDN). Inside each building, different types of generators are considered, dispatchable generators, and renewable generators, as well as loads and energy storage systems (EESs). DG, distributed generation; PLC, power-line communications. Reproduced from Arboleya P, Garcia P, Mohamed B, Gonzalez-Moran C. Distributed resources coordination inside nearly-zero energy buildings providing grid voltage support from a symmetrical component perspective. Electr Power Syst Res 2017;144:208–14.

Fig. 14 Laboratory setup for validation purposes implemented at LEMUR Microgrid (University of Oviedo) with a snapshot of the graphical user interface for real time microgrid (MG) monitoring. Reproduced from Arboleya P, Garcia P, Mohamed B, Gonzalez-Moran C. Distributed resources coordination inside nearly-zero energy buildings providing grid voltage support from a symmetrical component perspective. Electr Power Syst Res 2017;144:208–14.

In global SG era, wireless and wired technologies, have been successfully implemented for various applications such as environmental, health, smart home/buildings control/monitoring applications, surveillance, and vehicular communications. Nowadays SMs data collection problems are minimized using vehicular ad-hoc networks (VANET), is also helpful to eliminate the need for manpower [42]. Wireless sensor home area networks (WSHANs), such as home appliances coordination scheme for energy management (HACS4EM) in smart homes using ZigBee protocol is an appliance coordination system-based application, which is quite efficient in terms of reducing peak load demand, electricity consumption charges with an increase comfort level of customers [43]. Although the wireless sensor networks (WSNs) are promising solutions for SG applications but fulfilling the quality of service (QoS) requirements are difficult due to their sensor nodes power constraints and harsh SG channel (i.e., radio frequency (RF) interference, various types of noise, multipath fading, and node contentions) conditions. To overcome this for WSNs in harsh SG environments, the proposed link-quality-aware capacitated minimum hop spanning tree (LQ-CMST) and the priority and channel-aware multichannel (PCA-MC) scheduling algorithm are useful to work [44]. For low-voltage (LV) customers regarding data collection and transmission efficiency of smart electricity information collection system, original data can be

486

Smart Grid Energy Management

Internet access and control Multiple programs allow you to control and see the status of your home from anywhere via the internet.

Energy management Be comfortable at home; save energy when away, control temperatures in greenhouses, humidors, wine cellars, aquariums, and attics.

Multi-room audio Share your favorite music throughout every zone of your home. No audio rack or expensive proprietary components required.

Pool and Spa

Motion detection

Secure pool areas with access control. Control pumps, filters, timers, heating, temperatures, solar control, and more.

Detect intrusion, automatically turn on lights, and activate automation functions when entering a room.

Home theater Use smartphone or iPhone to control A/V equipment, lower projection screens, and close window coverings.

Lighting Set warm and comfortable moods for dining, movies or entertaining. Have lights automatically turn off when leaving your home. Provide architectural quality lighting control and passive security for the ‘lived-in’ look when on vacation.

Irrigation Control irrigation solenoid valves for lawn sprinklers, plus inputs for rain sensing,

Access control Limit admission to designated areas. When you swipe a card or key tag, security is arms or disarms and door strikes are activated.

Surveillance cameras View and / or record guests arriving at the front door, or check on kids in the pool from any Touchscreen or the internet.

Security Professional quality UL listed security is built-in. Keep your family safe with wireless sensors.

Telephones Check and adjust security, temperatures, and lights via any phone at home or away. Monitor and control with your smartphone or iPhone.

Vehicle detection Announce visitors, turn on lights, and switch on a television to view the driveway or other outdoor area.

Fig. 15 Integrated home automation system and its benefits. Reproduced from Vujovic V, Maksimovic M. Raspberry Pi as a sensor web node for home automation. Comput Electr Eng 2015;44:153–71.

reconstructed with the help of observation matrix and TwIST algorithm [45]. Nowadays Raspberry Pi [41], as a sensor web node as shown in Fig. 16, is inexpensive, fully customizable and programmable small computer with support for a large number of peripherals/network communication solutions for smart home/residential buildings applications. Raspberry Pi as a part of “the internet of things” (IoT) version is able to interact and communicate, maximizes safety, security, comfort, convenience. Cognitive radio networks (CRNs) [46], as shown in Fig. 17, which have been emerged as a promising and energy-efficient wireless communications solution (i.e., from home area networks (HANs) covering neighborhood area networks (NANs) to wide area networks (WANs), which solves the problem of spectrum scarcity and improve spectrum utilization by opportunistic use of available spectrum. CRNs utilize the spectrum which is licensed to primary radio users when they are not utilizing it (i.e., when the spectrum is idle). Thus, the performance of CRNs are highly dependent upon the activity of primary radio users [47,48]. As compared to the wired communication solutions, the wireless communication solutions offer many benefits, such as lower cost of equipment and installation, quicker deployment, wider spread access, and greater flexibility [35]. WiMAX and 3G/4G networks can also provide wireless communication solutions for data-concentrators (DCs). Table 3 summarizes wireless communication-based various EMSs for smart home/residential buildings applications. HEMS may be any hardware/software system or program/algorithm that can monitor/control and provide the feedback about home’s energy usage, and/or enable advanced ICT of energy-using systems/devices in the home [49]. Under the sustainable SG paradigm, the HEMS as shown in Fig. 18, performs an important role to improve the efficiency, economics, reliability, and energy conservation for distribution systems. Based on present researches and achievements, it has proved that buildings are responsible for more than one-third of the total world energy use and greenhouse gas emissions [50].

5.13.2.1.1

Stochastic model analysis for residential building energy management

In Ref. [51], authors described and presented a taxonomy of types of home appliances (i.e., activity-dependent short-term appliances, activity-dependent long-term “shiftable” appliances, and activity-independent long-term “non-shiftable” appliances) as shown in Fig. 19.

Smart Grid Energy Management

487

Fig. 16 Raspberry Pi as a sensor web node for home automation. Reproduced from Vujovic V, Maksimovic M. Raspberry Pi as a sensor web node for home automation. Comput Electr Eng 2015;44:153–71.

HAN gateway Distributed power sources

NAN gateway Power plant

Base station

HAN gateway

Control room NAN gateway Base station

(HAN)

(NAN)

(WAN)

Fig. 17 Illustration of organization of smart grid (SG) communication networks into three architectural layers: HAN, NAN, and WAN. When a cognitive radio network (CRN) is incorporated into this architecture, then the HAN gateway becomes the HAN cognitive gateway, the NAN gateway becomes the NAN cognitive gateway, and the base station becomes the CR base station. AC, alternative current; DC, direct current; HAN, home area network; NAN, neighborhood area network; PV, photovoltaic; WAN, wide area network. Reproduced from Khan AA, Rehmani MH, Reisslein M. Cognitive radio for smart grids: survey of architectures, spectrum sensing mechanisms, and networking protocols. IEEE Commun Surv Tutor 2016;18:860–98.

As per the partially defined vector-valued Markov chain, activity and individual home appliance random variables corresponding to activity-appliance combinations for each type based on the quantized Watt value of individual operating modes respectively of the random variable C can be expressed as: X Vtl ð1Þ Ct ¼ lA SV l jA

488

Wireless communication-based energy management schemes for smart home/residential buildings applications

S. No.

Technique/scheme

Wireless communication type

Advantages

Application type

Mode of operation Real-time

Simulation

Country

1.

Bluetooth home energy management scheme (BluHEMS)

2.4 GHz ISM band (GFSK modulation)

WSHANs

–

✓ Network simulator-2

Italy [122]

2.

Universal plug and play (UPnP)

IEEE 802.15.4 (ZigBee compatible with TinyOS)

Home appliances such fan, TV, lamp, washing machine, etc.

✓

✓

South Korea [123]

3.

Demand response (DR) algorithm (Algorithm 2) based on the randomized dual consensus – alternating direction method of multipliers (DC-ADMM)

ZigBee

Solar power-based residential energy hubs

–

✓ MATLAB

Taiwan [124]

4.

Smart home energy management system (SHEMS)

Zigbee and Wi-Fi

Home appliances (i.e., electrical water heater (EWH), heating, ventilation, and air conditioning (HVAC), electrical vehicle (EV), dishwasher, washing machine, and dryer

✓

✓

United States [125]

5.

DijCostMin algorithm

Zigbee and Wi-Fi

Home appliances such as refrigerator, TV, PC, washing-machine, etc.

–

✓

Pakistan [126]

6.

Premises automation system (PAS)

Zigbee

Useful for residential energy management, smart appliances and wireless sensor home area networks (WSHANs) Automatically recognizes appliance which is plugged into the system and guarantees a high level of identification accuracy in real environments In the neighborhood with the help of subgradient method, improves the real-time power balance substantially and outperforms the existing distributed demand response (DDR) schemes for consumers With sensors not only it detects human activities but also a machine learning algorithm intelligently help consumers to reduce total electricity payment with little or without consumer involvement Yields significant improved performance as compared to the existing methods and the solution without optimization Aims at accommodating both the consumer need of efficient energy management and the grid need of consumer interoperation

Office appliances (using plug-load meter), home appliances, smart equipment (e.g., LED lightings, solar panels and energy storage and electric vehicle (EV) charging stations

✓

✓

South Korea [127]

(Continued )

Smart Grid Energy Management

Table 3

7.

(i) Quality of

Zigbee

8.

Cognitive radio (CR) base channel selection mechanism

(i) Schedules

controlled loads based on customers’ profile preferences and time-of-use (TOU) electricity prices (ii) Modifies the working schedule of appliances whenever a surplus of energy, which has been made available by renewable sources

experience (QoE)aware cost saving appliance scheduling (QCSAS) algorithm (ii) Quality of experience (QoE)aware renewable source power allocation (QRSPA) algorithm

Cognitive radio (CR)

In real-time environments during large number of nodes, it can also take into account the different sensing abilities of various licensed users

Smart home appliancesbased renewable sources

–

✓

Italy [128]

Cognitive radio (CR) based smart grid (SG) applications

–

✓

Jordan [129]

Smart Grid Energy Management 489

490

Smart Grid Energy Management

Renewable energy Electrical power flow network Communication network Solar energy

Wind energy Home energy storage system

Non-schedulable appliances

Refrigerator Smart meter

Monitoring

Smart HEMS center

Alarm

Power utilities

Printer Logging

Microwave Management

Control

Television Main panel Hair dryer

Washing machine

Iron

Water heater Air conditioning

Schedulable appliances

Electric vehicles

Fig. 18 Overall architecture of a representative home energy management system (HEMS). Reproduced from Zhou B, Li W, Chan KW. et al., Smart home energy management systems: concept, configurations, and scheduling strategies. Renew Sustain Energy Rev 2016;61:30–40.

Consumer Estimation

Energy request

EMS

Control

Grid signals

Appliances Fig. 19 Consumer-centric energy management system. Reproduced from Ahmed N, Levorato M, Li GP. Residential consumer-centric demand side management. IEEE Trans Smart Grid 2017:1–12.

where SV l jA is the set of appliances involved in the activity. To get the elements of the transition matrix of C for a particular At ¼ ai by 0 ! X X l l ð2Þ Vtþ1; At ¼ ai j Vt; At ¼ ai PðCtþ1; Atþ1 ¼ ai jCt; At ¼ ai Þ ¼ PðCtþ1 jCt; At ¼ ai Þ ¼ P @ lA SV l jA

lA SV l jA

Smart Grid Energy Management ¼

X

  l ∏ P Vtþ1; jVtl

491 ð3Þ

lA SV l jA

For a given activity A¼ ai when At þ 1 ¼ajaAt ¼ ai, the stationary distribution can be termed as lim

n   1X P Cm ; jC0 ; A0 ¼ aj nm¼1

ð4Þ

As the initial distribution of  moving into the new activity and appliances dependencies, the state space, SC ; is defined by cj ; where j ¼ 1; …; ∏Li ¼ l maxSV i As per the definitions of random variables A, C, E, and F, the vector-valued Markov chain Zt ¼[At ¼ai, Ct ¼ ci, Et ¼ei, Ft ¼fi]T where tAN þ represents the hidden state and the observation Xt ¼ Ct þ Et þ Ft is the aggregate residential consumption. To fully characterize the HMM transition matrix P(Zt þ 1|Zt) ¼ P¼ (At þ 1, Ct þ 1, Et þ 1, Ft þ 1|At, Ct, Et, Ft), the chain rule for Atþ1 ¼ At ; P ðZtþ1 jZt Þ ¼ P ðAtþ1 jAt ÞP ðCtþ1 jCt ; At ÞP ðEtþ1 jEt ; At ÞP ðFtþ1 jFt Þ

ð5Þ

Y Y ð6Þ For Atþ1 a At ; P ¼ ðZtþ1 jZt ;Þ ¼ P ðAtþ1 jAt Þ C ði; Atþ1 Þ E ði; Atþ1 ÞP ðFtþ1 jFt Þ Q Q For 1rirmax (Sc) and 1rirmax (SE). C and E are the initial distribution of the aggregate consumption of the short term activity dependent appliances and the initial distribution of the single activity dependent “shiftable” long term appliance. The element wise initial distribution of the HMM is Y Y Y Y Y Y ¼ Α ; C ; E ; F ¼ Α ðA1 ¼ aÞ C ði; A1 Þ E ðj; A1 Þ F ðkÞ ð7Þ Ζ 1

1

1

1

1

For 1rarmax ðSA Þ, 1rirmax ðSC Þ, 1rjrmax ðSE Þ, 1rkrmax ðSF Þ. The emission matrix is defined as: P ðXt jZt Þ ¼ PðXt jAt ; Ct ; Et ; Ft Þ

ð8Þ

For the first time step the probability of the joint distribution of the first hidden state and consumption observation using the initial distribution of the hidden chain. YðiÞ   ðiÞ a1 ðsÞ ¼ PðX1 ¼ xj; Z1 ðiÞ ¼ Z ðsÞP X1 ¼ xj jZ1 ðiÞ ¼ sÞ 1

Or

ðiÞ

a1 ¼ the remaining forward calculations for 1rtrT

1 as ðiÞ

ðiÞ 

atþ1 ¼ at To classify the user authors evaluated

YT

ð9Þ

P li li

   diag BXtþ1 ¼ x li Pli

ð10Þ

X

ð11Þ

aiT ðr Þ

r A SZli

where T is the training period. The model li that results in the maximal value is then identified as the approximate user reference class based on the sequence of real-time observations sensed by the system.

5.13.2.1.2

State estimation based on the model

Once the reference class for a sequence of training observations X1 ¼ xj,....., XT ¼ xj is determined (li ¼ l) the state estimate may be calculated by propagating the forward a posteriori probability value for the new real-time consumption observations using the Q statistics, ð l; Ρl; Βl Þ; of the reference class. In other words, given the sequence XT þ 1 ¼ xa....., Xt ¼ xb can be calculated as at ðsÞ r A SZ at ðr Þ

P ðZt ¼ sjXtþ1 ¼ xa ; …Xt ¼ xb Þ ¼ P

ð12Þ

The state Zt ¼ s that results in the greatest probability given the observation sequence is defined as the maximum likelihood estimate.

5.13.2.2

Commercial Buildings Energy Management Analysis

Developed countries like the United Kingdom, the United States, and Japan has already developed building’s Life Cycle Assessment (LCA) standards to find suitable energy advice schemes to support green building design. As per LCA standard, a sustainable building or a green building refers to user-friendly environment structure with efficient energy consumption. Iranian green building assessment tool (IGBT), has proved that the energy efficiency and the water efficiency up to 39%, which are the most significant factors to assess the green offices/buildings with respect to their vital circumstances of energy and water conservation. As per “Green Economy for Sustainable Development 2012–2021” program in the UAE, soon the smart plug systems will reduce the per capita energy consumption, which will be one of the highest in the world [52]. In Italy real-time tool are in practices for management/control of thermal energy storage and smart poly-generation grids in the real-time/simulated manner [53]. Icethermal storages storing/releasing energy system [54], power information (e.g., generation capabilities and power demand

492

Smart Grid Energy Management

response control strategy) [55], and integrated energy management (IEM) for micro-generation using renewable and nonrenewable sources, storage technologies such as EVs performances [56], can be improved with active participant of buildings. The hybrid cooling systems (HCSs) for building air conditioning are energy efficient technology, which offers a great reduction in energy consumption and a coefficient of performance improvement as per different climates and system designs. HCSs as per the combination of cooling processes or machines are classified into five categories:

• • • • •

Vapor compression-based cooling, absorption-based cooling, adsorption-based cooling, desiccant-evaporative, and multi-evaporator cooling

Properly selected HCSs such as vapor compression cooling system (VCC) and the absorption cooling system (ABSC) has greater coefficient of performance (COP) as compared to other thermally driven cooling systems. The daily operations of an electric utility like fuel resources planning and taking strategic decisions to balance the supply/demand of electricity are now mostly influenced by the load forecasts (LFs), as shown in Fig. 20. When the worldwide electricity markets have undergone a revolution, LFs also gained a lot of significance across business departments like energy trading, financial planning, etc. Accurate LFs are the backbone for spot price establishment for the system to gain the minimum electricity purchasing cost in the market environment, and provides intelligence to energy management. Presently worldwide utilities are showing great interest in SG implementations, and load forecasting is important due to its various applications (i.e., DSM applications, storage maintenance and scheduling, renewable energies integration, etc.). It also benefits the global electricity customers to understand the important relationship between the demand and price and varying the electricity consumption pattern according to the net price. In Ref. [57], the proposed LF strategy has been compared with recent LF strategies, and found that the proposed LF strategy is good for accurate load predictions, maximizes the overall system reliability, resilience, and stability. According to Ref. [58], forecasting models of photovoltaic (PV) and solar power are divided in to four classes:

• • • •

Statistical models, AI-based models, Physical models, and Hybrid models.

Smart grid

Raw data

Data pre-processing Test data

Outlier rejection

Electrical load database

Load estimation

Hybrid KNN-NB predictor Feature selection

Predicted load Fig. 20 Load forecasting strategy in smart grid (SG). CVPP, commercial virtual power plants; DG, distributed generation; DSO, distribution system operators; DR, demand response; TVPP, technical virtual power plants; VPP, virtual power plants. Reproduced from Saleh AI, Rabie AH, Abo-Al-Ez KM. A data mining based load forecasting strategy for smart electrical grids. Adv Eng Inform 2016;30(3):422–48.

Smart Grid Energy Management

493

It is also expected that in future solar forecasting methodologies could be chosen to ensure the overall SG performance. Department of Defense (DoD), which is an executive branch department of the federal government of the United States driven by energy policy and increasing energy costs, pursues latest and innovative technology to improve energy efficiency and monitor energy consumption at domestic installations and deployed locations [59]. The DoD occupies nearly 276,770 buildings/apartments totaling over 2.2 billion square feet real estate sits largely within military installations, which resemble small cities with loads encompassing, residential, industrial/commercial sectors peaking in the tens of megawatts. These loads (i.e., residential, industrial/commercial sectors) depend on a single energy meter. Many installed traditional distribution systems are also aged and there is a need of renovation and modernization. SG technology helps to underpin many of these advancements and enables the DoD to better understand consumption patterns and make informed decisions regarding efficient energy use. However, higher operational security requirements and maintenance costs pose a unique challenge to select/implement suitable SGEM framework throughout the DoD and also requires additional planning and development to ensure their successful adoption. Due to significant effects on global CO2 and GHGs emissions, light emitting diodes (LEDs) may also be used as an alternative for managing the energy use in developing and developed countries. Presently high-power white LEDs, as shown in Fig. 21, have attracted the global attention due to their versatility in a variety of applications (i.e., residential/commercial buildings, automotive lamps, communications devices, and medical devices) and growing demand in markets [60]. Major LED manufacturers from Nichia (Japan), Cree (The United States), Philips Lumileds (Netherlands), and Osram (Germany), claims the LEDs lifetime nearly 50,000–100,000 h. Indonesian cities typically have suboptimal street lighting systems, with illegal connections, limited metering coverage, and poor service standards [61]. Only in the selected cities including Yogyakarta and Makassar have already implemented full metering infrastructure and are switching to LEDs. During the year 2011, about 3068 GWh or 2.3 million tons of CO2 resulted from public street lighting’s power consumption. Due to the slow pace of installing metering in Indonesian cities and the current billing practices, the government has launched a smart street light initiative (SSLI) in the year 2014 under the Nationally Appropriate Mitigation Actions (NAMA) framework established to tackle the above issues. With the help of following efficient lighting technologies and management schemes, up to 40% of CO2 emission reductions can be achieved:

Silicone lens

Phosphor layer

Wire bond

Phosphor layer

ESD diode

LED chip

Plastic lens Gold-plated leadframe (also serves as heat-sink)

Anode

Cathode

Silicone encapsulate Substrate

Package house

Anode

(A) Cree XLamp

Plastic lens Golden wire Anode

Package house

(C) Philips LUXEON K2

LED die

Bond wire

Cathode

(B) Osram LUW

Phosphor layer

Silicone encapsulate (phosphor) LED chip

Silicone lense

LED die Silicone encapsulate Substrate Heatsink slug

Transient voltage suppressor (TVS)

Bond layer Cathode Ceramic substrate

Thermal pad

Metal interconnect layer

(D) Philips LUXEON Rebel

Fig. 21 Some snapshots of light emitting diodes (LEDs) with different packaging type from worldwide LED manufacturers such as (A) Cree (United States), (B) Osram (Germany), (C) and (D) Philips Lumileds (Netherlands). Reproduced from Sun B, Jiang X, Yung KC, Fan J, Pecht MG. A review of prognostic techniques for high-power white LEDs. IEEE Trans Power Electr 2017;32:6338–62.

494

• • •

Smart Grid Energy Management

Increase the energy efficiency of traditional street lighting by replacing with latest efficient street lighting technologies in cities and urban areas. Reduce energy consumption on the supply side, thus leads to the reduction in GHGs emissions resulting in a more efficient, stable and less carbon intensive energy system. Achieve 400,000 tCO2e emission reductions by the year 2020; considering the current average lifetime of LED-based street lightings (10 years), the SSLI NAMA would achieve up to approx. 1,400,000 tCO2e by the year 2024.

Kimco Realty owns nearly 745 leasable shopping establishments across 39 states in the United States as well as in different countries such as Canada, Puerto Rico, Chile, and Mexico [62]. Recently Kimco upgraded the shopping center’s metal halide (MH) fixtures with 79 high-performance 217 W LED fixtures bundled with 0–10 V dimmable drivers. The shopping center parking lot lighting utilized the Lumewave (i.e., wireless lighting control system) and reduced the energy use by 56%. Each LED fixture contains a passive infrared (PIR) motion sensor as well as a wireless controller. The latter provides bidirectional communication via a central gateway device and cellular modem. The intelligent system contributes main four benefits:

• • • •

Additional savings of approximately 30% (compared to dusk-to-dawn operation) through late-night scheduled dimming and motion-activated control Fixture-level motion-responsive control enhances safety and security by actively deterring unwanted activity Fault detection notifications reduce maintenance inspection costs and provide rapid resolution of lighting outages Real-time performance monitoring and reporting via central management system software.

In Fig. 22 the comparison of the “Before” and “After” lighting plans illustrate the significant improvement in increased light levels as well as uniformity (reduced contrast between light and dark spots). As per the global growth in urban cities population demands [63], the IoT-based system as shown in Fig. 23 are fulfilling the various needs of city inhabitants. According to Ref. [64], the IoT is about to interconnect the embedded systems and bring together two evolving technologies (i.e., wireless connectivity and sensors). These connected embedded systems are the independent microcontroller unit (MCU)-based computers that use sensors to collect data as shown in Fig. 24. These IoT systems, which are networked together usually by a wireless protocol (i.e., WiFi, Bluetooth, 802.11.4, or a custom/dedicated communication system. The dedicated networking protocol selection is based on the distribution of nodes and the amount of data to be collected. The proposed IoT-based combined (four-tier) system, as shown in Fig. 25, includes smart home sensors, vehicular networking, weather and water sensors, smart parking sensors, and surveillance objects, which can increase the decision-making power for SC development and urban planning. The proposed IoT-based system using big data analytics can make intelligent/effective decisions for society at appropriate times. According to Ref. [65], fully realizing 30 billion IoT devices by the year 2020 requires a backend support framework to automate large numbers of remotely-performed functions. There is a broad spectrum of cloud solutions available to IoT developers, and are grouped into four broad categories:

Fig. 22 The Lumewave by Echelon wireless lighting control system enables highly granular control and monitoring of the lighting system. (A) This site map reveals the poor lighting uniformity. The red and yellow areas denote below minimum illuminance levels. (B) This site map reveals the improved lighting uniformity throughout the parking area. The green areas denote acceptable illuminance levels. Reproduced from ECHELON. Kimco realty cuts energy costs by 86% at bay area’s metro shopping center. Available from: http://www.echelon.com/assets/blt67852012056a770a/ Kimco%20Case%20Study.pdf?utm_source=Subscribed þ List&utm_campaign=7e6fa3ef12-&utm_medium=email&utm_term=0_3851e61b5d7e6fa3ef12-117274157&goal=0_3851e61b5d-7e6fa3ef12-117274157; 2017 [accessed 23.08.17].

Smart Grid Energy Management

Environmental pollution

495

Surveillance

Vehicular traffic

Carbon mono-oxide CCTV Pedestrian count Sulfur-di-oxide Time Emergency button voice Noise sensors Front screen Ozone Pollution Data Free slots others Location Smoke water Temperature others Electricity consumption Rain Total slots Humidity Gas consumption No of vehicles River/lake water Temperature Time Pressure Wind speed Smart home Smart parking Weather and water Aggregator system

IoT platform

Smart city and urban planning Fig. 23 Sensors deployment. BESS, battery energy storage system. EMS, energy-management-system; Reproduced from Rathore MM, Ahmad A, Paul A, Rho S. Urban planning and building smart cities based on the internet of things using big data analytics. Comput Netw 2016;101:63–80.

External conditions

Environmental conditions

Smart sensors Digital sensors RFID/NFC

A/D converters Data converters

Cloud storage

Analog front end

Flash memory

RAM memory Data storage Memory modules

Opto sensors WiFI Video

Connectivity Microcontroller

Bluetooth

Environmental sensors 802.15.4 Wireless Low dropout regulators

DC/DC converters

Custom wireless

Battery management

Wired

Power Power management

Fig. 24 Block diagram of IoT sensor node. Reproduced from Mouser Electronics. IoT sensor node block diagram. Available from: http://www. mouser.in/applications/internet-of-things-block-diagram/; 2017 [accessed 23.08.17].

Smart Grid Energy Management

Top-tier

Intermediate tier-II

Intermediate tier-I

Bottom tier

496

Data generation and collection

Source Smart home

Smart parking

Vehicular networking

Surveillance

IoT

Weather and water

Environmental pollution

Communicator Technologies 3G/LTE/3GPP

Zig BEE Wi-Fi

WiMAX

Real-time Spark/VoltDB/Storm Data management Hadoop and eco system processing

Offline MapReduce

HDFS

Govt. Data interpretation

Ethernet

Citizens

HBASE

Travelers

Smart city plan

Application E&G management

Citizen security

HIVE

Analysis SQL

Entrepreneur

Travelers guide line Fire detection

Storage

Hospitals

Citizens healthcare

Entrepreneur benefits

Security organization

Health planning Flood safety

Industry planning

Fig. 25 Four tier architecture for internet-of-things (IoT)-based big data analytics for remote smart city and urban planning. Reproduced from Rathore MM, Ahmad A, Paul A, Rho S. Urban planning and building smart cities based on the internet of things using big data analytics. Comput Netw 2016;101:63–80.

• • • • •

General cloud providers that provide IoT-focused features Dedicated IoT Cloud providers Embedded hardware manufacturers that provide a dedicated Cloud IoT backend Cloud providers that cater to the Do-it-yourself (DIY) or pro-maker market There is still the wild west of IoT-focused technology, their pricings and feature sets, which are also constantly evolving.

Cloud service providers are changing their pricing structures widely, and a combination of the number of devices, the number of messages over time (per day or month), and the size of payload/message (e.g., kilobytes or megabytes) influence the global pricing across the IoT backend providers. In the 21st century, the world is going to shift from sustainability assessment to “smart sustainable cities.” But currently there is a large gap between SC and sustainable city frameworks, and need to develop SC frameworks or redefining the SC concept [66]. The proposed decision hierarchical decision-making strategy for smart city energy management (SCEM) can enable the energy manager to govern city energy system as a whole while addressing different urban sectors with an integrated, structured, and transparent planning [67]. And the result obtained by this holistic approach with an optimal set of action plans in specific urban area:

• •

improves globally the city energy performances, by the given budget constraint, and deals with conflicting objectives and requirements, fragmented decision making, and difficult subsystem cross optimization.

The proposed tool also supports the energy manager of the public administration (PA) to define strategic and multi-sectorial action plans for the city energy management. It has been observed from the Swedish experience, that the small and medium-sized enterprises (SMEs) can supplement their efforts to improve energy efficiency by participating in industrial energy-efficiency networks (IEENs) [68]. The global contribution from buildings toward energy consumption services, both residential and commercial, the use of HVAC systems is significant and consists of 50% of the total energy consumption in buildings in developed countries [69]. Additionally, the need for HVAC changes over hours and days as the electric energy prices [70]. Dynamic HVAC control under dynamic energy pricing model to meet an acceptable level of occupants’ comfort is also critical and unable to achieve energy efficient buildings. But the building embodied with hybrid electrical energy storage (HEES) system as shown in Fig. 26, where the target building can enable peak power shaving by adopting a suitable charging/discharging schedule of electrical energy storage (EES) elements, avoids the co-scheduling problem of HVAC control and helps to achieve energy-efficient smart buildings. Experimental results as shown in Fig. 27 demonstrate that the proposed HEES algorithm minimizes the combination of building electricity bill and battery aging cost up to 10% in the total electric utility cost compared with other baseline methods. During “GreenPAD” project in Paderborn, Germany, it has been proved that data center’s can also benefit from variable energy prices in SGs [71]. The suppliers in this medium voltage (MV) grid were wind farms and PV collectors on rooftops as shown in

Smart Grid Energy Management

Utility company

497

HVAC control

Electricity price

Temperature

Supply sources

Energy demand

Co-scheduling

Capacity

Building load

HEES system

Building tasks

Fig. 26 The energy co-scheduling framework. Reproduced from Cui T, Chen S, Wang Y, Zhu Q, Nazarian S, Pedram M. An optimal energy coscheduling framework for smart buildings. Integration, the VLSI J 2017;58:528–37.

400 Proposed HEES 375

Greed HEES Proposed bat

Total daily cost ($)

350

Greedy bat No-bat

325 300 275 250 225 200 0.25

0.28

0.31

0.34

0.37

0.4

Peak energy price ($/kWh) Fig. 27 Relationship between daily cost and battery storage capacity for proposed hybrid electrical energy storage (HEES) algorithm and other baseline schemes. Reproduced from Cui T, Chen S, Wang Y, Zhu Q, Nazarian S, Pedram M. An optimal energy co-scheduling framework for smart buildings. Integration, the VLSI J 2017;58:528–37.

Smart Grid Energy Management

498

400 kV (EHV)

110 kV (HV)

Electrical substation paderborn Windenergy metering

MV metering

10−25 kV (MV)

LV metering

230 V−1 kV (LV)

Industry GCC University campus

University data center

Residential area

Solar panel

Fig. 28 Proposed smart grid (SG) architecture in Paderborn, Germany. MV, medium voltage; LV, low voltage. Reproduced from Masker M, Nagel L, Brinkmann A, Lotfifar F, Johnson M. Smart grid-aware scheduling in data centres. Comput Commun 2016;96:73–85.

Fig. 28. The fossil-fueled power plants were located in the extra high voltage (EHV) grid outside the MV grid. Although the SG was not yet in place, the necessary values were made available by Westfalen Weser Energie (WWE), the local grid provider. Besides the data center’s usage, data from additional metering points were used to train the linear models for energy prediction. These metering were the energy flow between MV grid and high voltage (HV) grid, the energy generated by wind farms and the contribution of solar panels. However, since the panels were located in residential areas, these last values were actually the difference between production and consumption in the respective low voltage (LV) grids. Energy simulation is an efficient way to examine data center’s air-conditioning energy consumption and has been used by various data centers to study energy conservation measures. It has been proved from more than 100 building energy simulation programs that EnergyPlus and DOE-2 simulation tools are very popular among data center industries [72]. EnergyPlus over DOE-2 simulation tools has advantages during data center’s energy simulation. Short term operating reserve (STOR) tool is useful to manage high inaccuracies in demand forecast or unforeseen problems in generation availability [73]. Grid STOR alerts vary across the year and are related to load penalties for large end users (i.e., triad warnings). Table 4 summarizes various EMSs for SG-based applications (i.e., distributed EMS, distribution networks and data centers, etc.).

5.13.2.2.1

Commercial building heating, ventilation, and air-conditioning model

According to Ref. [74], authors focused on commercial and residential HVAC systems as shown in Fig. 29 which represent nearly 50% of a building’s electricity consumption. A commercial HVAC model with n zones, where each temperature zone i ¼ 1,..., n its thermal dynamics are given by the first order model, Ci i

 dyi ðt Þ yo yi ðt Þ _ i ðt Þ yc þ Cp m ¼ dt Ri

 yi ðt Þ þ wi ðt Þ

ð13Þ i

i

where y is the zonal temperature, yo is the outside temperature, yc is the cooing coil discharge air temperature, C , R are _ i is the supply airflow, and wi is the external respectively its thermal capacitance and thermal resistance, cp is specific heat of air, m disturbances from solar, occupancy, etc. The total airflow supplied by the fan is equal to the summation of the supply airflow into each individual zone; _ i ðt Þ. The supply fan power can be modeled as a polynomial function of the total supply airflow, _ ðt Þ ¼ ðSÞni¼ 1 m m _ ðt ÞÞ þ C4 i _ ðt ÞÞ2 þ C3 ðm _ ðt ÞÞ3 þ C2 ðm Pfan ðt Þ ¼ C1 ðm

ð14Þ

Smart Grid Energy Management

499

Table 4 Energy management schemes for smart grid-based applications (i.e., distributed energy management systems, distribution networks, data centers) S. No.

Technique/scheme

Advantages

Application type

Simulation/type

Country

1.

Neighborhoodwatch-based distributed energy management algorithm

Smart grid (SG) based distributed energy management systems

✓ IEEE-14 bus and IEEE-30 bus system with birictional communication network

United States [130]

2.

Consensus-based energy management algorithm (CEMA)

Distributed energy management systems

✓ IEEE-39 bus system

China [131]

3.

Unified energy portfolio optimization framework

Data centers

✓ CPLEX and MOSEK

United States [132]

4.

Nonlinear least square support vector machine (NLSSVM) algorithm

Guarantees the accurate control computation to solve the economic dispatch problems during presence of compromised generation units It can achieve the social welfare maximization solution asymptotically during the non-convex optimization problem Solves the tractable linear mixed-integer programs for single and coordinated multiple data centers The average computational time for the proposed hybrid forecasting algorithm is less than ten minutes for all electricity price/load forecasting markets Particle swarm optimization (PSO)-based algorithm, which can lead distribution grids to achieve the higher levels of optimization and efficiency Can handle large number of controllable devices (i.e., residential, commercial, and industrial consumers) and also achieves substantial savings to reduce the SG peak load demand The distributed grid can be operated within the regulated margins at all times, and guarantees to converge the optimal operational point Minimizes the impact of dynamic adjustments to the system frequency

5.

Advanced volt-var optimization (VVO) solution

6.

Heuristic-based evolutionary algorithm (EA)

7.

Ergodic energy management (EEM) framework

8.

Robust real-time distributed optimal control algorithm

SG-based demandside management (DSM) applications

Smart distribution networks (SDNs)

✓

Iran [133]

MATLAB

✓ 33-node distribution test feeder and MATLAB

Canada [134]

Singapore [135]

SG-based demandside management (DSM) applications

✓

Smart power inverters

✓ 56-bus distribution grid and the IEEE-123 bus feeder with MATLAB and CVX

United States [136]

Distributed generations and controllable loads

✓ IEEE-9 bus, 39-bus systems, 200-unit system, java agent development framework (JADE) platform and SIMULINK

China [137]

where c1, c2, c3, and c4 are constants. Moreover, its chiller power can be described by a simple control-oriented model, Pchiller ðt Þ P i i m y ðt Þ i where ym ðt Þ ¼ δ P þ ð1 _i m

_ ðt Þðym ðt Þ Cp m ZCOP

yC Þ

ð15Þ

δÞyo is the mixed air temperature, δA[0, 1] is the outside air fraction, Z is the efficiency factor of

i

the cooling coil, coefficient of performance (COP) of the chiller. The total power consumption of the commercial building HVAC system is given by P total ðt Þ ¼ P fan ðt Þ þ Pchiller ðt Þ

500

Smart Grid Energy Management

Aggregator

Generalized battery models

Commercial HVAC

Residential ACs

Energy storage

Fig. 29 Schematic of coordination of building loads and energy storage. HVAC, heating, ventilation, and air-conditioning. Reproduced from Hao H, Wu D, Lian J, Yang, T. Optimal coordination of building loads and energy storage for power grid and end user services. IEEE Trans Smart Grid 2017:1–11.

5.13.2.2.2

Residential building heating, ventilation, and air-conditioning model

The temperature dynamics of each AC (indexed by i) can be described by the following hybrid model Ci

dyi ðt Þ yo yi ð t Þ ¼ dt Ri

Si ðt ÞP i COPi þ wi ðt Þ

ð16Þ

where yi is the indoor air temperature, yo is the outside temperature, Ci, Ri are respectively its thermal capacitance and thermal resistance, and is the outside temperature and si is dimensionless binary variable that indicates the operating state of each AC (1 when it is ON and 0 when it is OFF). In addition, Pi is its rated power when it is ON, and COPi is its coefficient of performance. Each AC has a temperature set-point yi r with a hysteretic ON/OFF local control within a temperature band ½yi r Di =2; yi r þ Di =2Š. In the cooling mode, the operating state si ðtÞ evolves as 8 9 i i i=2 > > > < 0 if y ðt þ eÞoyr D > = Si ðt þ eÞ ¼ 1 if yi ðt þ eÞ4yir þ Di=2 > > > > : Si ðt Þ otherwise ;

where e{ 1 is a small time increment, and the continuous model is given by

dyi ðt Þ yo yi ð t Þ ¼ ui ðt ÞCOPi þ wi ðt Þ ð17Þ dt Ri i I i where each AC has continuous power input u A[0, P ] instead of binary power  input of {0, P }. Moreover, maintain the temperature yi(t) within the user specified temperature band yir Di \2; yir þ Di \2 is treated implicitly as a constraint on the power input. For residential ACs, their control input can be written in a vector u ¼(u1,...., un) and their total power consumption is given by Ci

P total ðt Þ ¼

n X

ui ðt Þ

i¼1

5.13.2.2.3

Energy storage system model

For each ESS, the energy state is modeled by 8 i 9 > P ðt ÞZiþ ; P i ðt Þ40; charging > = dxi ðt Þ < 1 ¼ i i > dt : P ðt Þ Zi ; P ðt Þo0; discharging > ;

ð18Þ

where xi(t) denotes the energy stored in the battery, pi(t) is the battery power measured at the grid connection point, and Zi þ A ð0; 1Þ and Zi A ð0; 1Þ are the charging and discharging efficiencies, respectively. Additionally, the charging/discharging power needs to satisfy, i P i rP i ðt ÞrPþ

ð19Þ

Smart Grid Energy Management

501

where pi þ 40 and pi 40 are its charging and discharging power limits. Moreover, the energy state of the ESS must be within the user-specified limits xi rxi ðt Þrxiþ

ð20Þ

where xi þ 40 and xi  0 are its upper and lower energy limits.

5.13.3

AC, DC, and AC/DC (Hybrid)-Based Microgrids Energy Management Analysis

RERs have been confirmed as a key tool to counter the climate change and enhance energy security. Developed and developing countries across the globe have been promoting this sector by several policy measures. According to Ref. [75], India is committed to fighting against the climate change and has created an agenda to install RERs (about 2,25,000 MW) by the year 2022, would probably be one of the fasted growing RERs installations in the world. RERs integration in our current energy supply system has its own special significance and should be employed to reduce pollution and the flow of greenhouse gases. Therefore, it is necessary to assess the energy scheduling DERs such as storage and advanced renewable technologies can help facilitate the transition to a much smarter grid. DERs problems (i.e., from different viewpoints, procedures, limitations, and objectives) can be solved by MGs [76] as shown in Fig. 30. MGs and virtual power plants (VPPs) both are termed as promising ingredients for SGs to integrate renewable energies in reliable and economic manner. The VPP surrounded with red circles in Fig. 31 is a set of geographically sparse DERs (i.e., HEMS, ESS, DG and electric vehicle (EVs)), which are aggregated and decided in away to perform/participate as a single power facility [77]. The VPP control strategies can be centralized, hierarchical or fully distributed. In order to achieve these requirements, the proposed geographical communication routing protocol such as GRACO, supports multipoint-to-point (M2P), point-to-multipoint (P2M), and point-to-point (P2P) communications. Grid

Grid

Communication and control Elecrical network

Isolated

Connected

Local control PCC

Storage

DG

PQ control

MC Microgeneration control MGCC Microgrid system central control

MGCC

LC

AC

DC

MC LC Storage

MC

Load Load

LC

Storage

DG sources

DG

Diesel or RE

Diesel and RE

With

Without

MC LC

MC MC DG

LC

Frequency

Load

Load

Line frequency (A)

High frequency

(B)

Fig. 30 (A) Microgrid (MG) architecture and (B) MG structure. Reproduced from Mariam L, Basu M, Conlon, MF. Microgrid: architecture, policy and future trends. Renew Sustain Energy Rev 2016; 64:477–89.

502

Smart Grid Energy Management

DSO

DR

Energy market

TVPP CVPP VPP management system

Fig. 31 Illustration of virtual power plant (VPP). The entities of the grid surrounded in red circles decided to join the VPP. DSO, distribution system operators. Reproduced from Rekik M, Chtourou Z, Mitton N, Atieh A. Geographic routing protocol for the deployment of virtual power plant within the smart grid. Sustain Cities Soc 2016;25:39–48.

5.13.3.1

Classification of Microgrids

MG is an effective solution for accessing of the DG system/network, which embodies various distributed power supplies, thermal/ battery storage systems, departments for load, supervisory system, and protection, measurement, and communication systems. The power balance of DG system/network and loads is achieved by local controlling, which also increases the efficiency of RERs. MGs can be categorized into four categories:

• • •

AC-based MG DC-based MG AC/DC-based hybrid microgrid (HMG).

Fig. 32 shows an AC-based MG system with combination of DGs such as PV, wind, fuel cell, and diesel. The battery has been used as the storage system, where AC and DC loads are being served. AC MGs systems normally are operated at line frequency, where DGs are connected to a common bus in MG system [76]. The generated DC current from the DGs are the transformed to 50 Hz AC by a suitable power electronics converter and through to the load side. DC-based MG architecture for residential/ commercial buildings applications is shown in Fig. 33. Here 300–400 V DC bus is the system backbone, which interconnects the sources and high power loads. The main DC bus can also be derived into several LV busses up to 48 V to supply low power loads such as consumer electronics and room lighting applications, and also provide a safer environment for the human occupants of buildings [78]. According to Ref. [79], the voltage gain and power handling capability of non-isolated DC/DC converters are employable for PV fed and DC-based grid/MG applications and can be classified into four categories

• • • •

Low gain low power (LGLP) DC/DC converters Low gain high power (LGHP) DC/DC converters High gain low power (HGLP) DC/DC converters High gain high power (HGHP) DC/DC converters.

With the help of proposed methodology, five hybrid HGHP converter topologies are capable of handling up to 3 kW for a particular application. Earlier traditional converters were able to connect only one type of DGs with different methods (i.e., multilevel converter, Z-source converter, and buck-boost converter [80]). But now these converters are being replaced by multiinput DC/DC converters, as shown in Fig. 34, which are able to connect with DG units such as wind turbines (WTs), solar panels, batteries, fuel cells, and micro turbines. Multi-input DC/DC converters are in practice for various applications (i.e., DC-based MG, battery charger, EV charging stations and DC-link of flexible alternating current transmission system (FACTS) devices. Nowadays AC/DC HMGs are becoming more attractive options than individual AC or DC-based MGs due to easier and less expensive connection of AC and AC DERs and loads to proper feeders [81]. HMG as the future distribution network is shown in Fig. 35,

Smart Grid Energy Management

Wind turbine

Sunny boy PV 1

503

Sunny boy PV 2

Sunny webbox Loads

ACI loads/sunny boys

Utility

Con in

Dig in AC2 Gen/grid

Relay 1/2 +)

Relay 1/2 Generator

BatTmp

BatCur

DC

Bat VigOut

DC load

Bat cur sensor BatteryTmp sensor

DC/DC controller Fuel cell

Battery

Charge controller

PV

Fig. 32 AC-based microgrid (MG) system. Reproduced from Mariam L, Basu M, Conlon MF. Microgrid: architecture, policy and future trends. Renew Sustain Energy Rev 2016;64:477–89.

which utilizes both benefits of AC and DC, renewable energy-based distributed generators, controllable generators and EES, and a utility interactive inverter (UII), and provides a bidirectional connection between AC and DC subsystems [82]. Nested EMS is useful for networked HMGs with dispatchable generators, renewable distributed generators, battery energy storage system (BESS), and electrical loads, which reduces the operation cost in grid-connected mode, and provides layered privacy to the customers in islanded mode [83]. The fuzzy logic controller (FLC)-based EMS design can minimize the grid power profile fluctuations of residential gridconnected MG, which also includes RERs [84]. The proposed FLC-based EMS design approach uses both the MG energy rate-ofchange and the battery state-of-charge (SOC) within secure limits (up to 75% of the rated battery capacity). The proposed FLCbased EMS design was experimentally tested on a real residential MG implemented at the Public University of Navarre (UPNa). The power architecture of residential grid-connected MG is shown in Fig. 36, which corresponds to modified INGECON HYBRID MS30 commercial power stage. The MG configuration as shown in Fig. 37, which includes a domestic AC load with a rated power of 7 kW, a PV array of 4 kW, a small WT of 6 kW, and an ESS formed by a lead-acid battery bank with a rated capacity of 72 kWh. Microgrid-energy-management-system (MEMS) as shown in Fig. 38, is associated with a policy, electricity market, load/DER/ price forecast, customers, utility, loads, and DERs in an MG [85]. MEMS receives the load and energy resource forecasting data, customer information/preference, policy, and electricity market information to determine the best available controls on power flow, utility power purchases, load dispatch, and finally preformation of DER scheduling. In Ref. [86], a multi-agent system-based distributed EMS was tested and validated on a hybrid renewable energy system (HRES) in National University of Singapore, to perform optimal energy allocation and management for renewable (i.e., PV arrays and WTs), storage and DG. The complete HRES architecture as shown in Fig. 39, which consists of conventional generators as the secondary source of energy, power supplies, EESs and a supervisory control and data acquisition (SCADA) system for data acquisition and monitoring applications. EMS optimizes the usage of energy resources by curtailing the operational costs and CO2 emissions to a minimum level. Hybrid energy system in Lancaster University campus’s hybrid energy system [73], as shown in Fig. 40, also has gained lots of attention due to the promising prospects of SGs. Various simulation tools like hybrid optimization model of electric renewable (HOMER), the hybrid power system simulation model (HYBRID2), and hybrid optimization using genetic algorithm (HOGA) are useful for the design/ optimization and performance improvement of the hybrid systems [87].

504

Smart Grid Energy Management

To high power loads

Renewable source =

=

Energy storage system

=

To AC loads

=

=

Utility Main DC bus = Communication network

Smart grid features

Low voltage DC bus

=

Power management controller

= To low power loads

Utility communication network

Internet, cloud server Fig. 33 DC-based microgrid (MG) architecture for residential and commercial applications. Reproduced from Oliveira TR, Silva WW, DonosoGarcia PF. Distributed secondary level control for energy storage management in dc microgrids. IEEE Trans Smart Grid 2016;PP:1–11.

Pwind

Ppv

Multi-input DC/DC converter

DC link

DC/AC converter

Pload

Pbattery

Pfuelcell

Fig. 34 The general form of multi-input converters. Reproduced from Khosrogorji S, Ahmadian M, Torkaman H, Soori, S. Multi-input DC/DC converters in connection with distributed generation units – a review. Renew Sustain Energy Rev 2016;66:360–79.

Smart Grid Energy Management

VLL=380 (Vrms)

UII =

VDC 100% recirculation

RTI

100%

< 100% by-pass

80% 60% 40% 20% 0 Case A

Case B

Case C

Rack power dissipation (W/rack) Fig. 60 Variation of return temperature index (RTI) of the rack rows for different power loading configurations.

Table 7

Details of experimental program

Ranges of controlling variable

Measured/studied variables

1. Power density (W m 2): 379, 759, and 1139. 2. Schemes of server’s cooling flow rate: a. Uniform cooling flowrates (case 1). b. Nonuniform flowrates specified for case 2. c. Nonuniform flowrates specified for case 3. d. Nonuniform flowrates specified for case 4.

1. Temperature distribution throughout data center room. 2. Servers’ surface temperature. 3. Supply/return heat indices (SHI and RHI).

effect of these two factors are investigated while other parameters of the data center (such as perforated tile opening ratio of 25% and blower discharge air temperature 131C) are kept constant; see Table 7 for more details. The first group of experiments was performed under conditions of homogenous power density with homogenous cooling air flow rates over different servers along the test rack, abbreviated as case 1. Then the cooling load over different servers has been scattered with the purpose of approaching uniform servers’ surface temperature, abbreviated to be case 2, case 3, and case 4. To properly show the influence of any proposed cooling load scheme on the modeled data center thermal analysis both groups of experiments have been plotted on the same relevant figures. As shown in Figs. 61–63, the use of homogenous cooling loads leads to almost uniform temperature distribution at the rack inlets with a slight increase in the servers’ front temperature due to the influence of heat generation servers on its surroundings (within 11C). At the rear of servers, the temperature is increased owing to heat transferred from servers with nearly ascending order as the exhaust gases receive more energy along the rack except at the top server where the influence of cooling air bypass leads to a slight decrease in the exhaust air temperature. This behavior is independent of data center room power density, as the overall cooling load is synchronized with the room power density to

696

Energy Management in Data Centers

Case (1)

Case (2)

Case (1)

Case (2)

Case (3)

Case (4)

Case (3)

Case (4)

Height (cm)

40 30 20 10 0 10

12

14

16 18 20 22 Temperature (°C)

24

26

28

Fig. 61 Temperature profile at front (solid lines) and at rear (dashed lines) of the rack at room power density of 379 W m 2.

Case (1)

Case (2)

Case (3)

Case (4)

Case (1)

Case (2)

Case (3)

Case (4)

Height (cm)

40 30 20 10 0 10

12

14

16 18 20 22 Temperature (°C)

24

26

28

Fig. 62 Temperature profile at front (solid lines) and at rear (dashed lines) of the rack at room power density of 759 W m 2.

Case (1)

Case (2)

Case (3)

Case (4)

Case (1)

Case (2)

Case (3)

Case (4)

Height (cm)

40 30 20 10 0 10

12

14

16

18

20

22

24

26

28

Temperature (°C) Fig. 63 Temperature profile at front (solid lines) and at rear (dashed lines) of the rack at room power density of 1139 W m 2.

maintain the servers’ surface temperatures within the allowable operating range (correspondingly the exhaust air temperature is nearly kept constant). From the first view, it can be decided the nonnecessity of additional thermal management as a suitable degree of homogenous temperature distributions are observed. But when inspecting the servers’ surface temperature (the main target to keep the high processing efficiency of data centers) shown in Figs. 64–66, a significant ascending temperature rise along the rack from bottom to

Energy Management in Data Centers

Case (1)

Case (2)

Case (3)

697

Case (4)

Servers temperature (°C)

140 120 100 80 60 40 20 0

10

15 20 Server height (cm)

25

Fig. 64 Variation of servers’ temperature along rack height for different scheme of server cooling at room power density of 379 W m 2.

Case (1)

Case (2)

Case (3)

Case (4)

Servers temperature (°C)

140 120 100 80 60 40 20 0

10

15 20 Server height (cm)

25

Fig. 65 Variation of servers’ temperature along rack height for different scheme of server cooling at room power density of 759 W m 2.

Case (1)

Case (2)

Case (3)

Case (4)

Servers temperature (°C)

140 120 100 80 60 40 20 0

10

15 20 Server height (cm)

25

Fig. 66 Variation of servers’ temperature along rack height for different scheme of server cooling at room power density of 1139 W m 2.

top is observed. This result is mainly due to the accumulation of energy absorbed by cooling air as the exhaust is directed from the bottom to the exhaust fan. This temperature rise varies according to the power density in the range of around 60–801C for the bottom server and varies from around 85–1351C for the top server. This discrepancy in servers’ surface temperature is known as heterogeneous temperature distribution and may lead to the overheating of local servers even if the exhaust air temperatures are kept within the designed value.

698

Energy Management in Data Centers

Power 379 W m−2

Power 759 W m−2

Power 1139 W m−2

Power 379 W m−2

Power 759 W m−2

Power 1139 W m−2

Mean temperature (°C)

100 20 90 15 80

10

70

60

5

1

2 3 Cooling scheme case number

4

Standard temperature deviation

25

0

Fig. 67 The variation of the mean temperature along the rack (solid lines) and the corresponding standard of variation (dashed lines) at the four studied cooling schemes.

Case(1)

Case(2)

Case(3)

Case(4)

0.2

SHI

0.15

0.1

0.05

0

379

759 1139 Room power density (W m−2)

Fig. 68 Variation of supply heat index (SHI) with data center room power density for all studied cooling schemes.

Case(1)

Case(2)

Case(3)

Case(4)

0.8

RHI

0.6 0.4 0.2 0

379

759 1139 Room power density (W m−2)

Fig. 69 Variation of return heat index (RHI) with data center room power density for all studied cooling schemes.

Energy Management in Data Centers

699

For the purpose of keeping surface temperatures of all servers within a narrow range of discrepancy, an additional thermal management procedure based on the cooling load of servers along the rack is proposed. In this regard, four mentioned schemes are studied to recommend the best one of them that would keep all servers almost at the same temperature while maintaining a high degree of temperature distribution at both the front and rear of the racks. In fact, any of these schemes can be realized as most of the servers’ house fans are currently programmable, and so fan power supply can be varied according to the server location along the rack. The first choice was to remove both effects of bypass cooling, and accumulation of energy in exhaust air occur at the top server, so most of the cooling air is directed to that server; case 2. Due to the blockage of cooling air at low-level servers, these servers are heated more and affect their surroundings, so both temperatures of inlet and exit of low-level servers are increased (see Figs. 61–66). As the cooling air mass flow rate over the top server is high, the absorbed heat leads to the lowest temperature rise of the exhaust air while its surface temperature is the lowest. This scheme to cool the data center leads to the descending temperature rise of the servers’ surface temperature, which should oppose the natural convection of heat transfer, correspondingly the other

3.18e+02 3.16e+02 3.15e+02 3.13e+02 3.11e+02 3.10e+02 3.08e+02 3.06e+02 3.05e+02 3.03e+02 3.01e+02 3.00e+02 2.98e+02 2.97e+02 2.95e+02 2.93e+02 2.92e+02 2.90e+02 2.88e+02 2.87e+02 2.85e+02 Contours of Static Temperature (k)

Sep 03, 2015 ANSYS FLUENT 14.0(3d,dp,pbns,rke)

3.18e+02 3.16e+02 3.15e+02 3.13e+02 3.11e+02 3.10e+02 3.08e+02 3.06e+02 3.05e+02 3.03e+02 3.01e+02 3.00e+02 2.98e+02 2.97e+02 2.95e+02 2.93e+02 2.92e+02 2.90e+02 2.88e+02 2.87e+02 2.85e+02 Contours of Static Temperature (k)

Sep 03, 2015 ANSYS FLUENT 14.0(3d,dp,pbns,rke)

Fig. 70 Temperature distribution at the (A) first and (B) middle racks with roof top containment.

700

Energy Management in Data Centers

RTI 160 Without roof containment With roof containment

140

RTI %

120 100 80 60 40 20 0

1

2

3

4

Rack no SHI 0.6 Without roof containment With roof containment

0.5

SHI

0.4 0.3 0.2 0.1 0.0 1

2

3

4

3

4

Rack no RHI 1.0

Without roof containment With roof containment

RHI

0.8 0.6 0.4 0.2 0.0 1

2 Rack no

Fig. 71 Effect of adding roof top containment of the cold aisle of layout 1 on the return temperature index (RTI), supply heat index (SHI), and return heat index (RHI) parameter.

schemes are studied. The third scheme was proposed as the middle servers were observed to have almost the same temperature at the rear of the rack, so equal air cooling flowrates are used; case 3. The use of case 3 reduces the blockage of cooling behind the front of the rack, so air is slightly heated but with a level lower than the previous cases (case 1 and case 2). Correspondingly the absorbed heat provides exhaust air at the rack rear with lower temperature no matter the applicable room power density (see Figs. 61–63). Under this scheme, even the servers’ surface temperatures of the bottom two servers are almost equal, and the top is still the lowest while the third one possesses the maximum temperature (see Figs. 64–66). So additional lowering for cooling air of the top server is needed with a slight increase of the third one in comparison with the second to compensate for the effect of buoyancy. Accordingly, the final fourth scheme is proposed (case 4). Case 4 exhibits the best temperature distributions among other studied cases as shown in Figs. 61–63. As the cooling air is uniformly sucked by the server fans with almost no blockage, the increase of the cold aisle temperature (due to the effect of hot servers on their surroundings) is the lowest, correspondingly the exhaust air temperature is also the lowest (as the overall cooling load is fixed) under all studied room power densities. From Figs. 64–66, it is observed that the surface temperatures of all servers are approximately equal due to the proper distribution of cooling air flow rates. The average surface temperature of all servers along the rack and the corresponding standard of deviation (to estimate the degree of discrepancy between local and mean values) are determined and plotted on Fig. 67. The following results can be observed:

Energy Management in Data Centers

701

RCI HI 120 Without roof containment With roof containment

100

RCI %

80 60 40 20 0 1

2

3

4

Rack no RCI LO 120 Without roof containment With roof containment

100

RCI %

80 60 40 20 0

1

2

3

4

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1. 2. 3. 4.

The mean temperature value is increased by the increase of the power density. Under uniform cooling flowrates, the standard deviation is the maximum among studied cases for all studied power densities. All the studied cases lead to a reduction in the average temperature but with a different standard of deviation. Case 4 provides not only the lowest average temperature but also with the minor standard of deviation indicating almost equal temperatures of all servers along the rack.

To test the economy of the cooling system of the data center, the performance metrics are determined: SHI and RHI. The recommended values of SHI are to be lower than 0.2 while that of RHI should be higher than 0.8. The computed values for SHI and RHI for all studied cases are shown in Figs. 68 and 69, respectively. It can be noticed that case 2 provides the worst data center thermal performance, while case 4 provides the best performance where SHI is the lowest (approaches 0.1), and RHI is the maximum (approaches 0.9).

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Enhancement of Data Center Energy Management Using Cold Aisle Containment

As discussed in the previous sections, the existence of hot air recirculation and cold air bypass adversely affects RTI and SHI and data center performance and energy efficiency and management. Hot air recirculation and cold air bypass can be avoided using aisle partitions and containments to make a complete physical separation between the hot and cold aisles as shown in Figs. 9 and 11. Recently, several CFD simulations and experimental works were conducted to study the effect of using aisle partitions and containments on data center performance and energy management. Fig. 70 gives the air temperature distribution at the rear and front of the terminal and middle racks of a rack row obtained in the CFD case study considering cold aisle containment on top of cold aisles. Comparison between this temperature distribution and the temperature distributions obtained without using top containment profess that installing containments on cold aisles eliminates the cold air bypass and hot air recirculation at the middle and terminal racks, respectively. Fig. 71 compares the performance parameters SHI, RHI, and RTI of the data centers with and without top roof containment. The figure shows that installing cold aisle top containment enhances the performance parameters; where a reduction of RTI and SHI by 18–20% occurred, and their values become within the recommended values (RTI ¼ 110% and SHI ¼ 0.2). This is attributed

Temp. at front for (under-floor air cooling) vs height Temp. at rear for (under-floor air cooling)at vs height Temp. at front for (under-floor air cooling+aisle partition) vs height Temp. at rear for (under-floor air cooling+aisle partition) vs height Temp. at front for (under-floor air cooling+aisle enclosure) vs height Temp. at rear for (under-floor air cooling+aisle enclosure) vs height

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to the fact that using top containments eliminates the existence of hot air recirculation and cold air bypass at the terminal and middle racks, respectively. Fig. 72 compares the performance parameters RCI of the two data centers with and without roof top containment of cold aisle. The figure reveals that adding cold aisle roof top containment enhances RCI; where 8–15% enhancing ratios of RCIHI were noticed moving the value of RCIHI of the data center to be within the recommended range (about 95%). This enhancement is also attributed to the absence of hot air recirculation and cold aisle bypass with using roof top containments of the cold aisles. Experiments were also conducted using the scale physical model to study the effect of adding cold aisle partitions and containments as shown in Fig. 73 on the thermal performance of data centers. For the sake of this study, three groups of experiments were carried out for a data center without partitions or containments, a data center with cold aisle partitions, and data center with cold aisle containments, respectively, as shown in Fig. 73. In the group of experiments the data center power levels was varied in the 379–1898 W m 2 by step 380W m 2. The opening percentage of the perforated tiles was set at 25% and the powers of all servers of the racks and the speed of the fans of the servers were maintained at the same value to achieve uniform server power scheme configurations.

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Fig. 79 Variations of return temperature index (RTI), supply heat index (SHI) with power density for the three air distribution system configurations.

Figs. 74–78 gives the air temperature distribution around the racks (at front and rear of the rack) and the servers’ surface temperature distribution for the different arrangements of using and not-using aisle partitions at different data center power densities. The distributions for the three arrangements are superimposed on the same figures for comparison purposes. For the three arrangements, air temperatures at the front and rear of the rack were recorded at different points along the rack height. The averages of these temperatures are used for the sake of comparison between the different configurations of using and not-using aisle partitions and containments. As discussed before, Figs. 74–78 show that for the three arrangements the air distribution around the racks and the server surface temperatures increase with the increase of the server power. The figures also show that for the three arrangements, there is a remarkable increase in the server’s surface temperature with increasing its height from the floor reaching its maximum value at the top cabinet of the rack; server 4 (H ¼25 cm). Figs. 74–78(A) show that using aisle partitions and aisle containments improve the rack intake average air temperature whatever the value of rack power. This can be attributed to the fact that the existence of aisle partitions as aisle containments prevents hot air from recirculating from the hot aisle to the cold aisle. For example, using aisle partitions and containment reduces the average air temperature at rack intake from 26.1 to 22.51C at 1898 W m 2 showing an improvement of 13%. Accordingly, the surface temperature of rack servers is expected to be decreased by using cold aisle partitions and containment as shown in Figs. 74–78(B); for example, the surface temperature of the server located in the top cabinet of the rack decreased by 11% when the

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data center power density was 1898 W m 2. The effects of the aisle partitions and containments on the surface temperature of the server located at the bottom rack cabinet are negligible as the hot air recirculation has approximately no effect on the server located in the bottom cabinet. Using cold aisle enclosures (cold aisle containments) to completely isolate the cold aisle from the hot aisle instead of aisle partitions further improves the rack intake temperature and the server surface temperature (see Figs. 74–78(B)); for example, the rack intake air average temperature drops from 26 to 22.11C at a power density of 1898 W m 2 due to using cold aisle containments. The trend is the same for all power densities. This further improvement can be attributed to the fact that using cold aisle containments completely prevents the hot air recirculation and cold air bypass between the hot aisles and cold aisles. The figures also show that using cold aisle containments leads to an improvement of cooling of all the servers of the racks with an overall enhancement of about 15.5% and not only a cooling improvement of the top server as in the case of using aisle partitions. Fig. 79 compares the data center performance and energy management as measured by SHI and RHI of the three arrangements of without using cold aisle partitions and containments, using cold aisle partitions, and using cold aisle containments. The comparison is given at different rack powers. As discussed before, the figure shows a remarkable decrease in SHI and remarkable increase of RHI occurs with increasing the data center power density, which indicates the improvements of the data center performance and energy management. The figure also shows that a small enhancement in the performance and energy management efficiency of the data center occurs after using cold aisle partition and the enhancement increases with increasing the data center power density; for example, SHI was enhanced by the range 13–62% when the data center power density varied in the range 379–1898 W m 2. Fig. 79 also shows that additional enhancement in the performance and energy management of a data center is obtained by using cold aisle containment/enclosure instead of aisle partitions. The figure shows that SHI is enhanced by 70% as a result of using cold aisles containments. The enhancement of the thermal performance of the data center due to using cold aisle enclosure increases with the increase in data center power density.

References [1] EPA. Report to congress on server and data center energy efficiency, Public Law 109-431, USA; 2007. [2] TC 9.9. Mission critical facilities, technology spaces, and electronic equipment, thermal guidelines for data processing environments. American Society of Heating, Refrigerating and Air-Conditioning Engineers Inc.; 2008. [3] Cho J, Lim T, Kim BS. Measurements and predictions of the air distribution systems in high compute density (internet) data centers. Energy Build 2009;41(10):1107–15. [4] LBNL and Rumsey Engineers. High-performance for high-tech buildings. Data center energy benchmarking case study. Facility Report 2003; 8. [5] Schmidt R, Karki K, Kelkar K, Radmehr A, Patankar S. Measurements and predictions of the flow distribution through perforated tiles in raised-floor data centers. In: Proceedings of the Pacific Rim/ASME International Electronic Packaging Technical Conference and Exhibition (IPACK’01), Kauai, Hawaii; 2001. [6] ASHRAE. Datacom equipment power trends and cooling applications. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc.; 2005. [7] Rasmussen N, Cooling strategies for ultra-high density racks and blade servers. Schneider electric – data center science center, White Paper 46, Revision 7; 2012. [8] Magnus KH. Thermal management in telecommunications central offices: Generic requirements NEBS GR-3028-CORE. Piscataway, NJ: Telcordia Technologies, Inc.; 2001. [9] U.S. Department of Energy, U.S. Data Centers. Save Energy Now: Industrial Technologies Program, DOE-energy efficiency and renewable energy; 2009. [10] Cho J, Kim BS. Evaluation of air management systems thermal performance for superior cooling efficiency in high-density data centers. Energy Build 2011;43:2145–55. [11] Kang S, Schmidt RR, Kelkar KM, Rdmehr A, Patankar SV. A methodology for the design of perforated tiles in raised floor data centers using computational flow analysis. ITherm 2000;1. doi:10.1109/ITHERM.2000.866828. [12] Schmidt R, Karki K, Kelkar K, Radmehr A, Patankar S. Measurements and predictions of the flow distribution through perforated tiles in raised floor data centers. In: Advanced in electronic packaging, proceedings of IPACK’01, and the pacific Rim/ASME international electronic packaging technical conference and exhibition, Kauai, vol. 2, Paper IPACK2001-15728; 2001.p. 905–914. [13] Karki K, Radmehr A, Patankar SV. Use of computational fluid dynamics for calculating flow rates through perforated tiles in raised-floor data centers. Int J Heat Vent AirCond Refrig Res 2003;9(2):153–66. [14] Abdelmaksoud WA, Khalifa HE, Dang TQ, et al. An experimental and computational study of perforated floor tile in data centers. ITherm; 2012. [15] Kim S. Effectiveness of specialized floor tile designs on air flow uniformity. Binghamton, NY: State University of New York; 2009. [16] Schmidt RR, Cruz E. Cluster of high-powered racks within a raised-floor computer data center: effect of perforated tile flow distribution on rack inlet temperatures. ASME J Electron Package 2004;126:510–8. [17] Cho J, Lim T, Kim BS. Measurements and predictions of the air distribution systems in high compute density (Internet) data centers. Seoul, South Korea: Yonsei University; 2009. p. 120–749. [18] Schmidt RR. Airflow uniformity through perforated tiles in a raised-floor data center. In: Proceedings of IPACK; 2005. [19] Bhopte S, Schmidt RR, Agonafer D, Sammaika B. Optimization of data center room layout to minimize rack inlet air temperature. J Electron Packag 2006; [20] Karki KC, Patankar SV. Airflow distribution through perforated tiles in raised-floor data centers. Plymouth, MN: Innovative Research, Inc.; 2005. [21] Karki KC, Patankar SV, Radmehr A. Techniques for controlling airflow distribution in raised-floor data centers. In: Proceedings of IPACK03, The Pacific Rim/ASME international electronic packaging. Technical conference and exhibition, July 6–11, Maui, Hawaii; 2003. [22] Sharma RK. Dimensionless parameters for evaluation of thermal design and performance of large-scale data centers. Hewlett-Packard Laboratories; 2002. [23] Nada SA, Said MA, Rady MA. Numerical investigation and parametric study for thermal and energy management enhancements in data centers buildings. Appl Therm Eng 2016;98:110–28. [24] Nada SA, Said MA, Rady MA. CFD investigations of data centers’ thermal performance for different configurations of CRACs units and aisles separation. Alexandria Eng J 2016;55(2):959–71. [25] Nada SA, Rady MA, Elsharnoby M, Said MA. Numerical investigation of cooling of electronic servers racks at different locations and spacing from the data center cooling unit. Int J Curr Eng Technol 2015;5(5):3448–56. [26] Tannehill JC, Anderson DA, Pletcher RH. Computational fluid mechanics and heat transfer. 2nd ed. Washington DC: Taylor & Francis; 1997. [27] Fernando H. Can a data center heat-flow model be scaled down. Parkville, VIC: The University of Melbourne; 2012. [28] Abdelmaksoud W. Experimental and numerical investigations of the thermal environment in air-cooled data centers [Ph.D. thesis]. Syracuse, NY: Department of Mechanical and Aerospace Engineering, Syracuse University; 2012. [29] White FM. Fluid mechanics. 4th ed New York, NY: McGraw-Hill; 2001.

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[30] Fernando H., Siriwardana J., Halgamuge S. Can a data center heat-flow model be scaled down? In: Information and automation for sustainability (ICIAfS) IEEE; 2012. p. 273–278. [31] Awbi H, Nemri M. Scale effect in room airflow studies. Energy Build 1990;14(3):207–10. [32] Rasmussen N. Cooling strategies for ultra-high density racks and blade servers, American Power Conversion, Washington, DC, White Paper. [33] Industrial Perforators Association. Designers, specifiers and buyers handbook for perforated metals. Milwaukee, WI: Industrial Perforators Association; 1993. [34] Premnath R. Qualitative air flow modeling and analysis of data center air conditioning as multiple jet array [Ph.D. dissertation]. Kerala: Cochin University of science and technology, kochi-68202; 2011. [35] Smith JF, Abdelmaksoud WA, Erden HS, et al. Design of simulated server racks for data center research. In: Proceedings of ASME InterPACK, Portland, OR, July 6–8; 2011. [36] Nada SA, Elfeky KE, Attia AMA. Experimental investigations of air conditioning solutions in high power density data centers using a scaled physical model. Int J Refrig 2015;63:87–99. [37] Nada SA, Elfeky KE, Attia Ali MA, Alshaer WG. Thermal management of electronic servers under different power conditions. Int J Emerg Trends Electr Electron (IJETEE) 2015;11(4):145–50. [38] Nada SA, Elfeky KE. Effects of servers racks location and power loading configurations on the thermal management of data centers racks array. J Therm Sci Eng Appl (ASME) 2016;9(4): doi:10.1115/1.4036009. [39] Nada SA, Elfeky KE. Experimental investigations of thermal managements solutions in data centers buildings for different arrangements of cold aisles containments. J Build Eng 2016;5:41–9. [40] Nada SA, Attia AMA, Elfeky KE. Experimental study of solving thermal heterogeneity problem of data center servers. Appl Therm Eng 2016;109:466–74. [41] Nada SA, Elfeky KE, Attia AMA, Alshaer WG. Experimental parametric study of cooling servers management in data center buildings. Heat Mass Transfer 2017; doi:10.1007/s00231-017-1966.y.

5.17 Energy Management in Wind Energy Systems Eftichios Koutroulis, Technical University of Crete, Chania, Greece r 2018 Eftichios Koutroulis. Published by Elsevier Science Inc. All rights reserved.

5.17.1 Introduction 5.17.2 The Operational Characteristics of Wind Turbines 5.17.3 Power Converters and Generators for Wind Energy Conversion Systems 5.17.4 Maximum Power Point Tracking in Wind Energy Conversion Systems 5.17.5 Energy Management in Stand-Alone Systems With Wind Energy Conversion Systems 5.17.6 Energy Management in Microgrids With Wind Energy Conversion Systems 5.17.7 Energy Management in Grid-Connected Wind Energy Conversion Systems 5.17.8 Case Study 5.17.9 Closing Remarks References Relevant Websites

5.17.1

707 709 711 715 722 729 732 738 739 739 741

Introduction

The cumulative wind capacity installed all over the world has exhibited an exponential increase during the past 15 years, reaching approximately 433 GW by 2015. The total installed power capacity of wind energy production systems during 2015 exceeded 63 GW, corresponding to a cumulative growth which is higher than 17% and also approximately half of the global electricity growth [1]. China, United States, and Germany were the top countries in terms of the amount of new installed capacity during that year. Since wind generation is now considered as a mature and mainstream form of energy production, this growth is anticipated to continue in the next years. The structure of a wind energy conversion system (WECS) is illustrated in Fig. 1 [2,3]. The mechanical subsystem comprises the wind turbine (also commonly referred to as “wind generator”) blades, gearbox, and shaft, which rotates due to wind flow on the wind turbine blades. Thus, wind energy is converted to mechanical energy, which rotates the wind turbine shaft. An example of the wind speed variation during an hour of a day, which has been measured by an anemometer, is depicted in Fig. 2. The wind speed varies continuously, which affects the corresponding energy production of the wind turbine and the design of the wind turbine energy management system, as will be analyzed next. Depending on the orientation of their rotation axis with reference to the ground level, wind turbines are classified into horizontal- and vertical-axis types, with the former dominating in modern WECS because of their higher efficiency [4]. Modern large-scale wind turbines feature lengths of blades which can exceed 80 m. An electric generator is coupled to the shaft of the wind turbine for converting the wind energy captured by the mechanical subsystem to electric energy. When the wind turbine speed of rotation is low (e.g., in modern, multi-MW wind turbines), a gearbox is used to link the shaft of the wind turbine with the electric generator, thus increasing the speed of the generator shaft and reducing its torque [5]. Such configurations are referred to as indirect-drive WECSs. In contrast, in direct-drive WECSs, a gearbox is not used [6]. The electric energy that is produced by the generator is then transferred to the electric load of the WECS (e.g., battery bank and electric grid) through a power electronic converter which adapts the amplitude/frequency of the voltage and current produced by the generator to a form suitable for power supplying the load while regulating the corresponding power flow. In the case of grid-connected WECSs, a transformer is also

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employed for connecting the power converter output to the electric grid by adapting the voltage level of the power converter output to that required by the electric grid [5,7]. Frequently, multiple (typically) large-scale wind turbines, which are interconnected with the electric grid, are installed in a site, forming a wind farm (or wind park) [4]. Also, depending on the type of the installation field, WECSs are divided into onshore systems, which are installed on land, and offshore systems, which are installed on specially designed and constructed foundations in the sea. WECSs are used in various energy production schemes. Small-scale wind turbines are mainly used in stand-alone systems for providing power to isolated loads (e.g., remote houses and telecommunication systems) without connection to the electric grid. Distributed generation has also evolved during the past years, where the energy production units and storage devices are decentralized and installed close to the consumers, thus reducing the power supply dependence from the distribution and transmission network. Distributed generation aims to increase the degree of renewable energy sources’ (RESs) penetration into the electric networks and therefore enables one to limit the dependence from fossil fuels and reduce the environmental pollution caused by the (conventional) thermal generation of electric energy. Additionally, distributed generation reduces the requirements of constructing additional central generation and transmission infrastructure for covering the continuously growing electricity demand, increases the electric energy supply reliability, and contributes to the achievement of power quality by supporting the voltage/frequency adjustment process [8]. WECSs are also employed in microgrids, which are local and of small-scale integrated power systems, comprising energy generation units (conventional and renewable), energy storage structures, and consumers, which are interconnected through a common distribution system. Microgrids can be either interconnected with the main electric grid or operate isolated from it. Furthermore, the development of smart grids where WECSs are integrated has evolved during the past years. Smart grids comprise multiple types of energy production units (conventional and renewable), distributed energy production and storage, power electronic converters that regulate the power flow, as well as energy consumers (conventional or new types such as electric vehicles), which are connected to various levels of the electric network (distribution, low voltage, etc.). Additionally, they incorporate intelligent communication and control for ensuring the reliability, sustainability, and economic viability of the electric energy supply [9]. In all of the aforementioned applications, the targets of the WECS energy management process are the following: (1) to maximize the wind turbine energy production and (2) to transfer the wind-generated energy to the consumer with high efficiency and also with the required power quality (e.g., output-voltage amplitude and frequency within the specifications) as set in regulations and international standards. The energy management process is continuously executed, and simultaneously, it must conform to the operational constraints imposed by the energy production/storage systems that the WECS operates in coordination with (e.g., the electric grid, batteries, diesel generators, etc.). The energy management process of a WECS is implemented by the following: (1) selecting the appropriate architecture of the power conversion and conditioning system, as well as the types and topology of the hardware devices that it comprises (i.e., generator and power converters) and (2) applying the appropriate control strategy for continuously extracting the maximum possible power from the wind turbine and adjust its flow from the wind turbine to the load. Thus, in order to implement an energy management process, a microelectronic unit (e.g., using digital signal processors, microcontrollers, etc.) must be used for controlling the operation of the WECS subsystems (Fig. 1) in terms of the following basic functions:

•

• • •

Regulation of the wind turbine power generation through pitch and yaw control. [10] In the case of a pitch control scheme, the blades’ angle is regulated during high wind speed time intervals, such that the power produced by the wind turbine is restricted to its rated value. In yaw control, the wind turbine nacelle is oriented toward the direction of the wind, based on measurements acquired by an anemometer. Maximization of the power generated by the wind turbine through a maximum power point tracking (MPPT) technique, by properly adjusting the generator torque and speed of rotation. Regulation of the power flow between the wind turbine, the consumer load, and the complementary energy generation/storage devices. Control of the power electronic converter switching devices (e.g., insulated gate bipolar transistors (IGBTs), metal oxide semiconductor field effect transistors (mosfets), etc.) according to the pulse width modulation (PWM) technique.

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In the case of grid-connected WECS, the control unit performs additionally the following operations:

• •

synchronization of the power electronic interface with the electric grid and execution of appropriate actions in case of grid malfunctions (e.g., disconnection, low-voltage ride-through (LVRT) operation, etc.) and management of the active and reactive power supplied to the electric grid according to the commands received by the operator of the distribution/transmission system (DSO/TSO) that the WECS has been interconnected with (e.g., for supporting the frequency/voltage of the electric grid).

The aim of this chapter is to provide an overview of the energy management techniques applied in stand-alone systems, microgrids, and electric grids incorporating WECSs. For that purpose, the application of energy management processes is examined in terms of both the WECS hardware architecture employed and the control methods that are applied. Initially, in Section 5.17.2, the main operational characteristics of wind turbines are analyzed, since they define the operating principles and constraints of the energy management processes that can be implemented in WECS. Topologies of generators and power electronic converters for WECS, which comprise the power conversion and processing interface between the wind turbine and the electric load, are presented in Section 5.17.3. The MPPT techniques, which enable the maximization of the power that is produced by the wind turbine, thus being a major component of a WECS energy management system, are then analyzed in Section 5.17.4. In Sections 5–7, energy management configurations for WECS integrated in stand-alone systems, microgrids, and electric grids (i.e., gridconnected WECSs), respectively, are presented. Finally, in Section 5.17.8, concluding remarks are discussed.

5.17.2

The Operational Characteristics of Wind Turbines

According to Betz’s law, the maximum possible percentage of the power available in the air that can be captured by a wind turbine is 59.26%. The mechanical output power of a wind turbine, Pw, is calculated as follows [11]: PW ¼

1 1 rACp ðl; bÞvw3 ¼ rpR2 Cp ðl; bÞvw3 2 2

ð1Þ

where r is the air density (1.225 kg/m3 at sea level and 151C), R (m) is the radius of the blades, A¼ pR2 is the swept area of the blades (m2), Vw (m/s) is the wind speed, and Cp(l,b) is the power coefficient of the turbine, which depends on the tip–speed ratio l and the blades’ pitch angle b (degrees). Therefore, considering Betz’s limit, the maximum possible value of Cp is 0.5926. The tip–speed ratio is given by the following equation: l¼

O
R vw

ð2Þ

where O (rad/s) is the rotational speed of the wind turbine shaft. The Cp coefficient can be approximated by a mathematical function of the following form [12]:   a5 a2 a3
b a4
e l c þ a6
l Cp ðl; bÞ ¼ a1
lc

ð3Þ

where 1 1 ¼ lc l þ a7
b

a8 1 þ b3

ð4Þ

and a1–a8 are constants, whose values depend on the wind turbine aerodynamic design. An example of the shape of the power coefficient curve for a given value of b is illustrated in Fig. 3. The value of Cp is maximized for a single value of l (i.e., lopt in Fig. 3), where the power extraction efficiency is maximized. Thus, considering (2), the wind turbine rotational speed can be calculated by using the following equation: Oopt ¼ lopt

VW R

ð5Þ

where Oopt is the optimal rotational speed when the wind speed is equal to VW. The output power versus rotational speed curves of a wind turbine under various wind speed conditions are plotted in Fig. 4, indicating that the output power of a wind turbine depends on its rotational speed and the wind speed. Fixed-speed wind turbines operate at a single/constant rotational speed, while in variable-speed WECSs, the wind turbine speed is controlled by regulating the electric generator torque. Also, it is observed that for each value of wind speed, the power produced by the wind turbine is maximized at a single point that is typically referred to as “maximum power point” (MPP). The optimal tip–speed ratio value, lopt, is constant for all possible MPPs of the wind turbine power–rotational speed characteristics. The locus of MPPs under all possible wind speed values form the maximum power production curve of the wind turbine, PW,MPP, which is given by the following: PW;MPP ¼ k
O3 where k is a constant depending on the wind turbine aerodynamic design.

ð6Þ

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Energy Management in Wind Energy Systems

Power coefficient (Cp)

Cp,opt

opt Tip seed ratio () Fig. 3 The power coefficient curve of a wind turbine for a given value of b.

Wind turbine power, PW (W)

Locus of MPPs (PW,MPP)

MPP

V1

Wind speed: V1 >
Dt during charging > < CB ðt 1Þ þ VDC CB ðtÞ ¼ ð19Þ PB ðtÞ=nB;d > >
Dt during discharging > : CB ðt 1Þ þ V DC

where CB(t) and CB(t 1) (Ah) represent the battery capacity at time steps t and t 1, respectively; nB,c and nB,d (%) represent the battery efficiency during charging and discharging, respectively; PB(t) is the power flowing in/out of the battery bank (i.e., PB(t)40 during charging and PB(t)o0 during discharging); VDC is the DC-bus voltage level, which is equal to the nominal voltage of the battery bank; and Dt is the time step.

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Energy Management in Wind Energy Systems

In battery energy storage systems, the charging/discharging power levels must be lower than the corresponding maximum permissible limits that are defined by the battery manufacturer. The battery bank nominal capacity, CB,nom (Ah), is given by the following: CB;nom ¼ nB;p
Cb

ð20Þ

where nB,p is the total number of battery strings connected in parallel and Cb (Ah) is the nominal capacity of each battery. The load power consumption, PL(t), is related to the DC/AC inverter input power, PINV(t), as follows: PL ðtÞ ¼ nINV
PINV ðtÞ

ð21Þ

where nINV (%) is the DC/AC converter efficiency. Thus, the net power transferred to the battery bank is given by the following: PB ðtÞ ¼ PRES ðtÞ

PINV ðtÞ

ð22Þ

where PRES(t) is the total power produced by all power generation blocks of the hybrid RES system. Therefore, if PRES(t)4PINV(t) then the surplus energy PRES(t) PINV(t) charges the battery bank till it reaches the 100% state of charge, while if PRES(t)oPINV(t) then the battery bank is discharged by an amount of PINV(t)–PRES(t) in order to cover the load power requirements till it reaches the minimum permissible remaining capacity CB,min. The energy management of a stand-alone hybrid wind/PV system comprising a lead-acid battery storage unit has been investigated in Ref. [34]. As shown in Fig. 28, in that system, the power flow between the power production/storage devices and consumer loads is regulated by turning ON and OFF the electromechanical switches (i.e., relays) S1–S5. A control unit executes an energy management algorithm, which, at each sampling instant, derives the state (i.e., ON/OFF) of switches S1–S5 and the optimal amount of power of each source (i.e., wind turbine, PV, and battery), such that the power demand of the DC and AC loads is totally covered and simultaneously the total operational cost of the hybrid system is minimized. For that purpose, at each sampling instant, the energy management algorithm initially estimates the RES-generated power by measuring the solar irradiation, ambient temperature, and wind speed that prevail and using appropriate models of the wind turbine and PV sources. Also, it measures the loads power, as well as the battery bank current in order to estimate its state of charge. Then, the state of switches S1–S5 is derived by a fuzzy-logic-based control algorithm. Finally, a cost minimization algorithm is executed, which is based on genetic algorithms, that calculates the optimal amount of power of each source (i.e., wind turbine, PV, and battery) required for totally covering the consumer load requirements with the minimum total operational cost of the hybrid system (e.g., because of battery replacements in case of high depths of battery bank discharge). In Ref. [35], a hybrid system comprising a wind turbine with DFIG, a PV generator, and a lead-acid battery storage unit has been considered. The main electric grid is also used as a backup energy source. Targeting to reduce the dependence on the electric grid, management of the consumer loads is performed, which is based on the assignment of three different priorities to the loads according to their importance (e.g., priority #1 (highest): house loads, priority #2: cooling system, and priority #3 (lowest): pump). The loads are switched on and off according to their priority and the state of charge of the battery bank. Another configuration of a stand-alone hybrid energy production system that comprises a wind turbine, a PV generator, a leadacid battery bank, and a proton exchange membrane (PEM) fuel cell, which is used as a backup power source, is depicted in Fig. 29 [36]. The hydrogen produced by an electrolyzer, which is connected in parallel with the AC loads, is stored in a tank for feeding the fuel cell. The power flow between the power production and storage devices of the system and the AC loads to be supplied at time DC/DC converter with MPPT control

PV array

S4 S1

S5

DC/AC converter AC load

Control unit Generator Wind turbine

AC/DC converter with MPPT control

S2

Battery bank

S3

DC load Fig. 28 A stand-alone hybrid wind/photovoltaic (PV) system comprising a lead-acid battery storage unit and electromechanical switches controlling the power flow between the power production/storage devices and consumer loads. Reproduced from Yahyaoui I, Yahyaoui A, Chaabene M, Tadeo F. Energy management for a stand-alone photovoltaic-wind system suitable for rural electrification. Sustain Cities Soc 2016;25:90–101.

Energy Management in Wind Energy Systems

727

DC bus Generator AC/DC converter

Wind turbine

Battery bank AC bus DC/DC converter

PV array

AC loads DC/AC converter

DC/DC converter

Fuel cell

Hydrogen supply Electrolyzer Hydrogen storage tank Fig. 29 A stand-alone hybrid energy production system that comprises a wind turbine, a photovoltaic (PV) generator, a lead-acid battery bank, and a proton exchange membrane (PEM) fuel cell. Reproduced from Dursun E, Kilic O. Comparative evaluation of different power management strategies of a stand-alone PV/Wind/PEMFC hybrid power system. Int J Electr Power Energy Syst 2012;34:81–9.

instant t depends on the battery bank state of charge SOC (%), which is calculated as follows: Z t IB ðtÞ dt SOCðtÞ ¼ SOCðt0 Þ þ t0 CB;nom

ð23Þ

where CB,nom is the total nominal capacity (Ah) of the battery bank and IB(t) is the battery bank charging/discharging current (A). The value of IB is considered to be positive when the battery is charged and negative during battery discharging, and it is calculated considering the power balance in the overall system, as follows: IB ¼

PPV Zc1 þ PWG Zc2 þ PFC Zcf VB

PL =Zinv

ð24Þ

where PPV and PWG represent the power produced by the PV generator and wind turbine, respectively; PFC is the power provided by the fuel cell; PL is the power consumed by the loads of the DC/AC inverter; Zc1 and Zc2 represent the efficiency of the power converters of the PV source and wind turbine, respectively; Zcf is the efficiency of the fuel cell power converter; Zinv is the efficiency of the DC/AC inverter; and VB is the battery bank voltage. An example of an energy management strategy that can be applied in such a hybrid system is the following: in case that the total power production of the PV generator and wind turbine is higher than the power required by the AC loads and also the battery bank is not fully charged, then the electrolyzer will operate and the battery bank will be charged. In case that the battery bank is fully charged, then the excess RES power is provided to the electrolyzer exclusively, till the hydrogen storage tank is completely filled. If the total power produced by the RES units is lower than the power required by the load, then the battery bank and the fuel cell will provide the required power till the state of charge of the battery bank drops below the lowest permissible limit (e.g., 20%), or the stored hydrogen has been depleted, respectively. A bioethanol reformer is employed in a stand-alone hybrid wind/PV system in Ref. [28] for providing hydrogen to a PEM fuel cell, which together with RES sources and a battery bank provides power to the load (Fig. 30). PEM fuel cells have the advantage of higher efficiency and pollution-free operation compared with other types of generators (e.g., diesel generators). Bioethanol has the advantages of high hydrogen content and safety of storage and can be produced by using biomass residues (e.g., available from

728

Energy Management in Wind Energy Systems

DC bus Generator Wind turbine

AC/DC converter Bidirectional DC/DC converter

PV array

Battery bank

DC/DC converter

DC/AC converter

Fuel cell

AC load

DC/DC converter

Hydrogen supply Bioethanol reformer

Bioethanol supply Fig. 30 A stand-alone hybrid wind/photovoltaic (PV) system comprising a bioethanol reformer for providing hydrogen to a proton exchange membrane (PEM) fuel cell. Reproduced from Feroldi D, Degliuomini LN, Basualdo M. Energy management of a hybrid system based on wind-solar power sources and bioethanol. Chem Eng Res Des 2013;91:1440–55.

agricultural applications). The power balance equation in the system of Fig. 30 is the following: PL ðtÞ ¼ Z1
½Z2 PPV ðtÞ þ Z3 PWG ðtÞ þ Z2 PFC ðtÞ þ Z2 PB ðtÞ

PRH ðtފ

ð25Þ

where PPV, PWG, PFC, and PB are the output power of the PV generator, wind turbine, fuel cell, and battery bank, respectively; PRH is the power consumption of the bioethanol reformer heater; and Z1, Z2, and Z3 are the efficiency of the DC/AC, DC/DC, and AC/DC power converters, respectively (typically their values are assumed to be constant). An energy management strategy which can be applied in this hybrid system is the following:

• • • •

an MPPT process is performed to the wind turbine for covering as much as possible of the power required for satisfying the load demand and charging the battery bank, while the rest is covered by the PV source. in case that the wind turbine and PV sources are not capable of covering the load power demand, an MPPT process is applied to both of them for maximizing their power production, while the rest of the power demanded by the load is covered by the battery bank. when the battery bank has been fully charged, then the power production of the wind turbine and PV sources is suspended and the battery bank provides power to the load. when the battery bank has been discharged, then the fuel cell and the reformer are set to operate. The energy demand of the bioethanol reformer heater is covered by the wind turbine and PV sources. Also, the battery bank is charged in this case.

Multigeneration energy systems are capable of producing simultaneously several different products (e.g., electric energy, heat, hydrogen, etc.) by using one or more energy sources (e.g., solar collectors, biomass-based energy production units, etc.) [37]. By recovering the waste energy (e.g., heat) produced by individual processes that are executed within a multigeneration system, the overall efficiency is increased, compared with an energy system that is based on separately operating units [38]. In Ref. [38], a wind

Energy Management in Wind Energy Systems

729

turbine is incorporated in a multigeneration system for the production of electricity supplied to a building. Also, the electricity surplus is forwarded to an electrolyzer producing hydrogen, which is then stored in a tank.

5.17.6

Energy Management in Microgrids With Wind Energy Conversion Systems

In the case of microgrids interconnected with the main electric grid, the operating voltage and frequency are defined by the main grid, while in the case of isolated microgrids (also referred to as islanded operation), a local generator must control the microgrid voltage and frequency. For that purpose, WECSs with DFIGs can be controlled according to the indirect stator flux orientation (ISFO) technique in order to operate as a controllable voltage/frequency source, where the power produced is defined by the load, similarly to conventional power generation units [8]. Autonomous microgrids (i.e., not connected with the main electric grid) may employ a diesel generator for compensating the power production fluctuation of the wind turbines that they comprise. Also, battery energy storage units can be used to store any energy surplus available and release it back to the microgrid loads when required. Battery storage units can assist such configurations to achieve the energy production-demand balance under dynamically changing operating conditions (due to, for example, changes in wind speed, load power requirements, etc.). An energy management technique of a wind farm controlling the voltage and frequency of an isolated microgrid is presented in Ref. [8]. The microgrid under consideration comprises a wind farm with ISFO-controlled wind turbines and droop control, which adjust the voltage and frequency of the local grid, as well as an auxiliary generator (e.g., using fossil fuels), a DC energy storage unit (any type of storage capable of providing/accepting DC electric energy can be used in this scheme), and a controllable AC load (Fig. 31). The energy management scheme employed for this configuration operates as follows: in case that the consumer power demand is higher than the power generated by the wind farm, then the deficit is covered by the energy storage system. When the energy available in the energy storage system drops below a predefined level, then the auxiliary generator covers the energy deficit. When the wind-generated power is higher than the consumer demand, then any surplus of energy is stored in the energy storage system. When the stored energy reaches a predefined threshold, pitch angle control of the wind turbines is initially applied for reducing the corresponding power production. When the stored energy reaches a second threshold, then the controllable load is activated to absorb the energy surplus. In this energy management scheme, the electrical torque of the DFIG, which cannot be controlled when applying the ISFO technique, is controlled for performing the MPPT process of the wind turbine by adjusting the power of the energy storage unit, according to the following power-balance equation: PWG ¼ PES þ PL

ð26Þ

where PWG is the output power of the wind turbine generator, PES is the power provided to the energy storage system, and PL is the consumer power demand. The DFIG electrical torque is calculated as follows: TWG ¼ PWG =OWG

ð27Þ

where OWG is the DFIG shaft speed. Thus, according to Eqs. (26) and (27), by controlling the value of PES, the value of PWG and, consequently, the value of TWG can also be regulated such that operation at the MPP of the wind turbine is achieved. In order to provide electric energy to consumers in isolated areas, where an electric grid is not available, the remote area power supply (RAPS) systems are frequently employed, which are implemented in the form of a stand-alone microgrid (i.e., not interconnected with the main electric grid) [39]. A RAPS system comprising a WECS is depicted in Fig. 32 [40]. This RAPS system comprises a wind turbine coupled to a DFIG. A fuel cell, which is supplied by a hydrogen tank, and an electrolyzer have been coupled to the DC-link of the power converters controlling the DFIG. Also, the DFIG electrical output is connected to the output of a diesel generator, both providing power to the isolated AC loads. The diesel generator has been designed such that it is capable to operate in either a power generation mode or a synchronous-condenser mode and aims to adjust the RAPS output-voltage amplitude and frequency to the desired values. Also, the diesel generator is prohibited from operating during light load conditions, in order to achieve a reduction of the fuel cost, which is higher at light loads. In order to maximize the wind-generated energy production, an MPPT process is performed, which is based on regulating the operation of the fuel cell, electrolyzer, and diesel generator such that the total torque set on the DFIG is equal to that of the corresponding MPP of the wind turbine. The reactive power demand of the AC loads is covered by the diesel generator. The active power of the AC loads is provided by the wind turbine and any power surplus is transferred to the electrolyzer. Power balance is achieved through pitch control of the wind turbine, in order to regulate its power production. If the wind turbine output power is less than that required by the load, then the diesel generator covers the power deficit, unless the deficit is lower than a predefined percentage of the rated demand of the load (e.g., 20%). In this case, the deficit is covered by the fuel cell, while the diesel generator operates in the synchronous-condenser mode. In case that power is not produced by the wind turbine (due to e.g., low wind speed, malfunctions, etc.), load shedding can be applied such that only the most important AC loads are power supplied by the RAPS. Another RAPS scheme, employing a PMSG wind turbine and a hybrid energy storage, which comprises a nickel–cadmium battery bank and a supercapacitor, for handling any sudden changes of either the wind-generated power or the AC loads demand, is shown in Fig. 33(A) [41]. The use of energy storage in an isolated power supply system not only enables one to maintain power balance but also aims to decrease the fuel consumption of the diesel generator that may be employed, while simultaneously

730

Energy Management in Wind Energy Systems

Wind farm AC bus Doubly fed induction generator

Transformers

Wind turbine #1

DC/AC and AC/DC converters with ISFO control

Bidirectional AC/DC converter

DC energy storage unit

Auxiliary generator with fossil fuel

Wind turbine #2

DC/AC and AC/DC converters with ISFO control

AC loads

Controllable AC load

Wind turbine #N

DC/AC and AC/DC converters with ISFO control

Fig. 31 A microgrid comprising a wind farm with indirect stator flux orientation (ISFO)-controlled wind turbines, an auxiliary generator, an energy storage unit, and a controllable AC load. Reproduced from Fazeli M, Asher GM, Klumpner C, Yao L, Bazargan M. Novel integration of wind generator-energy storage systems within microgrids. IEEE Trans Smart Grid 2012;3:728–37.

guaranteeing uninterruptible power supply to the load. Additionally, in contrast to supercapacitors, the service lifetime of batteries is highly degraded as the number of charge/discharge cycles is increased [42]. Thus, the use of hybrid energy storage configurations employing batteries and supercapacitors aims to extend the battery service lifetime, as analyzed in the following. In the system of Fig. 33(A), the wind turbine is the primary source of power, while the energy storage and dump load are employed for preserving power balance in the system. A synchronous condenser is also used for providing the reactive power required by the AC loads, supplementary to the reactive power provided by the DC/AC inverter. If the wind-generated energy is higher than the AC loads demand, then the surplus is stored in the energy storage units. When both types of energy storage are fully charged (i.e., the battery state of charge reaches its maximum permissible value and the supercapacitor voltage reaches its maximum value permitted), then a dump load (e.g., water heater) is activated to absorb the corresponding surplus. If the power absorbed by the dump load reaches its maximum permissible limit, then the wind turbine active power production is reduced through pitch angle control. In case that the wind-generated power is less than that required by the load, then the deficit is covered by the energy storage units. The DC/AC inverter adjusts the amplitude and frequency of the voltage supplied to the AC loads. The wind turbine is connected to an

Energy Management in Wind Energy Systems

731

Doubly fed induction generator Wind turbine

AC loads DC-link capacitor Three-phase DC/AC converter

Three-phase AC/DC converter Diesel generator

Fuel cell

DC/DC converter

DC/DC converter

Hydrogen supply Electrolyzer Hydrogen storage tank Fig. 32 A remote area power supply (RAPS) system comprising a wind energy conversion system (WECS). Reproduced from Mendis N, Muttaqi KM, Perera S. An effective power management strategy for a wind-diesel-hydrogen based remote area power supply system to meet fluctuating demands under generation uncertainty. In: 2013 IEEE industry applications society annual meeting; 2013. p. 1–8.

uncontrolled rectifier, which feeds a boost-type DC/DC converter controlling the DC-link voltage, where the energy storage devices are also connected through bidirectional DC/DC converters. The wind turbine MPPT process is implemented by controlling the power flow of the two energy storage units, such that the wind turbine speed is adjusted to its optimal value. In order to regulate the power flow of the energy storage units, the controller illustrated in Fig. 33(B) is employed. A high-pass filter is used to split the difference between the wind turbine power production and the load power consumption into two parts: the high-frequency component (of frequency typically higher than 0.5 Hz) is used to control the operation of the supercapacitor DC/DC power converter, while the low-frequency component is used for controlling the power converter of the battery bank. In this control scheme, initially the wind turbine MPP output power is estimated by using Eq. (6). Then, the difference between the MPP power of the wind turbine and the power consumption of the AC loads is calculated. The resulting difference is used to produce the control signals of the DC/DC converters which interconnect the battery and supercapacitor banks, respectively, with the DC-link capacitor (i.e., control signals S1 and S2 in Fig. 33(A)). Thus, the supercapacitor supports the high-frequency mismatch between generation and demand, while the battery bank regulates the corresponding low-frequency component. Through this technique, the battery bank operation with high-frequency currents and depths of discharge at high rates is avoided, thus aiming to extend its service lifetime. An isolated microgrid comprising a wind farm, a lithium-ion battery energy storage unit, a diesel generator, and AC loads is presented in Fig. 34 [43]. The target of an energy management system in such a configuration is to control the power flow such that the system stability and reliability are ensured with the minimum possible operational cost [44]. Therefore, dynamic programming can be applied for calculating the optimal power flow between the energy production/storage units and the microgrid AC loads (i.e., scheduling how much power will be produced/absorbed by each unit at each time instant), such that the microgrid operational cost is minimized [43]. In Ref. [43], this process is based on the use of time series of wind speed predictions a few hours ahead (e.g., 24 h), which have been derived by using a Kalman filter. The ability of wind speed forecasting and then scheduling the power flow is important for the energy management system operation, since if it is known in advance that the wind speed will be lower in the subsequent time interval, then the diesel generator can be requested to cover any energy deficit instead of the battery bank, in order to ensure that adequate stored energy will be available when the wind-generated energy will be low. The energy management system used in Ref. [45] performs the optimal power production scheduling for each hour of a day (24 h) in a microgrid which is interconnected with the electric grid and consists of a wind turbine, a PV generator, a fuel cell, a micro-turbine, and a battery-based energy storage unit. For that purpose, the energy management algorithm calculates the optimal power production of the energy sources and energy storage unit, as well as the optimal amount of power exchanged with the electric grid, such that the total operational cost and total pollutant atmospheric emissions (i.e., NOx, SO2, and CO2) are

732

Energy Management in Wind Energy Systems

AC bus

Permanent magnet synchronous generator

Rectifier DC-link capacitor Boost-type DC/DC converter

Wind turbine

DC/DC converter

DC-link voltage control

Bidirectional DC/DC converter

+

Battery bank

Control of the AC-load voltage amplitude and frequency

AC loads

Dump load

Synchronous condenser

Control signal S1 Power flow controller Control signal S2 Bidirectional DC/DC converter

Supercapacitor banK (A)

+

Controller of the battery bank DC/DC converter

Control signal S1

Controller of the supercapacitor bank DC/DC converter

Control signal S2

–

Wind turbine MPP power

(B)

+ –

High-pass filter

Load power consumption

Fig. 33 An isolated power supply system employing a permanent magnet synchronous generator (PMSG) wind turbine and a hybrid energy storage structure, with two different types of energy storage devices: (A) a block diagram of the power supply system and (B) a block diagram of the controller managing the power flow of the supercapacitor and battery bank, respectively. Reproduced from Mendis N, Muttaqi KM, Perera S. Management of battery-supercapacitor hybrid energy storage and synchronous condenser for isolated operation of PMSG based variable-speed wind turbine generating systems. IEEE Trans Smart Grid 2014;5:944–53.

minimized and simultaneously the load power demand is covered. The power production of the wind turbine is estimated by forecasting the wind speed with the use of an artificial neural network (ANN).

5.17.7

Energy Management in Grid-Connected Wind Energy Conversion Systems

Currently, wind turbines are available with a power production capability of up to several megawatts. The increased penetration level of modern wind energy production systems in smart grids, where they operate in parallel with other types of energy production units (e.g., thermal units, PV generators, etc.), necessitates the application of energy management techniques, ensuring that the energy production efficiency is maximized and also that the power quality of the electric grid is not deteriorated. Because of the continuous fluctuations of wind speed, the power produced by a WECS changes continuously. Thus, increasing the penetration level of grid-connected WECSs imposes difficulties for the electric network to achieve the power supply demand balance and avoid power quality degradation in the form of voltage and frequency variations and disturbances [46]. Also, maintaining the power supply demand balance, under the influence of wind-generated energy variability, may result in the increase in the electric network operating costs. The curtailment of the power produced by a WECS can be imposed by the electric grid operator, within the framework of active network management schemes, in order to ensure that the power demand of the

Energy Management in Wind Energy Systems

Wind turbine #N

733

AC bus Transformers

Wind turbine #2 Wind turbine #1

AC loads

Li-ion battery bank

+

Bidirectional DC/AC converter

Diesel generator

Fig. 34 An isolated microgrid comprising a wind farm, a lithium-ion battery energy storage unit, a diesel generator, and AC loads. Reproduced from Babazadeh H, Gao W, Wu Z, Li Y. Optimal energy management of wind power generation system in islanded microgrid system. In: North American power symposium (NAPS); 2013. p. 1–5.

consumers is covered and the electric grid operates continuously according to its technical specifications (e.g., voltage amplitude/ frequency limits) [47]. For enhancing the stability of the electric network, the power production of grid-connected WECSs can be regulated by applying the following methods:

• • • •

•

blades’ pitch angle control, where the power produced by the wind turbine is limited when the wind speed exceeds a predefined value; control of the active and reactive power produced, through appropriate control of the power electronic converter which interfaces the WECS output energy to the electric grid; employing energy storage systems, where the wind-generated surplus energy is initially stored, and it is then retrieved during time periods of low wind speed, thus stabilizing the injection of wind energy into the electric grid; combining WECSs with other types of energy production units with complementary energy production characteristics (e.g., PV energy production systems, diesel generators, etc.), thus developing hybrid energy generation configurations. As an example, by combining wind turbines and PV generators, where the wind turbine energy production is high during the winter, while the PV generators produce more energy during the summer, achieves a more uniform total energy production during the entire year; and planning of the operation of the electric network energy production units (e.g., unit commitment and economic dispatch of thermal units) based on forecasts of wind speed, which enable the estimation of the WECS energy production.

The control of the blades’ pitch angle has been investigated in Ref. [48] for smoothing the WECS power production under highly turbulent wind speed conditions. Pitch angle control can be effective for curtailing the WECS power production in order to suppress the overvoltages developed in low voltage distribution systems during high-production/low-demand time intervals. This can be implemented by calculating the required pitch angle with the voltage droop control technique, as described in Ref. [49]. The drawback of this approach is that it results in a reduction of the WECS power production, compared with its maximum possible power production capability. Thus, both the WECS efficiency and the expected economic benefit during its operation are reduced, which, consequently, affects the WECS economic viability. Therefore, alternative techniques are desirable which enable harvesting the maximum possible wind energy and simultaneously achieve its smooth integration into the electric grid.

734

Energy Management in Wind Energy Systems

Permanent magnet synchronous generator Wind turbine

DC-link capacitor

Interconnection transformer

Three-phase AC/DC converter (active rectifier)

DC/AC converter

MPPT control

DC-link voltage control

Electric grid

Fig. 35 A variable-speed wind energy conversion system (WECS) consisting of a direct-drive permanent magnet synchronous generator (PMSG) which is interconnected with the electric grid through an AC/DC and a DC/AC power converter with a common DC-link. Reproduced from Yuan X, Li Y. Control of variable pitch and variable speed direct-drive wind turbines in weak grid systems with active power balance. IET Renew Power Gener 2014;8:119–31.

A variable-speed WECS consisting of a direct-drive PMSG that is connected to the electric grid through an AC/DC and a DC/AC power converter with a common DC-link is illustrated in Fig. 35 [50]. The power converter connected to the wind turbine generator is used for performing the MPPT process by regulating the generator speed, while the grid-side converter controls the DClink voltage in order to inject the wind-generated energy into the electric grid. However, in case of interconnection with a weak-grid (i.e., an electric grid which does not behave as an ideal voltage source), then the voltage and frequency at the point of common coupling (PCC) may vary with the amount of injected power. In order for the WECS to assist in maintaining the voltage and frequency of the electric grid within their specifications, active power is required to be injected by the grid-side inverter and the active power produced must also be controlled. The active power production is regulated in Ref. [50] by controlling the generator speed and the pitch angle of the wind turbine blades, such that the DC-link voltage remains constant, indicating that a balance between power production and load demand has been achieved, since in case that the power production is higher than the load power demand then the DC-link voltage increases and vice versa. Considering the power–speed curve shown in Fig. 16, the wind-generated power can be increased by reducing the generator speed when operating at the right side of the MPP, or by increasing the generator speed when operating at the left side of the MPP. Compared with the power production regulation through adjustment of the blades’ pitch angle, the control of the electric power produced has the advantage that it does not cause wear to the mechanical systems of the wind turbine in case that it is frequently applied and also has a faster dynamic response. In case that the wind speed is high while the load power demand is low, then a pitch angle regulation process can be activated in order to further reduce the generated power and to protect the wind turbine from damage due to over-speeding. By combining wind turbines with energy storage systems as well as applying power management techniques enables the stabilization of the energy production variability of the overall energy production system caused by the continuously fluctuating wind speed. Also, it provides the ability of controlling the active and reactive power transferred to the electric grid. Thus, WECSs are transformed from passive generators, with intermittent energy production characteristics, to active energy production units, with energy regulation capabilities similar to those of conventional energy generators [51]. Using energy storage units reduces the requirements for employing conventional operating reserve units in order to match the power generation and demand. This is especially important at high penetration levels of wind turbines, where the fluctuations of wind-generated power have a significant magnitude compared with the normal variability of the electric grid load [52]. Thus, the electric network operational costs can be reduced and the system reliability can be enhanced, with simultaneous environmental benefits due to the reduction of emissions. The energy storage units enable the application of time-shifting schemes, where the wind-generated energy surplus is stored during periods of low energy consumption and it is released back to the electric network during periods of high energy demand [52]. Also, the stored energy can be used to enhance the electric grid power quality and stability by smoothing the fluctuations of wind-generated energy, thus reducing their impact on the amplitude/frequency stability of the electric grid voltage, as well as by applying frequency/voltage regulation schemes where active and reactive power are injected by the energy storage system to the electric grid [52]. Energy storage units and power converters with a fast dynamic response are required in such a case, in order to quickly absorb/release the necessary amount of power during suddenly changing wind speed conditions. Also, within the framework of smart grids where dynamic pricing schemes may be applied, energy storage systems can be installed for storing electric energy during the time periods that its price is low and selling it back to the grid when the electricity price has been increased [52]. Energy storage systems using batteries (e.g., lead-acid, nickel–cadmium and lithium-ion), as well as supercapacitors and fuel cells, have been proposed for supporting the operation of WECSs [52]. Hydrogen systems that comprise fuel cells and electrolyzers have the advantage of high energy density, but they exhibit a relatively poor dynamic performance. For improving the dynamic response and peak power-supply capability of the energy storage system, batteries and/or supercapacitors can be used complementarily with fuel cells in hybrid configurations [31]. Supercapacitors can achieve charging and discharging faster and more

Energy Management in Wind Energy Systems

Wind turbine #N

735

AC bus Transformers

Wind turbine #2 Wind turbine #1

Interconnection transformer Electric grid Fuel cell Hydrogen supply

Bidirectional DC/AC converter

Electrolyzer Hydrogen storage tank Fig. 36 A wind energy conversion system (WECS) coupled with a hydrogen system for supporting the electric energy demand of the electric grid during load peak time periods. Reproduced from Bernal-Agustín JL, Dufo-López R. Hourly energy management for grid-connected wind-hydrogen systems. Int J Hydrogen Energy 2008;33:6401–13.

efficiently than batteries and they also feature a longer cycle life [31]. However, they exhibit a lower energy density than batteries. Thus, due to their fast dynamic response, supercapacitors are more suitable energy storage devices than batteries and fuel cells, for smoothing the wind-generated energy produced by WECSs during very fast fluctuations of wind speed [51]. Furthermore, electric energy can be stored through pumped hydro-storage schemes, where during off-peak time periods water is pumped from a low elevation tank to a tank located at a higher elevation. The water stored in the high-elevation tank can be supplied to hydro generators for producing electricity during the load peak time intervals. Such systems exhibit a slow response, thus are not been suitable for smoothing short-term wind fluctuations, but they are useful for storing long-term surplus wind-generated energy, which cannot be absorbed by the electric grid during off-peak time periods and providing it back to the electric network when required [52]. Other energy storage types that can be used in combination with a WECS include the compressed air and the flywheel energy storage units [52,53]. The coupling of WECSs with hydrogen systems enables the avoidance of rejecting the wind-generated energy that cannot be absorbed by the electric grid and facilitates the matching of energy production and demand. A wind-hydrogen system, targeting to cover the electric energy demand of the electric grid during load peak time periods, is shown in Fig. 36 [54]. Apart from the wind turbines, it also comprises a fuel cell, an electrolyzer, and a hydrogen storage tank, as well as a bidirectional DC/AC converter for interconnection with the AC bus, where the wind turbines and the electric grid are also connected through transformers. The energy management scheme of such a system operates as follows:

• •

during the time periods that the electric energy demand is low, the wind-generated energy is used to produce hydrogen, which is then stored in the storage tank, while the excess energy is supplied to the electric grid, and during the time periods of high electric energy demand, both the energy produced by the wind turbines and the energy produced by the fuel cell, which is produced using the previously stored hydrogen, are injected into the electric grid.

Energy storage systems can also be used for smoothing the wind-generated energy variability by directly interconnecting them with the electric grid, in order to enhance the stability and power quality of the electric system, which is influenced by the intermittency of the energy produced by wind farms. Such a configuration is illustrated in Fig. 37 [55]. A DC/AC power converter is used for converting the battery energy storage system DC voltage to AC. The wind farm and the AC output of the DC/AC power converter of the battery energy storage system are interconnected with the PCC through transformers. The power converter of the battery storage is bidirectional, enabling power flow toward either the batteries (for charging) or the electric grid (during batteries

736

Energy Management in Wind Energy Systems

Wind turbine #N

AC bus Transformers

Wind turbine #2 Wind turbine #1 Point of common coupling (PCC) Interconnection transformer

Battery + bank

Bidirectional DC/AC converter

Electric grid

Interconnection transformer

Fig. 37 Combination of a wind farm with a battery energy storage system, both interconnected with the electric grid. Reproduced from Li K, Xu H, Ma Q, Zhao J. Hierarchy control of power quality for wind-battery energy storage system. IET Power Electron 2014;7:2123–32.

Wind speed forecasting

WECS model

Windgenerated energy forecasting

Electric grid planning

Commands to the electric grid generators

Fig. 38 The process of planning the electric grid operation through wind speed forecasting. Reproduced from Flores P, Tapia A, Tapia G. Application of a control algorithm for wind speed prediction and active power generation. Renew Energy 2005;30:523–36.

Permanent magnet synchronous generator

Three phase rectifier

+

Wind turbine Filter

Vin –

Step-down DC/DC converter

+ Vo –

+

24 V battery bank

Duty cycle (D) control Fig. 39 A stand-alone wind energy conversion system (WECS) for charging a 24 V battery bank.

discharging). The controller of the battery energy storage system measures the power provided by the wind farm to the electric grid and controls the operation of the battery energy storage system DC/AC power converter such that the active power supplied to the grid is smoothed. Also, by measuring the grid voltage at the PCC, the generation/absorption of reactive power is regulated in order to support the PCC voltage stability. In modern smart grids, where WECSs are interconnected with the electric grid, the availability of wind speed forecasts during the next hours is important, in order to calculate the corresponding energy production of the WECSs and optimize the decisions taken by the producers about selling the wind-generated energy. Also, because of the intermittent nature of the WECSs energy production, wind speed forecasts enable the electric grid operators to plan the energy production of other energy production units, such that the energy production/consumption fluctuations are compensated (Fig. 38) [56]. Therefore, adequate energy is always supplied to the consumers and, simultaneously, the power quality is not deteriorated (e.g., due to instability of the electric grid frequency and/or

Energy Management in Wind Energy Systems

737

voltage amplitude) through the application of methods controlling the amount of active and reactive power injected into the electric grid. The use of energy storage systems complementary to the grid-integrated WECSs can also assist to the optimal scheduling of the electric grid energy production resources, by exploiting the flexibility characteristic of energy storage units [52]. The use of energy storage systems, together with the application of dynamic economic emission dispatch and demand side management techniques, can also assist in increasing the wind-generated energy penetration in electric grids [57]. A wind speed prediction scheme employing an ANN, which is using a sigmoid activation function and is trained by using the back-propagation method, is presented in Ref. [56]. A power management strategy for wind farms is proposed in Ref. [58] such that they participate in the frequency regulation process of an electric grid in coordination with the conventional energy production units of the grid. By implementing such a process, the time required for adjusting the frequency level to the desired value and the corresponding burden imposed on the electric grid generators can be reduced. In this power management scheme, whenever the power system frequency deviates from its nominal value by a predefined limit (e.g., 0.1 Hz), then the active power produced by the wind turbines of the wind farm is

WInd turbine power (W)

8000 5 m s−1 7 m s−1 9 m s−1 11 m s−1 13 m s−1 15 m s−1

7000 6000 5000 4000 D

3000

B

2000

C

1000 0

0

20

40

A 60 80 100 Rotational speed (rad s−1)

120

140

Fig. 40 The power vs. rotational speed curves of a wind turbine with R¼1.5 m and b¼0 degrees.

Wind speed (m s−1)

15

10

5

0

0

2

4

6

8

10 Time (s)

12

14

16

18

20

0

2

4

6

8

10 Time (s)

12

14

16

18

20

(A)

DC/DC converter duty cycle

1 0.8 0.6 0.4 0.2 0 (B)

Fig. 41 The variation of (A) wind speed and (B) duty cycle of the DC/DC converter, during the simulation time interval.

738

Energy Management in Wind Energy Systems

adjusted by controlling the wind turbines blades’ pitch angle or rotor speed of rotation, such that the total power production of the wind farm matches the desired value that has been defined by the electric grid operator. The power produced by the wind turbines that participate in such a power adjustment scheme is estimated by short-term forecasting.

5.17.8

Case Study

In order to demonstrate the impact of energy management on the energy production performance of a wind turbine, the operation of the stand-alone system presented in Fig. 39 has been simulated in MATLAB/SIMULINK. The WECS under study consists of a wind turbine (power rating: 7 kW at a wind speed of 15 m/s) with a PMSG that is connected to a step-down (Buck-type) DC/DC converter through a three-phase diode-based rectifier and a low-pass filter [59]. The DC/DC converter charges a battery bank with a 24 V nominal voltage level and its duty cycle (D) is adjusted by a control unit. The power–rotational speed curves of the wind turbine for various values of wind speed are illustrated in Fig. 40. These curves have been calculated by applying R¼ 1.5 m and b¼ 0 degrees in Eqs. (1) and (2), while the values of parameters a1- a8 in Eqs. (3) and (4) have been set equal to a1 ¼0.5176, a2 ¼ 116, a3 ¼0.4, a4 ¼ 5, a5 ¼ 21, a6 ¼ 0.0068, a7 ¼ 0.08, and a8 ¼0.035 according to Ref. [12]. The step-down DC/DC converter has been assumed ideal and its DC input and output voltage levels, Vin and Vo, respectively, are related as follows [24]: Vo ¼ D
Vin

ð28Þ

where D is the duty cycle, with values in the range of 0–1. If a battery bank is connected to the output of the DC/DC converter, then the value of Vo is relatively constant during a narrow time interval. In such a case, by adjusting the value of D in Eq. (28), the value of Vin can be modified, which, in turn, affects the rotational speed and power production of the wind turbine [24]. Thus, if for each value of wind speed, the duty cycle D is regulated to the appropriate value by applying an MPPT algorithm, as discussed in Section 5.17.4, then the wind turbine can be regulated to operate at the corresponding MPP. As an example, it has been hypothesized that the wind speed and the duty cycle of the DC/DC converter vary during a 20 s simulation time interval, as depicted in Fig. 41(A) and (B), respectively. The resulting variations of the wind turbine power and rotational speed (i.e., PW and O in Eqs. (1) and (2)) are depicted in Fig. 42. Under steady-state conditions, the power flowing into the battery bank depends on PW according to Eq. (7).

Rotational speed (rad s−1)

100 80 60 40 20 0

0

2

4

6

8

10 12 Time (s)

14

16

18

20

0

2

4

6

8

10 12 Time (s)

14

16

18

20

(A)

Wind turbine power (W)

3000 2500 2000 1500 1000 500 0 (B)

Fig. 42 The operation of the wind turbine during the simulation time interval: (A) rotational speed and (B) mechanical output power.

Energy Management in Wind Energy Systems

739

Initially, the wind speed is constant at a value of vw ¼7 m/s, but since the duty cycle D ¼0.36 has not been set to its optimal value, the wind turbine operates at point A in Fig. 40. This operating point is located far from the corresponding MPP and the resulting power of the wind turbine is approximately equal to PWE71 W. At t¼0.5 s, wind speed increases to vw ¼ 11 m/s, thus initially moving the wind turbine operating point to the transient point B in Fig. 40. However, due to the inertia of the interconnected wind turbine/PMSG mechanical system, the wind turbine operating point is progressively moved to point C in Fig. 40 after the transient state elapses, where PWE2365 W. However, point C also deviates from the corresponding MPP power level, since the value of D applied to the DC/DC converter is still not optimal. At t ¼ 10 s, the value of the duty cycle of the DC/DC converter is increased to its optimal value D ¼ 0.505 (for a wind speed of vw ¼11 m/s), resulting in the movement of the operating point of the wind turbine toward the steady-state point D in Fig. 40, which corresponds to the MPP for vw ¼ 11 m/s. At point D, the wind turbine power is higher by 16.96% compared with the power at point C. Therefore, by employing a variable-speed WECS scheme and applying the MPPT techniques described in Section 5.17.4, the power production of a WECS (either stand-alone or interconnected with the electric grid) can be improved substantially.

5.17.9

Closing Remarks

WECSs are widely employed nowadays in stand-alone systems for providing power to isolated loads, as well as in distributed generation systems, microgrids, and smart grids. In all of these applications, appropriate energy management processes must be performed in order to maximize the energy production of the wind turbines and transfer the wind-generated energy to the consumer with high efficiency and adequate power quality. In order to develop the energy management system of a WECS, the architecture of the power conversion and conditioning system and the types of the hardware devices that it comprises must be selected. Also, an appropriate control strategy must be applied by using microelectronic systems based on microcontrollers, DSPs, etc., which will enable the extraction of the maximum possible power from the wind turbine and control its flow toward the electric load(s). The former target is achieved through the application of an MPPT process, as well as by appropriately controlling the electric generator of the wind turbine. Depending on the operational characteristics of the target application (i.e., stand-alone system, microgrid, or grid-connected system), special requirements may be imposed on the functionality of the WECS energy management system, such as the production of predefined output-voltage amplitude and frequency levels in case of stand-alone systems and the synchronization with the electric grid in grid-connected WECSs. The functionality of energy management systems for WECSs is highly affected by the continuous variation of wind speed. For that purpose, energy storage systems, consisting of batteries, fuel cells, and/or supercapacitors, can be incorporated in the energy management systems of WECSs, for smoothing the wind-generated power fluctuation. In the case of grid-connected WECSs, this option additionally enables the improvement of the electric grid power quality and stability. Also, hybrid configurations can be implemented, where one or more energy sources of different types are combined to operate complementary with the wind turbines (e.g., PV sources, diesel generators, etc.). Toward the same direction, wind speed forecasting provides the ability to plan the energy production of other energy production units that operate complementary with the wind turbines (e.g., thermal units of electric grids), such that the energy production/consumption fluctuations are compensated. The successful operation of the WECS energy management system in satisfying the energy production requirements also depends on the appropriate sizing of the energy production and storage devices that it comprises. Suitable design optimization techniques have been developed (e.g., Ref. [33]), which take into account the meteorological conditions of the installation site and calculate the optimal capacities of the energy production and storage devices, such that the required amount of energy is generated during the year (thus completely satisfying the load energy requirements) with the minimum possible cost.

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[53] Díaz-González F, Sumper A, Gomis-Bellmunt O, Bianchi FD. Energy management of flywheel-based energy storage device for wind power smoothing. Appl Energy 2013;110:207–19. [54] Bernal-Agustín JL, Dufo-López R. Hourly energy management for grid-connected wind–hydrogen systems. Int J Hydrogen Energy 2008;33:6401–13. [55] Li K, Xu H, Ma Q, Zhao J. Hierarchy control of power quality for wind-battery energy storage system. IET Power Electron 2014;7:2123–32. [56] Flores P, Tapia A, Tapia G. Application of a control algorithm for wind speed prediction and active power generation. Renew Energy 2005;30:523–36. [57] Alham MH, Elshahed M, Ibrahim DK, Abo El Zahab EED. A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management. Renew Energy 2016;96:800–11. [58] Lin L, Wang S. Investigation of the strategy of wind farm power regulation considering system frequency regulation demand. In: 4th international conference on electric utility deregulation and restructuring and power technologies (DRPT); 2011. p. 1138–43. [59] Vilathgamuwa DM, Jayasinghe SDG. Rectifier systems for variable speed wind generation – a review. In: 2012 IEEE international symposium on industrial electronics; 2012. p. 1058–1065.

Relevant Websites http://www.gwec.net/ Global Wind Energy Council. http://www.irena.org International Renewable Energy Agency (IRENA). https://windeurope.org WindEurope. http://www.wrenuk.co.uk/ World Renewable Energy Congress/Network (WREC/WREN).

5.18 Energy Management in Geothermal Energy Systems Francesco Calise, University of Naples Federico II, Naples, Italy Adriano Macaluso, University of Naples Parthenope, Naples, Italy Antonio Piacentino, University of Palermo, Palermo, Italy Laura Vanoli, University of Naples Parthenope, Naples, Italy r 2018 Elsevier Inc. All rights reserved.

5.18.1 Introduction 5.18.1.1 Enhanced Geothermal Systems, Flash Systems and Double Flash Systems 5.18.1.2 Binary Power Plants 5.18.1.3 Direct Uses and District Heating and Cooling 5.18.2 System Layout 5.18.3 Case Study 5.18.4 Simulation Model 5.18.5 Results and Discussion 5.18.5.1 Daily Results 5.18.5.2 Monthly and Annual Results 5.18.5.3 Parametric Analysis and Economic Analysis 5.18.6 Conclusions References Relevant Website

Nomenclature A c cp eMED Fsol h I _ m

Area (m2) Specific cost (€ Sm 3), (€ kWh 1), (€ m 3) Specific heat (kJ kg 1 K 1) Specific consumption of MED (kWh m 3) Solar fraction Thermal conductivity (W m 1 K 1) Solar radiation (W m 2) Mass flow rate (kg s 1)

Abbreviations and acronyms ACH Absorption chiller COP Coefficient of performance Config. Plant configuration CSP Concentrated solar power EF CO2 emission factor GHG Greenhouse gases HTF Heat transfer fluid IHE Internal heat exchanger LHV Lower heating value (kWh S MED Multieffect desalination MedHE MED heat exchanger NCG Noncondensable gases ORC Organic Rankine cycle

Subscripts ACH brine chil,w cold cond

742

Absorption chiller Brine at the MED system Chilled water Cold side Condensation, condenser

1

m 3)

N Q _ Q R V W _ W z Z

PS PTC RecHE RES RO RPS SCF SPB SecHE TENORM TK TRS

743 745 750 751 753 758 759 763 764 765 769 771 772 777

Useful life (year) Thermal energy, (kWh), (MWh) Thermal energy flow (MW) Revenue(€) Volume rate (m3) Mechanical energy (kWh), (MWh) Mechanical power (MW) Depth (m) Capital resource (€)

Primary source Parabolic trough collector Recuperative heat exchanger Renewable energy sources Reverse osmosis Renewable polygeneration system Solar collector fluid Simple payback period Secondary heat exchanger Technically enhanced naturally occurring radioactive material Thermal storage tank Thermal recovery subsystem

Cooling mode “Cooling Mode” of operation of the TRS subsystem crit Critical CW Cooling water design Design condition of operation des Desalinated water

Comprehensive Energy Systems, Volume 5

doi:10.1016/B978-0-12-809597-3.00537-X

Energy Management in Geothermal Energy Systems

dist el fresh geo GHE grid hot HW in MED net O&M oil

Distillate at the MED system Electric Fresh water, drinkable water Geothermal Geothermal heat exchanger Grid Hot side Hot water Inlet Multieffect desalination Net Operation and maintenance Diathermic oil

Greek symbols D Variation, quantity

5.18.1

ORC out PTC RecHE sea SecHE Selfcons. th tot tp w well wf

Organic Rankine cycle Outlet Parabolic trough collector Recuperative heat exchanger Sea water Secondary heat exchanger Self-consumed Thermal energy Total Thermoelectric plant Water Well Working fluid

Z

Efficiency

743

Introduction

In this chapter, a thermoeconomic and environmental analysis of a hybrid renewable polygeneration system (RPS) linked to a district heating and cooling (DHC) network is proposed. The analyzed system is capable of supplying energy to a small district and it is based on parabolic through collector (PTC), organic Rankine cycle (ORC), multieffect desalination (MED), and absorption chiller (ACH) technologies. The scientific community agrees that efforts should be focused on the research and development of completely sustainable, innovative, and renewable technology systems in order to deal with increasing issues related to climate change and scarcity of fresh and pure water [1]. The aim of research activity is to find a hard compromise between the independence from fossil fuel utilization and the economic feasibility of such technologies, taking into account the gradual increase of global energy and water demands. Moreover, human perception and attitude about environmental problems [2,3] and daily habits [4] must be taken into account. Distributed generation, deep exploitation of renewable sources, distributed generation and polygeneration [5] are some of the roads to be undertaken. Significant efforts must be performed to deal with the typical issues related to exploitation of renewable energies, such as the programmability of the source (affected by its availability and variability [6]), forecasting, and management and control of the process of energy conversion. Typical examples are given by several attempts of forecasting of solar energy [7], wave energy [7], tide energy [8,9], and wind energy [10]. Moreover, this latter requires a great amount of land area (depending on capacity and turbine dimensions), with respect to other renewables [11]. Even if great off-shore plants provide large amounts of electricity stably and the small on-shore ones allow stand-alone operation, all the energy distribution networks with large penetration of wind energy are integrated with auxiliary plants (thermopower plants, renewable systems) and storage systems [6]. The thermochemical conversion of biomass and waste (as combustion, pyrolysis, and gasification [12–14]) shows excellent prospects for employment in waste treatment [15,16], biorefinery (biofuel syngas production [17]), hydrogen production [18], and electricity production [19]. Despite this, several issues need to be taken into account regarding types of products obtained [20–22], management operation, and reliability of such complex processes, both of pyrolysis [23–25] and gasification [26,27]. Moreover, nowadays the potentialities of the biomass exploitation technology is limited by hours and maintenance costs, hard applicability of large scale, and by management of waste and toxic residues [28–31]. In order to deal with the complex management and control of renewable energy systems, the adoption of storage systems [32–34] and of hybrid systems (fed simultaneously with different renewable sources) is deeply necessary. The energy storage systems are needed to overcome the issues related to the intermittence of the energy source and to mitigate the fluctuations of the energy gain supplied to the energy conversion system or to the distribution network. The hybrid systems smartly manage the integration of different technologies for the simultaneous exploitation of different energy sources, in order to constantly supply any useful material/energy product in an efficient and sustainable way and to overcome the issues related to a single energy source. In this way, a large part of the literature is dedicated to the design [35,36] operation management of hybrid systems [37–40]. Geothermal source can be considered one of the most promising renewable sources to be employed in hybrid energy conversion systems, characterized by higher programmability and stability with respect to the other renewables. Despite this feature, several critical points must be taken into account for a smart and sustainable management of the geothermal source, both considering power generation and direct uses (Fig. 1).

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Energy Management in Geothermal Energy Systems

Temperature (°C)

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 20 30 40 50 60 70 80 90 10 11 12 13 14 15 16 17 18 19 21 22 23 24 25 26 27

Brine or water Reservoir

Vapor Binary power plants Electricity production

Flash power plants

Industrial uses Agriculture and greenhousing

Heat pumps

Heating

Bathing

Cooling production

Fig. 1 Geothermal source usages.

A suitable planning of geothermal energy exploitation is extremely complex due to the plurality of the energy management and policy aspects to be addressed [41,42]. Such aspects as economic profitability, environmental concerns (local pollution, air quality, water source preservation), human resource development projects [42,43] and existing human activities [44] belong to different social actors and they are often in conflict with each other when proper planning is needed in terms of sustainability and quality of life. Therefore, the selection of the optimal configuration is extremely complex due to the duality of the mentioned issues. A geothermal resource management that aims at maximizing the economic profitability and the opportunity of employment could negatively affect the environmental impact [41]. Geothermal plants could affect the environment where the systems are installed by accidental and unexpected releases of ground-water toxics and wind-borne pollutants [45]. Closed-loop geothermal plants (where rejection practice is carried out) require deep drilling that could be located in seismically unstable geological formations. Moreover, drilling and reinjection processes themselves could induce further seismic activities. Geothermal plants could permanently change the characteristics and peculiarity of the local community. Even if some positive effects could be obtained (employment, commerce, and infrastructure), unwanted issues, varying on the specific site, can be caused [45]. Conversely, a management optimized from the ecological point of view may dramatically limit its economic profitability [41]. Moreover, it is worth noting that the long-term geothermal energy exploitation could affect not only the local environment but even the other human activities (agriculture, animal breeding, tourism) and it could affect the security of residential areas. In some cases, environmental benefits can be obtained, such as springs to be used for tourism after a subsidence caused by an underground pressure fall [46]: this was possible when chemical composition of the water was good enough for this usage and surface disposal of the brackish water was adopted. Moreover, in some geothermal fields a development of the local ecosystem was registered [46], thanks to higher steam-heated activity near the rejection areas or abandoned wells. Finally, in some geothermal fields the seismic induced activity caused formation fractures enabling further exploitation of the rock [46]. Several works deal with finding an acceptable compromise for planning a geothermal power system and considering the multicriteria evaluation approach as a valuable tool [41]. In all the cases, the solution consists in finding the best option among a set of available alternatives. The optimal configuration could be defined using different criteria in contrast with each other and it must be determined by solving a mathematical model. An interesting example of planning a geothermal system project is presented in Ref. [41]. The first step consisted in collecting the input information of the problem, represented by the social actors involved in the project and by their preferences and expectations. The analysis was carried out by reviewing various documents (laws, policy documents, press releases, and newspapers) and by means of interviews with the exponents and representatives of the social actors. In the mentioned case, the actors were the geothermal system operators, the municipalities involved in the project, resident and political associations, and committees and the preference model is based on the technical consequences that a specific

Energy Management in Geothermal Energy Systems

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criterion would cause. The mathematical model is structured in order to be simple and not compensatory and its results are represented by a rank of three alternatives for each criterion. Once the information has been reviewed, seven scenarios were designed and 11 criteria were chosen: electricity production, profitability, municipalities’ revenue, direct heat uses, avoided greenhouse gas (GHG) emissions, contaminants emissions, impact on aquifer, and visual impact. For each criteria weights and thresholds of preference were set. Weights assumed for each criterion are equal and further analyses were carried out by changing the relative weights for each criterion. The thresholds were usually selected on common sense and they are subject to arbitrariness. They were selected considering two approaches: if external benchmark was known (electricity production and GHG emissions), the value was set as the minimum percentage of achievement of the objective given by the specific criteria; if any benchmark is not available (other cases), the values were set as the minimum percentage of actual levels. In order to assess the exactness of the results and to verify if arbitrariness with which related values were set, further analyses were carried out by changing both the weights and thresholds. The methodology applied to the case study presented has shown that the consequences of the specific options vary as a function of the priority assigned to a specific criterion and to a specific points of view of an actor. In this framework, in Refs. [47–49] a development of an indicator assessment framework for the sustainable development of geothermal energy systems (GESs) is presented, applying the approach for case studies of Iceland, Kenya, and New Zealand. Nowadays, many set of energy indicators, sustainability assessment tools, accreditation systems, guidelines, or thematic frameworks are already available for sustainable projects development, like the ones provided by the International Atomic Energy Agency (IAEA), by the International Hydropower Association (IHA), World Wind Energy Association (WWEA), World Energy Council (WEC), Gold Standard Foundation (GSF), Commission for Sustainable Development (CSD), European Commission, etc. None of these mentioned tools is suitable for geothermal energy projects. Given that the impact and the implications could be significant and specific monitoring tools are needed to manage them in a sustainable manner, the sustainability assessment frameworks must be highly well categorized and customized [47–49]. The purpose of the proposed work is to review the literature of developing sustainability indicators and to describe the approach adopted and the phases to realize the assessment framework for geothermal systems. Several stakeholders were involved in assessing the framework by means of interviews and specific review of their comments. People involved belonged to academic institutions, to the energy industry, to government and nongovernmental institution or associations, other business institutions and from the United Nations University Geothermal Training Program (UNU-GTP). Moreover, invited people were from Iceland, Kenya, and from New Zealand. Stakeholders are defined as any person, group of people or organization, governmental or not, that are actively involved or could be affected by any organization’s activity, products, or services and associated performances regarding the issues to be addressed [50]. In the case of geothermal projects, the stakeholder could be any local community, geothermal system operator, local government or authorities or other business involved in the project [47–49]. The review deals with renewability, water resource usage, environment management, efficiency, economic management and profitability, security and reliability, research and innovation, community involvement, and dissemination of knowledge. Such topics were trivially strictly related to geothermal systems planning. Interesting results have been extrapolated. In fact, analyzing the first indicators chosen and the related comments, it can be argued that the concern about different topics varied depending on the specific belonging to a social category or nationality. Some of the stakeholders were more interested/concerned about environmental and economic aspects, others in renewability or to water usage, others in further technical aspects. In other words, it is important to involve various categories of stakeholders in the process of the development of a sustainability framework, since different opinions between the groups cause different weights to be related to the different criteria with which a proper scenario of geothermal project planning is assessed. From a technical point of view, many papers dealt with planning of geothermal systems and with energy and economic management of existing plants. Issues concerning economic aspects are related to the typology of geothermal reservoir, dimension of the plant, lifetime of the system, local energy demands and local needs, energy market conditions, and local policy in terms of legislation and disbursement of public funds.

5.18.1.1

Enhanced Geothermal Systems, Flash Systems and Double Flash Systems

The first main aspect [51] for a correct planning of a geothermal plant – and in particular for a proper sizing – is the definition of the geothermal resource [52] potential in terms of possible operating temperature, pressure, maximum flow rate producible for a long-term exploitation, and chemical properties of reservoir (and consequently of the fluid) [52]. Modeling studies, based on available extensive data of the specific basin structure of the reservoir (geological features, physical state, response to production [53]) and on the bases of technical experience of existing plants along several years of operation, are very powerful tools for assessing the potential and the best strategy of utilization and management [53]. Several works dealt with numerical models of geothermal systems. More complex models [54–56] can reproduce the main characteristics of the reservoir structure and their response to production [53], while more simple models, such as lumped based ones [57], can count on the simplicity of approach and can simulate pressure changes of reservoir, an important operation parameter among those characterizing the short and the long-term exploitation [53]. The accuracy reliability of such studies based on numerical model lies on the availability of extensive and detailed data about (1) the geological structure and physical state of the of the reservoir to be exploited [53], (2) a detailed measurement of the operational parameters of the plant for the years of operation, along with recording of maintenance actions (type and how

746

Energy Management in Geothermal Energy Systems

frequently), management techniques (in terms of production optimization techniques and end-use management), environmental monitoring [58–60], and variation of reservoir features. In this aspect, informatic reports and web-oriented tools could represent powerful instruments in supporting all steps of geothermal projects, i.e., development, research, planning, management, and sustainable exploitation for future generations [61–63]. An interesting report about enhanced geothermal system (EGS) in the United States is presented in Ref. [64]. Some of the main advantages provided by geothermal energy are that no storage is required and it can easily be coupled with other renewables. Moreover, geothermal energy has a high potential in covering base-load demand, providing a “buffer function” against fluctuations of fossil prices and supply interruption or disposal of nuclear power plants [64]. Nowadays, the obtained progresses allow one to easily deal with the typical issues of this technology (flow short circuiting, high injection pressure, water losses, geochemical impacts, induced seismicity [65]) throughout proper monitoring and inoperation changes [64]. Furthermore, this technology, thanks to the development in drilling technology and power conversion systems, is mature enough to stimulate large rock volumes and drill such regions in order to create connected fractures and fluid circulation without large pressure drops in an economic and effective way. Regardless of the grade and typology of reservoir (from hydrothermal and convective-dominated systems of high- and midgrade scale EGS plant to the very large and conductiondominated basement and sedimentary rock formations), EGS has a very high exploitable potential and can be considered as an attractive investment technology [64]. As mentioned in Ref. [64], development in drilling technology could be achieved both by improvements in traditional machineries, techniques, sensors and electronics, and by new methods of rock penetration. Further evolution of drilling technology allows access to deeper and hotter formations. As regard the power conversion technology, very high improvements both in reservoir performance and heat-to-power conversion efficiency could be achieved insisting on heat transfer performance for lower temperature fluid and in designing plants suitable for high resource temperature working with supercritical water [64]. As regards the reservoir technology, cost reduction could be induced by increasing the well specific production and extending lifetime of the reservoir. The latter can be achieved improving the downhole lift system of working fluid, stimulating specific targeted formations and maximizing the swept volume fractures to maximize the heat removal from the rock [65]. For proper planning, an accurate characterization of the reservoir is required, whether it is dry rock, water, or vapor dominated, or high or low-medium enthalpy. There have been several attempts in this regard and dealing with the potential of heat recoverable from the source. When in absence of detailed data concerning reservoir characterization or flow and transport models, tracer testing represented a useful tool for reservoir characterization and management [66]. In fact, several studies confirmed that tracer tests can be analyzed and used to predict thermal breakthrough of single [67–69] and double [66,70] phase fluid in porous media and yield information such as heat transfer area, reservoir volume, fluid velocities, and thermal sweep efficiency [70]. In Ref. [71], a campaign of experimental measurement of the pore volume of different geothermal reservoir is reported. The methodology consisted of the utilization of tracer mixed with water and injected in the bottom of the reservoir (injection region) and collected in the production section (production region). The pore volume, both for single or multiple injection and production regions, was estimated by measuring the tracer return curve along several days. In particular, two methods were adopted, namely the first moment analysis and the analysis of the long tailing portion of a tracer return curve. The first method, which as suggested provides in a simple way a first order estimation of the pore volume of the fracture in a hot dry geothermal reservoir, presented higher reliability of the results and it is based on the estimation of the average time spent by the tracer in the reservoir. Results that yield information about the enthalpy production and the thermal recovery efficiency have shown that the approach is reliable even for multiple fractures with variable properties but it can be complex in problems characterized by greater flow in the matrix-fracture system [70,72]. Several studies [73–76] showed that coupling geothermal carbon storage (GCS) with geothermal energy production could have economic and environmental benefits. First analyses proposed to use CO2 as working fluid from a carbon storage reservoir, even if the gas could combine itself with some formation water that makes such mix very corrosive for the equipment. Another possibility could consist of managing the CO2 injection as a pressure-support fluid and using water formation as working fluid for power production. Moreover, is some cases, depending on the nature of the geological reservoir, formation water production is needed to properly manage the pressure process during the GCS storage and consequently an exploitation of such hot water must be planned. Planning a coupled GCS–GES depends on several key parameters and may not be cost-effective. In fact, economic feasibility depends on reservoir temperature and permeability formation: this latter affects both the pressure management for storage and the potential of heat removal. Moreover, it must consider the high cost of the facilities to be added to the existing plant, which must minimize the environment footprint. Accurate evaluation of energy market (both for electricity production and direct-uses) is needed and brackish water disposal must be precisely planned. One option, depending on the chemical composition, is given by further utilization for agriculture or industrial process upon a distillation process, which requires further installation of facilities. Another possibility is given by the injection process, which requires further wells. Further decrease of development cost related to drilling, power plant, and rock stimulation could be achieved only through a well-structured research program and the development of pilot systems. As mentioned before, monitoring and registering the maintenance activities could be very useful tools for future planning and managing of geothermal systems. In Ref. [77] authors reported an analysis about wellhead maintenance along several years of operation. The study was conducted by interviewing the personnel, by retrieving maintenance diaries and by registering detailed

Energy Management in Geothermal Energy Systems

747

descriptions of the operations performed. Moreover, further data were collected by the dynamic maintenance management system installed in the plants. The best maintenance intervention was highlighted together with the frequency: this allowed building a statistical model whose analysis results were compared with recommendations provided by the personnel. The developed statistical model can be useful for future wellhead management of other geothermal power plants, and it could be useful to transfer the approach to other subsystems of geothermal fields. The proper management of geothermal systems is also based on optimization techniques that can be focused on any subsystem key parameter or key practice to minimize cost/maximize economic profitability. In Ref. [78], the optimization aimed at minimizing the net present value (NPV). An empirical correlation was used to relate the production and injection temperature to the power output: namely the mathematical model, together with a plausible estimation of future energy prices and interest rates, relates the injection process of fractured geothermal reservoir, in terms of flow rate scheduling, to the output electrical power. The objective function, as well as the correlation relating the operation temperatures and the power output, were based on an analytical thermal transport model and the solution was constrained by the minimum temperature achievable in the injection. The analysis was applied to two case studies: one simpler, composed of two productions and two injections wells, and one more complex, based on the observation of a real enhanced geothermal field in Soultz-sous-Forêts. In both cases, with good results, they determined the optimal values of the injection rates for each well and for all the wells combined. Accuracy of results strictly depend on data regarding actual fracture aperture or heat transfer area for each injector–producer pair. Moreover, the method is more suitable for liquid dominated reservoir and for fractured reservoirs characterized by a few dominant low paths connecting the wells. Another interesting example is presented in Ref. [79], where authors performed an optimization analysis coupling multivariate adaptive regression spline (MARS) model and bound optimization by quadratic approximation (BOBYQA) in order to optimize the well placement of injection wells and the management of the optimal production rate that maximizes the net profit after 50 years of operation. This method has proved to be efficient and reliable in reducing computational demand. Even in this case, uncertainty of the result strictly depends on the detailed data available regarding reservoir structure and geophysical features: in fact, lifetime of system and profit could be overestimated by optimizations without appropriate constraints on natural conditions. However, the bottom hole pressure (BHP) of the injection wells could significantly affect the profitability, so a compromise between pressure support and thermal breakthrough is a key parameter in field management. Moreover, variation of pumping strategy at the production well could be a more flexible and better option to control reservoir operation pressure and improve economic performances. In Ref. [80] an interesting economic analysis was performed, focusing on how the production power cost is affected by the capital cost, operation and maintenance costs, make-up well drilling costs, resource characteristics (in terms of well productivity and rate of decline), operational option (in term of installed plant capacity, number of years of make-up drilling cost and useful life), economic factors (like interest rate and inflation rate). As expected, power cost is deeply minimized maintaining the full generation capacity. Moreover, power cost strongly decreases as a function of the number of years (included between 10 and 16) of make-up well drilling, mainly for higher capacity power plants. After 20 years, make-up well drilling does not involve further decreases. With time, deposition of heavy metals and minerals occur, affecting produced flow rate, then power production. Consequently, further drilling is needed in order to restore initial productivity [81]. Plant capacity does not seem to involve significant effect on the minimum power cost. Conversely, specific power cost is much more sensitive to the O&M cost and by unit capital cost. O&M cost, as discussed later on, strongly depends on the selection of the geothermal site, with particular reference to the chemical composition of the geothermal fluid and related processes (in terms of facilities, consumables, etc.) needed for the treatment and with reference to the waste water, sludge, and solids disposal. Finally, power cost is affected by economic aspects such as inflation and interest rates, while well productivity, drilling cost per well, and well productivity decline rate do not involve appreciable affect. Total power cost, as expected, decreases as a function of the capacity of the plant; conversely make-up well drilling increases with the plant capacity. Most of the total plant cost is represented by operational costs, namely make-up well drilling cost and O&M cost. Long-term reliability is one of the most important aspects in maximizing the exploitation of the reservoir. As mentioned before, O&M costs represent a great amount of whole plant cost. Processes related to the management of geothermal fluid require specific facilities and parasitic energy consumption, which obviously affect the maximum potential of economic profitability. Typical issues related to operation and maintenance of the facilities are related to the chemistry of the geothermal fluid (water or vapor), whose typical chemical composition of geothermal fluid strictly depends on the geothermal site and temperature [82–84]. The major constituents include sodium (Na), chloride (Cl), bicarbonate (HCO3), sulfate (SO4), silica (SiO2), calcium (Ca), and potassium (K), including total dissolved solids (TDS), noncondensable gases (NCGs), heavy metals, and naturally occurring radioactive material (NORM) [83]. The majority of the mentioned constituents potentially affect geothermal power plant operations since they are related to scale and corrosion. Scale removal and corrosion control practices must be periodically performed and they involve interruptions of the operation of wells (both production and reinjection) and surface facilities, which are often expensive.

748

Energy Management in Geothermal Energy Systems

Scaling is the formation of hard layers inside ducts, tubes, heat exchangers, kittles, etc. due to the high content of natural chemicals with high chemical affinity with the facilities materials. This phenomenon causes:

• •

obstruction of producer wells, ducts, pipes, kittles (calcite and silica scaling) and consequently loss in mass production (then in electrical generation), and decrease of thermal exchange between the working fluids in the heat exchangers; increase of costs generated by mechanical cleaning of wells and pipelines, damages in wells, pipelines, and surface.

Typical chemical composition of scaling is generally composed of silica (SiO2), metals sulfides [85], calcium carbonate (CaCO3), galena (PbS), celestine (SrSO4) [85,86], barite [85], barium sulfate (BaSO4), calcium fluoride [87], strontium rich barite (Ba0.6Sr0.4SO4), Pb2 þ , As, Sb [85,88], Fe-mixed sulfides (like FeS) [85], and Fe-mixed carbonate (like FeCO3) and other metals sulfides [89]. In some cases, scale formation must be considered as NORM, since radionuclides, Ra-226, into barite and Pb-210 into galena are present [88]. Other NORMs are represented by Ra-228 and K-40 [90]. Silica (SiO2), metals sulfides, and calcium carbonate (CaCO3) are the most common ones [83] and they are related to different stages of the power production process, since precipitation occurs depending on temperature, pressure, and specific volume of the geothermal fluid [83]. The most common methods employed to mitigate and control silica scaling are [83] specific flash pressures and temperature levels to prevent silica over saturation, binary systems, adjustment of brine pH and controlled precipitation, dispersant [91], and chemical inhibitors. Silica scale inhibition allowed the development of efficient flash power plants. Silica scale can be chemically controlled and avoided by crystallizer-clarifier technology [87] as pretreatment of geothermal brine exiting the production well. Collateral effect of this practice is given by the production of solid wastes and sludge contaminated with NORMs (Ra-226 and Ra-228), coprecipitate with barium sulfate in the clarifier sludge, and scale deposits in the injection system (at temperatures below 1601C). The system presented in Refs. [87,92] consisted of the adoption of commercial inhibitors containing phosphonate and acrylate functionality that prevented or inhibited radioactive geothermal scale sludge precipitation without interfering with chemical inhibition of silicate scaling and deposition. Despite the further management costs needed for such chemical processes, the quantities of solid wastes requiring disposal is highly reduced, then high disbursement saving is obtained by avoiding expensive dispose cost for sludge and scale containing unacceptable contents of NORMs. In order to reduce scale formation, both in the surface and subsurface installations, several chemical inhibitors, based on phosphoric acids, were tested first in laboratory experiments then with geothermal brine in real applications [88]. In the first phase, laboratory tests concerned the inhibition capacity, the calcium tolerance, the effectiveness, the thermal stability and rock/brine/inhibitor interaction, dose rate adjustment both for short and for long-term operation. In particular, long-term inhibition is important to avoid scale formation and deposition at the injection wells, the open-hole sections, and the near wellbore region [88]. Results showed that small-scale laboratory experiment scans represent a very useful tool for selection of chemical inhibitors for geothermal power plants. Different types of inhibitor were selected, presenting high efficiency in a short period or in a long period and presenting promising results. As technique of silica removal from geothermal brine, in Ref. [93] liquid fluidized bed heat exchangers are used. In particular, a shell and tube exchanger is employed, where a bed of sand is placed on the shell side. When the geothermal brine passes through the shell the sand bed is fluidized, and a scrubbing action on the heat exchangers surfaces is carried out, thus avoiding scaling. Some further useful effects are given by an enhancement of the heat transfer coefficient with respect to those reported for conventional shell and tube heat exchangers and by removal of silica particles thanks to the attraction effect of sand particles. Diminishing the concentration of silica has positive effects in reinjection of the geothermal fluid and finally silica can be obtained as a by-product. The most common method applied in flash power plants to control silica deposition, both considering efficiency and economic aspects, as less cost related to the process waste disposal, is pH modification: decreasing the pH has positive effect in the inhibition by adding reactive fluids such as hydrochloric acid, sulfuric acid, and sulfurous acid [83]. Despite this circumstance, this method cannot be applied in every geothermal field because of the specific chemical composition of the geofluid. Moreover, increasing the acidity leads to higher corrosion management problems and to higher formation of metal carbonate and hydroxide precipitation [83]. Another alternative is given by using other kinds of compounds that do not modify the pH during the reaction process of silica deposition inhibition: cationic nitrogen-containing compounds, formic acid, oxygen scavengers [83]. Combining the different methods just described leads to higher efficiency process, adoption of less expensive equipment, less impact, less waste disposal requirement, no ponding, and no surface disposal of brine [94]. As regards carbonate deposition, even in this case most common practice is represented by adoption of inhibitors [83,95]. Other practices are represented by acidification of reservoir and pressure process control, like pressurization of geothermal brine in the wellbore or limitation of production, in order to not induce unacceptable pressure drops, which favor carbonate scale deposition. Finally, other scale formation is represented by metals sulfide deposition, whose chemical reaction is less know with respect to the silica and carbonate ones and which is induced by lower process temperature, higher acidity of the brine or, depending on metal, higher reducing [83]. Metals observed in chemical composition of scale layers are Cu, Hg, Pb, Ag, Fe, Zn, and As. Despite the hardness that characterizes sulfide metal scale, the most common practices are represented by mechanical removal and chemical dissolution; combined brine oxidation and acidification were proposed [83].

Energy Management in Geothermal Energy Systems

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Corrosion is the chemical and/or electrochemical reaction that gradually destroys the facilities’ metals converting them into more stable chemical forms, such as oxides and hydroxides. Basically, corrosion in carbon steel is due to acidic geothermal fluid (pH 5.2–5.8) conditions caused by the CO2 and by the turbulence, which prevents the formation of protective films [89]. Corrosion is managed during the design step of the geothermal power plant by accurate selection of specific materials, such as corrosion-resistant alloys, or by adoption of corrosion-resistant coatings. Among the TDS, predominant constituents are sodium chloride (NaCl), bicarbonate, sulfate, silica, calcium, and potassium [96]. Higher TDS concentration affects the number and the difficulty of the specific geothermal water treatments, consequently heavily affecting the production costs. The most mature technologies for removing TDS are membrane process-based and the thermal process ones. Among the membrane processes most common are the reverse osmosis (RO) and the electrodialysis and the electrodialysis reversal one. The first is characterized by low operation cost and low energy consumption, thanks to the low cost of membrane and low operation pressure needed. Conversely, the adopted membrane needs regular maintenance because of fouling and it requires a constant quality of the water; moreover it is not suitable for high TDS content [83]. The electrodialysis-based methods are characterized by higher recovery rates and it can be adopted for higher content of TDS. Even in this case membrane fouling leads to maintenance operation and related cost increase at higher TDS content [83]. Among the thermal methods, the most mature technologies are represented by multistage flash (MSF) and multieffect distillation (MED). Despite the advantages given by high capacity of processing greater mass flow rate of water with high efficiency (then high quality of distillated water) [97–99], these technologies are characterized by higher facilities cost and by higher specific energy consumption [97–99]. Even in this case, scale and fouling occur, requiring pretreatment of the water. Moreover, separate disposal of processed brine with very high content of TDS is needed [83]. As aforementioned, small contents of NORMs are present in geothermal fluid, whose unacceptable radium activity is related to higher concentrations of TDS [85,100] or to higher plant circulation volume and wherever temperature of geothermal fluid is lower [83,101]. In this case, NORM can be defined as technologically enhanced naturally occurring radioactive material (TENORM), namely concentrated or exposed NORMs to the environment after any technological process [102] and that can be considered as byproducts traceable in any waste streams, as scale layers, sludge, slags, etc. The most common TENORMs found are radionuclides, Ra-226, into barite and Pb-210 into galena [88], Ra-228, K-40, Th [83,90], Pb-210, Po-210 [90,103]. NORMs’ presence poses serious issues related to environmental impact (workers exposure, contamination from waste disposal [83]) and related to cost production, also related to opportune waste disposal methods. Dissolving-based methods (by using water, chemicals, acid, or bases) did not present efficiency in reducing TENORMs concentration in sludge. Almost-good results have been obtained by using particular chemical inhibitors that presented positive effects on barite inhibition: if the latter is properly treated, NORMs deposition is mitigated. Despite this, collateral effects were obtained that affected the silica inhibition and then further chemical processes (and combination of them) are required. Definitively, more experimentation and know-how are required [87]. Among the NCGs the main constituents are nitrogen (N2), carbon dioxide (CO2), hydrogen sulfide (H2S), methane (CH4), argon (Ar), oxygen (O2) [96], and nitrogen oxides [104]. In case of direct utilization of geothermal brine in flash power plants, except for hydrogen sulfide, gases are usually released into the atmosphere [96] before entering the turbine: in fact, they do not condense at the condensing operation temperature of steam and they gather in the condenser causing an increase of the backpressure at the turbine outlet and consequently causing a decrease of the power output [96]. Several common [96,105–107] and innovative methods [108,109] are available for toxic H2S abatement, whose reaction products are represented by (depending on the process) elemental sulfur, H2, steam, sulfurous acid (H2SO3) and/or sulfuric acid (H2SO4). The latter two are used in brine pH modification processes and acidizing activities [105,106]. While the other by-products could be sold in the market to obtain additional revenue [83,96]. The management of produced fluid must match opposite aspects, as cost-effective option on one side and minimized environmental impacts and local government restrictions on the other side. The first option is represented by rejection. This practice is adopted by most of the operators, since it prevents any pollution issue [42]. Other ones could require further treatment and accurate economic feasibility evaluation. One option is given by surface disposal, in particular by discharge into rivers or other water surface bodies and by disposal in natural storages, sedimentation, evaporation, and final disposal of dense waste in ponds [83]. The first category implies accurate treatments in order to obtain enough quality of water [42,83] to be discharged and satisfy requirements of local standards and specific government regulation. The second category implies issues related to air quality and salt deposition [42,83] and it could represent a dangerous site because of the long time needed for evaporation [83]. Another option of waste water management can be represented by reuse and recycle of the produced water for industry, agriculture, fresh water supply (residential sector), other nondrinkable water uses, hydrological uses. In these cases the water quality required, depending on chemical composition after power production and on the specific final use, is much more constrained and deeper further treatments (removal of metals or any toxic substance, distillation, pH adjustment) are needed [83]. In both cases, surface disposal and recycle, geothermal water is not rejected in the reservoir, affecting the long-term sustainability. The most appropriate solution for a particular geothermal field depends on many factors like chemical composition and produced volume of geothermal water, treatments and disposal options allowed by local government standards and restrictions, technical (environment, facilities) and economic feasibility (involved costs) of the specific option [83].

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Energy Management in Geothermal Energy Systems

An interesting option to be taken into account regards the possibility to consider the geothermal fluid produced as a costeffective feedstock resource of metals, minerals [110,111], acids, bases and gases (either liquefied or not) [112,113]: the presence of these elements usually represents an obstacle for the resource exploitation, both from a technologically and an economic viewpoint; however, if the concentration of such compounds (silica, carbonate, potassium, zinc, iron, cesium, rubidium, sodium, titanium, acids and bases, precious metals as gold and silver, chloride compounds, H2S, CO2, and many others) is sufficiently high, they represent a further economic resource. The feasibility of such option strictly depends on the market price of the aforementioned elements and minerals and on the operating cost of geothermal fluid chemical processes adopted to separate such compounds. Of course, regardless of whether the by-product is sold to the market or not, the extraction facilities and chemical separation processes are needed for the exploitation of the geothermal source. After any chemical process needed for either a proper processing of geothermal fluid or for recovering elements and minerals, several tons of solid waste are produced that must be disposed of in specific hazardous-waste fill or ponds [114]. Chemical composition of solid waste and disposal methods vary, from a geothermal field to another one, depending on the same factors mentioned above for water disposal [87,110,111]. Produced sludge can be encapsulated by cement and other materials to make it inert. Another option is represented by chemically reducing solid wastes into soluble forms before being reinjected. In some cases, it is possible to adopt biotechnology methods (specific bacteria) to dissolve, separate, or immobilize hazardous geothermal sludge [42]. A sustainable and efficient use of geothermal energy [53,115] cannot be separated by an appropriate case-oriented system design, based on appropriate methodologies [116] and considering the source availability. Several works could be found in literature, which are specifically focused on design, performance assessment, and optimization of multipurpose systems, simultaneously ensuring power production and supply for building thermal energy demands [117,118]. The polygeneration systems can be powered simultaneously by different energy sources (hybrid configurations), which can be either based on renewables or fossil fuels. In fact, interesting examples could be found in Refs. [119,120], where an optimization of a hybrid multipurpose system powered by natural gas and biomass is proposed, and in Ref. [121], where the authors investigate a fully renewable plant powered by biomass and solar energy. Obviously, several types of energy resources can be exploited and integrated within such polygeneration systems. An interesting example is provided in Refs. [122,123], where three trigeneration systems powered by three different energy sources are compared, while in Ref. [124], ocean thermal energy conversion (OTEC) technology is combined with the photovoltaic/thermal (PV/T) technology for the exploitation of solar energy. Moreover, several works deal with the exploitation of solar energy alone or in combination with other renewable sources (e.g., biomass [121,125–129], wind [128,130–133]), including geothermal energy [134–137]. As reported in the cited papers, different technologies could be employed for power generation, as the PV/T systems [138,139], fuel cells [140], and ORC modules [141,142].

5.18.1.2

Binary Power Plants

ORC technology consists of a Rankine thermodynamic cycle using an organic fluid as operating fluid, which may either be in the group of hydrofluorocarbons (HFCs), hydrocarbons (HCs) and fluorocarbons (PFCs). Due to the peculiar thermodynamic properties of these substances, in particular as regards their low critical temperature (and the consequently low boiling temperature), high molecular weight, and high density, the ORC allows one to exploit low-medium temperature heat sources in various applications [143–146]. Bottoming ORCs coupled with gas turbine [147,148], waste heat recovery from internal combustion engines [145,149], and thermal energy exploitation from biomass [126,127,150] and solar sources [141,151,152] only represent some of the several applicative examples of this technology that could be found in literature. On the basis of the statistics reported in Ref. [153], only 32.0% of geothermal energy at the global level is available at temperatures higher than 1301C, while the remaining 68.0% is available at lower temperatures. Thus, the adoption of ORCs for the exploitation of geothermal sources may be considered the best solution for electricity generation [154,155]. Geothermal binary power plants are characterized by no emissions, except for the vapor generated in cooling towers and the eventual losses of working fluid. Hence, this technology is not affected by issues concerning GHG emissions and toxics and pollutants release that characterize EGS and flash geothermal systems [52]. In Ref. [52] the authors present an interesting multidisciplinary analysis, based on both geological/geophysical and energy engineering aspects, pointing out that such an approach is needed for a sustainable long-term exploitation of geothermal sources [52]. Proper planning and correct management of power production of a binary power plant are based on the following two important aspects [52]: (i) the assessment of the geothermal resource potential and (ii) the choice of a rejection strategy [156]. The former is defined in terms of temperature and pressure of geothermal fluid and the maximum flow rate allowed for a long-term exploitation, while the second one is based on the thermal and chemical properties of the fluid in the reservoir. In particular it is important to define the minimum rejection temperature to avoid excessive scaling deposition on the heat exchanger, piping, and reinjection wells and in addition, to take into account the geofluid circulation inside the reservoir [52]. In fact, since the development step of the project, attention must be paid on how to obtain an efficient recharge of the geofluid (enthalpy and flow rate) after reinjection into the reservoir. Consequently, it is important to assess the number of production (included the compensation ones) and reinjections wells, the mutual distance among them and the time interval to avoid an undesirable depletion of production (in terms of geofluid flow rate or temperature) [52].

Energy Management in Geothermal Energy Systems

751

Once the potential of the reservoir has been defined (by means of detailed data provided by geophysical exploration and geochemical analysis [52]) and its response to production assessed (maximum temperature, maximum flow rate, hydraulic head and pressure), the design, calibration, and optimization of the power plant, based on thermodynamic considerations and environmental impacts, can be performed [52]. Numerical simulation of the reservoir can be considered, once again, a powerful instrument not only for designing the energy conversion system, but even to model the potential evolution of the field along several years of operation and consequently, for defining a proper management of the source [52]. An overview of the available models for the simulation of the thermofluid dynamic phenomena in geothermal systems is available in Ref. [157]. A correct design of energy systems based on the geothermal resource should take into account a proper modeling of the thermofluid dynamic phenomena occurring in the reservoir, and of the interaction between the system and the subsoil. In particular, in Ref. [158] a numerical model to study the performance of an innovative system for the exploitation of low enthalpy geothermal energy is developed, proposing a simplified thermal and fluid dynamic model with lumped parameters to quantify the heat flow rate that can be transferred from a geothermal aquifer by a downhole heat exchanger with a natural convection promoter. Several improvements of the original model have been consequently proposed [159–161]. In Refs. [162–164] a single domain approach is used to calculate the velocity and temperature distribution in the well, the aquifer, and the heat exchanger, avoiding the need to consider different computational domains and use approximate boundary conditions for each subdomain. An organic fluid can be considered a good candidate for an ORC system when it shows high latent heat of evaporation, high specific heat, positive or null slope of the vapor saturation curve, high thermal conductivity, low viscosity, chemical and thermal stability at high temperature, safety, and low environmental impact. The appropriate selection of ORC working fluid is a crucial issue, since it could affect the thermodynamic and economic performances. In fact, several works analyzed the best cycle configuration considering different typologies of substances [165–168], including pure or zeotropic mixtures [169–172]. Different layouts have been investigated, identifying the configurations achieving the best performances [173–176], considering both the subcritical and the supercritical Rankine cycles [177–179]. Finally, off-design working operation [180–183] and thermodynamic and economic optimizations of ORC systems [184–186] were deeply investigated. In Refs. [179,187,188] the performances of the system powered by solar and geothermal energies, such as the one proposed in the present study, were investigated. As mentioned above, exploitation of solar energy is achieved by means of a PTC solar field, which is considered the most mature technology among the concentrated solar power (CSP) ones [189–191]. It consists of a linear parabolic-shaped receiver that only concentrates the beam radiation in a linear duct (Dewar tube) located in the focus of the mirror and a tracking system that moves the mirror focusing the beam radiation as efficiently as possible. Different working fluids can be employed [192,193] for this collector, which also shows good versatility in being coupled with different energy sources [121] and technologies [194,195]. A commercial single stage lithium bromide (LiBr) water ACH provides the cooling production. This technology has been deeply analyzed in the literature (Refs. [196–198] are only some interesting examples). Several works are available in literature that analyze possible combinations of this technology with renewable sources to supply space cooling. This is due to the ORC capability to be powered by low-medium temperature heat sources, eventually produced by renewable sources [194,195]. For large-scale fresh water production, MED technology is one of the most common thermal desalination technologies: the advantage provided by this facility lies in the possibility to be driven by low-medium temperature heat sources (60.0–90.01C), thus being easy to adopt for exploitation of renewables [199–202]. Despite this peculiarity, MED technology is characterized by high thermal energy consumption (144  103–324  103 kJ m 3) [97,98,203] and by consequently high fresh water production cost. Several interesting studies can be found in literature about MED systems powered by geothermal sources [204,205].

5.18.1.3

Direct Uses and District Heating and Cooling

As mentioned above, the proposed RPS is supposed to be integrated in a DHC network supplying energy to a small district. Recently, the scientific research has been focusing on the development of smart energy systems [206], characterized by smart management [207] of the network of multienergy (heat, cool, and power) and material streams and by optimal design [208]. DHC may offer a significant contribution to achieve a higher utilization of renewable sources [209] and allows for a more intense exploitation of surplus of heat coming from centralized power plants, industries, and waste treatment plants, thus contributing to the development of a polygeneration system [210] and to the efficient management of a multicarrier energy network [211]. Therefore, DHC has a primary role in the decarbonization of the energy conversion process [212] and in the achievement of more sustainable scenarios. In this context, numerical analyses and optimizations of DHC networks are useful means to develop smart energy and water systems [213,214]. In Ref. [115] authors report a comprehensive review about the management of DHC network in Iceland, which could represent a sort of precious guideline for the long-term sustainable exploitation of low-temperature geothermal reservoir and for the management of direct uses. The operation of DHC networks resulted to be affected by several problems, some of them being represented by the overexploitation of the geothermal source, whose symptom is represented by excessive pressure draw-down, gradual cooling down of the well temperature, variation of chemical composition of the production well, variation of the reservoir condition induced by seismic activity and, finally, corrosion and scaling on the equipment required for appropriate maintenance of wells and mechanical equipment.

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Energy Management in Geothermal Energy Systems

The solutions adopted to deal with the aforementioned issues comprise improvement of energy efficiency of heating systems, deeper depth wells, directional drilling, new wells, reinjection, chemical inhibition, reduction of production, and transfer of a portion of production to other geothermal fields. Icelandic geothermal DHC fields are characterized by a high longevity (emblematic is the case of Laugarnes field, in activity since 1930) obtained not only due to the large scale of Icelandic geothermal reservoirs, but also due to the deep expertise acquired along several decades of operation under a forward-looking perspective of future exploitation, which is one of the bases of the “sustainability” concept. Still in regard of the management of direct uses of geothermal DHC network, in Ref. [215] the authors reported an interesting analysis concerning the exploitation of low-temperature geothermal source. In this study, emphasis is given to the importance of achieving the maximum temperature drop (minimum outlet temperature, compatibly with the chemical composition of geofluid, which can cause scaling) in order to manage the system with the minimum flow rate extracted from the reservoir and ensure an optimal pump operation [215]. Direct use of geothermal fluid in house heating applications is the best option whenever the chemical composition of the geothermal fluid permits it. Special attention must be paid in the heat exchanger selection, since this component must be designed with materials resistant against scaling and corrosion and it must be accurately sized to achieve the maximum temperature drop. Investment and operation costs are related to the extension of the DHC network, to the processed flow rate, and the time of operation. In order to allow for a cost-effective exploitation of the geothermal source and rapidly recovery the initial investment, geothermal energy must be used to cover the base load, thus achieving a more prolonged annual operation. Also, the heating equipment (radiators, fan coils) should be large and efficient, possibly with dual pipe circulation and equipped with thermostatic control in each room. In case no modification of existing heating system can be made, “cascade exploitation” represents the best solution, with the thermal energy content of the geothermal fluid exploited in series while feeding different heating systems operated at different temperature levels [215]. In this case, the “high temperature” use may be represented by space heating, while low grade heat can be exploited for soil heating of greenhouses, swimming pool heating, spas, aquaculture, etc. Peak loads are covered by traditional technology only when necessary. In the case that chemical composition or environmental conditions do not allow for a direct use of the geofluid, indirect use must be adopted. In this case geothermal fluid does not leave the geothermal field and before reinjection it is used to heat secondary clean water in a closed-loop configuration. If the fluid temperature is high enough, geofluid can be used in binary systems for electricity production and subsequently for space heating [215]. As underlined by the authors, the whole geothermal project planning strictly depends on the geological conditions, the features of the resource, and on socioeconomic conditions such as the type of energy users and other aspects related to the local energy market. Multipurpose systems capable of exploiting the thermal energy of geothermal fluid at different temperature levels (either directly or indirectly, i.e., receiving heat from a secondary fluid such as clean water), can improve the flexibility of the system in managing the variable energy loads and thus contribute to increasing the overall energy efficiency. In Ref. [78], the authors present an optimization based on a genetic algorithm and aimed at minimizing the cost of a DHC scheme by adopting an appropriate design. Cost fractions that depend on the examined DHC scheme were the annual pumping cost and the amortization of the piping, which transfers the hot water from the productions well to the storage tank, supposed to be installed in the border of the geothermal field. The analysis was carried out considering different types of geothermal fields, characterized by two zones of different conductivity and four zones with different temperatures, the highest one located in the center of the field. Solving the optimization model for a specific case study (which determines the total required flow rate from each well and the plausible location of new ones) becomes more difficult when the complexity of the geothermal field increases. However, some considerations could be drawn from an analysis of results: (1) the pumping cost is lower when new wells are placed in zones characterized by higher conductivity and far from each other, (2) costs related to piping is minimized when new wells are placed close to the storage tank. The total required mass flow rate is reduced if more water is pumped by high temperature zones, provided that these zones are not coincident with those with a high conductivity. In Ref. [216] a management strategy simulation is proposed, based in the case study of the Paris Basin geothermal district heating scheme, composed of 70 production and injection wells still in operation and supplying heat to ca. 150,000 buildings. The operation management of the examined DHC, based on a systematic water injection practice, on a continuous exploitation monitoring and on periodical well inspections, represents an effective management protocol to be applied in geothermal DHC systems. The examined case study is characterized by a 30-year detailed documentation about the operation, the chemical properties of the reservoir, and composition of the produced fluid. The study was aimed at designing and implementing exploitation strategies that allow extending reservoir longevity for further decades. Taking into account the actual physical state of the reservoir and considering reliable extraction technologies along with make-up drilling, new well drilling, chemical inhibition, and new anticorrosion well techniques, the simulation results showed that DHC longevity could be increased for several decades by excesses of heat supplied with respect to the forecasted local demand. In this chapter the authors propose a thermoeconomic and environmental analysis for a fully RPS simultaneously powered by geothermal and solar energy and connected to a small DHC network. The examined system is supposed to supply energy to a small district. The system configuration integrates three different subsystems, namely the hybrid power plant (HPP), the thermal recovery subsystem (TRS), and the MED subsystem in order to produce simultaneously electricity and desalinated water, and to supply the space heating and cooling requests of a small district.

Energy Management in Geothermal Energy Systems

753

Although all the adopted technologies are commercially available and have been deeply investigated in the scientific literature, such a complex layout has never been investigated, except in Ref. [217]. In fact, the referenced works deal with only some of the adopted technologies and do not investigate such a highly integrated layout and plurality of material and energy flows obtained by renewable sources. The HPP subsystem is based on the integration of geothermal wells with a PTC field to collect thermal energy by mediumtemperature geothermal source and solar energy respectively, while an ORC module guarantees power production. The TRS is based on shell and tube heat exchangers and heat storages, also including a single stage LiBr/water ACH and two MEDs. From May 1 till November 30 the TRS produces chilled water for space cooling while it produces hot water for space heating from December 1 till April 30. The third subsystem consists of a MED unit (in particular the module indicated as “MED3” module) directly powered by geothermal source. Pantelleria Island has been selected as a representative case study, in order to assess the potential of the proposed system. Pantelleria is a small Italian island located in the southern part of the Mediterranean Sea. It is characterized by mild weather conditions with a large availability of solar radiation and moderately high temperatures. Moreover, geothermal sources are largely available (approximately at 1601C) and natural fresh water resources are very scarce. Most of the dwellings are concentrated in a single area, with large uninhabited areas in the rest of the island. Therefore, Pantelleria Island is identified as a suitable candidate for possible installation of the proposed system, since the aim of the present study consists of designing a fully RPS, as much as possible independent from the grid. The plant is supposed to be connected to a DHC network; in particular it has been designed and calibrated to cover energy demand from a cluster of 800 buildings within the most densely populated area. An efficient control strategy has been developed, aimed at managing efficiently such a plurality of technologies and devices and to supply the different energy uses. Moreover, the control strategy operates in order to match the appropriate operating temperature levels in all the components, thus avoiding any heat dissipation and any risk of reinjection of the geothermal fluid in the well at an excessively low temperature. Energy production must match the real-time dependent demands for space heating and cooling and electricity. The electrical demand has been estimated on the basis of measured data, while thermal and cooling demands have been obtained by performing dynamic simulation. In fact, a dynamic simulation model of a representative building of Pantelleria Island was developed, first by modeling it in a SketchUP environment and successively by connecting it to the RPS, which was implemented and simulated in TRNSYS environment in order to evaluate the time-dependent cooling and heating loads. In fact, if the analysis is based on the assumption that all the energy vectors are effectively consumed by the user, an unacceptable overestimation of system profitability would be obtained. As regards the TRNSYS code, both built-in and user-defined components were used. In particular the MED and the ORC modules were first implemented in an EES environment and then linked to the TRNSYS project, while a model of fan coils has been implemented directly in TRNSYS software. The authors investigated the potential of the plant considering an alternative configuration of the same RPS when powered only by geothermal source, i.e., without installing a PTC system for the exploitation of solar energy. Moreover, the authors investigated the potential of the aforementioned two configurations by varying some key parameters, as explained below and reported in details in the “Result and Discussion” section. The present analysis is divided into the following sections:

• • • • •

Preliminary and brief description of the methodology and system layout; Presentation of the case study and preliminary assessment of energy loads of Pantelleria Island; Brief description of the economic model; Presentation of results on a daily, monthly, and yearly basis and comparison with the performance achieved by the same RPS when powered only by geothermal source; Presentation of results of a parametric analysis performed by varying the values of some relevant parameters and reassessing the plant performance. The independent variables chosen to perform such sensitivity analyses are the weather conditions (so as to consider the possible installation of the same plant in sites characterized by different climates), the mass flow rate and the inlet temperature of the geothermal fluid, and annual time of operation of the DHC network.

5.18.2

System Layout

The layout of the proposed RPS is reported in Fig. 2. The plant consists of an integration of three subsystems: HPP, TRS, and MED3. The HPP is based on the integration of geothermal and solar sources whose thermal energy is collected using a shell and tube heat exchanger and a PTC solar field respectively. Power generation is provided by an ORC module. The geothermal fluid powers in series the HPP, the TRS, and the MED3. The TRS is based on a network of shell and tube heat exchangers, thermal storage tanks, two MED modules (in particular MED1 and MED2) and a single stage LiBr-water ACH. The TRS simultaneously produces desalinated water, through the MED1 and MED2 units, and hot or chilled water depending on the season of the year. Chilled water is produced by the ACH. Finally, the third subsystem consists of a MED module, namely the MED3, which is powered by geothermal fluid and contributes to the total production of fresh water.

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Energy Management in Geothermal Energy Systems

SCF GF

To sea 3

P2

HTF HW of TRS

ORC 5 4

6

HW for MED1/2 HW for scace heating

From sea

ChW from ACH CW for space cooling HW MED3 system

GHE

7

TK2

Pgeo

SW of MED system

P4

13 D2

MED1

From sea to user

MED2

From sea to user

P5

M2

16 14

11

20

TK4

P6

P8

Production well

21

TK3

D3 M3

15

10

17

22 P7

P3

From net

M4 SecHE

FC

To net D4 19

12

To net

From net

26 8

25

18

From sea

23 P9

ACH To sea 27

P12

Med HE

TK5

P10

29 From sea MED3

TK6 9

24

To user

30 P11

28 Injection well

Fig. 2 System layout – Hybrid configuration.

The following main loops are included in the plant:

•

The geothermal fluid (GF) loop (green line), namely the loop of the geothermal brine powering consecutively the three subsystems, HPP, TRS, and MED3, passing through the geothermal heat exchanger, the secondary heat exchanger (SecHE), and the MED heat exchanger (MedHE);

Energy Management in Geothermal Energy Systems

•

• • • • • • • •

755

The heat transfer fluid (HTF) loop (orange line), namely the loop of the heat transfer fluid, in this case diathermic oil Dowtherm A, used to power the ORC module. It is heated in two steps: first, it receives thermal energy from the geothermal source through the GHE, then it receives thermal energy in the thermal storage tank TK1, which is coupled with the PTC system and collects the thermal energy coming from the solar field; The solar collector fluid (SCF) loop (yellow line): the diathermic oil loop, which flows from the tank TK1 to the PTC system and collects solar thermal energy; The hot water (HW) for TRS loop (red line), namely the hot water produced in the SecHE, which first drives the MED1 and MED2 modules through the thermal storage tank TK2 and that is successively stored in thermal storage tank TK3 in order to feed the TK4 or to power the ACH depending on the season; The hot water (HW) for MED1 and MED2 modules loop (dark purple line), namely the loop of the hot water feeding the MED1 and MED2 modules and stored in the TK2; The hot water (HW) powering the ACH and feeding the TK4 loop (light purple line), namely the loop of the hot water stored in TK3, which feeds the TK4 or powers the ACH depending on the season; The hot water (HW) for space heating loop (pink line), namely the hot water stored in TK4, which feeds the DHC network at a temperature ranging between 42.01C and 47.01C; The chilled water (ChW) loop (blue line), namely the loop of the inlet and outlet flows of the chilled water produced by the ACH and feeding the TK5; The cool water (CW) for space cooling loop (light blue line), namely the loop of the cool water stored in TK5 and feeding the DHC network at 7.001C; The hot water (HW) for MED3 module loop (violet line), namely the loop of the hot water produced through the MedHE, stored in TK6 and feeding the MED3 unit.

As mentioned in the Introduction, the authors propose a comparison between the above described configuration, respectively, when powered simultaneously by solar and geothermal energies (hybrid configuration) and when powered only by the geothermal source (geothermal configuration). This latter layout of the system is presented in Fig. 3. As it is shown, differences between the proposed configurations consist of the lack of the SCF loop, the PTC system, and the TK1. Table 1 shows the main design parameters of the plant. The present layout of the plant was obtained by iterative procedure developed on the basis of the findings from previous analyses regarding hybrid ORC systems [217–221] powered by renewable sources and about different RPS configurations [222–225]. Further details are included in Refs. [141,142,217–221], which are strictly focused on the phases of development of the actual system configuration. Special attention was paid to the selection of system capacity. In fact, system design parameters were varied in order to match energy demand of the selected cluster of buildings and achieve an average temperature of geothermal brine exiting the plant not lower than 70.01C. The HPP includes the SCF loop and the HTF loop connected through the stratified thermal storage tank TK1. As stated above, electricity is produced by an ORC unit fueled by diathermic oil Dowtherm A, which is heated up in two consecutive steps: initially it receives thermal power by geothermal source through the GHE, then it is heated up in the TK1. TK1 performs a dual function: it stores the thermal energy coming from the PTC system mitigating the temperature fluctuations and meanwhile it makes the heat supply to the diathermic oil independent from the instantaneous operation of the PTC field. In fact an on/off controller pump drives the operation of the variable speed pump P1 by comparing the temperature of diathermic oil at the bottom of the TK1 and the outlet temperature of the fluid exiting the PTC system: if the latter temperature is lower than the former one, P1 is deactivated, avoiding any heat dissipation. Similarly, another on/off controller drives the operation of geothermal pumps by comparing the inlet temperature of the diathermic oil entering the GHE and the temperature of the geothermal fluid: if the latter temperature is lower than the former one, the geothermal pumps are deactivated. The ORC unit, whose layout consists of a steam generator, a superheater, a condenser, a pump, and a turboexpander, works according to a Rankine thermodynamic cycle using a “dry fluid” (n-pentane) as working fluid: the advantage of this fluid consists in the possibility to feed the turboexpander with dry saturated steam, avoiding the superheating process. In any case, the use of the IHE allows one to consider the possibility to superheat the working fluid: the higher the superheating, the higher the potential heat recoverable, leading to higher first law efficiency. Moreover, the superheating process prevents any possibility of liquid drops at the turboexpander inlet. The condensation is here provided by a shell and tube heat exchanger where sea water flows on the tube side and it is used as cooling source. Mass flow rate of sea water is continuously adjusted in order to maintain outlet temperature below 35.01C, as recommended by Italian legislation. Geothermal brine exiting the GHE is supplied subsequently to the SecHE and lastly to the MedHE, powering the TRS and the MED3 respectively. In the SecHE 175 kg s of hot water is produced in order to feed the TRS network. An on/off controller pump drives the operation of the pump P3 as long as the temperature of the water exiting the SecHE is higher than 961C. From May 1 till November 29 (“Cooling Mode” of operation), TRS produces chilled water (for space cooling purpose) and desalinated water. Hot water coming from SecHE is first stored in in TK2 and then in the TK3. TK2 performs the twofold function of feeding the MED1 and MED2 units, while mitigating the temperature fluctuations, and making the operations of the MED1 and MED2 modules independent of each other. An on/off controller pump drives the operation of P4 and P5 deactivating them when the temperature of hot water feeding the units is above 96.01C or when it is preferable to deliver thermal energy to the network. For the same reason, operation of P4 and P5, and consequently the operation of the MED1 and MED2 units, are independent, in order to achieve the best regulation depending on the magnitude of the DHC network loads. The operation of the MED modules must be as stable as possible, avoiding rapid changes in the temperature and mass flow rate of the feeding source. Therefore, TK2 performs

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Energy Management in Geothermal Energy Systems

SCF GF

To sea 2 TK1

PTC

3

P2

HTF

1

HW of TRS

ORC P1

5

From sea

4

6

HW for MED1/2 HW for scace heating ChW from ACH CW for space cooling HW MED3 system

GHE

7

TK2

Pgeo

HW of MED system

P4

13 D2

MED1

From sea To user

MED2

From sea To user

P5

M2

16 14

11

20

TK4

P6

P8

Production well

21

TK3

D3 M3

15

10

17

22 P7

P3

From net

M4 SecHE

FC

To net D4 19

12

To net

From net

26 8

25

18

From sea

23 P9

ACH To user 27

P12

Med HE

TK5

P10

29 From sea MED3

TK6 9

24

To user

30 P11

28 Injection well Fig. 3 System layout – Geothermal configuration.

the important function of enhancing the thermal capacity of the network and making the inlet temperature constant as much as possible. MED1 and MED2 are fed with 300 kg s 1 of motive hot water and 30.0 kg s 1 of sea water to be desalinated. They are always active (being their product easy to store), allowing one to recover thermal energy from the geothermal fluid mainly during

Energy Management in Geothermal Energy Systems

Table 1 Main parameters of the renewable polygeneration system (RPS) HPP PTC system Mass flow rate Area Concentration ratio Intercept efficiency Efficiency slope Number of IAM points Tank volume Power pump P1 P_ rated;ORC Power of the pump Pgeo Geothermal fluid mass flow rate Geothermal source temperature HTF mass flow rate Power of the pump P2 GHE Tube length Shell diameter Tube outlet diameter Tube thickness Pitch tube Tube number Tube number Tube passages TRS SecHE Tube length Shell diameter Tube outlet diameter Tube thickness Pitch tube Tube number Tube passages Hot water mass flow rate Power of the pump P3 Power of the pump P4 Power of the pump P5 Power of the pump P6 Power of the pump P7 Power of the pump P8 Power of the pump P9 Power of the pump P10 Volume tank TK2 Volume tank TK3 Volume tank TK4 Volume tank TK5 P3 mass flow rate P4 mass flow rate P5 mass flow rate P6 mass flow rate P7 mass flow rate P8 mass flow rate P9 mass flow rate P10 mass flow rate ACH Rated capacity C.O.P. Ch. water mass flow rate

40.0 kg s 1 1.00  104 m2 35.0 0.700 15.0 10.0 100 m3 5.50 kW 1.20 MW 30.0 kW 40.0 kg s 1 1601C 50.0 kg s 1 7.50 kW 15.0 1.50 19.1 19.1 1.50 25.4 60 2.00

m m mm mm mm mm

11.0 m 1.50 m 19.1 mm 1.50 mm 25.4 mm 600 2.00 170 kg s 1 15.0 kW 20.0 kW 20.0 kW 5.00 kW 15.0 kW 20.0 kW 20.0 kW 20.0 kW 120 m3 120 m3 120 m3 50.0 m3 175 kg s 1 250 kg s 1 250 kg s 1 45 kg s 1 169 kg s 1 261 kg s 1 361 kg s 1 261 kg s 1 9.00  103 kW 0.800 361 kg s 1 (Continued )

757

758

Energy Management in Geothermal Energy Systems

Table 1

Continued

Ch. water inlet temp. Ch. water outlet temp. Cooling water mass flow rate Cooling water inlet temp.

12.01C 7.001C 233 kg s 30.01C

MedHE Tube length Shell diameter Tube outlet diameter Tube thickness Pitch tube Tube number Tube passages Mass flow rate Volume tank TK6 Power of the Pump 11 Power of the Pump 12

18.0 m 1.50 m 19.1 mm 1.50 mm 25.4 mm 4.00 2.00 50.0 kg s 120 m3 30.0 kW 30.0 kW

MED 1 Effects Hot water mass flow rate Sea water mass flow rate

8.00 300 kg s 30.0 kg s

MED 2 Effects Hot water mass flow rate Sea water mass flow rate

8.00 300 kg s 30.0 kg s

MED 3 Effects Hot water mass flow rate Sea water mass flow rate

8.00 150 kg s 15.0 kg s

1

1

1 1

1 1

1 1

the night, when the DHC network is deactivated. Hot water is stored in thermal storage TK3 before being sent again to the SecHE. Activation time of the district heating network is from 8:00 to 18:00. In this time period 169 kg s 1 of hot water stored in TK3 is supplied to the ACH passing through the diverter D3. The ACH unit produces 361 kg s 1 of chilled water at 7.001C, whose cooling energy is stored in TK5. Finally, 261 kg s 1 of cool water is supplied to the DHC network for space cooling. From November 30 until April 30 (“thermal recovery mode” period) TRS produces hot water (for space heating purposes) and desalinated water. Operation logics of MED1 and MED2 modules are the same as explained above. During activation time of the DHC network, 45.0 kg s 1 of hot water stored in TK3 are supplied to the TK4 passing through the diverter D3: TK4 is used to store hot water for space heating before being supplied to the network. Here the temperature of the tank is maintained as much as possible in the range between 42.01C and 47.01C by calibrating the mass flow rate of the pump P6. An on/off controller drives the operation of P6, which is deactivated as soon as the temperature entering the TK6 is above 50.01C. A regulation valve controls the temperature of the water exiting the TK4 so that the water returning from the network is mixed with the one exiting the TK4. In this way, heat dissipation is avoided and the network is supplied at as constant a temperature as possible. As mentioned before, MED3 is the last subsystem powered by geothermal brine before exiting the plant. MED3 is driven by hot water produced in the MedHE and stored in TK6. The unit is powered by 150 kg s 1 of hot water and 15.0 kg s 1 of sea water to be desalinated. Moreover the unit is active when the DHC network is deactivated, namely from 18:00 until 8:00. An on/off controller pump drives the operation of pump P11, deactivating it when the temperature of hot water exiting the MedHE is above 96.01C. Similarly, another controller deactivates pump P12 as soon as the temperature of the water feeding the MED3 unit is above 96.01C. Finally, a comparison between the two aforementioned configurations is proposed. For the sake of clarity, the two plant configurations examined in the following section have been distinguished as reported below:

• •

Configuration A: renewable hybrid plant powered by solar and geothermal source. Configuration B: renewable plant powered only by geothermal source.

5.18.3

Case Study

The plant is assumed to be installed in Pantelleria Island, in the South of Italy (latitude 361390 N, longitude 111580 E) and Meteonorm database [226] was used to obtain weather data in the simulation for that location. As mentioned before, the plant is

Energy Management in Geothermal Energy Systems

759

designed and calibrated to supply fresh water, electricity, and thermal and cooling energy to a small district in the island where building urban density is sufficiently high to make the installation of DHC network viable. In particular, the proposed system produces thermal and cooling energy to be supplied to a cluster of 800 buildings. The electrical energy provided by the ORC is first self-consumed by the buildings of the cluster and the eventual excess is delivered to the public network and sold to other users taking into account the time-dependent trends of selling and purchase prices of the Italian electricity market [227]. Similarly, desalinated water is first self-consumed by the cluster of 800 buildings and the excess is sold to the network. Similarly, desalinated water is first self-consumed by the cluster of 800 buildings and the excess is sold to the water network. As regards thermal and cooling energy, possible excess cannot be delivered to other users. Therefore, the thermodynamic and economic analyses require the accurate estimation of the time-dependent demands for space heating and cooling and electricity. In particular, electricity demand was estimated on the basis of measured data for the entire Pantelleria Island. Then, the electricity demand of the cluster of buildings has been calculated on the basis of the available floor area [228]. Space heating and cooling demands cannot be estimated, since no meters are installed for measuring such energy loads in buildings located in Pantelleria Island. Consequently, in this work a detailed building dynamic simulation had to be preliminarily performed, in order to calculate the time-dependent demand of thermal and cooling energy. Initially, typical building size and envelope features have been set up using the ISTAT [228] (Italian Institute of National Censuses and Social and Economic Surveys) data, which are shown in Table 2. As reported, about 94.0% of all buildings include 1 or 2 apartments and consist of 1 or 2 floors above the ground, 69.0% were built between 1940 and 1990 and 89.0% have a structure based on bearing walls. On this basis, a representative model of a “typical” building has been first modeled in SketchUp environment, then it was implemented in TRNBUILD environment and linked to the RPS in TRNSYS environment. In Table 3 stratification of the building envelope is reported, while in Table 4 the main features of the model have been reported. All residential buildings of Pantelleria Island amount to 5281, with only 3300 occupied by local residents throughout the year, while the remaining ones are occupied only in the summer period when the tourist flow increases [229]. Then, the authors decided to focus only on the energy demands of buildings actually occupied throughout the whole year. The duration curves of electric loads are presented in Fig. 4. The total demand is curtailed of the electric consumption from the sea water desalination units currently supplying the entire Pantelleria Island [229], while the electric demand of the 3300 residential buildings is reduced by subtracting the electric consumption related to space heating, which is prevalently supplied by electric heaters [229]. The electric loads related to global demand and to the residential sector increase in the summer period due to the high presence of tourists; conversely, the loads related to permanent residents achieve higher values during the rest of the year: this can be seen more clearly in Fig. 5. As regards the electric consumption of permanent residents, 31.0% of the total is represented by space heating and cooling [229] and 30% by DHW [229]. Electric consumption for desalination is reported in Fig. 5. The total fresh water demand of the whole Pantelleria Island is supplied by electrically-driven desalination units (prevalently based on RO) [229]. Then, the desalination heavily influences the total electric energy consumption, accounting for a 23.0% share [229]. If only the manufacturing sector is considered, desalination systems account for 65.0% of the total demand [229].The annual fresh water consumption on the island is equal to 682,600 m3 [229] and an additional amount of 84,000 m3 of water is imported by cargo ships to cover summer peak requests [229]. Then in summary 766,600 m3 represents the total annual fresh water consumption of Pantelleria Island [229]. In Fig. 6 the model of the building is shown. Once implemented in the TRNSYS environment, the dynamic simulation was performed to obtain and plot the thermal and cooling timedependent load profiles for all the considered residential buildings. Fig. 7 shows the duration curves of thermal and cooling loads.

5.18.4

Simulation Model

A detailed description of the model (governing equation, methodology) is available in Refs. [97,98,141,142,218–221], whilst it is here only briefly summarized for sake of brevity. The ORC model was first developed in Engineering Equation Solver (EES) and Table 2

Residential buildings of Pantelleria Island

No. of Build.

Number of apartments 1 2 4154 777

No. of Build.

Number of floors above the ground 1 2 3 4554 529 131

4 and more 67

No. of Build.

Year of construction 1918 and before 1919–1945 358 459

1961–1970 822

No. of Build.

Material Bearing wall 4 697

3–4 185

1946–1960 860

5–8 86

9–15 68

16 and more 11

Total 5281 Total 5281

1971–1980 1043

Steel reinforced concrete 466

1981–1990 914

1991–2000 448

2001 and after 377

Total 5281

Other 118

Total 5281

760

Table 3

Energy Management in Geothermal Energy Systems

Stratigraphy of the building envelope Thickness

2

Internal wall U¼2.585 W m Internal plaster Tuff Internal plaster

2

K

K

1

1

K )

Specific heat (kJ kg

1

(W m

2.00 36.0 2.00

1000 1600 1500

0.770 0.700 0.550

0.840 0.840 0.840

2.00 12.0 2.00

1000 1600 1000

0.770 0.700 0.550

0.840 0.840 0.840

150 10.0 12.0 7.00 0.0200

1500 500 600 2000 2300

1.80 1.00 0.250 1.60 1.00

1118 0.880 0.880 0.900 1.00

2.00 16.0 4.00 5.00 0.0200

1000 1500 2000 2200 2300

0.770 0.570 1.60 1.40 1.00

0.840 0.840 0.900 0.840 1.00

2.00 16.0 4.00 5.00 2.00 5.00

1000 1500 2000 600 1100 2000

0.770 0.570 1.60 1.40 0.050 1.40

0.840 0.840 0.900 0.840 1.00 0.670

K 1)

1

1

Floor on the ground U ¼1.093 W m Ground Lean concrete Aerated floor forming system Concrete screed Tile Floor U ¼1.094 W m 2 K Internal plaster Hollow flooring block Concrete slab Concrete screed Tile

Conductivity 3

(kg m )

(cm) External wall U ¼1.321 W m Internal plaster Tuff External plaster

Density

2

K

1

1

Roof U¼ 1.035 W m 2 K 1 Internal plaster Hollow flooring block Concrete floor slab Concrete screed Bitominous waterproof barrier Concrete tile

consecutively linked to the RPS and implemented in TRNSYS environment. The EES code was developed with the aim to relate the input parameters and design features to the ORC outputs, as mass flow rate of working fluid, thermodynamic state of the cycle, and cycle performances. Results were obtained in form of polynomial expressions of the mentioned outputs as a function of the inlet temperature of diathermic oil, given a constant mass flow rate. Therefore, the model calculates the off-design operation of the plant as a function of the thermal source powering the unit. Similarly, a code for a MED module has been first developed in EES environment and then linked to the RPS and implemented in TRNSYS environment. Polynomial expressions of the main output parameters have been extrapolated as a function of the inlet temperature of the hot water feeding the module. The MED system was simulated by means of detailed energy and mass balances for all its main subunits, paying special attention to the calculation of the latent heat transfer and to the heat exchange effectiveness in each evaporator. Therefore, the EES code of the MED calculates the off-design operation as a function of the temperature and mass flow rate of the motive hot water. The RPS was developed in TRNSYS, using both built-in and user-defined models. In particular, as mentioned before, novel models have been implemented for: MED, ORC, fan coils installed in the buildings, economic and primary energy calculations, and control strategies. Regarding all the other components, the TRNSYS library of built-in components, validated by experimental data, was used. A brief description of the thermoeconomic model is reported in Table 5. The simple payback period was used to assess the economic performances: it represents the time period needed to recover the capital investment and it is calculated as the ratio between the total plant cost Ztot and the total annual revenue Rtot related with power, fresh water, and heating and cooling production. All the revenues have been calculated on the basis of data regarding unit energy costs and feed-in tariffs for renewable sources provided by GSE [227] (public company which is in charge of the promotion of energy efficiency and renewables in Italy), AEEGSI [230] (the National Authority for Electricity, Natural Gas and Water Systems) and TERNA [231] (the Italian institution managing the national electricity transmission system). The revenue Rel is calculated taking into account the different incentives related to energy production by solar energy and geothermal source [227]. An average yearly solar fraction was assessed to this scope. Moreover, the model takes into account the incomes and the outlays due to the surplus or deficit of power production, calculated as DEel ¼ Eel,net Eselfcons, multiplied by the hourly selling price [227] (if DEel40) or hourly purchase price [227] (if DEelo0) on the electricity market. The revenues provided by space heating Rth and space cooling Rcool have been calculated in terms of avoided cost, namely the cost that should have been sustained if the same quantity of energy recovered was produced by a conventional technology. In this case, the electric heater was assumed as a

Table 4

Main features of the building model

Number of floors Number of apartments per floor Number of rooms per apartment Window area Fan coil Season:

Running time: 9:00–18:00 Nominal Power

Water flow rate

2.637 kW

304.2 kg h

1

405.7 kg h

1

201C

1.953 kW

304.2 kg h

1

405.7 kg h

1

261C

Light and machineries gains Activation time Power Radiative power Convective power Persons gain

7  6.66 m2 2.80 m 2 (0.022pers. m 2) 4

Room area Room height Pers. per apartment Number of window

Air flow rate

Tset.amb.

Sens. to Tot. Heat

0.76

Persons occupancy 8:00–22:00 10.0 W m 2 0.800  W 5.00  W 120 (65–55)

Lun – Fri

Sat–Sun

00:00–08:00 08:00–17:00 17:00–24:00 0:00–24:00 Total (sens./lat)

0.022 pers. m 0.00 pers./m2 0.022 pers. m 0.022 pers. m

2

2 2

Energy Management in Geothermal Energy Systems

Heating mode 30 November / 1 April Cooling mode 2 April/29 November

2 1 2 1.4  2.28 m2

761

762

Energy Management in Geothermal Energy Systems

Total Residential, 3300 Bld.

8

Residential, 800 Bld. (MW)

6 4 2 0

(h) Fig. 4 Duration curves of electric demands.

Space heating Residents

800 Buildings

Tourists

Desalination

MWh 1000 800 600 400 200 0 Jan Feb Mar

Apr May June July

Aug Sept Oct

Nov Dec

Fig. 5 Monthly electric energy consumption.

5.6

1.4

2.3

N

13.3

7.0

Fig. 6 Simplified model of the building.

(kW) 10,000

Cooling load

8000 6000

Thermal load

4000 2000 0 0

365

(h)

730 1000

Fig. 7 Duration curves of thermal and cooling loads of the considered buildings.

Energy Management in Geothermal Energy Systems

Table 5 SPB ¼

763

Economic model (1)

Ztot Rel þRth þRdes:water ZM & M

Ztot ¼ ZPTC þ Zwell þ ZHE þ ZORC þ ZMED þ ZACH þ Ztank þ ZBOP þ ZO & M ZPTC ¼ 600  APTC Zwell ¼ 2:00  1000  z
0:780 A ZHE ¼ Zgeo;HE þ ZSec;HE þ ZMED;HE Zgeo;HE ¼ 17:5  103 þ 699  A0:93 ZMed;HE ¼ 150  HE;geo ZSec;HE ¼ 150  0:0930 3 ZORC ¼ 4:00  10  P_ rated;ORC 
 ZMED ¼ 0:500  800  MðD;totÞ  3:60  24:0 þ cp  ðAtot Þg ZACH ¼ 250  Q_ ACH Ztank ¼ 494:9 þ 808  Vtank 2 ZBOP ¼ 15:0   10  ðZPTC þ ZHE þ Ztank þ ZACH þ ZMED Þ  ZðO & MÞ ¼ 30  P_ rated;ORC þ 32  P_ rated;ORC þ 26  P_ rated;ORC þ 13  P_ rated;ORC þ 160; 000 Rel ¼ ESelfcons:  Fsol ð0:296 þ 0:166Þ þ ESelfcons:  ð1 DEel;surplus=deficit ¼ Eel;net ESelfcons Rth ¼ Z Qth  cel;purch: ; Zel;heat: ¼ 0:950


0:780 A 0:0930

Fsol Þ  ð0:128 þ 0:166Þ þ DEel;surplus  cel;sell þ DEel;deficit  cel;purch:

(2) [233] (3) [234–236] (4) [118,237] (5) [238–240] (6) [222,225] (7) [234,235] (8) [241] (9) (10) [242] (11) (12) (13) (14)

el;heat:

cool  cel;purch: ; COP ¼ 3:00 Rcool: ¼ QCOP Rdes ¼ mdes  cdes

(15) (16)

conventional technology for space heating, considering a 95% average efficiency. Conversely, vapor compression chiller was assumed to represent the conventional technology for space cooling, adopting an average seasonal COP equal to 3.00. Both for space heating and space cooling, based on the requests the consequent amount of electricity needed was easily calculated. Even in this case, revenues take into account the time-dependent trends of the thermal and cooling loads and the variable purchase price of the electric energy. Finally, the revenue related with fresh water production was simply calculated as the amount of fresh water potentially saleable multiplied by the specific cost cdes, which was assumed equal to 0.7 € m 3. In order to assess the environmental performances of the plant, the avoided primary energy consumption and the avoided CO2 emissions have been calculated. All the energy production in Pantelleria Island is currently guaranteed by reciprocate engine-based power plants fueled by diesel gasoline. Since desalination, heating, and cooling processes are currently supplied by consuming electricity, all the material (desalinated water) and energy outputs (electricity, thermal and cooling energy supplied to the network DHC) of the plant were first converted into the equivalent amount of electricity that would be needed to produce the same outputs by the aforementioned conventional equipment. Eel;th ¼

Qth ;Z ¼ 0:95 Zel;heat: el;heat:

ð1Þ

Qcool ; COP ¼ 3:00 COP

ð2Þ

Eel;ch ¼

As mentioned earlier in this chapter, the currently installed desalination units are based on RO technology and characterized by an average unit energy consumption ZRO in the order of 3.3 kWh m 3 (11.9  103 kJ m 3). Consequently, the electricity consumption for sea water desalination was calculated as the amount of saleable fresh water multiplied by the specific consumption of the RO units.
Eel;des ¼ mdes  ZRO ; ZRO ¼ 3:30 kWh m 3 11:88  103 kJ m 3 ð3Þ Then, the primary source (PS) was calculated taking into account the efficiency Ztp of the power plant currently installed in Pantelleria [228]: DPS ¼

Eel;tot ; Ztp ¼ 0:390 Ztp

ð4Þ

Finally, avoided emissions have been assessed multiplying the PS (expressed in kg) by the emission factor of the diesel gasoline [232]. DCO2 ¼ DPS  EF; EF ¼ 3:11  103 kgCO2 10 3 kg

5.18.5

ð5Þ

Results and Discussion

One-year (8760 h) dynamic simulations were performed and results collected on daily, monthly, and yearly bases. All the simulations were performed assuming a 0.04 h time-step in order to promote the convergence while ensuring sufficient accuracy of results, mainly for components characterized by higher thermal capacities.

764

Energy Management in Geothermal Energy Systems

As mentioned in the Introduction section, the two considered configurations have been distinguished as reported below:

• •

Configuration A: renewable hybrid plant powered by solar and geothermal source. Configuration B: renewable plant powered only by geothermal source.

5.18.5.1

Daily Results

Daily results offer a detailed view of plant operation and allow one to analyze the behavior of each component observing the realtime fluctuations of its main thermodynamic parameters. For sake of brevity, only one representative day of the “cooling mode” operation period is reported. In Fig. 8 the temperatures calculated in a typical operation day are reported, while in Fig. 9 the main energy flows are presented. It is shown that the PTC operation strongly affects the operation of the HPP while it moderately influences the TRS and MED3 operation. In particular, it enhances the temperature levels along the whole plant, including the operating temperatures of the ORC cycle. This leads to higher power production rates and a higher first law efficiency. Higher temperatures of the HTF exiting the ORC induce a decrease in the heat flow rate exchanged in the GHE. The inlet temperature of the geothermal fluid is stable at 1601C and it always remains above 701C, except for some short time periods (activation and deactivation of the DHC) where it falls below 701C; this is due to the transient operation required to increase the tank temperatures up to the set point together with the high demand of thermal power by the network. When DHC network is deactivated, the operation of TRS and MED3 subsystems is very stable. This can be observed from the daily temperature profiles of the geothermal fluid, the TRS network hot water and the motive hot water feeding the MED modules. When the DHC network is active, temperature fluctuations can be observed. As regards MED modules, MED1 and MED2 are uninterruptedly active, while MED3 is active only when the network is off. Electricity production is well above the total load of the cluster. The network is supplied with cool water at a constant 7.001C temperature. As regard the “thermal recovery mode,” the system qualitatively shows the same behavior. Small differences consist of the lower temperatures and energy flows characterizing the PTC and the HPP. The proposed graphs refers to Config. A, since more interesting findings emerged with respect to Config. B. Significant differences between these two configurations deal with the operating conditions of the HPP, due to the strong influence of the PTC on the ORC operation. Temperature levels are lower for Config. B, leading to a quite lower net power production compared to Config. A. Power production maintains lower than the power load for long time periods throughout the year. Conversely TRS and MED unit operation for Config. B slightly differs from the case of Config. A. In particular, the TRS subsystem presents slightly lower temperatures during daylight hours, although it is suitably capable of covering thermal and cooling demands. Operation of all MED units (in terms of temperature levels and fresh water production) is almost the same as the one described above for Config. A. In brief, Config. B is perfectly capable of cover thermal and cooling energy demand and the desalination process occurs without significant variations with respect to the desalination process in Config. A. Finally, both configurations A and B do not allow one to cover entirely the electric loads by the cluster of buildings, thus being not capable to make the RPS independent from the grid. 250 T2 225 T3 200

T6

T5

175

T7

(°C)

150

T4 T8

125

T10

100

T12

T1 T14

75 50

T9 T29

25

T30

0 0

1

2

3

4

5

6

7

Fig. 8 Main temperatures of whole plant – Cooling mode.

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 (h)

Energy Management in Geothermal Energy Systems

765

1800

14,000 13,000

1600 12,000

QSecHE

11,000

1400 1200

9000

(KW)

8000 7000

Pel

Pel,net P 800, Build

QPTC

1000 QMED1

QGEO

6000

800

5000

600

4000

PEL, PEL,NET, PEL,LOAD (KW)

10,000

400

3000 2000 QMED3

1000 0

200 0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 (h) Fig. 9 Main energy flow – Cooling mode.

5.18.5.2

Monthly and Annual Results

Monthly and annual results at system level are shown in Table 6 for both the examined configurations, while in Table 7 a comparison between the economic and environmental performance indices is proposed. The electricity production is obviously higher in Config. A, due to the contribution of solar radiation, which enhances the performance and the production rate of the ORC module. Electricity production clearly is higher in summer months, due to the higher availability of solar radiation. This can be observed by looking at the average value of the first law efficiency for the ORC module, ZORC, which is higher in case A (15.3%) and varies month by month depending on the solar gain. In fact it ranges between 14.9% and 15.6%, achieving the highest values in summer. Conversely, in case B the ORC efficiency is constant and equal to 14.6%. Geothermal energy is predominant and stable, while solar energy provides a much lower contribution to the total production. This can be observed by looking at the average annual solar fraction, Fsol, which ranges between 7.90% and 20.1%. As concern power production, in both configurations the net power production is higher than the total load of the examined cluster of buildings, in particular the hybrid plant presents a net production 16.1% higher than the geothermal one. In case of the hybrid plant (Config. A) the net production slightly exceeds the load, although there are periods during the year (in particular 835 h) where the net power production is lower than the current load and the deficit must be covered by electricity purchase from the grid. In case B the net production is slightly higher than the load (only 130 MWh [468  103 MJ]) and the time periods of power deficit amount to 2280 h, namely the plant needs power from the grid for 26% of the time throughout the year. In both cases, the plant is perfectly capable of covering the space heating and cooling loads. As regard the desalination process, in both cases the total fresh water production is higher than the total fresh water need in the island of Pantelleria, although in case A it is only slightly higher, presenting a limited surplus of about 50  103 m3. As regards the exploitation of renewable thermal energy, relevant differences may be observed in the operation of the HPP, while the operation of the TRS and MED3 subsystems is almost unmodified. In fact, thermal energy exploited at the GHE is about 6.00% lower in the case of the HPP. As explained in the previous section, this is due to the higher HPP temperatures during the operation of the PTC system. Solar gain enhances the inlet temperature of diathermic oil entering the ORC unit, as well as its outlet temperature. Consequently, average temperature difference between the fluids at the GHE decreases, causing a decrease in the exchanged heat rate. Conversely, the heat rates transferred in the SecHE and in the MedHE (respectively supplied to the TRS and MED3 subsystems) in cases A and B are similar, presenting a deviation of about 1.00% and 0.07%, respectively. The total plant capital costs of the two configurations are very different, in fact the cost of the geothermal configuration plant accounts for only 46.0% of the cost of the hybrid one. It is worth noting that the PTC system, the ACH unit, and the ORC module account for 67.6% of the total investment cost of the hybrid plant. In particular, the PTC system only accounts for 27.3% of the total cost. Then, in the examined case study the CSP technology weighs heavily on the investment cost and therefore the benefits deriving from its inclusion in the plant layout must be accurately evaluated.

766

Table 6

Eel,ORC Eel,net Eel,load Eel,surplus Qth,GHE Qth,SecHE Qth,MedHE Qth,load Qch,load Qth,PTC Qth,MED1 Qth,MED2 Qth,MED3 (%) ZORC Fsol

Energy Management in Geothermal Energy Systems

Monthly and annual results Jan Feb Mar Apr  103 MWh (  3.60  106 MJ)

May

June

July

Aug

Sept

Oct

Nov

Dec

Year

A B A B A B A B A B A B A B A B A B A B A B A B A B

0.540 0.505 0.430 0.395 0.360 0.360 0.213 0.035 3.34 3.45 7.09 7.25 1.10 0.83 1.03 1.03 0.00 0.00 0.29 – 3.23 3.15 3.23 3.15 1.10 0.82

0.510 0.456 0.410 0.357 0.359 0.359 0.224 0.003 2.96 3.11 6.43 6.53 1.01 0.75 0.81 0.81 0.00 0.00 0.40 – 2.93 2.85 2.93 2.85 1.00 0.74

0.580 0.505 0.470 0.396 0.359 0.359 0.235 0.037 3.24 3.45 7.00 7.03 1.12 0.83 0.50 0.50 0.00 0.00 0.56 – 3.29 3.22 3.29 3.22 1.12 0.82

0.570 0.488 0.460 0.384 0.428 0.428 0.032 0.045 3.11 3.34 6.54 6.51 1.11 0.81 0.034 0.034 0.00 0.00 0.60 – 3.26 3.20 3.26 3.20 1.11 0.80

0.600 0.505 0.480 0.386 0.360 0.360 0.120 0.026 3.20 3.45 6.76 6.67 1.13 0.83 0.00 0.00 0.00 0.00 0.67 – 3.37 3.32 3.37 3.32 1.13 0.83

0.590 0.488 0.480 0.374 0.366 0.366 0.114 0.007 3.05 3.34 6.81 6.78 1.09 0.80 0.00 0.00 0.35 0.35 0.77 – 3.20 3.12 3.20 3.12 1.09 0.80

0.600 0.505 0.480 0.386 0.388 0.388 0.092 0.002 3.19 3.45 7.27 7.34 1.11 0.83 0.00 0.00 0.65 0.65 0.67 – 3.23 3.13 3.23 3.13 1.11 0.82

0.590 0.505 0.480 0.386 0.386 0.386 0.094 0.001 3.20 3.45 7.29 7.36 1.10 0.83 0.00 0.00 0.77 0.77 0.65 – 3.22 3.12 3.22 3.12 1.09 0.82

0.580 0.488 0.470 0.374 0.368 0.368 0.102 0.006 3.08 3.34 6.83 6.82 1.11 0.80 0.00 0.00 0.36 0.36 0.68 – 3.18 3.11 3.18 3.12 1.10 0.80

0.580 0.505 0.460 0.386 0.360 0.359 0.101 0.027 3.25 3.45 6.75 6.69 1.13 0.83 0.00 0.00 0.01 0.01 0.54 – 3.36 3.31 3.36 3.31 1.12 0.83

0.540 0.488 0.430 0.374 0.362 0.362 0.087 0.012 3.19 3.34 6.53 6.47 1.09 0.81 0.05 0.05 0.00 0.00 0.40 – 3.23 3.21 3.23 3.21 1.09 0.80

0.540 0.505 0.430 0.395 0.361 0.361 0.191 0.034 3.34 3.45 7.08 7.25 1.12 0.83 0.80 0.80 0.00 0.00 0.30 – 3.24 3.16 3.24 3.16 1.12 0.82

6.82 5.94 5.48 4.59 4.46 4.46 1.60 0.133 38.1 40.6 82.4 82.7 13.2 9.77 3.19 3.19 2.14 2.14 6.53 – 38.7 37.9 38.7 37.9 13.2 9.70

A B A B

14.9 14.6 7.90 –

15.2 14.6 11.8 –

15.3 14.6 14.7 –

15.4 14.6 16.1 –

15.4 14.6 17.3 –

15.6 14.6 20.1 –

15.4 14.6 17.4 –

15.4 14.6 17.0 –

15.5 14.6 18.1 –

15.3 14.6 14.3 –

15.1 14.6 11.1 –

15.0 14.6 8.30 –

15.3 14.6 14.6 –

The SPBs achieved by the two examined configurations significantly differ, since the geothermal system (Config. B) presents a SPB equal to 4.12 years, instead of the 8.42 years SPB achieved by the hybrid plant. As for the revenues related to output productions, it is worth noting that the higher power production rate achieved by the hybrid plant does not result in a proportional economic benefit. In fact, the revenue from the electricity production essentially depends on the public funding for renewable technologies, while the incomes related to the average market prices of electricity are much lower. Also, the avoided cost related to space heating and the revenue provided by the desalination process represent almost 44.0% of the total annual revenue from plant operation. The amount of electricity that should be produced by conventional technologies is equal to about 13.50  103 MWh [48.60  106 MJ] in case A and 12.60  103 MWh [45.36  106 MJ] in case B, which correspond to 37.50  103 MWh [135.0  106 MJ] and 34.9  103 MWh [125.6  106 MJ] of primary energy consumption, respectively. As a consequence, the hybrid plant achieves a 6.79% higher energy savings compared to the geothermal system. The amount of primary energy source (gasoline oil) to be used in case A is equal to 3039  103 kg while in case B it is equal to 2833  103 kg; based on the appropriate emission factor, the avoided CO2 emissions respectively amount to 9451  103 kg and 8810  103 kg. In Table 8 operating results for the MED modules have been reported. Since the desalination process is included in both configurations with no substantial differences, only the results obtained for Config. A have been presented. Similarly, results regarding the geothermal energy utilization have been reported only for Config. A in Table 9, since geothermal exploitation in Config. B is qualitatively similar. It is worth noting that in both the examined configurations the total fresh water production is higher than the annual demand of Pantelleria, which amounts to 767 103 m3. The ratio between the desalinated water and the total processed sea water ranges between 44.4% and 46.6% in case of MED1 and MED2 modules, while it ranges between 38.33% and 39.1% for MED3 module. All the MED units work with a specific consumption of thermal energy approximately equal to 90 kWh m 3 (324.0  106kJ m 3). In Table 9 the shares of geothermal energy exploitation by the three subsystems are reported. The TRS exploits the largest amount of geothermal energy, accounting for almost 61.6% of the total, followed by the HPP, which averages 28.5%, and finally the MED3 system, accounting for 9.9% (since it is active only when the DHC network is deactivated).

Table 7 Eel,ORC

Comparison of main results Eel,net

Eel,load

Surpl.- def.

Qth,Bld

Qcool,Bld

Qth,PTC

Qth,GHE

Qth,SecHE

Qth,MedHE

 103 MWh (  3.60  106 MJ) A B

6.83 5.94

Rsur

4.46 4.46 Rdef

1.02 0.13 Rel

Rth

3.75 3.75 Rcool

2.70 2.70 Rdes

(M€) A B

22.0 10.1

6.51 0.00 SPB (year)

0.021 0.008

0.008 0.032

1.434 1.272

0.662 0.662

0.151 0.151

0.693 0.682

8.422 4.119

38.14 40.59 Eel,net

82.39 81.38

13.22 13.12

(%Eel,net)

Eel,th

ZORC

(  103 m3)

(%)

1006.55 956.26 Eel,ch

Eel,des

Fsol

(h) 15.30 14.64

14.59 0.00

(%DPS)

DPS

(  103 MWh (  3.60  106 MJ) 5.475 4.592

(16.1)

Hours of deficit

835 2280 DPS

DCO2

(  103 kg) 3.952 3.952

0.901 0.901

3.166 3.127

34.60 32.25

(6.79)

2805 2615

9431 8708

Energy Management in Geothermal Energy Systems

Ztot

5.47 4.59

mdes

767

768

Table 8

Energy Management in Geothermal Energy Systems

Monthly and annual results – multieffect desalination (MED) units, Config. A

MED1 MED2

Hours of op.

msea (  103m3)

mdes (  103 m3)

mdes/msea (%)

mbrine (  103 m3)

xbrine (g kg 1)

Spec. Cons (kWh m 3) (  3.60  103 kJ m 3)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year

744 672 744 720 744 720 744 744 720 744 720 744 8760

80.3 72.6 80.4 77.8 80.4 77.8 80.4 80.4 77.8 80.4 77.8 80.4 946

35.8 32.5 36.5 36.2 37.5 35.5 35.8 35.7 35.3 37.3 35.9 35.9 430

44.6 44.8 45.4 46.6 46.6 45.7 44.6 44.4 45.4 46.4 46.2 44.7 45.5

44.52 40.1 43.8 41.5 42.9 42.2 44.5 44.6 42.4 43.0 41.8 44.4 516

68.6 68.8 69.6 71.1 71.2 70.0 68.6 68.4 69.7 70.9 70.6 68.7 69.7

90.24 90.2 90.1 89.9 89.9 90.1 90.2 90.2 90.1 90.0 90.0 90.2 90.1

MED3 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year

448 404 448 433 448 433 448 448 433 448 433 448 5270

31.9 29.0 32.2 31.5 32.1 31.4 32.2 31.8 31.7 32.1 31.1 32.4 379

12.2 11.1 12.4 12.3 12.5 12.1 12.3 12.1 12.3 12.5 12.1 12.4 146

38.2 38.4 38.6 39.1 39.0 38.7 38.3 38.2 38.7 38.9 38.8 38.4 38.6

19.7 17.9 19.7 19.2 19.6 19.2 19.9 19.7 19.4 19.6 19.0 20.0 233

61.5 61.7 61.9 62.4 62.3 62.0 61.6 61.4 61.9 62.2 62.1 61.7 61.9

90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0 90.0

Table 9

Thermal energy utilization, Config. A Qth;GHE Qth;tot;brine

Qth;TRS Qth;tot;brine

Qth;MedHE Qth;tot;brine

Qth;MED1=2 Qth;tot;brine

Qth;MED3 Qth;tot;brine

Qth;MED1=2 Qth;SecHE

61.5 61.8 61.6 60.8 61.0 62.2 62.8 62.9 62.0 60.7 60.4 61.3 61.6

9.60 9.70 9.90 10.3 10.2 10.0 9.60 9.50 10.0 10.1 10.1 9.70 9.90

56.1 56.4 57.9 60.5 60.8 58.4 55.9 55.6 57.8 60.3 59.9 56.2 57.9

9.50 9.60 9.80 10.3 10.1 10.0 9.60 9.40 10.0 10.1 10.1 9.70 9.90

91.2 91.2 94.0 99.6 99.6 93.9 88.9 88.4 93.2 99.4 99.1 91.6 94.1

(%) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year

29.0 28.5 28.5 28.9 28.8 27.8 27.6 27.6 28.0 29.2 29.5 28.9 28.5

In particular, the largest fraction of thermal energy supplied to the TRS is exploited by the MED modules (94.1%), which are uninterruptedly active. This is due to the low energy requests from the network serving space heating and cooling uses. This last result suggests that, due to the temperate climate of Pantelleria Island, a new calibration of the system could be attempted in order to supply a larger cluster of buildings. In fact, in the examined case thermal energy is mainly consumed to drive the desalination units. In summary, the hybrid configuration turned out to be very expensive compared to the geothermal configuration, while the thermodynamic advantages in terms of additional electricity and fresh water production do not result in proportional economic benefits.

Energy Management in Geothermal Energy Systems 5.18.5.3

769

Parametric Analysis and Economic Analysis

In order to better assess the economic profitability of the proposed hybrid and the geothermal configurations, a parametric analysis was performed by varying some of the key design parameters of the dynamic simulation model. All the selected cases are reported in Table 10. As shown in Table 10, the first independent variable to be investigated is related with the context condition. In particular, two distinct cities located in different geographical regions (Pisa, Italy and Mougla, Turkey) of the Mediterranean area have been identified, the choice being driven by the comparable availability of geothermal sources. The different climatic conditions of these two sites obviously influence the heating and cooling loads and the resulting operation of the RPS (since the HPP operation closely depends on the solar energy gain). This might lead to completely different results for the economic assessment of the examined plant configurations. As a second sensitivity analysis, the potential of the RPS has been investigated by assuming different availabilities of the geothermal source. In particular, a lower and a higher availability of geothermal source were considered (compared to the reference case), modifying both the inlet temperature (1401C and 1801C instead of the reference 1601C temperature) and the mass flow rate of geothermal fluid (35 kg s 1 and 45 kg s 1 instead of the reference 40 kg s 1 flow rate). Finally, different daily operation schedules of the DHC were considered (8:00–21:00 and 8:00–24:00, instead of the reference 8:00–18:00) in order to assess the economic advantages that could be achieved by ensuring a prolonged exploitation of thermal energy for space heating and cooling or a higher exploitation of thermal source to drive the desalination units. In Table 11 the main results of the thermodynamic analysis have been reported, while in Table 12 the main economic results are presented. Data were aggregated so as to offer the basis for an easier comparison between the two configurations when the value of each specific context variable was changed. The first set of dynamic simulations, given by cases A, B, C, D, E, and F, represents the parametric analysis performed by varying the hypothetical site of the installation. Case E is characterized by higher solar radiation, as evident from the higher thermal energy production by the PTC system Qth,PTC (9.45  103MWh [34.02  106 MJ] compared to the 6.51  103MWh [23.44  106 MJ] achieved in case A and 6.49  103MWh [23.36  106 MJ] in case C). In this case, a higher net electricity is obtained compared to the other sites (see the 5.90  103MWh [21.24  106 MJ] achieved in case E, compared to the 5.47  103MWh [19.69  106 MJ] obtained in case A and 5.45  103MWh [19.62  106 MJ] of case C). As already mentioned, PTC operation closely affects the HPP behavior, while it moderately affects the TRS and MED3. In fact, the operation of these two last components does not change substantially when considering different localities, as evident from a comparison between the amounts of heat exchanged in the SecHE for TRS and MedHE for MED3. Furthermore, the operation of these units does not change substantially between the hybrid and the geothermal configurations, though a slightly higher fresh water production is achieved by hybrid configurations (A, C, and E) with respect to the geothermal ones (B, D, and F). Conversely, this consideration is not applicable when the operating conditions of the HPP are concerned, since they significantly change as a function of solar radiation if the PTC system is installed. Looking to the cases B, D, and F, related with the geothermal configurations, it can be observed that plant operation does not vary, since the different climate conditions do not affect the heat exchanges between the different subsystems. Conversely, different environmental conditions induce corresponding changes in space heating and cooling loads, which ultimately lead to different Table 10

Parametric analysis Locality

Inlet temperature of geothermal fluid (1C)

Mass flow rate of geothermal fluid (kg s 1)

Time of operation

Source

A B

Pantelleria (Italy) Pantelleria (Italy)

160 160

40 40

8:00–18:00 8:00–18:00

Hybrid Geothermal

C D E F

Pisa (Italy) Pisa (Italy) Mugla (Turkey) Mugla (Turkey)

160 160 160 160

40 40 40 40

8:00–18:00 8:00–18:00 8:00–18:00 8:00–18:00

Hybrid Geothermal Hybrid Geothermal

G H I L

Pantelleria Pantelleria Pantelleria Pantelleria

(Italy) (Italy) (Italy) (Italy)

140 140 180 180

40 40 40 40

8:00–18:00 8:00–18:00 8:00–18:00 8:00–18:00

Hybrid Geothermal Hybrid Geothermal

M N O P

Pantelleria Pantelleria Pantelleria Pantelleria

(Italy) (Italy) (Italy) (Italy)

160 160 160 160

35 35 45 45

8:00–18:00 8:00–18:00 8:00–18:00 8:00–18:00

Hybrid Geothermal Hybrid Geothermal

Q R S T

Pantelleria Pantelleria Pantelleria Pantelleria

(Italy) (Italy) (Italy) (Italy)

160 160 160 160

40 40 40 40

8:00–21:00 8:00–21:00 8:00–24:00 8:00–24:00

Hybrid Geothermal Hybrid Geothermal

Energy Management in Geothermal Energy Systems

770

Table 11

Main thermodynamic results of the parametric analysis

Eel,ORC

Eel,net

Eel,load

3

Surpl.- Def.

Qth,Bld

Qcool,Bld

Qth,PTC

Qth,GHE

Qth,SecHE

Qth,MedHE

mdes

ZORC 3

6

3

 10 (m )

 10 (MWh) (  3.60  10 MJ)

Fsol

(%)

Hours of deficit (h)

C D A B E F

6.83 5.94 6.83 5.94 7.28 5.94

5.45 4.59 5.47 4.59 5.90 4.59

4.46 4.46 4.46 4.46 4.46 4.46

0.99 0.13 1.02 0.13 1.44 0.13

3.70 3.70 3.75 3.75 3.79 3.79

2.84 2.84 2.70 2.70 2.68 2.68

6.49 0.00 6.51 0.00 9.45 0.00

38.15 40.59 38.14 40.59 37.13 40.59

83.78 82.74 82.39 81.38 84.19 82.71

9.78 9.77 9.78 9.77 9.78 9.77

994.93 947.33 959.46 947.7 963.83 947.50

15.30 14.64 15.30 14.64 15.63 14.64

14.54 0.00 14.59 0.00 20.29 0.00

818 2278 835 2280 545 2282

G H A B I L

4.95 4.19 6.83 5.94 9.04 8.02

3.57 2.84 5.47 4.59 7.67 6.67

4.46 4.46 0.00 4.46 4.46 4.46

0.88 1.62 1.02 0.13 3.21 2.21

3.74 3.74 3.75 3.75 3.74 3.74

2.70 2.70 2.70 2.70 2.70 2.70

6.52 0.00 6.51 0.00 6.51 0.00

29.17 31.98 38.14 40.59 48.00 50.09

74.66 73.40 82.39 81.38 91.28 90.93

8.46 8.45 9.78 9.77 12.57 11.11

847.13 834.51 959.46 947.7 1081.40 1062.72

13.88 13.10 15.30 14.64 16.59 16.02

18.26 0.00 14.59 0.00 11.94 0.00

7492 8760 835 2280 1 1

M N A B O P

6.67 5.77 6.83 5.94 6.96 6.07

5.29 4.42 5.47 4.59 5.58 4.72

4.46 4.46 0.00 4.46 4.46 4.46

0.83 0.04 1.02 0.13 1.12 0.26

3.75 3.75 3.75 3.75 3.74 3.74

2.70 2.70 2.70 2.70 2.70 2.70

6.51 0.00 6.51 0.00 6.51 0.00

37.36 39.79 38.14 40.59 38.71 41.17

77.03 75.88 82.39 81.38 89.14 88.34

8.55 8.54 9.78 9.77 11.35 10.90

872.88 860.96 959.46 947.7 1041.26 1027.16

15.19 14.51 15.30 14.64 15.38 14.73

14.85 0.00 14.59 0.00 14.41 0.00

1093 3027 835 2280 619 1870

A B Q R S T

6.83 5.94 6.83 5.94 6.83 5.94

5.47 4.59 5.42 4.56 5.39 4.53

4.46 4.46 4.46 4.46 4.46 4.46

1.02 0.13 0.97 0.10 0.93 0.07

3.75 3.75 4.18 4.18 4.52 4.52

2.70 2.70 3.00 3.00 3.21 3.21

6.51 0.00 6.51 0.00 6.51 0.00

38.14 40.59 38.14 40.59 38.14 40.59

82.39 81.38 84.56 83.55 85.30 84.31

13.22 13.12 9.78 9.77 5.58 5.58

1006.55 956.26 959.46 947.7 902.44 890.62

15.30 14.64 15.30 14.64 15.30 14.64

14.59 0.00 14.59 0.00 14.59 0.00

835 2280 835 2336 926 2396

DPS

(%DPS) DPS

Table 12 Ztot

Economic results of the parametric analysis Rsur

Rdef

Rel

Rth

Rcool

Rdes

(M€)

SPB

Eel,net

(%Eel,net) Eel,th

Eel,ch

(year)

 103 MWh (  3.60  106 MJ)

Eel,des

DCO2

(  103 kg)

C D A B E F

21.6 10.1 22.0 10.1 22.3 10.1

0.022 0.008 0.021 0.008 0.030 0.008

0.008 0.032 0.008 0.032 0.005 0.032

1.434 1.264 1.434 1.272 1.488 1.296

0.651 0.651 0.662 0.662 0.669 0.669

0.158 0.158 0.151 0.151 0.150 0.150

0.692 0.682 0.693 0.682 0.697 0.682

8.257 4.138 8.422 4.119 8.307 4.069

5.452 (15.8) 4.592 5.475 (16.1) 4.592 5.901 (22.2) 4.592

3.890 3.890 3.952 3.952 3.992 3.992

0.945 0.945 0.901 0.901 0.895 0.895

3.164 3.126 3.166 3.127 3.181 3.127

34.49 (6.66) 32.19 34.60 (6.79) 32.25 35.82 (9.71) 32.43

2797 2610 2805 2615 2904 2622

9314 8693 9431 8708 9670 8733

G H A B I L

20.7 9.20 22.0 10.1 23.9 11.1

0.004 0.000 0.021 0.008 0.063 0.044

0.173 0.257 0.008 0.032 0.000 0.000

1.279 0.578 1.434 1.272 1.463 1.355

0.660 0.660 0.662 0.662 0.659 0.659

0.151 0.151 0.151 0.151 0.151 0.151

0.592 0.580 0.693 0.682 0.802 0.786

8.757 5.559 8.422 4.119 8.673 4.216

3.574 (20.6) 2.839 5.475 (16.1) 4.592 7.665 (13.0) 6.673

3.938 3.938 3.952 3.952 3.937 3.937

0.901 0.901 0.901 0.901 0.901 0.901

2.796 2.754 3.166 3.127 3.569 3.507

28.74 (6.91) 26.75 34.60 (6.79) 32.25 41.21 (6.53) 38.52

2330 7759 2169 7223 2805 9431 2615 8708 3342 11127 3123 10399

M N A B O P

22.0 10.1 22.0 10.1 22.0 10.1

0.018 0.006 0.021 0.008 0.023 0.009

0.011 0.042 0.008 0.032 0.006 0.026

1.430 1.239 1.434 1.272 1.436 1.284

0.661 0.661 0.662 0.662 0.660 0.660

0.151 0.151 0.151 0.151 0.151 0.151

0.615 0.604 0.693 0.682 0.766 0.754

8.698 4.317 8.422 4.119 8.189 3.985

5.288 (16.4) 4.423 5.475 (16.1) 4.592 5.579 (15.5) 4.715

3.946 3.946 3.952 3.952 3.942 3.942

0.901 0.901 0.901 0.901 0.901 0.901

2.880 2.841 3.166 3.127 3.436 3.390

33.37 (6.91) 31.07 34.60 (6.79) 32.25 35.53 (6.53) 33.21

2705 2519 2805 2615 2880 2693

9009 8384 9431 8708 9590 8968

A B Q R S T

22.0 10.1 22.0 10.1 22.0 10.1

0.021 0.008 0.021 0.007 0.020 0.006

0.008 0.032 0.008 0.032 0.008 0.032

1.434 1.272 1.433 1.268 1.432 1.264

0.662 0.662 0.735 0.735 0.792 0.792

0.151 0.151 0.167 0.167 0.178 0.178

0.693 0.682 0.667 0.656 0.641 0.631

8.422 5.475 (16.1) 4.119 4.592 8.225 5.424 (15.9) 4.020 4.559 8.099 5.391 (16.0) 3.961 4.527

3.952 3.952 4.401 4.401 4.755 4.755

0.901 0.901 1.002 1.002 1.069 1.069

3.166 3.127 3.071 3.032 2.978 2.939

34.60 (6.79) 32.25 35.63 (6.46) 33.33 36.39 (6.33) 34.09

2805 2615 2889 2702 2951 2763

9431 8708 9621 9000 9941 9203

Energy Management in Geothermal Energy Systems

771

economic profitability (due to the difference in avoided costs), primary energy savings, and avoided emissions. In fact, the revenues related to space heating and cooling change strictly depending on the site where the plant is assumed to be installed. The higher power production achieved by the hybrid configuration does not really induce proportional increases in the revenues. Conditions of electricity market do not allow this plant configuration to fully exploit the additional electricity production from an economic point of view. In fact, revenues related to electricity mainly depend on public funding availability, rather than on incomes from the sale of surplus electricity. The revenues of all cases given by electric production range between 45.7 and 49.5%, then the total revenue strongly depends on the desalination process, ranging between 23.2 and 24.7%, and on space heating, ranging between 22.2 and 23.8%. Consequently, the energetic advantages of the hybrid configuration induce a limited benefit in terms of operational profitability. Moreover, the high cost of the CSP and ORC systems contributes to significantly increasing the total plant cost, thus leading to almost double the SPB. This can be seen looking at the case E (hybrid configuration). Despite a 22.2% increment of the net electricity produced with respect to case F (geothermal), the SPB is more than twice than in case F and the energy saving is only 9.71% higher. Let us also observe that although case E is characterized by higher revenues from power and fresh water production (related with the higher solar gain) compared to cases A and C, it achieves a SPB (8.37 years) slightly lower with respect to case A (8.42 years) and higher with respect to case C (8.26 years). This is due to the higher cost of ORC unit, which must be designed with a higher nominal capacity. The second set (cases A, B, G, H, I, and L) and the third set (cases A, B, M, N, O, and P) of simulations resume the results of parametric analyses performed by varying the availability of geothermal source. In particular, in the second set the inlet temperature of geothermal fluid was varied, while in the third set the mass flow rate was changed. Considering the second set of results, it is interesting to note that the operation of all the subsystems significantly change with the inlet temperature of the geothermal fluid. It is also worth noting that the operation of the whole plant is more significantly affected by the variation of the inlet temperature of geothermal source rather than by the use of the PTC system. In fact, power production in case I was 53.4% higher than in case G, while power production in case L resulted 57.4% higher than in case H. This means that the variation of power production is much higher if comparing systems working with different inlet temperatures of geofluid, but it does not change substantially if considering hybrid and geothermal layouts. Once again, the solar gain mainly affects the HPP operation, while the operation of the TRS and MED3 subsystems directly depends on the geothermal source. This consideration is applicable even for the third set of simulations, where the mass flow rate was varied. From a further comparison between the cases A, G, and I (hybrid configurations at different inlet temperatures of geothermal source), it is worth noting that the increased electricity production at higher temperature of the geothermal source does not lead to economic advantages, as proven from the higher SPB (8.67 years) compared with the one achieved in case A (8.42 years). Despite the higher revenues guaranteed by the higher electricity production, the total plant cost is much higher due to the increased cost of the ORC unit (related with its higher capacity, due to the higher temperature of the geothermal source), thus resulting in an increase of the SPB. This last consideration is not applicable to the third set of simulations. In fact, by comparing the hybrid configurations identified by cases I and O, (namely the one at highest temperature and the one at highest mass flow rate, respectively), it can be observed that in case O a lower SPB (8.19 years) is achieved, compared to cases A (8.42 years) and M (8.69 years). In other words, the higher the mass flow rate of geothermal fluid, the lower the SPB. Conversely, higher temperature of the geothermal source do not improve SPB, as observed above. In case O, the temperature of geothermal source is assumed equal to case A, thus observing negligible changes in the nominal capacity of the ORC unit and in its investment cost. It is worth observing that electricity production is mainly affected by variations in the inlet temperature of the geothermal fluid, rather than by its mass flow rate. In fact, net power output in case O was only 5.2% higher than in case M (comparison between hybrid configurations), and the electricity production in case P is 6.40% higher than in case N (comparison between geothermal configurations). As evident, the variations of electricity production with the mass flow rate of geothermal fluid (third set of simulations) are much lower than those observed with respect to the inlet temperature of geothermal brine (second set of simulations). Considering the third set of simulations, a higher availability of thermal source leads to higher annual production and increased profitability. Hybrid configurations again lead to high investment costs, a result that is scarcely attractive. Finally, the time schedule of operation of the DHC network has been varied in the last set of simulations. This parametric analysis revealed that exploiting as much as possible the geothermal source for space heating and cooling could be economically convenient, in spite of the consequently lower annual production of fresh water. The higher the time of operation of the network, the lower the net electricity produced (and the revenues from electricity production). This is due to the energy absorbed by the auxiliaries of the plant, which are active for a longer time period. Also, depending on the selected control strategy, revenues related to fresh water production decrease with the increase of the network time operation. This is due to lower time operation of MED3, which is active when the DHC network is off. Despite the decreasing of the electric and fresh water production, the avoided costs related to space heating and cooling lead to lower SPBs. HPP operation is not affected by network operation.

5.18.6

Conclusions

A thermoeconomic analysis of a completely RPS powered by solar and geothermal sources is proposed. The plant can produce simultaneously power, desalinated water, heating and cooling, and it is supposed to be connected to a DHC network, supplying

772

Energy Management in Geothermal Energy Systems

energy to a small district of Pantelleria Island, which was chosen as case study. Time-dependent demand of electric energy was obtained by available data, while time-dependent demands of heating and cooling were obtained by a dynamic simulation of a representative building for the Pantelleria area. The dynamic simulation model of the whole plant was developed in EES and TRNSYS environments, using both built-in and user defined components. A performance comparison between two configurations is proposed, in order to better assess the thermodynamic advantages and the economic profitability of the PTC technology:

• •

the hybrid configuration, powered by solar and geothermal (case A); the geothermal configuration, powered only by geothermal source (case B).

The performance assessment was executed by performing a 1-year dynamic simulation, whose results were processed and shown on daily, monthly, and yearly bases. Moreover, a parametric analysis is proposed in order to better understand the plant operating conditions once some key parameters have been varied, namely the environmental conditions, the temperature of the geothermal fluid entering the plant, the mass flow rate of geothermal fluid, and the scheduled time operation of the DHC network. The plant is capable of supplying the heating and cooling demands of a large cluster of buildings and the fresh water demand of the entire Pantelleria Island. Conversely, in both cases A and B the system cannot be completely independent from the grid, since power production is lower than the current load for 835 h throughout the year for Config. A and 2280 h for Config. B. This suggests a further calibration of main design parameters of the plant in order to cover electricity demand completely and to increase the thermal recovery for space heating and cooling rather than for the desalination process: this would allow making the plant independent from the grid and would contribute to reduce the SPB. Config. B is economically more attractive, achieving much lower values of the SPB. PTC operation strongly affects the HPP operation, leading to higher power production of the ORC at higher efficiency, and it slightly affects the TRS and MED3 operations. Moreover, PTC technology, despite allowing for a slightly higher production of electricity and fresh water, does not induce proportional economic benefits. SPB is mostly influenced by the ORC and PTC systems, since they amount, together with the ACH, to 67.6% of the total investment cost. Thermodynamic and economic performances are highly affected by the availability of the geothermal source; the higher is geothermal source availability, the higher the thermodynamic performances of the plant. This same result is not always applicable for economic performances. In fact, a higher inlet temperature leads to higher power production, but at the same time causes a higher SPB because of the higher cost of ORC module. Conversely, a higher mass flow rate of geothermal source leads to higher power production without increasing the SPB. At least, the thermodynamic and the economic performances are more influenced by the variation of the inlet temperature of geothermal fluid rather than by the variation of the mass flow rate. Finally, pushing for the exploitation of thermal energy for space heating and cooling provides more economic benefit than fresh water production.

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Relevant Website www.uniparthenope.it Università degli Studi di Napoli “Parthenope”.

5.19 Energy Management in Ocean Energy Systems Faraz Junejo, Atif Saeed, and Sarmad Hameed, SZABIST, Karachi, Pakistan r 2018 Elsevier Inc. All rights reserved.

5.19.1 5.19.1.1 5.19.1.1.1 5.19.1.1.2 5.19.1.1.2.1 5.19.1.1.2.2 5.19.1.1.3 5.19.2 5.19.2.1 5.19.3 5.19.3.1 5.19.3.1.1 5.19.3.1.2 5.19.3.1.3 5.19.3.1.4 5.19.3.2 5.19.3.2.1 5.19.3.2.1.1 5.19.3.2.1.1.1 5.19.3.2.1.1.2 5.19.3.2.1.1.3 5.19.3.3 5.19.3.3.1 5.19.3.3.2 5.19.3.4 5.19.3.5 5.19.4 5.19.4.1 5.19.4.1.1 5.19.4.1.1.1 5.19.4.1.1.1.1 5.19.4.1.1.1.2 5.19.4.1.2 5.19.4.1.2.1 5.19.4.1.2.1.1 5.19.4.1.2.1.2 5.19.4.1.2.1.3 5.19.4.1.2.1.4 5.19.4.1.2.1.5 5.19.5 5.19.5.1 5.19.5.1.1 5.19.5.2 5.19.5.2.1 5.19.5.2.2 5.19.5.3 5.19.5.3.1 5.19.5.3.2 5.19.5.4 5.19.5.4.1 5.19.5.4.1.1 5.19.5.4.1.2 5.19.5.4.2

778

Introduction Why Sustainable Energy? Limited availability Environmental concerns Global warming and climate change Acid rain Economical concerns Background “Ocean Energy” History of Development as Sustainable Source (United Kingdom) Systems and Applications Wave Energy Oscillating, water column Absorber systems Overtopping devices Inverted pendulum devices Tidal Energy Applications of tidal energy Tidal electricity Barrages Tidal fence Tidal turbine Ocean Thermal Energy Conversion Closed cycle ocean thermal energy conversion system Open cycle ocean thermal energy conversion system Marine Currents Salinity Gradient Analysis and Assessment Ocean Energy Management (Sustainable Ocean Energy) Functional approach toward ocean energy management Implementation of sustainable energy management (key step approach) Energy audit Advanced monitoring and metering solutions Detect, measure, analyze, improve, and control approach toward ocean energy management Implementation of six sigma approach (detect, measure, analyze, improve, and control methodology) Detect Measure Analyze Improve Control Case Studies Case study: The Land Installed Marine Power Energy Transmitter Wave Power Project Performance Case Study: La Rance Tidal Power Station Physical aspects Technical aspects Case Study: Makai, Ocean Thermal Energy Conversion Service Provider Ocean Energy Research Center Makai’s involvement in ocean thermal energy conversion based research Case Study: Exploring the Potential to Install Marine Current Turbines in Southern Brazilian Shelf Region Methodology Hydrodynamic model Energy conversion module Initial boundary condition

Comprehensive Energy Systems, Volume 5

779 779 780 781 781 781 782 782 783 784 784 784 785 785 786 786 787 787 788 789 789 789 790 791 791 792 792 792 793 794 795 795 795 796 796 796 796 797 797 797 797 797 798 798 798 799 799 799 799 799 799 800 800

doi:10.1016/B978-0-12-809597-3.00539-3

Energy Management in Ocean Energy Systems 5.19.5.4.3 Results and discussion 5.19.5.4.4 Conclusions 5.19.5.5 Case study: The Power of Salinity: An Australian Example 5.19.5.5.1 Energy generation 5.19.5.5.2 Energy generation through pressure-retarted osmosis in Australia 5.19.5.6 Case Study: Optimization and Management of Low Head Hydropower Plant 5.19.6 Future Directions 5.19.7 Concluding Remarks References Further Reading Relevant Websites

Nomenclature APFM CZEDF DMAIC DMADV EDF FO GNP GHG LIMPET

5.19.1

Adjustable proportion fluid mixture Company Eletricite de France Detect, measure, analyze, improve and control Define, measure, analyze, design and verify Electricite De France Forward osmosis Gross national product Greenhouse gasses Land Installed Marine Power Energy Transmitter

NOAA OWC OTEC OERC PRO RET ROI SEM SBS UCC

779 800 800 800 801 802 802 804 805 806 807 807

National Oceanic and Atmospheric Administration Oscillating water column Ocean thermal energy conversion Ocean Energy Research Center Pressure-retarted osmosis Renewable energy target Rate of investment Sustainable energy management Southern Brazilian Shell Upper Chenab Canal

Introduction

With the current advancement of human society, energy is considered to be a backbone and most essential factor for economic growth [1]. The sufficient availability of energy and industrialization are closely linked with one another; or it can be said that these two factors are proportional to one another, i.e., the more the available energy will be, the more industrialize and advance nation will be. Since it is required for human beings to move on the cart of life including all the essential daily routine, i.e., lightening homes, transportation, and electricity for industrial and commercial sectors, it also has a vast impact on countries per capita gross national product (GNP). Nowadays, various methods have been developed to generate this all important required form of energy, i.e., electricity, but most of them causes some harmful effect on environment and also they are not sustainable [2]. For stable economic growth, energy and sustainability has to balance up. Ecological studies suggest that resource consumption and technology systems have to move toward sustainability in order for human beings to survive on Earth’s surface [3]. The environmental movements highly emphasize on the use and development of sustainable energy. Most of the renewable energy sources, such as solar, wind, and ocean, are sustainable, i.e., we can utilize them to generate electricity without putting them into danger of getting vanished or depleted [4]. Moreover, they are available in bulk quantity in every part of the world, unlike fossil fuel; of which reserves are not available everywhere. Among these number of resources from which sustainable energy can be harnessed, ocean energy has its own importance in number of ways. About 70% of the Earth surface has been covered with ocean [5], so it is available in vast quantity which can be utilized again and again to generate electricity without putting it into danger of getting depleted. Due to its massive size, ocean has the potential to generate higher power as compare to any other form of energy. Study suggests, that by using correct technology and selecting suitable site, ocean energy can be harnessed for up to the efficiency of 60%, i.e., higher than any other resources [6].

5.19.1.1

Why Sustainable Energy?

Sustainable energy can be defined as a form of energy that can be utilized again and again without putting a source in danger of getting depleted, expired, or vanished. The use and development of clean sustainable energy are widely encouraged as it do not harm our environment and is available all around us free of cost. All renewable sources like ocean, wind, and solar are sustainable energy sources due to the fact that they are stable and available worldwide. The reason why these sources are said to be sustainable is that, Sun will continue to shed its rays on Earth and heat caused by those rays generates winds [7]. As this cycle repeat itself again and again, this keep Earth warmer, the relative movement of Earth around Sun and Moon around Earth will keep on producing those waves in ocean [8] and the process of evaporation will cause water to evaporate and then again fall down in the form of ice or rain through glaciers, streams, and rivers merges in ocean to produce energy from hydropower [9]. These are ongoing processes, that is, this cycle will keep rotating itself and never going to stop until or unless something misfortunate happens.

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All of the above discussed energy sources can be incorporated by countries to generate electricity and stop the use of fossil fuels. Sustainable energy does not include any source which aside of energy generation, generates harmful waste and emit greenhouse gasses (GHG) in our environment [10]. Fossil fuels are not considered as a sustainable energy sources due to the reason that they are limited, cause immense pollution and are not available everywhere on Earth’s surface. There are various kind of availability, environmental and economic concerns with the use of fossil fuels which enable the researcher to search for its alternative so that nations and mankind continues to progress even if these resources get depleted.

5.19.1.1.1

Limited availability

Globally, every year, we are consuming fossil fuel, approximately 11 billion tons, i.e., oil. Study suggests that crude oil reserves are vanishing rapidly from Earth, that is, approximately 4 billion tons a year [11]. If we continue to utilize oil at this rate then as per Fig. 1 we will end up its reserves by 2052 [12]. World will still left with coal and natural gas, but if we increase the production of natural gas to fill the gap produce by oil, we are left with only 8 more years. Another fact that needs to be considered is that, as the world population is increasing annually, the rate of consumption of fossil fuel also increasing to fulfill our daily requirements and needs, making the rate of usage unstable; hence, it exponentially increases. Researchers suggest that we have enough coal to fulfill our energy requirements for hundreds of years, but once this oil and natural gas will be depleted then the rate at which coal will be consumed will make it at-most available till 2088 as per Fig. 1, emitting immense GHG in our environment. So, what after it? Probably in a meantime we will find some more reserves of fossil fuels but again it will have to last at some point of time. Table 1 shows the distribution of fossil fuel reserves worldwide.

Fossil fuel reserves 1.87%

2.45%

0.57%

0.23%

4.83% 8.57% 36.35% 16.04%

29.10%

Petroleum 36.35%

Natural gas 29.10%

Coal 16.04%

Nuclear 8.57%

Biomas 4.83%

Hydro 2.45%

Wind 1.87%

Solar 0.57%

Geothermal 0.23%

Fig. 1 World fossil fuel reserves. Reproduced from Gore A. The end of fossil fuels. Available from: https://www.ecotricity.co.uk/our-green-energy/ energy-independence/the-end-of-fossil-fuels.

Table 1

World fossil fuel reserves

Region

North America South America Europe Africa Russia Middle East India China Australia and East Asia Total

Fossil fuel reserves (giga tons of oil equivalent)

Fossil fuel reserves (%)

Oil

Coal

Gas

Sum

Oil

Coal

Gas

Sum

8 15 2 16 18 101 1 2 2 165

170 13 40 34 152 0 62 76 60 607

7 16 5 13 52 66 1 2 10 162

185 34 47 63 222 167 64 80 72 934

0.86 1.61 021 1.71 1.93 10.81 0.11 0.21 0.21 17.67

18.20 1.39 4.28 3.64 16.27 0.00 6.64 8.14 6.42 64.99

0.75 0.64 0.54 1.39 5.57 7.07 0.11 0.21 1.07 17.34

19.81 3.64 5.03 6.75 23.77 17.88 6.85 8.57 7.71 100.00

Source: Reproduced from British Petroleum. BP statistical review of world energy. London: British Petroleum; 2006.

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Anomaly temperature rise °C 0.5 0.4 0.3 0.2 0.1 0 −0.1 −0.2 −0.3 −0.4

1880

1900

1920

1940

1960

1980

2000

Anomaly temperature Fig. 2 Global temperature rise. Reproduced from NASA. Anomaly temperature difference. Available from: https://www.nasa.gov/topics/earth/ features/upsDownsGlobalWarming.html.

5.19.1.1.2

Environmental concerns

Also, beside its limited availability, there are many of the environmental problems that arise due to it, such as: climate change, air pollution, oil spills, and acid rain [13]. The process of extracting energy from fossil fuel generates heat trapped gasses or GHG, which is the main reason of ongoing annual average temperature rise globally as shown in Fig. 2. In spite of this growing list of global warming indicators, companies, and nations continues to exploit fossil fuel reserves in order to fulfill daily needs of human beings. In addition to this ecological disturbances, there are also certain consequences for the people living around pacific rims. These communities, many Indigenous, are threatened by the depletion of specific resources they depend upon for their livelihoods and culture. 5.19.1.1.2.1 Global warming and climate change Both of these terms are associated with the annual observed rise in temperature of Earth’s surface and oceans [14]. Studies suggest that one of the major reason of climate change is the human expansion of greenhouse effect that results when the atmosphere traps heat, while radiating from Earth to outer surface. Human activities are constantly changing greenhouse effect. Over the past centuries, the extensive use of coal and oil increases the concentration of CO2 in Earth’s environment. As, the process of extracting energy from fossil fuel includes the burning of it which alternatively produce carbon which reacts with oxygen in air to make CO2. The consequences of this increase in temperature are enlisted below [15]:

• • • • • • • • •

With the increase in global temperature, Earth will become warmer. Some regions may welcome it and some will not. Annual increase in temperature will lead to more evaporation and precipitation. With the climate change, ice started melting with higher rates in worldwide, especially at the Earth poles. With the melting of ice, sea levels are expected to rise between 7 and 23 in. by the end of century. Also, if this ice continues to melt with same rate at poles, this could add in further rise of 4–8 in. This will make hurricane and other natural storms to further strengthen up. Due to increase in evaporation and precipitation, floods and drought will become more common. Due to rise in sea levels, less fresh water will be available. This will also affect agricultural sector, as some crops which grow under low climatic regions could diminish or vanish from the surface of Earth, if annual temperature rises. It will also put an unpleasant effect on human health, as increase in CO2 concentration in air could give birth to certain kind of diseases, among which asthma is the common.

5.19.1.1.2.2 Acid rain Excessive use of fossil fuel and emission of GHG in our environment could also give birth to acid rain [16]. Acid rain causes when GHG gases like sulfur dioxide and nitrogen dioxide are released in air. These gases can rise up very high into atmosphere and reacts with water, oxygen, and other chemicals to form acidic pollutants, known as acidic rain. These GHG gasses can very easily dissolve with water and flow a long side with winds, as a result, two compounds can travel long distances and became a part of rain, sleet, snow, or fog. There are various effects of acidic rain on human and wildlife, which are discussed below [17]:

•

•

When acidic rain falls, it reaches streams and river flowing through soil or clay on ground. These acids react with soil and leach aluminum from its particle and then flow into lakes, streams, or rivers. There are various kind of marine species which can survive even after this high value of aluminum presence, but again there are some species too who cannot survive the higher value of pH and dies. Table 2 shows the tolerable amount pH of marine species. Due to the presence of sulfuric and nitric acids, when acid rain comes in contact with building materials, such as iron or paint and manmade structures. It not only corrodes its surface but also weaken its strength. The acidic particle corrodes material and causes paint or other coatings to deteriorate quickly.

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Table 2

Critical pH level for marine species

Animal

Critical pH level

Snails Clams Bass Crayfish Mayfly Frogs Salamanders’

6 6 5.5 5.5 5.5 4 5

Source: United States Environmental Protection Agency. Effects of acid rain. Available from: https://www.epa.gov/acidrain/effects-acid-rain.

3−Jul−2008

160

143.95

Brent crude oil ($/barrel)

140 120 100

9−Aug−2006 78.26

80 60

27−Sept−1991 41.45

40

9−Jul−2000 37.43 45.13

26−Dez−2008

20

13−Jan−2015

33.73

1-Jan-1988 1-Jan-1989 1-Jan-1990 1-Jan-1991 1-Jan-1992 1-Jan-1993 1-Jan-1994 1-Jan-1995 1-Jan-1996 1-Jan-1997 1-Jan-1998 1-Jan-1999 1-Jan-2000 1-Jan-2001 1-Jan-2002 1-Jan-2003 1-Jan-2004 1-Jan-2005 1-Jan-2006 1-Jan-2007 1-Jan-2008 1-Jan-2009 1-Jan-2010 1-Jan-2011 1-Jan-2012 1-Jan-2013 1-Jan-2014 1-Jan-2015 7-Dez-2015

0

Fig. 3 Trend of oil prices. Reproduced from Tverberg G. What happens when oil and other fossil fuels deplete. OilPrice.com; 2006.

• •

The acidic particles can react with other particle with ozone and transform into sulfates. This will make air hazy and make it difficult to see for humans, results in various road accidents. There is a very dangerous effect of inhaling these sulfates. As studies suggest that inhaling this sulfates can cause a certain disorder in human lungs and can cause asthma or bronchitis.

5.19.1.1.3

Economical concerns

The fairly low cost of extracting energy from fossil fuel sources creates its importance globally. But the major problem that arises due to its enormous increase in use, that is, as fossil fuels sources are limited and they have to deplete or vanish sometime around, there are many economic concerns also which could arise at a point when the amount to extract energy from fossil fuel like coal, natural gas, and oil will become greater than its value due to its limited availability. Some estimates suggest that the world could consume oil for 20 years before a sharp increase in its prices could be observed due to its limited availability [18]. This situation will lead the world to global economic crises. Prices would go up following a simple model of supply and demand. Fig. 3 clearly shows the rise in oil prices which began from 2004. Fossil fuels can never be considered as a sustainable energy source due to its limited availability; which not only causes enormous pollution, but is also, it is not available everywhere on Earth surface. It normally includes the use of coal, natural gas, and oil for the production of energy. Globally, several steps have been considered to reduce our dependency on fossil fuels as a consequence of which, around 20% of world’s energy comes from renewable and sustainable energy sources [19].

5.19.2

Background

Globally, at a time, need of energy is larger than ever before. The developing nations and modern industrialization require more and more sources from which energy can be generated or extracted. With the growing standards of human living the use of energy in our daily life has become inevitable, i.e., they are required in every aspect of our life. However, this fact is also need to be considered that the consumption of source should not affect ecological cycle of our planet. The Earth has been blessed with enormous amount of water in the form of oceans or seas. About 70% of Earth is covered with oceans, which is the reason that it seems to be blue from outer space and said to be as “Blue Planet.” Oceans and seas around us

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have the potential to fulfill all of our current energy demands by providing a clean energy in a bulk quantity; which can be harnessed in number of ways and forms: by exploiting the kinetic energy of waves, the streams of tides and ocean current and by the temperature gradient present in water. The kinetic energy present in ocean waves is generated through concentrated solar energy that is transferred through complex wind–wave interaction. The effect of variation of Earth temperature due to solar heating, combined with numerous of atmospheric phenomena generate winds globally. On the other hand, ocean tides are generated by a cyclic relative revolution of Earth w.r.t Sun and Moon w.r.t Earth and interaction of their gravitational forces. A number of phenomenon including: Earth, Sun, and Moon formation, Earth rotational tilt and rate of spinning can cause the change in tide conditions which are oftenly known as high tide or low tide. These conditions vary significantly with respect to time but are predictable. As the ocean tides are predictable, this gives the researcher a clearer picture to estimate the efficiency of plant to be installed at location unlike solar or wind resources which are quite unpredictable. Tides conditions are capable of more easily perceived or understood in coastal areas where constrained channels strengthen the water flow resulting in increased power density. Various energy forms are present within ocean out of which sustainable energy can be harnessed. They are broadly characterized to: tides, waves, and temperature gradient [20]. Tides possess potential energy which can be harnessed in order to convert it into the most important form of energy, i.e., electricity, by building a tidal barrage or other turbine equipped construction along estuary. Kinetic energy associated by ocean waves can be harnessed by developing modular technologies. Thermal energy generated due to temperature difference between above and deep sea levels can be harnessed using ocean thermal energy conversion (OTEC) processes. Harnessing energy from tides using tidal barrages and fences has far most the most utilized and developed technology in order to extract energy from oceans. It is the most mature technology in its form which is potentially adding energy in global grid. La Rance tidal power barrage is one of the biggest facility developed in France, which potentially adding 240 MW of energy in local grid [21]. Moreover, Nova Scotia Power in Canada has successfully designed and implemented their system to produce 20 MW [22]. Let us say, if all three of mentioned technologies are in place, the power produces will be ample for nearly about quarter of current electricity consumption. However, economics, environmental impacts, land-use, and grid interconnection constraints will likely impose further limits to how much of the resource can be extracted. Although technology is still at a relatively immature pilot project stage, economic projections indicate that ocean energy could become cost-competitive over the long-term. The advantage or the significant factor of sustainable energy is that, it could be utilized in both rural and urban areas. If proper consideration are made with respect to its efficient usage, most of our current energy needs can be fulfilled by using solar collectors, hydroelectric power plant, wind plants, and tidal plants; helping in minimizing the load on national grid of any society. If government make proper energy policies, then most of the cities can generate their required electricity on rooftop, i.e., by installing solar energy harvesting system or waste-to-energy conversion systems through pyrolysis and gasification – this could also be a successful idea, by a fact that waste is something in which most of the cities are sufficient enough. Although making energy locally can help, but there is still a need of larger power generation facilities that could help in accompanying the requirements. Most of the countries rely on fossil fuel or nuclear feed power stations for their energy generation developed on rural side. Cities rely on rural areas for most of their resources that include food and water. But in 2008, a petition was received by United Kingdom high court in which some of the rural resident’s complaints about their local amenity losses due to these large power plants. These local impacts of fossil fuel are lesser as compared to its global causes, but even so, there will inevitably be limits to how much land we can use for energy production.

5.19.2.1

“Ocean Energy” History of Development as Sustainable Source (United Kingdom)

As the consequences of 1974 oil crises, United Kingdom government launches the renewable energy assessment and support program which primarily focuses on wave and tidal options to extract that useful energy. But at that time, wind power has been considered as the most essential source of energy. In 1973, the United Kingdom Department of energy concluded in their energy paper 21 that “although aerogenerators might be considered economic on certain hill sites… a clear economic case cannot be made for a program large enough to make a significant contribution to the nation’s energy supply.” Afterwards, some new concepts of wind energy generation were emerged but never followed up. But after several years; now, wind energy is considered to be the major source of energy extraction and around the world, almost 60,000 MW of energy generating capacity has been installed, but United Kingdom still struggling to catch-up and still importing wind turbines from Germany and Denmark. Unfortunately, United Kingdom also at that time seemed to lose its initial lead in the area of wave power generation. United Kingdom was the pioneer in introducing a wave energy extraction program in late 1970s under the labor administration and yields some excellent designs, which includes Stephen Salter’s Duck System. But following the election, conservative government promised to cut the public expenditure, the advisory committee on energy research had concluded in 1980 that further work on deep sea energy projects should be halted, due to the fact of some high initial investment requirement. These assessments were strongly opposed at that time, some of the critics said that the suspicion of wave energy “was effectively withdrawn before the race began.” In 1994, government’s tidal barrage program also wound up, following the long-run assessment of Severn, and other potential sites on the basis of high initial cost and it environmental intrusive cause. It was not until after a change of government, in 1997, that perspectives started to change. Albeit given the subsidizing reductions, not a ton of new work had been done on wave or tidal vitality in the United Kingdom in the interim, the political atmosphere had obviously changed, to some degree as a result of

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developing worries about environmental change. A progression of reassessments of wave vitality and tidal vitality were done, at first, as a major aspect of the United Kingdom Technology Foresight program, in March 2001, with an affirmation by the Department of Trade and Industry that the decision of 1992 to forsake wave vitality was not right. Wave power has now been revived as an alternative, and tidal current vitality is additionally being sought after with more eagerness, and despite the fact that it is still too soon to state whether these new sources will demonstrate as fruitful as trusted, their joined vitality potential is huge, and the underlying choice to make light of these alternatives seems to have been untimely.

5.19.3

Systems and Applications

5.19.3.1

Wave Energy

This form of ocean energy has the potential to generate much larger amount of energy as compare to tidal power systems. Wave energy or wave power is the essential source of extracting energy from the movement of waves and converts it into all important electricity. Waves are generated by the interaction of wind with the surface of water or sea. This transfers the kinetic energy of blowing winds to sea and results a wave generation. The amount of energy transfers or power density of wind depends upon: wind speed, the distance between wind and surface of sea, and the time for which it blows. Ecological studies suggest that largest concentration of wave potential energy can be found at 40 and 60 degrees latitude in both the northern and southern hemispheres. The core principle to generate electricity from wave is to utilize the movement of waves into suable mechanical energy and then in a lump use that mechanical energy to generate electricity. These systems can be floating or fixed to the seabed offshore, it can also be constructed near the coastline and can extract energy from a to-and-fro motion of beach waves. Wave electricity generation devices can directly be used to extract energy from the surface of sea or through pressure fluctuations below the surface. Researchers believe that wave energy has the potential to generate 2 TW of energy if properly utilized. With a substantial resource potential, there are number of developed technologies used to harness this all important energy from waves. If we compare the wave energy with wind energy, this resource can cause equal impact in the modernization of our society, but it is still less developed. In 1970s, numerous steps and researches have been conducted in United Kingdom to harness this all important source of energy efficiently, but this initiative was halted in 1980s, following some adverse and disputed initial cost requirements. But, even though now, many projects came forward which resembles the interest of nation in harnessing this all important form of energy. Similarly, tidal energy generation concept was also backed in 1970s with the emphasis on large tidal barrages, one of the example is 8.6 GW tidal barrage established on Severne estuary. This project has also faced some delays and hurdle due to the fact of being expensive and causing several environmental invasive. Nowadays, more emphasis and research have been done on tidal current turbines and offshore tidal lagoons, in spite of big and costly tidal barrages. These different technologies not only employs different method to harness energy but also different techniques through which electricity can be generated efficiently based on the location on which it has to be constructed, waves power density and density of water. These factors tend to blur the boundaries when a large facility is planned. On the basis of above characterization, wave energy power plant has been differentiated on the basis of harnessed energy and electricity generation. There are various ways by which these system can work, few systems extract energy from surface waves. Others extract energy from pressure fluctuations below water surface or from the full wave. Some systems are fixed in position and let waves pass by them, while others follow the waves and move with them. Some systems concentrate and focus on waves, which increases their height and their potential for conversion to electrical energy. However, many research and development goals remain to be accomplished, including cost reduction, efficiency and reliability improvements, identification of suitable sites, interconnection with the utility grid, better understanding of the impacts of the technology on marine life and the shoreline. Also essential is a demonstration of the ability of the equipment to survive the salinity and pressure environments of the ocean, as well as, weather effects over the life of the facility. Some of the well-developed wave energy techniques are: oscillating water column (OWC), absorber systems, overtopping devices, and inverted pendulum devices. Each of which working, advantages, disadvantages is discussed in quite detail.

5.19.3.1.1

Oscillating, water column

Air turbines are mostly consumed in this type of ocean energy harnessing technique. The most basic form of OWC is the closed chamber that is open from the bottom allowing water to move in and out through a vent, while it is open to the air via one or more air turbines depending upon the size of system. The device works on the technique of expansion and compression of the air trapped within a closed chamber due to rise and fall of the waves, enabling a turbine to rotate and generate electricity. Since the airflow direction reverse the half way within each cycle, the method is needed to rectify the airflow. In order to overcome this issue, turbine with one-way valve has been utilized, i.e., they only spins in one direction regardless the direction of airflow. Flywheel is also made a part of this system to store momentum which is utilized during a period of reverse direction comparatively for a smaller period of time. Even after this flywheel motion, the output of the turbine generator is highly variable; therefore, careful designing, selecting correct location, and significant amount of power electronics are involved in order to make a design more efficient. Fig. 4 represents an onshore OWC plant, while Fig. 5 represents an offshore OWC plant.

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Generator

Windturbine

Fig. 4 Onshore oscillating water column. Reproduced from Khan J, Bhuyan GS. Ocean energy: global technology: report prepared by Powertech Labs Inc. for the IEA-OES; 2009.

Turbine

Generator

Fig. 5 Offshore floating oscillating water column. Reproduced from Khan J, Bhuyan GS. Ocean energy: global technology: report prepared by Powertech Labs Inc. for the IEA-OES; 2009.

Buoy Generated power Shaft

Dynamo

Fig. 6 Point absorber buoy.

5.19.3.1.2

Absorber systems

Absorber systems involves a careful designing of buoy whose mass and buoyancy are selected in such a way that it resonates along with the moving waves. The waves cause this buoy to perform a relative movement against a fixed reference; i.e., it can be tied with cable to seabed in order to make it fixed. The linear generator is the direct method to harness this linear motion of buoy into all important electricity. Linear generator comprises of a set permanent magnets and a piston, and a stator consisting of several coils arranged in tabular form. In this kind of absorber system, magnetic piston of the linear generator is anchored to the seabed in order to make it fixed. When buoy moves up and down due to reciprocating motion of waves, it makes the coils linearly move around the stationary piston results an induced voltage. Each of such buoy has the potential to generate 250 KW of energy which can be scaled-up or down depending upon the energy needs as shown in Fig. 6.

5.19.3.1.3

Overtopping devices

Overtopping devices are both offshore and onshore devices that are anchored to the seabed and store the energy of waves in action. It consist of a reservoir that stores water from the waves until a suitable head difference is obtained between a water level in reservoir and surrounding sea level. After obtaining a sufficient head difference, energy is then released from the reservoir to fall on turbine or generator in order to generate electricity similar to dams. Among various type of water turbines, Kaplan turbine is

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Air back in

Air out

Turbine Generator

Wave dircetion Fig. 7 Overtopping devices. Reproduced from Khan J, Bhuyan GS. Ocean energy: global technology: report prepared by Powertech Labs Inc. for the IEA-OES; 2009.

→

1 →

2

Trunk (center of mass)

Massless leg

→

→

F1

F2 Walking surface

Fig. 8 Inverted pendulum devices. Reproduced from Klebnikov S. Wave energy. Available from: https://www.researchgate.net/figure/ 272921986_fig1_Figure-1-Modeling-walking-as-an-inverted-pendulum. 2016.

mostly used in this systems due to its low head working characteristics. This technology do possess some advantage relating to several above discussed wave energy harnessing technology as it uses hydropower turbine, which is already developed and well understood technology (Fig. 7).

5.19.3.1.4

Inverted pendulum devices

Inverted pendulum devices use a back and forth motion of a fixed lever arm with the seabed in order to generate electricity. When the waves pass over the device, the buoyant force of the waves moves the anchored lever arm back and forth. This back and forth motion actuates the power takeoff system, such as hydraulic pump. Hydraulic pump pressurize the fluid and this oscillating motion is converted into the linear motion of fluid that is fed into the onshore generating station to derive the electrical generators (Fig. 8).

5.19.3.2

Tidal Energy

In this form of energy, tides of ocean or sea are used to generate electricity. Tides move a large quantity of water twice a day, and by utilizing it we can generate all important electricity. In spite of the fact that tide could provide sufficient amount of energy which can be utilized in our daily routine, the process of harnessing it; is not that simple. Tide is generated in ocean due to the incline and decline in the level of water relative to coastline. This is originated by the relative motion of Earth around Sun and Moon around Earth. The gravitational pull of Sun and Moon, along with the revolution of Earth results in tide. Studies suggest that Moon exerts comparatively large gravitational energy to Earth as compare to Sun due to

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the fact that it is much nearer to Earth, though it is smaller in mass. This gravitational force causes the ocean to bulge around the axis toward the Moon producing tides. Hence, tides are produced by the rotation of Earth beneath this lump, resulting periodic rise and fall of ocean levels. Similarly, gravitational attraction of Sun also effects tides, but to lesser extent. However, when the Sun, Moon, and Earth are positioned in a straight line, i.e., in full Moon, the gravitational force combined resulting in large tides. Whereas, in half Moon condition, they are positioned at right results in lower tides. To build a tidal power station, the selection of correct location plays a vital role for its successful working. To generate enough amount of power that can be put in use, deviation of at least 5 m is required between high and low tides. Geographically, only 40 sites around the world are considered to qualify certain parameters of tidal range. The higher the tide the more will be the amount of electricity generated. Similarly, this is inversely proportional to the cost of electricity. Exploiting sites with such parameter are also very economical as study suggests that almost 3000 GW of energy are available from these tides worldwide. However, only 2%, i.e., around 60 GW has been potentially generated consuming tidal energy. Few years ago, the only method to harness energy from tides is the construction of barrages at suitable location, but nowadays there are other options too. This source of harnessing energy is a clean and green and does not involve any usage of fossil fuel. However, some environmental concerns still exists which mainly has to deal with slit formation at the shore (due to blocking tides to reach the shore and washing away slit), harming marine life who dwell near the basin. In terms of reliability, tidal energy project are considered to be more predictable source of energy as compare to wind or solar since its occurrence are almost predictable.

5.19.3.2.1

Applications of tidal energy

Tidal energy is one of the many types of renewable energy like solar, wind, and geothermal energy. It is harnessed from the development of waves or tides because of the gravitational attraction of the Earth and Moon. Tidal energy is a type of gravitational energy which can be utilized to do work or be changed over in different types of energy. Tidal energy is still a juvenile innovation with progressions in tidal energy not as fast as in the field of several other types. While exclusive and way breaking methodologies are being produced to outfit the uninhibitedly accessible renewable wave and tidal energy, the full business improvement is still some way away. On the other hand, tidal barrage is a developed innovation; however, its advancement too has been moderate on account of high venture and long building time. Tidal energy has been utilized for several years. Just like wind mills, it was firstly utilized for the mechanical pulverizing of grains in grain mills. The development of turbines because of tidal energy was utilized as a part of the smash grains. However, with the approaches of fossil fuels, this use of tidal energy has turned out to be very low. Tidal energy can likewise be utilized as a source to store energy. Like a significant number of the hydroelectric dams which can be utilized a vast energy storage, so tidal barrages with their repositories can be altered to store energy. Though this has not been attempted out, with reasonable changes tidal energy can be put away too, however; expenses may end up being high. Tidal barrages can counteract damage to the coast amid high storms furthermore give a simple transport strategy between the two arms of a bay or an estuary on which it is manufactured. 5.19.3.2.1.1 Tidal electricity The process of harnessing energy from tides is in practice for about 100 years. Anciently, tide mills were used for this process. It works on a principle that when tide comes in, the water comes in through passage into the storage pond and when it is returned, the water flows back into ocean using water wheel. Significance difference between tide mill and modern tidal power plant is its capacity to generate electricity and storage. Nowadays, tidal power plants also known as barrage build across bay allowing water to flow in and flow out through a series of sluice. At high tide, the sluice of barrage is closed, creating a head of water on the ebb side. Simultaneously releasing water in series of turbines result in generation of electricity. Modern technologies of harnessing kinetic energy from tides revolve around two major concepts: tidal range and tidal stream. Tidal range (Fig. 9) utilizes the head pressure built when the sluice is closed. When passages are opened it allows the water to flow through turbine and results in generation of electricity. Whereas, in tidal stream (Fig. 10) it captures the kinetic energy of tidal current similar to wind turbine. In this case most of the energy escapes through sides but in spite of it even small facility can generate large amount of energy. Devices designed to extract the all important energy from this source comes in different forms and their applications also differs on the basis of geographical location and power needs. But, indeed they all are extracting either kinetic energy or potential energy from tides to generate electricity. Nowadays, out of various devices three devices are considered to be most efficient in terms of

High tide Tidal range Low tide

Fig. 9 Tidal range. Reproduced from IRENA. Tidal energy: technology brief. Abu Dhabi: IRENA; 2014.

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Fig. 10 Tidal stream. Reproduced from IRENA. Tidal energy: technology brief. Abu Dhabi: IRENA; 2014.

tidal energy extraction: barrages, tidal fences, and tidal turbine. Barrages work on the principle of tidal range which was discussed in quite detail in our previous section, whereas tidal fence and tidal turbines work on the principle of tidal stream. 5.19.3.2.1.1.1 Barrages The tidal barrage or tidal power plant as it is likewise known, is a type of “marine renewable vitality” system that consist of long dividers, dams, floodgate entryways, or tidal locks to catch and store the potential vitality of the sea. A tidal barrage is a sort of tidal power technology conspires that includes the development of a genuinely low-walled dam, known as a “tidal barrage.” The base of the barrage is constructed at the depth of sea with the gates just above the water level. Beneath the surface of water there are number of passages which controls the flow of water through turbine to generate electricity. These passages are known as sluice gates. The water which moves in and out through sluice gates, possess enormous amount of kinetic energy which is extracted through this device as much as it can and then converted into electricity. This method of energy extraction is very similar to hydroelectric power generation, the only difference is that in tidal barrage the direction of flow of water is two rather than one. On incoming tides, i.e., high tides fill the reservoir with water, while outgoing ebbing tide, it flows in opposite direction and empties it. As, tide is the vertical movement of water due to exerted gravitational force due to Sun and Moon, this method exploit this natural phenomenon and generates the most important form of energy, i.e., electricity. The consequence of funneling the majority of this water is that the stature of the ocean level once inside these tunnels can increase vertically many meters each day as it is being pushed forward by the approaching ocean water behind it as appeared in Fig. 11. This expansion in the ocean level can make a tidal scope of more than ten meters in stature in a few estuaries and areas which can be utilized to create power. A tidal barrage technique uses the head difference of high and low tides to generate energy. There are three different formations of barrages which can be utilized with each having its own significance and importance to harness energy: flood generation, ebb generation, and two-way generation. 1. Flood generation: this formation utilizes energy of incoming high tides as it moves toward the land. The tidal basin is emptied using a sluice gates located on barrage and hence the lower tide gets effectively empty. As the tides flows back the sluice gates a closed creating a head difference on either side of barrage. The barrage reservoir is filled up passing through turbine tunnel. This flow of water spins the turbine and generates electricity. This formation of barrage is a one-way system, i.e., one can only utilize the energy flowing through high tide to tidal basin which makes it restricted to about 6 h per tidal cycle. 2. Ebb generation: a tidal barrage ebb generation utilizes the vitality of an active or falling tide, alluded to as the “ebb tide,” as it returns back to the ocean making it the inverse of the past surge tidal barrage formation. At low tide, all the floodgate and sluice gates along the barrage are completely opened permitting the basin to fill gradually at a rate controlled by the approaching high tide. At the point when the sea or ocean level encourages the basin achieves its most astounding point at high tide, everyone of the floodgates and sluice are then shut entangling the water inside tidal basin. This repository of water may keep on filling up because of inland waterways and streams associated with it from the land. 3. Two-way formation: preciously we have seen “flood formation” and “ebb tide” formation of barrages which only utilizes the one directional flow of water. But in order to increase power generation and efficiency of system a new formation has been introduced which is enable of harnessing energy from tides in both directions. Two-way tidal barrage utilizes a part of both high and low tide in order to rotate turbine and generate electricity. This formation requires the more accurate and precise controlling of sluice gates. Sluice gates should remain closed until the heads of either side is sufficient enough. This will enable water in a reservoir to move back and forth hence moving a generator in both directions producing electricity. But in any case, this two-way barrage is as a rule less proficient than one-way barrage or ebb barrage as the required head is lesser which decreases the period over which typical one-way barrage may have generally work. Likewise, bidirectional tidal turbine generators are developed to work in both directions which make them generally more costly and less productive than unidirectional tidal generators.

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Fig. 11 Tidal ranges. Reproduced from IRENA. Tidal energy: technology brief. Abu Dhabi: IRENA; 2014.

5.19.3.2.1.1.2 Tidal fence A tidal fence is another type of tidal stream innovation, which uses quick streaming submerged sea ebbs and flows for energy conversion. From multiple points of view, a tidal fence construction is a cross between a tidal barrage and a tidal turbine stream system. Unlike submerged tidal turbines which exclusively rotate around its own vertical axis, tidal fence is made out of individual vertical-axis turbines that are mounted together inside a solitary fence like structure. The motivation behind a tidal fence, otherwise called a “caisson,” is to harness maximum available kinetic energy of stream. These tidal fence acts like a submerged tidal barrages over a channel or estuary, with the tidal streams being compelled to flow across the turbine edges, making them pivot, which thus controls generators and generate electricity. When we compare tidal barrage and tidal fence, tidal fence does not hinder the stream of water permitting the water to ceaselessly back and forth movement through it making them less expensive to install than a strong cemented tidal barrage. As per its name, a “tidal fence” is a wall like structure made out of concrete or steel. Tidal fence is utilized as a part of quick streaming territories, for example, the channels between two land masses where it guides the ocean water to the turbines when it goes through the fence as shown in Fig. 16. As their structure is open, tidal wall has less effect on nature than a strong tidal barrage or dam; however, they can even effects the movement of fish and other substantial marine creatures. To conquer this issue, wide openings between the caisson wall and pivoting turbines permit fish to swim through, unlike tidal barrage which prevents these creatures to swim in by closing passages. 5.19.3.2.1.1.3 Tidal turbine From many aspects tidal turbines are same to that of wind turbines. These turbines generators are also called as “stream generators” or “marine ebb and flow turbines,” they are placed on the sea floor, the stream flows through turbine sharp edges driving a generator creating electricity. In-fact, some locations where tidal stream facilities are installed, resembles to that of wind farm in which array of turbines moves simultaneously with flow to generate electricity. The generated energy then supplied to the local grids through long especially fabricated submarine wire. These offshore tidal turbines can be either partially or fully submerged beneath the surface of the water, with partially submerged turbines being easier and less costly for maintenance.

5.19.3.3

Ocean Thermal Energy Conversion

OTEC is another method to harness energy from the temperature difference between the topmost Sun warmed layer of sea and deep colder layer. Some fraction of emitted rays from Sun is absorbed by thermal masses like sea present on the surface of Earth and remaining is reflected back in the atmosphere. This result in an average yearly mean temperature of around 281C. Whereas, in deep, where water forms at higher latitude and descends to flow along the seabed toward the equator which makes it colder and

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Fig. 12 Temperature difference between surface and depth water around the globe. Reproduced from Masutani SM, Takahashi PK. Ocean thermal energy conversion (OTEC). Cambridge, MA: Academic Press; 2001.

less sensitive to irradiation from the Sun. The studies suggest that temperature difference ∆T, between the topmost layer of ocean and depth of almost 100–200 m came out to be 10–25oC. This technique utilizes this temperature difference in order to generate most important source of energy. Since, OTEC exploits ocean energy generated by the temperature difference makes the cost of electrical power generation minimal. However, the initial cost to install an OTEC system is quite high due to the fact that large pipelines and numbers of heat exchangers are required to generate a relatively modest amount of electricity. This economical factor restricts the fast developments in this technology. This is the reason that currently this system cannot compete with other renewable energy developments procedures in terms of cost and value. OTEC electrical power generation system works similar to heat engine. The system consumes the thermal energy from the topmost layer of sea and converts the portion of that energy into electrical energy. This system works on the principle of second law of thermodynamics, i.e., the heat extracted from the warm water must be released to a colder thermal sink. Colder depths of seawater are used as a thermal sink in OTEC through a submerged pipeline. A steady-state energy analysis yields that the total electrical power produced is directly proportional to the rate of heat transfer from the warmer water to colder sink. For this reason as Fig. 12 shows that temperature difference around the globe is quite low (i.e., at maximum places); making the overall efficiency of OTEC low as well. This low power conversion efficiency of OTEC yields that 90% of the total thermal energy harnessed from topmost surface is wasted and rejected at thermal sink. But, in spite of its relatively high inefficiency, unlike harmful fossil energy generation systems; OTEC does not cause any bad impacts on our environment. OTEC has been further classified into two types on the basis of its construction and design, namely open cycle and closed cycle OTEC. Both of these technologies have been further classified below on the basis of its advantages, disadvantages, and applications.

5.19.3.3.1

Closed cycle ocean thermal energy conversion system

This concept was first introduced by D’Arsonval. In this system, working fluid is used which evaporates at the temperature of warm seawater. The generated vapor will continuously expand before being condensed in colder seawater so that it could be used again and cycles continues. This closed loop process continuously repeats itself with the same working fluid; hence, it is named as “closed cycle.” The process works on the principle of Rankin cycle of which simplified schematic is shown in Fig. 13. This system comprises of heat exchangers, turbogenerator, and water supply and discharge system. This system utilizes heat transfer rate from warm seawater and produces saturated vapor in evaporator from working fluid. Electricity is generated when this gas vapors expands to lower pressure through turbogenerator. At the end, latent heat is transferred to cold seawater which condenses these saturated vapors resulting liquid is pumped to repeat the cycle. The success of this system depends upon the consequence that energy generated by the expanded vapor through turbine should be greater to that it consumes to repressurize vapor in condenser.

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5 25°C 20°C 6 7

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6 Line to the grid 7 Waste water ~ 7°C 8 Condenser 9 Deep water ~ 5°C 10 Circulation pump

Fig. 13 Closed loop ocean thermal energy conversion (OTEC) process flow. Reproduced from Masutani SM, Takahashi PK. Ocean thermal energy conversion (OTEC). Cambridge, MA: Academic Press; 2001.

Mechanical and thermal constraints like frictional and thermal losses results in lower efficiency of turbogenerator and heat exchangers. Thermal irreversibility in heat exchangers occurs when energy is transferred over a large temperature difference; therefore, it is required to select the working fluid which changes its phase at the observed temperature difference between the topmost layer and selected depth of seawater. More constraint regarding the correct selection of working fluids includes cost, availability, toxicity, corrosion property, and certain environmental hazards. Ammonia and some fluorocarbons are considered to be the most suitable working fluids but; however, their disadvantages are that they cause certain harm to our environment posed by leakages. Solution to this problem is the use of adjustable proportion fluid mixture (APFM) also known as “the Kalina.” The OTEC which utilizes Kalina fluid in order to generate electricity is said to work on Kalina cycle. The Kalina cycle employs the working fluid of ammonia mixed with water in varying proportion at certain different selected points in the system. The advantage of using this binary solution is that it has ability to evaporate and condense over range of different temperatures at given pressure.

5.19.3.3.2

Open cycle ocean thermal energy conversion system

Certain concerns regarding the usage of closed cycle OTEC like potential biofouling and cost, led researchers to think that how it can be overcome. Therefore, Claude suggested that in spite of using a working fluid they could utilize steam generated directly from seawater in order to drive their turbine. He designed and proposed certain number of steps which since then known as Claude’s or open cycle steps. These steps include: (1) partial evaporation of warm seawater under vacuum, (2) expansion of vapor particle through generator in order to generate electricity, (3) condensing of warm vapors through heat transfer by directly contacting it with cold seawater, and (4) discharge of condensate liquid and other noncondensable gasses. Therefore, open cycle OTEC eliminates the need of additional surface heat exchangers. As due to the fact that in this process working fluid, steam, is discharged after using it for the first time unlike the “closed cycle” in which it has to be used for several times it is named as “open cycle”; hence, the flow path and process are open looped. The additional features of this technology have been presented in Fig. 14. This entire system, from evaporator to condenser works at partial vacuum, i.e., typically pressure 1%–3% lesser than atmospheric pressure. This low pressure enables this framework to induce boiling of warm seawater and converts it into steam. Partial evaporation takes place when seawater is exposed to pressure below then saturation pressure corresponding to its temperature. This state is achieved by pumping the working fluid into evacuated chamber through spouts, especially designed to get maximum heat and mass transfer rates. This produced steam then expanded through turbogenerator results in electricity generation. After, the utilization of vapor it is further introduced into condenser where phase transfer from steam to liquid take places and cold condensed vapor is discharged into sea.

5.19.3.4

Marine Currents

There is no doubt in a finding that ocean currents possess of enormous energy, it has a great potential of fulfilling our daily needs of electricity. To harness this all important energy, there are number of formation and devices have been proposed. Some projects consist of turbine anchored to the seabed, other suggests them to be placed with current itself, allowing several units to be ties with one another using single cable. As separations to the buyer may be, in a few cases, industrial complexes were proposed amidst the sea and the made item would then be conveyed by ship to the mainland.

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Turbine Working fluid

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Condensor

Pump 24°C

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7°C

27°C

4°C Cold deep water (1 Km) Fig. 14 Open cycle ocean thermal energy conversion (OTEC). Reproduced from Masutani SM, Takahashi PK. Ocean thermal energy conversion (OTEC). Cambridge, MA: Academic Press; 2001.

A Canadian firm, after successfully testing six prototypes, collaboratively decided to build a 2200 MW ocean current energy conversion plant in Philippines, while utilizing Davis vertical hydro turbine scheme. The activities conflict, however, with worries about route security, atmosphere alteration, peril for sea life, cleaning of buoys on the off chance that they were utilized. In the wake of dismissing harnessing the Mediterranean's waves – their tallness being much more humble – Italians are again considering a marine current focal in the Straits of Messina.

5.19.3.5

Salinity Gradient

Membrane issues, especially their cost, remain a noteworthy hindrance to advance in tapping that kind of sea vitality. A current proposition prompted to the advancement of a model plan, wherein the surface of the sea assumes the part of membrane. In an adjacent range fresh water can be put away. Based upon the osmosis standard, it will move toward the salty seawater mass, going through a turbine and blends with the seawater on the opposite side. An impair is the span of turbines required, however, in the event that saltiness control must be created, this appears to be, today the most inexpensive approach to utilize ocean energy. The saltiness inclination has been utilized for power generation through batteries. The rule included is turn around electrodialysis; substituting cells of new and salt water are put beside each other. Streaming seawater go up against the part of electrolyte. Lockheed constructed an 180 MW exploratory focal. Such batteries are voluminous also; the framework goes through a decent piece of the created flow to actuate the water pumps.

5.19.4 5.19.4.1

Analysis and Assessment Ocean Energy Management (Sustainable Ocean Energy)

To survive in today’s market, the demand of organizational efficiency in factors like energy, investments, and workforce is increasing day by day. Nowadays, energy is considered to be a most vital factor for the reduction of operating margin. However, firms need to evaluate the effect of rapid price fluctuation on its operations in order to sustain in volatile market. The key step to control this problem is to smartly reduce the cost relating to energy sector and utilize it somewhere else. In this highly competitive market, organizations should reduce all the extra cost in their operations, in order to sustain longer. Energy is one of the factors, which consumes a large proportion of organizational budget. However, recently a large decline in energy cost has been noticed; but cost of energy will remain volatile. Therefore, sustainable energy generation along with sustainable energy management (SEM) practices must be realigned with normal industrial operations in order to extract as much from it as we can. For example, United States is strongly emphasizing on energy independence and efficiency, for this purpose a bill “American Reinvestment and Recovery Act (ARRA)” has been passed by congress. The main features of this bill are discussed below: American Reinvestment and Recovery Act: It is also commonly known as The Stimulus. This act was enforced by 111th congress on February 2009 and later signed into law by President Mr. Barack Obama on February 17, 2009. The primary objective of ARRA was

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to create vacancies on immediate basis. The secondary objective was to directly invest in infrastructure, health, education, and most importantly renewable energy and its management schemes. The initial allocated budget for ARRA was $787 billion, which was later revised to $832 billion during 2009–19 [23]. Total budget allocated for investment in energy sector was $27.2 billion, mainly in renewable vitality. However, this budget was further distributed in following manner to utilize it more efficiently: $6.3 billion to state and local governments; allowing them to invest in energy efficiency projects, $4.5 billion to federal buildings to increase their energy efficiency, $6 as independents renewable power generation loans at easy markup; allowing small firms to generate their own electricity, and $11 for the modernization and maintenance of United States electrical power grid. Beside material cost, energy cost is a major pressure factor for several organizations and manufacturers. A sound and easy to implement business strategy can yield more production stability by reducing cost. In order to work in profit and with efficiency, modern operations largely depend on the low cost of energy it consumes. Energy conservation and independence are also considered as major strategies for creating a competitive advantage in business. Realizing the fact, that energy management could play a vital role in addressing social, economic and environmental concerns, organizations are readily adopting these practices to minimize the risks. Overall, energy efficiency and management practices are among the most important option to increase the profit of organization, as well as, to reduce their dependencies on highly volatile fossil fuel prices. Economic concerns: Energy management and saving are really an important at any all levels of human interference, whether it is an organization, a nation, a small-scale institute or an individual. This practice reduces the energy cost and increases the overall profitability of organization. For example, Thailand started to take steps for energy conservation and efficiency after the first oil crises in 1973. In this regard, “Energy Conservation Act” was put into action in 1992. Moreover, the announcement of “National Energy Conservation Strategic Plan” (2002–11) and “Five Year Conservation Plan” (2002–06) took place. The nationwide joined effort toward energy efficiency plays a vital role in reducing their dependency on costly energy resources, i.e., crude oil. Private organizations are widely affected by energy cost; this not only directly affects their profitability but also affects their viability to sustain in global market. The higher the cost of consumed energy will be, the higher will be the cost of product in world market, which can never be a good sign for national trade. Environmental concerns: SEM is also concerned with the environmental problems of the nation. Environmental concerns mainly have to deal with the emission of carbon footprints and other GHG in Earth’s atmosphere. These problems are stated as global warming or climate change. These factors are not only rising the Earth’s annual average temperature but are also considered as the major reason of ozone depletion. SEM, especially minimizing use of fossil fuel is the major among various countermeasures of this problem. For the solution of the said issue, there have been numerous worldwide or universal participation activities. One of those is Intergovernmental Panel on Climate Change (IPCC), which began in November 1988. It has three working groups and one task force. One of six directors of the group originates from Thailand. There are also numerous steps have been taken after the formation of United Nations Framework Convention on Climate Change (UNFCC) in which various nations cooperates for the effort of reducing GHG emission.

5.19.4.1.1

Functional approach toward ocean energy management

These days, corporates decision taking and action planning are decided on the basis of strategic approach to make the action or decision sustain longer; successfully. Otherwise, the action or plan can never be successful enough under the rapidly changing circumstances and soon corporate will find itself in an uncertain situation of fighting for its existence. Key steps for successful strategic approach have been discussed in quite detail in this section, so that user could grasp its essence and take steps immediately without any further dely. It consist of following key steps: 1. Commitment of top management 2. Understanding the issues like • Grasp current energy use • Identify management strength and weakness • Analyze stakeholder needs • Anticipate barriers to implement • Estimate the future trend 3. Plan and organize, including • Develop a policy • Make out a plan/program 4. Implementation 5. Controlling and monitoring performance 6. Management review 1. Commitment of top management: it is the most essential for the accomplishment of energy conservation exercises inside organizations or industrial facilities to have clear and authority duty of top administration – either the corporate top (senior) administration or manufacturing plant chiefs. The top (senior) administration should express responsibility toward the energy

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management (or vitality conservation) and carry on along this line, for example, they should take a part in energy conservation activities themselves and encourage their staff as well. Understanding the issue: before attempting to make out any future projects or activity arranges, it is fundamental for the organization or production line administration to comprehend the present circumstance in a legitimate and precise way. This incorporates the status of their own operation, as well as other significant data, for example, contenders' operation, conditions around the organization and their pattern in future, positioning the organization itself in the neighborhood and in worldwide markets, and so on. The key steps for this purpose are: • Grasp the current energy use: the information regarding current consumption of energy should be gathered through measurements, estimations, or calculations of every individual unit under the premises of organization, with the classification on the basis of type of energy. The data should be collected regularly and arranged in daily, weekly, monthly, or yearly manner depending upon the requirement and precision set by its stakeholders. Then the data should be analyzed and a relation should be obtained between different operational modes and production scales. This data can also be utilized in the prediction of future trends. • Identify management strength and weakness: after the data collection, it should be compared with the pioneers or benchmarks in the industry. If such reference data are not easily available, then there historical data can be compared with the present data of their competitor so that right steps could be taken to get an edge over their competitor. Along with it, the strength and weaknesses of the company should also be evaluated considering the competitor situation in local and global market. • Analyze stakeholders needs: in an organization, stakeholders are basically top level senior managers, directors, staff/engineers, and workers/operators. The need and expectation of these stakeholders must be taken into account so that everyone could adopt the changes caused by SEM easily and large benefits can be extracted out of it. • Anticipate barriers to implement: designing an easy to implement and practically possible program also need consideration of expected barriers that could come along in its way of creating an organization that follows all the steps of SEM and contributes toward its social, economic and environmental amenability. Some possible barriers could be: ○ insufficient support of top management; ○ inadequate level of understanding and willingness of cooperation between multiple managers of same organization; ○ untrained workforce; and ○ insufficient budget allocation for SEM implementation activities. • Estimate the future trend: the future trend of energy demand could be estimated by using the historical data of the organization. This estimation enables the organization to increase or decrease in its power generation capabilities depending on rapidly changing circumstances of global market. It also provides a check and balance between the energy consumed and production of the organization for the particular period of time. Plan and organize: based on the analysis of previously collected data and understanding the position of company in local and global market and also identifying the strength and weakness of organization, the following step should be taken in order to design a relevant and good strategic plan to get a maximum out of this effort: • Develop a policy: it is fancied that the top (senior) administration announce the “energy policy statement.” This is exceptionally viable to let individuals inside and outside the organization unmistakably knows the administration's dedication to energy management (or energy protection). The configuration of the energy strategy statement is different; however, it generally incorporates the objective or goal of the organization and the more concrete focuses in the field of energy management (or energy conservation). • Make out a plan/program: any plan under consideration should be easy to implement, practical, and attainable. It should also take into an account, all the resources and related elements of the company which can be classified into measurable or quantifiable. It should also include the awareness campaign relating to SEM, motivation techniques, training, and so on. Implementation: the accepted plan should be enforced within an organization and all the organizational resources should be consumed in order to ensure smooth implementation of the plan. The responsible person or committee shall continue to work for the promotion of activities and training of workforce which is essential for the plan to survive. Controlling and monitoring performance: after the implementation, all the processes should be closely monitored in order for it to work smoothly. If any problem arise, or any variance between estimated and observed value noted; then necessary steps should be taken in order to overcome and stabilize it. Management review: after the plan or program has been completed, a report mentioning all the events, success and failures faced during its implementation should be submitted to top management. In it all the results should be analyzed in quite detail for any good and bad points with possible recommendations. This report shall be utilized as a feedback for subsequent program. Thus all activities could be repeated to form a cyclic movement.

5.19.4.1.1.1 Implementation of sustainable energy management (key step approach) In order to implement SEM program effectively, key factors approach which is discussed in quite detail in our previous section should be utilized. The major step toward the implementation of SEM program in any organization is the energy audit. Energy audit enables the organization to identify the problems or factors which could become a hurdle in the way of its implementation. Energy audit can be conducted by hiring an expert consultancy agency or by utilizing internal technical and trained staff.

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SEM system Parameters

Alerts Data collection unit Unit consumed

Production

Fig. 15 Energy efficient operation. SEM, sustainable energy management. Reproduced from Siemens ARC-White Papers. Seimens energy efficiency; 2009.

5.19.4.1.1.1.1 Energy audit There are number of stages in energy audit process, each having its own importance. The process includes: collection and analysis of data, site investigations, cost and benefit analysis, preparation of concise report, creating an action plan for the project implementation and monitoring and controlling. Energy audits act as a foundation of developing an SEM program that will give an edge to the organization, while creating more efficient operations. It also enables the analysis on where the most effective use of limited capital should be employed to achieve energy goals. Through energy audit, specific type of system can be monitored throughout the operation, which can be optimized, modified, or replaced based on the requirement. It also helps to identify the operations which yield greatest rate of investment (ROI), so that it could be modified and kept up-to-date in order to compete with the changing circumstances of global market. Monitoring systems energy consumption throughout the day and night cycle, and correlating it with the production delivers an important information regarding that systems efficiency. With newly introduced wireless energy monitoring technologies, this equipment’s can be installed in a very cost effective manner on existing system. Data collection with every 15 min interval will be sufficient enough to estimate the efficiency of the system. The process cycle governing energy audit is shown is Fig. 15. 5.19.4.1.1.1.2 Advanced monitoring and metering solutions As discussed previously that to conduct a successful energy audit, monitoring devices need to installed within premises in order to prepare a successful plan of action and correct estimation of future trends. These frameworks offer both modes, check of the utilities overwhelmed by a far reaching report, including droops and surges, and the capacity to power factor, harmonically disturbed waves and different parameters consistently. These solutions are adequate for obtaining metrics without any high capital investment or changing of existing system flows. It provides an important statistics of the real time process on the basis of which decision can be taken for corrective actions. Whether it is effectively measuring a capacitor bank to enhance control elements, performing load shedding, or deciding squandered vitality utilization, advance metering offers many preferences basically from gathering precise information from dissimilar sources. Hence, advanced metering is an approach to successful implementation of SEM on a distributed architecture and topology that will grow according to the requirement of organization. It will act as an essential strategic tool for optimization and evaluation of already installed process, operation or a system.

5.19.4.1.2

Detect, measure, analyze, improve, and control approach toward ocean energy management

Concerns regarding the importance of conservation and effective utilization of energy are increasing day by day. As the evidence of the above statement it can be given that nowadays people are in a habit of switch off all the necessary equipment’s, when not in used so as to use energy effectively. Bulk amount of energy generated by any nation has been consumed by the production or manufacturing industries to increase the countries GNP; therefore, certain approaches are required so that effective energy can be utilized by these sectors so as to increase their efficiency. The implementation of such systematic approach not only makes the nation industrialize and modern but also effect the lifestyle of each individual of a society in a better perspective. Cost associated with energy consumption is no longer considered as a minor component of total production expenditure. In spite of its greater importance and influence, there are certain facilities which do not take its advantage by properly managing it and minimize its effect on expenditure sheet; which directly minimize the production costs. Facilities without proper power managing systems and determined energy managing approaches do not have proper understanding regarding their energy usage and production ratio; such facilities cannot consume their resources to its fullest being efficient at the same time. While optimizing power monitoring investments, it is necessary to identify both intended application and prioritize energy consuming units within a facility. SEM is an effective tool which gives a certain edge to any facility over other (i.e., its competitor) by implementing it in terms of effective savings. These savings increases the GNP of overall nation, i.e., if the manufacturer invest less on a product, they will further sell product in lesser amount to the end user. It also increases the purchasing power of the individual of any society; hence, larger the trade yields better GNP of nation. In addition to these advantages, SEM also minimizes the air pollution which we

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Detect

Measure

Analyze

Improve

Control

Fig. 16 Detect, measure, analyze, improve, and control methodology (six sigma steps).

generate by burning fossil fuels. We cannot visualize it as we do when we start a car but whenever we switch on a light, we generate some amount of pollution in power plant, which is then released in air in terms of carbon footprint and is a reason of global warming. The necessity of an hour is to utilize this all important form of energy is such a manner that it gives us advantage economically, as well as ecologically. The six sigma is a proven approach in terms of quality management and implementation, as an extension to same principles this approach has been tested over the phenomenon of energy efficiency/conservation in number of facilities and there result came out to be unique and attractive. Six sigma at numerous associations just means a measure of value that takes a stab at close flawlessness. Six sigma is restrained, information-driven approach, and system for taking out deformities in any procedure – from assembling to value-based and from item to benefit. The core objective of six sigma methodology is to develop a measurement-based strategy that primarily focuses in reducing the process variations and improve process outcomes. This objective is attained by implementing two six sigma sub methodologies namely: detect, measure, analyze, improve, and control (DMAIC) and define, measure, analyze, design, and verify (DMADV) in a facility. DMAIC mainly focuses on the improvement of existing processes falling below expected values; whereas, DMADV is methodology used to design new processes and system considering specific requirements. Energy conservation plan mainly developed keeping DMAIC process in consideration as it mainly used to implement on the existing processes to enhance efficiency (Fig. 16). 5.19.4.1.2.1 Implementation of six sigma approach (detect, measure, analyze, improve, and control methodology) 5.19.4.1.2.1.1 Detect The phenomenon of energy saving is considered to be more where its consumption is higher. Therefore, the key is to attack the larger energy consumer rather than implementing it and worrying about the minor one. From this point of view, while designing a plan for energy management first target larger energy consumer within a facility, i.e., heating systems, cooling systems, lightning, etc. Those points also need to be detected in a process where energy has been wasted or exhausted for effective and long lasting saving. In order to detect/define such points and elements in a process, traditional approach of installing metering gives snapshot data of energy consumption which is not sufficient enough, for effective monitoring real time data logging devices need to be installed. There are certain rules to install power monitoring devices which are given below: 1. Advanced monitoring systems need to be installed with main electrical switchgear, whereas less sophisticated metering devices should be deployed to each of the identified bulk energy consumer. The advantage of installing advance monitoring system with main grid is that it will not only monitor the electrical parameters of the facility but also the power quality or power factor it is receiving. This approach enables its user to monitor basic electrical parameter and on the same time grasping the firsthand knowledge of the quality of power facility is receiving through electric utility. 2. As discussed, continuous monitoring of large loads allow to identify and predict accurate energy savings; therefore, the more the monitoring points will be, the better electrical model can be generated for statistical predictions. 5.19.4.1.2.1.2 Measure After identifying/detecting which load to measure, accurate measurement devices need to be installed in order to do quantitative analysis. Properly installed and verified measuring system could be a valuable asset for any organization. Annual energy consumption and production are the major concerns of an organization. An electrical measuring system could contain one or discrete points which are interconnected on a single station so as to enable a single user to monitor all the happening at a single point. An efficient measuring system contain three major components: metering devices to measure data, application software to manage, accumulate, display data and matched communication module in order to link metering devices with application software. This measuring system should be robust enough in order to work and gather real time data 24/7. This continuous extraction of important information mostly with the frequency of every 15 min enables the user in correct decision making. Also, this will give accurate information regarding how much energy is consumed in which part of the day the consumption is greater and what unit/ load consumes larger energy. This knowledge plays a vital role in reducing the energy consumption and increase the efficiency of the process. 5.19.4.1.2.1.3 Analyze Two type of analysis is mostly done in order to come up with an accurate energy management plan which is of energy consumption and quality. All the gathered data are then analyzed with respect to these two segments and parameter of interest are current and voltage consumption during the startup of load, power factor, and energy consumption. These observed parameters then can be compared with the actual in order to identify deviation of each load. These analysis helps the production engineer with energy consumption pattern for planning shift activities, such as production rates, reducing production breakdowns, maintenance engineer to check that whether the equipment is due for maintenance or not and planners to plan appropriate sizing of facility.

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5.19.4.1.2.1.4 Improve This analysis is then used in creating an appropriate energy management strategies for optimum and efficient plant operation, this includes: 1. Enabling the organization to predict the energy consumption pattern in manufacturing and production facilities with respect to any season, part of the day, or year. 2. Standardize the energy consumption patterns for different points, loads, or facilities within the plant. 3. Enable to shift the operations in the off-peak times, this is mostly suitable for the countries in which load shedding is commonly done. 4. Prediction of possible energy interruption during the operation which could affect the process a great deal. 5. Automatically improves power factor by adding a capacitor banks if correct prediction of its arrival can be made. 5.19.4.1.2.1.5 Control After taking steps to improve the power efficiency of the system, certain controls are needed to make it long lasting. The remarkable work in this field enables the development of devices like adjustable speed control motor drives and shunt capacitors for power factor correction and reduce losses.

5.19.5

Case Studies

5.19.5.1

Case study: The Land Installed Marine Power Energy Transmitter Wave Power Project

With the collective effort of Queen’s University Belfast, Wavegen Ireland Ltd., Charles Brand Ltd., Kirk McClure Morton, and I.S.T Portugal a 500 KW onshore wave energy power plant have been constructed along the west coast shoreline of Scotland known as Land Installed Marine Power Energy Transmitter (LIMPET). This power plant was commissioned in spring 2001 and since then it is working remotely and adding up the substantial amount of electricity in national grid of United Kingdom to overcome daily energy needs. LIMPET consist of onshore OWC wave energy harnessing technique, it was built in duration of 3 years and in 2001 commissioned to replace 75-KW power plant located on the adjacent site Whittaker. LIMPET consist of three water column housed in a concrete structure each having a dimension of 6 m  6 m and inclined at 401C with the horizontal to give a total water surface area of 169 m2. The upper section of the three columns are interconnected so that power could be harnessed using a single induction generator installed in the middle of rear wall. The power takeoff system of the OCW consist of a 2.5 m diameter well turbines connected to 250 KW induction generator. The output from the generator is rectified and then inverted using certain power electronics circuitry to assure the smooth transition of power in spite of high variable speed between 700 and 1500 r.p.m. Data acquisition system is also made a part of this power plant so as to monitor all the operational characteristics and parameters throughout the energy harnessing and electricity generation process. LIMPET power plan has proven to be extremely robust, and since its installation continues to adding up all important source of energy, i.e., electricity in national grid and is also withstood against a storm successfully. Fig. 17 shows a schematic diagram of LIMPET.

5.19.5.1.1

Performance

The power generated by this power plant was lower than the estimated value. This is due to the fact that wave’s excitation of water column along the shoreline was lower than the estimated value which was calculated through data analysis. Furthermore, mechanical losses of well turbine were also observed to higher affecting the efficiency of the system. Three different types of power conversion technologies accompanied in LIMPET to generate most important electricity. Firstly, wind energy is converted in

Fig. 17 Land Installed Marine Power Energy Transmitter (LIMPET) schematic. Reproduced from Siemens ARC-White Papers. Seimens energy efficiency; 2009.

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120 100 80 60 40 20 0 Supplied power Inverter losses Generator losses Windage losses Turbine losses

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Fig. 18 The optimized results of LIMPET Reproduced from Whittaker TJT, Beattie W, Folley M, Boake C, Wright A, Osterried M. The LIMPET wave power project – the first years of operation. In: Seminar on the Hydraulic Aspects of Renewable Energy, no. 1997, Scottish Hydraulics Study Group; 2004, p. 1–8.

pneumatic energy. Pneumatic energy is further converted in mechanical energy and at the end mechanical energy is consumed to generate electrical energy. To optimize the performance of LIMPET, three scenarios were considered along with the present electrical power generation and observed losses. The optimized results have been represented in Fig. 18. Bar A shows the current situation where the observed turbine loss accounts for 93 KW, total mechanical and electrical losses account for 43 KW leaving behind 12 KW for electricity generation and adding it to national grid. Bar B shows that if generator control algorithm has been altered to rotate the generator much faster, i.e., the time taken for it to be in a stall will be much lesser, the power generation would be increased to 12–20 KW. Bar C shows up to 33 KW of power can be extracted from system by balancing the turbine performance during intake and outtake flow. This could be done by adding guide vanes within a chamber. Whereas, Bar D shows that 58 KW of power can be extracted from same system if turbine works efficiently minimizing all the practically observed error and turbine works ideally to its theoretical readings, which can never be the case.

5.19.5.2

Case Study: La Rance Tidal Power Station

This power station is located on the estuary of Rance River, France. After almost 25 years of hard work and study, this 240 MW tidal power barrage becomes the first commercial scale plant in world. The low head hydro technology was commercially used for the first time on such large scale. This project was initiated in 1961 and completed in 1967.

5.19.5.2.1

Physical aspects

The reason of building it on Rance estuary is that, it possesses world’s highest tidal range, i.e., for an average of 8–14.5 m during spring tides. This tidal range makes estuary an attractive location for the construction of world’s first ever large-scale tidal power generation plant. The physical dimension of this barrage consist of 750 m length and 13 m height with a basin spread on area of 22 km2 which makes it capable of storing 180 million cubic meters of water. This project also includes a dam which is 330 m long, for the housing of turbines. After 30 years of successful operation, in 1996 the plant has gone through a complete shutdown to carry out much needed and prevented overhaul of all equipment by Electricite De France (EDF). Nowadays, plant is operating successfully with an annual power generation of 600 KWH.

5.19.5.2.2

Technical aspects

This plant utilizes 24 bulb type hydroelectric turbines each having a rating of 10 MW. These bulb type turbines were specifically designed for this power plant as it possesses a feature of utilizing both, high tides and low tide (edd tides) with great efficiency and under low head condition making this barrage technology a two-way formation so that maximum energy can be generated. The total cost of barrage was come up to be 620 million francs equivalent to 94.5 million euros [24].

Energy Management in Ocean Energy Systems 5.19.5.3

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Case Study: Makai, Ocean Thermal Energy Conversion Service Provider

OTEC is a procedure that can create power by utilizing the temperature contrast between profound chilly seawater and warm tropical surface waters. OTEC plants pump substantial amounts of profound frosty seawater and surface seawater to run a power cycle to generate power. OTEC is firm power (day in and day out), a perfect vitality source, ecologically maintainable and possess the potential to generate massive amount of energy. As of the higher electricity prices, environmental concerns and political commitment to secure energy develop the initial commercialization of OTEC. OTEC technology can be quite useful to tropical islands where they depend highly on fossil fuel-based energy generation. Makai is the OTEC service provider and is pioneering OTEC-based research since its working involvement in the first ever netpower producing plant in 1979. Since then Makai has been a prime contractor for number of various and unique research-based contracts in OTEC and recently they have been involved in the study to develop a 100 MW OTEC power plant to benefit the certain island communities like Hawaii and Guam. They have developed international expertise in OTEC in the area of commercial or pilot designs, economic modeling and feasibility study, and warm/cold water supply systems.

5.19.5.3.1

Ocean Energy Research Center

Makai has developed its first onshore Ocean Energy Research Center (OERC) in kailua-kona, Hawaii, United States. This research facility is entirely dedicated to demonstrate different new methods and equipment which could increase the overall efficiency of OTEC systems. The primary focus of this facility is to develop OTEC technology, but alongside they also study some of the new emerging technologies like: seawater air conditioning (SWAC), marine heat exchangers, and marine corrosion research. This research facility is the only facility of its kind with continuous approach to shallow and deep water to study its characteristics and effects using a turbine generator that completes OERC land-based OTEC closed cycle power plant connected to the national United States grid in summers 2015.

5.19.5.3.2

Makai’s involvement in ocean thermal energy conversion based research

As discussed that Makai has long and diverse involvement in OTEC-based research and technologies. Table 3 shows various OTECbased projects.

5.19.5.4

Case Study: Exploring the Potential to Install Marine Current Turbines in Southern Brazilian Shelf Region

The Southern Brazilian Shell (SBS) is located in Rio Grande do Sul State between 281S and 351S. The location is renowned of having quiet rugged shoreline that is spread from northeast to southwest. While having rugged shoreline, the bathymetry of this area is quite soft, with comparatively higher slope and shelf then other shore lines of Brazil [25].

5.19.5.4.1

Methodology

This case study was based on the utilization of tridimensional numeric model (TELEMAC3D) to extract the energy results. Several criteria were applied in order to study and compare different sustainability parameter. 5.19.5.4.1.1 Hydrodynamic model Company Eletricit de France (CZEDF) is the company responsible for the designing of TELEMAC system. The system mainly used for hydrodynamic simulations, it solves the Navier–Stokes equations by taking in account all the variations that are producing in the free surface of fluid, by neglecting mass conservation equation and considering all the hydrostatic pressure and approximations given by Boussinesq. The model entirely based on finite element techniques (FET) to substantially solve hydrodynamic equation [26]. Along with it the turbulence analysis was performed using Smagorinsky model. This model is generally used in large-scale

Table 3

Makai’s based ocean thermal energy conversion (OTEC) development projects

Makai’s involvement in OTEC projects OTEC project

Makai’s Involvement

Year

Funding source

1

Designed and built testing facilities

2009–17

OTEC process designing Overall designing of water handling and OTEC system attributes Evaluates onshore and offshore OTEC technologies along with the cos and commercial development plan Complete designing and construction

2009–11 2009

Hawaii Natural Energy Institute and Office of Naval Research NAVFAC Lockhead Martin

2009

Office of Naval Research

1978–79

State of Hawaii, Lockhead Martin, and Dillingham Corporation

2 3

Corrosion and performance testing of OTEC heat exchangers Pilot plant design 100 MW plant design

4

Guam OTEC feasibility: roadmap to commercialization

5

Mini OTEC 50 KW floating plant

Source: Masutani SM, Takahashi PK. Ocean thermal energy conversion (OTEC). Cambridge, MA: Academic Press; 2001.

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Table 4

Technical parameter of turbines

Parameters

Values

Start-in speed Cut-in speed Efficiency coefficient Nominal power Turbine head Turbine ray

0.2 m/s 1.5 m/s 0.35 170 KW 14 m 10 m

maritime applications having eddy phenomena. It analyses the mixing coefficient with the size of meshing element in order to determine velocity field [27]. 5.19.5.4.1.2 Energy conversion module With the advancement of human society, energy from moving ocean waves can also be extracted to generate electrical power similar to that of wind. As per the recent statistics, almost 76 equipments can be found in order to utilize hydrokinetic energy which includes turbines, generators, and rotors [28]. As discussed that the hydrodynamic simulation in this study were generated using TELEMAC3D; thus, the analysis of energy harnessing from mechanical tides to electrical power were performed using energy module [29]. The energy turbine, Eq. (1) is used to calculate power in watts by this module from the incident flow velocity where r is the density of fluid, Z is the efficiency coefficient, A is the exposed blade area and n is the incident velocity of fluid. The technical parameters of turbine used in this module is given in Table 4. P ðW Þ ¼

5.19.5.4.2

Initial boundary condition

1 rZAv3 2

ð1Þ

In this project, salinity and temperature fields are taken as initial boundary condition around the shoreline. Readings were obtained using circulation and climate advanced modeling project (OCCAM). Further, numerical computational model was designed using parameters like water level and the average number of tides in the region. The model was named Grenoble Model FES95.2. The humidity, temperature, and moisture variability of the wind around the shore was computed using data of National Oceanic and Atmospheric Administration (NOAA) website. The site was monitored for 2 years and data extracted at regular intervals. After the analysis of data, it was concluded that first year gives some anomalous data points due to El Niño Southern Oscillation (ENSO) influence having moderate discharge throughout the year. However, the second year yields normal conditions in which tides follows the natural pattern.

5.19.5.4.3

Results and discussion

After analysis of data, locations were selected to install turbines. The regions were particularly selected on the basis of average ocean current velocity. The mean average current velocity value of this region came out to be around 0.4 m/s which was enough to drive the current turbines and generate sufficient energy. Isobaths play a vital role in harnessing the mechanical energy of currents, the areas in which isobaths are closer with stronger vertical gradient could generate almost 7 kW/day. In this region the isobaths were apart from each other having a basin area in between of almost 20 m long. This further characterize the suitability of this location as it was expected that the chosen site could generate almost 10 kW of mechanical energy per day. So, two locations were selected on the basis of above specifications, the northern region was highly influenced by strong vertical gradient yielding stretching power currents and enhancing power generation, while on the other hand, the southern region was greatly dominated by topographic features that yields the flow to diverge resulting the twisting of the marine current, increasing the power generation.

5.19.5.4.4

Conclusions

So finding suggests that there are two suitable locations on SBS were marine current energy convertors can be installed. The northern area of the region has the highest potential to generate a power of 10 kW/day which makes it around 3.5 MW/year; whereas, the southern region comparatively have less potential of power generation and could generate around 7.5 kW/day, 1.5 MW/year. However, both locations have required oceanographic features that are influenced by current velocities. The local sites at SBS were evaluated against all of the geological, enviromental, and socioeconomic factors. The study also aims to highlight the possible constraints to install marine current turbine facility that were depth, sensitive biological resources, and human activities.

5.19.5.5

Case study: The Power of Salinity: An Australian Example

This is newly introduced technique of harnessing energy from mixing two waters having different salt concentrations. Excessive energy is released when rivers meets ocean, i.e., around 0.7–0.75 kW h is dissipated in the environment when 1 m3 of seawater get

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mixed with 1 m3 of river water [30]. This dissipated energy can be harnessed using pressure-retarted osmosis (PRO) technique, which works on the basis of separating salty water from fresh water by getting it flowed through membrane to harness the energy of salinity gradient. This salinity gradient power is usually referred to as osmotic power, study suggests that using PRO technique in one tenth of global river discharge water is used mixing it with seawater, more than 1300 TW of energy could be generated. This energy is equivalent to half of the annual energy generation of whole European Union (EU) [31] and equal to the global energy which we are currently generating from hydroelectric power systems. Table 1 shows some of the major fresh water sources that if they can be mixed with seawater, to what extent of energy could be generated from each. In spite of the fact that PRO technology was discovered in 1973, this research is still in its early stages till 2008. But due to that rapid advances in membrane technology it started to be considered as one of the promising source for renewable energy. The feasibility of PRO extensively depend on various criteria under the domain technical and economical both. These criteria’s are relatively high water permeability of membrane, resistance of membrane toward high applied pressures and extensively high salt rejection rate. Till right membrane technology will be available in local markets, PRO is expected to become a competitive source of renewable energy like water and solar.

5.19.5.5.1

Energy generation

Osmosis refers to a pathway to transport pure seawater across a semipermeable membrane from a solution having low salt concentration. The solution with lower salt content is known as “feed solution,” whereas the solution with high salt content is called “draw solution.” The system works by allowing pure water solution to flow through membrane and get mixed with draw solution so that the salt concentration could get balanced. The membrane is designed in such a way to allow passage of water, while separating the molecules or ions. As, every solution is characterized on several parameters; one of them is “osmotic pressure” that is determined through measuring the amount of salt present in the solution and calculating through Van’t Hoff equation [32]. In its simplest terms, osmotic pressure is referred to such pressure which is, when applied to certain solution, would block the flux generation of pure water across the membrane, needed to balance the salt concentration. Take an example of seawater in which concentration of NaCl varies between 3% and 4% or around 30 and 40 g/L, the osmotic pressures measures out to be between 25 and 33 bars, that is, relatively lower than the normal is the surrounding temperature is around 251C. This observation means that if a pressure of 25–33 bar is applied to the seawater it will prevent the flow of pure water and allowing them to get separated through membrane. When these two solutions are separated through membrane, the difference of the pressures between two solutions is directly proportional to the maximum flux generated through membrane and also to its permeability [33]. J ¼ AðDp

ð2Þ

DPÞ

where J is the water flux through membrane, which is typically measured in L\m2\h; ∆p is the difference of osmotic pressure of feed and draw solutions; ∆P is the difference of hydrostatic pressure of two solutions and A is the water permeability coefficient of the membrane, which depends on the properties of membrane and of which unit is L\m2\h\bar. In forward osmosis (FO), the flux generated through membrane solely depends upon the osmotic pressure difference of the two solutions, that is, J ¼ ADp. When hydrostatic pressure difference ∆P is increased, it pressurized the draw side and increases the water flux to 0–Dp through the membrane, so it can be observed that the membrane is dominated by osmotic pressure difference; however, the solution flow is declined by increasing ∆P. This cycle is known as PRO. The production of osmotic power depends upon the capacity of PRO membrane, for a steady and continues power production, the hydrostatic pressure of the draw solution needs to be maintained on a constant value. This allows the steady flow of pure seawater through the membrane yielding an increased volume on the side of draw solution. This additional volume can then be used to generate electrical power as shown in Fig. 19.

Draw solution: Sea water

Intake

Pretreatment Turbine PRO-module (membrane) River water bleed

Feed solution: River water Intake

Pretreatment

Fig. 19 Pressure-retarted osmosis (PRO) process flow.

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Table 5

Various combination of draw and feed solution

S/No.

Feed solution

Draw solution

1

River water

Seawater

2

Seawater

3

Brackish groundwater

4

River water

5

Brackish groundwater

Concentrated brine from salt lake Concentrated brine from brackish groundwater Concentrated brine from brackish groundwater Hypersaline groundwater

5.19.5.5.2

Water flux (L/m2/h)

21.3 174

Observed power density (W/m2)

Power production (per m3/s of feed solution)

8.4 564

1.4 11.6

43.3

34.8

2.9

81.3

73.5

3.3

116

250

Australian example and osmotic power estimate

Brisbane River vs. seawater, 10 MW Seawater vs. brine lake Torrens, 2.6 GW Surat and Bowin basin, 22 MW Surat and Bowin basin, 33 MW

7.7

Energy generation through pressure-retarted osmosis in Australia

With the same mindset as of several other European countries, Australia is also planning and working on policies to fulfill its nation requirement of energy through different renewable sources [34] and decrease its dependency on fossil fuel for same requirement. Some of the highlighted policies and plans are renewable energy target (RET) [35], the clean and green energy plan [36] and the proposed plan to reduce the carbon content emission by 2020. Take an example of RET, through this policy Australian government is determined to generate 20% of its required energy through renewable energy sources by 2020. Where at time, only 7% of the total energy they are generating, is from renewable sources and rest from fossil fuels. They are intended to achieve this target by increasing the subsidy and by investing in the research and development in the field of renewable energy. Currently out of 7%, the share of each energy sources contributing in local grid are wind 22.9%, hydro energy 63.4%, bioenergy 11.5%, and solar energy 2.1% [37]. The remaining share is of tidal wave and geothermal techniques of renewable power generation. Australia can be a country having huge potential for salinity gradient energy as various natural solutions exists here, by combining those, sufficient energy can be harnessed. The combination of solution required for the implementation of PRO technology are discussed in Table 5. Various sources can be utilized the only requirement is that they should have different salt concentration in order to implement PRO. To study the feasibility of installing a PRO in Australia; an example of Brisbane River located in Queensland is taken. The river has a flow of 7 m3/s, the flow which equals or exceeds for most of the time. If a turbine is installed having power production rate of 1.4 MW/m3/s of flowing river water, then 9.8 MW of electrical power could be harnessed through it making it competitive with other natural resources to generate electricity [38]. Huge part of capital investment depends on the power generating density of membrane. Most of the commercially available membrane designed for FO and RO have a power output from 0.3 to 2.7 W/m2. But however; modified membrane have been designed to extract almost 11.5 W/m2, under laboratory conditions. To make PRO viable, several studies have been conducted and concluded that to make PRO viable, At least 5 W/m2 of power density is required. With this power generation, 1.96 km2 of total membrane area is required to for a PRO to be installed at Brisbane River side. The cost of membrane installed at Perth in a desalination plant was approximately AU$ 452 M having capacity of 144 ML/day. The installed membrane area was around 700,000 m2. Applying this estimated cost to our membrane desired area of 1.96 km2, it can be calculated that around AU$ 885 million will be the installation cost of PRO at Brisbane River. It can be compared to several other renewable energy like wind, solar for better understanding. Solar farms would cost around AU$ 6800–7700 k/W [39]. Whereas, wind energy has an estimated capital investment cost of around 2000–2500 k/W. In order to compare these given figures with osmotic power resource, than PRO has capital investment cost of around AU$ 4000–8000; making it competitive with other renewable energy sources.

5.19.5.6

Case Study: Optimization and Management of Low Head Hydropower Plant

As Pakistan is going through severe power shortfall for last few decades, correct steps need to be taken to control this situation so that it could not get further worsen. For this purpose several small hydropower projects can play a vital role. For any plant to be operational efficiently, some factors need to be optimized like efficiencies of components, energy produced, and financial/ economic parameters. In this case study optimal sizing of Upper Chenab Canal (UCC) at bambanwala is presented. UCC has been located on Chenab River. Initially, canal was designed to discharge 340 m3/s of water in 1915 which was then further improved to 470 m3/s in 2006. This project is divided into two parts UCC (Upper) and UCC (Lower) to link it with River Ravi. The Babanwala River Bedian Dipalpur (BRBD) canal takes off from the left and link UCC cross regulator through babanwala regulator designed to discharge 206 m3/s. Whereas, Nokhar canal regulator is on the right side which is designed to discharge 20.5 m3/s as shown in Figs. 19 and 20.

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Chenab river

UCC upper

BRBD link ca

anal

nal

c ranch

rb Nokha

UCC lower

Ravi river Fig. 20 Location of plant. BRBD, Babanwala River Bedian Dipalpur; UCC, Upper Chenab Canal. Reproduced from Mohsin Munir M, Shakir AS, Khan NM. Optimal sizing of low head hydropower plant – a case study of hydropower project at Head of UCC (Lower) at Bambanwala. Pak J Eng Appl Sci 2015;16:73–83.

Table 6

Hydropower design parameters

Number of units Required discharge (m3/s) Head (m) Discharge per turbine (m3/s)

2 170 2.39 85

3 170 2.39 56.66

Optimization techniques have been used to determine size of plant. For this purpose 10 samples of flow rate from daily routine have been picked up for 20 years. This data helped to estimate the average flow rate of the streams so that right number of units can be installed. From collected data, the mean came out to be 170 m3/s. Based on the calculated value two or three horizontal shaft double regulated pit turbine are considered to be sufficient enough to generate electricity from available flow. For selecting correct number of unit comparison, i.e., 2 or 3, comparison between main components of power house has been discussed in Table 6. Due to increase in number of unit, substantially increases the project overhead cost. In light of the foregoing study, 2 units are considered suitable for the power house. So, the design discharge for two turbines came out to be 85 m3/s. In Table 5 a brief comparison between 2 and 3 units are given. The available head and required discharge rate arises the need of selecting correct type of turbine. Fig. 20 shows the industrial turbine selection chart used for the selection of turbine on the basis of head and flow. Kaplan horizontal pit type turbine came out to be most suitable for this application (Fig. 21). For plant sizing, “TURBNPRO” has been used to determine the size, speed, flow, and head. Following data was fed as an input to the software to determine turbine size:

• • •

required discharge: 170 m3/s head: 2.39 m site elevation: 229 m The solution and information obtained after analysis were as below:

• • • • • • • •

type: horizontal pit subtype: Kaplan draft type: straight spout opening: 3988 mm speed can attain: 83.3 rpm no of blades: 3 hub diameter: 1608 mm rated output: 1.788 MW

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2000 1000 Pelton 500

10

00

M

W

Franc

50

0

Head (m)

100

M

W

50 Vertical Kaplan

10

0

M

W

50

10

M

W

Kaplan horizontal PIT and bulb

5

10 5

1M

W

M

W

M

W

1 1

5

10

50 100 Flow rate (m3/s)

500

1000

Fig. 21 Industrial chart for selection of turbine [40].

• •

efficiency: 89.7% installed capacity: 3.58 MW

So, by these steps it is concluded that 3.58 MW hydropower plant utilizing Kaplan pit turbine with 2 units of equivalent size is essentially and monetarily attainable to fulfill desired requirement.

5.19.6

Future Directions

Ocean power has an extensive potential of overcoming the current energy crises. Nowadays, around a globe extensive researches are going on to find out new techniques to harness this all important energy. The ocean power has gained that much importance due to the following reasons: firstly, these technologies are based on/derived from well-developed hydrodynamic physics, marine design, and construction fields. Unlike solar which relies on the development of innovative materials and innovative advanced technologies to reap the advantage. The studies, fabrication, and development processes for marine power technologies are in practice from hundreds of years, which made the energy cost paths for marine technologies relatively predictable. Secondly, ocean energy, i.e., tides, waves are plentiful, dense, and foreseeable resource. Wave’s forms deep in thousand miles of ocean; therefore, their size and energy content can be estimated from 4–5 days in advance. In addition to it, the study suggests that tides and marine currents are 832 times denser than the flowing air used to extract energy and are predictable for around 100 years in advance. The balance and justify the energy generation from ocean resources, rapidly declining course and efficient systems are primary factors. Around the globe there are thousands of researches conducted for to justify same goals so that this technology could also become comparative with other sources of renewable energy. What advances in the technologies will bring the industries at that point along with what policies should be made for the growth of this industries are the questions that need to be answered for the sake of its commercialization. As of today, only 10 MW of the global grid, comes from marine energy, while it is expected that incoming times, the industry has the potential to add-in 1 GW of installed capacity on an annual market size of over $500 million. In this section, all the future/expected projects that world could experience near times are listed along with their expected power generation capabilities. Comparatively, United Kingdom is blessed with the most suitable sites for the extraction of wave and tidal energy. But several other European countries along with other countries of world also have a reasonable tidal and wave resources. It was estimated that EU’s total wave electricity generation is around 250 TWh, while that of tidal is 48 TWh, while other countries commutatively is generating 2000–4000 TWh from waves and around 800 TWh from tides [41].

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The growing interest of many countries in exploiting this all important resource is not surprising at all, as its export potential is an obvious attraction. DT/Carbon report suggested that by 2050, there could be an addition of 200 GWh of wave energy globally and around 20 GWh tidal current energy generating facilities. Another report in 2005 by Douglas-Westwood consultants EU would invest an addition of around €600 billions in further development of wave power generation facilities, while for tidal it could be around €155–444 billion [42]. Danes, one of the renowned energy equipment development firm has developed “Wave Dragon,” a device consist of two wave reflectors, targeting the waves to drive up into a storage and then use the head difference in order to drive a turbine. They are also working on “Novel Waveplane” that works on the basis of funneling waves of different height into a series of channels, creating a vortex that finally drives a turbine. Moreover, Dutch, developed a device named “Archimedes Wave Swing device.” This device consist of several air chambers that are connected with each other and are submerged around the shore, these chambers rise and fall along with waves and pump air through turbines to generate electricity [43]. Several test of the prototype has been carried out, and in 2004, a Scottish firm Atlantic Wind Connection (AWC) oceans finally took over the device and worked further for its enhancement. Energetechs have tested there prototype of 300 KW producing, novel offshore device at port kimbala. The device basically works on OWC phenomenon but has uniqueness of utilizing variable pitch turbine to enhance its efficiency. Currently, BC hydro in Canada is working in utilizing this device in a 100 MW wave power project at Vancouver; whereas, renowned group of United States, Aqua energy are utilizing this device in their 1 MW wave power extraction project at long the bay of Neah in Washington State. OPT, one of the United States firms are deploying overtopping buoys to extract wave energy along the coast of Spain. On the other hand, Japan is also extensively working on this technology and installed several OWC on breakwaters. They have also designed a floating wave energy test bed – “the mighty whale,” in order to formulate the potential of wave energy. Consortium, a norwegian company has developed a propeller type device similar to that of installed at Devon, to extract marine current power, it is a windmill type of device which is tested along a Kvalsund at the Arctic tip of Norway. Hydro WGC, Russia, have developed an experimental 1500 KW tidal power extraction system, launched at Sevmash Plant in Severodvinsk [44]. Meanwhile, in case of warner water, United States current-to-current technologies are working on setting up a novel 10 MW power plant, utilizing ducted-rotor tidal current turbine in Bermuda. Neptune power have proposed to install and array of 1 MW floating turbines in the tidal currents of the Cook Strait between New Zealand’s North and South Islands, the project includes more than 7000 tidal turbines anchored to the sea floor 40 m below the surface of Earth. Whereas, United States verdant power is currently working on a project to install 6.35 KW tidal turbine New York’s east river, with UN headquarters on of its beneficiary. On a larger scale, blue energy, a Canadian firm is working on a concept of tidal fence. They have proposed a design to mount several H-shaped tidal turbine in a modular structure. The design consist of a 4-km long tidal fence to be lain in between the islands of Samar and Dalupiri in the Philippines. The estimated generating capacity of this project is around 2200 MW at peak tidal flow. Dr. Alexander Gorlov, professor of mechanical engineering at Boston University, has been extensively working on vertical-axis turbine in order to utilize ocean currents at its max by moving it further out to sea, including the Gulf Stream. Gulf Stream is strictly not a tidal power energy, as it is not generated as a result of lunar gravitational force, but in this case, it is a part of planetary ocean current, driven ultimately by solar heat. Gorlov notes that “the total power of the kinetic energy of the Gulf Stream near Florida is the equivalent to approximately 65,000 MW.” At last, albeit routine tidal floods are no longer observed as appealing choices by most investigators, it has been proposed that building limited stores out to ocean in shallow water, as opposed to dams over estuaries, could be less expensive and less intrusive.

5.19.7

Concluding Remarks

As an alternate source of energy, wave and tidal energies have nowadays emerged as a best option. Number of notable work has been done in this field in order to exploit this source of energy, out of which the highlighted one are Pelamis wave device and Seagen tidal current turbine, which have reached there commercial values. Around the world, there are around 60 projects under testing phase, among which one third are of United Kingdom. Initially wave power gets more attention and is considered to be a better source, but now tidal energy is also catching up due to the fact that, tidal energy extraction technology is similar to that of wind power in numerous ways. Similarly, tidal barrages and lagoons are also developed on proved technology but it has some functional drawback, such as that it can only work on the basis of head difference from high tides, every 24 h a day. Although, there are barrages that works on both modes, i.e., that can extract energy from both incoming and ebb tides but then it consumes two-way tidal turbines which is more expensive and has to bear more stress. By complexity, tidal current turbines have the real favorable position of having the capacity to work effectively on the ebb and the stream, four times every 24 h, by swiveling around to confront the course of tidal current. As a result, power generation level is expected to be during lower neap tide cycles, but the power generation is nearly continuous. Moreover, if different of this devices are installed along the particular coast, different tides timing at different position would also smoothen the overall output. Unquestionably tidal ponds lowers its natural effects since they try not to square estuaries, and there might be areas and settings where tidal barrages are considered as appropriate – they are after all extremely site particular. South Korea has been taking a gander at the likelihood of finding a 252 MW plant in Sihwa Ho, a substantial tidal lake in Gyeonggi territory, flanking the West Ocean, where there is a 5.64 m tidal range.

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Energy Management in Ocean Energy Systems

However, environmental concerns regarding the development of large tidal barrage is still an issue. In some of the sites, the effect of blocking estuary me yields positive results, but most of the times, its causes severe ecological impact. But in contrast, precautions and care obviously has to be taken in any of the scientific findings, but overall, tidal current and wave systems do not seem to have a major impact in harming the ecological cycle of this planet. Ocean energy obviously has a noteworthy potential. While up to this point we have looked to the ocean bed as a wellspring of fossil vitality, it might well be that the oceans themselves may start to give a spotless and renewable substitution. The United Kingdom Governments Foresight Board has recommended that exploiting only 10% of the renewable vitality accessible on our planet, seas would give five times enhanced power than is as of now utilized all around. Also, survive in today’s market, the demand of organizational efficiency in factors like energy, investments, and workforce is increasing day by day. Nowadays, energy is considered to be a most vital factor for the reduction of operating margin. However, firms need to evaluate the effect of rapid price fluctuation on its operations in order to sustain in volatile market. The key step to control this problem is to smartly reduce the cost relating to energy sector and utilize it somewhere else. In this highly competitive market, organizations should reduce all the extra cost in their operations, in order to sustain longer. Energy is one of the factors, which consumes a large proportion of organizational budget. However, recently a large decline in energy cost has been noticed; but cost of energy will remain volatile. Therefore, sustainable energy generation along with SEM practices must be realigned with normal industrial operations in order to extract as much from it as we can.

References [1] Sonam T, Bharat T. Key to sustainable, socio-economic development of Bhutan. Energy 2010;2(1):118–20. [2] Fulkerson W, Judkins RR, Sanghvi MK. Energy from fossil fuel. Sci Am 1990;263(3):128–35. [3] Levy JI, Dilwali KM. Economic incentives for sustainable resource consumption at a large university – past performance and future considerations. Int J Sustain High Educ 2006;1(3):252–66. [4] Sapkota A, Lu Z, Yang H, Wang J. Role of renewable energy technologies in rural adaptation to climate change in Nepal. Renew Energy 2014;68(1):793–800. [5] Peel MC, McMahon TA, Finlayson BL. Continental differences in the variability of annual runoff: update and reassessment. J Hydrol 2004;295(4):185–97. [6] Bahaj A, Myers L. Fundamentals applicable to the utilisation of marine current turbines for energy production. Renew Energy 2003;28(14):2205–11. [7] Fisk LA. Acceleration of the solar wind as a result of the reconnection of open magnetic flux with coronal loops. J Geophys Res Space Phys 2004;108(4):1157. [8] Einstein A, Rosen N. On gravitational waves. J Frank Inst 1937;223(1):43–54. [9] Durack PJ, Wijffels SE, Matear RJ. Ocean salinities reveal strong global water cycle intensification during 1950 to 2000. Science 2012;336(1):455–8. [10] Caprotti F. China's cleantech landscape: the renewable energy technology paradox. Sustain Dev Law Policy 2009;9(3):6–10. [11] CIA World Factbook. All fossil fuel reserve and consumption data; 2015. [12] McKibben B. Crossing the red line. The New York Review of Books, vol. 51 (no. 10); 2010. [13] Gohlke JM, Hrynkow SH, Portier CJ. Health, economy, and environment: sustainable energy choices for a nation. Environ Health Perspect 2008;116(6):A236–7. [14] Schineider SH. The global warming debate heats up: an analysis and prespective. Bull Am Metrol Soc 1990;71(9):1292–304. [15] Alexander WJR. An assessment of the likely consequences of global warming on the climate. University of Pretoria, Water Resource and Flood Resource Department; 2005. [16] Berresheim H, Wine PH, Davis DD. Sulfur in the atmosphere. In: Sing HB, editor. Composition, chemistry and climate of the atmosphere. New York, NY: Van Nostrand Reinhold; 1995. [17] Galloway J, Dianwu Z, Jiling X, Likens G. Acid rain: China, United States, and a remote area. Science 1987;236(4808):1559–62. [18] Basedau M, Lay J. Resource curse or rentier peace? The ambiguous effects of oil wealth and oil dependence on violent conflict. J Peace Res 2011;46(6):757–76. [19] Eurostat. Renewable energy statistics. Available from: http://ec.europa.eu/eurostat/statistics-explained/index.php/Renewable_energy_statistics. [20] AEA Technology, IEA-OES. Review and analysis of ocean energy systems development and supporting policies: report for sustainable energy Ireland; 2006. [21] Frau JP. La Rance, a successful industrial scale experiment. IEEE Trans Energy Convers 1993;8:552–8. [22] Nova Scotia Power Inc. Nova Scotia power. A tidal power pioneer. Electrical Line Magzine; 2005. p. 28–30. [23] Congressional Budget Office (CBO). Congressional budget report; 2012. [24] Boyle G. Renewable energy: power for sustainable future. Milton Keynes: Open University; 1996. [25] Marques WC, Fernandes EHL, Rocha LAO, Malcherek A. Energy converting structures in the southern brazilian shelf: energy conversion and its influence on the hydrodynamic and morphodynamic processes. Earth Sci Geotech Eng 2011;1:61–85. [26] Hervouet J-M. Hydrodynamics of free surface flows: modelling with the finite element method. Hoboken, NJ: John Wiley & Sons; 2007. [27] Smagorinsky J. General circulation experiments with the primitive equation, I. The basic experiment. Weather Rev 1963;1:99–164. [28] Khan MJ, Bhuyan G, Iqbal MT, Quaicoe JE. Hydrokinetic energy conversion systems and assessment of horizontal and vertical axis turbines for river and tidal applications: a technology status review. Appl Energy 2009;86:1823–35. [29] Yue CD, Wang SS. GIS-Based evaluation of multifarious local renewable energy sources: a case study of the chigu area of southwestern Taiwan. Energy Policy 2006;34:730–42. [30] Elimelech M, Yip NY. Thermodynamic and energy efficiency analysis of power generation from natural salinity gradients by pressure retarded osmosis. Environ Sci Technol 2012;46:5230–9. [31] Skilhagen SE. Osmotic power – a new, renewable energy source. Desalin Water Treat 2010;15:271–8. [32] Helfer F, Lemckert C, Anissimov YG. Osmotic power with pressure retarded osmosis: theory, performance and trends – a review. J. Membr Sci 2014;453:337–58. [33] Fritzmann C, Löwenberg J, Wintgens T, Melin T. State-of-the-art of reverse osmosis desalination. Desalination 2007;216:1–76. [34] Syed A, Melanie J, Thorpe S, Penney K. Australian energy projections to 2029–30: ABARE research report 10.02. Canberra, ACT: Department of Resources, Energy and Tourism; 2010. [35] Climate Change Authority. Renewable energy target. Available online. Canberra, ACT: Australian Government. Available from: http://climatechangeauthority.gov.au/ret; 2012 [accessed: 03.12.13]. [36] Clean Energy Future. An overview of the clean energy legislative package. Canberra, ACT: Australian Government. Available from: http://www.cleanenergyfuture.gov.au/ clean-energy-future/an-overview-of-the-clean-energy-legislative-package/; 2012. [37] Dopita M, Williamson R. Australia’s renewable energy future. Canaberra, ACT: Australian Academy of Science; 2010. p. 37. [38] Department of the Environment Water Heritage and the Arts. Map of operating renewable energy generators in Australia. Available from: http://www.ga.gov.au/renewable/.

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[39] International Renewable Energy Agency. Renewable energy technologies: cost analysis series – Wind power, volume 1: power sector, issue 5/5; 2012. p. 57. [40] Mohsin Munir M, Shakir AS, Khan NM. Optimal sizing of low head hydropower plant – a case study of hydropower project at Head of UCC (Lower) at Bambanwala. Pak J Eng Appl Sci. 2015;16:73–83. [41] DTI/Carbon Trust. Renewables innovation review. London: Department of Trade and Industry and the Carbon Trust; 2004. [42] Adam W. Refocus marine renewable energy report: global markets, forecasts and analysis 2005–2009. London: Elsevier; 2005. [43] Smith D. Why wave, tide and ocean current promise more than wind. Modern Power Syst 2005;25(5):47–53. [44] MPS. Modern power systems. May 25th, June 28th and Nov 25th and Nov 29th web news reports. Available from: www.modernpowersystems.com; 2006.

Further Reading Afgan NH, Al Gobaisi D, Carvalho MG, Cumo M. Sustainable energy development. Renew Sustain Energy Rev 1998;2(3):235–86. Baños R, Manzano-Agugliaro F, Montoya FG, Gil C, Alcayde A, Gómez J. Optimization methods applied to renewable and sustainable energy: a review. Renew Sustain Energy Rev 2011;15(4):1753–66. Chu S, Majumdar A. Opportunities and challenges for a sustainable energy future. Nature 2012;488(7411):294–303. Chwieduk D. Towards sustainable-energy buildings. Appl Energy 2003;76(1–3):211–7. Dincer I. Renewable energy and sustainable development: a crucial review. Renew Sustain Energy Rev 2000;4(2):157–75. Ghaffarianhoseini A, Dahlan ND, Berardi U, Ghaffarianhoseini A, Makaremi N, Ghaffarianhoseini M. Sustainable energy performances of green buildings: a review of current theories, implementations and challenges. Renew Sustain Energy Rev 2013;25:1–17. Goldemberg J. Ethanol for a sustainable energy future. Science 2007;315(5813):808–10. Hegger M. Energy manual: sustainable architecture. Basel: Birkhaü ser; 2008. Kothari R, Tyagi VV, Pathak A. Waste-to-energy: a way from renewable energy sources to sustainable development. Renew Sustain Energy Rev 2010;14(9):3164–70. Lund H. Renewable energy strategies for sustainable development. Energy 2007;32(6):912–9. Mackay DJC. Sustainable energy – without the hot air, vol. 78. Cambridge: UIT 2009 (no. 2). Mandelli S, Barbieri J, Mattarolo L, Colombo E. Sustainable energy in Africa: a comprehensive data and policies review. Renew Sustain Energy Rev 2014;37:656–86. Oh TH, Pang SY, Chua SC. Energy policy and alternative energy in Malaysia: issues and challenges for sustainable growth. Renew Sustain Energy Rev 2010;14(4):1241–52. Omer AM. Energy, environment and sustainable development. Renew Sustain Energy Rev 2008;12(9):2265–300. Ong HC, Mahlia TMI, Masjuki HH. A review on energy scenario and sustainable energy in Malaysia. Renew Sustain Energy Rev 2011;15(1):639–47. Patlitzianas KD, Doukas H, Kagiannas AG, Psarras J. Sustainable energy policy indicators: review and recommendations. Renew Energy 2008;33(5):966–73. Serrano E, Rus G, Garcia-Martinez J. Nanotechnology for sustainable energy. Renew Sustain Energy Rev 2009;13(9):2373–84. Shafie SM, Mahlia TMI, Masjuki HH, Andriyana A. Current energy usage and sustainable energy in Malaysia: a review. Renew Sustain Energy Rev 2011;15(9):4370–7. Van Der Schoor T, Scholtens B. Power to the people: local community initiatives and the transition to sustainable energy. Renew Sustain Energy Rev 2015;43:666–75. Vera I, Langlois L. Energy indicators for sustainable development. Energy 2007;32(6):875–82.

Relevant Websites https://www.acs.org/content/acs/en/sustainability/understandingsustainability/sustainable-energy.html American Chemical Society – Sustainable Energy. http://www.conserve-energy-future.com/SustainableEnergy.php Conserve Energy Future – What Is Sustainable Energy and Its types. https://alcse.org/what-is-sustainable-energy/ Energy Alabama – What Is Sustainable Energy. https://www.federalregister.gov/agencies/ocean-energy-management-bureau Federal Register– Ocean Energy Management Bureau. http://listverse.com/2009/05/01/top-10-renewable-energy-sources/ Listverse – Top 10 Renewable Energy Sources. https://www.nrel.gov/workingwithus/learning.html NREL– Learning About Renewable Energy. http://www.ramma.is/english/history-and-ideas/sustainable-energy-resources/ Rammaáætlun – Energy Resources and Sustainability. http://www.renewableenergyworld.com/index/tech.html Renewable Energy World – Renewable Energy Overview. http://www.se4all.org/ Sustainable Energy for All (SEforALL). https://www.cnbc.com/sustainable-energy/ Sustainable Energy – A CNBC Special Report. https://www.doi.gov/hurricanesandy/boem US Department of the Interior – Bureau of Ocean Energy Management. https://www.eia.gov/energyexplained/?page=renewable_home US Energy Information Administration – Renewable Energy Sources – Energy Explained, Your Guide To Understanding Energy. https://www.usa.gov/federal-agencies/bureau-of-ocean-energy-management USA.gov – Bureau of Ocean Energy Management. https://www.usgs.gov/partners/bureau-ocean-energy-management-boem USGS – Bureau of Ocean Energy Management (BOEM). https://en.wikipedia.org/wiki/Bureau_of_Ocean_Energy_Management Wikipedia – Bureau of Ocean Energy Management. http://www.world-nuclear.org/information-library/energy-and-the-environment/sustainable-energy.aspx World Nuclear Association – Sustainable Energy: Renewable Energy: World Nuclear Association.

5.20 Energy Management in University Campuses Dionysia Kolokotsa, Technical University of Crete, Crete, Greece Junjing Yang and Lee Siew Eang, National University of Singapore, Singapore r 2018 Elsevier Inc. All rights reserved.

5.20.1 5.20.2 5.20.3 5.20.3.1 5.20.3.2 5.20.3.3 5.20.4 5.20.5 5.20.6 5.20.7 5.20.7.1 5.20.7.1.1 5.20.7.1.2 5.20.7.1.3 5.20.7.1.4 5.20.7.1.5 5.20.7.2

Introduction Energy Management of University Campuses: Techniques and Tools Mathematical Models for University Campus Energy Management Development of Building Models Modeling the University Campus Outdoor Areas Data Driven Mathematical Models Energy Use of University Campuses Energy Technologies for University Campus Buildings Users’ Behavior and Indoor Environmental Quality Case Studies The Case Study of Technical University of Crete Modeling of the Campus buildings and outdoor spaces Development of prediction models for the energy load and exterior conditions Development of control and optimization algorithms The web-based campus energy management infrastructure Energy performance Energy and Indoor Environment Quality Management of University Campus: The Case Study of National University of Singapore 5.20.7.2.1 Energy information network infrastructure 5.20.7.2.2 Instrumentation 5.20.7.2.3 Data cleaning 5.20.7.2.4 Energy monitoring 5.20.7.2.5 Smart tools 5.20.7.2.6 Indoor environment quality monitoring 5.20.7.2.7 Mobile apps for user feedback 5.20.8 Future Directions 5.20.9 Closing Remarks Acknowledgments References Further Reading Relevant Websites

5.20.1

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Introduction

University campuses can be considered as small towns due to their size, number of users and mixed and complex activities, including numerous actions, usually met in urban environments. Energy wastage in various space types, such as teaching auditoriums, working areas (offices, laboratories, computer rooms, etc.) or residential buildings (dormitories) can be encountered. Furthermore, overheating phenomena, by human energy release and solar radiation absorption from dark surfaces and buildings, are possible, thus creating an urban-kind climate in campuses. The energy and environmental impact of universities could be considerably reduced by applying organizational, technological, and energy optimization measures. To design and operate a sustainable campus, it is necessary to holistically and strategically integrate all aspects and information into the planning and operational process. High-quality open University Campus spaces can contribute to the improvement of the quality of life of students, thus allowing them to spend productive time in the studying environment. The major factor that determines the quality of the open urban spaces is the climate conditions that occur in the microscale environment. The strategies to improve urban environment include the use of smart materials, the increase of vegetation, ventilation, shading, and evaporation. On the other hand, Information & Communication Technology (ICT) for energy management has evolved considerably during the last decades. Advances in the design, operation optimization, and control of energy influencing building elements and systems (e.g., Heating Ventilation and Air Conditioning System (HVAC), solar, fuel cells, CHP, shading, natural ventilation, etc.) unleashed the potential for achieving significant energy savings and efficiencies in the operation of both new and existing building sites worldwide.

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Currently, the issue of Energy Management for large sites, such as University Campuses, is addressed by the Energy Information Systems (EIS) which have evolved out of the electric utility industry in order to manage time-series electric consumption data. However, other energy management technologies have also expanded their functionalities, and have partly come to merge with EIS technology. Since EIS products are relatively new technologies, they are changing quickly as the market unfolds [1]. Moreover, recent developments in smart grids’ sector create new opportunities for interdisciplinary impacts in energy management for University campuses such as [2]:

• • • •

Integration of renewable energy sources (RES), smart buildings, and distributed generators into the network; Implementation of demand response and comprehensive control and monitoring; Improvement of indoor environmental quality; The increase of end-users’ energy awareness.

While energy technologies in the built environment are expanded in the district or community level in order to reach the zero energy objectives [3], the use of University Campuses, as a field of application, is considerably advantageous compared to a community or city district as the overall area belongs to a single owner. The aim of the present chapter is to outline the recent technological developments in the energy management for University Campuses. The chapter starts with a discussion on the techniques and tools for energy management and continues with various modeling methodologies, energy technologies for University Campus buildings and continues by outlining the role of the users in the energy management practices. Two case studies are presented in Section 5.20.7 analyzing the role of a web-based energy management system in reducing the energy consumption and improving the environmental quality of Campus users.

5.20.2

Energy Management of University Campuses: Techniques and Tools

Campuses have different functionalities ranging from lecture rooms, seminar rooms, conference/meeting rooms, and laboratories [4]. The Energy Management for large sites such University Campuses are addressed by the EIS which have evolved out of the electric utility industry in order to manage time-series electric consumption data. However, the other energy management technologies have also expanded their functionalities, and have partly come to merge with EIS technology. The various categories of EIS are depicted in Ref. [5] (Fig. 1):

• • • •

Basic EIS which gather, archive, summarize, and display whole building electricity data. Demand Response Systems that Communicate between utilities and customers to facilitate demand response programs. Enterprise Energy Management which manages the overall energy costs by facilitating energy benchmarking and procurement optimization over a business enterprize. Web-Based Energy Management and Control Systems that can integrate multiple building systems (e.g., HVAC, lighting, generation), and/or monitor and control building systems at the component-level by communicating via the Internet. Energy information systems (EIS)

Basic EIS

Enterprise energy management

Web EMCS

Demand response (DR)

Energy management and control system (EMCS)

Fig. 1 The Energy Information Systems (EIS) and their interrelation. Reproduced from Motegi N, Piette MA. Web-based energy information systems for large commercial buildings. Berkeley: Lawrence Berkeley National Laboratory; 2003.

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Examples of basic EIS are the typical Building Energy Management Systems that operate on building level to perform monitoring and data gathering can be found in Ref. [6]. The basic EIS can only perform uncomplicated data analysis of historic building energy data. Demand response is a crucial aspect of energy management in Campus level as (1) it can support energy demand and supply balancing, (2) electric power is not economically storable at the scale of large power systems, and (3) generation costs vary remarkably with peaks in power demand. As a result, large consumers such Campuses are exposed to significant price fluctuations over time, system instability, power losses, etc. Moreover, demand response programs along with intelligent and automated systems and storage in a microgrid scale are considered of vital importance in overcoming the intermittency of RES power generation as well as for providing operational and capacity reserves that are necessary for Campus level. RES is a prerequisite for demand responsive University Campuses [7]. In addition, there are Enterprise Energy Management Systems available in the market that attempt to increase energy efficiency, reduce energy costs, reduce greenhouse gas emissions, and to automate and streamline energy management processes. However, the state of the art in such systems has limitations when it comes to addressing complex environments. Current offerings are made mainly on a consultancy basis, having an expert spend long periods assessing the site’s activities and then build a software application to model the energy usage of the organization. Or vice versa, where the consultant provides a software system to monitor and analyze the energy consumption and then provide a consulting and analysis service to help implement changes. This is an expensive cost that some organizations would not be willing to pay if the purpose does not fit with their overall company strategy. Also, this is not a flexible solution as it is difficult to make changes to the application if there has been a major change in the organization. It is also a time-consuming process and does not give a true overall view of the full energy usage pattern of the organization as there are so many energy consuming aspects (assets and operations) within an organization (especially larger ones) that does not get fully represented. Other more generic software solutions only model common energy uses, and becomes very complex when trying to do an energy management for a large organization with a wide variety of activities and are not capable of recommending optimization solutions. Some also have a limitation in what kind of data is being used, with most of the data being inputted manually having limited access to up-to-date data. For that reason various Universities have developed their own web-based energy management dashboards or have integrated various tools. Some examples are:

• • •

The energy management dashboard of Cornell University that provides real-time utility data in an effort to reduce our energy consumption and carbon footprint. It also provides information and links about campus sustainability initiatives, energy conservation tips, and an interactive discussion board [8]. The integrated EIS at the University of New Mexico in Albuquerque. The EIS collects electricity, natural gas, chilled water and steam energy consumption and production data. The software provides a campus energy balance, consumption reports, and individual facility or building energy production and consumption reports [9]. UC Davis developed a comprehensive web-based EIS to support campus-wide occupant engagement and energy savings. Different energy data monitoring platforms are available. Some examples of data platforms include:

• • •

A web-based energy cloud platform for university campus environment proposed by Ref. [10]. The specific platform forecasts future energy demand and controls various power appliances. The web-based Camp-IT platform developed by Ref. [11] that is further discussed in Section 5.20.4. The Standard Energy Efficiency Data (SEED) platform developed by US Department of Energy which is an open source software application that manages energy performance data of large groups of buildings [12].

5.20.3

Mathematical Models for University Campus Energy Management

The energy management of University Campus requires various mathematical modeling procedures. Those can be grouped into the following categories and analyzed briefly in this section:

• • • •

Modeling of University buildings using various thermal modeling tools. Modeling of outdoor spaces in order to assess the outdoor thermal conditions. Data driven mathematical models in order to analyze energy data in Campus level and predict the energy usage. Modeling and testing of control and optimization procedures in order to optimize the overall performance.

5.20.3.1

Development of Building Models

Various simulation tools have been used by various researchers such as EnergyPlus, TRNSYS, and ESP-r in order to simulate the Campus buildings [13–17]. For example, for Technical University of Crete Campus buildings, both ESP-r and EnergyPlus energy modeling tool are selected [13,14]. Esp-r has various computational subroutines that exchange information (interaction between the parameters of the various thermal zones) in order to accurately calculate the interactions between the systems of the building. One of the most important characteristics of ESP-r is that it cooperates with other simulation tools to provide a wider range of very accurate results.

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Moreover, ESP-r was selected for the specific study as it can model all the electric devices and the systems producing and storing energy from RES, it has a very wide range of control algorithms for the various systems, including fuzzy logic algorithms, it uses of airflow networks which enable the calculation of the movement of air masses inside or outside buildings, etc. The parameters that were simulated and validated, based on the respective measurements, are the air temperature and the internal gains due to the use of lighting and other appliances. The developed model was validated by comparing the simulation results with the respective thermal and energy measurements. The model was adapted to provide results of adequate accuracy. The results are presented in detail in Section 5.20.7. Another approach includes the development of simplified thermal network models [18]. This approach has been widely used to represent the heat transfer and dynamic thermal process through building envelope and the subsequent effects on indoor air temperature [19]. Also, Bernal et al. [20] propose the use of Matlab-EnergyPlus MLE þ to extend the capability of EnergyPlus to co-simulate a campus with multiple buildings connected to a chilled water distribution to a central chiller plant with control systems in Matlab.

5.20.3.2

Modeling the University Campus Outdoor Areas

The simulation of outdoor environmental conditions enables the design of urban areas that have the minimum environmental and energy impact on the surrounding constructions. Moreover, this type of simulations may have an effect on the energy management of the neighboring buildings. Exterior environmental conditions can be predicted by complex microscale or mesoscale computer models (CFD, OpenFoam, MIST, ENVIMET, WW5, etc.). For example, at the Technical University of Crete, a three-dimensional microclimate model ENVI-met was used to model the outdoor thermal environment. The materials and the sizes of the buildings, the exterior surfaces, and the trees/plants were as accurately reproduced as possible. The weather parameters that were used as input (air temperature and relative humidity) were acquired by the University’s weather station. More details are included in Section 5.20.7 (Fig. 2).

5.20.3.3

Data Driven Mathematical Models

Data driven approaches and forecasting have been used to address different aspects of the building energy related problems. Examples of data driven modeling techniques are [21]:

• • •

Simple regression models and multiple linear regression models [22–24]. Decision trees [25,26]. Artificial neural networks (ANN) [27,28]. Data driven models rely on the availability of prior data to forecast energy behavior for Campus buildings.

Fig. 2 Simulation of the outdoor environment of the Technical University of Crete. Reproduced from Kolokotsa D, Gobakis K, Papantoniou S. Development of a web based energy management system for University Campuses: the CAMP-IT platform. Energy Build 2016;123:119–35.

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To achieve forecasting capabilities, data-driven models must be trained on large detailed (hourly or sub-hourly readings) data, collected by smart metering systems or the building energy management systems [29,30]. At large scale, studies such as university campuses the training procedure is accomplished on aggregated measured data which is often deficient due to the high costs of metering. This limits the scope of a data-driven framework for energy consumption prediction and forecasting.

5.20.4

Energy Use of University Campuses

The energy use characteristics of campus buildings are the fundamental information, for a good campus energy planning. In order to make a comprehensive understanding of energy use of campus buildings from the demand side various efforts have been performed [31]. Some examples include:

•

•

• •

•

An energy data analysis was performed at a university campus in southern China. A set of measurement meters were installed on various buildings in the campus to obtain hourly end-user energy consumption data from September 1st, 2009 to August 31st, 2010. The energy consumption of the research laboratory buildings accounted the library was the highest. Among the proportion of the total energy consumption for the whole campus, the cooling consumption accounted for 52% while electricity consumption of lighting and electrical socket accounted for 23%. Moreover the peak energy was estimated. Bonnet et al. [32] studied electricity consumption of large university campuses. Through their work, a tool is developed allowing to address the diversity of activities and end-uses when analyzing energy demand and environmental impact on a campus. Electricity and water uses on the campus of the University of Bordeaux were studied. The method which is presented relies on the evaluation of floor surface area-based ratios for each activity and requires the assessment of reference values to be used on the campus. As implemented, the method shows the relative share of major uses and allows to estimate water conservation potential at the campus scale. For both electricity and water, a special attention is paid to R&D uses, with that sector being the most significant in terms of consumption and annual growth. Ó Gallachóir et al. [33] explored the use of simple performance indicators, for the assessment of building energy performance at the University College Cork Ireland. Their research connected the electricity bill to the number of students and the fuel consumption to the floor area. Agarwal et al. [34] presented data collected from various buildings from residence halls to data centers at the San Diego University Campus. The campus has an extensive energy generation, storage, and management system in place to deliver both electricity and thermal energy in the form of high temperature and chilled water to buildings. The 1200 acres Campus has two 13.5 MW gas turbines, a 3-MW steam turbine, and a solar cell installation. The cogeneration plant operates at a combined efficiency of 74% and enables the University to self-generate almost 80% of its electricity demand. Zhou et al. [35] carried out a questionnaire-based analysis for the energy use of colleges and universities in Guangdong Province of China, including electricity, water, gas, and cooling for a period of 6 years. The questionnaire survey revealed that there is a great difference in per unit energy use between different types of schools and Universities.

In this framework, it is noticeable that different metrics and indicators are used to assess the energy performance of University Campuses. An organization called International Sustainable Campus Network is established among Universities around the globe called International Sustainable Campus Network (ISCN). This network provides universities and corporations a common framework to formalize their commitments and goals on campus sustainability, and a platform to publicly share achievements within a group of peer and leading organizations [36]. The common framework addresses sustainability holistically into a nested hierarchy by encompassing three principles related to:

•

•

•

Individual campus buildings: respect for natural resources and social responsibility, and embraces the principle of a low carbon economy. To ensure buildings on campus can meet these goals in the long term, and in a flexible manner, useful processes include participatory planning (integrating end-users such as faculty, staff, and students) and life-cycle costing (taking into account future cost-savings from sustainable construction). Campus-wide planning and target setting: campus should be considered as a whole, and not just individual buildings. This includes processes that include comprehensive master planning with goals for impact management (e.g., limiting use of land and other natural resources and protecting ecosystems), responsible operation (e.g., encouraging environmentally compatible transport modes and efficiently managing urban flows), and social integration (ensuring user diversity, creating indoor and outdoor spaces for social exchange and shared learning, and supporting ease of access to commerce and services). Integration of research, teaching, outreach, and facilities for sustainability: on a sustainable campus, the built environment, operational systems, research, scholarship, and education are linked as a “living laboratory” for sustainability. Users (such as students, faculty, and staff) have access to research, teaching, and learning opportunities for connections between environmental, social, and economic issues. Campus sustainability programs have concrete goals and can bring together campus residents with external partners, such as industry, government, or organized civil society.

Organizations that enter the network commit to implementing these three principles, set their own concrete and measurable targets under each principle, and report regularly to the ISCN on their initiatives and performance on campus sustainability.

Energy Management in University Campuses

5.20.5

813

Energy Technologies for University Campus Buildings

Hence the technologies to achieve an energy-efficient and environmentally friendly campus are numerous [37]. Some examples of the energy technologies are: 1. Green buildings and technologies for improvement of the energy efficiency, such as improvement of lighting efficiency, improvement of envelope performance, etc. 2. Promote green power purchases and local renewable power projects to reduce reliance on fossil fuels. 3. Promote energy storage systems for effective management of energy production [38]. Other examples are tabulated in Table 1. A critical point in analyzing the energy performance of university buildings is the lack of available data. This is underlined in various studies concerning university buildings. For example, UK Universities are very high-energy consumers [39] while most of the United Kingdom’s colleges and universities are now required to report on their energy use and improve their efficiency. Another example can be found in Greek University Campuses. Since 2015, Technical University of Crete has developed a strategy to support its sustainability by investing in energy-efficient technologies. In this framework, 19 smart meters were installed at various points of the campus power grid to provide energy consumption breakdown and monitoring. Simultaneously a raising awareness campaign was initiated and messages are sent to the Campus users in order to motivate the reduction of the energy waste. Through the energy map of the Campus, real-time energy data and information is available on-line. This strategy led to a reduction of the energy consumption by 17% simply through monitoring and informing the end-users [11]. Moreover, data can be used for identifying peak demand profiles of various buildings and their contribution to the overall campus peak demand. This is performed by Yarbrough et al. [43] for Indiana University in the United States. The specific campus has 160 smart meters, mainly for buildings’ monitoring. By organizing those meters into a tree structure, the Campus Energy Management System can calculate demand and usage information even for buildings not directly metered. In these cases, buildings not directly metered are assigned a “virtual meter” and their usage and demand data is stored alongside data from actual meters. Data points are collected for each meter and calculated for each virtual meter every 15 min. The calculations that produce each building’s virtual meter have been validated through a careful quality control process for many, but not all of the buildings on campus. With real-time metering available, and energy visualization tools, electricity users and managers can investigate the timing of building demand peak and help identify target buildings for savings on the peak demand charge. Data mining-based approaches for predicting next-day energy consumption and peak power demand, with the aim of improving the prediction accuracy are valuable tools for developing strategies of operation, optimization, and interactions between Campus buildings and the power utility companies. For example, genetic algorithms optimization techniques have been applied to the Smart Leaf Community Microgrid [44] in order to reduce energy costs, optimize revenues, achieve near zero net energy, minimize emissions and maintain occupant satisfaction and comfort [44,45]. In this context, predictive techniques for renewable energy production and energy demand forecasting are combined with the existing pricing policies applied by the Italian energy utility. The microgrid adjusted the energy stored to the batteries in each hour according to the energy consumptionproduction matching in order to reduce the energy costs. The advanced real time microgrid management of energy resources has led to an almost 6% reduction in operational costs with no extra investment cost. Appropriate graphical interfaces of various energy, cost and environmental indicators and features with respect to operational profiles, schedules and load priorities may be of a great importance for improving users’ energy and environmental awareness.

5.20.6

Users’ Behavior and Indoor Environmental Quality

Another critical perspective in the energy management of University Campuses is the role of users and their active engagement. Increasing users’ engagement by providing interaction with the energy systems while allowing the exchange of information concerning the energy demand and indoor environmental quality may reduce significantly the Campuses’ carbon footprint. User’s engagement may be enforced through seamless interaction with the energy data platforms described in the previous section through various channels such as a web private portals and applications for PCs, mobiles, smart devices, in-home displays, etc. University infrastructure and wireless media provide an excellent basis for such applications. In this context, the recent fast development of smart grids’ concept can play a significant role in the Campus level energy management due to the physical proximity between consumers, energy production and energy resources management that helps to increasing end users’ awareness and engagement. Smart metering systems add more information than conventional meters and offer many advantages as cost effectiveness, accuracy and interactivity with the users. Once in place, interactive smart meters can allow users to control and manage their individual preferences and consumption patterns, providing incentives for energy efficient use through behavioral change. User engagement and acceptance is a critical factor for the successful roll-out of smart meters. Smart meters can be a useful tool in giving users up-to-date information on energy use. The advanced metering infrastructure can reduce environmental impact via lowering consumption and/or load shift. Nevertheless, to achieve reduction in energy use the engagement of the user is crucial. This can be translated to the transition of users from consumers to prosumers [46,47]. If University Campus users can be

814

Table 1

Energy Management in University Campuses

Energy technologies studies for University Campus buildings

Campus

Short description of the study

Energy technologies

Energy performance

Reference

The Republic of Korea

A number of Campus buildings are studied and categorized based on their age and typology

10% reduction of plug load Improvement of lighting efficiency Improvement of the building’s envelope (roofs, walls, windows) Change of set-points Change of occupants behavior

[37]

Heriot-Watt (HW) University, Edinburgh, Scotland

One building is studied that and provides both educational and social facilities for postgraduate (PG) students 96 buildings spread over 10 campuses in a total of 7 municipalities are studied

The detailed information on the occupancy patterns supported a more effective operation of Building Management System (BMS). Control strategies are adopted to optimize the energy performance of the building Optimization of the operation of the heating system

The potential for energy conservation is in the range 6%–29% Older buildings can reduce energy by 10–22% by replacing windows and adding insulation to the building envelope N/A

Southeast England, United Kingdom

1 example institutional building is studied

Faculty of Engineering University of Bologna

The building is designed by Giuseppe Vaccaro in 1929, it was built between 1933 and 1935 The building has a total area of 30,932 m2

UC Davis

The Campus of UC Davis

Improved envelope performance Biomass boiler using pellets Gas fired boiler (high efficiency condensing boiler Advertised energy efficiency ratio: 3.26 Natural ventilation is assisted by passive stack in the atrium Mechanical ventilation in toilet rooms Renewables 29.68 kWp solar PV panels (106 panels) on southeast face of roof Installation of thermostatic valves to improve the control system of the heating plant Improvement of the heat generation by replacing the existing three conventional gas boilers with three new condensing boilers Replacement of single pane windows with ultra-thin vacuum insulating glass with the same thickness and restoring the frames Design new buildings (except for laboratories and acute care facilities) to meet the equivalent standards of silverlevel Leadership in Energy and Environmental Design (LEED), from the U.S. Green Building Council. Strive to meet the Gold-level standards

Technical University of Catalonia (UPC) in Barcelona, Spain

[39]

Gas consumption for heating during the the period from November 2006 to March 2007 inclusive has been reduced to 62.7% due to changes in the management of the heating system The building is rated as BREAAM Outstanding, A

[40]

The results of the energy analysis show an energy saving of about 15% with operations building management and over 30% with improvements of the heating system and the windows

[42]

[41]

Design new buildings (except for acute care facilities) to exceed energy efficiency standards of California Energy Code (Title 24) by at least 20 percent, striving for 30 percent or more

transformed to prosumers they might be willing to change their behavior driven by various reasons, such as environmental awareness or responding to specific prize incentives. Numerous studies exist that monitor the users’ behavior. For example, a pilot study was undertaken aiming at understanding the reasons for excessive energy consumption for a university building in operation [39]. Interaction with building users was carried out via a questionnaire to determine the influence that they exert on the electrical demand of the building. Interviews with key management personnel were conducted to understand how the building is routinely managed and operated and to illicit any issues relating to energy consumption. Similar studies are tabulated in Table 2 showing the impact of users’ behavior in the management of Campus buildings.

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Table 2

Occupancy and indoor environmental quality studies

Campus

Short description of the study

Methodology

Performance

Reference

National University of Singapore

Identification models are developed to predict the cooling loads

Improvement of the accuracy of the simulation models by understanding the occupancy patterns of the Campus buildings

[4]

University of Sheffield, United Kingdom

Little differences are noticed in comfort levels while the energy needs of the passive building are significantly lower than the nonpassive one

[48]

National Technical University of Athens, Greece

Two buildings are studied where different energy technologies and approaches are used Study of indoor environmental quality in Campus buildings

The analysis of the accumulated data led to observations concerning the users’ awareness on issues such as energy conservation, recycling, etc. as contrasted to their views and wishes on personal comfort expressed by daily use patterns in their own work environment

[49]

University of Passo Fundo (UPF), Brazil

Two buildings are studied

Identification models are developed to predict the real daily energy consumption data. The variability of the daily occupancy is identified and is used as inputs into energy simulation programs, to perform detailed energy analysis Passive energy technologies are used in one of the buildings while the other one is a conventional building Analysis of users’ comfort conditions combined with measurements in various buildings of the Campus. Measurements include air temperature, relative humidity and daylighting levels The energy consumption and thermal performance of the two buildings are studied

Simulation results allow the identification of comfort sensations through PMV. The research indicates that by giving comfortable conditions to the users will increase energy consumption, but there is a potential reduction of lighting and equipment that could minimize this impact

[50]

5.20.7

Case Studies

5.20.7.1

The Case Study of Technical University of Crete

The Campus of the Technical University of Crete hosts five University departments, two libraries, administrative buildings and student dormitories. The Campus is one of the major energy consumers in the electricity grid of Crete, Greece with a peak power demand of 1.2–1.5 MW. It should be noted here that Crete is supported by an autonomous energy system, not interconnected with Greece’s mainland power network [51,52]. In the Campus, 19 smart energy meters have been installed to monitor the energy consumption for electricity in the various building blocks. In the Campus, a web-based energy management system is designed and implemented that allows:

• • • •

Improvement of indoor environmental quality of the buildings interconnected to the web-based energy management. Interconnection of the thermal models of the buildings with the models developed for the outdoor Campus spaces. Prediction of energy demand at least 12 h ahead that allows effective management of energy costs. Implementation of optimization techniques that support the minimization of the energy consumption without compromising comfort. The details of the web-based energy management system are analyzed in the following sections.

5.20.7.1.1

Modeling of the Campus buildings and outdoor spaces

The ESP-r energy-modeling tool is employed for simulation of the conditions inside the University buildings. ESP-r can simulate complicated elements of the building envelope and any electrical/mechanical equipment available [53]. The developed models using ESP-r are depicted in Fig. 1. A feature not supported by ESP-r is the import of 3D building models. Especially in buildings with complicated shapes and forms, the import of 3D models in ESP-r may be time-consuming. For that reason, a specific plugin is

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Energy Management in University Campuses

developed to simplify the process. The plugin, developed in the programing language Ruby [54], allows the export from SketchUp to ESP-r. Along with the creation of the geometry in ESP-r, the plugin also connects the various surfaces of the building model, either between each other or with the ground. Moreover, the plugin has the ability to transform the floor 2D diagram into thermal zones. With this functionality, the time for the development of the model is drastically reduced. In addition for the simulations of the conditions in the exterior area between the buildings of the Technical University of Crete, the three-dimensional microclimate model ENVI-met is used [55–58]. ENVI-met is a three-dimensional non-hydrostatic microclimate model including a simple one-dimensional soil model, a radiative transfer model, and a vegetation model. ENVI-met uses a uniform mesh with a maximum of about 300  300  35 cells with the horizontal extension ranging between 0.5 and 10 m and a typical vertical height of 1–5 m. The materials and the sizes of the buildings, the exterior surfaces, and the trees/plants were as accurately reproduced as possible. The weather parameters that were used as input (air temperature and relative humidity) were acquired by the University’s weather station and the parameters that were simulated, are: (1) surface temperature (oC); (2) air temperature at a height of 1.80 m above ground level (oC) and (C) wind velocity (m/s). A coupling mechanism between the ESP-r thermal models and ENVI-met is also developed and tested [59]. This coupling mechanism allows the integration and quantification of the impact microclimatic changes in the indoor environmental quality.

5.20.7.1.2

Development of prediction models for the energy load and exterior conditions

Neural networks [60,61] are used for the prediction of the energy loads of the TUC Campus buildings. The input parameters for the neural network predicting the energy load of the buildings are the exterior temperature, measured by a weather station positioned opposite to the studied buildings, the day of the week and minutes of the day. Also, the energy demand, recorded every 5 min, by smart meters, is used. The training of the neural network is repeated every day in order to avoid possible errors’ accumulation from the weather conditions of the previous day. The weather data are then forwarded to the neural network to be used as input. The comparison of the predicted energy load with the load measured by the smart meters, shows that the neural networks predict the load with acceptable accuracy. Accordingly, the parameters used as inputs for the network predicting the exterior temperature for the 24 h following a specific moment are the exterior temperature, the total horizontal radiation, relative humidity, wind speed and direction, as well as the time of day. The weather data also come from the weather station positioned opposite to the studied buildings. The neural networks’ training is repeated in regular intervals (i.e., on daily basis) in order to adapt to the various changes. The values predicted by the developed neural networks have been compared with the data collected from the smart energy meters (Fig. 3).

5.20.7.1.3

Development of control and optimization algorithms

The developed control algorithms are based on fuzzy logic techniques [62,63]. The characteristics of the fuzzy logic algorithms for the indoor environmental quality and visual comfort are tabulated in Table 3. The algorithm for the control of the lighting can dim or turn on/off the luminaires, depending on the available daylight and the desired lighting levels. The algorithm for the indoor environmental quality, controls the HVAC using the difference of the PMV index parameters (temperature, humidity, radiation, air velocity, metabolic rate, and clothing) and the difference of the CO2 concentration level from the required ones, in order to change the heating/cooling system or increase/decrease the flow of fresh air through the ventilation system. The algorithm for the thermal comfort uses the PMV index parameters (temperature, humidity, radiation, air velocity, metabolic rate, and clothing), in order to adjust the heating/cooling system or increase/decrease the flow of fresh air through the ventilation system. The development of these algorithms is made using Matlab, since it provides the appropriate libraries and graphical representation of the controls behavior. The performance of the control algorithms in reducing the energy consumption and in providing comfortable indoor conditions has been assessed, by linking the control algorithms with the simulation models for the interior and exterior environment (Fig. 4). The control algorithms in Matlab environment exchange data in real-time with the thermal models and ENVI-met, through the Building Control Virtual Test Bed (BCVTB) software. The optimization technique is developed using genetic algorithms. The specific technique is selected as it provides near optimal solution in nonlinear problems [44,64,65]. The objective function is expressed as:  !  32  32  32  X X X Energy demand jPMVj ½CO2 Š

ð2Þ þ w min w1
þ w 2 3 3 2000 9
105 i¼1 i¼1 i¼1 s:t: ( ) r3 during non occupancy hours jPMVj r0:5 during occupancy hours (

½CO2 Š

r2000 during non occupancy hours r800 during occupancy hours

Energy Management in University Campuses

817

Fig. 3 Building models using ESP-r. Reproduced from Kolokotsa D, Gobakis K, Papantoniou S. Development of a web based energy management system for University Campuses: the CAMP-IT platform. Energy Build 2016;123:119–35.

Table 3

Monthly energy consumption for the years 2014 and 2015

Months

Energy consumption (kWh) 2014

Energy consumption (kWh) 2015

Cooling degree days (2014)

Cooling degree days (2015)

Energy consumption per cooling degree day for 2014

Energy consumption per cooling degree day for 2015

Percentage of change (%)

May June July August September October

17,188 22,770 33,765 22,313 31,117 19,458

17,340 21,239 29,695 18,756 24,592 17,344

84 188 257 288 193 71

85 173 252 282 205 91

205.4 120.9 131.3 77.4 161.3 272.6

204.7 122.8 117.9 66.5 120.1 190.3

0% 2% 10% 14% 26% 30%

where PMV corresponds to the thermal comfort index [66], [CO2] corresponds to the concentration of carbon dioxide indoors, w1, w2,w3 are the weights of the decision variables defined by the decision maker. The weights are defined in the range [0,1] and w1 þ w2 þ w3 ¼ 1. The decision variables are the PMV index set point for the next 8 h in steps of 15 min (32 variables) and the relevant [CO2] concentration values that will drive the ventilation system (32 variables). The total number of decision variables is equal to 64. All decision variables including the energy demand are normalized in the range [0,1] by dividing them with its maximum value. The output of the optimization algorithm depends on the weight of the decision variables.

818

Energy Management in University Campuses

TUC campus buildings

120.00

90.00

Surface temperature

y

4700°C

60.00

30.00

0.00

N 0.00

30.00

60.00

90.00 x

120.00

150.00

Fig. 4 The surface temperature of the Technical University of Crete (TUC) Campus on 23/6/2015 at 12:00. Reproduced from Kolokotsa D, Gobakis K, Papantoniou S. Development of a web based energy management system for University Campuses: the CAMP-IT platform. Energy Build 2016;123:119–35.

The decision variables are strongly related to each other, since they affect the overall energy consumption of the HVAC system for the selected areas over the optimization period (8 h), while they are used to describe the satisfaction of indoor environmental quality through their application in the defined cost function. A potential distributed optimization approach, which uses less decision variables might not take under consideration, for example, the effect of fresh intake air to the thermal comfort and thus the energy required to achieve good comfort during the next steps.

5.20.7.1.4

The web-based campus energy management infrastructure

The existing Campus IP infrastructure is exploited by using sensor networks where nodes communicate their information using Web services, allowing direct integration in modern IT systems. To guarantee the system scalability and respect consolidated and diffused standards, the logical/architectural level of the whole Campus Energy Management System is linked with the existing infrastructure based on Internet Protocol (IP). The IP choice leads to wired networks realization in combination with Wi-Fi networks. Another advantage of the Ethernet protocol is its capability to integrate networks even in already existing buildings. The system is exposed toward the external part of the network by means of Web Services, enabling the XML information exchange through communications on Internet channels. The changes in the overall hardware architecture is depicted in Fig. 5. The hardware system operation is constantly monitored due to the fact that its characteristics may change in time. The hardware parts whose operational characteristics change mainly over time are the sensors. The computers, energy meters, HVACs, relays, cabling, etc. have self-diagnostic procedures for faulty operation and are monitored using the software. The sensors operation drifts in time (1) due to dirt or dust deposited on the sensor, or (2) due to inherent degradation of the sensor behavior. In the case of dirt, a regular annual maintenance is foreseen to restore the sensor’s operation. To cope with the sensors’ functionality degradation, a yearly recalibration of the sensors characteristic, using reference measurements, is applied. After the recalibration, the appropriate parameters in the control algorithm are adjusted accordingly.

5.20.7.1.5

Energy performance

The energy consumption of the buildings before and after the overall installation is monitored via the smart energy meters. The reduction of the energy use is analyzed on monthly basis. The energy consumption of the 2014 corresponding months versus the 2015 ones are tabulated in Table 3. As it is observed in Table 3 the energy requirements’ reduction ranges from 10% to 30% depending on the month of the year. The reduction of 20% as an average, is considered satisfactory due to the fact that the overall energy efficiency is mainly attributed to the optimal operation and management of the various buildings’ facilities and systems. Also, the energy demand in the Campus

Energy Management in University Campuses

819

× 104

Measured power demand Predicted power demand

9 8

R-square = 0.77 rmse = 7059

Power demand (W)

7 6 5 4 3 2 1 4400

4600

4800 Time

5000

5200

5400

Fig. 5 Comparison of the Measured (blue line) and the Predicted power demand (green line). Reproduced from Kolokotsa D, Gobakis K, Papantoniou S. Development of a web based energy management system for University Campuses: the CAMP-IT platform. Energy Build 2016;123:119–35.

buildings cannot be reduced more than 20%, since some laboratory equipment operate constantly and cannot be switched off. A further considerable improvement would be achieved by closely monitoring the overall performance of the HVAC operation by means of the Camp-IT monitoring platform. The use of Web-based platform operation has reduced the system’s failure and the maintenance efforts of the Campus technical services. Moreover the Camp-IT installation is used as a Living Lab for the energy related educational activities of the University.

5.20.7.2

Energy and Indoor Environment Quality Management of University Campus: The Case Study of National University of Singapore

The Campus of the National University of Singapore (NUS) hosts more than 270 buildings, covering administrative buildings, academic buildings, student dormitories, etc. The Campus is supporting the academic activities of more than 37,000 students and around 10,000 staffs. Considering its scale and complexity, occupancy levels and multifunctional purpose, it can be considered as a small town. If the energy and indoor environment quality can be well monitored by a common management platform, it will not only lead to an energy saving potential but also establish the campus as a leader in sustainability.

5.20.7.2.1

Energy information network infrastructure

The EIN takes whole National University of Singapore campus as a living lab, with its first phase to include 10 buildings to be instrumented an integrated energy and environment monitoring and management system. Among the 10 buildings, each of them can be communicated with a centralized and monitored Cockpit. The “Energy Cockpit” is located in the Department of Building. Energy analytics will be used together with the integrated monitoring system with a goal of reducing energy consumption. This will help determine any discrepancies between benchmarked performance and actual energy usage thereby unlocking energy savings opportunities and identifying areas for improvement or energy efficient upgrades. With the success of phase one, the second phase of the cockpit integrates 105 campus buildings under the same common energy and indoor environmental quality management platform.

5.20.7.2.2

Instrumentation

Different instruments are installed to collect relevant information about building performance. In specific,

• •

Smart meters – collect the electricity consumption from equipments, lighting fixtures, and air handling units (AHU) fans. British thermal units (BTU) meters – collect the cooling load energy consumptions.

820

• •

Energy Management in University Campuses

Occupancy counting sensors/solutions – Wifi-Based crown level estimation solution is installed across the whole campus and face recognition-based camera counting system is installed in one of the lecture theater as the testbed. IEQ sensor kits – 60 IEQ sensor kits including temperature, RH, CO2, and illuminance are installed at different locations of six buildings.

5.20.7.2.3

Data cleaning

Studies [67,68] have shown the potential errors in data, such as outliers, missing values and time stamp issues, which can happen with any data collection system any time randomly, sometime for no reason. In the energy consumption data and indoor environment data collecting system in NUS, it is impossible to spot and correct them by the huge reliance on manpower, time, resource, and finance. Since the energy consumption data collected is the basic for any data anlysis and monitoring, the data cleaning script is run at the background of the Cockpit to provide automatic data cleaning. The methodology involves data imputation and outlier detection. Fig. 6 is the dashboard of the Cockpit data collection and cleaning status. The green light in the last column shown the smooth data collection with no error currently. The total number of missing data imputed, time stamp issue data and the outliers identified thought the automatic data cleaning process are shown in columns 4, 5, and 6 respectively.

5.20.7.2.4

Energy monitoring

The cockpit consists of three main parts for an integrated and intelligent energy management. The basic information for each building is shown as the first part, including the gross floor area, building category, age of building, operating hours, occupancy per square meter as well as the EUI for past 12 months and the chiller plant efficiency. Then energy consumption of the air-conditioning part, electrical part, and the total energy consumption part are monitored simultaneously. It is also possible for the users to choose the time period that the data can be shown on a yearly basis (y), monthly basis (m), weekly basis (w), or daily basis (d). Comparison of energy consumption is also possible for up to four buildings. This comparison is a way of indicating the different pattern of energy consumption, and also bring opportunities to look into operational efficiency and cost control, system stability and reliability, as well as energy efficiency and environmental issues.

5.20.7.2.5

Smart tools

Apart from the simple data collection and monitoring, smart tools is developed in line with the big data analysis trend. All the novel methodologies developed in the research group are implemented to provide an intelligent profile for the building facility manager and the users.

Building models ESP-r ESP-r and sketch up plugin Coupling mechanism of indoor outdoor models

BCVTB

Envimet

Control and optimisation + Prediction algorithms using Matlab

Fig. 6 The connectivity of the various models. BCVTB, building control virtual test bed. Reproduced from Kolokotsa D, Gobakis K, Papantoniou S. Development of a web based energy management system for University Campuses: the CAMP-IT platform. Energy Build 2016;123:119–35.

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Automation server

PLC - BMS

Fire safety

Fire safety

TCP/IP RS-485

Power distribution boards

Power distribution boards

Lighting

Electric lighting

Lifts

USB

UPS

Heating and cooling

Modbus/IP

Fans Fault detection

Pumps

Fans Lifts

VRV

CsNet

UPS

Fault detection

Heating and cooling

Pumps

VRV H-link

H-link

Indoor unit

Indoor units CsNet Web

(A)

(B)

Fig. 7 The hardware architecture (A) before and (B) after the installation of the web-based Campus Energy Management. Reproduced from Kolokotsa D, Gobakis K, Papantoniou S. Development of a web based energy management system for University Campuses: the CAMP-IT platform. Energy Build 2016;123:119–35.

Table 4

Indoor environment quality sensor kit

Environmental parameter

Sensor

Temperature and relative humidity

Sensirion SHT75

CO2 Illuminance

K-30, CO2 Meter Inc. ROHM B17xx series

Specifications Range

Accuracy

Resolution

40–123.81C 0–100% RH 0–5000 ppm 0–65,000 lx

70.31C (@251C) 71.8% RH 730 ppm þ 3% of measured value –

0.011C 0.03% RH – 1 lx

The first smart tool called energy forecasting tool predicts the electrical energy consumption and the HVAC energy consumption of a building for the next 2 weeks by using artificial neuron network [29]. As shown in Fig. 7, with the development of forecasting model based on machine learning techniques and historical energy consumption data only, a set of boundary conditions can be provided and targets for the building facility managers and owners within which the buildings energy consumption should ideally fall (daily, weekly, monthly, and annual targets). The second smart tool implemented in the cockpit is called identification tool [4]. It is developed in order to learn the building energy consumption characteristics as well as get the knowledge of dynamic energy budget interrelationships by using the available data. The visualization part of the smart tool is able to provide the occupancy variation information in the current building.

5.20.7.2.6

Indoor environment quality monitoring

Among the 105 buildings, three buildings are selected to be implemented with 60 detailed indoor environment quality monitoring sensor kits. The parameters and specifications are listed in Table 4. Fig. 8 is the user interface in the cockpit as the user is able to select the specific space to have the real time information on the temperature, RH, CO2 concentration level and the illuminance level (Figs. 9 and 10). In summary an integrated energy and indoor environment quality monitoring cockpit developed as the energy information network in NUS campus not only include the massive smart meter reading data, but also the huge story hidden behind the amount of data, such as building characteristics, occupancy activity pattern, and the knowledge of dynamic energy budget interrelationships. It assists campus building energy management from data cleaning, data monitoring, data analysis, and smart tool analysis with four horizontal dimensions (comparison up to four buildings) and four longitudinal dimensions (yearly, monthly, weekly, and daily). With the development of the Energy Cockpit prototype, a log in page with different levels of access is designed which meets the identified data security for a data controlling. Hence, it provides different end users different levels of energy related figures with a more straightforward way.

5.20.7.2.7

Mobile apps for user feedback

Mobile apps are developed for occupancy feedback purpose. Occupants’ preference on temperature and indoor air quality will be collected with a connection to the office of facility management so that necessary changes can be adjusted according to the users’ feedback.

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Fig. 8 Automatic data cleaning dashboard.

Fig. 9 Energy forecasting dashboard.

Energy Management in University Campuses

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Fig. 10 The indoor environment quality monitoring user interface.

5.20.8

Future Directions

University Campuses are small communities with significant environmental impact and energy needs. On the other hand, University Campuses are incubators for innovative approaches and new energy technologies. Significant research has been performed during the last years for the energy management of University Campuses. In this research efforts, the various research teams have used their workplace as an experimental living lab. The existing knowledge should be integrated in a sufficient manner leading to zero energy or zero carbon campuses. Through the minimization of Campuses’ carbon footprint, a suitable research environment is created that resources may be relocated to cross cutting technologies and innovation. Moreover, significant research should be put in understanding the interactions between the indoor with the outdoor environment. In this context, the existing infrastructure (smart meters, sensors, smart devices, etc.) installed in the various Campuses around the globe can provide a robust basis for data gathering and monitoring. The understanding of the indoor–outdoor environment interaction can provide significant insights for the urban thermal environment and comfort. This knowledge can enhance the research for the urban comfort both indoors and outdoors and the means that they interact for the benefit of the user. Another significant aspect of the future research in energy management of University campuses is the integration of sustainable mobility. Electrical vehicles should be considered in the energy management process and smart sensors should provide the necessary data for reducing the energy demand for transportation. Moreover, education and training of various disciplines such as engineers, architects is reinforced by being close to innovative energy and environmental technologies for urban populations that can attract industry, businesses, tourism, and workforces.

5.20.9

Closing Remarks

University Campuses are small cities with complex and mixed activities. The energy and environmental impact of Campuses are of a great importance as they can be used to train young people and future generations toward sustainable energy management practices. Moreover a number of University Campuses worldwide have been committed to become zero energy or even zero carbon communities by investing in innovative energy production technologies, improve energy efficiency, reduce energy waste and increase young students energy and environmental awareness. This requires the active integration of users in a demand side management process. This is a great opportunity for Universities to become lighthouses of excellence in the energy and environmental sector.

Acknowledgments Part of the work described in the chapter is funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement SMART GEMS: SMART GRIDS ENERGY MANAGEMENT STAFF (Contract No 645677).

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[8] Sustainable Campus. Available from: http://www.sustainablecampus.cornell.edu/initiatives/building-dashboards. [9] McBride JR, Schuster L, Rickey D. Implementation of an integrated energy information system in a large university campus. Energy Syst Lab 2003. Available from:http:// hdl.handle.net/1969.1/91017 [10] Newaz SHS, Yang JH, Mohammed AFY, Lee GM, Choi JK. A web based energy cloud platform for campus smart grid for understanding energy consumption profile and predicting future energy demand. In: International conference on ICT convergence; 2014. p. 173–8. [11] Kolokotsa D, Gobakis K, Papantoniou S, et al. Development of a web based energy management system for University Campuses: the CAMP-IT platform. Energy Build 2016;123:119–35. [12] Lasternas B, Aziz A. SEED platform enhancements to support energy efficiency at the portfolio level report. Department of Energy; 2016. [13] Kolokotsa D, Gobakis K, Papantoniou S, et al. Development of a web based energy management system for University Campuses: the CAMP-IT platform. Energy Build 2016;123:119–35. [14] Papadaki N, Papantoniou S, Kolokotsa D. A parametric study of the energy performance of double-skin façades in climatic conditions of Crete, Greece. Int J Low Carbon Technol 2013;2. doi:10.1093/ijlct/cts078. [15] Fung AS, Taherian H, Hossain M, Rahman MZ, Selim MM. Energy audit and base case simulation of Ryerson University buildings. ASHRAE Trans 2015;121:84–98. [16] Pyloudi E, Papantoniou S, Kolokotsa D. Retrofitting an office building towards a net zero energy building. Adv Build Energy Res 2015;9(1):20–33. [17] Carbonara E, Tiberi M. Assessing energy performance and economic costs of retrofitting interventions in a university building. In: EEEIC 2016 – international conference on environment and electrical engineering; 2016. [18] Dong B, Oneill Z, Luo D, Bailey T. Development and calibration of an online energy model for campus buildings. 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Predictive control techniques for energy and indoor environmental quality management in buildings. Build Environ 2009;44(9):1850–63. [25] Kapelan Z, Savic DA, Walters GA. Decision-support tools for sustainable urban development. Proc Inst Civ Eng Eng Sustain 2005;158(3):135–42. [26] Rokach L, Maimon O. Decision trees. In: Data mining and knowledge discovery handbook; 2010. p. 165–92. [27] Kalogirou SA. Applications of artificial neural-networks for energy systems. Appl Energy 2000;67:17–35. [28] Gobakis K, Kolokotsa D, Synnefa A, Saliari M, Giannopoulou K, Santamouris M. Development of a model for urban heat island prediction using neural network techniques. Sustain Cities Soc 2011;1(2):104–15. [29] Deb C, Eang LS, Yang J, Santamouris M. Forecasting diurnal cooling energy load for institutional buildings using artificial neural networks. Energy Build 2015;121:284–97. [30] Suganthi L, Samuel AA. Energy models for demand forecasting – a review. Renew Sustain Energy Rev 2012;16(2):1223–40. [31] Guan J, Nord N, Chen S. Energy planning of university campus building complex: energy usage and coincidental analysis of individual buildings with a case study. Energy Build 2016;124:99–111. [32] Bonnet J-F, Devel C, Faucher P, Roturier J. Analysis of electricity and water end-uses in university campuses: case-study of the University of Bordeaux in the framework of the Ecocampus European Collaboration. J Clean Prod 2002;10(1):13–24. [33] Gallachóir BPÓ, Keane M, Morrissey E, O’Donnell J. Using indicators to profile energy consumption and to inform energy policy in a university – a case study in Ireland. Energy Build 2007;39(8):913–22. [34] Agarwal Y, Weng T. The energy dashboard: improving the visibility of energy consumption at a campus-wide scale. BuildSys 2009;55–60. doi:10.1145/1810279.1810292. [35] Zhou X, Yan J, Zhu J, Cai P. Survey of energy consumption and energy conservation measures for colleges and universities in Guangdong province. Energy Build 2013;66:112–8. [36] Global University Leaders Forum. Implementation guidelines to the ISCN-GULF sustainable campus charter – suggested reporting contents and format; 2010. p. 1–19. [37] Chung MH, Rhee EK. Potential opportunities for energy conservation in existing buildings on university campus: a field survey in Korea. Energy Build 2014;78:176–82. [38] Papanicolas C, Lange MA, Fylaktos N, et al. Design, construction and monitoring of a near-zero energy laboratory building in Cyprus. Adv Build Energy Res 2015;9 (1):140–50. [39] Gul MS, Patidar S. Understanding the energy consumption and occupancy of a multi-purpose academic building. Energy Build 2015;87:155–65. [40] Mata É, López F, Cuchí a. Optimization of the management of building stocks: an example of the application of managing heating systems in university buildings in Spain. Energy Build 2009;41(12):1334–46. [41] Gupta R, Gregg M. Empirical evaluation of the energy and environmental performance of a sustainably-designed but under-utilised institutional building in the UK. Energy Build 2016;128:68–80. [42] Semprini G, Marinosci C, Ferrante A, et al. Energy management in public institutional and educational buildings: the case of the school of engineering and architecture in Bologna. Energy Build 2016;126:365–74. [43] Yarbrough I, Sun Q, Reeves DC, Hackman K, Bennett R, Henshel DS. Visualizing building energy demand for building peak energy analysis. Energy Build 2015;91:10–5. [44] Provata E, Kolokotsa D, Papantoniou S, Pietrini M, Giovannelli A, Romiti G. Development of optimization algorithms for the leaf community microgrid. Renew Energy 2015;74:782–95. [45] Comodi G, Giantomassi A, Severini M, et al. Multi-apartment residential microgrid with electrical and thermal storage devices: experimental analysis and simulation of energy management strategies. 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[46] NCE Smart Energy Markets. A prosumer oriented energy market, IMPROSUME Publ. Ser; 2012. p. 1–114. [47] Karnouskos S. Demand side management via prosumer interactions in a smart city energy marketplace. In: IEEE PES innovative smart grid technologies conference Europe; 2011. [48] Lawrence R, Keime C. Bridging the gap between energy and comfort : post-occupancy evaluation of two higher-education buildings in Sheffield. Energy Build 2016;130:651–66. [49] Triantis E, Bougiatioti F, Oikonomou A. Users’ perception of comfort and well-being in university buildings. In: PLEA 2006 – 23rd international conference on passive and low energy architecture, conference proceedings; 2006. p. I405–10. [50] Frandoloso MAL, Brandli LL, Dias FP. How to improve eco-efficiency and indoor comfort at university of passo fundo – Brazil. In: Proceedings – 28th international PLEA conference on sustainable architecture þ urban design: opportunities, limits and needs – towards an environmentally responsible architecture, PLEA 2012; 2012. [51] Giatrakos GP, Tsoutsos TD, Zografakis N. Sustainable power planning for the island of Crete. Energy Policy 2009;37:1222–38. [52] Michalena E, Angeon V. Local challenges in the promotion of renewable energy sources: the case of Crete. Energy Policy 2009;37(5):2018–26. [53] Strachan P. ESP-r summary of validation studies. Analysis 2000;0–8. [54] Carlson L, Richardson L. Ruby cookbook. Cambridge, MA: O’Reilly; 2006. [55] Tsilini V, Papantoniou S, Kolokotsa D-D, Maria E-A. Urban gardens as a solution to energy poverty and urban heat island. Sustain Cities Soc 2015;14:323–33. [56] Yang X, Zhao L, Bruse M, Meng Q. Evaluation of a microclimate model for predicting the thermal behavior of different ground surfaces. Build Environ 2013;60:93–104. [57] Lahme E, Bruse M. Microclimatic effects of a small urban park in a densly build up area: measurements and model simulations. In: The fifth international conference on urban climate; 2003. [58] Huttner S, Bruse M, Dostal P. Using ENVI-met to simulate the impact of global warming on the microclimate in central European cities. 5th Jpn-German Meet. Urban Climatol 2008;18(18):307–12. [59] Gobakis K, Kolokotsa D. Coupling building energy simulation software with microclimatic simulation for the evaluation of the impact of urban outdoor conditions on the energy consumption and indoor environmental quality. Energy Build 2017;157:101–15. [60] Papantoniou S, Kolokotsa D. Prediction of outdoor air temperature using neural networks; application in 4 European cities. In: Third internation conference on countermeasures to urban heat island; 2014. [61] Zhang GP. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 2003;50:159–75. [62] Kolokotsa D. Comparison of the performance of fuzzy controllers for the management of the indoor environment. Build Environ 2003;38(12):1439–50. [63] Papantoniou S, Kolokotsa D, Kalaitzakis K. Building optimization and control algorithms implemented in existing BEMS using a web based energy management and control system. Energy Build 2014; doi:10.1016/j.enbuild.2014.10.083. [64] Caldas LG, Norford LK. Genetic algorithms for optimization of building envelopes and the design and control of HVAC systems. J Sol Energy Eng 2003;125:343. [65] Caldas LG, Norford LK. Genetic algorithms for optimization of building envelopes and the design and control of HVAC systems. J Sol Energy Eng 2003;125:343. [66] ISO. ISO 7730: ergonomics of the thermal environment analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria. Management 2005;3(7–10):605–15. [67] Barbato G, Genta G, Levi R. Outlier Detection, CIRP STC P. Precision engineering and metrology, Paris; 2009. [68] Figueroa García JC, Kalenatic D, Lopez Bello CA. Missing data imputation in multivariate data by evolutionary algorithms,. Comput Hum Behav 2011;27(5):1468–74.

Further Reading Allab Y, Pellegrino M, Guo X, Negzaoui E, Kindinis A. Energy and comfort assessment in educational building: case study in a French university campus. Energy Build 2017;143:202–19. Filho WL, Morgan EA, Godoy ES, et al. Implementing climate change research at universities: barriers, potential and actions. J Clean Prod 2018;17:269–77. Hasapis D, Savvakis N, Tsoutsos T, Kalaitzakis K, Psychis S, Nikolaidis NP. Design of large scale prosuming in Universities: the solar energy vision of the TUC campus. Energy Build 2017;141:39–55. Jradi M, Sangigboye FC, Mattera CG, Kjaergaard MB, Veje C, Jorgensen BN. A world class energy efficient university building by danish 2020 standards. Energy Procedia 2017;132:21–6. Li L, Tong Z, Linhua Z, Hongchange S. Energy consumption investigation and data analysis for one university of Guangzhou. Procedia Eng 2017;205:2118–25. Luo R, Han Y, Zhou X. Characteristics of campus energy consumption in North China university of science and technology. Proc Eng 2017;205:3816–23. Mytafides CK, Dimoudi A, Zoras S. Transformation of a university building into a zero energy building in Mediterranean climate. Energy Build 2017;155:98–114. Soini K, Jurgilevich A, Piertikainen J, Kurki KK. Universities responding to the call for sustainability: a typology of sustainability centers. J Clean Prod 2018;170:1423–32. Song K, Kim S, Park M, Lee HS. Energy efficiency-based course timetabling for university buildings. Energy 2017;139:394–405. Yoshida Y, Shimoda Y, Ohashi T. Strategies for a sustainable campus in Osaka University. Energy Build 2017;147:1–8.

Relevant Websites https://energyandsustainability.fs.cornell.edu/em/default.cfm Cornell University. http://www.distech-controls.com/en/us/colleges-universities/ Distech Controls. https://facilities.princeton.edu/news/campus-energy-management Facilities Princeton University. http://www.instepsoftware.com/solutions/campus-energy-management Instep. https://sustainability.unl.edu/campus-energy-management-plan N Sustainability. https://www.facilities.upenn.edu/sustainability/energy-management Penn Facilities and real estate services. https://smart-energy-analytics.org/case-studies Smart Energy Analytics Campaign. http://www.tuc.gr/3879.html Technical University of Crete.

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https://prezi.com/uhrzvtz1svwh/university-campus-energy-management/ University Campus Energy Management. https://facm.umn.edu/about-fm/energy-management University of Minnesota. https://facm.umn.edu/energy-management/building-dashboards University of Minnesota. http://facilities.wfu.edu/maintenance-utilities/energy-management-tracking/ Wake Forest University. https://wmich.edu/facilities/engineering/energy-management Western Michigan University. https://facilities.yale.edu/utilities-engineering Yale University.

5.21 Energy Management in Hospitals Fabrizio Ascione, Nicola Bianco, Claudio De Stasio, and Gerardo M Mauro, University of Naples Federico II, Napoli, Italy Giuseppe P Vanoli, University of Molise, Campobasso, Italy r 2018 Elsevier Inc. All rights reserved.

5.21.1 5.21.2 5.21.2.1 5.21.3 5.21.3.1 5.21.3.2 5.21.4 5.21.4.1 5.21.4.1.1 5.21.4.1.2 5.21.4.1.3 5.21.4.2

Introduction and State of Art: The Energy Issue in Hospitals Energy Assessment of Hospital Buildings Definition of a Hospital Reference Building Measures for Efficient Energy Design and Management in Hospitals Energy Efficiency Measures for the Demand Side Management Energy Efficiency Measures for the Supply Side Management Methodologies to Address Energy Design and Management of Hospitals Methodological Approaches for Hospitals’ Energy Retrofit at Different Climates Heating-dominated climate Balanced climate Cooling-dominated climate From an Reference Building to All Represented Hospitals: Assessment of Energy Performance and Retrofit Potentials Application 1: “D” pavilion of the “A. Cardarelli” hospital facility in Naples (Campania, South Italy) Application 2: “M. Chiello” hospital in Enna (Sicily, South Italy) Multistage and Multiobjective Optimization Application 1: Reference building of hospitals built in South Italy between 1991 and 2005 Conclusions

5.21.4.2.1 5.21.4.2.2 5.21.4.3 5.21.4.3.1 5.21.5 References Relevant Websites

Abbreviations

839 841 843 845 851 852 852 854

GA GC HVAC IC MCDM PEC PV RB RBh RES TED

Nominal energy efficiency ratio of absorption chillers (Wt Wt 1) Fabbisogno energetico normalizzato (normalized energy need) (J m 3 DD 1) Genetic algorithm Global cost (€) Heating, ventilating, and air conditioning Investment cost (€) Multicriteria decisionmaking Primary energy consumption (Whpr m 2 a 1) Photovoltaic Reference building Hospital reference building Renewable energy source Thermal energy demand (Wht m 2 a 1)

Subscripts cool Referred to space cooling el Referred to electrical energy or power heat Referred to space heating

pr roof t w

Referred Referred Referred Referred

Nomenclature d Prefix that denotes the difference of an indicator compared to the BB F Objective functions

Z

Nominal efficiency of boilers related to the low calorific value (Wt Wp 1) Thermal transmittance (Wt m 2 K 1) Vector of design variables

ACH BB BPO BPS CCHP CHP COP DHW EED EEM EER

EERass

827 829 830 834 834 835 836 836 837 838 839

Air changes per hour (h 1) Base building (before retrofit) Building performance optimization Building performance simulation Combined cooling, heating, and power Combined heating and power Nominal coefficient of performance of heat pumps (Wt Wel 1) Domestic hot water Electrical energy demand for direct electric uses (Whel m 2 a 1) Energy efficiency measure Nominal energy efficiency ratio of electric chillers (Wt Wel 1)

Comprehensive Energy Systems, Volume 5

FEN

U x

doi:10.1016/B978-0-12-809597-3.00541-1

to to to to

primary energy or power the roof thermal energy or power windows

827

828

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5.21.1

Introduction and State of Art: The Energy Issue in Hospitals

Hospitals and, in general, healthcare facilities should be a place of comfort and interaction among people, where the principles of sustainability are translated into an architectural language combined with functional and management expedients. Indeed, these facilities have to satisfy patients’ care needs, but they also should testify the advancement of medical and scientific research. They usually include various functions with different needs, since some thermal zones require a strict control of indoor conditions (operating theaters, intensive care units, etc.) whereas other ones (administrative offices, ambulatories, generic wards, etc.) are similar to typical functions of the tertiary sector. Definitely, the energy design and management of these buildings – concerning the thermal envelope, the heating, ventilating and air conditioning (HVAC) systems, and the primary energy systems – are complex tasks, especially if the goal is not to build a new healthcare facility but to refurbish an existing one. Indeed, infections and diseases due to poor indoor air quality (IAQ) are common in hospitals, and therefore a strict control of indoor conditions (i.e., air temperature, relative humidity, air tightness, filtration, and quality) is necessary to protect fragile patients from contamination. Moreover, the study of thermal comfort has to take into account that there are two different groups of users, staff and patients, who have different needs [1]. In this frame, over the years, several guidelines for a proper energy design of healthcare facilities were proposed by national and international associations, such as the American Institute of Architects [2], the American Society for Heating, Refrigerating and AirConditioning Engineers [3,4], and the German Committee for Standardization [5]. In Italy, the most relevant requirements and indications are set by a dedicated Ministerial Act [6] and by a Presidential Decree [7], as well as by the National Institute for Prevention and Job Security (ISPESL [8]). Despite the mentioned guidelines, hospitals are the most energy-intensive facilities in the building sector [9], and therefore their efficient design and management may produce huge energy savings, as well as environmental and economic benefits [10]. The high energy-intensity of these facilities is mainly related to the specific activities that must be carried out, which require large amounts of energy in order to guarantee the best service quality and comfort to users as well as to power devices and diagnostic tools. A fundamental issue affecting the energy performance of hospitals is the indoor air conditioning, especially concerning the achievement and maintenance of the high air quality required by healthcare activities, such as the specific aseptic conditions needed in operating theaters or in the rooms where patients with critical diseases are hosted. This implies substantial energy demands for air conditioning, mainly due to: 1. high ventilation rates, which in some zones are even greater than 15 air changes per hour (ACH); 2. need of a strict control of temperature and relative humidity; 3. need to control both sensible and latent heat loads, which determines the necessity of subcooling and consequently postheating of the supply air. Given these premises, the characteristics of thermal insulation and inertia of the building envelope may seem marginally influential, but, actually, they need to be carefully designed in order to keep a stable indoor microclimate, to avoid overheating and to exploit heat gains [11]. It should be noticed that, in recent years, the combined management of hospital activities induced the development of hospital districts, where several departments that carry out specialized and hyperspecialized activities are located in the same facility. Usually, hospital districts offer admission, day hospital, and first aid services. They are strategically placed on a territory in order to offer the same services to different parts of a city and allow one to realize two important objectives: the reduction of management costs related to hospital activities and the grouping of different services in the same facility, thereby making their fruition easier for users. Consequently, this leads to the development of energy districts, characterized by the presence of several buildings that conduct different activities and, thus, have heterogeneous and specific patterns of energy consumption. Normally, the conversion of thermal energy for space heating and domestic hot water (DHW) production is centralized. In this case, each building is supplied by a district heating ring that feeds the thermal substations where energy reconversion is performed. Heat exchangers are located in the thermal substations that, from the primary heat transfer fluid (vapor or overheated water) coming from the primary heating systems, produce DHW and the secondary heat transfer fluid that feeds the heating system. Conversely, the conversion of thermal energy for space cooling is, generally, not centralized and each building has its own chillers. Definitely, the evaluation of energy performance and the choice of energy efficiency measures (EEMs) for hospital districts are more complex compared to the case of single buildings. Therefore, the energy design and management of healthcare facilities differs if districts or single buildings are investigated. This chapter focuses on hospital buildings, because they are the vast majority of healthcare facilities. Most existing hospitals were built with poor attention to energy issues. Indeed, over the last few decades, because of low energy costs and low political and social interest for the economic and environmental sustainability of human activities, the design and construction of hospitals was aimed at fulfilling the required healthcare standards, by ignoring the energy efficiency of the interaction building-system. In fact, the turnover rate of the building stock, and especially of hospitals, is very low, particularly in the developed countries, which account for a major amount of world energy consumption [12]. Therefore, although the design of new hospitals with high-energy performance is necessary, the deepest focus must be addressed to the energy retrofit of existing hospital buildings and districts, in order to achieve a substantial reduction of the environmental impact of this sector. In addition, an effective retrofit design can also imply significant economic benefits, for instance, when a robust cost-optimal analysis is performed according to the recent guidelines of Energy Performance of Buildings Directive 2010/31/EU (EPBD Recast) [13] and Commission Delegated Regulation (EU) No. 244/2012 [14]. As depicted in Fig. 1, the cost-optimal design produces the minimization of predicted global costs (GCs) over the building lifecycle (that includes investment, operating, and maintenance costs).

Global cost (GC) [€]

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829

Current building performance Cost-optimality

Primary energy consumption (PEC) (kWh m−2a−1) Fig. 1 Cost-optimal curve.

At the same time, it allows a reduction of building primary energy consumption (PEC) compared to standard approaches (in the case of new building design) or to the existing condition (in the case of retrofit). From the shown introductive notes, it is clear that the existing hospital stock, in most of the world, causes considerable energy wastes, the costs of which fall on profit and loss accounts of hospitals, and consequently (given the high incidence of the public health system) on citizens (i.e., taxpayers). In this period of economic crisis, there is the crucial need to limit the public expenditure and the cost of services for citizens without affecting the quality level of offered services. In this context, the rationalization of hospitals’ energy design and management is de facto mandatory. The conflict between the need to limit expenditure and the necessity of high-qualitative standards can be resolved only by upgrading the efficiency of the examined systems, especially, for what concerns the issue here addressed, by optimizing energy performance. The solution of this problem requires new tools and knowledge, as well as a different approach compared to the one used in the past, which should pay more attention to the economic, energetic, and environmental sustainability.

5.21.2

Energy Assessment of Hospital Buildings

The first step to optimize the energy design and management of hospitals is the reliable assessment of energy performance as well as the impacts of EEMs. These measures can be applied at different levels, which go from the single ward or room [15] to the whole facility [16]. For example, in the first case, sensors controlling radiator valves can be installed only in a room or thermal zone to manage space heating and/or cooling. On the other hand, an exemplification of the second type of approach can be represented by the installation of a system to manage the supply water temperature of boilers as a function of outdoor temperature. The selection of the most proper EEMs has to be performed through comprehensive analyses that require proper tools, which are fundamental to analyze how the considered EEMs may improve the building energy performance. These tools should also allow one to identify the best solutions by providing energy, economic, and comfort indicators such as the reduction of energy demand, GC or discomfort hours, the payback period, the lifecycle costs, and so on. In this frame, the use of dynamic energy simulations is highly recommended to find out which are the weakest points of a hospital in terms of energy issues, especially when analyzing the performance of the building envelope or HVAC and primary energy systems. In this regard, only dynamic simulations that consider the transient behavior of the building year-round may provide reliable outcomes. Indeed, all steady or semi-steady methods are improper because they cannot consider the actual heat transfer through the building envelope as well as the dynamic behavior of energy systems, and therefore the impact of the high variability of forcing conditions (e.g., internal and external temperatures, solar radiation, heat gains, and so on). Definitely, validated dynamic building performance simulations (BPSs) should be employed to achieve reliable outcomes concerning building energy predictions by carefully defining all boundary conditions, such as hourly weather data files. In a detailed review, Poel et al. [17] delineated the methodologies and software for BPS that are dominant in scientific studies. Several tools are available and, definitely, the correct choice depends on many factors, for example, the required accuracy levels, the available computational times, and the inputs’ quality. Among the most popular and reliable BPS software, there is EnergyPlus [18], TRNSYS [19], IDA-ICE [20], or ESP-r [21], all of which are whole building energy simulation tools that allow a detailed analysis of the impact of different kinds of EEMs on the building performance. This software is frequently used in the research field, as shown by Crawley et al. [22] who performed a comparison among the 20 most used BPS tools. Concerning the dynamic energy assessment of hospital buildings, unfortunately, there are few scientific studies, mainly because of the high complexity in HVAC systems’ modeling and of the huge required computational burden. Indeed, the majority of these studies only analyzed small areas of a hospital, for example, a room or department, since the analysis of EEMs for an entire healthcare facility generally requires substantial computational times, thereby reducing the number of simulations that can be carried out to find optimal

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solutions. Definitely, the most used BPS tools for the energy assessment of hospitals are EnergyPlus and TRNSYS. EnergyPlus was used in Ref. [23] to demonstrate that significant energy savings, around 50%, can be achieved for most hospitals in all United States climate zones. In the same vein, Ascione et al. [11] analyzed the effects achievable by improving the thermal properties of the envelope of a hospital building located in Naples (South Italy), by performing dynamic simulations through EnergyPlus, supported by the graphical interface DesignBuilder [24]. The adopted values of thermal transmittance were set according to measured/ calculated input data. On the other hand, TRNSYS was used in several studies about this topic, focused on the optimization of energy systems. For instance, in Ref. [25] the energy retrofit of an existing hospital district in Naples was investigated by installing a renewable polygeneration system. The data related to cooling, heating, and electricity demands were analyzed for a year of operation. The results were investigated from both “energy” and “economic” points of view and they demonstrated that the installation of the system is feasible (payback period ¼ 12 years). Rasouli et al. [26] coupled TRNSYS and MATLAB [27] to investigate the impact of the installation of a novel heat and moisture recovery system called a run-around membrane energy exchanger (RAMEE). In cold climates, the installation of this system can lead to 60% reduction of the annual heating energy demand, whereas energy savings of around 15–20% can be achieved in warm climates. The estimated payback period is lower than 1 year for cold climates and varies between 1 and 3 years for warm ones. Measured and estimated data of energy consumptions of healthcare facilities, and associated bills, are scarce, especially in the Mediterranean area. The results found in the scientific literature are strongly related to the load patterns of the investigated building/facility and it is not possible to extend them to buildings with different functions or locations [28]. On the other hand, the energy analysis of each hospital facility, by running the mentioned BPS tools, is hardly feasible because it would require excessive computational complexity. Therefore, the best solution to investigate hospitals’ energy performance is to develop reference building (RB) models that can represent different categories of the existing hospital stock. As regards this topic, the EPBD Recast [13] stated the need for all member states (MSs) to develop RBs. These should be subjected to cost-optimal analyses (see Fig. 1) by identifying the most cost-effective solutions concerning building energy design or retrofit in order to set minimum requirements for building energy performance. The definition of RB is provided by the annex III of the EPBD Recast and is the following: “buildings that are characterized by and representative of their functionality and geographic location, including indoor and outdoor climate conditions.” In addition, according to Ref. [14], “the main purpose of an RB is to represent the typical and average building stock in a certain MS.” Notably, each MS has to define one RB for new buildings and at least two for existing ones that are subjected to refurbishment for the categories reported below:

• • •

residences occupied by a single family; apartment and/or multifamily buildings; office buildings.

The chosen buildings can be grouped based on their date of construction, architectural materials, dimensions, climate zones, and so on, in order to be as representative as possible of the analyzed building context. The above-mentioned list of categories for which RBs have to be developed can be widened by adding buildings with specific energy performance requirements such as hospitals, sport facilities, and hotels. In this regard, the definition of RBs for complex and big structures like hospitals can be particularly effective, because it allows one to investigate the energy performance of a limited groups of buildings, i.e., the RBs, extending the outcomes to the represented structures with a good approximation. This can yield huge savings of computational times and burden, since, as previously mentioned, the energy modeling and simulation of the considered buildings is quite computationally expensive and time-consuming. In this vein, the next subsection delineates an original methodology to define a hospital reference building (RBh), proposed in Ref. [10] by the same authors of this chapter.

5.21.2.1

Definition of a Hospital Reference Building

In this subsection, a methodology to define a RBh, which represents hospitals built between 1991 and 2005 in South Italy, is reported, as proposed in Ref. [10]. The time range (1991–2005) was chosen because during this period the Italian regulations regarding energy design of new buildings did not change [29], and thus the hospitals built in this time range have similar energy characteristics. The goal of the methodology is defining a model of a hospital pavilion that represents the mentioned building category by including all main use classifications typical of healthcare facilities, such as laboratories, generic wards, operating theaters, and offices. The developed RBh can be classified as theoretical RB and it was built using statistical data related to hospital buildings located in the district of Naples (South Italy, Mediterranean climate), which were provided by the “Azienda Sanitaria Locale of Naples” (ASLNAP1). Since the construction techniques used to build healthcare facilities can be considered quite homogeneous in South Italy, it is reasonable to assume that the building sample related to the hospital stock of Naples represents the whole category with a sufficient reliability. Furthermore, Naples presents average weather conditions within the considered geographical area (South Italy) and thus the weather data file of this city was used in EnergyPlus simulations, thereby providing average climatic conditions [10]. According to Ref. [30], the data required for the definition of an RB can be collected into four groups, related to (1) the building’s geometrical and type/shape characteristics, (2) functions and schedules of operation/use, (3) size and type of HVAC systems, and (4) thermophysics characteristics of the building envelope’s components. The geometry of the RBh (see Fig. 2) was defined on statistical bases, after a deep investigation of some of the main hospitals managed by ASLNAP1. Table 1 lists the geometry and shape parameters, which were obtained as a weighted average, in which the total floor area of each investigated hospital is the weight.

Energy Management in Hospitals

831

2nd floor Generic ward all-air 1021 m2

Generic ward heating and cooling 1083 m2 Common area 150 m2

Office heating 1531 m2

(D)

(A) Ground floor

3rd floor

Ambulatory heating and cooling 965 m2

Laboratory all-air 236 m2

Intensive care unit all-air 478 m2

Generic ward heating 1565 m2

Common area 150 m2

(B)

Common area 150 m2

Ambulatory heating 1250 m2

Office heating 706 m2

Office all-air 597 m2

Office heating 1473 m2

N



1st floor

(E) 4th floor

Office all-air 1231 m2

Operating theatre all-air 1125 m2 Common area 150 m2

Office heating and cooling 966 m2 (C)

Generic ward all-air 836 m2

Diagnostics all-air 511 m2 Ambulatory all-air 564 m2

Office heating 469 m2

Common area 150 m2

Ambulatory all-air 1569 m2 (F)

Fig. 2 Hospital reference building (RBh): rendering (A) and thermal zoning (B)–(F). Note that this figure is integrally taken from Ascione F, Bianco N, De Stasio C, Mauro GM, Vanoli GP. Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: a new approach to assess cost-optimality. Appl Energy 174;2016:37–68. Table 1

Geometrical characteristics of the hospital reference building (RBh)

Parameters

RBh

Total floor area Useful floor area Building shape Aspect ratio Number of floors Floor to floor height Azimuth

22,711 m2 18,926 m2 Rectangular 0.2 6 (1 buried) 3.96 m 0 degree

Window fraction (windows to wall ratio) South East North West Total Shading geometry

26% 24% 15% 23% 23% None

The definition of the thermal zoning of the RBh is related to the amount of useful floor area occupied by each use destination (expressed as percentage). Definitely, such distribution highly affects the energy performance of hospitals [11]. The statistical analysis of investigated data, from ASLNAP1, allowed to determine which are the most common use destinations and the related percentage of the occupied floor area. These are:

• •

“high-tech” zones (operating theaters, diagnostics, intensive care units, and laboratories): 12.4% of the useful floor area; generic wards: 23.8% of the useful floor area;

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• • •

Energy Management in Hospitals

ambulatories: 23.0% of the useful floor area; offices: 36.8% of the useful floor area; common areas: 4.0% of the useful floor area.

The aforementioned percentage values are preliminary, since the HVAC systems also have to be defined in order to subdivide the RBh into thermal zones in a detailed and rigorous way. In this regard, according to the explored data, the following options were taken into account for what concern in-room HVAC systems:

•

• • •

All-air: this option represents all-air systems supplied by air handling units, without exhaust air recirculation for hygiene reasons. The control of temperature and relative humidity can be performed in both heating and cooling operational modes, through the variation of inlet air supply temperature, and thus by using postheating coils. The setting of a suitable airflow guarantees proper levels of IAQ; Heating: this type of system is made of two-pipe fan coils for space heating operation, and therefore only temperature control (on/off) in wintertime is possible. Natural ventilation allows one to partially control IAQ; Heating and cooling: the elements of this type of system are the same as the heating one, but they also work in cooling operation, and therefore only the temperature control (on/off) is possible year-round. Natural ventilation allows one to partially control IAQ; No HVAC: no air-conditioning systems are installed, and thus temperature or relative humidity cannot be controlled. Natural ventilation allows one to partially control IAQ.

Table 2 and Fig. 2 provide the subdivision of the useful floor area of the RBh into thermal zones, determined by use classifications and HVAC system types, taking into account the analyzed building sample. High-tech zones require a strict control of microclimatic conditions and therefore all-air systems are usually present in these zones, whereas elsewhere, fan coils are generally installed. Common areas are characterized by the absence of HVAC systems. Concerning microclimatic control, Table 3 summarizes the set points required for the indoor temperature (T), indoor relative humidity (F), and ventilation rate (ACH), for heating (first value) and cooling (second value), respectively. Such values are defined in compliance with regulatory references [31–34] in the field of air conditioning of hospitals. In addition, the DesignBuilder database [24] was used to set internal heat gains, hourly occupancy profiles, HVAC systems’ operation, and DHW demand for the various thermal zones. As previously stated and reported in Table 2, the subdivision of the RBh into thermal zones was performed according to the percentages of floor area occupied by the different use classifications and HVAC system types. These thermal zones were arranged in the RBh’s five floors above the ground (i.e., the underground level is used for technical services) according to a typical layout obtained from the analysis of the hospital sample, as shown in Fig. 2. According to this layout, generic wards are usually placed on the highest floors, whereas the operating theaters mainly are located at the first floor. Fig. 2 does not show the buried floor because this hosts plant rooms, and thus it was not considered for what concerns the determination of the useful floor area and was simulated as a whole thermal zone without microclimatic control (air changes per hour (ACH) equal to 1 h 1 and heat internal gains ¼ 50 W m 2) [35]. The analysis of the building sample also focused on primary energy systems and allowed to determine the most common configurations. The related performance indices, efficiency of boilers (Z), and energy efficiency ratio (EER) of electrical chillers were obtained by means of weighted averages, by considering each hospital’s conditioned floor area as weight. The characteristics of such systems are listed below:

•

Primary heating system: this includes a natural gas boiler with peak thermal power of 1200 kWt and nominal lower calorific value (LCV) efficiency, denoted as Z, equal to 0.90;

Table 2 Percentage subdivision of the hospital reference building (RBhs) useful floor area into thermal zones (total, 100%) Thermal zones

Percentage of useful floor area

Common areas (no heating, ventilating and air conditioning (HVAC)) Offices (heating and cooling) Offices (heating) Offices (all-air) Ambulatories (heating and cooling) Ambulatories (heating) Ambulatories (all-air) Generic wards (heating and cooling) Generic wards (heating) Generic wards (all-air) High tech (all-air)

4.0% 5.1% 22.1% 9.6% 5.1% 6.6% 11.3% 5.7% 8.3% 9.8% 12.4%

Energy Management in Hospitals

Table 3

• • • •

833

Requirements for microclimatic control as a function of the thermal zone T (1C)

F (%)

Air changes per hour (ACH) (h 1)

Offices (heating and cooling) Offices (heating) Offices (all-air) Ambulatories (heating and cooling) Ambulatories (heating) Ambulatories (all-air) Generic wards (heating and cooling) Generic wards (heating) Generic wards (all-air)

20–26 20 20–26 20–26 20 20–26 20–26 20 20–26

– – 40–60 – – 40–60 – – 40–60

– – 3 – – 6 – – 3

Hi-tech zones Post mortem facility Radiology Sterilization rooms Intensive care units Operating theaters

20–26 20–26 24 24 24

40–60 40–60 50 50 50

6 6 15 6 15

Primary cooling system: this features two identical air-cooled chillers, each one with thermal power of 1000 kWt and EER equal to 2.5 at rated conditions; Primary DHW system: this includes a natural gas boiler with peak thermal power of 300 kWt and Z equal to 0.88; its size allows one to fulfill DHW demand; Electricity is taken from the grid. As regards direct electric uses (i.e., generic equipment and artificial lighting), the base electrical load is about 200 kWel, whereas the maximum one is around 800 kWel; No renewable energy source (RES) systems are installed.

After the definition of the building envelope’s thermal characteristics, the peak thermal loads for space heating and cooling were estimated and used to determine the size of heating and cooling systems. In particular, the types and thermal properties of envelope elements were selected as the most common in the analyzed building sample. Thus, the defined RBh has a structural frame made of reinforced concrete and vertical walls made of hollow bricks, whereas the horizontal components (e.g., ceiling and floors) are mixed structures in bricks and reinforced concrete. The windows are double-glazed with a gap filled by air and the frame is made of aluminum. The thermal properties of these envelope components were set in order to comply with the Italian law n. 10/91 [29], which was in force between 1991 and 2005 to regulate the design of new buildings. According to this law, new buildings had to be characterized by the energy index Fabbisogno energetico normalizzato (FEN) lower than a given threshold FENlim, which depended on climatic conditions, geometry, distribution of use destinations, and primary energy systems’ efficiency. The FEN [J m 3 DD 1] was defined as “the primary energy required over the year for space heating to keep a constant indoor temperature of 201C, with an adequate air change, per unit of conditioned volume and degree days (DD)” and it had to be calculated according to the procedure reported in Ref. [35]. The FENlim for the analyzed case study is equal to 113 kJ m 3DD 1. Therefore, the thermal characteristics of the RBh’s envelope have been properly chosen in order to obtain a FEN equal to FENlim, which implies the following values of thermal transmittances (U [W m 2 K 1]):

• • • •

external vertical walls: U¼ 0.72 W m roof: U¼ 1.20 W m 2 K 1; basement: U¼1.20 W m 2 K 1; windows: U ¼ 2.70 W m 2 K 1.

2

K 1;

The solar heat gain coefficient (SHGC) of windows has been set according to the EnergyPlus database and it is equal to 0.764, for the considered window type. Once the RBh was defined and modeled, a dynamic simulation of building energy performance was carried out by running EnergyPlus and using the IWEC weather file of Naples [36]. The main results are reported below:

• • • •

thermal energy demand (TED) for space heating per unit of conditioned area: TEDheat ¼ 51.2 kWht m 2a 1; TED for space cooling per unit of conditioned area: TEDcool ¼58.2 kWht m 2a 1; total PEC per unit of conditioned area: PEC ¼ 558.2 kWhpr m 2a 1; GC over building lifecycle (20 years): GC ¼ 11,982,610 € (633 € per unit of useful floor area) by considering the price of electricity equal to 0.18 € kWhel 1 and of natural gas equal to 0.62 € Nm 3, as well as a conversion factor from electricity to primary energy equal to 2.18, according to Italian standards.

It is noticed that TEDheat is higher than TEDcool for the significant entity of internal heat gains in hospital buildings, which highly penalizes the summer energy performance in balanced or cooling-dominated climates.

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Energy Management in Hospitals

5.21.3

Measures for Efficient Energy Design and Management in Hospitals

As outlined in the previous sections, hospitals are critical applications of the wide issue concerning energy conservation in buildings, microclimatic control, and air conditioning, because of the high complexity and strict requirements in terms of comfort and safety related to special functions, such as surgery blocks, white rooms, and intensive care units [11]. Normally, hospitals are quite big and complex structures that can be considered a kind of small city, since they include several zones and environments with different use destinations and functions, such as offices, ambulatories, generic wards, operating theaters, and so on. Thus, there are sundry heterogeneous thermal zones, which are characterized by different profiles in terms of comfort requirements, ventilation needs, demands of thermal and electric energy. Regarding the latter, all components of energy demand – namely for space heating, cooling, district hot water, and direct electric uses (i.e., artificial lighting and equipment) – are generally significant. Furthermore, as aforementioned, a rigid control of indoor conditions – in terms of air temperature, relative humidity, IAQ, air tightness and filtration – has to be guaranteed. Thus, besides a careful design of active energy systems, a central role is played by a suitable organization of functions, the space distributions, and the selection of underpressurized or pressurized rooms. This is fundamental because environmental conditions can affect specific diseases and syndromes, as discussed by Van Hoof et al. [37], who evidenced the implications produced by indoor environment on dementia, exploring also the role played by thermal conditions [38]. Finally, hospital buildings are very complex structures, which need a rigid microclimatic control and present high values of energy demand. They represent the most energy-intensive building type, considering the energy consumption per unit of net floor area [9]. Therefore, the efficient and effective energy design of the building envelope and design and management of energy systems are extremely critical issues. Notably, a multiobjective approach is recommended [10,39–47] because sundry competitive objective functions subsist, such as the minimization of heating and cooling demand, needs for artificial lighting, and thermal discomfort. Definitely, the proper energy design and management in hospital buildings requires consideration and optimization of all levers that influence energy performance, namely: 1. the characteristics of the building envelope in terms of thermal transmittance and inertia; 2. the characteristics, efficiency and operation strategy of energy systems concerning space heating, space cooling, DHW production, various devices and equipment, artificial lighting; 3. the exploitation of RESs; 4. the proper assessment of energy use patterns. Globally, EEMs should be implemented in order to optimize all mentioned levers. The levers (2) and (3) are linked because efficient primary energy systems are often integrated by renewables, for instance, heat pumps, biomass heating systems, electric chillers coupled with photovoltaics (PVs). Concerning hospitals, EEMs concerning to lever (4) are very limited because the occupants’ requirements are very rigid concerning comfort, microclimatic control, and the need of equipment and devices for healthcare activities. Thus, they are not investigated in this chapter, and the next subsections are focused on levers (1–3) by delineating the most widespread and effective EEMs that can be implemented to optimize the design and management of hospitals. In particular:

• •

Subsection 5.21.3.1 describes EEMs addressed to the demand side management, aimed at the reduction of TED and electrical energy demand for direct electric uses (EED), i.e., artificial lighting and equipment; thus, these EEMs affect lever (1) and some points of lever (2). Subsection 5.21.3.2 describes EEMs addressed to the supply side management, aimed at the improvement of efficiency and operation strategies of primary energy systems for reducing the total PEC; thus, these EEMs affect some points of lever (2) and lever (3). These levers are strictly linked as previously argued.

All told, the potential energy, economic, and environmental benefits produced by all types of EEMs are highly changeable because they depend on many factors. In particular, the effectiveness and the most proper characteristics of the cited EEMs are extremely affected by building geographical/climatic location and typology in terms of construction practice. However, an efficient thermal envelope (in containing heat losses, reducing the effects of impulsive external and endogenous loads, limiting indoor temperature variation), suitable air-conditioning systems (microclimatic stability, quality and quantity of ventilation, filtration efficacy and adequate airflow to guarantee healthiness), energy conversion systems, and transport of thermal fluids are always fundamental aspects for energy savings and indoor comfort in hospitals.

5.21.3.1

Energy Efficiency Measures for the Demand Side Management

These EEMs address the demand side management in order to minimize the values of TED and/or EED, and indirectly PEC. They can be classified in the following macrocategories that are aimed, respectively, at: 1. the reduction of TED for space heating and/or cooling; 2. the reduction of TED for the production of DHW, which is significant in hospitals [48]; 3. the reduction of electric energy demand for direct electric uses, i.e., artificial lighting and equipment. The category (1) collects several EEMs for the reduction of TED related to the space conditioning, such as:

•

renovation or thermal insulation of the building envelope (i.e., roof, floor, and walls) in order to achieve low values of the opaque components’ thermal transmittance (Uo0.5 W m 2 K 1); new innovative materials with extremely low thermal

Energy Management in Hospitals

•

• • • • • • • • • •

835

conductivity, for example, vacuum-insulated panels (VIPs), can be used to reduce issues concerning needed space and size. This EEM is particularly effective in heating-dominated climates, whereas in balanced and cooling-dominated climates it should be carefully investigated in order to avoid the risk of summer overheating, which is critical in hospitals because of the high internal heat gains; installation of energy-efficient windows (e.g., multiple glazing, argon-filled with low-emissive coatings and PVC frames) with low values of thermal transmittance (Uo2.0 W m 2 K 1) and, in cooling-dominated climates, of (SHGCo0.5); several kinds of windows, coatings, frames can be employed, and thus the detection of the most efficient choice needs the implementation of optimization algorithms and highly depends on building climatic location; installation of low-emissive, selective, or reflective coatings on existing windows, especially in balanced and cooling-dominated climates; installation of solar shading systems, especially in balanced and cooling-dominated climates; installation of cool roofs and cool coatings on the external walls, especially in balanced and cooling-dominated climates; exploitation of natural ventilation and free cooling in the thermal zones that do not need a very rigid microclimatic control (e.g., offices); increase of building air tightness, especially in heating-dominated climates; proper choice (in the case of new design) or retrofit (in the case of refurbishment) and operation/control of HVAC emission terminals (e.g., radiators, fan coils, radiant panels, air vents, and outlets); proper choice (in the case of new design) or retrofit (in the case of refurbishment) and operation/control of the subsystems for the regulation and distribution of heat transfer fluids; installation of heat recovery systems, when possible and by taking into consideration safety and hygiene issues; installation of thermal storage systems; control upgrade of HVAC systems by means of the installation of building management or automation systems (BASs).

Category (2) of EEMs for the demand side management collects measures for the reduction of TED due to DHW production, such as:

• • •

proper choice (in the case of new design) or retrofit (in the case of refurbishment) of DHW terminals (e.g., taps and showers); proper choice (in the case of new design) or retrofit (in the case of refurbishment) of the subsystem for the distribution of heat transfer fluids; installation of thermal storage systems for DHW. Finally, Category (3) collects ERMs for the reduction of EED due to direct electric uses, such as:

• • •

artificial lighting upgrade by installation of efficient systems, for example, light-emitting diode (LED) systems; installation of energy-efficient equipment, devices and appliances; control upgrade by means of the installation of BASs.

Concerning the described EEMs for the demand side management, as highlighted by the results reported in Ref. [11], the starting point to optimize energy performance is the proper thermal design of the building envelope. In this regard, the influence of the thermal quality, in terms of thermal transmittance and inertia, of the building envelope can seem poor because of the huge entity of ventilation loads. Actually, this is not completely true, since proper thermal characteristics of the building shell are fundamental to allow a stable indoor microclimate. Furthermore, these characteristics should be carefully designed in order to minimize heat losses in wintertime by avoiding, at the same time, summer overheating, which is quite likely in hospitals by virtue of the high internal heat gains [28]. In this regard, the building resilience to climate change should be deeply explored, as done by Lomas et al. [49] for Addenbrooke’s Hospital in Cambridge (England), by means of both measured and simulated data.

5.21.3.2

Energy Efficiency Measures for the Supply Side Management

These EEMs concern the supply side management and aim at the improvement of efficiency and operation strategies of energy systems for reducing PEC. They can be classified in the following two macrocategories that are aimed, respectively, at: I. the implementation and optimized management/operation of efficient primary heating/cooling systems; II. the installation and optimized management/operation of systems for the exploitation of RESs. Concerning the category (I), the choice of the most efficient and effective energy systems is highly affected by the climatic conditions. Common efficient options, among which the stakeholders can make a selection, are:

• • • •

efficient boilers, for example, the condensing ones; air-source or ground-source heat pumps, which can also be reversible (i.e., chillers for the space cooling); efficient air-cooled or water-cooled chillers; combined heating–power (CHP) and combined cooling–heating–power (CCHP), which provide electricity in addition to thermal energy.

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Energy Management in Hospitals

Category (II) of the cited EEMs concerns the installation of systems for exploitation of RESs, such as:

• • • • •

thermal solar collectors; PV generators; innovative PV thermal hybrid solar collector, also known as PVT systems (hybrid PV/T); biomass systems; micro-wind turbines.

Please, note that, in some cases, heat pumps can be considered as partial RES systems. Globally, the purpose of the ERMs for supply side management is providing the building with innovative and efficient energy supply systems. It is clear that the energy performance of healthcare facilities highly depends on the efficiency of HVAC and primary energy systems, since the mainly “energy-intensive” aspect in this sector is the high ventilation load, and the related temperature and humidity control, due to high mandatory air changes. Thus, huge energy savings are achievable only if energy systems are properly designed and characterized by an efficient operation strategy. In this regard, Čongradac et al. [50] showed the significant potential savings produced by improving the efficiency of control strategies for the HVAC system operation. Still in the matter of control, its role for achieving a reduction of energy demand was also investigated by Ma and Wang [51], with reference to the management of chillers, as well as by Buonomano et al. [52], who demonstrated that high energy savings can be obtained by adopting thermostatic valves for radiators and regulation of air handling units. Concerning energy savings, each component of HVAC systems should be properly selected and sized, including auxiliaries (pumps and fans), as demonstrated with quantitative outcomes in Refs. [53,54] for various institutional buildings, among which hospitals. The same issue concerns special equipment usually installed in high-care applications and in particular heat and cold storages, as evidenced in different studies [55–57]. Clearly, the efficiency of primary energy systems plays a fundamental role. In this regard, CHP and CCHP systems are extremely effective in hospitals, because the demands of electricity and heat are significant and quite constant along the whole year [58,59]. In this vein, Gimelli and Muccillo [60] employed a multiobjective genetic algorithm (GA) in order to optimize engine size, plant configuration, management strategies, absorption chiller size, and number of engines. The method was applied to the San Paolo hospital in Naples. Likewise, Costa and Fichera [61] proposed a mixed-integer linear programming model to detect the optimal configuration of a CHP plant in terms of size and annual operational time, still with reference to an Italian hospital facility. Recently, also Zheng et al. [62] applied a new methodology, based on the minimum distance approach, in order to optimize the operation strategy of CCHP systems for a healthcare facility.

5.21.4

Methodologies to Address Energy Design and Management of Hospitals

The previous section shows that the optimization of energy design and management of hospital buildings requires the solution of a complex problem because there are several potential EEMs that provide design variables to optimize. Each variable can assume sundry values and, finally, a huge domain of possible design scenarios should be investigated to find out the optimal solution. Definitely, an exhaustive research is not feasible because it would imply prohibitive computational times. Therefore, the implementation of optimized methodologies is fundamental to address to choice of EEMs. The next subsections provide a comprehensive picture of the most recent methodologies provided by current scientific literature. Furthermore, the application of these methodologies to worthwhile case studies, taken from literature, is illustrated.

5.21.4.1

Methodological Approaches for Hospitals’ Energy Retrofit at Different Climates

This section focuses on recent studies on the energy retrofit of hospitals, according to different climates and strategies. Three different investigations are proposed, by taking into consideration the following different cities and climates, in order to be representative of various weather conditions:

•

•

•

Novi Sad: this Serbian town has a typical continental climate, quite cold, characterized by four well-distinct seasons. According to the Köppen–Geiger classification [63], the city is within the "Cfs" climate (temperate climate, uniform precipitation along the year), with around 20 days of air temperatures below 01C and an average temperature in January of about 21C. The coldest months are January and February, the warmest month is July. This climate is heating-dominated; Naples: the city is on the Tyrrhenian coastal side of Italy, on the southern part of the peninsula, with mild weather conditions, quite moderate, as typical for Mediterranean coasts. In general, summers are quite warm and the winter season is not so cold, with exceptional events of daily temperatures slightly above 01C and few hours under this value. According to the Köppen– Geiger classification, the city is located in the "Csa" zone, with average temperatures in January of about 81C, in July of about 241C. The low peak of rain is in July, and the rainiest month is November. This is a balanced climate; Alexandria: according to the Köppen–Geiger classification, this Egyptian city is at the border between “hot desert” (BWh) and “semi-arid” climate (BSh), characterized by the mitigation effects of the Mediterranean Sea. The winters are mild, with daily temperatures in general between 12 and 181C, with moderate rainfalls, and warm-hot summers (average daily air temperatures also higher than 301C, on the hottest days), characterized by high rates of humidity. The coldest months are January and February; the warmest ones are July and August. This is a cooling-dominated climate.

Energy Management in Hospitals

837

A summary of the main average parameters of the explored climates is reported in Fig. 3, in which, on a monthly basis, air temperature (A), rainfall (B), and relative humidity (C) are provided. In addition, the investigated hospital buildings and their locations are shown in Fig. 4. In the next subsections, with reference to these climates and case studies, a brief presentation of methodologies, performed investigations, and achieved outcomes is proposed.

5.21.4.1.1

Heating-dominated climate

With reference to the cold climate of Novi Sad (Serbia), a deep investigation was performed by Čongradac et al. [50], who studied several EEMs particularly suitable for the energy retrofit of hospital buildings. The authors focused on the application of several management methods for handling the indoor control, implemented these in a software program for energy assessment, and then evaluated the impacts of such strategies. The authors identified two macrotypologies of control methods, based on the adoption of sensors and actuators, and thus (1) controllers at room and (2) at building levels. Among the usable devices, some of these are worthy to be cited, such as detector of people, timers, measurements of indoor parameters and CO2. They support an effective local or global management of space heating, cooling, ventilation, and lighting. Of course, this implies actions on set points, dead bands, window opening, or activation of blinds. In the cited study, the authors proposed a new comprehensive set of equations for calculating energy requirements and potential savings by implementing different available techniques regarding the indoor control of the environment. To calculate energy savings, many opportunities were found, and thus change in set points, time schedules and timers for managing the energy usages, detection of people in rooms, adoption of dead bands, demand controlled ventilation for avoiding oversized supply of outdoor air (ventilation loads). Other important techniques effective for energy savings are the application of windows’ sensors for the switching off of the thermal control when the windows are opened, presence and proper management of window shadings, and, with reference to the whole building, the variation of hot water temperature. Obviously, each kind of room can be suitable or not for the adoption of the aforementioned opportunities. For instance, according to the authors, the adoption of time-based controllers in hospital wards makes no sense, while, conversely, it could be useful in office and administrative spaces. More in detail, the authors – who formulated a suitable set of equations for determining the energy savings achievable by the applications of the aforementioned techniques, at the room and building levels respectively – have implemented these in a proper datasheet. The development of the numerical model was already presented in a previous paper [65]. Furthermore, they calibrated the outcomes on the basis of suitable tests performed in the emergency center of Novi Sad (see Fig. 4(A)). The tests have concerned measurements and predictions of electric demands of a variable refrigerant volume (VRV) system for the microclimatic control, by applying, compared to the base case, changes in set points, time schedule of HVAC use, running depending on occupants’ presence. It is noted that, in the test phases, highly fluctuating ambient conditions were recorded, so that the outcomes were scaled according to a

Daily average dry bulb temperature 25 Rainfall (mm)

Air temperature (°C)

30

20 15 10 5 0

180 160 140 120 100 80 60 40 20 0

Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Daily relative humidity (%)

(A)

Novi Sad

100 90 80 70 60 50 40 30 20 10 0

Napoli

Alexandria

Monthly average rainfall

(B)

Novi Sad

Napoli

Alexandria

Daily average air relative humidity

Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

(C)

Novi Sad

Napoli

Alexandria

(D)

Fig. 3 Novi Sad (Serbia), Naples (Italy), and Alexandria (Egypt). Main monthly data of the climate: (A) air temperature, (B) rainfall, (C) relative humidity, and (D) location of the cities.

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Energy Management in Hospitals

A

B

C

Fig. 4 Presentation of the case studies concerning energy retrofit of hospital buildings according to different methodologies and climates: please note that graph (C) evidences a large part of Alexandria and not only the building area considered in Ref. [64].

polynomial interpolation. The outcomes, referred to November 2011, revealed that measured and expected energy requests are well correspondent. Moreover, the indoor control strategy based on people presence was very effective, followed by the timescheduled management of active energy systems. Conversely, the adjustment of set points gave less significant results. Definitively, the proposed methodology, based on simple tools and identification of equations for assessing energy savings, may offer, during the design phase, useful information concerning the potentials of reduction of energy demands due to the adopted indoor control. Hospitals are highly complex buildings, so that fast, reliable tools and estimative ways for predictions are welcomed.

5.21.4.1.2

Balanced climate

For applications in which ventilation loads are predominant because of the high necessity of pure air, the efficiency of building thermal envelope can play a lower role, being the main part of sensible heating, cooling, and latent loads due to the large supply of outdoor air. This occurs when the requirements of IAQ become predominant. For instance, this can be the case in educational buildings (even 5 ACH according to the Italian standards) or in hospital buildings, in which, depending on the specific use of the indoor environment, 15 ACH can also be required (i.e., in surgery blocks or white rooms). Actually, hospitals can represent an exception because they are characterized by a high level of complexity, as described by Ascione et al. [11]. Indeed, besides hospital wards, high-care rooms, and sensible spaces, many other functions are those typical of the tertiary sectors, such as common spaces and offices, in which a mere diurnal use is expected without the necessary control of humidity or need of high ventilation rates. In this frame, with reference to the mild climate of South Italy, the authors [11,66] investigated the so-called “day hospital” pavilion (see Fig. 4(B)) of the “G. Pascale” hospital (the Italian National Institute for Cancer Treatment) in Naples, by means of transient energy simulations performed with EnergyPlus. The numerical studies were based on input data achieved by in situ surveys and deep preliminary investigations. The considered building, located in a balanced Mediterranean climate (in which both energy demands for heating and cooling are significant and similar), is characterized by an overall conditioned area and volume equal to 10,192 m2 and 41,834 m3, respectively. In Ref. [11], the authors explored the impact of the energy retrofit of the building envelope components. In particular, the study focused on diversified solutions for incrementing the thermal resistance of the envelope, according to the specific elements of the shell. For instance, the vertical walls were supposed to be refurbished by means of extruded polystyrene (installed on the parts above and below windows), insufflation of expanded perlite into the cavity of walls, and external insulation of perimeter beams and pillars. Analogously, external insulation was applied to the building roof, and new windows, low-emissive provided with solar screens, replaced the old ones. To deeply investigate the impact of these EEMs, three different HVAC systems were simulated for the building: case (a) fan coil without outdoor air control, case (b) outdoor-air fan coils, and case (c) fan coils plus dedicated outdoor air systems (DOAS). The overall retrofit design considered also the types of HVAC systems and equipment, since these can significantly affect annual energy demand for air conditioning. In particular, this

Energy Management in Hospitals

839

demand can be lowered by about 50% in case (a), 31% in case (b), and 16% in case (c). These outcomes are expected. Indeed, if the ventilation loads increase cases (b) and (c) or the latent loads are also controlled case (c), the role of the building envelope decreases progressively. In any case, a careful refurbishment of the building envelope was found profitable also for hospital buildings, with payback period always lower than 22 years. Furthermore, the outcomes also showed the favorable impact of the envelope refurbishment on the stability of indoor microclimate. As cited, with reference to the same building, a second paper [66] was focused on the refurbishment of the HVAC system. The study was published recently and carefully divided the studied building in several typologies of thermal zones, depending on the kind of use and different needs concerning the control of indoor microclimate. The considered technologies were different, and included all-water systems and mixed air–water ones, DOAS plus hydronic terminals or VRV. The outcomes showed that the “thermal zoning” of the building requires high care in order to combine suitable control without too expensive energy costs. Moreover, HVAC systems based on direct expansion technologies can be profitable, allowing energy and cost savings because of the low energy demanded by the auxiliaries and the high conversion efficiency. Heat recovery is effective, even if it must be only sensible (to avoid any risk of contamination) and rotary systems cannot be used for safety reasons. In this regard, the authors also recommended systems that decouple latent and sensible controls.

5.21.4.1.3

Cooling-dominated climate

This last case study concerns the warm climate of Alexandria, Egypt, which implies high-energy need for the building space cooling and air conditioning in general. The issue of potential energy savings, without affecting the quality of indoor control, was deeply investigated by Radwan et al. [64]. In this regard, even if Egypt has huge availability of energy, the increasing demands and consumptions, especially as regards the microclimatic control of buildings, are a crucial risk for the country’s energy balance. As a case study, the cited paper investigated a large hospital building concerning both design and management. More in detail, the main attention was paid to the cooling loads, by considering all heat transfer phenomena and heat gains, such as conduction through the building envelope, radiation through windows, endogenous gains due to persons and equipment, and lighting gains. The feasibility study was performed by means of energy simulations by comparing a baseline model with an energy-optimized one, which provides an energy retrofit scenario, by taking into consideration the variation of exterior walls, roofs, windows, occupancy density, amount of internal loads, and HVAC systems. Notably, great attention was paid to the role of ventilation loads, by varying the amount of supplied outdoor air, based on use destination of rooms. Analogously, in order to improve the energy efficiency of the HVAC systems, the initial constant air volume system, provided with four air handling units, was compared with a new system equipped with CO2-based demand controlled ventilation (DCV). Moreover, variable air volume (VAV) fans were simulated in the zones with highest utility rates. To estimate the achievable energy and cost savings, the transient energy program developed by Carrier (i.e., HAP 4.9) was used. The numerical investigations were very detailed, with a significant number of simulations aimed at analyzing the impact of any EEM. For each energy retrofit action, the impact on energy and economic performance indicators were calculated and also a new building orientation was investigated to evaluate the variation of cooling loads. Indeed, the exposure to environmental conditions, mainly in summertime, can also have important effects on the energy demand for the space cooling. The outcomes showed that the demand control ventilation is a very efficient energy saving strategy, with allowable savings around 41% in terms of the electric energy demand. Moreover, it was found that the proper energy design of building envelope can reduce the energy demand of about 8%, and that the building orientation plays a significant role. All told, all levers of energy efficiency have to be considered, and thus all opportunities of reduction of energy demands must be taken into consideration, with reference to both new constructions and major building renovations, in order to ensure effective building design and management. This is especially valid for very complex and big structures, such as hospitals.

5.21.4.2

From an Reference Building to All Represented Hospitals: Assessment of Energy Performance and Retrofit Potentials

In Ref. [67], the same authors of this chapter proposed a methodology to predict energy performance and retrofit potentials for hospital buildings by exploiting the outcomes achieved for RBs. The methodology addresses all hospitals included in an investigated stock, and it is organized in the following three main stages (see Fig. 5):

•

•

Stage 1: definition of RBh. The methodology starts from the definition of RBh models, as shown in Section 5.21.2.1. These represent the investigated hospital buildings’ stock as concerns geometrical characteristics, use destinations’ distribution, and types of HVAC and energy systems. Different RBhs are defined in order to consider the different configurations of the building envelope that characterize the investigated stock. Thus, the RBh models differ only for the components of an opaque and transparent envelope; Stage 2: assessment of specific energy indices. For each RBh model and use destination, specific thermal energy indices are assessed by performing dynamic energy simulations through EnergyPlus. These indices form a database that expresses the hourly values of TEDs per unit of net floor area for space heating, cooling, and DHW, respectively. They are evaluated for the different climatic zones that characterize the stock. It is noted that hospitals include several heterogeneous use destinations, which highly differ for internal loads, ventilation need, required IAQ levels, occupation, activity, and operation schedules. In this regard, the methodology provides energy indices for the main use destinations, namely, ambulatories, wards, high-tech areas (operating theaters, diagnostics, intensive care units, laboratories), and offices;

840

Energy Management in Hospitals

Reference building Envelope characterization

Transparent envelope Ug1

Ug2

Uop1 Opaque envelope

Uop2 Uop3

9 Models

Ug3

Subdivision into destination Uses: 1. Ambulatory 2. Generic ward 3. High-Tech 4. Office

Simulations Real building Hourly indices of thermal energy demand for space heating, cooling and DHW

Subdivision into destination Uses: 1. Ambulatory 2. Generic ward 3. High-Tech 4. Office

Thermal energy demand for space heating, cooling and DHW

Primary energy consumptions

Post process analysis Definition of HVAC system

Comparison

Monitored data of primary energy consumptions

Cost-optimal analysis

Fig. 5 Framework of the methodology proposed in Ref. [67] for the assessment of energy performance and retrofit potentials of hospitals, starting from reference buildings (RBs). This figure is integrally taken from Ascione F, Bianco N, De Rossi F, et al. From a hospital reference building to all represented healthcare facilities: a new approach to assess energy performance and retrofit potentials. In: Proceedings of the 29th international conference on efficiency, cost, optimisation, simulation and environmental impact of energy systems (ECOS 2016), June 19–23, Portorož, Slovenia; 2016.

•

When a specific hospital of the considered stock is investigated, the most proper specific energy indices are taken from the mentioned database, depending on the building’s climatic zone and envelope’s characteristics. Hence, the net floor area of the building is subdivided into the four main use destinations, and the specific indices are multiplied by the obtained area extensions in order to assess the hourly values of TEDs for each destination use, and thus for the entire building. Then, the MATLAB postprocess allows one to convert the hourly values of TED into the annual values of PEC – and then of GC over 20 years, as recommended in Refs. [13,14] – by exploiting the dynamic performance curves of the primary energy systems. Stage 3: cost-optimal analysis of energy retrofit. Some EEMs are considered, aimed at improving the thermal insulation of the building envelope, at increasing the efficiency of primary heating/cooling/DHW energy systems, and at exploiting REDs. Each possible combination of these EEMs, i.e., retrofit package, is investigated by assessing investment cost (IC), difference in primary energy consumption (dPEC), and global cost (dGC) compared to the base building (BB). This stage is performed in MATLAB without needing further simulations with EnergyPlus. In particular, the impact of EEMs for energy systems and RES exploitation is evaluated by using new performance curves. On the other hand, the impact of EEMs for the envelope’s thermal insulation is evaluated by using different specific thermal energy indices from the database that is generated in stage 2. The new indices refer to the same building climatic location but to higher levels of thermal insulation. Clearly, only a few EEMs for the envelope’s insulation can be analyzed, based on the different configurations of the building envelope that are considered in the definition of the RBh models (previous stage). Finally, the outcomes are reported onto a three-dimensional space by representing the values of IC, dPEC, and dGC for each solution. Then, all solutions are projected onto the dPEC–dGC plane in order to achieve the cost-optimal curve (see Fig. 1), thereby detecting the “unconstrained” cost-optimal retrofit package. Constraints can be set on IC or PEC in order to identify “constrained” cost-optimal solutions that comply with a limited economic budget or minimum levels of required energy performance.

Energy Management in Hospitals

Table 4

841

RBh models as a function of climatic zone and building envelope’s thermal characteristics

Climatic zone C (Naples, Campania) a

E11C Uw ¼2.00 W m Uop ¼0.36 W m

2

K 2 K

1 1

E12C Uw ¼2.0 W m 2 K 1 Uop ¼0.80 W m 2 K

E21C Uw ¼2.7 W m 2 K Uop ¼0.36 W m 2 K

E22Cb Uw ¼2.7 W m 2 K 1 Uop ¼0.80 W m 2 K

E31C Uw ¼5.0 W m 2 K 1 Uop ¼0.36 W m 2 K

E32C Uw ¼5.0 W m 2 K 1 Uop ¼0.80 W m 2 K

1

Climatic zone E (ENNA, Sicily)

1

1

1

E13C Uw ¼2.0 W m Uop ¼1.2 W m E23C Uw ¼2.7 W m Uop ¼1.2 W m E33Cc Uw ¼5.0 W m Uop ¼1.2 Wm

2

K 2 K

2

K 2 K

2 2

K K

1 1

1 1

1 1

E11Ea Uw ¼1.4 W m 2 K 1 Uop ¼0.32 W m 2 K E21E Uw ¼2.6 W m 2 K 1 Uop ¼0.32 W m 2 K

1

1

E31E Uw ¼5.0 W m 2 K 1 Uop ¼0.32 Wm 2 K 1

E12E Uw ¼1.4 W m 2 K 1 Uop ¼0.70 W m 2 K E22Eb Uw ¼2.6 W m 2 K 1 Uop ¼0.70 W m 2 K E32E Uw ¼5.0 W m 2 K 1 Uop ¼0.70 W m 2 K

1

1

1

E13E Uw ¼1.4 W m 2 K 1 Uop ¼0.12 W m 2 K E23E Uw ¼2.6 W m Uop ¼1.2 W m E33Ec Uw ¼5.0 W m Uop ¼1.2 W m

2 2

2 2

K K

1

K K

1

1

1

1

a

Limit configuration after 2005. Limit configuration between 1991 and 2005. c Limit configuration before 1991. Note that this table is integrally taken from Ascione F, Bianco N, De Rossi F, et al., From a hospital reference building to all represented healthcare facilities: a new approach to assess energy performance and retrofit potentials. In: Proceedings of the 29th international conference on efficiency, cost, optimisation, simulation and environmental impact of energy systems (ECOS 2016), June 19–23, Portorozˇ, Slovenia; 2016. Uw ¼ average value of windows’ thermal transmittance; Uop ¼ average value of opaque envelope’s thermal transmittance. b

In Ref. [67], the methodology was applied to hospital buildings of South Italy. To cover a wide segment of the considered stock, different weather data and types of opaque and transparent envelopes were considered. This produced 18 RBh models, which are characterized in Table 4. In particular, for example, purposes, two Italian climatic zones were considered, namely zones C and E, respectively, in the range of HDD (heating degree days, baseline 201C) 901–1400 Kd and 2101–3000 Kd. The weather data files of the cities of Naples (zone C) and Enna (zone E) have been associated, respectively. These files were used in EnergyPlus simulations. All RBh models have the same geometry and distribution of use destinations of the RB defined in Section 5.21.2.1 of this chapter. The methodology’s outcomes were compared against real data, as regards current energy performance, for two case studies, namely: the “D” pavilion of the “A. Cardarelli” hospital in Naples (Campania) and the “M. Chiello” hospital in Enna (Sicily). In both cases, a discrepancy around 10% was found concerning annual energy needs. Furthermore, for both case studies, the methodology was applied to address energy retrofit by considering the following EEMs. Concerning the building envelope:

• • • • • • • • •

installation of a 5-cm-thick thermal insulant layer in polyurethane on the external side of external walls. This EEM was investigated only for the “D” pavilion, since the “M. Chiello” hospital was already characterized by a good level of walls’ thermal insulation; installation of argon-filled double-glazed windows with low-emissive coatings and PVC frames (Uw ¼ 1.71 W m 2 K 1); concerning energy systems for space heating and cooling: installation of heat recovery systems for the air handling units; installation of energy-efficient natural gas boilers with nominal LCV efficiency, denoted as Z, equal to 0.95; installation of condensing natural gas boilers with nominal Z equal to 1.05; installation of air-source heat pumps with coefficient of performance (COP) equal to 3.5 at rated conditions; installation of reversible ground-source heat pumps with geothermal vertical probes with COP equal to 4.5 in heating operation and EER equal to 6.2 in cooling operation at rated conditions; installation of air-cooled chillers with magnetic levitation (maglev) compressors, characterized by EER equal to 3.5 at rated conditions; installation of water-cooled chillers equipped with cooling towers, characterized by EER equal to 5.5 at rated conditions. Concerning the DHW production:

• •

installation of an energy-efficient natural gas boiler with nominal Z equal to 0.95; installation of flat-plate solar thermal collectors on the roof, south-oriented and with 34-degree tilt angle. These can be selective and not selective, alternately. The size of the system is expressed by the percentage of the covered roof area, which can vary between 10 and 60% with a step equal to 10%. The next subsections show the outcomes achieved for the two case studies.

5.21.4.2.1

Application 1: “D” pavilion of the “A. Cardarelli” hospital facility in Naples (Campania, South Italy)

The “D” pavilion is one of the 21 pavilions of the “A. Cardarelli” hospital in Naples (Italian climatic zone C). It hosts the department of gastroenterology and it includes all use destinations of the RBh defined in Section 5.21.2.1. The data concerning thermal characteristics of the building envelope components, subdivision of net floor area into use destinations, and primary

Energy Management in Hospitals

842

energy system types were provided by the Siram S.p.A, which supplies the energy service to the hospital. The average values of thermal transmittance of the opaque envelope (Uop) and windows (Uw) are 0.717 W m 2 K 1 and 2.72 W m 2 K 1, respectively. The four main use destinations occupy the following net floor areas:

• • • •

ambulatory area: 1239 m2; generic ward area: 2134 m2; high-tech area: 1956 m2; office area: 3323 m2.

The current energy performance of the building was assessed by using the specific energy indices related to the RBh model E22C (see Table 4), because this latter refers to the considered climatic zone and provides the envelope configuration that is closest to the case study. The application of the methodology allowed an assessment of TEDs for heating, cooling, and DHW on hourly, monthly, and annual bases. In Fig. 6, the outcomes related to the monthly demands for space heating and cooling are shown and compared against real collected data provided by the Siram SpA. The simulated demands are always lower than the real ones, with the exceptions of January for heating, and September and October for cooling. The results are good on a monthly basis, since the discrepancy is between 20 and 30%, and very satisfying on an annual basis, since the discrepancy between simulated and real data is equal to 9.85% as for heating demand, 9.39% as for cooling demand and 6.63% as for DHW demand. The building has the following energy systems:

• • • •

the primary heating system consists of a natural gas boiler with nominal Z equal to 0.90; the primary cooling system consists of two identical air-cooled chillers with nominal EER equal to 2.5; the primary DHW consists of a natural gas boiler with nominal Z equal to 0.88; no RES systems are present.

By using the simulated hourly values of TED and the performance curves of energy systems, the values of PEC and GC, related to space conditioning and DHW production, were assessed for the BB configuration: PECBB ¼ 227.2 kWh m 2 a 1 and GCBB ¼ 2,091,400 €. Then, all the combinations among the explored EEMs (see previous subsection) were investigated by evaluating IC, dPEC, and dGC (see Fig. 7(A)), thereby achieving the cost-optimal curve for the energy retrofit (see Fig. 7(B)). The outcomes provide the “unconstrained” cost-optimal retrofit package, which includes five EEMs:

• • • • •

installation installation installation installation installation

of of of of of

heat recovery systems; an energy-efficient gas boiler for DHW production; an energy-efficient natural gas boiler for space heating; water-cooled chillers; nonselective solar collectors on the 60% of roof area (maximum size).

This cost-optimal package does not include EEMs for the building envelope’s thermal insulation because the BB’s envelope already presents a satisfying thermal resistance. In addition, as previously mentioned, the envelope’s insulation can play a minor role for hospitals given the high ventilation loads and internal heat gains, which can cause the risk of summer overheating.

Cooling demand comparison

Heating demand comparison

350 300

200

Simulated data Collected data Percentage difference

30

160

30

20

250

20 140 120

10

200

MWh

MWh

Simulated data Collected data Percentage difference

180

150

0

100

10

100 0

80 60

−10

−10 40

50 −20 %

0

(A)

20

−20 %

0 Jan Feb Mar Apr May Jun

Jul Aug Sep Oct Nov Dec

(B)

Jan

Feb

Mar

Apr

May Cooling Oct season

Nov

Dec

Fig. 6 “D” pavilion of “A. Cardarelli” hospital. Comparison between simulated and real collected data concerning monthly thermal energy demands (TEDs) for space cooling (A) and space heating (B), respectively. This figure is integrally taken from Ascione F, Bianco N, De Rossi F, et al., From a hospital reference building to all represented healthcare facilities: a new approach to assess energy performance and retrofit potentials. In: Proceedings of the 29th international conference on efficiency, cost, optimisation, simulation and environmental impact of energy systems (ECOS 2016), June 19–23, Portorož, Slovenia; 2016.

Energy Management in Hospitals

843

dGC, difference in GC compared to the BB (k€)

‘D’ pavilion: cost-optimal analysis of energy retrofit measures (ERMS)

PECBB = 227.2 kWh

Absence of ERMs addressed to the building envelope Thermal insulation of the opaque building envelope Installation of low-e windows Insulation of the opaque envelope + low-e windows

m−2a−1

GCBB = 2′091′400 €

200

BB

0 −200 −400 −600 0

−20

−40

−60

−80

−100 −120 (A) dPEC, difference in PEC compared to the BB (kWh m−2a−1) 0

200

600

400

800

1000

1200

IC, Investment cost (k€)

dGC, difference in GC compared to the BB (k€)

300 200 100

Absence of ERMs addressed to the building envelope Thermal insulation of the opaque building envelope Installation of low-e windows Insulation of the opaque envelope + low-e windows

BB 0 −100 −200 −300 PECBB = 227.2 kWh m−2a−1

−400

GCBB = 2′091′400 €

−500 −600 Cost-optimality −700

−120

−100

−80

−60

−40

−20

0

dPEC, difference in PEC compared to the BB (kWh m−2a−1)

(B)

Fig. 7 “D” pavilion of “A. Cardarelli” hospital: (A) assessment of IC, difference in primary energy consumption (dPEC) and difference in global cost (dGC) for all retrofit packages; (B) cost-optimal curve of energy retrofit. Note that this figure is integrally taken from Ascione F, Bianco N, De Rossi F, et al., From a hospital reference building to all represented healthcare facilities: a new approach to assess energy performance and retrofit potentials. In: Proceedings of the 29th international conference on efficiency, cost, optimisation, simulation and environmental impact of energy systems (ECOS 2016), June 19–23, Portorož, Slovenia; 2016.

All told, the cost-optimal retrofit allows huge benefits: PEC savings of 116.8 kWh m required investment is 499.8 k€.

5.21.4.2.2

2

a

1

and GC savings of 639.6 k€. The

Application 2: “M. Chiello” hospital in Enna (Sicily, South Italy)

The “M. Chiello” hospital is located in Enna (Italian climatic zone D) and offers several health services, including an emergency room. It was built in 1900 and refurbished several times, the last of which was in 2013. Also in this case, the energy service is supplied by the Siram S.p.A., which provided the data used in our investigation concerning building characteristics and energy consumption. The average values of thermal transmittance of the opaque envelope (Uop) and windows (Uw) are 0.455 W m 2 K 1 and 2.9 W m 2 K 1, respectively. The four main use destinations occupy the following net floor areas:

• •

ambulatory area: 3392 m2; generic ward area: 5986 m2;

844

• •

Energy Management in Hospitals

high-tech area: 5622 m2; office area: 3023 m2.

The current energy performance of the building was assessed by using the specific energy indices related to the RBh model E21E (see Table 4). The building presents the same primary energy systems of the “D” pavilion, obviously with different thermal capacities (kWt). The comparison between simulated and real data was carried out by considering the annual natural gas consumption, since other data on energy consumption were not available. Also for this case study, the outcomes were very satisfying, since the discrepancy was around 12%. Notably, the BB is characterized by (simulated data): PECBB ¼ 277.2 kWh m 2 a 1 and GCBB ¼ 5,042,600 €. All the combinations among the explored EEMs were investigated by evaluating IC, dPEC, and dGC (see Fig. 8(A)), thereby achieving the costoptimal curve for the energy retrofit (see Fig. 8(B)). The outcomes provide the “unconstrained” cost-optimal retrofit package, which includes five EEMs:

•

installation of heat recovery systems;

dGC, difference in GC compared to the BB (k€)

‘M.Chiello’ hospital: cost-optimal analysis of energy retrofit measures (ERMs) Absence of ERMs addressed to the building envelope Installation of low-e windows

PECBB = 272.2 kWh m−2a−1 GCBB = 5′042′600 € 0

BB

−200 −400 −600 −800 −1000 −1200 0 −50 1500

−100 (A)

2000

1000 500

−150 dPEC, difference in PEC compared to the BB (kWh m−2a−1) 0

IC, Investment cost (k€)

dGC, difference in GC compared to the BB (k€)

200 0 −200 −400 −600 −800

PECBB = 272.2 kWh m−2a−1 GCBB = 5′042′600 €

−1000 −1200 −1400

(B)

BB

Absence of ERMs addressed to the building envelope Installation of low-e windows

Cost-optimality −160

−140

−120

−100

−80

−60

−40

−20

0

dPEC, difference in PEC compared to the BB (kWh m−2a−1)

Fig. 8 “M. Chiello” hospital: (A) assessment of IC, difference in primary energy consumption (dPEC) and dGC for all retrofit packages; (B) cost-optimal curve of energy retrofit. Note that this figure is integrally taken from Ascione F, Bianco N, De Rossi F, et al., From a hospital reference building to all represented healthcare facilities: a new approach to assess energy performance and retrofit potentials. In: Proceedings of the 29th international conference on efficiency, cost, optimisation, simulation and environmental impact of energy systems (ECOS 2016), June 19–23, Portorož, Slovenia; 2016.

Energy Management in Hospitals

• • • •

845

installation of an energy-efficient gas boiler for DHW production; installation of a condensing gas boiler for space heating; the installation of highly efficient chillers with magnetic levitation (maglev) compressors; installation of nonselective solar collectors on the 20% of roof area.

Compared to the first case study, more efficient heating systems are preferred (i.e., a condensing boiler is preferred instead of an energy-efficient traditional boiler), because the heating demand is higher. The opposite occurs for cooling systems. In addition, the effective exploitation of solar RES is lower (20% of roof area versus 60%), because of the lower solar direct radiation for the climatic zone E. All told, also in this case, the cost-optimal retrofit yields huge benefits: PEC savings of 102.9 kWh m 2 a 1 and GC savings of 1036.4 k€. The required investment is 677.8 k€.

5.21.4.3

Multistage and Multiobjective Optimization

In previous investigations [10], the same authors proposed another original methodology, based on multistage and multiobjective optimization, in order to address the cost-optimal energy design or retrofit of complex buildings, such as hospitals. Compared to the previous one (see Section 5.21.4.2), this methodology allows a more detailed energy analysis, and thus it provides more reliable energy predictions and more robust outcomes. Furthermore, it ensures an accurate investigation of all levers affecting building energy performance, from envelope to primary energy systems, considering also RESs. In this regard, it can be considered the most comprehensive methodology available in current scientific literature to address the issue of efficient and effective energy design and management of hospitals. That’s why, here, a higher focus is dedicated to this methodology. As shown in the previous sections, the aroused issue is very complex because it requires an investigation into a wide domain of solutions as well as consideration of different objective functions. Most of the described methodologies (see Sections 5.21.4.1 and 5.21.4.2) solved this issue by exploring a few energy measures and packages, chosen through expertise or previous studies, thereby cutting the investigated domain. Definitely, this hampers comprehensive investigations and thus the achievement of robust outcomes. Conversely, the methodology delineated in this section solves the issue through the implementation of proper, i.e., building performance optimization (BPO), algorithms in order to carry out “smart” research within the solution domain, and therefore obtain robust and reliable optimal or suboptimal solutions (the true optimum is generally unknown [44]. Normally, as shown in Section 5.21.2, an analytical formulation of the objective function(s) of BPO problems in not available because the indicators concerning building energy performance are provided by BPS tools – for example, EnergyPlus, TRNSYS, IDA-ICE, ESP-r, and so on – which operate as black box functions. That’s why the most popular BPO algorithms are simulation-based, derivate-free methods that carry out interactive research in the solution domain until a stop criterion is satisfied and an acceptable suboptimal (i.e., tolerably close to the optimum) is obtained. Among these algorithms, the most frequently used in BPO are particle swarm optimization (PSO) [39,40] and, especially, GAs [41–47], which are the dominant one. GAs are a family of metaheuristic stochastic population-based algorithms, which realize the “Darwinian” evolution of a population of individuals, i.e., solutions, by means of the processes of crossover, mutation, and survival of the best individuals. The evolution goes on throughout a series of iterations, called generations, until the fulfillment of a convergence criterion. GAs are widespread in BPO because they ensure a good compromise between computational times and robustness of the solution, are preprogrammed in many software packages such as MATLAB, and allow multiobjective optimization. This latter is more proper for BPO compared to the single-objective one because the optimization of building energy performance can be conducted by considering diverse objective functions that are often divergent, such as the minimization of different components of energy demand, of thermal discomfort, of operating, lifecycle or IC, of polluting greenhouse emissions, and so on. Thus, the multiobjective approach is usually necessary in building applications because, even with well-coordinated research, it is difficult to find the optimal solution that allows satisfying perfectly all identified necessities. The main goal of multiobjective optimization algorithm is to find out the Pareto front, which is the set of nondominated solutions as represented in Fig. 9. Please note that a solution is “nondominated” if there is not any other solution that simultaneously improves all objective functions. Once the Pareto front is identified, the so-called multicriteria decisionmaking (MCDM) must be carried out. This consists of selecting a solution from the Pareto front by following the stakeholders’ wills and needs. Different methods can be used for MCDM, such as:

• •

the utopia point method [44,45], in which the chosen solution is the closest to the ideal point (i.e., the “utopia point”) that minimizes all objective functions; the comfort method [44–46], in which the chosen solution is the one that ensures, at least, an established level of thermal comfort and minimizes the other objective functions.

In addition, if multistage complex methodologies are used, the MCDM can be carried out in more phases by also performing cost-optimal analyses [10,42,44]. In this case, the GA aims to find recommended solutions that are, then, subjected to cost-optimal analysis to find the solution that minimizes building lifecycle costs. Studies concerning multiobjective BPO consider, very often, energy consumption (or operating costs) and thermal discomfort as the two competitive objective functions to be minimized [41,44–46], since these functions express the divergent wills of occupants to consume less energy, and thus money, to feel good concerning comfort. Definitely, the mentioned studies performed a holistic optimization of energy design or retrofit of residential or office buildings. Nevertheless, the investigation of hospital

Energy Management in Hospitals

Pareto front Investigated solutions

Objective function 2 to be minimized

846

Objective function 1 to be minimized Fig. 9 Qualitative example of Pareto biobjective optimization.

buildings needs a different, more complex approach, which is implemented in the described methodology [10]. First of all, the consideration of thermal comfort as objective function is very worthy in residential and office applications, because the comfort requirements drastically depend on occupants’ needs and wills, which are highly changeable. Contrariwise, in hospital buildings, comfort requirements represent a strict constraint, which must always be satisfied. In other words, there is no flexibility concerning comfort conditions in hospitals, and therefore comfort cannot be an objective but a need that must be guaranteed in any situation and does not depend on occupants’ wills and adaptability. Furthermore, hospitals are very complex and wide structures, and therefore the optimization of energy performance requires an investigation into a wider domain of scenarios compared to residential or office buildings. For instance, compared to the latter, hospitals require a deeper analysis of CHP and CCHP systems, which can be very efficient and cost-effective [60,61]. Thus, in order to address the energy design and management of hospital buildings, comprehensive procedures that allow one to explore all levers affecting energy performance with reasonable computational times are necessary. In this regard, the described methodology performs a complete energy optimization of hospitals, by using a multistage and multiobjective approach to detect robust cost-optimal energy design solutions. Notably, the methodology was proposed to address energy retrofit and management but it can be also applied to optimize the design of new constructions. Compared to other approaches, the multistage framework ensures a robust and reliable achievement of optimal solutions by exploring a huge domain of energy scenarios with a feasible computational burden. The procedure is performed thanks to the coupling between EnergyPlus and MATLAB according to the scheme reported in Fig. 10, where the methodology is applied to an RB. For complete details, the reader is invited to refer to [10]. In particular, the procedure includes the following three main stages: ● Stage 1: preliminary analysis. A reference design approach (in the case of new construction) or the current building status (in case of the retrofit), i.e., the BB configuration, is investigated by assessing energy performance. Based on outcomes, expertise, and physical observations, several EEMs are proposed for the reduction of TED for space conditioning (demand side management), and the most energy-efficient (i.e., the “best”) ones are found by means of Latin hypercube sampling and sensitivity analysis. ● Stage 2: first optimization stage. A control elitist GA, variant of NSGA II [68], is implemented in order to find nondominated packages of the EEMs (previously proposed) that minimize TEDheat and TEDcool by performing a biobjective Pareto optimization (see Fig. 11). The optimization problem can be formulated as follows:
min F 0 ðx 0 Þ ¼ TEDheat ðx 0 Þ; TEDcool ðx 0 ފ 0



0

0

0

x ¼ x1 ; …xn1 ; … …xPd0

i¼1

subject to 0

ni



nd0 þ1

; …; xPd0

i¼ 1

ni

0

with xj ¼

 Xd0 0 for j ¼ 1; … …; n i¼1 i 1

where F0 is the vector of the objective functions of this stage, while x0 is a vector of bits that encode the design variables (their number is denoted with d0 ) of this stage. In this regard, a design variable is associated to each EEM, and a string of ni bits encodes the ith variable. It is important to highlight that the design variables are assumed as discrete. This allows one to converge quickly to the optimization algorithm without affecting the accuracy and the generality of the method. Moreover, it is more realistic, considering that the construction industry is usually characterized by a limited number of design solutions, depending on commercial availability. Clearly, the discrete values that the variables can assume must be properly selected according to expertise and physical observations. The pseudocode of the GA, implemented to solve the formulated problem, is reported in Table 5. ● Stage 3: second optimization stage. Other EEMs are proposed for the improvement of the efficiency and operation strategies of primary energy systems as well as for the exploitation of RESs (supply side management). Then, a “smart” exhaustive sampling

Energy Management in Hospitals

847

Development of the reference building (RB)

Cost-optimal analysis of building energy retrofit by multi-stage multi-objective optimization Preliminary analysis of energy retrofit measures (ERMS) for the reduction of thermal energy demand (TED) Latin hypercube sampling (LHS) LHS allows to assess the impact of ERMs on: - TED for space heating (TEDheat) - TED for space cooling (TEDcool) - Total TED for space conditioning (TEDtot)

Energy assessment of the RB and selection of ERMs for TED reduction: The energy performance of the RB is investigated and proper energy retrofit measures (ERMs) for the reduction of thermal energy demand for space conditioning (TED) are identified based on building peculiarities and best-practice

Sensitivity analysis (SA) The standardized rank regression coefficients (SRRCs) of ERMs in relation to TEDheat, TEDcool and TEDtot are evaluated in order to identify the best (i.e., most energyefficient) ERMs

1st Optimization stage: bi-objective optimization of ERMs for the reduction of TED Setting decision variables and parameters of the optimization genetic algorithm (GA) The outcomes of the sensitivity analysis are exploited for properly setting the decision variables of GA, in order to minimize the running time. Inefficient ERMs are excluded, whereas the best ERMs are fixed (always present) in the optimization study. Population size (s) and maximum number of generations (gmax) are set to ensure the trade-off between computational burden and reliability

Bi-objective optimization of the ERMs for TED reduction The Pareto optimization is performed by coupling EnergyPlus and MATLAB®, by which the GA is implemented in order to identify the packages of ERMs that minimize TEDheat and TEDcool The outcome is a bi-dimensional Pareto front, that collects the non-dominated (optimal) combinations of ERMs for TED reduction

Non-optimized combinations of the best ERMs for TED reduction The non-dominated combinations of ERMs (provided by GA) are compared with the nonoptimized combinations of the best ERMs (provided by SA), in terms of TEDheat and TEDcool

2nd Optimization stage: tri-objective optimization of the whole energy retrofit and assessment of cost-optimality Exhaustive sampling of energy retrofit packages Selection of ERMs addressed to primary energy systems and RESs Proper ERMs for the improvement of the energy efficiency of the primary heating/cooling/electricity system and for the exploitation of Renewable Energy Sources (RESs) are identified based on building peculiarities and best-practice

Tri-objective optimization of the whole energy retrofit and cost-optimal analysis

A smart exhaustive sampling is carried out in order to assess Investment Cost (IC), difference in Primary Energy Consumption (dPEC) and in Global Cost (dGC) compared to RB, of the energy retrofit packages achieved by combining all the proposed ERMs addressed to primary energy systems and RESs with the non-dominated combinations (provided by GA) and the best non-optimized combinatlons (provided by SA) of the ERMs for TED reduction (also the case of absence of ERMs for TED reduction is considered)

the achieved values of IC, dPEC, and dGC for the investigated energy retrofit packages are represented in order to achieve a tridimensional Pareto front that identifies the non-dominated energy retrofit solutions. Then, the cost-optimal retrofit package is detected both in presence of a limitless economic budget, and in presence of different limited economic budgets

Fig. 10 Framework of the methodology proposed in Ref. [10] for the cost-optimal design of building energy retrofit by means of multistage, multiobjective optimization. Note that this figure is integrally taken from Ascione F, Bianco N, De Stasio C, Mauro GM, Vanoli GP. Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: a new approach to assess cost-optimality. Appl Energy 2016;174:37–68.

is conducted (the GA is not implemented in this stage) by applying all combinations of the EEMs for the supply side management to: ○ the BB configuration; ○ the nondominated packages of the EEMs for the demand side management, provided by the GA; ○ the best nonoptimized packages of the EEMs for the demand side management, provided by the sensitivity analysis of stage 1. The IC, PEC, and GC over 20 years [13,14], and GC are assessed for all the obtained whole design solutions (concerning both demand and supply side management), thereby performing a three-objective Pareto optimization. The optimization problem of this second stage can be formulated as follows:
 min F 00 ðx 00 Þ ¼ ICðx 00 Þ; dPECðx 00 Þ; dGCðx 00 Þ subject to  00 00 00 x 00 ¼ x1 ; …; xn1 ; … …; xPd00

n ii ¼ 1 ii

00



nd00 þ1

; …; xPd00

n ii ¼ 1 ii



00

with xj ¼



Xd00 0 n ICðx 00 ÞrB for j ¼ 1; … …; ii ¼ 1 ii 1

Energy Management in Hospitals

Thermal energy demand for cooling (TEDcool) (kWh m−2a−1)

848

ERMs for the reduction of TED: selected Pareto front (20 generations)

59

Points on the Pareto front with lowest TEDheat 58

P.1

P.14

Points on the Pareto front with lowest TEDcool Intermediate points on the Pareto front Non-optimized combinations of the best ERMs

P.2

57 P.3 56

P.15

P.4 TEDheat,RB = 51.2 kWh m−2a−1

55 P.5

TEDcool = 58.2 kWh m−2a−1

54 P.6

P.16

53 P.7 52

P.8 P.9

51

P.10

P.17 P.11 P.12

50 49 14

P.13 15

16

17

18

19

20

21

Thermal energy demand for heating (TEDheat) (kWh m−2a−1) Fig. 11 Pareto biobjective optimization by minimizing TEDheat and TEDcool. Note that this figure is integrally taken from Ascione F, Bianco N, De Stasio C, Mauro GM, Vanoli GP. Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: a new approach to assess cost-optimality. Appl Energy 2016;174:37–68.

Table 5

Genetic algorithm (GA) pseudocode

t ¼1 (generations’ index) Create the initial population P(1){x0 i(1)}i ¼ 1, …,s of s individuals Calculate F0 (x0 i(1)) for i ¼1, …,s Evaluate the rank value and the average crowding distance for each individual of P(1) DO UNTIL at least one stop criterion is satisfied t ¼t þ 1 Select the parents from P(t 1) Generate P(t){x0 i(t)}i ¼ 1,…,s from crossover and mutation of the parents: elite parents survive Calculate F0 (x0 i(t)) for i¼ 1,…,s Evaluate the rank value and the average crowding distance for each individual of P(t) END Return the Pareto front Note that this optimization sequence is proposed by Ascione F, Bianco N, De Stasio C, Mauro GM, Vanoli GP. A new methodology for cost-optimal analysis by means of the multi-objective optimization of building energy performance. Energy Build 2015;88:78–90.

where F00 is the vector of the objective functions and x00 is a vector of bits that encode the design variables (their number is denoted with d00 ) of this stage. Also a constraint is considered on the IC, since this latter cannot exceed the available economic budget, denoted with B. Hence, the cost-optimal solutions are found both in correspondence of a limitless budget (see Fig. 12) and of diverse limited budgets (see Fig. 13, e.g., purposes). Indeed, often the stakeholders impose a limit to the maximum investment to be disbursed, and the methodology allows one to select the “constrained” cost-optimal solution that fulfills such limit. In addition, a constraint could be set on PEC in order to find optimal solutions that ensure minimum levels of energy performance. Clearly, the proposed procedure can be used to address the energy design and management of any building, and in particular of large, complex facilities, like hospitals. However, its implementation to an RB is highly worthwhile, because, in this case, the outcomes can be considered valid for most represented buildings. This allows one to save a large amount of computational time because only the RB needs to be modeled from the energy point of view and investigated to optimize energy performance. Evidently, this benefit is substantial for complex structures that require a significant computational burden to be analyzed. That’s why, in Ref. [10], the methodology has been applied to the RBh described in Subsection 5.21.2.1 of this chapter, and the achieved results are shown in the following subsection.

Energy Management in Hospitals

849

dGC, difference in GC compared to the RB (k€)

Limitless economic budget

0

PECRB = 558.8 kWh m−2a−1

RB

Absence of ERMs for the reduction of thermal energy demand (TED) Points on the Pareto front with lowest TED for heating Points on the Pareto front with lowest TED for cooling Intermediate points on the Pareto front Non-optimized combinations of the best ERMs

GCRB = 11′982′610 €

−500 −1000 −1500 −2000 −2500

−20 −40 −60 −80 −100

(A)

−120 dPEC, difference in PEC compared to the RB (kWh m−2a−1)

dGC, difference in GC compared to the RB (k€)

500

0

500

1500

1000

2000

2500

3000

IC, investment cost (k€)

Absence of ERMs for the reduction of thermal energy demand (TED) Points on the Pareto front with lowest TED for heating Points on the Pareto front with lowest TED for cooling Intermediate points on the pareto front Non-optimized combinations of the best ERMs

PECRB = 558.8 kWh m−2a−1 GCRB = 11′982′610 €

0

RB

−500

−1000

−1500

−2000

−2500 Cost-optimality −3000 −140

(B)

−120

−100

−80

−60

−40

−20

0

dPEC, difference in PEC compared to the RB (kWh m−2a−1)

Fig. 12 Triobjective optimization of retrofit design by minimizing investment cost (IC), primary energy consumption (PEC) and global cost (GC) (A), and assessment of cost-optimality (B) in presence of a limitless budget. Note that this figure is integrally taken from Ascione F, Bianco N, De Stasio C, Mauro GM, Vanoli GP. Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: a new approach to assess cost-optimality. Appl Energy 2016;174:37–68.

Energy Management in Hospitals

850

Economic budget = 900 k€

dGC, difference in GC compared to the RB (k€)

Absence of ERMs for the reduction of thermal energy demand (TED) Points on the Pareto front with lowest TED for heating Points on the Pareto front with lowest TED for cooling Intermediate points on the Pareto front Non-optimized combinations of the best ERMs RB

PECRB = 558.8 kWh m−2a−1

0

GCRB = 11′982′610 € −500 −1000 −1500 −2000 −2500

−20 −40 −60 −80 −100

(A)

−120 dPEC, difference in PEC compared to the RB (kWh m−2a−1)

dGC, difference in GC compared to the RB (k€)

500

0

0

500

1500

1000

2000

2500

3000

IC, Investment cost (k€)

Absence of ERMs for the reduction of thermal energy demand (TED) Points on the Pareto front with lowest TED for heating Points on the Pareto front with lowest TED for cooling Intermediate points on the Pareto front Non-optimized combinations of the best ERMs

PECBB = 558.8 kWh m−2a−1 GCBB = 11′982’610 €

RB

−500

−1000

−1500

−2000

−2500 Cost-optimality

(B)

−3000 −140

−120

−100

−80

−60

−40

−20

0

dPEC, difference in PEC compared to the RB (kWh m−2a−1)

Fig. 13 Triobjective optimization of retrofit design by minimizing IC, PEC, and GC (A), and assessment of cost-optimality (B) in presence of a budget of 900 k€. Note that this figure is integrally taken from Ascione F, Bianco N, De Stasio C, Mauro GM, Vanoli GP. Multi-stage and multiobjective optimization for energy retrofitting a developed hospital reference building: a new approach to assess cost-optimality. Appl Energy 2016;174:37–68.

Energy Management in Hospitals 5.21.4.3.1

851

Application 1: Reference building of hospitals built in South Italy between 1991 and 2005

In Ref. [10], the methodology was applied to the RBh (whose detailed definition is illustrated in Section 5.21.2.1 of this chapter) that represents hospitals built in South Italy in the period 1991–2005. The energy retrofit and management of this building were optimized by considering several EEMs, as shown below. In particular, concerning the demand side management, notably for TED reductions, the following EEMs were investigated:

• • • • • • • • •

installation of heat recovery systems for the air handling units; installation of internal solar shading systems, composed of diffusive blinds; installation of argon-filled double-glazed windows with PVC frames. Three options were explored with different coatings: solar control, low-emissive coatings and their combination; plastering of the external side of the roof by using paints with a value of solar absorptance that can vary in the range 0.10–0.90; plastering of the external side of the roof by using paints with a value of thermal emissivity that can vary in the range 0.10–0.90; installation of a thermal insulant layer in polyurethane on the external side of the roof. The layer’s thickness can vary in the range 0.00–0.10 m; plastering of the external side of the walls by using paints with a value of solar absorptance that can vary in the range 0.10–0.90; plastering of the external side of the walls by using paints with a value of thermal emissivity that can vary in the range 0.10–0.90; installation of a thermal insulant layer in polyurethane on the external side of the walls. The layer’s thickness can vary in the range 0.00–0.10 m. On the other hand, for the supply side management, the following EEMs were investigated. Concerning space heating and cooling:

• • • • • •

installation of an energy-efficient natural gas boiler with Z equal to 0.95; installation of a condensing natural gas boiler with Z equal to 1.05; installation of air-source heat pumps with COP equal to 3.5 at rated conditions; installation of reversible ground-source heat pumps with geothermal vertical probes with COP equal to 4.5 in heating operation and EER equal to 6.2 in cooling operation at rated conditions; installation of air-cooled chillers with magnetic levitation compressors, characterized by EER equal to 3.5 at rated conditions; installation of water-cooled chillers equipped with cooling towers, characterized by EER equal to 5.5 at rated condition. Concerning DHW production:

•

installation of an energy-efficient natural gas boiler with Z equal to 0.95. Concerning cogeneration and trigeneration systems:

• •

installation of a CHP system, which consists of a gas natural internal combustion engine and a waste heat recovery boiler. Recovered heat can be employed for both space heating and DHW production. Different options were considered concerning the system size; installation of a CCHP system, which consists of a natural gas internal combustion engine, a waste heat recovery boiler, and an air-cooled single-effect absorption chiller. Recovered heat can be employed for space heating, cooling, and DHW production. Different options were considered concerning the system size, all characterized by the same value of the EER of the chiller, set equal to 0.7. Concerning the exploitation of RESs:

• •

installation of flat-plate solar thermal collectors on the roof, south-oriented and with 34 degree tilt angle. These can be selective and not-selective, alternately. The size of the system is expressed by the percentage of the covered roof area, which can vary between 10 and 60% with a step equal to 10%; installation of solar PV panels on the roof, south-oriented and with 34-degree tilt angle. These can be in poly- or monocrystalline silicon, alternately. Also in this case, the size of the system is expressed by the percentage of the covered roof area, which can vary between 10 and 60% with a step equal to 10%.

It is noticed that some EEMs are the same measures that were investigated in Ref. [67] (see Section 5.21.4.2), and they have been chosen based on the authors’ expertise, hospitals’ peculiarities, and best practices. Finally, 596,700 energy scenarios were explored, and the cost-optimal solution in the presence of a limitless economic budget (see previous Fig. 12) is composed of the following EEMs:

• • • •

heat recovery systems, solar shading systems, and plastering of the roof with low values of solar absorptance, as regards the EEMs for demand side management; energy-efficient natural gas boiler for DHW; CCHP system characterized by a power of 600 kWel; in this regard, it should be noted that the BB electric load for direct electric uses is about 200 kWel, whereas the maximum is about 800 kWel; not selective solar collectors for DHW production with a roof coverage equal to 30%;

852

•

Energy Management in Hospitals

monocrystalline PV panels with a roof coverage equal to 30%.

This solution produces potential PEC savings around 67.9 kWh m 2 a 1 (12.2%), GC savings around 2932 k€ (24.5%), as well as a reduction of polluting emissions around 1260 t CO2-eq per year (which is close to the annual emissions of 1000 new cars). Furthermore, since it requires an investment of 1236 k€, the cost-optimal solution was found also in correspondence of 12 economic budgets from 100 to 1200 k€ with a step of 100 k€ (for instance, the previous Fig. 13 refers to the budget of 900 k€). The outcomes showed that the ranking of the most cost-effective EEMs is as follows:

• • • • •

CCHP systems for higher budgets, and CHP systems for lower economic availabilities; not selective solar collectors for DHW production, mainly in presence of CCHP systems; monocrystalline PV panels for higher economic availabilities, polycrystalline PV panels for lower budgets; heat recovery systems, and cool roof if the budget allows; efficient natural gas boiler for DHW, mainly coupled with CCHP systems or in the case of low budgets.

Clearly, when the budget decreases, also the potential energy, economic, and environmental benefits decrease, but they are still substantial compared to the BB configuration.

5.21.5

Conclusions

The chapter offers a comprehensive overview of the main issues concerning efficient and effective energy design and management of hospital buildings. This is a crucial topic because hospitals are the most energy-intensive facilities of the building sector. Therefore, the optimization of their energy performance can allow substantial environmental and economic benefits. In this regard, the deepest attention should be addressed to the energy retrofit of existing hospitals, because most of them were built without properly considering the energy issues (and the building turnover is very low in most countries). In the case of both new construction and retrofits, the proper energy design and management of these complex edifices is extremely critical because it must consider all levers affecting energy performance, from the characteristics of the thermal building envelope to the peculiarities, operation, and efficiency of active energy systems. Thus, there are several design variables to optimize, and this leads to a huge number of scenarios to be investigated. Furthermore, as occurs in any problem concerning BPO, different objective functions can be considered, depending on stakeholders’ needs and wills, such as the minimization of different components of energy demand, thermal discomfort, investments, lifecycle costs, and polluting emissions. Definitely, in order to properly address the energy issues concerning hospitals’ design and management, complex optimization problems must be solved by means of comprehensive approaches. In this frame, after the discussion of the main energy issues concerning hospital buildings, the chapter offers an overview of the mentioned optimization approaches, provided by current scientific literature, by also showing the implementation to different interesting case studies. In all cases, huge potential benefits are achieved, in terms of energy and economic savings as well as reduction of polluting emissions. These outcomes demonstrate that the efficient and effective energy design and management of hospital buildings is fundamental to promote a sustainable future for the building sector.

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[31] Italian Government Ministerial Circular. Circolare Ministeriale LL.PP. n.13011/22.11. Requisiti fisico-tecnici per le costruzioni edilizie ospedaliere. Proprietà termiche, igrometriche, di ventilazione e di illuminazione; 1991 [in Italian]. [32] UNI – Italian Organization for Standardization. Norma UNI 10339: Impianti areaulici ai fini di benessere. Generalità, classificazione e requisiti. Regole per la richiesta di offerta, l’offerta, l’ordine e la fornitura; 1995 [in Italian]. [33] UNI – Italian Organization for Standardization. Norma UNI 8199: Misura in opera e valutazione del rumore prodotto negli impianti di riscaldamento, condizionamento e ventilazione; 1981 [in Italian]. [34] ISPESL, Istituto Superiore per la Prevenzione e la Sicurezza dei Lavoro. Linee Guida per la definizione degli standard di sicurezza e di igiene ambientale dei reparti operatori. Consiglio Superiore di Sanità; 2002 [in Italian]. [35] UNI – Italian Organization for Standardization. Norma UNI 13790: Prestazione energetica degli edifici – Calcolo del fabbisogno di energia per il riscaldamento e il raffrescamento; 2008 [in Italian]. [36] Energy.Gov. Available from: www.apps1.eere.energy.gov/buildings/energyplus/weatherdata_about.cfm/. [37] Van Hoof J, Kort HSM, Duijnstee MSH, Rutten PGS, Hensen JLM. The indoor environment and the integrated design of homes for older people with dementia. Build Environ 2010;45(5):1244–61. [38] Van Hoof J, Kort HSM, Hensen JLM, Duijnstee MSH, Rutten PGS. Thermal comfort and the integrated design of homes for older people with dementia. Build Environ 2010;45(2):358–70. [39] Ferrara M, Fabrizio E, Virgone J, Filippi M. A simulation-based optimization method for cost-optimal analysis of nearly zero energy buildings. Energy Build 2014;84:442–57. [40] Delgarm N, Sajadi B, Kowsary F, Delgarm S. Multi-objective optimization of the building energy performance: a simulation-based approach by means of particle swarm optimization (PSO). Appl Energy 2016;170:293–303. [41] Chantrelle FP, Lahmidi H, Keilholz W, Mankibi ME, Michel P. Development of a multicriteria tool for optimizing the renovation of buildings. Appl Energy 2011; 88(4):1386–94. [42] Hamdy M, Hasan A, Siren K. A multi-stage optimization method for cost-optimal and nearly-zero-energy building solutions in line with the EPBD-recast 2010. Energy Build 2013;56:189–203. [43] Echenagucia TM, Capozzoli A, Cascone Y, Sassone M. The early design stage of a building envelope: multi-objective search through heating, cooling and lighting energy performance analysis. Appl Energy 2015;154:577–91. [44] Ascione F, Bianco N, De Stasio C, Mauro G,M, Vanoli GP. A new methodology for cost-optimal analysis by means of the multi-objective optimization of building energy performance. Energy Build 2015;88:78–90. [45] Ascione F, Bianco N, De Masi RF, Mauro GM, Vanoli GP. Design of the building envelope: a novel multi-objective approach for the optimization of energy performance and thermal comfort. Sustainability 2015;7(8):10809–36. [46] Ascione F, Bianco N, De Stasio C, Mauro G,M, Vanoli GP. Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort. Energy Build 2016;111:131–44. [47] Ascione F, Bianco N, De Masi RF, et al. Multi-objective optimization of the renewable energy mix for a building. Appl Therm Eng 2016;101:612–21. [48] Bujack J. Heat consumption for preparing domestic hot water in hospitals. Energy Build 2010;42:1047–55. [49] Lomas KJ, Giridharan R. Thermal comfort standards, measured internal temperatures and thermal resilience to climate change of free-running buildings: a case-study of hospital wards. Build Environ 2012;55:57–72. [50] Čongradac V, Prebiracˇević B, Petrovacˇki N. Methods for assessing energy savings in hospitals using various control techniques. Energy Build 2014;69:85–92. [51] Ma Z, Wang S. Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm. Appl Energy 2011;88(1):198–211. [52] Buonomano A, Calise F, Ferruzzi G, Palombo A. Dynamic energy performance analysis: case study for energy efficiency retrofits of hospital buildings. Energy 2014;78:555–72. [53] Saidur R, Hasanuzzaman M, Yogeswaran S, Mohammed HA, Hossain MS. An end-use energy analysis in a Malaysian public hospital. Energy 2010;25(12):4780–5. [54] Saidur R, Hasanuzzaman M, Mahlia TMI, Rahim NA, Mohammed HA. Chillers energy consumption, energy savings and emission analysis in an institutional buildings. Energy 2011;36(8):5233–8. [55] Yang KH, Su CH, Hwang RL. The analysis on intelligent control strategies of a thermal energy storage air-conditioning system. Energy 1996;21(4):319–24. [56] Arteconi A, Brandoni C, Polonara F. Distributed generation and trigeneration: energy saving opportunities in Italian supermarket sector. Appl Therm Eng 2009;28 (8–9):1735–43. [57] Arteconi A, Hewitt NJ, Polonara F. State of the art of thermal storage for demand-side management. Appl Energy 2012;93:371–89. [58] Ruan Y, Liu Q, Zhou W, et al. Optimal option of distributed generation technologies for various commercial buildings. Appl Energy 2009;86(9):1641–53.

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[59] Piacentino A, Barbaro C, Cardona F. Promotion of polygeneration for buildings applications through sector- and user-oriented “high efficiency CHP” eligibility criteria. Appl Therm Eng 2014;71(2):882–94. [60] Gimelli A, Muccillo M. Optimization criteria for cogeneration systems: multi-objective approach and application in an hospital facility. Appl Energy 2013;104:910–23. [61] Costa A, Fichera A. A mixed-integer linear programming (MILP) model for the evaluation of CHP system in the context of hospital structures. Appl Therm Eng 2014; 71(2):921–9. [62] Zheng CY, Wu JY, Zhai XQ. A novel operation strategy for CCHP systems based on minimum distance. Appl Energy 2014;128(1):325–35. [63] Peel M, Finlayson B, McMahon T. Updated world map of the Köppen–Geiger climate classification. Hydrol, Earth Syst Sci 2007;1633–44. [64] Radwan AF, Hanafy AA, Elhelw M, El-Sayed AEHA. Retrofitting of existing buildings to achieve better energy-efficiency in commercial building case, study: hospital in Egypt Alexandria Eng J 2016 doi:10.1016/j.aej.2016.08.005. [65] Čongradac V, Prebiracˇevièć B, Jorgovanović N, Stanišić D. Assessing the energy consumption for heating and cooling in hospitals. Energy Build 2012;48:146–54. [66] Ascione F, Bianco N, De Masi RF, et al. Energy, audit of health care facilities: dynamic simulation of energy performances and energy-oriented refurbishment of system and equipment for microclimatic control AJEAS Am J Eng Appl Sci 2016 doi:10.3844/ajeassp.2016. [67] Ascione F, Bianco N, De Rossi F, et al., From a hospital reference building to all represented healthcare facilities: a new approach to assess energy performance and retrofit potentials. In: Proceedings of the 29th international conference on efficiency, cost, optimisation, simulation and environmental impact of energy systems (ECOS 2016), June 19–23,Portorozˇ, Slovenia; 2016. [68] Deb K. Multi-objective optimization using evolutionary algorithms, vol. 16. Chichester, NJ: John Wiley & Sons; 2001.

Relevant Websites https://www.ashrae.org/ American Society of Heating, Refrigeration and Air-Conditioning Engineers (ASHRAE). http://www.epbd-ca.eu/ Concerted Action EPBD (CA EPBD). http://www.psychology.emory.edu/lcpc/bailout.high.html DesignBuilder Software Ltd. http://www.din.de/en DIN – German Committee for Standardization. http://www.enea.it/en/home-luglio-2015?set_language=en&cl=en/ ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development). http://www.apps1.eere.energy.gov/buildings/energyplus/weatherdata_about.cfm/ Energy.Gov. https://ec.europa.eu/energy/en/topics/energy-efficiency/buildings/ European Commission. https://www.cen.eu/work/areas/construction/buildingsenergyperf/Pages/default.aspx/ European Committee for Standardization. http://www.eceee.org/members/MembersForum/BPIE/ European Council for an Energy Efficient Economy (ECEEE) – Buildings Performance Institute Europe (BPIE). http://www.governo.it/ Italian Government. https://www.inail.it/cs/internet/home.html/ National Institute for Accident Insurance at Work (INAIL). http://www.nrel.gov/ National Renewable Energy Laboratory (NREL). http://newbuildings.org/hubs/zero-net-energy/ New Buildings Institute. http://www.uni.com/ UNI – Italian Organization for Standardization. http://apps1.eere.energy.gov/buildings/energyplus/ US Department of Energy – Energy Efficiency and Renewable Energy. http://www.usgbc.org/ U.S. Green Building Council.

5.22 Energy Management in Hotels Shiming Deng and Wilco Chan, The Hong Kong Polytechnic University, Hong Kong, China r 2018 Elsevier Inc. All rights reserved.

5.22.1 Introduction 5.22.2 Energy Use Characteristics in Hotels 5.22.2.1 Breakdowns of Energy Use in a Hotel by Type and End Uses 5.22.2.2 Seasonal Variation of the Total Energy Use in Hotels 5.22.2.3 Seasonal Variation Profiles for Electricity, Diesel and Gas Use in Hotel Buildings 5.22.2.4 Hotel Class and Energy Use 5.22.2.5 Other Factors That May Influence Energy Use in Hotel Buildings 5.22.2.6 Multiple Regression for Hotel Energy Benching 5.22.3 Energy Management in Hotels 5.22.3.1 Assessing Energy Use Performance for Hotel Buildings 5.22.3.2 Energy Management Adopted by Hotels 5.22.4 The Energy Aspects of Environmental Assessment in Hotels 5.22.5 Carbon Audit as a Means to Reduce Energy Usage in Hotels in Hong Kong 5.22.6 Case Study 5.22.7 Concluding Remarks References Further Reading Relevant Website

5.22.1

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Introduction

Hotels provide short-term lodging, and depending on the level of facilities and services provided, they can be classified into different classes ranging from basic bed and breakfast type budget hotels to expensive five-star hotels. In higher-class hotels, luxury features, such as en-suite bathrooms and other functional areas, such as food and beverage outlets, for example, restaurants, coffee shops and bars, a swimming pool, business center, childcare, conference and banquet facilities and social function services, even shopping arcades, may be included. For example, there can be up to 10 restaurants in a Hong Kong hotel, providing eastern and western cuisines. Hotel buildings are, therefore, as compared to other types of buildings, unique in their design and operations. This includes variable hotel occupancy levels throughout a year; different operation schedules for different functional areas/ facilities; numbers of functional facilities (in-house laundry, business center, restaurants, etc.); highly varied personal preference of indoor built environment and user behaviors by hotel guests, etc. Consequently, the differences have led to varied operation schedules for building services (energy) systems and thus different energy use levels in different hotel buildings. Like any other buildings, in hotel buildings, different building services systems are installed in order to provide and maintain a suitable indoor built environment (thermal, visual, and indoor air quality (IAQ), etc.) and providing their guests and staff with quality services, such as lift services and hot water supply, corresponding to the rating/class of a hotel, but at the expense of consuming a sizable amount of energy. These building services installations mainly include heating, ventilating, and air-conditioning (HVAC), hot water supply, indoor lighting, and vertical transportation (V/T). HVAC and lighting systems are usually powered by electricity, but hot water supply for domestic use and sometimes space heating can be powered by either electricity or diesel or even gas. In addition, since there are usually food and beverage outlets in a hotel, a considerable amount of energy in the form of gas and electricity is used in its kitchens. For example, in a medium-sized hotel of 4–5 star rating in Hong Kong, its annual costs for electricity, gas, and diesel fuel may reach several tens of million Hong Kong dollars, and this contributes significantly to the total annual hotel operating cost. Therefore there are strong justifications to manage and conserve energy use in hotels [1]. These justifications would include the increased profitability due to reduced operational cost and the potential for improved market share due to improved indoor built environments, as well as preserving limited natural resources for sustainable development. Because a large amount of energy is consumed in a hotel building to operate its various building services system installations, there have been increasing concerns on hotel energy use from, and the efforts to look into the energy use performance and to promote good operational practices in hotels by, various stakeholders involved. For example, in Australia, Hong Kong, and the United Kingdom, general guidelines for achieving improved energy use performances in hotel buildings have been established by relevant government departments [2–5]. In these guidelines, good operational practices that may lead to reduced energy use in a hotel, such as good housekeeping practices and technical guidelines to efficiently operate building engineering services systems are suggested. Furthermore, good operational practices for better energy efficiency have also been promoted by hotel professional associations, for example, the International Hotels Environment Initiative [6]. Normally, managing energy use in a hotel building

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may be seen as part of its overall environmental management framework, although it has been noted that “most examples of successful environmental management are in the area of energy management, where there are clear motivations in terms of financial saving” [7]. On the other hand, energy use performance in various buildings including hotel buildings has also been extensively studied. Earlier reported studies include the survey on energy use performance in hotels in Ottawa, Canada [7]. The yearly averaged energy use based on per unit floor area was reported at 688.7 kWh/m2, according to the survey results of 43% of the hotels located in Ottawa area in the year of 1991. The percentage breakdown of different fuel types was as follows: electricity at 28.9%, gas 26.4%, and steam 44.7%, as compared to 40.9% for electricity and 51.9% for gas in the lodging buildings in the United States [8]. Since then there have been a growing number of studies on energy use performance in hotels in different parts of the world [9–13]. These included the extensive studies on the energy use performance and management in hotels in Hong Kong, where, based on the energy use data from 16 hotels, a yearly averaged energy use weighted per unit floor area of 563.88 kWh/m2 was reported [14]. In this chapter, typical energy use characteristics in hotels, using hotels in Hong Kong as examples, will be firstly presented. These are followed by the discussions on assessing energy use in hotel buildings and suitable energy management programs/ frameworks that may be applied to hotels. Finally, the energy aspects in hotel environment assessment and Hong Kong government’s carbon audit approach for the hotel sector are discussed.

5.22.2 5.22.2.1

Energy Use Characteristics in Hotels Breakdowns of Energy Use in a Hotel by Type and End Uses

To effectively operate a hotel, the following three kinds of energy are normally provided: electricity, gas, and diesel fuel. Electricity can be used as a power source for all building engineering services systems (HVAC, lighting, and V/T) as well as domestic hot water heating and cooking in a kitchen. The use of diesel fuel and gas is on the other hand specific; the former is for steam generation and hot water or space heating, while the latter is usually for cooking in kitchens, and maybe sometimes for hot water heating. Usually, electricity takes the lion’s share of the total energy use in a hotel building, at more than 50% or greater. Fig. 1 shows the breakdowns of the total energy consumption and the total cost in a hotel in Hong Kong. As seen, electricity takes 58% of the total energy use, but at more than 80% of the total cost, reflecting that electricity is more expensive, because it can power almost everything, and hence, more has to be paid for its use convenience. Furthermore, the breakdown of energy use in a hotel based on its end uses, such as air conditioning (A/C), lighting, V/T (lifts and escalators), hot water heating, etc., should also be made. Fig. 2 [10] shows the averaged percentage breakdown of the total energy use in 16 hotels in Hong Kong based on the following five end uses A/C, lighting, V/T, lifts, and escalators, and miscellaneous electrical items including small power, kitchen Towngas 9%

Diesel fuel oil 33%

Electricity 58%

(A)

Fuel oil 6%

Towngas 11%

Electricity 83%

(B)

Fig. 1 Percentage breakdowns of energy use (A) and cost (B) by fuel types in a hotel in Hong Kong.

Other electrical 23%

Non electrical 28%

Vertical transpotation 5% Lighting 12%

Air conditioning 32%

Fig. 2 Averaged percentage breakdown of the total energy use in the 16 hotels surveyed in Hong Kong. Reproduced from Deng S, Burnett J. A study of energy performance of hotel buildings in Hong Kong. Energy Build 2000;31(1):7–12.

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equipment consumption, etc., and nonelectrical items including gas and diesel for cooking and services water heating. As observed, on average, 32% of the total energy use is for space A/C in hotels in Hong Kong, due to its subtropical climate. Nonetheless, as a matter of fact, it must be pointed out that the shares of the total energy use by A/C or space heating will be dependent on the climate condition under which is hotel is located. For hotels located in cold climates, energy use by space heating can take the lion’s share of the total energy use, while for those located in temperate zone where summer can be very hot and winter very cold, energy use by HVAC can dominate. For example, in a study [15] reporting the energy use characteristics of hotel buildings in Shanghai, which is located in a hot summer and cold winter climate zone, it was revealed that energy use in both summer months and winter months was higher than those in spring and autumn months, because of the strong demands for space heating in winter and space cooling in summer.

5.22.2.2

Seasonal Variation of the Total Energy Use in Hotels

7000

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Like all other types of buildings, seasonal variation patterns of energy use similar to the seasonal variation patterns of outdoor air temperature can be exhibited, which is largely due to the fact that HVAC dominates the total energy consumption and the operation of building HVAC systems is strongly weather-related, with however the energy use for other service systems (lighting, services, and water heating) being fairly constant over different seasons in a year. Fig. 3 illustrates the monthly total energy use profile in a Hong Kong hotel, with the monthly variation profile of mean outdoor air dry-bulb temperature also included in the graph. As seen, the variation of energy consumption appeared correlated to that of weather condition as represented by outdoor air temperature, which is particularly true in the second half of the year. On the other hand, hotel buildings are different from other types of buildings in terms of occupancy, which does not stay unchanged but is highly variable throughout a year. Nonetheless, studies have suggested that the monthly variation of energy consumption in a hotel may not be directly related to that of average hotel occupancy level, as shown in Fig. 4. This may be explained by the fact that even at a very low occupancy rate, a hotel building will still need to operate its energy consumption systems in order to make the building functional. For example, in subtropical Hong Kong, where it can be hot and humid, therefore, even if a guest room is not occupied, A/C will still have to be provided to remove dampness/odor due to high humidity to ensure freshness for preparing walk-in guests or late check-in guests.

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Fig. 3 The monthly variation profiles for the total energy use and the outdoor mean air dry-bulb temperature for a Hong Kong hotel.

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However, for hotels located in different climate zones, different variation profiles may be resulted in. For example, as mentioned earlier, for the hotel buildings in Shanghai, China, a seasonal variation profile of total energy use that is remarkably different from that in hotels in Hong Kong is reported and reproduced in Fig. 5 [15].

5.22.2.3

Seasonal Variation Profiles for Electricity, Diesel and Gas Use in Hotel Buildings

In Section 5.22.2.2, using a hotel in Hong Kong as an illustrative example, the seasonal variation of the total energy use in hotels is examined. Since the total energy consumption is made of the consumptions by electricity, diesel fuel, and gas, it is also important to know the seasonal variation profiles for electricity, diesel, and gas use in hotel buildings. In this section, the seasonal variation profiles for the consumptions of electricity, diesel fuel, and gas for the same hotel and during the same period as that presented in Section 5.22.2.2 are shown as examples. Figs. 6–8 show the variation profiles of monthly electricity, diesel fuel, and gas use in the hotels in Hong Kong. Also included in these figures is the variation profile of the monthly mean outdoor air temperature. In Fig. 6, it shows that the monthly electricity use variation pattern follows the variation pattern of outdoor air temperature, which may be explained by the fact that in subtropical Hong Kong, as shown in Fig. 2, the total electricity use is dominated by A/C to deal with space cooling load whose variation pattern closely follows that of outdoor air temperature. However, as shown in Fig. 6, the monthly variation pattern of diesel fuel use inversely follows that of outdoor air temperature, which is also considered reasonable as the use of diesel fuel in the hotel is primarily for water heating, so that at a higher outdoor air temperature, or summer, the demand for water heating will be less and water entering a diesel boiler will be at a higher temperature, leading to less diesel fuel use. Finally it is noted from Fig. 7 that gas use does not appear to be related to weather changes since it is mainly used in kitchens for cooking purposes. Given this purpose, it can be seen that the gas consumption peaks during December and January, when there are two major festivals, Christmas and Chinese New Year, so that there will be more visitors to hotel restaurants. 8.0

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Fig. 5 Relationship between monthly energy use intensity (EUI) and outdoor temperature for hotels in Shanghai. Reproduced from Yao Z, Zhuang Z, Gu W. Study on energy use characteristics of hotel buildings in Shanghai. Procedia Eng 2015;121:1977–82.

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Fig. 6 The profile of monthly electricity use in a hotel in Hong Kong. Reproduced from Deng S, Burnett J. Energy use and management in hotels in Hong Kong. Int J Hosp Manag 2002;21(4):371–80.

Energy Management in Hotels

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Month (1995) Fig. 7 The profile of monthly diesel use in a hotel in Hong Kong. Reproduced from Deng S, Burnett J. Energy use and management in hotels in Hong Kong. Int J Hosp Manag 2002;21(4):371–80.

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Month (1995) Fig. 8 The profile of monthly gas use in a hotel in Hong Kong. Reproduced from Deng S, Burnett J. Energy use and management in hotels in Hong Kong. Int J Hosp Manag 2002;21(4):371–80.

5.22.2.4

Hotel Class and Energy Use

A previously reported study on hotel energy use has suggested that no clear relationship between the class of a hotel and its energy use based on energy use intensity (EUI, which is defined as the total energy use per unit floor area per year, MJ/m2/year) may be established [10]. This means that a luxury hotel may actually consume less energy than a budget hotel on a per unit floor area per year basis. This seems contradictory to the traditional perception that EUIs for a high-class hotel should have been higher. One of the reasons for this may well be that the guest rooms in a higher-class hotel will be larger than that in a budget hotel. Therefore if the total energy consumption is weighted on the basis of unit floor area, the resulted EUI for higher-class hotels might well be smaller than that for lower-class hotels. However, it must be pointed out that higher-class hotels are usually large in terms of gross floor area (GFA) and number of guest rooms, with more functional areas and facilities, and consequently, the total energy consumption will be larger than that in lower-class hotels. On the other hand, guests staying in higher-class hotels would also expect better indoor built environment (thermal, visual, and IAQ) and higher quality services, and this may also lead to a higher total energy use in a higher-class hotel in order to provide quality services and maintain better indoor built environment, through, for example, supplying more pretreated outdoor air for ensuring better IAQ.

5.22.2.5

Other Factors That May Influence Energy Use in Hotel Buildings

Apart from those mentioned earlier, there are still a number of factors that can affect energy use in hotels on an individual basis. These would include constructions of a hotel, types of building services systems installed, etc. For example, a hotel with an aircooled chiller plant would consume more energy for space cooling than with a water-cooled one, if all else is the same. Likewise,

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for a hotel where energy saving lights are installed, a lower energy consumption for lighting the hotel can be expected. In general, measures/technologies that can be taken to reduce energy use in other types of conventional buildings, such as better thermal insulation, reduced solar heat gains through shadings, variable speed operation of fans and chillers, and energy-efficient lights, may be applied to hotel buildings. Since these measures/technologies are extensively covered elsewhere, they are not repeated here.

5.22.2.6

Multiple Regression for Hotel Energy Benching

Multiple linear regression method has been employed to analyze the relationship between the electricity consumption and three explanatory variables, namely, GFA, annual cooling degree days (CDD), and occupied room (OCC). GFA represents the size of hotels and has been identified in earlier studies to have a strong explanatory power with electricity consumption in hotels [16]. CDD is the weather parameter and has also been proved useful in the assessment of heating and cooling requirements [17–20]. It is, however, found that the views on the base temperature for calculating CDD are divided. Based on more than 20 years’ worth of data, studies showed that 181C was the best base temperature to correlate the electricity consumption in commercial buildings in the subtropical city of Hong Kong [19]. On the other hand, discussions with senior hotel facility managers indicated that there would be a significant amount of body heat released via guest and staff activities in hotels. In order to remove the heat away from hotel occupants, A/C would very often need to be activated at 141C outdoor temperature or below [21]. Thus, CDD variables under various base temperatures at 14, 16, and 181C were investigated. The third variable is the number of OCC that acts as a proxy variable for the activities that consume electrical energy. Stepwise linear regression processing accepts OCC and CDD18 as the best valid variables and excludes the GFA variable, at 0.367 significant level, in the modeling of electricity consumption in the hotel sector. The resulting regression equation is as follows: Ee ¼ 0:052  OCC þ 151:665  CDD18

163; 415

ð1Þ

The R2 is 0.96 indicating that 96% of the variation in annual electricity use can be explained by the variations in the yearly OCC and CDD18 variables.

5.22.3 5.22.3.1

Energy Management in Hotels Assessing Energy Use Performance for Hotel Buildings

Different from other types of buildings, hotel buildings are unique in terms of various facilities installed, services and functions provided, and 24/7 operation schedule. In addition to guest rooms, there might be restaurants, kitchens, ballrooms, in-house laundry, retail outlets, swimming pool, etc., in a hotel. A hotel building is normally operated 24 h, all year round, although part of the building might be closed, for example, a ballroom or the kitchens. The guest occupancy varies significantly from time to time. Restaurants in hotels are open to not only in-house guests, but also to the general public. Also depending on the age of a hotel building, energy saving measures/technologies, such as those mentioned in Section 5.22.2.5 may be present in newer hotel buildings. Consequently, all of these would make the energy performance evaluation for hotel buildings more complex and difficult than for other commercial buildings. Therefore the energy use performance in a hotel building is expected to be affected by many factors in a collective manner and energy use performance assessment for hotel buildings can well be different from that for other types of buildings. For the purpose of assessing energy use performance, based on function, a hotel building may be separated into two functional areas: guest floors and non-guest floors. The non-guest floor area would include all other facilities except guest rooms in a hotel building. The results shown in Section 5.22.2 of this chapter are based on the entire hotel building and these are therefore considered inadequate, because factors pertinent to hotel buildings, when compared to other types of commercial buildings, such as hotel occupancy level, cannot be properly accounted for in assessing the energy performance of hotel buildings. It is therefore recommended that the energy use performance assessment for the two areas be separated, because the energy use for guest floors is likely more related to the head counts of hotel guests. The energy performance assessment in guest floors in a hotel should be preferably based on both unit floor area and occupancy level. However, on the other hand, a much more complicated evaluation for the energy consumption in non-guest floor areas than that in guest floor areas can be expected. An example is the number of restaurants in a hotel. A systematical approach for studying the energy use performances in non-guest floor areas in a hotel building should be made available. It is well understood that purposes of installing various building services systems in a hotel building are to provide and maintain a suitable indoor built environment and quality of services to hotel occupants at the expense of consuming energy. Therefore when assessing the energy use performances in hotel buildings, it is also necessary to examine and thus ensure that the indoor built environment and quality of services provided to both hotel guests and staff working in a hotel building are at an appropriate level pertaining to the class of the hotels concerned.

5.22.3.2

Energy Management Adopted by Hotels

Very often, energy management is an important component of an overall environmental management program and should therefore be viewed as an indispensable part of the overall environmental management framework. Nonetheless, energy use in a

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hotel building is normally by various hotel engineering services systems and energy management is more technically biased and should therefore be directed more toward engineering staff. Based on the extensive experiences obtained in implementing energy management programs in hotels in Hong Kong, the following key managerial and technical elements of a successful energy management program are summarized: Managerial Energy management should be fully integrated into a hotel’s overall management framework, and treated as equally important to other management functions, such as human resources and financial management. • An environmental manager or energy manager should be appointed as part of a hotel management team. • There should be a clearly defined energy management and use policy as well as an action plan, with the responsibility for the implementation vested in a member of senior executives or an energy manager if appointed. • All hotel staff should be encouraged to get involved in the energy management program. Good housekeeping practices should be exercised in all departments. The relevant information on hotel energy use and its associated costs should be communicated to all staff on a regular basis to raise their awareness on energy conservation issues. • Whenever possible, hotel guests are invited to get involved in a hotel’s energy management program, such as setting indoor air temperature at a higher but comfortable level, turning off indoor lights as and when appropriate, etc., by presenting guests a leaflet on tips for energy conservation, during check-in or by placing such a leaflet inside a guest room. • On many occasions, hoteliers are subject to pressure of passive acceptance and social support for energy saving facilities stemming from manufacturers’ claims and the green movement, respectively. At the same time, hotels have faced constraints in finding expertise and measuring equipment to verify the energy saving potential. Thus hoteliers hope that more resources can be allocated to independent research about the energy and economic performance of the current energy saving facilities, so as to assist them in making decisions for adoption of these energy saving facilities. Technical • Allowing part of the total annual operating budget for upgrading the existing building energy system installations, with a particular focus on upgrading/replacing energy intensive equipment such as A/C water chillers, cooling towers or air handling units (AHUs), and lighting installations. • Educating and training engineering staff so that every staff member is aware that everything he/she does may significantly affect the energy use in a hotel. • Establishing a well-defined operation and maintenance program for engineering staff to follow, so that wastes due to oversupply may be avoided and poor operating efficiency alleviated through good operating maintenance work. Preventative maintenance should also be considered before an operational problem occurs. • Carrying out regular energy audits, preferably once every year, and installing or adding suitable submeters for electricity and gas to monitor energy use in various electricity end uses and in kitchens, in order to facilitate the breakdown of the total energy use to identify major energy end users. Follow-up measures based on the outcomes of energy audits should be developed to address the problems identified and to prioritize these measures to be implemented. • Adopting technically advanced high efficiency equipment including energy saving lights, variable speed water chillers and energy-efficient motors, etc. • It is also suggested that hoteliers adjust the temperature settings for A/C and space heating following the seasonal change of temperature and the room status – occupied or unoccupied. For guest room hot water supply, the water temperature should not be higher than 48oC. Hoteliers should also make sure that lights in unoccupied guest rooms and meeting rooms are turned off, whilst doors and windows in air-conditioned spaces have to be closed. Equally important is that guest room HVAC units should be clean and their filters regularly replaced.

•

5.22.4

The Energy Aspects of Environmental Assessment in Hotels

Environmental awareness is gaining momentum in the lodging industry across the world. Apart from the tightening legislative controls, the increased “green demand” from customers is also a major driving force of such a movement. This is evidenced by the result of a recent survey that nearly 90% of British tourists considered it part of a hotel’s responsibility to actively protect and support the environment [22]. It is believed that these green tourists are more likely to book a lodging facility with a responsible environmental attitude. An increasing number of hoteliers have recognized the importance of environmentally responsible actions. They have started establishing an environmental management system, implementing green practices, and realizing the consequent benefits. However, hoteliers could hardly find an objective and systematic approach to measure the effectiveness and efficiency of their green actions. As a consequence, various assessment methods have been introduced to evaluate and benchmark the environmental performance of hotels, such as the Hotel Building Environmental Assessment Scheme (HBEAS), Green Globe 21, and ECOTEL, and so on. Collectively they are called “environmental assessment methods” (EAMs), which share the same core notion – providing operational guidelines and assessment criteria for hotel managers in respect to environmental issues. EAMs are not laws, but rather accreditations that encourage green practices through awarding credits or points for various addressed criteria. The assessment process can be done by self-declaration, certification by independent agents, or by the

862

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government [23]. It is usually conducted by benchmarking against a set of prescribed quantitative (such as energy input) and qualitative performance (such as use of sustainable raw material) indicators of diverse objectives. These indicators are relevant and achievable by a proportion of the industry [24]. The total score awarded is the aggregation of the total number of credits obtained, and this will be categorized into one of the preset performance grades. If the applicant achieves the performance grades, it will be awarded with an eco-label. Initiated by the Hong Kong Hotel Association, the HBEAS aims at promoting eco-friendly management and operation practices for hotels [25]. More specifically, it encourages reduction of natural resources consumption, reduction of waste and effluents, whilst maintaining a comfortable, healthy, and productive indoor environment. The assessment framework is clearly divided into two sections: (1) environmental management, operations, and maintenance practices (32 criteria); and (2) facilities and building performance (50 criteria). The former considers the actions by a hotel in endeavoring to reduce environmental impacts through effective operating practices, i.e., the software. The latter, on the other hand, looks at the hardware of a hotel, such as the building services systems, energy input and output, etc. The scores of the two sections are rated separately. A most important element under energy-related issues is energy consumption, since it is a significant benefit that EAM could bring to hotels. For HBEAS, energy-related issues are most valuable among other issues. It is noted that HBEAS puts a heavy weighting on energy-related issues. A maximum score of 82 can be attained in this part, which accounts for around 34% of the total score in the Scheme. It was pointed out that the current EAMs do not help investors and designers to reveal the actual resources requirement for fulfilling various assessed points. In other words, the incremental costs and benefits associated with each credit are unknown. This economic consideration is especially imperative as firms would only invest in specific environmental strategies that have paybacks within an economically viable timeframe. In this regard, environmental costing studies should be promoted so as to generate useful economic information for quantifying the impact of credit. Earlier and recent research about environmental costing could serve as a reference for developing an economic link to these credits in EAM [26]. Furthermore, benefits from attaining the certificate have been challenged by the gap between attitudes and behavior as well as the emerging of green washing [27]. Also, there is inadequate guidance for investors to prioritize the measures in an EAM. Yik, Burnett, and Prescott [28] pointed out that there was an “inherent trade-off between assessment criteria.” For example, hotel developers and operators would try to achieve higher scores for reduced energy consumption at the expense of worse indoor environmental quality by lowering ventilation rate. If insufficient guidance is offered, the developers cannot decide their priorities, which may thus deter their participation in the voluntary assessment. However, the scoring systems in most current EAMs are so complex that investors can hardly cost alternatives, or judge the value of any additional investment to improve the assessment outcome [29].

5.22.5

Carbon Audit as a Means to Reduce Energy Usage in Hotels in Hong Kong

Within the tourism industry, hotels are generally regarded as one of the major energy end users. In particular, the rapid growth in the number of hotels in the past three decades in Hong Kong has brought a remarkable increase in energy consumption. For example, in 2011, there were about 184 hotels in Hong Kong, consuming in total at least HK$13,838 million worth of energy [30]. Table 1 shows the estimated amount of pollutants directly or indirectly emitted from the hotel sector in Hong Kong. The reduction in the emission in the late 1990s was mainly due to the installation of flue gas desulfurizers and precipitators, which can effectively help clean up to 90% of the sulfur and ash. A slight reduction in NO2 and CO2 was also observed during the same period. However, it can be witnessed that emissions rebounded following an increase in the number of new hotels in the early 2000s. This indicates that existing green measures may not be able to cope with the estimated rise in pollutant emissions. Following a tourism expansion policy, it is projected that the number of hotel rooms will rise from 62,470 in 2011 to 71,024 by 2016 [31]. The surging up of rooms implies that the carbon activities and associated emissions will also be on the increase then. In recent years, the Environmental Protection Department of Hong Kong has encouraged carbon audits in various enterprises. A dedicated portal for assisting the estimation of greenhouse gas emitted by the business has been established. However, local hotels have been slow in following the steps in estimating the greenhouse gas. As observed, hotels merely share the environmental protection practices and some achievement on the web, and there were no quantified figures of greenhouse gas reduction. These environmental protection practices include the application of variable speed drive, LED lighting, the use of building management systems (BMS), the use of sensors for turning on/off rooftop signage, participating in a used cooking oil recycling program, diverting bathroom exhaust for cooling down lift machine rooms, and so on. However, it is noted that one of the local hotels has already launched a carbon-offset program – the counter balancing of carbon emission through the purchase of carbon credit to Table 1

1990 1995 1999 2003

Estimated amount of pollutants produced by the hotel sector SO2 (ton)

NOx (ton)

CO2 (ton)

Particulate (ton)

2772 2105 1576 1889

3187 3147 2742 3022

655,039 613,251 515,584 605,014

151 150 89 104

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help fund projects that reduce or offset overall greenhouse gas emission. The program was to donate funds to a Chinese project that was certified in 2009 under the United Nations Clean Development Mechanism, Executive Board and the Board of Climate, Community and Biodiversity Standard. A stakeholder in this low-carbon movement is local hotel associations. The support that these trade or professional associations can render would include the following: (1) provision of regular technical seminars about the trend or experience in using energyefficient equipment or renewable energy technology, (2) writing support letters to research institutes for application of research funds in developing energy saving devices, and (3) offering support or grants to demonstrative projects about the application of energy saving technology. In addition, hotel associations may also present awards to those supporters in the low-carbon campaign by acknowledging their endeavor and recognizing innovative breakthroughs in energy saving actions. Such recognitions and encouragements would give incentive and momentum to hotel professionals to keep on supporting low-carbon campaigns.

5.22.6

Case Study

To illustrate the effectiveness of successful implementation of an effective energy management in hotels, the following case study for energy conservation program in a five-star hotel in Hong Kong is included. The hotel is located in the central business and tourist area of metropolitan Hong Kong. It was built in 1988, having a total floor area of 37,000 m2, with 15 stories above the ground and two levels of basement. There are more than 450 guest rooms, and a number of retail outlets, an outdoor swimming pool on the rooftop and a ballroom, etc. There are also seven food and beverage outlets, and an in-house laundry. Given the subtropical climate, the hotel building is centrally provided with A/C, all year round. The major electricity consuming building services installations include HVAC, lighting, lifts, small services, and power in guest floors and in various hotel departments, such as the laundry and kitchens. For HVAC installations, the central A/C plant consists of four identical direct sea water-cooled centrifugal chillers, each at 580 TR cooling capacity. Other HVAC equipment includes fans used in AHUs and primary air handling units (PAU), and primary and secondary chilled water pumps. Since 1993, the hotel has implemented a well-organized energy management program, similar to that presented in Section 5.22.3. The following technical actions have been taken for conserving energy use in the hotel:

• • • • • •

• • • •

Investigation on indoor thermal and visual environment (temperature, relative humidity, and illuminance levels), and appropriate corrective measures have been taken: resetting indoor air temperature (from 18 to 221C), adjusting lux level to satisfy requirements by professional organizations. Improving IAQ to ensure an acceptable indoor environment for both guests and staff. Improving the air balance of air supplying to and exhaust from the building. Problems of excessive exhaust from the hotel building causing internal negative pressure have been identified and rectified. Rebalancing of supply air from central A/C units has been carried out for better air distribution in the building. The study for the ventilation system in the car park has resulted in better operating efficiency. A study on the reuse of toilet exhaust air for A/C in the lift machine room has been carried out to save energy. Energy audits show that the nearly 25% of total energy consumption in the hotel is by its central chiller plant. Therefore a detailed study on the chillers’ operating efficiency has been undertaken. With the installation of appropriate instrumentation, the performance of the chiller plant was assessed and problems relating to design and operations identified: a butterfly valve in chilled pipe was mistakenly half closed, causing a depression of COP; chiller oversizing and chilled water pump undersizing; malfunctioning of a BMS system for the chiller plant, etc. Adequate follow-up measures have been taken: the valve’s opening readjusted and normal COP was restored; investigation of retrofitting the existing chiller plant with a smaller chiller is under way; optimizing the operating sequencing of existing chillers in the plant. Currently the central chiller plant is being operated satisfactorily, very much in line with the recommended operating characteristics, given that these chillers are almost 10 years old. In the boiler plant, air/fuel ratio and firing rate have been suitably adjusted to ensure the highest possible boiler efficiency. The pneumatic control system for one of the AHUs was recommissioned for better energy efficiency of the unit. This recommissioning process also served as a tool for training hotel operating staff. Replacement of existing lighting fixtures with the ones of higher energy efficiency to save electricity consumption in lighting systems. Installing a water conservation system (Platypus) to reduce water consumption, thus indirectly saving energy consumption.

The following good housekeeping practices for conserving energy has also been fully implemented in various departments of the hotel. For kitchen staff: Switch off or turn down kitchen equipment, in particular gas cookers, when they are not in use. Minimize door opening for cold stores and freezers. Switch on water tap only when it is needed but do not let water run continuously. Adjust water flow rate and water temperature to suit different kitchen jobs and cleaning.

• • • •

Energy Management in Hotels

864

Turn off ventilation and lights in the kitchen when it is not in use. Operate dishwashers at or near their full load to reduce the number of washer operations. Keep kitchens clean at all times to reduce the amount of water used for final cleanup at the end of a business day. Clean daily and check frequently all kitchen cooking equipment to maintain burning efficiency. Follow the operating instructions written by kitchen equipment manufacturers.

100

11,000,000

Electricity consumption (kWh)

10,800,000

90

Occupancy

10,600,000

80

10,400,000

70

10,200,000

60

10,000,000

50 Electricity consumption

9,800,000

40

9,600,000

30

9,400,000

20

9,200,000

10

9,000,000

Occupancy (%)

• • • • •

0 1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

Year Fig. 9 Electricity use in the hotel from 1990 to 19999.

8,500,000

100 90 Occupancy

8,000,000

70

7,500,000

60 7,000,000

Gas consumption

50 40

6,500,000 30 20

6,000,000

10 5,500,000

0 1990

1991

1992

1993

1994

1995 Year

Fig. 10 Gas use in the hotel from 1990 to 1999.

1996

1997

1998

1999

Occupancy (%)

Gas consumption (MJ)

80

Energy Management in Hotels

865

Kitchen doors adjacent to dining areas should normally be kept closed to prevent excessive kitchen exhaust as its make-up air can be drawn from the dining areas (in consultation with the Engineering Department staff). For housekeeping staff: • Make sure that window drapes and/or blinds are closed when a room is not occupied. • In consultation with engineering staff, ensure that temperature and fan speed settings are set for guest rooms. • The room thermostats are correctly adjusted.

•

100

800,000

750,000

90

Occupancy

80 70 650,000

60

600,000

Diesel fuel consumption

50 40

550,000

Occupancy (%)

Diesel fuel consumption (L)

700,000

30 500,000 20 450,000

10 0

400,000 1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

Year Fig. 11 Diesel fuel use in the hotel from 1990 to 1999.

100

200,000

90

Occupancy

190,000

180,000

70 60

170,000

50 Water consumption

160,000

40 30

150,000

20 140,000 10 0

130,000 1990

1991

1992

1993

1994

1995 Year

Fig. 12 Water use in the hotel from 1990 to 1999.

1996

1997

1998

1999

Occupancy (%)

Water consumption (cubic metre)

80

866

Energy Management in Hotels

Report any leaking taps, running toilets, and similar faults. Ensure all room windows are closed unless opened for special reasons. Ensure that all power and lighting is off in unoccupied rooms as soon as guests have checked out (unless rooms have automatic access control system). For laundry staff: • Switch off lights and ventilation supply or A/C when the laundry is not in use. • Run full loads for washing machines to reduce the number of machine operations. • Loads should be weighed if necessary. • Ensure that water temperature and amount of water used in washing machines are in accordance with the washing machine manufacturer’s instructions. For front office staff: • Ensure that the main entrance door is kept closed all the time, to avoid air filtration. • The implementation of the energy management program has resulted in a significant reduction in energy and water use in the hotel since 1993, as illustrated in Figs. 9–12.

• • •

From these figures, it may be observed that the consumptions for electricity, gas, and diesel fuel in the hotel started to drop since 1993 when the energy management program was started, although the actual hotel room occupancy rate was on the contrary actually increased over the same period. For water consumption, a water management program was started in 1995, and a reduction in water use was clearly visible after 1995.

5.22.7

Concluding Remarks

Hotel buildings are a very special type of building. Firstly they are still buildings and therefore energy saving measures/technologies that may be applied to conventional buildings can also be applied to hotel buildings. Secondly, as compared to all other types of buildings, hotels are unique in the numbers and types of functional areas included, and have a 24/7 operational schedule, as well as a highly variable occupancy rate. Therefore energy management for hotel buildings must take these uniquenesses into account. In this chapter, typical energy use characteristics in hotels, using hotels in Hong Kong as examples, are presented. The key components of a suitable energy management program/framework that may be applied to hotels are highlighted. Moreover, the energy aspects of hotel environment assessment and the Hong Kong government’s carbon audit approach for the hotel sector are discussed. The case study included in this chapter on implementing a well-organized energy management program in a five-star hotel in Hong Kong clearly demonstrated the effectiveness of energy management in hotels.

References [1] Deng S, Burnett J. Water use in hotel buildings in Hong Kong. Int J Hosp Manag 2002;21:57–66. [2] Australian Government. Energy efficiency opportunities in the hotel industry sector: Project report. Canberra, ACT: Department of Industry, Tourism and Resources; 1999. [3] Department of Environment (UK), Guide 36: energy efficiency in hotels – a guide to owners and managers; Harwell: Energy Efficiency Office Best Practice programme; 1993. [4] Energy Efficiency Advisory Committee. Energy conservation within the hotel industry: hotels, guidelines for energy efficiency. Hong Kong: Hong Kong Government; 1992. [5] Energy Efficiency Office. Introduction to energy efficiency in hotels. Garston: Department of the Environment; 1994. [6] International Hotels Environment Initiative. Environmental management for hotels. Oxford: Butterworth-Heinemann; 1993. [7] Kirk D. Environmental management in hotels. Int J Contemp Hosp Manag 1993;7(6):3–8. [8] Energy Information Administration. Commercial buildings energy consumption surveys. Available from: https://www.eia.gov/consumption/commercial/data/1995/pdf/cb958. pdf; 1995. [9] Santamouris M, Balaras CA, Dascalaki E, Argiriou A, Gaglia A. Rgy conservation and retrofitting potential in Hellenic hotels. Energy Build 1996;24(1):65–75. [10] Deng S, Burnett J. A study of energy performance of hotel buildings in Hong Kong. Energy Build 2000;31(1):7–12. [11] Deng S, Burnett J. Energy and water use and their performance explanatory indicators in hotels in Hong Kong. Energy Build 2003;35(8):775–84. [12] Priyadarsini R, Wu X, Lee SE. A study on energy performance of hotel buildings in Singapore. Energy Build 2009;41:1319–24. [13] Trung DN, Kumar S. Resource use and waste management in Vietnam hotel industry. J Clean Prod 2005;13:109–16. [14] Deng S, Burnett J. Energy use and management in hotels in Hong Kong. Int J Hosp Manag 2002;21(4):371–80. [15] Yao Z, Zhuang Z, Gu W. Study on energy use characteristics of hotel buildings in Shanghai. Procedia Eng 2015;121:1977–82. [16] Lam JC, Chan ALS. Characteristics of electricity consumption in commercial buildings. Build Res Inf 1994;22(6):33–42. [17] Redlin MH, deRoos JA. , Gauging energy savings: further applications of multiple-regression analysis. Cornell Hotel Restaur Q 1980;20(4):48–52. [18] Stram DO, Fels MF. The applicability of PRISM to electric heating and cooling. Energy Build 1986;9(1):101–10. [19] Lam JC. Degree-day climate parameters for Hong Kong. Int J Ambient Energy 1995;16(4):209–18. [20] Lam JC. Climatic and economic influences on residential electricity consumption. Energy Convers Manag. 1998;39(7):623–9. [21] Ling S. Personal communication with hotel engineer; 2000. [22] International Hotels Environment Initiative (IHEI) Research. Consumer attitudes towards the role of hotels in environmental sustainability. Available from: http://www.hotelonline.com/Neo/News/PR2002_3rd/Jul02_IHEI.html#Them; 2002. [23] Lee WL, Chau CK, Yik FWH, Burnett J, Tse MS. On the study of the credit-weighting scale in a building environmental assessment scheme. Build Environ 2002;37:1385–96. [24] Font X, Harris C. Rethinking standards from green to sustainable. Ann Tour Res 2004;31(4):986–1007.

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[25] Hotel Building Environmental Assessment Scheme (HBEAS). Hotel building environmental assessment scheme: an environmental assessment for existing hotels, Version 1/100. Hong Kong: Hong Kong Hotels Association; 2000. [26] Chan WW. Partial analysis of the environmental costs generated by hotels in Hong Kong. Int J Hosp Manag. 2005;24(4):517–31. [27] Wong W, Chan K, Lo J. Hong Kong hotels’ sewage: environmental cost and saving technique. J Hosp Tour Res 2009;33(3):329–46. [28] Wearing S, Cynn S, Ponting J, Matthew M. Converting environmental concern into ecotourism purchases: a qualitative evaluation of international backpackers in Australia. J Ecotour 2001;1(2-3):133–48. [29] Burnett RT, Bartlett S, Jessiman B, et al. Measuring progress in the management of ambient air quality: the case for population health. J Toxicol Environ Health 2005;68 (13–14):1289–300. [30] Hong Kong Tourism Board. A statistical review of Hong Kong tourism. Hong Kong: Hong Kong Tourism Board; 2010. [31] Hong Kong Tourism Board. Hotel supply situations. Hong Kong: Hong Kong Tourism Board; 2011.

Further Reading Zmeureanu RG, Hanna ZA, Fazio P, Silverio JG. 1994. Energy performance of hotels in Ottawa. ASHRAE Trans 1994;100(1):314–22.

Relevant Website http://www.hotel-online.com/News/PR2002_3rd/Jul02_IHEI.html Hotel Online.

5.23 Energy Management in District Energy Systems Tahir Abdul Hussain Ratlamwala, National University of Sciences and Technology, Islamabad, Pakistan Ibrahim Dincer, University of Ontario Institute of Technology, Oshawa, ON, Canada r 2018 Elsevier Inc. All rights reserved.

5.23.1 Introduction 5.23.2 Energy 5.23.3 District Energy Concept 5.23.4 The Need for District Energy 5.23.4.1 Benefits of District Energy Network 5.23.4.2 Applications of District Energy Systems 5.23.4.2.1 Urban area 5.23.4.2.2 Rural area 5.23.5 Historical Developments 5.23.5.1 History of Development 5.23.5.2 History of Legislations 5.23.6 Energy Management 5.23.6.1 Energy Management Techniques 5.23.7 District Energy Systems 5.23.7.1 District Power Generation 5.23.7.2 District Heating 5.23.7.3 District Cooling 5.23.7.4 District Energy 5.23.7.5 Integrated District Energy Systems 5.23.8 System Analyses and Assessments 5.23.8.1 Occupant Behavior Model 5.23.8.1.1 Stochastic model for occupant behavior 5.23.8.2 Building Energy Demand Model 5.23.8.3 District Energy Supply System Model 5.23.8.4 Energy Analysis 5.23.8.5 Exergy Analysis 5.23.8.6 Environmental Analysis 5.23.8.7 Economic Analysis 5.23.9 Case Studies 5.23.9.1 Case Study: Geothermal Based Multigenerational System for Meeting Districts Energy Need 5.23.9.1.1 System description 5.23.9.1.2 Analysis 5.23.9.1.3 Results and discussion 5.23.9.1.4 Final remarks 5.23.9.2 Case Study: Combine Solar and Geothermal Energy Based System for a District 5.23.9.2.1 System description 5.23.9.2.2 Results and discussion 5.23.9.2.3 Final remarks 5.23.10 District Energy Systems Projects 5.23.10.1 Sweden: Advantage of District Energy System 5.23.10.2 District Energy System of Edmonton, Canada 5.23.11 Future Directions 5.23.12 Closing Remarks References Future Reading Relevant Websites

5.23.1

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Introduction

With the rapid modernization and technological developments, more and more people are attracted toward urban life style. Estimates suggests, back in 1980, 30% of the world’s total population lived in cities while rest of them in rural areas; however, by

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2011 these figures increased to 50%, as per United Nations. It is also predicted, that these numbers could go up to 70% by the year 2050, which means that out of 9.6 billion population, 6.4 billions will be living in cities by 2050 [1]. Some study also suggests, that by 2100, all of the world’s population will be urbanized [2], while the world’s population by the year would jump to 11 billion [3]. To support the needs of this growing population, things should be planned before to avoid hassle in future. In spite of this rapid growth, the rate of increase in world’s energy production would not be linear, there are certain factors like climate change and the urban heat island effect, and this would significantly decrease the heating load of the buildings while on the other hand cooling load will be increased. If this happens, it will make heat pumps more popular, especially in the areas where round the clock noted temperature is mild. Larger number of transportation vehicles, due to larger number of population will yield massive release of pollution in our atmosphere; therefore, the need of clean and healthy indoor air will be increased. This could be another reason for the sharp increase in energy demand in near future. However, as per today’s growth in the field of generating clean, green energy, it can be said that we need to cope up in order to come up with a technology, system, approaches or practices that could end up the future dilemma. World need a smarter technique that could address the top pillars of energy, i.e., security, sustainability, and affordability, all at a same time. The solution to the above stated problem is a district energy system (DES). A DES is recognized as a system or method or option that is capable of supplying required energy in the form of heating, cooling, or electricity, by harnessing them through available local waste or renewable energy resources. The DES system could compose of several components out of which the highlighted one would be: thermal energy storage device that ensures the supply of energy to its consumers in day time and during maintenance [4–7] and an intelligent network algorithm that could cope up the energy demand by energy supply using user behavior analysis and meteorological data. Another advantage of the system would be, previously well-developed technologies, such as, wind, solar, geothermal marine, and hydro, could be easily merged with DES to make a single platform in order to address the global dilemma. This chapter will give its reader an overview regarding the developments, application, and drawback of DESs systems by comparing various economic, environmental, and technical claims.

5.23.2

Energy

Energy plays a pivotal role in the development of any country. In this fast moving era, gap between energy generation and demand has become a norm due to unprecedented demand for energy. The world has shifted its focus from more energy generation to the smart and wise energy usage. The smarter use of energy includes generating multiple output from single source of energy and setting up a centralized energy systems capable, such as, DESs. DESs are capable of meeting (COM) both heating and cooling requirement of an entire district in a more efficient and smarter way. Energy analysis is the backbone to define the efficiency of any system, network, or application. The highlighted difference between ancient systems and DES is that, DES has the capability to store thermal energy. This component brings an additional component in the system, its analysis is directly affected by the thermal losses of storage and transportation. Depending upon the need, the system could be configured to have longer time period of storage, but in that case, thermal losses would also increase. This thing may be neglected in day to day application, but in case of seasonal energy storage system, these losses are significant. For this reason, time is considered as the main parameter for the energy analysis of any DESs.

5.23.3

District Energy Concept

DESs are capable of fulfilling different needs of mankind such as (1) heating affect, (2) cooling effect, (3) provide electricity, (4) provide low temperature hot water for domestic use. Number of energy sources, i.e., renewable or non-renewable can be used in combination to give a solution to current world energy crises. The energy generation of United States using several resources is shown in Fig. 1. The figure illustrates that how the dependency of United States changes within the span of 9 years. It can be noted that the use of coal and oil have been decreased while the utilization of renewable energy sources such as wind, hydro, solar, and natural gas is increased. The demand of nuclear energy resources remains same. If we do a comparison between Figs. 1 and 2, then this can be noticed that natural gas and renewable energy sources are becoming major shareholder of United States energy market. Increasing interest in utilization of renewable energy sources to generate cleaner and sustainable energy made this thing clear that in order to notice some significant increase in the demand of DES, renewable energy sources need to be embeded. In Table 1, each of these resources have been discussed on the basis of their possible advantages and disadvantages associated with their use with DES. For the successful implementation of any system and to make it economically/commercially viable, this thing is considered with great importance that whether the input sources is readily and easily available or not and to what extent it will affect our environment. In addition to the availability of these natural resources, there viability also largely depends on local and national policies, which should be considered during system designed. The right move and consideration during designing phase of the system is a key to insure the sustainably of future generation DES. For future designs, along with the consumption of clean and green energy sources, sustainable designs, and improvements should also be considered.

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US energy generation (2005)

US energy generation (2014)

1% 13%

9% 19%

19% 67%

72%

Renewable

Nuclear

Renewable

Fossil fuels

Nuclear

Fossil fuels

Other

Fig. 1 Energy generation trend of United States for the year 2005 and 2014. Data from IEA. Energy and climate change. World Energy Outlook Special Report; 2015. p. 1–200.

Projected US energy generation (2040)

0%

18%

34% 16% 31%

1%

Renewable

Nuclear

Natural gas

Oil

Coal

Other

Fig. 2 Projected energy generation trend of United States for the year 2040. Data from IEA. Energy and climate change. World Energy Outlook Special Report; 2015. p. 1–200. Table 1

Detailed discussion of each available resource

Source

Detail

Advantages

Disadvantages

Geothermal

Specifically need to be built above large geothermal sources Oftenly use wood or crop based material to provide heat Combustion of urban waste to provide heating

Can provide year long heating and cooling using district heating plant Renewable source that has strong advantages in sustainable future Utilization of heat generated from building waste

Geologically limited and usually is only efficient in areas where temperature is moderate Limited availability

Fossil fuels

Burning of coal or natural gas to provide heat

Solar thermal

Using sunlight and solar collectors to provide hot water

Processes and infrastructure already in place and operation Passive and active systems with the option to also provide cooling during warmer seasons using appropriate chillers

Biomass

Waste incineration

5.23.4

Potential health effects from emission when improperly managed Large source of greenhouse gas emission Geographic assessment as well as proper planning need to be carried out

The Need for District Energy

Climate change is a severe concern for many organizations around the globe. High levels of greenhouse gas (GHG) emission have caused many devastating changes in our depletion of ozone layer and other pollution related matters. Precipitation of acid levels has risen to extreme levels in the past few decades due to industrialization and process engineering.

Energy Management in District Energy Systems

Grid dependency

871

District heating and cooling system

DH system without CHP

System in combination of buildings

System installed on single building

Combined heat and power system (CHP)

Self energy productivity

CHP for building i.e., generation from waste

Fig. 3 Ideal characteristic for building energy supply system. CHP, combined heat and power; DH, district heating.

To counteract such calamities in climate it is important to address issues like using energy efficiently and intelligently. Recycling of waste energy that is emitted in the environment and supplying it directly to the municipal or domestic level network. Carbon fuel emission needs to be reduced from our industries and factories to help minimalize the GHGs coming out from these processes. This will promote a greener environment with less efficient systems to be disregarded and need to be termed as obsolete. The major challenges for the last few generations have been to combat the energy crisis with something sustainable. There have been several issues domestically as well as internationally on how to tackle such immense global warming crisis. Energy and environment management is necessary to have an energy based economy with clever use of fossil fuels and renewable energy. District energy is an interaction of district heating (DH), cooling, domestic electricity, and hot water supply. Certain techniques can be implemented to overcome the heat loss by bringing back the rejected heat again to the system. It is essential that these systems are economically attractive toward converting heat from industrial, commercial, and residential sectors to effective energy. Fig. 3 showcases various characteristics of ideal building energy supply system.

5.23.4.1

Benefits of District Energy Network

There are various benefits of using DES. Some of them are enlisted in this chapter below:

• •

• •

DES have economic, environmental, and social benefits for those people who are using this technology in a more planned economy. The control of energy can be managed from one central unit rather than distributed supplies which result in low emission of carbon dioxide gases. Carbon dioxide storage system often known as harmful gas sequestration allows long term storage of such gases so that they may not deplete ozone layer. This is an ecological benefit for the environment and is a source of reduction in cost of the complete life cycle of the system. When DH is applied to geothermal systems they tend to increase operation stability and reliability with an increase in life span. Cost of running the system is affordable and fuel costs are almost negligible in terms of the overall energy cost. It helps in reducing the regional air and noise pollution coming out of the industrial sector. Promoting technology that emits lesser amount of gases and harmful substances in the environment especially for heating and cooling processes.

872

• • • • • •

Energy Management in District Energy Systems

More infrastructure development in this energy system can generate jobs and improve the existing setup as well. Through ice storage and chilling water tanks the DESs can reduce the overall electric consumption for the plant. On the other hand, if thermal energy is stored in the central unit of the plant the management of such systems would be more flexible and productive. These systems can be efficient if load is calculated according to the energy that is being recycled for every part to decrease the cost of fuel consumption. Energy cannot be misused once it has been conserved for majority of the parts that have losses. Therefore it gives energy security for several years to the consumer. Operating and maintenance costs are far lower because installation and running of these systems increase energy efficiency. Specialized employees at the central unit of the plant improve the ergonomic and plant management. Setup of a better economic model will not require any personnel to keep an eye on chiller or boiler functionality. DES are capable of future expansions in the central networking unit and other facilities. It often is a source of low energy cost distribution with metering units.

5.23.4.2

Applications of District Energy Systems

5.23.4.2.1

Urban area

DES can be implemented in both urban and rural areas. It can be utilized at its peak or with higher efficiency in the areas with dense population [9]. It reliably can supply its consumer, both heating and cooling effect while reducing/eliminating the maintenance fees at same time. In order to extract the most of its offered features, DES needs an efficient distribution and piping network, for which significant capital amount is required. Users always expect the supply with no delays or cutoffs. For this a proper network needs to be designed with several in/out routes, so that if for some reason one route got damaged; supply could be resumed from the other one. The main lines are often located in tunnels or underground channels. For this reason, the DES system could only be successfully implemented if its network and construction bring into account at the time of urban development, if this not happens then it will yield the increased capital investment and more complexity. All of the main electricity consuming buildings in the area could be connected to a centralized energy provider through smart thermal grid, this will not only fulfil heating and cooling requirement of the buildings but also enables monitoring of its consumption. This concept is very similar to that of smart electricity grid system. The main challenge for smart thermal grid is to consume low-temperature heat source, while to that of smart electricity grid the main challenge is to overcome fluctuations caused during renewable energy productions. In near future, this is expected that of these technologies could be used at same time in same system. Such system could bring a revolution in real time district and building monitoring systems.

5.23.4.2.2

Rural area

DES does not have its applications restricted to urban areas only, around the globe one can found enough places where electricity is unavailable due to its harsh topography and comparatively low population density [10]. The best possible solution for such places to overcome this problem is to install multigenerational systems which could consume renewable energy and get it coupled through district heating and cooling (DHC). Such system would be capable of providing electricity as well as hot and cold effect wherever needed. In study [11], they have considered embedding parabolic hot water solar generator with absorption chillers. Through analysis they have concluded that system could give better energy and exergy efficiencies; however, it would not be economically viable as the estimated payback period came out to be 18 years.

5.23.5 5.23.5.1

Historical Developments History of Development

Energy management through centralized DESs has been done since 14th century [12]. The reason of its rapid demand is that, this centralized system could consume any of the available energy, from geothermal, fossil, biomass, and waste incineration [13]. The primary heat carrier fluid for DESs was steam until 1930s; the system used pipes flowing through concrete ducks having steam traps and compensator, but they did not get fame as they were on high risk from steam explosion; moreover, there were large amount of heat losses also, making system incompatible. These initial systems were mainly installed in apartments to reduce the risk of boiler explosions. The more advance system of district heat transport system were accompanied of pressurized hot water pipes in concrete, efficient heat exchangers and valves. These systems also failed to provide its users a control over heat demand but showed a bit of improvement in fuel savings, this era is characterized as second generation of DH systems. In 1970s previously modified systems were modified which said to be the third generation of the said technology. In this modified system they used water under extensive pressure but at relatively lower temperature than that of previous generation, this technology was referred to as “Scandinavian district heating modification,” this system was composed of perforated buried pipes along with the compact substations and is currently in use throughout the world [14]. The technology advancement of district heat energy system is shown in Table 2 along with the energy resources which they consumed. Fossil fuel, such as, coal is still in use for the DESs in China, extensive exergy and energy analysis have been conducted to enhance the efficiency of the system. Lio et al. [15] in 2013 carried out energy analysis using the first law, and exergy analysis while taking into account chemical, thermochemical, kinetic, and potential exergy. He concluded that the extraction ratio can be used as

Energy Management in District Energy Systems

Table 2

873

Generations of district energy system

Peak technology period Heat energy source

First generation

Second generation

Third generation

Fourth generation

1880–1930

1930–80

1980–2020

2020–50

Steam, boiler, and coal

Combined heat and power, coal, and oil

Biomass waste and fossil fuel

Heat recycling, renewable sources

a design criteria for the system to enhance its efficiency. Presently, European nations are the top most users of district energy technology, number of countries have took large steps to move toward sustainable advancement in DESs. Sweden took first steps in this united effort by moving their traditional district system based on oil to coal in 1973, since then Sweden has increased the usage of biomass in order to cut the demand of fossil fuel [16]. Various feasibility studies are also conducted to make the DESs “sustainable,” and cope up with the world’s current need of energy shortage and efficiency. The studies suggested to make this system efficient and decrease losses, renewable energy sources need to be combined with the existing system which will make the overall system more competitive [13,17–19]. In most of the system, the energy carrier or heat transfer fluid used are: low pressure steam, hot water, and hot air. The transmitted thermal energy is characterized into three broad spectrum according to its application: heating, cooling, and heating and cooling. Along with this, the energy resources which DES utilize are: fossil fuel, nuclear power, energy from waste, solar energy, heat pumps, and biomass. In Europe, Asia, and America, renewable energy resources such as geothermal and solar has been effectively used with DES, these cogeneration plants are often referred to as combined heat and power plant (CHP). Various new technologies have been studied to make the overall DES more cost effective, smaller and efficient [20]. Cogeneration plants can be further divided into topping cycles and bottoming cycle, which can further be revised into utility cogeneration, industrial cogeneration and desalination systems. One of the key factor to make DES liable is to utilize waste heat normally found at industrial facilities to supply heat to nearby town and buildings, it is important to characterize DES for better comparison and analysis based on several parameters.

5.23.5.2

History of Legislations

For successful energy generation, various steps need to be taken, among these steps the designing of proper legislations and polices governed by the government has its own significance. The main emphasis of these polices should be climatic change due to energy generation, CO reduction, and energy efficiency. Several countries made different policies which leads to a single goal of energy conservation. DH systems are also a component of same legislations which leads to the centralized heating or cooling so that proper control on energy wasted could be taken. Some of the same policies are: European Community and United Kingdom Energy White paper, energy saving program (ESP), renewable energy strategy (RES), renewable heat incentive (RHI), etc. All of these policies are aimed to support DH network, their advancement and development through financial aid from government. In general, a series of white paper has been issued [21–23], which clearly emphasise on application of DESs fuelled with low carbon resources such as solid waste or biomass. It is necessary to formulate such policies, especially where private firms could get encouraged through a subsidy. On the other hand ESP was aimed to reduce the electricity cost in those areas where net income is below the average income of the nation, these could be done by improving energy/component efficiency [8,24]. If we discuss regarding RES, the most highlighted feature of the policy was the introduction of “clean energy cash back” for those small scale industries/communities who took an initiative to support local grid through their own clean energy generation so that district community based electricity generation could catch eyes [25]. The newly implemented RHI scheme offers financial assistance for installation of DH systems [25,26]. HESS is a key policy proposal which will be introduced in near future, the policy will focus on making DH economically viable by installing a system at a location where heating density is 43000 kW/km. The policy took a model of United Kingdom which recently identified and installed centralized DH system total land of 5.5 m only (i.e., the places with large heat densities), in spite of such a small area, this system is contributing 20% in the overall heat demand of country. This DES is the largest and the most successful project in its own [27]. There are number of papers that outlined the developments in this field of DH. Study conducted by Lund et al. concluded that DH system could make a huge difference in the field of renewable energy systems, in account of environment, capital cost and fuel demand [28]. At a time, 46% of the net heat demand of Denmark is covered using DES, they researched and found out that the best solution to deal with their local energy crises is the exponential expansion in district schemes, with small scale heat pumps installed in local homes at remote areas. This thing would both in both conditions, with fossil fuel and with renewable energy system. On the other hand, Egeskog et al. [29] investigated the probability of biomass gasification, based on cogeneration of biofuels for both, transportation and DH purposes in European Union. Kalina [30] investigated the economical and technical benefits of modifying normal coal-fired heating plant with bio steam gasification, the research proposes that this system could be helpful in reducing both, the carbon emission and fossil fuel consumption which alternatively yields in energy savings. Some more researches were conducted in order to document the economic feasibility of integrating cooling cycles into CHP system (i.e., trigeneration), via absorption chiller, so that energy savings and increased efficiency could be studied [31,32]. Knuttson et al. [33] and Cakembergh et al. [34] modeled such systems using simulation based approaches and analysis techniques.

874

Energy Management in District Energy Systems

5.23.6

Energy Management

Energy management is a fusion of technology and management to increase the efficiency of production and enhance the results of output energy performance. It is necessary that management is related to renewable energy so that proper integration of energy systems can be achieved. It is important to control the budget and cost of energy consumption within the required regulations so that a company can have proactive growth for future investments. Energy needs to be utilized in a productive manner so that there is an increase in the chance of DESs to be implemented with effective cost budget. Management in energy sector has been a real problem for many developed economies due to huge losses in heating and cooling systems. To maintain energy efficiency in a system it is vital to manage renewable energy sources with comparatively low heat loss. This will certainly bring down the cost per unit price of energy and lessen the burdens of GHG emissions as well. DES once implemented through proper industrial and domestic channel, it improves the productivity and reduces the vulnerability of high waste heat energy. A few organizations are working for development of DESs so that high tech technology investment could be a source of savings for the government in long term. Revenue generated with DESs is basically utilized in maintenance and up gradation of the systems involved in recycling heat waste energy.

5.23.6.1

Energy Management Techniques

Energy savings are hard to come by if proper rules and regulations are not laid down by government and public sector. Policies which are outdated and cannot support the vision of clean sustainable energy tend to deviate from the main issue of recycling energy back into the system. Therefore, a strategic plan needs to be devised so that the policies are set clear with proper energy management techniques. Large organizations need a clear cut plan and technique to recycle heat waste energy whereas small organizations can still have rough draft strategy. These techniques and strategies need not be set up by a single individual or by one ruling authority. It is preferred that stakeholders devise strategies which benefit the energy management system and those who are implementing various technological facilities. Typical management standards ISO 14001, BS EN 16001, or ISO 50001 need to be implemented for recognition of how the system is going to produce cost effective solution. Cost management system needs to have a proper framework so that energy plans can be executed with other organization activities. The techniques that are laid down need to be in the form of a functional document that ensures that the DES aims are fully met with proper deadlines. Four main issues need to be intact which include the whole system, processes, operations, and resources. The initial position of an organization might not be that strong to implement such techniques but it is necessary to cover all the policies based on merit. These techniques include: 1. Allocating of energy with specific tasks given to the staff and important commitments need to be considered across the platform. Employee budget, hierarchy, and time factor needs to be assigned so that they deliver accordingly. 2. Organizational structure needs to be self-sufficient to improve the processes and other activities related to energy management. 3. Carbon emission related policies and agreements need to be followed thoroughly with energy based solutions. Every organization needs to recycle and pump back its waste energy into useful cost minimal energy. 4. Energy efficient projects need to be audited over time so that proper financial budget could be set up for such procedures. 5. Procedures related to procurement of energy related equipment need to be developed with high tech budget. Such equipment need to be serviced with regards to its use of energy and its life. 6. Energy analysis reports need to be published in relation the energy recycled back into the system. 7. Monitoring and metering of DESs is essential for increasing efficiency with time and allocated budget every year. 8. Management trainee programs need to be conducted to educate staff and energy related personnel to meet the long term goals. This includes all the specialized and technical employees across the board who can manage energy efficiently. 9. Communication network should be upsized with proper advancements in energy policies within the organization. External factors relevant to such energy systems are to taken into account when working on district based management.

5.23.7

District Energy Systems

There are four basic components of DESs, which include district power, heating, cooling and combined power, heating, and cooling (Fig. 4).

5.23.7.1

District Power Generation

The fast rate modernization with the use of electrical dependent appliances has led to extreme spike in power. To cater to this unexpected spiked in demand, the governments around the world are setting up more and more fossil fuel based power plants. These fossil fuel based power plants have led to unprecedented GHG emissions which is also blamed at for current natural disasters such as hurricanes and typhoons. The use of district power generation and supply has the capabilities of lowering the

Energy Management in District Energy Systems

875

District power Power generation system

Storage

Distribution network Excess power

Supply

Control chamber

Supply power Central PMT

Local PMT

Residential/ industrial

Consumer Fig. 4 Main components of district power system. PMT.

demand and supply gap of power with the help of renewable based power generation systems. The biggest contestant in the area of district power generation is solar energy. The concentrated solar energy systems such as heliostat field systems have capacity of generating power in gigawatts while utilizing barren land and solar energy. Similar to the solar is wind energy and geothermal energy based power generation systems. These renewable energy systems can generate centralized power which can then be supplied to a nearby district. These renewable based power generation systems will also help in reducing the carbon footprint as they are well known for low to near zero GHG emissions. The use of these systems will also help in reducing the pollution and temperature in the district as they will be setup outside the district. The district power generation in usual is comprised of four components which are fuel, storage, control and distribution, and consumer as displayed in Fig. 5.

5.23.7.2

District Heating

DH is a combination of geothermal, waste to energy power plant, heating boiler, heat recovery system, combined heat plus power system, heat pumps, and solar energy. Geothermal energy systems require their heat input from brine that is from underground reservoir and use heat exchangers as energy conversion units. Location and depth of wells in the field is the basic commodity to supply base load heat to fulfill the demand. The running cost is negligible whereas the fuel cost is near to free. It is environmental friendly with low carbon dioxide emissions. Waste to energy heating system is basically dependent on the combustible waste as a fuel source with burning of waste as its conversion technology. They basically cause drastic air pollution therefore they need to be located far from cities and towns. This burning of waste generates electricity for heating systems in the DH network. These waste incinerators are efficient in producing low budget heat for DH plants. Remaining waste can be used for other purposes like construction and recycling plants. DH boiler has a few fuel sources such as natural gas, oil, electricity, wood chips, biogas, and coal. It can provide peak load for fuels such as coal, electricity, and gas. These boilers can also give base load for wood chips and pellets. The peaking load in these boilers helps reduce the overall system cost and has carbon dioxide free emissions for biogas and landfill gas as a fuel source. Waste heat recovery system uses heat exchangers to extract heat from industrial processes and sewage. Boilers are required to use this heat for supply with DH technology to convert low-exergy waste heat into useful supply for domestic heating. CHP systems run effectively on gas, biogas, coal and biomass as fuel source. Usually after second or third stage the heat is captured after steam and gas turbines have performed there processes. It is basically used for base load generation of heat demand or electricity. It is best to use CHP with the combination of boilers and storage system. Heat pumps have mechanisms that can take an input of ambient air, water, ground, or waste heat from industrial processes to drive its energy processes. Basic purpose of heat pumps is to produce base load generation or peaking generation for the required amount of demand and capital investment for such projects. The system consumes underground water, wastewater, and sewage water whilst recycles water from district cooling (DC) systems very efficiently. Solar thermal energy systems collect their energy from sun onto the collectors. They require huge land areas to install these collectors and require boilers for backup/peak load generation.

876

Energy Management in District Energy Systems

DH plant

Heat recovery from waste

Controlled boiler

Solar or geo thermal energy

TES element

Distribution network Return hot water

Supply hot water Control chamber

Supply air Supply hot water Heat exchanger

AHU

Room

Return air Return hot water Consumer Fig. 5 Main components of district heating (DH) system. TES, thermo efficiency system; AHU, air handling unit.

The district heat system comprised of four main components, each having its own significance and important, namely: heat source, energy storage system, control and distribution system, and user as shown in Fig. 5 [35–37].

5.23.7.3

District Cooling

DC consists of electric chillers, free cooling, and absorption chiller. Electric chillers require minimum amount of electricity for cooling. These chiller systems need to stay competitive with the energy requirement so government subsidies in residential and commercial electricity consumption are essential. Free cooling is another method where water from lakes, oceans, and rivers is used as fuel source and conversion method of energy is from heat exchangers. It is important that the plants are close enough to the path of water where it has a reliable cooling source. If the demand of the required electricity load is much more than what is required than the plant may need backup energy sources. Absorption chiller systems are operated on renewable sources or driven by surplus heat from waste incinerations. These chillers are incorporated with a heat source where the process of absorption uses heat that comes from waste incineration so the energy efficiency is quite high at the primary level. These cooling systems can be integrated with CHP plant and can give a trio combination of cooling, heating, and power [38–40]. The schematic of basic DC network is shown in Fig. 6.

5.23.7.4

District Energy

There are certain places in the world where temperature in night goes down whereas in days it rises up. At these places, power, heating and cooling features are required at a time. District power, heating and cooling provides the solution for stated problem, it enables the supply of power, heating and cooling load simultaneously. The combined supply could be extracted at a same time or as per season or even daily requirement. The DES is the combination of all three district power, heating and cooling, in this system the input energy source can vary from fossil fuel to renewable energy or heat recovery from waste. The required cooling feature can be provided using absorption chillers that can utilize the available input source for generating cooling effect. The energy storage device could be designed as per end user requirement varying from ice thermal storage from hot water thermal storage. Number of studies have been conducted to develop techniques to enhance the efficiency of DHC systems [41–45].

5.23.7.5

Integrated District Energy Systems

In modern era DESs consisting of power generation systems, heat generation system, and cooling generation system can be coupled with other energy systems for provision of more than three useful outputs. The integrated system used for

Energy Management in District Energy Systems

877

DC plant Heat rejection system

Chiller

TES element

Distribution network Return chilled water

Control chamber

Supply chilled water Heat exchanger

Supply chilled water

Supply air

AHU

Room

Return chilled Return air water Consumer Fig. 6 Main components of district cooling (DC) system. AHU, air handling unit; TES, thermo efficiency system.

Power Power

H2 produced

Electrolyzer

Energy source

Hot water Heat Cooling system (absorption cooling system)

Power

Heating

Cooling Cooling Air drying process (cooling with dehumidification)

Dried air

Fig. 7 Efficient design model for integrated district energy system.

multigenerational purposes are more beneficial from both economic and environmental perspective as they use a single energy source to meet multiple needs of a district. These integrated systems are capable of supplying hot water, heating, cooling, power, hydrogen, and conditioned air to a district to meet its both residential and commercial needs as shown in Fig. 7.

5.23.8

System Analyses and Assessments

Modelling an energy efficient DNS system is as important as to have an economical car so that one can afford its fuel consumption and mileage. One of the major drawback of DNS system is its high capital investment and slower payback period. Therefore, to overcome these, we need to come up with an economical DNS model which could be easily commercially viable and have lesser

878

Energy Management in District Energy Systems

Start

Input

Occupant behavior model

Define occupant

Input climatic condition

Occupant behavior modelling

Building energy model

Heat load calculations

Electrical load calculations

Air conditioning model

Electrical load summation

Energy demand profile

District energy supply model

Energy supply type

District energy supply

Output

End Fig. 8 Efficient design model for district energy system.

complexity. In Fig. 8 the flow of designing such model is showed. The given model comprises of three main components, occupant behavior model, energy demand model, and district energy supply model. We will discuss each and every model in detail, in other section so as to have a first-hand knowledge of its modelling. The basic overview of the system is; occupant behavior model simulates the total load of each of its consumer or occupant. This load comprises of the total number of appliances installed in their homes. Then energy demand and profiles of each building is generated and at the end the total energy supplied or required by the district is quantified.

5.23.8.1

Occupant Behavior Model

The main purpose of this component is to monitor real time condition and load consumption of buildings in order to come up with an energy profile. In previous modellings, the operational conditions of various buildings were standardize; however, in real life these conditions will vary respective to the number of load attached, their specifications and their life. These varying conditions will change the overall energy profile of the building and also the strategies to save energy such as usage of more energy efficient appliances and improvement in overall operational condition; so that life of the load could be increased. Therefore, to address these parameters, an occupant behavior model is developed which stochastically simulates all of these practical operating condition of energy consuming devices and occupant schedule, to resemble as much as possible to real world conditions.

5.23.8.1.1

Stochastic model for occupant behavior

Occupant behavior model works on transitional approach simulating the real time behavior of each and every component, Fig. 9 shows the flow cycle of occupant behavior model. The detailed description of working of model is given below. The system starts by identifying the number of occupants attached to it, then the model identifies the working hours of each of its occupant based of distribution function T1–T4 as shown in Table 3. Afterwards, working state of each occupant is developed, in an office environment there are limited number of working states, it is briefly shown in Table 4. There are two assumption made, firstly, that there will be random mobility among working states. Secondly, that working state of each occupant is depending on occupants attributes. Transition states of overall system can be modeled based on first assumption

Energy Management in District Energy Systems

879

Start

Data input

Generation of distribution function Generation of Markov matrices Decision of hours Decision of working states Calculations of heating and electrical load Estimation of occupant schedule

Data output

End Fig. 9 Flow diagram of occupant behavior model.

Table 3

Table 4

Definition of time interval T1–T4

Time

Definition

T1 T2 T3 T4

Time Time Time Time

when when when when

occupant occupant occupant occupant

starts work finishes work leaves for lunch comes back from lunch break

Defining working states of each occupant

State

Definition

Location

Load source of appliance

A B C D

Using PC Not using PC Being out Using two PC

Seat Seat Outside of office Seat

One PC þ one monitor Standby power of one PC and monitor Standby power of one PC and monitor Two PC and two monitor

which is extracted using Markovian technique. Markovian technique is significant because in it, transition state of next state depends on the present one. Moreover, due to second assumption made, transition probabilities of each occupant solely depends on its attributes; whereas, the occupant attributes are simple data that need to be gathered in advance for modelling, this data includes occupant age, type of job, consumption etc. In this example we are only considering type of job as occupant attributes on which basis the modelling is done. In order to represent transition probabilities from one state to other, Markov matrices is used. The Markovian process, generates occupant attributes on which basis the Markov matric is generated. Two kind of data is required for function development, firstly, the average duration of each of its occupant working in a state, i ¼1, 2…n and percentage distribution of every working states. From first data, the probability (ri) of each transition occurrence from one working state to other is decided. Percentage distribution (Sj) determines the working of each occupant with different attributes in working state j ¼ 1, 2…n. Both of these data will be further utilized to determine the transition probabilities of occupant for state i to state j (Pij). The mathematical equations to

880

Energy Management in District Energy Systems

Table 5

Percentage distribution of working states

State

A B C D

Table 6

Position Manager

Clerk

Sales

Engineer

30 30 40 0

60 20 20 0

20 20 60 0

30 20 20 30

Electrical load

Term

Example

Load calculation

Lighting Appliance controlled by single occupant Appliance controlled by occupant group Appliances always switched on i.e., fans for ventilation and fans for air supply Other electrical loads

– PC and monitors Copy machine Network servers

Capacity  schedule Input from occupant behavior model Capacity  schedule Capacity  schedule

Emergency, sanitary, security, etc.

Capacity  schedule

develop Markov matrices is given as follows: Pij ¼ 12ri þ ri Sj ði ¼ jÞ

ð1Þ

Pij ¼ ri Sj ðia jÞ

ð2Þ

Afterwards, using the Markov matrices with the inverse function method [46], each working state of every occupant can be effectively simulated. The measured data ri and Sj will be fed in the model to come up with exact simulation results. In Table 5 the measure data ri is fed in the system with Sj ¼ 6.25% for all states and every attributes, the results of this fed data came out to be: the expected state transition of the system is 1.5 h based on a 5 min. interval should be used in occupant behavior model. The calculated result of each occupant if further fed into occupant schedule, so that the power consumption and heat release from the appliances can easily controlled. Further, by inputting these parameters to building energy demand model, the energy demand profile of each and every building can be generated.

5.23.8.2

Building Energy Demand Model

The second most important component of DES model is building energy demand model. This model calculates the hourly consumption of energy whether electrical or heat and generates energy profile of each building. Weighing factor method has been utilized to calculate the space heat load. The air conditioning model converts space heat loads into hot water coil loads. Throughout the modelling it was assumed that air conditioning unit is installed on every floor of given building. Then, the total load of the system is calculated by taking a summation of both hot and cold water coil load. The total electrical energy consumption of building is calculated by taking a sum of all electrical loads attached to the system as shown in Table 6. This table also showcased the method of calculation of each and every load. The total capacity of each and every load is calculated depending on its number and wattage consumption. Furthermore, energy consumption in the field of comfort, sanitary, security, etc. are also calculated which came out to be 31 W/m2. The schedule is showed in Fig. 10. This value of total capacity is taken from statistical data [47]; whereas, the schedule pattern is taken with reference to the IBEC electrical demand profiles [48].

5.23.8.3

District Energy Supply System Model

This component measures total amount of energy flow within the targeted district. It takes energy demand profiles, environmental conditions, climatic conditions, energy supply type (i.e., heat or electricity) and location of the buildings as parameters so that efficient energy supply pipeline network could be designed. This model is designed to deal with multiple buildings in a single district at a time. The pipelines are designed (i.e., its inner/outer diameter and type of insulator coating) on the basis of peak energy consumption demand; whether it is heating or cooling. Each part load characteristic is made for all the components installed; whereas, for refrigerators, in addition to it coefficient of performance (COP) model is also generated by doing a regressing between chilled water temperature and condensed water temperature.

Energy Management in District Energy Systems

881

25

Schedule (%)

20 15 10 5 0

1

3

5

7

9

11 13 15 17 19 21 23 Time

Fig. 10 Obtained schedule for electricity load.

5.23.8.4

Energy Analysis

The advantage of DESs is that, it yields better economic and environmental benefits to its users [49]. To enhance the efficiency of any system, a 4E toolkit or analysis is done so that it could be optimized, these 4E are: energy, exergy, environmental, and economic. Various analysis and researches has been done previously under the same domain, De Carli et al. [9] performed the energy and economic analysis of DHC system. The system consist of a closed loop heat pump and the analysis was performed in a mild climate. By doing this analysis they concluded, by utilizing DHC system in the climatic conditions of Italy, the consumption of primary energy sources could be reduced to 50%–80%, if we compare it to traditional system. Udomsri et al. [50] used TRANSYS, a software used for simulation and modelling purposes, to perform an effective simulation and yield a parametric study on decentralized cooling within DH systems. The research concludes that COP’s of thermally and electrically driven components, could rise up to 0.5–0.46. The prediction of load which would be applied on a grid during any particular interval of day cycle is also very important. Powell et al. [51] designed an empirical model for DESs so that the heating and cooling demand can be predicted based on the climate and time. Through this model they showed that reliability and efficiency of the system could be increased by proactive working of system that requires forecast as a parameter to control building demand. One more study relevant to the topic has been presented by Powell et al. [52] in this research they put forward their findings on the chiller in a DC system having an energy storage device. The overall operating cycle of DES is very much important to increase the efficiency and decrease the energy consumption, yielding an increased life [53]. The general energy balance equation is written as _ in þ W _ out þ W _ out -ðm _ in ¼ ðm _ out _ in ¼ En _  hÞin þ Q _  hÞout þ Q En

5.23.8.5

ð3Þ

Exergy Analysis

Exergy is the amount of useful energy needed to perform some useful task. Exergy analysis is also one of the premier tool for the system analysis, by performing it we can actually relate the system with its overall surroundings. Exergy analysis yields ideal parameters that would be beneficial for the maintenance/tuning of the system. This analysis is also used to stop the careless utilization of energy resources by putting forward the indirect side-effects which it causes on our environment. Through monitoring the consumption of resources, the overall efficiency of system also increases while it would also be useful to calculate the total waste a system generates during the overall process [54]. There are number of articles/studies found that conducted exergy analysis and used the results to increase the systems efficiency. Zhai et al. [55] conducted a study in which he performed the exergy analysis of DHC system by embedding it with solar energy collector, the proposed system would be able to provide electricity, heating, and cooling; all at the same time. Another study was conducted by Gon [56] in which he optimized the heating and cooling of simple residential AC system using exergy analysis; whereas, Ozgener et al. [57] used exergy analysis to figure out the effect of reference state to evaluate the performance of geothermal DH system. The generalized exergy balance equation is written as

 T0 _ T0 _ _ out -ðm _ in ¼ ðm _ out _ in ¼ Ex _  exÞin þ 1 _  exÞout þ 1 ð4Þ Ex Qin þ W Qout þ W Tin Tout

5.23.8.6

Environmental Analysis

One of the most important reasons of utilizing DES is their minimum environmental effects. The DES could help us minimizing the GHG emission through several ways, some of the important are: it could spread the use of renewable energy on district level effectively and it could easily replace our current low efficient HVAC systems by centralized efficient energy systems [54]. There are number of method to perform environmental analysis on DES, among which the most famous one is the analysis of CO2 payback

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Energy Management in District Energy Systems

time (CPT). This analysis reveals the CO2 produced during the process which could be used in order to reduce the emission GHGs. The research conducted by Ihara et al. [58] used this method to evaluate the environmental impact of DES in Tokyo. For this analysis he used equation below CPT ¼

5.23.8.7

CO emission during the initial construction phase Annual CO emissions reduction by the introduction of new systems

ð5Þ

Economic Analysis

The last part of 4E toolkit is economic analysis. This analysis is also very important in order to evaluate the viability of the system. Economic analysis is the major backbone, this analysis decides the direction and design of the project. When four types of DES were compared on the basis of economical superiority, DH found to be more beneficial on places that are densely populated i.e., urban areas, highly populated buildings, and modern industrial towns. However, for low population areas such as rural, the economic advantages of DH systems are less clear. There are three main factors on which economic viability of district energy networks lies [59]: (1) overall production cost of thermal energy, (2) the efficient designing of thermal energy supply network through district that largely depends upon the number of occupants connected to the centralized system, and (3) customer connection costs. The connection cost of overall system can be reduced to greater extent if the DES designed and installed in parallel to the urbanization of any district. The cost of installing this system exponentially increased when one try to retrofit it in an already developed area/community. The economic analysis was performed in a small town of Canada of which population was around 2500, in this system the excess heat produced by the Kraft pulp mill was utilized for heating purposes. This analysis revealed that, to justify the installation and economic viability of DES, the population of the small town located near pulp mill should be increased along with the surface area and heating capacity. It was concluded that thermal heating network is not economically feasible for such small population. DES systems are more economically attractive for the places having high energy demands such as public offices/buildings, commercial markets, and highly dense residential zones. In such scenario, a partial thermal network was proposed that includes half of a town, a government organization for the management and maintenance of DES and a proper government legislation to give some initial subsidy to its customer; could make this system economically viable as well as attractive. To come up with an economical model, environmental factors should be strictly taken into account. A study was conducted [60] which proposed a model considering several economic and environmental factors with their effects on the performance of DH system. For the sake of comparison, the data was collected from 300 km Brescia DES in Italy which was considered to be the most efficient system of that time. The outcome of analysis reveals that district thermal energy networks recovers the capital investment in a few years by yielding effective energy savings and minimizing cost needed to again and again maintain the system as it is required in traditional energy plants. Additionally system also fulfills all of the environmental requirements which are expected by the modern system to yield by minimizing the GHG emission. Another factor to design an economical DES is the designing of cost effective supply network. The important parameters to design a transport systems are: quantity of heat delivering from the generation plant to its users, plant thermal efficiency, temperatures of input and output fluids, and type of insulation to minimize heat losses are significant. There are number of methods found in literature that are used to decrease the overall cost of the system by minimizing the initial cost of distribution network. In study [59], it was proposed the use of air conditioning network jointly with dynamic energy storage component will be effective to cover energy requirement of peak demand periods. It also minimizes the load on the system and increases its life. In Finland, the Jyvaskyla DES was tested. The aim of this test was to eliminate the utilization of expensive fuels during peak hours of day [61]. Higher production of electricity with lower input/capital investment have been studied to make cogeneration plants for small districts more economically viable in Nordic countries, in which the winters are long. In another study, Persson and Wernor [16] analyzed that thermal heat energy transportation cost, he explained that heat energy distribution network cost includes yearly payback of original capital investment plus the operational costs to cover all the thermal losses caused due to heat transportation. They found out that DH systems are only fruitful when the total cost is lower than that of needed for heat energy production, they suggested that when the heat transportation costs are high, the use of recycled heat can compensate effectively for the total cost of DH system to get stable. Moreover, they also documented, four major categories in which the total cost of DESs need to be divided. They are: (1) Heat transportation cost, from heat generation till user end, this categories consumes almost 50% of the total investment, (2) heat energy loss cost, this consumes almost 50% of the cost requires for heat transportation cost, it also relates to the total price of recycled heat in DH system, (3) heat energy pressure lost cost, this cost relates to the pressure lost during heat transportation from one end to other and needs to be recovered, and (4) service and maintenance cost. The main focus of this research was to notify the costs, which a DH system consumes during heat transportation and its impact on overall cycle. Another aspect of the study showed that the total capital investment to design the transportation system of DH system in populated areas like cities are low. They also claimed that in future the efficiency of the systems could also increase if the proper planning and architectural concerns would take into account while designing district heat transportation system.

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In another study, Reihav and Werner [62] evaluated the net present value (NPV), i.e., currency per house on an investment of DH transportation system can be calculated as follows:
 Cprod Q Cserv& maint NF 1 NPV ¼ PQ ð6Þ 1 h In the equation above, P denotes unit price to the customer (currency/GJ), Q denotes the DH annual heat delivery to the consumer (GJ/house), Cprod is marginal heat production cost (currency/GJ), h denotes annual heat loss (GJ/year), Cserv&maint expresses annual service and maintenance costs (currency/house), and NF denotes the NPV factor, which is dependent on the interest rate and the period. Zhai et al. [63] also performed a cost analysis as a part of feasibility study of solar DH system. For this analysis they used life cycle approach in which they calculated the total cost that includes construction costs (initial capital investment requires for equipment installation and insulation), operational costs (cost required for maintenance, fueling, and operation) and demolition costs which is needed at the end of DESs life. They also calculated the dynamic payback period of the of the capital investment through equation which was documented by Kong et al. [64]. h i 1 IC ln 1 Cpower þCcool þCheat þC hotwater þCfuel þCOM ð7Þ PF ¼ lnð1 þ iÞ Here, i denotes interest rate, IC the initial capital (currency), Cpower the cost of power (currency), Ccool the cost of cooling (currency), Cheat the cost of heating (currency), Chotwater the hot water cost (currency), Cfuel the fuel cost (currency), and COM operation and management cost (currency).

5.23.9

Case Studies

5.23.9.1 5.23.9.1.1

Case Study: Geothermal Based Multigenerational System for Meeting Districts Energy Need System description

A multigenerational DES capable of producing hextuple useful outputs namely power, heating, cooling, hot water, hydrogen, and dry air was studied by Ratlamwala and Dincer [65]. In this study they used geothermal energy as an energy source integrated with quadruple effect absorption cooling system (QEACS), water electrolyzer and cooling with dehumidification system for provision of the required sextuple outputs as shown in Fig. 11. The part of power generated by the geothermal based quadruple flash power plant (QFPP) was supplied to the district and the remaining part was supplied to the absorption cooling system, water electrolyzer and air conditioning systems to meet their energy needs. In the electrolyzer, hydrogen and oxygen are produced by breaking the water molecule with the help of energy received from the power plant. The waste heat from the geothermal power plant is supplied to the QEACS for provision of heat and cooling from the condenser and the evaporator, respectively. Some part of heat from geothermal power plant is supplied to the district in the form of hot water. The part of the cooling produced by the absorption cooling system is supplied to the air-conditioning process in order to supply the dry air to the district.

5.23.9.1.2

Analysis

This section provided energy and exergy modelling of an integrated system studied in this paper. The assumptions made during the modelling are all systems and subsystems are modeled based on steady state conditions, the heat losses and pressure drops in heat exchangers and connecting piping system are considered negligible and the parasitic losses are taken into account in calculating the net power output from the plant. The net power produced by the QFPP is expressed as _ turb _ netgeo ¼ W W

_p W

_ parasitic W

ð8Þ

where _ turb ¼ m _ 3 ðh3 W

and

_ 5 ðh5 h4 Þ þ m

_ 13 ðh13 h14 Þ þ m _ 18 ðh18 h6 Þ þ m   _ turb W _p _ parasitic ¼ 0:2 W W _ p ¼m _ 1 ðh2 W

h19 Þ

h1 Þ

_ netgeo represents net power produced by the QFPP, W _ turb represents total power produced by the turbines, W _ parasitic Here, W _ p represents power consumed by the QEACS. represents parasitic loses occurring in the system, and W In the electrolyzer, power supplied is used to produce hydrogen, and the rate of hydrogen production is then calculated as Zelectrolyzer ¼

_ H2  HHV m _ electrolyzer W

ð9Þ

_ electrolyzer _ H2 represents hydrogen production rate in kg/s, and W where HHV represents higher heating value of hydrogen, m represents power supplied to the electrolyzer.

884

Energy Management in District Energy Systems

Buildings

Geothermal power plant

Geothermal liquid

Hot water Power

Reinjection

Water

Geothermal liquid from state 20 Absorption cooling system

Heating

Geothermal liquid to reinjection

Water electrolyzer

Cooling

Hydrogen

Hydrogen storage cylinder

Cooling coils 1 T1 Air P

Oxygen

Oxygen storage cylinder

2 T2 Conditioned air

Condensate Air-conditioning system

Cold storage

Fig. 11 Schematic of the geothermal energy based multigeneration system for a district. Adapted from Ratlamwala TAH, Dincer I. Development of a geothermal based integrated system for building multigenerational needs. Energy Build 2013;62:496–506.

The exergy content of hydrogen produced is calculated as follows:   _ H2 ¼ m _ H2 ex H2 ;ch þ ex H2 ;ph Ex

ð10Þ

where

ex H2 ;ch ¼

236:1  1000 and exH2 ;ph ¼ ½ðhH2 MWH2

h0 Þ

T0 ðsH2

s0 ފ:

_ H2 , ex H2 ;ch , exH2 ;ph , MWH2 ,T0 and s, represent exergy rate of hydrogen produced, specific chemical exergy of where, Ex hydrogen, specific physical exergy of hydrogen, molecular weight of hydrogen, ambient temperature, and specific entropy, respectively. The rate of heat supplied to the QEACS by the QFPP is calculated as   _ VHTG ¼ m _ 20  h20;QFPP hQEACS;o Q ð11Þ _ VHTG represents rate of heat supplied to the very high temperature generator (VHTG) of the QEACS, m _ 20 represents mass where Q flow rate of geothermal liquid at state 20 of the QFPP, h20;QFPP represents specific enthalpy at state 20 of the QFPP, and hQEACS;o represents the specific enthalpy of stream leaving the QEACS. The mass balance and energy balance equations for the VHTG are given as follows: The energy and exergy COPs are defined as COPen ¼

_ eva þ Q _ con Q _ VHTG þ W _P Q

ð12Þ

COPex ¼

_ con _ eva þ Ex Ex _P _Ex VHTG þ W

ð13Þ

Energy Management in District Energy Systems

885

The energy and exergy efficiencies equations are written on the principal of useful output over required input. The single generation energy and exergy efficiencies are defined as Zen;SG ¼

_ 1;qf h1;qf m

Zex;SG ¼

_ 1;qf Ex

_ net;geo W _ 21;qf h21;qf m

_ 20;qf h20;qf m

_ net;geo W _Ex 21;qf Ex _ 20;qf

ð14Þ

ð15Þ

where Zen,SG and Zex,SG represents energy and exergy efficiencies of the single generation system. The double generation energy and exergy efficiencies are defined as Zen;DG ¼

_ hw _ net;geo þ En W _ 1;qf h1;qf m _ 21;qf h21;qf þ m _ w hw;in m

ð16Þ

_ hw _ net;geo þ Ex W _ 21;qf þ Ex _ hw;in _ 1;qf Ex Ex

ð17Þ

Zex;DG ¼

where Zen,DG and Zex,DG represent energy and exergy efficiencies of the double generation system. The triple generation energy and exergy efficiencies are defined as Zen;TG ¼

_ hw þ m _ bld þ En _ H2 hH2 W _ _ _ w hw;in m1;qf h1;qf m21;qf h21;qf þ m

ð18Þ

_ hw þ Ex _ H2 _ bld þ Ex W _ 21;qf þ Ex _ hw;in _ 1;qf Ex Ex

ð19Þ

Zex;TG ¼

where Zen,TG and Zex,TG represents energy and exergy efficiencies of the triple generation system. The quadruple generation energy and exergy efficiencies are defined as Zen;QG ¼

_ cooling _ hw þ m _ bld þ En _ H2 hH2 þ Q W _ 1;qf h1;qf m _ 21;qf h21;qf þ m _ w hw;in þ m _ a;eva ha;in;eva m

Zex;QG ¼

_ 1;qf Ex

_ hw þ Ex _ H2 þ E _ bld þ Ex W _Ex 21;qf þ Ex _ hw;in þ Ex _ a;in;eva

ð20Þ

ð21Þ

_ cooling and Ex _ eva represents where Zen,QG and Zex,QG represents energy and exergy efficiencies of the quadruple generation system. Q rate of cooling produced by the QEACS and rate of exergy carried by cooling produced by the QEACS, respectively. The pentuple generation energy and exergy efficiencies are defined as Zen;PG ¼

_ 1;qf h1;qf m Zex;PG ¼

_ cooling þ Q _ con _ bld þ En _ hw þ m _ H2 hH2 þ Q W _ 21;qf h21;qf þ m _ w hw;in þ m _ a;eva ha;in;eva þ m _ a;con ha;in;con m

_ 1;qf Ex

_ hw þ Ex _ H2 þ Ex _ eva þ Ex _ con _ bld þ Ex W _ 21;qf þ Ex _ hw;in þ Ex _ a;in;eva þ Ex _ a;in;con Ex

ð22Þ

ð23Þ

where Zen,PG and Zex,PG represents energy and exergy efficiencies of the pentuple generation system. The overall energy and exergy efficiencies of the hextuple generation system are defined as Zen;ov ¼

_ con þ Q _ cooling þ En _ hw þ m _ bld þ Q _ H2 hH2 þ m _ 2;d h2;d W _ 21;qf h21;qf þ m _ a;eva ha;in;eva þ m _ a;con ha;in;con þ m _ w hw;in _ 1;qf h1;qf m m

ð24Þ

_ bld þ Ex _ H2 þ Ex _ eva þ Ex _ con þ Ex _ hw þ m _ 2;d ex 2;d W _ 21;qf þ Ex _ a;in;eva þ Ex _ a;in;con þ Ex _ hw _Ex 1;qf Ex

ð25Þ

Zex;ov ¼

where Zen,ov and Zex,ov represents energy and exergy efficiencies of the overall system. ex2,d represents specific exergy carried by conditioned air (dry air).

5.23.9.1.3

Results and discussion

The geothermal liquid pressure plays an important role in studies concerning geothermal flash systems because the amount of steam produced due to flashing is highly dependent on the pressure of geothermal liquid exiting the first expansion valve. The effect of increase in geothermal liquid pressure at state f2 on net power produced by the QFPP and the hydrogen production rate is displayed in Fig. 11. The net power produced by the QFPP and the hydrogen production rate increases from 103.2 to 124.6 kW and 2.7 to 3.2 L/s, respectively, with increase in pressure at state f2 from 500 to 1000 kPa. Such behavior is observed because increase in the pressure of stream exiting the first expansion valve helps in generating more steam out of the same amount of water as compared to stream exiting at lower pressure. As the amount of steam in the exit stream increases the power produced by the turbine also increases and as a result net power generated by the QFPP increases. The part of the net power generated by the QFPP

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Energy Management in District Energy Systems

130

3.8

125

3.6

120

3.4

115

3.2

110

3.0

105

2.8

100 500

600

700 800 Pf2 (kPa)

900

VH2 (L/s)

Wnet, geo (kW)

is supplied to the electrolyzer for hydrogen production so there is a direct relation between increase in net power generated and hydrogen production rate. With increase in net power generated, higher amount of power is supplied to the electrolyzer and with increase in power; the bond dissociation capability of the electrolyzer increases and as a result higher rate of hydrogen is produced (Fig. 12). The geothermal liquid temperature plays an eminent role in the integrated systems which utilize geothermal liquid as and energy source. The effect of increase in geothermal liquid temperature at inlet of the QFPP on the net power output of the QFPP and the hydrogen produced by the system can be seen in Ref. [65]. The net power produced and rate of hydrogen produced are found to be increasing from 46.2 to 111 kW and 1.2 to 2.9 L/s, respectively, with increase in geothermal liquid temperature from 450 to 500K. Such behavior is observed because increase in temperature results in higher content of energy carried by the stream and as the energy content of the geothermal liquid being provided to the QFPP increases, the power production capacity of the QFPP also increases. The part of power produced by the QFPP is supplied to the water electrolyzer; the increase in net power produced also results in higher rate of hydrogen production due to the fact that the water electrolysers required power to break the water bond in order to produce hydrogen (Fig. 13). It is a known fact that deploying a multigeneration system which uses single source of energy enhances the performance of the system from efficiency perspectives. In the present study a hextuple generation integrated system is studied which is capable of producing power, hot water, heating, cooling, hydrogen, and conditioned air (dry air). It displays the effect of increase in geothermal liquid temperature on the exergy efficiencies of the multigenerations. The exergy efficiency of the single, double, triple, quadruple, pentuple, and hextuple generations is found to be increasing from 0.20 to 0.23, 0.13 to 0.24, 0.15 to 0.25, 0.17 to 0.26, 0.20 to 0.28, and 0.21 to 0.28, respectively, with increase in geothermal liquid temperature from 450 to 500K. It is observed that

2.6 1000

120

3.8

105

2.6

90

2.2

75

1.8

60

1.4

45 450

460

470

480

490

VH2 (L/s)

Wnet,geo (kW)

Fig. 12 Effect of geothermal liquid pressure at f2 on net power generation and volumetric rate of hydrogen production. Adapted from Ratlamwala TAH, Dincer I. Development of a geothermal based integrated system for building multigenerational needs. Energy Build 2013;62:496–506.

1.0 500

Tf1 (K) Fig. 13 Effect of geothermal liquid temperature at f1 on net power generation and rate of hydrogen production. Adapted from Ratlamwala TAH, Dincer I. Development of a geothermal based integrated system for building multigenerational needs. Energy Build 2013;62:496–506.

Energy Management in District Energy Systems

887

0.30 0.28 0.26

ex

0.24 0.22 0.20 Single generation Double generation Triple generation Quadruple generation Pentuple generation Hextuple generation

0.18 0.16 0.14 0.12 450

460

470

480

490

500

Tf1 (K) Fig. 14 Effect of geothermal liquid temperature at f1 on exergy efficiencies of multigeneration system. Adapted from Ratlamwala TAH, Dincer I. Development of a geothermal based integrated system for building multigenerational needs. Energy Build 2013;62:496–506.

0.30

0.28

ex

0.26

0.24 Single generation Double generation Triple generation Quadruple generation Pentuple generation Hextuple generation

0.22

0.20 500

600

700

800

900

1000

Pf2 Fig. 15 Effect of geothermal liquid pressure at f2 on exergy efficiencies of multigeneration system. Adapted from Ratlamwala TAH, Dincer I. Development of a geothermal based integrated system for building multigenerational needs. Energy Build 2013;62:496–506.

for higher geothermal liquid temperature above 475K, the increase in number of generation leads to higher exergy efficiencies. The effect of increase in number of generations is seen to be fading out with each additional generation and for the pentuple and hextuple generations the exergy efficiencies are almost similar. The effect of increase in geothermal liquid pressure at state f2 on the exergy efficiencies of the multigeneration systems are presented in Ref. [65]. The exergy efficiency of the single, double, triple, quadruple, pentuple, and hextuple generations is found to be increasing from 0.20 to 0.24, 0.23 to 0.25, 0.24 to 0.26, 0.25 to 0.27, 0.27 to 0.29, and 0.27 to 0.29, respectively, with increase in geothermal liquid pressure at state f2 from 500 to 1000 kPa. The trend seen in the graphshows that increase in number of generations is beneficial from exergy efficiency perspective till it reached pentuple generation then it becomes almost similar to hextuple generation system. The another point observed is that as number of generations increase, the difference in exergy efficiency with the previous generation reduces gradually till it reaches pentuple generation and then the difference between efficiencies become negligible. Other potential benefits of multigeneration systems include reduced environmental, reduced cost, reduced payback time, increased efficiency, and increased effectiveness (Fig. 14).

5.23.9.1.4

Final remarks

The authors of the above mentioned case study used geothermal energy as an energy source for their multigeneration system in order to cater to the needs of a district in an environmentally friendly manner. The authors reported that increasing number of outputs from a system enhances the exergetic efficiency of the system. This increase in efficiency is useful as it shows that multigeneration systems can be more effective in providing a sustainable solution to the energy demand in a friendlier manner (Fig. 15).

Energy Management in District Energy Systems

888 5.23.9.2

Case Study: Combine Solar and Geothermal Energy Based System for a District

5.23.9.2.1

System description

A district based multigeneration system capable of generating five outputs by integrating solar parabolic trough, geothermal, organic Rankine cycle (ORC), absorption cooling system, heat pump system, and an air-conditioning process was studied by Islam and Dincer [66] as shown in Fig. 16. The studied system is capable of producing five outputs namely, power, heating, cooling, hot water, and dry air for storing food. The heat generated by the solar energy system is supplied to the ORC, drying process, and heat pump system to produce power, hot water, and dry air. The generator of the absorption cooling system is used as a condenser for the ORC to produce the required cooling. A second loop consisting of geothermal energy source is used to generate the required power by using single flash power plant and the second organic Rankine cycle.

5.23.9.2.2

Results and discussion

The energy and exergy efficiencies associated with multigeneration system are found to be 51% and 62%, respectively. The highest possible work potential of the input energy is achieved by coupling chiller and TES with exhaust of turbines. Moreover, R410a driven heat pump and drying system both are integrated to conserve waste heat of Therminol VP-1. As depicted in Fig. 17, the energy efficiencies for single generation, cogeneration, and trigeneration are found to be 22%, 34%, and 44.11%, respectively, and the exergy efficiencies for the single generation, cogeneration and tri-generation systems are found to be 54%, 60%, and 60.4%, respectively. It is significant to witness that increase in energy efficiency in case of the multigeneration is due to the contribution of high COP value of heat pump. It can also be noted that the high output value of turbine 2 is because of the high temperature gained by the working fluid isobutane from Therminol VP-1 in the solar heat exchanger. Exergy destructions occurring in the major components of the present multigeneration system are displayed in Fig. 18. It can be noted that the highest amount of the exergy destruction takes place in solar cycle followed by the geothermal. The third highest exergy destruction occurs in heat pump because of the high exergy losses in the compressor. The chiller of the absorption refrigeration cycle has the fourth highest exergy destruction because of the increased temperature difference across generator of ORC turbine2 11

25 Solar heat exchanger

Solar collectors

Air in 16 15 14 Evaporator

Pump 4

4

39

Ta2

TES2

31

Condenser 1

Preheater

Td2

ORC Tc2 turbine1

6

10

Thermal energy storage 2

42

3

Mixing chamber

5

43

Tb2 44

45

46

Evaporator

40

Electricity generator

Ta1

Td1 TES1

Pump1

41

27

Dry Prod

Superheater

2

Geothermal reservior

Condenser 2 28

19

20

1

Absorber

Condenser

Condensor

Heat

36

37 HEX

34 35 33 Absorption refrigeration system 38

Hot air Dryer

32 Generator

Pump 6

22

Wet product

17

26

Pump 5 29

18

Flash seperator

Heat pump

23

21

Hex 2 Compressor

24

12

13

Electricity generator

7

Tc1 9c 9 Tb1 Pump 3 Thermal energy storage 1

8

30 Pump1a

Pump2

Reinjection

Fig. 16 Schematic of multigeneration system. Adapted from Islam S, Dincer I. Development, analysis and performance assessment of a combined solar and geothermal energy-based integrated system for multigeneration. Sol Energy 2017; 147: 324–343.

46 Cooling

47

Energy Management in District Energy Systems

Energy efficiencies Exergy efficiencies

0.7 Energy and exergy efficiencies

889

0.6 0.5 0.4 0.3 0.2 0.1 0 Multigeneration system

Trigeneration system

Cogeneration system

Single generation system

Fig. 17 Energy and exergy efficiencies comparison of multigeneration system. Adapted from Islam S, Dincer I. Development, analysis and performance assessment of a combined solar and geothermal energy-based integrated system for multigeneration. Sol Energy 2017; 147: 324–343.

Exergy destruction rate (kW)

10,000

1000

100

10

Ab

H

m er

th

eo G

So

la

rc

ol

al

le

ct

or

s ea cyc le t so rp pum tio p n C chi om lle pr r H ess ea o tp r um p TE S 2 TE S 1 H E 2 Su Dr y pe e rh r ea te r H E 3

1

Fig. 18 Energy and exergy efficiencies comparison of multigeneration system. Adapted from Islam S, Dincer I. Development, analysis and performance assessment of a combined solar and geothermal energy-based integrated system for multigeneration. Sol Energy 2017; 147: 324–343.

absorption chiller and ORC working fluid. It is possible to offset the amount of exergy destruction taking place in chiller by operating the generator through a lower temperature source and replacing the absorber with an efficient one. The compressor of the heat pump is at number five in exergy destruction because of the high temperature difference between ambient and refrigerant. The rest of the subunits, thermal energy storages, dryer, and heat exchangers etc., have the lowest exergy destruction because of the minimum heat losses. The temperature of working fluid of the solar collector is varied from 593 to 660K to see its effect on the energy and exergy efficiencies of the multigeneration system as shown in Fig. 19. The energy efficiencies of the single generation, cogeneration, trigeneration, and multigeneration systems are increased from 22% to 31%, 34% to 41.3%, 44% to 50%, and 51% to 55.8%, respectively, whereas the exergy efficiencies of the single generation, cogeneration, tri-generation, and multigeneration systems are increased from 54% to 68.2%, 60% to 72.7%, 60.4% to 73%, and 62% to 74.3%, respectively. The high amount of work output through the ORC turbine 2 is the main contributing factor toward this increasing trend of both efficiencies of single generation. The increase in the efficiencies of the cogeneration is due to the fact that the high temperature of the generator improves the performance of absorption chiller, while in the case of tri-generation this increase is because of the reason that the high temperature at state 14 transfers more heat to R410a. This high inlet evaporator temperature of the heat pump increases the net amount of work output by the heat pump. The enhanced efficiencies of the multigeneration system are because of the increase in the energy and exergy efficiencies of TES 2, which conserves the waste heat of condenser 2.

Energy Management in District Energy Systems

Energy and exergy efficiencies

890

Energy efficiency of multigeneration system

0.7

Energy efficiency of trigeneration system

0.6

Energy efficiency of cogeneration system

0.5

Energy efficiency of single generation system

0.4

Exergy efficiency of multigeneration system

0.3

Exergy efficiency of trigeneration system Exergy efficiency of cogeneration system

0.2 0.1 590

Exergy efficiency of single generation system

610 630 650 Temperature of solar heat transfer fluid (K)

Fig. 19 Effect of change in temperature of heat transfer fluid on energy and exergy efficiencies of the system. Adapted from Islam S, Dincer I. Development, analysis and performance assessment of a combined solar and geothermal energy-based integrated system for multigeneration. Sol Energy 2017; 147: 324–343.

Energy and exergy efficiencies

0.7

Energy efficiency of multigeneration system

0.6

Energy efficiency of trigeneration system

0.5

Energy efficiency of cogeneration system

0.4

Energy efficiency of single generation system

0.3

Exergy efficiency of multigeneration system

0.2

Exergy efficiency of trigeneration system

0.1

Exergy efficiency of cogeneration system

0.0 0.5

0.55 0.6 0.65 0.7 0.75 Efficiency of parabolic trough solar collectors

Exergy efficiency of single generation system

Fig. 20 Effect of change in parabolic trough collector efficiency on energy and exergy efficiencies of the system. Adapted from Islam S, Dincer I. Development, analysis and performance assessment of a combined solar and geothermal energy-based integrated system for multigeneration. Sol Energy 2017; 147: 324–343.

The effects of variation in the efficiency of the solar collectors on energy and exergy efficiencies of the single generation, cogeneration, tri-generation and multigeneration systems are displayed in Fig. 20. A considerable improvement in the energy and exergy efficiencies of all subsystems and hence the overall system can be observed when the efficiency of the solar collectors is varied from 50% to 75%. The energy efficiencies of the single generation, cogeneration, tri-generation, and multigeneration systems increase from 14.7% to 22%, 22.6% to 34%, 29.4% to 44%, and 34% to 51%, respectively; whereas the exergy efficiencies of the single generation, cogeneration, tri-generation, and multigeneration systems increase from 36% to 54%, 40% to 60%, 40.3% to 60.4%, and 41.5% to 62%, respectively.

5.23.9.2.3

Final remarks

The authors of the above discussed case study combined two energy sources namely, solar thermal and geothermal to generate five useful outputs for a district. The study focused on energy and exergy analyses of the system to find out the systems efficiencies and the exergy destructions. The study concluded that the multigeneration systems help in enhancing the overall energy and exergy efficiencies of the system as compared to conventional system. This efficiency enhancement also reflects in terms of lower carbon footprint and better energy sustainability for the generations to come.

5.23.10 5.23.10.1

District Energy Systems Projects Sweden: Advantage of District Energy System

The Goteborg DH system is the largest one in Sweden consisting of a pipeline network spread on more than 1000 km. This system covers around 90% of the total multifamily residents and around 9000 single family houses present in the city and is further

Energy Management in District Energy Systems

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Sweden heat demand chart Ground heating Industry Services buildings Single family homes Multi-family homes 0%

20%

40%

Market share

60%

80%

100%

Composition of

Fig. 21 Swedish heat demand.

December

0

November

0

October

0

September

16.83% 12.73%

3.33%

August

6.90% 8.52% 26.80%

1.92%

July

1.34%

June

1.56%

May

32% 22.06% 10.62%

2.39%

April

0

March

0

February

0

January

0

0.00%

7.34% 12.95% 14.05% 18.67% 5.00%

10.00%

15.00% Cooling

20.00%

25.00%

30.00%

35.00%

Heating

Fig. 22 Edmonton heating and cooling demand.

connected to smaller DH systems present in the neighborhood. The system is known to yield around 3300 GWh of heat energy as demanded by the city network [67]. Fig. 21 displays the share of DH in Sweden. This figure shows how important role a DES plays in reducing the load on national grid. Almost 90% of multifamily homes and 80% of service buildings are provided heat by a DH system.

5.23.10.2

District Energy System of Edmonton, Canada

The DES of Edmonton has 230 MW capacity for heating, 100 MW for cooling, and 15 MW of electrical power which is used for electrical chillers being deployed for providing cooling. The plant is designed such that it has the potential to expand the heating supply capacity by another 170 MW [68]. The DES is made of screw chillers which are planned to be replaced by the absorption chillers in the future for lower dependency on electrical power. Fig. 22 illustrated the monthly heating and cooling demand breakdown of the Edmonton city. It is observed that in summer season from May till September cooling is required and heating is required almost throughout the year with higher concentration in the winter months from October till April.

5.23.11

Future Directions

The idea of coming up with a system that could utilize the waste energy and supply it in the neighborhood in order to fulfill their daily heating requirements came, back in 1880–1930 from Lund et al. [11]. This could be said as the first generation of DH systems which is more likely to focus on heat recovery systems or techniques. The second generation of DH systems were considered when

892

Energy Management in District Energy Systems

Heat, power, cooling, hot water, fresh water, and alternative fuels (synthetic fuels)

H2

Heat, power, cooling, hot water, and fresh water Evolution of district energy concept Heat, power, cooling, and hot water

Heat, power, and cooling Heat and power Heat Fig. 23 An evolution of district energy systems for useful commodities.

systems were made advanced enough to utilize multiple energy resources that could supply enough energy to small districts and storage temperature was reduced to around 1001C. After further advancement in the field of DH, when renewable resources such as geothermal, biomass, and solar thermal, started to utilize for the same purpose; this era of DH systems are considered as the third generation, whereas; presently, we are witnessing fourth generation of DH systems in which certain advancement is made in cooling technologies as well and same system is used to fulfill the cooling and heating demand of the neighborhood. They are integrated with more than one renewable energy sources and have smart control systems that could enable smooth and controlled energy flow in commercial, residential as well as industrial sectors. The future DESs, that will be known as a fifth generation of DH, is expected to have some distinct smart features in it which would enable ensure the security as well as stability of complete network also, great involvement of renewable energy sources is expected. These fifth generation DES will fulfill heating, cooling as well as electricity requirement of a district at time. Moreover, unlike previous systems, these systems could expands with the city population and installation of storage element such as battery or thermal storage tanks would also help to improve the overall systems efficiency and overcome demand side requirements. Rismanchi [69] believes that in near future, the energy generation pattern of district energy networks would experience some new trades. As of now, the energy production and consumption model has not changed, it is in the similar way to that energy suppliers generates and sell energy; whereas, energy consumers buy and use it. However, it is expected that with the sharp increase in the usage of renewable energy sources, especially in residential sectors, the pattern is expected to change. The latest set trend of utilization of green buildings and passive housing architecture will push the residential energy consumption not only to its minimum but also will make photo vortices more attractive than ever before. Some of the found architecture like net-zero buildings has already reduced the dependency of residential sectors on national grid. This new concept of re-generative buildings, where the host building generates the electrical power more than it requires will provide an opportunity for individual to be energy positive and such architecture would be able to sell excessive energy to district energy networks. Furthermore, it is noted that the basic usage of district energy began for heating purposes, later included heat and power (under the subject of CHP systems). It has been attracting attention to include more useful outputs for district applications as illustrated in Fig. 23. The future DESs will include integrated DESs for multigenerational outputs with heat, power, cooling, hot water, fresh water, alternative fuels (including carbon free fuels, such as, hydrogen and ammonia and synthetic fuels).

5.23.12

Closing Remarks

DH technology emphasis on distributing/supplying heat to a single neighborhood through centralized location so that residential and commercial daily requirements could be met. The main aim of district systems is to meet heating and cooling requirement of a district. In previous system, the heat was extracted through burning fossil fuel and then supplied; whereas, now with the advancement in this technologies various renewable energy resources have been integrated with same systems in order to increase

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their efficiency and make them economical viable. The efficiency of such systems can also be increased by integrating localized boilers. Also, various claims have been given in above chapter that shows that DH with combined heat and power (CHPDH) is the cheapest method of cutting carbon emission in our environment. Climate change is a severe concern for many organizations around the globe. High levels of GHG emission have caused many devastating changes in our depletion of ozone layer and other pollution related matters. Precipitation of acid levels has risen to extreme levels in the past few decades due to industrialization and process engineering. To counteract such calamities in climate it is important to address issues like using energy efficiently and intelligently. Recycling of waste energy that is emitted in the environment and supplying it directly to the municipal or domestic level network. Carbon fuel emission needs to be reduced from our industries and factories to help minimalize the GHGs coming out from these processes. This will promote a greener environment with less efficient systems to be disregarded and need to be termed as obsolete. The solution to the above stated problem is a DES, this is a system/technique that is capable of supplying required energy in the form of heating, cooling, or electricity, by harnessing them through available local waste or renewable energy resources. DES system could be composed of several components out of which the highlighted one would be: thermal energy storage device that ensures the supply of energy to its consumers in day time and during maintenance and an intelligent network algorithm that could cope up the energy demand by energy supply using user behavior analysis and meteorological data. Another advantage of the system would be, previously well-developed technologies such as: wind, solar, marine, and hydro, could be easily merged with DES to make a single platform in order to address the global dilemma. This chapter will give its reader an overview regarding the developments, application, and drawback of DES systems by comparing various economical, environmental, and technical claims.

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Advanced control systems engineering for energy and comfort management in a building environment – a review. Renew Sustain Energy Rev 2009;13(6–7):1246–61. Gustavsson L, Karlsson Å. Heating detached houses in urban areas. Energy 2003;28(8):851–75. Heller A. Solar energy – a realistic option for district heating. Euroheat Power/Fernwarme Int 2001;30(1–2):46–51. Hinrichs RA, Kleinbach M. Energy: its use and the environment. 5th ed. Australia: Brooks/Cole Cengage Learning; 2013. Holmgren K, Amiri S. Internalising external costs of electricity and heat production in a municipal energy system. Energy Policy 2007;35(10):5242–53. Holmgren K. Role of a district-heating network as a user of waste-heat supply from various sources – the case of Göteborg. Appl Energy 2006;83(12):1351–67. Bonilla JJ, Blanco JM, López L, Sala JM. Technological recovery potential of waste heat in the industry of the Basque Country. Appl Therm Eng 1997;17(3):283–8. Zhai XQ, Yang JR, Wang RZ. Design and performance of the solar-powered floor heating system in a green building. Renew Energy 2009;34(7):1700–8. Kim K-G. Risk assessment in urban planning and management∗. Habitat Int 1990;14(1):177–90. Ozgener L, Hepbasli A, Dincer I. A key review on performance improvement aspects of geothermal district heating systems and applications. Renewable and Sustainable Energy Rev 2007;11(8):1675–97. Ihara T, Genchi Y, Sato T, Yamaguchi K, Endo Y. City-block-scale sensitivity of electricity consumption to air temperature and air humidity in business districts of Tokyo, Japan. Energy 2008;33(11):1634–45. Marinova M, Beaudry C, Taoussi A, Trépanier M, Paris J. Economic assessment of rural district heating by bio-steam supplied by a paper mill in Canada. Bull Sci Technol Soc 2008;28(2):159–73. Curti V, Favrat D, von Spakovsky MR. An environomic approach for the modeling and optimization of a district heating network based on centralized and decentralized heat pumps, cogeneration and/or gas furnace. Part II: application. Int J Therm Sci 2000;39(7):731–41. Wigbels M, Bøhm B, Sipilä K. Operational optimisation: dynamic heat storage and demand side management strategies. Euroheat Power (English Ed.) 2005;11(2):58–61. Reidhav C, Werner S. Profitability of sparse district heating. Appl Energy 2008;85(9):867–77. Zhai H, Dai YJ, Wu JY, Wang RZ. Energy and exergy analyses on a novel hybrid solar heating, cooling and power generation system for remote areas. Appl Energy 2009;86(9):1395–404. Kong XQ, Wang RZ, Huang XH. Energy efficiency and economic feasibility of CCHP driven by stirling engine. Energy Convers Manag 2004;45(9–10):1433–42. Ratlamwala TAH, Dincer I. Development of a geothermal based integrated system for building multigenerational needs. Energy Build 2013;62:496–506. Islam S, Dincer I. 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Future Reading Department of Energy and Climate Change. Renewable heat incentive: consultation on the proposed RHI financial support scheme. Incentive; 2010. p. 1–23. Gadd H, Werner S. 18 – Thermal energy storage systems for district heating and cooling. In: Cabeza LF, editor. Advances in thermal energy storage systems. Cambridge: Woodhead Publishing; 2015. p. 467–78. Gang W, Augenbroe G, Wang S, Fan C, Xiao F. An uncertainty-based design optimization method for district cooling systems. Energy 2016;102:516–27. Hawkey D, Webb J, Winskel M. Organisation and governance of urban energy systems: district heating and cooling in the UK. J Clean Prod 2013;50:22–31. Kaarup Olsen P. Guidelines for low-temperature district heating. In: EUDP 2010-II full-scale demonstration of low-temperature. District heating in existing buildings; 2014. p. 1–43. Li H, Svendsen S. Energy and exergy analysis of low temperature district heating network. Energy 2012;45(1):237–46. Nielsen JE, Sørensen PA. Renewable heating and cooling; 2016. Available from: https://www.epa.gov/rhc [accessed on July 2017] Ondeck AD, Edgar TF, Baldea M. Optimal operation of a residential district-level combined photovoltaic/natural gas power and cooling system. Appl Energy 2015;156:593–606. Passerini F, Sterling R, Keane M, Klobut K, Costa A. Energy efficiency facets: innovative district cooling systems. Entrep Sustain Issues 2017;4(3):310–8. Werner S. International review of district heating and cooling. Energy 2017;137:617–31. Werner S. District heating and cooling in Sweden. Energy 2017;126:419–29.

Relevant Websites http://www.c40.org/case_studies/98-of-copenhagen-city-heating-supplied-by-waste-heat C40 Studies. http://district-heating.danfoss.com/home/#/ Danfoss Heating.

Energy Management in District Energy Systems

http://district-heating.danfoss.com/applications/what-is-district-heating/#/ District-heating. http://enwavetoronto.com/district_heating_system.html Enwave Services. https://www.gminsights.com/industry-analysis/district-heating-market Global Market Insights. https://globenewswire.com/news-release/2017/05/01/974914/0/en/District-Heating-Market-to-surpass-280bn-by-2024-Global-Market-Insights-Inc.html GlobeNewswire. http://www.districtenergy.org/viewdocument/development-trends-in-chinese-distr International District Energy Association. https://www.theguardian.com/big-energy-debate/2014/aug/20/DESmark-district-heating-uk-energy-security The Guardian. https://www.transparencymarketresearch.com/district-heating-cooling-market.html Transparency Market Research. https://corporate.vattenfall.com/about-energy/energy-distribution/district-heating/how-it-works/ Vattenfall.

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5.24 Sectoral Energy and Exergy Management Ibrahim Dincer, University of Ontario Institute of Technology, Oshawa, ON, Canada Muhammad F Ezzat, University of Ontario Institute of Technology, Oshawa, ON, Canada and Minia University, Minya, Egypt r 2018 Elsevier Inc. All rights reserved.

5.24.1 Introduction 5.24.2 Energy Utilization 5.24.3 Energy Management 5.24.3.1 Maintenance Measures 5.24.3.2 Low-Cost Measures 5.24.3.3 Retrofit Measures 5.24.3.4 Energy Management Systems 5.24.4 Exergy Management 5.24.5 Sectoral Energy and Exergy Analysis 5.24.5.1 Energy and Exergy Values for Commodities in Macrosystems 5.24.5.2 The Reference Environment for Macrosystems 5.24.5.3 Efficiencies for Devices in Macrosystems 5.24.5.3.1 Heating 5.24.5.3.2 Cooling 5.24.5.3.3 Work production 5.24.5.3.4 Electricity generation 5.24.5.3.5 Kinetic energy production 5.24.6 Case Study: Energy and Exergy Utilization in the United States 5.24.6.1 Analysis of the Residential Sector 5.24.6.1.1 Energy utilization data for the residential sector 5.24.6.1.2 Efficiencies of principal devices in the residential sector 5.24.6.1.3 Mean efficiencies for the overall residential sector 5.24.6.2 Analysis of the commercial sector 5.24.6.2.1 Energy utilization data for the commercial sector 5.24.6.2.2 Efficiencies of principal devices in the commercial sector 5.24.6.2.3 Mean efficiencies for the overall commercial sector 5.24.6.3 Analysis of the Transportation Sector 5.24.6.3.1 Energy utilization data for the transportation sector 5.24.6.3.2 Energy efficiencies for the transportation sector 5.24.6.3.3 Exergy efficiencies for the transportation sector 5.24.6.4 Analysis of the Industrial Sector 5.24.6.4.1 Methodology and energy data for the industrial sector 5.24.6.4.2 Process-heating efficiencies for the product heat temperature categories in each industry 5.24.6.4.2.1 Electrical process heating in the petroleum and coal industry 5.24.6.4.2.2 Fossil fuel process heating in the petroleum and coal industry 5.24.6.4.3 Mean process heating efficiencies for each industry of the industrial sector 5.24.6.4.4 Overall efficiencies for the industrial sector 5.24.6.5 Analysis of the Utility Sector 5.24.6.5.1 Energy utilization data for the utility sector 5.24.6.5.2 Energy efficiencies for the utility sector 5.24.6.5.3 Exergy efficiencies for the utility sector 5.24.6.6 Energy and Exergy Efficiencies and Flows for the Sectors and Country 5.24.6.7 Discussion 5.24.6.7.1 Summary of key findings 5.24.7 Future Directions 5.24.8 Closing Remarks References Further Reading Relevant Websites

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doi:10.1016/B978-0-12-809597-3.00545-9

Sectoral Energy and Exergy Management

Nomenclature

_ Qr Ex

E_ E Exacc ExQ Exd _ Q Ex

_ W Ex ex _ m _ Q Q

Thermal exergy rate associated with heat transfer rate (kW) Exergy rate associated with work (kW) Specific exergy (kJ/kg) Mass flow rate (kg/s) Heat rate (kW) Heat (kJ)

Greek letters g Exergy grade of the fuel

Z c

Energy efficiency Exergy efficiency

Subscripts 0 e f h

ke O P S

Kinetic energy Overall Product Stream

SCADA SLT

Supervisory control and data acquisition Second law of thermodynamics

Exergy rate (kW) Exergy of flow (kJ) Exergy accumulation (kJ) Thermal exergy (kJ) Exergy destruction (kJ) Thermal exergy rate (kW)

Reference environment Electric Fuel heat

Abbreviations EMS Energy management system FLT First law of thermodynamics

5.24.1

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Introduction

Designing efficient and cost effective systems in an environmentally benign way is one of the foremost challenges for engineer to overcome. In a world with finite natural resources and large energy demands, it is important to understand mechanisms that degrade energy and resources and to develop systematic approaches for improving systems in terms of such factors as efficiency, cost, environmental impact, etc. The undesirable effects of poor utilization of energy resources, especially regarding economics and ecology, demonstrate that the designing of appropriate energy systems requires careful analysis and planning. In this regard, exergy analysis is beneficial, providing a useful tool for:

• • • • • •

improving the efficiency of energy resource utilization, assessing the locations, types, and true magnitudes of wastes and losses, distinguishing between high- and low-quality energy resources and services, and better matching the quality of energy required for a service with the quality of the energy supplied, determining whether or not and by how much it is possible to design more efficient energy systems by reducing inefficiencies, reducing the impact of energy resource utilization on the environment, and enabling the achievement of some of the criteria for sustainable development, such as a sustainable supply of energy resources that are usable with minimal environmental degradation.

Many of these points are illustrated in Table 1, which lists energy and exergy efficiencies for several processes. The energy efficiencies in Table 1 represent the ratio of the energy of the useful streams leaving the process to the energy of all input streams, while the exergy efficiencies represent the ratio of the exergy of the products of a process to the exergy of all inputs. The exergy efficiencies in Table 1 are lower than the energy efficiencies, mainly because process irreversibilities destroy some of the input exergy. Exergy is also useful since it provides a link between engineering and the environment. Exergy analysis determines the true efficiency of systems and processes, making it particularly useful for finding appropriate improvements. For these and other reasons, exergy analysis is strongly recommended by many for use in the design of engineering systems and analyses of regional, national, and global energy systems as well as sectors of the economy. Recently, the use of energy and other resources in the industrial world has reached levels never before observed, leading to reduced supplies of natural resources and an additional damage to the natural environment due to the upsurge in the harmful released emissions. At the same time, energy conversion networks have become more complicated. Sometimes improvement efforts are focused on inappropriate resource conversions, in that the potential to improve the resource use is not significant. By describing the use of energy resources in society in terms of exergy, important knowledge and understanding are gained, and the areas are better identified where large improvements can be attained by applying measures to increase efficiency.

898

Sectoral Energy and Exergy Management

Table 1

Energy and exergy efficiencies of selected devices

Device

Energy efficiency (%)

Exergy efficiency (%)

Residential space heater (fuel) Domestic water heater (fuel) High-pressure steam boiler Tobacco dryer (fuel) Coal gasification system Petroleum refining unit Steam-heated reboiler Blast furnace

60 40 90 40 55 B90 B100 76

9 2–3 50 4 46 10 40 46

Source: Gaggioli RA. Second law analysis for process and energy engineering. ACS Symp Ser 1983;235:3–50. doi:10.1021/bk-1983-0235.ch001. Kenney WF. Energy conservation in the process industries. New York: Academic Press; 1984. Rosen MA, Dincer I. Sectoral energy and exergy modeling of Turkey. J Energy Resour Technol 1997;119:200. doi:10.1115/1.2794990.

Analyses of this nature provide insights into how effectively a society uses natural resources and balances such factors as economics and efficiency. Such insights can help identify areas in which technical and other improvements should be undertaken, providing an indication for the priorities that should be assigned to measures. Assessments and comparisons of various societies throughout the world can also be of fundamental interest in efforts to achieve a more equitable distribution of resources. During the past few decades, exergy has been increasingly applied to the industrial sector and other sectors of the economy, particularly to attain energy savings, and hence financial savings. The energy utilization of a region like a country can be assessed using exergy analysis to achieve better insights into its efficiency. This approach was first introduced in a landmark paper by Reistad [1], who applied it to the United States. Since then, several other countries, e.g., Canada by Rosen [2], Japan, Finland, and Sweden by Wall [3,4], Italy by Wall et al. [5], and Turkey by Ozdogan and Arikol [6], and Rosen and Dincer [7], have been examined in such a way using modified versions of this approach. In this chapter, a comprehensive exergy analysis of countries, regions, and economic sectors and services is introduced and discussed. Later, as an illustration, it is aimed to examine energy and exergy utilization for the United States.

5.24.2

Energy Utilization

The growth of energy is associated with comfort and wealth across the world. Achieving this growth in an environmental way utilizing renewable energy has been adopted by many developed countries. Currently, natural gas, coal, liquid fuels such as gasoline and diesel, renewables, and nuclear energy are the main sources of energy. The United States Energy Information Administration's (EIA) estimated that global energy use would witness an upsurge by around 48% between 2012 and 2040 (see Fig. 1). China and India will be responsible for half of the global overall rise in energy use during the previously mentioned period, this is mainly due to the estimated strong economic growth. Renewables and nuclear power are estimated to record the highest growth as energy sources between 2012 and 2040 with an average rate of growth of 2.6% and 2.3% per year, respectively. Although fossil fuels such as natural gas, petroleum, and coal are estimated to have a low rate of growth compared to other energy sources, fossil fuels will share around 75% of the world energy use until 2040. Natural gas will be the fastest growing fossil fuel with a growth rate of 1.9% annually. Increasing of tight and shale gases along with the coalbed methane are the main reasons for such growth. Liquid fuels use is predicted to be reduced from 33% to 30% between 2012 and 2040 due to the expected increase in the oil prices, which will result in a global switch toward the use of the technologies that can achieve better energy efficiency along with using other sources of energy, particularly renewables and nuclear energy. China, United States, and India will be consuming around 70% of the global coal use; a growth rate of 0.6% per year for coal utilization is expected. Currently, China is responsible for 50% of the world's overall coal use. However, a slowing economy and the Chinese government’s plans to reduce greenhouse gases emission are predicted to have a positive influence in reducing coal use by China. India is also expected to record higher coal consumption compared to the United States in 2030. Fig. 2 displays energy consumption by country between 2010 and 2050; all the data were obtained from EIA. China will have the highest energy consumption in the world with an energy use increasing from 105.6 to 191.6 EJ between 2010 and 2050, United States will come in the next rank with an energy consumption of 102.8 to 112.8 EJ for the same period of time. Canada, Japan, South Korea, Russia, India, and Brazil are expected to record a change in the energy consumption from 14.67 to 18.04 EJ, 22.68 to 19.31 EJ, 11.29 to 19.1EJ, 31.44 to 30.81 EJ, 24.9 to 80.7 EJ, and from 15.3 to 23.3 EJ, respectively, between 2010 and 2050. Fig. 3 shows energy use based on kg of oil equivalent per capita and kWh per capita for some selected countries in 2013; all the data are obtained from the United Nation. Qatar came in the first place with a 19,120 kg of oil equivalent per capita and 222,369 kWh of energy use per capita followed by Iceland, Canada, United States, Germany, France, Japan, China, and Brazil as illustrated in Fig. 3. The high energy use per capita in Qatar can be interpreted by the low prices of fossil fuel byproducts, leading to a higher

Sectoral Energy and Exergy Management

899

280 Natural gas

Coal

Liquid fuels

240 Energy consumption (EJ)

Renewables

Nuclear

200 160 120 80 40 0 1990

1995

2000

2005

2010

2015

2020

2025

2030

2035

2040

Years Fig. 1 World energy consumption by source, 1990–2040. Data obtained from U.S. Energy Information Administration (EIA). EIA projects 48% increase in world energy use by 2040 – Today in Energy – U.S. Energy Information Administration (EIA). Available from: https://www.eia.gov/ todayinenergy/detail.php?id=32912; 2017 [accessed 23.10.17].

200

Energy consumption (EJ)

180 Canada

160

Russia

140 120

Brazil

India

China

USA Japan South Korea

100 80 60 40 20 0 2010

2020

2030 Years

2040

2050

Fig. 2 Energy consumption of the countries that are using energy extensively, 2010–50. Data obtained from U.S. Energy Information Administration (EIA). U.S. Energy Information Administration – EIA – Independent Statistics and Analysis. Available from: https://www.eia.gov/ outlooks/aeo/data/browser/#/?id=1-IEO2017®ion=00&cases=Reference&start=2010&end=2050&f=A&linechart=BBReference-d082317.3-1IEO2017BReference-d082317.4-1IEO2017BReference-d082317.8-1-IEO2017BReference-d082317.9-1-IEO2017BReference; 2017 [accessed 23.10.17].

consumption compared to countries that are selling fossil fuels with higher prices. Moreover, using cheap domestic energy resources such as natural gas in an energy intensive industry such as aluminum smelters and LNG industry and electricity production increase the use per capita. Furthermore, some small countries set very low taxes on petroleum products, which encourages consumers from nearby countries to buy their fuel supplies from these countries.

5.24.3

Energy Management

Energy management can be defined as the process of utilizing energy prudently to reduce the cost or achieve other purposes. Measures of energy management can be categorized into the following groups: maintenance, low-cost, and retrofit. Many energy

900

Sectoral Energy and Exergy Management

l az i Br

at es St

U

Ki ni te d U Kg of oil equivalent per capita

ni te d

ng do m

at ar Q

Ja pa n

el an d Ic

an y er m

G

Fr

an ce

hi na C

C

an ad a

2,25,000.00 2,00,000.00 1,75,000.00 1,50,000.00 1,25,000.00 1,00,000.00 75,000.00 50,000.00 25,000.00 0.00

kWh per capita

Fig. 3 Energy use based on kg of oil equivalent per capita and kWh per capita for some selected countries in 2013. Data obtained from United Nation. UNdata | record view | Energy use (kg of oil equivalent per capita). Available from: http://data.un.org/Data.aspx?q=energy&d= WDI&f=Indicator_Code%3AEG.USE.PCAP.KG.OE; 2017 [accessed 23.10.17].

management measures are outlined here, along with their potential energy savings. This list is not intended to be inclusive; for example, it does not include all the available opportunities for air conditioning, cooling, and heating equipment; it is intended to assist those involved in management, operations, and maintenance to detect any opportunities of energy savings for each particular type of facility, considering the fact that other opportunities for energy management are existing. The approach of energy management widely permits the exploration of the previous approved inefficient practices. Enhancing the awareness of the staff managing part, facility operation, and maintenance along with the aid of the experts can result in better energy utilization and cost mitigation. Some of the practical energy management measures are discussed in the following sections [8].

5.24.3.1

Maintenance Measures

Maintenance opportunities for energy management is usually done on a steady basis, generally not exceeding one year, and incorporates the succeeding steps:

• • • • • • • •

Valves, gaskets, and fittings leaks are sealed Repairing damaged insulation Pressure and temperature controls and steam traps maintenance Surfaces associated with heat transfer should be cleaned Guaranteeing that the steam quality is appropriate for the application Confirming that the pressure and temperature of the steam are within the correct ranges identified by the designers for the equipment Checking the steam traps’ size to ensure that they are correct to eliminate all condensate Confirming the slope of the heating coils from the location where steam is entering to the location where it is trapped to avoid coils from submerging with condensate

5.24.3.2

Low-Cost Measures

Low-cost opportunities are generally once-off actions at which the cost is not greatly measured and are incorporated in the subsequent actions:

• • • • •

Equipment shut down when not needed Lockable covers for control equipment like thermostat should be provided to avoid any unauthorized tampering Equipment operation at or close capacity when it is possible and preventing operating multiple units at reduced capacity Provide the system with measuring and monitoring devices to acquire the required date to enhance system operation Evaluating control devices location to guarantee efficient operation

5.24.3.3

Retrofit Measures

Retrofit opportunities are generally once-off actions with substantial costs that incorporate alterations to current equipment. A lot of these measures need a comprehensive analysis and are beyond the extent of this chapter. Worked examples are provided for

Sectoral Energy and Exergy Management

Table 2

901

Some energy management opportunities related to different industrial processes

Process

Energy management issues

Process heating

Maintain satisfactory heating and acceptable temperature via observing and regulation Prevent unnecessary process shutdown to avoid wasting energy Carry out maintenance plan for the different mechanical devices that are operating utilizing oil for lubrications and actuations, replace worn belts that are used in transmission systems, etc. Managing system and equipment operation periods Maintaining chillers loaded in a proper way to achieve highest possible efficiency via managing the cooling charge Reduce the periods at which the process is subjected to the surrounding ambient conditions Monitoring the levels of voltage and current to ensure that they are appropriate Frequent checking of any electromagnetic interventions, hysteresis, phase balance, power factor, and appropriate insulation Controlling the circulation rate and following the maintenance schedule Appropriate adjustment of the flowing air via the dryer with respect to the supplied charge Manage the charge and storage times for maximum efficiency Appropriate controlling of combustion and stabilizing the flame along with fuel management

Mechanical processes

Process cooling Electrical processes

Pumps, fans, compressors Drying Combustion systems

Source: Dincer I, Zamfirescu C. Energy conservation. Sustainable energy systems and applications, Boston, MA: Springer US; 2011. p. 119–45. doi:10.1007/978-0-387-95861-3_4.

some of the listed opportunities. However, in further cases, there is only commentary. Energy management measures in this category comprise the following steps:

• • • • • • •

Converting from direct to indirect steam-heated equipment and recuperation of condensate Equipment insulation installation and or upgrading of equipment Removing equipment that is being heated by steam from building center to location with exterior exposures to use any equipment heat loss in providing heating to the area Revising general building heating concepts as opposed to task-heating concepts Adjusting processes to allow for steady or decreased steam or water required for system operation Examining the schedules of process operations to avoid high steam or water demands Investigating the possibility of utilizing hot water streams that are leaving the facility as waster for further use in other heat recuperation options

5.24.3.4

Energy Management Systems

Appropriate monitoring and controlling are very essential to achieve better management for any activity. Energy management systems (EMS) are computer-assisting tools consisting of pieces of equipment along with several sensors and actuators. Moreover, software for energy management, auditing, and monitoring is provided to control energy management process. Their informatics system is called SCADA (supervisory control and data acquisition); it gathers all the required data from the system and makes decisions to regulate the system in a manner that achieves the highest possible performance under energy conservation constraints. Any EMS is applied to satisfy the following:

• • • •

Comparing and recording energy utilization during the day, every week, during the whole month and year Screening all utility services such as electricity and gas consumption, water and steam demand Assess and optimize strategies for energy storage such as load shaving Detecting and identifying any errors that may lead to inefficient subsystems

An effective EMS installed in an industrial facility is able to mitigate the cost of energy by 2 to 3%; this percentage is fair enough to defend the investments in such systems. Table 2 shows a list of some energy management opportunities related to different industrial processes, applying such measures will definitely enhance system efficiency.

5.24.4

Exergy Management

The exergy of a system is defined as the maximum shaft work that can be done by the composite of the system and a specified reference environment [8]. Exergy has the characteristics which are not conserved where all processes occurring in a system with its surroundings environment are irreversible. Exergy is destroyed whenever an irreversible process occurs. When an exergy analysis is performed on a plant such as a power station, a chemical processing plant, or a refrigeration facility, the thermodynamic imperfections can be quantified as exergy destructions, which represent losses in energy quality or usefulness. The exergy method of analysis overcomes the limitations of the FLT. The concept of exergy is based on both the FLT and the SLT. Exergy analysis clearly indicates the locations of energy degradation in a process and can, therefore, lead to improved operation or technology. Exergy analysis can also quantify the quality of heat in a waste stream. The main aim of exergy analysis is

902

Sectoral Energy and Exergy Management

Energy conservation mentality

Exergy conservation mentality

Energy is always managed due to FLT

Exergy can partially be managed only

Fig. 4 Difference between energy management and exergy management. FLT, First law of thermodynamics.

to identify meaningful (exergy) efficiencies and the causes and true magnitudes of exergy losses depending on the properties of both a matter or energy flow and the environment. Energy conservation is utterly meaningless since energy analysis generally fails to identify waste or the effective use of fuels and resources. If one aims for better use of resources, exergy conservation becomes a logical and meaningful target. Therefore, an energy conservation mentality should be changed to an exergy conservation mentality. When it comes to exergy management versus energy management we can clearly state that exergy can partially be managed based on the SLT that states “exergy cannot be conserved. It can only be minimized if the measures are taken properly.” However, energy is already managed due to the FLT, which states “energy is neither destroyed nor created, it is always conserved.” Fig. 4 shows the differences between energy management and exergy management.

5.24.5

Sectoral Energy and Exergy Analysis

Exergy is the “fuel” of dissipative systems, i.e., systems that are sustained by converting energy and materials, for example, a living cell, an organism, an ecosystem, the earth’s surface with its material cycles. Societies are also dissipative systems, and can, therefore, be assessed by exergy analysis. Exergy analysis has mostly been applied in industrial systems and processes, but its application can straightforwardly be extended to macrosystems, allowing the examination of regional, national, and global energy and material conversions. Such applications describe the use of resources and related environmental impacts. Natural resources are traditionally divided into energy and other resources, especially in regional assessments. The separation often is vague. For example, oil is usually considered an energy resource and wood a material resource. Yet oil can also be converted to useful materials and wood can be used as a fuel. It is more appropriate to assess these resources with one unifying measure, and exergy provides such a resource measure. The following subsections will highlight the methodology of applying energy and exergy approaches in analyzing macrosystems.

5.24.5.1

Energy and Exergy Values for Commodities in Macrosystems

The exergy of an energy resource can for simplicity often be expressed as the product of its energy content and a quality factor (the exergy-to-energy ratio) for the energy resource. Quality factors for some energy forms are listed in Table 3. Energy resources are usually measured in energy units, as are exergy resources. Other resources are usually measured in purely quantitative units such as weight or volume. A material can be quantified in exergy units by multiplying its quantity by an exergybased unit factor for the material. Using such measures could allow for an expanded resource budgeting and provide the first step towards an integration of exergy with traditional energy budgeting. The exergy per unit quantity is a measure of the value or usefulness of a resource relative to the environment. This value relates to the price of the material or resource, which is also partly defined by the environment through, for instance, demand. In assessments of regions and nations, the most common material flows often are hydrocarbon fuels at near-ambient conditions. The physical exergy for such material flows is approximately zero, and the specific exergy reduces to the specific chemical exergy exf, which can be written as ex f ¼ ϒf Hf

ð1Þ

where ϒf denotes the exergy grade function for the fuel, which can be defined as the ratio of fuel chemical exergy to fuel higher heating value Hf. Table 4 lists typical values of Hf, exf and ϒf for fuels typically encountered in regional and national assessments. The specific chemical exergy of a fuel at T0 and P0 is usually approximately equal to its higher heating value Hf.

5.24.5.2

The Reference Environment for Macrosystems

The reference environment used in many assessments of macrosystems is based on the model of Gaggioli and Petit [9], which has a temperature T0 ¼ 251C, pressure P0 ¼ 1 atm, and a chemical composition consisting of air saturated with water vapor and the following condensed phases at 251C and 1 atm: water (H2O), gypsum (CaSO4
2H2O), and limestone (CaCO3). This reference environment model is used in this chapter, but with a temperature of 101C.

Sectoral Energy and Exergy Management

Table 3

903

Quality factors for some common energy forms

Energy form

Quality factor

Mechanical energy Electrical energy Chemical fuel energy Nuclear energy Sunlight Hot steam (6001C) District heating (901C) Moderate heating at room temperature (201C) Thermal radiation from the earth

1.0 1.0 B1.0a 0.95 0.9 0.6 0.2–0.3b 0–0.2b 0

a

This value may exceed 1, depending on the system definition and state.

b

This value depends significantly on the environmental temperature. Source: Wall G. Exergy – a useful concept. Göteborg, Sweden: Chalmers University of Technology; 1986.

Properties of selected fuelsa

Table 4 Fuel

Hf (kJ/kg)

Chemical exergy (kJ/kg)

ϒf

Gasoline Natural gas Fuel oil Kerosene

47,849 55,448 47,405 46,117

47,394 51,702 47,101 45,897

0.99 0.93 0.99 0.99

a

For a reference-environment temperature of 25C, the pressure of 1 atm and chemical composition as defined in the text. Source: Reistad GM. Available energy conversion and utilization in the United States. J Eng Power 1975;97:429. doi:10.1115/1.3446026.

5.24.5.3

Efficiencies for Devices in Macrosystems

Energy Z and exergy c efficiencies for the principal processes in macrosystems are usually based on standard definitions: Z ¼ ðEnergy in productsÞ=ðTotal energy inputÞ

ð2Þ

c ¼ ðExergy in productsÞ=ðTotal exergy inputÞ

ð3Þ

Exergy efficiencies can often be written as a function of the corresponding energy efficiencies by assuming the energy grade function gf to be unity, which is commonly valid for typically encountered fuels (kerosene, gasoline, diesel, and natural gas).

5.24.5.3.1

Heating

Electric and fossil fuel heating processes are taken to generate product heat QP at a constant temperature TP, either from electrical energy We or fuel mass mf. The efficiencies for electrical heating are

Zh;e ¼

QP We

ExQP ch;e ¼ We ¼ Ex





1

  T0  QP TP We

ð4Þ

 Zh;e

ð5Þ

Combining these expressions yields
ch;e ¼ 1

T0 TP



For fuel heating, these efficiencies are Zh;f ¼

QP mf  Hf

ð6Þ

904

Sectoral Energy and Exergy Management

and Ex QP mf ex f

  T0
1  QP TP ≌ 1 ¼ mf ϒf Hf

ch;f ¼

ch;f

ð7Þ T0 TP



 Zh;f

where double subscripts indicate processes in which the quantity represented by the first subscript is produced by the quantity represented by the second, for example, the double subscript h,e means heating with electricity.

5.24.5.3.2

Cooling

The efficiencies for electric cooling are QP We

Zc;e ¼ ExQP cc;e ¼ We ¼ Ex




cc;e ¼ 1

5.24.5.3.3

ð8Þ T0 TP

1



 QP

We

To TP





 Zc;e

ð9Þ

ð10Þ

Work production

Electric and fossil-fuel work production processes produce shaft work W. The efficiencies for shaft work production from electricity are W We

ð11Þ

ExW W ¼ Zm;e ¼ We Ex We

ð12Þ

W mf Hf

ð13Þ

Zm;e ¼

cm;e ¼ For fuel-based work production, these efficiencies are

Zm;f ¼

cf ¼

5.24.5.3.4

ExW W ¼ ≌ Zm;f mf ex f mf ϒf Hf

ð14Þ

We mf Hf

ð15Þ

ExWe We ≌ Ze;f ¼ mf ϒf Hf mf ex f

ð16Þ

Electricity generation

The efficiencies for electricity generation from fuel are Ze;f ¼

ce;f ¼

5.24.5.3.5

Kinetic energy production

The efficiencies for the fossil fuel-driven kinetic energy production processes, which occur on some devices in the transportation sector (e.g., turbojet engines and rockets) and which produce a change in kinetic energy Dke in a stream of matter ms, are as follows: ms Dkes mf Hf

ð17Þ

ms Dkes ms Dkes ¼ ≌ Zke;f mf exf mf ϒf Hf

ð18Þ

Zke;f ¼

cke;f ¼

5.24.6

Case Study: Energy and Exergy Utilization in the United States

The methodology discussed in the previous section is used to analyze overall and sectoral energy and exergy utilization in the United States. The study comprises four economic sectors: residential, commercial, industrial and transportation. The country is

Sectoral Energy and Exergy Management

905

Product Resedential Waste

Waste

Product Commercial Electricity

Utility

Waste

Product Transportation Waste Resources Product Industrial

Waste

Fig. 5 Model for the energy flows in a macrosystem like a country or a region.

modeled as a macrosystem as shown in Fig. 5. The analysis is carried out during the period from 2007 to 17 utilizing the available information and data from various local and international sources. Energy and exergy efficiencies are determined to assess the performance of the different economic sectors in the United States.

5.24.6.1

Analysis of the Residential Sector

Energy and exergy utilization in the residential sector is evaluated and analyzed.

5.24.6.1.1

Energy utilization data for the residential sector

To determine energy and exergy efficiencies for the residential sector, the consumption of total electrical and fossil fuel energy within the sector is determined. In the United States, about 42% of the total residential energy consumption was consumed in space heating in 2009, followed by water heating, which consumed around 18% of the total energy consumption [10]. However, space heating utilization has dropped to around 39% in 2017, and water heating use has dropped to about 16.2% (see Table 5). The values of the total electrical and fossil fuel consumption in the residential sector for the period from 2007 to 17 are presented in Table 5.

5.24.6.1.2

Efficiencies of principal devices in the residential sector

Energy and exergy efficiencies of principal devices in the residential sector are determined. The energy efficiencies, processes, and reference environment temperatures are assumed to be the same as those used by Rosen et al. [7] and Reistad [1]. The processes and operating data of the principal devices in the residential sector for the United States are listed in Table 6. Exergy efficiencies of the different devices are evaluated using these data and following the methodology in Section 5.24.5.1. For air conditioning, we follow the approach of Reistad [1], who noted that it is reasonable to assume for an air-conditioning system that the “energy efficiency” is Z ¼ 100%, the environment temperature is T0 ¼283K and the “product” heat is delivered at TP ¼ 293K. Although this treatment of air conditioning does not follow the conventional use of coefficients of performance to evaluate the merit of the device, it facilitates the sectoral assessment considered here. Using these values and Eqs. (8) and (10), we find for air conditioning Z¼
c¼ 1

T0 TP






QP ¼ 1 we

QP ¼1 We  283  1 ¼ 0:0341 ¼ 3:41% 293

For other devices in the residential sector, energy and exergy efficiencies can be obtained following the methodology used for air conditioning or other devices in Section 5.24.5.1 and the data in Table 6.

5.24.6.1.3

Mean efficiencies for the overall residential sector

Weighted mean energy and exergy efficiencies are calculated for the residential sector using a three-step process. First, weighted means are obtained for the electrical energy and exergy efficiencies for the device categories listed in Table 6, where the weighting factor is the ratio of electrical energy input to the device category to the total electrical energy input to all device categories in the sector. Second, weighted mean efficiencies for the fossil fuel-driven devices are similarly determined. Third, overall weighted means are obtained for the energy and exergy efficiencies for the electrical and fossil fuel processes, where the weighting factor is the ratio of total fossil fuel or electrical energy supplied to the residential sector divided by the total energy input to the sector. An

906

Table 5 Year

Sectoral Energy and Exergy Management

Energy consumption in the residential sector of United States for 2007–17 Device

Breakdown of energy use in sector, by type (EJ) Electrical

Natural gas

Fuel oil

LPG

2007

Space heating Water heating Lighting Air conditioning Othersa

0.29 0.4 0.75 0.91 2.5

3.45 1.39 – – 0.28

0.585 0.11 – – –

0.27 0.09 – – 0.14

2008

Space heating Water heating Lighting Air conditioning Othersa

0.2954 0.453 0.76 0.918 2.521

3.587 1.403 – – 0.2848

0.59 0.116 – – –

0.274 0.095 – – 0.18

2009

Space heating Water heating Lighting Air conditioning Othersa

0.2954 0.453 0.75 0.875 2.54

3.46 1.403 – – 0.2848

0.527 0.1055 – – –

0.274 0.0844 – – 0.20

2010

Space heating Water heating Lighting Air conditioning Othersa

0.30 0.46 0.75 1.18 2.55

3.47 1.42 – – 0.284

0.53 0.10 – – –

0.27 0.08 – – 0.20

2011

Space heating Water heating Lighting Air conditioning Othersa

0.29 0.47 0.73 0.84 2.54

3.43 1.44 – – 0.29

0.55 0.10 – – –

0.26 0.08 – – 0.20

2012

Space heating Water heating Lighting Air conditioning Othersa

0.29 0.48 – 0.86 2.56

3.43 1.45 – – 0.29

0.53 0.09 – – –

0.26 0.07 – – 0.2

2013

Space heating Water heating Lighting Air conditioning Othersa

0.30 0.86 0.65 0.86 2.56

3.44 1.45 – – 0.29

0.52 0.09 – – –

0.25 0.07 – – 0.21

2014

Space heating Water heating Lighting Air conditioning Othersa

0.3 0.49 0.61 0.87 2.57

3.45 1.46 – – 0.29

0.52 0.09 – – –

0.25 0.07 – – 0.21

2015

Space heating Water heating Lighting Air conditioning Othersa

0.348 0.474 0.474 0.833 2.9

3.186 1.276 – – 0.54

0.485 0.042 – – 0.01055

0.358 0.0633 – – 0.074

2016

Space heating Water heating Lighting Air conditioning Othersa

0.348 0.485 0.4642 0.8862 2.89

2.97 1.276 – – 0.54

0.4 0.042 – – 0.01055

0.316 0.0633 – – 0.074

2017

Space heating Water heating Lighting

0.37 0.485 0.4642

3.186 1.276 –

0.485 0.042 –

0.3376 0.0633 – (Continued )

Sectoral Energy and Exergy Management

Table 5

907

Continued

Year

Device

Breakdown of energy use in sector, by type (EJ)

Air conditioning Othersa

Electrical

Natural gas

Fuel oil

LPG

0.77 2.95

– 0.54

– 0.01055

– 0.074

a

Others include refrigeration, electronics, and wet-clean mostly clothes dryer. Source: EIA Data: 2011 United States Residential Sector Key Indicators and Consumption – Datasets – OpenEI Datasets. Available from: https://openei.org/datasets/dataset/eia-data2011-united-states-residential-sector-key-indicators-and-consumption; 2017 [accessed 27.10.17]. EIA. Annual Energy Outlook 2017 with projections to 2050; 2017 doi:DOE/EIA0383(2017).

Table 6

Process and operating data for the residential sector of United States

Device

Energy and exergy efficiencies (%)

Product heat temperature Tp (K)

Electrical

Air conditioning Lighting Space heating Water heater Othersa

Fuel

Electrical

Natural gas

LPG

Fuel oil

Ze

ce

Zf

cf

293 – 328 350 –

– – 328 374 –

– – 328 374 –

– – 328 374 –

100.0 25.0 100 93 –

3.4 24.3 17.1 25.4 –

– – 65 62 –

– – 11.1 14 –

a

Others include refrigeration, electronics, and wet-clean mostly clothes dryer. Energy efficiency and Tp values are from Ref. [11]. Corresponding reference environment temperatures are 272K for space heating and 283K for all other devices. Source: Rosen M.A. Evaluation of energy utilization efficiency in Canada using energy and exergy analyses. Energy 1992;17:339–50. doi:10.1016/0360-5442(92)90109-D. [2,8]. Dincer I, Rosen M. EXERGY: energy, environment and sustainable development. Amsterdam: Elsevier Science; 2013.

illustration for the calculation of the overall weighted mean energy and exergy efficiencies for the principal devices in the residential sector for the year 2016 is provided as follows: Step 1: Calculating weighted means for the electrical energy and exergy efficiencies for the device categories in the residential sector ½ð0:348  100Þ þ ð0:485  93Þ þ ð0:4642  25Þ þ ð0:8862  100ފ ¼ 82:5% ð0:348 þ 0:485 þ 0:4642 þ 0:8862Þ ½ð0:348  17:1Þ þ ð0:485  25:4Þ þ ð0:4642  24:3Þ þ ð0:8862  3:4ފ ¼ 14:91% ce ¼ ð0:348 þ 0:485 þ 0:4642 þ 0:8862Þ

Ze ¼

Step 2: Calculating weighted means for the fuel energy and exergy efficiencies for the device categories in the residential sector ½ð2:97  65Þ þ ð0:4  65Þ þ ð0:316  65Þ þ ð1:276  62Þ þ ð0:042  62Þ þ ð0:063  62ފ ¼ 64:18% ð2:97 þ 0:4 þ 0:316 þ 1:276 þ 0:042 þ 0:063Þ ½ð2:97  11:1Þ þ ð0:4  11:1Þ þ ð0:316  11:1Þ þ ð1:276  14Þ þ ð0:042  14Þ þ ð0:063  14ފ cf ¼ ¼ 11:89% ð2:97 þ 0:4 þ 0:316 þ 1:276 þ 0:042 þ 0:063Þ

Zf ¼

Step 3: Overall mean energy and exergy efficiencies are calculated for the residential sector as Z0 ¼ ð82:5  0:3011Þ þ ð64:18  0:6988Þ ¼ 69:69% c0 ¼ ð14:9  0:3011Þ þ ð11:89  0:6988Þ ¼ 12:8% The weighted mean electrical, fuel, and overall energy and exergy efficiencies for the residential sector for the 11 years from 2007 to 2017 are given in Table 7. The overall weighted mean energy and exergy efficiencies for the residential sector are illustrated for the same period in Fig. 6.

5.24.6.2

Analysis of the commercial sector

Energy and exergy utilization in the commercial sector is evaluated and analyzed. This sector analysis includes the energy supplied to provide space and water heating, lighting, air conditioning, and refrigeration.

908

Sectoral Energy and Exergy Management

Table 7

Mean efficiencies for the residential sector in the United States for 2007–2017

Year

Weighted mean electrical efficiencies (%)

Weighted mean fuel efficiencies (%)

Overall efficiencies (%)

Energy

Exergy

Energy

Exergy

Energy

Exergy

74.87 75.20 74.96 77.89 75.09 75.37 79.49 78.33 81.74 82.5 81.71

15.50 15.72 15.90 14.51 16.09 16.02 17.11 15.57 15.19 14.91 15.57

64.19 64.20 64.18 64.18 64.17 64.17 64.17 64.12 64.23 64.18 64.23

11.88 11.87 11.89 11.89 11.90 11.90 12.16 11.90 11.84 11.89 11.84

67.23 67.34 67.29 68.49 67.27 67.58 68.98 68.13 69.17 69.7 69.11

12.91 12.97 13.04 12.71 13.09 13.15 13.72 12.93 12.78 12.80 12.88

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

100 ηoo

90

oo ψ

80

Efficiency

70 60 50 40 30 20 10 0 2007

2009

2011

2013

2015

2017

Years Fig. 6 Overall energy and exergy efficiencies for the residential sector in the United States from 2007 to 2017.

5.24.6.2.1

Energy utilization data for the commercial sector

To determine exergy efficiency for the commercial sector, the consumptions of electrical and fuel energy within the sector are required. In 2012, United States consumed about 25% of the total energy consumption in the commercial sector in heating space, 10% for both lighting and refrigeration, 9% for cooling space, and 7% for heating water. Annual energy consumption data for the commercial sector for the period from 2007 to 17 are presented in Table 8.

5.24.6.2.2

Efficiencies of principal devices in the commercial sector

The operating and process data for the devices considered in the commercial sector analysis for the United States are listed in Table 9. Both energy and exergy efficiencies of the devices evaluated with the same methodology provided in Section 5.24.5. The process and operating data for the commercial sector are also listed in Table 9.

5.24.6.2.3

Mean efficiencies for the overall commercial sector

Mean energy and exergy efficiencies for commercial sector are calculated using information from Tables 8 and 9. These mean efficiencies are then used to determine the overall sector energy and exergy efficiencies (see Table 10 and Fig. 7). For illustration, the overall weighted mean energy and exergy efficiencies for the principal devices in the commercial sector for the year 2016 are evaluated as follows. Step 1: Calculating weighted means for the electrical energy and exergy efficiencies for the device categories in the commercial sector Ze ¼

½ð0:12  100Þ þ ð0:02  93Þ þ ð0:51  25Þ þ ð0:55  100Þ þ ð0:64  100ފ ¼ 79:14% ð0:12 þ 0:02 þ 0:51 þ 0:55 þ 0:64Þ

Sectoral Energy and Exergy Management

Table 8 Year

909

Energy consumption (in EJ) in the commercial sector of United States from 2007 to 2017 Device

Breakdown of energy use in sector, by type (EJ) Electrical

Natural gas

Fuel oil

2007

Space heating Water heating Lighting Air conditioning Refrigeration Othersa

0.178 0.10 1.13 0.59 0.43 2.41

1.53 0.46 – – – 1.16

0.18 0.02 – – – 0.23

2008

Space heating Water heating Lighting Air conditioning Refrigeration Othersa Space heating

0.19 0.10 1.12 0.54 0.43 2.521 0.19

1.63 0.46 – – – 1.25 1.66

0.17 0.02 –

Water heating Lighting Air conditioning Refrigeration Othersa

0.10 1.11 0.53 0.42 2.51

0.04 – – 1.05

0.02 – – – 0.2

Space heating Water heating Lighting Air conditioning Refrigeration Othersa

0.18 0.10 1.12 0.57 0.42 2.63

1.58 0.46 – –

0.17 0.02 – –

1.23

0.19

2011

Space heating Water heating Lighting Air conditioning Refrigeration Othersa

0.18 0.10 1.12 0.58 0.41 2.69

1.59 0.47 – – – 1.24

0.17 0.02 – – – 0.19

2012

Space heating Water heating Lighting Air conditioning Refrigeration Othersa

0.18 0.10 1.13 0.59 0.41 2.75

1.59 0.48 – – – 1.24

0.16 0.02 – – – 0.19

2013

Space heating Water heating Lighting Air conditioning Refrigeration Othersa

0.18 0.10 1.14 0.59 0.4 2.82

1.6 0.48 – – – 1.25

0.16 0.02 – – – 0.18

2014

Space heating Water heating Lighting Air conditioning Refrigeration Othersa

0.18 0.10 1.15 0.59 0.4 2.9

1.61 0.49 – – – 1.26

0.16 0.02 – – – 0.18

2015

Space heating Water heating Lighting Air conditioning Refrigeration Othersa

0.12 0.02 0.55 0.57 0.67 2.96

1.73 0.32 – – – 1.41

0.26 – – – – 0.11 (Continued )

2009

2010

– 0.21 0.18

910

Table 8

Sectoral Energy and Exergy Management

Continued

Year

Device

Breakdown of energy use in sector, by type (EJ) Electrical

Natural gas

Fuel oil

2016

Space heating Water heating Lighting Air conditioning Refrigeration Othersa

0.12 0.02 0.51 0.55 0.64 2.81

1.63 0.31 – – – 1.26

0.24 – – – – 0.16

2017

Space heating Water heating Lighting Air conditioning Refrigeration Othersa

0.12 0.02 0.50 0.50 0.65 2.82

1.71 0.31 – – – 1.29

0.26 – – – – 0.18

a

Others like ventilation, cooking, office equipment (PC), office equipment (non-PC), etc. Source: EIA. Annual Energy Outlook 2017 with projections to 2050; 2017 doi:DOE/EIA-0383(2017). EIA Data: 2011 United States Commercial Sector Key Indicators and Consumption – Datasets – OpenEI Datasets. Available from: https://openei.org/datasets/dataset/eia-data-2011-united-states-commercial-sector-key-indicators-and-consumption; 2017 [accessed October 30, 2017].

Table 9

Process and operating data for the commercial sector of United States

Device

Energy and exergy efficiencies (%)

Product heat temperature Tp (K) Electrical

Air conditioning Lighting Space heating Water heater Refrigerator Othersa

293 – 328 350 278 –

Natural gas

– – 328 374 – –

LPG

Fuel oil

– – 328 374 – –

– – 328 374 – –

Electrical

Fuel

Ze

ce

Zf

cf

100.0 25.0 100 93 100 –

3.4 24.3 17.1 25.4 1.8 –

– – 65 62 – –

– – 11.1 14 – –

a

Others like ventilation, cooking, office equipment (PC), office equipment (non-PC), etc.

Table 10 Year

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Mean efficiencies for the commercial sector in the United States for 2007–17 Weighted mean electrical efficiencies (%)

Weighted mean fuel efficiencies (%)

Overall efficiencies (%)

Energy

Exergy

Energy

Exergy

Energy

Exergy

64.80 64.41 64.28 64.56 64.56 64.54 64.23 64.07 78.55 79.13 78.97

14.75 14.96 15.03 14.86 14.87 14.86 14.96 15 9.88 9.77 9.82

64.34 64.37 64.90 64.35 64.35 64.33 64.37 64.33 64.58 64.57 64.59

11.73 11.71 11.19 11.72 11.73 11.74 11.74 11.75 11.50 11.51 11.49

64.59 64.39 64.56 64.46 64.46 64.44 64.28 64.20 70.94 71.23 70.92

13.32 13.37 13.31 13.35 13.35 13.36 13.40 13.42 10.76 10.71 10.76

Sectoral Energy and Exergy Management

911

80 70 60

Efficiency

50 o ηo

o ψo 40 30 20 10 0 2007

2009

2011

2013

2015

2017

Year Fig. 7 Overall energy and exergy efficiencies for the commercial sector in the United States from 2007 to 2017.

ce ¼

½ð0:12  17:1Þ þ ð0:02  25:4Þ þ ð0:51  24:3Þ þ ð0:55  3:4Þ þ ð0:64  1:8ފ ¼ 9:77% ð0:12 þ 0:02 þ 0:51 þ 0:55 þ 0:64Þ

Step 2: Calculating weighted means for the fuel energy and exergy efficiencies for the device categories in the commercial sector ½ð1:63  65Þ þ ð0:24  65Þ þ ð0:31  62ފ ¼ 64:57% ð1:63 þ 0:24 þ 0:31Þ ½ð1:63  11:1Þ þ ð0:24  11:1Þ þ ð0:31  14ފ ¼ 11:51% cf ¼ ð1:63 þ 0:24 þ 0:31Þ Zf ¼

Step 3: The overall mean energy and exergy efficiencies are calculated for the commercial sector as Z0 ¼ ð79:14  0:457Þ þ ð64:57  0:5422Þ ¼ 71:23% c0 ¼ ð9:77  0:457Þ þ ð11:51  0:5422Þ ¼ 10:714%

5.24.6.3

Analysis of the Transportation Sector

Energy and exergy utilization in the transportation sector is evaluated and analyzed. The transportation sector in the United States comprises three main modes namely, road, which incorporates rail, road and urban transit, and freight trucks, air, and marine. Mean energy and exergy efficiencies are calculated by multiplying the energy used in each mode by the corresponding efficiency. Then, these values are added to obtain the overall efficiency of the transportation sector.

5.24.6.3.1

Energy utilization data for the transportation sector

A breakdown for the energy consumption along with the process data by mode of transport for the United States transportation sector is presented in Table 11. Table 11 also includes the main fuel types that are used for each mode of transport along with the energy consumption for each mode and the estimated energy efficiency, rated load, and estimated operating.

5.24.6.3.2

Energy efficiencies for the transportation sector

Table 11 provides energy efficiencies for the three main modes of transport along with the pipelines and others that include military use, lubricant, and international shipping. These values are based on United States devices [1] and are assumed representative of United States devices. Since vehicles generally are not operated at full load, a distinction is made between rated load (full load) and operating (part load) efficiencies [1]. A weighted mean is obtained for the transportation mode energy efficiencies in Table 11, where the weighting factor is the fraction of the energy supplied for each mode of transport deducted from the total energy supplied to the whole transportation sector. The weighted mean overall energy efficiency of the transportation sector for the year 2016, for example, is calculated as follows: Zo ¼ ð0:0201  28Þ þ ð0:084  28Þ þ ð0:0089  15Þ þ ð0:6088  22Þ þ ð0:1938  22Þ þ ð0:0244  29Þ þ ð0:0602  22Þ ¼ 22:74%

912

Table 11 Year

Sectoral Energy and Exergy Management

Energy consumption and process data for the transportation sector in United States Mode of transport

Main fuel types

Energy consumption

Energy efficiencies (%)

EJ

%

Rated load

Estimated operating

2007

Rail Air Marine Road and urban transit Freight trucks Pipelines Othersa

Diesel Jet fuel Diesel, gasoline Gasoline, diesel, propane, electricity Diesel Natural gas Gasoline, diesel, propane, electricity

0.69 2.9 0.27 18.5 5.29 0.68 2.24

2.25 9.49 0.873 60.54 17.31 2.21 7.32

35 35 – 28 28 – 28

28 28 15 22 22 29 22

2008

Rail Air Marine Road and urban transit Freight trucks Pipelines Othersa

Diesel Jet fuel Diesel, gasoline Gasoline, diesel, propane, electricity Diesel Natural gas Gasoline, diesel, propane, electricity

0.66 2.79 0.26 17.86 4.98 0.68 2.15

2.24 9.49 0.88 60.8 16.95 2.32 7.33

35 35 – 28 28 – 28

28 28 15 22 22 29 22

2009

Rail Air Marine Road and urban transit Freight trucks Pipelines Othersa

Diesel Jet fuel Diesel, gasoline Gasoline, diesel, propane, electricity Diesel Natural gas Gasoline, diesel, propane, electricity

0.61 2.8 0.26 17.57 4.45 0.67 2.09

2.14 9.83 0.90 61.78 15.66 2.34 7.34

35 35 – 28 28 – 28

28 28 15 22 22 29 22

2010

Rail Air Marine Road and urban transit Freight trucks Pipelines Othersa

Diesel Jet fuel Diesel, gasoline Gasoline, diesel, propane, electricity Diesel Natural gas Gasoline, diesel, propane, electricity

0.60 2.74 0.26 17.93 4.48 0.67 2.11

2.1 9.52 0.91 62.26 15.54 2.32 7.34

35 35 – 28 28 – 28

28 28 15 22 22 29 22

2011

Rail Air Marine Road and urban transit Freight trucks Pipelines Othersa

Diesel Jet fuel Diesel, gasoline Gasoline, diesel, propane, electricity Diesel Natural gas Gasoline, diesel, propane, electricity

0.63 2.76 0.27 18.28 4.7 0.65 2.12

2.16 9.4 0.91 62.16 15.97 2.2 7.2

35 35 – 28 28 – 28

28 28 15 22 22 29 22

2012

Rail Air Marine Road and urban transit Freight trucks Pipelines Othersa

Diesel Jet fuel Diesel, gasoline Gasoline, diesel, propane, electricity Diesel Natural gas Gasoline, diesel, propane, electricity

0.66 2.80 0.27 18.30 4.91 0.65 2.11

2.21 9.44 0.91 61.63 16.55 2.18 7.1

35 35 – 28 28 – 28

28 28 15 22 22 15 22

2013

Rail Air Marine Road and urban transit Freight trucks Pipelines Othersa

Diesel Jet fuel Diesel, gasoline Gasoline, diesel, propane, electricity Diesel Natural gas Gasoline, diesel, propane, electricity

0.67 2.84 0.27 18.25 5.06 0.63 2.11

2.24 9.51 0.91 61.2 16.96 2.12 7.1

35 35 – 28 28 – 28

28 28 15 22 22 29 22

2014

Rail Air Marine Road and urban transit Freight trucks

Diesel Jet fuel Diesel, gasoline Gasoline, diesel, propane, electricity Diesel

0.68 2.89 0.27 18.29 5.14

2.28 9.66 0.91 60.8 17.18

35 35 – 28 28

28 28 15 22 22 (Continued )

Sectoral Energy and Exergy Management

Table 11 Year

913

Continued Mode of transport

Main fuel types

Energy consumption

Energy efficiencies (%)

EJ

%

Rated load

Estimated operating

Pipelines Othersa

Natural gas Gasoline, diesel, propane, electricity

0.64 2.11

2.12 7.05

– 28

29 22

2015

Rail Air Marine Road and urban transit Freight trucks Pipelines Othersa

Diesel Jet fuel Diesel, gasoline Gasoline, diesel, propane, electricity Diesel Natural gas Gasoline, diesel, propane, electricity

0.61 2.49 0.27 17.83 5.85 0.73 1.65

2.07 8.47 0.89 60.58 19.9 2.48 5.61

35 35 – 28 28 – 28

28 28 15 22 22 29 22

2016

Rail Air Marine Road and urban transit Freight trucks Pipelines Othersa

Diesel Jet fuel Diesel, gasoline Gasoline, diesel, propane, electricity Diesel Natural gas Gasoline, diesel, propane, electricity

0.60 2.49 0.27 18.12 5.77 0.73 1.79

2.01 8.4 0.89 60.88 19.38 2.44 6.02

35 35 – 28 28 – 28

28 28 15 22 22 29 22

2017

Rail Air Marine Road and urban transit Freight trucks Pipelines Othersa

Diesel Jet fuel Diesel, gasoline Gasoline, diesel, propane, electricity Diesel Natural gas Gasoline, diesel, propane, electricity

0.60 2.54 0.27 18.29 5.93 0.69 1.65

2.02 8.5 0.90 61 19.79 2.30 5.49

35 35 – 28 28 – 28

28 28 15 22 22 29 22

a

Others include military use, lubricant, domestic and international shipping. Source: EIA. Annual Energy Outlook 2017 with projections to 2050; 2017 doi:DOE/EIA-0383(2017). EIA Data: 2011 United States Commercial Sector Key Indicators and Consumption – Datasets – OpenEI Datasets. Available from: https://openei.org/datasets/dataset/eia-data-2011-united-states-commercial-sector-key-indicators-and-consumption; 2017 [accessed October 30, 2017].

5.24.6.3.3

Exergy efficiencies for the transportation sector

Before evaluating the overall mean exergy efficiencies for the transportation sector, it is noted that the outputs of transportation devices are in the form of kinetic energy (shaft work). The exergy associated with shaft work (W) is by definition equal to the energy, i.e., ExW ¼ W Thus, for electric shaft work production, the energy and exergy efficiencies of transportation devices can be shown to be identical: W We

ð19Þ

ExW W ¼ Zm;e ¼ We Ex We

ð20Þ

Zm;e ¼

cm;e ¼

For fossil-fueled shaft work production in transportation devices, the exergy efficiency can be shown to be similar to the energy efficiency: W mf Hf

ð21Þ

ExW mf ϒf Hf

ð22Þ

Zm;f ¼

cm;f ¼ When gf is unity, as is often assumed for most fuels [12],

cm:f ¼ Zm:f

ð23Þ

Thus, the overall mean exergy efficiencies for the transportation sector are equal to the overall mean energy efficiencies. For the year 2000, for instance, co ¼ Zo ¼ 22:24% The overall mean energy and exergy efficiencies for the transportation sector for 2007–17 are illustrated in Fig. 8 and Table 12.

914

Sectoral Energy and Exergy Management

25

Overall efficien

cy %

30

20 15 10 5 0 2007 2008 2009 2010 2011 2012 2013 2014 2015 Years Energy efficiency

2016

2017

Exergy efficiency

Fig. 8 Overall energy and exergy efficiency of the transportation sector in the United States from 2007 to 2017.

Table 12 2007–17

5.24.6.4

Overall energy and exergy efficiency of the transportation sector in the United States,

Year

Overall energy efficiency %

Overall exergy efficiency %

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

22.79 22.8 22.81 22.79 22.78 22.78 22.79 22.8 22.74 22.73 22.72

22.79 22.8 22.81 22.79 22.78 22.78 22.79 22.8 22.74 22.73 22.72

Analysis of the Industrial Sector

Energy and exergy utilization in the industrial sector is evaluated and analyzed. The industrial sector of the United States is composed of many industries. The four most significant ones are petroleum and coal products, chemical, paper, and metal.

5.24.6.4.1

Methodology and energy data for the industrial sector

To simplify the analysis of energy and exergy efficiencies for this complex sector, the four most significant industries, which account for more than 78% of the total sector energy use, are chosen to represent the overall sector. In the industrial sector of United States, the energy used to generate heat for production processes accounts for 64% of the total energy consumption, and mechanical drives account for 14%. For simplicity, we analyze here heating and mechanical end use only. This simplification is considered valid since these processes account for 78% of the energy consumption in the industrial sector. Assumptions and simplifications made for the heating and mechanical processes are as follows:

• • •

Heating processes for each industry are grouped into low-, medium-, and high-temperature categories as shown in Table 13. The temperature ranges given in Table 13 are based on Rosen [2], and the heating data are from Brown et al. [13]. The efficiencies for the low-temperature category are assumed to be the same as those for heating in the residential sector [2]. The efficiencies for the medium- and high-temperature categories are from Reistad [1]. All mechanical drives are assumed to be 90% energy efficient.

Three steps are used to derive the overall efficiency of the sector. First, energy and exergy efficiencies are obtained for process heating for each of the product-heat temperature Tp categories. Second, mean heating energy and exergy efficiencies for the four industries are calculated using a two-part procedure: (1) weighted mean efficiencies for electrical heating and fuel heating are evaluated for each industry; and (2) weighted mean efficiencies for all heating processes in each industry are determined with these values, using as weighting factors, the ratio of the industry energy consumption (electrical or fuel) to the total consumption of both electrical and fuel energy. Third, weighted mean overall (i.e., heating and mechanical drive) efficiencies for each industry

Sectoral Energy and Exergy Management

Table 13 TP category

Low Medium High

915

Process heating temperatures and efficiencies for the industrial sector TP range (1C)

o121 121–399 4399

Heating energy efficiencies (%) Electrical, Zh,e

Fuel, ch,e

100 90 70

65.5 60 50

Source: Rosen MA. Evaluation of energy utilization efficiency in Canada using energy and exergy analyses. Energy 1992;17:339–350. doi:10.1016/0360-5442(92)90109-D.

Table 14

Process heating data for the industrial sector

Industry

TP range

Breakdown of energy used in each TP range (%)

Mean TP in range (1C)

Electricity

Fuel

Petroleum and coal

Low Medium High

57 227 494

10.0 9.4 80.4

13.8 22.6 63.6

Chemical

Low Medium High

42 141 494

62.5 37.5 0.0

0.0 100.0 0.0

Paper

Low Medium High

67 160 732

100 0.0 0.0

0.0 83.0 17

Metal

Low Medium High

45 – 983

4.2 0.0 95.8

0.0 0.0 100.0

Source: Brown, HL, Hamel BB., Hedman BA. Energy analysis of 108 industrial processes. Lilburn, GA: Fairmont Press; 1996.

are evaluated using the weighting factor for the fractions of the total sectoral energy input for both heating and mechanical drives. In the determination of sector efficiencies, weighted means for the weighted mean overall energy and exergy efficiencies for the major industries in the industrial sector are obtained, using the weighting factor for the fraction of the total industrial energy demand supplied to each industry. For comprehensive illustration, the efficiencies for the petroleum and coal are calculated in the following subsections.

5.24.6.4.2

Process-heating efficiencies for the product heat temperature categories in each industry

Product-heat temperature data for each industry are separated into the categories defined in Table 13. The resulting breakdown is shown in Table 14, with the percentage of heat in each category supplied by electricity and fossil fuels. The evaluation of efficiencies for electrical and fossil fuel process heating for the oil and gas industry are shown in the next two subsections. The same process is applied to each industry in the industrial sector. 5.24.6.4.2.1 Electrical process heating in the petroleum and coal industry In the petroleum and coal industry, electric heating is used to supply all categories of heat as shown in Table 14. With Table 13 and Eq. (15), the energy efficiency for low-temperature electric heating is shown to be Z¼

QP ¼ 1ðor 100%Þ We

For the medium- and high-temperature categories, the energy efficiencies are similarly found to be 90% and 70%, respectively. Using Eq. (10) with T0 ¼283K, the exergy efficiencies for the three categories are:

•

Low-temperature: TP ¼330K (mean value in category)

 T0 QP ¼ 1  c¼ 1 TP We

 283  1 ¼ 0:142 or ð14:24Þ 330

916

Sectoral Energy and Exergy Management

Table 15

Energy and exergy data and efficiencies for all categories of product-heat temperature Tp in the industrial sector of United States TP range

Industry

Breakdown of energy and exergy efficiencies for each Tp category, by type Electrical heating

Fuel heating

Zh

ch

Zh

ch

Petroleum and coal

Low Medium High

100.00 90.00 70.00

14.24 39.06 44.17

65.00 60.00 50.00

9.26 26.04 31.55

Chemical

Low Medium High

100.00 90.00 –

10.16 28.48 –

60.00 –

18.99 –

Paper

Low Medium High

100.00 90.00 70.00

16.76 31.17 50.28

65.00 60.00 50.00

10.89 20.78 35.92

Metal

Low Medium High

100.00 – 70.00

11.01 – 54.23

– – 50.00

– – 38.73

•

Medium-temperature: Tp ¼ 500K (mean value in category)

 T0 QP 283 ¼ 1 c¼ 1  0:9 ¼ 0:3906 ðor 39:06%Þ  TP We 500

•

High-temperature: Tp ¼ 767K (mean value in category)
c¼ 1

T0 TP






QP ¼ 1 We

 283  0:7 ¼ 0:441ðor 44:1%Þ 767

5.24.6.4.2.2 Fossil fuel process heating in the petroleum and coal industry The petroleum and coal industry requires fossil fuel heating at all ranges of temperatures in Table 14. The energy efficiency for lowtemperature heating is found using Eq. (6) and data from Table 13: Z¼

QP ¼ 0:65 ðor 65%Þ mf  Hf

ð24Þ

Similarly, the energy efficiency for medium- and high-temperature heating are found to be 60% and 50%, respectively. The corresponding exergy efficiency for low-temperature process heating is found using Eq. (7), a reference-environment temperature T0 of 283K and a process-heating temperature TP from Table 14 of 330K, as follows:    1 283 330  QP ð25Þ c¼ ðmf ϒf Hf Þ Assuming g, we can combine Eqs. (24) and (25) to obtain the exergy efficiency for low-temperature process heating as
 283 c¼ 1  0:65 ¼ 0:092 ðor 9:25%Þ 330 Similarly, the exergy efficiencies for the medium- and high-temperature process heating are found to be 26.04% and 31.55%, respectively. See Table 15 for the breakdown of energy and exergy efficiencies for each TP category, by type.

5.24.6.4.3

Mean process heating efficiencies for each industry of the industrial sector

Prior to obtaining the overall energy and exergy efficiencies for the industrial sector, the overall heating efficiencies for each industry are evaluated. Again, the methodology in this section is implemented on the petroleum and coal industry. A combined mean efficiency for the three temperature categories for electric and fossil fuel processes is evaluated to obtain an overall heating efficiency in a given industry. Using data from Table 16, the fraction of total energy utilized by the oil and gas industry for electrical (Ee) and fossil fuel (Ef) heating is found for the year 2016 as follows:

•

For electrical energy: Ee ¼ ðElectrical energy inÞ=ðElectrical energy in þ Fuel energy inÞ ¼ 0:176=ð0:176 þ 4:22Þ ¼ 0:04 ðor 4%Þ

Sectoral Energy and Exergy Management

Table 16 Year

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

•

917

Energy consumption data (in EJ) for the industrial sector in the United States from 2007 to 2017 Industry Petroleum and coal Electrical Fuel

Chemical Electrical

Fuel

Paper Electrical

Fuel

Metal Electrical

Fuel

0.150 0.156 0.163 0.169 0.17 0.172 0.174 0.176 0.176 0.176 0.176

0.53 0.51 0.49 0.47 0.477 0.479 0.48 0.483 0.483 0.483 0.483

4.86 4.84 4.82 4.795 5.14 5.48 5.82 6.16 6.16 6.16 6.16

0.25 0.239 0.228 0.217 0.21 0.21 0.205 0.202 0.202 0.202 0.202

2.17 2.12 2.061 2.007 2.006 2.004 2.00 2.00 2.00 2.00 2.00

0.468 0.45 0.44 0.422 0.430 0.44 0.448 0.456 0.456 0.456 0.456

1.33 1.31 1.29 1.27 1.274 1.274 1.274 1.274 1.274 1.274 1.274

6.9 6.70 6.50 6.30 5.78 5.26 4.74 4.22 4.22 4.22 4.22

For fossil fuel energy: Ef ¼ 1 Ee ¼ 1:00 0:0353 ¼ 0:96 ðor 96 %Þ

Using energy fractions from Table 14 and energy and exergy efficiencies in Table 15, an average heating efficiency for the petroleum and coal industry can be calculated. The energy efficiency for electrical heating Zh;e can be evaluated in the petroleum and coal industry as follows: P Zh;e ¼ ðFraction in categoryÞ  ðEnergy efficiencyÞ ¼ ð0:1  100Þ þ ð0:094  90Þ þ ð0:805  70Þ ¼ 74:89%

Similarly, the corresponding exergy efficiency ch,e is calculated as ch;e ¼ ð0:1  14:24Þ þ ð0:094  39:06Þ þ ð0:805  44:17Þ ¼ 40:69% Using data in Tables 14 and 15, energy and exergy efficiencies for fossil fuel heating in the petroleum and coal industry for the year 2016 are found as follows: Zh;f ¼ ð0:138  65Þ þ ð0:225  60Þ þ ð0:635  50Þ ¼ 54:33% ch;f ¼ ð0:138  9:26Þ þ ð0:225  26:04Þ þ ð0:635  31:55Þ ¼ 27:23% With the energy efficiencies Zh,e and Zh,f, and the fractions of electrical energy Ee and fossil fuel energy Ef used by the oil and gas industry, overall mean energy and exergy efficiencies for heating can be determined: Zh ¼ ð0:04  74:89Þ þ ð0:96  54:33Þ ¼ 55:15% ch ¼ ð0:04  40:69Þ þ ð0:96  27:23Þ ¼ 27:76% Due to the lack of information for this sector the available published data are used; data in Table 16 for the years from 2010 to 2014 are collected from the reports published by energy information and administration [14,15], the data for the years from 2007 to 14 has been projected by the authors utilizing linear regression for the data provided in the previous two references along with data published by the Energy Information Administration for industry consumption in 2006 [16]. Data for 2015, 2016, and 2017 are assigned based on the assumption that they have the same values of energy consumption for each sector in 2014. Following the same methodology, mean heating energy and exergy efficiencies for the other three industries considered are determined (see Table 17). The mean heating energy and exergy efficiencies for the year 2016 are illustrated in Fig. 9.

5.24.6.4.4

Overall efficiencies for the industrial sector

Overall energy and exergy efficiencies for the industrial sector are obtained using process heating efficiencies (see Table 14), the mechanical drive efficiency (assumed to be 90%), and the total energy consumption for each industry (see Table 15). For years from 2007 to 2017, the overall mean heating energy (Zh,o) and exergy efficiencies (ch,o) are presented in Fig. 10, and overall energy and exergy efficiencies for the industrial sector are presented in Fig. 11.

5.24.6.5

Analysis of the Utility Sector

Energy and exergy utilization in the utility sector is evaluated and analyzed. The main electricity generation sources in the United States are provided from fossil fuels (diesel, crude oil, natural gas, fuel oil) along with nuclear and renewables.

918

Sectoral Energy and Exergy Management

Table 17 Year

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Process-heating energy and exergy efficiencies for the main industries in the industrial sector of United States Industry Petroleum and coal ch Zh

Chemical Zh

ch

Paper Zh

ch

Metal Zh

ch

54.76 54.79 54.83 54.86 54.91 54.97 55.05 55.14 55.14 55.14 55.14

63.56 63.45 63.34 63.23 63.07 62.91 62.76 62.63 62.63 62.63 62.63

22.82 22.71 22.59 22.47 22.30 22.12 21.96 21.82 21.82 21.82 21.82

62.60 62.5 62.45 62.36 62.25 62.25 62.17 62.12 62.12 62.12 62.12

22.67 22.68 22.69 22.71 22.72 22.72 22.74 22.74 22.74 22.74 22.74

55.53 55.43 55.40 55.30 55.36 55.45 55.53 55.60 55.60 55.60 55.60

42.29 42.22 42.21 42.14 42.18 42.24 42.29 42.33 42.33 42.33 42.33

27.51 27.53 27.55 27.57 27.61 27.65 27.70 27.76 27.76 27.76 27.76

Overall efficiency %

60 50 40 30 20 10 0 Petroleum and coal Chemical

Paper Exergy efficiency

Metal

Fig. 9 Heating energy and exergy efficiencies for the industrial sector for the year 2016 in the United States.

100 90

h,o

h,o

Overall heating efficiency %

80 70 60 50 40 30 20 10 0 2007

2009

2011

2013

2015

2017

Years Fig. 10 Overall heating energy and exergy efficiencies for the industrial sector in the United States.

5.24.6.5.1

Energy utilization data for the utility sector

For power plants for 2007–2016, the energy input, the electricity generated, along with estimated energy efficiencies are listed in Table 18. The overall energy efficiency can be determined by dividing total electrical energy produced by the total input energy.

Sectoral Energy and Exergy Management

919

100 o

o

90

Overall efficiency %

80 70 60 50 40 30 20 10 0 2007

2011

2009

2013

2015

2017

Years Fig. 11 Overall energy and exergy efficiencies for the industrial sector in the United States.

5.24.6.5.2

Energy efficiencies for the utility sector

Using data in Table 18, we can determine energy efficiencies for the power plants. Then, we can calculate the overall mean energy efficiencies of the utility sector. Sample calculations are shown below for the year 2016. Ze ¼

5.24.6.5.3

½4:31 þ 0:068 þ 4:21 þ 2:87 þ 0:88 þ 0:061 þ 0:115 þ 0:0072 þ 0:2 þ 0:738Š ¼ 31:7% ½13:47 þ 0:21 þ 13:15 þ 9:57 þ 0:98 þ 0:38 þ 0:96 þ 0:034 þ 0:87 þ 2:84Š

Exergy efficiencies for the utility sector

Since for fossil fuel energy, we assume gf ¼ 1, the exergy efficiencies for electricity generation from power plants are the same as the energy efficiencies. This equivalence is shown earlier for the industrial sector. Moreover, for simplification of the calculations, exergy efficiency is assumed to be equal to energy efficiency for the different renewable energy sources. Thus, the mean overall exergy efficiency is equal to the mean overall energy efficiency. That is, for the year 2016 it equals to: co ¼ Zo ¼ 31:7% Overall mean energy and exergy efficiencies for the utility sector for 2007–17 are shown in Table 19 and Fig. 12.

5.24.6.6

Energy and Exergy Efficiencies and Flows for the Sectors and Country

Overall energy and exergy efficiencies for all sectors of United States are evaluated. Using the efficiencies in Sections 5.24.6.1 through 6.5 and energy consumption data in each sector, energy, and exergy flow diagrams are constructed for the year 2016 (see Figs. 13 and 14). The resources in the energy and exergy flow diagrams are representing only fossil fuels resources such as coal, natural gas, and petroleum, all renewable sources are not included due to the shortness of information regarding the utilization of the renewable energy sources in industrial, commercial, and residential sectors. Note that in the energy and exergy flow diagram, the total of the electric consumption by the four sectors is less than the electricity supplied by the utility with around 0.5 EJ; presumably this amount of electricity is the electricity consumed by the other industrial sectors that are not considered in this study. Energy and exergy efficiencies for the five sectors and the overall United States economy for the year 2016 are illustrated in Fig. 15. For illustration, overall energy and exergy flows and efficiencies for the sectors in the United States for the year 2016 are evaluated as follows: Total energy input ¼ 89 EJ Residential sector product energy ¼ Zo  Total energy input to residential sector ¼ 0:6911  10:76 ¼ 7:44 EJ Commercial sector product energy ¼ Zo  Total energy input to public and private sector ¼ 0:7123  8:25 ¼ 5:87 EJ Transportation sector product energy ¼ Zo  Total energy input to transportation sector ¼ 0:227  29:74 ¼ 6:75 EJ Industrial sector product energy ¼ Zo  Total energy input to Industrial sector ¼ 0:64  14:97 ¼ 9:58 EJ Total product energy ¼ 7:44 þ 5:87 þ 6:75 þ 9:58 ¼ 29:64 EJ Overall energy efficiency ¼ 29:64=89 ¼ 33:3%

920

Table 18

Sectoral Energy and Exergy Management

Process data for electricity generation in the utility sector for the United States from 2007 to 2017

Year

Mode of generation

Electrical energy generated (EJ)

Energy input (EJ)

Energy efficiency, Ze %

2007

Coal Petroleum Natural gas Nuclear Hydropower Geothermal Solar photovoltaic Solar thermal power Biomass Wind

7.06 0.2052 2.47 2.9 0.5 0.032 0.064 0.0036 0.1152 0.4212

22.0625 0.64 7.72 9.67 0.556 0.2 0.533 0.017 0.50 1.62

32 32 32 30 90 16 12 21 23 26

2008

Coal Petroleum Natural gas Nuclear Hydropower Geothermal Solar photovoltaic Solar thermal power Biomass Wind

6.98 0.14 2.455 2.9 0.53 0.036 0.068 0.0036 0.118 0.446

21.81 0.44 7.67 9.67 0.589 0.225 0.567 0.017 0.513 1.715

32 32 32 30 90 16 12 21 23 26

2009

Coal Petroleum Natural gas Nuclear Hydropower Geothermal Solar photovoltaic Solar thermal power Biomass Wind

6.343 0.147 2.5 2.91 0.58 0.04 0.076 0.0036 0.1332 0.49

19.82 0.46 7.81 9.7 0.64 0.25 0.63 0.017 0.58 1.88

32 32 32 30 90 16 12 21 23 26

2010

Coal Petroleum Natural gas Nuclear Hydropower Geothermal Solar photovoltaic Solar thermal power Biomass Wind

6.41 0.144 2.38 2.93 0.666 0.047 0.086 0.0036 0.0151 0.0562

20.031 0.45 7.44 9.77 0.74 0.294 0.717 0.017 0.065 0.22

32 32 32 30 90 16 12 21 23 26

2011

Coal Petroleum Natural gas Nuclear Hydropower Geothermal Solar photovoltaic Solar thermal power Biomass Wind

6.8 0.147 2.217 2.93 0.74 0.05 0.097 0.0036 0.17 0.62

21.25 0.46 6.93 9.77 0.82 0.313 0.81 0.017 0.74 2.38

32 32 32 30 90 16 12 21 23 26

2012

Coal Petroleum Natural gas Nuclear Hydropower Geothermal Solar photovoltaic Solar thermal power

7.02 0.147 2.06 2.94 0.824 0.0576 0.108 0.0072

21.9 0.46 6.44 9.8 0.92 0.36 0.9 0.034

32 32 32 30 90 16 12 21 (Continued )

Sectoral Energy and Exergy Management

Table 18 Year

921

Continued Energy input (EJ)

Energy efficiency, Ze %

Mode of generation

Electrical energy generated (EJ)

Biomass Wind

0.1872 0.691

0.81 2.66

23 26

2013

Coal Petroleum Natural gas Nuclear Hydropower Geothermal Solar photovoltaic Solar thermal power Biomass Wind

7.17 0.144 1.764 2.95 0.893 0.0612 0.1152 0.0072 0.202 0.752

22.40 0.45 5.51 9.83 0.99 0.383 0.96 0.034 0.88 2.89

32 32 32 30 90 16 12 21 23 26

2014

Coal Petroleum Natural gas Nuclear Hydropower Geothermal Solar photovoltaic Solar thermal power Biomass Wind

7.26 0.144 1.67 2.99 0.91 0.612 0.119 0.0072 0.205 0.767

22.69 0.45 5.22 9.97 1.01 3.83 0.99 0.034 0.89 2.95

32 32 32 30 90 16 12 21 23 26

2015

Coal Petroleum Natural gas Nuclear Hydropower Geothermal Solar photovoltaic Solar thermal power Biomass Wind

4.76 0.086 4 2.87 0.81 0.055 0.104 0.0072 0.184 0.68

14.88 0.27 12.5 9.57 0.9 0.34 0.87 0.034 0.8 2.61

32 32 32 30 90 16 12 21 23 26

2016

Coal Petroleum Natural gas Nuclear Hydropower Geothermal Solar photovoltaic Solar thermal power Biomass Wind

4.31 0.068 4.21 2.87 0.88 0.061 0.115 0.0072 0.2 0.738

13.47 0.21 13.15 9.57 0.98 0.38 0.96 0.034 0.87 2.84

32 32 32 30 90 16 12 21 23 26

2017

Coal Petroleum Natural gas Nuclear Hydropower Geothermal Solar photovoltaic Solar thermal power Biomass Wind

4.37 0.061 4.08 2.85 0.93 0.065 0.19 0.0072 0.212 0.781

13.65 0.19 12.75 9.5 1.03 0.41 1.58 0.034 0.92 3.0

32 32 32 30 90 16 12 21 23 26

Source: [2,17–19]. Rosen MA. Evaluation of energy utilization efficiency in Canada using energy and exergy analyses. Energy 1992;17:339–50. doi:10.1016/0360-5442(92)90109-D. EIA Data: 2010 United States Electricity Supply, Disposition, Prices, and Emissions – Data.gov. Available from: https://catalog.data.gov/dataset/eia-data-2010-united-states-electricitysupply-disposition-prices-and-emissions; 2017 [accessed 30.10.17]. U.S. EIA. Annual Energy Review 2011; 2012 DOI:/EIA-1384(2011).EIA. Annual Energy Outlook 2017 with projections to 2050; 2017 doi:DOE/EIA-0383(2017).All the efficiencies corresponding to the mode of generation were taken from Rosen MA. Evaluation of energy utilization efficiency in Canada using energy and exergy analyses. Energy 1992;17:339–50. doi:10.1016/0360-5442(92)90109-D. U.S. EIA. Annual Energy Review 2011; 2012. DOI:/EIA-1384(2011).

922

Sectoral Energy and Exergy Management

Overall efficie

ncy %

Table 19

Overall energy and exergy efficiency of the utility sector in the United States 2007–17

Year

Overall energy efficiency %

Overall exergy efficiency %

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

31.6462 31.6481 31.6317 32.0573 31.6744 31.6862 31.7052 30.5753 31.6952 31.6957 31.4445

31.6462 31.6481 31.6317 32.0573 31.6744 31.6862 31.7052 30.5753 31.6952 31.6957 31.4445

33 30 27 24 21 18 15 12 9 6 3 0 2007

2008

2009

2010

2011

2012

2013

2014

Years

2015

2016

2017

Energy efficiency Fig. 12 Overall energy and exergy efficiency of the utility sector in the United States, 2007–17.

29.74 22.9 Transportation: 29.74 Fuel: 52.65

Waste: 58.85

6.75 13.65

5.39 Industrial: 14.97

Resources: 89.00

5.69 3.57

9.58

2.38 Residential: 10.76

Utility: 36.40

Electricity: 11.54

7.44

1.32 5.07 4.65

Product: 29.64 Commercial: 8.25

2.38 5.87

Fig. 13 Energy flow diagram for the United States for the year 2016. Numerical values are in EJ/yr. Losses represent waste energy emissions.

Sectoral Energy and Exergy Management

923

29.7 22.9 Transportation: 29.70 Fuel: 52.60 6.75

13.6 5.6 3.6

Resources: 89.00

Waste: 73.77

9.26

Industrial: 14.96

5.7

9.38

2 Residential: 10.76

1.3

1.38

5.08

Electricity: 11.54

Utility: 36.40

4.6 Commercial: 8.20

7.37

Product: 14.63 0.8

80 70 60 50 40 30 20 10 0

n

Exergy efficiency

l

O

U

ve r

til

al

l

ity

st du In

Tr an

sp

or

ria

ta

er C

om

m

id es R

tio

ci

en

al

tia

l

Overall efficiency %

Fig. 14 Exergy flow diagram for the United States for the year 2016. Numerical values are in EJ/yr. Losses represent waste energy emissions and external exergy consumption.

Energy efficiency

Fig. 15 Energy and exergy efficiencies for the sectors and the overall economy of the United States for 2016.

Similarly, exergy flows and efficiencies are evaluated for the year 2016 as follows, with the assumption that the fuel energy grade function is unity, so exergy inputs are equal to energy inputs: Residential sector product exergy ¼ co  Total exergy input to residential sector ¼ 0:1288  10:76 ¼ 1:38 EJ Commercial sector product exergy ¼ co  Total exergy input to public and private sector ¼ 0:1071  8:25 ¼ 0:88 EJ Transportation sector product exergy ¼ co  Total exergy input to transportation sector ¼ 0:22  29:74 ¼ 6:75 EJ Industrial sector product exergy ¼ Total exergy input to industrial sector ¼ 0:381  14:97 ¼ 5:7 EJ Total product exergy ¼ 1:6 þ 0:8 þ 6:76 þ 5:715 ¼ 14:71 EJ Overall exergy efficiency ¼ 14:71=88:9 ¼ 16:54%

924

Sectoral Energy and Exergy Management

5.24.6.7

Discussion

Exergy analysis indicates a less efficient picture of energy use in the United States than does energy analysis. The residential sector has the lowest exergy efficiency of all sectors, followed closely by the commercial sector. The reason for the low exergy efficiencies in these sectors is the inefficient utilization of the work potential or quality of the input energy. In these sectors, the primary use of energy is to produce cold or heat at near environmental temperatures. With the production of such products from a fossil fuel or electrical energy source, there is a loss in energy quality that can only be reflected with exergy analysis. The nearer to the temperature of the environment is the temperature of the heat produced, the lower is the exergy efficiency. An energy analysis of United States energy utilization does not provide a true picture of how well the economy utilizes its energy resources. An assessment based on energy can be misleading because it often indicates the main inefficiencies to be in the wrong sectors and a state of technological efficiency higher than actually exists. In order to accurately assess the true efficiency of energy utilization, exergy analysis must be used. Exergy flow diagrams are a powerful tool for indicating to industry and government where the emphasis should be placed on programs to improve the use of the exergy associated with the main energy resources (e.g., oil). Furthermore, the results provide important insights for future research and development allocations and projects. Energy utilization also causes environmental concerns such as global warming, air pollution, acid rain and stratospheric ozone depletion. These issues must be addressed if humanity is truly planing to achieve a sustainable energy future. Since all energy use leads to some environmental impact, some environmental concerns can be overcome through increased efficiency and some through use of sustainable energy resources. The former method is used in this chapter to evaluate and understand the efficiency of a country and to assist in increasing efficiency and reducing environmental impact.

5.24.6.7.1

Summary of key findings

The overall and sectoral energy and exergy assessments of United States and its main economic sectors have yielded several interesting findings:

• •

•

The overall energy and exergy efficiencies for the overall United States economy sectors in 2016 are found to be at 33.3% and 16.54%, respectively. Sectoral energy and exergy efficiencies, respectively, for the year 2016 are found to be 69.7% and 12.88% for the residential sector, 71.23% and 10.71% for the commercial sector, 64% and 38.1% for the industrial sector, 22.73% and 22.73% for the transportation sector, and 31.7%, and 31.7% for the utility sector. Thus, the most energy efficient sector for the year 2016 is the commercial sector (71.23%), and the most exergy efficient sector is the industrial sector (38%). In analyzing the relationship between energy and exergy losses (which can be viewed as representing perceived and actual inefficiencies, respectively), it is seen that actual inefficiencies in the residential, industrial, and commercial sectors are much higher than the perceived inefficiencies.

5.24.7

Future Directions

Energy management has been a crucial subject since the early ages of civilization. As presented in Fig. 16, it was more dependent on coal and wood and mostly the usage of the energy sources is limited to heating applications. Then, with the Digitalization where exergy must to incorporated

Nuclear energy

Steam power and oil

Primitives, sectors depending on coal and wood Fig. 16 Industrial development of the economic sector from the primitive use of energy to the digitization and application of exergy as an effective tool.

Sectoral Energy and Exergy Management

925

industrial development and the steam turbine technology, the energy management has become more critical for all sectors including transportation, residential, and industrial. The more complicated systems require smart solutions for energy management. New technologies towards digitalization will direct near-future developments. In the near future, everything from the milling machine to the welding systems will be networked to each other. Moreover, all the devices will have their embedded system that will be logging all the relevant data including energy usage. All of the connected machines will work together to plan the processing sequence. The result is a "self-organizing" adaptive manufacturing process without the need of constant human intervention [20]. The digitalization of the sector should be accompanied by the exergy management. Smart buildings, plants, etc. should be developed in a way that they can exergetically optimize their manufacturing/heating/transporting processes simultaneously. By the digitalization, it is evident that the industry as we know it today will evolve and may look much different in the next decade. Mike Zimmerman, the CEO of Building IQ, listed these developments and briefly explained as follows [21]:

• • • • • • • • •

The price of the energy sources will be higher than today, and it would be much more dynamic. The prices of the peak cost would proportionally change with the base load. That results in a way that the industries that optimize their energy usage will pay much less than those who cannot or do not optimization. The energy and exergy optimization will be an integrated part of the energy and exergy management plan of the industries’ operators. The operators would have exergy management strategies shaping the long-term plans instead of having daily/weekly exergy management strategies. The building operators will be monitoring online energy consumption and hence optimize their systems accordingly. These systems will allow forecasting the energy consumption and building various strategies to attain the desired result with a better exergy management. Demand response would be entirely different from now. In the next decade, it will mainly be integrated into tariffs as part of real-time or semi-real-time pricing. The cloud technology would play a critical role in exergy management systems. Within a decade, all the relevant data related to the manufacturing processes will be accessible from everywhere online. The real-time feedbacks would play more critical role in the exergy management process. Energy and exergy management and the optimization technologies would also play a vital role in the buildings’ long-term portfolio. The market value of the buildings will be based on those systems, and it will be one of the primary parameters for buying, leasing, and investment decisions of the buildings. The technologies such as smart buildings and smart grids will offer more efficient and responsive systems. The smart buildings will optimize their consumption based on the feedback from the grid prices. Moreover, internal microgrids with hybrid renewables and conventional electricity generation systems will manage the demand of new loads such as EV charging. The energy storage and distributed generation systems will play a more important role in the renewable energy driven systems. These systems will need to be incorporated into building energy and exergy optimization to minimize dependence on the grid as much as possible. Social networking will also be a crucial part of exergy management operations. For instance, building operators will use the social tools to share best practices and get in contact with residents to inform them of peak energy periods. Residents will also utilize the social tools to report real-time feedback to the operator regarding comfort issues, maintenance requirements, etc.

5.24.8

Closing Remarks

Exergy analyses have the potential to offer a lot of useful information regarding sectoral and overall energy and exergy use in any macrosystem such as a country or a region. Therefore, it can help attaining energy savings via efficiency and/or conservation actions. Furthermore, it can assist in launching standards to enable adequate energy plans in the whole macrosystem along with its sectors. In this chapter, Global energy consumption and worldwide energy utilization is provided. Energy and exergy management importance and the key difference between them are incorporated. A Methodology for economic sectoral analysis using energy and exergy approaches is included. A case study is applied on the economic sectors of the United States to clarify the methodology of energetic and exergetic sectoral analysis. The case study evaluation that is applied in this chapter validates the ease and the importance of utilizing exergy when assessing economic sectors of a region or country.

References [1] [2] [3] [4] [5] [6]

Reistad GM. Available energy conversion and utilization in the United States. J Eng Power 1975;97:429. doi:10.1115/1.3446026. Rosen MA. Evaluation of energy utilization efficiency in Canada using energy and exergy analyses. Energy 1992;17:339–50. doi:10.1016/0360-5442(92)90109-D. Wall G. Exergy conversion in the Japanese society. Energy 1990;15:435–44. doi:10.1016/0360-5442(90)90040-9. Wall G. Energy conversions in the Finnish. Japanese and Swedish Societies; 1991. http://dx.doi.org/10.1080/15567240491922238 Wall G, Sciubba E, Naso V. Exergy use in the Italian society. Energy 1994;19:1267–74. doi:10.1016/0360-5442(94)90030-2. Özdoĝan S, Arikol M. Energy and exergy analyses of selected Turkish industries. Energy 1995;20:73–80. doi:10.1016/0360-5442(94)00054-7.

926 [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21]

Sectoral Energy and Exergy Management Rosen MA, Dincer I. Sectoral energy and exergy modeling of Turkey. J Energy Resour Technol 1997;119:200. doi:10.1115/1.2794990. Dincer I, Rosen M. EXERGY: energy, environment and sustainable development. Amsterdam: Elsevier Science; 2013. Gaggioli RA, Petit PJ. Use the second law first. Chemtech 1977;7:496–506. EIA. Energy Use in Homes – Energy Explained, Your Guide To Understanding Energy – Energy Information Administration 2013. Available from: http://www.eia.gov/ energyexplained/?page=us_energy_homes; 2017 [accessed 27.10.17]. EIA Data: 2011 United States Residential Sector Key Indicators and Consumption – Datasets – OpenEI Datasets. Available from: https://openei.org/datasets/dataset/eiadata-2011-united-states-residential-sector-key-indicators-and-consumption; 2017 [accessed 27.10.17]. Terkovics PI, Rosen MA. Energy and exergy analysis of Canadian energy utilization. Toronto, Canada; 1988. Brown HL, Hamel BB, Hedman BA. Energy analysis of 108 industrial processes. Lilburn, GA: Fairmont Press; 1996. U.S. Energy Information Adminstration. Manufacturing Energy Consumption Survey (MECS) – Data – U.S. Energy Information Administration (EIA). Available from: https:// www.eia.gov/consumption/manufacturing/data/2010/; 2014 [accessed 31.10.17]. U.S. Energy Information Adminstration. Manufacturing Energy Consumption Survey (MECS) – Data – U.S. Energy Information Administration (EIA). Available from: https:// www.eia.gov/consumption/manufacturing/data/2014/; 2017 [accessed 31.10.17]. U.S. Energy Information Adminstration. Manufacturing Energy Consumption Survey (MECS) – Data – U.S. Energy Information Administration (EIA). Available from: https:// www.eia.gov/consumption/manufacturing/data/2006/#undefined; 2010 [accessed 31.10.17]. EIA. Annual Energy Outlook 2017 with projections to 2050; 2017 doi:DOE/EIA-0383(2017). EIA Data: 2010 United States Electricity Supply, Disposition, Prices, and Emissions – Data.gov. Available from: https://catalog.data.gov/dataset/eia-data-2010-united-stateselectricity-supply-disposition-prices-and-emissions; 2017 [accessed 30.10.17]. U.S. EIA. Annual Energy Review 2011; 2012 DOI:/EIA-1384(2011). 10 Predictions on Commercial Energy Management by 2020 | GreenBiz n.d. https://www.greenbiz.com/blog/2012/02/16/10-predictions-commercial-energy-management2020; 2017 [accessed 01.11.17]. Digitalization Is Changing the Future of Manufacturing. Available from: https://www.fraunhofer.de/en/research/current-research/production-4-0.html; 2017 [accessed 01.11.17].

Further Reading Bejan A, Dincer I, Lorente S, Reis AH, Miguel AF. Porous media in modern technologies: energy, electronics, biomedical and environmental engineering. New York, NY: Springer Verlag; 2004. Dincer I. Refrigeration systems and applications. 3rd ed. London: John Wiley & Sons Ltd; 2017. Dincer I, Ratlamwala T. Integrated absorption refrigeration systems: comparative energy and exergy analyses. New York, NY: Springer Verlag; 2016. Dincer I, Rosen MA. Thermal energy storage systems and applications. London: John Wiley & Sons Ltd; 2002. Dincer I, Rosen MA. Exergy analysis of heating, refrigerating and air conditioning. Oxford: Elsevier Science Ltd.; 2015. Dincer I, Rosen MA, Ahmadi P. Optimization of energy systems. London: John Wiley & Sons Ltd.; 2017. Dincer I, Zamfirescu C. Advanced power generation systems. Oxford: Elsevier Science Ltd.; 2014. Dincer I, Zamfirescu C. Sustainable energy systems and applications. Boston: Springer US; 2011.

Relevant Websites https://energy.gov/ ENERGY.GOV, Department of Energy. https://www.iea.org/ International Energy Agency. https://www.nrel.gov National Renewable Energy Laboratory. http://www.nrcan.gc.ca/home National Resources Canada. http://www.worldbank.org/en/topic/energy The World Bank. https://www.eia.gov U.S Energy Information Administration. https://www.epa.gov U.S Environmental Protection Agency. https://www.worldenergy.org/ World Energy Council.

5.25 Concluding Remarks Tahir Abdul Hussain Ratlamwala, National University of Sciences and Technology, Islamabad, Pakistan Ibrahim Dincer, University of Ontario Institute of Technology, Oshawa, ON, Canada r 2018 Elsevier Inc. All rights reserved.

5.25.1 5.25.2 5.25.2.1 5.25.2.2 5.25.2.3 5.25.2.4 5.25.3

5.25.1

Introduction Energy Management Dimensions Energy Management Basics Energy Management in Energy Sources Smart Innovations in Energy Management Energy Management Application Areas Conclusions

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Introduction

With the rapid modernization and technological developments, more and more people are attracted toward urban life style. Estimates suggests, back in 1980, 30% of the world’s total population lived in cities while rest of them in rural areas; however, by 2011 these figure increases to 50%, as per United Nations. It is also predicted that these numbers could go up to 70% by the year 2050, which means that out of 9.6 billion population, 6.4 billion will be living in cities by 2050. To support the needs of this growing population, things should be planned before to avoid hassle in future. Despite of this rapid growth, the rate of increase in world’s energy production wouldn’t be linear, there are certain factors like climate change and the urban heat island effect, and this would significantly decrease the heating load of the buildings while on the other hand cooling load will be increased. Larger number of transportation vehicles, due to larger number of population will yield massive release of pollution in our atmosphere; therefore, the need of clean and healthy indoor air will be increased. However, as per today’s growth in the field of generating clean, green energy, it can be said that we need to cope up in order to come up with a technology, system, approaches or practices that could end up the future dilemma. World need a smarter technique that could address the top pillars of energy, i.e., security, sustainability and affordability all at a same time. This fact cannot be denied that presently, oil is the major source for the generation of this all important form of vitality. The vast impact which oil has put on human life, cannot be replaced by any other sources, currently. Oil in its various forms like diesel, kerosene and other, together has a unique and distinct characteristic. These include: ease of transportation and storage, relative safety and great versatility at end use. Sustainable energy sources are considered to be a substitution of oil when all of these aspects are considered. None appears to completely equal oil. But oil, like other fossil fuels is a finite source that it has to eliminate from the surface of earth at some point of time; moreover, as the reserves of oil has been continuously depleting, the cost to recover what remains will be beyond the value of oil. Also, a time will be reached when the energy needed to recover the oil will be equal or exceed the value which can be generated from it and that will be the break-even, or net energy loss situation. Realizing the above phenomenon in its practical terms, researchers are giving importance to find out all the renewable/ sustainable energy sources that can replace oil. As sustainable energy means the form of energy that can be extracted through resources without putting them into danger of getting depleted or vanished; moreover, which also doesn’t put an unpleasant effect on our environment. Sustainable energy resources are the best alternative to fossil fuel through which we can not only fulfill our current energy needs, but also it cannot be vanished from the surface of earth as they are renewable (i.e., they can be utilized again and again). These are the sole reasons that why currently, every society urges on the study of different methods through which this resources can be utilized successfully. Energy management is a fusion of technology and management to increase the efficiency of production and enhance the results of output energy performance. It is necessary that management is related to renewable energy so that proper integration of energy systems can be achieved. It is important to control the budget and cost of energy consumption within the required regulations so that a company can have proactive growth for future investments. Energy needs to be utilized in a productive manner so that there is an increase in the chance of district energy systems to be implemented with effective cost budget. Management in energy sector has been a real problem for many developed economies due to huge losses in heating and cooling systems. To maintain energy efficiency in a system it is vital to manage renewable energy sources with comparatively low heat loss. This will certainly bring down the cost per unit price of energy and lessen the burden of greenhouse gas emissions as well. District energy systems once implemented through proper industrial and domestic channel, it improves the productivity and reduces the vulnerability of high waste heat energy.

Comprehensive Energy Systems, Volume 5

doi:10.1016/B978-0-12-809597-3.00555-1

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Concluding Remarks

5.25.2

Energy Management Dimensions

Energy management is a fusion of technology and management to increase the efficiency of production and enhance the results of output energy performance. It is necessary that management is related to renewable energy so that proper integration of energy systems can be achieved. It is important to control the budget and cost of energy consumption within the required regulations so that a company can have proactive growth for future investments. Energy needs to be utilized in a productive manner so that there is an increase in the chance of district energy systems to be implemented with effective cost budget. Management in energy sector has been a real problem for many developed economies due to huge losses in heating and cooling systems. To maintain energy efficiency in a system it is vital to manage renewable energy sources with comparatively low heat loss. This will certainly bring down the cost per unit price of energy and lessen the burden of greenhouse gas emissions as well. District energy systems once implemented through proper industrial and domestic channel, it improves the productivity and reduces the vulnerability of high waste heat energy.

5.25.2.1

Energy Management Basics

In the chapter on energy auditing, three classifications of energy audits namely industrial, commercial, and residential energy audits are discussed. Also discussed in details are different auditing levels of American society of heating, refrigerating, and airconditioning engineering. Energy audit procedures including preparation phase, executing phase, reporting phase, and post-audit phase are discussed. The chapter also discussed in details, the instrumentations of energy auditing and efficiency measures detailed out in the energy auditing methods. The chapter concludes by presenting different case studies related to the energy auditing technique employed in different places. The current volume of the book also discusses in details the energy conservation and its techniques. The chapter discusses thermal analysis and energy and economic analysis of different sectors including industrial sector, agriculture sector, transportation sector, and residential sector. Energy conservation methods play an important role in energy sector as they help in reducing emissions of harmful gasses, saving money, creating economic activities, and technological development for moving toward sustainable and affordable future. The chapter on waste energy management focusses on different techniques that can be used toward better handling of waste let it be industrial or residential and its impact on the environment and society. The chapter starts with explaining what waste is and then classify the waste into municipal, industrial, and hazardous waste. The chapter then moves on to detailing down the components of a waste namely organic, inorganic, and microbiological. The chapter further discusses the waste management legislations and points out that these legislations vary drastically from country to country and from developed to developing country. The chapter also discusses in details waste management techniques such as refuse derived fuel, recycling, thermal and landfills. The chapter points out that landfills method is the most common method deployed but that needs to change as it causes several health problems and suggest moving toward gasification technique for lower emissions of harmful gasses. The chapter concludes by presenting a very detailed case study including both analysis and techniques discussion. The chapter on energy reliability and management bridges gap between the two sections of energy which are energy reliability and its management. The chapter explains that the current trend of using renewable energy sources for intermittent power has brought it with a major hurdle of energy sustainability and security. However, supply and demand side energy management technique can help in solving these issues caused by new power generation techniques. Among all the techniques available, demand response program seems to be most promising due to its energy shaving and shifting capabilities as it helps in catering to the both high and low energy demand times in a most proficient way. This shifting in peaks helps in reducing the need for more power plants and also substantially decreases the risk of load shedding. The chapter on energy management software and tools discusses in details the different tools and software available for better management of energy. The chapter first starts with detailing out energy modeling tools such as power and energy systems modeling and analysis tools, power flow and short-circuit analysis tools, power generation, transmission, and distribution systems modeling and analysis, power quality analysis tools, switchgear and protection systems analysis tools, energy and power systems security analysis tools, communication and information transfer analysis tool, energy generation and demand forecasting, energy management tools, energy-economy and energy-market modeling and analysis tools, energy systems planning, policy and decision-making tools, energy systems optimization tools, customer’s end energy systems modeling and analysis tools, and distributed renewable energy resources and integration tools. After discussing the modeling tools, the chapter moves on to explaining different energy management tools available including but not limited to advanced distribution management systems, agent-based modeling of electricity systems, cepel toolkit, d-gen pro, doe-2, visual doe, equest, ea-psm, efen, e-load forecast, e-isof forecast, e-power forecast, e-accu wind, e-solar forecast, ez Sim, general algebraic modeling system, genopt, investigation of cost and reliability in utility systems, international network for sustainable energy, integrated planning model, long-range energy alternatives planning, load seer, grid store, ds-more, dr-pricer, xact-fit, smart spotter, meteodyn toolkit, modelica, mosaik, network security simulator, object modular network testbed in C þþ , padee, plexus, integrated energy model, power system analysis package, power-systems computer-aided design, pss netomac, pss sincal, pss pdms, ret screen, smart grid co-simulator, smart grid toolbox, e-sim, e-varient, e-valuate, e-sensitivity, transient security assessment tool, and yalmip.

Concluding Remarks 5.25.2.2

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Energy Management in Energy Sources

To provide power to the district energy systems, stand-alone systems, micro-grids or smart-grids, wind energy conversion systems are widely applied. In all of these applications, appropriate energy management processes must be performed, in order to maximize the energy production of the wind turbines and transfer the wind-generated energy to the consumer with high efficiency and adequate power quality. The chapter on energy management in wind energy systems provides an overview of the modern energy management techniques applied to the wind energy conversion systems both in terms of hardware architecture and controls. The architecture of the power conversion and conditioning system and the types of the hardware devices that it comprises must be selected first for the development of an energy management technique for the wind energy conversion system. Alongside this, in order to extract the maximum power from the wind turbine and control its flow toward the electric load, microelectronic systems based on microcontrollers, etc. needs to be utilized for corrective control strategy by utilizing maximum power point tracking process. The energy storage systems also need to be installed alongside the wind energy systems to cater for the drastic fluctuation in the wind characteristics. Fuel cells, batteries, and super capacitors can be incorporated in the energy management systems of wind energy for smoothing the wind-generated power fluctuation for stand-alone systems. Hybrid systems can also be developed by installing wind energy systems alongside other renewable or nonrenewable energy sources for better energy reliability. Wind speed forecasting helps in planning the energy production of other energy such sources such that the energy production and consumption fluctuations are compensated. Optimization techniques also needs to be employed to the energy management techniques of the wind energy systems for the appropriate and efficient sizing of the energy generation and storage systems. The optimization needs to take into account the meteorological conditions of the site in order to calculate the optimal capacities of the energy production and storage devices, such that the required amount of energy is generated during the year thus completely meeting the energy demand while spending the minimum cost. Geothermal energy stores in itself great potential for solving the energy demand shortfall problems around the globe. The chapter on energy management in geothermal energy systems starts with discussing different geothermal energy sources such as hot water reservoir, natural steam reservoir, geopressured reservoir, normal geothermal gradient, hot dry rock, and molten magma. The usage of geothermal energy is divided into two categories namely direct use and indirect use. The direct use covers the domain of space heating, air conditioning, industrial processes, drying, agriculture, hot water, melting snow, etc. However, indirect use of geothermal source covers the domain of power generation and hydrogen generation. Power can be generated from the geothermal source by utilizing either one of the following technologies binary cycle, flash steam cycle, and hybrid cycle. The hydrogen generation through geothermal source include supply of geothermal energy to the thermochemical water splitting cycles. A thermos-economic analysis of a renewable poly-generation system utilizing solar and geothermal energy sources is studied. The plant is capable of generating power, fresh water, heating, and cooling in order to meet the energy needs of a small district of Pantelleria Island. A comparative performance study is carried out for two cases namely the hybrid configuration, powered by solar and geothermal and the geothermal configuration, powered only by geothermal source in order to see the feasibility of attaching the solar energy source to the system. The ambient conditions, the temperature of the geothermal fluid entering the plant, the mass flow rate of geothermal fluid and the scheduled time operation of the district heating and cooling network are varied in order to conduct the parametric study of the two systems. The results obtained show that in both the cases, the system cannot be completely independent from the grid, since power production is lower than the current load requirements of the island. This suggests a further calibration of main design parameters of the plant in order to cover electricity demand completely and to increase the thermal recovery for space heating and cooling rather than for desalination process: this would allow to make the plant independent from the grid and would contribute to reduce the simple payback period. Parabolic trough collectors technology, despite allowing for a slightly higher production of electricity and fresh water, does not induce proportional economic benefits. The simple payback period is greatly influenced by the organic rankine cycle and parabolic trough collector systems, since they amount, together with the absorption chiller, to 67.6% of the total investment cost. The thermodynamic and the economic performances are noticed to be greatly influenced by the variation of the inlet temperature of geothermal fluid as opposed to the variation of the mass flow rate. It is concluded in the chapter that pushing on the exploitation of thermal energy for space heating and cooling provides more economic benefit than the fresh water production. Almost 70% of the earth surface is covered with water which is the reason that it seems to be blue from outer space and is referred to as blue planets. Oceans and seas around us have the potential to fulfill all of our current energy demands by providing a clean energy in a bulk quantity, which can be harnessed in number of ways and forms: by exploiting the kinetic energy of waves, the streams of tides and ocean current, and by the temperature gradient present in water. The chapter on energy management in ocean energy systems discusses various methods and technologies introduced to harness this all important form of energy along with its significance, application, advantages, and disadvantages. Around the world, there are around 60 projects under testing phase, among which one third are of United Kingdom. Initially wave power was considered to be a best way of harnessing energy from ocean and river, but now tidal energy has become the best option due to the fact that the energy extraction technology applied by tidal energy system is similar to that of wind power in many ways. By complexity, tidal current turbines have the real favorable position of having the capacity to work effectively on the ebb and the stream, four times every 24 h, by swiveling around to confront the course of tidal current. As a result, power generation level is expected to be during lower neap tide cycles, but the power generation is nearly continuous. However, environmental concerns regarding the development of large tidal barrage are still an issue. In some of the sites, the effect of blocking estuary may yield positive results, but in most of the scenarios it causes severe ecological impact. Till today we have looked to the ocean bed as a wellspring of fossil fuels, however, it might well be the time that

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Concluding Remarks

we start focusing our energy toward extracting energy from the ocean itself which is both sustainable and renewable. The chapter on ocean energy starts with discussing sustainable energy and the background of the ocean energy system is provided. The chapter then moves on to discusses different ways of harnessing energy from ocean such as wave energy, tidal energy, ocean thermal energy conversion, marine currents, and salinity gradients. The chapter then moves on to energy management applications and tools applied to the ocean energy systems and provides their mathematical models. Different case studies are also discussed in details in order to appraise the reader with the different usages of ocean energy systems.

5.25.2.3

Smart Innovations in Energy Management

Besides improvement in energy efficiency and better use of energy, energy quality management has become a major topic of interest as discussed in energy quality management chapter in this book. The techniques used toward optimizing the exergetic use of energy sources is known as energy quality management. The chapter starts with defining the concept of energy quality and then discusses in depth the concept of energy quality management. The chapter then introduces the energy quality management in two areas of application namely built environment and energy quality technology. After explaining the basics and techniques of energy quality management, the chapter presents five case studies. The first case study discusses the application of energy quality management in renewable energy sources due to their unpredictable and variable power generation curve. The case study discusses the system reliability as a constraint function and optimizes its usage under the given constraints. The second case study discusses the application of the energy quality management techniques and tools for the optimal use of solar energy systems for varying district classifications. A parametric study is carried out to see the effects of variations in different solar energy parameters on the patterns of solar utilization. In the end, an optimization study is carried out for maximizing the exergetic efficiency, minimizing the life cycle carbon dioxide emissions, and minimizing the cost. The third case study discussed in the energy quality management chapter focusses on the application of energy quality management in an area of standalone micro-grid systems. The standalone systems are usually used to cater to the energy needs of a far-flung area in a smart and independent manner. In this case study, a new formulation of energy quality management for the smarter use of standalone micro-grid system is proposed. In the fourth case study, energy quality management techniques are applied to the 100 kW capacity ejector refrigeration system. The sum of the costs of the electricity, capital investment, operational and maintenance expenses, and brine side fluids are considered as the objective functions for the energy quality management analysis of the said system. The optimization study is carried out to minimize the objective functions. The fifth case study focusses on the application of energy quality management technique in an area of novel ejector refrigeration system using zeotropic mixtures. The results obtained from this study showed that the irreversibility occurring in the ejector amounts to approximately 50% of the total exergy loss in the overall system. In the end, a novel advanced energy quality management technique is introduced and is applied to the ejector refrigeration system. It is concluded that the novel technique and result in avoiding 35% of the overall exergy destruction in the system. The depleting fossil fuels reserved has led to study of better energy conservation and usage mechanisms. Many renewable energy sources are introduced recently, however, there is a need to carry out sustainable energy management study of these renewable energy systems for their smart usage. The chapter on sustainable energy management discusses different ways of harnessing and using energy from sustainable sources such as solar, wind, hydro, and tidal along with their advantages and disadvantages, backed with current industrial trends and case studies. Also, key factors and steps involve in developing and implementing sustainable energy programs are discussed in quite details with examples. The word sustainable energy sources reflect to those sources of energy that are available in nature in bulk and will not get depleted if they are used on large scale. All renewable forms of energy like: geothermal, solar, tidal, biomass, and wind are known as sustainable as they are not only stable but also available naturally in bulk throughout the day and can easily be converted and stored after the successful study and inventions of different modules like lead acid battery, chillers, methane controlled chambers, turbines, etc. The chapter also discusses in depth the environmental aspects of energy, the sustainability factor and the management of energy. The chapter also sheds light on the smart concepts that are being integrated into sustainable energy management techniques to make them more effective. The chapter closes itself by discussing three case studies namely, borehole thermal energy storage at university of Ontario institute of technology, solar energy management and integration at Boston college and optimization and management of low head hydro power plant. The authors of the chapter conclude that the cost and environmental are two major concerns of the modern society in terms of sustainable energy and needs to be addressed for successful implementation of sustainable energy program within the organization, town, state, or country. Optimization plays an important role toward achieving the smart utilization of energy sources available to humankind. The chapter on optimization in energy management provides an overview of optimization techniques, their usages, their advantages and disadvantages, and different tools. The chapter starts with providing information on how to select system boundaries, objective functions, constraints, and decision variables. The chapter then moves on to discussing different optimization such as classical and numerical optimization. Different multi-objective optimization techniques and basics are also covered in this chapter. Four case studies namely energy management optimization of steam power plant, energy management optimization of energy storage tanks, energy management optimization of photovoltaic-wind hybrid energy system, and heat exchanger optimization for the thermal energy management of electric vehicles. In the first case study, the authors conclude that utilizing the optimal design parameters will result in increase in the power generation and reduction in the cost of power generation. The results obtained from the second case study suggest that optimizing the storage system results in reducing the overall size of the system because of better utilization of the resources. The optimization of the hybrid system done in third case study resulted in

Concluding Remarks

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the better distribution of energy as the energy is being stored during off-peak hours which is then released during the peak hours to meet the demand. In the fourth case study, authors deliberated on the idea of replacing the water as the coolant for the battery with phase change materials. For this purpose, a heat exchanger using phase change material was designed and optimization for providing cooling to the batteries. Results obtained showed that using the phase change material will result in better energy management and higher battery performance. At the end of the chapter the authors concluded that carrying out the energy management optimization not only results in higher efficiencies but also helps in reducing the cost of the system and lowering the greenhouse gas emissions. Wireless technology plays an important role in achieving the energy management protocols. The wireless technology is required to transmit the energy consumption data in a fast and uninterrupted manner. The cable network is vulnerable to weather and can lead to breakdown of data transmission if it is damaged for a considerable amount of time. In this fast growing world, all the atomization of industries, buildings, stores, residence, etc. has been done over wireless sensor network. In order to make the overall system efficient we have to make the network nodes to optimally utilize energy in order to perform their task effectively and efficiently at same time. The chapter on wireless technology energy management provides a complete overview of the wireless technologies available for better management of the energy. It discusses in details the wireless network formation and architecture by using either logical system architecture or physical system architecture. The chapter then moves on to describe two types of wireless communication techniques such as radio frequency spectrum and infrared spectrum. Different energy management techniques are discussed in the light of wireless technologies. Different types of wireless networks such as Bluetooth and ZigBee categorized as wireless personal area network and Wi-Fi categorized as wireless local area network are explained and analyzed thoroughly. The chapter also provides several case studies to ensure that reader understand how a wireless technology can be used toward achieving better sustainability. The chapter then provides a vision for future by discussing the wireless electricity transmission in the near future. Applying energy management techniques is one thing but applying it in a smarter way in an another. The smart energy management chapter starts with introducing the different concepts of smart energy management. The chapter also provides four stages of evolution of energy systems and then proposed the three distinct dimensions of the smart energy systems. The four stages of evolution are namely primitive energy systems, industrialized energy systems, district energy systems, and smart energy systems. The chapter also discussed the three dimensions of smart energy management such as participating object dimension, energy production dimension, and management science dimension. The participating object dimension cover the area of smart city energy management, smart building energy management, and smart household energy management. The energy production dimensions include generation management, transmission management, distribution management, and consumption management. Whereas, the management science dimension covers optimization, decision-making, evaluation and forecasting domains of the smart energy management. The upfront technologies and the framework of the smart energy systems are discussed in details. The key technologies related to smart energy management are divided into four categories namely energy production, internet of things, big data analytics, and security and privacy protection technologies. The basic architecture of the smart energy management consists of external big data sources such as weather, social media, etc., the big data applications including energy efficiency services, demand response, smart home, and asset management, the network communication such as Wi-Fi and ZigBee, and infrastructure including both software and hardware. The chapter then moves on to discussing in details the smart energy management applied to the residential, industrial, and transportation sector as case studies. It is also important to realize that the smart energy management is still in its infant age and the specific roadmap for the future is still hazy, but at the same time this concept is attracting lots of attention due to its foreseen benefits. The future direction of research in the area of smart energy management should be focused on strategy development, data gathering, behavioral characteristics, security of data and system, and regularizing the smart energy management systems and techniques. A collection of detailed energy consumption data from a consumer’s premise has been made possibly by using smart grid technology. In order to facilitate the decision making of a consumer regarding their energy consumption and helping governments and institutions with collecting accurate peak load data, smart grid energy management is used. The technologies and tools used by energy management companies will soon be replaced by smart energy management techniques and tools in order to make it more effective and efficient. Smart grid energy management topic has become a hot cake in the circle of nations, universities, companies, etc. due to its capabilities of solving complex energy needs in order to achieve higher sustainability, lower operation and maintenance cost and lower carbon emissions. The smart grid energy management chapter starts with discussing different strategies which are being considered for residential buildings, commercial buildings, micro-grids such as hybrid-based, energy storage, electric vehicles, fuel cell vehicles, fossil fuel-based vehicles, autonomous vehicles, renewable energy sources such as solar, wind, geothermal, ocean, etc., based on smart grid energy management techniques such as real time load shaving, modeling and simulation which are being applied for reaching sustainable development and goals. The chapter then moves on to explain different smart grid energy management technologies available in the market and identifies how each of their functionality will support achieving the goal of energy consumption reduction and load shifting and monitoring. It is expected that the consumer’s interaction the with smart grid energy management application will lead to the true demand side flexibility potential. The chapter also explores the domain of how the addition of incentives such as time-of-use and time-ofday tariffs, and easy to implement rules such as automating the appliances/applications to reduce operation when time-of-use and time-of-day tariffs electricity prices are high can help stimulate further benefits to both customers and the grid. Smart grid energy management-based technologies for worldwide smart grid supervision from various governments and standardization enforcing bodies are necessary and in order to achieve this, semiconductor chip design companies have to take initiatives toward its

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technologies. For cottage and large-sized industry it may be expensive at present, but in the long run it can help in moving toward secure green technologies suitable for transmitting data in a secure manner for achieving higher sustainability, lower generation cost, and lower carbon footprint. Recognizing a pattern of energy consumption plays a vital role in managing the energy demand and production cycle. The chapter on pattern recognition in energy management discussed different aspects of pattern recognition in different sectors of life. The chapter starts with presenting basics of clustering analysis before moving on to clustering algorithms for the time factor group formation. Time series-based pattern recognition are being used usually for the detection and diagnosis of faults, characterization of the consumption profiles, and optimization of processes. Univariate time series-based clustering algorithms are known to use point prototype grouping mechanism whereas multivariate time series algorithm use more complex instinct characteristics. The chapter also discusses in details the methods used for selection, clustering, and separation of pattern recognition techniques. Based on the multivariate statistics, a novel multivariate time series-based clustering method is proposed and applied to the energy efficiency program carried out by the energy company of Maranhão and Alagoas. The results reveal the viability of the method in recognizing patterns consistent with the reality of the electricity sector. The novel method is also useful to support decision-making at management level. The case study discussed in this chapter focusses on replacing 5250 old refrigerators with new ones for lowincome consumers. The results obtained by using selection, typification, and load curve clustering method and Fuzzy C-Means method reveal the viability and potential of the former in recognizing patterns and in generating conclusions coherent with the reality of the electric power sector.

5.25.2.4

Energy Management Application Areas

The advancement in technologies has made it possible to utilize system networks in all walk of our lives from household chores to power generation. The energy management in networked systems chapter introduces and explain different energy management techniques and tools used in networked systems. The chapter starts with presenting the different types of energy resources and their usages in daily life. The chapter then discusses the harmful effects of energy systems on environment in terms of greenhouse gas emissions and economic losses associated with implemented energy systems in a random and inefficient manner. Making this as the basis, the chapter moves on to the concepts of energy management with focus on energy management in networked systems. The chapter also provides history of energy management from ancient times to the modern industrial age. The main focus of the chapter is on supervisory control and data acquisition systems as standard algorithms and tools of energy management in networked systems. Few important things need to be considered whenever energy management systems are under design, implementation, and operation phase. The security of networks should be looked at very carefully while applying energy management systems to networks. There must be a smart balance between the degree of automation and mediation of human for security and reliability. It is extremely important to decide how and what portion of the system will work independently and which level, part or aspect will be controlled by humans. The green computing methods should be developed and installed for transformation of large chunks of data over the networks in an efficient manner. The global education and training of professionals and individuals should be planned for better implementation of energy policies on a ground level. This is important in order to have a cluster positive impact on the overall environment, as at present the people in developing countries still lack in basic health and education and are least concerned with energy management techniques and tools. The chapter on energy management in data centers provides a detailed overview of energy management especially thermal energy management systems and techniques applied in the data centers. The trends and predictions from the past two decades related to the power density and heat rejection of the data center are being reviewed. The comparison shows that the data centers power density and heat rejection from the computer servers have almost tripled in past couple of decades. The chapter also discusses in details different cooling strategies and thermal management techniques developed for the data centers. Computational fluid dynamics simulation and scale physical modeling techniques for data centers studies and researches are presented. Two case studies of computation fluid dynamic works and physical scale modeling works of data centers are presented and utilized to study the effects of the different operating and geometric design parameters on the thermal and energy management in data centers. University Campuses are small cities with complex and mixed activities. The energy and environmental impact caused by institutional buildings via activities and operations in teaching and research, as well as provision of support services can be greatly reduced by an effective choice of energy technologies and management. The energy and environmental impact of Campuses are of a great importance as they can be used to train young people and future generations toward sustainable energy management practices. Many universities around the globe are moving toward a net zero campus by heavily investing in innovative energy production technologies, energy efficiency, reduction of energy waste and by educating young generation with the issues face by the mother earth. The chapter on energy management in university campuses starts with discussing different tools and techniques utilized for energy management in university campuses. The chapter then provides detailed mathematical models for the study of university building, and university outdoor areas. The chapter then sheds lights on different types of energy usage patterns of university campuses. Different energy management technologies for university campuses and user behavior and indoor environmental quality requirements are also discussed in depth. The chapter also presents two detailed case studies of two university campuses namely technical university of Crete and national university of Singapore who have installed energy management tools and techniques on their campuses for better management of energy at their campuses in order to move toward the goal of net zero energy usage. Significant research efforts need to be put in understanding the interactions between the indoor and outdoor environments of the university campuses. The existing infrastructure such as smart meters, sensors, smart devices, and wireless

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technologies installed in the various campuses around the globe can provide a robust basis for data gathering and monitoring. Another significant aspect of the future research in energy management of university campuses is the integration of sustainable mobility such as electrical vehicles in order to reduce the carbon footprint of the university. The universities provide a great platform to train the future generation toward using energy in a more responsible way by applying different energy management technique and tools applied on the campus at their respective homes and offices in order to achieve sustainable and bright future for generations to come. Hospitals are complex structures that require the highest energy intensity of the building sector. The optimization of their energy performance can allow substantial environmental and economic benefits. The deepest attention should be addressed to the energy retrofit of existing hospitals, because most of them were built without properly considering the energy issues (and the building turn-over is very low in most countries). In both cases of new constructions and retrofits, the proper energy design and management of these complex edifices is extremely critical because it must consider all levers affecting energy performance, from the characteristics of the thermal building envelope to the peculiarities, operation, and efficiency of active energy systems. Thus, there are several design variables to optimize, and this leads to a huge number of scenarios to be investigated. Furthermore, as occurs in any problem concerning building performance optimization, different objective functions can be considered, depending on stakeholders’ needs and wills, such as the minimization of different components of energy demand, thermal discomfort, investments, life cycle costs, and polluting emissions. Definitely, in order to properly address the energy issues concerning hospitals’ design and management, complex optimization problems must be solved by means of comprehensive approaches. In the chapter on energy management in hospitals, after the discussion of the main energy issues concerning hospital buildings, the chapter offers an overview of the mentioned optimization approaches provided by current scientific literature. Different case studies are also presented to show that by applying energy management techniques in hospital, huge benefits can be achieved in terms of energy and economic savings as well as of polluting emissions’ reductions. The results obtained in the case studies demonstrate that the efficient and effective energy design and management of hospital buildings is fundamental to promote a sustainable future of the building sector. Hotels provide short-term lodging, and depending on the level of facilities and services provided, they can be classified into different classes ranging from basic bed and breakfast type budget hotels to expensive five star hotels. In higher class hotels, luxury features such as en-suite bathrooms and other functional areas such as food and beverage, a swimming pool, business center, childcare, conference and banquet facilities and social function services, even shopping arcades, may be included. Hotel buildings are therefore, as compared to other types of buildings, are unique in their design and operations. This includes variable, etc. Consequently, the differences in hotel occupancy levels throughout a year; operation schedules for different facilities; numbers of functional facilities such as in-house laundry, business center, and restaurants; personal preference of indoor built environment and user behaviors by hotel guests have led to varied operation schedules for energy systems in different hotel around the globe. Like any other buildings, in hotel buildings, different building services systems are installed in order to provide and maintain a suitable indoor built environment and providing the guests and staff with quality services, such as lift services and hot water supply, corresponding to the rating of a hotel, but at the expense of consuming a sizable amount of energy. The chapter on energy management in hotels starts with discussing typical annual energy usage characteristics of hotels. The chapter then moves on to breaking down the total energy consumption by fuel types and by end-users, seasonal variations of energy consumption and their correlations with a number of factors that may affect the energy usage in hotels. An extract of regression method used to predict hotel energy usage is illustrated for benchmarking and monitoring hotel energy use. Discussions on assessing energy usage in hotel buildings and suitable energy management programs and frameworks are also presented in the chapter. Finally, the energy aspects in hotel’s environment assessment and the Hong Kong government’s carbon audit approach for the hotel industry are discussed in details to appraise the readers with the techniques and tools that can be applied to better manage the energy usage in hotels. The unprecedented increase in the demand of energy has led to many economical and environmental problems worldwide. A very reasonable solution to meeting the energy needs is a district energy network. District energy network is a technique that is capable of supplying required energy in the form of heating, cooling or electricity, by harnessing them through available local waste or renewable energy resources Another advantage of the system is well-developed technologies such as: wind, solar, marine, and hydro that can be easily merge with district energy network to make a single platform in order to address the global dilemma. The chapter on energy management in district energy systems provides its reader with an overview regarding the developments, application and drawback of district energy network systems by comparing various economic, environmental and technical claims. The chapter also discusses several case studies to show the benefits of the district energy systems and to appraise readers with the possible uses of the district energy systems. The main aim of district energy system is to meet electricity, heating, and cooling requirement of a district in an efficient and sustainable manner. In past, the heat was extracted through burning fossil fuel and then supplied; whereas, now with the advancement in the technologies, various renewable energy resources have been integrated together in order to increase their efficiency and make them economically viable. Also, various examples are also being provided in the chapter that show that district energy system is the cheapest method of cutting carbon emission in our environment. Climate change is a severe concern for many organizations around the globe. To counteract the climate problems, it is important to address issues like using energy efficiently and intelligently by applying the concept of district energy systems. District energy system could compose of several components out of which the highlighted one would be: thermal energy storage device that ensures the supply of energy to its consumers in day time and during maintenance and an intelligent network algorithm that could cope up with the energy demand by supply energy using user behavior and meteorological data. Another advantage of the

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Concluding Remarks

district energy system is its easy merger with renewable energy system in order to come up with a singular platform that could address the global dilemma. It is very important for countries to develop their economic sectors, ranging from industrial sector to the residential sector, for their better economy and welfare. In this regard, the main issue goes beyond energy utilization and management. It requires a careful exergy management practices in the sectors. The chapter on sectoral energy and exergy management discusses applying energy and exergy analyses to huge and complex systems such as countries, regions, and economic sectors. Demonstrating the utilization of energy resources in society from the exergy perspective is recommended as it will result in better understating of the energy utilization and its effectiveness. The chapter also highlights the concept of energy and exergy management. A case study is presented with respect to energy use in the United States. The world energy consumption data are also provided so the reader can be aware of the current global energy use. The chapter also suggest that the price of the energy sources will be higher than today, and it would be much more dynamic in future. The energy and exergy optimization will be an integrated part of the energy and exergy management plan of the industries’ operators. Demand response would be entirely different from now. In the next decade, it will mainly be integrated into tariffs as part of real-time or semi-real-time pricing. The cloud technology would play a critical role in exergy management systems. The real-time feedbacks would play more critical role in the exergy management process. Energy and exergy management and the optimization technologies would also play a vital role in the buildings’ long-term portfolio. The technologies as smart buildings and smart grids will offer more efficient and responsive systems. Moreover, internal micro-grids with hybrid renewables and conventional electricity generation systems will manage the demand of new loads such as electric vehicle charging. The energy storage and distributed generation systems will play more important role in the renewable energy driven systems. Building operators will use the social tools to share best practices and get contact with residents to inform them of peak energy periods.

5.25.3

Conclusions

The current chapter presents the conclusions achieved by the authors who have contributed to different chapters in this volume of the book. The volume starts with providing basics of energy management including energy auditing, energy conservation, waste energy management, energy reliability, and different tools and software of energy management. The volume then moves on to discussing energy management in different energy sources such as wind, geothermal, and ocean energy systems. Smart technologies and developments with regards to energy management are also covered in present volume in chapters relating to energy quality management, energy sustainability, energy optimization, energy wireless technologies, smart grid and smart meters, and pattern recognition in energy management. The volume also presents different applications of energy management covering a vast domain of energy management in networks systems, data centers, university campuses, hospitals, hotels and in independent districts. The overall conclusion of the volume is that energy management is still in its developing phase but the results obtained from its application are very promising. The application of energy management and techniques has results in better use of existing facilities, reduction in energy production cost, reduction in energy consumption rate, reduction in greenhouse gas emissions, and better sustainability.