The Smart Grid As an Application Development Platform [1 ed.] 9781630814915, 9781630811099

Citation preview

The Smart Grid as an Application Development Platform

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For a listing of recent titles in the Artech House Power Engineering Library, turn to the back of this book.

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The Smart Grid as an Application Development Platform George Koutitas Stan McClellan

artechhouse.com

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Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the U.S. Library of Congress British Library Cataloguing in Publication Data A catalog record for this book is available from the British Library. ISBN-13: 978-1-63081-109-9 Cover design by John Gomes © 2017 Artech House 685 Canton St. Norwood, MA All rights reserved. Printed and bound in the United States of America. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the publisher. All terms mentioned in this book that are known to be trademarks or service marks have been appropriately capitalized. Artech House cannot attest to the accuracy of this information. Use of a term in this book should not be regarded as affecting the validity of any trademark or service mark. 10 9 8 7 6 5 4 3 2 1

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This book is dedicated to my parents, Christopher Koutitas (civil engineering professor) and Mary Koutitas (mathematics professor) Dr. George Koutitas

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Contents Preface

1

xiii

Smart Grid Business Model

1

1.1 Introduction 1.2 Vision 1.3 Problem 1.4 Solution 1.5 Growth Strategy Business Model 1.6 1.7 Risks References

1 1 2 2 3 4 4 5

2

The Power Grid at a Glance

7

2.1 Introduction 2.2 Useful Data 2.2.1 Power and Energy 2.2.2 Capacity, Generation, Consumption, and Demand

7 7 7 8

vii

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2.3

2.4

2.5

2.6

2.7

3

2.2.3 Alternating Current, Direct Current, Active Power, and Reactive Power 9 14 2.2.4 Example from Smart Meter Data Grid Architecture 17 2.3.1 Organization, Players, and Regions 17 2.3.2 Production 20 2.3.3 Transmission 24 25 2.3.4 Distribution Drawbacks of Current Network Design 26 26 2.4.1 Waste of Resources and Pollution 2.4.2 Adaptation to Time-Variable 28 Production and Consumption 2.4.3 Passive Nature of the End Consumer 30 2.4.4 Business Models 31 2.4.5 Security/Outages 32 Energy Markets 34 34 2.5.1 Wholesale Market 2.5.2 Retail Market 38 2.5.3 Analyzing the Bill 39 Understanding the Consumer 39 39 2.6.1 Appliances Footprint 2.6.2 Electricity Usage Analysis 42 43 2.6.3 Archetypes of Consumers Lessons Learned from the Telecommunications 44 Industry References 46 Smart Grid Elements

3.1 Introduction 3.2 The System of Systems 3.2.1 Evolution of the Grid 3.2.2 Architecture and Standards 3.2.3 Interoperability and Protocols 3.3 Business of Businesses 3.3.1 Utility of the Future 3.3.2 New Business Models and Players 3.3.3 Business-to-Consumer Providers

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49 49 49 49 52 55 60 60 64 67

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Contentsix



3.3.4 Utility Customer Beyond 2020 67 3.3.5 The Social Smart Grid 69 3.3.6 Start-Up Ecosystem 71 The ICT Layer 72 3.4 3.4.1 Smart Metering 72 3.4.2 Networking 75 3.4.3 Advanced Metering Infrastructure 77 3.4.4 Meter Data Management Systems 80 80 3.4.5 Example of In-Home Smart Metering 3.5 Evolution of Prosumers 81 81 3.5.1 The Path to Off-Grid 3.5.2 Connected Homes 83 88 3.5.3 Standards 3.6 Microgrids 91 3.6.1 Architecture 91 3.6.2 Types of Microgrids 92 3.7 Virtual Power Plants 93 93 3.7.1 Architecture 3.7.2 Emerging Trends 94 Electric Vehicles 95 3.8 3.8.1 Electric Vehicle Types and Charging Technologies 95 3.8.2 Effect on Consumption Patterns 96 98 3.8.3 V2G Concept 3.9 Smart Grid Pricing 98 98 3.9.1 Pricing Models 3.9.2 Net Metering 99 3.9.3 Renewable Energy Credits and Peak Load Credits 100 102 References 4

The Cloud Environment of Application Providers

4.1 Introduction 4.2 Overview of Services Introduction to Cloud Computing 4.3 4.3.1 Web Services and APIs 4.3.2 Reserving Resources in the Cloud

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105 105 105 108 108 111

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4.3.3

4.4

4.5

4.6

4.7

5

Example of Web Services for Home Automation 113 Product Development in the Cloud 115 4.4.1 Defining the Pricing Model of SaaS Service 115 4.4.2 Web App or Mobile App? 116 4.4.3 Security and Privacy 117 4.4.4 Steps for Accessing Open APIs with 118 Product Innovators 4.4.5 White Labeling 119 Open Data and APIs 120 4.5.1 Energy Information Administration 120 123 4.5.2 Green Button 4.5.3 Orange Button 125 4.5.4 PVWatts API 126 4.5.5 Microinverter APIs 128 4.5.6 Smart Thermostat and Connected Home Device APIs 129 4.5.7 Energy Usage Datasets 133 4.5.8 MultiSpeak 134 Open ADR 135 135 4.6.1 Key Actors and Services 4.6.2 Demand Response Event 136 137 4.6.3 Communication Architecture 4.6.4 Rush Hour Example 138 Conclusions and Concerns 139 References 140 User-Centric Applications

143

143 5.1 Introduction Data Processing Overview 143 5.2 5.3 Energy Analytics 144 5.3.1 Hourly and Daily Energy Analytics 144 146 5.3.2 Bill Forecasting 5.4 Load Disaggregation 148 5.4.1 Hidden Information in Appliance Footprints 149 5.4.2 Signal Processing on Smart Meter Data 153

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Contentsxi



5.4.3

5.5

5.6

5.7

5.8

6

Event Detection by Extracting Power Pulses from Smart Meter Data 154 5.4.4 Clustering 156 157 5.4.5 Pulse to Appliance Association 5.4.6 NIALM Results and Business Intelligence 158 Direct Load Control 159 5.5.1 Modeling User Comfort 161 5.5.2 Command Flow for DLC Demand Response 163 5.5.3 Fairness Issues Related to DR Commands 163 165 5.5.4 Simplified DLC Pseudocode Load Scheduling 165 5.6.1 Elastic Demand and Consumer Behavior 165 5.6.2 Objective of LS: Example of the EV Charging Garage 167 5.6.3 Types of LS Implementation 169 5.6.4 Simplified LS Pseudocode 170 Gamification Demand Response 170 170 5.7.1 Participatory Games 5.7.2 Rewards and Social Recognition 173 174 5.7.3 Objectives of Gamification 5.7.4 Simplified DR Gamification Pseudocode 175 Example: A Day of Smart Living in 2017 175 5.8.1 Energy Usage Analysis 176 5.8.2 Active Utility Customer 177 177 5.8.3 Home Automation 178 References Transactive Energy Economy

179

6.1 Introduction 179 6.2 Energy in the Sharing Economy 179 6.2.1 Evolution of Smart Cities: From Centralized to Distributed Architectures 179

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6.2.2 The Concept of Energy Giving 181 6.2.3 Value Proposition and Business Impact 182 6.3 The Transactive Grid 183 183 6.3.1 Foundations of Transactive Energy 6.3.2 Examples at the Retail and Distribution Levels 185 6.3.3 Modes of Operation and New Entities 187 6.3.4 Analysis of Transactions 188 6.3.5 End-User Transactive Energy Implementation 190 Cryptocurrencies: Their Role in the Energy 6.4 Sector 192 192 6.4.1 The Blockchain 6.4.2 Bitcoin 195 6.4.3 Smart Contracts and Ethereum 197 6.4.4 The Concept of an Energy Coin 198 6.5 Evolution of Collaborative Prosumers 199 6.5.1 System Model 199 6.5.2 Coalition Games and the Shapley Value 201 6.5.3 Various Pricing Schemes 203 6.6 Implementation Challenges 206 207 6.7 Conclusion References 207 7

Summary and Conclusions

209

About the Authors

211

Index 213

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Preface The utility industry is experiencing a significant evolution. The integration of information and communication technologies (ICTs) with the power grid— that is, the smart grid—has created a new environment of interdisciplinary and multidisciplinary innovation. The most important innovation in this environment is related to new services and business models that are changing the way utilities and consumers interact with each other. The smart grid is gradually transforming to a customer-centric or even a human-centric grid with the end user at its epicenter. This transition is familiar because it parallels a similar evolution in the telecommunications industry. With the advent of sophisticated new technologies, the telecom sector transformed from a focus on infrastructure to a content-based industry in which data management and applications became the real asset. As a result, service providers now focus on content delivery and customer engagement by providing an application development platform to the end user. The purpose of this book is to explore the evolution of the power grid toward the development of an application-centric environment. Along the way, we empower the reader to understand the primary motivation of the smart grid, and to explore how new technologies are creating a cleaner and more sustainable ecosystem in which new business models can blossom. Our goal is to present how the orchestration of these technologies is transforming the utility sector toward a human-centric grid. The end game of this transformation is for the xiii

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consumer to play an active role in the operation of the business as well as in the transfer of electrons. Following the modern trends toward a distributed system architecture and the sharing economy, the new smart grid becomes a distributed system that supports decentralized services. Grid intelligence and energy production migrates to the edge of the network. Consumers are transformed into prosumers of energy and providers of critical data that dramatically change their relationship with the electric utility. This relationship must be managed by new business concepts and entities, and the resulting platform must enable new applications and services. Cloud-based technologies, application programming interfaces (APIs), open data, smart meters, communication networks, home battery storage, electric vehicles (EVs), smart thermostats and appliances, and distributed generation (solar and wind) are the most critical components of the smart grid. New customer-centric services such as bill forecasting, load disaggregation, demand response, home energy management, and energy sharing are now available in the market due to the growth of third-party application providers and product innovators, thus transforming the smart grid into an application development platform. In this new era, utilities are migrating from a business philosophy of obligation to serve to a new philosophy of commitment to optimize, and the vertical operation of utilities is transforming to an ecosystem of partners of partners. The philosophy of infrastructure-based assets is changing, and the real asset is becoming data management and community engagement. We hope that everyone will enjoy the fruits of this evolution toward a smarter, cleaner, and more human-centric smart grid. We are all on the same grid! We are all connected with energy!

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1 Smart Grid Business Model

1.1  Introduction This chapter presents the executive summary of the business model for a smart grid for a startup company. The simplicity of this heuristic approach will help the reader better understand the reasons for the creation of the smart grid, the problems that it solves, and the new business opportunities that can emerge. At the end of the chapter, the reader will have a clear picture of the origins, vision, and business model of the smart grid.

1.2  Vision The vision of the smart grid is to create the next-generation power grid by leveraging and orchestrating newly developed information and communication technologies (ICTs) and renewable energy sources (RESs) into a single integrated, functional system. The outcome of this integration is an efficient smart grid that is a reliable, ecofriendly, and self-optimized network that can increase the number and types of service offerings to end users, reduce energy waste, and create new business models to support the economy. 1

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1.3  Problem Approximately 65% of energy converted from fossil fuels to electricity is wasted within the power plant due to conversion losses. In addition, 6% of the produced electricity is lost during its transmission and distribution from the power plant to the end consumer [1, 2]. Today’s 71% waste from production to consumption of the electricity industry results in billions of dollars in losses every year. Furthermore, the centralized network architecture of the power grid is vulnerable to blackout events and operational malfunctions that affect the quality of service offered to millions of people every year. Finally, technical innovation in the energy industry and the energy market has been focused primarily on service continuity and cost reduction for the past 50 years. The disruptive business models available via the integration of Internet-related architectures and modern communications technologies into the power network can be revolutionary. Clearly there is an untapped market currently being addressed by outdated technologies. It will be addressed via the integration of ICT and renewable energy technologies.

1.4  Solution One approach to modernizing the existing power network is to create a distributed system of RESs that enables the local production, consumption, and storage of electricity. The smart grid, then, is the next generation of the existing power grid network, and it incorporates and leverages modern communication and computing technologies. A fully functional smart grid can result in several significant benefits, including (1) significant reduction of energy losses and CO2 emissions, (2) improved reliability and quality of electric supply, and (3) development of the foundations from which new business models can emerge. This is achieved by creating the required infrastructure to support real-time command and data flow between all nodes of the network (production units and loads). The result of this structure is an autonomous, self-optimized network capable of adjusting to time-variable production and consumption needs. Some of the specific products/outcomes of the smart grid are: • •

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Clean energy technologies focused on energy harvesting and storage (e.g., solar panels, wind turbines, home batteries); Machine-to-machine (M2M) and Internet of Things (IoT) systems capable of reliable data and command flow in the network;

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Smart Grid Business Model3



• •

Platforms and portals for efficient energy management, automation and monitoring; and Business models that create value for vendors, consumers, third-party application providers, utilities, and retailers.

1.5 Growth Strategy The evolution of smart grid technologies has been divided into four phases. The first phase focused on efficient and clean energy technologies. The milestone was to develop novel solar energy, wind energy, and battery technologies that enable the production of energy near the consumer. The outcome of phase 1 was a multibillion dollar solar, wind, and battery industry [3]. In the second phase, the objective was to provide real-time monitoring and management of various nodes in the network to help equalize the timevarying energy production from RESs with the needs of consumers. The milestone was to enhance IoT and M2M systems and create a communication network integrated with and intrinsic to the power delivery network. This phase created new opportunities for hardware development and networking. Currently, the advanced metering infrastructure and home energy management industry is a multimillion dollar market [3]. The goal of the third phase, which is called Smart Grid 3.0 [4], is to provide customer-centric (or user-centric) applications. The first two phases created a foundation for new services and software development. In simple words, Smart Grid 3.0 is the application layer of the smart grid network. Third-party application development companies and vendors are now able to provide complete energy monitoring and management solutions to energy providers and utilities as well as to facility managers of commercial buildings and residential users. Phase 4 takes advantage of the interconnected entities of the smart grid and is focused on peer-to-peer interactions. The connected homes concept, smart charging applications such as vehicle-to-grid (V2G) or vehicle-to-vehicle (V2V) applications, or even the transactive energy concept will be dominant pillars of this evolution. The outcome of phase 4 will be the real implementation of the Internet of Energy (IoE) where units of energy or units of demand reduction will be transacted autonomously between distributed human or nonhuman nodes in the network. Table 1.1 presents the estimated costs and benefits of a fully operational smart grid in the United States based on published data [3].

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The Smart Grid as an Application Development Platform

Table 1.1 Estimated Costs and Benefits of Smart Grid Market in the United States 20-Year Total

In Billions of Dollars

Net investment required

338–476

Net benefit

1,290–2,030

Benefit-to-cost ratio

2.8–6.0

1.6 Business Model The business model of the energy industry is evolving from a strictly businessto-business (B2B) paradigm as new services emerge in the smart grid. Businessto-customer (B2C) and even hybrid combinations of B2B and B2C services are developing. Vendors and third-party application companies provide new services to energy providers, utilities (in B2B form), or even directly to the end user (in B2C form) or both (in hybrid form). Characteristic examples are hardware companies that provide innovative and well-designed products to end users such as smart thermostats, electric vehicles, and home batteries, and software companies that build web and mobile applications for the end user. The sales channels for these products and services can be through the energy provider or via a retail mechanism (shops that sell high-tech or energy products).

1.7  Risks The success of the smart grid depends on various parameters, including adoption of services by end users, regulation issues in different markets and regions, response of utilities in new technologies, long selling cycles that occur between vendors/third-party application providers and utilities, and interoperability of various technologies that are defined by standardization bodies. The evolution of the power grid will follow a similar trajectory as that of the telecommunications industry. In contrast to the previous model, in which the vertically operated telecom company deployed assets for the network infrastructure, we are now experiencing an environment of multiple players such as application providers, cloud operators, and broadband service providers in which the important asset is data management and ownership. In the previous model conventional telephone services were made available to consumers via

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Smart Grid Business Model5

essentially a local monopoly; now we experiencing multimedia content delivery and social networking that are being provided via a large number of content and service providers. It is inevitable that the electricity sector will follow a similar trajectory, because the end users require multiple degrees of freedom from which to choose and interact. We are now transforming from a domain of vertically integrated electric companies where the asset is the infrastructure ownership to enablers of innovative, user-centric energy services where the valuable asset is customer engagement and satisfaction.

References [1]

Energy Information Administration (EIA), www.eia.gov.

[2] http://energyliteracy.com. [3]

Electric Power Research Institute, “Estimating the Costs and Benefits of the Smart Grid: A Preliminary Estimate of the Investment Requirements and the Resultant Benefits of a Fully Functioning Smart Grid,” March 2014.

[4]

Andres Carvallo and John Cooper, The Advanced Smart Grid: Edge Power Driven ­Sustainability, 2nd ed., Norwood, MA: Artech House, 2015.

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2 The Power Grid at a Glance 2.1  Introduction This chapter provides an overview of the power grid network, with a focus on the network architecture, the electricity market, and the main drawbacks of today’s technologies. It gives the reader a broad overview of how the power grid network operates, from production, transmission, and distribution of electricity to consumption by end consumers.

2.2 Useful Data 2.2.1  Power and Energy Power and energy are complex quantities that are sometimes difficult to understand. Power is usually symbolized as P (in watts, W) and represents the rate at which energy is transferred. Energy is usually symbolized as E (in watt-hours, Wh) and represents the capacity to do work [1]. The watt-hour is not a standard unit in any formal system, but it is commonly used in electrical applications. If the power demand P is constant for a given time period t, then energy can be computed according to:

E = P ⋅ t (2.1) 7

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The Smart Grid as an Application Development Platform

The watt-hour (Wh) is an aggregate unit of energy equivalent to an average of one watt (W) of power delivered at a consistent rate during one hour (h) of time. Energy can be transferred from one place to another and converted from one form to another. At the power plant, energy is converted from chemical form (e.g., via the burning of fossil fuels) to electrical form. The electrical form of energy is then transferred through the transmission and distribution network of the power grid to the point at which is it consumed or used for another purpose. For example, energy can be transferred from the power plant to the air conditioning unit or the light bulb of a household. The following example provides a clear explanation of the difference between power and energy. Assume that a light bulb has a labeled power value of 100W (which is 0.1 kW). This value describes the rate at which energy will be used to provide lighting in the house. When the light bulb is turned on for 10 hr, it consumes 1 kWh (0.1 kW × 10 hr). When five light bulbs of 100W each are turned on for 2 hr, they will consume the same 1 kWh of energy (5 × 0.1 kW × 2 hr). However, the required power for the operation of one bulb is 0.1 kW (100W) and the required power for the operation of five bulbs is 0.5 kW (500W). This information is important to understanding the difference between consumption and demand. These concepts are explained in the next section. The cost of electricity is usually measured in $/kWh and defines the amount of dollars a consumer is charged for the amount of electricity consumed. This pricing is what affects a household’s utility bill. (Various mechanisms affecting the utility bill are discussed in detail in Section 2.5.) Referring to the previous example, if the cost of electricity is 0.10 $/kWh then the operation of one bulb for 10 hr or the operation of five bulbs for 2 hr will result in a cost to the user of $0.10 (1 kWh × 0.10 $/kWh). 2.2.2  Capacity, Generation, Consumption, and Demand Capacity is associated with power (kW), and models the rate of the consumption. Capacity can be viewed as the output that an electric generator (or a power plant) can produce under specific conditions. For example, the capacity of a gas-fueled electric power plant may be 1 MW. On the other hand, generation is associated with energy (kWh), and is the amount of electricity a generator or a power plant produces over a specific period of time. For example, if a power plant with a capacity of 1 MW operates for 2 hr, then the total generation is 2 MWh. If the generator operates at half of its maximum capacity for 2 hr, then the total generation would be 1 MWh. In general, many power plants do not operate at their maximum

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The Power Grid at a Glance9

capacity. This below-capacity operation of power plants is necessary for various reasons including fuel costs, demand capacity, environmental conditions, and safety or backup. Consumption refers to energy values, and is the total amount of energy used by a device, system, or operation that must consume the delivered power in order to function. For example, the consumption of one light bulb operating for 10 hr is equivalent to the consumption of five light bulbs operating for 2 hr. In both of these cases, consumption is 1 kWh. Demand refers to power values and describes the rate of consumption. To better understand the difference, the demand for one light bulb that operates for 10 hr is 0.1 kW and the demand for five light bulbs that operate for 2 hr is 0.5 kW (5× larger). 2.2.3  Alternating Current, Direct Current, Active Power, and Reactive Power Almost every household or office electrical outlet is supplied with power in the form of alternating current (AC). However, the majority of electronic devices use power in the form of direct current (DC). The AC form of power delivered to users today is the result of the battle of the currents from the late 18th century [2]. At that time, several technical factors drove the adoption of AC over DC. DC power systems generate and distribute electrical power at the same voltage as that used by the customers’ loads. This requires the use of a large, costly distribution infrastructure and forces power plants to be closer to the loads than for AC power systems. With the development of practical transformers, AC power could be transmitted over long distances at a conveniently high voltage using relatively small wires. The reduction of voltage to levels required by the consumer is also simplified via transformers. The transmission of AC power at high voltages reduces the losses associated with imperfections and other necessary components in the transmission infrastructure. Higher transmission voltages mean lower currents, and lower currents mean less heat generated in the power line due to resistance, as described by Ohm’s law and the power law, respectively:

V = I ⋅ R (2.2)



P = I ⋅V (2.3)

In the above equations, V is the voltage measured in volts, I is the current measured in amperes, R is the resistance measured in ohms, and P is the power measured in watts.

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Although DC power is not used generally for the transmission of energy from power plants into homes as Edison and others may have envisioned, it is still common when transmission distances are relatively small. DC power is used widely in all modern electronic devices such as computers, telephones, and automotive systems. Thus, the most important reason for the adoption of AC for the power generation and distribution system was the fact that it could be transferred over long distances, minimizing the need for local production of energy close to the end consumer (the load). However, due to the modern evolution of smart grid technologies that enable a distributed network of RES to efficiently generate and supply electricity locally and close to the end user, DC transmission is gaining momentum. In AC systems, the direction and flow of charge change periodically. This creates a wave pattern that is usually mathematically described by a sinusoidal function of time, which repeats periodically. As a result, voltage and current can be represented by:

V (t) = V A ⋅ sin(2pft + j) (2.4)



I (t) = I A ⋅ sin(2pft + j) (2.5)

where VA and IA are the amplitude of the sinusoid representing voltage and current respectively, f is the frequency of the sinusoid and is related to the period of repetition, t is the independent time variable, and φ is the phase, or offset of the sinusoid. In the United States power is delivered to the user with a voltage and frequency of 120Vac and 60 Hz, respectively, whereas in Europe power is delivered to the user with a voltage and frequency of 240Vac and 50 Hz, respectively. In general, higher frequencies are more beneficial for the end user. For example, with the 60-Hz distribution frequency used in the United States, light bulbs do not appear to flicker because the human eye is unable to distinguish 60 alternations per second. Direct current is much simpler to understand. Instead of providing an oscillating voltage, a DC power system provides a relatively constant voltage to the load, which consumes power via the amount of current required for its resistance [see (2.2) and (2.3)]. For example, an AA battery provides a relatively constant DC voltage of 1.5V and can provide a constant or variable current as required by the device being energized (up to a maximum level). It is straightforward to convert AC power to DC power by using a device called, somewhat obviously, an AC/DC converter. This process is also called rectification and is one of the most fundamental operations of electrical systems.

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The Power Grid at a Glance11

It is also possible to convert DC power to AC power with a somewhat more complex device called an inverter. As a result of the time dependence of AC power systems and the complexity of interactions between producers and consumers of this type of power, a secondary form of power flow is created inside of AC systems. This secondary power flow is typically called reactive power because it results from the complex reactions of the transmission/distribution system to the generated, sinusoidal power signal. The power that is effectively transferred from the distribution system to the load system is similarly called active power. To understand the difference between active and reactive power, let’s explore the interaction of AC power with different types of electrical loads that exist in a typical house (Figure 2.1). The three common types of loads are broadly differentiated based on the internal electrical structure of the load. These general load categories are called resistive, inductive, and capacitive, and a single device or system typically exhibits some level of all three load categories simultaneously. Resistive loads are devices that exhibit some resistance (R in ohms). Resistance converts part of the offered electric energy into heat inside of the load. Examples of loads that are approximately purely resistive are simple light bulbs and conventional ovens. In resistive loads, the electrical current (I) and the voltage (V ) are in phase with each other (Δφ = 0). This means that voltage and current rise and fall at the same time. Purely resistive loads consume active power, and do not consume or create reactive power. Inductive loads are devices that exhibit some inductance (L in henries). An inductive device stores current in the same way that a compressed spring stores kinetic energy. Examples of loads that have a large inductive component are motors for large equipment, washing machines, and the compressor of an air conditioning unit or refrigerator. In an inductive load, the sinusoidal voltage and current waveform are out of phase, and the voltage peaks and current peaks are not aligned. The current waveform lags behind the voltage waveform. Capacitive loads are devices that exhibit some capacitance (C in farads). A capacitive device stores charge (or, voltage) in the same way that a stretched spring stores kinetic energy. Examples of loads that have a large capacitive

Figure 2.1  Examples of different types of loads in a typical house.

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component are devices that exchange energy for other purposes, such as radios and power supplies. In a capacitive load, the sinusoidal voltage and current waveforms are not aligned, and are out of phase in a different way than for inductive loads. The voltage waveform lags behind the current waveform. Active power (P, measured in watts) is the real power that does the work (heat, light, etc.) and corresponds to the components of the voltage and current waveforms, which are in phase. The change in phase resulting when AC power interacts with inductive or capacitive loads creates reactive power (Q and measured in volt-amperes reactive or VAR). Reactive power is due to the voltage and current components that are out of phase and can be regarded as energy stored temporarily in inductive or capacitive components. Because this energy is stored temporarily, it is not producing any useful work and therefore is not active. According to the type of load, the reactive component can be negative (delayed in phase as a result of a capacitive load) or positive (advanced in phase as a result of an inductive load). A simplified sketch of the active and reactive power is given in Figure 2.2. The complex power, S, is a vector quantity that is the sum of active and reactive components, as shown in Figure 2.2. The higher the phase difference between current and voltage, the larger the reactive component and thus the smaller the active component. Complex power is a combined result of voltage and current and, hence, is measured in volt-amperes (VA). The apparent power is the magnitude of the complex power, or the length of the vector S. Mathematically, the magnitude of S is represented by |S|. The apparent power corresponds to the amount of energy used each month by a utility customer, or the cost of that customer’s monthly electric bill. The equations for computing the active, reactive, complex, and apparent power are as follows:

P = VRMS ⋅ I RMS ⋅ cos(j) (2.6a)



Q = VRMS ⋅ I RMS ⋅ sin(j) (2.6b)



S = P + jQ (2.6c)



S = P 2 + Q 2 (2.6d)

Note that the active and reactive components of the complex power S are important data that helps load disaggregation algorithms classify individual appliances from smart meter recordings for an entire house. Algorithms describing the load disaggregation problem are investigated in Chapter 4. All

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The Power Grid at a Glance13

Figure 2.2  (a) Normalized values of voltage and current that are out of phase and (b) vector diagram of active (P), reactive (Q), and complex or apparent power (S).

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The Smart Grid as an Application Development Platform

appliances and electronic devices create active power but some of them are responsible for the creation of reactive power. Examples of the existence of reactive power in typical house appliances are presented in Table 2.1. The power factor (PF) is the ratio of active power to apparent power. In an ideal power system, the power factor should be close to 1: PF =



P , ≤ 1 (2.7) S

Electric installations within buildings and houses should be deployed in such a way to maximize the power factor. This creates maximum efficiency in the delivery of power from the distribution system to the load(s). In practical scenarios, the power factor can never reach the value of 1 since electrical systems inside the load have inductive and capacitive elements. Characteristic values of the power factor for different buildings are presented in Table 2.2 [3]. 2.2.4  Example from Smart Meter Data One of the most important applications of the smart grid is energy analytics. Numerous vendors provide web-based and mobile apps to inform the end user about daily power demand, energy consumption, and costs of electricity. The following section describes how real smart meter data can be used to provide these valuable insights. Figure 2.3 presents the power readings in watts as recorded by a smart meter installed in the electrical panel of a typical one-bedroom apartment. Table 2.1 Reactive Power from Different Types of Loads Existence of Reactive Power

Appliance

Existence of Reactive Power

Oven/toaster/ hotplate

—

Dryer

✓

Lights

—

Refrigerator

✓

Air heater

—

Water heater

—

Hair dryer

—

PC/laptop

✓

Microwave

✓

Gaming

✓

Vacuum cleaner

✓

Television

—

Washing machine/ dish washer

✓

Air conditioning

✓

Appliance

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The Power Grid at a Glance15



Table 2.2 Approximate Values of the Power Factor of Typical Buildings Building Type

Power Factor (%)

Houses

95

Offices

85

Restaurants

88

Retail shops

89

Refrigerated warehouse

79

School and colleges

81

Hospitals

85

Industrial manufacturing

77

From: [3].

The smart meter gathering this data used a noninvasive sensor (a current transformer) deployed around the main electrical cable of the residence. The electrical current flowing inside a cable creates a magnetic field around the outside of the cable. The current transformer estimates the electrical current flowing inside the cable based on this external magnetic field. By knowing

Figure 2.3  Power demand, energy consumption, and cost of the use of electricity every 7 sec derived from a smart meter deployed in a typical one-bedroom house.

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16

The Smart Grid as an Application Development Platform

the voltage used in the power system (e.g., 120Vac in the United States), the power reading can be easily calculated according to (2.3). In this case, current measurements were made once every 7 sec, for a sampling rate of f = 0.14 Hz. The power readings were transferred to a computer for further processing based on the measurement setup presented in Table 2.3. The smart meter transmits a power reading in watts that corresponds to the power demand of the residence pi, i = 1, 2, …, N every 7 sec to the computer. At the end of one 24-hour period, the database contains approximately N = 12,300 recordings as show in Figure 2.3.To convert each power reading (pi in watts) to energy consumption (ei in watt-hours), the following formula should be applied [as in (2.1)]: ⎡ 1 1 ⎤ ei = pi ⋅ ti =  pi ⋅ ⎢ ⋅  (in Wh) (2.8) ⎣ f 3600 ⎥⎦



The energy consumption ei for each 7-sec increment is shown in the center plot of Figure 2.3. The total energy consumption for the 24-hour period can be computed as the sum of the energy in all of the individual 7-sec increments: N

Edaily = ∑ei = 2,100 Wh (2.9)



i=1

From the energy data ei it is now easy to compute the instantaneous cost of electricity usage. Assuming a cost of electricity equal to c = $0.12/kWh, the cost for every measurement of the example is computed as: Table 2.3 Measurement Setup for the Smart Meter Data Field

Description

Sensor

Noninvasive clamp sensor

Voltage

120V

Sampling frequency

0.14 Hz

Number of samples per day

12,300

Communication unit

434-MHz radio connection

Formatting

XML

Database

SQL

Software used for processing data

Matlab 2014

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The Power Grid at a Glance17



ci =



ei ⋅ c (in $) (2.10) 1,000

Note that the division by 1,000 converts the watt-hour measurement of ei to kilowatt-hours. The bottom plot of Figure 2.3 presents the cost of using electricity ci in dollars for each 7 sec of the 24-hour period. The total cost of the electricity consumption in the residence can be computed as: N

C daily = ∑ci = $2.1 (2.11)



i=1

2.3 Grid Architecture 2.3.1  Organization, Players, and Regions The electrical power grid is one of the largest and most resilient networks in the world. It satisfies the demand and consumption needs of electricity by generating and providing power required by users on a consistent basis … every day of the week, every week of the month, and every month of the year. The current architecture of the electrical grid is based on a centralized system that is segmented in different regions of the United States. The North American Electric Reliability Council (NERC, now called the North American Electric Reliability Corporation) was established in 1968 to ensure that the power grid in the United States was reliable, adequate, and secure [4]. NERC played a crucial role in the latter half of the 20th century because many local grids had been created in various regions of the United States at different times and were later interconnected to form a larger network covering the entire country. Currently, the United States has more than 3,000 electric distribution utilities and energy retailers, more than 200,000 miles of transmission and distribution lines, approximately 134,000,000 million customers [5], and more than 7,200 power plants and generating facilities, each having at least 1 MW of generating capacity [6]. The U.S. power grid is comprised of eight regional organizations: • • • • •

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Florida Reliability Coordinating Council (FRCC), Midwest Reliability Organization (MRO), Northeast Power Coordinating Council (NPCC), Reliability First Corporation (RFC), SERC Reliability Corporation (SERC),

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18

The Smart Grid as an Application Development Platform

• • •

Southwest Power Pool, RE (SPP), Texas Reliability Entity (TRE), and Western Electricity Coordinating Council (WECC).

The regional organizations are referred to as independent system operators (ISOs) and regional transmission organizations (RTOs) and are part of a national standard advocated by the Federal Energy Regulatory Commission (FERC). Electricity generated by power plants in these regions is provided to the end customer through different entities known as public utilities and energy retailers. Public utilities provide electricity to the general public, and can be privately owned or publicly owned. Publicly owned utilities include electric cooperatives (COOPs) and municipality-owned utilities (MOUs). COOPs were originally created to provide access to energy in remote/rural areas where the public utilities centered around larger urban/metropolitan areas could not deliver the required electric services. Publicly owned utilities are tax-exempt, nonprofit organizations according to U.S. Internal Revenue Code 26 U.S.C. § 501(c). COOPs are owned by the customers they serve, who are usually referred to as members. More than 900 COOPs exist in the United States, serving roughly 19 million customers, or 12% of the total number of meters. Interestingly, COOPs serve roughly seven customers per mile of distribution line, and produce roughly $2,000 of revenue per customer. MOUs may actually include territories outside of city limits or may not even serve an entire city. More than 2,000 MOUs exist in the United States, serving over 20 million customers, or 15% of the total number of meters. In contrast with COOPs, MOUs serve almost 50 customers per mile of distribution line (6´ the customer density of COOPs) and produce almost 8´ the revenue per customer. On the other hand, private utilities are called investor-owned utilities (IOUs), and operate with the intent of producing profit for their investors. Approximately 200 IOUs are operating in the United States, serving over 100 million customers, or 72% of the total number of meters. IOUs serve fewer than 40 customers per mile of distribution line (about 5× the customer density of COOPs) and produce 5× the revenue per customer. Finally, retail energy providers (REPs) are private companies that buy large amounts of energy from the wholesale market and resell it to the end customer at competitive prices. REPs are allowed to operate only in deregulated markets, which are discussed later in the chapter.

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The Power Grid at a Glance19

Currently, the electricity is provided to the end user following a four-step process as presented in Figure 2.4. The four elements responsible for the delivery of electric services are (1) a generating station or power plant, (2) a transmission network, (3) a distribution network, and (4) consumers (end users). Power plants produce electricity by converting different forms of energy to electricity. The most commonly used resources are fossil fuels. Power plants are usually deployed near a fuel source and away from densely populated areas since the conversion of chemical energy to electricity can create pollution and other negative outcomes for the surrounding environment. The transmission network transports electricity large distances from the power plants to the distribution network. The transmission network is supplied with energy from the power plant that is processed for transmission. This processing includes the step-up conversion of voltage generated by the power plant to high-voltage (HV) power, and it is accomplished by large electric transformers. Depending on the distance and location, HV values used for transmission are 756, 500, 345, 230, and 138 kV. The distribution network provides electricity to the end consumer. The HV power from the transmission network is converted to medium-voltage (MV) or low-voltage (LV) power using large step-down transformers. The conversion from HV to MV or LV is important because it adapts the distributed power to the standard or conventional power values expected by the consumer. The orientation of the distribution network, with higher voltage being distributed to consumers at a lower voltage via step-down transformers, is also critical to the efficiency and scalability of the overall transmission and distribution system. The technical specifics of this operation are beyond the scope of this book, but effectively exchange higher voltage and lower average current in the distribution system for lower voltage and higher per-meter

Figure 2.4  Current architecture of the power grid.

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20

The Smart Grid as an Application Development Platform

current in the consumer’s premises. The result of this system is the production of an efficient and highly controllable form of force at a distance. Consumers are the end users of the electricity and can be related to transportation, industrial, commercial, and residential uses. Depending on the type of the consumer, MV distribution values may be 69, 26, 13, or 4 kV for industrial and commercial consumers, and the LV distribution values are 240V and 120V for residential consumers. Of course, all of these voltage values are for AC distribution, and are typically presented in root-mean-square form (RMS or Vac), which is the power equivalent of a DC value. The aggregated information about the production and consumption of energy in the United States for 2015 is presented in Figure 2.5. The graph is based on data published by the U.S. Department of Energy (DOE), the Energy Information Administration (EIA), and Lawrence Livermore National Labs. A casual observation of the figure results in the conclusion that approximately 40% of all energy resources are used for electricity production to cover the needs of the consumers. From the conversion of energy from fossil fuels, nuclear power, or renewable energy sources approximately 65% is rejected energy. Rejected energy models the loss or waste of energy due to several factors including the conversion process at the power plant or heat produced in the transmission and distribution network (cables and transformers). Approximately 35% of the energy produced is actually delivered to the end consumer. 2.3.2  Production Power plants produce electricity from different types of natural resources, including fossil fuels, nuclear power, or renewable energy. To produce this electricity, the power plant transforms chemical, kinetic, or thermal energy via different mechanisms, which depend on the natural resource being used. The electricity produced via this transformation is then supplied to the transmission network for transportation to the distribution network and, finally, to the consumers. Today, electric utilities and independent power producers are responsible for almost of 95% of total energy production. Smaller players can also be involved in the production of energy, such as commercial and industrial units that are focusing on RESs such as solar and wind [9]. Chemical Energy to Electricity: Fossil fuel, nuclear, and biomass power plants convert chemical energy to electricity. The general principle of the conversion is illustrated in Figure 2.6. The fossil fuel is burned in the combustion chamber (or nuclear reactor in the case of nuclear energy). The outcome is heat. Heat is

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The Power Grid at a Glance21

Figure 2.5  Energy use in the United States for 2015 [7, 8].



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22

The Smart Grid as an Application Development Platform

Figure 2.6  Production and conversion of energy in a power plant.

supplied to water tanks, which turn it into steam (the steam boiler). The steam travels into a steam turbine and causes it to rotate at a high speed. The electric generator is a collection of wire coils fixed to one end of the turbine’s shaft. The rotation of the turbine causes the coils to turn, creating an electromagnetic field following Maxwell’s law [10]. This induces a large flow of electrons in surrounding equipment, and this flow of electrons is generated electricity. Kinetic Energy to Electricity: Two types of power stations convert kinetic energy to electricity: hydroelectric power stations and wind turbines. Hydroelectric power stations use the kinetic energy released by falling water to rotate a turbine in a fashion similar to fossil fuel power plants. The falling water causes the turbine to rotate a metal shaft in an electric generator. Water turbines are built at sources of running water with large drops in elevation. A dam may be used as a reservoir to store and release water in a controlled fashion. This provides the required flow of water to rotate the turbine. Following Maxwell’s law, the rotation of a turbine with wire coiled around magnetic material creates oscillating magnetic fields that are converted to electric power. In a similar manner, wind turbines use the kinetic energy of the wind. Wind blows on the angled blades of the turbine and creates lift, similar to

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The Power Grid at a Glance23

the wings of an airplane. This lift produces rotational motion around the axis of the rotor assembly. The rotor is part of a generator consisting of magnetic material and coiled wire. A shaft is also connected to permanent magnets that surround the wire. The motion of the magnets around the conductor induces a voltage in the coil of wires, and the resulting generated electricity is sent to transmission lines [11]. The wind turbine contains a gearbox to optimize generation based on various wind speeds. The optimal wind range for the operation of wind turbines is approximately 15 to 20 m/s. Thermal Energy to Electricity: Two types of power stations convert thermal energy to electricity: solar panel stations and geothermal stations. Solar panels are usually deployed in large numbers to form a solar farm. A single solar panel’s output is limited in the same sense that a single milk cow’s production of milk is limited. As a result, the output of several individual solar panels is typically aggregated to produce large volumes of electric power in the same way that large numbers of cows are herded together in a dairy farm to produce large volumes of milk. Solar panels contain silicon-based photovoltaic cells that transform incoming sunlight into small amounts of electricity. Each cell contains a positive and negative film of silicon with a protective glass covering. The photons contained in sunlight strike the cells and transfer energy to the electrons in the silicon. This transferred energy results in voltage or current captured by wires that connect several cells. The process is very similar to the way a digital camera captures an image via photons striking and exciting a specially formulated charge-coupled device (CCD). In the case of a CCD, the energy captured is very small and must be converted to a usable form that is often a visible image. In the case of solar panels, the energy captured is relatively larger but still must be converted to a usable form that is compatible with conventional electric systems. The conventional, compatible form of electricity is AC power, suitable for transmission to the electric power network or use directly by conventional electric appliances and other electrical devices. This conversion of collected solar power often involves two stages: some form of temporary storage (such as a battery) followed by subsequent conversion from DC (battery) to AC (grid). An inverter system transforms DC electric power into AC electric signals that can be put to immediate use by connecting the inverter directly to a dedicated circuit breaker in the electrical panel or the power grid (also referred as grid-tied inverters). Geothermal stations use thermal energy present in the earth to heat water into steam, rotate a turbine, and thus create electricity. In most cases, water tanks are boiled underground and the created steam spins a turbine to

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24

The Smart Grid as an Application Development Platform

create electricity similar to the principles discussed previously. The three types of geothermal power plants are binary cycle power plants, dry steam power plants, and flash steam power plants [4]. The United States has approximately 20,000 power plants that provide sufficient energy capacity to satisfy annual demand and consumption. Their characteristics are presented Table 2.4. In 2015, the total production of electricity in the United States was 4,000,000,000 MWh. 2.3.3  Transmission Power plants usually generate power at fairly low voltages. It is safer and more economical to step up LV generation to HV for transmission via large transformers than to directly generate power at higher voltages. This process is illustrated in Figure 2.7. HV transmission is preferable because it involves lower current on the transmission cables, which results in less heat through resistive loss. Although DC transmission has some advantages (including no requirement for synchronization), three-phase AC transmission is dominant because HV AC signals can be easily manipulated to optimize transmission and distribution networks. Some drawbacks of AC transmission include the requirement for synchronization, as well as skin effects on the cables and the corona effect [12]. Table 2.4 Characteristics of Power Plants in the United States for 2015 [6]

Resource

Number of Power Plants in the United States

Net Generation (in thousands of MWh)

Percentage (%)

Conversion to Electricity Efficiency (%)

Coal

1,145

1,356,000

33

Gas

5,800

1,350,000

33

34–43

99

797,000

20

30–38

Nuclear

32–39

Petroleum

3,573

40,000

1

30–38

Hydro

4,100

251,000

6

80–90

Wind

1,032

180,000

4.5

30–45

Biomass

2,200

54,000

1.5

25–30

Solar/solar thermal

1,249

26,000

0.6

12–20

0.4

25–35

Geothermal Total

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194

16,000

19,392

4,070,000

100

—

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The Power Grid at a Glance25

Figure 2.7  Diagram of the transmission network.

2.3.4  Distribution The distribution network (Figure 2.8) steps down the HV power from the transmission network and distributes high-quality electricity to residential, commercial, industrial, and transportation consumers. A substation converts HV to MV, and distributes power to the local grid via several MV links. Depending on the type of consumer, the MV conversion may be a twostep process. For example, residential consumers are also connected to polemounted transformers that step down distribution links from MV (possibly several kilovolts) to LV (120V or 240V). Commercial consumers often use 4- or 13-kV links directly from the substation, whereas industrial and transportation consumers may use 26- or 69-kV links. Substation transformers may be dedicated to one consumer or

Figure 2.8  Diagram of the electric distribution network.

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The Smart Grid as an Application Development Platform

shared between several consumers based on power needs. Within the distribution network, small-scale RES power generation may also coexist. This is achieved with inverter installations that transform the DC production from the RES to AC that can be fed into the distribution network. More details concerning distributed generation and off-grid applications are provided in Chapter 3. Table 2.5 gives an overview of power consumption and pricing in the United States. By comparing the U.S. electric power data in Tables 2.4 and 2.5, we can see that the total net generation (4,070,000 thousand MWh) is approximately 9% larger than the consumption (3,724,525 thousand MWh). This difference is due to losses from cables and transformers within the transmission and distribution networks, and is usually around 5% to 10%.

2.4  Drawbacks of Current Network Design 2.4.1  Waste of Resources and Pollution Today’s power grid has a centralized architecture that results in critical losses and inefficiencies. From the production of electricity to consumption, there is an average waste of 65.4% of the available energy. The main source of waste (approximately 62%) is due to the conversion of fossil fuels to electricity. The secondary source of waste (approximately 9%) is loss due to transportation and distribution of electricity to the end consumer as shown in Figure 2.9 and Tables 2.6 and 2.7. Table 2.6 provides data on generation, consumption, and losses in conversion and transmission for the three primary fossil fuels used

Table 2.5 Number, Consumption Needs, and Retail Price of Different Consumers in the United States

Consumer

Number

Consumption (in Average Retail thousands of MWh) Cost ($/kWh)

Residential

128,680,000

1,399,884

0.127

Commercial

17,853,995

1,358,419

0.105

839,212

958,563

0.069

79

7,659

0.069

147,373,702

3,724,525

Industrial Transportation Total

0.104 (average)

From: [6].

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The Power Grid at a Glance27



Figure 2.9  End-to-end losses in fossil fuel electrical distribution.

in electricity production. Table 2.7 provides data similar to Table 2.6, but the energy lost in generation and transmission is expressed in terms of financial metrics (dollars) and emitted pollutants (carbon dioxide). There are two ways to overcome this waste of resources: 1. Bring production close to consumption. This will reduce transportation and distribution losses (around 9%). Table 2.6 Generation and Transportation Efficiency for Fossil Fuels

Sector

Consumption for Electricity Production (in millions of MWh)

Losses on Conversion (%)

Net Generation (in millions of MWh)

Losses on Transportation (%)

Coal

4,118

67

1,360

8.8*

Gas

3,016

55

1,340

8.8*

Petroleum

52.2

66

17.5

8.8*

Total

7,186

62

2,717

8.8*

*Losses at the transportation/distribution network are the same for all types of generation.

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The Smart Grid as an Application Development Platform

Table 2.7 Wasted Dollars and CO 2 Emission Due to Inefficient Generation and Transmission of Electricity from Fossil Fuels Wasted in Transportation/ Wasted in Generation Distribution

Sector

Price ($/ MWh)

CO2 (kg/ MWh)

$ (in billions)

CO2 (in millions of tons)

$ (in billions)

CO2 (in millions of tons)

Coal

7.2

338

19.9

933

0.87

40.5

Gas

9.2

182

15.5

304

1.1

21.4

Petroleum

35.9

250

1.2

9

0.05

0.4

Total

—

—

36.6

1,246

2.03

62

2. Invest in renewable energy sources and distributed generation (DG). This will reduce losses associated with the conversion of fossil fuels to electricity (around 62%). It is clear that there is a need to minimize the waste of available resources and deliver high-quality electric services to the consumer. These challenges are addressed by the smart grid system, which aims to minimize the energy wasted in the production and transportation/distribution of electricity. 2.4.2  Adaptation to Time-Variable Production and Consumption The second drawback of the power grid is that it cannot adapt to time-variable production and consumption of electricity. Renewable energy sources, and more precisely solar panels and wind turbines, are widely used to support the capacity and production needs of the power network. The production of RESs varies with time according to weather and environmental conditions. For example, the production from a solar panel depends on the position of the sun during the day, and the production from a wind turbine depends on the speed of the wind [4, 11, 13, 14]. To better understand the time-varying nature of solar production, let’s explore a simplified formula. The instantaneous production from a solar panel is given by:

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Psolar (t) = A ⋅ r ⋅ PR ⋅ H (t) (in kW) (2.12)

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The Power Grid at a Glance29

where A (m2) is the area of the solar panel installation, r (%) is the solar panel yield or efficiency calculated from the ratio of electrical power (kWp) of one solar panel to the area of one solar panel, and PR (%) is the performance ratio that considers the efficiency of the installation. For the computation of PR, factors typically include inverter losses (4% to 11%), temperature losses (5% to 15%), DC and AC cable losses (1% to 3%), and shadow losses. Finally, H (in kW/m2) is the average solar radiation on a tilted panel. This depends on the time of day since it is associated with the position of the sun relative to the solar panels. Wind energy production also depends on the time of the day since wind speed is not constant. To compute the output power of a wind turbine, we can use the following simplified formula:

1 Pwind (t) = k ⋅ ⋅ c p ⋅ r ⋅ A ⋅V 3 (t) (in kW) (2.13) 2

where k is a constant to convert power to kilowatts; cp is the efficiency coefficient (0.25 to 0.45); ρ (in kg/m3) is the air density, which depends on the elevation of the wind turbine (typically 1.27 kg/m3 for dry air at 20°C); A (in m2) is the cross-sectional area swept out by the wind turbine blades; and V (in m/s) is the wind speed. In addition, the consumption of residential or commercial buildings is also a time-varying function and depends on the human activity within the house or building. Typically, production and consumption are not synchronous, making the integration of RES in the network a complex problem. The production of energy from RES might be enough to satisfy the overall energy consumption needs, but the capacity might not be adequate to satisfy the instantaneous demand. An example is given in Figure 2.10. During the day, the total wind and solar energy production is greater than the consumption. One can calculate the area below the curves of Pwind and Psolar and C and compare their values. On the other hand, there are periods during the day when the solar and wind capacity is not adequate to satisfy the instantaneous demand. For example, between 6 p.m. and midnight the consumption needs are always greater than the sum of solar and wind production, or Psolar(t) + Pwind(t) < C(t). On the other hand, between midnight and 6 p.m. in the afternoon the offered capacity is always larger than the demand, or Psolar(t) + Pwind(t) > C(t). To overcome these problems, load management (energy management) and energy storage techniques must be applied. These techniques require automation and intelligence in the network, which are addressed by smart grid technologies.

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The Smart Grid as an Application Development Platform

Figure 2.10  Daily energy production and consumption curves for solar panels, wind turbines, and residential houses.

2.4.3  Passive Nature of the End Consumer The third drawback of the power grid is the one-way communication flow from the provider to the consumer. The centralized architecture of the power grid does not enable consumers to participate in the operation and processes of the grid. Until recently, this one-way relationship between the energy provider and the consumer took the form of a monthly utility bill. Following the advances of other sectors of the economy, such as the telecommunications, mobile, and cloud industry where end users choose their provider and interact with the provider and other customers of the provider, it was clear that the energy sector had to evolve. The industry is transitioning from a phase in which the only communication between the provider and the consumer is the utility bill to a phase where the consumer can [15]: • • • •

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Select a provider from a pool of providers offering competitive prices for their services, Select different financial services such as energy packages, similar to the data packages found in the mobile industry, Use innovative services and mobile apps that offer analytics of energy consumption and bill forecasting, Control and manage energy consumption in their premises,

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The Power Grid at a Glance31



• • • •

Communicate in real time and provide feedback with the use of social media and notifications, Use and send energy credits to friends and family members, similar to the airtime transfer services found in the mobile industry, Enjoy energy roaming services similar to those found in the mobile industry, and Participate in demand response (DR) programs and support the operation of the grid and penetration of RES in the grid.

2.4.4 Business Models The fourth drawback of the power grid is related to its business model. The electricity industry is experiencing a huge transformation in its operational and business models. In numerous areas, vertically integrated utilities are the dominant entities for electricity production, transmission, and distribution. In these areas, the utility produces, transports, and distributes energy and provides billing services to the consumers. The disadvantage of this model is that it does not allow for competition [16, 17]. The main business model in the vertically operated utility was previously a business-to-business (B2B) one. This model is now transitioning to a business-to-consumer (B2C) model, or a combination of B2B and B2C. In recent years, the evolution of RESs and the business models of other sectors of the economy, such as the mobile and cloud industries, have created concerns about the efficiency of the vertical utility model. The most important factors driving the evolution of the utility industry are: 1. Disruption from clean energy technologies, which increased the consumer appetite for low carbon emissions; 2. Changing customer expectations of innovative services and quality of service (QoS) from their providers; 3. Goals imposed by regulators and policymakers via introduction of new rules in the operation of the grid; 4. Pressure from new corporate players entering the energy market; 5. Innovation in smart home devices and appliances (IoT) and the resulting need for automation and development of user-centric services; 6. Rising costs of energy, which stimulate consumer needs for an open market; and 7. CO2 emissions that threaten the environment and create the need for smart and clean production and distribution of energy.

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The Smart Grid as an Application Development Platform

The transition to a healthier business model is already happening and will open the door to new players who can provide B2C services. On the production side, distributed energy resources (DERs) and new energy producers are now joining the market. In addition, the corporate sector is also expected to play a dominant role. Corporations have already deployed large solar and wind farms and are now entering the wholesale and retail markets by moving from long-term power purchase agreements (PPAs) to active market participation [18]. On the transmission and distribution side, new independent transmission and distribution companies are emerging, and ICT companies are providing real-time monitoring and management of the network. On the retail side, the tremendous influx of new players is driving innovation and modernization. These new players include retail energy providers, ICT companies providing smart home analytics and automation, IoT devices and consumer goods, as well DR services. This transition has numerous benefits for the end consumer who can now play an active role in the operation of the network. The decentralization of the electricity business has a direct effect on the customer, who is now more connected and interactive. Figure 2.11 presents an overview of the business landscape. Competition in the areas of asset management and service offering improve the quality of service to the end user. The most valuable asset is now the ownership of data rather than the ownership of infrastructure. 2.4.5 Security/Outages The final drawback of the power grid is related to security. Today, all sectors of the economy rely on the secure operation of the power grid. Unfortunately, the centralized architecture of the power grid is vulnerable to different types of attacks. Natural disasters, terrorist attacks, or even cyberattacks can result in a blackout over a city or region. The impact of such an attack on the grid network of today could be huge, since it is based on a centralized architecture that is unable to adapt to rapid changes. The distributed architecture of the smart grid is more capable of managing the impact of attacks by adapting to changes in real time. Let’s explore this concept in the following scenario. Imagine that part of the transmission network is destroyed in a specific location and energy needs to be routed from different nodes of the network. Based on today’s technology a huge blackout event would ensue because the centralized power plant would not be able to route electricity to the impacted areas. In contrast, distributed energy sources and the automation of the smart grid would minimize the impact of such a scenario by routing the energy and managing the consumption accordingly.

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The Power Grid at a Glance33

Figure 2.11  (a) Transition of business models from vertically operated utilities to a decentralized approach and (b) the effect on the end consumer.

Even though the self-optimized capabilities of the smart grid can minimize the impact of a natural disaster or terrorist attack, new forms of problems become possible, including cyberattacks. The integrated ICT infrastructure on top of the energy network is more vulnerable than today’s grid network to cyberattacks such as malware, denial of service (DoS) attacks, and advanced persistent threats (APTs) [19]. This is because the entire ICT infrastructure is

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The Smart Grid as an Application Development Platform

connected to the Internet and incorporates some form of embedded computer system. To minimize these risks, critical infrastructures such as hospitals, data centers, and telecommunications networks must have a backup energy system, and their control units should be isolated from the Internet.

2.5 Energy Markets The energy market is the arena where the selling and buying of electricity follows supply and demand principles. There are two types of markets in most regions of the world: regulated and deregulated markets. In a regulated market, the utility is vertically operated and owns the generation, transmission, distribution, and retail segments of the business. In this market there is no room for other business models and players to evolve since the entire electricity flow is owned by the utility. In a deregulated market, the utility divests its ownership in generation and transmission and creates opportunities for other players to enter the market. For example, energy producers can actively participate by selling energy to energy retailers, who resell the energy and provide billing to end consumers. 2.5.1 Wholesale Market Electricity is a commodity that can be bought, sold, and traded. Because electricity cannot be easily stored in large quantities, and because it needs to be available on demand, there is a need for a market that can orchestrate the supply and demand in real time [20]. The benefits of the wholesale market include: • • •

Pricing transparency, Increased security of supply, and Easier entry of new players.

To better understand the operation of the wholesale market, we must discuss architecture and pricing (Figure 2.12). Architecture: The main players in the wholesale market are the independent system operators (ISOs), market operators (MOs), transmission system operators (TSOs), generators, retailers, and consumers. The ISO manages the spot market in different regions and provides an operational umbrella for the market. The MO clears and settles electricity transactions. Market operators do

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The Power Grid at a Glance35

Figure 2.12  Architecture and operation of the electricity market.

not clear trades but often require knowledge of the trade in order to maintain generation and load balance. The TSO is a controlling agency that coordinates supply and demand (e.g., the dispatch of the generating energy units to the area of demand). If a mismatch occurs between supply and demand, the TSO has the right to intervene in grid operation to keep the reliability of the network at high standards. The generators produce electricity and bid it to the market. The retailers purchase large amounts of electricity and resell it to consumers via supply contracts. Commodities/Pricing: The commodities transacted in the market are energy, power, ancillary services, and congestions or losses and these define the final price. Energy (in MWh) represents the actual commodity that is consumed by the end customers. Energy markets trade net generation in time intervals such as 5, 15, and 60 min. Power (in MW) is the capacity that satisfies the demand, and is managed by the market operator. Management of power is important for reliability issues because blackout events can occur when power capacity does not satisfy demand. Ancillary services are additional services contributing to the reliability of the grid, such as administration and management of the process. Transmission and congestion are associated with losses from insufficient capacity at the transmission lines or losses due to transmission over

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The Smart Grid as an Application Development Platform

long distances. For example, the cost of electricity may rise if a generator must supply demand in areas that are a long distance from the point of production. The price of a unit of electricity (in MWh) is usually referred to as the locational marginal price (LMP). The term locational refers to the costs that are associated with transporting electricity from the location of production to the location of consumption. The term marginal refers to the costs associated with the time-varying nature of the resource mix, where the price is based on the most expensive resource in the mix. For example, if the production at a given point in time is based on petroleum, then the price of electricity will increase. On the other hand, if production is based on free energy provided by RESs, then the price of electricity may decrease. In some cases, negative electricity prices might occur during a day when the supply from RESs is larger than the demand. In the wholesale market, three types of transactions take place: the auction market, the real-time market, and bilateral contracts. The auction market is a short-term trade that uses supply and demand principles to define the pricing. The process is designed to match the supply and demand at the lowest possible price using the concept of uniform clearing price. The algorithm is described as follows: 1. The ISO or MO predicts hourly demand and broadcasts this information to the market. 2. Each generator offers a specific amount of capacity (MW) at specific prices ($/MW). The offered price depends on the resource mix for generation as well as the operational costs of the generator. 3. The ISO or MO sorts the offers in ascending order. 4. The winning bids are those with the lowest price that meets the demand. 5. The clearing price is set as the most expensive unit (marginal) of generation taken from the winning bids. Consider the following example. To satisfy the hourly demand of a residential area, four generators provide the required supply: a wind farm, a coal plant, and two gas plants. Each plant is managed by different generators. For each generator, we know the hourly production of power in megawatts and the associated cost in dollars per megawatt. The algorithm shown in Table 2.8 computes the clearing price of the supply and the number of generators participating in transactions (bid) for every hour of the day. Figure 2.13 shows that during nighttime, when demand is low, the electricity price is also low and few generators participate in the transaction. The

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The Power Grid at a Glance37



Table 2.8 Pseudo Code Used for the Computation of the Clearing Price in the Energy Market %-----Inputs-----Step 1

Define hourly capacity in MW of the resource mix per day: Supply_Wind, Supply _Coal, Supply _Gas_1, Supply _Gas_2 (vectors with integer values of MW production of the relevant resource mix)

Step 2

Define hourly cost in $/MW of the resource mix per day: Cost_Wind, Cost_Coal, Cost_Gas_1, Cost_Gas_2 (vectors with integer values of $/MW of the production of the relevant resource mix)

Step 3

Define hourly demand in MW per day: Demand (vector with integer values of the demand for the day)

%----Processing----Step 4

∀ hour of day

Step 5

Sort Costs of resource mix in ascending order

Step 6

Find the resource mix required such that the Total Supply = Demand

%----Outputs----Step 7

Price is the max Cost from step 6

Step 8

Used_Resources are the resource mix found in step 6

Step 9

Plot results

electricity price is relatively low because wind energy production is cheap and is adequate to satisfy the demand. However, during peak daytime hours, the electricity price rises because expensive generators based on fossil fuels begin to participate in the transactions. Real-time (spot market) pricing models the adjustments of price based on real-time market conditions, such as use of fuel types and congestion. Bilateral contracts are contracts between a generator and a buyer that do not include an ISO to oversee the transaction. Bilateral contracts are usually long-term contracts similar to the PPAs used by large consumers, such as industrial units or retailers. Today, bilateral contracts may also exist in retail markets where consumers bypass the trader and buy energy directly from the generator. Although this process can reduce the cost of electricity, it also imposes risks associated with bypassing of the market.

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Figure 2.13  Supply, demand, pricing, and number of energy units in the resource mix used to provide the supply.

2.5.2 Retail Market The retail market exists in deregulated areas where a retailer (or the electric company) buys energy from the wholesale market and resells it to end consumers. The retailer is typically a private company that satisfies specific criteria, including the following: • • • •

Pool or spot market agreement, Billing capabilities, Credit control, and Customer support with efficient call centers.

The main risks faced by energy retailers include issues such as regulatory compliance and the ability to efficiently collect bills from end consumers. Retail markets require open access to distribution and transmission facilities. So, prices for the transmission and distribution process must provide the appropriate return on investment (ROI) to the owners of the network infrastructure. To maximize profits and reduce cost of offered services, RES

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The Power Grid at a Glance39

production must be deployed close to the point of consumption, thus minimizing the costs of transfer and conversion losses. In an effort to minimize financial risks and attract new customers, energy retailers have created innovative pricing models. The simplest model is flat pricing where the customer pays a specific amount per consumed energy unit. An emerging pricing model is based on time of use, where the cost is related to the hours of the day or days of the week when the energy is consumed. For example, during the night or during the weekend when the demand is low, retailers offer electricity at lower prices. A very attractive model is based on energy packages, which are similar to the data plans used by the mobile industry. A retailer may offer a flat monthly fee for usage up to a specific level (kWh), and above this level the price may rise significantly. These types of models are more efficient when the network employs smart meters that enable retailers and consumers to monitor consumption and cost. 2.5.3  Analyzing the Bill A utility or electric bill has three components. The first component is the use of electricity for the billing period. The second component includes taxes based on consumption. The third component is the access fee for use of the infrastructure. Human activity in residential or commercial units and humans’ different energy consumption habits are the main factors affecting a utility bill. The use of electricity is associated with the operation of appliances in the premise but also with the socioeconomic situation of the consumer. All of these factors are examined in the next chapter.

2.6  Understanding the Consumer 2.6.1 Appliances Footprint Residential houses and commercial buildings have numerous appliances and electronic devices that operate during the day and create load on the power grid. When an appliance operates, it creates a unique consumption footprint that is modeled by instantaneous active power and reactive power components [21]. The main components of this footprint are as follows: 1. Power value: the magnitude of the active power when the appliance is on. 2. Reactive value: the magnitude of the reactive power for inductive and capacitive appliances (zero for resistive appliances).

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The Smart Grid as an Application Development Platform

3. Periodicity: the periodic utilization of appliances that are usually related to thermostatic loads; for example, air conditioning units, refrigerators, ovens, and water heaters. 4. Cycles: the change of utilization for appliances having various cycles of operation, such as a washing machine with hot water preparation, wash, and rinse cycles. Figure 2.14 provides typical examples of the footprint of the active power component of an oven, a hotplate, a refrigerator, a washing machine, and an air conditioning unit. The data in Figure 2.14 represents the power demand of different appliances recorded every 7 sec. The consumption of each appliance is modeled by the variable xi(t) (in watts), t = 1, 2, …, T where T is the duration of operation of each appliance and i Î M where M = 5 is the set of appliances monitored. The energy consumption of each appliance ei, during each duration T, can be computed as the sum of energy in each 7-sec interval: T



xi (t) (in Wh) (2.14) 3,600 ⋅ f t=1

ei = ∑

Figure 2.14  Power consumption footprint of typical home appliances [22].

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The Power Grid at a Glance41



The oven, refrigerator, and air conditioning unit are characterized by periodic pulses. These systems incorporate thermostatic sensors, which keep the temperature at almost constant values. On the other hand, hotplates, which use resistive loads, display constant power consumption over time since a constant amount of current interacts with a resistance to create heat. Finally, the operation of the washing machine displays several types of consumption due to the different cycles of operation. The heating, wash, and rinse cycles can be easily identified in Figure 2.14. Table 2.9 presents the general characteristics of typical residential unit appliances. Table 2.9 Mapping of the Characteristics of Typical Household Appliances

Type Resistive

Inductive

Capacitive

Appliance

Power (in W)*

Reactive Power

Periodicity

Cycles

Oven

1,000–2,500

—

✓

—

Hotplate

900–1,300

—

—

—

Toaster

800–1500

—

✓

—

Water heater

3,000–4,000

—

✓

—

Air heater

1,000–1,500

—

—

—

Lights

40–100/bulb

—

—

—

Hair dryer

1,000–1600

—

—

—

Coffee

1,100–1,400

—

—

—

Refrigerator/ freezer

160–500

✓

✓

—

Air conditioner

1,200 wall mounted/3,000 central

✓

✓

—

Washing/ dishwasher

400–600

✓

—

✓

Dryer

2,200–3,000

✓

✓

—

Vacuum cleaner

1,500–2,000

✓

—

—

TV/PC/laptop

150/130/60

✓

—

—

Gaming

100–200

✓

—

—

Microwave

600–1,500

✓

—

—

*Represents typical power ranges found in the market.

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2.6.2  Electricity Usage Analysis The operation of appliances in a residential or commercial unit typically dominates the power needs of the premises. Total consumption is computed by adding the power footprints of the appliances that are active at any given time. Following the example given above, the total consumption of a premises is a vector P(t) (in watts), t = 1, 2, …, T where t = 1, 2, …, T spanning of T = 24 hr. In mathematical form this can be written as: P(t) =

m≤M

∑ xi (t) (in W) (2.15) i=1

where M is the total number of appliances in the premise and m ≤ M is the number of active appliances at any given point in time t. The total energy consumption of the premises over a day is then equal to: T

P(t) (in Wh) (2.16) 3,600 ⋅ f t=1

Ei = ∑



Based on the user activity in the residential or commercial unit, the geographical location, and the type of the unit, the energy usage analysis may vary. Tables 2.10 and 2.11 present the usage analysis over a year for residential and commercial units in the United States [6]. In the residential sector, consumers in southern households require more electricity for air conditioning because of the warmer climate. Additionally,

Table 2.10 Electricity Usage Analysis for Households

Region

Space Heating* (%)

Water Heating (%)

Air Conditioning (%)

Refrigerators (%)

Other Appliances (%)

United States

9

10

14

11

55

Northeast

7

8

7

14

65

Midwest

9

8

7

12

64

South

10

12

21

9

48

West

9

8

11

13

59

* Some regions may use natural gas or other energy sources instead of electricity for heating.

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The Power Grid at a Glance43



Cooling (%)

Ventilation (%)

Water Heating (%)

Lighting (%)

Refrigeration (%)

Computing (%)

Other (%)

United States

2

15

16

1

17

16

6

10

18

Northeast

2

10

18

1

18

16

6

11

19

Midwest

3

11

17

1

18

15

7

9

20

South

2

20

14

1

16

15

6

8

17

West

2

11

16

1

17

17

7

11

18

Region

Office Equipment (%)

Heating* (%)

Table 2.11 Electricity Usage Analysis for Commercial Sector

*Commercial buildings use other forms of energy such as natural gas instead of electricity for heating.

consumers in southern households use electricity for heating, whereas northern households prefer cheaper forms of energy for heating such as natural gas. 2.6.3  Archetypes of Consumers The daily and monthly energy consumption of residential consumers is very diverse. Depending on the location, climate zone, marital status, number of children, and profession, the daily energy footprint of each household varies significantly. Figure 2.15 presents the most typical archetypes of energy consumption depending on various human behaviors. The load curves are based on data derived from 82,000 meters of residential homes in the United States from a demand response company [23]. Some consumers use energy during the daytime. Others use the energy at night. Still other consumers do not consume much energy since they are often away from home. In most cases, consumers exhibit a small peak of consumption in the morning and then another peak in the afternoon/evening when returning home. The load curves provide significant information to utilities, energy retailers, and DR companies. Using this data, providers can evaluate system performance and customer engagement during energy management and DR programs. As electric vehicles penetrate the market, the daily consumption pattern is expected to change significantly based on the charging time of

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The Smart Grid as an Application Development Platform

Figure 2.15  Archetypes of residential electricity consumption. This figure is a reconstruction of measurements and published data taken from [23].

the vehicle. A detailed investigation of DR and electric vehicles is given in Chapter 3.

2.7  Lessons Learned from the Telecommunications Industry The business models and customer engagement models in the telecommunications sector evolved very rapidly at the end of the 20th century. The ability to communicate is a fundamental human need, and the consumption of data for communication quickly became a commodity. The telecommunications industry experienced different steps of growth moving from a vertically operated structure, where the telephone companies owned the infrastructure and provided the services to end users, to a more distributed system with multiple players and partners in the business. During this transition, the quality of experience and the quality of offered services improved dramatically. The telecommunications provider became the enabler or optimizer instead of the asset owner. Products moved from wireless devices to services and the telecommunications network became a development platform. With the current innovation of 5G (fifth-generation) mobile networks and the evolution of the Internet of Things, a new era of connected devices and people is expected to evolve. A primary factor during this transition was the tremendous pressure exerted on telecommunications providers by wireless and voice-over-IP technologies. In response to these technology and business pressures, the network service changed from telephony to content delivery. Instead of remaining a

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The Power Grid at a Glance45

voice network providing some data services, the telecommunications network evolved into a data platform that also provides voice services. The following key lessons were learned from the evolution of the telecommunications industry: 1. Open the market and create a foundation for partnership, collaboration, and competition. 2. Focus on customer-centric services and create innovation that satisfies modern customer behavior. 3. Enable multidisciplinary innovation. 4. Realize that data management is more important than infrastructure management. 5. Understand that the cloud creates strong foundations for emerging services. The electricity sector has been quite static for most of the previous century. During the past decade, the entire industry has experienced rapid, valuable evolution akin to the prior evolution of the telecommunications sector [16]. Figure 2.16 describes the evolution and convergence of the telecommunications and energy sectors. Figure 2.16 highlights the changing role of the providers

Figure 2.16  Evolution and convergence of the telecom and electricity sector.

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over time, and the adaptation of the business models in the marketplace sector. Consumer behavior is changing, and business models are adapting. Instead of transporting and delivering energy to the end consumer, utilities and electric companies are now focusing on optimizing the energy sources, the software, applications, and financial services offered to the customers. The energy market is becoming more open, new players are joining, and useful partnerships are being created. Asset ownership is important, but the electricity sector must echo the business and operational trends experienced in the telecom industry revolution. Customer engagement and data management will become the most important business asset. The Internet of Energy brings an entirely new world of opportunities, a fascinating landscape where the smart grid is transformed into an application development platform.

References [1]

Giovanni Petrecca, Energy Conversion and Management Principles and Applications, New York: Springer, 2014.

[2] https://en.wikipedia.org/wiki/War_of_Currents [3]

Austin Energy, “Customer Demand Load and Revenue evaluations,” PUC Docket No. 40627, Response to OPC 1-47, pp. 11–28, 2014.

[4] www.nrel.com. [5] www.census.gov. [6] www.eia.gov. [7] https://flowcharts.llnl.gov. [8] http://www.eia.gov/state/maps.cfm?v=Electricity. [9]

International Energy Agency, “Key Electricity Trends,” White Paper, www.iea.org, 2016.

[10] I. D. Mayergoyz and P. McAvoy, Fundamentals of Electric Power Engineering, Singapore: World Scientific Publishing Company, 2014. [11]

Fang Lin Luo and Hong Ye., Renewable Energy Systems: Advanced Conversion Technologies and Applications, Abingdon, UK: Taylor & Francis, 2013.

[12] Anthony J. Pansini, Power Transmission and Distribution, Boca Raton, FL: CRC Press, 2005. [13] www.nrel.gov/news/press/2016/37730.

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[14] Roland Wengenmayr and Thomas Buhrke, Renewable Energy: Sustainable Energy Concepts for the Energy Change, 2nd ed., New York: Wiley, 2012. [15] Andres Carvallo and John Cooper, The Advanced Smart Grid: Edge Power Driven Sustainability, 2nd ed., Norwood, MA: Artech House, 2015. [16] Accenture, “Power Play: Three New Models for Growth in the Utilities Industry,” www.accenture.com, 2015. [17] Richard E. Brown, C. Scott, and Hugo van Nispen, “Becoming the Utility of the Future,” IEEE Power and Energy Magazine, Vol. 14, No. 5, 2016, pp. 57–65. [18] www.greentechmedia.com. [19] http://smartgrid.ieee.org/newsletters/october-2015/cyber-attacks-and-the-smart-grid. [20] Chris Harris, Electricity Markets: Pricing, Structures and Economics, New York: Wiley, 2006. [21] George Koutitas and Leandros Tassiulas, “Low Cost Disaggregation of Smart Meter Sensor Data,” IEEE Sensors J., Vol. 6, No. 6, 2016, pp. 1665–1673. [22] George Koutitas and Leandros Tassiulas, “Periodic Flexible Demand: Optimization and Phase Management in the Smart Grid,” IEEE Trans. on Smart Grid, Vol. 3, No. 5, 2013, pp. 1305–1313. [23] www.opower.com.

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3 Smart Grid Elements 3.1  Introduction This chapter presents the architecture of the smart grid and analyzes the most important technological elements of the system. Starting with modern consumer behavior and expanding on concepts such as demand response and advanced metering infrastructure, the reader will learn about key players as well as the most important functionalities of the network.

3.2  The System of Systems 3.2.1  Evolution of the Grid To better understand the need for modernization of the power grid, we can consider differences between two of the most critical and widespread networks in operation today: the electrical power network and the telecommunications network. If Nicola Tesla observed today’s transmission of electricity, he would have a nearly complete knowledge and understanding of its operation, because many of the fundamental concepts and operations remain unchanged from the origin of the electrical power network. In contrast, if Alexander Graham Bell observed the modern telecommunications network and its applications, he would be amazed by the amount of technological innovation! This disconnect 49

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between applications of technology and the waste of resources can be considered the Achilles heel of today’s power grid. As a result, the evolution of ICT is one of the key drivers in grid modernization. The resulting smart grid is a system of systems that orchestrates ICT and power grid technologies, thereby creating a distributed architecture in which the intelligence migrates to the edge of the network. At the edge of the network are smart meters, smart appliances, and home automation systems. A similar evolution is happening in the computer and telecommunications industry where intelligence is deployed in smartphones [1]. The transformation into an application-enabled smart grid depends on real-time data and command flow between the network nodes of the power grid. As a consequence of migration of intelligence toward the edge of the network, this process entails the creation of novel services, automation, and data-driven decision making. In today’s smart grid, many important activities now happen in a fraction of a second, including these: 1. Data is collected from the edge of the network (smart meters, appliances, etc.). 2. Data is converted to information at the core network (databases, third-party application companies, utilities, vendors). 3. Information is processed and converted to knowledge and intelligence. 4. Intelligence is converted to command flow to enable automation and interaction at the edge of the network. These basic functionalities have transformed the grid into an autonomous, self-optimized network capable of adjusting to time-varying energy production and consumption. As a result of its transformation from a utility-centric network, the smart grid has become a customer-centric network. Transforming from a centralized network with passive customers, the smart grid has become a distributed system of smart devices with active customers who can manage certain aspects of the operation of the grid. In this transition, smart meters deployed at low voltage (household or even outlet) have become game-changing technologies. New services based on supervisory control and data acquisition (SCADA) schemes have emerged. The most important of these schemes are the advanced metering infrastructure (AMI), meter data management systems (MDMSs) and outage management systems (OMSs) [2]. With the evolution of these technologies, the deployment of RESs in the form of distributed generation (DG) became feasible, and new operational concepts emerged, such as microgrids. This was the beginning of the first generation of the smart grid, Smart Grid 1.0. Figure 3.1 describes the evolution of the electricity grid, from prior to the Smart

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Smart Grid Elements51

Figure 3.1  Products and services from the smart grid evolution.

Grid (version 1.0) through to the future (version 3.0 and beyond). The figure provides a holistic perspective on products and services provided by the Smart Grid in context with the evolution of the system from a centralized, passive system to a distributed, active system. Smart Grid 2.0 went a step further and created novel services within the premises of the consumer. Electric vehicles (EVs) and smart appliances emerging from the IoT sector have become the catalyst of this transformation. Intelligence is now supported at the edge of the network, and the customer can play an active role in the operation of the network. Analytics are now available in mobile apps and home energy management system (HEMS) displays. Smart thermostats and smart appliances empower the user with the ability to manage the energy in the household, and utilities can deploy demand response (DR) programs to automatically manage the operation of specific appliances in the house. For example, novel EV charging applications allow users to charge their cars at off-peak hours, minimizing the risk of congestion and peak generation. Smart Grid 3.0 is expected to further increase the intelligence and automation at the consumer level and to provide direct access to the energy services market. The full implementation of the Internet of Energy (IoE) will allow for not only bidirectional communication and energy flow between the consumer and the energy provider, but also directly between consumers. As a result, the producer and consumer of energy merge, creating the new class of prosumers who both produce and consume energy. Further, the concepts of connected homes and transactive energy will create new services such as virtual power plants, energy roaming, and peer-to-peer interactions.

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The Smart Grid as an Application Development Platform

As a general observation, the smart grid will be called upon to meet the future demand worldwide. This new, multifunctional, multiuser, distributed power network must: • • • • • • • • • • • •

Use ICT to improve the reliability, security, and efficiency of the grid and the distributed energy sources; Support dynamic adaptation to changes and self-healing actions; Allow efficient penetration of RESs in the grid; Enable smart services such as real-time monitoring, automation, and interaction between users and the energy provider; Integrate smart appliances and controllers with middleware; Provide consumers with insights and real-time information; Develop standards for interoperability of various equipment and appliances that are reliant on the grid infrastructure; Support new business models and open barriers to new players; Transform the passive consumer to an active prosumer; Provide a reliable and high-quality, clean energy supply that minimizes the wasting of resources; Orchestrate energy production and storage infrastructure; Enable new products, services, and markets.

3.2.2  Architecture and Standards Every transformation and evolution has its drawbacks. Such issues are often related to increased system complexity. The smart grid introduces new entities requiring segmentation of the system, addition of new layers, and cross-layer collaboration among different entities. Thus, the smart grid can be regarded as a three-layer structure of new entities, services, and domains as shown in Figure 3.2. The smart grid entities are new technologies that have emerged from the power grid. The most important of these items are distributed generation (DG) or distributed energy resources (DERs), smart homes and smart buildings, microgrids, and virtual power plants (VPPs). These items are discussed in detail later in this chapter. DG/DER can be small- or large-scale RESs deployed at the distribution or the transmission segments of the grid. Smart homes/buildings are consumers that may incorporate connected devices and smart devices such as RESs, batteries, smart metering equipment, smart appliances/thermostats, and EVs. These customers are also referred to as prosumers because they can produce and consume energy in different time intervals. Microgrids are small-scale versions of the grid with local production and storage of energy. They are connected

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Smart Grid Elements53

Figure 3.2  Architecture of the conceptual model of the smart grid.

to smart homes/buildings. Microgrids may operate in island mode without physical connection to the rest of the grid. Finally, VPPs are groups of DER and smart homes/buildings that can be viewed as a single power plant, where production of power is greater than consumption. VPPs are amorphous and do not require a physical grid connection between them. For example, a large number of homes with rooftop solar panels or smart thermostats in different geographical areas can be regarded as a VPP since the overall production of energy may be larger than the consumption, offering extra capacity to the remainder of the network. All smart grid entities are interconnected among different subnetworks that may have bidirectional power and data flow between them. Following Figure 3.2, the service layer incorporates new services emerging from the integration of the ICT infrastructure with the power grid. The most important of these services are energy management systems (EMSs), work and asset management systems (WAMSs), distributed automation (DA) or distributed management systems (DMSs), demand response (DR), HEMSs,

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or building management systems (BMSs), and MDMSs, which are related to the AMI [3]. For example, EMSs are information systems that provide real-time data about large-scale variations in production and consumption of energy, usually in the HV and MV segments of the network. WAMSs are systems that provide information about the status and condition of critical infrastructure such as power plants, transmission networks, and substations. DA/DMSs are information and actuator systems that provide real-time management of the distribution network (MV to LV). DRs are mechanisms that are usually coupled with the DMS and enable energy management, such as peak shaving from MV and LV consumers. HEMSs are located near the consumer, such as in smart home/building deployments; they enable data and command flow between the distribution network and the consumer. Furthermore, HEMSs provide a convenient interface between the user and the smart home/building. BMSs are a more complex version of the HEMSs. They deal with larger structures or campus scenarios. MDMSs are portals and databases of real-time and historical consumption for the smart homes/buildings. Finally, AMI is the smart metering infrastructure coupled with the MDMS. The AMI provides data and command flow between the consumer and the utility, and is often connected to customer information systems (CISs) for billing purposes. All of these systems and services are interconnected through multiple subnetworks and protocols/standards. The domain layer of Figure 3.2 represents the structured management of the different services and entities. Based on recommendations of the Smart Grid Interoperability Panel (SGIP) [4], the conceptual domains of the smart grid are the markets, transmission entities, distribution entities, service providers, operators, generators, and customers: • • • • • •

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Markets are the operators and participants in the overall electricity market. Transmission entities carry bulk electricity over long distances. Distribution entities manage the distribution of bulk electricity to and from customers (two-way power flow). Service providers are the corporations or organizations that provide electrical services to customers and utilities. Operators are the managers of the movement of electricity (transmission and distribution network). Generators are the producers of electricity. Generators can be based on fossil fuels or nuclear but also DER such as solar, wind, hydro, and may also include storage of energy for later distribution.

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•

Customers are the end users of electricity, the smart homes and buildings. Customers manage the generation, storage, and/or use of energy. Traditionally, power networks are comprised of residential, commercial, and industrial customers.

Various protocols provide the required data format and communication interface between the different services and entities of the smart grid. Finally, communication networks provide data and command flow between the several domains, services, and entities on the smart grid. Depending on the application, several communication networks may be involved, including the Internet; wide-area networks (WANs), which cover large distances; neighborhood-area networks (NANs), which are used for small-scale multibuilding coverage; and home-area networks (HANs), which are used for individual building scenarios.

3.2.3  Interoperability and Protocols The integration of multiple systems, services, and domains results in a very complex distributed network. Each entity of the smart grid utilizes different data structures, communication networks, and information systems, all of which need to be connected and orchestrated. For example, the smart meter of a residential home, which is part of the AMI, needs to share data with the MDMS through a communication network. The MDMS should also be connected to the CIS and EMS to provide data management at the operations end of the utility. These systems use different data formats and software bridges, which results in a collection of interoperability issues. The most important initiative in this regard is the SGIP [4]. The charter of the SGIP is to provide an architectural solution to the connection of the multiple layers, protocols, and entities. A representative example of the complex interoperability requirements for the smart grid is shown in Figure 3.3 [4]. Protocols and standards are the most important issues associated with interoperability, and there are literally hundreds of them involved in orchestrating the smart grid [2, 4–7]. Table 3.1 presents the most important standards and protocols for transferring data, formatting data, and applying control to various smart grid elements. Much effort has been devoted to simplifying interoperability in smart grid networks because there is a great variety in the technologies available from competitor companies in the market. The standardization landscape is expected to keep changing its shape for the next few years.

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.

Figure 3.3  Smart grid interoperability based on SGIP model [4].

Table 3.1 Sample of Protocols and Standards for Smart Grid Interoperability Standard/Protocol

Description

Domain Used

ANSI C12.18

Revenue metering for end devices tables.

Customer, service provider

C12.20

Transport of measurement device data over telephone networks.

Customer, service provider

C12.21

Protocol and optical interface for measurement device.

Customer, service provider

NSI/CEA 709 and Consumer Electronics Association 852.1 LON Protocol Suite

A general-purpose local-area networking (LAN) protocol in use for various applications including electric meters, street lighting, home automation, and building automation.

Customer, service provider

IEEE 1815 (DNP3)

Standard used for substation and feeder device automation, as well as for communications between control centers and substations.

Generation, transmission, distribution, operations, service provider

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Table 3.1 (continued) Standard/Protocol

Description

Domain Used

IEC 61850 Suite: Communication Networks and Systems in Substations

Standard that defines communications within transmission and distribution substations for automation and protection.

Transmission, distribution

IEC 61968/61970 Suites

Operations, Standards that define information customer exchanged among control center systems using common information models. They define application-level energy management system interfaces and messaging for distribution grid management in the utility space.

IEEE 1901-2010

IEEE Standard for Broadband over Power Line Networks.

Customer

MultiSpeak

A specification for application software integration within the utility operations domain; a candidate for use in an enterprise service bus. It is widely used for integration with MDMSs and CISs.

Distribution

NAESB REQ18, WEQ19 Energy Usage Information

Standards that specify two-way flows of energy usage information based on a standardized information model.

Customer, service provider

NAESB REQ-21 Energy Services Provider Interface (ESPI)

Standard that helps enable retail customers to share energy usage information with third parties who have acquired the right to act in this role.

Customer, service provider

OPC-UA Industrial

A platform-independent specification for a secure, reliable, high-speed data exchange based on a publish/subscribe mechanism. It is a modern service-oriented architecture (SOA) designed to expose complex data and metadata defined by other information model specifications (e.g., IEC 61850, BACnet, OpenADR).

Customer

Open Automated Demand 2.0 Response (OpenADR)

Specification that defines messages exchanged among the DR service providers (e.g., utilities, independent system operators, and customers) for priceresponsive and reliability-based DR.

Operations, service providers

Open Geospatial Consortium Geography Markup Language (GML)

Standard for the exchange of locationbased information addressing geographic data requirements for many smart grid applications.

Transmission, distribution

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The Smart Grid as an Application Development Platform Table 3.1 (continued)

Standard/Protocol

Description

Organization for the Advancement of Structured Information Standard (OASIS) Energy Interoperation (EI)

Energy interoperation describes an informa- Markets tion model and a communication model to enable DR and energy transactions. XML vocabularies provide for the interoperable and standard exchange of DR and price signals, bids, transactions, and presents options, and customer feedback on load predictability and generation information.

Energy Market Information eXchange (EMIX)

Provides an information model to enable the exchange of energy price, characteristics, time, and related information for wholesale energy markets, including market makers, market participants, quote streams, premises automation, and devices.

Markets

Smart Energy Profile 2.0

HAN device communications and information model.

Customer

Internet Protocol Suite, Request for Comments (RFC) 6272, Internet Protocols for the Smart Grid

The Internet protocols for IP-based Smart grid networks IPv4/IPv6 are the foundation protocols for delivery of packets over the Internet network. IPv6 is a new version of the Internet Protocol that provides enhancements to IPv4 and allows a larger address space.

Cross-cutting

NISTIR 7761v1, NIST Guidelines for Assessing Wireless Standards for Smart Grid Applications

This report is a draft of key tools and methods to assist smart grid system designers in making informed decisions about existing and emerging wireless technologies. An initial set of quantified requirements has been proposed for AMI and initial DA communications. These two areas present technological challenges due to their scope and scale. These systems will span widely diverse geographic areas and operating environments and population densities ranging from urban to rural.

Guideline

OpenHAN

A specification for a HAN that can connect to the utility advanced metering system; includes device communication, measurement, and control.

Requirement

Use Cases for Communication Between Plug-in Vehicles and the Utility Grid (SAE J2836/1)

Requirement This document establishes use cases for communication between plug-in EVs and the electric power grid for energy transfer and other applications.

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Domain Used

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Table 3.1 (continued) Standard/Protocol

Description

Domain Used

Security Profile for Advanced Metering Infrastructure, Version 1.0

Advanced Security Acceleration Project— Smart Grid, December 10, 2009. This document provides guidance and security controls to organizations developing or implementing AMI solutions. This includes the MDMS up to and including the HAN interface of the smart meter.

Cybersecurity

IEC 62351: Power Systems Management and Associated Information Exchange—Data and Communications Security

Open standard that defines security requirements for power system management and information exchange, including communications network and system security issues, Transmission Control Protocol (TCP) and Manufacturing Messaging Specification (MMS) profiles, and security for Inter-Control Center Protocol (ICCP) and substation automation and protection. It is for use in conjunction with related IEC standards, but has not been widely adopted yet.

Cybersecurity

IEEE 1686-2007

Standard that defines functions and features Cybersecurity to be provided in substation intelligent electronic devices (IEDs) for critical infrastructure protection programs. The standard covers IED security capabilities including access, operation, configuration, firmware revision, and data retrieval.

IEEE 802.11 (Wi-Fi)

IEEE standard for information technology. Telecommunications and information exchange between LANs and metropolitanarea networks (MANs).

Networking

Networking

IEEE 802.15.4 (ZigBee)

IEEE standard for LANs and MANs

IEEE 802.16

IEEE Standard for Air Interface for Broadband Wireless Access Systems.

3G/4G/LTE

Wireless cellular networks for data transfer.

Networking

Thread

Thread uses 6LoWPAN, which in turn uses the IEEE 802.15.4 wireless protocol with mesh communication, as does ZigBee and other systems. Thread, however, is IP addressable, with cloud access and AES encryption. It is widely used for smart home automation systems.

Networking

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The Smart Grid as an Application Development Platform Table 3.1 (continued)

Standard/Protocol

Description

Domain Used

Simple Object Access Protocol (SOAP)

Protocol used for exchanging structured information in the implementation of web services in computer networks. It uses XML for its message format, and relies on application layer protocols, HTTP or SMTP.

Data structure

Extensible Markup Language (XML)

A markup language that defines a set of rules for encoding documents in both a human-readable and machine-readable format.

From: [6].

3.3  Business of Businesses 3.3.1  Utility of the Future The new technologies and collaboration layers introduced by the smart grid have influenced modern customer behavior. These two considerations are primary factors in driving a paradigm shift for business models and participants in the energy sector. Several forces are challenging the traditional business models of the utility industry, including new clean energy technologies such as RES and storage, service providers that can be regarded as nontraditional competitors, modern customer behavior that is correlated to the world of services and applications, new regulatory pressures, and of course the rising costs of electricity. The same transformation was encountered in the telecom industry (1995–2005+) when wireless and data-driven content and multimedia began to displace conventional telephony. During this period, telecommunications systems transformed from a voice-optimized network that happened to carry some data to a data-optimized network that also carries some voice. The integration of ICT in the power grid is creating a similar paradigm shift in the utility sector. The emerging business models focus on the traditional aspects of transporting and delivering energy, but are also being transformed to include multidimensional and multicomponent value. These new servicedriven additions include data-intensive services such as optimizing energy sources, orchestrating distribution systems with demand, and integrating new services while addressing customers’ unique needs. Let’s explore some important drivers of the evolution. By 2025, new energy technologies such as smart thermostats are anticipated to produce

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an almost 15% reduction in demand. In addition, 57% of consumers are likely to purchase connected and smart home devices and services, such as smart thermostats and HEMSs. As a result of load reduction, the revenue risk to conventional energy providers is estimated at $18 billion to $48 billion in the United States and €39 billion to €61 billion in the European Union. Finally, an 89% increase in the cost of customer service was experienced between 2005and 2013 [8]. These factors are driving a transformation among energy providers to (1) focus on low-carbon and clean energy production, (2) become optimizers of various distribution platforms, and (3) become integrators of energy solutions and services. It is clear that there is a paradigm shift in the utility industry from an obligation to serve to a commitment to optimize. To survive and thrive in this environment, the utility of the future must adapt and move from an asset-based business model, where ownership and management of network assets are most important, toward a service-based business model, where customer engagement and data ownership are most important. Reference [9] describes several useful business archetypes in the generation, transportation/distribution, and retail segments of the energy industry. The gentailer model is applied in areas where generation and retail are competitive, but transmission and distribution are regulated. The gentailer may own generation assets producing energy that is sold to retail customers in competitive markets. This model is applied in regions where the main risk is customer migration to a different retailer in cases where behind-the-meter products and services are more attractive. The gentailer of the future should be aware of customer behavior and how the customers use smart technologies, such as connected devices and products. The gentailer should be able to harness these preferences into innovative mobile and/or tariff solutions. To leverage its operation, the gentailer must identify new partners to provide engaging behind-the-meter products and DER business plans. The pure merchant play model owns and operates generation assets and monetizes them by selling into competitive wholesale markets or by using bilateral agreements. These utilities prefer deregulated regions, like the gentailer model. A successful business player should have strong risk management capabilities, including the ability to execute a variety of complicated purchase and sales agreements (e.g., using derivatives) and should try to minimize costs of operations by implementing sophisticated operational procedures. The grid developer model develops and maintains transmission assets that connect generators to distribution system operators. They are usually found in regulated areas and operate as a monopoly. For successful operation, the grid developer should select optimum locations to identify new large-scale

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renewable generation and should be able to monitor risk based on regulatory settlements and changes. The network manager model allows generators and retail service providers to access their network. Similar to the grid developer model, the network manager usually operates as a monopoly in a regulated market. The conventional network manager model has specific, expanded responsibilities for network integration of incumbent systems and distributed energy resources, and is enhanced through new concepts such as the distribution system operator (DSO). An important aspect of network manager utilities is the ability to manage electricity and demand in real time by efficiently integrating power from distributed generation and RESs. It is important for network manager utilities to invest in the evolution of the network and the deployment of smart grid technologies throughout the system. A network manager should move toward a management model that is big data–centric and that accumulates real-time network information in terms of equipment status, power quality, RES production, circuit risks, and so forth. The product innovator model is the most dynamic model in the energy sector since it offers electricity and behind-the-meter products. Behind-themeter products may involve various retail financial packages including the provision of green energy options, time of use and real-time pricing that enhances consumption, and the provision of value-added devices such as smart thermostats in the connected home. The incorporation of connected home devices in the product innovator model creates tremendous business leverage. Intelligent end devices provide a link to the network to advance insights into consumption patterns, and this information can directly impact network stability. The product innovator model is most useful in markets where the regulatory framework allows retail choice, and in urban environments where the customers are tech savvy. A product innovator model is most successful when the business is effective at customer acquisition and retention. Living in a world of infinite connectivity, the product innovator should focus on brand awareness as well as community engagement. These can be achieved by smart corporate social responsibility (CSR) programs and/or with co-branding initiatives involving progressive companies. It is also of vital importance for the product innovator to understand the customer’s needs and to focus on Millennial and GenX customers who constitute the largest buying sector in the United States. The partner-of-partners model involves a utility company that transitions from traditional power delivery services to a wider range of energyrelated services. This model is gaining enormous momentum as customers demand more digital service offerings from their energy providers. Examples

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of advanced services include life-cycle EV battery change-outs, home-related conveniences like new service setup coordination, and management of grid sell-back via net metering. Similar to the product innovator, the partner-of-partners model requires co-branding initiatives with progressive companies that can achieve customer satisfaction and retention. The partner-of-partners model is most effective in markets where there is a high proliferation of energy technology and choice and where customers are seeking ways to simplify their lifestyle while lowering up-front costs. To be an effective player in the partner-of-partners model, an energy company should optimize customer acquisition and satisfaction by providing superb and state-of-the-art service delivery. These offerings provide a clear competitive differentiation that customers would not expect from their traditional energy companies. Since the model is based on collaboration, it is important to evaluate the brand strength of all partners, and invest in understanding customer behavior and needs. The value-added enabler model involves the evolution of a traditional utility that owns tremendous amounts of customer data, but does not have the expertise to use the data in developing useful insights and smart service offerings for their customers. This model encourages collaboration with manufacturers of smart devices and information technology companies. These collaborations enable the utility to create useful information for the end customer. The most dominant strategy behind the value-added enabler is to provide customers with a set-and-forget experience for smart devices, such as thermostats. By leveraging a data-centric infrastructure, the customer is provided with real-time and predictive insights into energy consumption patterns. The benefits for the customers are clear in the value-added enabler model, since they can learn about their energy consumption footprint while saving energy via smart thermostats that do all the work for them. For the utility, the primary benefits are reductions in peak-hour consumption and an increase in customer satisfaction. For the successful implementation of this model, it is important for the utility to invest in customer education. Further, the utility must demonstrate to customers that best practices concerning security and privacy are always up to date. In addition, utilities need to invest in additional information technology capacity to support, leverage, and protect this data. The virtual utility model is an innovative model that is emerging from the integration of ICT and clean energy technologies. The virtual utility aggregates generation from various distributed systems and acts as the intermediary between energy markets. A virtual utility becomes an integrator of smart services that are provided to end customers. Despite the fact that the virtual utility does not own infrastructure and assets, its role is important since it is

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the integrator of services that acts on behalf of the traditional energy providers. Its operation is important for the new roles addressed by DSOs. The virtual utility model is most successful in competitive markets with relatively low regulation but high penetration of distributed generation and demand response initiatives. For the successful implementation of this model, the virtual utility should be efficient at collaborating and interfacing with local distributed networks, and it should be able to provide a real-time balance between demand and supply. The implementation of the model is highly related to sophisticated information technologies and smart pricing algorithms that benefit the end customers as well as the energy provider. 3.3.2  New Business Models and Players The evolution of the energy sector has created new entities and business models capable of adapting to the forthcoming changes. New entities such as aggregators, as well as the transition from B2B models to B2C models, are the most significant transformations [10–12]. An aggregator can be considered to be the middleman between the consumer and the energy market. The aggregator bundles many consumers to deliver a reliable service to the energy system. Aggregation can be defined as the act of grouping distinct agents in a power system (i.e., consumers, producers, prosumers, or any mix thereof) to act as a single entity when engaging in power system markets (both wholesale and retail) or when selling services to the system operator [13]. One of the main functions of the aggregator is to deliver services such as DR and negawatt capacity. These aggregator characteristics serve to create the required foundations for DERs. The main contributions of the aggregator are allowing consumers to be aware of the performance of the DER and DR systems, empowering them with the ability to participate in the market and meet the market/system requirements, and fostering competition in the energy market. Services such as DR or EMS, energy storage, and community solar are considered the most significant models to have emerged from the transition of the energy sector [11]. Figure 3.4 presents the basic structure of the models and the relationships between the service provider, the utility, and the end customer. Demand Response Business Model: The DR service provider sells energy management products and capacity to the utility or the ISO/DSO. DR providers sell DR resources at prices determined by negotiation with the utility and the regulator. The DR provider most commonly leverages heavy customer loads by implementing command flow to actuators connected to the loads. Heavy

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loads include large-scale systems such as lighting, HVAC units, refrigeration units, variable-frequency drive units, idiosyncratic industrial process loads, and customer-sited generation such as backup diesel or gas units, fuel cells, or batteries. The command flow from the DR provider can be direct load control or load shedding, as discussed in the next chapter. Another interesting application is behavioral DR, in which the service provider engages customers in saving energy with the use of notifications, gamification, and social media. Monetization of this business model is most commonly based on a portion of the revenues generated from the sales of these services—that is, by brokering market revenues (brokerage fees)—and/or by charging for the use of the energy management software that enables the demand control (subscription fees). Energy Management System and Solar-Plus-Storage Business Models: These models provide energy management systems that focus on on-site management and optimization without market interaction. EMS providers are focused primarily on the optimization of local energy usage in response to energy prices and local needs. EMSs are not providers of electric service. Rather, they are regarded as enablers of smart energy services based on information technologies. EMS monetization is based on a shared profit structure from savings arrangements (a type of brokerage fee), subscription fees (for the software provided), and asset sales of monitoring and control equipment. In the case of the solar-plus-storage (SPS) model, the customer deploys production and storage equipment behind the meter. The main incentive for these types of technologies is related to profitability issues of solar photovoltaic (PV) systems through increasing self-consumption (i.e., minimizing the export of energy produced on site). With advances in technology, SPS can be an effective investment for the customer. The monetization of this business models is based on a form of shared or guaranteed savings arrangements, such as brokerage fees or the sale and financing of the PV and storage assets. These businesses operate in a B2C mode and they tend to sell products directly to residential and commercial/industrial customers. They also structure revenue streams around the sales and financing of solar and storage assets. Community Solar Business Models: Solar installations in many residences or businesses are not feasible for many reasons, including the size of the effective area of installation, shading, or building ownership challenges in urban environments. Community solar providers offer a solution to this problem by installing large solar PV plants located away from the customer site. In such cases, customers purchase the rights to a portion of the output of the solar

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Figure 3.4  Business models for (a) DR service provider; (b) EMS, energy storage, and solar provider; and (c) community solar provider.

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installation. Alternatively, customers can purchase an equity stake or share in revenues from a portion of the plant outright. The monetization of this business model is based on charging the customer for access to the PV system outputs (brokerage fees). The community solar provider will typically sell the plant’s output under a long-term PPA, and distribute the associated revenues to the project’s shareholders. 3.3.3 Business-to-Consumer Providers The world of infinite connectivity that today’s consumers experience, along with the evolution of technologies in social media, mobile apps, and IoT, have incentivized utilities and energy providers to focus on customer-centric services. For that reason, the B2C model, which helps utilities provide user-engaging services to end customers, has seen tremendous growth. The product innovator, partner-of-partners, and virtual utility models focus on the delivery of B2C services and products, as discussed in Section 3.3.1. In most cases, utilities collaborate with third-party application providers and with companies providing user-friendly and well-branded products. B2C providers usually follow one of two business approaches. In the first approach, a B2C provider delivers a white-label product (software or device) to the utility that brands it and offers it to the end customers. In the second approach, the B2C provider delivers the product directly to the end customers without utility collaboration. In some cases, both approaches may be followed by a B2C provider. A characteristic example is the bottom-up business approach of vendors described in [1]. For specific products and business models, a bottom-up growth strategy may be beneficial for the vendor since it creates the awareness and branding of its product that customers demand from their utilities. The B2C provider usually bases its services on open data and APIs that help create the services without a requirement for utility integration. A characteristic example is the case of energy analytics and usage analysis based on the Green Button open database [14]. Open data and existing APIs are discussed in detail in Chapter 4. Figure 3.5 presents the general principles of the B2C provider business model. 3.3.4  Utility Customer Beyond 2020 The electricity grid is transforming from a unidirectional grid with passive customers to a bidirectional grid with active participation of the end consumer. Consumer-driven technologies (CDTs), social media networks, and mobile devices are constantly evolving and affecting the way people interact with

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Figure 3.5  Business model for B2B-B2C services.

the world. One study [15] modeled the utility customer of 2020 as a digital, connected, and social customer. The digital 2020 customer will use a variety of mobile devices for constant connectivity and social interaction. Approximately 95% of utility customers are expected to be fully tech and digital savvy and to prefer conveniences such as online bill payment methods. This contrasts sharply with today’s 35% of tech savvy customers. For utilities, this means that they should expect their customers to prefer online and mobile interactions as well as cloud-based solutions. The digital customer of 2020 will expect instant access to information—anywhere and at any time—through the use of sophisticated, user-friendly apps. The connected 2020 customer will require connectivity with people and devices. The concept of shared payments that is used by financial technology companies has seen exponential growth during the past couple of years. This situation is expected to happen in utilities where customers would like to share bill payment information or split the bill payment in shared-room houses. Another example concerns the case of clean energy and energy conservation where people will expect to see the savings and clean energy production of their friends. By 2022, it is projected the average household will contain approximately 50 Internet-connected devices. The concept of a connected home is happening now, and customers will expect to have infinite access using one

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mobile app and a consistent platform to connect and communicate with their smart home devices. The social 2020 customers will require interaction not only with their utility but also with other utility customers and friends. Some utilities have already started using social media to track customer feedback. Today, the main use of social media from utilities concerns energy efficiency and safety tips, outage announcements and emergencies, response to customer complaints, and promotion of products and services. In the future, it is expected that utilities will use social media for behavioral demand response and gamification as well as for community development. Finally, the social utility customer will expect smart social responsibility initiatives from their utilities to engage them for the benefit of the environment. This social smart grid is discussed in the next section. Today, with the deployment of millions of smart meters, utilities have important information about customer segmentation. Customer segmentation is often cited as a critical next step for utilities in deploying smart grid products and services. The segments listed in Table 3.2 have been identified [16, 17]. 3.3.5  The Social Smart Grid Smart Grids 2.0 and 3.0, described in Figure 3.1, have created a new market of technology-driven utility customers who can be regarded as prosumers of energy. Residential solar energy production and negawatt production from smart thermostats and load controllers have the potential to be used for bill savings as well as to provide help to people who may be experiencing energy poverty. The donation market in the United States is equal to approximately $370 billion per year [18], and only a tiny fraction of this amount is used to provide help to people with limited resources to spend on energy. Currently, utilities and energy retailers implement customer assistance programs (CAPs) to help their low-income customers. However, these programs do not speak to smartphone users, Millennials, and GenX customers. At the same time, 64% of consumers feel it is important to buy goods or services from socially responsible companies, and approximately 65% of them bought goods or services from socially responsible companies in the past 12 months [19]. It is clear that social responsibility initiatives are an important factor in customer satisfaction/retention and community engagement for utilities. So what is energy poverty? Energy poverty is a term used to describe a person who is denied access to energy or cannot afford to cover basic energy needs [20, 21]. This denial of access can be due to either a lack of technology, which is more common in countries such as Africa and India, or financial

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Table 3.2 Customer Segmentation Based on Smart Grid Consumer Collaborative Studies

Customer Segment

Perspectives

Key Demographics

Awareness and Interest in Smart Grid Technologies

Green Champions

Youngest, more likely Smart energy to be college educated technologies fit their environmentally aware, high-tech lifestyle

Relatively highest levels of awareness and interest in all types of solar and EV, nearly four times the interest level of Status Quo

Saving Seekers

Looking at how smart energy programs can help them save money

Younger, more likely to be college educated

Lower levels of awareness and interest

Status Quo

Are not interested in new smart energy technologies

Middle age, lower income renters, living in non–single-family dwellings, less likely to be educated

Relatively lowest level of awareness and interest

Technology Cautious

Want to use energy wisely, but do not know how technologies can help

More likely homeowners who are older in age, less likely to be college educated

Marginally higher than Savings Seekers on awareness and moderate interest

Movers and Shakers

Expecting to be impressed with smart energy technologies to start engaging with their utilities

More likely middle age, higher income, singlefamily homeowners, college educated

High levels of awareness comparable to Green Champions on average, but moderate interest levels

From: [16, 17].

hardship, which can happen to anyone in any country or region. In the United States, energy poverty occurs when people cannot afford to pay for electricity and other utilities. In some cases, people have to choose between buying food for their family or paying for their energy needs. As a result, households may be disconnected from the energy network due to bad debt with the electric utility. According to the U.S. Department of Health and Human Services Low Income Home Energy Assistance Program (LIHEAP) [21], approximately 48 million people are at or below the poverty line and may be experiencing energy poverty. In 2014, LIHEAP provided energy assistance to roughly 6.9 million households nationwide. Many electric utilities and federal initiatives offer weatherization programs to support funding for low-income households. In addition, many

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utilities, in collaboration with local nonprofit organizations, offer financial assistance to low-income households. Unfortunately, the participation of donors and corporations in these fund-raising efforts is small. In recent years a new concept has emerged that applies smart grid technologies to help alleviate the problem of energy poverty. The social smart grid is a new form of digital customer assistance program that leverages smart grid technologies and educates utility customers about the ability to save energy and donate energy. The benefits to utilities are enormous because they can attract new socially conscious customers and improve customer satisfaction and community engagement. 3.3.6 Start-Up Ecosystem During the past 10 years, tremendous growth has been seen in the area of early-stage start-up companies that provide significant assistance to utilities and energy retailers as well as to end consumers. The majority of these startups focus on customer-centric, cloud-based applications that leverage alreadydeployed smart grid technologies. In addition, there has been an exponential growth of companies developing smart devices such as thermostats and load controllers, which are part of the emerging market called the connected home. The most important challenge of energy start-up companies is the adoption of services by users and/or energy providers. The three main types of growth (go-to-market) strategies usually followed by these start-ups are the top-down approach, the bottom-up approach, and the independent approach. In the top-down approach, companies usually follow a B2B model by establishing collaborations with utilities and energy retailers who market their solutions and products to their customers, usually as white label products. In the bottom-up approach, companies create engaging products and applications and engage the users before entering into agreements with utilities and energy retailers. Growth hacking and community development are the most efficient marketing strategies followed by start-ups in creating appropriate momentum. In the independent approach, companies establish a presence in the market without collaborating with utilities and energy retailers. These companies usually address a niche market. Alternatively, they may provide significant value to commercial or industrial customers, thus minimizing the need for collaboration with energy providers for a positive cash flow in their accounts. Each growth strategy has pros and cons. The strategy selected by a startup company depends on the business model, the nature of the application, the product/technology, and the market that is being addressed. Table 3.3

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The Smart Grid as an Application Development Platform Table 3.3 Start-Up Ecosystem for Customer-Centric Services

Sector

Business Model

Characteristic

Energy conservation programs/analytics

B2B with B2C applications

Cloud-based solutions for energy analytics, usage analysis, and customer engagement

Demand response

B2B with B2C applications

Mobile apps for demand response, gamification, and customer engagement

Smart thermostats

B2C product with B2B go-to-market strategy

Well-designed thermostats provided to end users. Utilities may apply rush hour events

Customer assistance programs

B2B with B2C applications

Cloud-based solutions and apps for smart energy donations and community engagement

Smart bill payment

B2B with B2C applications

Cloud-based solutions and apps for micro bill payments

Community solar

B2C product and B2B go-to-market strategy

Community solar companies mainly in deregulated markets

Energy harvesting

B2B

Technologies to convert heat waste to electricity

Energy choice

B2C

Energy marketplace usually met in deregulated markets

Software integration

B2B

Development of APIs for third-party application providers

Blockchain

B2C

Development of blockchain technologies with application in energy

Electric vehicles/smart charging apps

B2C

Cloud-based technologies for smart EV charging

presents the ecosystem for start-ups offering customer-centric services in the energy sector.

3.4  The ICT Layer 3.4.1 Smart Metering The information and communication technologies (ICT) layer is the foundation of the smart grid, and enables the real-time data and command flow between the nodes of the network. The network nodes include sensors, meters,

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databases, and servers. They are all part of a machine-to-machine (M2M) communication network capable of satisfying the challenges of data and command transmission in large geographical areas [22, 23]. Smart meters are fundamental elements of the smart grid and are responsible for many functions, including: 1. Observing activity on the power network, such as power demand (kW), energy (kWh), current (A), and so forth; 2. Converting these observations into data; 3. Processing and formatting the data for transmission; and 4. Transmitting the data using a communication network. Figure 3.6 presents a simplified block diagram of the operation of a wireless smart meter. With the use of a sensor unit, the smart meter monitors and measures some activity on the power network, which might be a physical signal (voltage, current, etc.). For example, the signal being observed may represent the power demand, in kilowatts, of the residential unit, and the observations may be made with a sampling frequency of 0.1 Hz (one measurement per 10 sec). A sensor is a transducer, a device that measures the magnitude of a specific physical signal and converts this measurement to an electric signal. The output of the sensor is digitized by an analog-to-digital (A/D) converter. The output of the A/D converter is a discrete value that can be processed by the smart meter as a number or as a single item of data. The smart meter processes one or several discrete data values for transmission in a series of steps that depends on the application and the communication network used.

Figure 3.6  Simplified block diagram of a wireless smart meter (data capture to data transmission).

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The block diagram of the general digital communication system shown in Figure 3.6 includes an additional stage called channel encoding, which adds some extra data for error correction. In addition, the multiplexing stage enables multiple signals from multiple inputs of the smart meter to be included into a single stream for transmission. At this point, the data stream is a sequence of seemingly arbitrary binary digits (bits) that can be reconverted to analog waveforms for transmission over the physical channel. The conversion occurs in the stage labeled modulation, and may involve manipulation of the amplitudes, frequencies, and phases of several sinusoidal carriers customized for a specific wireless or wired medium. For example, consider a smart meter measuring the power demand of a home with a sampling frequency of 0.1 Hz and then transmitting the observed data via a Wi-Fi channel operating at 2.4 GHz. The sensors’ measurements are converted to data. The data is processed by the embedded computer system in the meter, producing a sequence of bits. The sequence of bits may represent data from several different sensors and may incorporate additional bits for error control. The stream of bits is then modulated into a collection of sinusoidal carriers with frequencies around 2.4 GHz according to the standards that define Wi-Fi systems (IEEE 802.11a/b/g/n, and so on). The final stage shown in Figure 3.6 is labeled multiple access. In this stage, multiple Wi-Fi devices collaborate to allow the simultaneous transmission from multiple smart meters that might be deployed in the home or neighborhood. For example, a single building may have several wireless smart meters, all of which need to transmit the data they collect. The multiple-access process orchestrates the transmissions from several devices, enabling the data from each smart meter to be efficiently transmitted. The modulated signal is then fed to a transmitter, such as an antenna or cable. The transmitted signal is comprised of several components, including layers of protocols, addresses, data formats, error correction processes, and, of course, the measured power data or payload. The primary categories of smart meters are those used for utility AMIs and those focused on customer-centric services (in home). AMI meters are usually installed outside a customer’s premises, while in-home meters are usually installed inside a customer’s premises. In some cases, in-home meters include actuators that can switch on/off specific circuits of the electrical panel to provide energy management capabilities. Depending on the application, smart meters can measure the power demand, energy consumption, active and reactive power components, current harmonics, frequency of electric supply, and so forth. Table 3.4 presents a description of the two categories.

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Table 3.4 Types of Smart Meters Meter Type

Sensor

Sampling Frequency

In-home meters

Nonintrusive (usually clamp sensor) deployed at electrical panel

HF (high frequency of the order of 10 kHz to 0.1 Hz)

Used for energy usage analysis and energy management. They can extract lots of information such as current harmonics and frequency of electric supply. They require high data rate communication networks and storage at meter level.

Utility (AMI)

Intrusive sensor deployed at main power consumption cable of building

LF (low frequency of the order of one sample per 15-, 30-, 60-min interval)

Used to monitor demand, low CPU, and network needs. They cannot extract lots of information, which makes load disaggregation a difficult task. They require low data rate communication networks and have low CPU demands.

Description

3.4.2  Networking Command and data flow in the smart grid system are achieved through wired and wireless communication networks. The main challenge of the deployment of such communication networks is the provision of coverage from large geographical areas to indoor environments. Data transfer between the network nodes is achieved via different network topologies, as shown in Figure 3.7. With a star network topology, the smart meter is connected directly to an access point (AP) or gateway or even directly to the backhaul network. With a mesh network topology, smart meters might behave as relays and hop data received from neighboring meters to the AP or the gateway. In either case, the AP and the gateway act as a network sink, aggregating information from a large number of smart meters and forwarding data via the backhaul network. Ad hoc networks are not typical in smart grid systems since smart meters are fixed to buildings, and there are no mobility issues. The final choice of the network topology depends on the application and the deployment environment of the network. For example, in remote areas mesh networking might be preferred to minimize problems associated with the lack of coverage from WANs, such as the telecommunications network. Since overlay and underlay networks are needed to provide the required coverage, the system is managed with the use of supervisors and agents. An agent is the main processing unit

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of a HEMS. It orchestrates the network of smart meters and smart appliances (smart devices) in the premise as part of a home-area network (HAN) or building-area network (BAN). The agent acts as a border router or gateway connecting the HAN/BAN with the WAN to the backhaul network. The network connectivity between the agent and the smart devices may be a star or mesh topology. The most commonly used standard for the star network topology is IEEE 802.11 (Wi-Fi) and for the mesh network topology is IEEE 802.15.4 (Thread) [23, 24]. The agent communicates with the supervisor to aggregate data from a large number of consumers and transfer them to the service provider through the WAN backhaul network. This is part of a neighborhood-area network (NAN) and data transfer between agents to supervisors can be achieved in a star or mesh network topology. One of the most important networking challenges encountered in the smart grid, IoT and M2M applications, is that most of the existing protocols and standards for IPv4 transmission were designed to support bulk data transfer such as video and images. This makes the use of such protocols inefficient for smart devices. Smart meters do not support transmission of large quantities of data since the useful data payload includes samples of slowly evolving phenomenon such as power usage. Thus, as shown in Figure 3.6, the header of the packet is often larger than the payload of the packet, making the efficiency of the transmission low and the energy waste due to communication high. A solution to this problem may lie in approaches such as modification of the IEEE 802.15.4 communication standard, which is specifically designed

Figure 3.7  Mesh and star network topologies.

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for low-rate, low-power wireless personal-area networks (WPANs). To further satisfy the connectivity requirements of the smart home, Thread [24] employs an IPv6 architecture that allows devices to communicate with one another, access services in the cloud, or interact with the user through Thread-based mobile applications. The need to unify IPv6 and 802.15.4 technologies was resolved by the development of a layer that provides smooth adaptation between the IPv6 networking layer requirements and the 802.15.4 link layer capabilities. This adaptation is called 6LoWPAN. The main modification of 6LoWPAN is the adaptation layer between the IPv6 networking layer and the 802.15.4 link layer, which fragments IPv6 packets at the sender and reassembles them at the receiver. The 6LoWPAN adaptation also provides a compression mechanism that reduces the IPv6 header sizes and thus reduces transmission overhead. The main functionalities of 6LoWPAN are IPv6 packet encapsulation, IPv6 packet fragmentation and reassembly, and IPv6 header compression. 3.4.3  Advanced Metering Infrastructure An advanced metering infrastructure (AMI) is the communication system that provides command and data flow between connected devices. The basic building blocks of this smart grid communications architecture are identified and defined in Figure 3.8 [25]. This architecture defines the key segments of the communication networks and maps each segment onto associated power and energy system layers. Wide-Area Networks: The WAN provides network coverage over large geographical areas and connects the customer and distribution network to the

Figure 3.8  Smart grid communication architecture.

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service provider. It is comprised of the core network/backbone that connects to major service provider backbones or interutility backbones along the high-power electric transmission lines. Metropolitan-area networks (MANs) provide network coverage to smaller geographical areas such as the transmission network. Characteristic technologies used are the OPGW (optical fiber composite overhead ground wire), 2.5G/3G cellular such as General Packet Radio Service (GPRS), or 3G/4G cellular such as Long-Term Evolution (LTE). Backhaul Networks: The backhaul networks connect the WAN to the last-mile network. The backhaul network can be owned by the utilities or provided by third-party service providers (telcos, cables, etc.). In some cases, the backhaul network may exist without the need for a WAN. The main scope of the backhaul network is to aggregate smart meter data derived from the customers, substations, distribution smart meter devices, and mobile workforce information, and to transport this data to or from the utility head end and to or from the last-mile network. Last-Mile Networks: The last-mile network is overlaid on top of the energy distribution network and provides coverage in small-scale geographical regions. Neighborhood-area networks, field-area networks, or AMI architectures provide data transfer between microgrids and the service provider, or aggregate a group of smart meters deployed in neighborhoods and connect them to the backhaul. This segmentation enables the service provider to efficiently manage and monitor the large-scale network of smart meters. The last-mile network was one of the first important milestones of the smart grid where monitoring was necessary below the medium-voltage stations, toward the consumer. Customer–Premises Network: The customer-premises network is comprised of the residential or HAN, BAN, and industrial-area network (IAN). These networks are also connected to the ancillary elements outside the customer premises like the plug-in vehicle (PEV), solar/wind energy sources (microgrids), and storage devices. This network can also be connected to the public Internet network through a service provider–provided energy management gateway or energy services interface (ESI). The most important standards used for AMI applications are as follows: • •

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ANSI C12.1: Establishes acceptable performance criteria the AC watthour smart meters. ANSI C12.10: Covers the physical aspects of both detachable and bottom-connected watt-hour meters and associated registers.

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•

•

•

ANSI C12.19: Defines the utility industry end-device data tables, a table structure for utility application data to be passed between an end device and a computer. ANSI C12.20: Establishes the physical aspects and performance criteria for a meter’s accuracy class. It supersedes certain details in ANSI C12.1 and ANSI C12.10. ANSI C12.22: Defines protocol specification for interfacing to data communication networks. ANSI C12.22/IEEE Standard 1703 describes a protocol for transporting ANSI C12.19 table data over networks, for the purpose of interoperability among communications modules and meters. This standard uses AES encryption to enable strong, secure communications, including confidentiality and data integrity.

The RFC (RFC 6142) is used to define the transmission of C12.22 data over IP networks. The C12.22 IP-based communication system can be modeled similar to the network architecture of Figures 3.7 and 3.8. The C12.22 IP system includes the following elements: •

•

•

•

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The C12.22 IP node is the smart device of Figure 3.7 and is comprised of a C12.22 communications module and a C12.22 device. It is located within a C12.22 IP network segment. –– The C12.22 communications module is a communication module (hardware) that attaches a C12.22 device to a C12.22 network segment. –– The C12.22 device is the sensor that hosts a C12.22 application (which may also contain a C12.19 data table structure). The C12.22 IP network segment is the agent of Figure 3.7 and is a collection of all C12.22 IP nodes that implement the IP-based protocols and can communicate with each other segment using gateways such as IP routers, switches, and bridges. The C12.22 IP network is a C12.22 IP communications infrastructure composed of C12.22 IP network segments, interconnected using C12.22 IP relays. The C12.22 IP relay is a C12.22 node that performs the function of a relay. Compared to Figure 3.7, the C12.22 IP network is the NAN mesh network and the C12.22 IP relay is the mesh network node (agent). The C12.22 IP master relay is the supervisor of Figure 3.7 and operates at the top of the C12.22 relay hierarchy, aggregating data from different network segments and transferring the data to the utility or service provider.

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3.4.4  Meter Data Management Systems The meter data management system (MDMS) is considered the brain of the smart grid. The vast amount of data collected from the AMI infrastructure and smart meters of the consumer, distribution, and transmission energy network is stored in utility databases and processed using big data techniques. The data collected is mainly comprised of per-meter energy usage (e.g., watt-hours) and events such as demand response. Referring to Figure 3.3, the MDMS is part of the operations of the utility and is connected with the billing and CIS. The MDMS’s main processes include data storage and import, data validations, data cleansing and preprocessing, and data transfer to the billing and CIS system. The processed data of MDM systems also provides customercentric applications such as bill forecast, bill analysis, and load disaggregation. In some cases, third-party organizations like the U.S. Department of Energy may create open databases such as Green Button to help application service providers minimize the need for integration with utilities. This is discussed in Chapter 4 where open data and open APIs are discussed. 3.4.5  Example of In-Home Smart Metering In-home meters are usually nonintrusive, meaning that there is no need for electric cables or the electrical panel to be disconnected during installation. In most cases, the sensor is a coil that can be clamped around the main electric supply cable of the electrical panel. This clamp sensor is a current transformer (CT), which senses, via induction, the alternating current flowing in the cable (amps, A), and translates it to power (power, kW) via Ohm’s law (P = V × I) since the voltage is known (volts, V). An example is presented in Figure 3.9. The CT clamp sensor, which measure the current, is deployed around the main power supply cable of the electrical panel of the residential home. The CT is connected to a smart meter that translates current measurements to power consumption and formats/modulates the data for transmission to the gateway following the process described in Figure 3.6. In the example of Figure 3.9, transmission to the gateway is performed at the unlicensed band of 433 MHz using a mesh network. The gateway is the agent of the home as described in Figure 3.7. The agent sends the data via the Internet to the service provider’s server and database. The service provider stores the data and converts it to specific formats for further processing by end-user mobile and web apps. The process presents the most typical loop encountered in the utility industry for customer-centric services related to energy analytics. The orchestration of the AMI and MDMS provides new services to the end consumer.

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Figure 3.9  From smart metering to customer-centric services.

3.5  Evolution of Prosumers 3.5.1  The Path to Off-Grid The magic part of the smart grid is the integration of advanced ICT technologies with clean energy technologies, and the resulting B2C energy market. This integration provides energy management and monitoring services that enable consumers to produce or even store energy in their homes. During recent years, the availability of innovative products for rooftop solar energy production and home energy battery storage solutions has increased dramatically. A game changer in the prosumer sector will be the development of solar roof tiles that compete with conventional roofing materials, but provide energy conversion and management capabilities. In the long term and with the proper market penetration, these technologies could threaten the business model of conventional utilities since many existing customers may be able to go offgrid with manageable capital expense (say, less than $10,000). This potential justifies the need for utilities to become service providers by changing their business philosophy to include advanced, customer-centric services and novel approaches to customer engagement. The utility models of partner-of-partners, virtual utility, and value-added enabler discussed in Section 3.3.1 become vitally important in this scenario. The penetration of rooftop solar panels in the market increased exponentially as the price per watt declined. In 1977, the price per solar watt was $75/W. By 2015, the price per solar watt had fallen to $3/W for a 5-kW installation [26]. This cost is expected to continue to decline with the penetration into the

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market of the solar roof model. This reduction in price made solar-generated energy cheaper than grid-purchased electricity, and the return on investment to the consumer more attractive. The outcome of solar cost reduction is that the installed solar capacity in the United States went from 876 MW in 2010 to 10,727 MW in 2016, for a compound annual growth rate of over 50%. The increase of solar penetration is also due to attractive PPAs and financing, or net metering as discussed in Section 3.9. Table 3.5 presents typical values for solar energy production for different cities in the United States. A prosumer is a residential or commercial unit that can consume and produce energy. A prosumer has two different instantaneous energy states, the consumption state and the production state. In the consumption state the instantaneous total residential energy consumption, c(t) (in Wh), is larger than the instantaneous offered energy production from the solar system, p(t) (in Wh). During the production state, the energy consumption is smaller than the energy production. During a typical day, a prosumer may alter the state of operation according to solar energy production and residential energy consumption values. It is clear that during the consumption state, the prosumer needs to use electricity from the grid. To overcome this limitation, home battery storage can be deployed to help prosumers minimize the need to use grid electricity. The available battery level can be modeled with parameter s(t) (in Wh). The home battery is in the charging state, s(t)+, when p(t) > c(t) and in the discharging state, s(t)–, when p(t) < c(t). In a mathematical form the prosumer states can be modeled as: Production state:

p(t) + s(t) ≥ c(t) (3.1)

Consumption state:

p(t) + s(t) ≤ c(t) (3.2)

Table 3.6 presents an estimate of the costs for different residential unit sizes to go off-grid. Figure 3.10 presents the simulation results for a two-bedroom house in Austin, Texas, with daily energy consumption of 24 kWh and a 5-kW solar panel producing 21 kWh of energy on average. The simulation results concern two cases. In the first case, the prosumer was assumed to have no home battery energy storage, whereas in the second case the prosumer was assumed to

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Table 3.5 Solar Energy Production for Residential 5-kW and Commercial 200-kW Installations Production/year (kWh)

Production/Year Assuming Net Metering ($)

Location

Sector

Austin

Residential

7,584

890

Commercial

303,570

35,400

New York

Residential

5,940

1,100

Commercial

237,700

44,160

San Francisco

Residential

7,000

1,220

Commercial

286,308

48,700

have a 14-kWh home battery energy storage system. During a typical sunny day, the prosumer without the home battery storage alters the state from consumption during the night or evening (indicated by 0 in the bottom plot) to production (indicated by 1) during the daytime where the solar production is larger than the consumption. When the prosumer has a home battery storage system (as in the right side of the figure), it is observed that the prosumer operates in the production state all day since the stored energy can satisfy the consumption needs. 3.5.2 Connected Homes The clean energy technologies that empower consumers to locally produce and store energy are a game changer in the energy sector. To further enhance the Table 3.6 Simplified Off-Grid Planning Costs for Prosumers [27] Required Battery Capacity (kWh)

Battery Cost to Go OffGrid ($)

Home Size (Bedrooms)

Daily Consumption (kWh)

Solar Capacity (kW)

Energy Independency

1

10

5

100

14

5,000–6,000

2

20

5

87

14

5,000–6,000

3

30

6

77

28

10,000–12,000

4

40

7

70

42

16,000–20,000

Note: Home batteries are designed to provide at least one day of backup power in the event of cloudy days.

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Figure 3.10  (a) Prosumer consumption, production, and state without home battery storage and (b) prosumer consumption, production, and state with home battery storage. Prosumer state 0 indicates consumption state, whereas 1 indicates production state.

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prosumer role in the grid, companies have begun to commercialize products that enable customers to directly manage energy consumption. These smart devices constitute the basis of the connected home concept. Their operation provides for automation, better management of electricity, and a better quality of living. The result of this process creates negawatt capacity or effective energy production in the prosumer home or the grid. Even though solar energy production and home battery storage systems do not require an ICT infrastructure for operation, the process of energy management requires the orchestration of connected devices that communicate over wireless or wired networks. The connected devices act as a smart metering system that provides information about the consumption to end users. They also control actuators to enable management of devices with on/off commands or parameter adjustments (e.g., the thermostat temperature). This is an important part of the general architecture of HEMSs and BMSs, which are computer-based control systems installed in homes and/or buildings to control and monitor appliances, loads, and mechanical or electrical equipment. End users can be individuals, facility managers, or even the service provider or utility. Negawatt energy, symbolized by n(t) (in Wh), is associated with management of the operation of smart devices under user preferences or DR commands, as described in Chapter 4. For example, a smart thermostat may provide 9 kWh of energy savings in a two-bedroom house per day [28]. Smart devices can be appliances such as washing machines, ovens, and refrigerators, as well as smart thermostats that control heating/cooling and charging for electric vehicles. The effect of negawatt energy in the prosumer’s home is bill reduction, the extension of the prosumer production state, or even the protection of the home battery from energy drain. Following the equations described earlier, the prosumer state can be modeled as follows: Production state:

p(t) + s(t) + n(t) ≥ c(t) (3.3)

Consumption state:

p(t) + s(t) + n(t) ≤ c(t) (3.4)

The total prosumer available capacity or energy production at any time now becomes P(t) = p(t) + s(t) + n(t), and the negawatt energy can maximize the time during which the prosumer can act as producer of energy in the grid. Assuming a net-metered prosumer connect home, Figure 3.11 gives an overview of the operation and orchestration of smart devices and clean energy sources.

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Figure 3.11  The connected prosumer home.

As shown in the figure, the home can use the energy produced from the solar panels, the energy stored in the battery, or the power supplied from the electric grid when the prosumer is in the consumption state. The net meter captures the total energy received from or delivered to the grid. All electronic equipment and energy sources are connected to the energy box. The energy box contains the necessary inverters for DC/AC conversion as well as a controller (optimizer) to select the most appropriate sources of energy. For example, during the day when the battery is fully charged and consumption is less than the solar production, electricity in the premises is provided by the solar panels. On the other hand, if consumption during the night is expected to be high, the battery is kept fully charged and the controller uses electricity from the grid as required. The controller is of most importance when realtime electricity is available and will be discussed later in this chapter. The connected appliances communicate with the agent using wireless networks such as Thread or Wi-Fi, or wired connections such as HomePlug. The agent captures, stores, and processes all of the information for transmission to the cloud and the service provider. It acts as the intelligent unit of the HEMS and the gateway that connects the smart devices to the backhaul network. The service provider converts raw data to information and delivers engaging customer-centric services and applications to the prosumer. The effect that negawatt energy can have in the prosumer’s daily consumption and production patterns is presented in Figure 3.12, which contrasts negawatt production and prosumer state with and without home battery storage.

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Figure 3.12  (a) Prosumer consumption, solar production, negawatt production, and state without home battery storage; and (b) prosumer consumption, solar production, negawatt production, and state with home battery storage. Prosumer state 0 indicates consumption state, whereas 1 indicates production state.

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A comparison of Figures 3.10 and 3.12 clearly shows the effect of the negawatt concept on a prosumer’s consumption of energy. The two-bedroom house has a daily consumption of 24 kWh with a 5-kW solar panel that produces on average 21 kWh. Due to the operation of smart appliances and smart thermostats, the prosumer also produced 8.5 kWh of negawatt energy. The prosumer’s agent initiates energy-saving commands during times when the solar production is at minimum values, and during the period when the family was active in the house cooking, washing clothes, and so forth. In the case where the prosumer has no storage, the prosumer production state was increased by 2 hr, whereas in the case where the prosumer has a home battery, the battery minimum level reached 6.7 kWh instead of the 0.5 kWh of Figure 3.10. This has a significant effect on bill reduction or extension of home battery life. During a billing period, the prosumer may switch between production and consumption states several times. At the end of the billing period, the overall activity of the prosumer can be modeled following (3.5) and (3.6), where P(t) = p(t) + s(t) + n(t) is the total available energy from solar, management, or storage. Prosumer production role during billing period:



T

T

0

0

∫ P(t) ⋅ dt ≥

∫ c(t) ⋅ dt (3.5)

Prosumer consumption role during billing period:



T

T

0

0

∫ P(t) ⋅ dt ≤

∫ c(t) ⋅ dt (3.6)

3.5.3  Standards To orchestrate the operation of a large number of connected devices in a HEMS and BMS, standards have been developed that pertain to communication, interoperability, and energy management [29]. These standards can be categorized according to the system applications, such as home devices, networking, smart metering, home energy devices, and EVs. Table 3.7 presents a quick overview of some of the most widely used standards for connected homes/buildings.

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Table 3.7 Common Standards for Connected Home/Building Applications Category

Standard

Description

Home to Grid/Home to Device Networking

IEEE 1675

A standard for broadband over power lines developed by the IEEE Standards Association. It provided electric utility companies with a comprehensive standard for safely installing the hardware required for Internet access capabilities over their power lines.

IEEE 2030

A guide for smart grid interoperability of energy technology and information technology operation with the electric power system (EPS) and end-use applications and loads.

IEEE802.11

A set of media access control (MAC) and physical layer (PHY) specifications for implementing wireless local-area network (WLAN) computer communication. ZWave is a series of applications developed over Wi-Fi for home automation and control.

IEEE802.15.4

A technical standard that defines the operation of low-rate WPANs. Thread, ZigBee, and 6LoWPAN are enhancements to better fit the needs of smart devices.

KNX

An OSI-based network communications protocol for building automation with applications in lighting control, heating ventilation control, and energy management for BMSs.

ModBus

A serial communications protocol originally published by Modicon (now Schneider Electric) in 1979 for use with its programmable logic controllers (PLCs). Simple and robust, it has since become a de facto standard communication protocol, and it is now a commonly available means of connecting industrial electronic devices.

IEEE 1815 (DNP)

A standard used for substation and feeder device automation, as well as for communications among control centers and substations.

LonWorks

Refers to a local operating network, a networking platform specifically created to address the needs of control applications. The platform is built on a protocol created by Echelon Corporation for networking devices over media such as twisted pair, power lines, fiber optics, and RF. It is used for the automation of various functions within buildings such as lighting and HVAC; see building automation.

IEEE 1901/ HomePlug

A standard for high-speed (up to 500 Mbps at the physical layer) communication devices via electric power lines, often called broadband over power lines (BPL).

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Category

Standard

Description

Smart metering

IEEE 1377

A common structure is provided in this standard for encoding data in communication between end devices (meters, home appliances, IEEE 1703 nodes) and utility enterprise collection and control systems using binary codes and XML content.

1702-2011

IEEE Standard for Telephone Modem Communication Protocol to Complement the Utility Industry End Device Data Tables.

1703-2012

IEEE Standard for Local Area Network/Wide Area Network (LAN/WAN) Node Communication Protocol to Complement the Utility Industry End Device Data Tables.

Energy Devices

IEEE 1547

IEEE Standard for Interconnecting Distributed Resources with Electric Power Systems. It provides a solution for the interconnection of distributed resources with electric power systems.

Energy Management

OpenADR

The Open Automatic Demand Response standard is used to implement DR commands over connected smart grid devices.

ISO 50001

Provides organizations with an internationally recognized framework for implementing an EMS.

SEP

Smart ENERGY PROFILE (SEP): The Smart-Energy Profile 2.0 is being developed to create a standard and interoperable protocol that connects smart energy devices in the home to the smart grid. Although the original work for SEP 2.0 was done via a joint liaison agreement between the ZigBee Alliance and the HomePlug Alliance, the standard itself is designed to run over Transmission Control Protocol/Internet Protocol (TCP/IP) and is therefore MAC and PHY agnostic. The SEP 2.0 protocol is built around the notion of function sets; each function set represents a minimum set of device behaviors required to deliver a particular functionality. Some of the core function sets defined in the specification include metering, pricing, and demand response load control (DRLC).

1609.12-2016

IEEE Standard for Wireless Access in Vehicular Environments (WAVE).

Electric Vehicles

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3.6  Microgrids 3.6.1  Architecture The electric grid is transforming from a centralized power generation/distribution network to a decentralized or distributed architecture where DERs provide capacity closer to the end consumer. This transformation is a direct effect of ICTs that provide automation and monitoring of energy consumption and production as well as efficient, low-cost RESs and storage. One of the most important elements of the smart grid is the microgrid. A microgrid can be modeled as a localized subsection of the grid where production and consumption occur within the same MV and LV network. A direct consequence of this configuration is the reduction of energy waste due to transmission. Following the observation of Chapter 2, transmission losses are approximately 8% due to the transformation of high voltage to medium and low voltages. This loss is avoided in microgrids. Microgrids also use RESs for production capacity, and this further reduces the energy conversion inefficiencies associated with fossil fuel generation. These losses are on the order of 60% (see Chapter 2) and are avoided in microgrids. Theoretically, a microgrid is the most efficient grid—the overall energy waste can always be kept below 5% to 10%. The general architecture of a microgrid is presented in Figure 3.13. A DER provides the capacity to satisfy the demand of a set of consumers. The consumers are smart homes (or connected homes as described in Figure 3.11) and are part of a MV or LV network connected to the DER of the microgrid.

Figure 3.13  Microgrid architecture.

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The agents of the smart homes communicate with the DER and, more precisely, with a control/monitoring unit called the supervisor. This connection is usually performed via a NAN using standards similar to the prosumer. The supervisor captures the aggregated energy demands of the smart homes as well as the available energy from the DER. The DER usually includes solar panels and wind turbines that provide time-variable clean energy in the microgrid as well as a large battery bank to store energy for utilization during periods where the RES is not enough to satisfy the demand. The DER are controlled by the supervisor and the energy box. The energy box contains inverters for DC/AC conversion as well as optimizers/controllers that select the most appropriate energy resource to use at any given time. This is similar to the case of the prosumer as discussed in previous sections. The supervisor monitors overall consumption and production patterns, and initiates a command flow to the end consumers under demand response signals to maximize the efficiency of the microgrid. The supervisor may also communicate with the utility or the service provider using a backhaul network or the Internet. 3.6.2  Types of Microgrids There are two types of microgrid operation. The connected microgrid is connected to the grid and may use grid electricity when demand is higher than capacity. This type of microgrid is usually found in urban environments where grid electricity is used for emergency and backup requirements. A recent example is the Brooklyn microgrid [30], a small-scale, street-level microgrid that utilizes solar energy production and local storage. New concepts such as the blockchain of energy also emerged in this microgrid and will be discussed in Chapter 5. The island microgrid is disconnected from the grid and is usually deployed in remote areas where there is no grid infrastructure. An example is the Kythnos microgrid on the Greek island of Kythnos [31]. The microgrid is deployed in a small community of residential homes on Kythnos, which has no grid infrastructure. The vacation residential homes are powered by a solar farm and battery bank deployed near the premises. Controllers are used for energy management when solar energy and storage are adequate to satisfy the demands. Island microgrids require the implementation of large DER installations as well as efficient demand response and load control algorithms to minimize the probability that there will be insufficient capacity to satisfy the demand.

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Following the architecture of Figure 3.13, the energy sources are the solar panels that provide P(t) (in Wh) of energy, the wind turbines that provide W(t) (in Wh), and the battery bank that stores S(t) (in Wh) of energy. Each smart home may provide negawatt energy in the microgrid equal to ni(t) (in Wh), where i is the identifier of the set M of smart homes in the microgrid. The total offered negawatt production is given by: N (t) =

∑ ni (t) (3.7)

i∈M

Each smart home creates a demand that is equal to ci(t) (in Wh), thus the total microgrid demand is equal to: C(t) =

∑ ci (t) (3.8)

i∈M

Taking into account the aforementioned parameters, the microgrid’s total production is B(t) = P(t) + W(t) + S(t) + N(t). The microgrid can operate in island mode only when the following formula applies:

B(t) ≥ C(t) (3.9)

3.7  Virtual Power Plants 3.7.1  Architecture The virtual power plant (VPP) is the most recent advancement of the smart grid. A VPP is an amorphous system of DER and prosumers (DG), controlled by a single entity so as to provide a reliable overall power supply and participate in the energy market [32–36]. The DGs of the VPP are not required to be connected to the same MV grid network since the overall power production is not used for local consumption. Rather, the VPP behaves as an aggregation entity that participates in the energy market. VPP production can be based on any form of clean energy technology, such as solar, wind, and micro combined heat and power (microCHP), as well as negawatt energy from demand response and load control. The storage of energy is usually based on battery banks and home batteries which are part of the DER and owned by the prosumers. Figure 3.14 provides a simple description of the architecture of a VPP. The energy produced by the DGs, like that produced by DERs and prosumers,

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Figure 3.14  Architecture of a virtual power plant.

is larger than the consumption of the VPP members, following (3.9). This enables the VPP to participate in the market. During a period of time T, the VPP can offer the following amount of energy, EVPP :



EVPP =

T

∫ B(t) − C(t) ⋅ dt,  EVPP > 0 (3.10) 0

Efficient participation in the energy market requires an accurate forecast of the available energy from the VPP. This is achieved by the VPP controller, which can be regarded as the aggregator of energy. The controller monitors the excess of energy production in real time, performs forecasts of excess production, and initiates DR commands to achieve the promised energy market targets. The real-time data and command flow in the network of the VPP are achieved with communication networks between the prosumers, the DERs, and the controller. VPPs create new business models such as the aggregator and the virtual utility model presented in Section 3.3.1. 3.7.2 Emerging Trends VPPs have already laid important foundations for the transactive energy model [35], which virtually decouples energy from its physical delivery. During the coming years, new business models and engagement techniques for prosumers will emerge that can be regarded as collaborative communities of prosumers [36, 37]. Collaborative prosumers can be viewed as dynamic VPPs that are

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formed based on socioeconomic criteria among utility customers (consumers and prosumers), having as main objectives: •

• • •

To hold an active role in the energy market under the coordination and synergy with the utility/energy retailer of the service territory by transacting, trading, or sharing energy; To provide the gift of electricity to low-income houses and increase sustainability and social bonds; To allow efficient penetration of renewable energy sources and electric vehicles in the network; and To digitize and mobilize electricity and create new services such as energy roaming.

Collaborative prosumers can be coordinated using microeconomic models, based on coalitional games (cooperative games), which are discussed more in detail in Chapter 5.

3.8 Electric Vehicles 3.8.1  Electric Vehicle Types and Charging Technologies Electric vehicles (EVs) are regarded as a significant technological achievement in the energy and transportation sectors. Despite the fact that the concept of EVs has existed for many years, it is only in the past few years that a significant market penetration has been observed. This is because of the advancement of battery and smart grid technologies. Battery technologies have dramatically increased the capacity and thus the offered mileage of EVs, whereas the smart grid has improved the charging stations and customer-centric services for EV owners. Of course, the market penetration of EVs is also supported by the dramatic reduction in the cost per kilowatt-hour of battery storage—from $1,300/kWh in 2005 to $400/kWh in 2015 [38]. Two types of EVs are being marketed. The fully electric EVs are those that operate only on electric energy propulsion. For this type of EV, the battery capacity ranges from 20 to 100 kWh according the manufacturer and offered mileage without a recharge. The current mileage ranges from 60 to 320 miles, which makes EV market penetration more attractive for end consumers. The hybrid EVs use a combination of a typical fossil fuel engine with electric propulsion. The battery capacity used in hybrid EV cars is much smaller than that of fully electric EVs and usually ranges from 7 to 20 kWh. Hybrid EVs can

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be plug-in or non–plug-in. Plug-in hybrids can be charged in typical charging stations, whereas non–plug-in EVs charge their batteries using energy harvesting technologies from the fossil fuel engine or the breaks. Different battery technologies are used in EVs but the most commonly used is batter is the lithium-ion battery (LIB), which is expected to remain dominant into the next decade. The main advantage of LIBs is the high cyclability, which increases the number of times the battery can be recharged while still maintaining its efficiency. The main drawback of LIB is the low energy density that reduces the amount of energy that can be stored in a unit volume. Regarding the EV charging stations, there are three charging technologies. Level 1 charging refers to connecting the EV to a typical household outlet of 120V with AC of 8 to 12A. The time to charge a typical EV may vary from 12 to 24 hr according to the battery capacity. Level 1 EV charging corresponds to approximately 1.2 kW of additional demand to the grid during charging. Level 2 charging refers to specialized EV charging stations that provide 240V with AC. The time to charge a typical EV is significantly reduced since more current (30 to 40A) can be used to accelerate the charging time, which usually falls between 3 and 12 hr. Level 2 EV charging stations are usually deployed at the EV owner’s premises and correspond to approximately 7 to 10 kW of additional demand to the grid during the charging. Finally, level 3 or DC supercharging refers to specialized charging stations that are deployed in shopping malls or gas stations and implement 480V DC of 100A for fast charging. The time to charge a typical EV using level 3 charging varies from 20 to 60 min. Level 3 EV charging corresponds to approximately 50 kW of additional demand to the grid during charging. 3.8.2  Effect on Consumption Patterns EVs are expected to dramatically change residential consumption patterns. This is because EV charging creates additional demand to the power grid and much larger power peaks when compared to the operation of typical household appliances. For most residential units, the EV charging energy is much larger than the overall residential consumption and this has a significant effect on the cost. Figure 3.15 illustrates the case of a typical residential unit, mentioned in the previous examples, when a 45-kWh EV battery is fully charged, creating an extra demand of 7 kW. For the specific example, the owner of the EV was assumed to start charging the vehicle at 7 p.m. until 2 a.m. using the level 2 rate. It is observed

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Figure 3.15  Change of consumption pattern for a residential unit with and without an EV.

that the peak demand increased from 2 kW at 7 p.m. to 9 kW and the peak was maintained until 2 a.m., which was originally an off-peak period for the consumer. For the specific example, the total energy consumption of the residential unit without the EV was 24 kWh/day; with the EV it is now 69 kWh/ day assuming that the EV is fully discharged during the day. For a flat pricing of $0.12/kWh the residential owner was paying $2.90/day; with the EV the cost of electricity increases to $8.28/day. To minimize the costs of EV charging, RES and smart pricing models with demand response and load control optimizations are used and these will be discussed in Chapter 4. The effect of EVs on the power grid is significant and is expected to increase the peak demand even during off-peak hours, the electricity costs of consumers, and the need for DG to enable production close to the place of consumption. Most modern level 2 and level 3 charging stations are equipped with controllers that empower the EV owners or the service provider with the ability to automate the charging process and migrate the charging during off-peak hours.

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3.8.3 V2G Concept Electric vehicles further enhance the active participation of prosumers in the operation of the power grid. As with solar roofs and photovoltaic systems, the vehicle-to-grid (V2G) scenario supports two-way power flow to and from the grid. An EV owner may consume energy from the grid to charge the EV or may provide energy to the grid. From the grid perspective, a large number of EVs may be regarded as a distributed battery bank or a DG system. A simple V2G architecture was shown earlier in Figure 3.11. The EV is connected to the grid through a DC/AC converter. The EV power flow is controlled by an intelligent device, the controller, which decides if it needs to consume or offer energy to the grid. The decision is based on the consumption/production levels of the prosumer or the grid. There are many potential pitfalls in the road to making V2G a reality. For one thing, auto manufacturers need proof that using EV batteries to store energy will not diminish vehicle performance or EV miles travelled, and that customers would be willing to pay for such features. The most significant application of V2G is the peak load reduction during peak periods. In such case, the EVs operate as a DG system that helps the power grid during critical time periods. The ISO functions as a central control system to facilitate communication between an EV and the grid by emitting control and DR signals to the EVs.

3.9  Smart Grid Pricing 3.9.1 Pricing Models The evolution of clean energy technologies and related business models has enabled the end consumer to assume an active role in grid operation. The evolution of prosumers and DR services has resulted in the development of new pricing schemes that will make new services and new business models more attractive. As a result, energy providers and utilities are changing from flat pricing models, in which the end consumer pays a constant monetary amount per consumed kilowatt-hour, to more dynamic and time-varying pricing models [39]. Time-of-Use Pricing: A time-of-use (TOU) pricing model divides the day into segments and applies flat prices per segment that do not change often. The prices paid for energy consumed during these periods are preestablished and marketed to consumers. Usually, the prices are high during peak hours such

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as noon to 6 p.m., somewhat lower during partial-peak hours such as 8 a.m. to noon and 6 p.m. to 9 p.m., and at a minimum during off-peak hours. TOU prices may also change on a seasonal basis. The outcome of TOU pricing is behavioral demand response, in which consumers shift their usage toward times of the day when the price is low. For example, a consumer might initiate energy-intensive activities such as laundry during the night when the prices are low. Critical Peak Pricing: Critical peak pricing (CPP) is a more dynamic TOU model in which TOU prices are in effect except for certain peak days. During peak days, the prices may change to absorb the extra costs of generating and/ or purchasing electricity at the wholesale level by the utility or the retailer. In most cases, CPP takes place 15 days/yr and 6 hr/day and mainly targets commercial or industrial consumers. Real-Time Pricing: Real-time pricing (RTP) is the most dynamic pricing model of the smart grid. With RTP the price of electricity may change every hour or even every 15 min. Using the ICT infrastructure of the smart grid, price signals are broadcast to the user on an advanced or forward basis, reflecting the utility’s cost of generating and/or purchasing electricity at the wholesale level. The HEMS or BMS of the consumer processes pricing signals and performs demand response and load control to minimize costs of usage. The agents of the connected homes and the VPP controllers are expected to take full advantage of the RTP model to maximize the benefits of the consumers/ prosumers. 3.9.2 Net Metering Prosumers usually encounter difficulties consuming 100% of the energy produced by their solar roofs because solar energy production occurs during time periods when people are at work and not at home. To minimize the waste of this energy, power purchase agreements (PPAs) are used to allow prosumers to feed their excess energy to the power grid. A PPA is a bilateral agreement between the solar owner and the utility or energy provider that establishes a flat purchase price for the produced kilowatt-hours. A feed-in-tariff (FIT) mechanism is performed and the solar owner requires two meters. One meter captures the energy produced and the other meter captures the energy consumed. Assuming that X in ($/kWh) is the cost of using electricity and Y (in $/kWh) is the PPA price of produced solar energy, a prosumer can pay or earn, during the billing period T, the following amount:

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T

T

0

0

Q = X ⋅ ∫ c(t) dt − Y ⋅

∫ p(t) dt (3.11)

where c symbolizes the consumption and p the production of energy. In most cases, Y < X, which creates disincentives for consumers to adopt solar or clean energy production. To overcome such problems, net metering billing services are now used in many countries and states that allow consumers who generate some or all of their own electricity to use that electricity anytime, instead of when it is generated. Net metering requires only one meter, which can roll backward when the production is larger than the consumption (p > c). A net metered prosumer can pay or earn, during the billing period T, the following amount: Q=X⋅

T

∫ ( p(t) − c(t)) dt 0

(3.12)

The implementation of (3.12) for the prosumer of the previous examples is presented in Figure 3.16. The prosumer with 24 kWh/day consumption and a 5-kW solar roof generating 21 kWh of energy is with the utility that provides real-time pricing (third graph in Figure 3.16) and net metering billing. The real-time pricing is modeled as X, which varies over time and should be incorporated with the interval of T, as shown in (3.12). The effect of solar energy production is obvious in the fourth graph of the figure where the prosumer earns money (minus sign) from 10 a.m. to 5 p.m. At the end of the day, the prosumer earns Q = –$6.4 due to net metering billing. 3.9.3  Renewable Energy Credits and Peak Load Credits Renewable Energy Certificates (RECs) are the legal tender form of renewable energy credits. One REC represents the proof that 1 MWh of renewable energy was produced and routed into the commercial electrical grid. In most cases, a REC is credited to the owner of renewable energy resources, such as wind and solar farms. A REC generated via solar conversion is called a solar REC (SREC). The REC is a tradable, nontangible energy commodity in the United States and many other countries, and it is used to track the ownership of the environmental and social benefits of renewable energy. The form of the credit provides an easy-to-implement mechanism for the purchase of renewable energy that is delivered to and absorbed from the grid. Many clean energy providers buy RECs, which are then sold to end consumers who

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Figure 3.16  Net metered payments and earnings for a prosumer.

are regarded as clean energy consumers. These certificates can be sold and traded or bartered, and the owner of the REC can claim to have purchased renewable energy. For example, for a number of years, the Empire State Building in New York has been reported as operating on clean energy [40]. This is because the Empire State Building is a customer of a retailer that buys a large amount of RECs. The process of generating the REC is as follows: 1. A green energy provider (such as a wind farm) is credited with one REC for every 1,000 kWh or 1 MWh of electricity it produces. 2. A certifying agency gives each REC a unique identification number to make sure it does not get double counted. 3. The green energy is then fed into the electrical grid (by mandate). 4. The accompanying REC can then be sold on the open market. 5. Retirement occurs when a REC is used by the owner of the REC. The price of the REC depends on factors such as the year the REC was produced, location, type of clean energy, and whether the REC was used to satisfy a tight supply/demand condition. In the United States, spot prices for SRECs generally decreased from 2010 to 2014. In New Jersey, the spot price for a 2010 SREC was $665.04 in July 2010 and about $160 in May 2014 for

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SRECs generated in different years. In Delaware, the spot price for a 2010 SREC was $255 in July 2010 and about $50 in May 2014 for SRECs generated in different years. Peak load credits (PLCs) are used for consumers who are responsible for large loads. Whenever a consumer reduces its peak consumption time, it enters into preestablished peak load reduction agreements that reduce a utility’s planned capacity obligations. PLCs are similar to RECs but are also similar to the negawatt capacity discussed in previous chapters. With n(t) being the amount of negawatt production (load reduction) and o(t) the offer (in $/kWh) for peak reduction, the total incentive for the participant in the load reduction scheme is given by: I=

T

∫ n(t) ⋅ o(t) dt (3.13) 0

RECs and PLCs are important innovations that drive the adoption of clean energy and demand response programs in the residential and commercial sector. Chapter 4 will provide more details about the applications and the big picture of the smart grid.

References [1]

Andres Carvallo and John Cooper, The Advanced Smart Grid: Edge Power Driven Sustainability, 2nd ed., Norwood, MA: Artech House, 2015.

[2]

Takuro Sato et al., Smart Grid Standards: Specifications, Requirements, and Technologies, New York: Wiley, 2015.

[3]

Stephen F. Bush, Smart Grid: Communication-Enabled Intelligence for the Electric Power Grid, New York: Wiley, 2014.

[4] www.sgip.org. [5]

Vehbi Cagri Gungor et al., Smart Grid Technologies: Applications, Architectures, Protocols, and Standards, Boca Raton, FL: CRC Press, 2013.

[6]

National Institute of Standards and Technology, “NIST Framework and Roadmap for Smart Grid Interoperability Standards,” Release 3.0, 2014.

[7] https://standards.ieee.org. [8]

Accenture, “Power Play: Three New Models for Growth in the Utilities Industry,” White Paper, www.accenture.com, 2015.

[9]

Pricewatershousecoopers, “Looking Ahead: Future Market and Business Models: Future Utility Business Models,” 2016.

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[10] Hussein T. Mouftah and Melike Erol-Kantarci, “Smart Grid: Networking, Data Management, and Business Models,” Boca Raton, FL: CRC Press, 2016. [11] Scott P. Burger, “Business Models for Distributed Energy Resources: A Review and Empirical Analysis,” MIT Energy Initiative Working Paper, 2016. [12] Scott P. Burger et al., “The Value of Aggregators in Electricity Systems,” MIT Center for Energy and Environmental Policy Research, January 2016. [13] Scott P. Burger, “Business Models for Distributed Energy Resources: A Review and Empirical Analysis,” MIT Energy Initiative Working Paper, April 2016. [14] www.greenbuttondata.org. [15] www.smartgridnews.com. [16] http://smartgridcc.org/research/sgcc-research/sgccs-wave-5-consumer-pulse-and-market -segmentation-study-summary. [17] Smart Grid Consumer Collaborative, “Consumer Driven Technologies,” White Paper, 2016. [18] www.nptrust.org. [19] www.goodmustgrow.com/ccsindex. [20] www.gridmates.com. [21] http://liheap.org. [22] Harvey Lehpamer, Introduction to Power Utility Communications, Norwood, MA: Artech House, 2016. [23] Qie Sun et al., “A Comprehensive Review of Smart Energy Meters in Intelligent Energy Networks,” IEEE Internet of Things J., Vol. 3, No. 4, August 2016, pp. 464–479. [24] www.threadgroup.org. [25] National Institute of Standards and Technology, “NIST Framework and Roadmap for Smart Grid Interoperability Standards,” Release 3.0, 2014. [26] Donald Chung et al., “U.S. Photovoltaic Prices and Cost Breakdowns: Q1 2015 Benchmarks for Residential, Commercial, and Utility-Scale Systems,” National Renewable Energy Laboratory, 2015. [27] www.tesla.com. [28] www.nest.com. [29] www.standards.ieee.org. [30] http://brooklynmicrogrid.com. [31] www.microgrids.eu/index.php?page=kythnos&id=2. [32] Aaron Zurborg, “Unlocking Customer Value: The Virtual Power Plant,” U.S. Department of Energy, 2010.

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[33] Khalil El Bakari, Smart Power Systems and Markets with Virtual Power Plants: Development of Distributed Energy Resources Aggregation System, Lambert Academic Publishing, 2014. [34] Eko Adhi Setiawan, Concept and Controllability of Virtual Power Plant, Kassel, Germany: Kassel University Press GmbH, 2007. [35] Melvin Olken, “Editorial: Transactive Energy: Providing and Enabling Environment,” IEEE Power and Energy Magazine, Vol. 14, No. 3, May/June 2016, p. 4. [36] Andres Carvallo and George Koutitas, “Collaborative Prosumers About to Emerge,” Energy Central, 2014. [37] A. Satsiou, G. Koutitas, and L. Tassiulas, “Reputation Based Coordination of Prosumers,” Proc. 1st International Conference on Internet Science, Brussels, Belgium, April 9–11, 2013, FP7 European Network of Excellence in Internet Science, pp. 187–192. [38] Junwei Lu and Jahangir Hossain, Vehicle-to-Grid: Linking Electric Vehicles to the Smart Grid, Stevenage, UK: Institution of Engineering and Technology, 2015. [39] Scott P. Burger, “Business Models for Distributed Energy Resources: A Review and Empirical Analysis,” MIT Energy Initiative Working Paper, 2016. [40] http://green.blogs.nytimes.com/2011/01/06/green-power-for-the-empire-state -building/?_r=0.

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4 The Cloud Environment of Application Providers

4.1  Introduction This chapter provides an overview of existing open data and APIs that are important for sharing data between utilities and application providers. These technological components enhance existing business models and empower third-party application providers with the ability to connect with utilities’ systems and exchange data and commands. These processes create the foundations for the development of customer-centric smart grid services.

4.2  Overview of Services IP-based communication and cloud technologies have enabled the creation of new frontiers in all industrial sectors. A common result of this new style of interaction is the transformation toward customer-centric applications and new business models. The dominant example of this transformation is the mobile telecommunications industry, which spawned an application development platform where the focal point is the end user experience and the quality of 105

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the experience. For the mobile industry, IP communication carries intelligence to the edge of the network (e.g., smartphones) and creates the foundation of the business transformation. An ubiquitous layer of cloud-based services has almost replaced conventional circuit-switched phone services and has also transformed the business of mobile communications into an application development platform. Today, a huge portion of the mobile industry relies on collaboration with third-party application providers who develop and publish customer-centric services in this application development platform. These applications are offered directly to the end consumers and focus on areas related to social media, gaming, and analytics. The energy sector is now experiencing a transformation similar to that of the mobile communications sector. The advent of the smart grid has created all of the necessary technological foundations for reliable energy delivery, and in the process has spawned a secondary application-based platform for automation, monitoring, and control. This infrastructure, in combination with cloud-based data and communication solutions, has broadened the horizons of the smart grid business model. The smart grid has become an application development platform. This is a key feature of the third generation of the smart grid, called Smart Grid 3.0. The most significant applications offered by a variety of smart grid service providers are those related to energy/bill analytics, energy usage analysis (also known as load disaggregation), demand response and home energy management, gamification, and microbill payment. These applications are discussed in the following paragraphs. Energy analytics applications leverage data that is stored in meter data management (MDM) systems. Utilities provide MDM access to third-party application developers in a B2B model. These application developers create white-label applications leveraging MDM data, and the utility offers the applications in a B2C model to the end consumer. In some instances, federal entities such as the U.S. Department of Energy (DOE) have created incentives for utilities to make MDM data available to third-party developers via open APIs. A characteristic example of this situation is the DOE’s Green Button and Orange Button initiative, which is discussed later in this chapter [1, 2]. In other instances, product innovators offer behind-the-meter devices for home energy monitoring. In behind-the-meter offerings, a third-party application provider offers energy analytics solutions and devices directly to the end consumer in a B2C model. The consumer deploys the technology on the consumer-side of the power meter, without the intervention of the utility and/or energy provider. Energy analytics applications typically implement

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features such as bill forecasting, seasonal bill analysis, and daily consumption analysis. These features help end users understand their energy usage and thus optimize their use of energy, which results in saving money and reducing consumption. Energy usage analysis (also called load disaggregation) leverages MDM data to provide end users with detailed analysis of the energy consumption of individual appliances. Load disaggregation is more powerful than aggregate energy analytics, and it is an important service for end consumers due to the detailed insights it provides about appliance energy consumption. As a consequence, the end consumer can save energy by adopting behavioral energy efficiency programs. Third-party application providers may offer services such as load disaggregation in a B2B fashion to the utility/energy provider who then sells the service to the end users. In some cases, product innovators have developed behind-the-meter equipment connected directly to the home electrical panel that provides real-time energy usage analysis directly to the end user in a B2C manner. In applications of this nature, the intervention of the utility is not necessary. Demand response and home energy management focus on load management and automation rather than monitoring. Demand response (DR) is performed by the utility or the service provider to manage the demand during critical events, such as peak hours. In contrast, home energy management (HEM) provides individuals with the ability to manage the consumption in their homes according to personal preferences. For example, to save money, a user may want appliances with high-power consumption to operate in offpeak hours when the price of electricity is lower. HEM platforms are often connected with DR programs, because the combination of these processes provides additional flexibility and efficiency. For DR and HEM implementations, a third-party application provider may cooperate with the utility (B2B) and the utility may offer the service to the end consumer (B2C). Alternatively, the third-party application provider may provide the service directly to the end consumer via open APIs (B2C). Gamification is a technique that is widely used to engage consumers in DR and HEM services by providing incentives for participation. For example, a service provider may compare the demand response performance of several neighbors and engage consumers by means of friendly competition to save energy. Gamification may also be used when participatory sensing is necessary for the operation of the services. Participatory sensing refers to applications where the end users hold an active role in the operation. For example, an application that monitors the traffic in streets may require the end users

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to upload GPS and speed data to the system, which is necessary for the computation of the overall traffic. Microbill payment solutions follow the recent trends of utility customer to do all of their transactions online, using their smartphones. Microbill payment solutions provide innovation in financial technologies (usually called fintech) and some examples include the case of prepaid electricity, electricity gift cards, and smart energy donations. These types of services fulfill the needs of the sharing economy that has recently appeared in smart city environments. In addition, they solve the problem of the mobile nature of utility customers who are expected to consume energy or share energy outside their meters, for example, when they use their electric vehicles. Microbill payments follow the trends met in the banking system where a dozen bill-sharing apps have emerged that combine social media and payment solutions. Finally, new technologies such as the blockchain protocol and smart contracts supply the fertile foundation for the creation of new digital currencies based on one of the most basic commodities, such as electricity.

4.3  Introduction to Cloud Computing 4.3.1  Web Services and APIs The development of applications by third-party providers requires the orchestration of data that is managed by energy service providers (ESPs). This data may be maintained in back-office systems and/or stored in other databases. In these cases, cloud technologies provide a convenient mechanism for the integration and interoperability of various systems. The application provider may collect data from the MDM using customized software bridges, web services, or APIs, and then process the data to deliver useful insights to the end user via web-based or smartphone applications. A typical architecture is presented in Figure 4.1. A web service is a collection of open protocols and standards used for the exchange of data among various network nodes. For example, a web service may be used to connect a smart thermostat or the HEMS of a residential customer to the MDM system of the ESP. The web service uses IP-based, open communication standards for the connection of the network nodes [3]. The basic web services platform uses XML [28] for platform-independent representation of data and the HTTP [29] for retrieval, sequencing, and management of data on remote systems.

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Figure 4.1  Typical architecture of the collaboration of application providers with third parties.

Web services typically use the following components: • • •

SOAP (Simple Object Access Protocol), UDDI (Universal Description, Discovery and Integration), and WSDL (Web Services Description Language).

The web services platform is effective due to its low communication costs and its interoperability with different systems and technologies. These technologies provide a common, ubiquitous language between different systems connected to different networks. An application programming interface is a set of routines, data structures, and protocols that supports the building of applications that require the orchestration of different systems. An API provides a client (a software system) with the ability to execute specific actions or request specific data from a server (a different software system). The client and server may be different computer nodes in different locations, or they may be different pieces of software on the same physical computer node. The most commonly used actions in a web-based API are the commands GET/POST/PATCH/DELETE, which are basic actions supported by HTTP. These basic actions will be explained

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later in this chapter. A web service is a more complicated interface that leverages simpler HTTP-based client/server interactions to create a more robust, distributed software system. Web services are an approach to wrapping a higher-level API for delivery via HTTP. They are designed to support interoperable, machine-to-machine interaction over a network. Web services utilize HTTP for network transport, whereas a more general API may use any type of network transport [4]. Application providers usually need access to the MDM, advanced metering infrastructure (AMI), or CIS systems of the ESPs. For security purposes, ESPs typically store data locally in their own infrastructure (their back office). As a result, application providers must use customized software bridges and secure APIs to access the ESP’s back-office data. To simplify this process, ESPs typically collaborate with their IT infrastructure vendors to provide open APIs. Communication with the ESP back office is not the only requirement for application development. Application providers may also need to establish a system interconnection with other databases or systems in order to complete the function of their application. An example of a secondary system interconnect is a weather database or a weather information provider. Fortunately, open APIs exist that can be accessed using web services to enable these types of communication. Once the application provider has established access to the required data sources, algorithms and associated software must be developed to process the data and provide intelligence in the form of customercentric applications. One of the most significant difficulties that arises when connecting systems from different parties is the bridging of the systems. Because each party has already developed and is operating their own applications, software customization is required in order to enable interconnections to be made. In general, three general approaches are used for interconnecting an ESP’s system with that of the application provider: 1. The ESP provides an API. In this case, the application provider needs to customize its back-end system to communicate with the API of the ESP. 2. The application provider provides an API. In this case, the ESP needs to customize its back-end system in order to communicate with the API of the provider. 3. Both parties provide their own APIs, usually for security reasons. In this case, the back-end systems of both the ESP and the application provider must be customized to communicate via a common middleware layer.

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To reduce the integration complexity and make the overall process less burdensome for the ESP, the first option is usually followed and the application provider must develop required customizations that allow it to interconnect with the ESP’s systems. The API communication should be private and should restrict access to other parties or applications. For that reason, an authentication approach is leveraged, where every user holds an API key (or token) issued by the API owner. The token is unique, is included in the header of every API request, and may be issued per user, per platform, or per application. A request occurs when a user calls an API endpoint because there is a need to get data (GET request), send data (PATCH/POST/PUT request), or delete data (DELETE request). A complete API request contains two sections: a header and a body. The header section contains important information such as authentication or status of the transaction. The body section contains the actual request and associated data. After the API request is processed, the servicing endpoint responds using a similarly structured API response. The API request and the API response use a specific format for packaging data such as JavaScript Object Notation (JSON) [27] or XML [28]. Table 4.1 below presents a simple glossary of the most widely used terms. REST (representational state transfer) [30] is an architecture style for designing networked applications. It relies on a stateless, client/server, cacheable communications protocol such as HTTP. On many occasions, a set of APIs and the required documentation are incorporated into a software development kit (SDK). SDK is a programming package that enables a programmer to develop applications for a specific platform. For example, a billing integration vendor might provide an SDK to an application provider to help with the integration process for the CIS of a utility. 4.3.2  Reserving Resources in the Cloud One of the most important benefits of cloud computing, also called infrastructure as a service (IaaS), is that it offers affordable and reliable processing power and storage, minimizing the need for local investment in a computing infrastructure. Thus, an application provider may store and process data in the cloud by using a specific cloud provider and service. The main resources available via a cloud are virtual machines (VMs). A VM is a software-based emulation of a computer system that provides the complete functionality of a physical computer. VM systems are used in cloud computing applications because they scale easily and simply to handle highly variable workloads.

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The Smart Grid as an Application Development Platform Table 4.1 Glossary Examples for API and Web Services

Glossary

Description

Authentication

Identifies the user of the API. Common techniques for authentication include API keys and OAuth.

API key (token)

An authorization code passed to an API request via a header or parameter to identify the requester.

Header

The header is sent preceding the body of an HTTP request or response.

JSON

JavaScript Object Notation is a data format commonly used for API request parameters and the response body.

XML

XML is a format that is used to describe documents and data.

Client

The client sends an API request when communication is initiated. Many clients may use the same API.

Body

The body of a request including functional data such as an API key.

Request

A request with GET/POST/PATCH/PUT functions. Requests comprise the header and body.

Response

A response following a request with the status and the information in the body.

REST

A representational state transfer (REST) is an architectural pattern for interacting with resources via HTTP methods.

GET

The HTTP method for retrieving resources from a RESTful API.

DELETE

The HTTP method for deleting resources with a RESTful API.

POST

The HTTP method for creating resources with a RESTful API.

PUT/PATCH

The HTTP method for updating resources with a RESTful API.

Rate limiting

Limiting the consumption of an API to a certain number of requests per period of time. It is used to minimize congestion.

Status Code

HTTP status codes are what the server sends in the response back to the client with regard to the status of the request.

An implementation of a VM or a collection of VMs may involve specialized hardware, software, or a combination. In allocating virtual machines, one of the most important questions asked by application providers is: What amount of processing power should be reserved to run the application? In general, four different types of reservation policies are used for computing power in the cloud: on-demand, bid-spot, reserved, and dedicated.

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In an on-demand policy, the application provider pays for computing and storage capacity by the hour without a long-term commitment or upfront payments to the cloud provider. On-demand workload capacity offers the flexibility to increase or decrease computing and storage capacity depending on the demands of the application. This type of policy is best for startup companies or applications that do not have a constant user flow. With on-demand VM usage, users minimize their up-front costs and long-term infrastructure commitments. A bid-spot policy allows application providers to bid on spare and underutilized cloud computing capacity. Bid-spot capacity leverages a time-varying workload to offer computing capacity at lower costs to users who successfully bid for services. Bid-spot cloud capacity is not appropriate for applications that cannot be interrupted. Instead, it is preferred for applications with flexible operational times, low costs, or an urgent need for large amounts of computing capacity. As is obvious from the terminology, a reserved policy provides a significant discount compared to on-demand pricing and is the best choice when an application has predictable usage patterns. With reserved capacity, the application provider reserves specific time zones and a specific amount of cloud resources. This type of reservation is preferred by large application providers that experience constant traffic for their services, and it can result in cost savings of 50% to 75% over on-demand services. In a dedicated policy, a physical server is allocated for the use of the specific application rather than a VM. This type of cloud reservation is preferred in cases where the applications have a constant steady state or predictable usage and reliability is of the utmost importance. 4.3.3  Example of Web Services for Home Automation Following the principles of operation described previously, let’s explore how a smart application utilizes APIs and web services to access energy consumption data and control a smart thermostat. Consider the application scenario where a HEMS is provided to an end user by an application service provider or a utility via web services. The Smart Home app on the user’s smartphone monitors energy consumption of a home by accessing smart meter data and manages the temperature setting of a smart thermostat. The connection between the Smart Home app, the smart meter, and the smart thermostat is performed via web service APIs provided by the vendors of the smart devices. For the purposes of the example, we will assume that the Smart Home app uses the endpoint api.smartmetervendor.com/energy

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to obtain information about the energy consumption of the residential unit. The smart meter provides this information when it receives a GET request from the Smart Home app. Further, the Smart Home app uses the endpoint api.smartthermostatvendor.com/temperature to control the temperature setting of the smart thermostat. The smart thermostat sets the temperature requested when it receives a PATCH request from the Smart Home app. A simple schematic diagram is presented in Figure 4.2. In a simplified manner, the process of home energy monitoring and control is as follows: 1. The Smart Home app reads the energy consumption by sending a GET request to the smart meter endpoint. The GET request includes the header with the API key (token) of the application provider, and the body of the request is encoded in XML format. 2. The smart meter endpoint responds with the instantaneous kilowatthour consumption of the home and historical data, which are included in the body of the response in JSON format. 3. The Smart Home app processes the received data and creates a graph of energy consumption. 4. The user decides if it is important to change the temperature setting of the thermostat in order to save energy.

Figure 4.2  Interaction of Smart Home app with various systems.

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5. The Smart Home app sends a control command to the endpoint of the smart thermostat using a PATCH request with the new temperature setting, encoded in XML format. 6. The smart thermostat system updates its temperature setting and responds to the Smart Home app with a JSON-encoded success indicator.

4.4  Product Development in the Cloud 4.4.1  Defining the Pricing Model of SaaS Service In most cases, application providers host their services in the cloud. This significantly reduces the costs and makes the service more flexible and affordable. An application presented in the form of software as a service (SaaS) typically runs in the cloud and is not hosted in a specific location. In the SaaS model, a utility may rent an application from the application provider on a monthly or annual basis. The application provider may also rent the same application to other clients. Sometimes, large utility clients require exclusive use of the software in markets where competition is present. For the case of enterprise software, the utility client buys a license for the software and needs to install an IT infrastructure to operate and maintain the application in its own servers (i.e., it is self-hosted). This significantly increases the cost of the application, but self-hosted systems are more secure since there is no communication with other parties. Despite the fact that enterprise software is less vulnerable and may be more secure, in smart grid applications, applications offered in the SaaS format have gained tremendous momentum. SaaS applications offer different pricing mechanisms. The most widely used mechanisms are as follows: •

•

•

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Pay per user: The application is provided to the utility and the application provider charges a specific fee per user (often per month or per year). This pricing model is very common in cases where the application has a B2C component. Pay as you go: The application provider charges a fee according to the resources required to operate the application (storage, CPU usage, etc.). This pricing model is most common in B2B scenarios. Flat software license fee: The application provider charges a monthly or annual fee for the operation of the application. The fee often depends on the size and requirements of the utility client. For example, pricing

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•

is different for a utility with 5,000,000 customers versus one with 100,000 customers. Hybrid: Depending on the nature of the application, a combination of the aforementioned models may be applied in a hybrid billing scheme. For example, an application provider may require a small, fixed software license fee for the maintenance of the platform, which is customized for the utility client, while the client also pays a variable per-user fee according to the traffic requirements.

The application provider needs to keep the SaaS pricing as low as possible in order to make the solution attractive to utility clients. For every pricing model, the monetary charge depends on the operational expenses of the application provider, the complexity of software integration, and any customization required by the utility client. Additionally, the return on investment (ROI) the utility client will experience by using the application may be a factor in the pricing model. 4.4.2  Web App or Mobile App? One of the most important questions an application provider must answer before beginning development is whether the app should be offered in a native mobile format or in a responsive web format. There is no direct answer to this question because multiple variables affect the final decision. New applications and services are often launched using a bottom-up model. In the bottom-up model, the application provider first develops a beta version of the service. In this case, development in a responsive web format is a more suitable approach due to reduced complexity of development and maintenance. Development and maintenance of applications in the native mobile format can consume significant resources of the start-up company, whereas the responsive web format can be less complex. Web-based applications must be designed carefully to be responsive to the constraints of mobile devices. For example, relatively simple factors such as different screen dimensions in laptops, tablets, smartphones, or even smart watches can make web-based applications more difficult to construct. The web-based format also limits the user experience (UE), but makes updates and customizations for the various utility clients easier. Web-based apps can also be developed by a smaller number of programmers who are chosen from a larger pool of available talent. The native mobile format can significantly improve the UE, but the update and customization process of the application can be substantially more

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difficult. The native mobile format is usually preferred when the application provider offers an application that is connected to a hardware device. For example, a smart meter/load controller may offer an application in a specific native-mobile format to perform energy management and monitoring. On the other hand, when the application is related to a cloud platform offering energy analytics and bill forecasting, offering the application in a web-based format may be preferred since users may tend to use laptop or desktop computers to access the application. 4.4.3  Security and Privacy The application provider may store personal user data in the system, making security and privacy issues of major importance. Most applications require registration of personally identifiable information (PII) such as name, address, username, email, utility account number, size of home, and number of occupants. For a utility client to collaborate with an application provider, industrial standards and best practices must be followed to protect PII. In addition to static PII, other forms of dynamic PII may be produced when the application is used. Dynamic PII may include data such as cookies, web beacons, and embedded scripts. The privacy policy of the application provider should empower users with the ability to delete their personal information and never share data with third parties. In other words, data must be owned by the user. The cloud provider hosting the application services is responsible for firewall protection and external attacks on the cloud infrastructure. However, the application provider should protect the application itself and services provided by the application. For security purposes, it is important for a web-based application to use encrypted transport technologies, such as transport layer security (TLS) [31], which is capable of different encryption algorithms such as elliptic curve cryptography (ECC), RSA, and digital signature algorithms (DSAs). In addition, frequent malware scanning and vulnerability assessments should be performed on all systems. When an application provider establishes a business collaboration with a utility client, a complete IT security assessment should be performed. The most important items in the security assessment are (1) firewall implementation and cloud provider security audits, (2) strategy for monitoring personnel, (3) server security, (4) user registration and logging implementation strategies, (5) encryption algorithms, (6) authorization policies, (7) backup strategy and system duplication, and (8) service level agreement. A circumspect posture on web-based or Internet-based solutions is mandatory, because shortcuts or

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inadequate implementations can lead to disasters if PII is inadvertently released or if business processes are subverted due to malicious actors. 4.4.4  Steps for Accessing Open APIs with Product Innovators In many cases, the services and applications developed by application providers require integration with existing APIs of product innovators (vendors). An example is the smart thermostat business, where application providers require permissions to control smart thermostats in order to offer their final home automation service to the end user. For collaboration with other vendors, the application provider should follow specific business and marketing criteria related to the UE, user interface (UI), and branding and other marketing issues. In general, the front-end developer and the marketing team of the application provider should comply with the following guidelines: •

•

•

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Step 1. Satisfy the UE guidelines of the vendor. UE is an important factor for the adoption of the service by end users. In most cases, UE guidelines force application providers to clearly describe to end users the authorization process and personal data that will be used by the application. The UE guidelines also require the navigation within the app to be user friendly, and the context of the application to be clear and easy to understand. For example, a smart thermostat vendor will deny access to its API if the application provider has a complex UE. This happens to protects the brand of the vendor. Step 2. Satisfy the UI and branding guidelines of the vendor. The UI also protects the brand of the vendor who requires the use of specific icons and branding colors. The objectives of the vendor are to protect its brand, and to make clear to the end user that part of the services are enabled by the use of the vendor’s API and technology. Step 3. Present a solid marketing plan to the vendor. The vendor needs to know how the application provider plans to market the product to the end user. This helps the vendor understand the technology, estimate its required resources (cloud resources), and market the solution. In some cases, important business collaborations have begun between application providers and vendors. The marketing plan should include the following information: –– Program description. The application provider should present a solid plan about the marketing program and strategy including key messaging, description of the application, value proposition, and benefits [3].

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––

––

–– –– ––

Target users and audience of the application. It is important to understand who is the most probable user of the application so that the messaging and marketing can be focused on a specific audience. Pilot launches should be sharply focused on specific audiences/ users in specific geographical locations to make the success of the launch more controllable. Media and public relationships communication plan. A list of media partners and awareness steps is necessary. On some occasions, the vendor may also provide help establishing PR relationships. Images and graphics of application. This is a list of high-resolution graphics and screenshots of the app. Demographics and geography of marketing strategy. This includes a list of the locations for the launch and the expansion to other regions. Estimated outreach. This is an important indicator for the vendor to estimate the required cloud resources.

The collaboration between a vendor (product innovator) and an application provider is a win–win situation for both parties. Vendors create awareness about their products, and application providers do not need to spend capital on developing new hardware or software technologies. Recently, these types of collaborations have gained tremendous momentum that is reflected in the quality of services to the end users. 4.4.5 White Labeling Utilities and energy providers provide the billing to end users. As a result, the need exists for a strong customer relationship with their brand. At the same time, utilities and energy providers do not hold the technological expertise to create web-based and native mobile applications to leverage vast quantities of data collected from smart meters and to create customer-centric services. For that reason, mutually beneficial collaboration between utilities and application providers is necessary. To enable these collaborations, application providers capable of creating B2C products and services usually offer white-label products. The term white label originates from the image of the packaging of products that can be filled in with the marketer‘s trade dress and branding. Applications and web-based cloud platforms may be offered via a white label to meet the branding needs of the utility client, which customizes the offering using its branding. Application providers customize their products according to the needs of their clients, and then utilities offer their solution to the end

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users. The objective is for the final product to have the look and feel of having been developed by the utility and not the application provider.

4.5  Open Data and APIs Private companies, nonprofit organizations, and governments understood the necessity for data sharing and are now offering open data and APIs to application providers. There is a great diversity of data that is valuable for application providers, including the energy consumption characteristics and demographics of different geographical regions, raw smart meter data that are used for energy analytics applications and load disaggregation, solar energy production data that can be used for net metering and billing applications, and finally access to smart thermostats and smart appliances that can be used for home automation and energy management systems. This section presents the most widely used open data platforms and APIs for smart grid applications. 4.5.1  Energy Information Administration The Energy Information Administration (EIA) is an initiative of the DOE to impartially and independently collect, analyze, and disseminate information about the sources and uses of energy. The primary goal of EIA activities is to enhance the public understanding of the interaction of energy with the economy and the environment. The EIA maintains a variety of databases that provide important data regarding energy production, consumption, and efficiency in the United States. This data is maintained in the public domain so that it can be used by application providers at no cost and without specific permission [5]. The EIA databases allow users to explore and navigate datasets manually or automatically by connecting to the open data using an API [6]. Focusing on the electricity sector, EIA data includes information about the following fields of interest: • • • •

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Sales of electricity, revenue and prices, retail cost of electricity, fuel use for generation, demand and emissions, and so forth; Energy usage analysis in homes, commercial buildings, manufacturing, and transportation; Renewable energy production; and Analysis and projections of energy for different geographical and energy sectors.

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The EIA API uses a modified RESTful architecture, where a separate uniform resource identifier (URI) is used for each query command. Access to the EIA API requires an api_key, which can be obtained during the registration process of the application provider. The API also allows the user to specify data encoding formats (e.g., JSON or XML). The format of the EIA database is based on a hierarchical structure with the following elements: •

Category –– Children Category –– Child Series

For example: •

Category Electricity [id=0]. The following endpoint provides access to Children Category datasets such as net generation, total consumption, consumption for electricity generation, and retail sales of electricity: ht t p://api.eia .gov/c ate gor y/?api _ ke y=YOU R _ A PI _ K E Y_ HERE&category_id=0 –– Children Category retail sales of electricity [id=40]. The following endpoint provides access to Children Category datasets such as residential, commercial, industrial and transportation: http://api.eia.gov/categor y/?api_key=YOUR _ A PI_K EY_ HERE&category_id=40 –– Children Category retail sales of electricity for residential [id=1012]. The following endpoint provides access to Child Series datasets for the different states, such as Texas or California: http://api.eia.gov/categor y/?api_key=YOUR _ A PI_K EY_ HERE&category_id=1012 –– Child Series retail sales of electricity for residential in Texas. The following API call will return the average monthly retail price of electricity for the state of Texas and for the year 2016: ht tp://api.eia.gov/series/?api _ key=YOU R _ A PI _ K EY_ HERE&series_id=ELEC.PRICE.TX-RES.M

An example of an API call to the EIA data for the retail price of electricity in Texas is presented in Table 4.2. The API request must contain the unique API key assigned to the application provider by the EIA. The response is a dataset that includes the average retail price of electricity in Texas for residential users per month. In the example of Table 4.2, the electricity price for October 2016 is found to be $0.11/kWh.

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The Smart Grid as an Application Development Platform Table 4.2 EIA API Request Format for Retail Price of Electricity in Texas [7]

http://api.eia.gov/series/?api _ key=YOUR _ API _ KEY _ HERE&series _ id=ELEC.PRICE.TX-RES.M { “request”:{ “command”:”series”, “series _ id”:”ELEC.PRICE.TX-RES.M” }, “series”:[ { “series _ id”:”ELEC.PRICE.TX-RES.M” “name”:”Average retail price of electricity : Texas : residential : monthly”, “units”:”cents per kilowatthour”, “source”:”EIA, U.S. Energy Information Administration”, “data”:[ [“Sept 2016”,11.13], [“Oct 2016”,11], ... } ] } {END CODE}

The EIA data can also be displayed graphically in the form of charts by using an inline frame (IFrame). An IFrame is an HTML document embedded inside another HTML document. Data contained in an IFrame can be used by the application provider or website to automatically provide graphical representations of data. For example, the monthly residential retail cost of electricity can be displayed by integrating the IFrame shown in Table 4.3 into Table 4.3 IFrame Format for Presentation of Retail Price of Electricity in Texas [7]

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a web app. The IFrame structure includes information about the presentation of the embedded graph in the HTML document, found as width and height values in Table 4.3. 4.5.2 Green Button Green Button (www.greenbuttondata.org) is an open database and API that offers a unique business innovation in the energy sector. Green Button is an industry-led project based on federal initiatives that decouples the ownership of smart meter data from the administrative domain of electric utilities. Electric utilities volunteer to share anonymous smart meter data to a common database that can be accessed by third-party application providers using a simple API. This independent database reduces the complexity of business agreements between the application providers and the utility clients. It also dramatically accelerates the evolution of new applications related to big data and energy analytics. The Green Button database provides access to smart meter data without a requirement for business collaboration between the application provider and the utility. The Green Button API provides flexible access to energy usage information through a set of RESTful interfaces. Green Button represents energy usage information as a standardized set of resources as defined in the North American Energy Standards Board’s Energy Services Provider Interface (ESPI, REQ.21) [32]. This interface uses RESTful APIs to provide standard access to information for metered resources such as electricity, gas, and water, and may be used to access and manage the metered data through streams of energy usage information (EUI) encapsulated within an Atom feed [8]. Green Button allows data to be exchanged between three entities: the retail customer, the data custodian, and the third party. The retail customer is any customer (residential, commercial, industrial) served by a utility that offers smart meter data to the Green Button database. The data custodian is the enterprise (utility or energy provider) holding the smart metered data. Finally, the third party is an application provider willing to access smart meter data to develop and offer new services to the retail customer. The relationship between the actors in the data-sharing scheme is given in Figure 4.3. Green Button offers two types of APIs for data sharing: The Download My Data (DMD) API and the Connect My Data (CMD) API. The DMD service connects the retail customer with the data custodian. It enables the customer to download his or her smart meter data in a comma-separated value format for further analysis or to view the data in a web portal. The CMD API provides application developers with an automated technique to access

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Figure 4.3  Relationship of actors in Green Button [8].

consumer energy information while maintaining security and privacy. For an application provider to gain access to the CMD API, an authorization process should be followed using best practices such as the Internet Engineering Task Force’s OAuth 2.0 Authorization Framework, defined in RFC6749 and RFC6750 [9]. Once the retail customer has authorized access to smart meter data, the data custodian publishes a Green Button data stream for subscription by the third party. Through the Green Button CMD, an application provider has access to different types of data and resources. The most important are as follows[8]: •

•

•

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UsagePoint. A UsagePoint is where a resource is measured. Typically, it is the utility smart meter ID, but it could also be a wall outlet. UsagePoints provide the reference for all meter readings that are contained within the Green Button data. UsagePoints have a ServiceCategory that defines what kind of resource—such as an electricity, gas, or water measurement—is being reported. MeterReading: A MeterReading is a container for all of the measured IntervalBlocks within the Green Button data captured at a UsagePoint. It is the actual smart meter reading in kilowatt-hours or kilowatts. IntervalBlock: IntervalBlocks are the primary data carrier within the Green Button data. IntervalBlocks may have one or more Intervals, each with a start and duration, as well as the specific interval reading.

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•

ReadingType: A ReadingType provides the specifics of the reading data that is being obtained. Green Button follows international standards and has the ability to represent large industrial resources as well as those used in a residence.

Green Button uses the Atom Syndication Format Standard [9] to represent structured energy usage information encoded in XML format that may be exchanged on the Internet. The resources defined within Green Button, including UsagePoints and MeterReadings, are expressed in XML format within the Atom feed‘s entry tags. This results in a uniform way to expose full-featured data APIs that reference a retail customer’s encapsulated EUI. The Green Button API supports all functions of accessing, posting, or updating data in the database. An example for the case of processing data from a specific usage point (meter ID) is given in Table 4.4. 4.5.3 Orange Button Orange Button [10] is an initiative of the DOE [11] and the Sunshot Initiative [12]. The concept is similar to Green Button, but is focused on solar energy data. The intent of Orange Button is to increase solar market transparency and pricing by establishing data standards for the industry. It is expected to contribute to the adoption of industry-led open data standards for rapid and seamless data exchange across the solar value chain, from production to

Table 4.4 Example of API Calls for a Specific Meter Reading of a Usage Point at Green Button [8] API Function

Action

Description

GET

Retrieve a MeterReading using its MeterReading ID

Returns an XML representation of the MeterReadings.

POST

Add a new MeterReading to an existing UsagePoint

Creates a new field in which data can be stored.

PUT

Update an existing MeterReading within UsagePoint

Updates the reading, which can be accessed at a later time. The UsagePoint and MeterReading must exist.

DELETE

Delete a MeterReading from UsagePoint

Deletes the data entry. The UsagePoint and MeterReading must exist.

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consumption. Despite the fact that currently there is no open API to support solar data exchange, Orange Button will provide two important data platforms for solar data sharing: The Solar Data Translation Platform (SDTP) and the Solar Data Exchange Platform (SDEP). The SDTP is focused on translating data structures into standardized formats, whereas the SDEP is focused on connecting industry to the standardized data, improving access, and advancing the solar marketplace. The eventual goal for the Orange Button data platforms is to enable a marketplace for solar data exchanges, purchases, and connections. 4.5.4 PVWatts API PVWatts is an API and solar calculator that was developed by the National Renewable Energy Laboratory [13] to empower third parties with the ability to calculate the energy production of grid-connected photovoltaic (PV) energy systems. The service estimates the performance of hypothetical residential and small commercial PV installations based on real weather data. The estimations are based on a simulation model, called the PVWatts calculator, which processes input parameters related to the PV installation and weather data to provide an output describing the theoretical performance and energy production of the system in kilowatt-hours [14, 15]. To estimate the solar energy produced by a system, the model requires the inputs as described in Table 4.5. The outputs can be environmental data accompanied by energy production values, as shown in the table. The access to PVWatts API is given by a unique API key that is provided to the third-party application provider upon registration in the system. The GET command provides access to output of the PVWatts model and calculator, which replies with a RESPONSE [14]. An example is presented in Table 4.6. For this specific application, a 4-kW system placed at Boulder, Colorado, was modeled and the PVWatts API estimated the monthly AC energy production (in kWh), the hourly plane of array irradiance (in W/m2), the monthly solar radiation values (SOLRAD; in kWh/m2/day), the monthly DC energy production (in kWh), the annual AC production (in kWh), and the annual solar radiation (in kWh/m2/day). The most obvious application of the PVWatts API is the estimation of solar energy production and bill savings for a residential or commercial PV system. For example, a third-party application provider may use the PVWatts API to inform its users about the monthly production and the estimated savings in their bill. To compute the dollar savings in the bill, the EIA API [6] as described previously can be used to provide the pricing of electricity (in $/ kWh). Examples about bill analytics and forecasting are presented in Chapter 5.

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Table 4.5 Input Parameters for the Simulation Model of PVWatts and Output of PVWatts API Input Field

Unit

Description

Zip code OR latitude/longitude of location

—

Helps compute the solar intensity and environmental data for the location of PV system.

System size

kW DC

Helps compute the production of energy. Default value is 4 kW.

Module type

Different modules give different performances Standard, premium, thin that define the overall performance. Default value is standard. film

System losses

%

System losses may vary. Average value is 16%. Default value is 14%.

Array type

Fixed open rack, fixed roof mount, one-axis, two-axis

Different solar arrays provide difference performances. Rooftop solar are usually fixed open racks. Default value is fixed open rack.

Tilt angel

Degrees

Tilt angle changes the performance of the system. Default value is the latitude of the site.

Azimuth angle

Degrees

Default value is 180 deg in Northern Hemisphere and 0 deg in Southern.

DC/AC ratio

Ratio

Losses from DC-to-AC conversion. Default value is 1.1.

Inverter efficiency

%

The efficiency of the inverter. Default value is 96%.

Output Field

Unit

Description

Solar radiation

kWh/m2

Array irradiance

kWh/m2

This is an environmental parameter that presents the average hourly irradiance at the solar array.

Temperature

C

This is an environmental parameter that presents the temperature at the location of the solar array.

Wind speed

m/s

This is an environmental parameter that presents the wind speed at the location of the solar array.

AC output

kWh AC

This is the AC output of the solar array. This value presents the estimation of the produced energy. Data can be presented per hour, per month, or per year.

DC output

kWhDC

This is similar to AC power but represents the DC output.

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/day

This is an environmental parameter that presents the average monthly or annual solar radiation values.

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GET

RESPONSE

/api/pvwatts/v5.json?api _ key=DEMO _ KEY&lat=40&lon=-105&system _ cap acity=4&azimuth=180&tilt=40&ar ray _ type=1&module _ type=1&losses=10 { “inputs” : { “lat” : “40”, “lon” : “-105”, “system _ capacity” : “4”, “azimuth” : “180”, “tilt” : “40”, “array _ type” : “1”, “module _ type” : “1”, “losses” : “10” }, “station _ info” : { “location” : “94018”, “city” : “BOULDER”, “state” : “CO”, }, “outputs” : { “ac _ monthly” : [474.2, 465.6, 628.2, … ], “poa _ monthly” : [136.0, 136.0, 185.72,…], “solrad _ monthly” : [4.3, 4.8, 5.7, … ], “dc _ monthly” : [495.0, 487.5, 657.7, … ], “ac _ annual” : 6681.81640625, “solrad _ annual” : 5.5322184562683105 }

4.5.5 Microinverter APIs Solar microinverters are devices that convert DC power produced from PVs into AC power. Because microinverters are connected to the solar panel itself, some microinverter manufacturers have created an API to provide users and application providers with the ability to access real-time and historical measurements from solar energy production. The model is similar to a privatized version of the Green Button initiative. Companies offering programmatic access to energy production from their

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solar panels have important market advantages. First of all, the users can easily access solar energy production data directly, without requiring applications or software from other companies. Additionally, solar panel installers may use the specific microinverters so they can engage their customers with additional service offerings such as value-added software and management technologies. Finally, third-party application providers may require access to the API that can bring a new channel of income to the microinverter company. In this case, an SaaS pricing scheme may be used where the third-party application provider pays a monthly fee to the microinverter company to access the solar data. The fee is usually a function of the number of data requests (hits) per minute and the total monthly hits. One of the most widely used microinverter API’s is Enlighten, which is offered by Enphase [16]. The Enlighten API is JSON based and provides access to performance data for a PV system. As with many similar APIs, before using the API, a third-party application provider needs an API key, which is obtained upon registration. The rooftop solar owner can provide credentials (such as user ID) that allow the third-party application provider to connect to the Enlighten API and receive the solar data. The process is very similar to the Green Button CMD API discussed previously. The data returned from the Enlighten system may include the instantaneous power production values, daily production, production between time intervals, or the total production of the system to date. An example of the use of the Enlighten API is shown in Table 4.7. The output of the API call provides information about the production of energy as well as some ID and inventory information. For example, the “system_id” and the “modules” of the response in Table 4.7 are the ID of the microinverter and the number of solar modules used in the array. The “size” data is measured in watts and represents the capacity of the PV. The “current_power” is measured in watts, and the “energy_today” value is the daily solar production in watt-hours measured at the time of the API call. The “energy_lifetime” value represents the total energy produced in watt-hours from the first measurement. The user can customize the output of the calls to create a database of measurements. 4.5.6  Smart Thermostat and Connected Home Device APIs The smart thermostat is one of the most important elements of the smart grid. Its operation is directly related to quality of living and energy efficiency. Numerous vendors and product innovators provide smart thermostats directly to the end user (B2C model) or in collaboration with the smart energy plans of utilities and energy retailers (B2B model). The smart thermostat manufacturers,

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The Smart Grid as an Application Development Platform Table 4.7 JSON API Call for the Enlighten System of Solar Microinverters

GET

GET /api/v2/systems/67/summary HTTP/1.1 Host: api.enphaseenergy.com

RESPONSE

HTTP/1.1 200 OK Content-Type: application/json; charset=utf-8 Status: 200 {“system _ id”:67, “modules”:35, “size _ w”:6270, “current _ power”:907, “energy _ today”:1485, “energy _ lifetime”:59796267,

similar to microinverter manufacturers, provide APIs to enable third-party application providers to access data and control the devices. The benefits are obvious since the API makes the product more attractive to the end user or the home manufacturer. The API can also create new business opportunities involving third parties using the API. Many smart thermostat manufacturers also provide a portfolio of connected home devices and products such as cameras, light bulbs, smoke detectors, and water leakage detectors [16–20]. The APIs of these devices offer near-real-time data sharing and control through subscription-based access. By means of APIs, a third-party application provider can build products, applications, and advanced services to access and use the data from connected home devices. The process of retrieving or reading data is based on GET API calls, whereas the process for storing or writing data is based on PUT/PATCH API calls. In many cases, vendors do not allow POST/DELETE API calls because they need to protect their databases. A great variety of data are available from the APIs of connected home devices that enable write (PUT) and read (GET) functionalities. The GET function only allows access to the relevant data, whereas the PUT function enables the change of the operational status of the device. For example, a demand response program may require an automatic change to the smart thermostat temperature or even the ability to power off specific appliances. Table 4.8 presents the most important data values that are provided by connected home devices.

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Table 4.8 Examples of Read and Write Values of Connected Home Devices Device

Parameter

Action

NEST/ HONEYWELL smart thermostat

Current temperature

Provides current temperature setting (read-only function).

Target temperature

Allows a new temperature to be set (write function).

Humidity

Provides current humidity value (readonly function).

Location

Provides zip code information (readonly function).

Mode

Provides information about the state of operation. It can be cool, heat, or auto (write function).

Ambient temperature

Provides room temperatures (read-only function).

Status

Provides status of device. It can be online or offline (read-only function).

Carbon Monoxide (CO) status

Provides current CO value (read-only function).

Battery health state

Provides battery status (read-only function).

Status

Provides status of device. It can be online or offline (read-only function).

Location

Provides zip code information (readonly function).

Content related to events

Triggers a notification, including sound or motion event detected, event start/ stop times, or deep links to image and gif files.

Snapshot on demand

Allows user to take a snapshot.

System mode

Provides information about the state of operation of the device.

Humidity

Provides current humidity value (readonly function).

Ambient temperature

Provides room temperatures (read-only function).

NEST Protect smoke

NEST cam

HONEYWELL water leak

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Let’s explore two use cases that most of the application providers experience. The first example is related to reading the ambient temperature of the room where a NEST smart thermostat (https://nest.com) is placed. The second example is related to changing the target temperature of a NEST thermostat. For the examples below, it is assumed that the application provider has previously obtained authorization to access the devices via an API key or equivalent. Changing the Ambient Temperature of a NEST Thermostat: For REST calls, the GET function is used to read the relevant data. The endpoint of the API call is the URLhttps://developer-api.nest.com. In the example shown in Table 4.9, we first read the specific device ID (THERMOSTAT_ID) and then read the ambient temperature. The call is formatted using JSON and assumes an existing “Bearer” authentication scheme to transmit the access token. This is presented in the first two lines of the code. The last line, which is the GET request, returns the device ID and the ambient temperature from the NEST API endpoint. Changing the Target Temperature of a NEST Thermostat: For REST calls, PUT or PATCH requests are used to write data. To make a PUT call, the field and value that need to be updated should be specified. For the change of the Target_temperature, the field is “{“ “target_temperature_f”: X }”, and the root path should include …/devices/thermostats and the DEVICE_ID (see Table 4.10). Table 4.9 NEST Example to Read the Smart Thermostat ID and the Ambient Temperature in Fahrenheit GET Device ID

GET Ambient Temperature

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curl -v -L -H “Content-Type: application/json” -H “Authorization: Bearer c.lPg4Z...” -X GET “https://developer-api.nest.com/ devices/thermostats/DEVICE _ ID” curl -v -L -H “Content-Type: application/json” -H “Authorization: Bearer c.lPg4Z...” -X GET “https://developer-api.nest. com/devices/thermostats/DEVICE _ ID/ Ambient _ temperature _ f”

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Table 4.10 NEST Example to Change the Target Temperature of the Thermostat PUT (change) the target temperature

curl -X PUT -H “Content-Type: application/json” -H “Authorization: Bearer c.lPg4Z...” -d “{“target _ temperature _ f”: 72}” ‘https://developer-api.nest.com/devices/ thermostats/DEVICE _ ID’

The PUT command is used in Table 4.10 to change the field Target_temperature to the desired integer value, which for this example is 72°F. With this process, a mobile application may inform the user about the current temperature of the thermostat, and may allow the user to change the temperature from a mobile device. 4.5.7  Energy Usage Datasets Datasets that contain raw data about residential energy consumption play a crucial role in the development of new applications related to load disaggregation. These datasets provide low-frequency (LF) and high-frequency (HF) data related to the total energy consumption of the house as well as the consumption of individual circuits within the house. The dataset provides the ability to validate the accuracy of newly developed load disaggregation algorithms. Note that load disaggregation algorithms are examined in a later section of this chapter. The most widely used datasets are the REDD [21] and the Pecan Street datasets [22]. The REDD dataset contains two main types of home electricity data: HF current/voltage waveform data for the two power mains (as well as the voltage signal for a single phase), and LF power data including the mains and individual, labeled circuits in the house. The data is logged at a frequency of about once a second (1 Hz) for a mains and once every 3 sec (0.33Hz) for the circuits. The HF data contains AC waveform data, which contains the sinusoidal variation of the current and voltage. In most nonintrusive load disaggregation algorithms, LF data is preferred since smart meter electronic equipment cannot measure the HF components of the power. The data structure of the REDD dataset that is open to the public is presented in Table 4.11.

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Table 4.11 REDD dataset structure Data field

Description

Data type

house_{1..n}/

Includes directories for each monitored house. REDD provides data from 20 houses.

Integer

labels.dat

Includes device category labels for every channel. For example, a house with 10 circuits may include labels such as 1 mains, 2 oven, 3 refrigerator, 4 dishwasher, 5 kitchen_outlets, 6 lighting, 7 washer_dryer, 8 microwave, 9 electric_heat, 10 stove.

String

channel_{1..k}.dat

This is a vector that presents time and wattage readings for each channel. Time is presented in DD/MM/YY and Hr:Min:Sec, whereas wattage is provided as watt-hour numerical value.

Date/time structure and numerical values

4.5.8  MultiSpeak In some cases, the existing APIs and open data are not sufficient to provide the required integration and interoperability with existing utility systems. In most business models, third-party application providers need to integrate their applications and services with utility systems such as an MDM or CIS. For that reason, standards and specifications from federal or private coalitions provide the required foundations for system interoperability [23, 24]. MultiSpeak is a specification that minimizes the need for extensive software integration between a third-party application provider and a utility for the exchange of data. The specification defines standardized software bridges and/ or interfaces among software applications. The business benefit of MultiSpeak is a reduction in the complexity and time required for system integration between application providers and utilities. Software interfaces, data objects, and message structures are defined by MultiSpeak to permit vendors to write a common interface that facilitates communication with another type of software via XML-encoded data. The interfaces established in the MultiSpeak specification are defined on the basis of information flows between software functions using web services. The most important message exchange between the systems of the vendor and the utility are as follows: •

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Batch communication: Periodic data transfer from the owner of the data to the requestor. The data is transferred in groups and not in

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•

real time. The most significant application of batch communication is daily transfer of smart meter data from the user to the database of the application provider. Request/response communication: Commands sent between a client (data consumer) and a server (data provider) requesting the server to take a specific action. When the action is completed, the server responds with a message outlining the results. For example, the application of the vendor may request the server (utility) to send the current smart meter reading of the user.

4.6 Open ADR The Open Automated Demand Response (OpenADR) standard is a nonproprietary, open, standardized DR communications model. It facilitates the communication of DR signals between utilities or third-party application providers and electricity consumers via existing language and communications technologies. The standard relies on IP and web technologies (web services) to transmit DR signals. OpenADR also provides automation to DR events through predesigned programs and strategies within a scalable architecture able to support different forms of DR programs [25]. 4.6.1  Key Actors and Services The key actors in the exchange of OpenADR signals are the virtual top node (VTN) and the virtual end node (VEN). Following the concept of server/ client communication, VTNs and VENs communicate using web services. For example, a utility can be a VTN (server), while the agent of the HEMS or BMS or even the connected home devices of the consumer can be a VEN (client). Similarly, an aggregator (third-party application provider) can be a VEN from the utility’s perspective, or a VTN from the consumer’s perspective. The architecture of the communication of the actors is presented in Figure 4.4. The utility or the third-party application provider initiates and executes DR events via a demand response automated server (DRAS). A DRAS is a server that communicates with connected home devices, such as smart thermostats, water heaters, ovens, or even EVs, and initiates command flow to the smart devices. The OpenADR standard uses a web service to create a logical request/ response system in which each service has a single common endpoint. The root element defines the service and the operation and the devices communicate

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Figure 4.4  Key actors and communication architecture for the OpenADR.

using an XML-encoded payload. The concept is similar to the API structures described previously. 4.6.2  Demand Response Event Demand response is performed by means of DR event exchanges between the VTNs and VENs. An OpenADR event contains three time-dependent states, as shown in Figure 4.5. The first state is the notification state, where the notification about the event and the transition into the event takes place. The ramp time is the transition time required by the device until the event takes place. The second state is the active state, where the actual DR signal takes place. The active state lasts for the entire event duration. Finally, the recovery state is the state after the event where the device recovers from the event and no other events can take place [25]. During the active state of an OpenADR event, signals are transmitted to the device that carry information about the duration of the interval and the electricity price during that interval. For example, an OpenADR event can be a 10% reduction of energy used by a smart building. During the active state, control signals delivering the command for energy reduction are transmitted to connected devices. The DR signal may carry information about the

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Figure 4.5  OpenADR demand response event structure.

electricity price, the maximum consumption value, the thermostat temperature, and so forth. The DR signal may be continuous during the active period of the DR event, or segmented in high-resolution commands. For example, four different electricity pricing signals may exist during the DR event. An OpenADR event can also be targeted to different participants. For example, an OpenADR event can target a VEN, a group of devices, a class of devices, a service area, or a specific resource. 4.6.3 Communication Architecture The OpenADR standard uses standard HTTP commands, as have been described previously. HTTP is ideal for pull clients and possible for push clients if security issues are handled appropriately. The OpenADR standard also uses the Extensible Messaging and Presence Protocol (XMPP) as a transport protocol. XMPP is ideal for push applications and fast DR execution, and it also enables pull applications. Thus, the OpenADR standard event communication process is a push–pull action between the VTN and the VEN. The VTN pushes an event to an HTTP universal recourse identifier (URI), which is exposed by the VEN. On the other hand, a VEN may periodically pull upgrades from the VTN.

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4.6.4  Rush Hour Example OpenADR first provided an implementation architecture for DR in industrial and commercial applications; that implementation was then extended to residential customers. The residential application is a simplified use case since DR is implemented to a small number of connected devices, such as a smart thermostat. One of the most widely used implementations is the Rush Hour DR service provided by NEST [26]. Rush Hour is a B2B cloud service provided by the smart thermostat manufacturer to electric utilities that helps utilities reduce peak power consumption during critical hours. Smart thermostat owners can volunteer to join the Rush Hour program and receive DR signals according to the utility’s needs or objectives. Customers who participate in the program receive incentives or rewards, often in the form of bill credits. Despite the fact that the Rush Hour program is not a direct implementation of the OpenADR standard, it is based on concepts and techniques described by OpenADR. The Energy Rush Hour event follows an architecture similar to that presented in Figures 4.4 and 4.5. Following the communication architecture of Figure 4.4, the utility is the VTN, NEST cloud servers are the VEN (from the perspective of the utility), and they receive the DR request. The NEST servers, which are the DRASs, have control access to the connected devices of consumers, which are the final VENs. Thus, NEST servers act as VTNs from the perspective of the end users. When a utility predicts a large rise in demand for electricity, possibly due to environmental conditions, an energy rush hour event is scheduled. The energy rush hour event is sent to the NEST servers, which process and forward the event notification to all smart thermostats registered in the Rush Hour rewards program. The energy rush hour event is similar in structure to the OpenADR event presented in Figure 4.5. During the notification period, a smart thermostat owner receives a notification indicating that a temperature control will take place in the near future, at least an hour before it starts. During the ramp period, a precooling or preheating phase may take place, which is important to keep the comfort level in the living environment at an acceptable level during the active period of the DR event. Precooling and preheating adjusts the temperature of the home ahead of the rush hour so the participants can stay comfortable during the rush hour event. During the active period, the smart thermostat adjusts the home’s temperature to save the required amount of energy, according to commands received by the DRAS. During summer, the target temperature of the thermostat may be increased by a few degrees in order to reduce cooling needs and save energy. During winter, the target temperature of the thermostat may be decreased by a few degrees in order to

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reduce heating needs and save energy. The structure of the command is shown in Table 4.10. A PUT API request is used to change the target temperature of the smart thermostats to the preferred value. When the rush hour period ends, and during the recovery period, as presented in Figure 4.5, the smart thermostat will return to its regular temperature schedule and cannot accept any other DR commands until the temperature of the home reaches the users’ initial preferences. In general, electric utilities avoid frequent DR events in an effort to keep the user’s quality of experience at acceptable levels. In most cases, DR for residential users takes place 6 to 12 times per year according to weather conditions and consumption needs. User engagement in DR events is an important factor that is encouraged by offering reward and incentive programs as well as gamification programs.

4.7  Conclusions and Concerns Cloud computing, open data, and APIs provide a solution to the problem of data transfer and data exchange among the system of systems of the smart grid. The great diversity of the existing systems and the large number of vendors and protocols create interoperability issues. Web services and APIs create the necessary foundations for the development of software bridges for the connection of the systems. Because of security and reliability issues and also because of the increasing number of third-party application providers, some criticisms have been leveled at the existing solutions. The main concerns and criticisms of the existing APIs are as follows: •

•

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Adoption of services: Despite the fact that a large number of corporations and government initiatives promote the use of APIs and open data, the adoption of the services in the market is still low. This condition is related to the low speed of the evolution of the utility market and is expected to change in the near future. Difficulty: Existing APIs are a bit confusing for the average user. Users cannot easily share their smart meter data with application providers because of the complicated user experience at the relevant sites. In addition, the standards for the automated transfer of smart meter data from residential customers to third-party application providers are incomplete and create security and reliability concerns. For example, authorization and handshaking protocols are not well established and are difficult to implement.

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•

•

Latency: Many APIs provide a threshold on the number of call requests that can be accepted by a given third-party application provider over a specific period of time. In some cases, this limits the number of possible applications. In addition, some of the existing APIs are resource intensive and increase the cost of cloud hosting at the application provider. Appliance health: APIs make possible the automated control of specific smart appliances in a residential household. These control commands are usually based on on/off schemes that may affect the lifetime of the appliance. For example, an air conditioning unit that receives frequent off commands may malfunction in the near future.

References [1] www.greenbuttondata.org. [2] www.orangebuttondata.org [3]

Johan Zuidweg, Creating Value Added Services and Applications for Converged Communications Networks, Norwood, MA: Artech House, 2015.

[4]

Dominique Guinard and Vlad Trifa, Building the Web of Things: With Examples in Node.js and Raspberry Pi, Shelter Island, NY: Manning Publications, 2016.

[5]

Adrian Ryans et al., Winning Market Leadership: Strategic Market Planning for Technology-Driven Businesses, New York: Wiley, 1999.

[6] www.eia.gov. [7] www.eia.gov/opendata. [8] www.greenbuttondata.org/developers. [9] https://tools.ietf.org/html/rfc4287. [10] www.orangebuttondata.org. [11] https://energy.gov. [12] https://energy.gov/eere/sunshot/sunshot-initiative. [13] www.nrel.gov. [14] https://developer.nrel.gov/docs/solar/pvwatts-v5. [15] Aron P. Dobos, “PVWatts Version 5 Manual,” Technical Report, National Renewable Energy Laboratory, 2014. [16] https://developer.enphase.com. [17] https://developers.nest.com.

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[18] http://developer.honeywell.com. [19] https://www.ecobee.com/developers. [20] https://www.hivehome.com/us. [21] J Zico Kolter and Matthew J. Johnson, “REDD: A Public Data Set for Energy Disaggregation Research,” Proc. SustKDD Workshop on Data Mining Applications in Sustainability, 2011, http://redd.csail.mit.edu. [22] https://dataport.pecanstreet.org. [23] National Institute of Standards and Technology, “NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 3.0.” NIST Special Publication 1108R3, May 2014. [24] G. A. McNaughton and W. P. McNaughton, “MultiSpeak Version 3 User’s Guide,” National Rural Electric Cooperative Association, 2006. [25] www.openadr.org. [26] https://nest.com/support/article/What-happens-during-a-Summer-or-Winter -Rush-Hour. [27] ECMA International, “The JSON Data Interchange Format”, ECMA-404 1st Edition, October 2013, http://www.json.org/. [28] Bray, T., et al., Extensible Markup Language (XML) 1.0, Fifth Edition, W3C Recommendation 26, November 2008, https://www.w3.org/TR/xml/. [29] Internet Engineering Task Force, “Hypertext Transfer Protocol Version 2 (HTTP/2),” RFC 7540, May 2015, https://tools.ietf.org/html/rfc7540. [30] Fielding, R. T., “Representational State Transfer (REST)”, Architectural Styles and the Design of Network-based Software Architectures, Doctoral Dissertation, University of California at Irvine, 2000 http://www.ics.uci.edu/~fielding/pubs/dissertation/rest_ arch_style.htm. [31] Internet Engineering Task Force, “The Transport Layer Security (TLS) Protocol Version 1.2,” RFC 5246, August 2008, https://tools.ietf.org/html/rfc5246. Updated by RFC 6176, March 2011 https://tools.ietf.org/html/rfc6176. [32] North American Energy Standards Board, “Energy Services Provider Interface,” ESPI REQ.21, https://www.naesb.org/ESPI_Standards.asp.

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5 User-Centric Applications

5.1  Introduction The previous chapter presented an overview of the communication architecture of the utility’s and application provider’s systems. It was observed that web services, APIs, and software bridges are the most widely used technologies for the interaction of different systems connected to different networks. This chapter goes one step further and provides a detailed description about the mathematical tools and frameworks used for the processing of the collected data.

5.2  Data Processing Overview Data processing is the necessary step to convert raw data to useful information and insights that are used for the development of customer-centric services. The general system architecture is presented in Figure 5.1. Through the use of web services and APIs such as RESTful, an application provider can collect raw data to be stored in a cloud computing infrastructure. The data is typically stored in a database (e.g., SQL, NoSQL, or MongoDB) and a computing system (virtual machine or CPU) is available to process the data, create useful information, and deliver a helpful service to 143

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Figure 5.1  System architecture between an application provider and utility or vendor.

the end user. The end user accesses the service via a connected device such as a laptop, tablet, or smartphone. The most significant applications are those related to energy analytics (or bill analysis/forecasting), energy usage analysis (or load disaggregation), and demand response, which can be implemented in direct load control, load scheduling, or gamification approaches.

5.3 Energy Analytics Energy analytics is the foundation of smart grid services. In resolving problems related to the adage “You cannot manage something that you cannot measure,” energy analytics provide previously unavailable perspectives to the end user regarding the consumption of energy, the optimization of usage, and other actionable insights. For example, a user might have access to his or her household’s daily, weekly, monthly, or seasonal energy consumption. A service using energy analytics processes smart meter data, determines trends and actionable optimizations, and presents this information to the user in a convenient, understandable format. Two examples are presented below that fall within the service area of energy analytics: daily energy consumption analysis and bill forecasting. 5.3.1  Hourly and Daily Energy Analytics Let’s assume that a GET API call collects the raw power consumption data of a residential customer from the Green Button API or REDD dataset as

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discussed in Chapter 4. This data is stored by the application provider’s servers, and needs to be processed in order to produce useful information. Let’s assume further that the raw data has a sampling frequency of 1 Hz (1W measurement per second) and is stored as text file with a timestamp YYYYMM-DD HH:MI:SS and a power reading, pi (in W). Thus, each hour has 3,600 power readings and each day has 86,400 power readings. The objective of the energy analytics service is to process the raw data and present hourly and daily energy consumption values to the end user. This information can help the user understand household consumption trends and energy waste. The application provider’s processing system should implement specific steps to transform the raw data to useful information and convert simple power readings to useful, actionable insights. The first processing step requires a transformation of the power readings (in W) to energy consumption data (in Wh). Following the equations presented in Chapter 2, the conversion is based on the following formula, where f is the sampling frequency:



⎡ 1 1 ⎤ ei = pi ⋅ ti =  pi ⋅ ⎢ ⋅ (in Wh) (5.1) ⎣ f 3,600 ⎥⎦

The second step requires the computation of the hourly energy consumption data from the high-resolution readings, which can be computed according to: 3,600/f



ehr , j =

∑ i=1

ei (in Wh) (5.2)

The last step converts the hourly readings to daily energy consumption data by using the following transformation, where j = 1, 2, …, 24 for each day and k = 1, 2, …, 30 for each month: 24

eday,k = ∑ehr , j (in Wh) (5.3)

j=1

The data processed in this fashion can be stored in a database, and web presentation tools can be easily used for end-user visualization. The conversion of raw data to hourly and daily insights is presented in the Figure 5.2, where the observation period is 5 days, and the household observed is a onebedroom residential apartment.

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Figure 5.2  Hourly and daily energy consumption data from raw smart meter power readings.

A multitude of techniques are available for storing and processing large datasets, and the preferred strategy depends on the resources available from the application provider. In many cases, application providers need to minimize storage and processing needs. For that reason, raw data older than 1 day are compressed to hourly values and the rest of the raw data is deleted. In addition, historical data older than 1 month is compressed to daily data and the hourly data is deleted. In this fashion, each user of the service is responsible for a small, controllable amount of storage. Several different database management systems and techniques can be used to maximize the efficiency of the database and the storage of the data [1]. 5.3.2 Bill Forecasting Another important application derived from smart meter data is bill forecasting. To effectively perform bill forecasting, the application provider must process smart meter data and predict the total energy consumption up to the end of the current month. This estimation helps the user understand the dollar amount of the bill. The bill estimation is usually performed by processing the hourly energy consumption data as presented in (5.2). Let’s assume for the

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specific example that the first 5 days of the month correspond to N = 120 (5 days ´ 24 hr) values of ehr,j with j = 1, 2, …, N, as shown in (5.2). The aggregated energy consumption can be computed according to: k−1

E = { E1 , E2 ,…EK } ,   Ek = ∑ehr , j (5.4) j=1



In this equation, the values Ei constitute a time series that represents the aggregated energy consumption values for every 1-hr time interval. An example is show in Figure 5.3. A mathematical process such as linear regression or leastsquares curve fitting can be performed by the application provider to improve the accuracy of the bill forecasting services [2]. The objective of the linear regression is to estimate the best-fit linear equation on the aggregated measurements E. The linear equation has the form:

y = b ⋅ t + a (5.5)

where t is the time in hours and y is the estimated energy consumption in watt-hours (or kilowatt-hours). The objective of the process is to estimate coefficients a and b, which describe the line with the best fit to the observed data.

Figure 5.3  Bill forecast based on linear regression.

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Future consumption values can then be estimated according to the equation of the best-fit line. Linear regression is based on several important mathematical principles. First, the mean or average of the observed ehr,j values is computed using the equation: em =

N

1 ⋅ ∑ e (5.6) N j hr , j

The coefficients of the best-fit line can then be computed according to:

∑ j=1((ehr , j − em )( j − N /2)) (5.7) b= N 2 ∑ j=1( j − N /2) N



a = em − b

N (5.8) 2

Figure 5.3 presents an example for the linear regression of 120 observed energy consumption values. For this example, the coefficients of the best-fit line are b = 0.268 and a = 2.99 kWh. Thus, for t = 720 hr (1 month), the estimated bill forecast is y = 0.268 ´ 720 + 2.99 = 196 kWh. Assuming a cost of electricity c = $0.22/kWh, the estimated amount of the bill by the end of the month is $43.12. Most application providers develop bill forecasting and energy analytics services to offer useful insights to end users and engage the end users in conservation of energy. Another important service is load disaggregation, which is explored in the next section.

5.4 Load Disaggregation Load disaggregation or energy usage analysis converts a household’s raw smart meter data into a detailed map of the consumption of the individual appliances in the household. At the heart of load disaggregation are methods for recognizing, classifying, and extracting the usage patterns or signal characteristics for individual appliances from data that contains the total household energy consumption. This process involves complex mathematical models and machine learning algorithms. In most cases, load disaggregation is performed by third-party application providers who offer the service in a B2B manner to the utilities. Utilities may then provide the service to the end consumer.

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The classification of existing load disaggregation algorithms is based on two general categories: intrusive and nonintrusive monitoring. Intrusive monitoring performs appliance disaggregation based on meter readings from devices attached to specific appliances. Nonintrusive monitoring uses sophisticated signal processing techniques to disaggregate individual appliance signals from the total household meter reading. The nonintrusive approach is subdivided into supervised and unsupervised training algorithms [3, 4]. Unsupervised training is based on a plug-and-play approach in which the algorithm does not need any inputs from the users. In contrast, supervised training requires some basic appliance registration before the execution of the algorithm. The performance of load disaggregation algorithms is directly related to the quality and amount of available smart meter data as well as the sampling frequency, which can be in the greater than 3-kHz (HF) or less than 1-Hz (LF) range. Utilities or product innovators usually offer smart meter systems to their customers for free, and they allow access to load disaggregation and energy analytics services as part of the utility fees. This business model requires low hardware costs and low computational complexity at the user level. Thus, smart meters need to operate as data transmitters or command receivers in the advanced metering infrastructure (AMI) network, and all processing takes place in a cloud computing architecture. In addition, data traffic for such systems needs to be limited to avoid interference with existing wireless networks. These constraints bound the applicability of HF data sampling (>3 kHz) of active and reactive power readings. The most preferred methodology is to implement nonintrusive techniques on the LF data (0.01 to 5 Hz) of the active power component to reduce the cost of offered services and the computational complexity of the algorithms. A large number of different algorithms and mathematical techniques exist that can perform nonintrusive load disaggregation, and many of these are presented in [5]. The most important algorithms are based on factorial hidden Markov models (FHMMs), neural networks (NNs), pattern recognition, and machine learning techniques. Based on the energy analytics service, the application provider develops algorithms that will process the collected smart meter data and provide insights to the end user describing the percentage of energy consumption per appliance. 5.4.1  Hidden Information in Appliance Footprints Before moving on to the details of load disaggregation algorithms, it is important to observe some basic characteristics of the energy footprint of the most commonly used appliances. Each appliance has its own energy footprint that

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is modeled by instantaneous active power and reactive power components, following the discussion of Section 2.6.1. Assuming that the instantaneous demand (active power) of each appliance is modeled by the variable xi(t) (in W), t = 1, 2, …, T where T is the duration of operation of each appliance and i ∈ M where M is the set of the appliances in a household, then the total power consumption of the house p(t) is given by: M



p(t) = ∑ xi (t) (5.9) i

The superposition of the power consumption of each appliance in the household creates the instantaneous power consumption needs of the entire house, which is captured by the smart meter. An example is shown in Figure 5.4. Each appliance has its own energy footprint, which can be modeled as pulse-shaped functions. This observation was also discussed in Section 2.6.1. Load disaggregation algorithms use the unique appliance characteristics to associate a portion of the power consumption of the household with each appliance. The most important components of the appliance footprint, analyzed by load disaggregation algorithms are:

Figure 5.4  Raw smart meter data and hidden information on appliance footprint.

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1. Power value: the magnitude of the active power when the appliance is on; 2. Reactive value: the magnitude of the reactive power for inductive and capacitive appliances (zero for resistive appliances) that usually creates a large variance in the active power component; 3. Periodicity: the periodic pulse shaped consumption of appliances that are usually related to thermostatic loads; for example, air conditioning units, refrigerators, ovens, water heaters; 4. Cycles/multi state: the change of utilization for appliances having various cycles of operation, such as a washing machine with hot water preparation, wash, and rinse cycles that created multistate consumption; 5. Spike: once an appliance is on, a spike in power usage associated with its operation may occur; this is usually seen in inductive appliances that incorporate a motor; 6. Variance: a fluctuation of power around the average power consumption; this characteristic is seen in inductive appliances, and its presence is correlated to a positive reactive power component (see point 2); and 7. Slope: once an inductive appliance is on, a slope of the power curve is observed for a short time interval; this slope is a unique characteristic of a motor. An example of the flowchart for a typical nonintrusive, LF, supervised load disaggregation algorithm is presented in Figure 5.5. Each step of the algorithm is analyzed in detail in the sections below. The required inputs are (1) the raw smart meter readings, p(t), which are gathered by means of web services and GET API calls as described in previous chapters, and (2) the registration of the household’s appliance, which is performed by the home owner, usually using a web interface. The user is required to register each appliance in the residential unit by indicating the tabulated power value and name of the appliance. Thus, each appliance, i, will carry information about tabulated power values Xtab i (in W). The load disaggregation operation is the most significant part of the overall algorithm and involves two stages (Figure 5.5). In the first stage, the input smart meter data is processed and filtered for noise rejection. The output of this stage is a processed time series that works as input for the machine learning part of the algorithm. In the second stage, a machine learning algorithm converts the smart meter readings into pulses (or events) and then associates each pulse with an appliance. Thus, the original smart meter data is transformed to a set of power pulses, each one associated with the operation of an appliance. Since each power pulse is described by a time initiation point, a

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Figure 5.5  Flowchart of load disaggregation algorithm.

time duration, and a power value, the detailed energy characteristics for each appliance can be accurately estimated. During the registration process, the user registers the tabulated values of the power consumption for each appliance. Thus, a table is created that contains these reference values. In addition, two other types of information are updated during the appliance registration process. The first corresponds to values that include personalized user activity behavior, such as time of use probability and duration of pulse. The time of use probability is a binary vector that indicates the expected time of operation of the appliances. The duration of pulse is a maximum threshold for the expected duration of a power pulse. For example, a power pulse created by an oven cannot exceed 2 hr in

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duration and cannot be found at 3 a.m. since people do not typically cook at that time. The second category corresponds to characteristics that exist in typical appliances and for the purpose of this discussion are the multistate, periodicity, spike, slope, and variance as described in Table 5.1. This table is significant since it holds some a priori information about the characteristics of the appliance that is used during the process, which associates a pulse to an appliance. The following sections provide a more detailed explanation of each step of the algorithm. 5.4.2  Signal Processing on Smart Meter Data The raw data must first be processed via signal processing algorithms to filter noise and condition the signals for subsequent algorithms. Noise in the raw

Appliance Type

Name of appliance

Tabulated Power (W) (Example)

Variance

Spike

Slope

Periodicity

Multistate

Table 5.1 Map of Appliance Pulse Characteristics

Resistive

Electric storage heater

2,000

X

X

X

✓

X

Oven

1,500

X

X

X

✓

X

Hotplate

900

X

X

X

✓

X

Hot water

2,400

X

X

X

✓

X

Toaster

1,800

X

X

X

✓

X

Hair dryer

1,500

X

X

X

X

X

Coffee

1,100

X

X

X

✓

X

Lights

50

X

X

X

X

X

Vacuum cleaner

1,600

X

X

X

X

X

Fridge/freezer

350

✓

✓

X

✓

X

Air conditioning

1,200

✓

✓

✓

✓

X

Inductive

Capacitive

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Washer/dryer

2,300

✓

X

X

X

✓

Dishwasher

1,900

✓

X

X

X

✓

TV

200

X

X

X

X

X

Stereo

100

X

X

X

X

X

Game/entertainment

200

X

X

X

X

X

PC

300

X

X

X

X

X

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signals can be related to conditions including voltage/current spikes, externally coupled signals, and amplitude variance in the HF components. Spikes can be easily detected since they create a high power value with a very short time duration on the smart meter signal. Refer back to Figure 5.4, which highlights a spike in the raw data. In all cases, spikes have a duration of shorter than 5 sec that corresponds to one discrete measurement. Spikes in the power signal are detected and stored as information that will be used by the load disaggregation algorithm. By denoting as ts the point in time when a spike is detected, the detection process involves the following logic:

p ( t s ) − p ( t s −1) > d and p ( t s ) − p ( t s +1) > d (5.10)

where δ is a threshold and can be safely set to δ = 1,000W [5]. A verbal interpretation of (5.10) might be “If the difference in power between subsequent amplitude values is large (over 1,000W) and occurs over a short time period, then the segment is noted as a spike.” Spikes need to be removed by the signal processing algorithm since they can create unwanted distortion in the load disaggregation algorithm. Removal of spikes is simple and it can be based on the following smoothing process, which replicates a nearby sample:

p ( t s ) = p ( t s +1) (5.11)

An example of spike removal is present in the top graph of Figure 5.6. Once the spikes have been removed, a subsequent smoothing process is also used to de-noise the signal further. The simplest and most efficient way to remove unwanted HF noise from the power signal is via a moving average filter. A moving average or low-pass filter is a well-known signal processing tool that is very simple to implement and very effective in removing HF noise from a signal. There are a number of ways to implement a moving average or low-pass filter, and some expertise is required in order to avoid unnecessary residual distortion in the filtered output. The effect of the smoothing process is also presented in the top graph of Figure 5.6. 5.4.3  Event Detection by Extracting Power Pulses from Smart Meter Data Pulse extraction replaces the time series of the power signal with a set of discrete pulses. These discrete pulses represent events that are correlated to the change in power state (on/off or off/on) for specific appliances. The first step in pulse

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Figure 5.6  Processing of smart meter data and conversion to pulses.

extraction, which is also called edge detection, is to use the first derivative of the de-noised power signal to help estimate the start and end points (or edges) of events. An example is presented in the middle graph of Figure 5.6. The first derivative is computed according to:

⎞ ⎛ dp(t) dP = ⎜ : t = 1,…,T ⎟ (5.12) ⎝ dt ⎠

The positive and negative parts of the of the first derivative can be easily extracted according to:

⎛ dp(t) ⎞ dPk+ = ⎜ > 0 : k = 1,…,K ⎟ (5.13a) ⎝ dt ⎠



⎛ dp(t) ⎞ dPq− = ⎜ < 0 : q = 1,…,Q ⎟ (5.13b) ⎝ dt ⎠

with K = Q and K + Q = N, where N is the set of samples of the power signal. Similar to spike detection, a verbal interpretation of (5.12) and (5.13) might be “If the amplitude of the power changes quickly over a short time period, then the segment is noted as an edge, or the beginning/end of a pulse.”

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A pulse event is noted when the pulse detection algorithm encounters a leading edge (i.e., a change of state from off to -on) or a falling edge (a change of state from on to off) in the power signal of an appliance. Detected pulse events for the raw and conditioned signals of Figure 5.6 are shown in the bottom graph of the figure. The event detection process starts from the first negative value of dP–q that indicates an appliance has stopped its operation, or has switched from on to off. This trailing edge event needs to be associated with the corresponding off-to-on switch or leading edge event of the appliance indicated by dP+k. The correlation of neighboring on/off and off/on events is performed by computing the distance between events, taking into account the power and temporal relationships of dP–q and dP+k. The transition that occurs when an appliance is switched on or off creates disturbances in the amplitude of the power signal. These disturbances can be measured at the smart meter. The time component of the power signal indicates that, in most cases, these positive and negative disturbances are in proximity. To capture the higher importance of the amplitude component of the power signal (vertical axis) versus the time component (horizontal axis), we use weighting factors ω = 0.75 and ψ = 0.25 for the amplitude and time components in a heuristic association function. The association function for the pulse is given by (assuming normalized values):

(

f : q ! k = min w ⋅ dPq− − dPk+ k

2

) +y ⋅(t

− q

− t k+

2

)

(5.14)

The association of the negative component of the first derivative of the smart meter reading with a positive component defines an event or a power pulse that indicates the operation of a specific appliance. These relationships are shown graphically in Figure 5.6. Each pulse provides a variety of descriptive information, including initiation, duration, and amplitude, as described in the equations in Table 5.2. The total consumption of the house is the superposition (addition) of n power pulses created by the M appliances. 5.4.4  Clustering With the aforementioned methodology, a time series of raw power data is converted to a set of pulses (events) that carry important information. The next step is to define the appropriate association between pulses and appliances via a clustering algorithm. The clustering algorithm creates a group or cluster of the most probable candidate appliances for each extracted pulse. The selection process is based on the Euclidean distance [similar to (5.14)] between

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Table 5.2 Characteristics of Pulses for Load Disaggregation Algorithm Pulse Characteristic

Symbol

Pulse amplitude

Pn

Description



Pn =

dPq− + dPk+ 2

(5.15a)

Pulse initiation

tn

Pulse duration

τn

Pulse spike

sn

A binary indicator sn = [0,1] that indicates the presence of a spike detected in the smart meter data at the beginning of the pulse.

Pulse slope

βn

A binary indicator β n = [0,1] that indicates if a slope in the smart meter data was detected. The slope can be computed by a linear regression of the raw power signal p(t) for the time interval tn → tn + 14 sec, where 14 sec is a heuristic threshold.

Pulse variance

rn

A binary indicator rn = [0,1] that indicates if high variance was observed in the power signal during the operation of the appliance. It is determined by computing the variance of the raw smart meter data for the time interval tn → tn + τ n . A detailed description of the computations for these characteristics is presented in [5].



t n = t k+ (5.15b)



tn = t q− − t k+ (5.15c)

the registered appliance tabulated power value, Xtab i , also shown in Table 5.1, and the amplitude of the power pulse Pn. The easiest clustering algorithm to implement is the K-nearest neighbors (KNN) algorithm [6] with K = 5. This creates a cluster of the five most probable candidate appliances for each pulse. These appliances have a tabulated power level that is close to the amplitude of the extracted power pulse. On most occasions, the power value is only used for the clustering phase since it constitutes the most significant part of the appliance energy footprint. 5.4.5  Pulse to Appliance Association Association of one appliance to each pulse is performed via a classification process. Numerous techniques exist that can be used for classification. Most techniques are based on maximum likelihood estimation (MLS) [7] or heuristic scoring systems [5]. Usually an n-dimensional space is created for decision

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making that takes into account the appliance characteristics, the pulse characteristics, the user activity, and external conditions, as described in Table 5.1. For the purposes of our investigation, a simplified scoring system was used to model the maximum likelihood for pulse-to-appliance association [see (5.16)]. Using this approach, the appliance with the highest score is associated with the relevant pulse. Coefficient Ci includes normalized metrics in the range 0 ≤ M ≤ 1 that model the correlation of the pulse characteristics to the appliances: C i = w1 ⋅ ( M P + MT + M D + M PR ) + w2 ⋅ ( M M + MV + M SL + M S ) − Φ

(5.16) In our example, the weighting factors w1 and w 2 were set to 0.7 and 0.3, respectively, to give priority to metrics that hold a priori information of the appliances during the registration process. The appliance i with the highest metric C will be chosen to be associated with pulse n. In (5.16), MP correlates the power amplitudes, MT correlates the operation of specific appliances with the expected time of use, MD correlates the duration of the detected pulse with the expected pulse duration of the appliances, and MPR detects the periodicity of the detected pulse. Additionally, MM models the multistate operation of the appliance, MV models the existence of variance during the operation of an appliance, MSL models the existence of a slope at the initiation of an appliance, and MS models the existence of a spike at the initiation of an appliance. These are shown in Table 5.1. The model also allows for penalties to be applied through the use of a metric Φ, which is subtracted from the scoring coefficient. This factor corrects an inappropriate pulse-to-appliance association according to the maximum expected daily energy consumption of appliances. For example, an oven cannot consume more than 10 kWh of energy per day, or a hair dryer more than 1 kWh/day. 5.4.6  NIALM Results and Business Intelligence Nonintrusive appliance load monitoring (NIALM) algorithms provide important insights into the operation of appliances in residential houses. These intelligent algorithms transform raw smart meter data into power pulses, which are then associated with the operation of specific appliances. In this fashion, the user, the application provider, or the utility can develop a better understanding regarding which appliance was switched on or off and how long it may have been operating. An example of the outcome of NIALM algorithm

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and business intelligence for electric utilities is given in Figure 5.7. The first part of the figure presents the transformation of smart meter data to a set of pulses that are associated with appliances. This is also obvious in the third part of the figure, which presents the estimated probabilities that an appliance might be on during a specific 1-hr time interval of the data. This information is derived from the monthly smart meter data of the household. This information is mainly important for the application provider or the utility who might be willing to implement demand response programs. The knowledge of the operation of appliances is important since personalized demand response programs can be implemented for each residential customer. In some cases, third-party application providers leverage this information for security or health issues. For example, monitoring the everyday activity of an elderly person can help the application provider identify anomalous patterns and inform the relatives of the individuals of the presence of unexpected events. The middle part of the figure presents a disaggregation of the daily energy consumption according to the operation of the appliances. This result can be computed in daily, monthly, or annual increments and can be useful in helping the homeowner understand the energy waste in the house and the operating costs of each appliance in the utility bill. The energy consumption of the appliances can be computed according to (5.15). The energy consumption of appliance i, which has been associated with a set of pulses j∈m, is computed as follows: Ei =

∑ Pj ⋅ t j (5.17) j∈m

The benefits of load disaggregation are enormous for all parties. As a result, application providers are developing sophisticated algorithms and techniques to tackle this difficult scientific problem. Because the development and the operation of these algorithms requires scientific research as well as high computational capacity, application providers implement a SaaS business model with the electric utilities using cloud-based computational services.

5.5  Direct Load Control Demand response (DR) is the most fundamental mechanism of the smart grid since it balances time-varying production with time-varying consumption by transmitting pricing signals to load controllers. Pricing signals can hold information about the production of renewable energy sources (RESs),

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Figure 5.7  Results from NIALM and business intelligence for energy usage in residential units: (a) daily association of a pulse with an appliance, (b) daily disaggregation of energy usage according to registered appliances, and (c) probability of appliance operation averaged over a month of measurements [5].

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the cost of electricity, the available battery storage, and other key parameters. DR programs are appropriate and available for all energy consumers, including residential, commercial, and industrial environments, and create a win–win situation for all parties. Consumers can reduce their bills or receive incentives, and utilities can reduce the amount of imported energy or the cost of switching on additional power plants (capacity). The implementation of DR may contain several mechanisms, including direct load control (DLC) and load scheduling (LS). The DLC mechanism, also called load shedding, is used to reduce certain types of demand, such as from smart thermostats, and keep overall system consumption below a specific threshold. This mechanism is often associated with an outcome called peak shaving. An example of a DLC algorithm is the Rush Hour scenario described in Chapter 4. The LS mechanism is used to transfer delay-tolerant power tasks, such as washing machines or EVs, to off-peak hours [8–12]. Examples of load control and load scheduling are presented in Figure 5.8. Finally, gamificationbased DR is used to engage users in efforts to manually save energy in their homes when needed. A typical building or household has three types of appliances. The first type of appliances is the standard appliances, which create a load that cannot be managed because the quality of living will be immediately and adversely affected. Examples of standard appliances include TVs, computers, lighting, and refrigerators. The second type of appliances is the flexible type, for which a manageable load can be created because external control of their operation will not significantly affect the living comfort of the end consumer. Examples of flexible appliances include thermostatic loads such as those from air conditioning or heating units. DLC algorithms are very effective in the management of flexible appliances. The third type of appliances is related to delay-tolerant or nonemergency power loads. Examples of delay-tolerant appliances include loads such as those from washing machines, water heaters, and EV charging stations. For delay-tolerant appliances, the user can predefine a point in time when the clothes are to be washed or the water to be heated or the car to be charged without caring about when the function of the appliance will actually occur. LS algorithms are very effective in the scheduling of delaytolerant appliances. 5.5.1  Modeling User Comfort DLC algorithms are used to manage the operation of flexible devices (such as air conditioning units) by directly adjusting the temperature of connected smart thermostats or by switching the appliance on or off. In the case of smart

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Figure 5.8  Effect of DLC and LS to the total demand of the network.

thermostat management, each smart thermostat is associated with a power consumption value that is related to the power characteristics of the air conditioning unit it controls. In addition, each smart thermostat is also associated with a potential energy-saving value that is related to the temperature adjustment. Finally, a user comfort value is also incorporated into the algorithm that is related to the user’s temperature preferences. An example is presented in Figure 5.9. When the air conditioning unit is on, the temperature in the living environment is at the preferred comfort level and the power consumption is high. If the thermostat temperature is raised, energy needs will decrease and reach a new level. The temperature will also slowly adapt to the new settings according to the household’s insulation and heat loss characteristics. The delay

Figure 5.9  Characteristics of flexible appliances for DLC.

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between comfort reduction and energy reduction can produce a significant benefit for DLC programs. An effective DLC implementation requires several input parameters, including the potential power reduction from each user and the maximum time period that a user may accept a DR command, τ DR . This maximum period is related to the comfort level and the maximum allowable time duration of the DR event, described as the active period in Figure 4.5. 5.5.2  Command Flow for DLC Demand Response For the implementation of large-scale DR, a demand response automation server (DRAS) is used in the utility’s or third-party application provider’s back-end system. The DRAS holds information about the connected devices, including the ID for each device, the maximum time period that it can receive a DR command, τ DR, and the estimated power reduction it can provide to the system, DP = PON − POFF. A simplified example of the system configuration is presented in Figure 5.10. in this example, a network of a large number of residential users will create time-varying appliance activations, and the DRAS will have to manage these appliances in an optimal way. The overall objective of the process, assuming that the DRAS manages i∈N flexible devices of N residential users, is given by:



N

N

i=1

i=1

PT (t) = ∑ pi (t) − ∑ DPi (t) < B (5.18)

subject to τ i,DR < Ti,max where Ti,max is the maximum DR duration a user can experience in order to keep the comfort index above a threshold, PT is the total demand, pi is the demand created by each residential customer, t is the time of the DR implementation called TDR, and B is the maximum peak load of the system as defined in Figure 5.8. 5.5.3  Fairness Issues Related to DR Commands Every residential user who switches on a flexible appliance provides information to the DRAS. The arrival of this information can be modeled as a Poisson process of average rate of λ requests per unit time [10]. The DRAS server must formulate the structure of the DR command (event) that will be sent to the residential user. The DR event follows the principles discussed in Figure 4.5 and primarily includes the active period τ j,DR, and the amount of the

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Figure 5.10  Simplified architecture of DR implementation.

required load reduction. This can be translated to a number of specific actions, signals, or benefits, including items such as a smart thermostat temperature adjustment, a pricing signal, and an energy-saving value. For the case of smart thermostat management, the command structure is given by a PUT API call as described in Table 4.10. The PUT API call will adjust the smart thermostat temperature to the preferred value. The management of flexible appliances creates fairness issues since it is not clear which user’s flexible appliance should be managed first and for how long. The DRAS server performs a complex scheduling process to maintain fairness among the DR participants. Examples of fair scheduling processes include algorithms such as round robin and high power next [10]. Round Robin: Round robin (RR) scheduling is considered to be the fairest scheduling algorithm since DR events are assigned to each user’s flexible load in equal portions and in a circular order. With RR scheduling, all users will experience DR in equal events until (5.18) is satisfied. Unfortunately, RR scheduling does not have the ability to unequally schedule events, so activities that may be clearly more important cannot be discriminated. High Power Next: In high power next (HPN) scheduling, users creating the largest power consumption in the network will experience DR events first. The largest power consumption is provided by parameter pi which is available to the DRAS from the smart meters via GET API calls, as discussed previously (see Table 4.4). The HPN algorithm follows queuing theory where the queue at the DRAS can be modeled as an (M/M/1):(GI/∞/∞) or an (M/M/1):(SJN/∞/∞) service system. In this case we have one server, infinite waiting space, zero

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service time and population, and service discipline that can be either general independent or based on priority criteria similar to shortest job next (SJN). HPN is a heuristic approximation of SJN, but is implemented for the DR use case. The benefit for residential participants in a DLC demand response program can be based on a reward point system or bill credits. Bill credits are usually assigned by utilities that implement a DR program, whereas a reward point system may be provided by third-party application providers. The users earn points ri, for each user i, which are usually computed as a linear or increasing convex function of the amount of energy saved by the user’s flexible appliances during the DR event. The reward points can be defined as: ri ~ f ( DPi ⋅ ti ) (5.19)



5.5.4  Simplified DLC Pseudocode A pseudocode for the implementation of a DLC for a third-party application provider is given in Table 5.3. Of course, there are numerous ways to execute and implement a DR program. The simplified algorithm presented in Table 5.3 is a representative example for illustration only. An important observation of DLC demand response programs is that they provide peak shaving as well as energy savings since they manage flexible appliances with on/off commands. The potential energy savings during a DLC program can be computed as follows: ESAV =



TDR

∫ DPT (t) ⋅ dt > 0 (5.20) 0

N

where DPT (t) = ∑ DPi (t) . i=1

5.6 Load Scheduling 5.6.1  Elastic Demand and Consumer Behavior Load scheduling (LS) is the second most significant DR implementation and is related to the management of delay-tolerant or nonemergency power tasks. Delay-tolerant power tasks are loads created by appliances whose operation can be interrupted and/or delayed until a later time [12]. In some cases, delaytolerant power tasks are referred to as elastic demand tasks. An example is presented in the left part of Figure 5.11.

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Table 5.3 Simplified Pseudocode for DLC Implementation of a Third-Party Application Provider

FOR each user i ∈ N GET pi (the power consumption of each user from the smart meter GET API call) GET IDi (IDs of smart thermostats that can be managed using the GET API call) COMPUTE PT = ∑i ∈N pi (total network demand) IF PT >B (demand exceeds DR threshold) SORT pi (HPN algorithm sorting in descending order) SELECT j ∈ M ⊆ N [choose smart thermostats so that (5.18) can be satisfied] FOR each IDj SEND demand response command using PUT API call for each ID to reduce thermostat temperature SET timer tj IF tj > τ j (demand response active period exceeds the max threshold) COMPUTE rj [the reward according to (5.19)] SELECT k ∈ K ⊆ N,K ∩ N = ∅ [choose other IDs of smart thermostats so that (5.18) can be satisfied] LOOP until t = TDR (execute for the total demand response time) DISTRIBUTE rewards rj to accounts of participant users

Characteristic examples of delay-tolerant loads are those of washing machines, water heaters, and EV charging stations. Each power demand request n, n = 1, 2, …, N from a set of N users, has an initiation point in time (an), a time duration (sn), and a power consumption value (pn in watts). Each task is associated with a delay tolerance, dn > an, that provides degrees of freedom to the DRAS for load management. Some appliances have zero delay tolerance such as lights, entertainment equipment, and ovens. However, for other appliances, such as washing machines and EV charging stations, the delay tolerance can be up to an interval of 12 hr. For example, during the night, an EV owner does not care what time the car will be charged, as long as the car is fully charged by the following morning. The user comfort for elastic

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Figure 5.11  Delay-tolerant power tasks and LS demand response implementations.

demands is associated with the imposed deadline, which is user dependent. If a power task is not executed until the deadline, then the discomfort value will increase according to a function that considers the total time delay compared to the deadline. Similar to the DLC case, elastic demand requests can be assumed that arrive at the DRAS as a Poisson process with average arrival rate λ . The time duration and the deadline can be modeled using the exponential distribution Pr(sn ≤ x) = 1 − e–sx [12]. All demand tasks are eventually activated by their deadlines, so there are no demand losses. Thus, the LS demand response program does not save energy for the user or the system, but it does delay the consumption to off-peak hours. 5.6.2  Objective of LS: Example of the EV Charging Garage LS demand response has a crucial difference compared to the DLC case. In DLC the management of flexible appliances is related to the change of their operational state. Thus, to provide peak shaving, the total energy consumption of the network is reduced during a specific period. This is presented in (5.20) and Figures 5.8 and 5.9. On the other hand, LS manages the time of execution of the appliance but not its power consumption. Thus, the total energy of the network (ET) and the energy consumption of each appliance (en) is reserved as presented in Figures 5.8 and 5.11. The product en = pn ⋅ sn (in Wh) for every type of implementation of LS is kept the same. The objective of LS is load balancing and cost minimization rather than energy efficiency. With LS, the network or the consumer can transfer

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delay-tolerant power tasks to off-peak hours, when the electricity price is low. This reduces the total grid costs associated with operation of the elastic demand. For example, an application provider of a multilevel EV charging station garage may manage the charging times of the parked EVs so that the costs of electricity are minimized and the profits are maximized. An example is presented in Figure 5.12. Let’s assume that the total instantaneous power demands of the EV N charging garage are denoted by PT (t) = ∑ i−1 pi (t) where pi is the demand for each EV charger. The garage is supplied with electricity for a large solar array with instantaneous capacity B(t) which is also connected to the grid to satisfy the demand when PT (t) > B(t). The total electricity costs C(⋅) for the facility manager of the EV garage are computed as an increasing convex function according to this equation:



k ⎪⎧ C ( PT (t) − B(t))   =   ⎨ ( PT (t) − B(t)) ,PT (t) > B(t) (5.21) 0,PT (t) ≤ B(t) ⎩⎪

where k is an exponent that associates the costs of electricity according to the demand. It is clear that when the total demand is lower than the solar capacity, then the EV charging is free. On the other hand, when the demand for EV charging exceeds the solar capacity, then electricity from the grid needs to be used, which increases the costs. The objective of the LS demand response program is to minimize the long-term average costs in a network of consumers with elastic demand. This can be formulated according to:

Figure 5.12  Load scheduling implementation for an EV charging garage with solar production.

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⎡ 1 min⎢ lim ⎣T →∞ T

T

⎤

0

⎦

∫ Et (C(t)) ⋅ dt ⎥ (5.22)

Various mathematical approximations are used to solve the optimization algorithms, and for simple scenarios closed-form solutions can be implemented [12]. 5.6.3  Types of LS Implementation The objective of (5.22) can be achieved by using nonpreemptive or preemptive scheduling approaches. In the nonpreemptive scheduling case, once a power task is started, it should be active until its completion. For example, when the DRAS server decides to initiate EV charging, the operation should not be interrupted until the EV is fully charged. In the preemptive scheduling case, the operation of the power tasks can be interrupted and continued later, but must be completed before the deadline. For example, the EV charging can be started and stopped when the total demand is larger than the capacity PT (t) >B(t) and started again once the demand falls below the capacity PT (t) < B(t). In terms of mathematical modeling, we can distinguish three approximations. The threshold postponement (TP) and controlled release (CR) algorithms are nonpreemptive approaches to deal with the optimization problem, whereas the fully elastic (FE) algorithm is a preemptive approach [12]. Examples of these approximations are presented in the right part of Figure 5.11. The preemptive fully elastic approximation is also presented in Figure 5.12. Threshold Postponement: With the TP algorithm for every received power task, if the total demand is larger than the offered capacity, the power task is delayed until its deadline. This creates a control space {0, dn} for each demand n. That is, each demand n is either activated immediately upon arrival if PT (t) < B(t), or it is postponed to the end of its deadline and is activated when its deadline expires. This creates a queue of delayed power tasks named as Q(t). Following the top right part of Figure 5.11, the TR approach will postpone power task 1 and initiate it at time a1,DR = d1 − s1 because at the time of arrival of the power task, the consumption was larger than the production, or PT (t) > B(t). This approach has the minimum level of intelligence and thus computational complexity. The drawback of this implementation is that it cannot guarantee optimum results and savings. Controlled Release: With the CR algorithm, at time of arrival t of a new demand request n, the DRAS decides whether the demand will be served immediately

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or sometime later toward the end of its deadline. If the instantaneous power consumption is smaller than the production PT (t) < B(t), the request is served immediately; otherwise, it is queued. Queued requests, Q(t), are activated either when their deadline expires (as in TP), or at some point in time before the deadline expiration when the power consumption P(t) drops below B(t). The control space of CR is {[an, dn]}, which is larger than that of TP since the activation time decision an is also added. This can be observed in the bottom right part of Figure 5.11 where a3 < a3,DR < a3,DR − (d1 − s1). The CR algorithm is more complex than the TP case and significantly more efficient [12]. Fully Elastic: The FE algorithm is a preemptive policy that provides additional degrees of freedom for better management and cost minimization. FE is also a more flexible approximation compared to CR since power tasks can be interrupted at any time when the power consumption PT (t) > B(t), and start again at any time when PT (t) < B(t). An example is presented in the middle right part of Figure 5.11 where a2 < a2,DR1 < a2,DR2, a2,DR1 < a2,DR2 < a2,DR2 − (d3 − s3,DR2), s3,DR1 + s3,DR2 = s3. 5.6.4  Simplified LS Pseudocode According to the application and the objectives of the demand response program, any type of implementation and policy scheme can be used. In general, by taking into consideration the trade-off between computational complexity and performance, the CR nonpreemptive LS algorithms and policy are the most preferred solution. Table 5.4 presents simplified pseudocode for the implementation of the solution for the use case of the solar-powered EV charging garage example. The objective is to transfer the charging of EVs to hours when the solar production is larger than the demand, minimizing the operational costs of the facility.

5.7  Gamification Demand Response 5.7.1 Participatory Games Direct load control and load scheduling demand response programs are based on real-time data and command flow between smart meters, load controllers, and the DRAS. They implement principles derived from the OpenADR protocol. The drawback of these approaches is that they cannot be implemented in areas where AMI and DR are not fully deployed. An alternative in such cases is DR using gamification and user engagement. This approach is part of the

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Table 5.4 Simplified Pseudocode for Load Scheduling Continuous Release Policy

MONITOR P(t) (the total power consumption of the EV garage from the smart meter GET API call) MONITOR B(t) (the total power consumption of the EV garage from the solar microinverter GET API call) FOR each received power task i ∈ N IF PT(t) + pi(t) > B(t) (check if total demand is larger than capacity) PUT power task pi(t) in the queue Q(t) IF PT(t) + pi(t) < B(t) & t < di SET ai = t and execute power task pi(t) ELSEIF t + si > di SET ai = di – si and execute power task pi(t) (execute the tasks before its deadline) LOOP for all tasks

general concept of participatory sensing or games, and is based on the human desire for engagement and recognition. In a typical DR gamification scenario, the utility has assigned to a third-party application provider the objective of engaging consumers and convincing them to reduce their consumption during peak or critical hours. Thus, instead of a command flow to the load controllers of the consumers, such as smart thermostats, the application provider offers incentives and engaging communication tools directly to the consumer smart phones to reduce the consumption when necessary. The simplest form of gamification is the neighbor comparison concept that is implemented by various DR companies. In this case, the consumer has access to anonymous and aggregated data from the neighborhood and a ranking system is used to present the energy efficiency score of the consumer compared to the neighbors. It is generally observed that savings of the order of 1.5% to 3.5% can be achieved by this type of engagement [13]. The implementation of DR using gamification can be modeled by the general term of serious games [14]. At the simplest class of serious games, a third-party application provider or else the game designer publicly announces a list of top-K consumers and a list of bottom-M consumers from a set of N consumers in the network or area of implementation, according to their respective energy-consumption reduction during a DR event. From the perspective of the game designer, the general objective is to decide on the values K and M.

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In simple terms, how many people should be recognized in the competition and what type of feedback should be provided to them. From the consumer perspective, the objective is to save energy, and thus receive rewards, by sacrificing the comfort index in their household. These types of problems fall in a category called Stackelberg games [14]. Let’s explore the following case in which a utility has assigned to a game designer (third-party application provider) the task of creating DR programs based on user engagement. The game designer organizes daily contests involving the percentage of energy-demand reduction among consumers separately for each peak time slot; that is, in each contest, consumers compete among themselves according to their consumption reduction at a specific peak time slot. It is assumed that the game designer has access to the energy consumption for each player by accessing Green Button data (see Section 4.5.2). The outcome (i.e., win or loss) of each competition and the serious game parameters K and M are announced to the customers via a mobile app and may also be shared with customer’s friends in various social applications. An example is presented in Figure 5.13. A player i∈N at time instance t is creating a demand pi(t) and is willing to reduce the consumption to a new value p′i(t). The normalized participation level of player i is computed according to [14]:

dpi (t) =

pi (t) − ptʹ′ (t) (5.23) pi (t)

The consumers are ranked by the game designer according to their participation level dpi in the serious game. The strategy for consumer i is to adjust

Figure 5.13  Serious game design for demand response.

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her power consumption p′i, thus to choose dpi. Similar to the DLC case, the reduction of the power load of the consumer creates a discomfort index that is analogous to the amount of dpi. Following the observation of Figure 5.9, the discomfort experienced by reducing the consumption can be modeled as an increasing convex function of the load reduction, modeling the increasing marginal dissatisfaction for the consumer as a function of deviation from the reference consumption pattern. In mathematical form, the discomfort metric is:

ai ( dpi ) = A ⋅ dpin (5.24)

with dpi ∈ [0 1] and ai = 0 if dpi < 0, A is the discomfort constant and n is the exponent that can be safely set to n = 2. 5.7.2  Rewards and Social Recognition A question that many readers might have is what is the benefit to the consumer and why should he or she participate in a DR gamification scenario. There are two rewards for the consumer: First, the consumer experiences a reduction in power consumption by a factor dpi, which will reduce the consumer’s utility bill. Assuming a cost of electricity equal to c (in $/kWh) the first reward for the consumer is given by: t



ri ( dpi ) = c ⋅ ∫ dpi (t) ⋅ dt (5.25) 0

where τ is the time of the demand reduction for player i. Another important incentive for player participation is social recognition. Social recognition can be defined as the appreciation an observer holds for the person she observes. It is a three-part normative phenomenon, since one can receive positive, neutral, or negative social recognition. If at the end of the game, the player belongs to the top-K players, then social recognition is positive; if the player belongs to the bottom-M players, then social recognition is negative. Social recognition is neutral for all other cases [15]. The ranking of the players is presented in Figure 5.13. A social sensitivity index, h, can be used to model the social recognition of each user that takes the form [14]:



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⎧⎪ h, if i ∈ top-K si ( dpi )   =   ⎨ −h, if i ∈ bottom-M (5.26) 0, otherwise ⎪⎩

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This approach for winner determination aims to exploit the desire of customers for social approval. 5.7.3  Objectives of Gamification An efficient gamification implementation needs to be a win–win for both the players who belong in the top-K group and also for the game designer or the utility. The utility collaborates with the game designer to solve the problem of high grid operational costs, due to high peak-hour demand, by inducing the appropriate user consumption behavior. The game designer creates a game with ranking of top-K and bottom-M parameters to engage and incentivize the consumers (the players) to reduce their consumption during peak hours. Assuming that consumers are rational self-interested entities who seek to optimize their expected utility, the knowledge of values K and M incurs different load consumption strategies by consumers. The benefits for the players are the rewards, ri, and social recognition, si, and the cost is the discomfort index ai as described previously. Thus, the total participation reward for the consumer can be computed according to:

ui ( dpi ) = ri ( dpi ) + si ( dpi ) − ai ( dpi ) (5.27)

Thus, a player i selects a strategy dpi such that the total participation reward is maximized for the values of the parameters K and M set by the game designer:

max ⎡⎣ri ( dpi ) + si ( dpi ) − ai ( dpi )⎤⎦ (5.28) dpi

It is obvious that the saved energy of player i is a function of parameters K and M, dpi(K,M). On the other hand, the game designer needs to optimally select the ranking values K and M so that the game provides benefits for itself or for the utility company that it serves. Assuming that the grid operating costs C(⋅) can be modeled as an increasing convex function of the demand, following the rational of (5.21), the total costs after the game can be computed as the total consumption minus the total energy saved:



N

N

i=1

i=1

C ( PT (K , M )) = ∑ pi − ∑ dpi (K , M ) (5.29) N

where the factor S(K , M ) = ∑ dpi (K , M ) represents the saved energy of i=1 the players according to game parameters K and M. The objective of the game

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designer is to maximize the savings, and thus reduce the costs, by selecting appropriate values for K and M. In a mathematical form this is written as:



N

∑ dpi (K , M ) (5.30) K ,M ∈{1,2,…,N /2} max

i=1

The impact of a given set of values (K, M) is different for different consumers depending on consumer parameters of social recognition h and discomfort a. The solution of (5.28) and (5.30) can be computed in a closed form if parameters ai, hi, and dpi are known to the game designer. In other cases, a numerical solution to the equilibrium of the Stackelberg game can be computed [14]. 5.7.4  Simplified DR Gamification Pseudocode Gamification algorithms for DR applications can be implemented and executed in any of numerous ways. In the following example, we present a simplified version to help the reader understand the process. Let’s consider that the game designer or the application provider has access to smart metering information and has a detailed historical database of each player’s consumption per hour using the Green Button API as described previously. In addition, the game designer has a mobile app or a web-based portal that can send text notifications and rewards to the players. Rewards can be based on bill credits or tokens in the form of a digital currency and/or a point system. Table 5.5 presents the simplified pseudocode for the implementation of a demand response gamification program.

5.8  Example: A Day of Smart Living in 2017 We now introduce Mary, a 35-year-old woman who is a customer of a progressive utility that provides customer-centric services. Mary is tech savvy, ecofriendly, and she is always connected and loves sharing news with her friends in social networks. Mary is a mobile user and makes most of her transactions and communications using a smartphone. She has numerous connected home gadgets such as smart thermostats, a house with a rooftop solar panel, a home battery, and an EV. Mary’s utility is progressive and is considered to be a product and service innovator. Her utility collaborates with third-party application providers to provide state-of-the-art technology and services to the customers. Let’s explore how Mary experiences a day of smart living in 2017.

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Table 5.5 Simplified Pseudocode for DR Gamification Serious Game Scenario: The game designer and the utility have agreed to initiate a gamification algorithm to reduce the demand during peak hours on a hot summer day.

DEFINE Mtemp, Ktemp (create a first pair of game values M and K and broadcast game to users) FOR i = 1:N (each user) COMPUTE dpi [from (5.28)] END COMPUTE M, K [from (5.30) these will be the new values according to first dpi computations] LOOP until equilibrium is achieved SAVE M, K, dpi (create a database containing the final values for M and K that will be associated with users’ savings dpi) COMPUTE rewards ri (deliver the rewards to users’ mobile app accounts) ANNOUNCE the top-K and bottom-M winners in the mobile app or web portal

5.8.1  Energy Usage Analysis It is Saturday morning and Mary wakes up and prepares to do her laundry. When she starts the laundry, a notification appears in her mobile app and notifies her about the consumption of the washing machine and compares the weekly consumption with other appliances. The app presents the energy disaggregation of Mary’s daily activities such as entertainment, clean clothes, lighting, heating/cooling, and cooking. Mary has a deep knowledge of her energy footprint and the app informs her how she can reduce her consumption by following specific personalized energy-saving tips according to her energy footprint and behavior. The app also presents data bout the energy produced by her solar panel, the stored energy in her home battery, and the consumption of her EV. She can watch the costs associated with each activity and observe the forecast value of her utility bill by the end of the month. Her utility provides important information about her usage and ways to save energy. The benefit for her utility is that Mary is an

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engaged and active customer and, when needed, the utility can communicate with her. Mary has full transparency and knowledge for her consumption and she receives important help to reduce her consumption. 5.8.2  Active Utility Customer It is a very hot summer day and the utility needs to reduce the consumption during peak hours and minimize the amount of imported energy. The utility has asked an application provider to create a game that will engage customers in saving energy between 1:00 and 2:00 p.m., with the goal of reducing the load by 10%. The game designer sends the game information to the users using the mobile app notification system and presents a great incentive. For every 500 Wh saved during the peak hour, the user will receive one token that is equivalent to one free coffee from a famous coffee shop. Mary receives the notification, she likes the challenge, and shares it with her friends. A large number of people in her neighborhood are engaged and participate in the game. By the end of the day, the utility has saved money because it did not import energy from the market and customers feel happy that they protected the environment—and the next morning they all have a free coffee at the local coffee shop in their neighborhood. A market place is born where utilities, retailers, application providers, and customers have a win–win experience. There is fruitful bidirectional communication between the utilities and Mary. 5.8.3 Home Automation It is a cold winter weekday and Mary just left her office to go home. Her mobile app understands that Mary is driving back to her home because it is monitoring the GPS data. The app sends a command to the smart thermostat to set the desired temperature for Mary because the house is cold due to energy savings. Mary arrives at home and she feels comfortable. The smart home agent monitors the electricity prices of her home and manages the temperature so that by the end of the month Mary can save more than $10, which was Mary’s goals for bill savings. This was the only communication between Mary and her smart home software agent. Mary plugs in her EV and expects that the charging will occur during the time when electricity costs are at the minimum level. Mary has informed the charging station that she will need the car again next day at 9:00 a.m. The agent of the smart home monitors the current and forecast electricity prices and performs load scheduling to minimize the cost of electric charging.

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References [1]

Wen-Chen Hu and Naima Kaabouch, Big Data Management, Technologies, and Applications, Hershey, PA: IGI Global, 2013.

[2]

Mourad Barkat, Signal Detection and Estimation, 2nd ed., Norwood, MA: Artech House, 2005.

[3]

A. G. Ruzzelli, C. Nicolas, and G. M. P. O’Hare, “Real-Time Recognition and Profiling of Appliances Through a Single Electricity Sensor,” paper presented at 7th Annual IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks (SECON), 2010.

[4]

H. Kim et al., “Unsupervised Disaggregation of Low Frequency Power Measurements,” Proc. 11th International Conference on Data Mining, 2011, pp. 747–758.

[5]

G. Koutitas and L. Tassiulas, “Low Cost Disaggregation of Smart Meter Sensor Data,” IEEE Sensors J., Vol. 16, No. 6, March 2016.

[6]

Charu C. Aggarwal and Chandan K. Reddy, Data Clustering: Algorithms and Applications, Boca Raton, FL: CRC Press, 2016.

[7]

Russell B. Millar, Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB, New York: Wiley, 2011.

[8]

M. H. Albadi and E. F. El-Saadany, “A Summary of Demand Response in Electricity Markets,” Electric Power Systems Research, Vol. 78, No. 11, November 2008.

[9]

Ruilong Deng et al., “A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches,” IEEE Trans. on Industrial Informatics, Vol. 11, No. 3, 2015.

[10] G. Koutitas, “Control of Flexible Smart Devices in the Smart Grid,” IEEE Trans. on Smart Grid, Vol. 3, No. 3, 2012. [11] G. Koutitas and L. Tassiulas, “Periodic Flexible Demand: Optimization and Phase Management in the Smart Grid,” IEEE Trans. on Smart Grid, Vol. 4, No. 3, 2013. [12] Iordanis Koutsopoulos and Leandros Tassiulas, “Optimal Control Policies for Power Demand Scheduling in the Smart Grid,” IEEE J. on Selected Areas in Communications, Vol. 30, No. 6, 2012. [13] D. Levitan, “How Data and Social Pressure Can Reduce Home Energy Use,” Yale Environment 360 Report, 2012. [14] Thanasis Papaioannou, Vassiliki Hatzi, and Iordanis Koutsopoulos, “Optimal Design of Serious Games for Consumer Engagement in the Smart Grid,” IEEE Trans. on Smart Grid, No. 9, 2016. [15] S. Rottiers, “The Sociology of Social Recognition: Competition in Social Recognition Games,” Working Paper No. 1004, Heman Deleeck Center of Social Policy, University of Antwerp, 2010.

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6 Transactive Energy Economy 6.1  Introduction One of the most significant recent technology disruptors is the fast penetration of the sharing economy in everyday life. From housing to transportation, the sharing economy has upended legacy business models and has positioned end users at the epicenter of the system [1]. In this chapter, applications of the sharing economy in the power grid business are briefly described, and the new concept of transactive energy, focusing on the customer-centric dimension of the power grid sector, is presented and discussed in detail.

6.2  Energy in the Sharing Economy 6.2.1  Evolution of Smart Cities: From Centralized to Distributed Architectures The everyday lifestyle in modern cities has changed dramatically from prior eras. The sharing economy is a primary catalyst for this disruption of legacy economic processes and business models. A significant outcome of this change is the placement of the end user at the epicenter of the system, and the conversion of business models from centralized to distributed architectures, as shown in Figure 6.1. But what is the relation between the sharing economy 179

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Figure 6.1  Centralized, distributed, and decentralized system architectures.

and smart cities? In an abstract sense, the concept of a smart city is similar to the smart grid. From a centralized architecture where resource allocation and intelligence are managed in a top-down manner, the system transitions to a distributed or even decentralized architecture where intelligence migrates to the edge of the city network. The edge of the smart city is the citizen. In this model, each citizen has an important role in the operation and resource allocation of the smart city. Characteristic examples include participatory sensing [2] and emerging business models in housing and transportation sectors. Let’s explore the steps that enable development of the sharing economy. To facilitate this exploration, we observe the transportation and housing industries. Every person needs a house or some form of shelter to carry out daily life routines such as managing personal/family activities, eating, and sleeping. Similarly, every person needs a car or some form of transportation to move around the extended environment. The rooms of the house and the space in the car are resources to be leveraged in daily activities. These resources are idle most of the time, and the sharing economy provides a business framework to monetize them. Following the concepts of supply and demand, there are always requests for using a resource during a specific period of time (demand), and there are always car owners or house owners with idle resources who are willing to share them (supply). The traditional business sectors that have been drastically disrupted by today’s sharing economy are the taxi and hotel businesses. These enterprises have historically been centralized in nature, and funded by a single entity (e.g., real estate fund). In funding the enterprise, some investment of capital, or capital expenditure (CAPEX), has been used to create the resource. The CAPEX required to create the business and the operational expenditures (OPEX) necessary to operate the business are typically large. As a result, the resources may be offered to end users via relatively high purchase and use prices. It is clear that this centralized model has two distinct planes of supply

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(e.g., hotel owner or taxi fleet owner) and demand (e.g., hotel visitor or taxi patron) as shown in Figure 6.1. In a sharing model, the owner of a car or home may make his or her idle resources available to another person for a specific period of time. In this model, the owner of the shared resource is not burdened by an extremely large CAPEX or OPEX because the resources are also used to fill personal needs for housing and transportation. The sharing economy leverages this principle by providing a secured, broadly available foundation to monetize idle personal resources. Application providers in the shared economy define the pricing, facilitate secure transactions between parties, and provide system reliability and availability. Simply put, the role of the application provider in the sharing economy is to connect people willing to share a certain type of resource with people who are in need of that resource. To survive this evolution of the economy, a business must enable a service through an online, crowd-based platform rather than own a fixed infrastructure asset. For example, the largest housing business today is a private company that does not own a single room. Instead, this business simply facilitates transactions between room suppliers and visitors. In addition, the largest transportation company today does not own a single car. Instead, this business simply connects drivers with travelers. Clearly, in the sharing economy a distributed platform connecting sharers of a resource with people who need that resource can produce tremendous economic benefits. 6.2.2  The Concept of Energy Giving Will the energy sector experience a sharing revolution similar to that experienced by the transportation and housing sectors? Following the observations in Chapters 4 and 5, it is obvious that the energy business is undergoing a transformation parallel to the telecommunications industry, where the demand for mobility and ubiquity of communication technologies evolved into an application development platform. However, although the mobile industry is one of the most progressive industry segments, it has not adopted the concepts of the sharing economy. This lack of adoption is due to the fact that a mobile user does not own—and therefore cannot offer to share—network resources (spectrum, bandwidth, and so forth). In contrast, a residential prosumer in the energy sector who owns physical energy assets such as a solar panel or a home energy storage system may offer to share these resources (energy) with other consumers. Similar to the hotel and taxi examples described previously, there are many periods of time during which energy produced or stored by a

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distributed resource will be in excess, and thus could be shared or exchanged with another consumer who needs energy to satisfy immediate demand. There are critical milestones in the residential energy sector that make the concept of sharing energy an inevitable outcome. Recently, residential consumers have become empowered with the ability to produce energy (using solar panels), manage energy (using smart thermostats), and store energy (using home batteries). All of these activities are performed automatically and are transparent to the user. If one observes this evolution, it is clear that the missing element is the ability to share or transact energy (Figure 6.2). 6.2.3  Value Proposition and Business Impact The role of the electric utilities in the new era of the sharing economy is the topic of some debate between two distinct parties. On one hand, those who support this evolution argue in favor of the new business model. On the other hand, those who criticize the new business models argue against the evolution. Since the sharing economy is a new phenomenon, only time will settle the debate. However, it is clear that the sharing economy has dramatically improved the quality of offered services, and has created tremendous opportunities for monetization and new channels of revenue. The sharing economy will also affect the energy market, and will disrupt the operations of electric utilities. Much of this disruption is driven by the fact that local energy production and storage are becoming more efficient and affordable. In the near future it is estimated that a small residential customer will be able to go off the grid by deploying a solar panel and a home

Figure 6.2  The missing block to close the energy business cycle and ecosystem. Production of energy, management of energy, and storage of energy are now feasible. What is next?

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battery storage unit for less than $10,000 (which is approximately 8 years of paying utility bills). Electric prosumers are expected to reduce the need for utility-scale production, thereby reducing the revenues of electric utilities. As a result, in the sharing economy, electric utilities will need to adjust their business models to become enablers and optimizers of such services. As discussed in Chapter 3, the real asset of the electric utility is customer engagement and data management, and new business models have already become dominant. These new concepts include the partner of partners, the virtual utility and the product innovator business models. The value proposition and opportunities of the sharing economy can be summarized in terms of three concepts: 1. Utility as a bank: Currently, the electric utility sector operates with a seller-buyer business model. Utilities sell electricity, and consumers buy a specific amount. With the concept of energy sharing, forwardthinking utilities may adopt financial services from the banking system, including transaction fees, energy loans, and energy futures applied at the retail level. 2. New financial products: By adapting their business model away from the seller–buyer concept, electric utilities can offer a completely new suite of products to end customers. These new products, which may include energy gift cards and financial plans, can increase overall revenues even if revenue from actual sold energy to end users is reduced. 3. Reduction of unpaid bills: In the United States more than 48 million people are living at or below the poverty level. This situation results in unpaid bills totaling over $11 billion [3]. In contrast, the donation market produces transactions totaling over $350 billion [4]. Unfortunately, the number of donations in the energy sector is negligible. The sharing economy and the revolutionary concepts of energy giving and energy donation can enable utilities to leverage the untapped donation market, thereby reducing the costs of collection and increasing profits.

6.3  The Transactive Grid 6.3.1  Foundations of Transactive Energy The market for energy resources is transitioning from a generation-driven paradigm to a demand-driven paradigm. This transition is due to the rapid penetration of distributed energy resources (DERs), electric vehicles (EVs), home energy storage, and smart appliances. All of these disrupting technologies

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are appearing at the residential level and causing a paradigm shift in the energy market. The existing vertical utility business models and simplistic pricing schemes limit the customer’s degrees of freedom, and leave large quantities of usable resources untapped. In addition, the energy market is experiencing a transition toward mobile users similar to the transition encountered with the advent of wireless telecommunications. EV owners are now roaming and need to consume a large amount of energy outside their metered boundaries. In fact, an EV owner may need to consume more energy while charging a roaming vehicle than is consumed by his or her household. Furthermore, the excess energy available from residential prosumers, microgrids, or virtual power plants (VPPs) needs to migrate to the location of the demand. The sharing economy and the evolution of new digital currencies has created a need to decouple the energy transaction from its physical delivery to an online platform that supports automated smart payments. Transactive energy (TE) is expected to increase the degrees of freedom available to energy prosumers and to create the necessary framework for the direct exchange of energy among parties. Transactive energy will enable all parties to behave in a price-reactive and price-proactive manner in the operation of the grid. In the TE paradigm, these active parties (EVs, smart appliances, prosumers, or any other connected smart grid entity) are now considered to be transactive energy agents that can interact directly and in real time. Although it is a compelling concept, the complete definition of TE is still somewhat abstract. Based on [5], TE is a “system of economic and control mechanisms that allows the dynamic balance of supply and demand across the entire electrical infrastructure using a value of a key operation parameter.” The critical concept in this definition is that of the “entire electrical infrastructure,” which creates new services at the retail level. Simply put, transactive energy is a business process combined with a technology that connects consumers, producers, and prosumers of electricity to enable retail energy transactions. The architecture of an energy system supporting TE can be distributed or even decentralized in nature depending on applications. The TE concept is presented in Figure 6.3. In the figure, the transactive layer or transactive system interface is a horizontal software layer (middleware) enabling peer-to-peer and peer-tomarket interaction between transactive agents. This middleware implements the protocols and standards necessary to enable transactions among parties. In the transactive energy grid, the distribution system operator (DSO) and the aggregator play important roles by providing distributed management of a group of transactive agents. For example, a group of transactive agents can be the smart appliances within a smart home that is coordinated by a smart home device. In this case, the aggregator is the smart home device, or agent.

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Figure 6.3  Simplified architecture of transactive energy.

Another example is the case of a VPP. The aggregator or the DSO is the management entity of a large number of prosumers. The transactive agents in this case are the prosumers. Thus, the DSO aggregates and represents a group of transactive agents in the energy market. In addition, TE can be implemented in a fully decentralized approach because each transactive agent can interact directly with the market through the TE layer [6]. The concept of transactive energy is built on specific guidelines and principles that are still under development and discussion. Table 6.1 presents an overview of the transactive energy paradigm [5]. 6.3.2  Examples at the Retail and Distribution Levels At this point, it is important to explore two simplified use cases. These scenarios (1) concern the implementation of TE at the retail and distribution levels in the grid, (2) are simplified for illustrative purposes, and (3) represent only a small portion of the application of TE. Retail TE Use Case: A simplified process for the retail use case is summarized and discussed below. 1. The retailer performs a forward buy (i.e., energy is purchased in a quantity in excess of demand) and then sells tenders for unbundled energy subscriptions to customers.

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Field

Description

Architecture

Distributed or decentralized.

Extent

Usually within a specific geographic area.

Transactive parties

Transactive agents that can be automated devices such as solar panels, EVs, smart meters, smart appliances, smart home software agents, aggregators, demand response providers, VPPs, microgrids, or even humans.

Transacted commodities

Energy is the main product.

Types of transactions

Forward transactions are used to coordinate investments and manage risk. Spot transactions are used to coordinate operating decisions.

Temporal variability

Transactive systems may interact across multiple timescales. For example, transactions within a single system may range from a subsecond in length to several minutes or longer.

Interoperability

Technical and semantic (syntax format) interoperability issues should be considered. Transactions include the exchange of information between different parties that may be in different networks and using different protocols.

Market clearing

Peer to peer, bilateral, retail tariff, or exchange.

Stability

The stability of grid control and economic mechanisms is required and must be ensured. Consideration of system stability must be included in the formulation of TE techniques and should be demonstrable.

Value assignment

End users of electricity may have nonquantitative values such as comfort that must be quantified to enable a transaction.

Principles

• Should implement some form of highly coordinated self-optimization. • Should maintain system reliability and control while enabling optimal integration of renewable energy and DERs. • Should provide for nondiscriminatory participation by qualified participants. • Should be observable and auditable at interfaces. • Should be scalable, adaptable, and extensible across a number of devices. • Should be accountable for standards of performance.

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2. The customer agent may accept or ignore tenders as transactions for subscriptions. 3. The net metered quantity is computed (metered quantity minus accumulated subscription quantity) for each settlement interval and then a payment of credit occurs. Let’s explore an example for a consumer of electricity in the retail TE use case. A customer can make an agreement with a supplier for consumption of a fixed quantity of energy (in kWh) in each hour of the year(s) and for a fixed monthly payment (subscription). If the customer uses less energy in an hour than was originally subscribed, then the customer will get paid the difference at an hourly spot price. On the other hand, if the customer uses more energy in an hour than was originally subscribed, then the customer will pay the difference at an hourly spot price. At any point in time, the customer may automatically buy or sell a quantity of energy at current tendered prices. This process is performed by the smart home agent of the home energy management system (HEMS). Distribution TE Use Case: A simplified process for the distribution use case, also described as a VPP or microgrid, is discussed and summarized below. 1. A group of customers (consumers and/or prosumers) may subscribe to a future part of the distribution grid. 2. The distribution grid owner processes the subscriptions payments and recovers all fixed and variable distribution costs. 3. A group of customers using more energy than their subscription levels can automatically buy from other customers at variable prices. 4. A group of customers using less energy than their subscription levels can automatically sell to other customers at variable prices. 6.3.3  Modes of Operation and New Entities The additional degrees of freedom offered to end users of a transactive grid network necessitates creation of a new entity, the distribution system operator (DSO) and the aggregator. The DSO represents a group of transactive agents such as DERs and prosumers, and provides the interface to the independent system operator (ISO) and the wholesale market. The regional transmission operator (RTO) of the wholesale market protects reliability and efficiency in grid operations and protects the benefits of all entities [6]. The DSO also leverages demand-side management (DSM), which creates a new negawatt

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market. The DSO operates in a distributed architecture and enables/coordinates peer-to-peer exchanges between prosumers by facilitating transactions while maintaining grid reliability. This activity requires communication between the wholesale market (ISOs and RTOs) and the transactive agents. The DSO may be an independent entity (called an IDSO), or it may be managed by the utility. When managed by the DSO, the following TE operating modes are apparent [7, 8]: • • • •

Prosumer centric based on local preferences, Bilateral bid, Market priced, and Operator dependent.

Variations between production and consumption levels as well as changes in ambient conditions may alter the TE operating mode at any time. The prosumer-centric and bilateral-bid TE modes increase the degrees of freedom offered to prosumers who can act as independent transactive entities. In such cases, the DSO functions in an independent fashion (IDSO). The marketpriced and operator-dependent TE modes are more utility centric and focus on grid reliability. In such cases, the DSO functions as a utility-based DSO. Pricing schemes for transactive energy are similar to the case of wholesale market operation, with heavy dependence on the locational marginal price (LMP). A new pricing model has also recently emerged that is adapted to the new role of the DSO for retail market operation. This new pricing model is called the distribution LMP (DLMP). DLMP may be based on long-term and short-term pricing strategies and operational goals. The long-term DLMP incentivizes investments in the DER market, whereas the short-term DLMP provides instantaneous settlements between the TE agents. The concepts here are very similar to financial instruments that incorporate elements of risk and the time-value of money. In the transactive energy market, energy is an asset or commodity that is traded by individual investors (users) and institutional investors (distributors) in order to optimize demand or value, while minimizing risk or liability. 6.3.4  Analysis of Transactions Let’s explore the basic steps of a typical transaction. A supplier makes an offer or tender of a product to a consumer at a specific price. The consumer may accept or reject the offer. In the case of acceptance, the product is delivered

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for an exchange of money. Each of the two parties makes transactions that are considered beneficial and meet the needs of the other. The concept of transactive energy is used to facilitate this approach for energy transactions at the retail level. An energy transaction is the exchange of an energy commodity for a payment between two or more parties. The commodity is a quantity of energy that can be delivered to a location within a specific time interval. For this delivery, a payment is a transfer of a specific currency (debit and credit) from one party to another. The price is the payment per unit of the commodity (in $/kWh). Table 6.2 lists the elements present in the information model of the energy transaction [9, 10]. Transactions can occur in advance of service (forward transactions) or at the time of service (real-time transactions). Forward transactions are preferred to real-time transactions because they provide stability in costs and the ability to adapt to the physical limits of the devices involved. In forward transactions, the transactions may be executed years, months, days, hours, minutes, or seconds in advance of delivery of the product or at the time of delivery of the product. The product associated with the transaction is a specific amount Table 6.2 Energy Transaction Information Model Element

Description

Price

The total cost of the transaction over the delivery period at the rate of delivery.

Rate of delivery

The constant rate of delivery (R) over the delivery period. The seller is obligated to deliver at this rate and the buyer is obligated to consume at this rate.

Delivery period

The interval of time during which the energy was, is, or will be available for physical delivery. A delivery period (D) is designated by a start time (Ts ) and an end time (Te ). The duration of the delivery period is D = Te − Ts .

Amount

The amount of energy (A) to be delivered is the rate of delivery (R) times the duration of the delivery period (D); that is, A = R × D.

Buyer

The party buying energy.

Seller

The party selling energy.

Time

Time stamp of transaction.

Location

Geospatial location for delivery of the energy.

Meter IDs

The meter IDs if the transaction occurs between smart meters.

Currency

The currency used for the transaction.

Units

The units of measure for the energy.

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of energy that should be consumed within a specific time interval. In real-time transactions, the transaction occurs at the time of delivery of the product. In this case, energy production and usage must be instantaneously balanced to ensure grid stability. For forward or real-time transactions to occur, a distributed or coordinated decentralized control scheme must be implemented. The DSO or aggregator integrated with the TE layer facilitates the transactions. Centralized control is not preferred because it is very complex and expensive to implement due to the amount of information that needs to be transferred between all parties. 6.3.5  End-User Transactive Energy Implementation The transactive energy concept is still under development and there are no widely accepted standards. However, the Energy Market Information Exchange (EMIX) and the Transactive Energy Market Information Exchange (TEMIX) initiatives [9, 10] have made significant strides in the establishment and prioritization of standards efforts. EMIX and TEMIX are supported by the OpenADR alliance and the Smart Grid Interoperability Panel (SGIP) [11, 12]. The proposed methodologies and standards leverage concepts that are well understood in the wholesale market and are adapted for the retail case. Energy interoperability defines a service-oriented architecture (SOA) approach to energy interactions following the OASIS Standard Reference Model for SOAs. SOA describes how message exchange patterns (MEPs) are leveraged for this purpose. Web services enable communication between parties and create a platform for energy transactions. As in many Internet-driven protocols, the communication between the parties occurs via web services over TCP/IP, and different functions enable the creation, distribution, cancellation, and request of a tender or energy transaction. Let’s explore the following example of a consumer-driven transactive energy activity as presented in Figure 6.4. The home of a prosumer has a set of smart appliances, local storage, and a solar panel. These resources are part of a connected home that can communicate with a HEMS local via a local communication network. The HEMS function or agent has a transactive energy interface that provides communication and interaction with the TE market (DSO or aggregator). The agent connects to external databases, such as weather information and electricity price databases, using APIs as described in Chapter 4. The prosumer defines a set of inputs for the agent, such as comfort levels, preferred home temperature, and so on, as well as operating objectives such as minimize cost of operation or maximize profits from the TE market.

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Figure 6.4  System diagram for TE implementation.

The TE interface has three functions: (1) Determine the device’s optimal operating level, (2) receive and make optimal priced offers, and (3) enter into optimal transactions. Thus, control and optimization functions execute within the prosumer’s software agent to achieve the objectives based on the current market conditions and defined inputs. The variety of control and optimization algorithms that can be applied to such cases does not suffer from a shortage of options. However, a subset of approaches known as model predictive control (MPC) or receding horizon control (RHC) is typically used for industrial applications [13]. For example, MPC is used for building management systems (BMSs) and minimizing the cost of operation of smart buildings based on forward information such as weather forecasts, electricity prices, and building occupancy. MPC is a general-purpose control scheme that involves repeatedly solving a constrained optimization problem using predictions of future costs, disturbances, and constraints over a moving time horizon to choose the optimal control action. In general, the MPC involves the orchestration of the following principles:

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• •

•

Define a future time horizon and use the model to predict the output of the system being controlled in different time steps. Calculate a control command flow or sequence to the smart appliances or smart devices of the connected environment to optimize a performance index and the objectives (typically related to net costs or net benefits). Repeat this process using a receding horizon strategy so that, at each time step, the horizon is moved forward in time. This involves the application of the first control signal of the sequence calculated at each time step.

For the example presented in Figure 6.4, the end-user optimization problem can be the orchestration of the smart appliances (manageable loads) with the energy storage and production so that the prosumer home maximizes monthly earnings. In this case, earnings are gathered from participation in the TE market by selling to tenders or by minimizing consumption when electricity prices are high.

6.4  Cryptocurrencies: Their Role in the Energy Sector 6.4.1 The Blockchain The fact that transactive energy enables peer-to-peer exchange of electricity at the retail level creates the need for secure and reliable microtransactions. The exponential growth of mobile and online payments has provided some pressure to the existing centralized banking system to explore the applicability of alternative currencies. One of the main reasons for the creation of alternative currencies is the fact that the existing banking system becomes expensive and, for some applications, unreliable or nonoptimal. For example, the fact that a large number of transactions every day are processed by a small number of trusted servers creates concerns for cyberattacks. Furthermore, capital controls placed by governments may limit access to capital as well as circulation of capital. Finally, banks charge fees for processing transactions, and these fees can be significant. For example, during 2014 banks charged approximately $1.7 trillion dollars to process transactions [14] using the existing centralized system architecture. For these reasons, new approaches have emerged that leverage the connectivity of the Internet and the ubiquity of personal computing devices to enable reliable, secure financial transfers among peers in different geographical regions. The most widely used approach is the blockchain protocol [15, 16]. Blockchain is a set of peer-to-peer protocols that protect the integrity of pieces

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of information, including information used in a financial transaction. In this context, a single piece of information can be a digital currency, a digital vote, or even a digital signature. The protection of the integrity of this information, or several pieces of related information in a transaction, is performed through a distributed cryptography algorithm (a hash) that is virtually impossible to compromise. In a very simplified manner, blockchain can be considered to be a personal electronic banker or ledger, or a personal electronic notary that operates inside of a network-connected computer. Why is cryptocurrency necessary? In modern society, all people use paper bills or coins or ATMs or e-banking to send and receive money. This is required because a central bank is authorized to keep track of all transactions, check if a transaction is authorized, issue new money if necessary, transfer money in a safe way, and guarantee that the parties in the transaction will not lose their money to theft or mismanagement. In this centralized system architecture, the banks involved in the banking system keep records of information associated with each transaction. Today, billions of transactions are executed every day. This volume of activity pushes the existing centralized system architecture to its limit because a huge number of transactions must be processed by each of a small number of trusted and secure systems. In contrast to the centralized banking system, blockchain technology implements a distributed or peer-to-peer philosophy where transactions are processed by a large number of personal computers. Security is achieved by using cryptography and by broadcasting the transactional information to a large number of personal computers for observation and verification. All transactions are stored and processed between all computers acting as a distributed ledger system that becomes more reliable as more systems are added. Thus, blockchain is a database that is distributed among a large number of personal computers. It maintains a continuously growing list of ordered records called blocks. Each block contains a timestamp and a link to a previous block. The chain of blocks cannot be altered retroactively once data is recorded. Since the blocks are hosted in decentralized system, the blockchain is less vulnerable to hacks and cyberattacks than a centralized banking system. The timestamp of each block and the link to a previous block enables a verified, continuous ability to retrospectively audit all transactions. As a result, each member of the blockchain network can act as a distributed ledger and holds a public bookkeeping. For a transaction to occur and be valid, all distributed ledgers must agree. The system can only be hacked if a cyberattack can obtain simultaneous access to more than 51% of the blockchain network, which is impossible. An example of blockchain and a comparison with conventional banking is presented in Figure 6.5. In the network of four people, the bank keeps

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Figure 6.5  From a centralized banking system to a decentralized blockchain.

track of all transactions, creating a huge file of individual transaction orders. If the central database of the bank is attacked successfully, then the integrity of the transactions may not be maintained. On the other hand, the blockchain protocol keeps records of all transactions in a decentralized fashion, using the local storage available via the network of participating computer systems. As a result, each user acts as a ledger and if one user is attacked successfully, replicas of the transactions exist in the distributed database of the remaining computers. The distributed system survivability concept is identical to the original thought process that resulted in today’s Internet. Bookkeeping information is stored in a block using a cryptography technique called hash chain. The hash chain technique recursively encrypts the transactional information, making the data invisible to members while enabling tracking of the transaction event. A hash chain is the successive application of a cryptographic hash function to a piece of data. In computer security, a hash chain is a method to produce many one-time-use encryption keys from a single master encryption key or a password. For nonrepudiation, a hash function can be applied successively to additional pieces of data in order to record the chronology of the data’s existence and enable simple verification. An example is presented in Figure 6.6, where the hash function is used to recursively embed encrypted information about each transaction into the public sequence of transactions. The information in a block related to an individual transaction includes important quantities such as amount, sender, and receiver. Two keys provide encryption: the signing key and the verification key.

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Figure 6.6  The blockchain and hash concepts.

To describe the utility of the blockchain concept, we will explore an example. Alice wants to send coins to George. Alice creates two keys simultaneously. The signing key (SK), which is private, and the verification key (VK), which is public. The transaction information or message inside the block is created using the hash function multiplied with the SK. The SK depends on the message and is not always static. Thus a person does not have a unique signature. The verification message is the output of the multiplication of message with the SK and the VK and is a binary output (YES or NO) indicating if a transaction is real or not. Historic transactions can be traced by following the sequence of the proof of work (POW) in each block. The POW is the result of decoding by a member of the blockchain (a network node). The POW involves scanning for a value in the chain of blocks that, when hashed with a well-known approach such as SHA-256 [16], the hash begins with a certain number of zero bits (usually four). Once the CPU effort has been expended to make it satisfy the POW, the block cannot be changed without redoing the work. As later blocks are chained after it, the work to change the block would include rehashing all of the succeeding blocks, which is impossible. The computational complexity of the POW is what defines a bitcoin, a new approach to currency, which is discussed next. 6.4.2  Bitcoin Blockchain provides a platform for a secure, distributed ledger in which each network node verifies all transactions. These nodes, or personal computers, act as ledgers and create a collection of timestamps that is almost impossible to

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hack. The POW of the network nodes includes the provision of a small portion of computing time to find the necessary value (an integer) that will begin with a certain number of zeros when hashed. The new currency called bitcoin is created by the tokenization of effort spent by the nodes in the blockchain (POW) while creating the transaction system with distributed ledgers. The network nodes providing valid POW are called bitcoin miners. Table 6.3 summarizes the steps in the process of creating bitcoins from the blockchain network. One logical question at this point is how the first bitcoins were created. The blockchain platform provides a secure transaction system for mining bitcoins, but the first bitcoin was issued out of thin air. This is actually the case for all currencies that are created from nothing to satisfy the transactional needs for the exchange of goods. The fact that the bitcoin has a very large market value is because only a limited number of bitcoins are issued to tokenize the work (CPU and electricity consumption) of the computers actively mining bitcoins. Approximately 25 bitcoins are generated worldwide every 10 minutes, and it is estimated that the total number of bitcoins issued will be capped at 21 million. That number halves every 4 years. This logarithmic scale is intended to get more bitcoins into circulation in the early years, and reduce the reward for bitcoin miners as the value increases over time. Figure Table 6.3 Sequence of Bitcoin Network Processes Step

Process

Description

1

Create transaction

Private keys, verification keys, and the hash are used to encrypt transaction data between two nodes in the blockchain network that are executing a transaction.

2

Broadcast transaction

Encrypted transaction information is broadcast to the blockchain network. It is not necessary for all nodes of the network to collect this information.

3

Collect transaction

Network nodes collect the transactions into a block. If a node has a missing block, then it searches for the missing blocks.

4

Proof of work

Nodes work on finding the POW for the block.

5

Broadcast POW

When a node finds the POW, it broadcasts it to the rest of the nodes in the blockchain network.

6

Accept block

Nodes accept the block only if all transactions in it are valid. The validation process requires the POW sequence and the verification message (multiplication of hash and verification key).

7

Issue of bitcoin

The nodes that find POW are rewarded with a bitcoin.

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Figure 6.7  Market price of the bitcoin from its origin until March 2017.

6.7 presents the market price of bitcoins compared to the U.S. dollar from its origin in 2009 until March 2017. 6.4.3  Smart Contracts and Ethereum Blockchain technology can be used for different types of applications. For the banking system, bitcoin is the most significant application. The second most significant application is that of smart contracts, in which blockchain technology can be used between transacting parties to remove reliance on centralized systems. The significant outcome of such contracts is that untrusted parties can transact directly with each other in a peer-to-peer fashion. Similar to the bitcoin philosophy, a smart contract is stored using a blockchain network, and all participating nodes have an encrypted copy. Thus, a smart contract is a collection of digital information and processes (software, data, objects) that stores various critical elements of a contractual arrangement. Critical contracting elements include rules for negotiating the terms of the contract, policies for verifying the contract, and techniques for executing the agreed terms. This network can be thought of as a distributed system of electronic notaries. All the encrypted contract transactions are stored in chronological order in the blockchain using the concept of blocks. Thus, if anyone tries to change the terms of a smart contract, all participating nodes will detect evidence of tampering, and will not accept this change. Ethereum is the most widely used decentralized platform implementing smart contracts [17]. The effort spent by participating nodes in the blockchain

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network to validate and track the contracts is called proof of stake, similar to the POW tokenization of bitcoin. This effort is monetized via the creation of a new cryptocurrency called the Ether. Ether is a form of payment made by the clients of the Ethereum platform to the network nodes executing the requested operations of smart contracts. Simply put, Ether is an incentive ensuring that network nodes act as a distributed notary network. Today, Ether has reached approximately 30% of the market capitalization of bitcoin. As a result, many participants are suggesting that Ether may become the dominant cryptocurrency rather than bitcoin. 6.4.4  The Concept of an Energy Coin Extending the concepts of blockchain, bitcoin, Ether, and cryptocurrency, it is logical to conclude that a similar approach might be very useful in the energy sector. When compared with the energy market, the banking and contract markets are easier to disrupt since many banking transactions and contract interactions are virtual and do not require an exchange of a physical quantity (other than paper or other documents). In comparison, the energy sector is substantially more complicated because of the supply of energy, which is a physical quantity. As a result, there will always be a need for a utility or energy service company (ESCO) to provide energy (in the form of electrons, for electric power) to satisfy the demand of a business or household user. The utility sector, however, is being confronted with a new trend and a new clientele comprised of energy-independent prosumers. Energy-independent prosumers are the residential units that can operate off-grid, thus creating 100% of their production and storage without reliance on the local electric grid. Recent technological developments, such as smart thermostats, HEMSs, rooftop solar panels and tiles, and battery banks, are expected to significantly change the energy ecosystem. In addition, the evolution of independently managed operational entities such as the microgrid, community solar, and VPP technologies may soon make the concept of neighbor-to-neighbor, neighborto-business, and anyone-to-anyone energy distribution feasible. Irrespective of the outcomes of this trend, the role of the electric utilities will always be important and significant. However, new business models are expected to develop that will place the utility or the ESCO as the clearing house or the facilitator of transactions. This disruption in the energy market translates into a business opportunity through tokenization of activities and auditable, distributed transactions. Tokenization in the energy sector can be based on the negawatt market (energy saving potential of a household) and the local production (wind and

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solar). Home batteries may act as energy wallets or repositories of value that can be managed and utilized by autonomous agents acting under the direction of the homeowner, neighborhood, or local energy entity. Thus, the evolution of peer-to-peer cryptocurrencies using blockchain, bitcoin, Ether, and related technologies, coupled with the growth of the independent prosumer community, will bring new forms of currency and new opportunities for secured transactions in the energy business in the form of bilateral agreements between prosumers. It is expected that the new transactive energy system will not be 100% decentralized, but that a distributed approach will be the best fit since DSOs and utilities will continue to sustain the flow of electrons in the network.

6.5  Evolution of Collaborative Prosumers 6.5.1 System Model The increasing number of energy prosumers, the development of microgrids and VPPs, and the simultaneous evolution of the transactive energy concept is expected to create a new form of socioeconomic relationship in the form of collaborative prosumers. The concept of collaborative prosumers [18–20] describes how a community of prosumers can create a coalition that will optimize the use of energy to maximize profits and operational efficiencies. A new name for collaborative communities of prosumers or condumers [19] comes from a clever portmanteau of the term collaborative community of producers/consumers. A primary reason for the evolution of this new market is that fact modern technologies, business models, and use cases require a more flexible approach to the management of electric power. The distributed architecture of the smart grid and the decentralization of microtransactions are enabling the active role of end users in energy production, consumption, and storage. These factors depend on a flexible modeling of electricity. Energy must be decoupled from its physical delivery to allow new business models and services to develop that correspond to real-time conditions and leverage distributed architectures. Collaborative prosumers (or, again, condumers), can be considered as mates (or buddies) of the grid and can be regarded as dynamic VPPs that form among utility customers (consumers and prosumers) based on socioeconomic criteria. These coalitions have several primary objectives: 1. To hold an active role in the energy market by leveraging coordination and synergy with the utility/energy retailer of the service territory; 2. To benefit from transacting, trading, or sharing energy in a peer-topeer approach;

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3. To share electricity with low-income houses and increase sustainability and social bonds; and 4. To allow efficient penetration of renewable energy sources and electric vehicles in the network. To maximize the efficiency of such a coalition, a diversity of daily net production must exist between the prosumers and their production, and this production must be uncorrelated with weather phenomena. One of the simplest condumer settings is the local neighborhood of prosumers, as presented in Figure 6.8. Let’s assume that prosumers in a specific neighborhood are connected via the low-voltage grid to one substation of the DSO. Prosumers can be rooftop solar owners or consumers capable of reducing load and thus creating negawatt energy. Each prosumer has a smart home software agent that is connected to the DSO software agent. The DSO software agent is also connected to the energy market, where it represents the prosumers and maximizes the objective of the condumers. All software agents are connected to the cloud and may also be integrated through APIs to external databases. The software agent that represents condumers in the market is also connected to the wholesale market and other cloud-based applications for market forecast and market analysis. In addition, the software agents may be connected to cryptocurrency networks in the cloud. This entire system can be modeled as a multiagent system of

Figure 6.8  A DSO and two collaborative prosumers.

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software agents that are connected to the power grid. Thus, optimization of energy management and supply can be achieved to maximize the profits of the condumers. The decoupling of the energy transaction from physical delivery and the migration of the market to a cloud-based platform enables different condumers in different geographic areas to interact and exchange energy credits. Prosumers may have access to online information that contains real-time data about individual energy production and consumption, community-based production and consumption, energy credits balance, market forecast, and so forth. At any point in time t, the solar energy production of a prosumer i∈M, where M is the set of prosumers in the community is pi(t) and the negawatt energy production from a consumer that can reduce load is pi(t) = ci(t) − si(t), where c represents the consumption and s the load reduction for consumer i. Load reduction can be executed through a demand response program such as direct load control or load shifting as presented in Chapter 5. The total condumer production that is captured and managed by the DSO software agent is: M



P(t) = ∑ pi (t) (6.1) i=1

6.5.2  Coalition Games and the Shapley Value A microeconomic model based on coalitional games (i.e., cooperative games) can be used to define a fair allocation of profits among the prosumers of the coalition. This model can provide a fair distribution of profits derived from the coalition to the players. A mathematical way to achieve that is by using the Shapley theorem for coalitional games [21–23]. To allow such an environment to develop, access to the pool of energy must be presented to the utility customer or DSO. Additionally, these parties must be able to establish bilateral agreements among the retail market participants. Following the model described in the previous section, each prosumer may agree to participate in a condumer coalition if (1) the prosumer will increase revenues due to the DSO representation to the market, and (2) the prosumer will receive a fair portion of the total profits realized by the coalition. To study whether these conditions can be satisfied for all prosumers, we define the operators’ transferable utility (TU) coalitional game GM = {M,u} where M is the set of all prosumers, and u is the so-called characteristic function that assigns a positive scalar value to each coalition. This positive scalar value may represent the profits or gains of each coalition.

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The critical question in TU coalitional games is how the profits of each coalition will be shared among its members. In turn, this determines the coalitions that will be formed and if the grand coalition will be stable. In a stable grand coalition, no prosumer will be incentivized to leave or deviate from the coalition and act independently. The Shapley value of (6.2) provides a closed-form solution for the fair distribution of profits among players in a coalitional game. For each player i ∈ M participating in a coalition S ⊆ M, the Shapley value φ i(S, u) is defined as:



ji (S,u) =

∑

S⊂M

S !( M − S −1)! (u( S ∪ {i}) − u(S)) (6.2) M!

The coalition S is actually a group of the total players M that can collaborate. If they all collaborate with each other, then the coalition S is actually the players M. The Shapley value, φ , provides the information about the fair distribution of profits for each coalition. To function effectively, the coalitional game GM must be superadditive and supermodular. The superadditivity property can be easily verified for the case of prosumer cooperation since the cooperation among the players does not add any extra costs. Therefore, when two or more prosumers cooperate, they achieve the same performance as in stand-alone or disjoint operation. That is, they can revert back to their noncooperative behavior to obtain their noncooperative payoffs. The supermodular characteristic means that no given subset of prosumers has an incentive to deviate from the grand coalition. Practically, supermodularity implies that as more prosumers join the coalition and cooperate, the profits increase. Let’s explore the following example. Based on the architecture of Figure 6.8, three prosumers decide to cooperate and form a coalition that is represented to the market by a DSO. Of the three prosumers, two are solar prosumers that produce p1 = 100 kWh and p2 = 85 kWh and one is a negawatt prosumer that produces p3 = 30 kWh. The payoff for the production of energy to the market is assumed to be given as a convex function of the offered energy similar to the following equation:

u = 0.12 ⋅ p2 (6.3)

This equation describes how the market purchases the offered capacity, and it is a convex function modeling the need for more production in the network. By applying the Shapley value equation for the three prosumers, the revenues can be estimated, as shown in Table 6.4.

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Table 6.4 Fair Distribution of Profits (Shapley Value) for a Network of Three Collaborative Prosumers

Coalition

Prosumer

Profits Without Cooperation

Profits with Cooperation

Profit Increase (%)

{1, 2, 3}

i=1

1,200

2,580

115

i=2

867

2,193

152

i=3

108

774

616

Total

2,175

5,547

155

i=1

1,200

2,220

85

i=2

867

1,887

117

Total

2,067

4,107

98

i=1

1,200

1,560

30

i=3

108

468

333

Total

1,308

2,028

55

i=2

867

1,173

35

i=3

108

414

283

Total

975

1,587

62

{1, 2}

{1, 3}

{2, 3}

From the data in Table 6.4, we can see that the collaboration of prosumers in the market can provide significant profits, based on the pricing schemes used. For other pricing schemes, the collaboration may not be efficient and the formation of a coalition may be a disincentive for the prosumers. For example, if the pricing scheme of (6.3) was a linear function, then the benefits from the coalition and the individual player would be the same. 6.5.3  Various Pricing Schemes One of the main concerns related to the concepts of transactive energy, VPPs, and collaborative prosumers is the technique for modeling trading in the dynamic local prosumer–DSO market. In this scenario, the real-time relationship between the offered capacity (p) and the payoff, u ~ f(p) is an important consideration. To maintain stability, the pricing should be based on the energy conditions of the prosumer–DSO network as well as the conditions between the DSO and the market. An example of interesting pricing implementations is given in [24]. Two pricing schemes are presented that concern the purchase

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of energy from the prosumers (via the DSO) when excess is available, and the purchase of energy for the prosumers (via the DSO) when production is less than consumption. In the locally produced energy market described in [24] it is assumed that trading is based on real-time usage and production conditions rather than forecast values. The energy produced by each prosumer can be used to satisfy local (personal) demand, or excess can be fed directly to the microgrid to satisfy the demand of others. Information exchange about the net production between the prosumers and the DSO takes place via a communication network and is based on a 15-min time horizon. The prosumer–DSO information exchange is used to determine pricing. In case of excess of energy, the prosumer is rewarded for the produced energy. In case of inadequate production, the prosumer consumes energy from the grid and is billed for the withdrawn energy. In this scenario, the pricing schemes must incentivize the software agents of both the prosumers and the DSO. By proper incentive design and pricing structures, supply and demand are balanced and production and consumption peaks are lowered. For the selling of energy from prosumers to the DSO, a relationship that motivates production to match demand is critical. The following function can provide a supply–demand balance, based on a bell-shaped curve: p⋅q

u ( p,PT ,CT ) = e



2

( PT −CT )

(6.4)

a

where p is the production, q is the maximum rate of reward, PT is the total production, CT is the total consumption, and a is a scaling factor for the case where PT ≠ CT. When total energy production equals consumption, the relationship is optimized. On the other hand, when production is different than consumption (larger or smaller), then an incentive must exist that can motivate a restabilization of supply and demand. The relationship that defines the purchase of energy of the prosumers from the DSO is a pricing function that defines how much consumers pay for their withdrawn energy. Consider this example:



h ( c,PT ,CT ) =

c ⋅ r ⋅ CT (6.5) CT + PT

where c is the consumption, and r is the maximum cost of energy delivered by the DSO when the prosumers a produce minimum amount of energy. When

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production matches consumption, the substation charges consumers at a fixed rate per kilowatt-hour. However, when production is greater than consumption, the cost of consumed energy during overproduction decreases sharply, motivating consumers to shift their energy usage to periods of overproduction. This function encourages the condumers away from overproduction. The main drawbacks of this pricing model are that (1) it does not consider comfort and quality of living for the negawatt prosumers (e.g., smart thermostat owners) and (2) it does not create incentives for the prosumers that provide reliable excess of energy when needed for the coalition. A reputationbased pricing model is presented in [19] that models the reward based on the reputation of the condumer. Reputation can be defined as the ratio of the predicted energy production and the actual production over a given period of time as given in this equation:



Ri =

⎡ T p ⎤ 1 ⋅ min⎢1,∑ ∗i ⎥ (6.6) T ⎣ t=1 pi ⎦

where p∗i is the predicted net production of prosumer i and T is the time horizon. It is assumed that the market will pay the DSO based on the following equation:

U = RM ⋅ log ET ⋅ r ⋅ ET (6.7)

where R M represents the reputation of the entire coalition, ET the total condumer production, and r the price equivalent per unit of energy production. For the system to be stable, the actual price offered by the grid to the condumer must depend on the reliability of the production estimates made by the condumer. The predicted energy production should be as accurate as possible and condumers should comply with any agreements for a specific amount of energy in order for the grid operators to determine an efficient scheduling process. The allocation of payments from the DSO to the prosumers can be modeled as follows: ui =

Ri ⋅ Ei

∑i∈M ( Ri ⋅ Ei )

⋅U (6.8)

where Ri is the reputation of prosumer i, Ei is the energy offered by prosumer i, and U is the reward of the DSO by the market. The DSO may pay each prosumer in proportion to (1) the amount of energy offered at a particular time

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period, and (2) the reliability-based reputation of the prosumer in providing accurate estimates of its available energy.

6.6 Implementation Challenges The aforementioned pricing models and cooperation strategies represent some approaches to structuring the actual implementation of a transactive energy market. Many alternative approaches can be defined for structuring pricing and forming coalitions. The concepts described in this chapter are still under discussion, and several initiatives are under way that attempt to implement various technologies. For example, the work in [24] describes the trading in a local production/consumption environment and how the concept of an energy coin can be used to create incentives for the members of the coalition. In addition, the work presented in [25] describes a microgrid where the neighbors can collaborate and exchange energy credits. Based on the recent momentum of decentralized management and transactions, new business models and concepts are likely to emerge in the energy sector that will enable the active role of prosumers in this quickly changing market. However, in considering these factors and implementations, several important influences need to be taken into account, such as these: •

•

•

•

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Legislation: The power grid and the energy market need to adopt policies and other approaches that increase flexibility. Currently, personal transactions are forbidden and prosumer participation in the market is limited to net metering or power purchase agreements. Energy currency: Concepts such as bitcoin will not be compatible with the energy market, because transactions cannot be 100% decentralized due to the important roles of the DSO and utility. Although new digital currency technologies enable a totally decentralized system, the adaptation of these concepts into the energy market must allow utilities to continue playing a key role in grid operation. Prosumers: New energy market entities such as prosumers are solar owners, demand response participants, and negawatt producers. These prosumers may suffer some amount of discomfort in their living environment due to the lure of energy savings. New incentives and rewards should be created to monetize their participation and prevent systemic abuse. Coalitions: New energy market entities comprised of coalitions of prosumers and other participants, such as VPPs and microgrids, are

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•

based on cooperative and noncooperative games. New mathematical frameworks should be created to efficiently define pricing algorithms to encourage more active prosumer participation in the market. Pricing: Reputation-based pricing can provide a new foundation for dynamic energy prices and costs. Novel approaches aligned with socially conscious interaction will be important tools to incentivize prosumers in establishing reliable energy production and producing a stable market.

References [1]

Arun Sundararajan, The Sharing Economy: The End of Employment and the Rise of Crowd-Based Capitalism, Cambridge, MA: The MIT Press, 2016.

[2]

R. Shabo et al., “Framework for Smart City Applications Based on Participatory Sensing,” paper presented at IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom), 2013.

[3] https://www.census.gov. [4] https://givingusa.org. [5]

GridWise Architecture Council, “GridWise Transactive Energy Framework,” PNNL22946 Ver1.0, January 2015.

[6]

S. Barrager and E. Cazalet, Transactive Energy: A Sustainable Business and Regulatory Model for Electricity, Baker Street Publishing, October 2014.

[7]

F. Rahimi, A Ipakchi, and F. Fletcher, “The Changing Electrical Landscape,” IEEE Power and Energy Magazine, April 2016.

[8]

R. Masiello and J. Aguero, “Sharing the Ride of Power,” IEEE Power and Energy Magazine, June 2016.

[9]

E. Cazalet, “Transactional Energy Market Information Exchange (TeMIX): An Information Model for Energy Transactions in the Smart Grid,” OASIS Energy Market Information Exchange Technical Committee White Paper, 2010.

[10] https://www.oasis-open.org/committees/tc_home.php?wg_abbrev=emix. [11] http://www.openadr.org. [12] http://www.sgip.org. [13] Eduardo F. Camacho and Carlos Bordons Alba, Model Predictive Control,” New York: Springer, 2007. [14] http://www.economist.com

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[15] Arvind Narayanan et al., Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction, Princeton, NJ: Princeton University Press, 2016. [16] Nakamoto, Satoshi, “Bitcoin: A Peer-to-Peer Electronic Cash System,” https://bitcoin. org/en/bitcoin-paper. [17] https://www.ethereum.org [18] www.energycentral.com/c/um/collaborative-prosumers-about-emerge. [19] A. Satsiou, G. Koutitas, and L. Tassiulas, “Reputation Based Coordination of Prosumers,” paper presented at 1st International Conference on Internet Science, April 9–11, 2013. [20] M. Coddington and D. Sciano, “Change in Brooklyn and Queens,” IEEE Power and Energy Magazine, April 2017. [21] A. Roth, “The Shapley Value,” in Essays in Honor of Lloyd S. Shapley, Cambridge, UK: Cambridge University Press, 1988. [22] G. Mankiw, Principles of Microeconomics, 7th ed., Mason, OH: South-Western College Publishing, 2014. [23] G. Koutitas et al., “Greening the Airwaves with Collaborating Mobile Network Operators,” IEEE Trans. on Wireless Communications, Vol. 15, No. 1, 2016. [24] http://www.scanergy-project.eu. [25] http://brooklynmicrogrid.com.

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7 Summary and Conclusions This book has presented the customer-centric dimension of the electric grid and its evolution toward an application development platform. Key topics included the basics of electricity and the conventional grid structure, as well as the relationships between conventional economic models and emerging models based on transactive energy and the sharing economy. Each of these new concepts and emerging markets is enabled by technology advancements from multiple sectors. The confluence of these factors has resulted in the smart grid as we know it today: a platform for revolutionary new applications. The primary vision of the smart grid is the establishment of a dynamic new platform on which new business models can flourish, enabled by technologies and applications yet to be imagined. The results of this paradigm shift will include new economic opportunities and fresh social imperatives that will transform modern life as profoundly as the invention of the telephone, as fundamentally as the development of the original electric grid, and as rapidly as the spread of the Internet. Advanced metering, ubiquitous communications, and virtualization of power production and consumption entities have driven the development of important new terms and concepts, such as prosumers and condumers, and where business functions that were previously managed by regulated, vertical, and physical entities are now opening up to dynamic partnerships based on 209

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mobile, ephemeral, and sometimes new business structures, including digital currencies and virtualized payments. Collaborative prosumers and federations of technology-savvy and socially conscious entities are poised to emerge in the energy market. This trend has the potential to benefit the environment as well as the economy. The initial disruption of the energy sector, represented by the emergence of the smart grid, is only the beginning of the energy services evolution. As society evolves towards carbon-free, net-zero or even net-positive homes and businesses, new approaches to production, consumption, and management of energy will continue to disrupt traditional business models and will shape the next generation of the energy market. Efficiency levels that were impossible in past decades will become commonplace. Radical currencies, transaction models, and partnerships that evolve in real time will augment or supplant conventional relationships. New services, new business models, and new applications will be created using a revolutionary new technology: the smart grid as an application development platform!

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About the Authors Dr. George Koutitas is an academic and entrepreneur in electrical and computer engineering. He has more than 10 years of academic and business experience in smart grids, internet of things, energy efficient networking, and wireless networks. Dr. Koutitas is an assistant professor at Ingram School of Engineering at Texas State University, where he applies his scientific expertise on optimization algorithms, game theory, and signal processing for the benefit of the community and the environment. His vision is to develop innovative and impactful services and technologies in the energy and wireless industry. Dr. Koutitas is also the CEO and cofounder of Gridmates. Gridmates (www.gridmates.com) is the world’s first cloud platform for smart energy donations and energy gift cards that helps electric utilities and retailers transform their bill assistance programs and billing services to digital innovation. Gridmates cloud platform is one of the first implementations of transactive energy technologies applied to alleviate the problem of energy poverty in the United States and also around the world. His work as the founder of Gridmates was recognized by important media such as Forbes, CNBC, MSNBC, and Fast Company, as well as the U.S. Department of Energy and Sunshot Catalyst program that provided a grant indicating Gridmates as one of the most innovative energy startups. Dr. Koutitas holds a B.Sc. degree in physics from Aristotle University of Thessaloniki in Greece, a M.Sc. degree (with distinction and prizes from Nokia) in mobile and satellite communications from the University of Surrey, UK, and a Ph.D. in electrical engineering under EPSRC scholarship from the Centre for Communications Systems Research, UK. His postdoctoral studies 211

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were funded by the Department of Electrical and Computer Engineering at the University of Thessaly, and the European Union. He is a member of IEEE and IET, and has published more than 40 scientific publications in peer-reviewed journals and conferences as well as 2 books. His research is cited in more than 700 scientific publications. Dr. Stan McClellan is the director of the Ingram School of Engineering at Texas State University, where he is a professor of electrical engineering and also researches advanced communication and networking technologies. Dr. McClellan has held notable positions in the commercial, military/ aerospace, and academic industries, including Hewlett-Packard, ZNYX Networks, SBE Inc., General Dynamics, LTV Aerospace, and Rockwell International. He served as chief technologist, chief architect, or lead engineer for several distributed real-time systems, developing technologies including real-time interactive telepathology, highly available systems for telecommunications networks, real-time flight simulators using reconnaissance imagery, and a flight-worthy digital terrain system for the AFTI/F-16 test bed aircraft. He has also served as a technology and business consultant for commercial entities, including BellSouth, Motorola, Cisco, 3Com, Newbridge/Alcatel, BNR/Nortel, Network Equipment Technologies (NET), MCI/Worldcom, LSU Medical Center, and others. Most recently, Dr. McClellan was a founder and chief technology officer for a startup company in the smart grid space, where he developed a revolutionary approach to smart grid systems using advanced signal processing, on-wire communications, and a sophisticated system architecture incorporating endpoint mobility, autonomous device registration, and command/ control capability. As the author of numerous peer-reviewed technical publications and U.S. and international patents, Dr. McClellan is an expert in networking and distributed system optimization, particularly for voice/video transport with quality of service (QoS) constraints. He has made invited contributions to well-known references, including Advances in Computers, The IEEE/CRC Electrical Engineering Handbook, and The Encyclopedias of Electrical & Electronics Engineering.

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Index

Application programming interfaces (APIs) adoption of services, 139 appliance health and, 140 concerns, 139–40 connected home devices, 129–33 defined, 109 difficulty of, 139 ESP and, 110 glossary examples, 112 JSON, 128, 129 latency of, 140 microinverter, 128–29 PVWatts, 126–28 request, 111 response, 111 smart thermostat, 129–33 steps for accessing, 118–19

Active power, 12, 13, 150 Adoption of services, 4, 139 Advanced metering infrastructure (AMI) backhaul networks, 78 customer-premises network, 78 defined, 77 importance of, 50 last-mile networks, 78 smart meters for, 74 standards, 78–79 wide-area networks (WANs), 77–78 Advanced persistent threats (APTs), 33 Alternating current (AC), 9–11 Appliance footprints characteristics mapping, 41 components, 39–40 cycles, 40 defined, 149–50 hidden information in, 149–53 periodicity, 40 power consumption, 40 power value, 39 reactive value, 39 Appliances delay-tolerant, 161 health, 140 pulse association, 157–58 pulse characteristics, 153

Backhaul networks, 78 Bill analysis, 39 Bill forecasting, 146–48 Bitcoin, 195 Bitcoin miners, 196 Blockchain concept, 195 decentralized, 194 defined, 192–93 example, 193–94 213

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Blocks, 193 Bottom-M consumers, 171 Bottom-up approach, 71 Brooklyn microgrid, 92 Building-area network (BAN), 75–77 Building management systems (BMSs), 190 Business intelligence, 159 Business-to-consumer (B2C) providers, 67 Capacitive loads, 11–12 Capacity defined, 8 DERs and, 91 EVs and, 95 negawatt, 64, 85 solar, 29, 82 U.S. power plants, 24 wind, 29 Capital expenditures (CAPEX), 180–81 Channel encoding, 74 Characteristic function, 201 Charge-coupled device (CCD), 23 Cloud computing APIs, 109–11 conclusions and concerns, 139–40 DR and HEM, 107 energy analytics, 106–7 energy usage analysis, 107 gamification, 107–8 introduction to, 108–15 microbill payment, 108 OpenADR, 135–39 overview of services, 102 product development, 115 reserving resources, 111–13 web services, 108–11 Clustering, 156–57 Coalition games, 201–3 Coalitions, 206–7 Collaborative prosumers coalition games, 201–3

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coalition objectives, 199–200 defined, 199 distribution of profits, 203 DSO and, 200 emergence of, 210 evolution of, 199–206 microeconomic models and, 95 pricing schemes, 203–6 Shapley value, 201–3 system model, 199–201 Community solar models, 65–67 Complex power, 12, 13 Condumers, 209 Connected home devices, 129–33 Connected homes, 83–88 defined, 71 standards, 88–90 Connected microgrid, 92 Consumer-driven technologies (CDTs), 67 Consumers appliances footprint, 39–41 archetypes of, 43–44 beyond 2020, 67–69 bottom-M, 171 elastic demand and, 165–67 passive nature of, 30–31 retail price of, 26 top-K, 171 understanding, 39–44 Consumption daily curves, 30 defined, 9 EVs and, 96–97 time-variable, 28–30 Controlled release (CR) algorithm, 169–70 Cooperatives (COOPs), 18 Critical peak pricing, 99 Cryptocurrencies bitcoin, 195–97 blockchain, 192–95 energy coin, 198–99

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Index215

in energy sector, 192–99 Ethereum, 197–99 need for, 193 smart contracts, 197–98 Current transformer (CT), 80–81 Customer assistance programs (CAPs), 69 Customer-premises network, 78 Cycle/multi state, 151 Cycles, 40 Data processing, 143–44 Delay-tolerant appliances, 161 Demand, 9 Demand response (DR), 53, 159 coupled with DMS, 54 gamification, 170–75 load management and automation, 107 serious game design for, 172 Demand response automation server (DRAS), 163–64 Deregulated markets, 34 Digital signature algorithms (DSAs), 117 Direct current (DC), 9–11 Direct load control (DLC) algorithms, 161 appliance characteristics for, 162 command flow for demand response, 163 defined, 161 effect of, 162 fairness issues, 163–65 implementation, 163 pseudocode, 165, 166 user comfort modeling, 161–63 Distributed automation (DA), 53 Distributed energy resources (DERs), 32 aggregator characteristics and, 64 importance of, 52 rapid penetration of, 183 solar panels, 92 VPPs as groups of, 53 Distributed generation (DG), 50, 52 Distributed system survivability, 194

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Distribution, 25–26 Distribution LMP (DLMP), 188 Distribution system operator (DSO), 187–88, 200 Distribution TE use case, 187 Duration of pulse, 152 Elastic demand, 165–66 Electricity chemical energy to, 20–22 kinetic energy to, 22–23 thermal energy to, 23–24 usage analysis, 42–43 Electric vehicles (EVs) charging garage example, 167–69 charging stations, 96 consumption patterns and, 96–97 fully electric, 95 hybrid, 95–96 overview, 95 rapid penetration of, 183 types of, 95 V2G concept, 98 Elliptic curve cryptography (ECC), 117 End-user transactive energy implementation, 190–92 Energy chemical, 20–22 defined, 8 kinetic, 22–23 power and, 7–8 thermal, 23–24 uses (U.S.), 21 See also Transactive energy Energy analytics applications, 106–7 bill forecasting, 146–48 hourly and daily, 144–46 as smart grid services foundation, 144 Energy coin, 198–99 Energy currency, 206 Energy giving, 181–82

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Energy Information Administration (EIA), 120–23 Energy management systems (EMSs), 53, 54, 65 Energy Market Information Exchange (EMIX), 190 Energy markets bill analysis, 39 clearing price, 37 deregulated, 34 regulated, 34 retail, 38–39 wholesale, 34–38 Energy service company (ESCO), 198 Energy service providers (ESPs), 108, 110 Energy Services Provider Interface (ESPI), 123 Energy transaction information model, 189 Energy usage analysis cloud computing, 107 defined, 107, 148 See also Load disaggregation Energy usage datasets, 133–34 Ethereum, 197–98 Falling edge, 156 Fossil fuels end-to-end losses, 27 generation/transportation efficiency, 27 wasted dollars and CO2 emission, 28 Fully elastic (FE) algorithm, 169–70 Fully electric EVs, 95 Gamification defined, 107–8 objectives, 174–75 Gamification demand response participatory games, 170–73 pseudocode, 175, 176 rewards and social recognition, 173–74 Generation, 8–9

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Gentailer model, 61 Grand coalition, 202 Green Button, 123–25 Grid architecture chemical energy to electricity, 20–22 cooperatives (COOPs), 18 distribution, 25–26 illustrated, 19 independent system operators (ISOs), 18 kinetic energy to electricity, 22–23 municipality-owned utilities (MOUs), 18 production, 20–24 regional transmission organizations (RTOs), 18 retail energy providers (REPs), 18 thermal energy to electricity, 23–24 transmission, 24–25 U.S. regional organizations, 17–18 See also Power grid Grid developer model, 61–62 Hash chain, 194 High power next (HPN) scheduling, 164–65 Home-area network (HAN), 75–77 Home energy management systems (HEMSs), 51, 53, 54, 107 Hourly and daily energy analytics, 144–46 Hybrid EVs, 95–96 ICT layer advanced metering infrastructure (AMI), 77 meter data management systems (MDMSs), 80 networking, 75–77 smart metering, 72–75, 80–81 See also Information and communication technologies (ICTs)

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Index217

Independent approach, 71 Independent system operators (ISOs), 18, 34, 187, 188 Inductive loads, 11 Information and communication technologies (ICTs) evolution of, 50 in functional system, 1 integrated infrastructure, 33 integration of, 60 See also ICT layer Infrastructure as a service (IaaS), 111 Internet of Energy (IoE), 46, 51 Internet of Things (IoT), 2, 3 Interoperability, 4 Intrusive monitoring, 149 Island microgrid, 92 JSON API, 128, 129 K-nearest neighbors (KNN) algorithm, 157 Last-mile networks, 78 Latency, 140 Leading edge, 156 Legislation, 206 Linear regression, 147–48 Load disaggregation algorithm flowchart, 152 algorithms, 149, 150–51 benefits of, 159 clustering, 156–57 defined, 107, 148 event detection by extracting power pulses, 154–56 hidden information in appliance footprints, 149–53 intrusive monitoring, 149 NIALM results and business intelligence, 158–59 nonintrusive monitoring, 149

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operation, 151 pulse to appliance association, 157–58 signal processing on smart meter data, 153–54 smart living example, 176–77 supervised training, 149 unsupervised training, 149 Loads, 11–12 Load scheduling (LS) controlled release (CR) algorithm, 169–70 defined, 161 effect of, 162 elastic demand and consumer behavior, 165–67 EV charging garage, 167–69 fully elastic (FE) algorithm, 169–70 implementation, 168, 169–70 objective of, 167–69 pseudocode, 170, 171 threshold postponement (TP) algorithm, 169 Locational marginal price (LMP), 35, 188 Low Income Home Energy Assistance Program (LIHEAP), 70 Machine-to-machine (M2M), 2, 3 Market operators (MOs), 34 Meter data management systems (MDMSs), 50, 54, 55, 80, 106 Microbill payment, 108 Microgrids architecture, 91–92 Brooklyn, 92 connected, 92 island, 92 types of, 92–93 Microinverter APIs, 128–29 Model predictive control (MPC), 190 Modulation, 74

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MultiSpeak, 134–35 Municipality-owned utilities (MOUs), 18 Negawatt energy, 85, 86 Neighbor comparison, 171 NEST thermostat, 132–33 Net metering, 99–100 Network design drawbacks, 26–34 business models, 31 passive nature of consumer, 30 security/outages, 32–34 time-variable production and consumption, 28 waste of resources and pollution, 26 Networking, 75–77 Network manager model, 62 Nonintrusive appliance load monitoring (NIALM) algorithms, 158–59, 160 Nonintrusive monitoring, 149 OASIS Standard Reference Model, 190 Ohm’s law, 9 OpenADR communication architecture, 136, 137 defined, 135 demand response event, 136–37 HTTP commands, 137 key actors, 135–36 rush hour example, 138–39 services, 135–36 VTN/VEN, 135, 136 Open APIs, 119–35 Operational expenditures (OPEX), 180–81 Orange Button, 125–26 Outage management systems (OMS), 50 Participatory games, 170–73 Partner-of-partner model, 62–63 Partner of partners, 183 Peak load credits (PLCs), 102 Peak shaving, 161

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Periodicity, 40, 151 Plug-in hybrids, 96 Power AC, 9, 11 active, 12, 13, 150 complex, 12, 13 DC, 10–11 defined, 7 energy and, 7–8 reactive, 11, 13, 14, 150 Power demand, 15 Power factor (PF), 14, 15 Power grid capacity, 8 consumer and, 39–44 consumption and, 9 current network design drawbacks, 26–34 data, 7–17 demand and, 9 energy markets and, 34–39 generation, 8–9 grid architecture, 17–26 power and energy and, 7–8 telecommunications industry lessons and, 44–46 U.S. regional organizations, 17–18 Power plants, 24 Power purchase agreements (PPAs), 32 Power value, 39, 151 Price-proactive, 184 Price-reactive, 184 Pricing collaborative prosumers, 203–6 critical peak, 99 implementation and, 207 models, 98 net metering, 99–100 peak load credits (PLCs), 102 real-time, 99 renewable energy credits (RECs), 100–102 time-of-use, 98–99

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Product development (cloud) open, accessing, 118–19 pricing model of SaaS service, 115–16 security and privacy, 117 web app versus mobile app, 116–17 white labeling, 119–20 Product innovator model, 62, 183 Proof of work (POW), 195, 196 Prosumers available capacity, 85 collaborative, 95, 199–206 concept importance, 209 connected homes, 83–88 consumption state, 82, 84, 85 defined, 51, 82 DSO information exchange, 204 dynamic local, 203 evolution of, 81–90 implementation and, 206 negawatt energy and, 85, 86 off-grid planning costs, 83 path to off-grid and, 81–83 production state, 82, 84, 85 standards, 88–90 switching between consumption and production states, 88 Pure merchant play model, 61 PVWatts API, 126–28 Reactive power, 11, 13, 14, 150 Reactive value, 39, 151 Real-time pricing, 99 Real-time pricing models, 37 Receding horizon control (RHC), 190 REDD datasest, 133–34 Regional transmission operator (RTO), 187, 188 Regional transmission organizations (RTOs), 18 Regulated markets, 34 Regulation issues, 4 Renewable energy credits (RECs), 100–102

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Index219 Renewable energy sources (RESs) deployment, 50 distributed system, 2 in functional system, 1 production of, 159–61 time-varying energy production from, 3 Resistive loads, 11 Retail energy providers (REPs), 18 Retail market, 38–39 Retail TE use case, 185–87 Round robin (RR) scheduling, 164 RSA, 117 Security power grid, 32–34 product development (cloud), 117–18 Selling cycles, 4 Serious games, 171 Shapley value, 201–3 Shortest job next (SJN), 165 6LoWPAN, 77 Slope, 151 Smart cities, evolution of, 179–81 Smart contracts, 197–98 Smart Grid 2.0, 51, 69–71 Smart Grid 3.0, 51, 69–71, 106 Smart grid business business-to-consumer (B2C) providers, 67 community solar models, 65–67 demand response (DR) model, 64–65 energy management systems (EMSs) model, 65 market, 4 new models and players, 64–67 social smart grid, 69–71 solar-plus-storage (SPS) model, 65 start-up ecosystem, 71–72 utility consumer beyond 2020 and, 67–69 utility of the future, 60–64

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Smart grid business model business-to-business (B2B), 4 business-to-consumer (B2C), 4 gentailer model, 61 grid developer model, 61–62 growth strategy, 3–4 network manager model, 62 partner-of-partner model, 62–63 problem, 2 product innovator model, 62 pure merchant play model, 61 risks, 4–5 solution, 2–3 summary, 1 virtual utility model, 63–64 vision, 1 Smart Grid Interoperability Protocol (SGIP), 54, 55, 56, 190 Smart grids architecture and standards, 52–55 communication architecture, 77 conceptual model architecture, 53 electric vehicles (EVs), 95–98 elements, 49 evolution, 49–52 ICT layer, 72–81 interoperability, 55–60 microgrids, 91–93 pricing, 98–102 primary vision, 209 products and services, 51 prosumers and, 81–90 protocols and standards, 56–60 summary and conclusions, 209–10 virtual power plants (VPPs), 93–95 Smart Home app, 114 Smart living example active utility customer, 177 energy usage analysis, 176–77 home automation, 177 overview, 175

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Smart meter data event detection by extracting power pulses from, 154–56 example, 14–17 hidden information in appliance footprints, 150 measurement setup, 16 noninvasive sensor in, 15 processing and conversion to pulses, 155 signal processing on, 153–54 transmission, 16 Smart meters for AMIs, 74 block diagram, 73, 74 functions, 73 in-home example, 80–81 in ICT layer, 72–75 types of, 75 Smart thermostats GET and PUT functionalities, 130 importance of, 129 manufacturers, 130 NEST, 132–33 Social smart grid, 69–71 Software as a service (SaaS), 115–16 Solar Data Translation Platform (SDTP), 126 Solar energy, 29, 65, 82, 83 Solar-plus-storage (SPS) model, 65 Spike, 151 Stackelberg games, 172 Start-up ecosystem, 71–72 Superadditive, 202 Supermodular, 202 Supervised training, 149 Supervisor, 92 Supervisory control and data acquisition (SCADA), 50 Telecommunications industry lessons, 44–46

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Index221

Threshold postponement (TP) algorithm, 169 Time-of-use pricing, 98–99 Time of use probability, 152 Time-variable production and consumption, 28–30 Top-down approach, 71 Top-K consumers, 171 Trailing edge, 156 Transaction analysis, 188–90 Transactive energy agents, 184 analysis of transactions, 188–90 concept, 3 defined, 184 distribution system operator (DSO), 187–88 end-user implementation, 190–92 foundations of, 183–85 grid, 183–92 independent system operators (ISOs), 187, 188 modes of operation, 187–88 paradigm overview, 186 regional transmission operator (RTO), 187, 188 retail and distribution examples, 185–87 simplified architecture, 185 system diagram for implementation, 191 Transactive energy economy energy giving, 181–82 evolution of smart cities, 179–81 value proposition and business impact, 182–83 Transactive Energy Market Information Exchange (TEMIX), 190 Transferable utility (TU), 201–2 Transmission, 24–25 Transmission system operators (TSOs), 34, 35

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Transport layer security (TLS), 117 Unpaid bills, reduction in, 183 Unsupervised training, 149 User-centric applications data processing, 143–44 direct load control, 159–65 energy analytics, 144–48 gamification demand response, 170–75 load disaggregation, 148–59 load scheduling, 165–70 smart living example, 175–77 Utilities, response of, 4 Utility as a bank, 183 Variance, 151 Vehicle-to-grid (V2G) application, 3, 98 Vehicle-to-vehicle (V2V) application, 3 Virtual machines (VMs), 111, 112 Virtual power plants (VPPs), 52, 53 architecture, 93–94 defined, 93 dynamic, 94–95 emerging trends, 94–95 transactive energy model, 94 Virtual utility model, 63–64, 183 Web-based applications, 116 Web services defined, 108 glossary examples, 112 home automation example, 113–15 platform, 109 White labeling, 119–20 Wholesale market architecture, 34–35 benefits, 34 clearing price, 37 commodities/pricing, 35 independent system operators (ISOs), 34

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Wholesale market (Cont.) market operators (MOs), 34 transaction types, 35 transmission system operators (TSOs), 34, 35

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Wide-area networks (WANs), 77–78 Work and asset management systems (WAMSs), 53

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Recent Artech House Titles in Power Engineering Andres Carvallo, Series Editor Advanced Technology for Smart Buildings, James Sinopoli The Advanced Smart Grid: Edge Power Driving Sustainability, Second Edition, Andres Carvallo and John Cooper Battery Management Systems, Volume I: Battery Modelings, Gregory L. Plett Battery Management Systems for Large Lithium Ion Battery Packs, Davide Andrea Battery Power Management for Portable Devices, Yevgen Barsukov and Jinrong Qian Big Data Analytics for Connected Vehicles and Smart Cities, Bob McQueen Design and Analysis of Large Lithium-Ion Battery Systems, Shriram Santhanagopalan, Kandler Smith, Jeremy Neubauer, Gi-Heon Kim, Matthew Keyser, and Ahmad Pesaran Designing Control Loops for Linear and Switching Power Supplies: A Tutorial Guide, Christophe Basso Electric Power System Fundamentals, Salvador Acha Daza Electric Systems Operations: Evolving to the Modern Grid, Mani Vadari Energy Harvesting for Autonomous Systems, Stephen Beeby and Neil White GIS for Enhanced Electric Utility Performance, Bill Meehan Introduction to Power Electronics, Paul H. Chappell Introduction to Power Utility Communications, Dr. Harvey Lehpamer IoT Technical Challenges and Solutions, Arpan Pal and Balamuralidhar Purushothaman Plug-in Electric Vehicle Grid Integration, Islam Safak Bayram and Ali Tajer

Power Line Communications in Practice, Xavier Carcelle Power System State Estimation, Mukhtar Ahmad A Systems Approach to Lithium-Ion Battery Management, Phil Weicker Signal Processing for RF Circuit Impairment Mitigation in Wireless Communications, Xinping Huang, Zhiwen Zhu, and Henry Leung The Smart Grid as An Application Development Platform, George Koutitas and Stan McClellan Synergies for Sustainable Energy, Elvin Yüzügüllü Telecommunication Networks for the Smart Grid, Alberto Sendin, Miguel A. Sanchez-Fornie, Iñigo Berganza, Javier Simon, and Iker Urrutia For further information on these and other Artech House titles, including previously considered out-of-print books now available through our In-Print-Forever® (IPF®) program, contact: Artech House 685 Canton Street Norwood, MA 02062 Phone: 781-769-9750 Fax: 781-769-6334 e-mail: [email protected]

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