Operation of Smart Homes (Power Systems) 3030649148, 9783030649142

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Power Systems

Mehdi Rahmani-Andebili  Editor

Operation of Smart Homes

Power Systems

Electrical power has been the technological foundation of industrial societies for many years. Although the systems designed to provide and apply electrical energy have reached a high degree of maturity, unforeseen problems are constantly encountered, necessitating the design of more efficient and reliable systems based on novel technologies. The book series Power Systems is aimed at providing detailed, accurate and sound technical information about these new developments in electrical power engineering. It includes topics on power generation, storage and transmission as well as electrical machines. The monographs and advanced textbooks in this series address researchers, lecturers, industrial engineers and senior students in electrical engineering. **Power Systems is indexed in Scopus**

More information about this series at http://www.springer.com/series/4622

Mehdi Rahmani-Andebili Editor

Operation of Smart Homes

Editor Mehdi Rahmani-Andebili Department of Engineering Technology State University of New York, Buffalo State Buffalo, NY, USA

ISSN 1612-1287 ISSN 1860-4676 (electronic) Power Systems ISBN 978-3-030-64914-2 ISBN 978-3-030-64915-9 (eBook) https://doi.org/10.1007/978-3-030-64915-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The world’s ever-growing energy consumption necessitates the application of innovative energy-efficient technologies and advanced energy management techniques. The construction sector has a high potential to increase its energy efficiency and reduce its operation cost. The U.S. Energy Information Administration (EIA) has estimated that about one-third of the electrical energy consumption and carbon emissions are attributed to residences in the USA. A smart home is a building equipped with devices and energy resources that coordinate with one another to achieve a common set of goals for the benefit its occupants. Nowadays, smart homes are becoming popular and older buildings are being converted to smart homes, allowing occupants to control their energy consumptions. Smart homes can be connected to one another to exchange electricity. Moreover, a smart home can sell back its surplus electrical energy to the local electrical grid. Smart homes are being advertised by governments and environmentalists, since they can mitigate the energy security and environmental issues by advanced energy management techniques and optimal utilization of free and clean renewable energy sources. This book presents recent research advancements in the operation of smart homes. The first chapter analyzes approximately 11,000 studies published about smart homes and indexed in the Scopus database from 1985 to 2019. The studies are categorized based on different viewpoints, including the field and the subject of the researches, the name of the institutions, and the nationality of the authors. In the second chapter, a multi-time scale stochastic model predictive control (MPC) is applied to solve the cooperative distributed energy scheduling problem of smart homes. In this chapter, every smart home can transact electrical energy with the retailer and other connected smart homes. In addition, applying multi-time scale approach in the stochastic MPC simultaneously considers the precise resolution for the problem variables and the vast vision for the optimization time horizon. In the third chapter, a three-layer framework to react to malicious cyberattacks, reported by the decentralized DG retailers and smart home retailers, is proposed. v

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The performance of the framework is verified by its implementation on an electrical distribution network. The fourth chapter is related to demand-side management (DSM) implementation and presents various energy management frameworks for different residential buildings. The presented demand response frameworks are validated by several case studies. The fifth chapter presents a framework for the design of smart homes to support residents’ wellness and pleasurable experience and contribute to the heathy and happy living of their occupants by incorporating various technologies and devices into a domestic setting. Buffalo, NY, USA

Mehdi Rahmani-Andebili

Contents

1

Worldwide Research Trends on Smart Homes . . . . . . . . . . . . . . . . . Esther Salmerón-Manzano, Mehdi Rahmani-Andebili, Alfredo Alcayde, and Francisco Manzano-Agugliaro

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Multi-time Scale Stochastic Model Predictive Control for Cooperative Distributed Energy Scheduling of Smart Homes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mehdi Rahmani-Andebili

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How to Employ Competitive Smart Home Retailers to React to Cyberattacks in Smart Cities? . . . . . . . . . . . . . . . . . . . . . Arash Asrari

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Demand Response Frameworks for Smart Residential Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. L. Arun and M. P. Selvan

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Smart Homes to Support the Wellness and Pleasurable Experience of Residents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Mi Jeong Kim, Myung Eun Cho, and Han Jong Jun

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

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Chapter 1

Worldwide Research Trends on Smart Homes Esther Salmerón-Manzano, Mehdi Rahmani-Andebili, Alfredo Alcayde, and Francisco Manzano-Agugliaro

Abstract Research in smart homes is still quite recent; however, there is no doubt that it will become a pervasive research topic in the near future. This chapter analyzes the whole research production on smart homes indexed in the Scopus database from 1985 to 2019, yielding a total of 11,000 studies. One of the goals of this chapter is to identify the main countries and institutions that have published on this topic and what their interest has been throughout the time. Four out of 116 countries stand out in this field, namely China, USA, India, and South Korea. In terms of the main institutions, the three with the highest scientific output are Ulster University (UK), CNRS Centre National de la Recherche Scientifique (France), and Universite Grenoble Alpes (France). Another aim of the chapter is to determine the research fields and subfields investigated about smart homes. The publications are mainly focused on two scientific fields, that is, computer science and engineering, accounting for 64% of the overall scientific production. In an aggregate analysis of all publications, four main clusters have been identified, namely Internet of Things, Activity Recognition, Security, and Energy. Keywords Automation · Intelligent buildings · Smart home · Internet of Things (IoT) · Domestic appliances · Energy utilization · Ubiquitous computing · Artificial intelligence · Smart power grids · Energy management · Sensors · Wireless sensor networks · Energy efficiency

E. Salmerón-Manzano Faculty of Law, Universidad Internacional de La Rioja (UNIR), Logroño, Spain e-mail: [email protected] M. Rahmani-Andebili Department of Engineering Technology, State University of New York, Buffalo State, Buffalo, NY, USA e-mail: [email protected] A. Alcayde · F. Manzano-Agugliaro (*) Department of Engineering, ceiA3, University of Almeria, Almeria, Spain e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Rahmani-Andebili (ed.), Operation of Smart Homes, Power Systems, https://doi.org/10.1007/978-3-030-64915-9_1

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1.1

E. Salmerón-Manzano et al.

Introduction

The concept of smart home refers to the availability of a set of systems or devices that automate equipment providing a wide range of comforts to the inhabitants of a house. In a broad sense, smart homes can be understood from the perspectives of comfort, leisure, and safety [1]. The key idea is that smart homes can operate on their own with hardly any human intervention based on the concepts such as the Internet of Things (IoT) or home automation [2]. For example, the fridge can send an email or SMS to the smartphone if it detects that the door has been opened for a long time when the temperature is above the values of its optimal operation. To achieve this objective, sensors are the key due to their low cost, and their connection to the Internet via IoT [3], both factors are becoming increasingly widespread. All this allows to gather, record, and share the data in real time with other devices and control systems. Therefore, the data can be linked to a data center, and by using artificial intelligence, the established target can be achieved [4]. That is, to create an intelligent environment at home which can detect what is happening inside and outside through sensors, and act accordingly, depending on the event. Clear examples are the detection of water installation losses [5], firefighting systems [6], and gas alerts [7]. The main concerns of users include safety and energy saving. For example, according to the user’s lifestyle, energy can be saved by means of air-conditioning control [8]. The system may detect people’s absence in the smart home and the air conditioning may move to an energy-saving program [9]. Passive measures can also be implemented, such as raising and lowering blinds, or folding or unfolding awnings. Another example would be the operation of the washing machine, dryer or dishwasher can be configured with its start time in accordance with the energy rate signed up. But the control is not only for the absence of people in a house, but also to increase their comfort. Another relevant benefit that is increasing in smart houses is linked to the control and management of devices by smartphone or through a virtualvoice assistant, Virtual assistants act as control panels or command centers controlling the smart home, either with a single button or by means of an individual voice [10]. It should be noted that virtual assistants and smart home speakers are among the technological innovations of the year 2019 with a highly significant volume of business in developed countries, and it is envisaged that they will continue rising in the coming years. Some examples are highly popular devices such as Google Home or Alexa that allow turning devices on and off, closing or opening doors, and managing the temperature or lights of the home, thus beginning the transition to smart homes. Smart homes can also take care of the health of their inhabitants [11], since connectivity is also an unquestionable partner regarding health. Examples include the blood pressure monitor to measure heart activity [12], or the balance that register body weight on the cell phone that allows users to keep a record of the weight in the cloud.

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Given the present and future relevance of smart homes, this chapter aims to analyze all the publications indexed on the smart home to identify current research trends in the field. Therefore, firstly a bibliometric study of these publications will be made to illustrate the main interests of the countries and scientific institutions that are engaged in this research field. Secondly, an analysis of communities will be used to detect those issues that most concern scientists, and to establish the clusters in which these researches can be classified, to identify (future) research opportunities.

1.2

Data

In 1934 the concept of employing the quantity of scientific articles as a quantitative index of research output was established, and it was defined that “science is what is published in scientific journals” [13]. It can be understood that scientific journals are those that are indexed in scientific databases. Although there are scientific databases specific to certain fields of knowledge, overall, there are two main databases: Web of Knowledge and Scopus. There are studies that demonstrate that the Scopus database has a greater coverage in certain scientific areas [14], and, therefore, it is the database used in this book chapter. All the publication data collected in the Scopus database for this study were gathered through the search concept “Smart home” up to the year 2019. More than 11,000 results were obtained from 1985 to 2019. The flowchart of the followed methodology in this chapter can be seen in Fig. 1.1.

Fig. 1.1 Flowchart of the methodology

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Fig. 1.2 Periods and trends in smart home research

Figure 1.2 shows the number of publications on smart homes during the whole period of analysis. There is an anecdotal publication in 1985 and a second one in 1986. But as there is no time continuity, it is considered that the evolution in this field was from 1990 onwards, which is when there is continuity in time. Therefore, it is assumed that the beginning of the research output in this field was in 1990, resulting in three periods to be considered. The first period includes less than 10 publications per year, i.e., until 2001. Then, there is a second period, which may be considered as consolidation stage, with a great upward trend in the number of publications, but with less than 1000 publications per year, from 2002 to 2015. Finally, there is a third period of greater growing trend, with more than 1000 publications per year since the end of the second period to date.

1.3

Subjects from Worldwide Publications

The Scopus database classifies publications according to scientific or thematic categories. It should be noted that the same publication could belong to several categories at the same time. The subject categories in which all these publications are classified are shown in Fig. 1.3. Computer science is seen to lead the field of the research output on smart homes with 39% of overall research production, followed by Engineering (25%). It is striking that the third place is for mathematics, perhaps because of the high mathematical component of the previous two categories. For example, there is a use of advanced mathematics, especially in artificial intelligence. On the other hand, it is remarkable that the energy category is only in fourth place, even though energy saving could be an additional stimulus.

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Fig. 1.3 Percentages of smart home publications according to the Scopus categories

Fig. 1.4 Worldwide map of scientific production on smart homes

1.4

Countries, Affiliations, and Their Main Topics

Among the overall number of publications analyzed, there are studies from 116 different countries (Fig. 1.4). South America and especially Africa have, apparently, a limited scientific interest in this issue. The top 10 countries that show the highest interest in this research topic are: China, United States, India, South Korea, United Kingdom, Germany, France, Italy, Canada, and Taiwan. Figure 1.5 shows the evolution of the 5 top countries in the field. The same trend is observed for these countries as in the global periods identified, except for South Korea, which has a more limited growth in the third period. In this last period, the USA leads the research in this field.

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Fig. 1.5 Annual trend in the number of publications from the leading countries in smart home research

Fig. 1.6 Main institutions producing scientific publications on Smart homes

Figure 1.6 shows the 21 institutions with more than 50 publications in this field. Of all these, three stand out, with more than 100 contributions: Ulster University (UK), CNRS Centre National de la Recherche Scientifique (France), and Universite Grenoble Alpes (France). Overall, it can be seen that the affiliations belong to a large number of countries: UK, France (3), Pakistan, Canada (2), USA, New Zealand, South Korea (2), China (4), Saudi Arabia, Italy, Taiwan (2), Austria, and

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Netherlands. It is noteworthy that there is only one institution from the USA. Regarding the affiliations with the greatest number of contributions, China and France can be highlighted. The three main keywords of the institutions leading this research output have are listed in Table 1.1. Two terms are particularly relevant: Automation and Intelligent Buildings. Thus, it is the third keyword that identifies the specific research of these institutions. Relatedly, Activity Recognition is being investigated in: Ulster University, COMSATS University Islamabad, Université du Québec à Chicoutimi, Washington State University Pullman, and National Taiwan University. Speech Recognition as a keyword is used by Recherche Scientifique and Laboratoire d’Informatique de Grenoble, both institutions from France, perhaps showing a specialization of this country in this topic. Internet of Things (IOT) is a term widely used by many institutions such as: Electronics and Telecommunications Research Institute, Chinese Academy of Sciences, and Ministry of Education China, King Saud University, Beijing University of Posts and Telecommunications, Tsinghua University, or National Cheng Kung University. It is observed that this particular field of research is popular with institutions in Asian countries such as China, Taiwan, or South Korea.

1.5

Keywords from Worldwide Publications

Keywords are essential to identify works related to a certain research area, as they are used to list and index the articles. Therefore, keywords are an indispensable tool to conduct a bibliographic search, as they allow access to all related studies in large databases. Table 1.2 shows the keywords that index these publications, indicating the highest frequency of keywords that appear with the search terms used. It should be noted that the search terms have been removed from this table: Smart Homes and Smart Home. As expected, the two main keywords are those that appeared as terms in the main affiliations, i.e., Automation, and Intelligent Buildings. The third keyword in terms of relevance is Internet of Things, which also appeared in many of the major affiliations as an important term. Activity Recognition now appears in 16th place, not too significant. Also, it is noteworthy the presence of Domestic Appliances, which was not covered by any of the major affiliations as a main research topic. Energy-related keywords do not appear overall in a remarkable position: Energy Utilization, Smart Power Grids, Energy Management, or Energy Efficiency. Figure 1.7 shows a cloud of keywords from the scientific production of the smart homes. Here, automation and intelligent buildings have not been represented to establish a benchmark with the other keywords. It is remarkable that the Internet of Things is written as a keyword in three different ways: Internet of Things, Internet of Things (IOT), and IoT. This places the concept IoT in the first position, as observed in Fig. 1.7

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Table 1.1 Main affiliations and their main keywords Affiliation Ulster University

Country UK

Keywords 1 Automation

CNRS Centre National de la Recherche Scientifique Universite Grenoble Alpes

France

Automation

France

Automation

Pakistan

Automation

Canada

Automation

USA New Zealand

Intelligent Buildings Automation

COMSATS University Islamabad Université du Québec à Chicoutimi Washington State University Pullman Massey University Manawatu Electronics and Telecommunications Research Institute Chinese Academy of Sciences

South Korea

Automation

China

Automation

Ministry of Education China

China

Automation

Université de Sherbrooke

Canada

King Saud University

Saudi Arabia

Intelligent Buildings Automation

Beijing University of Posts and Telecommunications Laboratoire d’Informatique de Grenoble Università Politecnica delle Marche

China

Automation

France

Automation

Italy

Automation

National Taiwan University

Taiwan

Kyung Hee University

South Korea

Technische Universitat Wien

Austria

Intelligent Buildings Intelligent Buildings Automation

Technische Universiteit Eindhoven Tsinghua University

Netherland

Automation

China

Automation

National Cheng Kung University

Taiwan

Automation

2 Intelligent Buildings Intelligent Buildings Intelligent Buildings Intelligent Buildings Intelligent Buildings Automation Intelligent Buildings Intelligent Buildings Intelligent Buildings Intelligent Buildings Automation Intelligent Buildings Intelligent Buildings Intelligent Buildings Intelligent Buildings Automation Automation Intelligent Buildings Intelligent Buildings Intelligent Buildings Intelligent Buildings

3 Activity Recognition Speech Recognition Ubiquitous Computing Activity Recognition Activity Recognition Activity Recognition Wireless Sensor Networks Internet of Things Internet of Things Internet of Things Activities Of Daily Living Internet of Things Internet of Things Speech Recognition Ambient Assisted Living Activity Recognition Activity Recognition Energy Efficiency Data Mining Internet of Things Internet of Things

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Table 1.2 Top 20 keywords related to smart home Keyword Automation Intelligent Buildings Internet of Things Domestic Appliances Internet of Things (IOT) Energy Utilization Ubiquitous Computing Artificial Intelligence Smart Power Grids Energy Management Sensors Wireless Sensor Networks Energy Efficiency IoT Smart-home System Activity Recognition Smart Grid Home Automation Electric Power Transmission Networks Pattern Recognition

Fig. 1.7 Wordcloud of keywords of the scientific production on smart homes

N 7.924 6.785 2.273 891 793 756 737 661 626 609 603 587 548 543 537 520 503 490 437 428

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1.6

E. Salmerón-Manzano et al.

Worldwide Research Trends

Scientific communities or clusters of publications establish the trends of research in a particular field. Figure 1.8 shows the relationship between the total number of publications (11,000) analyzed in this study, Two levels can be identified. First, the central core, which includes the publications related to each other, around 65% of the total. Second, the outer circle, with 35% of the publications, which consists of publications that have no relationship with any other of this subject, shown in Fig. 1.8. The core was analyzed by means of a community analysis, Gephi software. Fourteen clusters were detected, Fig. 1.8. Only five had a level higher than 1% and, thus, can be considered as significant clusters in the subject. Of these five clusters, four are very significant (>20%) and the ones that define current smart home research worldwide, illustrated in Fig. 1.9, include Internet of Things, Activity Recognition, Security, and Energy.

1.6.1

Internet of Things

Considering the number of publications, the most important cluster is Internet of Things. Table 1.3 shows the main keywords associated with it. The first work recorded with these keywords dates from the mid-80s. Some examples are in 1985 “The Smart Home program” [15] that represented the optimization of four Fig. 1.8 Relationship between smart home publications

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Fig. 1.9 Relationship between the core of smart home publications

technologies: thermal envelope design, solar system design, HVAC interface logic, and economic optimization. One year later, in 1986, the Smart House Project discussed customary home services such as heating or security [16]. In the 1990s, the potential of robotics and artificial intelligence in relation to health began to be analyzed [17], with the primary concern for energy saving in relation to the environment and the CO2 emissions produced by energy consumption [18]. Within this subject, wireless communications (Wireless Sensor Networks) are the most frequently cited [19] as they allow for the connection between devices without the need for large installations that would be cost-prohibitive at home. Furthermore, the huge amount of information gathered by these devices needed the so-called Edge Computing to give more independence to all these devices, making them somewhat smarter [20]. Linked to this cluster is the term Ubiquitous Computing, understood as the integration of computing in the environment of the person. Within this last concept would be the wearable sensors that allow the integration of wearable and ambient sensors to achieve home monitoring and to help elderly people and people with chronic impairments [21]. Therefore, monitoring inhabitants would be possible even without hospitalization, providing support to the healthcare system [22].

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Table 1.3 First cluster of smart home publications (cluster 0 ¼ Internet of Things) Keyword Internet of Things Home Automation Ambient Assisted Living IoT Ambient Intelligence Smart Home Technology Ubiquitous Computing Assistive Technology Ontology Smart Environments Wireless Sensor Networks Sensors OSGI Pervasive Computing Context Awareness Privacy Elderly Aging in Place Zigbee Energy Efficiency

1.6.2

N 177 119 92 68 67 62 54 49 47 47 47 46 41 39 38 37 35 32 32 31

Activity Recognition

The second most important cluster regarding the number of associated publications is Activity Recognition. Table 1.4 summarizes the main keywords related to this cluster. This cluster is closely linked to the clusters of: IoT, Machine Learning, and Energy. Linked to the research of smart home, the Activity Recognition concept started in 2006. This first study showed the advantage of gathering a large amount of information to help users according to their lifestyle [23]. And within this, it is noteworthy the assistance to people with diseases such as Alzheimer [24]. Researchers also often use the term Activities of Daily Living (ADL), where the knowledge-driven approach to real time, continuous activity recognition is based on multisensor data streams in smart homes [25]. The Healthcare context is one of the most promising research lines within this cluster. Smart homes offer outstanding opportunities to provide supervision and support to people who are experiencing difficulties when living alone at their homes [26]. Automated activity tracking that identifies frequent activities in an individual’s routine allows, for example, registering the performance of regular activities to monitor an individual’s functional life health [27]. The other term linked to this cluster is Ambient Assisted Living (AAL). The concept is quite close to the previous one. In this case, a network of intelligent sensors, usually wireless, detects people’s activities and movements. Through a

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Table 1.4 Second cluster of smart home publications (cluster 10 ¼ Activity Recognition) Keyword Activity Recognition Internet of Things Machine Learning Activities Of Daily Living Ambient Assisted Living Wireless Sensor Networks Sensors Ambient Intelligence IoT Smart Environments Context Awareness Healthcare Pervasive Computing Data Mining Ontology Sensor Networks Ubiquitous Computing Home Automation Anomaly Detection Deep Learning

N 360 152 109 84 80 76 64 62 56 56 46 45 40 40 35 34 32 31 31 29

behavioral recognition component that automatically detects deviations from normal behavior, it issues an alarm to caregivers [28]. These technologies are targeted for modern societies where the elderly can live in their homes, smart homes, communicating with the outside world in an intelligent and goal-oriented way of health care [29]. For this purpose, deep learning for human activity recognition is very useful [30].

1.6.3

Security

The third cluster by the number of publications is the one focused on security. Table 1.5 includes the main keywords associated with this cluster. As can be observed in Fig. 1.9, this cluster is next to the IoT cluster. The safety of mechanisms linked to smart homes was one of the first topics to be studied, in the mid-1980s [16]. Therefore, it is not surprising that, in this cluster, references to devices based on low-cost technologies such as Zigbee [31], Raspberry Pi [32], or Arduino [33] appear. In this cluster, they are later attached to concepts such as: Privacy, Blockchain, or Authentication, since IoT security and privacy is a major challenge, mainly due to the massive scale and distributed nature of IoT networks [34]. This

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Table 1.5 Third cluster of smart home publications (cluster 13 ¼ Security) Keyword Internet of Things IoT Security Home Automation Wireless Sensor Networks Privacy Blockchain Authentication Zigbee Cloud Computing Smart Grid Sensors Machine Learning Arduino Access Control Raspberry Pi Smart City Wi-Fi Smart Home System Fog Computing Edge Computing IoT Security

N 541 287 185 98 80 74 68 68 53 47 43 40 36 35 33 31 28 26 26 25 25 25

cluster is located between the IoT and the energy clusters (Fig. 1.9) in the last concept, particularly related to the safety of devices. Regarding to energy, the energy consumption of the Smart home systems is primarily analyzed [35].

1.6.4

Energy

The main keywords associated with the fourth cluster are listed in Table 1.6. These are focused on energy. The main concepts are linked to the smart grid or power grid. Here, one of the first studies focuses on power-grid load balancing by using smart home appliances [36]. On the other hand, in this cluster, the Smart Meter is crucial, since it improves energy savings first [37], and in a more advanced stage it even reaches the net-zero energy home [38]. Thus, energy efficiency and demand management with smart meters will require smart grids and smart homes [39]. So, the combination of the three elements would allow managing energy consumption from the generation to the user in a more efficient way. Related to energy, the concepts of optimization [40], and especially with the price of the energy tariff are especially relevant aiming to encourage consumers to reduce their demand during peak load

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Table 1.6 The fourth cluster of smart home publications (cluster 13 ¼ Energy) Keyword Smart Grid Internet of Things Home Energy Management System Energy Management Demand Response Demand Side Management Home Automation Zigbee IoT Wireless Sensor Networks Sensors Nx Smart Meter Energy Efficiency Optimization Electric Vehicle Machine Learning Raspberry Pi Arduino Microgrid Multi-agent System Security

N 270 181 175 157 156 92 68 68 64 55 51 45 43 43 38 37 36 36 35 31 31

hours [41]. The concept of Home Energy Management System appears prominently. Here, the user can maximize the net benefits with decision-making tools. End users can be allowed to schedule their available distributed energy resources [42]. In short, the aim is to achieve the highest energy efficiency possible [43].

1.6.5

Machine Learning

The Machine Learning cluster has limited relevance compared to the ones above. It is an emerging cluster that will have its own identity in a short time. This cluster is closely linked to the Activity Recognition, Fig. 1.9. The main keywords are Fall Detection and KNX. The latter is an open communication standard protocol that has been adopted by more than 400 manufacturers in its 25-year history. Since the installation of a home automation system is a long-term investment, it seems appropriate to install it with open-source software. If a manufacturer stops supplying a product installed in the home, or if it stops its activity, all the homes that have this product will be forced to change the automation system. Additionally, this technology can be implemented in both the desktop system and mobile devices version (i.e.,

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Table 1.7 Fifth cluster of smart home publications (cluster 1 ¼ Machine Learning) Keyword Fall Detection KNX Activity Recognition Smart Home Care Internet of Things Home Automation Control Monitoring Artificial Neural Network Prediction Hidden Markov Model Machine Learning Deep Learning Visualization Depth Silhouettes Wireless Sensor Networks Smart Home System Elderly People Voice Recognition C# Ets Fieldbus Wavelet Transformation

N 13 12 11 9 7 7 7 7 7 7 6 5 5 5 5 4 4 4 4 4 4 4 4

smartphones, tablets) [44]. Furthermore, in this cluster the term Voice Recognition appears, which is also linked to several studies on KNX [45]. In short, smart homes need to be taken to the next level, as they have the potential to extend the independent lives of the elderly [46] (Table 1.7).

1.7

Research Trends by Country

As mentioned above, the four countries leading research in the field of smart home are China, USA, India, and South Korea. To explore the research trend followed by these countries, an analysis of the evolution of their keywords has been conducted.

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1.7.1

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China

Eight clusters have been identified in the Chinese research output on smart homes. Figure 1.10 shows the detected clusters of the publication related to smart home by Chinese affiliations. Table 1.8 presents the detected clusters and their percentages in relative terms according to the number of publications. The most cited articles of those published by authors from China related to smart home are those belonging to this cluster of sensors and communications. The studies related to IoT, for example that focused the research on networked smart homes [47], i.e. on a paradigmatic class of cyber-physical systems with cooperating objects.; or the integration of Smart home as sensors in Urban planning for building smart cities [48]. In the cluster of energy, the relationship between the arrival of smart grid and the concept of Smart home energy management systems can be underscored [43]. Finally, it should be noted that in the cluster of health, the advantages of IoT in healthcare and medicine are presented considering a holistic architecture of IoT eHealth ecosystem [49]

Fig. 1.10 Clusters of the Smart home research in China

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Table 1.8 Chinese clusters in smart home research (Figure 1.10) Color Red Green

Blue

Yellow

Purple

Cian Orange Brown

Main keywords Smart home system, wireless sensor network, personal communications systems, gateways, Zigbee, sensors Automation, intelligent buildings, artificial intelligence, ubiquitous computing, big data, human-computer interaction, learning system Domestic appliances, energy conservation, energy management, smart grid, energy utilization, costs, optimization Internet, cloud computing, network architecture, web services, information management, distributed computer systems Intelligent control, control systems, Bluetooth, smart homes, smartphones, phones, telephone sets, remote control, android Network security, cryptography, authentication, Internet of Things, blockchain, privacy protection Health care, human, fall detection, home care, electroencephalography Multimedia services, Zigbee network, environmental monitoring, network routing

Cluster name Sensors and communications Automation and artificial intelligence

% 24

Energy

14

Internet and computing

13

Smartphones and remote control

10

IOT and security

9

Health care

6

Environmental monitoring

1

22

Figure 1.11 shows the evolution of China’s smart home research production from 2012 to 2018. At the beginning of the period, in 2012, there was Wireless Telecommunication Systems, e.g., a design of the model for smart home gateway [50]; and then, in 2016, there was Automation and Energy Utilization, e.g., the research related to smart home energy management with plug-in electric vehicle battery energy storage and photovoltaic array [51]. At the end of the period, in 2018, research was done on pattern recognition and learning systems, e.g., Distance metric optimization driven convolutional neural network for age invariant face recognition [52].

1.7.2

USA

Table 1.9 shows the seven clusters found for the research conducted in the USA. The relationship between them is shown in Fig. 1.12. The most important cluster is the one related to remote control and smartphones. E.g. Activity Recognition Using Smartphone Sensors [53] or the hybrid approach to ambient and smartphone sensor assisted ADL (activities of daily living) recognition [54]. In the second cluster focused on Energy, the problem of scheduling deferrable appliances and energy resources of a smart home [55], or the use of stochastic model predictive control to distributed energy resources scheduling problem [56, 57] can be highlighted. The third cluster is focused on IOT and security, e.g., the effect of IoT new features on

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Fig. 1.11 Evolution of smart home research in China Table 1.9 USA clusters in smart home research (Fig. 1.12) Color Red

Green

Blue

Yellow

Purple Cian Orange

Main keywords Smart homes, smart environment, ubiquitous computing, human interaction, wireless telecommunication, wearable sensors, smartphones Domestic appliances, energy efficiency, energy management, smart grid, costs, optimization, electric power transmission network, air conditioning, energy utilization Automation, Internet of Things, network security, cryptography, distributed computer systems, information systems, web services Smart home technology, machine learning, human, daily life activity, telemedicine, activities of daily living, home care, assistive technology Embedded systems, security systems, learning systems, emerging technologies, artificial intelligence Monitoring, health care, patient monitoring, vital signs, sensors, Home automation, design, social networking, mobile computing, smart devices, human engineering, social aspects, ambient assisted living

Cluster name Smartphones and remote control

% 24

Energy

22

IOT and security

20

Machine learning

18

Artificial intelligence Health care

7

Social aspects

4

5

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Fig. 1.12 Cluster of the smart home research in the USA

security and privacy [58]. In the fourth cluster, the research is primarily devoted to machine learning techniques to discover patterns in resident’s daily activities [59]. This last cluster is related to healthcare because assistance technology will help individuals to live autonomously in their own homes [60]. Figure 1.13 shows the evolution of the research in this field in the USA from 2012 to 2018. Here we can see how in the initial stage attention was paid to health issues such as Alzheimer’s disease, daily life activity, disability, medical computing, in other words, surrounding the human issue. It must be noted that the situation was highly serious since Alzheimer’s disease is a progressive illness with no effective cure or treatment that affects more than 5.5 million people in the USA [61]. Later on, the topics of artificial intelligence were dealt with in 2014, and then the energy topics were dealt with in 2016, as can be seen with energy utilization, energy management, demand response, renewable energy, and electricity pricing, all with automation as an integrating element. In the last period, 2018, the Internet of Things appeared, as it included security issues such as network security, or privacy and security.

1.7.3

India

Table 1.10 shows the seven clusters found in the analysis of the Indian publications, which are shown in Fig. 1.14. One of the most cited papers in this country is A novel GSM-based control for e-devices [62]. The main cluster is that of Automation and

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Fig. 1.13 Evolution of Smart home research in the USA Table 1.10 India clusters in smart home research (Fig. 1.14) Color Red

Green

Blue

Yellow Purple Cian Orange

Main keywords Automation, artificial intelligence, learning systems, machine learning, big data, health care, home health care, pattern recognition Smartphones, home automation, domestic appliances, raspberry pi, Arduino, cost-effectiveness, remote control, face recognition, cameras, wireless communications Intelligent building, energy efficiency, energy utilization, smart meters, smart power transmission network, costs, smart power grids, electric vehicles Internet of Things, IoT, security, cryptography, authentication, blockchain, mobile security, cloud computing Wireless sensor networks, digital storage, sensor nodes, fog computing, clouds, smart cities Sensors, health, air quality, wsn, water quality, temperature, Standards, m2m, machine-to-machine, open systems, hardware, monitor and control, mptt

Cluster name Automation and Artificial intelligence

% 21

Smartphones and remote control

20

Energy

19

IOT and security

17

Wireless sensor networks Sensors

13

Informatics

6 4

Artificial intelligence, e.g., the integration in the near future of data from several sources such as sensors, smartphones, smart homes, and smart vehicles, which will also be of a temporary basis [63]. The second main cluster is focused on smartphones and remote control, using GSM [64] or Bluetooth [65]. The third cluster is devoted to energy, e.g., Smart Home intelligent system for monitoring the electrical energy

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Fig. 1.14 Cluster of the Smart home research in India

consumption based on the real time [66], and the fourth cluster relates to IOT and security, e.g., authentication in cloud-driven IoT-based big data environment [67]. Figure 1.15 shows the evolution of this research in India. The period which shows clear differences is from 2016 to 2018. At the beginning of this period, the topic of optimization and specific hardware was discussed as Zigbee, e.g., developing residential wireless sensor networks for electrocardiogram monitoring as Zigbee devices can offer a low-cost solution [68]. In 2017, the focus was on automation and domestic appliances. Already in 2018, the country’s research focused on network security, big data, learning systems, and blockchain.

1.7.4

South Korea

In South Korea, seven clusters related to smart homes were found, see Fig. 1.16. Each of them is represented in one color. The clusters are shown in Table 1.11. The main cluster has been named Computing, as the main keywords in these papers show related issues, such as: ubiquitous computing, user interfaces, middleware, user interfaces, semantics, or data mining. Among these studies, the most cited publications are those related to IEEE 802.15.4 and Zigbee [69]. As a curious fact, the

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Fig. 1.15 Evolution of Smart home research in India

Kongju National University has a specific department for this subject, the Green Home Energy Technology Research Department. The second cluster is focused on health care, where they highlight studies related to health surveillance. As an example, there is the research related to the future development of an intelligent residential space based on health monitoring adapted to human needs [70]. The third cluster is that of Automation, IoT and security, where the study of urban planning and building smart cities based on the Internet of Things using Big Data analytics stands out [48]. The fourth cluster is focused on energy, for example, on reducing electricity consumption based on the future development of the Smart grid [71]. Figure 1.17 shows the evolution of research in South Korea. The period with the greatest differences is from 2010 to 2018. At the beginning of 2010, research was primarily conducted on health issues and the technology associated with ubiquitous computing. Automation reaches its maximum in 2014, related, as in other countries, with domestic appliances. At the end of the period analyzed, 2018, terms such as Internet of Things, cryptography, blockchain, and the technology connected to fog computing appeared.

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Fig. 1.16 Cluster of the Smart home research in South Korea

Table 1.11 South Korea clusters in smart home research (Figure 1.16) Color Red

Green

Blue

Yellow Purple Cian Orange

Main keywords Smart home, ubiquitous computing, user interfaces, middleware, user interfaces, semantics, mobile devices, cellular telephone systems, data mining Health care, home environment, pattern recognition, learning systems, human activity, home health care, gesture recognition, deep learning, human-computer interaction Automation, Internet of Things, security, authentication, cryptography, internet protocols, privacy, data privacy, network security Energy utilization, optimization, cost, smart power grid, energy management, commerce, energy conservation Wireless sensor networks, personal communications systems, home networks, zigbee, networks protocols Remote control, face recognition, speech recognition, intelligent buildings, cameras, smartphones Consumer electronics, air conditioning, indoor positioning systems, residential environment

Cluster name Computing

% 21

Health care

18

Automation, IoT and security

18

Energy

8

Wireless sensor networks Remote control and smartphones Electronics

8 6 6

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Fig. 1.17 Evolution of Smart home research in South Korea

1.8

Relationship Between the Authors and Their Countries

Figure 1.18 shows the relationship between the authors from the different countries. In this figure, each node is an author, the thickness of the node indicates the number of publications by this author, and the color indicates the author’s affiliation country. For example, the hallmark of the authors from Pakistan is Nadeem Javaid from COMSATS University Islamabad, with more than 180 publications in this topic of smart home. As Table 1.12 shows, China stands out in terms of number of authors with 15%, followed by USA, with 11%, and India with 9%. It is noteworthy that Germany has such a high number of authors, higher than South Korea. As can be observed in the central area of the network (Fig. 1.18), European countries such as France, Germany, or the United Kingdom have a great relationship with each other, as expected. Countries with a large number of authors have two trends. First, to be an isolated cluster, like those of the USA or Pakistan. Second, to be very spread out, this means collaborating with many other authors from other countries. This is the case of China or India. Table 1.13 shows the 26 affiliations with

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Fig. 1.18 Relationship between the authors and their countries

Table 1.12 Number of different authors per country Affiliation country China United States India Germany South Korea United Kingdom Taiwan Italy France Spain Canada Japan Australia Malaysia Pakistan Indonesia Greece Brazil Netherlands Turkey

Number of different authors 4217 2901 2404 1571 1489 1266 942 899 734 639 580 547 541 503 497 366 340 328 270 245

% 15 11 9 6 5 5 3 3 3 2 2 2 2 2 2 1 1 1 1 1 (continued)

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Table 1.12 (continued) Affiliation country Austria Iran Finland Sweden Russian Federation Singapore Switzerland Saudi Arabia Portugal Czech Republic Belgium Norway Bangladesh United Arab Emirates

Number of different authors 235 231 228 226 225 215 192 178 173 169 162 148 144 144

% 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Table 1.13 Number of different authors per affiliation Affiliation COMSATS University Islamabad Beijing University of Posts and Telecommunications Vellore Institute of Technology, Vellore Electronics and Telecommunications Research Institute Korea Advanced Institute of Science & Technology Universite Grenoble Alpes K L Deemed to be University Universitá Politecnica delle Marche Rheinisch-Westfaelische Technische Hochschule Aachen National Cheng Kung University University Politehnica of Bucharest Ulster University Huazhong University of Science and Technology Tianjin University National Taiwan University Shanghai Jiao Tong University Tsinghua University University of Electronic Science and Technology of China Sungkyunkwan University Korea University National Chiao Tung University Taiwan Jilin University Technische Universiteit Eindhoven University of New South Wales (UNSW) Australia Washington State University Pullman Chinese Academy of Sciences

Number of different authors 140 129 112 111 96 88 82 79 76 75 74 72 71 71 69 65 64 64 61 59 59 58 58 57 57 56

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more than 55 authors. The institutions with more than 100 authors researching on smart homes are: COMSATS University Islamabad (Pakistan), Beijing University of Posts and Telecommunications (China), Vellore Institute of Technology known also as VIT (India), and Electronics and Telecommunications Research Institute (South Korea).

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Chapter 2

Multi-time Scale Stochastic Model Predictive Control for Cooperative Distributed Energy Scheduling of Smart Homes Mehdi Rahmani-Andebili

Abstract In this chapter, a multi-time scale stochastic model predictive control (MPC) approach is applied to solve the cooperative distributed energy scheduling problem of smart homes. In this problem, a variety of energy resources are considered for each smart home, and every smart home has a capability of power transaction with the retailer through the electrical grid as well as with the other connected smart homes. The challenges of the problem include modeling the technical and economic constraints of the energy resources and addressing the variability and uncertainty issues of renewables’ power that change the optimization problem to a stochastic, dynamic (time-varying), and mixed-integer nonlinear programming (MINLP) problem. To deal with the variability and uncertainty issues, a multi-time scale stochastic MPC is applied. Applying the multi-time scale approach in the stochastic MPC is able to simultaneously consider the precise resolution for the problem variables and the vast vision for the optimization time horizon. In addition, linear programming (LP) and genetic algorithm (GA) are combined (GA-LP) and applied in the problem as an effective and fast optimization technique. The numerical studies about the small and large systems demonstrate the competences of the proposed approach. Keywords Cooperative distributed energy scheduling · Multi-time scale model predictive control (MPC) · Photovoltaic (PV) panels · Smart homes · Stochastic optimization

M. Rahmani-Andebili (*) Department of Engineering Technology, State University of New York, Buffalo State, Buffalo, NY, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Rahmani-Andebili (ed.), Operation of Smart Homes, Power Systems, https://doi.org/10.1007/978-3-030-64915-9_2

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2.1

M. Rahmani-Andebili

Introduction

Increasing penetration of renewable energy resources into power systems can mitigate the energy security and environmental issues of fossil fuels [1]. In this regard, the building sector has a remarkable capability to decrease the carbon footprint and energy supply cost as well as increase the energy efficiency [2]. The U.S. Energy Information Administration (EIA) estimates that almost 37% of end use electricity consumption and 36% of the carbon emissions in the United States are related to the building sector [3, 4]. Figure 2.1 illustrates the energy consumption by sector in the United States (Quadrillion Btu) during the period of 1950–2019 [5]. As can be noticed, the energy consumption of building sector, as the combination of residential and commercial energy demands, has had the largest share among all the sectors in the recent decades. Moreover, the energy consumption of the residential sector in the United States (Quadrillion Btu) during the period of 1950–2019 is shown in Fig. 2.2 [6]. As can be seen, the electricity consumption of the residential sector in the United

Fig. 2.1 Energy consumption by sector in the United States (Quadrillion Btu) during the period of 1950–2019 [5]

Fig. 2.2 Energy consumption of residential sector in the United States (Quadrillion Btu) during the period of 1950–2019 [6]

2 Multi-time Scale Stochastic Model Predictive Control for Cooperative. . .

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Fig. 2.3 A real smart home designed and built-in Denmark [12]

States has had a rapid growth during the period, while the consumption of other types of energy sources have decreased or had a fluctuating pattern. Smart homes are defined as the small microgrids serving a single energy-aware building or a load with a rated power in the range of some kilo Watts. Advances in the technology can change a building to a smart home to precisely control and manage its energy consumption [7, 8]. Vast utilization of smart homes is one of the applications of smart grid which is being advertised by the governments [9– 11]. Figure 2.3 shows a real single smart home designed and built-in Denmark [12]. This smart home includes rooftop solar panels which is being operated under net-zero-energy condition while generating more power than its consumption. Smart homes can be equipped with devices and resources to coordinate with one another to achieve a common set of goals to benefit the occupants [13]. They can connect to each other, share their energy resources, and exchange electricity. In other words, each smart home can provide energy to other smart homes and purchase energy from other smart homes. Moreover, every smart home can deliver its additional electrical energy to the electrical grid and sell it to the retailer based on the price proposed by the retailer [14]. By scheduling energy resources of a smart home, the optimal power of the diesel generator (DG), the optimal power of the energy storage, the optimal purchasing/ selling power from/to the other smart homes, the optimal purchasing/selling power from/to the retailer can be determined at each time step, while the demand of the smart home is supplied and its operation cost is minimized.

2.1.1

Related Works

In [15], the power of renewables and the demand of consumers have been predicted to schedule the electricity purchases from the power market in a smart grid. In [16], a

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decentralized economic dispatch problem has been studied, where the problem solution is obtained by computing the weighted averages of the variables under some hypotheses. In [17], a hierarchical energy management approach for the single microgrid has been presented. In [18], demand response programs have been implemented for the smart homes in a distribution system to prevent the congestion of the feeders. However, there are some papers that have investigated the energy scheduling problem for the set of smart homes, presented in [19–24] and [25–30]. In [19–21] and [23, 24], the problem is not a cooperative distributed energy scheduling problem, since the cooperation of the smart homes has been disregarded and the problem has been solved by a centralized approach. However, the centralized optimization techniques have the so-called curse of dimensionality when the problem is large and complex [31]. In other words, the computational complexity and computation latency of the problem grow exponentially when the size of the problem increases. Moreover, this phenomenon is very likely to happen when the problem is a dynamic optimization problem due to the presence of renewables or fluctuated demands, since the problem must be optimized at every time step. Therefore, the centralized optimization is not applicable when the problem has a large number of variables or the state of the problem is changed dynamically. In addition, the privacy of the smart homes might be jeopardized in a centralized optimization approach because all the economic and technical information of the smart homes must be available for the control center [32]. The presence of renewables has been neglected in [19], [21, 22], and [26]. In addition, the existence of energy storages has not been considered in [19, 22, 27]. Moreover, the presence of DG has been disregarded in [19] and [26–30]. In addition, the energy scheduling problem does not have any dynamic and adaptive characteristics in [20, 21], [24], and [26–29], since the problem has been optimized once for the whole operation period (one day). Nonetheless, in such problems, the optimization problem must be updated at each time step (e.g., every hour, every five minutes, or . . .) due to the time-varying nature of renewables power or load demand. In [19–24] and [25–30], the energy scheduling problem of the smart homes is based on a cooperative distributed approach; however, these studies have not applied a multi-time scale optimization technique. In [33], a multi-time scale stochastic model predictive control (MPC) has been applied for the energy scheduling of appliances and energy resources of a single smart home.

2.1.2

The Contributions of the Study

In this study, the above-mentioned challenges and issues are addressed by applying the following proposed techniques.

2 Multi-time Scale Stochastic Model Predictive Control for Cooperative. . .

2.1.2.1

37

Applying Cooperative Distributed Optimization in the Energy Scheduling Problem of Smart Homes

Every smart home solves its own energy scheduling problem with the goal of minimum total cost considering its energy resources and the available power of the connected smart homes as well as the prices of power transactions with the connected smart homes. The price of the transacted energy between two smart homes is evaluated based on the marginal cost of the installed DG in the power exporter smart home; however, if there is no DG in the power exporter smart home, the price is determined based on the marginal cost of the installed DG in the power importer smart home. In addition, if both smart homes have a DG, the price of electricity transaction is determined based on the average value of generation costs of the DGs.

2.1.2.2

Applying Stochastic Approach to Address the Uncertainty of Renewables’ Power

In order to deal with the uncertainty of photovoltaic (PV) panels’ power, a stochastic approach, that includes predicting the value of solar irradiances over the optimization time horizon and defining the appropriate scenarios for the estimated solar irradiances, is applied.

2.1.2.3

Applying Multi-time Scale Optimization to Have Vast Vision for the Optimization Time Horizon and Precise Resolution for the Problem Variables

The multi-time scale approach with the five-minute and one-hour time scales is applied in the problem. The applied multi-time scale with the short time step (five minutes) and long time step (one hour) has the characteristics of having a vast vision for the optimization time horizon (12 hours) and precise resolution for the problem variables (5-min time step).

2.1.2.4

Applying MPC to Address the Time-Varying Power of Renewables

In order to deal with the variability concerned with the power of the PV panels, MPC technique is applied in the problem. The duration of the optimization time horizon for both five-minute and one-hour time scales is assumed to be 12-time steps.

38

M. Rahmani-Andebili

2.1.2.5

Modeling the Technical and Economic Constraints of Resources

All the economic and technical constraints of the energy resources of the smart homes including DG, the battery of plug-in electric vehicle (PEV), and PV panels are modeled.

2.1.2.6

Applying GA-LP as an Effective and Fast Optimization Technique to Deal with Both Desecrate and Continuous Variables

Linear programming (LP) is combined with a genetic algorithm (GA), and then GA-LP is applied to solve the energy scheduling problem of the smart home. Herein, the GA, dealing with the discrete variables of the problem, addresses the nonlinearity of the problem, and the LP, handling the continuous variables of the problem, quickly finds the globally optimal solution. In Sect. 2.2, the technique proposed to solve the cooperative distributed energy scheduling problem of the smart homes is presented. The problem formulation is presented in Sect. 2.3. In Sect. 2.4, the numerical studies are presented, and finally Sect. 2.5 concludes the chapter.

2.2 2.2.1

The Proposed Technique The Cooperative Distributed Energy Scheduling

Figure 2.4 illustrates the concept of the cooperative distributed optimization for a system with five smart homes. As can be seen, each smart home electrically and informationally connects to some other smart homes. In other words, every smart home can provide electrical energy to its connected smart homes or purchase it from them. In this study, it is assumed that every smart home can exchange the information just with its connected smart homes. The information includes the value of available energy and price to transact power with a smart home. The price of the transacted energy between two smart homes is evaluated based on the marginal cost of the installed DG in the power exporter smart home; however, if there is no DG in the power exporter smart home, the price is determined based on the marginal cost of the DG of the power importer smart home. Moreover, if every smart home has a DG, the price of electricity transaction is determined based on the average value of generation costs of the DGs.

2 Multi-time Scale Stochastic Model Predictive Control for Cooperative. . .

39

Fig. 2.4 A system including five smart homes [25]

Based on the proposed approach for the cooperative distributed energy scheduling, at every time step, every smart home randomly selects its counterpart (one of the connected smart homes) and solves its own energy scheduling problem considering the received information from the selected cooperator. Then, every smart home randomly changes its cooperator and share the updated information with one another. This process is repeated several times until no significant improvement is observed in the value of objective function of each smart home. At the end, the power of each energy resource of each smart home as well as the transacted power of the smart home with the grid and the other connected smart homes are known.

2.2.2

The Multi-time Scale Stochastic MPC

2.2.2.1

The Stochastic Approach

In this study, stochastic approach and multi-time scale MPC technique are applied to address the uncertainty and variability concerned with the power of the PV panels, respectively. The stochastic approach includes forecasting the solar irradiances and modeling the uncertainty of the predictions by defining some effective scenarios. This approach has been presented in [23, 25]. The values of solar irradiances are predicted for every time step of the optimization time horizon for both time scales (five-minute scale (t1) and one-hour scale (t2)), as can be seen in (2.1).

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M. Rahmani-Andebili

  ½e ρtþ1 , . . . , e ρtþN τ ρt  ¼ e t 2 ft 1 , t 2 g 8t 1 2 T 1

ð2:1Þ

8t 2 2 T 2   T 1 ¼ 1, ⋯, N T1   T 2 ¼ 1, ⋯, N T2

2.2.2.2

The Multi-time Scale MPC

The concept of single-time scale MPC is illustrated in Fig. 2.5 which is presented in references [23, 25, 34]. The detailed description of single-time scale MPC has been presented in [23, 25]. The considered time scales in the multi-time scale stochastic MPC include fiveminute and one-hour time scales. However, the energy scheduling problem of the smart home is optimized in every five-minute time step of the operation period (one day as the operation period includes 288-time steps). Figure 2.6 illustrates the applied multi-time scale MPC with t1 (five-minute scale) and t2 (one-hour scale) as

Fig. 2.5 Concept of single-time scale MPC [23, 25]

Fig. 2.6 Concept of multi-time scale MPC

2 Multi-time Scale Stochastic Model Predictive Control for Cooperative. . .

41

its time steps. Moreover, the forward-looking objective function (FFL) for the multitime scale MPC is presented in (2.2). As can be seen, the forward-looking objective function is the sum of the values of time step objective functions (Ft) over the optimization time horizon. F FL t ¼

Nτ X

F tþτ

τ¼1

t 2 ft 1 , t 2 g

ð2:2Þ

8t 1 2 T 1 8t 2 2 T 2 In the multi-time scale MPC, the decision variables of the problem are identified by comparing the value of weighted stochastic forward-looking objective functions FL 5 5 (FL t 1 and 60  t 2 ), as can be seen in (2.3). Herein, the multiplier (60) is used to make the value of stochastic forward-looking objective functions comparable with one another, by shrinking the value of hourly stochastic forward-looking objective function (FL t 2 ). As can be seen in (2.3), the values of discrete and continuous FL FL 5 variables achieved from FL t 1 , that is, Xt 1 are chosen if t 1  60  t 2 ; otherwise, FL the values of discrete and continuous variables obtained from t2 , that is, Xt2 are selected as the decision variables (Xt).

Xt ¼

8 > < Xt 1

FL t1 

> :X

FL t1

t2

5  FL t2 60 5  FL > t2 60 ð2:3Þ

t 2 ft 1 , t 2 g 8t 1 2 T 1 8t 2 2 T 2 Considering the short time scale (five-minute) and the large time scale (one-hour) in the multi-time scale MPC contributes to have precise resolution in the value of variables and vast vision in the optimization time horizon, respectively. Based on this, the applied multi-time scale MPC has 12 hours vision as its optimization time horizon (the value of Nτ is 12) and precise resolution about five minutes for the variables. Nonetheless, although a five minute-time scale MPC has a good resolution for the problem variables, its optimization time horizon is 60 minutes (because of Nτ ¼ 12), which is very short. In addition, a one-hour scale MPC has good optimization time horizon (12 hours), but the resolution for the problem variables (one hour) is not good enough. Therefore, a multi-time scale MPC can cover the disadvantages of any single-time scale MPC.

42

2.2.3

M. Rahmani-Andebili

The Optimization Technique

The energy scheduling problem of the smart homes is a mixed-integer nonlinear programming (MINLP) problem. In this chapter, GA-LP technique as the combination of GA and LP is applied to solve the energy scheduling problem of each smart home. Reference [25] includes the details of the optimization technique and its application in the problem.

2.3 2.3.1

The Problem Formulation The Objective Function

The goal of each smart home is to minimize the value of stochastic forward-looking objective function over the optimization time horizon (FL) subject to the constraints presented in (2.12)–(2.21). The value of stochastic forward-looking objective function is determined by summing up the values of forward-looking objective functions (FFL) weighted by the corresponding occurrence probability of each scenario (ΩPV), as can be seen in (2.4). The forward-looking objective function (FFL) has been presented in (2.2). The different cost and income terms of the time step objective function (F) is presented in (2.5). These terms include the fuel cost of the DG (CF _ DG), the carbon emissions cost of the DG (CE _ DG), the start-up cost of the DG (CSTU _ DG), the shutdown cost of the DG (CSHD _ DG), the switching cost of the PEV’s battery (CSW _ PEV), the cost or benefit due to power transactions with the grid (PGrid  π´ Retailer ), and the cost or benefit because of power transactions with the connected smart homes (∑PC  π C). min FL sh,t ¼ min

X

PV F FL sh,t,s  Ωs

s2S

8sh  2 SH  SH ¼ 1, ⋯, N SH t 2 ft 1 , t 2 g 8t 1 2 T 1 8t 2 2 T 2 8s 2 S   S ¼ 1, ⋯, N S

ð2:4Þ

2 Multi-time Scale Stochastic Model Predictive Control for Cooperative. . .

F sh,t,s ¼

43

8h i h i h i9  F DG E DG STU DG > DG DG > C  C þ C þ 1  x  x > > sh,t1 sh,t sh,t sh,t sh > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > h i h i > >   > > SHD DG SW PEV PEV DG DG > > þ x´ sh,t  C sh = < þ xsh,t1  1  xsh,t  Csh > > > > > > > > > > > > > > > > > > > > :

2   6 ´ Retailer þ PGrid þ4 t sh,t  π

X sh0 2 SH 0

PCsh,t,sh0

3> > > > > > 7> C >  π sh,t,sh0 5 > > > > > > > > > > > > ;

ð2:5Þ

8sh 2 SH t 2 ft 1 , t 2 g 8t1 2 T 1 8t 2 2 T 2 8s 2 S  0 SH 0 ¼ 1, ⋯, N SH where

x´ PEV sh,t

¼

8 0 t

φ  π Retailer t

PGrid 0 and PGrid < 0 mean that the smart home purchases power from the retailer and the smart home sells power to the retailer, respectively. The price of the transacted electricity between two smart homes is determined based on the marginal cost of the installed DG in the power exporter smart home; however, if there is no DG in the power exporter smart home, the price is determined based on the marginal cost of the installed DG in the power importer smart home. Moreover, if every smart home has a DG, the price of electricity transaction is determined based on the average value of generation costs of the DGs. The marginal cost of a DG can be determined using (2.8) [35, 36].

π Csh,t ¼

 F DG E DG ∂ C sh,t þ Csh,t ∂PDG sh,t 8sh 2 SH

ð2:8Þ

t 2 ft 1 , t 2 g 8t 1 2 T 1 8t 2 2 T 2 The fuel cost function and carbon emissions function of every DG are quadratic polynomials presented in (2.9) and (2.10), respectively [35, 36]. Herein, the sets of zF1 , zF2 , zF3 and zE1 , zE2 , zE3 are the fuel cost coefficients and carbon emissions coefficients of the DG, respectively. In addition, βE is the value of penalty for carbon emissions.

2 Multi-time Scale Stochastic Model Predictive Control for Cooperative. . . F DG

C sh,t

45

  G 2  G  F F F ¼ xDG  z  P þ z  P þ z sh,t 1,sh sh,t 2,sh sh,t 3,sh 8sh 2 SH t 2 ft 1 , t 2 g

ð2:9Þ

8t 1 2 T 1

DG C Esh,t

8t 2 2 T 2     E E G 2 E ¼ xDG þ zE2,sh  PG sh,t  β  z1,sh  Psh,t sh,t þ z3,sh 

8sh 2 SH t 2 ft 1 , t 2 g

ð2:10Þ

8t 1 2 T 1 8t 2 2 T 2 The switching cost of the battery of a PEV is determined based on the value of total cumulative ampere-hours throughput of the battery (ξPEV) in its life cycle and the value of initial price of its battery (PricePEV). In fact, considering this cost term prevents the PEV’s battery from unnecessary switching that is harmful to its life span. SW PEV

C sh

¼

PricePEV sh ξPEV sh

ð2:11Þ

8sh 2 SH

2.3.2

The Constraints

In the following, the constraints of the problem that must be held in every smart home and at every time step of the operation period are presented and described.

2.3.2.1

The Supply-Demand Balance

The sum of power of the DG, the power of the PV panels, the power of the PEV’s battery, the transacted power with the connected smart homes, and the transacted power with the grid through the grid must be equal to demand of the load (DL) in

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M. Rahmani-Andebili

every smart home and at every time step of the operation period. Herein, the transacted power with the connected smart homes is considered positive if the smart home imports power and it is negative if the smart home exports power. DG PEV PEV PV Grid xDG sh,t  Psh,t þ xsh,t  Psh,t þ Psh,t,s þ Psh,t þ

X 0

sh 2SH

0

PCsh,t,sh0 ¼ DLsh,t

8sh 2 SH

ð2:12Þ

t 2 ft 1 , t 2 g 8t 1 2 T 1 8t 2 2 T 2 The output power of the PV panels is a nonlinear function of the estimated solar irradiance (ρ) presented in [37].

2.3.2.2

The Power Limits of the DG

The DG must be operated between its maximum power limit (PDG ) and minimum power limit (PDG ), as can be seen in (2.13).  DG DG DG xDG sh,t  Psh  Psh,t  Psh 8sh 2 SH t 2 ft 1 , t 2 g

ð2:13Þ

8t 1 2 T 1 8t 2 2 T 2

2.3.2.3

The Minimum Up/Down Time Limits of the DG

The DG is not able to be started up/shut down earlier than the rated minimum downtime (MDTDG) and minimum uptime (MUTDG), respectively.

2 Multi-time Scale Stochastic Model Predictive Control for Cooperative. . . DG OFF

Δt sh

 MUT DG sh

47

ð2:14Þ

8sh 2 SH DG ON

Δt sh

 MDT DG sh

ð2:15Þ

8sh 2 SH

2.3.2.4

The Power Limits of the PEV’s Battery

 , PPEV  ], The value of power of the PEV’s battery must be in the rated range [PPEV as is shown in (2.16).  PEV   PEV PEV xPEV sh,t  Psh  Psh,t  Psh 8sh 2 SH t 2 ft 1 , t 2 g

ð2:16Þ

8t 1 2 T 1 8t 2 2 T 2

2.3.2.5

The Depth of Discharge Limit of the PEV’s Battery

The PEV’s battery should not be discharged more than the allowable depth of discharge (DOD) limit, to prolong the life span of each PEV’s battery. Moreover, the battery cannot be charged more than 100%. PEV DODPEV sh  SOC sh,t  100

8sh 2 SH t 2 ft 1 , t 2 g 8t 1 2 T 1 8t 2 2 T 2

ð2:17Þ

48

2.3.2.6

M. Rahmani-Andebili

The Unviability of the PEV for the Smart Home

The PEV will not be available for the smart home in some time periods (ΔtDep  Arr), since it will be used by the occupant for the driving purpose. In fact, the smart home will not have any energy storage, therefore the status of the PEV’s battery is considered to be zero in this interval. xPEV sh,t ¼ 0 8sh 2 SH   t 2 Δt DepArr

2.3.2.7

ð2:18Þ

The Full Charge Constraint for the PEV’s Battery Before Departure

The owner of the PEV needs his/her vehicle to have a full charge at the desirable time, that is, before driving the PEV (tDep). SOC PEV sh,t Dep ¼ 100

ð2:19Þ

8sh 2 SH

2.3.2.8

The Maximum Accessible Power from a Connected Smart Home

The power that the smart home can import from a connected smart home (PC) must be less than the available power of the connected smart home (PA) at every time step of the optimization period. PCsh,t,sh0  PAsh0 ,t 8sh 2 SH 8sh0 2 SH 0 t 2 ft 1 , t 2 g 8t 1 2 T 1 8t 2 2 T 2 where

ð2:20Þ

2 Multi-time Scale Stochastic Model Predictive Control for Cooperative. . .

PAsh0 ,t

 8 DG  0  PDG0 þ PPEV   PPEV > < xsh0 ,t  PDG sh ,t sh0 ,t sh sh0 ¼  > : xDG0  PDG0  PDG0 sh ,t sh ,t sh 8sh0 2 SH 0 t 2 ft 1 , t 2 g

49

xPEV sh0 ,t ¼ 1 xPEV sh0 ,t 6¼ 1 ð2:21Þ

8t 1 2 T 1 8t 2 2 T 2 Based on (2.21), if the PEV’s battery of a connected smart home is in discharging  PEV ¼ 1), the battery has the available generation capacity of PPEV status (xPEV sh0  Psh0 ,t sh0 ,t to supply the smart home. Moreover, if the DG of a connected smart home is in “on” DG status, the DG has the available generation capacity of PDG sh0  Psh0 ,t to help the smart home.

2.4

The Numerical Study

In this study, two case studies, including a five-smart home and fifty-smart home systems, as the small and large case studies, are investigated. The problem is simulated in MATLAB using the Intel Xeon Server (64 GB RAM).

2.4.1

Case Study 1

The configuration of the first case study has been illustrated in Fig. 2.4. Table 2.1 presents the availability of the resources for each smart home including PV panels installed on the roof of the smart home, DG, PEV, connection to the electrical distribution grid, and connection to the other neighboring smart homes. The technical data for different types of the DGs are presented in Table 2.2. Furthermore, the value of other parameters of the system and problem are presented in Table 2.3. The electricity price proposed by the retailer at every time step (five minutes) of the operation period (one day) has been presented in [23]. Table 2.4 presents the demand pattern of the load, the power pattern of the PV panels, the type of the DG, and the type of the PEV for every smart home.

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Table 2.1 The resources availability for the smart homes Sources Smart home ID SH1 SH2 SH3 SH4 SH5

Load Yes Yes Yes Yes Yes

PV availability No Yes Yes Yes Yes

DG availability Yes No Yes No Yes

PEV availability No No No Yes Yes

Connection to the grid Yes Yes Yes Yes Yes

Connection to other SHs Yes Yes Yes Yes Yes

Table 2.2 Technical data of different types of the DGs Parameter zF1 (¢/kWh2) zF2 (¢/kWh) zF3 (¢) zE1 (kg/kWh2) zE2 (kg/kWh) zE3 (kg) DG

P (kW) PDG (kW) MUTDG (min) MDTDG (min) CSTU _ DG (¢) CSHD _ DG (¢)

Type 1 0.00324 3.96 0 0.0007 0.39 0 5 20

Type 2 0.00243 9.94 0 0.0008 0.94 0 5 10

Type 3 0.00491 7.85 0 0.0008 0.61 0 5 15

10 10 100 100

10 10 100 100

10 10 100 100

Table 2.3 Value of parameters of the problem Parameter Nτ

Value 12

Parameter  (kW) PPEV

Value 10

SOC PEV tDep (%)

Value 100

φ

0.5

 PPEV Type 2 (kW)

15

SOC PEV tArr (%)

50

1 [38]

CapPEV Type 1 CapPEV Type 2 PEV

(kWh)

50

ΔtDep  Arr

9–10 and 16–17

(kWh)

75

PricePEV (¢)

200,000

20

ξ

10,000

β (¢/kg) PPV (kW) E

2

PPV 3 (kW)

10 10

Type 1

DOD

(%)

Parameter

PEV

(Ah)

Table 2.4 The different resources of every smart home with different pattern and type in Case Study 1 Smart home ID SH1 SH2 SH3 SH4 SH5

Pattern and type of resources Load Pattern PV Pattern 1 – 2 1 3 2 4 3 5 4

DG type 1 – 2 – 3

PEV type – – – 1 2

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51

Table 2.5 The operation cost of every smart home and the system ($/day) without energy scheduling and with non-cooperative and cooperative distributed energy scheduling in Case Study 1 – Without energy scheduling

Non-cooperative energy scheduling

Cooperative distributed energy scheduling

2.4.1.1

Smart home 1 2 3 4 5 Total 1 2 3 4 5 Total 1 2 3 4 5 Total

Time scale of MPC Five-minute One-hour 10.21 11.47 14.54 13.38 16.38 66.01 3.54 3.46 11.47 11.47 13.76 13.98 13.38 9.17 8.52 4.00 43.59 35.16 4.66 5.01 9.52 7.20 10.20 8.54 10.55 5.11 5.21 2.67 30.82 18.51

Multi

3.54 11.47 13.76 9.17 3.91 34.77 5.31 7.20 8.54 5.11 2.33 17.87

The Problem Simulation Without Energy Scheduling

Table 2.5 presents the operation cost of every individual smart home and the set of smart homes without scheduling the energy resources of the smart homes. In this condition, the power of the PV panels is considered as the negative demand and then it is added to the load demand of every individual smart home. In addition, at every time step, the extra power of every smart home is directly delivered to the grid and sold to the retailer. As can be seen, the total operation cost of the set of smart homes is about $66.01/day.

2.4.1.2

The Problem Simulation with Non-cooperative Energy Scheduling

The daily operation cost of every individual smart home and the set of smart homes with non-cooperative energy scheduling is given in Table 2.5. The energy scheduling of the set of smart homes, by applying five-minute scale, one-hour scale, and multi-time scale stochastic MPC approaches, results in about 33.9%, 46.7%, and 47.3% cost reductions, respectively. The reason of the superiority of the multi-time

52

M. Rahmani-Andebili

Fig. 2.7 The demand level and optimal power of the DG of SH1 in the cooperative distributed energy scheduling applying the five-minute scale stochastic MPC (Case Study 1)

scale stochastic MPC is the ability of this technique for having a precise resolution for the problem variables and vast vision for the optimization time horizon.

2.4.1.3

The Problem Simulation with Cooperative Distributed Energy Scheduling

Applying Five-Minute Scale Stochastic MPC The daily operation cost of the set of smart homes and every individual smart home and the optimal schedule of the energy resources of the smart homes in the cooperative distributed energy scheduling applying the five-minute scale MPC are presented in Table 2.5 and Figures 2.7, 2.8, 2.9, 2.10, 2.11, 2.12, and 2.13, respectively. As can be seen, the operation cost of every smart home decreases and SH1 not only removes its operation cost, but also makes income. Moreover, the total operation cost of the set of smart homes is decreased to about $30.82/day. In fact, the cooperation of the smart homes contributes to 29.2% cost saving compared to non-cooperative energy scheduling. The demand pattern and power of the DG of SH1 at every time step of the operation period are illustrated in Fig. 2.7. As can be seen, the DG of SH1 is shut down in the seventh time step and the needed electricity is purchased from the retailer between the 7th and 78th time steps of the operation period. For the rest of the operation period, SH1 starts up its DG, supplies its demand, and exports its extra power to the connected smart homes and the retailer, as can be seen in Figures 2.7 and 2.8.

2 Multi-time Scale Stochastic Model Predictive Control for Cooperative. . .

53

Fig. 2.8 The optimal transacted powers between SH1 and the connected smart homes and the grid in the cooperative distributed energy scheduling applying the five-minute scale stochastic MPC (Case Study 1)

Fig. 2.9 The demand level, power of the PV panels, and optimal power of the DG of SH3 in the cooperative distributed energy scheduling applying the five-minute scale stochastic MPC (Case Study 1)

The demand pattern, the power pattern of the PV panels, and the generation of the DG of SH3 are shown in Fig. 2.9. As can be seen, the DG is turned off and on several times over the operation period, since this DG is the most expensive and pollutant DG, as can be realized from Table 2.2. In addition, the transacted power of SH3 with the retailer and the connected smart homes is demonstrated in Fig. 2.10.

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Fig. 2.10 The optimal transacted powers between SH3 and the connected smart homes and the grid in the cooperative distributed energy scheduling applying the five-minute scale stochastic MPC (Case Study 1)

Fig. 2.11 The demand level, power of the PV panels, and optimal power of the DG and the PEV’s battery of SH5 in the cooperative distributed energy scheduling applying the five-minute scale stochastic MPC (Case Study 1)

The demand pattern, the power pattern of the PV panels, the power of the DG, and the power of the PEV’s battery related to the SH5 are shown in Fig. 2.11. As can be seen, SH5 starts up its DG in the 103rd time step and keeps it “on” until the 282nd time step; however, in some periods, sets the power of the DG at a minimum power limit and avoids shutting it down. In addition, the PEV’s battery does not have any

2 Multi-time Scale Stochastic Model Predictive Control for Cooperative. . .

55

Fig. 2.12 The optimal transacted powers between SH5 and the connected smart homes and the grid in the cooperative distributed energy scheduling applying the five-minute scale stochastic MPC (Case Study 1)

charging and discharging pattern. Moreover, the transacted power of the SH5 with the connected smart homes and the grid is illustrated in Fig. 2.12.

Applying the One-Hour Scale Stochastic MPC The daily operation cost of the set of smart homes and every individual smart home for the one-hour scale stochastic MPC can be seen in Table 2.5. Herein, the total operation cost of the set of smart homes is about $18.51 that has 47.3% reduction compared to the result of the non-cooperative energy scheduling. By applying the one-hour scale stochastic MPC in the cooperative distributed energy scheduling problem of the set of smart homes, the performance of the battery of each PEV is improved. Figures 2.13 and 2.14 illustrate the optimal charging and discharging patterns of the batteries of the PEVs in SH4 and SH5.

Applying Multi-time Scale Stochastic MPC The daily operation cost of the set of smart homes and every individual smart home for the multi-time scale stochastic MPC are presented in Table 2.5. As can be seen, the total operation cost of the set of smart homes is $17.87 that has about 48.6% reduction compared to the result of non-cooperative energy scheduling. Figure 2.15 shows the demand level, the power pattern of the PV panels, and the optimal power pattern of the DG and the PEV’s battery in SH5. As can be seen,

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Fig. 2.13 The demand level, power of the PV panels, and optimal power of the PEV’s battery of SH4 in the cooperative distributed energy scheduling applying the one-hour scale stochastic MPC (Case Study 1)

Fig. 2.14 The demand level, power of the PV panels, and optimal power of the DG and the PEV’s battery of SH5 in the cooperative distributed energy scheduling applying the one-hour scale stochastic MPC (Case Study 1)

the DG is able to program its generation in the small scales (five minutes) between the 121th and the 132th time step, and the PEV’s battery is capable of having vast vision (12 hours) for the optimization time horizon to have optimal charging/ discharging pattern.

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Fig. 2.15 The demand level, the power of the PV panels, and the optimal power of the DG and the PEV’s battery of SH5 in the cooperative distributed energy scheduling applying the multi-time scale stochastic MPC (Case Study 1)

2.4.2

Case Study 2

Figure 2.16 shows the configuration of the second case study that includes 50 smart homes with different sets of resources. As can be seen, every smart home has electrical and informational connections to some of the neighboring smart homes. In addition, different energy resources of each smart home with different types and power patterns are presented in Table 2.6. Table 2.7 presents the value of total daily operation cost of the problem without energy scheduling, with non-cooperative energy scheduling, and with cooperative distributed energy scheduling in Case Study 2. As can be seen, applying the multitime scale stochastic MPC in the non-cooperative and the cooperative distributed energy scheduling problems has better result compared to the single-time scale stochastic MPC with five-minute or one-hour scale. Furthermore, the cooperative distributed energy scheduling problem with the multi-time scale stochastic MPC has the most cost reduction. Figure 2.17 shows the operation cost of each smart home ($/day) without energy scheduling and with cooperative distributed energy scheduling applying multi-time scale stochastic MPC in Case study 2. As can be seen, every smart home is benefitted due to the cooperation in the energy scheduling problem; however, the value of cost reductions for the smart homes are not equal, since every smart home has various resources and different set of connections to the other smart homes. Table 2.8 presents the cost reduction percentage in the cooperative distributed energy scheduling problem of the set of smart homes compared to the result of the problem without energy scheduling in Case Studies 1 and 2. As can be seen, the

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Fig. 2.16 The system of Case Study 2

cooperation of the smart homes in the energy scheduling problem in the large system (Case study 2) has more potential for cost reduction compared to the small system (Case study 1).

2.5

Conclusion

In this chapter, the single-time scale stochastic model predictive control (MPC) and multi-time scale stochastic MPC, as the adaptive and dynamic optimization technique, were applied in the cooperative distributed energy scheduling problem of the set of smart homes with different resources of energy. Herein, the stochastic and MPC techniques managed the uncertainty and variability issues of the photovoltaic (PV) panels’ power, respectively. In addition, the multi-time scale MPC had a vast vision for the optimization time horizon and precise resolution for the problem variables. After simulating the problem, it was observed that the cooperation of the smart homes in the distributed energy scheduling problem results in the significant cost saving in both small and large case studies. In fact, the reason for this achievement is related to the cooperation of the smart homes for sharing their energy resources,

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Table 2.6 The resources of every smart home with different pattern and type in Case Study 2

Smart homes SH1, SH7, SH13, SH33, SH38 SH14, SH21, SH29, SH43, SH48 SH2, SH8, SH22, SH34, SH39 SH15, SH23, SH30, SH40, SH44 SH9, SH16, SH24, SH35, SH45 SH3, SH4, SH17, SH25, SH31 SH10, SH18, SH26, SH41, SH46 SH5, SH11, SH27, SH36, SH49 SH12, SH19, SH32, SH42, SH47 SH6, SH20, SH28, SH37, SH50

Pattern and type of resources Load PV DG PEV pattern pattern type type Pattern Pattern Type Type 1 4 1 2 Pattern Pattern – – 2 1 Pattern Pattern Type – 3 2 2 Pattern Pattern – Type 4 3 1 Pattern – Type – 5 1 Pattern Pattern Type Type 1 4 2 2 Pattern Pattern – Type 2 3 1 Pattern Pattern Type – 3 1 3 Pattern – – – 4 Pattern Pattern Type – 5 2 3

Connection to the grid Yes

Connection to other SHs Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Table 2.7 The operation cost of every smart home and the system ($/day) without energy scheduling and with non-cooperative and cooperative distributed energy scheduling in Case Study 2 – Without energy scheduling Non-cooperative energy scheduling

Cooperative distributed energy scheduling

Time scale of MPC – Five-minute One-hour Multi Five-minute One-hour Multi

Total operation cost ($/day) 652.10 423.91 272.37 263.53 288.38 154.79 144.87

including diesel generator (DG), PV panels, and the battery of the plug-in electric vehicle (PEV). Furthermore, it was proven that the cooperation of more smart homes in the energy scheduling problem has more potential for cost reduction. In addition, due to considering the small and large time scales (five-minute and one-hour scales) in the multi-time scale MPC, the DG could precisely adjust its power generation (every five-minute), and the PEV’s battery was able to have optimal charging and discharging patterns due to having vast optimization time horizon (12 hours).

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Fig. 2.17 The operation cost of every smart home ($/day) without energy scheduling and with the cooperative distributed energy scheduling applying the multi-time scale stochastic MPC in Case study 2 Table 2.8 The value of cost reduction (percent) in the cooperative distributed energy scheduling problem compared to the result of the problem without energy scheduling in Case Studies 1 and 2 Time scale of MPC Five-minute One-hour Multi-time scale

Case Study 1 53.3% 71.9% 72.9%

Case Study 2 55.7% 76.2% 77.7%

References 1. D. Pengwei, L. Ning, Appliance commitment for household load scheduling. IEEE Trans. Smart Grid 2, 411–419 (2011) 2. A. Zipperer, P.A. Aloise-Young, S. Suryanarayanan, R. Roche, L. Earle, D. Christensen, P. Bauleo, D. Zimmerle, Electric energy management in the SH: perspectives on enabling technologies and consumer behavior. Proc. IEEE 101(11), 2397–2408 (2013) 3. U.S. Energy Information Administration (EIA), Annual energy review 2011, Washington, DC, EIA-0384 (2012) 4. OECD/IEA, International Energy Agency, 2013. [Online]. Available: http://www.iea.org/ aboutus/faqs/energyefficiency 5. U.S. Energy Information Administration (EIA), [Online]. Available: https://www.eia.gov/ totalenergy/data/monthly/pdf/sec2_2.pdf 6. U.S. Energy Information Administration (EIA), [Online]. Available: https://www.eia.gov/ totalenergy/data/monthly/pdf/sec2_4.pdf 7. [Online]. Available.: http://www.homes.com/blog/2012/08/7-recent-advances-in-home-secu rity-technology/

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8. U.S. Congress, H. R. 6, Energy Independence and Security Act of 2007. 110th Congress, 1st Session, January 4, 2007. [Online]. Available: http://www.gpo.gov/fdsys/pkg/BILLS110hr6enr/pdf/BILLS-110hr6enr.pdf 9. N.G. Paterakis, A. Tascikaraoglu, O. Erdinc, A.G. Bakirtzis, J.P.S. Catalao, Assessment of demand-response-driven load pattern elasticity using a combined approach for smart households. IEEE Trans. Ind. Inf. 12(4), 1529–1539 (2016) 10. K.H.S.V.S. Nunna, S. Doolla, Responsive end-user-based demand side management in multimicrogrid environment. IEEE Trans. Ind. Inf. 10(2), 1262–1272 (2014) 11. V.C. Gungor et al., A survey on smart grid potential applications and communication requirements. IEEE Trans. Ind. Inf. 9(1), 28–42 (2013) 12. IEEE spectrum, [Online]. Available: https://spectrum.ieee.org/green-tech/buildings/denmarksnetzeroenergy-home 13. L. Jiang, D.Y. Liu, B. Yang, Smart home research. Proc. 2004 Int. Conf. Mach. Learn. Cybern. 2, 659–663 (2004) 14. Federal energy regulatory commission, [Online]. Available: http://www.ferc.gov/industries/ electric/indus-act/section-1241.pdf 15. C.K. Tham, T. Luo, Sensing-driven energy purchasing in smart grid cyber-physical system. IEEE Trans. Syst. Man, Cybern 43(4), 773–784 (2013) 16. V. Loia, A. Vaccaro, Decentralized economic dispatch in smart grids by self-organizing dynamic agents. IEEE Trans. Syst., Man, Cybern 44(4), 397–408 (2014) 17. E. Mojica-Nava, C.A. Macana, N. Quijano, Dynamic population games for optimal dispatch on hierarchical microgrid control. IEEE Trans. Syst. Man Cybern. 44(3), 306–317 (2014) 18. A. Asrari, M. Ansari, K.C. Bibek, Real-time congestion prevention in modern distribution power systems via demand response of smart homes. 2019 North American Power Symposium (NAPS) (2019), pp. 1–6 19. Y. Liu, S. Hu, H. Huang, R. Ranjan, A.Y. Zomaya, L. Wang, Game-theoretic market-driven SH scheduling considering energy balancing. IEEE Syst. J.. https://doi.org/10.1109/JSYST.2015. 2418032 20. M.A.A. Pedrasa, T.D. Spooner, I.F. MacGill, Coordinated scheduling of residential distributed energy resources to optimize SH energy services. IEEE Trans. Smart Grid 1(2), 134–143 (2010) 21. N. Gatsis, G.B. Giannakis, Residential load control: distributed scheduling and convergence with lost AMI messages. IEEE Trans. Smart Grid 2(3), 1–17 (2012) 22. T. Chang, M. Alizadeh, A. Scaglione, Real-time power balancing via decentralized coordinated home energy scheduling. IEEE Trans. Smart Grid 4, 1490–1504 (2013) 23. M. Rahmani-andebili, H. Shen, Energy scheduling for a smart home applying stochastic model predictive control. The 25th International Conference on Computer Communication and Networks (ICCCN), Waikoloa, Hawaii, USA, August 1–4, 2016 24. M. Rahmani-andebili, H. Shen, Price-controlled energy management of smart homes for maximizing profit of a GENCO. IEEE Trans. Syst. Man Cybernetics Syst. (2017). https://doi. org/10.1109/TSMC.2017.2690622 25. M. Rahmani-Andebili, H. Shen, Cooperative distributed energy scheduling for smart homes applying stochastic model predictive control. International Conference on Communications (ICC), Paris, France, May 21–25, 2017 26. I.Y. Joo, D.H. Choi, Distributed optimization framework for energy management of multiple smart homes with distributed energy resources. IEEE Access 5 (2017) 27. V. Pilloni, A. Floris, A. Meloni, L. Atzori, Smart home energy management including renewable sources: a QoE-driven approach. IEEE Trans. Smart Grid 9(3) (2018) 28. B. Celik, R. Roche, D. Bouquain, A. Miraoui, Decentralized Neighborhood energy management with coordinated smart home energy sharing. IEEE Trans. Smart Grid 9(6) (2018) 29. C. Keerthisinghe, G. Verbič, A.C. Chapman, A fast technique for smart home management: ADP with temporal difference learning. IEEE Trans. Smart Grid 9(4) (2018) 30. O.V. Cutsem, D.H. Dac, P. Boudou, M. Kayal, Cooperative energy management of a community of smart-buildings: a Blockchain approach. Electr. Power Energy Syst. 117 (2020)

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31. L. Chua, L. Yang, Cellular neural networks. IEEE Int. Symposium Circuits Syst. 2, 985–988 (1998) 32. M. Lisovich, D. Mulligan, S.B. Wicker, Inferring personal information from demand response systems. IEEE Secur. Privacy Magaz. (2010) 33. M. Rahmani-Andebili, Scheduling Deferrable Appliances and Energy Resources of a Smart Home Applying Multi-Time Scale Stochastic Model Predictive Control, Sustainable Cities and Societies, Vol. 32 (Elsevier, 2017), pp. 338–347 34. J.B. Rawlings, D.Q. Mayne, Model predictive control: Theory and design (Nob Hill Publishing, LLC, Madison, 2009). [Online]. Available: http://jbrwww.che.wisc.edu/home/jbraw/mpc/ electronic-book.pdf 35. J.J. Grainger, W.D. Stevenson Jr., Power System Analysis (McGraw-Hill, New York, 1994) 36. A.J. Wood, B.F. Wollenberg, Power Generation, Operation and Control, 2nd edn. (Wiley, New York, 1996) 37. M. Rahmani-Andebili, Chapter 5: optimal operation of a plug-in electric vehicle parking lot in the energy market considering the technical, social, and geographical aspects. In Planning and Operation of Plug-In Electric Vehicles: Technical, Geographical, and Social Aspects. Springer, Cham, 2019 38. U.S. energy information administration (EIA), [Online]. Available: http://www.eia.gov/ todayinenergy/detail.cfm?id¼9310

Chapter 3

How to Employ Competitive Smart Home Retailers to React to Cyberattacks in Smart Cities? Arash Asrari

Abstract The growing implementation of information/communication technology accompanied with increasing integration of distributed generation (DG) and smart homes (SHs) have made smart cities more prone to malicious cyberattacks. This chapter proposes a three-layer framework to react to cyberattacks reported by decentralized DG retailers and SH retailers. In the first layer, the system operator administratively modifies the topology of the system to enhance the network reliance on the non-attacked retailers. In the second layer, non-attacked SH retailers are handled by the market operator to manage congestions caused by the administrative action of the system operator in outages prevention. In the third layer, the system operator applies a forced load curtailment on the non-attacked SH retailers who were passive in the second-layer market mechanism to relieve the remaining congestions. The performance of the introduced framework is verified by its implementation on a distribution network modified to contain DG retailers and SH retailers. Keywords Cyberattack · distributed generation (DG) retailer · load curtailment · market operator · network reconfiguration · passive retailer · smart home (SH) retailer · system operator

Abbreviation AFLC DG DISCO DR EV FDI

administrative forced load curtailment distributed generation distribution company demand response electric vehicle false data injection

A. Asrari (*) School of Electrical, Computer, and Biomedical Engineering, Southern Illinois University, Carbondale, IL, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Rahmani-Andebili (ed.), Operation of Smart Homes, Power Systems, https://doi.org/10.1007/978-3-030-64915-9_3

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FR GENCO LC SCADA SH SHR SHRMCM

3.1 3.1.1

fast reconfiguration generation company load curtailment supervisory control and data acquisition smart home smart home retailers SHR market-based congestion management

Introduction Cybersecurity Analysis of Power Systems

One of the reasons for further research efforts recently focused on cybersecurity analysis of power systems is associated with the successful cyberattack on the Ukrainian power system in December 2015 [1]. In that complex cyberattack, several substations were isolated for several hours leading to power outages for almost 225,000 people. Thus, the operators had to curtail 73 MWh of the overall electric load. The following strategies were taken into account in that complicated cyberattack [2, 3]: 1. The corporate network of the targeted distribution entity was compromised via several superfishing emails. 2. In order to remotely isolate the substations, the supervisory control and data acquisition (SCADA) systems were hacked. 3. The information technology (IT) assets of the attacked system including remote terminal units were manipulated. 4. Master boot files on stations were attacked via a sophisticated KillDisk malware. 5. Denial of service (DOS) cyberattacks were implemented against phone calls in order to deny blackout information of customers. Although there are not several cyber-related incidents reported to the public due to security issues and privacy protocols of different countries, power system researchers and operators are recommended to proactively scrutinize different shapes and types of cyberattacks to protect power systems against them. As is reported in [4], power systems are under cyberattacks minute-by-minute. Among different types of attacks designed for power systems, false data injection (FDI) attacks against state estimation are further examined by researchers due to their effectiveness. The idea of FDI cyberattacks was first proposed by Liu et al. [5, 6]. With the assumption of having access to the configuration of power system, the authors introduced a novel strategy to manipulate the measurements of meters such that the launched FDIs are not detected by the system operator. Inspired by [5, 6], researchers examined more challenging cases to enhance the effectiveness of FDI attacks for different scenarios of targeting power systems. A summary of them is reported in the rest of this subsection.

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Xie et al., for the first time, formulated the economic impact of FDI attacks on real-time electricity markets [7]. They introduced a novel mechanism to falsify nodal price of ex-post real-time market to bypass the bad data detection algorithms. The authors verified guaranteed financial profit of attacker via the identified optimal FDI targeting a limited number of sensors. Liang et al. introduced a new model for FDIs targeting the wholesale electricity market by applying small manipulations on price data in each single attack related to an extended attack over a single day [8]. This model is referred to as coordinated attack, which misleads the customers to pay higher electricity bills causing huge economic losses for market retailers. The common assumption in modeling FDI attacks targeting power systems was that the attacker has information about configuration of the network. This assumption cannot be always the case if data control unit of the system operator is securely protected by advanced technologies. In [9], Esmalifalak et al. enhanced the conventional FDI attack model targeting electricity markets considering that the attacker does not have knowledge about the network topology. A linear independent component methodology was proposed for the attacker to estimate the Jacobian matrix multiplied by the eigenvalues of the covariance matrix. This model provides a platform for the attacker, having no prior information about system configuration, to manipulate the electricity price without being detected by the state estimation. In [10], Esmalifalak et al. modeled a holistic zero-sum game theory between the attacker and the defender in the wholesale electricity market. Two different strategies of decreasing congestion and increasing congestion were proposed for the attacker in this game model. By decreasing the congestion, the congestion price paid to the attacker in the real-time market will be less than the expected congestion price in the day-ahead market leading to financial benefit for the attacker. By increasing the congestion, the attacker will invest on purchasing financial transmission right (FTR) to be sold with a higher price to load-serving entities (LSEs) when the designed attack is launched. Other examples of reported investigations on cyberattacks targeting power systems are summarized as follows. A decentralized model based on the “directed acyclic graph” concept was proposed in [11] to increase the reliance of networked microgrids against cyberattacks. It was validated that the developed framework (1) omits the need for a central security management node and (2) leads to an incentive price causing an effective demand response when severe cyberattacks are experienced. In [12], a two-level optimization framework was introduced for optimal sizing of supercapacitors in order to improve the resiliency of adaptive protection schemes against communication outages caused by cyberattacks. In [13], a holistic approach was presented to assess the electrical safety of large-scale electric vehicle (EV) supply equipment taking into account the “cyberattack challenges” when communications between EV charging stations and electric utilities are activated. In [14], the issue of data integrity attacks against optimal power flow (OPF) in smart grids was scrutinized from two viewpoints. Primarily, the optimization problem of data integrity attacks against OPF with the least number of manipulated nodes was tackled. Then, two different defensive mechanisms, named “protecting critical nodes” and “detection based on the difference of phase angles and nonparametric

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cusum” were implemented to result in a secured smart grid against such attacks. In [15], catastrophic impacts of “feeder automation” in active distribution systems were studied, which can be abused by hackers and result in severe outages. Effective coordination between remote terminal units was addressed in [15] by the development of a Bayesian attack graph model to compute the probability of successfully targeting specific circuit breakers. In [16], a cyber-physical microgrid platform was developed to be utilized for the distributed secondary control of islanded networks. The event-triggering conditions were designed within the structure of the framework to reduce the cyber-system communication burden. In [17], a protection mechanism was developed to respond to detected attacks by allocating secure phasor measurement units (PMUs) on strategic buses of the network, where it was assumed that only a small part of a large power grid is successfully attacked by hackers. In [18], false data injection attacks were analyzed through using an interactive PMU simulation environment to provide a scheme for cyberattacks detection on larger smart grids. In [19], “data-driven” and “physics-based” approaches were investigated to develop a platform for effective utilization of PMUs in transmission networks. It was verified that the mentioned approaches can lead to efficient anomaly detection of cyberattack which improves system security. In [20], an event-trigger scheme was developed to present a system under data deception and system operator attacks. The Lyapunov stability theory was employed to yield the stability of load frequency control multiarea smart grid under such attacks. Moreover, a hybrid triggered scheme was proposed in [21] for the design of a network cascade control system to represent stochastic cyberattacks, where linear matrix inequality techniques were utilized to evaluate the stability of system under such attacks. Although extensive research efforts are focused on intelligent modeling of cyberattacks targeting smart grids, less research papers in the literature have examined reaction strategies for the system operator to minimize the negative impacts of such cyberattacks on operation of power systems. Some examples of proposed techniques coping with cyberattacks targeting power systems are as follows. In an earlier step of this research, Asrari et al. proposed a network reconfiguration-based mechanism to prevent blackout caused by FDI cyberattacks on smart grids [22]. However, the following question remained unanswered in [22]: “what if the implemented reconfiguration results in decentralized congestions in the system?”. In [23], Ansari and Asrari proposed a bi-level framework relying on Kalman filtering to replace the suspicious data with predicted data to mitigate the consequences of cyberattacks targeting microgrids. However, the following concern was not addressed in [23]: “what if the predicted data are also manipulated by attackers having access to the data control unit of operator?” In [24], Krishnan et al. developed a cyber-resilient distributed remedial action scheme to (1) react to failures in the computing nodes of network and (2) perform wind power curtailment for cyber-resilient operation of power system. In [25], Singh and Govindarasu proposed a more sophisticated reaction mechanism referred to as special protection scheme (SPS). This framework provides a platform based on decision tree-based anomaly detection to distinguish cyberattacks from smart grid physical disturbances in proposing remedial actions. During normal tripping of relays, generation/load shedding

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is adopted. On the other hand, during malicious relays tripping, the developed SPS recloses the tripped relays and provides situational awareness for the system operator. This mechanism will prevent malicious overloading designed by the attackers targeting smart grid. In [26], Wang and Govindarasu challenged the centralized system protection and control schemes, which are vulnerable to malicious cyberattacks. To address the challenge associated with the centralized framework, the authors proposed a multi-agent decentralized framework which can detect coordinated attacks and implement adaptable remedial actions to preserve the system stability. Despite recent research efforts on proposing reaction mechanisms against cyberattacks on smart grids, a framework is not yet developed in the existing literature to react to decentralized cyberattacks targeting decentralized retailers such that: (1) outages are prevented, (2) congestions are managed via a marketbased mechanism, and (3) a proper action is adopted for passive retailers in the process of reaction to cyberattacks. The mentioned three items are defined as the first motivation of this chapter. A three-layer framework is proposed in this chapter to react to decentralized cyberattacks targeting market retailers such that outages are prevented via an administration action ( first contribution), congestions are managed through a market mechanism (second contribution), and passive retailers are approached with an administrative action to relieve the remaining partial congestions (third contribution).

3.1.2

Smart Homes Analysis

Smart homes (SHs) are considered as an integral asset of smart grids due to their ability in providing a mutual flow of information and power with the upstream network managed by the system operator. Due to the significance of SHs in the operation of modern power systems, researchers have recently paid more attention to their effective management leading to less reliance on the upstream large-scale power plants. A summary of research papers in this area are covered in this subsection. In [27], Rahmani-Andebili and Shen examined a price-controlled energy management of SHs reacting to electricity price fluctuations. A bi-level framework was developed to take into account the optimization problems of (1) SHs minimizing their daily operation cost and (2) GENCO (generation company) maximizing its daily profit. As is validated in this work, GENCO is recommended to decrease the electricity price at peak hours motivating SHs to buy more electric power from GENCO. In [28], Liu et al. scrutinized the effect of smart home scheduling on electricity market operation. The authors proposed a bi-level market model where customers focus on minimizing the electricity bill via optimal scheduling of smart home appliances, while aggregators concentrate on minimizing the expense associated with energy purchased from utilities in the market level. As is verified in this work,

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the introduced framework not only leads to decrease of average monetary cost of customers but also results in reduction of peak-to-average ratio (PAR) for energy consumption and peak generation requirement. In [29], Rahmani-Andebili investigated the scheduling problem of energy sources and appliances of a smart home, where randomness and time-varying nature of PV panels were taken into account. In this work, the hour-scale deferrable appliances and day-scale deferrable appliances were distinguished from each other. According to the results, shifting the hour-scale and day-scale deferrable appliances to the optimal hours of the corresponding day/week decreases the SH’s operation cost while applying no impact on the occupant’s willingness. In [30], Rahmani-andebili and Venayagamoorthy approached the problem of SmartParks placement (SPP) from the viewpoint of plug-in electric vehicle (PEV) aggregator. The problem was formulated as a cooperation between PEV aggregators with the local distribution company (DISCO) in order to improve (1) system reliability and (2) aggregators’ participation in market transactions. As it is demonstrated in [30], aggregator is expected to identify the optimal incentive (i.e., charging cost discount) based on the reaction of PEV’s owners to the incentive leading to an improved profit for the aggregator and an enhanced system reliability. In [31], Rahmani-andebili and Shen presented an effective approach to solve the cooperative distributed energy scheduling problem by applying model predictive control on the set of SHs equipped with different sources of energy. In this model, each SH can transact power with the local DISCO via the grid or other connected SHs. As it is validated in this work, SHs experience a noticeable cost saving due to the cooperation of SHs and sharing their energy sources such as PV and battery of PEV. More details about this model were provided in [32]. The authors verified that the DG adjusts its output within the small time step of 5 minutes in the proposed model while the performance of PEV’s battery is limited because of short optimization time horizon of 12 time steps (i.e., one hour). In [33], Aznavi et al. introduced an energy price tag (EPT) for energy storage devices of an SH to effectively address the problem of SH’s coordinated energy management. The authors also developed a rule-based prioritization algorithm to optimally establish a priority order among PEV, battery of SH, and the imported power from the grid. According to the simulation results, an improved contribution of energy storage assets was obtained when the proposed EPT was reflected. It is verified that such an enhanced contribution results in reduced electricity bill through storing cheaper energy via the PEV and the SH’s battery bank. Despite the extensive research efforts accomplished on analysis and management of SHs from homeowner’s and aggregator’s points of view, a framework is yet to be investigated for the scenario where a smart city is attacked and contribution of SH retailers can alleviate the consequences of such a cyberattack. As a result, the reaction framework proposed in this chapter is designed for a smart city targeted by a cyberattack where the contribution of SH retailers in relieving congestions after administrative reaction of the system operator is scrutinized. The rest of this chapter is organized as follows. Section 3.2 elaborates the proposed framework. Section 3.3

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provides the problem formulation. Section 3.4 presents simulation results and the corresponding discussions. Finally, Section 3.5 concludes the chapter.

3.2

Proposed Framework

The proposed framework in this chapter contains three layers which are elaborated in this section.

3.2.1

Fast Reconfiguration

The intention of the proposed framework (see Fig. 3.1) is to react to the following situation: “In 20-60 minutes before the implementation of pre-dispatched schedule, system operator receives messages from several DG aggregators and SH aggregators indicating that they have become aware of manipulation of data on their clients and their data control units. Hence, their previously submitted bids/ offers are not fully reliable”. It should be noted that a DG aggregator refers to a retailer who has contracts with several private DG owners as his/her clients to participate in the market on their DG Retailers

SH Retailers #1 System Operator

#1

#2

...

#2

Attacker Targeting DG Retailers

.. .

Attacker Targeting SH Retailers

#H1 #G1

Legends Applied Attack Reported Attack Reliance After FR

#(H1)+1

.. .

#(G1)+1

Fast Reconfiguration

...

#H #G Upstream “Reserve” Peaking Plants

Fig. 3.1 Proposed framework: Layer #1

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behalf and increase their profit from the market. These aggregators are referred to as supply aggregators which are also known as wholesale marketers in the California Electricity Market [34]. In distribution power systems, DG aggregators enhance the effectiveness of distributed energy resources in providing their contribution at scale [35]. On the other hand, an SH aggregator refers to a retailer signing contracts with smart homeowners to participate in the market on their behalf and provide services such as demand response (DR) in order to decrease the energy bills for his/her clients. Interested readers are referred to [34] for further background regarding differences between supply aggregators and load aggregators in a deregulated electricity market. A simple strategy for the system operator reacting to the aforementioned situation is to rely on the power purchased from upstream large-scale peaking power plants in the “reserve” market for emergency situations. However, if a massive number of DG aggregators and SH aggregators are simultaneously attacked in a decentralized manner, an excessive supply from the upstream network can bring about upstream breakers tripping leading to cascading failures. To prevent such outages due to the attacked retailers, an effective strategy is fast reconfiguration (FR) to provide a platform for better utilization of reserve power compensating for the contribution of attacked retailers without tripping any breakers. Hence, in the first layer of the proposed framework, a reformulated reconfiguration is proposed for the system operator where the objective function is to minimize reliance on attacked entities. The formulation of the first layer reaction is presented in Sect. 3.3. As can be perceived from Fig. 3.1, the system operator becomes aware of attacks on G1 DG retailers out of G DG retailers and H1 SH retailers out of H SH retailers. Hence, he/she has a limited time to run the fast reconfiguration problem and identify the optimal topology in which the reliance on attacked retailers is minimized and potential outages due to the attacked entities are prevented. As can be inferred from Fig. 3.1, in the new topology, non-attacked retailers will have a chance to provide more contribution to the system after the administrative FR in case other challenges are experienced.

3.2.2

Market-Based Contribution of Non-attacked SH Retailers

The intention of designing the second layer of the proposed framework (see Fig. 3.2) is to address the following question: “What if the fast reconfiguration implemented by system operator in response to the attacked retailers prevents the outages but results in congestions?” Considering an organized attack on several aggregators at the same time (refer to Fig. 3.1), outages prevention by system reconfiguration might result in decentralized congestions specially in a large-scale distribution system. Hence, the second layer of this framework is developed to manage such congestions. Given that the severity of

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H2 Non-attacked SH Retailers

Winning Participants #1 #2

Running SHRMCM

Invited to SHRMCM

... .. . .. .

Alleviated Congestions

#H4

#H3

Decided to Participate in SHRMCM

#H2 Market Operator Fig. 3.2 Proposed framework: Layer #2

congestions is less than that of outages, the second-layer problem (i.e., congestion management) is handled via a market mechanism unlike the first-layer problem (i.e., outages prevention) which was addressed through an administrative action of the system operator. As can be observed from Fig. 3.2, the market operator reaches out to H2 non-attacked SH retailers (i.e., H2 ¼ H-H1) and invites them to a market named SHRMCM which is short for smart home retailers market-based congestion management. Out of the H2 invited retailers, H3 retailers decide to participate in the market to decrease the energy bills for their clients by providing demand response (i.e., load curtailment) for congestion management. To participate in this market, the H3 retailers submit their offers (i.e., ρ in cent/kWh) to provide ΔP load curtailment (LC) for congestion management. The market operator solves an optimization problem, to be formulated in Sect. 3.3, in order to identify the most economic offers leading to congestion alleviation. Out of H3 retailers participating in SHRMCM, H4 retailers are recognized by the market operator as the winning retailers in this market (refer to Fig. 3.2). The winning retailers will immediately provide their offered LC to alleviate the decentralized congestions after the administrative reconfiguration.

72

3.2.3

A. Asrari

Administrative Forced Load Curtailment for Passive Non-attacked SH Retailers

This section describes the third layer of the proposed framework as is depicted in Fig. 3.3. As explained, the first-layer reaction mechanism is carried out by the system operator to rapidly modify the system topology with the objective of minimum reliance on attacked retailers leading to the prevention of possible outages. If a massive number of retailers are attacked, after the reconfiguration preventing outages, it is possible to experience congestions in other parts of the system. Hence, in the second layer of the framework, a market-based mechanism is designed for SH retailers willing to participate in congestion management by providing load curtailment leading to a decreased energy bill for their clients. Possible congestions after the administrative reconfiguration are expected to be fully relieved in the second layer. However, in some rare cases, few partial congestions may exist after the second layer due to some passive SH retailers not participating in the second-layer market referred to as SHRMCM (refer to the previous subsection). In the third layer of the framework, an administrative forced load curtailment (AFLC) mechanism is developed for the system operator to apply forced curtailments on the passive retailers (i.e., the ones who did not participate in the second-layer market). As can be realized from Fig. 3.3, out of H2 invited retailers to SHRMCM, H5 retailers (H5 ¼ H2 – H3) have been passive meaning that they have submitted no offer to this market. Since the experienced congestions are not fully alleviated through SHRMCM, the system operator solves an optimization problem, to be formulated in Sect. 3.3, in order to apply minimum curtailments on the most effective H2 Non-attacked SH H Retailers Retail

System Operator

#1

#H2

Fig. 3.3 Proposed framework: Layer #3

List of Passive SH Retailers

#H3

H5 Passive SH Retailers

..

#H4

Decided to Participate in SHRMCM

Invited to SHRMCM

.. .. .

#2

Market Operator

SHRMCM

3 How to Employ Competitive Smart Home Retailers to React to Cyberattacks

73

passive SH retailers leading to full alleviation of remaining partial congestions. This administrative action is referred to as AFLC as is indicated in Fig. 3.3.

3.3 3.3.1

Problem Formulation Administrative Reconfiguration

As mentioned, the objective of the administrative reconfiguration is to minimize the reliance of the network on the attacked DG retailers and attacked SH retailers. This objective is formulated in (3.1). G X

Min

g¼1

xg  Pg þ

H X

! yh  Ph

ð3:1Þ

h¼1

where xg and yh are binary variables indicating attacks on gth DG retailer and hth SH retailer. For example, if x1 ¼ 0 and y1 ¼ 1, it means that the first DG retailer is not attacked while the first SH retailer is attacked. In (3.1), G and H refer to the number DG retailers and the number of SH retailers in the smart city. Plus, Pg and Ph, respectively, signify the power generation associated with the gth DG retailer and the demand response (i.e., offered power to be curtailed in emergency situations) related to the hth SH retailer. As can be inferred from (3.1), the reliance of the network on the offers of attacked assets (i.e., generation or demand response) will be minimized after implementing the new topology. The objective function presented in (3.1) has some constraints formulated as follows. The first constraint is the balance between generation and consumption after the administrative reconfiguration as is formulated in (3.2). PNet þ

G  X    xg  Pg þ 1  xg  Pg g¼1

¼

H X

F h  ½yh  Ph þ ð1  yh Þ  Ph 

ð3:2Þ

h¼1

where Fh stands for the overall load of the hth SH retailer. In (3.2), PNet represents the overall power to be supplied from the upstream network (i.e., substation). It is noted that PNet is the summation of the prescheduled power from grid in the energy market and the reserve power to be supplied from peaking power plants in emergency situations (refer to Fig. 3.1). As can be noted, system topology is updated to prevent possible outages due to the attacked retailers and provide a platform for (1) minimum reliance on the service from attacked retailers and (2) a better utilization of reserve power from upstream large-scale peaking plants. To ensure that no

74

A. Asrari

outages are experienced due to breakers tripping after the administrative reconfiguration, constraint (3.3) is formulated in the first-layer optimization problem. jI m j < I m TS

m2M

ð3:3Þ

where Im denotes the current flowing into mth branch and M is the total number of closed branches. In (3.3), ImTS stands for the trip setting current of the breaker on the mth branch. This constraint ensures that no breaker experiences a current equal to or higher than its trip current after implementing the fast reconfiguration leading to minimum reliance on the attacked entities. Another constraint of this problem is formulated in (3.4) to guarantee that voltage magnitudes are within their acceptable limits after performing the administrative reconfiguration. V min  jV n j  V max

n2N

ð3:4Þ

where Vmin/max refers to the minimum/maximum allowed voltage for buses within the system, and N represents the number of buses. Another important constraint of this problem is to ensure that the identified distribution network configuration is a radial topology. The constraints related to radiality checking are formulated in (3.5) and (3.6). M ¼N1 T ¼ ½a, b, . . . , z1L

a 2 l1 , b 2 l2 , . . . , z 2 lL

ð3:5Þ ð3:6Þ

According to (3.5), in a radial configuration, the number of closed branches (M) should be one unit less than the number of buses (N ). It is noted that loop-based strategy is employed in this work to define network topologies. In this strategy, each element of the coded topology is branch ID (i.e., branch No.) which is open in the corresponding loop. As can be inferred from (3.6), the coded topology (T ) contains L loops where the branches a, b, . . ., and z are open. In order for this topology to be radial, the open branches a, b, . . ., and z must belong to the l1st, l2nd, . . ., and lLth loops, respectively. To provide further clarity, an example is illustrated in Fig. 3.4. According to the provided explanations, the coded topologies T1 and T2 are as follows: T 1 ¼ ½1, 3, 6, 8

T 2 ¼ ½1, 3, 5, 6

ð3:7Þ

Although both the topologies satisfy the first constraint (i.e., M ¼ 5 ¼ N1 ¼ 6–1), T2 is not a radial configuration since it does not satisfy the second constraint. In other words, there is no open branch associated with l4 in T2. On the other hand, T1 is a radial topology since it meets (3.5) and (3.6). Further details about radiality checking of distribution systems can be found in [36].

3 How to Employ Competitive Smart Home Retailers to React to Cyberattacks

l3 7

6 4 3 1

8

l2

5

l1

2

l3 7

6 l4

4 3

9

1

T1

75

8

l2

5

l1

2

l4

9

T2

Fig. 3.4 Two sample configurations

Vn ∠ dn

Fig. 3.5 Typical buses n and n + 1

Vn+1 ∠ dn+1 Rn+jXn Pn+jQn

After identifying the desired configuration, the system operator runs a power flow to ensure outages will be successfully prevented through the reconfiguration and no overvoltage/undervoltage will be experienced. In this work, the backward/forward sweep power flow is utilized. The main constraints associated with this power flow are presented in (3.8)–(3.12). Interested readers are referred to [37] for further details about formulation and implementation of this power flow on radial distribution systems. To provide further clarity for readers, the bus n and bus n + 1 associated with (3.8)–(3.12) are depicted in Fig. 3.5. It is noted that in (3.10) and (3.11) PL,n + 1 and QL,n + 1 refer to the active/reactive load power of bus n + 1. Pn ¼ P0nþ1 þ Rn 

  2 P0nþ1 2 þ Q0nþ1 

Qn ¼ Q0nþ1 þ X n 

V 2nþ1 P0nþ1 2 þ Q0nþ1 V 2nþ1

P0nþ1 ¼ Pnþ1 þ PL,nþ1

2

ð3:8Þ

 ð3:9Þ ð3:10Þ

76

A. Asrari

Q0nþ1 ¼ Qnþ1 þ QL,nþ1 In ¼

3.3.2

ð3:11Þ

V n ∠δn  V nþ1 ∠δnþ1 Rn þ jX n

ð3:12Þ

Market-Based Load Curtailment

If there is no congestion after running the aforementioned power flow associated with the new topology, the system operator finalizes the procedure of reaction to the reported attacks and starts to closely work with the attacked entities to prevent any similar problem in the future hours. However, as mentioned earlier, it is possible to encounter partial congestions after applying a new reconfiguration which minimizes reliance of the network on several attacked DG/SH retailers. If such congestions are experienced after the administrative reconfiguration, the system operator contacts with the market operator and provides him/her with information about congested branches and non-attacked SH retailers who can compete in the market of SHRMCM and contribute in congestion management by performing further load curtailments leading to a reduced energy bill for their clients. After receiving the offers from retailers participating in the market, an optimization problem is solved by the market operator to identify the most economic redispatch for congestion management. The objective function of this optimization problem is formulated in (3.13). " Min

G  X g¼1



1  xg  αg 

PΔ g

 ρg þ

H X

# ð 1  yh Þ  β h 

PΔ h

 ρh

ð3:13Þ

h¼1

where α or β, PΔ, and ρ, respectively, stand for the binary variable indicating the result of corresponding retailer’s participance in the SHRMCM (i.e., α or β one if winning, α or β zero if not winning), and the submitted power (kW) and price (¢/ kWh) offers from the corresponding retailer for power redispatch. As can be inferred, this objective is formulated generally in case DG retailers are also invited for congestion management. However, as mentioned, the concentration of this work is to scrutinize the contribution of SH retailers in resolving consequences caused by cyberattacks in a smart city. Hence, in (3.13), αg is considered zero for all DG retailers. One of the constraints of this problem is formulated in (3.14) to check the balance between consumption and generation associated with redispatched schedule managing congestions.

3 How to Employ Competitive Smart Home Retailers to React to Cyberattacks

P0Net þ

G  X

77

   xg  Pg þ 1  xg  Pg

g¼1

¼

H X

   F h  yh  Ph þ ð1  yh Þ  ð1  βh Þ  Ph þ ð1  yh Þ  βh  Ph þ PΔ h

h¼1

ð3:14Þ where P0 Net represents the pre-scheduled power from the grid excluding the reserve power from upstream peaking plants which was utilized in the first-layer reaction. In the second layer, contributions from non-attacked SH retailers will compensate for the lack of extra supply from peaking plants. As can be inferred from (3.14), if the hth SH retailer is not attacked (i.e., 1 – yh ¼ 1) and has won in the competition of SHRMCM (i.e., βh ¼ 1), the corresponding retailer will provide the extra power curtailment of PhΔ increasing its total demand response to the value of Ph + PhΔ. Another constraint of this problem is presented in (3.15) to ensure that the current flowing into each branch is less than the defined rated value to manage congestions. It is noted that ImTS in (3.3) refers to trip setting current of the breaker installed on the mth branch which is usually greater than the rated current of the branch [38]. On the other hand, ImRated in (3.15) denotes the rated current of the mth branch. In other words, (3.3) was employed in the administrative reconfiguration to prevent outages or breakers tripping while (3.15) is utilized in the second layer to manage congestions via offers from proactive SH retailers. jI m j  I m Rated

3.3.3

m2M

ð3:15Þ

Administrative Forced Load Curtailment

If SH retailers proactively participate in the market-based congestion management of the second layer, congestions will be fully relieved and no further actions will be needed. However, in some rare cases, the contributions from SH retailers might not be sufficient in fully alleviating the congestions. In those scenarios, the third layer of this framework should be employed where the system operator administratively applies forced curtailments on the passive SH retailers. To accomplish this task, the system operator solves an optimization problem. The objective function of this problem, as is formulated in (3.16), is to apply minimum forced curtailments on the most effective passive SH retailers leading to full alleviation of congestions.

78

A. Asrari

Min

H  X h¼1

  K 1  ð1  yh Þ  ð1  βh Þ  ð1  γ h Þ  FC Δ h 

þK 2  ð1  yh Þ  ð1  βh Þ  γ h 

FC Δ h



ð3:16Þ

where K1 and K2 are, respectively, weighting factors associated with the retailers who have not participated in the second-layer market (γh ¼ 0) and the ones who have participated in that market but did not win (γh ¼ 1). As mentioned, the strategy of the system operator is to concentrate on the retailers who have been passive (i.e., not participated in SHRMCM). Therefore, K1 and K2 are, respectively, considered to be one and zero meaning that no forced curtailments will be applied on the retailers who have participated in the second-layer market but did not win. In (3.16), FChΔ signifies the applied forced curtailment on the hth passive retailer while PhΔ in (3.14) represented the offered load curtailment from the corresponding retailer participating in SHRMCM. One of the main constraints of this optimization problem is to ensure there is a balance between generation and consumption after applying the administrative load curtailment as is formulated in (3.17). P0Net þ

G  X    xg  Pg þ 1  xg  Pg g¼1

¼

H X

     Δ F h  yh  Ph þ ð1  yh Þ  ð1  βh Þ  Ph þ FC Δ h þ ð1  yh Þ  βh  Ph þ Ph

h¼1

ð3:17Þ Another constraint of this problem is formulated in (3.18) which ensures there is no congested line after implementing the third-layer reaction strategy. jI m j  I m Rated

m2M

ð3:18Þ

To sum up, the second layer will be utilized only if the administrative reconfiguration for outages prevention results in decentralized congestions. Moreover, the third layer will be employed only if the contributions from SH retailers participating in the second-layer market do not fully alleviate the experienced congestions.

3.4

Case Study and Numerical Results

The performance of the presented three-layer framework is verified on a 136-bus distribution system [39], which is modified to contain DG/SH retailers (see Fig. 3.6). Readers are referred to [40] for details about characteristics of the modeled DG

3 How to Employ Competitive Smart Home Retailers to React to Cyberattacks

79

SH Retailer

Distributed Generation (DG) Retailers Fig. 3.6 Case Study

retailers. It should be noted that the firm portion of Fh in (3.2), (3.14), and (3.17) is assumed to be 0.85 per unit meaning that the maximum load curtailment to be provided by a typical SH retailer is 15% of the aggregated rated load of his/her clients. Plus, the maximum and minimum allowed voltages are 1.1 per unit and 0.9 per unit, respectively. The formulated optimization problems are solved by the academic version of GAMS software [41]. The simulations are carried out on a PC with a processor of Intel(R) Core(TM) CPU i5–7500 @ 3.4 GHz. Figure 3.7 illustrates the attacks on the DG/SH retailers reported to the system operator. In area A1, 2 DG retailers and 5 SH retailers are attacked such that the manipulated generation offer has been 95 kW more than the actual submission and the falsified load data have been 350 kW less than the actual submission. This means

80

Fig. 3.7 Reported attacks

A. Asrari

3 How to Employ Competitive Smart Home Retailers to React to Cyberattacks

81

Table 3.1 Reported attacks and targeted lines Area A1 A2 A3 A4 A5 A6 A7 A8

ΔPDG 95 85 220 160 180 50 80 95

ΔPSH 350 175 202 320 95 220 150 185

ΔPTotal 445 260 422 480 275 270 230 280

Targeted Line 8 42 153 52 144 125 147 85

that 445 kW (i.e., 95 – (350)) should be provided for this area to prevent power outage for A1. Similarly, an extra generation of 260 kW, 422 kW, 480 kW, 275 kW, 270 kW, 230 kW, and 280 kW should be provided for the other areas shown in Fig. 3.7 to prevent decentralized outages in A2, A3, A4, A5, A6, A7, and A8, respectively. Table 3.1 displays a summary about the reported attacks illustrated in Fig. 3.7. As can be perceived from Fig. 3.7 and Table 3.1, the lines #8, #142, #153, #52, #144, #125, #147, and #85 are targeted by the attackers. In other words, the retailers within the mentioned 8 areas are attacked to submit falsified data to the system operator. When the operator becomes aware of the falsified data associated with the attacked retailers, he/she will have to immediately request for reserve power from upstream peaking plants. If the supplied power from upstream network is higher than the targeted lines of the attacked areas, the breakers on those lines will trip and the corresponding areas will experience outages. To provide further clarity, four examples are given here. In area A1, the overall demand is 2350 kW which is falsified to 2000 kW. The actual local generation is 440 kW which is manipulated to 535 kW. Hence, the requested power from the upstream network should increase from 1465 kW to 1910 kW to compensate for the attacked entities. The corresponding power associated with the trip setting current of the breaker on line #8 is 1655 kW which is less than 1910 kW. As a result, full reliance on the reserve power from the upstream network leads to the tripping of the breaker on the mentioned line resulting in an outage for A1. In area A2, the overall demand is 1200 kW which is falsified to 1025 kW. The actual local generation is 210 kW which is manipulated to 295 kW. Hence, the requested power from the upstream network should increase from 730 kW to 990 kW to compensate for the attacked entities. The corresponding power associated with the trip setting current of the breaker on line #42 is 800 kW which is less than 990 kW. Thus, full reliance on the reserve power from the upstream network results in tripping of the breaker on the mentioned line causing an outage for A2. In area A3, the overall demand is falsified to 3148 kW. The local generation is manipulated to 620 kW. Thus, the requested power from the upstream network should increase from 2528 kW to 2950 kW to compensate for the attacked aggregators. The corresponding power associated with the trip setting current of

82

A. Asrari

the breaker on line #153 is 2600 kW which is less than 2950 kW. As a result, full reliance on the reserve power from the upstream network leads to the tripping of the breaker on the mentioned line resulting in an outage for A3. In area A4, the overall demand is 4300 kW which is falsified to 3980 kW. The actual local generation is 195 kW which is manipulated to 355 kW. Hence, the requested power from the upstream network should be enhanced from 3625 kW to 4105 kW to compensate for the attacked retailers. The power associated with the trip setting current of the breaker on line #52 is 3850 kW which is less than 4105 kW. Thus, full reliance on the reserve power from the upstream network in the current topology brings about tripping of the breaker on the mentioned line resulting in an outage for A4. A similar explanation can be provided for the areas A5, A6, A7, and A8, where the power supply from upstream network should be enhanced to 695 kW, 1465 kW, 720 kW, and 1525 kW, respectively, in order to compensate for the lack of supply from the local attacked retailers which will result in tripping the lines #144, #125, #147, and #85, respectively, leading to decentralized outages for the mentioned areas. Figure 3.8 illustrates the consequences caused by the reported attacks if no proper reaction is implemented. As can be realized from this figure, the current flowing into the targeted lines associated with the 8 attacked areas will be higher than the trip setting current of the corresponding breakers. This will result in tripping of the breakers on lines #8, #42, #153, #52, #144, #125, #147, and #85 causing outages in areas A1, A2, A3, A4, A5, A6, A7, and A8. As can be gathered from Table 3.1 and Figs. 3.7 and 3.8, a fast reaction needs to be adopted by the system operator to prevent the described decentralized outages.

Actual Current Not Observed Due to the Attack

Trip Current

Branch ID Fig. 3.8 Breaker trippings if no reaction is provided

3 How to Employ Competitive Smart Home Retailers to React to Cyberattacks

83

Fig. 3.9 First-layer reaction

As explained in Sect. 3.2 and formulated in Sect. 3.3, the system operator solves an optimization problem to identify a new topology such that reliance on the attacked retailers is minimized and the reserve power from upstream large-scale peaking plants will not cause any outage. The computational burden of this algorithm is 7 minutes meaning that the system operator can rapidly find the new configuration after receiving the reports from the attacked retailers. The recognized configuration preventing outages due to simultaneous attacks on retailers in 8 different areas is presented in Fig. 3.9. As can be realized from this figure, lines #8, #42, #153, #52, #144, #125, #147, and #85 have become open to prevent outages in the eight mentioned areas. Instead, the branches highlighted in blue color in Fig. 3.9 have become closed to keep the radiality of the network. It should be noted that there

84

A. Asrari

Maximum Current

Rated Current for Security Concern

Current before SHRMCM

Branch ID Fig. 3.10 Currents after fast reconfiguration (i.e., before SHRMCM)

are 21 loops in this case study containing 136 buses and 156 branches. Therefore, there should be 135 closed branches to stand for a radial topology. Moreover, the 21 open branches should be associated with 21 loops of the network to form a radial system. These constraints are met for this identified configuration. After checking all of the constraints associated with the recognized topology, the system operator implements the reconfiguration to minimize the reliance of the system on the attacked retailers and prevent outages. It is noted that the dotted lines in black color in Fig. 3.9 (e.g., #131) represent the branches which have been open before and after the reconfiguration. Similarly, the solid black lines in Fig. 3.9 (e.g., #140) stand for the branches which have been closed before and after the administrative reconfiguration. After identification of the new topology, the system operator runs a power flow as was explained in Sect. 3.3. If there is no congested line after the administrative reconfiguration, the system operator finalizes the process. Otherwise, he/she reaches out to the market operator to handle the market mechanism in the second layer of the framework. Figure 3.10 depicts the currents flowing into different branches after the administrative reconfiguration preventing the decentralized outages. As can be found out from this figure, the currents flowing into all of the branches are less than the trip setting currents of the corresponding branches verifying that no breakers will be tripped after reconfiguration. However, the currents flowing into five branches are higher than the rated values defined for security of the system. This means that five decentralized congestions are experienced after the administrative reconfiguration although the eight decentralized outages are prevented. Hence, the market operator is called by the system operator to invite the non-attacked SH retailers into a market

3 How to Employ Competitive Smart Home Retailers to React to Cyberattacks

85

Fig. 3.11 Second-layer reaction

referred to as SHRMCM to compete for further curtailments leading to the alleviation of congestions. The eligible non-attacked SH retailers are encouraged to proactively participate in this market and decrease the energy bills of their clients through contributing in congestion management. Figure 3.11 depicts the participance of the invited SH retailers in the marketbased mechanism for the congestion alleviation of the system. In this figure, active SH retailers (i.e., SHRs participating in SHRMCM) are shown by green color while passive SH retailers (i.e., SHRs not participating in the second-layer market) are highlighted by the red color. As it can be observed from this figure, only three retailers indicated by J1, J16, and J19 have been passive SH retailers (SHRs). According to the congested line #39, the four SH retailers of J1, J2, J3, and J4 are invited to the SHRMCM to compete in providing further load curtailments leading to the alleviation of congestion in that line. Further curtailment of 280 kW is required to be provided by the invited retailers to relieve the congestion. Out of the mentioned retailers, J2, J3, and J4 have participated in the second-layer market. Regarding the congested line #48, the five SH retailers of J5, J6, J7, J8, and J9 are invited to the SHRMCM to compete in providing further load curtailments leading to the alleviation of congestion in that line. Further curtailment of 143 kW is needed to

86

A. Asrari

be provided by the invited retailers to alleviate the congestion. All of the invited retailers have participated in the second-layer market to address the congestion of line #48. As for the congested line #137, the four SH retailers of J10, J11, J12, and J13 are invited to the SHRMCM to compete in providing further load curtailments leading to the alleviation of congestion in that line. Further curtailment of 72 kW is requested to be provided by the invited retailers to relieve the congestion. All of the invited retailers have participated in the SHRMCM to alleviate the congestion of line #137. To address the congestion of line #121, the seven SH retailers of J14, J15, J16, J17, J18, J19, and J20 are invited to the SHRMCM to compete in providing further load curtailments leading to the alleviation of congestion in that line. Further curtailment of 360 kW is asked to be provided by the invited retailers to alleviate the congestion. As can be noticed, only five retailers out of the seven invited SH retailers have participated in the congestion management of line #121. Finally, for congestion alleviation of line #102, the four SH retailers of J21, J22, J23, and J24 are invited to the SHRMCM to compete in providing further load curtailments. Further curtailment of 107 kW should be provided by the invited retailers for congestion management. As can be observed, the four invited retailers have participated in the congestion management of line #102. Figures 3.12, 3.13, 3.14, 3.15, and 3.16 depict the impact of contributions provided by the SH retailers invited to SHRMCM by the market operator. As can be perceived from Fig. 3.12, the SH retailers J2, J3, and J4 have won in the market. However, the overall provided load curtailment by these active retailers is less than 280 kW meaning that it has not fully relieved the congested line #39. This issue is

Fig. 3.12 Result of SHRMCM for Branch #39 (horizontal axis: offered power in kW)

Fig. 3.13 Result of SHRMCM for Branch #48 (horizontal axis: offered power in kW)

Fig. 3.14 Result of SHRMCM for Branch #137 (horizontal axis: offered power in kW)

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87

Fig. 3.15 Result of SHRMCM for Branch #121 (horizontal axis: offered power in kW)

Fig. 3.16 Result of SHRMCM for Branch #102 (horizontal axis: offered power in kW)

due to the passive behavior of J1 who did not provide any offer to this market. In other words, the congested line #39 needs to be administratively addressed by the system operator in the third layer of the proposed framework. According to Fig. 3.13, out of the five SH retailers participating in the SHRMCM to address the congestion of line #48, only three of them offering lower prices have won in the competition. This contribution is sufficient to fully relieve the congestion of 143 kW. As can be inferred from Fig. 3.14, two SH retailers have won in the SHRMCM given that they have provided lower prices for their load curtailment. The provided demand response fully alleviates the congestion of 72 kW on line #137. As can be realized from Fig. 3.15, all of the SH retailers participating in the SHRMCM have been considered for further load curtailment which is not enough to fully address the congestion of 360 kW on line #121. This is due to the fact that the SH retailers J16 and J19 did not participate in this market (refer to Fig. 3.11). As a result, an administrative forced curtailment will be applied on them in the third layer to fully relieve the congested line #121. Finally, Fig. 3.16 reveals that the three retailers of J21, J22, and J23 have won in the SHRMCM because of offering lower prices compared to J24. This contribution fully alleviates the congestion of 107 kW on line #102. Therefore, no further forced curtailments will be needed to be applied on the retailers located in this area. Figure 3.17 compares the congestion management after SHRMCM in layer 2 and after administrative forced load curtailment in layer 3. As can be perceived from the figure, the red bar is only associated with passive SH retailers meaning that the administrative forced curtailments are only applied on the SH retailers not participating in the market-based congestion management in layer 2. Comparing the solid blue curve with the dashed red curve reveals the fact that the final schedule is associated with less electric demand compared to the initial schedule before applying the first-layer reaction mechanism referred to as the administrative fast reconfiguration. In other words, the administrative reconfiguration (computational burden of 7 minutes) is accompanied with market-based load curtailment (computational burden of 16 minutes) and the forced administrative load curtailment

88

A. Asrari

Initial Schedule Final Schedule

Curtailed in Layer 2 Curtailed in Layer 3

SH Retailer (SHR) ID Fig. 3.17 Comparison between initial and final schedules due to curtailments in layers 2 and 3 Rated Current

After Layer 2

After Layer 3

Branch ID Fig. 3.18 Relieved congestions after second-layer reaction and third-layer reaction

(computational burden of 1 minute) to not only prevent outages due to the attacked retailers but also manage congestions caused by the administrative reconfiguration. Figure 3.18 illustrates the reduced currents associated with the congestion managements after the second and third layers of the framework. As can be noticed, the market-based congestion managements applied on lines #48, #102, and #137 fully relieve the congested lines (compare the black dotted curve with the solid blue curve). However, as mentioned, the market-based load curtailments are not sufficient to fully relieve the congestions on lines #39 and #121. As a result, administrative forced load curtailments (AFLCs) are applied on (1) J1 to fully relieve the congested line #39 and (2) J16 and J19 to fully alleviate the congested line #121. Table 3.2 displays the bids and revenues of SH retailers in the process of congestion management. As can be gathered from the table, the SH retailers not

3 How to Employ Competitive Smart Home Retailers to React to Cyberattacks

89

Table 3.2 Comparison between passive (P) and active (A) SH retailers

Active/ Passive

Reduced Load (kW)

Revenue

Bids (cents/kWh)

J1

P

J2

A

110.0

0.0

0.0

60.0

234.0

3.9

J3

A

25.0

91.0

3.6

J4

A

85.0

258.9

3.0

J5

A

0.0

0.0

4.7

J6

A

60.0

192.0

3.2

J7

A

0.0

0.0

4.4

J8

A

38.0

152.0

4.0

J9

A

45.0

153.0

3.4

J10

A

0.0

0.0

3.6

J11

A

0.0

0.0

4.7

J12

A

63.0

195.3

3.1

J13

A

9.0

28.8

3.2

J14

A

25.0

87.5

3.5

J15

A

45.0

112.5

2.5

J16

P

35.0

0.0

0.0

J17

A

40.0

160.0

4.0

J18

A

65.0

237.3

3.7

J19

P

45.0

0.0

0.0

J20

A

25.0

125.0

5.0

J21

A

42.0

147.0

3.5

J22

A

30.0

99.0

3.3

J23

A

35.0

119.0

3.4

J24

A

0.0

0.0

4.6

SHR ID

highlighted have obtained revenue from SHRMCM leading to a reduced bill for their clients. This is due to the fact that they have been active in the second-layer market and, more importantly, they offered lower prices for their further load curtailments. On the other hand, the SH retailers highlighted in orange have not attained any revenue due to not winning in the SHRMCM. Finally, three SH retailers highlighted in red are the ones who did not participate in the SHRMCM. Therefore, an administrative load curtailment has been applied on these passive retailers to fully relieve the congestions. Although the retailers highlighted in orange did not obtain any revenue (similarity with the ones highlighted in red), they did not have to provide any further curtailment (difference with the passive retailers).

90

3.5

A. Asrari

Conclusions

In this chapter, a three-layer framework was proposed to react to cyberattacks targeting SH retailers and DG retailers in a smart distribution system. In the first layer of the framework, a fast reconfiguration was applied by the system operator to provide a platform such that outages are prevented via minimum reliance on the attacked retailers. In the second layer, proactive SH retailers participated in a market referred to as SHRMCM to alleviate congestions caused by the fast reconfiguration. In the third layer, the system operator applied a forced load curtailment on passive non-attacked SH retailers to fully relieve the remaining congestions. The simulation results verify that the fast reconfiguration prevents outages in 8 areas of the system. According to the results, the SHRMCM provides a platform for SH retailers to fully relieve three out of five congestions in the second layer after the first-layer outage prevention. Finally, as can be perceived from the results, the remaining two congestions are fully alleviated in the third layer by the administrative action of the system operator approaching the non-attacked retailers who were passive in the secondlayer market mechanism. The future step of this research is to propose a scheme to react to cyberattacks designed to result in market power or monopoly in smart distribution systems.

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Chapter 4

Demand Response Frameworks for Smart Residential Buildings S. L. Arun and M. P. Selvan

Abstract Nowadays, the electrical utilities are concentrating more on smart grid technologies in order to attain reliable, secure and profitable power system operation. Considering various techniques of smart grid, demand side management (DSM) is a promising technique for utility in which the end subscribers are motivated to participate directly in energy society activities. In DSM scheme, the utility proposes various pricing strategies and maximum demand limit (MDL) to get more profit and decrease the operational difficulties. The end subscribers are expected to respond (demand response) appropriately to decrease their electricity bill. Further, the recent advancements and extensive use of smart residential appliances and incorporation of communication and information technologies help consumers to reach minimum electricity bill by altering their demand pattern. Further, the residential consumers prefer battery back-up to reduce their demand during utility peak intervals. In addition to this, residential consumers use renewable power generations as an alternative to meet their demand either completely or partially. Further, they are also stimulated to export their surplus power generation to the grid at utility preferred price. These types of consumers are commonly called as prosumers. Consequently, utilities are introducing a time-dependent power injection limit to avoid grid operational difficulties. In order to attain more incentives from utilities without sacrificing the comfort, the end-user prefers to install building energy management systems. This chapter presents various energy management frameworks for different residential buildings. Further, the presented demand response frameworks are validated through different case studies on a smart residential building equipped with different kinds of household components. The results of the case studies demonstrate considerable yields for the end subscribers. S. L. Arun (*) School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India e-mail: [email protected] M. P. Selvan Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Rahmani-Andebili (ed.), Operation of Smart Homes, Power Systems, https://doi.org/10.1007/978-3-030-64915-9_4

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Keywords Demand response programs · Demand side management schemes · Energy management systems · Energy storage · Optimization · Renewable energy resources · Smart grid · Smart residential building

4.1

Introduction

The conservative electric power grid is experiencing various functional challenges due to rapid increment in electricity demand. Conservative power grid is structured with different sectors such as power generation, transmission, distribution and consumption. A number of conventional power plants like nuclear, coal and hydro are feeding power into the grid and always balancing the supply and demand equality. Further, the voltage levels of the generated electric power are altered at various stages with the help of the transformers in order to reduce the transmission loss. The electric utilities are delivering power to end subscribers in distribution and consumption sectors. Hence, the power flow in the conservative power grid is unidirectional. Due to faster growth in digitalization of industrial and commercial processes, the total electricity demand increases abruptly. On the other hand, the development in generation sectors is not as fast as the demand growth. Further, planning of new conventional power plants and extending the capability of transmission sectors to support the unmet demand are a highly challenging task. The challenges are mainly due to growth in population, constraints in city limit expansion, green-house gas emissions, economic and political reasons. In order to mitigate all these problems, the electric power utility begins to upgrade the conservative grid into the smart grid. Smart grid is expressed as a power system which adopts advanced technologies like cyber-secure two-way communication and artificial intelligence in a combined manner across various sectors in the existing electric system to attain a system that would be reliable, secure, efficient, sustainable, and resilient [1]. In smart grid, different techniques are introduced to improve the operation of existing electric power network. One such scheme is demand side management (DSM), which is a promising technique accepted by many power utility companies to have control on consumers’ energy consumption [2]. DSM schemes are usually easy to implement with the help of inexpensive components compared to either building new generation units or installing large energy storage devices [3]. Further, this scheme majorly comprises various programs such as efficient energy program, energy conservation program, demand management program and demand response program [4]. The efficient energy program includes all permanent changes on equipment such as replacing old light bulbs, which consume more energy with either compact fluorescent lamps (CFLs) or light emitting diodes (LED); and enhancements on the physical properties of the systems such as investing in the building shell by installing additional insulation [5]. Energy conservation is a part of efficient energy program and it gives more attention on end consumers and their behavioural changes in order to attain more reliable and efficient energy usage.

4 Demand Response Frameworks for Smart Residential Buildings

95

Direct demand control and time-dependent energy pricing are two approaches used in demand management program. In direct demand control, the time of operation of consumers’ loads is directly controlled by service providers [6, 7]. In the energy pricing approach, the utilities are proposing different pricing schemes while considering the locality demand variation, generation schedule and economic profit [8]. Simple tariff, flat rate tariff (FRT), block rate tariff, demand-based tariff, time of use tariff, critical peak pricing [9], day ahead tariff [10] and real-time pricing (RTP) [11] are a few energy pricing techniques followed by present utilities. In addition to this, utilities impose a time-dependent maximum demand limit (MDL) [12] to maintain the system load curve almost flat. Generally, utilities impose high demand limit during non-peak intervals and less demand limit during peak interval. Therefore, the consumer schedules most of their non-essential demand during non-peak intervals and hence the peak to average ratio of the utility improves. However, keeping the consumer demand under utility-defined MDL is not a mandatory task. Consumers are expected to pay the penalty only if the consumer’s net demand exceeds the utility MDL. The alteration from an end consumer such as installing efficient energy devices, managing the individual requirements and scheduling the demands to non-peak intervals are collectively termed as demand response [13]. Demand response is expressed as modifications in energy consumption by end subscribers from their regular demand patterns as a response to differences in utility electricity price over time, or to incentive payments planned to induce lesser energy consumption at times of peak electricity market prices or when system consistency is jeopardized [14]. With the help of demand response techniques, end subscribers are driven to have direct communication with the utility [15, 16]. Consumers are following different techniques to alter their energy consumption pattern like minimizing their energy consumption through demand reduction strategies, scheduling the operation of appliances to the different time period and using on site stand-by generated power. Consumer may attain the appreciable reduction in electricity bill by implementing suitable demand response techniques. Significant reduction in the bill is achieved by reducing the consumer’s demand during peak price intervals. Hence, automation, monitoring and control technologies of household appliances are essential in the smart grid environment to enrich the consumer’s comfort with minimum electricity bill.

4.2

Type of Household Appliances

Nowadays, the modern residential buildings are established with various household appliances. These appliances are capable to complete the assigned task easily and timely. Most of the appliances are included with the advanced features such as artificial intelligence, two-way communications and flexibility in operation and control. Based on the consumer usage, these appliances are broadly classified into

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the following types: Non-Interruptible and Non-Schedulable Demands (NINSDs), Interruptible and Non-Schedulable demands (INSDs) and Schedulable Demands (SDs).

4.2.1

Non-Interruptible and Non-Schedulable Demands (NINSDs)

NINSDs are primary loads, which need to perform the assigned task instantly whenever the user initializes them. All-time demands like security systems; critical demands like fan, lights, laptop/mobile charging, desktop and its peripherals; entertaining demands like home decorates, speakers, television and kitchen appliances such as induction stove, toaster and mixer are categorized under this type. As the operation of all NINSDs in a day is usually depend upon user comfort and desire, controlling of NINSDs may bother the well-being of the consumer.

4.2.2

Interruptible and Non-Schedulable Demands (INSDs)

The loads which control the operating temperature are considered as INSDs. Residential loads like refrigerator, air conditioner, space heater and electric water heater are a few examples of INSDs. Practically, the INSDs operating pattern in a day is merely influenced by the user comfort and environmental constraints. During operation of any INSD, the temperature is maintained at the user predefined set value but within the tolerance limit defined by the manufacturer. Whenever the temperature goes beyond the tolerance limit, the INSD moves to RUN mode and start to consume the rated power. However, INSD continues its operation in standby mode when the temperature is within the tolerance limit.

4.2.3

Schedulable Demands (SDs)

The loads whose time of operation can be scheduled within the user prefixed time span are considered as SDs [17]. Based upon the operational constraint, these loads are further divided as non-interruptible and schedulable demands (NISDs) and interruptible and schedulable demands (ISDs). The operation of NISDs should be continuous till the completion of task if they started once. Food grinder, cloth washer and dryer are a few examples of NISDs type. However, the operation of ISDs can be either continuous or discontinuous in the user predefined time span. Well pump, electrical vehicle, and dish washer are a few example of ISDs type.

4 Demand Response Frameworks for Smart Residential Buildings

4.3

97

Model of Household Components

In smart grid environment, end consumers can avail more incentives from the utility and improve the savings in annual electricity bill by adopting a suitable demand response programs. With the help of advanced energy management systems, consumers can easily and actively participate in such programs. Further, these advanced systems assist the end users to alter their demand pattern in such a way that the economic benefits of users will be increased. Moreover, the residential buildings are preferred to install different energy storage techniques to avoid power interruption and to meet the part of the demand during peak intervals. In addition to this, few consumers choose in-house renewable energy resources to reduce the grid dependency. Hence, the mathematical modelling of different household components is essential for the successful implementation of such energy management systems.

4.3.1

Model of NINSDs

As discussed earlier, the NINSDs are essential loads and perform the immediate operation as per the user instructions. Hence, the demand pattern of these NINSDs is dynamic and merely based on the availability of user. Further, the time at which the user will turn ON a NINSD at a particular day is highly random. In order to accomplish the user wish and comfort, the demand of all NINSDs available in the residential building is accumulated and assumed as a single load whose power consumption varies dynamically.

4.3.2

Model of INSDs

Let us define the set of available INSDs as A and the set of non-schedulable demand (NINSDs and INSDs) intervals as I . The INSD status
vector which represents  the Δ operating status (RUN / STAND-BY) of an INSD a a 2 A ¼ ½1, 2, . . . , A in any
 Δ non-schedulable demand interval i i 2 I ¼ ½1, 2, . . . , I  is defined as,   X a ¼ x1a , x2a , . . . , xia , . . . , xIa

8a 2 A

ð4:1Þ

Here, A represents the available number of INSDs in the considered residential building and
I denotes h the imaximum number of non-schedulable demand intervals over a day I ¼ 24  D60NS defined by user. Here, DNS is referred as the duration of a non-schedulable demand interval in minute assigned by the user. When the user set point temperature (Tseta), manufacturer set allowable tolerance limit (ΔTtola) and

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the actual temperature at the end of the non-schedulable demand interval i  1 are known, then the operating status of INSD a at interval i can be mathematically expressed as, When INSD a is performing cooling operation, 8 1 > > > < 0 xia ¼ > 1 > > : i1 xa

if INSD a is not initialized < Tset a if Tact i1 a i1 if Tact a > Tset a þ ΔTtola

ð4:2Þ

if Tset a  Tact i1  Tset a þ ΔTtola a

When INSD a is performing heating operation, 8 1 > > > < 0 xia ¼ > 1 > > : i1 xa

if INSD a is not initialized > Tset a if Tact i1 a if Tact i1 < Tset a  ΔTtola a

ð4:3Þ

if Tset a  ΔTtola  Tact i1  Tset a a

The total demand of non-schedulable demands (NSDs) during interval i can be computed as, PiNSD ¼ PiNINSD þ PiINSD PiINSD ¼ 8 > < 0 Pia ¼ SPa > : RPa

A X

ð4:4Þ

Pia

ð4:5Þ

if xia ¼ 1 if xia ¼ 0

ð4:6Þ

a¼1

if

xia

¼1

where PiNINSD and PiINSD represent the total power demand of all NINSDs and INSDs during interval i, respectively. SPa and RPa represent the stand-by power and rated power of the INSD a, respectively.

4.3.3

Model of SDs

Let us define a set of available SDs as L and the set
of schedulable demand intervals Δ as T : The scheduling vector Sl for each SD l l 2 L ¼ ½1, 2, . . . , L in any
 Δ schedulable demand interval t t 2 T ¼ ½1, 2, . . . , T  is defined as,

4 Demand Response Frameworks for Smart Residential Buildings

  Sl ¼ s1l , s2l , . . . , stl , . . . , sTl

8l 2 L

99

ð4:7Þ

where L is the number of SDs presents in the considered building and
T denotes h ithe  maximum number of schedulable demand intervals over a day T ¼ 24  D60S . Here, DS denotes the user-defined duration of a schedulable demand interval in a minute. The elements of scheduling vector stl represent the operating status of SD l at the tth schedulable demand interval.  stl ¼

0

if SD l status is OFF

1

if SD l status is ON

8l 2 L

ð4:8Þ

In order to control the SDs flexibly, the users are suggested to share the operational information of SDs such as the initialization interval βl (interval at which the SD l is added in the process of scheduling) and dead time interval ηl (interval at which the assigned task of SD l should be finished). Further, the scheduling algorithms schedule the initialized SDs only between these two-time intervals. Consumers can utilize the existing user interface module or manual settings available in the load to update the operational information. Nowadays, the upcoming residential loads are featured with the computational intelligence to calculate the required number of intervals ωl to perform the task (computation interval for SD l) at the time of starting itself based on the preliminary conditions like the existing water level in the water tank (if the SD being a smart well pump) and inserted weight of clothes (if the SD being a smart cloth washer). The critical constraint that should be satisfied by a user during the selection of operational intervals is expressed as, ωl  ηl  βl

8l 2 L

ð4:9Þ

As discussed earlier, based upon the working nature, the SDs are further classified into NISDs and ISDs. This classification is distinguished by user-defined preemptive status. Let ζ l represents the preemptive status of SD l and the value is set as,  ζl ¼

0

for preemptive loads ðISDsÞ

1 for non  preemptive loads ðNISDsÞ

ð4:10Þ

The aggregated demand of all SDs over an interval t is calculated as, PtSD ¼

L  X l¼1

where RPldenotes the power rating of SD l.

stl :RPl



ð4:11Þ

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4.3.4

Model of Battery

The energy storage devices help the residents by supporting the essential demand during peak intervals and avoiding the utility power interruption. Considering different energy storage methods, the residential consumers are mostly interested in battery storage techniques. The battery storage significantly reduces the consumers’ electricity bill by discharging the stored energy during high price intervals. In addition to this, charging the battery during less price intervals may reduce the bill further. Hence, the mathematical modelling of battery is important for optimal controlling of battery operation. The controllable parameters of battery for effective modelling are battery operating mode (charging mode/floating mode/discharging mode) and value of battery power exchange. In general, the battery is taken as an additional schedulable demand during charging mode whereas it is assumed to be an additional resource during the discharging mode. Let us represent the set of battery scheduling intervals as K . The  k operating vector which represents the battery operating mode (charging mode Bc , floating mode
  k k and discharging mode Bd ) during battery scheduling interval Bf
 Δ k k 2 K ¼ ½1, 2, . . . , K  is defined as,   B ¼ B1 , B2 , . . . , Bk , . . . , BK
 Bk ¼ Bkc , Bkf , Bkd

ð4:12Þ ð4:13Þ

where K represents
h i the maximum number of battery scheduling intervals over a day K ¼ 24  D60B . Here, DB is the time period of a battery scheduling interval in a minute set by the user as per the suggestions obtained from the manufacturer for extending the battery life. Each component of the battery operating vector in battery interval k is given as,


Bk ¼ Bkc , Bkf , Bkd



8 > < ð1, 0, 0Þ if battery in charging mode ¼ ð0, 1, 0Þ if battery in floating mode > : ð0, 0, 1Þ if battery in Discharging mode

ð4:14Þ

In order to optimally assign the battery controllable parameters, the state of charge (SoC) of battery is considered. The SoC expresses the remaining charge level of the battery which is related with battery capacity. At the starting of any battery scheduling interval k, existing SoC (SoCk) can be computed as,  k


PB DB 1 SoC k ¼ SoC k1 þ σ Bat  Capmax V Bus 60

ð4:15Þ

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where σ Bat is the battery round trip efficiency, VBus is the voltage of the DC bus in which battery is connected and Capmax is the rated capacity of battery  k  in Amperehour. In each battery interval k, the power exchange of the battery PB is calculated     with the help of charging PkBC and discharging PkBD power of battery during that particular interval. It is mathematically expressed as,
  PkB ¼ 1  Bkf Bkc  PkBC  Bkd  PkBD

4.3.5

ð4:16Þ

Model of RER

Nowadays, the interest on in-house renewable energy resources (RER)-based power generations is getting increased by the residential consumers to reduce their grid dependency. However, the generation of electric power from RER is highly irregular and site-dependent. Among various RER power generation approaches, the residential users are mostly preferred to install rooftop solar PV and small wind turbinebased power generation. In order to consider the dynamics in power generation from RER, the mathematical modelling of these components is essential. The amount of power generation from a solar PV is highly influenced by available solar irradiation and atmospheric temperature. The mathematical expression for solar PV power generation is expressed as, PSj

¼ f PV PSTC

!    GAj  1 þ T Cj  TSTC  C T GSTC

T Cj ¼ T Aj þ




 NOCT  20  GAj 0:8

ð4:17Þ ð4:18Þ

where fPV is the solar PV panel de-rating factor, PSTC is the nominal PV array power j in kW under standard test condition (STC), GAvg is the averaged solar irradiation
 Δ during RER interval j j 2 J ¼ ½1, 2, . . . , J  in kW/m2. Here, J is maximum
h i number of RER intervals over a day J ¼ 24  D60 and DRER is the time period RER of an RER interval in a minute set by the consumer as per the required accuracy. GSTC is the rated solar irradiation under STC (1 kW/m2), T Cj is the solar PV cell temperature during RER interval j in oC, TSTC is the rated temperature at STC in oC, CT is the temperature coefficient of solar panel, NOCT is the normal operating cell j temperature in oC and T Avg is the ambient temperature averaged over an RER o interval j in C. The power generation from the wind turbine is merely influenced by the wind speed and is calculated as,

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 3 PWj ¼ 0:5ρAw ν j Cp

ð4:19Þ

where PWj is the power generated (kW) by wind turbine in RER interval j, ρ is the air density (kg/m3), Aw is the swept area (m2), ν j is the averaged wind velocity (m/s) during the RER interval j and Cp is the power coefficient. The aggregated power generated from RER during interval j is the sum of power generated by in-house solar PV and wind turbine during that particular interval and it is expressed as, j PRER ¼ PSj þ PWj

4.4

ð4:20Þ

IRLMS with Priority-Based Load Scheduling Algorithm for Demand Response

Demand response schemes give the significant economic benefits to end user and reduce the utilities operational difficulties in smart grid environment. As part of demand side management techniques, utilities adopt various pricing schemes to have control on energy consumption at consumer premises. A very few utilities still follow the flat rate tariff scheme in which the electricity price is fixed and equal for all intervals. However, such utilities impose a time-dependent maximum demand limit (MDL) to bring the system load curve nearly flat. Further, the penalty also imposed if the user consumes beyond the defined MDL. In demand response schemes, the residents are expected to schedule their appliances and maintain their total power consumption under utility MDL to attain minimum electricity bill. However, this scheduling of appliances should be done with due consideration to well-being of consumer, dynamics in the operational behaviour of consumer and utility. Manual demand response in which the user has to analyse all the operational dynamics and then operate the appliances is highly inefficient and brings more practical difficulties. In order to support the user in the most economical way, a dedicated intelligent demand response system has to be installed in the residential buildings. It requires the ability to study the dynamics in consumer behaviour and utility operations. Further, this system is expected to take the decision on controlling of household appliances by its own. One such system is the intelligent residential load management system (IRLMS). The architecture of IRLMS is shown in Fig. 4.1. IRLMS includes smart NINSD module, smart INSD module and smart SD module to enable the exchange of data and operational instructions between the IRLMS processing unit and NINSDs, INSDs and SDs, respectively. The IRLMS obtains the instant updates of utility parameters such as maximum demand limit and other incentives with the help of smart meter interface. Generally, utilities and smart meters which are installed in the residential building are preferred with advance communication facility to send and receive the information. Smart NINSD module combines all running NINSDs power demands and display the warning indication

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Fig. 4.1 Architecture of IRLMS

when the aggregated demand of all NINSDs crosses the user pre-defined value. The operational parameters of INSDs such as user set point temperature, manufacturer tolerance limit, power consumption during RUN and STAND-BY mode and user operating status (switch ON/OFF) of all the INSDs are gathered by smart INSD module. This module also delivers the control instructions such as RUN or STANDBY suggested by the processing unit of IRLMS to individual INSDs. The smart SD module gets the load operational parameters such as initialization interval (βl), dead interval (ηl), computation intervals (ωl) and preemptive status (ζ l) from all SDs and conveys the control instructions to all SDs as per the suggestion received from the processing unit of IRLMS. The user interface module reads the user pre-defined limits such as allowable power consumption limit for NINSDs and INSDs extended tolerance limits. Further, this interface module can be utilized to indicate the warning signs and other power consumption details such as electricity bill, penalty payment, expected power consumptions. Based on the operating nature, IRLMS controls/schedules the operation of household appliances. The full-time

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horizon of IRLMS (24 hours) is distributed as schedulable demand interval duration DS and non-schedulable demand interval duration DNS decided by the consumers as per dynamics in their demand pattern. Typically, DNS is chosen to have a lesser value in order to study the practical variations of NSDs. IRLMS will be operated efficiently when the choice of various intervals durations fulfil the constraint as expressed in (4.21). DNS  DS

4.4.1

ð4:21Þ

Controlling of NINSDs

Since the operational dynamics of all NINSDs predominantly associated with the consumer’s requirement and comfort, the IRLMS does not have any influence on these loads. However, the IRLMS is featured to show a warning sign when the aggregated power consumption of all running NINSDs crosses the consumer set limit.

4.4.2

Controlling of INSDs

IRLMS offers an additional control on INSDs by considering the present operating status of individual INSDs and variations in temperature. The IRLMS extend the tolerance limit of INSDs which is pre-fixed by the manufacturer to a user set value. However, user can fix this extended limit to be zero if he/she gives the least importance to electricity bill and requires more comfort. If the actual temperature  of the working environment Tact ia varies in between the manufacturer tolerance limit (ΔTtola) and the user set extended tolerance limit (ΔTela), then the IRLMS takes a decision to either turn ON the INSD or continue in STAND-BY mode on basis of the utility MDL. If the number of working INSDs are more, IRLMS schedules them according to individual priority. The priority of INSD a is expressed as, If INSD a is performing cooling operation,

Priia

¼

8 > > < > > :

( 0

if INSD a is not yet initialized Tact i1 < Tset a a

1  ΔPriia otherwise

ð4:22Þ

4 Demand Response Frameworks for Smart Residential Buildings

ΔPriia ¼

Tset a þ ΔTtola þ ΔTela  Tact ia ΔTtola þ ΔTela

105

ð4:23Þ

If INSD a is performing heating operation,

Priia ¼

8 < if INSD a is not yet initialized

8 > > > < > > > :

0

:

> Tset a Tact i1 a

ð4:24Þ

1 þ ΔPriia otherwise

ΔPriia ¼

Tact ia  ðTset a þ ΔTtola þ ΔTela Þ ΔTtola þ ΔTela

ð4:25Þ

The operation of INSDs is instantly scheduled by IRLMS when the corresponding INSDs priority is greater than or equal to 1. In order to decrease the additional payment, IRLMS considers the utility MDL and operates the other INSDs in the descending order of priority. If the total residential demand goes beyond the utility MDL, the working of least priority INSDs is moved to forthcoming intervals.

4.4.3

Scheduling of SDs

Based on the utility electricity pricing scheme, the processing unit of IRLMS may adopt relevant scheduling technique. One such appropriate technique for consumers under flat rate tariff scheme is priority-based scheduling algorithm (PSA). The PSA technique decreases the excess payment by keeping the total residential demand under utility MDL. In PSA technique, the SDs are scheduled on basis of their priority. Considering the operational information of SDs such as dead time interval and computational interval, the priority of an initialized SD for the given interval will be calculated by the IRLMS processing unit. Let us denote the priority set of SD l as Γl and it is expressed as,  Δ  Γl ¼ γ 1l , γ 2l , . . . , γ tl , . . . , γ Tl

8l 2 L

ð4:26Þ

where γ tl represents the priority of SD l in interval t and it is computed as, γ tl ¼

t ηl  λtl þ 1

ð4:27Þ

where λtl represents the number of remaining intervals needed to finish the work of SD l from the scheduling interval t. The value of priority of any SD differs only between 0 and 1. Conversely, the value of priority of any SD will be 0 before its initialization and after dead time intervals as shown in (4.28).

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γ tl ¼ 0

if

t < βl

or

t > ηl

ð4:28Þ

The working of PSA technique is as follows: During starting of each schedulable demand interval t, the value of priority of all SDs ðl 2 L Þ will be computed using (4.27) and arranged in descending order by IRLMS processing unit. However, NISD with preemptive status (ζ l) 1 which is scheduled in last schedulable demand interval (t  1) and yet to finish the assigned task, will remain to have the value of priority equal to 1. Further, the IRLMS immediately schedule the SDs which are having the value of priority equal to 1 without considering any constraints imposed by the utility like MDL as shown in (4.29). stl ¼ 1;

if

γ tl ¼ 1

ð4:29Þ

The next highest priority of SD will be operated in the particular interval if the total power demand including this new SD is maintained under utility MDL. Subsequently, IRLMS schedules the next high  priority SDs by considering the MDL constraint. The power availability PtEP for scheduling new SD during a scheduling interval t is computed as, PtEP ¼ PtMDL  PtNINSD  PtINSD  PtSD

ð4:30Þ

where PtNINSD represents the expected demand of NINSDs and PtINSD represents the expected demand of INSDs during scheduling interval t. PtSD is the IRLMS reserved power for SDs operation in scheduling interval t. PtMDL is the utility defined MDL for interval t.

4.5

IRLMS with Optimization-Based Scheduling Algorithm for Demand Response

Nowadays, the utilities are interested in different pricing techniques in order to improve the profit and generation-demand balance. One such pricing scheme is real-time pricing (RTP) in which the electricity price is continuously varying for all intervals and the price details are known only just before the interval begins. In RTP, the utilities are maintaining the load curve nearly flat by applying higher electricity price during the peak demand period. When the RTP scheme is followed by utility, the priority-based scheduling algorithm will take more time to complete the computation because the processing unit of IRLMS has to find the number of combinations of initialized SDs which are obeying the utility MDL constraint in all the scheduling intervals. Among various combinations, the one which attains the minimum electricity price has to be considered. This procedure has to be repeated in every scheduling interval and hence it leads to an enumeration method of minimization. When many SDs are initialized then the number of combinations will

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107

increase enormously which leads to have large computation time for the process of scheduling SDs. Hence, the PSA is not an appropriate technique for residents under RTP scheme. In order to overcome this difficulty, the residents under RTP scheme may prefer another technique termed as optimization-based scheduling algorithm (OSA). The processing unit of IRLMS depicted in Fig. 4.1 has been upgraded by incorporating the OSA technique. The IRLMS obtains the timely updates of utility variations such as MDL and electricity price with the help of smart meter interface. However, the functioning of all other modules of IRLMS is same as discussed earlier. The full-time horizon of IRLMS (24 hours) is distributed as utility-defined pricing interval duration DP, user-defined schedulable demand interval duration DS and non-schedulable demand interval duration DNS. IRLMS with OSA technique will be operated efficiently when the choice of various interval durations fulfil the constraint as expressed in (4.31). DNS  DS  DP

ð4:31Þ

The electricity price for a specific pricing interval is fixed by the utility. Considering the price detail of the current interval and past data, the electricity price of upcoming intervals will be predicted with the help of artificial intelligent techniques. The NINSDs are operated based upon end users availability and their requirements. Hence, IRLMS does not have any influence on the operation of NINSDs. However, IRLMS controls the operation of INSDs by considering the user-defined extended tolerance limit. As discussed earlier, the operating status of INSDs (RUN/STAND-BY) for any non-schedulable demand interval is decided by the processing unit of IRLMS while considering the priority of INSDs and utilitydefined MDL. Further, considering the impact of NSDs while scheduling the operation of SDs leads further reduction in electricity bill and hence IRLMS reserves the estimated power consumption of NSDs for all intervals. In order to reduce the consumer electricity bill, the OSA technique shifts the operation of SDs from peak intervals (having a high-energy price and less MDL) to non-peak or mid-peak intervals (having a moderate energy price and sufficient MDL). Hence, the objective of the IRLMS is to minimize the consumer electricity bill subject to various hard and soft constraints.

4.5.1

Optimization Based Scheduling Algorithm

When the utility prefers RTP, an electricity subscriber may try to schedule the SDs at low price intervals to reduce the electricity bill. Therefore, the goal of IRLMS is not only keeping the total residential demand under utility demand limit but also minimizing the subscribers’ electricity bill. Let H represents the dynamic set of intervals and the elements of H will differ dynamically based upon the current

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scheduling interval (t) and the number of SDs which are initialized but not finished its task. H ¼ ½t, t þ 1, . . . , t md 

ð4:32Þ

where tmd represents the maximum of the dead time intervals of initialized and not yet completed SDs. Considering the residential demand and different price functions of utility, the fitness function for the OSA technique can be mathematically expressed as, ! X   h h min ℂU E tot 8h 2 H   ℂhU E htot ¼

(

ð4:33Þ

h

C hB  PhNet  Δh   ChB  PhMDL  Δh þ ChP  PhNet  PhMDL  Δh

if PhNet < PhMDL if PhNet  PhMDL ð4:34Þ

Δh ¼

DS 60

ð4:35Þ

PhNet ¼ PhNINSD þ PhINSD þ PhSD

ð4:36Þ

where h is the schedulable demand interval and an element of H. Further, E htot represents the aggregated energy consumption by all household appliances during a scheduling interval h. ℂhU is the price function of the utility. ChB and C hP are utility fixed base and penalty unit prices, respectively. The objective function defined in (4.33) is subjected to the following constraints:

4.5.1.1

Demand Scheduling Constraint

The component of the scheduling vector (stl) which expresses the functional status of SD l should be OFF during the interval t, if the interval t is not existed in user predefined schedulable load time span [βl,ηl]. This limitation is considered as hard constraint and mathematically expressed as, stl ¼ 0; t < βl ,

8l 2 L

stl ¼ 0; t > ηl ,

8l 2 L

ð4:37Þ

4 Demand Response Frameworks for Smart Residential Buildings

4.5.1.2

109

Load Computational Constraint

IRLMS must schedule the SDs only for the fixed number of scheduling intervals. The number of intervals that SD l be in RUN mode calculated during tth scheduling interval must be equal to the remaining number of intervals needed to finish the work by SL l from tth scheduling interval. This hard constraint is given in (4.38). Xηl

sb b¼t l

4.5.1.3

¼ λbl

8l 2 L

ð4:38Þ

Preemptive Constraint

As discussed earlier, based on the operation, the SDs are divided into two types. The NISDs are non-pre-emptible and they have to run continuously without any interruption till the end of task. Therefore, the total number of computational intervals (ωl) of NISD l must be reserved continuously within [βl,ηl] if the NISD l is scheduled to start. On the other hand, the operation of ISDs is preemptable and hence the ωl number of intervals for any ISD l may be reserved either uninterruptedly or in a discrete manner within [βl,ηl]. This constraint is expressed as a hard constraint as shown in (4.39).

4.5.1.4

υ1 X

βl þω l þθ1 Y

θ¼0

μ¼βl þθ

sμl ζ l ¼ ζ l

ð4:39Þ

υ ¼ ηl  β l  ω l þ 2

ð4:40Þ

Demand Constraint

The aggregated power consumption by all household of any scheduling  appliances  interval (t) shall be kept within the utility MDL PtMDL to reduce the additional payment. Hence, IRLMS schedules the operation SDs with due consideration to expected demand of NINSDs and INSDs. However, keeping the total demand of the consumer under MDL for all schedulable demand interval may occasionally disturb the well-being of the user. Hence, the demand constraint is considered as a soft constraint and mathematically expressed as,

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9 PtNINSD þ PtINSD þ PtSD  PtMDL > > > > > 1  tþ1  > tþ1 > > P  P MDL > > Rtþ1 SD = > ⋮> > > > > > > >   1 t md t md > ; P  P MDL t md SD R

ð4:41Þ

where Rt represents the reservation factor for non-schedulable loads at tth scheduling interval. The estimated NINSDs demand for the current schedulable demand interval (t) is assumed to be that of last non-schedulable interval (i  1) demand. The estimated INSDs demand for the current schedulable demand interval is calculated by processing unit of IRLMS with the help of building thermal dynamics. Further, IRLMS may fix a non-optimal schedule if the impact of non-schedulable demands is not accounted properly or considering the similar non-schedulable demand pattern for all upcoming schedulable demand intervals. Therefore, a reservation factor is adopted to keep a part of utility MDL for supporting the operational dynamics of non-schedulable demands. Generally, this reservation factor differs between 0 and 1. Further, the user has provision to assign the value of this factor by considering the past power consumption pattern or the personal requirement and desire. An electricity subscriber has to pay only the base electricity bill if the aggregated demand of all household appliances is retained under utility MDL. However, the subscriber is expected to pay the penalty, if the total residential demand crosses the utility MDL. Practically, the penalty payments significantly increases the user electricity bill. Further, preserving the total residential demand under utility MDL is not a mandatory task. The practical situations in which the total power consumption exceeds utility MDL are enumerated as (1) the SDs are uninterruptable over a schedulable demand interval (t) if they scheduled to start. Unexpected variations in the working pattern of non-schedulable demands may raise the total demand over the utility MDL; (2) if many SDs are included in the scheduling process and the number of intervals between the initialization interval and dead time intervals is less, then more number of SDs are scheduled to start simultaneously.

4.6

IRLMS for Demand Response in Buildings with Energy Storage Devices

In RTP environment, the residential users plan most of their demands during less price intervals without/with fulfilling their comfort so as to minimize the electricity bill. On the other hand, energy storage devices are greatly preferred to supply the consumers’ critical demands during high price intervals. Considering different energy storage methods, residential consumers mostly choose the battery storage

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111

techniques due to its operational flexibility and affordable investment cost. If the operating modes of battery such as discharging mode and charging mode are optimally time scheduled, then the electricity bill of the end user may reduce considerably without deteriorating the battery life. This necessitates to extend the use of IRLMS for scheduling battery. Hence, the functioning of IRLMS is extended to schedule the battery operational parameters such as battery operating mode (charging mode/floating mode/discharging mode) and value of battery power exchange by considering the dynamics in user demand pattern and utility factors such as MDL and electricity price. In addition to different kinds of household appliances (NINSDs, INSDs and SDs), the residential building is assumed to be included with battery banks to reduce the peak demand. In order to incorporate the battery into optimal scheduling, the IRLMS presented in Fig. 4.1 is modified as seen in Fig. 4.2. The functioning of smart NINSD module, smart INSD module, smart SD module and user interface module of modified IRLMS are similar with standard IRLMS. The smart battery module gathers the battery operational specifications like Ampere-hour rating, boundary limits of discharging current and charging current through user interface module. This battery module also calculates the current battery State of Charge (SoCk) using

Fig. 4.2 Architecture of modified IRLMS

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(4.15). The computed SoCk information is conveyed to the processing unit of IRLMS for optimal scheduling of battery operation. IRLMS coveys the battery operational instructions such as the operating mode and value of power exchange to the power conditioning unit with the help of smart battery module. The operation of battery will be controlled by the power conditioning unit.

4.6.1

Functioning of Modified IRLMS

The aim of the modified IRLMS is to attain more reduction in electricity bill without affecting the well-being of user. The full-time horizon of IRLMS is distributed into various intervals such as non-schedulable demand intervals, schedulable demand intervals, battery scheduling intervals and pricing intervals. The user can select the duration of battery scheduling interval DB and this selection generally depends upon the manufacturer suggestions on the continuous operation of battery. In each battery interval (k), IRLMS fixes the battery operating mode and value of battery power exchange. The modified IRLMS will be operated efficiently when the choice of various intervals durations fulfil the constraint as expressed in (4.42). DNS  DB  DS  DP

ð4:42Þ

As discussed earlier, IRLMS does not control the operation of NINSDs because it affects the comfort of the consumer. Succinctly, the IRLMS offers the optimal values of decision variables: xia ,stl ,Bk, PkBC and PkBD , for all intervals so as to decrease the electricity bill. The steps associated in the functioning of IRLMS for the battery equipped residential building are described as a flowchart in Fig. 4.3. The IRLMS schedules the operation of SDs and battery by employing OSA technique. At the time of scheduling, IRLMS assumes the battery as an additional schedulable demand during charging mode whereas it is assumed to be an additional resource during discharging mode. In order to include the battery into IRLMS scheduling process, the objective function defined in (4.33) is modified as expressed in (4.43). X   min ℂhU E htot ℂhU



Ehtot



( ¼

! 8h 2 H

ð4:43Þ

h

ChB  PhNet  Δh   ChB  PhMDL  Δh þ ChP  PhNet  PhMDL  Δh

if PhNet < PhMDL if PhNet  PhMDL ð4:44Þ

4 Demand Response Frameworks for Smart Residential Buildings

Fig. 4.3 IRLMS functional flowchart

113

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S. L. Arun and M. P. Selvan

Δh ¼

DS 60

ð4:45Þ

PhNet ¼ PhNINSD þ PhINSD þ PhSD þ PhB

ð4:46Þ

where PhNet and PhB are the net demand and net battery power during interval h, respectively. This fitness function of IRLMS is subjected to various soft and hard constraints. The operating constraints of SDs such as SDs can be scheduled only between the user-defined load initialization and dead time intervals (demand scheduling constraint), SDs should be planned only for a fixed number of computational intervals to finish the particular task (load computational constraint) and NISDs should operate continuously (preemptive constraint) are considered as hard constraints.

4.6.1.1

Demand Constraint

The total demand of the consumer which is the sum of total household demand and net power exchange of battery during any interval shall be within the utility-defined MDL. The mathematical representation of this soft constraint is expressed as. 9 PtNINSDs þ PtINSDs þ PtSDs þ PtB  PtMDL > > > > >  1  tþ1 > tþ1 tþ1 > >  P P þ P SDs B MDL > tþ1 > R =

1  tmd P Rtmd SDs

4.6.1.2

> ⋮> > > > > > > >  t md t md > ; P þP B

ð4:47Þ

MDL

Battery Operating Mode Constraint

The operating mode of the battery for any schedulable demand interval must be unique (charging mode/floating mode/discharging mode). This operational constraints is mathematically formulated as hard constraint and expressed as, Btc þ Btf þ Btd ¼ 1

ð4:48Þ

4 Demand Response Frameworks for Smart Residential Buildings

4.6.1.3

115

Battery Boundary Constraint

The battery operational parameters such as state of charge, value of power exchange during discharging and charging modes must be maintained between their minimum and maximum limits decided by the manufacturer for preserving the battery life. These hard boundary constraints are expressed as, SoC min  SoC t  SoC max PBCmin 

ð4:49Þ

 PBCmax

ð4:50Þ

PBDmin  PtBD  PBDmax

ð4:51Þ

PtBC

where SoCt is the existing battery SoC at the starting of schedulable demand interval t, SoCmin and SoCmax are the boundary limits for battery SoC. PBCmin and PBCmax are the boundary limits for charging power of battery. PBDmin and PBDmax are the boundary limits for discharging power of battery.

4.6.2

Controlling the Operation of Battery

If the time period of schedulable demand interval and battery interval is same then the power conditioning unit controls the battery operations such as operating mode and value of power exchange as per suggestion conveyed by IRLMS scheduling algorithm. When the interval durations are not similar then rescheduling of the battery operation within a schedulable demand interval will lead further decrement in electricity bill. However, this rescheduling of battery must be performed with due consideration to changes in NSDs demand pattern because the NSDs power demand is not constant for the entire schedulable demand interval. For instance, during a schedulable demand interval, consider that the scheduling algorithm of IRLMS selects the battery operating mode as charging mode but due to sudden increment in the number of users, the aggregated demand of all NSDs rises. In this condition, if the battery prefers the operating mode as charging for the complete schedulable demand interval then the total demand of the building may cross the utility MDL which lead to pay a high penalty. Hence, the operating mode of battery can be rescheduled within a schedulable load interval. On the other hand, operation of battery cannot be rescheduled in each non-schedulable demand interval which may have a severe impact on battery life. In order to extend the battery life, the battery rescheduling will be done by IRLMS only in every battery scheduling interval (k,  k where i < k < t) with due consideration to the total demand P and battery SoC EBAT  k  limitation. Here, PEBAT is the total residential demand excluding the scheduled battery power exchange during interval k. As a part of the flowchart representing the functioning of IRLMS shown in Fig. 4.3, the steps included in the battery rescheduling are given in Fig. 4.4.

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Fig. 4.4 IRLMS battery rescheduling sub-flowchart

4.7

IREMS for Demand Response in Buildings with Renewable Energy Resources

As the electricity demand of the nation is increasing at faster rate, the service providers give more attention to renewable energy resources (RER) to fulfil the generation and demand balance. It is anticipated that the incorporation of RER power generation will have a significant impact on dynamics in energy pricing. Though the RER-based distributed generation provides considerable economic benefits in transmission and distribution systems [18], the power system operators

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117

are facing additional operational challenges due to the intermittent nature of RER power production. In addition to the large-scale RER power generation projects, the government motivates the end consumers to install compact in-house RER power generation systems to support the residential demand either fully or partially. Further, the consumer can avail more decrement in electricity bill if the operation of residential demands optimally planned when RER generates more power [19]. In addition to this, the end consumers are motivated to trade their surplus power generation with the grid at utility preferred price [20]. These kinds of residential consumers are called as prosumers. A residential consumer may fix the capacity of RER by considering the demand pattern of the building, space availability and capital investment. Among various types of RER, residential buildings mostly prefer in-house solar PV and small rated wind turbine-based power generation systems. In order to include the RER components in the process of scheduling, the IRLMS has been upgraded as intelligent residential energy management system (IREMS).

4.7.1

Architecture of IREMS

The IREMS architecture is depicted in Fig. 4.5. IREMS contains smart NINSD module, smart INSD module, smart SD module, smart power converter module to realize the transfer of data and instruction signals between IREMS processing unit and NINSDs, INSDs, SDs and power conditioning unit of RER, respectively. Further, it also consists of a smart meter interface module and a user interface module to communicate with the grid and the consumer, receptively. The functioning of smart NINSD module, smart INSD module, smart SD module and user interface in the IREMS is same as IRLMS. However, the working of smart power converter module is advanced to convey the information about RER power generation to IREMS processing unit. With the help of the real time and the past data [21], the RER power generation for upcoming intervals will be forecasted by IREMS processing unit. Using the present and expected RER power availability, IREMS optimally time schedules the operation of various household appliances and battery.

4.7.2

Functioning of IREMS

The main aim of IREMS is to direct the consumers in decreasing the electricity bill by optimally scheduling the operation of household appliances and battery banks with due considerations to the available renewable power generation and variation in utility parameters such as MDL and electricity price. The full-time horizon of IREMS (24 hours) is distributed into schedulable demand interval duration (DS), non-schedulable demand interval duration (DNS), battery scheduling duration (DB),

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Fig. 4.5 Architecture of IREMS

RER interval duration (DRER) and pricing interval duration (DP). The effective operation of IREMS can be achieved only when the duration of various intervals follow the relation as expressed in (4.52) DRER  DNS  DB  DS  DP

ð4:52Þ

As discussed earlier, the operation of NINSDs is not controlled by IREMS due to consumer comfort. However, the INSDs are controlled by IREMS by considering the user-defined extended tolerance limit. Succinctly, the IREMS suggests the optimal values of decision variables: xia , stl , Bk, PkBC and PkBD for all the schedulable demand intervals so as to attain the significant reduction in consumer electricity bill. The basic steps associated in the functioning of IREMS is shown in Fig. 4.6 as a flowchart.

4 Demand Response Frameworks for Smart Residential Buildings

Fig. 4.6 IREMS functional flowchart

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4.7.3

Scheduling of SDs

The goal of IREMS is not only maximizing the utilization of RER but also minimizing the electricity bill. In order to consider the impact of RER, the objective function defined in (4.43) has been modified as, min

X

ℂhU



Ehtot



! 8h 2 H

ð4:53Þ

h

8 if PhNet > 0 > > > > > C hB  PhNet  Δh > > > ) > h > > CB  PhMDL  Δhþ >   <   ℂhU E htot ¼ ChP  PhNet  PhMDL  Δh > > > > if PhNet  0 > > > > > C hE  PhNet  Δh > > >   : C hE  PhEGmax  Δh

if 0 < PhNet < PhMDL if PhNet  PhMDL

ð4:54Þ

if 0 > > > >  1  tþ1 > tþ1 tþ1 tþ1 > > P þ P  P  P B RER MDL > > Rtþ1 SD =

1  tmd md P þ PtBmd  PtRER Rtmd SD

4.7.3.2

> ⋮> > > > > > > >  t md > ; P

ð4:56Þ

MDL

Power Injection Constraint

In the present scenario, the utilities are facing additional operational difficulties due to the large penetration of grid-connected in-house RER power generation systems. Hence, the service providers are expected to impose a time-dependent power injection limit (PIL) for prosumers [22]. The prosumers can be profited by exporting their surplus power to grid within utility PIL. RER power generation beyond utility PIL shall be either transferred to battery banks for future usage or dissipated with the help of dump loads. When the difference between power generation from RER and the aggregated demand of all household appliances exceeds the utility PIL, the IREMS processing unit suggests the power conditioning unit either to increase the power dissipation through dump load or to reduce the power extraction from RER. This perspective introduces a new constraint such as surplus power injected to grid t P EG  t atany scheduling interval t must be within the utility-defined maximum value PEGmax .

PtEG

4.7.4

PtEG  PtEGmax   ¼ PtRER  PtNINSD þ PtINSD þ PtSD þ PtB

ð4:57Þ ð4:58Þ

Scheduling of Battery

The scheduling algorithm of IREMS schedules the battery operating mode and value of power exchange in a schedulable demand interval by minimizing the fitness function defined in (4.53). The operation of battery is controlled by IREMS by conveying the scheduling instructions to the power conditioning unit. If the duration of battery scheduling interval and schedulable demand interval is same then the operating mode and power exchange of battery are fixed for the entire duration of schedulable demand interval. Else, the IREMS reschedules the operation of battery while considering the last interval operating mode, net residential demand, dump

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load power dissipation and SoC limitation. The rescheduling of battery operation is mathematically formulated as, 8 > > > > > > > > > > > > > > > > > > > > > > > > > > > > > >
PEGmax if

:

SoCk < SoC max

8 8 k1 k1 > > < PEG > PEGmax > > > > > > >: > > > > SoC k  SoCmax > > > > <
 Bkc , Bkf , Bkd ¼ ð0, 1, 0Þ; or if > > > > > > > > 8 > > > > k1 k1 > > > > < PNet > PMDL > >> > > > > > > > > > > > > > :> : > > SoCk  SoC min > > > > 8 > k1 k1 > > < PNet > PMDL > > > > ð0, 0, 1Þ; if > : : SoC k > SoCmin

ð4:59Þ

wher Pk1 EG is the RER power generation beyond the resident own demand for the battery scheduling interval (k  1) and Pk1 Net represents the net demand which combines the power demand of NINSDs, INSDs and SDs, the battery power exchange and the RER power generation for the battery scheduling interval (k  1).

4.8

Case Study

The case study demonstrates the demand response frameworks for handling residential demands and analyses the reduction in consumer’s electricity bill. The residential building considered for this study is presumed to be consisting of different household appliances. On basis of the operating nature, these appliances are classified into NINSDs, INSDs and SDs. The detailed information about operational timings and power rating of NINSDs, INSDs and SDs are listed in Tables 4.1, 4.2 and 4.3, respectively [23]. Nowadays, most of the residential buildings are equipped with energy storage devices to reduce the power consumption during peak intervals. Considering different types of energy storage devices, lead acid types of batteries are usually preferred by residential consumers due to its attractive features such as adequate initial investment and maintenance cost, market availability, better operational characteristics and longer life time [24]. The operational specifications of the battery which is installed in the considered residential building are listed in Table 4.4.

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Table 4.1 Non-interruptible and non-schedulable demands (NINSDs) S. No. 1

Appliances Fan

Rated power (kW) 0.10

2

Fluorescent lamp

0.04

3

CFL

0.02

4

Television (TV)

0.25

5

Laptop/mobile charging

0.05

Operating hours (h) 00.00–06.00 06.00–09.00 17.00–21.00 21.00–24.00 05.00–07.00 18.00–22.00 00.00–05.00 05.00–07.00 18.00–22.00 22.00–24.00 06.00–08.00 17.00–22.00 06.00–08.00 17.00–19.00

Quantity 4 2 2 4 3 6 4 8 8 4 1 1 2 2

Table 4.2 Interruptible and non-schedulable demands (INSDs) S. No. 1

Appliances Air conditioner (AC)

Rated power (kW) 1.0

2

Water heater

2.0

2

Refrigerator

0.5

Operating hours (h) 00.00–05.00 17.00–19.00 21.00–24.00 06.00–09.00 18.00–22.00 00.00–24.00

Table 4.3 Schedulable demands (SDs) S. No. 1 2 3

Load Cloth washer Cloth dryer Dish washer

Power (kW) 0.8 2.2 1.5

ζl 1 1 0

4

Well pump

1.2

0

5

Electrical vehicle charging

2.3

0

6

Grinder

0.5

1

Manual Start (h) 16.00 18.00 08.00 17.00 21.00 05.00 17.00 05.00 21.00 17.00

Time span βl ηl 08.00 13.00 13.00 19.00 08.00 12.00 14.00 18.00 21.00 24.00 00.00 06.00 09.00 18.00 00.00 05.00 21.00 24.00 13.00 18.00

ωl 2 1 1 1 1 1 1 2 1 1

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Table 4.4 Specifications of battery S. No. 1 2 3 4 5 6 7 8

Specifications Number of batteries Capacity (ah) DC voltage (V) Efficiency during charging (%) Efficiency during discharging (%) State of charge boundary limit (%) Current limit during charging Current limit during discharging

Rating 4 in series 75 12 85% 95% (30–90)% (5–20)% of nominal capacity (0–20)% of nominal capacity

Table 4.5 Specifications of solar PV panels S. No. 1 2 3 4 5 6 7

Specifications Number of PV panels De-rating factor STC power (kW) STC irradiation (kW/m2) STC temperature ( C) Temperature coefficient NOCT ( C)

Rating 30 0.8 0.1 1 25 0.0011 48

Table 4.6 Specifications of wind turbine S. No. 1 2 3 4 5 6 7 8

Specifications Number of wind turbines Efficiency Rotor diameter (m) Air density (kg/m2) Rated wind speed (m/s) Rated power (kW) Cut-in wind speed (m/s) Cut-out wind speed (m/s)

Rating 2 0.4 1.8 1.2 12 1 3 25

The residential consumers are encouraged by the government with several subsidies to install small-scale in-house renewable power generation systems. This case study presumes that the considered residential building is equipped with in-house RER such as solar and wind power generation systems. Each solar PV panel and wind turbine is rated as 0.1 kW and 1 kW, respectively. The detailed specifications of solar and wind power generation systems installed in the residential building are listed in Tables 4.5 and 4.6, respectively [25]. In flat rate tariff (FRT) scheme, the utility fixed per unit energy price for a specific day is taken as 5 cents/kWh. The utility-defined MDL is considered as 4 kW and is assumed to be constant over a particular day. The consumer penalty payment for consuming beyond utility MDL is computed as 2.5 times of the normal price fixed by the utility.

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Fig. 4.7 Utility electricity price Table 4.7 Duration of various intervals S. No. 1 2 3 4 5

Interval RER interval Non-schedulable demand interval Battery operating interval Schedulable demand interval Pricing interval

Duration DRER DNS DB DS DP

1 minute 1 minute 5 minute 15 minute 60 minute

On the other hand, the utility price variation of a specific day for the consumers under real-time pricing (RTP) scheme is shown in Fig. 4.7. Further, the utility MDL is taken as 4 kW and presumed to be constant over a particular day. The additional payment for consuming beyond utility demand limit is calculated as 2.5 times of the normal price fixed by the utility. The utility assigned prosumer power inject limit is taken as 0.5 kW. Further, the utility energy buying price is assumed to be same as the utility nominal selling price. As discussed earlier, the effective operation of residential energy management systems will be anticipated only when the duration of different intervals satisfies the relation given in (4.52). Considering this limitation, the time period of different intervals have been selected and listed in Table 4.7. Further, the considered additional operational constraints of various components while performing the scheduling are as follows: the aggregated power demand of all running NSDs at a particular non-schedulable demand interval i is constant; the operation of any SD is

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Fig. 4.8 Timeline diagram for a particular day

uninterruptable during a particular schedulable demand interval t, if it is planned to operate during that interval; scheduled battery parameters such as operating mode and value of power exchange are fixed for the entire duration of battery operating interval k; the total power generation by solar PV and wind turbine systems over a particular RER interval j is assumed to be constant; during a specific pricing interval, the utility price remains unchanged. The daily timeline diagram of various intervals is depicted in Fig. 4.8. In order to attain significant economic benefits through demand response programs, the residential users are expected to adopt a suitable demand response framework. The selection of framework may be influenced by the components present in the residential building. For example, the residence equipped with smart appliances (NINSDs, INSDs and SDs) under FRT scheme may prefer IRLMS with PSA technique, the residence with smart appliances under RTP scheme may prefer IRLMS with OSA technique, the residence equipped with smart appliances and battery back-up under RTP scheme may prefer IRLMS with modified OSA technique and the residence equipped with smart appliances, battery storage and RER under RTP scheme may prefer IREMS with OSA technique. The daily electricity bill of different residential buildings which are equipped with the appropriate demand response framework is listed in Table 4.8. For better comparison, the results obtained with different demand response frameworks are compared with that of when no scheduling algorithm (NSA) is employed. In order to demonstrate the effectiveness of various frameworks, the case studies have been extended for a period of one year. In addition to the variation in consumer behaviour and utility dynamics, the sessional variations are also considered for these studies. Further, time period of all the intervals (DRER, DNS, DB, DS and DP) are assumed to be 60 minutes for the better view of results.

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Table 4.8 Daily and annual electricity bill for different demand response frameworks

Pricing scheme FRT

Components NINSDs, INSDs and SDs

RTP

NINSDs, INSDs and SDs

Framework NSA IRLMS with PSA NSA IRLMS with OSA IRLMS with modified OSA NSA IREMS with OSA

NINSDs, INSDs, SDs and battery NINSDs, INSDs, SDs, battery and RER (with utility PEL) NINSDs, INSDs, SDs, batNSA tery and RER IREMS with OSA (without utility PEL) R*- percentage of reduction in electricity bill compared to NSA

Consumer’s electricity bill Daily Annual bill R* bill R* (cents) (%) ($) (%) 511 – 2486 – 445 12.95 2247 9.60 550 – 2488 – 457 17.00 2078 16.5 457 16.9 1901 23.59 488 368

– 24.58

1877 1501

– 20

446 357

– 19.96

1872 1470

– 21.47

On occasion, utilities feeding localities with meagre penetration of residential RER do not fix any power inject constraint. Considering this situation, the IREMS framework has been realized without PIL constraint. If the utility does not impose any PIL, prosumers can improve their profit by exporting their surplus generation to the grid at a utility fixed price. Further, effective scheduling of battery may improve the profit by selling the stored energy to the utility during peak intervals. In addition to these benefits, the prosumers can reduce the initial investment of the project by avoiding the dump load. From the results of different case studies shown in Table 4.8, it can be inferred that the residential consumer may attain the substantial reduction in electricity bill by adopting suitable demand response framework. The energy consumption per day with excess payment while employing different scheduling algorithms are compared and presented in Table. 4.9. Considering the impact of NSDs while scheduling the operation of SDs helps to maintain the total demand under MDL. Due to limited control, the average demand by NINSDs and INSDs is almost same for all schedulable load intervals in both NSA and OSA techniques. However, the demand response frameworks spread the operation of SDs all over a day to reduce the electricity bill. In addition to this, the IREMS reduces the total annual energy dissipated through dump load due to utility-defined PIL from 178 kWh to 40.86 kWh, which confirms 77% improved utilization of generated renewable energy.

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Table 4.9 Daily energy consumption with excess payment for different demand response frameworks

Pricing scheme FRT

Components NINSDs, INSDs and SDs

RTP

NINSDs, INSDs and SDs NINSDs, INSDs, SDs and battery

Framework NSA IRLMS with PSA NSA IRLMS with OSA IRLMS with modified OSA NSA IREMS with OSA

NINSDs, INSDs, SDs, battery and RER (with utility PEL) NINSDs, INSDs, SDs, battery and NSA RER IREMS with OSA (without utility PEL) R*- percentage of reduction in electricity bill compared to NSA

4.9

Daily energy consumption with penalty Energy R* (kWh) (%) 14.23 – 5.92 58.38 14.23 – 6.69 53 6.84 52 9.53 2.78

– 70.82

9.98 2.65

– 73.45

Summary

In smart grid environment, end consumers are gaining more benefits through demand response techniques. Further, the utilities are also adopting various demand side management techniques to control the power consumption at end-user premises. In order to attain more incentives from utilities without sacrificing the comfort, the end-user prefers to install building energy management systems. The intelligent residential load management system (IRLMS) with priority-based scheduling algorithm effectively schedules the operation of household appliances of the consumer under a flat rate tariff scheme. The IRLMS framework considerably reduces the total cost payable to utility by keeping the total demand under utility maximum demand limit (MDL) in most of the intervals. The IRLMS with optimization-based scheduling algorithm (OSA) optimally schedules the operation of different residential demands under real-time pricing (RTP) environment and significantly decrease the consumer electricity bill. In addition to the load management, the IRLMS with modified OSA technique optimally schedules the operating mode of battery (charging mode/floating mode/discharging mode) and the value of battery power exchange. Considering the battery into the scheduling process, the IRLMS framework attains further reduction in electricity bill without hindering the life of the battery. The advanced demand response framework such as intelligent residential energy management systems (IREMS) installed in the residential buildings which are equipped with renewable energy resources, effectively schedule the household appliances during the intervals in which renewable power generation more. This scheduling of demands yields a significant reduction in electricity bill. In addition to

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this, IREMS assists the prosumers to sell their excess renewable power generation without exceeding utility power injection limit to the grid at utility desired price which increases their savings in electricity bill.

References 1. X. Fang, S. Misra, G. Xue, D. Yang, Smart grid – the new and improved power grid: a survey. IEEE Commun. Surv. Tutorials 14(4), 944–980 (2012) 2. J. Sheen, Economic profitability analysis of demand side management program. Energy Conversion Manage. 46(18–19), 2919–2935 (2005) 3. P. Palensky, D. Dietrich, Demand side management, demand response, intelligent energy systems, and smart loads. IEEE Trans. Ind. Inf. 7(3), 381–388 (2011) 4. B. Ramanathan, V. Vittal, A framework for evaluation of advanced direct load control with minimum disruption. IEEE Trans. Power Syst. 23(4), 1681–1688 (2008) 5. M.A.A. Pedrasa, T.D. Spooner, I.F. MacGill, Scheduling of demand side resources using binary particle swarm optimization. IEEE Trans. Power Syst. 24(3), 1173–1181 (2009) 6. C.H. Gomes, A.G. Antunes, A. Martins, A multiple objective approach to direct load control using an interactive evolutionary algorithm. IEEE Trans. Power Syst. 22(3), 1004–1011 (2007) 7. N. Ruiz, I. Cobelo, J. Oyarzabal, A direct load control model for virtual power plant management. IEEE Trans. Power Syst. 24(2), 959–966 (2009) 8. C. Triki, A. Violi, Dynamic pricing of electricity in retail markets. 4OR 7(1), 21–36 (2009) 9. K. Herter, Residential implementation of critical-peak pricing of electricity. Energy Policy 35 (4), 2121–2130 (2007) 10. L. Wu, M. Shahidehpour, A hybrid model for day-ahead price forecasting. IEEE Trans. Power Syst. 25(3), 1519–1530 (2010) 11. P. Centolella, The integration of price responsive demand into regional transmission organization (RTO) wholesale power markets and system operations. Energy 35(4), 1568–1574 (2010) 12. G.T. Costanzo, G. Zhu, M.F. Anjos, G. Savard, A system architecture for autonomous demand side load management in smart buildings. IEEE Trans. Smart Grid 3(4), 2157–2165 (2012) 13. J. Conejo, J.M. Morales, L. Baringo, Real-time demand response model. IEEE Trans. Smart Grid 1(3), 236–242 (2010) 14. P. Siano, Demand response and smart grids-a survey. Renew. Sust. Energ. Rev. 30, 461–478 (2014) 15. G. Strbac, Demand side management: benefits and challenges. Energy Policy 36(12), 4419–4426 (2008) 16. C.L. Su, D. Kirschen, Quantifying the effect of demand response on electricity markets. IEEE Trans. Power Syst. 24(3), 1199–1207 (2009) 17. M. Rahmani-Andebili, Scheduling deferrable appliances and energy resources of a smart home applying multi-time scale stochastic model predictive control. Sustain. Cities Soc. (Elsevier) 32, 338, –347 (2017) 18. Department of energy, [Online]. Available: http://energy.gov/oe/services/technology-develop ment/smart-grid/role-microgrids-helping-advance-nation-s-energy-system 19. M. Rahmani-Andebili, H. Shen, Price-controlled energy management of smart homes for maximizing profit of a GENCO. IEEE Transactions on System, Man, and Cybernetics: Systems, 2017 20. M. Rahmani-Andebili, H. Shen, Cooperative distributed energy scheduling for smart homes applying stochastic model predictive control. International Conference on Communications (ICC), Paris, France, May 21–25, 2017

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21. M. Rahmani-Andebili, H. Shen, Energy scheduling for a smart home applying stochastic model predictive control. IEEE International Conference on Computer Communication and Networks (ICCCN 2016), Waikoloa, Hawaii, USA, August 1–4, 2016 22. N. Liu, X. Yu, C. Wang, C. Li, L. Ma, J. Lei, Energy-sharing model with price-based demand response for microgrids of peer-to-peer prosumers. IEEE Trans. Power Syst. 32(5), 3569–3583 (2017) 23. S.L. Arun, M.P. Selvan, Dynamic demand response in smart buildings using an intelligent residential load management system. IET Generation. Transm. Distrib. 11, 4348–4357 (2017) 24. S.L. Arun, M.P. Selvan, Intelligent residential energy management system for dynamic demand response in smart buildings. IEEE Syst. J. 12(2), 1329–1340 (2018) 25. S.L. Arun, M.P. Selvan, Smart residential energy management system for demand response in buildings with energy storage devices. Front. Energy 13, 715–730 (2019)

Chapter 5

Smart Homes to Support the Wellness and Pleasurable Experience of Residents Mi Jeong Kim, Myung Eun Cho, and Han Jong Jun

Abstract This chapter introduces the design of smart homes to support residents’ wellness and pleasurable experience. Smart homes should contribute to the heathy and happy living of their occupants by incorporating various technologies and devices into a domestic setting. Further, the design of smart homes should provide occupants with pleasurable experiences by the implementation of intuitive interfaces using a user-centered approach. The success of a smart home is dependent on its occupants’ acceptance of and engagement with it in the context of daily living; thus, more specific user studies should be conducted to implement a positive technology to fulfill users’ daily needs. This chapter first identifies the main issues for the design of smart homes by critically reviewing the related research and then frames the crucial factors, emphasizing wellness and pleasurable experience for consideration in the construction of smart homes. A framework for the design of smart homes was developed by focusing on the practicability of each variable from the perspective of supporting user experience. By utilizing the framework, more customized design factors that must be considered in the creation of smart homes could be developed to target user groups with support for their health and happiness. Keywords Smart home · User-centered approach · Wellness · Pleasurable experience · Conceptual framework

M. J. Kim (*) · H. J. Jun School of Architecture, Hanyang University, Seoul, Republic of Korea e-mail: [email protected]; [email protected] M. E. Cho School of Architecture, Hanyang University, Seoul, Republic of Korea Construction Research Institute, Hanyang University, Seoul, Republic of Korea © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Rahmani-Andebili (ed.), Operation of Smart Homes, Power Systems, https://doi.org/10.1007/978-3-030-64915-9_5

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Introduction

As advanced computing, which is ubiquitous and pervasive, has been incorporated into the fabric of daily life, and our built environment has been digitalized into responsive and intelligent surroundings. Information and communication technologies (ICTs), the Internet of Things (IoT), and artificial intelligence (AI) have developed smart environments that analyze occupants’ activities and enable communication between devices, objects, and humans [1, 2]. With the impact of advanced technologies on the design of environments, architects could not avoid considering the ways of using interactive technologies as architectural elements from a design perspective. Architects now need to see the combination of design and computing as a challenge and an opportunity for conventional architectural environments. When architects work on the design of an environment, they need not only expertise about design but also knowledge of computing [3]. Significant changes caused by such advanced technologies in architecture have been centered around homes where computing permeates all spaces and supports daily living [4–6]. Initially, a smart home (also termed an intelligent home, aware home, and adaptive house) was defined as a residence that included technologies to allow devices and systems, such as lighting, appliances, and security systems, to be controlled automatically [7]. More recently, the smart home has been described as an intelligent dwelling equipped with wired or wireless networks, sensors, and devices and features that can be remotely monitored and controlled and that provide customized services for its inhabitants by being aware of people’s behaviors and needs [8, 9]. The design and implementation of smart homes have been an important research and design issue of the past few decades. There has been significant research on smart homes with a focus on smart services and technologies in the domains of architecture, engineering, and construction (AEC) [10–12]. The goal of smart homes is to improve the quality of life of residents in domestic settings by providing customized smart services, resulting in an independent and comfortable life [13]. Most studies on smart homes deal with functionality, safety, security, and physiological monitoring and consider a restricted number of settings based on communities or laboratories to demonstrate the feasibility of the proposed technological innovations [14]. Further, following advancements in technology and devices, smart home services were developed based on the understanding of residents’ attitudes, activities, and needs for their well-being and safety [15]. Many studies on smart homes have been developed for the assistance of the elderly with reduced capabilities or of people with disabilities. Smart technology has been mainly used as an important means of supplementing physical problems for independent living. Most older adults prefer to live independently, maintaining current social networks and interactions with family members and close friends in a familiar home setting. The notion of “aging in place” can be realized with technologically mediated support; thus, people suffering from various diseases and handicaps can stay in their homes without moving into care institutions, thanks to the adoption of smart homes [2, 16]. Considerable studies have shown the positive impacts of smart technology on living environments, daily activities, and independence [17, 18].

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The development of smart homes has been facilitated by technological innovation to maximize residents’ comfort. Technology developers have claimed that the adoption of advanced technologies will make our lives more comfortable, and the devices and systems of smart living environments have been tested and implemented for demonstrating their prosed ideas. Research on smart homes has explored the technical challenges of delivering smart environments to support the daily lives of residents, including the difficulties faced in energy management, security, and monitoring and detecting incidents [19, 20]. For example, Rahmani-Andebili and Shen have worked on an optimal energy scheduling for smart homes to develop effective energy management [21–23]. Various prototypes have been implemented with new technologies, such as sensors, algorithms, and intelligent devices [2, 24]. With sensors installed in daily objects, such objects become smart and can interact with mobile devices, producing an ambient intelligence in the home setting [13]. To sum up, smart homes have the potential to improve access to in-home care and to offer support for older adults and people with disabilities in familiar dwellings. Despite such potential and benefits, the rate of rejection of smart devices and systems by users remains high as a result of the attempt to introduce the technology into homes without analyzing users’ activities and needs sufficiently [25, 26]. Since the adoption of smart homes could not be successful without a systematic understanding of users, it is necessary for the design of smart homes to be conducted from a more user-oriented perspective. Smart homes should provide users with optimal and pleasurable experiences beyond the furnishing of usable and practical spaces; thus, a user-centered approach needs to be adopted to design a pleasing smart environment. Further, the beneficiaries of smart homes should not only be the elderly and the disabled but also various user groups across different generations, including women, men, and children who require technological innovation to improve their quality of life and health at home. Recent trends in smart home research have highlighted healthcare services; thus, health-related assistance has become an important goal of smart homes. This is a common concern for people of all ages who want to live healthily and comfortably in their homes; the innovative technologies of the smart home could improve their lives and potentials. The challenge is to obtain an understanding of a variety of residents, with particular interest in their needs for and use of technology. Regarding the design of smart homes, the issues that researchers or architects need to address are highlighted below: the health smart home, prospective users, and acceptance.

5.1.1

The Health Smart Home

As a variation of the smart home, Noury et al. [11] presented the concept of health smart homes, with an emphasis on healthcare for the elderly in their own homes. Aging causes physical problems and impairments that require urgent assistance from technological systems, and smart technologies can facilitate safety and the

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maintenance of health under the conditions of the smart environment. For the elderly with chronic health problems, telehealth services can reduce medical expenses and provide independent living, since the services are capable of accessing personal health information, remote patient monitoring, and chronic disease management [27, 28]. Considerable efforts have been made to produce health smart homes, wherein monitoring and identifying behavior patterns are essential. For example, home-monitoring systems, such as personal emergency response systems, fall detection systems, and physiological monitoring systems, are developed with the aim of supporting safe and independent living at home. Such technologies enable a customized residential environment that autonomously tracks and records, even when they are not controlled manually [29]. Intelligent devices—from smart phones to furniture, picture frames, and kitchen appliances—are used to motivate residents to manage their diets, take medications, or continue exercising to improve physical health [30]. Further, the comfortable, familiar environment of the smart home provides them with emotional stability and healthy mental support. Active participation in social activities and the establishment of a sense of belonging as a social member are important to prevent residents from feeling isolated; thus, smart communication technology can be used for social connectedness in smart homes [31]. Telemedicine that connects patients with clinicians is becoming common to monitor physiological signals, such as heart rate, through wearable devices that are attachable to clothing or the skin, or to manage chronic diseases at home. However, all age groups—not only the elderly or patients—are concerned with living healthily and happily; therefore, the future of smart homes is to create a healthy and intelligent living environment for an expanded range of user groups [32]. Happiness is an essential concept encompassing emotional wellness, which enriches the aging life.

5.1.2

Prospective Users

The scope of smart homes has gradually varied to enhance residents’ overall quality of life, and numerous smart technologies are designed to promote occupant wellbeing, including health applications to support physical, social, and emotional activities. Wilson and Hargreaves [33] argued that prospective users who require technological benefits will expand to a wider range in the future, and a clear understanding of who these users may be and how they might use smart home technologies should be obtained for the design of smart homes. Recently, MIT Media Lab undertook a series of projects called Advancing Wellbeing, which reorganized the living environment using technology to improve health and wellbeing. The motivation for this is that people now spend more time sitting in front of screens; hence, the activities required for physical, mental, and social health are decreasing, leading to a weakening of the “social-emotional relationship.” Demanding work increases stress, causing workers to indulge in caffeine and non-nutrientrich foods, further producing unhealthy sleep behavior. Advancing Wellbeing

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emphasized the role of technology in realizing health culture, including physical, emotional, and social well-being for the future, taking into account the needs of people. Research into smart homes needs to be extended by considering ways to improve the well-being of the middle-aged and younger groups, thus moving beyond the elderly- and patient-oriented research. That is, smart homes need to be designed to support various user groups in consideration of their lifestyles, needs, and health, not limited to people having reduced capabilities as a consequence of age or disability. For example, middle age is a transitional period in which people must adapt to changing physical and psychological features and prepare to solve aging problems while performing their tasks; therefore, it is necessary to conduct smart home research that targets the entry level of the elderly. In addition, young single-person households are rapidly increasing and exhibit different lifestyles and behavior patterns from other generations. Such young age groups are predicted to become the biggest consumers of smart homes in a few years, as smart technologies are combined with ICTs, the IoT, and AI. The living patterns of the elderly, the middle aged, and younger groups and the statuses of their health are inevitably different; hence, the smart technologies and services that they require will vary. The important thing is to determine the different needs of the user groups and then to apply the demanded and preferred technologies to supporting these individuals in routine living through the design of smart homes.

5.1.3

Acceptance of Technologies

Most research has argued that smart homes can make occupants’ daily tasks easier and enhance their quality of life at home through embedded technologies and devices; yet, these promising potentials often do not translate into a willingness by residents to accept smart home technology in reality [34]. Older adults with decreased physical and cognitive capabilities often have negative attitudes toward the use of technologies at home. Their acceptance of smart technology is dependent on the complex relationships between cognitive and emotional components; thus, the concepts of engagement and positive experiences should be considered when designing smart homes for older adults, and not only an emphasis on efficiency and effectiveness [34]. Some researchers emphasize the users’ adoption of smart technologies from the householder’s perspective, focusing on their learning and the userfriendliness of the technologies. As the level of knowledge and ability required for the use of smart systems varies among users, smart education seems essential to facilitate the use of technologies [35, 36]. Accordingly, to enhance the acceptance of smart homes by users, a consideration of user ability and experience of technologies is critical. However, many studies on smart homes fail to explore users’ capabilities with technologies or to consider user experiences when interacting with smart homes. To implement optimal and enjoyable smart environments, user capabilities, needs, and preferences should be

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understood in detail, not only from a developer’s perspective but also from the user’s perspective. When innovative systems are provided in the smart environment, users’ awareness of the applications should be critically considered. As the end user of the smart environment, individuals must be provided with adequate educational information and feedback for ease of access to smart technologies without frustration [37]. More research needs to be conducted to identify the ways in which users are motivated to adopt smart technology as well as the factors supporting positive user experiences in smart homes. The provision of suitable services is complicated because it should take into consideration the needs and behavior of the actual users [38]. To design smart homes that will be accepted by users, various aspects, including ability, emotions, and perceptions, should be considered when conducting user-centered studies. The following sections present the critical issues and factors related to residents’ wellness and pleasurable experience that need to be considered for the design of smart homes, based on a critical review of the related research. First, by critically reviewing relevant studies, the concept of wellness associated with health smart homes was explored, emphasizing the potential of the wellness model. Second, key factors influencing users’ engagement and pleasurable experience during the use of smart technologies were identified to ensuring the facilitation of a pleasing smart environment. Through the case studies, the proposed ideas and argument were validated. After focusing on the practicability of each variable from the perspective of supporting user experience, a framework for the design of smart homes is to be developed with a focus on fun, fulfillment, play, and user engagement. It is expected that more customized factors for consideration in the design of smart homes could be constructed based on the framework, targeting specific user groups to ascertain their behaviors, needs, and preferences.

5.2

Promoting Residents’ Wellness in Health Smart Homes

According to Hettler (1976), while the term “health” has previously focused on disease-related conditions and viewed the body as a physiological system, the term “wellness” means a comprehensive state of well-being that includes not only physical aspects but also psychological, social, and emotional elements [39]. The meaning of health in today’s society has become more complex and is related to well-being, quality of life, and life satisfaction [40]. Wellness emphasizes the cognitive process of promoting happiness and satisfaction in various areas, such as via diverse lifestyles and intellectual, physical, emotional, and professional pursuits [41]. Wellness models have been proposed as a theoretical framework for measuring healthy and vital human activities related to individuals, communities, and the environment from an inter-relational and holistic perspective [42]. Georgia Tech’s Aware Home Research Initiative (AHRI), a group of interdisciplinary researchers, emphasized the concept of the “Happy Healthy Home” for residents, centering on the wellness model. Focusing on the understanding of individuals’ needs, this group

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Table 5.1 Dimensions of wellness

Categories Physical Emotional/ psychological Social Intellectual Spiritual Occupational Environmental

Hettler (1976) ✓ ✓

Helliwell (2005) ✓ ✓

May (2007) ✓ ✓

Myers et al. (2005) ✓ ✓

Ryan and Deci (2001) ✓ ✓

Ryff and Singer (2006) ✓ ✓

✓ ✓ ✓ ✓

✓

✓ ✓ ✓ ✓ ✓

✓ ✓ ✓ ✓ ✓

✓

✓

✓

✓

✓

✓

✓ ✓ ✓

Durlak (2000) ✓

✓ ✓

studied the development and application of necessary technologies for the living environment as related to health. They identified the technologies required by individuals for their routine lives in their homes and considered how these technologies could be accepted differently [32]. Wellness is a multidimensional construct encompassing the improvement of quality of life in various areas—such as emotions and the body—for human happiness and health. Many researchers have defined wellness via various components and interrelated areas, with some variations, depending on the researcher, as shown in Table 5.1. The intellectual and spiritual dimensions may be integrated or divided, and the importance of the occupational and environmental dimensions differs somewhat among researchers. For the design of the smart homes, Hettler’s wellness model [43] was chosen and extended, where subcategories for the six dimensions were developed, depending on the different user groups for the smart homes. The physical dimension is used to identify the need for regular physical activity, including diet and nutrition, the treatment of diseases, eating habits, and the use of appropriate medical systems. It is important to monitor residents’ vital signs and understand physical symptoms with a grasp of the relationship between nutrition and body performance. The physical benefits of looking good and feeling terrific often lead to the psychological benefits of enhanced self-esteem. The social dimension is related to the contribution to a sustainable environment and enhanced community life, emphasizing the interdependence between people and nature. It encourages healthy living and participation in improving the environment and communities through communication with neighbors. Social wellness actively seeks ways to maintain a healthier lifestyle and to enhance relationships with others. It involves harmonious living with others and the environment for the common good. The intellectual dimension concerns the pursuit of creative and intriguing activities. People can expand their knowledge and skills through intellectual and cultural activities via various channels both inside and outside the classroom. They may be interested in recent social issues, pursue personal interests, or devote time to reading books, magazines, and newspapers or engaging in creative endeavors. Intellectual wellness refers to activities such as developing intellectual curiosity and actively challenging oneself.

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The occupational dimension involves the realization of achievement and abundance through work. By participating in vocational activities, people convey their values and are satisfied with and contribute to meaningful and rewarding work using their talents and skills. This includes job choice and satisfaction, career ambitions, and achievement. The premise of occupational wellness is that occupational development is related to one’s attitude about one’s work. The spiritual dimension is concerned with the search for values, meanings, and purposes in life. It is related to affection for relationships, family, and society and to consideration of their existence and the meaning of life. In the spiritual dimension, people act on their own beliefs and values and discover joy and happiness in their spiritual lives. Although the value of this spiritual dimension is critical to healthy and happy living, it is not easy to define with specific subcategories. The emotional dimension is related to the acceptance of the various emotions of oneself and others. It is important to express one’s own emotions freely and to manage them effectively. Emotional wellness includes the positive assessment of marginal situations, responding effectively to stress and maintaining satisfactory relationships with others. Further, the solicitation of support and assistance from others when necessary is required for emotional wellness. This dimension also includes the ability to manage negative emotions and behaviors, such as anxiety, depression, and loneliness. The application of this wellness model has the potential to identify critical factors for consideration in the design of smart homes, since it provides an awareness of the interconnectedness of each dimension and its contribution to healthy living. Customized metrics could be developed for special user groups based on the wellness model with a focus on the combination of smart home services with technologies and derived from an analysis of studies that were conducted using such metrics, promising that smart home design could be proposed empirically. In the following sections, a case study utilizing the wellness model is presented. Derived from the wellness model, this study presents a framework for the prospective user group— young single-person households—and then further develops a metric for the survey that aims to understand the target user groups. Based on the results of the survey, a direction for the smart home design is proposed with attention drawn to challenging issues in conclusion.

5.2.1

Case Study: Smart Homes for Single-Person Households

5.2.1.1

Motivation and Users

This study investigated the characteristics and wellness-related aspects of young single-person households in a multidimensional way to identify the critical factors to be considered for the development of a smart home that would support quality of life [44]. The list of prospective users who would benefit from smart home technology was expanded. Departing from existing studies that mainly focused on elderly

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inhabitants, this case study selected people in their 20s to 40s who lived alone in Seoul, Korea, as the focal inhabitants for smart homes. The reason for the selection of young, single-person households as a prospective user group for smart homes was that this user group is rapidly expanding, and smart homes could potentially improve the well-being of such a group. Young, single-person households have different lifestyles and attitudes to smart technologies as well as different digital experiences and abilities from those of different generations. This study explored the perceptions and the development direction of smart homes for young, single-person households who are more familiar with smart devices and technologies, based on an understanding of their features and their use in daily living.

5.2.1.2

Wellness Framework

To propose a smart home design to support happy and healthy lifestyles in singleperson households, this study constructed a wellness framework, consisting of six dimensions—the physical, social, intellectual, occupational, spiritual, and emotional—and developed a metric, consisting of a total of 47 items in 21 categories (Table 5.2). The metric emphasized the wellness dimension to single-person households, along with these households’ perceptions of and preferences for smart homes and technologies. A significant difference in the responses to some items, depending on gender, was found. This proposed wellness framework could be utilized for further study on single-person households since it includes key factors associated with wellness in daily living for this specific context. Further, based on an understanding of the users’ experiences of wellness, an appropriate design for smart homes could be proposed to support the healthy and enjoyable living of the target user group.

5.2.1.3

Results and Discussion

As a result of the wellness analysis, it was found that young, single-person households do not have a balanced lifestyle in terms of physical, social, intellectual, occupational, spiritual, and emotional practices. The physical dimension score was the highest, reflecting relatively stable and positive approaches to physical health; however, the social dimension score was the lowest, showing a lack of active participation in volunteer work and low levels of interaction with neighbors. It is necessary to focus on support for more healthy and desirable social engagement in terms of exchange and belonging through the design of smart homes for this group. The highest score was obtained for the physical dimension, especially for the items related to exercise. Residents believed that exercise was essential for health and tried to exercise regularly, but most complained that they were too busy to exercise; thus, the provision of a conveniently manageable space with a flexible time schedule and smart equipment for exercise would be one effective support for them.

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Table 5.2 Wellness framework for single-person households Dimension Physical

Factors Regular physical activity Eating habits, diet, and nutrition

Medical self-care

Drinking and smoking

Social

Communicating with others Contributing to community

Preserving the environment Building a better living space and community

Intellectual

Intellectual and cultural activities

Intellectual curiosity

Intellectual growth and stimulation Occupational

Job choice

Job satisfaction Job ambition and achievement Reward and recognition

Question I try to exercise regularly I think exercise is essential for a healthy life I adjust my food intake for weight management I keep my mealtime regular I take care to eat foods that are good for the body, such as brown rice and organic food I take health supplements such as omega 3, vitamins, and lactic acid bacteria I think my health is good I use an appropriate medical system when I am sick I enjoy drinking beer, wine, etc., at home I drink alone I smoke I have a lot of friends to talk to I have a good relationship with my family I am undertaking volunteer work I have a club or meeting that I attend constantly I greet, get along with and interact with my neighbors I think it is important to preserve the environment I am participating in reducing waste, separating food, and conserving energy I use local sports spaces, libraries, and community centers I use parks or green spaces for walks or relaxation in the community I enjoy watching performances and culture I have a favorite hobby or cultural activity (e.g., sports, soccer, or games) I love to find and visit delicious restaurants I spend my time reading books, magazines, and news I am interested in the economy and politics I recently learned something new I tend to constantly strive for self-development I use my talents and skills to contribute to meaningful and rewarding work My job suits my aptitude I am happy with my work and job I feel rewarded and joyful in what I do I really want to succeed in what I do I am doing my best now for future rewards I set goals to achieve every day I am being recognized by my colleagues around me for my work I am getting sufficient rewards for my work (continued)

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Table 5.2 (continued) Dimension Spiritual

Factors Pleasure and happiness

Beliefs and values Religious life Emotional

Feelings of positivity or pessimism Ability to cope with stress

Satisfaction

Question Rewarding and loving human relationships are an important part of my life I tend to love work, family, and society I act according to my values and beliefs I think a lot about the meaning of my own life I tend to draw strength from my spiritual life I have a religious life I am pessimistic about my life and anxious about the future I sometimes feel lonely I become more frustrated or stressed out recently I am tired and find things hard, so I do not want to do anything I worry about who will take care of me when I’m sick I want to have time alone for myself and not for others

Although respondents maintained strong relationships with their families, they did not engage in many exchanges with neighbors. This was because, often, they did not know their neighbors well and did not have enough time to engage with them as a result of heavy workloads. Thus, linking with a virtual community through smart home technologies would facilitate a healthy and desirable social network, promoting social well-being and allowing for the development of relationships with neighbors. The scores for the intellectual dimension were not low overall; however, some scores were partially low for items related to intellectual growth, stimulation, and cultural activities. To enhance the intellectual wellness of single-person households, it is advisable to stimulate intellectual activities, such as learning something new or constantly striving for self-development. Contrary to expectation, participants did not experience much loneliness or anxiety about living alone and were relatively stable and positive, spiritually and emotionally.

5.2.1.4

Proposing Smart Homes for Young, Single-Person Households

Single-person households are mainly situated within small, multi-family houses and units. Therefore, it is necessary to design a smart home that can be applied to a smallscale house rather than a large-scale apartment complex. Since it is difficult to plan a large apartment complex for single-person households, alternative ways to utilize various community facilities and green spaces in the local society could be proposed as part of the smart home design. Contrary to expectation, this demographic’s awareness of smart technology was not strong; further, most were unfamiliar with

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or had never used smart home systems. Existing smart home systems are related to fields such as entertainment, information and management, security, housework, energy and eco-friendliness, and health. However, technical support for the fields related to community, learning, and education was found to be insufficient. Therefore, it is necessary to develop technology across a wider variety of fields and to raise awareness to disseminate and create acceptance of new technologies. Single-person households selectively accept only technologies that are necessary and useful to them at present; thus, technological development without consideration of their demands and acceptance is meaningless. The design of smart homes should be suitable for users. Based on the results of this study, a smart home for young, single-person households could be proposed with a focus on five areas: exercise, exchanges/belonging, safety, self-development, and hobbies/cultural activities. The development of the smart home and community includes units and common spaces in multi-family housing, community facilities available to residents, and the provision of a variety of smart home systems.

5.3

Supporting Residents’ Pleasurable Experiences with Smart Technologies

Within the context of the smart environment, a wide range of technologies are integrated into building components, and interactivity becomes an important issue in smart homes. Interactions between residents and smart systems in the home setting should be natural. However, there are some barriers preventing users from having a positive experience with the interaction. For example, some groups, such as older adults, middle-aged occupants, and homemakers, have experienced difficulties in manipulating smart systems because of their unfamiliarity with digital technologies. Even after exposure to technologies to access information and assistance in smart homes, some users do not have sufficient perceptual, motor, and cognitive capabilities to operate the systems [45]. Additionally, some are afraid of the use of innovative technologies and express concerns regarding the burden of the use of smart systems. They believe that smart home technologies are impractical to use, causing a significant workload; therefore, they become reluctant to use these technologies [46]. Thus, it is necessary to identify the end users’ perceptions and awareness of technologies carefully and to find ways to help users realize that smart homes can provide them with improved quality of life and independence. The development of user-friendly interfaces is an effective method to support users’ acceptance of smart technologies. Porteus and Browsell [47] argued that the role of the interface is crucial, since its design is the primary way of ensuring the user’s control of the system. To provide users with optimal performance and satisfaction in smart homes, the selection of appropriate modes for human-computer interaction (HCI) is critical [48]. User experience (UX), as a combination of users’ sensibilities, emotions, and cognition, has been heavily emphasized in interaction

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with technologies since the positive UX of a system is essential for its acceptance [49]. UX is something individual that emerges from interacting with a product, system, service, or object. Through a critical review of user studies on health smart homes, Kim et al. [50] found that residents’ interactions with smart systems should be direct and easy to use—otherwise, the smart services would be perceived as uncomfortable and displeasing, thus decreasing the acceptance of the services. Some users have more difficulties in handling devices, and badly designed interfaces could be quickly abandoned; hence, appropriate modality for the interaction needs is necessary for intuitive HCI [48]. Various technologies are being developed for the implementation of smart homes. Recently, positive technology has emerged as an alternative for smart homes. Positive technology aims to improve the overall quality of life by supporting satisfaction rather than focusing on device usability [51]. As positive technologies, it has been argued that augmented reality (AR) and virtual reality (VR) have the potential to improve occupants’ social relationships, wellness, and quality of life [52, 53]. AR, combined with physical representation, is a promising option for interfaces since it affords tangible interaction and augmented vision. Tangible interactions, such as touch and pressure, are direct and natural, and the augmented vision supports information access by adding digital data to a real world scene [54]. AR systems have moved into the context of people’s everyday lives, creating new opportunities for the accessing of contextually aware information. Context awareness is integrated to transform an AR system from dumb to intelligent and to realize the ubiquitous and pervasive features connected to everything through mobile devices [55]. Lee and Park [56] proposed that AR and VR could mitigate physical and spatial constraints by immersing users into the desired environment and would thus contribute substantially to maintaining the health of the elderly. To supporting residents’ pleasurable experiences with smart technologies, not only technological interventions but also the roles of design professionals are essential in the design of smart homes. Homes afford diverse sensory and emotional experiences, such as memories, smells, and familiarity with spaces. Emotion is a powerful factor influencing the way people make decisions, focus attention, and categorize information. Spaces affording positive emotions may make people desire to continuously remain in them [34]. Critical factors supporting positive emotions and experiences need to be identified, and their influence on the adoption of smart technologies should be considered based on the understanding of inhabitants’ characteristics and needs [57–59]. Challenges that provide residents with pleasurable experiences via advanced technologies should attract research attention when developing smart homes that afford sustainable smart living. Further, it is important to understand the key factors that influence occupant engagement as well as any negative experiences during the use of innovative devices to develop a pleasing smart environment. Interactions between people with technologies in the smart home should be intuitive, and designers should consider hedonic and experiential factors such as fun, fulfillment, play, and user engagement; otherwise, smart homes may become uncomfortable and displeasing environments [34, 60].

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5.3.1

Case Study: Enjoyable Smart Environments for Older Adults

5.3.1.1

Motivation and Users

By critically reviewing smart homes for older adults, this study developed an evaluation framework for the smart environment, focusing on pleasurable experiences [34]. The motivation for this study was that smart homes have the potential to support older adults’ well-being and independence and to enhance their quality of life; however, older adults often have negative experiences with technologies and thus are unwilling to use smart home systems. This study emphasized the strength of the hedonic factors of interacting with technology, such as fun, play, and user engagement, in the design of smart homes, providing a comprehensive review of smart homes to support positive aging and enjoyable user experiences in the architectural domain. Instead of emphasizing the efficiency of smart homes, this study focused on psychological wellness, positive experiences, and engagement based on the understanding of older adults’ cognitive and emotional aspects to make smart homes a more pleasing place for them, leading to an increase in their acceptance.

5.3.1.2

Evaluation Framework Focusing on Pleasurable Experiences

To enable enjoyment for older adults in smart homes, an evaluation framework was constructed by analyzing components of smart environments in context to extract principal factors for the optimal and positive experience. The evaluation framework consists of four categories: wellness, independence, acceptance, and design. Each category includes several factors for consideration in the design of smart environments. The primary goal of the smart environment is to promote the wellness of older adults; thus, providing a safe environment for the physical and emotional health of older adults is critical to the support of their wellness. Critically, key factors influencing the acceptance of smart technologies should be considered to enhance older adults’ emotional well-being in smart environments, focusing on fun and happiness. Eventually, the user experiences of older adults will depend on how the smart home has been designed. The design of the smart home should meet the individual’s needs and demands on the space based on a detailed understanding of the relationships between the individual, the community, and environment.

5.3.1.3

Proposing Enjoyable Smart Environments for Older Adults

This study identified the challenges to be addressed in the design of smart environments through a critical review of related research using the evaluation framework. Under the wellness category, it was found that smart home systems should mitigate

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Table 5.3 Evaluation framework for the smart environment, focusing on pleasurable experiencest Category Wellness

Factors Safety Health Interaction

Fun

Independence

Automation Affordance Physical support Cognitive support

Acceptance

Positive experiences Sustainability Perceived usefulness/benefits

Design

Identification of needs Human-centered approach Individual level Community level

Environment level

Clarification Providing a secure environment to ensure the safety of older adults Providing an active environment to promote the physical and emotional health of older adults Providing an environment enabling older adults to interact with nature, to interconnect with their families in the event of a specific problem or danger, and to participate actively in social activities to avoid being isolated Providing an enjoyable environment to enable a variety of activities that allow older adults to identify and pursue their interests and have fun Automating the system for older adults to be able to use the smart environment without extra effort or ability Providing clear perceptions of possible interactions between householders and artifacts in smart environments Supporting older adults who are physically frail to perform the activities of daily living Supporting older adults who are cognitively impaired to perform the activities of daily living Considering positive emotions (e.g., satisfaction, fun, and enjoyment) while using smart technology and residing in an independent smart home Making the smart environment more sustainable through smart technology Understanding the prospective older adults’ perceptions of the usefulness, benefits, and risks of smart home environments Understanding the needs of older adults to provide an enjoyable smart environment Designing a smart environment in consideration of the unique characteristics of older adults Considering only individual characteristics when designing smart environments Considering smart spaces in a connected smart community and smart neighborhood when designing smart environments Considering smart spaces in a connected smart city, infrastructure, and sustainable city when designing smart environments

the emotional loss experienced by older adults, and not merely support their functionality. Few studies considered social or entertaining factors for older adults in the smart environment. It is important to consider ways to support the emotional wellness of older adults in smart environments. Smart appliances or sensors could be design factors that encourage older adults to interact with the smart environment, producing pleasurable experiences. Further, with careful consideration of the

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strengths and potential risks, smart environments should be introduced to older adults. There may be issues related to financial accessibility or affordability for such adoption. Nevertheless, a correct and detailed understanding of older adults should be conducted, focusing on their needs, daily activities, and decreasing abilities to provide them with appropriate smart environments. Various innovative technologies are continually evolving, and people’s lifestyles are constantly changing; accordingly, spaces, and the houses in which they reside, are also transformed into smart environments. Architecture plays an important role in the encouragement of older adults to adapt to smart environments. A new, intelligent place for older adults should be designed and proposed based on the understanding of older adults through user studies.

5.4

A Conceptual Framework for the Design of Smart Homes

As the smart home is an interactive environment combined with a variety of technologies to support occupants’ daily activities, residents cannot avoid interacting with smart systems in the home setting. However, a technology-oriented approach to the design of smart homes may not optimize practical performance since users might have difficulties in handling such technologies that do not necessarily reflect their characteristics, behaviors, living patterns, and concerns properly. The importance of the user-centered approach to the design and research of smart homes has been proposed with the realization that, without in-depth knowledge of users, smart homes could not achieve substantial success in terms of practical usage. Positive technology, affording natural and intuitive interaction, should be applied to encourage user acceptance of smart technologies in dwellings. These issues—the health smart home, the prospective users, and acceptance, wellness, and pleasurable experience—are essential to the design of smart homes since they address critical factors affecting practical performance. User experience in smart homes depends on the ways in which smart homes are implemented, not only supporting users’ activities but also demonstrating the relationships between the individuals, technology, and spaces. For the performance of smart homes to be successful, a conceptual framework was strategically developed to provide a structured way of understanding the critical factors to be considered in the design of smart homes. A conceptual framework is one of the most effective means of characterizing important issues and elements to ensure that smart homes support residents’ well-being and enjoyment. The provision of adequate smart homes to residents has been a challenge since individuals’ abilities, needs, and preferences are varied, requiring specialized solutions. Different users’ perceptions and experiences of technologies should be understood to identify their responses to new devices, systems, and architectural design strategies. The framework for smart homes, established on work of Mynatt et al. [61] and Redden

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Wellness: healthy living Six dimensions: physical, social, intellectual, occupational, spiritual, emotional

Home: domestic setting

User: daily activities • basic, instrumental and enhanced activities • health-related activities

Human–Computer Interaction • Positive technology • Tangible interaction • User experiences • Engagement

• Information appliances • Augmented artifacts • Embedded infrastructure • Smart management

Pervasive computing and intelligent technology with ICT Evolutionary Nature of Smart Homes

Fig. 5.1 Conceptual framework for smart homes

and Benford [62], was developed via a framework by Kim et al. [63], combining expertise in HCI and wellness. The conceptual framework consists of four categories, emphasizing user experience in home settings: wellness for healthy living, the daily activities of users, the domestic setting of the home, and human-computer interaction in smart technologies [63]. The four categories and subcategories frame systematic ways for identifying driving factors and relationships among these elements with a focus on the practicability of each variable; these will be further extended by the continuous incorporation of promising elements. As shown in Fig. 5.1, the framework is described in terms of four concerns: healthy living, the user, the space, and HCI. The framework seeks to integrate crosscutting relationships among critical elements for each category based on understandings of wellness, users, homes, and interactive experiences with smart technologies. The focus of the framework is on multimodal interactions between users and smart homes that integrate spaces and technology. The HCI dimension focuses on UX, emphasizing users’ perceptions and acceptance of technology.

5.4.1

Wellness: Healthy Living

The wellness category refers to healthy living, which is critical to the improvement of quality of life as a primary goal of smart homes that emphasizes the understanding of users. The concept of wellness is multidimensional, consisting of six dimensions: the physical, social, intellectual, occupational, spiritual, and emotional. The focus of this category is to promote happiness and satisfaction by meeting individuals’ needs through the application of smart technologies in daily living.

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The physical dimension is related to users’ diet and nutrition, including their eating habits and the treatment of disease, and connected to assistive technologies. This physical dimension is different from the activities in the user category in that physical wellness seeks a relationship between nutrition and body performance rather than just the investigation of daily activities in terms of cognitive and physical capabilities. The social dimension deals with constructing a healthier relationship with community and environment. This social wellness encourages a sustainable and improved life with others, leading to harmonious well-being through smart technologies. The intellectual dimension is concerned with intellectual curiosity and challenges beyond the classroom through the help of smart technologies. This intellectual wellness seeks intriguing and cultural activities to enlarge inhabitants’ knowledge and capabilities through learning activities with creative endeavors. The occupational dimension refers to vocational activities, including career ambition as well as job choice and satisfaction. To achieve occupational wellness, people engage with meaningful work through technologies with a focus on achievement. The spiritual dimension is difficult to define, although it is crucial for quality of life. This spiritual wellness includes the search for values and purpose in life and the discovery of joy and happiness in daily living. The emotional dimension is related to the management of positive emotions as well as negative responses to oneself and others. This emotional wellness includes the search for support from others when necessary and the ability to express and control one’s own feelings effectively. The category of wellness has the potential to frame crucial factors for consideration when ensuring healthy living in smart homes.

5.4.2

User: Daily Activities

People seek increased comfort and improved wellness in smart homes, but the degree to which these are achieved may vary, depending on individuals’ conditions. Smart home technologies have been developed for standard users; hence, they may not be suitable for every inhabitant. Groups of users might differ in terms of their needs and uses of technology and may require different design solutions accordingly. For example, when a designer develops smart homes to assist users with dementia, the users’ needs and living methods should be understood first [64]. Studies on user characteristics relating to the ability to use smart technology have shown that the younger the person and the higher the level of education and income, the greater the ability to use technology [65]. Smart homes need to be designed for diverse user groups, and long-lasting relationships should be established [66]. As the technical experiences of users and their needs in daily living are important variables in the design of smart homes, the characteristics and health-related activities of the users need to be identified [67]. Smart home design might be usefully informed by the routines of the home, and its inhabitants’ daily activities should be explicated [68]. Smart homes should be designed and tailored to target users, and any difficulties encountered in completing

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daily activities should be identified. The category of daily activities has four subcategories: basic activities, instrumental activities, enhanced activities, and healthrelated activities. All activities in this category could potentially be assisted by advanced technologies in terms of recognizing behavior patterns, assisting daily routines, supporting social connections, and delivering consultation. Basic activities in daily living, such as sleeping and eating, need to be identified, in addition to instrumental activities such as maintaining the house and preparing meals. Further, other instrumental activities, such as social connection and self-development to adapt to a changing environment, need to be investigated. The most important issue is that concerns encountered in daily life may not be as easily detected as in a crisis but might still be significant for the long-term health and happiness of users living at home [61].

5.4.3

Home: The Domestic Setting

Smart homes should afford a comfortable and pleasing space for occupants. The domestic setting of smart homes should be adjusted according to changes in occupants’ living patterns [34]. Most of all, the characteristics of domestic settings are quite different from those of work-oriented settings; further, smart home settings are unlikely to be purpose-built. Thus, it is essential to understand the home from the perspective of supporting occupants’ daily activities in the evolutionary context. Technologies are spreading into routine existence by increasingly and invisibly being embedded into environments and artifacts. Interactions between people and spaces must be considered for the effective use of smart homes. Emphasizing the nature of spaces integrated with technology, our smart living environments have become interactive, with sensors and actuators comprising part of the building, to support occupants’ activities through things that think, spaces that sense, and places that play [32]. Further, thanks to the use of ICT devices and advanced technologies, the boundary between physical and digital spaces disappears. Physical objects that are tagged, labelled, monitored, and connected in houses became embedded intelligence, and the space around us become our interface through which to interact with digital information. To design smart environments that are acceptable to users, it is important to understand the use of each space constituting a smart home and to arrange smart technologies into spaces depending on users’ needs. The category of the domestic setting has four subcategories: information appliances, augmented artifacts, embedded infrastructure, and smart management. These subcategories show how interactive technologies can be manifested within domestic settings. Through intelligent home appliances, augmented objects, and furniture, domestic settings can be transformed into interactive environments that effectively assist residents to live healthily at home. Home settings should be understood using diverse approaches that are concerned with the functional forms of artifacts and devices, as well as interactive environments and technological infrastructures [62]. To develop

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solutions for the design of smart homes, the technical and spatial aspects of the problems should be identified, and promising ways for incorporating intelligent computing into domestic settings should be proposed. The adoption of intuitive control methods should be considered for the efficiency of smart home operation, and the design of computing to be diffused into homes in a way that is unobtrusive and reliable should be conducted.

5.4.4

Human-Computer Interaction: Smart Technology

Emerging intelligent technologies introduce a new level of complexity when it comes to usability. The matching of appropriate interfaces to the ability of users and the control of target devices is a critical issue affecting the support of HCI [49]. Technologies associated with smart homes act as embedded intelligent components, and occupants need to interact with such computing in the smart home. However, many users do not accept such technologies, and this non-usage is regarded as a failure of smart home designs and operation [69]. It is critical to understand the factors that support user engagement in smart homes and then to decide on acceptable technologies and functions, rather than being concerned with technological performance in isolation [70]. Encouraging users to be willing to adopt intelligent technologies is an important part of the design and implementation of smart homes; thus, the design of HCI is a vital component of intelligent settings for the home. The category of HCI has four subcategories related to interaction with smart technologies: positive technology, interface, UX, and engagement. The greater the desire to continue to use technologies, the greater the acceptance of smart homes. Fundamentally, the success of smart homes is dependent on their usage by occupants in the context of daily life; therefore, positive technology and tangible interaction could be considered to provide fun and intuitive experiences in smart homes. Smart homes should be designed not only to emphasize efficiency and effectiveness but also to engage occupants in positive experiences. To provide novel interfaces without being intrusive and to maintain normal capabilities in everyday lives, tangible and multimodal interaction could be included for the design of user interfaces, allowing intuitive and natural interaction [54, 71]. Above all, UX is emphasized in the design of smart homes since it is associated with how people are affected by and accept new technologies, and the social and psychological elements accompanying its use must not be overlooked [47]. Exploration of the cognitive aspects of human behaviors to support occupant engagement with smart technologies might increase acceptance of smart homes. As various smart technologies continue to evolve and be integrated into smart living spaces, the necessity of positive and pleasurable experiences is emphasized since these can make smart homes more comfortable and pleasing places to live. We introduced a conceptual framework with a focus on the critical issues, framing categories, subcategories, and elements that should be considered for the design of smart homes. This framework could aid researchers or developers in

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making preliminary judgements from a cognitive perspective about the design and implementation of smart homes for residents’ healthy living. The conceptual framework for the design of smart homes, established via a framework constructed by Kim et al. [63], was extended from a user and multidimensional perspective. Table 5.4 describes the four categories and associated subcategories in detail by focusing on the practicability of each variable from the perspective of supporting users’ wellness and pleasurable experiences. The framework largely reflects the evolutionary nature of the smart home and the smart layer surrounding the home setting. The proposed framework will help designers, architects, engineers, and researchers alike to explore and develop smart homes from a more expansive, integrated perspective. Utilizing this framework, more customized design factors that need to be considered for smart homes could be developed to target specific user groups with a consideration of their activities, demands, and experiences.

5.5

Challenges and Issues for the Design of Smart Homes

There has been significant research undertaken into the implementation of smart homes, and the value of the smart home on the market has been increasing, facilitated by global companies such as Google, Amazon, Apple, and Samsung [72]. The introduction of smart technologies into our homes has become unavoidable, and such incorporated smart systems have influenced residents’ cognitive experiences and daily activities. By critically reviewing the related research, we identified the main issues for the design of smart homes and developed a conceptual framework including crucial factors related to residents’ wellness and enjoyment in smart homes. The framework can be used for the design of smart homes, incorporating the relationship between user needs and technology adoption, since it allows the requirements of prospective users to be matched to smart technologies that will improve their quality of life in the domestic setting. This chapter explores the challenges and issues in the design of smart homes, emphasizing resident wellness and pleasurable experience from a user-centric perspective as follows.

5.5.1

Understanding of Prospective Users as a Way of Emphasizing Wellness

Smart homes have the potential to support the healthy and happy living of all age groups of users in a familiar home setting; however, embedding innovative technologies into domestic settings is not sufficient for the success of the smart home. The features and behavior of different prospective users are varied; thus, specific studies should be conducted to identify users’ daily needs, situations, and purpose in life, to allow for the optimal performance of smart homes and their acceptance by

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Table 5.4 Four categories of the conceptual framework for smart homes Category Wellness: Healthy living

Subcategory Physical Social Intellectual Occupational Spiritual

User: Daily activities

Emotional Basic activities

Instrumental activities Enhanced activities

Health-related activities Home: Domestic setting

Information appliances Augmented artifact

Factor Diet and nutrition Community and environment Creative and intriguing Vocational activities Value and meaning of life Satisfaction and stress Sleeping and movement Bathing and eating Medication regimen Maintaining house Preparing meals Social connection Self-esteem and development Telehealth Medical rehabilitation Stand-alone Networked Furniture Object

Embedded infrastructure Smart management

Human-computer interaction

Positive technology

Tangible interaction User experience (UX) Engagement

Instrumented Integrated Maintenance Control Tracking and monitoring Pervasiveness Affordance Intuitiveness Perception Emotion Acceptance Perceived usefulness

Element Health and disease Contribution Challenge Achievement Joyfulness Response Monitoring mobility Recognizing patterns Reminder Assistance Information Communication Education Consultation Treatment Intelligence Interactivity Embedded intelligence Augmented intelligence Equipment Embodiment Sustainability and reliability Context awareness Sensors, RFID, and tag Mobility Functionality Multimodality Cognitive behavior Sensibility and satisfaction Adaptation Benefits

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prospective users. Many smart home projects have been implemented with a focus on the technical aspects of the systems. They lack systematic methods for the investigation of users to determine the ways in which smart technologies can be used effectively in the domestic context; further, the approach to smart design is not a user-oriented one. Thus, the understanding obtained by this approach is not extensive, and does not reflect the characteristics, needs, and preferences of users properly. It is necessary to understand prospective users correctly through a systematic investigation to obtain in-depth information on them. Further, the investigation of users should have specific foci, addressing important values or meanings in their lives. We chose the wellness model, consisting of six dimensions, to match the significant values of the prospective users of smart homes and developed a conceptual framework for the smart home centered on wellness from a user perspective. Based on the proposed framework with a focus on wellness for healthy living, users’ daily activities, the domestic settings of their homes, and HCI, a systematic approach to the design of smart homes could be performed for prospective users, capturing each factor’s implications for smart homes. Based on insightful information and tailored solutions, smart homes could be designed to support residents’ wellness and positive experiences.

5.5.2

User Engagement and Perceptions of Technologies for Adoption

The factors affecting user engagement are not adequately considered in current smart homes, even though the user engagement of smart homes is crucial for their adoption. Higher levels of user engagement of smart homes would have positive effects on the satisfaction of users, facilitating their acceptance of technologies. Research on smart homes should focus on user engagement based on the adoption of and ability to use smart technologies in daily living to identify the critical factors to be considered for the design of smart homes. To achieve successful practical usage, the factors influencing the user perspective, such as accessibility, the provision of adequate information, and the quality of services, should be integrated into the design of the smart systems [34]. To encourage users to engage smart home technologies, users’ perceptions and experiences of technologies should be identified to provide promising accessibility and usability in their smart living. Explorations into user awareness and encouragement to accept the innovative technologies would empower users in the smart environment to maintain sustainable living. The level of knowledge and ability required for the use of the smart home system varies among users; proper pre-instruction and guidance could lead to strong knowledge of the application, which might positively affect the experience of smart homes [36]. Further, to achieve successful user engagement in smart homes, more attention should be directed to UX, emphasizing its HCI aspect, and, especially, intuitive, optimal, and pleasurable experiences. User engagement refers to the practical use of

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the given smart technologies, and the adoption of smart homes is dependent on user engagement associated with perception and experience. Supporting user experiences in the interaction with smart homes is one of the most promising ways to guarantee the adoption of smart homes.

5.5.3

Optimal and Pleasurable Experiences with Positive Technology

Over the past few decades, there has been a substantial increase in the use of technologies in all aspects of routine existence to make daily tasks easier and to support comfortable activities within the home. However, there are concerns about poor user experiences and the fear of technological utilization, producing negative feelings and an unwillingness to adopt smart homes. ICT applications and devices are often perceived as complex and difficult to control, and many studies on smart homes fail to explore user-friendliness or to consider the cognitive experience in the development of smart systems. Most significantly, optimal and pleasurable experiences in the interaction with technologies should be critical to maintain the functionality of all technologies incorporated into smart homes. It is no longer only essential for smart homes to be efficient and effective; to be successful, they must engage residents and provide them with positive and pleasurable experiences. To make smart homes more acceptable, a new approach to user interaction with the smart environment should be pursued from a HCI perspective. Smart homes should not only be designed as usable and practical spaces; they should be implemented with intuitive interfaces and positive technology, allowing residents to experience optimal and pleasurable feelings through interaction. For example, the combination of AR and smart technologies could be one promising way to transit conventional HCI into a new type of multimodal interaction with contextual information by coupling digital data to tangible objects and the environment. Positive technologies affect residents’ experience, satisfaction, and contentment, rather than only focusing on usability and performance [73].

5.5.4

A Multidisciplinary Approach and the Role of the Architectural Domain

The implementation of smart homes is associated with several multidisciplinary areas, such as engineering, architecture, information technology, and biomedicine. However, there is a lack of effective integration and information sharing in the design of smart homes, and many varying methods are being developed in individual projects; thus, it is necessary to adopt an integrated approach to smart homes. The collaboration between special fields is essential to solve the design problems

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affecting smart homes. Without proper understandings of the problems influencing the application of smart systems, the performance of smart homes cannot achieve success. A multidisciplinary approach, including related areas of expertise, should be conducted for the design of smart homes. Although smart homes involve spaces in which people reside, the architectural domain has been less involved with smart home research. This is because the development of smart homes has been driven by the information technology fields, with a focus on sensors, algorithms, and intelligent devices. Further, the architectural domain tends to focus on conventional space design rather than interactive and intelligent computing in smart homes. As the built environment is being digitalized, architecture needs to explore how smart technologies can be used as a design element to reshape homes into intelligent places. Since the domestic space provides the basis for the realization of smart homes, architecture should operate as a hub for the design of such homes, wherein knowledge-sharing and collaboration can be performed among related fields. This chapter contributes to the understanding of current and future smart homes and seeks to inspire the further design of and research on smart homes, addressing the challenges and issues affecting the enjoyable experiences of residents and supporting healthy and happy living. Future smart homes should promote the integration of all possible smart services into the home settings by extending the smart concept to the community and city levels. Acknowledgments This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2019R1A2C1087344/ NRF-2019R111A1A01043066).

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Index

A Activities of daily living (ADL), 12 Activity Recognition, 12, 13 Administrative fast reconfiguration, 87 Administrative forced load curtailment (AFLC) mechanism, 72, 73, 77, 78, 87, 88 Administrative reconfiguration, 73–75, 84 Advanced computing, 132 Advanced technologies, 132 Affiliations, 7 Aggregated power consumption, 109 Air-conditioning control, 2 Alzheimer’s disease, 20 Ambient Assisted Living (AAL), 12 AR system, 143 Artificial intelligence (AI), 2, 94, 132 Attacked aggregators, 81 Attacked retailers, 82 Attacked SH retailers, 73 Augmented reality (AR), 143 Automated activity tracking, 12 Automation, 7, 15, 18

B Battery, 124 Battery operational parameters, 115 Battery scheduling, 121 Battery scheduling intervals, 100 Bayesian attack graph model, 66 Bi-level framework, 67

C California Electricity Market, 70 Centralized optimization techniques, 36 Central security management node, 65 China’s smart home research production, 18 Commercial energy demands, 34 Compact fluorescent lamps (CFLs), 94 Computer science, 4 Computing, 22 Congestion alleviation, 86 Congestion management, 76, 86–88 Conservative power grid, 94 Consumers, 95 Context awareness, 143 Cooperative distributed energy scheduling problem cost reduction, 57 DISCO, 68 five-minute scale MPC, 52–54 multi-time scale stochastic MPC, 55, 57 objective function, 39 one-hour scale stochastic MPC, 53–55 problem formulation, 38 transacted energy, 38 Cyberattack challenges, 65 Cyberattacks adaptive protection schemes, 65 decentralized, 67 DOS, 64 FDI, 64 intelligent modeling, 66

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Rahmani-Andebili (ed.), Operation of Smart Homes, Power Systems, https://doi.org/10.1007/978-3-030-64915-9

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160 Cyberattacks (cont.) minute-by-minute, 64 strategies, 64 targeting microgrids, 66 targeting power systems, 65 Cyber-physical microgrid platform, 66 Cyber-secure two-way communication, 94 Cybersecurity analysis, 64

D Daily energy consumption, 128 Data integrity attacks, 65 Decentralized congestions, 78 Decentralized economic dispatch problem, 35 Decentralized outages, 82 Defensive mechanisms, 65 Demand response (DR), 70, 95, 102 Demand response frameworks, 122, 127 Demand side management (DSM) CFLs and LED, 94 consumers’ energy consumption control, 94 Denial of service (DOS), 64 Depth of discharge (DOD), 47 DG aggregators, 69 DG/SH retailers, 78, 79 Diesel generator (DG), 35, 59 Direct demand control, 95 Directed acyclic graph, 65 Distribution company (DISCO), 68 Domestic appliances, 7, 22, 23

E Economic and technical constraints, energy resources, 38 Edge Computing, 11 Elderly- and patient-oriented research, 135 Electric power grid, 94 Electric vehicle (EV), 65 Embedded technologies, 135, 136 Emotion, 143 Energy, 14, 15 Energy conservation, 94 Energy consumption, 34, 127 Energy efficiency, 7, 14, 15 Energy management, 7, 18, 20, 133 Energy price tag (EPT), 68 Energy-related keywords, 7 Energy-saving program, 2 Energy scheduling problem, 36, 37, 68 Energy utilization, 7, 18, 20 Enjoyment, 151 European countries, 25

Index F Fall detection, 15 False data injection (FDI), 64 Fast reconfiguration (FR), 69, 70, 84 FDI attacks, 65 Feeder automation, 66 Financial transmission right (FTR), 65 First-layer outage prevention, 90 Five-minute scale MPC, 52 Flat rate tariff (FRT), 95, 124 Forward-looking objective function, 42

G GAMS software, 79 Genetic algorithm (GA), 38 Google Home/Alexa, 2 Green Home Energy Technology Research Department., 23 GSM-based control, 20

H Hard boundary constraints, 115 Healthcare context, 12 Healthcare services, 133 Health smart homes healthcare, 133 intelligent devices, 134 monitoring and identifying behavior patterns, 134 promoting residents’ wellness, 136–138 telemedicine, 134 Hierarchical energy management approach, 36 Holistic zero-sum game theory, 65 Home automation system, 15 Home energy management system, 15 Household appliances advanced features, 95 INSDs, 96 NINSDs, 96 SDs, 96 Household components models battery, 100, 101 in-house RERs, 97 INSDs, 97, 98 NINSDs, 97 RER, 101, 102 SDs, 98, 99 smart grid environment, 97 Human-computer interaction (HCI), 142 HVAC interface logic, 11

Index I Industrial and commercial processes, 94 Information and communication technologies (ICTs), 132 Information technology (IT), 64 Intelligent buildings, 7 Intelligent dwelling, 132 Intelligent residential energy management systems (IREMS), 128 Intelligent residential load management system (IRLMS), 102, 128 Intelligent technologies, 150 Interactive technologies, 132 Internet, 2 Internet of Things (IoT), 2, 7, 132 Interruptible and non-schedulable demands (INSDs), 96, 123 IREMS architecture, 117 IREMS framework, 127 IREMS functioning, 117, 118 IRLMS demand response energy storage devices battery operational parameters, 111 battery operation controlling, 115 battery State of Charge, 111 consumers’ critical demands, 110 energy storage methods, 110 household appliances types, 111 modified IRLMS (see Modified IRLMS functioning) RER economic benefits, 116 electricity demand, 116 in-house solar PV, 117 IREMS architecture, 117 IREMS functioning, 117, 118 residential demands, 117 SDs scheduling, 120, 121 IRLMS scheduling algorithm, 115 IRLMS scheduling process, 112, 113 IRLMS with optimization-based load scheduling algorithm aggregated energy consumption, 108 dead time intervals, 108 demand constraint, 109 demand scheduling constraint, 108 enumeration method, 106 estimated INSDs demand, 110 goal, 107 load computational constraint, 109 MDL, 110 NINSDs, 107 OSA technique, 107

161 preemptive constraint, 109 pricing interval, 107 RTP, 106, 107 RUN/STAND-BY modes, 107 IRLMS with priority-based load scheduling algorithm INSDs controlling, 104, 105 manual demand response, 102 MDL, 102 NINSDs controlling, 104 SDs controlling, 105, 106 smart meter interface, 102, 104 smart NINSD module, 103 smart SD module, 103

K Keywords, 7 KNX, 15

L Light emitting diodes (LED), 94 Linear programming (LP), 38 Load curtailment (LC), 71, 86 Load-serving entities (LSEs), 65 Local generation, 81 Low-cost technologies, 13 Lyapunov stability theory, 66

M Machine learning cluster, 15, 16 Machine learning techniques, 20 Market-based congestion management, 71, 77 Market-based contribution, 70, 71 Market-based load curtailment, 76, 77, 87 Market operator, 71, 76, 84, 86 Market retailers, 65 Maximum demand limit (MDL), 95, 102, 128 Model predictive control (MPC), 58 Modern power systems, 67 Modified IRLMS functioning aim, 112 battery boundary constraint, 115 battery operating mode constraint, 114 battery power exchange, 112 decision variables, 112 demand constraint, 114 OSA technique, 112 scheduling process, 112 SDs, 114 Multi-agent decentralized framework, 67

162 Multidisciplinary approach, 155 Multi-time scale approach, 37 Multi-time scale MPC numerical study case study 1, 49 case study 2, 57, 59, 60 cooperative distributed energy scheduling, 52–56 MATLAB, 49 problem simulation with non-cooperative energy scheduling, 51 problem simulation without energy scheduling, 51 resources and pattern, 50 resources availability, 50 system and problem parameters, 49 Multi-time scale stochastic model predictive control (MPC) decision variables, 41 DG minimum up/down time limits, 46 DG power limit, 46 energy scheduling problem, 40, 42 full charge constraint, 48 maximum accessible power, 48, 49 numerical study (see Multi-time scale MPC numerical study) objective function, 42, 44, 45 optimization time, 41 PEV’s battery DOD limits, 47 PEV’s battery power limits, 47 PEV’s unviability, 48 stochastic approach, 39 supply-demand balance, 45

Index P Passive measures, 2 Passive non-attacked SH retailers, 72 Passive SH retailers, 85 Peak-to-average ratio (PAR), 68 PEV’s battery, 68 Phasor measurement units (PMUs), 66 Photovoltaic (PV) panels, 37, 58 Plug-in electric vehicle (PEV), 38, 59 Power injection limit (PIL), 121 Pre-defined schedulable load time span, 108 Priority-based scheduling algorithm (PSA), 105, 106 Prospective users, 134, 135 Prototypes, 133

N Net energy metering (NEM), 44 Network radiality, 83 Network reconfiguration, 66 Network topology, 65 Non-attacked retailers, 70 Non-attacked SH retailers, 70, 71, 85, 90 Non-interruptible and non-schedulable demands (NINSDs), 96, 123 Non-schedulable demand interval, 125 No scheduling algorithm (NSA), 126

R Radial distribution systems, 75 Real-time pricing (RTP), 95, 106, 125, 128 Renewable energy resources, 34 Reported attacks, 80–82 RER-based power generations, 101 RER mathematical modelling, 101 RER power generation, 122 Research trends by countries China, 17–19 India, 20–23 South Korea, 22–25 USA, 18–21 smart homes affiliations, 6, 8 countries, 5, 6 databases, 3 institutions, 7 keywords, worldwide publications, 7, 9 period of analysis, 4 subjects, worldwide publications, 4 worldwide (see Worldwide research trends) Reserve market, 70 Residential buildings, 122 Residential energy management systems, 125 Residents’ wellness, 151

O Optimal power flow (OPF), 65 Optimization-based scheduling algorithm (OSA), 126, 128 Optimization problem, 83

S Schedulable demands (SDs), 96, 115, 123 SDs scheduling, 120, 121 Second-layer market, 85, 86 Security, 13, 14

Index Sensors, 2 SH aggregators, 69, 70 SH retailers, 68, 87, 89, 90 SHRMCM, 76, 77, 85–87, 89, 90 SHs analysis, 67–69 SHs analysis and management, 68 SHs design challenges multidisciplinary approach, 154, 155 optimal and pleasurable experiences, 154 prospective users, 151, 153 user engagement and perceptions, 153 SHs design conceptual framework architectural design strategies, 146 categories and subcategories, 147, 152 critical factors, 146 cross-cutting relationships, 147 home: domestic setting, 149, 150 human-computer interaction, 150, 151 technology-oriented approach, 146 user-centered approach, 146 user: daily activities, 148, 149 user experience, 146 wellness: healthy living, 147, 148 SHs scheduling, 67 Single-person households motivation and users, 138, 139 smart homes, 141, 142 wellness analysis, 139–141 wellness framework, 139 Single-time scale MPC, 40 Small-scale in-house renewable power generation, 124 Smart devices, 133 Smart environments, 149 Smart grid, 94 The Smart Home program, 10 Smart homes (SHs) AI, 2 authors and countries relationship, 25–27 definition, 2 devices and resources, 35 energy management systems, 17 inhabitants health care, 2 IoT, 2 key idea, 2 microgrids, 35 optimal and pleasurable experiences, 133 research trends (see Research trends, smart homes) services and technologies, 132 virtual assistants, 2 Smart House Project, 11 Smart Meter, 14

163 Smart NINSD module, 102 Smart power grids, 7 Smart SD module, 103 Smart technologies, 132, 151 Social wellness, 148 SoC limitation, 122 Solar PV panels, 101, 124 Special protection scheme (SPS), 66 Standard test condition (STC), 101 Stochastic approach, 37 Supervisory control and data acquisition (SCADA), 64 Supporting residents’ pleasurable experiences challenges, 143 digital technologies, 142 evaluation framework, 144, 145 interactions, 142 motivation and users, 144 positive technology, 143 proposing enjoyable smart environments, 144, 146 smart environment, 142 smart technologies, 143 technologies perceptions and awareness, 142 user-friendly interfaces, 142 Systematic methods, 153 System operator administrative reaction, 68 AFLC mechanism, 72 attacks, 66 control unit, 65 decentralized outages, 82 FDI, 64 first-layer reaction mechanism, 72 FR, 90 G1 DG retailers, 70 non-attacked SH retailers, 84 optimization problem, 77, 83 passive, 78 power flow, 75 reaction strategies, 66 reverse market, 70 situational awareness, 67 topology, 76, 84 upstream network management, 67

T Technological innovations, 132, 133 Technologies acceptance, 135, 136 Telemedicine, 134 Third-layer reaction strategy, 78

164

Index

Three-layer framework, 90 Time-dependent energy pricing, 95 Time-dependent MDL, 95 Time-varying power, 37 Topology, 82–84

V Virtual assistants, 2 Virtual reality (VR), 143 Virtual-voice assistant, 2 Vocational activities, 148

U Ubiquitous computing, 11, 22, 23 Ukrainian power system, 64 Upstream network, 81, 82 U.S. Energy Information Administration (EIA), 34 User-centered approach, 133 User engagement, 153 User experience (UX), 142 Utility-defined MDL, 124 Utility-defined pricing interval duration, 107 Utility electricity price, 125

W Web of Knowledge and Scopus, 3 Wind turbine, 124 Wireless sensor networks, 11, 22 Wireless Telecommunication Systems, 18 Worldwide research trends Activity Recognition, 12, 13 community analysis, 10 energy, 14, 15 IoT, 10, 11 Machine Learning cluster, 15, 16 scientific communities, 10 security, 13, 14