Optimal Planning and Operation of Distributed Energy Resources (Energy Systems in Electrical Engineering) 9819927994, 9789819927999

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S. N. Singh · Naveen Jain · Umesh Agarwal · Manoj Kumawat Editors

Optimal Planning and Operation of Distributed Energy Resources

Editors S. N. Singh ABV-Indian Institute of Information Technology and Management Gwalior, Madhya Pradesh, India

Naveen Jain Department of Electrical Engineering College of Technology and Engineering Udaipur, Rajasthan, India

Umesh Agarwal Department of Electrical Engineering College of Technology and Engineering Udaipur, Rajasthan, India

Manoj Kumawat Department of Electrical Engineering National Institute of Technology Delhi New Delhi, Delhi, India

ISSN 2199-8582 ISSN 2199-8590 (electronic) Energy Systems in Electrical Engineering ISBN 978-981-99-2799-9 ISBN 978-981-99-2800-2 (eBook) https://doi.org/10.1007/978-981-99-2800-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

This book on Optimal Planning and Operation of Distributed Energy Resources attempts to present the state-of-the-art work done in distributed generation technologies such as renewable and non-renewable technologies, energy resource planning with optimization algorithms, analysis of advanced controllers and protection systems, reliability aspects, multi-criteria decision in energy system, integration of electric vehicles, economic dispatch of electric power plants and so on. The optimal planning of distributed energy resources under different aspects and important issues are to be considered while carrying out studies related to the planning and operational aspects of a power system grid. The planning of the electric system with the presence of distributed generation requires the attention of several factors, such as choice of the technology, number, allocation and capacity of the units. Further, selection of technology is depended on distribution system characteristics parameters as power loss, voltage profile, stability, reliability, etc. As a result, while dealing with the increased penetration of dispersed generation in the distribution system, the optimization method may be utilized for the near-to-best characteristics parameters in a distribution network. The goal of renewable energy-based distributed generation integration is to reduce carbon emissions through increased use of renewable energy and other clean distributed generation resources. Further, smart technology is enabling the efficient management and collaboration of renewable energy sources such as solar, ocean, wind and hydrogen with existing conventional energy sources. In the present domain of modern technology, a new trend of the Internet of things with artificial intelligence, utilities can quickly detect and resolve service issues through continuous self-assessment. In this book, an attempt to incorporate all the key requirements of the power system is considered, which help in the planning of an efficient grid. This book includes all the key topics of current trends in distribution system. Therefore, it is quite useful for postgraduates, research scholars and power industries, those who are working in the fields of power system optimization, renewable energy integration and power electronics application in power system.

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Preface

Further, this book fits in the following aspects of the global issues: • • • • • •

Global warming and climate change. Mitigation of energy crises. Application of advanced controller and protection system. Smart metres. Modern optimization approaches. Renewable energy-based distributed energy sources and electric vehicles integration in power system. • Reliability assessment in the presence of distributed energy resources. A brief overview of the importance of each chapter would give a wide understanding of this book. This book is dedicated to the most common and representative issues on operation and planning of distributed energy resources. Chapter 1 discusses the history of power system, global status of CO2 emission, electricity generation and consumption including a brief about various DG technologies. Further, this chapter also discusses the progress of India’s renewable energy market and achievements. Chapter 2 highlights various renewable and non-renewable DG technologies, including their benefits and shortcomings. This chapter also includes the post-effects (technical, economical and environmental) of DG integration in power system. Chapter 3 provides an insightful vision about designing of controller and protection coordinates before and after DER integration in the distribution power network. Chapter 4 includes the fundamentals of optimization techniques along with details about different analytical, meta-heuristic, nature and bio-inspired optimization algorithms. This chapter deals with application of optimization techniques for economic load dispatch problems. Chapter 5 provides the basic idea about optimal integration of renewable energy source in distribution system using the latest optimization algorithms. This chapter discusses the optimal allocation and capacity of RES for optimized benefits. Chapter 6 discussed the electric vehicles (EV) integration potential and its effects in terms of voltage balance, supply continuity and quality in energy pool. This chapter also covers the challenges and opportunities of EV integration. Chapter 7 describes the impact of accurate output prediction of renewable energy sources on reliable operation of power system. The accurate output prediction can help in optimal scheduling of power plant to satisfy the load demand. Chapter 8 discusses the strategies for designing and analysis of a standalone hybrid renewable energy source. This chapter also includes the benefits and drawbacks of using hybrid renewable energy source. Chapter 9 covers the incorporation of artificial intelligence techniques to support the demand response and management of the smart grid. Chapter 10 discussed the energy pool complications, benefits and various mechanisms. Chapter 11 evaluates the reliability in terms of energy not served and expected cost of interruption for distribution system with the integration of renewable energy

Preface

vii

sources. This chapter also highlights the effect of fuse failure probability on reliability evaluation. The effect of intermittent behaviour of renewable energy sources as DG is also included in this analysis. As mentioned in the above chapter-wise discussion, this book will impact all the important dimensions of the power system. Moreover, it would hopefully contribute to solving many issues of the modern power system and will be helpful for the students and academicians. Gwalior, India Udaipur, India Udaipur, India New Delhi, India

S. N. Singh Naveen Jain Umesh Agarwal Manoj Kumawat

Acknowledgements The editors would like to thank all the authors, whose works and support helped us to compile this project effectively on time. We are thankful to our parents, teachers, friends and relatives for their good wishes and support during this project. We acknowledge our sincere thanks to one and all, who inspired and helped directly or indirectly during the course of this study. As always, we are fortunate to express hearty thanks to the almighty God for the successful completion of this book. Our special gratitude to the Springer publication for continuous support to publish this book. Finally, the criticism and suggestions for the improvement of the book are always welcome.

Contents

1

Fundamentals of Power System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Umesh Agarwal, Naveen Jain, S. N. Singh, and Manoj Kumawat

1

2

The Energy Mix: An Emerging Trend in Distribution System . . . . . Sameer Bhambri, Manoj Kumawat, Vivek Shrivastava, Umesh Agarwal, and Naveen K. Jain

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3

Design of Efficient Distributed Energy Resources (DER) Controller and Protection System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. E. S. N. Raju and Trapti Jain

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5

Economic Dispatch for Unbalanced Active Distribution Systems Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . César Álvarez-Arroyo, Lázaro Alvarado-Barrios, Juan Manuel Escaño, Francisco González-Longatt, and Jose Luis Martínez-Ramos Optimal Siting and Sizing of Renewable Energy Sources in Distribution System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pavitra Sharma and H. D. Mathur

39

71

91

6

Scheduling of Electric Vehicle’s Charging–Discharging: An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Bhaskar Chauhan and Sachin K. Jain

7

Impact of Accurate Forecasting on Optimal Operation of Power System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Kailash Chand Sharma and Vivek Prakash

8

Optimal Design and Analysis of Standalone Hybrid Renewable Energy Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Sachin Jain and Venu Sonti

9

Machine Learning Applications in Smart Grid . . . . . . . . . . . . . . . . . . . 193 Arvind Kumar Jain ix

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Contents

10 Energy Pool Management Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . 215 Rajvir Kaur, Saurabh Kumar, and K. Vijayakumar 11 Reliability Analysis of Distribution System with Integration of Distributed Generation Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Umesh Agarwal, Naveen Jain, and Manoj Kumawat

About the Editors

S. N. Singh obtained his M.Tech. and Ph.D. in Electrical Engineering from the Indian Institute of Technology Kanpur, in 1989 and 1995. Presently, he is the Director of Atal Bihari Vajpayee-Indian Institute of Information Technology and Management Gwalior (India) (on leave from the Indian Institute of Technology Kanpur, India). Before joining IIT Kanpur as Associate Professor, Dr. Singh worked with UP State Electricity Board as Assistant Engineer from 1988 to 1996, with Roorkee University (now IIT Roorkee) as Assistant Professor from 1996 to 2000, and with the Asian Institute of Technology, Bangkok, Thailand as Assistant Professor from 2001 to 2002. Dr. Singh was Vice-Chancellor of MMM University of Technology, Gorakhpur, India from April 2017 to July 2022. Dr. Singh received several awards including the Young Engineer Award 2000 of the Indian National Academy of Engineering (INAE), the Khosla Research Award of IIT Roorkee, and the Young Engineer Award of CBIP New Delhi (India), 1996. Professor Singh receives the Humboldt Fellowship of Germany (2005, 2007). Professor Singh became the first Asian to receive the 2013 IEEE Educational Activity Board Meritorious Achievement Award in Continuing Education. He is also the recipient of the INAE Outstanding Teacher Award 2016, IEEE R10 region (Asia-Pacific) Outstanding Volunteer Award 2016, NPSC 2020 Academic Excellence Award, and 2021 IEEE Industry Application Society (IAS) Outstanding Educator/Mentor Award. His research interests include power system restructuring, FACTS, power system optimization and control, security analysis, wind power, etc. Professor Singh has published over 500 papers in International/ national journals/conferences and supervised 41 Ph.D. (eight Ph.D. under progress). He has also written 32 book chapters, eight edited books, and two textbooks. Professor Singh is FIEEE (USA), FIET (UK), FNAE, FIE(I), FIETE, FAIAA (Asia). Naveen Jain received his B.Eng. degree in Electrical Engineering and his M.Eng. degree in Power Systems from Malaviya National Institute of Technology Jaipur (Erstwhile MREC Jaipur), India, in 1997 and 1999, respectively. He received his Ph.D. degree in Electrical Engineering from the Indian Institute of Technology Kanpur, Kanpur, India, in 2013. He has over 20 years of teaching and research experience. Presently, he is working as an Associate Professor in the Department xi

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About the Editors

of Electrical Engineering at the College of Technology and Engineering, Udaipur, India. Presently, he is also the coordinator of TePP Outreach cum Cluster Innovation Centre (TOCIC) of the College of Technology and Engineering, Udaipur under Promoting Innovations in Individuals, Start-ups, and MSMEs (PRISM), Department of Scientific and Industrial Research (DSIR), Ministry of Science and Technology, New Delhi. He has published many research papers in reputed journals and National/International conferences. He is also having a Patent to his credit. He is a reviewer for several international journals and international conferences. He is a Fellow of the Institution of Electronics and Telecommunication Engineers (India), a Senior Member of the Institute of Electrical and Electronics Engineers (USA), a Life Member of the Indian Society for Technical Education, and a Fellow Member of the Institution of Engineers (India). His research interests include planning of distributed generations, renewable energy grid integration, and application of modern optimization methods in power systems, and technical issues in electricity markets. Umesh Agarwal graduated in Electrical Engineering from the College of Technology and Engineering (CTAE, Udaipur) India, in 2012. He received his M.Tech. degree in Power Systems in 2015 from Rajasthan Technical University (RTU, Kota), India. Further, he has completed his Doctoral degree from the College of Technology and Engineering (CTAE, Udaipur) India, in 2022. Currently, he is working as an Assistant Professor at JIET, Jodhpur. He has over 08 years of teaching, research, and industry experience. He has published many research papers in reputed journals and National/ International conferences. He is a reviewer for several international journals and international conferences. His areas of interest are power system restructuring, AI application to the power system, reliability analysis, and planning of distributed energy. Manoj Kumawat graduated in Electrical Engineering from the University of Rajasthan, Jaipur, India, in 2009. He received the M.Tech. degree in Power Systems in 2012 from Malaviya National Institute of Technology Jaipur (MNIT Jaipur), India. He received his Ph.D. degree in Planning Distributed Energy Resources in the Distribution System at the Department of Electrical Engineering, MNIT Jaipur. Presently, he is working as an assistant professor at the Department of Electronics and Electrical Engineering, National Institute of Technology Delhi, India. He has more than 9 years of industrial, teaching, and research experience and is a member of the Institute of Electrical and Electronics Engineers (USA). He is a reviewer for several international journals and international conferences. His areas of interest are AI application to the power system, power quality, and planning of distributed energy resources.

Chapter 1

Fundamentals of Power System Umesh Agarwal, Naveen Jain, S. N. Singh, and Manoj Kumawat

Abstract In the present scenario, the power system expansion has been elevated due to the growing demand of energy with increase in population. The modernization of society is also the key factor for increase in energy need. This chapter discusses the installed capacity for both renewable and non-renewable energy resources in India. Further, this energy need can only be mitigated by the integration of renewable energy sources (RES) as DG source in the existing distribution network. Also, RES integration in the distribution network will reduce the pollution level. This chapter includes the global CO2 emission trend since 2000. The current need of the installation of new power plant can also be mitigated by the installation of DG sources near the load point. This chapter includes the basic of DG and its technical, economical and environmental aspects. Keyword Distributed generation · Indian power generation · Power system · Renewable energy resources

Disclaimer: The presentation of material and details in maps used in this chapter does not imply the expression of any opinion whatsoever on the part of the Publisher or Author concerning the legal status of any country, area or territory or of its authorities, or concerning the delimitation of its borders. The depiction and use of boundaries, geographic names and related data shown on maps and included in lists, tables, documents, and databases in this chapter are not warranted to be error free nor do they necessarily imply official endorsement or acceptance by the Publisher or Author. U. Agarwal (B) · N. Jain Department of Electrical Engineering, College of Technology and Engineering, Udaipur, Rajasthan, India e-mail: [email protected] S. N. Singh Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India M. Kumawat Department of Electrical Engineering, National Institute of Technology Delhi, New Delhi, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. N. Singh et al. (eds.), Optimal Planning and Operation of Distributed Energy Resources, Energy Systems in Electrical Engineering, https://doi.org/10.1007/978-981-99-2800-2_1

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1.1 General Power system is the branch of electrical engineering where we study in depth for its design, operation, maintenance and analysis. The elements necessary for electric power generation, transmission and distribution combine to form a massively complex system, known as the electric power system (Kothari and Nagrath 2008). Energy is required and consumed in various forms for our industrial, commercial and day-to-day activities. Electrical energy is the most prominent among all forms of the energy. The reason is that it can be generated, transmitted and utilized with ease and effectively. Further, the cost effectiveness is also a remarkable benefit of electrical energy. The technological advancement makes it possible to generate electrical energy in bulk at centralized power stations and to transmit it easily over long distance. The electrical power system is a scientific marvel, and some consider electricity and its availability to be the greatest technical successes of the twentieth century, surpassing computers and aircrafts. It is impossible to deny the extreme need of electricity for the development of modern society. The entire world is in the race of electricity generation to satisfy the nation’s need. The China is the world leader (https://cea.nic.in/dashboard/?lang=en) in this race with generation capacity of 7798 TWh units, followed by the USA (4262 TWh), India (1557 TWh), Russia (1092 TWh) and Japan (1011 TWh). Globally installed electricity capacity, including renewables and non-renewables, is listed in Table 1.1. Global electricity consumption fell by 1.1% in 2020. It is the first drop since 2009. China, which consumes 29% of the world’s power, has rapidly recovered after COVID-19 and growth of 3.1% has been observed in 2020 (https://cea.nic.in/das hboard/?lang=en). It declined in USA by 3.9% and in European Union by 4.3%. It also fell in India, where power consumption has risen dramatically since 2000. The global electrical energy consumption since 2010 is included in Table 1.2. Table 1.1 Globally installed electricity capacity (https:// cea.nic.in/dashboard/?lan g=en)

S. No. Source of energy

Installed capacity (GW)

1

Fossil fuels

4284

2

Renewables

2501

3

Hydroelectricity

1140

4

Wind

622

5

Solar

587

6

Nuclear

369

7

Hydroelectric pump storage

168

8

Biomass and waste

136

9

Geothermal

10

Tidal, wave and fuel cell Total

14 2 9823

1 Fundamentals of Power System

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Table 1.2 Globally electrical energy consumption (https://cea.nic.in/dashboard/?lang=en) S. No.

Year

Consumption (TWh)

S. No.

Year

Consumption (TWh)

1

2010

18,706

7

2016

21,872

2

2011

19,397

8

2017

22,469

3

2012

19,809

9

2018

23,376

4

2013

20,469

10

2019

23,845

11

2020

23,582.71

5

2014

20,873

6

2015

21,289

33

33.4

31.5

32.7

32.2

32.2

32.3

32.2

31.6

31.3

30.4

29.1

28.7

28.9

27.8

27

26

24.9

23.8

25

23.5

30

23.1

35

33.5

Global CO2 emission 2000-2021 40

20 15 10 5 0

CO2 Emission (Gt of CO2)

Fig. 1.1 Global CO2 emission trend (2000–2021) (https://cea.nic.in/dashboard/?lang=en)

Due to this reduction in energy consumption, the CO2 emission also decreases by 6% (approx.) in 2020. Figure 1.1 represents the global CO2 emission trend since 2000 up to 2021.

1.2 Indian Power Sector Darjeeling’s first hydroelectric power plant was established over a century ago, in the 1890s. At the time of independence, total installed capacity was 1360 MW (https:// npp.gov.in/publishedReports). Except for a few licences, the entire power industry was controlled by state governments and generally managed by capital intensive state electricity boards (SEBs) with the enactment of the Electricity Act in 1948. In 1975, the central government joined the sphere of generation and transmission through central public sector corporations (NTPC, for example) to assist the efforts

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of cash-strapped SEBs. The Electricity Regulatory Commission Act of 1998 was enacted to form a regulatory commission (Kothari and Nagrath 2008). The first 765 kV transmission line, which was initially charged at 400 kV, was built in 1998. The Electricity Act of 2003 was passed in order to allow free access in transmission. The National Electricity Plan was completed in 2005–06 (https:// mnre.gov.in/). In 2006, a significant step towards the formation of a national grid was accomplished with the coordination of NR with ER-NWR-WR. All the national grids are marked in map as shown in Fig. 1.2. At present, lots of renewable technologies are available worldwide. Each has its merits–demerits. Some technologies may be abundantly available in nature at

Norhtern Powergrid (110.39 GW)

North-Eestern Powergrid (4.894 GW) Eestern Powergrid (34.222 GW)

Western Powergrid (127.71 GW)

Southern Powergrid (117.78 GW)

Islands - 78.06 MW Total India - 395075.06 MW

Fig. 1.2 Indian power grids

1 Fundamentals of Power System Table 1.3 Installed renewable energy capacity in India (31-01-2022) (https:// mnre.gov.in/)

5

S. No.

Sector

1

Wind power

40,100.93

2

Solar (ground mounted)

42,430.57

3

Solar (roof top)

6405.55

4

SPV (off-grid)

1467.44

5

Small hydro

4839.9

6

Biomass (bagasse’s) cogeneration

9403.56

7

Biomass (non-bagasse’s) cogeneration

772.05

8

Waste to power

199.14

9

Waste to energy (off-grid) Total

Cumulative capacity (MW)

234.97 105,854.11

free of cost like solar, wind, geothermal, ocean, tidal, etc., while others are rare to implement. Among all other technologies, solar is the most promising technology that can mitigate India’s energy needs. In Table 1.3, a detailed comparison of all renewable technologies is tabulated. In India, renewable energy sources come under the domain of MNRE. India is the first nation to set up a Ministry of Non-Conventional Energy Resources in the early 1980s (Khare et al. 2013). The large hydroelectric plants are not included in MNRE targets. Till 2030, as decided by the GOI through MNRE, 40% of the total electricity generation in India should be through renewable energy resources. India sets a milestone by generating 50.303 GW as of January 2022, contributed both by the solar park and by rooftop solar panels. India is home to three of the world’s top six solar parks. The world’s third-largest solar park with a capacity of 1000 MW is installed at Kurnool, Andhra Pradesh, India (https://mnre.gov.in/; Khare et al. 2013; Sharma et al. 2015; Sarat Kumar Sahoo 2016; Soni and Gakkhar 2014; Dawn et al. 2016; Razykov et al. 2011; Agarwal et al. 2021) (Fig. 1.3).

1.3 Distributed Generation (DG) The DG is depicted in as a source of power supply coupled to the distribution system’s radial structure near the consumer end. According to the International Council on Large Electric Systems, DG refers to any generating unit that is linked to a distribution network and has a capacity ranging from 50 to 100 MW (Ackermann et al. 2001; CIGRE Study Committee 2003). The Institute of Electrical and Electronics Engineer (IEEE) considers the DG as a facility that is lesser in size as compared to central power plant and having dispatchability (IEEE 2003). Taking into account all of the aforementioned perspectives on DGs, it is possible to infer that the DG is a modest source of electric power, rated up to 100 MW and linked near the load point. The

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Cumulative Capacity (MW) 772.05

199.14

234.97

1467.44 6405.55

9403.56

40100.93

42430.57

Wind Power

Solar (Ground Mounted)

Solar (Roof Top)

SPV (Off-grid)

Biomass (Bagasse’s) Cogeneration

Biomass (Non-Bagasse’s) Cogeneration

Waste to Power

Waste to Energy (Off-grid)

Fig. 1.3 Installed renewable energy capacity in India (31-01-2022)

DG is suitably smaller in size than the central power plants. The current polluted environment and greater sensitive loads as a result of technology advancements are encouraging consumers to adopt renewable energy. These are factors providing momentum for the use of renewable energy-based DG (Agarwal and Jain 2019). Being advantageous over conventional power plant, DG has evolved into a research hub for scientists, academics and environmentalists. Renewable energy sources like as DG are advantageous in terms of lowering greenhouse gas emissions, but power supply uncertainty is still a concern. Some renewable technologies need a huge space, although most, such as a biogas plant, may be contained in a small area. Furthermore, certain renewable technologies have substantial installation, operating and maintenance costs (Peças Lopes et al. 2007; Hadjsaid et al. 1999). However, it is still less costly than a centralized power generation source. To some extent, several of the DG technologies are still in the process of being developed. Solar photovoltaic (SPV) is the most prominent source of clean energy. It converts the solar radiation energy into electrical energy. It can be used as DG for the range of 1 kW–80 MW. Similarly, small hydro and micro-hydro plants convert the gravitational potential energy into electrical energy. The available power generation range is 5 kW–1 MW. The wind turbines convert the kinetic energy of wind into electrical energy. These can be used as DG in the range of 200 W–3 MW. Geothermal energy converts the heat energy of earth into electrical energy. This source of energy is

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available in the range of 5–100 MW range as DG. Tidal and ocean energy are the form of energy that converts the waves kinetic energy into electrical energy. These can be used as DG with the range of 0.1–1 MW. Biomass energy can be implemented as DG source with the range of 100 kW–20 MW. It converts the chemical energy in to electrical, thermal and biofuels. Although hydrogen energy systems also convert the chemical energy in to electrical, range (40–400 MW) is more as compared to biomass energy system (Agarwal and Jain 2019). Furthermore, some non-renewable energy resources can also be used as DG source like integrated gasification combined gas turbine (30 kW–3000+ kW), Micro-turbine (30 kW–1 MW), internal combustion (IC) engine (5 kW–10 MW) and fuel cell (FC) technologies (100 W–5 MW), etc.; all these non-renewable DG technologies can avoid grid expansion if these are dispatchable (Agarwal and Jain 2019).

1.4 Impacts of DG Technical, economical and environmental implications are the three broad areas in which the DG’s effects may be characterized (Pepermans et al. 2005; El-Khattam and Salama 2004; Prakash and Khatod May 2016). These impacts are discussed in Table 1.4.

1.5 Outlines of the Book This book is organized in 11 chapters including the fundamentals of power system. This book is dedicated to most common and representative issues on operation and planning of distributed energy resources. Chapter 1 discusses the history of power system, global status of CO2 emission, electricity generation and consumption including a brief about various DG technologies. Chapter 2 highlights various renewable and non-renewable DG technologies, including their benefits and shortcomings. This chapter also includes the aftereffects (technical, economical and environmental) of DG integration in power system. Chapter 3 provides an insightful vision about designing of controller and protection coordinate before and after DER integration in distribution power network. Chapter 4 includes the fundamentals of optimization techniques along with details about different analytical, meta-heuristic, nature and bioinspired optimization algorithms. This chapter also deals with the application of optimizations techniques for economic load dispatch problems. Chapter 5 provides the basic idea about optimal integration of renewable energy source in distribution system using some optimization algorithms. This chapter discusses the optimal location and capacity of RES for optimized benefits. The electric vehicles (EV) integration potential and its effects in terms of voltage balance, supply continuity and quality in energy pool are discussed in Chap. 6. This chapter also covers the challenges and opportunities of EV integration.

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Table 1.4 Impacts of DG S. No.

Category

Impacts

1

Technical

Enhanced voltage profile Improvement in reliability Improved energy efficiency of power supply As a backup source of energy Reduction in power loss Problem regarding protection coordination

2

Financial

Reduces depreciation costs of the fixed assets Delaying the need for investment in new energy infrastructure Reduction in operation and maintenance costs By establishing a positive market environment for new agents, DG lowers power tariffs

3

Environmental

Can be a source for green and clean source of power generation Reduction in deforestation Less requirement of space Wind turbines as DG creates noise pollution Wind turbines as DG are particularly not favourable to the bird species During the energy generation process, ocean wave energy can be hazardous to aquatic organisms

Chapter 7 describes the impact of accurate output prediction of renewable energy sources on reliable operation of power system. The accurate output prediction can help in optimal scheduling of power plant to satisfy the load demand. The strategies for designing and analysis of a standalone hybrid renewable energy source are discussed in Chap. 8. This chapter also includes the benefits and drawbacks of using hybrid renewable energy source. In particular, Chap. 9 covers the incorporation of artificial intelligence techniques to support the demand response and management of the smart grid. The energy pool complications, benefits and various mechanisms are discussed in Chap. 10. Chapter 11 evaluates the reliability in terms of energy not served and expected cost of interruption for distribution system with the integration of renewable energy sources. This chapter also highlights the effect of fuse failure probability on reliability evaluation. The effect of intermittent behaviour of renewable energy sources as DG is also included in this analysis. Overall, this book provides complete research prospective for optimal integration of renewable energy sources in distribution network considering power quality, reliability and cost effectiveness of power system.

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References Ackermann T, Andersson G, Söder L (2001) Distributed generation: a definition. Electr Power Syst Res 57(3):195–204 Agarwal U, Jain N, Singh SN, Kumawat M (2021) Solar photovoltaic (PV) generation. In: Singh SN, Tiwari P, Tiwari S (eds) Fundamentals and innovations in solar energy. Energy systems in electrical engineering. Springer, Singapore Agarwal U, Jain N (2019) Distributed energy resources and supportive methodologies for their optimal planning under modern distribution network: a review. Technol Econ Smart Grids Sustain Energy 4:3 Central Electricity Authority (CEA). Available at: https://cea.nic.in/dashboard/?lang=en. Accessed on: 24 Feb 2022 CIGRE Study Committee (2003) Impact of increasing contribution of dispersed generation on the power system Dawn et al (2016) Recent developments of solar energy in India: perspectives, strategies and future goals. Renew Sustain Energy Rev 62:215–235 El-Khattam, Salama MMA (2004) Distributed generation technologies, definitions and benefits. Electr Power Syst Res 71(2):119–128 Hadjsaid N, Canard JF, Dumas F (1999) Dispersed generation impact on distribution networks. IEEE Comput Appl Power 12(2):22–28 IIEEE, Institute of Electrical and Electronics Engineers, 1547–2003—IEEE draft standard for interconnecting distributed resources with electric power systems International Energy Agency (IEA). Available at: https://www.iea.org/reports/global-energy-rev iew-2021/co2-emissions. Accessed on: 24 Feb 2022 Khare et al (2013) Status of solar-wind renewable energy in India. Renew Sustain Energy Rev 27:1–10 Kothari, Nagrath (2008)“Power system engineering. Tata Mc-Graw Hills Publishers. ISBN 9780070647916 Ministry of New and Renewable Energy (MNRE). Available at: https://mnre.gov.in/. Accessed on: 24 Feb 2022 Peças Lopes JA, Hatziargyriou N, Mutale J, Djapic P, Jenkins N (2007) Integrating distributed generation into electric power systems: a review of drivers, challenges and opportunities. Electr Power Syst Res 77(9):1189–1203 Pepermans G, Driesen J, Haeseldonckx D, Belmans R, D’haeseleer W (2005) Distributed generation: definition, benefits and issues. Energy Policy 33(6):787–798 National Power Portal (NPP). Available at: https://npp.gov.in/publishedReports. Accessed on: 24 Feb 2022 Prakash P, Khatod DK (2016) Optimal sizing and siting techniques for distributed generation in distribution systems: a review. Renew Sust Energ Rev 57:111–130 Razykov TM et al (2011) Solar photovoltaic electricity: current status and future prospects. Solar Energy 85(8):1580–1608 Sahoo SK (2016) Renewable and sustainable energy reviews solar photovoltaic energy progress in India: a review. Renew Sustain Energy Rev 59:927–939 Sharma C, Sharma AK, Mullick SC, Kandpal TC (2015) Assessment of solar thermal power generation potential in India. Renew Sustain Energy Rev 42:902–912 Soni MS, Gakkhar N (2014) Techno-economic parametric assessment of solar power in India: a survey. Renew Sustain Energy Rev 40:326–334

Chapter 2

The Energy Mix: An Emerging Trend in Distribution System Sameer Bhambri, Manoj Kumawat, Vivek Shrivastava, Umesh Agarwal, and Naveen K. Jain

Abstract Decentralization of the power sector is currently gaining attention by power system planners to satisfy the growing energy demand of the society. Due to the deterioration of the worldwide environment and the continued depletion of global energy, renewable energy resources have gotten a lot of attention in recent years due to their self-renewal nature over time. The integration of renewable energy (RE) resources in existing power system suppress the need for new generation infrastructure for growing population. Distributed energy resources (DER) are referred as electricity generation by small generating units like solar photovoltaics, windmills, biomass, tidal wave, etc. installed near the load centers or installed at distribution grid. The integration of RE-based DG in distribution network is currently gaining more attention among transmission system operators (TSOs) as it fulfills the end user demands for reliable, economic and pollution-free energy source. Although most of the DER are renewable in nature, the output is intermittent in nature. The optimal siting and sizing of DER in distribution network for power loss minimization, voltage profile improvement and reliability enhancement are another aspect of DG integration. Still, DG integration can improve the system performance. This book chapter is confined to the review of existing distributed generation technologies, their potential capabilities to satisfy energy demand, types, basic operation, various aspects with DG penetration and relative merits and demerits. Keywords Biomass · Distributed · Generation · Grid · Generation · Photovoltaics · Renewable energy · Storage · Technology · Wind

S. Bhambri · M. Kumawat · V. Shrivastava Department of Electrical Engineering, National Institute of Technology Delhi, New Delhi, India U. Agarwal (B) · N. K. Jain Department of Electrical Engineering, College of Technology and Engineering, Udaipur, Rajasthan, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. N. Singh et al. (eds.), Optimal Planning and Operation of Distributed Energy Resources, Energy Systems in Electrical Engineering, https://doi.org/10.1007/978-981-99-2800-2_2

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Nomenclature a-Si AD Cd Td CIGS CAES CHP CC DG D-STATCOM EVs FESS KW Mono-Si MW MSC Poly-Si PVSTATCOM POD RE RPM SMES Si T/F TSPs TSO USA VAWT

Amorphous silicon Anno Domini Cadmium telluride Copper indium gallium selenide Compressed air energy storage Combined heat and power Central controller Distributed generation Distributed static compensator Electric vehicles Flywheel energy storage system Kilowatt Mono-crystalline silicon Megawatt Microsource controller Poly-crystalline silicon Photovoltaic static compensator Power oscillation damping Renewable energy Revolutions/min Supermagnetic energy storage Silicon Transformer Transmission system planners Transmission system operator United States of America Vertical axis wind turbine

2.1 Introduction In the present era, modern power systems are facing various technical, environmental and economical issues. The conventional distribution networks are already stressed with increased load demand, and it will affect the service continuity. Moreover, the conventional power plants are not emission free, and this may create issue of environmental pollution leading to health problems as well as other global concerns. Therefore, the necessity of renewable energy sources integration naturally emerges. Power system planners have been looking forward toward distributed generation units (solar, wind, biomass, tidal, etc.) as a solution to serve the society’s energy hunger. Small-scale generators built near load centers are referred to as DG units. The proper dispersion of DG units ensures the improved system reliability and health of power

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system. Modular power generation technologies are dispersed throughout the utility’s distribution system with objective of reducing transmission and distribution losses (Kumawat et al. 2021; Rezaee Jordehi 2016). The general structure of power system with distributed generation sources is represented in Fig. 2.1. Generally, an electrical power system consists of interlinked processes that produce, transmit and distribute electricity to the demand site (residential, commercial, industrial, institutional, etc.). Generation, transmission and distribution are the three stages of an electrical power system. In centralized power system, electricity is being transported over long transmission lines to supply load centers located far away from generation site. Due to the growing energy demand with increased population, these conventional power plants become less capable to satisfy the energy demand. Therefore, there may be a need of reconfiguration of the system by installing small

Fig. 2.1 General structure of power system with DGs

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energy sources in the existing power system that fulfills the demand with improving voltage profile, power quality and reliability. In (Ellabban et al. 2014), the DG is represented as a source of electrical energy that is connected to the radial structure of distribution system near the customer end. According to International Council on Large Electric System, any generation units, connected to distribution network and having capacity from 50 to 100 MW, without facility of central planning and dispatchability, are termed as DG (Carvalho et al. 2008). Institute of Electrical and Electronics Engineer (IEEE) considers the DG as facility, comparatively smaller than central power plant and can be allocate at anywhere in power system (León et al. 2022). The Electric Power Research Institute (EPRI) defines the DG as generation unit having maximum capacity up to 50 MW along with energy storage devices connected at consumers end or at distribution or subtransmission substations (Al-Tameemi et al. 2018). Considering all the above views about the DGs, it can be concluded that the DG is a small source of electric power, connected near the load point or in the distribution network. The size of the DG is sufficiently smaller than the central power generation source. The term DG can be classified as follows: . . . .

On the basis of connection On the basis of size On the basis of technology used On the basis of coupling methods.

On the basis of connection, the DG can be connected in grid connected mode and as a standalone device. The DG unit which delivers power to the utility grid is called grid connected DG; otherwise, it is called standalone DG. On the basis of size, Dg can be classified as micro, small, medium and large DG. The classification on the basis of size is shown in Table 2.1. On the basis of technology used, DG can be classified as renewable technology-based DG, non-renewable technology-based DG and storagebased DG technology. The classification based on technology used is represented in Fig. 2.2. Similarly, on the basis of coupling methods, DG can be classified as inverter-based DG and direct-coupled DG. Static fuel cells and photovoltaics are examples of inverter-based DGs. Another form (rotary energy conversion-based) includes micro-combined heat and power and direct driven wind turbine. On the other hand, direct-coupled DGs are synchronous generators and DFIG-based wind turbines (Adajah et al. 2021). There may be another classification based on the power output from DG. The output power may be active only or it may be reactive or combination of both. Table 2.1 DG types on the basis of size

S. No.

Size of DG

1

1 W–5 kW, micro

2

5 kW–5 MW, small

3

5 MW–50 MW, medium

4

50 MW–300 MW, large

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Non-renewable technology

Distributed generation Technology

Renewable or Sustainable technology

Storage technology

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Gas turbine Combustion turbine Reciprocating engine Micro turbine Wind Solar Tidal Geothermal Hydro Biomass Batteries Flywheel Super capacitor Pumped storage

Fig. 2.2 DG classification based on technology used

2.2 DG Technologies Renewable energy sources as DG are beneficial in contrast of reduction in greenhouse gases, but uncertainties in power supply are also an issue. Some of the renewable technologies require large space, but most can be concise at small place like biogas plant. Although the cost for installation and operation is high, it is still less than centralized power plants. Currently, to a certain extent, some of the DG technologies are still in research and underdevelopment phase. Some of the popular DG technologies are discussed here in detail (Kumawat et al. 2018).

2.2.1 Microturbines These are simple in structure with high-speed operation. The natural gas and biogas are the fuels used for the operation. Being at the developing phase, the cost is continuously decreasing with increased inefficiency. Although microturbine technology is not very much environment-friendly due to harmful emissions, these emissions are less severe than fossil fuel emissions (Agarwal et al. 2021).

2.2.2 Solar The effect of global warming on the world’s climate may lead to climate change as per experts. The main reason behind all this is emission of green-house gases through rapid industrialization. The amount of this emission is significantly high in developed countries. Even developing countries are producing green-house gases at a faster rate. In addition, these pollutants affect the health of urban/rural population in significant

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S. Bhambri et al. 1075 +215 860 +180 680 +150 530 +125

Previous year capacity Annual additions

405 +100 305 +76

229 +52

Annual solar additions

177 +39

15 +7

23 +8

40 +17

71 +31

101 +30

138 +37

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Fig. 2.3 Solar PV global capacity and annual additions (Kaygusuz 2009). Source Becquer institute and IEAPVPS

manner. Government bodies from developed/developing countries meet with agenda of cutting green-houses gases and to adopt ways of fulfilling demands through renewable energy. Solar energy is one of the highest potential energy resources around the globe, and its availability depends upon the geographical site. One of the ways to harness solar energy is to make use of solar panels. This technology, frequently called solar photovoltaics, converts the solar photon energy into direct current electricity. Through inverters, this power is interfaced with the utility grid (Agarwal et al. 2021; Mahdavi et al. 2021). Solar PV global capacity and annual addition are shown in Fig. 2.3.

2.2.2.1

Solar Technologies

Two technologies are available for producing electricity through solar: (i) solar photovoltaics and (ii) solar thermal. Solar photovoltaics represents clean, green and abundant source of electric power. Solar energy can be harnessed to produce electricity, heating in rooms and fluids like water, etc. Photovoltaic effect refers to conversion of light into electricity using semiconductor materials (solar panels) as shown in Fig. 2.4. Solar photovoltaic can be either on grid or off-grid system. Solar thermal technology

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SunLight Electron flow

Lamp

Metallic conducting strips

Glass lens _______ ++++++++

N type silicon Depletion layer P type silicon

-ve Electrons Substrate base

+ve Holes

Fig. 2.4 Photovoltaic effect of a solar cell

uses concentrating lenses to produce heat and then heat can be transferred to steam turbines for electricity production. Harnessing energy through solar has capability of fulfilling the increased demand of consumers, and it receives much more attention due to its availability across the globe. In addition, interest in solar photovoltaics has been gained in recent years due to its continually decreasing cost and increased solar panel efficiency (Agarwal et al. 2021). Worldwide installed capacity of this technology doubles in every couple of years. As per the records, the worldwide installed capacity of solar PV increases to 35 times (177 GW) in 2014 than what it was in 2006. The major disadvantage of solar photovoltaics is that its output is unpredictable (Rezaee Jordehi 2016; Agarwal and Jain 2019).

2.2.2.2

Components Used

In solar photovoltaics technology, solar panels/arrays (shown in Fig. 2.3) are the components that convert sunlight into direct current electricity. Solar cell is the smallest unit of any solar panel, and several solar panels may constitute an array. Apart from this, photovoltaics technology employs an inverter which inverts DC electricity to an AC electricity. Utility transformer (usually step up) also finds application in case electricity is to be injected to the existing grid (Agarwal et al. 2021). Figure 2.6 shows application of photovoltaics used to power household appliances with injection of power with the utility grid. In solar thermal technology, lenses/concentrators are used which focus solar radiation to produce enough heat. Further, this heat can be used to produce steam to run

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Fig. 2.5 Parabolic dish concentrator Sunlight Receiver Concentrator

Loads

Sunlight Solar Panal

DC Disconnect switch

DC/AC Converter

Utility grid

Utility meter Transformer

Solar charge controller

Battery

Fig. 2.6 Exchange of power with utility grid through solar panels

conventional generator to produce electricity (Agarwal et al. 2021). The parabolic dish concentrator employed in is shown in Fig. 2.5.

2.2.2.3

Solar Cell Technologies

Following is a brief classification and description of various solar cell technologies. Table 2.2 discusses the various solar cell technologies with their generation, including advantages and disadvantages of each generation. Crystalline solar cells provide a number of advantages over other types of solar cells, including higher efficiency and ease of use. These advantages have encouraged manufacturers to consider them as potential solar cell materials. Mono-Si solar PV

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Table 2.2 Various solar cell technologies (Richhariya et al. 2020) Generation

Technology

Merits

Demerits

I-Generation

Crystalline silicon wafer Mono-crystalline (17–18%) Poly-crystalline (12–14%)

• High photoconversion efficiency • Well-established technology • Abundance of availability

• Costly • Required heavy mechanical structure for support • Less optical absorption

II-Generation

Thin-film (4–8%) CIGS (10–15%) CdTe (9–11%)

• Relatively cheap • Can be made in a much thinner form • High optical absorption

• • • •

III-Generation

Organic material Dye synthesized (9–10%) Or Semiconductor synthesized solar cell (39–40%)

• High optical absorption • Cheaper

• Efficiency can be improved • Life is short which can also be improvised

IV-Generation

Perovskite PV & Hybrid (15–31%) Nanocrystal cell (7–8%)

• • • •

• Less stable when used in PV • Thermally stable

High efficiency More lifetime Non-toxic Low cost

Less efficient Less available material Toxic (cadmium) Less economic

cells come under first-generation technology. They have a lengthy history of demonstrating their durability and longevity. Mono-crystalline Si (Mono-Si) solar cells are more expensive than polycrystalline Si (Poly-Si) solar cells. Non-uniformity in the lattice structure of Poly-Si makes them less efficient as compared to Mono-Si cells. Rather than a single huge ingot, they have a series of smaller ingots extracted from a molten vat. Thin-film solar cells are known as second-generation cells. These are economically cheaper than silicon crystalline solar cells but have got lower efficiencies (Richhariya et al. 2020). China occupies the topmost position in solar installations with a capacity of nearly 205 GW till 2019. USA, European Union, Germany, India and Japan have a capacity of 131.7 GW, 63 GW, 49.2 GW and 42.8 GW, respectively (Agarwal et al. 2021). As per record of International Renewable Energy Agency (https://www.irena.org/news/pressreleases/2021/Apr/World-Adds-RecordNew-Renewable-Energy-Capacity-in-2020), Asia added (78 GW) in 2020. China added 49 GW of solar installations followed by Vietnam (addition of 11 GW), Japan (over 5 GW), India (more than 4 GW), Republic of Korea (more than 4 GW), the United States of America (added 15 GW), respectively.

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2.2.3 Wind Power Like solar photovoltaics, it is a clean, green and emission-free technology. India has the huge potential for electricity generation using wind power. Wind turbines are the machines that convert the energy from the wind into electricity (Rezaee Jordehi 2016). On the basis of structure, there are two types of wind turbines available: (i) horizontal axis wind turbine (HAWT) and (ii) vertical axis wind turbine (VAWT). On the other basis, onshore and offshore are the two types of wind farms. Performance and efficiency can be improved if used in hybrid with solar PV. Wind turbine output power is intermittent in nature as it depends on wind speed, which varies over time (Book Review 2006).

2.2.3.1

Historical Background

According to historical records, Persians invented the first windmill about 900 AD. The initial designs were drag-type devices with a vertical axis. As a result, they were naturally inefficient and prone to damage in strong winds. During the Middle Ages, wind energy became popular in Europe. The axes of these windmills were all horizontal. They were employed for a variety of mechanical tasks, including water pumping, grain grinding, wood sawing, and tool powering. Wind power remained an important source of energy in Europe until shortly before the industrial revolution, but it began to decline in importance after that (Book Review 2006).

2.2.3.2

Components

Following components are employed in a typical wind energy machine (Book Review 2006). Rotor: The rotor, as shown in Fig. 2.7, is made up of the hub and blades of the wind turbine. In terms of both performance and overall cost, these are usually regarded as the most crucial components. The majority of modern turbines have three-bladed upwind rotors. Drive train: The wind turbine drive train is made up of rotating components. A lowspeed shaft, a gearbox, and a high-speed shaft are common components on the rotor side (shown in Fig. 2.7). The other components of the drive train include support bearings, couplings, a brake and rotating sections of generator. The gearbox increases the rotor’s rotation from tens of rpm to several thousand of rpm. Generator: Grid-connected wind machine employs induction generators for penetrating power to the grid. Doubly fed induction generators (DFIG.) are machines

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Break Gear Box

21

Generator Necelle Electric control

Hub

Yaw Blades

Tower

Cable

Grid Foundation

Fig. 2.7 Components of windmill

that convert wind energy into electric power at both subsynchronous and supersynchronous speeds. Induction generators have the advantage of being durable, affordable and simple to connect to an electrical network. Windmill consisting of generator is shown in Fig. 2.7. Yaw and nacelle system: The yaw orientation mechanism, the machine bedplate or main frame and the wind turbine enclosure are all included in this category. The main frame secures and aligns the drive train components. The innards of the nacelle are kept dry by the nacelle cover. The rotor shaft is kept correctly aligned with the wind by a yaw orientation device. Large bearings connect the main frame and tower, as seen in Fig. 2.7. Foundation and tower: The bottom structure and the supporting foundation (shown in Fig. 2.7) are included in this category. The main types of these towers include free-standing steel tube, lattice tower and concrete tower. Controls: The control system of a wind turbine is crucial for both machine operation and electricity generation. Sensors, controllers, power amplifiers and actuators make up the wind turbine control system. The sensing system is made up of various sensors

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such as speed, position, flow, temperature, current, voltage and so on. Controllers are made up of mechanical mechanisms, electrical circuitry and computers. Power amplifiers include switches, electrical amplifiers, hydraulic pumps and valves. The actuation system includes motors, pistons, magnets and solenoids (Book Review 2006). Expansion in wind energy installations has become double in 2020 compared to 2019 (111 GW in 2020 to 58 GW in 2019). China added 72 GW of wind installations, followed by USA (14 GW wind installations). In 2020, ten additional countries expanded wind capacity by over 1 GW. Offshore wind capacity climbed to roughly 5% of total capacity in 2020 (https://www.irena.org/news/pressreleases/2021/Apr/ World-Adds-Record-New-Renewable-Energy-Capacity-in-2020).

2.2.4 Hydroelectricity It is the most widely used renewable energy source in the planet. It is a dispatchable source of electric power that converts energy from the gravitational force of falling water. Figure 2.9 depicts the basic operation of hydroelectric station employing gate valve, penstock, turbine, generator, transmission lines, transformer, etc. components. The potential energy of water at reservoir is converted into mechanical energy at turbines. Water turbines are coupled to generators to produce electricity that is transmitted at high voltages via power transformer. Although its emissions are modest and its cost is low, local ecosystems is affected, vast amount of land is occupied and people and wildlife are relocated (Rezaee Jordehi 2016). Small capacity of the reservoir can be used to produce power up to 10 MW on the concept similar to hydropower plant. Small hydropower station can be pico hydroelectric (less than 5 kW), micro hydroelectric (5–100 kW), mini hydroelectric (100 kW–1 MW) and small hydroelectric (1–10 MW). Medium type hydroelectric plants have capacities between 10 and 100 MW. Large hydroprojects have capacities beyond or equal to 100 MW (Boyle 2012; https://powermin.gov.in/en/content/faqshydropower). Capacities are not only the criteria based on which hydroplants are classified. This can be better understood by Fig. 2.8 (Fig. 2.9).

2.2.5 Fuel Cells Fuel cells have proven to be environmentally benign. Chemical energy is converted to electric energy by a chemical reaction between hydrogen ions and oxygen or another oxidizing agent. When hydrogen is used as a fuel, it produces water and heat in addition to electricity (shown in Fig. 2.10). Because fuel cells may generate electric energy as long as their inputs are applied, they are dispatchable energy sources (Adajah et al. 2021; https://www.energy.gov/eere/fuelcells/comparison-fuel-cell-tec hnologies).

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Hydroelectric plants

based upon the capacity

based upon the effective head of water

based upon type of turbine

high head, (>100 m)

Pico,(100 MW)

crossflow

Francis Kaplan Pelton

small, (1-10 MW)

Fig. 2.8 Classification of hydroelectric plants

Reservior/ Canal/ Dam

Penstock

Water flow Turbine

Electrical Utility

Step up transformer

Generator

Governor control

Fig. 2.9 Block diagram of mini hydropower station

Oxygen from air Lamp O2 Electric current _________

_ _Electric _ _ _ _ current ___ O2

Electric circuit

MeOH

H+

H+

Fuel

H+

H+

H+ H+

Polymer Electrolyte membrane Anode catalyst

Fig. 2.10 Operation of fuel cell

The type of fuel cells are shown in Table 2.3.

H2O, Exhaust Cathode catalyst

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Table 2.3 Type of fuel cells with operating conditions (https://www.energy.gov/eere/fuelcells/com parison-fuel-cell-technologies) Operating conditions (°C)

η (%)

Application

Advantages

Challenges

Alkaline fuel cell < 100 (alkaline polymer membrane)

(i) Space (ii) Transportation (iii) Backup power (iv) Military

(i) Wider range of stable materials allows lower cost components (ii) Low temp (iii) Quick start-up

(i) Sensitive to 60 CO2 in fuel and air (ii) Electrolyte management (aqueous) (iii) Electrolyte conductivity (polymer)

Proton exchange membrane fuel cell (perfluorosulfoic acid)

< 120

(i) Distributed generation (ii) Backup power (iii) Portable power (iv) Transportation (v) Special vehicles

(i) Solid electrolyte reduces corrosion and electrolyte management problems (ii) Low temp, Quick start-up and load following

(i) Expensive 60 catalyst sensitive to fuel impurities

Phosphoric acid fuel cell (phosphoric acid soaked in a porous matrix)

150–200

(i) Distributed generation

(i) Suitable for CHP (ii) Increased tolerance to fuel impurities

(i) Expensive catalysts (ii) Long start-up time (iii) Sulfur sensitivity

40

Molten carbonate fuel cell (potassium carbonates, soaked in a porous matrix)

600–700

(i) Distributed generation (ii) Electric utility

(i) High efficiency (ii) Fuel flexibility, suitable for CHP (iii) Hybrid/gas turbine cycle

(i) High temperature corrosion and breakdown of cell components (ii) Long start-up time, low power density

50

Solid oxide fuel cell

900

(i) Distributed generation (ii) Auxiliary power supplies (iii) Electric utility

(i) High efficiency, fuel flexibility (ii) Solid electrolyte suitable for CHP (iii) Hybrid/gas turbine cycle

(i) High temperature corrosion and breakdown of cell components (ii) Long start-up time (iii) Limited number of shutdowns

60

Fuel cell (electrolyte used)

(continued)

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Table 2.3 (continued) η (%)

Fuel cell (electrolyte used)

Operating conditions (°C)

Application

Advantages

Challenges

Direct methanol fuel cell

60–90

(i) Used to provide power for portable fuel cell applications

(i) Easier to transport

(i) Low 30–40 performance than proton exchange membrane fuel cell

Source Comparison of Fuel Cell Technologies, Department of Energy

2.2.6 Diesel Generators Diesel generators are combination of diesel engine and electric generator (alternator).These are used as emergency power supplies as the utility grid fails. It comes up with complete ancillary devices like sound attenuation, base, water heaters, canopy, circuit breakers and starting systems. It is very suitable for standalone operation and can be started and shut down almost spontaneously (Rezaee Jordehi 2016).

2.2.7 Biomass Biomass and its derived fuel can fulfill the growing energy demand in the context of rural development. Biomass is found in plenty in rural areas in the form of agricultural wastes. Its efficient utilization will lead to illuminate village life to a significant extent (Woldeyohannes et al. 2016). Industrialization in countries has led to increased energy consumption due mainly to the population growth, technology enhancement and improvements in standard of living. Currently, 80% of energy needs are fulfilled by fossil fuels. Increased energy demands, energy security concerns and environmental impacts caused by fuels have led to a trend toward renewable sources (https://www.irena.org/news/pressreleases/2021/Apr/World-Adds-Rec ord-New-Renewable-Energy-Capacity-in-2020; Shetty and Abhishek 2021). Organic materials like plants, trees, crops and materials derived from these come under biomass. It can also be considered a form of stored or gathered solar energy. Their useful forms such as heat, electricity and liquid fuels are termed as biomass energy. The raw material for biomass energy is derived from land, i.e., crops, crop wastes generated during processing of food, etc. Although biomass energy is renewable and sustainable, it shares many traits with fossil fuels. While biomass can be burned directly for energy, it can also be used as a feedstock for the production of other liquid or gas fuels (biofuels). Biofuels can be transported and stored, and they

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S. Bhambri et al. Biomass CHP system Heat

Treated biomass Raw Biomass Pre-treatment

Product conversion

Power generation Power

Fig. 2.11 Block diagram for conversion of raw biomass

provide an alternative to fossil fuels (https://www.irena.org/news/pressreleases/2021/ Apr/World-Adds-Record-New-Renewable-Energy-Capacity-in-2020). It is believed that by the end of 2050, biomass will occupy a considerable proportion of renewable energy. Conversion of raw material to useful form of energy is shown through block diagram in Fig. 2.11 (Shetty and Abhishek 2021). In 2020, there was deterioration in the net capacity growth (2.5 GW compared to 6.4 GW in 2019) of bioenergy. China added its bioenergy capacity by more than 2 GW. Europe also expanded in 2020, with 1.2 GW of new bioenergy capacity added, which is similar to what was added in 2019 (https://www.irena.org/news/pressrele ases/2021/Apr/World-Adds-Record-New-Renewable-Energy-Capacity-in-2020).

2.2.8 Geothermal Energy The Greek term ‘geo’ means earth and ‘therme’ means heat so geothermal refers to heat inside the earth. In suitable places, this heat can be used to warm buildings, fluids like water for household use, space heating and generate electricity. Heat from the earth’s interior is available in the form of lava flows, hot springs, geysers, fumaroles and mud pots. Most of these are found near earth’s tectonic plates. Figure 2.12 depicts the extraction of geothermal energy (Ellabban et al. 2014).

2.2.9 Tidal Energy Energy of high/low ocean water tides can be harnessed to generate electricity. Like geothermal energy, it also replenishes itself over time. This source of energy has not found much potential across the globe and still exists in its infancy. China, Japan, Canada, England and Russia have much more potential to harness energy from this type as compared to rest of the places. Special generators are used to transform tidal energy through different ways like: (i) tidal streams, (ii) barrages and (iii) tidal lagoons. Figure 2.13 shows the rotors that converts the energy of underwater tides (Shetty and Abhishek 2021).

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Grid

Turbine

Generator

Steam

Steam

Cooling tower

Injection well

Hot water

Fig. 2.12 Extraction of power through geothermal process

Fig. 2.13 Turbine converting energy of tides into electricity

Tidal Dam Reservior High Tide

Turbine Water Sea

2.2.10 Internal Combustion Engines The energy contained in a fuel is converted into mechanical power by a reciprocating or internal combustion (IC) engine. The engine’s shaft is turned by this mechanical power. The IC engine is equipped with a generator that converts rotational energy

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into electric energy. They range in size from small residential backup generators (up to 5 kW) to huge commercial generators (around 7 MW). Reciprocating engines run on gasoline, natural gas or diesel, which are all readily available (Adajah et al. 2021).

2.2.11 Combustion Turbines For DER, conventional combustion turbine generators (CT) typically range in capacity from 500 kW to 25 MW. They run on natural gas, oil or a combination of the two. At full load, modern single-cycle combustion turbines typically exhibit efficiencies ranging from 20 to 45%. When the load is less than full, efficiency suffers (Adajah et al. 2021).

2.2.12 Wave Energy Technology In simple words, this technology captures sea wave energy by suitable devices. The structure which is intercepted by sea waves will respond in an appropriate manner to forces exerted to it by the sea waves. The structure of shore mounted devices is firmly fixed to seabed, and for the other types, some part of the structure may be fixed but another part may be a float. In wave energy conversion devices, heave, surge and pitch are the movements that can be harnessed (Agarwal et al. 2022) (Fig. 2.14). The performance of wave energy conversion is determined by the physical size of the converter. In order to extract energy in the sea wave, the converter will have to have swept volume similar to the volume of water. The mode of operation will govern precise structure of each device, but as a rough idea, the swept volume must be of the order of several tens of cubic meters per meter width of the device. There are a number of ways of classifying wave energy converter like according to mode of operation, location, etc. According to device location, the three general classifications are (https://powermin.gov.in/en/content/faqs-hydropower) (Fig. 2.15). pitch

surge

heave

Fig. 2.14 Pitch, heave and surge responses of a floating object to an incident wave

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oyster

AWS III buoy

Shore based anchor

Near shore

floating

Fig. 2.15 Classification of wave energy converters according to location

(i) Fixed to seabed in shallow water (ii) Tethered in intermediate depths (iii) Floating offshore in deep water.

2.2.13 Storage Systems 2.2.13.1

What Is Actually Stored?

Usually, different forms of energy whether electrical, mechanical, thermal and chemical are received by an energy storage system and same as the one are delivered. Example is pumped hydrostorage plant and batteries. In pumped hydrostorage technology, water is elevated through motors to a particular height, and then it is allowed to fall on turbines to run alternator shaft to produce electricity. During this whole process, energy transformation can be seen, i.e., first electric energy is transformed to potential energy, and then kinetic energy is converted to electric energy. There are some instances where same form of energy is not seen at the discharged end of the storage system, power to heat, etc. The role of energy storage in electric system is to collect surplus of energy on its availability and act as a reservoir, releasing energy if required (Hauer 2022). Energy storage is critical for using renewable energy sources as a sustainable power source. It not only provides electricity to electrical equipment at times when generation through DG resources is not available but also improves the distribution network’s power quality. It can also be used as a backup power supply for emergency power. Energy is stored in a variety of components and ways, including lead-acid batteries, flywheels, and supercapacitors, etc. (Faisal et al. 2018; Kebede et al. 2022) (Fig. 2.16).

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Storage Technology

Charging

Discharging

Fig. 2.16 Three steps of storage process

2.2.13.2

Battery Storage

The battery storage technologies can be used to provide uninterruptible power supply. These are used to supply power over short periods of time. It also finds its function in correction of voltage flicker, sags and surges in case of switching of supplies or loads. The oldest example of rechargeable battery is the lead-acid battery having advantages of high power density and less construction price qualifying them to use in the automotive industry. The battery has been constantly improved in order to increase the system’s safety measures and low energy density. The half-cell reactions that occur during the discharge cycle are: − Pb + SO2− 4 = PbSO4 + 2e − PbO2 + 4H+ + SO2− 4 + 2e = Pb SO4 + 2H2 O

at the anode and cathode, respectively. During the discharge process, the anodic oxidation causes the produced Pb2+ ions to combine with the sulfate ions from the sulfuric acid, generating a PbSO4 precipitate on the negative electrode (the anode). The lead dioxide is converted back to PbSO4 by cathodic reduction at the positive electrode (cathode). Both electrodes become sulfated as a result of this, and the voltage changes during the discharge process due to the change in acidity caused by the creation of water and the consumption of sulfate ions. During the charging procedure, the process is reversed. (Hauer 2022).

2.2.13.3

Flywheel Energy Storage

Flywheel energy storage is an ingenious method of storing kinetic energy as electricity. Flywheels are said to be the earliest kind of energy storage, with a variety of uses including smoothing unequal torques in machinery. Excess electricity is stored and used to power a motor that rotates a flywheel at thousands of revolutions per minute, according to the concept (RPM). The flywheel is levitated in an evacuated chamber with magnets and highly efficient bearings, allowing it to move freely. The momentum of the flywheel is stored kinetic energy that can be used to power an

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Flywheel

Motor/generator

Electrical input or output

Fig. 2.17 Flywheel energy storage system

electricity generator in the system. Low maintenance costs, a long predicted lifetime, quick response, and 90% or more roundtrip efficiency are among the benefits of flywheel systems. At higher power levels, flywheels can be cost-effective, but even then, they are physically massive, and their installation comes with a plethora of safety and maintenance considerations. High cost, self-discharge risk and suitability for lesser capacity are the key drawbacks (Pullen 2022; Mditshwa et al. 2022; Javed et al. 2021; Safayatullah et al. 2021) (Fig. 2.17).

2.2.13.4

CAES

There is close resemblance between compressed air energy storage (CAES) plant and pumped storage hydropower plant in terms of their application. The name compressed air energy storage essentially encapsulates the technology’s basic operation. In CAES, ambient air or some other gas is compressed and stored under pressure in an underground container. The pressurized air/gas is heated and expanded in an expanded turbine to drive alternator to produce electricity (Breeze 2018). The detailed functioning of CAES plant is shown in Fig. 2.18. Compressed air energy storage plants have a large power rating and storage capacity, long lifetime when compared to other storage technologies. These qualities make it the cost-effective and promising method for bulk storage. This technology can provide a significant amount of world’s future storage needs as world has a large capacity for storing compressed air (Dooner and Wang 2020).

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Generator

Wind

Compressor

Air storage tank Turbine

Heat source if required

Compressor

Storage

Energy Extraction

Fig. 2.18 Process of CAES-based power generation

2.2.13.5

Supercapacitors

More energy at higher power outputs can be stored by supercapacitors. Also, high cyclability and stability make supercapacitors attractive devices for storing energy. They are found in many applications either standalone or in combination with batteries. A supercapacitor contains two parallel electrodes detached by a nonconductive material soaked with an electrolyte. On the application of potential between electrodes, electrolyte ions are attracted to the opposite charged electrodes. Total capacitance of the device is seen by two contributions derived from charge accumulation and the interaction with electrode surface. Due to the formation of electrostatic double-layer generation on each electrode (shown in Fig. 2.19), supercapacitors can handle high power. Because of its features like high charge/discharge current capability, very high efficiency and wide temperature range, supercapacitors can provide a high-performance and cost-effective solution at moderate power levels as a novel component (Kong et al. 2022).

2.3 Challenges and Benefits Dispersion of DGs in suitable sizes and places in conventional power system receives attention of utility designers as this reconfiguration without prior study may lead to power quality issues, increased losses, stability issues, etc. (Zhang et al. 2018). So endeavor of power system practicing engineers is to design or

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Electric double layer

Fig. 2.19 Basic diagram of supercapacitor with showing electrolyte, separator and EDL

+

+ + + + +

-

+ +

Electrolyte

+ + + -

+ + +

+ + -

+++++-

-

Separator

restructure the system in such a way as to reduce technical losses (Mahdavi et al. 2021; Lin and Lin 2008; Zhang et al. 2018; Shadman Abid et al. 2022). The analytical design process is carried out by formulating the problem in terms of objective function with constraints and applying suitable algorithms to minimize losses (https://www.irena.org/news/pressreleases/2021/Apr/World-AddsRecord-New-Renewable-Energy-Capacity-in-2020; Georgilakis and Hatziargyriou 2013; Ali et al. March 2021). DGs are being considered as major technological advancements with the goal of meeting future energy needs, overcoming power quality issues and carbon content in environment (Verma and Kashyap 2021; Singh and Jha 2021). It seems to be an attractive for policy makers, regulators and market operators along with providing an opportunity of reducing investment in transmission and distribution and minimizing ohmic losses in the system. Among all these, system operators have a challenge of maintaining power quality which consumers want in presence of volatile prices, insecure power sources and integration of new forms of sources. The main integration issues are technical, environmental and economical (Allan et al. 2015). Decentralizing the power sector is a way of achieving efficient energy and green energy provision as well as addressing issues over already existing power infrastructure. Therefore, economic viability of various district generation sources becomes a key issue (Li et al. 2021; Alarcon-Rodriguez et al. 2010). DG penetration issues on voltage and protection systems due to inappropriate sizing and siting like voltage reduction, electricity reliability, stability, protection settings and system islanding are too an area of power quality concern (Singh and Jha 2021; Rahman et al. 2015; Razavi et al. 2019). Conventional IC engine vehicles are gradually being replaced by eco-friendly electric vehicles (EV) across the globe. Definitely, there will be increased demand for electricity in coming years and increased penetration of EVs with grid will affect the distribution grid. In this context, appropriate controlling (charging and discharging of vehicle’s battery) is the need of an hour along with utilization of renewable sources (Yusuf et al. 2021). The energy grid is being transformed to meet the increased power demands and to support the rapid injection of renewable energy sources. Instead of a lot of research in selected renewable sources, there is still a need to identify suitable energy storage

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Commercial

Technical

Recovery of cost

Environental

Regulatory

Power quality voltage

Appropriate regulatory policy

Incentive scheme protection Creation of market mechanism

stability

Fig. 2.20 Types of DG challenges

means for grid tied applications. Although the most common form of storing energy is battery, research is being continued to identify potentials of other storage devices and their applicability. Capacitors, supercapacitors, SMES devices, flywheels and CAES are identified as suitable for high power applications. Apart from this, thermal energy storage finds application in bulk storage (Zhang et al. 2021; Boicea 2014; Kebede et al. 2022) (Fig. 2.20). Economically, DGs are becoming more popular with the advancement in technology. On their proper installation, system reliability and power quality are improved, relieve overload of the feeders, hold the upgrade of transmission and distribution system (Adajah et al. 2021; Carvalho et al. 2008). DG renewables are environment-friendly and good choice for electrifying rural areas. Their proper installation also ensures reduced losses, improved voltage profile, etc. (León et al. May 2022; Al-Tameemi et al. 2018).

2.4 Conclusion In this review chapter, author has presented the basic idea behind various distributed generation technologies, their importance, technical and economical aspects, need, challenges, benefits, etc. usually encountered in linking DGs into distribution network. Distributed energy resources play an outstanding role in diminishing economical and technical losses in transmission and distribution lines to a considerable extent. Along with this, utilities have to earn profit by increasing the energy efficiency of distributed generation and quality of power. Evidently, the efficient distributed energy management is the need of an hour. It helps in reducing power losses, improving voltage profile of distribution network, increasing reliability and

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quality of power and improving power factor of the distribution grid. Moreover, most of the distributed generation technologies replenish themselves over time fulfilling the objective of emission-free generation keeping the least impact on an environment.

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Chapter 3

Design of Efficient Distributed Energy Resources (DER) Controller and Protection System P. E. S. N. Raju and Trapti Jain

Abstract This chapter presents the design of efficient controllers and protection systems for distributed energy resources (DERs)-based microgrids. The control strategies for DERs include decentralized, centralized, and hierarchical controllers. These controllers have been designed based on a robust extended linear quadratic Gaussian (LQG) control, which combines the Kalman estimator with the linear quadratic regulator with prescribed degree of stability (LQRPDS). Finally, this chapter demonstrates the validation of the design procedure of the DER controllers using eigenvalue analysis, offline time-domain simulations, and RTDS-based simulations. Moreover, various protection systems for DERs-based microgrids have been discussed briefly.

Nomenculture ACMGs CPL DERs DQ ESSs IIDER IIMG IM LQG LHP LQR LQRPDS NSGA-II

AC microgrids Constant power load Distributed energy resources Synchronous reference frame Energy storage systems Inverter-interfaced distributed energy resource Islanded inverter-based microgrid Induction motor Linear quadratic Gaussian Left-hand plane Linear quadratic regulator Linear quadratic regulator with prescribed degree of stability Non-dominated sorting genetic algorithm-II

P. E. S. N. Raju (B) School of Energy Science and Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam 781039, India e-mail: [email protected] T. Jain Department of Electrical Engineering, Indian Institute of Technology Indore, Indore, MP 453552, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. N. Singh et al. (eds.), Optimal Planning and Operation of Distributed Energy Resources, Energy Systems in Electrical Engineering, https://doi.org/10.1007/978-981-99-2800-2_3

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R RIAL RL RMS RTDS

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Resistive Rectifier interfaced active load Inductive Root mean square Real-time digital simulator

3.1 Introduction Distributed energy resources (DERs) have received remarkable interest due to several economic and environmental concerns (Muhtadi 2021). The DER technologies include distributed power generation units such as photovoltaics, wind turbines, fuel cells, reciprocating engines, combustion turbines, microturbines, and co-generation and distributed energy storages as well as controllable loads. These DER technologies reduce greenhouse gas emission and transmission as well as distribution losses, enhance the power quality and reliability, and can provide voltage support. However, the design of efficient control and protection systems for DERs involves the coordinated management of both the distributed power generation units and distributed energy storages that are typically embedded within DERs-based microgrids. Furthermore, the output of these DERs is either a DC or a variable frequency AC. Therefore, these DERs are interfaced to the distribution network or the local loads through a front-end inverter. A recently evolved concept is to group a few of these inverterinterfaced DERs (IIDERs) and a cluster of loads together to form a small local power system, called an AC or inverter-based microgrids (Pogaku et al. 2007; Bottrell et al. 2013). These AC microgrids (ACMGs) can be operated either in an island mode or in a grid-connected mode of operation. Stability of ACMGs is not a critical issue in grid-connected mode of operation as the stiff grid would be responsible for its stable operation (Majumder 2010). However, in an island mode of operation, it is an important concern due to the low-inertial IIDER units such as solar photovoltaic panels and fuel cells. Stability of an islanded inverterbased microgrid (IIMG) mainly depends on the amount of physical disturbance and operating condition prior to the disturbance (Majumder 2010; Kundur et al. 2004). Depending upon the amount of disturbance, viz. small-signal and large-signal, stability can be classified as small-signal stability and transient stability, respectively (Kundur et al. 2004). At a given steady-state operating condition, the IIMG may be unstable when it is subjected to the large disturbance. However, the IIMG should operate satisfactorily if it is subjected to small-signal disturbance at a given steady-state operating condition. Therefore, small-signal stability is a fundamental requirement for the satisfactory and reliable operation of the IIMG. Stability of IIMGs is considerably influenced by the load dynamics (Kent et al. 1969; Majumder 2010) due to the presence of low-inertial IIDER units and poor damping of low-frequency modes associated with the droop or power-sharing controllers (Pogaku et al. 2007; Bottrell et al. 2013). Therefore, the impact of resistive (R) and inductive (RL) load dynamics has been studied in (Pogaku et al. 2007;

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Bottrell et al. 2013). It was found that the dominant low-frequency modes were very sensitive to the power-sharing controller parameters and network configuration, whereas the medium- and high-frequency modes were mainly affected by the inner loop controllers, network dynamics, and load dynamics. The impact of constant power load (CPL) and rectifier interfaced active load (RIAL), individually, revealed that the presence of RIAL caused an increase in the low-frequency modes, whereas the IIMG is destabilized due to the CPL dynamics (Bottrell et al. 2013). The impedance mismatch between IIDER units and induction motor (IM) loads, addressed in (Kahrobaeian and Mohamed 2014), showed that the presence of IM load causes medium frequency instabilities in the range of tens to a few hundreds of Hz. When IIMGs are subjected to small-signal disturbances and the primary level controller is not able to regulate deviations in the frequency and voltage, secondary control of IIMGs is often employed to eliminate these deviations (Ishaq et al. 2022; Choudhury 2022). In the literature, several DER controllers have been proposed to restore the system frequency and voltage in IIMGs (Ovalle et al. 2015; Ishaq et al. 2022; Choudhury 2022; Golsorkhi and Lu 2015; Kahrobaeian and Mohamed 2014; Yu et al. 2016; Sanjari and Gharehpetian 2013; Tsikalakis and Hatziargyriou 2008; Tan et al. 2012; Guo et al. 2015; Wang et al. 2017; Lou 2017; Zhao et al. 2016; Baghaee et al. 2016; Li et al. 2018). These are decentralized (Ovalle et al. 2015; Golsorkhi and Lu 2015; Kahrobaeian and Mohamed 2014; Yu et al. 2016), centralized (Sanjari and Gharehpetian 2013; Tsikalakis and Hatziargyriou 2008; Tan et al. 2012), distributed (Guo et al. 2015; Wang et al. 2017; Lou 2017) and hierarchical controllers (Zhao et al. 2016; Baghaee et al. 2016; Li et al. 2018). However, these controllers have been designed considering only a particular type of load to be fed by the IIMG. Thus, these are not able to maintain the stability under different load configurations. A more pragmatic approach would be to design the controller considering the presence of multiple types of static as well as dynamic IM loads in IIMGs. An appropriate design of controller requires rigorous investigation of load dynamics as well as load sharing among IIDER units on the stability of IIMGs. The main focus of this chapter is to provide general design procedure of efficient DER controllers to enhance the stability and dynamic performance of IIMGs feeding multiple types of passive loads, RIAL and dynamic IM load, simultaneously. Various types of passive loads include R load, RL load, and CPL. The generalized model of IIMGs feeding R load, RL load, CPL, RIAL, and dynamic IM load has been used to investigate the impact of load dynamics as well as load sharing among IIDER units on the stability and dynamic performance of IIMGs (Raju and Jain 2018, 2019). Based on these investigations, decentralized, centralized, and hierarchical DER controllers have been developed. The DER controllers are designed based on a robust extended Linear Quadratic Gaussian (LQG) control, which combines the Kalman estimator with the linear quadratic regulator with prescribed degree of stability (LQRPDS) (Franklin et al. 1990; Chen and Atherton 2007). In order to obtain the optimal values of the diagonal weighting matrices of Kalman estimator and LQRPDS, a bi-objective optimization problem has been formulated and solved using a fast and elitist multiobjective non-dominated sorting genetic algorithm-II (NSGA-II) (Deb et al. 2002).

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The DER controllers produce supplementary control signals to the local controllers of each IIDER unit and RIAL. Eigenvalue analysis, offline time-domain simulations, and real-time digital simulator (RTDS)-based simulations have been performed to validate the effectiveness of the DER controllers. It is found that the DER controllers provide robust control performance under various load configurations as well as small load disturbances. Finally, a brief description of the protection systems for DERs-based microgrids has also been provided.

3.2 Design of Efficient Controller for Distributed Energy Resources-Based Microgrids Controllers for DERs-based microgrids can be implemented in three levels such as primary, secondary, and tertiary levels, as shown in Fig. 3.1 (Ovalle et al. 2015; Ishaq et al. 2022). These controllers differ in their speed of response, requirements of communication infrastructure, and their operation time frame. The primary level DER controller performs the control of current, power, and local voltage. It normally follows the reference set points provided by secondary and tertiary level controllers. Furthermore, it maintains the stability of IIMGs by sharing the load among DER units, properly. Moreover, it is faster than secondary and tertiary level controllers and it operates on timescales of 10–100 milliseconds. The most common primary level DER controller is active and reactive power control (PQ control), droop control, and voltage and frequency control (V/F control). The secondary level controllers appear on top of primary level DER controller and operate on a slower timescale of 1–10 s (Ovalle et al. 2015; Ishaq et al. 2022). It is responsible for power flow & synchronization control and also ensures power quality by regulating the voltage and frequency deviations introduced by the small-signal disturbances. Furthermore, the secondary level controller can also be used to rectify the load sharing ratios done by the primary level DER controllers. Moreover, the secondary level DER controllers can be further classified into decentralized, centralized, distributed multiagent, hierarchical, and hierarchical distributed (Ovalle et al. 2015; Ishaq et al. 2022). Finally, the tertiary level controller is responsible for the supervision of DERs-based microgrids, forecasting and economic dispatch, and optimal operation of DERs. The tertiary level controller usually operates on a slowest timescale based on a static power flow model in which references are updated every 10–15 min (Ovalle et al. 2015; Ishaq et al. 2022). During the last decade, secondary level DER controllers such as decade, decentralized, centralized, and hierarchical controllers have been proposed in the literature to enhance the stability and dynamic performance of IIMGs (Ovalle et al. 2015; Ishaq et al. 2022; Choudhury 2022; Golsorkhi and Lu 2015; Kahrobaeian and Mohamed 2014; Yu et al. 2016; Sanjari and Gharehpetian 2013; Tsikalakis and Hatziargyriou 2008; Tan et al. 2012; Guo et al. 2015; Wang et al. 2017; Lou 2017; Zhao et al. 2016; Baghaee et al. 2016; Li et al. 2018). In a decentralized DER control strategy, each primary controller is controlled by its decentralized controller, as shown in Fig. 3.2.

3 Design of Efficient Distributed Energy Resources (DER) … UPPER

LEVEL

43

OPERATORS

Tertiary Level DER Controller Economic Dispatch and Operation Generation Forecasting

Microgrid Supervision

Secondary Level DER Controller Power Quality Control Voltage/Frequency Restoration

Power Flow/Synchronization Control

Primary Level DER Controller Local Control Voltage and Current Control

DER

Power Sharing Control

BASED

MICROGRID

MAIN

GRID

Fig. 3.1 Hierarchical control strategies in a DERs-based microgrids

The decentralized controller of ith IIDER unit receives only local information and is neither fully aware of system-wide variables nor actions of other decentralized controllers (Ishaq et al. 2022; Ovalle et al. 2015; Golsorkhi and Lu 2015; Kahrobaeian and Mohamed 2014). The decentralized DER control strategy provides sharing of total load in a proper way as well as active damping of oscillations between the output filters. Furthermore, it ensures the stability of IIMGs on a global scale because of local measurements. However, decentralized DER controllers suffer from the lack of uniformity/consistency and coordination among IIDER units (Ishaq et al. 2022; Choudhury 2022). To overcome these limitations, centralized DER controllers utilizing measurements based on either communication link or without communication link have been proposed in (Sanjari and Gharehpetian 2013; Tsikalakis and Hatziargyriou 2008; Tan et al. 2012). Centralized DER control strategy, shown in Fig. 3.3, depends on the data gathered in a dedicated centralized controller that performs the required calculations and generates the control actions for all the primary controllers at a single point. It requires an extensive communication between the centralized controller and primary controllers. Centralized controller determines the reference values, i.e., output power and/or terminal voltage, for the primary controller of each IIDER unit.

44

P. E. S. N. Raju and T. Jain Hierarchical Controller Centralized Controller Communication Infrastructure

Decentralized Controller

Decentralized Controller

···············

Decentralized Controller

Primary Control

Primary Control

···············

Primary Control

VSI

VSI

VSI

···············

DG or

DG or

ESS

ESS

ESS

DER1

DER2

DERn

DG or

Electrical/Microgrid Network

Fig. 3.2 Schematic diagram of decentralized and hierarchical DER control strategy

The main functions of the above decentralized and centralized DER controllers for IIMGs are proper load sharing as well as coordination among IIDER units and voltage as well as frequency regulation under small-signal disturbances. These requirements are of distinct timescales and importance. Thus, to address the aforementioned requirements, a hierarchical DER control strategy (Zhao et al. 2016; Baghaee et al. 2016; Li et al. 2018) was proposed, as shown in Fig. 3.2. This section provides general design procedure of the decentralized, centralized, and hierarchical controllers.

3.2.1 Design of Decentralized Controller A decentralized controller around each IIDER unit has been designed on the basis of robust extended LQG control scheme, which combines the Kalman estimator with the LQRPDS, as shown in Fig. 3.4 (Raju and Jain 2017; Franklin et al. 1990; Chen and Atherton 2007). A general design procedure of the decentralized controller, shown in Fig. 3.5, is given as follows: Step 1: Obtain a nonlinear dynamical model of IIMG system from the generalized modeling given in (Raju and Jain 2019). Step 2: Develop a small-signal linearized model of the IIMG system to produce frequency and voltage responses in the presence of load disturbances.

3 Design of Efficient Distributed Energy Resources (DER) …

45

Centralized Controller Communication Infrastructure

Primary Control

Primary Control VSI

VSI

···············

Primary Control VSI

···············

DG or

DG or

DG or

ESS

ESS

ESS

DER1

DER2

DERn

Electrical/Microgrid Network Fig. 3.3 Schematic diagram of centralized DER control strategy Linear Quadratic Regulator with Prescribed Degree-of-stability − &Δy ˆIIDER ΔˆIIDER x Extended LQG

ΔuIIDER = Δvdq

Linearized Model of Inverter Interfaced DER Unit

ΔyIIDER

Kalman State Estimator

Fig. 3.4 Decentralized controller of IIDER unit

Step 3: To consider the impact of measurement noises and model mismatch, augment the noise and disturbance models to the small-signal model of ith IIDER unit. Step 4: Use the augmented model of ith IIDER unit to design an extended LQGbased decentralized controller that generates control commands to the local controllers of the ith IIDER unit. Step 5: Formulate a bi-objective optimization problem and solve it using NSGA-II to get optimal parameters of the decentralized controller. Step 6: Finally, insert and combine the control signal obtained from the decentralized controller with the local controller of IIDER unit.

3.2.1.1

Extended LQG-based Decentralized Controller

Linear quadratic regulator (LQR) control scheme is well known for its robust performance (Franklin et al. 1990; Chen and Atherton 2007), but may not stabilize

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P. E. S. N. Raju and T. Jain

Microgrid

Dynamical Model

Steady State Initial Operating Conditions

Small Signal Model

Disturbance and Noise Model

Small Signal Model of the ith IIDER unit Decentralized Controller Based on extended LQG

Tuning of Parameters of Decentralized Controller Local Controller of ith IIDER unit

Fig. 3.5 Design procedure of the decentralized controller

the system with uncertain load parameters. Therefore, the decentralized controller has been designed based on the robust extended LQG control scheme, which combines the Kalman estimator with the LQRPDS, as shown in Fig. 3.4. The state-space linearized model of ith IIDER unit can be given as in (3.1). ˙ ] = [AIIDERi ][ΔxIIDERi ] + [BIIDERi ][ΔuIIDERi ] [ΔxIIDERi

(3.1a)

[ΔyIIDERi ] = [CIIDERi ][ΔxIIDERi ] + [DIIDERi ][ΔuIIDERi ]

(3.1b)

where ΔxIIDERi , ΔuIIDERi , and ΔyIIDERi are state, control, and output vectors, respectively; AIIDERi , BIIDERi , CIIDERi and DIIDERi are the state matrix, input matrix, output matrix and transfer matrix of the linearized model of the ith IIDER unit, respectively.

3.2.1.2

Optimal Kalman Estimator

An optimal Kalman state estimator (Franklin et al. 1990; Chen and Atherton 2007) has been designed to estimate the unmeasured states of an IIDER unit. The Kalman estimator can obtain state variables of the IIDER unit dynamically based on its local voltage and current measurements. The optimal Kalman state estimator for the ith IIDER unit given in (3.1) can be written as in (3.2) and (3.3). = AIIDERi Δ⩘ xIIDERi + BIIDERi ΔuIIDERi + KeIIDERi (ΔyMG − Δ⩘ yIIDERi ) Δx ⩘ ˙ IIDERi (3.2) xIIDERi + DIIDERi ΔuIIDERi (3.3) Δ⩘ yIIDERi = CIIDERi Δ⩘

3 Design of Efficient Distributed Energy Resources (DER) …

47

where Δ⩘ xIIDERi denotes an estimate of the state vector ΔxIIDERi and KeIIDERi is the Kalman estimator gain matrix given by (3.4), which is obtained by minimizing the steady-state error covariance given in (3.5). −1 KeIIDERi = PeIIDERi CTIIDERi RIIDERei

(3.4)

E{[ΔxIIDERi − Δ⩘ xIIDERi ]T [ΔxIIDERi − Δ⩘ xIIDERi ]}

(3.5)

Here, PeIIDERi is the unique symmetric positive semidefinite solution of (3.6) subjected to ReIIDERi > 0, QeIIDERi > 0 and the model of an IIDER unit is being detectable and has no uncontrollable modes on the imaginary axis (Franklin et al. 1990; Chen and Atherton 2007). −1 PeIIDERi ATIIDERi − PeIIDERi CTIIDERi RIIDERei CIIDERi PeIIDERi + AIIDERi PeIIDERi + QeIIDERi = 0 (3.6) where QeIIDERi and ReIIDERi are diagonal weighting matrices of the optimal Kalman estimator. The estimated outputs and state variables are fed to the state feedback controller as shown in Fig. 3.4.

3.2.1.3

LQRPDS with State Weighting

The gain matrix of the feedback controller has been obtained through LQRPDS, which ensures certain minimum distance between the closed-loop eigenvalues and the imaginary axis (Franklin et al. 1990; Chen and Atherton 2007). The optimal control law, ΔuIIDERi =−KcIIDERi ΔxIIDERi , minimizes the performance index given in (3.7). (∞ PI =

T T e2αt [(ΔxIIDERi QcIIDERi ΔxIIDERi ) + (ΔuIIDERi RcIIDERi ΔuIIDERi )]dt (3.7) t0

where α is a prescribed degree of stability, which will try to force ΔxIIDERi to die at least as fast as e−αt (Franklin et al. 1990; Chen and Atherton 2007). Therefore, the constant, α, can be used to force the controller to act faster and prevent any slowing down of the response caused by the additional decentralized controller. The gain matrix is calculated using (3.8). −1 KcIIDERi = RIIDERki BTIIDERi PcIIDERi

(3.8)

where PcIIDERi is obtained by solving the modified algebraic Riccati equation as given in (3.9).

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(AIIDERi + αI)T PcIIDERi + PcIIDERi (AIIDERi + αI) −1 −PcIIDERi BIIDERi RIIDERki BTIIDERi PcIIDERi + QcIIDERi = 0

(3.9)

The value of α can be selected according to the operating condition of the IIMG system. The larger value of α leads to more stability of the closed-loop IIMG system. However, a high degree of closed-loop stability can only be achieved at excessive control energy cost, or controller complexity cost (Franklin et al. 1990; Chen and Atherton 2007). Hence, in this work, the value of α is selected as 100 to force the decentralized controller to act faster as well as to minimize excessive control energy cost. It is evident from (3.4) and (3.8) that the optimal gain matrices, KeIIDERi and KcIIDERi , are dependent on the diagonal weighting matrices, QeIIDERi , ReIIDERi , QcIIDERi , and RcIIDERi . The selection of the diagonal elements of these matrices is still a challenging issue in the design of the extended LQG. The classical approaches to obtain these matrices are based on either heuristic or trial and error methods (Franklin et al. 1990; Chen and Atherton 2007). These methods are labor-intensive and do not guarantee optimal solutions. Therefore, the optimal values of these elements have been obtained as described in the following subsection.

3.2.1.4

Tuning of Parameters of the Decentralized Controller

Stability analysis of the IIMG described in (Raju and Jain 2018) showed that the low-frequency modes associated with the RIAL are lying in the unstable region. It was also observed that as the loading conditions vary, the eigenvalues shift either to the right half of the s-plane or closer to the imaginary axis. Therefore, in order to ensure the stable operation of the IIMG system, under different loading conditions, it is desirable that the low-frequency modes are shifted to the left-hand plane (LHP) while maintaining a certain minimum distance from the imaginary axis. Further, by maintaining a minimum damping of these modes, maximum overshoot can be limited in addition to the small-signal stable operation (Yassami et al. 2010). Based on this philosophy, a bi-objective optimization problem has been formulated to obtain the optimal values of diagonal weighting matrices of the Kalman estimator (QeIIDERi and ReIIDERi ) and LQRPDS (QcIIDERi and RcIIDERi ). The objectives are to minimize the deviation of the real part of the IIMG system eigenvalues from the desired relative stability value (σ0 ) and the deviation of the damping ratio from its desired value (ζ0 ). Mathematically, these two objective functions can be written as in (3.10).

3 Design of Efficient Distributed Energy Resources (DER) …

minimize x

f1 =

np Σ

n Σ

49

[σ0 − σi, j (x)]2

j=1 i=1, σi, j (x)≥σ0

f2 =

np Σ

n Σ

[ζ0 − ζi, j (x)]2

(3.10)

j=1 i=1, ζi, j (x)≤ζ0

subject to x

min

≤ x ≤ xmax

where n p is the number of critical operating points considered in the design process, n is the order of the AMG , σi, j (x), and ζi, j (x) are the real part and damping ratio of the ith eigenvalue of the jth operating point of the closed-loop IIMG system and x is a vector of the diagonal elements of the QeIIDERi , ReIIDERi , QcIIDERi , and RcIIDERi weighting matrices of the four IIDER units. In this work, the value of σ0 is selected as −10 so that the stability is guaranteed under all the possible operating conditions. Whereas, the value of ζ0 is chosen as 0.20 in order to obtain greater damping of the unstable as well as lightly damped modes. The objective functions f 1 and f 2 are conflicting in nature since the improvement in any one of them cannot be achieved without the deterioration of another. Therefore, in multi-objective optimization problems, the concept of a single optimal solution is superseded by a set of optimal trade-offs between the competing objectives, called Pareto optimal solutions. A fast and elitist multi-objective NSGA-II (Deb et al. 2002), which is known for its efficacy to converge near the true Pareto-optimal set, has been used to solve the above optimization problem.

3.2.1.5

Implementation of NSGA-II

In general, multi-objective genetic algorithms (GAs) differ with each other based on their fitness assignment, elitism, and diversification mechanism (Deb et al. 2002). Several GAs were in the literature for solving multi-objective problems (MOPs) (Deb et al. 2002). In this work, the ‘gamultiobj’ tool of MATLAB has been used to get the Pareto-optimal solution. It is a Pareto-ranking approach that explicitly utilizes the concept of Pareto non-dominance in evaluating fitness. Further, it reduces computational complexity and weakness of the NSGA technique. The implementation procedure of the NSGA-II is described as follows (Fig. 3.6): Step 1: Identify the control variables like diagonal elements of the state and control weighting matrices of LQG for the present problem. Step 2: Select the parameters like number of population, maximum number of generation, crossover, and mutation probabilities. Step 3: Generate the initial population. Step 4: Evaluate the objective functions for initial population. Step 5: Set the generation count. Step 6: Perform simulated binary crossover and polynomial mutation for the set of individuals.

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P. E. S. N. Raju and T. Jain

Fig. 3.6 Flow chart for NSGA-II technique

Step 7: Perform non-dominated sorting (i.e., sorting the population according to each objective function value in ascending order of magnitude). Step 8: Calculate crowding distance between the solutions. For each objective function, the boundary solutions (solutions with the smallest and largest function values) are assigned an infinite distance value. All other intermediate solutions are assigned a distance value equal to the absolute normalized difference in the function values of two adjacent solutions. This calculation is continued with other objective functions. The overall crowding distance value is calculated as the sum of individual distance values corresponding to each objective. Each objective function is normalized before calculating the crowding distance. Step 9: Perform tournament selection to identify the good individuals, thereby a higher fitness is assigned to individuals located on a sparsely populated part of the front. Step 10: Increment the generation count and repeat the steps from 6 to 9 until the count reaches the maximum number of generation. Using NSGA-II algorithm, a potential number of Pareto optimal solutions have been found.

3.2.1.6

Post-Pareto Analysis

Post-Pareto analysis (Taboada and Coit 2005), shown in Fig. 3.7, has been performed to identify the most effective Pareto-optimal solution. Based on the degree of similarity, the Pareto-optimal solutions are clustered into different groups using k-means algorithm. The optimal number of clusters is determined using the measure of silhouette coefficient (Taboada and Coit 2005). The number of clusters with the highest silhouette coefficient has been selected as the optimal number of clusters, which is

3 Design of Efficient Distributed Energy Resources (DER) … Fig. 3.7 Post-Pareto analysis flowchart

51

Start

Obtain the pareto-optimal solutions set using NSGA II algorithm Determine the optimal number of clusters using Silhouette plot Apply the cluster analysis and select representative solution closest to cluster centroid Analyze the objective functions with the representative solutions and find the knee cluster Select the single optimal solution closest to the ideal point

stop

found to be four. For each cluster, a representative solution has been selected based on its closeness to the cluster centroid. A cluster, wherein, a small improvement in one objective function leads to a large deterioration in the other objective function, is identified as a knee cluster.

3.2.2 Design of Centralized Controller The performance of the decentralized controller may degrade under certain operating conditions due to the lack of coordination among IIDER units. Therefore, a robust optimal centralized controller with an optimal Kalman state estimator has been designed, as shown in Fig. 3.8, to mitigate the instability caused by the RIAL (Raju and Jain 2017). Figure 3.9 shows general design procedure of the centralized controller, which is given as follows. Step 1: Obtain a nonlinear dynamical model of IIMG system with active and passive loads from the generalized modeling given in (Raju and Jain 2018). Step 2: Obtain steady-state operating point of IIMG system from the time-domain simulations (Raju and Jain 2018). Step 3: Develop a small-signal linearized model of the IIMG system, to produce frequency and voltage responses in the presence of small-signal disturbances.

52

P. E. S. N. Raju and T. Jain Proposed Centralized Controller based on LQRPDS −

ΔuMG

Reduced Order ΔyMG Model of the IIMG

Optimal Kalman State Estimator

Fig. 3.8 Closed-loop IIMG system Microgrid

Dynamical Model

Steady State Initial Operating Conditions Small Signal Model

Model Order Reduction Disturbance and Noise Model Centralized Controller with Kalman Estimator Tuning of Parameters of Estimator and Centralized Controller

Local Controller of IIDER Units

Local Controller of Active Loads

Fig. 3.9 Design procedures of the centralized controller

Step 4: Use the Schur balanced model-order reduction technique (Safonov and Chiang 1989) to reduce the order of the IIMG system and hence the complexity in the design of the centralized controller. Step 5: To consider the impact from measurement noises and model mismatch, add the noise models and disturbance models to reduced-order model of IIMG system. Step 6: Design the LQRPDS-based centralized controller with Kalman estimator, as shown in Fig. 3.8, with the help of augmented model obtained in the previous step. Step 7: Tune the parameters of the centralized controller by formulating a biobjective optimization problem and solve it using NSGA-II (Deb et al. 2002). Step 8: Finally, insert and combine the control signals obtained from the centralized controller with the local controller of IIDER unit and active loads.

3 Design of Efficient Distributed Energy Resources (DER) …

3.2.2.1

53

Model-Order Reduction

The order of a centralized DER controller is usually equal to or greater than the order of the IIMG system. This makes the design of the centralized controller impracticable. Moreover, analysis and stabilization of low-frequency unstable modes do not require full-order linear model of the IIMG system, which includes fast dynamic modes as well as states with negligible contribution to the input/output response. Hence, the design of the centralized controller has been simplified by reducing the order using Schur balanced model reduction procedure (Safonov and Chiang 1989). Using the Schur balanced approach, error bound (||G MG − G MGr ||∞ ), which is the infinity norm of the difference between the full-order (G MGr (s)) and reduced-order (G MGr (s)) linear models with respect to its order. Thus, the reduced-order model of the linearized model of the IIMG system can be represented as Δx˙MGr = AMGr ΔxMGr + BMGr ΔuMG

(3.11)

ΔyMG = CMGr ΔxMGr + DMG ΔuMG

(3.12)

where ΔxMGr is the vector of state variables, AMGr the state matrix, BMGr the input matrix, and CMGr the output matrix of the reduced-order model of the linearized IIMG system. This reduced-order model has been used to design the centralized controller.

3.2.2.2

Optimal Kalman Estimator

Since all the states may not be measurable in a practical IIMG system, an optimal Kalman state estimator has been designed, as shown in Fig. 3.8, to estimate the states and obtain IIMG system’s output variables. The Kalman estimator can obtain output variables of the IIMG system, dynamically, based on local voltage and current measurements of each DER unit and load. Furthermore, it reduces communication system burden and improves the flexibility and reliability of the centralized controller. The optimal Kalman state estimator for the reduced-order IIMG system given in (3.11) and (3.12) can be written as in (3.13) and (3.14). ˙ Δx⩘ xMGr + BMGr ΔuMG + KfMG (ΔyMG − Δ⩘ yMG ) MGr = AMGr Δ⩘ Δ⩘ yMG = CMGr Δ⩘ xMGr + DMG ΔuMG

(3.13) (3.14)

where Δ⩘ xMGr denotes an estimate of the state vector ΔxMGr and KfMG is the Kalman estimator gain matrix given by (3.15), which is obtained by minimizing the steadystate error covariance given in (3.16). −1 KfMG = PfMG CTMGr RfMG

(3.15)

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P. E. S. N. Raju and T. Jain

E{[ΔxMGr − Δ⩘ xMGr ]T [ΔxMGr − Δ⩘ xMGr ]}

(3.16)

Here, PfMG is the unique symmetric positive semidefinite solution of (3.17) subject to RfMG > 0, QfMG ≥ 0 and the IIMG system is being detectable and has no uncontrollable modes on the imaginary axis. −1 PfMG ATMGr + AMGr PfMG + QfMG − PfMG CTMGr RfMG CMGr PfMG = 0

(3.17)

where QfMG and RfMG are diagonal weighting matrices of the optimal Kalman estimator. The estimated outputs and state variables are fed to the centralized controller as shown in Fig. 3.8.

3.2.2.3

LQRPDS with Output Weighting

In the design of decentralized controller, the gain matrix has been obtained through the LQRPDS with state weighting. However, the complete model of the IIMG system has large number of states, and hence, it may be difficult to acquire all the states of the system for minimizing the performance index (PI) with state weighting. Therefore, the gain matrix of the centralized controller has been obtained through LQRPDS with output weighting (Franklin et al. 1990; Chen and Atherton 2007). The optimal control law for the reduced-order model of IIMG system given in (3.11) and (3.12) can be written as in (3.18), which is obtained by minimizing the quadratic cost function with output weighting given in (3.19). ΔuMG = −KkMG Δ⩘ xMGr

(3.18)

(∞ PI =

T e2αt [(Δ⩘ yMG QkMG Δ⩘ yMG ) + (ΔuTMG RkMG ΔuMG ]dt

(3.19)

t0

where α is a prescribed degree of stability, which will try to force the deviation in the output, ΔyMG , to die at least as fast as e−αt (Franklin et al. 1990; Chen and Atherton 2007). Therefore, the constant, α, can be used to force the controller to act faster and compensate any slowing down of the response caused by the additional centralized controller. The gain matrix is calculated using (3.20). −1 KkMG = RkMG BTMGr PkMG

(3.20)

where PkMG is the unique symmetric positive semidefinite solution of (3.21) subject to RkMG > 0, QkMG ≥ 0 and the IIMG system is being stabilizable and has no unobservable modes on the imaginary axis. −1 BTMGr PkMG = 0 (3.21) ATMGrα PkMG + PkMG AMGrα + QkMG − PkMG BMGr RkMG

3 Design of Efficient Distributed Energy Resources (DER) …

55

where AMGrα = (AMGr + αI), QkMG , and RkMG are state and control diagonal weighting matrices of the controller, respectively. It is evident from (3.15), (3.17), (3.20), and (3.21) that the optimal gain matrix of both the Kalman estimator and the centralized controller is dependent on their diagonal weighting matrices, represented by QfMG & RfMG and QkMG & RkMG , respectively. Therefore, these matrices are the tuning parameters of Kalman estimator and the centralized controller.

3.2.2.4

Tuning of Parameters of the Centralized Controller

It is desirable that the controller should be able to sustain small parameter perturbations while providing a good transient response so that the stability of the IIMG system is maintained under different operating conditions. Therefore, a bi-objective optimization problem has been formulated to obtain the optimal values of the diagonal elements of QfMG , RfMG , QkMG , and RkMG matrices. The objectives are to minimize the second norm of the gain matrix of the controller to improve transient response and the condition number of the eigenvector matrix of the closed-loop IIMG system to provide robustness against parameter perturbation. Mathematically, these two objective functions can be written as given in (3.22). minimize x

f 1 (x) = ||KkMG (x)||2 f 2 (x) = ||VMG (x)||2 ||VMG −1 (x)||2

(3.22)

subject to xmin ≤ x ≤ xmax where KkMG is the controller gain matrix, VMG is the eigenvector of the closed-loop IIMG system, and x is a vector of the diagonal elements of weighting matrices of the controller and Kalman estimator. Due to the conflicting nature of the two objective functions, NSGA-II (Deb et al. 2002) has been used to solve the above optimization problem. A potential number of Pareto-optimal solutions have been found using this algorithm. The most effective Pareto-optimal solution has been identified by carrying out a Post-Pareto analysis (Taboada and Coit 2005), as shown in Fig. 3.7.

3.2.3 Design of Two-Level Hierarchical Controller The presence of dynamic IM load introduces unstable, inter-area, and low-damping high-frequency oscillations in the IIMG (Raju and Jain 2018). The decentralized and centralized controllers may not be suitable to mitigate these undesirable oscillations. Therefore, a two-level hierarchical robust controller, utilizing synchronized phasor measurements supplied by PMUs, has been designed for the stability enhancement of the IIMG system feeding static and dynamic loads (Raju and Jain 2019). It consists of a local decentralized controller for each IIDER unit at the primary level accom-

56

P. E. S. N. Raju and T. Jain Small Signal Model

Microgrid

Selection of input/output signals Model Order Reduction

Decentralized Controller

Reduced Order Model with Selected input/output signals

Disturbance and Noise Model Centralized Controller Design Tuning of Centralized and Decentralized Controller’s Parameters

Local Controller of IIDER Units

Local Controller of Active Loads

Fig. 3.10 Design procedures of the hierarchical controller

panied by a MIMO centralized controller at the secondary level. The decentralized DER controllers are used to improve the stability as well as to avoid the circulating current flows among IIDER units (Liang et al. 2013). Whereas, the secondary centralized controller is employed to enhance the performance of each local decentralized controller by compensating for the voltage and frequency deviations caused by the different factors. These factors are virtual output impedances of primary level control, low-inertial IIDER units, and load disturbances. Figure 3.10 shows the general design procedure of the hierarchical controller. First, a decentralized controller is incorporated into the IIMG system model. It is worth mentioning that the decentralized controller adds additional auxiliary control terms to the conventional power-sharing controller based on the desired power sharing of each IIDER unit and the acquired information of total active and reactive power generations from the WAMS through PMUs (Raju and Jain 2019). Second, to produce frequency and voltage responses in the presence of load disturbances, small-signal linearized model has been developed by linearizing the nonlinear model of the IIMG system at a steady-state operating point (Raju and Jain 2019). Then, in order to reduce the costs associated with the installation of the PMUs, a reduced set of input/output signals have been obtained using geometric measures approach, as described in Sect. 3.2.3.1. Thereafter, to obtain reduced-order model with selected input/output signals, model-order reduction technique has been applied to the linearized model and selected input/output signals have been incorporated into the reduced-order model. Next, with consideration of impacts from measurement noises and model mismatch, the noise models and disturbance models are augmented to the reducedorder model with selected input/output signals, respectively. Then, the augmented model has been used to design the centralized controller, described in Sect. 3.2.3.2, to generate control commands to the local controllers of IIDER units and active

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57

loads. The parameters of the hierarchical controller have been tuned by formulating a bi-objective optimization problem and solve it using NSGA-II, as described in Sect. 3.2.1.4. Finally, the generated control commands are given to the decentralized controller of each IIDER unit and DC voltage as well as AC current controllers of the RIAL. It should be noted that delay-free communication channels have been considered during the design procedure with the assumption that microgrids are inherently established with a sophisticated communication infrastructure. Further, centralized controller has been designed with the assumption that the sampling frequency tends to infinity (Zolotas et al. 2007). In other words, the centralized controller is assumed to be represented as a continuous-time system.

3.2.3.1

Selection of Input/Output Signals

In order to reduce the costs associated with the installation of the PMUs and to increase the effectiveness of the controller, a reduced set of input/output signals have been obtained using geometric measures approach (Zolotas et al. 2007). This method utilizes normalized and orthogonal eigenvectors Ui and Wi corresponding to the ith eigenvalue, λi . Geometric measures of observability associated with the ith mode are defined as | | |CMGj Ui | || (3.23) m oi ( j ) = cos(θ (Ui , CMGj )) = || ||CMGj || ||Ui || where CMGj is jth row of CMG , θ (Ui , CMGj ) is the acute angle between the output vector and the right eigenvector Ui . A higher value of the observability measure represents the most observable output in damping the mode of interest. Geometric measures of controllability associated with the ith mode is defined as m ci (k) =

cos(θ (Wi , BTMGk ))

| T | |B | MGk Wi || || = ||BT || ||Wi || MGk

(3.24)

where BMGk is kth coloumn of BMG , θ (Wi , BMGk ) is the acute angle between the input vector and the left eigenvector Wi . A higher value of the controllability measure represents the most controllable input in damping the mode of interest. Using (3.23) and (3.24), the joint controllability/observability measure can be written as m coi (k, j ) = m ci (k)m oi ( j ).

(3.25)

A higher value of the joint controllability/observability measure indicates the better stability of the signals to be selected. To increase the robustness and quickness of the controller, the locations of its inputs as well as outputs have been selected by identifying the most measurable and most controllable signals corresponding to the modes of interest.

58

P. E. S. N. Raju and T. Jain Disturbance and Noise Models

Linear Quadratic Regulator with Prescribed Degree-of-stability −

Modal-based Extended LQG

ΔuMG

Modal form of Reduced ΔyMG Order Model of the MG with Selected input/output signals

Kalman State Estimator

Fig. 3.11 Centralized controller based on modal-based extended LQG control approach

3.2.3.2

Centralized Controller Design

Due to the presence of highly nonlinear dynamic IM load that couples voltage, supply frequency, active power, and reactive power dynamics, a centralized controller capable of performing effectively under the dynamics of IM load is required. Conventional LQG control approach (Franklin et al. 1990; Chen and Atherton 2007), which connects the Kalman estimator and the optimal output-feedback gain designed with LQR, is well known for its robust performance when active and passive loads were present in the IIMG. However, the conventional LQG control scheme is not able to stabilize the IIMG when the dynamic IM load is present. Therefore, the optimal output-feedback gain has been designed based on modal-based LQRPDS. Thus, the connection of Kalman estimator and optimal output-feedback gain designed with the modal-based LQRPDS forms a modal-based extended LQG control approach, as shown in Fig. 3.11. In addition to the above features, the modal-based extended LQG control approach offers simplicity and flexibility when targeting multiple modes such as inter-area, intra- and unstable modes without affecting stable modes and local decentralized controllers. This makes it highly suitable to PMUs supported two-level hierarchical controller. In this approach, first, the augmented model is transformed to the modal canonical form using the real Schur decomposition (Safonov and Chiang 1989) with the resulting transformation matrix, M, relating the original states variables and the decoupled modal variables. The modal-augmented model has been used to design the centralized controller based on extended LQG control approach. Next, this approach has been solved using the separation principle (Zolotas et al. 2007). This principle divides the central control problem into two subproblems: optimal output-feedback control problem and optimal estimate of the state. The former one is based on the LQRPDS theory and the later one is based on the Kalman estimator theory (Zolotas et al. 2007). The gain matrix of both the Kalman estimator and the optimal output-feedback control is dependent on their diagonal weighting matrices, represented by QfMG & RfMG and QkMG & RkMG , respectively. Therefore, these are the tuning parameters of the centralized controller.

3 Design of Efficient Distributed Energy Resources (DER) …

3.2.3.3

59

Tuning of Parameters of the Hierarchical Controller

The optimal values of the parameters of both decentralized and centralized controllers have been obtained by formulating a bi-objective optimization problem as given in (3.26). In addition to provide good transient response, the controller should be robust to sustain small parameter perturbations. This can be achieved by minimizing the second norm of the optimal output-feedback control gain matrix as well as the condition number of the eigenvector matrix of the closed-loop IIMG. Mathematically, these two objective functions can be written as minimize x

f 1 (x) = ||KkMG (x)||2 f 2 (x) = ||VMG (x)||2 ||VMG −1 (x)||2

subject to x

min

≤x≤x

(3.26)

max

where KkMG is the optimal output-feedback control gain matrix and VMG is the eigenvector of the closed-loop IIMG. The variable x is a vector of the real and reactive power control gains of decentralized controllers of IIDER units and diagonal elements of weighting matrices of the Kalman estimator and optimal output-feedback control. The tuned parameters of the hierarchical controller have been obtained using the NSGA-II (Deb et al. 2002) and the post-Pareto analysis (Taboada and Coit 2005).

3.2.4 Performance Assessment of DER Controllers Figure 3.12 shows a schematic diagram of a studied AC microgrid (ACMG) test system operating at a frequency 50 Hz and voltage of 230 V (per phase root mean square (RMS)). The ACMG system includes four IIDER units, three lines and locally connected loads such as R/RL, CPL, RIAL, and dynamic IM load. It also includes decentralized, centralized, and hierarchical DER controllers. Furthermore, the ACMG is interfaced to the main utility grid bus through an isolation switch and a 415 V/11 kV step-up transformer at its point of common coupling (PCC), which is bus 1. Moreover, the mircorgrid can be operated either in the islanded mode or the grid-connected mode based on the status of isolation switch. The islanded operation is realized by opening the isolating switch, which disconnects the microgrid from the main grid, as shown in Fig. 3.12. In the island mode of operation, IIDER units are responsible for maintaining the system frequency and voltage along with meeting the total power demand. The parameters of the studied ACMG system are given in the Appendix (Pogaku et al. 2007; Raju and Jain 2018; Bottrell et al. 2013). The design procedure of the DER controllers, such as decentralized, centralized, and hierarchical controllers, has been validated through eigenvalue analysis, off-line time-domain simulations and RTDS-based simulations. It is worth mentioning that the decentralized and centralized controllers have been validated considering the

60

P. E. S. N. Raju and T. Jain Secondary Level Centralized Control Energy Management System (EMS)

Information Flow Power Flow Robust Optimal

Secondary Level Decentralized Control

Grid

Robust Optimal

Centralized

Kalman

Controller

Estimator

100 kVA RTU/PMU 11 kV /415 V

Decentralized

Local

Controller

Control

Isolation Switch Bus 1

Inductive

VSI

(RL)/Dynamic Induction Motor idist1

(IM) Load

DER

IIDER1

Line 1

Source

RTU/PMU

Bus 2 Decentralized

Local

Controller

Control

VSI

Constant Power Load (CPL) idist2

DER

IIDER2

Line 2

Source

RTU/PMU Active Load

Bus 3 Decentralized

Local

Controller

Control

Rectifier

VSI

Interfaced Active idist3 DER

IIDER3

Local

Line 3

Source

Load (RIAL)

Control idist4

Decentralized

Local

Controller

Control

VSI

DER

IIDER4

Resistive (R) Load

Source

Bus 4 RTU/PMU

Fig. 3.12 Schematic diagram of a studied IIMG system with the decentralized, centralized and hierarchical DER controllers

static loads (i.e., the equivalent static inductive (RL) load model of dynamic IM load has been considered at bus 1), while the two-level hierarchical controller has been validated considering the static and dynamic loads (i.e., dynamic IM load at bus 1). The rationale behind this is that the decentralized and centralized DER controllers were not able to mitigate the instability of IIMG with static and dynamic IM loads. The performance assessment results for the decentralized controller, shown in Table 3.1, reveal that the unstable modes are shifted to the LHP and the system damping is also significantly improved with the decentralized controller. Furthermore, it can be confirmed from Fig. 3.13 that the decentralized controller is capable of actively mitigating the unstable oscillatory responses.

3 Design of Efficient Distributed Energy Resources (DER) …

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Table 3.1 Eigenvalue analysis-based performance assessment of the decentralized controller Unstable modes

S. No.

Without decentralized controller

Δφ AL &Δγd AL

1

With the decentralized controller

Eigenvalue

Damping factor

Eigenvalue

61.76±j49.89

−0.7778

−546.96 ± j336.93 0.8514

Damping factor

2

Δilq AL & Δvcd AL

33.87 ± j21.17 −0.8480

−434.77 ± j314.19

0.8105

3

Δi gq AL &Δvdc AL

14.01± j19.81

−100 ±j0.00

1.00

−0.5773

(a) 5

(b)

(a)

↓ 10 -6

(b) 1.06

0.02

V1

V3

-5

V (p.u)

ΔVD (V )

Δf (Hz)

0 0 ΔVD1 ΔVD2 ΔVD3 ΔVD4

-0.02 -10

V2

-0.04 5

10

15

0

2

Time (msec)

4

(c) 0.1

ΔVQ (V )

ΔiDQAL (A) & ΔVDCAL (V )

ΔVQ1 ΔVQ2 ΔVQ3 ΔVQ4

0.05 0 -0.05 -0.1 2

4

8

6

Time (msec)

8

10

0.15 ΔiDAL ΔiQAL ΔVDC AL

0.1

V4

0.94 1.01

10

(d)

0.15

0

6

Time (msec)

V (p.u)

0

1.0

V2

1

V1

0.99

V4

V3

0.05

0.98 9.98

0

9.985

9.99 Time (s)

9.995

10

-0.05 0

2

4

6

8

10

Time (msec)

Fig. 3.13 Simulation results. a Time-domain simulations. b Real-time simulations

The eigenvalue analysis-based performance assessment results for the centralized controller, shown in Table 3.2, reveal that both the LQRPDS-based and the LQR-based centralized controllers are able to stabilize the IIMG system; however, the LQRPDS-based centralized controller increases both damping and the distance of the eigenvalues from the imaginary axis. Furthermore, it can be seen from Fig. 3.14a that the LQRPDS-based centralized controller is more effective with respect to number of oscillations and settling time as compared to the LQR-based centralized controller. Moreover, it can be confirmed from Fig. 3.14b that the frequency oscillates with increasing amplitude during open-loop disturbance period, while these unstable oscillations are mitigated by the LQRPDS-based centralized controller during closed-loop disturbance period. The performance assessment of the hierarchical controller based on eigenvalue analysis, shown in Table 3.3, demonstrates its ability in significantly improving the damping of unstable and low-damping high-frequency modes as compared to the state-based hierarchical controller. Furthermore, the time-domain simulations based on linear model, shown in Fig. 3.15a, unveiled that the modal-based hierarchical controller responds to the input and stabilizes the deviation in frequency faster with lesser peak amplitude than the state-based hierarchical controller. Moreover, it can be confirmed from the dynamic response of the frequency, shown in Fig. 3.15b, that the unstable oscillations during open-loop disturbance period (1.0–2.0 s) are mitigated by the modal-based hierarchical controller in the closed-loop disturbance period (2.0–3.0 s).

Δφ AL & Δγd AL Δilq AL & Δvcd AL Δi gq AL & Δvdc AL

1

3

2

Unstable modes

S. No. Damping factor

34.03 ± −0.8303 j22.84 14.02±j21.57 −0.5448

62.29±j50.33 −0.7778

Eigenvalue

Without controller

−14.07 ± j21.49 −34.04 ± j22.84 −45.13 ± j0.00

Eigenvalue

1.00

0.8303

0.5475

Damping factor −313.36 ± j0.00 −267.63 ± j0.00 −232.48 ± j26.26

Eigenvalue

0.9937

1.00

1.00

Damping factor

With the LQR based centralized controller With the LQRPDS based centralized controller

Table 3.2 Performance assessment of the centralized controller using eigenvalue analysis

62 P. E. S. N. Raju and T. Jain

3 Design of Efficient Distributed Energy Resources (DER) …

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(b) Disturbance Power (kW)

(a) With the LQR based centralized controller

Δf (Hz)

0.5

0

-0.5 0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2.5 2 1.5 Disturbance Period

1 Steady-state Period

0.5 0 0

2

0.5

1

1.5

Steady-state Period

Closed-loop Period

Open-loop Period 2

2.5

3

3.5

With the proposed centralized controller

IIDG1 IIDG2 IIDG3 IIDG4

50.04

Frequency (Hz)

0.1

Δf (Hz)

4

Time (sec)

Time (seconds)

0.05 0 -0.05

50.02 50 49.98 49.96 49.94

-0.1 0

10

20

30

40

50

60

Time (milliseconds)

70

80

90

100

49.92 0

0.5

1

1.5

2

2.5

3

3.5

4

Time (sec)

Fig. 3.14 Performance evaluation using time-domain simulation. a Dynamic step response of the deviation in the frequency. b Dynamic response of the frequency of IIDG units

Finally, the performance of the decentralized, centralized, and hierarchical controllers has been compared in terms of the settling time of the dynamic step response of the deviation in frequency and voltage variables, as given in Table 3.4. It is evident that the centralized and hierarchical controllers are slower than the decentralized controller, whereas the hierarchical controller is slower than the decentralized controller and faster than the centralized controller. However, the hierarchical controller has the ability to mitigate the instability in IIMGs with static and dynamic loads unlike the decentralized controller. Thus, it can be concluded from the above validation results that the hierarchical controllers are quite robust to mitigate the instability in IIMGs with static and dynamic loads and to damp out the oscillations as well as to settle quickly at a new operating point under the application of small-signal disturbances.

3.3 Protection Systems for Distributed Energy Resources-Based Microgrids Protection issues/problems in DERs-based microgrids are raised from the power electronic interfaces of DER units due to the limitations with the power electronic switches (Rezaei and Uddin 2021). The protection strategies for DERs should be designed to provide optimum protection by considering the following factors. • • • • •

Bidirectional power flow could occur with the DERs integration. Location of fault occurrence. Location of DERs. Type of DERs like inverter-based DERs or synchronous machine-based DERs. Short-circuit current contribution by the inverter-based DERs is lower than the contribution of conventional synchronous generators. • Uncertainties with DER technologies like photovoltaics and wind.

62.98 − j39.21

42.05+j0.00

36.31+ j17.78

36.31 − j17.78

24.81 + j30.61

24.81 − j30.61

Δφ AL

Δil Q AL

ΔvcQ AL

Δi g D AL

Δi g Q AL

Δvdc AL

2

3

4

5

6

7

−135.30 ± j13.48e3

− 169.56 ± j12.94e3

ΔQ 1 &Δs Q1

Δϕ D Q1

8

9

Low-damping high-frequency modes

62.98 + j39.21

Δi C P L Q

1

0.013

0.010

−0.6296

−0.6296

−0.8981

−0.8981

−1.00

−0.8489

−0.8489

−11.33 ± j21.63e3

-58.35 ± j346.47

−181.51 − j79.37

−181.51 + j79.37

−173.71 − j0.00

−158 − j0.00

−198.32 − j12.50

−226.27 − j20.72

−226.27 + j20.72

Eigenvalue

0.4641

0.9999

0.9154

0.9154

1.00

1.00

0.9981

0.9958

0.9958

Damping factor

With the state-based hierarchical controller

Eigenvalue

Damping factor

Without controller

Unstable modes

S. No.

Table 3.3 Performance assessment of the hierarchical controller using eigenvalue analysis

−11.33 ± j21.63e3

−58.35 ± j346.47

−265.57 + j0.00

−257.99 − j40.05

−203 − j17.50

−231.26 − j9.26

−198.82 − j0.00

−231.26 + j9.26

−229.63 − j17.02

0.4641

0.9999

1.00

0.9882

0.9963

0.9992

1.00

0.9992

0.9973

Damping Factor

With the modal-based hierarchical controller Eigenvalue

64 P. E. S. N. Raju and T. Jain

3 Design of Efficient Distributed Energy Resources (DER) …

65

(a) 2

(b)

↓ 10 -3

50.04

1.5

Frequency (Hz)

Δf (Hz)

IIDG1 IIDG2 IIDG3 IIDG4

With the State-based

1 0.5 0 -0.5

50.02 50 49.98 49.96

-1 With the Proposed -1.5

49.94 0

0.5

1

1.5

2

2.5

3

3.5

4

-2 0

10

20

30

40

50

60

70

80

90

100

Time (sec)

Time (milliseconds)

Fig. 3.15 Performance evaluation using time-domain simulation. a Dynamic step response of the deviation in the frequency. b Dynamic response of the frequency of IIDG units Table 3.4 Performance comparison of the decentralized, centralized, and hierarchical controllers S. No. Deviation With the decentralized With the centralized With the hierarchical in output controller controller controller With static loads

1 2 3 4 5

Δ f (Hz) ΔV1 (V) ΔV2 (V) ΔV3 (V) ΔV4 (V)

Settling time (ms) 10.50 8.35 9.85 11.35 10.80

Settling time (ms) 49.00 40.60 40.80 39.80 45.20

With static and dynamic loads Settling time (ms) 40.70 38.30 35.30 33.80 43.33

Conventional power system protection systems are designed mainly for synchronous machine-based generators and for unidirectional power flow in distribution systems. However, the integration of DERs requires a new protective relay sensors to detect lower fault current values and more sophisticated & advanced protection coordination schemes due to the above-mentioned factors (Reno et al. 2021; Ropp and Reno 2021). The following are various protection methodologies for DERsbased microgrids according to the parameter used such as voltage, current, power, and frequency (Rezaei and Uddin 2021). • Voltage-based protection: In this, the relays and faulty zones will be operating only when the measured voltage exceeds the certain threshold value. • Impedance and admittance-based protections: In this scheme, the impedance or admittance between the fault point and relays is measured and compared with the threshold value. The relays will be opening if the impedance or admittance value is greater than the threshold value. • Current-based protection: In this scheme, the extraction of current traveling waves mostly carried out by wavelet analysis. The main advantage of this protection scheme is that the fault location can be detected by comparing the magnitude and polarity of current traveling waves before and after a fault incident.

66

P. E. S. N. Raju and T. Jain

• Differential protection: In this methodology, the relays will be operated to isolate the faulty zones when the difference between the fault currents surpasses the certain threshold value. • Distance protection: In this scheme, the distance protective relays will be used to provide adequate and reliable protection. The distance protective relays depend on the impedance at each relay that is measured by the division of voltage over current at the relay location. • Over current protection: In this protection scheme, over current relays with directional elements will be used to provide adequate security, for the DERs, feeders and loads, during disturbances and impending faults. • Total harmonic distortion (THD)-based protection scheme: In this protection scheme, the THD value of voltage and current samples will be calculated using fast Fourier transform (FFT) and the relays will be opening if the voltage and current THD exceeds the threshold value. • Adaptive and communication-based schemes: These techniques incorporates different topologies of the DERs-based microgrids while designing the modern microprocessor-based protective relays for different fault conditions. The relays settings will be stored in the program, and the measured parameter will be compared with threshold value to identify faulty zones. • Pattern recognition-based protection: In this methodology, fault patterns are recognized from the analysis of spectral energy density content of contours that are produced through S-transform originated from measured current at each bus in the microgrids (Rezaei and Uddin 2021). A tripping signal will be sent to the circuit breaker according to preset threshold value for current instigated on the differential energy. • Wide area measurement-based protection: In this protection scheme, intelligent electronic devices like phasor measurement unit (PMU) will be utilized. It requires the installation of comprehensive communication links to deliver fast communication of electrical information from various locations of the microgrids to the protective relays. • Artificial intelligence (AI) and nature-inspired algorithms (NIA)-based protection: In this, the following algorithms have been used for the relays setting optimization and optimal coordination due to their effectiveness, robustness, fast operation, and reliability. – – – – – – – – – –

Artificial bee colony optimization Biogeography-based optimization Particle swarm optimization (PSO) Grey wolf optimization (GWO) Cuckoo optimization algorithm (COA) Bat algorithm Gravitational search algorithm (GSA) Firefly algorithm Genetic algorithm (GA) Differential evolution (DE)

3 Design of Efficient Distributed Energy Resources (DER) …

67

– Harmony search algorithm (HSA) – Artificial neural network (ANN) • Digital signal processing (DSP)-based protection: In this scheme, wavelet transform-based DSP technique has been used to detect fault. It provides a timefrequency representation of transient signals and helps to discriminate between various types of faults with high precision. • Protection improvement by fault current limiter (FCL) devices: FCL devices are employed in the following applications. – To limit the fault current before reaching to maximum magnitude. – To diminish inrush current caused by capacitive loads. – To improve power quality. Furthermore, the following protection schemes have been suggested in the literature for the grid-connected and islanding modes of operation of the DERs-based microgrids (Raju and Jain 2013). • Grid-connected mode of operation of microgrids. – Improved current protection scheme. – Fault current limiter protection scheme. – Wide area protection scheme. • Islanding mode of operation of microgrids. – Voltage-based protection scheme. – Total harmonic distortion-based protection scheme. In conclusion, the network topology, load flow distribution, and the magnitude as well as direction of the fault current become completely different in DERs-based microgrids as compared to that in the traditional radial distribution network. Furthermore, the original relays, designed for the traditional radial distribution network, could not response correctly due to the impacts of the DERs. Moreover, the use of the traditional protection schemes in the DERs-based microgrids may lead to mis-operation and refusal of circuit breakers. Therefore, an adaptive protection in conjunction with the proper communication infrastructure based on IEC 61850-7420 standard would be recommended for DERs-based microgrids (Ustun et al. 2012). The rationale behind this is that the adaptive and communication-based protection technique has the capability of incorporating the dynamic changes; subsequently, it updates the relay settings according to the dynamic changes in the DERs-based microgrids.

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3.4 Conclusion In this chapter, the design procedure of the robust and efficient controllers such as decentralized, centralized, and hierarchical controllers has been presented for DERs-based microgrids. The DER controllers have been designed based on a robust extended linear quadratic Gaussian (LQG) control, which combines the Kalman estimator with the linear quadratic regulator with prescribed degree of stability (LQRPDS). Furthermore, the performance assessment results concluded that the DER controllers are quite robust to damp out the unstable oscillations as well as to settle quickly at a new operating point under the application of small load disturbances at different buses. Finally, a brief description of protection methodologies for DERsbased microgrids has also been provided.

Appendix IIDER Units Ratings: IIDER1 − (10 + j6) kVA; IIDER2 − (15 + j9) kVA; IIDER3 0− (20 + j12) kVA; IIDER4 − (25 + j15) kVA. Static Active and Reactive Power Droop mP2 = 4.18e − 4 rad/s/W, mP3 = 3.14e − 4 Gains: mP1 = 6.28e − 4 rad/s/W, rad/s/W, m P4 = 2.52e − 4, nQ1 = 1.66e − 3 V/VAR, nQ2 = 1.11e − 3 V/VAR, nQ3 = 8.33e − 4 V/VAR and nQ4 = 6.66e − 4 V/VAR. IIDER unit Parameters: L f = 1.35 mH, C f = 50 µF, Rf = 0.1 Ω, f sw = 8 kHz, wc =31.41 rad/s, K pv = 0.05, K iv = 390, K pi = 10.5, K ii = 16e3, F = 0.75, f nl = 50.5 Hz, Rc = 0.03 Ω, L c = 0.35 mH. RIAL Parameters: L f = 2.3 mH, C f = 8.8 µF, Rf = 0.1 Ω, f sw = 10 kHz, wc = 31.41 rad/s, K pv = 0.5, K iv = 150, K pi = 7, K ii = 25e3, Rc = 0.03 Ω, L c = 0.93 mH. Line Parameters: Line 1: (0.23 + j0.11) Ω, Line 2: (0.35 + j0.58) Ω, Line 3: (0.30 + j0.47) Ω. Load Parameters: Induction Motor Load: 10 HP, 400 V, 50 Hz, rs = 0.7834 Ω, L ss = 127.1 mH, rr = 7402 Ω, L rr = 127.1 mH, L m = 124.1 mH, P = 4, TL = 47.75 Nm; CPL: 12 kVA, r CPL = 13.224 Ω/phase and cos α = 0.85; RIAL: 12 kW and RRIAL = 40.833 Ω; R Load: 25 kW, RRLoad = 6.347 Ω/phase and VDC = 700 V.

References Baghaee HR, Mirsalim M, Gharehpetian GB (2016) Power calculation using RBF neural networks to improve power sharing of hierarchical control scheme in multi-DER microgrids. IEEE Trans Emerg Sel Topics Power Electron 04(4):1217–1225 Bottrell N, Prodanovic M, Green T (2013) Dynamic stability of a microgrid with an active load. IEEE Trans Power Electron 28(11):5107–5119 Chen Y, Atherton DP et al (2007) Linear feedback control: analysis and design with MATLAB. SIAM, USA Choudhury S (2022) Review of energy storage system technologies integration to microgrid: types, control strategies, issues, and future prospects. J Energy Storage 48:130966

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Ropp M, Reno M (2021) Influence of inverter-based resources on microgrid protection: Part 2: Secondary networks and microgrid protection. IEEE Power Energy Mag 19(3):47–57 Safonov MG, Chiang RY (1989) A Schur method for balanced model reduction. IEEE Trans Autom Control 34(7):729–733 Sanjari MJ, Gharehpetian GB (2013) Small signal stability based fuzzy potential function proposal for secondary frequency and voltage control of islanded microgrid. Electr Power Compon Syst 41(05):485–499 Taboada H, Coit D (2005) Post-Pareto optimality analysis to efficiently identify promising solutions for multi-objective problems. Rutgers University ISE Working Tan KT, Peng XY, So PL (2012) Centralized control for parallel operation of distributed generation inverters in microgrids. IEEE Trans Smart Grid 3(4):1977–1987 Tsikalakis AG, Hatziargyriou ND (2008) Centralized control for optimizing microgrids operation. IEEE Trans Energy Convers 23(1):241–248 Ustun TS, Ozansoy C, Zayegh, A Modeling of a centralized microgrid protection system and distributed energy resources according to IEC 61850-7-420. IEEE Trans Power Syst 27(3):1560– 1567 Wang Y, Wang X, Chen Z, Blaabjerg F (2017) Distributed optimal control of reactive power and voltage in islanded microgrids. IEEE Trans Ind Electron 53(01):340–349 Yassami H, Darabi A, Rafiei SMR (2010) Power system stabilizer design using Strength Pareto multi-objective optimization approach. Electr Power Syst Res 80(7):838-846 Yu K, Ai Q, Wang S, Ni J, Lv T (2016) Analysis and optimization of droop controller for microgrid system based on small-signal dynamic model. IEEE Trans Smart Grid 7(2):695–705 Zhao Z, Yang P, Guerrero JM, Xu Z, Green TC (2016) Multiple-time-scales hierarchical frequency stability control strategy of medium-voltage isolated microgrid. IEEE Trans Power Electron 31(8):5974–5991 Zolotas AC, Chaudhuri B, Jaimoukha IM, Korba P (2007) A study on LQG/LTR control for damping inter-area oscillations in power systems. IEEE Trans Control Syst Tech 15(1):151–160

Chapter 4

Economic Dispatch for Unbalanced Active Distribution Systems Management César Álvarez-Arroyo, Lázaro Alvarado-Barrios, Juan Manuel Escaño, Francisco González-Longatt, and Jose Luis Martínez-Ramos

Abstract A strategy for managing active distribution systems (ADS) requires the use of optimal control techniques that find a good solution to reduce active power losses. The solution to this type of problem presents two major difficulties: on the one hand, the optimisation problem is generically formulated as a mixed-integer nonlinear propagation problem, whose solution requires a high computational cost and, on the other hand, its application to complex distribution networks, which are characterised by their radial nature, large number of busbars, long length, and significant imbalances due to the presence of loads distributed unevenly between the phases. This chapter presents the development of an economic dispatch (ED) optimisation model for active distribution system management (ADSM). In this context, DIgSILENT Power Factory® provides useful tools to simulate complex systems. On the one hand, the DIgSILENT Programming Language (DPL) can be used for multiple purposes such as automation of simulations, automatic scenario generation, analysis of results, etc. On the other hand, DIgSILENT Power Factory® supports some external interfaces that can be used for data exchange and coupling with MATLAB® . The control and optimisation strategy are validated in IEEE 34-Node Distribution Test Feeder. C. Álvarez-Arroyo (B) · J. L. Martínez-Ramos Department of Electrical Engineering, Universidad de Sevilla, Seville, Spain e-mail: [email protected] J. L. Martínez-Ramos e-mail: [email protected] L. Alvarado-Barrios Department of Engineering, Universidad Loyola Andalucía, Seville, Spain e-mail: [email protected] J. M. Escaño Department of Systems Engineering and Automatic Control, Universidad de Sevilla, Seville, Spain e-mail: [email protected] F. González-Longatt Electrical Power Engineering, University of South-Eastern Norway, Porsgrunn, Norway e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. N. Singh et al. (eds.), Optimal Planning and Operation of Distributed Energy Resources, Energy Systems in Electrical Engineering, https://doi.org/10.1007/978-981-99-2800-2_4

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Keywords Active distribution network management · DIgSILENT Power Factory · Economic dispatching · Optimisation

4.1 Introduction Environmental sustainability and ways to limit carbon dioxide emissions (Gerbaulet et al. 2019) are leading to increased interest in accelerating the process of decarbonisation of the economy (Pye et al. 2019). As a contribution to this objective, electricity systems are currently undergoing a transition towards decentralised electricity grids, characterised by an increased penetration of distributed energy resources (DER) (Malik and Lehtonen 2016; Kakran and Chanana 2018). These resources comprise distributed generation units (DG), primarily renewable energy resources (RES), energy storage systems (ESS), and demand response (DR) (European Commission 2017). These changes are causing traditional distribution networks to undergo a transition from passive to active distribution systems (ADS) (Dubey et al. 2020). This transformation creates new technical challenges but provides new financial opportunities for system operators, energy market participants, and other entities such as aggregators. In this context, two ideas appear to be taken into account. On the one hand, the distribution system operator (DSO) is an independent body responsible for the reliable operation of ADS (Gao et al. 2017), which interacts with distribution companies (DisCO), which in turn act as energy trading entities (Bahramara et al. 2020), contract flexibility services from aggregators to deliver energy to end customers in a more cost-effective manner (Sheikhahmadi et al. 2021), and impose restrictions with respect to grid operating constraints under normal conditions, such as line capacity and voltage limits (Evangelopoulos et al. 2022). On the other hand, electricity sector regulation does not allow the DSO to own or operate generation or storage resources in the grids. Recent research highlights the need to provide the DSO with flexibility in the management of DERs (Heinrich et al. 2021; Olivella-Rosell et al. 2018) and to facilitate the exchange between operators, consumers, and aggregators (Jin et al. 2020), to increase the energy efficiency of these systems. Therefore, it is clearly accepted that it is necessary to provide the DSO with the capacity to operate generation and storage resources in its networks, either through specific bilateral contracts or by installing its own resources in duly justified cases (grid reinforcement), to maximise the penetration of DERs into the distribution grids (Oureilidis et al. 2020). Medium-voltage distribution networks generally have a radial topology, and the R/X ratio in these networks is much higher than in transmission systems, resulting in higher power losses and gradual loss of electrical energy along the distribution feeders (Rao 2010). Consequently, for many utilities around the world, loss minimisation is one of the most important issues. Two extensive methods to minimise losses in distribution networks are network reconfiguration and capacitor placement, which are well known and frequently used (Baran and Wu 1989; Rao et al. 2012).

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In this work, a new ED optimisation approach is proposed. The main objective is to minimise the active power losses in an unbalanced ADS, with high presence of DERs (RESs and ESSs), and to make use of the control resources available in the grid, in this case voltage-regulating transformers (OLTC) and batteries. The ED approach for ADSM is implemented in a joint MATLAB® -DIgSILENT® simulation platform, using Peer-to-Peer communication between them.

4.2 Economic Dispatch Optimisation Model for ADSMs This chapter explores optimal power management in medium-voltage (MV) distribution networks. These networks are normally operated radially (Willis 2004) and the presence of unbalanced voltage and current profiles caused by an uneven distribution of loads per phase (Short 2014) is very common, and this fact is worsened by the presence of DERs. RESs supply power directly to distribution grids and are affected by the variability of the power generated by RESs due to weather in one or more areas, depending on the location of these resources. This makes it more difficult for the DSO to control the MV network. One of the main objectives of the DSO is to optimise the efficiency of power transmission in distribution networks, with DERs being an important control element to minimise losses due to their location closer to the final consumer. This work presents an economic dispatch strategy to minimise active power losses in unbalanced ADS while maximising the power delivery of available DERs. Economic dispatch takes into account the power forecast of the RESs and the demand of the network. A schematic of this can be seen in Fig. 4.1.

4.2.1 Optimisation and Control Strategy Figure 4.2 shows the two-level control strategy proposed in this work. The first level considers the formulation of an optimisation problem whose objective is to minimise active power losses in the ADS, taking into account the power forecast of the wind, sun, and demand. The result is an ED every T s minutes, with a horizon of Np values, e.g. if T s is chosen as 15 min and 24 h of horizon are set, N = 96 for scheduling the ESSs that are under the control of the DSO. This process is performed in MATLAB® . The economic dispatch result is simulated in DIgSILENT® where the second level of optimisation is found, which acts on the taps of the regulating transformers, in a discrete way. DIgSILENT® performs the load flow and checks that the voltage and current values are within the limits but also optimises losses by acting on the OLTCs, trying to ensure that the network has high voltage values to reduce losses.

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Fig. 4.1 Economic dispatch strategy to reduce active power losses

Fig. 4.2 Workflow diagram of two-level optimisation strategy

As can be seen from the workflow scheme represented in Fig. 4.2, the control strategy requires communication between the two working platforms, MATLAB® and DIgSILENT® , which is explained in Sect. 4.3.

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4.2.2 Objective Function Generation (PtGen ) should be equal to demand (PtLoads ) plus losses (PtLosses ). Equation 4.1 can be rewritten leaving losses at one end of the equality. Minimising these losses constitutes the objective function of this problem and can be written as Eq. 4.2, where PtExt , PtBESSi , PtPV , PtWT and PtDem are the external grid power, the battery power i, the photovoltaic plant power, the wind turbine power and the power demand, respectively, for each time step t.   PtGen = PtLoads + PtLosses , ∀t ∈ 1, N p .

(4.1)

      min PtLoss = min PtExt + PtBESSi + PtPV + PtWT − PtDem , ∀t ∈ 1, N p . (4.2) To minimise power losses, costs must be assigned to generation power, both to external grid power (CtExt ) and to battery power (CtBESS ), in each time step (Tian et al. 2019). These generation costs are power dependent and can be expressed with Eqs. 4.3 and 4.4.     CtExt = CExt PtExt , ∀t ∈ 1, N p .

(4.3)

    CtBESS = CBESS PtBESSi , ∀t ∈ 1, N p , ∀i ∈ {1, n}.

(4.4)

Therefore, considering Eqs. 4.3 and 4.4, Eq. 4.2 can be rewritten as Eq. 4.5.     min CtExt = CtExt + CtBESSi , ∀t ∈ 1, N p , ∀i ∈ {1, n}.

(4.5)

Therefore, PtExt and PtBESSi will form the vector of variables in the optimisation process carried out by MATLAB® . Actually, the interesting variables to optimise are the battery powers, since the grid power is a variable that is free to comply with the power balance. Renewable sources, photovoltaic (PV), and wind turbine (WT) have been considered as zero generation costs, as well as OLTC operations. The problem is formulated as a nonlinear programming problem and is solved with an interior-point method algorithm.

4.2.3 OLTC Control Algorithm The study grid has two three-phase in-line regulator transformers, as can be seen in Fig. 4.1. The tap control constitutes the second level of the optimisation strategy and consists of modifying the position of the tap for each phase and each regulator transformer, preventing voltages from exceeding limit values Vmax and Vmin , but always looking for high voltage values, as this reduces losses. The tap control is

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implemented by Algorithm 1. The tap position of the phase ph and the regulator ph transformer i is represented as TAPi . The tap positions are considered integer values and must be within the maximum and minimum limits, represented by TAPmax and TAPmin , respectively.

The tap positions are not variables of the first level of optimisation, carried out by MATLAB® . These variables are controlled by DIgSILENT® using a script written in DIgSILENT Power Factory Language (DPL). Therefore, communication between MATLAB® and DIgSILENT® is needed.

4.2.4 System Modelling 4.2.4.1

Demand and RESs Forecasts

The power demand is not constant during the 24 h of the day but changes according to the curve shown in Fig. 4.3a. The PV and WT generators provide power as a function of solar power and wind power forecast (Fig. 4.3b and c). The figures show real power

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Fig. 4.3 a Power demand (pu), b wind power production (pu), c solar power production (pu)

curves, as the most likely predictions of demand and renewable generation. These are given in per unit (pu) in order to normalise them and observe their variability. In this way, scalability is allowed to adapt them to the generation and consumption powers of a specific grid.

4.2.4.2

Energy Storage System

The energy storage system consists of batteries and the state of charge of each battery in each time step (SOCit ) must not exceed the maximum and minimum storage limits (SOCimax and SOCimin ) to avoid damage. These restrictions can be expressed by Eq. 4.6 and their number depends on the number of batteries and the number of time steps.   SOCimin ≤ SOCit ≤ SOCimax , ∀t ∈ 1, N p , ∀i ∈ {1, n}.

(4.6)

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Batteries can operate as a generator or as a consumer, so the SOC must be updated in each time step using power and operating time through Eqs. 4.7.  SOCit

=

SOCit−1

−

  t · Pti · ηc for Pti < 0 , ∀t ∈ 1, N p . t·Pti i for Pt > 0 ηd

(4.7)

where ηc and ηc are, respectively, the charging and discharging efficiency, t is the time between samples, and Pti is the power supplied by the battery i in the instant t. The value of Pti can be positive (supply energy) or negative (storage energy). 4.2.4.3

Power Balance Constraint

The balance equation can be written by separating in one term the control variables of optimisation, external grid, and batteries power, and in a second term, the rest of the variables, power demand, power losses, and power from renewable sources (Eq. 4.8). This second term of equality is important since depending on the sign of this term, the objective function of the problem changes, as will be explained later. PtExt + PtBESS1 + PtBESS2 + · · · + PtBESSn = PtDem + PtLoss

  − PtPV + PtWT , ∀t ∈ 1, N p . (4.8)

4.2.4.4

Generation Limits

The power of the external grid can be positive to satisfy the generation defect of the renewables or negative if the renewables exceed the demand. Therefore, the power of the external grid can take any positive and negative value and must satisfy the Ext ) due to the connection capacity according to Eq. 4.9. power limits (Plim   Ext Ext −Plim < PtExt < Plim , ∀t ∈ 1, N p , ∀i ∈ {1, n}.

(4.9)

Batteries can absorb excess generation (negative power) like the external network if batteries reach their power or energy limits when photovoltaic and wind generation exceed the demand. In the opposite direction, batteries can work as generators (positive power) when there is a renewable generation defect. Therefore, the batteries i i and Pmin are the power limits. must satisfy Eq. 4.10 where Pmax   i i −Pmin ≤ Pti ≤ Pmax , ∀t ∈ 1, N p ,

(4.10)

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The photovoltaic generator has a minimum power of zero, in the case of no solar PV ) of the nominal power of the generator, so radiation, and a maximum power (Pmax its limits are those expressed in Eq. 4.11.   PV 0 ≤ PtPV ≤ Pmax , ∀t ∈ 1, N p .

(4.11)

Similarly, the wind turbine has a minimum power of zero in the case of a wind speed below a certain limit depending on the type of wind turbine and a maximum WT ) associated with the nominal power of the generator, so the generation power (Pmax limits are expressed in Eq. 4.12.   WT 0 ≤ PtWT ≤ Pmax , ∀t ∈ 1, N p .

4.2.4.5

(4.12)

Grid Operation Limits

For normal operation within the power quality limits of the network, the voltages at all nodes (Nn ) must be within a maximum value (Vmax ) and minimum (Vmin ) value, which correspond to ± 5% of the nominal voltage. Therefore, this type of constraint is expressed as Eq. 4.13 where Vtk is the voltage of node k in the time step t.   Vmin ≤ Vtk ≤ Vmax , ∀k ∈ {1, Nn }, ∀t ∈ 1, N p .

(4.13)

Another constraint in the operation of network is that the line from node k to node kj j does not exceed its ampacity value (Imax ) at each time step, to avoid overloads. kj These restrictions are expressed by Eq. 4.14 where Imax is the current from node k to node j in the time step t.   kj kj 0 ≤ It ≤ Imax , ∀k j, ∀t ∈ 1, N p .

(4.14)

4.3 MATLAB® -DIgSILENT® Simulation Platform Implementation The first step of the optimisation process is carried out by MATLAB® . It solves the optimisation problem whose objective function is stated in Eq. 4.5 subject to the constraints imposed by Eqs. (4.6)–(4.14). The network is modelled in DIgSILENT® and takes the results of the optimisation to configure the network at each moment, performing successive load flows acting on the OLTCs, which constitutes the second level of the optimisation. DIgSILENT® returns the simulation results to MATLAB® for further analysis and representation of the results. This implies that there is a

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programming in MATLAB® , another programming in DPL and a communication between them.

4.3.1 First-Level Optimisation Algorithm This section explains in detail each of the actions performed by MATLAB® and is shown in Algorithm 2 as a pseudocode. First, temporary files and variables are deleted, the simulation scenario is chosen, and all matrices, cells, and variables are defined. Then, the file with the nominal power of all loads is read and the information is saved in a matrix. After that, the files with the power demand, wind, and solar power predictions are read, allowing one to scale the demand and renewable generation for each time step. Lastly, before starting the optimisation loop, the configuration of the scenarios with the nominal powers of the renewable generators and batteries, as well as their storage capacity limits, is read from the scenario configuration files, and the file that controls the workflow between MATLAB and DIgSILENT (Switch.csv) is set. If the Switch file has a zero, MATLAB is working and DIgSILENT is waiting, while if the file has a one, DIgSILENT is working and MATLAB is waiting. The loop for each time step starts from this point (see loop For in Algorithm 2). The power demand can be calculated for each load and phase and the power from renewable sources can also be calculated. These data compose the right term of Equation 4.8, so the optimisation function can calculate the economic dispatch, i.e. the external grid and the battery powers. The actual power demands for each load and each phase are written in a csv file to be read later by DIgSILENT, the optimisation is carried out and the economic dispatch, and renewable powers are saved in a csv file to be read by DIgSILENT as well. Since the optimisation for the time step t is finished, DIgSILENT has to work. Once the optimisation for time t is complete, a 1 is written to the Switch file to pass the workflow to DIgSILENT and have MATLAB wait. After DIgSILENT works (this part is explained in Sect. 4.3.2), all results of the second level of optimisation, voltages, currents, power losses, and tap positions are saved in MATLAB variables. Here, the loop for is completed and repeated for other time steps.

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4.3.2 Second-Level Optimisation Algorithm This section describes the DPL programming and is shown in Algorithm 3 as a pseudocode. First, DigSILENT must have access to all MATLAB-written configuration files, and for it, the DIgSILENT work folder is set. In the DPL script, the declaration of variables, objects, and sets is started. A set of matrices has been created which are resized and initialised. These matrices are used to store the power provided by the external network, the demand for each load, the losses in the network, and the position of the taps, all at each time of day. After completing the optimization process, all the results are recorded in files for further analysis and visualization.

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Next, values are given to the attributes of the ComLdf object to configure the load flow, and the scenario setting files are read to save the information in variables and configure the number of time steps of a day (iterations N p ), the number of scenarios to simulate and the nominal powers and limits of the generators. At this moment, the loop that controls the time step begins and is repeated N p times. DIgSILENT reads the Switch file and goes into a While loop to check if it is its turn to work, waiting if DIgSILENT reads a zero value. Otherwise, read the file that has the economic dispatch powers and renewable source production for this time and sets the generators and batteries. The same applies for all loads; DIgSILENT reads the file with actual load powers and sets them. Now, the study network is configured for the time step t and the load flow is executed. Currents and voltages are determined, and the second level of optimisation (Algorithm 1) is carried out by moving the position of the OLTC for each phase and each in-line regulator transformer (TRX). The optimisation process is completed for the time step t. The results, external grid power, and power losses are saved in matrices, and all voltages and currents are written in files for analysis and depiction later using MATLAB. DIgSILENT finished its work writing a zero in a Switch file, and MATLAB changes to the next time step repeating the described process. Finally, DIgSILENT writes files with power losses, external grid power, and tap positions for each in-line regulator.

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4.4 Application Examples This section presents the data and characteristics of the network used as a case study. The circuit chosen for the test is a modification of the original IEEE 34-Node Test Feeder in (IEEE PES AMPS DSAS Test Feeder Working Group 1992) and is an MV distribution network that does not contain distributed generation resources to which PV, WT, and BESS have been incorporated with the aim of giving it an ADS character and is shown in Fig. 4.4.

4.4.1 Test System The IEEE 34-Node Test Feeder represents an actual feeder in Arizona, which displays a wide variety of components and topological characteristics in Owuor et al. (2011), which are representative of a typical rural distribution network with single and threephase laterals and long feeders. This distribution network is operated at 24.9 kV and has two in-line regulator transformers, two capacitor banks, and one low-voltage lateral at 4.16 kV. The substation is rated at 2500 kVA, with a 69 kV/24.9 kV transformer at the node 800. The network model was implemented in DIgSILENT Power Factory taking into account the data for each component provided in (IEEE PES AMPS DSAS Test Feeder Working Group 1992) and the contributions proposed to the modelling of this network by the authors in Alvarado-Barrios et al. (2020), which are summarised below. • The lines are modelled considering their geometrical configuration, which is defined by the type of tower and the type of conductor. In this case, there are five line configurations which are a combination of two types of towers (500 and 510, see IEEE PES AMPS DSAS Test Feeder Working Group (1992)) and three types of ACSR cables (1/0, #2 6/1, #4 6/1).

Fig. 4.4 Study network with distributed energy resources

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Table 4.1 DERs penetration scenarios

Case number

DERs penetration(%)

1

49.03

2

100.64

3

100.64

• In the system, there are two types of loads, spot and distributed. The former is located at the corresponding node, and the latter is modelled as a spot load located at the centre of the line. • The network presents important imbalances, with the presence of balanced and unbalanced three-phase loads, which can be connected in star or delta, and singlephase loads, some connected from line to line and others from line to ground. The representation of the loads in the software not only takes into account their characteristics but also includes impedance (Z), current (I), or constant power (PQ) models, as appropriate. • The two in-line regulator transformers are modelled as a transformer bank. • In this work, it was also considered to eliminate the line from node 864 to 858, the entire single-phase line from node 834 to 848, and the line from node 832 to node 890. • At node 806 is the BESS1 battery, at node 828 the wind turbine (WT), and at node 840 the other battery and photovoltaic plant (BESS2 and PV) are integrated into the study grid. Figure 4.4 shows the changes mentioned above. The different line types are represented with colour coding.

4.4.2 Simulation Results Three scenarios have been defined to study the influence of DER penetration levels (Table 4.1) on power losses due to energy transmission, understanding the DER penetration level as the fraction of the total system load provided by DER (PDERs ). In scenario 1, the study network has a penetration level of DER of 49.03%. In scenario 2, the penetration level has increased compared to scenario 1 by increasing the maximum power of the wind and photovoltaic generators, reaching a penetration level of 100.64%. And scenario 3 is similar to scenario 2 except that the level of battery storage is reduced by half to observe the effect of storage capacity.

4.4.2.1

Study Case 1

Scenario 1 presents a level of penetration of renewable energy of 49.03%. The maximum and minimum generator power limits and battery storage limits are presented in Table 4.2. The state of charge (SOC) and the energy limits of the batteries

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Table 4.2 DERs configuration. Scenario 1 Source

Node

Pmin

Pmax

Energymin

Energymax

SOCmin

SOCmax

(kW)

(kW)

(kWh)

(kWh)

(%)

(%)

WT

B828

50

100

–

–

–

–

PV

B840

0

100

–

–

–

–

BESS1

B840

−80

80

70

280

20.74

80

BESS2

B806

−100

100

70

280

20.74

80

(a) Economic dispatch powers

(b) State of charge of batteries

Fig. 4.5 Economic dispatch powers and SOC of scenario 1

are shown in Table 4.2 to study the effect of this variable on power losses because in scenario 3, this value changes. The result of the optimisation gives the power distribution for each time instant shown in Fig. 4.5a. It can be seen that, as the penetration level is very low, the power from renewable sources does not exceed the demand curve. Therefore, batteries only provide power at the beginning of the day because they have an initial charge but cannot be charged again because there is no excess of renewable generation. This can be seen in Fig. 4.5b where the state of charge of the batteries is depicted.

4.4.2.2

Study Case 2

In scenario 2, the WT and PV capacities increase from 100 to 300 kW, so the penetration level of renewable energy increases to 100.64%. The storage capacities of the batteries and their powers remain the same as in Scenario 1. The configuration of scenario 2 is shown in Table 4.3. As a result of the increase in renewable power, there are times when excess renewable generation exceeds demand, allowing batteries to act as both generators and loads storing energy. The power distribution can be seen in Fig. 4.6a. In the first moments of the day, the demand is satisfied by the power of WT and BESSs. From instants 8 to 23, the demand is lower than the power supplied by WT, so the batteries

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Table 4.3 DERs configuration. Scenario 2 Source

Node

Pmin

Pmax

Energymin

Energymax

SOCmin

SOCmax

(kW)

(kW)

(kWh)

(kWh)

(%)

(%)

WT

B828

50

300

–

–

–

–

PV

B840

0

300

–

–

–

–

BESS1

B840

−80

80

70

280

20.74

80

BESS2

B806

−100

100

70

280

20.74

80

(a) Economic dispatch powers

(b) State of charge of batteries

Fig. 4.6 Economic dispatch powers and SOC of scenario 2

are being charged by taking advantage of excess generation. After instant 23 and to cope with the increase in demand, in addition to WT, the batteries contribute to meet the demand. In the central zone of the day (41 to 54), when there is solar radiation and wind, the demand is satisfied with the excess, again allowing the batteries to charge. The charging and discharging of the batteries can be perfectly seen in Fig. 4.6b. Another fact to note is that in Fig. 4.6a, in contrast to Fig. 4.5a, the economic dispatch after simulation of the power flow in DIgSILENT® with the optimised scenario is represented. When MATLAB® performs the optimisation, the network losses are not known, but after simulation in DIgSILENT® they are. The power losses at each time step can be seen in Fig. 4.6a from the beginning of the day to instant 59 with small contributions from the external grid (PGrid ). It is the external grid that must provide power to feed the losses already reduced by the optimisation process.

4.4.2.3

Study Case 3

Scenario 3 is similar to scenario 2, but the maximum energy capacity of the batteries has been halved compared to Scenario 2 (Table 4.4). Having less energy storage capacity changes economic dispatch, as can be seen in Fig. 4.7a compared to Fig. 4.6a. Figure 4.7a shows how the number of moments in which batteries contribute power to the grid is significantly reduced. This fact can be seen in the moments when demand

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increases and photovoltaic (PV) power is still small (moments 34–40). This time interval in scenario 2 was covered by the batteries and is now supplied by external grid power. As there is less energy storage capacity in the batteries, they are discharged earlier, and the external grid supplies power to meet the demand. This discharge can be seen in Fig. 4.7b. It can also be seen how part of the renewable energy is not used to charge the batteries because they reach their maximum charge level at various times during the day (moments 50–54). Furthermore, during these moments when maximum storage has been reached, renewable generation is greater than demand, so the study grid exports power to the external grid. The external grid takes negative values (consumption) as can be seen in Fig. 4.7a. Figure 4.8 shows the evolution of active power losses during the day for the different scenarios simulated. It can be seen that in scenario 1, which has a lower level of penetration of DER (49.03%), the power loss curve is above the curves corresponding to the rest of the simulated scenarios, which means that scenario 1 has a higher level of losses. Scenarios 2 and 3 have the same level of DER penetration (100.64%); it should be remembered that in scenario 3, the storage capacity of the batteries is lower and it can be seen that the curves are very similar. However, if we look in detail, the scenario 3 curve has 3 time periods where the curve is above scenario 2. The periods are 33 to 39, 50 to 54, and 58 to 61. Table 4.4 DERs configuration. Scenario 3 Source

Node

WT

B828

PV BESS1 BESS2

Pmin

Pmax

Energymin

Energymax

SOCmin

SOCmax

(kW)

(kW)

(kWh)

(kWh)

(%)

(%)

50

100

–

–

–

–

B840

0

100

–

–

–

–

B840

−80

80

70

140

40

80

B806

−100

100

70

140

40

80

(a) Economic dispatch powers

(b) State of charge of batteries

Fig. 4.7 Economic dispatch powers and SOC of scenario 3

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Fig. 4.8 Active power losses

The power losses accumulated over time are the energy losses. Figure 4.9 represents the energy losses in each scenario. It can again be seen that in scenario 1, there is more loss of energy than in the other scenarios. The power differences observed in the 3 periods in Fig. 4.8 are cumulatively represented in Fig. 4.9 where it can be seen that in scenario 3, there is a higher level of loss corresponding to the 3 periods mentioned above and caused by the lower storage capacity of the batteries. Fig. 4.9 Energy losses in each scenario

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4.5 Conclusion This section presents conclusions on the impact of the presence of DERs in the ADS on network losses. As can be seen, the presence of BESSs in the ADS network effectively contributes to the reduction of active power losses. A higher penetration level of DERs improves loss reduction, but there must be adequate storage capacity values to be able to harness energy from renewable sources and contribute to further loss reduction. In this way, the DSO has the capacity to improve the operation of the ADS with the consequent economic and social welfare benefits. The presented optimisation and control strategy prove to be a very useful tool for the operation of ADSs in the DSO, contributing to the reduction of losses. This tool has been implemented through Peer-2-Peer communication between MATLAB® and DIgSILENT® and provides as a result for the DSO an economical dispatch and OLTCs’ operating commands for an efficient operation of its network.

References Alvarado-Barrios L, Álvarez-Arroyo C, Escaño JM, Gonzalez-Longatt FM, Martinez-Ramos JL (2020) Two-level optimisation and control strategy for unbalanced active distribution systems management. IEEE Access 8:197992–198009 Bahramara S, Mazza A, Chicco G, Shafie-khah M, Catalão JPS (2020) Comprehensive review on the decision-making frameworks referring to the distribution network operation problem in the presence of distributed energy resources and microgrids. Int J Electr Power Energy Syst 115:105466 Baran ME, Wu FF (1989) Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Power Eng Rev 9(4):101–102 Dubey A, Bose A, Liu M, Ochoa LN (2020) Paving the way for advanced distribution management systems applications: making the most of models and data. IEEE Power Energ Mag 18(1):63–75 Evangelopoulos VA, Kontopoulos TP, Georgilakis PS (2022) Heterogeneous aggregators competing in a local flexibility market for active distribution system management: a bi-level programming approach. Int J Electr Power Energy Syst 136:107639 European Commission (2017) Energy storage—the role of electricity, 1 Feb 2017. [En línea]. Available: https://ec.europa.eu/energy/sites/ener/files/documents/swd2017_61_document_trav ail_service_part1_v6.pdf Gao H, Liu J, Wang L (2017) Robust coordinated optimization of active and reactive power in active distribution systems. IEEE Trans Smart Grid 9(5):4436–4447 Gerbaulet C, von Hirschhausen C, Kemfert C, Lorenz C, Oei P-Y (2019) European electricity sector decarbonization under different levels of foresight. Renewable Energy 141:973–987 Heinrich C, Ziras C, Jensen TV, Bindner HW, Kazempour J (2021) A local flexibility market mechanism with capacity limitation services. Energy Policy 156:112335 IEEE PES AMPS DSAS Test Feeder Working Group (1992) IEEE PES test feeder, 34-bus feeder. [En línea]. Available: http://sites.ieee.org/pes-testfeeders/resources Jin X, Wu Q, Jia H (2020) Local flexibility markets: literature review on concepts, models and clearing methods. Appl Energy 261:114387 Kakran S, Chanana S (2018) Smart operations of smart grids integrated with distributed generation: a review. Renew Sustain Energy 81:524–535 Malik FH, Lehtonen M (2016) A review: agents in smart grids. Electr Power Syst Res 131:71–79

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Olivella-Rosell P, Lloret-Gallego P, Munné-Collado Í, Villafafila-Robles R, Sumper A, Ottessen SØ, Rajasekharan J, Bremdal BA (2018) Local flexibility market design for aggregators providing multiple flexibility services at distribution network level. Energies 11(4):822 Oureilidis K, Malamaki K-N, Gallos K, Tsitsimelis A, Dikaiakos C, Gkavanoudis S, Cvetkovic M, Mauricio JM, Maza Ortega JM, Ramos JLM, Papaioannou G, Demoulias C (2020) Ancillary services market design in distribution networks: Review and identification of barriers. Energies 13(4):917 Owuor AJO, Munda JL, Jimoh AA (2011) The IEEE 34 node radial test feeder as a simulation testbench for Distributed Generation. In: IEEE Africon’11, pp 1–6 Pye S, Li P-H, Keppo I, O’Gallachoir B (2019) Technology interdependency in the United Kingdom’s low carbon energy transition. Energ Strat Rev 24:314–330 Rao RS (2010) Capacitor placement in radial distribution system for loss reduction using artificial bee colony algorithm. [En línea]. Available: https://doi.org/10.5281/zenodo.1077229 Rao RS, Ravindra K, Satish K, Narasimham SVL (2012) Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation. IEEE Trans Power Syst 28(1):317–325 Sheikhahmadi P, Bahramara S, Mazza A, Chicco G, Catalão JPS (2021) Bi-level optimization model for the coordination between transmission and distribution systems interacting with local energy markets. Int J Electr Power Energy Syst 124:106392 Short TA (2014) Electric power distribution handbook. CRC Press, New York Tian K, Sun W, Han D, Yang C (2019) Coordinated planning with predetermined renewable energy generation targets using extended two-stage robust optimization. IEEE Access 8:2395–2407 Willis HL (2004) Power distribution planning reference book. CRC Press, New York

Chapter 5

Optimal Siting and Sizing of Renewable Energy Sources in Distribution System Pavitra Sharma and H. D. Mathur

Abstract An optimal siting and sizing of renewable energy sources (RES) play a substantial role in achieving the efficient operation and planning of the distribution system. It majorly affects the active power loss and voltage stability of the system. Therefore, this study formulates a multi-objective function that optimally allocates and sizes the RES in the distribution network by minimizing the total active power losses (TAPL) and voltage deviation (VD) of the network buses. The proposed algorithm is validated on an IEEE 69-bus model having two types of loads, i.e. consumer load and charging load of an electric vehicle. The consumer load is modelled as a polynomial type of load, and a total of four electric vehicle charging stations (EVSs) are integrated into the model. The formulated objective function also optimally allocates these four EVSs to minimize the TAPL and VD of the system. The objective function is minimized using four different optimization techniques, i.e. genetic algorithm (GA), grey wolf optimizer (GWO), particle swarm optimization (PSO), and hybrid particle swarm grey wolf optimization (HPSGWO). The simulation studies showed that the HPSGWO is superior to all other algorithms as it provides the solution having the lowest TAPL and VD in the system. Keywords Optimal siting · Optimal size · Electric vehicle · Renewable energy sources · Distribution system · Hybrid particle swarm grey wolf optimization

5.1 Introduction Renewable energy can be termed as the energy obtained from natural resources such as sunlight, wind, waves, tides, biomass, and geothermal heat (Elavarasan et al. 2020). These resources are mainly known as renewable energy sources (RES). They are used as the distributed generation (DG) units, as they can be installed near the load consumption points. According to the report of IEA, it is forecasted that P. Sharma · H. D. Mathur (B) Department of Electrical & Electronics Engineering, Birla Institute of Technology & Science, Pilani, Pilani, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. N. Singh et al. (eds.), Optimal Planning and Operation of Distributed Energy Resources, Energy Systems in Electrical Engineering, https://doi.org/10.1007/978-981-99-2800-2_5

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the annual increment to global renewable electricity capacity will reach 60% or an average of around 305 GW per year between 2021 and 2026 (IEA 2021). Though the integration of RES into the distribution system causes instability, however, if these RESs are optimally sized and allocated in the system, they can decrease the power losses, provide voltage support, and can improve the power quality and reliability of the system (Aljendy et al. 2019). Thus, optimal siting and sizing of RESs are the most crucial stage in the planning and operation of the distribution system. With RES, vehicles powered by electricity are also emerging as a probable substitute for petrol/diesel-powered vehicles, because of their eco-friendly and sustainable characteristics (Sharma et al. 2021a). But, an increase in the number of EVs leads to an increase in the overall load demand of the present distribution system (Sharma et al. 2021b). Moreover, it has been studied in a few literatures that some parameters of a distribution network such as power loss and voltage stability depend on the size and location of the generation sources as well as the load points (Bhamidi and Sivasubramani 2021; Sabarinath and Manohar 2018; Abass et al. 2019). Thus, if the integration of RESs and electric vehicle charging stations (EVSs) is planned strategically and optimally in a distribution system, then the system will have low power losses, high voltage margin stability, and an improved voltage curve. Further, power quality is also improved, and high system reliability is achieved. Within the previous years, various methods are been proposed that determine the optimal location and size of the RESs in the distribution network (Haider et al. 2021; Palanisamy and Muthusamy 2021; Ali et al. 2018; Tolba et al. 2017; Reddy et al. 2017; Rani et al. 2020; Abdul Kadir et al. 2018). In Haider et al. (2021), the optimal placement and sizing of the DG in a radial distribution network are estimated using a multi-objective PSO method. The authors of Palanisamy and Muthusamy (2021) determine the optimal location, power factor, and size of a DG unit using an artificial bee colony optimization algorithm. Ali et al. (2018) estimated the optimum sizing and allocation of solar photovoltaic cells using the Ant Lion optimizer algorithm. The hybrid particle swarm optimization algorithm (HPSO) is used to optimally locate and size the DG units (Tolba et al. 2017). Loss sensitivity analysis is presented for optimal capacity estimation, and it is concluded that outcomes obtained from HPSO are superior to various optimization methods. The study presented in Reddy et al. (2017) uses the whale optimization algorithm (WOA) for optimal placement and sizing of DG units. Rani et al. (2020) estimated the optimum siting and capacity of distributed generation units using the grasshopper optimization (GOA) technique. In Abdul Kadir et al. (2018), the improved gravitational search algorithm is proposed to obtain an optimal location and capacity of solar photovoltaics in order to minimize the total cost of the system. It can be stated from the performed literature review that many of the research focuses only focuses on the optimum capacity estimation and siting of the generation sources and not on the optimal location of the load points, i.e. EVS. Therefore, this study prepares a multi-objective function (MOF) to optimally size and locate the RESs and EVSs in the distribution network to minimize the total active power losses (TAPL) and voltage deviation (VD) at each bus of the network. This MOF is minimized using a hybrid particle swarm grey wolf optimization (HPSGWO) method

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which is established by Senel ¸ et al. (2019). This algorithm utilizes the grey wolf optimization (GWO) method to strengthen the particle swarm optimization (PSO) algorithm to diminish the probability of PSO trapping in local minima (Mishra et al. 2020). The study is structured as follows. Section 5.2 discusses consumer load modelling as well as charging load modelling of an electric vehicle. The problem formulation and operational constraints are presented in Sect. 5.3. In Sect. 5.4, the proposed algorithm has been discussed. The obtained results for different test cases are discussed in Sect. 5.5. Lastly, Sect. 5.6 concludes the paper.

5.2 Load Modelling This section discusses the modelling of consumer load and electric vehicle charging load of the system taken under the study. The RESs are developed as the constant power factor load with 0.9 lagging.

5.2.1 Consumer Load The load can be modelled in two different ways such as static and dynamic (Qian et al. 2011). The static load modelling is usually used for load flow problems, power loss calculation, and other system operations in a steady state, whereas dynamic load modelling is most commonly used to analyse the stability dynamics of the system, regulating the relays, and other system operations in the transient state (Rostami and Sadegh 2018). Further, the static load or voltage-dependent load is of two types exponential load model and polynomial load model (Anon 1993). These load models can be represented by a set of algebraic expressions between the voltage (magnitude and frequency) and power demand (real and reactive power demand). In a distribution power system, consumer load continuously varies with time. Hence, to simulate the practical situation, this study considers a polynomial voltagedependent static load. Generally, consumer load has three types of components that are residential, commercial, and industrial. In a polynomial load model, the load at each bus is a combination of these three components. Moreover, the share of each component in the total bus load varies with time, as shown in Table 5.1. A polynomial load model can be represented by a set of algebraic expressions between the amplitude of voltage and active and reactive power as represented in Eqs. (5.1) and (5.2). n pr

PLoad i = α PL0 Vi n qr

Q Load i = α Q L0 Vi

n pc

+ β PL0 Vi

n qr

+ β Q L0 Vi

n pi

+ γ PL0 Vi

n qr

+ γ Q L0 Vi

i = 1, 2, . . . Nb i = 1, 2, . . . Nb

(5.1) (5.2)

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Table 5.1 Share of each load component in the total bus load during a day (Rostami and Sadegh 2018) Hour load → 1 component

2

3

4

5

6

7

8

9

10

0.66 0.63 0.60 0.58 0.60 0.55 0.30 0.11 0.10 0.11

Residential Commercial

0.17 0.17 0.18 0.2

Industrial

0.17 0.20 0.22 0.22 0.17 0.3

Hour load → 13 component

14

15

16

0.23 0.15 0.14 0.32 0.34 0.33 17

18

11

12

0.12 0.17 0.37 0.46

0.56 0.57 0.56 0.56

0.51 0.37

19

23

20

21

22

24

Residential

0.14 0.14 0.15 0.18 0.20 0.33 0.60 0.70 0.74 0.76

0.75 0.71

Commercial

0.37 0.39 0.46 0.41 0.44 0.47 0.30 0.23 0.19 0.15

0.15 0.16

Industrial

0.49 0.47 0.39 0.41 0.36 0.20 0.10 0.06 0.07 0.009 0.10 0.13

Table 5.2 Exponent values concerning various load components Load component

Residential n pr

Summer Winter

Commercial n qr

n pc

Industrial n qc

n pi

n qi

Day

0.72

2.96

1.25

3.5

0.18

0.6

Night

0.92

4.04

0.99

3.95

0.18

0.6

Day

1.04

4.19

1.5

3.15

0.18

0.6

Night

1.3

4.38

1.51

3.4

0.18

0.6

0

0

0

0

0

0

Constant power

α+β +γ =1

(5.3)

where PLoad i and Q Load i represent the total active and reactive power demand at i th bus, whereas PL0 and Q L0 refer to the total real and reactive power demand at previous operating conditions, respectively. n pr , n pi , n pc are active power exponents that are dependent on various load components, i.e. residential, industrial, and commercial, respectively. Similarly, n qr , n qi , n qc are the reactive power exponents that are dependent on various load components, i.e. residential, industrial, and commercial, respectively. Vi is the amplitude of the voltage at the i th bus. Table 5.2 indicates the exponent values concerning various load components. This study considers a winter day and the average percentage share of different load components.

5.2.2 Charging Load of Electric Vehicle The intermittent nature of the charging load of EVs is an outcome of various features, like number of EVs, charging speed, EV distance covered on daily basis, time at which it is connected to the charger or disconnected to the charger, its battery capacity

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(Rawat and Niazi 2019). In addition, different types of EVs such as private and public behave differently which results in a rise in uncertainty in the EV charging pattern. The study models the EV charging load based on some parameters like the number of EVs, distance travelled by an EV on daily basis, its battery capacity, and time at which it is connected to the charger, i.e. connected time or disconnected to the charger, i.e., disconnected time. These parameters have their respective probability density functions, and by using the Monte Carlo simulation technique, these parameters are estimated for the modelling. It is assumed in the study that all the EVs are personal vehicles. At each EVS, the charging of the EV begins at 4:00 p.m. as soon as the owner reaches the home. The charging time TC,n for each n th EV can be acquired from (5.4) (Liu et al. 2015). TC,n =

EV Dn W100 ∀n ∈ Ne EV EV 100PC,n ηC

(5.4)

EV where W100 represents the energy used per 100 km in kWh/100 km; PCEV refers to the rate of charging EVs in kW; ηCEV is the value of EVs charging efficiency. Ne refers to the sum of no. of EVs. The stop time (Tend,n ) of EV is estimated using (5.5) (Liu et al. 2015).

Tend,n = Tstart,n + Tc,n − 1 ∀n ∈ Ne

(5.5)

where Tstart,n is the start time of EV charging. For a large no. of EVs, the total EV charging load in 24 h is calculated by Eq. (5.6). EVs PLoad (t) =

Ne 

EV PC,n (t)

(5.6)

n=1 EV where PC,n is the power at which charging occurs for n th vehicle. A total of four EVS are considered with a charging capacity of 15 vehicles each. Figure 5.1 presents the charging load curve of 15 EVs at each EVS. Table 5.3 represents the few parameters of EV taken into account. The average load of the EV charging load profile is considered while determining the optimal locations of four EVS.

5.3 Problem Formulation Power flow calculation is the first step to formulate the mathematical function which is to be minimized. Traditional load flow techniques like Newton–Raphson and Gauss– Seidel are incompatible for the distribution power system as it has a low X/R ratio. Therefore, the backward–forward sweep power flow method is most commonly used

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Fig. 5.1 24 h charging load curve of 15 EVs at each EVS

Table 5.3 Parameters of EV

EV parameters

Value

EV (kWh/100 km) W100

12

CnEV

24

(kWh)

min SOCmax n /SOCn (%)

100/20

EV /P EV,max PC,n D,n ηCEV

3

(kW)

85%

for the distribution power system to obtain fast and accurate results (Eminoglu and Hocaoglu 2009). Figure 5.2 shows the 2-bus radial distribution network. The total impedance Z r s for buses r and s can be expressed from Eq. (5.7). Z r s = Rr s + j X r s

(5.7)

Equations (5.8) and (5.9) represent the total active power loss (TAPL) and the sum of voltage deviation (VD), respectively, for the 2-bus radial distribution network.

Fig. 5.2 2-bus radial distribution network

5 Optimal Siting and Sizing of Renewable Energy Sources in Distribution …

TAPL =

Nbr 

 Rr s

s=1

Pr2s + Q r2s Vr2

97

 (5.8)

where Nbr is the total number of branches present in the network, r = 1 : Nb and Nb is the number of buses. VD =

Nb  |1 − Vr |

(5.9)

r =1

where Vr is the voltage at bus r, r = 1 : Nb . This study formulates two objective functions (OF), OF1 minimizes total active power loss (TAPL), and OF2 minimizes voltage deviation index (VD). The weighted sum approach is used to solve the multi-objective function (MOF) that is defined in Eq. (5.10). MOF = w1 ∗ TAPL + w2 ∗ TVD

(5.10)

where w1 , w2 are the weights of OF1 & OF2, respectively. The values considered for w1 and w2 are 0.7 and 0.3, respectively. This MOF is minimized subject to the following constraints. • Constraints of power balance

EVs PLoad + PLoad + PLoss = PRESs + Pgrid

(5.11)

where PRESs , PLoss , and Pgrid are the real power of RESs, real power losses in the system, and the electrical grid real power, respectively. Q Load + Q Loss = Q RESs + Q grid

(5.12)

where Q RESs , Q Loss , and Q grid are the reactive power of RESs, reactive power losses in the system, and the electrical grid reactive power, respectively. • Voltage bounds on the network’s buses

Vimin < Vi (t) < Vimax i = 1, 2, . . . Nb

(5.13)

where Vimax and Vimin are the maximum and minimum bus voltage bounds with values of 1.05 and 0.95, respectively. • Power bounds on RESs

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P. Sharma and H. D. Mathur min max PRES,m < PRES,m < PRES,m ∀m ∈ M

(5.14)

max Q min RES,m < Q RES,m < Q RES,m ∀m ∈ M

(5.15)

max min where PRES,m , Q max and PRES,m , Q min RES,m RES,m are the maximum and minimum thresholds on the real and reactive generation output of the mth RER, respectively.

5.4 Proposed Algorithm for Optimal Siting and Sizing of RES This study utilizes four different optimization techniques to determine the optimal sizing and siting of RES with EV charging load. The flow diagram of the proposed algorithm is shown in Fig. 5.3. In the optimization, the upper and lower thresholds of variables are set considering specific pre-assumptions. Firstly, in the test cases where RESs are incorporated into the considered distribution network, the overall load demand of the network is met only by RESs; i.e. there will be no power exchange between the grid and distribution network. This will diminish the dependency of the distribution network on the grid. Secondly, the addition of optimal sizes of all RESs in the system should be equal to or less than the entire load connected to the network. This will limit the oversizing of RESs. At last, the RESs are not allowed to be allocated on the 1st bus.

5.5 Case Study and Results This study considers an IEEE 69-bus radial distribution system having 12.66 kV base voltage and 100 MVA base. The considered power model comprises 68 branches and 69 buses as indicated in Fig. 5.4. The consumer real and reactive power load on the network is assumed to be 3800 kW and 2700 kVAr, respectively. The proposed algorithm is analysed for six test cases that are a distribution network without RES and EV load; without RES but with EV load; with one RES and EV load; with two RES and EV load; with three RESs, and EV load and with four RESs and EV load. In test case 2, optimal locations of EVS are determined, whereas, for other test cases, the optimal size and siting of RESs along with EVS are achieved through all the studied optimization methods. These outcomes for all the test cases are briefly described in Table 5.4. The four optimization methods are optimized for 200 iterations with 70 populations for each test case. Moreover, Fig. 5.5 shows the convergence characteristics for test case 5 with the four optimization methods. In test case 1, the TAPL is 166.03 kW which is increased in test case 2 as the EV load is considered. It can easily be interpreted from Table 5.4 that for every test case, HPSGWO provides the best result with

5 Optimal Siting and Sizing of Renewable Energy Sources in Distribution … Start

Input Load and Line Parameters Set the optimization factors like iteration count (i=1), population size count (p=1), minimum and maximum thresholds of locations of RESs and EVS and sizes of RESs. Update the network parameters using factors acquired from optimization method Perform the power flow and estimate the initial fitness using MOF

Check constraints

No

Add penalty factor to objective function

Yes Update RES factors and EVS location based on revised optimization values

Perform power flow and estimate the new fitness using MOF

Check for max. no. of population

p= p+1

Check for max. no. of iterations

i= i+1

Print optimal results

Fig. 5.3 Flow diagram of the proposed algorithm

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Fig. 5.4 IEEE 69-bus radial distribution system

the minimum TAPL and VD as compared to other studied optimization algorithms. In test case 3, 3.91 MW of RES should be installed at bus no. 9 to obtain the lowest value of TAPL, i.e. 125.56 kW. The maximum VD is 0.0523 at the 65th bus. Similarly, in test case 4, the optimal size and siting of two RESs are 1.95 MW and 1.92 MW at the 4th and 61st bus, respectively. The minimum value of TAPL obtained with these optimum values is 25.32 kW. Likewise in test case 5, the optimal sizes of the RESs are 0.6 MW, 1.4 MW and 1.87 MW at locations 17, 49 and 61, respectively, with the lowest TAPL and maximum VD of 10.44 kW and 0.0053 at the 69th bus. At last, in test case 6, with four RESs, the TAPL and maximum VD are the lowest among all the test cases, i.e. 6.91 kW and 0.0029, respectively. The optimal sizes of four RESs are 1.06 MW, 0.4 MW, 1.8 MW, and 0.62 MW with optimal locations 49, 17, 61, and 11, respectively. Figure 5.6 shows the voltage profile of all the buses under all the test cases optimized with the HPSGWO algorithm. It is evident from Fig. 5.6 that as the numeral of RESs is growing in the network, the TAPL and maximum VD are reduced, leading to a stable voltage profile of the distribution network.

5.6 Conclusion This research proposes an algorithm to optimally size and sites the renewable energy sources (RES) in a distribution network considering the electric vehicle charging load. The proposed algorithm also optimally allocates the four electric vehicle charging

N.A

N.A

N.A

PSO

GWO

HPSGWO

7 (RES 1)

N.A

GA

Test case 2—without RES but with EV load

GA

N.A

N.A

Base test case 1—without RES and EV load

Test case 3—with one RES and EV load

Optimal bus no. locations of RES

Different optimization techniques

Various cases

4.3093 (RES 1)

N.A

N.A

N.A

N.A

N.A

Optimal sizing of RES 166.03

TAPL (kW)

166.87

EVS 1 at 11, EVS 2 138.1 at 37 EVS 3 at 37, EVS 4 at 53

EVS 1 at 2, EVS 2 at 28 EVS 3 at 36, EVS 4 at 53

EVS 1 at 25, EVS 2 171.02 at 30 EVS 3 at 46, EVS 4 at 66

EVS 1 at 8, EVS 2 169.66 at 30 EVS 3 at 40, EVS 4 at 61

EVS 1 at 20, EVS 2 170.98 at 30 EVS 3 at 46, EVS 4 at 66

N.A

Optimal EVS bus no. sites

Table 5.4 Outcomes achieved from several optimization algorithms in all the studied test cases

0.057 at 65th bus

0.0788 at 65th bus

0.0800 at 65th bus

0.0795 at 65th bus

0.0800 at 65th bus

0.079 at 65th bus

Maximum voltage deviation (VD)

(continued)

96.85

117.30

120.21

119.26

120.18

N.A

MOF value

5 Optimal Siting and Sizing of Renewable Energy Sources in Distribution … 101

Test case 4—with two RESs and EV load

Various cases

GWO

PSO

2.15 MW (RES 1) 1.71 MW (RES 2)

60 (RES 2)

1.91 MW (RES 2)

63 (RES 2)

4 (RES 1)

1.95 MW (RES 1)

4 (RES 1)

1.92 MW (RES 2)

3.91 kW (RES 1)

1.95 MW (RES 1)

9 (RES 1)

HPSGWO

4.5093 (RES 1)

60 (RES 2)

8 (RES 1)

GWO

4.1 MW (RES 1)

Optimal sizing of RES

3 (RES 1)

7 (RES 1)

PSO

GA

Optimal bus no. locations of RES

Different optimization techniques

Table 5.4 (continued) TAPL (kW)

125.56

EVS 1 at 4, EVS 2 36.92 at 30 EVS 3 at 40, EVS 4 at 60

EVS 1 at 2, EVS 2 29.91 at 28 EVS 3 at 36, EVS 4 at 60

EVS 1 at 2, EVS 2 35.39 at 28 EVS 3 at 36, EVS 4 at 60

EVS 1 at 9, EVS 2 at 35 EVS 3 at 37, EVS 4 at 53

EVS 1 at 9, EVS 2 138.3 at 37 EVS 3 at 37, EVS 4 at 53

EVS 1 at 9, EVS 2 134.6 at 35 EVS 3 at 37, EVS 4 at 53

Optimal EVS bus no. sites

0.0279 at 27th bus

0.0268 at 27th bus

0.0267 at 27th bus

0.0523 at 65th bus

0.057 at 65th bus

0.0553at 65th bus

Maximum voltage deviation (VD)

(continued)

26.06

21.11

24.96

88.09

97.01

94.44

MOF value

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HPSGWO

GWO

PSO

0.24 MW (RES 2) 1.62 MW (RES 3) 0.44 MW (RES 4)

15 (RES 2)

62 (RES 3)

11 (RES 4)

1.87 MW (RES 3)

61 (RES 3) 1.55 MW (RES 1)

1.40 MW (RES 2)

48 (RES 1)

0.6 MW (RES 1)

1.64 MW (RES 3)

63 (RES 3)

49 (RES 2)

1.35 MW (RES 2)

17 (RES 1)

0.88 MW (RES 1)

1.89 MW (RES 3)

62 (RES 3)

47 (RES 2)

1.28 MW (RES 2)

20 (RES 1)

0.70 MW (RES 1)

1.74 MW (RES 3)

63 (RES 3)

47 (RES 2)

1.23 MW (RES 2)

50 (RES 2)

19 (RES 1)

0.91 MW (RES 1)

17 (RES 1)

1.92 MW (RES 2)

61 (RES 2)

GA

1.95 MW (RES 1)

4 (RES 1)

HPSGWO

Optimal sizing of RES

Optimal bus no. locations of RES

Different optimization techniques

Test case 6—with GA four RESs and EV load

Test case 5—with three RESs and EV load

Various cases

Table 5.4 (continued)

25.32

TAPL (kW)

10.44

EVS 1 at 17, EVS 2 13.37 at 30 EVS 3 at 43, EVS 4 at 60

EVS 1 at 17, EVS 2 at 32 EVS 3 at 40, EVS 4 at 57

EVS 1 at 17, EVS 2 21.44 at 32 EVS 3 at 40, EVS 4 at 57

EVS 1 at 17, EVS 2 14.74 at 32 EVS 3 at 40, EVS 4 at 57

EVS 1 at 17, EVS 2 19.01 at 32 EVS 3 at 40, EVS 4 at 57

EVS 1 at 2, EVS 2 at 28 EVS 3 at 36, EVS 4 at 60

Optimal EVS bus no. sites

0.0134 at 27th bus

0.0053 at 69th bus

0.0147 at 20th bus

0.0067 at 19th bus

0.0140 at 17th bus

0.0267 at 27th bus

Maximum voltage deviation (VD)

(continued)

9.47

7.34

15.10

10.37

13.38

18.0

MOF value

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Various cases

HPSGWO 0.4 MW (RES 2) 1.8 MW (RES 3) 0.62 MW (RES 4)

61 (RES 3)

11 (RES 4)

0.38 MW (RES 4)

11 (RES 4)

17 (RES 2)

1.52 MW (RES 3)

62 (RES 3) 1.06 MW (RES 1)

0.3 MW (RES 2)

49 (RES 1)

1.65 MW (RES 1)

0.46 MW (RES 4)

11 (RES 4)

13 (RES 2)

1.70 MW (RES 3)

62 (RES 3)

49 (RES 1)

0.34 MW (RES 2)

17 (RES 2)

GWO

1.38 MW (RES 1)

48 (RES 1)

PSO

Optimal sizing of RES

Optimal bus no. locations of RES

Different optimization techniques

Table 5.4 (continued) TAPL (kW)

EVS 1 at 17, EVS 2 at 30 EVS 3 at 43, EVS 4 at 60

6.91

EVS 1 at 23, EVS 2 16.61 at 28 EVS 3 at 46, EVS 4 at 60

EVS 1 at 17, EVS 2 10.51 at 30 EVS 3 at 43, EVS 4 at 60

Optimal EVS bus no. sites

0.0029 at 65th bus

0.0171 at 27th bus

0.0072 at 27th bus

Maximum voltage deviation (VD)

4.85

11.76

7.42

MOF value

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Fig. 5.5 Convergence characteristics for test case 5 with the four optimization algorithms

Fig. 5.6 Voltage curve under the studied test cases

stations (EVS) in a distribution network. The multi-objective function (MOF) is formulated which aims at minimizing the total active power losses (TAPL) and voltage deviation (VD) at each bus of the network. This MOF is solved using various optimization techniques such as genetic algorithm (GA), grey wolf optimizer (GWO), particle swarm optimization (PSO), and hybrid particle swarm grey wolf optimization (HPSGWO). The proposed method is being tested on an IEEE 69-bus radial distribution network having various RESs, polynomial-based voltage-dependent consumer load, and EV charging load. The EV charging load pattern is modelled using Monte Carlo simulations, based on parameters like the amount of EVs, EV distance covered on daily basis, its battery power, and moment at which it is connected to the charger,

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i.e. connected time or disconnected to the charger, i.e. disconnected time. It is evident from the results that HPSGWO is superior to the other considered algorithms, and it is found true for all the test cases. In addition, it is observed from the results that as there is an increase in the number of RESs in the considered network, the TAPL of the system and VD at each bus are reduced, leading to a stable and cost-efficient distribution network along with an improved voltage profile.

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Qian K, Zhou C, Allan M, Yuan Y (2011) Effect of load models on assessment of energy losses in distributed generation planning. Int J Electr Power Energy Syst 33(6):1243–1250. https://doi. org/10.1016/j.ijepes.2011.04.003 Rani KS, Saw BK, Achargee P, Bohre AK (2020) Optimal sizing and placement of renewable DGs using GOA considering seasonal variation of load and DGs. In: 2020 international conference on computational intelligence for smart power system and sustainable energy (CISPSSE), pp 1–6 Rawat T, Niazi KR (2019) Impact of EV charging/discharging strategies on the optimal operation of islanded microgrid. J Eng 2019(18):4819–4823. https://doi.org/10.1049/joe.2018.9335 Reddy PDP, Reddy VCV, Manohar TG (2017) Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems. Renewables Wind Water, Sol 4(1):1–13. https://doi.org/10.1186/s40807-017-0040-1 Rostami N, Sadegh MO (2018) The effect of load modeling on load flow results in distribution systems. Am J Electr Electron Eng 6(1):16–27. https://doi.org/10.12691/ajeee-6-1-3 Sabarinath g, Manohar TG (2018) Optimal sitting and sizing of renewable energy resources for power loss reduction in radial distribution systems using whale optimization algorithm. In: 2018 international conference on emerging trends and innovations in engineering and technological research (ICETIETR).https://doi.org/10.1109/ICETIETR.2018.8529136 Senel ¸ FA, Gökçe F, Yüksel AS, Yi˘git T (2019) A novel hybrid PSO–GWO algorithm for optimization problems. Eng Comput 35(4):1359–1373. https://doi.org/10.1007/s00366-018-0668-5 Sharma P, Mishra P, Mathur HD (2021a) Optimal energy management in microgrid including stationary and mobile storages based on minimum power loss and voltage deviation. Int Trans Electr Energy Syst 31(12):e13182. https://doi.org/10.1002/2050-7038.13182 Sharma P, Mishra AK, Mishra P, Dutt Mathur H (2021b) Optimal capacity estimation and allocation of distributed generation units with suitable placement of electric vehicle charging stations. In: 2021 IEEE Region 10 symposium (TENSYMP), pp 1–7. https://doi.org/10.1109/TENSYMP52 854.2021.9550958 Tolba MA, Tulsky VN, Zaki Diab AA (2017) Optimal allocation and sizing of multiple distributed generators in distribution networks using a novel hybrid particle swarm optimization algorithm. In: 2017 IEEE conference of Russian young researchers in electrical and electronic engineering (EIConRus), pp 1606–1612. https://doi.org/10.1109/EIConRus.2017.7910880

Chapter 6

Scheduling of Electric Vehicle’s Charging–Discharging: An Overview Bhaskar Chauhan and Sachin K. Jain

Abstract The electric vehicle (EV) market has seen remarkable growth in recent years. The number of EVs in the market is increasing day-by-day, demanding more energy in the power distribution system (PDS). So, it is of great significance to closely monitor and optimize every aspect related to EV’s charging–discharging (CD) planning. Controlled or coordinated and uncontrolled or uncoordinated CD scheduling are two types of techniques by which proper CD scheduling pattern is channelized for EVs. Without a well-coordinated schedule and a well-planned strategy for charging EVs, users (individual or parking lot owners/operators (PLO)) will typically apply an immediate charging, which burdens the system, increases the chance for grid instability, affect the voltages profile, produces harmonics, frequency deviations, increases the cost of charging, degrades vehicles battery life, damages charging station infrastructures efficiency, etc. By maintaining a CD equilibrium plan for EVs, various adverse effects of uncoordinated scheduling can be avoided, which are thoroughly discussed in this paper along with the challenges and issues faced by EV applications from the aggregators as well as customers’ point of view, curve shaping, PLO benefits while satisfying EV owners’ charging requirements. Along with these, this paper contributes toward the recently implemented scheduling methods which are helpful in selecting the charging points for EVs that arrive at parking stations to accomplish several objectives. Keywords Charging–discharging strategy · Electrical vehicle · Grid-to-vehicle · Optimization · Vehicle-to-grid

B. Chauhan · S. K. Jain (B) Department of Electronics and Communication Engineering, PDPM IIITDM Jabalpur, Jabalpur, MP 482005, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. N. Singh et al. (eds.), Optimal Planning and Operation of Distributed Energy Resources, Energy Systems in Electrical Engineering, https://doi.org/10.1007/978-981-99-2800-2_6

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6.1 Introduction Transportation sector is one of the most substantial sources of greenhouse gas (GHG) emission. Electric vehicles (EVs) have emerged as a cleaner and viable alternative to conventional combustion-based vehicles, which will help in the sustainable growth of the transportation sector. Technological changes always bring challenges with them, and in the smart-moving world, these challenges need smart and optimal solutions. Smart grid technology acts as a channel that provides flexibility in the energy system, and it may exploit charge storage facility in EV for demand response management at local levels. Hence, shifting toward electrified mobility is a win–win situation for the environment, users/public, power grid, and the market. The data of EV stock of different countries/regions, shown in Fig. 6.1, indicates almost exponential growth over the last decade, which is expected to rise further in upcoming years (IEA 2021). The associated high impact on the power grid (PG) and the environment has drawn great interest from both the industry as well as academia to study those areas to explore the opportunities, address the challenges and find the optimal solutions. This is evident from a large number of research publications related to EVs in the last couple of decades, special issues on EVs in the reputed journals, and emergence of many new journals dedicated to different aspects of EVs. Among key issues related to EVs, the charging and discharging of EVs is one of the most crucial issues. EV stochastic charging can lead to a large demand peak in the system because the pattern of electric power demand changes abruptly. Various studies show that grid integration of EV helps in improving financial and operational performance objectives and also resolves severe environmental issues (Mousavi Agah

Fig. 6.1 Stock of EVs in different regions (IEA 2021)

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and Abbasi 2012; Roe et al. 2009) despite its negative impact on the PG. Some of the negative impacts include an increase in system peak load, power losses, deteriorating PQ, increase in overall cost, charging infrastructure, safety, and efficiency. Several research studies suggest that EVs remain idle in parked conditions most of the time, which could be as high as 70–95% of their total time. Hence, EVs may act as an energy storage resource of electric power in the grid. These may deliver energy back to grid for several benefits such as incentives to grid operator or vehicle owner, grid stability by mitigating power quality (PQ) issues (voltage dips, harmonics, frequency deviations), or ancillary services (spinning reserves, regulation services) (Lin et al. 2014). The concept of vehicle-to-grid (V2G) was introduced by Kempton and Letendre (1997) to exploit this possibility. In an uncoordinated or uncontrolled charging–discharging (CD) strategy, the CD profiles of electric vehicles are random. Research reveals that with uncontrolled CD scheduling overall impact (grid stability, charging cost, GHG emissions—in case of plug-in hybrid EV (PHEV), etc.) of EVs reduces by only 10% as compared to 40% reduction with the controlled CD scheduling (Midlam-Mohler et al. 2009). So, it is always suggested to adopt intelligent optimization tools and scheduling strategies to minimize negative impacts and maximize profits. Many techniques related to the CD of EVs include vehicles-to-home (V2H), home-to-vehicle (H2V), grid-to-vehicles (G2V), which suggest intelligent and/or coordinated controlled CD scheduling of EVs. Price-based scheduling takes care of customers’ financial status and encourages EV owners to participate in CD schedules. In this regard, EV charging/parking station owners make their own plans to schedule EV CD demands, such as, when to charge or when to feed power back to the grid, which EV is chosen first, on what criteria CD process should take place based on their own benefits without affecting grid stability. The encouragement and punishment policy of scheduling are similar which puts an extra cost on EV owner when scheduler plan mismatches with customer requirements, and incentivizes customer in terms of charging fee reduction or free parking hours when requirements match with scheduler’s plan (Mirzaei and Kazemi 2021). The key contributions of this study are: 1. To study various works and challenges on EV CD strategies. 2. To study the CD scenarios at different charging sites (home, parking lots, and charging stations), V2X and ToU pricing schemes (forms, features), while satisfying EV owner’s requirements. 3. To study existing EV’s scheduling approaches. 4. An overview of major conventional (linear programming (LP), nonlinear programming (NLP), quadratic programming (QP), dynamic programming (DP)) and meta-heuristic (particle swarm optimization (PSO), genetic algorithm (GA)) optimization approaches with comparative evaluations. 5. A detailed literature review on optimization objectives and CD methods. 6. Components and procedure of optimization problems (OP).

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7. Motivations for customers, service providers, and start-ups to adopt EVs or proper CD scheduling. 8. Discussion on various key challenges and future scopes. The remaining sections of this paper are organized as: in II-CD Strategies, IIIApplication Sites, associated terms, and scheduling approaches, IV-Optimization in terms of EVs (Components, Objectives, and Algorithms), V-Literature studies and discussion, VI-Conclusion and future directions.

6.2 Charging–Discharging Strategies CD strategies can be broadly classified as controlled vs uncontrolled methods and centralized vs decentralized methods. The concept and relative merits of these strategies are discussed in brief. Figure 6.2 presents the overview of the objectives and scenarios of different CD strategies.

6.2.1 Controlled and Uncontrolled CD Methods Unfavorable effects on the distribution grid (power loss, voltage variations, overloading, harmonics, etc.), EV performance (Battery degradation, range anxiety, efficiency, etc.), owner profits (electricity bills, overall cost reduction), and increment in the charging costs are some of the consequences of a stochastic and an uncontrolled EV charging approaches (Ayyadi et al. 2019; Eltoumi et al. 2021). These issues generally arise during uncontrolled or uncoordinated CD of EVs, also known as the “According-to-User” system, because in uncontrolled CD, the grid operator has no

Fig. 6.2 Overview of the objectives and scenarios of different CD strategies

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Fig. 6.3 EV information EV travel time Arrival & Departure Time

Charging control strategy EV information

Driving Pattern

Charging facility

No. of EV

Battery Info.

information about the EV that connects to the grid. Once the EVs are plugged-in, it starts charging and when battery requirements are met, it stops. This operates without knowing the actual status of the PG. In the controlled CD method, the operator has complete information, as depicted in Fig. 6.3, about requested EVs for monitoring their scheduling process to avoid the above-listed issues while fulfilling the driver’s charging requirements and satisfying their objectives. The controlled or coordinated charging can improve the financial/economical and operational/technical objectives (Fig. 6.2) by managing the CD pattern smartly (Khatiri-Doost and Amirahmadi 2017). In (Leemput et al. 2011) and (Richardson et al. 2012), coordinated charging objectives and benefits of controlled charging for bulk penetration are discussed briefly. Similarly, in Ayyadi et al. (2019), when compared to an uncontrolled EVs charging strategy, the author states that for a residential area, the charging cost of EVs is lowered by 38% for 50% of EVs penetration rate and 50% for 100% of EVs penetration rate when using a controlled charging method. Several researchers classify controlled charging strategies in different ways (ElBayeh et al. 2021; Kong and Karagiannidis 2016; Leemput et al. 2011; Richardson et al. 2012). In (Kong and Karagiannidis 2016), the authors classified controlled charging as: (a) indirectly controlled charging, (b) smart charging, and (c) bidirectional charging (that supports V2G service). Similarly in El-Bayeh et al. (2021), both coordinated and uncoordinated strategies are classified into (a) direct, (b) delayed, and (c) random charging. An agent that takes care of all the charging activities in both centralized and decentralized frameworks is known as an aggregator (Xie et al. 2011). An aggregator serves as an interfacing unit between the service provider or network operator and multiple EVs, which optimally completes the CD requirements of the EV owners without compromising PG constraints (Sundström and Binding

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2012). The function of aggregator differs according to the CD frameworks. The prime functions of an aggregator are as follows: 1. Manages EVs lists (add when EV arrives and subtract when EV departs from the parked station or home). 2. Communicate with grid and EVs, i.e., V2G operation based on battery characteristics and EV information provided by EV owners (SOC, battery capacity, CD rate). 3. Taking care of both network operator’s constraints and EV owner’s benefits (Sundström and Binding 2012). 4. Distribution of EV load evenly. 5. Provide flexibility in case of random or frequent arrival of EV. 6. Provide updated load profile to EVs (decentralized case).

6.2.2 Centralized Versus Decentralized CD Method The centralized CD method is also known as the “Direct control method” (Gupta et al. 2018). In this method, the main network operator optimizes and plans the CD strategy for the bulk EVs simultaneously with the help of an aggregator which collects the requested EVs information through a communication channel. Each vehicle is required to follow the CD plans set by the aggregator. An aggregator sets the balance between the operator’s constraints and EV requirements along with the driver’s driving pattern. In centralized approach, an aggregator unit (AU) manages the fault that occurs in the distribution network by halting all CD sessions. It may include multiple local aggregators. In the smart grid environment of centralized CD, AU also forecasts the future demands and communicates with the main network operator. A typical working of centralized strategy is shown in Fig. 6.4a. When the amount of data inputs is more, computational complexity increases, and this will call for a big and efficient infrastructure to handle all the data. With a centralized method, highly optimal scheduling is obtained as all relevant information is taken into account in the scheduling process. Various studies (Gan et al. 2013; Ma et al. 2013; Xing et al. 2016) with a centralized coordinated approach for optimization have been proposed for maximizing the profits (operating cost minimization, etc.), peak load shaving, optimize CD, minimizing battery degradation, providing ancillary support (frequency and voltage control), minimizing PQ issues and stabilizing the grid. Centralized CD is efficient and provides accurate optimal scheduling, however, it suffers from some drawbacks. If AU fails due to some malfunctioning or false signal, the whole centralized system may collapse in centralized CD. It works with large database; hence, it is vulnerable to security and privacy issues and requires a strong communication infrastructure, which ultimately add-up in overall cost and complexity of the system. The decentralized CD control method is also known as indirect, distributed, and local control method. Decentralized denotes that each EV estimates its charging

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(a) Centralized

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(b) Decentralized

Fig. 6.4 Centralized and decentralized CD frameworks

requirements individually in coordination with the local AU. In other words, the EVs do not share their individual vehicle information (Fig. 6.3) to the aggregator, they determine their own charging patterns instead of being instructed by the main network operator or AU. After the local computation completes, the main network operator establishes communication channel with the AU. Figure 6.4 presents the framework of centralized and decentralized CD approaches. It can be observed that the difference lies in the position of the decision plane. In the case of centralized CD, an aggregator collects information and based on that optimizes desired parameters. Hence, the decision plane is the AU that takes care of scheduling. However, in a decentralized method, each EV locally computes all desired CD parameters. AU attracts customers for coordination by providing incentives through dynamic electricity pricing scheme and changes customer CD behavior but the final decision rests with EVs. This is why the decentralized CD method may not guarantee accurate and optimal scheduling of the CD process but computations are not much complex, and it provides flexibility to EV owner’s demands. Table 6.1 provides comparison of the two methods. Table 6.1 Centralized versus decentralized coordinated CD methods with their respective advantages and disadvantages Centralized coordinated CD method • Control action: direct control • Decision plane: AUs • Computational complexity: high

Decentralized controlled CD method • Control action: price-based control • Decision plane: EV user • Computational complexity: low

Advantages • Accurate and good optimal scheduling • Better utilization of network capacity • Fully support the ancillary services Disadvantages • Aggregator failure may disturb network stability and affects scheduling • Big budget with a highly efficient communication infrastructure is required • Complex • Data handling in bulk amount • Security and privacy issues • Restricted Scalability

Advantages • Consumer acceptable structure • Scalable or flexible • Communication infrastructure is less expensive Disadvantages • Low accuracy in scheduling or uncertainty in the final results • Forecasting of user behavior required for better results • Partially support ancillary services

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In (Ma et al. 2013), decentralized method is used for coordinated planning of the large population of PHEV, with the aim to minimize charging cost using dynamic electricity pricing scheme. Similarly, in Gan et al. (2013) and (Xing et al. 2016), the authors’ focused on maintaining electricity load profile and load shifting respectively based on CD demands using a decentralized coordination method. For real-time operations, EVs arrival is assumed as a random process; therefore, a more flexible or adaptive system is required that can handle the dynamic behavior, which is offered by the decentralized method. A general brief overview of the decentralized method is given in Bakule (2008) with past and present trends in decentralized control for large-scale interconnected complex systems.

6.3 Scheduling Approaches and Applications Sites The CD scheduling sites can be classified into three categories, viz. charging stations, parking lots, and residential parking. The developments in EVs sector are at naive stage in many countries across the world, and there is a lack of efficient EVs charging infrastructure. Therefore, charging at home parking is very popular at present among EV owner to charge their vehicles without any additional features.

6.3.1 Charging Stations Publicly accessible charging stations at dispersed geographical locations are needed like the fueling stations. As per the recent forecast in the global EV report (IEA 2021), 16.3 million (both slow and fast) charging sites will be operational by 2030. In Fig. 6.5, region-wise data of EV service station is shown. Now, these sites need proper care in terms of infrastructural maintenance, operational efficiency enhancement, smart working also optimal location, sizing, and scheduling of EVs at charging station become key interests of many researchers. In (Liu et al. 2013a), multi-objectives, aiming to identify the optimal sites locations and the optimal sizing of EVs charging station, are proposed. Minimization of charging time of EVs at charging stations (ultimately helps in minimizing waiting time) is also an emerging field of interests which is defined as the ratio of required capacity (total battery capacity minus SOC) with the power capacity. Charging time varies from 20 min to 20 h depending upon the type of plug-in capacity available (AC-DC fast or slow charging) which have been standardized by Society of Automotive Engineers (SAE), IEEE, etc. Intelligent CD strategy is generally implemented in charging stations in order to fulfill financial objectives for owners. Based on the type of charging facilities or technologies (V2G enabled) that it provides to the customers, such as slow charging or fast charging, the scheduler plans the operation of charging station for objectives which are minimizing the total cost of planning, minimizing queuing delay/waiting time at charging station, travel

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Fig. 6.5 EV publicly available charging points stocks data around the world (IEA 2021). *RoW = rest of the world

time, minimizing the power loss by optimizing or scheduling the charging station operations along with maximizing the charging infrastructure efficiency (Cheng and Gao 2018; Mehar and Senouci 2013; Moghaddam et al. 2017; Pal et al. 2019; Xu et al. 2017; Yenchamchalit et al. 2018) (Fig. 6.5).

6.3.2 Parking Lots and Residential Parkings With reference to the report (IEA 2021), the data reflects that up to 2020, there are 7 million home charging and 2.5 million workplace parking lot set-ups, which represent 40 GW and 15 GW of installed capacity, respectively. Such a huge amount of EVs charging in bulk must affect the PG. In the case of parking stations, vehicles may be scheduled to discharge in V2G mode or charge in G2V mode in bulk. The prime goal of a parking owner is to maximize the profits by selling available power present in the vehicles during the peak hours, when the electricity price is high, and buying it at low price during offpeak hours. Electric vehicle owners should be provided an incentive to participate in such scheduling. With large number of vehicles in a parking lot, it is difficult to implement this in manual mode and a proper scheduling mechanism is required using some intelligent optimization algorithms (OAs). An optimally managed bulk PHEV charging at parking station using PSO is proposed in Su and Chow (2011). Similar effort is reported in Somma et al. (2020) that aims to maximize the operator’s profits along with minimization of CO2 emission in PHEVs using multi-objective optimization. A single EV in residential parking is too small to contribute in dealing with the negative impacts (Eltoumi et al. 2021; Roe et al. 2009) on a large power system, although EV owners may get benefits of optimized scheduled charging, such as

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providing emergency supply during power outage at home, minimizing the electricity bills with V2G mode operation during peak hours, and charge EV at off-peak hours (Dubey et al. 2015; Gao et al. 2012; Liu et al. 2013b; Stefano et al. 2016). Many researchers focused on renewable energy sources integration along with EV. A combined analysis on PV-EV grid integration and their impacts are presented in Fachrizal et al. (2021) with hosting capacity assessment in a residential distribution grid. Fleets of electric vehicles and parking stations (residential, public, office building, etc.) have the potential to affect the grid negatively (Dubey and Santoso 2015) if not coordinated properly; otherwise, it may support PG operations and management using V2G framework (Jozi et al. 2017). With the exception of V2G under smart grid infrastructure (Shireen and Patel 2010), robust optimal scheduling techniques are necessary to be applied to predict and deliver smarter outputs for network operators (source) and consumers.

6.3.3 ToU Pricing Conventional utility tariff charges consumers based on their actual consumption at a fixed rate per kWh. However, a time-dependent sliding rate plan is structured according to peak and off-peak hours of a day known as the “time-of-use” (ToU) tariff. In the ToU structure, electricity bill will be estimated consumption of energy units and the time of consumption. It usually consists of three major divisions, peak, normal, and off-peak periods. During the peak period, the demand is high as compared to the normal period, whereas it is low during off-peak period. For better utilization of the resources, the utilities want to minimize the peak and fill the valley during the offpeak period. This tariff plan supports flattening of the demand curve by encouraging the consumers to shift their energy consumption from peak period to off-peak period by charging more for consumption during the peak and providing rebates during the off-peak. Various studies reveal that optimal scheduling and control of CD of EV with ToU tariff is crucial in minimizing the peak demand and maximizing the profit of EV owners. ToU-based optimal scheduling is proposed in Shi et al. (2019), which also establishes the benefits of ToU in EV scheduling. In (Skolthanarat et al. 2019), EV charging load is considered in pricing frame and authors showed that consumer using real-time pricing (dynamic) spend less on their electricity bills than those who are using flat rate pricing. Use of dynamic pricing in order to minimize the queuing delay (EVs at charging station) is proposed in Xu et al. (2017), it makes use of dynamic pricing scheme of electricity, the service fee, possible queuing delay, etc. On the basis of these data, coordination among different charging stations is established and information is provided to the EV owners. Different forms of ToU tariff and their key features are summarized in Table 6.2.

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Table 6.2 Various ToU tariff schemes and their key features Scheme

Features

Static (fixed nature)

• Time blocks are there, and the price for each block is either remains constant or predetermined, i.e., fixed using historical condition rather than current load condition • Blocks may be day-night based defined peak, mid-peak and off-peak pricing, season-based

Real Time (dynamic nature)

• Prices are real-time (dynamic) consumption of energy units based • Electricity prices are determined hourly-based or finer (10 min–20 min interval gap)

Variable Peak (combination of static and dynamic)

• Hybrid feature of static and dynamic pricing • Different blocks of time period are fixed but the on-peak period price show variation dynamically depending upon the electricity price at that instant or market situation

Critical Peak (combination of static and dynamic)

• Having hybrid features of static and dynamic • Prices are fixed for a year but on-peak during certain period of time (for some days, weeks of a year)

6.3.4 Vehicle-To-Everything (V2X) This concept is based on the availability of energy storage in EVs and its utilization in different applications. The most popular term V2G denotes transfer of power from vehicle-to-grid. This technology is in its initial stage of development and has a very promising future. V2X refers to applications of EV battery pack to support or fulfill other power requirements beyond mobility. Figure 6.6 shows possible applications under V2X, where X could be B (battery), H (home), L (load), V (another vehicle), or G (power grid) (Corchero and Sanmarti 2018). In V2G concept, EVs can provide ancillary services or maintain load profiles during peak hours (depends on network operator requirements). In (Tu et al. 2011) and (Oceano et al. 2020), authors briefly described various advantages of V2G and G2V services if the pools of vehicles are used. Some of the possible services include peak power shaving (maintaining the load curve by proper CD scheduling), ancillary services (spinning reserves), economics and social gains in terms of eco-friendly solutions of such issues. Fig. 6.6 Spectrum of V2X V2G V2B

V2V V2X

V2H

V2L

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In case of emergency, an EV can provide power to another EV using vehicle-tovehicle (V2V). EVs may act as a renewable energy storage and may provide power to homes or battery storage during power outages. This concept is termed as vehicle-tohome (V2H) or vehicle-to-battery (V2B), respectively (Dubey and Santoso 2015). Similarly, EVs can be used to provide power at remote sites like campsites, hill station stops, etc., that is known as vehicle-to-load (V2L). Despite several advantages of V2X concept, especially under a smart grid environment, there are several challenges, which demotivate EV consumers toward the V2X. In (Heuveln et al. 2021), authors took a survey on EV drivers and focus on their views on V2G services. Some are having a positive attitude as they got instant compensation for what they lose (battery power) and some show concern about battery life. Also, studies describe factors such as range anxiety after V2G, discomfort experience, unavailability of V2G infrastructure, uncertainty about the standards, and battery degradation that influence consumers and raise questions on V2G services. Among these factors, consumers worry more about battery life degradation. It is a fact that V2X/V2G affects EV’s battery life, however, with efficient optimization and scheduling these effects can be minimized and generated/saved revenue might be more than battery degradation cost. Battery aging is an important parameter that should be considered in V2X formulation. A complete analysis of battery degradation under V2G operation is presented in Lehtola and Zahedi (2021), and it can be asserted that EV owners can use the V2G facility and profit when calendar life is less than cycle life. V2G technology’s impact on battery degradation and the economic impact of V2G is evaluated in Bishop et al. (2013) and (Anastasiadis et al. 1968), respectively. It was stated that micro-grid operate more efficiently when EV’s pool connects with the grid because it leads to cost reduction under a coordinated approach (Anastasiadis et al. 1968). Apart from these effects of V2G, in Sovacool et al. (2018) authors briefly illustrated and reviewed some under-examined topics in V2G research that comes under societal and environmental impacts. In (Uddin et al. 2017), the authors proposed an OAs that minimizes the battery degradation considering V2G cycling. The following points suggest that V2X could be turned beneficial for the EVs: 1. When EV remains at rest for a longer duration (few hours of a day), the calendar aging dominates the cycle aging and degrades EV battery. 2. Degradation rate will depend upon the SOC of the battery. If SOC is high battery degradation rate will be high, i.e., battery degrades at a faster rate (Guo et al. 2019). 3. In V2X, battery degradation under calendar aging is minimized using appropriate OAs and may generate revenue. 4. Intelligent OAs might handle the battery degradation in such a way that EVs will undergo CD only when the cycle aging degrades less than the degradation caused by calendar aging, i.e., when V2X is more beneficial.

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6.3.5 Scheduling Approaches Scheduling of EV’s charging and/or discharging under V2X or G2V mode is very crucial for battery life, PG operations, EVs operation, economic benefits, etc. Many different approaches have been proposed in the literature based on different criteria. Some of these are introduced here in brief.

6.3.5.1

Priority-Based Algorithms (PBAs)

Latest scheduling approach used in Fahmy et al. (2020), for scheduling when to charge or discharge EVs at charging sites based on the priority of requirements. The working procedure of PBA is as follows: 1. EV’s information (Fig. 6.3) along with vehicle identity is received at a centralized controller. 2. Time taken by the vehicle to charge (Tc ) and discharge (Td ) is evaluated using expression– Tc/d =

(SOCdesired − SOCinitial ) ∗ (Battery Capacity) 100 ∗ Charger Power

(6.1)

3. Priority value is assigned to a vehicle based on Tc , Td and time of departure data. For example, maximum priority value is assigned to that EV whose Tc or Td is large and time of departure is ahead of scheduled actual departure time. 4. Available charging point (CP) at parking station will be assigned to the highest priority value EV. If CP is busy, then marked its starting time. Earliest starting time CP will be available to the highest priority value EV. 5. Evaluate the time when the EV will complete its CD. 6. Compare this finish time (FT) with the time of departure and plan according to the following rules. FT ≤ time of departure

Assign EV to that CP Update the starting time of CP (=FT of present EV)

FT > time of departure

(a) Assign to CP but EV may leave before desired SOC to achieve. Update the starting time of CP (=time of departure of present EV) (b) Refuse to link with CP if EV owner does not allow to leave with partial desired SOC

6.3.5.2

First-In-First-Served/Out (FIFS/O)

FIFO is a simple and straightforward EV charge scheduling approach. The EV which arrives first, based on the SOC requirement and parking lot maximum demand power,

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choose to be charged, and the rest EVs remain in the waiting area if CP is not available or maximum power demand reached. Again, from the waiting EVs whichever EV that comes first will be allowed to charge. In (Huang et al. 2012), two different variants of FIFS/O scheduling are made, in the first case an EV having minimum SOC requirement is served first, while in second case priority is given to an EV with maximum SOC requirement. The results with these two cases have been presented and compared with shopping mall and office parking scenario.

6.3.5.3

Processor Sharing (PS) Scheduling

PS scheduling is similar to FIFO scheduling before reaching the parking maximum demand level. After exceeding this limit, the controller distributes equally the available electrical power to the rest of the EVs, and hence, the charging rate will be adjusted accordingly. In (Kim et al. 2016), A comparison between two charge scheduling strategies is presented which says FIFO is superior over PS when maximum demand level is lower than decided upper limits but the author’s advised that the performance of these two strategies depends upon the kind of goal or situation the scheduler choose.

6.3.5.4

Charging and Departure Model

There are varieties of charging and departure models based on different scenarios and the place where scheduling is implemented. In (Huang et al. 2012), charging rate of each EV is considered as constant until its charging requirements are reached. Charging rate may assume as variable too but for that additional structure of different rates is required. This approach reduces complexity of scheduling problem when large set of EVs are taken into account. Two distinct examples of charging and departure model are: Office campus scenario: For departure model in office parking case, all EVs are considered to be departed at fixed time (which is not exactly possible in real-world environment). Shopping mall scenario: Parking vehicle in shopping complex demands different departure model as time of departure is random in this case. So, before the final deadline to depart from complex T d , T' is considered as randomly distributed variable in range between the time of arrival and departure. To deal with uncertainties in arrival or departure models, Monte Carlo simulation (MCS) is used in Ayyadi et al. (2019) considering residential area parking.

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6.4 Optimization for EVs Optimization is the process of selecting the best possible solution from the available options. Any problem needs to be optimized to find the best possible approximate solution and establish a true balance among reliability, availability, efficiency, and cost. By optimizing any problem, uncertainty can be reduced up to a great extent. Traditional methods of optimization include the estimation of the network’s state. In the present scenario where dynamic changes occur in many ways, like random arrival of EVs at charging station, impact of fluctuation loads of EVs on grids, dynamic pricing scheme to charge–discharge EVs, optimization tools that can handle these frequent changes are required. In this section components of OP, objectives, popular optimization algorithms, and key steps in OP are briefly discussed.

6.4.1 Components of OP The very first step to perform optimization is to formulate it in a mathematical form that is known as OP. It has components that are basically the raw material for any problem. Depending upon the application, the user defines the parameters of components. There are three major components in any OP, which are as follows:

6.4.1.1

Decision Variable

As the name suggests decision variable is a key parameter that decides the final output of the OP. These are the unknown variables that may vary during the optimization process (e.g., SOC) or remains fixed. They are responsible for final results so, it is very important to choose them wisely. Some of the decision variables in CD scheduling of EVs are battery information (Fig. 6.3), arrival and departure time, driving patterns, electricity pricing, distance of travel, etc.

6.4.1.2

Objective Functions

The objective function may be defined as a mathematical expression which is formulated using design/input/decision variables. There may be one or more objective functions present in an OP, depending upon the user requirements. Selection of suitable OAs depends upon the number of objective functions. Typical examples of objective functions are minimization of charging cost, maximizing battery life, waiting time minimization at the charging station, etc.

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6.4.1.3

Constraints

They are basically the bound on the decision variables, the range in which decision variable values are allowed and used to limit the solution/input parameters under the user-specified or safe acceptable range. Every parameter should have safe limits of operation. Although in most of cases constraints are inequality type, they are generally classified into—inequality and equality types. Constraints in CD scheduling problems—battery capacity (SOC) limits, charging power limits, charging voltage and current limits, generation and transmission limits, types of vehicles, parking pattern, etc.

6.4.2 Optimization Objectives With the centralized and decentralized CD methods, various optimization tools/ algorithms are used in either fashion. To enhance the efficiency of EVs or optimize performance with minimum negative impacts on grid stability, revenue generated, etc., a good objective function is very essential for OAs. A list of common objective function in CD optimal scheduling problem is provided below, and desired objectives may be selected depending upon the requirements and the problem priorities. 1. Cost minimization (Al-Karakchi et al. 2017; Chen et al. 2018; Maigha and Crow 2017) 1.1. 1.2. 1.3. 1.4. 1.5. 2. 3. 4. 5. 6. 7. 8.

Charging cost Parking fees Battery degradation cost Inconvenience cost Energy cost (charging station)

Minimizing peak load and reducing power losses (Binetti et al. 2015) Voltage and frequency regulations (PQ issues minimizing) Maximizing aggregator’s profits Maximizing EV owner profits (discharging rewards or V2G revenue) Customer satisfactions Emission control (PHEV) EV best route optimization.

6.4.3 Optimization Algorithms Optimization algorithm is defined as the set of rules, which needs to be followed in mathematical calculations or problem-solving operations for any OP. Numerous optimization approaches are there to solve EVs CD objectives which are classified as follows.

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Numerical Optimization Approaches

Numerical optimization approaches include linear programming (LP), nonlinear programming (NLP), and dynamic programming (DP), etc. Some other approaches under this category are: game theory (Vuelvas et al. 2021), queuing theory, which have been also used for EV scheduling. These techniques could be very efficient in cases where the underlying assumptions are fulfilled; however, these are susceptible to local solutions and unable to handle multi-objective problems efficiently. A. Linear Programming A first-order polynomial is formed in LP problems which offer a simple and effective optimization. Generally, computational complexity is less in such a problem, and they are not having multiple objectives. In (Sundström and Binding 2010a), to minimize the charging cost the author proposed a single objective with multiple constraint equation using the LP approach. The problem with the LP approach is inputs are fixed in nature, if there is any deviation in inputs, the output may not be accurate. Hence, LP does not fulfill OP involves dynamic parameters. Robust optimization approaches are introduced to handle such uncertainties. They may be combined with LP. A variant of the LP is mixed-integer linear programming (MILP). To maximize the environmental credits and minimize the generation as well as emission cost of generating units, in Hajimiragha et al. (2010), the author adopted MILP to deal with the LP problem. A piecewise linearization of constraints is being done with MILP. Similarly, in Hajimiragha et al. (2011), both MILP and robust optimization, with the aim to minimize net electricity and emission cost and tackle the uncertainty in constraints, are used, respectively. B. Nonlinear Programming A higher-order polynomial is formed in NLP problems. Among NLP, quadratic programming (QP) is widely used in EVs scheduling problems. QP extends the range of constraints and objective functions by allowing dynamics through a quadratic form, such as power loss (I 2 ter m). A QP approach has been used in Mousavi Agah and Abbasi (2012) and (Clement et al. 2009) for minimizing the power loss with appropriate constraints. Many scheduling problems include minimization of the variance. In EV route optimization, load shaving optimization, and forecasting problems errors/ deviations need to be optimized. A QP approach is proposed in Mets et al. (2010), solving an optimization problem with the goal of reducing peak load and flattening the load profile. The error expression in the objective function is the square of the difference between the optimal load and the real total load, which must be reduced. Similarly in Debnath et al. (2020); Jian et al. (2013); Sortomme et al. (2011); Zhang et al. (2012) minimization using QP has been implemented. C. Dynamic Programming The DP is capable of handling problems that include time-varying parameters. Unlike LP and NLP, DP provides more flexibility to the scheduler. A DP approach is proposed

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in Han et al. (2010), for optimal charging control of each EV. A charging cost, as well as revenue generation after providing the regulation services, are also investigated. Similarly in Rotering and Ilic (2011), two DP algorithms are used to minimize the daily electricity cost, support V2G and provide profits associated with ancillary services, optimize charging time and energy flow. It possesses the same drawbacks as other conventional methods.

6.4.3.2

Metaheuristic Approaches

OPs related to EVs are nonlinear, have multiple objectives, and require consideration of many diverse parameters as constraints. Therefore, metaheuristic optimization methods are employed to solve complex problems. Particle swarm optimization (PSO), genetic algorithm (GA), etc., are some of the popular metaheuristic algorithms used in EV scheduling problems (Alonso et al. 2014; Celli et al. 2012; Dixit et al. 2015; Faddel et al. 2017; Hutson et al. 2008; Wang et al. 2020; Mavrovouniotis et al. 2019). A. Particle Swarm Optimization PSO is an optimization algorithm based on social behavior of bird flocking. Each particle in swarm is considered as an optimal solution following the solution which is nearer to the expected results. There are three attributes assigned to each particle: velocity, location, and fitness. A possible solution is represented by the particle’s location and its velocity defines flying direction and distance. The particles update their velocity as they travel across the search space, eventually approaching the optimal solution. The fitness function is used to assess particle quality, which is problem dependent. The mathematical formulation of a general PSO algorithm. The position and velocity of ith particle in a kth iteration times can be denoted as X i and Vi respectively in Eqs. (6.2)–(6.3). (Celli et al. 2012) ( ) ( ) Vik+1 = Vik + q1i r1ik Pik − X ik + q2i r2ik Pgk − X ik

(6.2)

X ik+1 = X ik + Vik+1 ,

(6.3)

where i = 1, 2… n, and n is the size of the population, q1 and q2 are two positive constants, r1i and r2i are random number normalized in range [0:1], Pi best previous location of ith particle and Pg denotes the best particle of the group. With multi-objectives, various studies related to optimal scheduling of EV CD using PSO have been done (Amin et al. 2020; Hutson et al. 2008; Su and Chow 2011; Wang et al. 2020). In (Celli et al. 2012), with the objective to control the CD pattern of the fleet of EVs while satisfying EV owner requirements, energy prices, etc. Similarly, power loss minimization while placing the EVs in the distribution network optimally, using PSO is proposed in Dixit et al. (2015). A EVs route optimization

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problem in Siddiqi et al. (2011), using PSO with constraints, which include traveling time, time delays due to traffic signals, re-charging time of vehicle, etc. Maximizing the fuel economy and utilization in PHEV is optimized in Wu et al. (2008) using PSO, while taking care of vehicle performance which is considered under constraints. B. Genetic Algorithm Another population-based optimization algorithm is GA, which uses selection, mutation, and crossover as the genetic operators. This algorithm has been adopted in Mehar and Senouci (2013) to solve charging station’s location problem with some modifications, to avoid premature convergence, and improve the efficiency of existing GA. Similarly, in Hu et al. (2019), planning of EV charging station with additional features such as investment costs of charging station, energy losses in feeders has been proposed using Hierarchic-GA. Earlier efforts have been made by researchers using basic GA, to schedule charging of an EV with multiple objectives, such as minimizing the cost of charging under ToU pricing and maximizing the profit of charging station. Apart from these, research results reveal that maximum parking station’s profits are obtained by adopting integration of renewable energy resources (photovoltaic rooftop) with V2G technology (Fahmy et al. 2020; Shi et al. 2019; Sugii et al. 1999). Charging in bulk with random arrivals of EVs demands for a forecasted scheduling. An optimal day-ahead schedule of a PHEV load using GA is proposed in Mehboob et al. (2014) that minimizes the system power peaks. An efficient V2G model based on GA is proposed in Fahad and Beenish (2019) considering smart grid scenario to save energy in power system.

6.4.4 Steps in Optimization Problems To solve any OP, six basic steps are involved which needs to be followed in sequence: 1. 2. 3. 4. 5. 6.

Identifies the need of optimization. Selection of decision variables. Constraint’s formulation. Objective function formulation. Apply suitable algorithm. Observe the output and modify constraints/decision variable according to required results.

OP also includes optimization of electric buses charging station based on charging demands, energy storage system, etc. (Pan et al. 2020). PSO-based daily operation model of charging station is proposed with an objective to minimize CD and battery loss cost. With the aim to minimize the system power loss, a PSO-based optimization is proposed in Yenchamchalit et al. (2018) that finds optimal locations of charging stations and their size. In (Cheng and Gao 2018), an adaptive PSO-based optimization

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model has been adopted for optimal allocation of an EV charging station. Optimization takes care of the convenience of EV, charging demands, economic parameters involved in operational charging station and PQ issues. Earlier the OPs were relatively simple but with V2G facilities complexity in OPs is increasing; hence, researchers have focused on intelligent optimization algorithms that can handle more number of constraints. In (Pal et al. 2019), a grey wolf optimization (GWO) and whale optimization algorithms (WOA) have been used to optimally allocate the EV fast charging stations, thus enabling V2G facilities. In (Linru et al. 2020), the authors tried to analyze the safety measures on the PG side charging along with the safety of charging equipment.

6.5 Literature Summary and Discussions EVs are more cost-effective and environment-friendly than internal combustion engine (ICE)-based vehicles, still there is ample scope in EVs for optimization of cost. Almost every research related to the optimal solutions for EVs considers cost minimization as one of the prime objectives; however, it is not the only objective that can be optimized. CD scheduling has emerged as one of the crucial issues in EV operation. The reviewed literature on EVs has been classified and summarized under these two categories.

6.5.1 Formulation of Objective Function Objective function is the most important part of any OP that determines the success of optimization. Formulation of objective function is crucial, and it may involve single or multiple parameters. In EVs, many different parameters have been considered for formulating the objective function, e.g., charging cost, flattening of demand profile, etc. Table 6.3 summarizes the literature that deals with the formulation of objective functions in EVs optimal operation. A. Charging Cost The charging cost of EVs changes during peak, mid-peak, and off-peak hours of the day. Several authors focus on maintaining load profile (flattening of load profile, peak shaving (Alonso et al. Apr. 2014; Debnath et al. 2020)) by using ToU pricing (Dubey et al. 2015) scheme that ultimately provides incentives to the EV/parking lot owner (PLO) in other words overall cost of charging reduces (charging cost and discharging rewards balances each other) (Hutson et al. 2008). A comprehensive study of minimizing charging cost and valley load filling is evaluated in Maigha and Crow (2017), also illustrating static and dynamic charging scheme. The static charging scheme focuses on the day-ahead plan (forecasted load is required), whereas

CD power bounds and SOC limits Platform: MATLAB

Focus on regulation services provided by EVs

Lin et al. (2014)

CD power (flattening of load curve)

CD power limits of EV batteries Tool: GAMS

Model uncertainty in Maximizing the electricity price for profits of the EVs EV scheduling aggregator/network operator

Constraints and platform used

Cao et al. (2019)

Objective function

Motive

References

Table 6.3 Literature with focus on formulation of objectives Features

x

x

x

x

x

x

x

x

MO HC PL CS

Convex, ODC

R-MIP

Model

(continued)

Comparison between forecast-based and online-based scheduling is provided. The result shows that later is more accurate toward objectives completion as prediction of regulation demands are highly inaccurate in the real-time scenario

Proposed model handles electricity price uncertainty using robust optimization approach and compare outcomes with deterministic strategies

Results

6 Scheduling of Electric Vehicle’s Charging–Discharging: An Overview 129

Motive

Objective function

Minimization of Alonso et al. (2014) Demand-side management of EVs main transformer in a low-voltage (LV) load system

References

Table 6.3 (continued) Features x

✔ x

MO HC PL CS

Parking pattern, x power flow, voltage, current, and apparent power limits Platform: MATLAB

Constraints and platform used GA

Model

(continued)

Comparison among dumb, conventional, smart (G2V), and smart (G2V + V2G) charging strategy is shown on residential network. Results reveal that smart (G2V + V2G) strategy performs better than others in load profile improvement and stress reduction from the distribution transformer

Results

130 B. Chauhan and S. K. Jain

Motive

Time zone Khatiri-Doost and Amirahmadi (2017) (TZ)-based selection of priorities (charging or discharging)

References

Table 6.3 (continued)

Minimization of active power losses and distribution of system demands

Objective function Power load constraints (used to avoid the overload condition) and voltage constraints Platform: MATLAB

Constraints and platform used

Features ✔

✔ x

x

MO HC PL CS

Results

(continued)

TZ (Based on price) The proposed model shows that coordinated CD will help in reducing overall load during peak hours and maintaining grid power losses Comparison among different PEVs penetration level with an uncontrolled CD and controlled CD is presented

Model

6 Scheduling of Electric Vehicle’s Charging–Discharging: An Overview 131

Objective function Minimization of power loss

Motive

Integrated approach is proposed with network reconfiguration for power loss minimization during charging of EVs

References

Amin et al. (2020)

Table 6.3 (continued) Features ✔ x

x

MO HC PL CS

Power load, voltage x constraints, and SOC Tested On: modified IEEE 33-node medium-voltage network

Constraints and platform used Bi-PSO

Model

(continued)

Results of the proposed model compared with the existing controlled charge scheduling model and claims that energy saving improved by 31% with improved quality and minimal power loss

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132 B. Chauhan and S. K. Jain

Objective function Minimizing total cost of EV charging station (operation, maintenance, investment, network loss cost)

Motive

Aim is to optimally locate the EV charging station simultaneously focusing on improving its performance and determining the capacities

References

Liu et al. (2013a)

Table 6.3 (continued) Features x

x

✔

MO HC PL CS

Main transformer ✔ capacity, reactive power limits, voltage and current limits, daily average load limits, minimal load power factor limits Tested On: IEEE 123-node test feeder

Constraints and platform used MPDIPA

Model

(continued)

Reduction in network loss and voltage profile improved after optimization of charging station Convergence characteristics of modified primal–dual interior point algorithm (MPDIPA) are good, and it helps in searching the optimal capacities of charging station with minimal power loss rates

Results

6 Scheduling of Electric Vehicle’s Charging–Discharging: An Overview 133

Minimization of total running cost (fuel cost of thermal unit, start-up cost, V2G cost)

Aim is to determine how V2G support grid in order to minimize dependencies on conventional expensive units. V2G scheduling is prime motive to achieve

Saber et al. (2009)

Minimize EVs Ayyadi et al. (2019) Coordinated charging of multiple energy consumption EVs at the residential cost area is formulated as an optimization problem to minimize charging cost and EV battery degradation cost

Objective function

Motive

References

Table 6.3 (continued) Features

SOCs limit and charging power limits Platform: MATLAB

x

✔

x

x

✔

x

x

MO HC PL CS

System power x balance limit, generation power limits, SOC limits, parking lot space constraints, charger and inverter efficiencies, initial state of units taken into account Platform: visual C++

Constraints and platform used

LP, MCS

PSO

Model

(continued)

Results of the proposed model claim that with 100% penetration rate of an EV, the charging cost cut shot by 50% and for 50% rate charging cost is reduced up to 38%

Results show that constructive V2G scheduling reduces operational costs On comparing results with other optimization approaches, in terms of minimizing operational cost while undergoing V2G scheduling in a constrained parking lot, proposed model of PSO works efficiently although implementing proposed PSO model needs more time

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134 B. Chauhan and S. K. Jain

Study of ToU-based EV charging models

Gao et al. (2012) Minimize daily load curve, peak valley difference, change of electricity sales

Objective function Peak and marginal price limits, battery capacity loss constraints Platform: MATLAB

Constraints and platform used

Features ✔ x

x

x

MO HC PL CS Fuzzy logic

Model Results reflect that with V2G capabilities peak load valley difference reduces effectively

Results

PV Photovoltaic; MO Multi-Objective; HC Home Charging; PL Parking Lot; CS Charging Stations; ODC Optimal Decentralized Charging; MCS Monte Carlo Simulation; MMPP Markov-Modulated Poisson Process; EDF Earliest Deadline First; SCFS Shortest Charging First Serve; LCFS Longest Charging First Serve; SA Scheduling Algorithms; OA Optimization Algorithms; OP Optimization Problems

Motive

References

Table 6.3 (continued)

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in the dynamic charging scheme, it emphasizes on real-time charge scheduling. “Cost” includes—charging cost, parking fees, EV battery degradation cost, inconvenience cost, discharging rewards (under V2G services) (Zhou et al. 2020). EV battery degradation is based on various factors like operational temperature, low SOC, high DOD, uneven and frequent CD (i.e. number of cycles), C-rate, and total energy withdrawn. In (Zhou et al. 2020), collective cost function is formed containing all costs listed in the objectives section. Although in Ju et al. (2018) modeling of battery degradation cost is given. Shifting transportation toward electric-driven does not mean that it is free from the environmental effect or eco-friendly. Studies reveal that it is just like shifting emission directly from vehicle (ICE-based vehicles) to emission from the power plant (emission of poisonous gases); this concept with a new term, in Fang et al. (2018), is defined as the social cost of charging EV, which vary by time of day and by the level of harmful gas emission while generating electricity from the power plant. Optimization of home electricity cost, home energy, and EV charge optimization while satisfying user comfort is briefly discussed in Nguyen and Le (2014). B. Peak load shaving Integration of EVs in the grid increases rapidly as the growth of the EV market is exponential. For safe and reliable system operations, it is required to avoid uncoordinated CD scheduling as it increases the peak load and disturbs system stability. Peak load shaving (maintaining load curve) with efficient, coordinated CD scheduling, helps the power system to continue its stable operations. At a particular charging station, CD scheduling and optimal allowable loads are optimized in Debnath et al. (2020), while taking care of load curve shaping. In (Binetti et al. 2015), a real-time greedy algorithm-based optimization is proposed to efficiently minimize the grid power loss and peak load, while taking care of the EV owner’s demand and desired SOC at the departure time and similarly in Chen et al. (2018), minimize charging cost and peak valley differences based on ToU pricing. Coordinated charging using binary PSO algorithms is presented in Amin et al. (2020) to decrease the network power losses and improve voltage profile. QP- and DP-based OAs are used in Mousavi Agah and Abbasi (2012), the author gives a description of the impacts of PHEV charging on the residential distribution grid, and also the optimization is used to minimize the power loss and voltage deviations (other aspects like power factor control is also included in this study). C. Frequency Imbalance As there is always a difference in power generation and consumption, the distribution network has to face a certain imbalance in frequency. For grid demands 50/ 60 Hz frequency for smooth operation, according to standard only 1–2% frequency deviation is allowed. Maintaining frequency within an acceptable limit (1–2%) is a very important and critical part of any grid integration application whether it is the integration of renewable energy resources or EVs. The EVs battery storage response is swift and speedy, due to which researchers found EVs battery storage is suitable for providing ancillary services such as spinning reserves and regulation services

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(voltage and frequency). In (Lin et al. 2014), optimal CD scheduling is investigated with V2G systems for the provision of frequency regulations using a decentralized approach. Similarly, in Pillai and Bak-Jensen (2010), the authors describe the uses of EVs battery storage power (V2G) for frequency regulation and concluded that the pool of EVs with V2G facilities is more responsive, stable, and faster than other conventional generating units for maintaining frequency. D. Revenue Whether an EV owner or retailer/aggregator/service provider/network operator both demand minimum overall cost and maximum revenue generation. Not only this, but it is also expected that by optimal scheduling the durability of charging infrastructure/ equipment is maintained. From the retailer’s point of view, a day-ahead stochastic programming optimization has been proposed in Balram et al. (2013) that works on the CD scheduling of EV for maximizing the retailer’s profits, considering the uncertain demand of EV charging. The retailer’s profit is the difference between the rewards generated by selling electricity and the cost of purchasing the equivalent amount of power by EV owners for charging their vehicles. Sometimes retailer provides discount also to attract EV owners to participate in the process. Similarly, a fuzzy logic-based optimization approach has been used in Faddel et al. (2017), for optimal scheduling of EVs in order to maximize PLO’s profit while satisfying EV owner’s charging requirements. Also, consumer benefits in terms of minimum charging cost and penalty for unscheduled EV (profit to service provider or promoting coordinated scheduling) are presented in Gupta et al. (2018).

6.5.2 EV Charging and Discharging In initial days of EV development, the number of EVs used to be small and, generally, EVs are used in the daytime and charged immediately after it reaches the parking or at night. It has been observed that with increasing number of EVs, unplanned, and random charging could affect the grid in terms of stability, power quality, and rise in peak demand (Jia and Long 2020). Therefore, a planned and systematic charging and discharging scheduling is utmost required for optimal resource utilization. Several works have been reported, which continues with rising trend, for optimizing the CD pattern of EVs. Table 6.4 presents a summary of the literature that focuses on scheduling of EVs. Efforts are made in Sugii et al. (1999), using GA-based optimization techniques to minimize the cost of charging infrastructure and leveling of power load curve. ToU scheme-based EV charge scheduling is proposed in various research papers which provides daytime EVs charging plans for improving the grid sustainability, load profile, etc. (Dubey et al. 2015; Gao et al. 2012; Shi et al. 2019). In (Vuelvas et al. 2021), the authors focused on minimizing the energy cost using ToU pricing strategy and the game theory approach, assuming the charging station offers unidirectional energy flow, i.e., G2V only.

Improve the profits of Priority-based charging station and algorithms (PBA) for reduce energy EVs CD demands from the grid

Fahmy et al. (2020)

Scheduling base

Motive

References

Table 6.4 Literature with focus on scheduling

Constraints on SOCs. Energy balancing (PV, Grid, and V2G generation), constraints on power consumed and delivered Platform: MATLAB/ Simulink

Constraints and platform used V2G ✔

✔

Focus PV x

HC ✔

PL PSO, GA

Model

(continued)

Five different cases considered and implemented using PSO, GA in terms of computation time and accuracy The result shows that maximum profit will be generated when PV (installed at rooftop) is used along with V2G service in a parking station. The GA had more computational time than PSO rest of the results are approximately similar

Results

138 B. Chauhan and S. K. Jain

Scheduling base

Dynamic charging scheduling scheme (DCSS) based on model predictive control (MPC)

Motive

Maximize the profit of parking lot owner through dynamic charging and predictive control model powered by PV and grid systems

References

Zhang and Cai (2018)

Table 6.4 (continued) Focus PV

Upper bound on ✔ the charging load, range of charging decision, charging requirements of all the EVs Platform: MATLAB

Constraints and platform used x

V2G x

HC ✔

PL DCSS, MPC

Model

(continued)

Results obtained are compared with FIFO-based scheduling and two-stage scheduler and found that proposed model provides nearly 200% higher benefits to PL

Results

6 Scheduling of Electric Vehicle’s Charging–Discharging: An Overview 139

Joint time interval (JTI) Scheduling Based on electricity price

Optimally manage the EVs parking lots and proposed the model which is capable of determining the time of charge and discharge

Mirzaei and Kazemi (2021)

Scheduling base

First-In-First-Served/ Out (FIFO), Processor Sharing (PS)

Motive

Kim et al. (2016) Performance analysis of two kinds of scheduling strategies considering EV parking station as a base for implementation

References

Table 6.4 (continued)

Constraints on maximum power inject or absorbs, Platform: MATLAB

Maximum power demand limits on parking station

Constraints and platform used

Focus

x

x

PV x

x

✔

HC

x

V2G

✔

✔

PL

GA

FIFO, PS, MMPP

Model

(continued)

With the proposed model, charging cost is reduced by nearly 8.04% and EV owner’s profit increased by 4.33% compared with the existing scheduling approach in Honarmand et al. (2014)

Comparison between two charge scheduling strategies is presented saying that FIFO is superior to PS when maximum demand level is lower than decided upper limits Finally, the authors mentioned that these two strategies’ performance depends upon the kind of goal or circumstances the scheduler chooses

Results

140 B. Chauhan and S. K. Jain

Motive

Comparing performance of different scheduling methods

References

Huang et al. (2012)

Table 6.4 (continued)

FIFS/O, SCFS, LCFS, EDF, McNaughtan (McN)

Scheduling base Parametric constraints of Queuing theory (QT), MCS, and SA Platform: MATLAB

Constraints and platform used

Focus x

PV x

V2G x

HC ✔

PL QT, MCS, and SA

Model

EDF scheduling model provides the best results in terms of service quality and utilization level

Results

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The charge scheduling process involves the only unidirectional flow of energy, i.e., energy flow from grid-to-vehicle, where specific objectives need to be fulfilled. Although these OP are computationally less complex than those involving both charging and discharging processes, OPs with bidirectional energy flow are more promising in maximizing customer rewards (financial benefits), reducing peak demand, supporting ancillary services and renewable resources. In (Dwwhu et al. 2007), charging at low cost, i.e., minimization of charging cost, is proposed by charging at off-peak hours in the Singapore context, but due to lack of bidirectional energy flow revenue is not generated by discharging EVs batteries and there will be no peak shaving. Another attempt is made regarding minimization of charging cost of a large population of EVs considering decentralized strategy in Ma et al. (2013). Decentralization methods will allow each EV owner to choose their own charge scheduling. In (Gan et al. 2013), a decentralized method is used for valley filling (load curve flattening) through optimal charge scheduling of EVs, and here also unidirectional energy flow is considered. EV battery charging behavior also needs to be studied before proceeding for scheduling (using either centralized or decentralized methods). A brief discussion on minimization of overall charging cost of the EV fleet presents in Sundström and Binding 2010b, using linear and quadratic approximations. A QP approach for PHEV charging in Mets et al. (2010) is presented, aiming to minimize the peak load. A unidirectional energy flow in EV is assumed and fuzzy logic-based optimization, aiming to maximize the PLO’s profit by the proper charge scheduling is proposed in Faddel et al. (2017). Uncertainties of market price and EVs mobility are considered and formulated using fuzzy sets. Workday and weekend scenario is considered in Ma et al. (2015) for optimal charge scheduling of an EV based on moving window optimization to optimize charging cost of an EV fleet with PG constraints. In the present scenario, optimization of bidirectional energy flow (G2V and V2G) is the topic of interest of many researchers. V2G works efficiently in the parking lot where a pool of vehicles interacts or charge–discharge in a bulk amount. In unit commitment problems, challenges come with scheduling a small micro-unit to handle random fluctuation, peak load, etc. (Saber et al. 2009). This problem of dependencies on expensive units is resolved using V2G services up to a great extent but pool of vehicle is required, which is possible only in parking lots where EVs goes under CD process frequently. In (Saber et al. 2009) proposed, a variant of PSO, used for minimization of the total running cost (fuel cost in thermal units), the startup cost of the units, and V2G cost. Binary PSO algorithm is used in Hutson et al. (2008), for maximizing the profits (in the parking lot) using bidirectional energy flow. BPSO determines buying and selling times of power from and to the grid, respectively. A ToU-based V2G scheduling is proposed in Sharma et al. (2018), for maximizing the benefits to AUs and customers and also protecting them from fluctuating market prices. A study in a stochastic optimization framework, to maximize the long-term profits of charging station owners is proposed in Bagherzadeh et al. (2020), considering random vehicle arrival, vehicle SOC, energy purchase cost as random parameters.

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6.5.3 Discussions The integration of the EVs to the grid brings many opportunities in improving the grid performance, especially in the modern power system with the integration of renewable energy sources in substantial amount. EVs not only provide energy storage option but can also act a load that is flexible and, hence, can be shifted to a suitable time to make demand profile more uniform. Grid-connected EVs have significant impact on many grid parameters, e.g., grid stability, power quality (PQ), protection system, communication requirements, etc. Therefore, grid infrastructure and operational system should have appropriate mechanism to handle such issues. The traditional way of mitigating PQ issues involves use of capacitor banks and tap changers (as a voltage regulator; for voltage profile-related issues), passive filters (for harmonics-related issues), etc. However, with technological evolution, these issues are now handled in a smart way, and an optimized operation of EVs may support this. This requires systematic scheduling of EVs, and for that participation of individual EV owner is essential. Monetary benefits without compromising comfort level of individuals could be a great motivation, which may be explored from V2G frameworks. One of the major issues in implementation of V2G is battery degradation during CD cycles. Other critical issues in EV scheduling include requirement of expensive infrastructure and effective communication network, privacy issues of the EV users, range anxiety among customers (while discharging EVs), etc. Hence, research focus is required on the development of systematically planned efficient charging infrastructure in phased manner to consider the economic issues, increasing battery capacities, mitigating the effects of V2G on battery degradation, etc. Parking stations with V2G capabilities needs to be developed at affordable cost with high level of personal data security. Development of smart charging schemes that optimizes between charging time and battery life based on the user requirement will be useful in enhancing the confidence of the EV owners to participate in grid-supportive schemes, such as V2G. As far as smart cities are concerned, wireless charging as well as charging while moving, green communication networks, Internet of things (IoT), AMI are top-notch technologies that need to be explored. However, some are under investigation and at the implementation stage. With these technological advancements, security and privacy issues also need to be taken care of. The centralized CD method requires EVs information such as SOC, EVs driving pattern, energy demand, and time of arrival/ departure. Such customer data is crucial and may cause serious security as well as the privacy issue and hence calls for an effective and efficient security mechanism at charging sites. In this regard, an effort had been made in Li et al. (2017), where the authors suggested an authentication protocol for charging stations to authenticate an EVs identity. CD scheduling of EVs has two categories: single EV and pool of EVs scheduling, i.e., at public charging stations/parking lots. Although a single EV does not help to mitigate grid issues, a cumulative effect may be analyzed. Further, individuals may

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get benefits, such as reduced electricity bill (Dwwhu et al. 2007) and emergency power supply. In EVs scheduling problems, minimization of charging cost and flattening of load curve are two objectives that are touched by almost in every research in different manners. Recently, the social cost of CO2 emissions has been included in the total cost function while studying PHEV cost minimization. Similarly, other costs such as the cost of importing the crude oil (minimized with the use of EVs and providing gifts in terms of incentives to customers), electricity generation cost, and charging infrastructure maintenance cost along with the scenario such as traffic jams, weather, and road conditions, also need equal concern and research. Adopting CD scheduling is beneficial to all the stakeholders and resembles a win–win situation, if implemented effectively. Table 6.5 summarizes the motivation/ benefit of different entities in participating CD scheduling. Table 6.5 Motivation/ benefits of CD scheduling to different stakeholders of EV industry

Consumer/parking lot owner

Service provider/ grid

Startups/tech-savvy

• Reduction in cost – Charging cost – Parking fees – Battery depreciation cost – Emission cost (PHEV) • Efficient use of battery – Power outage (V2H/B) – Camp sites/ Remote location – Emergency (V2V) • Electricity bill reduction • Range anxiety control – Location of charging station – Route optimization

• Grid stability • Load curve shaping • Mitigation of PQ issues • Reduced power loss

• Incentives for charging stations • Reduced risk due to growing market • Opportunity to grow as a brand

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6.6 Conclusion and Future Directions The growing EV market has set certain challenges in front of researchers and working professionals to come up with innovative solutions. Considering the influence of this sector on different industries/individuals, the complexities involved are more. This sector needs comprehensive structuring of the charging infrastructure, which requires significant alterations in the power system operation and planning. The complexity of the issue becomes enormous with close engagement of individual EV user with variety of driving habits, profile, and requirements. This review paper has tried to cover almost all important domains related to EV CD scheduling and provides an overview of existing challenges and open scope for further studies in scheduling of EV CD. Comprehensive literature study of CD methods, CD application sites (such as parking lot and residential areas), optimization objectives and techniques, different scheduling approaches, constraints, challenges, and motivations have been presented. Scheduling strategies for unidirectional energy flow (G2V) are found financially viable and fulfill objectives to a great extent, but to compete with future challenges and dynamics of the load, bidirectional energy flow (V2X/G) needs to be included. Accordingly, advancement in CD infrastructure, maintenance, optimal operations, impacts on PG, cost increments are some new challenges that need concerns and innovative solutions. Apart from this, regulations and policies related to EVs need to be changed based on the prevailing local circumstances and developments in the EVs sector. Table 6.6 summarizes the comparative comprehensiveness of this review article as compared to some of the recent review publications in the similar domain. Table 6.6 Comparison of review articles featured in similar area Features

Review Article Dai et al. (2016)

Habib et al. (2015)

Yang et al. (2014)

Kong and Karagiannidis (2016)

Mukherjee and Gupta (2015)

Nimalsiri et al. (2020)

(This paper)

Renewable energy

✔

✔

x

x

✔

x

x

Optimization algorithms

x

x

✔

✔

✔

✔

✔

Scheduling algorithms

x

x

x

x

x

x

✔

Review on objectives of scheduling

x

✔

✔

✔

✔

✔

✔

Detailed steps x of OP

x

x

x

x

x

✔

Review on CD strategies

✔

✔

✔

✔

✔

✔

x

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Future scope for research in EV scheduling domain includes: – Scheduling random EV arrival and departure uncertainties at the charging station, while satisfying the customer’s charging demands. Easy and minimum time-consuming selection of available charging points. – Smart interaction of renewable energy resources (wind/solar) with forecasted data and use of an efficient tool to handle uncertainties. – Modeling of practical EVs smart charging stations which includes efficient wireless charging, data security, V2G service, and solid vehicle-to-everything communication networks. – Battery swapping infrastructure modeling for heavy vehicles. – Solar-powered DC EV charging station for remote locations. – Efficient wireless charging or charging-in-motion. – Multi-objective hybrid optimizations to schedule CD operations of an EV at home as well as parking stations.

References Al-Karakchi AAA, Putrus G, Das R (Jan 2017) Smart EV charging profiles to extend battery life. In: 2017 52nd international university power engineering conference UPEC 2017, vol 2017, pp 1–4. https://doi.org/10.1109/UPEC.2017.8231961 Alonso M, Amaris H, Germain JG, Galan JM (2014) Optimal charging scheduling of electric vehicles in smart grids by heuristic algorithms. Energies 7(4):2449–2475. https://doi.org/10. 3390/en7042449 Amin A et al (2020) An integrated approach to optimal charging scheduling of electric vehicles integrated with improved medium-voltage network reconfiguration for power loss minimization. Sustain 12(21):1–15. https://doi.org/10.3390/su12219211 Anastasiadis AG, Polyzakis A, Vokas GA (2018) Economic impact of V2G technology in a smart microgrid. In: AIP conference proceedings, vol 1968. https://doi.org/10.1063/1.5039187 Ayyadi S, Bilil H, Maaroufi M (2019) Optimal charging of electric vehicles in residential area. Sustain. Energy, Grids Networks 19:100240. https://doi.org/10.1016/j.segan.2019.100240 Bagherzadeh E, Ghiasian A, Rabiee A (2020) Long-term profit for electric vehicle charging stations: a stochastic optimization approach. Sustain. Energy, Grids Networks 24:100391. https://doi.org/ 10.1016/j.segan.2020.100391 Bakule L (2008) Decentralized control: an overview. Annu Rev Control 32(1):87–98. https://doi. org/10.1016/j.arcontrol.2008.03.004 Balram P, Le AT, Bertling Tjernberg L (2013) Stochastic programming based model of an electricity retailer considering uncertainty associated with electric vehicle charging. In: International conference European energy market EEM. https://doi.org/10.1109/EEM.2013.6607404 Binetti G, Davoudi A, Naso D, Turchiano B, Lewis FL (2015) Scalable real-time electric vehicles charging with discrete charging rates. IEEE Trans. Smart Grid 6(5):2211–2220. https://doi.org/ 10.1109/TSG.2015.2396772 Bishop JDK, Axon CJ, Bonilla D, Tran M, Banister D, McCulloch MD (2013) Evaluating the impact of V2G services on the degradation of batteries in PHEV and EV. Appl Energy 111:206–218. https://doi.org/10.1016/j.apenergy.2013.04.094

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Chapter 7

Impact of Accurate Forecasting on Optimal Operation of Power System Kailash Chand Sharma and Vivek Prakash

Abstract Accurate prediction of electricity demand and renewable generation such as solar and wind power is required for secure and reliable operation of power systems. However, prediction of uncertain generation and demand parameters is formidable challenging task, and the accuracy of forecasting models is being continuously ameliorated. The prediction error significantly affects the system operation if wind and solar generation penetration are high. Hence, the characterization of prediction error is imperative for the accuracy of the decision-making problems like unit commitment and economic dispatch. This chapter provides a detailed overview of forecasting techniques and simultaneously investigates the effect of prediction error on power system operation through security-constrained unit commitment (SCUC) problem. The SCUC problem with wind generation, solar generation and generic battery energy storage system is modelled through two-stage stochastic programming approach. In the first stage, day-ahead SCUC problem is solved for point forecasts of uncertain parameters while in the second stage real-time SCUC is solved considering wind and solar power uncertainty. Uncertainty is modelled through probabilistic scenarios. The considered SCUC problem is illustrated through practical case studies based on modified IEEE 39 bus system. Numerical results obtained show that high error in forecasting would increase the balancing cost, wind and solar power spillage and load shedding. However, power spillage and shedding can be minimized significantly by the optimal utilization of energy storages. Keywords Forecasting · Wind power · Solar power · Electricity market · Security-constrained unit commitment K. C. Sharma (B) Department of Electrical Engineering, Dr B R Ambedkar National Institute of Technology Jalandhar, Jalandhar, Punjab, India e-mail: [email protected] V. Prakash Faculty of Electrical Engineering and Computing, University of Zagreb, UNSKA ULICA 3, 10000 Zagreb, Croatia e-mail: [email protected] School of Automation, Banasthali Vidyapith, Tonk, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. N. Singh et al. (eds.), Optimal Planning and Operation of Distributed Energy Resources, Energy Systems in Electrical Engineering, https://doi.org/10.1007/978-981-99-2800-2_7

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7.1 Introduction The environmental concerns and rapid depletion of fossil fuels are the driving forces behind increasing renewable power penetration in the electric power systems. Among the renewable energy sources, wind and solar photovoltaic generators are growing rapidly because of their widespread availability, mature technology and excellent infrastructure. However, both wind and solar generations depend on atmospheric conditions and hence are uncertain and variable in nature. Thus, a large-scale grid integration of these generation sources can have the detrimental impact on the power system security and reliability (Hong et al. 2020; Tawn and Browell 2022). The major challenges for power system planning and operations include generation scheduling, tariff structure design, system control and dispatch, reactive power supply, voltage control, energy imbalance service, operating synchronized reserves and supplemental reserves. Any chaos in these will directly influence the power quality, system security and system stability (Zhang et al. 2014). Hence, there is a need for specific technologies to enable smooth and proper integration of wind and solar powers to the grid. In this regard, the high-level forecasting techniques can be an appropriate study area to accurately forecast the value of available wind and solar power to integrate into the respective national grids. Hence, it is high time to analyse and develop mechanisms for efficient integration of wind and solar PV with minimized forecasting errors.

7.2 Wind and Solar Power Forecasting in Power Systems Forecasting is the process in which future predictions are obtained based on the present and historical data, considering trends and seasonality (Hong et al. 2020). In addition to this, any possibility of the upcoming event that might impact the forecast should also be included for accurate forecasting. Forecasting serves as an essential aid for effective and efficient planning irrespective of the scenario or time horizon (Zhang et al. 2014). Figure 7.1 depicts the forecasting concept and forms of forecast. A forecasting task usually involves five necessary steps: (i) problem description, (ii) collecting information, (iii) data analysis, (iv) selection and fitting of forecast model and (v) evaluation of a forecast model. Wind and solar power forecast mean the prediction of the expected generation of one or more wind turbines or solar power in future (Tawn and Browell 2022). Increase in renewable power penetration on the grid has affected various power system operations. Therefore, the power system regulators must prepare detailed scheduled plans and establish the reserve capacity for it. To reduce this reserve capacity and to increase the renewable power penetration, accurate forecasting is a necessity (Zhang et al. 2014). Very short-term and short-term probabilistic forecasting provide highly valuable information on the uncertainty of expected wind and solar generation to the forecast users (Dowell and Pinson 2015). Some of the advantages of accurate forecasting and adequate integration of renewable generation into the power system are (Zhang et al. 2014; Catalao et al. 2010): it lessens

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Fig. 7.1 Forecasting concept and form of forecasts

the need for additional balancing energy in the power system. It reduces the need for reserve power and other ancillary services. It enables better dispatch, scheduling and unit commitment of thermal generators, energy storage plants, hydro plants, etc. and thus enhances power system reliability and stability. It enables more competitive market trading as wind and solar power ramp up and down on the grid. It helps in bringing down the financial and technical risk of uncertainty of wind and solar power generation for all electricity market participants.

7.2.1 Types of Forecasting According to the prediction horizon (Shahidehpour et al. 2002), these forecasting methods are classified into four categories: (i) very short-term forecasting (VSTF), (ii) short-term forecasting (STF), (iii) medium-term forecasting (MTF) and (iv) longterm forecasting (LTF). Power systems find different applications in different time horizons as depicted in Fig. 7.2. Very short-term and short-term forecasting use statistical models for prediction due to less computational time. Medium-term and long-term forecasting use physical and hybrid methods for more accuracy.

7.2.2 Classification of Forecasting Models Based on the methodology, forecasting models are broadly classified into three categories: (a) physical model, (b) statistical models and (c) advanced models as shown in Fig. 7.3. Physical prediction models are based on deterministic methods and use

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Fig. 7.2 Types of forecasting along with propagation of error and accuracy

numerical weather prediction (NWP) model data such as surface roughness, pressure, temperature, scaling of the wind speed within wind farms area (Yunus et al. 2015). NWP models were used by meteorologists for weather prediction. Later, wind speed forecasted by NWP models considering meteorological effects were converted to wind power using wind turbine power curve. These methods are suitable for MTF and LTF due to high execution time and complexity although the accuracy is more. Statistical prediction methods use online measurements of historical wind resource data. Therefore, these models are relatively less complicated, have low computational time and are economical (Zhang et al. 2014). These models are more reliable for VSTF and STF as prediction error increases with prediction time increases. A brief classification of statistical models includes a time series approach and an artificial intelligence approach. Advanced models include multivariate time

Fig. 7.3 Broad classification of forecasting models

7 Impact of Accurate Forecasting on Optimal Operation of Power System

157

series models such as vector autoregressive models and machine learning models (Dowell and Pinson 2015). For accounting the nonlinearity of time series data, various deep learning and artificial neural network (ANN) approach have been developed. A deep learning time series forecasting based on LSTMs, SVRM and EO is introduced (Catalao et al. 2010). In (Sharma et al. 2013), wind speed forecasting in 1 h, in advance horizon is done using a nonlinear autoregressive exogenous model. AI methods are competent in dealing with complex and nonlinear data but don’t give much insight about the structure of the model. The objective of the hybrid model is to combine the traits of different models and obtain a globally optimal prediction performance (Tawn and Browell 2022). A hybrid forecasting system comprising three models (a data pre-processing module, forecasting module, optimization module) is propounded to enhance the wind speed prediction accuracy (Zhang et al. 2014). Individual methods have their own constraints and quality. So, the idea is to get the best information from each model and maximize the available information and integrate them into one. It reduces the uncertainty in modelling the data and enhances the forecasting accuracy. The hybrid model combines the individuality of different models such as combining statistical and physical methods or mix STF and VSTF models (Zhang et al. 2014). Many researchers have worked in this direction and come out with a fruitful result. In (Catalao et al. 2010), three different methods are combined, namely wavelet transform, particle swarm optimization and adaptive network-based fuzzy inference system for short-term wind power forecasting. This model results in less computational time.

7.3 SCUC with Wind–Solar Generation and Energy Storage SCUC refers to the economic scheduling of generating units for serving the hourly load demand while satisfying temporal and operational limits of generation and transmission facilities in power systems (Fu et al. 2013; Gupta et al. 2020a, 2020b; Kushwaha et al. 2022; Shahidehpour et al. 2002; Wen et al. 2015; Day-Ahead and Real-Time Energy Markets Data). In evolving power systems, SCUC is utilized by the independent system operators and transmission system operators to clear electricity markets in day-ahead and real-time frameworks.

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7.3.1 Mathematical Formations 7.3.1.1

Day-Ahead SCUC Formulation

The objective of day-ahead SCUC model given in (1) is to minimize the total operation costs, including energy production cost, no-load cost, start-up cost and shutdown cost, for supplying forecasted hourly demand over the 24-h time horizon. The first two terms of (7.1) indicate the operation cost related to conventional generation sources, and the last two terms discuss the hourly energy storage and production cost for energy storage systems. (a) Objective function: ⎧  ⎫ I K   ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ Ck,i,t Pk,i,t ⎪ Cimin ui,t + Cisu yi,t + Cisd zi,t + ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ i=1 ⎪ ⎪ k=1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ T ⎨ I S ⎬    up up



p p dn dn st st min TC = + Ci Ri,t + Ci Ri,t + Cs,t Ss,t + Cs,t Ss,t ⎪ ⎪ ⎪ t=1 ⎪ s=1 i=1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ S ⎪ ⎪  ⎪ ⎪ str str

⎪ ⎪ pr pr ⎪ ⎪ C S + C S + ⎪ ⎪ s,t s,t s,t s,t ⎭ ⎩ s=1

(7.1) (b) Up- and Downtime constraints of conventional generators Constraints (7.2)–(7.11) are framed to consider the generator minimum up and downtime for the considered scheduling horizon. These constraints are similarly framed as discussed in our previous work (Eckroad and Gyuk 2003). yi,t − zi,t = ui,t − ui,t−1 ∀i, ∀t

(7.2)

yi,t + zi,t ≤ 1 ∀i, ∀t

(7.3)

up,min



1 − ui,t = 0 ∀i

Li

(7.4)

t=1 t+UT i −1

up,min

ui,t ≥ UTi yi,t ∀tt = Li

+ 1 . . . T − UTi + 1

(7.5)

tt=t T  tt=t

ui,t − yi,t ≥ 0, ∀tt = T − UTi + 2 . . . T

(7.6)

7 Impact of Accurate Forecasting on Optimal Operation of Power System

159

   = max 0, min T , UTi − Ui0 ui,t=0

(7.7)

up,min

Li

Lidown,min



ui,t = 0, ∀i

(7.8)

1 − ui,t ≥ DTi zi,t ∀tt = Ldown,min + 1 . . . T − DTi + 1 i

(7.9)

t=1 t+DT i −1 tt=t T 

1 − ui,t − zi,t ≥ 0, ∀tt = T − DTi + 2 . . . T

(7.10)

tt=t



   min Ldown, = max 0, min T , DTi − Si0 1 − ui,t=0 i

(7.11)

(c) Scheduling and ramping constraints The scheduled, maximum and minimum generator output along with the ramp up and ramp down limits in each hour for all the generators are given in (7.12)–(7.15). Pi,t =

K 

Pk,i,t , ∀i, ∀t

(7.12)

k=1

Pimin ui,t ≤ Pi,t ≤ Pimax ui,t , ∀i, ∀t

(7.13)



Pi,t−1 − Pi,t ≤ RDi ui,t + SDi 1 − ui,t , ∀i, ∀t

(7.14)



Pi,t − Pi,t−1 ≤ RUi ui,t−1 + SUi 1 − ui,t−1 , ∀i, ∀t

(7.15)

(d) Line flow constraints The power flow constraints and line flow limits are given by (7.16)–(7.17) as PFl,t = PFn,b,t =

  δn,t − δb,t /Xn,b Sbase , ∀l, n = b, ∀t

−fl max ≤ PFl,t ≤ flmax , ∀l, ∀t

(7.16) (7.17)

(e) Reserve capacity Constraints The available reserves for balancing services such as constraints on maximum and minimum limits for reserves available, limit of generator ramping for reserves are formulated using constraints (18)-(21). up

Pi,t + Ri,t ≤ Pimax ui,t , ∀i, ∀t

(7.18)

160

K. C. Sharma and V. Prakash min Pi,t − Rdn i,t ≥ Pi ui,t , ∀i, ∀t

(7.19)

up,min

, ∀i, ∀t

(7.20)

dn,max Rdn,min ≤ Rdn , ∀i, ∀t i,t ≤ Ri i

(7.21)

Ri

up

up,max

≤ Ri,t ≤ Ri

(f) Storage Constraints Constraints (7.22)–(7.27) discuss the energy storage constraints. The constraint (7.22) discusses the state-of-charge (SOC), SOCs,t of storage unit, at the end of the hour t depends on the previous hour SOC, the power storage and production efficiencies, respectively. Equation (7.23) provides the minimum and maximum SOC limits for each time interval. Equations (7.24) and (7.25) discuss the power storage and production limits of the storage units. st SOCs,t = SOCs,t−1 + ηss Ss,t −

1 p p Ss,t , ∀s, ∀t ηs

(7.22)

SOCmin ≤ SOCs,t ≤ SOCmax s s , ∀s, ∀t

(7.23)

st,max st 0 ≤ Ss,t ≤ Ss,t , ∀s, ∀t

(7.24)

p

p,max

0 ≤ Ss,t ≤ Ss,t

, ∀s, ∀t

(7.25)

Constraint (7.26) limits the minimum and maximum energy capacities of the storage units while constraints (7.27) and (7.28) give the available power storage and production limits in a day ahead framework. SOCmin,e ≤ SOCs,T ≤ SOCmax,e , ∀s, T s s

(7.26)

str 0 ≤ Ss,t ≤ SOCmax − SOCs,t , ∀s, ∀t s

(7.27)

pr

p

0 ≤ Ss,t ≤ SOCs,t − Ss,t , ∀s, ∀t (g) Power Balance constraints I 

Pi,t +

w∈wn

i∈in

+

W 

W df

Pw,t +

PV  pv∈pvn

PV f

Ppv,t +

I  up

Ri,t − Rdn i,t i∈in

S S   st str p

pr

Ss,t − Ss,t + Ss,t − Ss,t − PDn,t s∈sn

s∈sn

(7.28)

7 Impact of Accurate Forecasting on Optimal Operation of Power System

=

L 

PFl,t , ∀n, ∀t

161

(7.29)

l∈ ln

Constrain (7.29) provides the scheduled power balancing constraint for each hour. The power generation from conventional sources and renewable sources like wind and solar generators along with the available power from the storage units are used to serve the hourly demand.

7.3.2 Real-Time SCUC Formulation The real-time formulation of SCUC framework is modelled considering the uncertain generation characteristics of wind and PV power sources. The stochastic parameters are represented using the probabilistic scenarios w. The accommodation of scheduled generation to fulfil the real-time demand and the uncertain generation characteristics incurs additional cost. The additional cost includes the spinning reserve cost for each scenario and storage devices to replace the thermal generators. (a) Objective function Min BC = ⎧ I ⎫⎤ ⎡ S    ⎪   up ⎪ ⎪ ⎪ pr pr str ˆ str ⎪ Ci Rˆ i,t,ω + Cidn Rˆ dn Cs,t Ss,t,ω + Cs,t Sˆ s,t,ω ⎪ ⎥ ⎢N ⎪ ⎪ i,t,ω + ⎪ ⎪ T ω ⎢ ⎨ ⎬⎥  ⎢ s=1 i=1 ⎥ πω N ⎥ ⎢ W PV ⎥ ⎢ ⎪ ⎪    ⎪ ⎪ t=1 ⎣ ω=1 spill spill ⎪ ⎪ ⎦ shed shed spill spill ⎪ ⎪ C PD + C PW + C PV ⎪ ⎪ w,t,ω pv,t,ω n,t,ω w pv ⎩ ⎭ n=1

w=1

pv=1

(7.30) The objective function for real-time SCUC demonstrated in (7.30) includes the first term as up and down spinning reserve cost, the second term depicts the storage cost as reserve, the third term includes the cost of load shedding and last terms includes the cost associated with wind and solar power availability for each scenario. (b) Generator scheduling and reserve constraints Constraints (7.31)–(7.33) provide the generator scheduling for each scenario ω, and the limits of real-time generator power variation ramp up and ramp down for the reserve services. up Pˆ i,t,ω = Pi,t + Rˆ i,t,ω − Rˆ dn i,t,ω , ∀i, ∀t, ∀ω

(7.31)

162

K. C. Sharma and V. Prakash up up 0 ≤ Rˆ i,t,ω ≤ Ri,t , ∀i, ∀t, ∀ω

(7.32)

dn 0 ≤ Rˆ dn i,t,ω ≤ Ri,t , ∀i, ∀t, ∀ω

(7.33)

(c) Storage constraints Constraints (7.34)–(7.36) provide the real-time power storage and production constraints of storage units including the SOC for each scenario w for each time interval. st st 0 ≤ Sˆ s,t,ω ≤ Ss,t , ∀s, ∀t, ∀ω

(7.34)

p p 0 ≤ Sˆ s,t,ω ≤ Ss,t , ∀s, ∀t, ∀ω

(7.35)

    1 ˆp 1 p s ˆ st s st ˆ ˆ + η SOC S S S − = SOC + η S − s,t,ω s,t−1,ω p s,t p s,t,ω , ∀s, ∀t, ∀ω s s,t,ω s s,t ηs ηs (7.36) (d) Wind and PV Power Constraints The wind and PV power spillage constraints for each scenario w along with the load shedding at bus n is given by (7.37)–(7.39). spill

0 ≤ PWw,t,ω ≤ PWw,t,ω , ∀w, ∀t, ∀ω

(7.37)

n spill

0 ≤ PVpv,t,ω ≤ PVpv,t,ω , ∀pv, ∀t, ∀ω

(7.38)

shed ˆ n,t,ω , ∀n, ∀t, ∀ω 0 ≤ PDn,t,ω ≤ PD

(7.39)

(e) Power Balance constraint The real-time power balance constraint for each scenario w is shown in (40) which includes the power output I  i∈in

Pi,t +

S 

W  p

st Ss,t − Ss,t + PWw,t,ω

s∈sn

+

PV  pv∈pvn

w∈wn

PVpv,t,ω +

I    up Rˆ i,t,ω − Rˆ dn i,t,ω i∈in

7 Impact of Accurate Forecasting on Optimal Operation of Power System

+

S  W    spill p st Sˆ s,t,ω − Sˆ s,t,ω − PWw,t,ω s∈sn

−

163

PV 

w∈wn shed ˆ n,t,ω + PDn,t,ω PVpv,t,ω − PD spill

pv∈pvn

=

L 

ˆ l,t,ω , ∀n, ∀t, ∀ω PF

(7.40)

l∈ ln

7.4 Case Study 7.4.1 Input Data and Test System The modified IEEE 39 bus system is considered in this study to carry out the case studies. The bus system consists of ten conventional generators along with three wind and three PV generators with individual capacity of 472 MW. The peak demand of system is 5896.6 MW. The maximum of 40% penetration of renewable generation is considered for this study. Two storage units of 300 MW each are included. Figure 7.4 depicts the single line diagram of the modified IEEE 39 bus system. Table 7.1 details out the other parameters specific to the generators, and Table 7.2 shows the transmission line data used in this study (Prakash et al. 2017).

7.4.2 Wind, Solar Power and Demand Forecast Results Figure 7.5 provides the forecasting results of three PV generators, three wind generators and the demand profile for 24-h time horizon. The historical data for renewable generation and demand forecasting is collected from Australian Electricity Market Operator and ISO New England data portals, respectively (Dowell 2015; Pozo et al. 2014). The variable nature of wind, PV and demand profile can be observed with frequent up and down ramping of the power profiles. This indicates the complexity of maintaining the system operation with bulk penetration of renewable generation sources. The accurate forecasting of these resources along with demand is a must need to maintain the system parameters like voltage and frequency within the permissible operating range. In Fig. 7.6, 80% confidence interval is considered for the prediction of wind and PV power plant profiles separately. The wind power profiles for w1 , w2 and w3 and PV power profiles pv1 , pv2 and pv3 shown in Fig. 7.6 show the mean, upper and lower bounds of prediction power profiles with 20% prediction error. Hence, this error will impact the overall decision-making for the system operation

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Fig. 7.4 Single line diagram of modified IEEE 39 bus system (Prakash et al. 2017)

and cause additional cost for the grid balancing resources such as additional reserves or storage units.

7.4.3 Case Study In this work, three cases are considered as: Case 1: SCUC without RE generation and energy storage. Case 2: SCUC with RE generation and without energy storage. Case 3: SCUC with RE generation and energy storage.

158.2

ci

19.58 155.6

22.54 192.4

10 Interconnection 0.00951 to rest of USA/Canada

Nuclear

9

16.51 102.7

23.23 139.4

0.10908

0.00211

0.0048

Fossil

Nuclear

7

8

75

65

77

93

75 200

65 160

77

93

159 159 400

up,max

RUi RDi Ri

508 508 1000

260 260

169 169

107 107 120

17.87 101.75 129 129

0.000079 21.62 180.29

0.00056

Fossil

Nuclear

171.6

5

23.9

20.81 104.97

21.05 313.6

21.6

bi

6

0.00039

0.0007

Nuclear

Fossil

3

4

0.00043

0.00063

Hydro

Nuclear

1

2

ai

Type

i

Table 7.1 Generator data

1000

120

200

160

400

24

18

23

24

22

up

Rdn,max Ci i Pimax 398

8

107

−23 1320

1030

1 1

1117 1

649

676.8 265

−16 696

1

1

1

3

3

6

1

−22 609.6 183 824.4 294

5 5

1

187

8

−24 782.4 156

870

1 1

8

2

1

1

3

3

6

5

5

8

8

1

1

1

3

3

6

5

5

8

8

1

1

3

3

6

5

5

8

70

80

50.6 850

50.6 850

42.6 870

113.5 850

141.5 850

56.6 850

45

45

35

50

60

55

55

45

60

80

10 130

47.1 850 240 250

57.1 850

50.6 900 130 140

90 110

Cidn SDi SUi 42.6 800

up

Pimin Ui0 UTi DTi Ui,t=0 Si0 Ci

775.2 228

−22 1240

Cidn

7 Impact of Accurate Forecasting on Optimal Operation of Power System 165

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K. C. Sharma and V. Prakash

Table 7.2 Transmission line data (Prakash et al. 2017) Bus

X (pu)

1

2

0.0411

600

Bus

Limit (MW)

Bus

Bus

X (pu)

14

15

0.0217

Limit (MW) 600

1

39

0.025

1000

15

16

0.0094

600

2

3

0.0151

500

16

17

0.0089

600

2

25

0.0086

500

16

19

0.0195

600

2

30

0.0181

900

16

21

0.0135

600

3

4

0.0213

500

16

24

0.0059

600

3

18

0.0133

500

17

18

0.0082

600

4

5

0.0128

600

17

27

0.0173

600

4

14

0.0129

500

19

20

0.0138

900

5

6

0.0026

1200

19

33

0.0142

900

5

8

0.0112

900

20

34

0.018

900

6

7

0.0092

900

21

22

0.014

900

6

11

0.0082

480

22

23

0.0096

600

6

31

0.025

1800

22

35

0.0143

900

7

8

0.0046

900

23

24

0.035

600

8

9

0.0363

23

36

0.0272

900

900

9

39

0.025

900

25

26

0.0323

600

10

11

0.0043

600

25

37

0.0232

900

10

13

0.0043

600

26

27

0.0147

600

10

32

0.02

900

26

28

0.0474

600

12

11

0.0435

500

26

29

0.0625

600

12

13

0.0435

500

28

29

0.0151

600

13

14

0.0101

600

29

38

0.0156

1200

Table 7.3 shows the SCUC results for all the three cases. It is observed that the overall cost of system operation is lowest for the Case 3. This is because of the minimum commitment of the conventional generation sources and high penetration of RE generation sources with negligible operation cost. However, balancing cost is highest for Case 3. This cost is high because of the uncertain characteristics of RE generation, additional storage capacity is required to maintain the system security and reliability. The balancing cost requirement for each scenario and for all the considered cases is also given. Further, the line power flow limits are also analysed, and impact of high penetration of wind and PV power is observed for all the three cases. Figure 7.7 depicts that the line congestion can be minimized with combining the energy storage with high penetration of renewable generation. The line congestion is high in case of the system with conventional and renewable generation. Hence, storage units are advantageous for the optimal power flow.

7 Impact of Accurate Forecasting on Optimal Operation of Power System

Fig. 7.5 Forecasted demand, solar and wind power

Fig. 7.6 Forecasted wind and PV power for each scenario for 20% confidence interval

167

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Table 7.3 Operation cost analysis for each scenario for the considered cases Cases

TC

BC

BC(ω1 )

BC(ω2 )

BC(ω3 )

Case 1

2.7120E + 6

1.0969E + 6

−6432.931

3.0678E + 5

4.5708E + 6

Case 2

2.3683E + 6

1.5374E + 5

9561.981

−2009.857

7.6517E + 5

Case 3

2.1566E + 6

1.1999E + 5

40646.662

9135.500

5.3189E + 5

Fig. 7.7 Line power limits for the considered cases

The characteristics of both storage units considered for this study are shown in Fig. 7.8. The parameters like SOC, power storage, power production for 24-h time horizon is depicted. It is observed that when there is high available power generation from wind and PV generators as shown in Fig. 7.5, the storage units are charging and for low output from renewable units’ storage is discharging and providing the grid balancing service.

7.4.4 Impact of Forecasting Errors on Balancing Cost This study considers the 80% confidence interval for the prediction of wind, PV power and demand to show the impact of prediction error on the system operation. It is observed from the Fig. 7.9 that as the prediction error increases the balancing cost increases rapidly. Hence, the prediction with 20% error has high balancing cost because of the addition cost for the balancing resources like energy storage. If the prediction error is below 5%, the required resources for system operation can be estimated accurately in day-ahead for the real-time operation. Hence, the under- or

7 Impact of Accurate Forecasting on Optimal Operation of Power System

169

Fig. 7.8 Storage units state-of-charge, storage and production for DASCUC (Case 3)

Fig. 7.9 Impact of error on system balancing cost

overestimation of the required resources can be avoided with significant monetary benefits.

7.5 Conclusion This book chapter details out the fundamental concepts of forecasting in the modern renewable rich power systems. The challenges associated with the uncertain renewable generation such as wind and PV generation and their impact on the optimal operation of the power system are discussed in SCUS framework. It is observed that use of energy storage devices with high penetration of wind and PV generation

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can reduce the overall system cost significantly and reduce the line congestion. The techno-economic studies carried out in this chapter with three different cases and provides an understanding of system operation requirements with 20% prediction error and 40% renewable penetration. The role and value of energy storage systems to maintain the optimal system operation are analysed. It is observed that energy storage is one of the potential and optimal solution to counter the uncertainty and variability in the renewable-rich power systems.

Appendix A: Notations The notations used in this chapter are described below: Set and Indices i∈I

Index/set of conventional generators

s∈S

Index/set of energy storages

w∈W

Index/set of wind generators

pv ∈ PV

Index/set of solar photovoltaic generators

t, tt ∈ T

Index/set of time period

k∈K

Index/set of cost function block of conventional generators

n, b ∈ N

Index/set of buses/nodes

l∈L

Index/set of transmission line

ω ∈ Nω

Index/set of scenarios

Scalars and Parameters Ck,i,t Cisu /Cisd Cimin st Cs,t p

Cs,t up

Ci /Cidn pr

str /C Cs,t s,t up dn Cˆ i,t,ω /Cˆ i,t,ω prs str /C ˆ s,t Cˆ s,t

C ls C ws C pvs

  Cost of k th segment of i th generator at time t $/MWh   Start-up/shut-down cost of i th generator $/h   Minimum cost of i th generator $/h   Cost of storage for energy storage unit s at time t $/MWh   Cost of production for energy storage unit s at time t $/MWh   Cost of upward/downward reserve of i th generator $/MWh Cost of reserve storage/production for energy storage unit s at time   t $/MWh Cost of real-time upward/downward reserve of i th generator at time   t $/MWh Cost of real-time reserve storage/production for energy storage unit s   at time t $/MWh   Cost of load shedding $/MWh   Cost of wind power spillage $/MWh   Cost of solar PV power spillage $/M W h (continued)

7 Impact of Accurate Forecasting on Optimal Operation of Power System

171

(continued) Set and Indices RDi /RUi

Ramp up and down limit for i th generator [MW/h]

SDi /SUi

Shut-down/start-up ramp limit for i th generator [MW/h]

Pimin /Pimax up,min up,max Ri /Ri Rdn,max /Rdn,min i i

Minimum/maximum output limit of ith generator [MW]

st,max Ss,t

Maximum power storage for storage unit s[MW]

p,max Ss,t SOCsmin /SOCsmax SOCsmin,e /SOCsmax,e

Maximum power production for storage unit s[MW]

flmax

Maximum power flow limit for lth transmission line [MW]

PDn,t

Forecasted power demand at nth bus at time t[MW]

W df Pw,t

Forecasted power output of wth wind generator at time t[MW]

PV f Ppv,t

Forecasted power output of pvth solar generator at time t[MW]

ˆ n,t,ω PD Pˆ w,t,ω

Real-time power demand at nth bus at time t and scenario ω[MW]

Pˆ pv,t,ω

Real-time power output of pvth solar generator at time t and scenario ω[MW]

p

Minimum/maximum limit for upward reserve of ith generator [MW] Minimum/maximum limit for downward reserve of ith generator [MW]

Minimum/maximum state-of-charge for storage unit s[MWh] Minimum/maximum state-of-charge for storage unit s at the end of study horizon [MWh]

Real-time power output of wth wind generator at time t and scenario ω[MW]

ηsst /ηs

Efficiency rate to produce and storage energy for storage unit s

πω

Occurrence probability of scenario ω

Sbase

Base MVA of the system

Xn,b

Reactance of transmission line connected between bus n and b (pu)

Variables Pk,i,t

Scheduled generation for kth segment of ith generator at time t[MW]

Pi,t

Scheduled generation of ith generator at time t[MW]

Pi,t,ω

Real-time scheduled generation of ith generator at time t and scenario ω[MW] p

st /S Ss,t s,t

Scheduled power storage/production of storage unit s at time t[MW]

SOCs,t

State-of-charge for storage unit s at the end of time t[MW]

SOCs,t,ω

Real-time state-of-charge for storage unit s at the end of time t and scenario ω[MW]

up

Ri,t /Rdn i,t

Scheduled up/down reserve for generator i at time t[MW]

str /S pr Ss,t s,t ˆRup /Rˆ dn i,t,ω i,t,ω

Scheduled reserve storage/production of storage unit s at time t[MW] Real-time scheduled up/down reserve for generator i at time t and scenario ω[MW] (continued)

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K. C. Sharma and V. Prakash

(continued) Set and Indices p st /S ˆ s,t,ω Sˆ s,t,ω

Real-time scheduled reserve storage/production of storage unit s at time t and scenario ω[MW]

shed PDn,t,ω

Real-time load shedding at bus n at time t and scenario ω[MW]

spill PWw,t,ω spill PVpv,t,ω

Real-time wind power spillage at bus n at time t and scenario ω[MW]

Pw,t

Scheduled power output of wth wind generator at time t[MW]

Ppv,t

Scheduled power output of pvth solar generator at time t[MW]

PFn,b,t

Power flow in transmission line connected between n and b bus (n = b) at time t[MW]

ˆ n,b,t,ω PF

Real-time flow in transmission line connected between n and b bus (n = b) at time t and scenario ω[MW]

ui,t

ON/OFF status of generator i at time t [1/0]

yi,t

Start-up status of generator i at time t [1 = start, 0 = not start]

zi,t

Shut-down status of generator i at time t [1 = shutdown, 0 = not shutdown]

TC

Total cost or objective function variable for day-ahead market ($)

BC

Balancing cost or objective function variable for real-time market ($)

δn,t , δb,t

Bus angle at bus n and b at time t[radian]

Real-time PV power spillage at bus n at time t and scenario ω[MW]

7 Impact of Accurate Forecasting on Optimal Operation of Power System

173

References Catalao JPS, Pousinho HMI, Mendes VMF (2010) Hybrid wavelet-PSO-ANFIS approach for shortterm wind power forecasting in Portugal. IEEE Trans Sustain Energy 2(1):50–59 Day-ahead and real-time energy markets data, ISO New England [Online]. Available: https://www. iso-ne.com/markets-operations/markets/da-rt-energy-markets Dowell J (2015) Australian electricity market operator (AEMO) 5 minute wind power data. University of Strathclyde, [Online]. Available: https://bit.ly/38vReAU Dowell J, Pinson P (2015) Very-short-term probabilistic wind power forecasts by sparse vector autoregression. IEEE Trans Smart Grid 7(2):763–770 Eckroad S, Gyuk I (2003) EPRI-DOE handbook of energy storage for transmission & distribution applications, pp 3–35 Fu Y, Li Z, Wu L (2013) Modeling and solution of the large-scale security-constrained unit commitment. IEEE Trans Power Syst 28(4):3524–3533 Gupta PP, Jain P, Sharma KC, Bhaker R (2020a) Optimal scheduling of electric vehicle in stochastic AC SCUC problem for large-scale wind power penetration. Int Trans Electr Energy Syst 30(4):e12145 Gupta PP, Jain P, Kalkhambkar V, Sharma KC, Bhakar R (2020b) Stochastic security-constrained unit commitment with battery energy storage and wind power integration. Int Trans Electr Energy Syst 30(10):e12556 Hong T, Pinson P, Wang Y, Weron R, Yang D, Zareipour H (2020) Energy forecasting: a review and outlook. IEEE Open Access J Power Energy 7:376–388 Jiang C, Mao Y, Chai Y, Yu M, Tao S (2018) Scenario generation for wind power using improved generative adversarial networks. IEEE Access 6:62193–62203 Kushwaha P, Prakash V, Bhakar R, Yaragatti UR (2022) Synthetic inertia and frequency support assessment from renewable plants in low carbon grids. Electr Power Syst Res 209:107977 Pozo D, Contreras J, Sauma EE (2014) Unit commitment with ideal and generic energy storage units. IEEE Trans Power Syst 29(6):2974–2984 Prakash V, Sharma KC, Bhakar R, Tiwari HP, Li F (2017) Frequency response constrained modified interval scheduling under wind uncertainty. IEEE Trans Sustain Energy 9(1):302–310 Shahidehpour M, Yamin H, Li ZY (2002) Market operations in electric power systems. Wiley, USA, NY, New York Sharma KC, Jain P, Bhakar R (2013) Wind power scenario generation and reduction in stochastic programming framework. Electr Power Compon Syst 41(3):271–285 Tawn R, Browell J (2022) A review of very short-term wind and solar power forecasting. Renew Sustain Energy Rev 153:111758 Wen Y, Guo C, Pandži´c H, Kirschen DS (2015) Enhanced security-constrained unit commitment with emerging utility-scale energy storage. IEEE Trans Power Syst 31(1):652–662 Wu T, Zhang YJA, Wang S (2021) Deep learning to optimize: security-constrained unit commitment with uncertain wind power generation and BESSs. IEEE Trans Sustain Energy 13(1):231–240 Yunus K, Thiringer T, Chen P (2015) Member, ARIMA-based frequency—decomposed modeling of wind speed time series. IEEE Trans Power Syst 31:2546–2556 Zhang Y, Haghani A, Zeng X (2014) Component GARCH models to account for seasonal patterns and uncertainties in travel-time prediction. IEEE Trans Intell Transp Syst 16(2):719–729 Zhang Y, Wang J, Wang X (2014) Review on probabilistic forecasting of wind power generation. Renew Sustain Energy Rev 32:255–270

Chapter 8

Optimal Design and Analysis of Standalone Hybrid Renewable Energy Sources Sachin Jain and Venu Sonti

Abstract The usage of renewable energy sources (RES) with energy storage like battery for standalone household and commercial purposes is becoming popular. These systems have to manage the given amount of generated energy from the RES. In this chapter, an efficient hybrid standalone system with its power management strategy has been developed. The proposed configuration is implemented to deal with the intermittent nature of the energy generated by the photovoltaic (PV) array and processing its power through a parallel combination of two converters. The two converters consist of a DC-DC boost converter and a buck converter. Apart from processing the PV power, the battery power is processed through a DC-DC fullbridge converter to maintain the required load voltage. A load voltage-based power management scheme along with the maximum power extraction from the PV source is also proposed in this chapter. The proposed power management scheme has been verified with both simulation and experimental results. An experimental prototype with a capacity of 150 watts is also made to show the different modes of operation of the proposed system and its smooth transition between various modes. Keywords Renewable energy systems · Power management scheme · Photovoltaic systems · Maximum power point extraction

8.1 Introduction In the present era, the world is highly dependent on fossil fuel resources. Fossil fuels are getting depleted day by day, and above all, these resources are scarce. Hence, there is a need for an alternate reliable energy source with no greenhouse effect. Among the non-conventional sources of energy, the solar photovoltaic (SPV) (Philip et al. 2016; Alam et al. 2016) is more popular, due to its features like low maintenance cost, zero noise operation, and free abundant Sun (solar) energy. However, the cost of SPV cells is high, which makes the installation cost bit expensive. Still, due to S. Jain · V. Sonti (B) Department of Electrical Engineering, NIT Raipur, Raipur, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. N. Singh et al. (eds.), Optimal Planning and Operation of Distributed Energy Resources, Energy Systems in Electrical Engineering, https://doi.org/10.1007/978-981-99-2800-2_8

175

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the advancements in solar cell manufacturing technology the cost of production has declined considerably. Hence, nowadays solar power generation growing at a faster rate. However, the power output from the solar PV is not reliable, as it depends on environmental conditions. Thus, the solar PV systems need to be supported with other renewable sources (Abeywardana et al. 2017; Herrera et al. 2016; Pan et al. 2016; Wang and Zhang 2016) like battery and fuel cell to increase their reliability. Therefore, such hybrid solar PV systems are becoming more popular in remote locations, where the grid supply is inaccessible. The hybrid solar PV systems when used in remote areas in the absence of a grid are referred as standalone PV systems. This chapter gives the detailed working principle, operation, and design of the PV battery-based standalone systems. The circuit schematic for the employed configuration for the standalone system (Dhara et al. 2018; Jain et al. 2021) is shown in Fig. 8.1. The objective of the given system is to extract the maximum power from the solar PV source to meet all requirements of the standalone load. The given hybrid standalone system consists of three power conversion stages: power conversion stage A, power conversion stage B, and power conversion stage C. The power conversion stage A is used to step up the input voltage available from the PV source along with the extraction of maximum power from the PV source. Therefore, a boost converter with a maximum power point tracking (MPPT) algorithm is used. The power conversion stage B is used to charge the battery from the PV source. As the operating voltage PV source is higher than the battery voltage, therefore a buck converter is employed. The employed buck converter takes care of the excess PV power by charging the battery. The power conversion stage C is used to deliver the deficit power from the standard battery to the load. An isolated full-bridge DC-DC converter with battery discharging can be used. Detailed working and control strategy for the three converters are as follows.

8.1.1 Power Conversion Stage A: Boost Converter The boost converter is a non-isolated step-up DC to DC converter. It is widely used in the PV-based standalone system as it boosts the input voltage to a higher level. In the proposed system, the boost converter operates on MPPT algorithm to extract the maximum power from the PV source. In Fig. 8.2, the circuit diagram for the boost converter with its control scheme (Jain et al. 2021) is shown. In the control scheme, a P&O algorithm has been used to generate the voltage reference (V ref ) and further the PV voltage is being controlled with linear regulator PI through duty reference. In Table 8.1, (Dhara et al. 2018; Jain et al. 2021) the list of components for building a boost converter is mentioned. Now to build an experimental prototype, a PCB is designed and fabricated with components mentioned in Table 8.1. Figure 8.3a (Dhara et al. 2018; Jain et al. 2021) shows the experimental waveforms of input voltage (V PV ), inductor current (I L ), load output voltage (V LOAD ), and diode blocking voltage (V DBOOST ) for the boost converter. It can be observed from Fig. 8.3a that the input voltage (V PV ) of the

8 Optimal Design and Analysis of Standalone Hybrid Renewable Energy …

177

Power Conversion Stage A IPV

DPV

L1 CPV

VPV

PV

Dboost Sw1

VL

Co1

RLoad

Lbd L2

Lbc

Rb

IB

EB

Sw2

S1

S3 D1 N1

CB

Dbuck

Co2

N2

S4 D2

S2 Power Conversion Stage B

D3

D4

Power Conversion Stage C

Fig. 8.1 Given hybrid standalone configuration used for extracting the maximum power from the standalone hybrid PV system IPV

DPV

ILBOOST L1

Dboost VDBOOST

CPV PV

VPV IPV

P&O

VPV

Sw1

VPV

+ -

Vref

Co1

VL

RLoad

PI Sw1

Fig. 8.2 Boost converter with MPPT control Table 8.1 Parameters used for boost converter

1

PV capacitance C PV

1 mF, 315 V

2

Load capacitor (C o1 )

1 mF, 315 V

3

Load resistance (RLoad )

500 Ω

4

Boost inductor (L 1 )

1.5 mH

5

MOSFET:47N60C3

600 V, 47 A

6

Diode: DPH30IS600HI

600 V, 30 A

7

Switching frequency

20 kHz

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S. Jain and V. Sonti

VPV (40 V/DIV)

VPV (40 V/DIV)

ILBOOST (1 A/DIV)

IPV (500 mA/DIV)

VLOAD (100 V/DIV)

(a)

VDBOOST (50V/DIV)

PPV (5 W/DIV)

(b)

Fig. 8.3 Experimental waveforms of a input voltage (V PV ), inductor current (I L ), load output voltage (V LOAD ), and diode blocking voltage (V DBOOST ) for the boost converter b input voltage V PV , input current I PV and PV Power PPV for MPPT operation

converter is 40 V, whereas the output voltage (V LOAD ) of the converter is 100 V. From the nature of inductor current (I LBOOST ), it can be stated that the converter is working in discontinuous current conduction mode (DCM). Now, in Fig. 8.3b (Dhara et al. 2018; Jain et al. 2021) the working of the MPPT algorithm is shown. The current reference for PV source is gradually changed with respect to time. It can be observed that the tracking of PV power is also varied according to the change in current reference.

8.1.2 Power Conversion Stage B: Buck Converter The buck converter is a non-isolated step-down DC to DC converter. The voltage of the battery is much lower than the operating voltage of the PV panels. So, there is a requirement for stepping down the voltage in case of battery charging from a PV panel. The circuit diagram of the buck converter along with its control scheme (Jain et al. 2021) is shown in Fig. 8.4. The battery charging reference is activated when the load voltage (V LOAD ) is crossed the limit of V DC_High . After that, the current reference for battery charging will be varied proportionately with the difference between V DC(ref_High) . According to the current reference, duty cycle for the buck converter is generated. Table 8.2 (Dhara et al. 2018; Jain et al. 2021) gives the specifications of the components for building the buck converter. Experimentation has been performed for the validation of the converter. Figure 8.5 (Dhara et al. 2018; Jain et al. 2021) shows the experimental waveforms of output voltage (V OBUCK ), inductor current (I LBUCK ), and diode blocking voltage (V DBUCK ) for the buck converter along with battery charging control. It can be observed that the nature of inductor current (I LBUCK ) is also working in discontinuous current conduction mode (DCM).

8 Optimal Design and Analysis of Standalone Hybrid Renewable Energy … DPV

ILBUCK

L2

Lbc

179

Rb IB

CPV

VDC_High VLOAD

VDBUCK

PV

Electronic Load

CB

Dbuck

Compare

IBMAX

VDC(ref_High) (Battery Charging)

Sw2

+ -

PI

X

VLOAD

iref

+ -

PI Sw2

ILBUCK

Fig. 8.4 Buck converter with battery charging control

Table 8.2 Specifications of the components for a buck converter

1

PV capacitance (C PV )

1 mF, 315 V

2

Load capacitor (C o1 )

1 mF, 315 V

3

Load

150 Ω

4

Buck inductor (L 2 )

800 uH

5

Charging filter inductor (L bc )

10 uH

5

MOSFET:47N60C3

600 V, 47 A

6

Diode: DPH30IS600HI

600 V, 30 A

7

Switching frequency

10 kHz

VOBUCK (20 V/DIV)

ILBUCK (500 mA/DIV)

VDBUCK (50V/DIV)

Fig. 8.5 Experimental waveforms of output voltage (V OBUCK ), inductor current (I LBUCK ), and diode blocking voltage (V DBUCK ) for the buck converter

8.1.3 Power Conversion Stage C: Full-Bridge Converter The full-bridge converter is an isolated step-up DC to DC converter. The main motivation behind the use of this converter is to obtain a very high gain at the output voltage.

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The requirement of this converter is raised in connection to deliver the power from a standard battery (max. voltage level 48 V) to a DC bus, which is being used to deliver power to a single-phase inverter. In the proposed system, a full-bridge converter is used to supply the deficient power from the battery to load. The circuit diagram of the full-bridge converter and its control scheme (Jain et al. 2021) is shown in Fig. 8.6. The battery discharging reference is activated when the load voltage (V LOAD ) is crossed the limit of V DC_Low. After that, the phase reference for battery discharging will be varied proportionately with the difference between V DC ( ref_Low ) . According to the phase reference, the PWM for all four switches will be generated. Table 8.3, (Dhara et al. 2018; Jain et al. 2021) gives the specifications of components for building the full-bridge converter. Experimentation has been performed for the validation of the full-bridge converter with battery discharging control. Figure 8.7 (Dhara et al. 2018; Jain et al. 2021) shows the primary input voltage (V PFBRIDGE ), input current (I PFBRIDGE ), secondary output voltage (V SFBRIDGE ), and output current (I SFBRIDGE ) of the transformer. It can also be observed that the nature of transformer input current is also discontinuous in nature. Lbd

IB

S1

S3 D1

VPFBRIDGE

EB

VLOAD VDC(ref_Low)

N2 VSFBRIDGE

N1

S4

S2 VDC_Low VLOAD

D3

ISFBRIDGE

IPFBRIDGE

D2

Co2

VL

RLoad

D4

Compare

+ -

PI

X

Phase reference ZOH

Phase Overlap Logic

S1 S2 S3 S4

(Battery Dis-Charging)

Fig. 8.6 Full-bridge converter with battery discharging control

Table 8.3 Specifications of the components for a full-bridge converter

1

High-frequency transformer (N1 :N2 )

1:8

2

Load capacitor (C o2 )

1 mF, 315 V

3

R load

500 Ω

4

Battery discharging inductor

1 mH

5

Charging filter inductor (L bc )

10 uH

5

MOSFET:47N60C3

600 V, 47 A

6

Diode: DPH30IS600HI

600 V, 30 A

7

Switching frequency

20 kHz

8 Optimal Design and Analysis of Standalone Hybrid Renewable Energy …

181

ISFBRIDGE (5 A/DIV) EB (12.5 V/DIV) ISFBRIDGE (10 A/DIV)

IPFBRIDGE (1 A/DIV)

VPFBRIDGE (12.5 V/DIV)

IPFBRIDGE (1 A/DIV) VLOAD (100 V/DIV)

VSFBRIDGE (50 V/DIV)

(a)

(b)

Fig. 8.7 Experimental results of full-bridge converter

7 6.5

Voltage Gain

6 5.5 5 4.5 4 3.5 3 10

20

30

40

50

60

70

80

90

Phase Overlap

Fig. 8.8 Plot of voltage gain versus phase overlap in the full-bridge converter

Further, to verify the voltage boosting capability of the full-bridge converter a set of experimentations have been performed where the phase shift between two legs has been gradually increased and voltage gain is measured with a ratio of the output voltage to the input voltage. The plot between voltage gain vs phase overlap is shown in Fig. 8.8.

8.2 Operation of the Integrated Configuration The objective of the given hybrid standalone configuration is to utilize maximum PV power by operating it near to MPP and also maintaining the required output voltage. For operating PV source near to MPP, a P&O-based simple MPPT algorithm is used. The maximum power extracted from the PV source is shared by both load and the

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battery for storage. As the system maintains the required output DC link voltage, it indirectly controls the power requirement of the load at the output of the boost converter. The excess power generated in the PV source decides or controls the charge and discharge of the battery. Thus, the output DC link voltage is maintained at the required value by managing the PV and battery power. When the PV power is less, then the deficit power is drawn from the battery to meet the load demand. In other words, the battery maintains the required power to meet load demand and the output voltage during low or zero insolation at the PV source, and during high insolation conditions with PV source having excess power, the surplus power is being diverted to battery for its charging via buck converter. Thus, the presence of the battery source minimizes the unreliable of the PV power to a greater extent. The varying nature of the environmental conditions is taken care by the battery while maintaining the load requirement. Based on the above, the possible modes of the operation (Dhara et al. 2018; Jain et al. 2021) based on the above discussion are: (a) (b) (c) (d)

Mode I: Battery charging with excess PV power Mode II: PV power meeting the load demand Mode III: Battery discharging with deficit PV power Mode IV: Battery meeting the load demand.

(a) Mode I operation: Mode I typically represents the high insolation condition. In this mode, the power generated in the PV source operating at MPP is higher than the load requirement. The excess power generated in the PV source is diverted to the battery via buck converter. Thus, the output voltage across the load is maintained near to V DC(ref_High) with battery charging. This mode gets activated when the output voltage exceeds V DC(ref_High) . PV source is operated near MPP, and it feeds both load and the battery via boost and buck converter, respectively. The duty cycle of the boost converter is defined by P&O MPPT algorithm. Thus, the boost converter operates the PV source near MPP. The output voltage at the load terminal indirectly defines the duty cycle of the buck converter. The actual output voltage is compared with the reference output voltage, and the error is given to the PI controller which defines the required buck inductor current to be extracted from the PV source. The computed required buck inductor current is again compared with the actual one and error is given to the PI controller. The output of the PI controller is the required duty cycle of the buck converter. Thus, based on output voltage control excess power in the PV source is diverted to the battery. This helps in operating PV source near MPP by properly managing the power generated between load and the battery as shown in Fig. 8.9 (Dhara et al. 2018; Jain et al. 2021). This further helps in maintaining the required output voltage across the load. Two loop controls, i.e., the outer voltage and inner average current control, are used for the buck converter to fasten the response of the system. Thus, excess power in the PV source is conditioned by the buck converter for being fed to the battery as shown in Fig. 8.9.

8 Optimal Design and Analysis of Standalone Hybrid Renewable Energy …

DPV

L1 CPV

PV

VPV

183

Dboost Sw1

Co1

VL

PPV = Preq +PB

RLoad

Lbd L2

Lbc

Sw2 Dbuck

CB

Rb

S1

D3

S3 D1 N1

EB

N2 S4 D2

S2

Co2 D4

Fig. 8.9 Given hybrid standalone configuration at Mode I

(b) Mode II operation: When PV source MPP power matches the load requirement, this mode gets activated. In this mode, only PV source supports the load requirement. Output voltage across the load terminal is maintained by the PV source as shown in Fig. 8.10 (Dhara et al. 2018; Jain et al. 2021). The boost converter at the output terminals of the PV source conditions the PV power and operates it near MPP. When the output voltage retains between V DC(ref_Low) to V DC(ref_High), the PV source meets the load requirement. Thus, buck and full-bridge converters are not operating. (c) Mode III operation: Mode III typically represents the low insolation condition. In this mode, the power generated by the PV source operating at MPP is lesser than the load requirement. The deficit power generated in the PV source is extracted from the battery via a full-bridge converter. Thus, the output voltage across the load is maintained near to V DC(ref_Low) with battery discharging. This mode gets activated when the output DPV PV

L1 CPV

VPV

Dboost Sw1

VL

Co1

PPV = Preq Lbd

L2 Sw2 Dbuck

Lbc CB

Rb

S1

S3 D1 N1

EB

S2

N2 S4 D2

Fig. 8.10 Given hybrid standalone configuration at Mode II

D3 Co2 D4

RLoad

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S. Jain and V. Sonti

voltage falls below V DC(ref_Low) . With PV source is operated near MPP and battery feeding deficit power via boost and full-bridge converter respectively as shown in Fig. 8.11 (Dhara et al. 2018; Jain et al. 2021), system maintains the required output voltage. As discussed, the duty cycle of the boost converter is defined by the MPPT algorithm. The output voltage at the load terminal directly defines the duty cycle of the full-bridge converter. The actual output voltage is compared with reference output voltage and the error is given to PI controller which defines the phase reference for the full-bridge converter. The phase is then given to a zero-order hold circuit to determine the overlap angle. The overlap angle again controls the gain or power drawn from the battery. In other words, overlap angle controls regulate the deficit power of the load, and thus, it helps in maintaining the output voltage. Thus, based on output voltage control, the deficit power is drawn from the battery to meet the load demand. As PV source operated near MPP and by properly managing the power from the battery, the given system ensures maximum utilization of the PV source. Thus, even a very small power from the PV source operating at MPP can be extracted to support the load. Thus, deficit power drawn from the battery is conditioned using full-bridge converter as shown in Fig. 8.11. (d) Mode IV operation: The given mode activates when PV power is very little, and it is disconnected. The output load demand is taken care by battery source via full-bridge converter. This mode is activated seamlessly via Mode III where the full-bridge converter is already activated and is managing deficit PV power before moving to Mode IV. Thus, the battery power is conditioned via a full-bridge converter to meet the load requirement while maintaining the DC link voltage at V DC(ref_Low) volts (Dhara et al. 2018; Jain et al. 2021) as shown in Fig. 8.12. DPV PV

L1 CPV

VPV

Dboost Sw1

Co1

VL

PPV +PB= Preq

Lbd L2 Sw2 Dbuck

Lbc CB

Rb

S1

S3 D1 N1

EB

S2

N2 S4 D2

Fig. 8.11 Given hybrid standalone configuration at Mode III

D3 Co2 D4

RLoad

8 Optimal Design and Analysis of Standalone Hybrid Renewable Energy …

DPV PV

L1 CPV

VPV

185

Dboost Sw1

PB= Preq

Co1

VL

RLoad

Lbd L2 Sw2 Dbuck

Lbc CB

Rb

S1

S3 D1 N1

EB

S2

N2 S4 D2

D3 Co2 D4

Fig. 8.12 Given hybrid standalone configuration at Mode IV

8.3 Control Strategy Used for the Given Hybrid Standalone Configuration The proposed system consists of three converters boost, buck, and full-bridge DC-DC converters which are dedicated to their respective work. Boost converter takes care of the operation of the PV source near MPP. Buck converter diverts the excess PV power for the battery charging, and the full-bridge DC-DC converter takes care of the deficit PV power by discharging the battery. Activation of charging and discharging converter depends on the output load voltage which can be DC bus of inverter or DC load having a tolerance near to 10%. Using the given tolerance, the overlap or oscillatory operation between the charging and discharging conditions for the two converters can be avoided. In the proposed system, a difference of 20 V is kept between the reference voltages of the charging and discharging conditions for the two converters. Thus, for the output voltage between V DC(ref_Low) and V DC(ref_High) only the PV source pumps power into the load. And, when the output voltage falls below V DC(ref_Low) battery discharging gets activated. Therefore, before the start of the event for discharging, the only PV source is supporting the load. Similarly, the event of charging starts only when the output load voltage becomes more than V DC(ref_High) . Therefore, before the start of the event for charging, the only PV source is supporting the load. Thus, the overlap or oscillations between charging and the discharging condition is avoided and there are smooth transitions between the operation of each converter. In Fig. 8.13, (Dhara et al. 2018; Jain et al. 2021), the band of voltage range for different modes of operation has been clearly mentioned. When the output voltage is between V DC(ref_Low) and V DC(ref_High) only the PV source will feed the power to the load. Once the load voltage crossed the limit of V DC(ref_High), battery will also be charged from the PV. The control for the duty cycle calculation for the charging of the battery is active until load voltage dips below V DC_High . However, for calculation of duty cycle required for charging the battery is calculated using V DC(ref_High) which has a higher value than V DC_High . Thus, when the magnitude of the actual load voltage is lesser than V DC(ref_High), the error becomes negative. This will eventually reduce

186 Fig. 8.13 Voltage band-based control

S. Jain and V. Sonti

VDC(max) PV and Battery Charging

VDC(ref_High) (Battery Charging)

VDC_High Only PV Zone

VDC_Low

VDC(ref_Low) (Battery Dis-Charging)

PV and Battery Dis-charging

VDC(min)

the duty cycle for charging the battery to zero value leading to a smooth transition between Modes I and II. In a similar way, the smooth transition between Modes II and III is done. The voltage band between V DC(ref_High) and V DC_High is kept to avoid oscillation between the start and stop command for battery charging. Similarly, a band between start and stop (V DC(ref_Low) and V DC_Low ) command for discharging is also kept to avoid oscillation. Moreover, to avoid faults in the system a maximum limit for the DC bus has been kept as V DC(max) . To avoid deep discharge of battery for a sudden peak load, a minimum limit for DC bus is also kept V DC(min).

8.4 Simulation Results The given standalone PV-battery-based system along with its control strategy is simulated in the PSIM environment. The PV and battery sources are realized in the PSIM functional blocks. The simulation is performed for the variable environmental condition. The four modes of operation and the given control methodology are verified. The parameters used in the simulation are listed in Table 8.4 (Dhara et al. 2018; Jain et al. 2021). Simulation results (Dhara et al. 2018; Jain et al. 2021) showing mode transition from Mode I and Mode III for the given standalone system are given in Fig. 8.14a–g. The mode transition is achieved by changing the insolation. The PV source operating near MPP can be observed from the PV power waveform which aligns with available maximum power. As PV power extraction or its operation is given more importance, the DC-link voltage will be first developed by the PV source as shown in subplot (g). Once the PV source operates near MPP and if the output load voltage exceeds

8 Optimal Design and Analysis of Standalone Hybrid Renewable Energy … Table 8.4 Parameters used for simulation

187

1

PV open-circuit voltage (V OC )

190 V

2

PV capacitance (C PV )

1 mF

3

Load capacitor (C o1 , C o2 )

100 uH

4

Load resistance (RLoad )

800 Ω

5

Boost inductor (L 1 )

0.1 mH

6

Buck inductor (L 2 )

1 mH

7

Battery charging inductor (L bc )

10 uH

8

Battery voltage (E b )

48 V

9

High-frequency transformer (N1 :N2 )

1:8

10

Battery discharging inductor (L bd )

1 mH

11

V DC(max)

330 V

12

V DC(ref_High) (Battery charging)

320 V

13

V DC_High

315 V

14

V DC_Low

305 V

15

V DC(ref_Low) (Battery discharging)

300 V

16

V DC(min)

270 V

V DC(ref_High) , the battery charging corresponding to Mode I is started as can be seen from the subplots of Fig. 8.14e and f. The transition from Mode I to Mode III can be verified from the subplot Fig. 8.14f. The negative and positive values of the battery power justify charging Mode I and discharging Mode III. Further, an important thing to be noted the batter power is zero for a small period supporting Mode II, during the transition between Mode I and Mode III. Further, a low value of output load voltage VDC(ref_Low) in Mode III corresponding to the discharge mode can also be observed in Fig. 8.14a–g. Similarly, simulation results for different parameters are obtained and shown in Fig. 8.14h–n (Dhara et al. 2018; Jain et al. 2021) for Mode II and Mode IV. Here also transition between two modes is being shown by changing the insolation. In the Mode II operation, the load voltage is maintained between V DC(ref_High) and V DC(ref_Low) and the load gets the power from the PV source only. After changing the insolation to zero level, the PV power becomes zero which is verified with the subplot (i). At that transition, the battery starts pumping the power to maintain the load voltage constant at V DC(ref_Low) which further can be justified by subplot (m), (n).

8.5 Experimental Results An experimental prototype is developed. In the prototype to emulate PV characteristics, a DC voltage source with series resistance is being used. In place battery charging, an electronic load is used, and to show the battery discharging, a DC voltage

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source is being used. The parameters used in the experiment are listed in Table 8.5 (Dhara et al. 2018; Jain et al. 2021). Now, experimentation has been performed to show different modes of operation and smooth transition among different modes. In Fig. 8.15, the experimental waveforms of PV voltage (V PV ), PV current (I PV ), PV power (PPV ), battery current (I B ), load voltage (V LOAD ) are (Dhara et al. 2018; Jain et al. 2021) plotted in a subplot (a) for decreasing insolation condition, whereas in a subplot (b) the waveforms are plotted for the increasing insolation condition. Now, the changing of the insolation condition can be supported by changing the maximum current limit from the DC source. So, the experimental results are taken for both increasing and decreasing the current reference. Figure 8.15 subplot (a) is started with Mode I which can be supported by negative battery current (i.e., charging current) and load voltage is constant at 120 V.

8 Optimal Design and Analysis of Standalone Hybrid Renewable Energy … Table 8.5 Parameters used for experimental prototype

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Now, the current reference of the source (I PV ) is gradually decreased which is also shown in Fig. 8.15. Once the load voltage falls below 110 V, the battery discharging is stopped and the circuit will operate in Mode II where the load voltage is maintained by the PV source only. Further decrement of the source current makes the system to operate in Mode III where the PV source and battery both powers are integrated to maintain the load voltage at 100 V. Now, further reduction of current reference makes the system operate in Mode IV where load voltage is maintained through battery power only. This mode can also be supported by zero PV power and positive battery current (i.e., discharging current) as shown in Fig. 8.15. To justify the non-overlap of the charging and discharging mode, other results were taken where the output voltage, PV input current, buck inductor current, primary

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current of the full-bridge transformer, and load voltage were captured for Modes I and IV. Figure 8.16 (Dhara et al. 2018; Jain et al. 2021) subplot (a) shows the results captured during mode I where the PV source current is high enough to maintain both DC bus voltage and battery charging. Further, it can be noticed that the battery discharging current is nearly zero. This supports the non-overlapping of the charging and discharging conditions. Now, in Mode III and Mode IV, battery discharging operation is started to maintain the load voltage. Figure 8.16 subplot (b) presents the discharging operation of the battery. From Fig. 8.16 subplot (b), it can be stated that in Mode IV operation the PV current is reduced, and the battery starts discharging as can be seen from the primary winding current of the full-bridge transformer but the charging current becomes zero. Now, the load resistance (RLoad ) connected across the common output terminal of the boost converter and full-bridge converter in the experimental prototype is replaced with a single-phase inverter. The output of the single-phase inverter is connected to an RL load (where R = 100 Ω, L = 10 mH) to justify the operation of the system with a ceiling fan. Now in this integrated system, different modes of operation and transitions among them due to changes of power input (variation of insolation) and changing of load requirements are further analyzed. In Fig. 8.17 (Dhara et al. 2018; Jain et al. 2021), the load current flowing through the single-phase inverterfed RL load, battery current, the voltage at the input terminal of boost converter, power extracted from PV source are captured for changing insolation and changing load condition. The results shown in Fig. 8.17 are obtained by changing the current reference of the programmable DC source. It can be observed that different modes and their transition is shown by changing the power extraction from the PV source. The Mode I is justified with meeting the load requirement and battery charging current. Decrement in the magnitude of the battery current reference which ultimately becomes zero, make the system operate in Mode II where the PV source also meets load requirement with zero battery current.

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8.6 Conclusion This chapter presents an integrated converter circuit used for hybrid standalone PV system. The integrated converter circuit used is the combination of three power conversion stages. The design of individual power conversion stage along with their control is also discussed in the chapter. The integrated converter used in this chapter generally operates in four modes, i.e., (a) Mode I: battery charging with excess PV power (b) Mode II: PV power meeting the load demand (c) Mode III: battery discharging with deficit PV power (d) Mode IV: battery meeting the load demand. The operation of the integrated converter circuit into each mode is also discussed in the chapter. This verifies the operation of the integrated converter circuit, and the simulation and experimental waveforms in different modes are also presented in the chapter.

References Abeywardana DBW, Hredzak B, Agelidis VG, Demetriades GD (Feb 2017) Supercapacitor sizing method for energy-controlled filter-based hybrid energy storage systems. IEEE Trans Power Electron 32(2):1626–1637 Alam MJE, Muttaqi KM, Sutanto D (2016) Effective utilization of available PEV battery capacity for mitigation of solar PV impact and grid support with integrated V2G functionality. IEEE Trans Smart Grid 7(3):1562–1571 Dhara S, Jain S, Agarwal V (2018) A novel voltage-zone based power management scheme for PV-battery based standalone system. In: 2018 8th IEEE India international conference on power electronics (IICPE), pp 1–6 Herrera VI, Gaztañaga H, Milo A, Saez-de-Ibarra A, Etxeberria-Otadui I, Nieva T (July–Aug 2016) Optimal energy management and sizing of a battery—supercapacitor-based light rail vehicle with a multiobjective approach. IEEE Trans Ind Appl 52(4):3367–3377 Jain S, Dhara S, Agarwal V (Jan–Feb 2021) A voltage-zone based power management scheme with seamless power transfer between PV-battery for OFF-grid stand-alone system. IEEE Trans Ind Appl 57(1):754–763

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Pan L, Gu J, Zhu J, Qiu T (2016) Integrated control of smoothing power fluctuations and peak shaving in wind/PV/energy storage system. In: 2016 8th international conference on intelligent human-machine systems and cybernetics (IHMSC), pp 586–591 Philip J et al (July–Aug 2016) Control and implementation of a standalone solar photovoltaic hybrid system. IEEE Trans Ind Appl 52(4):3472–3479 Wang H, Zhang J (2016) Research on charging/discharging control strategy of battery-super capacitor hybrid energy storage system in photovoltaic system. In: 2016 IEEE 8th international power electronics and motion control conference (IPEMC-ECCE Asia), pp 2694–2698

Chapter 9

Machine Learning Applications in Smart Grid Arvind Kumar Jain

Abstract The sheer volume of real-time data related to grid security, power quality, energy price, energy demand, etc., is the main challenge in smart grid. Integration of renewable energy has introduced more uncertainty in the grid. Hence, real-time prediction, analysis, and control of smart grid are difficult. As machine learning algorithms are capable to efficiently process data and find the hidden relationships among variables, the focus lies on utilizing the machine learning techniques in smart grid applications to predict the decisions, even in uncertain conditions. This chapter covers some important machine learning techniques. Further, how to use machine learning techniques in power system security analysis, calculation of available transfer capability of tie lines, and forecasting of electricity price/load are presented. Keywords Artificial neural network · Decision tree · Random forest · Supervised learning · Unsupervised learning · Power system security · Price forecasting · Load forecasting · Available transfer capability

9.1 Introduction Across the globe, power sector has seen an enormous growth in its energy consumption requirement, power production capacity, transmission, and distribution systems. The recent advancements in technology are pushing it to deploy novel sensor-based instruments and information communication technology, adopt novel monitoring, control and energy management methods with an intent of rapid implementation of smart grid techniques at distribution as well as transmission levels. Smart grid (SG) definition given by the US Department of Energy (DOE) is as follows. “An automated, widely distributed energy delivery network, the smart grid will be characterized by a two-way flow of electricity and information and will be capable of monitoring everything from power plants to customer preferences to individual A. K. Jain (B) Department of Electrical Engineering, National Institute of Technology, Agartala, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. N. Singh et al. (eds.), Optimal Planning and Operation of Distributed Energy Resources, Energy Systems in Electrical Engineering, https://doi.org/10.1007/978-981-99-2800-2_9

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appliances. It incorporates into the grid the benefits of distributed computing and communications to deliver real-time information and enable the near-instantaneous balance of supply and demand at the device level.” At large, a traditional power grid along with electronic devices and two-way communication is called smart grid. The benefits of the smart grid are: • Efficient generation, transmission, distribution, and consumption • Improves reliability through automatic outage prevention and restoration • Reducing greenhouse gas productions by allowing integration of renewable energy • Allows demand response for load management and peak avoidance. The main challenge of smart grid is to handle power and communication networks, where the amount of real-time information related to power quality, price, energy demand, etc., is linked to the nodes of the power system. To optimize the smart grid operation, various machine learning techniques, such as ANNs, fuzzy systems, genetic algorithm, particle swarm optimization, artificial bee colony algorithm, and other artificial intelligence methods and their hybrid combinations can significantly contribute. Therefore, the focus lies on developing artificial intelligence models to make decisions in uncertain conditions. This chapter includes basics of machine learning algorithms and deliberates about the artificial neural network, random forest, decision tree algorithms as these algorithms are extensively used in electrical engineering. Issues and challenges in smart grid as well as selecting an appropriate machine learning algorithm to solve the smart grid problems are discussed subsequently. Approach to use machine learning algorithms in power system security analysis, calculation of available transfer capability of tie lines, and forecasting of electricity price/load is presented in ensuing sections along with future directions and conclusion.

9.2 Background Machine learning (ML) is a data analytic approach instructing computational devices to develop the capability to learn from historical data or past incidences. ML methods utilize computational techniques to “learn” hidden patterns solely from data without using mathematical equations as a model. The performance of these algorithms improves adaptively with an increase in the number of training patterns available. Further, machine learning techniques recognize hidden relationships among various attributes that create understanding to predict the outcome and take better decisions. For example, electric utilities rely on machine learning to predict the electric energy price and quantity in competitive electricity markets. With the increase in the amount of data available, use of machine learning seems to be beneficial and indispensable to solve problems related to power system security, electricity price, and load forecasting.

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When to use machine learning? How machine learning works? These are the obvious queries, which come to an individual user’s mind. From the literature, it is established that machine learning techniques are useful when a mathematical formula or equation does not exist for a complex problem having a huge volume of data and n-numbers of variable. For example, ML may be a good alternative to handle the problems given below: • There is no fixed pattern in data and is altering continuously such that the software needs to adjust: as in electrical load forecasting. • Handwritten rules and equations are very intricate. To predict the output, machine learning uses supervised and unsupervised learning techniques as shown in Fig. 9.1.

Fig. 9.1 Machine learning techniques include both unsupervised and supervised learning

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9.2.1 Supervised Learning Supervised learning constructs a model capable of predicting the outcomes for unknown cases utilizing the target output for known cases even if the uncertainty exists. It utilizes known input and output data to train a model to predict future outputs. Models developed for prediction purposes utilizing this type of learning can be based either on classification or regression techniques. Classification techniques: Classification techniques predict discrete responses and divide input data into classes. Whether a tumor is cancerous or non-threatening is the example of discrete response. Algorithms mostly used for classification tasks are support vector machine (SVM), boosted and bagged decision trees, knearest neighbor, Naïve Bayes, discriminant analysis, logistic regression, and neural networks (Anath 2018). Regression techniques: Regression techniques forecast uninterrupted responses, for example, deviations in temperature or variation in power supply. Regression techniques are applicable if data range is given or if the nature of response is a real number. Linear model, nonlinear model, regularization, stepwise regression, boosted and bagged decision trees, neural networks, and adaptive neuro-fuzzy learning are some common regression algorithms.

9.2.2 Unsupervised Learning Unsupervised learning is the training of a machine learning algorithm to realize hidden patterns or inherent structures in data using information that is neither classified nor labeled. Clustering is used to explore any kind of relationship among various attributes in data or to ascertain the natural associations in the data. K-means and k-medoids, hierarchical clustering, Gaussian mixture model, hidden Markov models, selforganizing maps, fuzzy c-means clustering, and subtractive clustering are some common algorithms used for performing clustering.

9.2.3 Popular Machine Learning Algorithms Machine learning algorithms like ANN, SVM, decision tree (DT), and random forest (RF) are used in various engineering applications. Brief details about the ANN, DT, RF machine learning algorithms are given in the following subsections as these algorithms are widely used in electrical engineering.

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Artificial Neural Network: ANN is a computational algorithm, which mimics the human brain. The human brain comprises of several billion cells known as neurons. These neurons are connected through dendrites and synapses. Synapses allow neurons to pass signals in the form of electrical and chemical signal. Therefore, neurons are responsible to process information by carrying information toward (inputs) and away (outputs) from the brain. Further, large number of simulated neurons form artificial neural network. These artificial neurons are processing units, which are interconnected by nodes. These processing units are made up of input and output units. Based on an internal weighting system, the input units receive information in different forms and structures (Bhattacharya 2017). The neural network is trained to learn about the input patterns and produce output. Similar to human beings, ANNs also use a set of learning rules and guidelines to produce desired output results. Initially, an ANN undergoes training and during training it learns to recognize patterns in data, whether visually, aurally, or textually. In supervised learning, the actual output produced by the network is compared with the target output and the difference between them is minimized using backpropagation. In backpropagation, as the name indicates, first the weights connected between the output layer and hidden layer are updated and then the weights connecting hidden layer to the input layer are adjusted until the error between the target output, and the actual output is reduced below some tolerance (Chakrabarti and Jeyasurya 2007; Shafi et al. 2006). ANN can be used for pattern recognition, data classification, and data mining and for regression of continuous target attributes. Artificial Neural Network Layers ANN consists of various layers. These layers comprise of many interconnected “nodes” containing an “activation function.” The following three layers are usually present in a neural network: a. Input layer For each observation, input layer receives the values of the explanatory attributes. The number of neurons in the input layer is decided on the basis of number of independent variables. The training patterns comprising of the independent attributes are presented to the neural network, and then these are passed on to the neurons present in subsequent hidden layers. The data is not changed by the passive neurons present in the input layer. They receive a single value on their input and duplicate the value to their many outputs. b. Hidden layer The hidden layer is a layer whose output is not visible, and it is hidden between input and output layers. Hidden layer may be single or multiple. The hidden layers derive the net input by performing computation on the weighted inputs. Net input is applied with activation function to drive the actual output.

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c. Output layer Output layer is connected with hidden layer or input layer, and it gives output value based on prediction. Usually, the output layer will contain only one neuron unit if the problem under consideration is to classify the patterns. The active nodes of the output layer combine and change the data to produce the output values. Structure of a Neural Network ANN consists of various layers. It has an interconnected weight adjustment mechanism. The selection of the ANN structure plays a pivotal role while implementing a neural network in a particular problem as it depends on the desired outcomes. The simplest neural network consists of an input layer and an output layer. By increasing number of hidden layers, ANN predictive power increases but more number of hidden layers create the problem of overfitting. Advantages • ANNs deal proficiently with linear and nonlinear data but they need a huge number of distinctive numbers of training patterns to capture real-world operation. • ANN is robust as it can work even if some units fail to respond to network. However, large size processing and storage resources are required to implement ANN software. • ANNs learn from the examined data and do not need to reprogramme but they are called black box models as they simply take inputs from the users and provide outputs after being trained. Drawbacks • ANN is hardware-dependent. • ANN does not give any clue regarding probing solution. This unexplained behavior of ANN decreases trust in the network (Bhesdadiya et al. 2015). • There is no standard rule for ascertaining the proper network structure of ANNs. Therefore, a suitable network structure is attained through hit and trial. • ANNs can work with numerical values of variables. Therefore, before utilizing ANNs, problems have to be converted into numerical values. However, this conversion influences the functioning of the network. Decision Trees Decision tree is used to build a training model for predicting class of target variables by learning decision rules deduced from input data. DT uses branching technique to demonstrate each plausible result of a decision depending on certain circumstances. In a DT, each node represents an attribute, each branch represents the decision (rule), and each leaf node represents an outcome, i.e., the decision made after evaluating all the features or attributes. Researchers prefer decision tree algorithms because it is

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capable to make decisions under uncertainty. These algorithms help the researchers to select an optimal decision using forward and backward calculation paths. Since decision trees are robust to errors, therefore to deal with the training data having errors, decision tree algorithms are considered as the most suitable. Decision trees are the ideal choice if the training pattern contains some missing data as they can handle missing values of training data agreeably by examining the data in different columns. Further, decision tree algorithms are useful when the objective function has discrete output. Types of Decision Trees a. Classification Trees: When a dataset needs to be divided into different classes, classification trees are preferred. b. Regression Trees: In case, the output variable is expected to be continuous or numerical, regression trees are employed. They are applied if the problem is to predict certain outcome. Advantages • Decision tree algorithms are very intuitive, easy to explain, and can handle large dataset. • Type of data is not a constraint for decision tree machine learning algorithms as they accept both numeric and unconditional data and can handle missing data too. • These algorithms do not assume any linearity in the data; hence, they are capable enough to handle the situation where the parameters are nonlinearly related. • Feature selection, which plays a very important role in prediction, is performed implicitly by decision trees. Therefore, it finds it application in data exploration. • Decision trees are insensitive to missing values as well as outliers. Hence, missing values are not hurdle in dividing data for developing a decision tree. Further, data splitting takes place on some samples within a split range, therefore, outliers do not influence the decision trees. This reduces the training time also. Drawbacks • One of the drawbacks of decision tree algorithms is that the results may be based on anticipations. Therefore, the payoffs and resulting outcomes may be different or may not be as per the expectations while making decisions in real time. This type of situation may result in impractical decision trees, which may be prone to make wrong decisions. • These algorithms are not well-suited for continuous variables and give rise to instability and classification plateaus. • They are simple to use in comparison with other decision-making models. However, it is a complex task to create large decision trees with several branches. In addition, it is time-exhaustive also. • These algorithms use only one feature at an instant and may not be the most suitable for actual data in the decision space. • These algorithms need careful adjustment through pruning.

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Random Forests • Random forests are a popular collaborative or group learning technique that can be utilized to create analytical model for both classification and regression problems. Collaborative or group learning methods employ several learning models to gain better analytical outcomes. Further, the model constructs an entire forest of random uncorrelated decision trees to reach at the potential response. This algorithm utilizes a bagging approach to form a group of decision trees with random subset of the data. To get the acceptable and satisfactory performance of the random forest algorithm, a model is trained repeatedly on random dataset. In this collaborative method, the final prediction is obtained by combining the output of all the decision trees in the random forest. This algorithm is popular among the research community because it is resistant to outliers and also maintains the accuracy when there is missing data. It is adept at handling numerical, binary and categorical features, without scaling, transformation, or modification. Advantages • Since overfitting issue with random forest algorithms is less as compared to decision tree algorithms. Therefore, pruning is not required in the random forest. • This algorithm is more robust to noise; therefore, it is fit for classification and regression tasks. • These algorithms are not sensitive to the parameters; hence, implementation of these algorithms is easy as a decent model may be developed without much tuning. • Its performance is efficient even on large databases. Drawbacks • Decision trees give unsatisfactory performance on unknown data, which limits the use of DT in analytical method. • During real-time predictions, the algorithm becomes slow due to the presence of a huge number of decision trees. • Random forest algorithm gets influenced toward the features having more levels. In such cases, variable importance scores may not be authentic. • For regression tasks, these algorithms predict within the range of training data of the response values.

9.2.4 Main Issues and Challenges in Smart Grid The conventional power grid follows top-down approach for power flow in which electricity flows unidirectional from power source to sink. In this grid, generation follows load and operation is based on past incidences/events. Further, in conventional system, grid accessibility for new producers is limited. This system has served well for the twentieth century. However, power grid has witnessed changes due to deregulation of power industry, penetration of renewable energy sources, multidirectional power flow, consumers become presumers and grid operation based more

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on real-time data. These changes in the power grid have imposed challenges and enhanced complexity like making it more vulnerable to fluctuations/uncertainty, required real-time observability and control, need of millions of devices and many entities. Under these circumstances, the future electric power system, referred as the “smart grid” may be a favorable alternative to: • Enhance the resiliency in the grid to accommodate changing conditions in electricity network. • Forecast grid behavior such as generation, peak demand, and faults. • Enhance power quality to satisfactory operation of customers’ apparatus. • Provide fault tolerance and self-healing capabilities for reliable operation of grid. • Integrate distributed renewable energy sources. • Provide security from attacks or deliberate disruptions, etc. Considering the complexity and heterogeneity of grid, challenges for smart grid can be summarized in the following categories: Technical challenges: These challenges mainly address the execution of distributed communication strategies required for real-time information exchange, advanced metering infrastructure for dynamic pricing, efficient huge data processing methods, etc. Economic challenges: Restructuring of power system introduced challenges to adopt new business models under the competitive electricity markets. Further, communication and advance metering infrastructure allow a customer to participate in active demand response strategies for shifting their demand from high price periods to low price periods which may lead to imbalance in the system (Jain and Srivastava 2017). Regulatory challenges: The regulatory challenges are associated with the policies and standards required at different levels to make smart grids feasible. Though the above-mentioned challenges are quite distinct in nature, one of the biggest challenges for smart grid development will be handling the huge amount of data that is expected to be gathered from various sources and treated to optimize its operation.

9.2.5 Challenge to Select Appropriate Machine Learning Algorithm for Smart Grid Choosing the right algorithm for solving smart grid problem can seem to be a massive task because of the availability of huge number of supervised and unsupervised machine learning algorithms with each one having a different approach to learning. No method can be considered the best one, which may be suitable to solve all kind of problems. Only trial and error can help in selecting the correct algorithm. It would be very difficult for veteran data scientists also to suggest whether an algorithm will work or not without applying it. The selection of an algorithm depends on

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the size and nature of data available in a particular problem, the in-depth knowledge/understanding desirable from the data, and how that understanding has to be utilized. Some recommendations’ for selecting between supervised and unsupervised machine learning algorithm for smart grid are given below: • A supervised learning should be selected in case a model needs to be trained for prediction, for example, the future value of a continuous variable, such as energy price or load variation. • An unsupervised learning should be selected in case a model is expected to determine good internal representation by exploring the data, such as splitting data into clusters.

9.3 Machine Learning Applications in Smart Grid Machine learning algorithms can be used in determining power system stability, security, reliability, power quality, economic load dispatch, available transfer capability of tie lines, price forecasting, load forecasting, etc. Brief summary of machine learning techniques and its applications in smart grid is given in table below (Dong and Zhang 2010) (Table 9.1).

9.4 Solutions and Recommendations To show the utility of machine learning techniques in power systems, some examples like power system security assessment, estimation of ATC of tie lines, and electrical energy price/load forecasting are given in forthcoming sections for better understanding.

9.4.1 Machine Learning Approach to Power System Security Assessment Due to the large interconnected power system and involvement of various entities in deregulated power grid, a high level of system security is required even under intact condition. The main objective of a utility is to provide uninterrupted quality power supply to customer. If power system runs in normal condition satisfying operating as well as load constraints and remains in normal condition following the contingency out of the credible contingencies list then system is secure. The process of detecting power system states such as normal state, emergency state, and insecure state is called power system security monitoring and assessment. Further, power system security

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Table 9.1 Machine learning techniques and its applications in power system Category

Techniques

Supervised learning

• Regression techniques, • Regression or prediction • Neural network: – Security assessment Multilayer neural network – Available transfer capability (MLNN), self-organizing map (ATC) assessment – (SOM), recurrent neural network Generation/load/price/weather (RNN), forecast, • SVM – Predictive maintenance • Decision tree – Theft detection – Demand response • Classification – Security assessment – Fault detection – Non-intrusive load monitoring

Unsupervised learning

• Principal component analysis • K-means • Generative adversarial networks (GANs) • Autoencoders

Semi-supervised learning • Graph-based algorithms • Self-learning Reinforcement learning

Q-Learning • Temporal difference learning

Applications

– – – –

Dimensionality reduction Clustering Anomaly detection Scenario generation, etc.

– Regression or prediction classification (fault detection) – Optimized control – Control strategy/action selection – Parameter tuning – Procedure optimization

is divided into dynamic as well as static security. Under static security problem, steady-state performance of power system is evaluated considering credible contingencies, whereas dynamic security is evaluated considering transient disturbances in the system. The effect of a contingency on a power system transient security is usually assessed by time-domain simulation. However, due to nonlinear nature of physical phenomenon and high complexity of physical power system, time-domain method is not best fit for security assessment. Hence, there is a need of modern techniques for security assessment. An outline based on ML techniques for security assessment is shown in Fig. 9.2. Operating states of power systems determine the security of the system. Based on the load and operating constraints, power systems may be divided into five states as shown in Fig. 9.3. General procedure for security assessment: Suppose that voltage security problem is observed in some reactive power weak areas during contingency analysis. To address this problem, initially, random data based on system knowledge, normal, and abnormal operating conditions is generated. Further, a certain number of attributes

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Fig. 9.2 Machine learning outline for security assessment

are computed as input variables to drive security criteria. Pre-fault operating parameters such as voltage, reactive power generation, power flows, and topology indicators work as attributes under preventive mode security assessment. During emergency state, attributes are power flows, voltage magnitude, transformer ratios, circuit breakers status, etc. These attributes are depending on the disturbances and short-term system modeling. a. Data preprocessing: Due to strong correlation among geographically close components of a power system, many different attributes provide the equivalent information. Therefore, clustering techniques are used to identify significant attributes out of large number of basic variables. Like in practical power system security problem, correlation coefficient among pair of bus voltages magnitude may be computed using database. Further, with the help of clustering algorithm voltage coherent regions may be identified. Now in place of bus voltages, region voltage would be used as an attribute to reduce computational burden. b. Supervised learning of security criteria: To derive appropriate security criteria using supervised learning, database for security margins is derived using various contingencies and partitioned it in separate learning and test samples. The learning sample will be used to build the synthetic security criteria, whereas the

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Fig. 9.3 Power systems operating states

test set will be used to assess their reliability by comparing the security outcome predicted by them and the “actual” one ascertained by simulation. Now, there is a requirement to define security classes like secure, marginally secure, or insecure based on the appropriate threshold value of security margin. Decision tree is used to identify relevant subset of attributes and the threshold values for voltage security. Decision trees can be used either in a preventive or in an emergency state depending upon whether normal pre-disturbance or just after disturbance attribute values are used. If less number of insecure states is detected, then the security margin threshold value may be increased before rebuilding a tree. If more false alarms are detected, extra learning states should be used. To provide continuous security margin, a hybrid approach consisting of decision tree and multilayer perceptron (MLP) neural network is used. The MLP can model nonlinear. The DT can identify the attributes having strong correlation with the security class. Thus, in this method, the latter attributes are used as input variables to a MLP model, while normalized security margin is used as output variable. For determining MLP structure, trial-and-error approach is used to find out number of units in hidden layer and topology. Once MLP structure and weights are improved on the basis of the learning states, it gives a closed-form and differentiable security approximation,

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which may be used for rapid estimation of margin for any known or unknown patterns and also to evaluate margin sensitivities to attribute values.

9.4.2 Machine Learning Approach to Calculate Available Transfer Capability of Tie Line In a vertically integrated power system, role of tie line was to maintain inter-area security and reliability. In restructured power system, both power-generating companies and power purchasing companies submit bids in the market with an intent to have maximum profit. It may overload and cause congestion in certain tie lines or transmission paths making the operation of the market inefficient and the system insecure. Therefore, it is essential to quantify the available transfer capability (ATC) along different transmission system corridors. Available transfer capability (ATC) is defined as a measure of transfer capability remaining in the physical transmission network for further commercial activity over and above the existing commitment (Manikandan et al. 2008). Mathematically, ATC = TTC−TRM−ETC,

(9.1)

where TTC Total Transfer Capability TRM Transmission Reliability Margin ETC Existing Transmission Commitments. a. Total Transfer Capability (TTC): It is the amount of electric power that can be transferred over the interconnected transmission network in a reliable manner while meeting all the constraint. b. Transmission Reliability Margin (TRM): It is the amount of transmission transfer capability necessary to ensure that the interconnected transmission network is secure under uncertainties. c. Existing Transmission Commitments (ETC): It is the actual commitment of the interconnected transmission network at a particular time. ATC of a transmission system is computed at regular intervals. It is calculated as the extra power transfer capability available under the various outages, and the base load state. The various methods proposed to compute ATC are either slow or inaccurate. Recently, ANNs have emerged as a promising approach in various applications of power system. This is because they are able to accurately and rapidly map the highly nonlinear relationship between input and output. In recent years, radial basis function neural network (RBFNN) has gained significant attention from researchers because it possesses a simple structure and its training is efficient (Jain et al. 2003). Its main advantage is that it does not need to be retrained for the augmented new training

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data. RBFN consists of only one nonlinear hidden layer and its output layer is linear. The training of RBFNN is fast because it needs to adapt only the weights connected between the hidden layer and the output layer. There is no weight association between the input layer and the hidden layer. General procedure to determine ATC: Let us consider a radial basis function neural network-based method to compute ATC in restructured power system considering bilateral as well as multilateral transactions. A Euclidean distance-based clustering technique has been employed to find out the number of hidden units and unit centers for the RBFNN. Random forest technique has been used for feature selection to find out the appropriate number of inputs and the size of the neural network. The architecture of RBFNN model is shown in Fig. 9.4. This model consists of the input, hidden, and output layers. All the nodes in each layer are connected fully to the previous layer. Each node of the input layer represents the input variable assigned to it. The hidden layer receives these inputs directly without any associated weights. The hidden nodes (units) contain the radial basis functions and are analogous to the sigmoid function commonly used in the MLP neural networks. The RBF is similar to the Gaussian density function, which is defined by a center position and a width parameter (Madhusudhana Rao et al. 2010). The rate of decrease of the function is controlled by the width of the RBF unit. The output of the ith unit ai (Xp), in the hidden layer, is given by (Pandey et al. 2008): (

)

⎛

ai X p = exp⎝−

] r [ ∑ x j p − x ji 2 j=1

σi

where x j p jth variable of an input pattern p. x ji center of ith RBF unit for input variable j.

Fig. 9.4 Architecture of a radial basis function neural network

⎞ ⎠,

(9.2)

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width of ith RBF unit. The output of the RBFNN can be computed as y=

H ∑

( ) wi ai X p + wo

(9.3)

i=1

a. Input Feature Selection Any neural network will take a long time to train if the number of inputs is large, thereby increasing the interconnection weights also. This problem can be overcome by performing a feature selection task, which will help in identification of the features enhancing the discrimination ability of the neural network. The neural network should be trained using these selected features only. Selection of relevant features increases the predictive accuracy of ANNs and reduces their training time as well as their size. The ATC across any transmission corridor depends on various parameters such as network configuration, power output of generators, power demand of customers, and the bilateral as well as multilateral transactions of the market participants. However, for a particular network topology, the main parameters affecting the ATC are the active and reactive power demand in the system. Hence, these are selected as input variables to train the neural network. Further, in order to represent a particular contingency at which ATC has to be determined, an extra input is required. This extra input is represented in the form of bipolar digits to represent the corresponding contingency. Since only few active and reactive power demand will have larger influence on the ATC, the features having greater impact on the ATC have been selected using a random forest technique (Jain et al. 2010; Pindoriya et al. 2008). b. Random Forest Technique A random forest is an illustration of groups of trees that grows a forest of decision trees on bagged samples. The randomness arises from sampling the data as well as the variables. Each tree is grown using a bootstrap sample of training data. This bootstrap sampling typically leaves 30% of the data out of bag (OOB). The stepwise description of random forest algorithm is given below (Jain et al. 2007). • Take N bootstrap samples from the original data. • For each bootstrap sample, grow a tree by randomly picking m variables out of M to determine the best split at each node. • Predict new data by aggregating the predictions of the N trees. An estimate of the error rate can be obtained, based on the training data, using the following steps. a. At each bootstrap iteration, predict the data not present in the bootstrap sample (i.e., OOB data) using the tree grown with the bootstrap sample. b. Aggregate the OOB predictions. Calculate the mean square error (MSE) rate and call it the OOB estimate of the error rate, given by

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MSEOOB =

N ]2 1 ∑ [ OOB Y (X i ) − Yi N i=1

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(9.4)

To estimate the importance of the mth variable in the OOB cases for the kth tree, all values of the mth variable are randomly permuted. These altered OOB covariate values are then put down the tree, and function approximation is obtained. Then, a new internal error rate MSEm is calculated. The amount, by which this new error exceeds the original test set error, is defined as the importance of the mth variable, which is given by Importancem = MSEOOB −MSEm .

(9.5)

c. Solution Algorithm Figure 9.5 shows the block diagram of the method of ATC determination using RBFNN. A large number of patterns are generated by randomly varying the loads at each bus over a wide range. Then, the reduced number of inputs as well as size of the neural network is obtained by performing input feature selection (Block I). Next, normalization of the selected inputs as well as outputs is done (Jain et al. 2011). (Block II). The number of nodes in the hidden layer, cluster center, and its width are determined using Euclidean distance-based clustering technique (Block III). These hidden units receive the input data directly, and the supervised learning is used to alter the weights connecting the hidden layer to the output layer (Block IV).

9.4.3 Machine Learning Approach for Electricity Price/Load Forecast Forecasting electric energy consumption or energy price provides future trends and patterns of electric power consumption. Various factors such as temperature, humidity, seasons, holidays, working days, appliances usage, and number of occupants affect the electricity consumption. There are three types of forecasting (Zahid et al. 2019): • Short-Term Forecasting (STF): Duration from a few minutes to hours • Medium-Term Forecasting (MTF): Duration from one month to one year • Long-Term Forecasting (LTF): Duration from one year to several years. STF provides accurate forecasting compared to the others. Under smart grid scenario, consumers can participate in the operations of smart grid by shifting their load from peak period to off-peak period based on price signals. Therefore, price forecasting is done by customers or power traders to maximize savings/profits.

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Fig. 9.5 Conceptual diagram of learning model for ATC estimation

Electricity price/Load Forecasting Model With sufficient data, a machine learning model can look at a sequence of actions leading to the electricity price/load forecasting as shown in Fig. 9.6. a. Data Source Power utilities or market operators manage wheeling of power from sources to sinks. They generate big data pertaining to power demand and price, power generation and distribution. Same may be used for study purpose.

Fig. 9.6 Frame work for price/load forecasting

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b. Feature Extraction Feature extraction is a process used to select a subset of data out of actual data to obtain accurate results compared to actual data. Techniques like DT and recursive feature elimination are used to extract features from data. c. Feature Selection It is a process used to eliminate the less important features from the dataset. Hence, accuracy as well as computational efficiency is enhanced. Random forest, decision tree, etc., are used for feature selection. These techniques calculate the significance of all features in vector form with respect to the target, i.e., electricity price or load. Threshold ε is used to control feature selection. Features having importance greater than or equal to the threshold ε are considered, and rests of the features are dropped (Zahid et al. 2019). d. Parameters tuning and cross-validation Parameter tuning is very vital to do precise and effective forecasting. ML techniques like ANN and SVM are used as classifiers. e. Price/Load Forecasting algorithm Once the features are identified and the parameters are tuned, they are used in machine learning algorithm to predict electricity price/load. Algorithm for price/load forecast is as follows: • • • •

Input: price/load data. Distinct target data and features data. Divide data into training and testing datasets. Carry out feature extraction and feature selection using appropriate ML techniques like decision tree and random forest algorithm. Find out the significance of all the features with respect to target (price/load) and drop less important features. • The processed data used by ML technique like ANN to forecast price or load. • Compare forecasted price/load with actual price/load. f. Performance evaluation The performance of proposed ML approaches may be assessed using various indices such as mean average percentage error (MAPE), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). The formulas for calculating these performances are given below: MAPE =

| | T | Dv − Pv | 1 ∑ | 100|| T T =1 Dv |

(9.6)

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⎡ | T |1 ∑ RMSE = √ ( Dv − Pv )2 T T =1

(9.7)

MSE =

T 1 ∑ ( Dv − Pv )2 T T =1

(9.8)

MAE =

) T ( ∑ Pv − Dv , T T =1

(9.9)

where Dv = desired/actual value at time T, and Pv = predicted/forecasted value at time T.

9.5 Future Directions Smart grid is a future electricity grid. It allows two-way flows of electricity and information for maintaining grid. With the evolution of smart grid, realization of a reliable, secure, and stable power grid is essential. Hence, future research directions may include the following: • Hybrid machine learning approaches may be explored for determining smart grid security in expedite manner. • A hybrid optimization technique can be employed to optimize demand response under smart grid for real power balance. • In restructured power system, electricity price is volatile due to strategic behavior of participants and oligopoly nature of competitive electricity markets. Therefore, an evolutionary machine learning algorithm may be explored for developing optimal strategy of a participant. • Deep learning algorithm may be explored for power quality monitoring under smart grid environment.

9.6 Conclusion This chapter presents briefly the working principle of various ML algorithms like ANN, RF, and DT algorithms. Issues and challenges in smart grid are discussed considering the complexity of the system due to bulk volume of data. The selection of best-fit ML algorithm to solve a problem is a tough task; therefore, a basic idea pertaining to the selection of a suitable ML algorithm for solving a particular problem is deliberated. Further, a machine learning approach to predict power system security, to estimate available transfer capability of a tie line, and to forecast electricity price/load is discussed in length. Future research direction based on

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machine learning is also proposed under smart grid paradigm. From the discussion, it is emerged that handling of huge amount of data collected from various sources is the main challenge in smart grid. Various machine learning algorithms and other artificial intelligence methods and their hybrid combinations can efficiently solve the smart grid data handling problem.

References Ananth (2018) Machine learning-intelligent decisions based on data. Retrieved from https://witanw orld.com Bhattacharya S (2017) Machine learning classification techniques. Retrieved from https://www.lin kedin.com Bhesdadiya RH, Patel RM (Jan 2015) Notice of removal: review of available transfer capability calculation methods. In: 2015 international conference on electrical, electronics, signals, communication and optimization (EESCO). IEEE, pp 1–6 Chakrabarti S, Jeyasurya B (2007) An enhanced radial basis function network for voltage stability monitoring considering multiple contingencies. Electric Power Syst Res 77(7):780–787 Dong Z, Zhang P (2010) Conclusions and future trends in emerging techniques. In: Emerging techniques in power system analysis. Springer, Berlin, Heidelberg, pp 185–193 Jain T, Srivastava L, Singh SN (2003) Fast voltage contingency screening using radial basis function neural network. IEEE Trans Power Syst 18(4):1359–1366 Jain T, Singh SN, Srivastava SC (2010) Adaptive wavelet neural network-based fast dynamic available transfer capability determination. IET Gener Transm Distrib 4(4):519–529 Jain T, Singh SN, Srivastava SC (2011) Fast static available transfer capability determination using radial basis function neural network. Appl Soft Comput 11(2):2756–2764 Jain AK, Srivastava SC (2017) Price responsive demand management of an industrial buyer in day-ahead electricity market. Int J Emerg Electr Power Syst 18(1) Jain T, Singh SN, Srivastava SC (June 2007) A neural network based method for fast ATC estimation in electricity markets. In: 2007 IEEE power engineering society general meeting. IEEE, pp 1–8 Madhusudhana Rao G, Narasimhaswamy I, Kumar BS (Dec 2010) Deregulated power system load forecasting using artificial intelligence. In: 2010 IEEE international conference on computational intelligence and computing research. IEEE, pp 1–5 Manikandan BV, Raja SC, Venkatesh P, Kannan PS (2008) Available transfer capability determination in the restructured electricity market. Electr Power Compon Syst 36(9):941–959 Pandey SN, Tapaswi S, Srivastava L (2008) Locational marginal price projection using a novel RBFNN approach in spot power market. In: 2008 Joint international conference on power system technology and IEEE power India conference. IEEE, pp 1–7 Pindoriya NM, Singh SN, Singh SK (2008) An adaptive wavelet neural network-based energy price forecasting in electricity markets. IEEE Trans Power Syst 23(3):1423–1432 Shafi I, Ahmad J, Shah SI, Kashif FM (Dec 2006) Impact of varying neurons and hidden layers in neural network architecture for a time frequency application. In: 2006 IEEE international multitopic conference. IEEE, pp 188–193 Zahid M, Ahmed F, Javaid N, Abbasi RA, Zainab Kazmi HS, Javaid et al (2019) Electricity price and load forecasting using enhanced convolutional neural network and enhanced support vector regression in smart grids. Electronics 8(2):122

Chapter 10

Energy Pool Management Mechanisms Rajvir Kaur, Saurabh Kumar, and K. Vijayakumar

Abstract In this chapter, mechanism for energy pool management is discussed. In the following sections of the chapter, the benefits and challenges of distributed energy resources (DER) are presented in detail. Further, the need of optimal sizing and energy management of energy pool is elaborated. Moreover, a case study on base station of telecom tower is carried out to demonstrate the impact of energy management on hybrid energy pool. Firstly, the optimal sizing of the system is implemented. A multi-objective optimal sizing problem is formulated to estimate the size of PV panels and battery bank. A macrocell telecom tower base station is considered having peak load of 3.5 kW. Three objectives are formulated which are: annualized cost of electricity (LCOE), loss of power supply probability (LPSP), and excess energy generation (EE). Further, a VES-based DRM is implemented, and the performance of the system is evaluated based on the performance index defined. A VES-based DRM algorithm is implemented on same random day which align the turn on and off of air conditioner with respect to availability of PV generation. Moreover, the comparative analysis is carried out to validate the effectiveness of VES-based DRM over without VES-based DRM. Keywords Distributed energy resources · Optimal planning · Energy management · Virtual energy storage · Telecom power supply · Renewable energy resources

10.1 Introduction The demand for electricity has grown in the last two decades significantly, mainly because of advanced living standard and population growth. Because of the environmental issue and limited availability of conventional energy resources (coal, petroleum products, etc.), it has shifted the emphasis on renewable energy resources R. Kaur (B) · S. Kumar · K. Vijayakumar Department of Electrical and Electronics Engineering, National Institute of Technology, Puducherry, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. N. Singhetal.(eds.), Optimal Planning and Operation of Distributed Energy Resources, Energy Systems in Electrical Engineering, https://doi.org/10.1007/978-981-99-2800-2_10

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for maintaining the demand and supply balance. Since these renewable sources of energy are intermittent in nature and some other limitation has to be used in coordination with conventional sources, there has been a change in the way electrical energy is produced, transmitted, and consumed, which led to the concept of energy pool (Ezugwu et al. 2019; Kumar et al. 2018). The energy pool has improved reliability, individual control, environment friendly and can be used as ancillary services. These advantages come with some technical and economic challenges like protection, synchronization, coordination control, power quality, reactive power compensation, standardization, communication, energy management, and market policy (Olatomiwa et al. 2016; Kumar et al. 2020). An energy pool consists of several distributed energy resources (DER), loads (AC/DC), storage units, and converters. DERs are small-scale, scalable, energy generator, and storage technologies that provide electric energy and are consumed at site (https://www.nrel.gov/docs/fy02osti/31570.pdf). DERs are also named as local/embedded/behind-the-meter generation. Generally, the capacity of DERs is in the range of 3 kW–50 MW, which are installed on site to meet the dedicated electricity demand (https://www.wbdg.org/resources/distributed-energy-resourcesder). DERs are alternative or enhancement to traditional centralized grid, which are either connected to the local electric power grid or isolated from the grid in standalone applications. DERs include renewable and non-renewable energy resources, energy storage, power converter such, electric vehicles (EV), and other controlled loads (Wojszczyk et al. 2008). Common examples of DERs include wind energy conversion system (WECS), photo-voltaics (PV), fuel cells, micro-turbines, reciprocating engines, combustion turbines, co-generation, and energy storage systems.

10.1.1 Benefits of DER The share of DERs in electricity market is expected to increase in near future. For example, the share of DER in Australia’s electricity generation capacity is estimated to increase upto 45% with the electricity network transformation roadmap (https://www.wbdg.org/resources/distributed-energy-resources-der). The arrival of DERs provides decentralized, locally generated energy which is transforming the conventional grid by its bidirectional flow of power. The increase in penetration of DERs into power grid gives newer opportunities and benefits for power system and consumers. • Affordability: The consumers with installed DER at their site may pay in-occur reduced electricity bill as they can sell surplus power back to main power grid. Stability: Electricity generated by DERs assists the grid to stabilize during peak hours. • Reduced network costs: As the electricity is generated and consumed locally, this helps in reducing the network expansion cost.

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• Reliability: DERs can help enhance the overall reliability by reducing the peak load and acting as backup. • Green power: Consumers can generate electricity at their site by installing environment-friendly resources such as WECS, PV, fuel cell, combined heat power systems. It helps to reduce emissions. • Improved power quality (PQ): DER may be designed to mitigate the power quality issues in the given site such as ride through momentary outages using a UPS or distributed storage device.

10.1.2 Challenges DERs incorporation into the grid do not require infrastructure up-gradation. However, grid modernization is required to safely integrate the DERs into the distribution network. The increasing DER penetration in distribution network poses challenge to the smooth operation of the grid within the safety and stability limits (Andrén et al. 2014). Few challenges associated with high penetration of DER into the grid are: • A flexible business model for the electricity market is required which allows energy trade from DER and participation of consumers in the market. The model should reduce the overall system cost while ensuring the system stability (Kushwaha et al. 2018). • In the modern digitized grid, the integration of DERs allows consumer participation and two way power flow and data communication. Thus, the digitization and automation of grid have increased the vulnerability toward the cyber-security (Kaur et al. 2021; Ghahremani et al. 2021). • The participation of consumer in electricity market with integration of DE, has increased the uncertainty due to unexpected consumer behavior (Chawda et al. 2021). • There is a need to develop productive data-enabled plug and play products and services (Miranda et al. 2018). • Network optimization techniques to optimize the distribution network capacity. • Development of techniques for the integration and coordination of flexible loads and energy pool (Kumar et al. 2021). • A framework is needed that reforms tariff, network capacity assessment, market structure for maximum utilization of DERs (Kaur et al. 2018). Consequently, the utility operators are forced to look for the development and implementation of intelligent approaches in planning and operation of power system in the presence of high penetration of DERs. In the modern power system with high penetration of DERs, the power balance of generation and demand is challenging. The power generated by DERs such as renewable resources (e.g., PV and WECS) is intermittent and uncertain in nature as get effected by varying weather and geography of the site. In standalone energy

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pools, the spinning reserve such as diesel generator is added to ensure power balance in presence of renewable resources. The operation of diesel generator needs regular maintenance and causes carbon emissions (Reddy et al. 2013). Nowadays, battery energy storage is deployed to overcome the intermittent and random nature of renewable resources. The size of battery banks is decided based on number of autonomous hours, demand and overall energy resource mix the energy pool (Frazier et al. 2020). A proper resource adequacy assessment should be carried to decide the size of different resources in the energy pool and capacity of battery bank to be installed while doing the system planning. The reliable operation economical operation of DER-based energy pool is ensured by optimal sizing and energy management techniques.

10.2 Optimal Sizing An undersized system will result in energy deficit situation; eventually, the continuity of power supply will break. Thus, the network quality degrades and the subscribers get substandard services. On the other hand, an oversized system requires higher initial investment and results in underutilization of resources (Kaur et al. 2019). It is challenging to solve multiple objective optimal sizing problem employing conventional techniques such as weighted linear combination or epsilon constraint method (Kaur 2018). In these strategies, the effect of any one objective may dominate the solution in a higher proportion. If the objective functions are not commensurable, then multi-objective algorithms are implemented. The variant of differential algorithm is implemented to get the optimal resource configuration (Zhou et al. 2017). Further, multi-objective particle swarm optimization (MPSO) is employed for optimal sizing. MPSO has fast convergence however stagnates at local minima and may result in a sub-optimal result (Sadeghi et al. 2020). The optimal size for PV-batterybased power supply for telecom base station using is proposed in multi-objective gray wolf optimization (MGWO) (Kaur et al. 2019).

10.3 Energy Management of DER The renewable power generation is estimated based on the weather forecast information; thus, exact generation-demand profile determination is difficult. Consequently, offline energy management algorithms are less effective in presence of DERs (Sheng et al. 2017). In DRM algorithms, encourage the consumers to shift or reduce their power consumption at the given time such that power balance of generation and load demand are met (Yi et al. 2013). DRM algorithms based on time of use or incentive concept are implemented for residential consumers in the form of home energy management system (HEMS) (Chellamani and Chandramani 2019). HEMS reschedules the inter-

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ruptible loads in peak demand hours as per market conditions to gain the financial incentive or lower the electricity bills (Hug-Glanzmann 2011). Interruptible loads are those loads which can readily change their consumption by fixed amount for the predefined time as directed by DRM algorithm (Wang et al. 2014). Consumption units such as air conditioner and refrigerator are thermostatically controlled units which have time constant of 30 min. A DRM algorithm is proposed in (Hao et al. 2014), which has demonstrated the energy management in commercial building by controlling the consumption by air conditioning units. Further, DRM in residential consumers based on thermostatically controlled loads is carried by developing state queuing mode based on price response (Lu and Chassin 2004; Hatziargyriou et al. 1990). In this chapter, a DRM algorithm based on controlling the thermal load with large time constant which generates the virtual storage capacity analogous to electrochemical batteries is implemented for the energy pool consisting of renewable DERs. The thermal loads are modeled based on Newton’ cooling law considering the cyclic on and off patterns (Pipattanasomporn et al. 2014).

10.4 A Case Study: DRM-Based Power Supply for Telecom Tower Base Station The exponential growth in telecom sector requires the installation of new telecom towers, especially in remote and rural areas. The remote and rural areas face frequent power outages or has poor quality power supply (Feng et al. 2012). Conventionally, telecom towers in remote and rural areas are supplied by diesel generator set along battery bank. The operation of diesel generator set causes both air and noise pollution. The connected load of one tower is in the range of 600–3000 W which is small in global perspective. However, if the total load demand of the base stations of more than 3 billion telecom towers worldwide becomes approximately 3% of energy consumption in the whole world (Alsharif et al. 2017). The operation of telecom towers with conventional power supply contributes approximately 170,000 kg of carbon emission (Chamola and Sikdar 2016; Renga et al. 2017). Thus, a DERbased base stations power supply for telecom towers installed in remote and rural is suggested in the present chapter. The proposed DER-based power supply consists of PV panels along with battery bank which is environment friendly. DER-based power supply generates electricity on site and consumed locally at base station meeting the requirements of the base station power supply. Generally, a telecom tower base station with a rating in the range 1–3 kW is installed in the rural and remote areas. Conventionally, the telecom tower base station has power supply from the grid along with diesel generator set and battery. The conventional power supply architecture of telecom tower base station is shown in Fig. 10.1. A telecom tower base station consists of electronic devices (such as transceiver, receiver, power amplifier, network equipment), lighting load, and air conditioner. The

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P M U

AC DC DC DC Converter Converter

48V

PWM Controller

D C B U S

Grid DG set

POL Converter

Battery DC DC Converter

Telecom Tower Base Station

Fig. 10.1 Architecture of conventional power supply for base station

base station load is categorized as non-critical such as lighting load and critical load which includes computer or network equipment and switching equipment. Critical load cannot support power outages greater than 3 ms; thus, an uninterrupted power supply unit is installed with battery backup of more than 30 min. The non-critical loads which can withstand small power cuts such as air conditioning and lighting loads are backed by diesel generator. The power supply consists of a DC distribution bus system of 48 V which supplies electronic equipment which are rated at different voltages in the range 3.3–48 V using point of load converters.

10.4.1 Need for DER for Base Station Power Supply The use of diesel generator set and battery bank for supplying base station loads has following issues: • The automatic switch which connects diesel generator to the load is a source of single point failure. If the switch fails to operate properly at the time of power outage, then the load could not be transferred to the diesel generator (Chandrasena et al. 2015; Onar et al. 2010). • There is a diesel generator in conventional base station power supply which is on standby during normal operation of the system. Sometimes, a well-maintained diesel generator fails to start which is 15% in 15 min for one day mission (Nguyen et al. 2019). • The operation of diesel generator contributes toward the largest share of overall operating expenditure of telecom operators which includes transportation cost for regular maintenance visits for the diesel generator, storage of fuel at the base station, and increasing fuel cost. • The diesel generator operation causes air and noise pollution (Payman et al. 2008).

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• Generally, battery bank provides back up for 6–8 h. Thus, the large size battery becomes the bulkiest element in the power supply which requires big room with special flooring and cooling arrangement. The life cycle of the battery is smaller among all equipment, which needs regular replacement. Further, the large size battery bank requires regular maintenance increases the operating expenditure of the telecom operator (Chamola and Sikdar 2016). Thus, the diesel generator set with battery bank supplying the base station cause increased operating expenditure, environment and reliability issues. Thus, there is a need for alternative resources which meets the requirement of the base station power supply which are: less operating cost, high power density, well-regulated DC bus voltage, uninterrupted power supply which does not cause carbon emissions. DERs which generate electricity on site are suitable alternative for the conventional power supply for base station. Therefore, PV panels along with battery bank are proposed in this chapter for supplying base station in remote and rural areas. Battery energy storage system supplies the base station load during the non-availability of electricity from PV panels which are the main source electricity generation. Thus, integration of battery with PV panels in power supply increases the flexibility and availability of the system. Batteries store the excess energy generated by renewables as well as provide energy during deficit. Also, optimal size of the system components is important for the reliable, continuous, and economical operation of the base station power supply. In planning stage, it is critical to decide the configuration of the DER-based power supply as well as size of the system. The architecture of proposed power supply for base station is shown in Fig. 10.2. DC–DC converter such as Cuk converter is used to interface PV panel directly to the DC distribution bus which ensures the voltage regulation under load and source disturbances. Usually, central hierarchical control is adopted base station power supply which has three control layers which are: primary layer, secondary layer, and tertiary layer. Primary-level controller ensures that the local current and voltages are maintained within the predefined set limits. Secondary-level controller is responsible for regulating the DC bus voltage . The tertiary-level controller does the power management. In this chapter, a virtual energy storage (VES)-based power management tertiary controller is proposed.

10.4.2 System Modeling It is assumed that PV panels installation on site has the provision of power point tracking. The power generation from PV panel is dependent on carrying weather condition and irradiation. Therefore, power generated by PV panels (Ppv ): Ppv = Pr

g [1 + K t (Ta + 0.0256g) − Tref ] gref

(10.1)

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PV Panels

DC DC Converter

48V

PWM Controller

D C B U S

DC AC Converter AC Load

DC Load POL Converter

Battery DC DC Converter

Base Station load

Fig. 10.2 Architecture of proposed power supply for base station

Pp = η p η M ηd Ppv

(10.2)

where η p , η M and ηd are conversion efficiency of PV panel, maximum power point tracking factor, and de-rating constant for PV panels, respectively. The battery capacity (E b max ) is calculated for connected load (PL ), battery efficiency (ηb), and converter efficiency (ηconv ) for desired autonomous days (ad) is as follows,

E b max =

PL ad DoDηconv ηb

(10.3)

10.4.3 Battery Charging and Discharging Strategy Battery energy storage system is integrated with PV panels to ensure the high availability and continuity of base station power supply. The size of battery bank is designed to supply power to base station when PV panels are not generating enough electricity to meet the base station load demand. The battery bank charging happens during the surplus energy generation by PV panels after fulfilling the load demand, i.e., (Pp,t − PL ,t > 0). To increase the life cycle of battery, the charging of batteries beyond the maximum state of charge (SoCmax ) limit is avoided. Thus, charging happens only if SoCt < SoCmax . SoCt+1 = (1 − b)SoCt + ηr t (Pp,t − PL ,t )t

(10.4)

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where SoCmax = E b max . Battery discharges during energy deficit, i.e., when the energy generated by PV panels is lesser than the base station load demand, i.e., (Pp,t − PL ,t < 0). Again, the battery is not discharged beyond the minimum state of charge limit (SoCmin ) such that Soct > SoCmin . SoCt+1 = (1 − b)SoCt + ηr t (Pp,t − PL ,t )t

(10.5)

where SoCmin = (1 − DoD)E b max .

10.4.4 Optimal Sizing Problem Formulation To meet the requirements of base station power supply which are high availability, continuity, economical, and environment friendly, a multiple objective optimal sizing problem is constructed to estimate the size of PV panels and battery bank. Three objectives are formulated which are: loss of power supply probability (LPSP), annualized cost of electricity (LCOE), and excess energy generation (EE). (1) LCOE: The economic analysis of PV-based base station power supply is carried to by calculating the cost of electricity generation (COE) of PV-battery-based power supply. COE is calculated considering total present cost of the system (TPC) and annual load (PLa . TPC consists of the investment, replacement, maintenance and operational cost of PV panels, battery bank, and integrating converters. The system components with their cost and lifespan are given in Table 10.1.

COE = 3

TPC($)

t=0

PLa (kWh)

C RF

(10.6)

i(1+i ) where, CRF = (1+i) n −1 . (2) LPSP: LPSP is defined as the probability of power deficit to the load. LPSP is the measure to estimate the availability of power supply. It is calculated when renewable power is fails to meet the total load demand at a given time and gets accumulated over the time period considered for analysis. LPSP is in the range 0–1, where LPSP = 0 depicts 100% available power supply. n

 LPSPt =

PL ,t − Pp,t + PSoCmin ($)  PL ,t

(10.7)

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Table 10.1 Cost and lifespan of the power supply equipment Investment cost of PV 1200 Lifespan of PV, project 25 ($/kW) Investment cost of battery ($/kWh) Investment cost of converter ($/kW) Maintenance cost of battery Real interest (p.a.)

182

Lifespan of battery

3

127

Lifespan of converter

15

10%

Rating of PV panel (kW) ηconv , ηb (%)

0.2

6%

94, 85

(3) Excess Energy: It is the tendency the sizing algorithm to get the oversized renewable resource configuration to get an economical and reliable power supply. However, the oversized system leads to under utilization of resources and unnecessarily increases the initial investment cost for operators. When the surplus energy is available from PV panels after meeting the load demand and battery charging, the excess energy factor is calculated. EEt = Pp,t − PL ,t only when SoCt = SoCmax

(10.8)

10.4.5 Problem Formulation A multiple objective optimal sizing problem is constructed to perform the technoeconomic analysis of the PV-battery power supply for base station. The objective functions are minimized in the process of optimization which are: minimization of COE, LPSP, and EE. The objectives are function of decision variable which are the rated capacity of PV (Pr ), and battery (E b max ). NSGA-II (Deb et al. 2002) is a multi-objective algorithm which minimizes all the objectives simultaneously to non-dominant Pareto optimal solution set. The operator can select any solution per the requirement without worsening any of the objectives. F1 minimize

=

26280  t=0

F2

COE, minimize =

26280  t=0

F3

LPSP, minimize =

26280  t=0

EE

(10.9)

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Evaluate objective function(F)

Rank population(non dominant sorting) Selection

Start

Crossover Stop

Mutation

Report final population

Select N individuals

Stopping criteria met?

Evaluate objective function(F)

Child population

Initialize the size of population, range for P_pr, ad. Maxiter.

225

Combine parent, child and rank populations

Fig. 10.3 Flow chart of optimization process based on NSGA-II flowchart

10.4.6 Optimization Process The flowchart in Fig. 10.3 shows steps involved in NSGA-II implemented for optimal sizing of PV-battery-based power supply. Each simulation step t simulates one hour in real-time scenario. Firstly, initial population of Ppr and ad is generated considering the upper and lower bounds of each decision variable. Then irradiation data is loaded for the PV power generation calculation. The rated capacity of battery is calculated for each member (decision variable ad) in the current population. For each simulation step, battery charging and discharging happens as per the strategy given in Sect. 10.4.3. If PV-battery power fails to meet the base station demand, then LPSPt is calculated for each simulation step, i.e., every hour. When surplus energy generation is available from PV panels after fulfilling the base station load demand and battery charging to its rated capacity, hourly EEt is calculated. Then offspring population is reproduced by following NSGA-II algorithm (Deb et al. 2002).

10.4.7 Energy Management in Base Station Power Supply Energy management is the basic need for future telecommunication industry to have economical, reliable, and energy-efficient operation as more and more telecom towers will be installed to avail network services in remote and rural areas. Conventional way of energy management is done at three levels: network, site sheltering, and energy consumption at base station. Due to limited floor area at the

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Fig. 10.4 Power consumption of different equipment at base station

17.50%

7.50%

10% 65%

Power amplifier (with feeders) Power supply

Signal processing Air conditioning

base station, the site sheltering energy management approaches place the electronic equipment compactly which demands proper cooling to maintain the temperature within safe limits. Among all the connected load at base station, air conditioning unit consumes 17.50% of total demand. The power consumption averages of the different load connected at base station is shown in Fig. 10.4 (Ayang et al. 2016). The power consumption at base station can be reduced by installing additional elements such as heat ex-changers, filters, and fans (Roy 2008). A demand response management-based algorithm is implemented at base station power supply for real-time power balance. Air conditioners which are thermal loads having time constant of 30 min are modeled analogous to batteries to develop virtual energy storage (VES) concept. VES-based energy management algorithm helps to balance the intermittency due to PV power generation.

10.4.8 VES-Based Energy Management Strategy The electro-chemical batteries store chemical energy which is converted to electrical energy for utilization and available as electrical state of charge (SoC). There is internal resistance in the battery, due to which energy get dissipated if battery terminals are connected to the load. On the other hand, air conditioner has a time constant of 30 min which stores thermal energy and represented in terms of kWh. In air conditioner, the stored energy is lost due to heat gain from atmosphere. The analogy of air conditioner with batteries is shown in Fig. 10.5. The air conditioner has cyclic pattern of turn on and off in order to maintain the room temperature (Troom ) in preset range 19– 25 ◦ C. The power consumption by the air conditioner is 1200 W when turned on.

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25

ON

ON

1 0.5

19

OFF

0

20

OFF

40

60

80

Status(ON/OFF)

Troom ( oC)

Fig. 10.5 Analogy of air conditioner with battery

0 100

120

Time (minutes) Fig. 10.6 Room temperature profile with air conditioner

There is no power consumption during the turn off. Generally, turn on duration of air conditioner is 15 min and turn off duration is 45 min. The ambient temperature (Tout ) and air conditioner coil (Tcoil ) are responsible for the change in Troom . dTroom = k(Tout − Troom ) + kac (Tcoil − Troom ) dt

(10.10)

where k and kac are cooling coefficient of room and air conditioner coil receptively. The change in Troom due to ambient temperature and air conditioner coil is shown in Fig. 10.6.

10.4.9 VES Algorithm The DRM algorithm disconnects the certain loads which can interrupted at the given time such that instantaneous power balance of generation and load demand can be met. In PV-battery-based power supply, PV panels are primary source. When PV power is insufficient to meet the load demand, battery bank supplies the load. Thus, the required battery power is given by

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Pb,t = PL ,t − PP,t

(10.11)

Now, virtual energy storage is created by air conditioner load by using DRM algorithm. The air conditioner is a flexible load and has time constant of 30 min. The default turn on and turn off time of air conditioner is 15 and 45 min which is synchronized with PV power generation using proposed VES-based DRM algorithm to create virtual storage capacity. When surplus power is available from PV panels after meeting the load demand and battery charging, the air conditioner is allowed to turn on beyond 15 min. This extended turn on of air conditioner is analogous to battery charging as during turn on period, air conditioner consumes 1200 W. When PV power generated is insufficient to meet the load demand, instead of discharging the battery, air conditioner is turned off. The extended turn off of air conditioner is analogous to battery discharging as now the air conditioner will not consume any power; thus, virtually 1200 W power is availed from air conditioner. In this process, it is ensured that the room temperature limits are not violated. The generation and utilization of virtual energy (discharging and charging powers) are governed by the following equations: Priority values to turn off thermal load: σoff,t = 1 −

Ton,max − Toff,t Ton,max

(10.12)

Toff,max − Toff,t Toff,max

(10.13)

where Ton,t ≥ Ton,max . Priority values to turn on thermal load: σon,t = 1 −

where Toff,t ≥ Toff,max . Virtual power generated by turning off the appliances at any interval is calculated as: Pvsc,t = Pac where,Ton,t ≥ Ton,max

(10.14)

Virtual power consumed by turning on the appliances is calculated as: Pvsc,t = 0 − Pac

where, Toff,t ≥ Toff,max

(10.15)

The flowchart shown in Fig. 10.7 gives the description of the VES-based DRM algorithm.

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Fig. 10.7 Priority-based strategic control for VES

10.4.10 Performance Analysis of Energy Management Algorithm In the present study, a macrocell telecom tower base station is considered having peak load of 3.5 kW. The optimal size of PV-battery-based powers supply is calculated using NSGA-II algorithm as described in Sect. 10.4.6. The Pareto optimal front obtained from NSGA-II algorithm which solves the multi-objective optimal sizing problem is shown in Fig. 10.8. By applying min max algorithm on the solution set obtained in the Pareto optimal front, the optimal rated capacity of PV panels and battery bank size are 6.9 kWp and 400 V, 15.1 Ah, respectively. The working of the VES-based DRM algorithm is analyzed for any random day considering a time step of 1 min. Figure 10.9 shows the PV power, load demand, and power needed from battery. Now, VES-based DRM algorithm is implemented on same day which will align the turn on and off of air conditioner with respect to PV power availability. The virtual storage capacity generation as a result of VESbased DRM algorithm is shown in Fig. 10.10. It is observed that surplus PV power is available during the daytime from 10:00 am to 4:00 pm. During this duration, the air conditioners are strategically turned on for effective utilization of surplus PV power

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-4

10

Excess energy factor(%)

2 1.5 1 0.5 0 0.3 1.5

0.2 1

0.1

0.5 0

Price($/kWh)

0

LPSP

Fig. 10.8 Pareto optimal front

PL,t

Power (kW)

5

PP,t

Pb,t

0

-5 0

200

400

600

800

1000

1200

1400

Time (minutes)

Fig. 10.9 Connected load demand, power generated by PV panels, and battery power requirement on a random day

and reduced the battery power consumption. The negative sign of power due to virtual stored capacity (Pvsc,t ) shows that power is consumed by air conditioner which is analogous to battery charging. When PV power is insufficient to meet load demand, first the air conditioner is turned off as per the VES-based DRM algorithm which virtually generates Pvsc,t which is shown by positive sign in Fig. 10.10. The overall switching frequency of air conditioner remains unchanged after the implementing algorithm proposed VES-based DRM algorithm. Further, the VES-based DRM algorithm is run for one year to analyze the performance of system. The strategically switching on and off duration of the load as per the VES-based DRM algorithm improves the overall reliability of the system. In general, the system tends to be oversized to enhance the reliability, resulting in increase in LCOE. Moreover, the resources are underutilized in an oversized system and thus increase in excess energy generation index. Table 10.2 shows the comparison of the performance index with and without VES-based DRM algorithm. The LPSP index is

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Fig. 10.10 Impact of VES-based DRM algorithm on a typical day Table 10.2 Performance index of system with and without VES Ap ad (h) LCOE ($) LPSP Without VES With VES

24 24

6.8 6.8

0.1302 0.1302

0.0832 0.041

EE 0.197 0.138

a measure of reliability of the power supply and has improved to 0.041 from 0.0832. Moreover, the EE index has decreased from the 0.197 to 0.138. It can be summarized from the above discussion that the reliability of the system is enhanced by 50% with implementation of VES-based algorithm in comparison to the system without energy management. Further, the utilization of resources is enhanced by 30%.

10.5 Conclusion Energy pool seems to be the solution of energy crisis by making the integration of renewable resources possible to the central grid and providing a platform for uses to the consumer with less conversion and transmission losses. Even though it has broad outlook, it faces certain challenge which are very critical for reliability, performance, and cost of the system. In actual practice, the implementation of technological advancement has made the operation and control accurate and precise but the cost of operation and control has increased significantly. It will make the system uneconomical. Since these renewable sources are intermittent and wide in nature making the coordination a difficult task, the protection of the system is still a concern. More research work has to be done to make the protection efficient. The use of power electronic devices has increased in control and operation making the power quality a big issue from consumer point of view. There is wide scope for research in control and operation of energy pool. In this chapter, a case study on base station of telecom tower is carried out to demonstrate the impact of energy management on hybrid energy pool. Firstly, the optimal sizing of the system is implemented. A

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multi-objective optimal sizing problem is formulated to estimate the size of PV panels and battery bank. A macrocell telecom tower base station is considered having peak load of 3.5 kW. Three objectives are formulated which are: annualized cost of electricity (LCOE), loss of power supply probability (LPSP), and excess energy generation (EE). Further, a VES-based DRM is carried out, and the performance of the system is evaluated based on the performance index defined. A VES-based DRM algorithm is implemented on same random day which aligns the turn on and off of air conditioner with respect to availability PV generation. Moreover, the comparative analysis is carried out to validate the effectiveness of VES-based DRM over without VES-based DRM.

References Alsharif MH, Kim J, Kim JH (2017) Green and sustainable cellular base stations: an overview and future research directions. Energies 10(5):587 Andrén F, Bründlinger R, Strasser T (2014) IEC 61850/61499 control of distributed energy resources: concept, guidelines, and implementation. IEEE Trans Energy Convers 29(4):1008– 1017 Ayang A, Ngohe-Ekam P-S, Videme B, Temga J (2016) Power consumption: base stations of telecommunication in Sahel zone of Cameroon: typology based on the power consumption-model and energy savings. J Energy 2016 Chamola V, Sikdar B (2016) Solar powered cellular base stations: current scenario, issues and proposed solutions. IEEE Commun Mag 54(5):108–114 Chandrasena RP, Shahnia F, Rajakaruna S, Ghosh A (2015) Dynamic operation and control of a hybrid nanogrid system for future community houses. IET Gener Transmiss Distrib 9(11):1168– 1178 Chawda S, Mathuria P, Bhakar R (2021) Bi-level approach for load serving entity’s sale price determination under spot price uncertainty and renewable availability. Technol Econ Smart Grids Sustain Energy 6(1):1–11 Chellamani GK, Chandramani PV (2019) Demand response management system with discrete time window using supervised learning algorithm. Cognitive Syst Res 57:131–138 Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2):182–197 Distributed energy resources (DER) [online]. Available https://arena.gov.au/renewable-energy/ distributed-energy-resources/ Distributed energy resources (DER) [online]. Available https://www.wbdg.org/resources/ distributed-energy-resources-der Ezugwu EO, Dike DO, Okozi SO, Olubiwe M, Paulinus-Nwammuo CF (2019) Design of power pool scheme for demand-side management of co-located banks. Am J Electr Power Energy Syst 8(4):86–94 Feng D, Jiang C, Lim G, Cimini LJ, Feng G, Li GY (2012) A survey of energy-efficient wireless communications. IEEE Commun Surv Tutor 15(1):167–178 Frazier AW, Cole W, Denholm P, Greer D, Gagnon P (2020) Assessing the potential of battery storage as a peaking capacity resource in the united states. Appl Energy 275:115385 Ghahremani S, Sidhu R, Yau DK, Cheung N-M, Albrethsen J (2021) Defense against power system time delay attacks via attention-based multivariate deep learning. In: 2021 IEEE international conference on communications, control, and computing technologies for smart grids (SmartGridComm), pp 115–120

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Chapter 11

Reliability Analysis of Distribution System with Integration of Distributed Generation Resources Umesh Agarwal, Naveen Jain, and Manoj Kumawat

Abstract Due to the increased demand for energy and depletion of the fossil fuels, the energy production using renewable energy resources simultaneously with the conventional power generating plant will make a significant contribution to sustainable power production. Wind and solar energy resources are the promising sources of renewable energy. They have huge potential that can satisfy the energy demand and can reduce the emission of harmful greenhouse gases emitted by the conventional power plants. Reliability assessment is one of the key indicators to measure the impact of the renewable energy-based distributed generation (DG) units in the distribution networks and to minimize the cost that is associated with power outage. Being the only link between consumers and utility, it becomes the prime need to enhance the reliability of distribution network in terms of reduction in expected interruption cost and energy not served. This chapter presents the reliability assessment of distribution network connected at Bus-2 of Roy Billinton Test System (RBTS) with and without DG. To get an in-depth assessment on DG impact, multiple DG placement is also considered in this chapter. The results obtained from the case studies have demonstrated the effectiveness of using DG to enhance the reliability of the conventional distribution system. Keywords Distributed generation · Distribution network · Reliability assessment · Renewable energy

U. Agarwal (B) · N. Jain Department of Electrical Engineering, College of Technology and Engineering, Udaipur, Rajasthan, India e-mail: [email protected] M. Kumawat Department of Electrical Engineering, National Institute of Technology Delhi, New Delhi, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 S. N. Singh et al. (eds.), Optimal Planning and Operation of Distributed Energy Resources, Energy Systems in Electrical Engineering, https://doi.org/10.1007/978-981-99-2800-2_11

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11.1 Introduction Since the beginning of electricity evolution, the electric power system has been used to generate, transmit and distribute power, and an electrical utility’s primary goal is to provide affordable, reliable and high-quality power to its customers. The electrical power network is incredibly complicated; a breakdown could result in power outages for a significant number of consumers or even catastrophic events like blackouts, and it’s difficult to analyse the whole system simultaneously. Therefore, from reliability evaluation prospective of the system, the power system is divided into three functional zones such as generation, transmission and distribution. As indicated in Fig. 11.1, these functional zones in series can be considered the hierarchical stages of power system reliability studies (Roy and Jonnavithula 1996). The hierarchical level 1 includes only the generation system and evaluates the reliability by analysing the indices loss of load expectation (LOLE), loss of energy expectation (LOEE), failure frequency and failure duration. Hierarchical level 2 includes both the generation and transmission facilities and evaluates the reliability for both systems. All three functional zones are included in hierarchical level III, which refers to the entire electric power system. It is concerned with assessing the reliability of generation, transmission and distribution networks. In the conventional structure of electrical power systems as depicted in Fig. 11.2, the power is generated at large generating plants. These plants are generally located far away from the load centres and at limited locations. These are termed as central power stations. The power is then transmitted by long transmission line to the distribution substations and then delivered to the consumers. During this process, lot of energy got wasted and huge infrastructure is required to maintain the power quality. As a result, a new term known as distributed generation (DG) is created by the assimilation and incorporation of unconventional or renewable energy sources into the network. In order to serve a customer on-site and support distribution networks at the same time, DG is characterized as small-scale power generation units of electricity (a few kilowatts kW) connected directly to the grid, distribution network and on the consumer side of the metre. These units can also operate independently with various rating levels. On the basis of generation capacity, these DG are classed as

Fig. 11.1 Power system’s functional areas and degrees of hierarchy

11 Reliability Analysis of Distribution System with Integration …

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Fig. 11.2 Conventional large power system

Micro, Small, Medium and Large DG. Table 11.1 displays the formation of DG at various levels (Viral and Khatod 2012). The most cost-effective way to fulfil rising demand due to load growth in the traditional system is to integrate DGs at suitable locations in the distribution network. While also supplying ecologically friendly energy and assisting in meeting the growing demand economically. It may play a major part in the electrical power network, and using this technology in power systems can provide several benefits, including the following: • Improves the system reliability by reducing the interruption duration during the event of fault or power disturbance. • Decreases the power loss that occurs in transmission lines as a result of longdistance power transport. • As demonstrated in Fig. 11.3, improved voltage support is employed to sustain peak demand and deliver power during peak hours. • Improving the power quality of the supply. • Making the customers to have more choices for selecting the power provider utility by creating a competitive market. • Lowering the necessity of expanding generation facilities when load demand rises. • It serves as a backup power supply to ensure continuous power delivery. Presently, most of the power in developed countries or in industrial countries are generated by large centralized facilities, such as fossil fuel powered by coal or gas, nuclear, large solar power plants or hydropower plants. Nowadays, the

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Table 11.1 Impacts of distributed generation S. No.

Category

Impacts

1

Technical

Enhanced voltage profile Improvement in reliability Improved energy efficiency of power supply As a backup source of energy Reduction in power loss Problem regarding protection coordination

2

Financial

Reduces depreciation costs of the fixed assets Delaying the need for investment in new energy infrastructure Reduction in operation and maintenance costs By establishing a positive market environment for new agents, DG lowers power tariffs

3

Environmental

Can be a source for green and clean source of power generation During the energy generation process, ocean wave energy can be hazardous to aquatic organisms Less requirement of space Wind turbines as DG creates noise pollution Wind turbines as DG are particularly not favourable to the bird species

Fig. 11.3 DG in parallel to the main grid

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Fig. 11.4 Conventional power plant with DG integration

advanced system structure, on the other hand, can supply these characteristics through automated operation and renewable energy sources such as sunshine, wind and geothermal. Besides this, these small-scale plants have other benefits like less space requirement, small in size, less capital investment, less commissioning time and moreover less contaminants emission (more preciously CO2 ). With high penetration in the existing distribution network, the DG will definitely affect the power system operation. Figure represents the power distribution network with DG spread at each level of consumers. The distribution network is generally designed with radial configuration (Fig. 11.4). Due to the radial design of the networks, it has been noted in numerous publications that the distribution system provides the largest percentage of power outages at the end-point users (Billinton and Allan 1996). As a result, a lot of scholars are interested in how reliable the distribution system is when wind energy generator, energy storage system and solar PV are present. Studies on the effects of battery storage and renewable distributed generation on the dependability of traditional distribution systems may be found in Saboori et al. (2015); Li and Zio (2012). The parameters of the ESS have been studied by Arifujjaman et al. (2015) to optimize power system losses, efficiency, reliability and energy cost. The cost-effective hybridization techniques have been proposed in Arifujjaman (2015) and genetic algorithm in (Ogunjuyigbe et al. 2016), to quantify the effects of PV, wind and microhydro units on the environmental sustainability. They investigated the properties of RES in order to reduce the objective function of the proposed power system.

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With the goal of reducing the overall cost of the system, (Bhandari et al. 2014) have introduced a novel method for the best combination of renewable energy sources in standalone systems. A network reconfiguration technique to raise a traditional distribution system’s dependability index has been described by Duan et al. (2015). However, the strategy is dependent on the utilities’ ability to provide a power supply and the capacity of the power system’s components. A pseudo-dynamic planning method has been provided by Souza et al. (Ahadi et al. 2016) to quantify the effect of reliability evaluation on the distribution system. The approach is evaluated on a 54-bus distribution network, and the outcomes have shown that the pseudo-dynamic planning method works well on a traditional distribution network. To increase the dependability of a traditional distribution system, (Duan et al. 2015) suggested distribution system expansion planning. This strategy aims to maximize the system’s operational expenses, power system investment and reliability indices. In order to increase dependability, (Souza et al. 2015) suggested allocating switches optimally inside a traditional distribution system. This method reduces the price of energy that isn’t delivered in a power grid. Different approaches to assess the reliability worth and the cost of the power system have been provided by Abbasi and Seifi (2015). These methods are based on expected interruption cost and customer damage functions (CDF) evaluation in the network. The goal of the effort is to maximize the advantages and disadvantages of employing DG units to lessen power system disruption. One method for determining the dependability indices of a traditional distribution system with the integration of renewable DG and ESS units is Monte Carlo simulation (Alkuhayli 2012; Billinton and Gao 2008; Borges 2012; Gao and Billinton 2009; Ghajar and Billinton 2006; Locatelli et al. 2015). The methods outline the unpredictable properties of renewable energy resources and how they affect the system dependability. The simulated outcomes from the Monte Carlo simulation techniques show how the random behaviour of renewable energy sources influences the distribution system reliability indices in a significant way. The power system issues may often be somewhat solved using the Monte Carlo simulation method described in Alkuhayli (2012); Billinton and Gao (2008); Borges (2012) Gao and Billinton (2009); Ghajar and Billinton (2006); Locatelli et al. (2015). But the results are not much reliable as compared to other methods. This method becomes time-consuming and hard to execute with multi-objective complex system. Only modest electrical systems may use it due to the calculation time. A typical distribution system’s dependability index can also be calculated using an analytical technique that can accommodate several producing units (Zhanga et al. 2011). When evaluating the reliability of a power system, the analytical approach for measuring the reliability indices of a traditional distribution system in the presence of DG units is used (Zhanga et al. 2011). The foundation of this method is the assumption that in the event of a utility power loss, the traditional DG units will provide the load requirement. Only generation units with non-intermittent output power may be estimated as reliable using the analytical approach. The reliability assessment of a small-scale power system is the only used for analytical models (Aghaebrahimi and Mehdizadeh 2011). More accurate results can be obtained for

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small-scale power system with less computational speed and efforts as compared to Monte Carlo simulation. These advantages, however, are only applicable to the failure of the approach to be utilized for stochastic characteristics of the key components of a power system in various operational states. This chapter discusses the modelling of reliability indices with states of distribution network. Also, the modelling for customer interruption cost is done. Further, this chapter discusses the distribution network with DG integration at various locations and analyses the impact on expected interruption cost, energy not served, system average interruption frequency index and system average interruption duration index. According to the study’s findings, renewable DG technologies may be used to increase a power system’s reliability.

11.2 Distribution System Power is delivered from bulk power systems to retail consumers via distribution networks. Distribution substations accept power from sub-transmission lines and use power transformers to step-down the voltages. These transformers provide power to primary distribution systems, which are made up of a large number of distribution feeders. The feeders consist of a main three-phase trunk, one phase laterals, feeder interconnections, fuses and distribution transformers. Distribution transformers reduce voltages to levels that are suitable for use and supply secondary mains. Electric utility distribution planning departments have traditionally focused on capacity challenges, focusing on designs that satisfy the maximum demand of all the customers within prescribed voltage limits without going beyond the equipment ratings. Almost all capacity planning is done using rigorous analytical methods like power flow models. Although reliability is essential, it has traditionally been treated as a secondary priority, with extra capacity and feeder links included to ensure that certain loads may be restored once a failure occurs. The capacity planning is only the half part of the distribution network planning. A distribution network planned for capacity (and basic protection schemes) costs about 40% and 50% of a typical overhead design. This system does not have any switches, fuse cutouts, tie switches, extra capacity and lightning protection. Feeders are only protected by fuses at substations, and poles and hardware are as cheap as feasible. Any money invested over and beyond this ‘minimum capacity design’ goes toward improving reliability. In view of this, around half of the expense of a distribution system is for reliability, while the other half is for capacity. Utilities must migrate from capacity planning to integrated capacity and reliability planning in order to spend distribution reliability funds as efficiently as capacity dollars. This department will retain accurate historical reliability data on hand, employ predictive reliability models, develop

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systems to specified reliability objectives and optimize expenditure based on costper-reliability benefit ratios. The impact of distribution reliability on customers is even more profound than cost. For a typical residential customer that has 90 min of power outage per year, 70 to 80 min will be attributed to distribution system issues. The radial design of most distribution systems, the vast number of components involved, the scarcity of protective devices and sectionalizing switches, and the distribution system’s proximity to end-use consumers all contribute to this.

11.2.1 Objectives of Distribution Systems The prime objectives of any distribution systems are as follows: • • • • • • • •

Adequate performance with advanced equipment’s Automation Minimum interruption duration Better power factor Voltage magnitude must be in prescribed limits Maximum system security Higher reliability Safety protection.

11.2.2 Classification of Distribution System The distribution network can be classified as nature of current, type of construction and type of connection. All these classifications are discussed here in brief for reader’s convenience. A. Nature of current: The distribution network can be classified into two different categories on the basis of nature of current. • AC distribution system • DC distribution system. AC distribution system is the most commonly used system due to simplicity and economic operation as compared to the DC distribution system. B. Construction type There are two different types of construction available so far. • Underground system • Overhead system.

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The overhead system is most usually used since it is 6 to 10 times less expensive than the underground system. In most cases, the underground system is used in metropolitan areas. The well-developed countries generally follow the underground system. C. Types of connection The distribution network can be classified into three different structures according to the connections. These are as follows: • Radial system • Interconnected system • Ring main system. Each and every system has its own unique benefits. All these systems are discussed here with their connection diagram.

11.2.3 Radial Distribution System This approach has been used in distribution system planning because it is highly easy and cost-effective compared to other methods. Figure 11.5 shows a single-line representation of the radial distribution system. Only when electricity is generated at low voltage and the substation is located in the centre of the load, the radial connection is used.

Fig. 11.5 Single-line diagram of radial distribution system

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Although the radial distribution network has lots of benefits being simple and economic, it also suffers from the following drawbacks. 1. The neck end of the distributors will suffer from severe heavy load. 2. Being radial in nature, if a fault occurs at any feeder, the whole remaining load points will suffer the interruption until the faulted feeder becomes healthy. 3. Voltage fluctuation will be observed at the end-user terminal. Hence, this system is preferred for small-distance power delivery.

11.2.4 Ring Main System In the ring main distribution system, the loop structure is created by the primary transformer. Figure 11.6 shows the single-line diagram of the ring main distribution system and the closed terminal ABCDEGHI is fed by the substation at A. The distributors are tapped from the points C, E and H feeders through distribution transformer. The main advantages of the ring main distribution system are as follows: 1. 2. 3. 4.

Less voltage fluctuation for the end user. More reliable as compared to radial distribution network. In case of any fault at ant feeder, the rest of the system will operate continuously. Suppose the fault is occurred in the F section, then we can isolate the B & D feeder. The remaining section will not have any interruption.

Fig. 11.6 Single-line diagram of ring mains system

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11.2.5 Interconnected System When two or more producing stations are linked to the feeder, the system is called an interconnection system. Figure 11.7 shows the single-line diagram of the interconnected system. The closed path feeder ABCD is supplied by two generating station S1 and S2 at points B and D, respectively. In case of peak load demand, the load will be supplied by the other generation station. This configuration will increase the service availability and reliability at consumers end.

11.3 Distributed Generation The DG is depicted as a source of power supply coupled to the distribution system’s radial structure near the consumer end. According to the International Council on Large Electric Systems, DG refers to any generating unit that is linked to a distribution network and has a capacity ranging from 50 to 100 MW. The Institute of Electrical and Electronics Engineer (IEEE) considers the DG as a facility that is lesser in size as compared to central power plant and having dispatchability. Taking into account all of the aforementioned perspectives on DGs, it is possible to infer that the DG is a modest source of electric power, rated up to 100 MW and linked near the load point (Zhang and Sun 2015). The DG is suitably smaller in size than the central power

Fig. 11.7 Single-line diagram of interconnected system

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plants. The current polluted environment and greater sensitive loads as a result of technology advancements are encouraging consumers to adopt renewable energy. These are factors providing momentum for the use of renewable energy-based DG. Being advantageous over conventional power plant, DG has evolved into a research hub for scientists, academics and environmentalists. Renewable energy sources like DG are advantageous in terms of lowering greenhouse gas emissions, but power supply uncertainty is still a concern. Some renewable technologies need a huge space, although most, such as a bio-gas plant, may be contained in a small area. Furthermore, certain renewable technologies have substantial installation, operating and maintenance costs. However, it is still less costly than a centralized power generation source. To some extent, several of the DG technologies are still in the process of being developed. Solar photovoltaic (SPV) is the most prominent source of clean energy. It converts the solar radiation energy into electrical energy. It can be used as DG for the range of 1 kW to 80 MW. Similarly, small hydro and microhydro plants convert the gravitational potential energy into electrical energy. The available power generation range is 5 kW–1 MW. The wind turbines convert the kinetic energy of wind into electrical energy. These can be used as DG in the range of 200 W to 3 MW. Geothermal energy converts the heat energy of earth into electrical energy. This source of energy is available in the range of 5–100 MW range as DG (Li and Zio 2012). Tidal and ocean energy are the form of energy that converts the waves kinetic energy into electrical energy. These can be used as DG with the range of 0.1–1 MW. Biomass energy can be implemented as DG source with the range of 100 kW–20 MW. It converts the chemical energy into electrical, thermal and biofuels. Although hydrogen energy systems also convert the chemical energy into electrical, range (40–400 MW) is more as compared to biomass energy system. Furthermore, some non-renewable energy resources can also be used as DG source like integrated gasification combined gas turbine (30 kW–3000 + kW), microturbine (30 kW–1 MW), internal combustion (IC) engine (5 kW–10 MW) and fuel cell (FC) technologies (100 W–5 MW) all these non-renewable DG technologies can avoid grid expansion if these are dispatchable (Agarwal and Jain 2019, 2020; Agarwal et al. 2021).

11.3.1 Impacts of DG Technical, economical and environmental implications are the three broad areas in which the DG’s effects may be characterized. These impacts are discussed in Table 11.1.

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11.4 Mathematical Modelling In this chapter, modelling for the reliability-centric strategies is presented in detail. The modelling includes the modelling for the state of distribution network, modelling for failure rate and modelling for cost worth reliability indices.

11.4.1 Distribution System State The state diagram for the components of the radial distribution network is generally a three-state model as shown in Fig. 11.8a. The state before fault, where the system operates normally, is termed as ‘normal state’. The state between isolation and fault occurrence is called ‘switching state’. The state before completing the repair but after isolation is termed as ‘repair state’. In some circumstances, fuses and circuit breakers are not necessary to operate and then two-state model is used to depict the components model as shown in Fig. 11.8b. In case, the repair process is same with previous one then both models can be combined to generate a ‘single space model’ as shown in Fig. 11.8c. There is always significant possibility of two modes of failure: (i) active failure (leads the system in switching state) whereas (ii) passive failure (leads the system in repair state). In Fig. 3.1, λa and λp represent the active and passive failure rates,

(a) (b)

(c)

Fig. 11.8 Distribution system components state diagram. a three-state model b two-state model c combined state model

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respectively and r s and r p represent the switching and repair time for the components. For single-state weather, the failure rate is considered as constant and the radial distribution network gets less affected due to the adverse weather (storm, disaster). Therefore, it is not considered in calculation (Li and Zio 2012).

11.4.2 Reliability Indices of Load Point In power system, the load point reliability indices are average failure rate λi , outage duration for ith end consumer r i and annual outage duration U i . Equations (11.1)– (11.3) are used to calculate these indices as depicted below: λ=

k 

λj

(11.1)

r j ∗ λj

(11.2)

j=1

U=

k  j=1

k r=

j=1 r j

k

j=1

∗ λj λj

=

U λ

(11.3)

11.4.3 Reliability Indices for the System The system average interruption frequency (SAIFI) is the annual average of the number of times, and customer service was interrupted. k SAIFI =

j=1

q

N j ∗ λj

t=1

Nt

(11.4)

How well a system functions during an interruption is measured by the system average interruption duration (SAIDI). This index counts all interruptions that the typical customer experiences during the course of a specific time period. k SAIDI =

j=1 N j ∗ U j q t=1 N t

(11.5)

The customer average interruption duration index (CAIDI) is used to determine the typical amount of time the system takes to restore service after an interruption. Equation (11.6) may be used to calculate the CAIDI.

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k j=1

Nj ∗Uj

j=1

N j ∗ λj

CAIDI = 1 − k

249

(11.6)

The proportion of a consumer’s annual service time (24*365 = 8760) or the designated reporting period is shown by this indicator. It is described in (11.7) as follows: ASAI = 1 −

SAIDI 8760

(11.7)

This index shows, typically stated in percentages, the percentage of a customer’s annual or designated reporting period that has gone without being served. It is shown as: ASUI =

SAIDI 8760

(11.8)

11.4.4 Cost Worth Reliability Indices (A) Expected interruption cost (ECOST) The customer interruption cost (CIC) for the four distinct customer sectors has been examined in this section (postal survey-based). The sector customer damage function (SCDF), which is shown in Table 11.2, was computed using survey data. Figure 11.9 presents its plot, which depicts the interruption cost for the specified kind of client as a function of interruption interval. In the literature, four categories of consumers are typically studied, and the composite customer damage function (CCDF) is calculated using the greatest peak load from each type of consumer as input (11.9). CCDF =



(%)Peak of Sector∗SCDF

(11.9)

Table 11.2 Sector customer damage function ($/kw) Consumer’s category

Outage time (in min.) 1

20

Residential

0.001

0.09

0.48

4.91

15.690

Small users

4.78

9.88

21.07

68.83

119.160

Commercial

0.38

2.97

8.55

31.32

83.008

Govt. & Inst

0.04

0.37

1.49

6.56

26.040

60

240

480

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Table 11.3 Composite customer damage function ($/kw)

Interruption Cost ($/kW)

Fig. 11.9 Sector customer damage function for each sector

Outage time (in min.)

CCDF

1

1.0

20

2.80

60

6.72

240

24.51

480

55.44

Fig. 11.10 CCDF for various interruption durations

Interruption Cost ($/kW)

The CCDF for various time periods is shown in Table 11.3. In addition, Fig. 11.10 shows the graphical description of the CCDF. The overall cost of a system interruption is calculated by averaging the estimated costs of a disruption at each load point from the CCDF. The ECOST and ENS, together with load point and system reliability indices, can aid in a better comprehension of dependability (Fig. 11.10).

11 Reliability Analysis of Distribution System with Integration …

ECOST =

k 

ECOST j =

k 

j=1

λj ∗ C j ∗ L j ,

251

(11.10)

j=1

where L i is defined as load on ith consumer end, C i is the CCDF, which is the function of interruption duration and λi is the malfunction frequency for ith consumer end including all contingencies. The total ECOST for the system is obtained by adding all the ECOST for individual customers’ end points. ECOSTsystem =

LP 

ECOSTi

(11.11)

i=1

Estimating the distribution network’s reliability in terms of outage costs of consumers entails the following five fundamental stages: Calculate the indices λi , r i and U i for all types of failure for each load point. Use CCDF and r i to calculate the interruption cost. Taking into consideration the ECOST at each LP for each of the probable failure types. 4. For each different network configuration, repeat steps one to three. 5. After that, the overall ECOST is calculated by (11.11). (B) Energy not served (ENS) 1. 2. 3.

ENS =

LP  i=1

ENSi =

LP 

U i × Li

(11.12)

i=1

The total ENS for the system is obtained by adding all the ENS for individual customers’ end points.

11.5 Problem Formulation The main objective of this paper is to minimize the cost that is associated with the power outage in the distribution system while considering the diverse customers (residential, small users, commercial and government institutions), cost per customer and varied cost for different interruption durations, provided the following constraints (Li and Zio 2012): • The structure of the system will remain radial throughout the evaluation process. • Supply is always available to the LPs. • Fuse failure probability is not considered in the analysis at this point. Two objective functions are developed in order to assess how DG units affect the distribution system’s dependability. The expected energy not supplied (EENS)

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goal function is to be minimized, and the expected interruption cost index (ECOST) objective function is to be decreased in the distribution system. By contrasting the target function before and after the DG units are added to the power system, this goal may be achieved. Objective function = minimize

LP 

ECOST j&ENS j .

j=1

11.6 Solution Strategy In this research work, greedy search algorithm has been employed to find optimum location of DGs in the distribution system. A greedy search algorithm is an algorithmic that follows the problem-solving heuristic of making the locally optimal choice at each stage in order to find a global optimum solution. This algorithm has five components: • Candidate Set: It refers to the set of all possible solutions. In other words, it is set, from which a solution will be created. In our problem, the sections, where switches are available, are the locations at which DG can be placed. • Objective Function: It assigns a value to a candidate solution or a partial solution. In the problem considered. • Selection Function: It chooses the best candidate to be added to the solution after each iteration. For this problem, the selection function is minimization of objective function. • Feasibility Function: It checks whether the achieved solution is practically viable or not. • Solution Function: It indicates when we have determined a complete solution, i.e., global optimum. Figure 11.11 gives the flowchart for the implementation of greedy search algorithm in the problem under consideration.

11.7 Network Topology The topology of the evaluated distribution network at Bus-2 is shown in Fig. 11.12. The studied network is a part of RBTS 6-Bus system (Li and Zio 2012). This network at Bus-2 consists of four circuit breakers connected at the starting point of each feeder. There are four feeders (F1, F2, F3 and F4) of 11 kV each. The network consists 20 transformers, 14 sectionalizing switches, 20 fuses and 22 load points. The total number of consumers at the network is 1908. The load data of various load points

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Fig. 11.11 Flowchart for reliability analysis in the presence of DG

and consumers is shown in Tables 11.4 and 11.5. It can be seen that F1 and F4 have the highest load as compared to other feeders, and feeder F2 has the minimum load (2 consumers). Both loads of feeder F2 are directly connected to the feeder as these are large load points and don’t require any transformation of voltage. Reliability data of the system components is included in Table 11.5.

11.8 Computational Results The objective to minimize the energy not served (ENS) and expected interruption cost (ECOST) is obtained by placing the DG at various locations and comparing the results

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Fig. 11.12 Radial distribution network at Bus-2 of RBTS Table 11.4 Consumers loading data for RBTS Bus-2 system Number of load points

Load points

Customer type

Load level per load point (MW) Average

Number of customers

Peak

Bus-2 5

1–3, 10, 11

Residential

0.535

0.8668

210

4

12, 17–19

Residential

0.450

0.7291

210

1

8

Small users

1.00

1.6279

1

1

9

Small users

1.15

1.8721

1

6

4, 5, 13, 14, 20, 21

Govt./ Inst

0.566

0.9167

1

5

6, 7, 15, 16, 22

Commercial

0.454

0.7500

10

Table 11.5 Peak load data at various customer terminal for Bus-2

Consumer type

Peak load (MW)

Residential

7.25

Small users

3.50

Govt. and institutional

5.55

Commercial

3.70

Total

20

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Table 11.6 Reliability evaluation of RBTS Bus-2 network with DG Cases

SAIFI (fail./Cust.*Yr.)

SAIDI (Intr./Cust.*Yr.)

ECOST ($/yr.)

ENS (kWh/ yr.)

Without DG

0.271

0.923

121,799

19,950

DG in F1

0.271

0.896

113,820

18,510

DG in F2

0.271

0.922

119,791

19,585

DG in F3

0.271

0.877

113,616

18,481

DG in F4

0.271

0.875

112,518

18,280

DG in F1F2

0.271

0.896

111,812

18,148

DG in F1F3

0.271

0.851

105,631

17,040

DG in F1F4

0.271

0.848

104,533

16,844

DG in F2F3

0.271

0.877

111,514

18,116

DG in F2F4

0.271

0.875

110,576

17,915

DG in F3F4

0.271

0.830

104,334

16,811

DG in F1F2F3

0.271

0.850

103,630

16,680

DG in F1F3F4

0.271

0.803

96,349

15,375

DG in F2F3F4

0.271

0.83

102,332

16,446

DG in F1F2F4

0.271

0.848

102,530

16,480

DG in F1F2F3F4

0.271

0.803

94,350

15,010

for reliability indices. The placement of DG has been done using the greedy search optimization algorithm. Table 11.6 shows the results for DG placement in RBTS Bus-2 distribution network. It can be depicted from the results that DG placement in F2 does not have any significant impact on the reduction in ECOST and ENS. Without any DG in the distribution network, the ENS is 19950 kWh/yr., while it is reduced by 24.76% in the presence of DG. Similarly, the expected interruption cost with any DG integration was 121,799 $/yr., while with DG integration, it reduces by 22.54%. The results reveal that the SAIFI will remain the same for all locations of DG placement as DG will reduce the interruption duration during any interruption. It will not reduce the interruption frequency. Figures 11.13, 11.14 and 11.15 represent the variation of ECOST, ENS and SAIDI with and without DG with saving.

11.9 Conclusion Reliability assessment of the distribution network is the key performance indicator to measure the impacts of DG integration on technique conventional power distribution system. A reliability assessment procedure with DG at various feeders is proposed in this paper with the aim to reduce the ECOST and ENS for the network that is associated with the power outage. Based on the results, it is obvious that the DG has significant impact on the interruption cost and energy unserved. There is a

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VARIATION IN ECOST WITH DG PLACEMENT DG in F1F2F3F4

Saving with DG

27449

94350

121799

Without DG

WITHOUT DG

DG IN F1F2F3F4

SAVING WITH DG

Fig. 11.13 Variation of ECOST with DG placement at various locations

VARIATION OF ENS (kWh/Yr.) WITH DG PLACEMENT DG in F1F2F3F4

Saving with DG

4940

15010

19950

Without DG

WITHOUT DG

DG IN F1F2F3F4

SAVING WITH DG

Fig. 11.14 Variation of ENS with DG placement at various locations

considerable reduction in the cost that is associated with the power outage with DG resources. The simulation results indicate that power companies should encourage the incorporation of renewable energy into their distribution systems as an option to increase network reliability. This research may be expanded to include the effect of system components failure probability like fuse failure probability. Further, effect of RES integration in large power system can also be included for future research. In addition, reliability evaluation can guide the power system operators and planners to optimize the outage duration with RES integration to increase the service availability for the consumers. The findings of the analysis have proven that the DG can not only improve the cost saving in the conventional distribution network but also can improve the reliability.

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DG in F1F2F3F4

Reduction with DG

0.12

Without DG

0.803

0.923

VARIATION OF SAIDI WITH DG PLACEMENT

WITHOUT DG

DG IN F1F2F3F4

REDUCTION WITH DG

Fig. 11.15 Variation of SAIDI with DG placement at various locations

References Abbasi AR, Seifi AR (2015) Considering cost and reliability in electrical and thermal distribution networks reinforcement planning’. Energy 84:25–35 Agarwal U, Jain N (2019) Distributed energy resources and supportive methodologies for their optimal planning under modern distribution network: a review. Technol Econ Smart Grids Sustain Energy 4:3 Agarwal U, Jain N (2020) Reconfiguration of radial distribution network for reliability enhancement considering renewal energy sources. In: 2020 International conference on electrical and electronics engineering (ICE3), pp 162–167 Agarwal U, Jain N, Singh SN, Kumawat M (2021) Solar photovoltaic (PV) generation. In: Singh SN, Tiwari P, Tiwari S (eds) Fundamentals and innovations in solar energy. Energy systems in electrical engineering. Springer, Singapore Aghaebrahimi MR, Mehdizadeh M (2011) A new procedure in reliability assessment of wind–diesel islanded grids. Elect Power Compon Syst 39(14):1563–1576 Ahadi A, Kang SK, Lee JH (2016) A novel approach for optimal combination of wind, PV and energy storage system in diesel - free isolated communities. Appl Energy 170:101–115 Alkuhayli AA (2012) Reliability evaluation of distribution system contained renewable distributed generations. In: Proceeding IEEE North American power symposium (NAPS). Champaign, Illinois, pp 1–6 Arifujjaman MD (2015) A comprehensive power loss, efficiency, reliability and cost calculation of a 1 MW/500 kWh battery-based energy storage system for frequency regulation application. Renew Energy 74:158–69 Bhandari B, Lee KT, Lee CS, Song C, Maskey RK, Ahn S (2014) A novel off-grid hybrid power system comprised of solar photovoltaic, wind, and hydro energy sources. Appl Energy 133:236– 242 Billinton R, Allan RN (1996) Reliability evaluation of power systems, 2nd edn. Plenum Press, New York Billinton R, Gao Y (2008) Multistate wind energy conversion system models for adequacy assessment of generating systems incorporating wind energy. IEEE Trans Energy Convers 23(1):163–170 Borges CLT (2012) An overview of reliability models and methods for distribution systems with renewable energy distributed generation. Renew Sustain Energy Rev 16:4008–4015

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