Microgrids: Modeling, Control, and Applications 032385463X, 9780323854634

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Microgrids

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Microgrids Modeling, Control, and Applications

Edited by

JOSEP M. GUERRERO Center for Research on Microgrids (CROM), Aalborg University, Aalborg East, Denmark

RITU KANDARI Department of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women, Delhi, India

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2022 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-323-85463-4 For Information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Joe Hayton Acquisitions Editor: Lisa Reading Editorial Project Manager: Aleksandra Packowska Production Project Manager: Prasanna Kalyanaraman Cover Designer: Greg Harris Typeset by MPS Limited, Chennai, India

In the honor of My father Late Mr. Matwar Singh Kandari I cannot find words to express my gratitude to my late father who always supported me unconditionally and made me an independent and determined person. He always pushed me to be more than what I think I'm capable of doing! I would also like to dedicate this to my beloved mother, Mrs. Trilochana Devi, my siblings, Mrs. Manju, Mrs. Meenu, and Mr. Deepak Kandari, my nephew and niece, Adit and Adhya without whom I would never be able to achieve my objectives and succeed in life. I am also thankful to my grandfather, Mr. Bhawan Singh Kandari, R/O Paithani, Uttarakand for always being so supportive and understanding my thought process. I am grateful to my coeditor, Prof. Josep M. Guerrero and all the contributors for working so hard with me for the timely completion of this book. Last, but not the least, I would like to express my gratitude to the publisher, Elsevier, Aleksandra Packowska, Prasanna Kalyanaraman, Greg Harris, Lisa Reading, Joe Hayton and team for their continuous support, feedback, and suggestions throughout this process and publishing this book with Elsevier. Ritu Kandari

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Contents List of contributors

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Section I Introduction to microgrids 1. Microgrids, their types, and applications

3

Ayush Mittal, Aryan Rajput, Kamya Johar and Ritu Kandari 1.1 1.2 1.3 1.4 1.5

Introduction Microgrid classification Structure Modes of operation Control of AC microgrid 1.5.1 Hierarchical control schemes 1.6 Control of DC microgrid 1.6.1 Control structures 1.7 Control of hybrid (AC/DC) microgrid 1.8 Microgrid research areas 1.9 Solar 1.9.1 Independent (or stand-alone) PV system 1.9.2 Grid-connected PV system 1.9.3 PV modeling 1.10 Maximum power point tracking 1.10.1 P&O method 1.11 Wind turbine system 1.12 Battery 1.12.1 Lithium-ion battery 1.12.2 Lead
acid battery 1.12.3 Battery modeling 1.12.4 Sizing batteries correctly 1.13 Fuel cell 1.14 Advantages and applications of microgrid 1.15 Conclusion References

3 4 6 8 9 10 12 13 16 17 20 21 21 22 24 24 25 26 26 27 27 28 29 32 34 34

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Section II AC microgrids 2. Disturbance observer
aided adaptive sliding mode controller for frequency regulation in hybrid power system

43

Vivek Patel, Dipayan Guha and Shubhi Purwar 2.1 Introduction 2.2 System modeling 2.2.1 Model of reheated thermal power system 2.2.2 Distributed energy resources 2.3 Disturbance observer
aided adaptive sliding mode load frequency controller 2.3.1 Traditional sliding mode load frequency controller (SMLFC) 2.3.2 Adaptive sliding mode LFC with disturbance observer 2.4 Results and discussion 2.4.1 Performance analysis of isolated HPS against multiple load perturbation 2.4.2 Performance analysis of isolated HPS with multiple-step loads and random wind power perturbation 2.4.3 Performance analysis of isolated HPS with GRC and GDB 2.4.4 Performance analysis of interconnected two-area HPS with multiple-step load and RWPP 2.4.5 Performance analysis of two-area HPS with GRC and GDB 2.4.6 Robust stability analysis 2.5 Conclusion References

3. Recent advancements in AC microgrids: a new smart approach to AC microgrid monitoring and control using IoT

43 46 47 48 51 51 52 55 55 57 57 58 61 62 63 63

67

P. Madhumathy and Shweta Babu Prasad 3.1 3.2 3.3 3.4 3.5 3.6

Introduction Problem statement Literature survey Block diagram Methodology Details of hardware and software used 3.6.1 LCD display (JDH162A): a 16 3 2 LCD is a display unit used in different activities 3.7 Details about the web portal: ThingSpeak 3.8 Algorithm 3.9 Software development flowchart

67 73 74 77 79 79 79 80 81 81

Contents

3.10 Results and discussions 3.10.1 Hardware section of the model 3.11 Graphical analysis 3.12 Conclusion and future scope References Further reading

ix 82 82 83 85 86 87

Section III DC microgrids 4. DC microgrid

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Ritu Kandari, Neeraj and Ayush Mittal 4.1 4.2 4.3 4.4 4.5 4.6

Introduction DC microgrid Mode of operation Advantages of DC microgrid Standards DC microgrid architecture 4.6.1 Photovoltaics cell/solar 4.6.2 DC
DC converters 4.7 Principle of chopper 4.8 Boost converter 4.9 Case-I (switch S is ON) 4.10 Case-II (switch S is OFF) 4.11 Buck-boost converter 4.12 Case-I (switch S is ON) 4.13 Case-II (switch S is OFF) 4.13.1 Maximum power point tracking controller 4.13.2 Storage device—battery 4.14 Working principle 4.15 Discharging mechanism 4.16 Charging mechanism 4.17 State of charge and state of health 4.18 Types of batteries 4.18.1 Modeling 4.19 Types of modeling methods 4.20 Equivalent circuit model 4.21 Data-driven model 4.22 Case study 4.23 Conclusion References

91 92 96 101 101 102 106 110 111 111 111 112 112 113 113 114 118 118 119 119 121 121 124 127 128 130 131 134 134

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5. Role of dual active bridge isolated bidirectional DC-DC converter in a DC microgrid

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Anupam Kumar and Abdul Hamid Bhat 5.1 Introduction 5.2 Microgrid 5.3 Dual-active bridge converter 5.3.1 DAB parameter design 5.4 Fuzzy logic controller 5.5 Performance evaluation 5.5.1 Single-phase shift technique 5.5.2 Forward conduction mode 5.5.3 Reverse conduction mode 5.6 Experimental verification 5.7 Conclusion References

141 142 143 146 148 149 149 151 151 153 154 155

Section IV Hybrid AC/DC microgrids 6. Introduction to hybrid AC/DC microgrids

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Shivani Mishra and R.K. Viral 6.1 Introduction 6.1.1 Hybrid micro-grid 6.1.2 The topographies of hybrid micro-grid 6.1.3 Need of hybrid micro-grid 6.1.4 Comparison between conventional grid and hybrid micro-grid 6.2 Architecture of hybrid micro-grid 6.3 Architecture of AC-coupled hybrid micro-grid 6.4 Architecture of DC-coupled hybrid micro-grid 6.5 Architecture of AC-DC coupled hybrid micro-grid 6.6 Modeling of hybrid micro-grid components 6.6.1 PV system model 6.6.2 Wind energy system model 6.6.3 Biomass energy model 6.6.4 Small-hydro system model 6.6.5 Battery model 6.6.6 Fuel cell model 6.7 Power quality issues in hybrid micro-grid 6.8 Control strategies and energy management system for hybrid micro-grid

159 160 162 162 162 163 164 165 166 167 167 168 169 169 170 171 172 172

Contents

6.8.1 6.8.2 6.8.3 6.8.4

AC-coupled hybrid micro-grid DC-coupled hybrid micro-grid AC-DC coupled hybrid micro-grid Transition between grid-connected and standalone operation mode for energy management 6.9 Modeling of hybrid micro-grid 6.9.1 Modeling of PV and wind hybrid micro-grid 6.9.2 Modeling of PV, wind and biomass hybrid micro-grid 6.9.3 Modeling of PV, wind, biomass and small hydro hybrid micro-grid 6.10 Mathematical modeling of hybrid micro-grid 6.10.1 Modeling of AC micro-grid 6.10.2 Modeling of DC micro-grid 6.11 Coordination control of the converters 6.11.1 Isolated mode 6.12 Grid-connected mode 6.13 Economic potential and their benefits for hybrid micro-grid 6.13.1 Credit risk 6.13.2 Commercial risk 6.13.3 Returns 6.14 Case study regarding hybrid micro-grid 6.15 Conclusion References

7. Control of hybrid AC/DC microgrids

xi 172 173 174 175 176 176 177 177 178 178 179 179 179 180 181 183 183 184 184 186 187

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P. Shambhu Prasad, Alivelu M. Parimi and L. Renuka 7.1 Introduction 7.1.1 Microgrid stability 7.1.2 Frequency stability 7.2 Literature review 7.3 Theoretical approach—different control techniques 7.3.1 Structures of robust controllers 7.3.2 General mixed sensitivity problem 7.3.3 H Infinity control problem 7.3.4 Structured singular value- μ control theory 7.4 Methodology 7.5 Results and discussion—case studies 7.5.1 H infinity controller frequency response 7.5.2 Mu synthesis controller frequency response 7.5.3 μ synthesis controller with parametric variations 7.5.4 Order reduction of the controller

191 194 195 196 198 199 202 205 206 209 212 212 214 215 216

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7.5.5 Case studies—comparison of control techniques 7.6 Conclusion 7.7 Summary References

216 220 220 223

8. Recent advancements in hybrid AC/DC microgrids

227

P. Shambhu Prasad and Alivelu M. Parimi 8.1 Introduction 8.2 Challenges in hybrid AC/DC microgrid and possible solutions 8.2.1 Operational aspects 8.2.2 Compatibility issues 8.2.3 Uncertainty, and perturbations in the renewable sources of energy 8.2.4 Protection 8.2.5 Reliability 8.3 Advances in hybrid microgrids 8.3.1 System modeling 8.3.2 K-nearest neighbors 8.3.3 Control law formulation 8.4 Case study 8.4.1 Preparation of data set 8.4.2 Data labeling 8.4.3 Data division for training and testing 8.4.4 Training the model 8.4.5 Training accuracy 8.4.6 Testing accuracy 8.4.7 Making predictions 8.4.8 Evaluating testing accuracy 8.4.9 Evaluating training accuracy 8.4.10 Plotting 8.4.11 Using logistic regression 8.5 Conclusion References Index

228 231 231 232 232 233 234 234 235 236 236 238 238 238 240 240 240 240 241 241 242 242 242 243 244 247

List of contributors Abdul Hamid Bhat National Institute of Technology, India Dipayan Guha Electrical Engineering Department, Motilal Nehru National Institute of Technology Allahabad, India Kamya Johar IEEE Member, India Ritu Kandari Indira Gandhi Delhi Technical University for Women, India Anupam Kumar Modern Institute of Technology and Research Centre, India P. Madhumathy Dayananda Sagar Academy of Technology and Management, India Shivani Mishra Department of Electrical & Electronics Engineering, Amity School of Engineering and Technology, Amity University, India Ayush Mittal Open Systems International, Inc., India Neeraj Indira Gandhi Delhi Technical University for Women, India Alivelu M. Parimi Department of Electrical and Electronics Engineering, BITS Pilani, Hyderabad Campus, India Vivek Patel Electrical Engineering Department, Motilal Nehru National Institute of Technology Allahabad, India P. Shambhu Prasad Department of Electrical and Electronics Engineering, BITS Pilani, Hyderabad Campus, India Shweta Babu Prasad Dayananda Sagar Academy of Technology and Management, India Shubhi Purwar Electrical Engineering Department, Motilal Nehru National Institute of Technology Allahabad, India Aryan Rajput HMR Institute of Technology and Management, India

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List of contributors

L. Renuka Department of Electrical and Electronics Engineering, BITS Pilani, Hyderabad Campus, India R.K. Viral Department of Electrical & Electronics Engineering, Amity School of Engineering and Technology, Amity University, India

SECTION I

Introduction to microgrids

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CHAPTER 1

Microgrids, their types, and applications Ayush Mittal1, Aryan Rajput2, Kamya Johar3 and Ritu Kandari4 1 Open Systems International, Inc., India HMR Institute of Technology and Management, India 3 IEEE Member, India 4 Indira Gandhi Delhi Technical University for Women, India 2

1.1 Introduction With an exponential rise in the demand of electrical energy, a huge change in the utilization of energy is observed. To fulfill the growth and cutoff the dependency on fossil fuels and aged power transportation networks, numerous renewable energy resources, including and not limited to—solar photovoltaics (PV), wind energy, and fuel cells (FCs) are explored along with the evolution of various techniques, including—geographically distributed (and interfaced) energy resources, power electronic converter(s) (PECs), and energy storage systems (ESSs). The proficiency in the researched techniques paved the path for the operation of a new entity which came to be known as microgrid. Several engineers and researchers along with institutions have proffered varied definitions for the term “microgrid.” For example, the definition accepted by the International Electro-Technical Commission as proposed by Advance Grid Research at US Department of Energy for the microgrid is, “A microgrid is a group of interconnected loads and distributed energy resources within clearly defined electrical boundaries that acts as a single controllable entity with respect to the grid. It can connect and disconnect from the grid to enable it to operate in gridconnected or island-mode.” Nejabatkhah, Li, and Tian (2019), Olivares et al. (2014), Parhizi, Lotfi, Khodaei, and Bahramirad (2015) define microgrid as, “the concept of roaming DERs and various loads in the existing power system, such as solar-PV, wind turbines, micro-turbines, and storage devices which can be operated either in grid-connected mode or in stand-alone mode.”

Microgrids DOI: https://doi.org/10.1016/B978-0-323-85463-4.00008-3

© 2022 Elsevier Inc. All rights reserved.

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Microgrids

Generally, microgrid is the composition of distributed generation (DG), loads, ESS, PECs, and control devices; but the basis of microgrid is distributed resource (DR) that is the summation of DGs and ESS, that is, DR 5 DG 1 ESS. DGs refer to small-scale power system that may be independent of the large electrical grid and are primarily located on the consumer side to meet their demands, whereas ESS stores energy in batteries, flywheel, regenerative FC, and other devices. The DG and DR technologies are subset of distributed energy resources (DER) that is defined as the generation or production of electricity or heat on the load (or demand) end for local application (Gupta, Kandari, & Kumar, 2021).

1.2 Microgrid classification The base for the classification of microgrids can be broadly divided into two categories—system topology and market segments (or, utility areas). System topology (or, architecture) can classify microgrids in three subsets—(1) DC microgrid, (2) AC microgrid, and (3) hybrid AC/DC microgrid, whereas the area of application can classify the same into five broad categories—(1) utility, (2) commercial/industrial, (3) institutional, (4) transportation, and (5) remote-area microgrid(s). The same is depicted in the next flowchart (Fig. 1.1) (Mohammed, Refaat, Bayhan, & AbuRub, 2019).

Figure 1.1 Classification of microgrid.

Microgrids, their types, and applications

5

1. DC microgrid Owing to the advancements in technology and PECs, DC microgrid has emerged as a modern marvel in the power system. The flexibility due to its capability of natural interfacing with DC-based DG, renewable energy source (RES), loads, and ESS along with the efficiency due to minimal power conversion has created an opportunity for the DC microgrids. Moreover, an increased research in the fields such as RES and DC microgrid has showed the pros of the same that helps in bringing the technology one step closer to real-time application. Fig. 1.2 shows the basic architecture of the DC microgrid (Che, Shahidehpour, Alabdulwahab, & Al-Turki, 2015; Ferreira, Barbosa, Braga, & Ferreira, 2013; Lonkar & Ponnaluri, 2015). 2. AC microgrid This is the commonly applied conventional type of microgrid. Several types of DERs such as PV, wind turbines, and FCs are connected and merged into the large power network or existing utility grids. Due to laid down network, AC microgrid requires minimum modification and it brings out minimal alteration to the topology. This system is commonly merged with low and medium voltage levels due to its capability to inflate the distribution network with reduced transmission losses. Though the interconnection of microgrid with conventional architecture is beneficial, it brings in additional cons, such as system stability, power quality, reactive power deficiency, and DERs synchronization.

Figure 1.2 Schematic of DC microgrid architecture.

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Figure 1.3 Schematic of AC microgrid architecture.

Fig. 1.3 shows an architecture of AC microgrid (Alfergani, Alfaitori, Khalil, & Buaossa, 2018; Kzaviri, Pahlevani, Jain, & Bakhshai, 2017; Wu, Wu, Guerrero, Vasquez, & Li, 2019). 3. Hybrid AC/DC microgrid Hybrid microgrid is the interconnection of AC and DC microgrid(s). Though the network architecture of hybrid microgrid system is complex, it offers pros linked with both the microgrid(s) such as flexibility, increased efficiency and reliability along with economic operation (Fusheng, Ruisheng, & Fengquan, 2016). The hybrid topology facilitates smooth interconnection with conventional grid due to AC microgrid architecture, whereas DC microgrid architecture helps in amalgamation of DC-based RES, DERs, ESSs, and loads with minimal alteration in existing system. The hybrid microgrid topology drastically reduces the number of PECs required followed with the cutting down of unnecessary losses due to power conversion (Ahmed, Meegahapola, Vahidnia, & Datta, 2020; Nejabatkhah et al., 2019; Pati, Mohanty, Choudhury, & Kar, 2017). The architecture of the hybrid AC/DC microgrid is depicted in Fig. 1.4.

1.3 Structure The most basic structure of the microgrid is divided into three layers, as depicted in Fig. 1.5—local control (LC) layer in the bottom, followed by centralized control (CC) layer, and in the uppermost is the distribution network and dispatch layer. Fig. 1.6 describes the composition of three

Microgrids, their types, and applications

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Figure 1.4 Schematic of AC/DC microgrid architecture.

Figure 1.5 Three-layer structure of the microgrid.

Figure 1.6 Composition (or components) of three-layer microgrid.

layers of microgrid. The first layer from top is the distribution layer handling network and dispatching the power to the utility end, followed by the middle layer that is known as CC layer. CC layer forecasts the load

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Figure 1.7 Schematic of three-layer microgrid control structure.

demand, DG output and accordingly proposes the operational plans required in the real time. The last layer, or the LC layer, is responsible to systematize the DGs, ESs, and local load within microgrid (Bidram & Davoudi, 2012; Mariam, Basu, & Conlon, 2016; Raveendran Nair and Costa Castelló, n.d.; Tembo, 2016) (Fig. 1.7).

1.4 Modes of operation Microgrid primarily operates in two modes of operation—islanded mode or grid-connected. The latter can further be subclassified into power matched or mismatched operation that can be understood with the help of point of common coupling (PCC) (Fig. 1.8). Generally, the interconnection between the distribution network and microgrid is via PCC and both active (P) and reactive (Q) power flows through the PCC only, that is, power exchange between distribution network and microgrid occurs via PCC link ( Jain, Gupta, Masand, Agnihotri, & Jain, 2016; Prakash, Tech, Paul, & Professor, n.d.; Sharma & Saini, 2018). The power exchanged, that is, change in active power and reactive power can be represented as ΔP and ΔQ, respectively. When the value

Microgrids, their types, and applications

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Figure 1.8 Different modes of operation via PCC link. PCC, Point of common coupling.

of ΔP and ΔQ is 0 (ΔP 5 0 and ΔQ 5 0), the power flow via PCC is 0, depicting that the DG output is stabilized with the load demand and no power is exchanged (or transferred) via PCC. This mode of operation is known as power-matched operation and is considered the most economical mode (Prakash et al., n.d.). But, unlike previous case, if the value of either ΔP or ΔQ is not 0 (ΔP6¼0 or ΔQ6¼0), there is flow of charges via PCC link, depicting that exchange in power took place between the microgrid and distribution network. This mode of operation is known as power-mismatched operation and can be classified into following cases (Gupta, 2014): Case I: ΔP ðorΔQÞ , 0 If the exchanged active (or reactive) power is less than 0, that is, ΔP (or ΔQ) , 0, the power generated by DGs is excessive and after fulfilling the load demand, the microgrid has injected power into the distribution network. Case II: ΔP ðor ΔQÞ . 0 If the exchanged active (or reactive) power is more than 0, that is, ΔP (or ΔQ) . 0, the power generated by DGs is deficient for fulfilling the load demand, thus, requiring additional power to be transferred from the distribution network to microgrid. Fig. 1.9 shows the several switching (or transfers) between different modes of operation of microgrid.

1.5 Control of AC microgrid To operate the AC microgrid in stable condition along with economic operation, robust control techniques are necessary. Some of the control

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Figure 1.9 Switching (or transfer) between various operating modes.

Figure 1.10 Issues associated with AC microgrid.

techniques that help in reducing the stability issues associated with the AC microgrid are mentioned in Fig. 1.10.

1.5.1 Hierarchical control schemes To fulfill the control aspects for the abovementioned techniques, three levels of control strategies are proposed and it is known as hierarchical control scheme (Fig. 1.11). The three levels of the hierarchical schemes which are applied in AC microgrids can be classified as—(1) primary control, (2) secondary control, and (3) tertiary control schemes.

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Figure 1.11 Hierarchical control scheme of AC microgrid.

1. Primary control scheme The primary control scheme is directly connected to the microgrid and controls the fluctuations during the transition mode of microgrid, that is, switching (or transition) from grid-connected to islanded mode. The disruption in the generation of the power and load demand could cause the instability in the two prime parameters—voltage and frequency—of the AC microgrid; hence, this scheme needs to be furious in operation in comparison to other two schemes. The principal task of the primary control scheme is to maintain the stable voltage and frequency within the acceptable limits (Lopes, Moreira, & Madureira, 2006). Thus failing to control the earlier two parameters could cause the grid to become instable. The other major functions of this scheme includes—(1) ensuring plug and play operation and (2) active and reactive power sharing between parallel operating DERs (Bidram, Nasirian, Davoudi, & Lewis, 2017). This scheme is consisted of current control or/and voltage control loops of DERs. In this the voltage source converters (VSCs) could be utilized either as current-controlled or voltage-controlled voltage source inverter. The common methods based on droop control and virtual impedance are utilized as primary control strategies for taking care of power sharing among DERs connected in AC microgrid (Guerrero, Vasquez, Matas, De Vicuña, & Castilla, 2011). 2. Secondary control scheme Although voltage and frequency are maintained by primary control scheme, still deviations in the steady-state parameters—voltage and frequency—of the AC microgrid are observed. Therefore to regulate and minimize the deviations in the steady-state parameters, secondary

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control scheme is proposed, which is usually unhurried in comparison to primary scheme. This scheme utilizes optimization strategies as control methodology and, thus, requires communication channel configured as bidirectional in nature. Further, this scheme is subclassified into decentralized and CC schemes. The former scheme is used in large microgrids, whereas the latter one is utilized in smaller AC microgrids (Katiraei & Iravani, 2006; Savaghebi, Jalilian, Vasquez, & Guerrero, 2012). 3. Tertiary control scheme The primary control scheme manages voltage and frequency, the secondary control regulates deviations in the steady-state parameters, that is, voltage and frequency, whereas the tertiary control scheme looks after economic operation of the microgrid along with power exchange between the traditional grid and microgrid by adjusting the DERs power references. Though this scheme accelerates the economic operation of the microgrid, it is the slowest control scheme when compared with other two control schemes, that is, primary and secondary control schemes (Bidram & Davoudi, 2012). Optimum economic operation is achieved when all DERs are operated at the same marginal cost. Therefore different optimization algorithms—such as particle swarm optimization, game theory, and grossing algorithm—are engaged to ensure effective and accelerated economic operation (Barklund, Pogaku, Prodanovic, HernandezAramburo, & Green, 2008; Pantoja & Quijano, 2011). All the three hierarchical control schemes are combined and the same is depicted in Fig. 1.12.

1.6 Control of DC microgrid Several control methodologies such as distributed, decentralized, and centralized are proposed to achieve stable operation of DC microgrid. The following list depicts various aspects of the control strategies: 1. stable voltage 2. reduced power loss 3. smooth transitions 4. power flow control among interconnected DC microgrids 5. load sharing 6. power sharing 7. economic operation

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Figure 1.12 Flowchart for all three control schemes of AC microgrid.

1.6.1 Control structures The main components of the DC microgrid include—battery storage systems and parallel DERs based on the converters. Each converter is regulated by a local controller having voltage, current, and droop control as variables. Some examples of the controller are as follows: PV system is operated with a source-dependent controller known as maximum power point tracking (MPPT) and battery (or ESS) system works with the state of charge controller. These local controllers are centrally regulated for efficient management of energy and the control is defined as CC system. The following section briefly describes the varied types of controllers that could be classified on the basis of communication links as—(1) distributed control, (2) CC, and (3) decentralized control. 1. Centralized control As the name suggests, the DC microgrid having CC (Fig. 1.13) has a CC system along with digital communication networks which

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Figure 1.13 Framework of centralized control scheme.

connects the generation sources and the loads. DERs in small-scale DC grids are controlled by CC having high bandwidth communication links (or networks) utilizing the master and slave methodology, whereas hierarchical control is used in large-scale DC microgrids (Jin, Wang, Xiao, Tang, & Choo, 2014; Valenciaga & Puleston, 2005). The hierarchical control methodology comprises locally controlled inverters along with digital communication link (DCL)-based synchronized control. Due to the introduction of substantial and considerable distinguished control between varied hierarchical levels, DC microgrid continues to operate even after the CC failure. Though CC provides the worthy performance, it cannot allow a single point of failure in communication network and, therefore, requires an expensive redundancy in the complete communication network system (Che & Shahidehpour, 2014; Jin et al., 2014; Wang, Sechilariu, & Locment, 2012). 2. Decentralized control This control method (Fig. 1.14) is based on the LC and some of the utilized methodologies include—adaptive control of droop coefficient, power line signaling (PLS), and data bus signaling (DBS). The adaptive control of droop coefficient is an extended version of the conventional droop control method. This method is helpful in regulating the state of charge of ESSs during both charging and discharging processes. The adjustment and setting up of droop curve requires expertise and even the slightest difference may lead to microgrid

Microgrids, their types, and applications

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Figure 1.14 Framework of decentralized control scheme.

unstable operation (Dragiˇcevi´c, Guerrero, & Vasquez, 2014; Stefanutti, Saggini, Mattavelli, & Ghioni, 2008). The DBS control methodology takes the variation in DC bus voltage as input to regulate several DERs, ESS, and parallel connected utility grid. This control could be further subclassified into three basic modes of operation as—storage dominating, utility dominating, and generation dominating. The mode of operation is totally regulated by the DC bus voltage. Therefore effectiveness and reliability are totally dependent on the measured voltage on common DC bus. The prime function of the DBS is the regulated and coordinated operation and control among several resources (or DERs) such as PV, wind, and ESS, which is realized with the help of droop characteristics of current voltage plot. This control scheme is employed locally over DC bus voltage; thus communication links are absent making it simpler in implementation (Gkountaras, Dieckerhoff, & Sezi, 2014; Schönberger, Duke, & Round, 2006). Communication-based method PLS is also employed but is more complex in comparison to the other two discussed methods. 3. Distributed control In this control scheme, there is no CC unit established and all local controllers communicate with each other through committed and customized DCLs. This control could bear communication failure for a period of time as compared to CC scheme. This control has better information consciousness and data realization as compared to CC structure. However, the high level of complexity and stability margin

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Figure 1.15 Framework of distributed control scheme.

are some of the major challenges among the distributed control structure (Hossain, n.d.; Nasirian, Moayedi, Davoudi, & Lewis, 2015; Zhao & Dörfler, 2015). The framework of distributed control scheme is depicted in Fig. 1.15. Table 1.1 summarizes the key points of the three DC microgrid control schemes mentioned in the earlier section.

1.7 Control of hybrid (AC/DC) microgrid The hybrid microgrid, as the name suggests, is the combination of two microgrids—AC and DC. The AC microgrid is widely configured and utilized due to minimal alterations required in the existing infrastructure and utility grids, whereas DC microgrid is gaining popularity due to its own advantages, such as—no reactive power requirement or compensation, no synchronization issue, increasing DC loads (electrical vehicles, battery operated devices, etc.), minimum transmission losses, and economical to transmit power over long distances. Therefore the hybrid AC/DC microgrid configuration is an optimum infrastructure due to the involvement of pros from both the AC and DC microgrids (Katiraei, Iravani, Hatziargyriou, & Dimeas, 2008; Nejabatkhah & Li, 2015; Planas, Gil-DeMuro, Andreu, Kortabarria, & Martínez De Alegría, 2013). The hierarchical control schemes, as shown in Fig. 1.16, of this system also contain three control levels: tertiary, secondary, and primary. The key functions of tertiary control include—minimizing the deviations occurred by secondary control, managing the demand, power exchange, and market pricing. The

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Table 1.1 Key points for all three control schemes of DC microgrid. Approach

Distributed control

Decentralized control

Centralized control

Communication Communication link Control decision

Yes DCL

No Power line communication Local

Yes DCL

Depends on the reliability of the communication protocol Easy to implement

Single point of failure damage total control

Functional reliability

Cost effectiveness

Processed locally Tolerate some point of failure Cost effective

Global

Costly as redundant communication line is required

DCL, Digital communication link.

primary functions of secondary control are ensuring good power quality and minimization of V/F deviation occurred at primary level. And, the main tasks of the primary control system are to ensure the quality of V/F and P/Q in permissible limits (Gupta, Kandari, & Kumar, 2021) (Piagi & Lasseter, 2006). Table 1.2 briefly summarizes the characteristics of the AC/DC hybrid microgrid control schemes.

1.8 Microgrid research areas Microgrid consists of several fragmented renewable resources and varied weather conditions that bring in the key challenge of ensuring stable operation of the system. The control system needs to be designed keeping in focus some of the major issues and the prime research areas are discussed in the following section. 1. Voltage and frequency stability Microgrid comprises DERs and ESSs that are connected by PECs known as inverters. Thus these systems are also defined as inverterdominated small-scale utility (or power) grid that can either work in autonomous mode or grid-connected mode. The fluctuations or variations arising in the voltage and frequency parameters are due to mismatch

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Microgrids

Figure 1.16 Framework of AC/DC hybrid microgrid control scheme.

in the generated power and the load. The challenging task for the microgrid is to keep the deviations within permissible limits, which is even more difficult in autonomous mode due to the deprivation of reactive power support and inertia which is available in grid-connected mode. 2. Distributed energy storage Due to the indefinite behavior of DERs along with irregular weather condition, the power output is varied leading to voltage instability and potential costs. The issue is resolved with an involvement and deployment of ESS. But, the major challenge in employing ESS is regulating the dynamic balance between the power demand and generation along with maintaining voltage and frequency within permissible limits. The system’s effective and efficient operation depends on the optimum management of ESS (Li et al., 2017; Zeng, Xu, Ding, Yukita, & Ichiyanagi, 2015; Zhao, Yan, Xue, & Zhang, 2017).

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19

Table 1.2 Key points for AC/DC hybrid microgrid. Primary

Secondary

Tertiary

Grid forming grid following Based on local control

Centralized decentralized Based on local and global control Can be applicable for both autonomous and grid-connected mode Communication infrastructure is required

Centralized distributed

Can be applicable for both autonomous and grid-connected mode No communication network or infrastructure is required

Can be implemented easily

Complex control

Based on global control Applicable for only grid forming mode Two communication links are required. • First link is dedicated to tertiary control • Second link is dedicated for primary and secondary control Complex control and costly

3. Reactive power compensation Among AC microgrid primary issues, reactive power compensation is the major one that directly influences the voltage stability and power quality problems. The reactive power demand is raised by the inductive load and the power quality can be drastically improvised by maintaining the reactive power exchange controlled and compensated. However, the issue becomes critical in the absence of utility grid, that is, while the system is working as autonomous due to absence of reactive power and inertia from the grid (Gayatri, Parimi, & Pavan Kumar, 2018; Shahidehpour, 2010). 4. Harmonics mitigation The key component in the microgrid is DERs (PV, wind, FC, micro-turbine), interconnected with the help of VSCs and are controlled via high-frequency switching controller. This introduces harmonics in the system, further affecting the power quality along with dynamic stability of the microgrid. The problem is resolved with the help of LCL filters, which eliminates harmonics, thus improving the quality of the power. Though LCL filter’s worthy performance and minimal cost lead to the widespread use of the device, its performance is highly influenced with the impedance of the line leading to

20

Microgrids

resonance and instability (Micallef, Apap, Spiteri-Staines, & Guerrero, 2017; Said-Romdhane, Naouar, Belkhodja, & Monmasson, 2017). 5. Protection issues The protection systems required in microgrids are modern and advanced due to bidirectional exchange of power between microgrid and traditional utility grid. The protection system needs to cater an additional protection to resolve grid following and grid forming faults. The former is required to isolate the microgrid from the utility grid, whereas the latter is present to effectively isolate fraction of the microgrid. Since the fault nature and operation mode define the direction and amount of faulty current, the protection scheme employed is also distinct in nature from conventional systems.

1.9 Solar PV technology is the backbone of the renewable energy sector and plays one of the most crucial roles in designing of microgrid. It is among the important renewable energy resources, distributed geographically, which is more or less available in every microgrid (Tomar, Mittal, & Sharma, 2018). PV technology is a direct method of energy conversion and generates electricity by converting solar energy into electricity. The core component for the conversion of energy into electrical is the solar cell. It utilizes the energy of the incoming photons to generate electrical energy. PV systems are primarily classified according to the application requirements, configuration of the components, and the way connections are performed to other power sources and electrical loads. The two principal operations (as shown in Fig. 1.17) of the system could be either independent (stand-alone) or in parallel with traditional electrical grid (utility interactive system).

Figure 1.17 Types of photovoltaics system.

Microgrids, their types, and applications

21

1.9.1 Independent (or stand-alone) PV system As the name suggests, this PV system is deployed independently and unhitched from the traditional electric grid. This type of system is installed in remotely located off-grid areas to meet the local demands. The electrification using PV is possible only during day time, therefore, to serve the load and deliver the energy around the clock along with minimal blackouts, the system must be equipped with the ESS. The basic design of the independent PV system (as shown in Fig. 1.18) comprises solar cell array, MPPT controller, battery (as backup) along with battery charge controller, off-grid inverter, step-up transformer, and load (Alok, Bhagyashree, & Singh, 2019; El-Shahat & Sumaiya, 2019).

1.9.2 Grid-connected PV system The designed PV system is connected with traditional power grid using power conditioning unit (PCU) and operates in parallel with the utility grid. It injects power to the grid with the help of PCU that converts the PV DC output into AC power with grid’s required level of voltage and power quality. Grid-connected PV system could be further classified into centralized-type and distributed-type grid. The former directly transfers power into the utility grid for distribution to consumers, whereas latter is a type of DG in microgrid, where the power is directly transmitted to

Figure 1.18 Schematic of stand-alone PV system. PV, Photovoltaics.

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Microgrids

Figure 1.19 Schematic of grid-connected PV system. PV, Photovoltaics.

serve the load and fulfill the demand. The surplus or deficit in energy is cared by utility grid (Chouder, Silvestre, Sadaoui, & Rahmani, 2012; Marion et al., 2005). The generic structure of the grid-connected PV system is depicted in Fig. 1.19. The structures for both the PV systems along with functions of major component installed are similar; however, the difference lies in inverter. Though both the inverters, that is, grid-tie inverter and off-grid inverter convert direct current to alternating current, the former is the current source for P/Q output, whereas the latter is the voltage source for V/f output. Also, grid-tie inverter performs some additional functions such as— MPPT (discussed in the following section), ensuring good power quality by suppressing the harmonics in output current, and V/f automatic tracking.

1.9.3 PV modeling The following section will administer some brief information regarding the PV. PV may be defined as a technology that converts solar energy into DC electrical energy by utilizing photon energy from the sun rays. In a solar panel or a solar module/array, PV cells (schematic is shown in Fig. 1.20) are the basic building blocks that form a p
n junction using a silicon semiconductor material. Due to the illumination of p
n

Microgrids, their types, and applications

23

Figure 1.20 Schematic of PV cell. PV, Photovoltaics.

junction, the photons from sunlight with large energy gets absorbed, and it knocks down the valence electrons, thus producing electric energy. The generic solar cell structure is shown in the figure consisting of metal grid, rear contact, antireflective layer, and p
n junction diode (Tomar, Mittal, & Pattnaik, 2021) (Jiang, Qahouq, & Orabi, 2011). The PV cell mathematical model is shown in Fig. 1.21 having five prime parameters—current source dependent on sunlight connected in parallel with diode along with shunt and series resistor connected with the load (Dey, Khan, Abhinav, & Bhattacharjee, 2016). Applying Kirchhoff’s Current Law I 5 Iph 2 Id 2 Ish and the diode current, Id , is calculated as
Id 5 I0 3 e



Vd η 3 VT 21

where I0 is the diode saturation current; η is the ideal constant of diode (1
2); Vd is the diode voltage; and VT is the thermal voltage.

24

Microgrids

Figure 1.21 PV cell mathematical model (El-Shahat & Sumaiya, 2019). PV, Photovoltaics.

The common electrical characteristics evaluated for any PV module include rated power, open and short-circuit voltage, voltage and current at maximum power, and no. of cells connected in series or/and parallel (Tomar et al., 2018). The photovoltaic’s V
I curve is of nonlinear nature and the reason for this type of characteristics is due to its dependency on external parameter—irradiance and temperature. The PV cell characteristics also depict three prime parameters—short-circuit current, open-circuit voltage, and maximum power point (Alam, Kumar, Srivastava, & Dutta, 2018; Islam, Merabet, Beguenane, & Ibrahim, 2013).

1.10 Maximum power point tracking MPPT is an acronym for maximum power point tracking and is a sourcedependent controller. The energy is extracted from the solar using PV cell, but for the optimum performance and maximum power, MPPT controller is employed. There are numerous MPPT algorithms available in literature such as perturbation and observation (P&O), incremental conductance, fractional short-circuit current method, fractional opencircuit voltage method, and artificial neural network but the following section briefly describes one of the commonly using MPPT algorithm, P&O method (Dolara, faranda, & leva, 2009; Johnson & Professor, 2007).

1.10.1 P&O method This method is based on the association between the components of the PV, that is, output power and voltage. Once the output current increases, the current’s perturbation will persevere on the same course along the direction of MPP. Further, power P(t) is calculated and compared with P (t 2 1) which leads to two outcomes: 1. P(t) . P(t 2 1) 2. P(t) , P(t 2 1)

Microgrids, their types, and applications

25

Figure 1.22 Perturbation and observation (El-Shahat & Sumaiya, 2019).

In the first case, measured voltage V(t) is compared with V(t 2 1), if measured voltage V(t) comes out to be greater than V(t 2 1), the duty cycle D(t) is incremented by a constant ΔD otherwise decremented by ΔD. In the second case, measured voltage V(t) is compared with V(t 2 1), if measured voltage V(t) comes out to be less than V(t 2 1), the duty cycle D(t) is decremented by a constant ΔD otherwise incremented by ΔD. The variations are maintained and controlled by a constant value ΔD; therefore P&O algorithm fails to cater the system during the rapid change in irradiance level and abnormal variation(s) in the atmospheric condition. The steps for the P&O method is summarized in Fig. 1.22 (Elgendy, Zahawi, & Atkinson, 2012; El-Shahat & Sumaiya, 2019; Hlaili & Mechergui, 2016).

1.11 Wind turbine system A wind turbine is a machine that converts kinetic energy of the wind into torque that causes the turbine blades to rotate and drive the electrical generator. The amount of kinetic energy in wind depends upon its speed as wind is considered mass, and mass has some kinetic energy. To study a wind turbine model in a microgrid, we consider an aerodynamic input torque that helps drive a generator. For this model, we consider a Permanent Motor Synchronous Generator. The following equation tells about the mechanical power of a wind turbine system ( Jess, n.d.): 1 Pw 5 Cp ðλ; β ÞρAV 3 2 where ρ is density of air (kg/m3 ), Cp is coefficient of power, A is rotor blade area of interception (m2 ), V is avg speed of wind (m/s), and λ is the tip speed ratio (TSR).

26

Microgrids

Theoretically, max value of Cp which is the coefficient of power is 0.593 is termed Betz’s coefficient. The TSR for a wind turbine is defined as the ratio of rotational speed of the tip of a blade to the wind velocity. Mathematically, λ5

Rω V

where R is turbine radius (m), ω is angular speed (rad/s), and V is average wind speed (m/s). The total wind energy generated can be obtained by Qw 5 P 3 ðtimeÞ½kWh The speed at which the turbine starts to rotate and generate power is known as cut-in speed. Cut out speed is the high wind speed that acts on the turbine, resulting in a very high risk of damage to the rotor. A braking system is an essential tool that is employed to save the rotor from being damaged. The braking system helps get the position of the rotor to a stopped position (Gilbert M Masters, 2004; Ragheb & M, 2011). The maximum capable power output of a generator is called the rated power output. This rated output results from rated output speed which is between the cut-in and cut out speed.

1.12 Battery Battery is an electrochemical device and converts the energy stored in chemical into electrical doable energy. It is a form of energy storage (ES) and is commercially available in several types but widely used includes—lithium-ion battery, lead
acid battery, and nickel
cadmium battery (Brodd, 2013; Schumm, 2021).

1.12.1 Lithium-ion battery A type of rechargeable battery is called lithium-ion battery, mostly applied for applications in electric vehicles. In a Li-ion battery, during discharge, the li ions transport from the negative (2ve) electrode to the positive (1ve) electrode through an electrolyte and during charge period, Lithium-ion battery employs li compound as the material at 1 ve side and graphite at the 2 ve side. Li-ion batteries have high energy density and low self-discharge. CoO2 ðsÞ 1 Li1 ðaqÞ 1 e2 -LiCoO2 Li1 ðaqÞ 1 C6 ðsÞ 1 e2 -LiC6

Microgrids, their types, and applications

27

The main components of functionality of a li-ion battery are 1 ve electrode, 2 ve electrodes, and the electrolyte. The 2 ve electrode is mainly made of carbon, the 1 ve electrode is generally a metal oxide, and the electrolyte is a lithium salt in an organic solvent. The electrochemical roles of the electrodes alter between anode and cathode which mainly depends on the direction of flow of current through the cell (He, Xiong, & Fan, 2011; Hu, Sun, & Zou, 2013). Despite high cost of manufacturing, additional protection circuitry along with hassling and complex integration of high capacity, lithium-ion battery has impressed with its several other characteristics, which is helping it to be among the popular choice for the practical applications. Some of the prime features include little self-discharge, high specific energy, low maintenance, and less pollution as compared to others (Barcellona & Piegari, 2017).

1.12.2 Lead
acid battery Lead
acid batteries are among the oldest rechargeable batteries that are currently in use. This battery utilizes sponge lead and lead peroxide to convert chemical energy into electric energy. It contains varied concentrations of water and acid (specifically, sulfuric acid). Though it has low specific energy and output power, short lifetime and causes pollution, it has its own merits such as—low price and manufacturing cost, high reliability, and advanced technology due to which it is employed in power sector, that is, power plants and substations. This battery is primarily used as an independent source of power for closing or opening circuit breakers, relay protection, communication, and emergency lighting in power substations (Bullock, 2013; Pang, Farrell, Du, & Barth, 2001).

1.12.3 Battery modeling In the case of electricity blackout or when the renewable energy resources are not capable of fulfilling the load demand, the battery operates and supplies power. Therefore the battery is an essential element in a microgrid. The system becomes more stable when the generation of power matches the load demand. So the battery is mostly set down between the RES and load. When power from the microgrid is unavailable, the battery must meet the load requirements. Thus battery sizing is an important

28

Microgrids

factor is battery modeling. The battery capacity can be calculated by (Alzahrani, Ferdowsi, Shamsi, & Dagli, 2017): Bsize 5

Eload XDaysoff DoDmax Xηtemp

where Eload is the load required when power is not available, Daysoff is days of storage when grid power is unavailable, DoDmax is the max depth of discharge (DoD) of battery, and ηtemp is the temp correction factor. DoD may be defined as the amount of capacity in percentage that has been removed from full-charged battery. Correction factor may be defined as the capacity batteries show changes with respect to temp; for lead
acid batteries, greater changes are seen on lower values of temp. Therefore battery sizing is very crucial so that sufficient standby time is available even under worst temperature conditions.

1.12.4 Sizing batteries correctly Sizing of the batteries is very important, as it is important to supply the load demand without any failure. There are a number of factors taken into considerations for the optimization so that the battery works as we expect. Many of these factors are secured in the chemistry and other depends on physical structure of cell. Temperature is one factor that solely influences the battery performance and these following factors are also considered for the optimal working of the cell. 1. minimum and maximum voltage 2. correction factor 3. duty cycle 1.12.4.1 Voltage of system (min and max) The battery is made up of several cells and these cells have a restricted voltage range that depends upon the kind of cell which is deployed. Considering the case of lead
acid batteries, the nominal voltage of cell which is also called the voltage of a fully charged cell without any input charge is 2 V. 1.12.4.2 Duty cycle In a cell, it is very important to compute the amount of power required for each function during the battery discharge period. Generally, different classes of load considered are as follows:

Microgrids, their types, and applications

1. 2. 3. 4.

29

continuous load communications emergency intermittent

1.12.4.3 Correction factor Batteries have a tendency to age with time and lose its power; therefore a margin of 25% is added initially to cover that factor. Also, as load may increase, a design margin about 10%
15% is added. The battery size provided by different suppliers depends upon the minimum voltage at the end of the cycle. Given a lead
acid battery, provided an 8-hour standby time, min voltage should not be less than 1.70 V/cell. Batteries play a role in off grid hybrid renewable energy system (HRES) and have big share in the initial cost. Batteries are generally used as a backup option that stores power when the power generated is greater than the required demand. During peak hours when power demand is much higher than the production, batteries are put forward that supplies the required power. A battery’s lifetime depends upon the rate of energy consumed from the system. To increase battery life, battery consumption rate is lowered but it is not applicable in the case of HRES. During the period when consumption is very high, the effective capacity of battery devalues and thus decreases the life of the battery. The battery can recover some of its life when there is zero consumption from the system.

1.13 Fuel cell FC is an electrochemical device that converts the chemical energy stored in fuels into electrical energy using chemical reactions (or, precisely pair of redox reaction). Unlike batteries, FCs can continuously produce electricity till fuel is fed into the device. The basis of FC lies in the late 1830s, when two scientists named William R. Grove (in 1838) and Christian Friedrich Schönbein (in 1839) independently experimented to generate an electric current using hydrogen and oxygen. Though the substructure of the FC was placed in the 1830s, the term “fuel cell” was introduced in 1889 by two researchers— Charles Langer and Ludwig Mond (Breeze, 2017a; Grove, 1838, 1839; Schönbein, 1839).

30

Microgrids

Both FC and batteries are forms of galvanic cells and convert the chemical energy into electrical and work on the similar principles, that is, converting one form of energy into another with the help of redox reactions. But the basic difference between both the devices are the constituents and parameters (Fuel Cell Technologies Office, 2015; Fuel Cells & Infrastructure Technologies Program Hydrogen, 2006). A battery is preloaded with all the necessary reactants required for generating the electrical energy, whereas FC is just a housing and all the reactants required to produce the electricity needs to be fed externally. The FC produces electrical energy till reactants are fed into the housing of the device and stops otherwise. Unlike battery, FC has the capability to produce energy continuously but is highly dependent on the external factors, such as—fuel availability and fuel feeding mechanism. The following schematic (Fig. 1.23) depicts that the basic construction of FC is not complex and primarily comprises—two electrodes (anode and cathode), two catalyst layers for each electrode, and an electrolyte solution (or medium) (Behling, 2013; Sheshpoli, Ajarostaghi, & Delavar, 2019; Sudhakar, Selvakumar, & Bhat, 2018). The electrolyte is sandwiched between catalyst layers followed by both the electrodes. Also, extrinsic methods are present to feed the

Figure 1.23 Basic structure of fuel cell (Angelopoulos, Antonakis, Nikolaou, & Takoudis, 2004).

Microgrids, their types, and applications

31

Figure 1.24 Schematic for working of fuel cell (Fuel Cells & Infrastructure Technologies Program Hydrogen, 2006).

fuel and extract the by-products resulting from the reaction taking place. The device uses hydrogen and oxygen as a raw product to produce electricity along with water and heat as by-products. The working principle of the FC could be understood with the help of following diagram (Fig. 1.24): • At anode The hydrogen fuel in diatomic gaseous form, that is, 2H2 , is continuously fed to the anode. The absorbed 2H2 molecule gets ionized with the release of four electrons (e2 ) and four protons of hydrogen. The released e2 are forced to flow externally through load before diffusing back to cathode (Mittal & Tomar, 2021). • At cathode The oxygen molecules break into atoms and are held in their receptive state on the electrode for interim period. The incoming e2 at cathode initiates another reaction, thus producing by-product as water along with heat or negatively charged ions. • In electrolytic medium This medium prevents direct contact between the fuel and oxidant gas on anode and cathode, respectively. This medium is electronically insulated and allows the freely movement of particular ions through it (Appleby, 1993; Breeze, 2017b; Srinivasan, Davé, Murugesamoorthi, Parthasarathy, & Appleby, 1993).

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Figure 1.25 Steps for working of fuel cell.

The steps involved in working of FC could be summarized in the following flowchart, which is demonstrated in Fig. 1.25. The redox reactions occurring inside the FCs are exothermic in nature and the chemical reaction(s) taking place on the electrodes anode and cathode are as follows: At anode ðoxidation occursÞ: 2H2 -4H1 1 4e2 At cathode ðreduction occursÞ: O2 1 4H1 1 4e2 -2H2 O 1 Δ Overall reaction : 2H2 1 O2 -2H2 O 1 Δ Several types of FCs are commercially employed for varied types of applications which can be classified primarily on the basis of type of fuel input or electrolyte used. The basic FCs known are—proton exchange membrane FC, molten carbonate FC, solid oxide FC, phosphoric acid FC, alkaline FC, direct methanol FC, and reversible FC. Table 1.3 summarizes the properties, pros and cons of few commercially employed FCs (Comparison of Fuel Cell Technologies | Department of Energy, 2011; Fuel Cell Technologies Office, 2015; Fuel Cells & Infrastructure Technologies Program Hydrogen, 2006; Sheshpoli et al., 2019; United States Department of Energy, 2006).

1.14 Advantages and applications of microgrid Traditionally, the concept of microgrid was introduced as a solution to meet the diverse demand and cater the energy requirement of the remote locations due to the deficiency of infrastructure. However, advancements and research in the microgrid sector has shifted the technology from remote only solution to

Table 1.3 Characteristics of fuel cell. Fuel cell technology

PEMFC

AFC

PAFC

MCFC

SOFC

Electrolyte

Ion exchange membranes

Immobilized liquid phosphoric acid

Immobilized liquid molten carbonate

Yttrium-stabilized zirconia

Operating temperature Charge carrier Catalyst Power output Pros

80°C

Mobilized or immobilized KOH 120°C
150°C

200°C

650°C

800
1000°C

H1 Platinum ,250 kW Lightweight, compact, low level of nitrogen oxides

OH2 Platinum 300 W
5 kW High efficiencies, produce potable water, long life cycle

H1 Platinum 200 kW Operates very quietly

Requires relatively pure hydrogen, complex engineering units

Costly platinum catalyst to speed up reaction, needs very pure hydrogen

Large and heavy

CO322 Nickel 250 kW
2 MW High efficiencies, operates with unreformed fuels, nickel electrode cheap Significant start-up time, demands on corrosion stability and component life

O22 Perovskites 100 kW Suitable for large and small plants, tolerant to fuel impurities, low cost design Significant start-up time, sensitive to operating temperature

Cons

AFC, Alkaline fuel cell; MCFC, molten carbonate fuel cell; PAFC, phosphoric acid fuel cell; PEMFC, proton exchange membrane fuel cell; SOFC, solid oxide fuel cell.

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Microgrids

an alternative for serving the power surge demand in the urban areas. They are potentially utilized in the high-resolution optimal scheduling along with flexible operation, localized solution along with economical operation. Microgrid is getting introduced in various sectors, such as—farms, mission critical infrastructures (defense), municipal and government facilities, colleges, hospitals, airports, homeowner, and industrial units. The microgrid is also finding its way based on requirements into various organizations, such as—organizations wanting to lower energy costs, organizations requiring huge amount of reliable energy, and organizations pursuing sustainability. The microgrids have some specific advantages from the perspective of the application that includes promoting renewable energy consumption at local level, improvising the quality and reliability of power supply and resisting emergency, saving power transmission losses over large distances, and increasing the energy efficiency (Wei & Chen, 2019).

1.15 Conclusion The chapter deals with various aspects of the microgrid and briefly discussed about the classification along with different types of microgrid. The layered structure of the microgrid is explained followed by brief explanation of modes of operation, control, and hierarchical control scheme of the each microgrid. The concept and modeling of PV, MPPT algorithms, wind turbine system, batteries, and FC is also discussed. The chapter ends with the brief overview of the advantages and applications associated with the microgrid.

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SECTION II

AC microgrids

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CHAPTER 2

Disturbance observer
aided adaptive sliding mode controller for frequency regulation in hybrid power system Vivek Patel, Dipayan Guha and Shubhi Purwar Electrical Engineering Department, Motilal Nehru National Institute of Technology Allahabad, India

2.1 Introduction Distributed generation (DG) resources are progressively practiced to match the increasing energy requirement in the current energy scenario. DG technologies have given electrical energy solutions to environment-friendly, economical, and reliable consumers compared to the conventional generating system. DG system practices a small-scale generation placed near to the consumers or load centers. The commonly used power generation resources are solar energy, wind energy, diesel generator, fuel cells (FCs), etc. One of the simplest and convenient ways of matching the ever-rising load demand is the coordinated operation between the conventional power system and DGs, which results in a hybrid power system (HPS). This coordination further confirms the delivery of quality and uninterrupted power to the end users. However, the performance of HPS is highly dependent on the controller used in the system. Owing to the intermittent output of DGs and continuous variation in load demand, the system frequency deviates from the steady-state level, which disturbs the power system's equilibrium operation. Moreover, considerable droop in frequency causes instability. Thus to restore frequency and damp out system oscillations, a load-frequency controller (LFC) is employed in HPS (Pandey et al., 2014). Many control strategies have been adopted and applied for powerfrequency control of HPS. The performance of an autonomous renewable energy
based HPS without controllers is studied in Lee and Wang (2008); Ray et al. (2010). An HPS with proportional-integral (PI)-controller is discussed in the study of Senjyu et al. (2005). The controller settings in the study Microgrids DOI: https://doi.org/10.1016/B978-0-323-85463-4.00001-0

© 2022 Elsevier Inc. All rights reserved.

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of Senjyu et al. (2005) were derived from a trial-and-error approach. Ziegler
Nichols-based proportional-integral-derivative (PID) controller for LFC of a microgrid that is available in the study of Mallesham et al. (2011). Nevertheless, HPS experiences different types of uncertainties, namely, random variation in load demand, transients of renewable energy resources (RERs), modeling error, and parameter variation. The PI controller with advanced control methodology integrated is studied in the work of Sahu et al. (2013) to tackle these uncertainties. Different control methodologies like optimization-based controller (Das et al., 2012; Bevrani et al., 2012; Shankar and Mukherjee, 2016; Guha et al., 2018), robust controller (Jiang et al., 2012; Bevrani and Hiyama, 2009), fuzzy logic
based controller (Kocaarslan and Ertugrul, 2005) have been employed to improve the dynamic performance of the power system. A robust HN controller for the HPS has been discussed in the study of Pandey et al. (2014). However, the effect of system nonlinearities on the system performance has not been shown in the same work. In the work of Tan et al. (2017a,b), the LFC of power systems without DGs is discussed considering the effects of system nonlinearities. The sliding mode controller (SMC) is applied in the study of Gao (1995) for frequency control and shows its robustness. Pole assignment techniquebased sliding mode load frequency control (SMLFC) has been presented in the study of Sivaramkrishnan et al. (1984). Genetic algorithm
tuned SMC for LFC is used in the study of AI-Hamouz and Al-Duwaish (2000). Optimization-based SMLFC of power systems has been studied in the work of Mohanty (2015). Decentralized LFC with SMC for a multiarea power system considering generation rate constraints (GRC) and parameter uncertainty is presented in the study of Yang et al. (2013). However, this study does not show the impacts of the governor dead-band (GDB) and DGs penetration on system performance. The mastery of a nonlinear SMLFC (Prasad et al., 2017), observer-based nonlinear SMLFC without RERs (Prasad et al., 2019), and with RERs (Prasad et al., 2019) for LFC of power systems is assessed. In the study of Mi et al. (2017), SMLFC for a multiarea power system considering time delay is available. A second-order SMC and an extended disturbance observer (DOB) for LFC are presented in the study of Liao and Xu (2018). A multiarea HPS performance by using adaptive integral higher order SMC is presented in the study of Sarkar et al. (2018). LFC with time delays is investigated using super twisting SMC in the study of Dev et al. (2019). In the work of Mi et al. (2019), frequency control of a multiarea HPS based on SMC has been presented. DOB
based integral

Frequency regulation in hybrid power system

45

SMC is discussed in the works of Zhou et al. (2018); Yuan and Gao (2019); Zhang et al. (2016); and Li et al. (2014). The double SMC with DOB for the islanded microgrid is available in the study of Wang et al. (2016). In the study of Mi et al. (2016), SMLFC with DOB is implemented for HPSs. The references mentioned earlier investigated the performances of SMLFC with DOB for an isolated power system only. In the study of Tummala et al. (2018), nonlinear DOB
based SMC is utilized to study the system performance. The main motivation of the present work appears from the following aspects: 1. The performance of DGs for the development of HPS, for reliable and stable power supply, has been examined. 2. For delivering reliable and stable electrical power to the end users, the energy storage device is explored. 3. Inspired by the existing control strategies’ theories, a robust control algorithm is implemented in a complex HPS considering parameter variation and random disturbances. 4. It is well known that the main limitation of SMC is the chattering effect, which is effectively tackled by the proposed control strategy. 5. The proposed controller effectively minimizes the effect of load disturbances. The review of existing literature reveals that the consideration of nonlinearities in HPS is very less. Furthermore, the study of HPS performance with a double-stage reheat turbine has not been considered. In this study an effort has been given to analyze the performance of HPS associated with a double-stage reheat thermal power system using the DOB
aided adaptive sliding mode controller (DOB-ASMC) considering GRC and GDB nonlinearities. The DOB estimates the variable wind power and load fluctuation integrated with ASMC to generate appropriate control signals. In SMC the uncertainty bound defined in the switching control term is tough to derive in practical cases. To tackle this problem the switching term’s gain is designed as a function of the sliding surface and the system states, while adaptive law is used to estimate the unknown uncertainty bound of the system. The notable contribution of the current work is précised next. 1. A higher order model of isolated and interconnected HPS has been developed to observe its dynamic performance against multiple load variations. The effect of DGs penetration on system outputs is also examined.

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2. The ASMC is developed to minimize the frequency deviation and control DGs output power to assess the performance of HPS in isolated and interconnected modes. Moreover, to improve the control performance and reducing chattering effects, a DOB is integrated into ASMC. 3. The results obtained with DOB-ASMC are compared with SMC, linear quadratic regulator (LQR), and PI controller to quantify proposed controller’s supremacy. 4. The consequences of GRC and GDB nonlinearities on the system performance have been inspected. 5. The robust stability of the HPS is examined by using Kharitonov’s theorem after a 650% variation of the system parameters. The organization of the chapter is as follows. Section 2.2 provides the model of considered HPS. The design methodology of the proposed DOB-ASMC is provided in Section 2.3. Section 2.4 outlines the simulation results and comparative discussion. Finally, Section 2.5 concludes the present work and future proposal.

2.2 System modeling A practical power system is a sophisticated nonlinear system with different types of uncertainties. Since the LFC study is generally performed for the small changes in load and output powers of RERs, the linearized model can be developed to study the dynamic performance of HPS. The control law for frequency regulation has been derived from the linearized model. The conventional linearized reheated power system model is given in Fig. 2.1A. The dynamic equations are computed as follows. •

Δf 5 2 •

Kp Kp 1 Δf 1 ΔPg 1 ð 2ΔPd 1 ΔPDG Þ Tp Tp Tp

(2.1)

 1 1  ΔPg 1 ΔXrh1 1 Xg TT TT

(2.2)

ΔPg 5 2 •

ΔXrh1 5 2

ðTrh1 1 Trh2 Þ ðαðTrh1 1 Trh2 Þ 2 βTrh2 2 ðTrh1 1 Trh2 ÞÞ ΔXrh1 1 ΔXrh2 1 ΔXg Trh1 Trh2 Trh1 Trh2

(2.3) •

ΔXrh2 5 2

ΔXrh1 Trh1 Trh2

(2.4)

Frequency regulation in hybrid power system

47

(A)

(B)

(C)

Figure 2.1 (A) Reheated thermal power system model, (B) double-stage reheated turbine, (C) comparative study between single- and double-stage reheat turbine. •

ΔXg 5

1 1 1 1 Δf 2 ΔXg 2 ΔE 1 uðt Þ RTg Tg Tg Tg •

ΔE 5 KE Δf

(2.5)

(2.6)

where ΔPg ; Δf ; ΔXg ; ΔXrh1 ; ΔXrh2 , and ΔE are the deviation in turbine output, frequency, governor valve position, reheater stages 1 and 2, and integral control, respectively; ΔPd and ΔPDG are the deviation in load and DGs output, respectively; TP ; Tg ; TT ; Trh1 , and Trh2 are the time constants of power system, governor, turbine, and reheater stages 1 and 2, respectively. The comparative study between single- and double-stage reheat turbines is shown in Fig. 2.1C.

2.2.1 Model of reheated thermal power system A single- and double reheat turbines’ transfer function model is described in the works of Kundur (1994); Nanda et al. (2006). In the present work the performance of a one-stage reheat turbine is initially observed, and then the study is extended to double-stage reheat power system. 2.2.1.1 Transfer function model of double-stage reheat turbine The double-stage turbine has double-stage tandem-compound reheater (Kundur, 1994) and four cylinders, a very high pressure, high-pressure,

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intermediate-pressure, and low-pressure turbines, with the pu MW rating of α; β; χ;and δ, respectively. The approximated linear model of a double-stage turbine is shown in Fig. 2.1B. The sum of pu MW rating must be equal to 1, that is, α 1 β 1 χ 1 δ 5 1 (Nanda et al., 2006). Eqs. (2.7) and (2.8) provides the transfer function representation of a double-stage reheat turbine. GTur ðsÞ 5

1 1 sfαðTrh1 1 Trh2 Þ 1 βTrh2 g 1 s2 Trh1 Trh2 ð1 1 sTrh1 Þð1 1 sTrh2 Þð1 1 sTT Þ

 GTur ðsÞ 5 α 1

 β 12α2β 1 1 1 1 sTrh1 ð1 1 sTrh1 Þð1 1 sTrh2 Þ ð1 1 sTT Þ

(2.7)

(2.8)

where GTur ðsÞ is the transfer function of a double-stage reheater turbine. The deviation in frequency of the isolated power system, as depicted in Fig. 2.1A, with single- and double-stage reheater following constant load perturbation, is shown in Fig. 2.1C. It is realized from Fig. 2.1C that the test system with a double-stage reheat turbine exhibits better output compared to a single-stage reheat turbine considering peak overshoot, undershoot, and speed of response. Hence, the rest of the simulations have been performed considering a double-stage reheat turbine power system.

2.2.2 Distributed energy resources The presented work examines the potency of the proposed DOB-ASMC for frequency control of isolated and interconnected HPS against sudden load changes and intermittent RERs output. The DGs integrated into the conventional power system are wind power generation (WPG), FC, diesel engine generator (DEG), aqua-electrolyzer (AE), and battery energy storage system (BESS). A brief description of the DGs mentioned earlier is given in the subsequent sections. 2.2.2.1 Wind power generation WPG mainly depends on wind speed VW, which changes continuously with time. The wind turbines’ output power is a cubic function of wind speed and is defined in Eq. (2.9). PW 5

3 ρair Ab CP VW 2

(2.9)

where ρair is the air density in kg=m3 ; Ab is the blade’s swept area in m2 ; and Cp is the power coefficient. The WPG model is shown by a

Frequency regulation in hybrid power system

49

first-order time-lag transfer function, neglecting all nonlinearities, as depicted in Eq. (2.10). GWTG ðsÞ 5

ΔPWTG KWTG 5 ΔPW 1 1 sTWTG

(2.10)

where ΔPWTG is variation in output power of WPG; ΔPW is variation in available wind power; KWTG and TWTG are the gain and time constant of WPG, in order. 2.2.2.2 Fuel cell FCs are electrochemical cells that convert the chemical energy of the hydrogen into electrical power by adding gaseous hydrogen with air in the absence of combustion. FCs are integrated with HPS because of its high efficiency with low carbon emission. The transfer function representation of FC is given in Eq. (2.11). GFC ðsÞ 5

ΔPFC KFC 5 u2 1 1 sTFC

(2.11)

where ΔPFC is the variation in output power of FC; TFC and KFC are the time constant and gain of FC, in order. 2.2.2.3 Aqua-electrolyzer Some output of WPG is utilized in AE to generate hydrogen and subsequently supplied to the FC for electric power generation. The linearized model of AE is given in Eq. (2.12). GAE ðsÞ 5

ΔPAE KAE 5 u2 1 1 sTAE

(2.12)

where ΔPAE is the variation in output of AE; TAE and KAE are the time constant and gain of AE, in order. 2.2.2.4 Diesel engine generator DEG autonomously supplies the required power to hybrid power generation when solar or wind power does not fulfill the load demand. It is used as a standby unit. The transfer function form of DEG is given in Eq. (2.13). GDEG ðsÞ 5

ΔPDEG KDEG 5 u2 1 1 sTDEG

(2.13)

where ΔPDEG is the variation in DEG output power; KDEG and TDEG are the gain and time constant of DEG, in order.

50

Microgrids

2.2.2.5 Battery energy storage system The BESS is exploited in HPS to offer adequate damping of system oscillations, thereby enhancing system stability. The BESS transfer function model is given in Eq. (2.14). GBESS ðsÞ 5

ΔPBESS KBESS 5 u2 1 1 sTBESS

(2.14)

where ΔPBESS is the variation in BESS output power; TBESS and KBESS are the time constant and gain of BESS, in order. Fig. 2.2 shows the linearized model of the concerned isolated HPS. The state-space representation of the test system (Fig. 2.2) (Eqs. 2.1
2.6), and (Eqs. 2.11
2.15) can be obtained by using Eq. (2.15). •

x1a ðt Þ 5 A1a x1a ðt Þ 1 B1a u1a ðt Þ 1 W1a ΔP1a

(2.15)

d1a ðtÞ 5 W1a ΔP1a

(2.16)

where system matrix A1a Aℝ11 3 11 , input matrix B1a Aℝ11 3 2 , and disturbance matrix W1a Aℝ11 3 2 are constant matrices. The states of the system, control input, and disturbance input are chosen as follows  T x1a ðtÞ 5 Δf ΔPg ΔXrh1 ΔXrh2 ΔXg ΔE ΔPWTG ΔPAE ΔPFC ΔPBESS ΔPDEG ,  u1a ðtÞ 5 u1

u2

T

 T ; ΔP1a 5 ΔPd ðtÞ ΔPWTG ðtÞ

Figure 2.2 Isolated HPS model. HPS, Hybrid power system.

51

Frequency regulation in hybrid power system 2

1 6 2 TP 6 6 6 6 0 6 6 6 0 6 6 0 6 1 6 62 6 RTg 6 6 KE 6 6 A1a 5 6 6 0 6 6 6 0 6 6 6 6 6 0 6 6 6 6 6 0 6 6 6 4 0

A3 3 3 5 2

KP TP 1 2 TT 0 0

1 TT A3 3 3 A4 3 3

0

0

0

0

0

0

1 TT A3 3 5 0 1 2 Tg 0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0 0 1 0

0

0

KP TP

0

0

0

0

0

0

0 0

0 0

0 0

0 0

0 0

0 0

0

0

0

0

0

0

0

0

0

0

0

0

0

1 TAE

0

0

0

1 TFC

0

0

1 Tg 0

2

0 2

2

1

TWTG 2

KP TP

KP TP

2

2

2

KP TP

KP TP

1 TBESS 0

0 2

1 TDEG

3 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 5

ðTrh1 1 Trh2 Þ 1 ðαðTrh1 1 Trh2 Þ 1 βTrh2 2 ðTrh1 1 Trh2 Þ ; A4 3 3 5 2 ; A3 3 5 5 Trh1 Trh2 Trh1 Trh2 Trh1 Trh2

2 60 B1a 5 6 4 0 2 6 W1a 5 4

3T

1 Tg

0

0

0

0

0

0

K

0

0

0

0

0

0

0

0

KAE TAE

KFC TFC

KBESS TBESS

0 7 7 5 K DEG

TDEG

2 Tpp

0

0

0

0

0

0

0

0 0

0

0

0

0

0

0

0

KWTG TWTG

0

0 0

0

3T 7 5

2.3 Disturbance observer
aided adaptive sliding mode load frequency controller 2.3.1 Traditional sliding mode load frequency controller (SMLFC) The literature review unfolds that the efficacy of SMLFC has been shown for conventional interconnected power systems. However, its mastery after penetration of RERs needs further investigation. In this section, initially, the traditional SMC has been developed for Fig. 2.2, and then its

52

Microgrids

performance is improved considering DOB. To design SMLFC, Eq. (2.15) is rewritten as in Eq. (2.17). •

x1a ðt Þ 5 A1a x1a ðt Þ 1 B1a u1a ðt Þ 1 d1a ðt Þ

(2.17)

The following assumptions have been taken while designing SMLFC: 1. The matrix pair ðA1a ; B1a Þ is fully controllable and B1a matrix has full rank. 2. :d1a ðtÞ: # ξ; ξ is greater than zero, where:::indicates standard vector norm. The sliding surface sðt Þ is derived from an integral sliding surface as given in Eq. (2.18). ðt sðt Þ 5 Gx1a ðt Þ 2 ðGA1a 2 GB1a K Þx1a ðιÞdι (2.18) 0 2 3 11

2 3 11

where G 5 ℝ and K 5 ℝ are the constant matrices. The matrix K is chosen to ensure that eigenvalues of ðA1a 2 B1a K Þ must be negative and jGB1a j is nonsingular. The reaching criterion can be defined as sðt Þ_sðt Þ , 0 for the system with sliding surface mode (Wang, et al. 2016). The following equality hitting condition is used for developing the SMLFC. s_ðt Þ 5 2 ηsðt Þ 2 ψsgnðsðt ÞÞ

(2.19)

The sliding mode controller is derived by using Eqs. (2.17)
(2.19) and shown in Eq. (2.20).  u1a 5 2 Kx1a ðt Þ 2 ðGB1a Þ21 Gd1a ðt Þ 1 ηsðt Þ 1 ψsgnðsðt ÞÞ (2.20) The disturbance d1a(t) is not known in the design SMLFC. A boundary value ξ has been chosen by the trial and error method instead of having actual disturbance. Thus Eq. (2.20) is modified as  (2.21) u1a 5 2 Kx1a ðt Þ 2 ðGB1a Þ21 Gξ 1 ηsðt Þ 1 ψsgnðsðt ÞÞ where η and ψ are greater than zero; “sgn” is sign function. The designed controller of Eq. (2.21) is not appropriate for studying the system behavior due to its boundary limitation for the load disturbance and fluctuated renewable power. Hence, the control law is further updated by adding a DOB with it.

2.3.2 Adaptive sliding mode LFC with disturbance observer The conventional SMLFC controller is designed assuming certain boundary conditions in load disturbance and fluctuated renewable power

Frequency regulation in hybrid power system

53

output. This assumption may cause inaccurate controller design (Tummala et al., 2018). To accelerate the controller’s robustness and accuracy, this section redesigned the SMLFC by estimating the load profile and fluctuated renewable power output (Mi et al., 2016; Tummala et al., 2018). The augmented model of Eq. (2.17) is represented as follows:          A1a I x1a ðt Þ B1a x_ 1a ðt Þ 0 5 1 u1a 1 _ (2.22) 0 d 1a ðt Þ d_ 1a ðt Þ 0 0 d1a ðt Þ From Eq. (2.22) the DOB can be constructed as       h i  B A L I x ^ ð t Þ _ 1a 1a 1a 1 u1a 1 1 ðx1a ðt Þ 2 x^ 1a ðt ÞÞ x_^ 1a ðt Þd^ 1a ðt Þ 5 0 L2 0 0 d^ 1a ðt Þ (2.23) X

X

where x1aðt Þ is the estimated state; d1a ðt Þ is the estimated value of disturbance; I is identity matrix; L1 and L2 are the designed observer  gain matrices. _ _ Defining x 1a ðt Þ 5 ðx1a ðt Þ 2 x^ 1a ðt ÞÞ, d 1a ðt Þ 5 d1a ðt Þ 2 d^ 1a ðt Þ and Subtracting Eq. (2.23), from Eq. (2.22), the observation error of the system is as _    h i 0 _ x1a ðt Þ _ _x 1a ðt Þd__ 1a ðt Þ 5 A _ 2 • d1a ðt Þ  d 1a ðt Þ  (2.24) A1a 2 L1 I _ where; A 5 0 2 L2 The following lemma is used to calculate the gain of the DOB. Lemma 1: The estimated disturbance d^ 1a ðtÞ can follow the disturbance d1a ðtÞ of the system Eq. (2.22), if observer gains L1 , L2 and are selected such that observer error Eq. (2.24) is asymptotically stable (Yuan and Gao, 2019; Zhang et al., 2016; Li et al., 2014). L1 5 A1a 1 2Δ; L2 5 Δ2 I; Δ 5 diagðγ 1 ; γ 2 ; γ 3 . . .γn Þ; γi . 0; i 5 1; 2; 3. . .n To enhance the performance of the controller, control law of Eq. (2.20) is redesigned by using a DOB as given in Eq. (2.23). The   observer error d1a ðt Þ 2 d^ 1a ðt Þ is calculated to satisfy the accuracy by selecting proper L1 and L2.

54

Microgrids

2.3.2.1 Adaptive law To enhance the controller performance, the ASMC can be reconstructed by using the adaptive law and DOB as follows: ^ 1 ηsðtÞ 1 ω:G:sgnðsðtÞÞ ^ u1a 5 2 K x^ 1a ðtÞ 2 ðGB1a Þ21 ½G dðtÞ

(2.25)

^ is the estimated disturbance value,:dðtÞ 2 dðtÞ: ^ where dðtÞ , ω,ω . 0, the adaptive law of ω^ is as follows. ω_^ 5 Γ :G::s:

(2.26)

Theorem 1: The system (Eq. 2.17) under the developed control law, Eq. (2.25), then, the DOB-ASMC-based closed-loop system is asymptotically stable if the switching gains in the extended control law Eq. (2.25) fulfill the sliding surface Eq. (2.18). The observer gains, L1 and L2, are selected such that the observer error dynamics Eq. (2.24) is globally stable for all x. Proof: The Lyapunov function candidate is chosen as υðtÞ 5 0:5s2 ðtÞ 1 0:5ω~ 2

(2.27)

where ω_~ 5 ω_ 2 ω_^ 5 2 ω_^ derivative of υðtÞ becomes _ 5 sðtÞ_sðtÞ 2 υðtÞ

1 _ ω~ ω^ Γ

(2.28)

derivative of sðtÞ is as _sðt Þ 5 G x_ 1a ðt Þ 2 ðGA1a 2 GB1a KÞx1a ðt Þ

(2.29)

Substitute Eqs. (2.25), Eq. (2.26), and Eq. (2.29) into Eq. (2.28) _ 5 sðtÞ½G x_ ðtÞ 2 ðGA1a 2 GB1a KÞx1a ðtÞ 2 υðtÞ

1 _ ω~ ω^ Γ

(2.30)

^ 1 ηsðtÞ 1 ω:G:sgnðsðtÞÞgg ^ _ 5 sðtÞ½GA1a x1a ðtÞ 1 GB1a f 2 Kx1a ðtÞ 2 ðGB1a Þ21 fG dðtÞ υðtÞ 1 _ 1 Gd1a ðtÞ 2 GA1a x1a ðtÞ 1 GB1a Kx1a ðtÞ 2 ω~ ω^ Γ

(2.31)

55

Frequency regulation in hybrid power system



 1 ^ 5 sðtÞ G d1a ðtÞ 2 d^ 1a ðtÞ 2 ηsðtÞ 2 ωjjGjjsgnðsðtÞÞ 2 ω~ ω_^ Γ ^ # jjsðtÞjj jjGjjω 1 ηjjsðtÞjj 1 ωjjGjj jjsðtÞjj 2 υ_ðt Þ 5 jjsðt Þjj jjGjjω 2 ηjjsðt Þjj2 2 ω^jjGjj jjsðt Þjj 2

1 ω~ ω_ Γ

1B ω ΓjjGjj jjsðt Þjj Γ (2.34)

~ 2 ηjjsðtÞjj2 5 jjsðtÞjj jjGjjðω 2 ω^ 2 ωÞ 2

υ_ðtÞ 5 2 η:sðtÞ: , 0

(2.32)

(2.35) (2.36)

From Eq. (2.36), υ_ðtÞ , 0 can be ensured that the system is stable asymptotically with appropriate choice of η by using control law Eq. (2.25) and the DOB error system Eq. (2.24). Remarks: The SMC with DOB is designed for isolated and interconnected HPS following the same procedures, as discussed in this section. The nominal values of system parameters are offered in Table 2.1. However, the system, input, and disturbance matrices for interconnected HPS are different from isolated HPS.

2.4 Results and discussion This section demonstrates the developed DOB
based SMC’s effectiveness to stabilize the frequency and DGs’ output power deviation of the concerned HPS. Five different cases have been studied to assess the efficacy of the applied control mechanism.

2.4.1 Performance analysis of isolated HPS against multiple load perturbation Fig. 2.3A illustrates the load profile applied to isolated HPS for investigating its dynamic performance. To quantify the mastery of the developed DOB-ASMC, the outputs with DOB-ASMC are compared with conventional SMC, LQR, and PI controller and presented in Fig. 2.3C. It is

56

Microgrids

Table 2.1 System parameters. Parameters

Nominal value

Kp Tp TT Tg Trh2 Trh1 TAE TDEG KDEG KFC TFC KWTG TBESS KBESS β α

120 20 0.3 0.08 10 10 0.5 2.3 0.03 0.01 3 1 0.1 20.003 0.2 0.8

(A) Dgs output power deviation

(B)

10 -3

4

SMC DOB-ASMC

2

0 10 -3

4

-2

2 0 20

21

22

-4 0

10

20

30

40

50

60

Time(sec)

(C)

(D)

0.02 0.01

Frequency deviation(Hz)

0.015

0 -0.01

0.01

-0.02

0.005

20

21

22

23

0 -0.005 -0.01

SMC PI controller LQR controller DOB-ASMC

-0.015 -0.02 0

10

20

30

40

50

60

Time(sec)

Figure 2.3 Dynamic performance of isolated HPS (A) multistep load deviation, (B) DGs output power, (C) frequency deviation (D) frequency deviation with 6 50% parameter variation in nominal value. DGs, Distributed generators; HPS, hybrid power system.

Frequency regulation in hybrid power system

57

worthy to note from Fig. 2.3A that the developed DOB efficiently tracks the fast-changing actual load profile, ensuring the usefulness of the same in the proposed work. Fig. 2.3C shows that all the designed controllers make frequency deviation zero. However, PI-controller offers oscillatory output with significant overshoot and settling time. Conversely, DOB-ASMC yields a faster response with little rise time, minimum peak overshoot, undershoot, and settling time than other controllers shown in Fig. 2.3C. Hence, the mastery of the proposed DOB-ASMC is founded for the modeled HPS. In Fig. 2.3B the DGs output power is shown only for SMC and DOBASMC. It is inferred from Fig. 2.3B that the results obtained DOBASMC quickly attain steady-state value than conventional SMC. Fig. 2.3D gives the satisfactory outputs with 650 parameter variations in the nominal value.

2.4.2 Performance analysis of isolated HPS with multiple-step loads and random wind power perturbation In this phase an random wind power perturbation (RWPP) is simultaneously applied to HPS along with the load perturbation (Fig. 2.3A). The profile of RWPP is given in Fig. 2.4A. It is confirmed from Fig. 2.4A that the developed DOB smoothly tracks the wind power profile. The dynamic performance of isolated HPS after these perturbations is plotted in Fig. 2.4B and C. To establish the mastery of the DOB-ASMC controller, the obtained results are compared with the results of SMC, LQR, and PI controllers in Fig. 2.4C. The results obtained from this case show that the proposed DOB-ASMC quickly alleviates the frequency and power oscillation. Furthermore, DOB-ASMC offers a response with small peak overshoot and undershoot. Similar enhancement in the output power of DGs is also noticed with DOB-ASMC. Hence, it may conclude that DOBASMC is better in comparison to other control actions. The control effort of the proposed controller with multiple-step load and RWPP is given in Fig. 2.4D.

2.4.3 Performance analysis of isolated HPS with GRC and GDB In this phase of the simulation result, the impacts of GDB and GRC on the isolated HPS performance has been closely inspected. The deviation of frequency and power with the proposed DOB-ASMC is revealed in

58

Microgrids

(A)

(B) 10 -3

6

w r(p.u) we wer(p.u)

DOB-ASMC

4

-2 0

10

20

30

40

50

60

Time(sec)

(D)

(C)

0.05 DOB-ASMC

u 1(p.u)

0.01

0.02

-0.01

0.01

0

-0.02 20

21

22

23

24

-0.05 0

10

20

30

40

50

0

10

20

30

40

50

60

0

5

SMC PI Controller LQR Controller DOB-ASMC

-0.02 0

10

20

30

Time (sec)

40

50

60

0

2

-0.01

u (p.u)

Frequency deviation(Hz)

0

DOB-ASMC

-5

60

Time(sec)

Figure 2.4 Dynamic performance of isolated HPS after RWPP (A) actual and estimated wind power, (B) DGs output power, (C) frequency deviation, and (D) u1(t) and u2(t). DGs, Distributed generators; HPS, hybrid power system; RWPP, random wind power perturbation.

Fig. 2.5A and B respectively. The results are obtained, considering both multiple load and RWPP. To highlight the efficacy of DOB-ASMC, the system outputs are also derived from SMC and compared in Fig. 2.5A. However, both SMC and DOB-ASMC effectively controlled the impacts of GRC and GDB and preserved the system stability. It is also evident from Fig. 2.5A that frequency deviation with DOB-ASMC reaches the steady-state level with small overshoot significantly faster as compared to SMC, in comparison to SMC. The control effort of the proposed controller is given in Fig. 2.5C and D.

2.4.4 Performance analysis of interconnected two-area HPS with multiple-step load and RWPP To quantify the capability of the developed DOB-ASMC, the study has been forwarded to an interconnected HPS model. The model of interconnected HPS is shown in Fig. 2.6. The dynamic behavior of the modeled HPS has been assessed considering the similar load (Fig. 2.3A) and

59

Frequency regulation in hybrid power system

(A)

(B)

10 -3

10

6

DOB-ASMC without GRC SMC without GRC

-10 -15 0

2

4

6

8

10

12

DOB-ASMC with GRC SMC with GRC

0.02 0

DGs output power(p.u)

Frequency deviation (Hz)

0 -5

-3

DOB-ASMC SMC

4

2 0

-2

-0.02 -4 0

2

4

6

8

10

0

12

2

4

Time (sec)

(C)

8

10

12

(D)

DOB-ASMC

0

2

0

u (p.u)

5

0.1

1

u (p.u)

6

Time(sec)

DOB-ASMC

-5

-0.1 0

2

4

6

Time(sec)

8

10

12

0

2

4

6

8

10

12

Time(sec)

Figure 2.5 Dynamic performance of isolated HPS with GRC and GDB (A) frequency deviation, (B) DGs output power, (C) u1(t), and (D) u2(t). DGs, Distributed generators; GDB, governor dead-band; GRC, generation rate constraints; HPS, hybrid power system.

Figure 2.6 Interconnected two-area HPS models. HPS, Hybrid power system.

wind power (Fig. 2.4A) perturbations, as discussed in the case of isolated HPS. The deviation in frequency and tie-line power with DOB-ASMC is depicted in Fig. 2.7A
C. For the assessment of the improvement of the results with DOB-ASMC, the system response obtained with SMC,

60

Microgrids

(A)

10 -4

0 -0.01

0.01

-0.02 20

22

24

0

-0.01

SMC LQR controller PI contoller DOB-ASMC

-0.02 0

10

20

30

40

50

Tie-line power deviation

Frequency deviation(Hz)

0.02

(C)

10 -4

4

0.01

0 -2

2

-4 22

24

26

28

SMC LQR controller PI controller DOB-ASMC

-2

-4

60

0

10

20

30

Time(sec)

(B)

60

DOB-ASMC

0 -0.02 -0.04

1

0

-0.06

-0.01

0.01 0

-0.08

-0.01 -0.02

-0.02

20

0

10

20

21

30

40

22

23

50

-0.1

24

0

60

10

20

(D)

10 -4

10

30

40

50

60

Time(sec)

Time(sec)

(F)

1

DOB-ASMC

DOB-ASMC

0.5 5

0

2

u (p.u)

Dgs output power(p.u)

50

(E)

0.02

u (p.u)

Frequency deviation(Hz)

0.01

40

Time(sec)

SMC LQR controller PI contoller DOB-ASMC

0.02

30

0

0

-0.5

-1

-5 0

10

20

30

40

Time(sec)

50

60

-1.5 0

10

20

30

40

50

60

Time(sec)

Figure 2.7 Dynamic performance of interconnected two-area HPS (A) frequency deviation of area-1, (B) frequency deviation of area-2, (C) tie-line power deviation, (D) DGs output power, (E) u1(t), and (F) u2(t). DGs, Distributed generators; HPS, hybrid power system.

LQR, and PI controller is also depicted in Fig. 2.7A
C. It is identified that the results obtained with the PI controller are more oscillatory, and it requires more time to reach the final value. Conversely, DOB-ASMC yield better response compared to others concerning minimum undershoot and settling time. A similar kind of enhancement in the tie-line power is identified with DOB-ASMC. Hence, it may infer that DOBASMC outperforms SMC, LQR, and PI controller. The control effort of the DOB-ASMC is given in Fig. 2.7E and F.

61

Frequency regulation in hybrid power system

2.4.5 Performance analysis of two-area HPS with GRC and GDB To establish the mastery of DOB-ASMC, the performance of interconnected HPS has been inspected with GRC and GDB nonlinearities. The dynamic responses obtained with DOB-ASMC are shown and compared with SMC in Fig. 2.8. It is depicted in Fig. 2.8A and B that the response obtained with DOB-ASMC settles before than that of SMC. A notable enhancement in the tie-line power deviation has been seen with (A)

(B)

0.03

Frequency deviation (Hz)

0.02

Frequency deviation (Hz)

0.03

SMC DOB-SMC

0.01 0 -0.01 -0.02 -0.03 -0.04

0.01 0 -0.01 -0.02 -0.03 -0.04

-0.05 0

5

Time(sec)

10

0

15

5

10

15

Time (sec)

(C)

10 -4

4

(D)

10 -3

DOB-SMC

1

DGs output power(p.u)

DOB-ASMC SMC

2

Tie-line power deviation

DOB-ASMC SMC

0.02

0 -2 -4 -6

0.5

0

-0.5

-8 -1

-10 0

5

Time(sec)

10

0

15

5

(E)

10

(F) 0.3

0.02

DOB-ASMC

DOB-ASMC

0.2

0

0.1

u (p.u)

0.01

0

2

-0.01

1

u (p.u)

15

-0.02

-0.1

-0.03

-0.2 -0.3

-0.04

-0.4

-0.05 0

5

10

Time(sec)

15

0

5

10

15

Time(sec)

Figure 2.8 Dynamic performance of interconnected two-area HPS with GRC and GDB (A) frequency deviation of area-1, (B) frequency deviation of area-2, (C) tie-line power deviation, (D) DGs output power, (E) u1(t) and (F) u2(t). DGs, Distributed generators; GRC, generation rate constraints; GDB, governor dead band; HPS, hybrid power system.

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DOB-ASMC (Fig. 2.8C). Fig. 2.8D gives the DGs output power. It is illustrating from Fig. 2.8D that the DGs output power reaches steady-state level i with DOB-ASMC. The control effort of DOB-ASMC has given in Fig. 2.8E and F.

2.4.6 Robust stability analysis The Routh
Hurwitz criterion examines the system’s stability when the characteristic polynomial’s coefficients are fixed. To access robust stability of interval system Eq. (2.37) with lumped uncertainties, Kharitonov stability method is applied (Saxena and Hote, 2016). Eq. (2.38) shows four Kharitonov’s polynomials computed for interval polynomial Eq. (2.37). All polynomials in qðs; CÞ passed Hurwitz test to ensure that Eq. (2.37) is robustly stable (Saxena and Hote 2016). 1 qðs; CÞ 5 e0 1 e1 s 1 e2 s2 1 . . .en sn _ ei CC; ei Aðe2 i ; ei Þ

(2.37)

1 2 1 2 1 where e2 i ; ei Aℝ with ei # ei # ei , i 5 0; 1; 2. . .n and ei ; ei 6¼ 0 2 1 1 2 1 3 2 4 q1 ðsÞ 5 e2 0 1 e1 s 1 e2 s 1 e3 s 1 e4 s . . . 1 1 2 1 3 2 4 q2 ðsÞ 5 e2 0 1 e1 s 1 e2 s 1 e3 s 1 e4 s . . . 1 2 2 2 1 3 4 q3 ðsÞ 5 e0 1 e1 s 1 e2 s 1 e3 s 1 e1 4 s ... 1 1 2 2 2 3 1 4 q4 ðsÞ 5 e0 1 e1 s 1 e2 s 1 e3 s 1 e4 s . . .

(2.38)

To give a geometrical description of Eq. (2.38), it has seen that the value set for the constant frequencys 5 jω with ωAℝ is a rectangle with level edges corresponding to the imaginary and real axis. Where q1 ðjωÞ,q2 ðjωÞ,q3 ðjωÞ, and q4 ðjωÞ are the four vertices of the rectangle, and for stable operation of such Kharitonov polynomials, its phase increases strictly along with ω increases. This theorem states that the qðjω; CÞ is robustly stable, when origin is not included in Kharitonov’s rectangle for entire range of frequencies ω2 ; ω1 . The characteristics equation of isolated HPS is calculated, as shown in Eq. (2.39). The 650% parameter variation from the nominal value has been considered for the present investigation. The plot of Kharitonov’s four polynomials (rectangle) with a change of frequency is revealed in Fig. 2.9, which shows that the rectangles do not include the origin, and, thus, robust stability of the proposed system is confirmed with parametric uncertainty. pðsÞ 5 s11 1 26:9s10 1 370:7s9 1 3522s8 1 1:816 3 104 s7 1 5:079 3 104 s6 1 8:607 3 104 s5 1 8:652 3 104 s4 1 4:679 3 104 s3 1 1:209 3 104 s2 1 1303s 1 54:14

(2.39)

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Figure 2.9 Kharitonov’s rectangle of isolated HPS for 0 , ω , 15 rad/s. HPS, Hybrid power system.

2.5 Conclusion In this study the ASMC with DOB is developed for load frequency control of an HPS comprising double-stage reheat thermal power plant and DGs. In sliding mode control the boundary value of switching gain is difficult to obtain in practical cases. To tackle this difficulty the switching gain is designed as a function of the sliding surface and the system states, while adaptive law is used to estimate the unknown uncertainty bound of the system, and to enhance the control performance and reducing chattering effects, a DOB is integrated into ASMC. The performance of the system has been assessed both in isolated and interconnected modes with multiple load and fluctuated wind power perturbations. The result obtained with DOB-ASMC is compared with conventional SMC, LQR, and PI controllers. The obtained results confirm the mastery of the DOB-ASMC over the controllers mentioned earlier in terms of time
response measurement. The impacts of GDB and GRC nonlinearity on the system outputs have been outlined and mastery of DOB-ASMC is confirmed over SMC, LQR, and PI controller. The robust closed-loop stability of the concerned HPS with DOB-ASMC has been affirmed applying Kharitonov’s theorem considering 6 50% parameters variation. In future the presented work will be focused on the large-scale power systems with communication delays.

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9861.

CHAPTER 3

Recent advancements in AC microgrids: a new smart approach to AC microgrid monitoring and control using IoT P. Madhumathy and Shweta Babu Prasad Dayananda Sagar Academy of Technology and Management, India

3.1 Introduction Electricity plays a very important role for various applications. A transformer is an essential element for transmission and distribution of electric power. It is used for the conversion of input AC voltage to a greater or reduced voltage as output. Monitoring is performed by collecting data online through different measurement techniques from the sensors. Information cannot be obtained through manual monitoring due to various physical drawbacks and excessive heating of transformer. Since these variables might potentially diminish transformer longevity, various approaches are presently being employed for offline and online observation of transformer. The proposed system helps us locate and identify unusual conditions and service the transformer thereby increasing the quality and life of the transformers. Microgrids (MGs) are electrical structures that integrate distributed load and generation and also combine managing of thermal and electrical load, thermal with electrical storage, or a “smart” interface with the grid, working alongside or separately from it. While functioning parallelly, MGs offer an aggregation of energy, capacity, and other functions to the grid. MGs work efficiently in numerous ways. They make use of cogeneration to offer service to adjusted electric and thermal loads, to accomplish efficiencies over 80% contrasted with 30%
50% during regular generation. Additionally, sustainable power source permits MGs to attempt proficient and adaptable crossbreed generation activities. Utilizing thermal and electrical capacity to oversee the duration of utilization of power and fuel, MGs assist Microgrids DOI: https://doi.org/10.1016/B978-0-323-85463-4.00002-2

© 2022 Elsevier Inc. All rights reserved.

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moderation with driving costs by productively moving burden to times of decreased request and estimating. Building temperatures for the most part move gradually, and by “smart” management of thermal loads, they can adequately utilize structures themselves as thermal stockpiling to oversee load shape. These and comparative proficiency and vitality in addition to the board procedures save money as well as essentially lessen the ecological effect of giving vitality administrations. MGs in addition to nanogrids give manageable explanations for improved access to areas insufficiently served by conventional electricity grids. They can also provide economic and environmental benefits to these secluded areas. The word nanogrid has been used to identify a small MG. Nanogrids are able to function in both grid-connected and islanded mode. To assess performance and long-term effects on the connected equipment in the nanogrid, lengthy time period measurements of the power quality indices for a nanogrid during insulated operation are needed. MGs belong to the class of small-scale grids and incorporate DGs (Distributed Generation), units to store energy, linear or nonlinear loads to function in grid-connected or islanded mode. Here, DGs may produce renewable or nonrenewable energy, in which the aspects are connected to power converters. The CIGRE working group C6.22 Microgrid Evolution Roadmap (WG6.22) offer a general interpretation: MGs are power supply models having immense energy systems (e.g.,: dispensed generators, storage units, or administrable loads) to function in such a way that can be operated in a restrained and regulated manner both while being connected to the primary energy network and while islanded. Initially, when the renewable energy sources were proposed, the energy generated via them was not sizeable in evaluation to the massive traditional generators supplying powering to the grid; hence, their influence on the overall administration of the grid was not of much importance. During this period the whole idea was to grant them to produce enormous amounts of energy inject it into the grid by way of the use of their personal algorithms. The predominant conventional mills may want to adjust the small unbalances and fluctuations induced by using these DGs. Typically, the major communication necessities for a smart or MG are illustrated as follows: • Data rate: It is a vital necessity as only few linked networks furnish the necessary data rate. When a home or industrial area network is considered, the appropriate data rate is ,100 kbps compared to WAN requiring .10 Mbps.

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69

Range: There are innumerable DGs, energy storage systems, and clients for an MG all connected to the primary link using inverters to facilitate communication. Reckoning the dimensions and of MG and separation of the units that extend to hundreds of kilometers, it can be concluded that not many networks can satisfy this need. • Security: If there are gaps in the security system, the entire architecture is prone to physical and cyberattacks. This facet needs to be strengthened by implementing appropriate security fixes over the entire architecture thereby improving the security aspect of the MG. • Latency: When the features of the gadget used in the grid is considered, different limits for the latency are considered. When inverter signals are studied, the limit is ,10 Ms. • Reliability and scalability. Researchers’ attempt in the 1980s to connect humans via technology gave forth the prevalent computing area to use embedded systems in businesses carried out on a daily basis. Presently the technology being beyond the normal PC, and using handheld smart devices has made communication and interaction easier and faster. A smart environment is one where there is free and easy usage of sensors, actuators, screens, computing units all embedded logically in items surrounding us and which are a part of our everyday lives all linked to each other in a logical and effortless manner. To materialize a well-rounded Internet of Things (IoT) architecture, a capable, adequate, economical, dynamic structure is needed. Here cloud computing steps in to proclaim that it provides the necessary infrastructure and technology required to materialize the need of a successful IoT system by providing state-of-the-art and advanced data repositories and other services that provide dependable services. Sensing and actuating units are linked together to create a unique structure working in a combination to make new and inventive application a successful reality. This accepted common aim is materialized by adopting logical sensing on a big scale, analysis of data and depiction of results by utilizing prevalent sensor technologies in addition to cloud computing. Security can be compromised when networks are deployed extensively. Attackers can use these methods to harm the network: by impairing availability of network resources, insert wrong information into network, get access to confidential information, and so on. The important units of IoT, namely, RFID (radio frequency identification), WSN (Wireless Sensor

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Networks), cloud, are susceptible to compromise. Cryptography is the important tool to defend data from these attacks. RFID is most susceptible due to its allowing to be tracked. Cryptographic solutions can be employed to the complicated issues and are prone to deeper study before being prevalent widely. Confidentiality and message authentication codes are encryption tools that protect data by providing data integrity and authenticity. To protect from internal invasion, encryption is not sufficient, other noncryptographic tools must be employed. At regular intervals, novel applications for the sensors have to be installed in addition to updating the old ones that can be carried out remotely and wirelessly. In the old ways of updating and installing, a data dissemination tool was used to circulate code to all hubs void of identity proof leading to gaps in security. While using a protocol that allows updating of all nodes in a secure manner by employing authentication security threats can be avoided. More research needs to be carried out to employ security in the cloud. As cloud deals with financial aspects of IoT in addition to data and tools, it is more vulnerable to attacks. Protection of identity becomes very important in the case of hybrid clouds, which comprises both private and public clouds. Heterogeneous networks by nature offer multiple services via multiple applications. This characteristic allows different types of traffic and data to be carried in the network by all the applications that offer the different services without there being any changes in Quality of Service (QoS). The different types of applications are as follows: • Throughput and delay tolerant elastic traffic used for observing weather conditions at low sampling rates. • Bandwidth and delay sensitive inelastic (real-time) traffic used to observe noise or traffic. Hence, a restrained and optimum path needs to be tread to provide service for variety of traffic on the network which have a different QoS need. This is not an easy task to promise certain QoS in wireless networks, as there are parts where the allocation of resources is not uniform. QoS in cloud computing provides a vast area for advanced where main area to be considered is the data and tools. Dynamic scheduling and resource allocation algorithms are developed keeping particle swarm optimization as the basis. As the capacity for different networks and tools grows along with the IoT impediments could be faced. An embedded system is computer systems committed to definitive functionality and it is comprised as a part of mechanical or electrical system,

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generally having dynamic computing restraints, and controls various devices. It is embedded as a component and has hardware and mechanical elements. A typical family of significant processors is the digital signal processor and are designed and developed to cut down the price and stature thereby increasing the accuracy and performance. Embedded systems are intended to carry out explicit errands, while a PC performs numerous errands. Some also consist of real-time performance restraints that need to be addressed for safe operation, while others may have low or no performance needs, thereby cutting down costs when hardware is simplified. Embedded systems are known to possess the functionality listed here: 1. processing: the capacity of a system to measure and understand the analog/digital signals, 2. communication: the phenomenon of transmitting signals from one system to another, and 3. storage: safeguarding the received information on a temporary basis. Any application that makes the use of an embedded system requires all or a combination of the previous functionalities. The use of embedded systems is advised when it is necessary to have economical implementation, increased dependency, and superior to ordinary hardware. There is feasibility of a single low-cost microcontroller to compensate for the use of a large number of physical logic gates, timing circuits, input buffers, output drivers, etc. VLSI (very large scale integration) and embedded systems are interdependent and there would be no existence of one without the other. Real-time operating system and embedded system serve extremely important roles in latest technologies. The former comprises three types of real-time systems having their basis on the timing requirement. 1. Soft real-time system—This is used only if there is a deterioration in performance just because the particular function was not able to keep up to a certain time limit. The quality of an output is reduced once the time limit is crossed, reducing the QoS of the system in the process. 2. Hard real-time system—The system fails if the time limit is not satisfied. The hardware or software in this system functions by being restricted to a time limit. The entire system fails if the system does not operate within the time period, for example, pacemaker units, antilock brakes, and aircraft control systems. 3. Firm real-time system—This is a hybrid of the previous two. An embedded system having hardware and software needs to accurately function to solve a particular problem and have the following characteristics:

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a. The size of the processors has reduced invariably but the performance has remarkably increased. b. They consume very less power. c. The prices of the processors have reduced to a large extent. d. There is better understanding in the masses currently about the hardware unit and that it is more feasible to have a programmable processor in the unit to make the structure increasingly potent with reduced latency time. e. Several modeling and experimental tools can be employed to simulate results to reduce the build time and develop prototype. f. The use of the common runtime languages like JAVA that are allowed to be used in a variety of applications was not possible in the near past. g. With the coming up of multiple integrated software platforms, it is now possible to develop applications in an easier manner. h. With the incorporation of embedded systems with the Internet, it is now feasible to make embedded systems a part of networking and be functional in different architectures like local area network (LAN), WAN, and the Internet. Obtaining and distributing of important information from network via the linked devices and a protected functional layer is the definition of IoT. Simply put, IoT is a set of network devices linked wirelessly to distribute information and set up communication to form latest information and store it for future analysis. IoT is fully functional when it uses units like smart objects that comprise sensors and actuators, and they are constructed to take complete advantage of their networking features for interfacing and fully utilize the freely available Internet sources for interaction on different levels. The linked IoT devices synthesize data on massive scale and to process this is a difficult feat. This hurdle can be overcome by accumulating and analyzing data on a large scale. It was aimed to step out of the conventional desktop age into an advanced age for computing. Considering the IoT standard, several objects by which we are enveloped will be in some way linked on the network this can be facilitated by RFID and sensor network technologies where data and communication devices are built in such a way so that they are not visible to the human eye. Data are generated in massive forms that need storage, processing, and need to be conferred in an esthetic way to allow for easy interpretation. Cloud computing gives a virtual model to assimilate several monitoring, storage devices, and other

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tools. The economic solution hence provided assists businesses and clients to use tools to run their business virtually. Smart connectivity with extant networks and context aware computing is an integral part of IoT. As there is advances made in Wi-Fi and 4G-LTE, the advances made in sensing and gathering information from everywhere is indisputable. A profound development of the present Internet into a web of linked objects is to obtain information from the surroundings (sensing) in addition to cooperating with the hardware units by using the current norms of the Internet to serve for a variety of purposes. As a large number of technologically advanced devices were developed like the Bluetooth, RFID, Wi-Fi, and telephonic data services in addition to sensor and actuator units, IoT has assisted to develop the stationary Internet into an advanced futuristic form. This transformation has led to the communication of people and devices, and Internet revolution led to the interconnection between people and devices at remarkable proportions. Researchers aim to create smart environment by linking objects. When the entire IoT architecture is being examined, a capable, competent, protected, expandable, and economical structure is needed with appropriate computing and storage components. Cloud computing is an emerging science that ensures stable benefits to be generated via advanced data centers that employ virtual storage units. Here, data are received from sensors placed everywhere, information is inspected and interpreted and also presented esthetically to the user, all this while being hidden from the user. In the proposed system, different parameters like temperature, pressure, level, humidity, voltage, current, vibration, and movement are recorded by Arduino Atmega connected to a PC or a laptop. The data collected are transmitted to a server that can be located anywhere across the globe via the Internet.

3.2 Problem statement An MG consists of components like generator, protective relays, lightening arrestors, wave trap, isolators, and loads. Usually the transformers used in the MG are damaged due to the sudden rise in the temperature, that is, whenever there is a huge amount of current that flows through the winding which leads to blackouts in the MG. These blackouts that occur for a few microseconds may lead to the collapse of the entire MG. Hence, for the protection microcontrollers are used which act as a crucial component

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in controlling the entire system. By using the proposed model, the drawbacks can be overcome and the MG can be protected from damage. Objectives of the proposed model are as follows: • To collect the values of electrical in nature and also transmit them via the network in real time, a network module attached to a PC is used at the power station. • System designed should send alarms when the relay trips or in the cases when the voltage or current readings are over the preset values. • If voltage or current consumed by load exceeds threshold, the loads should switch off automatically. • Along with this, the system should calculate the total power and energy consumption and store in the cloud. • System developed should be able to monitor and control the substation with the help of microcontroller. • To reduce and manage time efficiently.

3.3 Literature survey Thiyagarajan et al. proffered an architecture to shield from increase in current because of over burdening the equipment. Framework depends on microcontroller utilized to note the appropriation transformer’s current (Thiyagarajan & Palanivel, 2010). Kumar et al. discuss the building of automatic control circuits for programmable logic controller (PLC) system to screen the states of transformer such as temperatures, current, voltage. Here framework assists in recognizing the internal along with external flaws of transformer. This equipment is helpful for checking and managing the parameters of a distribution transformer continuously (Behera, Masand, & Shukla, 2014). Vishwanath et al. discuss about a configuration that utilizes a temperature sensor, PIC (peripheral interface controller) microcontroller, liquid crystal display (LCD) display Global Service Mobile (GSM) board, Xbee to notify electricity board. By utilizing this framework, one is able to recognize numerous flaws of three-phase transmission lines where temperature, voltage, and current can be screened by GSM modem (Vishwanath, Shetty, Shamilli, & Thanuja, 2015). Sachin Kumar et al. propose a structure and improvement of remote monitoring system for a three-phase transformer. Arduino microcontroller and Zigbee-based wireless devices are utilized for checking the operating point of three-phase transformer from isolated locations. The Arduino

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microcontroller assists in observing the three-phase current, voltage, temperature, and power parameters. The prepared parameters are shown on LCD that allows it to be understood easily. All sensors that screen the three-phase parameters are controlled by individual microcontroller; hence, the design is minimal (Sachin Kumar & Prabhu, 2016). Kumar et al. suggest an online monitoring design that includes a GSM modem, with PLC along with sensor packages. The current activity state of the transformer is collected as an SMS. By utilizing the recommended framework, the administrators can be assisted for an increased period of time (Kumar, Raj, Kumar, Prasad, & Kumar, 2012). Landage et al. have suggested to advance reduced cost design to screen the well-being condition of isolated distribution transformers by utilizing GSM technology to detect the failure of distribution transformers and to provide and efficiency and stability to the customers. The IC is manufactured to gain knowledge via electrical sensing framework. A ground-breaking GSM network is established to transmit data to and from networks for appropriate restorative activity immediately. Variations in monitored values of communication are detected to secure the whole communication and dissemination (Landage, Khsrche, Vadirajacharya, & Kulkarni, 2012). Sujatha and Vijay Kumar (2011) proposed a technology to protect people from being harmed by electricity by immediately recognizing faults and cease flow of electricity to the faulty line thereby also informing the electricity board to repair the faults. Mohamadi et al. presents structure and persistently checking and discovering errors of transformer and make note of the important parameters such as load current, voltage, transformer, temperatures, and humidity. The aim of this proposed framework is to also enhance efficiency, accuracy, and stability with reduced endeavors. The important values of devices like voltage, current (over voltage, under voltage, over current) are detected, and this information is transmitted to microcontroller that checks parameter limits, which additionally transmits to the IoT web server. Adafruit software having Wi-Fi module along with the data ensures that legitimate information is provided to the administrator thereby judging the situation in a right way before any cataclysmic before the system collapses (Mohamadi & Akbari, 2012). He et al. (2019) discuss about the issue of inadequate strength in the studies performed for multi-MG scheme access distribution network.

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Here, multi-MG day-ahead scheduling optimization scheme is proposed to govern power interactively. The upper optimization model aims to reduce the power between the MG and the distribution network while controlling the output among MGs. The lower development scheme tries to find a solution to develop the working of MGs economically. Several approaches are used to lower the entire expenditure of the system while there is energy exchange and also lower the effect of variations in power over the distribution network that comprises several MGs. Meng et al. (2015) discusses about the evolution of an MG central controller in an inverter-based intelligent MG lab. It is developed to arrange for an adjustable foundation to perform experiments for detailed research on MGs. The entire control unit is layered and comprises primary, secondary, and tertiary control. For the primary control aspect is built in MATLAB/Simulink and compiled to dSPACEs to be controlled locally. For effective administration of secondary and tertiary layers, a LabVIEW-based MG central unit is established. The software and hardware units are also established. Results obtained from experiments illustrate the working of the entire unit. Ni et al. (2019) discuss about a bi-level optimized scheduling scheme established on Stackelberg game and economical functioning of electric vehicle charging
swapping
storage integrated station (CSSIS). The MG reaping the highest benefit devises the cost of electricity transaction with CSSIS which follows by being flexible to charge or discharge is the idea of the fixed price. Che, Shahidehpour, Alabdulwahab, and Al-Turki (2015) discusses about a community MG with several AC and DC MGs. Independent MGs having varied frequency and voltage needs function autonomously complying with other MGs in the vicinity to provide redundancy. A layered architecture having primary, secondary, and tertiary units is discussed to provide economically feasible solution. This solution is also used on a grid-connected community MG. Experimental results demonstrate that this architecture is competent to distribute loads effectively. Mírez (2017) discusses about the linking of DC MGs via experiments and tools along with utility network with autonomous connection to every MG, having power sources and repository, main control unit, coupling points. By research and studies, novel approaches for linkages are found and concluded that (1) in spite of a nominal voltage existing on the DC MG bus, it is important to have three mini-voltage scales. (2) A provisional repository is required at the power units; also the current are stable for an

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indefinite time period. (3) The expenditure occurring due to generating and storing power needs to be enhanced for the MG to be functional in accordance with the novel scale of measurement. (4) Unique and novel approaches are developed for additional control within the central control units. These novel approaches are necessary to conduct research in latest patterns and plans of electrical network, linkage of devices. Zhang et al. (2015) analyzes the pros and cons of DC MG for commercial establishment. The inputs procured from the DC MG at the Xiamen University show that DC MG equipped with a solar unit on its roof efficiently controls fluctuations in the establishment. To make sure that the DC MG works without interruption, a correct energy repository along with a 2-way AC/DC inverters is necessary. Hence, it is accepted that the solar unit can be used for matched DC loads, and maintain AC power to support the rest loads. It is budgetary to install a DC MG at $2.2/W and can be easily marketed. Son et al. (2009) discuss about a repository incorporated with a grid to be used as an MG devoid of voltage source inverters. There is no necessity for the system to be modified in view of the inverters’ control unit that already exists with the users. This new design makes sure that there is smooth progression from the grid connected to islanded application that facilitates its connection to the upper network. The research carried out in this behalf supports that the repository can distribute increased power to users. Khayat et al. (2019) propose a decentralized frequency control of AC MG by using by P
ω droop nature. The method is implemented by the utilization of an effective power evaluation to dispose of the system required for communication in the secondary control layer. The idea proposed can be accomplished by utilizing an exclusive frequency characteristic as a global variable in independent AC MGs. The idea rebuilds the MG frequency to nominal value to preserve the right way of power distribution in droop mechanism. The consensus protocol is used as an evaluation tool that does not need an independent communication system. The outcome of the experiment show efficient distribution of power and recovery of frequency.

3.4 Block diagram The proposed system has the components: (1) data collector, (2) data processor, and (3) communication unit. Here, the block diagram of the combined system or control unit to oversee the well-being of the transformer is shown in Fig. 3.1. Here, sensors are utilized to detect the different

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Figure 3.1 Block diagram of the proposed system.

transformer specifications; this detected information is transmitted to the microcontroller that analyses the necessary values and transmits the values to the LCD, driver, and RS-232 which is interfaced with the PC or workstation. The gathered information is further transferred by means of the web to the server. Since the reports are conveyed by means of the web, it can be ensured that the legitimate information reaches the authentic individuals.

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3.5 Methodology Arduino Mega is the controlling unit for the system. The current sensor (ACS712) measures the current fluctuation at substation, which will measure the current and transfers the signal to Arduino, which, in turn, displays values on LCD and also will send the data to cloud server for further process. The voltage sensor (Potentiometer) measures the voltage fluctuation at substation, which measures the voltage and transfers signal to Arduino, which, in turn, displays values on LCD and also will send the data to cloud server for further process. The temperature sensor (LM35) measures the temperature fluctuation at critical places in substation and measures the temperature and signals the Arduino and displays value on LCD. Rain sensor monitors rains; if there is heavy rain, the output power lines are shut down. Relay unit is used to control the output supply. The output voltage or relay can also be controlled from remote places. Passive infrared sensor (PIR) sensor senses and alarms when anybody enters main power transformer area or restricted area. This system will give all substation parameters to cloud server through Wi-Fi module (ESP8266). That data will be helpful for further analysis.

3.6 Details of hardware and software used Arduino Mega 2560: It is a microcontroller board established on the ATmega2560 (datasheet). It has 54 digital input/output pins [of which 14 can be used as PWM (pulse width modulation) outputs], 16 analog inputs, 4 UARTs (universal asynchronous receiver transmitter) (hardware serial ports), a 16-MHz crystal oscillator, a USB connection, a power jack, an ICSP (in-circuit serial programming) header, and a reset button. ACS712 Current Sensor: It allows for alternating or direct current detection in industrialized, commercial, and network systems. ESP8266 Wi-Fi Module: It is an inexpensive Wi-Fi IC with complete TCP/IP stack and microcontroller capabilities synthesized by Espress if Systems.

3.6.1 LCD display (JDH162A): a 16 3 2 LCD is a display unit used in different activities Voltage sensor: A potentiometer is used as a voltage sensor that is a three-terminal resistor consists of sliding/rotating contact resulting in a flexible voltage divider.

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Relay: This switch controls the operation of a circuit by an independent low-power signal, or in which numerous circuits must be inhibited by individual signal. ULN2003 relay driver: It is an electromagnetic device utilized to employ a low-voltage circuit to turn a light bulb ON and OFF while deriving power from a 220 V mains supply. Temperature sensor: The LM35 series are precision IC temperature equipment having the resultant voltage corresponding in a linear manner to the temperature in Celsius. PIR sensor: The electronic sensor employed to recognize and sense the motion of humans within a particular range of the sensor is called PIR sensor or PIR. Buzzer: It is an electrical device that is used to make a buzzing sound. It consists of two terminals, that is, positive and negative terminal. Power supply circuit (PSU): A PSU provides different forms of energy to an output or a set of different loads. Arduino software: The Arduino has its own application software that enables the programmer to download and upload programs and other functionalities such as debugging. The Arduino integrated development environment (IDE) is downloaded from the Arduino download page. It uses C language for programming. The open-source Arduino Software (IDE) makes it easy to write code and upload it to the board and runs on Windows, Mac OS X, and Linux.

3.7 Details about the web portal: ThingSpeak ThingSpeak is an open-source IoT application and API to store and retrieve data from things using the HTTP protocol over the Internet or via a LAN. ThingSpeak enables the creation of sensor logging applications, location tracking applications, and a social network of things with status updates. Access to this website can be obtained as follows: Step 1: Create an account of the user/MG in the website. Step 2: Once the account is created, provide the mail ID of the user for signing in the account. Step 3: After providing the mail ID, provide the password for security purpose. Step 4: After successful login, the user can create his/her own channel. This channel can be provided with any name. The channel created contains eight different fields, which can be named according to the users’ wish. Each field will contain the information of the user, in the form of a graphical data.

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Step 5: The channel created in the previous step will have recorded information not only about the user but also about the information about the channel itself, as to how many days ago was the channel created, when was the user information last entered onto the website, and recorded by the channel.

3.8 Algorithm The next steps of the algorithm depict the course of action taken by the system in monitoring of the substation: Step 1: Initialize the pins to which the sensors, buzzer, ULN2003 and LCD are connected. Step 2: Declare the pins of LCD connections with Arduino. Step 3: Set the baud rate. Step 4: Assigning pins as input and output pins. Step 5: Set all the channels to high initially. Step 6: Check all the parameters like voltage, current, and temperature. Step 7: Calling accelerometer function. Step 8: Temperature, voltage, and current measurement and corresponding output. Step 9: Uploading the data to the cloud. Step 10: Analyze values of each parameter.

3.9 Software development flowchart Decision-making steps are given in a flowchart, which indicates how system takes decision. The initialization of sensors, processing controller, and LCD takes place. Next, values are noted from the sensors and other components. There are values stored on the EEPROM memory which are used by the microcontroller to compare with the incoming data. The microcontroller sends a message to the controller cell when a difference is noticed in the compared values. When there is no difference noticed, the microcontroller continues with the testing process. The entire process repeats till a negative result is obtained in the logic of decision-making. But when it is positive, the microcontroller follows steps to execute further. Once the information has been sent, the loop repeats. The corresponding flowchart is shown in Fig. 3.2.

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Figure 3.2 Flowchart of the system.

3.10 Results and discussions 3.10.1 Hardware section of the model The results derived and measured by microcontroller are communicated to the chosen control room via Wi-Fi unit as shown in Fig. 3.3. The following results are obtained when the proposed system is tested (Fig. 3.4): 1. Current .10 A 5 current fault 2. Temperature .400°C 5 temperature fault 3. Voltage ,220 V 5 voltage fault (under voltage) 4. Voltage .230 V 5 voltage fault (over voltage) 5. Vibration .normal transformer vibrations 5 vibration fault

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Figure 3.3 Hardware circuit.

Figure 3.4 Output messages displayed on the LCD screen. LCD, Liquid crystal display.

3.11 Graphical analysis When the values of voltage, temperature, and power are greater or lesser than the threshold value, then the corresponding measured values are uploaded to the cloud as shown in the next figures.

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Figure 3.5 Field chart for voltage.

Figure 3.6 Field chart for temperature.

Fig. 3.5 shows the different voltage levels recorded at the MG by the monitoring system on different days. These readings are displayed on the LCD. Fig. 3.6 shows the different temperature levels recorded at the MG by the monitoring system on different days. These readings are displayed on the LCD. Fig. 3.7 shows the different power levels recorded at the MG by the monitoring system on different days. These readings are displayed on the LCD.

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Figure 3.7 Field chart for power.

The proposed system can be utilized in industries and also for monitoring and controlling home appliances. It can be made use of in city power grids. The system can be used in solar power station and hydropower station. The proposed system can also be made use of in wind power generator stations. The advantages of the proposed system are as follows: the entire system can be shut down for quick repairs and reinstallations. Remote monitoring can be performed to avoid further power loss and time, low maintenance, efficient and low-cost design, and produce high accuracy.

3.12 Conclusion and future scope The last phase of dissemination of electricity is its conveyance from the source power plants to the end clients. Distribution framework conveys power by the conveyance framework and this reaches the load centers. Thus it is necessary to possess increased efficiency along with reliability and QoS in a distribution network. The study undertaken supplies solutions for deciding faults taking place in transformer and it conquers the disadvantages of past working techniques. This proposed system has been realized by a technique established on IoT to monitor the well-being of transformer and this is advantageous and reliable in comparison to hand-operated screening of current, temperature, power, vibrations, and humidity. The framework is dependent on an ATMEGA2560 microcontroller that functions as a data collection and

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conveyance framework. At the observation hub, once information of any faults or variations is received, one can promptly dive in to resolve the errors to avert any large-scale damage to the transformer. The framework concentrates on how effectively the transformer uses the wireless links in the place of large cables that are expensive which are unreliable and are of high maintenance. The IoT communication structure assists in enhances the transmission process. The use of ATMEGA2560 microcontroller in the design and framework is highly beneficial for industrial purposes. This structure is linked to a distribution transformer capable of transmitting data about the variations in operating values information to a mobile gadget by utilizing an IoT network. The system hardware is built from the peripherals as mentioned earlier. The experimental results obtained are as expected.

References Behera, S. K., Masand, R., & Shukla, D. S. P. (2014). A review of transformer protection by using PLC system. International Journal of Digital Application & Contemporary Research, 3(2), September. Che, L., Shahidehpour, M., Alabdulwahab, A., & Al-Turki, Y. (2015). Hierarchical coordination of a community microgrid with AC and DC microgrids. In: IEEE transactions on smart grid. He, L., Wei, Z., Yan, H., Xv, K.-Y., Zhao, M.-yu., & Cheng, S. (2019). A day-ahead scheduling optimization model of multi-microgrid considering interactive power control. In: 4th International conference on intelligent green building and smart grid (IGBSG). Khayat, Y., Heydari, R., Naderi, M., Dragicevic, T., Shafiee, Q., Fathi, M., . . .Blaabjerg, F. (2019). Estimation-based consensus approach for decentralized frequency control of AC microgrids. In: 21st European conference on power electronics and applications (EPE '19 ECCE Europe). Kumar, A., Raj, A., Kumar, A., Prasad, S., & Kumar, B. (2012). Method for monitoring of distribution transformer. Undergraduate Academic Research Journal (UARJ), 1(3), ISSN: 2278-1129. Landage, V., Khsrche, A., Vadirajacharya, K., & Kulkarni, H. (2012). Transformer health condition monitoring through GSM technology. International Journal of Scientific & Engineering. Research, 3(12). Meng, L., Savaghebi, M., Andrade, F., Vasquez, J. C., Guerrero, J. M., & Graells, M. (2015). Microgrid central controller development and hierarchical control implementation in the intelligent microgrid lab of Aalborg University. In: IEEE applied power electronics conference and exposition (APEC). Mírez, J. (2017). A modeling and simulation of optimized interconnection between DC microgrids with novel strategies of voltage, power and control. In: IEEE second international conference on DC microgrids (ICDCM). Mohamadi, S. H., & Akbari, A. (2012). A new method for monitoring of distribution transformers. IEEE, 978-1-4577-1829-8/12. Ni, K., Wei, Z., Yan, H., Xu, K.-Y., He, L.-J., & Cheng, S. (2019). Bi-level optimal scheduling of microgrid with integrated power station based on Stackelberg game. In: 4th international conference on intelligent green building and smart grid (IGBSG).

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Sachin Kumar, B. S., & Prabhu, D. N. (2016). Simulation and analysis of compact remote monitoring system. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, 4(Special Issue 2), Nitte conference on advances in electrical engineering NCAEE-2016. Son, K. M., Lee, K., Lee, D.-C., Nho, E.-C., Chun, T.-W., & Kim, H.-G. (2009). Grid interfacing storage system for implementing microgrid. In: Transmission & distribution conference & exposition: Asia and Pacific. Sujatha, M. S., & Vijay Kumar, M. (2011). On-line monitoring and analysis of faults in transmission and distribution lines using GSM technique. Journal of Theoretical and Applied Information Technology, 33(2), 1817
3195, ISSN: 1992-8645, E-ISSN. Thiyagarajan, V., & Palanivel, T. G. (2010). An efficient monitoring of substations using microcontroller based monitoring system (pp. 56
60) IJRRAS 4(1) July 2010. Vishwanath, R., Shetty, A. V., Shamilli, P., & Thanuja, M. (2015). A new approach to monitor condition of transformers incipient fault diagnosis based on GSM & XBEE:. International Journal of Science, Engineering and Technology Research (IJSETR), 4(11), 3826
3829. Zhang, F., Meng, C., Yang, Y., Sun, C., Ji, C., Chen, Y., . . .Yang, G. (2015). Advantages and challenges of DC microgrid for commercial building a case study from Xiamen University DC microgrid. In: IEEE first international conference on DC microgrids (ICDCM).

Further reading Agarwal, M., & Akshaypandya. (2014). GSM based condition monitoring of transformer. IJSRD—International Journal for Scientific Research & Development, 1(12), ISSN (online): 2321-0613. Cheng, X.-h., & Wang, Y. (2011). The remote monitoring system of transformer fault based on The Internet of Things. In: International conference on computer science and network technology. Dharanya, S., Priyanka, M., Rubini, R., & Umamakeswari, A. (2013). Real time monitoring and controlling of transformers. Journal of Artificial Intelligence, 6(1), 33
42. Available from https://doi.org/10.3923/jai.2013.33-42, ISSN 1994-5450. DOI. Furundzic, D., Djurovic, Z., Celebic, V., & Salom, I. (2012). Neural network ensemble for power transformers fault detection. In: 11th symposium on neural network applications in electrical engineering NEUREL. Mao, H. (2010). Research of wireless monitoring system in power distribution transformer station based on GPRS (p. 5) IEEE, 978-1-4244-5586-7/10. More, K., Khaire, A., Khalkar, S., & Salunke, P. G. (2015). XBEE based transformer protection and oil testing. International Journal of Scientific Research Engineering & Technology (IJSRET), 4(3), ISSN 2278-0882. Nagaraju, N., & Kiruthika, M. S. (2013). Fault sensing in a remote transformer using GSM. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2(10). Pathak, A. K., Kolhe, A. N., Gagare, J. T., & Khemnar, S. M. (2016). GSM based distribution transformer monitoring and controlling system. International Journal of Engineering Research & Technology, 2(2), IJARIIE-ISSN (O)-2395-4396. Rahman, A., Ali, A., Khaliq, A., & Arshad, M. (2004). GSM-based distribution transformer monitoring system. Dubrovnik: IEEE MELECON. Suresh, D. S., Prathibha, T., & Kouser, T. (2014) Oil based transformer health monitoring, IJSR Volume 3 Issue 6, June 2014

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Xin, Z., Ronghui, H., Weizhao, H., Shenjing, Y., Dan, H., & Min, Z. (2014). Real-time temperature monitoring system using FBG sensors on an oil-immersed power transformer. Materials Science, 40(Suppl. 2), 253
259. Available from https://doi.org/ 10.13336/j.1003-6520.hve.2014.S2.048. Zanzad, R. T., Umare, N., & Patle, G. (2016). ZIGBEE wireless transformer monitoring, protection and control system. International Journal of Innovative Research in Computer and Communication Engineering, 4(2).

SECTION III

DC microgrids

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CHAPTER 4

DC microgrid Ritu Kandari1, Neeraj1 and Ayush Mittal2 1 Indira Gandhi Delhi Technical University for Women, India Open Systems International, Inc., India

2

4.1 Introduction For increasing the complete functionality of the power system, the smart grid uses the sensors, computational capacity, communications, and controller techniques. When an intelligent network is used, a silly system can also become smart for optimal operation of the power system from generation, distribution, transmission, energy storage, and load consumption for consistency and resilient operation of the grid. In the power system generation, transmission, and distribution, the whole scenario has been changed after 2003 because of the implementation of a new policy called Electricity Act 2003. Before 2003, the power system’s definition was mainly based on the combination of generation, distribution, and transmission. But after 2003, it is considered as a single term as commodity. In other words, electricity cannot be bought and sold from the government. In 2003, the government recognized the need to make the load demand and involved private players, and thereby distributed generation came into the picture (Hartono, Budiyanto, & Setiabudy, 2013; Rai, Ravishankar, & R, 2021). A general structure of microgrid consisting of generation, transmission, and distribution is represented in Fig. 4.1. The components of a microgrid are as follows: 1. Generation unit—The distributed generation generates power next to the consumer. It indicates installing solar power plants on the rooftop. Mainly one side is distributing, and the other side is consuming. Therefore, the government has been regulated to distinguish the poultry restructured power system. The power system has controlled the observation section in the distribution section, but still, with the transmission path, it is not regulated but is owned by the government. The private players can do generation and distribution through private industries, and this is the current power system scenario. The generation is available in different current and voltage ratings. Microgrids DOI: https://doi.org/10.1016/B978-0-323-85463-4.00007-1

© 2022 Elsevier Inc. All rights reserved.

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Figure 4.1 Structure of a microgrid.

2. Transmission and distribution unit—In the transmission and distribution case, the High Voltage Direct Current (HVDC) transmission has economic generation.

4.2 DC microgrid Fig. 4.2 represents the general structure of a DC microgrid. DC microgrid concept is the same as the conventional microgrid, but power is available in the DC form. It is the integration of energy storage devices and the main grid. DC microgrid can operate in both the ways, grid-connected mode and islanded mode of operation. In isolated DC microgrid operation, two major operational issues, such as standalone DC microgrid system and feasible, adaptable, and realizable and interconnection of two local dc grids, are prevalent. It has various topologies of the microgrid. HVDC transmission is dealing with unipolar, bipolar, and homopolar technologies. Similarly, microgrid distribution is in terms of unipolar DC microgrid and bipolar DC microgrid. In the unipolar DC microgrid, one is a positive terminal, and the other is a negative terminal (Kroposki, Basso, & DeBlasio, 2008; Olivares, 2014). 1. Storage System—If the generation is more than a load, it can start charging the storage. If the battery is fully charged, it has to make the battery ideal and do not operate at photovoltaic (PV) or wind at its maximum power point (MPP). However, if the P generation is more than PL load and the battery has not been fully charged, it can charge it. But if the PG is less than the load, it has to check the state of the charge (SOC) of the

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Figure 4.2 General structure of a DC microgrid.

battery. If the battery’s SOC is not less than 40 %, it can undoubtedly discharge the battery to meet the load, but it is already 40% less, then it cannot take the shortage of energy from the battery. So, it has to switch on the diesel generator. If the diesel generator can meet the gap, then the excess diesel generator energy available to it can be used for battery charging. But if it is not so, it has to see to the state of charge the battery. It has to keep on discharging to meet the loads, and slowly because the battery is getting discharged, the diesel generator is not having the excess energy to meet the difference. So, that case, it has to curtail the loads. First of all, it has to curtail one noncritical load, and then the second noncritical load and the critical load can never be curtailed. So, that case from 40% to 30% even after 20% even slightly below 20%, it can discharge the battery to protect the critical load (Tomasov, Kajanova, Bracinik, & Motyka, 2019). 2. Fuel cell technology—Fuel cell indicates a DC source. Dc residential load is solar power produced in the DC power, converting DC to DC power. It changes the voltage magnitude to DC Converter so that the converter is also required for the residential load. In the case of wind power, AC power is generating and converting it into DC. In the case of an energy storage system, various types of converters such as bidirectional DC to DC converter, buck-boost converter, dual active bridge converter, and the grid side conversion and integration have been used. For taking power from the ac grid, it is converting into DC to be done with an active front end converter that is power

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electronic converters. Generation, the source is available like fuel cell, solar power that is directly generated dc power. It has storage in the form of a battery bank, supercapacitors, ultra-capacitors, or flywheels (Mittal & Tomar, 2021; Alam, Kumar, Srivastava, & Dutta, 2018). 3. Diesel generator—If the diesel generator is available, it can meet the difference between load and generation. So, whatever the gap between load and generation can be taken out of the diesel generator. If it is an excess diesel generator, it can allow the battery to charge, and if not, it will come for to this point. If the battery that has the state of charge is less than 30%, then it will charge if it is less than 30%, then it will go further. It is less than 20% and if it is yes. Then, it can go for battery discharging, and a single critical load can be met. Suppose the diesel generator has excess energy, which is more than the difference between the load and the generation. If it is more significant, then charge the battery if it is not greater, then it has to take some energy because the diesel generator cannot meet load generation different. So, that case, whether SOC of the battery and if it is less than 30%. So, then it is not a good state to discharge. If it is SOC is less than if it is not less than 30%, then it can discharge. The critical load and noncritical loads are in operation, but if it is less than 20%, it can go to one noncritical and one crucial load, and the battery can discharge. But if it is less than 20%, it will only switch on critical load and start discharging the battery. 4. Unipolar Topology—At a unipolar DC technique, tons and sources are all attached in between the constructive and the negative pole of the DC bus illustrated in Fig. 4.3. The vitality has been sent across the DC bus at the same voltage degree; hence array of DC bus voltage degree is just a

Figure 4.3 Unipolar topology.

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primary element inside this technique. A high voltage degree advances the energy transmission capacity of this body; however, it also requires greater DC
DC converters as a way to coordinate with the enduser compression degree. Moreover, larger voltage rates could potentially increase protection threats. Having a very low voltage amount, the transmission convenience of this strategy is restricted by a brief space. Nevertheless, the correct choice of minimal voltage amount might prevent the installation of some high numbers of most DC
DC converters in nonelectrical power gridconnected gear. Even the unipolar technique is feasible for many homes in distant rural locations, wherever no more usefulness power infrastructure is different. Just lately 48 V DC unipolar techniques are implemented together with all the use of PV panel at microgrids for ninth homes in rural regions of India. All in all, the unipolar process isn’t hard to execute and there’s not any prospect of needing any asymmetry in between your DC sticks. Yet this strategy doesn’t supply any recourse, and so a single fault often leads into a shutdown of the entire technique. What’s more, this technique doesn’t offer you diverse voltage degree options towards their clients (Ahmed, Meegahapola, Vahidnia, & Datta, 2020). i. Bipolar Topology—The AC grid is stepping down by using converters and reducing the dc voltage. This dc voltage may be different such as 48, 110, 200, and 380 V or any other voltages. In the case of application requirement for designing, it depends on the number of experiments found that 48-V dc and 380-V dc is more economical. They are very economical compared to the rest of the voltage technologies. Nowadays, a train is using to see the DC microgrid working on different voltage ratings. It will not be aware Bureau of Indian Standards that is the Indian Standards. In the bipolar dc microgrid, which is the middle part of the converter. So, it has a positive and negative DC voltage rating. It may have a positive 350
380 V and negative 380 DC voltage rating. If it is combined with a pole to pole voltage, it will be 700
760 V. If it is 350, it will be doubled, that is, 700 V. If it is 380, then it is 760 V dc bipolar technology. The residential load is connected to the distribution network. The distribution network is bipolar dc microgrid DC distribution. Fig. 4.4 shows the bipolar topology (Bidram & Davoudi, 2012; Tembo, 2021). The role of converters in bipolar DC microgrids is represented as the way of power flow. There is a need for a bidirectional converter in the energy storage device that can convert AC power to DC and AC power to ac bidirectional. It should operate in the same way that whatever

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Figure 4.4 Bipolar topology.

converter is required for energy storage devices. There has been confusion for renewable integrations energy, whether it is a bidirectional or unidirectional. So, it is clear from the energy storage system. AC green electrical vehicles will require a bidirectional converter in main grid energy storage devices. They will work on a bidirectional concept to convert AC to DC and DC to AC for residential purposes. The unidirectional converters and energy sources like Power Electronics converters. From this, it takes the idea that for what kind of converters are required. The bidirectional converter may be a dual active bridge converter, and bidirectional buck to boost converters. So this converter is nothing but utilized to integrate sources and storage devices to the grid and form the dc microgrid.

4.3 Mode of operation The utilization of DC source is better than the AC source due to converting issue into the AC. DC can directly be transmitted and installed. For avoiding multiple conversions like AC
DC
AC or DC
AC
AC, a DC microgrid is preferable. DC microgrid can be operated with isolated mode or grid-connected mode of operation. But, commonly, the DC microgrid is an isolated mode prevalent because it is easily achievable. In the grid-connected method, the DC microgrid is not preferred due to the hybrid AC
DC grid (Bidram, Nasirian, Davoudi, & Lewis, 2017; Lopes, Moreira, & Madureira, 2006; Gupta, 2014).

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1. Isolated Mode In isolated DC microgrid, Fig. 4.5 represents the general structure of the DC microgrid. It has PV panels, diesel generators, battery, supercapacitors, and wind connected to the system by AC
DC
DC
DC conversions. It has a different type of loads, where some are critical, and some could be noncritical system consisting of renewable energy sources that could be PV or wind. Hybrid storage systems combine battery, supercapacitor, and nonrenewable energy sources such as diesel generator. To electrify a rural, remote area through the DC grid, the presence of PV, as well as wind along with the battery and supercapacitor, is encouraged. Further, it can have a diesel generator looking into evening hours, that is, load catering. If the renewable energy source is not enough to meet the load, then the diesel generator can certainly come to active participation (Fig. 4.6). In an isolated system where the first part represents renewable power generation, the second one represents the DC bus voltage. It contains the battery’s state of charge, the battery’s power, the supercapacitor, the load power, the load is taken care of, and the diesel generator output. Renewable power generation is kept on varying over a while. The voltage, which is almost excluding a few transients, and the state of charge of the battery, kept changing from 80% to 90% and then 40%. Finally, it represents the battery and supercapacitor’s characteristic, the power, and this is how the load is kept on changing and the diesel generator characteristic. When the state of charge of the battery is low, it can see the diesel generator is operating with its maximum capacity because, indeed, it is active. After all, the battery cannot meet the load. And if this characteristic has been assumed, to this picture, when the supercapacitor and battery behave during transients. And this is one more characteristic where the power is varying and the voltages the SOC of the battery, battery and

Figure 4.5 General diagram of isolated PV system with DC microgrid.

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Figure 4.6 Isolated DC microgrid.

supercapacitor, power characteristic, load power, and diesel generator. So, in one case, the diesel generator is not at all active because it is not required for the diesel generator to be switched on. In other matters, at 14 seconds, the diesel generator came into action, perhaps because the load is slightly low so that the load is at two and the load is reduced. When the generation is more than load, then the diesel generator may not be brought into the system operational conditions; whereas, the generation is less from the renewables the DG comes to play. But the characteristics depends on how the battery status and how they have been designed. So, when the battery falls below a particular SOC, then the DG comes to active operation (Micallef, Apap, Spiteri-Staines, & Guerrero, 2017; Rajesh, Dash, Rajagopal, & Sridhar, 2017). 2. Grid-connected mode Fig. 4.7 represents the two DC microgrids and the interconnection of these two local DC microgrids. It is apparent that both the DC microgrids are not necessarily operating at the same voltage level. One is running at 48 V, and the other may operate at 380 V. The critical challenge here when we have 2 DC microgrids operating at different voltages can be interconnected and complement each other. The present energy system’s impacthas integrated more than one DC microgrid or is being managed together. A single DC microgrid does have a condition where the generation is more than load, and it has seen the other case where the

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Figure 4.7 Grid-connected DC microgrid.

generation is less than load. When the generation is less than load, the diesel generator must be brought actively once the battery cannot meet the load increase. If it has two DGs and one has excess energy and the other has a shortage of power. Now instead of bringing the DG in a different scenario where the generation is less from renewable. Perhaps it can take the excess renewable energy available in the other microgrid to meet the other microgrid’s energy shortage. Maybe it is an attractive opportunity for the DC microgrids to incorporate and complement or support each other. First of all, it can achieve local energy security, effective renewable energy sources utilization, cost reduction, high reliability, Echo-friendly generation, and supply quality improvement. The system consists of two autonomous DC microgrid, mostly apart from each other, with considerable line resistance (Che & Shahidehpour, 2014). Each DC microgrid consists of one PV source, battery equivalent to a group of sources from renewable sources and storage devices. PV sources are an interface to DC bus through a boost converter and a bidirectional buck-boost DC
DC converter to connect the battery storage. The interconnection of 2 DC microgrids is realized by considering DC as an interfacing unit that provides galvanic isolation and high power feeding capability in both directions and a large conversion ratio through highfrequency transformers. Two full H-bridge converters are connected to either side of the transformer to produce the high-frequency AC from DC. Now, this is the power control management strategy between

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autonomous DC microgrids. It enhanced the system reliability and efficient utilization of resources. It includes overcharging and under discharging of storage (Shahidehpour, 2010). The loads are considered in this where two of them are deemed to be noncritical and considered as critical. Means, under no circumstances, one load can be taken out or compromised. In contrast, two noncritical loads can be compromised if the renewable energy sources and diesel generator cannot meet those. Now, PV is connected through the boost convertor to an isolated DC microgrid. The output of the wind turbine is processed through
AC
DC
AC conversion to feed power. Battery and supercapacitors are connected to isolated DC microgrid through a bidirectional buck-boost converter for bidirectional power flow because they can be charged or discharged. The diesel generator is an interface to isolated DC microgrid through the controlled rectifier. Now, this is one of the control algorithms which has been developed for the DC microgrid system. The generation may be more than the load, or sometimes the Power generation may be less than the load power. So, for optimal utilization of resources available to operate DC microgrid, either the generation is more than PL (Load Power) or the load or less than PL. Certainly, it needs to store when the generation is excess and have to discharge the battery when the generation is minimum, but let us depending upon the state of charge conditions of the battery. Now, if the P generation is more remarkable than PL if it is true that means, the battery is at a state of charge which is 90%, and if it is not charged up to 90% then, it can charge the battery and allow PV and wind to operate at its maximum power point tracking (MPPT). If the generation is more than loaded, it has excess energy to check whether the battery has been charged up to 90%. If it has not been charged up to 90%, and if the generation is more, it can allow battery to charge and keep generating do not reduce the generation from PV and wind. It will enable them to operate at MPPT mode. If it is already being charged up to 90%, then PV and wind bus will go to regulation mode, and the battery will be ideal and not charge further. But, if P generation is not greater than PL, it will check whether the battery with the state of charge is less than 40%. If it is not discharged below 40%, then the battery can discharge to meet the shortage of generation and allow to operate its MPPT of PV and wind certainly. But if it has already been discharged below 40, then it has no option generation and has a low battery, which has already been discharged below 40. The Ac grid is stepping down by using converters and reducing the dc voltage. This dc voltage may be different such as 48, 110, 200, and 380 V

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or any other voltages. In the case of application requirement for designing, it depends on the number of experiments found that 48-V dc and 380-V dc is more economical. They are very economical compared to the rest of the voltage technologies. Nowadays, a train is using to see the DC microgrid working on different voltage ratings. It will not be aware Bureau of Indian Standards that is the Indian Standards. In the bipolar dc microgrid, which is the middle part of the converter. So, it has a positive and negative DC voltage rating. It may have a positive 350
380 V and negative 380 DC voltage rating. If it is combined with a pole to pole voltage, it will be 700
760 V.If it is 350, it will be doubled, that is 700 V. If it is 380, then it is 760 V dc bipolar technology. The residential load is connected to the distribution network. The distribution network is bipolar dc microgrid DC distribution.

4.4 Advantages of DC microgrid It is using the DC microgrid. From that, the saving of energy is approximately 35%. It is an effortless electrical technology. Here one more advantage is that synchronization. In the case of the AC microgrid, we have to go for the voltage frequency impedance matching so that this kind of problem is not there in the DC microgrid, and there are no screen effects. Due to that, it may have 15% to 20% of national energy savings, according to the Bureau of Indian Standards working on the 48 V 5 A maximum. Because it is observed that residential power consumption of single-phase loads has two different power consumption categories: having the 5 and 16 A power sockets. So that existing infrastructure has been used if it is adopting the 48 V dc standard.

4.5 Standards There were no such exit final standards available, but some authorities were working on to form the standards of DC microgrid. So here are some of these standards and their descriptions. It is applicable for data and telecommunication equipment, and it is suitable up to the voltage level of 400 V. DC power supply forms the standards for data centers in microgrid and finds where this can be used and the standard. So, it suggests the architecture for DC microgrid IEEE 946 used to design the DC auxiliary power system for generating stations. And the scopes will help for sizing and determination of duty cycle and home

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maintenance, and it will also give guidelines for the typical architecture of these systems. The voltage reading from DC equipment has been extended. IC SG port is used for distribution networks up to 1500 V. The standards are developed by and are helping to establish standards for standalone DC microgrid. The residential houses are the next power distribution giving scopes and give the guidelines for using this with the existing system to take from the standards. According to the new concept in India, DC microgrid infrastructure will find the immediate difference in the number of convergence if it will find that in infrastructure as it needs approximately ten converters and indicates DC in microgrid infrastructure. It needs at least five converters because this DC microgrid is introduced to minimize the conversion status by reducing the convergence. From that, the number of losses will be reduced. So, it has several convergences.

4.6 DC microgrid architecture Renewable energy sources and energy storage systems are gradually receiving participating to distribution microgrid system. If it generates energy at DC power, their addition in DC microgrids is tracing in the research community. Nevertheless, energy capability of any demand energy response is flexible and unreliable owing to its reliance on climate condition. So, an interface with the AC grid is very significant in order to recover the consistency and accessibility of energy in a DC microgrid system. There are rare choices to interface a DC microgrid with an AC grid such as (Mariam, Basu, & Conlon, 2016): 1. Radial configuration Just one line diagram of this radial DC microgrid process is displayed in Fig. 4.7, where several renewable energy source (RES), energy storage system (ESS), and heaps (both AC and DC) are directly on the DC bus. This bus might also be unipolar or bipolar based on requirements and applications. This structure can be utilized in residential buildings, even by which low voltage DC bus is more advised to coordinate with the compression level of several appliances and also in order to steer clear of additional DC
DC conversion stages. Additionally such techniques, heaps and AC grid port can be located near to each other so as to decrease the supply declines. The same theory can be expand to some multi DC microgrid platform like a multistory construction or perhaps a neighborhood community, by which each microgrid could have RES along with

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ESS along with various loads. Such techniques, the DC bus of each microgrid might be inter connected in series or in parallel based upon the physical design of their communities or buildings. This manner, every construction functions as a bunch of those microgrids and can absorb or exude ability towards the neighboring microgrids. The concurrent aerodynamic architecture may raise the trustworthiness of the machine by obeying just faulty buses just in the event of faults, hence enabling the healthful buses keep their usual functioning. The series U-shaped structure might possess some stability problems throughout islanding modes. The radial DC microgrid congurations could provide quite a few advantages like ease, multi-voltage degree (in bi polar) and capacity to split the capacity from neighboring bicycles (in multi-bus structure). Nevertheless, the show aerodynamic architecture isn’t exible throughout fault states. As an instance, just one fault may impact each of customer linked to single bus network. In the event there is series radial multi-bus system, if a bus is dispersed by circuit-breakers, the trucks before the bus won’t need an option to talk about their power with the full system (Alfergani, Alfaitori, Khalil, & Buaossa, 2018) (Fig. 4.8). 2. Ring main configuration The ring architecture is a closed-loop and intelligent electronic device for DC distribution. It has different alternators and reference buses that can be opted for any purpose and for many buses that can consume the power.

Figure 4.8 Radial architecture.

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In mesh type of architecture in DC microgrid have different kinds of appearing on the earth system, it will look like the unipolar thing. They are putting one additional resistor and resistance connected to the ground. It is preferable in case of an earthing and neutral point. If this kind of thing at the middle point of a converter is connected yet, this is called a neutral point, forming the bipolar. The pole’s addition to pole volts is the total dc voltage, like positive and negative volt. This method is used in the system that has circulating current problem because when it is integrated on the grid side converter, the converter forms a closed-loop, which is the problem of small circulating current, and is dangerous for the appliance. There may be changes that can minimize losses through this solar earthing system to limit the present, determining resistance after removing the grounding resistance so that the charge here, in this case, can easily detect the losses. Because of this, current and voltage are not minimized. It will get the relative magnitude of current and voltage in the DC microgrid until it is very problematic. 3. Interconnected configuration: a. Mesh type—In a mesh style DC microgrid, also referred to as a multiterminal grid, more than 1 AC grid ports are connected to the DC grids, each through an AC DC converter. Different DC microgrid architectures are possible in accordance with this conguration where several DC and AC power supplies are on the DC feeders. Fig. 4.9 shows one architecture. The Mesh Type DC (MTDC) is significantly more reliable compared to radial or the ring DC grids on account of the access to other feeders to furnish capacity to various parts of the procedure. Much like architectures are used in HVDC system such as off-shore wind farms and underground urban subtransmission and distribution platform. A “handshaking” method was proposed to locate and isolates the faulty DC bus and restore the MTDC platform without any internal communication within AC DC converters from the device (Fig. 4.10). b. Zonal type—To help enhance the trustworthiness of the machine, a zonal electric supply platform was suggested in a variety of newspapers, at which in fact the supply process will be inserted right in to many of zones and each zone includes 2 redundant DC buses as exhibited at Fig. 4.11. The truth is that this DC grid design is composed of cascaded DC microgrid approaches with a symmetrical arrangement. Even the ZTDC microgrid technique comprises several grid things, like electrical power converters, power storage programs, generations and also switchgears using the purpose of supplying a set of heaps. Each station is closely joined using just two redundant DC buses driven with the AC

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Figure 4.9 Ring main configuration.

grid and also spread DC and AC vitality resources. Such a design stipulates an improved dependability and accessibility for those heaps which might be furnished through a few of those motors. Let’s assume that an error happens while in the top bus of both Zone-1, the buttons in the top side is going to be switched away as the buttons in the decrease facet are retained on that the ability is slowly moved into the heaps along with other motors. What’s more, due to the fact just about every zone is additionally attached having its power source, numerous flaws in the lower and upper feeders of just about every zone may split the DC grid into couple segments. This setup is much more modular and flexible because of this greater quantity of buttons and also is acceptable for supply preparation. Even the ZTDC grid offers multiple choices to furnish capacity to heaps. Power could be furnished from several bicycles concurrently, either sequentially or just from 1 bus only. Nevertheless, the ability drawn from numerous buses may reevaluate the plan and functioning of this supply platform. Because of this, A-BUS pick plan was suggested. Predicated on the particular strategy, lots bring power in your bus with all an maximum voltage amount. Nevertheless, force

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Figure 4.10 Mesh configuration.

may swap to a different bus based upon the essential problems. Such a setup is normally utilized at shipboard electricity provides (Fig. 4.11). As mentioned above, the DC microgrid is the hybrid energy system, which might consist of various nonconventional and renewable energy resources such as solar, wind, hydro, etc. along with secondary power generation setup such as Fuel cells, storage devices consisting of batteries, supercapacitors, and power electronic devices including converters, inverters, etc. The microgrid system might have all or part of it. But, for the system’s optimum sizing and output, modeling of individual component(s) and system [or subsystem(s)] is the first step, which enables in identifying the right component and system (or subsystem) with the assistance from their characteristics. Although, accuracy as designing a perfect model is too complex and extremely time consuming; appropriate model should be trade-off between accuracy and complexity. General methodology for microgrid system is described below:

4.6.1 Photovoltaics cell/solar This section will provide brief information regarding the PV. PVs is the technology, which uses the energy of the photons to convert the solar energy into the DC electrical energy. Solar is an eco-friendly and inexhaustible form of energy and hence is the primary choice in the field of renewable sector (Jiang, Qahouq, & Orabi, 2011) (Fig. 4.12). PV cell is the basic unit and building block of the solar panel (or array/module). It is simply a p
n junction diode formed using the

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Figure 4.11 Zonal configuration.

Figure 4.12 Structure of solar cell (Dey, Khan, Abhinav, & Bhattacharjee, 2016).

semiconductor (primarily, silicon) material. When the p
n junction of the diode gets illuminated by the sunlight, then the photons with higher energy, that is, energy greater than band-gap of the semiconductor material, are absorbed following which the valence electrons are knocked loose leading to the generation of the electricity. The basic conventional solar structure is depicted in the figure consisting of metal grid, anti-reflective

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Figure 4.13 Solar cell circuit diagram (Jiang et al., 2011).

layer and metal contact in addition to p-n junction diode (Dey et al., 2016; Jungbluth, Stucki, Frischknecht, & Tuchschmid, 2021). The mathematical model for the basic unit of solar panel, that is, PV cell can be formulated using the 5 basic parameters, which are depicted in Fig. 4.13. The diagram shows the simplified equivalent circuit of the single diode PV cell consisting of—solar induced photocurrent (Iph), diode current (Id), shunt resistance current (Ish) along with PV cell’s output parameters, that is, current (Ipv) and voltage (Vpv) (Islam, Merabet, Beguenane, & Ibrahim, 2013; Tomar, Mittal, & Pattnaik, 2021). Applying the Kirchhoff’s First Law (i.e., Kirchhoff’s Current Law), Ipv 5 Iph 2 Id 2 Ish The solar induced photocurrent, Iph is calculated as (Islam et al., 2013; Pon Vengatesh & Rajan, 2011): Iph 5 ½Isc 1 Ki ðTC 2 TR Þ 3 Ir where Isc 5 short-circuit current of cell at Standard Test Condition (STC), Ki 5 PV cell short-circuit current/temperature coefficient (A/K), Ir 5 irradiance in kW/m2, TC 5 cell working temperature, and TR 5 cell reference temperature at STC. The diode current, Id is calculated as:
 Id 5 Isat 3 e

V η 3 VT 21

where Id 5 diode saturation current, η 5 ideal constant of diode (1
2), V 5 output voltage (including the ohmic loss), and VT 5 thermal voltage respectively. VT is calculated as: VT 5

kT q

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where k 5 Boltzmann constant (1.38e223J/K), T 5 Temperature (in °K), q 5 electron charge (1.6e219 C). V is calculated as:   V 5 Vpv 1 Ipv 3 Rse where Rse 5 PV cell’s series resistance. The PV cell shunt resistance current, Ish is calculated as:    Vpv 1 Ipv 3 Rse Ish 5 Rsh Combining all the equations, the final equation can be formulated as:



Ipv 5 ½Isc 1 Ki ðTC 2 TR Þ 3 Ir 2

8 < :


Isat 3 e

Vpv1ðIpv 3 Rse Þ 21 η 3 k:T q

9 = ;

2

   Vpv 1 Ipv 3 Rse Rsh

The characteristics of the PV cell is nonlinear in nature. The presence of factors such as irradiance (Ir ) and temperature in the PV’s output current equation, effects the nature of the curve. The following diagram depicts the PV and IV characteristics of the solar cell. The 3 important parameters that can be visualized from the plot of the PV cell are—ShortCircuit current (corresponding to short-circuit condition with voltage drop equal to zero), Open Circuit Voltage (OCV) (corresponding to open circuit condition with current becoming zero) and Maximum Power Point (Tomar, Mittal, & Pattnaik, 2021) (Fig. 4.14). The below table shows the common electrical characteristics for any PV module or panel, which are measured at the STC, that is, at irradiance of 1000 W/m2, air mass 1.5 g and cell temperature 25°C. The PV module type SunPower make SPR
305
WHT data (Fig. 4.15) is used as an example for the same. Parameters

Symbol

Rated power Open circuit voltage Short-circuit current Voltage at maximum power Current at maximum power Total no. of cells in series Total no. of cells in parallel

PMP VOC ISC VMP IMP NS NP

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Figure 4.14 IV curve of solar cell (PVEducation, 2021).

Figure 4.15 PV module type SunPower make SPR
305
WHT (SolarDesignTool, 2021).

4.6.2 DC
DC converters This section deals with the modeling aspect of the DC/DC converters (or, Choppers), which acts as a building block for the purpose of interconnectivity between various sources of power generation along with transmission and distribution. As briefed by the name, the DC/DC converter’s primary task is to convert one DC level to other, that is, shift the voltage level in either direction (upwards or downwards). Choppers primarily consist of energy storing elements, that is, inductors and capacitors and semiconductor devices such as—power transistors, IGBTs, GTOs, Power MOSFETs, thyristors, etc.

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4.7 Principle of chopper The principle for the operation of the chopper is switching. They are designed using the semiconductor devices acting as switches, which are triggered with the help of external signals known as gate signals. The average output voltage is controlled by varying the ON-period or OFFperiod of the switch. The chopper’s operation can be primarily classified as Boost converter (step-up chopper), Buck converter (step-down chopper) and Buck-Boost converter (step-up/down chopper) (Rosas-Caro, Ramirez, Peng, & Valderrabano, 2010; Shen, Qin, & Wang, 2018).

4.8 Boost converter The chopper configuration depicted in Fig. 4.16 can provide output voltage (Vout) greater than the input voltage (Vin), that is, Vout . Vin . The configuration consists of elements such as—inductor (L), capacitor (C), Diode (D), and switch S with gate signal (Hasaneen & Mohammed, 2008).

4.9 Case-I (switch S is ON) When the switch (S) is in ON state, inductor L is connected to the supply Vin and stores the energy during the Ton period. Mathematically, voltage across inductor, VL can be represented as: VL 5 L

dis dt

The energy input (Ei ) to the inductor during the Ton period is given by: Ei 5 Vin 3 IS 3 Ton

Figure 4.16 Boost converter.

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4.10 Case-II (switch S is OFF) When the switch (S) is in OFF state, inductor L is disconnected from the supply and connected to the load and current is forced to flow during the Toff period. Mathematically, Vout can be represented as: Vout 5 VL 1 Vin The energy released (Eo ) by the inductor during the Toff period is given by: Eo 5 ðVout 2 Vin Þ 3 IS 3 Toff Considering the lossless steady-state system, the two energies, that is, energy absorbed and released by L should be equal. Mathematically, Ei 5Eo Vin 3 IS 3 Ton 5 ðVout 2 Vin Þ 3 IS 3 Toff 

Ton 1 Toff Vout 5 Vin 3 Toff











T Vout 5 Vin 3 T 2Ton 1 Vout 5 Vin 3 12D

where T is the total time period of switch (S) open and close, that is, Ton 1 Toff and D (Ton =T ) is the duty cycle which is defined as the ratio of ON-period, Ton , and total time period, T.

4.11 Buck-boost converter The following configuration of converter is capable of performing both the operations of the chopper, that is, step-up and step-down, by continuously varying the switching characteristic (or Duty Cycle) (Mashinchi Mahery & Babaei, 2013; Zhou & He, 2015).

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4.12 Case-I (switch S is ON) When the switch (S) is in ON position, then the inductor is connected to the supply, Vin and energy is stored in the inductor, L. Hence, the energy is stored by the inductor, L during the Ton period. Mathematically, energy stored is given by Ei 5 Vin 3 IS 3 Ton

4.13 Case-II (switch S is OFF) When switch (S) is in OFF state, the polarity of the induced emf in the inductor changes and its forces the current, IS to flow through the load. Thus, the inductor is discharged for the period of Toff and the energy given by the inductor to the load can be mathematically represented as Eo 5 Vout 3 IS 3 Tof f Considering the lossless system at steady-state condition, the complete energy stored is released by the inductor and thus, Ei 5Eo Vin 3 IS 3 Ton 5 Vout 3 IS 3 Toff 

Ton Vout 5 Vin 3 Toff 



Ton Vout 5 Vin 3 T 2Ton



! 1  Vout 5 Vin 3  T =Ton 2 1 ! 1  Vout 5 Vin 3  1=D 2 1

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Figure 4.17 Buck-boost converter.

Or,



D Vout 5 Vin 3 12D



where D is the duty cycle and for, 0 , D , 0:5 the circuit acts as Buck converter, whereas for, 0:5 , D , 1 it acts as a Boost converters (Fig. 4.17).

4.13.1 Maximum power point tracking controller In the above section, modeling of two DC/DC converters (Boost converter and buck-boost converter) is explained. The working principle for the same is switching, which is accomplished with the help of MPPT controller(s). Unlike conventional resources, PV’s generation and control of power cannot be achieved with the variation in the fuel inflow rate or amount of energy transferred to the generator; the additional controller (MPPT) is used to guide and track the MPP of the PV system. The MPP is highly affected due to nonlinearity in the relation(s) between voltagecurrent and voltage-power because of varied insolation level, weather condition and temperature. Thus, to overcome these effects, MPPT techniques are exerted based on various algorithms proposed to extract the maximum possible power from the PV module. Few of the MPPT algorithms mentioned in the literature are depicted below (Christopher & Ramesh, 2013; MathWorks, 2021b) (Fig. 4.18): Among these algorithms, incremental conductance (INC) and P&O are briefly explained below:

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Figure 4.18 Types of MPPT algorithms.

•

Perturbation & Observation (P&O) Algorithm In the P&O MPPT algorithm, the output of the PV module is first perturbed and voltage, V(t) is made unsettled. Accordingly, power, P(t) is calculated and compared with P(t 2 1) followed by comparison between the last measured voltage, V(t 2 1) and present voltage, V(t). If the measured power, P(t) is greater than P(t 2 1), along with higher V(t) as compared to V(t 2 1), then the Duty cycle (D) is incremented by a constant factor ΔD, else D is decremented by the same factor, that is, ΔD. Similarly, if P(t) is found lesser than P(t 2 1) followed by comparison between V(t) and V(t 2 1), the Duty cycle (D) is incremented (by a factor ΔD) if V(t) is greater than V(t 2 1), else decremented by a factor ΔD. The variations are maintained and controlled by a constant value ΔD, therefore P&O algorithm fails to cater the system during the rapid change in irradiance level and abnormal variation(s) in the atmospheric condition (Azad et al., 2017; Villalva & Ruppert F, 2009) (Fig. 4.19). • INC Algorithm The principle of INC algorithm is the comparison of PV module INC with instantaneous conductance and the following characteristics of PV’s derivative power: (1) zero value of dP=dV at MPP, (2) positive value of dP=dV at MPP’s left, and (3) negative value of dP=dV on MPP’s right, forms the basis and monitors an algorithm (Mishra, Das, Kumar, & Pattnaik, 2019). Mathematically, the former relations can be denoted as: At MPP, dP 50 dV At MPP’s Left dP .0 dV

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Figure 4.19 P&O.

At MPP’s Right dP ,0 dV

The principle of INC can be mathematically represented as: dP dðIV Þ 5 dV dV     dðIV Þ dI ΔI 5I 1V DI 1 V dV dV ΔV

For attaining the condition of MPP, dP 50 dV Or,

 I 1V

ΔI ΔV

 50

ΔI I 5 2 ; @MPP ΔV V ΔI I 0 . 2 ; @MPP sLeft ΔV V

ΔI I 0 , 2 ; @MPP sRight ΔV V where

ΔI ΔV 5 incremental I V 5 instantaneous

conductance conductance

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Depending on theresult, the Vref (previous MPP voltage) is increased or decreased and gets replaced by the new voltage at MPP. The condition is maintained at MPP and Vref until a change is occurred in the parameters such as—ΔI, irradiance level, abnormal and sudden change in atmospheric conditions. INC has an edge over P&O as this can cater the system with a rapid change in the parameters (Anowar & Roy, 2019; MathWorks, 2021a; Selvan, 2013) (Fig. 4.20).

Figure 4.20 InC algorithm.

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4.13.2 Storage device—battery This section, deals with the modeling of batteries (primarily, Li-ion) and covers varied type of modeling topologies. In microgrid(s), the necessity of energy storage devices arises due to the need of uninterrupted supply of energy along with catering the uncertainty in the demand (or load transients) which lasts for interim period of time. For this purpose, the primarily included storage devices are—Fuel cells, battery-banks and supercapacitors. The building block of the battery is a cell, which is the static electrochemical device, and converts the energy stored in the chemicals into the do-able energy (or electricity). Number of cells packed together, connected in serial or/ and parallel to obtain the desired level and rating(s) is termed as a battery. Chemically, it converts the chemical energy into electrical energy with the help of exothermic redox reactions without any intermediate thermal or mechanical process. The classification can be varied on the basis of—chemical composition, size, form factor, application based but the broad classification of batteries includes: Nonrechargeable (or, primary) and Rechargeable (or, secondary). As the name points, the former is made for the one-time consumption, whereas, the latter can be used multiple times. Examples of the primary batteries include—alkaline and coin-cell whereas, for secondary batteries it includes— lead acid, Ni-Cd (Nickel Cadmium), Ni-MH (Nickel metal hydride), Li-ion (lithium ion), Li-Po (lithium polymer) (Koehler, 2018; Schumm, 2021). Each cell consists of a series connection of two half cells via an electrolyte (conductive in nature), comprising anions and cations. One half cell includes the combination of electrolyte and anode, whereas the other half cell is the combination of electrolyte and cathode. Among various batteries invented, the prominent ones with promising performance are Li-ion batteries. High power density, lower selfdischarge rate, longer cycle life are some of the traits which helps in sturdy performance of Li-ion batteries. Li-ion batteries primarily consist of—electrodes, electrolyte and partitions (porous in nature). The Li-ion is provided via positive electrode, which is composed of Li packed with transition metal oxides, such as—LiNiO2 , LiMn2 O4 , LiCoO2 , etc.

4.14 Working principle The construction of the cell (or battery) includes—positive (1ve) electrode (termed as Cathode) and negative (2ve) electrode (termed as

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Anode) along with an electrolyte solution (or medium). The mechanism of the battery charging and discharging is based on the redox reactions taking place inside the cell. The process differs depending on the composition of the electrode and the electrolyte medium (Woodbank Communications Ltd, 2021a; Matsusada Precision Inc, 2020).

4.15 Discharging mechanism The electrons emitted due to the oxidation of 2 ve electrode is absorbed by the 1 ve electrode thus causing the reduction at cathode. Thus, the surplus electrons generated at the anode move through the guided media (or external circuit), to compensate for the deficiency of electrons occurred due to reduction reaction at cathode. The process persists and battery keeps generating the power, till the circuit is complete and necessary substance (or chemicals) are available to keep the chemical reaction sustained, until fully discharged (Bhatt, Forsyth, Withers, & Wang, 2021; Zschornak et al., 2019) (Figs. 4.21 and 4.22).

4.16 Charging mechanism Charging in simple terms is storing the charge in the batteries for reuse. In the fully discharged condition, the battery maintains the state of chemical equilibrium with no chemical reactions taking place. However, with the chemical reaction, status quo could be achieved, with the extraction of electrons back from the cathode to anode. During the charging, cathode undergoes oxidation reaction, whereas reduction occurs at negative electrode. The current in the reverse direction from the external power supply, causes a reverse electrochemical reaction in the secondary battery (Bhatt et al., 2021; Zschornak et al., 2019) (Fig. 4.23).

Figure 4.21 Redox reaction process (Matsusada Precision Inc, 2020).

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Figure 4.22 Discharging mechanism (Matsusada Precision Inc, 2020).

Figure 4.23 Charging mechanism.

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4.17 State of charge and state of health State of charge, can be viewed as a thermodynamic quantity which is defined as the measure of the available capacity (in Ah) in the cell, expressed as a percentage of the rated capacity. SoC helps in assessing the potential energy of the battery (He, Xiong, & Fan, 2011). SOC 5

QC QN 2 QðIn Þ 5 QN QN

QðIn Þ 5

ðt In dt t0

where QC 5 battery remaining capacity, QN 5 battery rated capacity, QðIn Þ 5 standard discharge current, and In 5 t0 2 t time the battery release power. The primary aspects of the SoC depicting its significance are—(1) to maintain the performance of the battery uniform, (2) to extend the charging and discharging cycle life, (3) to protect and avoid the battery overcharge or/and over-discharge, (4) rational allocation and effective use of energy, (5) forecast the remaining work ability (Haizhou, 2017; Murnane & Ghazel, 2017). SoC estimation is a nonlinear, highly complex and scientific problem. Real time accurate estimation is difficult and is highly effected by the parameters summed up as follows: rate and time of charging and discharging, self-discharging, polarization effect, temperature and battery aging (Xiong & Xiong, 2020; Topan et al., 2017; Gkountaras, Dieckerhoff, & Sezi, 2014).

4.18 Types of batteries With the advancements in technology, more and more number of batteries are getting introduced. But, the highly used practically employed and preferable secondary batteries, consists of—Lead acid, NiCd, NiMH, and Li-ion. • Lead Acid Lead acid batteries are among the oldest storage devices which are practically employed in various applications till date and are preferable technology choice in automotive sector. They were invented in 1859 by Gaston Planté. These batteries are robust, tolerant to abuse and economical. These are composed of cathode (lead dioxide), anode (sponge metallic lead) and electrolyte (sulfuric acid solution). The metal elements used can

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makes these batteries toxic and improper disposal is hazardous to the environment (Brodd, 2013; Bullock, 2013; Battery University, 2021; Pang, Farrell, Du, & Barth, 2001). The working of the lead acid can be explained using the following equations depicting chemical reactions: Discharging

H2 SO4

"

Charging

PbðmetalÞ 1HSO2 4

Discharging

"

Charging

2 PbO2 13H1 1HSO2 4 12e

PbðmetalÞ 1PbO2 12H2 SO4

•

H1 1HSO2 4

PbSO4 1H 1 12e2

Discharging

"

Charging

Discharging

"

Charging

PbSO4 12H2 O

2PbSO4 12H2 O

During discharge, the lead dioxide and lead react with the sulfuric acid to create lead sulfate, water, and energy, whereas, during charging, the cycle is reversed and lead sulfate and water are electrochemically converted to lead, lead oxide and sulfuric acid by an external electrical charging source. The various applications include automotive applications, Standby/ Back-up/Emergency power for electrical installations, Submarines, Uninterruptible Power Supplies (UPS), etc. (Bullock, 2013; Woodbank Communications Ltd, 2021b; BYJUS, 2021). NiCd Ni-Cd batteries are created utilizing alkaline chemistry with energy density about double that of lead acid batteries. These were invented in 1899 but introduced on large scale in early 1960s.These use nickel hydroxide ½NiðOHÞ2  as cathode, Cd as anode and potassium hydroxide (KOH) as an electrolyte. The batteries small size and high discharge rate capacity has made it practically possible to make portable tools and other appliances. The charging process is endothermic in nature and reaction for charging and discharging can be explained by following equations (Woodbank Communications Ltd, 2021c; Omar et al., 2014):

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123

Reaction at Cathode: Cd12OH2

Discharging

"

Charging

CdðOHÞ2 12e2

Reaction at Cathode: 2NiOðOHÞ12H2 O12e2

Discharging

"

Charging

2NiðOHÞ2 12OH2

Overall Reaction: Discharging

2NiOðOHÞ1Cd12H2 O

•

"

Charging

2NiðOHÞ2 1CdðOHÞ2

The various applications of these batteries include; power tools, radios, electric razors, medical instrumentation, emergency lighting, motorized tools, etc. (Wilberforce, Thompson, & Olabi, 2020). NiMH Nickel metal hydride batteries consist of a positive plate containing nickel hydroxide as its principal active material, a negative plate mainly composed of hydrogen-absorbing alloys, a separator made of fine fibers, an alkaline electrolyte, a metal case and a sealing plate provided with a self-resealing safety vent. Their basic structure is identical to that of NiCd batteries. With cylindrical nickel metal hydride batteries, the positive and negative plates are separated by the separator, wound into a coil, inserted into the case, and sealed by the sealing plate through an electrically insulated gasket (Hariprakash, Shukla, & Venugoplan, 2009; Kang, Yan, Zhang, & Du, 2014; Woodbank Communications Ltd, 2021d). Reaction at positive plate: NiOOH1H2 O1e2

Discharging

"

Charging

NiðOHÞ2 1OH2

Reaction at negative plate MH1OH2

Discharging

"

Charging

M 1H2 O1e2

Overall Reaction: NiOOH 1 MH

Discharging

"

Charging

NiðOHÞ2 1 M

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Microgrids

The pros of NiMH batteries over NiCd includes: robust nature, high tolerance to overcharge and over-discharge, higher energy density which is about 50% better than NiCd, and typically 3000 charging cycles as compared with 500 for NiCd. The applications of these batteries include—UPS, energy storage in smart grids, telecom sector, mobile phones, camcorders, medical instruments, etc. (Tsai & Chan, 2013). • Li-ion Li-ion batteries are making the future of the energy storage system in every domain ranging from automobile, utility grid to gadgets such as— mobile phones, toys, appliances, etc. Li is the lightest of metals and has the greatest electrochemical potential which helps in achieving very high energy densities in high power battery applications. The generic construction of the Li-ion cells uses carbon as anode and lithium cobalt dioxide or compound of lithium manganese as cathode. However, electrolyte used is common and usually based on lithium salt (Barcellona & Piegari, 2017; Meng et al., 2018). The following two reactions depict the chemistry of lithium ion batteries. Reaction at positive plate: Li12x MO2 1 xLi1 1 xe2

Discharging

"

Charging

LiMO2

Reaction at negative plate: Discharging

Lix C6

"

Charging

xLi1 1 xe2 1 C6

There are numerous advantages which helps in replacing other batteries from the market, but few of them are—high cell voltage of 3.6 V which means fewer cells for same rating battery, very high energy density (about times of lead acid), no memory effect, tolerate micro-cycles, fast charging capability, long cycle life, and very fast response rate towards charging and discharging calls (Clean Energy Institute, 2021). Table 4.1 summarizes the technical specifications and various parameters of Lead acid, NiCd, NiMH and Li-ion batteries (Jiangmen TWD Technology Co. Ltd., 2021; Battery University, 2019).

4.18.1 Modeling Battery modeling is a critical aspect during the system designing process as it is important to balance the modeling complexity and accuracy for the

Table 4.1 Comparison of commonly used rechargeable batteries. Specifications

Lead acid

NiCd

Li-ion1

NiMH Cobalt

Manganese

Phosphate

100
150 Low 500
1000 1
2

90
120 Very low 1000
2000 1
2

Specific energy (Wh/kg) Internal resistance Cycle life2 (80% DoD) Charge time4 (h)

30
50 Very low 200
300 8
16

45
80 Very low 10003 1
2

60
120 Low 300
5003 2
4

150
250 Moderate 500
1000 2
4

Overcharge tolerance Self-discharge/month (room temp)

High 5%

Moderate 20%5

Low 30%5

Low. No trickle charge , 5% (Protection circuit consumes 3%/ month)

Cell voltage (nominal)

2V

1.2 V6

1.2 V6

3.6 V7

Charge cut-off voltage (V/cell)

2.40 Float 2.25

Discharge cut-off voltage (V/cell, 1 C)

1.75 V

Full charge detection by voltage signature 1.00 V

Typically 4.20, but, some go to higher V 2.50
3.00 V

Peak load current Best result

5C8 0.2 C

20 C 1C

2C ,1 C

Charge temperature Discharge temperature Maintenance requirement

220°C to 65°C 220°C to 65°C 3
6 months10

0°C to 45°C9 2 20°C to 60°C Maintenance-free

Safety requirements

Thermally stable

0°C to 45°C 2 20°C to 65°C Full discharge every 90 days when in full use Thermally stable, fuse protection

Practically used since

Late 1800s

1950

1991

5C 0.5 C

1990

3.7 V7

3.2
3.3 V 3.60 2.50 V

. 30 C , 10 C

Protection circuit mandatory11 1996

1999 (Continued)

Table 4.1 (Continued) Specifications

Lead acid

NiCd

Li-ion1

NiMH Cobalt

Toxicity Coulombic efficiency Cost

12

Very high

Very high

B90%

B70% Sow charge B90% Fast charge Moderate

Low

Low

Manganese

Phosphate

Low 99% High13

The figures are based on average ratings of commercial batteries at time of publication. Specialty batteries with above-average ratings are excluded. 1 Combining cobalt, nickel, manganese and aluminum raises energy density up to 250 Wh/kg. 2 Cycle life is based on the depth of discharge (DoD). Shallow DoD prolongs cycle life. 3 Cycle life is based on battery receiving regular maintenance to prevent memory. 4 Ultra-fast charge batteries are made for a special purpose. 5 Self-discharge is highest immediately after charge. NiCd loses 10% in the first 24 hours, then declines to 10% every 30 days. High temperature and age increase self-discharge. 6 1.25 V is traditional; 1.20 V is more common. 7 Manufacturers may rate voltage higher because of low internal resistance (marketing). 8 Capable of high current pulses; needs time to recuperate. 9 Do not charge Li-ion below freezing. 10 Maintenance may be in the form of equalizing or topping charge to prevent sulfation. 11 Protection circuit cuts off below about 2.20 V and above 4.30 V on most Li-ion; different voltage settings apply for lithium-iron-phosphate. 12 Coulombic efficiency is higher with quicker charge (in part due to self-discharge error). 13 Li-ion may have lower cost-per-cycle than lead acid.

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optimum outputs. The modeling of the batteries can be sub-classified into several categories but the four broad categories comprises of—Empirical model, Equivalent circuit model (ECM), Electrochemical Model and DataDriven model. Among these four modeling methodologies, equivalent and data-driven model is briefly described in the following subsection.

4.19 Types of modeling methods The following flowchart shows the various types of modeling methods available in the literature (Fig. 4.24). The types consist of empirical model, ECM, electrochemical model and data-driven model. The ECM comprises of two SoC estimation models—General Nesting Logit (GNL) and partnership for a new generation of vehicle (PNGV) model. Moreover, data-driven model comprises of machine learning (ML)-based support vector machine model and big data/ML-based extreme Learning Machine model (Meng et al., 2018).

Figure 4.24 Types of battery modeling techniques.

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Microgrids

4.20 Equivalent circuit model The ECMs are based on the electrical circuits and are designed to capture the dynamic characteristics of batteries with the help of sources (voltage/ current), capacitors and resistors. Conventionally, a large capacitor or an ideal voltage source is used to depict the OCV whereas the left over components and parameters depicted the battery’s internal resistance and dynamic characteristics. SoC could be inferred on the basis of OCV model using a lookup table. ECMs are much easier to understand along with the freedom to design the structure as per the application. This method of modeling is widely used in simulating, designing and studying in the fields such as—EVs, battery management system, etc. • Rint Model The Rint model in the following diagram implements the parameters—OCV (VOC), internal resistance (RO) and terminal voltage (Vt). The circuit also employs the load current, I, whose polarity is positive at the point of discharge and negative during charging. Both, VOC and RO are the functions of SoC, State of Health and temperature (Arianto, Yunaningsih, Astuti, & Hafiz, 2016; He et al., 2011) (Fig. 4.25). Mathematically, following equation can be obtained from the figure: Vt 5 VOC 2 ðI 3 RO Þ •

Thevnin Model Thevnin Model is a modified version of Rint Model consisting of parallel RC network in addition to OCV, VOC and internal resistance, RO. With an additional RC element connected in the series with Rint Model, the designing aspect of the dynamic characteristics improvised. The capacitance, C1 , is used to describe the transient response of the battery during charging and discharging, V1 symbolizes the voltage across the RC network and R1 depicts the polarization resistance (Fang, Qiu, & Li, 2017; He, Xiong, & Fan, 2011; Arianto et al., 2016) (Fig. 4.26).

Figure 4.25 Rint model.

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Figure 4.26 Thevnin model.

Mathematically, following equations depicts the electrical behavior of the model: Vt 5 VOC 2 ðI 3 Ro Þ 2 V1 V1 5 2

V1 I 1 C1 R 1 3 C1

•

PNGV Model PNGV model is the modified Thevnin model with an addition of Ccap in series thevnin network. Ccap is a very large capacitance and it helps in describing the variation in OCV with an accumulation of the discharging current I. VC and V1 are the voltage drops across Ccap and RC network respectively. The electrical behavior could be depicted using the following equations (Fotouhi, Auger, Propp, & Longo, 2016; He et al., 2011; Pei, Zhao, Yuan, Peng, & Wu, 2018) (Fig. 4.27): Vt 5 VOC 2 VC 2 ðI 3 Ro Þ 2 V1 VC 5

V1 5 2 •

I Ccap

V1 I 1 C1 R 1 3 C1

GNL Model GNL model is the modified PNGV model and is more accurate due to presence of an additional RC branch connected in the series which helps in accounting the concentration polarization effect of the battery. An additional parameter V1 is introduced which is nothing but the voltage across the second RC element. Electrically the relation could be

130

Microgrids

Figure 4.27 PNGV model.

Figure 4.28 GNL model.

devised as follows (Barcellona & Piegari, 2017; He et al., 2011) (Fig. 4.28): Vt 5 VOC 2 VC 2 ðI 3 Ro Þ 2 V1 2 V2 VC 5

I Ccap

V1 5 2

V1 I 1 C1 R 1 3 C1

V2 5 2

V2 I 1 C2 R 2 3 C2

4.21 Data-driven model Owing to the advancement in technology and evolution of data mining techniques, ML found a way in the modeling aspect of the batteries, thereby making the analysis and modeling much easier. Moreover, relationship(s) could be easily established even without any prior knowledge. This modeling technique is solely related to the historical data measured over a period of time and hence named data-driven model (Tomasov et al., 2019).

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Figure 4.29 Data-driven model.

Fig. 4.29 briefly depicts the three main components of Data-driven model: (1) training data (or database), (2) training process, and (3) battery model. The training data is primarily a database including the data for various parameters, such as—Current (I), terminal voltage (V), temperature (T), SoC. measured or/and calculated. The training data is analyzed and the model is trained using the ML techniques. Finally, the Data-Driven model is formulated which directly reflects and expresses the relationship between the terminal voltage, V and input parameters I, T, and SoC (Li, Li, & Wang, 2019; Li, He, Su, & Zhao, 2020; Energsoft, 2019). Mathematically, the function could be expressed as: f ðV Þ 5 fI; T ; SoCg Though, data-driven model can predict the terminal voltage effectively, its accuracy is highly influenced by the type of training dataset provided. Owing to dependence on the dataset and prior measured reading (or observations), the use of the model gets limited.

4.22 Case study A proposed test microgrid consisting of a solar PV, utility grid, and a battery bank is simulated in MATLAB shown by Fig. 4.30. The sources and the battery energy storage device are connected to the DC bus using their respective power converters. Fig. 4.31 shows the PV power variation with time. It is observed that the PV output is intermittent in nature. It varies in the form of a bell shaped structure. During the initial hours it is zero and it increases till it reaches it maximum point. After that the PV power starts decreasing and eventually reaches zero. Fig. 4.32 shows the variation in load with respect to time. It is observed that initially even when the PV power is zero, load requirement

132

Microgrids

Figure 4.30 Test microgrid.

Figure 4.31 PV power.

is still there. The load power required needs to be served by some other means in the absence of PV power. Hence a battery is connected to the grid which takes care of the load in the absence of PV power. Also, when the PV output is more and load requirement is less, the battery takes the excess power in charging itself. It is this power which is further utilized in the absence of PV power to serve the load. The variation in battery power is shown in Fig. 4.33. Now it can be seen that at t 5 4.2 seconds, battery is turned off. During this time the grid

DC microgrid

133

Figure 4.32 Load power.

Figure 4.33 Battery power.

is connected to the circuit to balance the system. It can be observed from figure d that any extra amount of power generated by the PV is sent to the grid. During this time if load requirement is more as compared to the generated PV power, the deficit amount of power is served by the grid. Fig. 4.34 shows the role of the grid in the test microgrid. It can be observed from the diagram that the grid does not participate when the battery is working. It starts working when the battery is off at t 5 4.2 seconds to t 5 6.4 seconds. When the load power is more than the power generated by the PV, the grid starts participating and delivering its power to the load. While when the power generated by the PV is more than that required by the load, the excess amount of power is sent to the grid. Grid allows bidirectional flow of power.

134

Microgrids

Figure 4.34 Grid power.

4.23 Conclusion This chapter provides an introduction of DC microgrid system in detail. The components of a DC microgrid system are well explained. Different modes of operation of a DC microgrid are further explained in the chapter. Different types of configuration and architecture as well as a detailed description of the components of a basic DC microgrid system are also discussed. A small case study on a DC microgrid system is performed over MATLAB/Simulink. The results show that for the test microgrid, due to the intermittent nature of PV source, dynamic load cannot be served all the time and hence a battery energy storage device and a utility grid are connected to the system.

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147). Available from https://doi.org/10.1109/ICETECT.2011.5760106. PVEducation. IV curve. (2021). ,https://www.pveducation.org/pvcdrom/solar-cell-operation/iv-curve. Accessed 03.02.21. Rai, I., Ravishankar, S., & R, A. (2019). Review of DC microgrid system with various power quality issues in “Real Time Operation of DC Microgrid Connected System”. Majlesi Journal of Mechatronic Systems. Available from http://journals.iaumajlesi.ac.ir/ms/ index/index.php/ms/article/view/410, Accessed 21.03.21. Rajesh, K. S., Dash, S. S., Rajagopal, R., & Sridhar, R. (2017). A review on control of ac microgrid, . Renewable and Sustainable Energy Reviews (Vol. 71, pp. 814
819). Elsevier Ltd. Available from http://doi.org/10.1016/j.rser.2016.12.106. Rosas-Caro, J. C., Ramirez, J. M., Peng, F. Z., & Valderrabano, A. (2010). A DC-DC multilevel boost converter. IET Power Electronics, 3(1), 129
137. Available from https://doi.org/10.1049/iet-pel.2008.0253. Schumm, B. (2021). Battery | electronics. Britannica. ,https://www.britannica.com/technology/battery-electronics. Accessed 23.03.21. Selvan, S. (2013). Modeling and simulation of incremental conductance MPPT algorithm for photovoltaic applications-Indian journals. International Journal of Science, Engineering and Technology, 2 (7), 681
685, Accessed: Feb. 03, 2021. [Online]. Available. Available from https://www. indianjournals.com/ijor.aspx?target 5 ijor:ijset1&volume 5 2&issue 5 7&article 5 013. Shahidehpour, M. (2010). Role of smart microgrid in a perfect power system. IEEE PES General Meeting, 1. Available from 10.1109/PES.2010.5590068. Shen, Y., Qin, Z., & Wang, H. (2018). Modeling and control of DC-DC converters. Control of Power Electronic Converters and Systems (pp. 69
92). Elsevier. SolarDesignTool. Sunpower SPR-305-WHT-U (305W) solar panel. (2021). ,http://www. solardesigntool.com/components/module-panel-solar/Sunpower/514/SPR-305WHT-U/specification-data-sheet.html.. Accessed 03.02.21. Tembo, G. Overview of the microgrid concept and its hierarchical control architecture. (2021). ,http://www.ijert.org.. Accessed 21.03.21. Tomar, A., Mittal, A., & Pattnaik, A. (2021). In A. Iqbal, H. Malik, A. Riyaz, K. Abdellah, & S. Bayhan (Eds.), Renewable Power for Sustainable Growth. Lecture Notes in Electrical Engineering (723). Singapore: Springer. Available from https://doi.org/ 10.1007/978-981-33-4080-0_73. Tomasov, M., Kajanova, M., Bracinik, P., & Motyka, D. (2019). Overview of battery models for sustainable power and transport applications. Transportation Research Procedia, 40, 548
555. Available from https://doi.org/10.1016/j.trpro.2019.07.079. Topan, P. A., Ramadan, M. N., Fathoni, G., Cahyadi, A. I., & Wahyunggoro, O. (2017). State of Charge (SOC) and State of Health (SOH) estimation on lithium polymer battery via Kalman filter. In: Proceedings—2016 2nd international conference on science and technology-computer, ICST 2016 (pp. 93
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165). Springer Singapore. Zschornak, M., et al. (2019). Fundamental principles of battery design. Physical Sciences Reviews, 3(11). Available from https://doi.org/10.1515/psr-2017-0111. Zhou, X., & He, Q. (2015). Modeling and simulation of buck-boost converter with voltage feedback control. 2015 7th International Conference on Mechanical and Electronics Engineering (ICMEE 2015), MATEC Web of Conferences, 31(2015) 10006. Available from https://doi.org/10.1051/matecconf/20153110006.

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CHAPTER 5

Role of dual active bridge isolated bidirectional DC-DC converter in a DC microgrid Anupam Kumar1 and Abdul Hamid Bhat2 1 Modern Institute of Technology and Research Centre, India National Institute of Technology, India

2

5.1 Introduction In the current times of “sustainable energy for all,” the requirement of DC microgrid (Lasseter, 2011; Majumder, Ghosh, Ledwich, & Zare, 2010) has become a necessity. Electricity at cheap cost, without causing pollution or health hazards, and affordable at the same time is the need of the hour. DC microgrid plays a pertinent role in achieving the abovementioned goals and abiding the economic and environmental constraints. DC microgrid is basically a power system designed for integration of renewable energy sources (Guerrero, Berbel, Matas, Sosa, & De Vicuña, 2007; In WSEAS Transactions on Systems & Control, 2015) with storage elements and works at the distribution network. Various control strategies broadly categorized into three categories: centralized, decentralized, and distributed. Master
slave control is the most common strategy utilized in centralized control strategy in which one of the distributed generators (DGs) acts as the master and rest of the DGs act as slaves. In this approach the master DG acts as a voltage source and carries on the voltage regulation. For secondary and tertiary control of DC microgrid, distributed control strategy is used. On the basis of consensus protocol, several algorithms are developed for improving power share and DC voltage regulation. In decentralized control strategy, the communication is totally ignored. As the microgrid consists of nonconventional sources of energy and energy storing elements, requirement of a power electronics interface (Bayoumi, 2014; Mi, Bai, Wang, & Gargies, 2008) becomes unavoidable. The importance of power electronics
based energy conversion system Microgrids DOI: https://doi.org/10.1016/B978-0-323-85463-4.00006-X

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that facilitates energy transfer in both the directions (forward as well as backward) owning high efficiency has increased manifolds in DC microgrids. As the power electronics
based energy conversion system finds its application in DC microgrid, it has to be light in weight, has high power density and reduced switching losses. Applications utilized in industries based upon power electronics (electric vehicle, transformers working on solid-state devices) are finding isolated bidirectional DC
DC converters (De Doncker, Divan, & Kheraluwala, 1991; In Electromotion Scientific Journal, 2014; In Operation Design & Control of Dual H-Bridge-Based Isolated Bidirectional DC-DC Converter, 2008; Zhao, Song, Liu, & Sun, 2014) very useful and are incorporating these in their central power unit ( Jeong, Kim, Baek, Kim, & Kim, 2016). In the present chapter the elementary structure and execution of DC microgrid is discussed. Modeling and composition of dual-active bridge (DAB) converter along with the converter waveforms in both the modes (forward as well as reverse) are presented. Optimum operating range for DAB using fuzzy logic
based single-phase shift (SPS) control strategy is decided. Various performance parameters [battery voltage, battery current, plot of state of charge (SOC) for battery, and converter efficiency] of DAB converter working in SPS mode for both the forward and reverse directions of power flow are shown. The working of DAB as a power electronics interface for facilitating bidirectional power in DC microgrid is discussed. The SPS control strategy of converter operation along with simulation results is presented. Validation of closed-loop controller is validated by varying the load, as the load is varied, the voltage at the load end is kept at the reference value. The increment in the load is met by discharging the battery on the LV (low-voltage) side. Mathematical calculations are carried out for designing the DC capacitors.

5.2 Microgrid Microgrids can be defined as power systems designed for integration of renewable energy sources with storage elements and work at the distribution network. Various control strategies are broadly categorized into three categories: centralized, decentralized, and distributed. In centralized control strategy the master DG acts as a voltage source and carries on the voltage regulation. In this strategy the current supplied by master is measured and used for setting the reference for slave DGs. Voltage regulation is performed by the master and is centrally coordinated

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by the master DG and thus is totally independent of slave DGs. The only disadvantage of this approach is that the voltage regulation is deteriorated if any fault occurs in master DG. In this approach the master DG acts as the voltage source, while the slave DGs act as current sources. For secondary and tertiary control of DC microgrid, distributed control strategy is used. On the basis of consensus protocol, several algorithms are developed for improving power share and DC voltage regulation. These algorithms improve voltage regulation and facilitate power sharing across the microgrid. Although this control strategy is dependent on communication network, still this method does not fall in centralized category. In this strategy a voltage correction term is generated by the voltage regulator for adjusting the voltage set point in droop mechanism. The algorithm will not converge if intermittent information or considerable unwarranted delays are presented by the communication network. In AC systems the active power is controlled by changes in frequency, while the reactive power flow is governed by the changes in the voltage magnitude. The DC version of this method is used for reducing circulating current and facilitating load sharing in a decentralized manner. Furthermore dynamic gain droop control is the basis of many advanced decentralized strategies that are used for accurate voltage regulation. A diagrammatic representation of grid-connected DC microgrid is shown in Fig. 5.1.

5.3 Dual-active bridge converter DAB converter is a bidirectional converter introduced for the purpose of bidirectional power flow with improved efficiency, reduced size, and low cost (Hofer, Svetozarevic, & Schlueter, 2017). DAB is controlled in various phase-shifted modulation schemes of which SPS is the most widely used and studied. A comprehensive comparative analysis of DAB operating in SPS and extended phase shift mode based upon its application as solid-state transformer is shown in the study of Kondrath (2017). It can be observed from the schematic diagram of DAB converter shown in Fig. 5.2 that two DC/AC converters are connected through a high-frequency linking transformer. The inductance value of primary side combined with the referred secondary side inductance value forms the inductor in the DAB. The operating duty ratio of each bridge is 50%. The line diagram of DC microgrid studied in the present work is shown in Fig. 5.3.

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Figure 5.1 Schematic diagram of grid-connected DC microgrid.

Figure 5.2 Diagrammatic representation of dual-active bridge converter.

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LOAD HV bus LV bus DC-AC Converter Solar PV Array

DC-DC Converter

Battery DAB Converter

Figure 5.3 Dual-active bridge converter as an energy conversion interface in DC microgrid.

It consists of a renewable energy source (e.g., solar photovoltaic [PV] array) injecting power at the high-voltage (HV) bus. The power electronics interface (DAB) is connected between the HV bus and LV bus. The load is connected at the HV bus. The PV array has a maximum power rating of 1000 VA. The battery storage system consists of two batteries connected in series each having a voltage rating of 34 V. From the previous modeling of DAB (Kumar, 2019) operating in SPS mode, the equations of inductor current are derived as i LðtÞ 5



N

 4X 1 Vin Vout Np sinð½2n 1 1 ωs t 2 φ 2 θn Þ sinð½2n 1 1ωs t 2 θn Þ 2 π n50 ½2n 1 1 Z ½n Z ½n Ns

pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi |Z[n]| 5 RL 2 1 ð½2n11ωs LÞ2 , Impedance. θn 5 tan21

(5.1) here

½2n 1 1ωs L RL

Z[n] 5 nth

harmonic

(5.2)

where Vin(t) is voltage at the input side, Vout (t) is voltage at the output side, iL is inductor current, RL is resistance of leakage inductance, L is leakage inductance, Np is primary side winding turn number, and Ns is secondary side winding turn number. The phase shift of the voltage at the primary side and the voltage at the secondary side is [. ωs is the switching frequency of the square wave. The normalized transmitted power is defined as P NV1 V2 Pt 5 4Dð1 2 DÞ 5 where PN 5 (5.3) PN 8Lfs

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1 0.8

pt

0.6 0.4 0.2 0 1 0.5 D1

0

0

0.2

0.4

0.6

0.8

1

D2

Figure 5.4 3D graph of unified transmitted power with duty ratio D1 5 D2 5 D.

The three-dimensional graph of this normalized transmitted power is plotted in Fig. 5.4. The converter characteristics for DAB working in forward conduction mode (power flow from primary to secondary) for SPS control mode is plotted in Fig. 5.5. As shown in this figure, the transformer secondary voltage leads the primary voltage by a certain phase angle which causes power flow in the forward direction, that is, from HV to LV DC bus. Similarly, for the reverse conduction mode, the waveforms can be observed in Fig. 5.6. As shown in this figure, the transformer primary voltage leads the secondary voltage by a certain phase angle which causes power flow in the forward direction, that is, from LV to HV DC bus.

5.3.1 DAB parameter design The design of DAB consists of selection of input and output capacitors that are discussed in this section. Cinput 5

ðΔIL Tsw Þ 8ΔVo

(5.4)

where Cinput is capacitor value for the input side, ΔIL is inductor current ripple (peak-to-peak value), Tsw is total time period (1/fsw ), and fsw is switching frequency. Now taking fsw 5 20,000, ΔVo 5 1% of 35 V (output voltage) 5 0.35 V, ΔIL # 20% (15.6), we get

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300

(A) Primary Voltage Vp

-300

Time(sec) 35

(B) Secondary Voltage

Vs

-35

Time(sec) 4

(C) Inductor Current Il 0 -2

t0 t1

t3 Time(sec)

t2

t4

t5

Figure 5.5 (A) Bridge voltage for primary side, (B) bridge voltage for secondary side, and (C) current through the inductor for forward power conduction mode.

313

(A) Primary Voltage Vp

-313

Time(sec) 28

(B) Secondary Voltage Vs

-28

Time(sec) 4

(C) Inductor Current

Il 0

-4

t0 t1

t2

t3

Time(sec) t4

t5

t6

Figure 5.6 (A) Bridge voltage for primary side, (B) bridge voltage for secondary side, and (C) current through the inductor for reverse power conduction.

15:6 3 1 15:6 Cinput 5 20;000 3 8 3 0:35 5 56;000 5 278:57 μF; we are taking it at 300 μF for slight less ripples

Coutput 5

ðIo DTsw Þ ΔVo

(5.5)

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Coutput is capacitor value for the output side, Io is current value for the output side, D is duty ratio pertaining to outer phase shift among the two sides, Tsw is total time period (1/fsw ), fsw is switching frequency, ΔVo is output voltage ripple (peak-to-peak value). Now taking fsw 5 20; 000, ΔVo 5 1% of 35 V (output voltage) 5 0.35 V, ΔIL # 20% (15.6), we get 3 0:94 3 1 16:45 Coutput 5 17:5 20;000 3 0:18 5 3600 5 4569:44μF; Coutput is taken as 4500 μF.

5.4 Fuzzy logic controller Remedies for tedious and lengthy mathematical problems are obtained using fuzzy logic controller (FLC). Due to its uncomplicated and elegant nature, fuzzy logic is used in huge and complicated systems for which mathematical models are difficult to obtain. One of the major advantages of controller based upon fuzzy logic is that it very easily reflects the thinking of a human mind and it is highly susceptible to changes in system. Controller incorporating fuzzy logic utilizes Sugeno fuzzy method. This inference reduces the calculation and processing burden and adds versatility to the controller and makes it preferable over other fuzzy inference. The schematic diagram of proposed controller is shown in Fig. 5.7. In the closed-loop controller, a comparison between the output voltage with reference voltage is carried on, from the comparison the error is generated. The differential of error and error are taken as the input for FLC. Voref

+

Fuzzy Logic controller

-

Vo

Sawtooth Wave at 20KHz -

Phase shift modulator

+

A Constant wave of magnitude 0.5 Control pulses

Figure 5.7 Controller based upon fuzzy logic.

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The FLC’s output along with a square wave of frequency 20 kHz is given to a phase shift modulator. The components of FLC are sequentially discussed next. Fuzzification: Fuzzification involves conversion of input parameters [error (e) and variation in error (de)] and resulting parameters into fuzzified signals. The degree of belonging of these signals leads to their categorization or recognition in fuzzy sets. Linguistic variables resulting from these lead to the selection of two fuzzy levels listed next: 1. zero 2. positive big The number of rules is reduced to 4 by the two fuzzy levels; thus the computational burden on the system is eased. Decision-making: Constant output membership functions are obtained from Sugeno implication method. As there is a reduction in membership functions (two functions for error and two for change in error), only a total of 4 (2 3 2) rules are possible. Table 5.1 shows the fuzzy logic rules implied in the controller. Defuzzification: Solution parameters are obtained from the controller in the form of linguistic parameters. Defuzzification is the reversal of fuzzification. Defuzzification involves center of gravity method. The whole DC microgrid controller based upon the previously discussed FLC is shown in Fig. 5.8.

5.5 Performance evaluation 5.5.1 Single-phase shift technique DAB converter is simulated in MATLAB/SIMULINK and SimPowerSystems software. Solar PV array is connected at the input side/ HV of the DAB converter. Energy storage element (battery) is connected at the LV side. The connection of load is carried out at the HV bus. Table 5.2 specifies the system parameters of the simulation. Table 5.1 Fuzzy logic controller rule base. Error (e)\change in error (de)

ZE

PB

ZE PB

ZE PB

PB PB

PB, Positive big; ZE, zero.

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Figure 5.8 Schematic diagram of DC microgrid control.

Table 5.2 System parameters.

Voltage at the input side (Vin) Voltage at the output side (Vout) Capacitor value at the input side (Cin) Capacitor value at the output side (Cout) Leakage inductor (L) Transformer turn ratio (N) SOC of battery Pin (VA) Pload (VA) Power stored in battery (VA) Load voltage (V) Efficiency (%)

SPS (forward)

SPS (reverse)

95.65 V (HV side) 66.68 V (LV side) 300 μF 4500 μF 450 μH 300:100 90% 975.13 915.52 38.6 95.64 64.75

65.56 V (LV side) 95.23 V (HV side) 4500 μF 300 μF 450 μH 100:300 90% 973.56 1062.75 115.4 95.23 77.28

HV, High-voltage; LV, low-voltage; SOC, state of charge; SPS, single-phase shift.

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5.5.2 Forward conduction mode The voltage of the solar PV array connected at the input/HV side is maintained at 95.65 V for forward conduction mode. The battery voltage at the LV side is kept at 66.68 V. The transformer transformation ratio is 300:100 during the forward power flow mode. The power injected by PV array is 975.13 VA. The power injected is plotted in Fig. 5.9. Power of 38.6 VA is stored by the battery and the load requirement is 915.52 VA in forward power flow mode. Battery characteristics are plotted in Fig. 5.10. As the battery SOC rises during the forward conduction mode, it can be established that the power is being sent from the primary side to the secondary side of converter. Also the battery current is having a negative value establishing the charging of battery. The voltage of battery bank is 66.68 V and the voltage of single battery is 33.34 V.

5.5.3 Reverse conduction mode The battery bank at the LV side is kept at 65.5 V and a power of 115.5 VA is being delivered in reverse conduction mode. The PV array injects 973.56 VA from the HV side. For the reverse power flow mode, 100:300 is the transformation ratio. Due to the variation in load, the load power demand changes to 1062.75 VA. Thus the converter delivers a power of 89.19 VA. As the battery SOC decreases in this mode, it can be established that the power is being sent from the secondary/LV side to the primary/HV side of converter. Thus the suitability of DAB as a power

Figure 5.9 Injected power of solar PV array. PV, Photovoltaic.

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Figure 5.10 Battery characteristics on the LV side. LV, Low-voltage.

Figure 5.11 Battery power characteristics.

electronics interface for bidirectional power flow is established. The DC microgrid comprising solar PV array as the source connected at the input side of converter and battery bank as the storage element connected at the secondary side of DAB converter is tested in MATLAB environment. Battery power characteristics are plotted in Fig. 5.11. The performance of controller is plotted in Fig. 5.12. The load voltage is kept constant at 95 V despite a step change in load current.

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Figure 5.12 Load characteristics for DAB converter. DAB, Dual-active bridge.

Figure 5.13 Laboratory setup of OPAL-RT with host PC and DSO. DSO, Digital storage oscilloscope.

5.6 Experimental verification The MATLAB/Simulink results obtained for DAB are validated with a real-time digital simulator (OPAL-RT). The OPAL-RT in which the proposed system is tested is shown in Fig. 5.13. Real-time simulation results for DAB operating in SPS mode depicting converter waveforms is shown in Fig. 5.14. The phase shift is evident

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Figure 5.14 DAB converter waveforms for forward power flow mode. DAB, Dualactive bridge.

Figure 5.15 DAB converter waveforms for reverse power flow mode. DAB, Dualactive bridge.

from the waveforms depicting power flow from primary to secondary side. Similarly, the converter waveforms for SPS mode in the reverse conduction direction are presented in Fig. 5.15.

5.7 Conclusion It can be concluded that DAB converter operating in SPS controlled with controller based on fuzzy logic implying reduced rule base is suitable for application as a power electronics interface in DC microgrid. The converter efficiency in forward direction is 64.75%, while the converter efficiency is 77.28% in the reverse direction.

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References De Doncker, R. W. A. A., Divan, D. M., & Kheraluwala, M. H. (1991). A three-phase soft-switched high-power-density DC/DC converter for high-power applications. IEEE Transactions on Industry Applications, 27(1), 63
73. Available from https://doi. org/10.1109/28.67533. Guerrero, J. M., Berbel, N., Matas, J., Sosa, J. L., & De Vicuña, L.G. (2007). Droop control method with virtual output impedance for parallel operation of uninterruptible power supply systems in a microgrid. In: Conference proceedings—IEEE applied power electronics conference and exposition—APEC (pp. 1126
1132). Available from https:// doi.org/10.1109/apex.2007.357656. Hofer, J., Svetozarevic, B., & Schlueter, A. (2017). Hybrid AC/DC building microgrid for solar PV and battery storage integration. In: 2017 IEEE 2nd international conference on direct current microgrids, ICDCM 2017 (pp. 188
191). Institute of Electrical and Electronics Engineers Inc. Available from https://doi.org/10.1109/icdcm.2017. 8001042. Bayoumi, E.H.E. (2014). Dual-input DC-DC converter for renewable energy, Electromotion Scientific Journal, (Vol. 21, Issue 1
2, pp.77
84). Mi. C, Bai.H, Wang. C, Gargies. S . (2008). Operation, design and control of dual Hbridge-based isolated bidirectional DC-DC converter ,IET Power Electron., (Vol. 1, Issues 4, pp. 507
517). In Electromotion Scientific Journal (2014) (Vol. 21, Issues 1
2, pp. 77
84). In Operation Design and Control of Dual H-Bridge-Based Isolated Bidirectional DC
DC Converter (2008) (Vol. 1, Issue 4, pp. 507
517). In WSEAS Transactions on Systems and Control (2015) (Vol. 10, pp. 493
502). Jeong, D. K., Kim, H. S., Baek, J. W., Kim, J. Y., & Kim, H. J. (2016). Dual active bridge converter for energy storage system in DC microgrid. In: 2016 IEEE transportation electrification conference and expo, Asia-Pacific, ITEC Asia-Pacific 2016 (pp. 152
156). Institute of Electrical and Electronics Engineers Inc. Available from https://doi.org/ 10.1109/itec-ap.2016.7512939. Kondrath, N. (2017). Bidirectional DC
DC converter topologies and control strategies for interfacing energy storage systems in microgrids: An overview. In: 2017 5th IEEE international conference on smart energy grid engineering, SEGE 2017 (pp. 341
345). Institute of Electrical and Electronics Engineers Inc. Available from https://doi.org/ 10.1109/sege.2017.8052822. Kumar. (2019). A single phase shift based isolated bidirectional DC
DC converter for bidirectional energy transfer in DC-microgrid with identification of optimum operating zone. International Journal of Industrial Electronics and Drives, 5. Lasseter, R. H. (2011). Smart distribution: Coupled microgrids. Proceedings of the IEEE, 99 (6), 1074
1082. Available from https://doi.org/10.1109/jproc.2011.2114630. Majumder, R., Ghosh, A., Ledwich, G., & Zare, F. (2010). Power management and power flow control with back-to-back converters in a utility connected microgrid. IEEE Transactions on Power Systems, 25(2), 821
834. Available from https://doi.org/ 10.1109/tpwrs.2009.2034666. Zhao, B., Song, Q., Liu, W., & Sun, Y. (2014). Overview of dual-active-bridge isolated bidirectional DC
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SECTION IV

Hybrid AC/DC microgrids

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CHAPTER 6

Introduction to hybrid AC/DC microgrids Shivani Mishra and R.K. Viral Department of Electrical & Electronics Engineering, Amity School of Engineering and Technology, Amity University, India

6.1 Introduction Due to the global initiatives, the renewable energy system has been developed and used as a renewable power generating system. This type of system is capable of generating electricity by the use of more than one renewable energy sources (Jia, Zhu, Du, & Wang, 2018). (“Autonomous Control of Interlinking Converter with Energy Storage in Hybrid ACDC Microgrid,” 2013). The most commonly used hybrid renewable energy systems are solar and wind. There is also a different combination of renewable energy sources for the generation of hybrid power because of the intermittent nature of the storage unit for working as a standalone system. Different control techniques are used for the hybrid renewable energy system for transferring resourceful power. The design of hybrid system depends on which types of renewable energy are taken, their conversion, and the use of converters in different locations. Hybrid energy structures are designed as per the location, and its size depends on the demand profile by the consumer of that location. Weather data also play an important role (Mohamed, Shaaban, Ismail, Serpedin, & Qaraqe, 2019; Nejabathhah, Li, & Tian, 2019; Nejabatkhah & Li, 2015; Satish Kumar, Chandrasena, Ramu, Sreenivas, & Victor Sam Moses Babu, 2017). (“An Efficient Planning Algorithm for Hybrid Remote Microgrids,” 2019; “Energy Management System for Small Scale Hybrid Wind Solar Battery Based Microgrid,” 2020; “Overview of Power Management Strategies of Hybrid AC/DC Microgrid,” 2015; “Power Quality Control of Smart Hybrid AC/DC Microgrids: An Overview,” 2019) Tariff-based policies are recently introduced in India in which competitive bidders bid for lowest energy price. In this policy, consumers are benefited to get electricity at lowest price but it is used in generation only. Microgrids DOI: https://doi.org/10.1016/B978-0-323-85463-4.00005-8

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Transmission cost in India is still in higher side, which increases the price of electricity at consumer end (Zhou et al., 2020). (“Compartmentalization Strategy for the Optimal Economic Operation of a Hybrid AC/DC Microgrid,” 2020) To resolve the above problem generation should be nearby loads (consumer end). This phenomenon will reduce the electricity cost at consumer end where transmission charges will be very negligible. Hybrid micro-grid will be a good approach to deliver cost effective electricity at remote location like: mine sides, remote villages, islands etc. and individual building (Som & Chakraborty, 2014). (“Studies on Economic Feasibility of an Autonomous Power Delivery System Utilizing Alternative Hybrid Distributed Energy Resources,” 2014)

6.1.1 Hybrid micro-grid Hybrid micro-grids are defined as per the amalgamation of distribution generation sources within defined boundary, which is interconnected with consumers load. In the hybrid micro-grid uses of natural resources at nearby load centers which reduce the cost of electricity as well as consumption of fossil fuel like: coal etc. The transmission cost is also very low. Due to lesser line length the line losses will be negligible (Mohamed et al., 2019). (“An Efficient Planning Algorithm for Hybrid Remote Microgrids,” 2019) There are AC micro-grid, DC micro-grid and hybrid AC/DC micro-grid. In AC micro-grid, AC supply is given to the consumer via. AC sub-grid in which all the generated power is converted into AC form by direct or by use of converters. DC micro-grid, DC supply is connected through consumer via. giving supply in DC form for the transmission purposes, converting DC supply into AC to supply residential consumers and others (Jia et al., 2018). (“Analysis of the Transition Between Multiple Operational Modes for Hybrid AC/DC Microgrids,” 2018) (Ahmadi and Kazemi, 2020). Hybrid micro-grid using of AC and DC supply were used to provide electricity to the consumer by supplying residential consumer (AC load) through AC sub-grid and DC load through DC sub-grid. Hybrid micro-grid having advantages in terms of quality of power and reliability (Hamad, Azzouz, & El Saadany, 2016). (“A Sequential Power Flow Algorithm for Islanded Hybrid AC/DC Microgrids,” 2016) The hybrid micro-grid is designed using renewable energy sources such as solar PV array, wind turbine, biomass energy, and BES (Battery energy storage) as shown in Fig. 6.1. By these natural resources electricity

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Figure 6.1 Basic diagram of hybrid micro-grid with renewable energy sources (Parida & Choudhury, 2018). (“Microgrid Based Hybrid Energy Co-Operative for GridIsolated Remote Rural Village Power Supply for East Coast Zone of India,” 2018).

is generated, solar system and wind turbine are the renewable energy system which cannot be backed down (or controlled) because of its nature of resources but the biomass which is flexible resource is to be shut down in the time of exceeding demand. The excess generation is used for charging the battery and its deposited energy is utilized once the lower generation is found by these natural resources (Duan et al., 2019; Karimi, Oraee, Golsorkhi, & Guerrero, 2017). (“Decentralized Method for Load Sharing and Power Management in a PV/Battery Hybrid Source Islanded Microgrid,” 2017; “Reinforcement-Learning-Based Optimal Control of Hybrid Energy Storage Systems in Hybrid AC/DC Microgrids,” 2019). The battery energy storage system is very economical for the system because when there is off-peak hour it stored the excess amount of electricity and when there is peak hour then it supplies the stored energy to the system to fulfill the system requirement of electricity. The micro-gird is designed for self-sufficient criterion in this condition main-grid not connected to the system. The electrical energy engendered by the PV system and wind turbine depends on atmospheric condition and batteries uses with system for storage of energy in long-term basis. In the power electronic system ultra-capacitors are used for the storage of energy on the short-term basis because it can charge and discharge in an instantaneous condition. While the designing and installation of micro-grid, distributed generations (DG’s) are based on the inverter which having the very lowinertia that causes the high magnitude of transients which makes the operation unstable. Due to fast response of the inverter-based sources require the time for response time for the transient by it ensures the supply of dynamic load regardless of slow rotating machine (Peyghami, Mokhtari, & Blaabjerg, 2018). (“Autonomous Operation of a Hybrid AC/DC Microgrid with Multiple Interlinking Converters,” 2018)

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6.1.2 The topographies of hybrid micro-grid (Che, Shahidehpour, Alabdulwahab, & Al-Turki, 2015; Li & Shahidehpour, 2019; Mahmood & Jiang, 2018; Mohamed et al., 2019) 1. Amalgamation of renewable energy sources is used in micro-grid for electricity generation with the interconnected loads 2. Micro-grid work as grid connected and also as in island mode 3. Micro-grid work as a single controlled entity 4. Micro-grid is designed as per the total energy requirement of the system.

6.1.3 Need of hybrid micro-grid (Aprilia, Meng, Hosani, Zeineldin, & Dong, 2019; Eghtedarpour & Farjah, 2014; Karimi, Oraee, & Guerrero, 2017; Nejabathhah et al., 2019; Patel, Mohanty, & Hasanien, 2020; Xiao, Luo, Shuai, Jin, & Huang, 2016) 1. It is designed to fulfill the local or remotely located consumer demand 2. When it connects with the main grid, the reliability of the main grid enhances 3. It enhances reliability of electrical supply by enhancing the stability of grid with the minimized cost of electricity 4. It is the integration of distributed energy system with energy storage system by which system reliability increases with the system intelligence 5. Hybrid micro-grid the group renewable sources is to be substance of smart grid.

6.1.4 Comparison between conventional grid and hybrid micro-grid To improve the electricity resources of any country, there is a need of improvement in all the three sectors: generation, transmission, and distribution. Previously, conventional grids were used to provide electricity to the consumer, but various issues are associated with the use of conventional grids such as customer satisfaction, power quality, capacity generation, and asset identification. To resolve such issues, hybrid micro-grid is used to enhance consumer centric approach with the improvement of power quality and power loss. Table 6.1 provides a comparison between conventional grids and hybrid microgrids.

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Table 6.1 Comparison between conventional grid and hybrid micro-grid (Duan et al., 2019; Farhadi & Mohammed, 2015; Loh, Li, Chai, & Blaabjerg, 2013; Mohamed et al., 2019; Nejabathhah et al., 2019; Nejabatkhah & Li, 2015; Satish Kumar et al., 2017). S. no.

Characteristic

Conventional grid

Hybrid micro grid

1

Customer involvement

Uniformly customer distributed and no involvement

2

Dominated by central generation

3

Generation and capacity building Depend on central generation Power quality

Informed and customer actively participated Many distributed generation systems interlinked Power quality is top priority

4

Optimize assets

5

Self-healing

6

Natural disaster

Focus on outages and less response on power quality Depend on regulatory commission minimize on consumer effect Depend on protective devices Very heavy effect

Minimizing impact on consumer Self-healing process available Islanding option available

6.2 Architecture of hybrid micro-grid Architecture of hybrid micro-grid defines how generating stations of renewable energy sources are connected with the loads like residential, commercial, community etc. The renewable energy sources are connected through AC bus, DC bus, AC and DC bus by which supply is given to all the required consumers. The simple architecture design is as shown in Fig. 6.2, each grid is connected through the star/delta transformer further a circuit breaker is connected through three phase four wire micro-grid but in Fig. 6.2, circuit breaker is open and the system work as island micro-grid (Karimi et al., 2017). (“Decentralized Method for Load Sharing and Power Management in a Hybrid Single/Three- Phase Islanded Microgrid Consisting of Hybrid Source PV/Battery Units,” 2017) The source of the unit is depending only on PV, wind, small-hydro,

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Figure 6.2 Typical single and three phase Hybrid micro-grid.

battery or the hybrid system renewable sources with battery. Three-phase and single-phase units are connected with the system and loads are also connected with the phase through which electricity is supply to loads. The different architectures are:

6.3 Architecture of AC-coupled hybrid micro-grid In AC micro-grid system, single AC bus is used for the transfer of electricity form one position to another position. For the conversion of DC supply to AC supply and vice-versa can be done by using the bi-directional converter. Hence in this system no alternative supply point available at the time of any fault occur in bus (Loh et al., 2013; Loh, Li, Chai, & Blaabjerg, 2013). (“Autonomous Control of Interlinking Converter with Energy Storage in Hybrid AC-DC Microgrid,” 2013) Fig. 6.3 shows, that renewable energy sources are linked through AC bus, storage system is connected through the bidirectional converter (Xiao et al., 2016). (“An Improved Control Method for Multiple Bidirectional Power Converters in Hybrid AC/DC Microgrid,” 2016) because of charging and discharging conditions. The sources like: wind, biomass and hydro are linked through AC bus directly as for generation of electricity in AC form. The generated electricity supplies the AC load and work as isolated grid and it work as grid-connected mode when connects with the utility grid (Kalla, Singh, Murthy, Jain, & Kant, 2018; Nejabatkhah & Li, 2015). (“Adaptive Sliding Mode Control of Standalone Single-Phase Microgrid Using Hydro, Wind and Solar PV Array Based Generation,” 2018; “Overview of Power Management Strategies of Hybrid AC/DC Microgrid,” 2015)

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Figure 6.3 Hybrid AC micro-grid.

Figure 6.4 Hybrid DC micro-grid.

6.4 Architecture of DC-coupled hybrid micro-grid In the DC micro-grid, electricity is transferred form one position to another via DC bus. The conversion of electricity from DC to AC is done by converter due to the presence of single bus (Loh et al., 2013; 2013). (“Autonomous Control of Interlinking Converter with Energy Storage in Hybrid AC-DC Microgrid,” 2013) When there is any occurrence of fault no alternative supply is available in this system. In Fig. 6.4, the renewable energy sources solar PV and wind with converters connecting with DC bus to supply the DC loads and by the application of inverter it also supplies electricity to the AC loads. This system works as both isolated and grid-connected mode (Nejabatkhah & Li, 2015)

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(“Overview of Power Management Strategies of Hybrid AC/DC Microgrid,” 2015)

6.5 Architecture of AC-DC coupled hybrid micro-grid In AC-DC micro-grid, micro-grid is connected with the AC and DC buses. AC and DC buses are linked over bi-directional converters. The AC bus system is linked through distribution system via. transformer. The AC and DC loads are entre through power electronic converters in micro-grid. The system operates through the isolating switches at point of common coupling (PCC) and bi-directional converters. Operations which can be executed are (Farhadi & Mohammed, 2015; Gupta, Member, Doolla, & Chatterjee, 2018). 1. Operating process through grid-connected mode 2. DC system work as islanded mode when AC operated as grid-connected mode and the bi-directional converter is in switch-off mode 3. AC and DC operates as an islanding mode when the PCC switch is off 4. AC and DC switches operates independently, when PCC isolating switches and bi-directional converters are off. The assembly in Fig. 6.5 shows the AC/DC hybrid micro-grid in which both AC and DC have both storage elements (SEs) and distribution generations. Both the buses are linked with the interlinking converter’s (ILC’s). The system requires extra synchronization in voltage and control of power between AC and DC subsystems. The ILC’s are designed to

Figure 6.5 Hybrid AC/DC micro-grid.

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link with the AC and DC buses which used to increase reliability and capacity of the hybrid system (Eghtedarpour & Farjah, 2014; Li & Shahidehpour, 2019; Nejabatkhah & Li, 2015). With the reduced number of power converters which is connected through source and load to AC and DC buses by means of minimized requirement of conversion of power to reduces the system cost and improves the efficiency of the overall system (Xiao et al., 2016). For AC and DC power sources the wind turbines and solar systems are used towards supply to the AC and DC grid. Micro-grid system contains two types of control architecture. a. Source control b. Load control

6.6 Modeling of hybrid micro-grid components 6.6.1 PV system model Photovoltaic system is a design in which one or more solar panels are connected with each other with the inverter and additional electrical equipment that system uses the sun’s energy and convert it into electrical energy. It works as a power system. Solar panels are used to absorb the sun light, inverter converter the generated DC electricity into AC form and supply to the consumer. In the solar panel, solar tracking system are used which improve the system performance. The photovoltaic system is allied with battery which is used to store excess amount of energy into battery. It uses to generate the electrical energy from few kW to hundreds of MW. A photovoltaic system is work stand-alone system as well as grid-connected system. Importance of the system that it does not harm the environment. Fig. 6.6 defines the basic operating structure of Solar PV system. The process of that model is the extract solar radiation and supply the generated electricity to the consumer as per their need whether in DC or AC form (Krishan & Suhag, 2019; Guan et al., 2015). The model can also

Figure 6.6 Solar PV model.

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work as grid connected mode, when the system is proficient for generating and storing the excess amount of electricity. The output of solar PV cell depends on the temperature and irradiance in the solar PV cell. By the application of irradiance solar PV characteristics is affected. In Fig. 6.7 the generation of electrical power depends upon the cell current I, I-V characteristics of solar cell identifies current and voltage at maximum power point. Eq. (6.1) I 5 IL 2 I0

½expðqVoc =AKT Þ 2 1 2 Voc =Rsh

(6.1)

where I 5 Cell current, I0 5 Current of diode in reverse saturation, k 5 Boltzmann’s constant (13,807 10 kJ), q 5 Electronic charge (1.6022 1019 C), T 5 Temperature of cell, Rsh 5 Shunt resistor, Rs 5 Series resistor.

6.6.2 Wind energy system model Wind energy is the combination of one or more wind turbines by which mechanical energy is converted to electrical energy through electrical power transmission. Wind turbines can be established in the onshore landscape of wild or rural areas and in offshore areas but offshore farms construction and maintenance is higher in comparison to onshore wind farms. Wind turbine can produce the electricity which is variable nature as per recorded the wind speed day by day but consistent year by year. The production of electrical energy which is generated from the wind farms can supply the electricity to the grid or work as isolated grid. In Fig. 6.8, By the weather forecasting it predict average wind speed which is require to establish the wind farms (Zhou, Wang, & Ge, 2019). The wind turbine system is connected with the battery energy storage system by which access amount of electricity stored

Figure 6.7 Basic model of solar PV system.

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Figure 6.8 Wind energy model.

and supply to the area when there is deficiency in wind energy. The production of wind energy does not harm the environment. For the conversion of wind energy into electricity, there system consists generator. Charge controller and battery. By the generation of electricity, it will provide directly to AC load and for DC loads through converter. Extraction of power PWIND from the wind turbine (Satish Kumar et al., 2017), Eq. (6.2) P WIND 5 0:5Cp Aρv 3

(6.2)

where Cp 5 power coefficient, ρ 5 density (kg/m3), A 5 swept area (m2), ν 5 velocity of wind (m/s).

6.6.3 Biomass energy model Biomass has stored energy from sun. This energy is generated from the plant (by photosynthesis process plants absorb the sun energy) or animal waste which can be burnt and the gas is formed which is called biogas (or ethanol) if generated in the form of liquid is called as biofuel (or bio-diesel). In Fig. 6.9, the raw biomass product like: wood, cow dung etc. are used and this product gone through the CHP system by which the convertible product is used for the production of electricity and it can also use in the form heat.

6.6.4 Small-hydro system model Mini hydro power plant is used in hybrid micro-grid. It is used where there is abundance of water resources or creation of water canal/linkage of river. The construction cost of hydro power plant is high but operation and maintenance cost is almost zero. This power plant is very useful due to fuel cost not involved. Its efficiency is very good (Singh & Balchandra, 2019). This type of power plant is also useful to provide back-up at the time of shut-down. Due to the availability of water resources 24 3 7, it’s not depended on day and night scenario.

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Figure 6.9 Biomass energy model.

Figure 6.10 Small-hydro system model.

In the hydro power plant, Fig. 6.10 converts the pressure of head water into the speed governor through penstock turbine and then the mechanical power is converted to electricity after going through the generator, then the generated electricity is used for applications in power system (Jaszczur, Hassan, Palej, & Abdulateef, 2020). Eq. (6.3) P 5 m 3 g 3 Hnet 3 η

(6.3)

where P 5 power in watts, m 5 rate of mass flow in kg/s, g 5 gravitational force is 9.81m/s2, Hnet 5 Hgross 3 0.9, η 5 efficiency.

6.6.5 Battery model Parameters that define the state of battery is defined by SOC (state of charge) and terminal voltage Vb, Eqs. (6.4) and (6.5)    ð  ð Vb
Vo 1 Rb :ib
K Q=Q 1 ib dt 1 A:exp B ib dt (6.4)   ð SOC 5 100 1 1 ib dt=Q

(6.5)

where Rb 5 internal resistance of battery, Vo 5 open circuit voltage of battery, ib 5 battery charging current, Vb 5 polarization voltage, Q 5 battery capacity.

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6.6.6 Fuel cell model When there is the existence of contact of different material than there is a charge transfer from one to other. When there is abrupt change in current then it takes some time to response due to double layer charge form voltage. Capacitance C is functions as electrode with slack properties in charge double layer (Fig. 6.11). The voltage across capacitance C given in equation, Eq. (6.6)   Vc 5 1
dvc =dt ðRact 1 Rconc Þ (6.6) Output voltage of fuel cell VFC, Eq. (6.7) VFC 5 E
Vc
Vact
Vohmic

(6.7)

where Rohmic, Ract, Rconc are the ohmic, activation and concentration resistance, C is the capacitance of membrane due to the effect of double layer due to that effect PEM fuel cell output voltage incorporates.

Figure 6.11 PEM fuel cell equivalent circuit.

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6.7 Power quality issues in hybrid micro-grid Due to the increasing dispersion of non-linear loads, single phase unbalance loads, single phase unbalance DG, power quality issues are most imperative issues for the incorporation of hybrid micro-grid in future. Harmonics and voltage variations are the power quality issues in the DC sub-grids. Unbalances, harmonics and voltage variations are the power quality issues in AC sub-grids. Momentary interruption, voltage sags and harmonics are the power quality issues in the AC data-centers which can be resolved by the back-up generators and uninterruptible power supply system. To improve the power quality issues in the system, compensation devices are added additionally. Power electronic based devices like: static synchronous compensator, unified power quality conditioner etc. are used to compensate the unbalance condition in three phase AC-side (Nejabathhah et al., 2019). Active and passive power filters are used to moderate the harmonics in the AC sub-grid. Tuned filleter and large capacitor are used to strainer the variations and harmonics in DC-side. The occurrence of power quality issues in different sections like: 1. Data centers 2. Electric vehicles 3. Residential distribution systems To mitigate the power quality issues, traditional compensation methods are used. When the power quality issues are spread widely in hybrid micro-grid then traditional compensation method does not work then SEs, DC-AC subgrids interfacing converters (IFCs), distribution generator’s IFC and loads can help to address power quality issue with their target for power management.

6.8 Control strategies and energy management system for hybrid micro-grid For the appropriate operations of hybrid micro-grid, the control strategies and energy management systems are the vital features. The frequency and voltage are controlled with the same time active and reactive power are resolute with the support of energy management strategies (Nejabatkhah & Li, 2015; Satish Kumar et al., 2017).

6.8.1 AC-coupled hybrid micro-grid In Fig. 6.12, AC-coupled hybrid micro-grid, it’s control strategies and energy management basically focused on:

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Figure 6.12 AC-coupled hybrid micro-grid.

• • •

Power balancing Voltage control of AC bus Frequency control of AC bus The control strategies are described in two ways, grid connected mode and stand-alone mode with the DGs and SEs which is shown as parallel current and voltage AC sources. AC-coupled method can be elaborated in standalone and grid-connected mode. In standalone micro-grid voltage and frequency can be controlled by the methods like: master slave, droop and power balancing for DGs-SEs, AC bus. In grid-connected mode, it divides in dispatched and un-dispatched output power. Dispatch output power operate in control mode with power balancing and un-dispatch output power operate in DGs MPPT with control mode and SEs by charging mode.

6.8.2 DC-coupled hybrid micro-grid In Fig. 6.13, DC-Coupled hybrid micro-grid, the control strategies and energy management are done for voltage control in DC-link, voltage and frequency control for AC-link and power balancing among generation

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Figure 6.13 DC-coupled hybrid micro-grid.

and demand. For power management operations, the system is divided into the standalone and grid-connected mode. Standalone system is subdivided into DC and AC bus voltage control and grid-connected system is subdivided into the dispatch and un-dispatch output power. AC bus deals with the voltage control mode for IFCs and DC bus deals with voltage control mode for DGs and SEs. In grid-connected mode, power output for un-dispatched is using of control mode for IFC DC link voltage, MPPT for DGs and charging mode for SEs. For power output for dispatched control mode operate in IFC DC link, DGs and SEs DC link voltage mode, IFC power control and DGs with SEs power balancing.

6.8.3 AC-DC coupled hybrid micro-grid In Fig. 6.14, AC-DC Coupled hybrid micro-grid, AC and DC buses are connected with DGs and SEs, coordination is also required between AC and DC subsystems. For balancing the power and voltage in the AC and DC subsystems is done by the application of different control strategies and energy management process. In AC-DC coupled method, ILC’s are used for the power control mode bi-directionally. The couple system is divided into the standalone and grid-connected and the sub-divided into

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Figure 6.14 AC-DC coupled hybrid micro-grid.

the standalone with DC and AC bus voltage control and grid-connected with the dispatched and un-dispatched power output. Voltage control methods are used in standalone for AC and DC links with droop method and power balancing. In grid-connected method, voltage and power are control with AC and DC bus, ILC’s are used also.

6.8.4 Transition between grid-connected and standalone operation mode for energy management To minimize the frequency and voltage disturbances and deviation transition within the grid-connected to standalone operations. Circulating power and overloading in distribution generations prevent to ensure the balanced flow of power. From Fig. 6.15, The transition from gridconnected to standalone is done in a condition first is to switch from power/current control mode from grid-connected to standalone’s voltage control mode. By this switching the DG which is current control mode shifted towards the voltage control mode that’s the reason earlier it supplies the power to the main grid now afterwards in standalone mode it supplies the continuous power to the loads. Its transition is applied through DC/DC converters with the battery energy storage system and

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Figure 6.15 Techniques for transition from grid-connected to standalone mode and vice-versa.

also with DC/AC converter with energy storage system and interfacing or ILC’s are used. The transition from standalone to grid-connected mode, the reconnection is done when the micro-grid voltage is harmonized with grid voltage. The harmonization is done first as active method and second as passive method. In passive method voltage from micro-grid and main grid are monitored and connects when both have same phase angle. The passive method is more practical method and the method based on assumptions by which magnitude of voltage and frequency are slightly different but it’s not a fast and controllable process. Other than that active process is fast and controllable. In this process, distribution generation and energy storage system are in co-ordinations. There is an islanding detection algorithm which have the disconnection time through which microcontroller switch from grid-connected mode to standalone mode and vice-versa.

6.9 Modeling of hybrid micro-grid There is various way to utilize above model with the multiple combination as per availability of natural resources and conditions. To describe the models of hybrid micro-grid with use of HOMER-Software some of combination described.

6.9.1 Modeling of PV and wind hybrid micro-grid In Fig. 6.16, the combination of PV and Wind is used. This combination is where demand/load is comparatively low. It uses where solar radiation

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Figure 6.16 Modeling of PV and wind hybrid micro-grid.

and average speed of wind is available. In the Fig. 6.10 wind generating station is available with PV generation and battery storage (Karimi et al., 2017). Wind is allied through AC-bus system and PV with battery are connected with DC-bus system. Converter is also installed to convert DC supply to AC supply. The reason behind this the consumer load is connected only in AC-bus system (Parida & Choudhury, 2018).

6.9.2 Modeling of PV, wind and biomass hybrid micro-grid In the Fig. 6.17, the combination of PV, wind and biomass are used. This combination is used mainly at medium load system. It installed at where solar radiation/average wind speed with proper transportation or availability of wood/methane gas etc. to generate bio-gas. Bio-gas and wind is directly allied through AC-bus system and PV and battery directly linked through DC-bus system with converter which uses to convert DC supply to AC supply. The reason behind that the consumer load is connected to AC-bus system.

6.9.3 Modeling of PV, wind, biomass and small hydro hybrid micro-grid In the Fig. 6.18, the combination of PV, wind, biomass and small hydro are used. This system is where high consumer load is available. This system is used where radiation of solar and wind average speed/bio-gas and canal system available. Canal system is used to produce electricity through small hydro system where small hydro, wind and biomass are linked to AC-bus system and PV system links through DC-bus system. Converter is allied in between DC and AC bus to convert the DC form of electricity into AC to supply the electricity to the consumer load (Che et al., 2015; Li et al., 2019).

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Figure 6.17 Modeling of PV, wind and biomass hybrid micro-grid.

Figure 6.18 Modeling of PV, wind, biomass and small-hydro hybrid micro-grid.

6.10 Mathematical modeling of hybrid micro-grid In the hybrid micro-grid both AC and DC sub-grids are used and the combination of both are also used which is connected with the ILC. The distribution generation in the micro-grid operate with the different control strategies. Due to coupling of voltage and frequency, power flow algorithm is for both sub-grids iteratively (Hamad et al., 2016). So different modeling techniques describe as below:

6.10.1 Modeling of AC micro-grid Pac;i 5 Vac;i Qi 5 Vac;i

X

I V Y cosðδi jЄ ac ac;j ij

X

I V Y sinðδi jЄ ac ac;j ij


δj 2 θij Þ


δj 2 θij Þ

(6.8) (6.9)

where Pac,i Eq. (6.8) is an injected active power, Qi Eq. (6.9) is an injected reactive power, Vac,i is voltage magnitude, Yij Eq. (6.10) is magnitude of

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Y-bus admittance matrix, δ and Θ Eq. (6.11) is angle. Due to the AC sub-grid admittance is not constant because of the variation in frequency. Yij 5 1=OR2 ij 1 ω2 L 2 ij

(6.10)

θij 5 Π
tan21 ðωLij =Rij Þ

(6.11)

where Rij 5 resistence, Lij 5 inductance, i,j 5 buses, ω 5 frequency.

6.10.2 Modeling of DC micro-grid The power injected into the DC micro-grid, Eq. (6.12) X Pdc;i 5 Vdc;i jЄ Idc Vdc;j Gij ’i ЄIdc

(6.12)

where Pdc,i 5 injected power, Vdc, j 5 voltage of bus j, G 5 conductance matrix, Idc 5 current through DC bus.

6.11 Coordination control of the converters For the uninterrupted, high quality with high efficiency of power supply into the utility grid, the coordination control in the converter has to be required. This uninterrupted power supply is given to variable AC and DC load which is generated hybrid renewable energy sources in both the modes whether isolated or grid-connected mode (Liu & Chiang Loh, 2011). Algorithm is designed for the converter’s coordination control.

6.11.1 Isolated mode Boost converter and back to back boost AC/DC/AC converter are used to operate for power balance and energy constraints of the system like: wind turbine doubly-fed induction generator (DFIG) operated with onMPPT or off-MPPT. For AC grid’s stable voltage and frequency, the main converters act as voltage source. In between the AC-link and DClink it works as converter or inverter for power exchange smoothly (Thakar, Vijay, & Doolla, 2019) Eq. (6.13). The unbalance in power occur due to various load and supply conditions, taken solar and wind as the renewable energy sources for hybrid micro-grid, Ppv 1 Pw 5 PacL 1 Ploss 1 Pb

(6.13)

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where Ppv 5 real power of solar PV, Pw 5 real power of wind energy, PacL 5 real power load connected to AC, Pb 5 power injected into the battery, Ploss 5 total grid loss. In standalone mode the supply is given to the load via. renewable energy sources. In Fig. 6.19 shown that switch is open through which grid supply is disconnected and the switch through renewable energy sources is closed by which supply given to the load for fulfillment of their requirement.

6.12 Grid-connected mode When the hybrid micro-grid operates in grid-connected mode than the main objective of boost converter is to regulate the terminal voltage. When the renewable energy sources are taken as solar PV and wind than boost converter has to trail the terminal voltage of PV arrays MPPT. To synchronize the AC grid back to back AC/DC/AC converter DFIG of wind turbine is controlled and it can regulate the current to rotor-side by which MPPT of solar panel is also synchronized. The excess amount of electricity from the hybrid micro-grid is fed to the utility grid. Battery energy storage system is used by hybrid micro-grid but its working is less significant due to the connectivity through utility grid. The existence of battery in the system is only for elimination of frequent power transfer within the AC link and DC link Eq. (6.14). For the amalgamation of solar PV system and wind energy characteristics the operation of main converter is to operate bi-directionally in the system. Eq. (6.15) Ppv 1 Pac 5 PdcL 1 Pb

(6.14)

Ps 5 Pw
PacL
Pac

(6.15)

Figure 6.19 Standalone system (without grid connection).

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where PdcL 5 real power loads connected to the DC bus, Pac 5 power exchange between AC link and DC link, Ps 5 power injected from hybrid to utility grid. By the implementation of PQ control with the use of main converter for current controlled voltage source the power getting exchanged within AC to DC grid and supply reactive power to AC-link. In Fig. 6.20, grid-connected mode supply is given to the load via. grid and renewable energy system are used as an additional electricity supply source (Thakar et al., 2019). When any fault occur in the system and grid supply is getting disturbed the renewable energy sources supply that area and in other condition like in summer electricity need is increasing then grid-connected mode is very beneficial.

6.13 Economic potential and their benefits for hybrid micro-grid For economic feasibility of hybrid micro-grid system uncertainty should be minimized, siting, sizing and number of distribution generation should define as per requirement of the system, management of load operations should be done, for the forecasting of hybrid micro-gird short term management process should be installed, for efficient working of hybrid micro-grid multi party community is present. For every project, government or private company identified economically and financial feasibility. There are many financial tools are available to identify financial analysis of new project like: net present value (NPV), internal rate of return etc. NPV is the one of the best ways which can be used for project viability. To start any new project government and private company prepared detailed project report (DPR). DPR shows that how the project is viable in terms of economically, financially, customer benefit and social growth of that area. DPR also shows the utilization of available natural resources

Figure 6.20 Grid-connected system.

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in the current site and as well as how this come help to improve social life of stake holders (Dykes et al., 2020). The planning of any project like hybrid grid techno-economical analysis is very crucial. In techno-economical analysis firstly identified that which technology is suitable as per the resource’s availability. The load identification and consumer mix are also very useful to maintain power quality with load mitigation. Hybrid micro-grid define that how to maintain electricity with and without grid connected at the remote location. Now a days it is very famous in cluster define area where simple and small resources are available. DG is key part of hybrid micro-gird. As per the Fig. 6.21, hybrid micro-grid is the combination of conventional generation, renewable generation with storage generation as per the required demand with minimum tariff. This planning shows that how to maintain hybrid micro-grid in profitable scenario. The techno-economic analysis is totally depending on the risk involved at the project and how to get revenue where renewable energy costing is very high which does not meet the countries conventional energies cost. To identify the risk and revenue involved in the project is defined by Fig. 6.22, For identification of risk, its divided into two parts, one is credit risk and second is commercial risk.

Figure 6.21 Hybrid energy planning.

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Figure 6.22 Evaluation of risk and return of cost.

6.13.1 Credit risk As per the regulatory rules all projects of electricity are divided into 70 3 30 ratio where 70 is the dead ration and 30 is the equity ratio. Dead ration is also called sponsor credit risk in which any financial institution provides dead in the project as per the project profitability. 30% equity ratio is also called country ratio in which companies equity involved to maximization of profit.

6.13.2 Commercial risk Commercial risk is divided into many parts the main parts of commercial risk is resource. Resource planning will maximize the profit if resource identification is in correct direction. To convert resources into the electricity generation, proper technology should be used. Operating cost is also having critical scenario if technology identification is right. Environmental clearance of any project with the ministry of environment is necessary and statuary. If any clearance get wrong project might be affected.

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If above points are in right direction revenue generation will be on positive side and help the technology upgradation.

6.13.3 Returns Any company growth is totally depending on how much return from the project and involvement in the social growth. Project cost is basic scenario to identify its returns after paying the debt and consolidation of equity. Electricity business is totally depending on consumer paying capacity to increase and simplified the consumer paying capacity maximum payment kiosk to be provided which will help to provide maximum revenue generation. In the capital expenditure regulatory commission gives 16% of return to the equity up to 20years of its newly installed assets.

6.14 Case study regarding hybrid micro-grid For development of hybrid micro-grid, a case study is designed and planned in the village Kharolwas because the state of Rajasthan has planned to target 30 GW of solar capacity by 2025. For the preparation of hybrid micro-grid, selection of natural resources is a main task. Being one of the hottest areas of Indian region, selection of PV system will be favorable as well as at the time of emergency situation diesel genset and battery storage will be taken as back-up. Selection of area is the most important factor, Nagaur district is one of the developing areas of Rajasthan. Kharolwas is the one of the best villages of district Nagaur the details of location are provided in Table 6.2. To plan a future development of the said area, configuration of hybrid micro-grid for the above said village planned in HOMER software. The project location is suitable for solar power generation, due to average radiation is very high as well as to provide a backup diesel genset with battery storage system. The basic designed model is shown in Fig. 6.23: showed Table 6.2 Location of Kharolwas, Rajasthan. Location

Kharolwas, Rajasthan

Latitude Longitude Time zone

260 45.11’ North 740 21.33’ East Asia/Kolkata

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Figure 6.23 Proposed model of hybrid micro-grid.

Figure 6.24 Solar radiation of the kharolwas village on daily basis.

the proposed hybrid micro-grid model which is amalgamation of solar PV and biomass. A battery storage system uses which is used for storing the excess amount electricity by the hybrid system and bi-directional converter is used to convert AC form of electricity to DC vice-versa as per the load demand. The solar radiation per day of the areas graph shown in Fig. 6.24, graph is drawn to show the yearly solar radiation with the clearness index. Their exists an deviation of radiation and clearness index as per month availability of solar radiation. In the kharolwas village, the total population is around 869. According to the population per hour demand curve is provided in Fig. 6.25. As per the future planning of state government and maintain the grid stability above model has proposed. The graph shows the per day demand of the area with the variation in timing. Fig. 6.26 shows the demand generation graph of that area on the yearly basis. The graph is drawn with the axis in hour per day/ day of the year. This graph shows the variation of need of electricity as per variation in hours. Consumption of electricity varied as per weather conditions.

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Figure 6.25 Demand generation graph per hour.

Figure 6.26 Yearly demand generation graph.

The excess amount of electricity will be provided to the state load dispatch center, to maintain the grid frequency and electricity to nearby areas.

6.15 Conclusion In the increase of the emission and utilization of fossil fuel, greenhouse gas effect plays an important role, which increases the temperature of earth. For overcoming such scenario, utilization of natural resources will be a huge task as there are many natural resources such as wind, smallhydro, solar, biomass etc. These natural resources are used either alone or in combination with any of these. Hybrid micro-grid is one of the finest solutions for future prospective. It will be helpful to utilize natural resources with minimum cost, and hybrid micro-grid can help the grid to maintain its frequency and reliability in critical situations. To develop any model, planning is necessary. In the above model, as per available natural resources, solar PV is the primary generating unit and to provide a backup generation solution biomass and diesel genset are mainly used because of

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the availability of petroleum product and easy availability of husk. After the simulation of planned model, desirable result was obtained, which shows the effectiveness of this.

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CHAPTER 7

Control of hybrid AC/DC microgrids P. Shambhu Prasad, Alivelu M. Parimi and L. Renuka Department of Electrical and Electronics Engineering, BITS Pilani, Hyderabad Campus, India

7.1 Introduction This chapter addresses the control of hybrid microgrid from the stability point of view. A hybrid microgrid is considered with the inclusion of both AC and DC bus and sources like wind turbine generator (WTG), solar photovoltaic (PV), microturbine (MT), diesel engine generator (DEG), fuel cell (FC), flywheel energy storage (FES) and battery energy storage (BES). In a hybrid microgrid, one of the major stability issues is caused due to the uncertain and perturbed nature of renewable energy sources mentioned above. This is fundamental because of their dependence on natural resources and intrinsic characteristics of microgrids like the high value of R/X ratio, low inertia, the poor coupling of active power and frequency, etc. In this regard, it becomes important to consider these uncertainties while maintaining frequency regulation. In this chapter, the robust control of hybrid microgrids for frequency stability has been addressed. Robust control provides better closed-loop stability in presence of disturbances and uncertainties. Synthesis methods like H2 optimal problem, standard H infinity control, and μ synthesis method have been used to achieve robust stability and performance. For a complete analysis, mathematical modeling of the hybrid microgrid and the controller in the form of state-space equations have been investigated. Comparative analysis of these methods along with standard PID control has been done along with modeling of uncertainties and disturbances with time-domain simulations to ascertain theoretical validations. The chapter would provide a comprehensive approach to the mathematical base of the robust control techniques, along with an understanding of the implementation of robust control for the hybrid microgrid to maintain frequency stability and investigate the behavior of microgrid under different scenarios. For the case study, the frequency stability has been investigated under Microgrids DOI: https://doi.org/10.1016/B978-0-323-85463-4.00003-4

© 2022 Elsevier Inc. All rights reserved.

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different scenarios like change in load, fluctuations in wind power, change in solar irradiance, etc. and comparative analysis has been done for different controllers. Microgrids are considered a group of distributed energy resources (DERs) coupled to loads with a power electronic interface (Lasseter et al., 2011). It overcomes the economical and ecological drawbacks of conventional power systems but usually has operational complexities for reliable and seamless deployment (Dragiˇcevi´c, Lu, Vasquez, & Guerrero, 2016). Fig. 7.1 shows the structure of the hybrid microgrid. Both AC [diesel engine, microturbine (MT), and wind turbine] and DC [photovoltaic (PV) array, battery, flywheel, fuel cell (FC)] have been considered for the structure. As shown in the figure, the renewable energy sources are interconnected via an electrical network and by the control structures. Control structures for such systems are hierarchical in structure. The hierarchical structure of the control provides effective regulation on frequency and voltage (Fan, 2017). To establish a strong coupling between active power and frequency, synchronization issues with the main grid and neighboring microgrids, coordination of active and reactive power, etc., hierarchical control structure plays a critical role. The frequency instabilities are often caused due to mismatch in generated power and load demand (Vasquez, Guerrero, Luna, Rodriguez, & Teodorescu, 2009). Each of these issues are addressed in different levels of the

Figure 7.1 Hybrid microgrid structure hybrid.

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hierarchical structure and assigned as specific functions (Bevrani, Francois, & Ise, 2017). The hierarchical levels are named primary, secondary, and central as shown in Fig. 7.2. The functionalities and significance of each level are explained in brief as follows. The first level, also known as primary control, consists of a droop controller, voltage control loop, and current control loops (Hirsch, Parag, & Guerrero, 2018). This loop forms the decentralized control of the microgrid (Tayab, Roslan, Hwai, & Kashif, 2017). The droop controllers play an important role to suppress the frequency deviations for any change in active power load. The inner loops such as voltage control and current control are employed to generate the references, sense the local signals, and maintain stability (Yamashita, Vechiu, & Gaubert, 2020). However, due to impedance mismatching, droop controllers have

Figure 7.2 The hierarchical control structure for hybrid microgrid.

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their drawbacks. Adaptive droop controllers, proportional integral and derivative (PID) controllers, master
slave approach, virtual impedance controllers, etc. have found wide applications in improving frequency stability and maintain voltage levels. However, these controllers do not address the uncertain nature of renewable energy sources especially in hybrid microgrid and its operational complexities (Meng et al., 2016). Secondary level control is assigned to address the frequency deviations caused due to mismatch in power generated and demanded (Dao, Dehghani-Pilehvarani, Markou, & Ferrarini, 2019). They play an important role in maintaining the voltage and frequency regulation within the standards. Consensus-based approach, predictive approach, etc. are few examples of the controllers used at this level. These methods are also used to improve power quality (Naderi et al., 2020). For the reliable and sustained operation of a microgrid in gridconnected mode, the central level plays a critical role. Synchronization with the interlink converters, and overcome the operational complexities in grid-connected mode are other aspects of central level control. The communication requirements and power management issues are addressed in global level control. At this level, communication links are employed to communicate with neighboring systems (Han, Solanki, & Solanki, 2013).

7.1.1 Microgrid stability Power system stabiliy is defined as the ability for a sytem to be in its operating equilibrium under normal operating states, and regain its state of equilibrium after being subjected to a disturbance, is defined as stability (Kundur, 1994). In conventional power systems, stability issues can be further categorized as rotor angle stability, voltage stability, etc. However, in microgrids, stability issues arise due to intrinsic characteristics of microgrids such as system size, higher penetration of renewable energy sources, lower system inertia, and mismatch in line impedance (Farrokhabadi et al., 2020). Hence, the reliable operation of microgrid becomes more challenging under such scenarios. Broadly the stability issues in microgrids can be classified as voltage stability and frequency stability. Voltage stability is not critical in microgrids, due to the short length of transmission lines, resulting in smaller voltage drops and fast recovery of any loss in loads. However, voltage stability often arises in microgrids due to mismatch in active power and frequency coupling. This again is the outcome of the low length of transmission lines (Shuai et al., 2016). The occurrence of low steady-state and dynamic voltages is one more voltage stability issue.

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7.1.2 Frequency stability Frequency stability is a critical aspect of microgrids. Frequency deviations are often caused due to mismatch in load and generated power. In other words, it is caused due to poor coupling of active power and frequency. Other intrinsic aspects such as low inertia, low reactance, and high resistance especially for low voltage (LV) grids also cause poor frequency regulation. One fundamental reason is the low length of feeder lines in LV feeder, which results in low reactance and high resistance. To establish the relationship between frequency and active power, let us consider a microgrid system as shown in Fig. 7.3. Mathematically, the active power and reactive power can be expressed as P5 Q5

Vs  RðVs 2 Vg cosθÞ 1 XVg sinδ 2 1X

(7.1)

Vs  2 RðVg sinδÞ 1 XðVs 2 Vg cosθÞ 2 1X

(7.2)

R2

R2

where Vs +δ is the bus voltage at Distributed Generation (DG), and Vg +0 is the grid voltage. Zejθ 5 R 1 jX 5 Zcosθ 1 jZsinθ represents the impedance of the line. Ignoring the resistance component and assuming δ to be small such that Eqs. (7.1) and (7.2) can be modified as Vs XP Vg sinδ.δD Vs Vg X

(7.3)

 Vs  XQ Vs 2 Vg cosδ .Vs 2 Vg D Vs X

(7.4)

P5

Q5

From Eqs. (7.3) and (7.4), it is very much clear that active power depends on and reactive power depends on. This coupling is only possible when the resistance component is neglected. But in LV microgrids, the

Figure 7.3 Simple microgrid structure.

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resistance is high and hence cannot be ignored. This leads to poor coupling between active power and frequency. Further, due to the line resistance, a strong coupling between and is formed, which complicates frequency regulation. For any change in load or the system, it leads to frequency instability in the form of low-frequency oscillations. These oscillations must be damped else may lead to the collapse of the system. The low-frequency oscillations can also be observed by plotting the eigenvalue spectrum, and the oscillating modes near the imaginary axis may be observed. Mismatch inline impedance is one of the primary reasons for frequency instabilities. Low inertia is one more prime reason for the failure of the system to damp the low-frequency oscillations. Moreover, due to the small size of the system, any change in the DERs side is instantaneously reflected on the load side. These changes are categorized as perturbations in system dynamics. And considering a hybrid microgrid, where more DERs are considered, the perturbations make frequency regulations a tedious process. Poor response in the frequency controllers also hampers the protection system that is designed more dynamically. Any trigger in a change of load leads to undamped frequency oscillations, in a span of few seconds to minutes. This must be identified by frequency protection schemes to trip the system immediately, failure to which may lead to the collapse of the system. Hence, the perturbations, uncertainties, and disturbances must be addressed while analyzing the stability of the microgrid. Robust stability and performance are desired in the presence of these uncertainties. The controller modeling should address these aspects along with system modeling. Robust control theory
based controllers provide optimum controllers while considering all uncertainties. The next section includes the mathematical base and review of the controllers which is an important prerequisite for stability analysis.

7.2 Literature review Robust control theory
based HN and H2 controllers have been widely used in the past (Bevrani, Feizi, & Ataee, 2015; Bidram, Davoudi, Lewis, & Guerrero, 2013; Modabbernia, Alizadeh, Sahab, & Moghaddam, 2020). In the study of Gu, Petkov, and Konstantinov (2005) the robust control theory as a multivariable feedback problem has been defined. The control and dynamics of microgrid (Fan, 2017), intelligent microgrid operation, and control along with frequency and voltage regulation have been well explained in the study of Bevrani et al. (2017). Further, in the study of

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Bevrani et al. (2017) the modeling of renewable energy sources has been considered in state-space form and the selection of weighting functions has been considered. The weighting functions act as pre- or postcompensators to obtain the desired frequency response. In the study of Hu and Bhowmick (2020), complete design of a fully distributed and robust secondary control scheme for voltage and frequency restoration along with real power sharing for an islanded microgrid has been demonstrated. A consensus-based design has been addressed for robust control. Similarly in the study of Sedhom, El-Saadawi, Elhosseini, Saeed, and Abd-Raboh (2020), the harmony search
based HN controller method is used to overcome the drawbacks of the droop controller. Harmony search
based HN controller is proved to be effective in maintaining the frequency and voltage regulations as compared to other controllers. A microgrid system, with inner voltage and current controllers, has been designed for controller stability analysis. In the study of Baghaee, Mirsalim, Gharehpetian, and Talebi (2017), robust control design of an autonomous microgrid has been done, addressing the communication delay. Communication delay holds importance while designing the system, especially for synchronized phasor measurements. In the study of Li et al. (2017), robust control of microgrid with superconducting magnetic energy storage (SMES) has been analyzed. The robust control allows the optimum flow of charge/discharge between battery and SMES by making the droop gains dynamic. This allows to improve the battery life and enhance system performance. In the study of Krishna, Schiffer, and Raisch (2020), the effect of clock drifts has been considered one of the uncertainties for the design of robust controllers. The clock drift has been modeled and included in the dynamics of a microgrid for stability analysis. In a dynamic event-triggered robust secondary frequency control has been designed for an islanded microgrid. A communication delay is also included for the modeling of the same. In the study of Boukerdja et al. (2020) a robust controller has been designed to address instability issues specifically for DC microgrids with constant power loads (CPL). The drawbacks with CPL are their intrinsic characteristics as incremental negative impedance, which causes critical voltage stability issues. The controller on the converter side overcomes the instability issues due to CPL. In the study of Golpîra and Bevrani (2019) the low inertia characteristic of the microgrids has been addressed with robust frequency control. In the study of Fathi and Bevrani (2019),various mathematical aspects for optimization for a multivariable feedback problem have been addressed. In the study of Lam et al. (2020) the robust control

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for diesel and PV array has been verified on a real-time basis for the frequency regulation problem. Dynamic change in the load has also being addressed. From a power quality point of view, in the study of Vu et al. (2017), the current sharing and voltage stability issues have been considered together a robust control problem. The controller adjusts the droop gains of the microgrid in an adaptive method to improve bus stability. In the study of Kumar and Hote (2018) a coefficient diagram algorithm
based robust control scheme has been proposed for frequency control in an interconnected power system. In the study of Lam et al. (2020) a centralized robust controller is being designed with the upper and lower bounds of the uncertainties being determined by the Lebesgue-measurable matrix and a norm reduction criterion is applied for disturbance rejections. Other applications such as robust control for boost inverter, small pressurized water reactor (Yan, Wang, Qing, Wu, & Zhao, 2020), and stability issues have been addressed in the study of Kolluri et al. (2018); Lam et al. (2020); Shivam and Dahiya (2018). In the next section the theoretical approach to robust control theory and the mathematical prerequisite to understanding the norms of the controller have been discussed in detail.

7.3 Theoretical approach—different control techniques In this section the theoretical approach for robust control of a multivariable linear feedback system is discussed for various control techniques. Consider a system shown in Fig. 7.4 where PðsÞ represents the plant model, KðsÞ represents controller, wðtÞ and uðtÞ are the inputs to the system, and zðtÞ and yðtÞ are the outputs of the system. It is a multiinput
multioutput (MIMO) system with linear feedback. The model PðsÞ can represent any system, like a microgrid or a simple spring-mass-damper system. As discussed before, the robust control theory includes the modeling of the disturbances like noise or any parametric change in dynamics of the system

Figure 7.4 Linear fractional transformation structure.

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such as fluctuation in wind power and change in load. The input wðtÞ is categorized as input disturbance, uðtÞ is the controlled input, zðtÞ is the controlled output like error signals or cost function, and yðtÞ is the measured output. Such structures are called linear fractional transformation. To ensure robust stability and performance, the norms of such systems as shown in Fig. 7.4 are defined such that it becomes an optimization process where the model for controller KðsÞ is obtained. The optimization process calculates a controller KðsÞ such that it satisfies the norm of the system. The norm indicates the size or frequency response in our case. The controller norms are defined such that it results in the least gain for the transfer function Twz ðsÞ such that a stable and robust performance is achieved in the presence of disturbances. In the next section the system shown in Fig. 7.4 is expressed in the form of state-space equations.

7.3.1 Structures of robust controllers The system shown in Fig. 7.4 can be expressed in state-space form as x_ 5 AxðtÞ 1 B1 wðtÞ 1 B2 uðtÞ

(7.5)

zðtÞ 5 C1 xðtÞ 1 D11 wðtÞ 1 D12 uðtÞ

(7.6)

yðtÞ 5 C2 xðtÞ 1 D21 wðtÞ

(7.7)

The state-space representation for the controller KðsÞ is given as x_ f 5 Af xf ðtÞ 1 Bf yðt Þ

(7.8)

uðtÞ 5 Cf xf ðtÞ 1 Dxf ðtÞ

(7.9)

The input for the system PðsÞ is partitioned into two vector signal components wðtÞ (disturbance) and uðtÞ which is the controlled input and also the output of KðsÞ. The output of PðsÞ is partitioned into two vectors signals, zðtÞ error or cost, and yðtÞ measured output, which is also the input of KðsÞ. Since uðtÞ does not enter directly into the expression for in Eq. (7.7), the modified block diagram can be represented as ð_xcl Þ 5 Acl xcl ðtÞ 1 Bcl wðtÞ

(7.10)

eðtÞ 5 Ccl xcl ðtÞ 1 Dcl wðtÞ

(7.11)

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where 

    x B1 1 B2 Df D21 A 1 B2 Df C2 xcl 5 B 5 Acl 5 xf cl Df D21 Bf C2

 Ccl 5 C1 1 D12 Df C2

B2 Cf Af



D12 Cf Dcl 5 D11 1 D12 Df D21

(7.12) (7.13)

The controller KðsÞ is required to be stabilized in the sense that Acl is a Hurwitz matrix. A standard feedback design has an objective formulated as a real-value function of the closed-loop system model given by Eqs. (7.10) and (7.11) to be minimized concerning KðsÞ. The H2 or HN optimization process is the task of minimizing the gain represented by GðλÞ 5 Dcl 1 Ccl ðλI 2Acl Þ21 Bcl

(7.14)

The split model representation for the sytstem can be expressed as   P11 P12 P5 (7.15) P21 P22 To derive P11 the term is made zero in Eqs. (7.5)
(7.7) like the twoport network parameters approach to derive the ðA; B; C; DÞ parameters. Resulting in x_ 5 AxðtÞ 1 B1 wðtÞ

(7.16)

zðtÞ 5 C1 xðtÞ 1 D11 wðtÞ

(7.17)

yðtÞ 5 C2 xðtÞ 1 D21 wðtÞ

(7.18)

Therefore P11 ðsÞ that is essentially the transfer function between zðsÞ and wðsÞ can be expressed as P11 ðsÞ 5 C1 ½sI 2A21 B1 1 D11

(7.19)

Similarly, the transfer function between zðsÞ and uðsÞ, by equation wðtÞ 5 0, can be expressed as P12 ðsÞ 5 C1 ½sI 2A21 B2 1 D12

(7.20)

The transfer function between yðsÞ and wðsÞ, by making uðsÞ 5 0, can be expressed as P21 ðsÞ 5 C2 ½sI 2A21 B1 1 D21

(7.21)

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Figure 7.5 Linear fractional transformation representing the portioned P(s).

And transfer function between yðsÞ and uðsÞ, by making wðsÞ 5 0, can be expressed as P22 ðsÞ 5 C2 ½sI 2A21 B2 1 D22

(7.22)

In terms of block diagram representation, it is represented in Fig. 7.5. From, input, output equation can be written as zðtÞ 5 P11 wðtÞ 1 P12 uðtÞ

(7.23)

yðtÞ 5 P21 wðtÞ 1 P22 uðtÞ

(7.24)

uðtÞ 5 KyðtÞ

(7.25)

Equation (7.24) can be modified as .yðtÞ 5 ½I 2P22 K21 P21 w ðt Þ

(7.26)

From Eqs. (7.20) and (7.23) uðtÞ 5 K½I 2P22 K21 P21 w ðt Þ

(7.27)

Substituting Eq. (7.22) in Eq. (7.18) zðtÞ 5 P11 wðtÞ 1 P12 ðK½I 2P2 2K21 P21 wðtÞÞ

(7.28)

zðtÞ 5 wðtÞðP11 1 P12 ðK½I 2P2 2K21 P21 ÞÞ

(7.29)

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Microgrids

Equation (7.29) represents the transfer function between input and output which can be expressed as Twz 5 ðP11 1 P12 ðK½I 2P22 K21 P21 ÞÞ

(7.30)

Twz 5 FðP; KÞ

(7.31)

Equation (7.31) can be used to express the transfer of any system in augmented form.

7.3.2 General mixed sensitivity problem In the mixed sensitivity problem, weighting functions are added to the linear fractional transformation structure to get the desired loop (frequency response). The weighting functions act as pre- and postcompensators to get the desired response. Consider the following block diagram. From Fig. 7.6, it can be seen that it has three outputs ½z1 z2 y, and inputs as w. The state-space expression can be written as z1 ðtÞ 5 wðtÞ 1 ð2 GÞuðtÞ

(7.32)

z2 ðtÞ 5 uðt Þ

(7.33)

yðtÞ 5 wðtÞ 2 ð2 GÞuðtÞ

(7.34)

For the previous equations the P matrix can be written as     I 2G P11 5 P 5 P21 5 ½IP22 5 ½G 0 12 I and

2

I PðsÞ 5 4 0 I

3 2G I 5 2G

(7.35)

(7.36)

Adding weights to the function to the system is modified as shown in Fig. 7.7. For the previous system, z1 5 W1 e and z2 5 W2 u. Therefore Eq. (7.30) can easily be modified as 2

W1 I PðsÞ 5 4 0 I

3 2W1 G W2 I 5 2G

(7.37)

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Figure 7.6 Closed loop structure.

Figure 7.7 Weighted control structure.

Using Eq. (7.30), the transfer functions can be expressed as Twz1 5 ðW1 1 ð2 W1 GÞðK½I 1GK21 ÞÞ

(7.38)

Twz2 5 ððW2 IÞðK½I 1GK21 ÞÞ

(7.39)

Considering more weighting functions, the system can be modified as shown in Fig. 7.8. The PðsÞ matrix for the previous system can be written as 2 3 W1 ðsÞ ^ 2W1 ðsÞGðsÞ 6 0 7 ^ W2 ðsÞ 6 7 6 PðsÞ 5 6 0 (7.40) ^ W3 ðsÞGðsÞ 7 7 4 ? 5 ^ ? I ^ 2GðsÞ

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Figure 7.8 Block diagram representation of a system with three weights and two inputs.

And the transfer function can be expressed as Twz1 5 ðW1 1 ð2 W1 GÞðK½I 1GK21 ÞÞ

(7.41)

Twz2 5 ððW2 IÞðK½I 1GK21 ÞÞ

(7.42)

Twz2 5 ððW3 IÞðK½I 1GK21 ÞÞ

(7.43)

This structure is also known as the general mixed sensitivity problem in HN control design. Under such a problem, linear fraction expression can be written as Twz ðsÞ 5 ½W1 ðsÞSðsÞ; W2 ðsÞFðsÞSðsÞ; W3 ðsÞT ðsÞT

(7.44)

where is the controller model, is the sensitivity function, is the input transfer function and is the complementary sensitivity function. Each of them is expressed as SðsÞ 5 ½I 1GðsÞFðsÞ21

(7.45)

RðsÞ 5 FðsÞ½I 1GðsÞFðsÞ21

(7.46)

Control of hybrid AC/DC microgrids

T ðsÞ 5 GðsÞFðsÞ½I 1GðsÞFðsÞ21

205

(7.47)

In the next section the norms of the controller are being discussed, which are important for the frequency response.

7.3.3 H Infinity control problem In the HN control problem the objective is to design a stabilizing controller u2 ðsÞ 5 KðsÞyðsÞ, such that the norm of the closed-loop system Twz ðsÞ takes a value less than γ   Twz ðsÞ , γ (7.48) From Eq. (7.48), it can be seen that three kinds of robust control problems can be formulated as H2 optimal control problem. The problem to solve is   (7.49) min Twz ðsÞ2 FðsÞ

HN optimal control problem. The problem is to solve   min Twz ðsÞN FðsÞ

(7.50)

Standard HN control problem. Any controller FðsÞ satisfying the following inequality can be regarded as a solution to the problem   Twz ðsÞ , γ (7.51) N where Twz ðsÞ is the transfer function from input w(disturbance) to output z(error). KðsÞ is a dynamic HN provided, for any symmetric matrix X . 0 the following inequality is satisfied. 2 T 3 Acl X 1 XAcl XBcl CclT 4 (7.52) BTcl 2Iγ DclT 5 , 0 Ccl Dcl 2Iγ where Acl , Bcl , Ccl , and Dcl are defined by Eqs. (7.12) and (7.13). It is also important to note that the matrices ðA; B2 and C2 Þ as described in Eqs. (7.5)
(7.7) must be stabilizable and detectable. The selected value of γ for our hybrid microgrid structure is 1, such that the objective function 51 can be modified as   Twz ðsÞ , 1 (7.53) N In the next section the mathematical base for structured singular value μ is being discussed.

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7.3.4 Structured singular value- μ control theory In this section, system modeling by adding uncertainties μ is being considered. The linear fractional transformations shown in Fig. 7.4 can now be modified as shown in Fig. 7.9. The uncertainty block ΔðsÞ has been augmented to the plant model PðsÞ along with the controller KðsÞ. Such structures are known as M 2 Δ configuration. The μ value provides sufficient and necessary conditions for robust stability and performance of the system where the standard configuration is shown in Fig. 7.9. To elaborate the small gain theorem, the standard configuration can be looked into input
output terminals of the perturbed block and remodeled as shown in Fig. 7.10. As shown in Fig. 7.10 is the plant model and is the perturbation model. Assume that M ðsÞ is stable, then the closed-loop system is stable for all stable ΔðsÞ if and only if the small gain condition expressed next is satisfied     M ðsÞ ΔðsÞ , 1 (7.54) N N This means that the infinity norm of M ðsÞ and ΔðsÞ should be less than 1. For a linear system the previous small gain condition can be interpreted as, for any disturbance model ΔðsÞ, the norm of the open-loop transfer function is smaller than 1, which means the open-loop Nyquist plot will never encircle point ð2 1; j0Þ on the real axis. Thus the closedloop system will be always stable, irrespective of the structure of the

Figure 7.9 M 2 Δ configuration for μ synthesis.

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Figure 7.10 Illustration of the small gain theorem.

stable model ΔðsÞ. This is known as robust stability. The small gain theorem can also be extended to nonlinear systems in the same manner. The system is shown in Fig. 7.9 and includes the parametric uncertainties δi and block uncertainties Δi . These can be modeled as

 Δ 5 diag½δ1 Ir1 :::δk Irk ::Δ1 :::Δf ; δi AC; Δj 5 AC ki kj (7.55) According to μ synthesis theorem, the controller KðsÞ satisfies the robust stability and performance on satisfying the norm condition expressed as inf sup μ½M ðjwÞ , 1

(7.56)

k wAR

where μ is defined as μΔ ðM Þ 5

1

 minΔ σðΔÞ:jI 2 ΔI j 5 0; ΔAΔ

(7.57)

When the upper bound of μ is considered, Eq. (7.56) can be rewritten as

 min inf sup σ DM ðjωÞD21

(7.58)

  min DM ðPðjωÞ; KðjωÞÞD21 N

(7.59)

K

Also expressed as

K;D

D ωAR

Equation (7.59) is iteratively solved for D and K. In Eq. (7.59), D is defined as a positive definite symmetric matrix with appropriate dimensions and σðÞ represents the maximum singular value of a matrix. A flow chart representation of the D
K iteration is shown in Fig. 7.11.

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Figure 7.11 Flowchart for D
K iterations.

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7.4 Methodology For frequency stability investigation, a hybrid microgrid with DER like wind turbine generator (WTG), solar PV, MT, diesel engine generator (DEG), FC, flywheel energy storage (FES), and battery energy storage are considered. A microgrid system consisting of state-space representation of DER and the controller is shown in Fig. 7.12. The parameters of the dynamic model are given in Table 7.1 in the appendix. The total power contributed by each of the DERs can be expressed as PLoad 5 PDEG 1 PMT 1 PWTG 1 PPV 1 PFV 6 PBES 6 PFED

Figure 7.12 Microgrid dynamic model.

Table 7.1 The parameters of the frequency response model. Parameter

Value

D(pu/Hz) M(pu/s) TFC(s) TBESS(s) TFESS(s) TDEG(s) TMT(s) TWTG(s) TPV

0.014 0.3 4 0.1 0.1 2 2 1.5 1.8

(7.60)

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Microgrids

It can be seen that the robust controller addresses the sources MT, DEG, and FC. WTG and PV are not considered for frequency regulation, as their output depends on environmental conditions. So these two sources have been considered disturbances like fluctuations in wind power and change in solar irradiance. They are considered the input wðtÞ for the system. Similarly, input to the controller is the controlled input and is Δf the measured output. The perturbations in DERs associated with frequency regulation can be expressed as ΔPLoad 1 ΔPDEG 1 ΔPMT 1 ΔPFC 1 ΔPPV 1 ΔPWTG 1 ΔPBES 1 ΔPFES 5 0

(7.61)

The linearized state-space model of the system shown in Fig. 7.12 can be expressed as x_ 5 Ax 1 B1 w 1 B2 w

(7.62)

z 5 C1 x 1 D12 u

(7.63)

y 5 C2 x

(7.64)

where the state vectors are defined as xT 5 ½ΔPWTG ΔPPV ΔPDEG ΔPFC ΔPMT w T 5 ½ΔPWTG

ΔPBES ΔPFES Δf 

ΔPϕ ΔPLoad 

(7.65) (7.66)

y 5 Δf

(7.67)

The state-space matrices are defined as 3

2

21 6 TWTG 6 6 6 6 0 6 6 6 6 0 6 6 6 6 6 0 6 6 x_ 5 6 6 0 6 6 6 6 6 0 6 6 6 6 6 0 6 6 6 4 0

0

0

0

0

0

0

21 TPV

0

0

0

0

0

0

21 TDEG

0

0

0

0

0

21 TFC

0

0

0

0

21 TMT

0

0

0

21 TBES

0

0 0 0

0 0

0

0

0

0

0

0

21 TFES

0

0

0

0

0

0

0

7 7 7 7 7 2 7 1 7 7 2 6 TWTG 3 6 0 7 ΔPWTG 7 6 7 6 ΔP 7 6 7 6 6 0 PV 7 7 6 6 7 ΔP 7 DEG 0 7 6 7 6 0 7 6 ΔP 7 6 FC 7 6 0 76 6 ΔPMT 7 1 6 7 6 0 7 7 6 6 0 7 6 7 6 ΔPBES 7 6 7 4 6 ΔPFES 5 6 0 1 7 7 6 0 Δf 6 TBES 7 7 4 0 7 1 7 7 TFES 7 7 2 2D 7 5 M 0

0 1 TPV 0 0 0 0 0 0

3 3 2 0 07 6 0 7 7 6 1 7 7 7 6 7 6 07 7 6 TDEG 7 7 7 2 3 6 7 6 1 7 ΔP 7 7 6 Wind 07 7 6  4 ΔPϕ 5 1 6 TFC 7 07 7 6 1 7 ΔPLoad 7 6 07 7 7 6 6 TMT 7 07 7 7 6 6 0 7 07 7 7 6 25 4 0 5 M 0

(7.68)

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 y5 0

0

0 0

0

0

0

1 x

211

(7.69)

The closed-loop structure of the system along with the controller is shown in Fig. 7.13. Fig. 7.13 represents the closed-loop structure of the microgrid. KðsÞ is controller function, GðsÞ is the plant model, ΔðsÞ are the uncertainties in the system. w1 , w2 ;and w3 are the input disturbances. PðsÞ is the perturbed model of the system, including disturbances and uncertainties. z123 , z4 and z5 represent the error signals. And Wd , We ;and Wu being the weighting functions assigned to these error signals. They are expressed as We ðsÞ 5 0:01

s3 1 5s2 1 10s 1 60 s3 1 100s2 1 15s 1 3

Wu ðsÞ 5

2ðs 1 1Þ 0:01s 1 9

3 0:01 0 0 Wd 5 4 0 0:01 0 5 0 0 0:01

(7.70)

(7.71)

2

(7.72)

For stability analysis, ΔPWind , ΔPϕ ; and ΔPLoad are considered input disturbances to the system, and Δf is the measured output fed to the controller. The output of the controller is fed to plant model GðsÞ. The modeling of the closed-loop system with weighting functions is being discussed. System stability and different case studies would be covered in the next section.

Figure 7.13 Closed-loop system block diagram.

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Microgrids

7.5 Results and discussion—case studies In this section, stability analysis of the perturbed system, the frequency response of the closed-loop system, and case studies for different input will be discussed. Fig. 7.14 represents the bode plot for the perturbed system PðsÞ. The inputs considered are ΔPWind , ΔPϕ, ΔPLoad , and controlled input uðtÞ. ΔPWind , ΔPϕ , and ΔPLoad are considered disturbances and uðtÞ is considered controlled input to the controller. Similarly, yðtÞ is considered measured output Δf . As can be seen from the bode plot, the frequency response is within the bounded limits. The bode plot has been considered for four inputs and one output. Considering the input ΔPWind and measured output Δf , it can be concluded that, for changes in wind power fluctuation, the change in frequency is within stable limits. Similarly for other inputs ΔPϕ and ΔPLoad the system frequency response is bounded. The closed-loop stability of the system can be obtained from the open-loop response.

7.5.1 H infinity controller frequency response The frequency response of the closed-loop system is shown in Fig. 7.15 using the HN controller as expressed by Eq. (7.51). Since the closed-loop system is MIMO, the singular values of the system are plotted. Singular values of the frequency response are an extension of the bode plot for MIMO systems. Fundamentally, the singular values can be obtained from

Figure 7.14 Bode plots for the perturbed system P(s).

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the eigenvalues for a system. For a MIMO system the singular values represent the magnitude of the system at a particular frequency. The singular values are shown in Fig. 7.15 and reflect the frequency response of two functions, the closed-loop system, and gamma. As discussed before, the HN controller norms should be less than gamma, and for standard HN optimal problem, it should be less than 1 as expressed by Eq. (7.53). The three red lines in the frequency response represent the frequency response of three functions, that is, sensitivity SðsÞ, input transfer function RðsÞ, and complementary function T ðsÞ as defined by the Eqs. (7.45)
(7.47) of the closed-loop system. To obtain the frequency response the singular value decomposition (SVD) of the closed-loop transfer function is obtained. The robust stability criteria for the system can also be expressed as    We ðI 1GPÞ21    (7.73)  Wp ðI 1GPÞ21  , γ N where We and Wp are the weighting functions as defined by Eqs. (7.70) and (7.71). The obtained value of γ for robust stability is 0.2562, which indicates that the controller satisfies the norms of robust stability and performance under the influence of disturbances. The method for modeling the uncertainties as explained by Eq. (7.55), also known as “unstructured uncertainty modeling,” is being used for the HN controller. The configuration for unstructured uncertainty for the response shown in Fig. 7.15 is a multiplicative perturbation method. It depends on the position of the

Figure 7.15 Singular values of the closed-loop system.

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Microgrids

uncertain block as shown in Fig. 7.13. Other configurations that can also be used for the same system are additive perturbation, inverse additive perturbation, etc. It is also noted that the HN norm of the open-loop system is 8.7006 and 0.2562 for the closed-loop system. It represents the peak gain or the largest value of the frequency response. For the unstable system the HN norm and peak value of frequency both are infinity.

7.5.2 Mu synthesis controller frequency response The frequency response of the system with the μ synthesis controller is shown in Fig. 7.16. As shown in Fig. 7.16, the controller should satisfy the norms bounded by the μ synthesis controller. It also represents the robust performance index for the perturbed system sensitivity function as discussed for the previous controller. As expressed in Eq. (7.59) the bound should be less than 1. To obtain the robust stability and performance, the norms for the controller can be defined as    We ðI 1Fu ðG; ΔÞK 21    (7.74)  WP KðI 1FU ðG; ΔÞK 21  , 1 N where We and Wp are the weighting functions as defined in Eqs. (7.70) and (7.71), GðsÞ represents the plant model, Δ represents the uncertainty block, and FU represents the upper linear fraction system consisting of GðsÞ and ΔðsÞ. The obtained value of bound to satisfy the robust stability and performance under the influence of uncertainties is 0.3319. In other words,

Figure 7.16 Singular values of the closed-loop system.

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1 jΔjN , 0:3319 . To obtain the value of the bound, the D
K iteration method is followed. The iterations continue, till the bound value less than 1. For better analysis the weighting functions are being considered for both controllers.

7.5.3 μ synthesis controller with parametric variations In this part the robustness of the controller is verified with parametric uncertainties in a structured form. With age the parameters of the system often vary due to environmental and characteristic nature. Such variations also should be considered for robust stability and performance. In this case the considered parametric variation is in inertia ðMÞ and damping ðDÞ of the microgrid. A 6 50% deviations for D and M are considered in the modeling. The deviations are included in the model as shown in Eq. (7.55). The modeled perturbed system is taken as PðsÞ 5 GðsÞðI 1 ΔðsÞÞ

(7.75)

As shown in Fig. 7.17, with the deviations in the parameteric specifications of the system, the frequency response of the closed-loop system is within the bound limits. The value of bound obtained in this case is 0.9771. It also represents that the inequality given by Eq. (7.76) is satisfied.    We ðI 1GPÞ21    (7.76)  Wp KðI 1GPÞ21  , 1 N For the modeling of deviations, ΔðsÞ is taken with an order 4 3 4: Then uncertainly modeling of the subsystem has been done on the

Figure 7.17 Singular value response of the system with parametric deviations.

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Microgrids

Simulink platform. The response as shown in Fig. 7.17 is being obtained for 10 samples of the uncertainty.

7.5.4 Order reduction of the controller One of the drawbacks of the HN controller is that they result in very high order controllers. The order of the controller obtained in Section 7.5.1 for the HN case was 16. The implementation of such a controller is complex. However, the order of the controller can be reduced by using Hankel-norm approximation. It removes the unobservable and uncontrolled modes of the augmented plant and reduces the burden on the controller. It returns the Hankel singular values in the process. The other norms of the controller remain unaffected. Fig. 7.18 shows the singular value response of the HN controller considered in Section 7.5.1 with reduced controller order. The order of the reduced controller is 6. The remaining frequency response is under the norms as indicated in Fig. 7.18. Similarly, the order of the controller in μ synthesis can also be reduced by Hankel-norm approximation.

7.5.5 Case studies—comparison of control techniques In this section, three different cases are being investigated for frequency regulation. For each case, one input disturbance is considered cumulatively. Initially, the frequency response for change in load is considered, then change in load and wind power fluctuation are considered together.

Figure 7.18 Singular value response of the H infinity controller with a reduced-order controller.

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And for the last case, input disturbance of the PV array is included. The frequency response for each case is explained. Case 1: Load deviation. In this case a change in input load power being considered disturbed input. The frequency regulation has been observed for three controllers, that is, HN, μ synthesis, and PID controller as shown in Figs. 7.19 and 7.20. A comparative analysis for the three different controllers has been made as shown in Figs. 7.19 and 7.20. It can be seen that the performance of the μ

Figure 7.19 Load deviation.

Figure 7.20 Frequency response.

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Microgrids

synthesis controller is better than the other two when there is a change in load at 5 and 10 seconds. The deviations are less as compared to other controllers. The response of PID is poor as it not addressing the uncertainties and disturbances. The robust performance of the other two controllers can be observed. Case 2: Deviation in wind power and load. In this case, simultaneous deviations have been considered for both wind and load as shown in Figs. 7.21 and 7.22. The deviations are taken

Figure 7.21 Wind power and load deviation.

Figure 7.22 Frequency response.

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at the same instants. In this case also, frequency regulation due to the μ synthesis controller is better than the other two. However, it settles with a slight delay as compared to the HN controller. Case 3: Deviation in solar irradiance, wind power, and load. In this case a multiinput disturbance has been considered input to the system as shown in Figs. 7.23 and 7.24. As solar irradiance is more dynamic, different amplitudes are considered at more number of instances. As again, the

Figure 7.23 Solar irradiance, wind power, and load deviations.

Figure 7.24 Frequency response.

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Microgrids

performance of the μ synthesis controller stands out in this case also. The main reason being as it models the uncertainties along with the plant model. The settling time is slightly poor as compared to others but has zero steady-state error.

7.6 Conclusion In this chapter a robust control HN and μ synthesis controllers have been implemented for the frequency regulation of a hybrid microgrid. As discussed, the robust controllers address the perturbations and uncertainties in the DERs. These uncertainties are inevitable in the energy sources due to their dependence on naturally available energy. Maintaining the frequency stability within the regulated limits in the presence of perturbations requires a robust approach to the modeling and design of the system. The disturbances considered for the modeling were deviations in load, deviations in wind power, and deviations in solar irradiance. These three factors were usually the prime reasons for any frequency instability issues. To analyze the stability the system was modeled with a multiplicative perturbation method for uncertainty modeling. The norms of the HN controller and μ synthesis controller were defined such that the system satisfies robust performance and stability index. The frequency response plots were analyzed using the SVD method. The frequency response of both the controllers was plotted considering the same weighting functions. Parametric uncertainties modeling was included in the controller design, and stability investigation was done in the concerned scenario. Also, the order of the controller was reduced using the Hankel-norm approximation. Further, different case studies were observed for single- and multiinput disturbances to the system. Initially, load deviations were considered and later wind power fluctuations and solar irradiance were added to it. It is observed that, under all scenarios, the performance of the μ synthesis controller is superior as compared to the HN controller and optimal PID controller. The main reason being that is μ synthesis controller addresses the uncertainties in its modeling, whereas in the HN controller only the disturbances were included. Except in its settling time, the μ synthesis controller stability index is superior to the other two. For both synthesis methods, it results in a high-order controller, which was reduced, and stability was investigated. Overall, the robust controllers improve the dynamic response of hybrid microgrids.

7.7 Summary This chapter explains the robust control theory to maintain frequency regulation for a hybrid microgrid. The hybrid microgrid, which consists of both AC

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and DC sources, often suffers frequency stability in the form of undamped low-frequency oscillations. Also due to the intrinsic characteristics of the microgrids, these oscillations remain undamped and may cause a collapse of the system causing serious economic effects. To extract the true advantages of green energy, seamless and reliable operation of microgrids is highly desired. For robust performance the intrinsic characteristics of the system and their dependence on naturally available sources must be addressed. In such operating conditions the controller which maintains the stability for the system must be designed keeping because of perturbations and disturbances. Robust control theory
based HN and μ synthesis controllers are two such controllers that perform under the presence of these disturbances. For stability analysis the state-space modeling of the hybrid microgrid is obtained which includes the disturbances and uncertainties. The performance of the controller is tested by plotting the open-loop and closed-loop frequency responses of the system. It is observed that the frequency response must be bounded by the limits, as defined by the norms of the controller. Similarly, the performance of the controllers is tested under different scenarios such as load deviation, wind power fluctuations, and change in solar irradiance. It is observed that under these disturbances, whether the controller satisfies the stability norms or not. Additionally, the performance of the controller is being compared with a standard controller such as an optimal PI controller. It can be seen that under all test cases, the performance of the μ synthesis controller is superior to the other two controllers, as it includes the modeling of uncertainties. Other aspects such as order reduction of controller and modifications of weighting functions can be taken as future work. The glossary of the terms has been shown in Table 7.2. List of abbreviations has been shown in Table 7.3. Table 7.2 Glossary. Microgrid

A cluster of renewable energy resources interconnected via power electronic devices

Hybrid microgrid Stability

A microgrid consisting of both AC and DC sources

Robust Frequency Hierarchical

Balanced and bounded response of a system for a certain input Strong and unlikely to fail under tough circumstances Number of cycles per second Ordered or ranked as per function for different levels (Continued)

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Microgrids

Table 7.2 (Continued) Microgrid

A cluster of renewable energy resources interconnected via power electronic devices

Islanded microgrid Norm of controller Transfer function Perturbations

Microgrid not connected to the main grid Size of controller Laplace transform on output versus input Small change due to any disturbance

Table 7.3 List of abbreviations and symbols. AC

Alternating current

DC PID WTG PV MT DEG FC FES BES DER SMES CPL SoC inf sup min MIMO SVD HN μ R X Z P Q V θ δ

Direct current Proportional integral and derivative controller Wind turbine generator Photovoltaic Microturbine Diesel engine generator Fuel cell Flywheel energy Battery energy system Distributed energy resource Super magnetic energy storage Constant power load State of charge Infimum Supremum Minimum Multi input
multi output Singular value decomposition H infinity controller Mu synthesis controller Resistance Reactance Impedance Active power Reactive power Voltage Impedance angle Bus angle

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References Baghaee, H. R., Mirsalim, M., Gharehpetian, G. B., & Talebi, H. A. (2017). A generalized descriptor-system robust HN control of autonomous microgrids to improve small and large signal stability considering communication delays and load nonlinearities. International Journal of Electrical Power & Energy Systems, 92, 63
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119). Academic Press, http://www.sciencedirect.com/science/article/ pii/B9780128164457000049. Sedhom, B. E., El-Saadawi, M. M., Elhosseini, M. A., Saeed, M. A., & Abd-Raboh, E. E. (2020). A harmony search-based H-infinity control method for islanded microgrid. ISA Transactions, 99, 252
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CHAPTER 8

Recent advancements in hybrid AC/DC microgrids P. Shambhu Prasad and Alivelu M. Parimi Department of Electrical and Electronics Engineering, BITS Pilani, Hyderabad Campus, India

This chapter addresses the recent advancements in hybrid microgrids. Microgrids have harnessed the advantages of green energy and overcome the environmental drawbacks of fossil fuel
based conventional sources of energy. Hybrid microgrids are superior as compared to AC and DC microgrids, as they overcome the drawbacks of AC and DC microgrids. Hybrid microgrids have gained a lot of attention in recent times and developed specific applications, such as data centers and military, and are growing at an exponential pace. However, along with the advantages, complications are also there for overcoming the challenges for seamless operation and efficient performance. This involves stability, active and reactive power sharing among microsources, overcoming the load fluctuations, maintaining power quality, protection against undesired contingencies, etc. Other challenges such as microgrid control, tuning the gain parameters, incorporating energy storage systems, electric vehicle charging, and applying machine learning concepts have gained popularity in recent times. Maintaining microgrid stability has been a challenging task, keeping given uncertainties and disturbances of the nature-based renewable energy sources. Other factors that influence system stability, especially frequency stability, are the low X=R ratio, low inertia, intermittent nature of the sources, etc. Considering all these, maintaining system stability becomes the prime objective, especially for hybrid microgrids that involve both AC and DC grid complexities. In this chapter an approach has been made to introduce machine learning concepts for the objective of frequency stability in hybrid microgrids. Machine learning technology has found its applications in all domains, due to its ability to predict scenarios in complex circumstances. Incooperating machine learning makes the system more robust to face any undesired circumstances by making complex calculations in the presence of huge data, in a short time. It is a step ahead by adding more smartness to the existing system and making it a smart grid from existing hybrid microgrids. Microgrids DOI: https://doi.org/10.1016/B978-0-323-85463-4.00004-6

© 2022 Elsevier Inc. All rights reserved.

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8.1 Introduction Hybrid microgrids combine the features of AC and DC microgrids (Lasseter et al., 2011; Unamuno & Barrena, 2015). AC microgrid that is a more commonly used configuration has the advantage of direct integration to distributed generated units with fewer complexities and converter stages (In Microgrid Dynamics & Control, 2017). The AC microgrids have low switching losses, due to less number of converting stages, but suffer from more conduction losses due to synchronization issues and the flow of reactive power (Fan, 2017). Circulating currents, low-frequency transformers, harmonic distortions, etc. are some of the challenges that need to be investigated in AC microgrids. On the other hand, DC microgrids have fewer losses and more converting stages (Dragicevic, Lu, Vasquez, & Guerrero, 2016). They are mostly interfaced with power electronic
based converters that act as the interfacing link between grid and micro sources. The converters also play a crucial role in impedance matching and isolation purposes (Sahoo, Sinha, & Kishore, 2018). With the presence of high efficient converters and lack of synchronizing units, the overall efficiency of the DC microgrids is more as compared to AC microgrids. However, designing the triggering circuits and controlling the output parameters, cost of converters units, switching losses, etc. are few drawbacks of DC microgrids. Hybrid microgrids combine the advantages of both AC and DC microgrids (Aboli, Ramezani, & Falaghi, 2019; Li et al., 2017; Liu, Hossain, Lu, Rafi, & Li, 2018). A few sources such as microturbines, diesel engine generators, wind turbines, where energy is available in the form of AC, are connected to the AC grid via the synchronizing units. Similarly, other DC sources such as solar arrays, fuel cells, battery storage systems, etc. provide direct DC voltage which are connected to the DC grid via the power electronic interfaces (Microgrid Stability Defintition PES Report, n.d.). Thus hybrid microgrids provide direct integration of AC, DC, and battery management systems link at a common point. This also facilitates in adding a new feature like charging stations for electric vehicles to the common point. The modifications to be made are highly simplified and provide feasible solutions to many complex architectures. Besides, in hybrid microgrids, there is no need for synchronization of the generating and storage units, as AC gird exists in architecture. This is one of the drawbacks of DC microgrids, which is overcome in hybrid microgrids. Voltage transformation is also carried out with ease, with the

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existing AC grid. For DC side voltage transformation, converters are used to step up or down the magnitude (Mirsaeidi, Dong, & Said, 2018). Fig. 8.1 shows a typical structure of a hybrid microgrid. With the numerous advantages, there are few drawbacks in hybrid microgrids that need to be addressed. Due to the presence of synchronizing units on the AC side, and converting units on the DC side, protection becomes one of the challenging aspects in hybrid microgrids. Good literature is available for protecting the AC units with the help of advanced breakers and relays with communication links, protecting DC units still needs a lot of attention. Due to all these aspects, and additionally, more number of converting stages, the reliability of hybrid microgrid decreases as compared to AC or DC microgrid. One more challenging aspect, which this chapter also further focuses on, is the complex control structure of hybrid microgrids. The implementation of a conventional hierarchical control structure is adapted in the hybrid microgrid; however, the control objectives are different. For AC microgrids, seamless power flow between the micro sources, decoupling the active power and frequency, similarly reactive power and voltage, damping the frequency oscillations, low inertia, etc. are some of the objectives for the control hierarchy of AC units (Farrokhabadi et al., 2020; In Robust Power System Frequency Control, 2014; Wind Power &

Figure 8.1 Hybrid microgrid structure.

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Frequency Control, 2014). Similarly, triggering units for the converters, commutation circuits, optimizing switching losses, maximum power point tracking, droop controller, etc. are some of the control objectives of the DC units (Guerrero, Matas, de Vicuna, Castilla, & Miret, 2007; Guerrero, Vasquez, Matas, De Vicuña, & Castilla, 2011; Guo et al., 2014). In hybrid microgrids the presence of both units makes the control hierarchy complex and needs a meticulous approach. In this chapter an attempt has been made to study the frequency stability approach of a hybrid microgrid by machine learning. Machine learning is like training the machine with a set of data and making it able to predict the outcome for any set of data with the help of a particular algorithm. With the help of a set of data fed to the machine, the computer program is said to learn from experience, and predict the outcome for any given set of data. Based on the available data, for more accuracy, the data are classified into training and testing purposes, and accuracy for each set of data can be evaluated individually. There are many algorithms available in the literature for training and testing the data. Fundamentally, the data that are fed to the machine are classified under a certain pattern by the algorithms. Once the pattern is prepared by the algorithm, a model is prepared, which can recognize the patterns created and make predictions for any unknown set of data. Machine learning has found huge applications in the field of computer science and engineering, and many more uses such as the medical field and economics commerce have found versatile applications of machine learning algorithms. With precise pattern creation and efficient prediction, machine learning algorithms have been able to solve complex problems that could not be solved by a conventional mathematical approach. In the field of electrical engineering, especially microgrids, machine learning is the recent advancements, and its applications have been found in all classical challenges, like frequency stability, voltage stability, protection, load demand response, active power-sharing among microsources, and calculating the optimum cost of equipment and evaluating losses (Atique & Bayne, 2020; Karim, Currie, & Lie, 2016; Leonori, n.d.). In this chapter a machine learning approach has been made for maintaining frequency stability in a hybrid microgrid. The controller design has been obtained based on H infinity
based robust controller. In the H infinity controller the weighting functions play a crucial role in evaluating the sensitivity function and predicting the stability (Hu & Bhowmick, 2020; Kumar & Kumar, 2020; Vu et al., 2017; Yan, Wang, Qing, Wu, & Zhao, 2020; Lam et al., 2020; Shajiee, Hosseini Sani, Shamaghdari, & Naghibi-

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Sistani, 2020). In general, the gains of the weighting functions can be tuned and optimum values of the gains are obtained by the trial and error method. In this chapter, several weighting functions are considered, for different parameters, and simulations have been carried out to evaluate the stability of the system. Based on the simulation results obtained, a data set for the system analysis has been prepared which is used to train the machine by using the K-nearest neighbors (KNN) algorithm. Once the machine is trained, for any given set of weighting functions, the system can predict the stability and a conclusion on the optimum value of weighting functions can be made. With this approach, system performance and complexity can be eased, especially for hybrid microgrids. And due to the machine learning approach, the system scale can be increased to higher levels, as machine learning algorithms can be solved for higher order systems with several busses. The huge data can be processed with ease by using machine learning algorithms. The rest of the chapter discusses the challenges faced by hybrid microgrids, algorithms, and a case study with different gains of the weighting functions, and by using a machine learning algorithm, the system stability was evaluated. Section 8.2 discusses the challenges in hybrid microgrids and their possible solutions with relevance to machine learning algorithms. Section 8.3 deals with the detailed description of machine learning algorithms, and the classification of machine learning problems. Section 8.4 discusses one case study where machine learning algorithms have been used to classification-based problem. The chapter ends with a discussion on results along with a conclusion and future scope.

8.2 Challenges in hybrid AC/DC microgrid and possible solutions Due to the presence of AC and DC grids, synchronization units, converting interface, etc., hybrid microgrids have been facing a lot of challenges in seamless execution of the grid and terms of performance metrics. A few of the challenges and how machine learning can be used to address these have been discussed in brief in this section.

8.2.1 Operational aspects The presence of an AC grid needs a synchronizing unit such the micro source and grid are synchronized and feed the loads. Sources such as wind turbines generator, diesel engine generators produce electricity at nominal

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frequency. To get synchronized with the existing grid, the phase sequence and frequency must be matched. This needs further synchronizing units. Synchronizing units are not required for the DC grid, but due to the presence of converting stages, commutation and triggering units need to be synchronized with the existing DC grid (Kumar & Kumar, 2020). The synchronizing units may be tuned with the help of machine learning algorithms. Based on previous synchronizing data, the machine could be learned, and before actual synchronization, issues could be predicted. This comes under the classification of supervised learning.

8.2.2 Compatibility issues External loads such as electric vehicle charging units, communication systems for the control loops, battery management systems need to be compatible with the existing system. This facilitates the plug and plays characteristics of the hybrid microgrids. The characteristic features of these external links must match with the existing system architecture to avoid any compatibility issues and provide seamless operation of the unit. Machine learning algorithms can be trained to predict any compatibility issues of external loads (Adibi & Woude, 2019). Based on the predictions made, important conclusions could be drawn.

8.2.3 Uncertainty, and perturbations in the renewable sources of energy We have seen that there could be several uncertainties in the system, such as damping, inertia, and time constants of the sources, and perturbations like load deviations, change in wind speed, and solar irradiance. The system must have a robust controller, to address these issues. For such, cases H infinity
based robust controllers could be preferred as they have a high degree of robust stability and robust performance, in the presence of uncertainties and perturbations. We have also seen the role of weighting functions that act as pre- and postcompensators and bound the system response within the norms. Machine learning algorithms can be used to tune the gains of the weighting functions, as they play a crucial role. The system may be simulated for different gains and a data set could be prepared based on the results. This data set is fed to an algorithm, which creates a pattern and understands the model. For any given set of gains of the controller, the system could predict response and important conclusions could be derived. This also helps in designing the compensator and

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controller specifications (Mbuwir, Ruelens, Spiessens, & Deconinck, 2017; Yolda¸s, Önen, Muyeen, Vasilakos, & Alan, 2017; Kolluri & de Hoog, 2020). In this chapter a detailed analysis has been made on the same aspect and by using the algorithm KNN, the system stability against low-frequency oscillations has been predicted for a set of gains. These gains were obtained by simulating the system with different combinations.

8.2.4 Protection Protection in hybrid is a challenging aspect, due to the presence of both AC and DC units. With regards to AC units, protection units need to be designed based on the magnitude of fault current. However, fault current in this case also depends on microgrid operating mode, so fault current calculation is a challenging aspect. For example, when the microgrid is operating under the grid-connected mode, most of the frequency deviations are taken care of by the grid. In this case the fault current magnitude severity is less. However, when operating in autonomous mode, the fault current depends on several aspects such as sequence impedance of the low voltage feeder connecting the sources, diesel engine generators, wind turbines, etc., as in conventional grid, evaluating the sequence networks in complex tasks considering the renewable energy sources and respective synchronizing units. Additionally, when the distributed generating units are integrated with the grid, the direction of fault current becomes bidirectional, as compared to unidirectional in the case of conventional sources of energy. This further complicates the protection aspect, as some of the relay principles fail in this aspect. Protecting the DC units is further complex, as there is no current zero instance in this case. The magnitude of fault current in this aspect is more severe, and protection units have to be designed to withstand the levels. Still, a lot of research gaps are existing in the case of the protection of DC units. In grid-connected mode the microgrid is connected to the grid via the static switch, so protection aspects may be easy. However, in the islanding mode of operation, this task is challenging and needs more attention. Machine learning algorithms can be incorporated with this aspect, concerning communication links within the protection units(Fahim, Sarker, Muyeen, Sheikh, & Das, 2020; Zamora & Srivastava, 2010; Tsao & Thanh, 2021). A fast response could be designed by incorporating the previous database and training the system and creating the data model. However, the dependence of communication link and reliability aspects relevant to link could be the major

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drawback for the proposed system. For different types of fault, studies could give rise to different possibilities.

8.2.5 Reliability Reliability is one important aspect in hybrid microgrids and is more concerned due to the presence of both AC and DC grids. With the presence of converter stages, synchronizing units, etc., reliability is affected. However, for individual aspects, reliability could be highly improved by employing future predictions. The machine learning algorithms have good prediction accuracy based on the data used for training and testing.

8.3 Advances in hybrid microgrids In this section the application of machine learning algorithms concerning frequency stability has been discussed in brief, as shown in Fig. 8.2, to apply the machine learning algorithm. First, the data are prepared. This is termed a data set. This data set is used for training algorithms and finding patterns. This acts as the experience for the machine. Based upon the input data, the machine prepares a pattern and a model that can recognize the pattern. Based on the pattern formation, the machine can predict the response for any given set of new data. The accuracy for the prediction can be calculated based on the input data and algorithm used. The input data are generally classified into two

Figure 8.2 Machine learning for pattern recognition.

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labels. This comes under the classification of supervised learning using machine learning. The labeled data are further classified as data for testing and data for training. The model prediction accuracy can be evaluated for each set of data, which is labeled as testing and training data. The system description used for the case purpose and the algorithm used for the machine learning are explained in the following sections.

8.3.1 System modeling A hybrid microgrid was considered to study the frequency deviation. The hybrid microgrid is shown in Fig. 8.3. The system structure is shown in Fig. 8.3. It consists of sources like a wind turbine, solar panels, microturbine, diesel engine generator, fuel cell, flywheel, and battery energy storage systems. All the sources are represented in the form of first-order transfer functions. The conventional system has been represented with the damping D, and inertia constant M. The system is designed for an H infinity
based robust controller. The perturbation considered for the system is load deviation, change in wind power, and change in solar irradiance. This perturbation occurs naturally and needs to be addressed for robust stability and performance criteria. The controller is derived based on H infinity
based robust control theory. The frequency stability is maintained when the frequency response is within the norms of the controller. The same frequency response has already been discussed in the previous chapter. In this chapter, we attempt to tune the gains of the weighting functions with the help of a machine learning algorithm. A data set has been obtained by simulating the system shown in Fig. 8.3, many times with different values of the gain parameters. Based on the values of

Figure 8.3 Block diagram representation of hybrid microgrid.

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gains, system behavior was observed. It was seen whether the system is becoming unstable or stable for different values of the gains of weighting functions. Those values and the system behavior were assigned data labels. A machine learning
based classification for the prepared data set was attempted and even system performance was checked for a random set of data. The system predictability was observed to be correct.

8.3.2 K-nearest neighbors Nearest neighbors algorithms are one of the simplified algorithms used in machine learning. It trains the machine, based on the input data set, and labels assigned. In general, the features that are assigned to define the operating points are near to the labeling. K represents the number of data sets considered nearest to the given label. This makes for assigning labels for any given set of data close to the original labeling. We assume that the operating point X is assigned with a metric function ρ. That is ρ: XxX-R is a function that returns the distance between any two elements of X. For example, if X 5 Rd then ρ can be the Euclidean distance, qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  0  Pd  0 0 2 ρ x; x 5 :x 2 x : 5 i51 xi 2xi .

Let S 5 ðx1 ; y1 Þ . . . ðxm ; ym Þ be a sequence of the set of data, used for training purposes. For each x ЄX; let π1 ðxÞ; . . . πm ðxÞ be a reordering of f1 . . . mg according to their distance to x, ρðx; xi Þ. That is for all i , m;     ρ x; xπiðxÞ # ρ x; xπi11 ðxÞ (8.1) For a number K; the KNN rule for binary classification is defined as follows: Input: a training sample S 5 ðx1 ; y1 Þ . . . ðxm ; ym Þ Output: for every point x Є X

 Return the majority label among yπi ðxÞ : i # K When K 5 1, we have the 1-NN rule: hs ðxÞ 5 yπ1 ðxÞ

(8.2)

8.3.3 Control law formulation The control law used to obtain the frequency stability response based on the H infinity controller has already been discussed in the previous chapter in detail. As shown in Fig. 8.4, GðsÞ represents the system transfer function, including the micro sources in their first-order transfer function form as shown in Fig. 8.4. The frequency deviations are the measured output of

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Figure 8.4 Closed-loop system block diagram.

the block. PðsÞ is the perturbed model, wherein the perturbations are load deviations, wind speed deviations, and changes in solar irradiance. ΔðsÞ is the block for uncertainty, wherein the parameters D and M are considered to be uncertain. They change their values with time and age. Similarly, the time constants of the sources can also be considered uncertain, since most of them depend on system parameters, which change with time. The We ; Wu and Wd are the pre- and postcompensators. They act to make the open-loop shape of the sensitivity and complementary sensitive functions stable. The functions are chosen as shown next. We ðsÞ 5 0:01

s3 1 5s2 1 10s 1 60 s3 1 100s2 1 15s 1 3

Wu ðsÞ 5

2ðs 1 1Þ 0:01s 1 9

3 0:01 0 0 Wd 5 4 0 0:01 0 5 0 0 0:01

(8.3)

(8.4)

2

(8.5)

The weights of the gain as shown are selected based on the trial and error method. The same gains can be obtained based on different tuning methods. In this chapter the gains have been chosen based on the trial and error method, and simulations were carried out for different values of

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the gains. As mentioned, a data set is prepared for different gains of the weighting functions, and the data set is used to train the machine. KNN algorithm is used to train the machine and make predictions for any given set of data. For each set the prediction accuracy has been calculated individually and the results are presented in the next sections as a case study.

8.4 Case study In this section a case study for machine learning
based classifications for frequency stability of a hybrid microgrid has been explained. For this the system shown in Fig. 8.4 has been considered, and simulations were performed with different gains of the weighting functions. The results were tabulated and used as data sets. Then the machine was trained using this data set using the KNN algorithm with K 5 5. The testing and training accuracy were evaluated and plotted, respectively. The prediction for any set of gains was also evaluated, to check the system prediction performance. And finally, the predictions were also checked with regressionbased algorithms and important conclusions were drawn.

8.4.1 Preparation of data set As shown in Table 8.1 for different gains of the pre- and postcompensators, system stability was evaluated. These gains represent the coefficients of the transfer functions represented by Eqs. (8.3)
(8.5). By varying the gains the system was simulated and stability was evaluated. It could be seen that one gain at a time was varied. System prediction can also be improved for different combinations. A total of 104 combinations were taken, and a few of them have been represented in Table 8.1.

8.4.2 Data labeling The next important step is to label the data. This classification of machine learning comes under supervised learning. We need to label the data. The whole data are classified into two groups, X; and Y : The group X consists of all the gains of the compensator. array(['Stable', 'Unstable'], dtype 5 object) And the group Y consists of label stability. The label Y has two objects, stable or unstable. And the group X has labels from W1 to W3 A3 .

Table 8.1 Data set for frequency stability evaluation. W1

W2B0

W2B1

W2A0

W2A1

W3B0

W3B1

W3B2

W3B3

W 3A 0

W3A1

W3A2

W3A3

Stability

1 1 0.01

2 3 2

2 2 2

9 9 9

0.01 0.01 0.01

0.6 0.6 0.6

0.1 0.1 0.1

0.05 0.05 0.05

0.01 0.01 0.01

3 3 3

15 15 0.01

100 100 100

1 1 1

Stable Stable Unstable

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Microgrids

8.4.3 Data division for training and testing To improve the accuracy of the prediction, the whole data are not used for training. The available data are classified into two categories, training and testing. Generally, 75% of data are taken for training the algorithm, and the remaining is utilized for testing. (104, 13) (78, 13) (26, 13) 

(104, 1) (78, 1) (26, 1) As shown earlier, out of the available 104 sets, 78 have been assigned for training the model, and the rest is assigned for testing the model. During computation, this classification can be changed.

8.4.4 Training the model The next important step is to train the model with a machine learning algorithm. For our case study, we have chosen the KNN algorithm as mentioned before. As shown next, the KNN KNeighborsClassifier (algorithm 5 'auto', leaf_size 5 30, metric 5 'minkowski’, metric_params 5 None, n_jobs 5 None, n_neighbors 5 5, p 5 2, weights 5 'uniform') algorithm with K 5 5 is assigned to the machine. The assignment has been done by python programming and the necessary algorithm has been imported from the database.

8.4.5 Training accuracy Based on the assigned data for training and testing, the accuracy is calculated for each assignment. The accuracy of training data is found to be 98.71%. Training accuracy Stability 0.987179 dtype: float64

8.4.6 Testing accuracy The accuracy of testing data is also checked. It is found to be 88.46%. Stability 0.884615 dtype: float64

Recent advancements in hybrid AC/DC microgrids

241

8.4.7 Making predictions The next important step is to make predictions. Since the machine has been trained and it prepares a pattern based on the algorithm, now the machine can predict the output for an unknown set of data. As shown next, two sets of data that are not parts of the data set are array(['Stable'], dtype 5 object) array(['Unstable'], dtype 5 object) obtained and fed to the machine to check the stability of the hybrid microgrid. The accuracy of the prediction was later verified by again feeding the simulation with the proposed set of data. And the prediction done by the machine learning program was found to be correct. To make more analysis, different sets of data can be fed to the machine, and system prediction could be evaluated. This implies the efficacy of the algorithm and the data set. The system data can be further analyzed by increasing the number of iterations, which is the next step. The result is stored in an array labeled “Stable” or “Unstable.”

8.4.8 Evaluating testing accuracy Now, the machine is trained for more iterations. As mentioned in Section 8.4.4, the value of K is taken as 5. This means that the machine takes the nearest 5 value around and calculates the Euclidean distance from the desired set point. [Stability 1.0 dtype: float64, Stability 0.987179 dtype: float64, Stability 0.987179 dtype: float64, Stability 0.987179 dtype: float64, Stability 0.987179 dtype: float64, Stability 0.910256 dtype: float64, Stability 0.910256 dtype: float64, Stability 0.910256 dtype: float64, Stability 0.910256 dtype: float64, Stability 0.910256 dtype: float64, Stability 0.910256 dtype: float64] CodeText A loop is formed to run the program for 12 iterations, and the accuracy of each case is evaluated. This is repeated for testing and training data.

242

Microgrids

8.4.9 Evaluating training accuracy [Stability 0.923077 dtype: float64, Stability 0.923077 dtype: float64, Stability 0.923077 dtype: float64, Stability 0.884615 dtype: float64, Stability 0.884615 dtype: float64, Stability 0.846154 dtype: float64, Stability 0.846154 dtype: float64, Stability 0.846154 dtype: float64, Stability 0.846154 dtype: float64, Stability 0.846154 dtype: float64, Stability 0.846154 dtype: float64] In this scenario the accuracy for training has been evaluated for 12 iterations. And in most cases, it is found to have greater than 84%.

8.4.10 Plotting Finally, both the training accuracy data and testing accuracy data have been plotted as shown in Fig. 8.5. It has been observed from the graph that, when iterations are less, the predictions are very high. For example, for K 5 1 the training prediction is 100%. But such models are not advisable, as the number of iterations is very low. Similarly, the testing prediction decreases as we move toward more iterations. This also increases system complexity. One important conclusion that can be made is for K 5 3, the training and testing prediction accuracy are the same as 92%. This means that for the most efficient prediction, we can choose the KNN algorithm with the K value set to 3. The plot also tells about the closeness of both predictions for different sets of data. This implies that even if we take more iterations, prediction accuracy will not deviate much.

8.4.11 Using logistic regression The same analysis has been made using logistic regression, which involves more system complexities. 0.9871794871794872 0.9230769230769231 The prediction for testing and training data was calculated and found to be much higher than the KNN algorithm. This implies that the logistic

Recent advancements in hybrid AC/DC microgrids

243

Figure 8.5 Training and testing accuracy.

regression algorithm can also be adapted. However, in our case of the classification-based machine learning approach, such complexity need not be required. If we involve more system dynamics, like predicting system controllability and observability along with stability, then in that case it could be adapted.

8.5 Conclusion In this chapter a machine learning approach for the classification-based problem under the category of supervised learning has been attempted. The problem selected for the classification case is the evaluation of robust frequency stability for a hybrid microgrid. The proposed system is simulated with different gains of weighting functions, and the system stability was investigated for the case of frequency stability. Simulation studies were carried out, and a proper data set was prepared, which was used to feed the machine for training purposes. The data set was again classified under the labels of training and testing to increase the prediction accuracy. The KNN algorithm was chosen to train the machine with the data set. After simulations the training and testing accuracy were observed and evaluated for more iterations. The training and testing accuracy were found to be good, and system prediction was tested for an unknown set of data. The number of iterations was increased and a plot was obtained between the testing and training data accuracy. It was found that for K 5 3, system performance reaches the optimum value. The prediction accuracy was also evaluated by

244

Microgrids

using the logistic regression method. The study can be further extended by evaluating the confusion matrix for the system and an important conclusion could be derived regarding system dynamics. Machine learning programs could be used to analyze important specifications of the system such as stability, voltage regulations, and protection aspects. It provides a high degree of prediction accuracy, which can answer the crucial drawbacks, in relevance to seamless operation and stable performance of the system. This recent advancement certainly increases the degree of reliability and takes us a step closer toward the green energy goals.

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Index Note: Page numbers followed by “f” and “t” refer figures and tables, respectively.

A Adafruit software, 75 Adaptive droop controllers, 193
194 Adaptive law, 54
55 Aided adaptive sliding mode controller (ASMC), 45
46 Alternating current (AC), 222t. See also Direct current (DC) AC-coupled hybrid micro-grid, 164, 165f, 172
173, 173f AC-DC coupled hybrid micro-grid, 166
167, 166f, 174
175, 175f green electrical vehicles, 95
96 grid, 100
101, 228
229, 231
232 microgrids, 5
6, 9
12, 160, 178
179, 228 algorithm, 81 block diagram, 77
78 graphical analysis, 83
85 hardware and software, 79
80 hardware section of model, 82 LCD display, 79
80 literature survey, 74
77 methodology, 79 problem statement, 73
74 recent advancements in, 67
73 results, 82 software development flowchart, 81 system, 164 web portal, 80
81 power, 95
96 sub-grid, 160 units, 229
230 Aqua-electrolyzer (AE), 48
49 Arduino mega, 79 Arduino Mega 2560, 79 Arduino microcontroller, 74
75

B Battery energy storage system (BESS), 48, 50
51, 50f, 160
161

Battery/batteries, 26
29, 100, 121
127, 191
193 bank, 151
152 characteristics, 151 lead-acid, 27 lithium-ion, 26
27 modeling, 27
28, 170 state of the charge (SOC), 92
93 Biomass energy model, 169, 169f, 170f Biomass hybrid micro-grid, 177 Bipolar topology, 95 Boltzmann’s constant, 168 Boost converter, 111, 179
180 Buck-boost converter, 112, 114f

C Capacitance, 171 Centralized control (CC), 6
8, 13
14 Charging-swapping-storage integrated station (CSSIS), 76 Chopper, principle of, 111 Closed-loop, 103
106 controller, 142 structure, 211 system model, 200 Cloud computing, 72
73 Commercial risk, 183
184 Communication systems, 232 Consensus-based approach, 193
194, 197
198 Constant power loads (CPL), 197
198 Control law formulation, 236
238 Control strategies system for hybrid microgrid, 172
176 Control techniques, 216
220, 218f, 219f Controller, order reduction of, 216 Conventional grid, 162, 163t Coordination control of converters, 179
180 isolated mode, 179
180 Country ratio, 183

247

248

Index

Credit risk, 183 Cryptographic solutions, 69
70

D Data bus signaling (DBS), 14
15 Data division for training and testing, 240 Data labeling, 238
239 Data set, 234
236 preparation of, 238 Data-driven model, 130
131, 131f load power, 133f PV power, 132f test microgrid, 132f Decentralized control, 14
15 Defuzzification, 149 Depth of discharge (DoD), 27
28 Diesel engine generator (DEG), 48
49, 209 Diesel generator, 43, 94, 97
98 Digital communication link (DCL), 13
14 Direct current (DC), 222t DC-coupled hybrid micro-grid, 165
166, 165f, 173
174, 174f DC
DC converters, 94
95, 110 grid, 231
232 design, 104
106 microgrids, 5, 12
16, 91
96, 142
143, 160, 165
166, 179, 228 advantages of, 101 architecture, 102
110 battery power, 133f bipolar topology, 96f boost converter, 111 buck-boost converter, 112 case study, 131
133 case-I (switch S is ON), 111, 113 case-II (switch S is OFF), 112
118 charging mechanism, 119
120, 120f comparison of commonly used rechargeable batteries, 125t control of, 141
142 DAB converter, 143
148, 154f data-driven model, 130
131 DC
DC converters, 110 discharging mechanism, 119 ECM, 128
130 experimental verification, 153
154

FLC, 148
149 forward conduction mode, 151 framework of centralized control scheme, 14f general diagram of isolated PV system, 97f general structure of, 93f grid power, 134f grid-connected DC microgrid, 99f isolated DC microgrid, 98f laboratory setup, 153f mesh configuration, 106f mode of operation, 96
101 modeling, 124
127, 127f performance evaluation, 149
152 photovoltaics cell/solar, 106
109 principle of chopper, 111 radial architecture, 103f reverse conduction mode, 151
152 single-phase shift technique, 149
150 solar cell circuit diagram, 108f standards, 101
102 state of charge and state of health, 121 structure, 92f structure of solar cell, 107f types of batteries, 121
127 working principle, 118
119 zonal configuration, 107f sub-grid, 160 units, 229
230 Distributed control, 15
16 Distributed energy resources (DERs), 4, 48
51, 191
193 AE, 49 BESS, 50
51 DEG, 49 FC, 49 WPG, 48
49 Distributed energy storage, 18 Distributed generation (DG), 4, 43, 160
161 Distributed generators, 141 Distributed resource (DR), 4 Disturbance observer (DOB), 44
45 Disturbance observer
aided ASMC for frequency regulation in HPS, 43
46

Index

disturbance observer-aided adaptive SMLFCC, 51
55 adaptive sliding mode LFC with disturbance observer, 52
55 traditional SMLFC, 51
52 results, 55
62 dynamic performance of isolated HPS, 56f, 59f interconnected two-area HPS, 60f interconnected two-area HPS models, 59f interconnected two-area HPS with GRC and GDBi, 61f interconnected two-area HPS with multiple-step load and RWPP, 58
60 isolated HPS against multiple load perturbation, 55
57 isolated HPS with GRC and GDB, 57
58 isolated HPS with multiple-step loads and RWPP, 57 robust stability analysis, 62 system parameters, 56t two-area HPS with GRC and GDB, 61
62 system modeling, 46
51 distributed energy resources, 48
51 model of reheated thermal power system, 47
48 reheated thermal power system model, 47f DOB-aided adaptive sliding mode controller (DOB-ASMC), 45
46, 58
60 Double-stage reheat turbine, transfer function model of, 47
48 Doubly-fed induction generator (DFIG), 179
180 Dual-active bridge converter (DAB converter), 142
148 3D graph of unified transmitted power, 146f bridge voltage for primary side, 147f diagrammatic representation of dualactive bridge converter, 144f dual-active bridge converter, 145f

249

parameter design, 146
148 Duty cycle, 28
29

E Electricity, 91, 141, 165
166 business, 184 Electrolyte, 27, 30
31 Electrolytic medium, 31 Embedded system, 70
71 Energy management system for hybrid micro-grid, 172
176 Energy storage systems (ESSs), 3 Equivalent circuit model (ECM), 124
130 External loads, 232

F Flywheel, 191
193 Flywheel energy storage (FES), 209 Forward conduction mode, 151 battery characteristics, 152f DC microgrid control, 150f fuzzy logic controller rule base, 149t injected power of solar PV array, 151f system parameters, 150t Frequency stability, 195
196 Fuel cells (FCs), 3, 29
32, 43, 48
49, 106, 191
193, 209 basic structure of, 30f model, 171 PEM fuel cell equivalent circuit, 171f schematic for working of, 31f steps for working of, 32f technology, 93
94 Fuzzification, 149 Fuzzy logic controller (FLC), 44, 148
149, 148f controller based upon fuzzy logic

G Gate signals, 111 General mixed sensitivity problem, 202
205 Generation rate constraints (GRC), 44
45, 57
58 Generation unit, 91 Global Service Mobile (GSM), 74 GNL model, 129, 130f

250

Index

Governor dead-band (GDB), 44
45, 57
58 Graphical analysis, 83
85 field chart for temperature, 84f voltage, 84f hardware circuit, 83f output messages, 83f Grid-connected mode, 98, 180
181, 180f, 181f, 233
234 Grid-connected PV system, 21
22, 22f, 23f, 24f

H H infinity control problem, 205, 206f, 207f H infinity controller frequency response, 212
214, 214f H infinity-based robust controller, 230
231, 235
236 H-bridge converters, 99
100 Handshaking method, 104 Harmonics, 172 mitigation, 19
20 Heterogeneous networks, 70 Hierarchical control schemes, 10
12, 13f Hierarchical levels, 193
194 High Voltage Direct Current (HVDC), 92, 104 High-voltage (HV), 145 HOMER-Software, 176 Hybrid AC/DC microgrids, 6, 16
17, 159
162 advances in hybrid microgrids, 234
238 block diagram representation of, 235f control law formulation, 236
238 K-nearest neighbors, 236 system modeling, 235
236 architecture of, 163
164 of AC-coupled hybrid micro-grid, 164 of AC-DC coupled hybrid microgrid, 166
167 of DC-coupled hybrid micro-grid, 165
166 typical single and three phase, 164f case study, 184
186, 185f, 212
220, 238
243

control techniques, 216
220 data division for training and testing, 240 data labeling, 238
239 demand generation graph per hour, 186f evaluating testing accuracy, 241 evaluating training accuracy, 242 H infinity controller frequency response, 212
214 using logistic regression, 242
243 making predictions, 241 μ synthesis controller frequency response, 214
215 μ synthesis controller with parametric variations, 215
216 order reduction of controller, 216 plotting, 242 preparation of data set, 238 singular values of closed-loop system, 213f solar radiation, 185f testing accuracy, 240 training accuracy, 240 training model, 240 yearly demand generation graph, 186f challenges in, 231
234 compatibility issues, 232 machine learning for pattern recognition, 234f operational aspects, 231
232 protection, 233
234 reliability, 234 uncertainty, and perturbations in renewable sources of energy, 232
233 comparison between conventional grid and hybrid micro-grid, 162 control strategies and energy management system for hybrid micro-grid, 172
176, 191
196 AC-coupled hybrid micro-grid, 172
173 AC-DC coupled hybrid micro-grid, 174
175 DC-coupled hybrid micro-grid, 173
174

Index

transition between grid-connected and standalone operation mode for energy management, 175
176, 176f control techniques, 198
208 general mixed sensitivity problem, 202
205 H infinity control problem, 205 structured singular value control theory, 206
208 structures of robust controllers, 199
202 coordination control of converters, 179
180 data set for frequency stability evaluation, 239t economic potential and benefits for hybrid micro-grid, 181
184 commercial risk, 183
184 credit risk, 183 returns, 184 flowchart for D-K iterations, 208f frequency stability, 195
196 grid-connected mode, 180
181 hierarchical control structure for, 193f hybrid micro-grid, 160
161 literature review, 196
198 linear fractional transformation structure, 198f mathematical modeling of, 178
179 modeling of AC micro-grid, 178
179 modeling of DC micro-grid, 179 methodology, 209
211 bode plots, 212f closed-loop system block diagram, 211f microgrid stability, 194 modeling of, 167
171, 176
177 battery model, 170 biomass energy model, 169 fuel cell model, 171 PV and wind hybrid micro-grid, 176
177, 177f PV system model, 167
168 PV, wind and biomass hybrid microgrid, 177, 178f small-hydro system model, 169
170

251

wind energy system model, 168
169 need of, 162 power quality issues in, 172 recent advancements in, 227
231 simple microgrid structure, 195f topographies of, 162 Hybrid energy structures, 159 system, 106 Hybrid micro-grid, 162, 163t Hybrid microgrids, 6, 159
161, 161f, 227 Hybrid power system (HPS), 43 Hybrid storage systems, 97 Hydro power plant, 170

I Incremental conductance (INC), 114
115, 117f Interconnected configuration, 104 Interfacing converters (IFCs), 172 Interlinking converter (ILC), 166
167 Internet of Things (IoT), 69, 72 Isolated mode, 97, 179
180

K K-nearest neighbors algorithm (KNN algorithm), 230
231, 236, 237f Kharitonov’s theorem, 46 Kirchhoff’s Current Law, 23 Kirchhoff’s First Law, 108

L Lead acid, 121
122 batteries, 27, 118 Linear quadratic regulator (LQR), 46 Liquid crystal display (LCD), 74, 79
80 Lithium ion battery (Li-ion battery), 26
27, 118, 124 Lithium polymer battery (Li-Po battery), 118 Load deviation, 217, 217f, 218f, 232
233, 236
237 Load-frequency controller (LFC), 43 Loads, 100, 163
164 Local area network (LAN), 72 Local control (LC), 6
8 Logistic regression, 242
243

252

Index

Low inertia, 196 Low voltage (LV), 142, 195

M Machine learning, 230
231 algorithms, 230
231 technology, 227, 230 Master
slave control, 141 Maximum power point tracking controller (MPPT controller), 13, 24
25, 100, 114
117, 115f Mesh Type DC (MTDC), 104 Microgrids (MGs), 3
4, 67, 142
143, 191
193, 227 advantages and applications of, 32
34 battery, 26
29 battery modeling, 27
28 classification, 4
6, 4f AC microgrid architecture, 6f AC/DC microgrid architecture, 7f characteristics of fuel cell, 33t composition of three-layer microgrid, 7f three-layer structure of microgrid, 7f control of AC microgrid, 9
12 control of DC microgrid, 12
16 control of hybrid (AC/DC) microgrid, 16
17, 19t control structures, 13
16 control schemes, 17t framework of AC/DC hybrid microgrid control scheme, 18f framework of decentralized control scheme, 15f framework of distributed control scheme, 16f correction factor, 29 duty cycle, 28
29 FC, 29
32 grid-connected DC microgrid, 144f grid-connected PV system, 21
22 hierarchical control schemes, 10
12 independent PV system, 21 microgrid research areas, 17
20 photovoltaics system, 20f modes of operation, 8
9

hierarchical control scheme of AC microgrid, 11f issues associated with AC microgrid, 10f MPPT, 24
25 P&O method, 24
25 PV modeling, 22
24 sizing batteries correctly, 28
29 solar, 20
24 stability, 194 structure, 6
8 different modes of operation via PCC link, 9f three-layer microgrid control structure, 8f voltage, 176 voltage of system, 28 wind turbine system, 25
26 Microturbine (MT), 191
193, 209 Mini hydro power plant, 169 Modeled perturbed system, 215 μ synthesis controller frequency response, 214
215 singular value response, 215f with parametric variations, 215
216 singular value response, 216f Multiinput
multioutput system (MIMO), 198
199

N Nanogrids, 68. See also Microgrids (MGs) Nearest neighbors algorithms, 236 Net present value (NPV), 181 Neutral point, 103
106 Nickel cadmium batteries (Ni-Cd batteries), 118, 122
123 Nickel metal hydride batteries (Ni-MH batteries), 118, 123
124

O OPAL-RT, 153

P Perturbation & Observation (P&O), 24
25, 25f, 115, 116f Photovoltaics (PV), 3, 92
93, 176
177 array, 145, 191
193

Index

cell, 23, 106
109 grid-connected PV system, 21
22 independent PV system, 21, 21f modeling, 22
24 solar, 106
109 boost converter, 111f IV curve of solar cell, 110f PV module type SunPower, 110f sources, 99
100 system model, 167
168 solar PV model, 167f technology, 20 PNGV model, 129, 130f Point of common coupling (PCC), 8, 166 Power conditioning unit (PCU), 21
22 Power electronics converters (PECs), 3, 96
97 Power electronics-based energy conversion system, 141
142 Power generation resources, 43 Power line signaling (PLS), 14
15 Power quality issues in hybrid micro-grid, 172 Primary control scheme, 11 Programmable logic controller (PLC), 74 Project cost, 184 Proportional integral and derivative (PID), 193
194 Protection issues, 20

Q Quality of Service (QoS), 70

R Radial configuration, 102 Radio frequency identification (RFID), 69
70, 72
73 Random wind power perturbation (RWPP), 57, 58f Reactive power compensation, 19 Real-time operating system, 71
72 Reheated thermal power system, 47
48 transfer function model of double-stage reheat turbine, 47
48 Renewable energy sources (RES), 5, 102, 142, 191
193 Renewable energy system, 159

253

Renewable power generation, 97
98 Renewable sources of energy, uncertainty, and perturbations in, 232
233 Reverse conduction mode, 151
152, 152f, 153f. See also Distributed energy resources (DERs) Ring main configuration, 103 Rint model, 128, 128f Robust control, 232
233 theory, 196
197 Robust controllers closed loop structure, 203f linear fractional transformation, 201f structures of, 199
202 Robust stability analysis, 62, 63f

S Secondary control scheme, 11
12, 198
199 Single-phase shift technique (SPS technique), 142
143, 149
150 Singular value control theory, 206
208, 208f, 209t Singular value decomposition (SVD), 213
214 Sizing batteries, 28
29 Sliding mode controller (SMC), 44
45 Sliding mode load frequency controller (SMLFC), 44
45, 51
52 with disturbance observer, 52
55 adaptive law, 54
55 Small-hydro system model, 169
170, 170f Smart interface, 67 Software development flowchart, 81, 82f Solar, 20
24, 106 deviation in solar irradiance, wind power, and load, 219 energy, 43 panels, 167 PV cell, 168, 209 system, 160
161 Sponsor credit risk, 183 Standard Test Condition (STC), 108 State of charge (SOC), 92
93, 142 Storage device, 118, 120f Storage elements (SEs), 166
167 Storage system, 92
93

254

Index

Sugeno fuzzy method, 148
149 Supercapacitors, 100 Superconducting magnetic energy storage (SMES), 197
198

T

V Voltage and frequency stability, 17
18 stability, 194 variations, 172 Voltage of system, 28 Voltage source converters (VSCs), 11

Tariff-based policies, 159
160 Tertiary control scheme, 12 Testing accuracy, 240 Thevnin model, 128, 129f ThingSpeak, 80
81 Training accuracy, 240 model, 240 Transfer function model of double-stage reheat turbine, 47
48 Transmission cost, 160 and distribution unit, 92 Trial-and-error approach, 43
44

Weather data, 159 Web portal, 80
81 Wi-Fi module, 79 Wind energy, 43 system model, 168
169, 168f Wind hybrid micro-grid, 176
177 Wind power and load, deviation in, 218 Wind power generation (WPG), 48
49 Wind turbine generator (WTG), 209 Wind turbine system, 25
26, 160
161 Wireless Sensor Networks (WSN), 69
70

U

Z

Uninterruptible Power Supplies (UPS), 121
122 Unipolar topology, 94
95 Unstructured uncertainty modeling, 213
214

Ziegler
Nichols-based PID controller, 43
44 Zigbee-based wireless devices, 74
75 Zonal type, 104
106

W