Electric Power Systems Resiliency: Modelling, Opportunity and Challenges considers current strengths and weaknesses of various applications and provides engineers with different dimensions of flexible applications to illustrate their use in the solution of power system improvement. Detailing advanced methodologies to improve resiliency and describing resilient-oriented power system protection and control techniques, this reference offers a deep study on the electrical power system through the lens of resiliency that ultimately provides a flexible framework for cost-benefit analysis to improve power system durability.
Aimed at researchers exploring the significance of smart monitoring, protecting and controlling of power systems, this book is useful for those working in the domain of power system control and protection (PSOP).
- Features advanced methodologies for improving electrical power system resiliency for different architectures, e.g., smart grid, microgrid and macro grid
- Discusses resiliency in power generation, transmission and distribution comprehensively throughout
- Includes case studies that illustrate the applications of resilience in power systems
| Contributors |
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ix | |
| Editors' biographies |
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| Authors' biographies |
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xv | |
| Foreword |
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xxvii | |
| Preface |
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1 Overview and architecture of resilient energy systems |
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2 Understanding the causes of outages |
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2 | (1) |
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2 | (9) |
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4 Resiliency versus reliability of electrical power systems |
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11 | (1) |
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5 Matrices for measurement of resiliency and reliability |
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11 | (6) |
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6 Means of improving resiliency |
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17 | (3) |
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20 | (5) |
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21 | (4) |
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2 Resilient smart-grid system: Issues and challenges |
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25 | (46) |
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25 | (14) |
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2 Resilient smart-grid system |
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39 | (12) |
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3 Smart-grid resiliency enhancement |
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51 | (13) |
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64 | (7) |
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65 | (6) |
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3 Remedial action scheme to improve resiliency under failures in the Central American power grid |
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71 | (16) |
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71 | (1) |
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2 Central American power grid |
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72 | (3) |
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3 Operating premises and flowchart of the RAS |
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75 | (3) |
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78 | (6) |
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84 | (3) |
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84 | (3) |
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4 Protective relay resiliency in an electric power transmission system |
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87 | (62) |
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87 | (7) |
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2 Benchmark of overcurrent coordination |
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94 | (16) |
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3 Enhanced DOCR coordination |
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110 | (6) |
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4 On-line coordination system |
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116 | (16) |
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5 Improve the resilience of the protection scheme in electrical systems |
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132 | (10) |
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142 | (7) |
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142 | (5) |
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147 | (2) |
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5 Smart grid stability prediction using genetic algorithm-based extreme learning machine |
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149 | (16) |
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149 | (2) |
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151 | (3) |
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3 GA-based extreme learning machine (ELM) |
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154 | (4) |
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158 | (3) |
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161 | (4) |
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162 | (3) |
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6 Enhancing relay resiliency in an active distribution network using latest data-driven protection schemes |
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165 | (16) |
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165 | (1) |
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2 Microgrid protection challenges |
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166 | (3) |
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169 | (7) |
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4 Comparative analysis of data-driven protection schemes |
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176 | (1) |
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177 | (4) |
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178 | (3) |
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7 Microgrids as a resilience resource in the electric distribution grid |
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181 | (32) |
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181 | (2) |
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2 Key resources offered by microgrid within distribution system |
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183 | (3) |
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3 Assessment of distribution system resilience with microgrid |
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186 | (7) |
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4 Strategies for enabling distribution system resiliency with microgrids |
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193 | (9) |
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5 Barriers and challenges |
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202 | (5) |
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207 | (6) |
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208 | (5) |
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8 Internet of things and fog computing application to improve the smart-grid resiliency |
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213 | (18) |
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213 | (1) |
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2 Internet of things in smart-grid resiliency |
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214 | (2) |
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3 Fog computing in smart-grid resiliency |
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216 | (3) |
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4 Integration of loT and fog computing in smart grid |
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219 | (2) |
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5 General discussion and futures directions |
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221 | (4) |
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6 Conclusion and futures directions |
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225 | (6) |
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227 | (4) |
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9 Load forecasting using ANN and their uncertainty effect on power system reliability |
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231 | (14) |
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231 | (1) |
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2 Purpose of load forecasting and classification |
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232 | (2) |
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3 Relative ranking of input data by analyzing performance index |
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234 | (1) |
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4 ANN-based load forecasting techniques |
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235 | (1) |
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5 LEVENBERG-MARQUARDT solution methodology |
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236 | (1) |
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6 Effect of load forecast uncertainties on reliability of power system |
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237 | (1) |
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238 | (4) |
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242 | (3) |
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242 | (3) |
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10 Cost-benefit analysis for smart grid resiliency |
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245 | (16) |
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245 | (1) |
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2 Elements of resilience framework |
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246 | (2) |
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3 Cost-benefit analysis tool |
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248 | (1) |
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4 Cost-benefit analysis approach |
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249 | (3) |
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5 Cost-benefit analysis: Advantages |
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252 | (1) |
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6 Why we are using resiliency in a project? |
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252 | (3) |
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7 Economic analysis of a photovoltaic system |
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255 | (3) |
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258 | (3) |
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258 | (3) |
| Index |
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261 | |
Ramesh C. Bansal is a Professor at University of Sharjah and Extraordinary Professor at University of Pretoria. He has over 25 years of teaching and research experience, and previously worked at University of Pretoria, University of Queensland, University of South Pacific, and BITS, Pilani. He has published over 400 journal/conf. papers, books/book chapters, and is he AE/Editor of many reputed journals. He has Google citations of over 16000 and h-index of 60. He has supervised 25 PhD, 5 Post Docs. He is a Fellow of IET-UK, IE (India), SAIEE (South Africa). Manohar Mishra is an Associate Professor in the Department of Electronics and Electrical Engineering, Faculty of Engineering and Technology, Siksha O Anusandhan Deemed to be University, Bhubaneswar, Odisha, India. He received his PhD in Electrical Engineering, MTech in Power Electronics and Drives, and BTech in Electrical Engineering in 2017, 2012, and 2008, respectively. He has published more than 50 research papers in various peer-reviewed international journals and conferences. He has edited three research books, and has served as a reviewer for journal publishers such as the Institute of Electrical and Electronics Engineers (IEEE), Springer, Elsevier, and Inderscience. At present, he has more than 10 years of teaching experience in the field of electrical engineering. He is a senior member of the IEEE, and is currently guiding four PhD scholars. His areas of interest include power system analysis, power system protection, signal processing, power quality, distribution generation systems, and micro-grids. He has served as Convener and Volume Editor of the International Conference on Innovation in Electrical Power Engineering, Communication and Computing Technology (IEPCCT-2019, IEPCCT-2021) and the International Conference on Green Technology for Smart City and Society (GTSCS-2020). Currently, he is serving as Guest Editor for different journals such as International Journal of Power Electronics (Inderscience), International Journal of Innovative Computing and Application (Inderscience), and Neural Computing and Application (Springer). Prof. Yog Raj Sood is currently working as Vice-Chancellor, Jaypee Institute of Information Technology, Noida. He has authored and co-authored several national and international publications and also working as a reviewer for reputed professional journals. He is having an active association with different societies and academies around the world. He has received several awards for the contributions to the scientific community. He has successfully completed three MHRD research projects and one mega projects under TIFAC- CORE of Rs. 53 million. Ten research scholars have already completed Ph.D. under his guidance. He is a Senior member of IEEE, life member of Indian Society for Technical Education (ISTE), fellow of Institution of Engineers (India), and member of many other technical societies. His major research interests involve Deregulated Power Sector, Congestion Management, and Microgrid
