Robustness of Evolving Power Grids

Nyhet

Modeling and Analysis

Inbunden, Engelska, 2026

2 049 kr

Kommande

Tse and co-authors take a multidisciplinary approach to address the complex issues facing the sustainable and robust management of modern power grids – particularly the integration of renewable energy, artificial intelligence, and cyber networks – and the resulting necessity for enhanced resilience.This book concentrates on three aspects central to robustness. First, it presents a methodology for modeling cascading failures in power grids using complex network theory to develop cascading failure models that capture the propagation of failures triggered by initial events. Next, it proposes new evaluation metrics to accurately assess the robustness of complex power grids against major blackout incidents caused by cascading failures. Unlike traditional structure-based metrics, these metrics incorporate network characteristics, electrical variables, and learning-based methods. Last, it presents new strategies for enhancing the robustness of evolving power grids, focusing on both the system planning phase and system control/protection phase. Throughout, the book offers comprehensive and in-depth analyses to ensure that readers gain a comprehensive understanding of the topic. It also draws from the latest advances in the field of complex circuits and systems and incorporates findings from academic studies, industry reports, and real-world case studies, providing a solid foundation for the technical descriptions. The content is designed with practical applicability in mind; readers will benefit from actionable strategies directly implementable in real-world scenarios. Readers will acquire a holistic and multidisciplinary understanding of the robustness of evolving power grids and will be able to take a holistic perspective on modern power grid challenges and opportunities.This is a vital and cutting-edge resource for research students, undergraduate students, and researchers in power engineering. Engineers and policymakers will also find valuable recommendations and methodologies to enhance robustness and mitigate risks.

Produktinformation

Utgivningsdatum2026-03-30
Mått156 x 234 x undefined mm
FormatInbunden
SpråkEngelska
Antal sidor232
FörlagTaylor & Francis Ltd
ISBN9781041030744

Tillhör följande kategorier

Dong Liu received the BEng (Hons) degree in electronic engineering with first-class honors from The Hong Kong Polytechnic University in 2014, BEng in microelectronics from Sun Yat-sen University in Guangzhou, China, in 2014, and PhD in electrical engineering from the Hong Kong Polytechnic University in 2019. He previously held a postdoctoral position at the Institute of Textile and Clothing at the Hong Kong Polytechnic University and currently serves as a research fellow at the Centre for Complexity and Complex Networks and the Department of Electrical Engineering at City University of Hong Kong. He is currently a full member of the IEEE Power and Energy Circuits and Systems Technical Committee and a member of the IEEE Power and Energy Society. He is the author or co-author of 14 publications in peer-reviewed journals, including various IEEE transactions, and has delivered over 10 invited presentations at international conferences. His research interests encompass cascading failure modeling and analysis, cyber-physical systems, and machine learning for assessing robustness in smart grids.Xi Zhang received the B.Eng. degree in Automation from Beijing Jiaotong University, Beijing, China, in 2013, and the Ph.D. degree in Electronic and Information Engineering from the Hong Kong Polytechnic University, Hong Kong, China, in 2017. He is currently an Assistant Professor at the School of Automation, Beijing Institute of Technology, Beijing, China. His research interests focus on power system resilience, applications of AI technologies to the analysis and decision-making in power networks, etc. He served as Associate Editor of IEEE Transactions on Circuits and Systems-II: Express Briefs (2022-2023), and Guest Editor of IEEE Journal on Emerging and Selected Topics in Circuits and Systems (Q3 2023). He received the Best Associate Editor Award for his editorial services for IEEE Journal on Emerging and Selected Topics in Circuits and Systems in 2023.Chi Kong Tse graduated from the University of Melbourne, with a BEng (Hons) and PhD degrees in electrical engineering. He is currently a Chair Professor of Electrical Engineering at City University of Hong Kong and concurrently being appointed as Associate Vice President (Innovation). He is/was honorary professor and distinguished fellow with several Australian, Canadian and Chinese universities, including Melbourne University, RMIT University, University of Western Australia, University of Calgary, Huazhong University of Science and Technology (being Chang Jiang Scholars Chair), Northeastern University, etc. His research interests are in power electronics and nonlinear systems. Prof. Tse has been awarded numerous invention, research and teaching prizes. Among them, the IEEE CASS Charles A. Desoer Technical Achievement Award 2022 recognized his "outstanding contributions and continued leadership in the development of research in complex behavior of power electronics and energy systems". He was the Editor-in-Chief of the IEEE Transactions on Circuits and Systems II (2016-2019) and of the IEEE Circuits and Systems Magazine (2013-2016). He was elected an IEEE Fellow in 2005 for his contributions to power electronics systems.

1. Introduction 2. Power Network Representation and Power Flow Models 3. Power System Protection Strategies 4. Cascading Failure Models of Evolving Power Grids 5. Robustness Assessment of Evolving Power Grids 6. Robustness Enhancement of Evolving Power Grids Via System Planning 7. Robustness Enhancement Via System Control and Protection 8. Forward Perspectives on Power System Robustness. Appendix A: Failure Propagation in the Network, Appendix B: Derivation of Power Grid Resilience Metric, Appendix C: Parameter Settings in Monte Carlo Case Study, Appendix D: Real-World Data Sources