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Enhancing Resilience in Distribution Systems
- 1st Edition - July 1, 2025
- Authors: Fangxing Fran Li, Qingxin Shi, Jin Zhao
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 3 6 4 0 - 2
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 3 6 3 9 - 6
Enhancing Resilience in Distribution Systems presents practical guidance for readers on the challenges and potential solutions for resilience in modern power systems. The book begi… Read more
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Request a sales quoteEnhancing Resilience in Distribution Systems presents practical guidance for readers on the challenges and potential solutions for resilience in modern power systems. The book begins by explaining the risks and problems for resilience presented by renewable-based power systems. It goes on to clarify the current state of research and propose several novel methodologies and technologies for analysis and improvement of power system resilience. These methods include deep learning, linear programming, and generative adversarial networks.
Packed with practical steps and tools for implementing the latest technologies, this book provides researchers and industry professionals with guidance on the resilient systems of the future.
- Breaks down novel methodologies and tools from deep learning to generative adversarial networks
- Supports readers in implementing practical steps towards resilient renewable energy
- Presents practical guidance for readers on the challenges and potential solutions for resilience in modern power systems
Academics in the energy field, including graduate students and researchers, Energy industry professionals
1. Resilience in Modern Distribution Systems
2. Solutions, Current Issues, and Future Challenges
3. Components in Distribution Systems
4. Optimal Planning to Enhance Distribution Resilience
5. Optimal Operation to Enhance Distribution Resilience
6. Machine Learning Can Help Form Microgrids for Better Resilience
7. More on Machine Learning: When the Extreme Event Data is Scarce
8. Conclusions
2. Solutions, Current Issues, and Future Challenges
3. Components in Distribution Systems
4. Optimal Planning to Enhance Distribution Resilience
5. Optimal Operation to Enhance Distribution Resilience
6. Machine Learning Can Help Form Microgrids for Better Resilience
7. More on Machine Learning: When the Extreme Event Data is Scarce
8. Conclusions
- No. of pages: 250
- Language: English
- Edition: 1
- Published: July 1, 2025
- Imprint: Elsevier
- Paperback ISBN: 9780443236402
- eBook ISBN: 9780443236396
FL
Fangxing Fran Li
Fangxing ‘Fran’ Li is the James W. McConnell Professor in Electrical Engineering and the Campus Director of CURENT at the University of Tennessee at Knoxville, USA. His current research interests include resilience, artificial intelligence in power, demand response, distributed generation and microgrid, and energy markets. From 2020 to 2021, he served as the Chair of the IEEE PES Power System Operation, Planning and Economics (PSOPE) Committee. He has been the Chair of IEEE WG on Machine Learning for Power Systems since 2019 and the Editor-In-Chief of IEEE Open Access Journal of Power and Energy (OAJPE) since 2020. Prof. Li has received numerous awards and honours including R&D 100 Award in 2020, IEEE PES Technical Committee Prize Paper award in 2019, 5 best or prize paper awards at international journals, and 6 best papers/posters at international conferences.
Affiliations and expertise
James McConnell Professor, Dept. of Electrical Engineering and Computer Science, University of Tennessee at Knoxville, USAQS
Qingxin Shi
Qingxin Shi is an Assistant Professor in the School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China. His research interests include demand response, resilient urban power systems, and hydrogen-electric integrated energy systems. He serves as the Associate Editor of Protection and Control of Modern Power Systems and the IEEE Open Access Journal of Power and Energy (OAJPE).
Affiliations and expertise
Assistant Professor, North China Electric Power University, Baoding, Hebei, ChinaJZ
Jin Zhao
Jin Zhao is an Alexander von Humboldt Researcher hosted by Technical University Dortmund, Germany. She was previously a Research Scientist at the University of Tennessee (UTK), USA, and a 2021 outstanding reviewer of IEEE Transactions on Power Systems and the IEEE Open Access Journal of Power and Energy. She has been the Chair of IEEE PES TF on Advanced Intelligence Techniques for Resilient Power System Restoration since 2022. Her research interests include power system resilience, transmission & distribution system restoration, renewable energy integration, resilient micro-grids, and machine-learning.
Affiliations and expertise
Alexander von Humboldt Researcher, Department of Electrical Engineering and Information Technology, Technical University Dortmund, Germany