Enhancing Resilience in Power Distribution Systems

Erscheinungsjahr 2025
ISBN: 978-0-443-23640-2
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 152 mm x 229 mm, Gewicht: 450 g

Enhancing Resilience in Power 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.

Autoren/Hrsg.

Li, Fangxing Fran

Shi, Qingxin

Zhao, Jin

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Energieverteilung, Stromnetze
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Zuverlässigkeitstechnik
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik

Inhaltsverzeichnis

1. Resilience in Modern Distribution Systems
2. Solutions, Current Issues, and Future Challenges
3. Components in Distribution Systems
4. Resilience-Oriented Long-term Planning in Distribution systems
5. Resilience-Oriented Short-term Planning in Urban-Level Power Networks
6. Optimal Operation to Enhance Distribution Resilience
7. Machine Learning for Pre-Event Preparation
8. Machine Learning for During-Event Mitigation
9. Machine learning for post-event restoration
10. Conclusions

Über Autor(innen)

Zhao, Jin
Jin Zhao is an Assistant Professor in the Department of Electronic & Electrical Engineering, Trinity College Dublin, Ireland. Her research interests include power system resilience, climate adaptive energy systems, microgrids and machine learning. She currently serves as Senior Editor for IET Generation, Transmission & Distribution, Associate Editor for the IEEE Trans. on Smart Grid, Chair of the IEEE Task Force AISR, and as a member of the Steering Committee and PES representative for IEEE DataPort.

Shi, Qingxin
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).

Li, Fangxing Fran
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.