Enhancing Resilience in Power Distribution Systems

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. 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). 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.

• 1. Resilience in Modern Distribution Systems2. Solutions, Current Issues, and Future Challenges3. Components in Distribution Systems4. Resilience-Oriented Long-term Planning in Distribution systems5. Resilience-Oriented Short-term Planning in Urban-Level Power Networks6. Optimal Operation to Enhance Distribution Resilience7. Machine Learning for Pre-Event Preparation8. Machine Learning for During-Event Mitigation9. Machine learning for post-event restoration10. Conclusions