Enhancing Resilience in 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 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.

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