Optimal Operation of Active Distribution Networks

Feifan Shen received the B.Eng. degree in Electrical Engineering and Automation from Hohai University, Nanjing, China, in 2015, and the M.Eng. degree from the Department of Electrical Engineering, Wuhan University, Wuhan, China, in 2017, and the joint Ph.D. degree from the Department of Electrical Engineering, Centre of Electric Power and Energy, Technical University of Denmark, Lyngby, Denmark, and the Interdisciplinary graduate school, Nanyang Technological University, Singapore, in 2021. He is currently an assistant professor in the school of electrical and information engineering, Hunan University. His research interests include congestion management and self-healing of distribution networks, wind farm operation and control, and the application of AI in smart grid operation. Zhaoxi Liu received his Ph.D. degree from the Technical University of Denmark in 2016. He is currently a Professor at the South China University of Technology, Guangzhou, China. He was a Research Associate at the University of Wisconsin-Milwaukee, a Postdoctoral fellow in the Technical University of Denmark and a visiting scholar at the University of California, Berkeley. He has practical industrial experience in more than ten large power industry projects as an Electrical Engineer. His research interests mainly include operations research of energy systems, power system security and resilience, and integration of distributed energy resources and renewable energy sources in power systems. He serves as an active reviewer for several journals on power and energy systems. He is also a guest editor of the journal Frontiers in Energy Research. Wenshu Jiao received the B.S. and a M.S. degree in Electrical Engineering in 2018 and 2021 respectively from Shandong University, Jinan, China, where he is currently working toward his Ph.D. degree in Electrical Engineering. His research interests include renewable energy generation, active distribution networks, voltage control and distributed optimization algorithms with applications to power system. Menglin Zhang is a professor with the School of Automation, China University of Geosciences, Wuhan. She received the B.S. degree in Electrical Engineering and Automation from Southwest Jiaotong University, Chengdu, China, in 2011, and the Ph.D. from the Department of Electrical Engineering, Wuhan University, Wuhan, China, in 2017. She was a Postdoctoral Researcher with the Department of Electrical and Electronic Engineering, Huazhong University of Science and Technology (HUST), Wuhan, China from 2017 to 2019. She was a Postdoctoral Researcher with the Center for Electric Power and Energy, Technical University of Denmark (DTU) from 2019 to 2021. Her research includes the optimal operation of renewable-based multi-energy systems under uncertainty, including the modeling of multi-energy systems considering flexibility, robust and stochastic optimization, and the solution for bulk energy systems. She is a young editor for Applied Energy and IET Renewable Power Generation. Qiuwei Wu is currently a Chair Professor at the School of Electrical and Information Engineering at Tianjin University, China. Prior to this he was a tenured Associate Professor at the Tsinghua-Berkeley Shenzhen Institute of Tsinghua University, China. His research interests are in decentralized/distributed optimal operation and control of power systems with high penetration of renewables, including distributed wind power modelling and control, decentralized/distributed congestion management, voltage control and load restoration of active distribution networks, and decentralized/distributed optimal operation of integrated energy systems. Dr. Wu is an Associate Editor of IEEE Transactions on Power Systems and IEEE Power Engineering Letters, Deputy Editor-in-Chief and Associate Editor of the International Journal of Electrical Power and Energy Systems and the Journal of Modern Power Systems and Clean Energy, and a subject editor for IET Generation, Transmission & Distribution and IET Renewable Power Generation.

1. Introduction to Active Distribution Networks 2. Distribution locational marginal pricing for congestion management in distribution networks 3. Distribution Locational Marginal Pricing for Optimal Electric Vehicle Charging through Chance Constrained Mixed-Integer Programming 4. Robust Dynamic Tariff Method for Day-Ahead Congestion Management of Distribution Networks 5. Comprehensive Congestion Management for Distribution Networks based on Dynamic Tariff, Reconfiguration and Re-profiling Product 6. ADMM-based Market Clearing and Optimal Flexibility Bidding of Distribution-level Flexibility Market for Day-Ahead Congestion Management of Distribution Networks 7. Two-Tier Demand Response with Flexible Demand Swap and Transactive Control for Real-Time Congestion Management in Distribution Networks 8. Distributed Coordinated Voltage Control for Distribution Networks with DG and OLTC based on MPC and Gradient Projection 9. Double-Time-Scale Distributed Voltage Control for Unbalanced Distribution Networks Based on MPC and ADMM 10. Distributed Self-healing Scheme for Unbalanced Electrical Distribution Systems Based on Alternating Direction Method of Multipliers 11. Distributed Risk-Limiting Load Restoration in Unbalanced Distribution Systems with Networked Microgrids 12. Hierarchical Service Restoration Scheme for Active Distribution Networks based on ADMM