Reinforcement Learning in Power Distribution System: Theory, Algorithms and Applications
* 21PESGM2335, Deep Reinforcement Learning in Power Distribution Systems: Overview, Challenges, and Opportunities: N. YU, UCR * 21PESGM2336, Adversarial reinforcement learning for Inverter-based Volt-VAR Control in Active Distribution Networks: W. WU, Tsinghua University * 21PESGM2337, Control Power Distribution Systems with Sample-efficient and Communication Efficient Reinforcement Learning Algorithms: Y. GAO, UC Riverside * 21PESGM2338, Deep Reinforcement Learning-based Capacity Scheduling for PV-Battery Storage System: J. WANG, Southern Methodist University
-
Members: $5.00PES
IEEE Members: $10.00
Non-members: $20.00Length: 02:03:07
26 Jul 2021
This panel session covers the theory, algorithms, and applications of reinforcement learning in power distribution system. Model-based for distribution system control, energy scheduling, and protection may not be reliable when electric utilities do not have complete and accurate distribution network topology and parameters. The model-free reinforcement learning-based approach addresses the limitations of model-based algorithms. The theoretical advancement of reinforcement learning, unique algorithms and their applications in power distribution systems will be presented.
Chairs:
Nanpeng Yu, UCR
Sponsor Committees:
(AMPS) Distribution System Analysis
