基于物理引导强化学习的停电缓解方法
Blackout Mitigation via Physics-guided RL
AI总结：
本文提出物理引导强化学习框架，利用潮流灵敏度因子引导探索，在Grid2Op平台上验证了结合线路移除与发电机调整的序贯决策能有效防止或延迟停电。
摘要：
本文考虑针对系统异常进行补救控制动作的序贯设计，最终目标是防止停电。设计了一个物理引导的强化学习（RL）框架，以识别有效的实时补救前瞻决策序列，并考虑对系统稳定性的长期影响。本文考虑的控制动作空间既包括离散取值的输电线路开关决策（线路重连和移除），也包括连续取值的发电机调整。为了识别有效的停电缓解策略，设计了一种物理引导方法，利用与输电网络相关的潮流灵敏度因子在智能体训练过程中引导强化学习的探索。使用开源Grid2Op平台进行的全面实证评估表明，将物理信号纳入强化学习决策具有显著优势，确立了所提出的物理引导方法相比其黑箱对应方法的收益。一个重要观察结果是，策略性地移除输电线路，并结合多次实时发电机调整，通常能够产生有效的长期决策，从而可能防止或延迟停电。
英文摘要：
This paper considers the sequential design of remedial control actions in response to system anomalies for the ultimate objective of preventing blackouts. A physics-guided reinforcement learning (RL) framework is designed to identify effective sequences of real-time remedial look-ahead decisions accounting for the long-term impact on the system's stability. The paper considers a space of control actions that involve both discrete-valued transmission line-switching decisions (line reconnections and removals) and continuous-valued generator adjustments. To identify an effective blackout mitigation policy, a physics-guided approach is designed that uses power-flow sensitivity factors associated with the power transmission network to guide the RL exploration during agent training. Comprehensive empirical evaluations using the open-source Grid2Op platform demonstrate the notable advantages of incorporating physical signals into RL decisions, establishing the gains of the proposed physics-guided approach compared to its black box counterparts. One important observation is that strategically~\emph{removing} transmission lines, in conjunction with multiple real-time generator adjustments, often renders effective long-term decisions that are likely to prevent or delay blackouts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
