用物理敏感度引导强化学习，实时优化电网切线操作防崩溃
RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors
- 用功率潮流敏感度因子指导强化学习探索方向
- 在Grid2Op平台测试中显著提升电网资源利用与防崩溃能力
- 适合关注电力系统韧性与智能控制的研究者
由于技术和政策决策的相互影响，电力系统韧性与气候变化密切相关。本文提出一种基于物理信息的机器学习框架，以增强电网韧性。当遭遇扰动事件时，设计补救控制措施防止停电。所提出的物理引导强化学习（PG-RL）框架能实时确定有效的线路切换操作，兼顾功率平衡、系统安全与电网可靠性。为制定有效防黑启动策略，PG-RL利用功率潮流敏感度因子引导强化学习训练过程中的探索行为。在Grid2Op平台上的全面评估表明，将物理信号融入强化学习可显著提升电网资源利用率并获得更优的停电预防策略，这对应对气候变化至关重要。
原文摘要 · Abstract (English)
Electricity grid's resiliency and climate change strongly impact one another due to an array of technical and policy-related decisions that impact both. This paper introduces a physics-informed machine learning-based framework to enhance grid's resiliency. Specifically, when encountering disruptive events, this paper designs remedial control actions to prevent blackouts. The proposed Physics-Guided Reinforcement Learning (PG-RL) framework determines effective real-time remedial line-switching actions, considering their impact on power balance, system security, and grid reliability. To identify an effective blackout mitigation policy, PG-RL leverages power-flow sensitivity factors to guide the RL exploration during agent training. Comprehensive evaluations using the Grid2Op platform demonstrate that incorporating physical signals into RL significantly improves resource utilization within electric grids and achieves better blackout mitigation policies - both of which are critical in addressing climate change.
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