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.

本研究解决电网韧性与气候变化相互影响的关键问题，提出了一种基于物理指导的强化学习框架（PG-RL）以增强电网的韧性。研究结果表明，PG-RL显著改善了电网资源利用率并提升了黑outs缓解策略的效果，从而在应对气候变化方面具有重要意义。

利用强化学习缓解连锁故障：通过灵敏度因子的有针对性探索