RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors
This addresses grid resiliency challenges for energy systems, which is critical for climate change adaptation, but appears incremental as it builds on existing RL methods by incorporating physical signals.
The paper tackles the problem of preventing blackouts in electricity grids by designing real-time remedial control actions using a physics-informed reinforcement learning framework, achieving improved resource utilization and better blackout mitigation policies as demonstrated on the Grid2Op platform.
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.