RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors

TL;DR

This paper presents a physics-informed reinforcement learning framework that uses sensitivity factors to improve real-time remedial actions, significantly enhancing grid resilience and blackout mitigation capabilities amid climate change challenges.

Contribution

It introduces PG-RL, a novel physics-guided RL approach that leverages power-flow sensitivity factors for targeted exploration in grid blackout mitigation.

Findings

Enhanced resource utilization in electric grids

Improved blackout mitigation policies

Effective real-time remedial control actions

Abstract

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.