Real-Time Cascade Mitigation in Power Systems Using Influence Graph Improved by Reinforcement Learning

TL;DR

This paper develops a reinforcement learning-based approach to real-time cascade mitigation in power systems, modeling the problem as a Markov decision process to improve decision-making under uncertainty.

Contribution

It introduces a novel MDP framework for cascade mitigation and a policy gradient algorithm that accelerates learning and handles invalid actions.

Findings

Reinforcement learning effectively reduces cascading outage risk.

Proactive line disconnections improve system resilience.

Certain lines are identified as critical for mitigation.

Abstract

Despite high reliability, modern power systems with growing renewable penetration face an increasing risk of cascading outages. Real-time cascade mitigation requires fast, complex operational decisions under uncertainty. In this work, we extend the influence graph into a Markov decision process model (MDP) for real-time mitigation of cascading outages in power transmission systems, accounting for uncertainties in generation, load, and initial contingencies. The MDP includes a do-nothing action to allow for conservative decision-making and is solved using reinforcement learning. We present a policy gradient learning algorithm initialized with a policy corresponding to the unmitigated case and designed to handle invalid actions. The proposed learning method converges faster than the conventional algorithm. Through careful reward design, we learn a policy that takes conservative actions without deteriorating system conditions. The model is validated on the IEEE 14-bus and IEEE 118-bus systems. The results show that proactive line disconnections can effectively reduce cascading risk, and certain lines consistently emerge as critical in mitigating cascade propagation.