Power Grid Cascading Failure Mitigation by Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based strategy to mitigate cascading failures in power grids, utilizing physics-informed rewards and neural networks, showing promising results on the IEEE 118-bus system.

Contribution

It presents a novel RL-based approach with physics-informed components for cascading failure mitigation in power systems.

Findings

Significant reduction in system collapses on IEEE 118-bus system

Effective use of shallow and deep neural networks

Demonstrates potential for real-world power grid resilience

Abstract

This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL). The motivation of the Multi-Stage Cascading Failure (MSCF) problem and its connection with the challenge of climate change are introduced. The bottom-level corrective control of the MCSF problem is formulated based on DCOPF (Direct Current Optimal Power Flow). Then, to mitigate the MSCF issue by a high-level RL-based strategy, physics-informed reward, action, and state are devised. Besides, both shallow and deep neural network architectures are tested. Experiments on the IEEE 118-bus system by the proposed mitigation strategy demonstrate a promising performance in reducing system collapses.