Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation

TL;DR

This paper introduces a reinforcement learning approach to mitigate multi-stage cascading failures in power grids, addressing the complexity overlooked by traditional single-stage strategies, and validates its effectiveness on standard test systems.

Contribution

It formulates multi-stage cascading failure mitigation as a reinforcement learning problem and develops a simulation environment for training and testing the approach.

Findings

Effective mitigation of cascading failures demonstrated on IEEE 14-bus system.

Successful application of reinforcement learning to complex power grid scenarios.

Improved resilience compared to traditional methods.

Abstract

Cascading failures in power grids can lead to grid collapse, causing severe disruptions to social operations and economic activities. In certain cases, multi-stage cascading failures can occur. However, existing cascading-failure-mitigation strategies are usually single-stage-based, overlooking the complexity of the multi-stage scenario. This paper treats the multi-stage cascading failure problem as a reinforcement learning task and develops a simulation environment. The reinforcement learning agent is then trained via the deterministic policy gradient algorithm to achieve continuous actions. Finally, the effectiveness of the proposed approach is validated on the IEEE 14-bus and IEEE 118-bus systems.