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

Meng, Bo; Xu, Chenghao; Zhu, Yongli

Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation (Papers Track)

Bo Meng (Sun Yat-sen University); Chenghao Xu (Sun Yat-sen University); Yongli Zhu (Sun Yat-sen University)

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Disaster Management and Relief Reinforcement Learning

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.