Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation
This addresses power grid stability for grid operators, but it is incremental as it applies an existing RL method to a new scenario.
The paper tackled the problem of multi-stage cascading failures in power grids by treating it as a reinforcement learning task and training an agent using deterministic policy gradient, achieving validation on IEEE 14-bus and 118-bus systems.
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.