Power Grid Cascading Failure Mitigation by Reinforcement Learning
This addresses power grid stability for energy systems, but it is incremental as it applies RL to an existing problem.
The paper tackles cascading failure mitigation in power grids by proposing a reinforcement learning strategy with physics-informed components, demonstrating promising performance in reducing system collapses on the IEEE 118-bus system.
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.