Workshop: Machine Learning for Engineering Modeling, Simulation and Design

Rethink AI-based Power Grid Control: Diving Into Algorithm Design

Xiren Zhou · siqi wang · Ruisheng Diao · Desong Bian · Jiajun Duan · Di Shi


Recently, deep reinforcement learning (DRL)-based approach has shown promise in solving complex decision and control problems in power engineering domain. In this paper, we present an in-depth analysis of DRL-based voltage control from aspects of algorithm selection, state space representation, and reward engineering. To resolve observed issues, we propose a novel imitation learning-based approach to directly map power grid operating points to effective actions without any interim reinforcement learning process. The performance results demonstrate that the proposed approach has strong generalization ability with much less training time. The agent trained by imitation learning is effective and robust to solve voltage control problem and outperforms the former RL agents.

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