Distributed Optimization Method for Power Grids Based on Multi-Agent Deep Reinforcement Learning

This paper investigates the distributed optimization problem for a class of power grids. A distributed optimization method based on multi-agent deep reinforcement learning is developed by leveraging the multi-agent trust region policy optimization (MA-TRPO) algorithm. In the proposed algorithm, the power grid is divided into several sub-regions, which is managed by an agent. To achieve coordinated optimization among agents, deep neural networks are used to approximate the optimal policy of each agent. Unlike existing works, the proposed algorithm outperforms not only deep reinforcement learning methods for centralized control but also distributed online optimization methods. A case study is presented to verify the effectiveness and feasibility of the proposed approach.