Multi-agent deep reinforcement learning strategy for distributed energy

Abstract The strong random disturbance issues caused by the large-scale grid connections of distributed energy, such as wind energy, photovoltaic energy storage and electric vehicles, must be resolved. In this paper, we propose a Multi-agent deep reinforcement learning strategy, namely DDQN-CDP, which deeply integrate the improved actor-critic strategy with the neural network. This approach also solves the problem of the lack of continuous action controlling ability of traditional deep reinforcement learning, and obtains an optimal solution by multi-region collaboration. By simulating the modified IEEE standard two-area load frequency control power system model and Hubei power grid model, our results indicate that the proposed strategy can solve the strong random disturbance problem caused by the large-scale grid connections of distributed energy and achieve faster convergence and better control performance than other strategies.