Multi-agent deep reinforcement learning strategy for distributed energy. (November 2021)

• Record Type: Journal Article
• Authors: Xi, Lei; Sun, Mengmeng; Zhou, Huan; Xu, Yanchun; Wu, Junnan; Li, Yanying
• Is Part Of: Measurement. Volume 185(2021)
• Publication Date: 2021-11
• Subjects: Distributed energy -- Automatic generation control -- Deep reinforcement learning -- Multi-region collaboration; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109955
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The strong random disturbance caused by distributed energy must be resolved. A deep reinforcement learning strategy DDQN-CDP can obtain an optimal solution. DDQN-CDP can realize the control of continuous action space. Simulation results show that DDQN-CDP can solve the strong random disturbance problem. 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.