Research on the multi-area cooperative control method for novel power systems

As new energy sources increasingly penetrate novel power systems, the coordinated control of frequency regulation units has become more challenging, leading to the degraded performance of power grid control. The automatic generation control methods based on traditional reinforcement learning overly depend on exploration, making it difficult quickly obtain optimal solutions for multi-area cooperative control and resulting in suboptimal performance. To address this issue, this paper proposes a multi-area coordinated control method using a greedy actor-critic algorithm enhanced with expert experience replay. This method decouple the role of entropy for exploration and policy collapse, utilizing a proposal policy to secure high-value actions for policy updates. Thus the exploration and exploitation are balanced and the local optima is avoided. Additionally, the expert experience replay collects expert demonstration data with high-value to assist the learning of multi-agent. Therefore, the meaningless exploration in early training is reduced and the rapid attainment of optimal solutions is facilitated. This paper validates the proposed method through simulations on the model of the improved IEEE standard two-area load frequency control and the model of the Sichuan, Chongqing, and Hubei three-area. Compared with other various reinforcement learning methods, the proposed method is demonstrated with superior control performance.