一种基于云边智能的配电网分区与运行优化方法

The increasing emergence of distributed renewable generation and varying load demand adversely affect the security of distribution network operation. In this paper, a cloud-edge intelligence-based optimization method is proposed for distribution network partitioning and operation to derive the near-optimal real-time control strategies of switches, energy storage systems, static var compensators, and capacitor banks. It realizes centralized training in the cloud and real-time execution at edge. To address the computational burden in large-scale distribution networks, a novel partitioning method is devised to facilitate network division for operation optimization. Then, a new switch importance calculation approach is introduced to reduce the dimensionality of switch action space. Next, a multi-agent Markov Decision Process is established, where each agent corresponds to a type of controlled devices in each sub area. Finally, considering the specific inaccuracies in the distribution network model, a modified domain randomization method and an improved mixed multi-agent soft Actor-Critic algorithm is developed to enhance the robustness of policies under mismatch between the simulation model and the practical system. Numerical studies in IEEE 33-bus system and a practical 445-node distribution network are implemented to validate the effectiveness and merits of the proposed optimization method.