Dynamic Collaborative Optimization Strategy for Multiple Area Clusters in Distribution Networks Considering Topology Change

To tackle the challenges arising from missing real-time measurement data and dynamic changes in network topology in optimizing and controlling distribution networks, this study proposes a data-driven collaborative optimization strategy tailored for multi-area clusters. Firstly, the distribution network is clustered based on electrical distance modularity and power balance indicators. Next, a collaborative optimization model for multiple area clusters is constructed with the objectives of minimizing node voltage deviations and active power losses. Then, a locally observable Markov decision model within the clusters is developed to characterize the relationship between the temporal operating states of the distribution network and the decision-making instructions. Using the Actor–Critic framework, the cluster agents are trained while considering the changes in cluster boundaries due to topology variations. A Critic network based on an attention encoder is designed to map the dynamically changing cluster observations to a fixed-dimensional space, enabling agents to learn control strategies under topology changes. Finally, case studies show the effectiveness and superiority of the proposed method.