A Dynamic Clustering Method for Distribution Networks Based on GNN-FOCOPS
[1]HAO Wubang,DUAN Wenqiang,LI Yan,et al.A Dynamic Clustering Method for Distribution Networks Based on GNN-FOCOPS[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-9.[doi:10. 13705 / j. issn. 1671-6833. 2026. 06. 003]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
48
Number of periods:
2027 XX
Page number:
1-9
Column:
Public date:
2027-12-10
- Title:
- A Dynamic Clustering Method for Distribution Networks Based on GNN-FOCOPS
- Keywords:
- distribution network cluster division; graph neural networks; constrained reinforcement learning; FOCOPS; voltage regulation; resource diversity entropy
- CLC:
- TM734 文献标 志码：A doi:
- Abstract:
- High penetration of distributed energy resources caused drastic variations in net load and power-flow directions in distribution networks, so static clustering did not adapt well to photovoltaic (PV) fluctuations and peak–valley load transitions. To address this issue, a dynamic clustering method based on a graph neural network (GNN) and constrained reinforcement learning was proposed. A comprehensive evaluation index system was first constructed by integrating variable-weight modularity, energy-sustainable voltage regulation capability, active–reactive coordination capability, and resource diversity entropy. Then, a GNN–Tree-DP–FOCOPS decision-making framework was developed, in which the GNN extracted topology- and operation-aware features, Tree-based dynamic programming (Tree-DP) imposed a hard connectivity projection on the clustering result, and first-order constrained optimization in policy space (FOCOPS) optimized the policy under operational constraints. Case studies on the IEEE 33-bus and 123-bus systems showed that, compared with the hourly-restarted particle swarm optimization method and K-means clustering, the proposed method significantly improved the comprehensive index F during high-PV-generation and peak-load periods; the trained policy required only 8 ms for single-step inference. The method also maintained good generalization to unseen operating conditions within the trained topology, achieving a 0% constraint-violation rate under a PV ramp-down scenario. However, because the learned GNN policy was tightly coupled with the training network structure, the performance degraded markedly in cross-topology transfer, where the comprehensive index dropped to 0.6561 and the violation rate increased to 70%, indicating that retraining was required. Overall, the proposed method enabled fast and safe dynamic clustering within the trained topology and provided technical support for cluster-based autonomous operation in distribution networks with high renewable penetration
- References:
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2026-06-12
