Data–Physics Fusion-Driven Dynamic Partitioning of Active Distribution Networks for Fast Coordinated Power Control

High penetrations of distributed energy resources make active distribution networks strongly time-varying, nonlinear, and spatially coupled, which limits the online applicability of centralized voltage/reactive-power optimization. This paper proposes a data–physics fusion dynamic partitioning method for fast power coordination. A physics-based rolling partition baseline is first developed by integrating node operating behavior, voltage/reactive sensitivity, electrical distance, and feeder topology, providing an interpretable and efficient partitioning scheme for normal operating conditions. For high-volatility and strongly coupled scenarios, a heterogeneous dynamic graph and a heterogeneous spatio-temporal graph attention network are introduced to learn control-oriented latent node embeddings. Physical regularization, boundary-coupling penalties, and temporal smoothing constraints are further embedded into soft clustering to reduce cross-partition coupling and partition fluctuation. Tests on the IEEE 33-bus, IEEE 123-bus, and practical Feeder Z systems show that the dynamic partition closely approximates global OPF results, achieving normalized costs of 1.00017 and 1.00099 on the two IEEE systems with 74.3% and 83.2% time reductions. It further reduces the Feeder Z fixed-partition cost gap by 88.0%, while HST-GAT lowers boundary P/Q exchanges by 1.55%/6.57% under volatile conditions.