Data-Driven Power Flow Calculation Method for Active Distribution Network Based on PIGAT

With the increasing penetration of electric vehicles (EVs) and distributed energy resources, source-load uncertainty, spatiotemporal variability, and nonlinear coupling make rapid and accurate assessment of operating states more difficult in active distribution networks. This paper proposes a Physics-Informed Graph Attention Network (PIGAT) surrogate model for power flow prediction. PV output is represented by a Beta distribution, and EV charging loads are generated by category-based Monte Carlo sampling. The graph attention mechanism captures the topological relationships among buses. A physics-informed loss combines nodal power balance and system active power conservation constraints to improve the physical consistency of the predictions. The model maps source-load inputs to bus voltage magnitudes, voltage phase angles, and system active power loss. Tests on the IEEE 33-bus and 69-bus systems evaluate prediction accuracy, physical consistency, inference time, performance under light- and heavy-load conditions, and performance with limited training data. On the IEEE 33-bus system, the MAE and RMSE of bus voltage magnitude predictions are below 5 × 10⁻⁴ p.u.; the MAE and RMSE of system active power loss predictions are 0.635 and 0.712 kW, respectively; and the MAE of the active power conservation deviation is 0.862 kW. The pure forward-pass inference time is 1.58 ms, compared with 8.19 ms for MATPOWER; including the optional physics-residual check, the total evaluation time is 2.64 ms.