Data-Driven Power Flow Calculation Method for Active Distribution Network Based on PIGAT
TL;DR
A Physics-Informed Graph Attention Network (PIGAT) surrogate model for power flow prediction, which captures the topological relationships among buses and combines nodal power balance and system active power conservation constraints to improve the physical consistency of the predictions.
Abstract
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.