Abstract
The high penetration of renewables into the active distribution network (ADN) brings voltage deviation and difficulties to the optimal power flow (OPF) problem. The optimal operation of the distribution grid aims to efficiently manage the flow of electricity from sources to end-users, ensuring a resilient and sustainable grid. To perform the optimal operation, OPF plays a pivotal role in solving a complex optimization problem due to the system's operational constraints and the provided OPF solutions. Implementing traditional OPF algorithms can be challenging for large-scale networks with complex topologies and constraints. The most recent advancement in learning-based models has shifted the paradigm towards data-driven approaches. This paper proposes a high-fidelity graph attention networks (GAT) model that leverages the attention mechanism and graph convolution feature mapping property to learn neighbor informative node representations for OPF solutions. We validated the proposed model on the IEEE-33, 69, and modified 123-bus power distribution networks. The proposed GAT model outperformed the state-of-the-art MPGCN and DNN models, achieving improvement of 86.33% and 62.71%, respectively, under 60% DG penetration condition. The robustness assessment of DNN, MPGCN, and GAT models are also compared in all three test cases. The GAT model exhibited less variability in its median error 0.22, 0.21, and 0.038, respectively, in each case. For computational efficiency analysis, the GAT model was processed on an IEEE-123 bus with 13,810 samples in 782 seconds, which remains within the steady-state OPF calculation time limit of 15 minutes (900 seconds). The proposed GAT model showcases its effectiveness and promises results for addressing the OPF problem in the distribution network, as evidenced by performance evaluation metrics.