Data-Driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Reactive power optimization of distribution networks is traditionally addressed by physical model based methods, which often lead to locally optimal solutions and require heavy online inference time consumption. To improve the quality of the solution and reduce the inference time burden, this paper proposes a new graph attention networks based method to directly map the complex nonlinear relationship between graphs (topology and power loads) and reactive power scheduling schemes of distribution networks, from a data-driven perspective. The graph attention network is tailored specifically to this problem and incorporates several innovative features such as a self-loop in the adjacency matrix, a customized loss function, and the use of max-pooling layers. Additionally, a rule-based strategy is proposed to adjust infeasible solutions that violate constraints. Simulation results on multiple distribution networks demonstrate that the proposed method outperforms other machine learning based methods in terms of the solution quality and robustness to varying load conditions. Moreover, its online inference time is significantly faster than traditional physical model based methods, particularly for large-scale distribution networks.