GAT‐OPF: Robust and Scalable Topology Analysis in AC Optimal Power Flow With Graph Attention Networks

ABSTRACT As power systems rapidly grow in scale and complexity, existing data‐driven methods are limited when applied to large‐scale networks due to issues with low prediction accuracy and constraint violations. This paper proposes an innovative hybrid framework, GAT‐OPF, which, for the first time, combines graph attention networks (GAT) with deep neural networks (DNN) to form the GAT‐DNN model, designed to dynamically adapt to topology changes in the AC optimal power flow (AC‐OPF) problem. A hybrid loss function is also developed, combining prediction error with a constraint violation penalty term and incorporating a dynamic Lagrange multiplier adjustment mechanism to ensure constraint compliance throughout training. The model was tested under topology changes on the IEEE 30‐bus system and validated for scalability on larger systems, including IEEE 300‐bus, 1354‐bus, and 9241‐bus systems. The results show that the proposed model significantly enhances the computational efficiency of large‐scale power systems while effectively balancing high prediction accuracy and low constraint violations without post‐processing, highlighting its potential for real‐time optimization in large‐scale power systems.