Graph Neural Network-Accelerated Network-Reconfigured Optimal Power Flow
This work addresses the problem of real-time grid operations for power systems by reducing computing time, though it is incremental as it builds on existing ML approaches for OPF.
The paper tackles the computational challenge of network-reconfigured optimal power flow (NR-OPF) by proposing a graph neural network (GNN)-based method to accelerate the solution process, achieving superior performance in case studies.
Optimal power flow (OPF) has been used for real-time grid operations. Prior efforts demonstrated that utilizing flexibility from dynamic topologies will improve grid efficiency. However, this will convert the linear OPF into a mixed-integer linear programming network-reconfigured OPF (NR-OPF) problem, substantially increasing the computing time. Thus, a machine learning (ML)-based approach, particularly utilizing graph neural network (GNN), is proposed to accelerate the solution process. The GNN model is trained offline to predict the best topology before entering the optimization stage. In addition, this paper proposes an offline pre-ML filter layer to reduce GNN model size and training time while improving its accuracy. A fast online post-ML selection layer is also proposed to analyze GNN predictions and then select a subset of predicted NR solutions with high confidence. Case studies have demonstrated superior performance of the proposed GNN-accelerated NR-OPF method augmented with the proposed pre-ML and post-ML layers.