Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions
This addresses the need for efficient and reliable grid operations, offering a domain-specific incremental improvement over existing methods.
The paper tackles the real-time ac-OPF problem in electricity grids by developing a topology-aware GNN that predicts optimal solutions, achieving improved prediction accuracy, flow feasibility, and adaptivity to varying grid topologies in numerical tests.
Solving the optimal power flow (OPF) problem is a fundamental task to ensure the system efficiency and reliability in real-time electricity grid operations. We develop a new topology-informed graph neural network (GNN) approach for predicting the optimal solutions of real-time ac-OPF problem. To incorporate grid topology to the NN model, the proposed GNN-for-OPF framework innovatively exploits the locality property of locational marginal prices and voltage magnitude. Furthermore, we develop a physics-aware (ac-)flow feasibility regularization approach for general OPF learning. The advantages of our proposed designs include reduced model complexity, improved generalizability and feasibility guarantees. By providing the analytical understanding on the graph subspace stability under grid topology contingency, we show the proposed GNN can quickly adapt to varying grid topology by an efficient re-training strategy. Numerical tests on various test systems of different sizes have validated the prediction accuracy, improved flow feasibility, and topology adaptivity capability of our proposed GNN-based learning framework.