Topology-aware Graph Neural Networks for Learning Feasible and Adaptive AC-OPF Solutions

• Published in: IEEE transactions on power systems Vol. 38; no. 6; pp. 1 - 11
• Main Authors: Liu, Shaohui, Wu, Chengyang, Zhu, Hao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Contingency; Electric power grids; Electricity distribution; Feasibility; feasibility regularization; graph neural network; Graph neural networks; Learning; Load flow; Network topologies; Network topology; Neural networks; Optimal power flow; Power flow; Predictive models; Real time; Regularization; Stability analysis; Task analysis; Topology; topology adaptivity; Transmission line matrix methods; Voltage
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2022.3230555

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.