Transferable Graph Learning for Transmission Congestion Management via Busbar Splitting
This addresses the challenge of near-real-time congestion management for power grid operators, offering improved generalization and transferability, though it is incremental as it builds on existing ML methods.
The paper tackles the problem of solving network topology optimization for transmission congestion management via busbar splitting, which is intractable for large-scale systems, by proposing a graph neural network approach that achieves up to 4 orders-of-magnitude speed-up and delivers AC-feasible solutions within one minute with a 2.3% optimality gap on a 2000-bus system.
Network topology optimization (NTO) via busbar splitting can mitigate transmission grid congestion and reduce redispatch costs. However, solving this mixed-integer non-linear problem for large-scale systems in near-real-time is currently intractable with existing solvers. Machine learning (ML) approaches have emerged as a promising alternative, but they have limited generalization to unseen topologies, varying operating conditions, and different systems, which limits their practical applicability. This paper formulates NTO for congestion management problem considering linearized AC PF, and proposes a graph neural network (GNN)-accelerated approach. We develop a heterogeneous edge-aware message passing NN to predict effective busbar splitting actions as candidate NTO solutions. The proposed GNN captures local flow patterns, achieves generalization to unseen topology changes, and improves transferability across systems. Case studies show up to 4 orders-of-magnitude speed-up, delivering AC-feasible solutions within one minute and a 2.3% optimality gap on the GOC 2000-bus system. These results demonstrate a significant step toward near-real-time NTO for large-scale systems with topology and cross-system generalization.