Physics-Informed Graph Learning Acceleration for Large-Scale AC-OPF with Topology Changes
Enables fast, feasible AC-OPF solutions for real-time power system operation under topology changes, addressing a critical bottleneck for grid operators.
GraphOPF accelerates AC-OPF solving by up to 66x and NN training by up to 200x for large-scale power systems with topology changes, achieving over 99% feasibility.
In power systems, alternating current optimal power flow (AC-OPF) has been a challenging problem for decades due to its nonconvexity, but fast and efficient solutions are even more needed because of high penetration of large scale renewable generation and load growth. Recently, neural networks (NN) have gained attention in solving AC-OPF, but it is still in an early stage to be applicable for real and large-scale power system operation with topology-changing characteristics. To end this, we propose a novel framework called GraphOPF that considers topology-adaptability, scalability, NN training time, self-supervision, and feasibility altogether. Extensive experiments show that the proposed framework against the baselines is up to 200 times faster in NN training and up to 66 times faster in solving AC-OPF for large-scale power systems including the real Korean power system, while achieving more than 99% feasibility.