CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations
This addresses the trade-off between speed and accuracy in power grid optimization, reducing costly uplift payments and carbon emissions, though it is incremental as it builds on existing neural network methods for OPF.
The authors tackled the slow speed of solving the exact AC-OPF problem in power systems by developing CANOS, a deep learning system that predicts near-optimal solutions within 1% of the true cost while running in as little as 33–65 ms and scaling to grids with up to 10,000 buses.
Optimal Power Flow (OPF) refers to a wide range of related optimization problems with the goal of operating power systems efficiently and securely. In the simplest setting, OPF determines how much power to generate in order to minimize costs while meeting demand for power and satisfying physical and operational constraints. In even the simplest case, power grid operators use approximations of the AC-OPF problem because solving the exact problem is prohibitively slow with state-of-the-art solvers. These approximations sacrifice accuracy and operational feasibility in favor of speed. This trade-off leads to costly "uplift payments" and increased carbon emissions, especially for large power grids. In the present work, we train a deep learning system (CANOS) to predict near-optimal solutions (within 1% of the true AC-OPF cost) without compromising speed (running in as little as 33--65 ms). Importantly, CANOS scales to realistic grid sizes with promising empirical results on grids containing as many as 10,000 buses. Finally, because CANOS is a Graph Neural Network, it is robust to changes in topology. We show that CANOS is accurate across N-1 topological perturbations of a base grid typically used in security-constrained analysis. This paves the way for more efficient optimization of more complex OPF problems which alter grid connectivity such as unit commitment, topology optimization and security-constrained OPF.