  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.
