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In this paper, we develop an online method that leverages machine learning toobtain feasible solutions to the AC optimal power flow (OPF) problem withnegligible optimality gaps on extremely fast timescales (e.g., milliseconds),bypassing solving an AC OPF altogether. This is motivated by the fact that asthe power grid experiences increasing amounts of renewable power generation,controllable loads, and other inverter-interfaced devices, faster systemdynamics and quicker fluctuations in the power supply are likely to occur.Currently, grid operators typically solve AC OPF every 15 minutes to determineeconomic generator settings while ensuring grid constraints are satisfied. Dueto the computational challenges with solving this nonconvex problem, manyefforts have focused on linearizing or approximating the problem in order tosolve the AC OPF on faster timescales. However, many of these approximationscan be fairly poor representations of the actual system state and still requiresolving an optimization problem, which can be time consuming for largenetworks. In this work, we leverage historical data to learn a mapping betweenthe system loading and optimal generation values, enabling us to findnear-optimal and feasible AC OPF solutions on extremely fast timescales withoutactually solving an optimization problem.

Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow