In this paper, we develop an online method that leverages machine learning to
obtain feasible solutions to the AC optimal power flow (OPF) problem with
negligible optimality gaps on extremely fast timescales (e.g., milliseconds),
bypassing solving an AC OPF altogether. This is motivated by the fact that as
the power grid experiences increasing amounts of renewable power generation,
controllable loads, and other inverter-interfaced devices, faster system
dynamics and quicker fluctuations in the power supply are likely to occur.
Currently, grid operators typically solve AC OPF every 15 minutes to determine
economic generator settings while ensuring grid constraints are satisfied. Due
to the computational challenges with solving this nonconvex problem, many
efforts have focused on linearizing or approximating the problem in order to
solve the AC OPF on faster timescales. However, many of these approximations
can be fairly poor representations of the actual system state and still require
solving an optimization problem, which can be time consuming for large
networks. In this work, we leverage historical data to learn a mapping between
the system loading and optimal generation values, enabling us to find
near-optimal and feasible AC OPF solutions on extremely fast timescales without
actually solving an optimization problem.

本文介绍了一种基于机器学习方法的在线求解交流优化潮流问题的方案，通过利用历史数据来学习系统负载和最优发电量之间的映射关系，实现在毫秒级别上获得近乎最优的解决方案，避免了解决非凸优化问题带来的计算挑战。