The optimal power flow (OPF) problem is one of the most important optimization problems for the operation of the power grid. It calculates the optimum scheduling of the committed generation units. In this paper, we develop a neural network approach to the problem of accelerating the current optimal power flow (AC-OPF) by generating an intelligent initial solution. The high quality of the initial solution and guidance of other outputs generated by the neural network enables faster convergence to the solution without losing optimality of final solution as computed by traditional methods. Smart-PGSim generates a novel multitask-learning neural network model to accelerate the AC-OPF simulation. Smart-PGSim also imposes the physical constraints of the simulation on the neural network automatically. Smart-PGSim brings an average of 49.2% performance improvement (up to 91%), computed over 10,000 problem simulations, with respect to the original AC-OPF implementation, without losing the optimality of the final solution.

本文提出了一种基于神经网络的方法来解决电力网络的最优潮流问题，并通过智能的初始解决方案及其他输出的指导，使得模型产生更快的收敛速度并保持最终解的最优性，计算结果显示在不失最终解最优性的前提下，Smart-PGSim 取得了平均49.2%（最高可达91%）的性能提升。

Smart-PGSim: 使用神经网络加速 AC-OPF 电网模拟