论文标题
模拟AC OPF求解器，以获得亚秒可行的近乎最佳溶液
Emulating AC OPF solvers for Obtaining Sub-second Feasible, Near-Optimal Solutions
论文作者
论文摘要
由于绕过传统优化技术而导致的惊人速度，使用机器学习来获取AC最佳功率流的解决方案最近是一个非常活跃的研究领域。但是，通常在维持这些加速度的同时确保最终预测的可行性是一个具有挑战性的，尚未解决的问题。在本文中，我们训练一个神经网络，以效仿迭代求解器，以便廉价地迭代到最佳效果。一旦接近收敛，我们就会解决功率流以获得总体AC可行解决方案。最多1,354台总线的网络显示的结果表明，所提出的方法能够在笔记本电脑上的毫秒中找到可行的，近乎最佳的AC OPF解决方案。此外，结果表明，所提出的方法可以找到导致启动或DC-HER的“困难” AC OPF解决方案启动算法差异。
Using machine learning to obtain solutions to AC optimal power flow has recently been a very active area of research due to the astounding speedups that result from bypassing traditional optimization techniques. However, generally ensuring feasibility of the resulting predictions while maintaining these speedups is a challenging, unsolved problem. In this paper, we train a neural network to emulate an iterative solver in order to cheaply and approximately iterate towards the optimum. Once we are close to convergence, we then solve a power flow to obtain an overall AC-feasible solution. Results shown for networks up to 1,354 buses indicate the proposed method is capable of finding feasible, near-optimal solutions to AC OPF in milliseconds on a laptop computer. In addition, it is shown that the proposed method can find "difficult" AC OPF solutions that cause flat-start or DC-warm started algorithms to diverge.
