Optimal Power Flow (OPF) is a valuable tool for power system operators, but it is a difficult problem to solve for large systems. Machine Learning (ML) algorithms, especially Neural Networks-based (NN) optimization proxies, have emerged as a promising new tool for solving OPF, by estimating the OPF solution much faster than traditional methods. However, these ML algorithms act as black boxes, and it is hard to assess their worst-case performance across the entire range of possible inputs than an OPF can have. Previous work has proposed a mixed-integer programming-based methodology to quantify the worst-case violations caused by a NN trained to estimate the OPF solution, throughout the entire input domain. This approach, however, does not scale well to large power systems and more complex NN models. This paper addresses these issues by proposing a scalable algorithm to compute worst-case violations of NN proxies used for approximating large power systems within a reasonable time limit. This will help build trust in ML models to be deployed in large industry-scale power grids.

翻译：最优潮流（OPF）是电力系统运行人员的重要工具，但在大规模系统中求解非常困难。机器学习（ML）算法，特别是基于神经网络的优化代理，通过比传统方法更快地估计OPF解，已成为求解该问题的新兴工具。然而，这些ML算法如同黑箱，难以评估其在OPF全部可能输入范围内的最差性能。已有研究提出基于混合整数规划的方法，用于量化经训练以估计OPF解的神经网络在整个输入域内引发的最差违规。但该方法难以扩展至大型电力系统和更复杂的神经网络模型。本文针对这些问题，提出一种可扩展算法，能在合理时限内计算用于逼近大型电力系统的神经网络代理的最差违规。这将有助于建立对部署于工业级大型电网的机器学习模型的信任。