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.

该论文介绍了一种可扩展的算法，用于计算神经网络近似大型电力系统的最坏情况违规，并在合理的时间限制内构建对机器学习模型在大型工业规模电力网络中部署的信任。