This paper introduces, for the first time to our knowledge, physics-informed neural networks to accurately estimate the AC-OPF result and delivers rigorous guarantees about their performance. Power system operators, along with several other actors, are increasingly using Optimal Power Flow (OPF) algorithms for a wide number of applications, including planning and real-time operations. However, in its original form, the AC Optimal Power Flow problem is often challenging to solve as it is non-linear and non-convex. Besides the large number of approximations and relaxations, recent efforts have also been focusing on Machine Learning approaches, especially neural networks. So far, however, these approaches have only partially considered the wide number of physical models available during training. And, more importantly, they have offered no guarantees about potential constraint violations of their output. Our approach (i) introduces the AC power flow equations inside neural network training and (ii) integrates methods that rigorously determine and reduce the worst-case constraint violations across the entire input domain, while maintaining the optimality of the prediction. We demonstrate how physics-informed neural networks achieve higher accuracy and lower constraint violations than standard neural networks, and show how we can further reduce the worst-case violations for all neural networks.
翻译:本文首次为我们的知识介绍了物理信息丰富的神经网络,以准确估计AC-OPF结果并严格保证其性能。电源系统操作者和其他几个行为体正在越来越多地对包括规划和实时操作在内的大量应用使用最佳电流算法(OPF)算法(OPF)算法(OPF),然而,以其原始形式,AC最佳电流问题由于非线性和非线性和非线性,往往难以解决。除了大量近距离和放松之外,最近的努力还侧重于机器学习方法,特别是神经网络。然而,迄今为止,这些方法仅部分考虑到培训期间可使用的大量物理模型。更重要的是,它们没有为可能限制其产出提供保障。我们的方法(一)在神经网络培训中引入AC电流方程方程式,以及(二)整合能够严格确定和减少整个输入领域最坏情况限制违反情况的方法,同时保持最佳的预测。我们展示了物理学了解的神经网络如何进一步减少最准确性和最差的违反情况,并显示所有标准神经网络。