The AC Optimal Power Flow (AC-OPF) is a key building block in many power system
applications. It determines generator setpoints at minimal cost that meet the power demands …

Abstract The Optimal Power Flow (OPF) problem is a fundamental building block for the
optimization of electrical power systems. It is nonlinear and nonconvex and computes the …

This paper introduces, for the first time to our knowledge, physics-informed neural networks
to accurately estimate the AC-Optimal Power Flow (AC-OPF) result and delivers rigorous …

Deep Neural Networks approaches for the Optimal Power Flow (OPF) problem received
considerable attention recently. A key challenge of these approaches lies in ensuring the …

We develop, in this paper, a machine learning approach to optimize the real-time operation
of electric power grids. In particular, we learn feasible solutions to the AC optimal power flow …

Machine learning assisted optimal power flow (OPF) aims to reduce the computational
complexity of these non-linear and non-convex constrained optimization problems by …

To cope with increasing uncertainty from renewable generation and flexible load, grid
operators need to solve alternative current optimal power flow (AC-OPF) problems more …

In recent years, there has been significant interest in the development of machine learning-
based optimization proxies for AC Optimal Power Flow (AC-OPF). Although significant …

Due to the nonlinear and non-convex attributes of the optimization problems in power
systems such as Optimal Power Flow (OPF), traditional iterative optimization algorithms …

We develop a Deep Neural Network (DNN) approach, namely DeepOPF, for solving optimal
power flow (OPF) problems that are critical for daily power system operation. DeepOPF …