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 …

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 …

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 …

We explore machine learning methods for AC Optimal Powerflow (ACOPF)-the task of
optimizing power generation in a transmission network according while respecting physical …

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 …

Due to the increasing uncertainties of renewable energy and stochastic demands, quick-
optimal control actions are necessary to retain the system stability and economic operation …

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 …

Optimal power flow (OPF) is at the heart of many power system operation tools and market
clearing processes. Several mathematical and heuristic approaches have been presented in …

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 …

To shift the computational burden from real-time to offline in delay-critical power systems
applications, recent works entertain the idea of using a deep neural network (DNN) to …