The advances of deep learning (DL) techniques bring new opportunities to numerous
intractable tasks in power systems (PSs). Nevertheless, the extension of the application of …

The fields of power system engineering and operations research are growing rapidly and
becoming increasingly entwined. This survey aims to strengthen the connections between …

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 …

We develop DeepOPF as a Deep Neural Network (DNN) approach for solving security-
constrained direct current optimal power flow (SC-DCOPF) problems, which are critical for …

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 …

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 …

This paper studies how to train machine-learning models that directly approximate the
optimal solutions of constrained optimization problems. This is an empirical risk minimization …

Power networks, responsible for transporting electricity across large geographical regions,
are complex infrastructures on which modern life critically depend. Variations in demand …

Despite major advancements in nonlinear programming (NLP) and convex relaxations, most
system operators around the world still predominantly use some form of linear programming …

The security-constrained optimal power flow (SCOPF) is fundamental in power systems and
connects the automatic primary response (APR) of synchronized generators with the short …