Electric power systems and the companies and customers that interact with them are
experiencing increasing levels of uncertainty due to factors such as renewable energy …

Historically, centrally computed algorithms have been the primary means of power system
optimization and control. With increasing penetrations of distributed energy resources …

The power flow equations relate the power injections and voltages in an electric power
system and are therefore key to many power system optimization and control problems …

This article reviews recent works applying machine learning (ML) techniques in the context
of energy systems' reliability assessment and control. We showcase both the progress …

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 …

Big data has potential to unlock novel groundbreaking opportunities in power grid that
enhances a multitude of technical, social, and economic gains. As power grid technologies …

This tutorial summarizes recent advances in the convex relaxation of the optimal power flow
(OPF) problem, focusing on structural properties rather than algorithms. Part I presents two …

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 …

Distribution networks are usually multiphase and radial. To facilitate power flow computation
and optimization, two semidefinite programming (SDP) relaxations of the optimal power flow …

Higher levels of renewable electricity generation increase uncertainty in power system
operation. To ensure secure system operation, new tools that account for this uncertainty are …