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 …

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 …

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 …

The data-driven method with strong approximation capabilities and high computational
efficiency provides a promising tool for optimal power flow (OPF) calculation with stochastic …

With the increasing penetration of renewable energy, power grid operators are observing
both fast and large fluctuations in power and voltage profiles on a daily basis. Fast and …

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 …

Power dispatch is a core problem for smart grid operations. It aims to provide optimal
operating points within a transmission network while power demands are changing over …

Abstract The Security-Constrained Economic Dispatch (SCED) is a fundamental
optimization model for Transmission System Operators (TSO) to clear real-time energy …