Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

Modern power system is moving towards a smart and competitive grid, with competing
generating companies, power retailers, and strategically behaving consumers playing a …

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 …

Optimal power flow (OPF) is one of the most important optimization problems in the energy
industry. In its simplest form, OPF attempts to find the optimal power that the generators …

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 …

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 …

The article proposes a novel End-to-End Learning and Repair (E2ELR) architecture for
training optimization proxies for economic dispatch problems. E2ELR combines deep neural …

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

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

Foundation models, such as Large Language Models (LLMs), can respond to a wide range
of format-free queries without any task-specific data collection or model training, creating …