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 …

In recent years, machine learning has proven to be a powerful tool for deriving insights from
data. In this review, we describe ways in which machine learning has been leveraged to …

Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics
developed with decades of research to solve large-scale MIP instances encountered in …

Abstract Machine learning (ML) has been recently introduced to solving optimization
problems, especially for combinatorial optimization (CO) tasks. In this paper, we survey the …

Under the background of the rapid change of energy technology and the deep integration of
artificial intelligence into the power system, it is of great significance to study the intelligent …

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 …

This paper summarizes the technical activities of the IEEE Task Force on Solving Large
Scale Optimization Problems in Electricity Market and Power System Applications. This Task …

Optimized power system planning and operation are core to delivering a low-cost and high-
reliability transition path to net-zero carbon emissions. The major technological changes …

This paper proposes an intelligent Deep Learning (DL) based approach for Data‐Driven
Security‐Constrained Unit Commitment (DD‐SCUC) decision‐making. The proposed …

The rapid growth of electric vehicles (EV) in cities has led to the development of microgrids
(MGs) combined with photovoltaics (PV) and the energy storage system (ESS) as charging …