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 …

Abstract The Electricity Supply Chain is a system of enabling procedures to optimize
processes ranging from production to transportation and consumption of electricity. The …

This paper proposes a novel model-free/data-driven centralized training and decentralized
execution multi-agent deep reinforcement learning (MADRL) framework for distribution …

Microgrids are gaining popularity by facilitating distributed energy resources (DERs) and
forming essential consumer/prosumer centric integrated energy systems. Integration …

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 …

The energy sector is enduring a momentous transformation with new technological
advancements and increasing demand leading to innovative pathways. Artificial intelligence …

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 …

Accurate knowledge of the distribution system topology and parameters is required to
achieve good voltage control performance, but this is difficult to obtain in practice. This paper …

Smart Grid technology has been considered an attractive research issue due to its efficiency
in solving energy demand, storage, and power transmission. The integration of IoT …

The recent advances in computing technologies and the increasing availability of large
amounts of data in smart grids and smart cities are generating new research opportunities in …