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 …

We propose preventive learning as the first framework to guarantee Deep Neural Network
(DNN) solution feasibility for optimization problems with linear constraints without post …

A new computationally simple method of imposing hard convex constraints on the neural
network output values is proposed. The key idea is to map a latent vector to a point that is …

Abstract The Optimal Power Flow (OPF) problem is the cornerstone of power systems
operations, providing generators' most economical dispatch for power demands by fulfilling …

Ensuring solution feasibility is a key challenge in developing Deep Neural Network (DNN)
schemes for solving constrained optimization problems, due to inherent DNN prediction …

In recent years, there has been significant interest in the development of machine learning-
based optimization proxies for AC Optimal Power Flow (AC-OPF). Although significant …

Amid the increasing interest in the deployment of Distributed Energy Resources (DERs), the
Virtual Power Plant (VPP) has emerged as a pivotal tool for aggregating diverse DERs and …

The decision-making paradigms of future energy systems are increasingly becoming
decentralized and multi-entity/agent. The Alternating Direction Method of Multipliers (ADMM) …

Conventional solvers are often computationally expensive for constrained optimization,
particularly in large-scale and time-critical problems including AC optimal power flow (OPF) …

Recent years have witnessed increasing interest in optimization proxies, ie, machine
learning models that approximate the input-output mapping of parametric optimization …