End-to-end constrained optimization learning: A survey
J Kotary, F Fioretto, P Van Hentenryck… - arxiv preprint arxiv …, 2021 - arxiv.org
This paper surveys the recent attempts at leveraging machine learning to solve constrained
optimization problems. It focuses on surveying the work on integrating combinatorial solvers …
optimization problems. It focuses on surveying the work on integrating combinatorial solvers …
Recent developments in machine learning for energy systems reliability management
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 …
of energy systems' reliability assessment and control. We showcase both the progress …
Predicting ac optimal power flows: Combining deep learning and lagrangian dual methods
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 …
optimization of electrical power systems. It is nonlinear and nonconvex and computes the …
Powermodels. jl: An open-source framework for exploring power flow formulations
In recent years, the power system research community has seen an explosion of novel
methods for formulating and solving power network optimization problems. These emerging …
methods for formulating and solving power network optimization problems. These emerging …
DeepOPF: A feasibility-optimized deep neural network approach for AC optimal power flow problems
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 …
operators need to solve alternative current optimal power flow (AC-OPF) problems more …
Deepopf: deep neural networks for optimal power flow
X Pan - Proceedings of the 8th ACM International Conference …, 2021 - dl.acm.org
We develop a Deep Neural Network (DNN) approach, namely DeepOPF, for solving optimal
power flow (OPF) problems that are critical for daily power system operation. DeepOPF …
power flow (OPF) problems that are critical for daily power system operation. DeepOPF …
Data-driven screening of network constraints for unit commitment
The transmission-constrained unit commitment (TC-UC) problem is one of the most relevant
problems solved by independent system operators for the daily operation of power systems …
problems solved by independent system operators for the daily operation of power systems …
A survey on applications of machine learning for optimal power flow
Optimal power flow (OPF) is at the heart of many power system operation tools and market
clearing processes. Several mathematical and heuristic approaches have been presented in …
clearing processes. Several mathematical and heuristic approaches have been presented in …
Learning for DC-OPF: Classifying active sets using neural nets
The optimal power flow is an optimization problem used in power systems operational
planning to maximize economic efficiency while satisfying demand and maintaining safety …
planning to maximize economic efficiency while satisfying demand and maintaining safety …
Learning optimal power flow: Worst-case guarantees for neural networks
This paper introduces for the first time a framework to obtain provable worst-case
guarantees for neural network performance, using learning for optimal power flow (OPF) …
guarantees for neural network performance, using learning for optimal power flow (OPF) …