Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems

DOI10.1287/ijoc.2020.0976OpenAlexW3092319930MaRDI QIDQ4995099

Álinson S. Xavier, Feng Qiu, Shabbir Ahmed

Publication date: 23 June 2021

Published in: INFORMS Journal on Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1902.01697

zbMATH Keywords

mixed integer linear programmingmachine learningsecurity-constrained unit commitment

Mathematics Subject Classification ID

Mathematical programming (90Cxx)

Related Items (5)

A Polyhedral Study on Fuel-Constrained Unit Commitment ⋮ Scenario-dominance to multi-stage stochastic lot-sizing and knapsack problems ⋮ Learning optimal solutions via an LSTM-optimization framework ⋮ Learning to repeatedly solve routing problems ⋮ A novel solution approach with ML-based pseudo-cuts for the flight and maintenance planning problem

Uses Software

• CPLEX
• MATPOWER
• Scikit
• Julia
• JuMP
• GitHub

Cites Work

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• Random sampling and machine learning to understand good decompositions
• Learning MILP resolution outcomes before reaching time-limit
• Min-up/min-down polytopes
• A tight MIP formulation of the unit commitment problem with start-up and shut-down constraints
• On learning and branching: a survey
• The feasibility pump
• Julia: A Fresh Approach to Numerical Computing
• JuMP: A Modeling Language for Mathematical Optimization
• A Machine Learning-Based Approximation of Strong Branching

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