Use of Machine Learning Models to Warmstart Column Generation for Unit Commitment

TL;DR

This paper introduces machine learning models to effectively warmstart the column generation process in solving the unit commitment problem, leading to faster and more accurate solutions compared to traditional methods.

Contribution

It proposes using machine learning to generate initial dual values for column generation, improving efficiency and scalability in solving the unit commitment problem.

Findings

ML-based warmstarting yields tighter lower bounds.

ML approaches find accurate primal feasible solutions faster.

Method scales well to large problem instances.

Abstract

The unit commitment problem is an important optimization problem in the energy industry used to compute the most economical operating schedules of power plants. Typically, this problem has to be solved repeatedly with different data but with the same problem structure. Machine learning techniques have been applied in this context to find primal feasible solutions. On the other hand, Dantzig-Wolfe decomposition with a column generation procedure has been shown to be successful in solving the unit commitment problem to tight tolerance. We propose the use of machine learning models not to find primal feasible solutions directly but to generate initial dual values for the column generation procedure. Our numerical experiments compare machine learning based methods for warmstarting the column generation procedure with three baselines: column pre-population, the linear programming relaxation and coldstart. The experiments reveal that the machine learning approaches are able to find both tight lower bounds and accurate primal feasible solutions in a shorter time compared to the baselines. Furthermore, these approaches scale well to handle large instances.