Is Learning for the Unit Commitment Problem a Low-hanging Fruit?

This is a summary of the work that can be found in [1]. Open Access pdf is available at [2].

Abstract

The blast wave of machine learning and artificial intelligence has also reached the power systems community, and amid the frenzy of methods and black-box tools that have been left in its wake, it is sometimes difficult to perceive a glimmer of Occam’s razor principle. In this letter, we use the unit commitment problem (UCP), an NP-hard mathematical program that is fundamental to power system operations, to show that simplicity must guide any strategy to solve it, in particular those that are based on learning from past UCP instances. To this end, we apply a naive algorithm to produce candidate solutions to the UCP and show, using a variety of realistically sized power systems, that we are able to find optimal or quasi-optimal solutions with remarkable speedups. Our claim is thus that any sophistication of the learning method must be backed up with a statistically significant improvement of the results in this letter.

Citation

If you would like to cite this work, please use the following citation:

  1. Pineda and J.M. Morales, Is learning for the unit commitment problem a low-hanging fruit? doi.org/10.1016/j.epsr.2022.107851, 2022.

Alternatively you could use this bibtex entry:

@ARTICLE{PINEDA2022107851,
author={{S. Pineda and J.M. Morales}},
journal={Electric Power Systems Research},
title={Is learning for the unit commitment problem a low-hanging fruit?},
year={2022},
volume={207},
number={-},
pages={107851},
doi={doi.org/10.1016/j.epsr.2022.107851}}