Cost-driven Screening of Network Constraints for the Unit Commitment Problem

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

Abstract

In an attempt to speed up the solution of the unit commitment (UC) problem, both machine-learning and optimization-based methods have been proposed to lighten the full UC formulation by removing as many superfluous line-flow constraints as possible. While the elimination strategies based on machine learning are fast and typically delete more constraints, they may be over-optimistic and result in infeasible UC solutions. For their part, optimization-based methods seek to identify redundant constraints in the full UC formulation by exploring the feasibility region of an LP-relaxation. In doing so, these methods only get rid of line-flow constraints whose removal leaves the feasibility region of the original UC problem unchanged. In this paper, we propose a procedure to substantially increase the line-flow constraints that are filtered out by optimization-based methods without jeopardizing their appealing ability of preserving feasibility. Our approach is based on tightening the LP-relaxation that the optimization-based method uses with a valid inequality related to the objective function of the UC problem and hence, of an economic nature. The result is that the so strengthened optimization-based method identifies not only redundant line-flow constraints but also inactive ones, thus leading to more reduced UC formulations.

Citation

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

  1. Porras, S. Pineda, J. M. Morales and A. Jimenez-Cordero, “Cost-driven Screening of Network Constraints for the Unit Commitment Problem,” in IEEE Transactions on Power Systems, vol. 38, no. 1, pp. 42-51, Jan. 2023.

Alternatively you could use this bibtex entry:

@ARTICLE{9736690,
author={{Porras, Alvaro and Pineda, Salvador and Morales, Juan Miguel and Jimenez-Cordero, Asuncion}},
journal={IEEE Transactions on Power Systems},
title={Cost-driven Screening of Network Constraints for the Unit Commitment Problem},
year={2023},
volume={38},
number={1},
pages={42-51},
doi={10.1109/TPWRS.2022.3160016}}
ISSN={1558-0679},}