A Polyhedral Study on Unit Commitment with a Single Type of Binary Variables

TL;DR

This paper introduces a novel power unit commitment formulation using only one type of binary variable, along with strong valid inequalities, to improve solution tightness and computational efficiency.

Contribution

It develops a single-binary variable formulation for unit commitment and proposes strong valid inequalities with facet-defining conditions and separation algorithms.

Findings

Enhanced formulation tightness with strong valid inequalities

Effective computational performance on network-constrained UC problems

Applicability of inequalities to multi-variable binary formulations

Abstract

Efficient power production scheduling is a crucial concern for power system operators aiming to minimize operational costs. Previous mixed-integer linear programming formulations for unit commitment (UC) problems have primarily used two or three types of binary variables. The investigation of strong formulations with a single type of binary variables has been limited, as it is believed to be challenging to derive strong valid inequalities using fewer binary variables, and the reduction of the number of binary variables is often accompanied by a compromise in tightness. To address these issues, this paper considers a formulation for unit commitment using a single type of binary variables and develops strong valid inequality families to enhance the tightness of the formulation. Conditions under which these strong valid inequalities serve as facet-defining inequalities for the single-generator UC polytope are provided. For those large-size valid inequality families, the existence of efficient separation algorithms for determining the most violated inequalities is also discussed. The effectiveness of the proposed single-binary formulation and strong valid inequalities is demonstrated through computational experiments on network-constrained UC problems. The results indicate that the strong valid inequalities presented in this paper are effective in solving UC problems and can also be applied to UC formulations that contain more than one type of binary variables.