Tight and compact MILP formulation for the thermal unit commitment problem
Abstract
This paper presents a mixed-integer linear programming (MILP) reformulation of the thermal unit commitment (UC) problem. The proposed formulation is simultaneously tight and compact. The tighter characteristic reduces the search space and the more compact characteristic increases the searching speed with which solvers explore that reduced space. Therefore, as a natural consequence, the proposed formulation significantly reduces the computational burden in comparison with analogous MILP-based UC formulations. We provide computational results comparing the proposed formulation with two others which have been recognized as computationally efficient in the literature. The experiments were carried out on 40 different power system mixes and sizes, running from 28 to 1870 generating units.
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The EV model is based on the current traditional mobility behaviour in Flanders.
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References 34 · Cited by 419
- Authors
- Morales-Espana, Latorre, J. M., & Ramos, A.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2013-11
- Volume
- 28
- Issue
- 4
- Pages
- 4897-4908
- ISSN
- 0885-8950 (ISSN-L)
- 1558-0679
- OpenAlex
- W2125630204
- Cited by
- 419
- References
- 34
- Citations by year
- 2026: 28 · 2025: 28 · 2024: 27 · 2023: 35 · 2022: 35 · 2021: 33 · 2020: 46 · 2019: 49 · 2018: 30 · 2017: 29 · 2016: 31 · 2015: 28 · 2014: 14 · 2013: 5 · 2012: 1
- FWCI
- 15.60
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Power Systems (publishedVersion)
- Funders
- European Commission
- Erasmus+
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Power System Reliability and Maintenance
- Keywords
- thermal unit commitment, mixed-integer linear programming reformulation, power system dispatch, computational efficiency, search space reduction, generation scheduling
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