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A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods

Author

Listed:
  • Luis Montero

    (Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

  • Antonio Bello

    (Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

  • Javier Reneses

    (Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain)

Abstract

Optimizing the schedule of thermal generators is probably the most important task when the operation of power systems is managed. This issue is known as the unit commitment problem in operational research. It has been profoundly studied in the literature, where several techniques have been proposed to address a computationally tractable solution. In turn, the ongoing changes of paradigms in energy markets focus the attention on the unit commitment problem as a powerful tool to handle new trends, such as the high renewable energy sources penetration or widespread use of non-conventional energy-storage technologies. A review on the unit commitment problem is propo- sed in this paper. The easy understanding of the diverse techniques applied in the literature for new researchers is the main goal of this state-of-art as well as identifying the research gaps that could be susceptible to further developments. Moreover, an overview of the evolution of the Mixed Integer Linear Programming formulation regarding the improvements of commercial solvers is presented, according to its prevailing hegemony when the unit commitment problem is addressed. Finally, an accurate analysis of modeling detail, power system representation, and computational performance of the case studies is presented. This characterization entails a significant development against the conventional reviews, which only offer a broad vision of the modeling scope of their citations at most.

Suggested Citation

  • Luis Montero & Antonio Bello & Javier Reneses, 2022. "A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods," Energies, MDPI, vol. 15(4), pages 1-40, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1296-:d:746511
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    References listed on IDEAS

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    Cited by:

    1. Francesco Gulotta & Edoardo Daccò & Alessandro Bosisio & Davide Falabretti, 2023. "Opening of Ancillary Service Markets to Distributed Energy Resources: A Review," Energies, MDPI, vol. 16(6), pages 1-25, March.
    2. Awol Seid Ebrie & Chunhyun Paik & Yongjoo Chung & Young Jin Kim, 2023. "Environment-Friendly Power Scheduling Based on Deep Contextual Reinforcement Learning," Energies, MDPI, vol. 16(16), pages 1-12, August.
    3. Layon Mescolin de Oliveira & Ivo Chaves da Silva Junior & Ramon Abritta, 2022. "Search Space Reduction for the Thermal Unit Commitment Problem through a Relevance Matrix," Energies, MDPI, vol. 15(19), pages 1-16, September.
    4. Layon Mescolin de Oliveira & Ivo Chaves da Silva Junior & Ramon Abritta, 2023. "A Space Reduction Heuristic for Thermal Unit Commitment Considering Ramp Constraints and Large-Scale Generation Systems," Energies, MDPI, vol. 16(14), pages 1-15, July.
    5. Vikram Kumar Kamboj & Om Parkash Malik, 2023. "Optimal Unit Commitment and Generation Scheduling of Integrated Power System with Plug-In Electric Vehicles and Renewable Energy Sources," Energies, MDPI, vol. 17(1), pages 1-16, December.
    6. Donovin D. Lewis & Aron Patrick & Evan S. Jones & Rosemary E. Alden & Abdullah Al Hadi & Malcolm D. McCulloch & Dan M. Ionel, 2023. "Decarbonization Analysis for Thermal Generation and Regionally Integrated Large-Scale Renewables Based on Minutely Optimal Dispatch with a Kentucky Case Study," Energies, MDPI, vol. 16(4), pages 1-23, February.
    7. Ali, E.S. & Elazim, S.M. Abd & Balobaid, A.S., 2023. "Implementation of coyote optimization algorithm for solving unit commitment problem in power systems," Energy, Elsevier, vol. 263(PA).

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