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A Space Reduction Heuristic for Thermal Unit Commitment Considering Ramp Constraints and Large-Scale Generation Systems

Author

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  • Layon Mescolin de Oliveira

    (Laboratory of Power Systems, Department of Electrical Energy, Federal University of Juiz de Fora, José Lourenço Kelmer St., São Pedro, Juiz de Fora 36036-900, Brazil)

  • Ivo Chaves da Silva Junior

    (Laboratory of Power Systems, Department of Electrical Energy, Federal University of Juiz de Fora, José Lourenço Kelmer St., São Pedro, Juiz de Fora 36036-900, Brazil)

  • Ramon Abritta

    (Department of Geoscience and Petroleum, Norwegian University of Science and Technology, PTS Paviljong, 540, Valgrinda, S.P. Andersens veg 15, 7031 Trondheim, Norway)

Abstract

This paper expands the research around a recently proposed method to reduce the search space region for thermal unit commitment problems. The importance of such techniques comes from the combinatorial explosion regarding the variables of the problem when there are a large quantity of generating units in the system. The proposed heuristic approach utilizes sensitivity indices to gather information about the system and fix many of the binary decision variables over the planning horizon. This work further explores the method by demonstrating its effectiveness in large-scale systems subjected to ramp constraints. Despite the significantly increased complexity, the results of this paper indicate that the method can achieve high quality solutions notably faster than other approaches from the literature.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5370-:d:1194017
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    References listed on IDEAS

    as
    1. Wei Han & Hong-hua Wang & Xin-song Zhang & Ling Chen, 2013. "A Unit Commitment Model with Implicit Reserve Constraint Based on an Improved Artificial Fish Swarm Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-11, December.
    2. Shahbazitabar, Maryam & Abdi, Hamdi, 2018. "A novel priority-based stochastic unit commitment considering renewable energy sources and parking lot cooperation," Energy, Elsevier, vol. 161(C), pages 308-324.
    3. Aml Sayed & Mohamed Ebeed & Ziad M. Ali & Adel Bedair Abdel-Rahman & Mahrous Ahmed & Shady H. E. Abdel Aleem & Adel El-Shahat & Mahmoud Rihan, 2021. "A Hybrid Optimization Algorithm for Solving of the Unit Commitment Problem Considering Uncertainty of the Load Demand," Energies, MDPI, vol. 14(23), pages 1-21, November.
    4. Bernard Knueven & James Ostrowski & Jean-Paul Watson, 2020. "On Mixed-Integer Programming Formulations for the Unit Commitment Problem," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 857-876, October.
    5. Zhu, Xiaodong & Zhao, Shihao & Yang, Zhile & Zhang, Ning & Xu, Xinzhi, 2022. "A parallel meta-heuristic method for solving large scale unit commitment considering the integration of new energy sectors," Energy, Elsevier, vol. 238(PC).
    6. 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.
    7. 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.
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