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Search Space Reduction for the Thermal Unit Commitment Problem through a Relevance Matrix

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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

Given the combinatorial explosion related to the operation decisions in the thermal unit commitment problem, this paper presents a new strategy to reduce the search space and to start the multi-modal optimization process. To achieve such goals, a relevance matrix is obtained to indicate how important each generating unit is at each hour of the operational planning. This matrix is generated through the results of a constructive heuristic based on sensitivity indexes that account for operational and economic characteristics of the generating units and of the system under planning. The proposed method is shown to reduce the complexity of the problem, thus decreasing the combinatorial explosion and, consequently, the computational burden. Its effectiveness is verified by performing optimizations with and without its utilization. The results achieved with the proposed space-reduction approach enable solutions that present good quality. Furthermore, these solutions are retrieved with significantly reduced processing time.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7153-:d:928458
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    References listed on IDEAS

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    Cited by:

    1. Ebrie, Awol Seid & Kim, Young Jin, 2024. "Reinforcement learning-based optimization for power scheduling in a renewable energy connected grid," Renewable Energy, Elsevier, vol. 230(C).
    2. 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.

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