Two-stage multi-objective unit commitment optimization under future load uncertainty

Bo Wang, You Li, Junzo Watada

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    The unit commitment problem is to reduce the total generation cost as much as possible while satisfying future power demands. Therefore, optimization must be performed based on correct predictions of future demands. However, various uncertain factors affect these loads making an exact forecasting unsuccessful. This study mitigates this difficulty by applying fuzzy set theory and the objective is to build a two-stage multi-objective fuzzy programming model. To define the supply reliability effectively, we propose a new concept of maximal blackout time based on the fuzzy credibility theory. In addition, an improved two-layer multi-objective particle swarm optimization algorithm is designed as the solution. Finally, the performance of this study is discussed in comparison with experimental results from several test systems.

    Original languageEnglish
    Title of host publicationProceedings - 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012
    Pages128-131
    Number of pages4
    DOIs
    Publication statusPublished - 2012
    Event2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012 - Kitakyushu
    Duration: 2012 Aug 252012 Aug 28

    Other

    Other2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012
    CityKitakyushu
    Period12/8/2512/8/28

    Keywords

    • Fuzzy set theory
    • Load uncertainty
    • Maximal blackout time
    • Particle swarm optimization algorithm
    • Two-stage multiobjective

    ASJC Scopus subject areas

    • Biochemistry, Genetics and Molecular Biology(all)

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  • Cite this

    Wang, B., Li, Y., & Watada, J. (2012). Two-stage multi-objective unit commitment optimization under future load uncertainty. In Proceedings - 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012 (pp. 128-131). [6457196] https://doi.org/10.1109/ICGEC.2012.147