Re-scheduling the unit commitment problem in fuzzy environment

Bo Wang, You Li, Junzo Watada

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

    9 Citations (Scopus)

    Abstract

    The conventional prediction of future power demands are always made based on the historical data. However, the real power demands are affected by many other factors as weather, temperature and unexpected emergencies. The use of historical information alone cannot well predict real future demands. In this study, the experts' opinions from related fields are taken into consideration. To deal the uncertainty of historical data and imprecise experts' opinions, we employ fuzzy variables to better characterize the forecasted future power loads. The conventional unit commitment problem (UCP) is updated here by considering the spinning reserve costs in a fuzzy environment. As the solution, we proposed a heuristic algorithm called local convergence averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. The comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.

    Original languageEnglish
    Title of host publicationIEEE International Conference on Fuzzy Systems
    Pages1090-1095
    Number of pages6
    DOIs
    Publication statusPublished - 2011
    Event2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei
    Duration: 2011 Jun 272011 Jun 30

    Other

    Other2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011
    CityTaipei
    Period11/6/2711/6/30

    Keywords

    • Fuzzy set theory
    • Fuzzy Value-at-Risk
    • Local convergence averse binary particle swarm optimization
    • Power supply reliability
    • Test systems
    • Unit commitment problem

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence
    • Applied Mathematics
    • Theoretical Computer Science

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