Re-scheduling the unit commitment problem in fuzzy environment

Bo Wang*, You Li, Junzo Watada

*この研究の対応する著者

    研究成果: Conference contribution

    11 被引用数 (Scopus)

    抄録

    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.

    本文言語English
    ホスト出版物のタイトルIEEE International Conference on Fuzzy Systems
    ページ1090-1095
    ページ数6
    DOI
    出版ステータスPublished - 2011
    イベント2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei
    継続期間: 2011 6 272011 6 30

    Other

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

    ASJC Scopus subject areas

    • ソフトウェア
    • 人工知能
    • 応用数学
    • 理論的コンピュータサイエンス

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