A solution method of unit commitment by artificial neural networks

Hiroshi Sasaki*, Masahiro Watanabe, Junji Kubokawa, Naoto Yorino, Ryuichi Yokoyama

*この研究の対応する著者

    研究成果: Article査読

    186 被引用数 (Scopus)

    抄録

    The authors explore the possibility of applying the Hopfield neural network to combinatorial optimization problems in power systems, in particular to unit commitment. A large number of inequality constraints included in unit commitment can be handled by dedicated neural networks. As an exact mapping of the problem onto the neural network is impossible with the state of the art, a two-step solution method was developed. First, generators to be stored up at each period are determined by the network, and then their outputs are adjusted by a conventional algorithm. The proposed neural network could solve a large-scale unit commitment problem with 30 generators over 24 periods, and results obtained were very encouraging.

    本文言語English
    ページ(範囲)974-981
    ページ数8
    ジャーナルIEEE Transactions on Power Systems
    7
    3
    DOI
    出版ステータスPublished - 1992 8月

    ASJC Scopus subject areas

    • 電子工学および電気工学

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