A solution method of unit commitment by artificial neural networks

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

    Research output: Contribution to journalArticle

    173 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)974-981
    Number of pages8
    JournalIEEE Transactions on Power Systems
    Volume7
    Issue number3
    DOIs
    Publication statusPublished - 1992 Aug

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering

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