As global carbon emissions and fossil fuel consumption increase, the widely utilization of renewable energy have drawn more attention. Island regions, endowed with abundant renewable resources, present a critical opportunity for low-carbon development. This paper introduces a deep reinforcement learning based optimization framework to improve a multi-energy island microgrid. Furthermore, an innovative approach using a fusion model of Transformer and LSTM to predict wind and solar energy outputs, significantly improving the accuracy of renewable energy forecasts. Case analysis results indicate that with the support of the proposed reinforcement learning method, the economic and operation benefits of multi-energy microgrids on islands are achieved and the operational stability is also enhanced.


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    Titel :

    A Reinforcement Learning Based Operation Optimization of Multi-Energy Island Microgrid


    Beteiligte:
    Zhu, Wangwang (Autor:in) / Yu, Shu (Autor:in) / Wen, Shuli (Autor:in) / Zhang, Zhexi (Autor:in) / Zhao, Qiang (Autor:in) / Zhang, Bing (Autor:in)


    Erscheinungsdatum :

    10.10.2024


    Format / Umfang :

    421440 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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