In this paper, a Deep Neural Network (DNN)-based efficient interconnected energy management system and Reinforcement Learning (RL) technique are proposed. In this paper being investigated, interconnected M Gs are linked to a main grid, and to maintain local usage of power, they share power from the main grid. The objective from the viewpoint of the distribution system operator (DSO) is to reduce the demand-side peak-to-average ratio (PAR) but also to increase the profit from selling energy. DSO uses a DNN to train the Multi-MG response without having actual access to customer data in order to protect customer privacy. In addition, the DSO optimizes the prediction- based choice by applying the Monte Carlo method of reinforcement learning to determine its selling price strategy. Simulation findings from proposed computation deep learning method and comparison with conventional model-based techniques provide indication for the utility of the proposed method for resolving power system challenges with incomplete or ambiguous information.


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    Titel :

    Deep Neural Network Based Intelligent Multi-Microgrid Energy Management


    Beteiligte:
    Mavuri, Sri Suresh (Autor:in) / Nakka, Jayaram (Autor:in) / Kotla, Aswini (Autor:in)


    Erscheinungsdatum :

    2023-08-09


    Format / Umfang :

    609020 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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