With the continuous development and promotion of the new energy vehicle market, the management of charging loads for new energy vehicles has emerged as one of the focal issues in the energy domain. Rational planning for the layout of charging facilities for these vehicles, combined with effective load management, serves as a crucial approach to foster the sustainable development of the new energy vehicle sector, and also to reduce energy consumption and environmental pollution. This paper proposes a reliable methodology and system for load characteristic analysis and carbon emission computation, tailored to new energy vehicle charging scenarios. Initially, diverse charging scenarios are contemplated, encompassing residential communities, corporate campuses, public parking spaces, and highway service areas. Subsequently, depending on varied dates and meteorological conditions, models characterizing the charging load features of new energy vehicles are sequentially constructed. These models predominantly consist of an evaluation module for the time distribution of electric vehicle charging sessions and a multi-state charging demand assessment module for electric vehicles. Utilizing Monte Carlo simulations, the characteristic load curves for the targeted new energy vehicle charging scenarios are derived. Lastly, based on these characteristic curves, the charging modality, and associated carbon emission factors, the carbon emissions are calculated. The methodology presented here can effectively facilitate a comprehensive analysis and assessment of charging loads for electric vehicles across diverse scenarios, factoring in carbon emissions.


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    Titel :

    A Novel Charging Load Prediction and Carbon Emission Calculation Method for Electric Vehicle


    Beteiligte:
    Liu, Wenli (Autor:in) / Liu, Wensi (Autor:in) / Han, Te (Autor:in) / Zhao, Weigang (Autor:in)


    Erscheinungsdatum :

    02.11.2023


    Format / Umfang :

    499261 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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