Electricity network is leading to a low carbon future with high penetration of plug-in electric vehicles (EVs). However, it is extraordinarily difficult to acquire detailed information on regional EV electrification with an incomplete monitoring system for network operators. In this study, a flexible graph signal processing (GSP)-based non-intrusive monitoring on aggregated EVs is proposed to enhance the EVs visibility for operating power system safely and cost-efficiently. It can deduce the individual EV charging status with the highest possibility iteratively from the limited dataset using a GSP-based possibility calculation after processing a daytime EV characteristic charging patterns. The experiment is developed with realistic EV charging datasets collected in London, and the results show the daily EVs number in a specific region of 500 EVs daily aggregation can be estimated efficiently with an around 4.77% value of relative mean absolute deviation applying the proposed method.


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    Title :

    Regional non-intrusive electric vehicle monitoring based on graph signal processing


    Contributors:
    Li, Jiahang (author) / Li, Ran (author) / Wang, Shuangyuan (author) / Xiang, Yue (author) / Gu, Yunjie (author)

    Publication date :

    2021-02-17


    Remarks:

    Li , J , Li , R , Wang , S , Xiang , Y & Gu , Y 2021 , ' Regional non-intrusive electric vehicle monitoring based on graph signal processing ' , IET Generation, Transmission and Distribution , vol. 14 , no. 26 , pp. 6512-6517 . https://doi.org/10.1049/iet-gtd.2020.0845



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



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