This paper presents a framework and methods to estimate electric vehicles' possible states, regarding their demand, location and grid connection periods. The proposed methods use the Monte Carlo simulation to estimate the probability of occurrence for each state and a fuzzy logic probabilistic approach to characterize the uncertainty of electric vehicles' demand. Day-ahead and hour-ahead methodologies are proposed to support the smart grids' operational decisions. A numerical example is presented using an electric vehicles fleet in a smart city environment to obtain each electric vehicle possible states regarding their grid location. ; The present work has been developed under the EUREKA – ITEA2 Project SEAS (ITEA-12004), AVIGAE Project (P2020 – 3401), and has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013, and SFRH/BD/87809/2012 and SFRH/BD/94688/2013.


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    Title :

    Scenario generation for electric vehicles' uncertain behavior in a smart city environment


    Contributors:

    Publication date :

    2016-06-12


    Remarks:

    oai:zenodo.org:1067699
    Energy 111(15 September 2016) 664-675



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




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