• DocumentCode
    3665722
  • Title

    Probabilistic estimation of the state of Electric Vehicles for smart grid applications in big data context

  • Author

    João Soares;Nuno Borges;Bruno Canizes;Zita Vale

  • Author_Institution
    GECAD - Knowledge Engineering and Decision-Support Research Centre, Polytechnic of Porto (ISEP/IPP), Rua Dr. Antã
  • fYear
    2015
  • fDate
    7/1/2015 12:00:00 AM
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    This paper presents a framework and methodology to estimate the possible states of Electric Vehicles (EVs) regarding their location and periods of connection in the grid. A Monte Carlo Simulation (MCS) is implemented to estimate the probability of occurrence of these states. The framework assumes the availability of Information and Communication Technology (ICT) technology and previous data records to obtain the probabilistic states. A case study is presented using a fleet of 15 EVs considering a smart grid environment. A high accuracy was obtained with 1 million iterations in MCS.
  • Keywords
    "Smart grids","Electric vehicles","Monte Carlo methods","Batteries","Real-time systems","Accuracy"
  • Publisher
    ieee
  • Conference_Titel
    Power & Energy Society General Meeting, 2015 IEEE
  • ISSN
    1932-5517
  • Type

    conf

  • DOI
    10.1109/PESGM.2015.7286185
  • Filename
    7286185
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3665722