• DocumentCode
    45308
  • Title

    PEVs modeling and impacts mitigation in distribution networks

  • Author

    Shaaban, Mostafa F. ; Atwa, Yasser M. ; El-Saadany, Ehab F.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada
  • Volume
    28
  • Issue
    2
  • fYear
    2013
  • fDate
    May-13
  • Firstpage
    1122
  • Lastpage
    1131
  • Abstract
    This paper proposes a novel model to estimate the electric energy consumption of light duty fleet of plug-in electric vehicles (PEVs). This model can be used to evaluate the impacts of plugging such loads in distribution networks. Both vehicles users´ habits and diversity of usage are considered in the presented model, as well as different electric ranges and ambient temperature effect. Moreover, the paper proposes a method to optimally allocate distributed generation (DG) units in the distribution network to mitigate the impacts of high penetration of PEVs. The proposed model shall help the local distribution companies (LDC) to better assess the expected effects of PEVs on their networks and evaluate the required upgrades. Furthermore, the proposed DG allocation methodology helps to identify the optimal buses on which to connect these DG units in the presence of high PEVs penetration. A genetic based approach is utilized for the planning problem of determining the optimal locations and sizes of DG units, which is defined as a multi-objective mixed integer programming.
  • Keywords
    battery powered vehicles; distributed power generation; integer programming; power distribution planning; power generation planning; PEV impact mitigation; PEV modeling; allocation methodology; ambient temperature effect; distributed generation units; distribution networks; electric energy consumption; light duty fleet; multiobjective mixed integer programming; optimal location; planning problem; plug-in electric vehicle; Batteries; Electric vehicles; Energy consumption; Load modeling; All electric range; Monte Carlo methods; electric vehicles; probability density function;
  • fLanguage
    English
  • Journal_Title
    Power Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8950
  • Type

    jour

  • DOI
    10.1109/TPWRS.2012.2212467
  • Filename
    6307907