• English
    • العربية
  • العربية
  • Login
  • QU
  • QU Library
  •  Home
  • Communities & Collections
View Item 
  •   Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Electrical Engineering
  • View Item
  • Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Electrical Engineering
  • View Item
  •      
  •  
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Placement of electric vehicle fast charging stations in distribution network considering power loss, land cost, and electric vehicle population

    No Thumbnail [120x130]
    Date
    2022
    Author
    Ahmad, Fareed
    Iqbal, Atif
    Ashraf, Imtiaz
    Marzband, Mousa
    Khan, Irfan
    Metadata
    Show full item record
    Abstract
    Recently, electric vehicles (EVs) gained tremendous attention from government agencies and the automotive industry due to lower CO2 emissions, low maintenance, and operating costs. However, due to increasing EV penetration, the EV's load affects the distribution network parameters like power loss, voltage profile, and harmonic distortion. Therefore, the proper placement of EV fast-charging stations (FCSs) is required for the reliability of the distribution network. Further, this paper proposes two-stage processes for the placement of FCSs. In the first stage, the charging station owner decision index (CSODI) has been introduced considering the land cost index (LCI) and electric vehicle flow index (EVFI). The CSODI has been formulated to minimize the land cost and maximize the EVs flow for FCSs placement. In the next stage, an optimization problem is formulated for minimizing the total active power loss by considering the distribution system operator (DSO) constraints. In addition, the minimization problem has been solved using the hybrid gray wolf optimization-particle swarm optimization (GWOPSO) algorithm. Therefore, the best possible locations were obtained by the GWOPSO with 198.93 kW power loss. Furthermore, the average 2.02% power loss for the GWOPSO technique is lower when compared to the PSO technique. 2022 Taylor & Francis Group, LLC.
    DOI/handle
    http://dx.doi.org/10.1080/15567036.2022.2055233
    http://hdl.handle.net/10576/43112
    Collections
    • Electrical Engineering [‎2821‎ items ]

    entitlement


    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Home

    Submit your QU affiliated work

    Browse

    All of Digital Hub
      Communities & Collections Publication Date Author Title Subject Type Language Publisher
    This Collection
      Publication Date Author Title Subject Type Language Publisher

    My Account

    Login

    Statistics

    View Usage Statistics

    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Video

    NoThumbnail