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    Online optimal variable charge-rate coordination of plug-in electric vehicles to maximize customer satisfaction and improve grid performance

    245727_245727.pdf (2.409Mb)
    Access Status
    Open access
    Authors
    Hajforoosh, S.
    Masoum, Mohammad Sherkat
    Islam, Syed
    Date
    2016
    Type
    Journal Article
    
    Metadata
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    Citation
    Hajforoosh, S. and Masoum, M. and Islam, S. 2016. Online optimal variable charge-rate coordination of plug-in electric vehicles to maximize customer satisfaction and improve grid performance. Electric Power Systems Research. 141: pp. 407-420.
    Source Title
    Electric Power Systems Research
    DOI
    10.1016/j.epsr.2016.08.017
    ISSN
    0378-7796
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/21769
    Collection
    • Curtin Research Publications
    Abstract

    © 2016 Elsevier B.V. Participation of plug-in electric vehicles (PEVs) is expected to grow in emerging smart grids. A strategy to overcome potential grid overloading caused by large penetrations of PEVs is to optimize their battery charge-rates to fully explore grid capacity and maximize the customer satisfaction for all PEV owners. This paper proposes an online dynamically optimized algorithm for optimal variable charge-rate scheduling of PEVs based on coordinated aggregated particle swarm optimization (CAPSO). The online algorithm is updated at regular intervals of Δt = 5 min to maximize the customers’ satisfactions for all PEV owners based on their requested plug-out times, requested battery state of charges (SOCReq) and willingness to pay the higher charging energy prices. The algorithm also ensures that the distribution transformer is not overloaded while grid losses and node voltage deviations are minimized. Simulation results for uncoordinated PEV charging as well as CAPSO with fixed charge-rate coordination (FCC) and variable charge-rate coordination (VCC) strategies are compared for a 449-node network with different levels of PEV penetrations. The key contributions are optimal VCC of PEVs considering battery modeling, chargers’ efficiencies and customer satisfaction based on requested plug-out times, driving pattern, desired final SOCs and their interest to pay for energy at a higher rate.

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