Optimal plug-in electric vehicle charging with schedule constraints

Andres Cortes, Sonia Martinez

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    5 Citations (Scopus)

    Abstract

    This paper proposes a decentralized algorithm that allows a group of Plug-in Electric Vehicles (PEVs) to arrive at an optimal strategy to charge their batteries during the day. By communicating repeatedly with an energy coordinator, the PEVs adjust their battery-charging plans by means of a price-feedback signal that accounts for the aggregated demand. The algorithm allows PEVs to adjust their plan simultaneously while respecting schedule constraints at every iteration. The collective strategy is optimal in that it minimizes the overall price of the supplied energy and leads to an off-peak utilization of the grid. The algorithm is proven to converge to a solution by means of nonlinear analysis tools of discrete-time systems. In order to show convergence, we present a refinement of the LaSalle invariance principle for discrete-time systems. Simulations demonstrate the proficiency of the algorithm in two particular scenarios.

    Original languageEnglish
    Title of host publication2013 51st Annual Allerton Conference on Communication, Control, and Computing, Allerton 2013
    PublisherIEEE Computer Society
    Pages262-266
    Number of pages5
    ISBN (Print)9781479934096
    DOIs
    Publication statusPublished - 2013
    Event51st Annual Allerton Conference on Communication, Control, and Computing, Allerton 2013 - Monticello, IL, United States
    Duration: 2 Oct 20134 Oct 2013

    Publication series

    Name2013 51st Annual Allerton Conference on Communication, Control, and Computing, Allerton 2013

    Conference

    Conference51st Annual Allerton Conference on Communication, Control, and Computing, Allerton 2013
    Country/TerritoryUnited States
    CityMonticello, IL
    Period2/10/134/10/13

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