Probabilistic capacity planning framework for electric vehicle charging stations with overstay

Bayram, I. Safak; (2022) Probabilistic capacity planning framework for electric vehicle charging stations with overstay. In: Proceedings of the IEEE International Conference on Smart Grid Communications. IEEE, Piscataway, NJ.

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Abstract

Public charging stations provide charging services as well as parking for the growing population of electric vehicles (EVs). Effective management of these facilities is becoming crucial, with a significant proportion of drivers remaining parked even after the services are completed. This phenomenon, known as overstay, results in an underutilization of station resources and becomes a barrier to other electric vehicle drivers seeking charging services. To that end, this article presents (i) stochastic modeling of charging stations with overstaying customers, and (ii) a methodology to calculate station capacities with respect to a performance metric probability of loss of load that represents the percentage of unsatisfied demand. The station model is constructed using a two-dimensional Markov chain reflecting interactions among the idle, charging, and overstaying customers. Initially, the generalized small-scale charging station model is studied to investigate the behavior of the station parameters. Then, the general model is extended using the statistical large deviation theory to cover the case of large-scale charging stations. Effective demand, a deterministic quantity, for each charger is calculated, and station capacity is calculated in terms of the above-mentioned performance metric. The case studies demonstrate that calculating effective demand-based capacity leads to substantial savings when provisioning station resources. However, a significant proportion of these savings diminish with increasing rate of overstay customers and durations.

  • Item type:Book Section
    ID code:82167
    Dates:
    Date
    Event
    28 October 2022
    Published
    20 May 2021
    Accepted
    Notes:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:01 Sep 2022 13:59
    Last modified:14 Apr 2024 00:15
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/82167
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