• DocumentCode
    2362001
  • Title

    Avoiding overages by deferred aggregate demand for PEV charging on the smart grid

  • Author

    Pang, G. ; Kesidis, G. ; Konstantopoulos, T.

  • fYear
    2012
  • fDate
    10-15 June 2012
  • Firstpage
    3322
  • Lastpage
    3327
  • Abstract
    We model the aggregate overnight demand for electricity by a large community of (possibly hybrid) plug-in electric vehicles (PEVs) each of whose power demand follows a prescribed profile and is interruptible. The community is served by a regional electrical utility which is assumed to purchase electricity from a state/national distribution grid according to a flat-rate Φ per kilowatt-unit-time up to a threshold L, and thereafter overage (demand >; L) charges π >; Φ are leveed per kilowatt-unit-time. Rather than a spot-price system for household consumers (which would necessarily need to be operated by automated means overnight when most consumers sleep), the “grid” (regional utility) is “smart” in that it monitors its total load and, when overages threaten, can reduce load by signaling certain consumers to interrupt charging and defer their charging load by one unit of time. In this paper, we model the uninterrupted load by a Gaussian process which we justify by means of a functional central limit theorem (FCLT). This limiting Gaussian process is the arrival process of a discrete-time queue which is used to model the (partially) interrupted and deferred load over a finite time-horizon. We can then compute the mean amount of overage at the end of this time horizon (say at 6 AM when charging is to be completed ahead of the morning commute).
  • Keywords
    electric vehicles; smart power grids; PEV charging; aggregate demand; avoiding overages; smart grid; Aggregates; Batteries; Electricity; Energy consumption; Load modeling; Power demand; Smart grids;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communications (ICC), 2012 IEEE International Conference on
  • Conference_Location
    Ottawa, ON
  • ISSN
    1550-3607
  • Print_ISBN
    978-1-4577-2052-9
  • Electronic_ISBN
    1550-3607
  • Type

    conf

  • DOI
    10.1109/ICC.2012.6363650
  • Filename
    6363650