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
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