A scalable stochastic model for the electricity demand of electric and plug-in hybrid vehicles

Mahnoosh Alizadeh, Anna Scaglione, Jamie Davies, Kenneth S. Kurani

Research output: Contribution to journalArticle

69 Scopus citations

Abstract

In this paper we propose a stochastic model, based on queueing theory, for electric vehicle (EV) and plug-in hybrid electric vehicle (PHEV) charging demand. Compared to previous studies, our model can provide 1) more accurate forecasts of the load using real-time sub-metering data, along with the level of uncertainty that accompanies these forecasts; 2) a mathematical description of load, along with the level of demand flexibility that accompanies this load, at the wholesale level. This can be useful when designing demand response and dynamic pricing schemes. Our numerical experiments tune the proposed statistics on real PHEV charging data and demonstrate that the forecasting method we propose is more accurate than standard load prediction techniques.

Original languageEnglish (US)
Article number6595730
Pages (from-to)848-860
Number of pages13
JournalIEEE Transactions on Smart Grid
Volume5
Issue number2
DOIs
StatePublished - Mar 1 2014

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Keywords

  • Electric vehicles
  • load forecasting
  • load modeling
  • queueing theory
  • statistics

ASJC Scopus subject areas

  • Computer Science(all)

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