Direct load management of electric vehicles

Mahnoosh Alizadeh, Anna Scaglione, Robert J. Thomas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Electrical Vehicles are gaining increasing attention, due to the opportunities and challenges they present for the energy market. On the one hand, they will allow to drastically reduce the need for oil; on the other hand they may require a significant shift in the day to day management of the electricity generation. This paper is concerned with finding appropriate models for residential load in light of a widespread penetration of electric vehicles. The analysis is aimed at finding a SmartGrid solution that would enable us to optimize the generation dispatch in real time and allow to plug cars in any SmartGrid enabled plug. The key idea is to discriminate between regular load and the load due to the EVs, gathering in real time aggregate information about the sensed EV arrivals and their associated charging times in a demand matrix, that can be readily used to optimize the dispatch, while updating without real time constraints the billing record for the EV.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages5964-5967
Number of pages4
DOIs
StatePublished - Aug 18 2011
Externally publishedYes
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: May 22 2011May 27 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
CountryCzech Republic
CityPrague
Period5/22/115/27/11

Keywords

  • Communication
  • Direct Load Control
  • Electric Vehicles
  • Load Forecast
  • Optimization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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