Non-convex dynamic economic/environmental dispatch with plug-in electric vehicle loads

Zhile Zang, Kang Li, Cheng Zhang, Aoife Foley

Research output: Contribution to conferencePaperpeer-review

Abstract

Electric vehicles are a key prospect for future transportation. A large penetration of electric vehicles has the potential to reduce the global fossil fuel consumption and hence the greenhouse gas emissions and air pollution. However, the additional stochastic loads imposed by plug-in electric vehicles will possibly introduce significant changes to existing load profiles. In his paper, electric vehicles loads are integrated into an 5-unit system using a non-convex dynamic dispatch model. The actual infrastructure characteristics including valve-point effects, load balance constrains and transmission loss have been included in the model. Multiple load profiles are comparatively studied and compared in terms of economic and environmental impacts in order o identify patterns to charge properly. The study as expected shows ha off-peak charging is the best scenario with respect to using less fuels and producing less emissions.
Original languageEnglish
Number of pages7
DOIs
Publication statusPublished - 2014
EventComputational Intelligence Applications in Smart Grid (CIASG), 2014 IEEE Symposium on - Florida, Orlanda, United States
Duration: 09 Dec 201412 Apr 2015

Conference

ConferenceComputational Intelligence Applications in Smart Grid (CIASG), 2014 IEEE Symposium on
CountryUnited States
CityOrlanda
Period09/12/201412/04/2015

Keywords

  • RBF; TLBO ; battery model; neural network; heuristic method

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