Demand Modeling of a dc Fast Charging Station

Qian Deng, Sujit Tripathy, Daniel Tylavsky, Travis Stowers, Jeff Loehr

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

1 Scopus citations

Abstract

This paper presents the modeling and simulation of power loads due to plug-in electric vehicles' (EVs) charging events at a dc fast charging station. Two algorithms for modeling the loads are introduced and compared, one based on sampling and one based on statistical distribution built from the sample database. Simulation of load versus time was performed using a horizon of 7 days using both techniques. The cause for the difference in the result of these two approaches is explored. Regardless of the method used, the results show that a dc fast charging station with 6 fast chargers potentially serving 700 plugin EVs generally gets 105 charging events per day with a peak load of 375 kW.

Original languageEnglish (US)
Title of host publication2018 North American Power Symposium, NAPS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538671382
DOIs
StatePublished - Jan 2 2019
Event2018 North American Power Symposium, NAPS 2018 - Fargo, United States
Duration: Sep 9 2018Sep 11 2018

Publication series

Name2018 North American Power Symposium, NAPS 2018

Conference

Conference2018 North American Power Symposium, NAPS 2018
CountryUnited States
CityFargo
Period9/9/189/11/18

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Keywords

  • Dc fast charging station
  • Demand forecasting
  • Electric vehicles
  • Load modeling

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Control and Optimization

Cite this

Deng, Q., Tripathy, S., Tylavsky, D., Stowers, T., & Loehr, J. (2019). Demand Modeling of a dc Fast Charging Station. In 2018 North American Power Symposium, NAPS 2018 [8600618] (2018 North American Power Symposium, NAPS 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NAPS.2018.8600618