Flexibility of electric vehicle demand: Analysis of measured charging data and simulation for the future

Marte K. Gerritsma*, Tarek A. AlSkaif, Henk A. Fidder, Wilfried G.J.H.M. van Sark

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

52 Citations (Scopus)

Abstract

This paper proposes a method for analyzing and simulating the time-dependent flexibility of electric vehicle (EV) demand. This flexibility is influenced by charging power, which depends on the charging stations, the EV characteristics, and several environmental factors. Detailed charging station data from a Dutch case study have been analysed and used as input for a simulation. In the simulation, the interdependencies between plug-in time, connection duration, and required energy are respected. The data analysis of measured data reveals that 59% of the aggregated EV demand can be delayed for more than 8 h, and 16% for even more than 24 h. The evening peak shows high flexibility, confirming the feasibility of congestion management using smart charging within flexibility constraints. The results from the simulation show that the average daily EV demand increases by a factor 21 between the 'Present-day' and the 'High' scenario, while the maximum EV demand peak increases only by a factor 6, as a result of the limited simultaneity of the transactions. Further, simulations using the average charging power of individual measured transactions yield more accurate results than simulations using a fixed value for charging power. The proposed method for simulating future EV flexibility provides a basis for testing different smart charging algorithms.

Original languageEnglish
Article number14
JournalWorld Electric Vehicle Journal
Volume10
Issue number1
DOIs
Publication statusPublished - 1 Mar 2019
Externally publishedYes

Keywords

  • Charging power
  • Demand flexibility
  • Electric vehicles
  • Monte Carlo simulation
  • Smart charging

Fingerprint

Dive into the research topics of 'Flexibility of electric vehicle demand: Analysis of measured charging data and simulation for the future'. Together they form a unique fingerprint.

Cite this