evsim
Overview
evsim is part of a suite of packages to analyse, model and simulate
the charging behavior of electric vehicle users:
- evprof: Electric
Vehicle PROFiling
- evsim: Electric
Vehicle SIMulation
evsim package provides the functions for:
- Simulating new EV sessions based on Gaussian Mixture Models created
with package {evprof}
- Calculating the power demand from a data set of EV sessions in a
specific time resolution
- Calculating the occupancy (number of vehicles connected) in a
specific time resolution
Usage
If you have your own data set of EV charging sessions or you have
already built your EV model with evprof, the best place
to start is the Get
started chapter in the package website.
Installation
You can install the package from CRAN or the development version from
GitHub:
# CRAN stable release
install.packages("evsim")
# Latest development version
# install.packages("devtools")
devtools::install_github("mcanigueral/evsim")
Getting help
If you encounter a clear bug, please open an issue with a minimal
reproducible example on GitHub. For
questions and other discussion, please send me a mail to
marc.canigueral@udg.edu.
For further technical details, you can read the following academic
articles about the methodology used in this paper:
- Electric vehicle user profiles for aggregated flexibility
planning. IEEE PES Innovative Smart Grid Technologies Europe
(ISGT Europe). IEEE, Oct. 18, 2021. DOI
link.
- Flexibility management of electric vehicles based on user
profiles: The Arnhem case study. International Journal of
Electrical Power and Energy Systems, vol. 133. Elsevier BV, p. 107195,
Dec. 2021. DOI
link.
- Potential benefits of scheduling electric vehicle sessions
over limiting charging power. CIRED Porto Workshop 2022:
E-mobility and power distribution systems. Institution of Engineering
and Technology, 2022. DOI
link.
- Assessment of electric vehicle charging hub based on
stochastic models of user profiles. Expert Systems with
Applications (Vol. 227, p. 120318). Elsevier BV. May 2023. DOI link.
Acknowledgements
This work has been developed under a PhD program in the eXiT research group from the University
of Girona (Catalonia) in collaboration with Resourcefully, an energy transition
consulting company based in Amsterdam, The Netherlands.