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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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