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Modelling charge profiles of electric vehicles based on charges data
Natascia Andrenacci1, Federico Karagulian1, Antonino Genovese1
1Ente per le Nuove Tecnologie, l'Energia e l'Ambiente (ENEA), Rome, 00193, Italy.
Open Research Europe
|August 30, 2023
Summary
Analyzing electric vehicle (EV) charging data from Barcelona and Turku reveals distinct user habits for fast vs. slow charging. Different synthetic load profile approaches significantly impact renewable energy system sizing for charging infrastructure.
Area of Science:
- Electrical Engineering
- Sustainable Energy Systems
- Transportation Infrastructure
Background:
- Effective electric vehicle (EV) charging infrastructure design is crucial for user and manager benefits.
- Analyzing EV charging data aids in planning and managing charging infrastructure, including integration with renewable energy resources and storage systems.
- Understanding charging behaviors and patterns is key for optimizing infrastructure development.
Purpose of the Study:
- To analyze hourly, daily, and seasonal patterns in EV charge duration and energy consumption.
- To simulate power requests at charging stations (CSs) and determine the optimal size of an integrated renewable energy system (RES).
- To evaluate the impact of different synthetic load profile generation approaches on RES sizing.
Main Methods:
- Analysis of two EV charging datasets from Barcelona, Spain, and Turku, Finland.
- Investigation of charging patterns, including charge duration and energy delivered.
- Monte Carlo simulations using statistical and non-parametric distributions to model user influx and determine optimal RES size.
Main Results:
- Distinct user habits identified between fast and slow EV charging, with shorter durations for fast charges.
- Hourly request distributions varied significantly based on charging type (fast vs. slow) and dataset location.
- The choice of synthetic load profile generation approach substantially influenced the investment results for a renewable energy system (RES).
Conclusions:
- The study highlights the critical impact of different synthetic profile design approaches on determining the optimal size of photovoltaic (PV) systems for EV charging infrastructure.
- Economic viability, assessed using Net Present Value (NPV), is sensitive to the chosen load profile modeling method.
- Findings provide valuable insights for optimizing the design and integration of renewable energy sources within EV charging infrastructure.
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