ChaProEV 0.1.3

ChaProEV: Charging Profiles of Electric Vehicles

ChaProEV

This repository contains the ChaProEV (Charging Profiles of Electric Vehicles) model.

Status

The ChaProEV model is under construction and not yet ready for use. Please contact the authors below if you have questions or requests to get model runs at some point.

Authors and contact

Omar Usmani ([e-mail])

Licence

ChaProEV is released under the Apache 2.0 licence. All accompanying documentation and manual are released under the Creative Commons BY-SA 4.0 license.

Requirements

(See requirements.txt file for versions (corresponding to Python 3.11.1, which is the version used for developping and testing the model))

List of relevant files and modules

ChaProEV.py

Where you run the model

scenarios folder

This folder contains scenario configuration files. There is one per scenario, containing all necessary parameters to run the scenatio. To add a scenario, copy one of the existing files and add/remove/modify parasmeters paramters as required, including the first parameter, which is the scenario name, which should be the same as the file name (it might work with another name, but this is not tested and there is no reason to have different names for the same scenario). These scenario files contain the following sections:

• unit_conversions: Parameters to convert units
• files: File-related parameters (folders, output type choices, etc.)
• colors: Color-related parameters(defining new ones, as well as color bars)
• time: Time-related parameters and constants needed in formulas
• run: Parameters related to the run, such as timing, and elements such as locations, vehicles, legs, trips, etc.
• weather: Parameters related to the weather, such as elements to define what we want to download from various sources, where we store weather data, how we process it, or how we want to label quantititis
• plots: Parameters for plots (colors, sizes, etc.)

define.py:

This module defines and declares classes for the different objects that define the system (the parameters/defintions come from a parameters file), namely:

1. Legs: Legs are point-to-point vehicle movements (i.e. movements where the vehicle goes from a start location and ends/stops at an end location).
2. Vehicles: Each vehicle type (or subtype) is defined in this class.

weather.py

This module contains functions related to weather data and factors. The weather data is pulled from the CDS (Climate Data Store) ERA-5 weather data from the Copernicus institute. This data contains many quantities (such as temperature, precipitation, or solar radiation) at an hourly level (starting in 1950) for the whole world, at a one-decimal resolution for latitudes and longitudes. Note that the data sometimes has trailing digits, but the resolution still seems to be to the first decimal. This is the reason why we round the coordinate values in our processing functions.

It contains the following functions:

1. download_cds_weather_quantity: Downloads CDS ERA-5 weather data for a given quantity in a given area.
2. download_all_cds_weather_data: Downloads all the necessary CDS weather data.
3. make_weather_dataframe: This function makes a weather DataFrame into one we can use by removing empty data and processing data into forms useful for the model.
4. write_weather_database" This function writes the weather database.
5. get_hourly_values: This function takes a Dataframe for a given weather quantity. If this is a cumulative quantity, it adds hourly values to it.
6. get_all_hourly_values: This functions adds hourly values to cumulative quantities in the weather database.
7. get_EV_tool_data:: This gets the temperature efficiency data from the EV tool made by geotab.
8. **temperature_efficiency_factor:**This function returns the temperature efficiency factor that corrects the baseline vehicle efficiency.
9. plot_temperature_efficiency: Plots the temperature efficiency correction factor (source data versus interpolation)of electric vehicles.
10. get_run_location_weather_quantity: Returns a chosen weather quantity for a given location and a given runtime.
11. get_run_weather_data: Fetches the weather data and efficiency factors and puts it into a table that is saved to files/databases.
12. solar_efficiency_factor:* This gives us the efficiency factor of solar panels (i.e. how much of the solar radiation is converted into electricity). THIS IS A PLACEHOLDER FUNCTION
13. setup_weather: This runs all the functions necessary to get the run weather factors for a given case.
14. get_location_weather_quantity: Returns the value a a chosen weather quantity for a given location and time tag.

run_time.py:

This module defines time structures.

It contains the following functions:

1. get_time_range: This function returns the time range of the run, and the associated hour numbers, based on values found in the parameters file.
2. get_time_stamped_dataframe: This function creates a DataFrame with the timestamps of the run as index (and hour numbers as a column).
3. get_day_type: Tells us the date type of a given time_tag.
4. add_day_type_to_time_stamped_dataframe: Adds a column with the date type to a time-stamped_dataframe

writing.py:

This contains functions related to writting outputs. It contains the following functions:

1. write_scenario_parameters: This function writes the scenario parameters to the output files (either as separate files, or as tables/sheets in groupfiles.)

mobility.py:

This module computes the various functions related to mobility. It contains the following functions:

1. get_trip_probabilities_per_day_type: This function computes the trip probabilities per day type.

Temperature efficiency

This function returns the temperature efficiency factor that corrects the baseline vehicle efficiency. It uses a data file (extracted from EV tool made by geotab.

This information is based on 5.2 million trips by 4200 vehicles. The degree 6 polynomial fit is:

$$ C(T)=0.7078+0.01751\cdot T+0.0001611\cdot T^2-1.036\cdot 10^{-5}\cdot T^3 -3.581\cdot 10^{-7}\cdot T^4+1.746\cdot 10^{-9}\cdot T^5 +1.07\cdot10^{-10}\cdot T^6 $$

where T is the ambient temparture (in the weather data, it's the temperature at 2 meters), and C is the efficiency correction factor.

Plotting the fit verus the data shows that thisi a great fit:

Context, goals, and future developments

Driver

The primary driver for the publication and development of ChaProEv in this repository is the participation in the Mopo project (funded from European Climate, Infrastructure and Environment Executive Agency under the European Union’s HORIZON Research and Innovation Actions under grant agreement N°101095998).

Goal

The main goal of providing this repository is transparency regarding the assumptions and computations of the ChaProEV model.

Uses outside Mopo

Prior to Mopo

1. Afspraken maken: Van data tot informatie Informatiebehoeften, datastandaarden en protocollen voor provinciale systeemstudies – Deel II technische rapportage. Nina Voulis, Joeri Vendrik, Reinier van der Veen, Alexander Wirtz, Michiel Haan, Charlotte von Meijenfeldt, Edwin Matthijssen, Sebastiaan Hers, Ewoud Werkman (CE Delft, TNO, Quintel), April 2021 where a previous version of ChaProEV was used to provide charging profiles of electric vehicles at the province level (for the Dutch proivinces of North Holland and Limburg)

2. Elektrisch rijden personenauto's & logistiek: Trends en impact op het elektriciteitssyteem. Hein de Wilde, Charlotte Smit, Omar Usmani, Sebastiaan Hers (TNO), Marieke Nauta (PBL), August 2022 where a previous version of ChaProEV was used to identify potential moments where charging electric cars could result in local (i.e. neighbourhood/transformer level) network issues and see if these issues could be solved.

3. Verlagen van lokale impact laden elektrisch vervoer: De waarde en haalbarheid van potentiële oplossingen, Charlotte Smit, Hein de Wilde, Richard Westerga, Omar Usmani, Sebastiaan Hers, TNO M12721, December 2022 where a previous version of ChaProEV was used to identify and quantify potential solutions to potential local (i.e. neighbourhood/transformer level) issues due to a possible large-scale adoption of electric cars (with illustrative examples for neighbourhoods in ihe cities of Amsterdam and Lelystad).

4. TRADE-RES. A previous version of ChaProEV was used to generate reference charging profiles for a number of European countries, based on statistical differences per country

After/during Mopo

Future

The ChaProEV will be used in other projects that will be listed here, if deemed relevant and apprpriate within the context of these projects.

Acknowledgments

 

---

	This project was partly develop under funding from European Climate, Infrastructure and Environment Executive Agency under the European Union’s HORIZON Research and Innovation Actions under grant agreement N°101095998.