Python package datafev is a framework for the development, testing, and assessment of managemant algorithms for EV charging scenarios. The framework allows to develop scenario-oriented management strategies. It includes a portfolio of optimization- and rule-based algorithms to coordinate charging and routing operations in clustered charging systems. Furthermore, it provides statistical scenario generation tool to create EV fleet demand profiles. Its target users are researchers in the field of smart grid applications and the deployment of operational flexibility for local energy systems.
- Clone repository via SSH (
git clone [email protected]:erdemgumrukcu/datafev.git) or clone repository via HTTPS (git clone https://github.com/erdemgumrukcu/datafev.git) - Open an issue at https://github.com/erdemgumrukcu/datafev/issues
- Checkout the development branch:
git checkout development - Update your local development branch (if necessary):
git pull origin development - Create your feature/issue branch:
git checkout -b issueXY_explanation - Commit your changes:
git commit -m "Add feature #XY" - Push to the branch:
git push origin issueXY_explanation - Submit a pull request from issueXY_explanation to development branch via https://github.com/erdemgumrukcu/datafev/pulls
- Wait for approval or revision of the new implementations.
datafev requires at least the following Python packages:
- matplotlib>=3.5.1
- numpy>=1.21.5
- openpyxl>=3.0.9
- pandas>=1.4.2
- pyomo>=6.4.1
as well as the installation of at least one mathematical programming solver for convex and/or non-convex problems, which is supported by the Pyomo optimisation modelling library. We recommend one of the following solvers:
If all the above-mentioned dependencies are installed, you should be able to install package datafev via PyPI (using Python 3.X) as follows:
pip install datafev
or:
pip install -e '<your_path_to_datafev_git_folder>/src'
or:
<path_to_your_python_binary> -m pip install -e '<your_path_to_datafev_git_folder>/src'
Another option rather than installing via PyPI would be installing via setup.py:
py <your_path_to_datafev_git_folder>/setup.py install
or:
pyton <your_path_to_datafev_git_folder>/setup.py install
You can check if the installation has been successful by trying to import package datafev into your Python environment. This import should be possible without any errors.
import datafev
The documentation for the latest datafev release can be found in folder ./docs and on this GitHub page.
For further information, please also visit the FEIN Aachen association website.
import os
from datetime import datetime, timedelta
import matplotlib.pyplot as plt
from pyomo.environ import SolverFactory
from datafev.data_handling.fleet import EVFleet
from datafev.data_handling.cluster import ChargerCluster
from datafev.data_handling.multi_cluster import MultiClusterSystem
from datafev.routines.arrival import *
from datafev.routines.departure import *
from datafev.routines.charging_control.decentralized_fcfs import charging_routine
def main():
"""
This tutorial aims to show the use of datafev framework in a small example scenario
in the following the steps to set up a simulation instance will be given.
"""
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# SIMULATION SET-UP
# Simulation inputs
input_file = pd.ExcelFile("inputs/example_01.xlsx")
input_fleet = pd.read_excel(input_file, "Fleet")
input_cluster1 = pd.read_excel(input_file, "Cluster1")
input_capacity1 = pd.read_excel(input_file, "Capacity1")
# Getting the path of the input excel file
abs_path_input = os.path.abspath(input_file)
print("Scenario inputs are taken from the xlsx file:",abs_path_input)
print()
#Printing the input parameters related to the EV fleet
print("The charging demands of the EVs in the simulation scenario are given in the following:")
print(input_fleet[["Battery Capacity (kWh)", "Real Arrival Time", "Real Arrival SOC"]])
print()
# Printing the input parameters of the charging infrastructure
print("The system consists of one charger cluster with the following chargers:")
print(input_cluster1)
print()
print("Aggregate net consumption of the cluster is limited in the scenario (i.e., LB-UB indicating lower-upper bounds)")
print(input_capacity1)
print()
print()
# Simulation parameters
sim_start = datetime(2022, 1, 8, 7)
sim_end = datetime(2022, 1, 8, 9)
sim_length = sim_end - sim_start
sim_step = timedelta(minutes=5)
sim_horizon = [sim_start + t * sim_step for t in range(int(sim_length / sim_step))]
print("Simulation starts at:", sim_start)
print("Simulation fininshes at:", sim_end)
print("Length of one time step in simulation:", sim_step)
print()
print()
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# INITIALIZATION OF THE SIMULATION
cluster1 = ChargerCluster("cluster1", input_cluster1)
system = MultiClusterSystem("multicluster")
system.add_cc(cluster1)
fleet = EVFleet("test_fleet", input_fleet, sim_horizon)
cluster1.enter_power_limits(sim_start, sim_end, sim_step, input_capacity1)
print("Simulation scenario has been initalized")
print()
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# DYNAMIC SIMULATION
print("Simulation started...")
for ts in sim_horizon:
print(" Simulating time step:", ts)
# The departure protocol for the EVs leaving the charger clusters
departure_routine(ts, fleet)
# The arrival protocol for the EVs incoming to the charger clusters
arrival_routine(ts, sim_step, fleet, system)
# Real-time charging control of the charger clusters
charging_routine(ts, sim_step, system)
print("Simulation finished...")
print()
print()
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# ANALYSIS OF THE SIMULATION RESULTS
# Displaying connection data of cluster
print("Connection data")
print(cluster1.cc_dataset[["EV ID", "Arrival Time", "Leave Time"]])
print()
# Printing the results to excel files
system.export_results_to_excel(sim_start, sim_end, sim_step, "results/example01_clusters.xlsx")
fleet.export_results_to_excel(sim_start, sim_end, sim_step, "results/example01_fleet.xlsx")
# Path of the output excel file
abs_path_output_cluster = os.path.abspath("results/example01_clusters.xlsx")
abs_path_output_fleet = os.path.abspath("results/example01_fleet.xlsx")
print("Scenario results are saved to the following xlsx files:")
print(abs_path_output_cluster)
print(abs_path_output_fleet)
print()
#Line charts to visualize cluster loading and occupation profiles
fig1=system.visualize_cluster_loading(sim_start, sim_end, sim_step)
fig2=system.visualize_cluster_occupation(sim_start, sim_end, sim_step)
plt.show()
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if __name__ == "__main__":
main()The datafev package is released by the Institute for Automation of Complex Power Systems (ACS), E.ON Energy Research Center (E.ON ERC), RWTH Aachen University under the MIT License.
- Erdem Gumrukcu, M.Sc. [email protected]
- Amir Ahmadifar, M.Sc. [email protected]
- Aytug Yavuzer, B.Sc. [email protected]
- Univ.-Prof. Antonello Monti, Ph.D. [email protected]
Institute for Automation of Complex Power Systems (ACS)
E.ON Energy Research Center (E.ON ERC)
RWTH Aachen University, Germany
