This repository contains the codes and results which is published in the paper title: "Efficient Quantification and Representation of Aggregate Flexibility in Electric Vehicles" by Nanda Kishor Panda and Simon Tindemans.

✅ Exact aggregation of flexibility for a large number of Electric Vehicles (EVs) 🚗 🚙
✅ Additive minkowski summation for flexibility aggregation
✅ Efficient representation of feasible flexibility of EVs
✅ Function to plot 3-d Polytopes using MATLAB

The repository is organized as follows:

• 📁 data: Contains the data used in the paper
• 📁 figures: Contains the figures used in the paper
• 📁 matlab_functions: Contains the MATLAB functions used to generate 3-d polytope
• helper_functions.py: Contains all helper functions used in the rest of the code.
• run_me.ipynb: Contains the main code to run the results of the paper and other functions.
• .gitignore: Contains the files to be ignored by git
• LICENSE: Contains the license information

Step 1: Clone the repository

git clone <repo-link>

Step 2: Install the required packages The code is tested on . The required packages are listed in the requirements.txt file. To install the required packages, run the following command:

pip install -r requirements.txt

or

conda install --file requirements.txt

For the optimization solver, we used . You can install the Gurobi solver by following the instructions in the Gurobi Documentation for Mac and Linux and Gurobi Documentation for Windows.

Note The results presented for computational performance is based on the program being run on a machine configuration featuring the Apple M2 MAX chip with 12-core CPU, macOS Ventura Version 13.5.1, 32GB RAM, in conjunction with Python 3.10.11 and the Gurobi 10.0.2 optimization solver.

If you liked this work and want to use it in your research, please consider citing the original paper:

The paper can be found at arXiv

@misc{panda2023efficient,
      title={Efficient Quantification and Representation of Aggregate Flexibility in Electric Vehicles}, 
      author={Nanda Kishor Panda and Simon H. Tindemans},
      year={2023},
      eprint={2310.02729},
      archivePrefix={arXiv},
      primaryClass={eess.SY}
}

If you re-use part of the code or some of the functions, please consider citing the repository:

@software{nanda_kishor_panda_2024_10811010,
  author       = {Nanda Kishor Panda},
  title        = {nkpanda97/ul-flexiility: Version 0},
  month        = mar,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {pre-release},
  doi          = {10.5281/zenodo.10811010},
  url          = {https://doi.org/10.5281/zenodo.10811010}
}

The research was supported by the ROBUST project, which received funding from the MOOI subsidy programme under grant agreement MOOI32014 by the Netherlands Ministry of Economic Affairs and Climate Policy and the Ministry of the Interior and Kingdom Relations, executed by the Netherlands Enterprise Agency. The authors would like to thank ROBUST consortium partners for fruitful discussions during the preparation of this paper