First public release README file (v0.2, May 2025).

Here is a small guide to start experimenting with the Python Peer-to-Peer Energy Sharing Optimization Library (PyPESOL).

The software is shared under MIT license.

Disclaimer The code is highly experimental and has only been tested on Ubuntu and MacOS. Use with care!

If this software is used in research work, please cite the following publication:

• Duvignau, Romaric, Vincenzo Gulisano, and Marina Papatriantafilou. "PyPESOL: The Python P2P Energy Sharing Optimization Library." Proceedings of the Sixteenth ACM International Conference on Future Energy Systems (ACM e-energy '25). 2025. 1014-1015. https://dl.acm.org/doi/10.1145/3679240.3734691

The library implements energy and optimization models as well as matching methods from the following publications:

• Duvignau, R., Gulisano, V., Papatriantafilou, M., & Klasing, R. (2024). Geographical Peer Matching for P2P Energy Sharing. IEEE Access.
• Duvignau, R., & Klasing, R. (2023). Greediness is not always a vice: Efficient Discovery Algorithms for Assignment Problems. Procedia Computer Science, 223, 43-52.
• Duvignau, R., Gulisano, V., & Papatriantafilou, M. (2023, January). Cost-optimization for win-win P2P energy systems. In 2023 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) (pp. 1-5). IEEE.
• Duvignau, R., Gulisano, V., & Papatriantafilou, M. (2022, April). Efficient and scalable geographical peer matching for p2p energy sharing communities. In Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing (pp. 187-190).
• Duvignau, R., Heinisch, V., Göransson, L., Gulisano, V., & Papatriantafilou, M. (2021). Benefits of small-size communities for continuous cost-optimization in peer-to-peer energy sharing. Applied Energy, 301, 117402.
• Duvignau, R., Heinisch, V., Göransson, L., Gulisano, V., & Papatriantafilou, M. (2020, June). Small-scale communities are sufficient for cost-and data-efficient peer-to-peer energy sharing. In Proceedings of the Eleventh ACM International Conference on Future Energy Systems (pp. 35-46).

1. Clone the source code.

2. Packages to install before running the code: (required) pyomo, (optional for some sub-components) numpy, scipy, pandas, prophet (as of prophet v1.1, requires Python3.7), plotly eg using the current Python3.7 installation:

pip3 install numpy scipy pyomo pandas plotly prophet

or complete install via conda and a virtual environment for Python 3.7, including two optimization solvers:

conda update anaconda
conda create -n py37 python=3.7
conda activate py37
conda install numpy scipy pandas plotly prophet redis
conda install -c conda-forge pyomo
conda install -c conda-forge ipopt glpk

3. Install a LP solver program. The default one that is configured in the code is 'cbc' solver.

sudo apt-get install coinor-cbc

brew install cbc

Follow the jupyter notebook tutorial

Inspect the following files:

• cons.csv

Household consumptions, each line is 1 hour of consumption.

That is: h1, h2, ..., hn consumption for hour1, then a newline and the same for hour3 and so on.

The size is then N36524 numbers, N values per line, where N is number of households. (default unit kWh)

• price.csv

Yearly hourly price, so 365*24 numbers, 1 value per line. (default unit €/kWh)

• sun.csv

Solar profile, so 365*24 numbers, 1 value per line. (default unit kWh/kWp)

• pv.csv

File with PV capacities.

• battery.csv

File with battery capacities.