Awesome

PyPSA-PL: optimisation model of the Polish energy system

Introduction

PyPSA-PL is an implementation of the energy modelling framework PyPSA shipped with a use-ready dataset tailored for the Polish energy system. PyPSA-PL can be used to plan optimal investments in the power, heating, hydrogen, and light vehicle sectors – given the final use demand together with capital and operation costs for assets – or just to optimise the hourly dispatch of the utility units – given the final use demand and operation costs only. That makes it a useful tool to investigate the feasibility of decarbonisation scenarios for the Polish energy system in which a large share of electricity is supplied by variable sources like wind and solar.

Installation and usage

PyPSA-PL has been developed and tested using Python 3.10. The project dependencies can be installed using the Poetry tool according to the pyproject.toml file. Alternatively, you can use any other Python package manager – the dependencies are also listed in the requirements.txt file. Additionally, you will need to install an external solver (see PyPSA manual).

PyPSA-PL-mini notebooks can be deployed on the Google Colab platform. To do so, navigate to one of the PyPSA-PL-mini application notebooks in the notebooks directory. In the notebook, click the "Open in Colab" banner and follow the instructions provided therein.

Input data and assumptions

This table lists the main input data sources. More detailed source attribution can be found in the input spreadsheets themselves.

Input	Source
Technology and carrier definitions	Kubiczek P. (2024). Technology and carrier definitions for PyPSA-PL model. Instrat.
Technological and cost assumptions	Kubiczek P., Żelisko W. (2024). Technological and cost assumptions for PyPSA-PL model. Instrat.
Installed capacity assumptions	Kubiczek P. (2024). Installed capacity assumptions for PyPSA-PL model. Instrat.
Annual energy flow assumptions	Kubiczek P. (2024). Annual energy flow assumptions for PyPSA-PL model. Instrat.
Capacity utilisation assumptions	Kubiczek P. (2024). Capacity utilisation assumptions for PyPSA-PL model. Instrat.
Installed capacity potential and maximum addition assumptions	Kubiczek P. (2024). Installed capacity potential and maximum addition assumptions for PyPSA-PL model. Instrat.
Electricity final use time series	ENTSO-E. (2023). Total Load—Day Ahead / Actual. Transparency Platform. https://transparency.entsoe.eu/load-domain/r2/totalLoadR2/show
Wind and solar PV availability time series	De Felice, M. (2022). ENTSO-E Pan-European Climatic Database (PECD 2021.3) in Parquet format. Zenodo. https://doi.org/10.5281/zenodo.7224854 <br><br> Gonzalez-Aparicio, I., Zucker, A., Careri, F., Monforti, F., Huld, T., Badger, J. (2021). EMHIRES dataset: Wind and solar power generation. Zenodo. https://doi.org/10.5281/zenodo.4803353
Temperature data used to infer space heating demand and heat pump COP time series	IMGW. (2023). Dane publiczne. Instytut Meteorologii i Gospodarki Wodnej. https://danepubliczne.imgw.pl/
Daily space heating demand time series	Ruhnau, O., Muessel, J. (2023). When2Heat Heating Profiles. Open Power System Data. https://doi.org/10.25832/when2heat/2023-07-27
Traffic data used to infer light vehicle mobility and BEV charging time series	GDDKiA. (2023). Stacje Ciągłych Pomiarów Ruchu (SCPR). Generalna Dyrekcja Dróg Krajowych i Autostrad. https://www.gov.pl/web/gddkia/stacje-ciaglych-pomiarow-ruchu

Publications and full datasets

Here you can find the list of publications based on the PyPSA-PL results and links to the full datasets stored in Zenodo.

Kubiczek, P., Smoleń, M. (2024). Trzy dekady wyzwań. Scenariusz polskiej transformacji energetycznej do 2050 r. Instrat Policy Paper 03/2024. https://instrat.pl/trzy-dekady-wyzwan/
Kubiczek, P., Smoleń, M., Żelisko, W. (2023). Poland approaching carbon neutrality. Four scenarios for the Polish energy transition until 2040. Instrat Policy Paper 06/2023. https://instrat.pl/poland-2040/
Kubiczek P. (2023). Baseload power. Modelling the costs of low flexibility of the Polish power system. Instrat Policy Paper 04/2023. https://instrat.pl/baseload-power/
Kubiczek P., Smoleń M. (2023). Poland cannot afford medium ambitions. Savings driven by fast deployment of renewables by 2030. Instrat Policy Paper 03/2023. https://instrat.pl/pypsa-march-2023/

Acknowledgements

The current version of PyPSA-PL is a successor of the PyPSA-PL v1 developed by Instrat in 2021. The following publications were based on the PyPSA-PL v1 results:

Czyżak, P., Wrona, A. (2021). Achieving the goal. Coal phase-out in Polish power sector. Instrat Policy Paper 01/2021. https://instrat.pl/coal-phase-out
Czyżak, P., Sikorski, M., Wrona, A. (2021). What’s next after coal? RES potential in Poland. Instrat Policy Paper 06/2021. https://instrat.pl/res-potential
Czyżak, P., Wrona, A., Borkowski, M. (2021). The missing element. Energy security considerations. Instrat Policy Paper 09/2021. https://instrat.pl/energy-security

License

The code is released under the MIT license. The input and output data are released under the CC BY 4.0 license.