PyPSA-PL
An implementation of the energy modelling framework PyPSA shipped with a use-ready dataset tailored for the Polish energy system.
https://github.com/instrat-pl/pypsa-pl
Category: Energy Systems
Sub Category: Global and Regional Energy System Models
Last synced: about 6 hours ago
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PyPSA-PL: optimisation model of the Polish energy system
- Host: GitHub
- URL: https://github.com/instrat-pl/pypsa-pl
- Owner: instrat-pl
- License: mit
- Created: 2021-12-15T11:06:46.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-12-10T09:48:45.000Z (5 months ago)
- Last Synced: 2025-04-25T15:36:53.915Z (5 days ago)
- Language: Jupyter Notebook
- Homepage: https://instrat.pl/en/projekty/en-pypsa-pl/
- Size: 30.6 MB
- Stars: 23
- Watchers: 4
- Forks: 7
- Open Issues: 0
- Releases: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
README.md
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 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). Three challenging decades. Scenario for the Polish energy transition out to 2050. Instrat Policy Paper 03/2024. https://instrat.pl/three-challenging-decades/
- 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.
© Fundacja Instrat 2024
Owner metadata
- Name: Instrat
- Login: instrat-pl
- Email: info@instrat.pl
- Kind: organization
- Description: Warsaw-based think tank with a mission of supercharging policies and public opinion with open data and research for a fair, green and digital economy
- Website: www.instrat.pl
- Location: Warsaw, PL
- Twitter: fundacjainstrat
- Company:
- Icon url: https://avatars.githubusercontent.com/u/60134030?v=4
- Repositories: 5
- Last ynced at: 2024-05-29T13:40:21.037Z
- Profile URL: https://github.com/instrat-pl
GitHub Events
Total
- Release event: 1
- Watch event: 5
- Push event: 3
- Fork event: 1
- Create event: 1
Last Year
- Release event: 1
- Watch event: 5
- Push event: 3
- Fork event: 1
- Create event: 1
Committers metadata
Last synced: 1 day ago
Total Commits: 36
Total Committers: 4
Avg Commits per committer: 9.0
Development Distribution Score (DDS): 0.222
Commits in past year: 4
Committers in past year: 2
Avg Commits per committer in past year: 2.0
Development Distribution Score (DDS) in past year: 0.5
| Name | Commits | |
|---|---|---|
| Patryk Kubiczek | p****k@i****l | 28 |
| paczyzak | 5****k | 5 |
| micas-pro | m****2@g****m | 2 |
| Mateusz Sienkan | s****z@g****m | 1 |
Committer domains:
- instrat.pl: 1
Issue and Pull Request metadata
Last synced: 1 day ago
Total issues: 1
Total pull requests: 3
Average time to close issues: about 1 year
Average time to close pull requests: 5 months
Total issue authors: 1
Total pull request authors: 3
Average comments per issue: 0.0
Average comments per pull request: 0.0
Merged pull request: 3
Bot issues: 0
Bot pull requests: 0
Past year issues: 0
Past year pull requests: 1
Past year average time to close issues: N/A
Past year average time to close pull requests: about 1 month
Past year issue authors: 0
Past year pull request authors: 1
Past year average comments per issue: 0
Past year average comments per pull request: 0.0
Past year merged pull request: 1
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- LukeBlueLOx (1)
Top Pull Request Authors
- sin (1)
- micas-pro (1)
- patryk-kubiczek (1)
Top Issue Labels
Top Pull Request Labels
Dependencies
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- dill ==0.3.2
- docplex ==2.14.186
- docutils ==0.16
- entrypoints ==0.3
- et-xmlfile ==1.0.1
- geographiclib ==1.50
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- gitdb ==4.0.5
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- nbformat ==5.0.7
- netCDF4 ==1.5.3
- networkx ==2.4
- numexpr ==2.7.1
- numpy ==1.17.5
- olefile ==0.46
- openpyxl ==3.0.4
- pandas ==0.25.3
- parso ==0.7.0
- patsy ==0.5.1
- plotly ==4.13.0
- ply ==3.11
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- prompt-toolkit ==3.0.5
- psutil ==5.7.0
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- pvlib ==0.7.2
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- pyproj ==2.6.1.post1
- pypsa ==0.17.0
- pyrsistent ==0.16.0
- pyshp ==2.1.0
- python-dateutil ==2.8.1
- pytz ==2020.1
- pywin32 ==227
- pyzmq ==19.0.1
- ratelimiter ==1.2.0.post0
- retrying ==1.3.3
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- seaborn ==0.10.1
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- wincertstore ==0.2
- wrapt ==1.12.1
- xarray ==0.15.1
- xlrd ==1.2.0
- zipp ==3.1.0
- 153 dependencies
- adjusttext ^0.8
- gurobipy ^10.0.2
- highspy ^1.5.3
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- kaleido 0.2.1
- linopy 0.1.5
- numpy ^1.24.2
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- pandas ^1.5.3
- plotly ^5.13.1
- pypsa ^0.23.0
- python ^3.10
- seaborn ^0.12.2
- xarray ^2023.5.0
Score: 4.5217885770490405