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Model Factsheets

PyPSA

Name PyPSA-Earth: An Python-based Open Optimisation Model of the Earth Energy System
Acronym PyPSA-Earth
Methodical Focus Optimization
Institution(s)
Author(s) (institution, working field, active time period)
Current contact person Davide Fioriti
Contact (e-mail) dummi@mail.de
Website https://pypsa-meets-earth.github.io/projects.html
Logo /media/logos/PyPSA_Earth_logo.png
Primary Purpose The PyPSA meets Earth initiative works on open modelling, open data, open source solver support and open communities for energy system planning. Many of the data, model, solver activities are neutral to the model framework, opening the door for collaboration also outside of the PyPSA space. The tools are developed and maintained by members of the multi-organizational PyPSA developer team https://pypsa.org/.
Primary Outputs PyPSA-Earth is the first open-source global energy system model with data in high spatial and temporal resolution. It enables large-scale collaboration by providing a tool that can model the world energy system or any subset of it. This work is derived from the European PyPSA-Eur model using new data and functions. It is suitable for operational as well as combined generation, storage and transmission expansion studies. We work hard to extend the PyPSA-Earth model by end of this year to include sector-coupling, myopic and perfect pathway expansion capabilities.
Support / Community / Forum
Framework PyPSA
Link to User Documentation https://pypsa-earth.readthedocs.io/en/latest/
Link to Developer/Code Documentation https://pypsa.readthedocs.io/en/latest/
Documentation quality excellent
Source of funding Research and Industry
Number of developers less than 50
Number of users less than 100

Open Source
License GNU Affero General Public License v3.0
Source code available
GitHub
Access to source code https://github.com/pypsa-meets-earth/pypsa-earth
Data provided all data
Collaborative programming
GitHub Organisation

Modelling software Python
Internal data processing software
External optimizer Linopy
Additional software
GUI

Modeled energy sectors (final energy) electricity, heat
Modeled demand sectors -
Modeled technologies: components for power generation or conversion
Renewables -
Conventional -
Modeled technologies: components for transfer, infrastructure or grid
Electricity transmission
Gas -
Heat -
Properties electrical grid AC load flow, DC load flow, transshipment model, single-node / copper plate model
Modeled technologies: components for storage battery, kinetic, compressed air, pump hydro, chemical, heat, gas
User behaviour and demand side management
Changes in efficiency
Market models -
Geographical coverage
Geographic (spatial) resolution global, continents, national states, TSO regions, federal states, regions, NUTS 3
Time resolution hour, Flexible. But per default larger than hourly
Comment on geographic (spatial) resolution Flexible
Observation period 1 year, >1 year, Single period or multi-period possible
Additional dimensions (sector) -

Model class (optimisation) LP, MILP
Model class (simulation) Bottom up, Top down
Other
Short description of mathematical model class
Mathematical objective costs
Approach to uncertainty Deterministic
Suited for many scenarios / monte-carlo
typical computation time less than a day
Typical computation hardware Depends on complexity. Between 8 GB RAM - 250 GB RAM
Technical data anchored in the model Yes, but can be swapped out

Interfaces - PyPSA - Atlite - Linopy - Powerplantmatching - Earth-osm
Model file format .py
Input data file format csv, json, geojson, yaml, netCDF
Output data file format csv, json, geojson, yaml, netCDF
Integration with other models PyPSA-Earth-Sec
Integration of other models

Citation reference -
Citation DOI https://doi.org/10.1016/j.apenergy.2023.121096
Reference Studies/Models -
Example research questions -
Model usage Industry, NGOs, Research Institutes and Universities
Model validation cross-checked with other models
Example research questions -
further properties
Model specific properties -