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PyPSA-Earth: A flexible Python-based open optimisation model to study energy system futures around the world.

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PyPSA-Earth. A Flexible Python-based Open Optimisation Model to Study Energy System Futures around the World.

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Development Status: Stable and Active

Test workflows Documentation Status Size License: AGPL v3 REUSE status Code style: black pre-commit.ci status Discord Google Drive

PyPSA-Earth: A Global Sector-Coupled Open-Source Multi-Energy System Model

PyPSA-Earth is the first open-source global cross-sectoral energy system model with high spatial and temporal resolution. Originally it was derived from the European PyPSA-Eur model using new data and functions which provide capabilities for modelling the world energy system or any subset of it, enabling large-scale collaboration and transparent analysis for a sustainable energy future. It is suitable for operational studies, as well as expansion studies on combined generation, storage and transmission accounting for cross-sectoral interactions. The model provides two main features: (1) customizable data extraction and preparation scripts with global coverage for power and cross-sectoral modelling and (2) a PyPSA energy modelling framework integration. In particular, the data includes energy demand, generation and medium to high-voltage networks from open sources, yet additional data can be further integrated. A broad range of clustering and grid meshing strategies help adapt the model to computational and practical needs.

With the recent integration of PyPSA-Earth and the sector-coupled PyPSA-Earth model, all functionality is now combined into a single, comprehensive tool. This unified model allows for detailed optimization of multi-energy systems, covering electricity, heating, transport, and more. It is designed to adapt to the specific needs of any country or region, offering customizable data extraction, preparation scripts with global coverage, and a broad range of clustering and grid meshing strategies to meet computational and practical requirements.

PyPSA-Earth is capable to provide the modelling evidence needed to translate implications behind energy scenarios into the regional actions. By making this tool openly available, we aim to foster collaboration, innovation, and informed decision-making that leads to sustainable and efficient energy solutions worldwide.

For more details, the model is described in the Applied Energy article "PyPSA-Earth: A new global open energy system optimization model demonstrated in Africa," 2023. The preprint describing the sector-coupled functionalities is also available here. Additional information can be found in the documentation.

PyPSA meets Earth is a free and open source software initiative aiming to develop a powerful energy system model for Earth. We work on open data, open source modelling, open source solver support and open communities. Stay tuned and join our mission - We look for users, co-developers and leaders! Check out our website for results and our projects. Happy coding!

Figure: Example power systems build with PyPSA-Earth. See images of ~193 more countries at https://zenodo.org/records/10080766

The diagram below depicts one representative clustered node for the sector-coupled model with its generation, storage and conversion technologies.

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Livetracker. Most popular global models:

Get involved

There are multiple ways to get involved and learn more about our work. That's how we organise ourselves:

  • Discord NEW! (Open)
    • chat with the community, team up on features, exchange with developers, code in voice channels
    • registration and usage is for free

  • General initiative meeting (Open)
    • every forth Thursday each month Thursday 16-17:00 (UK time) download .ics
    • join for project news and high-level code updates
    • meeting hosted on Discord
    • open agenda. See what we will discuss. Invited members have edit rights.
  • Buddy talk (Open)
  • Specific code meeting (Open)
    • meeting hosted on Discord
    • join updates, demos, Q&A's, discussions and the coordination of each work package
      1. Demand creation and prediction meeting, on demand
      2. AI asset detection meeting, on demand
      3. Sector coupling meeting, every Thursday 09:00 (UK time), download .ics
      4. PyPSA-Earth meeting, every Thursday 16:00 (UK time), download .ics
  • Outreach meeting (Open)
    • every second week, Tuesday 17:00 (UK time)
    • planning, discussing events, workshops, communication, community activities
  • Google Drive
    • access to minutes, presentations, lists, documents (access to minutes)

Installation

  1. Open your terminal at a location where you want to install pypsa-earth. Type the following in your terminal to download the package from GitHub:

       .../some/path/without/spaces % git clone https://github.com/pypsa-meets-earth/pypsa-earth.git
  2. The python package requirements are curated in the envs/environment.yaml file. The environment can be installed using:

    .../pypsa-earth % conda env create -f envs/environment.yaml

If the above takes longer than 30min, you might want to try mamba for faster installation:

    (base) conda install -c conda-forge mamba

    .../pypsa-earth % mamba env create -f envs/environment.yaml
  1. For running the optimization one has to install the solver. We can recommend the open source HiGHs solver which installation manual is given here.

  2. To use jupyter lab (new jupyter notebooks) continue with the ipython kernel installation and test if your jupyter lab works:

       .../pypsa-earth % ipython kernel install --user --name=pypsa-earth
       .../pypsa-earth % jupyter lab
  3. Verify or install a java redistribution from the official website or equivalent. To verify the successful installation the following code can be tested from bash:

       .../pypsa-earth % java -version

    The expected output should resemble the following:

       java version "1.8.0_341"
       Java(TM) SE Runtime Environment (build 1.8.0_341-b10)
       Java HotSpot(TM) 64-Bit Server VM (build 25.341-b10, mixed mode)

Running the model in previous versions

The model can be run in previous versions by checking out the respective tag. For instance, to run the model in version 0.4.1, which is the last version before the repo pypsa-earth-sec was merged, the following command can be used:

git checkout v0.4.1

After checking out the tag, the model can be run as usual. Please make sure to install the required packages for the respective version.

Test run on tutorial

  • In the folder open a terminal/command window to be located at this path ~/pypsa-earth/

  • Activate the environment conda activate pypsa-earth

  • Rename config.tutorial.yaml to config.yaml. For instance in Linux:

    mv config.tutorial.yaml config.yaml
  • Run a dryrun of the Snakemake workflow by typing simply in the terminal:

    snakemake -j 1 solve_all_networks -n

    Remove the -n to do a real run. Follow the tutorial of PyPSA-Eur 1 and 2 on YouTube to continue with an analysis.

Training

  • We recently updated some hackathon material for PyPSA-Earth. The hackathon contains jupyter notebooks with exercises. After going through the 1 day theoretical and practical material you should have a suitable coding setup and feel confident about contributing.
  • The get a general feeling about the PyPSA functionality, we further recommend going through the PyPSA and Atlite examples.

Questions and Issues

  • We are happy to answer questions and help with issues if they are public. Through being public the wider community can benefit from the raised points. Some tips. Bugs and feature requests should be raised in the GitHub Issues. General workflow or user questions as well as discussion points should be posted at the GitHub Discussions tab. Happy coding.

Documentation

The documentation is available here: documentation.

Collaborators

FabianHofmann
Fabian Hofmann
fneum
Fabian Neumann
ekatef
Ekaterina
euronion
Euronion
Justus-coded
Justus Ilemobayo
mnm-matin
Mnm-matin
martacki
Martha Frysztacki
LukasFrankenQ
Lukas Franken
pz-max
Max Parzen
davide-f
Davide-f
koen-vg
Koen Van Greevenbroek
Eddy-JV
Eddy Jalbout
hazemakhalek
Hazem
energyLS
EnergyLS
AnasAlgarei
AnasAlgarei
yerbol-akhmetov
Yerbol Akhmetov
GbotemiB
Emmanuel Bolarinwa
DeniseGiub
DeniseGiub
doneachh
Anton Achhammer
Tomkourou
Thomas Kouroughli
finozzifa
Finozzifa
GridGrapher
GridGrapher
glenkiely-ieg
Null
cpschau
Cschau
Emre-Yorat89
Emre_Yorat
virio-andreyana
Null
giacfalk
Giacomo Falchetta
Ekaterina-Vo
Ekaterina-Vo
lkstrp
Lukas Trippe
Tooblippe
Tobias
arizeosalac
zeosalac
asolavi
Null
rajesh-ieg
Null
danielelerede-oet
Null
carlosfv92
Carlos Fernandez
stephenjlee
Stephen J Lee
rsparks3
Ryan
ollie-bell
Null
juli-a-ko
Juli-a-ko
squoilin
Sylvain Quoilin
siddharth-krishna
Siddharth Krishna
pitmonticone
Pietro Monticone
Netotse
Null
kma33
Katherine M. Antonio
jessLryan
Jess
jarry7
Jarrad Wright
HanaElattar
HanaElattar
EmreYorat
EmreYorat
AndreCNF
André Cristóvão Neves Ferreira
AlexanderMeisinger
Null