Installation

Installation#

The subsequently described installation steps are demonstrated as shell commands, where the path before the % sign denotes the directory in which the commands following the % should be entered.

Clone the Repository#

First of all, clone the PyPSA-Eur repository using the version control system git in the command line.

$ git clone https://github.com/PyPSA/pypsa-eur.git

Install Python Dependencies#

PyPSA-Eur relies on a set of other Python packages to function. We recommend using the package manager conda <https://docs.anaconda.com/miniconda/> or mamba to install them and manage your environments.

The package requirements are curated in the envs/environment.yaml file. There are also regularly updated locked environment files for each platform generated with conda-lock to ensure reproducibility. Choose the correct file for your platform:

  • For Intel/AMD processors:

    • Linux: envs/linux-64.lock.yaml

    • macOS: envs/osx-64.lock.yaml

    • Windows: envs/win-64.lock.yaml

  • For ARM processors:

    • macOS (Apple Silicon): envs/osx-arm64.lock.yaml

    • Linux (ARM): Currently not supported via lock files; requires building certain packages, such as PySCIPOpt, from source

We recommend using these locked files for a stable environment.

$ conda update conda

$ conda env create -f envs/linux-64.lock.yaml # select the appropriate file for your platform

$ conda activate pypsa-eur

Install a Solver#

PyPSA passes the PyPSA-Eur network model to an external solver for performing the optimisation. PyPSA is known to work with the free software

and the non-free, commercial software (for some of which free academic licenses are available)

For installation instructions of these solvers for your operating system, follow the links above. Commercial solvers such as Gurobi and CPLEX currently significantly outperform open-source solvers for large-scale problems, and it might be the case that you can only retrieve solutions by using a commercial solver. Nevertheless, you can still use open-source solvers for smaller problems.

See also

Instructions how to install a solver in the documentation of PyPSA

Note

The rules cluster_network solves a mixed-integer quadratic optimisation problem for clustering. The open-source solvers HiGHS, Cbc and GlPK cannot handle this. A fallback to SCIP is implemented in this case, which is included in the standard environment specifications. For an open-source solver setup install for example HiGHS and SCIP in your conda environment on OSX/Linux. To install the default solver Gurobi, run

$ conda activate pypsa-eur
$ conda install -c gurobi gurobi"=12.0.1"

Additionally, you need to setup your Gurobi license.

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