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
The path to the directory into which the
git repository is cloned, must not have any spaces!
If you do not have
git installed, follow installation instructions here.
/some/other/path % cd /some/path/without/spaces /some/path/without/spaces % 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 and environment management system
conda to install them.
Install miniconda, which is a mini version of Anaconda that includes only
conda and its dependencies or make sure
conda is already installed on your system.
For instructions for your operating system follow the
conda installation guide.
The python package requirements are curated in the envs/environment.yaml file. The environment can be installed and activated using
.../pypsa-eur % conda env create -f envs/environment.yaml .../pypsa-eur % conda activate pypsa-eur
Note that activation is local to the currently open shell! After opening a new terminal window, one needs to reissue the second command!
If you have troubles with a slow
conda installation, we recommend to install
mamba as a fast drop-in replacement via
conda install -c conda-forge mamba
and then install the environment with
mamba env create -f envs/environment.yaml
Install a Solver¶
PyPSA passes the PyPSA-Eur network model to an external solver for performing a total annual system cost minimization with optimal power flow. 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. It might be the case that you can only retrieve solutions by using a commercial solver.
simplify_network solve a quadratic optimisation problem for clustering.
The open-source solvers Cbc and GlPK cannot handle this. A fallback to Ipopt is implemented in this case, but requires
also Ipopt to be installed. For an open-source solver setup install in your
conda environment on OSX/Linux
conda activate pypsa-eur conda install -c conda-forge ipopt coincbc
and on Windows
conda activate pypsa-eur conda install -c conda-forge ipopt glpk
Set Up the Default Configuration¶
PyPSA-Eur has several configuration options that must be specified in a
config.yaml file located in the root directory.
An example configuration
config.default.yaml is maintained in the repository.
More details on the configuration options are in Configuration.
Before first use, create a
config.yaml by copying the example.
.../pypsa-eur % cp config.default.yaml config.yaml
Users are advised to regularly check their own
config.yaml against changes in the
when pulling a new version from the remote repository.