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
.
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!
Note
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.
Note
The rules cluster_network
and 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
Warning
On Windows, new versions of ipopt
have caused problems. Consider downgrading to version 3.11.1.
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 config.default.yaml
when pulling a new version from the remote repository.