Running the Model

Run Full Model

PyPSA-Earth utilizes the snakemake workflow management tool inherited from PyPSA-Eur. Snakemake decomposes a large software process into a set of subtasks, or 'rules', that are automatically chained to obtain the desired output.

To execute a full model run, the following command needs to be applied:

snakemake --cores 1 solve_all_networks

Here, the flag --cores 1 dictates the number of CPU cores allocated for the process. Notably, the solve_all_network rule within Snakemake orchestrates the process of solving the network.

Note

Snakemake, which is one of the major dependencies, will be automatically installed in pypsa-earth environment, thereby there is no need to install it manually.

Execute Different Parts of the Workflow

The snakemake included in the pypsa-earth conda environment can be used to execute any custom rule with the following command:

snakemake <your custom rule>

Starting with essential usability features, PyPSA-Earth Snakemake procedure enables users to execute the entire workflow flexibly with diverse options, requiring no Python coding. For example, users can model the global energy system or specific subsets of countries using only necessary data. Wildcards, serving as special generic keys, adapt to multiple values based on configuration options, facilitating the execution of extensive workflows with parameter sweeps and diverse options.

You can execute some parts of the workflow in case you are interested in some specific parts. For example, power grid topology may be extracted and cleaned with the following command which refers to the script name:

snakemake -j 1 clean_osm_data

Solar profile for the requested area may be calculated using the output name:

snakemake -j 1 resources/renewable_profiles/profile_solar.nc

Because of the wildcards in the build_renewable_profiles rule definition, the only available option to run the rule separately is by directly calling the output file in the snakemake command.

Choose the Solver

To solve a full optimization problem, it is important to pick a solver in config.yaml file. For instance, this tutorial uses the open-source solver glpk and does not rely on the commercial solvers such as Gurobi or CPLEX (for which free academic licenses are available).

solver:
  name: glpk

glpk can solve the network with low temporal and spatial resolution. To make a full model run, it is advised to use CPLEX, Gurobi, or open-source HiGHS.