PyPSA-eur/Clustering.md

@@ -26,12 +26,12 @@ It currently is still an [issue](https://github.com/PyPSA/pypsa-eur/issues/124)
 The goal was to define a model with higher resolution for Germany ([nuts2](https://ec.europa.eu/eurostat/de/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts)) and limit modelling of neighbouring countries to a minimum. While the current solution is specific to Germany, it can easily be adapted to other countries.
 
 **The way**
-- create a PyPSA-eur-network with desired config-settings (clustering.custom_busmap in the PyPSA-eur config file must be set to `false` at this step)
-- modify `custom_clustering.py.ipynb` (in backbone_tools) such that it meets your needs.
-- execute it. It will create a `custom_busmap_elec_s_{clusters}.csv` file where `clusters` is analogous to its use in the PyPSA-eur snakefile. It will be written to the PyPSA-eur-folder "data".
-- delete all files in the PyPSA-eur-folder "network", "results" and "resources"
-- enable clustering.custom_busmap in the PyPSA-eur config file by setting it to `true`.
-- create the final PyPSA-eur-network with desired settings (remember to match the wildcard `{clusters}` to `custom_busmap_elec_s_{clusters}.csv`)
+- Create a PyPSA-eur-network with desired config-settings (clustering.custom_busmap in the PyPSA-eur config file must be set to `false` at this step).
+- Modify `custom_clustering.py.ipynb` (in backbone_tools) such that it meets your needs.
+- Execute it. It will create a `custom_busmap_elec_s_{clusters}.csv` file where `clusters` is analogous to its use in the PyPSA-eur snakefile. It will be written to the PyPSA-eur-folder "data".
+- Delete all files in the PyPSA-eur-folder "network", "results" and "resources".
+- Enable clustering.custom_busmap in the PyPSA-eur config file by setting it to `true`.
+- Create the final PyPSA-eur-network with desired settings (remember to match the wildcard `{clusters}` to `custom_busmap_elec_s_{clusters}.csv`).
 
 **The result**
 Clustering as described above yields the following map. Note that colours only serve the purpose of visually separating clusters.