Basic Setup

A good starting point to customize your model are settings of the default configuration file config.default.yaml. You may want to do a reserve copy of your current configuration file and then overwrite it by a default configuration:

cp config.default.yaml config.yaml

Specify the country/region of interest

The model can be adapted to include any country, multiple countries (e.g. Nigeria and Benin) or full continents (currently whole regions, such as Africa, Asia, Europe, Oceania, NorthAmerica, and SouthAmerica, are available for simulation) using countries argument:

countries: ["NG", "BJ"]

Configure enable section to download/build data

For a successful model run, ensure the download of essential open-source data, including databundle and cost data, is activated in the enable section:

enable:
retrieve_databundle: true  #  Recommended 'true', for the first run. Otherwise data might be missing.
retrieve_cost_data: true  # true: retrieves cost data from technology data and saves in resources/costs.csv, false: uses cost data in data/costs.csv
download_osm_data: true  # If 'true', OpenStreetMap data will be downloaded for the above given countries
build_natura_raster: false # If True, than an exclusion raster will be build
build_cutout: false
# If "build_cutout" : true, then environmental data is extracted according to `snapshots` date range and `countries`

After the initial run, it is recommended to set the retrieval of databundle and cost data to false to prevent unnecessary redownloading of data.

When build_natura_raster: false is utilized, the exclusion raster for protected areas is sourced from the pre-compiled data/natura.tiff file downloaded with the databundle. Conversely, if build_natura_raster is set to true, the exclusion raster, delineating areas where renewables cannot be installed, is computed using the build_natura_raster rule.

When using the weather year 2013, it is recommended to use default build_cutout: false because pre-compiled cutouts are automatically downloaded with retrieve_databundle: true. On contrary, when simulating a weather year other than 2013, it is crucial to set build_cutout: true in order to generate custom cutouts. However, it is essential to first configure the Copernicus Climate Data Store (CDS) API. Detailed instructions for setting up the Copernicus API can be found in customization_copernicus. After initial run and successful generation of custom cutouts, build_cutout can be switched to false to avoid reconstructing the cutout.

Note

No need to configure the Copernicus API if the weather year 2013 is used, as pre-compiled cutouts are automatically downloaded.

Tip

Additionally, if you encounter issues with failed retrieve_databundle, you can use the following script to debug it through the command line interface (CLI):

python scripts/non_workflow/databundle_cli.py

Specify the weather year scope

Likewise, the example's temporal scope can be restricted (e.g. to 7 days):

snapshots:
  start: "2013-03-01"
  end: "2013-03-07"
  inclusive: "left" # end is not inclusive

Ensure that the selected date range aligns with the dates available in the cutout dataset. If the weather data within the cutouts corresponds to the year 2013, then the range of snapshots should fall within that same year.

Specify the demand year

Year-related parameters are also being used when specifying load_options:

load_options:
  ssp: "ssp2-2.6"
  weather_year: 2013
  prediction_year: 2030
  scale: 1

The weather_year value corresponds to the weather data which was used to generate the electricity demand profiles for a selected area while prediction_year corresponds to the point of a Shared Socioeconomic Pathways (SSP) trajectory. PyPSA-Earth uses SSP2-2.6 scenario within the Shared Socioeconomic Pathways framework, which is characterized by medium challenges to mitigation and adaptation efforts resulting in a global warming of approximately 2.6°C by the end of the 21st century. The available values for weather_year and prediction_year can be checked by looking into pypsa-earth/data/ssp2-2.6 folder. Currently, there are pre-calculated demand data for 2011, 2013, 2018 weather years and for 2030, 2040, 2050, and 2100 scenario prediction years.

Use custom demand data

It is possible to implement custom demand profiles. It can be done by creating a dedicated custom demand sub-folder in a scenario folder pypsa-earth/data/ssp2-2.6 and placing there a custom demand file. The name of a custom demand sub-folder should correspond to weather_year argument which stands in this case for general identification of a demand input. The name of a demand input file should be a continent name to which belongs a country of initerest. Both csv and nc formats can be used for demand files.

For example, to pypsa-earth/data/ssp2-2.6/2013_custom/

For example, to provide custom inputs for Nigeria, you can put the time-series into Africa.csv file and place the file into pypsa-earth/data/ssp2-2.6/2013_custom/ folder. To make it fetched, you'll need to specify weather_year: 2013_custom under load_options.

A format of the custom csv demand file should correspond to the csv files supplied with the model: there are region_code, time, region_name and Electricity demand columns, while a semicolon is used as a separator.

Configure atlite section

To accurately model the temporal and spatial availability of renewables such as wind and solar energy, we process historical weather data using atlite package. Atlite configurations can be adjusted in config.yaml:

atlite:
  nprocesses: 4
  cutouts:
    cutout-2013-era5:
      module: era5
      dx: 0.3  # cutout resolution
      dy: 0.3  # cutout resolution
      # The cutout time is automatically set by the snapshot range.

No adjustments are required when utilizing pre-compiled cutouts. When using custom cutouts generated by build_cutout rule, replace all entries of cutout-2013-era5 with the custom cutout name for a region of interest. For example, when simulating Kazakhstan with cutouts: asia-2013-era5, every occurrence of cutout-2013-era5 should be updated to asia-2013-era5 which refers to asia-2013-era5.nc file generated in cutouts folder.

Please note that a temporal dimension of the cutout should be consistent with the values set for snapshots parameter. A time range of the cutout is determined by the parameters set when building this cutout while the time resolution corresponds to those of the used climate archives. In case of ERA5 dataset used in PyPSA-Earth by default, hourly resolution is implied.

To delve into the specifics of the provided configurations and explore additional settings, please refer to the configuration page. There are many more configuration options beyond what is adapted for the tutorial!