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2. Basic customization

2. Basic customization#

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:

.../pypsa-earth (pypsa-earth) % 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 5. Setup Copernicus API. 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.

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

Note

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.

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.

Note

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!

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