.. SPDX-FileCopyrightText: PyPSA-Earth and PyPSA-Eur Authors .. .. SPDX-License-Identifier: CC-BY-4.0 .. _customization_basic1: ####################### 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: .. code:: bash .../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: .. code:: yaml 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: .. code:: yaml 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 :ref:`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. Specify the weather year scope ------------------------------ Likewise, the example's temporal scope can be restricted (e.g. to 7 days): .. code:: yaml 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`: .. code:: yaml 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/` .. note:: 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``: .. code:: 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 :ref:`config` page. There are many more configuration options beyond what is adapted for the tutorial!