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Guidelines for region names

The region column in the data format specifies the spatial scope of the timeseries data.

Levels of regions

The list of regions is grouped into several levels of spatial detail.

Aggregate regions

This list covers groupings of several countries, like 'World', various definitions of Europe and the EU, and other groupings of several countries (e.g., EFTA).

The definitions of regions chosen for this project are guided by energy modelling conventions and pragmatism rather than political considerations.

Given that models often have different regional scope or aggregation levels, the convention chosen in this nomenclature allows a flexible use of the terms Europe and European Union: the codelist defines several alternative versions of these regions named Europe (*) and EU*, respectively.

Each model should (if applicable) report data at the generic region name and specify in the model documentation which definition was used. If multiple definitions are possible, the model should use a specification earlier in the codelist.

For example, if Turkey is a separate region in a model, the scenario results for Europe should use definition Europe (excl. Turkey) (from line 12 rather than 15) and additionally report alternative region aggregation levels using the specific name Europe (incl. Turkey) (if relevant).

Countries

Each country in this list includes the ISO2 and ISO3 codes as an attribute as well as a flag on EU membership and (optional) a list of synonyms. The list also includes the alternatives to the ISO 3166-1 alpha-2 standard used by the European Commission (iso2_alt).

See countries.yaml for the codelist. The source data and script to generate the codelist are available in the data folder.

Example for using this codelist

The code snippet (Python) below shows how to obtain the list of countries and a mapping of ISO2/3-codes (including alternatives) to the common country names using the installable Python package.

>>> import nomenclature as nc
>>> list(nc.countries)
['Albania', 'Andorra', 'Austria', ..., 'United Kingdom']
>>> nc.iso_mapping['GR']
'Greece'
>>> nc.iso_mapping['GRC']
'Greece'
>>> nc.iso_mapping['EL']
'Greece'

Sub-country areas following the 'Nomenclature of Territorial Units for Statistics' (NUTS)

One set of disaggregation of countries follows the NUTS 2021 classification used by Eurostat.

  • Major socio-economic regions: nuts1.yaml
  • Basic regions for the application of regional policies: nuts2.yaml
  • Small regions for specific diagnoses: nuts3.yaml

Each file includes the mapping of the NUTS-x code to the country name (as defined in countries.yaml) and the "parent" region(s) for NUTS-2 and NUTS-3 areas.

The script to generate the codelist is available in the data folder. The source file NUTS2016-NUTS2021.xlsx can be downloaded from the NUTS 2021 classification website (last download March 27, 2020, per @erikfilias).

Example for using this codelist

The code snippet (Python) below shows how to obtain a recursive dictionary along the NUTS classification, i.e.,

nuts_hierarchy = {
    <country>: {
        <nuts1>: {
            <nuts2>: [<list of nuts3 areas>],
            ... },
        ... },
   ... },
}

The package also includes a regions dictionary with the names of all NUTS areas.

>>> import nomenclature as nc
>>> nc.nuts_hierarchy['Belgium']['BE2']['BE24']
['BE241', 'BE242']]
>>> nc.regions['BE241']['name']
'Arr. Halle-Vilvoorde'

Other sub-national area classification

Other sub-national disaggregations can be defined, ideally described as aggregations of NUTS1,2 or 3 regions.

ehighway2050 clusters

e-highway2050 was an EU-funded project (2012-2015), whose objective was to provide a modular and robust expansion plan for the Pan-European Transmission Network from 2020 to 2050.

It defined a cluster model of the Pan-European transmission grid (D2.2), see https://docs.entsoe.eu/baltic-conf/bites/www.e-highway2050.eu/e-highway2050/.

The cluster model is included in the definitions, see ehighway.yaml.

Classification at a more detailed level (municipality, district, etc.)

To be added at a later stage

Directional timeseries data

To represent data that refers to a flow or capacity between regions, any two regions names at the same level of spatial detail can be combined using a > character (without spaces before/after that character).

Bi-directional data must be declared separately for each direction using only the > character. No other characters (<>, =) are allowed to represent directional data.

Example:

Norway>Germany

To represent data that refers to a flow or cost contribution of a generator or a demand in a region to a connection between two regions, the name of the region of the contributing agent (generator or demand) can be combined with the bi-directional data using a : character before the bi-directional data (without spaces before/after that character).

The region of the contributing agent can be of a different level of spatial detail from the two regions specified in the bi-directional data.

Example:

DE30:France>Spain

Parallel connections of directional data

When timeseries data represents flows or capacity on one of several parallel connections between regions (e.g., power lines, natural gas pipelines), the convention above is not sufficient. For such cases, the convention for directional data can be extended with a | character (without spaces before/after).

Norway>Germany|E54