Data bundle for egon-data: A transparent and reproducible data processing pipeline for energy system modeling

egon-data provides a transparent and reproducible open data based data processing pipeline for generating data models suitable for energy system modeling. The data is customized for the requirements of the research project eGoⁿ. The research project aims to develop tools for an open and cross-sectoral planning of transmission and distribution grids. For further information please visit the eGoⁿ project website or its Github repository.

egon-data retrieves and processes data from several different external input sources. As not all data dependencies can be downloaded automatically from external sources we provide a data bundle to be downloaded by egon-data.

The following data sets are part of the available data bundle:

1. climate_zones_germany
   • Climate zones in Germany
   • source: Own representation based on DWD TRY climate zones
   • License: Attribution 4.0 International (CC BY 4.0)
2. emobility
   • Data on eMobility mit_trip_data:
     motorized individual travel - individual trips of electric vehicles (EV) generated with a modified version of simBEV v0.1.3 (https://github.com/rl-institut/simbev/tree/1f87c716d14ccc4a658b8d2b01fd12b88a4334d5). simBEV generates driving profiles for BEVs and PHEVs based upon MID data (BMVI) per RegioStaR7 region type (BBSR).
   • Reiner Lemoine Institut, June 2022
   • License: Attribution 4.0 International (CC BY 4.0)
3. geothermal_potential
   • Spatial distribution of deep geothermal potentials in Germany
   • source: Assessment and Public Reporting of Geothermal Resources in Germany: Review and Outlook
   • License: Attribution 4.0 International (CC BY 4.0)
4. household_electricity_demand_profiles
   • Annual profiles in hourly resolution of electricity demand of private households for different household types (singles, couples, other) with varying number of elderly and children.
     The profiles were created using a bottom-up load profile generator by Fraunhofer IEE developed in the Bachelor's thesis "Auswirkungen verschiedener Haushaltslastprofile auf PV-Batterie-Systeme" by Jonas Haack, Fachhochschule Flensburg, December 2012.
     The columns are named as follows: "<HH_TYPE_PREFIX>a<PROFILE_ID>", e.g. P2a0000 is the first profile of a couple's household with 2 children. See publication below for the list of prefixes. Values are given in Wh.
     A related conference paper can be obtained here: http://publica.fraunhofer.de/documents/N-374761.html
   • License: Attribution 4.0 International (CC BY 4.0)
5. household_heat_demand_profiles
   • Sample heat time series including hot water and space heating for single- and multi-familiy houses. The profiles were created using the loadprofile generator by Fraunhofer IEE developed in the Master's thesis "Synthesis of a heat and electrical load profile for single and multi-family houses used for subsequent performance tests of a multi-component energy system", Simon Ruben Drauz, RWTH Aachen University, March 2016
   • License: Attribution 4.0 International (CC BY 4.0)
6. hydrogen_storage_potential_saltstructures
   • The data are taken from figure 7.1 in Donadei, S., et al., (2020), p. 7-5..
   • Source: Flach lagernde Salze, (c) BGR Hannover, 2021.
     Datenquelle: InSpEE-Salzstrukturen, (c) BGR, Hannover, 2015. &
     Donadei, S., Horváth, B., Horváth, P.-L., Keppliner, J., Schneider, G.-S., &
     Zander-Schiebenhöfer, D. (2020). Teilprojekt Bewertungskriterien und
     Potenzialabschätzung. BGR. Informationssystem Salz: Planungsgrundlagen,
     Auswahlkriterien und Potenzialabschätzung für die Errichtung von Salzkavernen
     zur Speicherung von Erneuerbaren Energien (Wasserstoff und Druckluft) –
     Doppelsalinare und flach lagernde Salzschichten: InSpEE-DS. Sachbericht.
     Hannover: BGR.
   • License: The original data are licensed under the GeoNutzV, see https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf
7. industrial_sites
   • Information about industrial sites with DSM-potential in Germany from a Master's thesis by Danielle Schmidt. The data set includes own information on the coordinates of every industrial site.
   • source: Schmidt, Danielle. (2019). Supplementary material to the masters thesis: NUTS-3 Regionalization of Industrial Load Shifting Potential in Germany using a Time-Resolved Model [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3613767
   • License: Attribution 4.0 International (CC BY 4.0)
8. nep2035_version2021
   • Data extracted from the German grid development plan - power
   • source: Netzentwicklungsplan Strom 2035 (2021), erster Entwurf | Übertragungsnetzbetreiber (M) CC-BY-4.0
   • License: Attribution 4.0 International (CC BY 4.0)
9. pipeline_classification_gas
   • Parameters for the classification of gas pipelines
   • source: Single parameters extracted from Electricity, Heat and Gas Sector Data for Modelling the German System
   • License: Attribution 4.0 International (CC BY 4.0)
10. pypsa_eur_sec
    • Preliminary results from scenario generator pypsa-eur-sec
    • source: own calculation using pypsa-eur-sec fork (https://github.com/openego/pypsa-eur-sec)
    • License: Attribution 4.0 International (CC BY 4.0)
11. regions_dynamic_line_rating
    • German regions suitable to model dynamic line rating
    • source: Own representation based on Grundsätze für die Ausbauplanung des Deutschen Übertragungsnetze (2020)
    • License: Attribution 4.0 International (CC BY 4.0)
12. re_potential_areas
    • Eligible areas for wind turbines and ground-mounted PV systems.
    • Reiner Lemoine Institut, January 2022
    • License: Attribution 4.0 International (CC BY 4.0)
13. WZ_definition
    • Definitions of industrial and commercial branches
    • source: Klassifikation der Wirtschaftszweige (WZ 2008)
    • Extract from Terms of Use: © Statistisches Bundesamt, Wiesbaden 2008 Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellenangabe gestattet.
14. zensus_households
    • Dataset describing the amount of people living by a certain types of family-types, age-classes,sex and size of household in Germany in state-resolution.
    • source: Data retrieved from Zensus Datenbank by performing these steps:
      • Search for: "1000A-2029"
      • or choose topic: "Bevölkerung kompakt"
      • Choose table code: "1000A-2029" with title "Personen: Alter (11 Altersklassen)/Geschlecht/Größe desprivaten Haushalts - Typ des privaten Haushalts (nach Familien/Lebensform)"
      • Change setting "GEOLK1" to "Bundesländer (16)" higher resolution "Landkreise und kreisfreie Städte (412)" only accessible after registration.
    • Extract from Terms of Use: © Statistische Ämter des Bundes und der Länder 2021, Vervielfältigung und Verbreitung, auch auszugsweise, mit Quellennachweis gestattet.