DOI: 10.5281/zenodo.10226008
title: Data bundle for egon-data: A transparent and reproducible data processing pipeline for energy system modeling

description: 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:





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)






cutouts





Weather data from Europe in 2011. Source: ERA5






demand_regio_backup





Electricity and heat demands






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)






entsoe





 






gas_data





CH4 infrastructure




Biogas demand




CH4 demand




Source: SciGRID_gas






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)






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)






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)






hydrogen_network





Planned H2 infrastructure




Forecast H2 demand




Source: fnb-gas






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 undPotenzialabschätzung. BGR. Informationssystem Salz: Planungsgrundlagen,Auswahlkriterien und Potenzialabschätzung für die Errichtung von Salzkavernenzur 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






industrial_gas_demand




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)






mastr_geocoding




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)






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)






pypsa_eur




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)






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)






wind_offshore_status2019





 






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.






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.






zensus_population




district_heating_shares_egon.csv