DOI: 10.5281/zenodo.6520140
title: Data bundle for egon-data: A transparent and reproducible data processing pipeline for energy system modeling

description: <strong>egon-data</strong> 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 <strong>eGo<sup>n</sup></strong>. 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 eGo<sup>n</sup> 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: <strong>climate_zones_germany</strong> Climate zones in Germany source: Own representation based on DWD TRY climate zones License: Attribution 4.0 International (CC BY 4.0) <strong>emobility</strong> Data on eMobility mit_trip_data:<br> motorized individual travel - individual trips of electric vehicles (EV) generated with simBEV v0.1.2 (https://github.com/rl-institut/simbev). simBEV generates driving profiles for BEVs and PHEVs based upon MID data (BMVI) per RegioStaR7 region type (BBSR). Reiner Lemoine Institut, January 2022 License: Attribution 4.0 International (CC BY 4.0) <strong>geothermal_potential</strong> 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) <strong>household_electricity_demand_profiles</strong> 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.<br> 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.<br> The columns are named as follows: "&lt;HH_TYPE_PREFIX&gt;a&lt;PROFILE_ID&gt;", 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.<br> 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) <strong>household_heat_demand_profiles</strong> 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) <strong>hydrogen_storage_potential_saltstructures</strong> 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.<br> Datenquelle: InSpEE-Salzstrukturen, (c) BGR, Hannover, 2015. &<br> Donadei, S., Horváth, B., Horváth, P.-L., Keppliner, J., Schneider, G.-S., &<br> Zander-Schiebenhöfer, D. (2020). Teilprojekt Bewertungskriterien und<br> Potenzialabschätzung. BGR. Informationssystem Salz: Planungsgrundlagen,<br> Auswahlkriterien und Potenzialabschätzung für die Errichtung von Salzkavernen<br> zur Speicherung von Erneuerbaren Energien (Wasserstoff und Druckluft) –<br> Doppelsalinare und flach lagernde Salzschichten: InSpEE-DS. Sachbericht.<br> Hannover: BGR. License: The original data are licensed under the GeoNutzV, see https://sg.geodatenzentrum.de/web_public/gdz/lizenz/geonutzv.pdf <strong>industrial_sites</strong> 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) <strong>nep2035_version2021</strong> 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) <strong>pipeline_classification_gas</strong> 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) <strong>pypsa_eur_sec</strong> 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) <strong>regions_dynamic_line_rating</strong> 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) <strong>re_potential_areas</strong> Eligible areas for wind turbines and ground-mounted PV systems. Reiner Lemoine Institut, January 2022 License: Attribution 4.0 International (CC BY 4.0) <strong>WZ_definition</strong> 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. <strong>zensus_households</strong> 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.