Generation-based and consumption-based grid emission intensity time series for German federal states 2020 - 2023

The dataset contains time series estimating the consumption mix, generation-based and consumption-based grid emission intensity of German federal states for the period 2020 to 2023 as shown on the CO2Map website. For an explanation of the underlying methodology see the CO2Map Methology section, a detailed scientific article currently is in preparation. All time series have an hourly resolution (time stamps are given in UTC) and contain values for the 13 federal territorial states in Germany, with the city states merged into them:BW: Baden-WuerttembergBY: BavariaBB: Brandenburg and BerlinHE: HesseMV: Mecklenburg-Western PomeraniaNI: Lower Saxony and BremenNW: North Rhine-WestphaliaRP: Rhineland-PalatinateSL: SaarlandSN: SaxonyST: Saxony-AnhaltSH: Schleswig-Holstein and HamburgTH: Thuringia "Emission intensity"This folder contains the following files for 2020 to 2023:"region_consumption_based_emission_intensity.csv": Consumption-based emission intensity in kgCO2/kWh"region_generation_based_emission_intensity.csv": Generation-based emission intensity in kgCO2/kWh "Tables"The folder contains the raw data representing the hourly consumption and storage mix including imports for federal states in MWh. This data allows to calculate generation-based and consumption-based emission intensity time series using arbitrary technology-specific emission factors for the technology types Biomass, Gas, Hard coal, Hydro, Lignite, Nuclear, Other fossil, Other renewable, Solar, Storage, Wind Offshore, Wind Onshore. Each file contains time series for one region as given above and for one year. The columns represent the hourly amount of load/storage/load+storage originating from a certain region and technology type, with the regions including local generation (i.e. the same federal state), imports from other federal states in Germany, and imports from other European countries (the origin of the imports is determined using a flow-tracing algorithm, see the CO2Map Methology section). For each year and each federal state there is one file for load (representing electricity consumption), one file for storage (representing storage charging) and one file for total (sum of load and storage). The file "2023_import_table_load_DE_BW.csv", for instance, contains the mix of hourly load in Baden-Wuerttemberg. The first entry 01.01.2023 00:00 (UTC) then contains the mix for the electricity consumption in the first hour (UTC) of the year 2023 in BW. The sum over all columns for this row yields 4667.80 MWh, which is the approximated total load in this hour in BW (based on a regionalization method). The largest entry in the row is from "DE_NI,HB / Wind Onshore" (614.57 MWh), which is the amount of onshore wind power generated in Lower Saxony and Bremen and consumed in Baden-Wuerttemberg (based on the flow-tracing method). The second largest entry is from "DE_BW / Wind Onshore", which is the amount of onshore wind power generated and consumed locally in Baden-Wuerttemberg. The largest import to BW from other countries in this hour has been 115.18 MWh of nuclear power from Belgium. The file "2023_import_table_load_DE_BW.csv" contains an aggregated version of the same file, with the originating regions corresponding to the same federal state, imports from other federal states in Germany, and imports from other countries (each separated per technology). For the data sources and methods used to calculate the time series contained in this dataset see the CO2Map Methodology and About sections. Please communicate any questions, corrections or comments to mirko.schaefer [at] inatech.uni-freiburg.de All authors acknowledge funding from Elektrizitätswerke Schönau (EWS) through the Sonnencent program, ID 00009280. Tim Fürmann acknowledges funding from DFG (SPP 1984), project ID 450860949. Ramiz Qussous and Robin L. Grether would like to thank the German Federal Government, the German State Governments, and the Joint Science Conference (GWK) for their funding and support as part of the NFDI4Energy consortium, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 501865131.