DOI: 10.5281/zenodo.7510854
title: Load and generation time series for German federal states: Static vs. dynamic regionalization factors (data)

description: This dataset contains regionalization factors for electricity generation and demand time series in Germany for the years 2019 - 2022. The factors can be used to distribute national generation and demand time series available from SMARD or ENTSO-E to federal state level. The methods underlying the regionalization factors are described in [1], with a focus on the year 2021. However, an extended version of the dataset covering the years 2019-2022 is also included for comprehensive analysis. Moreover, the dataset comprises the corresponding regionalized generation and demand time series at the federal state level of Germany. This time series has been generated using the provided distribution factors for the years 2019-2022 and corresponding generation and demand time series from SMARD [2]. Addtionally, the regionalization methodology for the distributed generation and demand data for the year 2021 has been supplemented with validation data, as described in [1]. This data has been cross-checked against the available SMARD Transmission System Operator (TSO) data. A description of the preprocessing required to obtain the TSO data comparison is provided in a separate .txt file. A PDF document has been prepared, which includes scatter plots that illustrate a comparison between actual and allocated generation per production type or demand data for TSOs on an hourly basis for the year 2021. <strong>"static_regionalization_factors.2021[csv, xlsx]"</strong> Each column corresponds to one factor per federal state and per production type or demand. Regionalization factors are based on share of generation capacity in each state (generation) or population and GDP (demand). <strong>"dynamic_regionalization_factors_2021.[csv, xlsx]"<br> “dynamic_regionalization_factors_all.[csv, xlsx]”</strong> Each column corresponds to one factor per federal state and per production type or demand. Each row corresponds to a specific hour of the years 2019 through 2022. Regionalization factors are based on a combination of per unit generation data and share of generation capacity in each state, simulated renewable generation data based on spatio-temporal weather data and distribution of wind and solar generation capacities, and a regionalized load dataset for 2015 [3]. <strong>“time_series_federal_states_all.[csv, xlsx]”</strong> Each column corresponds to the allocated electricity generation or demand per federal state per production type or demand in units of MWh. Each row corresponds to a specific hour of the years 2019 through 2022. The regionalized generation and demand time series has been created by utilizing the dynamic regionalization factors provided in the dataset, in conjunction with the national electricity generation and demand data of Germany as provided by SMARD [2]. <strong>“TSO_actual.[csv, xlsx]”<br> “TSO_allocated.[csv, xlsx]”</strong> Each column corresponds to the spatially aggregated electricity generation per type or demand per TSO in units of GWh. Each row corresponds to one hour of the year 2021. The TSOs in Germany do not hold direct responsibility for individual federal states, but rather for specific regions. In order to assess the validity of the regionalization methodology employed, it was necessary to generate data at the NUTS3 level and subsequently aggregate it to correspond with the relevant TSOs. The data is pre-processed at NUTS3 level and then undergoes the same methodology as outlined in [1]. The preprocessing steps required to map the installed capacity to the TSO level are explained in the accompanying .txt file. The allocated generation and demand data are aggregated to correspond to the TSO level using a shapefile of mapped regions in Germany that correspond to the TSOs [4]. The actual TSO data is generation and demand as published by SMARD [2]. The accompanying PDF presents scatter plots that showcase the actual vs allocated hourly generation types or demand per TSO, expanding on the information provided in the article. [1] M. Sundblad, T. Fürmann, A. Weidlich and M. Schäfer, "Load and generation time series for German federal states: Static vs. dynamic regionalization factors," <em>2023 Open Source Modelling and Simulation of Energy Systems (OSMSES)</em>, Aachen, Germany, 2023, pp. 1-6, doi: 10.1109/OSMSES58477.2023.10089686. [2] Bundesnetzagentur | SMARD.de [3] Matthias Kühnbach, Anke Bekk, and Anke Weidlich (2021). Prepared for regional self-supply? On the regional fit of electricity demand and supply in Germany. Energy Strategy Reviews, 34:100609, 20 [4] Frysztacki, Martha Maria. (2023). Mapping of districts to control zones of German Transmission System Operators (TSOs) (v0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7530196