German Renewable Energy Unit Register

The UnitRegister dataset is a comprehensive compilation of all renewable energy units connected to the grid in Germany. It combines data from two primary sources: the Marktstammdatenregister published by Germany's Federal Network Agency (BNetzA), and individual publications from the four German Transmission System Operators (TSOs) on their conjoint reporting webpage.
The dataset encompasses active, decommissioned, and planned units, resulting from the merging and cleaning of these two unit registers. The Marktstammdatenregister dataset is regularly updated and was last downloaded in December 2022. The TSOs' unit register is updated annually around August, with a one-year lag, and the last data extraction took place in 2022, providing information until 2021.
While both datasets are similar, the main difference lies in the reporting format. The TSOs report units as bundles of installations under a common payment code (Unit_ID), whereas BNetzA publishes each installation as a separate unit with an individual identification code (BNetzA_Unit_ID). For example, a wind park with multiple turbines is reported as a single unit by the TSOs, whereas BNetzA provides information on each turbine separately.
The main dataset, "UnitRegister.csv," is located in the "3Output" folder. It comprises more than 2.4 million units categorized into ten technologies, including Geothermal, Hydropower, Gas_landfill, Gas_mine, Gas_sewage, Biomass, Wind_on, Wind_off, Solar_GM, and Solar_RM. This dataset consists of 13 columns/variables, including "Unit_ID," "BNetzA_EEG_ID," "BNetzA_Unit_ID," "Technology," "Capacity_kW," "Commissioning," "Decommissioning," "State," "PLZ," "Source," "RefYield," "Depth," and "Coast_dist."
In addition to the main dataset, this publication provides separate files for different stages of data processing. For example, "UnitRegister_TSOs.csv" and "UnitRegister_BNetzA.csv" offer clean datasets for the TSOs and BNetzA sources individually. The file "UnitRegister_BNetzA_Raw.csv" contains the compiled BNetzA register in its raw form, comprising 82 columns/variables. Detailed information on these variables can be found in the BNetzA user manual, which is included in the "Dokumentation MaStR Gesamtdatenexport" folder within the BNetzA directory.

Notes:

The dataset has undergone merging and cleaning processes aligned with the research objectives of the Chair of Energy Economics at BTU. It is important to note that these objectives may differ from other research purposes. Specifically, our research focused on analyzing payments associated with renewable energy projects, giving priority to the TSO data. The BNetzA data set was used to supplement the unit stock and fill in data gaps for TSO units whenever possible.
Users should exercise caution when selecting the data source that best aligns with their research goals. To assist in this decision-making process, we have included the R-code used for data processing. By downloading the data package and modifying the top-level directory (line 22), users can run the code. However, it is crucial to verify that any updated datasets maintain consistency with the previous file structure (which has not always been the case in the past) and update the file reading sections in the code accordingly.

This publication is part of a series of datasets on German Renewable Energy Sources Data, which also includes a PaymentRegister, a TariffRegister and the combination of this information to analyze individual bids in renewable auctions. To gain a comprehensive understanding of the datasets and their relationships, we recommend referring to two related publications:
•    Batz Liñeiro, T. B., Müsgens, F. (2023). "Evaluating the German onshore wind auction programme: An analysis based on individual bids." Energy Policy, 172, 113317. https://doi.org/10.1016/j.enpol.2022.113317 | Preprint

•    Batz Liñeiro, T., Müsgens, F. (2021). "Evaluating the German PV auction program: The secrets of individual bids revealed." Energy Policy, 159, 112618. https://doi.org/10.1016/j.enpol.2021.112618 | Preprint