Performing energy modelling exercises in a transparent way - the issue of data quality in power plant databases

  • In energy modelling, open data and open source code can help enhance traceability and reproducibility of model exercises which contribute to facilitate controversial debates and improve policy advice. While the availability of open power plant databases increased in recent years, they often differ considerably from each other and their data quality has not been systematically compared to proprietary sources yet. Here, we introduce the python-based ‘powerplantmatching’ (PPM), an open source toolset for cleaning, standardizing and combining multiple power plant databases. We apply it once only with open databases and once with an additional proprietary database in order to discuss and elaborate the issue of data quality, by analysing capacities, countries, fuel types, geographic coordinates and commissioning years for conventional power plants. We find that a derived dataset purely based on open data is not yet on a par with one in which a proprietary database has been added to the matching, even though the statistical values for capacity matched to a large degree with both datasets. When commissioning years are needed for modelling purposes in the final dataset, the proprietary database helps crucially to increase the quality of the derived dataset.

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Author:Fabian Gotzens, Heidi Heinrichs, Jonas HörschORCiDGND, Fabian Hofmann
Parent Title (German):Energy strategy reviews
Document Type:Article
Date of Publication (online):2018/11/24
Date of first Publication:2018/11/24
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2020/04/20
Tag:Energy system analysis; Europe; Open data data; Power plant; Power system model
Page Number:12
First Page:1
Last Page:12
This is an open access article under CC BY-NC-ND license. (
Institutes:Wissenschaftliche Zentren und koordinierte Programme / Frankfurt Institute for Advanced Studies (FIAS)
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
3 Sozialwissenschaften / 33 Wirtschaft / 333 Boden- und Energiewirtschaft / 333.7 Natürliche Ressourcen, Energie und Umwelt
Licence (German):License LogoCreative Commons - Namensnennung-Nicht kommerziell - Keine Bearbeitung 4.0