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In energy modelling, open data and open source code can help enhancetraceability and reproducibility of model exercises which contribute tofacilitate controversial debates and improve policy advice. While theavailability of open power plant databases increased in recent years, theyoften differ considerably from each other and their data quality has not beensystematically compared to proprietary sources yet. Here, we introduce thepython-based "powerplantmatching" (PPM), an open source toolset for cleaning,standardizing and combining multiple power plant databases. We apply it onceonly with open databases and once with an additional proprietary database inorder to discuss and elaborate the issue of data quality, by analysingcapacities, countries, fuel types, geographic coordinates and commissioningyears for conventional power plants. We find that a derived dataset purelybased on open data is not yet on a par with one in which a proprietary databasehas been added to the matching, even though the statistical values for capacitymatched to a large degree with both datasets. When commissioning years areneeded for modelling purposes in the final dataset, the proprietary databasehelps crucially to increase the quality of the derived dataset.

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