TY  - JOUR
A1  - Gotzens, Fabian
A1  - Heinrichs, Heidi
A1  - Hörsch, Jonas
A1  - Hofmann, Fabian
T1  - Performing energy modelling exercises in a transparent way - the issue of data quality in power plant databases
T2  - Energy strategy reviews
N2  - 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.
KW  - Open data  data
KW  - Power plant
KW  - Europe
KW  - Power system model
KW  - Energy system analysis
Y1  - 2018
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/54385
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-543855
SN  - 2211-4688
N1  - This is an open access article under CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/)
VL  - 23
SP  - 1
EP  - 12
PB  - Elsevier
ER  -