TY  - JOUR
AU  - Gotzens, Fabian
AU  - Heinrichs, Heidi
AU  - Hörsch, Jonas
AU  - Hofmann, Fabian
TI  - Performing energy modelling exercises in a transparent way - the issue of data quality in power plant databases
JO  - Energy strategy reviews
VL  - 23
SN  - 2211-467X
CY  - Amsterdam [u.a.]
PB  - Elsevier
M1  - FZJ-2019-00182
SP  - 1 - 12
PY  - 2019
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000458217800001
DO  - DOI:10.1016/j.esr.2018.11.004
UR  - https://juser.fz-juelich.de/record/859310
ER  -