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 c...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2019-01-01
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| Series: | Energy Strategy Reviews |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211467X18301056 |
| _version_ | 1819027920058843136 |
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| author | Fabian Gotzens Heidi Heinrichs Jonas Hörsch Fabian Hofmann |
| author_facet | Fabian Gotzens Heidi Heinrichs Jonas Hörsch Fabian Hofmann |
| author_sort | Fabian Gotzens |
| collection | DOAJ |
| description | 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. Keywords: Open data, Power plant data, Europe, Power system model, Energy system analysis |
| first_indexed | 2024-12-21T05:50:08Z |
| format | Article |
| id | doaj.art-01caa21bd78646629ab6f8b5896216c3 |
| institution | Directory Open Access Journal |
| issn | 2211-467X |
| language | English |
| last_indexed | 2024-12-21T05:50:08Z |
| publishDate | 2019-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Strategy Reviews |
| spelling | doaj.art-01caa21bd78646629ab6f8b5896216c32022-12-21T19:13:59ZengElsevierEnergy Strategy Reviews2211-467X2019-01-0123112Performing energy modelling exercises in a transparent way - The issue of data quality in power plant databasesFabian Gotzens0Heidi Heinrichs1Jonas Hörsch2Fabian Hofmann3Institute of Energy and Climate Research (IEK), Systems Analysis and Technology Evaluation (IEK-STE), Forschungszentrum Jülich, Wilhelm-Johnen-Straße, 52425 Jülich, Germany; Corresponding author.Institute of Energy and Climate Research (IEK), Systems Analysis and Technology Evaluation (IEK-STE), Forschungszentrum Jülich, Wilhelm-Johnen-Straße, 52425 Jülich, GermanyFrankfurt Institute for Advanced Studies (FIAS), Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Germany; Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, GermanyFrankfurt Institute for Advanced Studies (FIAS), Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, GermanyIn 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. Keywords: Open data, Power plant data, Europe, Power system model, Energy system analysishttp://www.sciencedirect.com/science/article/pii/S2211467X18301056 |
| spellingShingle | Fabian Gotzens Heidi Heinrichs Jonas Hörsch Fabian Hofmann Performing energy modelling exercises in a transparent way - The issue of data quality in power plant databases Energy Strategy Reviews |
| title | Performing energy modelling exercises in a transparent way - The issue of data quality in power plant databases |
| title_full | Performing energy modelling exercises in a transparent way - The issue of data quality in power plant databases |
| title_fullStr | Performing energy modelling exercises in a transparent way - The issue of data quality in power plant databases |
| title_full_unstemmed | Performing energy modelling exercises in a transparent way - The issue of data quality in power plant databases |
| title_short | Performing energy modelling exercises in a transparent way - The issue of data quality in power plant databases |
| title_sort | performing energy modelling exercises in a transparent way the issue of data quality in power plant databases |
| url | http://www.sciencedirect.com/science/article/pii/S2211467X18301056 |
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