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ERA5: The new champion of wind power modelling?
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity. Integration of Renewable Energy Sources Group, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID iD: 0000-0003-4921-8345
2018 (English)In: Renewable energy, ISSN 0960-1481, E-ISSN 1879-0682, Vol. 126, p. 322-331Article in journal (Refereed) Published
Abstract [en]

Output from meteorological reanalyses are used extensively in both academia and industry for modelling wind power. Recently, the first batch of the new ERA5 reanalysis was released. The main purpose of this paper is to compare the performance of ERA5 and MERRA-2 (a commonly used reanalysis today) in terms of modelling i) the aggregated wind generation in five different countries and ii) the generation for 1051 individual wind turbines in Sweden. The modelled wind power generation was compared to measurements. In conclusion, ERA5 performs better than MERRA-2 in all analysed aspects; correlations are higher, mean absolute and root mean square errors are in average around 20% lower and distributions of both hourly data and changes in hourly data are more similar to those for measurements. It is also shown that the uncertainty related to long-term correction (using one year of measurements and reanalysis data to predict the energy production during the remaining 1–5 years) is 20% lower for ERA5. In fact, using one year sample data and ERA5 gives slightly more accurate estimates than using two years of sample data and MERRA-2. Additionally, a new metric for quantifying the system size and dispersion of wind farms is proposed.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 126, p. 322-331
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:uu:diva-349172DOI: 10.1016/j.renene.2018.03.056OAI: oai:DiVA.org:uu-349172DiVA, id: diva2:1199866
Available from: 2018-04-23 Created: 2018-04-23 Last updated: 2018-05-09Bibliographically approved

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Olauson, Jon

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