Validation of European-scale simulated wind speed and wind generation time series

Juan Pablo Murcia, Matti Juhani Koivisto, Graziela Luzia, Bjarke T. Olsen, Andrea N. Hahmann, Poul Ejnar Sørensen and Magnus Als

Applied Energy, 2022, vol. 305, issue C, No S0306261921011296

Abstract: This paper presents a validation of atmospheric reanalysis data sets for simulating onshore wind generation time series for large-scale energy system studies. The three reanalyses are the ERA5, the New European Wind Atlas (NEWA) and DTU’s previous generation European-level atmospheric reanalysis (EIWR). An optional scaling is applied to match the microscale mean wind speeds reported in the Global Wind Atlas version 2 (GWA2). This mean wind speed scaling is used to account for the effects of terrain on the wind speed distributions. The European wind power fleet for 2015–2018 is simulated, with commissioning of new wind power plants (WPPs) considered for each year. A generic wake model is implemented to include wake losses that are layout agnostic; the wake model captures the expected wake losses as function of wind speed given the technical characteristics of the WPP. We validate both point measurement wind speeds and generation time-series aggregated at the country-level. Wind measurements from 32 tall meteorological masts are used to validate the wind speed, while power production for four years from twelve European countries is used to validate the simulated country-level power production. Various metrics are used to rank the models according to the variables of interest: descriptive statistics, distributions, daily patterns, auto-correlation and spatial-correlation. We find that NEWA outperforms ERA5 and EIWR for the simulated wind speed, but, as expected, no model is able to fully describe the auto-correlation function of the wind speed at a single point. The mean wind speed scaling is found to be necessary to match the distribution of generation on country-level, with NEWA-GWA2 and ERA5-GWA2 showing highest accuracy and precision for simulating large-scale wind generation time-series.

Keywords: Large scale energy system; Wind energy; Atmospheric reanalysis; European; Renewable energy; Wind generation; Validation (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (14)

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DOI: 10.1016/j.apenergy.2021.117794

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