Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean

Alain Ulazia, Jon Sáenz, Gabriel Ibarra-Berastegui, Santos J. González-Rojí and Sheila Carreno-Madinabeitia

Applied Energy, 2017, vol. 208, issue C, No S0306261917313144, 1232-1245

Abstract: In this article, offshore wind energy potential is measured around the Iberian Mediterranean coast and the Balearic Islands using the WRF meteorological model without 3DVAR data assimilation (the N simulation) and with 3DVAR data assimilation (the D simulation). Both simulations have been checked against the observations of six buoys and a spatially distributed analysis of wind based on satellite data (second version of Cross-Calibrated Multi-Platform, CCMPv2), and compared with ERA-Interim (ERAI). Three statistical indicators have been used: Pearson’s correlation, root mean square error and the ratio of standard deviations. The simulation with data assimilation provides the best fit, and it is as good as ERAI, in many cases at a 95% confidence level. Although ERAI is the best model, in the spatially distributed evaluation versus CCMPv2 the D simulation has more consistent indicators than ERAI near the buoys. Additionally, our simulation’s spatial resolution is five times higher than ERAI. Finally, regarding the estimation of wind energy potential, we have represented the annual and seasonal capacity factor maps over the study area, and our results have identified two areas of high potential to the north of Menorca and at Cabo Begur, where the wind energy potential has been estimated for three turbines at different heights according to the simulation with data assimilation.

Keywords: Offshore wind energy potential; WRF; WRFDA; Data assimilation; Mesoscale model; Fluid mechanics (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.apenergy.2017.09.030

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