Browsing by Keyword "Data assimilation"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula(MDPI AG, 2019) Ulazia, Alain; Ibarra-Berastegi, Gabriel; Sáenz, Jon; Carreno-Madinabeitia, Sheila; González-Rojí, Santos J.A constant value of air density based on its annual average value at a given location is commonly used for the computation of the annual energy production in wind industry. Thus, the correction required in the estimation of daily, monthly or seasonal wind energy production, due to the use of air density, is ordinarily omitted in existing literature. The general method, based on the implementation of the wind speed’s Weibull distribution over the power curve of the turbine, omits it if the power curve is not corrected according to the air density of the site. In this study, the seasonal variation of air density was shown to be highly relevant for the computation of offshore wind energy potential around the Iberian Peninsula. If the temperature, pressure, and moisture are taken into account, the wind power density and turbine capacity factor corrections derived from these variations are also significant. In order to demonstrate this, the advanced Weather Research and Forecasting mesoscale Model (WRF) using data assimilation was executed in the study area to obtain a spatial representation of these corrections. According to the results, the wind power density, estimated by taking into account the air density correction, exhibits a difference of 8% between summer and winter, compared with that estimated without the density correction. This implies that seasonal capacity factor estimation corrections of up to 1% in percentage points are necessary for wind turbines mainly for summer and winter, due to air density changes.Item Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean(Elsevier Ltd, 2017-12-15) Ulazia, Alain; Sáenz, Jon; Ibarra-Berastegui, Gabriel; González-Rojí, Santos J.; Carreno-Madinabeitia, SheilaIn 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.