The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential

Hywind-Scotland is a wind farm in Scotland that for many reasons is at the leading edge of technology and is located at a paradigmatic study area for offshore wind energy assessment. The objective of this paper is to compute the Capacity Factor ( CF ) changes and instantaneous power generation changes due to seasonal and hourly fluctuations in air density. For that reason, the novel ERA5 reanalysis is used as a source of temperature, pressure, and wind speed data. Seasonal results for winter show that CF values increase by 3% due to low temperatures and denser air, with economical profit consequences of tens of thousands (US$). Hourly results show variations of 7% in air density and of 26% in power generation via FAST simulations, emphasizing the need to include air density in short-term wind energy studying.This work was financially supported by the Spanish Government through the MINECO project CGL2016-76561-R, (MINECO/ERDF, UE) and the University of the Basque Country (UPV/EHU, GIU 17/002). ERA5 hindcast data were downloaded at no cost from the Copernicus Climate Data Store. All the calculations and plots were made using R: https://www.r-project.org

Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula

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.This work has been funded by the Spanish Government’s MINECO project CGL2016-76561-R (AEI/FEDER EU) and the University of the Basque Country (UPV/EHU funded project GIU17/02). The ECMWF ERA-Interim data used in this study have been obtained from the ECMWF-MARS Data Server. The authors wish to express their gratitude to the Spanish Port Authorities (Puertos del Estado) for being kind enough to provide data for this study. The computational resources used in the project were provided by I2BASQUE. The authors thank the creators of the WRF/ARW and WRFDA systems for making them freely available to the community. NOAA_OI_SST_V2 data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, through their web-site at http://www.esrl.noaa.gov/psd/ were used in this paper. National Centres for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce. 2008, updated daily. NCEP ADP Global Upper Air and Surface Weather Observations (PREPBUFR format), May 1997—continuing. Research Data Archive at the National Centre for Atmospheric Research, Computational and Information Systems Laboratory. http://rda.ucar.edu/datasets/ds337.0/ were used. All the calculations have been carried out in the framework of R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org

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

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.This work has been funded by the Spanish Government’s MINECO project CGL2016-76561-R (MINECO/FEDER EU), the University of theBasque Country (project GIU14/03) and the Basque Government (Elkartek 2017 INFORMAR project). SJGR is supported by a FPIPredoctoral Research Grant (MINECO, BES-2014-069977). The ECMWFERA-Interim data used in this study have been obtained from the ECMWF-MARS Data Server thanks to agreements with ECMWF and AEMET. The authors would like to express their gratitude to the Spanish Port Authorities (Puertos del Estado) for kindly providing data for thisstudy. The computational resources used in the project were providedby I2BASQUE. The authors thank the creators of the WRF/ARW and WRFDA systems for making them freely available to the community. NOAA_OI_SST_V2 data provided by the NOAA/OAR/ESRL PSD,Boulder, Colorado, USA, through their web-site athttp://www.esrl.noaa.gov/psd/was used in this paper. National Centers for Environmental Prediction/National Weather Service/NOAA/U.S.Department of Commerce. 2008, updated daily. NCEP ADP GlobalUpper Air and Surface Weather Observations (PREPBUFR format), May1997–Continuing. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory.http://rda.ucar.edu/datasets/ds337.0/were used. All thecalculations have been carried out in the framework of R Core Team(2016). R: A language and environment for statistical computing. RFoundation for Statistical Computing, Vienna, Austria. URLhttps://www.R-project.org/

Global estimations of wind energy potential considering seasonal air density changes

The literature typically considers constant annual average air density when computing the wind energy potential of a given location. In this work, the recent reanalysis ERA5 is used to obtain global seasonal estimates of wind energy production that include seasonally varying air density. Thus, errors due to the use of a constant air density are quantified. First, seasonal air density changes are studied at the global scale. Then, wind power density errors due to seasonal air density changes are computed. Finally, winter and summer energy production errors due to neglecting the changes in air density are computed by implementing the power curve of the National Renewable Energy Laboratorys 5 MW turbine. Results show relevant deviations for three variables (air density, wind power density, and energy production), mainly in the middle-high latitudes (Hudson Bay, Siberia, Patagonia, Australia, etc.). Locations with variations from −6% to 6% are identified from summers to winters in the Northern Hemisphere. Additionally, simulations with the aeroelastic code FAST for the studied turbine show that instantaneous power production can be affected by greater than 20% below the rated wind speed if a day with realistically high or low air density values is compared for the same turbulent wind speed.This work was funded by the Spanish Government's MINECO project CGL2016-76561-R (AEI/FEDER EU) and the University of the Basque Country (UPV/EHU-funded project GIU17/02). The ECMWFERA-5 data used in this study were obtained from the Copernicus Climate Data Store. All the calculations were carried out in the framework of R Core Team (2016). More can be learnt about R, alanguage and an environment for statistical computing, at the website of the R Foundation for Statistical Computing, Vienna,Austria (https://www.R-project.org/)

Long-term changes in offshore wind power density and wind turbine capacity factor in the Iberian Peninsula (1900–2010)

This study analysed temporal and spatial changes in offshore wind power density (WPD) and capacity factor (CF) around the Iberian Peninsula during the 20th century by analysing data from ERA20 and ERA5. Both WPD and CF were calculated using reanalysis data considering a wind turbine with a hub height of 90 m and incorporating the effect of air density changes. Since ERA5 assimilates more observations, the data from ERA20 was bias-corrected using quantile matching, with ERA5 reanalysis data as the reference. As both variables are based on wind speed (WS), this variable was also corrected and analysed. The results show that the mean values for WPD, CF, and WS during the 20th century were highest in the Atlantic zone and the Gulf of Lyon and lowest around the Balearic Islands. The results of the assessment of decadal trends using the Theil–Sen estimator show that all indicators increased significantly in the waters of the Iberian Peninsula during the study period (1900–2010). Considering the mean slope over this period, the change over the entire period could amount to 174 Wm-2 for WPD, 8.8% for CF, and 1.1 ms−1 for WS. Based on these changes, offshore wind turbines would have increased their returns by approximately 20% over the 11 decades.This work was financially supported by the Spanish Government through the MINECO project CGL2016-76561-R (MINECO/ERDF, UE) and the University of the Basque Country (UPV/EHU, GIU 17/002). Reanalysis data were downloaded at no cost from the ECMWF. All calculations and plots were carried out in the R framewor

Changes in the simulation of atmospheric instability over the Iberian Peninsula due to the use of 3DVAR data assimilation

The ability of two downscaling experiments to correctly simulate thermodynamic conditions over the Iberian Peninsula (IP) is compared in this paper. To do so, three parameters used to evaluate the unstable conditions in the atmosphere are evaluated: the total totals index (TT), convective available potential energy (CAPE), and convective inhibition (CIN). The Weather and Research Forecasting (WRF) model is used for the simulations. The N experiment is driven by ERA-Interim's initial and boundary conditions. The D experiment has the same configuration as N, but the 3DVAR data assimilation step is additionally run at 00:00, 06:00, 12:00, and 18:00 UTC. Eight radiosondes are available over the IP, and the vertical temperature and moisture profiles from the radiosondes provided by the University of Wyoming and the Integrated Global Radiosonde Archive (IGRA) were used to calculate three parameters commonly used to represent atmospheric instability by our own methodology using the R package aiRthermo. According to the validation, the correlation, standard deviation (SD), and root mean squared error (RMSE) obtained by the D experiment for all the variables at most of the stations are better than those for N. The different methods produce small discrepancies between the values for TT, but these are larger for CAPE and CIN due to the dependency of these quantities on the initial conditions assumed for the calculation of a lifted air parcel. Similar results arise from the seasonal analysis concerning both WRF experiments: N tends to overestimate or underestimate (depending on the parameter) the variability of the reference values of the parameters, but D is able to capture it in most of the seasons. In general, D is able to produce more reliable results due to the more realistic values of dew point temperature and virtual temperature profiles over the IP. The heterogeneity of the studied variables is highlighted in the mean maps over the IP. According to those for D, the unstable air masses are found along the entire Atlantic coast during winter, but in summer they are located particularly over the Mediterranean coast. The convective inhibition is more extended towards inland at 00:00 UTC in those areas. However, high values are also observed near the southeastern corner of the IP (near Murcia) at 12:00 UTC. Finally, no linear relationship between TT, CAPE, or CIN was found, and consequently, CAPE and CIN should be preferred for the study of the instability of the atmosphere as more atmospheric layers are employed during their calculation than for the TT index.The computational resources were provided by I2BASQUE, and the authors thank the creators of WRF/ARW and WRFDA systems. Authors also thank the anonymous reviewers for their comments, which have helped to improve the paper. Finally, most of the calculations were carried out with R (R Core Team, 2020), and the authors want to thank all the authors of the packages used for it

Sensitivity Studies for a Hybrid Numerical–Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)

This study evaluates the performance of statistical models applied to the output of numerical models for short-term (1–24 h) hourly wind forecasts at three locations in the Basque Country. The target variables are horizontal wind components and the maximum wind gust at 3 h intervals. Statistical approaches such as persistence, analogues, linear regression, and random forest (RF) are used. The verification statistics used are coefficient of determination (R2) and root mean square error (RMSE). Statistical models use three inputs: (1) Local wind observations; (2) extended EOFs (empirical orthogonal functions) derived from past local observations and ERA-Interim variables in a previous 24-h period covering a domain around the area of study; and (3) wind forecasts provided by ERA-Interim. Results indicate that, for horizons less than 1–4 h, persistence is the best model. For longer predictions, RF provides the best forecasts. For horizontal components at 4–24 h horizons, RF slightly outperformed ERA-Interim wind forecasts. For gust, RF performs better than ERA-Interim for all the horizons. Persistence is the most influential factor for 2–5 h. Beyond this horizon, predictors from the ERA-Interim wind forecasts led the contribution. Hybrid numerical–statistical methods can be used to improve short-term wind forecasts.This work was supported by the Spanish Government, MINECO project CGL2016-76561-R (MINECO/EU ERDF), and the University of the Basque Country (project GIU17/02)

An Energy Potential Estimation Methodology and Novel Prototype Design for Building-Integrated Wind Turbines

ROSEO-BIWT is a new Building-Integrated Wind Turbine (BIWT) intended for installation on the edge of buildings. It consists of a Savonius wind turbine and guiding vanes to accelerate the usual horizontal wind, together with the vertical upward air stream on the wall. This edge effect improves the performance of the wind turbine, and its architectural integration is also beneficial. The hypothetical performance and design configuration were studied for a university building in Eibar city using wind data from the ERA5 reanalysis (European Centre for Medium-Range Weather Forecasts’ reanalysis), an anemometer to calibrate the data, and the actual small-scale behavior in a wind tunnel. The data acquired by the anemometer show high correlations with the ERA5 data in the direction parallel to the valley, and the calibration is therefore valid. According to the results, a wind speed augmentation factor of three due to the edge effect and concentration vanes would lead to a increase in working hours at the rated power, resulting annually in more than 2000 h.The research leading to these results was carried out in the framework of the Programme Campus Bizia Lab EHU (Campus Living Lab) with a financial grant from the Office of Sustainability of the Vice-Chancellorship for Innovation, Social Outreach and Cultural Activities of the University of the Basque Country (UPV/EHU). This programme is supported by the Basque Government. We acknowledge also the availability given by the School of Engineering of Gipuzkoa-Eibar in the University of Basque Country, the EDP-Renewable awards in which we obtained the main award in September 2017, the Youth Enterprise Grant of UPV/EHU, and the project GIU17/02 of EHU/UPV. All computations and representations of this work were developed using the programming language

Evolución de la densidad de potencia eólica offshore en costas de la Península Ibérica estimada por reanálisis

Ponencia presentada en: XII Congreso de la Asociación Española de Climatología celebrado en Santiago de Compostela entre el 19 y el 21 de octubre de 2022.[ES]La demanda de energías renovables es cada vez mayor y es necesario saber cuáles son los recursos disponibles. Por este motivo este estudio analiza la evolución temporal y espacial de la densidad de potencia eólica (WPD) y el factor de capacidad (CF) “offshore” para la Península Ibérica en el siglo XX. Para ello, se ha obtenido tanto WPD como CF de los reanálisis ERA20 y ERA5 teniendo en cuenta la corrección por densidad de aire a 90 m. A continuación, se ha ajustado ERA20 con la técnica de “quantil matching” y los datos de reanálisis de ERA5. Este ajuste se realiza debido a que ERA5 es una reanálisis más completo en el que se asimilan más observaciones, pero con menor alcance temporal. Los resultados muestran que los valores medios de WPD y CF a lo largo de los 100 años son mayores en la zona Atlántica y en el Golfo de León, localizando los mínimos en la zona de las Islas Baleares. Los resultados de las tendencias decadales calculadas con la técnica de Theil-Sen muestran que WPD y CF aumentan de manera significativa en la zona de la Península Ibérica.[EN]Renewable energy demand is becoming an increasingly important and it is necessary to know what sources are available. For that reason, this study analyses temporal and spatial variation in offshore wind power density (WPD) and capacity factor (CF) around the Iberian Peninsula in the 20th century. For this, both WPD and CF have been calculated based on ERA20 and ERA5 reanalysis data, taking into account air density at 90 m. Then, the ERA20 dataset has been bias-corrected using quantile matching and ERA5 reanalysis data. The rationale underlying this adjustment is that ERA5, though it covers a shorter period, is a more observationally-complete reanalysis. The results show that the mean WPD and CF over the century are highest in the Atlantic zone and the Gulf of Lion and lowest around the Balearic Islands. The results of assessing the decadal trends with the Theil-Sen estimator show that WPD and CF both increased significantly in the waters of the Iberian Peninsula.Esta publicación es parte del proyecto de I+D+i PID2020-116153RB-I00, financiado por MCIN/ AEI/10.13039/501100011033. Y también ha recibido fondos de la Universidad del País Vasco (UPV/EHU), en concreto, del proyecto GIU20/008

Sensitivity Studies for a Hybrid Numerical-Statistical Short-Term Wind and Gust Forecast at Three Locations in the Basque Country (Spain)

This study evaluates the performance of statistical models applied to the output of numerical models for short-term (1–24 h) hourly wind forecasts at three locations in the Basque Country. The target variables are horizontal wind components and the maximum wind gust at 3 h intervals. Statistical approaches such as persistence, analogues, linear regression, and random forest (RF) are used. The verification statistics used are coefficient of determination (R2) and root mean square error (RMSE). Statistical models use three inputs: (1) Local wind observations; (2) extended EOFs (empirical orthogonal functions) derived from past local observations and ERA-Interim variables in a previous 24-h period covering a domain around the area of study; and (3) wind forecasts provided by ERA-Interim. Results indicate that, for horizons less than 1–4 h, persistence is the best model. For longer predictions, RF provides the best forecasts. For horizontal components at 4–24 h horizons, RF slightly outperformed ERA-Interim wind forecasts. For gust, RF performs better than ERA-Interim for all the horizons. Persistence is the most influential factor for 2–5 h. Beyond this horizon, predictors from the ERA-Interim wind forecasts led the contribution. Hybrid numerical–statistical methods can be used to improve short-term wind forecasts.This work was supported by the Spanish Government, MINECO project CGL2016-76561-R (MINECO/EU ERDF), and the University of the Basque Country (project GIU17/02)