Validation n 1 of the Wind Data Generator (WDG) software performance. Comparison with measured mast data - Complex site in Southern France

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1 Validation n 1 of the Wind Data Generator (WDG) software performance Comparison with measured mast data - Complex site in Southern France Mr. Tristan Fabre* La Compagnie du Vent, GDF-SUEZ, Montpellier, 34967, France Mr. Karim Fahssis**, Mr. Sushant Kumar, Mr. Malleswara Rao MeteoPole Renewable Energy Pvt. Ltd., Paris, 75018, France Category: Validation of correlation levels for long-term data adjustment to reduce wind resource annual variability uncertainties Key Words: WDG, Mesoscale, WRF, Correlation, MCP, GFS, ERA, MERRA, CFSR, NCEP, Validation Abstract The objective is to validate the performance of the Wind Data Generator software (WDG) by comparing simulated data from WDG (both WRF modeled and Global Reanalysis Data) against mast data measured on a complex site in Southern France.

2 Introduction Roqueredonde met mastis operated by La Compagnie du Vent, GDF-SUEZ and is known to be one of the most complex sites in France. La Compagnie du Vent, GDF-SUEZ and MeteoPole worked together on the validation of the performance of a mesoscale wind modeling software (The Wind Data Generator) by comparing the on-site measured wind data against simulated wind data. The Wind Data Generator (WDG) is a software calculating meteorological parameters based on the mesoscale model WRF. WRF is a fully compressible and non-hydrostatic model. Its vertical coordinate is a terrain-following hydrostatic pressure coordinate. The grid staggering is the Arakawa C-grid. The model uses the Runge-Kutta 2nd and 3rd order time integration schemes and 2nd to 6th order advection schemes in both horizontal and vertical directions. It uses a timesplit small step for acoustic and gravity-wave modes. The dynamics conserves scalar variables. The future wind farm project consists of 13 units of Enercon E70 - E4 wind turbine generators (WTGs) at 57 m hub height located at 5 kilometers along one distinct ridge. The ridge runs in a 0 to 180 direction and rise to approximately 140 m above the surrounding plains. The WTGs locations are generally on level terrain at the top of the ridgelines; with steep slopes on both sides of the WTGs. One measurement towers are installed on the site with data availability going up to 122 months. Due to the complexity of the environment, the met-mast measurements realized at Roqueredonde (10 years) allows us to make good long-term adjustment of data collected on the project location. Performing long-term adjustment of on-site measured wind data can be very beneficial to project owners as it enables reducing annual variability uncertainties and so it helps in building more bankable projects with higher financial returns. Site Details Site Name Roqueredonde Mast Location N, 3.189E Mast Elevation 910m Orography Complex Roughness Lush with low scrubs

3 Figure 1 Site Satellite View Data Summary Analysis period 01/08/ /07/2008 Analysis period duration 10 months WDG-WRF* outputs (Spatial Resolution) GFS (5.6 km), NCEP (5.6 km), ERA (5.6 km) WDG-GDR* outputs (Temporal Resolution) MERRA (hourly), ERA-interim (6-hourly), CFSR (hourly) *WRF: Weather Research Forecast Model (automatic WRF computations with the WDG software) **GDR: Global Data Retrieval (automatic access to reanalysis data with the WDG software) Selection of the WDG output Automatic WRF computations are run with the WDG software for 3 different input scenarios (GFS, NCEP and ERA data). Time series data from different reanalysis products MERRA (hourly), ERA-interim (6-hourly) and CFSR (hourly) are automatically retrieved from the Global Data Retrieval feature of the WDG. As ERA-interim has 6-hour temporal resolution, it has been decided to consider only the data sets which have hourly frequency to make the comparison with on-site measurement data consistent. After doing the quality test of resulting dataset, mean wind speed and correlation coefficient of different datasets are calculated and compared with mast data for a concurrent period of 6 months (August 1st January 31st 2008). The table below summarizes it as follows.

4 WDG outputs WRF*-GFS WRF*-NCEP WRF*-ERA GDR**- MERRA GDR**- CFSR Correlation Coefficient (R²) with mast data from August 1st 2007 to January 31st *WRF: Weather Research Forecast Model (automatic WRF computations with the WDG software) **GDR: Global Data Retrieval (automatic access to reanalysis data with the WDG software) The experiment output shows that WRF-GFS is the WDG output with the highest correlation level with concurrent mast data. It is therefore decided to use this WDG output as reference data set for MCP (Measure-Correlate-Predict) process. MCP calculation is then performed to generate wind data for the next 6-month period and validate the post-processed data with the mast observation data. Comparison of Long-Term Adjusted data with Mast data After performing MCP with 6-month measurement data and One-Year WDG selected output (WRF-GFS), a One-Year hourly data time series is generated and correlation coefficient and mean wind speed are compared with the valid data measured at mast location for the same period. It is noted that 10 months of measurement data are valid during this comparison year and the comparison is thus done for this concurrent period. Figure 2 Regression summary and correlation coefficient between mast and post-processed data

5 Figure 3 Summary of wind data comparison From figure 1 and 2, a correlation coefficient on hourly data of 0.92 (R²) is observed between the two datasets and the relative error on mean wind speed for the concurrent period is 0.11%. Diurnal profile, frequency distribution, monthly wind speed and wind rose are also compared. The analysis concludes that data quality thus generated considering WDG-GFS as reference could be considered as the best for this location for long term adjustment of on-site measured wind speeds.

6 Proportion of Total Wind Energy vs. Final direction Final speed - LLS W Proportion of Total Wind Energy vs. wd C5-Maximum 40m NE-10 min Average WPD % 0% % % % 60% % % Figure 4 Wind rose, sectorial energy and probability distribution function from WDG post-processed (left) and mast (right)

7 In figure 4, probability distribution functions from the two datasets are compared and it is observed that WDG post-processed data has managed to represent the measurement data consistently. Wind roses are also compared for wind direction and sector-wise energy yield. WDG has simulated them very well. The table below summarizes the different statistical parameters in predominant wind directions. WDG Mast Direction sector ( ) Mean wind speed (m/s) Median (m/s) Std Dev (m/s) Weibull k (-) Weibull c (m/s) Data Validation for Long Period To check the consistency of WDG data for long period WDG-GFS output for almost six and half years is compared with observation from mast for the same period. As the observation for such a long period has chance of having poor data recovery rate data quality test is performed and poor quality data have been removed. Based on comparison the obtained correlation coefficient between mast and WDG-GFS is 0.71 (refer figure 6). To understand the inter-annual variation, annual mean wind speed from the two datasets are plotted against each other (refer fig.5). Looking on the trend one could clearly infer that WDG-GFS is well capable of capturing the inter-annual variability. Mast WDG Figure 5 Inter-annual variation of wind speed from mast and WDG (bias corrected)

8 Figure 6 Correlation plot for 6.4 years of concurrent data Conclusion By selecting the most appropriate WDG output and then using this output as reference data for MCP process, long-term adjustment process can be optimized with excellent levels of correlation and reduced relative errors on annual mean wind speed. The WDG can be used for long-term data adjustment even in complex sites and can help project owners in reducing project uncertainties by considering the long-term annual variability in the wind resource assessment process. WDG further could be used for site selection well before the campaign starts and prioritization of projects based on approximate annual yield estimation just after a few months of campaign.