New developments on thermal stability in Meteodyn WT K. Fahssis, C.Bezault , D.Delaunay

1. New developments on thermal stability in Meteodyn WT K. Fahssis, C.Bezault, D.Delaunay

2. Stability effects modeling in Meteodyn WT Validation studies Integrating stability effects in the AEP estimation • Contents

3. Challenges for introducing thermal stability in a long term statistical assessment: Great number of meso-scale configurations Transient nature of thermal stability (diurnal cycle) Correlation with wind speed and direction Integration of the thermal stability effects at micro-scale Two approaches for the numerical methods 1/ NS equations + Heat transport equation Combining transient meso and micro scale computations Ground model (albedo, ground temperature, conductivity, soil humidity) Radiative fluxes (solar, infra-red) Selection of a limited number of « homogeneous events » 2/ NS equations + Turbulent length scale profiles Steady NS equations solved for given direction and stability class Statistical analysis of the triplet (wind direction and speed, stability class) A stability class defines a turbulent length scale profile and inlet boundary conditions • Thermal stability and AEP assessment

4. Equations • Reynolds Averaged NavierStokesequations- Stationary flow • Closure of the system (turbulence modeling): where

5. Equations Transport equation for the turbulent kineticenergy

6. Equations • Evaluation of the turbulent lengthscale: • Consideration of the thermal stratification • Models of Yamada (1983) and Arritt (1987) Flux Richardson number

7. Validations Log – linearlaw profiles on homogeneous terrain

8. Validations • 2D hill: Experiment of • Ross et al. (2004) • Boundary-Layer Meteorol. • 113, 427-459 Experiment: stable Experiment: neutral h = Hcos²(p x/L) H = 229 m (full-scale) L = 1000 m z0 = 1 m (canopy model) WT neutral WT stable: LMO=400 m

9. Integrating the thermal stratification • in AEP assessment

10. Integration Process Thermal stability and AEP assessment Meteorological Data AEP, IEC export • Time series • Speed/direction joint frequencies • Speed/direction/stability joint frequencies • Wind flow computation: • one direction sector • one stability class Orography map Roughness map Met masts and wind turbines locations Wind speed coefficients Turbulence intensity Wind shear Wind direction

11. Thermal stability and AEP assessment Regional data (weather station, meso-scale data) Mean Wind speed Solar radiation (daily) Snow (daily) Hour, season  StabilityClasses • On site Gradient measurements • (met mast, LIDAR, SODAR) • 10-min mean values of: • Mean wind speed • Mean air temperature • Richardson Number • ObukhovLength •  Stability Classes On site Turbulence measurements (LIDAR, SODAR, met mast) Standard deviation of: Vertical wind speed Horizontal wind speed Horizontal winddirection Heat and momentum vertical fluxes  Stability Classes Time series of wind speed, direction, stability class Tables of joint frequency tables speed/direction/stability

12. Thermal stability and AEP assessment AEP assessment of a windfarm in the North-East of France Speed Coefficients for 3 stability classes Wind direction: 60 deg stable : LMO = 500 m neutral unstable : LMO = - 80 m

13. Thermal stability and AEP assessment Wind profiles at the met mast (Maïa Eolismeasurements) Wind Direction 60 deg Roughnesslength = 6 cm – 65 cm

14. Thermal stability and AEP assessment

15. Conclusion Stabilityeffects: Works in progress • Analysis of Hovsore and HornsRev profiles (A.Peña, 2009) • New sites by Maïa Eolis (multiple 80 m masts) • Calibrating LMOinside WT code as a functionof « experimental » LMO • Most relevant parametersfrom a statistical point of view • Application to the Meteodynforecast module Acknowledgements • French Environment and Energy Management Agency Researchfunding • French Ministry for ResearchResearchfunding • Maïa EolisOn site measurements and scientificpartnership

16. THANK YOU FOR YOUR ATTENTION!