A NEW TURBULENCE PREDICTION METHOD FOR TURBINE SUITABILITY ANALYSIS

1. A NEW TURBULENCE PREDICTION METHOD FOR TURBINE SUITABILITY ANALYSIS ALEX CLERC, PETER STUART AND PETER DUDFIELD 5 FEBRUARY 2013 University of Cambridge

2. CONTENTS • Motivation • Vertical changes in turbulence • Horizontal changes in turbulence • Example calculation

3. EXAMPLE MAST AND TURBINE

4. ENERGY YIELD – MAJOR COMPONENTS Reference Long Term Wind Resource Wind Flow Model Wakes Loss Turbine Model Electrical Loss Adjustment Factors • Inputs: • Measured Wind Climate(s) • Topography • Outputs: • Wind Climate at each turbine including Turbulence Intensity Net Net Yield

5. ENERGY YIELD – MAJOR COMPONENTS Reference Long Term Wind Resource Wakes Loss Electrical Loss Adjustment Factors • Inputs: • Wind Speed • Air Density • Turbulence Intensity Wind Flow Model Turbine Model 1320kW at 13m/s, 5% TI 1120kW at 13m/s, 30% TI 10-minute Mean Power (kW) Net Net Yield Wind Speed (m/s)

6. ENERGY YIELD – MAJOR COMPONENTS Reference Long Term Wind Resource Electrical Loss Adjustment Factors Wind Flow Model Turbine Model Wakes Loss • Inputs: • Turbine Details • Wind Speed • Turbulence Intensity Net Net Yield

7. CAN’T WE JUST USE CFD? • Yes! • But wouldn’t you like a second opinion? • This presentation uses an industry-standard flow model very similar to WAsP (MS3DJH + empirical roughness and obstacle models) • Running this model takes only minutes on a PC • The accuracy of this flow model is well understood

8. CONTENTS • Motivation • Vertical changes in turbulence • Horizontal changes in turbulence • Example calculation

9. VERTICAL CHANGES IN TI TIU TIL

10. VERTICAL CHANGES IN TI • Dataset: 190 masts with at least two boom-mounted anemometers and 1 year of data. • Mean wind speed and TI calculated for upper and lower anemometer (concurrent data only). • Challenge is to predict TI at upper anemometer using other measurements

11. VERTICAL CHANGES IN TI • Model 1: Persistence • The upper TI is predicted to be the same as the lower TI • Model 2: Ratio of Wind Speed (RoWS) • The upper TI is predicted to be the same as the lower TI times the lower wind speed divided by the upper wind speed. • In other words, assume the standard deviation of wind speed does not change with height.

12. VERTICAL CHANGES IN TI • RoWS model is better for 186 out of 190 masts • Under-predictions of turbulence are small with both models • A few bad over-predictions for sites in the UK (a flat site in England and a hilly forested site in Wales)

13. CONTENTS • Motivation • Vertical changes in turbulence • Horizontal changes in turbulence • Example calculation

14. HORIZONTAL CHANGES IN TI TIA TIB

15. HORIZONTAL CHANGES IN TI • Dataset: 53 direction sectors from 7 mast pairs, max distance 2km, same height above ground level • Wind speed, shear and TI calculated by 30° sector (concurrent data only). • Challenge is to predict TI at second mast (Mast B) using measurements at first mast (Mast A)

16. HORIZONTAL CHANGES IN TI: DERIVATION OF NEW MODEL Two locations with simple vertical profiles and the same geostrophic wind: Use k-epsilon and at both points, assume: After some manipulation: with C = 0.1

17. HORIZONTAL CHANGES IN TI: MODEL VALIDATION (using measurements at Mast B) • Mast B measured wind speed and shear used as an input to validate the RES model. • Accounting for wind speed variation is the main reason for RES model’s better accuracy • Using shear gives a small additional improvement to RES model accuracy

18. HORIZONTAL CHANGES IN TI: MODEL VALIDATION • Here the RES TI model is integrated with the flow model (no measurements from Mast B) • Decrease in RES model accuracy due to flow model prediction error • RES model improves the TI estimate in 74% of cases and has better overall accuracy

19. RES Model Limitations • Flow separation • Non-neutral conditions • Extrapolations beyond a few km • Local equilibrium is assumed (turbulent energy production balances dissipation)

20. CONTENTS • Motivation • Vertical changes in turbulence • Horizontal changes in turbulence • Example calculation

21. EXAMPLE MAST AND TURBINE 10% TI at mast measurement 16% TI at turbine location

22. EXAMPLE CALCULATION: ENERGY YIELD • AEP of 10% curve: 9.02GWh • AEP of 16% curve: 8.89GWh • 1.5% loss in available energy • Potentially more energy loss due to turbine performance in non-standard conditions? 10% TI 16% TI Calculation uses method of A. Albers, “Turbulence and Shear Normalisation of Wind Turbine Power Curve”

23. EXAMPLE CALCULATION: SITE SUITABILITY Knowledge of ambient TI is essential to assessing suitability of turbine locations 16% ambient TI + wakes 10% ambient TI + wakes

24. CONCLUSIONS • Turbulence intensity can vary tremendously across a site, posing a challenge to wind farm development • The presented TI model can give accurate and reliable predictions about the variation of turbulence • The model is easy to understand, easy to use and very fast to compute • The results are easy to apply to energy yield and suitability calculations • The presented model is useful both on its own and in combination with a CFD model

25. ACKNOWLEDGEMENTS • Colleagues at the University of Cambridge: • Dr. C.P. Caulfield • Colleagues at RES: • Dr. Mike Anderson • Maciej Drahusz • Alan Duckworth • Alice Ely • Devin Gurbuz • Michelle James