1. Experience of Modelling Forested Complex Terrain Peter Stuart, Ian Hunter & Nicola Atkinson 30 th October 2009

2. Overview The Challenges of Forested Complex Terrain. Predicting the Breakdown of Linear Flow Models in Forested Complex Terrain. Tuning Canopy Model Parameters Energy Yield Prediction Verification in Forested Complex Terrain Future challenges: Modelling Non-Neutral Canopy Flow

3. The Challenges of Forested Complex Terrain

4. The Challenges of Forested Complex

5. The Challenges of Forested Complex Terrain – Some Observations Masts A & B are in simple non forested terrain Masts C & D are in complex forested terrain However masts A,B,C & D are all on the same wind farm site! 8%

6. Predicting the Breakdown of Linear Flow Models in Forested Complex Terrain

7. Predicting the Breakdown of Linear Models in Forested Terrain: Method & Models Calculate flow over idealised hills using both CFD and linear models for incrementally increasing slopes and tree heights. MS3DJH / RES Roughness Establish guidelines for where linear models fail by comparing to CFD. Linear CFD Use simple geometrical considerations to assess likely impact on real sites. Confirm predicted effects using CFD.

8. 8 Variation of Critical Terrain Slope with Tree Height (2D Symmetric Hill) Critical angle for recirculation reduced by ~ ¼° per metre of tree height. c.f. Kaimal and Finnigan (1994): 2D Critical slope ~10° for a very rough hill.

9. Variation of Critical Terrain Slope with Tree Height (2D Symmetric Hill) Critical angle for linear model break down reduced by ~½° per metre of tree height.

10. Predicting the Breakdown of Linear Models in Forested Terrain: Example Site

11. Predicting the breakdown of linear flow models in forested terrain: Example Site 11 Establish critical angle considering tree height (20m). Predominant wind direction

12. Tuning Canopy Parameters

13. The drag due to the canopy is taken into account via an additional term entering the momentum equation : α (in m 2 m -3 ) is the leaf foliage area per unit of volume C D is the canopy drag coefficient. The effects of the canopy on turbulence are accounted for by additional source terms S k and S ε in the transport equations of k and ε Lopes da Costa, J. C. P., “Atmospheric Flow Over Forested and Non-Forested Complex Terrain”, PhD Thesis University of Porto, July 2007. Ventos Canopy Model

14. European site with complex orography and extensive forest cover (H ~ 15m). 6 meteorological masts used for validation. Tuning Canopy Parameters: Example site

15. 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 M273M272M223M1M187M186 Shear Exponent Predicted and measured shear exponents for 330° direction. Measured Shear CFD Predicted Shear H = 15m, C D = 0.25 and α = 0.2 Tuning Canopy Parameters: Example site

16. Reducing the canopy density improves agreement, but even with α = 0.05 the predicted shear exponents are still too high. 2 nd Iteration: α → 0.13 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 M273M272M223 M1 M187M186 Shear Exponent Measured Shear CFD Predicted Shear 3 rd Iteration: α → 0.05 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 M273M272M223M1M187M186 Shear Exponent Measured Shear CFD Predicted Shear Tuning Canopy Parameters: Example site

17. Further improvement gained by using an effective tree height of ¾ the actual height. 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 M273M272M223M1M187M186 Shear Exponent Measured Shear CFD Predicted Shear Predicted and measured shear exponents for 330° direction. Final parameters: H = 11.25m, C D = 0.25, α = 0.05 Tuning Canopy Parameters: Example site

18. Optimized parameters derived from 330° direction applied to 300° direction. Shear Exponent 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 M273M272M223M1M187M186 Measured Shear CFD Predicted Shear Predicted and measured shear exponents for 300° direction. Tuning Canopy Parameters: Example site

19. Energy Yield Prediction Verification in Forested Complex Terrain

20. 20 Prediction Verification in Forested Complex Terrain - Site Overview Moderately complex terrain. 11 multi-megawatt class turbines. Inhomogeneous forest cover 5-20m in height. Two 40m Masts (turbine hub height is 65m, rotor diameter is 82m) Mast A Mast B Power Performance Mast T3

21. 21 How is the Wind Farm Performing? Power performance indicates that turbine T3 is operating as expected. However, the majority of turbines are found to be under producing.

22. 22 Prediction Verification in Forested Complex Terrain – Terrain Effect Plot error in yield vs. predicted terrain effect Strong correlation between predicted terrain effect and error in energy yield. Using WAsP provides very similar results.

23. 23 Prediction Verification in Forested Complex Terrain – Increased Roughness Increasing roughness in orography model improves agreement. Roughness increased from default 0.04m to 2m, potentially more representative of the site (tree height up to 20m). Site also modelled using CFD, including a canopy model. CFD and increased roughness orography model in very close agreement.

24. 24 Prediction Verification in Forested Complex Terrain – Shear Predictions Still an error in the predictions; other causes – high shear? Strong correlation between high shear and error in energy yield predictions. Power Performance Turbine Increasing Shear

25. Future challenges: Modelling Non-Neutral Canopy Flow

26. How does non-neutral atmospheric stability change canopy flows e.g. Internal Boundary Layer:

27. Conclusions Trees can cause very different site conditions to occur within the same wind farm e.g. 8% change in turbulence intensity. Tree height has a strong impact on the critical slope at which linear models breakdown. Tuning of canopy parameters help improve agreement between observations and CFD model predictions. Operational data suggests that tuning the roughness length in the linear orography model helps improve agreement with operational data and CFD predictions. Operational data suggests a link between turbine under performance and wind shear.