1. A METHODOLOGY FOR ESTIMATING WIND FARM PRODUCTION THROUGH CFD CODES. DESCRIPTION AND VALIDATION Daniel Cabezón, Ignacio Martí CENER, National Renewable Energy Centre (Spain) Wind Energy Department dcabezon@cener.com

2. INDEX 1.Introduction 2.Description of the methodology 3.1 Numerical model 3.2 Estimation of wind farm power 3.Alaiz wind farm 4.Experimental validation 5.Conclusions

3. 1. INTRODUCTION Complex terrain sites:  Increasing uncertainty when estimating power production with linear models  Higher uncertainties for larger wind farms and for larger distances to meteorological mast The proposed analysis:  Presents a methodology for estimating power production of large wind farms through a CFD (Computational Fluid Dynamics) code  Compiles power measurements of a real wind farm during a 4 years period  Validates the methodology in terms of power production for each wind turbine and compares it with conventional tools

4. 2. DESCRIPTION OF THE METHODOLOGY 2.1 NUMERICAL MODEL Digital Terrain Model MODULE 1 Raster topographical information High resolution 3D surface Grid generation MODULE 2 Structured mesh Horizontal resolution: 20m Vertical resolution: 0.5m CFD solver MODULE 3 RANS Navier Stokes Fluent 6.2 K-ε turbulence model Steady-state Neutral atmosphere

5. ¿How wind speed estimation is transformed into power estimation? Ratios Wind Turbine velocity – Mast velocity for sector φ and WTi CFD - Output 2. DESCRIPTION OF THE METHODOLOGY 0º 2040º 340º WT1 WT2 WT3 WT50....... The CFD model solves instantaneous situations for every direction 1 simulation for sector φ Field of V,TI,P… when wind comes from φ 2.2 ESTIMATION OF WIND FARM POWER

6. WAKE EFFECTS for sector φ and WTi Normalized POWER CURVE for WTi Wind speed and direction at MAST 2. DESCRIPTION OF THE METHODOLOGY Net Annual Energy Production / Wind Turbine Net Annual Energy Production at Wind Farm OUTPUTS RATIOS Wind Turbine velocity – Mast velocity for sector φ and WTi 2.2 ESTIMATION OF WIND FARM POWER INPUTS

7. 0º2040º 60º340º80º WT1 WT2 WT3 WT50............................................................................. 0º2040º 60º340º80º... ANNUAL FREQUENCY (HRS) FOR EACH WIND TURBINE & FOR SECTOR φ INPUT 2. DESCRIPTION OF THE METHODOLOGY WIND SPEED FOR EACH WIND TURBINE & FOR SECTOR φ...................................................... WT1... WT2WT3 WT50... bin_1 bin_2 bin_3 bin_30 WT1WT2WT3WT50 2.2 ESTIMATION OF WIND FARM POWER

8. ANNUAL FREQUENCY (HRS) POWER CURVES WAKE EFFECTS... PRODUCTION / WT (GWh) PARK 2.2 ESTIMATION OF WIND FARM POWER 2. DESCRIPTION OF THE METHODOLOGY... bin_1 bin_2 bin_3 bin_30 WT1WT2WT3 WT50... bin_1 bin_2 bin_3 bin_30 INPUT WT1WT2WT3 WT50... 20º 60º 340º INPUT WT1WT2 WT50 WT1WT2WT3WT4WT5WT50 40º

9. 3. ALAIZ WIND FARM ALAIZ 6 ALAIZ 2 ALAIZ 3 WT 1 WT 2 WT 3 WT 4 ALAIZ 9 Measurement campaign met masts Wind farm met mast Alaiz wind farm:  Installed power = 33.09 MW  49 WTs (660 kW) + 1 WT (750 kW)  Measurement campaign: 1996-1997  Wind farm measurements: 2000 (40 WTs) 2001-2003 (50 WTs) Alaiz hill site:  Complex terrain (global RIX = 16 %)  4 kilometers hill, E-W orientation  Prevailing wind direction: N  Highly roughed on the hilltop

10. 4. EXPERIMENTAL VALIDATION vs AEP (Anual Energy Production) MEASUREMENTS AEP 2000 AEP 2001 AEP 2002 AEP 2003 WT1_WT40 WT1_WT50 AEP Average AEP for just north direction at Alaiz_9 (20º sector) Production filtering: WT availability > 70% Modeling from Alaiz3_55 and Alaiz 6_40 WAsP CFD (AEP_WTi / AEP_ref) MODELLED AEP_ref = AEP corresponding to the nearest WT to the met mast (AEP_WTi / AEP_ref) REAL COMPARATIVE

11. I. AEP modelling from ALAIZ 3 – 55 m Underestimation on WT 13 to 19 1 2 3 4 4. EXPERIMENTAL VALIDATION 20º degrees turning clockwise! WT_ref=WT_28

12. 20º degrees turning clockwise around Alaiz 2 ALAIZ 6 ALAIZ 2 ALAIZ 3 Turning caused by an upstream hill Production moved to sector 2 (10º-30º) 9.7% 28.2% 23.2% 4. EXPERIMENTAL VALIDATION I. AEP modelling from ALAIZ 3 – 55 m

13. Similar trend for Alaiz3_55 y Alaiz 6_40 Underestimation for alignement 1(WT1_WT11) and 2 (WT12_WT19) 1 2 3 4 WT_ref=WT_35 4. EXPERIMENTAL VALIDATION II. AEP modelling from ALAIZ 6 – 40 m

14. 4. EXPERIMENTAL VALIDATION III. Global Error. Wind Farm AEP 2000200120022003 CFD-5.32-12.350.4615.73 WAsP-29.34-36.87-27.64-16.64 2000200120022003 CFD-4.58-11.071.9217.41 WAsP-31.76-39.50-30.66-20.12 AEP Error % from ALAIZ 3_55AEP Error % from ALAIZ 6_40

15. WT segregation according to similar RIX indexes ALAIZ 6 ALAIZ 3 4. EXPERIMENTAL VALIDATION IV. Combined WAsP simulation with 2 masts

16. Global Production Error with Alaiz6_40 = -31% Global Production Error with Alaiz6_40 + Alaiz3_55 = -29.2% 4. EXPERIMENTAL VALIDATION IV. Combined WAsP simulation with 2 masts 1 2 3 4

17. 5. CONCLUSIONS A specific methodology for the estimation of wind farm output power from CFD codes has been developped and validated in a complex terrain wind farm. Only conventional inputs needed (mast data, power curve…). Uncertainty decrease of 25% at the test site based on power measurements CFD annual absolute error variation in AEP are in the range 0.46% - 17.41% for a wind farm in complex terrain while with WAsP the error range is 16.64% - 39.5%. The reduction of errors with WAsP using two meteorological masts in this case was only 1.8%. A CFD simulation with CENER methodology can help to increase accuracy in AEP estimation reducing the number of meteorological masts.