A METHODOLOGY FOR ESTIMATING WIND FARM PRODUCTION THROUGH CFD CODES. DESCRIPTION AND VALIDATION

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 • Introduction • Description of the methodology 3.1 Numerical model 3.2 Estimationof wind farm power • Alaiz wind farm • Experimental validation • 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. MODULE 3 MODULE 2 MODULE 1 CFD solver Grid generation Digital Terrain Model RANS Navier Stokes Fluent 6.2 K-ε turbulence model Steady-state Neutral atmosphere Structured mesh Horizontal resolution: 20m Vertical resolution: 0.5m Raster topographical information High resolution 3D surface 2. DESCRIPTION OF THE METHODOLOGY • 2.1 NUMERICAL MODEL

5. 340º 0º 20 40º WT1 WT2 WT3 . . . . . WT50 2. DESCRIPTION OF THE METHODOLOGY • 2.2 ESTIMATION OF WIND FARM POWER ¿How wind speed estimation is transformed into power estimation? The CFD model solves instantaneous situations for every direction 1 simulation for sector φ Field of V,TI,P… when wind comes from φ CFD - Output Ratios Wind Turbine velocity – Mast velocity for sector φ and WTi

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

7. 60º 80º 340º 0º 20 40º 60º 80º 340º 0º 20 40º . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. DESCRIPTION OF THE METHODOLOGY • 2.2 ESTIMATION OF WIND FARM POWER WT50 WT1 WT2 WT3 WT1 WT2 WT3 WT50 bin_1 bin_2 bin_3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . INPUT . . . bin_30 WT1 WT2 ANNUAL FREQUENCY (HRS) FOR EACH WIND TURBINE & FOR SECTOR φ WT3 . . . WIND SPEED FOR EACH WIND TURBINE & FOR SECTOR φ . . WT50 INPUT

8. 2. DESCRIPTION OF THE METHODOLOGY • 2.2 ESTIMATION OF WIND FARM POWER WT1 WT2 WT3 WT50 WT1 WT2 WT3 WT50 WT1 WT2 WT50 bin_1 bin_1 20º bin_2 bin_2 40º bin_3 bin_3 60º . . . . . . . . . bin_30 bin_30 340º INPUT INPUT POWER CURVES ANNUAL FREQUENCY (HRS) WAKE EFFECTS PARK WT1 WT2 WT3 WT4 WT5 WT50 . . . PRODUCTION / WT (GWh)

9. ALAIZ 6 ALAIZ 2 WT 3 ALAIZ 3 WT 1 WT 2 ALAIZ 9 WT 4 3. ALAIZ WIND FARM • Alaiz hill site: • Complex terrain (global RIX = 16 %) • 4 kilometers hill, E-W orientation • Prevailing wind direction: N • Highly roughed on the hilltop 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)

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

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

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

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

14. 4. EXPERIMENTAL VALIDATION III. Global Error. Wind Farm AEP Results using one met. mast AEP Error % from ALAIZ 3_55 AEP Error % from ALAIZ 6_40 CFD errors below 18%. WAsP errors up to 39.5%

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

16. 1 3 2 4 4. EXPERIMENTAL VALIDATION IV. Combined WAsP simulation with 2 masts Global Production Error with Alaiz6_40 = -31% Global Production Error with Alaiz6_40 + Alaiz3_55 = -29.2% Using two met. masts with WAsP the error decreases in a 1.8%

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.