Challenges in modelling offshore wind – how to address them using observation

1. Challenges in modelling offshore wind – how to address them using observation Idar Barstad idar.barstad@uni.no

2. Improvements of resource estimates and forecast of energy yield rely primarily on the quality of numerical models and their input data. • There are many ways to set up the model suite, and the numerical model tool is normally tailored for generic use. • The system can sometimes be inherently unpredictable

3. Future wind power potential in Europe - Arpege/Ifs T159L60c3 -- (1972-2001) N/F --(2020-2049) – R[1-4] (A1B) SST = ERA40 + delta from CGCMs (variability from ERA40 =>get more realistic sea ice) Barstad et al. (2012), J. Renewable Energy [ Barstad et al. (2008), Clim.Dyn. ]

4. Wind speed at 100m ::Annual average (1972-2001) Solid line=8.5 m/s Current wind climate

5. Future power potential(2020-2049) Fractional power potential in reference to (1972-2001) Black line=1.0

6. The effect of surface waves Example 12UTC 29Feb 2008

7. Difference Two-way coupling No coupling Colour scale for roughness length / m Colour scale for roughness length difference / m WRF roughness length (m) after 12 h Work by: Alastair Jenkins, Alok Gupta, John Michalakes (NREL), Idar Barstad

8. The effect on U10 (wind speed) in WRF 2-way coupling :: Significant impact on the wind field! After 12 hrs simulation

9. BIAS (Obs-ERAI) 2 0 BIAS (Obs-3km) BIAS (Obs-9km) -2 High resolution downscaling (9-3km)

10. Qscat assimilation STD RMSE 2 Coastal effects 0 BIAS -2 Stations along the Norwegian Coast High resolution downscaling(9-3km)

11. BIAS (Courtesy M Zagar, Vestas

12. Inversion - Vosper’s regime diagram Vosper (2004) Weak inversion Strong inversion Strong inversion Hm/PBLH • Valentia Irland • - 6700 cases over 10 yrs (2000-2010) • - Conditions for lee waves • 15-20% of the time

13. Wind turbine drag in WRF from V3.3 • Drag from turbines in a single cell, distributed over several layers • - > works on both the TKE and the momentum eqs. (Blahak et al. 2010)

14. Simulation of a wind farm100 x 5MW wind turbines Fitch et al. (2012); MWR • Q: • How sensitive is the power output to the atmospheric characteristics?

15. Typical θ-profile over sea: side view top view The principle: Generation of pressure gradients by wind farm: - θ increases with height under typical stable conditions - As air lifted over farm, lower θ air brought up from below - This creates cold anomaly aloft and thus high pressure anomaly below –> pressure gradients deflect wind. Slide 3

16. Idealized study –sensitivity to upstream parameters Full model • WRF 1 km model • Ideal set-up • turbine drag 5MW top view Work by: Fitch and Barstad Slide 11

17. Demonstration of the WRF-turbine dragat Dogger bank (9km-3km-1km) 15 x15 km with a 5 MW turbine in each grid cell

18. Dogger bank wind farm (15 x15 km with a 5 MW turbine in each grid cell) 1km domain wind and wind speed 1km domain wind, wind speed & vorticity Conclusions: - 10% effect on wind speed (up to 60% on power) - Long wakes

19. 40km Fractional wind reduction -50 0 50 20km Reduced model –”single farm” Same model as in Smith (2009)

20. Reduced model – “single farm” The effect of the inversion strength double inversion strength

21. Two farms – reduced model Second farm (Lx=10km) Present in all runs (Lx=10km) Utop=15 m/s UBL=8.5 m/s H=500m dth/th=0.01

22. Two farms – reduced model Second farm (Lx=10km) Present in all runs (Lx=10km)

23. Conclusions • Models may produce data, but we have to be critical to their results • Models may be tailored to your specific needs -> talk to an expert! • Observational campaigns should be design to address scientific questions. Do we have these questions?

24. Alaska Thank you for your attention!Idar.Barstad@uni.no NOAA / MODIS 23 MAR 2010, Aleutians Islands