2. Wind power Part 2: Resource Assesment San Jose State University FX Rongère February 2009

3. Wind resource characterization Energy provided by the wind Available power is proportional to the cube of the wind velocity

4. Power Capacity Calculation Use of Probability Density Function (Pdf) Since We will use the Pdf(v): It has been shown that the Rayleigh’s approximation gives good results for wind power capacity calculation

5. Using the mathematical properties of the Rayleigh’s Distribution we can show that: The most frequent wind speed is equal to 0.8 times the average wind speed. Rayleigh’s Distribution

6. The most contributing wind speed is equal to 1.6 times the average wind. Most contributing wind

7. The average power is equal to 1.91 times the power corresponding to the average wind speed Average power

8. Example of power distribution Lee Ranch Facility in Colorado Actual measures probability of wind and power Curves use the Rayleigh’s distribution

9. More complex Pdf Weibull’s distribution is a more general form than Rayleigh’s distribution: Rayleigh’s distribution: k=2

10. Wind Shear In general wind is stronger with altitude because of the friction on the ground Wind shear is much more complex than friction on the ground. Analysis must be performed for each specific case

11. Class of wind power density Locations are rated following the table: Assuming a Rayleigh distribution and a wind shear provided by the power law with an exponent equals to.14

12. Wind scale

13. Wind resource in the USA

14. Wind Farms in the USA

15. Wind resource in the USA

17. Wind resource in California Solano 415 MW Altamont Pass 586 MW

18. Wind resource in California San Gorgonio 619 MW Tehachapi 665 MW Pacheco 16 MW

19. Altamont Pass 586 MW 6,000 wind turbines Early 80s Repowering has started 38 Mitsubishi (1MW in 2006)

20. Pacheco Pass 16 MW 167 wind turbines Mid 80s Project by Enel with Vestas 660kW

21. Tehachapi 665 MW 2,000+ wind turbines Early 80s Repowering started in 1999 Micon 700 kW GE 1.5 MW Mitsubishi 1 MW

22. San Gorgonio 619 MW 1,000+ wind turbines Early 80s Repowering started in 1999 Zond 750 kW Vestas 650 kW Mitsubishi 600 kW GE 1.5 MW

23. Wind Resource in California 45 miles

24. Projects ProjectUtility/DeveloperLocationStatusMW Cap Online date/ Turbine Alta Mesa IVTenderland Power/ CHI Enel San Gorgonio PassNA40NA Vestas - 660 kW (61) Altamont PowerAltamont Power, LLCAltamont PassNA36NA / NEG Micon 800kW (45) Pacific RenewablePG&ELompoc 83NA MontezumaFPL EnergySolana 32NA Pine Tree Wind Project Zilkha/ LA Dept of PWMojave (North)Proposed120NA Tehachapi Wind Project Western WindTehachapiProposed50NA San Gorgonio Wind Project SeaWest WindpowerSan GorgonioProposed37NA Tehachapi Wind Project Coram EnergyTehachapiProposed12NA

25. Recent Projects

26. Under-construction Projects Source: American Wind Energy Association

27. Assessment Techniques Wind Tower Expensive Punctual information Telecommunication Limited height (50 m) Source: Wes Slaymaker Commercial Wind Site Assessment Madison, WI February 2005 Wind vane anemometer

28. Sodar Acoustic signal modified by the wind velocity by Doppler effect: Frequency is higher in front of the moving source and lower behind f : emitted frequency f’ : observed frequency w : velocity of the wave v : velocity of the source

29. Sodar Sound velocity: R : Boltzmann’ constant 8.314 Jmol -1 K -1 T : Temperature K M : Mass of one mole of gas γ : 1.4 for dry air Depends on Temperature and Humidity

30. Sodar Sound is reflected and scattered by the eddies carried by the turbulent wind The amplitude of the received wave characterizes the stability of the atmosphere Using several sodar sources allows to capture the different components of the wind velocity Vertical range : 200 m. to 2,000 m. Frequency: 1,000 Hz 4,000 Hz

31. Sodar signal

32. Satellite based measurements Sea waves scatter and reflect radar signal Direction and Wave length of the waves provide wind information Accuracy of ±2m/s and ±20 o Not valid close to the coast because of effect on waves

33. Numerical simulation Objective: To get detailed wind calculation in specific location from general atmospheric observations Categories of models from the general to detailed Mesoscale models (n00 km x n00km x 10 km) ex KAMM Microscale linear models (n km x n km x n km) ex WAsP Navier-Stokes non-linear models with turbulence (n00 m x n00 m x n00 m) They are usually used in conjunction with local measured data to be adjusted

34. Source : Wind Flow Models over Complex Terrain for Dispersion Calculations COST Action 710 - 1997

35. References http://rredc.nrel.gov/wind/pubs/atlas/maps.html#2-1 http://www.wasp.dk/Courses/Index.htm http://www5.ncdc.noaa.gov/documentlibrary/pdf Companies to follow: www.awstruewind.com (Albany) www.awstruewind.com www.windlogic.com (St Paul)www.windlogic.com www.3tiergroup.com (Seattle)www.3tiergroup.com www.garradhassan.com (UK)www.garradhassan.com

36. Application At Ilio Point on Molokai (Hawaii) The average wind speed at 30m is 8.1m.s -1 The shear exponent is.14 and the wind follows the Rayleigh’s distribution What is the average speed at 50m? What is the class of the site? What is the power density available at 50m? What is the most probable wind speed at 50m? What is the most contributing wind speed at 50m? What is the probability to have a wind speed greater than 25 m.s -1 at 50m? Ilio Point