1. Evaluation of global wind power Cristina L. Archer Stanford University 5 October 2005 San Jose State University

2. Outline Introduction on wind energy Least Square (LS) Methodology Global wind maps at 80 m Validation Statistics Calculation of global wind power potential Benefits of networking

3. Introduction Wind turbines are: reliable non invasive Wind energy is: clean inexpensive abundant scalable increasingly popular More info: http://www.windpower.org Archer and Jacobson (2003) http://www.awea.org/projects/

4. Some Examples of Wind Turbines 32 m rotor blade of a 1.5 MW turbine (~54 m) Vindeby (DK) Copenhagen (DK)

5. Problems with wind power Intermittency Unreliability Noise Aesthetics Animals and birds Solutions: Siting Networking  frequency of low wind speeds  standard deviation of wind speed

6. Least Squares (LS) methodology Power law:Log law: Friction coefficient (1/7) Roughness length (0.01 m)

7. Least Squares (LS) methodology Power law:Log law:

8. LS methodology: examples

9. Mean 80m Wind Speed at Sounding Locations

10. Mean 80m Wind Speed in North America

11. Mean 80m Wind Speed in Europe

12. Validation: KSC towers

13. Four-level towers

14. Statistics: Global Frequencies ClassN. of stations Percent 1622275.9 293911.5 33374.1 42332.8 51321.6 61311.6 72052.5 Total8199100 Total ≥ 3 1038 12.7

15. Statistics: Global Mean 80m Speeds Station TypeMean V80Mean V80 for Class ≥3 (m/s) Surface over land 4.548.40 Buoys8.609.34 Soundings4.848.02 All4.59 8.44

16. Calculation of Global Wind Power P global = 72 TW =53,898 Mtoe P global = f x A land x  x P turb 12.7%1.3x10 8 km 2 6 km -2 0.087x8.44-1500/77 2 P turb = P rated x CF = P rated x (0.087V-P rated /D 2 )

17. Calculation of Global Wind Power

18. Problems with wind power Intermittency Unreliability Noise Aesthetics Animals and birds Solutions: Siting Networking  frequency of low wind speeds  standard deviation of wind speed

19. Methodology for hourly 80m- trends

20. Methodology for hourly 80m- patterns

21. The shear function (  )

22. The shear function (  ) at KSC

23. Networking – The arrays

24. Networking – Frequency Distributions

25. Networking - Statistics

26. Conclusions 1)~13% of world’s stations are in class ≥3 2)Mean wind speed is steady and reliable at “windy” locations 3)Data from KSC support LS methodology 4) Networking several wind farms virtually eliminates intermittency and reduces backups 5) Global wind power potential is 72 TW 6) More results at: http://www.stanford.edu/group/efmh/winds