1. Presented at Environmental Finance Workshop Series University of Toronto October 12, 2005 DEALING WITH UNCERTAINTY: Wind Resource Assessment D. C. McKay ORTECH Power Presented at Environmental Finance Workshop Series University of Toronto October 12, 2005

2. 10 Steps in Building a Wind Farm Understand Your Wind Resource Determine Proximity to Existing Transmission Lines Secure Access to Land Establish Access to Capital Identify Reliable Power Purchaser or Market Address Siting and Project Feasibility Considerations Understand Wind Energy’s Economics Obtain Zoning and Permitting Expertise Establish Dialogue with Turbine Manufacturers and Project Developers Secure Agreements to Meet O&M Needs

3. Considering a Wind Farm? Need to Consider Revenue Capital Costs Operational Costs All carry uncertainty

4. Why Estimate Uncertainty? Uncertainty is inevitable Understanding its origin is important to: –Know it –Control it –Be prepared for it

5. Who wants to know? You –To set contingencies –To conduct realistic sensitivity analyses with financial model –To assess project feasibility –To qualify for competitive financing Your lender/ financier

6. Uncertainty on Revenue side: Wind Resource Assessment Wind shear Long term Variation Monitoring Wake Estimate Noise Power Curve

7. Sources of Uncertainty: Wind Shear

8. Profiling & Extrapolation –Log law or power law U(z1)/U(z2)=(z1/z2)^p –p ~ height, roughness, terrain, direction & stability –wake & turbulence

9. Sources of Uncertainty: Wind Shear Can only be eliminated if wind is monitored at hub height Often no hub height measurement available when feasibility of project is assessed

10. Sources of Uncertainty: Wind Shear Uncertainty value for Wind Shear +/- 20-25% Sources: Wind Resource Analysis Program 2002, Minnesota Department of Commerce, http://www.state.mn.us/mn/externalDocs/WRAP_Rep ort_110702040352_WRAP2002.pdf Project specific estimates

11. Sources of Uncertainty: Long Term Variation

12. E.g. –25 years long term data available (d.o.f. = 24), standard deviation of sample (s = 15%) => good measure of year to year variation –99% confidence interval = 7% t…student-t, t(d.o.f., confidence level) => good measure of long term average

13. Sources of Uncertainty: Long Term Variation Climate Change –Mean levels of wind energy –Fluctuations of wind energy

14. Sources of Uncertainty: Wind Resource Monitoring Systematic error Calibration of instrument –Quality of instrument –Installation (effects of tower, mounting arrangements) –Surrounding terrain, obstructions, etc. –Instrument icing/ malfunctioning –Type B ≈ +/- 5% Random error –Data recovery rate, electronic noise –Reduced by increasing number of samples –Type A ≈ +/- 1%

15. Sources of Uncertainty: Wind Modelling Wind Models –Flow Model Vs Wind Climate Model –Diagnostic Vs Prognostic –Meso-scale Vs Micro-scale (Coupling) –Physics (hydrostatic / non-hydrostatic, compressible / non-compressible, friction, turbulence closure)

16. Sources of Uncertainty: Wind Modeling Input to Models –Land Use, Seasonal Variations –Terrain (resolution & accuracy) –External Forcing (pressure gradients, solar radiation, stratification, temperature difference between land and water)

17. Sources of Uncertainty: Wind Modelling Wake Modelling (project specific estimate of  2%) Model Validation Difference between WAsP and MS-Micro Models <2% on project example Difference between WAsP and more advanced models 25%+ Noise Modelling

18. Sources of Uncertainty: Turbine Power Curve

20. Other Factors in Production Estimating Power curve guarantee Availability and maintenance time Electrical losses Time dependent performance deterioration (blade soiling) Blade icing and extreme weather

21. Combination of Uncertainties Project example Contribution+/- Total Wind Shear20.0% Long Term Variation7.0% Monitoring5.1% Modeling5.0% Wake Estimate2.0% Power Curve7.0% 23.5%

22. Summary Rational quantification of revenue estimate uncertainty is essential Wind shear is often biggest contributor to uncertainty Redundant modeling helps to keep model uncertainty down Monitoring at as many locations as possible and as close as possible to hub height will reduce uncertainty Other loss factors need be considered