10 steps in building a wind farm

DEALING WITH UNCERTAINTY: Wind Resource

Assessment

D. C. McKay ORTECH Power

Presented atPresented atEnvironmental Finance Environmental Finance

Workshop Series Workshop Series University of TorontoUniversity of Toronto

October 12, 2005October 12, 2005

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

Considering a Wind Farm?

Need to Consider• Revenue• Capital Costs• Operational CostsAll carry uncertainty

Why Estimate Uncertainty?• Uncertainty is inevitable• Understanding its origin is

important to:– Know it– Control it– Be prepared for it

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

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

Sources of Uncertainty:Wind Shear

Sources of Uncertainty:Wind Shear• Profiling & Extrapolation

– Log law or power lawU(z1)/U(z2)=(z1/z2)^p

– p ~ height, roughness, terrain, direction & stability

– wake & turbulence

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

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_Report_110702040352_WRAP2002.pdf

• Project specific estimates

Sources of Uncertainty:Long Term Variation

Annual Variations in Wind Speed and Energy Production

0.5

0.6

0.7

0.8

0.9

1

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1.2

1.3

1.4

Year

No

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Win

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Normalized Wind Speed

Normalized Wind Energy

Sources of Uncertainty:Long Term Variation• 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

Sources of Uncertainty:Long Term Variation

• Climate Change– Mean levels of wind energy– Fluctuations of wind energy

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%

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)

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)

Sources of Uncertainty:Wind Modelling

• Wake Modelling(project specific estimate of 2%)

• Model ValidationDifference between WAsP and MS-Micro Models <2% on project example

Difference between WAsP and more advanced models 25%+

• Noise Modelling

Sources of Uncertainty:Turbine Power Curve

Sources of Uncertainty:Turbine Power Curve

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

Combination of Uncertainties• Project example

Contribution +/- TotalWind Shear 20.0%Long Term Variation 7.0%Monitoring 5.1%Modeling 5.0%Wake Estimate 2.0%Power Curve 7.0%

23.5%

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