10 steps in building a wind farm
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DEALING WITH UNCERTAINTY: Wind Resource Assessment D. C. McKay ORTECH Power Presented at Environmental Finance Workshop Series University of Toronto October 12, 2005. 10 Steps in Building a Wind Farm. Understand Your Wind Resource Determine Proximity to Existing Transmission Lines - PowerPoint PPT PresentationTRANSCRIPT
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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
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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
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Considering a Wind Farm?
Need to Consider• Revenue• Capital Costs• Operational CostsAll carry uncertainty
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Why Estimate Uncertainty?• Uncertainty is inevitable• Understanding its origin is
important to:– Know it– Control it– Be prepared for it
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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
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Uncertainty on Revenue side: Wind Resource Assessment• Wind shear• Long term Variation• Monitoring• Wake Estimate• Noise• Power Curve
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Sources of Uncertainty:Wind Shear
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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
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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
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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
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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
1.1
1.2
1.3
1.4
Year
No
rma
liz
ed
Win
d S
pe
ed
/ E
ne
rgy
Normalized Wind Speed
Normalized Wind Energy
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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
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Sources of Uncertainty:Long Term Variation
• Climate Change– Mean levels of wind energy– Fluctuations of wind energy
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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%
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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)
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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)
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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
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Sources of Uncertainty:Turbine Power Curve
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Sources of Uncertainty:Turbine Power Curve
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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
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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%
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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