a systematic method for quantifying flow model uncertainty in wind resource assessment
DESCRIPTION
A SYSTEMATIC METHOD FOR QUANTIFYING FLOW MODEL UNCERTAINTY IN WIND RESOURCE ASSESSMENT. ALEX CLERC, PETER STUART AND MIKE ANDERSON 16 APRIL 2012. CONTENTS. Background and motivation Dataset and derivation of method Example calculation. ENERGY YIELD – MAJOR COMPONENTS. Reference. - PowerPoint PPT PresentationTRANSCRIPT
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A SYSTEMATIC METHOD FOR QUANTIFYING FLOW MODEL UNCERTAINTY IN WIND RESOURCE ASSESSMENTALEX CLERC, PETER STUART AND MIKE ANDERSON
16 APRIL 2012
CONTENTS
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• Background and motivation
• Dataset and derivation of method
• Example calculation
ENERGY YIELD – MAJOR COMPONENTS
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EXAMPLE ENERGY YIELD
4Taken from “EWEA Comparison of Resource and Energy Yield Assessment Procedures” presented during EWEA Wind Resource Assessment Technology Workshop, Brussels, 11 May 2011
5Taken from “EWEA Comparison of Resource and Energy Yield Assessment Procedures” presented during EWEA Wind Resource Assessment Technology Workshop, Brussels, 11 May 2011
COMBINED STANDARD UNCERTAINTY
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Combined Standard UncertaintyAnnual Energy Production (AEP)Uncertainty in AEP due to flow modelNumber of flow predictions in Energy YieldSensitivity of AEP to flow predictions (wind speed) Standard uncertainty of flow predictionsCorrelation of flow prediction errors,
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DATASET AND METHOD
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• Dataset: mast pairs from Europe and North America, wind speed ratios calculated by 30° sector
• Speed-ups taken from a flow model very similar to WAsP
• The observed speed-up errors are used to study:
– uncertainty of a speed-up prediction for a particular wind direction (u)
– correlation of errors (ρ)
557 mast pairs
EFFECT OF DISTANCE ON SPEED-UP ERROR
8Log scale
1/D 1 LDMTeλ = 10%, L1 = 1km
EFFECT OF SPEED-UP ON SPEED-UP ERROR
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A = 0.5, S is the speed-up
EFFECT OF DIRECTION ON CORRELATION
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EFFECT OF DISTANCE ON CORRELATION
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2
11 /2/2 LDLD
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MMTT ee
DTT is distance between turbinesDMM is distance between masts
SUMMARY
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• Uncertainty (u) depends on:– distance from mast to turbine– speed-up predicted by the model
• Correlation of errors (ρ) depends on:– wind direction– distance from turbine to turbine– distance from mast to mast if applicable
• Sensitivity (c) mainly depends on the shape of the wind distribution and power curve
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EXAMPLE CALCULATION
13Taken from “EWEA Comparison of Resource and Energy Yield Assessment Procedures” presented during EWEA Wind Resource Assessment Technology Workshop, Brussels, 11 May 2011
EXAMPLE CALCULATION
14Modified from “EWEA Comparison of Resource and Energy Yield Assessment Procedures” presented during EWEA Wind Resource Assessment Technology Workshop, Brussels, 11 May 2011
With one mast flow model uncertainty is 3.14%
original mast
optimal second mast
3.14% with one mast2.00% with two masts
With two masts flow model uncertainty is 2.00%
CONCLUSIONS
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• The presented flow model uncertainty method is systematic and evidence-based
• Easy to use and applicable to linear models such as WAsP and MS3DJH
• Many possible applications– Optimisation of mast deployment– Optimisation of mast weight scheme– Quantify benefits of masts in financial terms
• Open-source software “DeltaWindFlow” available from RES website
– http://www.res-group.com/resources/download-area.aspx
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