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AWS Truewind Methodology

AWS Truewind Methodology. Timeline of AWS Truewind participation Key points Wind resource modeling Estimation of plant output Validation and adjustment. Timeline. 1999-2000: AWS Truewind develops MesoMap for high-resolution wind resource mapping

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AWS Truewind Methodology

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  1. AWS Truewind Methodology • Timeline of AWS Truewind participation • Key points • Wind resource modeling • Estimation of plant output • Validation and adjustment

  2. Timeline • 1999-2000: AWS Truewind develops MesoMap for high-resolution wind resource mapping • 2001-present: AWS Truewind, NREL, WPA collaborate to produce, validate, and publish MesoMaps for 30+ states (gaps in central and southeastern US) • 2007-2008: AWS Truewind privately remaps and revalidates all states and completes lower 48 states • May 2008: windNavigator v. 1.0 released (www.windNavigator.com) • Fall 2009: NREL approaches AWS Truewind about using windNavigator to update state-level wind potential estimates • Aug 2009-Feb 2010: wN capacity factor data provided under license to NREL, reviewed and approved by NREL & WPA • February 2010: New state-level potential estimates released by NREL and AWS Truewind

  3. Key Points • The resolution of the underlying wind speed and capacity factor data is 200 m. This is adequate to resolve most terrain, but some smoothing of sharp ridgelines still occurs. • The wind resource and capacity factor estimates have been thoroughly validated by AWS Truewind and NREL. However, errors may still occur, and uncertainty is always present. As more data are acquired, updates are likely. • The CF estimates reflect the particular generic turbine power curve and hub heights assumed. Different turbine models and heights may have higher or lower output. • The assumed turbine density of 5 MW/km2 is a rough average. A much higher effective density (up to 20 MW/km2) may be achieved on arrays with a single row, e.g., ridgetops.

  4. The MesoMap Process topography roughness vegetation greenness sea temps GeophysicalData Wind Maps MesoscaleSimulations(MASS) MicroscaleSimulations (WindMap) Validation Data Bases MetData full equations of motion dynamic 366 days from15 years 2.5 km resolution mass-conserving adjusts for localterrain and roughness 200 m resolution global reanalysis rawinsonde surface met data comparisonswith met data adjustments error estimates

  5. Map Validation & Adjustment • AWS Truewind data base of about 1600 monitoring towers • 550 ASOS; pre-ASOS stations excluded • 1050 “tall towers” • Observed mean speeds adjusted to long term and projected to map height using observed or estimated shear • Map bias at each point calculated and bias-correction map created • Reduces spatially-correlated biases • Does not eliminate bias at every tower • Objective error estimates derived • Raw deviations: 0.45 m/s • Adjusted for data uncertainties: 0.35 m/s • Errors tend to be higher in West and East (complex terrain), lower in Midwest and Plains states

  6. Validation Scatter Plots

  7. Estimation of Plant Output • For each point, wind speed distribution created from from 12 years of weather simulations (windTrends) • Then gross turbine output calculated for a generic IEC Class 2 turbine power curve, corrected for air density

  8. Validation of Speed Distributions • Predicted output compared to calculated from observed winds at 10 sites • For the same mean speed, 6% standard error • Largest errors occur in Western mountain passes, much smaller errors in open plains and eastern mountains • Combined with uncertainty in the mean wind speed, overall uncertainty about 10% for windy sites

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