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© 2007 AWS Truewind, LLC Wind Resource Assessment and Energy Production: From Pre-Construction to Post Construction July 2007 Eric White, Director of Engineering.

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Presentation on theme: "© 2007 AWS Truewind, LLC Wind Resource Assessment and Energy Production: From Pre-Construction to Post Construction July 2007 Eric White, Director of Engineering."— Presentation transcript:

1 © 2007 AWS Truewind, LLC Wind Resource Assessment and Energy Production: From Pre-Construction to Post Construction July 2007 Eric White, Director of Engineering AWS Truewind, LLC 463 New Karner Road Albany, NY 12205

2 © 2007 AWS Truewind, LLC AWS Truewind - Overview l Industry Leader & Consultant for 15,000+ MW l Full spectrum of renewable energy system planning, development and evaluation services Project roles in over 50 countries 50 employees; Albany, NY based Specializing in Mapping and Modeling, Project Engineering, Energy Assessment, Performance Evaluation, Forecasting

3 © 2007 AWS Truewind, LLC Topics Wind resource assessment process Energy production modeling Effects on wind farm siting and design Comparison with actual yields Operational plant evaluations

4 © 2007 AWS Truewind, LLC Wind Resources Determine: l Project Location & Size l Tower Height l Turbine Selection & Layout l Energy Production »annual, seasonal »on- & off-peak l Cost of Energy/Cash Flow l Warranty Terms l Size of Emissions Credits The wind energy industry is more demanding of wind speed accuracy than any other industry. Establishing Project Viability

5 © 2007 AWS Truewind, LLC Power in the Wind (Watts) Density = P/(RxT) P - pressure (Pa) R - specific gas constant (287 J/kgK) T - air temperature (K) = 1/2 x air density x swept rotor area x (wind speed) 3 A V3V3 Area = r 2 Instantaneous Speed (not mean speed) kg/m 3 m2m2 m/s Knowledge of local wind speed is critical to evaluating the available power

6 © 2007 AWS Truewind, LLC Wind Shear The change in horizontal wind speed with height A function of wind speed, surface roughness (may vary with wind direction), and atmospheric stability (changes from day to night) Wind shear exponents are higher at low wind speeds, above rough surfaces, and during stable conditions Typical exponent ( ) values: –.10 -.15: water/beach –.15 -.25: gently rolling farmland –.25 -.40+: forests/mountains = Log 10 [V 2 /V 1 ] Log 10 [Z 2 /Z 1 ] Wind Shear Profile Wind Shear Profile V 2 = 7.7 m/s V 1 = 7.0 m/s Z 2 = 80 m Z 1 = 50 m V 2 = V 1 (Z 2 /Z 1 ) Wind speed, and available power, generally increase significantly with height

7 © 2007 AWS Truewind, LLC Wind Resource Assessment Handbook Fundamentals for Conducting a Successful Monitoring Program Fundamentals For Conducting A Successful Monitoring Program WIND RESOURCE ASSESSMENT HANDBOOK Prepared for: National Renewable Energy Laboratory 1617 Cole Boulevard Golden, CO 80401 NREL Subcontract No. TAT-5-15283-01 Prepared By: AWS Scientific, Inc. 255 Fuller Road Albany, NY 12203 April 1997 Published by NREL – 22223.pdf Peer reviewed Technical & comprehensive Topics include: –Siting tools –Measurement instrumentation –Installation –Operation & maintenance –Data collection & handling –Data validation & reporting –Costs & labor requirements

8 © 2007 AWS Truewind, LLC Summary of Wind Resource Assessment Process Identify Attractive Candidate Sites Collect >1 yr On Site Wind Data Using Tall Towers Adjust Data for Height and for Long-Term Climatic Conditions Use Model to Extrapolate Measurements to All Proposed Wind Turbine Locations Predict Energy Output From Turbines Quantify Uncertainties

9 © 2007 AWS Truewind, LLC Siting Main Objective: Identify viable wind project sites Main Attributes: Adequate winds –Generally > 7 m/s @ hub height Access to transmission Permit approval reasonably attainable Sufficient land area for target project size –30 – 50 acres per MW for arrays –8 – 12 MW per mile for single row on ridgeline

10 © 2007 AWS Truewind, LLC Sources of Wind Resource Info Existing Data (surface & upper air) –usually not where needed –use limited to general impressions –potentially misleading Modeling/Mapping –integrates wind data with terrain, surface roughness & other features New Measurements –site specific using towers & other measurement systems

11 © 2007 AWS Truewind, LLC Modern Wind Maps Old and new wind maps of the Dakotas Source: NREL Utilize mesoscale numerical weather models High spatial resolution (100-200 m grid = 3-10 acre squares) Simulate land/sea breezes, low level jets, channeling Give wind speed estimates at multiple heights Extensively validated Std error typically 4-7% GIS compatible Reduce development risks

12 © 2007 AWS Truewind, LLC Wind Mapping

13 © 2007 AWS Truewind, LLC Example MesoMap

14 © 2007 AWS Truewind, LLC Typical Monitoring Tower Heights up to 60 m Tubular pole supported by guy wires Installed in ~ 2 days without foundation using 4-5 people Solar powered; cellular data communications

15 © 2007 AWS Truewind, LLC How and What To Measure Anemometers, Vanes, Data Loggers, Masts Measured Parameters –wind speed, direction, temperature –1-3 second sampling; 10-min or hourly recording Derived Parameters –wind shear, turbulence intensity, air density Multiple measurement heights –best to measure at hub height –can use shorter masts by using wind shear derived from two other heights to extrapolate speeds to hub height Multiple tower locations, especially in complex terrain Specialty measurements of growing importance –Sodar, vertical velocity & turbulence in complex terrain

16 © 2007 AWS Truewind, LLC Predicting Long-Term Wind Conditions From Short-Term Measurements Measure one year of data on- site using a tall tower Correlate with one or more regional climate reference stations –Need high r 2 –Reference station must have long-term stability –Upper-air rawinsonde data may be better than other sources for correlation purposes Predict long-term (7+ yrs) wind characteristics at project site Measure - Correlate - Predict Technique Airport A Regression y = 1.0501x + 0.4507 R 2 = 0.8763 Airport B Regression y = 1.4962x + 0.4504 R 2 = 0.875 Airport C Regression y = 1.7278x + 0.7035 R 2 = 0.8801 0 5 10 15 20 25 05101520 Reference Station Mean Wind Speed (m/s) Project Site 60 m Wind Speed (m/s) Airport A Airport B Airport C This plot compares a sites hourly data with three regional airport stations. A multiple regression resulted in an r 2 of 0.92.

17 © 2007 AWS Truewind, LLC Conceptual Project Estimated Net Capacity Factor ~ 29.0 – 31.5% Software tools (WindFarmer, WindFarm, WindPro) are available to optimize the location and performance of wind turbines, once the wind resource grid within a project area is defined.

18 © 2007 AWS Truewind, LLC Elements of Energy Production Analysis & Reporting Site/Instrument Description Wind Data Summary Long-term Speed Projection Turbine Power Curve Turbine Number & Layout Gross Energy Production Loss Estimates Uncertainty Analysis Net Annual Energy Production (P50, P75, P90, etc.)

19 © 2007 AWS Truewind, LLC Influences on Uncertainty l Measured Speed l Shear l Climate l Resource Model l Plant Losses Sensor Types, Calibration & Redundancy, Ice-Free, Exposure on Mast, # of Masts Height of Masts, Multiple Data Heights, Sodar, Terrain & Land Cover Variability Measurement Duration, Period of Record @ Reference Station, Quality of Correlation Microscale Model Type, Project Size, Terrain Complexity, # of Masts, Grid Res. Turbine Spacing (wakes), Blade Icing & Soiling, Cold Temp Shutdown, High Wind Hysteresis, etc. (2-4%) (Typical Range of Impact on Lifetime Energy Production) (1-3%) (4-9%) (5-10%) (1-3%)

20 © 2007 AWS Truewind, LLC Operational Plant Performance: Understanding operational wind farm performance can be non- trivial Need to separate wind variability from other effects to understand real long term expectations Plant Production Project Availability Typical Plant Operational Data

21 © 2007 AWS Truewind, LLC Characteristics of an Operational Assessment Information from actual operations improves estimate –Many sources of uncertainty can be removed (measured speed, shear, resource model, plant losses, actual turbine performance) Monthly farm level numbers help smooth and linearize results (Power vs. wind speed) Climatological adjustment with reference station data to provide long term trends Can focus directly on the bottom line data (Revenue Meter) Typical Wind Farm Power Curve (After correcting for Availability) 100% Avail Plant Output Nacelle Average Wind Speed

22 © 2007 AWS Truewind, LLC Case Comparison - Reference Wind 12 month Rolling Average Wind Speed Month Trends are similar Annualized Wind Speed shows low period in 04 and 05; should expect low production Wind Speed (m/s) Nacelle Average Ref Station 2 Ref Station 1

23 © 2007 AWS Truewind, LLC Case Comparison – Operational Performance

24 © 2007 AWS Truewind, LLC Conclusions Wind conditions are site-specific and variable, but predictable over the long term. Accuracy is crucial. Wind resource assessment programs must be designed to maximize accuracy. Combination of measurement and modeling techniques gives projections close to those experienced in actual operations. Operational plant evaluations can be used to improve projections

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