1. Eric White, Director of Engineering
Wind Resource Assessment and Energy Production: From Pre-Construction to Post Construction May 2008
Eric White, Director of Engineering
AWS Truewind, LLC
463 New Karner Road
Albany, NY
© 2007 AWS Truewind, LLC

2. AWS Truewind - Overview
Industry Leader & Consultant for 20,000+ MW
Full spectrum of wind farm development and evaluation services
Wind Assessment, Mapping, Engineering, Performance Assessment, Forecasting
In business 25 years
Project roles in over 50 countries
Albany, New York based; 55+ employees
© 2007 AWS Truewind, LLC

3. Topics Wind resource assessment process Energy production modeling
Wind Farms
Measurements
Siting
Wind resource assessment process
Energy production modeling
Effects on wind farm siting and design
Operational plants and actual yields
© 2007 AWS Truewind, LLC

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

5. Power in the Wind (Watts)
= 1/2 x air density x swept rotor area x (wind speed)3  A V3
Density = P/(RxT)
P - pressure (Pa)
R - specific gas constant (287 J/kgK)
T - air temperature (K)
Area =  r2
Instantaneous Speed
(not mean speed)
kg/m3 m2
m/s
Knowledge of local wind speed is critical
to evaluating the available power
© 2007 AWS Truewind, LLC

6. 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:
: water/beach
: gently rolling farmland
: forests/mountains
Wind
Shear
Profile
V2= 7.7 m/s
V1 = 7.0 m/s
Z2= 80 m
Z1= 50 m
Wind speed, and available power, generally
increase significantly with height
V2 = V1(Z2/Z1)
 = Log10 [V2/V1]
Log10 [Z2/Z1]
© 2007 AWS Truewind, LLC

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

8. 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
© 2007 AWS Truewind, LLC

9. Siting Main Objective: Identify viable wind project sites
Main Attributes:
Adequate winds
Generally > 7 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
© 2007 AWS Truewind, LLC

10. 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
© 2007 AWS Truewind, LLC

11. Alternative Sources of Wind Speed Data
Visual Indications and local knowledge
Beaufort Scale
Wind Speed Estimated by Visual Effects on Land Features
Accuracy : 15% Without Height Adjustment
National Weather Service (NWS)
Measurements of Wind Speeds
for Weather Conditions
Accuracy : +/- 1 m/s up to 10 m/s % above 10 m/s
Environmental Protection Agency
Accuracy: 0.25 m/s < 5 m/s
5% > 2 m/s not to exceed 2.5 m/s
World Meteorological Organization
Accuracy: 0.5 m/s < 5 m/s
10% > 5 m/s
Wind Industry expects 1-2% speed measurement accuracy!
Alternative sources and techniques have large uncertainty; the wind industry is more demanding of accuracy than traditional users
© 2008 AWS Truewind, LLC
© 2007 AWS Truewind, LLC 11

12. Old and new wind maps of the Dakotas
Modern Wind Maps
Utilize mesoscale numerical weather models
High spatial resolution ( 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
Old and new wind maps of the Dakotas
Source: NREL
© 2007 AWS Truewind, LLC

13. Wind Mapping
© 2007 AWS Truewind, LLC

14. 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
© 2007 AWS Truewind, LLC

15. 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
© 2007 AWS Truewind, LLC

16. Predicting Long-Term Wind Conditions From Short-Term Measurements
Measure - Correlate - Predict Technique
Measure one year of data on-site using a tall tower
Correlate with one or more regional climate reference stations
Need high r2
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
Airport A Regression
y = x R 2 =
Airport B Regression
y = x
= 0.875
Airport C Regression
y = x = 5 10 15 20 25
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 site’s hourly data with three regional airport stations. A multiple regression resulted in an r2 of 0.92.
© 2007 AWS Truewind, LLC

17. 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.
Estimated Net Capacity Factor ~ 29.0 – 31.5%
© 2007 AWS Truewind, LLC

18. 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.)
© 2007 AWS Truewind, LLC

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

20. So What’s a P90?
For a function with an assumed normal probability distribution,
P50 = mean of the distribution
P90 = the point where 90% of the results are expected to be above
Both are important
© 2007 AWS Truewind, LLC

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

22. 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
© 2007 AWS Truewind, LLC

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

24. Case Comparison – Operational Performance
© 2007 AWS Truewind, LLC

25. A Few Words on Plant Underperformance - Understanding the Issue
Plant underperformance vs preconstruction estimates is a real and significant issue for the industry
Many plants averaging ~ 8 to 10% below projections
Contributions from numerous sources
Can be hard to pinpoint and evaluate
Wind variability complicates the analysis
Many aspects can be addressed
Need to peel back the onion to understand the real issues involved - Studies in process
© 2008 AWS Truewind Confidential
© 2007 AWS Truewind, LLC

26. Early Findings – Some Key Contributors
Regional Climate and Variability
Resource Assessment Campaign Biases
Actual Plant Availability
As-Built Plant Characteristics
Changes from Plan
Sub-Optimal Operation
Many contributors – across the project development cycle
© 2008 AWS Truewind Confidential
© 2007 AWS Truewind, LLC

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