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© 2007 AWS Truewind, LLC
Wind Resource Assessment and Energy Production:
From Pre-Construction to Post Construction
May 2008
Eric White, Director of EngineeringAWS Truewind, LLC
463 New Karner RoadAlbany, NY 12205
© 2007 AWS Truewind, LLC
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
Topics
• Wind resource assessment process• Energy production modeling• Effects on wind farm siting and design• Operational plants and actual yields
© 2007 AWS Truewind, LLC
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.
Establishing Project Viability
© 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 V3
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
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
= Log10 [V2/V1]
Log10 [Z2/Z1]
WindShearProfile
WindShearProfile
V2= 7.7 m/sV2= 7.7 m/s
V1 = 7.0 m/sV1 = 7.0 m/s
Z2= 80 mZ2= 80 m
Z1= 50 mZ1= 50 m
V2 = V1(Z2/Z1)Wind speed, and available power, generallyincrease significantly with height
© 2007 AWS Truewind, LLC
Wind Resource Assessment HandbookFundamentals for Conducting a Successful Monitoring Program
Fundamentals For ConductingA Successful Monitoring Program
WIND RESOURCEWIND RESOURCEASSESSMENT HANDBOOKASSESSMENT HANDBOOK
Prepared for:
National Renewable Energy Laboratory
1617 Cole BoulevardGolden, CO 80401
NREL Subcontract No. TAT-5-15283-01
Prepared By:
AWS Scientific, Inc.
255 Fuller RoadAlbany, NY 12203
April 1997
• Published by NREL– www.nrel.gov/docs/legosti/
fy97/22223.pdf
• 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
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
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
© 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
© 2007 AWS Truewind, LLC© 2008 AWS Truewind, LLC
Alternative Sources of Wind Speed Data
Alternative sources and techniques have large uncertainty; the wind industry is more demanding of accuracy than traditional users
•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
10% above 10 m/s •Environmental Protection Agency
–Accuracy: 0.25 m/s < 5 m/s5% > 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!
© 2007 AWS Truewind, LLC
Modern Wind Maps
Old and new wind maps of the DakotasSource: 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
© 2007 AWS Truewind, LLC
Wind Mapping
© 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
© 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
© 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 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
Measure - Correlate - Predict Technique
Airport A Regressiony = 1.0501x + 0.4507
R2 = 0.8763
Airport B Regressiony = 1.4962x + 0.4504
R2 = 0.875
Airport C Regressiony = 1.7278x + 0.7035R 2 = 0.8801
0
5
10
15
20
25
0 5 10 15 20Reference Station Mean Wind Speed (m/s)
Pro
ject
Sit
e 60
m W
ind
Sp
eed
(m
/s)
Airport AAirport BAirport 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
Conceptual Project
Estimated Net Capacity Factor ~ 29.0 – 31.5%
0%
30%
60%N
NNENE
ENE
E
ESE
SESSE
SSSW
SW
WSW
W
WNW
NWNNW
Percent of Total Energy
Percent of Total Time
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.
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.
© 2007 AWS Truewind, LLC
Elements of Energy ProductionAnalysis & 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
Influences on Uncertainty
Measured Speed
Shear
Climate
Resource Model
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%)
© 2007 AWS Truewind, LLC
Normal Distribution
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140
P50 - Mean
P90
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
Cumulative Distribution
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
0 20 40 60 80 100 120 140
P50 - Mean
P90
© 2007 AWS Truewind, LLC
Operational Plant Performance:• Understanding operational wind farm performance can be non-
trivial Monthly Operational Data
10000
15000
20000
25000
30000
35000
40000
45000
50000
Janu
ary
Febru
ary
Mar
chApr
ilM
ayJu
ne July
Augus
t
Septe
mbe
r
Octobe
r
Novem
ber
Decem
ber
Month
Mo
nth
ly P
rod
uct
ion
(R
even
ue
Me
ter,
MW
h)
0
10
20
30
40
50
60
70
80
90
100
2004 prod
2005 prod
2006 prod
ave prod
2004 avail
2005 avail
2006 avail
ave avail
Need to separate wind variability from other effects to understand real long term expectations
Plant Production
Project Availability
Typical Plant Operational Data
© 2007 AWS Truewind, LLC
R2 = 0.9667
0
10000
20000
30000
40000
50000
60000
70000
4 5 6 7 8 9 10
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%
Ava
il P
lan
t O
utp
ut
Nacelle Average Wind Speed
© 2007 AWS Truewind, LLC
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02 Jan-04 May-05 Oct-06
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
Win
d S
peed
(m
/s)
Nacelle Average
Ref Station 2
Ref Station 1
© 2007 AWS Truewind, LLC
84.0%
86.0%
88.0%
90.0%
92.0%
94.0%
96.0%
98.0%
100.0%
102.0%
Oper Eval AWS EPR 2004 2005 2006
Actual Operations
100% Availability
Case Comparison – Operational Performance
84.0%
86.0%
88.0%
90.0%
92.0%
94.0%
96.0%
98.0%
100.0%
102.0%
Oper Eval AWS EPR 2004 2005 2006
Actual Operations
100% Availability
© 2007 AWS Truewind, LLC© 2008 AWS Truewind Confidential
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 onionto understand the real issues involved
- Studies in process
© 2007 AWS Truewind, LLC© 2008 AWS Truewind Confidential
Early Findings – Some Key Contributors
Resource Assessment Campaign Biases
As-Built Plant Characteristics Changes from Plan
Regional Climate and Variability
Actual Plant Availability
Sub-Optimal Operation
Many contributors – across the project development cycle
© 2007 AWS Truewind, LLC
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