© 2007 aws truewind, llc wind resource assessment and energy production: from pre-construction to...

© 2007 AWS Truewind, LLC

Wind Resource Assessment and Energy Production:

From Pre-Construction to Post Construction

July 2007

Eric White, Director of EngineeringAWS Truewind, LLC

463 New Karner RoadAlbany, NY 12205

[email protected]


AWS Truewind - Overview Industry Leader & Consultant for 15,000+ MW

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


Topics

• Wind resource assessment process• Energy production modeling• Effects on wind farm siting and design• Comparison with actual yields• Operational plant evaluations


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


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


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


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


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


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


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


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


Wind Mapping


Example MesoMap


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


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


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.


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.


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.)


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%)


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


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


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


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


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

© 2007 aws truewind, llc wind resource assessment and energy production: from pre-construction to...

Documents

wind direction

wind energy industry

llc wind resources

function of wind speed

horizontal wind speed

site wind data

wind farm siting

wind watts density