The Wind Resource: Prospecting for Good Sites

Bruce Bailey, President AWS Truewind, LLC

255 Fuller Road

Albany, NY 12203 bbailey@awstruewind.com

Talk Topics

  Causes of Wind

  Resource Impacts on Project Viability

  Siting Linkages to Resource Assessment

  Wind Resource Definition

  Energy Production Prediction

  Uncertainty Analysis

  Due Diligence Objectives For Financing

What Causes Wind?

  Uneven heating of the earth’s surface

  Daily heating and cooling cycles

  Earth’s rotation

  Weather systems – track and intensity

  Position of jet stream

  Local influences – sea breezes, slope winds, channeling through valleys, etc.

Wind Resources Determine:

  Project Location & Size

  Tower Height

  Turbine Selection & Layout

  Energy Production »  annual, seasonal

»  on- & off-peak

»  capacity credit

  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 (W/m2)

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 ρ A V3

Area = π r2 Instantaneous Speed (not mean speed)

kg/m3 m2 m/s

Summary of Wind Resource Planning Steps

  Identify Attractive Candidate Sites   Collect >1 yr 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

Siting

Main Objective: Find and design viable wind project sites

Main Attributes:

  Adequate winds

  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

Siting Attributes

  Winds »  Minimum Class 4 desired (>7 m/s @ hub height) for wind farms

  Transmission »  distance, voltage, excess capacity

  Permit approval »  land use compatibility »  public acceptance »  visual, noise, and bird/bat

impacts are leading issues

  Land area »  economies of scale with

larger project size »  number of landowners

Siting Tools

  Wind Maps & Other Regional Resource Data

  Topographic Maps

  Transmission Line Maps & Databases

  Property Maps

  Geographic Information Systems (GIS) and associated data layers

Old vs. New Wind Maps of the Dakotas

Local Wind Map Showing Transmission and Road Overlays

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

www.windexplorer.com/NewYork/NewYork.htm

Wind data projections available at a 200 m grid resolution statewide

Measurement

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

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

Typical Monitoring Tower

• Heights up to 60 m

• Tubular pole supported by guy wires

• Installed in 1-2 days without concrete using 3 people

• Solar powered; cellular data communications

Raising the Tower

Final Touches / Sensor Orientation

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 »  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

Data Analysis Wind Speed (m/s)

Hours/ Year

0 0.01 434.42 823.43 1,098.64 1,228.75 1,216.56 1,092.37 900.88 687.59 487.9

10 323.011 200.012 115.913 63.014 32.115 15.416 6.917 2.918 1.219 0.420 0.221 0.122 0.023 0.024 0.025 0.026 0.0

01002003004005006007008009001000110012001300

0 2 4 6 8 10 12 14 16 18 20 22 24 26

Wind Speed (m/s)

Pro

ba

bili

ty (

ho

urs

/ye

ar)

GE 1.5xle - 1.5 MW

0

200

400

600

800

1000

1200

1400

1600

0.0 5.0 10.0 15.0 20.0 25.0

Wind Speed (m/s)

Tu

rbin

e O

utp

ut

(kW

)

Speed Frequency Distribution Wind Turbine Power Curve: Output As a Function of Speed

Wind Direction Rose

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 (α) values: »  .10 - .15: water/beach

»  .15 - .25: gently rolling farmland

»  .25 - .40+: forests/mountains α = Log10 [V2/V1] Log10 [Z2/Z1]

α

Wind Shear Profile

V2= 7.7 m/s

V1 = 7.0 m/s

Z2= 80 m

Z1= 50 m

V2 = V1(Z2/Z1)α

Wind Shear is important when extrapolating wind speed data from a met. mast that is shorter than the intended hub height of the turbine

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 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

0 5 10 15 20 Reference Station Mean Wind Speed (m/s)

Pro

ject

Sit

e 60

m W

ind

Sp

eed

(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.

Energy Prediction For Wind Farm

Sample Energy Production Calculation Appendix 2: Preliminary P50 Energy Production Estimate for Ayia Anna-Kochi Project Area

GE 1.5xle (Altitude Adjusted)WS

(m/s) Output (kWh)0 0.01 0.02 0.0

cut in 3 1,323.34 60,484.45 168,173.66 297,296.57 431,266.78 539,540.49 582,520.010 534,351.411 415,751.912 281,729.813 173,933.014 101,064.215 55,702.516 29,138.217 14,472.918 6,828.319 3,061.0

cut out 20 1,304.121 0.022 0.0 39 On-Site Turbines23 0.0 6.738 80 Meter Weibull C Gross Output per Turbine 3,698 MWh/yr24 0.0 2.034 Weibull K at 50m Net Energy per Turbine 3,258 MWh/yr25 0.0 1.50 Turbine Capacity (MW) Number of Turbines 39

Gross Plant Production 144,220 MWh/yrNet Plant Production 127,048 MWh/yrNet Capacity Factor 24.8%

INPUTS

0100200300400500600700800900100011001200

0 2 4 6 8 10 12 14 16 18 20 22 24 26

Wind Speed (m/s)

Pro

bab

ilit

y (h

ou

rs/y

ear)

Wind Speed Frequency Distribution

GE 1.5xle - 1.5 MW

0

200

400

600

800

1000

1200

1400

1600

0.0 5.0 10.0 15.0 20.0 25.0

Wind Speed (m/s)

Tur

bin

e O

utp

ut (

kW)

Turbine Power Curve

Energy Production Projection

  Multiply wind speed frequency distribution data (annual hours per 0.5 or 1.0 m/s speed bin) by turbine power curve output values (for same speed bins)

»  Power curve must be adjusted for site air density

  Sum the product of all speed bins for the total gross energy production (MWh)

  Determine production loss factors and their magnitude »  Wakes, availability, electrical, blade soiling/icing, high wind

hysteresis, cold temperatures

»  Cumulative losses are typically 10-15%

  Deduct losses to calculate net energy production

  Determine net production for different probability levels (P75, P90, P95, etc.) based on uncertainty analysis

Micrositing – Predicting Wind Conditions at Every Turbine, and Optimizing Turbine Locations

Software tools (WindFarmer, WindFarm, WindPro) are available to optimize the location and performance of wind turbines, once the wind resource within a project area is defined.

Optimization Tools

Wind Resource Mapping & Turbine Energy Production

Turbine Noise Emissions

Photosimulations

Conclusions

•  The wind resource drives project viability.

•  Wind conditions are site-specific and time/height variable.

•  Accuracy is crucial. Wind resource assessment programs must be designed to maximize accuracy.

•  Combination of measurement and modeling techniques gives the most reliable result.

•  Know the uncertainties and incorporate into decision making.

•  Good financing terms depend on it.