Wind powerPart 2: Resource

Assesment

San Jose State UniversityFX Rongère

February 2009

Wind resource characterization

Energy provided by the wind

A

AA

A

AA

AA

AAAA

AA

AA

AOpen

vvmW

khmWQdt

dE

2.

2.

).(

32

Available power is proportional to the cube of the wind velocity

Power Capacity Calculation

Use of Probability Density Function (Pdf) Since

We will use the Pdf(v):

It has been shown that the Rayleigh’s approximation gives good results for wind power capacity calculation

33 .2

.,.2

. vAWvAW

dvvPdf

dvvPdfvWW

).(

).().(

0

0

2

2 .2

.exp.

.2

.)(

v

v

v

vvPdf

Using the mathematical properties of the Rayleigh’s Distribution we can show that: The most frequent wind speed is equal to 0.8 times the

average wind speed.

Rayleigh’s Distribution

vvatlocatedisvPdf .80.02

:)( max

Rayleigh's function

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 0.5 1 1.5 2

V/Vaverage

Pdf(

v)

"Vaverage=2m/ s" "Vaverage=5m/ s" "Vaverage=10m/ s"

The most contributing wind speed is equal to 1.6 times the average wind.

Most contributing wind

vvatlocatedisvPdfv .60.18

:)(. max3

Most contributing wind to the power

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 0.5 1 1.5 2

V/Vaverage

v3 . P

df(

v)

Vaverage=2m/ s Vaverage=5m/ s vaverage=10m/ s

The average power is equal to 1.91 times the power corresponding to the average wind speed

Average power

dvvPdf

dvvPdfvv

).(

).(.

0

0

3

3

333 .91.16

vvv

vWvW .91.1)(

33

.2

.

.2

.

vAW

vAW

Example of power distribution

Lee Ranch Facility in Colorado Actual measures probability of

wind and power Curves use the Rayleigh’s

distribution

More complex Pdf

Weibull’s distribution is a more general form than Rayleigh’s distribution:

Rayleigh’s distribution: k=2

kk

v

v

v

v

v

kvPdf

.2

.exp.

.2

..

.2

.)(

1

Wind Shear

In general wind is stronger with altitude because of the friction on the ground

Wind shear

0

20

40

60

80

100

1 1.2 1.4 1.6 1.8 2

v(z)/v(10m)

Altitude (m

)

smooth surfaceocean, sand

low grass or fallowground

high grass or low rowcrops

tall row crops or lowwoods

high woods withmany trees suburbs,small towns

ground cover n

smooth surface ocean, sand 0.1low grass or fallow ground 0.16high grass or low row crops 0.18tall row crops or low woods 0.2high woods with many trees suburbs, small towns 0.3

n

z

zzvzv

00 ).()(

Wind shear is much more complex than friction on the ground. Analysis must be performed for each specific case

Class of wind power density

Locations are rated following the table:

Assuming a Rayleigh distribution and a wind shear provided by the power law with an exponent equals to .14

Wind Power Class

Wind Power Density

(W/m2)

Average Speed m/s

(mph)

Wind Power Density

(W/m2)

Average Speed m/s

(mph)0 0 0 0

1000 9.4 (21.1) 2000 11.9 (26.6)

7.0 (15.7) 800 8.8 (19.7)  7

6.0 (13.4) 500 7.5 (16.8)  5

300 6.4 (14.3) 600 8.0 (17.9)  6

400

5.1 (11.5) 300 6.4 (14.3)  3

200 5.6 (12.5) 400 7.0 (15.7)  4

250

10 m (33 ft) 50 m (164 ft)

  1100 4.4 (9.8) 200 5.6 (12.5)

  2150

Wind scaleWind speed Wave height

Beaufort number

kt km/h mph m/s

Description

m ft

0 0 0 0 0-0.2 Calm 0 0

1 1-3 1-6 1-3 0.3-1.5 Light air 0.1 0.33

2 4-6 7-11 4-7 1.6-3.3 Light breeze 0.2 0.66

3 7-10 12-19 8-12 3.4-5.4 Gentle breeze 0.6 2

4 11-15 20-29 13-18 5.5-7.9 Moderate breeze 1 3.3

5 16-21 30-39 19-24 8.0-10.7 Fresh breeze 2 6.6

6 22-27 40-50 25-31 10.8-13.8 Strong breeze 3 9.9

7 28-33 51-62 32-38 13.9-17.1 Near gale 4 13.1

8 34-40 63-75 39-46 17.2-20.7 Gale 5.5 18

9 41-47 76-87 47-54 20.8-24.4 Severe gale 7 23

10 48-55 88-102 55-63 24.5-28.4 Storm 9 29.5

11 56-63 103-119 64-73 28.5-32.6 Violent storm 11.5 37.7

12 64-80 120 74-95 32.7-40.8 Hurricane 14+ 46+

Wind resource in the USA

Wind Farms in the USA

Wind resource in the USA

Wind resource in the USA

Wind resource in California

Solano415 MW

Altamont Pass586 MW

Wind resource in California

San Gorgonio619 MW

Tehachapi665 MW

Pacheco16 MW

Altamont Pass

586 MW6,000 wind turbinesEarly 80sRepowering has started38 Mitsubishi (1MW in 2006)

Pacheco Pass

16 MW167 wind turbinesMid 80sProject by Enel withVestas 660kW

Tehachapi

665 MW2,000+ wind turbinesEarly 80sRepowering started in 1999Micon 700 kWGE 1.5 MWMitsubishi 1 MW

San Gorgonio

619 MW1,000+ wind turbinesEarly 80sRepowering started in 1999Zond 750 kWVestas 650 kWMitsubishi 600 kWGE 1.5 MW

Wind Resource in California

45 miles

ProjectsProject Utility/Developer Location Status MW 

CapOnline date/

Turbine

Alta Mesa IV Tenderland Power/ CHI Enel

San Gorgonio Pass NA 40 NAVestas - 660 kW (61)

Altamont Power Altamont Power, LLC Altamont Pass NA 36 NA / NEG Micon 800kW (45)

Pacific Renewable PG&E Lompoc   83 NA

Montezuma FPL Energy Solana   32 NA

Pine Tree Wind Project

Zilkha/ LA Dept of PW Mojave (North) Proposed 120 NA

Tehachapi Wind Project

Western Wind Tehachapi Proposed 50 NA

San Gorgonio Wind Project

SeaWest Windpower San Gorgonio Proposed 37 NA

Tehachapi Wind Project

Coram Energy Tehachapi Proposed 12 NA

Recent Projects

Name Location

Power Capacity

(MW) UnitsTurbine

Mfr. Developer OwnerPower

PurchaserYear

Online

Edom Hills repower

20 8 ClipperBP Alternative Energy

BP Alternative Energy

SCE 2008

Alite Wind Farm

24 8 VestasAllco/Oak Creek Energy

California Portland Cement

2008

Dillon 45 45 MitsubishiIberdrola Renewables

Iberdrola Renewables

Southern California Edison

2008

Solano Wind Project

Solano 63 21 VestasSacramento Municipal Utility District

Sacramento Municipal Utility District

Sacramento Municipal Utility District

2007

Buena VistaAltamont Pass

38 38 MitsubishiBabcock & Brown

Babcock & Brown

Pacific Gas & Electric

2006

Shiloh Wind Power Project

Solano County

150 100 GE Energy PPM Energy PPM Energy

PG&E, Modesto I rrigation District & City of Palo Alto Utilities

2006

Solano I IASolano County

24 8 VestasSacramento Municipal Utility District

Sacramento Municipal Utility District

Sacramento Municipal Utility District

2006

Coram Energy (Aeroman repower)

Tehachapi 10.5 7 GE Energy Coram Energy Coram EnergySouthern California Edison

2005

Kumeyaay Wind Power Project

East of San Diego

50 25 GamesaSuperior Renewable Energy

Babcock & Brown

San Diego Gas & Electric

2005

Victorville Wind Project

Victorville prison

0.75 1 Vestas NORESCO NORESCOVictorville Prison

2005

Under-construction Projects

Name Location

Power Capacity

(MW) UnitsTurbine

Mfr. Developer OwnerPower

PurchaserYear

Online

Pine Tree Wind Project

north of Mojave

120 80 GE Energy

Los Angeles Department of Water and Power

Los Angeles Department of Water and Power

Los Angeles Department of Water and Power

San Gorgonio Farms repower

San Gorgonio

5 10 VestasSan Gorgonio Farms

San Gorgonio Farms

Shiloh I INorthern California

150 75 REPower enXco enXco PG&E

Source: American Wind Energy Association

Assessment Techniques

Wind Tower Expensive Punctual information Telecommunication Limited height (50 m)

Source: Wes Slaymaker Commercial Wind Site AssessmentMadison, WI February 2005

Wind vane anemometer

Sodar

Acoustic signal modified by the wind velocity by Doppler effect:

Frequency is higher in front of the moving source and lower behind

fvw

wf .'

f: emitted frequencyf’: observed frequencyw: velocity of the wavev: velocity of the source

Sodar

Sound velocity:

Cv

Cp

M

TRPw

...

R: Boltzmann’ constant 8.314 Jmol-1K-1

T: Temperature K

M: Mass of one mole of gas

γ: 1.4 for dry air

Effect of Temperature on Sound Speed

300

310

320

330

340

350

-10 0 10 20 30

Temperature (C)

Sound S

peed (

m/s

)

Depends on Temperature and Humidity

Sodar

Sound is reflected and scattered by the eddies carried by the turbulent wind

The amplitude of the received wave characterizes the stability of the atmosphere

Using several sodar sources allows to capture the different components of the wind velocity

Vertical range : 200 m. to 2,000 m.Frequency: 1,000 Hz 4,000 Hz

Sodar signal

Satellite based measurements

Sea waves scatter and reflect radar signal Direction and Wave length of the waves

provide wind information Accuracy of ±2m/s and ±20o

Not valid close to the coast because of effect on waves

Numerical simulation

Objective: To get detailed wind calculation in specific location

from general atmospheric observations

Categories of models from the general to detailed Mesoscale models (n00 km x n00km x 10 km) ex

KAMM Microscale linear models (n km x n km x n km) ex

WAsP Navier-Stokes non-linear models with turbulence

(n00 m x n00 m x n00 m)

They are usually used in conjunction with local measured data to be adjusted

Source : Wind Flow Models over Complex Terrain for Dispersion Calculations COST Action 710 - 1997

References http://rredc.nrel.gov/wind/pubs/atlas/

maps.html#2-1 http://www.wasp.dk/Courses/Index.htm http://www5.ncdc.noaa.gov/documentlibrary/pdf

Companies to follow:www.awstruewind.com (Albany) www.windlogic.com (St Paul) www.3tiergroup.com (Seattle) www.garradhassan.com (UK)

Application

At Ilio Point on Molokai (Hawaii) The average wind speed at 30m is 8.1m.s-1

The shear exponent is .14 and the wind follows the Rayleigh’s distribution

What is the average speed at 50m? What is the class of the site? What is the power density available at 50m? What is the most probable wind speed at 50m? What is the most contributing wind speed at 50m? What is the probability to have a wind speed

greater than 25 m.s-1 at 50m?

Ilio Point