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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