WIND RESOURCE ASSESSMENT FOR THE STATE OF WYOMING
Performed by Sriganesh Ananthanarayanan
under the guidance of Dr. Jonathan Naughton,
Professor, Department of Mechanical Engineering
University of Wyoming, Laramie, WY 82071
1
Table of Contents
LIST OF FIGURES ............................................................................................................ 2
LIST OF TABLES.............................................................................................................. 2
INTRODUCTION: ............................................................................................................. 3
ANALYSIS......................................................................................................................... 5
RESULTS ........................................................................................................................... 7
Wind resource reports ....................................................................................................... 11
CONCLUSION:................................................................................................................ 11
FUTURE WORK:............................................................................................................. 12
REFERENCES ................................................................................................................. 13
APPENDIX 1: WIND ROSE FOR EACH MONTH FOR LARAMIE ........................... 14
APPENDIX 2: WIND RESOURCE ASSESSMENT FOR CHEYENNE ....................... 17
APPENDIX 3: WIND RESOURCE ASSESSMENT FOR CASPER ............................. 22
APPENDIX 4: WIND RESOURCE ASSESSMENT FOR ROCK SPRINGS ................ 27
APPENDIX 5: WIND RESOURCE ASSESSMENT FOR SHERIDAN ........................ 32
APPENDIX 6: WIND RESOURCE ASSESSMENT FOR GILLETTE.......................... 37
APPENDIX 7: WIND RESOURCE ASSESSMENT FOR RAWLINS .......................... 42
LIST OF FIGURES
Figure 1- The state of Wyoming with seven wind sites chosen for South-East Wyoming 4
Figure 2- Probability density function and model distributions for Laramie, WY............. 8
Figure 3- Monthly wind speed profile averaged over six years.......................................... 9
Figure 4 Wind rose graphics for Laramie, WY during summer ....................................... 10
Figure 5-Wind rose graphics for Laramie, WY during winter ......................................... 10
LIST OF TABLES
Table 1: Mean wind speed variation with respect to height for Laramie ........................... 8
Table 2: Mean wind speed and Wind power density .......................................................... 8
Table 3: Wind direction during Winter & Summer .......................................................... 10
Table 4: Annual Energy Production Table………………………………………………11
2
INTRODUCTION:
Wyoming wind blows strong and persistently thanks to a combination of elevation,
topography and weather conditions. High wind speeds throughout the year is very
common in Wyoming, especially South-East Wyoming. Towards the central part of
Wyoming there is a low spot in the continental divide. This combined with down-flow on
the mountains makes for a rich resource of wind. Wind speeds are depicted by classes
ranging from one through seven. Class four (> 7.5 m/s) and higher is considered to be
highly attractive for power generation using wind energy [1]. Previous research indicates
that most of the locations in the south-east part of Wyoming have average wind speeds in
excess of those associated with Class 5 [2]. An important term often associated with wind
energy production is the capacity factor, which is defined as the ratio of the actual energy
produced in a given period, to the hypothetical maximum possible, i.e. running full time
at rated power. With a typical capacity factor of 45% [3], Wyoming contains one of the
best on-shore wind resource locations in the United States of America. This is very
impressive, but does not mean wind plants will be constructed just anywhere in
Wyoming. There are plenty of questions to be answered before going further. For
instance, are there large fluctuations in the wind speeds annually? What direction does
the wind come from? How much power can be generated during a year? To answer these
questions, the wind resource at a prospective development location needs to be assessed
in detail. A proper wind resource assessment forms a very important phase in the
development of utility scale wind farms. In addition, it provides common people with an
awareness of the wind resource possessed by Wyoming and the associated economic
potential that could be exploited to generate additional revenue for the state. From a
strategic standpoint, in addition to the significant coal and gas reserves possessed by
Wyoming, energy generated from wind could also significantly diversify the economic
portfolio of the state.
Wyoming’s phenomenal wind resource has not gone unnoticed. The National Renewable
Energy Laboratory and AWS Truepower developed a wind resource map of USA at 80
meters [4], which is the typical height at which utility scale wind turbines’ nacelles are
located. The map from the aforementioned study shows the prominence of Wyoming’s
wind resource compared to other states. Current wind industry standards show that most
of the wind project development is occurring at sites with mean wind speed at hub height
greater than 6.5 m/s [5]. All this looks very promising for Wyoming. However there are
some important issues we need to address. First and foremost, a readily accessible wind
resource report for any chosen location is necessary. A good wind resource report can
provide a quick overview of the wind characteristics of the chosen site. In addition, it can
point out whether the location needs to be further investigated by installing monitoring
stations.
The goal of this project was to perform an in-depth analysis of the wind resource for the
state of Wyoming. This is done by using the airport wind data recorded for the past few
years at chosen locations and using this data to determine the various wind and statistical
parameters such as mean wind speed, probability density function, wind power density,
and wind rose. As part of this study, all of the above have been incorporated into an
3
easily understood wind resource report that is readily available for the general public. The
report also determines estimated annual energy production for a typical utility scale wind
turbine. Using this report, the public can develop an understanding of the economic
potential of wind energy in Wyoming. Furthermore, the procedure developed as part of
this study can be used to conduct a similar assessment at any other location in the world,
provided wind data exists for that location.
For conducting any analysis associated with wind resource, wind data is required. The
more data available, the better the analysis results. The National Climatic Data Center
(NCDC), which is the world’s largest archive for climate data [6], keeps a record of wind
speeds measured from meteorological towers at a height of 10 meters above the ground,
better known as Automated Surface Observing System (ASOS). In total there are 16
ASOS stations around Wyoming. The measurements at the seven chosen sites were
representative of the wind resource needed for commercial wind development, with
special emphasis on South-East Wyoming. The wind data from January 1, 2006 to July 7,
2012 for the following sites were analyzed as part of this study: Laramie (KLAR),
Cheyenne (KCYS), Casper (KCPR), Rock Springs (KRKS), Gillette (KGCC), Sheridan
(KSHR), and Rawlins (KRWL). These locations are depicted in Figure 1 below.
Figure 1- The state of Wyoming with seven wind sites chosen for South-East Wyoming wind resource assessment
ASOS records the wind speed, direction, temperature, and pressure. For this study, wind
speed and direction were the two parameters considered. The raw data was reviewed for
errors, and the corrupted data was eliminated. The biggest advantage of collecting large
volumes of data is that it provides a good basis for characterizing the wind resource at
any given location.
For each of these sites, the probability density function (pdf) was developed. After
calculating the necessary parameters, the mean wind speed was determined from the pdf.
wind power density, average wind speed profile, wind rose, and potential annual energy
4
production for a utility scale wind turbine was determined. This procedure has been
automated using a MATLAB routine that can handle wind data of any location in the
world.
ANALYSIS
PROBABILITY DENSITY FUNCTION
A probability density function is a function associated with wind data that shows the
relative frequency with which a particular wind speed occurs or intuitively it is related to
the percentage of time that wind blowing at a particular location achieves a certain speed.
Hourly wind data implies discrete wind data. The bin spacing is determined by the
MATLAB routine according to the data collected. Each set of wind data has its own bin
spacing. The number of bins are then used to develop a histogram which provides us with
a good distribution of the data. Finally, the histogram is normalized using the empirical
formula below, to obtain the probability density function for the location.
pdf (u) = histogram(u)/(n*delta_u);
where delta_u= bin spacing ; n = total number of bins;
The mean wind speed for each location is obtained from the probability density function
using the equation.
u   ( pdf (u ) * u * delta u ,
u is the mean wind speed.
Distributions used for wind speed
In order to get a better approximation or fit to the pdf, the pdfs determined here have been
compared with two statistical distributions used commonly for wind resource assessment,
namely Rayleigh and Weibull probability distributions.
The simpler of the two is the Rayleigh distribution
f (u ) 
2u u / c 2
e
c2
0u ,
which has a single parameter c. The Weibull distribution
f (u ) 
k u
 
cc
k 1
e  u / c 
k
0u
has two parameters k, the shape parameter, and c, the scale parameter. The Rayleigh
distribution is actually a special case of the Weibull distribution with k = 2.
For both distributions, the minimum value of velocity is zero and the maximum value is
infinity.
5
WIND SPEED
Wind speed is the most important aspect of the wind resource, because there is a direct
relationship between wind speed and wind turbine power output. Currently, most of the
utility-based wind turbines are constructed with a hub height of 80 meters, so the focus of
this analysis is the wind resource at 80 meters above ground. The first step then is to
extrapolate the wind speed data collected (by NCDC) for 10 meters. Assuming that the
measurements were taken on a flat terrain, the 1/7th Power Law was used for the
extrapolation
u 80
 Z
 u 0 * 
 Z0




Where u80 stands for velocity of wind at 80 meters, u 0 stands for velocity at a reference
height (where the velocity is known), Z is height of interest which is 80 meters, Z0 is the
reference height, and α is the shear factor. For this analysis, α is assumed to be 1/7, which
is appropriate for neutral stability conditions. [7]
The extrapolated wind speed data was used to determine another important parameter
associated with wind resource –the Wind Power Density (WPD). The wind power
density, measured in watts per square meter, indicates how much power in the wind is
available at the site for conversion using a wind turbine. Wind speed generally increases
with height above ground. The wind power density was determined using
= ∗ ⍴ ∗ ( u80 )
where u 80 is the velocity at hub height determined using Power Law. The wind power
density equation implies that wind power density is proportional to the cube of wind
velocity.
AVERAGE MONTHLY WIND SPEED PROFILE
The average monthly wind speed profile shows the variation of the wind speed for every
month throughout the year. This representation of the wind resource will give significant
insights on the wind variation over a year. The profile is determined by averaging the
wind speed for each month of the monitoring period. The average wind speeds are plotted
for each month.
WIND ROSE
The principal purpose of developing a wind rose is to scope out potential locations for
siting wind turbines for commercial use. Obviously, the optimum turbine locations would
depend a great deal on the direction from which the wind blows at any particular site. The
wind rose is a polar plot that represents the percentage of time that the wind direction
falls within each sector of the compass. On the wind rose shown here, the length of each
bar is proportional to the fractional frequency at which that particular wind speed (and
below) was observed from that direction. Different colors on each bar indicate
percentages for different wind speeds.
6
ANNUAL ENERGY PRODUCTION
The Annual Energy Production was calculated by using the publically available power
curve data provided by the manufacturer. The manufacturer’s power curve was adjusted
for the density of air at each site. The next step was to interpolate the wind speeds with
the turbine manufacturer’s data, followed by computing the corresponding potential
power output. The annual energy production was determined using
=  ( pdf (u ) * ( P (u ) * delta u ) ∗ hours in the year ,
RESULTS
The analysis procedure discussed above was applied to the wind data obtained from
NCDC to assess the wind resource of Wyoming. The probability density function and
model distributions, namely the Rayleigh and Weibull distributions, generated from the
wind data are first described. This is followed by a discussion of the mean wind speed
and wind rose plots. Finally, the economic potential of a particular site, which will be of
most interest to utility wind developers, common public and other stakeholders, is
described using the results obtained for wind power density and potential annual energy
production.
The only site considered here is Laramie, WY. Resource reports of other sites are
presented in the appendices.
Probability density function (pdf) and model distributions
Figure 2 shows the probability density function and model distributions, both Rayleigh
and Weibull, for Laramie. The more the peak of the curves shift to the right, the windier
the location is. The vertical axis of this curve is related to the frequency with which a
particular wind speed occurs. The majority of the wind speeds are between 4 – 8 m/s.
Rayleigh is a single parameter model for the wind, whereas Weibull is a two parameter
model. Both Rayleigh and Weibull distributions give a good fit to the probability density
function. Weibull provides more of a conservative approach.
7
Figure 2- Probability density function and model distributions for Laramie, WY
The table 1 below is a comparison of the mean wind speed at 10 meters, 50 meters and 80
meters. The wind speed at 50 meters and 80 meters was extrapolated using the 1/7 Power
Law explained earlier. It is clear that the wind significantly increases with elevation
based on the power law.
Location
Laramie
10m
50m
80m
5.83
7.33
7.84
Table 1: Mean wind speed variation with respect to height for Laramie
Wind Power Density
The wind power density for Laramie at hub height is tabulated below in Table 2. Sites
having wind power density greater than 400 W/m2 are considered to be good. Higher
values imply that the site is an excellent source for wind power production. This implies
that this site is a good candidate for wind energy production.
LOCATION
Average wind speed at hub height (m)
WPD (W/m2)
LARAMIE
7.84
636.15
Table 2: Mean wind speed and Wind power density
Average monthly wind speed profile
Figure 3 below show the average monthly wind speed profile during the monitoring
period. The winter has the highest wind speeds, while Spring and Fall have slightly lower
winds. During summer there is a significant decrease in the wind speed.
8
Figure 3- Monthly wind speed profile averaged over six years
Wind Rose
Figure 4 shows the wind rose plotted for Laramie during the summer months June
through August. Figure 5 shows the wind rose for winter months, September through
May.
9
Figure 4 Wind rose graphics for Laramie, WY during
summer
Figure 5-Wind rose graphics for Laramie, WY during
winter
The typical direction of wind for Laramie obtained from the wind rose graphics above are
tabulated in Table 3. The individual wind rose for each month during the monitor period
is shown in Appendix 1.
Location
Laramie
Latitude
(degrees)
Longitude
(degrees)
Direction of
wind (Winter)
41.31
-105.58
W/SW
Direction
of wind
(Summer)
SW/NW
Table 3 Wind direction during Winter & Summer
Wind rose provides us with a visual representation of prevailing wind direction. The
majority of the time, the distribution of wind direction is strongly bipolar in nature with
wind blowing from the south-west or the north-west nearly all of the time. For the winter
months from September through May the winds blow from the south-west direction.
During the transition period from winter to summer the wind blows from the north-west
direction. This implies that siting wind turbines with these directions in mind would best
utilize the wind resource.
ANNUAL ENERGY PRODUCTION
In the context of wind energy production, what is of greatest interest is how the wind
characteristics are reflected in the actual performance of a particular wind turbine. In each
case, it depends on the shape of the wind turbine’s power curve (the amount of electric
power the turbine produces at each wind speed). The potential wind energy production
from a GE 1.5 MW xle wind turbine was evaluated. The GE 1.5 MW xle is typically
employed for commercial wind power generation, and over the years has been widely
used in USA [8].
10
The Annual Energy Production was calculated by using the publically available power
curve data provided by the manufacturer. The wind speed data was interpolated and the
corresponding potential power generation was determined. The results for Laramie are
shown in Table 4 below.
LOCATION
LARAMIE
Average
wind
speed at
hub
height
(m)
7.84
WPD
(W/m2)
AEP
2006
(MWhrs.)
AEP
2007
(MWhrs.)
AEP
2008
(MWhrs.)
AEP
2009
(MWhrs.)
AEP
2010
(MWhrs.)
AEP
2011
(MWhrs.)
AEP
2012
(MWhrs.)
636.15
3877.3
3769.1
3889.4
3698.4
3605.4
3796.8
2078.5
Table 4 Annual Energy Production Table
The results obtained for annual energy production are very promising. In addition, a
capacity factor for each of the above annual energy production values can be determined.
As an example, capacity factor for the year 2008 has been determined below
Capacity Factor = (3889.4) / (365*24*1.5) = 29.6%
The annual energy production of other chosen wind sites can be found in the appendix.
These values confirm that the south-east corner of Wyoming has one of the best wind
resources in the country. The capacity factor obtained using the airport wind data provide
a reasonable estimate of what was expected. Other wind sites like Laramie valley has a
capacity factor of 45% [9]. As part of this study, the maximum value for capacity factor
was 45%, obtained for Rawlins in the year 2011.
Wind resource reports
All the results were included in a standard wind resource report for each of the seven
locations. The Appendix contains these wind data reports for the monitoring period. The
report provides a summary of the wind resource capability of each location with
particular emphasis on the following items:
 pdf and model distributions,
 Mean wind speed and wind power density,
 Average monthly wind speed profile,
 Wind rose for each month averaged over six years, and
 Annual energy production from a GE 1.5 MW wind turbine.
CONCLUSION:
Wind resource assessment for Southeast Wyoming has been carried out using the wind
data obtained from National Climatic Data Center. One key aspect to keep in mind is that
11
the results obtained are based on the wind data collected for every hour. This implies that
the wind resource characterization provided here may not be an exact representation of
the site, but provides a very good estimate of what can be expected from the chosen site.
This study has established that, the South- East corner of Wyoming, especially Laramie,
Cheyenne, Casper, Rawlins and Rocksprings possess excellent wind resource that has
significant economic potential. Gillette and Sheridan have average wind resource
compared to other chosen sites. The resource reports attached in the appendix for the
chosen locations indicate that there is lot of potential for both wind energy and economic
development. These reports can be accessed by the general public.
The results of this study have several important implications. The probability density
function and model distributions prove that high quality wind blows steadily throughout
the Southeast corner of Wyoming. The high values obtained for wind power density is
another indication that Wyoming has one of the best wind resource in the country.
Average monthly wind speed profiles, and potential annual energy production for all the
locations is a good source of encouragement for both utility scale and residential scale
wind developers. In addition, the wind rose graphics provide a precise direction of the
wind. Other wind sites in the south-east corner of Wyoming may potentially have wind
resource than the sites chosen for this study. However, the potential wind resource at
other locations in southeastern Wyoming can only be investigated if data is collected for
those sites.
FUTURE WORK:
These obtained results are highly promising, but further work needs to be performed to
obtain a much more accurate representation of these chosen sites. Analysis done using
wind speed measurements at various heights would improve our knowledge in modeling
the winds more accurately. In addition, on-site measurements and wind data for every
five minutes would further verify the results presented herein. Although these are studies
that involve long time scales, this study goes a long way in creating an awareness both to
the public, and utility scale wind developers about the developable wind resource
possessed by Wyoming.
12
REFERENCES
[1] U.S. Department of Energy, Increasing Wind Energy’s contribution to U.S.
Electricity Supply; 20% Wind Energy by 2030, July 2008, Page 175 Table B-7
[2] Wind Powering America, Wyoming Wind map and Wind Resource Potential,
http://www.windpoweringamerica.gov/wind_resource_maps.asp?stateab=wy
[3] J. Naughton, J. Baker, and T. Parish, “Wind Diversity Enhancement of
Wyoming/California Wind Energy Projects”, Wyoming Infrastructure Authority report,
January 2013, Page 4
[4] U.S.–Land based and Offshore Annual Average Wind Speed at 80 meters
http://www.nrel.gov/gis/images/80m_wind/awstwspd80onoffbigC3-3dpi600.jpg
[5] “Wind Resource Assessment Handbook Final Report”, New York State Energy
Research and Development Authority, October 2010, Page 1-4
[6] National Climatic Data Center http://www.ncdc.noaa.gov/about-ncdc
[7] S. Wharton, J. K. Lundquist “Atmospheric Stability Impacts on Power Curves of
Tall Wind Turbines – An Analysis of a West Coast North American Wind Farm”
February 2010, Page 16
[8] General Electric news center, GE wind energy http://www.genewscenter.com/PressReleases/GE-s-15-000th-1-5-Megawatt-Wind-Turbine-Supports-Training-of-FutureWind-Technicians-at-Basin-Electric-s-Crow-Lake-Wind-Farm-2e63.aspx
[9] J. Naughton, J. Baker, and T. Parish, “Wind Diversity Enhancement of
Wyoming/Colorado Wind Energy Projects”, Wyoming Infrastructure Authority report,
April 2013, Page 21
Dr. Jonathan Naughton can be reached at naughton@uwyo.edu
Sriganesh Ananthanarayanan can be reached at sriganesh.ananthanarayanan@gmail.com
13
APPENDIX 1: WIND ROSE FOR EACH MONTH FOR LARAMIE
January
February
March
April
14
May
June
August
July
15
September
October
November
December
16
APPENDIX 2: WIND RESOURCE ASSESSMENT FOR CHEYENNE
Station ID: KCYS
Latitude:
41.14 degrees
Longitude: -104.81 degrees
Tower Type: ASOS
Sensor Heights: 10 m
Monitor Start: Jan 1, 2006
Monitor End: July 7, 2012
Wind speed is the most important aspect of the wind resource, because there is a direct relationship
between wind speed and wind turbine power output. A turbine with a 80m hub height was selected, so
now we are primarily interested at the wind resource at 80 m. The first step then is to intelligently
extrapolate the wind speed data collected at the lower heights.
* The mean wind speed at hub height (80m) is = 7.59 m/s
17
WIND POWER DENSITY
The wind power density, measured in watts per square meter, indicates how much energy is available
at the site for conversion by a wind turbine. Wind speed generally increases with height above ground.
* The Wind Power Density at hub height (80m) is = 538.12 W/m^2
PROBABILITY DENSITY FUNCTION
A probability density function is a curve associated with the wind data that shows the frequency with
which a particular wind speed occurs or the percentage of time that the wind spends at each speed.
This provides us with a very good description of nature of the wind in the location. This curve has also
been compared with two models used commonly for wind resource assessments, namely Rayleigh and
Weibull distributions.
WIND ROSE
A wind rose is a polar plot that represents the percentage of time that the wind direction falls within
each sector of the compass. Figures show an average wind rose for each month measured during the
period 1/2006 – 7/2012. These wind roses are based on the wind vane measurements at 10m, and
extrapolated to the hub height of 80 meters.
18
January
February
March
April
19
May
June
July
August
20
September
October
December
November
ANNUAL ENERGY PRODUCTION
In the context of wind energy production, what is of greatest interest is how the wind characteristics
are reflected in the actual performance of a particular wind turbine. There is not a simple or obvious
mathematical relationship between the energy contained in the wind and the amount of energy a given
wind turbine will produce. In each case, it depends on the shape of the wind turbine’s power curve
(the amount of electric power the turbine produces at each wind speed). In this section, we examine
the productivity of GE 1.5 MW
1) Annual Energy Production for 2006 = 3921.6 MW-hrs.
2) Annual Energy Production for 2007 = 3963 MW-hrs.
3) Annual Energy Production for 2008 = 4202.7 MW-hrs.
4) Annual Energy Production for 2009 = 3894 MW-hrs.
5) Annual Energy Production for 2010 = 3731.5 MW-hrs.
6) Annual Energy Production for 2011 = 4053.3 MW-hrs.
7) Annual Energy Production for 2012(data until 7/7/2012) = 1988.6 MW-hrs.
21
APPENDIX 3: WIND RESOURCE ASSESSMENT FOR CASPER
Station ID: KCPR
Latitude:
42.866 degrees
Longitude: -106.312 degrees
Tower Type: ASOS
Sensor Heights: 10 m
Monitor Start: Jan 1, 2006
Monitor End: July 7, 2012
Wind speed is the most important aspect of the wind resource, because there is a direct relationship
between wind speed and wind turbine power output. A turbine with a 80m hub height was selected, so
now we are primarily interested at the wind resource at 80 m. The first step then is to intelligently
extrapolate the wind speed data collected at the lower heights.
* The mean wind speed at hub height (80m) is = 7.53 m/s
22
WIND POWER DENSITY
The wind power density, measured in watts per square meter, indicates how much energy is available
at the site for conversion by a wind turbine. Wind speed generally increases with height above ground.
* The Wind Power Density at hub height (80m) is = 549.06 W/m^2
PROBABILITY DENSITY FUNCTION
A probability density function is a curve associated with the wind data that shows the frequency with
which a particular wind speed occurs or the percentage of time that the wind spends at each speed.
This provides us with a very good description of nature of the wind in the location. This curve has also
been compared with two models used commonly for wind resource assessments, namely Rayleigh and
Weibull distributions.
WIND ROSE
A wind rose is a polar plot that represents the percentage of time that the wind direction falls within
each sector of the compass. Figures show an average wind rose for each month measured during the
period 1/2006 – 7/2012. These wind roses are based on the wind vane measurements at 10m, and
extrapolated to the hub height of 80 meters.
23
January
February
March
April
24
May
June
July
August
25
September
October
November
December
ANNUAL ENERGY PRODUCTION
In the context of wind energy production, what is of greatest interest is how the wind characteristics are
reflected in the actual performance of a particular wind turbine. There is not a simple or obvious
mathematical relationship between the energy contained in the wind and the amount of energy a given wind
turbine will produce. In each case, it depends on the shape of the wind turbine’s power curve (the amount
of electric power the turbine produces at each wind speed). In this section, we examine the productivity of
GE 1.5 MW
1) Annual Energy Production for 2006 = 3592.8 MW-hrs.
2) Annual Energy Production for 2007 = 3323.9 MW-hrs.
3) Annual Energy Production for 2008 = 3416.3 MW-hrs.
4) Annual Energy Production for 2009 = 3360.7 MW-hrs.
5) Annual Energy Production for 2010 = 3396.2 MW-hrs.
6) Annual Energy Production for 2011 = 3664.9 MW-hrs.
7) Annual Energy Production for 2012(data until 7/7/2012) = 1754.1 MW-hrs.
26
APPENDIX 4: WIND RESOURCE ASSESSMENT FOR ROCK SPRINGS
Station ID: KRKS
Latitude:
41.58 degrees
Longitude: -109.2 degrees
Tower Type: ASOS
Sensor Heights: 10 m
Monitor Start: Jan 1, 2006
Monitor End: July 7, 2012
Wind speed is the most important aspect of the wind resource, because there is a direct relationship
between wind speed and wind turbine power output. A turbine with a 80m hub height was selected, so
now we are primarily interested at the wind resource at 80 m. The first step then is to intelligently
extrapolate the wind speed data collected at the lower heights.
* The mean wind speed at hub height (80m) is = 7.96 m/s
27
WIND POWER DENSITY
The wind power density, measured in watts per square meter, indicates how much energy is available
at the site for conversion by a wind turbine. Wind speed generally increases with height above ground.
* The Wind Power Density at hub height (80m) is = 684.81 W/m^2
PROBABILITY DENSITY FUNCTION
A probability density function is a curve associated with the wind data that shows the frequency with
which a particular wind speed occurs or the percentage of time that the wind spends at each speed.
This provides us with a very good description of nature of the wind in the location. This curve has also
been compared with two models used commonly for wind resource assessments, namely Rayleigh and
Weibull distributions.
WIND ROSE
A wind rose is a polar plot that represents the percentage of time that the wind direction falls within
each sector of the compass. Figures show an average wind rose for each month measured during the
period 1/2006 – 7/2012. These wind roses are based on the wind vane measurements at 10m, and
extrapolated to the hub height of 80 meters.
28
January
February
March
April
29
May
June
July
August
30
September
October
November
December
ANNUAL ENERGY PRODUCTION
In the context of wind energy production, what is of greatest interest is how the wind characteristics are
reflected in the actual performance of a particular wind turbine. There is not a simple or obvious
mathematical relationship between the energy contained in the wind and the amount of energy a given wind
turbine will produce. In each case, it depends on the shape of the wind turbine’s power curve (the amount
of electric power the turbine produces at each wind speed). In this section, we examine the productivity of
GE 1.5 MW
1) Annual Energy Production for 2006 = 3703.2 MW-hrs.
2) Annual Energy Production for 2007 = 3760 MW-hrs.
3) Annual Energy Production for 2008 = 3745.6 MW-hrs.
4) Annual Energy Production for 2009 = 3633.1 MW-hrs.
5) Annual Energy Production for 2010 = 3549 MW-hrs.
6) Annual Energy Production for 2011 = 3892.9 MW-hrs.
7) Annual Energy Production for 2012(data until 7/7/2012) = 1900.1 MW-hrs.
31
APPENDIX 5: WIND RESOURCE ASSESSMENT FOR SHERIDAN
Station ID: KSHR
Latitude:
44.79 degrees
Longitude: -106.95 degrees
Tower Type: ASOS
Sensor Heights: 10 m
Monitor Start: Jan 1, 2006
Monitor End: July 7, 2012
Wind speed is the most important aspect of the wind resource, because there is a direct relationship
between wind speed and wind turbine power output. A turbine with a 80m hub height was selected, so
now we are primarily interested at the wind resource at 80 m. The first step then is to intelligently
extrapolate the wind speed data collected at the lower heights.
* The mean wind speed at hub height (80m) is = 6.27 m/s
32
WIND POWER DENSITY
The wind power density, measured in watts per square meter, indicates how much energy is available
at the site for conversion by a wind turbine. Wind speed generally increases with height above ground.
* The Wind Power Density at hub height (80m) is = 410.04 W/m^2
PROBABILITY DENSITY FUNCTION
A probability density function is a curve associated with the wind data that shows the frequency with
which a particular wind speed occurs or the percentage of time that the wind spends at each speed.
This provides us with a very good description of nature of the wind in the location. This curve has also
been compared with two models used commonly for wind resource assessments, namely Rayleigh and
Weibull distributions.
WIND ROSE
A wind rose is a polar plot that represents the percentage of time that the wind direction falls within
each sector of the compass. Figures show an average wind rose for each month measured during the
period 1/2006 – 7/2012. These wind roses are based on the wind vane measurements at 10m, and
extrapolated to the hub height of 80 meters.
33
January
February
March
April
34
May
June
July
August
35
October
September
November
December
ANNUAL ENERGY PRODUCTION
In the context of wind energy production, what is of greatest interest is how the wind characteristics are
reflected in the actual performance of a particular wind turbine. There is not a simple or obvious
mathematical relationship between the energy contained in the wind and the amount of energy a given wind
turbine will produce. In each case, it depends on the shape of the wind turbine’s power curve (the amount
of electric power the turbine produces at each wind speed). In this section, we examine the productivity
GE 1.5 MW
1) Annual Energy Production for 2006 = 1672.6 MW-hrs.
2) Annual Energy Production for 2007 = 1545.4 MW-hrs.
3) Annual Energy Production for 2008 = 1652.2 MW-hrs.
4) Annual Energy Production for 2009 = 1640.2 MW-hrs.
5) Annual Energy Production for 2010 = 1436.8 MW-hrs.
6) Annual Energy Production for 2011 = 1646.1 MW-hrs.
7) Annual Energy Production for 2012(data until 7/7/2012) = 888.94 MW-hrs.
36
APPENDIX 6: WIND RESOURCE ASSESSMENT FOR GILLETTE
Station ID: KGCC
Latitude:
44.29 degrees
Longitude: -105.5 degrees
Tower Type: ASOS
Sensor Heights: 10 m
Monitor Start: Jan 1, 2006
Monitor End: July 7, 2012
Wind speed is the most important aspect of the wind resource, because there is a direct relationship
between wind speed and wind turbine power output. A turbine with a 80m hub height was selected, so
now we are primarily interested at the wind resource at 80 m. The first step then is to intelligently
extrapolate the wind speed data collected at the lower heights.
* The mean wind speed at hub height (80m) is = 7.44 m/s
37
WIND POWER DENSITY
The wind power density, measured in watts per square meter, indicates how much energy is available
at the site for conversion by a wind turbine. Wind speed generally increases with height above ground.
* The Wind Power Density at hub height (80m) is = 497.11 W/m^2
PROBABILITY DENSITY FUNCTION
A probability density function is a curve associated with the wind data that shows the frequency with
which a particular wind speed occurs or the percentage of time that the wind spends at each speed.
This provides us with a very good description of nature of the wind in the location. This curve has also
been compared with two models used commonly for wind resource assessments, namely Rayleigh and
Weibull distributions.
WIND ROSE
A wind rose is a polar plot that represents the percentage of time that the wind direction falls within
each sector of the compass. Figures show an average wind rose for each month measured during the
period 1/2006 – 7/2012. These wind roses are based on the wind vane measurements at 10m, and
extrapolated to the hub height of 80 meters.
38
January
February
March
April
39
May
June
July
August
40
September
October
December
November
ANNUAL ENERGY PRODUCTION
In the context of wind energy production, what is of greatest interest is how the wind characteristics
are reflected in the actual performance of a particular wind turbine. There is not a simple or obvious
mathematical relationship between the energy contained in the wind and the amount of energy a given
wind turbine will produce. In each case, it depends on the shape of the wind turbine’s power curve
(the amount of electric power the turbine produces at each wind speed). In this section, we examine
the productivity of GE 1.5 MW
1) Annual Energy Production for 2006 = 3402.7 MW-hrs.
2) Annual Energy Production for 2007 = 3337.8MW-hrs.
3) Annual Energy Production for 2008 = 3569.4 MW-hrs.
4) Annual Energy Production for 2009 = 3370.8 MW-hrs.
5) Annual Energy Production for 2010 = 3301.8 MW-hrs.
6) Annual Energy Production for 2011 = 3639.4 MW-hrs.
7) Annual Energy Production for 2012(data until 7/7/2012) = 1761.5 MW-hrs.
41
APPENDIX 7: WIND RESOURCE ASSESSMENT FOR RAWLINS
Station ID: KRWL
Latitude:
41.805 degrees
Longitude: -107.2 degrees
Tower Type: ASOS
Sensor Heights: 10 m
Monitor Start: Jan 1, 2006
Monitor End: July 7, 2012
Wind speed is the most important aspect of the wind resource, because there is a direct relationship
between wind speed and wind turbine power output. A turbine with a 80m hub height was selected, so
now we are primarily interested at the wind resource at 80 m. The first step then is to intelligently
extrapolate the wind speed data collected at the lower heights.
* The mean wind speed at hub height (80m) is = 17.68 m/s
42
WIND POWER DENSITY
The wind power density, measured in watts per square meter, indicates how much energy is available
at the site for conversion by a wind turbine. Wind speed generally increases with height above ground.
* The Wind Power Density at hub height (80m) is = 4262.14 W/m^2
PROBABILITY DENSITY FUNCTION
A probability density function is a curve associated with the wind data that shows the frequency with
which a particular wind speed occurs or the percentage of time that the wind spends at each speed.
This provides us with a very good description of nature of the wind in the location. This curve has also
been compared with two models used commonly for wind resource assessments, namely Rayleigh and
Weibull distributions.
WIND ROSE
A wind rose is a polar plot that represents the percentage of time that the wind direction falls within
each sector of the compass. Figures show an average wind rose for each month measured during the
period 1/2006 – 7/2012. These wind roses are based on the wind vane measurements at 10m, and
extrapolated to the hub height of 80 meters.
43
January
February
March
April
44
May
June
July
August
45
September
October
November
December
ANNUAL ENERGY PRODUCTION
In the context of wind energy production, what is of greatest interest is how the wind characteristics are
reflected in the actual performance of a particular wind turbine. There is not a simple or obvious
mathematical relationship between the energy contained in the wind and the amount of energy a given wind
turbine will produce. In each case, it depends on the shape of the wind turbine’s power curve (the amount
of electric power the turbine produces at each wind speed). In this section, we examine the productivity
GE 1.5 MW xle wind turbine
1) Annual Energy Production for 2006 = 4418.8 MW-hrs.
2) Annual Energy Production for 2007 = 3768.7 MW-hrs.
3) Annual Energy Production for 2008 = 5471.1 MW-hrs.
4) Annual Energy Production for 2009 = 5282 MW-hrs.
5) Annual Energy Production for 2010 = 5204.9 MW-hrs.
6) Annual Energy Production for 2011 = 5882 MW-hrs.
7) Annual Energy Production for 2012(data until 7/7/2012) = 2930.1 MW-hrs.
46