OWPI’s Wind Assessment for Oklahoma

OWPI’s Wind Assessment for Oklahoma
Wind speed is fundamental to a wind turbine’s
productivity. Because the amount of energy available in the
Tim Hughes
wind is proportional to the cube of the wind speed, small
The Environmental Verification
changes in wind speed result in relatively large changes in
and Analysis Center
wind power. Over varied terrain, it is not unusual for the wind
University of Oklahoma
speed to vary significantly over short distances.
3200 Marshall Ave, Suite 110
Norman, OK 73072-8032
Consequently, knowledge of the variability of the wind over
Phone: (405) 447-8412
time and space is critical to developers of wind farms.
E-mail: [email protected]
The Pacific Northwest Laboratory in conjunction with
the U.S. Department of Energy has established a wind power
classification system. The classification provides a practical reference for determining the
resource potential based upon an average annual wind speed. Average annual wind speeds are
categorized into seven wind power classes. Wind class 1 denotes very light winds (poor wind
resource), while larger class numbers indicate stronger winds.
One of Oklahoma Wind Power Initiative’s many goals is to develop high-resolution wind
power maps for Oklahoma at heights of 10 and 50 meters. Prior to this study, the best resource
map available for Oklahoma was developed by the U.S. Department of Energy and Pacific
Northwest Labs in 1987. Figure A shows their resource map for Oklahoma, which has
Longitude by 1/4 deg.
Latitude, or approximately 33
km (20 miles) in each
Since 1993, Oklahoma
has become home to one of
the premier surface weather
networks in the world - the
Oklahoma Mesonet.
114 stations and 5-minute
atmospheric data, this network
offers an opportunity to create
accurate high-resolution wind
Figure A. U.S. Department of Energy, Pacific Northwest
power density (WPD) maps to
Laboratory estimation of wind resources in Oklahoma,
1987. 1 – poor; 2 – marginal; 3 – fair; 4 – good.
placement of both large and
small wind turbines.
For More Information contact:
Selecting Mesonet stations to be used for Wind Assessment
Mesonet stations were sited to present the best overall estimate of regional environmental
conditions, but not all stations were optimally located to monitor wind resource. Stations with
poor exposure to the wind must be excluded from the assessment, because data from these sites
would likely bias the wind resource. The following information was used to evaluate the wind
exposure (i.e., fetch conditions) for each Mesonet station:
Version Date: August 2002
Panorama photos from the Oklahoma Mesonet Web site (okmesonet.ocs.ou.edu),
1-m resolution digital orthophotos (aerial photos), and
a 200-m resolution Land Use/Land Cover (LULC) grid derived from the USDA/NRCS
“poor”, “fair”, “good”, and
subjective criteria.
example, stations with short,
consistent vegetation cover
and no obstructions in the
immediate vicinity of the site
were rated as having excellent
or good fetch conditions.
Figure B. 79 Mesonet stations with “good” to “excelStations with tall, inconsistent
ent” fetch conditions.
vegetation cover or anomalous
vegetative cover too close to
the site (e.g. a wind break of trees) in prevailing wind directions, were rated as having fair to
poor fetch conditions. Of the 114 Mesonet stations, 79 sites were classified as having good or
excellent fetch conditions (Fig. B).
Analyses Performed
Computer models provide an objective method for estimating the effects of terrain on
wind flow and for interpolating wind data to locations where data does not exist. An exact
mathematical description of the wind flow across the terrain is provided by the Navier-Stokes
equations. Because of the great complexity of the equations, they require large amounts of input
data and are extremely difficult (often impossible) to solve. Furthermore, it is uncertain whether
the large efforts required to run the complex model are rewarded with significantly more
accurate wind simulations than those obtained from simpler models.
For projects like OWPI, the hardware (e.g., supercomputers) and input data necessary to
solve the Navier-Stokes equations is cost prohibitive. Instead, OWPI investigated the use of
simple models that can be run on personal computers. Currently, two categories of simple
analytical models exist for use in wind resource assessment: mass consistent and Jackson-Hunt.
Mass consistent models conserve mass, while Jackson-Hunt models conserve mass and attempt
to conserve momentum (approximations made to the Navier-Stokes equations). In general, mass
consistent models are used for surveying large areas, while Jackson-Hunt models have been used
more extensively for micrositing. Accordingly, the decision was made to use a mass consistent
model for the state assessment.
Because of the large number of input data-points, a modeling technique employing
“neural networks” was also considered a credible tool for wind assessment. Neural nets are
empirical models often used for statistical analysis and data modeling. They provide an
alternative to conventional analytical techniques for solving nonlinear problems. To our
knowledge, neural networks have never been used for the purpose of wind assessment.
Nonetheless, the use of neural nets in other fields of study has shown great promise.
Mass Consistent Model
WindMapTM, a software program by Brower & Company, uses a mass-conserving model
for predicting and mapping the wind over an area. The software has gained acceptance in the
wind industry as it has been used by several other states (e.g., Iowa, Massachusetts, Minnesota,
etc.) in the assessment of their wind resources.
The model can ingest four types of data: surface, elevation, roughness, and upper-air
data. For this project, the following data sets were used as inputs into the model:
Wind Speed and Direction
Mean wind speeds for 16 compass directions and a Weibull parameter were entered for
each Mesonet station incorporated into the model.1 A Weibull parameter describes the shape of
the frequency distribution of
wind speeds.
Of the 79
Mesonet stations with good to
excellent fetch conditions,
wind data for 76 stations were
included in the model. Two
stations were excluded from
the group because both
stations were not in existence
for the entire period, 1994 to
2000. A third station was
excluded because of its close
proximity to another Mesonet
Figure C. Locations of the 89 Mesonet stations used to
station. In addition to the 76
initialize WindMap.
stations, 13 Mesonet stations
with fetch ratings of fair were
included in the model. These stations were located in the eastern portion of the state in areas that
lacked stations with good or excellent fetch conditions (Fig. C).
WindMap requires a reference station to be chosen from one of the 89 stations. A
reference station defines the directional frequencies of the wind. These frequencies are often
displayed on a graph called a wind rose. An average wind rose was computed for Mesonet
stations with good to excellent fetch conditions. Of these Mesonet stations, ARNE (near Arnett,
OK) was determined to have a wind rose most comparable to the average (Fig. D). Hence,
ARNE was chosen as the reference station for the model.
Topography DEM Data
Topography information for the entire state was obtained from the Digital Atlas of
Oklahoma produced by the U.S. Geological Survey. The digital atlas provides a 60-m resolution
Mean wind speeds, Weibull parameters, and wind roses were all calculated using seven years worth of 5-minute,
scalar-average wind speeds (5-minute average of 100 counts taken at 3-second intervals).
Digital Elevation Model (DEM)
derived from 1:100,000 scale
digital topographic maps of
Land Use/Land Cover Data
"roughness length" represents a
height above ground below which
friction from obstacles (e.g.,
effectively stifles air currents. In
scientific terms, it is the height
above ground at which the neutral
wind profile extrapolates to zero.
The roughness values
landuse/landcover (LULC) grid
model put together by the GAP
Analysis Program (GAP). GAP’s
LULC grid describes specific land
Figure D. Wind rose for the Arnett Mesonet Station.
at a
Percentages represent averages over the period, 1994
resolution of 30 m. Roughness
to 2000.
values were assigned to these
specific land use practices and
natural vegetation covers. For example, urban areas were assigned roughness values of 1.0 while
agricultural regions were assigned values closer to 0.03.
Results from WindMap
Figure E illustrates the results from WindMap’s output at 50 meters. Evident in this
figure is the good to excellent wind resource in the western 1/3 of the state. A significant
amount of Class 5 winds exist near the town of Woodward in the northwest portion of the state.
This area has been under study and OWPI believes that it will be one of the first areas developed.
Other significant Class 5 areas include Beaver County (in the Panhandle) and the Wichita
Mountains/Slick Hills area located in the southwest. The tremendous wind resource in the
western half of the state is attributable to its geographic proximity to the Rocky Mountains and
Gulf of Mexico, as well as small roughness values due to a lack of trees or other tall vegetations.
The Class 3 areas are located in the flattest portions of the state between the arid, high
plains of the west and the rugged, woodlands in the east. Interestingly, the transition between
Classes 2 and 3 areas follows along the boundary between the “Cross-Timbers” and the tall grass
prairies of the southern Great Plains. The Cross Timbers consist of post oak and blackjack oak
woodlands that form the western boundary of deciduous forests in Texas, Oklahoma, and
southeastern Kansas.
Further east into the Cross Timbers, the woodlands become denser and the terrain
becomes more rugged. Consequently, the wind resources become highly variable (i.e., higher
Figure E
Wind Resource at 50 meters (164 ft) AGL
WindMap[TM] Computer Model
Creation Date: 03/22/02
terrain has Class 2 and even 3 winds while lower terrain has very little wind resource).
WindMap was able to emphasize the terrain’s affects on the wind resource for some of the more
predominant terrain features such as the Ouachita Mountains (southeast Oklahoma) and the
Arbuckle Mountains (south central Oklahoma). Unfortunately, these features are barely evident
in the figure provided. [Note: Mountains in Oklahoma typically have terrain relief of less than
1500 feet.]
Empirical Model
Neural networks represent a relatively new method for using computers to solve
problems. Specifically, a neural network or “neural net” is a linked assembly of processors or
processing elements whose interconnections are similar to those between neurons in a brain. By
a process of adaptation, the
computer is able to learn
from a set of training
Thus, neural
networks are often viewed as
intelligence (AI).
The neural network
(NN) model was "trained"
using 50 of the 76 Mesonet
stations with good and
Figure F. NN model developed using two sets of Mesonet
excellent fetch conditions.
stations – a Training group and a Control group.
The remaining 26 stations
were set aside as the control
group (Fig. F). The model incorporated the following information about the Mesonet stations:
• calculated wind power density,
• elevation,
• terrain exposure (or relative elevation)
• and roughness length (vegetative influence).
Calculated Wind Power Density
Using 7 years of data from Mesonet stations, wind power density (WPD) values were
calculated for the 10-meter level according to the equation below:
* ∑ ( ρ i * vi 3 )
2 * n i =1
where ρ is air density and v is wind velocity (scalar-averaged wind speed) for a particular station.
The above equation was applied to all valid five-minute data (n) for the time period. For each
station, n was approximately 735,000. Air density was explicitly calculated using temperature
and pressure data from the station.
Terrain Exposure
“Terrain Exposure” (also often referred to as "relative elevation") is defined as the
distance a point sits above or below the average elevation of a surrounding area. The
surrounding area can be defined in numerous ways. Some models have calculated terrain
exposure relative to a circular area with some diameter, typically 10 to 20 km, depending on the
scale of the region being modeled. For this model, terrain exposure values were calculated
relative to north and south "pie-wedge" areas. This method was based on the assumption that
surface terrain and vegetation characteristics in the north and south directions have the greatest
impact on the wind resource.
To test this assumption, an average wind-energy rose diagram was developed with data
collected from sites with excellent and good fetch ratings (Fig. G). The figure displays the mean
percent time and mean percent energy
of the wind in 16 compass directions
over the seven-year period. From the
wind rose, it was determined that the
wind direction was from the NW to
NE (inclusive) and SE to SW
(inclusive) sectors 77% of the time,
and more importantly 89% of the wind
energy was from these north and south
Furthermore, when one
considers that winds from east and
west sectors tend to be light (not
strong enough to turn large turbines),
the percentage of realizable wind
power from the north and south sectors
becomes even more significant.
Consequently the development and use
of the "wedge-method" appears
To determine the terrain
exposure inputs to the model, the
Figure G.
Average wind-energy rose for
average elevations were calculated
Mesonet stations with 'good' or 'excellent' fetch
using the north and south “pieconditions.
wedges” with 10-km (6.2-mi) radials.
The north wedge subtends the
northeast to northwest (Cartesian coordinates 34° to 146°), and the south wedge subtends the
southwest to southeast (Cartesian coordinates 214° - 326°). The degree readings correspond to
Cartesian degree coordinates rather than compass degrees, since ArcView (the GIS software
used) requires the former as inputs. ArcView "spatial analysis" tools were used to calculate the
average elevations in these pie-wedges. North and south terrain exposure values were then
determined for each Mesonet station used in the model by subtracting the average elevation from
the actual elevation at a site. A positive number represents a site that sits above an adjacent
wedge area on average; a negative number represents a site that sits below an adjacent wedge
area on average.
Results from NN Model
For the initial wind resource map, the NN model’s 50-m WPD estimates appear
conservative. Based on data collected from a single 40-m tower in northwest Oklahoma (near
Buffalo), the wind resource for that area should be closer to 500 W m-2 than the model predicted
386 W m-2. The model also appears to underestimate the part of the state with more vegetation
(roughly, east of I-35).
The underestimation of WPD at a height of 50 meters most likely is the result of the
extrapolation technique (power law profile) used on the 10-m model output. The exponent, m,
used in the power law is dependent on both the surface roughness and stability. Consequently, m
varies across the state, but how and by what amount? Unfortunately, Oklahoma lacks a network
that measures winds at heights above 100 feet, thus the mapping of m would be purely
Alternatively, a correction factor was applied to the entire map in order to reduce
underestimation. The 10-m WPD grid was multiplied by a correction factor of 1.23. The
correction factor was based on discrepancies between predicted versus actual wind power
densities at 10 meters (i.e., the slope of the trend is increased to equal one). The result was a
map with significantly more Class 3, 4, and 5 winds (Fig. H).
The neural network model predicts a good to excellent wind resource for the ridges and
hills in Western Oklahoma at the 50-meter level. The model output emphasizes the topography
across the state for two reasons. First, the neural network model has a relatively fine resolution
of 60 meters. The fine resolution allows the model to recognize terrain features that might not be
evident with larger resolutions (e.g., the current national resource atlas has a resolution of
roughly 20 miles across Oklahoma). Second, topography is emphasized because many of the
model inputs are directly or indirectly related to elevation.
Model Comparison
Based on DOE/PNL’s assessment (Fig. A), both the NN and WindMap models
successfully represented the large-scale variation of wind energy across the state. That is, wind
power increases from east to west for all assessments. As expected, the transition from east to
west follows the general rise in elevation across the state. However, the model output from the
NN and WindMap provides much finer resolution than PNL’s map. The increased resolution in
an assessment map is important to the wind power industry for which small changes in wind
power translate to millions of dollars.
In spite of this general agreement, large discrepancies are evident between DOE/PNL’s
map and OWPI’s maps. First, DOE/PNL’s map classifies the Ouachita Mountains in southeast
Oklahoma as having Class 4 winds, while the map produced from WindMap categorizes the
majority of the region as Class 1 or 2. Unfortunately, verification data does not exist for these
mountains; hence, two different conclusions are drawn.
A side-by-side comparison of OWPI’s models shows distinct contrasts between the two
models. The NN model shows greater detail in local variations of WPD than WindMap.
WindMap produced broad areas of winds with similar WPDs, while the NN model’s output
closely adheres to changes in topography, hence more local variation in WPDs. Some of the
detail can be attributed to the better resolution of the NN model (i.e., 60 vs. 372 m).
WPD estimates from both models appear to be conservative for certain areas (e.g.,
ridges). To determine the accuracy of the model's WPD estimates and to improve the output,
more wind data is needed from heights around 50 meters. Recently, OWPI help put into
Figure H
Wind Resource at 50 meters (164 ft) AGL
Neural Network Computer Model
Creation Date: 03/22/02
operation two more tall towers. Future plans are to instrument more towers. The locations of
these towers will likely be in areas with the greatest potential for development of wind energy,
but attempts will be made to have these towers spread evenly across the state.
Until further validation data from tall towers are obtained, the current results are OWPI’s
best estimates of the wind resource across Oklahoma. The NN model’s output may be viewed
in conjunction with WindMap’s output to provide a good idea of the energy potential for most
locations in Oklahoma. More specifically, the 50-m output from WindMap would be used to
provide a first estimate of the wind resource, because these results were derived from physical
equations and did not require the application of a correction factor. The 10 or 50-m WPD map
from the NN model could be used to adjust the estimate for a local area. For example, the NN
maps clearly show significant hills and ridges for the west half of Oklahoma. If the land in
question is located on one of these features, WindMap’s estimate may be viewed as conservative.
For more information, contact:
Tim Hughes, Project Director
The Environmental Verification and Analysis Center
The University of Oklahoma
3200 Marshall Ave, Suite 110
Norman, OK 73072-8032
Phone: (405) 447-8412
E-mail: [email protected]