NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Technical and Economic 

Feasibility Study of Utility-Scale 

Wind at the Doepke-Holliday 

Superfund Site

A Study Prepared in Partnership with the 

Environmental Protection Agency for the 

RE-Powering America’s Land Initiative: 

Siting Renewable Energy on Potentially 

Contaminated Land and Mine Sites

Joseph Owen Roberts and Gail Mosey

Produced under direction of the U.S. Environmental Protection 

Agency (EPA) by the National Renewable Energy Laboratory (NREL) 

under Interagency Agreement IAG-09-1751 and Task No. 

WFD4.1001.

Technical Report

NREL/TP-7A30-57674

May 2013
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Technical and Economic 

Feasibility Study of UtilityScale Wind at the DoepkeHolliday Superfund Site 

A Study Prepared in Partnership with the 

Environmental Protection Agency for the 

RE-Powering America’s Land Initiative: 

Siting Renewable Energy on Potentially 

Contaminated Land and Mine Sites

Joseph Owen Roberts and Gail Mosey

Prepared under Task No. WFD4.1001

Technical Report

NREL/TP-7A30-57674

May 2013
NOTICE

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express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

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or favoring by the United States government or any agency thereof. The views and opinions of authors 

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

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iii

Contacts

National Renewable Energy Laboratory U.S. Environmental Protection Agency

Gail Mosey, project leader

15013 Denver West Parkway 

Golden, CO 80401-3305 

Phone: 303-384-7356 

Fax: 303-384-7411 

Email: gail.mosey@nrel.gov 

Lura Matthews, EPA program manager

Office of Solid Waste and Emergency Response

Center for Program Analysis

Phone: 202-566-2539 

Email: matthews.lura@epa.gov

Joseph Owen Roberts, principal investigator

15013 Denver West Parkway 

Golden, CO 80401-3305 

Phone: 303-384-7151 

Fax: 303-384-7411 

Email: joseph.roberts@nrel.gov 
iv

Acknowledgments

We would like to thank Shelley Brodie, Lura Matthews, Shea Jones, and Jessica Trice of the

U.S. Environmental Protection Agency (EPA); Katie Brown, AAAS Science & Technology 

Policy fellow hosted by EPA; Mick Cossairt of Deffenbaugh Industries; and Travis Daneke of 

Kansas Department of Health and Environment for assisting with the site visit to the Johnson 

County Landfill.
v 

Executive Summary

The U.S. Environmental Protection Agency (EPA), in accordance with the RE-Powering 

America’s Land initiative, selected the Johnson County Landfill in Shawnee, Kansas, for a 

feasibility study of renewable energy production. Citizens of Shawnee, city planners, and site 

managers are interested in redevelopment uses for landfills in Kansas that are particularly well 

suited for wind turbines. The purpose of this report is to assess the Johnson County Landfill for 

possible grid-tied wind electrical generators (wind turbines) and to estimate the cost, 

performance, and site impacts of various wind turbines. In addition, the report outlines financing 

options that could assist in the implementation of a turbine. 

The feasibility of wind turbines installed on landfills is highly impacted by the available area for 

the turbine, wind resource, landfill operating status, landfill cap status, distance to transmission

and distribution lines, and distance to major roads. The Johnson County Landfill is in a suitable

area to have a utility-scale turbine, and the wind resource at the Doepke-Holliday (Doepke) 

Superfund site is appropriate for wind power generation. The Doepke Superfund site is part of 

the Johnson County Landfill but is a closed cell (a portion of the landfill that is no longer being 

filled and is capped), with other cells of the Johnson County Landfill still in use. The findings 

from this report can also be applied to other landfills in the surrounding area and through North 

America, depending on wind resource, incentives, siting constraints, and power prices. 

Installing wind turbines on landfills is a unique situation and requires penetration of the landfill 

cap. A landfill is typically sealed from water contamination by covering the refuse with clay or 

other non-permeable material and soil. The penetration of the landfill cap for the purpose of 

installing a wind turbine foundation has been successfully accomplished multiple times.1 The 

type of foundation required for a wind turbine is site-specific and typically requires vertical piles 

that penetrate the bedrock beneath the landfill.2 As such, a lined landfill presents additional 

technical concerns, and a successful implementation has not yet been attempted. The Doepke 

Superfund site is not lined, so the technical feasibility of installing a megawatt-scale turbine at 

the site is achievable. The additional cost of this foundation compared to a typical greenfield 

installation is dependent on the size of the turbine. 

The economics of the potential turbines were analyzed using the current Kansas City Power and 

Light (KCP&L) electric rate of $0.08391/kWh (average of the summer rate of $0.09469/kWh 

and the winter rate of $0.07312/kWh, with a 10% escalation)

3

 and calculations assume some 

federal incentives such as the production tax credit (PTC) and 5-year accelerated depreciation 

(MACRS). The avoided cost scenario assumes only a 5% annual escalation rate in the avoided 

cost, and thus sale price of energy, and only $0.03/kWh. There currently are not any state or 

 

1 Two such examples include Hull, Massachusetts (http://www.hullwind.org/), and Kingston, Massachusetts 

(http://www.kingstonmass.org/index.asp?SEC=EED177CD-B486-4F21-B2C7-3C545A7A0356&DE=4D0C9B80-

B70C-4FA0-BEE9-C711FA2AADFF&Type=B_BASIC). 2 For more information, see http://www.mass.gov/dep/energy/hull2.pdf. 3 For more information, see the Section 6 of this report. 
vi 

utility incentives offered for commercial wind power systems in Kansas for megawatt-scale 

turbines with the possible ownership scenarios.

4

 

The economics of a turbine on the Doepke site depend greatly on the cost of electricity. Based on 

past electric rate increases in Kansas, the current rate could increase to $0.10/kWh or higher in a 

relatively short amount of time. Table ES-1 summarizes the turbine performance and economics 

of potential systems that would use all available areas that were surveyed at the Doepke site. The 

table shows the annual energy output from the system along with the number of average 

American households that could be powered from such a system. The table lists results assuming 

the current electric rate of $0.08391/kWh and shows results assuming a hypothetical rate 

increase to $0.10/kWh. In the coming years, increasing electrical rates and potential increased 

demand for clean power will continue to improve the feasibility of implementing turbines at 

landfill sites. 

 

4 “Database of State Incentives for Renewables & Efficiency.” U.S. Department of Energy, 2012. 

http://www.dsireusa.org/incentives/index.cfm?re=0&ee=0&spv=0&st=0&srp=1&state=KS. 
vii 

Table ES-1. Turbine Performance and Economics, Including Job Creation Estimates5

Annual Cost Savings 

($/year)

Payback Period

(years)

Wind 

System 

Sizea

(kW) 

Annual 

Output

(kWh/year)

Number of 

Houses 

Poweredb

With Current 

Electrical Cost

With Rate 

Increase

Annual 

O&M 

($/year)

System 

Cost 

without 

Incentives 

($)

Net Meter 

Current 

Electrical 

Cost c

Avoided 

Costd

Estimated 

Potential 

Construction 

Job-Yearse

Estimated 

Potential 

Maintenance

Jobsf

IEC Class III Wind Turbine, 80-m Hub Height, 100-m Rotor Diameter

1,620 5,098,607g 462 $456,470 $544,000 $70,000 $5,440,000 10.4 >20 yr 59 0.4 a Data assume a maximum usable area of solely the Doepke site. b Number of average American households that could hypothetically be powered by the turbine when assuming 11,040 kWh/year/household.

6

c Assumes 10 year PTC, 5 yr MACRS, $0.08391/kWh, 10% utility rate escalation, $70,000 annual O&M, 7% debt interest, d Assumes 10 year PTC, 5 yr MACRS, $0.03/kWh, 5 % utility rate escalation, $70,000 annual O&M, 7% debt interest, e Job-years supported by this project capital investment, including direct, indirect, and induced jobs. f Permanent jobs (direct, indirect, and induced) sustained as a result of operation and maintenance (O&M) of the system. g Annual output corrected for long-term annual average, including 15% losses. 

 

5 This feasibility study assumes the ability to sell electricity at and above current commercial consumer rates which is very unlikely to be realistic in this case. Most likely 

only the avoided cost of electricity would be paid which is substantially less than the assumed rates. 6 “Frequently Asked Questions.” U.S. Energy Information Administration. Accessed Oct. 22, 2010: 

http://www.eia.doe.gov/ask/electricity_faqs.asp#electricity_use_home. 
viii

Table of Contents

1 Study Location...................................................................................................................................... 1

2 Wind Turbine Technology ................................................................................................................... 3

2.1 Wind Characteristics ........................................................................................................................ 3

2.2 Wind Turbines ................................................................................................................................. 5

2.3 Wind Power History/Background .................................................................................................... 6

3 Potential Turbine Locations ................................................................................................................ 7

3.1 Setbacks ........................................................................................................................................... 7

3.2 Site-Specific Considerations ............................................................................................................ 8

3.3 Permitting ......................................................................................................................................... 8

4 Site-Measured Wind Resource.......................................................................................................... 13

4.1 Utilization of Existing Communication Tower .............................................................................. 13

4.2 Data Recovery and Validation ....................................................................................................... 16

4.3 Wind Resource Assessment Summary .......................................................................................... 17

4.4 Long-Term Data Adjustment ......................................................................................................... 27

4.5 Energy Production Estimates ......................................................................................................... 29

5 Economics and Performance............................................................................................................ 31

5.1 Assumptions and Input Data for Analysis ..................................................................................... 31

5.2 Incentives and Financing Opportunities ........................................................................................ 31

5.3 Wind Project Employment ............................................................................................................. 32

6 Avoided Cost Electric Rate................................................................................................................ 34

7 Conclusions and Recommendations ............................................................................................... 35

Appendix A. Measure-Correlate-Predict Matrix Method ....................................................................... 36

1. Build the Joint Probability Distribution ........................................................................................... 36

2. Build the Percentile Time Series ...................................................................................................... 36

3. Synthesize Percentile Time Series Data ........................................................................................... 37

4. Transform Percentile Time Series Into Target Wind Speeds ........................................................... 37

Options ................................................................................................................................................. 38
ix

Table of Figures

Figure 1. U.S. national wind resource map ......................................................................................4

Figure 2. Kansas 80-m annual average wind speed map .................................................................4

Figure 3. Modern utility-scale wind turbines ...................................................................................5

Figure 4. Potential utility-scale turbine construction areas ..............................................................7

Figure 5. Views of the feasible area for a utility-scale wind turbine at the Doepke site .................8

Figure 6. FAA potential impact areas for the Doepke site ..............................................................9

Figure 7. Long-range radar impacts for the Doepke site ...............................................................10

Figure 8. NEXRAD radar impacts for the Doepke site .................................................................11

Figure 9. Military operation impacts for the Doepke site ..............................................................12

Figure 10. Riggers installing anemometry on the Doepke tower ..................................................14

Figure 11. Tower interference on anemometry ..............................................................................17

Figure 12. Seasonal wind resource variation .................................................................................19

Figure 13. Wind shear profile ........................................................................................................21

Figure 14. Diurnal shear variation .................................................................................................22

Figure 15. Diurnal wind resource variation ...................................................................................23

Figure 16. Wind speed frequency distribution ...............................................................................23

Figure 17. Wind energy rose ..........................................................................................................24

Figure 18. Average measured turbulence intensity at 81.2 m ........................................................25

Figure 19. Average turbulence intensity from the north ................................................................26

Figure 20. Average turbulence intensity from the south................................................................27

Figure 21. Long-term reference station locations ..........................................................................28

Figure 22. Levelized cost of energy comparison of modern turbines with historical 

performance .............................................................................................................................30

List of Tables

Table ES-1. Turbine Performance and Economics, Including Job Creation Estimates................ vii

Table 1. Data Logger Configuration and Tower Information ........................................................15

Table 2. Wind Speed Sensor Summary Statistics ..........................................................................18

Table 3. Wind Resource and Turbine Performance Annual Average Variation ...........................29

Table 4. Doepke Wind Project Estimated Job Creation ................................................................33

Table 5. Wind Project Performance and Economics Assuming Avoided Cost .............................34
1 

1 Study Location 

The Johnson County Landfill is located in Shawnee, Kansas, which is located to the west of the 

major metropolitan area of Kansas City, Missouri. As of the 2000 census, Shawnee had a 

population of approximately 62,000 people. It has a humid climate that is characterized by large 

seasonal temperature swings. The winters commonly experience temperatures below freezing 

with moderate snowfall. The summers are humid and commonly experience temperatures around 

90oF. The two electric utility companies serving the Johnson County Landfill are Kansas City 

Power and Light (KCP&L) and Westar Energy. 

Under the RE-Powering America’s Land initiative, the U.S. Environmental Protection Agency 

(EPA) provided funding to the National Renewable Energy Laboratory (NREL) to support a 

feasibility study of wind energy generation at the Doepke-Holliday (Doepke) Superfund site. The 

Doepke Superfund site is part of the Johnson County Landfill, which is still in operation today. 

Currently, there are 42 acres of capped landfill area at the Doepke site that can potentially be 

used for a megawatt-scale wind turbine. Other areas in the Johnson County Landfill that are 

capped or will possibly be capped in the future could also offer an economy of scale for a wind 

project, as the purchase of more than one turbine can produce a more cost-effective project in 

general. The Doepke site includes a former municipal and industrial waste landfill located on an 

80-acre parcel of land on the southern bluffs of the Kansas River in Shawnee. The site initially 

operated as a residential trash disposal service starting in 1952. During the 1950s and 1960s, it 

received residential, commercial, and industrial waste. The site closed in 1970 and was added to 

the EPA Superfund National Priorities List in 1983. In 1996, the cap was installed and approved 

by EPA. Due to the presence of contaminants, landfill sites have limited populated 

redevelopment potential. Therefore, renewable energy generation is a viable reuse. 

One very promising and innovative use of closed landfills is to install wind turbines. Landfills 

are typically located in areas away from residential zones and can also present the advantage of 

being higher than the surrounding area, thus offering access to a more energetic wind resource 

than the surrounding areas. Wind turbines can generate revenue on a landfill site that might

otherwise go unused. The Doepke Superfund site and the Johnson County Landfill are owned by 

Deffenbaugh Industries, which is interested in potential revenue flows from turbines on landfills. 

Wind turbines on landfills could give other landfill owners a reason to close landfills in a timely 

manner and to maintain the landfill cap after it is in place.

Like most states, Kansas relies heavily on fossil fuels to operate its power plants. About threefourths of Kansas’s electricity is generated from coal, and the remaining one-fourth is generated 

from nuclear.7 The cost of many renewable energy technologies is relatively high compared to 

conventional alternatives; however, there are many compelling reasons to consider moving 

toward renewable energy sources for power generation instead of fossil fuels, including: 

• Using renewable fuel sources is more sustainable in the long term

• Burning fossil fuels can have negative effects on human health and the environment, such 

as acid rain

 

7 “Kansas State Profile and Energy Estimates.” U.S. Energy Information Administration, 2012. Accessed March 2, 

2011: http://www.eia.gov/cfapps/state/state_energy_profiles.cfm?sid=KS. 
2 

• Extracting and transporting fossil fuels can lead to accidental spills, which can be 

devastating to the environment and communities 

• Depending on the use of foreign sources of fossil fuels can be a threat to national security 

• Fluctuating electric costs are associated with fossil-fuel-based power plants, whereas 

renewable sources like wind are known to ensure long-term price stability through power 

purchase agreements (PPAs)

• Burning fossil fuels could contribute to climate change 

• Generating energy without harmful emissions or waste products can be accomplished 

through renewable energy sources

• Using water for power generation is much lower for wind than for coal and nuclear 

electricity generation

• Utilizing abundant renewable resources available in Kansas, particularly wind energy, 

can be accomplished. 
3 

2 Wind Turbine Technology

Uneven heating of the earth’s surface creates motion of the atmosphere and thus kinetic energy 

in this movement. Variation in heating and factors, such as surface orientation or slope, rate of 

reflectivity, absorptivity, and transmissivity, also affect the wind resource. In addition, the wind 

resource can be affected (accelerated, decelerated, or made turbulent) by factors such as terrain, 

bodies of water, buildings, and vegetative cover. 

Wind is air with kinetic energy that can be transformed into useful work via wind turbine blades 

and a generator. Overall, wind is a diffuse resource that can generate electricity cost effectively 

and competitively in regions with a good wind resource, high cost of electricity, or both. 

2.1 Wind Characteristics

Winds vary with the season, time of day, and weather events. Analysis of wind data focuses on 

several critical aspects of the data—average annual wind speed, frequency distribution of the 

wind at various speeds, turbulence, vertical wind shear, and maximum gusts. These parameters

allow for estimation of available energy in the wind and the suitability of turbine technology for 

the site.

The wind speed at any given time determines the amount of power available in the wind. The 

power available in the wind is given by: 

P = 1⁄2 * A ρV3

 

where

P = power of the wind [W] 

A = windswept area of the rotor (blades) [m2

] = πD2/4 = πr

2

 

ρ = density of the air [kg/m3

] (at sea level at 15°C)

V = velocity of the wind [m/s] 

As shown, wind power is proportional to velocity cubed (V3

). This matters because if wind 

velocity is doubled, wind power increases by a factor of eight (23

 = 8). Consequently, a small 

difference (e.g., increase) in average speed causes significant differences (e.g., increases) in 

energy production. Examining ways to increase the wind velocity at a particular site should be 

considered. Normally, the easiest way to accomplish this is to increase the height of the tower. 

The wind industry has been moving toward higher towers, and the industry norm has increased 

from 30 m to 80 m over the last 15–20 years. 

A map of the national wind resource is shown in Figure 1. Wind maps give a visual 

approximation of the wind resource in an area but do not provide enough data for estimating 

annual electricity output at a particular site. On-site wind data collected for a period of 1–3 years 

is necessary to estimate wind turbine performance. This study used recently collected on-site 

wind data for its screening-level production estimates and analysis. 
4 

Figure 1. U.S. national wind resource map

Source: NREL, Wind Powering America, 2012

Figure 2. Kansas 80-m annual average wind speed map8

Source: NREL 2012

 

8 Accessed May 8, 2013: http://www1.eere.energy.gov/wind/images/wind_speed_map_lg.jpg. 
5 

2.2 Wind Turbines 

Wind turbines consist of rotating blades that convert the kinetic energy of the wind to electric 

power. They have a number of moving parts and require regularly scheduled and unscheduled 

maintenance. Manufacturer warranties cover the first 2–10 years. Professional wind turbine 

maintenance contractors are recommended after the warranty period. Figure 3 shows large wind 

turbines that could be considered for the Doepke site. 

Figure 3. Modern utility-scale wind turbines. Photo by Owen Roberts, NREL

Wind turbines are typically cost effective where the average wind speed is high, where the 

competing energy costs are high, or a combination of both. Large wind farms of 100–500 MW 

have been driving the industry because of lower installed costs due largely to economies of scale 

and improved low wind speed turbine technology, which results in an overall lower cost of 

energy. Only a single utility-scale turbine is appropriate for the Doepke site due to land, 

permitting, and neighbor constraints. But expansion to other closed sections of the Johnson 

County Landfill could be possible to create a larger, more cost-effective project. The small 

number of turbines and challenging construction sites would result in significantly higher 

installed costs than the typical rates for the wind farm industry. 
6 

2.3 Wind Power History/Background

In the United States, there have been about 58,000 MW of wind power installed.9 Turbines are 

available from as small as 250 W to as large as 5 MW. For the size of the wind plants considered 

here, large turbines in the range of 800 kW to 3,000 kW per turbine would be appropriate. 

Wind power became a commercial-scale industry more than 30 years ago. Over that time, wind 

power has moved from the fringes of the electric power sector to a mainstream resource 

responsible for 35% of U.S. new power capacity from 2007 through 2011; it is second in new 

capacity additions only to new natural gas power.10 In the best resource areas or localities with 

exceptionally high electricity costs, wind power can be cost effective even in the absence of 

direct financial incentives or subsidies. Recent technological improvements11 are expected to 

significantly lower the lifecycle cost of energy of wind energy. Initial investment costs for wind 

power are relatively high compared to natural gas or other forms of generation12; however, with 

zero fuel costs and relatively modest fixed annual operations expenditures, wind-generated 

electricity is often a favorable generation resource over the long term 

 

9 Wiser, R.; Bollinger, M. 2011 Wind Technologies Market Report. Washington, D.C.: Department of Energy, 2011. 

http://www1.eere.energy.gov/wind/pdfs/2011_wind_technologies_market_report.pdf. 10 Williams, E.; Hensley, P. AWEA US Wind Industry Annual Market Report 2012. Washington, DC: AWEA, 2013. 11 Wiser, R.; Lantz, E.; Bolinger, M.; Hand, M. (February 2012). Recent Developments in the Levelized Cost of 

Energy from U.S. Wind Power Projects. http://eetd.lbl.gov/ea/ems/reports/wind-energy-costs-2-2012.pdf. 12 U.S. EIA Updated Capital Cost Estimates for Utility Scale Electricity Generation Plants. Washington, DC: EIA, 

April 2013. http://www.eia.gov/forecasts/capitalcost/pdf/updated_capcost.pdf. 
7 

3 Potential Turbine Locations

3.1 Setbacks

The Doepke Superfund site is approximately 42 acres in size. Figure 4 shows the area (in red) 

that could be considered for siting a turbine when considering these setbacks and topography.13

Figure 4. Potential utility-scale turbine construction areas 

Illustration done in Google Maps

The topography of the Doepke site generally slopes down as one proceeds to the north, and thus 

the locations further south will offer a more energetic wind resource. Constraints, such as 

operations, drainage, and viewshed, will need to be considered when siting a potential turbine. 

Other considerations, such as the geotechnical conditions at various locations within this area 

and local turbulence, should be studied before finalizing a location for a turbine. 

 

13 Assumes setback of 130 m (the height of a large, utility-scale turbine of 80-m hub height and 100-m rotor 

diameter) from structures and property owners other than Deffenbaugh Industries.
8 

3.2 Site-Specific Considerations

Figure 5. Views of the feasible area for a utility-scale wind turbine at the Doepke site. Photos by 

James Salasovich, NREL 

As shown in Figure 5, the capped landfill area has sufficient space to allow for the installation of 

a utility-scale turbine. There are electrical points around the site where a turbine could tie into 

the grid. A detailed interconnection study would have to be conducted before a wind turbine is 

properly sited. Coordination with other states, such as the Massachusetts Department of 

Environmental Protection, is recommended, as multiple wind turbine foundations have 

penetrated the caps of older landfills in the state.

3.3 Permitting

One of the largest constraints to permitting utility-scale wind turbines can be avoiding 

interference with air traffic, weather radar, and military operations. The Doepke site is not within 

any direct flight paths that would automatically preclude it from the possibility of installing a 

utility-scale turbine (see Figure 6). 

View to North

View to West View to East

View to South
9 

Figure 6. FAA potential impact areas for the Doepke site

Source: Illustration generated using the Federal Aviation Administration’s Notice 

Tool: https://oeaaa.faa.gov/oeaaa/external/gisTools/gisAction.jsp?action=showNoNoticeRequiredToolForm.

Long-range radar can also be affected by the movement of the turbine blades and could cause 

interference for air traffic control if not mitigated. Figure 7 shows there is a high likelihood of 

some interference with long-range radar at the Doepke site, but many other considerations should 

be taken into account, and a full impact study should be conducted as one of the first steps if 

there is interest in pursuing wind at the Doepke site. Areas in the red-colored zone typically have 

some significant impacts on local radar, but many wind projects have been built within this highimpact area. Mitigation measures are possible and can vary from ignoring the interference to 

upgrading the software of the radar to filter this interference. 

Local ordinances may also apply to a potential turbine at the Doepke site and should be 

investigated further, as some local governments have regulations that constrain the overall height 

of structures for viewshed and other reasons. 
10 

Figure 7. Long-range radar impacts for the Doepke site

Source: Illustration generated using the Federal Aviation Administration’s DOD Preliminary Screening 

Tool: https://oeaaa.faa.gov/oeaaa/external/gisTools/gisAction.jsp?action=showLongRangeRadarToolForm. 

Figure 8 shows that the Doepke site has a very low probability of interfering with local 

weather radar.
11 

Figure 8. NEXRAD radar impacts for the Doepke site

Source: Illustration generated using the Federal Aviation Administration’s Notice Criteria 

Tool: https://oeaaa.faa.gov/oeaaa/external/gisTools/gisAction.jsp?action=showNoNoticeRequiredToolForm.

Figure 9 shows that there is a low probability of a wind turbine at the Doepke site interfering 

with military operations.
12 

Figure 9. Military operation impacts for the Doepke site

Source: Federal Aviation Administration
13 

4 Site-Measured Wind Resource

As previously mentioned, measurement of key parameters, such as wind speed, wind direction, 

air temperature, and air pressure, are key to understanding and predicting the suitability of a 

turbine at a specific site, as well as the economic performance of a project. The collection of data 

at this site greatly adds to the potential feasibility of a wind project on the site.

4.1 Utilization of Existing Communication Tower 

Deffenbaugh Industries allowed the utilization of an existing lattice communication tower for 

collecting meteorological data for the wind and solar resource assessment. The tower is over 

85 m tall and allowed data collection of wind speed at three different elevations up to 81 m 

above the local ground height. The tower presented large advantages compared to a typical 60-m 

tubular tilt-up tower, which is typically used for wind resource assessment as utility-scale 

turbines are typically 80 m to 100 m tall. The collection of data at 81 m above ground level 

eliminated the uncertainty, extrapolating 60-m data to an 80 m tall turbine hub height. The lattice 

tower is 29 inches wide on a face with a triangular cross section. The solidity of the tower was 

quite low as the tower interference on the anemometers was minimal as seen in Figure 11. 

Twelve-foot-long aluminum booms were installed on the tower to place the anemometers farther 

away from the tower than typical tubular tower booms. The mast-to-face width ratio was 

approximately 4.5, which is lower than typically recommended, but the low solidity of the tower 

produced a high quality dataset. Table 1 contains detailed information on the orientation, 

location, and type of instruments installed on the Doepke tower. 
14 

Figure 10. Riggers installing anemometry on the Doepke tower. Photo from Shelly Brodie, EPA
15 

Table 1 shows various details of the instrumentation, such as height of the instruments as well as how the data logger is configured.

Table 1. Data Logger Configuration and Tower Information

Tower Type: Lifting anchor direction, degrees to magnetic north n/a Guy wire direction, degrees to magnetic north

Tubular/Lattice

Vendor 

name Model Tower height Tower width at base Anchor 1 Anchor 2 Anchor 3 Anchor 4

LATTICE

OKC 

TOWER 280 # 85.34 meters 29 inches cm N/A N/A N/A N/A

Station

identification

Averaging time

Data Logger: Vendor name Model Logger time Logger date

1 

hour

10 

min other

Data Logger: NRG SYM 2011/KANSAS CENTRAL 1/17/11 X

Communications/ 

Manual Collection: ESN / IMEI SIM

PHONE 

NUMBER SID

Call Schedule

(i.e., Daily, 16:00 

MST)

Scheduled

Upload time

Service

Provider

Signal

Vendor name Type Strength

Communications Device: NRG GSM 890141032243879141999 602-748-3961 DAILY

6:30 

AM AT&T 28

Sensors: Sensor type

Logger

slope

Logger

offset

Sensor mounting height

(ft or m, preferably m)

Boom 

length 

(inches)

Upstand

(inches)

Boom 

direction

to 

magnetic 

N

If vane

D-B to MN

(e.g., 

Anemometer) Vendor name

Model 

number Serial number

Channel C1: ANEM NRG 40C 137164 0.755 0.39 266.0 ft 81.07 m 110 22.5 80

Channel C2: ANEM NRG 40C 137166 0.756 0.39 199.0 ft 60.65 m 110 22.5 80

Channel C3: ANEM NRG 40C 135386 0.752 0.40 103.0 ft 31.39 m 110 22.5 80

Channel C4: ANEM NRG 40C 137163 0.755 0.42 266.5 ft 81.22 m 110 22.5 260

Channel C5: ANEM NRG 40C 137169 0.757 0.37 199.5 ft 60.80 m 110 22.5 260

Channel C6: ANEM NRG 40C 137173 0.755 0.36 103.5 ft 31.54 m 110 22.5 260

Channel C7: WIND VANE NRG 200P N/A 259.0 ft 78.94 m 110 23.0 260 TOWARD

Channel C8: WIND VANE NRG 200P N/A 94.0 ft 28.65 m 110 23.0 260 TOWARD

Channel C9: TEMP NRG 110S N/A 7.0 ft 2.1 m

Channel C10: BP NRG BP20 N/A 7.5 ft 2.28 m

Channel C11: VOLTS ft m

Channel C12: PYRAN LI-COR 200 PY69430 10.0 ft 3 m
16 

4.2 Data Recovery and Validation

The data collected from the retrofitted meteorological tower was transmitted daily to NREL, 

EPA, and Deffenbaugh Industries. This allowed frequent reviews of the data quality and 

identification of instrument malfunction or weather-caused data loss, as well as tracking of the 

seasonal wind resource in near real time. 

There were some intermittent instrument failures of one anemometer and one wind vane, but the 

redundant sensors allowed a robust dataset that quantifies the wind resource consistent with and 

above current industry standards. There were some periods of lost data due to icing events, 

which stop the anemometers and wind vanes from reading the actual occurrences, but overall 

data collection averaged 93.3% for the wind speed sensors. The invalid data were excluded from 

calculations for each individual icing or erroneous data event.

Tower shading is a phenomenon where the influence of the tower on the wind influences the 

apparent wind that acts upon the sensors. This phenomenon can skew the wind data to appear 

higher or lower in velocity than in reality. Tower shading was also excluded for directions that 

the tower interference or shading was greatest, but the orientation of the tower and 

instrumentation minimized these occurrences, and the redundancy of the anemometry at each 

level allowed data to be collected without interruption at a specific instrument level. Figure 11 

shows the directions that were most affected by the tower shading, and the data from each 

specific anemometer were removed when the wind direction caused the illustrated change in the 

ratio of anemometers at a specific height. The overall peak impacts were quite low at only 2%, 

which adds to the quality of the dataset. 
17 

Figure 11. Tower interference on anemometry

4.3 Wind Resource Assessment Summary

The average annual wind speed at 80 m for the observation period was 6.59 m/s. This annual 

average wind speed represents a moderately developable wind resource when combined with 

modern utility-scale turbines. Table 2 shows the statistics for the wind speed sensors for the 

observation period from January 18, 2011, to January 19, 2012. 
18 

Table 2. Wind Speed Sensor Summary Statistics

Variable

Speed 

81.2 m

Speed 

81.1 m

Synthesized 

80 m

Speed 

60.8 m

Speed 

60.7 m

Speed 

31.5 m

Speed 

31.4 m

Measurement 

height (m) 81.2 81.1 80 60.8 60.7 31.5 31.4

Mean wind 

speed (m/s) 6.7 6.6 6.6 6.2 6.2 5.3 5.3

MoMMa wind 

speed (m/s) 6.7 6.6 6.6 6.2 6.2 5.3 5.3

Median wind 

speed (m/s) 6.4 6.4 6.3 6 5.9 5 5

Min wind 

speed (m/s) 0.4 0.4 0.4 0.4 0.4 0.4 0.4

Max wind 

speed (m/s) 24.9 24.3 28.1 23.6 23.1 21.5 20.3

Weibull k 2.425 2.398 2.383 2.357 2.38 2.172 2.185

Weibull c 

(m/s) 7.6 7.5 7.4 7 7 6 6

Mean power 

density 

(W/m2) 286 281 277 236 233 155 156

MoMM power 

density 

(W/m2) 286 281 277 236 233 155 156

Mean energy 

content 

(kWh/m2/yr) 2,503 2,465 2,423 2,070 2,044 1,356 1,366

MoMM 

energy 

content 

(kWh/m2/yr) 2,503 2,465 2,423 2,070 2,044 1,356 1,366

Energy 

pattern factor 1.612 1.627 1.64 1.653 1.644 1.773 1.767

Possible 

records 52,890 52,890 52,890 52,890 52,890 52,890 52,890

Valid records 47,376 50,051 52,489 47,783 49,094 49,005 49,672

Missing 

records 5,514 2,839 401 5,107 3,796 3,885 3,218

Data 

recovery rate 

(%) 89.57 94.63 99.24 90.34 92.82 92.65 93.92 a Mean of monthly means (MoMM) is used to remove seasonal biases when less than complete years are averaged 

into a dataset.

Figure 12 shows that the strongest winds occur during the spring and late fall at the Doepke site. 

This wind resource does not have the advantage of producing power during typical highest peak 

demands in the summer, and as such, seasonal pricing might not be as viable.
19 

Figure 12. Seasonal wind resource variation

4.3.1 Vertical Wind Shear

Vertical wind shear is defined as the change in wind speed with the change in height. Typically, 

wind speed increases as the height above the ground increases. This variation of wind speed with 

elevation is called the vertical profile of the wind speed or vertical wind shear. In wind turbine 

engineering, the determination of vertical wind shear is an important design parameter because 

(1) it directly determines the productivity of a wind turbine on a tower of certain height, and (2) 

it can strongly influence the lifetime of a turbine. Each turbine manufacturer will have their own 

limits as to how to count average wind shear for a site and the associated suitability of 

a turbine.14 

Analysts typically use one of two mathematical relations to characterize the measured wind 

shear profile: 

• Logarithmic profile, also known as log law

• Power law profile, also known as power law. 

For this study, the power law was used. 

4.3.2 Power Law

The power law equation is used for estimating wind shear and resultant wind speeds at different 

heights. Depending on what data are known and what are sought, the power law equation can be 

manipulated to solve for any of the variables. 

 

14 As outlined in IEC 61400-1 (wind turbine generator design requirements).
20 

 

V = wind speed at height of interest (e.g., hub height) 

Vref = wind speed measured at height Zref

Z = height of interest (e.g., hub height) 

Zref = height of measured data

α = wind shear exponent 

The wind shear exponent, α, is often referred to as the vertical wind shear factor (VWSF). It 

defines how the wind speed changes with height. When the actual wind shear value is not 

known, a typical value used to estimate the wind shear exponent is 0.14 (or 1/7 power law). 

Depending upon the type of terrain and surface roughness features, the VWSF can vary from 

0.0 to 0.4. When wind speed readings are available at multiple heights, the VWSF can be 

calculated using the power law equation, as shown in Figure 13. 

The average wind shear power law exponent of 0.244 at the Doepke site is relatively high, which 

increases the financial viability of taller turbines and reduces the financial viability of 

shorter turbines. 
21 

Figure 13. Wind shear profile

Figure 14 shows the strong pattern of higher shears at night compared to the daytime shear 

values. This meteorological phenomenon is typical of this region of the country due mainly to 

the nocturnal low-level jet.
22 

Figure 14. Diurnal shear variation

Figure 15 illustrates the daily pattern of wind speeds with the strongest winds at 80 m occurring 

at night and in the middle of the day. 
23 

Figure 15. Diurnal wind resource variation

Figure 16 shows the frequency of wind speeds as they occur for the full year of observed data. 

Figure 16. Wind speed frequency distribution

0 5 10 15 20 25

0

1

2

3

4

5

6

7

Frequency (%)

Probability Distribution Function

Speed 81.1 (m/s)

Actual data Best-fit Weibull distribution (k=2.34, c=7.54 m/s)
24 

Figure 17 shows the frequency of energy in the wind relative to the direction that the winds come 

from. The predominant winds are from the south with another large portion of the energy in the 

wind from the north. 

Figure 17. Wind energy rose

Figure 18 shows the average turbulence intensity measured at 81.2 m above the ground. This 

level of turbulence intensity might require a turbine that is designed to withstand these turbulent 

winds, but it is worth noting that the turbulence from the predominant wind directions, both 

north and south, is somewhat lower than the average turbulence intensity as seen in Figure 19

and Figure 20. 

Proportion of Total Wind Energy vs. Direction 78.9

0°

22.5°

45°

67.5°

90°

112.5°

135°

157.5°

180°

202.5°

225°

247.5°

270°

292.5°

315°

337.5°

0%

10%

20%

Speed 81.2 WPD

Speed 81.1 WPD

Speed 60.8 WPD

Speed 60.7 WPD

Speed 31.5 WPD

Speed 31.4 WPD
25 

Figure 18. Average measured turbulence intensity at 81.2 m 
26 

Figure 19. Average turbulence intensity from the north
27 

Figure 20. Average turbulence intensity from the south

4.4 Long-Term Data Adjustment

The observation period from January 18, 2011, to January 19, 2012, was used for the economic 

performance calculations and the long-term comparison and correction. This full year of data 

was compared to multiple long-term reference stations, and the station with the highest 

correlation with appropriate data quality and recovery rates was chosen.
28 

Figure 21. Long-term reference station locations 

Illustration done in Google Maps

The Kansas City International Airport (site 724460) produced the highest correlation coefficient 

and also had the highest data recovery rate, and thus was the chosen reference station to 

determine how the observation period compared to the 12 years of data from the reference 

station. This backward prediction of a local wind resource is an industry-standard method for 

determining if the observation period will indicate either excessive or underperformance of the 

turbines at a given site. This prediction is typically very important for large wind farm 

developments because the financial performance of the wind farm is dependent on the 

production of the turbines, and the financiers need to understand how the wind farm will, on 

average, perform in the coming years. 

The chosen algorithm was the Matrix Method due to its small mean bias and mean absolute error 

in predicting the turbine performance.15 The algorithm had a mean bias error of only -7.27 kW; 

that is to say the algorithm, on average, under-predicted the power production from a sample 

International Electrotechnical Committee (IEC) Class III turbine by 7.27 kW. The annual 

variations in this same sample IEC Class III turbine were compared to identify how the 

observation period (collected data) compared to the previous 12 years of data from the reference 

station. Table 3 shows that the generic Class III turbine power output for the 2011 observation 

 

15 Lybech Thøgersen, M.; Motta, M.; Sørensen, T.; Nielsen, P. Measure-Correlate-Predict Methods: Case Studies 

and Software Implementation. Aalborg, Denmark: EMD International A/S. 

http://www.emd.dk/files/windpro/Thoegersen_MCP_EWEC_2007.pdf. 
29 

period was roughly 1.76% higher than the average turbine output for the 12-year long-term 

average. Thus, for this generic IEC Class III turbine, the annual power production for the 

observation period will be 1.76% greater than on average, and the standard deviation of annual 

turbine output for this Class III turbine is 3.94%. 

Table 3. Wind Resource and Turbine Performance Annual Average Variation

Year

Mean 

(m/s) Weibull k Class III turbine Output Fraction from Average

2000 6.7 3.05 105.02%

2001 6.5 2.967 99.71%

2002 6.6 2.933 101.83%

2003 6.6 2.889 101.16%

2004 6.6 2.961 102.01%

2005 6.4 2.884 95.82%

2006 6.7 3.012 105.69%

2007 6.4 2.756 96.73%

2008 6.6 2.776 101.08%

2009 6.4 2.801 94.80%

2010 6.3 2.778 93.28%

2011 6.5 2.517 101.76%

Average 6.5 2.845 Standard Deviation 3.94%

4.5 Energy Production Estimates

The industry trend for utility-scale wind turbines is toward larger rotor diameters and smaller 

electrical nameplate capacities for lower wind speed regimes. This has been shown to be cost 

effective, especially for lower wind speed sites, as shown in Figure 22.

16 

 

16 Wiser, R.; Lantz, E.; Bolinger, M.; Hand, M. (February 2012). Recent Developments in the Levelized Cost of 

Energy from U.S. Wind Power Projects. http://eetd.lbl.gov/ea/ems/reports/wind-energy-costs-2-2012.pdf.
30 

Figure 22. Levelized cost of energy comparison of modern turbines with historical 

performance17,18

These data show that for turbines currently produced and installed in North America, the cost of 

energy, especially for lower wind speed sites, such as the Doepke site, now have a much better 

chance at producing cost-effective electricity, depending on incentives and project and turbine 

costs. It appears that the industry is continuing in this direction, and the next generation of 

turbines already being tested and installed could have a similar impact on the cost of energy as 

rotors become even larger. It is yet to be seen where turbine pricing for these new machines will 

fall. It is also worth noting that modern IEC Class III turbines with larger rotor to nameplate 

electrical capacity typically reduce the volatility of annual variations in the wind resource.

 

17 Wiser, R.; Lantz, E.; Bolinger, M.; Hand, M. (February 2012). Recent Developments in the Levelized Cost of 

Energy from U.S. Wind Power Projects. http://eetd.lbl.gov/ea/ems/reports/wind-energy-costs-2-2012.pdf. 18 This model assumes current turbine and installation pricing, reduced O&M costs, production tax credits (PTC) 

and modified accelerated cost-recovery system (MACRS) tax incentives, increased turbine availability, and the 

comparative capacity factors for the current and previous generation turbine technologies.

8 m/s

7 m/s

6 m/s

$0

$20

$40

$60

$80

$100

$120

$140

2002-03 Current, 2012-13

Standard Technology Technology Choice

Levelized Cost of Energy ($/MWh)

No Incentives

31% Cost Reduction

17% Cost Reduction
31 

5 Economics and Performance

5.1 Assumptions and Input Data for Analysis

It was assumed that the installed cost of an 80-m hub height 100-m rotor diameter IEC Class III 

wind turbine at the Doepke site for a single turbine installation, purchase, and interconnection is

approximately $3,450/kW of nameplate capacity. This cost includes the turbine, turbine 

transport, and the balance-of-system components for the project and installation.

The economics of a potential turbine at the Doepke site depend greatly on the cost of electricity. 

The average KCP&L electric rates of $0.08391/kWh are similar to those found in other 

Midwestern cities in the United States. The Database of State Incentives for Renewables and 

Efficiency (DSIRE) provides a summary of net metering, interconnection rules, and other 

incentives available to Kansas utility customers.19 Renewable energy systems, including 

commercial wind farms, are subject to interconnection and net-metering rules promulgated at the 

state level. Interconnection rules for Kansas were found on the DSIRE website. The turbine size 

limit for interconnection is 25 kW for residential and 200 kW for nonresidential systems. 

Similarly, the turbine size limit for net metering is 25 kW for residential and 200 kW for 

nonresidential systems. The capacity values for the interconnection limits are very low, and the 

owner of a larger turbine project would have to work out an arrangement with KCP&L 

before proceeding. This feasibility study assumes the ability to sell electricity at and above 

current commercial consumer rates which is very unlikely to be realistic in this case. Most likely 

only the avoided cost of electricity would be paid, which is substantially less than the 

assumed rates. 

There is little to no electricity use at a closed landfill, and all of the electricity generated by a 

proposed turbine is assumed to be sold back to the utility. There are possible electricity uses at 

the quarry operation where the electricity from a turbine could be used. From an economic 

standpoint, the current net-metering laws in Kansas are not advantageous for a turbine that can 

generate large amounts of excess energy because of the relatively low system size limit of 

200 kW for commercial systems. Setting up a PPA where KCP&L would agree to buy back 

electricity from larger turbines should be explored. The economics of the potential systems were 

analyzed assuming that a PPA with KCP&L would be used, and KCP&L would buy back the 

electricity at a rate of $0.08391/kWh.20 

5.2 Incentives and Financing Opportunities

State incentives or local utility incentives are not currently offered for commercial wind turbines 

in Kansas. It was assumed for this analysis that federal incentives are not received due to the 

complexities of ownership, power markets, profit margins, and demand for renewable energy. 

Identifying and leveraging state and federal incentives and grants is an important part of making 

a wind project cost effective. A private, tax-paying entity that owns wind projects can qualify for 

 

19 “Kansas Incentives/Policies for Renewables & Efficiency.” Database of State Incentives for Renewables & 

Efficiency, 2013. Accessed March 2011: 

http://www.dsireusa.org/incentives/index.cfm?re=1&ee=1&spv=0&st=0&srp=1&state=KS. 20 This feasibility study assumes the ability to sell electricity at and above current commercial consumer rates, which 

is very unlikely to be realistic in this case. Most likely only the avoided cost of electricity would be paid, which is 

substantially less than the assumed rates.
32 

a 30% federal business energy investment tax credit (ITC) or a production tax credit (PTC) and 

accelerated depreciation on the wind project. Because the federal government does not pay taxes, 

private ownership of the wind project is required to capture tax incentives or U.S. Treasury 

Section 1603 grant payments.21 

The system facilitator could potentially pursue an agreement with KCP&L that would negotiate 

both a higher price for the electricity produced by the potential system and the potential to sell 

renewable energy certificates (RECs). Any power that is produced by a wind project will help 

the state reach its renewable portfolio standard (RPS) and would be an opportunity for KCP&L

to accelerate the diversification of their energy mix with clean energy. It has been demonstrated 

across the country that people are willing to pay a premium for certified clean energy,

22 and 

KCP&L could start a voluntary green power purchase pilot program with energy from the 

landfills in Kansas.

23

Technical assistance to support project development is available through the U.S. Department of 

Energy’s (DOE) Office of Energy Efficiency and Renewable Energy (EERE). Technical 

assistance is provided to commercial power developers and other technology projects involving 

liquid fuels developed from biomass. EERE also makes information available to the public on 

renewable energy applications. EERE can assist commercial wind and solar developers by 

providing detailed renewable resource maps, interfacing with Kansas utilities, and contacting 

local economic developers. 

There are several options for financing a solar wind project. A potential alternative financing 

option is the third-party ownership PPA. This type of agreement works by having a wind 

developer install, finance, and operate the wind project while the utility company purchases the 

electricity generated by the system. The wind project is financed by the wind developer, and the 

payments are paid by the revenue from selling the generated electricity and RECs to the utility. 

In this configuration, the land that the wind project is on would need to be leased to the owner of 

the project for the duration of the contract. 

Another gap financing tool that could be available is tax increment financing (TIF). Connecticut, 

Iowa, Michigan, and Wisconsin have been leaders in structuring state-facilitated TIF financing as 

an effective and efficient means to enhance site reuse and redevelopment programs and to obtain 

successful cleanup and redevelopment results. Municipalities are good candidates for TIF 

because it is an incentive they can implement under their own control. 

5.3 Wind Project Employment

The Council of Economic Advisors (CEA) calculated the number of jobs (direct, indirect, and 

induced) from federal spending using economic models developed with real-world data. CEA 

found that $92,000 in federal spending is equivalent to one job-year. According to CEA, this 

 

21 “Kansas Incentives/Policies for Renewables & Efficiency.” Database of State Incentives for Renewables & 

Efficiency, 2013. Accessed March 2011: 

http://www.dsireusa.org/incentives/index.cfm?re=1&ee=1&spv=0&st=0&srp=1&state=KS. 22 “NREL Highlights Utility Green Power Leaders.” Transmission & Distribution World, 2009. Accessed July 20, 

2010: http://tdworld.com/customer_service/doe-nrel-utility-green-power-0409/. 23 An example of such a program is Xcel Energy’s Windsource program. For more information, see http://www

.xcelenergy.com/Colorado/Company/Environment/Renewable%20Energy/Pages/Wind_Power.aspx. 
33 

means that for every $92,000 of federal money that is spent, there is one job created that can be 

sustained for one year. Table 4 shows an estimate of jobs supported by a wind project at the 

Doepke Superfund site. This project represents a large amount of money that would support a 

significant number of jobs. A portion of these jobs, including the installation and system 

maintenance jobs, would be within the community. The short-term jobs column refers to the 

number of job-years that would be created as a result of the one-time project capital investment. 

This means that the jobs will be created and sustained for one year. The long-term jobs column 

refers to the number of jobs that would be sustained as a result of the O&M of the system. These 

jobs will be sustained for the life of the system due to the annual cost to keep the 

system operating. 

Table 4. Doepke Wind Project Estimated Job Creation 

Short-Term 

Jobs 

Potentiala

(job-years or 

jobs lasting 

one year)

Long-Term Jobs 

Potentialb (number 

of long-term jobs)

IEC Class III Turbine, 80m Tower 100m Rotor 59 0.4 a Job-years created as a result of project capital investment, including direct, indirect, and induced jobs. b Jobs (direct, indirect, and induced) sustained as a result of O&M of the system. 
34 

6 Avoided Cost Electric Rate 

As there are no RPS or other state incentives for this project it is also very unlikely that the 

turbine would be able to be net-metered. Utilities will pay the avoided cost of energy (which is 

much lower than the billed rate) for any generator connected to their system. Table 5 shows a 

summary of the system economics when assuming a hypothetical buyback electric rate of 

$0.03/kWh with federal incentives. 

Table 5. Wind Project Performance and Economics Assuming Avoided Cost

Wind 

System 

Sizea

Annual 

Output

Annual 

O&M 

($/year)

System 

Cost 

without 

Incentives 

($)

Net Meter 

Current 

Electrical 

Cost 

Payback 

(years) c

Avoided 

Cost 

Payback 

(years)d

Estimated 

Potential 

Construction 

Job-Yearse

Estimated 

Potential 

Maintenance 

Jobsf

(kW) (kWh/year)

IEC Class III Wind Turbine, 80-m Hub Height, 100-m Rotor 

Diameter

1,620 5,098,607g $70,000 $5,440,000 10.4 >20 59 0.4

a Data assume a maximum usable area of solely the Doepke site.

b Number of average American households that could hypothetically be powered by the turbine when assuming 

11,040 kWh/year/household.24

c Assumes 10-year PTC, 5-year MACRS, $0.08391/kWh, 10% utility rate escalation, $70,000 annual O&M, 7% debt interest d Assumes 10-year PTC, 5-year MACRS, $0.03/kWh, 5 % utility rate escalation, $70,000 annual O&M, 7% debt interest, e Job-years supported by this project capital investment, including direct, indirect, and induced jobs. f Permanent jobs (direct, indirect, and induced) sustained as a result of operation and maintenance (O&M) of the system. g Annual output corrected for long-term annual average, including 15% losses.

 

24 “Frequently Asked Questions.” U.S. Energy Information Administration. Accessed Oct. 22, 2010: 

http://www.eia.doe.gov/ask/electricity_faqs.asp#electricity_use_home. 
35 

7 Conclusions and Recommendations 

The Doepke site appears technically feasible for the installation of a utility-scale wind turbine. 

Using obtainable and accessible land that is unavailable for other purposes allows for reuse of 

land that would not otherwise contribute to productivity for Kansas. Installing a wind project and 

the associated facilities on landfills rather than on undeveloped land relieves “greenfields” of 

land-use impacts. Developing wind projects on landfills can provide an economically viable 

reuse option for landfills. The Doepke site has existing distribution voltage lines, roads, 

industrial zoning, and all other critical infrastructure in place for a wind project. One obstacle to 

wind projects on landfills is that landfills require little to no electricity once they are capped and 

closed. Therefore, finding a use for the electricity generated by the wind project is a key element. 

Another major obstacle is the very low interconnection and net-metering rules in Kansas, which 

limits a commercial size wind project to 200 kW. An arrangement with KCP&L would have to 

be worked out prior to installing a utility-scale wind turbine at the Doepke site. 

It is recommended that the party ultimately responsible for facilitating the implementation wind 

project contact KCP&L and attempt to set up an agreement in which KCP&L would purchase 

the electricity generated at the site. 

For this feasibility study, system calculations and sizes were based on the specific site area; 

however, actual system installation should be based on the availability of funds or on the amount 

of power that can be sold. Installing a small demonstration system could be advantageous, but 

the basic economics of turbine size work against this option. When the project goes out to bid, a 

design-build contract should be issued that requests the best performance (kWh/year) at the best 

price, and that allows vendors to optimize system configuration and turbine type. A third-party 

ownership PPA provides a feasible way for a system to be financed on these sites. Estimation by 

a third-party developer/owner of similar projects is recommended to examine the sensitivities of 

current or future power markets and current and future federal incentives, for example. 

In the coming years, increasing electrical rates and potential increased demand for clean power 

will continue to improve the feasibility of implementing wind projects at these sites due to 

transmission constraints and local motivation to generate renewable energy. 
36 

Appendix A. Measure-Correlate-Predict Matrix Method

Below is an explanation of the Matrix Method used in the long-term correlation of the data in 

this feasibility study, as described by Tom Lambert of Mistaya Engineering.25 The Matrix Time 

Series (MTS) algorithm is an adaptation of the classic Matrix Method, modified to produce 

realistic time series data. The algorithm comprises the following steps: 

1. Build the Joint Probability Distribution

The fundamental idea behind matrix-based MCP methods is to use the complete twodimensional joint probability distribution (JPD) of the target and reference wind speeds to 

generate the predicted wind speed data. Not only does this allow the algorithm to model arbitrary 

nonlinear relationships between the target and reference site, it also preserves information about 

the variance in each variable (as opposed to, for example, basic linear regression, which maps a 

single specific target wind speed to every given reference wind speed value). Therefore, the first 

step of the MCP method is to build this JPD. 

2. Build the Percentile Time Series

The next product of the MTS algorithm is the percentile time series. This is another set of time 

series data points that Windographer generates as an intermediate output of the MTS algorithm. 

In each time step, Windographer takes the concurrent target and reference wind speeds, chooses 

from the JPD the column corresponding to the appropriate reference speed bin, and from that 

constructs the cumulative distribution function (CDF) of target wind speeds observed in that bin. 

Finally, from this CDF, Windographer calculates the percentile value corresponding to the 

observed target wind speed. The percentile time series can be viewed using the percentiles graph

radio button. 

A simple way to think of the percentile time series is that each percentile value represents how 

windy the target site is compared to how windy we would expect it to be, given the current 

 

25 The material in this appendix was obtained using the Windographer software by Mistaya Engineering: 

http://www.windographer.com/. 
37 

reference wind speed. Therefore, a percentile value of 0.5 or 50% means that, in this time step, 

the target wind speed is exactly the average of what we would expect, given the reference wind 

speed in that time step. In the same way, a percentile value of 90% would indicate an unusually 

windy target site, and a value of 10% would indicate an unusually calm target site. Because the 

distribution of target wind speeds will vary depending on the range of reference wind speeds 

considered, the same value of the percentile time series can, of course, represent different target 

site wind speeds in different time steps.

3. Synthesize Percentile Time Series Data

In this step, Windographer uses its built-in gap-filling algorithm to fill gaps in the percentile time 

series. The synthesized data respects the seasonal and diurnal patterns in the original percentile 

time series, as well as the characteristic autocorrelation, and matches up with the edges of any 

gaps in the original target site data. 

4. Transform Percentile Time Series Into Target Wind Speeds

The final step is for Windographer to transform the synthetic percentile time series values into 

synthetic target wind speed values. Windographer does this by referring back to the JPD and 

using it to calculate the expected target wind speed value for the given percentile value and 

reference wind speed in each time step, essentially reversing the “build the percentile time 

series” step. Once again, Windographer calculates the CDF of target wind speed values for the 
38 

given reference wind speed, only this time it uses the percentile value to look up the predicted 

target wind speed value for that time step. Whereas the previous step was concerned with 

preserving the seasonal and diurnal patterns and autocorrelation of the data, it is really this step 

that is concerned with preserving the statistical relationship between target and reference wind 

speeds, as it does by consulting the JPD. 

Options

The MTS algorithm supports the following options: 

The Direction sectors drop-down box controls the number of direction sectors the data is split 

into for analysis. If this value is greater than one, the data in each time step is split up according 

to the value of the reference wind direction in that time step. Windographer calculates a separate 

JPD for each direction sector, but all of the percentile values are still put into the same percentile 

time series. Then, when the time comes to transform the percentile data into target wind speeds, 

the JPD to use for each time step is again determined based on the reference direction in that 

time step.

Reference speed bin size and Target speed bin size refer to the sizes of the bins used to 

produce the JPD. Smaller bins will produce a more fine-grained JPD, but if the bins are too small 

then the JPD will begin to suffer from sparsity problems (i.e., some bins will have little or 

no data).

The Moving average window controls the size of the moving average used when calculating the 

percentile time series. Because the percentile values are calculated based on a CDF of target 

wind speeds (as explained above), the percentile values can sometimes vary erratically, 

especially at high and low values where data is scarce. Therefore, Windographer offers the 

ability to smooth the percentile time series by calculating a moving average. For example, if the 

time step is 60 minutes and the moving average window is set to 3 hours, each percentile value 

will really represent the average of 3 percentile values (one central time step and one on either 

side). Figure B-6 shows the effect of increasing the moving average window on the smoothness 

of the percentile time series (in order, no moving average, 3 hours, 5 hours). 
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