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

Cost and Performance 

Assumptions for Modeling 

Electricity Generation 

Technologies

Rick Tidball, Joel Bluestein, Nick Rodriguez,

and Stu Knoke

ICF International

Fairfax, Virginia

Subcontract Report

NREL/SR-6A20-48595

November 2010
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

1617 Cole Boulevard

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Cost and Performance 

Assumptions for Modeling 

Electricity Generation 

Technologies

Rick Tidball, Joel Bluestein, Nick Rodriguez,

and Stu Knoke

ICF International

Fairfax, Virginia

NREL Technical Monitor: Jordan Macknick

Prepared under Subcontract No. KACX-8-88312-04

Subcontract Report

NREL/SR-6A20-48595

November 2010
This publication received minimal editorial review at NREL.

NOTICE

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

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

any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately 

owned rights. Reference herein to any specific commercial product, process, or service by trade name, 

trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

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.

Available electronically at http://www.osti.gov/bridge

Available for a processing fee to U.S. Department of Energy

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information

P.O. Box 62

Oak Ridge, TN 37831-0062

phone: 865.576.8401

fax: 865.576.5728

email: mailto:reports@adonis.osti.gov

Available for sale to the public, in paper, from:

U.S. Department of Commerce

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

phone: 800.553.6847

fax: 703.605.6900

email: orders@ntis.fedworld.gov

online ordering: http://www.ntis.gov/help/ordermethods.aspx

Cover Photos: (left to right) PIX 16416, PIX 17423, PIX 16560, PIX 17613, PIX 17436, PIX 17721

Printed on paper containing at least 50% wastepaper, including 10% post consumer waste.
iii

Table of Contents

1 Introduction............................................................................................................................. 1

1.1 Objective .............................................................................................................. 1

1.2 Technologies Covered ......................................................................................... 2

1.3 Report Organization............................................................................................. 5

2 Technical Performance Characteristics................................................................................... 6

2.1 Plant Size ............................................................................................................. 6

2.2 Heat Rate.............................................................................................................. 8

2.3 Availability Factor (Capacity Factor)................................................................ 10

2.4 Plant Lifetime..................................................................................................... 15

3 Cost Characteristics .............................................................................................................. 17

3.1 Capital Cost........................................................................................................ 17

3.2 Learning ............................................................................................................. 42

3.3 O&M Costs........................................................................................................ 55

4 Levelized Cost of Energy (LCOE) ....................................................................................... 59

4.1 Baseline Results (2015 and 2030)...................................................................... 60

4.2 Capital, O&M, and Fuel Costs (2015 and 2030) ............................................... 68

4.3 Time Trends (2005 through 2050)..................................................................... 75

4.4 Impact of Equipment Costs................................................................................ 83

4.5 Impact of Labor Costs........................................................................................ 83

4.6 Impact of Fuel Costs.......................................................................................... 88

4.7 LCOE Conclusions ............................................................................................ 91

5 Conclusions........................................................................................................................... 93

Appendix A. NEMS (AEO 2009 Data Set)............................................................................. 95

Appendix B. MARKAL (GPRA 2009 Data Set) .................................................................. 115

Appendix C. ReEDS (NREL-SEAC 2008 Data Set) ............................................................ 130

Appendix D. MiniCAM (MiniCAM 2008 Data Set) ............................................................ 150

Appendix E. IPM (EPA 2009 Data Set)................................................................................ 163

Appendix F. MERGE (MERGE 2009 Data Set) .................................................................. 172

Appendix G. Description of LCOE Tool............................................................................... 184

Appendix H. GDP Index ....................................................................................................... 195

Appendix I. LCOE Results .................................................................................................. 196
iv

List of Figures

Figure 1. Power plant size comparison ................................................................................... 7

Figure 2. Heat rate comparisons ............................................................................................. 9

Figure 3. 2010 Capacity factor comparison.......................................................................... 13

Figure 4. 2025 Capacity factor comparison.......................................................................... 14

Figure 5. Plant lifetime comparison...................................................................................... 16

Figure 6. Overnight capital costs—coal................................................................................ 21

Figure 7. Overnight capital costs—IGCC............................................................................. 22

Figure 8. Overnight capital costs—combustion turbine ....................................................... 23

Figure 9. Overnight capital costs—combined cycle ............................................................. 24

Figure 10. Overnight capital costs—nuclear......................................................................... 25

Figure 11. Overnight capital costs—biomass....................................................................... 26

Figure 12. Overnight capital costs—geothermal .................................................................. 27

Figure 13. Overnight capital costs—wind (onshore)............................................................ 28

Figure 14. Wind (offshore)—overnight capital costs........................................................... 29

Figure 15. Overnight capital costs—solar thermal ............................................................... 30

Figure 16. Overnight capital costs—PV ............................................................................... 31

Figure 17. Average overnight capital costs—conventional technologies............................. 33

Figure 18. Average overnight capital costs—renewable technologies................................. 34

Figure 19. Ratio of average overnight capital costs (renewable/conventional).................... 36

Figure 20. Capital cost versus plant size, 2010..................................................................... 37

Figure 21. Capital cost versus plant size, 2025..................................................................... 38

Figure 22. Overnight capital costs versus capacity factors, 2010......................................... 39

Figure 23. Overnight capital costs versus capacity factors, 2025......................................... 41

Figure 24. Normalized capital costs—coal........................................................................... 44

Figure 25. Normalized capital costs—IGCC........................................................................ 45

Figure 26. Normalized capital costs—combustion turbine................................................... 46

Figure 27. Normalized capital costs—combined cycle ........................................................ 47

Figure 28. Normalized capital costs—nuclear...................................................................... 48

Figure 29. Normalized capital costs—biomass .................................................................... 49

Figure 30. Normalized capital costs—geothermal................................................................ 50

Figure 31. Normalized capital costs—wind (onshore) ......................................................... 51

Figure 32. Normalized capital costs—wind (offshore)......................................................... 52

Figure 33. Normalized capital costs—solar thermal............................................................. 53

Figure 34. Normalized capital costs—PV ............................................................................ 54

Figure 35. Fixed O&M costs, 2015 ...................................................................................... 56

Figure 36. Variable O&M costs, 2015.................................................................................. 58

Figure 37. LCOE costs, 2015................................................................................................ 61

Figure 38. LCOE costs, 2030................................................................................................ 62

Figure 39. LCOE cost ratios relative to coal, 2015............................................................... 64

Figure 40. LCOE cost ratios relative to coal, 2030............................................................... 65

Figure 41. LCOE cost ratios relative to wind, 2015 ............................................................. 66

Figure 42. LCOE cost ratios relative to wind, 2030 ............................................................. 67

Figure 43. Capital, fuel, and O&M costs—coal ................................................................... 70

Figure 44. Capital, fuel, and O&M costs—IGCC................................................................. 70
v

Figure 45. Capital, fuel, and O&M costs—combustion turbine ........................................... 71

Figure 46. Capital, fuel, and O&M costs—combined cycle................................................. 71

Figure 47. Capital, fuel, and O&M costs—nuclear .............................................................. 72

Figure 48. Capital, fuel, and O&M costs—biomass............................................................. 72

Figure 49. Capital, fuel, and O&M costs—geothermal (HT)............................................... 73

Figure 50. Capital, fuel, and O&M costs—wind (onshore).................................................. 73

Figure 51. Capital, fuel, and O&M costs—wind (offshore)................................................. 74

Figure 52. Capital, fuel, and O&M costs—solar thermal (CSP) .......................................... 74

Figure 53. Capital, fuel, and O&M costs—PV..................................................................... 75

Figure 54. LCOE—coal ........................................................................................................ 77

Figure 55. LCOE—IGCC..................................................................................................... 77

Figure 56. LCOE—combustion turbine................................................................................ 78

Figure 57. LCOE—combined cycle...................................................................................... 78

Figure 58. LCOE—nuclear................................................................................................... 79

Figure 59. LCOE—biomass.................................................................................................. 79

Figure 60. LCOE—geothermal (HT).................................................................................... 80

Figure 61. LCOE—wind (onshore) ...................................................................................... 80

Figure 62. LCOE—wind (offshore)...................................................................................... 81

Figure 63. LCOE—solar thermal (CSP)............................................................................... 81

Figure 64. LCOE—PV.......................................................................................................... 82

Figure 65. Impact on LCOE of equipment cost, 10% change .............................................. 84

Figure 66. Impact on LCOE of equipment cost, 50% change .............................................. 85

Figure 67. Impact on LCOE of labor cost, 10% change....................................................... 86

Figure 68. Impact on LCOE of labor cost, 50% change....................................................... 87

Figure 69. Impact on LCOE of fuel cost, 10% change ......................................................... 89

Figure 70. Impact on LCOE of fuel cost, 50% change ......................................................... 90

Figure 71. AEO 2009, plant capacities in online year .......................................................... 99

Figure 72. AEO 2009, online year and lead times.............................................................. 100

Figure 73. AEO 2009, high and low heat rates................................................................... 102

Figure 74. AEO 2009, capacity factors............................................................................... 103

Figure 75. AEO 2009, overnight capital costs.................................................................... 107

Figure 76. AEO 2009, highest and lowest learning function values................................... 109

Figure 77. AEO 2009, learning function values for the 11 focus technologies.................. 110

Figure 78. AEO 2009, commodity price adjustment factor for all technologies................ 111

Figure 79. AEO 2009, overnight capital costs for the 11 focus technologies..................... 112

Figure 80. AEO 2009, variable O&M ................................................................................ 114

Figure 81. GPRA 2009, wind capacity factors ................................................................... 118

Figure 82. GPRA 2009, solar thermal capacity factors...................................................... 119

Figure 83. GPRA 2009, base overnight capital costs for 13 technologies ......................... 121

Figure 84. GPRA 2009, overnight capital costs ................................................................. 122

Figure 85. GPRA 2009, onshore wind class 4 capital costs (base and target cases) .......... 123

Figure 86. GPRA 2009, onshore wind classes 5 and 6, capital costs................................. 124

Figure 87. GPRA 2009, shallow offshore wind class 4, capital costs................................ 124

Figure 88. GPRA 2009, geothermal flash learning factor .................................................. 125

Figure 89. GPRA 2009, geothermal binary learning factor................................................ 126

Figure 90. GPRA 2009, geothermal EGS—convective learning factor ............................. 126
vi

Figure 91. GPRA 2009, residential PV learning factor ...................................................... 127

Figure 92. GPRA 2009, commercial PV learning factor.................................................... 127

Figure 93. GPRA utility PV learning factor ....................................................................... 128

Figure 94. GPRA 2009, fixed annual O&M costs for renewable technologies.................. 129

Figure 95. NREL-SEAC 2008, heat rates for six technologies .......................................... 134

Figure 96. NREL-SEAC 2008, capacity factors, onshore wind ......................................... 136

Figure 97. NREL-SEAC 2008, capacity factors, shallow offshore wind ........................... 137

Figure 98. NREL-SEAC 2008, capacity factors, deep offshore wind ................................ 137

Figure 99. NREL-SEAC 2008, capacity factors, solar thermal and PV ............................. 138

Figure 100. NREL-SEAC 2008, capacity factors, seven technologies............................... 139

Figure 101. NREL-SEAC 2008, plant lifetime for 11 technologies................................... 140

Figure 102. NREL-SEAC 2008, overnight capital cost for renewable technologies ......... 142

Figure 103. NREL-SEAC 2008, overnight capital cost for five technologies.................... 143

Figure 104. NREL-SEAC 2008, learning factor for renewable technologies .................... 144

Figure 105. NREL-SEAC 2008, learning factors for five conventional technologies ....... 145

Figure 106. NREL-SEAC 2008, fixed O&M costs ............................................................ 146

Figure 107. NREL-SEAC 2008, fixed O&M costs ............................................................ 147

Figure 108. NREL-SEAC 2008, variable O&M costs........................................................ 148

Figure 109. NREL-SEAC 2008 variable O&M cost for technologies with constant costs 149

Figure 110. MiniCAM, plant size ....................................................................................... 153

Figure 111. MiniCAM, heat rates for fossil and nuclear technologies............................... 155

Figure 112. MiniCAM, capacity factors............................................................................. 156

Figure 113. MiniCAM, power plant lifetime...................................................................... 157

Figure 114. MiniCAM, capital costs................................................................................... 159

Figure 115. MiniCAM, fixed O&M costs .......................................................................... 161

Figure 116. MiniCAM, variable O&M costs...................................................................... 162

Figure 117. EPA 2009, online year..................................................................................... 165

Figure 118. EPA 2009, heat rates ....................................................................................... 166

Figure 119. EPA 2009, capacity factors ............................................................................. 167

Figure 120. EPA 2009, capital costs................................................................................... 169

Figure 121. EPA 2009, learning factor ............................................................................... 170

Figure 122. EPA 2009, variable O&M costs...................................................................... 171

Figure 123. EPA 2009, fixed O&M costs........................................................................... 171

Figure 124. MERGE, plant size.......................................................................................... 174

Figure 125. MERGE, heat rates.......................................................................................... 175

Figure 126. MERGE, capacity factors................................................................................ 177

Figure 127. MERGE, overnight capital costs, fossil and nuclear....................................... 179

Figure 128. MERGE, overnight capital costs, renewables................................................. 180

Figure 129. MERGE, all inclusive O&M costs.................................................................. 183

Figure 130. Flow chart for the LCOE tool.......................................................................... 186
vii

List of Tables

Table 1. Six Data Sets Used by Six Models ........................................................................... 1

Table 2. New Electricity Generation Technologies by Data Set, ........................................... 2

Table 3. Eleven Focus Technologies for This Report............................................................. 4

Table 4. Technologies Covered in Each Data Set (11 technologies)...................................... 4

Table 5. Report Organization.................................................................................................. 5

Table 6. Plant Size (MW) ....................................................................................................... 6

Table 7. Heat Rate Values by Data Set (Btu/kWh, for 2010)................................................. 8

Table 8. Capacity Factors for 2010....................................................................................... 10

Table 9. Capacity Factors for 2025....................................................................................... 11

Table 10. Capacity Factor Changes in 2025 Relative to 2010.............................................. 12

Table 11. Plant Lifetime (years) ........................................................................................... 15

Table 12. Capital Cost Trends by Data Set and Technology (11 technologies)................... 20

Table 13. High and Low Capital Costs for Each Technology in 2030 ................................. 32

Table 14. Annual Change in Overnight Capital Costs, 2015 to 2030................................... 43

Table 15. Fixed O&M in 2015 (2007$/kW/yr)..................................................................... 55

Table 16. Variable O&M in 2015 (2007$/MWh)................................................................. 57

Table 17. LCOE Calculations............................................................................................... 59

Table 18. LCOE Calculation Matrix..................................................................................... 60

Table 19. Matrix of LCOE Calculations............................................................................... 91

Table 20. Eleven Core Utility Scale Power Generation Technologies................................. 93

Table 21. Cost and Performance Characteristics in AEO 2009 for 20 Technologies........... 96

Table 22. AEO 2009, Overnight Capital Costs in 2009 (reported in 2007$) ..................... 104

Table 23. AEO 2009, Learning Parameters........................................................................ 106

Table 24. AEO 2009, O&M Costs...................................................................................... 113

Table 25. DOE EERE R&D Programs............................................................................... 115

Table 26. Cost and Performance Characteristics in GPRA 2009 ....................................... 116

Table 27. GPRA 2009, Power Plant Sizesa......................................................................... 117

Table 28. GPRA 2009, Capital Cost for Non-Wind Renewable Technologies (2006$/Kw)

.......................................................................................................................... 120

Table 29. GPRA 2009, Capital Cost for Wind Technologies (2006$/kW) ........................ 120

Table 30. GPRA 2009, Fixed O&M Costs (2006$/kW/yr) for Renewable Technologies. 128

Table 31. NREL-SEAC 2008, Summary of Characteristics for 2005 ................................ 132

Table 32. NREL-SEAC 2008, Heat Rates for Six Technologies ....................................... 133

Table 33. NREL-SEAC 2008, Capacity Factors ................................................................ 135

Table 34. NREL-SEAC 2008, Overnight Capital Cost for Wind Technologies (2004$/kW)

.......................................................................................................................... 141

Table 35. NREL-SEAC 2008, Overnight Capital Cost for Geothermal and PV (2004$/kW)

.......................................................................................................................... 141

Table 36. NREL-SEAC 2008, Overnight Capital Cost for Five Technologies (2004$/kW)

.......................................................................................................................... 142

Table 37. NREL-SEAC, Fixed O&M Costs (2004$/kW/yr).............................................. 145

Table 38. NREL-SEAC Variable O&M (2004$/MWh)..................................................... 147

Table 39. MiniCAM, Cost and Performance Characteristics ............................................. 152

Table 40. MiniCAM, Heat Rates........................................................................................ 154
viii

Table 41. MiniCAM, Capacity Factors............................................................................... 154

Table 42. MiniCAM, Capital Costs.................................................................................... 158

Table 43. MiniCAM, Fixed and Variable O&M ................................................................ 160

Table 44. EPA 2009, Technical Data.................................................................................. 164

Table 45. EPA 2009, Cost Data (2006$) ............................................................................ 168

Table 46. MERGE 2008, Technologies Covered ............................................................... 172

Table 47. MERGE Characteristics for Eight Technologies................................................ 173

Table 48. MERGE, Heat Rates........................................................................................... 175

Table 49. MERGE, Capacity Factors.................................................................................. 176

Table 50. MERGE, Overnight Capital Costs...................................................................... 178

Table 51. LCOE Calculation Parameters............................................................................ 181

Table 52. MERGE, All Inclusive O&M Costs (2015 values, reported in 2008$).............. 182

Table 53. LCOE Calculation Parameters............................................................................ 185

Table 54. Macros ................................................................................................................ 188

Table 55. Worksheets in Excel Workbook ......................................................................... 189

Table 56. Overnight Capital Cost Components.................................................................. 191

Table 57. Eleven Technologies Included in Summary ....................................................... 194

Table 58. GDP Index .......................................................................................................... 195

Table 59. LCOE Results, 2015 ........................................................................................... 196

Table 60. LCOE Results, 2030 ........................................................................................... 199
1

1 Introduction

1.1 Objective

The goal of this project was to compare and contrast utility scale power plant characteristics used 

in data sets that support energy market models. Six specific data sets – each associated with a 

different model – were selected for the study. The data sets and corresponding models are shown 

in Table 1. Details concerning each data set and model are contained in Appendix A through 

Appendix F. It is important to note that two of the data sets (AEO 2009 and MiniCAM 2008) 

represent modeled results, not direct model inputs. These two data sets include cost and 

performance improvements that result from increased deployment (termed “learning by doing”) 

as well as resulting capacity factors estimated from particular model runs, whereas other data 

sets represent data before the model is run. The differences in capacity and availability factors 

that result from this discrepancy are discussed in further detail in Section 2.3, and learning rate 

differences are discussed in Section 3.2.

Table 1. Six Data Sets Used by Six Models

Data Set Model

Designation Time 

Horizon

Data 

Type Name Owner

AEO 2009 2030 Modeled

result

NEMS – National Energy 

Modeling System

Energy Information Administration 

(EIA)

GPRA 2009 2050 Model 

input

MARKAL – Market Allocation International Energy Agency1

NREL-SEAC 

2008

and 

Brookhaven National Laboratory

2

2050 Model 

input

ReEDS – Regional Energy 

Deployment System

National Renewable Energy 

Laboratory (NREL)

MiniCAM 

2008

2050 Modeled 

result

MiniCAM – Mini Climate 

Assessment Model

Pacific Northwest National 

Laboratory (PNNL)

EPA 2009 2035 Model 

input

IPM – Integrated Planning 

Model

ICF International

MERGE 

2009

2050 Model 

input

MERGE – Model for 

Estimating the Regional and 

Global Effects of 

Greenhouse Gas Reductions

Electric Power Research Institute 

(EPRI)

In addition to comparing and contrasting power plant characteristics, the levelized cost of energy 

(LCOE) was evaluated for the technologies contained in each data set. LCOE values were 

computed using an Excel spreadsheet – referred to as the “LCOE tool” – created during this 

project. The LCOE tool was developed to handle up to 29 unique technologies spanning seven 

energy sources – coal, natural gas, nuclear, biomass, geothermal, wind, and solar. A description 

of the LCOE tool is provided in Appendix G.

 1 The Energy Technology Systems Analysis Programme (ETSAP) was formed through an International Energy 

Agency (IEA) Implementing Agreement. ETSAP oversees the MARKAL model. 2 For the ReEDS model, three NREL-SEAC data sets were considered corresponding to three different years (2008, 

2009, and 2010). Based on discussions with NREL, the NREL-SEAC 2008 data set was selected for analysis in this 

report.
2

1.2 Technologies Covered

The six data sets cover a wide range of technologies that include both the existing fleet of power 

generation plants as well as new plants that may be built to meet future electricity needs. This 

project is focused on new power plants that may be built in coming years, and a list of new 

power plant technologies included in each data set is shown in Table 2.

Table 2. New Electricity Generation Technologies by Data Set3, 4

Technology

Data Set

AEO 

2009

GPRA 

2009

NRELSEAC 

2008

MiniCAM 

2008

EPA 

2009

MERGE 

2009

Coal

Pulverized 

Coal

Scrubbed X X X X X

Scrubbed, w/ biomass cofiring X

Adv, 1st gen, w/ carbon capture X

Adv, 2nd gen, w/ carbon capture X

IGCC 

(advanced 

coal)

Conventional X X X X X

Adv, 1st gen, w/ carbon capture X X X X X

Adv, 2nd gen, w/ carbon capture X

Natural Gas (includes dual fuel)

Combustion 

Turbine 

Conventional X X

Advanced X X X

Combined 

Cycle

Conventional X X

Advanced X X X X

Adv. w/ carbon capture X X X

Distributed 

Generation

Base X

Peak X

Fuel Cells X X

Nuclear X X X X X

Hydro

Conventional X X

Build-out on powered 

Build-out on unpowered

Upgrade

Small 

Wind

Onshore All wind classes X X X X X X

Offshore All wind classes X X X

 3 Table shows technologies that may be installed in the future. Table does not include plants that currently exist in 

the power generation fleet that are not expected to be installed in the future.

4 Technologies marked with boldface “X”s are the focus technologies in this report.
3

Technology

Data Set

AEO 

2009

GPRA 

2009

NRELSEAC 

2008

MiniCAM 

2008

EPA 

2009

MERGE 

2009

Solar

PV X X X X X X

Solar Thermal X X X X X X

Geothermal 

Hydrothermal X X X X X

EGS X X

Biomass

MSW, Landfill Gas X X X

Other X X X X X

Energy Storage

Battery X

Compressed air energy storage (CAES) X

ICE Storage X

Pumped Hydro X

Technical performance and cost characteristics were collected for each of the technologies listed 

in Table 2. Characteristics were gathered for each year within the time horizon covered by each 

data set. These characteristics were then reviewed for accuracy and entered into an Access 

database to facilitate data retrieval, analysis, and maintenance.

For the analysis discussed throughout this report, the technologies shown in Table 2 were 

narrowed to 11 focus technologies as indicated in Table 3. These 11 technologies represent 

power generation options that are generally regarded to have a significant role in meeting the 

future demand for electricity. This portfolio of power plants includes a cross section of fossil, 

nuclear, and renewable technologies
4

Table 3. Eleven Focus Technologies for This Report

Energy Source Utility Scale Power Generation Technology

Coal Coal (pulverized coal plant); Integrated Gasification Combined Cycle (IGCC)

Natural Gas Combustion Turbine (advanced) 5; Combined Cycle (advanced) 6

Nuclear Nuclear plant

Biomass Biomass gasification plant

Geothermal Hydrothermal

Wind Onshore7

; Offshore8

Solar Solar Thermal9, 10; Photovoltaic (utility scale PV)

The 11 technologies are not all contained in each data set. Some data sets (e.g., AEO 2009, 

NREL-SEAC 2008, and EPA 2009) have broad coverage of power generation plants and include 

most, or all, of the 11 technologies. Other data sets are focused on particular technologies (e.g., 

the GPRA data set is focused on the Department of Energy’s Energy Efficiency and Renewable 

Energy (EERE) technologies), and these data sets contain a subset of the 11 technologies. A 

matrix of the 11 technologies that are contained in each of the six data sets is shown in Table 4. 

The technologies contained in each data are also indicated in Table 2 (marked with boldface

“X”).

Table 4. Technologies Covered in Each Data Set (11 technologies)

Technology

Data Set

AEO 

2009

GPRA 

2009

NRELSEAC 

2008

MiniCAM 

2008

EPA 

2009

MERGE 

2009

Coal X --- X X X X

IGCC X --- X X X X

Combustion Turbine X --- X X X ---

Combined Cycle X --- X X X X

Nuclear X --- X X X X

Biomass X --- X X X X

Geothermal (hydrothermal) X X X X X ---

Wind (onshore) X X X X X X

Wind (offshore) X X X --- --- ---

Solar Thermal X X X X X X

PV X X X X X X

 5 The MiniCAM 2008 data set did not have an advanced technology, and the MiniCAM conventional technology 

was used for comparison with other data sets.

6 Ibid.

7 For NREL-SEAC 2008, Class 5 onshore wind is used for comparisons to other data sets. 8 For NREL-SEAC 2008, Class 5 offshore wind (shallow) is used for comparisons. 9 For NREL-SEAC 2008, Class 4 solar thermal is used for comparisons. 10 The MiniCAM 2008 and GPRA 2009 data sets include thermal energy storage with solar thermal power plants. 

No other data sets in this study include energy storage with solar thermal plants.
5

1.3 Report Organization

The report organization is shown in Table 5. In Section 2, the technical performance parameters 

for the electricity generation technologies contained in the data sets are discussed. In Section 3, 

the overnight capital cost and the operation and maintenance (O&M) costs are compared and 

contrasted. In Section 4, a summary of LCOE values is presented, including an analysis of the

sensitivity of LCOE results to several parameters.

Table 5. Report Organization

Section Title

1 Introduction

2 Technical Performance Characteristics

3 Cost Characteristics

4 Levelized Cost of Energy

5 Conclusions
6

2 Technical Performance Characteristics

The comparison of technical performance characteristics is organized as follows:

• Plant Size

• Heat Rate

• Availability Factor (Capacity Factor)

• Plant Lifetime

2.1 Plant Size

Table 6 shows the plant sizes for 11 technologies in each of the six data sets. The power plant 

sizes, including the standard deviation and the coefficient of variation, are shown in Figure 1. 

The coefficient of variation is expressed in percent and is equal to the standard deviation divided 

by the mean.

Table 6. Plant Size (MW)

Technology

Data Set

Standard 

Deviation

Coefficient 

AEO GPRA of Variation

NRELSEAC MiniCAM EPA MERGE

Coal 600 --- 600 600 600 675 34 5%

IGCC 550 --- 550 550 550 800 112 19%

Combustion Turbine 230 --- 160 160 230 --- 40 21%

Combined Cycle 400 --- 300 250 400 450 82 23%

Nuclear 1,350 --- 1,000 1,350 1,350 1,400 164 13%

Biomass 80 --- 100 80 80 75 10 12%

Geothermal 

(hydrothermal) 50 50 50 50 50 --- 0 0%

Wind (onshore) 50 50 100 50 50 100 26 39%

Wind (offshore) 100 100 100 --- --- --- 0 0%

Solar Thermal 100 100 200 100 100 125 40 33%

PV 5 5 100 5 5 20 38 163%

Note: Coefficient of variation (CV) is the standard deviation divided by the mean.

As indicated in Table 6 and Figure 1, the coefficient of variation (CV) is less than 50% for all 

technologies with the exception of PV systems, which have a CV of 163%. Depending on the 

data set, PV plant sizes range from 5 MW to 100 MW. PV for utility scale generation is an 

emerging technology, and many different plant sizes and configurations have been proposed. 

These variations have a wide range of capacities, leading to a large standard deviation (PV 

standard deviation equal to 38 MW; coefficient of variation equal to 163%). 
7

0

200

400

600

800

1,000

1,200

1,400

1,600

Coal IGCC Combustion

Turbine

Combined

Cycle 

Nuclear Biomass Geothermal Wind

(onshore)

Wind

(offshore)

Solar Thermal PV

Technology

Plant Size (MW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

CV=5%

CV=21%

CV=23%

CV=13%

CV=12%

CV=0%

CV=39%

CV=0%

CV=33%

CV=163%

CV=19%

Figure 1. Power plant size comparison
8

2.2 Heat Rate

Heat rate values in 2010 for fossil, nuclear, biomass, and geothermal technologies are shown in 

Table 7. In most data sets, heat rates remain constant over the modeling horizon or show a 

modest improvement (see following data set sections in this report for details on heat rate 

changes over time).

Table 7. Heat Rate Values by Data Set (Btu/kWh, for 2010)11

Technology

Data Set

Standard 

Deviation

Coefficient 

AEO GPRA of Variation

NRELSEAC MiniCAM EPA MERGE

Coal 9,200 --- 9,200 9,319 9,200 8,979 123 1%

IGCC 8,765 --- 9,000 8,005 8,765 8,979 406 5%

Combustion 

Turbine 9,289 --- 8,900 8,877 9,289 --- 231 3%

Combined 

Cycle 6,752 --- 6,870 6,164 6,752 7,260 393 6%

Nuclear 10,434 --- 10,400 10,339 10,434 10,339 48 0%

Biomass 9,646 --- 14,500 12,133 9,646 12,186 2,042 18%

Geothermal 34,633 --- --- 34,120 --- --- 363 1%

The heat rate values in Table 7 are shown in Figure 2 along with the coefficient of variation. The 

CV values are below 20% for all six technologies, and below 10% for six of the seven 

technologies.

The two technologies with large CV values are combustion turbines (13%) and biomass (18%). 

For biomass, the NREL-SEAC data set is at the high end of the heat rate range, and the AEO and 

EPA data sets are at the low end of the heat rate range.

 11 For some technologies in some data sets, the online year occurs after 2010. In these cases, the heat rates 

correspond to the on-line year.
9

Figure 2. Heat rate comparisons
10

2.3 Availability Factor (Capacity Factor)

Capacity factors are often outputs in energy-economic models, based on estimated generation 

and taking into account any curtailment that is necessary. The maximum availability factor is 

generally the input to the model and represents the highest possible capacity factor. AEO 2009 

and MiniCAM 2008 wind and solar data represent capacity factors from model outputs, whereas 

other data sets represent maximum availability factors for wind and solar technologies. Data for 

conventional, biomass, and geothermal technologies from all data sets are maximum availability 

factors. Capacity factors and availability factors are used interchangeably throughout the report, 

as generally the capacity factor is very close or equal to the maximum availability factor. An 

important exception to this is when models utilize supply curves for renewable technologies

where the quality of the resource is highly variable and depends upon the location of the facility. 

Technologies that make use of geothermal, wind, and solar resources are often modeled using a 

supply curve. In a supply curve, there is a limited amount of development that may occur in any 

resource class, and each resource class has an associated capacity factor. For AEO 2009 and 

MiniCAM 2008 data, the reported capacity factors for wind and solar technologies represent 

average capacity factors of all installations estimated in model outputs. EPA 2009 and MERGE 

2009 data represent average national capacity factor inputs, whereas GPRA 2009 and NRELSEAC 2008 data represent capacity factor inputs from particular classes within a supply curve. 

Capacity factors for 2010 are shown in Table 8 and Figure 3 (or the closest year in the data 

set).12

Table 8. Capacity Factors for 2010

For combustion turbines, the MiniCAM data has a significantly lower availability factor 

compared to other data sets (10%). This value is closer to the resulting capacity factor for all data 

sets for combustion turbine technologies. 

Technology

Data Set

Standard 

Deviation

Coefficient 

AEO GPRA of Variation

NRELSEAC MiniCAM EPA MERGE

Coal 85% --- 85% 80% 85% 80% 0.027 3%

IGCC 85% --- 81% 80% 85% 80% 0.026 3%

Combustion Turbine 92% --- 88% 10% 92% --- 0.404 57%

Combined Cycle 87% --- 85% 80% 87% 80% 0.035 4%

Nuclear 90% --- 90% 90% 89% 90% 0.004 0%

Biomass 83% --- 84% 80% 83% 85% 0.019 2%

Geothermal 

(hydrothermal)

90% 95% 85% 90% 87% --- 0.039 4%

Wind (onshore)13 44% 43%* 43%* 42% 39% 35% 0.034 8%

Wind (offshore)14 40% 36%* 45%* --- --- --- 0.045 11%

Solar Thermal15 31% 42%+ 32%16 73%+ 36% 22% 0.178 45%

PV 22% 23% 21% 25% 24% 26% 0.019 8%

 12 If the technology online year occurs after 2010, the table shows the capacity factor for the online year, not 2010. 13 “*” indicates a Class 5 wind resource was used. Otherwise, a national average capacity factor was used. 14 “*” indicates a Class 5 wind resource was used. Otherwise, a national average capacity factor was used. 15 “+” indicates thermal energy storage is included 16 Class 4 solar thermal resource
11

The capacity factors for solar thermal technologies are interesting from the perspective that 

MiniCAM and GPRA incorporate thermal storage, whereas the other four data sets do not. 

Thermal storage is the reason for the relatively high (73%) capacity factor in the MiniCAM data 

set. In the MiniCAM data set, the level of thermal storage is assumed to be constant over time 

(i.e., MiniCAM solar thermal capacity factor remains constant at 73% over time). However, in 

the GPRA data set the level of thermal storage increases over time, which leads to an increase in 

the solar thermal capacity factor over time. In GPRA, the solar thermal capacity factor doubles 

between 2007 (41%) and 2030 (82%), with intermediate values of 42% in 2010 and 74% in 

2025.

For PV, the capacity factors range from 21% to 26% with a standard deviation of 0.019 (8% 

coefficient of variation). The NREL-SEAC data set has the lowest PV capacity factor (21%), and 

the MERGE data set has the highest capacity factor (26%).

Capacity factors for the year 2025 are shown in Table 9 and Figure 4, and changes relative to 

2010 are shown in Table 10. Fossil and nuclear technology capacity factors show little or no 

change between 2010 and 2025. However, renewable technology capacity factors increase in 

several cases between 2010 and 2025. One interesting observation is that the capacity factor for 

onshore wind in the AEO data set actually declines between 2010 and 2025 (from 44% to 40%). 

For the AEO data set, capacity factors are endogenous variables that are generated by the NEMS 

model. In the case of onshore wind technologies, NEMS is forecasting a decline in capacity 

factors, perhaps due to the development of lower quality wind resources in later years.

Table 9. Capacity Factors for 2025

Technology

Data Set

Standard 

Deviation

Coefficient of 

AEO GPRA Variation

NRELSEAC MiniCAM EPA MERGE

Coal 85% --- 85% 80% 85% 80% 0.027 3%

IGCC 85% --- 81% 80% 85% 80% 0.026 3%

Combustion Turbine 92% --- 80% 10% 92% --- 0.394 58%

Combined Cycle 87% --- 85% 80% 87% 80% 0.035 4%

Nuclear 90% --- 90% 90% 89% 90% 0.003 0%

Biomass 83% --- 84% 80% 83% 85% 0.019 2%

Geothermal 

(hydrothermal)

90% 95% 85% 90% 87% --- 0.039 4%

Wind (onshore)17 40% 47%* 46%* 46% 39% 42% 0.035 8%

Wind (offshore)18 40% 45%* 48%* --- --- --- 0.038 9%

Solar Thermal19 31% 74% 32%20 73% 36% 22% 0.229 51%

PV 22% 32% 21% 25% 24% 26% 0.037 15%

 17 “*” indicates a Class 5 wind resource was used. Otherwise, a national average capacity factor was used. 18 “*” indicates a Class 5 wind resource was used. Otherwise, a national average capacity factor was used. 19 “+” indicates thermal energy storage is included 20 Class 4 solar thermal resource
12

Table 10. Capacity Factor Changes in 2025 Relative to 2010

Technology

Data Set

AEO GPRA NREL-SEAC MiniCAM EPA MERGE

Coal 0% --- 0% 0% 0% 0%

IGCC 0% --- 0% 0% 0% 0%

Combustion Turbine 0% --- 0% 0% 0% ---

Combined Cycle 0% --- 0% 0% 0% 0%

Nuclear 0% --- 0% 0% 0% 0%

Biomass 0% --- 0% 0% 0% 0%

Geothermal (hydrothermal) 0% 0% 0% 0% 0% ---

Wind (onshore) -10% 9% 6% 10% 0% 20%

Wind (offshore) 0% 25% 6% --- --- ---

Solar Thermal 0% 79% 0% 0% 0% 0%

PV 0% 36% 0% 0% 0% 0%
13

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Coal IGCC Combustion

Turbine

Combined

Cycle 

Nuclear Biomass Geothermal Wind

(onshore)

Wind

(offshore)

Solar Thermal PV

Technology

Capacity Factor (%)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

CV=3%

CV=58%

CV=4%

CV=0%

CV=2%

CV=4%

CV=8% CV=11%

CV=45%

CV=8%

CV=3%

Figure 3. 2010 Capacity factor comparison
14

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Coal IGCC Combustion

Turbine

Combined

Cycle 

Nuclear Biomass Geothermal Wind

(onshore)

Wind

(offshore)

Solar Thermal PV

Technology

Capacity Factor (%)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

CV=3%

CV=58%

CV=4%

CV=0% CV=2%

CV=4%

CV=8% CV=9%

CV=51%

CV=15%

CV=3%

Figure 4. 2025 Capacity factor comparison
15

2.4 Plant Lifetime

Plant lifetimes are shown in Table 11, and in Figure 5. It can be difficult to draw conclusions 

when comparing lifetimes. For example, some data sets, such as AEO and EPA do not use plant 

lifetimes. The models supported by these two data sets – NEMS and IPM, respectively – allow 

plants to run for as long as they are economic, with no pre-determined retirement age. In some 

data sets, lifetimes represent the maximum service life, whereas in other models, the reported 

lifetime is the value used to compute economic results, such as the levelized cost of energy 

(LCOE) or for retirement calculations.

Table 11. Plant Lifetime (years)

Technology

Data Set

Standard 

Deviation

Coefficient of 

AEO GPRA Variation

NRELSEAC MiniCAM EPA MERGE21

Coal X --- 60 45 X 30 15.0 33%

IGCC X --- 60 45 X 30 15.0 33%

Combustion Turbine X --- 30 45 X --- 10.6 28%

Combined Cycle X --- 30 45 X 30 8.7 25%

Nuclear X --- 60 60 X 30 17.3 35%

Biomass X --- 45 45 X 30 8.7 22%

Geothermal 

(hydrothermal)

X + 20 30 X --- 7.1 28%

Wind (onshore) X 20 20 30 X 30 5.8 23%

Wind (offshore) X 20 20 --- --- --- 0.0 0%

Solar Thermal X 30 30 30 X 30 0.0 0%

PV X 30 30 30 X 30 0.0 0%

--- Technology not included in data set

X Technology included in data set, but lifetime not pre-determined

+ Technology included in data set, but lifetime not reported

A few general trends, though, can be discerned regarding plant lifetimes.

• Renewable technologies – geothermal, wind, and solar – have lifetimes in the range 

of 20 to 30 years.

• Fossil plants have lifetimes in the range of 30 to 60 years.

• Nuclear plants in most data sets have a lifetime of 60 years (30-40 year initial license 

plus one 20-30 year license renewal).

• In the MERGE data set, all plants are assumed to have an economic life of 30 years. 

These 30-year plant lifetime values are for economic calculations, and are not 

intended to reflect service lifetimes.

 21 MERGE plant lifetimes represent economic lifetimes, not service lifetimes
16

Figure 5. Plant lifetime comparison
17

3 Cost Characteristics

The comparison of cost characteristics is organized as follows:

• Overnight Capital Costs22

• Learning

• O&M Costs (fixed and variable)

For cost comparisons, the capital and O&M costs in all data sets have been adjusted to 2007 

dollars using the GDP index shown in Appendix H.

3.1 Capital Cost

The overnight capital costs for the 11 technologies in the six data sets are presented in the 

following 11 graphs (Figure 6 through Figure 16). Each of the 11 technologies is included in a 

separate comparison chart. All costs have been adjusted to 2007 dollars. The following are a few 

observations concerning overnight capital costs in the following 11 figures:

• Coal – As indicated in Figure 6, MiniCAM is at the low end of the cost range. This 

result is likely because the MiniCAM characteristics are generally taken from the 

AEO 2008 publication (2007 calendar year data); while the other data sets generally 

have characteristics based on calendar year 2008 or 2009. There was a significant 

run-up in power plant costs starting in 2007, largely due to changes in commodity 

prices, and this cost increase is likely not captured in the MiniCAM data set.

The capital costs for coal plants in the MERGE data set are at the high end of the 

range, and are held constant across the modeling horizon (indicative of no learning 

effects). The coal costs in the NREL-SEAC data set fall below the MERGE costs, but 

above the costs for all other data sets. As indicated in Table 4, the GPRA data set 

examined for this project does not include coal.

• IGCC – For IGCC plants (Figure 7), MiniCAM costs are at the bottom end of the 

range, and show declining costs over time (indicative of learning effects pushing 

costs downward over time). MERGE and NREL-SEAC are at the high end of the 

range, with NREL-SEAC costs trending slightly above MERGE overnight capital 

costs. Both the NREL-SEAC and MERGE data are relatively flat, showing no 

significant learning effects. The other data sets – AEO, MiniCAM, and EPA – all 

show noticeable declines in overnight capital costs over time. The GPRA data set 

does not include IGCC.

• Combustion Turbine – For combustion turbines (Figure 8), the NREL-SEAC data 

set has the highest overnight capital costs, and the MiniCAM data set generally has 

the lowest capital costs. For both NREL-SEAC and MiniCAM, the costs are 

 22 Different data sets may have included different hardware for a particular technology, or used different definitions 

for “overnight capital costs.” The level of detail provided with each data set did not allow us to thoroughly check all 

technologies and all definitions across all data sets. Overnight capital costs are shown as they are reported. Future 

analyses that compare overnight capital costs between data sets could be improved with a more detailed examination 

of technology descriptions and financial definitions used in the data sets being compared.
18

relatively constant over the modeling horizon (NREL-SEAC costs increase through 

2010, and then remain constant). The other two data sets – AEO and EPA – both 

show declining combustion turbine costs over time. Starting in about 2025, AEO 

costs are less than or equal to MiniCAM costs. The GPRA and MERGE data sets do 

not include combustion turbines.

• Combined Cycle – For combined cycle plants (Figure 9), the MiniCAM data set has 

the lowest costs. The MiniCAM costs remain constant through 2020 (near $800/kW), 

then decline through 2050 (down to about $600/kW in 2050). The NREL-SEAC 

overnight capital costs start near the MiniCAM costs, but remain constant over the 

modeling horizon. The MERGE costs are higher (near $900/kW), and remain 

relatively constant over the modeling horizon. Compared to the other data sets, the 

AEO and EPA data sets do show noticeable declines in overnight capital costs 

starting in about 2015. The AEO costs are comparable to the MiniCAM costs in 2030 

(around $700/kW). The GPRA data set does not include combined cycle.

• Nuclear – For nuclear plants (Figure 10), overnight capital costs fall in the range of 

approximately $2,500/kW to $4,800/kW. The MERGE data set is at the top end of the 

range, while the MiniCAM costs are towards the low end of the range. Costs in the 

AEO, NREL-SEAC and EPA data sets fall in between. Starting in about 2015, the 

AEO data set shows a decline in costs over time, with projected costs falling below 

$2,500 by 2030. The GPRA data set does not include nuclear.

• Biomass – Overnight capital costs for biomass technologies (Figure 11) are between 

approximately $2,000/kW and $4,000/kW. The MiniCAM costs are at the low end of 

the range, and the MERGE costs are at the high end over the entire time horizon. 

AEO and EPA costs start at the high end, but then fall, with AEO having costs below 

$2,500/kW by 2030. NREL-SEAC overnight capital costs are constant at slightly 

under $3,000/kW over the modeling horizon. The GPRA data set does not include 

biomass.

• Geothermal – The geothermal category shown in Figure 12 represents geothermal 

hydrothermal plants. As indicated, the EPA data set has an unusually high overnight 

capital – nearly $10,000/kW. However, this cost represents an average cost, and 

actual overnight costs for plants adopted each year could be significantly different. In 

the EPA data set, geothermal hydrothermal costs range from approximately 

$1,400/kW to over $17,000/kW depending on site-specific parameters. The value 

shown in Figure 12 is a simple average of these cost extremes. Data showing 

overnight capital costs for geothermal plants actually adopted each year in connection 

with the EPA 2009 data set were not available. Other than the EPA data set, overnight 

capital costs for geothermal plants fall between approximately $2,000/kW and 

$5,000/kW. Excluding the EPA data set, the GPRA costs are at the high end of the 

range, and the MiniCAM costs are towards the low end of the range. The AEO data 

set starts at a relatively low value (under $2,000/kW), and then rises sharply to nearly 

$5,000/kW before declining to about $3,000/kW. The sharp rise in geothermal 

overnight capital costs for the AEO data set results from the modeling approach used 

with this data set. For the AEO data set, there is an input list of identified available 

geothermal sites, with individual cost and performance characteristics for each site. 
19

The NEMS model, which is run in conjunction with the AEO 2009 data set, adopts 

the least expensive site first. Once a specific geothermal site is selected to be built, it 

is removed from the list and the next most expensive geothermal site becomes 

available for adoption.

• Wind (onshore) – For onshore wind (Figure 13), the MERGE data set has overnight 

capital costs at the upper end, and the GPRA and MiniCAM data sets are at the low 

end. The AEO, NREL-SEAC, and EPA data sets are in between. With NREL-SEAC

and AEO, the overnight capital costs decline over time, indicating that learning is 

having an impact. The EPA costs remain constant over time.

• Wind (offshore) – Three data sets – AEO 2009, NREL-SEAC 2008, and GPRA 2009 

– have offshore wind (Figure 14). Overnight capital costs for AEO 2009 are at the 

upper end, starting slightly below $4,000/kW in 2007, and then falling below 

$3,000/kW by 2030. The GPRA cost curve starts slightly below $3,000/kW, and then 

declines relatively quickly crossing the NREL-SEAC cost curve in 2014. From 2030 

and beyond, the GPRA cost is near $1,500/kW. The NREL cost curve starts near 

$2,500/kW in 2005, and declines to approximately $2,200/kW by 2050.

• Solar Thermal – In 2005 (Figure 15), the NREL-SEAC data set has an overnight 

capital cost for solar thermal near $6,500/kW. The NREL-SEAC cost curve declines 

relatively quickly, reaching costs comparable to the other data sets by 2015. From 

approximately 2015 and beyond, the NREL-SEAC, EPA, and MERGE data sets have 

constant costs (i.e., no learning) in the range of $4,500/kW to $5,000/kW. The AEO, 

GPRA, and MiniCAM data sets all show declining costs. Between 2025 and 2030 

(end year for AEO), the AEO and GPRA data sets trend closely, and reflect the 

lowest overnight capital costs (reaching about $3,000/kW in 2030). From 2030 to 

2050, the GPRA data set has the lowest costs, with the MiniCAM costs trending 

slightly higher. Solar thermal plants in MiniCAM and GPRA include thermal energy 

storage, while solar thermal plants in the other four data sets do not. It is interesting to 

note that the overnight capital costs for the AEO and GPRA data sets are quite close 

between 2025 and 2030, even though GPRA has thermal storage and AEO does not.

• PV – As seen in Figure 16, the GPRA and NREL-SEAC data sets have overnight 

capital costs that track relatively closely, with PV overnight capital costs in these two 

data sets consistently falling below PV costs in the other data sets. The MiniCAM PV 

overnight capital costs start above $10,000/kW, but decline to about $2,200/kW by 

the end of the modeling horizon (2050). The MERGE data set and the EPA data set 

each have constant costs – slightly under $8,000/kW for MERGE and near 

$5,800/kW for EPA. The AEO data set shows cost improvements over time –

declining from around $6,000/kW in the early years to below $4,000/kW in 2030.

Table 12 summarizes the overnight capital cost trends in the six data sets. With the exception of 

the EPA and NREL-SEAC data sets, the data sets show similar cost trends for fossil fuel and 

renewable technologies. In the NREL-SEAC data set, capital costs for most fossil plants remain 

constant and capital costs for most renewable plants decline. Compared to the NREL-SEAC data 

set, the EPA data set generally shows different trends. In the EPA data set, capital costs for 

several fossil plants decline (or show up and down fluctuations), while capital costs for 

renewable plants typically remain constant.
20

As Table 12 shows, the AEO data set exhibits variable costs. The AEO data set includes a 

commodity cost adjustment based on a forecast of the metal and metal products producer price 

index. This index is forecast in the NEMS model, and it shows increasing capital costs over the 

next few years (through about 2015). This PPI forecast leads to cost increases in the early years 

(through about 2015) in the AEO 2009 data set. However, after the first few years, overnight 

capital costs for all technologies in the AEO data set decline as a result of learning effects.

Table 12. Capital Cost Trends by Data Set and Technology (11 technologies)

Technology

Data Set

AEO 

2009

GPRA 

2009

NREL-SEAC 

2008

MiniCAM

2008

EPA 

2009

MERGE

2009

Coal  ---    O

IGCC  ---    O

Combustion Turbine  ---    ---

Combined Cycle  --- O   

Nuclear  ---   O 

Biomass  --- O   O

Geothermal (hydrothermal)   O  O ---

Wind (onshore)     O O

Wind (offshore)    --- --- ---

Solar Thermal     O O

PV     O O

--- technology not included in data set

O constant costs

 declining costs 

 variable costs (cost trend shows rising, falling, and constant behavior over modeling horizon)
21

0

500

1,000

1,500

2,000

2,500

3,000

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capital Cost (2007$ / kW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Technology not incuded in following data set:

-- GPRA 2009

Figure 6. Overnight capital costs—coal 
22

0

500

1,000

1,500

2,000

2,500

3,000

3,500

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year 

Capital Cost (2007$ / kW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Technology not included in following data set:

-- GPRA 2009

Figure 7. Overnight capital costs—IGCC 
23

0

100

200

300

400

500

600

700

800

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capital Cost (2007$ / kW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Technology not included in following data sets:

-- GPRA 2009

-- MERGE

Figure 8. Overnight capital costs—combustion turbine 
24

0

200

400

600

800

1,000

1,200

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capital Cost (2007$ / kW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Technology not included in following data set:

-- GPRA 2009

Figure 9. Overnight capital costs—combined cycle 
25

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capital Cost (2007$ / kW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Technology not included in following data set:

-- GPRA 2009

Figure 10. Overnight capital costs—nuclear 
26

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capital Cost (2007$ / kW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Technology not included in following data set:

-- GPRA 2009

Figure 11. Overnight capital costs—biomass 
27

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capital Cost (2007$ / kW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Technology not included in following data set:

-- MERGE

Figure 12. Overnight capital costs—geothermal
28

0

500

1,000

1,500

2,000

2,500

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capital Cost (2007$ / kW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Figure 13. Overnight capital costs—wind (onshore) 
29

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capital Cost (2007$ / kW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Technology not included in following data sets:

-- MiniCAM

-- EPA 

-- MERGE

Figure 14. Wind (offshore)—overnight capital costs
30

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capital Cost (2007$ / kW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Figure 15. Overnight capital costs—solar thermal
31

0

2,000

4,000

6,000

8,000

10,000

12,000

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capital Cost (2007$ / kW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Figure 16. Overnight capital costs—PV 
32

Table 13 shows the data sets with the highest and lowest capital costs in 2030. This table shows 

that the MERGE data set has the highest frequency of high overnight capital costs – highest, or 

tied for highest, for eight of the eleven technologies. The MERGE data set may have high costs 

because this data set generally shows no exogenous learning effects; instead, cost reductions 

occur in the model (not the data set) as certain expanded capacity thresholds have been reached.

Table 13. High and Low Capital Costs for Each Technology in 2030

Technology Highest Capital Cost Lowest Capital Costs

Coal MERGE AEO, MiniCAM (tied)

IGCC NREL-SEAC, MERGE (tied) AEO, MiniCAM (tied)

Combustion Turbine NREL-SEAC AEO

Combined Cycle MERGE AEO, MiniCAM (tied)

Nuclear MERGE AEO, MiniCAM (tied)

Biomass MERGE MiniCAM

Geothermal (hydrothermal) EPA MiniCAM

Wind (onshore) MERGE GPRA, MiniCAM (tied)

Wind (offshore) AEO GPRA

Solar Thermal EPA, MERGE, NREL-SEAC (tied) AEO, GPRA (tied)

PV MERGE GPRA, NREL-SEAC (tied)

The MiniCAM data set is at the low end of the cost spectrum in 2030 – lowest, or tied for lowest

– for seven of eleven technologies. This observation is likely because the MiniCAM 

characteristics are generally taken from the AEO 2008 publication (2007 calendar year data),

while the other data sets generally have characteristics based on calendar year 2008 or 2009. 

Although calendar year is not a direct indication of the year when the underlying data was 

derived, it can be used as a proxy for the vintage of the underlying data, which was not made 

available for MiniCAM and most other data sets. There was a significant run-up in power plant 

costs starting in 2007, largely due to changes in commodity prices, and these increases are likely 

not captured in the MiniCAM data set. After MiniCAM, the AEO data set has the highest 

frequency of low costs – five of eleven technologies. The AEO data set has relatively aggressive 

learning factors, and these high learning rates lead to relatively low capital costs for most 

technologies by 2030.

Another comparison of overnight capital costs is offered in Figure 17 and Figure 18. These 

figures show simple average costs for conventional technologies and renewables, respectively. 

The conventional technology group consists of four types of power plants: coal, IGCC, 

combined cycle, and nuclear (combustion turbines are not included in every data set, and are 

therefore omitted). The renewable group consists of three renewable technologies: onshore wind, 

solar thermal and PV (biomass, geothermal, and offshore wind are not included in every data set 

and are therefore omitted).
33

0

500

1,000

1,500

2,000

2,500

3,000

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055

Year

Capital Costs (2007$/kW)

AEO 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Technology Basis: coal, IGCC, 

combined cycle, and nuclear

Data Set Not Included: GPRA 2009

Figure 17. Average overnight capital costs—conventional technologies
34

0

1,000

2,000

3,000

4,000

5,000

6,000

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055

Year

Capital Cost (2007$/kW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Technology Basis: onshore wind, 

solar thermal, and PV

Figure 18. Average overnight capital costs—renewable technologies 
35

The ratio of averaged renewable to conventional overnight capital costs is shown in Figure 19

(costs in Figure 18 divided by costs in Figure 17). This chart shows that the NREL-SEAC data 

set has the lowest differential between renewable and conventional costs. By 2025, the cost 

differential in the NREL-SEAC data set is reduced to 1.2, and by approximately 2035 the 

differential falls to 1.1 (i.e., the overnight capital cost of renewables is only 10% higher than 

conventional technologies). The EPA, MERGE, AEO and MiniCAM data sets all have higher 

costs differentials compared to the NREL-SEAC data set.

Scatter charts were prepared to examine the correlation between capital cost and plant size for 

2010 and 2025. These charts are helpful in examining how costs are clustered, and how costs 

change over time (based on two years – 2010 and 2025).

The first scatter chart is shown in Figure 20, which shows capital costs and plant sizes in 2010. 

As indicated on the left hand edge of this figure, PV has relatively high costs, but it is also the 

smallest sized power generation technology compared. Moving up in capacity, onshore wind is 

the lowest cost technology for power plants with capacities near 100 MW. Combustion turbines 

are the lowest cost technology near 200 MW, and combined cycle plants are the lowest cost in 

the range of 300-450 MW. Nuclear is the only technology evaluated with capacities above 1,000 

MW. To a certain extent, these observations are an artifact of the plant size ranges appearing in 

the various data sets. However, we know that new power plant construction in 2009 is dominated 

by onshore wind, combustion turbine, and combined cycle power plants, which is consistent with 

the overnight capital costs shown in Figure 20.

Figure 21 considers capital costs and plant sizes in 2025. Many of the cost points are somewhat 

lower in 2025 than in 2010, but the broad appearance of the scatter chart has not changed. The 

lowest cost plant technology for a given plant size has not changed over this time frame.

Scatter charts were also prepared to explore the correlation between capital cost and plant 

capacity factors for 2010 and 2025. The purpose of these charts is to anticipate the effect of 

capacity factors on the cost of energy. In general, the lowest levelized cost of energy (LCOE) 

will occur for capacity factors and low capital costs (the lower right corner of these charts).

Figure 22 considers the capital costs and capacity factors for 2010 (or the closest year in the data 

set). With the exception of the MiniCAM combustion turbine data point (which has a capacity 

factor corresponding to peaking power), the nuclear, biomass, and fossil fuel plants are at the 

right edge (high capacity factor). At low capacity factors, the technologies are stacked from 

lowest capital costs to highest capital costs in the following order: 1) onshore wind, 2) offshore 

wind, 3) solar thermal, and 4) PV.
36

0.0

0.5

1.0

1.5

2.0

2.5

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 2055

Year

Capital Cost Ratio

AEO 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Data Set Not Included: GPRA 2009

Technology Basis: 

 Conventional: coal, IGCC, combined cycle, and nuclear

 Renewable: onshore wind, solar thermal, PV

Figure 19. Ratio of average overnight capital costs (renewable/conventional)
37

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

0 200 400 600 800 1,000 1,200 1,400

Plant Size (MW)

Capital Cost (2007$ / kW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

PV

Nuclear

Coal

IGCC

Combined Cycle

Geothermal

Solar Thermal

Offshore Wind

Onshore Wind

Biomass

Comb Turbine

Off Scale

EPA IPM Geothermal = 9,613

MiniCAM PV = 8,486

Figure 20. Capital cost versus plant size, 2010
38

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

0 200 400 600 800 1,000 1,200 1,400

Plant Size (MW)

Capital Cost (2007$ / kW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

PV

Nuclear

Coal

IGCC

Combined Cycle

Geothermal

Solar Thermal

Offshore Wind

Onshore Wind

Biomass

Comb Turbine

Offscale

EPA IPM Geothermal = 9,613

Figure 21. Capital cost versus plant size, 2025
39

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Capacity Factor (%)

Capital Cost (2007$ / kW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009 Fossil, Nuclear, Biomass, 

and Geothermal

Solar 

Thermal

Offshore Wind

PV

Off Scale

EPA IPM Geothermal = $9,613

MiniCAM PV = $8,486

Combustion 

Turbine 

(peaking) Onshore Wind

Figure 22. Overnight capital costs versus capacity factors, 2010
40

Figure 23 considers the capital costs and capacity factors for 2025. Compared to 2010, the data 

points on the chart have generally moved down (i.e., lower capital costs) and to the right (i.e., 

higher capacity factors), corresponding to lower capital costs and high capacity factors. With the 

exception of the MiniCAM combustion turbine data point (peaking plant), the nuclear, biomass, 

and fossil fuel plants are again at the right edge (high capacity factor). Onshore wind and PV 

have moved slightly to the right, while offshore wind and especially solar thermal have moved 

noticeably to the right (higher capacity factors).
41

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Capacity Factor (%)

Capital Cost (2007$ / kW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Fossil, Nuclear, Biomass, 

and Geothermal

Solar Thermal

Offshore Wind

PV

Onshore Wind

Offscale

EPA IPM Geothermal = $9,613

Combustion 

Turbine 

(peaking)

Figure 23. Overnight capital costs versus capacity factors, 2025
42

3.2 Learning

For the data sets evaluated in this study, learning is in some cases treated as in input parameter in 

the data set, and in other cases, learning is handled as an output parameter generated from the 

model associated with the data set. This report is focused on data sets, not energy models, and a 

detailed examination of learning in individual models is beyond the project scope. However, a 

few observations concerning learning include the following:23

• In the AEO 2009 data set, learning is handled as an endogenous variable (i.e., model 

output), which is generated by the NEMS model. The overnight capital costs that are 

presented in this report do include learning effects generated from the NEMS 

modeling analysis that was conducted using the AEO 2009 data set characteristics.

• For the NREL-SEAC 2008 data set, the impact of R&D (referred to as “learning by 

R&D”) is included in the data set, but the impact of “learning by doing” is not. 

Instead, the ReEDS model calculates “learning by doing” based upon the installed 

capacity additions for each technology.

• The GPRA data set is developed by multiple technology programs in the Department 

of Energy, which may address learning using different methodologies. However, the 

data set is believed to handle learning in a similar fashion as the NREL-SEAC data 

set. That is, “learning by R&D” is included in the data set, but “learning by doing” is 

not. For the most part, GPRA uses accelerated assumptions for R&D investment 

relative to NREL-SEAC, and hence larger reductions generally occur in overnight 

capital costs over time.

• The EPA data set is constructed in a similar fashion as the AEO data set. That is, 

learning is a model output.

• How learning effects are handled in the MERGE and MiniCAM data sets, and the 

associated models, is not well understood due to a lack of publicly available 

documentation. The overnight capital costs in MERGE show little or no change over 

time, suggesting that learning is handled in the MERGE model, not the MERGE data 

set. For MiniCAM, overnight capital costs do decline over time, suggesting the 

MiniCAM data set includes endogenous learning. 

• Certain models may incorporate growth rate penalties wherein the capital cost of a 

technology may increase due to rapid increases in demand (i.e. demand exceeds 

supply). This dynamic is not addressed in detail in this study, but growth penalties 

have the potential to reduce the levels of learning reported here and are worth further 

inquiry.

• The influence of learning can be observed in Figure 24 through Figure 34, which 

show normalized overnight capital costs for all six data sets (normalized such that the 

cost in the first year is one). In Table 14, the annual change in overnight costs is 

shown for a 15-year period – 2015 through 2030.

 23 Additional comments on learning effects are briefly discussed for each data set in the 

appendices (see learning sections in Appendix A through Appendix F). 
43

Table 14. Annual Change in Overnight Capital Costs, 2015 to 2030

Technology

Data Set

AEO GPRA

NRELSEAC MiniCAM EPA MERGE

Coal 1.9% N/A no change 0.3% 1.2% no change

IGCC 2.2% N/A no change X 1.3% no change

Combustion Turbine 2.4% N/A no change 0.2% 1.5% N/A

Combined Cycle 2.2% N/A no change 0.6% 1.3% -0.2%

Nuclear 2.6% N/A 0.5% 0.1% no change 1.1%

Biomass 2.7% N/A no change 0.3% 0.9% no change

Geothermal 2.3% 0.6% no change 0.4% no change N/A

Wind (onshore) 1.6% 1.1% 0.5% 0.5% no change no change

Wind (offshore) 2.2% 2.4% 0.4% N/A N/A N/A

Solar Thermal 3.2% 2.2% no change X no change no change

PV 3.1% 2.1% 3.4% 3.8% no change no change

N/A Technology not included in data set

X Technology included in data set, but online year occurs after 2015

As Table 14 shows, costs in the AEO data set decline at an annual rate between 1.6% (onshore 

wind) and 3.2% (solar thermal). For technologies in the GPRA data set, the annual cost declines 

range from 0.6% to 2.4%. In the NREL-SEAC data set, all but one of the technologies show no 

change in costs or a modest change (<1%). PV is the only technology in the NREL-SEAC data 

set with an annual cost reduction greater than 1% per year for the period between 2015 and 2030. 

In MiniCAM, the only technology with a change greater than 1% is PV. For the EPA data set, all 

fossil technologies decline at a rate greater than 1%, while the renewable and nuclear 

technologies show a change less than 1% (most exhibit no change). In MERGE, most 

technologies show no change in overnight capital costs, although combined cycle actually shows 

a small increase (only entry in Table 14 that shows a cost increase between 2015 and 2030).
44

0.0

0.2

0.4

0.6

0.8

1.0

1.2

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Normalized Cost (first year = 1)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Technology not incuded in following data set:

-- GPRA 2009

Figure 24. Normalized capital costs—coal
45

0.0

0.2

0.4

0.6

0.8

1.0

1.2

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year 

Normalized Cost (first year = 1)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Technology not included in following data set:

-- GPRA 2009

Figure 25. Normalized capital costs—IGCC
46

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Normalized Cost (first year = 1)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Technology not included in following data sets:

-- GPRA 2009

-- MERGE

Figure 26. Normalized capital costs—combustion turbine
47

0.0

0.2

0.4

0.6

0.8

1.0

1.2

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Normalized Cost (first year = 1)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Technology not included in following data set:

-- GPRA 2009

Figure 27. Normalized capital costs—combined cycle
48

0.0

0.2

0.4

0.6

0.8

1.0

1.2

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Normalized Cost (first year = 1)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Technology not included in following data set:

-- GPRA 2009

Figure 28. Normalized capital costs—nuclear
49

0.0

0.2

0.4

0.6

0.8

1.0

1.2

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Normalized Cost (first year = 1)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Technology not included in following data set:

-- GPRA 2009

Figure 29. Normalized capital costs—biomass
50

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Normalized Cost (first year = 1)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Technology not included in following data set:

-- MERGE

Figure 30. Normalized capital costs—geothermal
51

0.0

0.2

0.4

0.6

0.8

1.0

1.2

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Normalized Cost (first year = 1)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Figure 31. Normalized capital costs—wind (onshore)
52

0.0

0.2

0.4

0.6

0.8

1.0

1.2

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Normalized Cost (first year = 1)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009 Technology not included in following data sets:

-- MiniCAM

-- EPA 

-- MERGE

Figure 32. Normalized capital costs—wind (offshore)
53

0.0

0.2

0.4

0.6

0.8

1.0

1.2

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Normalized Cost (first year = 1)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Figure 33. Normalized capital costs—solar thermal
54

0.0

0.2

0.4

0.6

0.8

1.0

1.2

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Normalized Cost (first year = 1)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Figure 34. Normalized capital costs—PV
55

3.3 O&M Costs

Fixed O&M costs in 2015 are shown in Table 15 and Figure 35. The GPRA data set does not 

include fossil and biomass technologies, and the MiniCAM and EPA data sets do not include 

offshore wind (hence, O&M costs are not presented for these entries). The MiniCAM data set 

does include IGCC and solar thermal technologies, but the online year for these technologies 

occurs later than 2015. As Table 15 shows, no O&M costs are reported for the MERGE data set. 

All-inclusive O&M costs were calculated for the MERGE data set, but a breakdown of fixed and 

variable O&M costs was not available for comparison with other data sets.

Table 15. Fixed O&M in 2015 (2007$/kW/yr)

Technology

Data Set

Standard 

Deviation

Coefficient 

AEO GPRA of Variation

NRELSEAC MiniCAM EPA MERGE

Coal 27.53 --- 36.78 30.10 27.52 --- 4.37 14%

IGCC 38.67 --- 39.70 --- 38.72 --- 0.58 1%

Combustion Turbine 10.53 --- 6.88 12.65 10.58 --- 2.40 24%

Combined Cycle 11.70 --- 15.00 13.22 11.71 --- 1.57 12%

Nuclear 90.02 --- 93.77 70.06 90.02 --- 10.75 13%

Biomass 64.45 --- 72.93 36.77 64.49 --- 15.77 26%

Geothermal 

(hydrothermal)

164.6

4 119.89 174.49 86.19 184.34 --- 41.50 28%

Wind (onshore) 30.30 24.73 11.98 12.59 30.29 --- 9.14 42%

Wind (offshore) 89.48 130.42 15.63 --- --- --- 58.18 74%

Solar Thermal 56.78 49.83 48.79 --- 56.79 --- 4.34 8%

PV (utility scale) 11.68 5.32 9.92 58.38 11.71 --- 21.94 113%

Coefficient of variation (CV) is the standard deviation divided by the mean.

The coefficient of variation is greater than or equal to 20% for six technologies – combustion 

turbines, biomass, geothermal, onshore wind, offshore wind, and PV. The largest coefficient of 

variation occurs for PV, where the MiniCAM data set has a fixed O&M cost that is significantly 

higher than those of any of the other data sets are.
56

0

20

40

60

80

100

120

140

160

180

200

Coal IGCC Combustion

Turbine

Combined

Cycle 

Nuclear Biomass Geothermal Wind

(onshore)

Wind

(offshore)

Solar Thermal PV

Technology

Fixed O&M (2007$ / kW)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

CV=14%

CV=24% CV=12%

CV=13%

CV=26%

CV=28%

CV=42%

CV=74%

CV=8%

CV=113%

CV=1%

Figure 35. Fixed O&M costs, 2015
57

Variable O&M costs are shown in Table 16 and Figure 36. A few observations concerning 

variable O&M costs include:

• For geothermal and PV, variable O&M costs are zero in all data sets, and the 

resulting coefficient of variation is 0%.

• The coefficient of variation for nuclear plants is 85%. The reason for this high CV is 

that most data sets report a variable O&M cost for near $0.50/MWh, while the 

MiniCAM data set has a significantly higher cost near $2/MWh.

• The coefficient of variation exceeds 100% for onshore wind, offshore wind, and solar 

thermal technologies. The reason for these large CVs occurs primarily because some 

data have zero variable O&M costs, while other data sets report a variable O&M cost 

component. In some cases, the absolute costs are small, but the coefficient of 

variation turns out to be large. For example, with solar thermal, three data sets report 

zero costs, and one data set (NREL-SEAC) reports a small cost ($0.11/MWh). Based 

on these costs, the coefficient of variation for solar thermal technologies turns out to 

be 200%.

Table 16. Variable O&M in 2015 (2007$/MWh)

Technology

Data Set

Standard 

Deviation

Coefficient 

AEO GPRA of Variation

NRELSEAC MiniCAM EPA MERGE

Coal 4.59 --- 1.77 5.02 4.59 --- 1.50 37%

IGCC 2.92 --- 4.06 --- 2.92 --- 0.66 20%

Combustion Turbine 3.17 --- 2.92 3.77 3.17 --- 0.36 11%

Combined Cycle 2.00 --- 3.13 2.22 2.00 --- 0.54 23%

Nuclear 0.49 --- 0.52 1.97 0.49 --- 0.74 85%

Biomass 6.71 --- 10.42 5.09 6.68 --- 2.26 31%

Geothermal 

(hydrothermal) 0.00 0.00 0.00 0.00 0.00 --- 0.00 0%

Wind (onshore) 0.00 0.00 5.21 5.91 0.00 --- 3.06 137%

Wind (offshore) 0.00 0.00 16.67 --- --- --- 9.62 173%

Solar Thermal 0.00 0.00 0.11 --- 0.00 --- 0.06 200%

PV 0.00 0.00 0.00 0.00 0.00 --- 0.00 0%
58

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

Coal IGCC Combustion

Turbine

Combined

Cycle 

Nuclear Biomass Geothermal Wind

(onshore)

Wind

(offshore)

Solar Thermal PV

Technology

Variable O&M (2007$ / MWh)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

CV=37%

CV=11%

CV=23%

CV=85%

CV=31%

CV=0%

CV=137%

CV=173%

CV=200% CV=0%

CV=20%

Figure 36. Variable O&M costs, 2015
59

4 Levelized Cost of Energy (LCOE)

A levelized cost of energy (LCOE) tool was developed, and this tool was used to calculate 

LCOE values for electricity generation technologies. Additional details on the LCOE tool are 

included in Appendix G.

The LCOE tool was designed for rapid comparison of technology cost and performance 

characteristics, not for project or location specific analyses. It is important to recognize that the 

LCOE tool does not represent the decision-making structure of the models discussed in this 

report. Models have complex decision-making processes and other energy system properties to 

determine electricity portfolios that are not incorporated in the LCOE tool. The LCOE tool 

utilizes very basic financial assumptions, as the intent is to compare technology cost and 

performance characteristics, not to analyze the impacts of detailed financial assumptions. Thus,

no taxes, tax incentives, or debt/equity financing structures are considered.

The exclusion of taxes from the analysis may have a favorable effect on certain technologies 

with high capital costs and low operational costs (e.g. renewable technologies). Similarly, the 

exclusion of tax credits and depreciation incentives may have a relatively favorable effect on 

conventional technologies compared with renewables. Future studies would benefit from 

performing sensitivity analyses of LCOE with changes in financial assumptions.

LCOE values were computed for 11 technologies (see Table 3) in six data sets (see Table 1). 

LCOE values were first computed for a set of baseline conditions as described in Table 17. In 

addition to the baseline conditions, several alternative scenarios were analyzed as noted in Table 

17.

Table 17. LCOE Calculations

Parameter Baseline Conditions Alternative Scenarios or 

Calculations

Year 201524 All years from 2005 through 2050

Currency Basis 2007 real dollars Same for all scenarios

Equipment Cost Default Values in LCOE Tool +10%, +20%, +50%

Labor Cost Default Values in LCOE Tool +10%, +20%, +50%

Fuel Cost From AEO 2009 data set +10%, +20%, +50%

Discount Rate (real) 7.0% No change

Lifetime From NREL-SEAC 2008 data set25 No change

LCOE values were calculated for 2005 through the last year in each data set26

Table 18

. For each year, a 

total of 10 different scenarios were computed as indicated in . The 10 scenarios were 

 24 The year 2015 was selected for the baseline year to allow coverage across all data sets. The EPA 2009 data set 

does not contain records for 2010. The first on-line year for technologies in the EPA 2009 data set is 2012. 25 To eliminate the effect of plant lifetimes on LCOE calculations, the same set of lifetimes were used for 

calculations. NREL-SEAC had the most complete set of plant lifetimes, and the NREL-SEAC lifetimes were 

therefore selected for all LCOE calculations. 26 The last year is 2050 for all data sets except AEO and EPA. The last year for AEO is 2030, and the last year for 

EPA is 2035.
60

selected to evaluate the parametric sensitivity of LCOE results to three input variables:

1) equipment costs, 2) labor costs, and 3) fuel costs

Table 18. LCOE Calculation Matrix 

Scenario Capital Cost Adjustment Fuel Cost 

Adjustment Equipment

(non-steel)

Labor

1 0% 0% 0%

2 10%

3 20%

4 50%

5 10% 0% 0%

6 20%

7 50%

8 0% 0% 10%

9 20%

10 50%

In this section, the LCOE results are discussed for the baseline scenario and the alternative 

scenarios. This section is organized as follows:

• Baseline results (2015 and 2030)

• Capital, O&M, and fuel costs (2015 and 2030)

• Time trends (2005 through 2050)

• Impact of equipment costs

• Impact of labor costs

• Impact of fuel costs

4.1 Baseline Results (2015 and 2030)

LCOE values for 2015, the baseline year, are shown in Figure 37, and for comparison LCOE 

values for 2030 are shown in Figure 38. All LCOE values are reported in 2007 dollars for the 11 

technologies that span the six data sets (tabular data in Appendix I).

In 2015, the mean values for fossil technologies range from $52/MWh (combined cycle) to 

$77/MWh (combustion turbine) across the data sets. In 2030, the mean LCOE values for fossil 

technologies show small changes, generally upward. In 2030, the costs range from $53/MWh 

(coal and combined cycle) to $80/MWh (combustion turbine). Concerning the combustion 

turbine, the cost is relatively high in the MiniCAM data set. The reason for the high MiniCAM 

combustion turbine cost is that the combustion turbine in this data set is regarded as a peaking 

turbine and has a relatively low capacity factor (10%), which drives up the LCOE. Other data 

sets have a combustion turbine capacity factor of 80% or higher.
61

LCOE by Data Set and Technology, 2015

0

100

200

300

400

500

600

Coal IGCC Combustion

Turbine

Combined

Cycle

Nuclear Biomass Geothermal

(HT)

Wind

(onshore)

Wind

(offshore)

Solar

Thermal

(CSP)

PV

Technology

LCOE (2007$ / MWh)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Mean = 53 

SD = 9.6 

CV = 18%

Mean = 62 

SD = 11.4 

CV = 18%

Mean = 77 

SD = 28.9 

CV = 38% Mean = 52 

SD = 3.6 

CV = 7%

Mean = 69 

SD = 21.3 

CV = 31%

Mean = 75 

SD = 13.5 

CV = 18%

Mean = 83 

SD = 41.6 

CV = 50%

Mean = 63 

SD = 29.5 

CV = 47%

Mean = 101 

SD = 31.2 

CV = 31%

Mean = 181 

SD = 80.6 

CV = 45%

Mean = 270 

SD = 137.5 

CV = 51%

Figure 37. LCOE costs, 2015
62

LCOE by Data Set and Technology, 2030

0

100

200

300

400

500

600

Coal IGCC Combustion

Turbine

Combined

Cycle

Nuclear Biomass Geothermal

(HT)

Wind

(onshore)

Wind

(offshore)

Solar

Thermal

(CSP)

PV

Technology

LCOE (2007$ / MWh)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Mean = 53 

SD = 11.1 

CV = 21%

Mean = 57 

SD = 14.3 

CV = 25%

Mean = 80 

SD = 28.6 

CV = 36% Mean = 53 

SD = 3.9 

CV = 7%

Mean = 65 

SD = 15.4 

CV = 24%

Mean = 70 

SD = 14.8 

CV = 21%

Mean = 78 

SD = 43.5 

CV = 56%

Mean = 56 

SD = 29.2 

CV = 52%

Mean = 73 

SD = 28.4 

CV = 39%

Mean = 139 

SD = 100.0 

CV = 72%

Mean = 211 

SD = 158.2 

CV = 75%

Figure 38. LCOE costs, 2030
63

As indicated in Figure 37 and Figure 38, nuclear costs show a slight decrease from a mean value 

of $69/MWh in 2015 to $65/MWh in 2030. Biomass and geothermal also show reductions –

biomass declines from $75/MWh to $70/MWh, and geothermal declines from $83/MWh to 

$78/MWh. Concerning geothermal, the EPA data set is clearly at the high end. In this data set, a 

wide range of capital costs is provided depending on actual site conditions. In the EPA data set, 

geothermal costs were averaged, but the resulting capital cost was still quite high, which drives 

up the LCOE values for the EPA data set.

All six data sets included onshore wind, and in these data sets, onshore wind is generally one of 

the most competitive power generation technologies. In 2015, the LCOE for onshore wind is 

$63/MWh, falling to $56/MWh in 2030. The MERGE data set has relatively high capital costs 

for wind, which drives up the LCOE. Three of the data sets reported offshore wind 

characteristics, and based on these three data sets the LCOE for offshore wind falls from 

$101/MWh in 2015 to $73/MWh in 2030.

Solar thermal and PV are the most expensive power generation technologies. In 2015, the mean 

solar thermal cost is $181/MWh, and the mean PV cost is $270/MWh. In 2030, these costs fall to 

$139/MWh and $211/MWh, respectively. There is a large standard deviation for both solar 

thermal and PV, particularly in 2030. In 2030, the GPRA data set has a noticeably lower LCOE 

than the other data sets, while MERGE shows a significantly higher result.

Another perspective of LCOE costs is presented in Figure 39 and Figure 40. In these figures, 

LCOE costs are normalized to a coal plant for 2015 and 2030, respectively. Looking at the mean 

values, coal plants are the least expensive option in both 2015 and 2030 (tied with combined 

cycle in 2015). However, interesting observations are apparent when looking at individual data 

sets. For example, the cost of onshore wind in 2030 in the NREL-SEAC data set is lower than 

the cost of a coal plant (ratio < 1). The MiniCAM data set has a lower cost for onshore wind 

compared to a coal plant in both 2015 and 2030.

Another set of LCOE ratio plots is presented in Figure 41 and Figure 42, where onshore wind is 

used for normalization. Given the mean values in these two charts, it is easy to see how the 

competitive position of onshore wind changes between 2015 and 2030. In 2015, the mean cost of 

a coal plant is 85% (ratio = 0.85) of the mean cost for onshore wind, and in 2030 the cost 

differential declines to 94%. The gap between onshore wind and other renewables also shrinks. 

For example, relative to onshore wind, the mean cost of solar thermal declines from a ratio of 

2.72 in 2015 to 2.29 in 2030.
64

LCOE Normalized to Coal, 2015

0

1

2

3

4

5

6

7

8

Coal IGCC Combustion

Turbine

Combined

Cycle

Nuclear Biomass Geothermal

(HT)

Wind

(onshore)

Wind

(offshore)

Solar Thermal

(CSP)

PV

Technology

LCOE Ratio

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Mean = 1.00 

SD = 0.00 

CV = 0%

Mean = 1.13 

SD = 0.09 

CV = 8%

Mean = 1.59 

SD = 0.67 

CV = 42%

Mean = 1.00 

SD = 0.10 

CV = 10%

Mean = 1.27 

SD = 0.13 

CV = 10% Mean = 1.41 

SD = 0.18 

CV = 13%

Mean = 1.76 

SD = 0.93 

CV = 53%

Mean = 1.24 

SD = 0.31 

CV = 25%

Mean = 2.15 

SD = 0.86 

CV = 40%

Mean = 3.54 

SD = 0.72 

CV = 20%

Mean = 5.59 

SD = 1.64 

CV = 29%

Figure 39. LCOE cost ratios relative to coal, 2015
65

LCOE Normalized to Coal, 2030

0

1

2

3

4

5

6

7

8

Coal IGCC Combustion

Turbine

Combined

Cycle

Nuclear Biomass Geothermal

(HT)

Wind

(onshore)

Wind

(offshore)

Solar Thermal

(CSP)

PV

Technology

LCOE Ratio

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Mean = 1.00 

SD = 0.00 

CV = 0%

Mean = 1.08 

SD = 0.09 

CV = 8%

Mean = 1.68 

SD = 0.64 

CV = 38%

Mean = 1.04 

SD = 0.14 

CV = 13%

Mean = 1.22 

SD = 0.06 

CV = 5% Mean = 1.34 

SD = 0.20 

CV = 15%

Mean = 1.70 

SD = 1.02 

CV = 60%

Mean = 1.14 

SD = 0.32 

CV = 28%

Mean = 1.80 

SD = 0.72 

CV = 40%

Mean = 2.85 

SD = 1.17 

CV = 41%

Mean = 4.37 

SD = 1.96 

CV = 45%

Figure 40. LCOE cost ratios relative to coal, 2030
66

LCOE Normalized to Onshore Wind, 2015

0

1

2

3

4

5

6

7

8

Coal IGCC Combustion

Turbine

Combined

Cycle

Nuclear Biomass Geothermal

(HT)

Wind

(onshore)

Wind

(offshore)

Solar Thermal

(CSP)

PV

Technology

LCOE Ratio

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Mean = 0.85 

SD = 0.21 

CV = 25%

Mean = 0.88 

SD = 0.24 

CV = 27%

Mean = 1.52 

SD = 0.96 

CV = 63%

Mean = 0.86 

SD = 0.26 

CV = 30%

Mean = 1.07 

SD = 0.20 

CV = 19% Mean = 1.19 

SD = 0.30 

CV = 25%

Mean = 1.58 

SD = 0.54 

CV = 34%

Mean = 1.00 

SD = 0.00 

CV = 0%

Mean = 1.99 

SD = 0.45 

CV = 23%

Mean = 2.72 

SD = 0.28 

CV = 10%

Mean = 4.36 

SD = 1.71 

CV = 39%

Figure 41. LCOE cost ratios relative to wind, 2015
67

LCOE Normalized to Onshore Wind, 2030

0

1

2

3

4

5

6

Coal IGCC Combustion

Turbine

Combined

Cycle

Nuclear Biomass Geothermal

(HT)

Wind

(onshore)

Wind

(offshore)

Solar Thermal

(CSP)

PV

Technology

LCOE Ratio

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Mean = 0.94 

SD = 0.28 

CV = 30%

Mean = 1.02 

SD = 0.33 

CV = 32%

Mean = 1.80 

SD = 1.15 

CV = 64%

Mean = 0.99 

SD = 0.34 

CV = 34%

Mean = 1.14 

SD = 0.30 

CV = 26%

Mean = 1.25 

SD = 0.40 

CV = 32%

Mean = 1.68 

SD = 0.56 

CV = 33%

Mean = 1.00 

SD = 0.00 

CV = 0%

Mean = 1.68 

SD = 0.24 

CV = 14%

Mean = 2.29 

SD = 0.77 

CV = 34%

Mean = 3.48 

SD = 1.25 

CV = 36%

Figure 42. LCOE cost ratios relative to wind, 2030
68

4.2 Capital, O&M, and Fuel Costs (2015 and 2030)

The next 11 figures (Figure 43 to Figure 53) show the contribution of capital, O&M, and fuel to 

LCOE costs. The costs in these 11 figures correspond to 2015 and 2030, and all costs are 

expressed in 2007 real dollars.

Coal, Figure 43– Coal costs show slight changes between 2015 and 2030. The costs go down in 

the AEO and EPA data sets, rise in the NREL-SEAC and MERGE data sets, and remain constant 

in MiniCAM. As indicated, MERGE O&M costs are relatively high. This result should be 

viewed with caution, however, because O&M costs were not provided by EPRI. Rather, MERGE 

O&M costs were calculated based on selected cost values provided in EPRI literature, and it is 

possible that the actual O&M costs developed by EPRI are lower than the calculated quantities.

IGCC, Figure 44 – Cost trends for IGCC plants are similar to coal plants – AEO and EPA 

decline from 2015 to 2030, and NREL-SEAC and MERGE rise during this same time period. 

The online year for IGCC in MiniCAM is 2020, and an IGCC cost for 2015 is not reported in the 

MiniCAM data set. Like coal plants, the MERGE O&M contribution is high compared to other 

data sets.

Combustion Turbine, Figure 45 – The MiniCAM combustion turbine capital cost component is 

noticeably high. This result occurs because the MiniCAM combustion turbine is modeled as a 

peaking unit with a low capacity factor (10%).

Combined Cycle, Figure 46 – Costs remain relatively constant between 2015 and 2030, 

although the NREL-SEAC data set does show a noticeable increase in overall LCOE costs as a 

result of higher fuel costs. For all combined cycle plants, fuel costs are the largest component of 

LCOE.

Nuclear, Figure 47 – For nuclear, the AEO and NREL-SEAC data sets have costs that are all 

relatively close in 2015. The AEO data set does show an overall decline in LCOE between 2015 

and 2030, whereas the NREL-SEAC data set has about the same total LCOE in both years. The 

MiniCAM data set is at the low end of the spectrum. One explanation for low MiniCAM costs is 

that the characteristics for MiniCAM are generally taken from the AEO 2008 publication (2007 

calendar year data); while the other data sets generally have characteristics based on calendar 

year 2008 or 2009. There was a significant run-up in capital costs starting around 2007, and this 

capital cost increase is likely not captured in the MiniCAM data set. The MERGE data set shows 

the highest capital and O&M costs compared to the other data sets. Nuclear fuel costs (uranium) 

in all data sets are relatively constant.

Biomass, Figure 48 – Four of the data sets – AEO, MiniCAM, EPA, and MERGE – show LCOE 

declines between 2015 and 2030. In contrast, the NREL-SEAC data set shows a slight increase 

over this period.

Geothermal (hydrothermal), Figure 49 – For geothermal, wind, and solar resources there is no 

fuel cost and there are only two contributors to LCOE – capital and O&M. The one noticeable 

outlier is the capital cost for the EPA 2009 data set. In this data set, there is a wide range of 

geothermal overnight capital costs depending on site conditions. An average value was used for 
69

the LCOE calculations, but the average capital cost was still significantly higher than other data 

sets. This high capital cost is the primary driver behind the high EPA geothermal LCOE values.

Onshore Wind, Figure 50 – MERGE O&M and capital costs are high compared to the other 

data sets. O&M costs for all other data sets are relatively consistent. The GPRA 2009 data set 

has the lowest capital costs and resulting LCOE values for 2015 and 2030.

Offshore Wind, Figure 51 – Three of the data sets – AEO, GPRA, and NREL-SEAC –report 

offshore wind costs. The AEO 2009 data set has the largest capital cost and the highest LCOE 

values. The GPRA 2009 data set shows a relatively large reduction in O&M between 2015 and 

2030. In 2015, the LCOE for GPRA is higher than NREL-SEAC, but by 2030, the GPRA LCOE 

is lower than NREL-SEAC.

Solar Thermal, Figure 52 – For solar thermal, the MERGE data set has relatively high O&M 

costs and relatively high capital costs. Within a given year (2015 or 2030), the GPRA data set 

has the lowest LCOE (MiniCAM online year for solar thermal is 2020 – no MiniCAM solar 

thermal result for 2015). Two of the data sets – GPRA and MiniCAM – have thermal energy 

storage integrated with solar thermal plants, while the other four data sets do not. Even with 

energy storage, the capital costs in the GPRA and MiniCAM data sets are at the low end 

compared to the other data sets, which do not incorporate thermal energy storage.

PV, Figure 53 – Like solar thermal, the MERGE data set has relatively high O&M costs and 

relatively high capital costs. With the exception of the MERGE data set, O&M costs for PV 

technologies are quite small relative to PV capital costs.
70

Coal 

$22.74 $24.57

$18.83 $22.74

$28.87

$8.05 $6.54

$9.04

$8.05

$18.69 $18.70 $22.63

$18.31

$18.91

$18.45

$17.12

$24.57

$18.09 $19.07

$28.87

$8.05

$6.54

$8.69 $8.05

$22.63

$19.90

$20.49

$19.56 $20.95

$20.44

$0

$10

$20

$30

$40

$50

$60

$70

$80

2015 2030 2015 2030 2015 2030 2015 2030 2015 2030 2015 2030

AEO 2009 GPRA 2009 NREL-SEAC

2008

MiniCAM 2008 EPA 2009 MERGE 2009

LCOE (2007$ / MWh)

Fuel

O&M

Capital

Figure 43. Capital, fuel, and O&M costs—coal

IGCC 

$27.80

$34.83

$27.80

$34.50

$7.88

$9.38

$7.88

$24.49

$17.39

$18.50

$18.01

$18.45

$20.03

$34.83

$21.78 $22.71

$34.50

$7.88

$9.38

$8.56 $7.88

$24.49

$16.96

$19.54

$17.60 $19.96

$20.44

$0

$10

$20

$30

$40

$50

$60

$70

$80

$90

2015 2030 2015 2030 2015 2030 2015 2030 2015 2030 2015 2030

AEO 2009 GPRA 2009 NREL-SEAC

2008

MiniCAM 2008 EPA 2009 MERGE 2009

LCOE (2007$ / MWh)

Fuel

O&M

Capital

Figure 44. Capital, fuel, and O&M costs—IGCC
71

Combustion Turbine

$6.91 $9.43

$53.16

$6.91

$4.35 $3.78

$17.68

$4.36

$49.98 $49.20

$49.07

$51.35

$4.80 $9.43

$51.65

$5.55

$4.35

$3.78

$17.17

$4.36

$52.47

$54.62

$53.67

$57.01

$0

$20

$40

$60

$80

$100

$120

$140

2015 2030 2015 2030 2015 2030 2015 2030 2015 2030 2015 2030

AEO 2009 GPRA 2009 NREL-SEAC

2008

MiniCAM 2008 EPA 2009 MERGE 2009

LCOE (2007$ / MWh)

Fuel

O&M

Capital

Figure 45. Capital, fuel, and O&M costs—combustion turbine

Combined Cycle

$11.18 $9.43 $9.75 $11.19 $10.57

$3.43 $5.00 $3.99 $3.44 $7.15

$36.66 $37.97

$34.07

$37.32

$40.13

$8.03 $9.43 $8.91 $9.15 $10.84

$3.43

$5.00 $3.76 $3.44

$7.86

$38.87

$42.16

$35.87

$41.44

$38.78

$0

$10

$20

$30

$40

$50

$60

$70

2015 2030 2015 2030 2015 2030 2015 2030 2015 2030 2015 2030

AEO 2009 GPRA 2009 NREL-SEAC

2008

MiniCAM 2008 EPA 2009 MERGE 2009

LCOE (2007$ / MWh)

Fuel

O&M

Capital

Figure 46. Capital, fuel, and O&M costs—combined cycle
72

Nuclear

$42.38 $40.62

$30.75

$58.66

$11.56 $12.02

$10.54

$31.65

$9.95 $9.92

$9.66

$9.86

$28.71

$37.72

$30.29

$38.24

$49.81

$11.56

$12.02

$10.54

$11.66

$28.22

$12.94

$12.90

$12.44

$12.94

$12.82

$0

$20

$40

$60

$80

$100

$120

2015 2030 2015 2030 2015 2030 2015 2030 2015 2030 2015 2030

AEO 2009 GPRA 2009 NREL-SEAC

2008

MiniCAM 2008 EPA 2009 MERGE 2009

LCOE (2007$ / MWh)

Fuel

O&M

Capital

Figure 47. Capital, fuel, and O&M costs—nuclear

Biomass

$40.08

$31.33

$23.89

$40.47 $37.88

$15.12

$19.73

$10.04

$15.09

$27.99

$17.58 $29.35

$19.83

$19.52

$24.66

$26.53 $31.33

$22.96

$35.57 $37.88

$15.12

$19.73

$9.65

$15.09

$21.81 $16.66

$31.11

$18.89

$20.69

$26.14

$0

$10

$20

$30

$40

$50

$60

$70

$80

$90

$100

2015 2030 2015 2030 2015 2030 2015 2030 2015 2030 2015 2030

AEO 2009 GPRA 2009 NREL-SEAC

2008

MiniCAM 2008 EPA 2009 MERGE 2009

LCOE (2007$ / MWh)

Fuel

O&M

Capital

Figure 48. Capital, fuel, and O&M costs—biomass
73

Geothermal (HT)

$61.17 $55.64

$43.04 $34.73

$130.43

$20.27

$13.99

$22.86

$10.61

$23.48

$43.09 $50.72 $43.04

$32.83

$130.43

$20.27 $12.93 $22.86

$10.03

$23.48

$0

$20

$40

$60

$80

$100

$120

$140

$160

$180

2015 2030 2015 2030 2015 2030 2015 2030 2015 2030 2015 2030

AEO 2009 GPRA 2009 NREL-SEAC

2008

MiniCAM 2008 EPA 2009 MERGE 2009

LCOE (2007$ / MWh)

Fuel

O&M

Capital

Figure 49. Capital, fuel, and O&M costs—geothermal (HT)

Wind (onshore)

$57.74

$31.47

$43.00

$31.71

$55.19

$74.25

$8.48

$6.23

$8.08

$8.96

$8.72

$43.95

$45.07

$25.08

$38.01

$27.51

$55.19

$74.25

$8.48

$4.64

$7.31

$7.52

$8.72

$36.15

$0

$20

$40

$60

$80

$100

$120

$140

2015 2030 2015 2030 2015 2030 2015 2030 2015 2030 2015 2030

AEO 2009 GPRA 2009 NREL-SEAC

2008

MiniCAM 2008 EPA 2009 MERGE 2009

LCOE (2007$ / MWh)

Fuel

O&M

Capital

Figure 50. Capital, fuel, and O&M costs—wind (onshore)
74

Wind (offshore)

$111.52

$55.40 $57.00

$24.79

$34.78

$19.95

$79.37

$35.48

$51.78

$24.79

$13.04

$14.74

$0

$20

$40

$60

$80

$100

$120

$140

$160

2015 2030 2015 2030 2015 2030 2015 2030 2015 2030 2015 2030

AEO 2009 GPRA 2009 NREL-SEAC

2008

MiniCAM 2008 EPA 2009 MERGE 2009

LCOE (2007$ / MWh)

Fuel

O&M

Capital

Figure 51. Capital, fuel, and O&M costs—wind (offshore)

Solar Thermal

$153.25

$96.86

$138.13 $130.28

$208.17

$20.30

$12.78

$17.03 $17.73

$110.97

$94.77

$36.84

$138.13

$48.94

$130.28

$20.30 $208.17

$5.26

$17.03

$5.18

$17.73

$110.97

$0

$50

$100

$150

$200

$250

$300

$350

2015 2030 2015 2030 2015 2030 2015 2030 2015 2030 2015 2030

AEO 2009 GPRA 2009 NREL-SEAC

2008

MiniCAM 2008 EPA 2009 MERGE 2009

LCOE (2007$ / MWh)

Fuel

O&M

Capital

Figure 52. Capital, fuel, and O&M costs—solar thermal (CSP)
75

PV

$290.48

$109.07 $140.35

$278.96 $254.84

$318.65

$6.47

$2.51

$5.76

$28.46

$5.94

$179.11

$182.12

$58.97 $83.61

$155.07

$254.84

$318.65

$6.47

$0.98

$3.43

$14.23

$5.94

$179.11

$0

$100

$200

$300

$400

$500

$600

2015 2030 2015 2030 2015 2030 2015 2030 2015 2030 2015 2030

AEO 2009 GPRA 2009 NREL-SEAC

2008

MiniCAM 2008 EPA 2009 MERGE 2009

LCOE (2007$ / MWh)

Fuel

O&M

Capital

Figure 53. Capital, fuel, and O&M costs—PV

4.3 Time Trends (2005 through 2050)

LCOE costs for 2005 through 2050 are shown in the next 11 figures (Figure 54 through Figure 

64).

• Coal – As indicated, with the exception of the MERGE data set, the LCOE trends 

remain near $50/MWh through 2050. The MERGE costs are in the range of $70 to 

$80/MWh. The high MERGE costs result from relatively high capital costs and 

relatively high O&M costs compared to the other data sets.

• IGCC – For IGCC plants, the MERGE data set has the highest LCOE costs, followed 

by the NREL-SEAC data set. The AEO, EPA, and MiniCAM data sets show similar 

cost trends, which decline over time as a result of learning (i.e., capital costs decline 

over time).

• Combustion Turbine – For combustion turbines, the MiniCAM data set is the 

highest due to the low capacity factor in this data set (10%). The other data sets show 

similar cost trends

• Combined Cycle – For combined cycle plants, LCOE values generally trend upward 

in most data sets. The AEO and MiniCAM data sets show stable prices after about 

2020, due to offsetting impacts of lower capital costs but increasing fuel costs.

• Nuclear – For nuclear plants, MERGE costs are the highest. The other data sets show 

relatively similar LCOE costs through 2050.
76

• Biomass –The MiniCAM data set shows the lowest LCOE costs. The MERGE data 

set is the highest, followed by the NREL-SEAC data set. The AEO and EPA data sets 

fall between the NREL-SEAC and MiniCAM values.

• Geothermal – The geothermal category is intended to represent geothermal 

hydrothermal plants. However, the EPA data set may be based on EGS as well as 

hydrothermal characteristics, which could explain the unusually high LCOE costs for 

the EPA data set. The fluctuations in the AEO data set result from the methodology 

used by EIA, which involves analyzing expected plant construction costs based on 

site-specific geothermal resources.

• Wind (onshore) – For onshore wind, the MERGE data set has the highest LCOE 

costs. The other data sets show similar LCOE trends.

• Wind (offshore) – Three data sets – AEO 2009, NREL-SEAC 2008, and GPRA 2009 

– have offshore wind. The GPRA LCOE costs are clearly the highest in early years, 

but then fall to the lowest costs in later years (after 2025). The AEO costs are the 

highest relative to the same years for the GPRA and NREL-SEAC data sets.

• Solar Thermal – MERGE LCOE costs are highest. The GPRA and MiniCAM costs 

are the lowest, and the other data sets fall in between. It is interesting to note that the 

EPA and NREL-SEAC data sets show no change starting from around 2015 and

beyond. This constant LCOE cost results from constant capital cost and constant 

O&M cost assumptions.

• PV – Like solar thermal, the MERGE costs are the highest. The MiniCAM costs start 

near the MERGE costs in 2005, but then quickly fall to levels similar to the other data 

sets. The GPRA and NREL-SEAC data sets have the lowest costs compared to the 

other data sets (GPRI generally lower than NREL-SEAC).
77

•

Pulverized Coal

0

10

20

30

40

50

60

70

80

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

LCOE (2007$ / MWh)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

LCOE not included for this technology in following data set:

-- GPRA 2009

Figure 54. LCOE—coal

IGCC

0

10

20

30

40

50

60

70

80

90

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

LCOE (2007$ / MWh)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

LCOE not included for this technology in following data set:

-- GPRA 2009

Figure 55. LCOE—IGCC
78

Combustion Turbine

0

10

20

30

40

50

60

70

80

90

100

110

120

130

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

LCOE (2007$ / MWh)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

LCOE not included for this technology in following data sets:

-- GPRA 2009

-- MERGE 2009

Figure 56. LCOE—combustion turbine

Combined Cycle

0

10

20

30

40

50

60

70

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

LCOE (2007$ / MWh)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

LCOE not included for this technology in following data set:

-- GPRA 2009

Figure 57. LCOE—combined cycle
79

Nuclear

0

10

20

30

40

50

60

70

80

90

100

110

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

LCOE (2007$ / MWh)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

LCOE not included for this technology in following data set:

-- GPRA 2009

Figure 58. LCOE—nuclear

Biomass

0

10

20

30

40

50

60

70

80

90

100

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

LCOE (2007$ / MWh)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009 LCOE not included for this technology in following data set:

-- GPRA 2009

Figure 59. LCOE—biomass
80

Geothermal (hydrothermal)

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

170

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

LCOE (2007$ / MWh)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

LCOE not included for this technology in following data set:

-- MERGE 2009

Figure 60. LCOE—geothermal (HT)

Wind (onshore)

0

10

20

30

40

50

60

70

80

90

100

110

120

130

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

LCOE (2007$ / MWh)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Figure 61. LCOE—wind (onshore)
81

Wind (offshore)

0

50

100

150

200

250

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

LCOE (2007$ / MWh)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009 LCOE not included for this technology in following data sets:

-- MiniCAM

-- EPA 2009

-- MERGE 2009

Figure 62. LCOE—wind (offshore)

Solar Thermal (CSP)

0

50

100

150

200

250

300

350

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

LCOE (2007$ / MWh)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Figure 63. LCOE—solar thermal (CSP)
82

PV

0

50

100

150

200

250

300

350

400

450

500

550

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

LCOE (2007$ / MWh)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Figure 64. LCOE—PV
83

4.4 Impact of Equipment Costs

Estimates were developed for the fraction of equipment costs that contribute to the capital cost 

component of power generation technologies. Equipment is defined to include all site 

construction materials as well as all power generation systems delivered to the plant site. The 

estimates for the fraction of equipment costs are shown in Appendix I. Calculations were then 

completed to determine the sensitivity of the LCOE baseline values in 2015 to 10%, 20%, and 

50% changes in the equipment cost. Figure 65 shows the impact of a 10% change, and Figure 66

shows the impact of a 50% change.

As indicated in Figure 65, a 10% change in the equipment cost changes the LCOE values from 

approximately 1% to 6%. The larger impacts occur for wind and solar technologies where 

equipment costs account for a larger fraction of capital costs compared to the other power 

generation technologies.

The impacts of a 50% increase in equipment costs are shown in Figure 66. The trends are similar 

to the 10% case, with wind and solar technologies showing the highest sensitivity to changes in 

equipment costs.

4.5 Impact of Labor Costs

The impact of labor costs on baseline LCOE values is shown in Figure 67 (10% change) and 

Figure 68 (50% change). A 10% increase in labor cost increases LCOE costs by a little over 3% 

for geothermal technologies. The impact is lower for other technologies, and quite small for a 

couple of technologies. For example, a 10% increase in labor has less than a 0.5% impact on 

LCOE costs for combustion turbine and combined cycle technologies.

The impact of a 50% increase in labor is shown in Figure 68. Like the 10% case, LCOE values 

for geothermal technologies are the most sensitive, and LCOE values for combustion turbine and 

combined cycle technologies are the least sensitive.
84

Impact of 10% Increase in Equipment Cost, 2015

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

Coal IGCC Combustion

Turbine

Combined

Cycle

Nuclear Biomass Geothermal

(HT)

Wind

(onshore)

Wind

(offshore)

Solar

Thermal

(CSP)

PV

Technology

Change Relative to Baseline (%)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Figure 65. Impact on LCOE of equipment cost, 10% change
85

Impact of 50% Increase in Equipment Cost, 2015

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

Coal IGCC Combustion

Turbine

Combined

Cycle

Nuclear Biomass Geothermal

(HT)

Wind

(onshore)

Wind

(offshore)

Solar

Thermal

(CSP)

PV

Technology

Change Relative to Baseline (%)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Figure 66. Impact on LCOE of equipment cost, 50% change
86

Impact of 10% Increase in Labor Cost, 2015

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

Coal IGCC Combustion

Turbine

Combined

Cycle

Nuclear Biomass Geothermal

(HT)

Wind

(onshore)

Wind

(offshore)

Solar

Thermal

(CSP)

PV

Technology

Change Relative to Baseline (%)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Figure 67. Impact on LCOE of labor cost, 10% change
87

Impact of 50% Increase in Labor Cost, 2015

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

Coal IGCC Combustion

Turbine

Combined

Cycle

Nuclear Biomass Geothermal

(HT)

Wind

(onshore)

Wind

(offshore)

Solar

Thermal

(CSP)

PV

Technology

Change Relative to Baseline (%)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Figure 68. Impact on LCOE of labor cost, 50% change
88

4.6 Impact of Fuel Costs

For the LCOE analysis completed in this report, the fuel costs for geothermal, wind, and solar 

technologies are assumed to be zero. However, fossil, nuclear, and biomass technologies do have 

fuel costs, and the LCOE values are sensitive to changes in these fuel costs.

Figure 69 shows the sensitivity of LCOE costs to a 10% change in fuel costs. As indicated, 

natural gas technologies (combustion turbine and combined cycle) are the most sensitive, and 

these technologies show a 7% to 8% increase in LCOE with a 10% increase in the cost of natural 

gas. Nuclear technologies are the least sensitive, with LCOE increases of 1% to 2% depending 

on the data set. Coal (pulverized and IGCC) and biomass plants show an LCOE increase of 2% 

to 4% with a 10% increase in the cost of fuel.

Figure 70 shows the change in LCOE costs with a 50% change in fuel costs. Like the 10% case, 

the natural gas technologies are the most sensitive, and nuclear technologies are the least 

sensitive. Coal and biomass plants fall in between.
89

Impact of 10% Increase in Fuel Cost, 2015

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

Coal IGCC Combustion

Turbine

Combined

Cycle

Nuclear Biomass Geothermal

(HT)

Wind

(onshore)

Wind

(offshore)

Solar

Thermal

(CSP)

PV

Technology

Change Relative to Baseline (%)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Figure 69. Impact on LCOE of fuel cost, 10% change
90

Impact of 50% Increase in Fuel Cost, 2015

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

Coal IGCC Combustion

Turbine

Combined

Cycle

Nuclear Biomass Geothermal

(HT)

Wind

(onshore)

Wind

(offshore)

Solar

Thermal

(CSP)

PV

Technology

Change Relative to Baseline (%)

AEO 2009

GPRA 2009

NREL-SEAC 2008

MiniCAM 2008

EPA 2009

MERGE 2009

Figure 70. Impact on LCOE of fuel cost, 50% change
91

4.7 LCOE Conclusions

LCOE values were calculated for 11 technologies that span six data sets (see Table 19). For each 

technology, LCOE values were first calculated for a baseline set of conditions, followed by 

alternative scenario calculations designed to assess the parametric sensitivity to the following 

four variables:

• Increasing equipment costs: +10%, +20%, +50%

• Increasing labor costs: +10%, +20%, +50%

• Increasing fuel costs: +10%, +20%, +50%

Table 19. Matrix of LCOE Calculations

Technology

Data Set

AEO

2009

GPRA 

2009

NREL-SEAC

2008

MiniCAM 

2008

EPA

2009

MERGE 

2009

Coal X --- X X X X

IGCC X --- X X X X

Combustion 

Turbine

X --- X X X ---

Combined Cycle X --- X X X X

Nuclear X --- X X X X

Biomass X --- X X X X

Geothermal 

(hydrothermal)

X X X X X ---

Wind (onshore) X X X X X X

Wind (offshore) X X X --- --- ---

Solar Thermal X X X X X X

PV X X X X X X

Technologies not included in a particular data set are marked with “---“

Observations from the parametric sensitivity study include the following:

Equipment Costs – A 10% increase in equipment costs increases LCOE values by 1% to 6%. 

The larger impacts occur for wind and solar technologies where equipment costs account for a 

larger fraction of capital costs compared to other power generation technologies. The impact 

scales linearly, and a 50% increase in equipment costs increases LCOE values by approximately 

5% to 30%.

Labor Costs – Geothermal technologies have a relatively high labor component (lots of site 

development), and as a result the impact of labor costs are greatest for geothermal technologies. 

In contrast, combustion turbines and combined cycle plants have relatively low on-site labor 

requirements, and these technologies have a weak dependence on changes in labor costs. The 

calculations show that a 10% increase in labor costs increases the LCOE value for geothermal 

technologies by slightly over 3%. The impact is less than 0.5% for combustion turbines and 

combined cycle plants. Like equipment costs, the impact of labor costs scales linearly, and a 50% 
92

increase in labor costs increases LCOE values by fivefold compared to a 10% increase in labor 

costs.

Fuel Costs – Fuel costs for geothermal, wind, and solar technologies are assumed to be zero for 

the LCOE calculations in this report. However, fossil, nuclear, and biomass technologies do have 

fuel costs, and the LCOE values are sensitive to changes in these fuel costs. Natural gas 

technologies (combustion turbine and combined cycle) are the most sensitive, and these 

technologies show a 7% to 8% increase in LCOE with a 10% increase in the cost of natural gas. 

Nuclear technologies are the least sensitive, with LCOE increases of 1% to 2% depending on the 

data set. Coal (pulverized and IGCC) and biomass plants show an LCOE increase of 2% to 4% 

with a 10% increase in the cost of fuel. The impact of fuel costs scales linearly (i.e., 50% 

increase in fuel costs produces a fivefold impact on LCOE relative to a 10% increase in fuel 

costs).
93

5 Conclusions

This report focused on an evaluation of technical performance and cost characteristics for power 

generation technologies described in data sets that support energy market models. The following 

six data sets were examined:

• AEO 2009 (NEMS model)

• GPRA 2009 (MARKAL model)

• NREL-SEAC 2008 (ReEDS model)

• MiniCAM 2008 (MiniCAM model)

• EPA 2009 (IPM model)

• MERGE 2009 (MERGE model)

The six data sets included characteristics for 29 different types of electricity generation 

technologies, spanning fossil, nuclear, and renewable resources. The specific technologies 

covered within each data set varied, and the time horizon described differed between data sets 

(AEO extended to 2030, EPA to 2035, and the other four data sets to 2050). To compare and 

contrast characteristics between data sets, a core group of 11 technologies was selected (see 

Table 20).

Table 20. Eleven Core Utility Scale Power Generation Technologies

Energy Source Utility Scale Power Generation Technology

Coal Coal (pulverized coal plant)

Integrated Gasification Combined Cycle (IGCC)

Natural Gas Combustion Turbine (advanced)

Combined Cycle (advanced)

Nuclear Nuclear plant

Biomass Biomass gasification plant

Geothermal Hydrothermal

Wind

Onshore

Offshore

Solar

Solar Thermal

Photovoltaic

Technical performance characteristics, such as the heat rate for fossil and nuclear plants, tended 

to track closely, although differences were observed. The largest variation in heat rate occurred 

for biomass plants, which is possibly a reflection of uncertainty in how this technology will 

evolve as it matures.

Capacity factor was another technical performance characteristic that was examined. For fossil 

and nuclear plants, capacity factors were generally 80% or higher and typically varied only a few 

percentage points between data sets (one exception occurred for one combustion turbine that was 

modeled as a peaking unit with a low capacity factor). Biomass and geothermal plants also had 
94

capacity factors exceeding 80%. Capacity factors for wind and solar were lower. Wind 

technologies (both onshore and offshore) had capacity factors near 40% in 2010, rising to 

slightly below 45% in 2025 (average values across all data sets). Solar thermal technologies had 

large capacity factor variations, in part due to inclusion of thermal energy storage in some, but 

not all, data sets. The average capacity factor for solar thermal technologies without storage 

remained constant from 2010 to 2025 at 30%, although the average capacity factor for solar 

thermal technologies with storage rose from 58% in 2010 to 74% in 2025. PV capacity factors 

were similar between data sets, with an average value of 24% in 2010, increasing to 25% in 

2025.

Overnight capital costs showed disparities for all technologies across the data sets. These 

disparities included absolute values as well as the rate of change in overnight capital costs over 

time. The variations tended to be smaller for mature technologies, such as coal and combined 

cycle plants, which are not heavily dependent on site conditions. Less mature technologies, such 

as solar thermal and PV, and those that are heavily dependent on site conditions, such as 

geothermal, tended to show the largest variations in overnight capital costs.

In addition to examining technical performance and cost characteristics, the levelized cost of 

energy (LCOE) was computed using a uniform algorithm for all data sets. These calculations 

highlighted the magnitude of disparities across the data sets.

Energy market models forecast market penetration of power generation technologies based on 

economics coupled with many other criteria (e.g., demand, resource availability, grid reliability, 

and policies). While only part of the decision making equation, LCOE values provide insights on 

how different data sets can lead to different projections – often significantly different – for the 

types of power generation technologies that will be built in the future.

Understanding power generation characteristics that drive energy market models is a first step in 

understanding different model projections for the future power plant portfolio in the United 

States. The disparities identified in this study suggest that work is needed to better understand 

how data set owners develop technical and cost characteristics, including the use of commodity 

price indices to adjust capital costs. Additional work is also recommended to track how data set 

characteristics change. The six data sets examined in this study are updated periodically, and 

these changes, and the subsequent impact on LCOE results, should be followed.
95

Appendix A. NEMS (AEO 2009 Data Set)

The AEO 2009 data set is based on the Annual Energy Outlook (AEO) prepared by the Energy 

Information Administration (EIA). The AEO 2009 report was initially released in March 2009, 

and then updated in April.27 The April update was prepared to reflect provisions in the American 

Recovery and Reinvestment Act (ARRA) that were enacted in February 2009. Assumptions used 

for the power generation technologies in the AEO 2009 data set are available online.28 However, 

not all details are available on-line, and ICF contacted EIA for additional information to augment 

the analysis.29

Power plant characteristics are summarized in Section 8 of the Assumptions to the Annual 

Energy Outlook 2009.30

• World Bank Report, Study of Equipment Prices in the Power Industry, June 2008 

draft.

The assumptions in this section pertain to the Electricity Market 

Module, which is one component of the NEMS model. In this documentation, EIA indicates that 

the electricity generation plant characteristics are intended to reflect cost and performance values 

for typical plants under normal operating conditions. EIA indicates that the data were derived 

from various sources, including internal EIA resources, DOE Field Offices, National 

Laboratories, and discussions with industry and government personnel. EIA also consulted 

industry reports, including:

• Lawrence Berkeley National Laboratory, Annual Report on U.S. Wind Power 

Installation, Cost, and Performance Trends: 2007, LBNL-275E.

• California Energy Commission, Integrated Energy Policy Report, CEC-100-2007-

008-CMF, December 2007.

• Nuclear Energy Institute presentation, “Assessing the Economics of New Nuclear 

Power”, Center for Strategic and International Studies, July 31, 2008.

o The AEO 2009 data set includes cost and performance characteristics for 20 

electricity generation technologies that could be built over a modeling horizon 

that extends to 2030.

The AEO 2009 data set includes cost and performance characteristics for 20 electricity 

generation technologies that could be built over a modeling horizon that extends to 2030. These 

characteristics are summarized in Table 21.

 27 Annual Energy Outlook 2009, DOE/EIA-0383, March 2009, http://www.eia.doe.gov/oiaf/archive/aeo09/, 

accessed September 2009. 28 Assumptions to the Annual Energy Outlook 2009, Table 8.2, p89, http://www.eia.doe.gov/oiaf/aeo/assumption/

electricity.html, accessed September 2009. 29 Data were provided during the course of several e-mail exchanges during the months of September and October 

2009. Laura Martin was the primary contact at EIA.

30 Assumptions to the Annual Energy Outlook 2009, Section 8, Electricity Market Module, 

http://www.eia.doe.gov/oiaf/aeo/assumption/electricity.html, accessed September 2009.
96

Table 21. Cost and Performance Characteristics in AEO 2009 for 20 Technologies

31

Technology Size 

(MW)

Online 

Year

Lead 

Time 

(yrs)

Heat Rate (Btu/kWh) Capacity 

Factor32 Plant 

Lifetime 

(yrs)

Base Overnight 

Capital Cost 

(2007$/kW)

O&M (2007$)

2008 nth of 

kind

Variable 

($/MWh)

Fixed 

($/kW)

Coal (pulverized) 600 2012 4 9,200 8,740 85% --- 1,923 4.59 27.53

Integrated Gasification 

Combined Cycle (IGCC)

550 2012 4 8,765 7,450 85% --- 2,223 2.92 38.67

IGCC w/ Carbon Capture 

and Sequestration (CCS)

380 2016 4 10,781 8,307 85% --- 3,172 4.44 46.12

Combustion Turbine 

(conventional)

160 2010 2 10,810 10,450 92% --- 638 3.57 12.11

Combustion Turbine 

(advanced)

230 2010 2 9,289 8,550 92% --- 604 3.17 10.53

Combined Cycle 

(conventional)

250 2011 3 7,196 6,800 87% --- 917 2.07 12.48

Combined Cycle (advanced) 400 2011 3 6,752 6,333 87% --- 877 2 11.7

Combined Cycle with CCS 400 2016 3 8,613 7,493 87% --- 1,683 2.94 19.9

Fuel Cells 10 2011 3 7,930 6,960 87% --- 4,640 47.92 5.65

Nuclear 1,350 2016 6 10,434 10,434 90% 60 2,873 0.49 90.02

Distributed Gen (base) 2 2011 3 9,050 8,900 50% --- 1,305 7.12 16.03

Distributed Gen (peak) 1 2010 2 10,069 9,880 5% --- 1,566 7.12 16.03

Biomass 80 2012 4 9,646 7,765 83% --- 3,339 6.71 64.45

Municipal Solid Waste 

(MSW) & Landfill Gas (LFG)

30 2010 3 13,648 13,648 --- --- 2,377 0.01 114.25

Geothermal 50 2010 4 34,633 30,301 90% --- 1,630 0 164.64

Hydro 500 2012 4 --- --- 57% --- 2,038 2.43 13.63

Wind (onshore) 50 2009 3 --- --- 44% --- 1,797 0 30.3

Wind (offshore) 100 2012 4 --- --- 40% --- 3,416 0 89.48

Solar Thermal 100 2012 3 --- --- 31% --- 4,693 0 56.78

PV 5 2011 2 --- --- 22% --- 5,750 0 11.68

 31 Highlighted technologies (11 total) correspond to those technologies that represent the focus group for this report. 32 Capacity factors are endogenous in the NEMS model, and can change over time. The values shown correspond to maximum availability factors in 2010, or the 

first year technologies come online. .
97

Power plant characteristics for the 11 focus technologies are discussed in the remainder of this 

appendix, which is organized as follows:

• Technical Performance Characteristics

o Plant Size

o Online Year & Lead Time

o Heat Rate

o Capacity Factor

o Plant Lifetime

• Cost Characteristics

o Overnight Capital Costs

o Learning

o Commodity Cost Adjustments – based on Metals and Metal Products PPI

o Operation and Maintenance (O&M) Costs

5.1.1 Technical Performance Characteristics

Plant Size

Each technology in the AEO 2009 data set is represented by a single typical plant size. Plant 

capacities for 11 technologies are compared in Figure 71. These 11 technologies represent a 

cross section of fossil, nuclear, and renewable technologies, and include those technologies that 

are expected to make a significant contribution in meeting the demand for future power. In the 

case of combustion turbine and combined cycle technologies, Figure 71 shows power plant size 

for advanced, rather than conventional, technologies.

As indicated in Figure 71, there is a large size range between technologies. Nuclear is the largest 

at 1,350 MW, and PV is the smallest at 5 MW. In the AEO 2009 data set, the maximum plant 

capacities for all technologies, including PV, remain unchanged over the modeling period.

Online Year & Lead Time

The online year represents the first year that a new unit could be completed based on an order 

date of 2008. The lead time represents the lag between the order date for a new plant and the date 

when the new plant is expected to go into service.

The term “vintage year” is used elsewhere in this report but has a different meaning than “online 

year.” Vintage year is used to describe power plant characteristics for a particular time period. 

For example, in data sets such as EPA 2009, a characteristic such as the heat rate for combustion 

turbines may be constant for a vintage year time period – say 2015 to 2020. If combustion 

turbine heat rates are expected to improve after 2020, a new vintage year time period – say 2021 

through the end of the modeling cycle – will be defined with an improved heat rate for 

combustion turbines. The term vintage year is not used in conjunction with the AEO 2009 data 

set.
98

For most technologies in the AEO 2009 data set, the online year coincides with the lead time 

added to the year 2008. However, there are exceptions. For geothermal and onshore wind 

technologies, the online year was advanced due to accelerated market activity occurring in 

anticipation of the expiration of Production Tax Credits33

Figure 72

. The advanced online year for 

geothermal and onshore wind power plants is illustrated in (blue diamond below top of 

column). Figure 72 also signifies technologies that were not commercially available in 2008 

(blue diamond above column). For example, nuclear technology (based on advanced technology) 

is not expected to be ready for commercial orders until 2010 (six year lead time results in online 

year of 2016).

 33 PTCs expected to expire in 2009 for wind and 2010 for geothermal and LFG.
99

600

550

230

400

1,350

80

50 50

100 100

5

0

200

400

600

800

1,000

1,200

1,400

Coal IGCC Comb

Turbine 

Comb

Cycle

Nuclear Biomass Geothermal Wind 

(onshore)

Wind

(offshore)

Solar

Thermal

PV

Technology

Capacity (MW)

Figure 71. AEO 2009, plant capacities in online year
100

2008

2009

2010

2011

2012

2013

2014

2015

2016

Coal IGCC Comb

Turbine 

Comb

Cycle

Nuclear Biomass Geothermal Wind 

(onshore)

Wind

(offshore)

Solar

Thermal

PV

Technology

Online Year

0

1

2

3

4

5

6

7

8

Lead Time (yrs)

Lead Time

Online Year

Figure 72. AEO 2009, online year and lead times
101

Heat Rate

The AEO 2009 data set contains two heat rates. The first heat rate represents existing equipment 

available in 2008. The “nth of a kind” heat rate represents a reduced heat rate resulting from 

technology improvements. The first heat rate is the highest (least efficient) value and 

corresponds to the heat rate for technology that is installed in the first online year (online years 

and corresponding heat rates are shown in Table 21). The “nth of a kind” heat rate is the lowest 

(most efficient) and corresponds to the heat rate in 2025. Heat rates decline linearly between the 

on-line year and 2025, and remain unchanged through 2030 (end of the NEMS modeling 

horizon).34

A comparison of the highest and lowest heat rates for 11 technologies in the AEO 2009 data set 

is shown in Figure 73. Heat rates are not applied to wind and solar technologies.

Capacity Factor

Table 21 lists the capacity factors for the technologies included in the AEO 2009 data set, and 

Figure 74 shows these factors for 11 focus technologies. In the AEO 2009 data set, capacity

factors are an output parameter, not an input characteristic, for the NEMS model.

EIA provided maximum capacity factors for all technologies, and the data shown correspond to 

maximum capacity factors. For wind, EIA provided minimum, maximum and average capacity 

factors, but for all other technologies, only maximum capacity factors were provided.

In general, the capacity factors remain relatively constant over the modeling horizon (2009 

through 2030), but fluctuations do occur. As indicated in Figure 74, the capacity factors are 

relatively high for fossil, nuclear, and geothermal technologies (85% to 92%). Wind and solar 

technologies have significantly lower capacity factors, which are in the range of 22% to 42%.

Plant Lifetime

The modeling approach in NEMS is to allow plants to operate as long as they are economic 

compared to the alternatives. When a plant is no longer economic, it is retired. As a result of this 

approach, the AEO 2009 data set does not contain pre-determined plant lifetimes. There are 

exceptions, however. For example, nuclear plants do have a pre-determined lifetime of 60 years 

(40 year initial license plus one 20 year license renewal).

 34 EIA indicated that heat rates for biomass and geothermal are handled differently. A detailed explanation of the 

differences was not provided (e-mail exchange between ICF and EIA, November 2, 2009).
102

9,200

8,765

9,289

6,752

9,646

10,434

6,333

8,550

7,450

8,740

7,765

0

2,000

4,000

6,000

8,000

10,000

12,000

Coal IGCC Comb

Turbine 

Comb

Cycle

Nuclear Biomass Geothermal Wind 

(onshore)

Wind

(offshore)

Solar

Thermal

PV

Technology

Heat Rate (Btu/kWh)

High

Low

N/A

34,633 30,301

Figure 73. AEO 2009, high and low heat rates
103

85% 85%

92%

87%

90%

83%

90%

44%

40%

31%

22%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Coal IGCC Comb

Turbine 

Comb

Cycle

Nuclear Biomass Geothermal Wind 

(onshore)

Wind

(offshore)

Solar

Thermal

PV

Technology

Capacity Factor

Figure 74. AEO 2009, capacity factors
104

5.1.2 Cost Characteristics

Overnight Capital Costs

In the EIA AEO 2009 data set, the base overnight costs shown in Table 21 are adjusted by two

parameters to develop a starting year (2009) overnight cost. These two adjustment factors are 

project contingency and technology optimism (both factors are shown in Table 22).

Table 22. AEO 2009, Overnight Capital Costs in 2009 (reported in 2007$)

Technology

Base Overnight 

Cost (2007$/kW)

Adjustment Factors Total Overnight 

Capital Cost 

(2007$/kW) Project 

Contingency

Technology 

Optimism

Coal 1,923 1.070 1.000 2,058

IGCC 2,223 1.070 1.000 2,378

IGCC w/ CCS 3,172 1.070 1.030 3,496

Combustion Turbine 

(conv.) 638 1.050 1.000 670

Combustion Turbine 

(adv) 604 1.050 1.000 634

Combined Cycle 

(conv.) 917 1.050 1.000 963

Combined Cycle (adv.) 877 1.080 1.000 948

Combined Cycle with 

CCS 1,683 1.080 1.040 1,890

Fuel Cells 4,640 1.050 1.100 5,359

Nuclear 2,873 1.100 1.050 3,318

DG (base) 1,305 1.050 1.000 1,370

DG (peak) 1,566 1.050 1.000 1,644

Biomass 3,339 1.070 1.054 3,766

MSW, LFG 2,377 1.070 1.000 2,543

Geothermal 1,630 1.050 1.000 1,711

Hydro 2,038 1.100 1.000 2,242

Wind (onshore) 1,797 1.070 1.000 1,923

Wind (offshore) 3,416 1.100 1.025 3,851

Solar Thermal 4,693 1.070 1.000 5,021

PV 5,750 1.050 1.000 6,038

The project contingency factor35

 35 The project contingency factor is only used in the AEO data set. The ReEDS data set, discussed later in this 

report, introduces construction cost multipliers, which are based on similar principles to the AEO project 

contingency factors. However, the AEO project contingency factors should not be confused with the ReEDS 

construction cost multipliers.

accounts for unforeseeable elements that may increase costs. 

EIA references the American Association of Cost Engineers as a source for developing 

contingency factors. The contingency factors range between 1.05 and 1.10, and remain constant 

over the NEMS modeling horizon (2009 through 2030).
105

EIA uses the technology optimism factor to account for a tendency to underestimate costs for 

emerging technologies or new designs that are unproven. For mature or proven technologies this 

factor is one, and for emerging or unproven technologies, the factor is greater than one. As 

experience is gained, the technology optimism factor is gradually reduced to one.

The starting points for total overnight capital costs used in the AEO 2009 data set are shown in 

Figure 75 for the 11 focus technologies. As indicated in this figure, costs range from $652/kW 

for a combustion turbine (advanced) to approximately $6,000/kW for PV.

Learning

Over the course of the NEMS modeling horizon (2009 through 2030), total overnight capital 

costs are reduced based on learning effects. Learning is based on installed capacity, and in the 

NEMS model, learning is divided into three periods depending on technology maturity level. The 

periods correspond to revolutionary (Period 1), evolutionary (Period 2), and mature (Period 3), 

with the learning rate declining from Period 1 through Period 3. In NEMS, learning is described 

for major components to account for cross-over effects that impact power generation systems 

that use the same major components. For example, the turbine generator for a combustion 

turbine, combined cycle plant, and an IGCC plant is basically the same, and the construction of 

any of these three plants will contribute to learning effects (i.e., cost reductions) for the 

combustion turbine. A summary of the learning parameters in the AEO 2009 data set is shown in 

Table 23.
106

Table 23. AEO 2009, Learning Parameters

Technology Component

Learning Rate by Period Capacity Doublings 

Required to 

Advance

Minimum 

Learning 

by 2025

1 2 3 1-2 2-3

Pulverized Coal --- --- 1% --- --- 5%

Combustion Turbine – conv. --- --- 1% --- --- 5%

Combustion Turbine – adv. --- 10% 1% --- 5 10%

Heat Recovery Steam 

Generator

--- --- 1% --- --- 5%

Gasifier --- 10% 1% --- 5 10%

Carbon 

Capture/Sequestration

20% 10% 1% 3 5 20%

Balance of Plant – IGCC --- --- 1% --- --- 5%

Balance of Plant – Turbine --- --- 1% --- --- 5%

Balance of Plant – Comb. 

Cycle

--- --- 1% --- --- 5%

Fuel Cell 20% 10% 1% 3 5 20%

Nuclear (adv) 5% 3% 1% 3 5 10%

Fuel Prep – Biomass IGCC 20% 10% 1% 3 5 20%

Dist Gen – Base --- 5% 1% --- 5 10%

Dist Gen – Peak --- 5% 1% --- 5 10%

Geothermal --- 8% 1% --- 5 10%

Municipal Solid Waste --- --- 1% --- --- 5%

Hydropower --- --- 1% --- --- 5%

Wind (onshore) --- --- 1% --- --- 1%

Wind (offshore) 20% 10% 1% 3 5 20%

Solar Thermal 20% 10% 1% 3 5 20%

Solar PV 15% 8% 1% 3 5 20%
107

2,058

2,379

652

921

3,318

3,766

1,712

1,923

3,852

5,022

6,038

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

Coal IGCC Comb

Turbine 

Comb

Cycle

Nuclear Biomass Geothermal Wind 

(onshore)

Wind

(offshore)

Solar

Thermal

PV

Technology

Cost (2007$ / kW)

Figure 75. AEO 2009, overnight capital costs
108

In Table 23, learning rates for Period 1 are supplied for those technologies that are grouped in 

this category in 2009. For example, offshore wind is regarded as being in a revolutionary 

development phase (Period 1) in 2009, and pulverized coal is regarded as a mature technology 

(Period 3) in 2009. In the NEMS model, technologies are advanced to the next learning period 

based on cumulative installed capacities. In all cases, the AEO 2009 data used three doublings to 

advance technologies from Period 1 to Period 2, and five doublings to move technologies from 

Period 2 to Period 3.

As noted in Table 23, a minimum learning by 2025 is set for all technologies, regardless of 

capacity additions. This minimum learning is intended to account for international development 

(i.e., technologies installed outside of the United States) or increased research and 

development.36

From a numerical perspective, learning factors are expressed as a multiplier that is applied to the 

overnight capital cost for a particular technology in the staring year for the modeling period. 

Learning factors for all technologies start at one in 2009, and then decline over time. The 

learning factors are relatively steep for technologies in a revolutionary development phase 

(Period 1), and relatively shallow for mature technologies (Period 3). In the AEO 2009 data set, 

the lowest learning occurs for onshore wind, and the highest learning occurs for solar thermal 

power plants. These two technologies are shown in 

It is interesting to note that all technologies, with the exception of onshore wind, 

have a minimum learning of at least 5% by 2025. For onshore wind, the minimum learning is set 

at 1%. It is unclear why the AEO 2009 data set uses a minimum learning of only 1% for onshore 

wind. Perhaps major breakthroughs from learning by R&D are not anticipated, or perhaps

significant advancements in cost reductions are not viewed as likely.

Figure 76. In Figure 77, the learning 

functions for the 11 focus technologies are shown for the time period of 2009 and 2030. The 

learning factors in both figures represent total learning effects, and include all cross-over 

learning that occurs at the component level (based on cumulative installed capacity as forecast by 

NEMS).

Commodity Cost Adjustments – based on Metals and Metal Products PPI

In addition to learning factors, the AEO 2009 data set also includes a commodity adjustment 

based on the producer price index (PPI) for metals and metal products. In the AEO 2009 data set, 

the same commodity adjustment factor is used for all 20 technologies over the modeling horizon. 

EIA recognizes that different technologies have different commodity price dependencies. 

However, the PPI adjustment has just recently been added, and it is an initial attempt to correct 

for fluctuating commodity prices. The PPI adjustment curve is shown in Figure 78.

In summary, overnight capital costs in the AEO 2009 data set change over time based on four 

factors: 1) project contingency, 2) technology optimism, 3) learning, and 4) metal and metal 

products PPI. Figure 79 shows the overnight capital costs for the 11 focus technologies in the 

AEO data set.

 36 EIA, Assumptions to AEO 2009, p 91.
109

0.70

0.75

0.80

0.85

0.90

0.95

1.00

2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030

Year

Learning Factor

Solar Thermal

Wind, Onshore

Figure 76. AEO 2009, highest and lowest learning function values
110

0.70

0.75

0.80

0.85

0.90

0.95

1.00

2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030

Year

Learning Factor

Solar Thermal

Geothermal

Wind (onshore)

Nuclear

IGCC Coal

Combustion Turbine

Combined Cycle

Wind (offshore)

Biomass

PV

Figure 77. AEO 2009, learning function values for the 11 focus technologies
111

0.80

0.85

0.90

0.95

1.00

1.05

1.10

2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030

Year

Commodity Factor

Figure 78. AEO 2009, commodity price adjustment factor for all technologies
112

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030

Year

Cost (2007$ / kW)

Solar Thermal

Geothermal

Biomass Nuclear

Combined Cycle

Combustion Turbine 

IGCC Coal

PV

Wind (onshore)

Wind (offshore)

Figure 79. AEO 2009, overnight capital costs for the 11 focus technologies
113

O&M Costs

Table 24 shows the variable and fixed operation and maintenance (O&M) costs used in the AEO 

2009 data set.

Table 24. AEO 2009, O&M Costs

Technology

O&M

Variable Fixed

(2007$/MWh) (2007$/kW/yr)

Coal 4.59 27.53

IGCC 2.92 38.67

IGCC w/ CCS 4.44 46.12

Combined Cycle (conv.) 2.07 12.48

Combined Cycle (adv.) 2.00 11.70

Combined Cycle with CCS 2.94 19.90

Combustion Turbine (conv.) 3.57 12.11

Combustion Turbine (adv.) 3.17 10.53

Fuel Cells 47.92 5.65

Nuclear 0.49 90.02

DG (base) 7.12 16.03

DG (peak) 7.12 16.03

Biomass 6.71 64.45

MSW, LFG 0.01 114.25

Geothermal 0 164.64

Hydro 2.43 13.63

Wind (onshore) 0 30.30

Wind (offshore) 0 89.48

Solar Thermal 0 56.78

PV 0 11.68

In some data sets, such as MERGE (which is described in a separate section of this report), 

variable and fixed O&M costs are rolled into a single “all-inclusive” category, and a single O&M 

cost is reported. However, the AEO 2009 data set does make a clear distinction between variable 

and fixed O&M costs, and these distinct costs are shown in Table 24. These values, which are 

expressed in 2007 dollars, do not change over the NEMS modeling horizon (i.e., O&M costs 

remain constant through 2030).

The O&M values for the 11 focus technologies are shown in Figure 80. Note that for geothermal, 

wind, and solar technologies, the variable O&M costs are zero. In the AEO 2009 data set, the 

O&M costs for these technologies are assumed to be dominated by fixed costs with a negligible 

contribution from variable factors.
114

28

39

11 12

90

64

165

30

89

57

12

4.59

2.92

3.17

2.00

0.49

6.71

0.00 0.00 0.00 0.00 0.00

0

1

2

3

4

5

6

7

8

9

Coal IGCC Comb

Turbine 

Comb Cycle Nuclear Biomass Geothermal Wind 

(onshore)

Wind

(offshore)

Solar

Thermal

PV

Technology

Variable O&M (2007$ / MWh)

0

20

40

60

80

100

120

140

160

180

Fixed O&M (2007$ / kW)

Fixed O&M

Variable O&M

Figure 80. AEO 2009, variable O&M
115

Appendix B. MARKAL (GPRA 2009 Data Set)

The Government Performance and Results Act (GPRA) of 1993 requires the U.S. Department of 

Energy (DOE) and other Federal agencies to report annual benefits. Within DOE, the 

responsibilities for developing power plant characteristics for the GPRA analysis are divided 

between multiple offices, including:

• Renewable Power – Office of Energy Efficiency and Renewable Energy (EERE)

• Fossil Plants – Office of Fossil Energy

• Nuclear Power – Office of Nuclear Energy

In compliance with GPRA, EERE prepares an integrated benefits analysis to evaluate the 

effectiveness of its R&D programs (programs listed in Table 25). The GPRA analysis includes 

three primary cases:

• No Funding (Baseline Case) – No DOE R&D funding

• Program Funding – New R&D funding for each program

• Portfolio Funding -- R&D “portfolio” where each program competes for pool of 

funding

Table 25. DOE EERE R&D Programs37

DOE EERE R&D Programs

Biomass

Buildings

Federal Energy Management 

Geothermal

Hydrogen & Fuel Cells

Industry 

Solar 

Vehicles

Weatherization & Intergovernmental 

Wind & Hydropower

Within the Program Funding case, there are two sub cases: 1) “target” and 2) “over target.” The 

target case corresponds to a realistic desired budget, and the over target case corresponds to a 

funding level that represents a maximum level that a particular program could manage.

For this particular project, ICF evaluated technology characteristics for the GPRA Program 

Funding Target case. NREL staff collected power generation technology characteristics 

developed by individual EERE programs for this case, and provided this information to ICF.

While the majority of technology characteristics are based on the GPRA Target Case, ICF did 

examine the GPRA Baseline Case (no R&D funding) to determine the impact of learning on 

 37 Programs are listed on DOE EERE home page, http://www.eere.energy.gov/.
116

overnight capital costs. Learning associated with R&D was estimated to be represented by the 

difference in overnight capital costs between these two cases.

The GPRA 2009 data set includes power plant characteristics for five electricity generation 

technologies as indicated in Table 26. As noted, the GPRA 2009 data set includes characteristics 

for photovoltaic, solar thermal (concentrating solar power, or CSP), onshore wind, offshore 

wind, and geothermal technologies. PV characteristics are available for utility generation, 

commercial buildings, and residential buildings. Wind technologies are separated by location 

(onshore and offshore) and wind resource (Class 4 to 6 for onshore and class 6 to 7 for offshore). 

Geothermal is divided into hydrothermal binary, hydrothermal flash, and enhanced geothermal 

systems (EGS).

Table 26. Cost and Performance Characteristics in GPRA 2009

Technology Description

Overnight Capital Cost O&M, Fixed Heat Rate Life

(2006$/kW) (2006$/kW/yr) (Btu/kWh) (yrs)

PV Utility Generation 5,576 8.08 --- 30

Commercial Buildings 7,040 23.95 --- 30

Residential Buildings 7,761 85.20 --- 30

Solar Thermal38 --- 5,356 61.14 --- 30

Wind, onshore Class 4 1,448 27.60 --- 20

Class 5 1,448 27.60 --- 20

Class 6 1,448 27.60 --- 20

Wind, offshore Class 5, Shallow 2,865 180.00 --- 20

Class 6, Shallow 2,865 180.00 --- 20

Class 7, Shallow 2,865 180.00 --- 20

Geothermal Hydrothermal - Binary 4,623 119.28 --- ---

Hydrothermal - Flash 3,295 75.44 --- ---

EGS – Convective 4,212 148.01 --- ---

Observations related to the technical performance and cost characteristics in the GPRA 2009 

data set are included in the remainder of this section, which is organized as follows:

• Technical Performance Characteristics

o Plant Size

o Heat Rate

o Capacity Factor

o Plant Lifetime

• Cost Characteristics

o Overnight Capital Costs

o Learning

o O&M Costs (fixed and variable)

 38 Solar thermal technology in the GPRA data set is based on concentrating solar power (CSP).
117

5.1.3 Technical Performance Characteristics

Plant Size

Power plant sizes were not provided for the GPRA data set. However, based on discussions with 

NREL personnel, the power plant sizes in the GPRA 2009 data set were assumed to be identical 

to the plant sizes in the AEO 2009 data set. These plant sizes are shown in Table 27.

Table 27. GPRA 2009, Power Plant Sizesa

Technology Plant Size (MW)

PV (all types) 5

Solar Thermal 100

Wind, onshore (all types) 50

Wind, offshore (all types) 100

Geothermal 50

a Each technology is assumed to be equivalent to AEO.

Heat Rate

ICF only evaluated renewable technologies in the GPRA 2009 data set. Heat rates are primarily 

used to describe the performance of fossil technologies. Because ICF only evaluated renewable 

technologies in the GPRA 2009 data set, heat rates are not assessed in this report for the GPRA 

data set.

Capacity Factor

Capacity factors are provided for solar thermal, PV, onshore wind, offshore wind, and 

geothermal technologies in the GPRA data set. Geothermal technologies have the highest 

capacity factors – 90% for hydrothermal flash and 95% for EGS.

The onshore and offshore wind capacity factors are broken down by wind class as indicated in 

Figure 81. This figure shows that wind capacity factors improve over time for both onshore and 

offshore resources. These capacity factor improvements result from technology advancements,

such as improved control systems and increased blade efficiencies, which expand the range of 

wind speeds that can be utilized. In general, the capacity factors increase by 10 to 15 percentage 

points for each wind class between the start of the modeling time period and approximately 

2030. Between 2030 and 2050, the capacity factors remain relatively constant. In 2050, the 

lowest capacity factor is 40% (offshore Class 4) and the highest capacity factor is 54% (offshore 

Class 7).
118

Onshore Class 4

Onshore Class 5

Onshore Class 6

Offshore Class 5

Offshore Class 6

Offshore Class 7

30%

35%

40%

45%

50%

55%

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capacity Factor

Figure 81. GPRA 2009, wind capacity factors

The solar thermal capacity factors for the GPRA data set are shown in Figure 82. As indicated, 

the capacity factor doubles between 2007 and 2030 – increase from 41% in 2007 to 82% in 2030 

(remains constant at 82% beyond 2030). The reason for the rise in the solar thermal capacity 

factor is that the GPRA data set incorporates increasing levels of thermal storage over time. 

Thermal storage extends the energy delivery time, thereby increasing the capacity factor.
119

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capacity Factor (%)

Figure 82. GPRA 2009, solar thermal capacity factors

Plant Lifetime

Plant lifetimes are shown in Table 26, and they remain constant over the GPRA 2009 modeling 

time frame. Plant life data are not reported in the GPRA 2009 data set for geothermal 

technologies (hydrothermal and EGS).

5.1.4 Cost Characteristics

Overnight Capital Costs

Table 28 shows the data points received from NREL for GPRA 2009 geothermal, PV, and solar 

thermal technologies. The data for geothermal technologies start in 2007 then continue in five 

year increments from 2010 to 2050. PV and solar thermal technologies also start in 2007, 

continue in five year increments from 2010 to 2030, and then jump to 2050. Overnight capital 

costs for geothermal, PV, and solar thermal technologies are expected to be decrease 

significantly during these years.
120

Table 28. GPRA 2009, Capital Cost for Non-Wind Renewable Technologies (2006$/Kw)

Year

Geothermal 

Hydrothermal 

Binary

Geothermal 

EGS

PV 

Commercial

PV 

Residential

PV 

Utility

Solar 

Thermal 

CSP

2007 $4,623 $4,212 $7,040 $7,761 $5,577 $5,356

2010 $4,533 $4,136 $4,019 $5,024 $3,919 $4,945

2015 $4,360 $3,989 $2,221 $3,316 $2,592 $4,223

2020 $4,232 $3,880 $1,888 $2,818 $2,204 $3,897

2025 $4,104 $3,772 $1,699 $2,396 $1,983 $3,586

2030 $3,975 $3,662 $1,614 $2,036 $1,884 $3,035

2035 $3,867 $3,563 -- -- -- --

2040 $3,754 $3,458 -- -- -- --

2045 $3,675 $3,374 -- -- -- --

2050 $3,639 $3,341 $1,533 $1,731 $1,790 $2,569

Table 29 shows capital costs for all wind technologies. Capital costs for onshore wind are similar 

in 2005; after 2010, however, onshore Classes 5 and 6 are about $100/kW lower than Class 4. 

All offshore wind classes have identical capital costs. The main differences between the wind 

categories are the capacity factors. In general, the higher the wind class the higher the capacity 

factor.

Table 29. GPRA 2009, Capital Cost for Wind Technologies (2006$/kW)

Year Wind Class 4

Wind Class 5-

6

Shallow Offshore

Wind Class 5-7

2008 $1,448 $1,448 $2,865 

2010 $1,422 $1,422 $2,812 

2015 $1,300 $1,193 $1,984 

2020 $1,225 $1,100 $1,700 

2025 $1,136 $1,048 $1,600 

2030 $1,118 $1,016 $1,468 

2040 $1,102 $986 $1,443 

2050 $1,080 $962 $1,418 

Figure 83 compares the overnight capital costs of each technology for their base year. Not all 

costs in Figure 83 are from the same base year. Wind technologies have their first year of data in 

2008 while the other technologies have their first year being 2007. There are 13 technologies 

because the GPRA data have variations within geothermal, PV, and wind.
121

4,212

4,623

3,295

7,040

7,761

5,577 5,356

1,448 1,448 1,448

2,865 2,865 2,865

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

Geothermal EGS Convective

Geothermal Hydrothermal Binary

Geothermal Hydrothermal Flash

PV Commercial

PV Residential

PV Utility

Solar Thermal

Wind Class 4Wind Class 5Wind Class 6

Wind Offshore Class 5 ShallowWind Offshore Class 6 ShallowWind Offshore Class 7 Shallow

Technology

Cost (2006$/kW)

Figure 83. GPRA 2009, base overnight capital costs for 13 technologies
122

In Figure 84 shows overnight capital costs. Costs have been linearly interpolated to allow 

comparisons during gap years. PV for residential buildings begins as the highest cost of any of 

the technologies shown, but quickly drops below solar CSP and geothermal hydrothermal binary. 

Wind power (including Wind Offshore Class 5-7, not shown) has the lowest overnight capital 

costs of the technologies shown, for all years.

Geothermal 

Hydrothermal - 

Binary

PV - Utility

PV - Commercial 

Buildings

PV - Residential 

Buildings

Solar CSP

Wind - Class 5

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

2007 2012 2017 2022 2027 2032 2037 2042 2047

Year

Cost (2006$/kW)

Figure 84. GPRA 2009, overnight capital costs

Learning

Capital costs are influenced by learning from R&D provided by the DOE in the GPRA data set. 

Learning is captured in the database by comparing the target case capital costs with the base case 

capital costs. Specifically, the formula below was used to calculate the learning factor:

Learning Factor = Target Case Capital Cost

Base Case Capital Cost
123

Learning factors were calculated for wind, PV, and geothermal technologies. The calculation 

was not made for solar thermal (CSP) because the characteristics of the technology in the target 

and base case were distinct. In the base case, CSP has no storage. In the target case, CSP has 

storage of 6 hours until 2015, 15 hours from 2020 to 2025, and 17 hour storage from 2030 

forward39

In 

.

Figure 85, capital costs for onshore Wind Class 4 are shown over time. The lines show the 

actual capital costs for the base and target cases corresponding to the left axis. The blue bars 

show the calculated learning factors corresponding to the right axis. Capital costs are reduced by 

about 20 percent in Class 4 wind technology by 2050.

Target

Baseline

-

200

400

600

800

1,000

1,200

1,400

1,600

2008 2010 2015 2020 2025 2030 2040 2050

Year

Baseline and Target Capital 

Cost (2006$/kW)

-

0.20

0.40

0.60

0.80

1.00

1.20

Learning Factor

Learning Factor

Figure 85. GPRA 2009, onshore wind class 4 capital costs (base and target cases)

Wind Classes 5 and 6 experience the same cost improvement from learning. Both wind classes 

are modeled with the same capital costs in the base and target cases. Capital costs decrease by 

over 30 percent by 2050. Figure 86 shows the capital costs and learning factor over time for 

onshore Wind Classes 5 and 6.

Offshore wind has the same target and base case costs across all classes. The decrease in cost due 

to learning is shown in Figure 87. Offshore wind capital costs are projected to decrease by nearly 

50 percent by 2050, perhaps due to advances in offshore structure designs.

 39 Information based on spreadsheet sent by NREL on September 11, 2009 (Solar-PDS FY10 Submission- CSPMehos revised- 4-13-08.xls).
124

Target 

Baseline

-

200

400

600

800

1,000

1,200

1,400

1,600

2008 2010 2015 2020 2025 2030 2040 2050

Year

Baseline and Target 

Captial Cost (2006$/kW)

-

0.20

0.40

0.60

0.80

1.00

1.20

Learning Factor

Learning Factor

Figure 86. GPRA 2009, onshore wind classes 5 and 6, capital costs

Target

Baseline

-

500

1,000

1,500

2,000

2,500

3,000

3,500

2008 2010 2015 2020 2025 2030 2040 2050

Year

Baseline and Target 

Capital Cost (2006$/kW)

-

0.20

0.40

0.60

0.80

1.00

1.20

Learning Factor

Learning Factor

Figure 87. GPRA 2009, shallow offshore wind class 4, capital costs
125

Learning in geothermal technologies is depicted in the following three figures. Although the 

target case data extends to 2050, the base case data only extends to 2030. Therefore, learning 

factors are only calculated to 2030.

Figure 88 and Figure 89 show the capital costs and learning factors for both geothermal 

hydrothermal technologies. Both technologies have the same learning factors. However, the 

actual costs are different. Binary technology is more expensive on a per kilowatt basis. Capital 

costs for geothermal binary start at $4,337 per kW while geothermal flash technology starts at 

$3,091 per kW in 2007. Both decrease by 25 percent by 2030.

Target

Baseline

-

500

1,000

1,500

2,000

2,500

3,000

3,500

2007 2010 2015 2020 2025 2030

Year

Baseline and Target Capital 

Cost (2006$/kW)

-

0.20

0.40

0.60

0.80

1.00

1.20

Learning Factor

Learning Factor

Figure 88. GPRA 2009, geothermal flash learning factor
126

Target

Baseline

-

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

2007 2010 2015 2020 2025 2030

Year

Baseline and Target 

Capital Cost (2006$/kW)

-

0.20

0.40

0.60

0.80

1.00

1.20

Learning Factor

Learning Factor

Figure 89. GPRA 2009, geothermal binary learning factor

Geothermal EGS – convective technology has a smaller decrease in cost due to learning than

either hydrothermal technology. By 2030, the decrease is less than 20 percent. Figure 90 shows 

the capital costs in the baseline and target cases and the learning factor for geothermal EGS.

Target

Baseline

-

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

2007 2010 2015 2020 2025 2030

Year

Baseline and Target 

Capital Cost (2006$/kW)

-

0.20

0.40

0.60

0.80

1.00

1.20

Learning Factor

Learning Factor

Figure 90. GPRA 2009, geothermal EGS—convective learning factor

The GPRA data have three classifications for photovoltaic (PV) technologies: residential, 

commercial, and utility. In the data provided by NREL, residential and commercial PV extend to 

2050, while utility only extends to 2025. Figure 91 and Figure 92 show the costs of residential 

and commercial PV in the different cases and their learning factors. The learning effect trend in 

residential and commercial PV is different from the learning found in the other GPRA renewable 
127

technologies. Gains from learning by DOE R&D are realized in the early part of the modeling 

horizon, and the baseline cost almost converges with the target case by 2050. Contrast these 

trends with geothermal and wind, where the baseline case never begins to converge with the 

target case.

Target

Baseline

-

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

2007 2010 2015 2020 2025 2030 2050

Year

Baseline and Target Capital 

Cost (2006$/kW)

-

0.20

0.40

0.60

0.80

1.00

1.20

Learning Factor

Learning Factor

Figure 91. GPRA 2009, residential PV learning factor

Target

Baseline

-

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

2007 2010 2015 2020 2025 2030 2050

Year

Baseline and Target 

Capital Cost (2006$/kW)

-

0.20

0.40

0.60

0.80

1.00

1.20

1.40

Learning Factor

Learning Factor

Figure 92. GPRA 2009, commercial PV learning factor
128

Figure 93 shows the capital costs in the target and baseline cases and learning factor of utility PV 

in GPRA. There is no convergence of capital costs between 2007 and 2025 in utility PV. To be 

fair, residential PV did not converge between 2007 and 2025, either. All of its convergence 

occurs between 2030 and 2050, when there is expected to be a significant decrease in the 

baseline capital cost.

Target

Baseline

-

1,000

2,000

3,000

4,000

5,000

6,000

2007 2010 2015 2020 2025

Year

Baseline and Target Capital 

Cost (2006$/kW)

-

0.20

0.40

0.60

0.80

1.00

1.20

Learning Factor

Learning Factor

Figure 93. GPRA utility PV learning factor

O&M Costs

In the GPRA data set, O&M costs change for all renewable technologies over the modeling 

horizon (2005 through 2050). O&M cost data received from NREL is shown in Table 30.

Table 30. GPRA 2009, Fixed O&M Costs (2006$/kW/yr) for Renewable Technologies

Year

Geothermal 

Hydrothermal 

Binary

PV 

Commercial

PV 

Residential

PV 

Utility

Solar 

Thermal 

CSP

Shallow 

Offshore 

Wind Class 5

Wind 

Class 5

2007 $119.28 $23.95 $85.20 $8.08 $61.14 -- --

2008 -- -- -- -- -- $180.00 $27.60

2010 $119.68 $12.06 $15.07 $7.00 $52.11 $180.00 $25.98

2015 $116.75 $4.44 $6.63 $5.18 $48.52 $127.00 $24.08

2020 $113.82 $3.21 $4.79 $3.75 $44.94 $110.00 $22.29

2025 $110.88 $2.60 $3.88 $3.03 $41.34 $80.00 $20.61

2030 $107.95 $2.35 $3.50 $2.74 $37.73 $55.00 $19.15

2035 $105.08 -- -- -- -- -- --

2040 $102.25 -- -- -- -- $50.00 $16.46

2045 $100.04 -- -- -- -- -- --

2050 $99.06 $2.12 $3.16 $2.47 $22.91 $45.00 $14.11
129

Figure 94 shows the fixed O&M costs for six of the renewable technologies. Costs from the table 

have been linearly interpolated in the figure to allow comparisons during gap years. Solar CSP 

has base O&M costs in 2005, but, from 2010 through 2050, it has costs in ten year increments.

Geothermal - 

Hydrothermal - 

Binary

PV - Utility

PV - Commercial 

Buildings

PV - Residential 

Buildings

Solar CSP

Wind - Class 5

$0.00

$20.00

$40.00

$60.00

$80.00

$100.00

$120.00

$140.00

2007 2012 2017 2022 2027 2032 2037 2042 2047

Year

Cost (2006$/kW)

Figure 94. GPRA 2009, fixed annual O&M costs for renewable technologies
130

Appendix C. ReEDS (NREL-SEAC 2008 Data Set)

NREL developed the ReEDS model and is responsible for developing the power plant 

characteristics that are used to support the model. In general, the available generator types in the 

ReEDS data set are based on the most likely types identified by EIA in the latest Annual Energy 

Outlook.40

While the generator types in the ReEDS data set are generally consistent with the EIA Annual 

Energy Outlook, the ReEDS power plant characteristics generally differ from AEO. NREL uses 

internal resources, including contractor support from firms such as Black & Veatch, to develop 

power plant characteristics. As part of the process for developing power plant characteristics for 

ReEDS, NREL analyzes the full spectrum of power generation technologies, including fossil and 

nuclear as well as renewable.

For this project, NREL provided ICF with a data set used in conjunction with a Department of 

Energy 20% wind penetration study conducted in 2008 and a 20% National Renewable Portfolio 

Standard study.

4142

The NREL-SEAC 2008 data set used in the ReEDS model has cost and performance 

characteristics for 22 electricity generation technologies projected to 2050. For each technology, 

the data include three cost parameters (overnight capital cost, variable O&M, and fixed O&M) 

and two technical performance characteristics (heat rate and plant life). 

This data set is referred to as NREL-SEAC 2008.

Table 31 lists the 22

technologies and the five characteristics – three cost and two technical performance parameters –

for the 2008 data set.

In the NREL-SEAC 2008 data set, wind and solar thermal are divided into five resource classes. 

Also, the offshore wind is separated into deep and shallow categories. Each wind category 

(onshore, shallow offshore, and deep offshore) has five wind resource classes that affect only the 

capacity factor. The solar resource class does not affect the solar thermal capacity factor. The

resource class does not affect the cost parameters or plant life of wind or solar thermal.

NREL-SEAC data cost projections are provided in five year intervals from 2000 to 2050 for all 

technologies except geothermal technologies. Geothermal technologies are separated by three 

characteristics: (1) hydrothermal or EGS, (2) geo classes, and (3) geographic regions. In NRELSEAC data, there is no distinction between flash and binary. Geothermal hydrothermal is used to 

model both flash and binary.

Wind and storage technologies are the main distinguishing characteristics in the NREL-SEAC

data set. Three storage technologies (battery, CAES, and ICE-storage) were included in the data 

set but were not found in the other data sets listed in Table 1. Also, NREL-SEAC is similar to 

only the GPRA data set in that wind is separated by wind class for both onshore and offshore 

resources.

 40 ReEDS Model Documentation, Base Case Data and Model Description, Conventional Generation (Section 2.5), 

p28, W. Short et al., August 2009.

41 20% Wind Energy by 2030, DOE EERE, DOE/GO-102008-2567, July 2008. 42 Evaluating a Proposed 20% National Renewable Portfolio Standard. NREL Technical Report. NREL/TP-6A2-

45161. February 2009.
131

Observations related to the technical performance and cost characteristics in the NREL-SEAC 

2008 data set are included in the remainder of this section, which is organized as follows:

• Technical Performance Characteristics

o Plant Size

o Heat Rate

o Capacity Factor

o Plant Lifetime

• Cost Characteristics

o Overnight Capital Costs

o Learning

o O&M Costs (fixed and variable)
132

Table 31. NREL-SEAC 2008, Summary of Characteristics for 200543

Technology Overnight Capital 

Cost (2004$/kW)

Variable O&M 

(2004$/MWh)

Fixed O&M 

(2004$/kW/yr)

Heat Rate 

(Btu/kWh)

Lifetime 

(Years)

Plant Size

(MW)

Advanced Combined Cycle (CC) 742 2.86 13.71 6,870 30 300

Adv CC with Carbon Capture 1,371 8.09 -- 7,790 30 400

Adv Combustion Turbine (CT) 595 11.42 7.33 11,560 30 160

Biomass 2,617 9.52 66.63 14,500 45 100

Cofire New 2,048 1.62 34.65 9,470 60 600

Cofire Old 1,234 3.40 24.46 10,000 60 600

Conventional Hydro 1,320 3.20 12.72 -- 100 200

Geothermal EGS44 6,998 -- 199.69 -- 20 50

Geothermal Hydrothermal45 2,818 -- 159.41 -- 20 50

IGCC 2,617 3.71 36.26 9,000 60 550

IGCC with Carbon Capture 3,475 8.09 30.00 9,700 60 380

MSW-Landfill Gas 3,475 9.52 66.63 15,628 30 600

Nuclear 3,103 0.48 85.66 10,400 60 1,000

Oil-Gas-Steam 396 3.16 25.26 9,000 50 250

PV 5,000 -- 70.00 -- 30 100

Scrubbed Coal New 2,018 1.62 33.60 9,470 60 600

Scrubbed Coal Old 1,204 3.40 23.41 10,000 60 600

Unscrubbed Coal 1,000 3.94 27.16 10,000 60 600

Wind, Onshore Classes 3-7 1,570 6.66 10.95 -- 20 100

Wind, Offshore Deep Classes 3-7 3,046 22.84 14.28 -- 20 100

Wind, Offshore Shallow Classes 3-7 2,284 19.99 14.28 -- 20 100

Solar Thermal Classes 1-5 5,850 0.10 55.72 -- 30 200

 43 In addition to the power generation technologies shown in the table, the NREL-SEAC 2008 data set also contains characteristics for four energy storage technologies: 

batteries, compressed air energy storage (CAES), ice storage, and pumped hydro. 44 Geothermal EGS capital costs and O&M costs were developed using the methodology described in Section 4.2 (geothermal cost methodology). For reference, capital 

costs had a standard deviation of $196 (2.8%), and fixed O&M costs had a standard deviation of $1.31 (0.7%). 45 Geothermal hydrothermal capital costs and O&M costs were developed using the methodology described in Section 4.2 (geothermal cost methodology). For 

reference, capital costs had a standard deviation of $650 (23.1%), and fixed O&M costs had a standard deviation of $35.07 (22%).
133

5.1.5 Technical Performance Characteristics

Plant Size

In the NREL-SEAC model, plant size is not a critical parameter, and is often only used for 

statistical purposes. Representative nominal plant sizes used in the NREL-SEAC model are 

shown in Table 31.

Heat Rate

Heat rates for six of the 26 technologies in each NREL-SEAC data set are listed in Table 32. 

Geothermal technologies do not have heat rates in NREL-SEAC. Many heat rates are constant 

with time (combined cycle, nuclear, and biomass), but the others drop about 20% from 2005.

Table 32. NREL-SEAC 2008, Heat Rates for Six Technologies

Year Comb Turbine Comb Cycle Coal Nuclear IGCC Biomass

2000 11,560 6,870 8,470 10,400 9,000 14,500

2005 11,560 6,870 9,470 10,400 9,000 14,500

2010 8,900 6,870 9,200 10,400 9,000 14,500

2015 8,900 6,870 9,100 10,400 9,000 14,500

2020 8,900 6,870 9,000 10,400 8,900 14,500

2025 8,900 6,870 9,000 10,400 8,900 14,500

2030 8,900 6,870 9,000 10,400 8,580 14,500

2035 8,900 6,870 9,000 10,400 8,580 14,500

2040 8,900 6,870 9,000 10,400 8,580 14,500

2045 8,900 6,870 9,000 10,400 8,580 14,500

2050 8,900 6,870 9,000 10,400 8,580 14,500

Figure 95 compares heat rates (linearly interpolated to provide annual data during gap years) 

between technologies in the NREL-SEAC 2008 data set. Biomass technology has the highest 

heat rate in both data sets. Combined cycle remains the most efficient in the data set, with a heat 

rate of 6,870 Btu/kWh.
134

Combustion 

Turbine

Combined Cycle

Coal

IGCC 

Nuclear

Biomass

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Heat Rate (Btu/kWh)

Figure 95. NREL-SEAC 2008, heat rates for six technologies

Capacity Factors

NREL provided capacity factors for all renewable technologies. For non-renewable technologies, 

capacity factors were calculated from planned outage rate data provided by NREL as follows:

Calculated Capacity Factor (%) = 100% - Planned Outage (%)

Capacity factors for technologies in the NREL-SEAC 2008 data set are shown in Table 33. This 

table contains data for renewable and non-renewable technologies in five year increments 

between 2000 and 2050.
135

Table 33. NREL-SEAC 2008, Capacity Factors

Technology

Capacity Factors (%) by Year

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Wind, 

onshore

Class 3 18.5 32.0 35.0 36.0 38.0 38.0 38.0 38.0 38.0 38.0 38.0

Class 4 18.5 36.0 39.0 41.0 42.0 42.5 43.0 43.0 43.0 43.0 43.0

Class 5 18.5 40.1 43.0 44.0 45.0 45.5 46.0 46.0 46.0 46.0 46.0

Class 6 18.5 44.0 46.0 47.0 48.0 48.5 49.0 49.0 49.0 49.0 49.0

Class 7 18.5 47.0 50.0 51.0 52.0 52.5 53.0 53.0 53.0 53.0 53.0

Wind, 

offshore, 

shallow

Class 3 34.0 34.0 37.0 38.0 39.0 39.5 40.0 41.0 42.0 42.0 42.0

Class 4 38.0 38.0 41.0 43.0 44.0 44.5 45.0 45.0 45.0 45.0 45.0

Class 5 42.0 42.0 45.0 46.0 47.0 47.5 48.0 48.0 48.0 48.0 48.0

Class 6 46.0 46.0 48.0 50.0 51.0 51.0 51.0 51.0 51.0 51.0 51.0

Class 7 50.0 50.0 52.0 54.0 55.0 55.0 55.0 55.0 55.0 55.0 55.0

Wind, 

offshore, 

deep

Class 3 38.0 38.0 38.0 38.0 39.0 39.5 40.0 40.0 40.0 40.0 40.0

Class 4 43.0 43.0 43.0 43.0 44.0 44.5 45.0 45.0 45.0 45.0 45.0

Class 5 46.0 46.0 46.0 46.0 47.0 47.5 48.0 48.0 48.0 48.0 48.0

Class 6 50.0 50.0 50.0 50.0 51.0 51.0 51.0 51.0 51.0 51.0 51.0

Class 7 54.0 54.0 54.0 54.0 55.0 55.0 55.0 55.0 55.0 55.0 55.0

Solar 

Thermal

Class 1 21.8 (same for all years)

Class 2 27.8 (same for all years)

Class 3 31.2 (same for all years)

Class 4 32.0 (same for all years)

Class 5 33.3 (same for all years)

PV 21.0 (same for all years)

Coal 84.6 (same for all years)

IGCC 81.0 (same for all years)

Combustion Turbine 80.0 (same for all years)

Combined Cycle 84.6 (same for all years)

Nuclear 90.2 (same for all years)

Biomass 84.0 (same for all years)

Geothermal 84.6 (same for all years)

Figure 96 compares capacity factors for onshore wind with a breakdown by wind class. As 

expected, capacity factors are higher for wind resources that have higher wind speeds (wind 

speeds increase as class designations increase). Across all onshore wind classes, capacity factors 

improve over time. The largest improvement for the time period examined is between 2000 and 

2005. After 2005, improvements still occur, but at lower rates compared to the initial five year 

period. By 2050, the onshore wind capacity factors range from 38% (Class 3) to 53% (Class 7).
136

Class 6 Class 5 Class 4 Class 3

Class 7 

0%

10%

20%

30%

40%

50%

60%

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capacity Factor

Figure 96. NREL-SEAC 2008, capacity factors, onshore wind

Offshore wind technologies – both shallow and deep – show modest improvements in capacity 

factor over time (see Figure 97 and Figure 98, respectively). Shallow offshore wind capacity 

factors are in the range of 34-50% in 2000, and then increase to a range of 42-55% in 2050. For 

deep offshore, capacity factors are 38-54% in 2000, and increase to 40-55% in 2050.
137

Class 3 

Class 4 

Class 6 Class 5

Class 7 

0%

10%

20%

30%

40%

50%

60%

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capacity Factor

Figure 97. NREL-SEAC 2008, capacity factors, shallow offshore wind

Class 4 Class 3 

Class 5 Class 6

Class 7 

0%

10%

20%

30%

40%

50%

60%

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capacity Factor

Figure 98. NREL-SEAC 2008, capacity factors, deep offshore wind
138

Figure 99 shows solar thermal and PV capacity factors in the NREL-SEAC 2008 data set. 

Capacity factors for solar thermal are divided into five classes – Class 1 through Class 5. For a 

given class, the capacity factors do not change over time. However, the capacity factors do 

increase with the quality of the solar thermal resource (quality increases as class increases). The 

solar thermal capacity factors range from approximately 22% (Class 1) to 33% (Class 5). In the 

NREL-SEAC 2008 data set, the capacity factor for PV technology remains constant at 21% –

slightly below Class 1 solar thermal.

Class 1 PV

Class 2

Class 3

Class 4

Class 5

0%

5%

10%

15%

20%

25%

30%

35%

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capacity Factor

Figure 99. NREL-SEAC 2008, capacity factors, solar thermal and PV

In the NREL-SEAC 2008 data set, capacity factors for coal, IGCC, combustion turbine, 

combined cycle, nuclear, biomass, and geothermal technologies remain constant over the time 

horizon of 2000 through 2050. These capacity factors are shown in Figure 100.
139

84.6%

81.0%

80.0%

84.6%

90.2%

84.0% 84.6%

75%

80%

85%

90%

95%

100%

Coal IGCC Combustion

Turbine

Combined

Cycle

Nuclear Biomass Geothermal

Technology

Capacity Factor

Figure 100. NREL-SEAC 2008, capacity factors, seven technologies

Plant Lifetime

Plant life does not improve over time for any technology in this data set. Also, plant life is the 

same between NREL-SEAC 2008, as seen in Figure 101.
140

Figure 101. NREL-SEAC 2008, plant lifetime for 11 technologies

5.1.6 Cost Characteristics

The NREL-SEAC cost characteristics were developed with assistance from Black & Veatch 

(B&V) using a three step process.46

The cost development process applies to overnight capital costs as well as O&M costs. In the 

case of overnight capital costs, B&V did not rely on learning curves. Rather, changes in 

overnight capital costs over time are based on engineering judgment coupled with data from 

actual projects and input from industry experts. In addition to not using learning curves, B&V 

also reported that commodity price indices are not used to develop cost projections.

First, B&V started with in-house data for projects that had 

actually been built in recent years. Second, B&V consulted government (e.g., DOE) and industry 

experts, and requested opinions on current as well as projected costs. Third, B&V used 

engineering judgment, coupled with the information developed during the first two steps, to 

develop a set of projected cost characteristics for individual power generation technologies.

47

Overnight Capital Costs

The following three tables and three figures compare overnight capital costs for 11 technologies 

in the NREL-SEAC 2008 data set.

 46 Conversation between J. Macknick (NREL) and J. Pietruszkiewicz (B&V), February 2010. 47 Ibid.
141

In Table 34, capital costs provided by NREL are shown for all wind and solar thermal

technologies and classes. In all cases, capital costs were identical for the various classes within 

wind and solar thermal technologies.

Table 34. NREL-SEAC 2008, Overnight Capital Cost for Wind Technologies (2004$/kW)

Year

Deep Offshore Shallow Offshore Onshore Wind Solar Thermal

Class 3-7 Class 3-7 Class 3-7 Class 1-5

2000 3,046 3,046 1,570 5,850

2005 3,046 3,046 1,570 5,850

2010 3,046 3,046 1,570 5,572

2015 3,046 3,046 1,530 4,179

2020 2,665 2,665 1,490 4,179

2025 2,570 2,570 1,451 4,179

2030 2,475 2,475 1,413 4,179

2035 2,380 2,380 1,413 4,179

2040 2,284 2,284 1,413 4,179

2045 2,284 2,284 1,413 4,179

2050 2,284 2,284 1,413 4,179

Table 35 shows the overnight capital costs for geothermal, PV, and biomass technologies. For 

the geothermal hydrothermal data in the NREL-SEAC data set, capital costs and O&M costs for 

all geo classes and regions are static throughout the modeling horizon (2000-2050). Geothermal 

cost data from the various geo classes and regions were aggregated to reflect typical geothermal 

plants. The aggregation methodology involved sorting geothermal plants by cost. At the point 

where the cost points diverged from the linear trend, the data were cut off. The points not cut off 

from the data are considered to be the most plausible sites for development. The average capital 

costs and fixed costs from these sites were used in this report and accompanying database. 

Capital costs for geothermal hydrothermal are constant at $2,818 per kilowatt.

Table 35. NREL-SEAC 2008, Overnight Capital Cost for Geothermal and PV (2004$/kW)

Year Geothermal PV Biomass

2000 2,818 5,000 2,617

2005 2,818 5,000 2,617

2010 2,818 3,480 2,617

2015 2,818 2,538 2,617

2020 2,818 2,100 2,617

2025 2,818 1,705 2,617

2030 2,818 1,512 2,617

2035 2,818 1,493 2,617

2040 2,818 1,474 2,617

2045 2,818 1,455 2,617

2050 2,818 1,436 2,617
142

The same data given by NREL on overnight capital costs for renewable technologies in NRELSEAC is shown in Figure 102. Costs have been linearly interpolated to allow comparisons across 

years without NREL data.

Onshore Class 3-7

Offshore Shallow 

PV Class 3-7

Geothermal

Biomass

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capital Costs (2004$/kW)

Solar Thermal Class 1-5

Offshore Deep 

Class 3-7

Figure 102. NREL-SEAC 2008, overnight capital cost for renewable technologies

The NREL-SEAC 2008 capital cost parameters provided by NREL are summarized for the five

conventional technologies in Table 36 and shown Figure 103. In most cases, base overnight 

capital costs are adjusted over the modeling horizon (2000 through 2050).

Table 36. NREL-SEAC 2008, Overnight Capital Cost for Five Technologies (2004$/kW)

Year Comb Turbine Comb Cycle IGCC Coal Nuclear

2000 595 742 2,617 2,018 3,103

2005 595 742 2,617 2,018 3,103

2010 714 742 2,703 2,075 3,016

2015 714 742 2,703 2,132 3,016

2020 714 742 2,703 2,132 2,874

2025 714 742 2,703 2,132 2,874

2030 714 742 2,703 2,132 2,801

2035 714 742 2,703 2,132 2,801
143

Year Comb Turbine Comb Cycle IGCC Coal Nuclear

2040 714 742 2,703 2,132 2,801

2045 714 742 2,703 2,132 2,801

2050 714 742 2,703 2,132 2,801

Comb Turbine

Comb Cycle

IGCC

Nuclear

Coal

$0

$500

$1,000

$1,500

$2,000

$2,500

$3,000

$3,500

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capital Cost (2004$/kW)

Figure 103. NREL-SEAC 2008, overnight capital cost for five technologies

Comparing Figure 102 and Figure 103, the highest capital cost between all 11 technologies is 

solar CSP. By the end of the modeling horizon, solar CSP decreases significantly, by nearly 30 

percent, but it is still the highest. The capital cost for geothermal hydrothermal is constant across 

geo classes and regions over the modeling horizon. Although conventional combustion turbine 

technology increases in cost during the modeling period, it remains the lowest overnight cost 

throughout the period.

Learning

Overnight capital costs change over time for some technologies in the NREL-SEAC data set. 

While these changes are not based on learning rates within the NREL-SEAC model, effective 

learning rates can be calculated for the 2008 NREL-SEAC data set based on reported capital 

costs using the following simple formula:

Learning Factor =

Starting Year Capital Cost

Future Year Capital Cost
144

All NREL-SEAC technologies begin with data in the year 2000. The future year capital cost is 

the year in which the learning factor is calculated. Figure 104 shows the learning factors for all 

renewable technologies.

Figure 105 shows learning factors for five conventional technologies. This figure shows that 

some technologies are expected to increase in cost over time.

Geothermal, combined cycle, and biomass technologies have a learning factor of one because 

they had no change in capital costs. These series are all indicated in the chart as part of the bold 

black line.

Offshore Shallow 

Class 3-7

PV

0.00

0.20

0.40

0.60

0.80

1.00

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Learning Factor

Onshore Wind Class 3-7

Offshore Deep 

Class 3-7

Solar Thermal Class 

Geothermal & Biomass

Figure 104. NREL-SEAC 2008, learning factor for renewable technologies
145

Comb Turbine

Comb Cycle

IGCC

Nuclear

Coal

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Learning Factor

Figure 105. NREL-SEAC 2008, learning factors for five conventional technologies

O&M Costs

Table 37 shows the fixed O&M costs for some of the technologies with changing costs. Fixed 

O&M costs changed over time for solar thermal, PV, combustion turbines, CAES, and batteries

in NREL-SEAC 2008. Wind and offshore wind technologies have different fixed costs but 

maintain those costs throughout the modeling period. Fixed O&M costs for geothermal 

hydrothermal and geothermal EGS are not expected to change over time.

Table 37. NREL-SEAC, Fixed O&M Costs (2004$/kW/yr)

Year Comb Turbine PV Solar Thermal

2000 7.33 70.00 55.72

2005 7.33 70.00 55.72

2010 6.28 22.00 51.07

2015 6.28 9.06 44.57

2020 6.28 7.50 44.57

2025 6.28 6.09 44.57

2030 6.28 5.40 44.57

2035 6.28 5.33 44.57

2040 6.28 5.27 44.57

2045 6.28 5.20 44.57

2050 6.28 5.13 44.57
146

Figure 106 shows fixed O&M costs for the same technologies listed in Table 37 with costs that 

have been linearly interpolated to allow comparisons between technology costs for gap years.

Figure 107 shows fixed O&M costs for technologies with constant costs. Geothermal has the 

highest fixed O&M cost. In NREL-SEAC 2008, combustion turbine is the technology with the 

lowest fixed O&M, but by 2025, PV is essentially tied with combustion turbines for the 

technology with the lowest fixed O&M cost.

Comb Turbine

PV

0

10

20

30

40

50

60

70

80

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Fixed O&M (2004$/kW)

Solar Thermal Class 1-5

Figure 106. NREL-SEAC 2008, fixed O&M costs
147

$34

$159

$11 $14 $14 $14

$67

$36

$86

0

20

40

60

80

100

120

140

160

180

Onshore Wind

Shallow Offshore Wind

Deep Offshore Wind

Comb Cycle 

Biomass 

Geothermal 

IGCC 

Nuclear 

Coal 

Fixed O&M Cost (2004$/kW)

Figure 107. NREL-SEAC 2008, fixed O&M costs

Table 38 shows variable O&M costs for four technologies that having changing costs (3 wind 

technologies and combustion turbines).

Table 38. NREL-SEAC Variable O&M (2004$/MWh)

Year Wind Class 3-7 Shallow Class 3-7 Deep Class 3 Comb Turbine

2000 6.66 19.99 22.84 11.42

2005 6.66 19.99 22.84 11.42

2010 5.19 17.13 22.84 2.67

2015 4.76 15.23 22.84 2.67

2020 4.41 13.33 19.99 2.67

2025 4.28 11.90 17.61 2.67

2030 4.16 10.47 15.23 2.67

2035 4.16 10.47 14.28 2.67

2040 4.16 10.47 13.33 2.67

2045 4.16 10.47 13.33 2.67

2050 4.16 10.47 13.33 2.67
148

Figure 108 shows variable O&M costs for the same four technologies found in Table 38 with

linearly interpolated values for gap years to make relative comparisons between technology 

costs. Deep offshore wind has the highest variable O&M cost in the NREL-SEAC 2008 data set.

Variable O&M costs in the NREL-SEAC 2008 data set do not change over time for CSP, PV, 

hydro, landfill, biomass, nuclear, combined cycle, combined cycle with CCS, new coal, IGCC, 

coal with CCS, new cofired, and pumped hydro. Figure 109 compares variable O&M costs for 

six technologies in NREL-SEAC 2008 with constant costs. In NREL-SEAC 2008, PV and 

geothermal technologies do not have variable O&M costs.

Onshore Wind

Shallow Offshore 

Wind

Deep Offshore 

Wind

Combustion 

Turbine

0

5

10

15

20

25

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Variable O&M ($2004/MWh)

Figure 108. NREL-SEAC 2008, variable O&M costs
149

$1.62

$2.86

$9.52

$3.71

$0.48 $0.10

0

1

2

3

4

5

6

7

8

9

10

Comb

Cycle

Biomass IGCC Nuclear Coal Solar

Thermal

Variable O&M Cost (2004$/MWh)

Figure 109. NREL-SEAC 2008 variable O&M cost for technologies with constant costs
150

Appendix D. MiniCAM (MiniCAM 2008 Data Set)

The MiniCAM modeling framework was developed by Pacific Northwest National Laboratory 

(PNNL), and PNNL maintains the power plant technology files that support this model. For this 

project, NREL obtained a MiniCAM data set from PNNL, and provided this data set to ICF.

The cost assumptions and efficiencies for the power plant technologies used in MiniCAM are 

generally consistent with the 2008 Annual Energy Outlook.48 However, there are differences. For 

example, in the MiniCAM data set the geothermal characteristics were adjusted based on 

information in addition to the AEO 2008 report.49

ICF contacted PNNL to determine if there are plans to update the power plant characteristics in 

MiniCAM, which are now based on AEO 2008 data, with AEO 2009 values.

Costs for solar and wind technologies were 

altered based on GIS data.

50

The data set obtained for the MiniCAM model has cost and performance characteristics for 12 

electricity generation technologies (see 

PNNL responded 

that they are aware that power generation capital costs have risen substantially since the AEO 

2008 report was released due to commodity price increases and other factors. However, PNNL 

reported that they have no current plans to update power plant costs in MiniCAM.

Table 39).51

• Technical Performance Characteristics

This data set has power plant characteristics 

in 15 year increments extending over a 45 year time period from 2005 through 2050. The key 

characteristics of the MiniCAM data set are described in this section, which is organized as 

follows:

o Plant Size

o Heat Rate

o Capacity Factor

o Plant Lifetime

• Cost Characteristics

o Overnight Capital Costs

o Learning

o O&M Costs

 48 CO2 Emission Mitigation and Technological Advance: An Updated Analysis of Advance Technology Scenarios, 

PNNL, PNNL-18075, December 2008, p 3.9. 49 CO2 Emission Mitigation and Technological Advance: An Updated Analysis of Advance Technology Scenarios, 

PNNL, PNNL-18075, December 2008, p 3.14. 50 E-mail exchange, November 10 and 11, 2009. 51 In addition to the 12 technologies described, the MiniCAM data set included a 13th technology for an oil-fired 

turbine. However, this technology is not discussed in this report because it is expected to have limited market 

penetration in future years. 
151

5.1.7 Technical Performance Characteristics

Plant Size

Power plant name plate capacities for 10 of the 12 MiniCAM technologies are shown in Figure 

110. These 10 technologies represent all of the MiniCAM renewable technologies and all of the 

MiniCAM fossil technologies with the exception of those fossil technologies that are expected to 

come online in future years with carbon capture capabilities. As indicated, the capacities range 

from 5 MW for PV to 1,350 MW for nuclear.

The MiniCAM capacities match the capacities used in the AEO 2008 report, which is to be 

expected since the source for much of the MiniCAM data is the AEO 2008 report. The power 

plant capacities did not change for most technologies between the AEO 2008 report and the AEO 

2009 report. Therefore, the power plant capacities for MiniCAM shown in Figure 110 generally 

match the power capacities for the AEO 2009 data set shown in Figure 71. One difference is that 

the MiniCAM data set only has conventional combustion turbine and combined cycle 

technologies, while the AEO 2009 data set has both conventional and advanced technologies 

(advanced technologies are shown in Figure 71).
152

Table 39. MiniCAM, Cost and Performance Characteristics

Technology

Online

Year 

Size (MW) Overnight 

Capital Cost 

(2004 $/kW)

O&M (2004$) Heat Rate 

(Btu/kWh)

Capacity 

Factor 

(%)

Life 

(yrs) Variable 

($/MWh)

Fixed 

($/kW/yr)

Coal, scrubbed 2005 600 1,545 4.58 27.50 9,319 80% 45

IGCC 2020 550 1,791 2.92 38.62 8,005 80% 45

IGCC, with carbon storage 2020 380 3,301 11.82 52.44 9,367 80% 45

Combustion Turbine, conventional 2005 160 492 3.36 11.31 8,877 10% 45

Combined Cycle, conventional 2005 250 725 2.03 12.07 6,164 80% 45

Combined Cycle, with carbon capture 2020 400 2,551 8.95 15.24 6,950 80% 45

Nuclear 2005 1,350 2,300 1.80 64.00 10,339 90% 60

Biomass 2005 80 1,899 4.65 33.60 12,133 80% 45

Geothermal 2005 50 2,419 0.00 78.74 34,120 90% 30

Wind (onshore) 2005 50 1,167 7.00 11.50 --- 40% 30

Solar Thermal (CSP) 2020 100 3,731 0.00 34.37 --- 73% 30

PV 2005 5 9,500 0.00 100.00 --- 25% 30
153

600

550

160

250

1,350

80 50 50

100

5

0

200

400

600

800

1,000

1,200

1,400

1,600

Coal IGCC Combustion

Turbine

(conv)

Combined

Cycel (conv)

Nuclear Biomass Geothermal Wind

(onshore)

Solar

Thermal

PV

Technology

Size (MW)

Figure 110. MiniCAM, plant size
154

Heat Rate

In the MiniCAM data, heat rates for fossil and nuclear power plants show improvement over 

time. The heat rates for fossil and nuclear technologies for the time period of 2005 through 2050 

are shown in Table 40 and Figure 111. The table also shows heat rate values for geothermal 

technologies. In the MiniCAM data set, geothermal technologies have a constant heat rate of 

34,120 Btu/kWh (10% thermal efficiency) over the entire modeling horizon.

Table 40. MiniCAM, Heat Rates

Technology

Heat Rate by Year (Btu/kWh)

2005 2020 2035 2050

Coal, scrubbed 9,319 8,719 8,525 8,336

IGCC --- 8,005 7,598 7,266

Combustion Turbine, conventional 8,877 8,877 8,680 8,487

Combined Cycle, conventional 6,164 6,164 5,698 5,334

Nuclear 10,339 10,035 10,035 10,035

Biomass 12,133 8,939 8,740 8,546

Geothermal 34,120 34,120 34,120 34,120

Wind (onshore) --- --- --- ---

Solar Thermal (CSP) --- --- --- ---

PV --- --- --- ---

Capacity Factors

The MiniCAM capacity factors remain constant over the modeling horizon (to 2050) for all 

technologies with the exception of onshore wind. For onshore wind, the capacity factor rises 

from 40% in 2005 to 49% in 2050. The capacity factor is relatively low for the combustion 

turbine (10%), because the MiniCAM model handles this technology as a peaking turbine. The 

capacity factors for 10 MiniCAM technologies are shown in Table 41 and Figure 114.

Table 41. MiniCAM, Capacity Factors

Technology

Capacity Factor by Year (%)

2005 2020 2035 2050

Coal, scrubbed 80% 80% 80% 80%

IGCC --- 80% 80% 80%

Combustion Turbine, conventional 10% 10% 10% 10%

Combined Cycle, conventional 80% 80% 80% 80%

Nuclear 90% 90% 90% 90%

Biomass 80% 80% 80% 80%

Geothermal 90% 90% 90% 90%

Wind (onshore) 40% 45% 47% 49%

Solar Thermal (CSP) --- 73% 73% 73%

PV 25% 25% 25% 25%
155

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

2005 2020 2035 2050

Year

Heat Rate (Btu/kWh)

Biomass

Nuclear

Coal

Combustion Turbne (conventional)

IGCC

Combinced Cycle (conventional)

Note: Geothermal heat rate not shown, 

but remains constant at 34,120 Btu/kWh

Figure 111. MiniCAM, heat rates for fossil and nuclear technologies
156

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2005 2020 2035 2050

Year 

Capacity Factor (%)

Combustion Turbine (conventional)

Nuclear & Geothermal (both at 90%)

Coal, IGCC, Combined Cycle, and Biomass (all at 80%)

Solar Thermal

Wind (onshore)

PV

Figure 112. MiniCAM, capacity factors
157

Plant Lifetime

In the MiniCAM modeling, plant lifetime values ranging from 30 to 60 years are used for 

computing the levelized cost of energy. The lifetimes are shown in Figure 113.

45 45 45 45

60

45

30 30 30 30

0

10

20

30

40

50

60

70

Coal IGCC Combustion

Turbine

(conv)

Combined

Cycel (conv)

Nuclear Biomass Geothermal Wind

(onshore)

Solar

Thermal

PV

Technology

Life (yrs)

Figure 113. MiniCAM, power plant lifetime
158

5.1.8 Cost Characteristics

Capital Cost

Table 42 and Figure 114 show the overnight capital costs for 10 technologies in the MiniCAM 

data set. Similar to other models, PV technology has the highest cost and combustion turbine and 

combined cycle technologies are at the low end of the overnight capital cost range.

Table 42. MiniCAM, Capital Costs

Technology

Overnight Capital Cost (2004$/kW)

2005 2020 2035 2050

Coal, scrubbed 1,545 1,545 1,455 1,370

IGCC --- 1,791 1,610 1,361

Combustion Turbine, conventional 492 509 479 451

Combined Cycle, conventional 725 725 632 551

Nuclear 2,300 2,266 2,232 2,199

Biomass 1,899 1,899 1,789 1,684

Geothermal 2,419 2,419 2,220 2,082

Wind (onshore) 1,167 1,082 1,004 931

Solar Thermal (CSP) --- 3,731 3,209 2,976

PV 9,500 4,258 2,879 2,246

Learning

Learning is not explicitly modeled in MiniCAM. Rather, changes in technology characteristics, 

such as capital costs over time, are exogenously specified. Based on discussions with PNNL 

staff,52

 52 E-mail exchange between PNNL and ICF, January 4, 2010.

the exact process that leads to changes in capital costs is left ambiguous. Capital cost 

changes could result from government or private R&D (learning by R&D), or spillover effects 

from other industries. 
159

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

2005 2020 2035 2050

Year

Cost (2004$ / kW)

Combustion Turbine (conventional)

Combined Cycle (conventional)

Wind (onshore)

Coal

IGCC

Biomass

Nuclear

Geothermal

Solar Thermal

PV

Figure 114. MiniCAM, capital costs
160

O&M

Fixed and variable O&M values for the MiniCAM data set are shown in Table 43. The fixed and 

variable O&M costs are graphed in Figure 115 and Figure 116, respectively.

Table 43. MiniCAM, Fixed and Variable O&M

Technology

Fixed O&M (2004$/kW/yr) Variable O&M (2004$/MWh)

2005 2020 2035 2050 2005 2020 2035 2050

Coal, scrubbed 27.50 27.50 25.89 24.38 4.58 4.58 4.32 4.06

IGCC --- 38.62 36.02 32.44 --- 2.92 2.72 2.45

Combustion Turbine, conventional 11.31 11.68 11.00 10.36 3.36 3.48 3.27 3.08

Combined Cycle, conventional 12.07 12.07 11.04 10.14 2.03 2.03 1.86 1.71

Nuclear 64.00 64.00 64.00 64.00 1.80 1.80 1.80 1.80

Biomass 33.60 33.60 31.64 29.79 4.65 4.65 4.38 4.12

Geothermal 78.74 78.74 72.25 67.75 0.00 0.00 0.00 0.00

Wind (onshore) 11.50 11.50 10.67 9.89 7.00 4.60 4.27 3.96

Solar Thermal (CSP) --- 34.37 29.56 27.42 --- 0.00 0.00 0.00

PV 100.00 30.00 25.00 20.00 0.00 0.00 0.00 0.00

As indicated, PV is projected to have a sharp drop in fixed O&M costs, starting at $100 per kW 

per year in 2005, and then dropping to between $20 and $30 per kW per year for 2020 and 

beyond. All other technologies in the MiniCAM data set show a constant or slightly declining 

fixed O&M cost over the modeling horizon.

Variable O&M costs for geothermal, solar thermal, and PV are set at zero in the MiniCAM data 

set. Variable O&M costs for other technologies decline or remain constant over the modeling 

time period (see Figure 116). Onshore wind technologies have the largest decline in variable 

O&M, falling from $7 per MWh in 2005 to under $4 per MWh in 2050. 
161

0

10

20

30

40

50

60

70

80

90

100

2005 2020 2035 2050

Year

Cost (2004$ / kW / yr)

Combustion Turbine, Combined Cycle, and Wind (onshore) -- all three nearly identical

PV

Geothermal

Nuclear

IGCC

Biomass

Coal

Solar 

Thermal

Figure 115. MiniCAM, fixed O&M costs
162

0

1

2

3

4

5

6

7

8

2005 2020 2035 2050

Year

Cost (2004$ / MWh)

Wind (onshore)

Coal & Biomass (nearly identical)

Combustion Turbine (conventional)

IGCC

Combined Cycle (conventional)

Nuclear

Figure 116. MiniCAM, variable O&M costs
163

Appendix E. IPM (EPA 2009 Data Set)

The IPM model, developed by ICF, has been used by many clients, including the U.S. 

Environmental Protection Agency (EPA). The EPA recently conducted an analysis with IPM in 

response to the 2009 American Recovery and Reinvestment Act (ARRA). The results of the EPA 

ARRA analysis are in the public domain, and for this project, the power generation technology 

characteristics used in this study were evaluated. The data set of power plant characteristics that 

corresponds to the EPA ARRA analysis is referred to as the EPA 2009 data set.

The power generation technology characteristics used in the EPA 2009 data set were taken from 

the AEO 2009 analysis. For the EPA 2009 data set, some adjustments were necessary to 

accommodate the IPM modeling structure, but these adjustments were, for the most part, minor. 

Other aspects of the EPA data set, such as carbon constraints, are significantly different 

compared to the AEO 2009 data set. However, this project is focused on power generation 

technologies, and in the case of power generation characteristics the EPA 2009 and AEO 2009 

data sets are nearly identical.

Observations related to the technical performance and cost characteristics in the EPA 2009 data 

set are included in the remainder of this section, which is organized as follows:

• Technical Performance Characteristics

o Plant Size

o Online Year

o Heat Rate

o Capacity Factor

o Plant Lifetime

• Cost Characteristics

o Overnight Capital Costs

o Learning

o O&M Costs (fixed and variable)

5.1.9 Technical Performance Characteristics

The EPA data set has 13 technologies (compared to 20 for AEO 2009). These 13 technologies 

are shown in Table 44 , along with the online year, heat rate, and availability.

Plant Size

Plant sizes remain constant in all years. Table 44 shows plant sizes for all IPM technologies.
164

Online Year

As noted in Table 44 (and shown in Figure 117 for eight technologies), the online years range 

from 2012 to 2020. Because nuclear is based on advanced technology, it has the latest online 

year (2020).

Table 44. EPA 2009, Technical Data

Technology

Online

Year

Heat Rate

(Btu/kWh)

Capacity

Factor (%)

Plant Size

(MW)

Coal 2015 9,200 85% 600

Coal (with CCS) 2015 10,781 80% 380

IGCC 2015 8,765 85% 550

Combustion Turbine (adv) 2012 9,289 92% 230

Combined Cycle (adv) 2012 6,752 87% 400

Nuclear 2020 10,434 89% 1,350

Biomass 2012 9,646 83% 80

MSW, LFG 2012 13,648 90% 30

Fuel Cells 2012 7,930 87% 10

Wind (onshore) 2012 --- 39% 50

PV 2012 --- 24% 5

Solar Thermal 2012 --- 36% 100

Geothermal 2012 --- 87% 50

Heat Rate

One difference between the EPA 2009 and AEO 2009 data sets is that EPA 2009 uses constant 

heat rates over the modeling horizon (2009 through 2035), while AEO 2009 allows for improved 

heat rates over time. The constant value heat rates in EPA data set are shown in Table 44 and 

Figure 118 for eight common technologies.

Capacity Factor

The EPA data set uses availability factors for fossil fuel, biomass, and MSW/LFG power plants, 

which are a measure of a plant’s mechanical integrity. Unlike capacity factors, availability 

factors do not account for resource availability. The availability factors for fossil fuel, biomass, 

and MSW/LFG power plants are shown in Table 44 and Figure 119 under the heading “capacity 

factor” and are all 80% or higher. The EPA data set uses capacity factors for nuclear, wind, and 

solar. Resource availability has a big impact on wind and solar. In all technologies, the capacity 

factor remains constant over the time horizon.

Plant Lifetime

EPA 2009 does not assume plant lifetimes for technology. Plants are retired based on economic 

optimization within the linear program. Furthermore, EPA 2009 has no degradation. A plant will 

run just as efficiently in the last year a plant is built in the model as it did in the first year it was 

built.
165

2015 2015

2012 2012

2020

2012 2012 2012 2012 2012

2008

2010

2012

2014

2016

2018

2020

2022

Coal

IGCC

Combustion Turbine (adv)

Combined Cycle (adv)

Nuclear

Biomass

Wind (onshore)

PV

Solar Thermal

Geothermal

Online Year

Figure 117. EPA 2009, online year
166

9,200

8,765

9,289

6,752

10,434

9,646

- - - -

0

2,000

4,000

6,000

8,000

10,000

12,000

Coal

IGCC

Combustion Turbine (adv)

Combined Cycle (adv)

Nuclear

Biomass

Wind (onshore)

PV

Solar Thermal

Geothermal

Heat Rates (Btu/kWh)

N/A

Figure 118. EPA 2009, heat rates
167

85% 85%

92%

87% 89%

83%

39%

24%

36%

87%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Coal

IGCC

Combustion Turbine (adv)

Combined Cycle (adv)

Nuclear

Biomass

Wind (onshore)

PV

Solar Thermal

Geothermal 

Capacity Factor (%)

Figure 119. EPA 2009, capacity factors
168

5.1.10 Cost Characteristics

Overnight Capital Costs

Overnight capital costs and O&M costs for technologies in the EPA 2009 data set are shown in 

Table 45. These costs correspond to the first year that a technology is adopted (online year).

Table 45. EPA 2009, Cost Data (2006$)

Technology

Overnight Capital Cost

(online year, 2006$/kW)

Variable O&M 

(2006$/MWh)

Fixed O&M 

(2006$/kW/yr)

Coal 2,113 4.47 26.80

Coal (with CCS) 3,503 4.32 44.90

IGCC 2,414 2.84 37.70

Combustion Turbine (adv) 603 3.09 10.30

Combined Cycle (adv) 903 1.95 11.40

Nuclear 2,995 0.48 87.70

Biomass 3,559 6.50 62.80

MSW, LFG 2,430 2.84 111.30

Fuel Cells 4,983 46.70 5.50

Wind (onshore) 1,831 0.00 29.50

PV 5,614 0.00 11.40

Solar Thermal 4,668 0.00 55.30

Geothermal 9,361 0.00 179.50

Overnight capital costs are shown for 10 technologies in Figure 120. A few notes concerning 

these costs:

• The geothermal cost of $9,361/kW needs to be viewed with caution. In the EPA data 

set, geothermal costs are assumed to range from $1,364/kW to $17,358/kW 

depending on site specific factors. The value of $9,361 is a simple average of these 

two extremes.

• Most technologies have an online year of 2012. However, the online year for coal and 

IGCC is 2015, and the online year for nuclear is 2020.

• Most technologies extend through 2035. However, coal, IGCC, combustion turbine, 

and combined cycle technologies end in 2031.

Learning

Learning factors were calculated using the following formula:

learning factor for year “n” = (capital cost in year n) / (starting year capital cost)

Geothermal, nuclear, PV, solar thermal, and wind show no improvement in costs over time, and 

have a learning factor of one for all years. Coal, IGCC, combustion turbine, combined cycle, and 

biomass technologies have learning factors not equal to one over the modeling horizon (see 

Figure 121).
169

Comb Turbine

Comb Cycle

Biomass

Geothermal

IGCC

Nuclear

PV

Coal

Solar Thermal

Wind

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

2010 2015 2020 2025 2030 2035

Capital Cost (2006$/kW)

Figure 120. EPA 2009, capital costs
170

Comb Turbine

Comb Cycle

Biomass

IGCC

Coal

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1.05

1.10

2010 2015 2020 2025 2030 2035

Learning Factor

Figure 121. EPA 2009, learning factor
171

O&M Costs

In the EPA data set, the O&M costs remain fixed over time. Variable O&M costs are shown in 

Figure 122, and fixed O&M values are compared in Figure 123.

10.30 11.40

62.80

179.50

37.70

87.70

11.40

26.80

55.30

29.50

$-

$20

$40

$60

$80

$100

$120

$140

$160

$180

$200

Comb Turbine

Comb Cycle

Biomass

Geothermal

IGCC

Nuclear

PV

Coal

Solar Thermal

Wind

Variable Cost (2006$/kWh)

Figure 122. EPA 2009, variable O&M costs

3.09

1.95

6.50

2.84

0.48

4.47

0.00 0.00 0.00 0.00

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

$7.00

Comb Turbine

Comb Cycle

Biomass

IGCC

Nuclear

Coal

PV

Geothermal

Solar Thermal

Wind

Fixed O&M (2006$/kW)

Figure 123. EPA 2009, fixed O&M costs
172

Appendix F. MERGE (MERGE 2009 Data Set)

The MERGE model is a private sector model developed by the Electric Power Research Institute 

(EPRI). The power generation technology characteristics used in MERGE are based on EPRI’s 

proprietary Technology Assessment Guide (TAG).53

ICF reviewed several MERGE publications, including the following recent publication: 

“Program on Technology Innovation: Integrated Generation Technology Options,” EPRI, Report 

No. 1019539, November 2009. This report contains overview information with highlights, but 

does not contain detailed characteristics for power generation technologies over the full 

modeling horizon. For the most part, characteristics are only reported for two years – 2015 and 

2025. One notable characteristic that is not reported is O&M costs.

The 2009 EPRI MERGE report referenced in the preceding paragraph describes characteristics 

for eight primary power generation technologies, as listed in Table 46. As indicated, two of the 

technologies – supercritical pulverized coal (SCPC) and integrated gasification combined cycle 

(IGCC) – include options for carbon capture. For the carbon capture options, performance 

characteristics are provided for both standard (first generation) and advanced (second generation) 

versions of the technology. For the SCPC and IGCC technologies, the carbon capture 

technologies are only reported for 2025, and the non-carbon capture designs are reported only for 

2015.

Table 46. MERGE 2008, Technologies Covered

Technology Technology Design Years Provided

CO2 Capture Version 2015 2025

Supercritical Pulverized Coal (SCPC)

No --- X ---

Yes 1st gen --- X

Yes 2nd gen --- X

Integrated Gasification Combined Cycle 

(IGCC)

No --- X ---

Yes 1st gen --- X

Yes 2nd gen --- X

Combustion Turbine Combined Cycle (CTCC) no --- X X

Nuclear --- --- X X

Biomass Circulating Fluidized Bed (CFB) --- --- X X

Wind (onshore)

54 --- --- X X

Solar Thermal Trough --- --- X X

Solar Photovoltaic (PV) --- --- X X

 53 Telephone call between ICF and EPRI, November 9, 2009. 54 The EPRI reports did not distinguish between onshore and offshore wind. However, a quick comparison of the 

EPRI wind data to the AEO 2009 data for onshore and offshore wind suggests that the EPRI wind data applies to 

onshore technologies.
173

Table 47 shows a summary of characteristics for the MERGE technologies for 2015. These 

characteristics were compiled based on the EPRI documentation as well as ICF estimates in 

some cases. For example, ICF calculated the O&M costs based on the reported levelized cost of 

energy (LCOE), overnight capital costs, and fuel costs.

Table 47. MERGE Characteristics for Eight Technologies

(Characteristics for 2015)

Power Generation 

Technology

Plant 

Size 

(MW)

Heat Rate Capacity 

Factor 

(%)

Life 

(yrs)

Costs (2008$) 

(%) (Btu/kWh) Overnight 

Capital 

($/kW)

O&M (all 

inclusive, 

$/MWh)

Coal, no CO2 capture 

(CC) 675 38% 8,979 80% 30 $2,650 $23.82

Coal, 1st gen CC no data in 2015

Coal, 2nd gen CC no data in 2015

IGCC, no CO2 capture

(CC) 800 38% 8,979 80% 30 $2,960 $25.78

IGCC, 1st gen CC no data in 2015

IGCC, 2nd gen CC no data in 2015

Combined Cycle 450 47% 7,260 80% 30 $880 $7.52

Nuclear 1,400 33% 10,339 90% 30/6055 $4,860 $33.31

Biomass 75 28% 12,186 85% 30 $3,580 $29.46

Wind (onshore) 100 N/A N/A 35% 30 $2,350 $46.26

Solar Thermal56 125 N/A N/A 22% 30 $4,851 $116.81

PV 20 N/A N/A 26% 30 $7,981 $188.54

The MERGE characteristics, including a discussion of how they were developed, are discussed 

in the remainder of this section, which is organized as follows:

• Technical Performance Characteristics

o Plant Size

o Heat Rate

o Capacity Factor

o Plant Lifetime

• Cost Characteristics

o Overnight Capital Costs

o Learning

o O&M Costs (all inclusive)

 55 For economic calculations, all plants (including nuclear) are assumed to operate for 30 years. However, in the 

actual MERGE analysis, plants are allowed to operate as long as they are economic. Nuclear plants have a fixed 

retirement age of 60 years.

56 MERGE contained three scenarios for solar thermal. The data shown in this table, and throughout this section, are 

based on a scenario with 100% solar and wet cooling.
174

5.1.11 Technical Performance Characteristics

Plant Size

The nameplate plant capacities are shown in Figure 124. As indicated, the capacities range from 

20 MW for solar PV to 1,500 MW for nuclear. The capacities remain constant between 2015 and 

2025 with the exception of nuclear (increase from 1,400 MW to 1,500 MW).

675

800

650 650

800 800

100

1,400

450

20

125

75

1,500

0

200

400

600

800

1,000

1,200

1,400

1,600

Coal, no

CC

Coal, 1st

gen CC

Coal, 2nd

gen CC

IGCC, no

CC

IGCC, 1st

gen CC

IGCC,

2nd gen

CC

Combined

Cycle

Nuclear Biomass Wind

(onshore)

Solar

Thermal

PV

Technology

Size (MW)

2015

2025

Figure 124. MERGE, plant size

Heat Rate

The EPRI MERGE documentation provides heat rates for two years – 2015 and 2025 – and these 

heat rates are shown in Table 48 and Figure 125. As indicated, the heat rates for nuclear and 

biomass technologies remain constant for the two years (10,339 and 12,186, respectively). The 

combined cycle system has an improved heat rate over time – declining from 7,260 Btu/kWh in 

2015 to 6,319 Btu/kWh in 2025.
175

Table 48. MERGE, Heat Rates

Power Generation Technology Heat Rate by Year (Btu/kWh)

2015 2025

Coal

Coal, no CC 8,979 ---

Coal, 1st gen CC --- 12,637

Coal, 2nd gen CC --- 10,339

IGCC

IGCC, no CC 8,979 ---

IGCC, 1st gen CC --- 11,006

IGCC, 2nd gen CC --- 10,035

Combined Cycle --- 7,260 6,319

Nuclear --- 10,339 10,339

Biomass --- 12,186 12,186

Wind (onshore) --- N/A N/A

Solar Thermal57 --- N/A N/A

PV --- N/A N/A

8,979

7,260

8,979

12,186

10,339 10,339

12,637

6,319

10,035

11,006

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Coal, no

CC

Coal, 1st

gen CC

Coal, 2nd

gen CC

IGCC, no

CC

IGCC, 1st

gen CC

IGCC,

2nd gen

CC

Combined

Cycle

Nuclear Biomass Wind

(onshore)

Solar

Thermal

PV

Technology

Heat Rate (Btu/kWh)

2015

2025

N/A

Figure 125. MERGE, heat rates

 57 MERGE contained three scenarios for solar thermal. The data shown in this table, and throughout this section, are 

based on a scenario with 100% solar and wet cooling.
176

Capacity Factors

The capacity factors are shown in Table 49, and graphed in Figure 126. All fossil technologies 

have a capacity factor of 80%, and nuclear is set at 90% (in both 2015 and 2025). Biomass, solar 

thermal, and PV are modeled with constant capacity factors of 85%, 22%, and 26%, respectively. 

The wind capacity factor increases from 35% in 2015 to 42% in 2025.

Table 49. MERGE, Capacity Factors

Power Generation Technology Capacity Factor by Year (%)

2015 2025

Coal Coal, no CC 80% ---

Coal, 1st gen CC --- 80%

Coal, 2nd gen CC --- 80%

IGCC IGCC, no CC 80% ---

IGCC, 1st gen CC --- 80%

IGCC, 2nd gen CC --- 80%

Combined Cycle --- 80% 80%

Nuclear --- 90% 90%

Biomass --- 85% 85%

Wind (onshore) --- 35% 42%

Solar Thermal58 --- 22% 22%

PV --- 26% 26%

 58 MERGE contained three scenarios for solar thermal. The data shown in this table, and throughout this section, are 

based on a scenario with 100% solar and wet cooling.
177

80% 80%

35%

80% 80% 80% 80%

42%

80%

90%

85%

22%

26%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Coal, no

CC

Coal, 1st

gen CC

Coal, 2nd

gen CC

IGCC, no

CC

IGCC, 1st

gen CC

IGCC,

2nd gen

CC

Combined

Cycle

Nuclear Biomass Wind

(onshore)

Solar

Thermal

PV

Technology

Capacity Factor

2015

2025

Figure 126. MERGE, capacity factors

Plant Lifetime

Based on an e-mail exchange with EPRI personnel,59

While LCOE values are calculated on a 30 year basis, the MERGE model allows plants to 

operate as long as they are economical. One exception is nuclear plants, which are retired after 

60 years of operation.

it was explained that levelized cost of 

energy (LCOE) calculations are computed using a plant lifetime value of 30 years for all 

technologies. The levelized costs were first developed in the EPRI Technical Assessment Guide 

(TAG), and then used as inputs for the MERGE analysis.

5.1.12 Cost Characteristics

Overnight Capital Costs

Overnight capital costs for 2015 and 2025 are shown in Table 50. These costs range from a low 

of $880/kW for a combustion turbine combined cycle plant in 2015, to $7,981/kW for PV 

technology (all costs reported in 2008 dollars). For coal and IGCC plants without CO2 capture, 

data are only reported for 2015. Coal and IGCC technologies with CO2 capture have costs 

reported in 2025. For those technologies that have costs reported in both 2015 and 2025, 

overnight capital costs remain constant except for combined cycle and nuclear plants. The 

combined cycle plant shows a slight increase from $880/kW to $902/kW (2.5% increase), while 

the nuclear plant shows a reduction from $4,860/kW to $4,127/kW (15% reduction).

 59 Revis James (EPRI) and Rick Tidball (ICF), December 18, 2009.
178

Table 50. MERGE, Overnight Capital Costs

Technology Overnight Capital Cost by Year (2008$/kW)

2015 2025

Coal Coal, no CC $2,650 ---

Coal, 1st gen CC --- $4,435

Coal, 2nd gen CC --- $3,678

IGCC IGCC, no CC $2,960 ---

IGCC, 1st gen CC --- $4,083

IGCC, 2nd gen CC --- $3,317

Combined Cycle --- $880 $902

Nuclear --- $4,860 $4,127

Biomass --- $3,580 $3,580

Wind (onshore) --- $2,350 $2,350

Solar Thermal60 --- $4,851 $4,851

PV --- $7,981 $7,981

The MERGE model extends to the year 2050, and the cost data shown in Table 50 provide only a 

glimpse of how overnight capital costs are expected to change over time. These limited data were 

interpolated and extrapolated to produce the cost curves shown in Figure 127 (fossil and nuclear) 

and Figure 128 (renewables). In these figures, the 2015 capital costs are assumed to apply for the 

time period of 2010 through 2024, and the 2025 capital costs are assumed to apply from 2025 

and beyond.

 60 MERGE contained three scenarios for solar thermal. The data shown in this table, and throughout this section, are 

based on a scenario with 100% solar and wet cooling.
179

0

1,000

2,000

3,000

4,000

5,000

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Capital Cost (2008$/kW)

Coal, no 

Coal, 1st gen 

Coal, 2nd gen CC

IGCC, no CC

IGCC, 1st gen CC

IGCC, 2nd gen CC

Combined Cycle

Nuclear

Figure 127. MERGE, overnight capital costs, fossil and nuclear
180

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

2005 2010 2015 2020 2025 2030 2035 2040

Year

Capital Cost (2008$/kW)

Solar Thermal

Wind (onshore)

PV

Biomass

Figure 128. MERGE, overnight capital costs, renewables
181 

Learning

EPRI indicated that learning is handled as an exogenous input to MERGE61

Based on the limited data available, it appears that no learning occurs for biomass, onshore wind, 

and solar thermal technologies, at least for the time period between 2015 and 2025 (i.e., the costs 

remain unchanged over this time period). Learning does seem apparent for nuclear technologies 

(decrease in cost between 2015 and 2025). The reason for the slight increase in combined cycle 

technology cost is probably due to introduction of advanced technology (GE 7F machine in 

2015, and GE 7H machine in 2025).

 (presumably 

developed in the EPRI Technical Assessment Guide). EPRI reported that, for most technologies, 

the learning rates are conservative, with the exception of fast growing technologies like wind and 

solar. 

62

O&M Costs (all inclusive)

The EPRI literature provided no details on either fixed or variable O&M costs. However, the 

EPRI literature did provide LCOE values and fuel costs, and, based on these LCOE values and 

fuel costs, an “all inclusive” O&M cost was calculated (in $/MWh). 

The all inclusive O&M costs, which include both fixed and variable O&M, were reverse 

calculated using the following LCOE formula63:

The variables used in the preceding LCOE equation are described in Table 51. In simple terms, 

the all inclusive O&M cost is equal to the LCOE cost less the annualized capital cost and less the 

annualized fuel cost (i.e., O&M Cost = LCOE – Annual Capital – Annual Fuel). 

Table 51. LCOE Calculation Parameters

Abbreviation Description Units

LCOEnominal Levelized Cost of Energy (nominal dollars) $/MWh

LCOEreal Levelized Cost of Energy (real dollars) $/MWh

OC Overnight Capital Cost $/kW

CT Construction Cost Multiplier ---

FC Fuel Cost (with inflation) $/MMBtu

FE Fuel Escalation Factor (annual change above inflation) %

FOM Fixed O&M (with inflation) $/kW/yr

 

61 E-mail exchange between R. James (EPRI) and R. Tidball (ICF), January 12, 2010. 62 Program on Technology Innovation: Integrated Generation Technology Options, EPRI, Report No. 1018329, 

November 2008, Tables 1-4 and 1-5, (GE machines referenced in combined cycle discussion). 63 LCOE equation discussed in greater detail in the following companion report: “Cost and Performance of 

Electricity Generation Technologies, Task 3 – Levelized Cost of Energy (LCOE) Tool Development.” 

ൌ௥௘௔௟ ܧܱܥܮ

ሿ௡ሻܨܦ െ 1ሺ כ ܹ݄ܯ כ ܯܸܱ ൅ ܵ כ ܯܱܨ ൅ ሻܧܨ ൅ 1ሺ כ ܥܨሾ ൅ ∑ ܵ כ ܶܥ כ ܥܱ

ሺ1 ൅ ܦܴ௟௔௡௜௠௢௡ሻ௡

∑ ሾܯܹ݄ כ ሺ1 െ ܨܦሻሿ௡

ሺ1 ൅ ܦܴ௟௔௘௥ሻ௡
182

Abbreviation Description Units

VOM Variable O&M (with inflation) $/MWh

DF Degradation Factor (annual decline in maximum plant capacity) % 

S Plant Capacity (nameplate) MW

CF Capacity Factor %

MWh Electricity Production (= S X CF X 8,760 hrs/yr) MWh/yr

n Number of Years (1 through end of plant lifetime) ---

e Inflation Rate %

DRnominal Nominal Discount Rate %

DRreal Real Discount Rate (= [DRnominal + 1] / [1 + e] - 1) %

The all inclusive O&M results for 2015 and 2025 are shown in Table 52 and Figure 129. The 

table shows the calculated O&M results as well as the LCOE and fuel costs used to calculate the 

O&M results. As indicated, the all-inclusive O&M costs range from a low of $7.52/MWh for a 

combined cycle plant to $188/MWh for a PV system.

Table 52. MERGE, All Inclusive O&M Costs (2015 values, reported in 2008$)

Technology Fuel 

(2008$/MMBtu)

LCOE 

(2008$/MWh)

All Inclusive O&M 

(2008$/MWh)

2015 & 2025 2015 2025 2015 2025

Coal Coal, no CC $1.80 $66.00 --- $23.82 ---

Coal, 1st gen CC $1.80 --- $101.00 --- $34.71

Coal, 2nd gen CC $1.80 --- $86.00 --- $31.28

IGCC IGCC, no CC $1.80 $71.00 --- $25.78 ---

IGCC, 1st gen CC $1.80 --- $92.00 --- $32.10

IGCC, 2nd gen CC $1.80 --- $78.00 --- $27.37

Combined Cycle --- $9.00 $81.50 $74.00 $7.52 $8.27

Nuclear --- $0.80 $84.00 $74.00 $33.31 $29.71

Biomass --- $1.72 $83.50 $77.00 $29.46 $22.96

Wind (onshore) --- --- $99.00 $82.00 $46.26 $38.05

Solar Thermal64 --- --- $290.00 $290.00 $116.81 $116.81

PV --- --- $456.00 $456.00 $188.54 $188.54

 64 MERGE contained three scenarios for solar thermal. The data shown in this table, and throughout this section, are 

based on a scenario with 100% solar and wet cooling.
183

$23.82 $25.78

$34.71 $31.28 $32.10

$27.37

$7.52

$33.31 $29.46

$46.26

$116.81

$188.54

$8.27

$29.71

$22.96

$38.05

$0

$20

$40

$60

$80

$100

$120

$140

$160

$180

$200

Coal, no

CC

Coal, 1st

gen CC

Coal, 2nd

gen CC

IGCC, no

CC

IGCC, 1st

gen CC

IGCC, 2nd

gen CC

Combined

Cycle

Nuclear Biomass Wind

(onshore)

Solar

Thermal

PV

Technology

Cost (2008$/MWh)

2015

2025

Figure 129. MERGE, all inclusive O&M costs
184 

Appendix G. Description of LCOE Tool

A calculation tool was created to compute the levelized cost of energy (LCOE) for power 

generation technologies. The tool was created in Excel and is named “LCOE_ver_x.xls” (x 

signifies the version number). This appendix describes the calculation methodology used in the 

Excel tool, and provides guidance on the input parameters required for the tool and the output 

results that are computed. The discussion is organized as follows:

• Methodology

• Excel Workbook Structure

• Excel Worksheet Descriptions

5.1.13 Methodology

LCOE results are calculated in both nominal and real dollars. The equations used for these 

LCOE calculations are shown below (first equation in nominal dollars and second equation in 

real dollars):

The variables used in the preceding LCOE equations are described in Table 53. 

ൌ௡௢௠ ܧܱܥܮ

ሿ௡ሻܨܦ െ 1ሺ כ ܹ݄ܯ כ ܯܸܱ ൅ ܵ כ ܯܱܨ ൅ ሻܧܨ ൅ 1ሺ כ ܥܨሾ ൅ ∑ ܵ כ ܶܥ כ ܥܱ

ሺ1 ൅ ܦܴ௟௔௡௜௠௢௡ሻ௡

∑ ሾܯܹ݄ כ ሺ1 െ ܨܦሻሿ௡

ሺ1 ൅ ܦܴ௟௔௡௜௠௢௡ሻ௡

ൌ௥௘௔௟ ܧܱܥܮ

ሿ௡ሻܨܦ െ 1ሺ כ ܹ݄ܯ כ ܯܸܱ ൅ ܵ כ ܯܱܨ ൅ ሻܧܨ ൅ 1ሺ כ ܥܨሾ ൅ ∑ ܵ כ ܶܥ כ ܥܱ

ሺ1 ൅ ܦܴ௟௔௡௜௠௢௡ሻ௡

∑ ሾܯܹ݄ כ ሺ1 െ ܨܦሻሿ௡

ሺ1 ൅ ܦܴ௟௔௘௥ሻ௡
185

Table 53. LCOE Calculation Parameters

Abbreviation Description Units

LCOEnominal Levelized Cost of Energy (nominal dollars) $/MWh

LCOEreal Levelized Cost of Energy (real dollars) $/MWh

OC Overnight Capital Cost $/kW

CT Construction Cost Multiplier ---

FC Fuel Cost (with inflation) $/MMBtu

FE Fuel Escalation Factor (annual change above inflation) %

FOM Fixed O&M (with inflation) $/kW/yr

VOM Variable O&M (with inflation) $/MWh

DF Degradation Factor (annual decline in maximum plant capacity) % 

S Plant Capacity (nameplate) MW

CF Capacity Factor %

MWh Electricity Production (= S X CF X 8,760 hrs/yr) MWh/yr

n Number of Years (1 through end of plant life) ---

e Inflation Rate %

DRnominal Nominal Discount Rate %

DRreal Real Discount Rate (= [DRnominal + 1] / [1 + e] - 1) %

The fuel cost (FC), fixed O&M (FOM), and variable O&M (VOM) factors include inflation 

based on the following equations:

FCj = FCj-1 * MWhj * (1+e)

FOMj = FOMj-1 * (1+e)

VOMj = VOMj-1 * (1+e)

where “j” is the particular year and “e” is the inflation rate.

The construction cost multiplier (CT) is also adjusted with inflation as follows:

CT = CT0 * (0.97 +e)

where CT0 is the baseline construction cost multiplier for a particular technology at 3% inflation.

5.1.14 Excel Workbook Structure

Figure 130 shows a flow chart for the LCOE tool. As indicated, the first step is to select a single 

comparison year for the analysis. Based on the comparison year selected, power plant 

characteristics from a companion Access database are imported into the LCOE tool for the data 

sets shown in Table 1. The Access database, which is named “TechChar.mdb,” needs to be 

saved in the same folder as the LCOE Excel file to establish a proper data transfer path.
186

Figure 130. Flow chart for the LCOE tool

Select Comparison 

Year

Import Power Plant Characteristics from 

Access Database to Excel Spreadsheet

Calculate LCOE Values Based on Characteristics Contained in 

Data Sets

Adjust Factors that Impact 

Overnight Capital Costs

Calculate LCOE Values Based on Adjusted Characteristics

Compare Original and 

Adjusted LCOE Values

End

Run another 

Scenario

Input Fuel Costs, Construction Cost Multipliers, and Plant 

Degradation

Choose Financial 

Parameters

Enter Plant Lifetime Data
187

The Access database contains technical performance and cost characteristics for power plant 

technologies. However, additional data is required to compute LCOE values. After the power 

plant characteristics are imported into the spreadsheet, the user is guided through a series of steps 

(see Figure 130) to supply additional data needed to run the LCOE calculations. After the 

required data has been entered, baseline LCOE values are computed for the data sets.

In addition to computing baseline LCOE values for the six data sets, the user can create 

customized technical performance and cost characteristics for a “user defined” data set. Up to 29 

unique power generation technologies can be characterized in this user defined data set. The 

LOCE values for the user defined data set are calculated in the LCOE spreadsheet.

After computing the baseline LCOE values in the six sets and the user defined data set, the user 

then has the ability to change a number of factors, such as learning and capital cost contributions. 

LCOE values are then re-computed based on the adjustments entered by the user. The user can 

then repeat the preceding steps to examine multiple scenarios for different parameter 

adjustments.

The workbook contains several macros to facilitate calculations. The principal macros are 

described in Table 54.

5.1.15 Excel Worksheet Descriptions

The Excel workbook is divided into several worksheets as listed in Table 55. In the following 

paragraphs, each of these worksheets is briefly discussed.

Introduction

This worksheet describes what calculations are completed and how the workbook is organized.

Year

This worksheet is used to enter the desired year for comparing power plant characteristics 

between the data sets. The user is prompted to enter a year, and based on the year selected, data 

is then imported from a companion Access database to the Excel spreadsheet.

Note that the Access database, which is named “TechChar.mdb,” and the Excel workbook, 

which is named “LCOE_ver_x.xls,” need to be saved in the same folder to ensure that a path can 

be created between the two files for data exchange.
188

Table 54. Macros

Macro Sub-macros Action

DatabaseLCOETable --- Imports data from Access database to Excel 

spreadsheet for a single year entered by the 

user.

copy_methodology --- The LCOE methodology is copied from the 

“Methodology” worksheet to the “Calculations” 

worksheet

baseline aeo_copy_data

calc_LCOE

aeo_fill_base_nom

aeo_fill_base_real

The “baseline” macro runs a set of four macros 

for each data set (including the user defined 

data set. Four macros for the AEO data set are 

shown for illustration. The real and nominal 

LCOE values are calculated for all technologies 

in each data set (up to 29 technologies per data 

set), and the key results are transferred to the 

“LCOE base nom” and “LCOE base real” 

worksheets.

adjust aeo_copy_adjusted

calc-LCOE

aeo_fill_adj_nom

aeo_fill_base_real

The “adjust” macro runs a set of four macros for 

each data set (including the user defined data 

set). The four macros for the AEO data set are 

shown for illustration. The real and nominal 

LCOE values are calculated for all technologies 

in each data set (up to 29 technologies per data 

set) based on adjusted overnight capital costs. 

Key results are transferred to the “LCOE adj 

real” and “LCOE adj nom” worksheets.

update --- This macro runs both the “baseline” and “adjust” 

macros
189

Table 55. Worksheets in Excel Workbook

Worksheet Title Type of Content Description

Intro Reference This worksheet describes what the spreadsheet calculates and how the 

workbook is organized. 

Year Imported Data 

from Access 

database 

This worksheet is used to select the comparison year used for the analysis. 

Based on the comparison year, data from a companion Access database 

will be imported for the data sets.

Currency Input On this worksheet, the user provides the currency year for reporting 

financial results, and applicable interest rates needed to run the financial 

calculations.

GDP Index Input This worksheet shows the macroeconomic index used to adjust capital and 

O&M costs. The default values are based on a GDP index. However, these 

default values can be changed. 

Fuel Input On this sheet, the user determines fuel costs for coal, natural gas, and 

uranium. Users can provide customized costs or select from a menu of fuel 

costs that have been previously developed.

Construction Input This sheet shows construction cost multipliers that are applied to all 

overnight capital costs. 

Degradation Input This sheet shows the expected annual decline in energy output from PV 

systems. 

Lifetime Input This sheet is used to insert user specified plant lifetime data.

LCOE Base 

Nom

Output This worksheet shows the baseline LCOE values calculated from the 

parameters input on the preceding worksheets as well as the plant 

characteristics contained in the data sets. The LCOE results are reported in 

nominal dollars.

LCOE Base 

Real

Output This worksheet is similar to the previous worksheet, except the LCOE 

results are reported in real dollars.

Cost 

Contribution

Input This worksheet shows a breakdown of overnight capital cost components 

for each of the power generation technologies. These values are used in 

conjunction with user specified adjustments (see next work sheet).

Cost Adj Input This worksheet allows the user to adjust factors, such as learning and cost 

components, that impact overnight capital costs. 

LCOE Adj Nom Output This worksheet shows the impact of the adjusted overnight capital costs on 

LCOE values (nominal dollars). 

LCOE Adj Real Output This worksheet shows the impact of the adjusted overnight capital costs on 

LCOE values (real dollars). 

LCOE Comp 

Nom

Output This sheet compares baseline and adjusted values (in nominal dollars).

LCOE Comp 

Real

Output This sheet compares baseline and adjusted values (in real dollars).

Methodology Equations This worksheet contains the LCOE equations.

Calculations Intermediate 

Calculations

This worksheet is used to complete all LCOE calculations. The methodology 

is copied from the preceding worksheet to this worksheet for each set of 

calculations. 

Data Set Sheets Data Summary These worksheets show the power plant characteristics for the individual 

data sets – one worksheet for each data set. 

User Defined Input This worksheet is used to enter customized power plant characteristics for a 

user defined data set.

Charts and 

Tables

Summary of 

Results

Following the “User Defined” worksheet, there are several summary tables 

and charts.
190

Currency

This sheet is used to enter the following parameters:

• Desired currency year

• Discount rate (real)

• Inflation rate

Based on the real discount rate and the inflation rate, a nominal discount rate is automatically 

computed.

GDP Index

This worksheet contains the indices used to adjust financial data to the currency year supplied by 

the user on the “Currency” worksheet. The default adjustments are based on a gross domestic 

product (GDP) index that is consistent with the index used in the AEO 2009 data set. An 

alternate index can be supplied. For example, if a producer price index (PPI) or power plant 

index is desired, the user can enter this data by following the instructions on the worksheet.

Fuel

On this worksheet, the user selects fuel costs and fuel escalation factors for coal, natural gas, and 

uranium. The fuel escalation factor accounts for cost increases that occur above the inflation rate. 

The user can enter customized fuel prices and escalation factors, or select from three predetermined fuel cost tables. The pre-determined fuel costs are based on the AEO 2009, NRELSEAC 2008, and EPA 2009 data sets. These three data sets represent a cross section of all data 

sets covered in the LCOE tool.

In addition to selecting the fuel price and escalation factor, the user must also select a currency 

year and an applicable year for each fuel. Fuel costs in the desired comparison year (entered on 

the “Year” worksheet) and in the desired currency, dollars (entered on the “Currency” 

worksheet) are automatically calculated.

Note that fuel costs only apply to fossil and nuclear technologies. In the LCOE spreadsheet tool, 

fuel costs for all renewable technologies are set to zero.

Construction

A uniform set of construction cost multipliers are applied to all technologies in all data sets and 

the user defined data set. There are a total of 29 technologies represented in the data sets, and 

default construction cost multipliers for these 29 technologies are shown on this worksheet. The 

default values can be changed by the user.

The default construction cost multipliers were derived from the ReEDS 2009 data set. For 

technologies not covered in the ReEDS 2009 data set, construction cost multipliers were adapted 

to provide full coverage across all 29 technologies in the LCOE tool.

Degradation

Solar photovoltaic (PV) power systems typically show a decline in maximum capacity as the 

systems age. On this worksheet, the user is prompted to enter a degradation factor for solar PV 
191

(suggested default is 1%). The degradation factor is expressed as an annual percentage reduction 

in maximum output. Degradation factors for all technologies other than solar PV are set to 0%.

Lifetime

On this worksheet, the user can enter unique power plant lifetime values for all 29 technologies. 

The LCOE values shown on subsequent worksheets are calculated based on plant lifetimes 

entered by the user as well as plant lifetimes contained in each data set.

Note that some data sets, such as AEO 2009 and EPA 2009, do not contain plant lifetime values. 

These data sets are used with the NEMS and IPM models, respectively, and these models do not 

use firm power plant lifetimes. In the case of NEMS and IPM, these models allow power plants 

to run until they are no longer economically viable – there is no pre-determined age limit. For 

data sets that do not have inherent plant lifetimes, the LCOE results are only computed using the 

lifetime values entered by the user.

LCOE Base Nom

This worksheet shows LCOE values calculated with the data entered on previous worksheets. 

The LCOE values are calculated in nominal dollars for the technologies in all data sets plus the 

user defined data set.

LCOE values are not calculated if a particular technology is not represented in a specific data set. 

If a technology is represented in a specific data set, but there is no pre-determined life, the LCOE 

results will only be reported for the lifetime entered by the user.

LCOE Base Real

This worksheet makes the same calculations as the previous worksheet. However, results are 

reported in real dollars (previous worksheet is based on nominal dollars).

Cost Contribution

This worksheet and the Cost Adjustments worksheet (see next section) are used to evaluate the 

impact on LCOE values as a result of changes in cost components that contribute to overnight 

capital costs. The overnight capital cost components that can be adjusted are shown in Table 56.

Table 56. Overnight Capital Cost Components

Overnight Capital Cost Components

Concrete

Steel

Other Onsite Materials

Equipment (major pieces delivered to site)

Labor

Other Cost Components
192

On the Cost Contributions worksheet, default values are provided for the contribution of each 

cost component to overnight capital costs for each of the 29 technologies included in the LCOE 

tool. The user can modify the cost contributions if desired. References used to estimate 

contributions to capital costs are shown below

5.1.16 References for Estimating Capital Cost Contributions

Energy Information Administration (EIA), Assumptions to AEO 2009, Report # DOE/EIA-0554, 

March 2009.

Engineering News Record, http://enr.construction.com/economics/default.asp

Hance, C. N., Factors Affecting Costs of Geothermal Power Development, prepared by 

Geothermal Energy Association, prepared for the U.S. Department of Energy, August 2005.

, accessed 

December 18, 2009.

Mahjouri, F., and A. Nunez, The Relative Cost of Solar Thermal Collector Installations, 

http://www.thermomax.com/

Meier, P. J., Life-cycle Assessment of Electricity Generation Systems and Applications for 

Climate Change Policy Analysis, UWFDM-1181, Aug. 2002

.

NETL, Cost and Performance Baseline for Fossil Energy Plants, Report No. DOE/NETL2007/1281, Vol. 1, August 2007 (revised).

NREL, Job and Economic Development Impact (JEDI) model, http://www.nrel.gov/analysis/

jedi/

Peterson, P., Future of Nuclear Energy, U Cal Berkeley, Aug. 2005

, default data, accessed January 2010.

Resource Dynamics Corporation, Assessment of DG Technology Applications, report prepared 

for Maine PUC, February 2001.

Spitzley, D. V., and G. A. Keoleian, Life Cycle Environmental and Economic Assessment of 

Willow Biomass Electricity: A Comparison with Other Renewable and Non-Renewable Sources, 

February 2005, CSS04-05R.

White, S. W. and G. L. Kulcinski, "Birth to Death" Analysis of the Energy Payback Ratio and 

CO2 Gas Emission Rates from Coal, Fission, Wind, and DT Fusion Electrical Power Plants, 

Fusion Technology Institute, Univ. of Wisconsin, UWFDM-1063, Feb. 1999

White, S. W. and G. L. Kulcinski, Net Energy Payback and CO2 Emissions from WindGenerated Electricity in the Midwest, Fusion Technology Institute, Univ. of Wisconsin, 

UWFDM-1092, Dec. 1998

Wiser, R., G. Barbose, and C. Peterman, Tracking the Sun, LBNL-1516e, February 2009.
193

The LCOE tool does allow for an interaction between steel prices and equipment prices. On the 

Cost Contribution worksheet, the estimated fraction of equipment costs attributed to steel – based 

on all equipment delivered to the construction site – is shown. The default factors shown for each 

of the 29 technologies can be modified. Data sources for estimating the cost contribution of steel 

to power generation technologies were scarce. The estimates in the spreadsheet should be 

regarded as rough approximations only.

Cost Adj

This worksheet is used to evaluate the impact of changes to the six overnight capital cost 

components shown in Table 56. In addition to the six capital cost components, the user can 

adjust learning rates as well as three factors that apply only to the AEO data set (metal and metal

products PPI, technology optimism, and project contingency). On this sheet, the user provides 

estimated changes (expressed as a percent), and based on these estimates the overnight capital 

costs in all data sets are adjusted by the same percentage levels.

LCOE Adj Nom

This worksheet shows the baseline (before) LCOE values and the adjusted (after) LCOE values 

reported in nominal dollars. Like the “LCOE (baseline)” worksheet, LCOE results are not 

reported for technologies not contained in a particular data set, or for technologies that are 

included but do not have a pre-determined life.

LCOE Adj Real

This worksheet is comparable to the previous worksheet, but the LCOE values are reported in 

real dollars.

LCOE Comp Nom

This worksheet compares the baseline and adjusted LCOE results in nominal dollars.

LCOE Comp Real

This worksheet compares the baseline and adjusted LCOE results in real dollars.

Methodology

This worksheet contains the LCOE equations.

Calculations

The LCOE methodology is copied from the Methodology worksheet to this worksheet for all 

calculations. As calculations are completed, the results are transferred using macros to the 

respective results tables. For example, the tables contained in the LCOE (baseline), LCOE (adj, 

nom), and LCOE (adj, real) worksheets are populated dynamically as the calculations are 

completed.

Data Set Worksheets (multiple sheets)

These worksheets contain information needed to compute LCOE results. There are six 

worksheets – one for each data set.

User Defined

This worksheet is used to create a customized data set with up to 29 unique power generation 

technologies.
194

Summary Tables and Charts

Following the “User Defined” worksheet, there are several charts and tables that summarize 

results for 11 key technologies as shown in Table 57.

Table 57. Eleven Technologies Included in Summary

Technology

Coal

IGCC

Combustion Turbine

Combined Cycle

Nuclear

Biomass

Geothermal (hydrothermal)

Wind (onshore)

Wind (offshore)

Solar Thermal

PV
195

Appendix H. GDP Index

Table 58. GDP Index

Year Relative GDP Values 

(2000 = 1) (2007 =1)

1990 0.816 0.681

1991 0.844 0.705

1992 0.864 0.721

1993 0.884 0.738

1994 0.903 0.753

1995 0.921 0.769

1996 0.939 0.783

1997 0.954 0.796

1998 0.965 0.805

1999 0.979 0.817

2000 1.000 0.835

2001 1.024 0.855

2002 1.042 0.870

2003 1.064 0.888

2004 1.095 0.914

2005 1.130 0.943

2006 1.167 0.974

2007 1.198 1.000

2008 1.225 1.022

2009 1.237 1.032

2010 1.243 1.038

2011 1.258 1.050

2012 1.274 1.063

2013 1.297 1.082

2014 1.324 1.105

2015 1.354 1.130

2016 1.385 1.156

2017 1.417 1.182

2018 1.450 1.210

2019 1.484 1.239

2020 1.521 1.270

Source: The 1990 through 2009 values are based on data from the U.S. Department of 

Commerce, Bureau of Economic Analysis. The 2010 through 2020 values are based on data from 

EIA’s 2009 Annual Energy Outlook.
196

Appendix I. LCOE Results

Table 59. LCOE Results, 2015

Data Set Technology Baseline Costs Contribution

Cost Breakdown ($/MWh) LCOE Cost Breakdown (%) to Capital (%)

Capital O&M Fuel ($/MWh) Capital O&M Fuel Equipment Labor

AEO 2009 Coal $23 $8 $19 $49 46% 16% 38% 0.50 0.30

GPRA 2009 Coal 0.50 0.30

NREL-SEAC 2008 Coal $25 $7 $19 $50 49% 13% 38% 0.50 0.30

MiniCAM Coal $19 $9 $18 $46 41% 20% 40% 0.50 0.30

EPA 2009 Coal $23 $8 $19 $50 46% 16% 38% 0.50 0.30

MERGE 2009 Coal $29 $23 $18 $70 41% 32% 26% 0.50 0.30

`AEO 2009 IGCC $28 $8 $17 $53 52% 15% 33% 0.50 0.20

GPRA 2009 IGCC 0.50 0.20

NREL-SEAC 2008 IGCC $35 $9 $19 $63 56% 15% 29% 0.50 0.20

MiniCAM IGCC 0.50 0.20

EPA 2009 IGCC $28 $8 $18 $54 52% 15% 34% 0.50 0.20

MERGE 2009 IGCC $35 $24 $18 $77 45% 32% 24% 0.50 0.20

AEO 2009 Combustion Turbine $7 $4 $50 $61 11% 7% 82% 0.75 0.10

GPRA 2009 Combustion Turbine 0.75 0.10

NREL-SEAC 2008 Combustion Turbine $9 $4 $49 $62 15% 6% 79% 0.75 0.10

MiniCAM Combustion Turbine $53 $18 $49 $120 44% 15% 41% 0.75 0.10

EPA 2009 Combustion Turbine $7 $4 $51 $63 11% 7% 82% 0.75 0.10

MERGE 2009 Combustion Turbine 0.75 0.10

AEO 2009 Combined Cycle $11 $3 $37 $51 22% 7% 71% 0.60 0.15

GPRA 2009 Combined Cycle 0.60 0.15

NREL-SEAC 2008 Combined Cycle $9 $5 $38 $52 18% 10% 72% 0.60 0.15

MiniCAM Combined Cycle $10 $4 $34 $48 20% 8% 71% 0.60 0.15

EPA 2009 Combined Cycle $11 $3 $37 $52 22% 7% 72% 0.60 0.15
197

Data Set Technology Baseline Costs Contribution

Cost Breakdown ($/MWh) LCOE Cost Breakdown (%) to Capital (%)

Capital O&M Fuel ($/MWh) Capital O&M Fuel Equipment Labor

MERGE 2009 Combined Cycle $11 $7 $40 $58 18% 12% 69% 0.60 0.15

AEO 2009 Nuclear $42 $12 $10 $64 66% 18% 16% 0.50 0.30

GPRA 2009 Nuclear 0.50 0.30

NREL-SEAC 2008 Nuclear $41 $12 $10 $63 65% 19% 16% 0.50 0.30

MiniCAM Nuclear $31 $11 $10 $51 60% 21% 19% 0.50 0.30

EPA 2009 Nuclear 0.50 0.30

MERGE 2009 Nuclear $59 $32 $10 $100 59% 32% 10% 0.50 0.30

AEO 2009 Biomass $40 $15 $18 $73 55% 21% 24% 0.50 0.30

GPRA 2009 Biomass 0.50 0.30

NREL-SEAC 2008 Biomass $31 $20 $29 $80 39% 25% 36% 0.50 0.30

MiniCAM Biomass $24 $10 $20 $54 44% 19% 37% 0.50 0.30

EPA 2009 Biomass $40 $15 $20 $75 54% 20% 26% 0.50 0.30

MERGE 2009 Biomass $38 $28 $25 $91 42% 31% 27% 0.50 0.30

AEO 2009 Geothermal (HT) $61 $20 $0 $81 75% 25% 0% 0.20 0.40

GPRA 2009 Geothermal (HT) $56 $14 $0 $70 80% 20% 0% 0.20 0.40

NREL-SEAC 2008 Geothermal (HT) $43 $23 $0 $66 65% 35% 0% 0.20 0.40

MiniCAM Geothermal (HT) $35 $11 $0 $45 77% 23% 0% 0.20 0.40

EPA 2009 Geothermal (HT) $130 $23 $0 $154 85% 15% 0% 0.20 0.40

MERGE 2009 Geothermal (HT) 0.20 0.40

AEO 2009 Wind (onshore) $58 $8 $0 $66 87% 13% 0% 0.70 0.10

GPRA 2009 Wind (onshore) $31 $6 $0 $38 83% 17% 0% 0.70 0.10

NREL-SEAC 2008 Wind (onshore) $43 $8 $0 $51 84% 16% 0% 0.70 0.10

MiniCAM Wind (onshore) $32 $9 $0 $41 78% 22% 0% 0.70 0.10

EPA 2009 Wind (onshore) $55 $9 $0 $64 86% 14% 0% 0.70 0.10

MERGE 2009 Wind (onshore) $74 $44 $0 $118 63% 37% 0% 0.70 0.10

AEO 2009 Wind (offshore) $112 $25 $0 $136 82% 18% 0% 0.70 0.10

GPRA 2009 Wind (offshore) $55 $35 $0 $90 61% 39% 0% 0.70 0.10
198

Data Set Technology Baseline Costs Contribution

Cost Breakdown ($/MWh) LCOE Cost Breakdown (%) to Capital (%)

Capital O&M Fuel ($/MWh) Capital O&M Fuel Equipment Labor

NREL-SEAC 2008 Wind (offshore) $57 $20 $0 $77 74% 26% 0% 0.70 0.10

MiniCAM Wind (offshore) 0.70 0.10

EPA 2009 Wind (offshore) 0.70 0.10

MERGE 2009 Wind (offshore) 0.70 0.10

AEO 2009 Solar Thermal (CSP) $153 $20 $0 $174 88% 12% 0% 0.50 0.30

GPRA 2009 Solar Thermal (CSP) $97 $13 $0 $110 88% 12% 0% 0.50 0.30

NREL-SEAC 2008 Solar Thermal (CSP) $138 $17 $0 $155 89% 11% 0% 0.50 0.30

MiniCAM Solar Thermal (CSP) 0.50 0.30

EPA 2009 Solar Thermal (CSP) $130 $18 $0 $148 88% 12% 0% 0.50 0.30

MERGE 2009 Solar Thermal (CSP) $208 $111 $0 $319 65% 35% 0% 0.50 0.30

AEO 2009 PV $290 $6 $0 $297 98% 2% 0% 0.60 0.10

GPRA 2009 PV $109 $3 $0 $112 98% 2% 0% 0.60 0.10

NREL-SEAC 2008 PV $140 $6 $0 $146 96% 4% 0% 0.60 0.10

MiniCAM PV $279 $28 $0 $307 91% 9% 0% 0.60 0.10

EPA 2009 PV $255 $6 $0 $261 98% 2% 0% 0.60 0.10

MERGE 2009 PV $319 $179 $0 $498 64% 36% 0% 0.60 0.10
199

Table 60. LCOE Results, 2030

Data Set Technology Baseline Costs Contribution

Cost Breakdown ($/MWh) LCOE Cost Breakdown (%) to Capital (%)

Capital O&M Fuel ($/MWh) Capital O&M Fuel Equipment Labor

AEO 2009 Coal $17 $8 $20 $45 38% 18% 44% 0.50 0.30

GPRA 2009 Coal 0.50 0.30

NREL-SEAC 2008 Coal $25 $7 $20 $52 48% 13% 40% 0.50 0.30

MiniCAM Coal $18 $9 $20 $46 39% 19% 42% 0.50 0.30

EPA 2009 Coal $19 $8 $21 $48 40% 17% 44% 0.50 0.30

MERGE 2009 Coal $29 $23 $20 $72 40% 31% 28% 0.50 0.30

`AEO 2009 IGCC $20 $8 $17 $45 45% 18% 38% 0.50 0.20

GPRA 2009 IGCC 0.50 0.20

NREL-SEAC 2008 IGCC $35 $9 $20 $64 55% 15% 31% 0.50 0.20

MiniCAM IGCC $22 $9 $18 $48 45% 18% 37% 0.50 0.20

EPA 2009 IGCC $23 $8 $20 $51 45% 16% 39% 0.50 0.20

MERGE 2009 IGCC $35 $24 $20 $79 43% 31% 26% 0.50 0.20

AEO 2009 Combustion Turbine $5 $4 $52 $62 8% 7% 85% 0.75 0.10

GPRA 2009 Combustion Turbine 0.75 0.10

NREL-SEAC 2008 Combustion Turbine $9 $4 $55 $68 14% 6% 81% 0.75 0.10

MiniCAM Combustion Turbine $52 $17 $54 $122 42% 14% 44% 0.75 0.10

EPA 2009 Combustion Turbine $6 $4 $57 $67 8% 7% 85% 0.75 0.10

MERGE 2009 Combustion Turbine 0.75 0.10

AEO 2009 Combined Cycle $8 $3 $39 $50 16% 7% 77% 0.60 0.15

GPRA 2009 Combined Cycle 0.60 0.15

NREL-SEAC 2008 Combined Cycle $9 $5 $42 $57 17% 9% 75% 0.60 0.15

MiniCAM Combined Cycle $9 $4 $36 $49 18% 8% 74% 0.60 0.15

EPA 2009 Combined Cycle $9 $3 $41 $54 17% 6% 77% 0.60 0.15

MERGE 2009 Combined Cycle $11 $8 $39 $57 19% 14% 67% 0.60 0.15

AEO 2009 Nuclear $29 $12 $13 $53 54% 22% 24% 0.50 0.30
200

Data Set Technology Baseline Costs Contribution

Cost Breakdown ($/MWh) LCOE Cost Breakdown (%) to Capital (%)

Capital O&M Fuel ($/MWh) Capital O&M Fuel Equipment Labor

GPRA 2009 Nuclear 0.50 0.30

NREL-SEAC 2008 Nuclear $38 $12 $13 $63 60% 19% 21% 0.50 0.30

MiniCAM Nuclear $30 $11 $12 $53 57% 20% 23% 0.50 0.30

EPA 2009 Nuclear $38 $12 $13 $63 61% 19% 21% 0.50 0.30

MERGE 2009 Nuclear $50 $28 $13 $91 55% 31% 14% 0.50 0.30

AEO 2009 Biomass $27 $15 $17 $58 46% 26% 29% 0.50 0.30

GPRA 2009 Biomass 0.50 0.30

NREL-SEAC 2008 Biomass $31 $20 $31 $82 38% 24% 38% 0.50 0.30

MiniCAM Biomass $23 $10 $19 $52 45% 19% 37% 0.50 0.30

EPA 2009 Biomass $36 $15 $21 $71 50% 21% 29% 0.50 0.30

MERGE 2009 Biomass $38 $22 $26 $86 44% 25% 30% 0.50 0.30

AEO 2009 Geothermal (HT) $43 $20 $0 $63 68% 32% 0% 0.20 0.40

GPRA 2009 Geothermal (HT) $51 $13 $0 $64 80% 20% 0% 0.20 0.40

NREL-SEAC 2008 Geothermal (HT) $43 $23 $0 $66 65% 35% 0% 0.20 0.40

MiniCAM Geothermal (HT) $33 $10 $0 $43 77% 23% 0% 0.20 0.40

EPA 2009 Geothermal (HT) $130 $23 $0 $154 85% 15% 0% 0.20 0.40

MERGE 2009 Geothermal (HT) 0.20 0.40

AEO 2009 Wind (onshore) $45 $8 $0 $54 84% 16% 0% 0.70 0.10

GPRA 2009 Wind (onshore) $25 $5 $0 $30 84% 16% 0% 0.70 0.10

NREL-SEAC 2008 Wind (onshore) $38 $7 $0 $45 84% 16% 0% 0.70 0.10

MiniCAM Wind (onshore) $28 $8 $0 $35 79% 21% 0% 0.70 0.10

EPA 2009 Wind (onshore) $55 $9 $0 $64 86% 14% 0% 0.70 0.10

MERGE 2009 Wind (onshore) $74 $36 $0 $110 67% 33% 0% 0.70 0.10

AEO 2009 Wind (offshore) $79 $25 $0 $104 76% 24% 0% 0.70 0.10

GPRA 2009 Wind (offshore) $35 $13 $0 $49 73% 27% 0% 0.70 0.10

NREL-SEAC 2008 Wind (offshore) $52 $15 $0 $67 78% 22% 0% 0.70 0.10

MiniCAM Wind (offshore) 0.70 0.10
201

Data Set Technology Baseline Costs Contribution

Cost Breakdown ($/MWh) LCOE Cost Breakdown (%) to Capital (%)

Capital O&M Fuel ($/MWh) Capital O&M Fuel Equipment Labor

EPA 2009 Wind (offshore) 0.70 0.10

MERGE 2009 Wind (offshore) 0.70 0.10

AEO 2009 Solar Thermal 

(CSP)

$95 $20 $0 $115 82% 18% 0% 0.50 0.30

GPRA 2009 Solar Thermal 

(CSP)

$37 $5 $0 $42 88% 12% 0% 0.50 0.30

NREL-SEAC 2008 Solar Thermal 

(CSP)

$138 $17 $0 $155 89% 11% 0% 0.50 0.30

MiniCAM Solar Thermal 

(CSP)

$49 $5 $0 $54 90% 10% 0% 0.50 0.30

EPA 2009 Solar Thermal 

(CSP)

$130 $18 $0 $148 88% 12% 0% 0.50 0.30

MERGE 2009 Solar Thermal 

(CSP)

$208 $111 $0 $319 65% 35% 0% 0.50 0.30

AEO 2009 PV $182 $6 $0 $189 97% 3% 0% 0.60 0.10

GPRA 2009 PV $59 $1 $0 $60 98% 2% 0% 0.60 0.10

NREL-SEAC 2008 PV $84 $3 $0 $87 96% 4% 0% 0.60 0.10

MiniCAM PV $155 $14 $0 $169 92% 8% 0% 0.60 0.10

EPA 2009 PV $255 $6 $0 $261 98% 2% 0% 0.60 0.10

MERGE 2009 PV $319 $179 $0 $498 64% 36% 0% 0.60 0.10
F1146-E(10/2008) 

REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, 

gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this 

collection of information, including suggestions for reducing the burden, to Department of Defense, Executive Services and Communications Directorate (0704-0188). Respondents 

should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a 

currently valid OMB control number. 

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION.

1. REPORT DATE (DD-MM-YYYY)

November 2010

2. REPORT TYPE

Subcontract Report

3. DATES COVERED (From - To)

 

4. TITLE AND SUBTITLE

Cost and Performance Assumptions for Modeling Electricity 

Generation Technologies

5a. CONTRACT NUMBER

DE-AC36-08-GO28308

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

R. Tidball, J. Bluestein, N. Rodriguez, and S. Knoke

5d. PROJECT NUMBER

NREL/SR-6A20-48595

5e. TASK NUMBER

SAO9.2502

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

ICF International

Fairfax, VA

8. PERFORMING ORGANIZATION

REPORT NUMBER

KACX-8-88312-04

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory

1617 Cole Blvd.

Golden, CO 80401-3393

10. SPONSOR/MONITOR'S ACRONYM(S)

NREL

11. SPONSORING/MONITORING

AGENCY REPORT NUMBER

NREL/SR-6A20-48595

12. DISTRIBUTION AVAILABILITY STATEMENT

National Technical Information Service

U.S. Department of Commerce

5285 Port Royal Road

Springfield, VA 22161

13. SUPPLEMENTARY NOTES

NREL Technical Monitor: Jordan Macknick

14. ABSTRACT (Maximum 200 Words)

The goal of this project was to compare and contrast utility scale power plant characteristics used in data sets that 

support energy market models. Characteristics include both technology cost and technology performance projections 

to the year 2050. Cost parameters include installed capital costs and operation and maintenance (O&M) costs. 

Performance parameters include plant size, heat rate, capacity factor or availability factor, and plant lifetime. 

Conventional, renewable, and emerging electricity generating technologies were considered. Six data sets, each 

associated with a different model, were selected. Two of the data sets represent modeled results, not direct model 

inputs. These two data sets include cost and performance improvements that result from increased deployment as 

well as resulting capacity factors estimated from particular model runs; other data sets represent model input data. 

For the technologies contained in each data set, the levelized cost of energy (LCOE) was also evaluated, according 

to published cost, performance, and fuel assumptions.

15. SUBJECT TERMS

cost and performance; electricity generation; power plants; utility scale; data; model inputs; model results; learning by 

doing; capacity factors; levelized cost of energy (LCOE); NEMS; National Energy Modeling System; EIA; Energy 

Information Administration; MARKAL; Market Allocation International Energy Agency; ReEDS; Regional Energy 

Deployment System; MiniCAM; Mini Climate Assessment Model; IPM; Integrated Planning Model; MERGE; Model for 

Estimating the Regional and Global Effects of Greenhouse Gas Reductions

16. SECURITY CLASSIFICATION OF: 17. LIMITATION 

OF ABSTRACT

UL

18. NUMBER 

OF PAGES

19a. NAME OF RESPONSIBLE PERSON

a. REPORT

Unclassified

b. ABSTRACT

Unclassified

c. THIS PAGE

Unclassified 19b. TELEPHONE NUMBER (Include area code)

Standard Form 298 (Rev. 8/98)

Prescribed by ANSI Std. Z39.18