GRATEFUL APPRECIATION TO PARTNERS 

The U.S. Department of Energy would like to acknowledge the in-depth analysis and 

extensive research conducted by the National Renewable Energy Laboratory and 

the major contributions and manuscript reviews by the American Wind Energy 

Association and many wind industry organizations that contributed to the production 

of this report. The costs curves for energy supply options and the WinDS modeling 

assumptions were developed in cooperation with Black & Veatch. The preparation of 

this technical report was coordinated by Energetics Incorporated of Washington, DC 

and Renewable Energy Consulting Services, Inc. of Palo Alto, CA. All authors and 

reviewers who contributed to the preparation of the report are listed in Appendix D. 

NOTICE 

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20% Wind Energy by 2030

Increasing Wind Energy’s Contribution to 

U.S. Electricity Supply 

DOE/GO-102008-2567 • May 2008 

More information is available on the web at: 

www.eere.energy.gov/windandhydro 

http://www.nrel.gov/docs/fy08osti/41869.pdf 

May 2008 

 

Table of Contents 

Acronyms .......................................................................................x 

Chapter 1. Executive Summary & Overview ...........................1
 

1.1 Introduction and Collaborative Approach......................................... 1
 

1.1.1	 Scope....................................................................................................2
 

1.1.2	 Contributors .........................................................................................3
 

1.1.3	 Assumptions and Process.....................................................................3
 

1.1.4	 Report Structure ...................................................................................4
 

1.1.5	 Setting the Context: Today’s U.S. Wind Industry ...............................5
 

1.2 Scenario Description............................................................................. 7
 

1.2.1 Wind Geography ..................................................................................7
 

1.2.2 Wind Power Transmission and Integration........................................10
 

1.2.3	 Electrical Energy Mix ........................................................................11
 

1.2.4	 Pace of New Wind Installations.........................................................12
 

1.3 Impacts................................................................................................. 13
 

1.3.1	 Greenhouse Gas Reductions ..............................................................13
 

1.3.2	 Water Conservation............................................................................15
 

1.3.3	 Energy Security and Stability.............................................................17
 

1.3.4	 Cost of the 20% Wind Scenario .........................................................18
 

1.4 Conclusion ........................................................................................... 20
 

1.5 References and Other Suggested Reading........................................ 20
 

Chapter 2. Wind Turbine Technology.....................................23
 

2.1 Introduction......................................................................................... 23
 

2.2 Today’s Commercial Wind Technology ........................................... 24
 

2.2.1	 Wind Resources .................................................................................24
 

2.2.2	 Today’s Modern Wind Turbine .........................................................25
 

2.2.3	 Wind Plant Performance and Price ....................................................26
 

2.2.4	 Wind Technology Development ........................................................28
 

2.2.5	 Current Turbine Size ..........................................................................29
 

2.2.6	 Current Status of Turbine Components..............................................30
 

2.3 Technology Improvements on the Horizon ...................................... 34
 

2.3.1 Future Improvements to Turbine Components ..................................35
 

2.3.2 Learning-Curve Effect .......................................................................39
 

2.3.3 The System Benefits of Advanced Technology.................................40
 

2.3.4 Targeted RD&D.................................................................................42
 

2.4 Addressing Technical and Financial Risks....................................... 43
 

2.4.1	 Direct Impacts....................................................................................43
 

2.4.2	 Indirect Impacts..................................................................................44
 

2.4.3	 Risk Mitigation Through Certification, Validation, and 

Performance Monitoring....................................................................46
 

2.5 Offshore Wind Technology ................................................................ 48
 

2.5.1 Cost of Energy ...................................................................................49
 

2.5.2 Current Technology ...........................................................................49
 

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2.5.3	 Technology Needs and Potential Improvements................................50
 

2.6	 Distributed Wind Technology ........................................................... 54
 

2.6.1	 Small Turbine Technology.................................................................54
 

2.6.2	 Technology Trends.............................................................................55
 

2.7	 Summary of Wind Technology Development Needs ....................... 56
 

2.8	 References and Other Suggested Reading........................................ 58
 

Chapter 3.	 Manufacturing, Material and Resources ............ 61
 

3.1	 Raw Materials Requirements ............................................................ 62
 

3.2	 Manufacturing Capability ................................................................. 64
 

3.2.1	 Current Manufacturing Facilities.......................................................66
 

3.2.2	 Ramping Up Energy Industries..........................................................69
 

3.3	 Labor Requirements........................................................................... 70
 

3.3.1	 Maintaining and Expanding Relevant Technical Strength.................70
 

3.4	 Challenges to 20% Wind Energy by 2030 ........................................ 72
 

3.4.1	 Challenges..........................................................................................72
 

3.5	 References and Other Suggested Reading........................................ 73
 

Chapter 4.	 Transmission and Integration into the U.S. 

Electric System....................................................... 75
 

4.1	 Lessons Learned.................................................................................. 76
 

4.1.1	 Wind Penetration Experiences and Studies........................................76
 

4.1.2	 Power System Studies Conclude that 20% Wind Energy
 

Penetration Can Be Reliably Accommodated....................................77
 

4.1.3	 Wind Turbine Technology Advancements Improve System
 

Integration ..........................................................................................83
 

4.1.4	 Wind Forecasting Enhances System Operation .................................86
 

4.1.5	 Flexible, Dispatchable Generators Facilitate Wind Integration .........86
 

4.1.6	 Integrating an Energy Resource in a Capacity World........................87
 

4.1.7	 Aggregation Reduces Variability .......................................................89
 

4.1.8	 Geographic Dispersion Reduces Operational Impacts.......................90
 

4.1.9	 Large Balancing Areas Reduce Impacts ............................................91
 

4.1.10	 Balancing Markets Ease Wind Integration.........................................92
 

4.1.11	 Changing Load Patterns Can Complement Wind Generation............93
 

4.2	 Feasibility and Cost of the New Transmission Infrastructure 

Required For the 20% Wind Scenario ............................................ 93
 

4.2.1	 A New Transmission Superhighway System Would Be 

Required.............................................................................................95
 

4.2.2	 Overcoming Barriers to Transmission Investment.............................98
 

4.2.3	 Making a National Investment in Transmission...............................100
 

4.3	 U.S. Power System Operations and Market Structure 

Evolution............................................................................................ 100
 

4.3.1	 Expanding Market Flexibility ..........................................................100
 

4.3.2	 Enhancing Wind Forecasting and System Flexibility ......................100
 

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4.4	 References and Other Suggested Reading...................................... 101
 

Chapter 5. Wind Power Siting and Environmental 

Effects ....................................................................105
 

5.1	 Wind Energy Today.......................................................................... 105
 

5.1.1 Site-Specific and Cumulative Concerns...........................................106
 

5.2 Environmental Benefits.................................................................... 107
 

5.2.1 Global Climate Change and Carbon Reductions..............................107
 

5.2.2 Improving Human Health through Reduced Air Emissions ............108
 

5.2.3 Saving Water....................................................................................109
 

5.3	 Potential Environmental Impacts.................................................... 110
 

5.3.1	 Habitat Disturbance and Land Use ..................................................110
 

5.3.2	 Wildlife Risks ..................................................................................111
 

5.3.3	 Managing Environmental Risk and Adaptive Management 

Principles..........................................................................................114
 

5.4	 Public Perception and Engagement ................................................ 116
 

5.4.1	 Public Attitudes................................................................................116
 

5.4.2	 Visual Impacts..................................................................................116
 

5.4.3	 Sound ...............................................................................................117
 

5.4.4	 Land Value.......................................................................................118
 

5.5	 Siting/Regulatory Framework ......................................................... 118
 

5.5.1	 Local.................................................................................................119
 

5.5.2	 State and Federal..............................................................................119
 

5.6	 Addressing Environmental and Siting Challenges ........................ 121
 

5.6.1 Expand Public-Private Partnerships.................................................121
 

5.6.2 Expand Outreach and Education......................................................123
 

5.6.3 Coordinate Land-Use Planning........................................................123
 

5.7	 Prospects for Offshore Wind Energy Projects in the United 

States and Insights from Europe ..................................................... 124
 

5.8	 Findings and Conclusions ................................................................ 126
 

5.9	 References and Suggested Further Reading................................... 128
 

Chapter 6. Wind Power Markets ...........................................133
 

6.1	 U.S. Market Evolution Background................................................ 133
 

6.2	 U.S. Electricity Market..................................................................... 134
 

6.2.1	 Electric Utilities ...............................................................................135
 

6.2.2	 Federal Agencies..............................................................................135
 

6.2.3	 Power Marketing Administrations...................................................136
 

6.2.4	 Compliance, Voluntary, and Emissions Markets.............................136
 

6.3	 Wind Power Applications................................................................. 137
 

6.3.1	 Large-Scale Wind Power Plants.......................................................138
 

6.3.2	 Offshore Wind..................................................................................138
 

6.3.3	 Community Wind.............................................................................139
 

6.3.4	 Small Wind ......................................................................................139
 

6.3.5	 Native American Wind Projects.......................................................140
 

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6.4 Stakeholder Involvement and Public Engagement........................ 140
 

6.5 Conclusions........................................................................................ 141
 

6.6 References.......................................................................................... 142
 

Appendix A. 20% Wind Scenario Impacts........................... 143
 

A.1 Introduction ...................................................................................... 143
 

A.2 Methodology...................................................................................... 146
 

A.2.1 Energy Generation Technologies.....................................................146
 

A.2.2 Transmission and Integration...........................................................148
 

A.2.3 Quantification of Impacts.................................................................149
 

A.3 Wind Capacity Supply Curves ........................................................ 149
 

A.4 Impacts .............................................................................................. 151
 

A.4.1 Generation Mix ................................................................................152
 

A.4.2 Carbon Emission Reduction.............................................................154
 

A.4.3 Reduced Natural Gas Demand.........................................................155
 

A.4.4 Land Use ..........................................................................................155
 

A.4.5 Transmission ....................................................................................158
 

A.5 Direct Electricity Sector Cost .......................................................... 161
 

A.5.1 Water Consumption Savings............................................................164
 

A.6 Other Effects .................................................................................... 166
 

A.7 References & Suggested Further Reading ..................................... 167
 

Appendix B. Assumptions Used for Wind Deployment 

System Model.................................................... 169
 

B.1 Financial Parameters ....................................................................... 169
 

B.2 Power System Characteristics ......................................................... 170
 

B.2.1 WinDS Regions................................................................................170
 

B.2.2 Electric System Loads......................................................................172
 

B.2.3 Growth Rate .....................................................................................174
 

B.2.4 Capacity Requirements....................................................................174
 

B.3 Wind................................................................................................... 175
 

B.3.1 Wind Resource Definition................................................................175
 

B.3.2 Wind Resource Data ........................................................................176
 

B.3.3 WinDS Seasonal and Diurnal Capacity Factor Calculations...........179
 

B.3.4 Wind Technology Cost and Performance ........................................181
 

B.4 Conventional Generation................................................................. 183
 

B.4.1 Conventional Generation Cost and Performance .............................186
 

B.4.2 Fuel Prices........................................................................................186
 

B.5 Transmission ..................................................................................... 187
 

B.6 Treatment of Resource Variability ................................................. 189
 

B.7 Federal and State Energy Policy ..................................................... 191
 

B.7.1 Federal Emission Standards.............................................................191
 

B.7.2 Federal Energy Incentives................................................................192
 

B.7.3 State Energy Incentives....................................................................192
 

B.7.4 State Renewable Portfolio Standards...............................................193
 

iv 20% Wind Energy by 2030 – Prepublication Version 
 

B.8 Electricity Sector Direct Cost Calculation...................................... 194
 

B.9 References & Suggested Further Reading...................................... 195
 

Appendix C. Wind-Related Jobs and Economic 

Development ......................................................198
 

C.1 The JEDI Model................................................................................ 199
 

C.1.1 Model Description ...........................................................................199
 

C.1.2 Caveats.............................................................................................201
 

C.2 Wind Scenario Inputs....................................................................... 202
 

C.3 Findings ............................................................................................. 203
 

C.4 Manufacturing Sector....................................................................... 206
 

C.5 Construction Sector .......................................................................... 207
 

C.6 Operations Sector ............................................................................. 208
 

C.7 Conclusion ......................................................................................... 210
 

C.8 References.......................................................................................... 210
 

Appendix D. Participants in the 20% Wind Scenario..........211
 

Appendix E. Glossary ..............................................................219
 

List of Figures 

Figure 1-1. Report chapters.......................................................................................4
 

Figure 1-2. Cumulative U.S. wind capacity, by year................................................5
 

Figure 1-3. Required growth in U.S. capacity (GW) to implement the 

20% Wind Scenario......................................................................7
 

Figure 1-4. 20% Cumulative installed wind power capacity required to 

produce 20% of projected electricity by 2030..............................8
 

Figure 1-5. Supply curve for wind energy—current bus-bar energy costs ...............8
 

Figure 1-6. Supply curve for wind energy—energy costs including 

connection to 10% of existing transmission grid capacity ...........9
 

Figure 1-7. 46 States would have substantial wind development by 2030 .............10
 

Figure 1-8. All new electrical generation including wind energy would 

require expansion of U.S. transmission by 2030........................11
 

Figure 1-9. Conceptual transmission plan to accommodate 400 GW of 

wind energy (AEP 2007)............................................................12
 

Figure 1-10. U.S. electrical energy mix ..................................................................12
 

Figure 1-11. Annual and cumulative wind installations by 2030 ...........................14
 

Figure 1-12. Annual CO2 emissions avoided (vertical bars) would reach 825 

million tons by 2030...................................................................16
 

Figure 1-13. Avoided emissions would nearly level projected growth in CO2
 

emissions from the electricity sector..........................................16
 

Figure 1-14. National water savings from the 20% Wind Scenario........................17
 

Figure 1-15. Incremental investment cost of 20% wind is modest; a 

difference of 2%.........................................................................19
 

20% Wind Energy by 2030 – Prepublication Version v 
 

 

Figure 2-1. The wind resource potential at 50 m above ground on land and 

offshore.......................................................................................24
 

Figure 2-2. Comparison of the wind energy resource at 50 m, 70 m, and 100 

m for Indiana ..............................................................................25
 

Figure 2-3. A modern 1.5-MW wind turbine installed in a wind power plant........26
 

Figure 2-4. Turbine capacity factor by commercial operation date (COD) 

using 2006 data...........................................................................27
 

Figure 2-5. Wind energy price by commercial operation date (COD) 

using 2006 data...........................................................................27
 

Figure 2-6. The development path and growth of wind turbines............................29
 

Figure 2-7. Growth in blade weight ........................................................................30
 

Figure 2-8. Typical power output versus wind speed curve ...................................32
 

Figure 2-9. Operation and maintenance costs for large-scale wind plants 

installed within the last 10 years for the early years of 

operation (Wiser and Bolinger 2007).........................................34
 

Figure 2-10. Curvature-based twist coupling..........................................................36
 

Figure 2-11. Twist-flap coupled blade design (material-based twist
 

coupling).....................................................................................36
 

Figure 2-12. Clipper Windpower multiple-drive-path gearbox ..............................39
 

Figure 2-13. Cost of wind turbines delivered from Spain between 1984 and
 

2000............................................................................................40
 

Figure 2-14. Unplanned repair cost, likely sources, and risk of 

failure with wind plant age.........................................................44
 

Figure 2-15. Average O&M costs of wind farms in the United States ...................45
 

Figure 2-16. Blade growth and startup dates for U.S. blade test facilities..............47
 

Figure 2-17. Types of repairs on wind turbines from 2.5 kW to 1.5 MW ..............57
 

Figure 3-1. a. Annual installed wind energy capacity to meet 20% of energy
 

demand. b. Cumulative installed wind energy capacity to 

meet 20% of energy demand......................................................65
 

Figure 3-2. Annual and cumulative installed wind energy capacity
 

represented in the 20% Wind Scenario ......................................66
 

Figure 3-3. Examples of manufacturers supplying wind equipment across 

the United States.........................................................................67
 

Figure 3-4. Percentage of 22-year-olds with a bachelor degree in science and 

engineering through 2050...........................................................72
 

Figure 4-1. Hourly load shapes with and without wind generation ........................79
 

Figure 4-2. Time scales for grid operations ............................................................80
 

Figure 4-3. Impact of wind on load-following requirements..................................81
 

Figure 4-4. GE turbine frequency response ............................................................83
 

Figure 4-5. Vestas wind turbine control capability.................................................84
 

Figure 4-6. GE wind plant controls.........................................................................84
 

Figure 4-7. Impact of wind generation on system dynamic performance...............85
 

Figure 4-8. Annual hourly capacity factor ..............................................................91
 

Figure 4-9. Annual transmission investments from 1975 through 1999 and 

projections through 2005............................................................94
 

Figure 4-10. Conceptual new transmission line scenario by WinDS region...........96
 

vi 20% Wind Energy by 2030 – Prepublication Version 
 

Figure 4-11. Cumulative savings versus total transmission cost for 

renewable energy zone (worst case)...........................................97
 

Figure 5-1. Electricity production is responsible for 39% of CO2 emissions 

in the United States ..................................................................108
 

Figure 5-2. Anthropogenic causes of bird mortality .............................................112
 

Figure 5-3. Linear decision strategy (command and control) and interactive 

model with adaptive management principles ...........................115
 

Figure 5-4. Decibel levels of various situations....................................................117
 

Figure 5-5. Actions to support 20% wind energy by 2030 ...................................126
 

Figure 6-1. U.S. wind energy capacity growth (shown in megawatts [MW]) 

slowed during years when the PTC expired.............................134
 

Figure A-1. Prescribed annual wind technology generation as a percent of 

national electricity demand from Laxson, Hand, and Blair 

(2006) and corresponding annual wind capacity 

installation for 20% Wind Scenario from WinDS model.........145
 

Figure A-2. Supply curve for wind energy—current bus-bar energy costs ..........150
 

Figure A-3. Supply curve for wind energy: energy costs including
 

connection to 10% of existing transmission grid capacity .......151
 

Figure A-4. Cumulative installed wind power capacity required to produce 

20% of projected electricity by 2030 .......................................151
 

Figure A-5. 20% Wind Scenario electricity generation mix 2000–2030..............152
 

Figure A-6. Generation by technology in 2030 ....................................................153
 

Figure A-7. Capacity by technology in 2030........................................................153
 

Figure A-8. Cumulative carbon emission reductions attributed to wind 

energy (compared to expanding the generation mix 

without wind energy) ...............................................................154
 

Figure A-9. Fuel usage and savings resulting from 20% Wind Scenario ............155
 

Figure A-10. Projected wind capacity installations in 2012 .................................156
 

Figure A-11. Projected wind capacity installations in 2018 .................................157
 

Figure A-12. Projected wind capacity installations in 2024 .................................157
 

Figure A-13. Projected wind capacity installations in 2030 .................................158
 

Figure A-14. Transport of wind energy over existing and new transmission 

lines projected for 2012............................................................159
 

Figure A-15. Transport of wind energy over existing and new transmission 

lines projected for 2018............................................................159
 

Figure A-16. Transport of wind energy over existing and new transmission 

lines projected for 2024............................................................160
 

Figure A-17. Transport of wind energy over existing and new transmission 

lines projected for 2030............................................................160
 

Figure A-18. Direct electricity sector costs for 20% Wind Scenario and nonew-Wind Scenario..................................................................163
 

Figure A-19. Annual water consumption savings due to deployment 

of wind energy..........................................................................166
 

Figure B-1. WinDS regions ..................................................................................170
 

Figure B-2. Wind region and Balancing Areas in WinDS base case....................172
 

Figure B-3. National load duration curve for base year in WinDS.......................173
 

Figure B-4. Projected coal and natural gas prices in WinDS to 2030...................187
 

20% Wind Energy by 2030 – Prepublication Version vii 
 

 

Figure B-5. Distance between wind sites and correlation with power output.......190
 

Figure C-1. Prescribed annual wind technology generation as a percentage 

of national electricity demand from Laxson, Hand, and 

Blair (2006) and corresponding annual wind capacity 

installation for 20% Wind Scenario from WinDS model.........199
 

Figure C-2. Wind's economic ripple effect ...........................................................201
 

Figure C-3. Annual direct, indirect and induced economic impacts from
 

20% scenario ............................................................................205
 

Figure C-4. Total economic impacts of 20% Wind Energy by 2030 on a 

relative basis.............................................................................206
 

Figure C-5. Potential manufacturing jobs created by 2030...................................207
 

Figure C-6. Direct manufacturing, construction, and operations jobs 

supported by the 20% Wind Scenario ......................................208
 

Figure C-7. Jobs per year from direct, indirect, and induced categories...............208
 

Figure C-8. Jobs and economic impacts by NERC region....................................209
 

List of Tables 

Table 2-1. Areas of potential technology improvement..........................................41
 

Table 3-1. Main components and materials used in a wind turbine (%).................63
 

Table 3-2. Yearly raw materials estimate (thousands of metric tons).....................63
 

Table 3-3. Locations of U.S. wind turbine component manufacturers ...................66
 

Table 3-4. U.S. Manufacturing firms with technical potential to enter wind 

turbine component market..........................................................68
 

Table 3-5. Toyota North America vehicle production and sales............................70
 

Table 3-6. Wind technology-related educational programs around the United 

States today ................................................................................71
 

Table 4-1. Wind integration costs in the U.S. .........................................................82
 

Table 4-2. Methods to estimate wind capacity value in the U.S.............................88
 

Table 4-3. Midwest ISO plant capacity factor by fuel type (June 2005–May
 

2006)...........................................................................................89
 

Table 4-4. Wind generation variability as a function of the number of 

generators and time interval .......................................................90
 

Table 5-1. Estimated water savings from wind energy in the interior West 

(2003) .......................................................................................110
 

Table 5-2. Estimated avian fatalities per megawatt per year ................................113
 

Table 5-3. Status of offshore wind energy applications in state and federal 

waters .......................................................................................125
 

Table A-1. Assumptions used for scenario analysis ............................................147
 

Table A-2. Distribution of wind capacity on existing and new transmission 

lines ..........................................................................................161
 

Table A-3. Direct electricity sector costs for 20% Wind Scenario and nonew-Wind Scenario (US$2006) ...............................................162
 

Table A-4. Incremental direct cost of achieving 20% wind, excluding certain 

benefits (US$2006) ..................................................................163
 

Table A-5. Water consumption rates for power plants .........................................165
 

viii 20% Wind Energy by 2030 – Prepublication Version 
 

Table A-6. U.S. states by region ...........................................................................166
 

Table B-1. Baseline financial assumptions...........................................................169
 

Table B-2. NERC regions used in WinDS............................................................171
 

Table B-3. WinDS demand time-slice definitions................................................173
 

Table B-4. Base load and load growth in the WinDS scenario.............................174
 

Table B-5. National capacity requirements in the WinDS base case....................175
 

Table B-6. Peak reserve margin............................................................................175
 

Table B-7. Classes of wind power density............................................................175
 

Table B-8. Data sources for land-based wind resource and environmental 

exclusions.................................................................................177
 

Table B-9. Data sources for the offshore wind resource and environmental 

exclusions.................................................................................178
 

Table B-10. Land-based wind technology cost and performance projections 

(US$2006)................................................................................181
 

Table B-11. Shallow offshore wind technology cost and performance 

projections (US$2006) .............................................................182
 

Table B-12. General assumptions for conventional generation technologies...B-184
 

Table B-13. Cost and performance characteristics for conventional 

generation (US$2006) ..............................................................185
 

Table B-14. National SO2 emission limit schedule in WinDS..............................192
 

Table B-15. Federal renewable energy incentives ................................................192
 

Table B-16. State renewable energy incentives ....................................................193
 

Table B-17. State RPS requirements as of August 2005 ......................................193
 

Table C-1. JEDI wind modeling assumptions ......................................................202
 

Table C-2. Wind plant expenditure data summary (in millions) ..........................203
 

Table C-3. U.S. construction-related economic impacts from 20% wind.............204
 

Table C-4. U.S. operations-related economic impacts from 20% wind................204
 

20% Wind Energy by 2030 – Prepublication Version ix
 
 

 

 

 

Abbreviations and Acronyms 

ACE area control error 

AEO Annual Energy Outlook 

AEP American Electric Power 

AGATE Advanced General Aviation Transport Experiments 

AGC automatic generation control 

ALA American Lung Association 

AMA American Medical Association 

API American Petroleum Institute 

APPA American Public Power Association 

ATTU Annual Turbine Technology Update 

AWEA American Wind Energy Association 

AWST AWS Truewind 

BACI before-and-after-control impact 

Berkeley Lab Lawrence Berkeley National Laboratory 

BLM Bureau of Land Management 

BPA Bonneville Power Administration 

BSH Bundesamt für Seeschiffahrt und Hydrographie 

BTM BTM Consult ApS 

Btu British thermal unit 

BWEC Bat and Wind Energy Cooperative 

CAA Clean Air Act 

CAIR Clean Air Interstate Rule 

CAISO California Independent System Operator 

CAMR Clean Air Mercury Rule 

CapX 2020 Capacity Expansion Plan for 2020 

CBO Congressional Budget Office 

CDEAC Clean and Diversified Energy Advisory Committee 

CEC California Energy Commission 

CEQA California Environmental Quality Act 

CESA Clean Energy States Alliance 

CF capacity factor 

CFRP carbon filament-reinforced plastic 

CNV California/Nevada 

CO2 carbon dioxide 

Coal-IGCC integrated gasification combined cycle coal plants 

Coal-new new pulverized coal plants 

COD commercial operation date 

COE cost of energy 

CREZ Competitive Renewable Energy Zones 

CT combustion turbine 

dB decibels 

DEA Danish Energy Authority 

DEIS draft environmental impact statement 

DOD U.S. Department of Defense 

DOE U.S. Department of Energy 

DOI U.S. Department of Interior 

DWT distributed wind technology 

x 20% Wind Energy by 2030 – Prepublication Version 
 

 

 

 

ECAR East Central Area Reliability Coordinating Agreement 

EEI Edison Electric Institute 

EERE Office of Energy Efficiency and Renewable Energy 

EFTA European Free Trade Agreement 

EIA Energy Information Administration 

EIR environmental impact review 

EIS environmental impact statement 

ELCC effective load-carrying capability 

EPA U.S. Environmental Protection Agency 

EPAct Energy Policy Act 

EPC engineering, procurement, and construction 

EPRI Electric Power Research Institute 

ERCOT Electric Reliability Council of Texas 

ERO Electric Reliability Organization 

EU European Union 

EUI Energy Unlimited Inc. 

EWEA European Wind Energy Association 

FAA Federal Aviation Administration 

FACTS flexible AC transmission system 

FEIR final environmental impact report 

FERC Federal Energy Regulatory Commission 

FL Florida 

FRCC Florida Reliability Coordinating Council 

FTE full-time equivalent 

GaAs gallium arsenide 

Gas-CC combined cycle natural gas plants 

Gas-CT gas combustion turbine 

GE General Electric International 

GHG greenhouse gas 

GIS geographic information system 

GRP glass fiber-reinforced-plastic 

GS3C Grassland/Shrub-Steppe Species Collaborative 

GVW gross vehicle weight 

GW gigawatt 

GWh gigawatt-hour 

Hg mercury 

HSIL high-surge impedance-loading (transmission line) 

HVDC high-voltage direct current 

Hz hertz 

IEA International Energy Agency 

IEC International Electrotechnical Commission 

IEEE Institute of Electrical and Electronics Engineers 

IGCC integrated gasification combined cycle 

IOU investor-owned utility 

IPCC Intergovernmental Panel on Climate Change 

IRP integrated resource planning 

ISET Institute for Solar Energy Technology (Institut für Solare 

Energieversorgungstechnik) 

ISO independent system operator 

20% Wind Energy by 2030 – Prepublication Version	 xi 
 

 

 

 

 

 

ISO-NE ISO New England 

ITC investment tax credit 

JEDI Jobs and Economic Development Impact (model) 

kg kilogram 

km2 square kilometers 

kV kilovolt 

kW kilowatt 

kWh kilowatt-hour 

lb pound 

LC levelized cost 

LDC load duration curve 

LIDAR light detection and ranging 

LLC Limited Liability Company 

LNG liquefied natural gas 

LOLP loss of load probability 

m meter 

m2 square meter 

MAAC Mid-Atlantic Area Council 

MACRS Modified Accelerated Cost Recovery System 

MAIN Mid-American Interconnected Network 

MAPP Mid-Continent Area Power Pool 

Midwest ISO Midwest Independent System Operator 

MMBtu million British thermal units 

MMS Minerals Management Service 

MMTCE million metric tons of carbon equivalent 

MNDOC Minnesota Department of Commerce 

MOU Memorandum of Understanding 

MRO Midwest Reliability Organization 

MTEP MISO Transmission Expansion Plan 

MVA megavolt amperes 

MW megawatt 

MWh megawatt-hour 

MW-mile megawatt-mile 

NAICS North American Industrial Classification System 

NAS National Academy of Sciences 

NCAR National Center for Atmospheric Research 

NCEP National Commission on Energy Policy 

NE New England 

NEMS National Energy Modeling System 

NEPA National Environmental Policy Act 

NERC North American Electric Reliability Corporation 

NESCAUM Northeast States for Coordinated Air Use Management 

NGOs nongovernmental organizations 

nm nautical mile 

NOAA National Oceanic and Atmospheric Administration 

NOI notice of intent 

NOx nitrogen oxides 

NPCC Northeast Power Coordinating Council 

NPV net present value 

xii 20% Wind Energy by 2030 – Prepublication Version 
 

 

 

 

 

NRC National Research Council 

NRECA National Rural Electric Cooperative Association 

NREL National Renewable Energy Laboratory 

NSTC National Science and Technology Council 

NWCC National Wind Coordinating Collaborative 

NWF National Wildlife Federation 

NWS National Weather Service 

NY New York 

NYISO New York Independent System Operator 

NYSERDA New York State Energy Research and Development Authority 

O3 ozone 

O&M operations and maintenance 

OE Office of Electricity Delivery and Energy Reliability 

OCS Outer Continental Shelf 

OMB Office of Management and Budget 

PBF Public Benefits Fund 

PGE Portland General Electric 

PJM Pennsylvania-New Jersey-Maryland Interconnection 

PMA Power Marketing Administration 

PNM Public Service Company of New Mexico 

POI point of interconnection 

PPA power purchase agreement 

PSE Puget Sound Energy 

PTC production tax credit 

PUC Public Utility Commission 

PURPA Public Utility Regulatory Policies Act 

QF qualifying or qualified facility 

R&D research and development 

RMA Rocky Mountain Area 

RD&D research, development & demonstration 

REC renewable energy credit 

REPI Renewable Energy Production Incentive 

REPP Renewable Energy Policy Project 

RFC ReliabilityFirst Corporation 

RGGI Regional Greenhouse Gas Initiative 

RMATS Rocky Mountain Area Transmission Study 

RPS Renewable Portfolio Standards 

RTO Regional Transmission Organization 

s second 

Sandia Sandia National Laboratories 

SCADA supervisory control and data acquisition 

SEAC	 Strategic Energy Analysis Center 

SEPA Southeastern Power Administration 

SERC Southeastern Electric Reliability Council 

SF6	 sulfur hexafluoride (one of six greenhouse gases identified in the 

Kyoto Protocol) 

SiC silicon carbide 

SO2	 sulfur dioxide 

SODAR sonic detection and ranging 

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SOx sulfur oxides 

SPP Southwest Power Pool 

ST steam turbine 

Std. Dev. standard deviation 

SWPA Southwestern Power Administration 

TRE Texas Regional Entity 

TVA Tennessee Valley Authority 

TWh terawatt-hours 

UCTE Union for the Co-ordination of Transmission of Electricity 

UKERC UK Energy Research Centre 

USACE U.S. Army Corps of Engineers 

USCAP U.S. Climate Action Partnership 

USDA U.S. Department of Agriculture 

USFS U.S. Department of Agriculture Forest Service 

USFWS U.S. Fish & Wildlife Service 

USGS U.S. Geological Survey 

UWIG Utility Wind Integration Group 

V volt 

VAR volt-ampere-reactive 

W watt 

WEST Western EcoSystems Technology 

Western Western Area Power Administration (formerly WAPA) 

WCI Western Climate Initiative 

WECC Western Electricity Coordinating Council 

WGA Western Governors’ Association 

Wh watt-hour 

WinDS Wind Energy Deployment System Model 

WindPACT Wind Partnerships for Advanced Component Technology 

WPA Wind Powering America 

WRA Western Resource Advocates 

WRCAI Western Regional Climate Action Initiative 

WWG Wildlife Workgroup 

xiv 20% Wind Energy by 2030 – Prepublication Version 
20% Wind Energy by 2030 – Prepublication Version xv 

 

 

1

Chapter 1.	 Executive Summary 

& Overview 

1.1 INTRODUCTION AND COLLABORATIVE APPROACH 

Energy prices, supply uncertainties, and 

environmental concerns are driving the 

United States to rethink its energy mix 

and develop diverse sources of clean, 

renewable energy. The nation is 

working toward generating more energy 

from domestic resources—energy that 

can be cost-effective, and replaced or 

“renewed” without contributing to 

climate change or major adverse 

environmental impacts. 

In 2006, President Bush emphasized the 

nation’s need for greater energy 

efficiency and a more diversified energy 

portfolio. This led to a collaborative 

effort to explore a modeled energy 

scenario in which wind provides 20% of 

U.S. electricity by 2030. Members of 

this 20% wind collaborative (see 20% 

Wind Scenario sidebar) produced this 

report to start the discussion about 

issues, costs, and potential outcomes 

associated with the 20% Wind Scenario. 

A 20% Wind Scenario in 2030, while 

ambitious could be feasible if the 

significant challenges identified in this 

report are overcome. 

This report was prepared by DOE in a 

joint effort with industry, government, 

and the Nation’s national laboratories 

(primarily the National Renewable 

Energy Laboratory and Lawrence 

Berkeley National Laboratory). The 

report considers some associated 

challenges, estimates the impacts and 

considers specific needs and outcomes 

20% Wind Scenario: 

Wind Energy Provides 20% of 

U.S. Electricity Needs by 2030 

Key Issues to Examine: 

•	 Does the nation have sufficient wind energy 

resources? 

•	 What are the wind technology requirements? 

•	 Does sufficient manufacturing capability exist? 

•	 What are some of the key impacts? 

•	 Can the electric network accommodate 20% wind? 

•	 What are the environmental impacts and benefits? 

•	 Is the scenario feasible? 

Assessment Participants: 

•	 U.S. Department of Energy (DOE) 

−	 Office of Energy Efficiency and Renewable Energy 

(EERE), Office of Electricity Delivery and Energy 

Reliability (OE), and Power Marketing 

Administrations (PMAs) 

− National Renewable Energy Laboratory (NREL) 

− Lawrence Berkeley National Laboratory (Berkeley 

Lab) 

− Sandia National Laboratories (SNL) 

•	 Black & Veatch engineering and consulting firm 

•	 American Wind Energy Association (AWEA) 

− Leading wind manufacturers and suppliers 

− Developers and electric utilities 

− Others in the wind industry 

in the areas of technology, manufacturing and employment, transmission and grid 

integration, markets, siting strategies, and potential environmental effects associated 

with a 20% Wind Scenario. 

20% Wind Energy by 2030 – Prepublication Version 1 
 

 

 

 

 

 

 

1 In its Annual Energy Outlook 2007, the U.S. Energy Information Agency estimates 

that U.S. electricity demand will grow by 39% from 2005 to 2030, reaching 5.8 

billion megawatt-hours (MWh) by 2030. To meet 20% of that demand, U.S. wind 

power capacity would have to reach more than 300 gigawatts (GW) or more than 

300,000 megawatts (MW). This growth represents an increase of more than 290 GW 

within 23 years.1 

The data analysis and model runs for this report were concluded in mid-2007. All 

data and information in the report are based wind data available through the end of 

2006. At the time this report was written (2007), the U.S. wind power fleet 

numbered 11.6 GW and spanned 34 states.2 American wind farms are expected to 

generate an estimated 48 billion kilowatt-hours of wind energy in 2008, more than 

1% of U.S. electricity supply. This capacity addition of 5,244 MW in 2007 exceeds 

the more conservative growth trajectory developed for the 20% Wind Scenario of 

about 4,000 MW/year in 2007 and 2008. The wind industry is on track to grow to a 

size capable of installing 16,000 MW/year, consistent with the latter years in the 

20% Wind Scenario, more quickly than the trajectory used for this analysis. 

1.1.1 SCOPE 

This report examines some of the costs, challenges, and key impacts of generating 

20% of the nation’s electricity from wind energy in 2030. Specifically, it 

investigates requirements and outcomes in the areas of technology, manufacturing, 

transmission and integration, markets, environment and siting. 

The modeling done for this report estimates that wind power installations with 

capacities of more than 300 gigawatts (GW) would be needed for the 20% wind in 

2030 scenario. Increasing U.S. wind power to this level from today’s 11.6 GW level 

would require significant changes in transmission, manufacturing and markets. This 

report presents an analysis of one specific scenario for reaching the 20% level and 

contrasts it to a scenario of no wind growth beyond the level in 2006. Major 

assumptions in the analysis have been highlighted throughout the document and 

have been summarized in the appendices. These assumptions may be considered 

optimistic. In this report, no sensitivity analyses have been done to estimate the 

impact that changes in the assumptions would have on the information presented 

here. As summarized at the end of this chapter, the analysis provides an overview of 

some potential impacts of these two scenarios by 2030. This report does not 

compare the Wind Scenario to other energy portfolio options, nor does it outline an 

action plan. 

To successfully address energy security and environmental issues, the nation needs 

to pursue a portfolio of energy options. None of these options by itself can fully 

address these issues; there is no “silver bullet.” This technical report examines one 

potential scenario in which wind power serves as a significant element in the 

portfolio. However, the 20% Wind Scenario is not a prediction of the future. Instead, 

it paints a picture of what a particular 20% Wind Scenario could mean for the 

nation. 

1 AEO data from 2007 were used in this report. AEO released new data in March of 2008, which were not 

incorporated into this report. While the new EIA data could change specific numbers in the report, it would not 

change the overall message of the report. 

2

 According to AWEA, the U.S. wind energy industry installed 5,244 MW in 2007, expanding the nation's 

total wind power generating capacity by 45% in a single calendar year and more than doubling the 

2006 installation of 2,454 MW. American Wind Energy Associations 2007 Market Report, January 2008. 

Government sources for validation of 2007 installed MW were not available at the time this report was written 

2 20% Wind Energy by 2030 – Prepublication Version 
 

 

 

1.1.2	 CONTRIBUTORS 1

Report contributors include a broad 

cross section of key stakeholders, 

including leaders from the nation’s 

utility sector, environmental 

communities, wildlife advocacy 

groups, energy industries, the 

government and policy sectors, 

investors, and public and private 

businesses. In all, the report reflects 

input from more than 50 key energy 

stakeholder organizations and 

corporations. Appendix D contains a 

list of contributors. Research and 

modeling was conducted by experts 

within the electric industry, 

government, and other organizations. 

This report is not an authoritative 

expression of policy perspectives or 

opinions held by representatives of 

DOE. 

1.1.3	 ASSUMPTIONS AND 

PROCESS 

To establish the groundwork for this 

report, the engineering company 

Black & Veatch (Overland Park, 

Kansas) analyzed the market potential 

for significant wind energy growth, 

quantified the potential U.S. wind 

supply, and developed cost supply 

curves for the wind resource. In 

consultation with DOE, NREL, 

AWEA, and wind industry partners, 

future wind energy cost and 

performance projections were 

developed. Similar projections for 

conventional generation technologies 

were developed based on Black & 

Veatch experience with power plant 

design and construction (Black and 

Veatch, forthcoming 2008). 

To identify a range of challenges, 

possible solutions, and key impacts of 

providing 20% of the nation’s 

electricity from wind, the 

stakeholders in the 20% Wind 

Scenario effort convened seven 

expert task forces to examine specific 

Wind Energy Deployment System Model 

Assumptions (See Appendices A and B) 

• Assumes that the contributions to U.S. electricity supplies from other 

renewable sources of energy would remain at 2006 levels in both 

scenarios. 

• Assumes capital costs would be reduced by 10% over the next two 

decades and capacity factors would be increased by about 15% 

(corresponding to a 15% increase in annual energy generation by a wind 

plant) 

• Assumes future environmental study and permit requirements are not 

expected to add significant costs to wind technology. 

• The assumptions used for the WinDS model were obtained from a number 

of sources, including technical experts (see Appendix D), the WinDS base 

case (Denholm and Short 2006), AEO 2007 (EIA 2007), and a study 

performed by Black & Veatch (2007). These assumptions include 

projections of future costs and performance for all generation 

technologies, transmission system expansion costs, wind resources as a 

function of geographic location within the continental United States, and 

projected growth rates for wind generation. 

• Assumes cost of new transmission is generally split between the 

originating project, be it wind or conventional generation, and the 

ratepayers within the region. 

• Assumes that 10% of existing grid capacity is available for wind energy. 

• Current long-term power purchase agreements are not implemented in 

WinDS. The model assumes that local load is met by the generation 

technologies in a given region. 

• Wind energy generation prescribed annually at national level to reach 

20% wind energy by 2030 

− A stable policy environment that supports accelerated wind 

deployment 

− Balance of generation economically optimized with no policy changes 

from those in place today (e.g., no production tax credit [PTC] beyond 

12/31/08) 

− Technology cost and performance assumptions as well as electric grid 

expansion and operation assumptions that affect the direct electric 

system cost. 

• Land-based and offshore wind energy technology cost reductions and 

performance improvements expected by 2030 (see tables A-1, B-10, and 

B-11) 

• Fossil fuel technology costs and performance generally flat between 2005 

and 2030 (see tables A-1 and B-13) 

• Nuclear technology cost reductions expected by 2030 (see tables A-1 and 

B-13) 

• Reserve and capacity margins calculated at the North American Electric 

Reliability Corporation (NERC) region level, and new transmission 

capacity added as needed (see sections A.2.2 and B.3) 

• Wind resource as a function of geographic location from various sources 

(see Table B-8) 

• Projected electricity demand, financing assumptions, and fuel prices based 

on Annual Energy Outlook (EIA 2007; see sections B.1, B.2, and B.4.2). 

20% Wind Energy by 2030 – Prepublication Version 3 
 

 

 

 

 

1 areas critical to this endeavor: Technology and Applications, Manufacturing and 

Materials, Environmental and Siting Impacts, Electricity Markets, Transmission and 

Integration, and Supporting Analysis. These teams conducted in-depth analyses of 

potential impacts, using related studies and various analytic tools to examine the 

benefits and costs. (See Appendix D for the task force participants.) 

NREL’s Wind Deployment System (WinDS) model3

 was employed to create a 

scenario that paints a “picture” of this level of wind energy generation and evaluates 

some impacts associated with wind. Assumptions about the future of the U.S. 

electric generation and transmission sector were developed in consultation with the 

task forces and other parties. Some assumptions in this analysis could be considered 

optimistic. Examples of assumptions used in this analysis are listed in the text box 

and are presented in detail in Appendix A and B. For comparison, the modeling 

team contrasted the 20% Wind Scenario impacts to a reference case characterized by 

no growth in U.S. wind capacity or other renewable energy sources after 2006. 

In the course of the examination, two workshops were held to define and refine the 

work plan, present and discuss preliminary results, and obtain relevant input from 

key stakeholders external to the report preparation effort. 

1.1.4 REPORT STRUCTURE 

A 20% wind energy scenario in 2030 would require improved turbine technology to 

generate wind power, significant changes in transmission systems to deliver it 

through the electric grid, and large expanded markets to purchase and use it. In turn, 

these essential changes in the power generation and delivery process would involve 

supporting changes and capabilities in manufacturing, policy development, and 

environmental regulation. As shown in Figure 1-1, the chapters of this report address 

some of the requirements and impacts in each of these areas. Detailed discussions of 

the modeling process, assumptions, and results can be found in appendices A 

through C. 

Figure 1-1. Report chapters 

3

 The model, developed by NREL’s Strategic Energy Analysis Center (SEAC), is designed to address 

the principal market issues related to the penetration of wind energy technologies into the electric 

sector. For additional information and documentation, see text box entitled “Wind Energy Deployment 

Model Assumptions,” Appendices A and B, and http://www.nrel.gov/analysis/winds/ 

4 20% Wind Energy by 2030 – Prepublication Version 
 

 

1.1.5 SETTING THE CONTEXT: TODAY’S U.S. WIND INDUSTRY 1

After experiencing strong growth in the mid-1980s, the U.S. wind industry hit a 

plateau during the electricity restructuring period in the 1990s, then regained 

momentum in 1999. Industry growth has since responded positively to policy 

incentives when they are in effect (see Figure 1-2). Today, the U.S. wind industry is 

growing rapidly, driven by sustained production tax credits (PTCs), rising concerns 

about climate change, and renewable portfolio standards (RPS) or goals in roughly 

50% of the states. 

U.S. Figure 1-2. Cumulative U.S. wind capacity, by year turbine 

(in megawatts [MW]) technology has 

advanced steadily to 

offer improved 

performance. In 2006 

alone, average turbine 

size increased by more 

than 11% over the 

2005 level to an 

average size of 1.6 

MW. In addition, 

average capacity 

factors have improved 

11% over the past two 

years. To meet the 

growing demand for 

wind energy, U.S. manufacturers have expanded their capacity to produce and 

assemble the essential components. Despite this growth, U.S. components continue 

to represent a relatively small share of total turbine and tower materials, and U.S. 

manufacturers are struggling to keep pace with rising demand (Wiser & Bolinger, 

2007). 

Initiatives to Improve Wind Turbine Performance 

Avoid problems before installation 

•	 Improve reliability of turbines and components 

•	 Full-scale testing prior to commercial introduction 

•	 Development of appropriate design criteria, specifications and standards 

•	 Validation of design tools 

Monitor Performance 

•	 Monitor and evaluate turbine and wind-plant performance 

•	 Performance tracking by independent parties 

•	 Early identification of problems 

Rapid Deployment of Problem Resolution 

•	 Develop and communicate problem solutions 

•	 Focused activities with stakeholders to address critical issues, e.g., Gearbox 

Reliability Collaborative 

20% Wind Energy by 2030 – Prepublication Version 5 
 

 

 

 

 

1 In 2005 and 2006, the United States led the world in new wind installations. By 

early 2007, global wind power capacity exceeded 74 GW, and U.S. wind power 

capacity totaled 11.5 GW. This domestic wind power has been installed across 35 

states and delivers roughly 0.8% of the electricity consumed in the nation (Wiser 

and Bolinger, 2007). 

A Brief History of the U.S. Wind Industry 

The U.S. wind industry got its start in California during the 1970s, when the oil shortage 

increased the price of electricity generated from oil. The California wind industry benefited 

from federal and state investment tax credits (ITCs) as well as state-mandated standard utility 

contracts that guaranteed a satisfactory market price for wind power. By 1986, California had 

installed more than 1.2 GW of wind power, representing nearly 90% of global installations at 

that time. 

Expiration of the federal ITC in 1985 and the California incentive in 1986 brought the growth 

of the U.S. wind energy industry to an abrupt halt in the mid-1980s. Europe took the lead in 

wind energy, propelled by aggressive renewable energy policies enacted between 1974 and 

1985. As the global industry continued to grow into the 1990s, technological advances led to 

significant increases in turbine power and productivity. Turbines installed in 1998 had an 

average capacity 7 to 10 times greater than that of the 1980s turbines, and the price of windgenerated electricity dropped by nearly 80% (AWEA 2007). By 2000, Europe had more than 

12,000 MW of installed wind power, versus only 2,500 MW in the United States, and 

Germany became the new international leader. 

With low natural gas prices and U.S. utilities preoccupied Energy Policy Act of

by industry restructuring during the 1990s, the federal 1992 

PTC enacted in 1992 (as part of the Energy Policy Act The PTC gave power [EPAct]) did little to foster new wind producers 1.5 cents installations until just before its expiration in June 1999. 

Nearly 700 MW of new wind generation were installed in the (increased annually with

inflation) for every last year before the credit expired—more than in any previous kilowatt-hour (kWh) of 12-month period since 1985. After the PTC expired in 1999, electricity produced it was extended for two brief periods, ending in 2003. from wind during the It was then reinstated in late 2004. Although this first 10 years of intermittent policy support led to sporadic growth, business operation. inefficiencies inherent in serving this choppy market 

inhibited investment and restrained market growth. 

To promote renewable energy systems, many states began requiring electricity suppliers to 

obtain a small percentage of their supply from renewable energy sources, with percentages 

typically increasing over time. With Iowa and Texas leading the way, more than 20 states 

have followed suit with RPS, creating an environment for stable growth. 

After a decade of trailing Germany and Spain, the United States reestablished itself as the 

world leader in new wind energy in 2005. This resurgence is attributed to increasingly 

supportive policies, growing interest in renewable energy, and continued improvements in 

wind technology and performance. The United States retained its leadership of wind 

development in 2006 and, because of its very large wind resources, is likely to remain a major 

force in the highly competitive wind markets of the future. 

6 20% Wind Energy by 2030 – Prepublication Version 
 

 

1.2 SCENARIO DESCRIPTION
 1

The 20% Wind Scenario presented here would require U.S. wind power capacity to 

grow from the current 11.5 GW to more than 300 GW over the next 23 years (see 

Figure 1-3). This ambitious growth could be achieved in many different ways, with 

varying challenges, impacts, and 

levels of success. The 20% Wind 

Figure 1-3. Required growth in Scenario would require an U.S. capacity (GW) to implement the installation rate of 16 GW per year 20% Wind Scenario after 2018 (See Figure 1-4). This 

report examines one particular 

scenario for achieving this dramatic 

growth and contrasts it to another 

scenario that—for analytic 

simplicity—assumes no wind growth 

after 2006. The authors recognize 

that U.S. wind capacity is currently 

growing rapidly (although from a 

very small base), and that wind 

energy technology will be a part of 

any future electricity generation 

scenario for the United States. At the 

same time, a great deal of uncertainty remains about the level of contribution that 

wind could or is likely to make. In the 2007 Annual Energy Outlook (EIA 2007), an 

additional 7 GW beyond the 2006 installed capacity of 11.6 GW is forecast by 

2030.4

 Other organizations are projecting higher capacity additions, and it would be 

difficult to develop a “most likely” forecast given today’s uncertainties. The analysis 

presented here sidesteps these uncertainties and contrasts some of the challenges and 

impacts of producing 20% of the nation’s electricity from wind with a scenario in 

which no additional wind is added after 2006. This results in an estimate, expressed 

in terms of parameters, of the impacts and benefits associated with increased 

reliance on wind energy generation under given assumptions. The analysis was also 

simplified by assuming that the contributions to U.S. electricity supplies from other 

renewable sources of energy would remain at 2006 levels in both scenarios (see 

Figure A-6 for resource mix). 

The 20% Wind Scenario has been carefully defined to provide a base of common 

assumptions for detailed analysis of all impact areas. Broadly stated, this 20% 

scenario is designed to consider incremental costs while recognizing realistic 

constraints and considerations (see the Considerations in the 20% Scenario sidebar 

in Appendix A). Specifically, the scenario describes the mix of wind resources that 

would need to be captured, the geographic distribution of wind power installations, 

estimated land needs, the required utility and transmission infrastructure, 

manufacturing requirements, and the pace of growth that would be necessary. 

1.2.1 WIND GEOGRAPHY 

The United States possesses abundant wind resources. As shown in Figure 1-5, 

current “bus-bar” energy costs for wind (based on costs of the wind plant only, 

excluding transmission and integration costs and the PTC) vary by type of location 

4

 AEO data from 2007 were used in this report. AEO released new data in March of 2008, which were 

not incorporated into this report. While new EIA data could change specific numbers in this report, it 

would not change the overall message of the report. 

20% Wind Energy by 2030 – Prepublication Version 7 
 

 

 

1 Figure 1-4. 20% Cumulative installed wind power capacity required to (land-based or offshore) 

produce 20% of projected electricity by 2030 and by class of wind power 

density (higher classes 

offer greater productivity). 

Transmission and 

integration will add 

additional costs which are 

discussed in Chapter 4. 

The nation has more than 

8,000 GW of available 

land-based wind resources 

(Black and Veatch, 

forthcoming 2008) that 

industry estimates can be 

captured economically. 

NREL periodically 

classifies wind resources 

by wind speed which forms 

the basis of the Black and 

Veatch study. See 

Appendix B for further details. 

Electricity must, however, be transmitted from where it is generated to areas of high 

electricity demand, using the existing transmission system or new transmission lines 

where necessary. As shown in Figure 1-6 on the next page, the delivered cost of 

wind power increases when costs associated with connecting to the existing electric 

grid are included. The assumptions used in this report are different than EIA’s 

assumptions and are documented in Appendix A &B. The cost and performance 

assumptions of the 20% Wind Scenario are based on real market data from 2007. 

Figure 1-5. Supply curve for wind energy—current bus-bar energy costs 

See Appendix B for wind technology cost and performance projections; PTC and transmission and 

integration costs are excluded. 

8 20% Wind Energy by 2030 – Prepublication Version 
 

 

 

 

Cost and performance for all technologies either decrease or remain flat over time. 1

The models suggest that as much as 600 GW of wind resources could be available 

for $60 to $100 per megawatt-hour (MWh), including the cost of connecting to the 

existing transmission system. Including the PTC reduces the cost by about 

$20/MWh, and costs are further reduced if technology improvements in cost and 

performance are projected. In some cases, new transmission lines connecting highwind resource areas to load centers could be cost-effective, and in other cases, high 

transmission costs could offset the advantage of land-based generation, as in the case 

of large demand centers along wind-rich coastlines. 

NREL’s WinDS model estimated the overall U.S. generation capacity expansion 

required to meet projected electricity demand growth through 2030. Both wind 

technology and conventional generation technology (i.e., coal, nuclear) were 

included in the modeling but other renewables were not. Readers should refer to 

Appendices A and B to see a more complete list of the modeling assumptions. Wind 

energy development for this 20% Wind Scenario optimized the total delivered costs, 

including future reductions in cost per kilowatt-hour for wind sites both near to and 

remote from demand sites from 2000 through 2030.5

 Chapter 2 presents additional 

discussion of wind technology potential. Of the 293 GW that would be added, the 

model specifies more than 50 GW of offshore wind energy, mostly along the 

northeastern and southeastern seaboards. 

Based on this least-cost optimization algorithm (which incorporates future cost per 

kilowatt-hour of wind and cost of transmission), the WinDS model further specified 

Figure 1-6. Supply curve for wind energy—energy costs including 

connection to 10% of existing transmission grid capacity 

Note: See Appendix B for wind technology cost and performance projections. Excludes PTC, includes 

transmission costs to access existing electric transmission within 500 miles of wind resource. 

5

 The modeling assumptions prescribed annual wind energy generation levels that reached 20% of 

projected demand by 2030 so as to demonstrate technical feasibility and quantify costs and benefits. 

Policy options that would help induce this growth trajectory were not included. It is assumed that a 

20% Wind Energy by 2030 – Prepublication Version 9 
 

 

 

 

1 the wind capacity needed by state by 2030. As shown in Figure 1-6, most states 

would have the opportunity to develop their wind resources. Total land requirements 

are extensive, but only about 2% to 5% of the total would be dedicated entirely to 

the wind installation. In addition, the visual impacts and other siting concerns of 

wind energy projects must be taken into account in assessing land requirements. 

Chapter 5 contains additional discussion of land use and visual impacts. Again, the 

20% Wind Scenario presented here is not a prediction. Figure 1-6 simply shows one 

way in which a 20% wind future could evolve. 

1.2.2 WIND POWER TRANSMISSION AND INTEGRATION 

Development of 293 GW of new wind capacity would require expanding the U.S. 

transmission grid in a manner that not only accesses the best wind resource regions 

of the country but also relieves current congestion on the grid including new 

transmission lines to deliver wind power to electricity consumers. Figure 1-7 

conceptually illustrates the optimized use of wind resources within the local areas as 

well as the transmission of wind-generated electricity from high-resource areas to 

high-demand centers. This data was generated by the WinDS model given 

prescribed constraints. The figure does not represent proposals for specific 

Figure 1-7. 46 States would have substantial wind development by 2030 

Land Requirements 

Altogether, new landbased installations 

would require 

approximately 50,000 

square kilometers (km2

) 

of land, yet the actual 

footprint of land-based 

turbines and related 

infrastructure would 

require only about 1,000 

to 2,500 km2

 of 

dedicated land—slightly 

less than the area of 

Rhode Island. 

The 20% scenario 

envisions 241 GW of 

land-based and 54 GW 

of shallow offshore wind 

resource to optimize 

delivered costs, which 

include both generation 

and transmission. 

transmission lines. 

Wind capacity levels in each state depend on a variety of assumptions and the national optimization of electricity generation expansion. 

Based on the perspectives of industry experts and near-term wind development plans, wind capacity in Ohio was modified and offshore 

wind development in Texas was included. In reality, each state’s wind capacity level will vary significantly as electricity markets evolve 

and state policies promote or restrict the energy production of electricity from wind and other renewable and conventional energy sources. 

stable policy environment that recognizes wind’s benefits could lead to growth rates that would result 

in the 20% Wind Scenario. 

10 20% Wind Energy by 2030 – Prepublication Version 
 

Figure 1-8 displays transmission needs in the form of one technically feasible 1

transmission grid as a 765 kV overlay. A complete discussion of transmission issues 

can be found in Chapter 4. 

Figure 1-8. All new electrical generation including wind energy would require 

expansion of U.S. transmission by 2030 

Until recently, concerns had been prevalent in the electric utility sector about the 

difficulty and costs of dealing with the variability and uncertainty of energy 

production from wind plants and other weather-driven renewable technologies. But 

utility engineers in some parts of the United States now have extensive experience 

with wind plant impacts, and their analyses of these impacts have helped to reduce 

these concerns. As discussed in detail in Chapter 4, wind’s variability is being 

accommodated, and given optimistic assumptions, studies suggest the cost impact 

could be as little as the current level - 10% or less of the value of the wind energy 

generated. 

1.2.3 ELECTRICAL ENERGY MIX 

The U.S. Energy Information Administration (EIA) estimates that U.S. electricity 

demand will grow by 39% from 2005 to 2030, reaching 5.8 billion MWh by 2030. 

The 20% Wind Scenario would require delivery of nearly 1.16 billion MWh of wind 

energy in 2030, altering U.S. electricity generation as shown in Figure 1-9. In this 

scenario, wind would supply enough energy to displace about 50% of natural gas 

electric utility consumption and 18% of coal consumption by 2030. This amounts to 

an 11% reduction in natural gas across all industries. (Gas-fired generation would 

probably be displaced first, because it typically has a higher cost.) 

20% Wind Energy by 2030 – Prepublication Version 11 
 

 

 

 

 

1 Figure 1-9. Conceptual transmission plan to 

accommodate 400 GW of wind energy (AEP 2007) 

The increased wind development in this scenario could reduce the need for new coal 

and combined cycle natural gas capacity, but would increase the need for additional 

combustion turbine natural gas capacity to maintain electric system reliability. These 

units, though, would be run only as needed.6 

1.2.4 PACE OF NEW WIND INSTALLATIONS 

Manufacturing capacity would require some time to ramp up to support rapid growth 

in new U.S. wind installations. The 20% Wind Scenario estimates that the 

installation rate would need to increase from installing 3 GW per year today to more 

than 16 GW per year by 2018 and continue 

Figure 1-10. U.S. electrical energy mix at roughly that rate through 2030, as seen 

in Figure 1-10. This increase in installation 

rate, although quite large, is comparable to 

the recent annual installation rate of 

natural gas units, which totaled more than 

16 GW in 2005 alone (EIA 2005). 

The assumptions of the 20% Wind 

Scenario form the foundation for the 

technical analyses presented in the 

remaining chapters. This overview is 

provided as context for the potential 

impacts and technical challenges discussed 

in the next sections. 

6

 Appendix B presents a full analysis of changes in the capacity mix and energy generation under the 

20% Wind Scenario. 

12	 20% Wind Energy by 2030 – Prepublication Version 
 

 

1.3 IMPACTS
 1

The 20% Wind Scenario presented here 

offers potentially positive impacts in 

terms of GHG reductions, water 

conservation, and energy security, as 

compared to the base case of no wind 

growth in this analysis. However, 

tapping this resource at this level would 

entail large front-end capital 

investments to install wind capacity 

and expanded transmission systems. 

The impacts described in this section 

are based largely on the analytical tools 

and methodology discussed in detail in 

appendices A, B, and C. 

Wind power would be a critical part of 

a broad and near-term strategy to 

substantially reduce air pollution, 

water pollution, and global climate 

change associated with traditional 

generation technologies (see Wind 

versus Traditional Electricity 

Generation sidebar). As a domestic 

energy resource, wind power would 

also stabilize and diversify national 

energy supplies. 

1.3.1	 GREENHOUSE GAS 

REDUCTIONS 

Supplying 20% of U.S. electricity from 

wind could reduce electric sector 

carbon dioxide (CO2) emissions by 

825 million metric tons. 

The threat of climate change and the 

growing attention to it are helping to 

position wind power as an 

increasingly attractive option for new 

power generation. U.S. electricity 

demand is growing rapidly, and 

cleaner power sources (e.g., renewable 

energy) and energy-saving practices 

(i.e., energy efficiency) could help 

meet much of the new demand while 

reducing GHG emissions. Today, 

Wind vs. Traditional Electricity Generation 

Wind power avoids several of the negative effects of 

traditional electricity generation from fossil fuels: 

•	 Emissions of mercury or other heavy metals into the air 

•	 Emissions associated with extracting and transporting 

fuels. 

•	 Lake and streambed acidification from acid rain or 

mining 

•	 Water consumption associated with mining or electricity 

generation 

•	 Production of toxic solid wastes, ash, or slurry 

•	 Greenhouse gas (GHG) emissions 

20% Wind Scenario: Projected Impacts 

•	 Environment: Avoids air pollution, reduces GHG 

emissions, and reduces water use in electricity 

generation. Reduces electric sector CO2 emissions by 825 

million metric tons. 

•	 U.S. energy security: Diversifies our electricity portfolio 

and represents an indigenous energy source with stable 

prices not subject to fuel volatility 

•	 Energy consumers: Wind potentially reduces demand 

for fossil fuels, in turn reducing fuel prices and 

stabilizing electricity rates 

•	 Local economics: Creates new income source for rural 

landowners and tax revenues for local communities in 

wind development areas. 

•	 American workers: Generates well-paying jobs in 

sectors that support wind development, such as 

manufacturing, engineering, construction, transportation, 

and financial services. The new manufacturing will cause 

significant growth in the wind industry supply chain. 

•	 Water savings: Reduce cumulative water use in the 

electric sector by 8% (4 trillion gallons) 

wind energy represents approximately 30% of new capacity additions (AWEA 

2008). Greater use of wind energy, therefore, presents an opportunity for reducing 

emissions today as the nation develops additional clean power options for tomorrow. 

20% Wind Energy by 2030 – Prepublication Version 13 
 

 

 

 

 

2 can be converted to carbon equivalent by multiplying by 12/44. Appendix A presents results in 

1

20% Wind Scenario: Major Challenges 

• Investment in the nation’s transmission system so the 

power generated is delivered to urban centers that need the 

increased supply; 

• Larger electric load balancing areas, in tandem with better 

regional planning, so that regions can depend on a 

diversity of generation sources, including wind power; 

• Continued reduction in wind capital cost and improvement 

in turbine performance through technology advancement 

and improved manufacturing capabilities; and 

• Addressing potential concerns about local siting, wildlife, 

and environmental issues within the context of generating 

electricity. 

Concerns about climate change have 

spurred many industries, policy 

makers, environmentalists, and 

utilities to call for reductions in GHG 

emissions. Although the cost of 

reducing emissions is uncertain, the 

most affordable near-term strategy 

likely involves wider deployment of 

currently available efficiency and 

clean energy technologies. Wind 

power is one of the potential supplyside solutions to the climate change 

problem (Socolow and Pacala 2006). 

Governments at many levels have 

enacted policies to actively support 

clean electricity generation, including 

the renewable energy PTC and state 

RPS. A growing number of energy and environmental organizations are calling for 

expanded wind and other renewable power deployment to try to reduce society’s 

carbon footprint. 

GHG Reduction 

Under the 20% Wind Scenario, a 

cumulative total of 7,600 million 

tons of CO2 would be avoided by 

2030, and more than 15,000 million 

tons of CO2 would be avoided 

through 2050. 

According to EIA, The United States annually emits 

approximately 6,000 million metric tons of CO2.

7

 These 

emissions are expected to increase to nearly 7,900 million 

metric tons by 2030, with the electric power sector 

accounting for approximately 40% of the total (AEO, 2007). 

As shown in Figure 1-11, based on the analysis completed for 

this report, generating 20% of U.S. electricity from wind 

could avoid approximately 825 million metric tons of CO2 in 

the electric sector in 2030. The 20% scenario would also 

reduce cumulative emissions from the electric sector through 

that same year by more than 7,600 million metric tons of CO2 

Figure 1-11. Annual and cumulative wind installations by 2030 

7

 CO 

carbon equivalent, not CO2. In addition, the WinDS model projects higher CO2 emissions than the EIA 

model because it assumes a higher share of coal-fired generation. 

14 20% Wind Energy by 2030 – Prepublication Version 
 

 

 

 

 

(2,100 million metric tons of carbon equivalent). See Figures 1-12 and 13. In 1

general, CO2 emission reductions are not only a wind energy benefit but could be 

achieved under other energy mix scenarios. 

The Fourth Assessment Report of the United Nations Environment Programme and 

World Meteorological Organization’s Intergovernmental Panel on Climate Change 

(IPCC) notes that, “Renewable energy generally has a positive effect on energy 

security, employment, and air quality. Given costs relative to other supply options, 

renewable electricity can have a 30% to 35% share of the total electricity supply in 

2030. Deployment of low-GHG (greenhouse gas) emission technologies... would be 

required for achieving stabilization and cost reductions” (IPCC 2007). 

More than 30 U.S. states have created climate action plans. The Regional 

Greenhouse Gas Initiative (RGGI) is a 10-state collaborative in the Northeast to 

address CO2 emissions. All of these state and regional efforts include wind energy as 

part of a portfolio strategy to reduce overall emissions from energy production 

(RGGI 2006). 

Wind turbines typically have a service life of at least 20 years and transmission lines 

can last more than 50 years, meaning that investments in 20% wind by 2030 would 

continue to supply clean energy at least through 2050. As a result, cumulative 

climate change impacts could grow to more than 15,000 million tons of CO2 avoided 

by mid-century (4,182 million metric tons of carbon equivalent). 

The 20% Wind Scenario constructed here would displace a significant amount of 

fossil fuel generation. According to the WinDS model, by 2030, wind generation is 

projected to displace 50% of electricity generated from natural gas and 18% of that 

generated from coal. Coal displacement is of particular interest because the carbon 

emissions reduction opportunity is relatively higher. Recognizing that coal power 

will continue to play a major role in future electricity generation, a large increase in 

total wind capacity could potentially defer the need to build some new coal capacity, 

avoiding or postponing the associated carbon emissions. Current DOE projections 

anticipate construction of approximately 140 GW of new coal plant capacity by 

2030 (EIA 2007); the 20% Wind Scenario could avoid more than 80 GW of new 

coal capacity.8 

Wind energy that displaces fossil fuel generation can also help meet existing 

regulations for emissions of conventional pollutants, including sulfur dioxide, 

nitrogen oxides, and mercury. 

1.3.2 WATER CONSERVATION 

The 20% scenario would potentially reduce cumulative water consumption in the 

electric sector by 8% from 2007 through 2030, or 4 trillion gallons—significantly 

reducing water consumption in the arid states of the interior West. In 2030, annual 

water consumption in the electric sector would be reduced by 17%. 

8

 Carbon mitigation policies were not modeled in either the 20% wind or no new Wind Scenarios, 

which results in conventional generation mixes typical of current generation capacity. Under carbon 

mitigation scenarios, additional technologies could be implemented to reduce the need for conventional 

generation technology (see Appendix A). 

20% Wind Energy by 2030 – Prepublication Version 15 
 

 

 

 

1 Figure 1-12. Annual CO2 emissions avoided (vertical bars) 

would reach 825 million tons by 2030. 

The cumulative 

avoided 

emissions by 

2030 would 

total 7,600 

million tons. 

Water scarcity is a significant problem in many parts of the country. Even so, few 

realize that electricity generation accounts for nearly half of all water withdrawals in 

the nation, with irrigation coming in second at 34% (USGS 2005). Water is used for 

cooling of natural gas, coal, and nuclear power plants and is an increasing part of the 

challenge in developing those resources. 

Figure 1-13. Avoided emissions would nearly level projected Although a significant 

growth in CO2 emissions from the electricity sector portion of the water 

withdrawn for electricity 

production is recycled back 

through the system, 

approximately 2% to 3% of 

the water withdrawn is 

consumed through 

evaporative losses. Even 

this small fraction adds up 

to approximately 1.6 to 1.7 

trillion gallons of water 

consumed for power 

generation each year. 

As additional wind 

generation displaces fossil 

fuel generation, each 

megawatt-hour generated by 

wind could save as much as 

600 gallons of water that would otherwise be lost to fossil plant cooling.9

 Because 

wind energy generation uses a negligible amount of water, the 20% Wind Scenario 

would avoid the consumption of 4 trillion gallons of water through 2030, with 

annual reductions shown in Figure 1-14. The annual savings in 2030 is 

approximately 450 billion gallons. This reduction would reduce the expected annual 

water consumption for electricity generation in 2030 by 17%. The projected water 

9

 See Appendix A for specific assumptions. 

16	 20% Wind Energy by 2030 – Prepublication Version 
 

 

savings are dependant on future generation mix, which is 1

discussed further in Appendix A. Wind Reduces Vulnerability 

Based on the WinDS modeling results, nearly 30% of the Continued reliance on natural gas for 

projected water savings from the 20% Wind Scenario new power generation is likely to put 

would occur in western states, where water resources are the United States in growing 

particularly scarce. The Western Governors Association competition in world markets for 

(WGA) highlights this concern in its Clean and Diversified liquefied natural gas (LNG)—some of 

Energy Initiative, which recognizes increased water which will come from Russia, Qatar, 

consumption as a key challenge in accommodating rapid Iran, and other nations in less-thangrowth in electricity demand. In its 2006 report on water stable regions. 

needs, the WGA states that “difficult political choices will 

be necessary regarding future economic and environmental 

uses of water and the best way to encourage the orderly transition to a new 

equilibrium” (WGA 2006). 

1.3.3 ENERGY SECURITY AND STABILITY 

There is broad and growing recognition that the nation should diversify its energy 

portfolio so that a supply disruption affecting a single energy source will not 

significantly disrupt the national economy. Developing domestic energy sources 

with known and stable costs would significantly improve U.S. energy stability and 

security. 

When electric utilities have a Power Purchase Agreement or own wind energy 

turbines, the price of energy is expected to remain relatively flat and predictable for 

the life of the wind project, given that there are no fuel costs and assuming that the 

machines are well maintained. In contrast, a large part of the cost of coal- and gasfired electricity is in the fuel, for which prices are often volatile and unpredictable. 

Fuel price risks reduce security and stability for U.S. manufacturers and consumers, 

as well as for the economy as a whole. Even small reductions in the amount of 

energy available or changes in the price of fuel can cause large economic disruptions 

across the nation. This capacity to disrupt was clearly illustrated by the 1973 

embargo imposed by the Organization of Arab Petroleum Exporting Countries (the 

“Arab oil embargo”); the 2000–2001 California electricity market problems; and the 

gasoline and natural gas shortages and price spikes that followed the 2005 hurricane 

damage to oil refinery and natural gas processing facilities along the Gulf Coast. 

Using wind energy increases security and stability by diversifying the national 

Figure 1-14. National water savings from the 20% Wind Scenario 

20% Wind Energy by 2030 – Prepublication Version 17 
 

 

 

 

1 electricity portfolio. Just as those investing for retirement are advised to diversify 

investments across companies, sectors, and stocks and bonds, diversification helps 

distribute the risks and stabilize rates for electricity consumers. 

Wind energy reduces reliance on foreign energy sources from politically unstable 

regions. As a domestic energy source, wind requires no imported fuel, and the 

turbine components can be either produced on U.S. soil or imported from any 

friendly nation with production capabilities. 

Energy security concerns for the electric industry will likely increase in the 

foreseeable future as natural gas continues to be a leading source of new generation 

supply. With declining domestic natural gas sources, future natural gas supplies are 

expected to come in the form of LNG imported on tanker ships. U.S. imports of 

LNG could quadruple by 2030 (EIA 2007). Almost 60% of uncommitted natural gas 

reserves are in Iran, Qatar, and Russia. These countries, along with others in the 

Middle East, are expected to be major suppliers to the global LNG market. Actions 

by those sources can disrupt international energy markets and thus have indirect 

adverse effects on our economy. Additional risks arise from competition for these 

resources caused by the growing energy demands of China, India, and other 

developing nations. Under the 20% scenario, according to the WinDS model results, 

wind energy could displace approximately 11% of natural gas consumption which is 

equivalent to 60% of expected LNG imports in 2030.10 This shift would reduce the 

nation’s energy vulnerability to uncertain natural gas supplies. Please see Appendix 

A for gas demand reduction assumptions and calculations. 

Continued reliance on fossil energy sources exposes the nation to price risks and 

supply uncertainties. Although the electric sector does not rely heavily on petroleum, 

which represents one of the nation’s biggest energy security threats, diversifying the 

electric generation mix with increased domestic renewable energy would still 

enhance national energy security by increasing energy diversity and price stability. 

1.3.4 COST OF THE 20% WIND SCENARIO 

The overall economic cost of the 20% Wind Scenario accrues mainly from the 

incremental costs of wind energy relative to other generation sources. This is 

impacted by the assumptions behind the scenario, listed in table A-6. Also, some 

incremental transmission would be required to connect wind to the electric power 

system. This transmission investment would be in addition to the significant 

investment in the electric grid that will be needed to serve continuing load growth, 

whatever the mix of new generation. The market cost of wind energy remains higher 

than that of conventional energy sources in many areas across the country. In 

addition, the transmission grid would have to be expanded and upgraded from windrich areas and across the existing system to deliver wind energy to many demand 

centers. 

Compared to other generation sources, the 20% Wind Scenario entails higher initial 

capital costs (to install wind capacity and associated transmission infrastructure) in 

many areas, yet offers lower ongoing energy costs than conventional power plants 

for operations, maintenance and fuel. Given the optimistic cost and performance 

assumptions of wind and conventional energy sources (detailed in Appendix B), the 

20% Wind Scenario could require an incremental investment of as little as $43 

billion NPV more than the base-case no new Wind Scenario. This would represent 

10 Compared to consumption of the high price scenario of EIA (2007), used in this report 

18 20% Wind Energy by 2030 – Prepublication Version 
 

 

less than 0.06 cent (6 one-hundredths of 1 cent) per kilowatt-hour of total generation 1

by 2030, or roughly 50 cents per month per household. Figure 1-18 shows this cost 

comparison. The base-case costs are calculated under the assumption of no major 

Figure 1-15. Incremental investment cost of 20% wind is modest; 

a difference of 2% 

changes in fuel availability or environmental restrictions. In this scenario, the cost 

differential would be about 2% of a total NPV expenditure exceeding $2 trillion. 

This analysis intended to identify the incremental cost of pursuing the 20% Wind 

Scenario. In regions where the capital costs of the 20% scenario exceed those of 

building little or no additional wind capacity, the differential could be offset by the 

operating cost and benefits discussed earlier. For example, even though Figure 1-18 

shows that under optimistic assumptions the 20% Wind Scenario could increase total 

capital cost by nearly $197 billion, most of that cost would be offset by the nearly 

$155 billion in decreased fuel expenditure, resulting in a net incremental cost of 

approximately $43 billion in NPV. These monetary cost do not reflect other 

potential offsetting positive impacts. 

An integrated approach to expanding the transmission system would need to include 

furnishing access to wind resources as well as meeting other system needs. 

As estimated by the NREL WinDS model, given optimistic assumptions, the specific 

cost of the proposed transmission expansion for the 20% Wind Scenario is $20 

billion in NPV. The actual required grid investment could also involve significant 

costs for permitting delays, construction of grid extensions to remote areas with 

wind resources, and investments in advanced grid controls, integration and training 

to enable regional load balancing of wind resources. 

The total installed costs for wind plants include costs associated with siting and 

permitting of these plants. It has become clear that wind power expansion would 

require careful, logical, and fact-based consideration of local and environmental 

concerns, allowing siting issues to be addressed within a broad risk framework. 

Experience in many regions has shown that this can be done, but efficient, 

20% Wind Energy by 2030 – Prepublication Version 19 
 

 

 

 

1 streamlined procedures will likely be needed to enable installation rates in the range 

of 16 GW per year. Chapter 5 covers these issues in more detail. 

1.4 CONCLUSION 

There are significant costs, challenges, and impacts associated with the 20% Wind 

Scenario presented in this report. There are also substantial positive impacts from 

wind power expansion on the scale and pace described in this chapter are not likely 

to be realized in a business-as-usual future. Achieving this scenario would involve a 

major national commitment to clean, domestic energy sources with minimal 

emissions of GHGs and other environmental pollutants. 

1.5 REFERENCES AND OTHER SUGGESTED READING 

AWEA 2007. American Wind Energy Association Web site, Oct.1, 2007: 

http://www.awea.org/faq/cost.html. 

BTM Consult. 2007. International Wind Energy Development, World Market 

Update 2006. Ringkøbing, Denmark: BTM. 

Edmonds, J.A., M.A. Wise, J.J. Dooley, S.H. Kim, S.J. Smith, P.J. Runci, L.E. 

Clarke, E.L. Malone, and G.M. Stokes. 2007. Global Energy Technology 

Strategy: Addressing Climate Change. Richland, WA: Global Energy 

Strategy Technology Project. 

http://www.pnl.gov/gtsp/docs/gtsp_2007_final.pdf 

EIA (Energy Information Administration). 2005. Electric Power Annual. 

Washington, DC: EIA. Table 2.6. 

http://www.eia.doe.gov/cneaf/electricity/epa/epat2p6.html. 

EIA. 2007. Annual Energy Outlook. Washington, DC: EIA. 

http://www.eia.doe.gov/oiaf/aeo/index.html. 

IPCC (Intergovernmental Panel on Climate Change). 2007. Climate Change 2007: 

Impacts, Adaptation and Vulnerability: Working Group II Contribution to 

the Fourth Assessment Report of the Intergovernmental Panel on Climate 

change. PCC Report presented at 8th session of Working Group II of the 

IPCC, April 2007, Brussels, Belgium. http://www.ipcc.ch/ipccreports/ar4­

wg2.htm 

Johnston, L., E. Hausman, A. Sommer, B. Biewald, T. Woolf, D. Schlissel, A. 

Roschelle, and D. White. 2006. Climate Change and Power: Carbon 

Dioxide Emissions Costs and Electricity Resource Planning. Cambridge, 

MA: Synapse Energy Economics, Inc. 

O'Connell, R., R. Pletka, et al. 2007. 20% Wind Energy Penetration in the United 

States: A Technical Analysis of the Energy Resource. Overland Park, KS: 

Black & Veatch. 

RGGI (Regional Greenhouse Gas Initiative). 2006. “About RGGI.” 

http://www.rggi.org/about.htm. 

Socolow, R.H., and S.W. Pacala. 2006. “A Plan to Keep Carbon in Check,” 

Scientific American, September. 

Teske, S., A. Zervos, and O. Schafer. 2007. Energy [R]evolution: A Blueprint for 

Solving Global Warming, USA National Energy Scenario. Amsterdam: 

20 20% Wind Energy by 2030 – Prepublication Version 
 

 

Greenpeace International. http://www.greenpeace.org/raw/content/usa/press­ 1

center/reports4/energy-r-evolution-introduc.pdf 

USCAP (U.S. Climate Action Partnership). 2007. A Call for Action. http://www.uscap.org/USCAPCallForAction.pdf 

USGS (U.S. Geological Survey). 2005. Estimated Use of Water in the United States 

in 2000. http://pubs.usgs.gov/circ/2004/circ1268/htdocs/figure01.html 

WGA (Western Governors’ Association). 2006. Water Needs and Strategies for a 

Sustainable Future, p. 4. http://www.westgov.org/wga/publicat/Water06.pdf 

Wiser, R., M. Bolinger, and M. St. Clair. 2005. Easing the Natural Gas Crisis: 

Reducing Natural Gas Prices through Increased Deployment of Renewable 

Energy and Energy Efficiency. Berkeley, CA: Berkeley Lab. Report No. 

LBNL-56756. http://eetd.lbl.gov/EA/EMP/reports/56756.pdf 

Wood Mackenzie. 2007. Impact of a Federal Renewable Portfolio Standard. 

Edinburgh, Scotland: Wood Mackenzie. 

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1

22 20% Wind Energy by 2030 – Prepublication Version 
 

 

 

Chapter 2.	 Wind Turbine 

Technology 

Today’s wind technology has enabled wind to enter 

the electric power mainstream. Continued 

technological advancement would be required 

under the 20% Wind Scenario. 

2.1 INTRODUCTION 

2

Current turbine technology has enabled wind energy to become a viable power 

source in today’s energy market. Even so, wind energy provides approximately 1% 

of total U.S. electricity generation. Advancements in turbine technology that have 

the potential to increase wind energy’s presence are currently being explored. These 

areas of study include reducing capital costs, increasing capacity factors, and 

mitigating risk through enhanced system reliability. With sufficient research, 

development, and demonstration (RD&D), these new advances could potentially 

have a significant impact on commercial product lines in the next 10 years. 

A good parallel to wind energy evolution can be derived from the history of the 

automotive industry in the United States. The large-scale production of cars began 

with the first Model T production run in 1910. By 1940, after 30 years of making 

cars and trucks in large numbers, manufacturers had produced vehicles that could 

reliably move people and goods across the country. Not only had the technology of 

the vehicle improved, but the infrastructure investment in roads and service stations 

made their use practical. Yet 30 years later, in 1970, one would hardly recognize the 

vehicles or infrastructure as the same as those in 1940. Looking back at the 

automobiles produced more than 30 years ago, we see how RD&D led to the 

continuous infusion of modern electronics; improved combustion and manufacturing 

processes; and ultimately, safer, more reliable cars with higher fuel efficiency. In a 

functional sense, wind turbines now stand roughly where the U.S. automotive fleet 

stood in 1940. Gradual improvements have been made in the past 30 years over 

several generations of wind energy products. These technology advances enable 

today’s turbines to reliably deliver electricity to the grid at a reasonable cost. 

Through continued RD&D and infrastructure development, great strides will be 

made to produce even more advanced machines supporting future deployment of 

wind power technology. This chapter describes the status of wind technology today 

and provides a brief history of technology development over the past three decades. 

Prospective improvements to utility-scale land-based wind turbines as well as 

offshore wind technology are discussed. Distributed wind technology (100 kilowatts 

(kW) or less) are also addressed in this chapter. 

20% Wind Energy by 2030 – Prepublication Version 23 
 

 

 

2.2 TODAY’S COMMERCIAL WIND TECHNOLOGY
 

2 Beginning with the birth of modern wind-driven electricity generators in the late 

1970s, wind energy technology has improved dramatically up to the present. Capital 

costs have decreased, efficiency has increased, and reliability has improved. Highquality products are now routinely delivered by major suppliers of turbines around 

the world and complete wind generation plants are being engineered into the grid 

infrastructure to meet utility needs. In the 20% scenario outlined in this report, it is 

assumed that capital costs would be reduced by 10% over the next two decades and 

capacity factors would be increased by about 15% (corresponding to a 15% increase 

in annual energy generation by a wind plant). 

2.2.1 WIND RESOURCES 

Wind technology is driven by the nature of the resource to be harvested. The United 

States, particularly the Midwestern region from Texas to North Dakota, is rich in 

wind energy resources as shown in Figure 2-1, which illustrates the wind resources 

measured at a 50 meter (m) elevation. Measuring potential wind energy generation 

at 100-m elevation, the projected operating hub height of the next generation of 

modern turbines, greatly increases the U.S land area that could be used for wind 

deployment, as shown in Figure 2-2 for the state of Indiana. Taking these 

measurements into account, current U.S. land-based and offshore wind resources are 

estimated to be sufficient to supply the electrical energy needs of the entire country 

several times over. For a description of U.S. wind resources, see Appendix B. 

Figure 2-1. The wind resource potential at 50 m above ground on 

land and offshore 

Identifying the good wind potential at high elevations in states such as Indiana and 

off the shore of both coasts is important because it drives developers to find ways to 

harvest this energy. Many of the opportunities being pursued through advanced 

24 20% Wind Energy by 2030 – Prepublication Version 
 

 

Figure 2-2. Comparison of the wind energy resource at 

50 m, 70 m, and 100 m for Indiana 

2

technology are intended to achieve higher elevations where the resource is much 

greater, or to access extensive offshore wind resources. 

2.2.2 TODAY’S MODERN WIND TURBINE 

Modern wind turbines, which are currently being deployed around the world, have 

three-bladed rotors with diameters of 70 m to 80 m mounted atop 60-m to 80-m 

towers, as illustrated in Figure 2-3. Typically installed in arrays of 30 to 150 

machines, the average turbine installed in the United States in 2006 can produce 

approximately 1.6 megawatts (MW) of electrical power. Turbine power output is 

controlled by rotating the blades about their long axis to change the angle of attack 

with respect to the relative wind as the blades spin about the rotor hub. This is called 

controlling the blade pitch. The turbine is pointed into the wind by rotating the 

nacelle about the tower. This is called controlling the yaw. Wind sensors on the 

nacelle tell the yaw controller where to point the turbine. These wind sensors, along 

with sensors on the generator and drivetrain, also tell the blade pitch controller how 

to regulate the power output and rotor speed to prevent overloading the structural 

components. Generally, a turbine will start producing power in winds of about 5.36 

m/s and reach maximum power output at about 12.52 m/s–13.41 m/s. The turbine 

will pitch or feather the blades to stop power production and rotation at about 22.35 

m/s. Most utility-scale turbines are upwind machines, meaning that they operate 

with the blades upwind of the tower to avoid the blockage created by the tower. 

The amount of energy in the wind available for extraction by the turbine increases 

with the cube of wind speed; thus, a 10% increase in wind speed creates a 33% 

increase in available energy. A turbine can capture only a portion of this cubic 

increase in energy, though, because power above the level for which the electrical 

system has been designed, referred to as the rated power, is allowed to pass through 

the rotor. 

20% Wind Energy by 2030 – Prepublication Version 25 
 

 

 

 

Figure 2-3. A modern 1.5-MW wind turbine installed in a wind power plant 

2

Rotor Hub 

Tower, 80 m 

Minivan 

Rotor Blades 

• Shown Feathered 

• Length, 37 m 

Nacelle Enclosing: 

• Low-Speed Shaft 

• Gearbox 

• Generator, 1.5 MW 

• Electrical Controls 

In general, the speed of the wind increases with height above the ground, which is 

why engineers have found ways to increase the height and the size of wind turbines 

while minimizing the costs of materials. But land-based turbine size is not expected 

to grow as dramatically in the future as it has in the past. Larger sizes are physically 

possible; however, the logistical constraints of transporting the components via 

highways and of obtaining cranes large enough to lift the components present a 

major economic barrier that is difficult to overcome. Many turbine designers do not 

expect the rotors of land-based turbines to become much larger than about 100 m in 

diameter, with corresponding power outputs of about 3 MW to 5 MW. 

2.2.3 WIND PLANT PERFORMANCE AND PRICE 

The performance of commercial turbines has improved over time, and as a result, 

their capacity factors have slowly increased. Figure 2-4 shows the capacity factors at 

commercial operation dates (CODs) ranging from 1998 to 2005. The data shows that 

turbines in the Lawrence Berkeley National Laboratory (Berkeley Lab) database 

(Wiser and Bolinger 2007) that began operating commercially before 1998 have an 

average capacity factor of about 22%. The turbines that began commercial operation 

after 1998, however, show an increasing capacity factor trend, reaching 36% in 2004 

and 2005. 

The cost of wind-generated electricity has dropped dramatically since 1980, when 

the first commercial wind plants began operating in California. Since 2003, 

however, wind energy prices have increased. Figure 2-5 (Wiser and Bolinger 2007) 

shows that in 2006 the price paid for electricity generated in large wind farms was 

between 3.0 and 6.5 cents/kilowatt-hour (kWh) with an average near 5 cents/kWh 

26 20% Wind Energy by 2030 – Prepublication Version 
 

Figure 2-4. Turbine capacity factor by commercial operation date (COD) 

using 2006 data 

2

Figure 2-5. Wind energy price by commercial operation date (COD) 

using 2006 data 

(1 cent/kWh = $10/megawatt-hour [MWh]). This price includes the benefit of the 

federal production tax credit (PTC), state incentives, and revenue from the sale of 

any renewable energy credits. 

Wind energy prices have increased since 2002 for the following reasons (Wiser and 

Bolinger 2007) 

z Shortages of turbines and components resulting from the dramatic 

recent growth of the wind industry in the United States and Europe 

z The weakening U.S. dollar relative to the euro (many major turbine 

components are imported from Europe and there are relatively few 

wind turbine component manufacturers in the United States) 

z A significant rise in material costs such as steel and copper as well 

as transportation fuels over the last three years 

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z The on-again, off-again cycle of the wind energy PTC (uncertainty 

hinders investment in new turbine production facilities and 

encourages hurried and expensive production, transportation, and 

installation of projects when the tax credit is available). 

Expected future reductions in wind energy costs would come partly from expected 

investment in the expansion of manufacturing volume in the wind industry. In 

addition, a stable U.S. policy for renewable energy and a heightened RD&D effort 

could also lower costs. 

2.2.4 WIND TECHNOLOGY DEVELOPMENT 

Until the early 1970s, wind energy filled a small niche market, supplying 

mechanical power for grinding grain and pumping water, as well as electricity for 

rural battery charging. With the exception of battery chargers and rare experiments 

with larger electricity-producing machines, the windmills of 1850 and even 1950 

differed very little from the primitive devices from which they were derived. 

Increased RD&D in the latter half of the twentieth century, however, greatly 

improved the technology. 

In the 1980s, the practical approach of using low-cost parts from agricultural and 

boat-building industries produced machinery that usually worked, but was heavy, 

high-maintenance, and grid-unfriendly. Little was known about structural loads 

caused by turbulence, which led to the frequent and early failure of critical parts, 

such as yaw drives. Additionally, the small-diameter machines were deployed 

mostly in densely packed arrays in the California wind corridors that were not 

aesthetically pleasing in such a rural setting. These densely packed arrays also often 

blocked the wind from neighboring turbines, producing a great deal of turbulence for 

the downwind machines. Reliability and availability suffered as a result. 

Recognizing these issues, wind operators and manufacturers have worked to develop 

better machines with each new generation of designs. Drag-based devices and 

simple lift-based designs gave way to experimentally designed and tested high-lift 

rotors, many with full-span pitch control. Blades that had once been made of sail or 

sheet metal progressed through wood to advanced fiberglass composites. The DC 

alternator gave way to the grid-synchronized induction generator, which has now 

been replaced by variable-speed designs employing high-speed solid-state switches 

of advanced power electronics. Designs moved from mechanical cams and linkages 

that feathered or furled a machine to high-speed digital controls. A 50-kW machine, 

considered large in 1980, is now dwarfed by the 1.5-MW to 2.5-MW machines 

being routinely installed today. 

Many RD&D advances have contributed to these changes. Airfoils, which are now 

tested in wind tunnels, are designed for insensitivity to surface roughness and dirt. 

Increased understanding of aeroelastic loads and the ability to incorporate this 

knowledge into finite element models and structural dynamics codes make the 

machines of today more robust but also more flexible and lighter on a relative basis 

than those of a decade ago. 

As with any maturing technology, however, many of the simpler and easier 

improvements have already been incorporated into today’s turbines. Increased 

RD&D efforts and innovation will be required to continue to expand the wind 

energy industry. 

28 20% Wind Energy by 2030 – Prepublication Version 
 

2.2.5 CURRENT TURBINE SIZE 

Throughout the past 20 years, average wind turbine ratings have grown almost 

linearly, as illustrated by Figure 2-6. Each group of wind turbine designers has 

predicted that its latest machine is the largest that a wind turbine will ever be. But 

with each new generation of wind turbines (roughly every five years), the size has 

grown along the linear curve and has achieved reductions in life-cycle cost of energy 

(COE). 

Figure 2-6. The development path and growth of wind turbines 

2

As discussed in Section 2.2.2, this long-term drive to develop larger turbines is a 

direct result of the desire to improve energy capture by accessing the stronger winds 

at higher elevations. (The increase in wind speed with elevation is referred to as 

wind shear.) Although the increase in turbine height is a major reason for the 

increase in capacity factor over time, there are economic and logistical constraints to 

this continued growth to larger sizes. 

The primary argument for limiting the size of wind turbines is based on the squarecube law. This law roughly states that “as a wind turbine rotor grows in size, its 

energy output increases as the rotor swept area (the diameter squared), while the 

volume of material, and therefore its mass and cost, increases as the cube of the 

diameter.” In other words, at some size, the cost for a larger turbine will grow faster 

than the resulting energy output revenue, making scaling a losing economic game. 

Engineers have successfully skirted this law by either removing material or using it 

more efficiently as they increase size. Turbine performance has clearly improved 

and cost per unit of output has been reduced, as illustrated in Figures 2-4 and 2-5. A 

Wind Partnerships for Advanced Component Technology (WindPACT) study has 

also shown that in recent years, blade mass has been scaling at an exponent of about 

2.3 as opposed to the expected 3 (Ashwill 2004), demonstrating how successive 

generations of blade design have moved off the cubic weight growth curve to keep 

weight down (see Figure 2-7). The latest designs continue to fall below the cubic 

line of the previous generation, indicating the continued infusion of new technology 

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Figure 2-7. Growth in blade weight 

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into blade design. If advanced RD&D were to result in even better design methods, 

as well as new materials and manufacturing methods that allow the entire turbine to 

scale as the diameter squared, continuing to innovate around this size limit would be 

possible. 

Land transportation constraints can also limit wind turbine growth for turbines 

installed on land. Cost-effective road transportation is achieved by remaining within 

standard over-the-road trailer dimensions of 4.1 m high by 2.6 m wide and a gross 

vehicle weight (GVW) under 80,000 pounds (lb; which translates to a cargo weight 

of about 42,000 lb). Loads that exceed 4.83 m in height trigger expensive rerouting 

to avoid obstructions and often require utility and law enforcement assistance along 

the roadways. These dimension limits have the most impact on the base diameter of 

wind turbine towers. Rail transportation is even more dimensionally limited by 

tunnel and overpass widths and heights. Overall widths should remain within 3.4 m 

and heights are limited to 4.0 m. Transportation weights are less of an issue in rail 

transportation with GVW limits of up to 360,000 lb (Ashwill 2004). 

Once turbines arrive at their destination, their physical installation poses other 

practical constraints that limit their size. Typically, 1.5-MW turbines are installed on 

80-m towers to maximize energy capture. Crane requirements are quite stringent 

because of the large nacelle mass in combination with the height of the lift and the 

required boom extension. As the height of the lift to install the rotor and nacelle on 

the tower increases, the number of available cranes with the capability to make this 

lift is fairly limited. In addition, cranes with large lifting capacities are difficult to 

transport and require large crews, leading to high operation, mobilization, and 

demobilization costs. Operating large cranes in rough or complex hilly terrain can 

also require repeated disassembly to travel between turbine sites (NREL 2002). 

2.2.6 CURRENT STATUS OF TURBINE COMPONENTS 

The Rotor 

Typically, a modern turbine will cut in and begin to produce power at a wind speed 

of about 5.37 m/s (see Figure 2-8 below). It will reach its rated power at about 12.52 

m/s to 13.41 m/s, where the pitch control system begins to limit power output and 

prevent generator and drivetrain overload. At around 22.35 m/s, the control system 

30 20% Wind Energy by 2030 – Prepublication Version 
 

pitches the blades to stop rotation, feathering the blades 

to prevent overloads and damage to the turbine’s 

components. The job of the rotor is to operate at the The Betz Limit 

absolute highest efficiency possible between cut-in and Not all of the energy present in a stream of rated wind speeds, to hold the power transmitted to the moving air can be extracted; some air must drivetrain at the rated power when the winds go higher, remain in motion after extraction. and to stop the machine in extreme winds. Modern Otherwise, no new, more energetic air can utility-scale wind turbines generally extract about 50% enter the device. Building a wall would of the energy in this stream below the rated wind speed, stop the air at the wall, but the free stream compared to the maximum energy that a device can of energetic air would just flow around the theoretically extract, which is 59% of the energy stream wall. On the other end of the spectrum, a (see The Betz Limit sidebar). device that does not slow the air is not 

extracting any energy either. The maximum Most of the rotors on today’s large-scale machines have energy that can be extracted from a fluid an individual mechanism for pitch control; that is, the stream by a device with the same working mechanism rotates the blade about its long axis to area as the stream cross section is 59% of control the power in high winds. This device is a the energy in the stream. Because it was significant improvement over the first generation of first derived by wind turbine pioneer fixed-pitch or collective-pitch linkages because the Albert Betz, this maximum is known as the blades can now be rotated in high winds to feather them Betz Limit. out of the wind. This reduces the maximum loads on 

the system when the machine is parked. Pitching the 

blades out of high winds also reduces operating loads, and the combination of 

pitchable blades with a variable-speed generator allows the turbine to maintain 

generation at a constant rated power output. The older generation of constant-speed 

rotors sometimes had instantaneous power spikes up to twice the rated power. 

Additionally, this pitch system operates as the primary safety system because any 

one of the three independent actuators is capable of stopping the machine in an 

emergency. 

Blades 

As wind turbines grow in size, so do their blades—from about 8 m long in 1980 to 

more than 40 m for many land-based commercial systems and more than 60 m for 

offshore applications. Rigorous evaluation using the latest computer analysis tools 

have improved blade designs, enabling weight growth to be kept to a much lower 

rate than simple geometric scaling (see Figure 2-7). Designers are also starting to 

work with lighter and stronger carbon fiber in highly stressed locations to stiffen 

blades and improve fatigue resistance while reducing weight. (Carbon fiber, 

however, costs about 10 times as much as fiberglass.) Using lighter blades reduces 

the load-carrying requirements for the entire supporting structure and saves total cost 

far beyond the material savings of the blades alone. 

By designing custom airfoils for wind turbines, developers have improved blades 

over the past 20 years. Although these airfoils were primarily developed to help 

optimize low-speed wind aerodynamics to maximize energy production while 

limiting loads, they also help prevent sensitivity to blade fouling that is caused by 

dirt and bug accumulation on the leading edge. This sensitivity reduction greatly 

improves blade efficiency (Cohen et al. 2008). 

Current turbine blade designs are also being customized for specific wind classes. In 

lower energy sites, the winds are lighter so design loads can be relaxed and longer 

blades can be used to harvest more energy in lower winds. Even though blade design 

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Figure 2-8. Typical power output versus wind speed curve 

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methods have improved significantly, there is still much room for improvement, 

particularly in the area of dynamic load control and cost reduction. 

Controls 

Today’s controllers integrate signals from dozens of sensors to control rotor speed, 

blade pitch angle, generator torque, and power conversion voltage and phase. The 

controller is also responsible for critical safety decisions, such as shutting down the 

turbine when extreme conditions are encountered. Most turbines currently operate in 

variable-speed mode, and the control system regulates the rotor speed to obtain peak 

efficiency in fluctuating winds. It does this by continuously updating the rotor speed 

and generator loading to maximize power and reduce drivetrain transient torque 

loads. Operating in variable-speed mode requires the use of power converters, which 

offer additional benefits (discussed in the next subsection). Research into the use of 

advanced control methods to reduce turbulence-induced loads and increase energy 

capture is an active area of work. 

Electrical controls with power electronics enable machines to deliver fault-ridethrough control, voltage control, and volt-ampere-reactive (VAR) support to the 

grid. In the early days of grid-connected wind generators, the grid rules required that 

wind turbines go offline when any grid event was in progress. Now, with penetration 

of wind energy approaching 10% in some regions of the United States, more than 

8% nationally in Germany, and more than 20% of the average generation in 

Denmark, the rules are being changed (Wiser and Bolinger, 2007). Grid rules on 

both continents are requiring more support and fault-ride-through protection from 

the wind generation component. Current electrical control systems are filling this 

need with wind plants carefully engineered for local grid conditions 

32 20% Wind Energy by 2030 – Prepublication Version 
 

The Drivetrain (Gearbox, Generator, and Power Converter) 

Generating electricity from the wind places an unusual set of requirements on 

electrical systems. Most applications for electrical drives are aimed at using 

electricity to produce torque instead of using torque to produce electricity. The 

applications that generate electricity from torque usually operate at a constant rated 

power. Wind turbines, on the other hand, must generate at all power levels and 

spend a substantial amount of time at low power levels. Unlike most electrical 

machines, wind generators must operate at the highest possible aerodynamic and 

electrical efficiencies in the low-power/low-wind region to squeeze every kilowatthour out of the available energy. For wind systems, it is simply not critical for the 

generation system to be efficient in above-rated winds in which the rotor is letting 

energy flow through to keep the power down to the rated level. Therefore, wind 

systems can afford inefficiencies at high power, but they require maximum 

efficiency at low power—just the opposite of almost all other electrical applications 

in existence. 

Torque has historically been converted to electrical power by using a speedincreasing gearbox and an induction generator. Many current megawatt-scale 

turbines use a three-stage gearbox consisting of varying arrangements of planetary 

gears and parallel shafts. Generators are either squirrel-cage induction or woundrotor induction, with some newer machines using the doubly fed induction design 

for variable speed, in which the rotor’s variable frequency electrical output is fed 

into the collection system through a solid-state power converter. Full power 

conversion and synchronous machines are drawing interest because of their faultride-through and other grid support capacities. 

As a result of fleet-wide gearbox maintenance issues and related failures with some 

designs in the past, it has become standard practice to perform extensive 

dynamometer testing of new gearbox configurations to prove durability and 

reliability before they are introduced into serial production. The long-term reliability 

of the current generation of megawatt-scale drivetrains has not yet been fully 

verified with long-term, real-world operating experience. There is a broad consensus 

that wind turbine drivetrain technology will evolve significantly in the next several 

years to reduce weight and cost and improve reliability. 

The Tower 

The tower configuration used almost exclusively in turbines today is a steel 

monopole on a concrete foundation that is custom designed for the local site 

conditions. The major tower variable is the height. Depending on the wind 

characteristics at the site, the tower height is selected to optimize energy capture 

with respect to the cost of the tower. Generally, a turbine will be placed on a 60-m to 

80-m tower, but 100-m towers are being used more frequently. Efforts to develop 

advanced tower configurations that are less costly and more easily transported and 

installed are ongoing. 

Balance of Station 

The balance of the wind farm station consists of turbine foundations, the electrical 

collection system, power-conditioning equipment, supervisory control and data 

acquisition (SCADA) systems, access and service roads, maintenance buildings, 

service equipment, and engineering permits. Balance-of-station components 

contribute about 20% to the installed cost of a wind plant. 

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Operations and Availability 

Operating and maintenance (O&M) costs have also dropped significantly since the 

1980s as a result of improved designs and increased quality. O&M data from the 

technology installed well before 2000 show relatively high annual costs that increase 

with the age of the equipment. Annual O&M costs are reported to be as high as 

$30-$50/MWh for wind power plants with 1980s technology, whereas the latest 

generation of turbines has reported annual O&M costs below $10/MWh (Wiser and 

Bolinger 2007). Figure 2-9 shows annual O&M expenses by wind project age and 

equipment installation year. Relative to wind power prices shown in Figure 2-5, the 

O&M costs can be a significant portion of the price paid for wind-generated 

electricity. Since the late 1990s, modern equipment operation costs have been 

reduced for the initial operating years. Whether annual operation costs grow as these 

modern turbines age is yet to be determined and will depend greatly on the quality of 

these new machines. 

Figure 2-9. Operation and maintenance costs for large-scale wind plants 

installed within the last 10 years for the early years of operation (Wiser and 

Bolinger 2007) 

SCADA systems are being used to monitor very large wind farms and dispatch 

maintenance personnel rapidly and efficiently. This is one area where experience in 

managing large numbers of very large machines has paid off. Availability, defined 

as the fraction of time during which the equipment is ready to operate, is now more 

than 95% and often reported to exceed 98%. These data indicate the potential for 

improving reliability and reducing maintenance costs (Walford 2006). 

2.3 TECHNOLOGY IMPROVEMENTS ON THE HORIZON 

Technology improvements can help meet the cost and performance challenges 

embedded in this 20% Wind Scenario. The required technological improvements are 

relatively straightforward: taller towers, larger rotors, and continuing progress 

through the design and manufacturing learning curve. No single component or 

design innovation can fulfill the need for technology improvement. By combining a 

number of specific technological innovations, however, the industry can introduce 

new advanced architectures necessary for success. The 20% Wind Scenario does not 

require success in all areas; progress can be made even if only some of the 

technology innovations are achieved. 

34 20% Wind Energy by 2030 – Prepublication Version 
 

 

2.3.1 FUTURE IMPROVEMENTS TO TURBINE COMPONENTS 

Many necessary technological advances are already in the active development 

stages. Substantial research progress has been documented and individual companies 

are beginning the development process for these technologies. The risk of 

introducing new technology at the same time that manufacturing production is 

scaling up and accelerating to unprecedented levels is not trivial. Innovation always 

carries risk. Before turbine manufacturers can stake the next product on a new 

feature, the performance of that innovation needs to be firmly established and the 

durability needs to be characterized as well as possible. These risks are mitigated by 

RD&D investment, including extensive component and prototype testing before 

deployment. 

The following are brief summaries of key wind energy technologies that are 

expected to increase productivity through better efficiency, enhanced energy 

capture, and improved reliability. 

The Rotor 

The number one target for advancement is the means by which the energy is initially 

captured—the rotor. No indicators currently suggest that rotor design novelties are 

on their way, but there are considerable incentives to use better materials and 

innovative controls to build enlarged rotors that sweep a greater area for the same or 

lower loads. Two approaches are being developed and tested to either reduce load 

levels or create load-resistant designs. The first approach is to use the blades 

themselves to attenuate both gravity- and turbulence-driven loads (see the Blades 

subsection that follows). The second approach lies in an active control that senses 

rotor loads and actively suppresses the loads transferred from the rotor to the rest of 

the turbine structure. These improvements will allow the rotor to grow larger and 

capture more energy without changing the balance of the system. They will also 

improve energy capture for a given capacity, thereby increasing the capacity factor 

(Ashwill 2004). 

Another innovation already being evaluated at a smaller scale by Energy Unlimited 

Inc. (EUI; Boise, Idaho) is a variable-diameter rotor that could significantly increase 

capacity factor. Such a rotor has a large area to capture more energy in low winds 

and a system to reduce the size of the rotor to protect the system in high winds. 

Although this is still considered a very high-risk option because of the difficulty of 

building such a blade without excessive weight, it does provide a completely 

different path to a very high capacity factor (EUI 2003). 

Blades 

Larger rotors with longer blades sweep a greater area, increasing energy capture. 

Simply lengthening a blade without changing the fundamental design, however, 

would make the blade much heavier. In addition, the blade would incur greater 

structural loads because of its weight and longer moment arm. Blade weight and 

resultant gravity-induced loads can be controlled by using advanced materials with 

higher strength-to-weight ratios. Because high-performance materials such as carbon 

fibers are more expensive, they would be included in the design only when the 

payoff is maximized. These innovative airfoil shapes hold the promise of 

maintaining excellent power performance, but have yet to be demonstrated in fullscale operation. 

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Figure 2-10. Curvature-based twist coupling 

2

One elegant concept is to build passive means of reducing loads directly into the 

blade structure. By carefully tailoring the structural properties of the blade using the 

unique attributes of composite materials, the internal structure of the blade can be 

built in a way that allows the outer portion of the blade to twist as it bends (Griffin 

2001). “Flap-pitch” or “bend-twist” coupling, illustrated in Figure 2-10, is 

accomplished by orienting the fiberglass and carbon plies within the composite 

layers of the blade. If properly designed, the resulting twisting changes the angle of 

attack over much of the blade, reducing the lift as wind gusts begin to load the blade 

and therefore passively reducing the fatigue loads. Yet another approach to 

achieving flap-pitch coupling is to build the blade in a curved shape (see 

Figure 2-11) so that the aerodynamic loads apply a twisting action to the blade, 

which varies the angle of attack as the aerodynamic loads fluctuate. 

Figure 2-11. Twist-flap coupled blade design (material-based twist coupling) 

To reduce transportation costs, concepts such as on-site manufacturing and 

segmented blades are also being explored. It might also be possible to segment 

molds and move them into temporary buildings close to the site of a major wind 

installation so that the blades can be made close to or actually at the wind site. 

36 20% Wind Energy by 2030 – Prepublication Version 
 

Active Controls 

Active controls using independent blade pitch and generator torque can be used to 

reduce tower-top motion, power fluctuations, asymmetric rotor loads, and even 

individual blade loads. Actuators and controllers already exist that can achieve most 

of the promised load reductions to enable larger rotors and taller towers. In addition, 

some have published control algorithms that could achieve the load reductions in the 

research literature (Bossanyi 2003). Sensors capable of acting as the eyes and ears of 

the control system will need to have sufficient longevity to monitor a highreliability, low-maintenance system. There is also concern that the increased control 

activity will accelerate wear on the pitch mechanism. Thus, the technical innovation 

that is essential to enabling some of the most dramatic improvements in performance 

is not a matter of exploring the unknown, but rather of doing the hard work of 

mitigating the innovation risk by demonstrating reliable application through 

prototype testing and demonstration. 

Towers 

To date there has been little innovation in the tower, which is one of the more 

mundane components of a wind installation. But because placing the rotor at a 

higher elevation is beneficial and because the cost of steel continues to rise rapidly, 

it is highly likely that this component will be examined more closely in the future, 

especially for regions of higher than average wind shear. 

Because power is related to the cube of wind speed, mining upward into these rich 

veins of higher wind speed potentially has a high payoff—for example, a 10% 

increase in wind speed produces about a 33% increase in available power. Turbines 

could sit on even taller towers than those in current use if engineers can figure out 

how to make them with less steel. Options for using materials other than steel (e.g., 

carbon fiber) in the tower are being investigated. Such investigations could bear fruit 

if there are significant adjustments in material costs. Active controls that damp out 

tower motion might be another enabling technology. Some tower motion controls 

are already in the research pipeline. New tower erection technologies might play a 

role in O&M that could also help drive down the system COE (NREL 2002). 

Tower diameters greater than approximately 4 m would incur severe overland 

transportation cost penalties. Unfortunately, tower diameter and material 

requirements conflict directly with tower design goals—a larger diameter is 

beneficial because it spreads out the load and actually requires less material because 

its walls are thinner. On-site assembly allows for larger diameters but also increases 

the number of joints and fasteners, raising labor costs as well as concerns about 

fastener reliability and corrosion. Additionally, tower wall thickness cannot be 

decreased without limit; engineers must adhere to certain minima to avoid buckling. 

New tower wall topologies, such as corrugation, can be employed to alleviate the 

buckling constraint, but taller towers will inevitably cost more. 

The main design impact of taller towers is not on the tower itself, but on the 

dynamics of a system with the bulk of its mass atop a longer, more slender structure. 

Reducing tower-top weight improves the dynamics of such a flexible system. The 

tall tower dilemma can be further mitigated with smarter controls that attenuate 

tower motion by using blade pitch and generator torque control. Although both 

approaches have been demonstrated, they are still rarely seen in commercial 

applications. 

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The Drivetrain (Gearbox, Generator, and Power Conversion) 

Parasitic losses in generator windings, power electronics, gears and bearings, and 

other electrical devices are individually quite small. When summed over the entire 

system, however, these losses add up to significant numbers. Improvements that 

remove or reduce the fixed losses during low power generation are likely to have an 

important impact on raising the capacity factor and reducing cost. These 

improvements could include innovative power-electronic architectures and largescale use of permanent-magnet generators. Direct-drive systems also meet this goal 

by eliminating gear losses. Modular (transportable) versions of these large 

generation systems that are easier to maintain will go a long way toward increasing 

the productivity of the low-wind portion of the power curve. 

Currently, gearbox reliability is a major issue and gearbox replacement is quite 

expensive. One solution is a direct-drive power train that entirely eliminates the 

gearbox. This approach, which was successfully adopted in the 1990s by EnerconGmbH (Aurich, Germany), is being examined by other turbine manufacturers. A less 

radical alternative reduces the number of stages in the gearbox from three to two or 

even one, which enhances reliability by reducing the parts count. The fundamental 

gearbox topology can also be improved, as Clipper Windpower (Carpinteria, 

California) did with its highly innovative multiple-drive-path gearbox, which divides 

mechanical power among four generators (see Figure 2-12). The multiple-drive-path 

design radically decreases individual gearbox component loads, which reduces 

gearbox weight and size, eases erection and maintenance demands, and improves 

reliability by employing inherent redundancies. 

The use of rare-earth permanent magnets in generator rotors instead of wound rotors 

also has several advantages. High energy density eliminates much of the weight 

associated with copper windings, eliminates problems associated with insulation 

degradation and shorting, and reduces electrical losses. Rare-earth magnets cannot 

be subjected to elevated temperatures, however, without permanently degrading 

magnetic field strength, which imposes corresponding demands on generator cooling 

reliability. Availability of rare-earth permanent magnets is a potential concern 

because key raw materials are not available in significant quantities within the 

United States (see Chapter 3). 

Power electronics have already achieved elevated performance and reliability levels, 

but opportunities for significant improvement remain. New silicon carbide (SiC) 

devices entering the market could allow operation at higher temperature and higher 

frequency, while improving reliability, lowering cost, or both. New circuit 

topologies could furnish better control of power quality, enable higher voltages to be 

used, and increase overall converter efficiency. 

Distributed Energy Systems (Wallingford, Connecticut; formerly Northern Power 

Systems) has built an advanced prototype power electronics system that will deliver 

lower losses and conversion costs for permanent-magnet generators (Northern 

Power Systems 2006). Peregrine Power (Wilsonville, Oregon) has concluded that 

using SiC devices would reduce power losses, improve reliability, and shrink 

components by orders of magnitude (Peregrine Power 2006). A study completed by 

BEW Engineering (San Ramon, California; Behnke, Erdman, and Whitaker 

Engineering 2006) shows that using medium-voltage power systems for 

multimegawatt turbines could reduce the cost, weight, and volume of turbine 

electrical components as well as reduce electrical losses. 

38 20% Wind Energy by 2030 – Prepublication Version 
 

 

Figure 2-12. Clipper Windpower multiple-drive-path gearbox 

2

The most dramatic change in the long-term application of wind generation may 

come from the grid support provided by the wind plant. Future plants will not only 

support the grid by delivering fault-ride-through capability as well as frequency, 

voltage, and VAR control, but will also carry a share of power control capability for 

the grid. Plants can be designed so that they furnish a measure of dispatch capability, 

carrying out some of the traditional duties of conventional power plants. These 

plants would be operated below their maximum power rating most of the time and 

would trade some energy capture for grid ancillary services. Paying for this trade-off 

will require either a lower capital cost for the hardware, contractual arrangements 

that will pay for grid services at a high enough rate to offset the energy loss, or 

optimally, a combination of the two. Wind plants might transition, then, from a 

simple energy source to a power plant that delivers significant grid support. 

2.3.2 LEARNING-CURVE EFFECT 

Progressing along the design and manufacturing learning curve allows engineers to 

develop technology improvements (such as those listed in Section 2.3.1) and reduce 

capital costs. The more engineers and manufacturers learn by conducting effective 

RD&D and producing greater volumes of wind energy equipment, the more 

proficient and efficient the industry becomes. The learning curve is often measured 

by calculating the progress ratio, defined as the ratio of the cost after doubling 

cumulative production to the cost before doubling. 

The progress ratio for wind energy from 1984 to 2000 was calculated for the high 

volume of machines installed in several European countries that experienced a 

healthy combination of steadily growing manufacturing output, external factors, and 

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research investment during that time. Results show that progress ratio estimates 

were approximately the same for Denmark (91%), Germany (94%), and Spain 

(91%) (ISET 2003). At the time this report was written, there was not enough 

reliable data on U.S.-based manufacturing of wind turbines to determine a U.S. 

progress ratio. Figure 2-13 shows the data for Spain. 

Figure 2-13. Cost of wind turbines delivered from Spain between 

1984 and 2000 

Note: The Y axis represents cost and is presented in logarithmic units. The data points shown fit the 

downward-sloping straight line with a correlation coefficient, r2 , of 0.85. 

Moving from the current level of installed wind capacity of roughly 12 gigawatts 

(GW) to the 20% Wind Scenario total of 305 GW will require between four and five 

doublings of capacity. If the progress ratio of 91% shown in Figure 2-13 continues, 

prices could drop to about 65% of current costs, a 35% reduction. The low-hanging 

fruit of cost reduction, however, has already been harvested. The industry has 

progressed from machines based on designs created without any design tools and 

built almost entirely by hand to the current state of advanced engineering capability. 

The assumption in the 20% Wind Scenario is that a 10% reduction in capital cost 

could accelerate large-scale deployment. In order to achieve this reduction, a 

progress ratio of only 97.8% is required to produce a learning effect of 10% with the 

4.6 doublings. With sustained manufacturing growth and technological 

advancement, there is no technical barrier to achieving 10% capital cost reduction. 

See Appendix B for further discussion. 

2.3.3 THE SYSTEM BENEFITS OF ADVANCED TECHNOLOGY 

A cost study conducted by the U.S. Department of Energy (DOE) Wind Program 

identify numerous opportunities for technology advancement to reduce the life-cycle 

COE (Cohen and Schweizer et al 2008). Based on machine performance and cost, 

this study used advanced concepts to suggest pathways that integrate the individual 

contributions from component-level improvements into system-level estimates of 

the capital cost, annual energy production, reliability, O&M, and balance of station. 

The results, summarized in Table 2-1, indicate significant potential impacts on 

annual energy production and capital cost. Changes in annual energy production are 

equivalent to changes in capacity factor because the turbine rating was fixed. A 

range of values represents the best, most likely, and least beneficial outcomes. 

40 20% Wind Energy by 2030 – Prepublication Version 
 

 

The Table 2-1 capacity factor improvement of 11% that results from taller towers 

reflects the increase in wind resources at a hub height of 120 m, conservatively 

assuming the standard wind shear distribution meteorologists use for open country. 

Uncertainty in these capacity factor improvements are reflected in the table below. 

Depending on the success of new tower technology, the added costs could range 

from 8% to 20%, but there will definitely be an added cost if the tower is the only 

component in the system that is modified to take the rotor to higher elevations. An 

advantage would come from a system design in which the tower head mass is 

significantly reduced with the integration of a rotor and drivetrain that are 

significantly lighter. 

The capital cost reduction shown for the drivetrain components is mainly attributed 

to the reduced requirements on the structure when lighter components are placed on 

the tower top. Performance increases as parasitic losses in mechanical and electrical 

components are reduced. Such components are designed specifically to optimize the 

Table 2-1. Areas of potential technology improvement 

2

Technical Area Potential Advances 

Performanc

Increm

(Best/Expe

Percent

e and Cost 

ents 

cted/Least 

ages) 

Annual Energy 

Production 

Turbine 

Capital Cost 

Advanced Tower Concepts 

* Taller towers in difficult locations 

* New materials and/or processes 

* Advanced structures/foundations 

* Self-erecting, initial, or for service 

+11/+11/+11 +8/+12/+20 

Advanced (Enlarged) Rotors 

* Advanced materials 

* Improved structural-aero design 

* Active controls 

* Passive controls 

* Higher tip speed/lower acoustics 

+35/+25/+10 -6/-3/+3 

Reduced Energy Losses 

and Improved Availability 

* Reduced blade soiling losses 

* Damage-tolerant sensors 

* Robust control systems 

* Prognostic maintenance 

+7/+5/0 0/0/0 

Drivetrain 

(Gearboxes and Generators 

and Power Electronics) 

* Fewer gear stages or direct-drive 

* Medium/low speed generators 

* Distributed gearbox topologies 

* Permanent-magnet generators 

* Medium-voltage equipment 

* Advanced gear tooth profiles 

* New circuit topologies 

* New semiconductor devices 

* New materials ( gallium arsenide 

[GaAs], SiC) 

+8/+4/0 -11/-6/+1 

Manufacturing and Learning 

Curve* 

* Sustained, incremental design and 

process improvements 

* Large-scale manufacturing 

* Reduced design loads 

0/0/0 -27/-13/-3 

Totals +61/+45/+21 -36/-10/+21 

*The learning curve results from the NREL report (Cohen and Schweizer et al 2008) are adjusted from 3.0 

doublings in the reference to the 4.6 doublings in the 20% Wind Scenario. 

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2

performance for wind turbine characteristics. The improvements shown in Table 2-1 

are in the single digits but not trivial. 

Without changing the location of the rotor, energy capture can also be increased by 

using longer blades to sweep more area. A 10% to 35% increase in capacity factor is 

produced by 5% to 16% longer blades for the same rated power output. Building 

these longer blades at an equal or lower cost is a challenge because blade weight 

must be capped while turbulence-driven loads remain no greater than what the 

smaller rotor can handle. With the potential of new structurally efficient airfoils, 

new materials, passive load attenuation, and active controls, it is estimated that this 

magnitude of blade growth can be achieved in combination with a modest system 

cost reduction. 

Technology advances can also reduce energy losses in the field. Improved O&M 

techniques and monitoring capabilities can reduce downtime for repairs and 

scheduled maintenance. It is also possible to mitigate losses resulting from 

degradation of performance caused by wear and dirt over time. These improvements 

are expected to be in the single digits at best, with an approximate 5% improvement 

in lifetime energy capture. 

Doubling the number of manufactured turbines several times over the years will 

produce a manufacturing learning-curve effect that can also help reduce costs. The 

learning-curve effects shown in Table 2-1 are limited to manufacturing-related 

technology improvements and do not reflect issues of component selection and 

design. As discussed in Section 2.3.2, the learning curve reflects efficiencies driven 

by volume production and manufacturing experience as well as the infusion of 

manufacturing technology and practices that encourage more manufacturing-friendly 

design in the future. Although these changes do not target any added energy capture, 

they are expected to result in continuous cost reductions. The only adjustment from 

the NREL (Cohen and Schweizer et al 2008) reference is that the 20% Wind 

Scenario by 2030 requires 4.6 doublings of cumulative capacity rather than the 3.0 

doublings used in the reference targeted at the year 2012. The most likely 13% cost 

reduction assumes a conservative progress ratio of 97% per doubling of capacity. 

However, there are a range of possible outcomes. 

The potential technological advances outlined here support the technical feasibility 

of the 20% Wind Scenario by outlining several possible pathways to a substantial 

increase in capacity factor accompanied by a modest but double-digit reduction in 

capital cost. 

2.3.4 TARGETED RD&D 

While there is an expected value to potential technology improvements, the risk of 

implementing them has not yet been reduced to the level that allows those 

improvements to be used in commercial hardware. The issues are well known and 

offer an opportunity for focused RD&D efforts. In the past, government and industry 

collaboration has been successful in moving high-risk, high-potential technologies 

into the marketplace. 

One example of such collaboration is the advanced natural gas turbine, which 

improved the industry efficiency standard—that had been capped at 50%—to almost 

60%. DOE invested $100 million in the H-system turbine and General Electric (GE) 

invested $500 million. Although it was known that higher operating temperatures 

would lead to higher efficiency, there were no materials for the turbine blades that 

42 20% Wind Energy by 2030 – Prepublication Version 
 

 

could withstand the environment. The research program focused on advanced 

cooling techniques and new alloys to handle combustion that was nearly 300°F 

hotter. The project produced the world’s largest single crystal turbine blades capable 

of resisting high-temperature cracking. The resulting “H system” gas turbine is 

11.89 m long, 4.89 m in diameter, and weighs more than 811,000 lb. Each turbine is 

expected to save more than $200 million in operating costs over its lifetime (DOE 

2000). 

A similar example comes from the aviation world. The use of composite materials 

was known to provide excellent benefits for light-jet airframes, but the certification 

process to characterize the materials was onerous and expensive. NASA started a 

program to “reduce the cost of using composites and develop standardized 

procedures for certifying composite materials” (Brown 2007). The Advanced 

General Aviation Transport Experiments (AGATE), which began in 1994, solved 

those problems and opened the door for new composite material technology to be 

applied to the light-jet application. A technology that would have been too high risk 

for the individual companies to develop was bridged into the marketplace through a 

cooperative RD&D effort by NASA, the Federal Aviation Administration (FAA), 

industry, and universities. The Adam aircraft A500 turboprop and the A700 very 

light jet are examples of new products based on this composite technology. 

Some might claim that wind technology is a finished product that no longer needs 

additional RD&D, or that all possible improvements have already been made. The 

reality is that the technology is substantially less developed than fossil energy 

technology, which is still being improved after a century of generating electricity. A 

GE manager who spent a career in the gas turbine business and then transferred to 

manage the wind turbine business noted the complexity of wind energy technology: 

“Our respect for wind turbine technology has grown tremendously. The practical 

side is so complex and forces are so dramatic. We would never have imagined how 

complex turbines are” (Knight and Harrison 2005). 

Already, there is a clear understanding of the materials, controls, and aerodynamics 

issues that must be resolved to make progress toward greater capacity factors. The 

combination of reduced capital cost and increased capacity factor will lead to 

reduced COE. Industry feels the risk of bringing new technology into the 

marketplace without a full-scale development program is too great and believes 

sustained RD&D would help reduce risk and help enable the transfer of new 

technology to the marketplace. 

2.4 ADDRESSING TECHNICAL AND FINANCIAL RISKS 

2

Risks tend to lessen industry’s desire to invest in wind technology. The wind plant 

performance track record, in terms of generated revenues and operating costs 

compared with the estimated revenues used in plant financing, will drive the risk 

level of future installations. The consequences of these risks directly affect the 

revenues of owners of wind manufacturing and operating capabilities. 

2.4.1 DIRECT IMPACTS 

When owners of wind manufacturing and operating capabilities directly bear the 

costs of failure, the impacts are said to be direct. This direct impact on revenue is 

often caused by: 

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z	 Increasing O&M costs: As discussed previously and illustrated in 

Figure 2-9, there is mounting evidence that O&M costs are 

increasing as wind farms age. Most of these costs are associated 

with unplanned maintenance or components wearing out before the 

end of their intended design lives. Some failures can be traced to 

poor manufacturing or installation quality. Others are caused by 

design errors, many of which are caused by weaknesses in the 

technology’s state of the art, generally codified by the design 

process. Figures 2-14 and 2-15 show steadily rising O&M costs for 

wind farms installed in the United States in the two decades before 

the turn of the century and the components that have caused these 

increasing costs, respectively. The numbers and costs of component 

failures increase with time, and the risk to the operators grows 

accordingly. In Figure 2-15 the solid lines represent expected repairs 

that may not be completely avoidable, and the dashed lines show 

potential early failures that can significantly increase risk. 

z	 Poor availability driven by low reliability: Energy is not 

generated while components are being repaired or replaced. 

Although a single failure of a critical component stops production 

from only one turbine, such losses can mount up to significant sums 

of lost revenue. 

z	 Poor wind plant array efficiency: If turbines are placed too close 

together, their wakes interact, which can cause the downwind 

turbines to perform poorly. But if they are placed too far apart, land 

and plant maintenance costs increase. 

Figure 2-14. Unplanned repair cost, likely sources, and risk of 

failure with wind plant age 

2.4.2 INDIRECT IMPACTS 

Although the wind industry has achieved high levels of wind plant availability and 

reliability, unpredictable or unreliable performance would threaten the credibility of 

this emerging technology in the eyes of financial institutions. The consequences of 

real or perceived reliability problems would extend beyond the direct cost to the 

plant owners. These consequences on the continued growth of investment in wind 

could include: 

44	 20% Wind Energy by 2030 – Prepublication Version 
 

 

Figure 2-15. Average O&M costs of wind farms in the United States 

0.000 

0.005 

0.010 

0.015 

0.020 

0.025 

0 2 4 6 8 10 12 14 16 18 20 

Year of Operation 

Annual O&M Cost, $/kWh 

Lemming & Morthorst - 600 kW (1999) 

Vachon - 600-740 kW (2002) 

Vachon - 2 MW (2002) 

WindPACT 1.5 MW - GEC (2003) 

WindPACT 1.5 MW - Northern Power (2004) 

2

z	 Increased cost of insurance and financing: Low interest rates and 

long-term loans are critical to financing power plants that are loaded 

with upfront capital costs. Each financial institution will assess the 

risk of investing in wind energy and charge according to those risks. 

If wind power loses credibility, these insurance and financing costs 

could increase. 

z	 Slowing or stopping development: Lost confidence contributed to 

the halt of development in the United States in the late 1980s 

through the early 1990s. Development did not start again until the 

robust European market supported the technology improvements 

necessary to reestablish confidence in reliable European turbines. 

As a result, the current industry is dominated by European wind 

turbine companies. Active technical supporters of RD&D must 

anticipate and resolve problems before they threaten industry 

development. 

z	 Loss of public support: If wind power installations do not operate 

continuously and reliably, the public might be easily convinced that 

renewable energy is not a viable source of energy. The public’s 

confidence in the technology is crucial. Without public support, 

partnerships working toward a new wind industry future cannot be 

successful. 

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2.4.3	 RISK MITIGATION THROUGH CERTIFICATION, VALIDATION, AND 

PERFORMANCE MONITORING 

To reduce risk, the wind industry requires turbines to adhere to international 

standards. These standards, which represent the collective experience of the 

industry’s leading experts, imply a well-developed design process that relies on the 

most advanced design tools, testing for verification, and disciplined quality control. 

Certification 

Certification involves high-level, third-party technical audits of a manufacturer’s 

design development. It includes a detailed review of design analyses, material 

selections, dynamic modeling, and component test results. The wind industry 

recognizes that analytical reviews are not sufficient to capture weaknesses in the 

design process. Therefore, consensus standard developers also require full-scale 

testing of blades, gearboxes, and the complete 

system prototype (see Industry Standards

Industry Standards sidebar). 

The American National Standards Institute (ANSI) Actively complying with these standards has designated the American Wind Energy encourages investment in wind energy by Association (AWEA) as the lead organization for ensuring that turbines reliably achieve the the development and publication of industry maximum energy extraction needed to expand consensus standards for wind energy equipment the industry. and services in the United States. AWEA also 

participates in the development of international 

wind energy standards through its representation Full-Scale Testing

on the International Electrotechnical Commission Testing standards were drafted to ensure that 

(IEC) TC-88 Subcommittee. Information on these accredited third-party laboratories are 

standards can be accessed on AWEA’s Web site conducting tests consistently. These tests

(http://www.awea.org/standards). reveal many design and manufacturing 

deficiencies that are beyond detection by 

analytical tools. They also provide the final 

verification that the design process has worked and give the financial community the 

confidence needed to invest in a turbine model. 

Full-scale test facilities and trained test engineers capable of conducting full-scale 

tests are rare. The facilities must have equipment capable of applying tremendous 

loads that mimic the turbulence loading that wind applies over the entire life of the 

blade or gearbox. Full-scale prototype tests are conducted in the field at locations 

with severe wind conditions. Extensive instrumentation is applied to the machine, 

according to a test plan prescribed by international standards, and comprehensive 

data are recorded over a specified range of operating conditions. These data give the 

certification agent a means for verifying the accuracy of the design’s analytical 

basis. The industry and financial communities depend on these facilities and skilled 

test engineers to support all new turbine component development. 

As turbines grow larger and more products come on the market, test facilities must 

also grow and become more efficient. New blades are reaching 50 m in length, and 

the United States has no facilities that can test blades longer than 50 m. Furthermore, 

domestic dynamometer facilities capable of testing gearboxes or new drivetrains are 

limited in capacity to 1.5 MW. The limited availability of facilities and qualified test 

46	 20% Wind Energy by 2030 – Prepublication Version 
 

 

engineers increases the deployment risk of new machines that are not subjected to 

the rigors of current performance validation in accredited facilities. 

At full-scale facilities, it is also difficult to conduct tests accurately and capture the 

operating conditions that are important to verify the machine's reliability. These tests 

are expensive to conduct and accreditation is expensive to maintain for several 

reasons. First, the scale of the components is one of the largest of any commercial 

industry. Because blades are approaching sizes of half the length of a football field 

and can weigh more than a 12.2-m yacht, they are very difficult and expensive to 

transport on major highways. The magnitude of torque applied to the drivetrains for 

testing is among the largest of any piece of rotating equipment ever constructed. 

Figure 2-16 shows the largest blades being built and the approximate dates when 

U.S. blade test facilities were built to accommodate their testing. 

Figure 2-16. Blade growth and startup dates for U.S. blade test facilities 

2

Although it is very expensive for each manufacturer to develop and maintain 

facilities of this scale for its own certification testing needs, without these facilities, 

rapid technological progress will be accompanied by high innovation risk. Wind 

energy history has proven that these kinds of tests are crucial for the industry’s 

success and the financial community’s confidence. These tests, then, are an essential 

element of any risk mitigation strategy. 

Performance Monitoring and O&M 

One of the main elements of power plant management is strategic monitoring of 

reliability. Other industries have established anonymous databases that serve to 

benchmark their reliability and performance, giving operators both the ability to 

recognize a drop in reliability and the data they need to determine the source of low 

reliability. The wind industry needs such a strategically designed database, which 

would give O&M managers the tools to recognize and pinpoint drops in reliability, 

along with a way to collectively resolve technical problems. Reliability databases 

are an integral part of more sophisticated O&M management tools. Stiesdal and 

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Madsen (2005) describe how databases can be used for managing O&M and 

improving future designs. 

In mature industries, O&M management tools are available to help maximize 

maintenance efficiency. Achieving this efficiency is a key factor in minimizing the 

COE and maximizing the life of wind plants, thereby increasing investor confidence. 

Unlike central generation facilities, wind plants require maintenance strategies that 

minimize human attention and maximize remote health monitoring and automated 

fault data diagnosis. This requires intimate knowledge of healthy plant operating 

characteristics and an ability to recognize the characteristics of very complex faults 

that might be unique to a specific wind plant. Such tools do not currently exist for 

the wind industry, and their development will require RD&D to study wind plant 

systems interacting with complex atmospheric conditions and to model the 

interactions. The resultant deeper understanding will allow expert systems to be 

developed, systems that will aid operators in their quest to maximize plant 

performance and minimize operating costs through risk mitigation. These systems 

will also produce valuable data for improving the next generation of turbine designs. 

2.5 OFFSHORE WIND TECHNOLOGY 

Offshore wind energy installations have a broadly dispersed, abundant resource and 

the economic potential for cost competitiveness that would allow them to make a 

large impact in meeting the future energy needs of the United States (Musial, 2007). 

Of the contiguous 48 states, 28 have a coastal boundary. U.S. electric use data show 

that these same states use 78% of the nation’s electricity (EIA 2006). Of these 28 

states, only 6 have a sufficient land-based wind energy resource to meet more than 

20% of their electric requirements through wind power. If shallow water offshore 

potential (less than 30 m in depth) is included in the wind resource mix, though, 26 

of the 28 states would have the wind resources to meet at least 20% of their electric 

needs, with many states having sufficient offshore wind resources to meet 100% of 

their electric needs (Musial, 2007). For most coastal states, offshore wind resources 

are the only indigenous energy source capable of making a significant energy 

contribution. In many congested energy-constrained regions, offshore wind plants 

might be necessary to supplement growing demand and dwindling fossil supplies. 

Twenty-six offshore wind projects with an installed capacity of roughly 1,143 MW 

now operate in Europe. Most projects were installed in water less than 22 m deep. 

One demonstration project in Scotland is installed in water at a depth of 45 m. 

Although some projects have been hampered by construction overruns and higherthan-expected maintenance requirements, projections show strong growth in many 

European Union (EU) markets. For example, it is estimated that offshore wind 

capacity in the United Kingdom will grow by 8,000 MW by 2015. Similarly, 

German offshore development is expected to reach 5,600 MW by 2014 (BSH & 

BWEA). 

In the United States, eight or nine offshore project proposals in state and federal 

waters total to a capacity of more than 1,500 MW. Two of these proposed projects 

are being treated separately by the Minerals Management Service (MMS) because 

the permits were submitted before MMS authority was established by the Energy 

Policy Act (EPAct) of 2005 (MMS). Two other projects in Texas state waters have 

already received state approval. 

48 20% Wind Energy by 2030 – Prepublication Version 
 

2.5.1 COST OF ENERGY 

The current installed capital cost of offshore projects is estimated in the range of 

$2,400 to $5000 per kW (Black and Veatch, forthcoming 2008, Pace Global 2007). 

Because offshore wind energy tends to take advantage of extensive land-based 

experience and mature offshore oil and gas practices, offshore cost reductions are 

not expected to be as great as land-based reductions spanning the past two decades. 

However, offshore wind technology is considerably less mature than land-based 

wind energy, so it does have significant potential for future cost reduction. These 

cost reductions are achievable through technology development and innovation, 

implementation and customization of offshore oil and gas practices, and learningcurve reductions that take advantage of more efficient manufacturing and 

deployment processes and procedures. 

2.5.2 CURRENT TECHNOLOGY 

Today’s baseline technology for offshore wind turbines is essentially a version of 

the standard land-based turbine adapted to the marine environment. Although 

turbines of up to 5 MW have been installed, most recent orders from Vestas 

(Randers, Denmark) and Siemens (Munich, Germany), the two leading suppliers of 

offshore wind turbines, range from 2.0 MW to 3.6 MW. 

The architecture of the baseline offshore turbine and drivetrain comprises a threebladed upwind rotor, typically 90 m to 107 m in diameter. Tip speeds of offshore 

turbines are slightly higher than those of land-based turbines, which have speeds of 

80 m/s or more. The drivetrain consists of a gearbox generally run with variablespeed torque control that can achieve generator speeds between 1,000 and 1,800 

rpm. The offshore tower height is generally 80 m, which is lower than that of landbased towers because wind shear profiles are less steep, tempering the advantage of 

tower height. 

The offshore foundation system baseline technology uses monopiles at nominal 

water depths of 20 m. Monopiles are large steel tubes with a wall thickness of up to 

60 mm and diameters of 6 m. The embedment depth varies with soil type, but a 

typical North Sea installation must be embedded 25 m to 30 m below the mud line. 

The monopile extends above the surface where a transition piece with a flange to 

fasten the tower is leveled and grouted. Its foundation requires a specific class of 

installation equipment for driving the pile into the seabed and lifting the turbine and 

tower into place. Mobilization of the infrastructure and logistical support for a large 

offshore wind plant accounts for a significant portion of the system cost. 

Turbines in offshore applications are arranged in arrays that take advantage of the 

prevailing wind conditions measured at the site. Turbines are spaced to minimize 

aggregate power plant energy losses, interior plant turbulence, and the cost of 

cabling between turbines. 

The power grid connects the output from each turbine, where turbine transformers 

step up the generator and the power electronics voltage to a distribution voltage of 

about 34 kilovolts (kV). The distribution system collects the power from each 

turbine at a central substation where the voltage is stepped up and transmitted to 

shore through a number of buried, high-voltage subsea cables. A shore-based 

interconnection point might be used to step up the voltage again before connecting 

to the power grid. 

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2

Shallow water wind turbine projects have been proposed and could be followed by 

transitional and finally deepwater turbines. These paths should not be considered as 

mutually exclusive choices. Because there is a high degree of interdependence 

among them, they should be considered a sequence of development that builds from 

a shallow water foundation of experience and knowledge to the complexities of 

deeper water. 

2.5.3 TECHNOLOGY NEEDS AND POTENTIAL IMPROVEMENTS 

Offshore, wind turbine cost represents only one-third of the total installed cost of the 

wind project, whereas on land, the turbine cost represents more than half of the total 

installed cost. To lower costs for offshore wind, then, the focus must be on lowering 

the balance-of-station costs. These costs, which include those for foundations, 

electrical grids, O&M, and installation and staging costs, dominate the system COE. 

Turbine improvements that make turbines more reliable, more maintainable, more 

rugged, and larger, will still be needed to achieve cost goals. Although none of these 

improvements is likely to lower turbine costs, the net result will lower overall 

system costs. 

The commercialization of offshore wind energy faces many technical, regulatory, 

socioeconomic, and political barriers, some of which may be mitigated through 

targeted short- and long-range RD&D efforts. Short-term research addresses 

impediments that are preventing initial industry projects from proceeding and helps 

to sharpen the focus for long-term research. Long-term research involves a more 

complex development process resulting in improvements that can help lower 

offshore life-cycle system costs. 

Short-Term RD&D Options 

Conducting research that will lead to more rapid deployment of offshore turbines 

should be an upfront priority for industry. This research should address obstacles to 

today’s projects, and could include the following tasks: 

z	 Define offshore resource exclusion zones: A geographically based 

exclusion study using geographic information system (GIS) land use 

overlays would more accurately account for all existing and future 

marine uses and sensitive areas. This type of exclusion study could 

be part of a regional programmatic environmental impact statement 

and is necessary for a full assessment of the offshore resource 

(Dhanju, Whitaker, and Kempton 2006). Currently, the developer 

bears the burden of siting during a pre-permitting phase with very 

little official guidance. This activity should be a jointly funded 

industry project conducted on a regional basis. 

z	 Develop certification methods and standards: MMS has been 

authorized to define the structural safety standards for offshore wind 

turbines on the OCS. Technical research, analysis, and testing are 

needed to build confidence that safety will be adequate and to 

prevent overcautiousness that will increase costs unnecessarily. 

Development of these standards will require a complete evaluation 

and harmonization of the existing offshore wind standards and the 

American Petroleum Institute (API) offshore oil and gas standards. 

MMS is currently determining the most relevant standards. 

z	 Develop design codes, tools, and methods: The design tools that 

the wind industry uses today have been developed and validated for 

50	 20% Wind Energy by 2030 – Prepublication Version 
 

land-based utility-scale turbines, and the maturity and reliability of 

the tools have led to significantly higher confidence in today’s wind 

turbines. Offshore design tools are relatively immature by 

comparison. The development of accurate offshore computer codes 

to predict the dynamic forces and motions acting on turbines 

deployed at sea is essential for moving into deeper water. One major 

challenge is predicting loads and the resulting dynamic responses of 

the wind turbine support structure when it is subjected to combined 

wave and wind loading. These offshore design tools must be 

validated to ensure that they can deal with the combined dominance 

of simultaneous wind and wave load spectra, which is a unique 

problem for offshore wind installations. Floating system analysis 

must be able to account for additional turbine motions as well as the 

dynamic characterization of mooring lines. 

z	 Site turbines and configure arrays: The configuration and spacing 

of wind turbines within an array have a marked effect on power 

production from the aggregate wind plant, as well as for each 

individual turbine. Uncertainties in power production represent a 

large economic risk factor for offshore development. Offshore wind 

plants can lose more than 10% of their energy to array losses, but 

improvements in array layout and array optimization models could 

deliver substantial recovery (Seawind 2003). Atmospheric boundary 

layer interaction with the turbine wakes can affect both energy 

capture and plant-generated turbulence. Accurate characterization of 

the atmospheric boundary layer behavior and more accurate wake 

models will be essential for designing turbines that can withstand 

offshore wind plant turbulence. Wind plant design tools that are able 

to characterize turbulence generated by wind plants under a wide 

range of conditions are likely necessary. 

z	 Develop hybrid wind-speed databases: Wind, sea-surface 

temperatures, and other weather data are housed in numerous 

satellite databases available from the National Oceanic and 

Atmospheric Administration (NOAA), NASA, the National 

Weather Service (NWS), and other government agencies. These 

data can be combined to supplement the characterization of coastal 

and offshore wind regimes (Hasager et al. 2005). The limitations 

and availability of existing offshore data must be understood. 

Application of these data to improve the accuracy of offshore wind 

maps will also be important. 

Long-Term R&D Options 

Long-term research generally requires hardware development and capital 

investment, and it must take a complex development path that begins early so that 

mature technology will be ready when it is needed. Most long-term research areas 

relate to lowering offshore life-cycle system costs. These areas are subdivided into 

infrastructure and turbine-specific needs. Infrastructure to support offshore wind 

development represents a major cost element. Because this is a relatively new 

technology path, there are major opportunities for reducing the cost impacts in this 

area. Although land-based wind turbine designs can generally be used for offshore 

deployment, the offshore environment will impose special requirements on turbines. 

These requirements must be taken into account to optimize offshore deployment. 

Areas where industry should focus efforts include: 

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z	 Minimize work at sea: There are many opportunities to lower 

project costs by reallocating the balance between work done on land 

and at sea. In addition, the portion of labor devoted to project O&M, 

land-based installation and assembly, and remote inspections and 

diagnostics can be rebalanced with respect to upfront capital 

enhancements, such as higher quality assurance, more qualification 

testing, and reliable designs. This rebalancing might enable a 

significant life-cycle cost reduction by shifting the way wind 

projects are designed, planned, and managed. 

z	 Enhance manufacturing, installation and deployment strategies: 

New manufacturing processes and improvements in existing 

processes that reduce labor and material usage and improve part 

quality have high potential for reducing costs in offshore 

installations. Offshore wind turbines and components could be 

constructed and assembled in or near seaport facilities that allow 

easy access from the production area to the installation site, 

eliminating the necessity of shipping large components over inland 

roadways. Fabrication facilities must be strategically located for 

mass-production, land-based assembly, and for rapid deployment 

with minimal dependence on large vessels. Offshore system designs 

that can be floated out and installed without large cranes can reduce 

costs significantly. New strategies should be integrated into the 

turbine design process at an early stage (Lindvig 2005; Poulsen and 

Skjærbæk 2005). 

z	 Incorporate offshore service and accessibility features: To 

manage O&M, predict weather windows, minimize downtime, and 

reduce the equipment needed for up-tower repairs, operators should 

be equipped with remote, intelligent, turbine condition monitoring 

and self-diagnostic systems. These systems can alert operators to the 

need for operational changes or to schedule maintenance at the most 

opportune times. A warning about an incipient failure can alert the 

operators to replace or repair a component before it does significant 

damage to the system or leaves the machine inoperable for an 

extended period of time. More accurate weather forecasting will 

also become a major contributor in optimizing service for lower 

cost. 

z	 Develop low-cost foundations, anchors, and moorings: Current 

shallow-water foundations have already reached a practical depth 

limit of 30 m, and anchor systems beyond that are derived from 

conservative and expensive oil and gas design practices. Costsaving opportunities arise for wind power plants in deeper water 

with both fixed-bottom and floating turbine foundations, as well as 

for existing shallow-water designs in which value-engineering cost 

reductions can be achieved. Fixed-bottom systems comprising rigid 

lightweight substructures, automated mass-production fabrication 

facilities, and integrated mooring and piling deployment systems 

that minimize dependence on large sea vessels are possible low-cost 

options. Floating platforms will require a new generation of 

mooring designs that can be mass produced and easily installed. 

z	 Use resource modeling and remote profiling systems: Offshore 

winds are much more difficult to characterize than winds over land. 

Analytical models are essential to managing risk during the initial 

52	 20% Wind Energy by 2030 – Prepublication Version 
 

siting of offshore projects, but are not very useful by themselves for 

micrositing (Jimenez et al. 2005). Alternative methods are needed to 

measure wind speed and wind shear profiles up to elevations where 

wind turbines operate. This will require new equipment such as 

sonic detection and ranging (SODAR), light detection and ranging 

(LIDAR), and coastal RADAR-based systems that must be adapted 

to measure offshore wind from more stable buoy systems or from 

fixed bases. Some systems are currently under development but 

have not yet been proven (Antoniou et al. 2006). The results of an 

RD&D measurement program on commercial offshore projects 

could generate enough confidence in these systems to eliminate the 

requirement for a meteorological tower. 

z	 Increase offshore turbine reliability: The current offshore service 

record is mixed, and as such, is a large contributor to high risk. A 

new balance between initial capital investment and long-term 

operating costs must be established for offshore systems, which will 

have a significant impact on COE. Offshore turbine designs must 

place a higher premium on reliability and anticipation of on-site 

repairs than their land-based counterparts. Emphasis should be 

placed on avoiding large maintenance events that require expensive 

and specialized equipment. This can be done by identifying the root 

causes of component failures, understanding the frequency and cost 

of each event, and appropriately implementing design 

improvements (Stiesdal and Madsen 2005). Design tools, quality 

control, testing, and inspection will need heightened emphasis. 

Blade designers must consider strategies to offset the impacts of 

marine moisture, corrosion, and extreme weather. In higher 

latitudes, designers must also account for ice flows and ice accretion 

on the blades. Research that improves land-based wind turbine 

reliability now will have a direct impact on the reliability of future 

offshore machines. 

z	 Assess the potential of ultra-large offshore turbines: Land-based 

turbines may have reached a size plateau because of transportation 

and erection limits. Further size growth in wind turbines will largely 

be pushed by requirements unique to offshore turbine development. 

According to a report on the EU-funded UpWind project, “Within a 

few years, wind turbines will have a rotor diameter of more than 

150 m and a typical size of 8 MW–10 MW” (Risø National 

Laboratory 2005). The UpWind project plans to develop design 

tools to optimize large wind turbine components, including rotor 

blades, gearboxes, and other systems that must perform in large 

offshore wind plants. New size-enabling technologies will be 

required to push wind turbines beyond the scaling limits that 

constrain the current fleet. These technologies include lightweight 

composite materials and composite manufacturing, lightweight 

drivetrains, modular pole direct-drive generators, hybrid space 

frame towers, and large gearbox and bearing designs that are 

tolerant of slower speeds and larger scales. All of the weightreducing features of the taller land-based tower systems will have an 

even greater value for very large offshore machines (Risø National 

Laboratory 2005). 

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2

RD&D Summary 

The advancement of offshore technology will require the development of 

infrastructure and technologies that are substantially different from those employed 

in land-based installations. In addition, these advances would need to be tailored to 

U.S. offshore requirements, which differ from those in the European North Sea 

environment. Government leadership could accelerate baseline research and 

technology development to demonstrate feasibility, mitigate risk, and reduce 

regulatory and environmental barriers. Private U.S. energy companies need to take 

the technical and financial steps to initiate near-term development of offshore wind 

power technologies and bring them to sufficient maturity for large-scale deployment. 

Musial (2007) and Bywaters and colleagues (2005) present more detailed analyses 

of actions for offshore development. 

2.6 DISTRIBUTED WIND TECHNOLOGY 

Distributed wind technology (DWT) applications refer to turbine installations on the 

customer side of the utility meter. These machines range in size from less than 1 kW 

to multimegawatt, utility-scale machines used to offset electricity consumption at the 

retail rate. Because the WinDS deployment analysis does not currently segregate 

DWT from utility deployment, DWT applications are part of the land-based 

deployment estimates in the 20% wind energy scenario. 

Historically, DWT has been synonymous with small machines. The DWT market in 

the 1990s focused on battery charging for off-grid homes, remote 

telecommunications sites, and international village power applications. In 2000, the 

industry found a growing domestic market for behind-the-meter wind power, 

including small machines for residential and small farm applications and 

multimegawatt-scale machines for larger agricultural, commercial, industrial, and 

public facility applications. Although utility-scale DWT requirements are not 

distinguishable from those for other large-scale turbines, small machines have 

unique operating requirements that warrant further discussion. 

2.6.1 SMALL TURBINE TECHNOLOGY 

Until recently, three-bladed upwind designs using tail vanes for passive yaw control 

dominated small wind turbine technology rated at less than 10 kW. Furling, or 

turning the machine sideways to the wind with a mechanical linkage, was almost 

universally used for rotor overspeed control. The drivetrains were direct-drive, 

permanent-magnet alternators with variable-speed operation. Many of these 

installations were isolated from the grid. Today, there is an emerging technology 

trend toward grid-connected applications and nonfurling designs. U.S. 

manufacturers are world leaders in small wind systems rated at 100 kW or less, in 

terms of both market and technology. 

Turbine technology begins the transition from small to large systems between 20 

kW and 100 kW. Bergey Windpower (Norman, Oklahoma) offers a 50-kW turbine 

that uses technology commonly found in smaller machines, including furling; 

pultruded blades; a direct-drive, permanent-magnet alternator; and a tail vane for 

yaw control. Distributed Energy Systems has a 100-kW turbine that uses a directdrive, variable-speed synchronous generator. Although most wind turbines in the 

100-kW range have features common to utility-scale turbines, including gearboxes, 

mechanical brakes, induction generators, and upwind rotors with active yaw control, 

54 20% Wind Energy by 2030 – Prepublication Version 
 

Endurance Windpower (Spanish Fork, Utah) has a 4-kW machine with such 

characteristics. 

Reliability and acoustic emissions are the prominent issues for small DWT 

applications. These installations usually consist of single machines that are widely 

scattered, requiring simplicity in design, ease of repair, and long maintenance and 

inspection intervals. Because DWT applications are usually close to workplaces or 

residences, limiting sound emissions is critical for market acceptance and zoning 

approvals. DWT applications are also usually located in areas with low wind speeds 

that are unsuitable for utility-scale applications, so DWT places a premium on lowwind-speed technologies. 

The cost of DWT turbines per kilowatt scales inversely with turbine size. Smallscale DWT installation costs are always higher than those for utility-scale 

installations because the construction effort cannot be amortized over a large number 

of machines. For a 1-kW system, hardware costs alone can be as high as $5,000 to 

$7,000/kW. Installation costs vary widely because of site-specific factors such as 

zoning and/or permitting costs, interconnection fees, balance-of-station costs, 

shipping, and the extent of do-it-yourself participation. Five-year warranties are now 

the industry standard for small wind turbines, although it is not yet known how this 

contributes to turbine cost. The higher costs of this technology are partially offset by 

the ability of these applications to compete with retail electricity rates. In addition, 

the use of small turbines makes connection to the electric distribution system 

possible, eliminating the need for an expensive interconnection between the 

substation and the transmission. 

Tower and foundation costs make up a larger portion of DWT installed cost, 

especially for wind turbines of less than 20 kW. Unlike utility-scale turbines that 

commonly use tapered tubular steel towers, multiple types, sources, and heights of 

towers are available for small wind turbines. 

2.6.2 TECHNOLOGY TRENDS 

Recent significant developments in DWT systems less than 20 kW include the 

following: 

z	 Alternative power and load control strategies: Furling inherently 

increases sound levels because the cross-wind operation creates a 

helicopter-type chopping noise. Aerodynamic models available 

today cannot accurately predict the rotor loads in the highly skewed 

and unsteady flows that occur during the furling process, 

complicating design and analysis. Alternative development 

approaches include soft-stall rotor-speed control, constant-speed 

operation, variable-pitch blades, hinged blades, mechanical brakes, 

and centrifugally actuated blade tips. These concepts offer safer, 

quieter turbines that respond more predictably to high winds, gusts, 

and sudden wind direction changes. 

z	 Advanced blade manufacturing methods: Blades for small 

turbines have been made primarily via hand lay-up manufacturing 

of fiberglass or pultrusion. The industry is now pursuing alternative 

manufacturing techniques, including injection, compression, and 

reaction injection molding. Advantages such as shorter fabrication 

time, lower parts costs, and increased repeatability and uniformity 

are often achieved using these methods, although the tooling costs 

are typically higher. 

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2

z	 Rare-earth permanent magnets: Ferrite magnets have long been 

the staple in permanent-magnet generators for small wind turbines. 

Rare-earth permanent magnets are now taking over the market with 

Asian suppliers offering superior magnetic properties and a steady 

decline in price. The result is more compact and lighter weight 

generator designs. 

z	 Reduced generator cogging: Concepts for generators with reduced 

cogging torque (the force needed to initiate generator rotation) are 

showing promise to reduce cut-in wind speeds, an important 

characteristic for enhancing low-wind-speed turbine performance. 

z	 Induction generators: Small turbine designs that use induction 

generators are under development. This approach, common in the 

early 1980s, avoids the use of power electronics that increase cost 

and complexity and reduce reliability. 

z	 Grid-connected inverters: Inverters used in the photovoltaics 

market are being adapted for use with wind turbines. Turbinespecific inverters are also appearing in both single- and three-phase 

configurations. Another new trend is obtaining certification of most 

inverters by Underwriters Laboratories and others for compliance 

with national interconnection standards. 

z	 Reduced rotor speeds: To reduce sound emissions, turbine designs 

with lower tip-speed ratios and lower peak-rotor speeds are being 

pursued. 

z	 Design standards and certification: The industry is increasing the 

use of consensus standards in its turbine design efforts for machines 

with rotor swept areas under 200 square meters (m2

; about 65 kW 

rated power). In particular, IEC Standard 61400-2 Wind Turbines – 

Part 2: Design Requirements of Small Wind Turbines. Currently, 

however, a limited number of wind turbines have been certified in 

compliance with this standard because of the high cost of the 

certification process. To address this barrier, a Small Wind 

Certification Council has been formed in North America to certify 

that small wind turbines meet the requirements of the draft AWEA 

standard that is based on the IEC standard (AWEA 1997–2006). 

2.7	 SUMMARY OF WIND TECHNOLOGY DEVELOPMENT 

NEEDS 

Wind technology must continue to evolve if wind power is to contribute more than a 

few percentage points of total U.S. electrical demand. Fortunately, no major 

technology breakthroughs in land-based wind technology are needed to enable a 

broad geographic penetration of wind power into the electric grid. However, there 

are other substantial challenges (such as transmission and siting) as well as 

significant costs associated with increased penetration that are discussed in other 

chapters of this report. No single component improvement in cost or efficiency can 

achieve the cost reductions or improved capacity factor that system-level advances 

can achieve. 

The wind capacity factor can be increased by enlarging rotors and installing them on 

taller towers. This would require advanced materials, controls, and power systems 

56	 20% Wind Energy by 2030 – Prepublication Version 
 

that can significantly reduce the weight of major components. Capital costs would 

also be brought down by the manufacturing learning curve that is associated with 

continued technology advancement and by a nearly fivefold doubling of installed 

capacity. 

The technology development required to make offshore wind a viable option poses a 

substantial potential risk. Offshore wind deployment represents a significant fraction 

of the total wind deployment necessary for 20% wind energy by 2030. Today’s 

European shallow-water technology is still too expensive and difficult to site in U.S. 

waters. Deepwater deployment would eliminate visual esthetics concerns, but the 

necessary technologies have yet to be developed, and the potential environmental 

impacts have yet to be evaluated. Work is needed on analysis methods, evaluating 

technology pathways, 

and fielding offshore Figure 2-17. Types of repairs on wind turbines from 2.5 kW to 1.5 MW 

prototypes to establish 

the offshore option. 

Today’s market success 

is the product of a 

combination of 

technology achievement 

and supportive public 

policy. A 20% Wind 

Scenario would require 

additional land-based 

technology 

improvements and a 

substantial development 

of offshore technology. 

The needed cost and 

performance 

improvements could be 

achieved with 

innovative changes in 

existing architectures 

that incorporate novel 

advances in materials, 

design approaches, control strategies, and manufacturing processes. Risks are 

mitigated with standards that produce reliable equipment and full-scale testing that 

ensures the machinery meets the design requirements. 

Fostering the Introduction of Technology Improvements 

The 20% Wind Scenario assumes a robust technology that will produce costcompetitive generation without preferential tax subsidies. Areas where industry can 

focus RD&D efforts include those which require the most frequent repairs (see 

Figure 2-17). 

z	 Increasing the capacity factors by placing larger rotors on taller 

towers (this can be achieved economically only by using lighter 

components and load-mitigating rotors that reduce the integrated 

tower-top mass and structural loads; reducing parasitic losses 

throughout the system can also make gains possible), and 

developing advanced controls and improved power systems. 

2

20% Wind Energy by 2030 – Prepublication Version 57 
 

 

 

 

2

z	 Reducing the capital cost with steady learning-curve improvements 

driven by innovative manufacturing improvements and a nearly 

fivefold doubling of installed capacity 

2.8 REFERENCES AND OTHER SUGGESTED READING 

Antoniou, I., H.E. Jørgensen, T. Mikkelsen, S. Frandsen, R. Barthelmie, C. Perstrup, 

and M. Hurtig. 2006. “Offshore Wind Profile Measurements from Remote 

Sensing Instruments.” Presented at the European Wind Energy Conference, 

February 27–March 2, Athens, Greece. 

Ashwill, T. 2004. Innovative Design Approaches for Large Wind Turbine Blades: 

Final Report. Report No. SAND2004-0074. Albuquerque, NM: Sandia 

National Laboratories. 

AWEA (American Wind Energy Association). 1996–2007. IEC Wind Turbine 

Standards. http://www.awea.org/standards/iec_stds.html#WG4. 

Behnke, Erdman, and Whitaker Engineering (BEW Engineering). 2006. Low Wind 

Speed Technology Phase II: Investigation of the Application of MediumVoltage Variable-Speed Drive Technology to Improve the Cost of Energy 

from Low Wind Speed Turbines. Report No. FS-500-37950, DOE/GO102006-2208. Golden, CO: National Renewable Energy Laboratory 

(NREL). http://www.nrel.gov/docs/fy06osti/37950.pdf. 

Bossanyi, E.A. 2003. “Individual Blade Pitch Control for Load Reduction,” Wind 

Energy, 6(2): 119–128. 

Brown, A. 2007. “Very Light and Fast.” Mechanical Engineering, January. 

http://www.memagazine.org/jan07/features/verylight/verylight.html. 

BSH (Bundesamt für Seeschifffahrt und Hydrographie.). Wind Farms. 

http://www.bsh.de/en/Marine%20uses/Industry/Wind%20farms/index.jsp. 

BTM Consult. 2005. World Market Update 2005. Ringkøbing, Denmark: BTM 

Consult ApS. http://www.btm.dk/Pages/wmu.htm. 

BWEA (British Wind Energy Association). “Offshore Wind.” 

http://www.bwea.com/offshore/info.html. 

Bywaters, G., V. John, J. Lynch, P. Mattila, G. Norton, J. Stowell, M. Salata, O. 

Labath, A. Chertok, and D. Hablanian. 2005. Northern Power Systems 

WindPACT Drive Train Alternative Design Study Report; Period of 

Performance: April 12, 2001 to January 31, 2005. Report No. SR-500­

35524. Golden, CO: NREL. http://www.nrel.gov/publications/ 

Cohen, J., T. Schweizer, A. Laxson, S. Butterfield, S. Schreck, L. Fingersh, P. 

Veers, and T. Ashwill. 2008. Technology Improvement Opportunities for 

Low Wind Speed Turbines and Implications for Cost of Energy Reduction. 

Report No. NREL/SR-500-41036. Golden, CO: NREL. 

Cotrell, J., W.D. Musial, and S. Hughes. 2006. The Necessity and Requirements of a 

Collaborative Effort to Develop a Large Wind Turbine Blade Test Facility 

in North America. Report No. TP-500-38044. Golden, CO: NREL 

Dhanju A., P. Whitaker, and W. Kempton. 2006. “Assessing Offshore Wind 

Resources: A Methodology Applied to Delaware.” Presented at the AWEA 

Conference & Exhibition, June 4–7, Pittsburgh, PA. 

58	 20% Wind Energy by 2030 – Prepublication Version 
 

DOE (U.S. Department of Energy). 2000. World’s Most Advanced Gas Turbine 

Ready to Cross Commercial Threshold. Washington, DC: DOE. 

http://www.fossil.energy.gov/news/techlines/2000/tl_ats_ge1.html. 

EIA (Energy Information Administration). 2006. “State Electricity Sales 

Spreadsheet.” http://www.eia.doe.gov/cneaf/electricity/epa/sales_state.xls. 

EUI (Energy Unlimited Inc.). 2003. Variable Length Wind Turbine Blade. Report 

No. DE-FG36-03GO13171. Boise, ID: EUI. 

http://www.osti.gov/bridge/servlets/purl/841190-OF8Frc/ 

Griffin, D.A. 2001. WindPACT Turbine Design Scaling Studies Technical Area 1 – 

Composite Blades for 80- to 120-Meter Rotor. Report No. SR-500­

29492.Golden, CO: NREL. 

Hasager, C.B., M.B. Christiansen, M. Nielsen, and R. Barthelmie. 2005. “Using 

Satellite Data for Mapping Offshore Wind Resources and Wakes.” 

Presented at the Copenhagen Offshore Wind Proceedings, October 26–28, 

Copenhagen, Denmark. 

IEC (International Electrotechnical Commission). 2007. “Technical Committee 88: 

Wind turbines, Standards 61400-x.” 

http://nettedautomation.com/standardization/IEC_TC88/index.html 

ISET (Institut fuer Solare Energieversorgungstechnik). 2003. Experience Curves: A 

tool for Energy Policy Programmes Assessment (EXTOOL). Lund, Sweden: 

ISET. http://www.iset.uni-kassel.de/extool/Extoolframe.htm. 

Jimenez, B., F. Durante, B. Lange, T. Kreutzer, and L. Claveri. 2005. “Offshore 

Wind Resource Assessment: Comparative Study between MM5 and 

WAsP.” Presented at the Copenhagen Offshore Wind Proceedings, October 

26–28, Copenhagen, Denmark. 

Knight, S., and L. Harrison. 2005. “A More Conservative Approach.” Windpower 

Monthly, November. 

Kühn, P. 2006. “Big Experience with Small Wind Turbines (SWT).” Presented at 

the 49th IEA Topical Expert Meeting, September, Stockholm, Sweden. 

Lindvig, K. 2005. “Future Challenges for a Marine Installation Company.” 

Presented at the Copenhagen Offshore Wind Proceedings, October 26–28, 

Copenhagen, Denmark. 

MMS (Minerals Management Service). Alternative Energy and Alternate Use 

Program. 

http://www.mms.gov/offshore/RenewableEnergy/RenewableEnergyMain.ht 

m. 

Musial, W. 2007. “Offshore Wind Electricity: A Viable Energy Option for the 

Coastal United States.” Marine Technology Society Journal, 42 (3), 32-43. 

Northern Power Systems. 2006. Low Wind Speed Technology Phase I: Advanced 

Power Electronics for Low Wind Speed Turbine Applications. Report No. 

FS-500-37945, DOE/GO-102006-2205. Golden, CO: NREL. 

http://www.nrel.gov/docs/fy06osti/37945.pdf. 

NREL. 2002. Addendum to WindPACT Turbine Design Scaling Studies Technical 

Area 3 – Self-Erecting Tower and Nacelle Feasibility: Report No. SR-500­

29493-A. Golden, CO: NREL. 

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O'Connell, R., and R. Pletka, et al. 2007. 20 % Wind Energy Penetration in the 

United States: A Technical Analysis of the Energy Resource. Overland Park, 

KS: Black & Veatch. 

Pace Global Energy Services, Aug. 2007, Assessment of Offshore Wind Power 

Resources, http://www.lipower.org/newscenter/pr/2007/pace_wind.pdf 

Peregrine Power. 2006. Low Wind Speed Technology Phase II: Breakthrough in 

Power Electronics from Silicon Carbide. Report No. FS-500-37943, 

DOE/GO-102006-2203. Golden, CO: NREL. 

http://www.nrel.gov/docs/fy06osti/37943.pdf 

Poulsen, S.F., and P.S. Skjærbæk. 2005. “Efficient Installation of Offshore Wind 

Turbines: Lessons Learned from Nysted Offshore Wind Farm.” Presented at 

the Copenhagen Offshore Wind Proceedings, October 26–28, Copenhagen, 

Denmark. 

Risø National Laboratory. 2005. Association Euratom - Risø National Laboratory 

Annual Progress Report 2005. Report No. Risø-R-1579(EN). Roskilde, 

Denmark: Risø National Laboratory. 

http://www.risoe.dk/rispubl/ofd/ofdpdf/ris-r-1579.pdf. 

SEAWIND, Altener Project, 2003. (Per Nielsen) “Offshore Wind Energy Projects 

Feasibility Study Guidelines,” Denmark. 

Stiesdal, H., and P.H. Madsen. 2005. “Design for Reliability.” Presented at the 

Copenhagen Offshore Wind Proceedings, October 26–28, Copenhagen, 

Denmark. 

Walford, C.A. 2006. Wind Turbine Reliability: Understanding and Minimizing Wind 

Turbine Operation and Maintenance Costs. Report No. SAND2006-1100. 

Albuquerque, NM: Sandia National Laboratories. 

Wiser, R., and M. Bolinger. 2007. Annual Report on U.S. Wind Power Installation, 

Cost, and Performance Trends: 2006. DOE/GO–102007-2433. Golden, CO: 

NREL. 

http://www.osti.gov/bridge/product.biblio.jsp?query_id=0&page=0&osti_id 

=908214 

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Chapter 3.	 Manufacturing, 

Material and 

Resources 

3

Stakeholders and decision makers need to know whether a generation mix of 20% 

wind energy by 2030 might be constrained by raw materials availability, 

manufacturing capability, or labor availability. This chapter examines the adequacy 

of these critical resources. 

Over the past five years, the wind industry in the United States has grown by an 

average of 22% annually. In 2006 alone, America’s wind power generating capacity 

increased by 27%. 

The U.S. wind energy industry invested approximately $4 billion in 2,454 

megawatts (MW) of new generating capacity in 2006, making wind the second 

largest source of new power generation in the nation—surpassed only by natural 

gas—for the second year in a row. Recently installed wind farms increased 

cumulative installed U.S. wind energy capacity to 13,884 MW—well above the 

10,000 MW milestone reached in August 2006 (AWEA 2007). On average, 1 MW 

of wind power produces enough electricity to power 250 to 300 U.S. homes. 

Based on estimates released by the U.S. Department of Energy (DOE) Energy 

Information Administration (EIA 2006), annual electricity consumption in the 

United States is expected to grow at a rate of 1.3% annually—from 3.899 billion 

megawatt-hours (MWh) in 2006 to about 5.368 billion MWh in 2030. Although 

wind energy supplied approximately 0.8% of total electricity in 2006, more and 

larger wind turbines can help to meet a growing demand for electricity. (See the 

Glossary in Appendix E for explanations of wind energy capacity and measurement 

units.) 

The most common large turbines currently in use have a rated capacity of between 1 

MW and 3 MW, with rotor diameters between 60 meters (m) and 90 m, tower 

heights between 60 m and 100 m, and capacity factors between 30% and 40% 

(capacity factor is an indicator of annual energy production). Although currently 

installed machines are expected to operate through 2030, larger turbines (with 

capacity factors that increase over time, as discussed in Chapter 2) are expected to 

become more common as offshore technology advances are transferred to landbased turbines. These larger turbines could reach rated power between 4 MW and 6 

MW and capacity factors between 40% and 50%. 

For the purpose of estimating the raw materials and investment effort needed to 

support the required level of wind penetration in the 20% Wind Scenario, industry 

leaders have assumed that most of the wind turbines used in the next two to three 

decades will be in the 1-MW to 3-MW class, with a modest contribution of the 

larger-sized machines (see Chapter 2). Today, approximately 2,000 turbines are 

installed each year, but that figure is expected to rise and to level out at about 7,000 

turbines per year by 2017. 

20% Wind Energy by 2030 – Prepublication Version 61 
 

 

 

3.1 RAW MATERIALS REQUIREMENTS
 

3

Wind turbines are built in many sizes and configurations, with a wide range of 

materials used in manufacturing large turbines. Reducing the weight and cost of the 

turbines is the key to making wind energy competitive with other power sources, 

and several research programs have significantly improved the efficiency of current 

machines. Throughout the next few decades, business opportunities are expected to 

expand in wind turbine components and materials manufacturing. To reach the high 

levels of wind energy associated with the 20% Wind Scenario, materials usage will 

also need to increase considerably, even as new technologies that improve 

component performance are introduced. 

To estimate the raw materials required for the 20% Wind Scenario, this analysis 

focuses on the most important materials used in building a wind turbine today (such 

as steel and aluminum) and on main turbine components. Table 3-1 shows the 

percentage of different materials used in each component and each component’s 

percentage of total turbine weight. The table applies to turbines in the 1.5-MW class, 

as well as to larger turbines of 4 MW or more. 

Using the materials consumption model in Table 3-1 as a basis, Table 3-2 further 

describes the raw materials required to reach manufacturing levels of about 7,000 

turbines per year. This analysis assumes that turbines will become lighter and annual 

installation rates will level off to roughly 7,000 turbines per year by 2017 and 

continue at that rate through 2030. Approximately 100,000 turbines will be required 

to produce 20% of the nation’s electricity in 2030. 

No single component dominates a wind turbine’s total cost, which is generally split 

evenly among the rotor, electrical system, drivetrain, and tower. The technological 

progress described in Chapter 2, however, could significantly reduce costs (e.g., 

through the use of lighter weight components for blades and towers). 

The availability of critical resources is crucial for large-scale manufacturing of wind 

turbines. The most important resources are steel, fiberglass, resins (for composites 

and adhesives), blade core materials, permanent magnets, and copper. The 

production status of these materials is reviewed in the following list: 

z	 Steel: The steel needed for additional wind turbines is not expected 

to have a significant impact on total steel production. (In 2005, the 

United States produced 93.9 million metric tons of steel, or 8% of 

the worldwide total.) Although steel will be required for any 

electricity generation technology installed over the next several 

decades, it can be recycled. As a result, replacing a turbine after 20+ 

years of service would not significantly affect the national steel 

demand because recycled steel can be used in other applications for 

which high-quality steel is not a requirement (Laxson, Hand, and 

Blair 2006). 

z	 Fiberglass: Additional fiberglass furnaces would be needed to build 

more wind turbines. Primary raw materials for fiberglass (sand) are 

in ample supply, but availability and costs are expected to fluctuate 

for resins, adhesives, and cores made from the petroleum-based 

chemicals that are used to impregnate the fiberglass (Laxson, Hand, 

and Blair 2006). 

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Table 3-1. Main components and materials used in a wind turbine (%) 

1.5 MW 

Rotor 

Weight % Magnet 

Permanent Concrete Steel Aluminum Copper GRP CRP Adhesive Core TOTAL

 Hub 6.0 100 100.0 

Blades 7.2 2 78 15 5 100.0 

Nacelle

 Gearbox 10.1 96 2 2 100.0 

Generator 3.4 65 35 100.0 

Frame 6.6 85 9 3 3 100.0 

Tower 66.7 2 98 

100.0 0.0 1.3 89.1 0.8 1.6 5.8 0.0 1.1 0.4 100.0 

4 MW 

Rotor

Magnet 

Permanent Concrete Steel Aluminum Copper GRP CRP Adhesive Core 

Hub 6.00 100 100.0 

Blades 7.6 2 68 10 15 5 100.0 

Nacelle

 Gearbox 10.10 96 2 2 100.0 

Generator 2.7 3 93 4 100.0 

Frame 6.60 85 9 3 3 100.0 

Tower 67.00 2 98 

100.0 0.08 1.34 89.63 0.80 0.51 5.37 0.76 1.14 0.38 100.0 

Notes: Tower includes foundation. GRP = glass-fiber-reinforced plastic. CRP = carbon fiber reinforced plastic 

Source: Sterzinger and Svrcek (2004) 

Table 3-2. Yearly raw materials estimate (thousands of metric tons) 

Year kWh/kg Permanent 

Magnet Concrete Steel Aluminum Copper GRP CRP Adhesive Core 

2006 65 0.03 1,614 110 1.2 1.6 7.1 0.2 1.4 0.4 

2010 70 0.07 6,798 464 4.6 7.4 29.8 2.2 5.6 1.8 

2015 75 0.96 16,150 1,188 15.4 10.2 73.8 9.0 15.0 5.0 

2020 80 2.20 37,468 2,644 29.6 20.2 162.2 20.4 33.6 11.2 

2025 85 2.10 35,180 2,544 27.8 19.4 156.2 19.2 31.4 10.4 

2030 90 2.00 33,800 2,308 26.4 18.4 152.4 18.4 30.2 9.6 

Notes: kg = kilograms; GRP = glass-fiber-reinforced plastic. CRP = carbon fiber reinforced plastic 

Source: Sterzinger and Svrcek (2004) 

z Core: End-grain balsa wood is an alternative core material that can 

replace the low-density polymer foam used in blade construction. 

Availability of this wood might be an issue based on the growth rate 

of balsa trees relative to the projected high demand. 

z Carbon fiber: Current global production of commercial-grade 

carbon fiber is approximately 50 million pounds (lb) per year. The 

use of carbon fiber in turbine blades in 2030 alone would nearly 

double this demand. To achieve such drastic industry scale-up, 

changes to carbon fiber production technologies, production 

facilities, packaging, and emissions-control procedures will be 

required. 

z Permanent magnets: By eliminating copper from the generator 

rotor and using permanent magnets, which are becoming more 

economically feasible, it is possible to build smaller and lighter 

generators. World magnet production in 2005 was about 40,000 

metric tons, with about 35,000 metric tons produced in China. 

Although supply is not expected to be restricted, significant 

additions to the manufacturing capability would be required to meet 

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3

the demand for wind turbines and other products (Trout 2002; 

Laxson, Hand, and Blair 2006). 

z	 Copper: Although wind turbines use significant amounts of copper, 

the associated level of demand still equates to less than 4% of the 

available copper. This demand level, then, would not have a 

significant impact on national demand (U.S. refined copper 

consumption was 2.27 million metric tons in 2005). Although 

copper ranks third after steel and aluminum in world metals 

consumption, global copper production is adequate to satisfy 

growing demands from the wind industry. Copper price escalation 

has exceeded inflation in recent years and could affect turbine costs. 

Despite the demand and supply status of these materials, new component 

developments are expected to significantly change material usage patterns. 

Generally, trends are toward using lighter-weight 

materials, as long as the life-cycle costs are low. In Material Usage Analysis addition to the findings of Ancona and McVeigh

(Ancona and McVeigh 2001) (2001; described in the Materials Usage Analysis 

sidebar), other trends in turbine components are •	 Turbine material usage is, and will continue 

outlined in the subsections that follow. to be, dominated by steel. 

• Opportunities exist for introducing Evolution of Rotors aluminum or other lightweight composites, 

provided that cost, strength, and fatigue Most rotor blades in use today are built from glassrequirements can be met. fiber-reinforced plastic (GRP). Steel and various 

composites and carbon filament-reinforced plastic • GRP is expected to continue to be used for (CFRP) are also used. As the rotor size increases blades. for larger machines, the trend will be toward high­ • The use of carbon fiber might help reduce strength, fatigue-resistant materials. Composites weight and cost. involving steel, GRP, CFRP, and possibly other 

new materials will likely come into use as turbine 

primary drivers. designs evolve. • Low costs and high reliability remain the 

• Variable-speed generators will become more Changes to Machine Heads common. 

The machine head contains an array of complex • Permanent-magnet generators on larger machinery including yaw drives, blade-pitch­ turbines will increase the need for magnetic change mechanisms, drive brakes, shafts, bearings, materials. 

oil pumps and coolers, controllers, a bedplate, the • Simplification of the nacelle machinery drivetrain, the gearbox, and an enclosure. Design might reduce raw material costs and also simplifications and innovations are anticipated in increase reliability. each element of the machine head. 

3.2 MANUFACTURING CAPABILITY 

In principle, a sustainable level of annual wind turbine installation would be best 

supported by a substantial domestic manufacturing base. If installation rates 

fluctuate greatly from one year to the next, however, manufacturing capability may 

or may not be able to grow or shrink as necessary. The National Renewable Energy 

Laboratory (NREL) created a simple model to explore sustainable installation rates 

that would maintain wind energy production at specific levels spanning several 

decades (Laxson, Hand, and Blair 2006). 

64	 20% Wind Energy by 2030 – Prepublication Version 
 

 

Figure 3-1. a. Annual installed wind energy The NREL study explored a number of capacity to meet 20% of energy demand. alternative scenarios for annual wind power b. Cumulative installed wind energy capacity expansion to understand their potential capacity to meet 20% of energy demand. impact on wind energy installation and 

manufacturing rates. The results indicate that 

achieving the 20% Wind Scenario by 2030 is 

possible without overwhelming the ability of 

U.S. industry to meet those demands (Laxson, 

Hand, and Blair 2006). 

NREL’s study assessed potential barriers that 

would prohibit near-term high wind penetration 

levels, such as manufacturing rates or resource 

limitations. To reach 20% electric generation 

from wind by 2030 in the United States, the 

authors noted, an annual installed capacity 

increase of about 20% would need to be 

sustained for a decade (Laxson, Hand, and Blair 

2006). Figure 3-1 shows a comparison of three 

models for the installation rates required to meet 

three energy supply goals of 10%, 20%, and 

30% of total national electrical energy 

production from wind by 2030. Figure 3-1(a) 

shows the annual rates and Figure 3-1(b). shows 

the cumulative capacity attained in each case. A 

manufacturing production level of 20 gigawatts 

(GW) per year by 2017—and maintained at this 

value thereafter—would reach levels close to 

400 GW of wind energy capacity by 2030. 

The NREL study assumed that the wind plant 

capacity factor would not change from year to 

year or from location to location, providing an 

upper bound on the annual installation rate and 

cumulative capacity required to produce 20% of 

electricity demand. Alternatively, the 20% Wind 

Scenario evaluation assumes that plant capacity 

factors will increase modestly with experience and technology improvements (see 

Chapter 2). The 20% scenario also accounts for regional variations in wind 

resources, as explained in Appendix A’s detailed description of the analytic 

modeling approach employed. Note that when these refinements are included, the 

20% curve in Figure 3-1(a) shifts downward, somewhat similar to that shown in 

Figure 3-2. 

This chapter discusses the materials and manufacturing needed to pursue 20% wind 

energy from 2007 through 2030 to meet the annual and cumulative installed capacity 

shown in Figure 3-2. This figure shows the forecasts for annual and cumulative 

installed wind energy capacity, which also forms the basis for estimates of new wind 

turbines and the raw materials required to produce them. In this scenario, annual 

installations climb to more than 16 GW per year, and the total installed wind 

capacity increases to 305 GW by 2030. Between 2007 and 2030, 293 GW are 

installed. (For more details on the modeling approach used, see Appendix A.) 

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Figure 3-2. Annual and cumulative installed wind 

energy capacity represented in the 20% Wind Scenario 

3

3.2.1 CURRENT MANUFACTURING FACILITIES 

A growing number of states and companies in the United States are ramping up 

capacity to manufacture wind turbines, or have the ability to do so. Jobs are 

expected to remain in the U.S. if investments are made in certain components and in 

advanced manufacturing technologies. 

A useful perspective on growing manufacturing requirements is provided by a nongovernment organization study released in 2004 called Wind Turbine Development: 

Location of Manufacturing Activity (Sterzinger and Svrcek 2004). This study 

investigated the current and future U.S. wind manufacturing industry, both to 

determine the location of companies involved in wind turbine production and to 

examine limitations to a rapidly expanding wind business. The report covered four 

census regions (the Midwest, Northeast, South, and West) and divided turbine 

manufacturing into 20 separate components. These components were grouped into 

five categories, as shown in Table 3-1. The table also shows the locations of U.S. 

Table 3-3. Locations of U.S. wind turbine component manufacturers 

Region Division Rotor Nacelle Gearbox & Generator & Tower Division 

and Drivetrain Power Total 

Controls Electronics 

Midwest East North Central 6 5 8 1 2 22 

West North Central 1 0 1 1 8 11 

Northeast Middle Atlantic 3 4 4 5 1 17 

New England 0 6 0 2 0 8 

South East South Central 0 0 0 0 2 2 

South Atlantic 3 2 1 1 2 9 

West South Central 4 5 0 1 6 16 

West Mountain 1 0 0 1 0 2 

Pacific 5 4 2 4 4 19 

Component Total: 23 26 16 16 25 106 

(Sterzinger and Svrcek 2004) 

66 20% Wind Energy by 2030 – Prepublication Version 
 

wind turbine component manufacturers in 2004, broken down by region. Among the 

106 companies surveyed, about 90 companies directly manufacture components for 

utility-scale wind turbines, with utility scale being roughly defined as 1 MW or 

greater. 

Figure 3-3 shows the locations of a number of the current manufacturers of wind 

turbines and components. These firms are widely distributed around the country and 

some are located in regions with, as yet, little wind power development. 

Figure 3-3. Examples of manufacturers supplying wind equipment 

across the United States 

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3

A large national investment in wind would likely spread beyond these active 

companies. To identify this potential, the North American Industrial Classification 

System (NAICS; http://www.census.gov/epcd/www/naics.html) was searched to 

identify companies operating under relevant industry codes. The manufacturing 

activity related to wind power development is substantial and widely dispersed 

(Sterzinger and Svrcek 2004). As shown in Table 3-4, more than 16,000 firms are 

currently producing products under one or more of the NAICS codes that include 

manufacture of wind components. These firms are spread across all 50 states. They 

are concentrated, however, in the most populous states and the states that have 

suffered the most from loss of manufacturing jobs. The 20 states that would likely 

receive the most investment and the most new manufacturing jobs from wind power 

expansion account for 75% of the total U.S. population, and 76% of the 

manufacturing jobs lost in the last 3.5 years. 

Table 3-4. U.S. Manufacturing firms with technical potential to 

enter wind turbine component market 

NAICS 

Code 

Code Description Total 

Employees 

Annual Payroll 

($1000s) 

Number of 

Companies 

326199 All Other Plastics Products 501,009 15,219,355 8,174 

331511 Iron Foundries 75,053 3,099,509 747 

332312 Fabricated Structural Metal 106,161 3,975,751 3,033 

332991 Ball and Roller Bearings 33,416 1,353,832 198 

333412 Industrial and Commercial Fans and Blowers 11,854 411,979 177 

333611 Turbines, and Turbine Generators, and 

Turbine Generator Sets 17,721 1,080,891 110 

333612 Speed Changer, Industrial 13,991 539,514 248 

333613 Power Transmission Equip. 21,103 779,730 292 

334418 Printed Circuits and Electronics Assemblies 105,810 4,005,786 716 

334519 Measuring and Controlling Devices 34,499 1,638,072 830 

335312 Motors and Generators 62,164 2,005,414 659 

335999 Electronic Equipment and Components, NEC 42,546 1,780,246 979 

Total 1,025,327 35,890,079 16,163 

A 2006 NGO report entitled “Renewable Energy Potential: A Case Study of 

Pennsylvania (Sterzinger and Stevens 2006) identified the bottlenecks in the 

component supply chain. Bottlenecks were identified for various components, but 

obtaining gearbox components was particularly problematic. Currently, only a few 

manufacturers in the world deliver gearboxes for large wind turbines. To sustain a 

growing number of wind installations, additional investment must be in place to 

support the development of a gearbox industry focused on wind applications. The 

manufacture of large bearings—as well as the number of large castings suppliers— 

will also need to expand. 

The wind equipment manufacturing sector also faces trade-offs between using 

domestic or foreign manufacturing facilities. An advantage to U.S.-based operations 

is a reduction in the often significant transportation cost of moving large 

components such as blades and towers. Manufacturing many significant wind 

turbine components is also a labor-intensive process. With U.S. labor wage rates at 

higher levels than those paid in many other countries, manufacturers have naturally 

been drawn to setting up their factories outside the United States (e.g., in Mexico 

and China). One wind blade manufacturer with significant international 

68 20% Wind Energy by 2030 – Prepublication Version 
 

manufacturing experience estimates that, to make a U.S. factory competitive, the 

labor hours per blade would need to be reduced by a factor of 30%–35%. To ensure 

that the bulk of these manufacturing jobs stay in the United States, automation and 

productivity gains through the development of advanced manufacturing technology 

are needed. These gains will allow the higher U.S. wage rates to be competitive. 

To attract these jobs, a number of U.S. states have set aside funds for RD&D, with 

plans to collaborate with industry and the federal government on a cost-shared basis. 

Collaboration among state, industry, and federal programs in advanced 

manufacturing can create competitive U.S. factories with U.S. employees whose 

jobs will remain secure for a long time. 

3.2.2 RAMPING UP ENERGY INDUSTRIES 

In the United States, several industries have experienced large rates of growth over a 

short period of time. The power plants most commonly used to produce electricity 

around the world are large in scale, such as thermal power stations fired with coal, 

gas or oil, or nuclear reactors. Nuclear power stations have been developed mainly 

since the middle of the twentieth century and have now reached a penetration of 

17.1% in the world’s power supply. Worldwide, nuclear power plant installations 

saw a 17% annual growth rate between 1960 and 1997 (BTM 1999). In the United 

States, despite a halt in new nuclear plant licensing in the early 1980s, nuclear plants 

generate about 20% of the nation’s electrical energy, and have done so for the last 

decade or more. The history of nuclear power shows that it is possible to achieve 

substantial levels of penetration with a new technology over two to three decades. 

Even though the time horizon of the 20% Wind Scenario is consistent with the 

historical development of nuclear power, it is nonetheless difficult to directly 

compare penetration patterns for nuclear power and wind technology. Wind power is 

a smaller scale technology, with a current typical commercial unit size of 2 MW–3 

MW compared to a typical 1,000-MW nuclear plant. Despite the smaller scales of 

wind power, its modularity makes it ideal for all sizes of installations—from a single 

unit (2 MW–3 MW) to a huge wind farm (1,000 MW). On the supply side, serial 

production of large numbers of similar units can reduce manufacturing costs. These 

factors suggest that manufacturing ramp-up for wind turbines should be less 

daunting than ramp-up for nuclear power plant equipment. 

Experiences with natural-gas-fired power plants over the past decade also provide 

important perspectives on the ability to rapidly expand manufacturing capability for 

wind power. From the early 1990s through the first half of the current decade, the 

U.S. electric sector experienced a rush toward new gas combined-cycle and 

combustion-turbine generation. This growth was driven by the expectation—now 

discounted—of continuing low natural gas prices. From 1999 through 2005, tens of 

gigawatts of natural gas power plants were manufactured and installed in the United 

States each year, with installations peaking in 2002 at more than 60 GW (Black and 

Veatch, forthcoming 2008). The experience with natural gas demonstrates that huge 

amounts of power generation equipment can be manufactured in the United States if 

sufficient market demand exists. 

As shown in Table 3-3, Toyota North America exemplifies the manufacturing scaleup of a modular technology and capability that is possible in the United States. 

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Table 3-5. Toyota North America vehicle 

production and sales 

3

Direct U.S. Employment (2005) 32,003 employees 

2005 Payroll $2,244,946,444 

Cumulative U.S. Production 12,374,062 vehicles 

Cumulative Sales $272,390,226,806 

U.S. Vehicle Sales (2005) 2,269,296 vehicles 

U.S. Vehicle Production (2005) 1,393,100 vehicles 

Average Engine Power 2004-2005 227 horsepower or 0.17 MW 

2005 U.S. Production in Power 

Output Terms 

275 million horsepower 

236 million kW or 236 GW 

2005 U.S. Sales in Power Output 

Terms 

448 million horsepower 

384 million kW or 384 GW 

Source: Adapted from website data 

http://www.toyota.com/about/operations/manufacturing/). 

Toyota has continued to establish its U.S. manufacturing capability since the mid1980s, and automobiles, like wind turbines, require large quantities of steel, plastics, 

and electronic components. There is no indication that Toyota’s domestic expansion 

has caused any strain on the nation’s manufacturing or materials-supply sectors. 

Today, the majority of vehicles Toyota sells in the United States are actually 

produced in this country. 

Table 3-3 shows that Toyota’s annual U.S. production, when expressed in terms of 

engine power output, increased to 236 GW by 2005. This annual production begins 

to approach in power capability the total amount of wind generation installed 

between 2007 and 2030 through realization of the 20% Wind Scenario. 

3.3	 LABOR REQUIREMENTS 

Beyond the raw material and manufacturing facilities required to create wind 

turbines and components, a skilled labor force would be required. These staff would 

need a range of skills and experience to fill many new employment opportunities. 

The likely outcome from developing new capabilities and capacity would be 

expansion of manufacturing in areas currently capable of competing or locations 

where logistic advantages exist. 

3.3.1	 MAINTAINING AND EXPANDING RELEVANT TECHNICAL 

STRENGTH 

Major expansion of wind power in the United States would require substantial 

numbers of skilled personnel available to design, build, operate, maintain, and 

advance wind power equipment and technology. Toward this end, a number of 

educational programs are already offered around the nation, including those shown 

in Table 3-6. 

Although this is an excellent beginning, many more programs of a similar nature 

will be needed nationwide to satisfy the needs stemming from the 20% Wind 

Scenario. One concern is that the number of students in power engineering programs 

has been dropping in recent years. Currently, U.S. graduate power engineering 

70	 20% Wind Energy by 2030 – Prepublication Version 
 

 

programs produce about 500 engineers per year; in the 1980s, this number 

approached 2,000. In addition, the number of wind engineering programs in U.S. 

graduate schools is significantly lower than in Europe. This concern is echoed in 

Figure 3-4, which shows that the number of college graduates receiving degrees in 

Table 3-6. Wind technology-related educational programs around the United States today 

School Location Degree or Program 

Wind Energy Applications Training 

Symposium 

Boulder, Colorado Workshops for Industry 

Colorado State University Fort Collins, Colorado 65-MW turbine on campus for research 

(engineering, environmental, etc.) 

Advanced Technology Environmental 

Education Center: Sustainable Energy 

Education and Training 

Bettencourt, Iowa Workshops for upper level high school 

and community college technology 

instructors 

Iowa Lakes Community College Estherville, Iowa One-year diploma for wind technician; 

two-year associate in applied science 

degree for wind technician 

University of Massachusetts at Amherst: 

College of Engineering, and Renewable 

Energy Research Laboratory (becoming 

University of Massachusetts Wind 

Energy Center in late 2008) 

Amherst, Massachusetts MS and Ph.D. level engineering 

programs specializing in wind energy 

Minnesota West Community and 

Technical College 

Canby, Maine Associate of applied science degree 

program in wind energy technology; 

diploma for wind energy mechanic; 

online certificate program for ”windsmith” 

Southwestern Indian Polytechnic Institute Albuquerque, New Mexico Under development: Integration of 

renewable energy technology 

experiential learning into the electronics 

technology, environmental science, 

agricultural science, and natural 

resources certificate and degree 

programs 

Mesalands Community College: North 

American Wind Research and Training 

Center 

Tucumcari, 

New Mexico 

Under development: Curriculum for 

operations and maintenance technician; 

two-year associate degree in wind farm 

management 

Wayne Technical and Career Center Williamson, New York New Vision Renewable Energy Program 

for high school seniors 

Columbia Gorge Community College Hood River, Oregon One-year certificate and two-year degree 

for renewable energy technician 

Lane Community College Eugene, Oregon Two-year associate of applied science 

degree for energy management 

technician; two-year associate of applied 

science option for renewable energy 

technician 

Texas Tech and other American 

universities: Wind Science & Engineering 

Research Center 

Lubbock, Texas Integrative graduate education and 

research traineeship 

Lakeshore Technical College Cleveland, Wisconsin Associate degree in applied science; 

electromechanical technology with a 

wind system Technician track 

Fond du Lac Tribal and Community 

College 

Fond du Lac, Wisconsin Clean Energy Technician Certificate 

Program 

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Figure 3-4. Percentage of 22-year-olds with a bachelor degree in 

science and engineering through 2050 

3

science and engineering has been declining, and that this trend is projected to 

continue for the foreseeable future (NSTC 2000). 

Even the level of U.S. graduate programs is well below similar graduate programs in 

Europe (Denmark, Germany, etc). At this rate, the United States will be unable to 

provide the necessary trained talent and manufacturing expertise. Unless this trend is 

reversed, despite major new wind installations in the United States, most of the 

technology will be imported, and a significant portion of the economic gains will be 

foreign, rather then domestic. 

3.4 CHALLENGES TO 20% WIND ENERGY BY 2030 

3.4.1 CHALLENGES 

Materials 

Several key materials are crucial to the production of a wind turbine. Among the raw 

materials that might potentially constrain the ability to reach high levels of wind 

power are fiberglass (about 9 metric tons required per megawatt of wind turbine 

capacity), resins, and permanent magnets. For example, the glass fiber requirements 

would be about half the level used domestically for roofing shingles (which is 

currently the largest consumer of fiberglass) and about double the amount now used 

in boat building. 

Manufacturing 

The 20% Wind Scenario would demand installations at a sustained growth rate of 

20% annually for nearly a decade and then maintain annual installations through 

2030. For turbine companies, it is no longer simply a matter of where to establish 

new manufacturing capacity. Investment decisions must now address strategies for 

building out and securing supply lines on a global basis; a proactive stance is 

essential to operate successfully in an environment of rapidly growing and shifting 

72 20% Wind Energy by 2030 – Prepublication Version 
 

 

demand for wind turbines (Hays, Robledo, and Ambrose 2006). Fortunately, the 

20% Wind Scenario could be feasible even with the potential challenges related to 

the availability of raw material or increased manufacturing demands. For rapid 

growth of manufacturing capacity to be achieved, stable and consistent policies that 

encourage investment in these new sectors of activity are needed. 

Labor 

One potential gap in achieving high rates of wind energy development is the 

availability of a qualified work force. In a report published by the National Science 

and Technology Counsel (NTSC) in 2000, the percentage of 22-year-olds earning 

degrees in science and engineering will continue to drop in the next 40 years (NSTC 

2000). More support from industry, trade organizations, and various levels of 

government could foster university programs in wind and renewable energy 

technology, preparing the work force to support the industry’s efforts. 

3.5 REFERENCES AND OTHER SUGGESTED READING 

Ancona and McVeigh. 2001. Princeton Energy Resources International, LLC. 

Rockville, MD 

http://www.generalplastics.com/uploads/technology/WindTurbineMaterialsandManufacturing_FactSheet.pdf 

AWEA (American Wind Energy Association). 2007. Wind Power Capacity In U.S. 

Increased 27% in 2006 and Is Expected To Grow an Additional 26% in 

2007. Washington, DC: AWEA. 

http://www.awea.org/newsroom/releases/Wind_Power_Capacity_012307.ht 

ml 

BTM Consult. 1999. Wind Force 10: A Blueprint to Achieve 10% of the World’s 

Electricity from Wind Power by 2010. Ringkøbing, Denmark: BTM Consult 

ApS. http://www.inforse.dk/doc/Windforce10.pdf 

EIA (Energy Information Administration). February 2006. Annual Energy Outlook 

2006. Report No. DOE/EIA-0383.Washington, DC: EIA. 

Hays, K., C. Robledo, and W. Ambrose. 2006. Wind Power at a Crossroads, Supply 

Shortages Spark Industry Restructuring, Strategy White Paper. Cambridge, 

MA: Emerging Energy Research. 

Laxson, A., M.M. Hand, and N. Blair. 2006. High Wind Penetration Impact on W.S. 

Wind Manufacturing Capacity and Critical Resources. Report No. 

NREL/TP-500-40482. Golden, CO: National Renewable Energy Laboratory 

(NREL). 

NSTC (National Science and Technology Council). 2000. Ensuring a Strong U.S. 

Scientific, Technical and Engineering Workforce in the 21st Century. 

Washington, DC:NSTC. 

O’Connell, R., and R. Pletka, et al. 2007. 20% Wind Energy Penetration in the 

United States: A Technical Analysis of the Energy Resource. Overland Park, 

KS: Black & Veatch. 

Sterzinger, G., and M. Svrcek. September 2004. Wind Turbine Development: 

Location of Manufacturing Activity. Washington, DC: Renewable Energy 

Policy Project (REPP). 

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3

Sterzinger, G., and M. Svrcek. 2005. Component Manufacturing: Ohio’s Future in 

the Renewable Energy Industry. Washington, DC: REPP. 

Sterzinger, G., and J. Stevens. October 2006. Renewable Energy Potential: A Case 

Study of Pennsylvania. Washington, DC: REPP. 

Trout, S.R. 2002. “Rare Earth Magnet Industry in the USA: Current Status and 

Future Trends.” Presented at the XVII Rare Earth Magnet Workshop, 

August 18–22, Newark, New Jersey. 

74 20% Wind Energy by 2030 – Prepublication Version 
 

 

Chapter 4.	 Transmission and 

Integration into the 

U.S. Electric System 

The ever-increasing sophistication of the operation 

of the U.S. electric power system—if it continues on 

its current path—would allow the 20% wind energy 

scenario to be realized by 2030. The 20% Wind 

Scenario would require the continuing evolution of 

transmission planning and system operations, in 

addition to expanded electricity markets. 

There are two separate and distinct power system challenges to obtaining 20% of 

U.S. electric energy from wind. One challenge lies in reliably balancing electrical 

generation and load over time with a large portion of energy coming from a variable 

power source such as wind, which, unlike many traditional power sources, cannot be 

accessed on demand or is “nondispatchable.” The other challenge is to plan, build, 

and pay for the new transmission facilities that will be required to access remote 

wind resources. Substantial work already done in this field have outlined scenarios 

in which barriers to achieving the 20% Wind Scenario could be removed while 

maintaining reliable service and reasonable electricity rates. 

This chapter begins with an examination of several detailed studies that have looked 

at the technical and economic impacts of integrating high levels of wind energy into 

electric systems. Next, this chapter examines how wind can be reliably 

accommodated into power system operations and planning. Transmission system 

operators must ensure that enough generation capacity is operating on the grid at all 

times, and that supply meets demand, even through the daily and seasonal load 

cycles within the system. To accommodate a nondispatchable variable source such 

as wind, operators must ensure that sufficient reserves from other power sources are 

available to keep the system in balance. Overall, it is the net system load that must 

be balanced, however, not an individual load or generation source in isolation. When 

seen in this more systemic way, wind energy can play a vital role in diversifying the 

power system’s energy portfolio. 

As the research discussed in this chapter demonstrates, wind’s variability need not 

be a technical barrier to incorporating it into the broader portfolio of available 

options. Although some market structures, generation portfolios, and transmission 

rules accommodate much more wind energy than others, reforms already under 

consideration in this sector can better accommodate wind energy. Experience and 

studies suggest that with these reforms, wind generation could reliably supply 20% 

of U.S. electricity demand. 

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Finally, this chapter assesses the feasibility and cost of building new transmission 

lines and facilities to tap the remote wind resources that would be needed for the 

20% Wind Scenario. Many challenges are inherent in building transmission systems 

to accommodate wind energy. If electric loads keep growing as expected, however, 

4

Wind Penetration Levels 

At least three different measures are used to describe wind penetration levels: 

energy penetration, capacity penetration, and instantaneous penetration. They 

are defined and related as follows: 

Energy penetration is the ratio of the amount of energy delivered from the 

wind generation to the total energy delivered. For example, if 200 megawatthours (MWh) of wind energy is supplied and 1,000 MWh is consumed during 

the same period, wind’s energy penetration is 20%. 

Capacity penetration is the ratio of the nameplate rating of the wind plant 

capacity to the peak load. For example, if a 300-MW wind plant is operating in 

a zone with a 1,000-MW peak load, the capacity penetration is 30%. The 

capacity penetration is related to the energy penetration by the ratio of the 

system load factor to the wind plant capacity factor. Say that the system load 

factor is 60% and the wind plant capacity factor is 40%. In this case, and with 

an energy penetration of 20%, the capacity penetration would be 20% × 

0.6/0.4, or 30%. 

Instantaneous penetration is the ratio of the wind plant output to load at a 

specific point in time, or over a short period of time. 

extensive new transmission will be required to connect new generation to loads. This 

is true regardless of the power sources that dominate over the coming decades, 

whether they are fossil fuels, wind, hydropower, or others. The U.S. power industry 

has renewed its commitment to a robust transmission system, and support continues 

to grow for cleaner generation options. In this environment, designers and engineers 

must find ways to build transmission at a reasonable cost and take a closer look at 

the alternatives to conventional power generation in a carbon-constrained future. 

4.1 LESSONS LEARNED 

4.1.1 WIND PENETRATION EXPERIENCES AND STUDIES 

The needs of system operators—reflected in grid codes—ensure that wind power 

will continue to be integrated in ways that guarantee the continued reliable operation 

of the power system. Grid codes are regulations that govern the performance 

characteristics of different aspects of the power system, including the behavior of 

wind plants during steady-state and dynamic conditions. Grid codes around the 

world are also changing to incorporate wind plants, including Federal Energy 

Regulatory Commission (FERC) Order 661-A in the United States. 

Several U.S. utilities are approaching 10% wind capacity as a percentage of their 

peak load, including the Public Service Company of New Mexico (PNM) and Xcel 

Energy (which serves parts of Colorado, Michigan, Minnesota, New Mexico, North 

76 20% Wind Energy by 2030 – Prepublication Version 
 

 

Dakota, South Dakota, Texas, and Wisconsin). Xcel Energy could actually exceed 

13% by the end of 2007. MidAmerican Energy in Iowa has already exceeded 10%, 

and Puget Sound Energy (PSE) in Washington expects to reach 10% capacity 

penetration shortly after 2010. 

4.1.2	 POWER SYSTEM STUDIES CONCLUDE THAT 20% WIND ENERGY 

PENETRATION CAN BE RELIABLY ACCOMMODATED 

Rapid growth in wind power has led a number of utilities in the United States to 

undertake studies of the technical and economic impacts of incorporating wind 

plants, or high levels of wind energy, into their electric systems. These studies are 

yielding a wealth of information on the expected impacts of wind plants on power 

system operations. 

General Electric International (GE), for example, has conducted a comprehensive 

study for New York State that examines the impact of 10% capacity penetration of 

wind by 2008 (Piwko et al. 2005). The state of California has set the ambitious goal 

of achieving 20% of its electrical energy from renewable sources by 2010 and 30% 

by 2020 (CEC 2007). The state of Minnesota has studied wind energy penetration of 

up to 25% to be implemented statewide by 2020 (EnerNex Corporation 2006). The 

Midwest ISO (Independent System Operator) has examined the impact of achieving 

a wind energy penetration of 10% in the region by 2020, with 20% in Minnesota 

(Midwest ISO 2006). 

U.S. experience with studies on wind were reviewed in a special issue of IEEE 

Power & Energy Magazine (IEEE 2005). The Utility Wind Integration Group 

(UWIG; 2006) also summarized these studies in cooperation with the three large 

utility trade associations—the Edison Electric Institute (EEI), the American Public 

Power Association (APPA), and the National Rural Electric Cooperative 

Association (NRECA). The UWIG (2006) summary came to the following 

conclusions: 

z	 “Wind resources have impacts that can be managed through proper 

plant interconnection, integration, transmission planning, and 

system and market operations.” 

z	 “On the cost side, at wind penetrations of up to 20% of system peak 

demand, system operating cost increases arising from wind 

variability and uncertainty amounted to about 10% or less of the 

wholesale value of the wind energy. These conclusions will need to 

be reexamined as results of higher-wind-penetration studies—in the 

range of 25%–30% of peak balancing-area load—become available. 

However, achieving such penetrations is likely to require one or two 

decades.” 

z	 “During that time, other significant changes are likely to occur in 

both the makeup and the operating strategies of the nation’s power 

system. Depending on the evolution of public policies, 

technological capabilities, and utility strategic plans, these changes 

can be either more or less accommodating to the natural 

characteristics of wind power plants.” 

z	 “A variety of means—such as commercially available wind 

forecasting and others discussed below—can be employed to reduce 

these costs.” 

4

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4

z	 “There is evidence that with new equipment designs and proper 

plant engineering, system stability in response to a major plant or 

line outage can actually be improved by the addition of wind 

generation.” 

z	 “Since wind is primarily an energy—not a capacity—source, no 

additional generation needs to be added to provide back-up 

capability provided that wind capacity is properly discounted in the 

determination of generation capacity adequacy. However, wind 

generation penetration may affect the mix and dispatch of other 

generation on the system over time, since non-wind generation is 

needed to maintain system reliability when winds are low.” 

z	 “Wind generation will also provide some additional load carrying 

capability to meet forecasted increases in system demand. This 

contribution is likely to be up to 40% of a typical project’s 

nameplate rating, depending on local wind characteristics and 

coincidence with the system load profile. Wind generation may 

require system operators to carry additional operating reserves. 

Given the existing uncertainties in load forecasts, the studies 

indicate that the requirement for additional reserves will likely be 

modest for broadly distributed wind plants. The actual impact of 

adding wind generation in different balancing areas can vary 

depending on local factors. For instance, dealing with large wind 

output variations and steep ramps over a short period of time could 

be challenging for smaller balancing areas, depending on the 

specific situation.” 

Load, Wind Generation, and Reserves 

The first phase in determining how to integrate wind energy into the power grid is to 

conduct a wind integration study, which begins with an analysis of the impact of the 

wind plant profiles relative to the utility load curve. By way of illustration, 

Figure 4-1 shows a two-week period of system loads in the spring of 2010 for the 

Xcel system in Minnesota. This system has 1,500 MW of wind capacity on a 

10,000-MW peak-load system (Zavadil et. al. 2004). Because both load and wind 

generation vary, it is the resulting variability—load net of wind generation—that 

system operators must manage, and to which the non-wind generation must respond. 

Although wind plants exhibit significant variability and uncertainty in their output, 

electric system operators already deal with these factors on similar time scales with 

current power system loads. It is critical to understand that output variability and 

uncertainty are not dealt with in isolation, but rather as one component of a large, 

complex system. The system must be operated with balance and reliability, taking 

into account the aggregate behavior of all of its loads and generation operating 

together. 

To maintain system balance and security, the electric system operator analyzes the 

regulation and load-following requirements of wind relative to other resources. 

Wind energy contributes some net increase in variability above that already imposed 

by cumulative customer loads. This increase, however, is less than the isolated 

variability of the wind alone on all time scales of interest. Although specific details 

vary, distribution of changes in the net load flattens and broadens when large-scale 

wind is added to the system. The resulting reserve requirements can be predicted 

with statistical analysis. It is not necessary, or economically feasible, to counter each 

78	 20% Wind Energy by 2030 – Prepublication Version 
 

 

Figure 4-1. Hourly load shapes with and without wind generation 

4

movement of wind with a corresponding movement in a traditional energy source. 

As a result, the net load of wind requires fewer reserves than would be required to 

balance the output of individual wind plants, or all the wind plants aggregated 

together, in isolation from the load. In the very short time frame, the additional 

regulation burden has been found to be quite small, typically adding less than 

$0.50/MWh to the cost of the wind energy (Zavidil, et. al. 2004). 

Operational impacts of nondispatchable variable resources can occur in each of the 

time scales managed by power system operators. Figure 4-2 illustrates these time 

scales, which range from seconds to days. “Regulation” is a service that rapidresponse maneuverable generators deliver on short time scales, allowing operators to 

maintain system balance. This typically occurs over a few minutes, and is provided 

by generators using automatic generation control (AGC). “Load following” includes 

both capacity and energy services, and generally varies from 10 minutes up to 

several hours. This time scale incorporates the morning load pick-up and evening 

load drop-off. The “scheduling” and “unit-commitment” processes ensure that 

sufficient generation will be available when needed over several hours or days ahead 

of the real time schedule. 

A statistical analysis of the load net of wind indicates the amount of reserves needed 

to cope with the combination of wind and load variability. The reserve determination 

starts with the assumption that wind generation and load levels are independent 

variables. The resultant variability is the square root of the sum of the squares of the 

individual variables (rather than the arithmetic sum). This means that the system 

operator, who must balance the total system, needs a much smaller amount of 

reserves to balance the load net of wind. Higher reserves would be needed if that 

operator were to try to balance the output of individual wind plants, or all the wind 

plants aggregated together in isolation from the load. 

Some suggest that hydropower capacity, or energy storage in the form of pumped 

hydro or compressed air, should be dedicated to supply backup or firming and 

shaping services to wind plants. Given an ideally integrated grid, this capacity would 

not be necessary because the pooling of resources across an electric system 

20% Wind Energy by 2030 – Prepublication Version 79 
 

 

 

Figure 4-2. Time scales for grid operations 

4

Source: Milligan et al. (2006) 

eliminates the need to provide costly backup capacity for individual resources. 

Again, it is the net system load that needs to be balanced, not an individual load or 

generation source in isolation. Attempting to balance an individual load or 

generation source is a suboptimal solution to the power system operations problem 

because it introduces unnecessary extra capacity and an associated increase in cost. 

Hydro capacity and energy storage are valuable resources that should be used to 

balance the system, not just the wind capacity. 

Reserve Requirements Calculation 

A hypothetical example is offered to calculate reserve requirements. Say that system peak 

load for tomorrow is projected at 1,000 MW with a 2% forecast error, which makes the 

forecast error (i.e., expected variability of peak load) equal to 20 MW. Wind generation for a 

200-MW wind plant in that balancing area is predicted at a peak hour output of 100 MW 

with an error band of 20%. The expected variability of peak wind generation, then, is 20 

MW. Assuming that these are independent variables, the total error is calculated as the 

square root of the sum of the squares of the individual variables (which is the square root of 

2 × 20 squared, or 1.41 × 20, which equals 28 MW). Adding the two variables to estimate 

reserve requirements would result in an incorrect value of 40 MW. 

80 20% Wind Energy by 2030 – Prepublication Version 
 

 

Figure 4-3 illustrates the incremental load-following impact of wind on an electrical 

system, as determined in the work of Zavadil and colleagues (2004). The histograms 

show more high-ramp requirements with wind than without wind, and a general 

reduction in small-ramp requirements compared to the no wind case. For these 

illustrative summer and winter hours, following load alone entails relatively fewer 

large-megawatt changes in generation (ramps). Following load net of wind 

generation, however, creates a wider variability in the magnitude of load change 

between two adjacent hours. A system with wind generation needs more active loadfollowing generation capability than one without wind, or more load-management 

capability to offset the combined variability of load net of wind. 

Figure 4-3. Impact of wind on load-following requirements 

4

Wind Integration Cost 

One impact of the variability that wind imposes on the system is an increase in the 

uncertainty introduced into the day-ahead unit-commitment process. Specifically, 

despite improvements in wind generation forecasting, greater uncertainty remains 

about what the next day’s load net of wind and resulting generation requirements 

will be. The impact of these effects has been shown to increase system operating 

cost by up to $5.00/MWh of wind generation at wind capacity penetrations up to 

20%, assuming that the wind turbine capacities have been appropriately derated. 

These figures are shown in the Unit-Commitment Cost column of Table 4-1. These 

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Table 4-1. Wind integration costs in the U.S. 

4

Date Study Wind 

Capacity 

Penetration 

(%) 

Regulati 

on Cost 

($/MWh) 

Load 

Following 

Cost 

($/MWh) 

Unit 

Commitment Cost 

($/MWh) 

Gas 

Supply 

Cost 

($/MWh) 

Total 

Operating 

Cost 

Impact 

($/MWh) 

May 03 Xcel-UWIG 3.5 0 0.41 1.44 na 1.85 

Sep 04 Xcel-MNDOC 15 0.23 na 4.37 na 4.60 

Nov 06 MN/MISO 35 

(25% energy) 

0.15 na 4.26 na 4.41 

July 04 CA RPS Multiyear Analysis 4 0.45 na na na na 

June 03 We Energies 4 1.12 0.09 0.69 na 1.90 

June 03 We Energies 29 1.02 0.15 1.75 na 2.92 

2005 PacifiCorp 20 0 1.6 3.0 na 4.6 

April 06 Xcel-PSCo 10 0.20 na 2.26 1.26 3.72 

April 06 Xcel-PSCo 15 0.20 na 3.32 1.45 4.97 

Source: Adapted from IEEE (2005) 

day-ahead cost impacts are significantly higher than the others, reflecting the high 

cost of starting up generating units on a daily basis—even when they might or might 

not be needed. 

The impact of wind’s variability depends on the nature of the dispatchable 

generation sources, their fuel cost, the market and regulatory environment, and the 

characteristics of the wind generation resources. The most recent study conducted 

for Minnesota, for example, examined up to 25% energy penetration in the Midwest 

ISO market context (EnerNex 2006). The study found that the cost of wind 

integration is similar to that found in a study done two years earlier for a 15% wind 

capacity penetration in a vertically integrated market (Zavadil et al. 2004). A 

comparison of these results illustrates the beneficial effect of regional energy 

markets, namely that large operational structures reduce variability, contain more 

load-following resources, and offer more useful financial mechanisms for managing 

the costs of wind integration. Handling large output variations and steep ramps over 

short time periods (e.g., within the hour), though, can be challenging for smaller 

balancing areas. 

Table 4-1 shows the integration cost results from recent U.S. studies. The wind 

integration issue is primarily a matter of cost, but the costs in the 20% Wind 

Scenario are expected to be less than 10% of the wholesale cost of energy (COE). 

Wind Penetration Impacts 

U.S. studies for capacity penetrations in the range between 20% and 35% have 

found that the additional reserves required to meet the intrahour variability are 

within the capabilities of the existing stack of units expected to be committed. In the 

high-penetration Minnesota study (EnerNex 2006), changes in total reserve 

requirements amounted to 7% of the wind generation needed to reach 25% wind 

energy penetration (5,700 MW). These reserves included 20 MW of additional 

regulating reserve, 24 MW of additional load-following reserve, and 386 MW 

maximum of additional operating reserve to cover next-hour errors in the wind 

forecast. Existing capacity is expected to cover these reserve needs, although over 

time, load growth could reduce this spare capacity without the construction of new 

dispatchable power plants. Because wind and load are generally uncorrelated over 

82 20% Wind Energy by 2030 – Prepublication Version 
 

short time scales, the regulation impact of wind is modest. The system operator will 

schedule sufficient spinning and nonspinning reserves so that unforeseen events do 

not endanger system balance, and so that control performance standards prescribed 

by the North American Electric Reliability Corporation (NERC) are met. 

4.1.3	 WIND TURBINE TECHNOLOGY ADVANCEMENTS IMPROVE 

SYSTEM INTEGRATION 

As described in more detail in the Wind Turbine Technology chapter, wind turbine 

technology has advanced dramatically in the last 20 years. From a performance point 

of view, modern wind power plants have much in common with conventional utility 

power plants, with the exception of variability in plant output. In the early days of 

wind power applications, wind plants were often thought of as a curiosity or a 

nuisance. Operators were often asked to disconnect from the system during a 

disturbance and reconnect once the system was restored to stable operation. With the 

increasing penetration of wind power, most system operators recognize that wind 

plants can and should contribute to stable system operation during a disturbance, as 

do conventional power plants. 

As grid codes are increasingly incorporating wind energy, new plants are now 

capable of riding through a serious fault at the point of interconnection and are able 

to contribute to the supply of reactive power and voltage control, just like a 

conventional power plant. The supply of reactive power is a critical aspect of the 

design and operation of an interconnected power system. Modern wind plants can 

perform this function and supply voltage support for secure grid operations. 

In addition, modern wind plants can be integrated into a utility’s supervisory control 

and data acquisition (SCADA) system. They can provide frequency response similar 

to that of other conventional machines and participate in plant output control 

functions and ancillary service markets. Figure 4-4 illustrates the ability of a wind 

power plant to increase its output (blue line) in response to a drop in system 

frequency (red line). Figure 4-5 illustrates various control modes possible via 

SCADA participation, including the ability to limit plant output power at any given 

time, control ramp rate in moving up or down, and carry spinning reserves as 

ordered (Saylors 2006). These plants also have the ability to tap frequencyFigure 4-4. GE turbine frequency response 

4

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responsive reserves. These Figure 4-5. Vestas wind turbine control capability control features come at a 

4

cost, however, which is that 

of “spilling” wind, a free 

energy resource. In any 

given geographic area, the 

cost of operating wind units 

in this manner so as to 

provide ancillary services 

would have to be compared 

with the cost of furnishing 

such services by other 

means. 

Wind plant control systems 

offer another mechanism for 

dealing with the variability 

of the wind resource. 

Controllers can hold system 

voltage constant at a remote 

bus, even under widely varying wind speed conditions. Figure 4-6 shows an 

example of the voltage control features on a GE wind plant built recently in 

Colorado. In this system, voltage can be controlled across a broad range of wind 

conditions and power plant output. Voltage disturbances at the point of 

interconnection (POI) on the remote bus trigger offsetting changes in the wind plant 

voltage, controlling variations in the bus voltage. 

Figure 4-6. GE wind plant controls Modern wind plants can be 

added to a power grid 

without degrading system 

performance. In fact, they 

can contribute to 

improvements in system 

performance. A severe test 

of the reliability of a 

system is its ability to 

recover from a three-phase 

fault at a critical point in 

the system. (For 

definitions of faults, see 

the Glossary in Appendix 

E.) System stability studies 

have shown that modern 

wind plants—equipped 

with power electronic 

controls and dynamic 

voltage support 

capabilities—can improve 

system performance by 

supporting postfault 

voltage recovery and 

damping power swings. 

84 20% Wind Energy by 2030 – Prepublication Version 
 

This performance is illustrated in Figure 4-7, which simulates a normally cleared 

three-phase fault on a critical 345-kV bus in New York State (the Marcy substation 

in central New York; Piwko et al. 2005). The simulation assumed a 10% wind 

penetration (3,300 MW on a 33,000-MW system) of wind turbines with doubly fed 

induction generators. It incorporated power electronics that allowed for independent 

control of real and reactive power. The top half of the figure shows the quicker 

recovery and increased damping in the system voltage transient at the Marcy 345-kV 

bus. The bottom half of the figure similarly shows that the flow on the east interface 

has less overshoot and is more highly damped with wind. And because the power 

electronics capabilities of these wind turbines remain connected to the grid and 

respond to grid conditions with or without real power generation, they manage 

voltage on the grid even when the turbine is not generating power. 

Utility planners use models to understand and represent the capabilities and 

performance of generators and transmission system assets. Detailed wind plant 

Figure 4-7. Impact of wind generation on system dynamic performance 

4

models that incorporate today’s sophisticated wind turbine and plant control features 

are being used to study future system configurations, as well as to improve the 

20% Wind Energy by 2030 – Prepublication Version 85 
 

 

 

4

power system performance of conventional technology. Wind turbine manufacturers 

and developers are giving a high priority to the development of improved models in 

response to the leadership of utility organizations such as the Western Electricity 

Coordinating Council (WECC). The models are critical tools that enable planners to 

understand wind plant capabilities and accurately determine the impact of wind 

plants on power system behavior. 

Improved performance features are likely to be incorporated into wind models as the 

utility interface and control characteristics of wind turbines and wind plants continue 

to evolve. Variable-speed designs with power electronic controls are improving real 

and reactive power control within wind turbines under both transient and steadystate conditions. 

4.1.4	 WIND FORECASTING ENHANCES SYSTEM OPERATION 

System operators can significantly reduce the uncertainty of wind output by using 

wind forecasts that incorporate meteorological data to predict wind production. Such 

systems yield both hour-ahead and day-ahead forecasts to support real-time 

operations. They also inform the scheduling and market decisions necessary for dayahead planning. 

Forecasting allows operators to anticipate wind generation levels and adjust the 

remainder of generation units accordingly. Piwko and colleagues (2005) found that a 

perfect wind forecast reduced annual variable production costs by $125 million. And 

a state-of-the-art forecast delivered 80% of the benefit of a perfect forecast. 

Improved short-term wind production forecasts let operators make better day-ahead 

market operation and unit-commitment decisions, help real-time operations in the 

hour ahead, and warn operators about severe weather events. Advanced forecasting 

systems can also help warn the system operator if extreme wind events are likely so 

that the operator can implement a defensive system posture if needed. The operating 

impact with the largest cost is found in the unit-commitment time frame. The 

seamless integration of wind plant output forecasting—into both power market 

operations and utility control room operations—is a critical next step in 

accommodating large penetrations of wind energy in power systems. 

4.1.5	 FLEXIBLE, DISPATCHABLE GENERATORS FACILITATE WIND 

INTEGRATION 

Studies and actual operating experience indicate that it is easier to integrate wind 

energy into a power system where other generators are available to provide 

balancing energy and precise load-following capabilities. In 2005, Energinet.dk 

published the preliminary results of a study of the impact of meeting 100% of 

western Denmark’s annual electrical energy requirement from wind energy 

(Pedersen 2005). The study showed that the system could absorb about 30% energy 

from wind without any excess (wasted) wind production, assuming no transmission 

ties to outside power systems. Surplus wind energy starts to grow substantially after 

the wind share reaches 50%. And if wind generates 100% of the total energy 

demand of 26 terawatt-hours (TWh), 8 TWh of the wind generation would be 

surplus because it would be produced during times that do not match customer 

energy use patterns. Other energy sources, such as thermal plants, would supply the 

deficit, including the balancing energy. In the Pedersen study, the cost of electricity 

doubled when wind production reached 100% of the load. The study made very 

86	 20% Wind Energy by 2030 – Prepublication Version 
 

 

Effective Load Carrying Capability 

The ELCC is the amount of additional load that can be served at the target reliability level with the 

addition of a given amount of generation (wind in this case). For example, if the addition of 100 MW 

of wind could meet an increase of 20 MW of system load at the target reliability level, it would have 

an ELCC of 20 MW, or a capacity value of 20% of its nameplate value. 

Consider the following example: There are 1,000 MW of wind capacity in a concentrated geographic 

area, with an ELCC of 200 MW or a capacity value of 20%. The peak load of the system is 5,000 

MW. On the peak load day of the year, there is a dead calm over the area, and the output of the wind 

plant is 0. The lost capacity is 200 MW (20% of 1,000 MW). If this system were planned with a 

nominal 15% reserve margin, it would have a planning reserve of 750 MW that would well exceed 

the reserves needed to replace the loss of the wind capacity at system peak load. 4

conservative assumptions, however, of no external ties or market opportunities for 

the excess wind energy. 

4.1.6 INTEGRATING AN ENERGY RESOURCE IN A CAPACITY WORLD 

Wind energy has characteristics that differ from those of conventional energy 

sources. Wind is an energy resource, not a capacity resource. Capacity resources are 

those that can be available on demand, particularly to meet system peak loads. 

Because only a fraction of total wind capacity has a high probability of running 

consistently, wind generators have limited capacity value. Traditional planning 

methods, however, focus on reliability and capacity planning. Incorporating wind 

energy into power system planning and operation, then, will require new ways of 

thinking about energy resources. 

Traditional system planning techniques use tools that are oriented toward ensuring 

adequate capacity. Most transmission systems, however, can make room for 

additional energy resources if they allow some flexibility for interconnection and 

operation. This flexibility includes choice of interconnection voltage, operation as a 

price-taker in a spot market, and limited curtailment. Economic planning tools and 

probabilistic analytical methods must also be used to ensure that a bulk power 

system has adequate generation and transmission capacity while optimizing its use 

of energy resources such as wind and hydropower. 

Many hydropower generators produce low-cost variable energy. Unlike wind 

energy, most hydropower energy can be scheduled and delivered at peak times, so it 

contributes greater capacity value to the system. But because the reality of droughts 

causes hydropower capacity to vary from year to year, the capacity value of this 

energy resource (effective load-carrying capacity [ELCC]) must be calculated using 

industry-standard reliability models. The capacity value is used for system planning 

purposes on an annual basis, not on a daily operating basis. Some combination of 

existing market mechanisms and utility unit-commitment processes must be used to 

plan capacity for day-to-day reliability. 

Planning techniques for a conventional power system focus on the reliable capacity 

offered by the units that make up the generation system. This is essential for meeting 

the system planning reliability criterion, such as the loss of load probability (LOLP) 

of 1 day in 10 years. The ELCC of a generation unit is the metric used to determine 

its contribution to system reliability. It is important to recognize that wind does offer 

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4

some additional planning reserves to the system, which can be calculated with a 

standard reliability model. The ELCC of wind generation, which can vary 

significantly, depends primarily on the timing of the wind energy delivery relative to 

times of high system risk. The capacity value of wind has been shown to range from 

approximately 5% to 40% of the wind plant rated capacity, as shown in Table 4-2. In 

some cases, simplified methods are used to approximate the rigorous reliability 

analysis. 

Reliability planning entails determining how much generation capacity of what type 

is needed to meet specified goals. Because wind is not a capacity resource, it does 

Table 4-2. Methods to estimate wind capacity value in the U.S. 

Region/Utility Method Note 

CA/CEC ELCC Rank bid evaluations for RPS (20%-25%) 

PJM Peak Period Jun-Aug HE 3 -7 p.m., capacity factor using 3-year rolling average (20%, fold 

in actual data when available) 

ERCOT 10% May change to capacity factor for the hours between 4 -6 p.m. in July (2.8%) 

MN/DOC/Xcel ELCC Sequential Monte Carlo (26%-34%) 

GE/NYSERDA ELCC Offshore/land-based (40%/10%) 

CO PUC/Xcel ELCC PUC decision (10%), Full ELCC study using 10-year data gave average 

value of 12.5% 

RMATS Rule of thumb 20% for all sites in RMATS 

PacifiCorp ELCC Sequential Monte Carlo (20%). New Z-method 2006 

MAPP Peak Period Monthly 4-hour window, median 

PGE 33% (method not stated) 

Idaho Power Peak Period 4 p.m. -8 p.m. capacity factor during July (5%) 

PSE and Avista Peak Period The lesser of 20% or 2/3 of January Capacity Factor 

SPP Peak Period Top 10% loads/month; 85th percentile 

not require 100% backup to ensure replacement capacity when the wind is not 

blowing. Although 12,000 MW of wind capacity have been installed in the United 

States, little or no backup capacity for wind energy has been added to date. Capacity 

in the form of combustion turbines or combined cycle units has been added to meet 

system reliability requirements for serving load. Thinking in terms of “backing up” 

the wind is not appropriate because the wind capacity was installed to generate, lowemissions energy but not to meet load growth requirements. Wind power cannot 

replace the need for many “capacity resources,” which are generators and 

dispatchable load that are available to be used when needed to meet peak load. If 

wind has some capacity value for reliability planning purposes, that should be 

viewed as a bonus, but not a necessity. Wind is used when it is available, and system 

reliability planning is then conducted with knowledge of the ELCC of the wind 

plant. Nevertheless, in some areas of the nation where access to generation and 

markets that spans wide regions has not developed, the wind integration process 

could be more challenging. (For more information on capacity terminology, see the 

Glossary in Appendix E.) 

Plant capacity factors illustrate the roles that different power technologies play in a 

bulk power system. The capacity factor (CF) of a unit measures its actual energy 

production relative to its potential production at full utilization over a given time 

period. Table 4-3 shows the capacity factors of different power plant types within 

the Midwest ISO for a year. The units with the highest capacity factors—nuclear 

88 20% Wind Energy by 2030 – Prepublication Version 
 

 

 

 

 

(75% CF) and coal (62% and 71% CF)—are the workhorses of the system because 

they produce relatively low-cost baseload energy and are fully dispatchable. Wind 

(30% CF) and hydro (27% CF) generate essentially free energy, so the wind is taken 

whenever it is available (subject to transmission availability) and the hydro is 

scheduled to deliver maximum value to the system (to the extent possible). The 

plants with the lowest capacity factors (combined cycle, combustion turbines, and 

oil- and gas-fired steam boilers) are operated as peaking and load-following plants 

and essential capacity resources. As illustrated in Table 4-3, many resources in the 

system operate at far less than their rated capacity for much of the year, but all are 

necessary components of an economic and reliable system. 

4.1.7 AGGREGATION REDUCES VARIABILITY 

Table 4-3. Midwest ISO plant capacity factor by fuel type (June 2005–May 2006) 4

Fuel Type Number of 

Units 

Max Capacity 

(MW) 

Possible 

Energy 

(MWh) 

Actual 

Energy 

(MWh) 

Capacity 

Factor 

(%) 

Combined Cycle 50 12,130 106,257,048 11,436,775 11 

Gas Combustion Turbine 

(CT) 

275 21,224 185,924,868 14,749,450 8 

Oil CT 187 7,488 65,595,756 2,292,288 3 

Hydro 113 2,412 21,129,120 5,696,734 27 

Nuclear 17 11,895 104,200,200 77,764,757 75 

Coal Steam Turbine 

(ST; <300 MW) 

230 25,432 222,786,948 137,771,172 62 

Coal ST Coal (≥300 MW) 113 51,155 448,116,048 320,014,108 71 

Gas ST 20 1,673 14,651,976 1,256,756 9 

Oil ST 12 1,790 15,676,896 560,910 4 

Other ST 10 345 3,021,324 1,722,434 57 

Wind 28 1,103 9,658,776 2,882,459 30 

Total 1055 136,646 1,197,018,960 576,147,844 

The greater the number of wind turbines operating in a given area, the less their 

aggregate production variability. This is shown in Table 4-4, which gives an analysis 

of wind production variability as a function of an increasing number of aggregated 

wind turbines in a large wind plant in the Midwest (Wan 2005). Table 4-4 shows the 

average and standard deviation of step changes in wind plant output for different 

numbers of turbines over different time periods. These results indicate that wind 

production changes very little over short time periods. As the time period increases 

from seconds to minutes to hours, the output variability increases because it is 

driven by changes in weather patterns. In addition, as a general trend, the more wind 

turbines that are operating in a given period, the lower the production variability 

during that period. Simply put, system operators in the United States have found that 

as more wind generating capacity is installed, the combined output becomes less 

variable. 

20% Wind Energy by 2030 – Prepublication Version 89 
 

 

 

Table 4-4. Wind generation variability as a function of the 

number of generators and time interval 

4

14 Turbines 

(%) 

61 Turbines 

(%) 

138 Turbines 

(%) 

250+Turbines 

(%) 

1-Second Interval 

Average 0.4 0.2 0.1 0.1 

Std. Dev. 0.5 0.3 0.2 0.1 

1-Minute Interval

 Average 1.2 0.8 0.5 0.3 

Std. Dev. 2.1 1.3 0.8 0.6 

10-Minute Interval 

Average 3.1 2.1 2.2 1.5 

Std. Dev. 5.2 3.5 3.7 2.7 

1-Hour Interval

 Average 7.0 4.7 6.4 5.3 

Std. Dev. 10.7 7.5 9.7 7.9 

Note: This table compares output at the start and end of the indicated time period in terms of the percentage of total 

generation from each turbine group. Std. Dev. is the abbreviation for standard deviation. 

A careful evaluation of integrating wind into current operations should include a 

determination of the magnitude and frequency of occurrence of changes in the net 

load on the system during the time frames of interest (seconds, minutes, and hours). 

This analysis, which should be conducted both before and after the wind generation 

is added, will help determine the additional requirements on the balance of the 

generation mix. 

Similarly, as more wind turbines are installed across larger geographic areas, the 

aggregated wind generation becomes more predictable and less variable. The 

benefits of geographical diversity can be seen in Figure 4-8, which shows the change 

in wind plant hourly capacity factor over one year for four different levels of wind 

plant aggregation. This figure shows the operational capacity factor of wind turbines 

aggregated over successively larger areas—first over southwest Minnesota, then 

across southwest and southeast Minnesota, then across the entire state, and finally 

across both Minnesota and central North Dakota. There is a decrease in the number 

of occurrences of very high and very low hourly capacity factors in the tails of the 

distribution as the degree of aggregation increases. A considerable benefit is also 

realized across a broad mid-range of capacity factors from 20% to 80% (EnerNex 

2006). 

4.1.8 GEOGRAPHIC DISPERSION REDUCES OPERATIONAL IMPACTS 

Actual wind production data and sophisticated mesoscale weather modeling 

techniques have shown that a sudden and simultaneous loss of all wind power on a 

system is not a credible event. This scenario would be prevented by spatial 

variations of wind from turbine to turbine in a wind plant, and to a greater degree, 

from plant to plant. Because of the higher capacities of existing thermal plants and 

transmission lines, the loss of a wind plant will seldom be the single largest first 

contingency event for planning purposes. Severe weather events can lead to the loss 

of wind plant output as individual turbines trip off line and/or restart as a storm front 

90 20% Wind Energy by 2030 – Prepublication Version 
 

Figure 4-8. Annual hourly capacity factor 

4

passes through. This kind of event happens on the time scale of tens of minutes to 

hours, however, rather than in seconds. 

4.1.9 LARGE BALANCING AREAS REDUCE IMPACTS 

To maintain stable operation of the electric system, the amount of generation 

supplied must balance with the load instantaneously. If the generation and load are 

not in balance, the system could potentially suffer a loss of either or lose stability 

and collapse. The system-balancing function is performed by authorities who 

operate a portion of the system called a “balancing area.” (For more information on 

balancing areas, see the Glossary in Appendix E.) Today there are about 130 

balancing areas in the U.S. grid. The largest is the PJM grid, which is part of the 

Eastern Interconnection with a peak load of 145,000 MW. A small balancing area, in 

contrast, might be a small utility with a peak load of a few hundred megawatts. 

Balancing areas are an outgrowth of the evolution of power systems. In some areas, 

the current patchwork nature of the grid resulted when a number of small isolated 

systems were combined into a single balancing area such as PJM. 

Systems became interconnected for a number of reasons, mostly having to do with 

reliability and economics. Consider this example: If three adjacent systems, each 

with a peak load of 3,000 MW, had a single largest contingency (loss of a line or 

generator) of 300 MW, each would carry 300 MW of reserves. If the three systems 

were interconnected, and the single largest contingency was still 300 MW, each 

system would need only 100 MW of reserves to cover contingency reserve 

requirements. In this example, and as another advantage, the peak load of the 

combined system would be less than 9,000 MW because of diversity in the load of 

the three systems. Finally, operators can call on the most efficient and lowest cost 

producers available across the combined system and shift production away from 

more expensive units. This approach ensures that the generation mix used to meet 

the aggregated system’s changing load is always relatively more efficient. Overall, 

the three interconnected systems are able to operate more efficiently at a reduced 

operating cost. 

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Wind units operate in a parallel situation across multiple balancing areas. As 

indicated previously, geographically dispersed wind units produce electricity more 

consistently and predictably. Similarly, when a system is operating across a larger 

area, more wind generators are available to offset customer demands, making the 

resulting load net of wind less variable and more predictable. 

The Energy Policy Act (EPAct) of 2005 created an Electricity Reliability 

Organization (ERO), overseen by FERC, to enforce mandatory reliability standards, 

with fines for rule violations. The resulting reliability standards, implemented by 

NERC, include 

z Operator training 

z Balancing authority performance criteria 

z Control room situational awareness capability 

z Control center hardware and software capability. 

These reliability requirements are likely to increase pressure on small balancing 

areas to consolidate. In addition to providing reliability benefits, consolidation of 

balancing areas offers economic advantages because it would reduce operating costs 

and lower the cost of increased penetration of wind power. Virtual balancing area 

consolidation can deliver the benefits of large-area aggregation without physically 

merging balancing areas under a single operator. Virtual consolidation can be 

accomplished through reserve sharing or pooling across a group of utilities, sharing 

of area control error (ACE) data among several balancing areas, and dynamic 

scheduling of wind plants from a smaller to a larger balancing area. All these 

methods can help deal with the challenges of high penetrations of wind power. (For 

further explanation of ACE, see the Glossary.) 

4.1.10 BALANCING MARKETS EASE WIND INTEGRATION 

Experience has shown that using well-functioning hour-ahead and day-ahead 

markets and expanding access to those markets are effective tools for dealing with 

wind’s variability. A deep, liquid real-time market is the most economical approach 

to providing the balancing energy required by wind plants with variable outputs 

(IEA 2005). The absence of a wind production forecast introduces significant costs 

into the day-ahead market. As a result, wind plant participation in day-ahead 

markets is important for minimizing total system cost. Price-responsive load markets 

and associated technologies are helpful components of a well-functioning electricity 

market, which allows the power system to better deal with increased variability. In 

some regions of the United States that lack centralized markets, access to balancing 

and related services is being pursued through instruments such as bilateral contracts 

and reserve-sharing agreements. 

The market allows energy from all generators across the area to be dispatched based 

on real-time prices. When wind blows strongly, the real-time price falls, signaling 

more controllable generators to reduce their output and save costly fuel. Conversely, 

when wind drops off, real-time prices rise and dispatchable generators increase their 

output. As an example, the Midwest ISO covers a footprint of 15 states, so there is a 

deep pool of generators that can ramp up and down in response to wind output. The 

EnerNex (2006) study in Minnesota examined up to 25% energy penetration in the 

Midwest ISO market context (33% capacity penetration). The integration costs were 

similar to the results of a study done two years earlier (Zavadil et al, 2004) for a 

92 20% Wind Energy by 2030 – Prepublication Version 
 

15% wind capacity penetration in a structure without the regional Midwest ISO 

balancing market. 

4.1.11 CHANGING LOAD PATTERNS CAN COMPLEMENT WIND 

GENERATION 

To date, the electric system has been planned and operated under the fundamental 

assumption that the supply system must perfectly meet every customer’s energy use, 

and that the demand is relatively uncontrolled. But this assumption is starting to 

change as policy makers work to create opportunities for customers to manage their 

energy use in response to price signals. Wider use of price-responsive demand is 

expected to boost the competitiveness of wholesale electricity markets, enhance grid 

reliability, and improve efficiency of resource use. Technology and regulatory 

options that enable customer energy management are gaining momentum because of 

increasing support from electricity regulators, regional transmission organizations 

(RTOs), and retail electricity providers. 

Several customer-driven energy trends could have a significant impact on wind 

development. Much wind generation occurs in hours when energy use is low. Two 

proposed off-peak electricity uses—deployment of plug-in hybrid vehicles with offpeak charging and production of hydrogen to power vehicles—could absorb much of 

this off-peak, low-cost wind generation. In addition, as more customers gain the 

ability to practice automated price-responsive demand or to automatically receive 

and respond to directions to increase or decrease their electricity use, system loads 

will be able to respond to, or manage, variability from wind and other energy 

sources. 

4.2	 FEASIBILITY AND COST OF THE NEW TRANSMISSION 

INFRASTRUCTURE REQUIRED FOR THE 20% WIND 

SCENARIO 

4

If the considerable wind resources of the United States are to be utilized, a 

significant amount of new transmission will be required. Transmission must be 

recognized as a critical infrastructure element needed to enable regional delivery and 

trade of energy resources, much as the interstate highway system does for the 

nation’s transportation needs. Every era of new generation construction in the 

United States has been accompanied by construction of new transmission. Federal 

hydropower developments of the 1930s, 1940s, and 1950s, for example, included the 

installation of integral long-distance transmission owned by the federal government. 

Construction and grid integration of large-scale nuclear and coal plants in the 1960s 

and 1970s entailed installing companion high-voltage interstate transmission lines, 

which were needed to deliver the new generation to loads. Even the natural gas 

plants of the 1990s, although requiring less new electric transmission, relied on 

expansion of the interstate gas transportation network. Significant expansion of the 

transmission grid will be required under any future electric industry scenario. 

Expanded transmission will increase reliability, reduce costly congestion and line 

losses, and supply access to low-cost remote resources, including renewables. 

Much of the current electric grid was built to deliver power from remote areas to 

load centers. During the past two decades, however, investment in gas-fired 

generation units located closer to load centers allowed the power system to grow 

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without major new transmission (Hirst and Kirby 2001). Transmission investment 

lagged substantially behind that of previous decades because of uncertainty about 

the outcome of electricity restructuring. The average level of investment for the last 

half of the 1990s was under $3 billion per year, as illustrated in Figure 4-9. These 

figures were down from investments of approximately $5.5 billion per year in the 

mid 1970s (adjusted for inflation). Although transmission investment declined for 

two decades, it has been steadily climbing since the late 1990s . 

Figure 4-9. Annual transmission investments from 1975 through 1999 and 

projections through 2005 

4

Transmission investment from investor-owned utilities and independent 

transmission companies climbed from $3.0 billion per year in 2000 to $6.9 billion in 

2006 (Eisenbrey 2007). Nearly $8 billion of investment is expected in 2007, with the 

figure growing to $8.4 billion in 2009. The steady increase in new transmission 

investment reflects not only catch-up in local transmission, but new commitments to 

backbone transmission systems for major new generation, intra- and inter-regional 

trade, and increased reliability. 

The 20% scenario of wind energy penetration would require continued transmission 

investment. Many new transmission infrastructure studies, plans, and projects are 

already under way. Current or recent activities include 

z	 Planning by the Western Governors’ Association’s (WGA) Clean 

and Diversified Energy Advisory Committee (CDEAC; 2006) 

z	 The collaboration of Minnesota utilities in the Capital Expansion 

Plan for 2020 (CapX 2020; see http://www.capx2020.com/) 

z	 The creation of Competitive Renewable Energy Zones (CREZ) by 

the state legislature in Texas (ERCOT 2006) 

z	 The creation of state transmission or infrastructure authorities in 

Wyoming, Kansas, South Dakota, New Mexico, and Colorado 

z	 The proliferation of large interstate transmission projects in the 

West (WIEB 2007) 

z	 The SPP “X Plan” and Extra High Voltage analysis (see 

http://www.spp.org) 

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z	 The Midwest ISO Transmission Expansion Plan 2006 (Midwest 

ISO 2006). 

4.2.1	 A NEW TRANSMISSION SUPERHIGHWAY SYSTEM WOULD BE 

REQUIRED 

Wind energy development requires two types of transmission. Trunk-line 

transmission runs from areas with high-quality wind resources and often carries a 

high proportion of energy from wind and other renewable sources. The second type 

is backbone high-voltage transmission across long distances to deliver energy from 

production areas to load centers. These superhighways mix power from many 

generating areas, sources, and shippers—just as a highway carries all types of 

vehicles traveling a range of distances. 

To determine how much transmission would be needed for the 20% Wind Scenario, 

the National Renewable Energy Laboratory’s (NREL) Wind Deployment System 

(WinDS) model was used (see Appendix D). The approach, described in appendices 

A and B, used the WinDS model to determine distances from the point of production 

to the point of consumption, as well as the cost-effectiveness of building wind plants 

close to load or in remote locations and paying the transmission cost. To account for 

the cost of transmission that would be required by coal and other resources, the 

analysis added the typical cost of transmission needed to interconnect those 

resources to the capital cost. This method, although providing balance in the overall 

cost assessment, is only a first step. More work must be done in regional 

transmission planning processes to evaluate the transmission required for the desired 

portfolio of resources. 

When determining whether it is more efficient to site wind projects close to load or 

in higher quality wind resources areas that are remote from load and require 

transmission, the WinDS optimization model finds that it is often more efficient to 

site wind projects remotely. In fact, the model finds that it would be cost-effective to 

build more than 12,000 miles of additional transmission, at a cost of approximately 

$20 billion in net present-value terms. Much of that transmission would be required 

in later years after an initial period where generation is able to use the limited 

remaining capacity available on the existing transmission grid. Transmission 

required for the 20% Wind Scenario can be seen in the red lines on the map in 

Figure 4-10. The red lines represent general areas where new transmission capacity 

would be needed, in addition to the existing transmission grid illustrated by green 

lines. As a point of comparison, more than 200,000 miles of transmission lines are 

currently operating at 230 kV and above. 

This analytical approach is consistent with other recent or current studies and plans, 

such as the following: 

z The CDEAC evaluated a “high renewables” case and found that it 

would require an additional 3,578 line miles of transmission at a 

total cost of $15.2 billion (CDEAC 2006). This transmission 

investment would access 68.4 GW of renewable generation 

(predominantly wind) and 84.6 GW of new fossil fuel generation. 

Under the CDEAC analysis, if half of the transmission cost is 

assigned to wind, the resulting cost would be approximately $120 

per new kilowatt of wind developed. This represents about a 7% 

increase in the capital cost of wind development (based on capital 

costs for a wind energy facility of about $1,800/kW). 

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Figure 4-10. Conceptual new transmission line scenario by WinDS region 

4

z	 The Midwest ISO compared the benefits and costs of bringing 8,640 

MW of new wind energy online. Using a natural gas price of $5 per 

million British thermal units (MMBtu; well below 2007 prices), the 

annual benefits of reduced natural gas costs from new transmission 

and development of wind generation were between $444 and $478 

million (Midwest ISO 2003). The Midwest ISO recently studied the 

costs of developing 16,000 MW of wind within its system, along 

with 5,000 miles of new 765-kV transmission lines to deliver the 

wind from the Dakotas to the New York City area. Although the 

overall generation and transmission costs reached an estimated 

investment of $13 billion, the project produced annual savings of 

$600 million over its costs. These savings are in the form of lower 

wholesale power costs and prices in the eastern part of the Midwest 

ISO footprint—such as Ohio and Indiana—resulting from greater 

access to lower cost generation in the western states such as Iowa 

and the Dakotas. 

z	 AEP, a large utility and transmission owner/operator, produced a 

conceptual transmission plan to integrate 20% electricity from wind. 

The conceptual plan provides for 19,000 miles of new 765-kv 

transmission line at a discounted or net present value cost of $26 

billion. This estimate is close to the WinDS model estimate (AEP, 

2007). 

z	 ERCOT, the independent transmission operator for most of Texas, 

evaluated 12 options to build transmission for additions of 1,000 

MW to 4,600 MW of wind energy. ERCOT found that the 

transmission addition would cost between $15 million and $1.5 

billion, depending on the distance required. The transmission cost 

averages $180/kW of wind energy, or about 10% of the $1,800/kW 

96	 20% Wind Energy by 2030 – Prepublication Version 
 

 

 

capital cost (ERCOT 2006). The benefits available from such 

transmission are often reported in terms of annual savings to 

consumers and the reduced cost of energy production. The graph in 

Figure 4-11 illustrates the cumulative benefits in the Texas study, 

for the weakest investment of the 12 analyzed by ERCOT. It should 

be noted that wind transmission cost estimates remain highly 

uncertain. For example, ERCOT recently updated their earlier study 

and found that for additions of 5,150 MW to 18,000 MW of wind 

energy, the transmission addition would cost between $2.95 billion 

and $6.38 billion, or in the range of $350/kW to $570/kW (ERCOT 

2008). 

z	 In another study analyzing transmission costs, the CDEAC Wind 

Task Force used NREL’s WinDS geographic information system 

(GIS) database to create wind energy supply curves for many states 

in the western United States. This analysis showed that the western 

states can build 30 GW of wind capacity that can be delivered at a 

price of $50/MWh (counting both generation and transmission 

costs). Building additional transmission to reach more wind 

resources and more loads would raise the marginal cost by 20% to 

$60/MWh. More than 100 GW of new wind capacity could be 

developed at that price, using 2005 equipment costs (CDEAC 

2006). 

Clearly, significant additional transmission capacity would be required to integrate 

high levels of wind across the country. As the studies described here demonstrate, 

however, meeting this challenge could be economically and technically feasible. In 

addition, sizable net reductions in the cost of delivering bulk electricity to load 

centers could be achievable. 

Figure 4-11. Cumulative savings versus total transmission cost for renewable 

energy zone (worst case) 

4

Source: ERCOT (2006) 

20% Wind Energy by 2030 – Prepublication Version 97 
 

 

 

 

4

Developing any major new generation sources in remote or semiremote locations 

will require new transmission to deliver the energy to loads. As long as load 

continues to grow, investment in transmission will be needed as well. Most highvoltage transmission additions serve multiple generation resources, not just wind. 

Once the marginal transmission cost for wind is balanced against its low energy cost 

and environmental impacts, the net costs might turn out to be not much greater in the 

portfolio context than the transmission costs of traditional fossil fuel resources. 

An investment of approximately $60 billion (in undiscounted terms) in transmission 

between now and 2030, as suggested by the NREL analysis, given optimistic 

assumptions, amounts to an expenditure of approximately $3 billion per year over 

the next 22 years. Current transmission investment level is nearly $8 billion per year 

and growing. Regardless of wind’s role, most analysts believe that this figure will 

continue to increase as utilities make up for decades of underinvestment in the grid. 

In the mid-Atlantic region, where several 765-kV projects have been proposed, 900 

miles of new transmission would cost approximately $3.5 billion, or 0.1 cent/kWh in 

amortized rates for consumers in the PJM system, a high-cost area for new 

transmission construction (Pfirrmann 2005). Under optimistic assumptions, the rate 

impact of the transmission portion of the 20% Wind Scenario could be as low as 

$1.30 per residential customer per month. But this cost would be partially offset by 

the benefits of access to lower cost resources and enhanced reliability. The cost of 

new transmission, then, might not be excessive or prohibitive for customers. In any 

case, and as long as electricity demands grow, new transmission will be required to 

serve any new generation developed, and incremental transmission costs will be 

unavoidable. 

4.2.2 OVERCOMING BARRIERS TO TRANSMISSION INVESTMENT 

Barriers to transmission investment include: 

z Transmission planning 

z Allocation of the costs of new transmission investments 

z Assurance of cost recovery 

z Siting of new transmission facilities. 

More details on each area are given in the following subsections. 

Transmission Planning 

Currently, generation companies are reluctant to commit to a new generation project 

unless it is clear that transmission will be available, but transmission developers are 

equally reluctant to step forward until generator interconnection requests have been 

filed (the “chicken or the egg” conundrum). Most electric utilities planned 

generation and transmission in an integrated process until the 1990s, when federal 

open access rules required the separation of transmission and generation businesses. 

The effects of this separation on planning can be reduced through open, transparent 

transmission planning processes, which are now required by FERC’s recently 

enacted ruling, Order No. 890 (FERC 2007). 

The 20% Wind Scenario would require a generic change in the way transmission 

planning is done in many areas of the country. Numerous parties across a wide 

98 20% Wind Energy by 2030 – Prepublication Version 
 

 

geographic area would need to collaborate on developing a common plan instead of 

individual entities planning in isolation. This approach yields major economies of 

scale in that all users would benefit by pooling solutions to their needs into a single 

plan that would be more productive in regional terms than simply summing the 

needs of individual organizations. FERC’s Order No. 890 is a large step toward this 

regional joint planning approach, but success will depend on collaborative followthrough at the regional level. 

Cost Allocation 

Transmission is often a “public good”─ meaning that its benefits are widely 

dispersed and that some parties can enjoy these benefits without incurring direct 

costs. In such situations, parties might have incentives to avoid paying their fair 

share of the costs. Accordingly, public good status cannot be achieved unless some 

government agency determines how the costs are to be allocated and is able to 

enforce that allocation. 

Under the Federal Power Act, FERC is responsible for determining how 

transmission costs are to be allocated. For regions with RTOs or ISOs, FERC has 

typically reviewed generic cost allocation plans proposed by these organizations and 

approved the plans with modifications the commission finds appropriate. In areas 

without RTOs or ISOs, prospective transmission developers propose cost allocation 

arrangements to FERC on a project-by-project basis. FERC reviews the proposals; 

calls for additional information if needed; and approves or rejects them, or approves 

them with certain conditions attached. 

Cost Recovery 

A new transmission facility, regardless of need or Creating RE Models for New 

merit, will not be built until the participating utilities Transmission 

(and the financial community) have a very high degree A few states that have good wind resources of certainty that the cost of the facility will be and RPS laws have decided to expand their recoverable in a predictable manner. FERC and state states’ transmission in advance of regulatory approval of a cost allocation plan and a rate generation to enable the modular of return on the investment are essential. 

development of location-constrained, clean, 

and diversified resource areas to meet state Transmission Siting goals. Texas, Minnesota, Colorado, and 

California, for example, are leaders in Local opposition to proposed transmission lines is renewable energy development, and have often a major challenge to transmission expansion. An created renewable energy models for new AC transmission line typically benefits all users along transmission. North Dakota, South Dakota, its path by increasing reliability, allowing for new Wyoming, Kansas, and New Mexico have generation and the associated economic development, also established new authorities to spur and providing access to lower cost resources. Local investment in additional transmission owners, however, do not always value such benefits infrastructure. and frequently have other concerns that must be 

addressed. Some transmission companies have been 

more effective than others at obtaining local input, identifying and dealing with 

landowners’ concerns, and selecting routes. Best practices in this area need to be 

identified and broadly applied. 

State agencies sometimes reject interstate transmission proposals if it appears that 

they would not result in significant benefits for intrastate residents. This concern led 

Congress to include a provision in the 2005 EPAct that establishes a federal 

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“backstop” transmission siting authority, which can be invoked if the U.S. 

Department of Energy (DOE) has designated the relevant geographic area as a 

“national interest electric transmission corridor” (i.e., a “national corridor”), and an 

affected state has withheld approval of a proposed transmission facility in the 

National Corridor for more than one year. 

4.2.3	 MAKING A NATIONAL INVESTMENT IN TRANSMISSION 

The 20% Wind by 2030 Scenario would require widespread recognition that there is 

national interest in ensuring adequate transmission. Expanding the country’s 

transmission infrastructure would support reliability of the power system; enable 

open, fair, and competitive wholesale power markets; and grant owners and 

operators access to low-cost resources. Although built to enable access to wind 

energy, the new transmission infrastructure would also increase energy security, 

reduce GHG emissions, and enhance price stability through fuel diversity. 

4.3	 U.S. POWER SYSTEM OPERATIONS AND MARKET 

STRUCTURE EVOLUTION 

4

The lessons summarized from research done to date illustrate a number of changes 

that would facilitate reaching 20% wind energy penetration. Expanding from 

approximately 12 GW at the time of this writing to over 300 GW will require most 

or all of these changes. This section summarizes the operational and market features 

that would support the 20% Wind Scenario. These features are also important to the 

long-term sustainability of the electric industry. 

4.3.1	 EXPANDING MARKET FLEXIBILITY 

The 20% Wind Scenario would be aided by the development of or access to energy 

spot markets where participants who have an excess or shortfall of power could 

trade at competitive prices that reflect the marginal cost of balancing load. Such 

markets were recently implemented in the 15-state Midwest ISO region, the midAtlantic PJM region, New York, New England, and the Southwest Power Pool 

(SPP), showing the feasibility of such reforms. It is certainly possible that other 

regions could pursue such reforms by 2030. 

Broad geographical markets and interarea trading would allow the benefits of 

geographic dispersion and aggregation of wind plant output to be realized. These 

benefits have been shown to reduce the variability of wind plant output on a large 

scale, which makes a market-based approach and trading system all the more 

worthwhile. The challenge is that energy spot markets have been subject to 

opposition as market prices have risen because of higher fuel costs. 

4.3.2	 ENHANCING WIND FORECASTING AND SYSTEM FLEXIBILITY 

The 20% Wind Scenario would require highly trained power system operators, 

equipped with state-of–the-art wind resource forecasting tools that would be fully 

integrated with power system operations. Forecasting is spreading rapidly and 

improving significantly, particularly in terms of its adoption and integration within 

power system operations. Some power system dispatchers, however, still need to be 

trained to operate systems with high wind penetration and to use forecasting and 

operations tools that predict and respond to wind plant output fluctuations. 

100	 20% Wind Energy by 2030 – Prepublication Version 
 

 

To achieve balance in a power system using wind energy, the 20% Wind Scenario 

would require the use of the existing fleet of flexible, dispatchable, mainly gas-fired 

generators designed for frequent and rapid ramping. There would need to be enough 

dispatchable units to balance the system as fluctuations occur in wind plant output 

and load. 

Transmission services vary across regions in the United States. Regions with RTOs 

have “financial transmission rights” that are more flexible than capacity reservations 

and allow for payment based on usage. In addition, under FERC Order 890 (FERC 

2007), all regions are now required to develop “conditional firm” services, which 

would allow for resources such as wind to be better integrated into the grid. 

The 20% Wind Scenario would require end users to be able (via price signals and 

technology) to respond to system needs by shifting or curtailing consumption. Timeshifting of demand would help reduce today’s large difference between peak and 

off-peak loads and encourage more flexible loads, such as plug-in hybrid cars, 

hydrogen production, and smart appliances that take energy from the grid during 

low-load periods. These practices would smooth electricity demand and open a 

larger market for off-peak wind energy. 

The 20% Wind Scenario would require a smarter, more flexible, and more robust 

high-voltage transmission grid than the one in place today. Greater reliance on 

flexible AC transmission system (FACTS) devices and wide-area monitoring and 

control systems would be necessary. Increased flexibility would accommodate 

variations in technology choices, resource mixes, market rules, and regional 

characteristics. Greater robustness would help ensure future reliability. Information 

technologies for distributed intelligence, sensors, smart systems, controls, and 

distributed energy resources would need to be standardized and integrated with 

market and customer operations. 

4.4 REFERENCES AND OTHER SUGGESTED READING 

Ackermann, T. 2005. Wind Power in Power Systems. Chichester, UK: Wiley Europe 

Ltd. 

AEP (American Electric Power). 2007. Interstate Transmission Vision for Wind 

Integration. 

http://www.aep.com/about/i765project/docs/windtransmissionvisionwhitepa 

per.pdf 

Bouillon, H., P. Fösel, J. Neubarth, and W. Winter. 2004. Wind Report 2004. 

Bayreuth, Germany: E.ON Netz. http://www.windwatch.org/documents/wp-content/uploads/EonWindReport2004.pdf 

Cardinal, M., and N. Miller. 2006. “Grid Friendly Wind Plant Controls: 

WindCONTROL – Field Test Results.” Presented at WindPower 2006, June 

4–7, Pittsburgh, PA. 

CDEAC (Clean and Diversified Energy Advisory Committee). 2006. Combined 

Heat and Power White Paper. Denver, CO: CDEAC. 

http://www.westgov.org/wga/initiatives/cdeac/CHP-full.pdf 

CEC (California Energy Commission). 2007. Intermittency Analysis Project: Final 

Report. California Energy Commission, July 2007. 

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4

http://www.energy.ca.gov/pier/final_project_reports/CEC-500-2007­

081.html 

Deutsche Energie-Agentur GmbH (dena). 2005. Energy Management Planning for 

the Integration of Wind Energy into the Grid in Germany, Onshore and 

Offshore by 2020. Cologne, Germany: Deutsche Energie-Agentur GmbH. 

http://www.uwig.org/Dena-2005_English.pdf 

Eisenbrey, C. 2007. EEI Energy Data Alert, Strong Upward Trend in T&D 

Investment Continued in 2006. Washington, DC: Edison Electric Institute 

(EEI). 

EnerNex Corporation. 2006. Final Report: 2006 Minnesota Wind Integration Study. 

Volumes I and II. Knoxville, TN: EnerNex. 

http://www.puc.state.mn.us/docs/#electric 

ERCOT (Energy Reliability Council of Texas). 2006. Analysis of Transmission 

Alternatives for Competitive Renewable Energy Zones in Texas. Austin: 

ERCOT. 

http://www.ercot.com/news/presentations/2006/ATTCH_A_CREZ_Analysi 

s_Report.pdf 

ERCOT (Energy Reliability Council of Texas). 2008. ERCOT’s Competitive 

Renewable Energy Zone Transmission Optimization Study. Austin: 

ERCOT. http://www.ercot.com/news/press_releases/2008/nr04-02-08.html 

ERCOT (Energy Reliability Council of Texas). 2008. ERCOT’s Competitive 

Renewable Energy Zone Transmission Optimization Study. Austin: 

ERCOT. http://www.ercot.com/news/press_releases/2008/nr04-02-08.html 

Eriksen, P., A. Orths, and V. Akhmatov. 2006. “Integrating Dispersed Generation 

into the Danish Power System – Present Situation and Future Prospects.” 

Presented at the IEEE Power Engineering Society (PES) Meeting, June 20, 

Montreal. 

Erlich, I., W. Winter, and A. Dittrich. 2006. Advanced Grid Requirements for the 

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Europe by End of 2006 (Cumulative). 

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publications/statistics/070129_Wind_map_2006.pdf 

FERC (Federal Energy Regulatory Commission). 2007. Order No. 890 Final Rule: 

Preventing Undue Discrimination and Preference in Transmission Service. 

Washington, DC: FERC. http://www.ferc.gov/industries/electric/indusact/oatt-reform/order-890/fact-sheet.pdf 

Gross, R., P. Heptonstall, D. Anderson, T. Green, M. Leach, and J. Skea. 2006. 

The Costs and Impacts of Intermittency: An Assessment of the Evidence 

on the Costs and Impacts of Intermittent Generation on the British 

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Centre. 

http://www.ukerc.ac.uk/ImportedResources/PDF/06/0604_Intermittency_re 

port_final.pdf 

Hirst, E., and B. Kirby. 2001. Transmission Planning for a Restructuring U.S. 

Electric Industry. Washington, DC: EEI. 

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http://www.eei.org/industry_issues/energy_infrastructure/transmission/trans 

mission_hirst.pdf 

Holttinen, H. 2004. The Impact of Large Scale Wind Power Production on the 

Nordic Electricity System. ISBN 951-38-6426-X. Espoo, Finland: VTT 

Technical Research Centre of Finland. 

http://www.vtt.fi/inf/pdf/publications/2004/P554.pdf 

IEA (International Energy Agency). 2005. Variability of Wind Power and Other 

Renewables: Management Options and Strategies. Paris: IEA. 

http://www.iea.org/Textbase/Papers/2005/variability.pdf 

IEEE. 2005. IEEE Power and Energy Magazine Special Issue: Working with Wind; 

Integrating Wind into the Power System, November–December, (3:6). 

Milligan, M. and K. Porter. 2006. “Capacity Value of Wind in the United States: 

Methods and Implementation,” Electricity Journal, 19(2): 91–99. 

Milligan, M., B. Parsons, J.C. Smith, E. DeMeo, B. Oakleaf, K. Wolf, M. Schuerger, 

R. Zavadil, M. Ahlstrom, and D. Nakafuji. 2006. Grid Impacts of Wind 

Variability: Recent Assessments from a Variety of Utilities in the United 

States. Report No. NREL/CP-500-39955. Golden, CO: National Renewable 

Energy Laboratory (NREL). http://www.uwig.org/Ewec06gridpaper.pdf. 

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Transmission Expansion Plan 2006. http://www.midwestmarket.org 

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Action Plan. Portland, OR: Northwest Power and Conservation Council, 

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Power Production. Report No. 238389. Fredericia, Denmark: Energinet.dk. 

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Coal-Fired Resources. FERC Docket No. AD05-3-000. Washington, DC: 

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m 

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WindLogics. 

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Chapter 5. Wind Power Siting 

and Environmental 

Effects 

The 20% wind energy by 2030 scenario offers 

substantial positive environmental impacts in 

today’s carbon-constrained world. Wind plant 

siting and approval processes can accommodate 

increased rates of installation while addressing 

environmental risks and concerns of local 

stakeholders. 

5.1 WIND ENERGY TODAY 

5

Wind energy is one of the cleanest and most environmentally neutral energy sources 

in the world today. Compared to conventional fossil fuel energy sources, wind 

energy generation does not degrade the quality of our air and water and can make 

important contributions to reducing climate change effects and meeting national 

energy security goals. In addition, it avoids environmental effects from the mining, 

drilling, and hazardous waste storage associated with using fossil fuels. Wind energy 

offers many ecosystem benefits, especially compared with other forms of electricity 

production. Wind energy production can also, however, negatively affect wildlife 

habitat and individual species, and measures to mitigate prospective impacts may be 

required. As with all responsible industrial development, wind power facilities need 

to adhere to high standards for environmental protection. 

Wind energy generally enjoys broad public support, but siting wind plants can raise 

concerns in local communities. Successful project developers typically work closely 

with communities to address these concerns and avoid or reduce risks to the extent 

possible. Not all issues can be fully resolved and not every prospective site is 

appropriate for development, but engaging local leaders and the public is imperative. 

Various agencies and stakeholders must also be involved in reviewing and 

approving projects. If demand increases and annual installations of wind energy 

approach 10 gigawatts (GW) and more, the wind energy industry and various 

government agencies would need to scale up their permitting and review 

capabilities. 

To date, hundreds of wind projects have been successfully permitted and sited. 

Although the wind energy industry must continue to address significant 

environmental and siting challenges, there is growing market acceptance of wind 

energy. If challenges are resolved and institutions are adaptive, a 20% windgenerated electricity scenario in the United States could be feasible by 2030. As 

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5

noted by the Intergovernmental Panel on Climate Change (IPCC), under certain 

conditions, renewable energy could contribute 30% to 35% of the world’s electricity 

supply by 2030 (IPCC 2007). 

This chapter reviews environmental concerns associated with siting wind power 

facilities, public perceptions about the industry, regulatory frameworks, and 

potential approaches to addressing remaining challenges. 

5.1.1 SITE-SPECIFIC AND CUMULATIVE CONCERNS 

About 10% to 25% of proposed wind energy projects are not built—or are 

significantly delayed—because of environmental concerns. Although public support 

for wind energy is generally strong, this attitude does not always translate into early 

support for local projects. Site-specific concerns often create tension surrounding 

new energy facilities of any kind. Although most wind energy installations around 

the United States pose only minor risks to the local ecology or communities, some 

uncertainties remain. Further research and knowledge development will enable some 

of these uncertainties to be mitigated and make risks more manageable. 

Local stakeholders generally want to know how wind turbines might affect their 

view of their surroundings and their property values. In addition, they might be 

concerned about the impact on birds and other wildlife. Weighing these risks and 

benefits raises questions about the best management approaches and strategies. 

Wind energy developments usually require permits or approvals from various 

authorities, such as a county board of supervisors, a public service commission, or 

another political body (described in more detail in Section 5.5). These entities 

request information from a project developer—usually in the form of environmental 

impact studies before construction—to understand potential costs and benefits. The 

results of these studies guide jurisdictional decisions. A single lead agency might 

consider the entire life-cycle effects of a wind energy project. This is in contrast to 

fossil fuel and nuclear projects, where the life-cycle impacts (e.g., acid rain and 

nuclear wastes) would be widely dispersed geographically. No single agency 

considers all impacts. 

For many government agency officials, the central issue is whether wind energy 

projects pose risks to the resources or environments they are required to protect. 

They want to know the net cumulative environmental impact (e.g., emissions 

reductions versus wildlife impacts) of using 20% wind power in the United States, 

whether positive or negative. Uncertainty can arise from inadequate data, modeling 

limitations, incomplete scientific understanding of basic processes, and changing 

societal or management contexts. Complex societal decisions about risk typically 

involve some level of uncertainty, however, and very few developers make decisions 

with complete information (Stern and Fineberg 1996). Because a great deal of 

experience exists to inform decision making in such circumstances, residual 

uncertainties about environmental risks need not unduly hinder wind energy project 

development. 

The wind industry may encounter difficulties entering a competitive energy 

marketplace if it is subject to requirements that competing energy technologies do 

not face. Risks associated with wind power facilities are relatively low because few 

of the significant upstream and downstream lifecycle effects that typically 

characterize other energy generation technologies are realized. Moreover, the 

potential risks are not commensurate between wind energy and other sources, such 

106 20% Wind Energy by 2030 – Prepublication Version 
 

as, nuclear and fossil fuels and comparative impact analyses are not readily 

available. These analyses would need to examine the broader context of the potential 

adverse effects of wind power on human health and safety (minimal), ecology, 

visibility, and aesthetics in relation to the alternatives. 

The acceptability of risks will vary among communities and sites, so it is important 

to understand these differences and build broad public engagement. Developing 

effective approaches to gaining the public’s acceptance of risks is a necessary first 

step toward siting wind energy facilities. 

5.2 ENVIRONMENTAL BENEFITS 

5.2.1 GLOBAL CLIMATE CHANGE AND CARBON REDUCTIONS 

Publicity related to wind power developments often focuses on wind power’s impact 

on birds, especially their colliding with turbines. Although this is a valid 

environmental concern that needs to be addressed, the larger effects of global 

climate change also pose significant and growing threats to birds 

and other wildlife species. The IPCC recently concluded that A single 1.5-MW wind turbine global climate change caused by human activity is likely to displaces 2,700 tons of CO2 per year seriously affect terrestrial biological systems, as well as many compared with the current U.S. other natural systems (IPCC 2007). A 2004 study in Nature 

average utility fuel mix, or the forecast that a midrange estimate of climate warming could 

cause 19% to 45% of global species to become extinct. Even equivalent of planting 4 square. 

when minimal temperature increases and climate changes are kilometers of forest every year

(AWEA 2007). projected, extinction of species would be in the 11% to 34% 

range (Thomas et al. 2004). The future for birds in a world of 

global climate change is particularly bleak. A recent article 

found that 950 to 1,800 terrestrial bird species are imperiled by climate changes and 

habitat loss. Species in higher latitudes will experience more effects of climate 

change; birds in the tropics will decline from continued deforestation, which 

exacerbates global climate change and land conversion (Jetz, Wilcove, and Dobson 

2007). Wind energy, which holds significant promise for reducing these impacts, 

can be widely deployed across the United States and around the world to begin 

reducing greenhouse gas emissions (GHGs) now. Although the effects of wind 

energy development on wildlife should not be minimized, they must be viewed in 

the larger context of the broader threats posed by climate change. 

A primary benefit of using wind-generated electricity is that it can play an important 

role in reducing the levels of carbon dioxide (CO2) emitted into the atmosphere. 

Wind-generated electricity is produced without emitting CO2, the GHG that is the 

major cause of global climate change. 

Today, CO2 emissions in the United States approach 6 billion metric tons annually, 

39% of which are produced when electricity is generated from fossil fuels (see 

Figure 5-1; EIA 2006). If the United States obtained 20% of its electricity from wind 

energy, the country could avoid putting 825 million metric tons of CO2 annually into 

the atmosphere by 2030, or a cumulative total of 7,600 million metric tons by 2030 

(see assumptions outlined in appendices A and B). 

A relatively straightforward metric used to understand the carbon benefits of wind 

energy is that a single 1.5-MW wind turbine displaces 2,700 metric tons of CO2 per 

5

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Figure 5-1. Electricity production is responsible for 39% of 

CO2 emissions in the United States 

5

Source: EIA (2006) 

year compared with the current U.S. average utility fuel mix, or the equivalent of 

planting 4 square kilometers of forest every year (AWEA 2007). 

The fuel displaced by wind-generated electricity depends on the local grid and the 

type of generation supply. In most places, natural gas is the primary fuel displaced. 

Wind energy can displace coal on electric grids with large amounts of coal-fired 

generation. In the future, wind energy is likely to offset more coal by reducing the 

need to build new coal plants. Regardless of the actual fuel supplanted, more 

electricity generated from wind turbines means that other nonrenewable, fossilbased fuels are not being consumed. In New York, for example, a study prepared for 

the independent system operator (ISO) found that if wind energy provided 10% of 

the state’s peak electricity demand, 65% of the energy displaced would be from 

natural gas, followed by coal at 15%, oil at 10%, and electricity imported from out 

of state at 10% (Piwko et al. 2005). 

In addition, manufacturing wind turbines and building wind plants generate only 

minimal amounts of CO2 emissions. One university study that examined the issue 

(White and Kulsinski, 1998) found that when these emissions are analyzed on a lifecycle basis, wind energy’s CO2 emissions are extremely low—about 1% of those 

from coal, or 2% of those from natural gas per unit of electricity generated. In other 

words, using wind instead of coal reduces CO2 emissions by 99%; using wind 

instead of gas reduces CO2 emissions by 98%. 

5.2.2	 IMPROVING HUMAN HEALTH THROUGH REDUCED AIR 

EMISSIONS 

Switching to a zero-emissions energy-generation technology like wind power 

contributes to cleaner and healthier air. Moreover, wind power generation is not a 

direct source of regulated pollutants such as nitrogen oxides, sulfur dioxide, and 

mercury. 

Coal-fired power plants are the largest industrial source of mercury emissions in the 

United States (NESCAUM 2003). The U.S. Environmental Protection Agency 

(EPA) (EPA 2007) and the American Medical Association (AMA) note that fetal 

exposure to methylmercury has been linked to problems with neurological 

development in children (AMA Council on Scientific Affairs 2004). 

108	 20% Wind Energy by 2030 – Prepublication Version 
 

Furthermore, according the American Lung Association (ALA), almost half of all 

Americans live in counties where unhealthy levels of smog place them at risk for 

decreased lung function, respiratory infection, lung inflammation, and aggravation 

of respiratory illness. And more than 76.5 million Americans are exposed to 

unhealthful short-term levels of particle pollution, which has been shown to increase 

heart attacks, strokes, emergency room visits for asthma and cardiovascular disease, 

and the risk of death. Some 58.3 million Americans suffer from chronic exposure to 

particle pollution. Even when levels are low, exposure to these particles can also 

increase the risk of hospitalization for asthma, damage to the lungs, and the risk of 

premature death (ALA 2005). 

5.2.3 SAVING WATER 

The nation’s growing communities place greater demands on water supplies and 

wastewater services, and more electricity is needed to power the expanding water 

services infrastructure. Future population growth in the United States will heighten 

competition for water resources. Especially in arid regions, communities are 

increasingly facing challenges with water and electric power shortages, resources 

that are interlinked. 

Water is a critical resource for thermoelectric power plants, which use vast 

quantities. These plants were responsible for 48% of all total water withdrawals in 

2000, or about 738 billion liters per day (Hutson et al. 2005). Much of the water 

withdrawn from streams, lakes, or other sources is returned, but about 9%—totaling 

about 68 billion liters per day—is consumed in the process. Although regulation will 

require the majority of new generation plants to use recirculating, closed-loop 

cooling technologies, which will lessen water withdrawals, this evolution will 

actually lead to an overall increase in water consumption (DOE 2006). 

Even some renewable technologies place a demand on water resources. For 

example, most ethanol plants have demonstrated a reduction in water use over the 

past years, but are still in the range of 13.25 to 22.7 liters of water consumed per 

3.79 liters of ethanol produced (IATP 2006). 

In contrast, wind energy does not require the level of water 

resources consumed by many other kinds of power 

generation. As a result, it may offer communities in waterstressed areas the option of economically meeting growing 

Wind energy has the potential

conserve billions of liters of wa

interior West, which faces dec

water reservoirs. 

to 

ter in the 

lining 

energy needs without increasing demands on valuable water 

resources. Wind energy can also provide targeted energy 

production to serve critical local water system needs such as irrigation and 

municipal systems. 

A non-governmental organization report entitled The Last Straw: Water Use by 

Power Plants in the Arid West, Baum and colleagues (2003) called attention to water 

quality and supply issues associated with fossil-fuel power plants in the interior 

West. Faced with water shortages, the eight states in this region are seeing water for 

power production compete with other uses, such as irrigation, hydropower, and 

municipal water supplies. Based on this analysis, the authors estimate that 

significant savings from wind energy are possible, as illustrated in Table 5-1. 

As the United States seeks to lessen the use of foreign oil for fuel, water use and 

consumption is high among other energy production methods. Most ethanol plants 

have demonstrated a reduction in water use in recent years, but are still in the range 

5

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Table 5-1. Estimated water savings from 

wind energy in the interior West (2003) 

5

Wind 

Energy (MW) 

Water Savings 

(billion gallons 

withdrawn) 

Water Savings 

(billion gallons 

consumed) 

1,200 3.15 1.89 

3,000 7.88 4.73 

4,000 10.51 6.31 

Adapted from "The Wind/Water Nexus" Wind Powering America 

(USDOE 2006) 

of 13.25 to 22.7 liters of water consumed per 3.79 liters of ethanol produced (IATP 

2006). An issue brief, prepared by the World Resources Institute, stated that coal-toliquid fuel production is a water intensive process, requiring about 10 gallons of 

water used for every gallon of coal-to-liquid product (Logan and Venezia 2007). 

Global climate change is also expected to impact water supplies. Mountains in the 

western United States will have less snowpack, more winter flooding, and reduced 

flows in the summer, all of which worsen the already fierce competition for 

diminished water resources (IPCC 2007). Because of increasing demands for water 

and decreasing supplies, some tough decisions will be needed on how this valuable 

resource should be allocated—especially for the West and Great Plains. Although 

wind energy cannot solve this dilemma, an increased reliance on wind energy would 

alleviate some of the increased demand in the electricity sector, thereby reducing 

water withdrawals for the other energy sources. 

5.3 POTENTIAL ENVIRONMENTAL IMPACTS 

5.3.1 HABITAT DISTURBANCE AND LAND USE 

Fuel extraction and energy generation affect habitat and land use, regardless of the 

type of fuel. Traditional electricity generation requires mining for coal or uranium 

and drilling for natural gas, all of which can destroy habitat for many species and 

cause irreversible ecological damage. With the global and national infrastructure 

required to move fuel to generating stations—and the sites needed to store and treat 

the resulting waste—processing fossil fuel and nuclear energy is also a highly landintensive endeavor. 

Coal mining is estimated to disturb more than 400 thousand hectares of land every 

year for electricity generation in the United States, and destroys rapidly disappearing 

wildlife habitat. In the next 10 years, more than 153,000 hectares of high-quality 

mature deciduous forest are projected to be lost to coal mining in West Virginia, 

Tennessee, Kentucky, and Virginia, according to the National Wildlife Federation 

(Price and Glick 2002). 

Wind development also requires large areas of land, but the land is used very 

differently. The 20% Wind Scenario (305 GW) estimates that in the United States 

about 50,000 square kilometers (km2

) would be required for land-based projects and 

more than 11,000 km2

 would be needed for offshore projects. The actual footprint 

for wind development projects, which ranges from 2% to 5% for the turbines and 

110 20% Wind Energy by 2030 – Prepublication Version 
 

 

related infrastructure, and totals about 1,000 to 2,500 km2 (100,000 to 250,000 

hectares11) of disturbed land under the 20% Wind Scenario —an amount of 

dedicated land that is slightly smaller than Rhode Island. For scale comparisons, 

available data for existing coal mining activities indicate that about 1,700,000 

hectares of land is permitted or covered and about 425,000 hectares of land are 

disturbed (DOI 2004). An important factor to note is that wind energy projects use 

the same land area each year; coal and uranium must be mined from successive 

areas, with total disturbed area increasing each year. In agricultural areas, land used 

for wind generation projects has the potential to be compatible with some uses 

because only a few hectares are taken out of production, and no mining or drilling is 

needed to extract the fuel. 

Although wind energy may be able to coexist with land uses such as farming, 

ranching, and forestry, wind energy development might not be compatible with land 

uses such as housing developments, airport approaches, some radar installations, and 

low-level military flight training routes. Wind turbines are tall structures that require 

an otherwise undisturbed airspace around them. The need for relatively large areas 

of undisturbed airspace can also directly or indirectly affect wildlife habitat. 

In a presentation to the National Wind Collaborative Committee, wildlife biologists 

described direct impacts include building wind turbines, service roads, and other 

infrastructure such as substations. Estimates of temporary construction impacts 

range from 0.2 to 1.0 hectare per turbine; estimates of permanent habitat spatial 

displacement range from 0.3 to 0.4 hectare per turbine (Strickland and Johnson 

2006). Indirect impacts can include trees being removed around turbines, edges in a 

forest being detrimental to some species, and the presence of turbines causing some 

species or individuals to avoid previously viable habitats. For example, a grassland 

songbird study on Buffalo Ridge in Minnesota found species displacement of 180 

meters (m) to 250 m from the wind turbines (Strickland and Johnson 2006). 

Indirect habitat impacts on grassland species are a particular concern, especially 

because extensive wind energy development could take place in grassy regions of 

the country. Peer-reviewed research has concluded, however, that one species, the 

Lesser Prairie Chickens, actively avoids electricity infrastructure such as 

transmission lines and frequent vehicle activity by as much as 0.4 km and fossil fuel 

power plants by more than 1 km (Robel et al. 2004). Displacements of already 

declining local populations are likely, but the magnitude of these effects is uncertain 

because data specific to wind energy are not yet available. The extent of unknowns 

surrounding this issue led the National Wind Coordinating Collaborative’s (NWCC) 

Wildlife Workgroup to form the Grassland/Shrub-Steppe Species Collaborative 

(GS3C), a four-year research program to study the effects of wind turbines on 

grassland birds (NWCC 2006). Like the Bat and Wind Energy Cooperative (BWEC) 

discussed later, the GS3C provides a vehicle for public and private funding and for 

third-party peer-reviewed studies. Issues regarding the conservation of sensitive 

habitats will need to be addressed over time. Strategic planning and siting to 

conserve and improve potentially high-value habitat can be constructive and 

beneficial for both wind energy and wildlife. 

5.3.2 WILDLIFE RISKS 

Wildlife—and birds in particular–are threatened by numerous human activities, 

including effects from climate change. Relative to other human causes of avian 

11 One hectare = 2.47 acres 

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mortality, wind energy’s impacts are quite small. Figure 5-2 puts the wind industry’s 

impacts into context and illustrates that many human (and some feline) activities 

pose risks to birds. 

Figure 5-2. Anthropogenic causes of bird mortality 

(per 10,000 avian deaths) 

5

Source: Erickson et al. (2002) 

As Figure 5-2 shows, anthropogenic causes of bird fatalities range from 100 million 

to 1 billion annually. Currently, it is estimated that for every 10,000 birds killed by 

all human activity, less than one death is caused by wind turbines. In fact, a recent 

National Research Council (NRC 2007) study concluded that current wind energy 

generation is responsible for 0.003% of human-caused avian mortality. Even with 

20% wind, turbines are not expected to be responsible for a significant percentage of 

avian mortality so long as proper precautions are taken in siting and design. 

Further comparative analyses are needed to better understand the trade-offs with 

other energy sources. Avian mortality is also caused, for example, by oil spills, oil 

platforms built on bird migration routes along the Gulf Coast, acid rain, and 

mountaintop mining. Wind energy will likely continue to be responsible for a 

comparatively small fraction of total avian mortality risks, although individual sites 

can present more localized risks. Some data relative to specific sites are offered in 

the list that follows: 

z	 The first large-scale commercial wind resource area developed in 

the world was Altamont Pass in California’s Bay Area in the 1980s. 

The Altamont Pass development has seen high levels of bird kills, 

specifically raptors. Although this facility has been problematic, it 

remains an anomaly relative to other wind energy projects. In 

January 2007, a number of the parties involved agreed to take steps 

112	 20% Wind Energy by 2030 – Prepublication Version 
 

to reduce raptor fatalities and upgrade the project area with newer 

technology. 

z	 An NWCC fact sheet (2004) reviewed the mortality figures from 12 

comparable postconstruction monitoring studies and found that the 

fatality rate averaged 2.3 bird deaths per turbine per year and 3.1 

birds per megawatt per year of capacity in the United States (outside 

California). Fatality rates have ranged from a low of 0.63 per 

turbine and 1 per megawatt at an agricultural site in Oregon to 10 

per turbine and 15 per megawatt at a fragmented mountain forest 

site in Tennessee (NWCC 2004). This information, which is shown 

in Table 5-2, will be updated in 2008 to incorporate newly available 

data. 

Table 5-2. Estimated avian fatalities 

per megawatt per year 

Wind Project and Location Total Fatalities 

Stateline, OR/WA 2.92 

Vansycle, OR 0.95 

Combine Hills, OR 2.56 

Klondike, OR 0.95 

Nine Canyon, WA 2.76 

Foote Creek Rim, WY (Phase 1) 2.50 

Foote Creek Rim, WY (Phase 2) 1.99 

Wisconsin 1.97 

Buffalo Ridge, MN (Phase 1) 3.27 

Buffalo Ridge, MN (Phase 2) 3.03 

Buffalo Ridge, MN (Phase 3) 5.93 

Top of Iowa 1.44 

Buffalo Mountain, TN 11.67 

Mountaineer, WV 2.69 

Source: Data adapted from Strickland and Johnson (2006) 

z	 Before 2003, bat kills at wind farms studied were also generally 

low. The frequency of bat deaths in 2003 at a newly constructed 

wind farm in West Virginia, though, led researchers to estimate that 

1,700 to 2,900 bats had been killed, and that additional bats 

probably died a few weeks before and after the six-week research 

period (Arnett et al. 2005). According to a USGS biologist, bat 

mortality has also been higher than expected at a number of sites in 

the United States and Canada (Cryan 2006). 

Wildlife collisions with wind turbines are a significant concern, particularly if they 

affect species populations. To date, no site or cumulative impacts on bird or bat 

populations have been documented in the United States or Europe. But that does not 

mean that impacts are nonexistent. This is a particular worry with bats because they 

are relatively long-lived mammals with low reproduction rates, according to a peer5

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5

reviewed study (Arnett et al. 2005). BWEC is currently conducting the necessary 

research to understand the risks to bats. 

Concerns about uncertain risks to birds and bats can lead permitting agencies and 

developers to conduct lengthy and costly studies that may or may not answer the 

wildlife impact questions raised. More research is necessary to understand more 

clearly the link between preconstruction surveys and postconstruction monitoring 

results. Well-designed research programs can, however, be costly for many projects 

and require care in assessing the appropriate levels of analysis. 

Addressing these uncertainties through additional, focused research would be 

necessary if the United States is to increase wind development. Although many 

factors influence decisions to build wind projects, wildlife and environmental 

concerns can cause site exclusion because of the following: 

z Concerns about potential wildlife impacts 

z Costly study requirements 

z Future risk mitigation requirements 

z Conflicts with other resources. 

The long term viability of the wind industry will be helped by acknowledging and 

addressing the challenges raised by these uncertain risks. Collaborative efforts such 

as BWEC and GS3C offer constructive models for this undertaking. 

5.3.3	 MANAGING ENVIRONMENTAL RISK AND ADAPTIVE MANAGEMENT 

PRINCIPLES 

Dealing with uncertainties associated with siting wind power facilities is a challenge 

for some institutions because it requires a management structure with high levels of 

social trust and credibility. As a result, various stakeholders are investigating how 

adaptive management principles might be applied to assess and manage wildlife and 

habitat risks at wind power sites. Under these models, developers and operators of 

the wind site, along with permitting agencies, could adjust the management of the 

site and the level of monitoring studies needed to the potential challenges that arise 

over the life of the project. 

Although the term is used often, “adaptive management” is not always well defined. 

Here adaptive management refers to an evolutionary management approach that 

purposely seeks to adapt management and decision-making processes to evolving 

knowledge of the technology or environmental risks in question (Holling 1978; 

Walters 1986; Lee 1993). “Social learning” is a centerpiece in this approach, with 

management seeking to enhance its capability to learn from experience and an 

expanding body of knowledge. Management solutions are regarded more as 

experiments than as definitive solutions to the challenges involved. Valuable 

experience with this approach exists in such areas as watershed planning (Lee 1993; 

NRC 2004), fisheries (Walters 1986), and forestry (Holling 1978). 

An adaptive management approach contrasts with the more typical regulatory 

approaches, which assume that sufficient knowledge exists at the outset to define 

environmental risks and effects. The basic differences between two decision 

approaches—a linear approach commonly called “command and control” and the 

adaptive management approach—are quite apparent in Figure 5-3. In the figure, 

114	 20% Wind Energy by 2030 – Prepublication Version 
 

Figure 5-3. Linear decision strategy (command and control) and interactive 

model with adaptive management principles 

Source: Morgan et. al. (2007) 

5

Model 1 assumes that sufficient research and assessment can be done before the 

technology or management system is deployed, allowing an appropriate 

management system and the needed regulatory requirements to be put in place at the 

outset. Risk analysis plays a critical role in this process, with the assumption that 

major risks can be identified and assessed and appropriate mitigation systems 

instituted. In Model 2, the assumptions address different types of situations—the 

risks are uncertain and unlikely to be resolved in the near future; the risks can be 

assessed only partially at the outset; and surprises are likely as experience unfolds. 

This model emphasizes the importance of flexible, rapid response to new knowledge 

or events. 

Accordingly, this risk management approach might well be suited for a technology 

such as wind energy, where experience and knowledge are still growing and where 

documented effects are strongly site-specific. Guiding principles and applications 

for this approach are still evolving, but adaptive management seems particularly 

well suited for situations of high uncertainties or conflict in the political process. 

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5.4 PUBLIC PERCEPTION AND ENGAGEMENT
 

5

Because the environmental benefits of wind energy are significant, public support 

for expanding wind energy development is widespread. The impacts of wind 

projects, however, are predominately local and can concern some individuals in the 

affected communities and landscapes. A primary challenge in achieving 20% of U.S. 

electricity from wind is to maximize the overall benefits of this form of energy 

without disrupting or alienating specific communities, especially prospective 

communities that do not have experience with wind turbines. 

5.4.1 PUBLIC ATTITUDES 

Wind energy development receives considerable general support among the U.S. 

population. Of those polled in a study conducted by Yale University in 2005, more 

than 87% want expanded wind energy development (Global Strategy Group 2005). 

Only a minority of the U.S. population appears to oppose wind energy, but that 

opposition can strengthen when particular sites 

are proposed. Some evidence indicates that, over 

Selected Public Opinion Surveys time, opposition might decrease and support 

might grow. Surveys commissioned in the United 

•	 RBA Research conducted a study for the Kingdom and Spain have found, for example, that 

British Wind Energy Association in April local support for a wind project increased once it 

2002 of people living near a small project in was installed and operating. 

the United Kingdom. It found that 74% of 

participants supported the wind farm—37% 

strongly—and only 8% were opposed. Of Communities must be consulted about the global 

those opposed to the project, about 25% impacts of wind, and this includes addressing 

remained opposed after the project was their concerns early on. Involving affected 

constructed. Sixty percent later supported the communities early is critical to identifying 

wind farm (RBA Research 2002). concerns and addressing them proactively. 

Stakeholder concerns must be taken seriously and •	 Although polls show broad statewide support a long-term commitment to understanding for the Cape Wind offshore project in stakeholder interactions must be made. Massachusetts, some opponents have been 

very vocal. When asked, however, some 

opponents say they might support the project if 5.4.2 VISUAL IMPACTS
 

it were part of a broader strategy to combat Wind turbines can be highly visible because of global climate change. More information on their height and locations (e.g., ridgelines and this topic can be found at open plains). Reactions to wind turbines are www.mms.gov/offshore/alternativeenergy subjective and varied. The best areas for siting 

wind turbines tend to be those with lower 

population densities. Although this can minimize the number of people affected, less 

populated areas may also be prized for tranquility, open space, and expansive vistas. 

Some people feel that turbines are intrusive; others see them as elegant and 

interesting. In either case, the visual impacts of wind energy projects may well be a 

factor in gauging site acceptability. 

Discourse with communities about the expected impacts is important. Wind project 

developers can conduct visual simulations from specific vantage points and produce 

maps of theoretical visibility across an affected community (Pasqualetti 2005). With 

this information, technical adjustments to the project layout can be made to 

accommodate specific concerns, or a developer might relocate wind turbines, reduce 

the tower height, or even propose screening devices (such as trees) to minimize 

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visual impact. All of these steps can, of course, affect the economic feasibility of a 

proposed project, so they should be weighed carefully in siting and development 

decisions. 

Because almost all commercial-scale wind turbines rise more than 60 m above the 

ground, proposed wind projects must be reviewed by the Federal Aviation 

Administration (FAA). In February 2007, the FAA updated an advisory circular 

(FAA 2007) dealing with obstruction lighting and marking, including new uniform 

recommendations for lighting wind energy projects. The new FAA suggestions are 

designed to allow pilots flying too low to be warned of obstructions and minimize 

intrusion to neighbors. The guidance recommends that wind energy projects should 

be lit at night, but now the lights can be up to 0.8 km apart and be placed only 

around the project perimeter, reducing the number of lights needed overall. The 

guidelines recommend red lights, which are less annoying than white lights to 

people nearby. No daytime lighting is necessary if the turbines and blades are 

painted white or off-white. 

5.4.3 SOUND 

All machinery with moving parts makes some sound, and wind turbines are no 

exception. The primary sound is aerodynamic noise from the blades moving through 

the air—the “whoosh-whoosh” sound heard as the blades pass the tower. When the 

wind picks up and the wind turbines begin to operate, the sound from a turbine 

(when standing closer than 350 m) is 35 to 45 decibels (dB; see Figure 5-4). This 

sound is equivalent to a kitchen refrigerator running. Less commonly heard in 

modern turbines are the mechanical sounds from the generator, yaw drive, and 

gearbox. Concerns about sound are primarily associated with older technology, such 

as the turbines of the 1980s, which were considerably louder. Advances in 

engineering and insulation ensure that modern turbines are relatively quiet. 

Figure 5-4. Decibel levels of various situations 

5

Source: BWEA (2007) 

When proposing a wind energy project, wind developers can conduct studies to 

predict sound levels in various places, including in nearby buildings or homes. 

Turbines might be slightly louder if, for example, they are located on a windy ridge 

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5

or if houses are located downwind in a sheltered valley. Changes can usually be 

made to a project if the sound levels at a particular location are deemed too high. In 

general, standard setbacks from residences and other buildings appear to reasonably 

ensure that sound levels from a wind project will be low and nonintrusive. 

5.4.4 LAND VALUE 

Residents can become concerned about possible declines in local property values 

when wind energy projects are proposed in their communities. To ascertain what 

effects they are likely to experience, they may look to other communities with 

existing wind facilities. The primary asset for many families is their home, so 

property values are a serious concern. Studies of the effects of wind projects on local 

property values should be done with great care, even though extensive studies have 

already been conducted on other energy facilities, such as nuclear plants. Because 

home values are a composite of many factors, isolating the effects of proximity to a 

wind project is important (though only a part of the full picture). Wind projects also 

tend to be located in areas of low residential density, which further compounds the 

difficulties of controlling the impact on property value. To date, two U.S. studies 

(see sidebar) have addressed these issues. Though neither is definitive and additional 

work in this area is needed, both studies found little evidence to support the claim 

that home values are negatively affected by the presence of wind power generation 

facilities. 

Wind Energy and Home Values 

In 2003, the Renewable Energy Policy Project (REPP) conducted a study of 24,000 

home sales surrounding 11 U.S. wind projects. It compared the average selling price 

over time of homes near the wind project with a nearby control area that was at least 

8 km from the project. No clear evidence of adverse effects on property values was 

found. In some communities, home values near the facilities rose faster than 

properties in the control group (Sterzinger, Fredric, and Kostiuk 2003). 

In April 2006 a study from Bard College focused on a 20-turbine wind project in 

Madison County, New York. Researchers visited each home, measured the distance 

to the nearest turbine, and ascertained to what degree the home could see the wind 

facility. This master’s thesis also concluded that there was no evidence that the 

facility affected home values in a measurable way, even when concentrating on 

homes that sold near to the facility or those with a prominent view of the turbines 

(Hoen 2006). 

Individuals with turbines on their properties might actually see an increase in their 

property values because of the lease payments paid by the wind project owner. 

Lease payments tend to be $2,000 to $5,000 (US$2006) per turbine per year, either 

through fixed payments or as a small share of the revenue. 

5.5 SITING/REGULATORY FRAMEWORK 

Currently, wind energy projects are governed by a complex set of laws. Projects are 

subject to the input of a diverse set of decision makers and different permitting 

regulations apply in different parts of the country. Authorities at local, state, and 

federal levels make siting decisions. These authorities have different responsibilities 

relative to a project, but there can be inconsistencies among them and even within 

the same agency. In some places, primary decisions rest with the local jurisdiction, 

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although federal and state requirements may still apply. A wide diversity of 

requirements means that projects across the country must adhere to different 

standards, and different information is often required before permits are issued. 

Differing levels of public involvement also occur in these processes. A dramatic 

increase in development is likely to make this situation even more complex for 

developers and decision makers alike. Increased uniformity in regulatory 

requirements across regions would greatly facilitate the increased deployment of 

wind projects necessary to reach the 20% Wind Scenario. 

5.5.1 LOCAL 

Locally elected officials make some siting decisions at the county level. This 

approach allows the community to maintain control of local land use decisions, 

which is especially attractive in states where local authority is highly prized. 

Responsibilities differ among local bodies, but local commissions are often 

responsible for property assessments, rural road maintenance, economic 

development, zoning, and water quality (NACO 2003). Local commissions typically 

are concerned with protecting the environment, enhancing tax revenue, and 

preserving the local quality of life. 

In some cases, however, local authorities may feel ill equipped to weigh the highly 

technical information presented by a wind project developer. They can be easily 

influenced by proponents or opponents armed with incomplete or inaccurate 

information. In communities where wind development has a history, decision 

makers are more comfortable rendering considered permit decisions. 

Most wind energy projects go through the local conditional use permit process and 

must spell out the conditions under which a project will operate. For example, a 

project permit might limit the sound level or require a setback distance from roads, 

houses, or property lines. Counties can also create ordinances to permit wind energy 

facilities. In Pike County, Illinois, the County Board created a permitted use 

ordinance that lays out standard conditions for wind projects. Decision makers in 

Klickitat County, Washington, designated specific areas to encourage and guide 

wind energy development. And the local authorities in Kern County, California, 

conducted a countywide environmental impact review to enable development of the 

Tehachapi Wind Resource Area. 

5.5.2 STATE AND FEDERAL 

States can control siting decisions either through specific decision-making bodies or 

by virtue of rules set for projects on state-controlled land. In addition, a state 

agency—such as a wildlife agency—might establish guidelines for siting wind 

projects. State guidelines can include requirements such as maintaining certain 

sound levels or conducting studies. 

A few states have an energy siting board, which means that they have placed the 

authority to review energy facilities with the state utility commission (i.e., a public 

service commission). The governor or legislature usually appoints representatives, 

and because they are more accountable to the public, they tend to be generally more 

familiar with this sector. The charge of these state commissions or boards often 

includes supplying reliable electric service at reasonable prices. Concerned 

individuals or project opponents have legal recourse to raise objections by formally 

challenging a commission decision. 

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5

The federal government participates in regulating wind energy projects through 

several different agencies, depending on the circumstances. Unless there is a federal 

involvement, such as when developers propose a project on federally managed land, 

wind energy projects are not usually subject to the National Environmental Policy 

Act (NEPA). An agency can trigger the provisions of NEPA by undertaking a major 

federal action, such as allowing construction of a large energy project on federal 

lands (NEPA 1969). 

The federal agencies that follow have mandates that may be related to wind energy: 

z	 The FAA conducts aeronautical studies on all structures higher than 

60 m above ground level for potential conflicts with navigable 

airspace and military radar, and ensures proper marking and 

lighting. Developers are required to submit an application for each 

individual turbine. From 2004 to 2006, the FAA approved almost 

18,000 wind turbine proposals and issued only eight determinations 

of hazard (Swancy 2006). The FAA completed nearly half of these 

turbine proposals in 2006 (Swancy 2006). 

z	 The Bureau of Land Management (BLM) manages 105 million 

hectares of public land, mostly in the western United States. In 

2005, the BLM finalized a programmatic environmental impact 

statement for wind energy development on BLM lands in the West. 

This statement includes best management practices for wind energy 

projects, sets standard requirements for projects, and allows for sitespecific studies. As an alternative, wind developers can rely on the 

previous programmatic NEPA document and provide a development 

plan without having to do a full EIS at each site, which can save 

valuable resources and time (BLM). 

z	 The U.S. Army Corps of Engineers (USACE) issues permits for 

any development that will affect wetlands. Roads, project 

infrastructure, and foundations at some wind project sites have the 

potential to affect wetlands. Projects must also comply with the 

Endangered Species Act if any threatened or endangered species 

will be adversely affected. 

z	 The U.S. Fish & Wildlife Service (USFWS) can pursue 

prosecution for violations of the Migratory Bird Treaty Act, which 

prohibits the killing or harming of almost all migratory birds. Some 

migratory birds, however, can be taken under a permit or license. 

The USFWS also enforces the Bald and Golden Eagle Protection 

Act, which gives additional protection to eagles. The USFWS 

exercises prosecutorial discretion under these statutes. To date, no 

wind energy companies have faced action under either law, but 

flagrant violations without mitigation could be subject to 

prosecution (USFWS). 

z	 The Minerals Management Service (MMS) oversees permitting 

for offshore ocean-based wind energy projects proposed for the 

outer continental shelf (OCS). MMS is developing the rules and 

issued a programmatic environmental impact statement for all 

alternative energy development on the OCS. New regulations are 

expected in 2008. 

z	 The U.S. Department of Agriculture Forest Service (USFS) 

manages 78 million hectares of public land in national forests and 

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grasslands (USDA). Projects sited on any Forest Service lands are 

subject to NEPA, and potentially to siting guidelines that the Forest 

Service is currently developing. 

z	 The U.S. Department of Defense (DOD) has no formal review 

process for wind energy projects, although DOD does participate in 

the FAA studies. Wind energy companies planning a project near an 

Air Force base, however, generally work with base leadership to 

address and avoid conflicts. 

z	 The U.S. Department of Energy (DOE) has taken the lead in 

creating an interagency project siting team. The team reviews how 

wind sites affect government assets such as radar installations, and 

decides how to plan for and mitigate those impacts (DOE 2007). 

5.6	 ADDRESSING ENVIRONMENTAL AND SITING 

CHALLENGES 

In order to install more than 10 GW of wind capacity per year by 2014, the United 

States will need to have a consistent way to review and approve projects. Examples 

below reflect what mature energy industries are doing to address concerns about 

wildlife and energy facility siting issues. The approaches described outline steps that 

could be adopted for a 20% Wind Scenario. The wind energy industry—in 

partnership with the government and nongovernmental organizations (NGOs)—will 

need to address environmental and siting issues. 

5.6.1	 EXPAND PUBLIC-PRIVATE PARTNERSHIPS 

States, collaboratives, and the National Academy of Sciences (NAS) have identified 

gaps in the knowledge base about wind energy and its risks. This situation is not 

surprising for a relatively new energy technology. The knowledge gaps are framed 

in questions such as 

z	 How can large deployments of wind energy generation contribute to 

national climate change goals and significantly reduce GHG 

emissions? 

z	 Can bats be deterred from turbines? 

z	 How high do night-migrating songbirds fly over ridgelines? 

Sometimes developers address these questions at specific sites, but broader research 

is urgently needed on a few of the most significant questions. 

Several research collaboratives have been formed (see sidebar entitled Examples of 

Existing Wind Energy Research Collaboratives) to ensure that the interests of 

various stakeholders are represented, that research questions are relevant, and that 

research results are widely disseminated. Collaboratives can help to avoid relying on 

industry-driven research, which critics often perceive as biased. Various 

combinations of technical experts and informed representatives from industry, 

relevant NGOs, and government agencies currently participate in ongoing 

collaboratives on wind energy. For example, BWEC is exploring the effectiveness of 

an acoustic deterrent device to warn bats away from the spinning blades of wind 

turbines. Although the risk to bats might be greater at some sites than at others, it is 

not necessarily feasible or appropriate for one company or one project to foot the 

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Examples of Existing Wind Energy Research Collaboratives 

Bats and Wind Energy Cooperative (BWEC) 

After learning in 2003 that thousands of bats had been killed at a West Virginia site, the wind energy 

industry immediately collaborated with Bat Conservation International, the USFWS, and the 

National Renewable Energy Laboratory (NREL) to form BWEC. This organization created a 

research program to explore ways to reduce fatalities. Its work currently centers on two areas: (1) 

understanding and quantifying what makes a site more risky for bats and (2) field-testing deterrent 

devices to warn bats away from wind turbine blades. 

National Wind Coordinating Collaborative Wildlife Workgroup 

The NWCC is a forum for defining, discussing, and addressing wind–avian interaction issues, with a 

focus on public policy questions. Supported by funds from DOE, the Wildlife Workgroup (WWG) 

serves as an advisory group for national research on wind–avian issues. The group released a report, 

Studying Wind Energy/Bird Interactions: A Guidance Document, which is the first-ever 

comprehensive guide to metrics and methods for determining and monitoring potential impacts on 

birds at existing and proposed wind energy sites. Additionally, the WWG has facilitated six national 

research meetings. It is subdivided into a number of groups focused on specific tasks, such as 

development of a “mitigation toolbox.” 

Grassland/Shrub-Steppe Species Collaborative 

The GS3C is a voluntary cooperative to identify the impacts, if any, that wind energy has on 

grassland and shrub steppe avian species. Established in 2005 as the Grassland/Shrub Steppe Species 

Subgroup, the GS3C includes representatives from state and federal agencies, academic institutions, 

NGOs, and the wind industry. 

5

entire bill for this research. A public–private partnership is often a more effective 

way to undertake and fund the research needed, and might also lead to more credible 

results. 

As development levels ramp up, an overarching research consortium that would 

combine the work of these collaboratives would likely provide a benefit. Such a 

program could focus on addressing potential risks and ensuring that the most critical 

uncertainties are research priorities. As the more focused groups come together in a 

region, they could examine some of the habitat and biological sensitivity issues to 

understand which areas are most appropriate for development. With the public– 

private nature of the consortium, the conversation might shift from where 

development is inappropriate to where it is most promising. These groups, or a 

larger institute, could also identify priority conservation areas and work toward 

enhancing key habitat areas. 

If the wind energy sector is to increase installations to more than 16 GW of capacity 

each year after 2018, large research consortia could be created to take part in 

sustainable growth planning. A region, for example, might decide to open to 

development because new transmission lines are planned. In this case, a 

collaborative research body could determine what baseline wildlife and habitat 

studies are needed; organize and fund researchers to begin the work; and determine 

what mitigation, habitat conservation, or other activities might be appropriate for the 

area. 

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5.6.2 EXPAND OUTREACH AND EDUCATION 

Public acceptance of wind projects may increase if the local community directly 

shares in the benefits from a new wind energy development. In Europe, for example, 

tax law allows individuals to invest directly in wind projects. Those individuals 

might well view their turbines as a source of income and feel more positive about 

the siting of turbines nearby. 

In the United States, examples of direct community impacts include: 

z	 Community wind: Groups of individuals join together to develop 

and own a project. Although this can be risky because of the 

significant complexity and capital required to successfully build a 

wind project, the rewards are significant. A town or municipality 

sometimes purchases a turbine to generate power and lower public 

electricity bills. These groups might develop a smaller project in 

conjunction with a commercial development to leverage the 

economies of scale available for turbine purchases, construction, 

and operations and maintenance. 

z	 Property tax payments: Wind projects are multimillion-dollar 

facilities that can make a significant contribution to a community’s 

tax base. Projects are usually on leased private property, with the 

project owner paying any related property taxes. 

z	 Payment in lieu of taxes: In places where property taxes are not 

required, project owners often contribute to a local community fund 

in lieu of taxes. 

In other energy facility siting programs, communities might protect property values. 

Desired facilities are also sometimes collocated in the community as a form of 

incentive. Many such options exist and any combination might be part of a siting 

strategy. Wind energy developers can engage residents in a potential host 

community to explain possible impacts, share information about the project, and 

learn about community concerns. This early involvement gives citizens an 

opportunity to ask questions and have their concerns addressed. 

5.6.3 COORDINATE LAND-USE PLANNING 

Successfully addressing numerous inconsistencies in permitting and regulation will 

require government and industry stakeholders to review the policies and procedures 

currently being implemented across multiple jurisdictions. In the long term, it may 

be necessary to create sustainable growth planning effort as new areas of 

development open. A number of NGOs already have ecoregional plans that may 

yield a solid baseline of biological data. In 2006, states also completed wildlife 

action plans that identify high-priority actions needed to preserve and enhance their 

wildlife resources. 

Numerous states and federal agencies have developed, or are in the process of 

developing, siting guidelines for wind power developments. Some states have 

created siting guidelines in conjunction with implementation of their state renewable 

portfolio standards (RPS). Other collaborative efforts to develop guidelines are 

moving forward through wildlife or energy agencies. Development of siting 

guidelines gives developers and agency officials a clear pathway to what may be 

required in certain jurisdictions, although time and cost considerations are involved. 

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5.7	 PROSPECTS FOR OFFSHORE WIND ENERGY 

PROJECTS IN THE UNITED STATES AND INSIGHTS 

FROM EUROPE 

5

Europe’s experience with offshore wind energy projects is instructive for how the 

United States might address environmental and siting challenges. European 

developments are supported by ambitious national goals for wind deployment, 

financial instruments and subsidies, and commitments to reduce GHGs. Direct 

comparisons and lessons learned would be 

instructive but need to be applied with appropriate Offshore Wind Plant Siting and cautions about different public policies.

Seabed Rights in the United States 

A growing awareness of the large potential for Various U.S. government agencies are electricity contributions from offshore wind responsible for evaluating and approving the energy has led to numerous proposals for siting siting, installation, and operation of wind offshore wind plants in European seas. Currently, power plants in the ocean. Until recently, 26 projects are installed in the North and the offshore siting was notably more complex Baltic Seas in eight nations with a combined than land-based siting because of unclear and capacity of more than 900 MW. A major scientific overlapping legal and jurisdictional effort is in progress to support these projects. authorities. More than 280 research studies and assessments 

are examining environmental and human effects Before the passage of the Energy Policy Act from installed offshore wind installations (EPAct) of 2005, the USACE assumed (SenterNovem 2005). Studies have also been permitting authority over proposed offshore conducted on birds, marine ecology, and animal wind energy developments. With EPAct physiology (Gerdes et al. 2005). Others have 2005, Congress delegated authority to grant addressed the planning, construction, operations, easements, leases, or rights-of-way in coastal maintenance, and decommissioning of turbines. waters to the MMS under the DOI. By contrast, the United States does not yet have 

any commercial-scale offshore wind power sites, Uncertainty about the extent of potential and proposals for developing them are still impacts of offshore wind projects—in limited. Preliminary environmental analyses addition to the lack of well-designed siting relating to offshore installations are restricted to strategies— and the lack of long-term NEPA-related requirements for specific projects in scientific information to fully evaluate the federal waters. (Table 5-3 lists proposed projects technology can contribute to delays in and the documentation relating to the permitting deployment (Musial and Ram, forthcoming). and NEPA process.) 

The state of knowledge and assessment of risks surrounding offshore wind energy 

are still emerging, which is characteristic of the early stages of any energy 

technology. To date, Denmark has conducted the most extensive before-aftercontrol-impact study in the world. The most recent environmental monitoring 

program (spanning more than five years) from this study has concluded that none of 

the potential ecological risks appear to have long-term or large-scale impacts (DEA 

2006). Denmark intends to do further research, however, to assess the effects over 

time of multiple projects within the same region. 

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Table 5-3. Status of offshore wind energy applications in state and federal waters 

Type of 

Initiativea Developer 

Project 

Location 

Number 

of 

Turbines 

Proposed 

Federal 

Application 

Filed 

Status as of 

June 2007 

Project Cape Wind 

Associates 

Nantucket 

Sound 130 November 2001 

Received permit approval for the 

met tower in 2002; USACE 

issued a draft environmental 

impact statement (EIS) in 

November 2004; MMS issued a 

notice of intent (NOI) to prepare 

a new EIS in May 2006. 

Massachusetts issued a final 

environmental impact report 

(FEIR); draft environmental 

impact statement (DEIS) in 

progress by MMS. 

Project 

LIPA & 

Florida 

Power and 

Light 

Long Island 

Sound 40 July 2005 

Joint application submitted to 

USACE April 2005; MMS issued 

an NOI to prepare an EIS in 

June 2006; project cancelled in 

October 2007. 

Project 

Wind Energy 

Systems 

Technologies 

(W.E.S.T.) 

Galveston, 

TX 50–60 

N/A 

(Texas state 

waters) 

Signed lease with Texas 

General Land Office in 2005. 

Meteorological tower installed to 

begin collecting data in 2007. 

Project Bluewater 

Wind LLC Delaware 200 TBD Won competition May 22, 2007, 

with Delmarva Power & Light 

Project Hull 

Municipal 

Boston 

Harbor 4 

N/A 

(Massachuset 

ts state 

waters) 

Collecting data. Received 

funding from Massachusetts 

Technology Collaborative to 

support permitting and siting 

analyses. 

Announced 

Patriot 

Renewables 

LLC 

Buzzards 

Bay, MA 90–120 

N/A 

(Massachuset 

ts state 

waters) 

Applied for state approval with 

Massachusetts Environmental 

Affairs, May 2006. Conducting 

feasibility studies. 

Announced Southern 

Company 

Off the coast 

of Savannah, 

GA 

3–5 No current 

plans 

Two-year collaborative study 

with Georgia Tech. Concluded 

that conditions are favorable but 

current cost and regulatory 

situation precludes development. 

a

 In this table, a “Project” is a planned commercial development or demonstration where complete state or federal applications 

have been submitted to appropriate permitting agencies. “Announced” refers to proposals at the feasibility study and data 

collection stage, with no commercial plans as yet and no permit applications completed. 

To date, members of the European wind industry and other stakeholders have largely 

mitigated risks related to wind energy or decided that the local siting risks are less of 

a concern than other factors, such as air emissions and the larger global risks of 

climate change. Precautionary principles apply during the adoption of facility siting 

and design, as well as risk management principles. Because risks are highly sitespecific, well-planned siting strategies are critical to future offshore wind 

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developments. Successful strategies in Europe have recognized the need to engage 

local populations in siting decisions and development planning. This allows local 

concerns to be addressed and builds community support for wind facilities. 

z Fish and benthic communities 

z Undersea sound and marine mammals 

z Electromagnetic fields and fish behaviors 

z Human intrusion on seascape environments 

z Competing commercial and recreational uses of the ocean 

z Other socioeconomic effects, including tourism and property values. 

As the United States establishes a regulatory process and siting strategies for 

offshore wind projects, much can be learned from Europe’s decades of experience 

with offshore wind. If the United States supports a major increase of offshore wind 

deployments over the next two decades, it will need to develop an ambitious and 

well-managed environmental research and siting program and lay the groundwork 

for collaborative approaches that engage the public and other interested 

stakeholders. 

5.8 FINDINGS AND CONCLUSIONS 

5

To scale up wind energy development responsibly, benefits and risks could be 

considered in context with other energy options. Remaining uncertainties associated 

with overall risks, cost-effective opportunities for risk mitigation, strategic siting 

approaches, enlarged community involvement, and more effective planning and 

permitting regimes can also be considered. Figure 5-5 offers a representation of 

activities that may be needed over the near and longer terms. More details are given 

in the subsections that follow. Some of the activities would begin now and continue 

through 2030. Given the significant ramp-up of wind installations by 2018 in the 

20% Wind Scenario, these actions would need to occur within the next decade, in 

time to anticipate and plan for siting strategies and potential environmental effects. 

Figure 5-5. Actions to support 20% wind energy by 2030 

126 20% Wind Energy by 2030 – Prepublication Version 
 

Near- and Mid-Term Actions 

Compare life-cycle effects of energy generation options: The knowledge base for 

comparing wind energy with other energy options—according to their climate 

change implications—is still uneven and incomplete. Such knowledge could prove 

helpful to wind energy developers; electric utilities; and national, state, and local 

regulators in evaluating wind energy developments. In fact, EPAct 2005 included 

authorization language for an NAS study of the comparative risk and benefits of 

current and prospective electricity supply options. The study, however, has not 

begun. 

Research wildlife and habitat effects: The current research program on wind 

energy is largely driven by the problems that have arisen at specific sites, such as 

bird mortality in California and bat mortality in West Virginia. Additional research 

on wildlife and habitat fragmentation, which takes a collaborative approach and 

involves interested parties, affected communities, and subject matter experts would 

be informative and should be placed within the context of other energy risks. 

Define risks: A systematic risk research program that addresses the full range of 

human, ecological, and socioeconomic effects from wind project siting is needed. 

Such a study would establish a systematic knowledge base to inform research 

priorities and decision makers. A comprehensive survey of risk issues that might 

arise at different sites has yet to be designed and undertaken, although several state 

agencies—such as the California Energy Commission—are developing these 

priorities. Along with these risk research programs, the associated cost and time 

implications must be demarcated. 

Engage national leaders: Evolving national and state policies and corporate 

programs are beginning to minimize human-caused emissions of greenhouse gases. 

Wind energy is an important part of the portfolio of energy technologies that can 

contribute to this goal. National leadership could help to facilitate rapid progress 

towards 20% wind energy. Many positive impacts are projected from wind energy 

comprising a larger share of the U.S. electricity grid, but these data must be 

quantified and made publicly available. 

Develop siting strategies: The risks associated with wind energy deployment are 

heavily site-specific, and public responses will vary among potential sites. Siting 

strategies are needed to identify sites that are highly favored for wind energy 

developments, but also to avoid potential ecological risks and minimize community 

conflict. The American Wind Energy Association (AWEA) is currently developing a 

siting handbook, which may be valuable as a first step in addressing this need. 

Further work could continue to enhance collaborative siting processes that engage 

states, NGOs, host community officials, and various other stakeholders. 

Address public concerns: Building public support is essential if wind energy is to 

supply 20% of the nation’s electricity by 2030. Although substantial national 

experience exists with siting different types of energy facilities, that experience has 

not yet been incorporated into wind siting strategies. The roots of public perceptions 

of and concerns about wind energy are not well understood. 

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5

Long-Term Actions 

Apply adaptive management principles: As with other technologies, wind energy 

will continue to pose new uncertainties as existing ones are reduced. The knowledge 

base is certain to evolve as new sites are developed and the scale of wind 

development expands in the United States, in Europe, and in other parts of the 

world. Adaptive management concepts and approaches, which have been applied to 

the development of numerous other technologies, should also be considered for 

incorporation into wind energy development. 

5.9 REFERENCES AND SUGGESTED FURTHER READING 

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AMA (American Medical Association) Council on Scientific Affairs. 2004. Mercury 

and Fish Consumption: Medical and Public Health Issues. Report No. A-04. 

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15842.html. 

AMA. 2006. “AMA Adopts New Policies on Mercury Pollution, Hormone 

Compounds and Smoke-Free Meetings.” American Medical Association, 

November 13. http://www.ama-assn.org/ama/pub/category/17086.html. 

Arnett, E., W. Erickson, J. Horn, and J. Kerns. 2005. Relationships between Bats 

and Wind Turbines in Pennsylvania and West Virginia: An Assessment of 

Fatality Search Protocols, Patterns of Fatality, and Behavioral Interactions 

with Wind Turbines. Bat and Wind Energy Cooperative. 

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AWEA (American Wind Energy Association). 2007. Wind Energy Basics. 

Washington, DC: AWEA. 

http://www.awea.org/newsroom/pdf/Wind_Energy_Basics.pdf. 

Baum, E., J. Chaisson, B. Miller, J. Nielsen, M. Decker, D. Berry, and C. Putnam. 

2003. The Last Straw: Water Use by Power Plants in the Arid West. 

Boulder, CO and Boston, MA: The Land and Water Fund of the Rockies 

and Clean Air Task Force. 

http://www.catf.us/publications/reports/The_Last_Straw.pdf. 

BLM. “BLM Facts.” U.S. Bureau of Land Management. 

www.blm.gov/nhp/facts/index.htm. 

BWEA (British Wind Energy Association). 2007. Noise from Wind Turbines: The 

Facts. London: BWEA. http://www.britishwindenergy.co.uk/pdf/noise.pdf. 

Cryan, P. 2006. “Overview of What We Know About the Bat/Wind Interaction as of 

November of 2004.” Presented at the National Wind Coordinating 

Collaborative (NWCC) Wildlife Research Meeting VI, November 14, San 

Antonio, TX. http://www.nationalwind.org/events/wildlife/2006­

3/presentations/bats/cryan.pdf. 

DEA (Danish Energy Authority). 2006. Danish Offshore Wind – Key Environmental 

Issues. ISBN: 87-7844-625-2. Stockholm, Sweden: DONG Energy. 

http://www.ens.dk/graphics/Publikationer/Havvindmoeller/danish_offshore_ 

wind.pdf. 

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DOE (U.S. Department of Energy). 2006. The Wind/Water Nexus. DOE/GO102006-2218. Golden, CO: National Renewable Energy Laboratory 

(NREL). http://www.eere.energy.gov/windandhydro/windpoweringamerica/ 

pdfs/wpa/wpa_factsheet_water.pdf. 

DOE. 2007. “Federal Interagency Wind Siting Collaboration.” DOE Office of 

Energy Efficiency and Renewable Energy, Wind & Hydropower 

Technologies Program. 

http://www1.eere.energy.gov/windandhydro/federalwindsiting/about_collab 

oration.html. 

DOI (U.S. Department of the Interior). 2004. State and Indian Regulatory Program 

Permitting: 2004. Washington, DC: DOI Office of Surface Mining. 

http://www.osmre.gov/progpermit04.htm 

EIA. 2006. Emissions of Greenhouse Gases in the United States 2005. Washington, 

DC: Department of Energy. Report No. DOE/EIA-0573. 

www.eia.doe.gov/oiaf/1605/ggrpt/index.html. 

EPA (U.S. Environmental Protection Agency). 2007. Mercury: Health Effects. 

Washington, DC: EPA. http://www.epa.gov/mercury/effects.htm. 

Erickson, W., G. Johnson, and D. Young. 2002. “Summary of Anthropogenic 

Causes of Bird Mortality.” Presented at Third International Partners in 

Flight Conference, March 20–24, Asilomar Conference Grounds, 

CA.http://www.dialight.com/FAQs/pdf/Bird%20Strike%20Study.pdf. 

FAA. 2007. (U.S. Department of Transportation, Federal Aviation Administration), 

Advisory Circular, AC 70/7460-1K, Obstruction Marking and Lighting. 

Washington, DC. 

https://oeaaa.faa.gov/oeaaa/external/content/AC70_7460_1K.pdf 

Flavin, C., and J. Sawin. 2006. American Energy: The Renewable Path to Energy 

Security. Washington, DC: Worldwatch Institute Center for American 

Progress. http://www.americanprogress.org/issues/2006/09/ 

american_energy.html/AmericanEnergy.pdf. 

GAO (Government Accountability Office). 2005. Wind Power: Impacts on Wildlife 

and Government Responsibilities for Regulating Development and 

Protecting Wildlife. Report No. GAO-05-906. Washington, DC: GAO. 

http://www.gao.gov/new.items/d05906.pdf 

Gerdes, G., A. Jansen, K. Rehfeldt, and S. Teske. 2005. Offshore Wind: 

Implementing a New Powerhouse for Europe; Grid Connection, 

Environmental Impact Assessment & Political Framework. Amsterdam: 

Greenpeace International. 

http://www.greenpeace.org/raw/content/international/press/reports/offshorewind-implementing-a.pdf 

Global Strategy Group. 2005. Survey on American Attitudes on the Environment – 

Key Findings. New Haven, CT: Yale University School of Forestry & 

Environmental Studies. http://www.loe.org/images/070316/yalepole.doc 

Government Accountability Office. 2005. Wind Power: Impacts on Wildlife and 

Government Responsibilities for Regulating Development and Protecting 

Wildlife. Report No. GAO-05-906. Washington, DC: Government 

Accountability Office. www.gao.gov/new.items/d05906.pdf 

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Hoen, B. 2006. Impacts of Windmill Visibility on Property Values in Madison 

County, New York. Unpublished M.S. thesis. Annandale on Hudson, NY: 

Bard College, Bard Center for Environmental Policy. 

http://www.aceny.org/pdfs/misc/effects_windmill_vis_on_prop_values_hoe 

n2006.pdf. 

Holling, C.S. 1978. Adaptive Environmental Assessment and Management. New 

York: John Wiley & Sons. 

Hutson, S.S., N. Barber, J. Kenny, K. Linsey, D. Lumia, and M. Maupin. 2005. 

Estimated Use of Water in the United States in 2000. Circular No. 1268. 

Reston, VA: U.S. Geological Survey. 

http://pubs.usgs.gov/circ/2004/circ1268/index.html. 

IPCC (Intergovernmental Panel on Climate Change). 2007. Climate Change 2007: 

Impacts, Adaptation and Vulnerability: Contribution of Working Group II 

Contribution to the Intergovernmental Panel on Climate Change [IPCC] 

Fourth Assessment Report. Report presented at 8th session of Working 

Group II of the IPCC, April 2007, Brussels, Belgium. 

http://www.usgcrp.gov/usgcrp/links/ipcc.htm 

IATP 2006. Water Use by Ethanol Plants: Potential Challenges. Institute for 

Agriculture and Trade Policy (IATP). October 2006. 

http://www.agobservatory.org/library.cfm?refid=89449 

Jetz, W., D. Wilcove, and A. Dobson. 2007. “Projected Impacts of Climate and 

Land-Use Change on the Global Diversity of Birds.” PLoS Biology, 5(6): 

1211–1219. http://www.pubmedcentral.nih.gov/ 

picrender.fcgi?artid=1885834&blobtype=pdf. 

Lee, K.N. 1993. Compass and Gyroscope: Integrating Science and Politics for the 

Environment. Washington, DC: Island Press. 

Morgan, G., et al. 2007. Best Practice Approaches for Characterizing, 

Communicating, and Incorporating Scientific Uncertainty in Climate 

Decision Making, Advance Copy. Washington, DC: The National 

Academies Press (NAP). 

Musial, W. and B. Ram, Large Scale Offshore Wind Deployments: Barriers and 

Opportunities, NREL, Draft, 2007. 

NACO (National Association of Counties). 2003. A Brief Overview of County 

Government. Washington, DC: NACO. http://www.naco.org/ 

NEPA 1969. The National Environmental Policy Act of 1969, as amended, 42 

U.S.C. 4321–4347. 

http://ceq.eh.doe.gov/nepa/regs/nepa/nepaeqia.htm 

NESCAUM (Northeast States for Coordinated Air Use Management). 2003. 

Mercury Emissions from Coal-Fired Power Plants: The Case for Regulatory 

Action. Boston, MA: 

NESCAUM.http://bronze.nescaum.org/airtopics/mercury/rpt031104mercury 

.pdf. 

NRC (National Research Council). 2004. Endangered and Threatened Fishes in the 

Klamath River Basin: Causes of Decline and Strategies for Recovery. 

Washington, DC: NAP. 

http://books.nap.edu/openbook.php?isbn=0309090970. 

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NRC. 2007. Environmental Impacts of Wind-Energy Projects. Washington, DC: 

NAP. http://dels.nas.edu/dels/reportDetail.php?link_id=4185 

NWCC. 1999. Studying Wind Energy/Bird Interactions: A Guidance Document. 

Metrics and Methods for Determining Or Monitoring Potential Impacts On 

Birds At Exisitng and Proposed Wind Energy Sites. Washington, DC: 

NWCC. 

NWCC. 2004. Wind Turbine Interactions with Birds and Bats: A Summary of 

Research Results and Remaining Questions. Washington, DC: NWCC. 

http://www.nationalwind.org/publications/wildlife/wildlife_factsheet.pdf. 

NWCC. 2006. “Collaborative Study of the Effects of Wind Power on PrairieChicken Demography and Population Genetics.” Press release from 

National Wind Coordinating Collaborative, May 9. 

http://www.nationalwind.org/workgroups/wildlife/060509­

press_release.pdf. 

Pasqueletti, M.J. 2005. “Visual Impacts.” Presented to National Wind Coordinating 

Collaborative Technical Considerations in Siting Wind Developments: 

Research Meeting, December 1–2, Washington, DC. 

http://www.nationalwind.org/events/siting/presentations/pasqualettivisual_impacts.pdf. 

Piwko, R., B. Xinggang, K. Clark, G. Jordan, N. Miller, and J. Zimberlin. 2005. The 

Effects of Integrating Wind Power on Transmission System Planning, 

Reliability and Operations. Schenectady, NY: GE Energy. 

http://www.nyserda.org/publications/wind_integration_report.pdf. 

RBA Research. 2002. Lambrigg Residents Survey. London: BWEA. 

http://www.bwea.com/ref/lambrigg.html. 

Regional Greenhouse Gas Initiative. “About RGGI.” Regional Greenhouse Gas 

Initiative. www.rggi.org/about.htm.SenterNovem. 2005. Concerted Action 

for Wind Energy Deployment (COD): Principal Findings 2003-2005. 

Utrecth, Netherlands: SenterNovem . 

http://www.offshorewindenergy.org/cod/COD-Final_Rept.pdf. 

Robel, R., Harrington, J., Hagen, C., Pitman, J., and Reker, R.. 2004. Effect of 

energy development and human activity of the use of sand sagebrush habitat 

by Lesser Prairie-chickens in southwest Kansas. Transactions of the North 

American Wildlife and Natural Resources Conference 68. 

http://kec.kansas.gov/wptf/robertrobel.pdf 

Stern, P.C., and H.V. Fineberg. 1996. Understanding Risk: Informing Decisions in a 

Democratic Society. Washington, DC: NAP. 

http://books.nap.edu/openbook.php?isbn=030905396X. 

Sterzinger, G., B. Fredric, and D. Kostiuk. 2003. The Effect of Wind Development on 

Local Property Values: Analytical Report. Washington, DC: Renewable 

Energy Policy Project (REPP). 

http://www.repp.org/articles/static/1/binaries/wind_online_final.pdf. 

Strickland, D., and D. Johnson. 2006. “Overview of What We Know About 

Avian/Wind Interaction.” Presented at the National Wind Coordinating 

Collaborative Wildlife Workgroup Research Meeting VI, November 14, 

San Antonio, TX. http://www.nationalwind.org/events/wildlife/2006­

3/presentations/birds/strickland.pdf. 

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Swancy, H. 2006. “Wind Power and Aviation.” Presented at the AWEA 2006 Fall 

Symposium, December 6–8, Phoenix, AZ. 

Thomas, C.D., A. Cameron, R. Green, M. Bakkenes, L. Beaumont, Y. Collingham, 

B. Erasmus, et al. 2004. Extinction Risk from Climate Change. Nature, 427: 

145–148. http://www.gbltrends.com/doc/nature02121.pdf. 

USDA. “Forest Service: About Us.” U.S. Department of Agriculture Forest Service. 

www.fs.fed.us/aboutus. 

DOE. 2006. The Wind/Water Nexus. Golden, CO: National Renewable Energy 

Laboratory. 

www.eere.energy.gov/windandhydro/windpoweringamerica/pdfs/wpa/wpa_ 

factsheet_water.pdf. 

DOE. 2007. “Federal Interagency Wind Siting Collaboration.” U.S. Department of 

Energy, Energy Efficiency and Renewable Energy, Wind and Hydropower 

Technologies Program. 

www1.eere.energy.gov/windandhydro/federalwindsiting/about_collaboratio 

n.html. 

USFWS (U.S. Fish & Wildlife Service). Migratory Birds & Habitat Programs. 

“Migratory Bird Treaty Act.” U.S. Fish & Wildlife Service. 

http://www.fws.gov/pacific/migratorybirds/mbta.htm. 

Walters, C.A. 1986. Adaptive Management of Renewable Resources. New York: 

MacMillan. 

White, S., and G. Kulsinski. 1998. Net Energy Payback and CO2 Emissions from 

Wind Generated Electricity in the Midwest. Report No. UWFDM-1092. 

Madison, WI: Fusion Technology Institute, University of Wisconsin. 

http://fti.neep.wisc.edu/pdf/fdm1092.pdf. 

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Chapter 6. Wind Power Markets 

Wind power suppliers and consumers span a broad 

range. Currently, wind power serves almost all 

large-scale utility markets, and smaller scale 

community-based projects are playing an 

increasing role in some regions. In addition, the 

eastern and Gulf Coast states are considering 

offshore proposals. 

If 20% wind energy by 2030 were to be reached, supply and demand markets would 

need to expand to deliver wind energy to end-use customers throughout the United 

States. This chapter presents a brief overview of U.S. electricity markets, major 

wind power supply chain segments, market drivers, and their potential impacts on 

U.S. wind power expansion. 

6.1 U.S. MARKET EVOLUTION BACKGROUND 

6

The U.S. Department of Energy (DOE) projects U.S. electricity demand to increase 

by 39% from 2005 to 2030 (EIA 2007). Taking into account projected plant 

retirements and the implementation of energy efficiency and demand reduction 

programs, meeting this increased demand could require new electricity generation to 

increase by more than 50% over that period. Wind power is a viable option for 

meeting a substantial portion of this growing demand for electricity. 

During the past seven years, the total number of wind installations worldwide has 

grown at an average annual rate of 27%. Recent growth of the wind power market in 

the United States has been driven by a dramatic reduction in the cost of wind energy, 

public interest in renewable energy, state renewable energy standards, federal 

production tax credits (PTCs), and volatile natural gas prices. Historically, however, 

periodic expiration and subsequent extensions of the federal PTC have resulted in 

intervals of no growth followed by explosive growth, as shown in Figure 6-1. 

The U.S. wind power industry has experienced two major transformations in its 

history. In 1940, more than 100,000 wind turbines—many of them Jacobs 

Windmasters—were in operation across the Midwest, producing electricity for 

isolated farms and ranches. Their use declined, however, as electrification connected 

rural U.S. regions to electricity grids in the 1940s and 1950s. The oil price shocks of 

the 1970s stimulated new interest in renewable energy and led to the establishment 

of the Public Utility Regulatory Policies Act (PURPA) of 1978. By requiring utility 

companies to buy electricity from independent power producers (including wind 

companies), PURPA provided the foundation for the emergence of a second wind 

energy market in a few states in the 1980s. A key catalyst for wind’s further 

development was California’s investment tax credit and supportive state policies that 

jump started the bulk power wind industry in the early 1980s. The addition of 

federal tax credits at that time also contributed to industry expansion. Several firms 

20% Wind Energy by 2030 – Prepublication Version 133 
 

 

 

Figure 6-1. U.S. wind energy capacity growth (shown in 

megawatts [MW]) slowed during years when the PTC expired 

6 pioneered modern wind turbine technology during this period, and by 1990 more 

than 6,000 turbines were operating in the state. 

The significantly broader and larger wind electric supply that we are witnessing 

today originated in the late 1990s. This most recent expansion is a technical 

revolution that is influencing electricity markets in dozens of countries around the 

globe. Public and private research and technological innovation have rapidly 

improved wind resource assessment and siting, wind turbine aerodynamics and 

component design, and power electronics. Turbine sizes have increased steadily, 

leading to improvements in wind generation economics. Wind plant reliability has 

also improved—today, manufacturers routinely guarantee the availability of their 

turbines at 97% or higher. Although the wind resource is variable, wind turbines are 

highly reliable and operate whenever the wind resource is sufficient to generate 

electricity. The current U.S. wind energy market is robust and expanding at 

unexpected rates. 

6.2 U.S. ELECTRICITY MARKET 

Electricity in the United States is supplied mainly by more than 3,000 utilities across 

the country. Some are owned by shareholders, others by the customers they serve. 

State public utility commissions and the Federal Energy Regulatory Commission 

(FERC) oversee these utilities and specific electricity markets. These utilities and 

commissions work within a regulatory framework based on federal and state 

legislation and jurisdiction-specific regulations that vary throughout the country. As 

a result of these regulatory differences, the roles of utilities and commissions also 

differ, creating a variety of market structures at the local and regional levels. To 

bring wind energy to customers nationwide, wind project developers must 

accommodate these local and regional market features. 

134 20% Wind Energy by 2030 – Prepublication Version 
 

6.2.1 ELECTRIC UTILITIES 

Approximately 200 investor-owned utilities (IOUs), 70 large municipal and federal 

or state systems, and 50 rural generation and transmission cooperatives supply 

power for 3,000 local distribution companies across the country. The largest of these 

utilities typically own power plants and generate much of the power they supply. 

They purchase the rest of the electricity needed to serve their customers from other 

utilities or from nonutility generators through power purchase agreements (PPAs). 

Utilities serve a variety of customers with differing needs and priorities, both retail 

and wholesale. Retail customers are divided into three categories: residential, 

commercial, and industrial. Residential customers use energy in a single dwelling 

for personal service. Commercial customers often have multiple dwellings, offices, 

or business enterprises located in a multifunction building. Industrial customers are 

typically large manufacturing or assembly plants that have hundreds of workers and 

multiple electricity applications. Special forms of commercial and industrial 

customers include the federal, state, and local public sectors. 

Retail electricity service to end-use customers is regulated by state commissions in 

many states and jurisdictions. Some states have implemented restructuring or 

deregulation of their electricity markets, resulting in increased competition among 

electricity providers and retailers. In states where competitive entities are vying to 

supply electric generation and to serve retail customers, wind developers have the 

opportunity to build wind projects and deliver wind energy. In states that have not 

restructured, wind developers can sell into wholesale markets or sell to the 

incumbent utilities under a PPA. Some utilities are pursuing options for owning and 

operating their own wind projects. 

At the national level, FERC policies have been implemented to foster competitive 

wholesale electricity markets and spur innovation and efficiency improvements. 

FERC continues to review and modify, as appropriate, its policies concerning 

competition in wholesale power markets. FERC policies cover some of the nation’s 

electricity grid system, which is made up of transmission lines. FERC treats 

transmission as a common carrier, meaning that it requires transmission providers to 

allow nondiscriminatory access to their wires. The large wholesale markets enable a 

more effective exchange of services and compensation for all electricity generators, 

including wind power generators, helping them compete for larger shares of 

generation markets. 

To regulate their utilities, roughly half of the states in the country have integrated 

resource planning (IRP) policies in place. An IRP policy requires utilities to evaluate 

opportunities to serve loads through energy efficiency and demand reduction 

programs on the same basis they use to plan new generation. In addition, utilities 

must compare supply alternatives—including fossil and nonfossil resources—on a 

risk-adjusted basis. In some decisions made under the IRP process, local customer 

preference is considered and can influence decisions made by commissions in 

selecting generation options. As a result, the IRP process has been an important 

factor in establishing wind power markets. 

6.2.2 FEDERAL AGENCIES 

In aggregate, the federal government is the largest single consumer of electricity in 

the world. Federal agency electricity consumption in 2005 was more than 55,000 

gigawatt-hours (GWh), which would equate to approximately 18 gigawatts (GW) of 

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wind capacity at a 35% capacity factor. As a result, federal agencies were 

encouraged to meet an executive order renewable energy goal of 2.5% of site 

electricity from new renewable energy sources by the end of 2005. Agencies 

exceeded the goal considerably with a final tally of about 3,800 GWh (6.9%) of the 

electricity they consumed coming from renewable sources (DOE 2006). There was a 

dramatic increase in 2004 and 2005, largely because of renewable energy certificate 

(REC) purchases by the Air Force, the General Services Administration, and the 

Environmental Protection Agency (EPA). Overall, 96% of federal renewable 

energy—outside the Department of Defense—was purchased with RECs. 

The Energy Policy Act (EPAct) of 2005 also guides federal agency energy use. It 

requires the agencies to incorporate renewable energy into their electricity supply 

mix at an escalating rate beginning at 3.0% in 2007 and increasing up to 7.5% by 

2013, to the extent economically feasible and technically practicable. Both EPAct 

2005 and the executive order goal will help advance wind power use across federal 

facilities. Wind energy could play a significant role in meeting this goal, 

particularly through projects sited on federal lands. 

6.2.3 POWER MARKETING ADMINISTRATIONS 

Starting in the 1930s, the federal 

government created Power Marketing 

Administrations (PMAs) to market 

electricity generated by governmentowned hydropower projects. The PMAs 

include the Bonneville Power 

Administration (BPA), the Western Area 

Power Administration (Western), the 

Southwestern Power Administration 

(SWPA), and the Southeastern Power 

Administration (SEPA). Though not 

technically a PMA, the Tennessee Valley 

Authority (TVA) has a similar purpose in 

the Tennessee Valley. Each of these 

entities operates as a utility, supplies 

power to other utilities, and often owns 

extensive transmission networks that are 

important to generators, including the wind 

industry. Western and BPA have extensive 

transmission grids in regions with 

significant wind potential. Generally, the PMAs and the TVA are mandated by 

Congress to set rates at the lowest possible levels consistent with sound business 

principles. The PMAs provide access to available transmission capacity on their 

systems under FERC-approved transmission tariffs. 

6.2.4 COMPLIANCE, VOLUNTARY, AND EMISSIONS MARKETS 

Under a scenario of significant wind energy expansion, multiple revenue streams 

and diverse markets for wind generation output will be increasingly important. 

Compliance and voluntary markets, which have the potential to create separate and 

complementary revenue streams for supporting wind energy generation, can reduce 

risks. Emerging emissions markets might also provide revenue streams. 

136 20% Wind Energy by 2030 – Prepublication Version 
 

Policy-Driven Markets 

Compliance markets, or markets where there are standards for renewable energy 

contributions, play an important role in supporting the development of wind energy 

resources. Today, 25 states plus the District of Columbia have established renewable 

portfolio standards (RPS) requirements, which proscribe the amount of renewable 

energy that must be produced within the state. These compliance markets have been 

growing rapidly in recent years and hold potential to substantially contribute to 

expansion of wind energy capacity. Current state RPS policies call for about 55 GW 

of new renewable energy capacity by 2020, and a number of states are considering 

increasing their targets. 

Voluntary or Green Power Markets 

Voluntary markets for renewable energy have also played a key role in supporting 

new wind energy development in recent years. Today, more than 500,000 electricity 

customers across the nation are purchasing green power products through regulated 

utility companies, from green power marketers in a competitive market setting, or in 

the form of RECs. 

These voluntary purchasers support about 2 GW of new renewable energy capacity, 

mostly wind. Sales have recently grown at annual rates exceeding 60%. Large 

nonresidential customers—including businesses, universities, and federal, state, and 

local governments—are driving much of the growth, and this trend is likely to 

continue. 

Voluntary REC markets can also be important because they might be able to support 

wind energy projects in regions that have good wind regimes but no compliance 

markets (e.g., RPS). Because RECs are sold separately from commodity electricity, 

they can be used to support wind energy facilities in regions with the best resources. 

Some factors do limit the effectiveness of RECs, though, including the lack of a 

national REC tracking system, the lack of a national REC trading system, and the 

difficulty of using RECs in project financing. 

Air Quality Markets 

Throughout the past several decades, approaches for controlling pollution from 

fossil-based power generators have moved from traditional command and control 

strategies to market-oriented trading regimes that allow the most cost-effective 

emission reduction techniques to be applied first. Sulfur dioxide (SO2) emissions 

were the first to be controlled with cap and trade programs, and now, nitrogen 

oxides (NOx) and mercury (Hg) programs have been added. Others, such as carbon 

dioxide (CO2) programs, are currently under serious consideration. Markets must 

have accurate price information to operate efficiently; and these programs help to 

incorporate the external costs of pollutants from carbon-based fuels into power 

prices. 

6.3 WIND POWER APPLICATIONS 

6

There are four basic wind applications: 

z Utility-scale wind power plants, both land-based and offshore 

z Community-owned projects, which often produce power for local 

consumption and sell bulk power under contracts 

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z Institutional and business applications 

z Off-grid home installations and behind-the-meter farm/ranch/home 

systems. 

Utility-scale wind power plants produce large amounts of wholesale power and 

account for more than 90% of all wind power generated in the United States. The 

size and number of turbines vary in each of these applications. Off-grid and behindthe-meter projects usually employ turbines smaller than 100 kilowatts (kW), and 

wind power plants typically use turbines larger than 1,000 kW. 

Wind projects range from less than 400 watts (W) to more than 400 megawatts 

(MW), with much larger projects expected in the future. The utility-scale technology 

that started in California in the early 1980s revolved around 50- to 100-kW 

machines. The standard size of today’s more efficient and reliable turbines ranges 

from 1,500 kW to 2,500 kW. A 1,000-kW turbine can supply electricity for about 

300 homes. 

6.3.1 LARGE-SCALE WIND POWER PLANTS 

Wind power plants consist of a number of individual wind turbines that are 

generally operated through a common control center. The number can range from a 

few, to dozens, to hundreds of energy-producing turbines. 

Wind projects that are 2,000 megawatts or larger have been proposed. Such largescale wind projects will bring about new challenges and benefits and will require 

(and are large enough to justify) dedicated large-scale transmission to carry power 

long distances on land or shorter distances offshore to urban demand centers. 

Accelerated growth of wind power in the United States would almost certainly 

require developing a number of these very large-scale projects, for several reasons: 

z	 Siting constraints on traditional projects: Installing large 

numbers of turbines in remote regions minimizes landowner 

objections to dense turbine siting in populated areas. 

z	 Geographic distribution of the wind resource: Most high-quality 

land-based wind resources in the nation are in mountain and plains 

states. The 20% Wind Scenario would require significant amounts 

of these resources to be captured. 

z	 Development pace and scale of development: A few very large 

projects can add as much wind generation capacity as hundreds of 

traditional 100-MW projects and can be developed and built much 

more quickly. 

z	 Restrictions on land-based deployment: Some energy-constrained 

coastal areas will depend on offshore wind resources that will 

require large-scale project development to reduce overall 

infrastructure costs. 

6.3.2 OFFSHORE WIND 

Coastal areas, especially in California and the northeastern United States, pay higher 

than average prices for electricity, so offshore wind developers have an added 

incentive—in the form of high market prices—to enter these markets. There are 

uncertainties with permitting requirements in federal waters. However, the Minerals 

138	 20% Wind Energy by 2030 – Prepublication Version 
 

Management Service (MMS) is in the process of developing proposed rules, along 

with a programmatic environmental impacts statement. The MMS program is 

expected to be in place toward the end of 2008. Still, technical, market and policy 

uncertainties are limiting the deployment of offshore wind turbines alone (see 

chapters 3 and 5 for more discussion of offshore wind). In addition, the cost of 

offshore wind projects is higher than land-based turbines by about 40%, according to 

a study conducted by Black & Veatch, an engineering company based in Overland, 

Kansas (Black and Veatch, forthcoming 2008). This higher cost can be attributed to 

the added complexity of siting wind turbines in a marine and potentially harsher 

environment, higher foundation and infrastructure costs, higher than expected 

operations and maintenance (O&M) because of accessibility issues, and the added 

cost of O&M associated with offshore turbine locations and the marine environment. 

In the next 10 years, the U.S. offshore wind market could play a more significant 

role in bringing new power generation online in selected regions of the country 

where electricity prices are higher than average, population density restricts power 

plant installations, shallow water sites are available, state governments have passed 

aggressive RPS requirements, and coastal communities support this energy option. 

6.3.3 COMMUNITY WIND 

Community stakeholders have started to evaluate wind development as a way to 

diversify and revitalize rural economies. Schools, universities, farmers, Native 

American tribes, small businesses, rural electric cooperatives, municipal utilities, 

and religious centers have installed their own wind projects. Although community 

wind projects can be of any size, they are usually commercial in scale with 

capacities greater than 500 kW, and they are connected on either side of the meter. 

Community wind includes both on-site wind turbines used to offset customer’s loads 

and wholesale wind generation sold to a third party. 

Community wind is likely to advance wind power market growth because it has the 

following advantages: 

•	 Strengthens communities: Locally owned and controlled wind development 

substantially broadens local tax bases and generates new income for farmers, 

landowners, and communities. 

•	 Galvanizes support: Local ownership and increased local impacts broaden 

support for wind energy, engage rural and economic development interests, and 

build a larger constituency with a direct stake in the industry’s success. Local 

investments and local impacts produce local advocates. 

6.3.4 SMALL WIND 

Small wind (sometimes called “distributed wind energy”) refers to wind turbines 

that are generally smaller than 100 kW each. Residences or businesses can install 

small wind turbines on site to meet their local electricity demands, often selling 

excess electricity sold back to the grid on distribution lines. The on-grid behind-themeter applications, where turbines are connected to distribution lines and supply 

electricity to partially meet local loads, comprise the primary market for small wind. 

On-grid installations are currently supported by a variety of state and utility financial 

incentives, which reduce up-front capital costs to the consumer. Small wind can also 

include small units for off-grid applications such as remote homes and livestock 

watering facilities, as well as the wind–diesel hybrid systems that are deployed in 

remote village settings, such as, Alaska. 

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Small wind has lower wind speed requirements, so more locations can accommodate 

and harvest wind. It also represents a form of distributed generation. The U.S. small 

wind manufacturing industry dominates today’s world markets, and deploying 

distributed wind energy in the rural or remote parts of the United States can help to 

build acceptance of future wind power plants. As markets continue to expand and 

manufacturers increase their volume, the result will be lower cost turbines. 

Although small wind systems have higher per-kilowatt costs than utility-scale 

systems, they compete with retail instead of wholesale electricity rates, which are 

6

also higher. 

Community Wind in Minnesota 

Minnesota took major steps to encourage the 

development of renewables by requiring the state’s 

largest utility, Xcel Energy, to acquire a growing 

amount of wind energy. The target was 425 MW in 

1994, 825 MW by 1999, and 1,125 MW by 2003. This 

created a reliable wind energy market in the state 

which, in turn, helped wind energy find its way into 

many areas of Minnesota’s economy, including 

construction, O&M, and engineering. It also forged 

the path for development of permitting rules that other 

states and counties use as models for writing their own 

regulations. 

Community wind began in the United States in 

Minnesota in 1997 when local advocates worked with 

the legislature to create the Minnesota Renewable 

Energy Production Incentive (REPI). Local ownership 

was a priority for those who created this incentive, 

which paid $0.01 to $0.015/kWh for the first 10 years 

of production for projects smaller than 2 MW. In the 

beginning, local wind developers had to individually 

negotiate with utilities for interconnection and PPAs. 

It was not until a special community wind tariff— 

establishing a set power purchase rate of $0.033/kWh 

and standard procedures for interconnection for wind 

projects below 2 MW—was created in 2001 as part of 

Xcel Energy’s merger settlement that community 

wind projects really became feasible. The initial 

Minnesota REPI allocation was then quickly 

subscribed, and a second round was fully subscribed 

within 6 months. Pairing of these complementary 

policies allowed the community wind market to really 

take off. 

Small wind energy market challenges 

include turbine availability (product gaps 

exist for 5-, 15-, and greater than 100-kW 

turbines); economics and lack of financial 

incentives across all market segments; 

turbine reliability; utility interconnections; 

and zoning and permitting. 

6.3.5	 NATIVE AMERICAN WIND 

PROJECTS 

Native American reservations constitute a 

special community with emerging interests 

in wind power development. Windgenerating potential on tribal lands, which is 

conservatively estimated at more than 1.5 

GW, could make an important contribution 

toward the 20% Wind Scenario. At least 39 

Native American reservations with 

significant wind power potential (Class 4 

and higher) are located in remote areas that 

could support development. Self-governed 

Native American tribes have a unique legal 

relationship with the U.S. federal 

government and are afforded increased 

opportunities under EPAct 2005. 

6.4	 STAKEHOLDER 

INVOLVEMENT AND 

PUBLIC ENGAGEMENT 

As wind energy development proceeds in 

the United States, site selection and 

development will require well-designed and 

effective stakeholder engagement. The 

preceding sections outlined the markets and supply segments that can contribute to 

the 20% Wind Scenario. The types of stakeholders and their perceptions of wind 

energy are likely to vary markedly from one location to another. An important part 

of any stakeholder initiative is to identify the full range of interested parties and 

decision makers, such as public utility commissions and their staffers, utilities and 

140	 20% Wind Energy by 2030 – Prepublication Version 
 

 

regional transportation organizations and their customers, state and federal 

legislators, and financiers. Understanding stakeholders’ interests and how to 

effectively communicate with these various groups is central to the pursuit of 20% 

wind energy by 2030. 

Experience with past wind and other energy facility development in the United 

States has brought home the critical importance of stakeholder involvement. The 

energy community now generally recognizes that effectively engaging stakeholders 

in siting-related decisions requires attention to a number of key factors: 

•	 State and local siting guidelines and procedures are needed to establish a known 

and deliberate siting process in which local concerns and siting issues are fully 

considered. Developers must also be able to plan for and manage a predetermined 

and predictable process. 

•	 The developer, state and local officials, and the host communities should 

collaborate on designing stakeholder outreach 

•	 A comprehensive list of stakeholders—including those who will be targeted in the 

engagement efforts— should be compiled early in the process. 

•	 Concerns and requirements of various stakeholders should be assessed. Needs 

should be identified and defined through interviews with stakeholders. 

•	 The stakeholder-engagement process should begin before the site is assessed and 

selected so that baseline information can be established. Stakeholders should 

continue to be actively engaged throughout the facility development and 

operational process, with an emphasis on two-way communications. 

•	 A neutral third party should carefully evaluate effectiveness of the engagement 

process along the way, to ensure that any initiatives incorporate new stakeholders 

that might appear and new concerns that might arise. This will also allow 

deficiencies in engagement and communications to be forthrightly addressed. 

Finally, no element in an engagement and communications effort is more important 

than building trust among the developers, state and local officials, and members of 

the host community. Although this is a much more difficult task than is generally 

understood, experience has shown that openness, serious consideration of local 

concerns, and a participatory process all contribute substantially to successful 

outcomes. 

6.5 CONCLUSIONS 

6

Within the 20% Wind Scenario, multiple revenue streams and multiple markets for 

wind generation output would be increasingly important. Standards for renewable 

energy contributions as well as voluntary markets have the potential to create 

separate and complementary revenue streams for supporting wind energy generation 

while reducing risks. Today, 25 states have established RPS requirements. 

Compliance markets, which have been growing rapidly in recent years, can make 

substantial contributions to the expansion of wind energy capacity. Emerging 

emissions markets can also be a source of revenue streams. 

To create the catalyst necessary to support aggressive wind energy growth, many 

different market drivers must converge. And if the significant increase in wind 

power development under the 20% Wind Scenario is to be realized, many 

stakeholders will need to embrace a robust wind future. Stakeholder interests are as 

diverse as stakeholder types. A long-term commitment to understanding and 

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working with stakeholders will be critical for deploying significant levels of wind 

power. All segments of the market must be taken into account when planning for the 

wide adoption of wind-generated electricity. Market forces need to be targeted and 

utilized efficiently to leverage stakeholder interests if 20% of U.S. electricity from 

wind is to be realized. 

6.6 REFERENCES 

DOE (U.S. Department of Energy). 2006. Annual Report to Congress on Federal 

Government Energy Management and Conservation Programs Fiscal Year 

2005. Washington, DC: DOE. 

http://www1.eere.energy.gov/femp/pdfs/annrep05.pdf. 

EIA (Energy Information Administration). 2007. Annual Energy Outlook 2007 with 

Projections to 2030. Washington, DC: EIA. Report No. DOE/EIA-0383. 

http://www.eia.doe.gov/oiaf/archive/aeo07/index.html 

O’Connell, R., and R. Pletka, et al. 2007. 20 % Wind Energy Penetration in the 

United States: A Technical Analysis of the Energy Resource. Overland Park, 

KS: Black & Veatch. 

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Appendix A. 20% Wind Scenario 

Impacts 

A.1 Introduction 

This appendix describes the analytic tool and assumptions that were used to identify 

some key components of the impacts, and technical challenges of providing 20% of 

the nation’s electricity from wind in 2030. The 20% level was chosen exogenously 

as the central assumption of the evaluation. The relative cost difference between a 

scenario including 20% wind-generated electricity and a scenario in which no 

additional wind technology is installed after 2006 is the primary metric. All 

modeling assumptions contribute to this incremental cost of wind energy. Thus 

changes to the assumptions increase or decrease the incremental cost of of the 20% 

Wind Scenario over the scenario that does not include wind energy. No sensitivities 

exploring changes to the assumptions were 

performed for this analysis. Modeling 

assumptions are described this appendix (See 

Table A-1) and Appendix B. 

The National Renewable Energy Laboratory’s 

(NREL) Wind Deployment System (WinDS) 

model was employed to simulate generation 

capacity expansion of the U.S. electricity sector 

through 2030. This model used a wind energy 

generation rate that would result in production of 

20% of projected electricity demand from wind 

by 2030. Carbon emission reductions in this 20% 

Wind Scenario have also been derived from the 

WinDS model outputs. Water savings associated 

with significant wind energy generation have 

been externally calculated as well. The 

assumptions used for these analyses were 

developed from a variety of sources and 

experiences that span the wind and electricity 

generation industries; model-specific details of 

these assumptions are presented in Appendix B. 

The 20% Wind Scenario would require U.S. wind 

power capacity to grow from the current 11–12 

gigawatts (GW) to more than 300 GW over the 

next 23 years. This ambitious growth could be 

reached in many different ways, with varying 

challenges, benefits, costs, and levels of success. 

This report examines one particular scenario for 

achieving this dramatic growth and contrasts it to 

another scenario called no new wind, which 

assumes no wind growth after 2006 for analytic 

simplicity. 

Considerations in the 20% Wind 

Scenario 

•	 Wind resources of varying quality exist across 

the United States and offshore. 

•	 Although land-based resources are less 

expensive to capture, they are sometimes far 

from demand centers. 

•	 Typically, wind power must be integrated into 

the electric grid with other generation sources. 

•	 Technology and power market innovations 

would make it easier to handle a variable 

energy resource such as wind. 

•	 New transmission lines would be required to 

connect new wind power sources to demand 

centers. 

•	 Transmission costs add to the cost of 

delivered wind energy costs, but today’s U.S. 

grid requires significant upgrading and 

expansion under almost any scenario. 

•	 Wind installations will require significant 

amounts of land, although actual tower 

footprints are relatively small. 

•	 Domestic manufacturing capacity might not 

be sufficient to accommodate near-term rapid 

growth in U.S. wind generation capacity The 

gap may be filled by other countries. 

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A

The authors recognize that U.S. wind capacity today is growing rapidly, although 

from a very small base, and that wind energy technology will be a part of any future 

electricity generation scenario for the United States. At the same time, there is still a 

great deal of uncertainty about what level of contribution wind could or is likely to 

make. In its Annual Energy Outlook with Projections to 2030 (shortened in this 

appendix to AEO 2007 or simply AEO), the Energy Information Administration 

(EIA) forecasts that an additional 7 GW—beyond the 2006 installed capacity of 11.6 

GW—will be installed by 2030 (EIA 2007).12 Other organizations are projecting 

higher capacity additions, and given today’s uncertainties, developing a “most 

likely” forecast would be difficult. The analysis presented here sidesteps these 

uncertainties and contrasts the impacts of producing 20% of the nation’s electricity 

from wind with a scenario in which no additional wind is added after 2006. This 

yields a parameterized estimate of some of the impacts associated with increased 

reliance on wind energy generation. 

The analysis was also simplified by assuming that the contributions to U.S. 

electricity supplies from other renewable sources of energy would remain at 2006 

levels in both scenarios. In addition, no sensitivity analyses have been done to 

identify how the results would differ if assumptions were changed. 

Broadly stated, this 20% Wind Scenario is designed to optimize costs while 

recognizing certain constraints and considerations (see sidebar above). Specifically, 

the scenario describes the mix of wind resources that would have to be captured, the 

geographic distribution of the wind power installations, estimated land needs, and 

required utility and transmission infrastructure changes associated with 20% wind in 

2030. It is not a definitive identification of the exact locations of wind turbines and 

transmission lines. 

The scenario reflects several assumptions about generation technology cost and 

performance as well as electric grid system operationg and expansion. For example, 

wind technology development is projected to continue based on a history of 

performance improvements. The national transmission system is assumed to evolve 

in ways favorable to wind energy development by shifting toward large regional 

markets. In addition, future environmental study and permit requirements are not 

expected to add significant costs to wind technology. 

The 20% Wind Scenario was constructed by specifying annual wind energy 

generation in each year from 2007 to 2030, based on a trajectory proposed in a 

previous NREL study (Laxson, Hand, and Blair 2006). These investigators forced 

the WinDS model to reach the 20% level for wind-generated electricity by 2030 and 

evaluated aggressive near-term growth rates. Next, they examined sustainable levels 

of wind capacity installations that would maintain electricity generation levels at 

20% and accommodate the repowering of aging wind turbine equipment in wind 

installations beyond 2030. The 20% wind by 2030 trajectory from the NREL study 

was implemented in WinDS by calculating the percentage of annual energy 

production from wind, an increase of approximately 1% per year. Figure A-1 

illustrates the energy generation trajectory proposed in the study, and the 

corresponding annual wind capacity installations that the WinDS model projects will 

meet these energy generation percentages. 

12 AEO data from 2007 were used in this report. AEO released new data in March of 2008, which were 

not incorporated into this report. While the new EIA data could change specific numbers in the report, 

it would not change the overall message of the report. 

144 20% Wind Energy by 2030 – Prepublication Version 
 

Figure A-1. Prescribed annual wind technology generation as a percent of 

national electricity demand from Laxson, Hand, and Blair (2006) and 

corresponding annual wind capacity installation for 

20% Wind Scenario from WinDS model 

The combined cost, technology, and operational assumptions in the WinDS model 

show that reaching an annual installation rate of about 16 GW/year by 2018 could 

result in generation capacity capable of supplying 20% of the nation’s electricity 

demand by 2030. This annual installation rate is affected by the quality of wind 

resources selected for development as well as future wind turbine performance. The 

declining annual installed capacity after 2024 is an artifact of the prescribed energy 

generation from the NREL study (Laxson, Hand, and Blair 2006), in which 

technology improvements and wind resource variations were not considered. The 

NREL study (Laxson, Hand, and Blair 2006) provides an upper level of about 20 

GW/yr, because turbine performance is unchanged over time. Based on the wind 

resource data and the projected wind technology improvements presented in this 

report, sustaining a level of annual installations at approximately 16 GW/year 

beyond 2030 would accommodate repowering of aging wind turbine equipment and 

increased electricity demand, so that the Nation’s energy demand would continue to 

be met at the 20% wind level. 

The 20% Wind Scenario does not include policy incentives such as a production tax 

credit (PTC) or carbon regulations, although such policies may make this growth 

trajectory more likely. It is implicitly assumed that a stable policy environment that 

recognizes wind’s impacts could lead to growth rates that would result in the 20% 

Wind Scenario. 

Some of the consequences of a 20% Wind Scenario in 2030, including carbon 

emission reductions and natural gas demand reduction, were calculated based on the 

results of the WinDS model. To estimate the impacts associated with incorporating 

electricity from wind into the grid at this level, a comparison has been made with a 

scenario in which no additional wind power would be installed after 2006. The 

differences between the two cases are attributed to the incorporation of wind power. 

From a planning and operational perspective, integrating wind generation into the 

U.S. electricity grid at the 20% level appears to be technically feasible without 

significantly and unrealistically constraining the WinDS model (e.g. assuming no 

new transmission will be built). In addition to modeling the expansion of the 

electricity grid to transmit power from wind-rich geographic areas to demand (load) 

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A

centers, the model treats wind resource variability on time scales ranging from 

multiyear capacity planning to minute-to-minute ancillary service requirements. 

(WinDS does not perform minute-to-minute ancillary service calculations, but it 

uses statistics to approximate these requirements. See Appendix B for a detailed 

discussion of the treatment of wind variability.) The 20% Wind Scenario presented 

here includes future reductions in wind technology costs and increased performance, 

coupled with transmission system expansion that is favorable to wind energy. These 

assumptions affect only the direct cost to the electricity sector associated with this 

level of wind energy expansion. These cost and performance assumptions differ 

from those used by EIA. The assumptions are based on 2006 market data 

developed by Black & Veatch for all generation technologies. Explicit cost and 

performance projections are used rather than learning algorithms generally used by 

EIA. See Appendix B for more information. 

A.2 Methodology 

The WinDS model was used to identify some key components of the impacts of 

producing 20% of the nation’s electricity from wind energy by 2030. WinDS is a 

geographic information system (GIS) and linear programming model of electricity 

capacity expansion for the U.S. wholesale market.13 The model operates over 

multiple regions and time periods. Generation capacity expansion is selected to 

achieve a cost-optimal generation mix to meet 20% wind generation over a 20-year 

planning horizon for each 2-year period from 2000 to 2030. 

The assumptions used for the WinDS model were obtained from a number of 

sources, including technical experts (see Appendix D), the WinDS base case 

(Denholm and Short 2006), AEO 2007 (EIA 2007), and a study performed by Black 

& Veatch (2007). These assumptions include projections of future costs and 

performance for all generation technologies, transmission system expansion costs, 

wind resources as a function of geographic location within the continental United 

States, and projected growth rates for wind generation. Appendix B describes these 

assumptions in detail. 

A.2.1 Energy Generation Technologies 

Wind-generation technologies are contained in the WinDS model, along with 

conventional technologies such as coal plants (pulverized coal and integrated 

gasification combined cycle [IGCC]), nuclear plants, and natural-gas-fired 

combustion turbine and combined cycle plants. The model does not include 

technologies installed “behind the meter” such as cogeneration or other distributed 

generation systems, nor does it include energy efficiency or demand response 

technologies. Table A-1 summarizes the modeling assumptions. 

Wind technology options include land-based and offshore technologies. Wind 

resource classes 3 through 7 (at 50 meters [m] above ground level) are specified for 

358 wind supply regions across the continental United States. Each wind supply 

region in WinDS includes a mix of these wind resource classes. Offshore wind 

resources are associated with coastal and Great Lakes regions. Resource maps 

reference those produced by the Wind Powering America (WPA) initiative or by 

individual state programs, and include environmental and land use exclusions. In 

13 The model, developed by NREL’s Strategic Energy Analysis Center (SEAC), is designed to address 

the principal market issues related to the penetration of wind energy technologies into the electric 

sector. For additional information and documentation, see http://www.nrel.gov/analysis/winds/. 

146 20% Wind Energy by 2030 – Prepublication Version 
 

 

 

Table A-1. Assumptions used for scenario analysis 

Scenario Assumptions 

Renewable Energy 

Technologies (other 

than wind) 

• Contributions to U.S. electricity supply from renewable 

energy (other than wind) are held constant at 2006 levels 

through 2030. 

Land-Based Wind 

Technology Cost • $1,730/kW in 2005 and 2010, decreasing 10% by 2030 

• Regional costs vary with population density, with an 

additional 20% in New England 

Shallow Offshore 

Wind Technology 

Cost 

• $2,520/kW in 2005, decreasing 12.5% by 2030 

Wind Technology 

Performance • Capacity factor improvements about 15% on average 

over all wind classes between 2005 and 2030 

Existing 

Transmission • 10% of existing transmission capacity available to wind 

plants at point of interconnection 

New Transmission • Transmission will be expanded 

• $1,600/megawatt-mile (MW-mile) 

• 50% of cost covered by wind project 

• Regional cost variations prescribed as follows: 40% 

higher in New England and New York, 30% higher in 

Pennsylvania-New Jersey-Maryland (PJM) East 

interconnection, 20% higher in PJM West, 20% higher in 

California 

Wheeling Charges • No wheeling charges between balancing areas 

Conventional 

Generation 

Technology Cost 

and Performance 

• Natural gas plant cost ($780/kW in 2005) and 

performance flat through 2030 

• Coal plant capital cost ($2,120/kW in 2005) increases 

about 5% through 2015 and then remains flat through 

2030 

• Coal plant performance improvement of about 5% 

between 2005 and 2030 

• Nuclear plant capital cost ($3,260/kW in 2005) decreases 

28% between 2005 and 2030 

• Nuclear plant performance stays flat through 2030 

Fuel Prices • Natural gas prices follow AEO 2007 high fuel price 

forecast 

• Coal prices follow AEO 2007 reference fuel price forecast 

• Uranium fuel price is constant 

addition to the geographic display of wind resources, seasonal and diurnal variations 

in capacity factor (CF) are computed based on wind resource data. Appendix B 

contains more information about the wind resource data used for this study. 

Experts at Black & Veatch Corporation developed wind technology cost and 

performance projections based on their experience and market knowledge, 

discussions with wind industry professionals, and review of cost and performance 

trends (Black & Veatch 2007). Wind technology costs in 2005 are assumed to be 

$1,730/kW14 (kilowatt; in real US$2006), which reflects recent cost increases 

14 All dollar values in appendices A and B are in $US2006. These capital costs include construction 

financing, which adds approximately 5% to the “overnight” capital cost given in Appendix B. The 

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A

attributed to current exchange rates between the euro and the dollar, increased 

commodity prices, a constrained supply of wind turbines, and construction 

financing. Wind technology costs are projected to decrease to $1,550/kW by 2030 

(in US$2006 including construction financing). The cost of offshore wind energy 

technology is projected to decrease 12.5% over the same period, from $2,500/kW in 

2005 to $2,200/kW in 2030 (real US$2006 including construction financing). 

Specialists at Black & Veatch developed wind technology performance projections, 

in the form of capacity factors, by extrapolating historical performance data from 

2000 to 2005. Appendix B gives more details on the cost and performance estimates 

for current and future years for both land-based and offshore wind technologies. 

Black & Veatch experts also developed conventional generation technology cost and 

performance projections based on reported engineering, procurement, and 

construction costs for currently proposed plants through 2015. Fossil plant costs 

were assumed to remain flat beyond 2015 with modest performance improvements 

for coal plants. Cost and performance projections for nuclear plants assume 

continued technology development. Appendix B presents these cost and 

performance assumptions. 

A.2.2 Transmission and Integration 

Wind energy can be used to meet local loads (i.e., loads in the same wind supply 

region), as well as those in other geographic locations. Local loads can be met either 

by transmitting on the existing grid where capacity is available (10% of the capacity 

of each existing line is assumed to be available for wind) or by building a short, 

dedicated transmission line directly to the local load. Wind energy shipped to 

another locale can also be transmitted, either on the existing grid when capacity is 

available or on a new transmission line. If the transmission line crosses between two 

balancing areas, there must be enough capacity on the line in each seasonal or 

diurnal time frame to take advantage of wind’s full nameplate capacity, as well as 

the energy associated with other generators transmitting power over that line. If no 

capacity remains on that transmission line, the model assumes that a dedicated 

transmission line will be constructed for wind. 

When integrating wind resources into the grid, the model considers both planning 

and operating reserve margins for all North American Electric Reliability 

Corporation (NERC) regions. For both types of reserve margin, WinDS accounts for 

the reduction in wind generation variability that occurs when wind generation is 

used from disparate wind sites whose output is not fully synchronized with one 

another. The wind plant’s capacity value is a function of the CF, seasonal and 

diurnal wind variations, and correlation with existing wind capacity installations. In 

this way the variability of the wind resource is assessed in combination with 

conventional generation within each NERC region. 

The transmission system is assumed to expand under large, regional operation and 

planning entities, which incorporate polices that favor wind energy. Operating grid 

systems on large, regional bases, such as through the Midwest Independent 

Transmission System Operator (Midwest ISO), mitigates the variability of wind 

power. The WinDS model calculates reserve and planning margins at the NERC 

regional level, which is representative of these large operating structures. A wind 

WinDS model applies financing costs in each solution period that requires overnight capital costs as 

input. 

148 20% Wind Energy by 2030 – Prepublication Version 
 

energy penetration limit of 25% has been assumed at the interconnect level. Also, 

based on the participant funding principle adopted by Midwest ISO, the cost of new 

transmission is assumed to be split equally between the originating project, be it 

wind or conventional generation, and the ratepayers within the region. This tariff 

structure is assumed to apply nationwide. The exception is for new transmission 

lines that are built at a project in one interconnection region to meet loads in another 

interconnection region. In this case, the transmission cost is borne entirely by the 

project. Consistent with the large, regional planning process, this scenario assumes 

that there are no wheeling charges between balancing areas. Finally, the 20% Wind 

Scenario assumes that 10% of existing grid capacity is available for wind energy. 

A.2.3 Quantification of Impacts 

Projected electricity demand estimates, as well as financing and economic 

assumptions, were obtained from the AEO reference case (EIA 2007). Total direct 

costs of all generation technologies were estimated over a 20-year planning horizon 

in each two-year solution period. The sum of these direct costs represents the total 

cost to the electricity sector for generation choices through 2030, including costs for 

capital investment, operations and maintenance (O&M), new transmission, and fuel. 

To calculate the impacts of 20% wind energy, the authors of this report have 

constructed a scenario in which no new wind systems are installed after 2006. This 

scenario assumes that the conventional generation mix expands to meet electricity 

demand with currently enacted policies. Table A-1 outlines the major assumptions in 

the scenario and supporting analyses. The difference between the two cases, 20% 

wind and no new wind, represents the impact of wind energy. 

A.3 Wind Capacity Supply Curves 

In economic analysis, a supply curve is used to determine the quantity of a product 

that is available at various prices. For this report, wind generation potential is plotted 

against its calculated levelized cost (LC) of electricity in ascending order. See 

Appendix B for more information. For example, the potential (in gigawatts) from 

high-speed wind resources has been plotted against its levelized cost (dollars per 

megawatt-hour [MWh]); lower speed wind projects have higher costs and represent 

the next step up on the supply curve. Cost and potential were estimated for each 

region based on a GIS optimization strategy developed by NREL. The regions were 

aggregated such that an overall supply curve for national wind potential could be 

developed. The following supply curves compare the quantities and costs for wind 

resources and show which products can be brought to market at the lowest cost 

(resources on the left side of Figure A-2 “Supply Curve for wind energy: current 

busbar energy costs”). See Appendix B for wind resource estimates. 

The national supply curve for bus-bar energy costs—for the wind plant alone, 

excluding transmission costs—is shown in Figure A-2. The figure illustrates that 

more than 8,000 GW of wind energy is available in the United States at $85/MWh 

or less. This is a huge amount of capacity, equivalent to roughly eight times the 

existing nameplate generating capacity in the country, which is estimated at 983 GW 

(EIA 2007). This price, however, excludes the cost of transmission or integration. 

The supply curve uses today’s cost and performance figures, which are projected to 

improve with future technology development. 

A

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Figure A-2. Supply curve for wind energy—current bus-bar energy costs 

A

See Appendix B for wind technology cost and performance projections; PTC and transmission and 

integration costs are excluded. 

The supply curve shows the simple relationship between wind power class and cost, 

as the higher classes are the lowest cost (and least abundant resources); Classes 3 

and 4 are much more prevalent. At today’s costs, offshore wind is not costcompetitive with land-based wind technologies. Finally, the national resource 

potential for land-based wind technologies exceeds the existing nameplate 

generating capacity in the country by a factor of eight. However, that does not mean 

capturing that full potential is economically, technically or politically viable. 

The national supply curve in Figure A-3 shows the costs of connecting to the 

existing transmission system, given that 10% of capacity is available for new wind 

generation. This supply curve also shows the cost of connecting directly to load 

centers that are in the same balancing area as the wind resource, given that a 

maximum of 100% of that load can be served by wind. This curve is produced as an 

input to the WinDS model. Please see Appendix B or (Black & Veatch 2007) for 

more information. 

Figure A-3 shows only the supply curve for wind projects that can enter the existing 

transmission system (or that can power nearby loads). It does not include wind 

projects that would require new transmission to deliver power to markets distant 

from the generation system. The supply curve, however, shows more than 1,000 GW 

of wind energy— approximately 600 GW of land-based and roughly 400 GW of 

offshore capacity. Developing all of this resource is not economical and would 

require significant modifications in the transmission system, but under certain 

conditions it could produce enough energy to greatly exceed 20% of the nation’s 

electricity supply in the future. The supply curve further illustrates that more than 

600 GW of wind are available at or below $100/MWh at current bus-bar energy 

costs and performance indicators. These supply curves do not factor in transmission 

or integration costs or technology improvements. 

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Figure A-3. Supply curve for wind energy: energy costs including connection to 

10% of existing transmission grid capacity 

A.4 Impacts 

Based on the assumptions used to create the 20% Wind Scenario, providing 20% of 

the nation’s projected electricity demand by 2030 would require the installation of 

293.4 GW of wind technology (in addition to the 11.4 GW currently installed) for a 

cumulative installed capacity of 304.8 GW, generating nearly 1,200 terawatt-hours 

(TWh) annually. Offshore wind technology would account for about 18%—54 

GW—of total wind capacity by 2030. Figure A-4 shows the cumulative installed 

capacity of land-based and offshore wind technologies required to generate 20% of 

projected electricity demand by 2030. 

Figure A-4. Cumulative installed wind power capacity required to produce 20% 

of projected electricity by 2030 

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A.4.1 Generation Mix 

This section presents impacts on the remaining generation mix and on the emissions 

of carbon from producing 20% of the nation’s electricity from wind in 2030. The 

geographic distribution of wind turbines and the transmission expansion required to 

accommodate them are also addressed. Sophisticated routines in the WinDS model 

use existing transmission or build new transmission while incorporating associated 

wind integration costs. This scenario shows that with wind technology advancement 

associated reductions in costs and changes in the grid system, producing 20% wind 

energy in the nation’s portfolio by 2030 could be technically feasible. 

The generation mix produced by the WinDS model based on the requirement that 

20% electricity generation will come from wind is pictured in Figure A-5. This 

scenario does not assume that carbon regulation policies are in place and reflects the 

assumptions listed in table A-1 as well as others. The resulting generation mix, 

excluding wind, is made up of the most cost-effective conventional technologies in 

place today. Wind energy grows as a percentage of the nation’s generation mix, and 

coal-generated electricity remains the major generation technology in 2030. Nuclear 

power generation declines slightly as a fraction of the total generation mix. Natural 

gas technologies make a greater contribution to the total mix through 2016 and then 

decline to a level similar to today’s level by 2030. Changes in assumptions would 

produce a different mix of conventional generation technologies. 

Figure A-5. 20% Wind Scenario electricity generation mix 2000–2030 

A

Figure A-6 illustrates the comparison in net generation in 2030 between 

conventional energy and wind energy generation, when applied to the 20% wind and 

the no new Wind Scenarios. The 20% Wind Scenario, of course, would result in 

dramatically higher levels of wind energy generation. This figure also shows a 

significant reduction of energy generated from combined cycle natural gas plants 

(Gas-CC) as well as reduced energy from new pulverized coal plants (Coal-New). 

152 20% Wind Energy by 2030 – Prepublication Version 
 

Figure A-7 compares generating capacity by 2030 between the 20% Wind Scenario 

and the no new Wind Scenario. Again, the contribution from wind is the primary 

Figure A-6. Generation by technology in 2030 

Figure A-7. Capacity by technology in 2030 

A

difference. The 20% Wind Scenario requires less Coal-New and Gas-CC capacity. 

The 20% Wind Scenario does require additional gas combustion turbine capacity 

(Gas-CT) to maintain grid reliability when wind resources vary. As shown in 

Figure A-6, relatively little electricity is generated from these plants in both 

scenarios. 

Several important assumptions could affect the resulting mix of conventional 

generation in output from the WinDS model, including 

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z	 Fuel price forecasts: The WinDS model uses regional gas and coal 

fuel price projections from the AEO 2006 (EIA 2007). The 

reference case coal fuel projections were implemented, but the 

natural gas price forecast from the high-price case was deemed more 

probable. Other gas and coal future price projections could be used, 

and modifying these prices would affect generation from gas, coal, 

and other sources. 

z	 Fuel price elasticity: For this analysis, the WinDS model does not 

include fuel price elasticity. This could be important in scenarios 

that differ significantly from the scenario assumed in the AEO 2006 

(EIA 2007). For example, assuming wind generation at 20% of U.S. 

electricity, the demand for gas and coal would decrease, resulting in 

a lower price for both (thereby conversely driving up demand) and 

settling on a cost value lower than that currently used in the model. 

z	 Carbon regulation: The imposition of a carbon constraint would 

also change this generation mix significantly, increasing future 

Coal-IGCC and Nuclear capacity, reducing future Coal-new and 

Gas-CC capacity, and leading to significantly more plant 

retirements and less use of existing coal plants. 

A.4.2 Carbon Emission Reduction 

Comparing the 20% Wind Scenario with the no new Wind Scenario, in which no 

additional wind technology is installed after 2006, provides one way of estimating 

the potential carbon emissions reductions that could be attributed to wind energy. 

This scenario assumes that the conventional generation mix is allowed to expand 

while optimizing total costs without any carbon regulation policy. Figure A-8 

illustrates the cumulative carbon emissions reduction of more than 2,100 million 

metric tons of carbon equivalent (MMTCE) attributed to producing 20% of the 

nation’s electricity from wind during the significant wind energy expansion period, 

Figure A-8. Cumulative carbon emission reductions attributed to wind energy 

(compared to expanding the generation mix without wind energy) 

A

154	 20% Wind Energy by 2030 – Prepublication Version 
 

2005 to 2030. Extrapolating cumulative carbon emissions avoidance over the 20­

year wind plant life through 2050 results in avoided emissions of more than 4,000 

MMTCE, and avoided carbon emission in 2030 alone of 225 MMTCE. 

A.4.3 Reduced Natural Gas Demand 

Figure A-9 demonstrates the decrease in coal and gas fuel use for the 20% Wind 

Scenario relative to the no new Wind Scenario. This graph indicates that a reduction 

in coal use across all coal technologies and a reduction in natural gas use comprise a 

significant fraction of the total amount that would be used without additional wind 

installations. Incorporating enough wind generation technology to produce 20% of 

the nation’s electricity demand by 2030 could reduce the electricity sector’s natural 

gas requirements by about 50% and its coal requirements by about 18%. This shift 

of natural gas from the electric sector translates into reduced national demand for 

natural gas of 11%. 

Figure A-9. Fuel usage and savings resulting from 

20% Wind Scenario 

A

Wind power offers the country important resource diversification benefits, including 

the prospect for moderating natural gas demand. In 2006, gas-fired generation 

accounted for nearly 20% of the nation’s electricity generation capacity. Because of 

the way electricity markets operate, the price of gas-fired generation determines the 

price of electricity. Wellhead natural gas prices, which hovered near $2/MMBty in 

the 1990s, have risen to more than $6/MMBtu and most forecasts expect prices to 

remain high relative to historical standards. Past efforts to forecast natural gas 

prices have not been very successful (see for example, Wiser and Bolinger 2004 and 

Bolinger and Wiser 2006). 

A.4.4 Land Use 

Under the 20% Wind Scenario, wind turbines required to supply 20% of the nation’s 

electricity (about 300 GW) would be broadly distributed across the continental 

United States; at least 100 MW would be installed in 43 of the 48 contiguous states. 

Hawaii and Alaska have not been represented in this study, but both states are 

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expected to install more than 100 MW of wind capacity. The WinDS model uses the 

best available assessment of local wind resources to expand wind technology 

capacity. Limitations of wind resource input data, which could significantly affect 

the wind technology capacity installed in a given state, are discussed in Appendix B. 

In addition to wind resources, other factors related to the model logic can influence 

the amount of wind capacity installed in a given state. For instance, current longterm power purchase agreements are not implemented in WinDS. The model 

assumes that local load is met by the generation technologies in a given region. 

The lack of wind capacity installed in Ohio is assumed to be primarily a result of the 

amount of existing conventional energy resources that supply the state, reducing the 

need for additional generating capacity, regardless of the fact that Ohio’s wind 

resources are sufficient to support wind technology development. Additionally 

Ohio’s wind resources are concentrated in the western part of the state. The 

transmission cost assumptions are higher in Ohio than in neighboring Indiana and 

Michigan, which makes Ohio’s wind resource appear less cost-effective in 

comparison. Some states such as Louisiana, Mississippi, and Alabama have lower 

quality wind resources than Ohio, but under the right economic circumstances some 

wind energy development could occur in those states. The WinDS model optimizes 

the installation of wind energy capacity within each of the three large 

interconnection areas in the United States. The model shows that broad geographic 

distribution of wind energy capacity serves to meet the broadly distributed national 

electricity load. Figures A-10 to A-13 illustrate capacity expansion of wind energy 

representing the years 2012, 2018, 2024, and 2030 (approximately 3%, 9%, 15%, 

and 20% electricity generation, respectively). The specific assumptions used in this 

model significantly affect each state’s projected wind capacity. See Table A-1 and 

Appendix B for more information on the assumptions. In reality, these levels will 

vary significantly as electricity markets evolve and state policies promote or restrict 

wind energy production. 

Figure A-10. Projected wind capacity installations in 2012 

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Figure A-11. Projected wind capacity installations in 2018 

Figure A-12. Projected wind capacity installations in 2024 

A

The black outline in each state in Figures A-10 to A-13 represents land area required 

for a wind farm, corresponding to the capacity shown on the green scale. These 

figures use standard exclusion practices, which are detailed in Appendix B. The total 

land area of the United States required for 305 GW of wind energy, assuming a 

turbine density of 5 MW per square kilometer (km2

), would be smaller than 61,000 

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Figure A-13. Projected wind capacity installations in 2030 

A

km2 (50,000 km2 for land-based projects and 11,000 km2 for offshore projects). Only 

about 2% to 5% of the wind farm area, which is represented by the brown square 

within each black outline, is occupied by towers roads, and other infrastructure 

components, and the balance of the area remains available for its original use (such 

as farming or ranching). 

A.4.5 Transmission 

To meet the nation’s growing demand for electricity, significant transmission 

expansion will be required. Meeting 20% of the demand from wind requires 

transmission expansion, specifically to accommodate such a geographically 

dispersed resource. Three types of transmission systems included in the WinDS 

model could be used to transport wind power around the country: 

z Existing grid: The model assumes that 10% of the existing grid 

could be used for new wind capacity, either by improving the grid 

or by drawing on existing unused capacity. 

z New lines: The WinDS model can evaluate the use of straight-line 

transmission lines in the 358 wind regions. The model assumes that 

appropriate planning will allow new transmission lines to be 

constructed as additional capacity is needed. 

z In-region transmission: In any of the 358 wind regions in the 

United States, the model can assess transmission lines directly from 

the wind site to loads within the same region. 

Figures A-14 to A-17 illustrate the expansion of the transmission system required 

under the 20% Wind Scenario for the years 2012, 2018, 2024, and 2030 

(approximately 3%, 9%, 15%, and 20% wind-electricity generation, respectively). 

158 20% Wind Energy by 2030 – Prepublication Version 
 

The 20% Wind Scenario assumes that transmission planning and grid operations 

occur on several levels—planning at the national level, reserve margin constraint 

Figure A-14. Transport of wind energy over existing and 

new transmission lines projected for 2012 

Figure A-15. Transport of wind energy over existing and 

new transmission lines projected for 2018 

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Figure A-16. Transport of wind energy over existing and 

new transmission lines projected for 2024 

A Figure A-17. Transport of wind energy over existing and 

new transmission lines projected for 2030 

160 20% Wind Energy by 2030 – Prepublication Version 
 

 

planning at the NERC level, and load growth planning and operations at the 

balancing area (BA) level. For visual clarity, these figures display wind capacity 

only at the balancing area level. 

The balancing areas are shaded in purple to depict the amount of locally installed 

wind, which is assumed to meet local load levels. Generally, the first wind system 

installed either uses the existing grid or is accompanied by a short transmission line 

built to supply local loads. In later years, as the existing grid capacity is filled, 

additional new transmission lines are built. New transmission lines built to support 

load in a balancing area with wind resources in that same balancing area are not 

pictured in the figure; only those that cross balancing area boundaries are illustrated. 

The blue arrows in each figure represent wind energy transported on existing 

transmission lines between balancing areas. The red arrows represent new 

transmission lines constructed to transport wind energy between balancing areas. 

The arrows originate and terminate at the centroid of a balancing area and do not 

represent the physical location of demand centers or wind resources. The location 

and relative number of red or blue arrows are dependent on the relative cost of using 

the existing grid or building new lines. 

Table A-2 summarizes projected installed wind capacity in 2030 by transmission 

type, number of megawatt-miles of transmission, and the resulting average distance 

traveled by each megawatt. Transmission options are based on a variety of factors. 

The cost of using existing transmission compared with new transmission can shift 

the relative amounts significantly. Appendix B contains a more complete discussion 

of transmission options used in the WinDS model. 

Table A-2. Distribution of wind capacity on existing and 

new transmission lines 

A

Transmission 

Type 

Existing Transmission 

Lines 

2030 

Wind Capacity 

71 GW 

2030 

MW-Miles 

20 million 

MW-miles 

Average Distance 

Traveled for Each 

MW 

278 miles 

New Capacity Lines 

within a WinDS region 67 GW N/A N/A (estimated at 50 miles) 

New Capacity Lines that 

Cross One or More 

WinDS Region 

Boundaries 

166 GW 30 million 

MW-miles 180 Miles 

A.5 Direct Electricity Sector Cost 

WinDS has been used to estimate the direct costs of meeting 20% of the nation’s 

electricity requirements with wind power in accordance with the 20% Wind 

Scenario (see Appendix B for detailed calculations of each cost element). Direct 

costs to the electricity sector for each scenario include the capital costs of wind and 

conventional energy equipment, as well as transmission, O&M, and fuel costs. 

External analyses based on the WinDS model have estimated water consumption 

reductions. By comparing this scenario with a reference case that involves no new 

wind generation after 2006, the potential costs of future wind development was 

estimated as the incremental change between these two scenarios. 

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A 

Although the model calculates costs through 2030 only, the cost implications of 20­

year investment decisions (fuel consumption and O&M costs) made after 2010 will 

extend beyond 2030 (e.g., to 2050 in the case of a project built in 2030 with a 20­

year life span). Capital and transmission expansion costs are calculated for 

generation capacity added through 2030. Other costs presented in this section 

assume a 20-year project life for wind technology installed after 2010. Thus, the 

incremental differences in fuel consumption, carbon emissions, and water 

consumption between the two scenarios in 2030 are reduced proportionally for wind 

systems that achieve a 20-year operational history between 2030 and 2050. 

Direct costs to the electricity sector for each scenario include the capital costs of 

wind and conventional energy equipment, as well as transmission, O&M, and fuel 

costs. Table A-3 and Figure A-18 illustrate these costs for the 20% Wind Scenario as 

well as the no new Wind Scenario with no additional wind capacity after 2006. 

These costs represent the effect of investment decisions made over 20 years. The 

primary difference between the two scenarios is the higher capital investment for the 

20% Wind Scenario, which is offset somewhat by additional fuel costs for the no 

Wind Scenario. Both scenarios show a significant investment—exceeding $2 

trillion—in generation capacity expansion through 2030. The capital costs include 

all financing costs applied to WinDS model investment selection, as described in 

Appendix B. The discounted capital costs, excluding financing, are $717 billion for 

the 20% Wind Scenario and $580 billion for the no Wind Scenario. 

Table A-3. Direct electricity sector costs for 20% Wind Scenario and 

no-new-Wind Scenario (US$2006) 

Present Value 

Direct Costs for 

20% Wind 

Scenario* 

(billion US$2006) 

Present Value 

Direct Costs for 

No New Wind 

after 2006* 

(billion US$2006) 

Wind Technology O&M Costs $51 $3 

Wind Technology Capital Costs $236 $0 

Transmission Costs $23 $2 

Fuel Costs $813 $968 

Conventional Generation O&M Costs $464 $488 

Conventional Generation Capital Costs $822 $905 

* 7% real discount rate is used, per Office of Management and Budget (OMB) guidance; 

the time period of analysis is 2007-2030. WinDS modeling is used through 2030 and 

extrapolations of fuel usage and O&M requirements for 2030-2050. 

The WinDS model assumes that conventional generation systems, including coal 

and nuclear plants, are sited near load centers (except for California, which restricts 

the installation of coal and nuclear plants). Wind resources, on the other hand, tend 

to be geographically distant from load centers, requiring transmission lines to move 

electricity to the load. 

Estimated costs of transmission expansion in the no Wind Scenario, then, are much 

lower than those for the 20% Wind Scenario, which might be overly conservative. 

Assuming that conventional plants are built near load centers is a simplifying 

162 20% Wind Energy by 2030 – Prepublication Version 
 

 

Figure A-18. Direct electricity sector costs for 

20% Wind Scenario and no-new-Wind Scenario 

assumption for modeling purposes, but may not reflect real siting issues that the coal 

and nuclear industries face today. 

The WinDS model estimates construction of a portion of a duplicate transmission 

line to maintain system reliability while expanding transmission capacity, but the 

model does not explicitly model system reliability conditions and resulting 

transmission upgrades. 

Table A-4 summarizes the key findings of this analysis, focusing on direct electricity 

sector costs and ignoring the benefits of wind generation in reducing carbon 

emissions, or reducing water consumption. All costs are shown in US$2006, and the 

difference between the present values of the two cost streams is the total cost 

difference. In effect, WinDS calculates the incremental cost of achieving 20% wind 

(considering costs of capital, O&M, transmission and integration, and 

decommissioning) relative to a no Wind Scenario in which only nonwind 

technologies compete for market share. 

Table A-4. Incremental direct cost of achieving 20% wind, 

excluding certain benefits (US$2006) 

Present Value 

Direct Costs 

(billion US 

$2006) a 

Average Incremental 

LC of Wind 

($/MWh-Wind)b 

Average Incremental 

Levelized 

Rate Impact 

($/MWh-Total) 

Impact on Average 

Household 

Customer 

($/month)c 

43 billion $8.6/MWh $0.6/MWh $0.5/month 

a

Per Office of Management and Budget (OMB) guidance, a 7% real discount rate is used. The 

time period of analysis is 2007–2030. WinDS modeling is used through 2030 and extrapolations 

of fuel usage and O&M requirements are used for 2030–2050.b

 The levelized cost per kilowatthour of wind produced is found by solving the following formula: 

∑ wind generation * LC /(1+d)t = PV of costs in “20% Wind” case–PV of costs in no new Wind 

Scenario. 

c

 Assumes 11,000 kWh/year average consumption. 

A

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A

The result of this analysis suggests rather modest incremental electricity sector 

costs.15 The direct incremental cost of 20% wind is estimated to be $43 billion in net 

present value terms, increasing electricity rates by only $0.6/MWh on average over 

the 2007–2050 analysis period, and raising average residential monthly electricity 

bills by just $0.5/MWh over that same time period. The average incremental LC 

imposed by each megawatt-hour of wind is estimated at $8.6/MWh. Because WinDS 

considers not just bus-bar energy costs, but also transmission costs and the cost of 

integrating wind’s variable output pattern into electricity grids, the analysis 

presented here suggests that the potential direct costs of achieving 20% wind, 

relative to meeting load with conventional technologies, need not be overwhelming. 

A.5.1 Water Consumption Savings 

In the energy sector, water is used primarily for cooling in steam plants, but it is also 

used in boilers and in air pollution reduction processes. Several technologies are 

used to condense steam (EPRI 2002; Feeley et al. 2005): 

z	 Recirculating steam plant cooling: Water is reused to cool steam 

in a closed loop system using a cooling tower or cooling pond. 

z	 Once-through cooling: Water from a lake, a river, or the ocean is 

used to condense steam and the water is returned to its source but at 

a higher temperature. 

z	 Dry cooling: Air cools steam, using far less water than the first two 

wet cooling technologies. Although dry cooling is not widely used, 

it can be the cooling technology of choice where water supplies are 

limited. 

Two types of water use are generally considered: 

z	 Water withdrawal: Water is removed from the ground or diverted 

from a surface source for use. 

z	 Water consumption: Water is withdrawn from a source but not 

directly returned to the source because it is evaporated, transpired, 

incorporated into products and crops, or consumed by people or 

livestock. 

In this analysis, water consumption projections were made by applying water 

consumption rates (gallons per megawatt-hour generated) to projected megawatthours of generation for each type of power plant. These calculations were made on a 

yearly basis for the no new Wind Scenario and the 20% Wind Scenario. Water 

savings from deploying large amounts of wind-generated electricity are calculated as 

the difference in water consumption between the two scenarios. 

Water consumption rates were developed from several data sources, the most 

important of which are: 

z	 EIA Form 767 for 2002: This database includes water consumption 

rates for each steam power plant, including the steam portion of 

combined cycle plants. Because these data often contain unrealistic 

values for water consumption (e.g., no water consumption or very 

15 These costs reflect the model inputs and could vary significantly with different fossil fuel 

price assumptions, carbon taxes or caps, or additional breakthroughs in renewable 

technologies. 

164	 20% Wind Energy by 2030 – Prepublication Version 
 

large amounts of water consumption per megawatt-hour), 

observations with extremely high and low values have been 

removed before computing average consumption rates for each type 

of power plant (EIA 2002). 

z	 EPRI’s water and sustainability study: This report contains 

typical water consumption values for steam and combined cycle 

power plants (EPRI 2002). 

z	 A Clean Air Task Force/Western Resource Advocates study: 

This report supplements the EPRI estimates with other sources of 

data (Baum et al. 2003). 

Because of the quality and availability of data from these sources, the authors have 

assumed in this study that existing and new power plants have the same water 

consumption rates. Although once-through cooling plants withdraw more water per 

megawatt-hour than recirculating plants, the mix of power plant cooling types (oncethrough and recirculating) was assumed to stay the same over the study period. No 

systematic regional variations in water consumption for coal-fired steam plants were 

found, and the number of realistic observations for the other technologies was too 

small to permit a useful geographic disaggregation. Therefore, only national average 

water consumption rates were used. 

Table A-5 illustrates water consumption rates used in the analysis. Figure A-19 

shows annual water savings resulting from the deployment of wind resources. 

Table A-5. Water consumption rates for power plants 

Generation 

Type 

Coal-Fired Steam 

Water 

Consumption 

Rate: Gallons 

per MWh 

541 

Source 

(see list of references for 

full citation) 

EIA Form 767 for 2002 

Gas-Fired Combined 

Cycle 

180 EPRI; Clean Air Task Force & Western 

Resource Advocates 

Nuclear 609 EIA Form 767 for 2002 

Oil- or Gas-Fired 

Steam 

662 EIA Form 767 for 2002 

Combustion Turbine 0-100 See note below 

Wind 0 Clean Air Task Force & Western 

Resource Advocates 

A

Note: Data on water consumption rates for combustion turbines are sparse. Estimated 

consumption rates range from 0 to about 100 gall/MWh. For example, the U.S. Department 

of Energy Environmental Assessment for the Installation and Operation of Combustion 

Turbine Generators at Los Alamos National Laboratory (December 2002) estimated that 

water use by planned combustions turbines would be 0 (p. 17). A California Energy 

Commission study (2005) indicates that water consumption for combustion turbines is less 

than 100 gal/MWh. We analyzed total water savings, assuming combustion turbine water 

consumption is 0 gal/MWh and 100 gal/MWh and found that the difference in total water 

savings in any year was only 0.3% or less. Therefore, water savings are not sensitive to 

assumptions about water consumption rates for combustion turbines. 

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Figure A-19. Annual water consumption savings due to 

deployment of wind energy 

A

Displacing large amounts of fossil-fueled power generation with wind energy 

reduces water consumption. Based on the authors’ estimates, if the current 

conventional generation mix is expanded to meet electricity needs, approximately 51 

trillion gallons of water will be consumed for electricity production from 2007 to 

2030. If wind energy deployment gradually increases to 20% of the nation’s 

electricity over the same time period, however, 47 trillion gallons of water will be 

consumed. This is a saving of 4 trillion gallons, an 8% reduction in water 

consumption during that time. Of the 4 trillion gallons of water saved nationally, 

29% will be in the West, 41% will be in the Midwest/Great Plains, 14% will be in 

the Northeast, and 16% will be in the Southeast (see Table A-6). Extrapolating the 

savings beyond the 2030 WinDS simulation period to account for the benefit of the 

20-year investment decision to install wind energy results in cumulative water 

consumption savings of 6 trillion gallons by 2050. 

Table A-6. U.S. states by region 

Region States 

West Alaska, Arizona, California, Colorado, Hawaii, Idaho, 

Montana, Nevada, New Mexico, Oregon, Washington, 

Wyoming, Utah 

Midwest/Great Plains Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, 

Missouri, Nebraska, North Dakota, Ohio, Oklahoma, South 

Dakota, Texas, Wisconsin 

Northeast Connecticut, Delaware, Maine, Maryland, Massachusetts, 

New Hampshire, New Jersey, New York, Pennsylvania, 

Rhode Island, Vermont 

Southeast Alabama, Arkansas, Florida, Georgia, Louisiana, 

Kentucky, North Carolina, South Carolina, Tennessee, 

Mississippi, Virginia, West Virginia 

A.6 Other Effects 

Appendix C describes the jobs and economic impacts directly associated with the 

manufacturing, construction, and operational sectors of the wind industry. 

Other benefits associated with wind energy include an improved environment and 

better health resulting from reduced particulate or other chemical emissions such as 

166 20% Wind Energy by 2030 – Prepublication Version 
 

acid rain or mercury, and market benefits including diversification of the electricity 

sector. These benefits, and others, have not been quantified in this study. 

A.7 References & Suggested Further Reading 

Awerbuch, S. 1993. “The Surprising Role of Risk in Utility Integrated Resource 

Planning.” The Electricity Journal 6(3): 20–33. 

Awerbuch, S. 2003. “Determining the Real Cost: Why Renewable Power is More 

Cost-Competitive than Previously Believed.” Renewable Energy World 

6(2), March-April 2003. 

Baum, E., J. Chaisson, B. Miller, J. Nielsen, M. Decker, D. Berry, and C. Putnam. 

2003. The Last Straw: Water Use by Power Plants in the Arid West. 

Boulder, CO and Boston, MA: The Land and Water Fund of the Rockies 

and Clean Air Task Force. 

http://www.catf.us/publications/reports/The_Last_Straw.pdf. 

Black & Veatch Corporation. 2007. Wind Supply Curves. September. Overland, KS: 

Black & Veatch. 

Bokenkamp, K., H. LaFlash, V. Singh, and D.B. Wang. 2005. “Hedging Carbon 

Risk: Protecting Customers and Shareholders from the Financial Risk 

Associated with Carbon Dioxide Emissions.” The Electricity Journal 18(6): 

11–24. 

Bolinger, M., and R. Wiser. 2005. Balancing Cost and Risk: The Treatment of 

Renewable Energy in Western Utility Resource Plans. LBNL-58450. 

Berkeley, CA: Lawrence Berkeley National Laboratory (Berkeley Lab). 

Bolinger, M., and R. Wiser. 2006. Comparison of AEO 2007 Natural Gas Price 

Forecast to NYMEX Futures Prices. LBNL-62056. Berkeley, CA: Berkeley 

Lab.. 

Bolinger, M., R. Wiser, and W. Golove. 2006. “Accounting for Fuel Price Risk 

When Comparing Renewable to Gas-Fired Generation: The Role of Forward 

Natural Gas Prices.” Energy Policy 34(6): 706–720. 

CEC (California Energy Commission). 2005. Page 43 in A Preliminary 

Environmental Profile of California’s Imported Electricity. CEC-700-2005­

017. Sacramento: CEC. 

http://www.energy.ca.gov/2005_energypolicy/documents/2005-06­

27+28_workshop/presentations/2005-06­

27_CECSTAFF+ASPEN_PRESENTATION.PDF 

Cavanagh, R., A. Gupta, D. Lashof, and M. Tatsutani. 1993. “Utilities and CO2 

Emissions: Who Bears the Risks of Future Regulation?” The Electricity 

Journal, 6(2): 64–75. 

Denholm, P., and W. Short. 2006. Documentation of WinDS Base Case. Version 

AEO 2006 (1). Golden, CO: National Renewable Energy Laboratory 

(NREL). http://www.nrel.gov/analysis/winds/pdfs/winds_data.pdf. 

DOE (U.S. Department of Energy), NNSA (National Nuclear Security 

Administration), and Los Alamos Site Office (US). 2002. Environmental 

Assessment for the Installation and Operation of Combustion Turbine 

Generators at Los Alamos National Laboratory. DOE/EA-1430. Los 

Alamos, NM: DOE, NNSA, and Los Alamos Site Office. 

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EIA (Energy Information Administration). 2002. A Steam-Electric Plant Operation 

and Design Report, Schedule 6, Cooling System Information. Form EIA767. Washington, DC: EIA. 

http://www.eia.doe.gov/cneaf/electricity/forms/eia767/eia767.pdf 

EIA (Energy Information Administration). 2007. Annual Energy Outlook 2007 with 

Projections to 2030. Washington, DC: EIA. Report No. DOE/EIA-0383. 

http://www.eia.doe.gov/oiaf/archive/aeo07/index.html. 

EPRI (Electric Power Research Institute). 2002. Water and Sustainability (Volume 

3): U.S. Water Consumption for Power Production – The Next Half 

Century. Report 1006786. Prepared by Bevilacqua-Knight, Inc. Palo Alto, 

CA: EPRI. 

Feeley, T. III, L. Green, J. Murphy, J. Hoffman, and B. Carney. 2005. Department of 

Energy/Office of Fossil Energy’s Power Plant Water Management R&D 

Program. Washington, DC: U.S. Department of Energy (DOE), National 

Energy Technology Laboratory (NETL), Science Applications International 

Corporation (SAIC). 

Hoff, T.E. 1997. Integrating Renewable Energy Technologies in the Electric Supply 

Industry: A Risk Management Approach. NREL/SR-520-23089. Golden, 

CO: NREL. Hutson, S., N. Barber, J. Kenny, K. Linsey, D. Lumia, and M. 

Maupin. 2004. Estimated Use of Water in the United States in 2000. 

Circular 1268. Denver, CO: U.S. Geological Survey. 

http://pubs.usgs.gov/circ/2004/circ1268/. 

Johnston, L., E. Hausman, A. Sommer, B. Biewald, T. Woolf, D. Schlissel, A. 

Roschelle, and D. White. 2006. Climate Change and Power: Carbon 

Dioxide Emissions Costs and Electricity Resource Planning. Cambridge, 

MA: Synapse Energy Economics, Inc. 

Laxson, A., M. Hand, and N. Blair. 2006. High Wind Penetration Impact on U.S. 

Wind Manufacturing Capacity and Critical Resources. NREL/TP-500­

40482. Golden, CO: NREL. http://www.nrel.gov/docs/fy07osti/40482.pdf. 

NREL. 2006. Projected Benefits of Federal Energy Efficiency and Renewable 

Energy Programs-FY2007 Budget Request. NREL/TP-320-39684. Golden, 

Colorado: NREL. http://www1.eere.energy.gov/ba/pdfs/39684_00.pdf. 

Rabe, B. 2002. Greenhouse & Statehouse: The Evolving State Government Role in 

Climate Change. Arlington, Virginia: Pew Center on Global Climate 

Change. http://www.pewclimate.org/docUploads/states_greenhouse.pdf. 

Repetto, R., and J. Henderson. 2003. Environmental Exposures in the US Electric 

Utility Industry. New Haven, CT: Yale School of Forestry & Environmental 

Studies. 

http://environment.yale.edu/doc/969/environmental_exposures_in_the_us_el 

ectric/. 

Wiser, R., and M. Bolinger. 2004. An Overview of Alternative Fossil Fuel Price and 

Carbon Regulation Scenarios. LBNL-56403. Berkeley, CA: Berkeley Lab. 

eetd.lbl.gov/EA/EMP/reports/56403.pdf. 

Wiser, R., M. Bolinger, and M. St. Clair. 2005. Easing the Natural Gas Crisis: 

Reducing Natural Gas Prices through Increased Deployment of Renewable 

Energy and Energy Efficiency. LBNL-56756. Berkeley, CA: Berkeley Lab. 

http://www.lbl.gov/ScienceArticles/Archive/sabl/2005/February/assets/Natural-Gas.pdf 

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Appendix B. Assumptions Used 

for Wind Deployment 

System Model 

To define the 20% Wind Scenario, a number of modifications were made to the 

National Renewable Energy Laboratory’s (NREL) Wind Deployment System 

(WinDS) base case assumptions (which are set forth in the WinDS documentation; 

see Denholm and Short 2006). These changes include updating wind resource maps, 

accounting for seasonal and diurnal capacity factor (CF) variations, and including 

offshore wind resources from South Carolina to Texas. Black & Veatch developed 

the wind and conventional generation technology cost and performance projections 

in consultation with American Wind Energy Association (AWEA) industry experts. 

The assumptions about the large regional planning and operation structure of the 

transmission system were developed through collaboration with the experts who 

contributed to Chapter 4. The financial assumptions and the region definitions are 

unchanged from the WinDS base case. This appendix outlines the assumptions used 

in constructing the 20% Wind Scenario. 

B.1 Financial Parameters 

WinDS optimizes the electric power system “build” based on projected life-cycle 

costs, which include capital costs and cumulative discounted operating costs over a 

fixed evaluation period. The “overnight” capital costs supplied as inputs to the 

model are adjusted to reflect the actual total cost of construction, including tax 

effects, interest during construction, and financing mechanisms. Table B-1 

summarizes the financial values used to produce net capital and operating costs. 

These assumptions are unchanged from the WinDS base case (Denholm and Short 

2006) and are designed to correspond to assumptions made by the Energy 

Information Administration (EIA) in the Annual Energy Outlook 2007 with 

Projections to 2030 (shortened in this appendix to AEO 2007 or simply AEO; EIA 

2007a). 

Table B-1. Baseline financial assumptions 

B

Name Value Notes and Source 

Inflation Rate 3% Based on recent historical inflation rates 

Real Discount Rate 8.5% Equivalent to weighted cost of capital. Based on EIA 

assumptions (EIA 2006) 

Marginal Income Tax Rate 40% Combined federal/state corporate income tax rates 

Evaluation Period 20 Years Base Case Assumption 

Depreciation Schedule 

Conventional

 Wind 

15 Year 

5 Year 

MACRS (Modified Accelerated Cost Recovery Schedule) 

MACRS (Modified Accelerated Cost Recovery Schedule) 

Nominal Interest Rate during 

Construction 

10% Base Case Assumption 

Dollar Year 2004 All costs are expressed in year 2004 dollars. 

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B.2 Power System Characteristics 

B.2.1 WinDS Regions 

Four types of regions are included in the WinDS model (see Figure B-1): 

z	 Interconnect regions: There are three major interconnects in the 

United States (all are electrically isolated): the Eastern Interconnect, 

Western Interconnect, and the ERCOT (Electric Reliability Council 

of Texas) Interconnect. 

z	 National Electric Reliability Council (NERC)16 subregions: 

WinDS uses 13 NERC subregions, which are listed in Table B-2. 

z	 Balancing areas: WinDS uses 136 balancing areas. 

z	 Wind resource regions: There are 358 wind resource regions in 

WinDS. 

Interconnect regions, NERC regions, and balancing areas are defined and operated 

by various regulatory agencies. 

Figure B-1. WinDS regions 

B

16For more information on NERC, see http://www.nerc.com/regional/. 

170	 20% Wind Energy by 2030 – Prepublication Version 
 

Table B-2. NERC regions used in WinDS 

NERC Region/ 

Subregion 

Abbreviation Region 

Name 

East Central Area Reliability 

Coordination Agreement 

1 ECAR 

2 ERCOT Electric Reliability Council of Texas 

3 MAAC Mid-Atlantic Area Council 

4 MAIN Mid-America Interconnected 

Network 

5 MAPP Mid-Continent Area Power Pool 

6 NY New York 

7 NE New England 

8 FRCC Florida Reliability Coordinating 

Council 

9 SERC Southeast Reliability Council 

10 SPP Southwest Power Pool 

11 NWP Northwest 

12 RA Rocky Mountain Area 

13 CNV California/Nevada 

Note: NERC regions in WinDS are based on the pre-2006 regional definitions 

defined by the EIA (2000). In January 2006, NERC regions were redefined;, 

however, the EIA has not incorporated these changes through publication of AEO 

2007; therefore, WinDS will continue to use pre-2006 definitions until the EIA 

modifies its data. Similarly, some of the recent changes to balancing area 

boundaries (now referred to as balancing authorities) are not yet reflected in 

WinDS (e.g. the formation of the Texas Regional Transmission Organization). 

Wind resource regions were created specifically for the WinDS model. These 

regions were selected using the following rules and criteria: 

z Incorporate buildup from counties (so the electricity load can be 

determined for each wind supply/demand region based on county 

population) 

z Avoid crossing state boundaries (so that state-level policies can be 

modeled) 

z Conform to balancing areas as much as possible (to better capture 

the competition between wind and other generators) 

z Separate major windy areas from load centers (so that the distance 

from a wind resource to a load center can be well approximated) 

z Conform to NERC region/subregion boundaries (so that the results 

are appropriate for use by integrating models that use the NERC 

regions and subregions). 

Figure B-2 illustrates all wind regions and balancing areas in the United States. 

B

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Figure B-2. Wind region and Balancing Areas in WinDS base case 

B

Several components of the WinDS model necessitate using four levels of geographic 

resolution. For example, electricity demand is modeled at the NERC region level 

and wind-generator performance is modeled at the wind-resource region level. 

B.2.2 Electric System Loads 

Loads are defined by region and by time. WinDS meets the energy and power 

requirements for each of 136 balancing areas. Energy is met for each balancing area 

in each of 16 time slices, and within each year modeled by WinDS. Table B-3 

defines these time slices. 

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Table B-3. WinDS demand time-slice definitions 

Slice 

Name 

Number of 

Hours Per 

Year 

Season Time Period 

H1 1,152 Summer Weekends, plus 11:00 p.m. to 6:00 a.m. 

weekdays 

H2 462 Summer Weekdays, 7:00 a.m. to 1:00 p.m. 

H3 264 Summer Weekdays, 2:00 p.m. to 5:00 p.m. 

H4 330 Summer Weekdays, 6:00 p.m. to 10:00 p.m. 

H5 792 Fall Weekends, plus 11:00 p.m. to 6:00 a.m. 

weekdays 

H6 315 Fall Weekdays, 7:00 a.m. to 1:00 p.m. 

H7 180 Fall Weekdays, 2:00 p.m. to 5:00 p.m. 

H8 225 Fall Weekdays, 6:00 p.m. to 10:00 p.m. 

H9 1,496 Winter Weekends, plus 11:00 p.m. to 6:00 a.m. 

weekdays 

H10 595 Winter Weekdays, 7:00 a.m. to 1:00 p.m. 

H11 340 Winter Weekdays, 2:00 p.m. to 5:00 p.m. 

H12 425 Winter Weekdays, 6:00 p.m. to 10:00 p.m. 

H13 1,144 Spring Weekends, plus 11:00 p.m. to 6:00 a.m. 

weekdays 

H14 455 Spring Weekdays, 7:00 a.m. to 1:00 p.m. 

H15 260 Spring Weekdays, 2:00 p.m. to 5:00 p.m. 

H16 325 Spring Weekdays, 6:00 p.m. 10:00 p.m. 

The electricity load in 2000 for each balancing area and time slice is derived from an 

RDI/Platts database (Platts Energy Market Data; see 

http://www.platts.com/Analytic%20Solutions/BaseCase/). Figure B-3 illustrates the 

WinDS load duration curve (LDC) for the entire United States for the base year, 

showing the 16 load time slices. As a reference, the actual U.S. coincident LDC— 

also derived from the Platts database—is depicted in the figure as well. The 

Figure B-3. National load duration curve for base year in WinDS 

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B

aggregated data for the United States that are shown in Figure B-3 are not used 

directly in WinDS, as the energy requirement is met in each balancing area. This 

curve does, however, give a general idea of the WinDS energy requirement. The 

LDC does not include the “super peak,” which occurs in most systems for a few 

hours per year. These peak requirements are discussed in Section B.2.4. 

B.2.3 Growth Rate 

Load growth is defined at the NERC region level. Loads in all balancing areas 

within each NERC region are assumed to grow at the same rate to 2050. Table B-4 

contains the 2000 load and annual growth rates for each NERC region 

Table B-4. Base load and load growth in the 

WinDS scenario
 

NERC 

Region/SubRegion 

Abbreviation 2000 Load 

TWh/year 

Annual Load 

Growth 

1 ECAR 370 1.010 

2 ERCOT 205 1.016 

3 MAAC 197 1.009 

4 MAIN 184 1.010 

5 MAPP 110 1.011 

6 NY 109 1.006 

7 NE 96 1.010 

8 FL 141 1.022 

9 SERC 589 1.015 

10 SPP 132 1.013 

11 NWP 176 1.017 

12 RA 97 1.022 

13 CNV 202 1.017 

Source: EIA (2007b) 

WinDS assumes that the growth rate in each time slice is also constant (i.e., the load 

shape remains the same over time). 

B.2.4 Capacity Requirements 

In each balancing area, WinDS requires that firm capacity be available to meet the 

demand in each time slice (see the national example of time-slice demand in 

Figure B-3). In addition, for every NERC and interconnect region, WinDS requires 

sufficient capacity to meet the peak instantaneous demand throughout the course of 

the year, plus a peak reserve margin. The instantaneous annual peak load is higher 

than the load in each of the 16 time slices, because the load in each time slice is the 

average load over the hours included in that time slice. The reserve margin 

requirement can be met by any generator type, although the generator must have the 

appropriate capacity value. In the case of wind power, the actual capacity value is a 

minority fraction of the nameplate capacity. Section B.6 discusses the treatment of 

resource variability within the model. 

Although these capacity requirements are implemented regionally, Table B-5 

illustrates their national impact. 

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Table B-5. National capacity requirements in the WinDS base case 

Capacity Requirement Total (GW) Annual 

Growth Rate 

% 

1.6 

2000 2050 

Average load in the summer peak time slice 571 1,249 

Annual peak instantaneous load 702 1,531 1.6 

Peak capacity value (not nameplate) to 

meet reserve margin 

875 1,730 1.4 

Table B-6 gives the peak reserve margin for each region. Reserve margin is ramped 

from its initial value in 2000 to the 2010 requirement, and maintained thereafter. It is 

assumed that energy growth and peak demand grow at the same rate, and that the 

load shape stays constant from one period to the next. 

Table B-6. Peak reserve margin 

NERC Region Abbreviation 2010 Required 

Reserve Margin 

1 ECAR 0.12 

2 ERCOT 0.15 

3 MAAC 0.15 

4 MAIN 0.12 

5 MAPP 0.12 

6 NY 0.18 

7 NE 0.15 

8 FL 0.15 

9 SERC 0.13 

10 SPP 0.12 

11 NWP 0.08 

12 RA 0.14 

13 CNV 0.13 

Source: PA Consulting Group (2004) 

B.3 Wind 

B.3.1 Wind Resource Definition 

Table B-7 defines wind power classes. 

Table B-7. Classes of wind power density 

Wind Power 

Class 

3 

Wind Power 

Density, W/m2 

300–400 

Speed 

m/s 

6.4–7.0 

4 400–500 7.0–7.5 

5 500–600 7.5–8.0 

6 600–800 8.0–8.8 

7 >800 >8.8 

B

Notes: W/m2 = watts per square meter; m/s = meters per second. Wind speed 

measured at 50 m above ground level. 

Source: Elliott and Schwartz (1993) 

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B

Wind power density and speed are not explicitly calculated in WinDS. Different 

classes of wind power are identified by resource level, capacity factor, turbine cost, 

and so forth, which are discussed in the subsections that follow. 

B.3.2 Wind Resource Data 

The basic wind resource input for the WinDS model is the amount of available 

windy land area (in square kilometers [km2

]) by wind power class (Class 3 and 

higher). The amount of available windy land is derived from state wind resource 

maps and modified for environmental and land use exclusions (as outlined in 

Tables B-8 and B-9). These are the most recent state wind maps available from the 

Wind Powering America (WPA; see 

http://www.eere.energy.gov/windandhydro/windpoweringamerica/) initiative and 

individual state programs. The maps depict estimates of the wind resource at 50 m 

above the ground. 

The WinDS base case (Denholm and Short 2006) used only two data sources, the 

WPA maps validated by NREL and the Wind Energy Resource Atlas of the United 

States (PNL 1987). For this report, however, the WinDS model uses recent wind 

maps from individual state programs where available (instead of maps from the 

1987 PNL atlas) and new WPA state maps. 

Using the recent maps offers an advantage in that modern mapping techniques and 

recent measurement data are incorporated into the mapping process, resulting in a 

finer horizontal resolution (1 km or smaller size grid cells) of the wind resource. The 

disadvantage is that not all updated maps were created using the same technique. 

The difference in techniques leads to a “patchwork quilt” pattern in some regions 

where notable resource discontinuities at state borders result from the different 

mapping techniques. For this project, several 50-m state maps were adjusted to 

produce more interstate compatibility. Table B-8 summarizes the state sources and 

land use exclusions for the land-based wind resource data used in WinDS, and 

Table B-9 presents the same information for offshore wind. 

Most state maps were completed with direct support from WPA and cost-sharing 

from individual states and regional partners. Under the WPA initiative, state wind 

resource maps were produced as described here. The preliminary resource map was 

produced by AWS Truewind (AWST; Albany, New York). NREL validated this 

map in cooperation with private consultants who had access to proprietary data, 

special data and knowledge of wind resources in each state, or both. The validation 

results were used to modify the preliminary map and to create a final wind map. 

NREL mapped three states—Illinois, North Dakota, and South Dakota—before 

AWST became involved. An important difference between the NREL and AWST 

maps is that the NREL mapping technique assumed low surface roughness 

(equivalent to short grasslands); AWST used digital land cover data sets for surface 

roughness values. Increases in surface roughness generally cause decreases in the 

50-m wind resource, so the NREL maps might overestimate the wind resource in 

areas that do not have low surface roughness. The 50-m wind power classes for 

individual grid cells on the WPA maps were used to determine available windy land 

for the WinDS model. 

Individual state programs have updated other (non-WPA) maps, which were created 

using a variety of mapping techniques. NREL has not, however, validated these 

176 20% Wind Energy by 2030 – Prepublication Version 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table B-8. Data sources for land-based wind resource and 

environmental exclusions 

Onshore Wind Resource Data Used in WinDS (10/23/2006) 

Resource Data (50 m height): 

State Data Source* State Data Source* State Data Source* 

Arizona Ohioa 2004, N/AWST 

Alabama 

2003, N/AWST Maine 2002, N/AWST 

Oklahomaa 2002, OTH 

Arkansas 

1987, PNL Maryland 2003, N/AWST 

2006, N/AWST** Massachusetts 2002, N/AWST Oregon 2002, N/AWST 

California Michigana Pennsylvaniaa 2003, N/AWST 2005, N/AWST 2003, N/AWST 

Colorado 2003, N/AWST Minnesota 2006, OTH Rhode Island 2002, N/AWST 

Connecticut 2002, N/AWST Mississippi 1987, PNL South Carolina 2005, AWST 

Delaware Missouria 2003, N/AWST 2004, N/AWST South Dakota 2000 NREL 

Florida 1987, PNL Montana 2002, N/AWST Tennessee 1987, PNL 

Georgia Nebraskaa 2006, AWST 2005, N/AWST Texas 2004, OTH/2000, NREL 

Idaho 2002, N/AWST Nevada 2003, N/AWST Utah 2003, N/AWST 

Illinois 2001, NREL New Hampshire 2002, N/AWST Vermont 2002, N/AWST 

Indianaa 2004, N/AWST New Jersey 2003, N/AWST Virginia 2003, N/AWST 

Iowa 1997, OTH New Mexico 2003, N/AWST Washington 2002, N/AWST 

Kansas New Yorka 2004, OTH 2004, AWST West Virginia 2003, N/AWST 

Kentucky 1987, PNL North Carolina 2003, N/AWST Wisconsin 2003, OTH 

Louisiana 1987, PNL North Dakota 2000 NREL Wyoming 2002, N/AWST 

* YrSource 

Yr = Year produced (1987 to present); Source = PNL, NREL, N/AWST (NREL with AWS TrueWind), AWST (AWS TrueWind alone 

not validated by NREL) or OTH (data from other sources) 

PNL data resolution is 1/4 degree of latitude by 1/3 degree of longitude, each cell has a terrain exposure percent (5% for

 ridgecrest to 90% for plains) to define base resource area in each cell. Ridgecrest areas have 10% of the area assigned to

 the next higher power class. 

NREL data was generated with the WRAMS model, and does not account for surface roughness. Resolution is 1 km. 

Texas includes the Texas mesas study area updated by NREL using WRAMS. 

N/AWST data was generated by AWS TrueWind and validated by NREL. Resolution is 400 m for the northwest states (WA, OR, 

ID, MT, and WY) and 200 m everywhere else. These data consider surface roughness in their estimates. 

N/AWST** data was generated by AWS TrueWind, and will be validated by NREL. Data used is preliminary. 

OTH data from other sources. The methods, resolution, and assumptions vary. These results have not been validated by NREL 

For most states, the data was taken at face value. However, some datasets were not available as 50 m power density. In those 

cases, assumptions were made to adjust the data to 50 m power density. a In these states, the class 2, 3 and 4 wind power class estimates were adjusted upwards by 1/2 power class to better represent the

 likely wind resource at wind turbine height. For Nebraska, only the portion of the state east of 102 degrees longitude was adjusted. 

Wind Resource Onshore Exclusions (last revised Jan 2004) 

Criteria for Defining Available Windy Land (numbered in the order they are applied): 

Environmental Criteria Data/Comments: 

USGS Federal and Indian Lands shapefile, Jan 2005 

Service managed lands 

3) 100% exclusion of federal lands designated as park, 

2) 100% exclusion of National Park Service and Fish and Wildlife 

USGS Federal and Indian Lands shapefile, Jan 2005 

wilderness, wilderness study area, national monument, national 

battlefield, recreation area, national conservation area, wildlife 

refuge, wildlife area, wild and scenic river or inventoried roadless 

area. 

4) 100% exclusion of state and private lands equivalent to criteria State/GAP land stewardship data management status 1, from 

2 and 3, where GIS data is available. Conservation Biology Institute Protected Lands database, 2004 

8) 50% exclusion of remaining USDA Forest Service (FS) lands USGS Federal and Indian Lands shapefile, Jan 2005 

(incl. National Grasslands)*** 

9) 50% exclusion of remaining Dept. of Defense lands*** USGS Federal and Indian Lands shapefile, Jan 2005 

10) 50% exclusion of state forest land, where GIS data is State/GAP land stewardship data management status 2, from 

available*** Conservation Biology Institute Protected Lands database, 2004 

Land Use Criteria 

5) 100% exclusion of airfields, urban, wetland and water areas. USGS North America Land Use Land Cover (LULC), version 2.0, 1993; 

ESRI airports and airfields (2003) 

11) 50% exclusion of non-ridgecrest forest*** Ridge-crest areas defined using a terrain definition script, overlaid with 

USGS LULC data screened for the forest categories. 

Other Criteria 

Derived from elevation data used in the wind resource model. 

6) 100% exclude 3 km surrounding criteria 2-5 (except water) 

1) Exclude areas of slope > 20% 

Merged datasets and buffer 3 km 

7) Exclude resource areas that do not meet a density of 5 km2

 of Focalsum function of class 3+ areas (not applied to 1987 PNL resource 

data) class 3 or better resource within the surrounding 100 km2

 area. 

***50% exclusions are not cumulative. If an area is non-ridgecrest forest on FS land, it is just excluded at the 50% level one time. 

B

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Table B-9. Data sources for the offshore wind resource and 

environmental exclusions 

Offshore Wind Resource Data Used in WinDS (10/23/2006) 

Resource Data (50 m height): 

State Data Source* State Data Source* State Data Source* 

Alabama 2006, NREL3 Maine 2002, NREL1 North Carolina 2003, NREL1 

California 2003, NREL1 Maryland 2003, NREL1 Ohio 2006, NREL2 

Connecticut 2002, NREL1 Massachusetts 2003, NREL1 Oregon 2002, NREL1 

Delaware 2003, NREL1 Michigan 2006, NREL2 Pennsylvania 2006, NREL2 

Florida 2006, NREL3 Minnesota 2006, NREL2 Rhode Island 2002, NREL1 

Georgia 2006, NREL3 Mississippi 2006, NREL3 South Carolina 2006, NREL3 

Illinois 2006, NREL2 New Hampshire 2002, NREL1 Texas 2006, NREL3 

Indiana 2006, NREL2 New Jersey 2003, NREL1 Virginia 2003, NREL1 

Louisiana 2006, NREL3 New York 2003, NREL1 Washington 2002, NREL1 

* YrSource 

Wisconsin 2006, NREL2 

Yr = Year produced (2002 to present); Source = NREL with different methods enumerated below 

NREL1: Validated near-shore data was supplemented with offshore resource data from earlier, preliminary runs which extended

 further from shore. In most cases, this still did not fill the modeling area of interest of 50 nm from shore. The resource estimates

 were extended linearly to obtain full coverage at 50 nm with little or no change in spatial pattern. 

NREL2: Similar to NREL1, but available resource data estimates and areas not covered by validated and preliminary data were

 evaluated by NREL meteorologist to establish a best estimate of resource distribution based on expert knowledge and available

 measured/modeled data sources. 

NREL3: No validated resource estimates existed to provide a baseline. NREL meteorologists generated an initial best estimate

 of resource distribution to be used in the model, based on expert knowledge and available measured/modeled data sources. 

Wind Resource Offshore Exclusions 

No exclusions were applied to the offshore resource data. It is 

characterized by power class and depth (0-30 m and >30m) 

B

maps, which do not necessarily show the 50-m wind power classes on the maps or 

the 50-m classes in geographic information system (GIS) format. For two states 

(Minnesota and Wisconsin) where the 50-m power classes for individual grid cells 

were unavailable, a methodology that applies basic assumptions to calculate wind 

power classes for each grid cell was used. This methodology calculates a 

combination of wind speed at the grid cells (direct or interpolated), extrapolates to 

adjust the wind speeds from map height(s) to 50 m, plots common wind speed 

frequency distribution, and takes air density into consideration. Next, environmental 

and land use exclusions were applied to arrive at the final windy land area totals. 

Updated wind resource maps were unavailable for six southeastern states— 

Alabama, Florida, Kentucky, Louisiana, Mississippi, and Tennessee. The underlying 

50-m wind power class data from the maps contained in the 1987 atlas (PNL 1987) 

were used to calculate windy land area for these states. The horizontal resolution of 

the atlas maps is quite a bit larger (approximately 25-km grid cells) than that of the 

updated state maps, which feature 1-km or smaller grid cells. Consequently, 

landform classifications and environmental and land use exclusions were used to 

calculate the available windy land for these states. 

As mentioned previously, several state maps were adjusted to produce more 

interstate compatibility. The Texas map was adjusted to include wind resources 

currently being developed on the mesas in western Texas. Because the mesas are 

relatively small terrain features, adequately depicting the available resources on 

these features is difficult. As a result, the Texas map underestimates the power class 

on the mesas where considerable wind energy development has taken place. In 

adjusting the maps, the power class values for the mesas were increased based on 

anemometer measurements, leading to a realistic representation of the mesas in the 

Texas map used for the WinDS model. The maps for eight states—Oklahoma, 

178 20% Wind Energy by 2030 – Prepublication Version 
 

 

Missouri, Nebraska (the eastern two-thirds of the state), Indiana, Michigan, Ohio, 

Pennsylvania, and New York—were adjusted because their 50-m wind power class 

maps underestimate the potential resource at modern turbine hub heights. The 

available resource increase results from the high wind speed shear that is present in 

the affected states. The available windy land in these states was increased based on 

the wind power density values of individual grid cells. Grid cells in classes 2, 3, and 

4 that had 50-m power density values greater than the midpoint of the associated 

wind power class were adjusted to the next highest class. The net result of these 

adjustments was to increase the windy land for classes 3, 4, and 5 in the eight states. 

For each of the 358 WinDS regions, the total available land area corresponding to a 

particular wind resource power class was multiplied by an assumed turbine density 

of 5 megawatts per square kilometer (MW/km2

). This calculation yields the total 

wind-generation capacity available within each WinDS region for each wind power 

class. 

The patchwork quilt effect that results from the varied resource input data affects the 

selection of wind energy capacity in the WinDS model. If a state’s resource is 

underestimated, the WinDS model may select less wind energy capacity than is 

currently being developed in a given state. Similarly, if a state’s resource is 

overestimated, the actual wind energy capacity could be significantly less than that 

calculated by the model. 

All these resource maps were based on wind power estimates at 50 m above ground 

level. Today’s wind turbines, however, have hub heights as high as 80 m to 100 m. 

As turbine technology improves and hub heights increase, wind resources could be 

significantly different. Many states that show poor wind capability for electricity 

generation at the 50-m level may have significantly improved wind speeds at heights 

of 80 m to 100 m. As an example, even though Missouri is currently developing 

several hundred megawatts of wind energy, WinDS does not specify significant 

wind energy capacity for the state. 

B.3.3 WinDS Seasonal and Diurnal Capacity Factor Calculations 

For each region and wind power class (classes 3 to 7), 16 time slices represent four 

seasons and four time periods (see Table B-3). The diurnal and seasonal variations 

of the wind are portrayed as the ratio of the average wind turbine output during the 

time slice with the annual average wind turbine output. Average capacity factors are 

calculated for each of the 358 WinDS regions for each power class. 

Monthly and hourly wind variations were obtained from two databases: 

z AWST text supplemental database files 

z National Commission on Energy Policy/National Center for 

Atmospheric Research (NCEP/NCAR) global reanalysis mean 

values (Kalnay et al. 1996). 

For states with AWST data, annual and monthly average wind speeds and power 

were selected from the fine map grid (400-m resolution in Washington, Oregon, 

Idaho, Montana, and Wyoming; 200-m resolution in all other states), and hourly 

wind speed profiles by season from the coarse model grid (10 km in Washington, 

Oregon, Idaho, Montana, and Wyoming; 2 km in all other states). States with AWST 

data are identified in Table B-8. 

B

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B

For monthly input data, only one 3 × 3-km cell for each region and power class was 

used. This cell was chosen because it has the lowest cost based on the existing grid 

usage optimization that is normally done as an input to WinDS (Sabeff et al. 2004). 

The resulting monthly pattern is the average of the monthly values within the 3 × 3­

km cell for all map points in the desired power class (plus or minus one class). For 

hourly input data, the closest grid point from the coarse grid for each 3 × 3 cell was 

used. The hourly pattern is the average of hourly values for up to twenty 3 × 3 cells 

for each region/power class combination. There are four patterns, one for each 

season. Seasons are three-month periods (March–May, June–August, September– 

November, and December–February). 

For states without AWST data and for certain offshore regions, NCEP/NCAR 

reanalysis data were used. Reanalysis uses a dynamic data assimilation model to 

create worldwide data sets of wind, temperature, and other variables on a 208-km 

resolution grid, four times daily, throughout the depth of the atmosphere. Average 

values of wind speed, wind power, and air density were used, by month and by day 

(four times daily), over a 46-year period of record. Reanalysis wind characteristics 

from 120 m above ground level have been found to have the best correlation with 

measured wind data and wind maps. Reanalysis data, though, are suitable for use 

only over fairly level terrain at lower elevations. Fortunately, AWST data are 

available for most states that are not suitable for reanalysis. 

For regions that use reanalysis, the reanalysis grid point closest to the geographic 

center of the region was chosen. For some offshore locations, the center of the 

offshore region was computed and the closest reanalysis grid point was used. 

Using the AWST and NCEP/NCAR databases, input data sources were used to 

populate matrices of average wind speed, wind power, and air density by month and 

hour of day (24 hours × 12 months). The 24 × 12 array of wind speed, wind power, 

and air density was then divided into desired seasonal and diurnal time slices (see 

Table B-3). For each time slice, the power output of the General Electric 

International (GE) 1.5-MW wind turbine as a function of air density was estimated, 

and a histogram of wind speed probability as a function of wind speed and Weibull k 

factor was calculated. 

These data were then combined to calculate wind turbine capacity factor for each 

time slice. In the AWST data, wind power is available only by month, so the 

Weibull k factor was calculated only once for each season. All times of day use the 

same Weibull k for calculating capacity factor. Finally, a weighted average of 

capacity factors from the four time slices was used to revise nighttime values into a 

“nights and weekends” capacity factor. Time-slice capacity factors were then 

normalized by the total annual capacity factor, resulting in values representing the 

ratio of power produced in the current time slice to annual average power produced. 

This is the desired input into the WinDS model. 

This process creates a desired array of capacity factor ratios only for regions and 

wind power classes with data. With reanalysis, each region has data from only one 

power class. A final data processing step is to populate the entire array of 358 

regions × 5 power classes with results. If a power class is missing, data from the 

next lower power class are chosen. If there are no available data from a lower power 

class, the next higher power class is chosen. For reanalysis regions, all five power 

classes are given the same array of capacity factor ratios. 

180 20% Wind Energy by 2030 – Prepublication Version 
 

 

B.3.4 Wind Technology Cost and Performance 

Black & Veatch analysts developed wind technology cost and performance 

projections for this report in consultation with AWEA industry experts (Black and 

Veatch, forthcoming 2008). Costs for turbines, towers, foundations, installation, 

profit, and interconnection fees are included. Capital costs are based on an average 

installed capital cost of $1,775 per kilowatt (kW) in 2007, which reduces to 

$1,650/kW in 2006 after adjusting for inflation and removing the construction 

financing charge. Additional costs reflecting terrain slope and regional population 

density are described later in this subsection. 

Capital costs are projected to decrease by 10% in the future as a result of technology 

development. Black & Veatch used historical capacity factor data to create a 

logarithmic best-fit line, which is then applied to each wind power class to project 

future performance improvements.17 Variable and fixed operations and maintenance 

(O&M) costs represent an average of recent project costs according to Black & 

Veatch’s experience. Approximately 50% of variable O&M cost is the turbine 

warranty. These costs are expected to decline as turbine reliability improves and the 

scale of wind turbines increases. Other variable O&M expenses are tied to labor 

rates, royalties, and other costs that are expected to be stable. Fixed O&M costs, 

including insurance, property taxes, site maintenance, and legal fees, are projected to 

stay the same because they are not affected by technology improvements. 

Table B-10 lists cost and performance projections for land-based wind systems 

(Black and Veatch, forthcoming 2008). 

Table B-10. Land-based wind technology cost and performance projections 

(US$2006) 

Wind Resource 

Power Class at 

50 m 

3 

Year 

Installed 

2005 

Capacity 

Factor 

(%) 

32 

Cost 

($/kW) 

1,650 

Fixed O&M 

($/kW-yr) 

11.5 

Variable 

O&M 

($/MWh) 

7.0 

3 2010 35 1,650 11.5 5.5 

3 2015 36 1,610 11.5 5.0 

3 2020 38 1,570 11.5 4.6 

3 2025 38 1,530 11.5 4.5 

3 2030 38 1,480 11.5 4.4 

4 2005 36 1,650 11.5 7.0 

4 2010 39 1,650 11.5 5.5 

4 2015 41 1,610 11.5 5.0 

4 2020 42 1,570 11.5 4.6 

4 2025 43 1,530 11.5 4.5 

4 2030 43 1,480 11.5 4.4 

5 2005 40 1,650 11.5 7.0 

5 2010 43 1,650 11.5 5.5 

5 2015 44 1,610 11.5 5.0 

5 2020 45 1,570 11.5 4.6 

5 2025 46 1,530 11.5 4.5 

5 2030 46 1,480 11.5 4.4 

6 2005 44 1,650 11.5 7.0 

B

17Capacity factors for 2000 and 2005 fit to actual data. For the higher wind power classes (6 and 7), 

however, limited data are available for operating plants, so capacity factors were extrapolated from the 

linear relationships between wind classes. 

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B

Wind Resource 

Power Class at 

50 m 

6 

Year 

Installed 

2010 

Capacity 

Factor 

(%) 

46 

Cost 

($/kW) 

1,650 

Fixed O&M 

($/kW-yr) 

11.5 

Variable 

O&M 

($/MWh) 

5.5 

6 2015 47 1,610 11.5 5.0 

6 2020 48 1,570 11.5 4.6 

6 2025 49 1,530 11.5 4.5 

6 2030 49 1,480 11.5 4.4 

7 2005 47 1,650 11.5 7.0 

7 2010 50 1,650 11.5 5.5 

7 2015 51 1,610 11.5 5.0 

7 2020 52 1,570 11.5 4.6 

7 2025 52 1,530 11.5 4.5 

7 2030 53 1,480 11.5 4.4 

Note: MWh = megawatt-hour 

Source: Black and Veatch, forthcoming 2008 et al. (2007)
 

Table B-11 lists cost and performance projections prepared by Black & Veatch for 

shallow offshore wind technology (in water shallower than 30 m). Capital costs for 

2005 were based on publicly available cost data for European offshore wind farms. 

Capital costs are assumed to decline 12.5% as a result of technology development 

and a maturing market. The capacity factor projection, which is based on the 

logarithmic best-fit lines generated for land-based turbines, we increased 15% to 

account for larger rotor diameters and reduced wind turbulence over the ocean. By 

2030 this adjustment factor is reduced to 5% as land-based development allows 

larger turbines to be used in turbulent environments. O&M costs are assumed to be 

three times those of land-based turbines (Musial and Butterfield 2004) with a 

learning rate commensurate to that projected by the U.S. Department of Energy 

(DOE; NREL 2006). 

Table B-11. Shallow offshore wind technology cost and performance 

projections (US$2006) 

Wind 

Resource 

Power Class 

at 50 m 

Year 

Installed 

Capacity 

Factor 

(%) 

34 

Capital 

Cost 

($/kW) 

2,400 

Fixed 

O&M 

($/kW-yr) 

15 

Variable 

O&M 

($/MWh) 

3 2005 21

3 2010 37 2,300 15 18 

3 2015 38 2,200 15 16 

3 2020 39 2,150 15 14 

3 2025 40 2,130 15 13 

3 2030 40 2,100 15 11 

4 2005 38 2,400 15 21 

4 2010 41 2,300 15 18 

4 2015 43 2,200 15 16 

4 2020 44 2,150 15 14 

4 2025 45 2,130 15 13 

4 2030 45 2,100 15 11 

5 2005 42 2,400 15 21 

5 2010 45 2,300 15 18 

5 2015 46 2,200 15 16 

5 2020 47 2,150 15 14 

182 20% Wind Energy by 2030 – Prepublication Version 
 

Wind 

Resource 

Power Class 

at 50 m 

Year 

Installed 

Capacity 

Factor 

(%) 

48 

Capital 

Cost 

($/kW) 

2,130 

Fixed 

O&M 

($/kW-yr) 

15 

Variable 

O&M 

($/MWh) 

5 2025 13

5 2030 48 2,100 15 11 

6 2005 46 2,400 15 21 

6 2010 48 2,300 15 18 

6 2015 50 2,200 15 16 

6 2020 51 2,150 15 14 

6 2025 51 2,130 15 13 

6 2030 51 2,100 15 11 

7 2005 50 2,400 15 21 

7 2010 52 2,300 15 18 

7 2015 54 2,200 15 16 

7 2020 55 2,150 15 14 

7 2025 55 2,130 15 13 

7 2030 55 2,100 15 11 

Source: Black and Veatch, forthcoming 2008. 

A number of adjustments, including financing, interest during construction, terrain 

slope, population density, and rapid growth were applied to the capital cost. 

Although financing has not been treated explicitly, it is assumed to be captured by 

the weighted cost of capital (real discount rate) of 8.5%. 

A slope penalty that increases one-fourth of the capital cost by 2.5% per degree of 

terrain slope was used to represent expected costs associated with installations on 

mesas or ridge crests. Costs associated with installation represent 25% of the capital 

cost. Wiser and Bolinger (2007) present regional variations in installed capital cost 

for projects constructed in 2006. Applying a multiplier related to population density 

within each of the 358 WinDS regions results in regional variations similar to that 

observed in data. An additional 20% must be applied to the base capital cost in New 

England to reflect observed capital cost variations. Slope and population density 

penalties have been applied to the capital cost listed in Tables B-10 and B-11 within 

the model to represent topographical and regional variations across the United 

States. 

WinDS does assume that the price paid per unit of wind capacity can rise above the 

capital costs of Tables B-10 and B-11 as well as the multiplier factors, if the demand 

for new wind capacity significantly exceeds that supplied in earlier years. In 

particular, if new wind installations are more than 20% greater than those of the 

preceding year, there is a 1% increase in capital cost for each 1% growth above 20% 

per year (EIA 2004). 

B.4 Conventional Generation 

B

U.S. conventional energy generation included in the WinDS model, and most likely 

to be built in the United States, has been included in EIA’s data reports (2007). 

Table B-12 illustrates expected construction time and schedules for conventional 

energy technologies. 

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Table B-12. General assumptions for conventional generation technologies 

B 

B

Technology 

Modeled 

Capability for 

new builds in 

WinDS 

Construction 

Time (years) 

(1) 

Construction Schedule (2) 

Fraction of Cost in Each Year 

Forced 

Outage 

Rate (%) 

(3) 

Planned 

Outage 

Rate (%) 

(3) 

Emis

(lbs/MM

1 2 3 4 5 6 SO2 NO

Conventional Hydropower -Hydraulic Turbine No NA - - - - - - 2.0% 5.0% 0 

Natural Gas Combustion Turbine Yes 3 0.8 0.1 0.1 - - - 10.7% 6.4% 0.0006 0.

Combined Cycle Natural Gas Turbine Yes 3 0.5 0.4 0.1 - - - 5.0% 7.0% 0.0006 0.

Conventional Pulverized Coal Steam Plant (No SO2 Scrubber) 

No-Scrubbers may be added to meet SO2 

constraints. Existing plants may also switch to low-sulfur coal. 6 0.1 0.2 0.2 0.2 0.2 0.1 7.9% 9.8% 0.2355 0.4

Conventional Pulverized Coal Steam Plant (With SO2 scrubber) No-see above 6 0.1 0.2 0.2 0.2 0.2 0.1 7.9% 9.8% 1.57 0.4

Advanced Supercritical Coal Steam Plant (with SO2 and Nox 

Controls) Yes 4 0.4 0.3 0.2 0.1 - - 7.9% 9.8% 0.157 0.

Integrated Coal Gasification Combined Cycle Turbine Yes 4 0.4 0.3 0.2 0.1 - - 7.9% 9.8% 0.0184 0.

Oil/Gas Steam Turbine 

No -Assumes Gas-CT or GasCC will be built instead. NA - - - - - - 7.9% 9.8% 0.026 0

Nuclear Yes 6 0.1 0.2 0.2 0.2 0.2 0.1 5.0% 5.0% 0 

Geothermal No NA - - - - - - 5.0% 5.0% 0 

Biomass (as Thermal Steam Generator) No NA - - - - - - 5.0% 5.0% 0 

Concentrating Solar Power with Storage Yes 3 0.5 0.4 0.1 - - - 35.0% 5.0% 0.00015 0.

Municipal Solid Waste / Landfill Gas No NA - - - - - - 5.0% 5.0% 0 

B-184 20% Wi
 

WinDS considers outage rates when determining the net capacity available for 

energy (as described in Section 2) and also when determining the capacity value of 

each technology. Planned outages are assumed to occur in all seasons except 

summer. Table B-12 shows outage rates for each conventional technology. 

Emission rates are estimated in Table B-12 for SO2, NOx, mercury, and CO2. 

Table B-13 provides input-specific emission rates (in pounds per million British 

thermal units) for plants that use combustible fuel. Output emission rates (in pounds 

per megawatt-hour) are calculated by multiplying input emission rate by heat rate. 

Table B-13. Cost and performance characteristics for 

conventional generation (US$2006) 

Gas CT 

Install 

Date 

2005 

Capital 

Cost 

($/kW) 

Fixed 

O&M 

($/MW/yr) 

Variable 

O&M 

($/MWh) 

Heat Rate 

(Btu/kWh) 

625 7,700 12.0 11,560 

2010 750 6,600 2.8 8,900 

2015 750 6,600 2.8 8,900 

2020 750 6,600 2.8 8,900 

2030 750 6,600 2.8 8,900 

Gas-CC 2005 780 14,400 3.0 6,870 

2010 780 14,400 3.0 6,870 

2015 780 14,400 3.0 6,870 

2020 780 14,400 3.0 6,870 

2030 780 14,400 3.0 6,870 

New Coal (SC) 2005 2,120 35,300 1.7 9,470 

2010 2,180 35,300 1.7 9,200 

2015 2,240 35,300 1.7 9,100 

2020 2,240 35,300 1.7 9,000 

2030 2,240 35,300 1.7 9,000 

Coal - IGCC 2005 2,750 38,100 3.9 9,000 

2010 2,840 38,100 3.9 9,000 

2015 2,840 38,100 3.9 8,900 

2020 2,840 38,100 3.9 8,800 

2030 2,840 38,100 3.9 8,580 

Nuclear 2005 3,260 90,000 0.5 10,400 

2010 3,170 90,000 0.5 10,400 

2015 3,020 90,000 0.5 10,400 

2020 2,940 90,000 0.5 10,400 

2030 2,350 90,000 0.5 10,400 

Notes: New nuclear plants may not be constructed before 2010. O&M costs do not 

include fuel. Heat rate is net heat rate (including internal plant loads). 

Source: Black and Veatch, forthcoming 2008 

B

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B

B.4.1 Conventional Generation Cost and Performance 

Table B-13 also gives capital cost values, heat rates (efficiency), and fixed and 

variable O&M costs for conventional technologies that might be added to the 

electric system. Cost and performance values for natural gas, nuclear, and coal 

technologies are based on recent project costs according to Black & Veatch 

experience. Pulverized coal plants continue to operate in WinDS, and SO2 scrubbers 

can be added to unscrubbed coal plants for $200/kW. Oil, gas, steam, and 

unscrubbed coal plants cannot be added to the electric system, but those currently in 

operation are maintained until retired. WinDS sites conventional generation 

technology where it is least expensive (generally adjacent to load centers) and does 

not require new transmission. California is the exception because its legislative 

requirements prohibit siting new coal plants. 

Capital costs for 2005, 2010, and 2015 are based on proposed engineering, 

procurement, and construction (EPC) estimates for plants that will be commissioned 

in 2010, 2015, and 2020. A wet scrubber is included in the EPC costs for new 

pulverized coal plants. Owners’ costs of 20% for coal, nuclear, and combined-cycle 

gas plants and 10% for simple-cycle gas plants provide an “all-in” cost. These 

owners’ costs are based on national averages and include transmission and 

interconnection, land, permitting, and other costs. As with wind systems, an 

additional 20% of the capital costs listed in Table B-3 is applied to coal and nuclear 

generation technology in New England, representing siting difficulties. 

B.4.2 Fuel Prices 

Fuel prices for natural gas and coal are derived from reference projections from the 

AEO (EIA 2007b). These tables provide the prices in each census region, which are 

then assigned to a NERC subregion used in WinDS. Prices in the AEO 2007 are 

projected to 2030. Beyond 2030, WinDS increases fuel prices at the same national 

annual average rate as the AEO 2007 projects between 2020 and 2030. 

Figure B-4 illustrates the projected fossil fuel prices in constant $US2005. The 20% 

Wind Scenario uses the reference AEO fuel price forecast for coal because 

government agencies and the private sector regularly use that forecast to make 

planning and investment decisions. The New York Mercantile Exchange futures 

prices for natural gas for May 2007 through 2012 exceed the AEO’s high fuel price 

forecast over that period. Also, under the current set of technology cost and 

performance assumptions, the WinDS model tends to select natural gas-fueled 

technology over coal-fueled technology. To provide a conservative estimate while 

representing a more traditional mix of conventional generation technology, the AEO 

high natural gas price forecast has been implemented. 

The price of uranium fuel in WinDS is constant at $0.5/MMBtu (Denholm and Short 

2006). 

186 20% Wind Energy by 2030 – Prepublication Version 
 

 

Figure B-4. Projected coal and natural gas prices in WinDS to 2030 

B.5 Transmission 

Three types of transmission systems can be used to transport wind power around the 

country: 

z	 Existing grid: It is assumed that 10% of the existing grid can be 

used for new wind capacity, either by improving the grid or by 

tapping existing unused capacity. A GIS optimization determines 

the distance at which a particular wind farm will have to be built to 

connect to the grid (based on the assumption that the closest wind 

installation will access the grid first at the least cost). In this way, a 

supply curve of costs to access the grid is created for each class of 

wind in each region. Additionally, a pancake-type fee for crossing 

between balancing areas may be charged within the model. The 

supply curves described earlier are based on this type of 

transmission and the GIS optimization described here. In the near 

term, one can expect that most wind will be built and use the 

existing grid without needing to build excessive amounts of new 

transmission lines, but as higher penetration levels are reached, the 

existing grid will be insufficient. 

Existing transmission capacity is estimated using a database of 

existing lines (length and voltage) from RDI/Platts (Platts Energy 

Market Data; see http://www.platts.com/Analytic%20Solutions/ 

BaseCase/). This database is translated into a megawatt capacity as 

a function of kilovolt (kV) rating and length (Weiss and Spiewak 

1998). 

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B

z	 New lines: The model has the ability to build straight-line 

transmission lines between any of the 358 wind regions. The line is 

built exactly to the size necessary to transmit the desired megawatts 

and the cost of building that transmission line is accounted for in the 

model. 

AWEA experts indicate that new transmission line capacity might 

be constructed for any generation technology for an average cost of 

$1,600/MW-mile. Based on input from the AWEA expert panel, 

regional transmission cost variations include an additional 40% in 

New England and New York; 30% in PJM East (New Jersey and 

Delaware); 20% in PJM West (Maryland, West Virginia, 

Pennsylvania, Ohio, parts of Illinois, Indiana, and Virginia); and 

20% in California. 

The WinDS model assumes that 50% of the cost of new 

transmission is borne by the generation technology for which the 

new transmission is being built (wind or conventional); the other 

half is borne by the ratepayers within a region (because of the 

reliability benefits to all users associated with new transmission). 

This 50–50 allocation, which is common in the industry, was 

recently adopted for the 15-state Midwest Independent 

Transmission System Operator (Midwest ISO) region. New wind 

transmission lines that carry power across the main interconnects 

are not cost-shared with other technology. In the WinDS model, this 

sharing of costs is implied by reducing the cost of new transmission 

associated with a particular capacity by 50%. This means that the 

relative costs of transmission and capacity capital are in line with 

the model’s assumption. The remaining 50% of transmission costs 

are integrated into the final cost value outputs from the model, 

resulting in accurate total transmission costs. 

z	 In-region transmission: Within any of the 358 wind regions 

around the country, the model can build directly from a wind 

resource location to a load within the same region. A second GISgenerated supply curve is used within the model to assign a cost for 

this transmission. 

A fourth type of transmission, used predominantly by conventional capacity and 

called general transmission, can be built as well. This is limited because 

conventional capacity can generally be built in the region where it is needed, thereby 

obviating the need for new transmission. 

WinDS uses a transmission loss rate of 0.236 kW/MW-mile. This value is based on 

the loss estimates for a typical transmission circuit (Weiss and Spiewak 1998). The 

assumed typical line is a 200-mile, 230-kV line rated at 170 megavolt amperes 

(MVA; line characteristics derived from EPRI [1983]). 

To emulate large regional planning structures based on that of the Midwest ISO, 

there is essentially no wheeling fee between balancing areas used in this analysis 

(although the model has the capability to model such a fee). The wind penetration is 

limited to 25% energy in each of the three interconnects: Western, Eastern, and 

ERCOT. 

188	 20% Wind Energy by 2030 – Prepublication Version 
 

B.6 Treatment of Resource Variability 

Variability of the wind resource can affect the electricity grid in several ways. One 

useful way to examine these impacts is to categorize them in terms of time, ranging 

from multiyear planning issues to small instantaneous fluctuations in output. 

At the longest time interval, a utility’s capacity expansion plans might call for the 

construction of more nameplate generation capacity. To meet this need, planners can 

plan to build conventional dispatchable capacity or wind. The variability of wind 

output precludes the planners from considering 1 MW of nameplate wind capacity to 

be the same as 1 MW of nameplate dispatchable capacity. The wind capacity cannot 

be counted on to be available when electricity demand is at its peak. Actually, 

conventional capacity cannot be considered 100% available, either. The difference is 

in the degree of availability. Conventional generators experience forced outages on 

the order of 2% to 20% of the time; wind energy is available at varying levels that 

average about 30% to 45% of the time, depending on the quality of the wind site. 

For planning purposes, this lack of availability can be handled in the same way—a 

statistical treatment that calculates how much more load can be added to the system 

for each megawatt of additional nameplate wind or conventional capacity or 

effective load carrying capability (ELCC). 

Wind’s ELCC is less than that of conventional capacity because (1) the wind 

availability is less than that of conventional generators and (2) at any given instant, 

energy from a new wind farm can be heavily correlated with the output from the 

existing wind farms. In other words, if the wind is not blowing at one wind site, 

there is a reasonable chance that it is not blowing at another nearby site. On the other 

hand, there is essentially no correlation between the outputs of any two conventional 

generation plants. 

Fortunately, there are ways to partly mitigate both the low availability of the wind 

resource and its correlation between sites. In the past 20 years, the capacity factors 

of new wind installations have improved considerably. This is attributable to better 

site exploration and characterization and to improvements in the wind turbines 

(largely higher towers). 

The correlation in wind output between sites can also be reduced. Increasing the 

distance between sites and the terrain features that separate them reduces the chance 

that two sites will experience the same wind at the same time. Figure B-5 shows this 

correlation as a function of distance between sites in an east–west direction and in a 

north–south direction (Simonsen and Stevens 2004). With its multiple regions, 

WinDS is able to approximate the distance between sites and, therefore, the 

correlation between their outputs. WinDS uses the correlation between sites to 

estimate the variation in wind output from the total set of wind farms supplying 

power to a particular region. 

Between each two-year optimization period and for each demand region, WinDS 

updates its estimate of the marginal ELCC associated with adding wind of each 

resource class in each wind supply region to meet demand within a NERC region. 

This marginal ELCC is a strong function of the wind capacity factor and the distance 

from the existing wind systems to the new wind site for which the ELCC is being 

calculated. It is also a weak function of the demand region’s LDC and the size and 

forced outage rates of conventional capacity. This marginal ELCC is assumed to be 

B

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Figure B-5. Distance between wind sites and correlation with power output 

B

the capacity value of each megawatt of that wind class added in the next period in 

that wind supply region to serve the NERC region’s demand. 

All other factors being equal, when expanding wind capacity, WinDS will select the 

next site in a region that is as far from the existing sites as possible to ensure the 

lowest correlation and the highest ELCC for the next wind site. (From a practical 

standpoint, all factors are never “equal,” and WinDS considers the trade-offs 

between ELCC and wind site quality, transmission availability and cost, and local 

siting costs.) 

Generally, for the first wind site supplying a demand region, these capacity values 

(ELCCs) are almost equal to the peak season capacity factor. As the wind penetrates 

to higher levels, though, the ELCC can decline to almost zero in an individual wind 

supply region. 

The next time frame of major interest is the day ahead. Utilities generally make 

decisions on which generating units to commit to generation the day before they are 

actually committed. To comply with these unit-commitment procedures, 

independent power plant owners can be expected to bid for firm capacity a day 

ahead. This can be problematic for wind generator owners. For example, if the wind 

owner bids to provide firm capacity and the wind does not blow as forecast, the 

owner may have to make up the difference by purchasing power on the real-time 

market. If that power costs more per kilowatt-hour than the owner is being paid for 

the day-ahead bid, the owner will lose money on those kilowatt-hours that must be 

purchased. 

Not all of today’s electric grid systems operate day-ahead and real-time markets. 

California, for example, allows a monthly balancing of bid and actual wind 

generation that is much more tolerant of the inaccuracies in forecasting wind a day 

ahead of time. In all cases, however, the imbalances can be offset with adequate 

operating reserves. To capture the essence of the unit-commitment issue, WinDS 

estimates the impact of wind variability on the need for operating reserves (which 

include quick-start and spinning reserves) that can rapidly respond to changes in 

wind output. The operating reserves are assumed to be a linear function of the 

190 20% Wind Energy by 2030 – Prepublication Version 
 

 

 

variance in the sum of generation (both wind and conventional) minus load. Because 

the variability of wind is statistically independent of load variability and forced 

outages, the total variance can be calculated as the sum of the variance associated 

with the normal (i.e., no wind) operating reserve and the total variance (over all the 

wind supply regions) in the wind output over the reconciliation period. 

Before each two-year optimization, WinDS calculates the marginal operating 

reserve additions required by the next unit of wind added in a particular wind supply 

region from a particular wind class. The resulting value is the difference between the 

operating reserve required by the total system with the new wind and the operating 

reserve required by the total system if there were no new wind installations in that 

wind supply region. This value is then used throughout the next two-year linear 

program optimization as the marginal operating reserve requirement induced by the 

next megawatt of wind addition in that region of that wind resource class. 

In the shortest time interval, regulation reserves must compensate for instantaneous 

changes in wind output. Regulation reserves are normally provided by automatic 

generation control of conventional generators whose output can be automatically 

adjusted to compensate for small voltage changes on the grid. Fortunately, these 

instantaneous changes in wind output do not all occur at the same time, even from 

wind turbines within the same wind farm. This lack of correlation over time and the 

ease with which conventional generators can respond allows this second-order cost 

to be reasonably ignored. 

WinDS assumes that energy generated by wind that is delivered to a specific 

demand region in a specific time slice and exceeds the total electricity load in that 

region/time slice will be lost. In addition, WinDS also statistically accounts for 

surplus wind lost within a time slice because of variations in load and wind within 

the time slice. 

WinDS has three endogenous options for mitigating the impact of resource 

variability. The first is to add conventional generators that can provide spinning 

reserve (e.g., gas-CC) and quick-start capabilities (combustion turbines). The 

second—and usually least costly—option is to allow the dispersion of new wind 

installations to reduce the correlation of the outputs from different wind sites. 

Finally, the model can allow for storage of electricity at the wind site, which is 

usually the most costly option. The storage option was not available within this 

analysis and is currently being developed for the model. 

B.7 Federal and State Energy Policy 

All currently enacted federal and state emission standards, renewable portfolio 

standards (RPS), and tax credits are implemented in WinDS. 

B.7.1 Federal Emission Standards 

WinDS has the ability to add a national cap on CO2 emissions from electricity 

production. It can also charge a tax for CO2 emissions. Neither a carbon cap nor a 

tax is implemented in the 20% Wind Scenario. 

Emissions of SO2 are capped at the national level. WinDS uses a cap that 

corresponds roughly to the 2005 Clean Air Interstate Rule (CAIR), replacing the 

previous limits established by the 1990 Clean Air Act Amendments (CAAA). The 

CAIR rule divides the United States into two regions. WinDS uses the U.S. 

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Environmental Protection Agency’s (EPA) estimate of the effective national cap on 

SO2 resulting from the CAIR rule (EPA 2005). Table B-14 shows the SO2 cap used 

in WinDS. 

Table B-14. National SO2 emission limit schedule in WinDS 

B

Year 2003 2010 2015 2020 2030 

National SO2 Emissions 

(Million Tons) 

10.6 6.1 5.0 4.3 3.5 

(EPA 2005) 

NOx emissions are currently unconstrained in WinDS. The NOx cap from CAIR can 

be added, but the net effect on the overall competitiveness of coal is expected to be 

relatively small (EIA 2003). 

Mercury emissions are currently unconstrained in WinDS. The Clean Air Mercury 

Rule (see http://www.epa.gov/camr/index.htm) is a cap and trade regulation, 

expected to be met largely via the requirements of CAIR. Control technologies for 

SO2 and NOx that are required for CAIR are expected to capture enough mercury to 

largely meet the cap goals. As a result, the incremental cost of mercury regulations 

is very low and is not modeled in WinDS (EIA 2003). . 

B.7.2 Federal Energy Incentives 

Several classes of incentives have been applied to wind systems at the federal level. 

These incentives generally have the effect of reducing the cost of producing energy 

from these and other renewable sources. A production tax credit (PTC) provides an 

offset to the tax liability of companies, based on the production of energy from the 

resource receiving the incentive. Within this analysis, a PTC has been available for 

wind up to 2008 (see Table B-15). 

Table B-15. Federal renewable energy incentives 

Name Value Notes and Source 

Renewable Energy PTC $19/MWh Applies to wind. No limit to the aggregated 

amount of incentive. Value is adjusted for 

inflation to US$2006. Expires end of 2008. 

(U.S. Congress 2005) 

B.7.3 State Energy Incentives 

Several states also offer production and investment incentives for renewable energy 

resource development. Table B-16 lists the values used in WinDS. These incentives, 

however, are overwhelmed in the 20% Wind Scenario by the specification of wind 

energy generation in each year through 2030. 

192 20% Wind Energy by 2030 – Prepublication Version 
 

 

Table B-16. State renewable energy incentives 

State 

Iowa 

PTC $/ 

MWh 

ITC 

5.00% 

Assumed State 

Corporate Tax Rate 

10.0% 

Idaho 5.00% 7.60% 

Minnesota 6.50% 9.8% 

New Jersey 6.00% 9.0% 

New Mexico 10 7.0% 

Oklahoma 2.5 6.0% 

Utah 4.75% 5.0% 

Washington 6.50% 0.0% 

Wyoming 4.00% 0.0% 

Investment and production tax credit data from IREC 2006
 

Tax rates from: www.taxadmin.org/fta/rate/corp_inc.html
 

B.7.4 State Renewable Portfolio Standards 

A number of states have addressed RPS, and states can put capacity mandates in 

place as an alternative or supplement to an RPS (see Table B-17). A capacity 

mandate requires a utility to install a certain fixed capacity of renewable energy 

generation. Unless prohibited by law, a state might also meet requirements by 

importing electricity. 

Table B-17. State RPS requirements as of August 2005 

State RPS 

Start 

Year2 

RPS Full 

Implementation3 

Penalty 

in 

$/MWh 

WinDS 

Assumed 

RPS 

Fraction4 

Legislated 

RPS 

Fraction 

(%) 

Load 

Fraction5 

Arizona 2001 2025 50 0.0079 1.1 1 

California 2003 2017 5 0.034 20 0.63 

Colorado 2007 2015 50 0.044 10 0.69 

Connecticut 2004 2010 55 0.013 10 0.94 

Delaware 2007 2019 25 0.056 10 0.75 

Illinois 2004 2013 10 0.062 15 0.92 

Massachusetts 2003 2009 50 0.026 4 0.85 

Maryland 2006 2019 20 0.045 7.5 0.8 

Minnesota 2002 2015 10 0.072 1,125 MW 1 

Montana 2008 2015 10 0.075 15 0.9 

New Jersey 2005 2008 50 0.029 6.5 1 

New Mexico 2006 2011 10 0.026 10 0.53 

Nevada 2003 2015 10 0.133 20 0.89 

New York 2006 2013 5 0.035 25 0.84 

Oklahoma 2005 2016 50 0.05 See Note 6 1 

Oregon 2002 2020 5 0.078 See Note 6 1 

Pennsylvania 2007 2020 45 0.014 8 0.98 

Rhode Island 2007 2019 55 0.069 15 0.99 

Texas 2003 2015 50 0.01 5,880 MW 1 

Vermont 2005 2012 10 0.05 See Note 6 1 

Wisconsin 2001 2011 10 0.006 2.2 0.75 

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20% Wind Energy by 2030 – Prepublication Version 193 
 

 

 

 

State RPS RPS Full Penalty WinDS Legislated Load 

Start 

Year2 

Implementation3 in 

$/MWh 

Assumed 

RPS 

Fraction4 

RPS 

Fraction 

(%) 

Fraction5 

B

Notes: 

1) RPS data as of 8/16/05. Source: IREC 2006. 

2) RPS Start Year is the “beginning” of the RPS program. The RPS is ramped linearly to the full 

implementation year. 

3) RPS Full Implementation is the year that the full RPS fraction must be met. WinDS assumes 

the fraction met is ramped up linearly between the start year and the full implementation year. 

4) WinDS Assumed RPS Fraction is the fraction of state demand that must be met by wind by
 

the full implementation year. This value is based on the total state RPS requirement and 

adjusted to estimate the fraction actually provided by wind since WinDS does not currently
 

include other renewables such as biomass cofiring and certain hydro projects. 

5) Load fraction is the fraction of the total state load that must meet the RPS. In certain locations, 

municipal or cooperative power systems may be exempt from the RPS. 

6) Several states have special funds set aside to promote renewables. The net increase in wind 

due to these funds was estimated and applied as an effective RPS. 

B.8 Electricity Sector Direct Cost Calculation 

The objective of the electricity sector direct cost calculation is to determine the 

difference in system-wide costs where 20% wind penetration is required compared 

to the case where no new wind penetrates after 2006. The goal was to estimate the 

cost per kilowatt-hour of wind produced and the cost per kilowatt-hour of total load 

met. The resulting numbers for both scenarios are reported in Appendix A. 

To gather necessary costs from the WinDS model, it was programmed to calculate 

costs incurred in each year of the simulation from 2008 through 2030 for both cases 

(with and without wind). These costs are then broken into subgroups, including wind 

capital costs; conventional energy capital costs; wind and conventional transmission 

build costs (including the full transmission cost, not just the portion shared by each 

generator); and conventional fuel costs. 

Because the benefits (especially fuel savings) of wind turbines installed in the years 

immediately preceding 2030 are not evident until after 2030, the cost impacts 

beyond 2030 are estimated. To arrive at the estimate, the model assumes that wind 

generation would linearly decay from 2030 to 2050 and that the conventional fuel 

and O&M savings would also linearly decay to 0 from 2030 to 2050. This is a 

conservative approach because it assumes that the wind farms are retired linearly. 

Finally, all costs (including the approximated costs after 2030) are discounted back 

to 2006. The WinDS model run is made with an 8.5% real weighted cost of capital 

to represent a typical utility perspective. In evaluating a policy such as an RPS, a 

social discount rate of 7% should be used in accordance with Office of Management 

and Budget guidelines (OMB 1992). This lower rate effectively places higher 

(higher than a utility’s 8.5% discount rate) value on benefits and costs encountered 

further in the future. The total cost difference then simply becomes the difference in 

the present value of the two cost streams. To find the cost per kilowatt-hour 

(levelized cost) of wind produced, the total cost difference is levelized to satisfy the 

following formula: 

∑ wind generationt * LC /(1+d)t

 = PV of costs in 20% case – PV of costs in no wind 

case 

194 20% Wind Energy by 2030 – Prepublication Version 
 

 

 

 

 

 

 

As a second result, to find the cost per kilowatt-hour of total generation, replace 

wind generation with total generation in the preceding formula. The complete 

equation that calculates the present value of the costs used in the preceding equation 

is as follows: 

PVCosts = a + b + c 

2030 

a = ( ( CapCostNewCapacityt + CapCostNewTransmissiont + O&MCostt + FuelCostt ) / ( 1 + d )( t – 2006 ) ) Σ

t=2006 

Σ

2050
 

/ ( 1 + d )( t – 2006 ) b = ( WindO&MCostsCapBuiltBy2030 ) t 

t=2031 

Σ

2050
 

) / ( 1 + d( t – 2006 ) c = ( ( ( ConvO&M2030 + FuelCost,2030 ) FractionNotRetiredWind ))

t=2031 

where

 FractionNotRetiredWind = Fraction of wind generation remaining from wind capacity installed prior to 2031
 

in the 20% wind case
 

B.9 References & Suggested Further Reading 

Denholm, P., and W. Short. 2006. Documentation of WinDS Base Case. Version 

AEO 2006 (1). Golden, CO: National Renewable Energy Laboratory 

(NREL). http://www.nrel.gov/analysis/winds/pdfs/winds_data.pdf. 

EIA (Energy Information Administration). 2000. Cross Reference of States to 

Federal Regions, NERC Regions, and Census Divisions. Washington, DC: 

EIA. http://www.eia.doe.gov/cneaf/electricity/ipp/html1/tb5p01.html. 

EIA. 2002. Upgrading Transmission Capacity for Wholesale Electric Power Trade. 

Washington, DC: EIA. 

http://www.eia.doe.gov/cneaf/pubs_html/feat_trans_capacity/w_sale.html. 

EIA. 2003. Analysis of S. 485, the Clear Skies Act of 2003, and S. 843, the Clean 

Air Planning Act of 2003. SR/OIAF2003-03(2003). Washington, DC: EIA. 

EIA. 2004a. The Electricity Market Module of the National Energy Modeling 

System; Model Documentation Report. DOE/EIA-M068(2004). 

Washington, DC: EIA. 

EIA. 2004b. Analysis of Senate Amendment 2028, the Climate Stewardship Act of 

2003. SR/OIAF/2004-06. Washington, DC: EIA. 

EIA. 2006a. Assumptions to the Annual Energy Outlook 2006 with Projections to 

2030. . Washington, DC: EIA. 

http://www.eia.doe.gov/oiaf/archive/aeo06/assumption/index.html 

EIA. 2006b. Supplemental Tables to the Annual Energy Outlook 2006. Washington, 

DC: EIA. 

http://www.eia.doe.gov/oiaf/archive/aeo06/supplement/index.html. 

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B

EIA. 2006c. Energy and Economic Impacts of H.R.5049, the Keep America 

Competitive Global Warming Policy Act. SR/OIAF/2006-03.Washington, 

DC: EIA. 

EIA. 2007a. Annual Energy Outlook 2007 with Projections to 2030. Washington, 

DC: EIA. Report No. DOE/EIA-0383. 

http://www.eia.doe.gov/oiaf/archive/aeo07/index.html 

EIA. 2007b. Supplemental Tables to the Annual Energy Outlook. Tables 60 through 

72. Washington, DC: EIA. 

http://www.eia.doe.gov/oiaf/aeo/supplement/index.html. 

EIA. 2007c. Assumptions to the Annual Energy Outlook 2007 with Projections to 

2030. Washington, DC: EIA. 

http://www.eia.doe.gov/oiaf/aeo/assumption/index.html 

EIA. 2007d. Energy Market and Economic Impacts of a Proposal to Reduce 

Greenhouse Gas Intensity with a Cap and Trade System. SR/OIAF/2007-01. 

Washington, DC: EIA. http://www.epa.gov/cleanenergy/egrid/index.htm. 

Elliott, D.L., and M.N. Schwartz. 1993. Wind Energy Potential in the United States. 

PNL-SA-23109. NTIS No. DE94001667. Richland, WA: Pacific Northwest 

Laboratory (PNL). 

EPA (U.S. Environmental Protection Agency). 1996. “Compilation of Air Pollutant 

Emission Factors, AP-42.” In Volume I: Stationary Point and Area Sources. 

Fifth edition. Washington, DC: EPA. http://www.epa.gov/ttn/chief/ap42/. 

EPA. 2005a. Clean Air Interstate Rule, Charts and Tables. Washington, DC: EPA. 

http://www.epa.gov/cair/charts_files/cair_emissions_costs.pdf. 

EPA. 2005b. eGRID Emissions & Generation Resource Integrated Database. 

Washington, DC: EPA Office of Atmospheric Programs. 

EPRI (Electric Power Research Institute). 1983. Transmission Line Reference Book, 

345-kV and Above. Second edition. Palo Alto, CA: EPRI. 

IREC (Interstate Renewable Energy Council). 2006. Database of State Incentives for 

Renewable & Efficiency (DSIRE). http://www.dsireusa.org/. 

Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, 

et al. 1996. “The NCEP/NCAR 40-Year Reanalysis Project.” Bulletin of the 

American Meteorological Society. 77, 437–471. 

http://ams.allenpress.com/perlserv/?request=resloc&uri=urn%3Aap%3Apdf%3Adoi%3A10.1175%2F1520­

0477%281996%29077%3C0437%3ATNYRP%3E2.0.CO%3B2. 

McDonald, A., and L. Schrattenholzer. 2001. “Learning Rates for Energy 

Technologies.” Energy Policy 29, 255–261. 

Musial, W., and S. Butterfield. 2004. Future for Offshore Wind Energy in the United 

States. NREL/CP-500-36313. Golden, CO: NREL. 

NREL. 2006. National Wind Technology Center Website, “About the Program,” 

http://www.nrel.gov/wind/uppermidwestanalysis.html. 

NREL. 2006. Projected Benefits of Federal Energy Efficiency and Renewable 

Energy Programs-FY2007 Budget Request. NREL/TP-320-39684. Golden, 

CO: NREL. http://www1.eere.energy.gov/ba/pdfs/39684_00.pdf. 

196 20% Wind Energy by 2030 – Prepublication Version 
 

O’Connell, R., R. Pletka, et al. 2007. 20 % Wind Energy Penetration in the United 

States: A Technical Analysis of the Energy Resource. Overland Park, KS: Black 

& Veatch. 

OMB (Office of Management and Budget). 1992. Guidelines and Discount Rates for 

Benefit-Cost Analysis of Federal Programs. Circular A-94.Washington, DC: 

OMB. http://www.whitehouse.gov/OMB/circulars/index.html. 

PA Consulting Group. 2004. “Reserve Margin Data.” Energy Observer 2, July. 

PNL. 1987. Wind Energy Resource Atlas of the United States. DOE/CH 10093-4. 

Richland, WA: PNL. 

Sabeff, L., R. George, D. Heimiller, and A. Milbrandt. 2004. Regional Data and GIS 

Representation: Methods, Approaches & Issues. Presentation to Scoping 

Workshop for GIS Regionalization for EERE Models. 

http://www.nrel.gov/analysis/workshops/pdfs/brady_gis_workshop.pdf. 

Simonsen, T., and B. Stevens. 2004. Regional Wind Energy Analysis for the Central 

United States. Grand Forks, ND: Energy and Environmental Research 

Center. 

http://www.undeerc.org/wind/literature/Regional_Wind.pdf. 

U.S. Congress. 2005. Domenici-Barton Energy Policy Act of 2005. Washington, 

DC: 109th Congress. 

Weiss, L., and S. Spiewak. 1998. The Wheeling and Transmission Manual. 

Lilburn,GA: The Fairmont Press Inc. 

Wiser, R., and M. Bolinger. 2007. Annual Report on U.S. Wind Power Installation, 

Cost, and Performance Trends: 2006. DOE/GO-102007-2433. Golden, CO: 

NREL. http://www.osti.gov/bridge. 

Notes: Many of the assumptions about conventional generation and fuel prices are 

drawn from the EIA’s National Energy Modeling System. This information is 

published in the AEO, which consists of three documents: the main AEO (which 

focuses on results); the supplemental tables (which contain additional details on 

results at the regional level); and the assumptions (which presents input details). 

Several sources for emissions data are available from the EPA, including the AP-42 

series of documents. Detailed emissions estimates for different combustion 

technologies and emissions controls can be found in the AP-42 series. The eGRID 

database estimates emissions rates from existing plants, based on measured fuel use 

and continuous emissions monitoring system data measurement 

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Appendix C.	 Wind-Related Jobs 

and Economic 

Development 

This appendix details the economic model used to project the employment and 

economic development impacts of the 20% Wind Scenario described in Appendix 

A. Ramping up wind capacity and electricity output from wind would displace jobs 

and economic activity elsewhere. However, identifying such transfers accurately 

would be very difficult so the impacts cited here do not constitute impacts to the 

U.S. economy overall but are specific to the wind industry and related industries 

from this 20% scenario.The impacts were calculated using the Jobs and Economic 

Development Impacts (JEDI) model, based in part on data from the Wind 

Deployment System (WinDS) model (developed by the National Renewable Energy 

Laboratory [NREL]). Appendix A summarizes the WinDS modeled scenario, and 

specific assumptions are described in Appendix B. Cost and performance projections 

for this analysis were supplied by Black & Veatch (Black and Veatch, forthcoming 

2008) and are detailed in Appendix B. 

The 20% Wind Scenario was constructed by specifying annual wind energy 

generation for every year from 2007 to 2030 based on a trajectory proposed in an 

NREL study (Laxson, Hand, and Blair 2006). The 2006 study forced the WinDS 

model to reach the 20% level for wind-generated electricity by 2030. The 

investigators evaluated aggressive near-term growth rates followed by sustainable 

levels of wind capacity installations that would maintain electricity generation levels 

at 20% and accommodate the repowering of wind installations beyond 2030. The 

20% wind by 2030 trajectory was implemented in WinDS by calculating the 

percentage of annual energy production from wind at an increase of approximately 

1% per year. Figure C-1 illustrates the energy generation trajectory proposed by the 

NREL study, along with the corresponding annual wind capacity installations that 

the WinDS model projects will meet these energy-generation percentages. 

The combined cost, technology, and operational assumptions in the WinDS model 

show that an annual installation rate of about 16 gigawatts per year (GW/year) 

reached by 2018 could result in generation capacity capable of supplying 20% of the 

nation’s electricity demand by 2030. This annual installation rate is affected by the 

quality of wind resources selected for development as well as future wind turbine 

performance. The declining annual installed capacity after 2024 is an artifact of the 

prescribed energy generation from the NREL study in which technology 

improvement and wind resource variation were not considered. The NREL study 

provides an upper level of about 20 GW/year, because turbine performance is 

unchanged over time and only one wind resource power class was assumed. Based 

on the wind resource data and the projected wind technology improvements 

presented in this report, sustaining a level of annual installations at approximately 16 

GW/year beyond 2030 would accommodate the repowering of aging wind turbine 

equipment along with increased electricity demand, so that the nation’s energy 

demand would continue to be met at the 20% wind level. This installation level 

C

198	 20% Wind Energy by 2030 – Prepublication Version 
 

Figure C-1. Prescribed annual wind technology generation as a percentage of 

national electricity demand from Laxson, Hand, and Blair (2006) and 

corresponding annual wind capacity installation for 20% Wind Scenario from 

WinDS model. 

could maintain energy production of 20% of the Nation’s demand. Additionally, this 

scenario shows that this level of wind development could accommodate the 

repowering of aging wind turbine equipment. Specific policy incentives necessary 

for this growth, such as a production tax credit (PTC) or carbon regulation policy, 

are not modeled. 

To obtain 20% of U.S. electricity from wind by 2030, changes in the wind power 

and electricity industries would need to be made. These changes, which are 

discussed in the body of this report, include advances in domestic manufacturing of 

wind turbine components; training, labor, and materials for installation of wind 

farms and operations and maintenance (O&M) functions; and improvements in wind 

technology and electric power system infrastructure. This appendix covers the 

output from the JEDI model, which shows the potential employment impacts from 

this scenario along with other impacts to the United States associated with new wind 

installations. 

C.1 The JEDI Model C

C.1.1 Model Description 

The JEDI model was developed in 2002 for NREL to demonstrate the state and local 

economic development impacts associated with developing wind power plants in the 

United States. These impacts include employment numbers created in the wind 

power sector, and the increase in overall economic activity associated with the 

construction and operating phases of new wind power. The JEDI spreadsheet-based 

model for wind is free and available to the public. It can be downloaded from the 

Wind Powering America website: www.windpoweringamerica.gov. Documentation 

is listed on the same site. For questions, please contact Marshall Goldberg at 

mrgassociates@earthlink.net or Suzanne Tegen at suzanne_tegen@nrel.gov. 

JEDI was initially designed to estimate economic impacts to state economies. 

Subsequent enhancements made the model capable of performing county, regional, 

and national analyses as well. This particular analysis focuses primarily on 

20% Wind Energy by 2030 – Prepublication Version 199 
 

 

 

 

 

C

economic impacts for the United States as a whole, although some state and regional 

results are presented. 

To calculate economic impacts, the model relies on investment and expenditure data 

from the 20% Wind Scenario for the period between 2007 and 2030. It also uses 

industry multipliers that trace supply linkages in the economy. For example, the 

analysis shows how wind turbine purchases benefit not only turbine manufacturers, 

but also the fabricated metal industries and other businesses that supply inputs 

(goods and services) to those manufacturers. 

The model evaluates three separate impacts for each expenditure: direct, indirect, 

and induced. 

z	 Direct impacts are the on-site or immediate effects created by 

spending money for a new wind project. In the JEDI model, the 

construction phase includes the on-site jobs of the contractors and 

crews hired to construct the plant as well as their managers and 

staffs. It also includes the jobs at the manufacturing plants that build 

the turbines and the jobs at the factories that produce the towers and 

blades, among others.18 

z	 Indirect impacts refer to the increase in economic activity that 

occurs, for example, when a contractor, vendor, or manufacturer 

receives payment for goods or services and in turn is able to pay 

others who support their business. This includes the banker who 

finances the contractor and the accountant who keeps the 

contractor’s books, as well as the steel mills, electrical part 

manufacturers, and suppliers of other necessary materials and 

services. 

z	 Induced impacts are the changes in wealth that result from 

spending by people directly and indirectly employed by the project. 

For example, when plant workers and other local workers receive 

income from expenditures related to the plant, they in turn purchase 

food, clothing, and other goods and services from local business. 

The sum of these three impacts yields a total impact that results from the 

construction of the turbine. Figure C-2 illustrates this ripple effect—the turbine 

installation spending that increases dollar flow in the economy. This figure excludes 

the impacts on other energy sectors as wind power displaces other sources of energy. 

JEDI relies on U.S.-specific multipliers and personal expenditure patterns. These 

multipliers—for patterns of employment, wage and salary income, output (economic 

activity), and personal spending (expenditure)—are adapted from the IMPLAN 

Professional Software model (Minnesota IMPLAN Group, Inc., Stillwater, 

Minnesota; see http://www.implan.com/software.html). The IMPLAN model is 

based on U.S. industry and census data. Spending from new investments (e.g., 

purchases of equipment and services) to construct and operate wind plants is 

matched with the appropriate multipliers for each industry sector (e.g., construction, 

18When an impact analysis is conducted in this manner, the definitions of direct and indirect are 

changed somewhat. Typically, the change in final demand to an industry (in this instance the wind 

industry) is seen as the direct effect. In the JEDI model, the direct effect includes what are usually 

called first-round indirect effects (e.g., demand to manufacturers and other goods and service 

suppliers). The JEDI indirect effects are all subsequent rounds of the industry indirect effects. 

200	 20% Wind Energy by 2030 – Prepublication Version 
 

Figure C-2. Wind's economic ripple effect 

electrical equipment, machinery, professional services, and others) affected by the 

change in expenditure. 

Outputs from the JEDI model are reported for two distinct phases: the construction 

phase and the annual operations phase. The construction period outputs represent the 

entire construction period (typically one year for a utility-scale wind project, 

although this can vary depending on the size of the project). The outputs for the 

operating period represent the jobs and economic impacts created for one year of 

operation. 

C.1.2 Caveats 

Before noting the specific economic impacts from the 20% Wind Scenario, it is 

important to underscore several caveats about the JEDI model. 

First, the model is considered static. As such, it relies on interindustry relationships 

and personal consumption patterns at the time of the analysis. The model does not 

account for feedback through demand increases or reductions that could result from 

price changes. Similarly, the model does not account for feedback from inflationary 

pressures or potential constraints on local labor and money supplies. In addition, the 

model assumes that adequate local resources and production and service capabilities 

are available to meet the level of local demand identified in the model’s 

assumptions. For new power plants, the model does not automatically take into 

account improvements in industry productivity over time, changes during 

construction, or changes in O&M processes (e.g., production recipe for labor, 

materials, and service cost ratios). To adjust for advancements in technology or 

changes in wages and salaries, the model is run with new cost assumptions (e.g., 

once with a construction cost of $1,650/kW and again with a construction cost – 

excluding construction financing - of $1,610/kW). 

Second, the intent of using the JEDI model is to construct a reasonable profile of 

investments (e.g., wind power plant construction and operating costs) to demonstrate 

the economic impacts that will likely result during the construction and operating 

periods. Given the potential for future changes in wind power plant costs beyond 

those identified, and potential changes in industry and personal consumption 

patterns in the economy noted earlier, the analysis is not intended to provide a 

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C

precise forecast, but rather an estimate of overall economic impacts in the wind 

energy sector from specific scenarios. 

Third, because the analysis and results are specific to developing new land-based 

and offshore wind power plants only, this is considered a gross analysis. This means 

that results do not reflect the net impacts of construction or operation of other types 

of electricity-generating power plants or replacement of existing power generation 

resources to meet growing needs. 

Fourth, the analysis assumes that the output from the wind power plants and the 

specific terms of the power purchase agreements generate sufficient revenues to 

accommodate the equity and debt repayment and annual operating expenditures. 

And finally, the analysis period is 2007 through 2030; additional impacts beyond 

these years are not considered. 

C.2 Wind Scenario Inputs 

To assess the economic development from the addition of 293 GW of wind 

technology in the United States, the authors relied on inputs from the WinDS model. 

The detailed cost and performance projections can be found in Appendix B of this 

report. 

Table C-1 summarizes the wind data assumptions used in the JEDI model. The cost 

data are allocated into expenditure categories. Each category includes the portion of 

the expenditure that goes to the local area, which in this case is the entire United 

States. 

Table C-1. JEDI wind modeling assumptions 

Category 

Period of Analysis 

Nameplate Capacity 

Number of Turbines 

Turbine Size 

Technology Cost1 per kW 

2007 

2010 

2015 

2020 

2025 

2030 

O&M Costs 

Fixed2

 Variable3

 2004 

2010 

2015 

2020 

2025 

Land-Based 

2007-2030 

239.5 GW 

79,130 

1500–5000 kW 

$1650 

$1650 

$1610 

$1570 

$1530 

$1480 

$11.50/kW 

$7.00/MWh 

$5.50/MWh 

$5.00/MWh 

$4.60/MWh 

$4.50/MWh 

Shallow 

Offshore 

2007-2030 

53.9 GW 

17,976 

3000 kW 

$2400 

$2300 

$2200 

$2150 

$2130 

$2100 

$15.00/kW 

$21.00/MWh

$18.00/MWh

$16.00/MWh

$14.00/MWh

$13.00/MWh 

Total 

293.4 GW 

97,106 

202 20% Wind Energy by 2030 – Prepublication Version 
 

 

 

 

Category Land-Based Shallow 

Offshore Total 

2030 

U.S. Spending

$4.40/MWh $11.00/MWh 

Labor 100% 100% 

Materials and Services 100% 100% 

Equipment (Manufacturing

 Transition)4 

Major Components 

Blades 50% in 2007 to 80% in 2030 

Towers 26% in 2007 to 50% in 2030 

Machine Heads 20% in 2007 to 42% in 2030 

Sub-Components 10% in 2007 to 30% in 2030 

Notes: 1. All dollar values are 2006 dollars. Technology costs exclude construction 

financing costs and regional cost variations that result from increased population density, 

elevation, or other considerations that are included in the WinDS model. Thus, the 

cumulative investment costs presented in this study are lower than those presented in 

Appendix A. 2. Fixed costs include land lease cost. 3. Variable costs include property taxes. 

4. Refers to U.S. manufacturing/assembly for turbines, blades and towers. For purposes of 

this modeling, the transition (percentage of U.S. manufacturing/assembly) is assumed to 

occur at an average annual rate over the 24-year period. 

As explained earlier, the JEDI model uses project expenditures—or spending—for 

salaries, services, and materials to calculate the total economic impacts. Table C-2 

summarizes the expenditure data used in the analysis. 

Table C-2. Wind plant expenditure data summary (in millions) 

Category Onshore Offshore All Wind 

Total Cumulative Construction Cost 

(2007-2030) 

$379,343 $115,790 $495,133 

Domestic Spending $200,192 $94,690 $294,882 

Total Annual Operational Expenses in 

2030 (300 GW) 

$63,618 $20,765 $84,383 

Direct O&M Costs $4,394 $2,861 $7,255 

Other Annual Costs $59,224 $17,904 $77,128 

Property Taxes $1,533 $345 $1,877 

Land Lease $639 $144 $783 

Notes: All dollar values are 2006 dollars. All dollars represent millions of dollars. Though 

some of the money spent during construction leaves the country, all O&M spending is 

domestic. 

C.3 Findings 

As Table C-3 indicates, developing 293 GW of new land-based and offshore wind 

technologies from 2007 to 2030 could have significant economic impacts for the 

entire United States. Cumulative economic activity from the construction phase 

alone will reach more than $944 billion for direct, indirect, and induced activity in 

the nation. This level of economic activity stimulates an annual average of more 

than 250,000 workers required for employment in the wind power and related 

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C

sectors from 2007 forward. Of these average annual positions, the wind industry 

supports 70,000 full-time workers in construction-related sectors, including more 

than 47,000 full-time workers directly in construction and 22,000 workers in 

manufacturing. As noted earlier, this estimate does not take into account the 

offsetting employment effects in other energy sectors. 

Table C-3. U.S. construction-related economic impacts from 20% wind 

Average Annual Impacts 

Direct Impacts 

Jobs Earnings 

$5,221 

Output

72,946 $12,217

Construction Sector Only 47,020 $3,547 

Manufacturing Sector Only 22,346 $1,446 

Other Industry Sectors 3,580 $228 

Indirect Impacts 66,035 $3,008 $11,377 

Induced Impacts 119,774 $4,483 $15,749 

Total Impacts (Direct, Indirect, 

Induced) 

Total Construction Impacts 

2007-2030 

Direct Impacts 

258,755 

Jobs 

1,750,706 

$12,712 

Earnings 

$125,305 

$39,343 

Output 

$293,197 

NPV of 

Output

$111,153 

Construction Sector Only 1,128,479 $85,129 

Manufacturing Sector Only 536,305 $34,706 

Other Industry Sectors 85,922 $5,471 

Indirect Impacts 1,584,842 $72,197 $273,057 $103,541 

Induced Impacts 2,874,582 $107,591 $377,984 $143,367 

Total Impacts (Direct, Indirect, 

Induced) 6,210,129 $305,093 $944,238 $358,061 

Note: All dollar values are millions of 2006 dollars. Average annual Jobs are full-time equivalent for 

each year of the construction period. Cumulative jobs are total full-time equivalent for the 24-year 

construction period from 2007 through 2030. The NPV column shows the net present value of the 

output column with a discount rate of 7%, per guidance from the Office of Management and 

Budget. 

Under this scenario, the wind industry would produce 300 GW/year. By 2020, the 

economic activity generated from annual operations of the wind turbines would 

exceed $27 billion/year. The number of wind plant workers alone would grow to 

more than 28,000/year, and total wind-related employment would exceed 215,000 

workers (see Table C-4). 

Table C-4. U.S. operations-related economic impacts from 20% wind 

Operation of 300 GW 

in 2030 

Direct Impacts 

Jobs 

76,667 

Earnings 

$3,643 

Output

$8,356 

Plant Workers Only 28,557 $1,617 

Nonplant Workers 48,110 $2,026 

Indirect Impacts 37,785 $1,624 $5,642 

Induced Impacts 102,126 $3,822 $13,429 

Total Impacts (Direct, Indirect, 

Induced) 216,578 $9,090 $27,427 

204 20% Wind Energy by 2030 – Prepublication Version 
 

Operation of 300 GW 

in 2030 

Total Operation Impacts 2007-

2030 

Direct Impacts 

Jobs 

Jobs 

1,163,297 

Earnings 

Earnings 

$55,907 

Output 

Output 

$122,463 

NPV of 

Output

$26,072 

Property Tax $1,877 $760 

Land Lease $783 $317 

Other Direct Impacts $119,804 $24,996 

Plant Workers Only 482,578 $27,458 

Nonplant Workers 680,719 $28,449 

Indirect Impacts 561,107 $24,118 $84,008 $17,674 

Induced Impacts 1,591,623 $59,572 $209,286 $42,569 

Total Impacts (Direct, Indirect, 

Induced) 3,316,027 $139,596 $415,757 $86,315 

Note: All dollar values are millions of 2006 dollars. Operation jobs in 2030 are full-time equivalent 

for operation of the 300 GW fleet existing in 2030. Cumulative jobs are total full-time equivalent for 

the 24-year construction period from 2007 through 2030. The NPV column shows the net present 

value of the output column with a discount rate of 7%, per guidance from the Office of Management 

and Budget. 

Economic impacts from direct, indirect, and induced impacts are shown per year in 

Figure C-3. 

Figure C-3. Annual direct, indirect and induced economic 

impacts from 20% scenario 

Figure C-4 displays the total economic impacts on a relative basis. The impacts of 

both the construction and the operation phases are included for the entire period 

from 2007 through 2030. 

The 20% Wind Scenario shows the U.S. wind industry growing from its current 3 

GW/year in 2007 to a sustained 16 GW/year by around 2018, as represented in 

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Figure C-4. Total economic impacts of 20% Wind Energy by 2030 

on a relative basis 

C

Figure C-1. In the following sections, employment impacts in the wind industry are 

divided into three major industry sectors: manufacturing, construction, and 

operations. Each sector is described during the year of its maximum employment 

supported by the wind industry. 

The JEDI model estimates the number of jobs supported by one project throughout 

the economy, as well as the total economic output from the project. Results from the 

JEDI model do not include macroeconomic effects; instead, the model focuses on 

jobs and impacts supported by specific wind projects. In other words, the 

employment estimates from the JEDI model look only at gross economic impacts 

from this 20% Wind Scenario. 

C.4 Manufacturing Sector 

The 20% Wind Scenario includes the prospect of significantly expanding wind 

power manufacturing capabilities in the United States. In 2026, this level of wind 

development supports more than 32,000 U.S. manufacturing full-time workers in 

land-based and offshore wind projects. These employment impacts are directly 

related to producing the major components and subcomponents for the turbines, 

towers, and blades installed in the United States. Although the level of domestic 

wind installations declines after 2021 in the scenario modeled, the manufacturing 

and construction industries have the potential to maintain a high level of 

employment and expand further to meet increasing global demand. 

To estimate the potential location for manufacturing jobs, data from a nongovernmental organization, Renewable Energy Policy Project (REPP), report were 

used (Sterzinger and Svrcek 2004). The REPP report identified existing U.S. 

companies with the technical potential to enter the wind turbine market. The map in 

Figure C-5 was created using the percentages of manufacturing capability in each 

state and JEDI’s manufacturing jobs output. Again, these potential manufacturing 

jobs from the REPP report are based on technical potential existing in 2004, without 

assuming increased productivity or expansion over time. The data also assume that 

existing facilities that manufacture components similar to wind turbine components 

are modified. Most of the manufacturing jobs in this scenario are located in the 

206 20% Wind Energy by 2030 – Prepublication Version 
 

 

 

 

Figure C-5. Potential manufacturing jobs created by 2030 

Great Lakes region, where manufacturing jobs are being lost. Even states without a 

significant wind resource can be impacted economically from new manufacturing 

jobs (e.g., southeastern US). 

C.5 Construction Sector 

The year 2021 represents the height of the wind plant construction period, with 16.7 

GW of wind having been brought online. In that year, more than 65,000 construction 

industry workers are assumed to be employed and $54.5 billion is generated in the 

U.S. economy from direct, indirect, and induced construction spending. 

To reach the 20% Wind Scenario, today’s wind power industry would have to grow 

from 9,000 annual construction jobs in 2007 to install 3 GW, to 65,000 new annual 

construction jobs in 2021 to install more than 16 GW. Construction jobs could be 

dispersed throughout the United States. Assuming the 16 GW/year capacity can be 

maintained into the future, including the replacement of outdated wind plants, the 

industry could maintain 20% electricity from wind as demand grows. In this 

scenario, the construction sector would experience the largest increase in jobs, 

followed by the operations sector, and then by the manufacturing sector. Figure C-6 

shows the direct employment impact on the construction sector, the manufacturing 

sector and the operations sector (plant workers only). 

Figure C-7 shows employment impacts during the same years, but adds the indirect 

and induced jobs. The bottom three bars (manufacturing, construction, and 

operations—including plant workers and other direct jobs) are direct jobs only. This 

chart depicts the large impact from the indirect and induced job categories, 

compared to the initial direct expenditures in the direct categories. 

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20% Wind Energy by 2030 – Prepublication Version 207 
 

 

 

Figure C-6. Direct manufacturing, construction, and operations 

jobs supported by the 20% Wind Scenario 

Figure C-7. Jobs per year from direct, indirect, and induced categories 

C

C.6 Operations Sector 

JEDI results indicate that in 2030, employment of more than 215,000 total 

operations workers (direct, indirect, and induced) will exist to maintain 293 GW of 

wind capacity installed between 2007 and 2030. This includes more than 28,000 

direct O&M jobs and 48,000 other direct jobs related to operating a wind plant (e.g., 

utility services and subcontractors). In 2030, JEDI predicts that land-based and 

offshore wind project operations will have a total economic impact of $27 billion. 

Operations employment would be dispersed across the country and is likely to be 

208 20% Wind Energy by 2030 – Prepublication Version 
 

near wind installations. Rural Americans, in particular, could realize significant 

positive impacts from this scenario in the form of landowner payments and property 

taxes. Counties use property taxes to improve roads and schools, along with other 

vital infrastructure. More than $8.8 billion is estimated in property taxes and land 

lease payments between 2007 and 2030, which could be an important boost for rural 

communities. 

Figure C-8 shows the results of JEDI analysis performed on a state-by-state basis in 

the form of impacts to each North American Electric Reliability Corporation 

(NERC) region. The individual state impacts were summed to calculate the NERC 

region impacts. These total impacts are lower than those from the JEDI analysis for 

the entire country because any job or dollar flowing out of state is considered 

monetary leakage (in the U.S. analysis, the model considers the whole country to be 

“local”). 

Figure C-8. Jobs and economic impacts by NERC region 

C

Figure C-8 shows jobs in job-years, which are FTE jobs counted in each year in 

which they exist. For example, if a maintenance worker holds one job for 20 years, 

this is shown as 20 job-years. For this figure, jobs during construction are assumed 

to last for one year. Jobs during the operations period are assumed to last for 20 

years. Economic impacts are direct, indirect, and induced. Because it represents 

impacts from 305 GW of new wind starting in 2004 and ending in 2030, Figure C-8 

shows three additional years when compared to other results. 

20% Wind Energy by 2030 – Prepublication Version 209 
 

 

 

 

C.7 Conclusion 

As a nation, the United States has made much progress recently in developing its 

wind resources. With advancements in wind technologies and increasing demand for 

electricity projected, however, significant opportunities to develop this domestic 

renewable resource will continue to arise. Actions toward this goal, as identified in 

the 20% Wind Scenario, offer residents and businesses in the rural and urban United 

States potential for economic development opportunities and potential for 

employment. 

The United States is a prime location for developing wind resources and new wind 

manufacturing facilities. At the same time, relocating or expanding existing 

industries can give businesses opportunities to meet many of the material needs 

associated with wind technology manufacturing, installation, and facility operation. 

In many areas of the country, renewable resources provide an opportunity to boost 

the local economy significantly. Wind plants offer employment during construction 

and continue to support permanent jobs during operation. Today, tax revenues from 

wind plants help to fund local schools, hospitals, and government services. 

Based on the scenario presented in this report, a new and expanding wind 

manufacturing industry can meet 20% of our domestic electricity needs through 

2030. 

C.8 References 

Laxson, A., M. Hand, and N. Blair. 2006. High Wind Penetration Impact on U.S. 

Wind Manufacturing Capacity and Critical Resources. NREL/TP-500­

40482. Golden, CO: National Renewable Energy Laboratory. 

MIG IMPLAN. “IMPLAN Professional Software.” Stillwater, MN: Minnesota 

IMPLAN Group, Inc. (MIG) www.implan.com/software.html. 

O’Connell, R., R. Pletka, et al. 2007. 20 % Wind Energy Penetration in the United 

States: A Technical Analysis of the Energy Resource. Overland Park, KS: 

Black & Veatch. 

Sterzinger, G., and M. Svrcek. 2004. Wind Turbine Development: Location of 

Manufacturing Activity. Washington, DC: Renewable Energy Policy Project 

(REPP). 

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210 20% Wind Energy by 2030 – Prepublication Version 
 

Appendix D. Participants in the 

20% Wind Scenario 

The U.S. Department of Energy would like to acknowledge the authors and 

reviewers listed below. Their contributions and support were important 

throughout the development of this report. The final version of this document 

was prepared by the U.S. Department of Energy. 

This technical report is the culmination of contributions from more than 90 

individuals and more than 50 organizations over 15 months beginning in June 2006. 

Contributions included strategic planning and guidance, workshop participation, 

topical task force participation, chapter writing, report review, management and 

coordination, and management oversight. Many participants contributed in several 

of these capacities. 

Members of the following advisory group supplied strategic guidance: 

Rashid Abdul Mitsubishi Power Systems 

Stan Calvert U.S. Department of Energy 

Edgar DeMeo* Renewable Energy Consulting Services, Inc. 

Robert Gates Clipper Windpower 

Robert Gramlich American Wind Energy Association 

Thomas O. Gray American Wind Energy Association 

Steven Lindenberg U.S. Department of Energy 

James Lyons GE Global Research 

Brian McNiff McNiff Light Industries 

Bentham Paulos Energy Foundation 

Bonnie Ram* Energetics Incorporated 

Janet Sawin Worldwatch Institute 

Brian Smith National Renewable Energy Laboratory 

J. Charles Smith Utility Wind Integration Group 

Randall Swisher American Wind Energy Association 

Robert Thresher National Renewable Energy Laboratory 

James Walker enXco 

*Also provided management and logistic support under subcontract to the National 

Renewable Energy Laboratory 

Individual chapters were prepared and reviewed by the following teams. 

Authors are shown in bold, task force members are underlined, and task force 

chairpersons are identified with an asterisk. 

Reviewers are shown in italics. Reviewers did not help to draft the chapters they 

reviewed. Their participation is not meant to imply that they or their respective 

organizations either agree or disagree with the findings of the effort. 

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D

Chapter 1. Overview of the 20% Wind Scenario 

Elizabeth Salerno American Wind Energy Association 

Robert Gramlich American Wind Energy Association 

Alison Silverstein Consultant 

Paget Donnelly Energetics Incorporated 

Edgar DeMeo Renewable Energy Consulting Services, Inc. 

Larry Flowers National Renewable Energy Laboratory 

Thomas O. Gray American Wind Energy Association 

Maureen Hand National Renewable Energy Laboratory 

Bonnie Ram Energetics Incorporated 

Brian Smith National Renewable Energy Laboratory 

Chapter 2. Wind Turbine Technology 

Michael Robinson* National Renewable Energy Laboratory 

Paul Veers Sandia National Laboratories 

Sandy Butterfield National Renewable Energy Laboratory 

Jim Greene National Renewable Energy Laboratory 

Walter Musial National Renewable Energy Laboratory 

Robert Thresher National Renewable Energy Laboratory 

Edgar DeMeo Renewable Energy Consulting Services, Inc. 

Robert Gramlich American Wind Energy Association 

Robert Poore Global Energy Concepts, LLC 

Scott Schreck National Renewable Energy Laboratory 

Alison Silverstein Consultant 

Brian Smith National Renewable Energy Laboratory 

James Walker enXco 

Lawrence Willey GE Energy 

Jose Zayas Sandia National Laboratories 

Rashid Abdul Mitsubishi 

Jim Ahlgrimm U.S. Department of Energy 

James Lyons GE Global Research 

Amir Mikhail Clipper Windpower 

Chapter 3. Manufacturing, Material Resources, and Impacts on the Nation’s 

Economy 

Lawrence Willey* GE Energy 

Corneliu Barbu GE Energy (formerly) 

Maureen Hand National Renewable Energy Laboratory 

Edgar DeMeo Renewable Energy Consulting Services, Inc. 

Kate Gordon Apollo Alliance 

Steve Lockard TPI Composites 

Brian O’Hanlon U.S. Department of Commerce 

Elizabeth Salerno American Wind Energy Association 

Brian Siu Apollo Alliance 

Brian Smith National Renewable Energy Laboratory 

Paul Veers Sandia National Laboratories 

James Walker enXco 

Stephen Connors Massachusetts Institute of Technology 

Brian McNiff McNiff Light Industries 

212 20% Wind Energy by 2030 – Prepublication Version 
 

Chapter 4. Transmission and Integration into the U.S. Electric System 

J. Charles Smith* Utility Wind Integration Group 

Robert Gramlich American Wind Energy Association 

Mark Ahlstrom WindLogics 

Jeff Anthony American Wind Energy Association 

Jack Cadogan U.S. DOE Retired 

James Caldwell Los Angeles Department of Water and Power 

Henri Daher National Grid USA 

Edgar DeMeo Renewable Energy Consulting Services, Inc. 

Ken Donohoo Electric Reliability Council of Texas 

Abraham Ellis Public Service Company of New Mexico 

Douglas Faulkner Puget Sound Energy 

Robert Fullerton Western Area Power Administration 

Stephen Gehl Electric Power Research Institute 

Jay Godfrey American Electric Power 

John Holt National Rural Electric Cooperative Association 

Karen Hyde Xcel Energy 

Mike Jacobs American Wind Energy Association 

Brendan Kirby Oak Ridge National Laboratory 

Ronald L. Lehr American Wind Energy Association 

Charles Linderman Edison Electric Institute 

Michael Milligan National Renewable Energy Laboratory 

Dale Osborn Midwest Independent System Operator 

Philip Overholt U.S. Department of Energy 

Brian Parsons National Renewable Energy Laboratory 

Richard Piwko GE Energy 

Steve Ponder Sierra Pacific Resources 

Craig Quist PacifiCorp 

Kristine Schmidt Xcel Energy 

Matthew Schuerger Energy Systems Consulting Services, LLC 

Alison Silverstein Consultant 

Beth Soholt Wind on the Wires 

John Stough AEP 

Robert Thomas Cornell University 

Gary Thompson Nebraska Public Power District 

Robert Zavadil EnerNex 

Ellen Lutz U.S. Department of Energy (formerly) 

Chapter 5. Wind Power Siting and Environmental Effects 

Laurie Jodziewicz* American Wind Energy Association 

Bonnie Ram Energetics Inc. 

James Walker enXco 

Wayne Walker Horizon Wind Energy 

Abby Arnold Resolve 

John Coequyt Greenpeace 

Edgar DeMeo Renewable Energy Consulting Services, Inc. 

Nathanael Greene Natural Resources Defense Council 

Alan Nogee Union of Concerned Scientists 

Janet Sawin Worldwatch Institute 

Alison Silverstein Consultant 

Tom Weis enXco Consultant 

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D

Katherine Kennedy Natural Resources Defense Council (formerly) 

Jim Lindsay Florida Power & Light Company
 

Laura Miner U.S. Department of Energy (formerly) 

Robert Thresher National Renewable Energy Laboratory 

Chapter 6. Wind Power Markets 

The contributions of this Task Force (Markets and Stakeholders) spanned a broad 

spectrum of issues and are reflected in many of the chapters in this report. 

Larry Flowers* National Renewable Energy Laboratory 

Ronald L. Lehr American Wind Energy Association 

David Olsen Center for Energy Efficiency and Renewable 

Technologies 

Brent Alderfer Community Energy 

Jeff Anthony American Wind Energy Association 

Lori Bird National Renewable Energy Laboratory 

Lisa Daniels Windustry 

Trudy Forsyth National Renewable Energy Laboratory 

Robert Gough Intertribal Council on Utility Policy 

Steven Lindenberg U.S. Department of Energy 

Walter Musial National Renewable Energy Laboratory 

Kevin Rackstraw Clipper Windpower 

Roby Robichaud U.S. Department of Energy 

Susan Sloan American Wind Energy Association 

Tom Wind Wind Utility Consulting 

Bob Anderson Bob Anderson Consulting 

Ruth Baranowski National Renewable Energy Laboratory 

Edgar DeMeo Renewable Energy Consulting Services, Inc. 

Robert Fullerton Western Area Power Administration 

Robert Gramlich American Wind Energy Association 

Karen Hyde Xcel Energy 

Bonnie Ram Energetics Incorporated 

Kristine Schmidt Xcel Energy 

Michael Skelley Horizon Wind Energy 

Brian Smith National Renewable Energy Laboratory 

Dennis Lin U.S. Department of Energy 

Roby Roberts Goldman Sachs 

Wayne Walker Horizon Wind Energy 

Appendices A, B, and C and Supporting Analysis Task Force 

Maureen Hand* (A, B, C) National Renewable Energy Laboratory 

Nate Blair (A, B) National Renewable Energy Laboratory 

Suzanne Tegen (C) National Renewable Energy Laboratory 

Mark Bolinger (A) Lawrence Berkeley National Laboratory 

Dennis Elliott (B) National Renewable Energy Laboratory 

Ray George (B) National Renewable Energy Laboratory 

Marshall Goldberg (C) MRG Associates 

Donna Heimiller (B) National Renewable Energy Laboratory 

Tracy Hern (A) Western Resource Advocates 

Bart Miller (A) Western Resource Advocates 

Ric O’Connell (A, B) Black & Veatch 

214 20% Wind Energy by 2030 – Prepublication Version 
 

Marc Schwartz (B) National Renewable Energy Laboratory 

Ryan Wiser (A) Lawrence Berkeley National Laboratory 

Jeff Anthony American Wind Energy Association 

Steven Clemmer Union of Concerned Scientists 

Edgar DeMeo Renewable Energy Consulting Services, Inc. 

Robert Gramlich American Wind Energy Association 

Christopher Namovicz U.S. DOE Energy Information Administration 

Elizabeth Salerno American Wind Energy Association 

Alison Silverstein Consultant 

Brian Smith National Renewable Energy Laboratory 

Ian Baring-Gould National Renewable Energy Laboratory 

Jack Cadogan U.S. DOE Retired 

Eric Gebhardt GE Energy 

Gary Jordan GE Energy 

Brian Parsons National Renewable Energy Laboratory 

Ryan Pletka Black & Veatch 

Walter Short National Renewable Energy Laboratory 

Martin Tabbita GE Energy 

Hanson Wood enXco 

Michael DeAngelis Sacramento Municipal Utility District 

Alejandro Moreno U.S. Department of Energy 

Workshops and Outreach 

Two strategic workshops took place during the course of this work. At the first of 

these, held August 17–18, 2006, attendees developed the initial statement of the 20% 

Wind Scenario and defined work plans. At the second, held November 9–10, 2006, 

participants shared and discussed preliminary results and obtained input from a 

group of invited individuals from key stakeholder sectors. Previously, these 

individuals had been external to the effort. Many of the authors, reviewers, and task 

force members listed in this appendix attended one or both of these workshops. 

The invited participants at the November workshop brought along important 

feedback and perspectives from their respective sectors that have helped to shape 

this report. Some also reviewed sections of the report. Their participation is not 

meant to imply that they or their respective organizations either agree or disagree 

with the findings of the effort. These participants are listed below: 

Aaron Brickman U.S. Department of Commerce 

Jennifer DeCesaro Clean Energy Group 

Michael Fry American Bird Conservancy 

Matt Gadow DMI Industries 

Stephen Gehl Electric Power Research Institute 

David Hamilton Sierra Club 

John Holt National Rural Electric Cooperative Association 

Robert Hornung Canadian Wind Energy Association 

Karen Hyde Xcel Energy 

Ed Ing Law Office of Edwin T. C. Ing 

Debra Jacobson DJ Consulting 

Miles Keogh National Association of Regulatory Utility 

Commissioners 

Charles Linderman Edison Electric Institute 

Steve Lockard TPI Composites 

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D

Craig Mataczynski Renewable Energy Systems Americas 

Christopher Namovicz U.S. Department of Energy, Energy Information 

Administration 

Alan Nogee Union of Concerned Scientists 

Jim Presswood Natural Resources Defense Council 

Kristine Schmidt Xcel Energy 

Linda Silverman U.S. Department of Energy 

Brian Siu Apollo Project 

Kate Watson Horizon Wind Energy 

On November 28, 2006, a topical outreach workshop was held with representatives 

from nongovernmental organizations concerned about wildlife conservation and the 

environment. Participants discussed the early findings of the Environment and Siting 

Task Force and offered insights into issues important to their organizations. 

Workshop attendees are listed below. Their participation is not meant to imply that 

they or their respective organizations either agree or disagree with the findings of the 

effort. 

Matthew Banks World Wildlife Fund 

Laura Bies The Wildlife Society 

Brent Blackwelder Friends of the Earth 

John Coequyt Greenpeace 

Amy Delach Defenders of Wildlife 

Tom Franklin Izaak Walton League 

Michael Fry American Bird Conservancy 

Robert Gramlich American Wind Energy Association 

Tony Iallonardo National Audubon Society 

Laurie Jodziewicz American Wind Energy Association 

Katie Kalinowski Resolve/National Wind Coordinating Collaborative 

Katherine Kennedy Natural Resources Defense Council 

Betsy Loyless National Audubon Society 

Laura Miner U.S. Department of Energy 

Amber Pairis Association of Fish and Wildlife Agencies 

Cliff Scher Energetics Incorporated 

Kate Smolski Greenpeace 

Robert Thresher National Renewable Energy Laboratory 

James Walker enXco 

Wayne Walker Horizon Wind Energy 

Tim Warman National Wildlife Federation 

Tom Weis enXco 

Peggy Welsh Energetics Incorporated 

Marchant Wentworth Union of Concerned Scientists 

The Communications and Outreach Task Force advised on outreach strategy and 

facilitated engagement of key stakeholders. These functions will continue beyond 

release of this report. Members of this task force include 

Mary McCann-Gates* Clipper Windpower 

Jill Pollyniak* Clipper Windpower 

Thomas O. Gray American Wind Energy Association 

Susan Williams Sloan American Wind Energy Association 

Peggy Welsh Energetics Incorporated 

216 20% Wind Energy by 2030 – Prepublication Version 
 

 

Document Coordination, Lead Editors and Other Contributions including 

managing report production/editing, and developing graphics and Web site 

materials. Members of this team include 

Bonnie Ram Energetics Incorporated, Team Manager 

Cliff Scher Energetics Incorporated 

Russell Raymond Energetics Incorporated 

Julie Chappell Energetics Incorporated, Graphics Lead 

Tommy Finamore Energetics Incorporated, Cover Graphic 

Susan Kaczmarek Energetics Incorporated, Document Coordinator 

Kathleen O’Dell National Renewable Energy Laboratory 

Donna Heimiller National Renewable Energy Laboratory 

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Appendix E. Glossary 

Area control error (ACE): The instantaneous difference between net actual and 

scheduled interchange, taking into account the effects of frequency deviations. 

Balancing area (balancing authority area): The collection of generation, 

transmission, and loads within the metered boundaries of the balancing authority. 

The balancing authority maintains load-resource balance within this area. 

Before-and-after control impact (BACI): A schematic method used to trace 

environmental effects from substantial anthropogenic changes to the environment. 

The overall aim of the method is to estimate the state of the environment before and 

after any change and the specific objectives is to compare changes at reference sites 

(or control sites) with the actual area of impact. 

Bus: An electrical conductor that serves as a common connection for two or more 

electrical circuits. 

Bus-bar: The point at which power is available for transmission. 

Cap and trade: An established policy tool that creates a marketplace for emissions. 

Under a cap and trade program, the government regulates the aggregate amount of a 

type of emissions by setting a ceiling or cap. Participants in the program receive 

allocated allowances that represent a certain amount of pollutant and must purchase 

allowances from other businesses to emit more than their given allotment. 

Capability: The maximum load that a generating unit, generating station, or other 

electrical apparatus can carry under specified conditions for a given period of time 

without exceeding approved limits of temperature and stress. 

Capacity: The amount of electrical power delivered or required for which 

manufacturers rate a generator, turbine, transformer, transmission circuit, station, or 

system. 

Capacity factor (CF): A measure of the productivity of a power plant, calculated as 

the amount of energy that the power plant produces over a set time period, divided 

by the amount of energy that would have been produced if the plant had been 

running at full capacity during that same time interval. Most wind power plants 

operate at a capacity factor of 25% to 40%. 

Capacity penetration: The ratio of the nameplate rating of the wind plant capacity 

to the peak load. For example, if a 300-megawatt (MW) wind plant is operating in a 

zone with a 1,000-MW peak load, the capacity penetration is 30%. The capacity 

penetration is related to the energy penetration by the ratio of the system load factor 

to the wind plant capacity factor. For example, say that the system load factor is 

60% and the wind plant capacity factor is 40%. In this case, and with an energy 

penetration of 20%, the capacity penetration would be 20% × 0.6/0.4, or 30%. 

Capital costs: The total investment cost for a power plant, including auxiliary costs. 

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Carbon dioxide (CO2): A colorless, odorless, noncombustible gas present in the 

atmosphere. It is formed by the combustion of carbon and carbon compounds (such 

as fossil fuels and biomass); by respiration, which is a slow form of combustion in 

animals and plants; and by the gradual oxidation of organic matter in the soil. CO2 is 

a greenhouse gas that contributes to global climate change. 

Carbon monoxide (CO): A colorless, odorless, but poisonous combustible gas. 

Carbon monoxide is produced during the incomplete combustion of carbon and 

carbon compounds, such as the fossil fuels coal and petroleum. 

Circuit: An interconnected system of devices through which electrical current can 

flow in a closed loop. 

Competitive Renewable Energy Zones (CREZ): A mechanism of the renewable 

portfolio standard in Texas designed to ensure that the electricity grid is extended to 

prime wind energy areas. The designation of these areas directs the Electric 

Reliability Council of Texas to develop plans for transmission lines to these areas 

that will connect them with the grid. See also “Electric Reliability Council of Texas” 

and “renewable portfolio standard.” 

Conductor: The material through which electricity is transmitted, such as an 

electrical wire. 

Conventional fuel: Coal, oil, and natural gas (fossil fuels); also nuclear fuel. 

Cycle: In AC electricity, the current flows in one direction from zero to a maximum 

voltage, then back down to zero, then to a maximum voltage in the opposite 

direction. This comprises one cycle. The number of complete cycles per second 

determines the frequency of the current. The standard frequency for AC electricity in 

the United States is 60 cycles. 

Dispatch: The physical inclusion of a generator’s output onto the transmission grid 

by an authorized scheduling utility. 

Distribution: The process of distributing electricity. Distribution usually refers to 

the series of power poles, wires, and transformers that run between a high-voltage 

transmission substation and a customer’s point of connection. 

Effective load-carrying capability (ELCC): The amount of additional load that 

can be served at the target reliability level by adding a given amount of generation. 

For example, if adding 100 MW of wind could meet an increase of 20 MW of 

system load at the target reliability level, the turbine would have an ELCC of 20 

MW, or a capacity value of 20% of its nameplate value. 

Electricity generation: The process of producing electricity by transforming other 

forms or sources of energy into electrical energy. Electricity is measured in kilowatthours. 

Electric Reliability Council of Texas (ERCOT): One of the 10 regional reliability 

councils of the North American Electric Reliability Council. ERCOT is a 

membership-based 501(c)(6) nonprofit corporation, governed by a board of directors 

and subject to oversight by the Public Utility Commission of Texas and the Texas 

Legislature. ERCOT manages the flow of electric power to approximately 20 

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million customers in Texas, representing 85% of the state’s electric load and 75% of 

the Texas land area. See also “North American Electric Reliability Council.” 

Energy: The capacity for work. Energy can be converted into different forms, but 

the total amount of energy remains the same. 

Energy penetration: The ratio of the amount of energy delivered from one type of 

resource to the total energy delivered. For example, if 200 megawatt-hours (MWh) 

of wind energy supplies 1,000 MWh of energy consumed, wind’s energy penetration 

is 20%. 

Externality: A consequence that accompanies an economic transaction, where that 

consequence affects others beyond the immediate economic actors and cannot be 

limited to those actors. 

Feed-in law: A legal obligation on utilities to purchase electricity from renewable 

sources. Feed-in laws can also dictate the price that renewable facilities receive for 

their electricity. 

Frequency: The number of cycles through which an alternating current passes per 

second, measured in hertz. 

Gearbox: A system of gears in a protective casing used to increase or decrease shaft 

rotational speed. 

Generator: A device for converting mechanical energy to electrical energy. 

Gigawatt (GW): A unit of power, which is instantaneous capability, equal to one 

million kilowatts. 

Gigawatt-hour (GWh): A unit or measure of electricity supply or consumption of 

one million kilowatts over a period of one hour. 

Global warming: A term used to describe the increase in average global 

temperatures caused by the greenhouse effect. 

Green power: A popular term for energy produced from renewable energy 

resources. 

Greenhouse effect: The heating effect that results when long-wave radiation from 

the sun is trapped by greenhouse gases produced by natural and human activities. 

Greenhouse gases (GHGs): Gases such as water vapor, CO2, methane, and lowlevel ozone that are transparent to solar radiation, but opaque to long-wave radiation. 

These gases contribute to the greenhouse effect. 

Grid: A common term that refers to an electricity transmission and distribution 

system. See also “power grid” and “utility grid.” 

Grid codes: Regulations that govern the performance characteristics of different 

aspects of the power system, including the behavior of wind plants during steadystate and dynamic conditions. These fundamentally technical documents contain the 

rules governing the operations, maintenance, and development of the transmission 

system and the coordination of the actions of all users of the transmission system. 

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Heat rate: A measure of the thermal efficiency of a generating station. Commonly 

stated as British thermal units (Btu) per kilowatt-hour. Note: Heat rates can be 

expressed as either gross or net heat rates, depending whether the electricity output 

is gross or net generation. Heat rates are typically expressed as net heat rates. 

Instantaneous penetration: The ratio of the wind plant output to load at a specific 

point in time, or over a short period of time. 

Investment tax credit (ITC): A tax credit that can be applied for the purchase of 

equipment such as renewable energy systems. 

Kilowatt (kW): A standard unit of electrical power, which is instantaneous 

capability equal to 1,000 watts. 

Kilowatt-hour (kWh): A unit or measure of electricity supply or consumption of 

1,000 watts over a period of one hour. 

Leading edge: The surface part of a wind turbine blade that first comes into contact 

with the wind. 

Lift: The force that pulls a wind turbine blade. 

Load (electricity): The amount of electrical power delivered or required at any 

specific point or points on a system. The requirement originates at the consumer’s 

energy-consuming equipment. 

Load factor: The ratio of the average load to peak load during a specified time 

interval. 

Load following: A utility’s practice in which more generation is added to available 

energy supplies to meet moment-to-moment demand in the utility’s distribution 

system, or in which generating facilities are kept informed of load requirements. The 

goal of the practice is to ensure that generators are producing neither too little nor 

too much energy to supply the utility's customers. 

Megawatt (MW): The standard measure of electricity power plant generating 

capacity. One megawatt is equal to 1,000 kilowatts or 1 million watts. 

Megawatt-hour (MWh): A unit or energy or work equal to1,000 kilowatt-hours or 

1 million watt-hours. 

Met tower: A meteorological tower erected to verify the wind resource found 

within a certain area of land. 

Modified Accelerated Cost Recovery System (MACRS): A U.S. federal system 

through which businesses can recover investments in certain property through 

depreciation deductions over an abbreviated asset lifetime. For solar, wind, and 

geothermal property placed in service after 1986, the current MACRS property class 

is five years. With the passage of the Energy Policy Act of 2005, fuel cells, 

microturbines, and solar hybrid lighting technologies became classified as five-year 

property as well. 

Nacelle: The cover for the gearbox, drivetrain, and generator of a wind turbine. 

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Nameplate rating: The maximum continuous output or consumption in MW of an 

item of equipment as specified by the manufacturer. 

Nondispatchable: The timing and level of power plant output generally cannot 

be closely controlled by the power system operator. Other factors beyond 

human control, such as weather variations, play a strong role in determining 

plant output. 

Nitrogen oxides (NOx): The products of all combustion processes formed by the 

combination of nitrogen and oxygen. NOx and sulfur dioxide (SO2) are the two 

primary causes of acid rain. 

Power: The rate of production or consumption of energy. 

Power grid: A common term that refers to an electricity transmission and 

distribution system. See also “utility grid.” 

Power marketers: Business entities engaged in buying and selling electricity. 

Power marketers do not usually own generating or transmission facilities, but take 

ownership of the electricity and are involved in interstate trade. These entities file 

with the Federal Energy Regulatory Commission (FERC) for status as a power 

marketer. 

Power Purchase Agreement (PPA): A long-term agreement to buy power from a 

company that produces electricity. 

Power quality: Stability of frequency and voltage and lack of electrical noise on the 

power grid. 

Public Utility Commission: A governing body that regulates the rates and services 

of a utility. 

Public Utility Regulatory Policies Act (PURPA) of 1978: As part of the National 

Energy Act, PURPA contains measures designed to encourage the conservation of 

energy, more efficient use of resources, and equitable rates. These measures 

included suggested retail rate reforms and new incentives for production of 

electricity by cogenerators and users of renewable resources. 

Production tax credit (PTC): A U.S. federal, per-kilowatt-hour tax credit for 

electricity generated by qualified energy resources. Originally enacted as part of the 

Energy Policy Act of 1992, the credit expired at the end of 2001, was extended in 

March 2002, expired at the end of 2003, was renewed on October 4, 2004 and was 

then extended through December 31, 2008. 

Radioactive waste: Materials remaining after producing electricity from nuclear 

fuel. Radioactive waste can damage or destroy living organisms if it is not stored 

safely. 

Ramp rate: The rate at which load on a power plant is increased or decreased. The 

rate of change in output from a power plant. 

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Renewable energy: Energy derived from resources that are regenerative or that 

cannot be depleted. Types of renewable energy resources include wind, solar, 

biomass, geothermal, and moving water. 

Regional Greenhouse Gas Initiative (RGGI): An agreement among 10 

northeastern and mid-Atlantic states to reduce CO2 emissions. Through the initiative, 

the states will develop a regional strategy to control GHGs. Fundamental to the 

agreement is the implementation of a multistate cap and trade program to induce a 

market-based emissions controlling mechanism. 

Renewable energy credit (REC) or certificate: A mechanism created by a state 

statute or regulatory action to make it easier to track and trade renewable energy. A 

single REC represents a tradable credit for each unit of energy produced from 

qualified renewable energy facilities, thus separating the renewable energy’s 

environmental attributes from its value as a commodity unit of energy. Under a REC 

regime, each qualified renewable energy producer has two income streams—one 

from the sale of the energy produced, and one from the sale of the RECs. The RECs 

can be sold and traded and their owners can legally claim to have purchased 

renewable energy. 

Renewable portfolio standard (RPS): Under such a standard, a certain percentage 

of a utility’s overall or new generating capacity or energy sales must be derived 

from renewable resources (e.g., 1% of electric sales must be from renewable energy 

in the year 200x). An RPS most commonly refers to electricity sales measured in 

megawatt-hours, as opposed to electrical capacity measured in megawatts. 

Restructuring: The process of changing the structure of the electric power industry 

from a regulated guaranteed monopoly to an open competition among power 

suppliers. 

Rotor: The blades and other rotating components of a wind turbine. 

Solar energy: Electromagnetic energy transmitted from the sun (solar radiation). 

Sulfur dioxide (SO2): A colorless gas released as a by-product of combusted fossil 

fuels containing sulfur. The two primary sources of acid rain are SO2 and NOx. 

Trade wind: The consistent system of prevailing winds occupying most of the 

tropics. Trade winds, which constitute the major component of the general 

circulation of the atmosphere, blow northeasterly in the northern hemisphere and 

southeasterly in the southern hemisphere. The trades, as they are sometimes called, 

are the most persistent wind system on Earth. 

Turbine: A term used for a wind energy conversion device that produces electricity. 

See also “wind turbine.” 

Turbulence: A swirling motion of the atmosphere that interrupts the flow of wind. 

Utility grid: A common term that refers to an electricity transmission and 

distribution system. See also “power grid.” 

Variable-speed wind turbines: Turbines in which the rotor speed increases and 

decreases with changing wind speeds. Sophisticated power control systems are 

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required on variable-speed turbines to ensure that their power maintains a constant 

frequency compatible with the grid. 

Volt (V): A unit of electrical force. 

Voltage: The amount of electromotive force, measured in volts, between two points. 

Watt (W): A unit of power. 

Watt-hour (Wh): A unit of electricity consumption of one watt over the period of 

one hour. 

Wind: Moving air. The wind’s movement is caused by the sun’s heat, the earth, and 

the oceans, which force air to rise and fall in cycles. 

Wind energy: Energy generated by using a wind turbine to convert the mechanical 

energy of the wind into electrical energy. See also “wind power.” 

Wind generator: A wind energy conversion system designed to produce electricity. 

Wind power: Power generated by using a wind turbine to convert the mechanical 

power of the wind into electrical power. See also “wind energy.” 

Wind power density: A useful way to evaluate the wind resource available at a 

potential site. The wind power density, measured in watts per square meter, indicates 

the amount of energy available at the site for conversion by a wind turbine. 

Wind power class: A scale for classifying wind power density. There are seven 

wind power classes, ranging from 1 (lowest wind power density) to 7 (highest wind 

power density). In general, sites with a wind power class rating of 4 or higher are 

now preferred for large-scale wind plants. 

Wind power plant: A group of wind turbines interconnected to a common utility 

system. 

Wind resource assessment: The process of characterizing the wind resource and its 

energy potential for a specific site or geographical area. 

Wind speed: The rate of flow of wind when it blows undisturbed by obstacles. 

Wind speed profile: A profile of how the wind speed changes at different heights 

above the surface of the ground or water. 

Wind turbine: A term used for a device that converts wind energy to electricity. 

Wind turbine rated capacity: The amount of power a wind turbine can produce at 

its rated wind speed. 

Windmill: A wind energy conversion system that is used primarily to grind grain.
 

Windmill is commonly used to refer to all types of wind energy conversion systems. 

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