Projected Benefits of 

Federal Energy Efficiency and 

Renewable Energy Programs

FY 2006 Budget Request

May 2005 NREL/TP-620-37931

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Technical Report 

NREL/TP-620-37931 

May 2005 

Projected Benefits of Federal 

Energy Efficiency and Renewable 

Energy Programs 

FY 2006 Budget Request 
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Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

March 2005 – Page i 

Contents 

Executive Summary................................................................................................................ ES-1 

Chapter 1 – Introduction........................................................................................................ 1-1 

 Role of Benefits Analysis in Performance Management................................................. 1-2 

 Benefits Framework......................................................................................................... 1-3

 Benefits Analysis Team................................................................................................... 1-5 

 Report Organization......................................................................................................... 1-6 

Chapter 2 – EERE Benefits-Analysis Process...................................................................... 2-1 

 Step 1: Baseline Case and Guidance................................................................................ 2-2 

 Step 2: Program and Market Inputs ................................................................................. 2-9 

 Step 3: Program and Portfolio Benefits Estimates........................................................... 2-12 

Chapter 3 – FY 2006 Benefits Estimates............................................................................... 3-1 

 Outcomes and Benefits Metrics....................................................................................... 3-1 

 Portfolio Benefits............................................................................................................. 3-4 

 Program Benefits ............................................................................................................. 3-12 

Chapter 4 – Midterm Benefits Analysis of EERE’s Programs........................................... 4-1 

 Introduction...................................................................................................................... 4-1 

 Biomass Program............................................................................................................. 4-2 

 Building Technologies Program ...................................................................................... 4-3 

 Distributed Energy Program ............................................................................................ 4-6 

 Federal Energy Management Program ............................................................................ 4-10 

 Geothermal Technologies Program ................................................................................. 4-11 

 Hydrogen, Fuel Cells, and Infrastructure Technologies Program ................................... 4-13 

 Industrial Technologies Program..................................................................................... 4-14 

 Solar Energy Technologies Program ............................................................................... 4-15 

 Vehicle Technologies Program........................................................................................ 4-18 

 Weatherization and Intergovernmental Program............................................................. 4-20 

 Wind Technologies Program ........................................................................................... 4-22 

Chapter 5 – Long-Term Benefits Analysis of EERE’s Programs 

 Introduction...................................................................................................................... 5-1 

 Biomass Program............................................................................................................. 5-2 

 Building Technologies Program ...................................................................................... 5-4 

 Distributed Energy Program ............................................................................................ 5-8 

 Federal Energy Management Program ............................................................................ 5-10 

 Geothermal Technologies Program ................................................................................. 5-12 

 Hydrogen, Fuel Cells, and Infrastructure Technologies Program ................................... 5-14 

 Industrial Technologies Program..................................................................................... 5-19 

 Solar Energy Technologies Program ............................................................................... 5-21 

 Vehicle Technologies Program........................................................................................ 5-23 

 Weatherization and Intergovernmental Program............................................................. 5-26 

 Wind Technologies Program ........................................................................................... 5-29 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

March 2005 – Page ii 

Appendix A – GPRA06 Benefits Estimates: MARKAL and NEMS Model Baseline Cases.. A-1 

Appendix B – GPRA06 Biomass Program Documentation .................................................... B-1 

Appendix C – GPRA06 Building Technologies Program Documentation ............................. C-1 

Appendix D – GPRA06 Distributed Energy Program Documentation ................................... D-1 

Appendix E – GPRA06 Federal Energy Management Program Documentation.................... E-1 

Appendix F – GPRA06 Geothermal Technologies Program Documentation......................... F-1 

Appendix G – GPRA06 Hydrogen, Fuel Cells, and Infrastructure Technologies Program 

Documentation............................................................................................... G-1 

Appendix H – GPRA06 Industrial Technologies Program Documentation............................ H-1 

Appendix I – GPRA06 Solar Energy Technologies Program Documentation........................ I-1 

Appendix J – GPRA06 Vehicle Technologies Program Documentation................................ J-1 

Appendix K – GPRA06 Weatherization and Intergovernmental Program Documentation.... K-1 

Appendix L – GPRA06 Wind Technologies Program Documentation................................... L-1 

 

Appendix M – GPRA06 Estimate of Penetration of Generating Technologies into Green Power 

Markets .......................................................................................................... M-1 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

March 2005 – Page iii 

Tables 

ES.1. EERE FY 2006 Benefits Metrics..................................................................................... ES-1 

ES.2. Summary of EERE Integrated Portfolio Benefits for FY 2006 Budget Request............. ES-2 

ES.3. U.S. Department of Energy Office of Energy Efficiency and Renewable Energy: 

FY 2006 Funding Summary and Selected 2025 and 2050 Benefits by Program ......... ES-6 

2.1. Summary of Baseline Changes from the AEO2004 ........................................................ 2-3 

3.1. Annual EERE Portfolio Benefits for FY 2006 Budget Request for Selected Years ....... 3-4 

4.1. Program Benefits Modeling by Primary Type of Model Used and Activity Area.......... 4-1 

4.2. FY06 Benefits Estimates for Biomass Program (NEMS-GPRA06) ............................... 4-3 

4.3. Modeling Approach for Building Technologies Program Activities............................... 4-4 

4.4. Building Technologies Program Energy Savings by End Use ........................................ 4-5 

4.5. FY06 Benefits Estimates for Building Technologies Program (NEMS-GPRA06)......... 4-6 

4.6. Distributed Energy Capacity............................................................................................ 4-9 

4.7. FY06 Benefits Estimates for DE (NEMS-GPRA06)....................................................... 4-10 

4.8. FY06 Benefits Estimates for FEMP (NEMS-GPRA06).................................................. 4-11 

4.9. Geothermal Capacity and Generation.............................................................................. 4-12 

4.10. FY06 Benefits Estimates for Geothermal Technologies Program (NEMS-GPRA06).... 4-13 

4.11. FY06 Benefits Estimates for Hydrogen, Fuel Cells, and Infrastructure Technologies 

Program (NEMS-GPRA06).......................................................................................... 4-14 

4.12. FY06 Benefits Estimates for Industrial Technologies Program (NEMS-GPRA06) ....... 4-15 

4.13. NEMS-GPRA06 Solar Capacity and Water Heaters....................................................... 4-16 

4.14. FY06 Benefits Estimates for Solar Energy Technologies Program (NEMS-GPRA06).. 4-17 

4.15. FY06 Benefits Estimates for Vehicle Technologies Program (NEMS-GPRA06) .......... 4-20 

4.16. FY06 Benefits Estimates for Weatherization and Intergovernmental Program 

(NEMS-GPRA06)......................................................................................................... 4-22 

4.17. Wind Capacity ................................................................................................................. 4-24 

4.18. FY06 Benefits Estimates for Wind Technologies Program (NEMS-GPRA06).............. 4-24 

5.1. Long-Term Benefits Modeling by Primary Type of Model Used and Activity Area ..... 5-2 

5.2. Bio-based Products Energy Savings by Year .................................................................. 5-3 

5.3. Projected Ethanol Demand .............................................................................................. 5-3 

5.4. FY06 Benefits Estimates for Biomass Program (MARKAL-GPRA06) ......................... 5-4 

5.5. Solid-State Lighting Technologies .................................................................................. 5-6 

5.6. Residential and Commercial Unitary DX System Technologies .................................... 5-6 

5.7. Building America Building Shell Cost Assumptions ...................................................... 5-6 

5.8. Residential Delivered Energy Savings by Demand and Fuel.......................................... 5-7 

5.9. Commercial Delivered Energy Savings by Demand and Fuel ........................................ 5-7 

5.10. FY06 Benefits Estimates for Building Technologies Program (MARKAL-GPRA06) .. 5-7 

5.11. Commercial-Sector Distributed Generation Technology Assumptions ......................... 5-8 

5.12. Industrial-Sector Distributed Generation Technology Assumptions .............................. 5-8 

5.13. Electric Utility Distributed Peaker Technology Assumptions ........................................ 5-9 

5.14. Installed Distributed Generation Capacity by Sector and Case ...................................... 5-10 

5.15. FY06 Benefits Estimates for DE Program (MARKAL-GPRA06).................................. 5-10 

5.16. FEMP Annual Energy Savings Projections..................................................................... 5-11 

5.17. FY06 Benefits Estimates for FEMP (MARKAL-GPRA06) ........................................... 5-11 

5.18. EGS Generation Assumptions ......................................................................................... 5-12 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

March 2005 – Page iv 

5.19. Total Geothermal Capacity by Type................................................................................ 5-13 

5.20. Total Geothermal Power Generation by Type................................................................. 5-13 

5.21. FY06 Benefits Estimates for Geothermal Technologies Program 

(MARKAL-GPRA06)................................................................................................... 5-14 

5.22. LDV and Commercial Light Truck Vehicle Miles Traveled .......................................... 5-14 

5.23. Cost and Efficiency Assumptions for Fuel Cell Vehicles ............................................... 5-15 

5.24. Hydrogen Production Costs by Technology and Component ........................................ 5-16 

5.25. 5 kW Residential Combined Heat and Power System Assumptions .............................. 5-17 

5.26. 200 kW Commercial Combined Heat and Power System Assumptions ........................ 5-17 

5.27. Light-Duty Vehicle Market Shares for the Hydrogen Case ............................................ 5-17 

5.28. Calculation of Petroleum Savings ................................................................................... 5-18 

5.29. Calculation of Carbon Emission Reduction..................................................................... 5-18 

5.30. Hydrogen Production by Feedstock................................................................................. 5-19 

5.31. FY06 Benefits Estimates for Hydrogen Fuel Cells and Infrastructure Technologies 

Program (MARKAL-GPRA06).................................................................................... 5-19 

5.32. Industrial Program Integration Factors............................................................................ 5-20 

5.33. Delivered Energy Savings in the Industrial Sector.......................................................... 5-20 

5.34. FY06 Benefits Estimates for Industrial Technologies Program 

(MARKAL-GPRA06)................................................................................................... 5-20 

5.35. Solar Energy Technologies Program Assumptions ......................................................... 5-21 

5.36. Solar-Generation Capacity by Case and Type................................................................. 5-22 

5.37. Solar Generation by Case and Type ................................................................................ 5-22 

5.38. FY06 Benefits Estimates for Solar Energy Technologies Program 

(MARKAL-GPRA06)................................................................................................... 5-23 

5.39. Projected Vehicle Miles Traveled by Vehicle Class ...................................................... 5-24 

5.40. Cost and Efficiency Assumptions for Light-Duty Vehicles ............................................ 5-24 

5.41. Cost and Efficiency Assumptions for Heavy Trucks....................................................... 5-25 

5.42. Light-Duty Vehicle Market Shares for the Vehicles Technologies Program Case ......... 5-25 

5.43. Petroleum Consumption by Vehicle Class and Case....................................................... 5-26 

5.44. FY06 Benefits Estimates for Vehicle Technologies Program (MARKAL-GPRA06).... 5-26 

5.45. Weatherization Assistance Program Projected Budget and Goals .................................. 5-27 

5.46. Projection of Baseline Case and Clean Cities Program Case 

Alternative-Fueled Vehicles ........................................................................................ 5-27 

5.47. Delivered Energy Demand Reductions in the Residential Sector ................................... 5-27 

5.48. Delivered Energy Demand Reductions in the Commercial Sector ................................. 5-28 

5.49. Delivered Energy Demand Reductions in the Industrial Sector ..................................... 5-28 

5.50. FY06 Benefits Estimates for Weatherization and Intergovernmental Program .............. 5-28 

5.51. Wind-Power Assumptions ............................................................................................... 5-29 

5.52. Total Wind Capacity and Generation .............................................................................. 5-30 

5.53. FY06 Benefits Estimates for Wind Program (MARKAL-GPRA06) .............................. 5-30 

5.57. FY06 Benefits Estimates for Hydropower Program (MARKAL-GPRA06)................... 5-30 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

March 2005 – Page v 

Figures 

ES.1. U.S. Nonrenewable Energy Consumption, 1980-2000, and Projections to 2050: 

Baseline and Portfolio Cases ........................................................................................ ES-7 

ES.2. U.S. Total Energy Expenditures, 1980-2000, and Projections to 2025: Baseline and 

Portfolio Cases.............................................................................................................. ES-7 

ES.3. U.S. Total Energy-System Cost Projections to 2050: Portfolio Case.............................. ES-8 

ES.4. U.S. Carbon Emissions, 1980-2000, and Projections to 2050: Baseline and Portfolio Cases

 ES-8 

ES.5. U.S. Oil Consumption, 1980-2000, and Projections to 2050: Baseline and 

Portfolio Cases.............................................................................................................. ES-9 

ES.6. U.S. Natural Gas Consumption, 1980-2000, and Projections to 2050: Baseline and 

Portfolio Cases.............................................................................................................. ES-9 

ES.7. U.S. Central Conventional Electricity-Capacity Addition Projections to 2025: 

Baseline and Portfolio Cases ........................................................................................ ES-10 

1.1. Generalized EERE Logic Model ..................................................................................... 1-3 

1.2. FY 2006 Benefits Metrics Reported ................................................................................ 1-4 

2.1. EERE Program and Portfolio Benefits-Analysis Process................................................ 2-1 

2.2. U.S. Nonrenewable Energy Demand and Energy Intensity, 1980-2000, and Baseline 

Projections to 2050 ....................................................................................................... 2-5 

2.3. Projected Energy Prices................................................................................................... 2-6 

2.4a. Potential Impacts of EERE R&D Programs on Technology Introduction ...................... 2-8 

2.4b. Potential Impacts of EERE Deployment Programs on Market Penetration .................... 2-8 

3.1. U.S. Nonrenewable Energy Consumption, 1980-2000, and Projections to 2050: 

 Baseline, Program, and Portfolio Cases........................................................................ 3-5 

3.2. U.S. Total Energy Expenditure, 1980-2000, and Projections to 2025: Baseline, 

Program, and Portfolio Cases ....................................................................................... 3-6 

3.3. U.S. Total Energy-System Cost Projections to 2050: Baseline, Program, and Portfolio 

Cases ............................................................................................................................. 3-7 

3.4. U.S. Carbon Emissions, 1980-2000, and Projections to 2050: Baseline, 

Program, and Portfolio Cases ....................................................................................... 3-8 

3.5. U.S. Oil Consumption, 1980-2000, and Projections to 2050: Baseline, Program, and 

Portfolio Cases.............................................................................................................. 3-9 

3.6. U.S. Natural Gas Consumption, 1980-2000, and Projections to 2050: Baseline, 

Program, and Portfolio Cases ....................................................................................... 3-10 

3.7. Impacts on Capacity Projections to 2025: Portfolio Case ............................................... 3-10 

3.8. U.S. Renewable Energy Capacity, 1980-2000, and Projections to 2050: Baseline, 

Program, and Portfolio Cases ....................................................................................... 3-11 

3.9. EERE Program FY 2006 Budget Requests...................................................................... 3-12 

3.10. Annual Nonrenewable Energy Savings: 2025 and 2050 ................................................. 3-14 

3.11. Annual Energy-Expenditure Savings: 2025 .................................................................... 3-14 

3.12. Annual Energy-System Cost Savings: 2050.................................................................... 3-15 

3.13. Annual Carbon Dioxide Savings: 2025 and 2050 ........................................................... 3-15 

3.14. Annual Oil Savings: 2025 and 2050................................................................................ 3-16 

3.15. Annual Natural Gas Savings: 2025 and 2050.................................................................. 3-16 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

March 2005 – Page vi 

3.16. Annual Electric-Generating Capacity – DER, Renewables, Energy Efficiency: 

2025 and 2050............................................................................................................... 3-17 

4.1. Commercial-Sector DG Adoption Rates ......................................................................... 4-8 

4.2. Industrial-Sector Combined Heat and Power Adoption Rates ....................................... 4-9 

4.3. Sales Shares in 2025 ........................................................................................................ 4-19 

4.4. Vehicle Miles Traveled in 2025....................................................................................... 4-19 

5.1. An Illustrative Reference Energy System........................................................................ 5-31 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Executive Summary – Page ES-1 

EXECUTIVE SUMMARY

The Office of Energy Efficiency and Renewable Energy (EERE) of the U.S. Department of 

Energy (DOE) leads the Federal Government’s efforts to provide reliable, affordable, and 

environmentally sound energy for America, through its 11 research, development, 

demonstration, and deployment (RDD&D) programs. EERE invests in high-risk, high-value 

research and development (R&D) that, conducted in partnership with the private sector and other 

government agencies, accelerates the development and facilitates the deployment of advanced 

clean energy technologies and practices. EERE designs its RDD&D activities to improve the 

Nation’s readiness for addressing current and future energy needs. 

This document summarizes the results of the benefits analysis of EERE’s programs, as described 

in the FY 2006 Budget Request. EERE has adopted a benefits framework developed by the 

National Research Council (NRC)1

 to represent the various types of benefits resulting from the 

energy efficiency technology improvements and renewable energy technology development 

supported by EERE programs. Specifically, EERE’s benefits analysis focuses on three main 

categories of energy-linked benefits—economic, environmental, and security. The specific 

measures or metrics of these benefits estimated for FY 2006 are identified in Table ES.1. These 

metrics are not a complete representation of the benefits or market roles of efficiency and 

renewable technologies, but provide an indication of the range of benefits provided. EERE is 

continuing to take steps to more fully represent the NRC framework.2

 

Table ES.1. EERE FY 2006 Benefits Metrics 

Primary Outcome 

 Energy displaced • Reductions in nonrenewable energy consumption (quadrillion Btu/yr) 

Resulting Benefits 

 Economic • Reductions in consumer energy expenditures (NEMS-GPRA06 - billion 2002 

dollars/yr) 

• Reductions in energy-system costs (MARKAL-GPRA06 - in billion 2002 dollars/yr) 

 Environmental • Reductions in carbon dioxide emissions (mmtc equivalent/yr) 

 Security • Reductions in oil consumption (mbpd) 

• Reductions in natural gas consumption (quadrillion Btu/yr) 

• Avoided additions to central conventional power3

 (cumulative gigawatts) 

• Table ES.2 shows the estimated energy displaced and resulting benefits to the Nation of 

realizing the EERE program goals associated with the FY 2006 budget request. These 

impacts are the benefits expected in the reported year—that is, the benefits are annual, not 

cumulative (with the exception of avoided additions to conventional central power). 

 

1 Energy Research at DOE: Was It Worth It? Energy Efficiency and Fossil Energy Research 1978 to 2000, National Research 

Council (2001). The NRC is the principal operating agency of the National Academy of Sciences (NAS) and the National 

Academy of Engineering (NAE), providing services to the government, the public, and the scientific and engineering 

communities. 

2

 In FY 2005, EERE made two key improvements: (1) adding an electricity security metric and (2) extending the analysis through 

the year 2050. EERE will be implementing additional portions of the framework in the future. 3

 Central conventional power includes centrally located fossil, nuclear, combined cycle, combustion turbine/diesel, and pumped 

storage. It does not include distributed power and renewable power (central or distributed). 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Executive Summary – Page ES-2 

Table ES.2. Summary of Annual EERE Integrated Portfolio Benefits for FY 2006 Budget Request4,5

EERE Midterm Benefits (NEMS-GPRA06) 2010 2015 2020 2025 

Energy Displaced 

• Primary nonrenewable energy savings (quadrillion Btu/yr) 

Economic 

• Energy-expenditure savings (billion 2002 dollars/yr)* 

Environment 

• Carbon dioxide emission reductions (mmtc equivalent/yr) 

Security 

• Oil savings (mbpd) 

• Natural gas savings (quadrillion Btu/yr) 

• Avoided additions to central conventional power (cumulative 

gigawatts)6

1.1 

12 

22 

0.1 

0.5 

5 

3.4 

37 

67 

0.6 

1.1 

49 

7.8 

87 

160 

1.3 

1.9 

96 

12.3 

123 

262 

2.3 

1.8 

137 

EERE Long-Term Benefits (MARKAL-GPRA06) 2020 2030 2040 2050 

Energy Displaced 

• Primary nonrenewable energy savings (quadrillion Btu/yr) 

Economic 

• Energy-system cost savings (billion 2002 dollars/yr)* 

Environment 

• Carbon dioxide emission reductions (mmtc equivalent/yr) 

Security 

• Oil savings (mbpd) 

• Natural gas savings (quadrillion Btu/yr) 

8.0 

43 

152 

1.3 

3.1 

17.7 

102 

364 

4.6 

2.8 

28.2 

188 

568 

9.0 

3.6 

33.6 

282 

699 

11.0 

2.4 

* Midterm energy-expenditure savings only include reductions in consumer energy bills, while long-term energysystem cost savings also include the incremental cost of the advanced energy technology purchased by the 

consumer. 

Under a business-as-usual energy future, realization of these goals and the associated projected 

market outcomes would: 

• Reduce the expected increase in U.S. energy demand by 39% in 2025 and 76% in 2050, 

resulting in a reduction in nonrenewable energy consumption starting in 2030. (Figure ES.1) 

• Reduce the expected increase in U.S. consumer energy expenditures by 43% in 2025. 

(Figure ES.2)

• Reduce the expected increase in U.S. energy system costs by 6% in 2050. (Figure ES.3)

• Reduce the expected increase in annual U.S. carbon emissions by 45% in 2025 and 71% in 

2050. (Figure ES.4) 

• Reduce the expected increase in U.S. oil consumption (most of which is expected to originate 

from outside the United States) by 34% in 2025 and 98% in 2050, resulting in declining oil 

consumption after 2025. (Figure ES.5) 

 

4 Estimates reflect the benefits associated with program activities from FY 2006 to the benefit year, or to program completion 

(whichever is nearer), and are based on program goals developed in alignment with assumptions in the president’s budget. 

Midterm program benefits were estimated using the NEMS-GPRA06 model, based on the Energy Information Administration’s 

(EIA) National Energy Modeling System (NEMS) and using the EIA’s Annual Energy Outlook 2004 (AEO2004) Reference 

Case. Long-term benefits were estimated using the MARKAL-GPRA06 model developed by Brookhaven National Laboratory. 

Results can differ among models due to structural differences. The models used in this analysis estimate economic benefits in 

different ways, with MARKAL reflecting the cost of additional investments required to achieve reductions in energy bills. 5

 For some metrics, the benefits estimated by MARKAL-GPRA06 do not align well with those reported by NEMS-GPRA06. 

Every attempt is made in the integrated modeling to use consistent baselines, input data and assumptions in both models to 

produce consistent results. However, NEMS and MARKAL are in some respects fundamentally different models (see Boxes 4.1 

and 5.1). Discrepancies in the estimated benefits often exist simply because of these model differences. 6 Small final changes in these estimates were not reflected in the FY 2006 Budget Request. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Executive Summary – Page ES-3 

• Reduce the expected increase in U.S. natural gas consumption, much of which is expected to 

originate outside the United States, by 21% in 2025 and 18% in 2050. (Figure ES.6) 

• Reduce the need for additions to central conventional power by 16% in 25. (Figure ES.7) 

EERE develops these benefits projections annually to help meet the requirements of the 

Government Performance and Results Act (GPRA) of 1993 and the President’s Management 

Agenda (PMA). GPRA requires Federal Government agencies to develop and report on output 

and outcome measures for each program. This analysis helps meet GPRA requirements by 

identifying the potential outcomes and benefits of realizing EERE program goals (outputs). The 

benefits estimates do not reflect the risk of realizing these goals, which is being addressed 

separately.7

The reported benefits reflect only the net annual improvement from 2005 to 2050 of program 

activities included in EERE’s FY 2006 Budget Request (including subsequent-year funding) and 

do not include the benefits from past work. The benefits estimates assume continued funding for 

program activities consistent with multiyear program plans.8 By basing estimated benefits on 

budget levels, the analysis addresses the performance-budget integration goal of the PMA. This 

analysis also provides the benefits called for in the R&D Investment Criteria, developed by the 

Office of Management and Budget (OMB) as part of the PMA. 

EERE uses two energy-economy models—NEMS-GPRA06 and MARKAL-GPRA06—to 

estimate the impacts of EERE programs on energy markets. The NEMS-GPRA06 model is a 

modified version of the National Energy Modeling System (NEMS), the midterm energy model 

used by the Department of Energy’s Energy Information Administration (EIA). The MARKALGPRA06 model is a modified version of the MARKet ALlocation (MARKAL) model developed 

by Brookhaven National Laboratory and used by numerous countries worldwide. EERE uses 

NEMS-GPRA06 to estimate the midterm benefits of its programs and MARKAL-GPRA06 to 

estimate the long-term benefits of its programs. Descriptions of these models are provided in 

Chapters 4 and 5. 

EERE uses a three-step process to estimate benefits across its portfolio: 

(1) Establishment of the Baseline Case and guidance 

(2) Determination of program and market inputs 

(3) Assessment of program and portfolio benefits. 

In Step 1, a Baseline Case and standard methodological approach (guidance) are developed to 

improve the consistency of estimates across EERE programs. The Baseline Case provides a 

representation of business-as-usual future energy markets without the effect of EERE programs. 

It also provides a consistent set of assumptions about future energy prices, conversion factors, 

economic growth, and other external factors, against which to analyze the impacts of EERE 

programs. To develop the Baseline Case through 2025, EIA’s Annual Energy Outlook 2004

(AEO2004) Reference Case forecast is modified to remove any identifiable effects of EERE 

 

7 A standard approach to risk assessment is being developed for EERE’s multiyear program plans. 8 Funding levels may increase, decrease, or remain constant, depending on the program. See Appendices B through M for 

information on individual multiyear program plans. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Executive Summary – Page ES-4 

programs already included in the forecast. This is done for both the NEMS-GPRA06 model and 

the MARKAL-GPRA06 model.9 

For the period after 2025, other credible sources are used to compile a set of economic and 

technical assumptions for MARKAL-GPRA06.10 A summary of the Baseline Case results is 

included in Appendix A. EERE also specifies common methodological approaches (guidance) 

used in developing benefits estimates. This guidance identifies common definitions, the basis for 

assessing benefits, data requirements, etc. An overview is provided in Chapter 2. 

In Step 2, analysts from throughout EERE characterize the results of the EERE programs in a 

format suitable for analysis within the NEMS and MARKAL integrated-modeling frameworks. 

For technology R&D programs, this usually requires expressing program outputs in terms of the 

cost and performance of a new (or improved) technology, which will compete against an 

existing, but improving, technology in the baseline. For deployment programs (e.g., information 

dissemination, or codes and standards), analysts develop approaches to characterizing outputs on 

a case-by-case-basis using alternative modeling techniques such as altering discount rates or 

fixing market penetration (in the case of minimum efficiency standards). In many cases, the 

NEMS and MARKAL frameworks are not suitable for directly analyzing programmatic 

activities; as a result, “off-line” analyses are conducted. The market analyses and off-line 

estimates used in the integrated modeling framework are documented in Appendices B 

through M.

In Step 3, the program- and market-specific information from Step 2 is incorporated into 

NEMS-GPRA06 and MARKAL-GPRA06. Modeling all the activities within a program together 

accounts for market feedbacks and interactions that can change the ultimate level of energy 

savings associated with realizing each program’s goals. EERE adjusts off-line estimates to 

account for areas of overlapping program impacts. This downward revision is based on how 

much of the overlap or integration was captured by the off-line analysis. The benefits analysis 

team, based on its expert judgment, determines the amount of revision. The resulting benefits 

estimates of individual program analyses are listed by program, along with FY 2006 program 

budgets, in Table ES.3. 

Analysts also run NEMS-GPRA06 and MARKAL-GPRA06 with all programs simultaneously 

represented, in order to derive estimates of the benefits of the overall EERE portfolio. This 

portfolio analysis accounts for interactions among EERE’s programs, and tends to report reduced 

benefits compared to the sum of the individual programs. The fully integrated results are listed in 

Table ES.2 and displayed in the graphs in this Executive Summary. Specific details on the 

representation of program outputs in NEMS-GPRA06 and the underlying program analysis and 

documentation are provided in Chapter 4. Representation of the program outputs in MARKALGPRA06 is provided in Chapter 5. 

EERE is continuing to pursue a number of improvements to its benefits analysis. Important 

changes planned for analysis of the benefits of the FY 2007 budget request include: 

 

9

 Slight differences in the NEMS-GPRA06 and MARKAL-GPRA06 baselines may occur from the differences inherent in the two 

models. 

10 For instance, the primary economic drivers of Gross Domestic Product (GDP) and population are based on the real GDP 

growth rate from the Congressional Budget Office’s Long-Term Budget Outlook and population growth rates from the Social 

Security Administration’s 2002 Annual Report to the board of trustees. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Executive Summary – Page ES-5 

• Developing alternative scenarios that reflect potential options facing the Nation in the 

future (e.g., higher fossil fuel prices, a carbon-constrained world). 

• Potential incorporation of new metrics for security and knowledge benefits. 

• Greater streamlining and consistency in the development of program-level benefits 

estimates. 

In addition, EERE is developing methods for linking estimates of benefits from both past and 

future program efforts into the overarching NRC benefits framework noted above. Finally, EERE 

is developing a more systematic way of representing program and technology risk. Although not 

part of this benefits analysis per se, information on risk is recognized as an important component 

in the application of benefits information to portfolio management. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Executive Summary – Page ES-6 

Table ES.3. U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (

FY 2006 Funding Summary and Selected 2025 and 2050 Benefits by Program11

Program 

FY 2006 

Request 

(thousands $) 

Nonrenewable 

Energy Displaced 

(quads/yr) 

Energy 

Expenditure 

Savings 

(billions 2002$/yr) 

Energy System 

Cost Savings 

(billions 2002$/yr) 

Carbon 

Dioxide 

 Emissions 

Reductions 

(million mtce/

 2025 2050 2025 2050 2025 2050 2025 2Biomass 72,164 0.1 1.1 0.0 N/A N/A 0.6 3 Building Technologies 57,966 1.2 4.2 11.5 N/A N/A 62.4 28 Distributed Energy Resources 56,629 0.3 0.3 1.6 N/A N/A 1.4 11 Federal Energy Management 19,166 0.1 0.1 0.5 N/A N/A 3.6 1 Geothermal Technologies 23,299 0.3 2.4 0.0 N/A N/A 5.2 8 Hydrogen, Fuel Cells, and Infrastructure Technologies 182,694 0.2 4.3 2.4 N/A N/A 26.4 5 Industrial Technologies 56,489 2.2 0.5 12.9 N/A N/A 3.2 44 Solar Energy Technologies 83,953 0.3 1.7 1.8 N/A N/A 2.3 8 Vehicle Technologies12 165,943 4.0 18.9 60.9 N/A N/A 177.4 76 Weatherization and Intergovernmental 310,067 1.2 1.1 9.6 N/A N/A 17.1 27 Wind and Hydropower 44,749 3.3 3.7 4.5 N/A N/A 3.6 81 Facilities and Infrastructure 16,315 N/A N/A N/A N/A N/A N/A N/A Program Direction and Management Support 110,980 

N/A N/A N/A N/A N/A N/A N/A 

Sum of programs ** 1,200,414 13.1 38.3 105.7 N/A N/A 300.4 292 ** The sum of program benefits differs from the EERE portfolio values in Table ES.2, because interactions among programs are not acco

estimates. Sums may not total due to rounding. 

11 Budget request from FY 2006 Budget-in-Brief, U.S. Department of Energy Office of Energy Efficiency and Renewable Energy, 

http://www.eere.energy.gov/office_eere/pdfs/fy05_budget_in_brief.pdf. 

12 The Vehicle Technologies Program is run by the Office of FreedomCAR and Vehicle Technologies.
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Executive Summary – Page ES-7 

 Figure ES.1. U.S. Nonrenewable Energy Consumption, 1980-2000, and Projections to 2050: 

Baseline and Portfolio Cases 

Note: The percentage change in the chart shown for 2025 and 2050 is the difference between the Baseline Case and 

the Portfolio Case, compared to the difference between the values of the Baseline Case in 2025 (or 2050) versus 

2005. Data Sources: 1980-2000, Energy Information Administration, Annual Energy Review 2003, DOE/EIA-0384 

(2003), Table 1.3, Web site http://www.eia.doe.gov/emeu/aer/contents.html; 2005-2025: NEMS-GPRA06; 2030-2050: 

MARKAL-GPRA06. 

Figure ES.2. U.S. Total Energy Expenditures, 1980-2000, and Projections to 2025: 

Baseline and Portfolio Cases 

Note: The percentage change in the chart shown for 2025 and 2050 is the difference between the Baseline Case and 

the Portfolio Case, compared to the difference between the values of the Baseline Case in 2025 (or 2050) versus 

2005. Data Sources: 1980-2000, Energy Information Administration, Annual Energy Review 2003, DOE/EIA-0384 

(2003), Table 3.4 and Table D1, Web site http://www.eia.doe.gov/emeu/aer/contents.html; 2005-2025: NEMSGPRA06; 2030-2050: MARKAL-GPRA06. 

0

20

40

60

80

100

120

140

160

1980 1990 2000 2010 2020 2030 2040 2050

Quadrillion Btu

Baseline Case

Portfolio Case

2005 Level

-39% -76%

0

200

400

600

800

1000

1200

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

- 43%

B illion 2002 D ollars

Baseline C ase

P ortfolio C ase

2005 Level
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Executive Summary – Page ES-8 

Figure ES.3. U.S. Total Energy-System Cost Projections to 2050: Portfolio Case 

Note: The percentage change in the chart shown for 2050 is the difference between the Baseline Case 

and the Portfolio Case, compared to the difference between the values of the Baseline Case in 2050 

versus 2005. Data Source: MARKAL-GPRA06. 

Figure ES.4. U.S. Carbon Emissions, 1980-2000, and Projections to 2050: 

Baseline and Portfolio Cases 

Note: The percentage change in the chart shown for 2025 and 2050 is the difference between the Baseline Case and 

the Portfolio Case, compared to the difference between the values of the Baseline Case in 2025 (or 2050) versus 

2005. Data Sources: 1980-2000, Energy Information Administration, Annual Energy Review 2003, DOE/EIA-0384 

(2003), Table 12.2, Web site http://www.eia.doe.gov/emeu/aer/contents.html; 2005-2025, NEMS-GPRA06; 2030-

2050, MARKAL-GPRA06. 

0

500

1000

1500

2000

2500

3000

1980 1990 2000 2010 2020 2030 2040 2050

Million Metric Tons Carbon Equivalent

2005 Level

Baseline Case

Portolio Case

- 45%

-71%

6000

7000

8000

9000

10000

11000

12000

13000

14000

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

2005 Level

Baseline Case

Portfolio Case

-6%

Billion 2002 Dollars
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Executive Summary – Page ES-9 

 Figure ES.5. U.S. Oil Consumption, 1980-2000, and Projections to 2050: 

Baseline and Portfolio Cases 

Note: The percentage change in the chart shown for 2025 and 2050 is the difference between the Baseline Case and 

the Portfolio Case, compared to the difference between the values of the Baseline Case in 2025 (or 2050) versus 

2005. Data Sources: 1980-2000, EIA, Annual Energy Review 2003, DOE/EIA-0384 (2003), Table 1.3, Web site 

http://www.eia.doe.gov/emeu/aer/contents.html; 2005-2025, NEMS-GPRA06; 2030-2050, MARKAL-GPRA06. 

 Figure ES.6. U.S. Natural Gas Consumption, 1980-2000, and Projections to 2050: 

Baseline and Portfolio Cases 

Data Sources: 1980-2000, EIA, Annual Energy Review 2003, DOE/EIA-0384 (2003), Table 1.3, Web site 

http://www.eia.doe.gov/emeu/aer/contents.html; 2005-2025, NEMS-GPRA06; 2030-2050, MARKAL-GPRA06. 

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

1980 1990 2000 2010 2020 2030 2040 2050

Million Barrells per Day

2005 Level

Baseline Case

Portoflio Case

- 34% - 98%

0

5

10

15

20

25

30

35

40

1980 1990 2000 2010 2020 2030 2040 2050

Quadrillion Btu

2005 Level

Baseline Case

Portfolio Case - 21%

- 18%
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Executive Summary – Page ES-10 

Figure ES.7. U.S. Central Conventional Electricity-Capacity Addition Projections to 2025: 

Baseline and Portfolio Cases 

Note: The percentage change in the chart shown for 2025 is the difference between the Baseline Case and the 

Portfolio Case, compared to the difference between the values of the Baseline Case in 2025 versus 2005. Data 

Source, MARKAL-GPRA06.

0

20

40

60

80

100

120

2005 2010 2015 2020 2025

Gigawatts

Baseline Case

(MARKAL-GPRA06)

Portfolio Case

(MARKAL-GPRA06)

- 16%
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Introduction (Chapter 1) – Page 1-1 

CHAPTER 1

INTRODUCTION

The Office of Energy Efficiency and Renewable Energy (EERE) develops—and encourages 

consumers and business to adopt—technologies that improve energy efficiency and increase the 

use of renewable energy. This report describes analysis undertaken by EERE to better understand 

the extent to which the technologies and market improvements funded by its fiscal year (FY)

2006 Budget Request1 will make energy more affordable, cleaner, and more reliable. 

This benefits analysis helps EERE meet the provisions of the Government Performance Results 

Act (GPRA) of 1993 and the President’s Management Agenda (PMA). GPRA requires Federal 

Government agencies to develop and report on output and outcome measures for each program.2

This EERE benefits analysis supports these GPRA requirements by developing an assessment of 

the benefits that may accrue to the Nation if the performance goals (outputs) of EERE’s 

programs are realized. The estimates of consumer energy-expenditure savings, energy-system 

cost savings,3 carbon emission savings, and reduced reliance on fossil fuels that are reported here 

result from the increased use of energy-efficient technologies and increased production of 

renewable energy resources—which are supported by the technology advances and market 

adoption activities pursued by EERE programs. 

Shortly after GPRA was enacted, EERE initiated a corporate approach to benefits analysis that 

examined the energy, economic, and environmental impacts of program efforts. Through the 

1990s, EERE program offices continued to refine their benefits-analysis methodologies and 

assumptions. An annual external review of the methodologies and assumptions employed was 

initiated in 1997 and continued through 2002 when EERE was reorganized. Although the 

benefits analysis has changed since it was initiated 11 years ago, the amount of energy saved or 

displaced continues to be the key measure of the EERE program impact. 

This benefits analysis also supports the President’s Management Agenda. The analysis 

summarized in this report is based on the modeling of program performance goals or outputs. 

EERE’s programs develop these goals based on the following key assumptions:4

 

1

 See http://www.eere.energy.gov/office_eere/budget.html. 2 See the Government Performance Results Act (GPRA) of 1993 at http://www.whitehouse.gov/omb/mgmt-gpra/gplaw2m.html

and http://www.whitehouse.gov/omb/circulars/a11/02toc.html 3 NEMS-GPRA06 estimates consumer-expenditure savings, which are the gross savings from avoiding purchased energy. They 

do not include all incremental investment required to achieve these savings. MARKAL-GPRA06 estimates energy-system costs 

savings, which includes both the savings from avoiding purchased energy and the incremental investment required for the 

advanced energy technology. 

4 Achieving program goals is generally not dependent on a single technical pathway, but instead encompasses a number of 

alternative approaches, of which some may fall short without jeopardizing realization of the final goal. The pursuit of multiple 

pathways can increase the likelihood of achieving program goals, thereby reducing the risk of the program. Risk is being 

addressed in a separate EERE effort to develop a standard approach to risk assessment. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Introduction (Chapter 1) – Page 1-2 

• Programs will be funded at levels consistent with DOE’s FY 2006 Budget Request. 

• Funding levels will remain constant in inflation-adjusted dollars or increase to 

accommodate key initiatives in particular cases, as indicated. 

By basing estimated benefits on budget levels, the analysis addresses the performance-budget 

integration goal of the PMA. This analysis also provides the benefits sought in the R&D 

Investment Criteria, developed by the Office of Management and Budget (OMB) for the PMA. 

Role of Benefits Analysis in Performance Management 

EERE employs a widely used logic model5 as the foundation for managing its portfolio of 

efficiency and renewable investments, and for ensuring that these investments provide energy 

benefits to the Nation. In its simplest form, a logic model identifies budget and other inputs to a 

program, activities conducted by the program, and the resulting outputs and outcomes of those 

activities. The logic model employed by EERE (Figure 1.1) provides an integrated approach that 

explicitly links requested budget levels to performance goals and estimated benefits—and helps 

ensure that estimated benefits reflect the funding levels requested. The elements of the logic 

model, which are specified in GPRA, are included in the annual budget request. 

Multiyear Program Plans (MYPPs), developed by each of EERE’s 11 programs, address the 

inputs required, the activities that will be undertaken with their requested budget, the 

performance milestones they expect to achieve as they pursue these activities, and the resulting 

products or outputs of this effort.6

 Inputs may include cost-shared or leveraged funds, as well as 

EERE program dollars—and may also include advances by others on which the program builds. 

Performance milestones capture intermediate points of discernable progress toward outputs and 

are used by program managers, DOE, OMB, and others to track program progress toward their 

outputs. Outputs, often referred to as “program goals” or “program performance goals,”7 are the 

resulting products or achievements of an overall area of activity. EERE’s R&D programs 

typically specify their outputs in terms of technology advances (e.g., reduced costs, improved 

efficiency), while deployment programs develop outputs related to their immediate market 

impacts (e.g., number of homes weatherized). Outputs evolve over time as the program pursues 

increasing levels of technology performance or market penetration.8

 

This benefits analysis links these program outputs to their market impacts or outcomes. EERE’s 

programs have discernable effects on energy markets, both by reducing the level of energy 

 

5 The logic model is a fundamental program planning and evaluation tool. For more information on logic models, see: Wholey, J. 

S. (1987). Evaluability assessment: developing program theory. Using Program Theory in Evaluation. L. Bickman. San 

Francisco, Calif., Jossey-Bass. 33. Jordan, G. B. and J. Mortensen (1997). "Measuring the performance of research and 

technology programs: a balanced scorecard approach." Journal of Technology Transfer 22(2). McLaughlin, J. A. and G. B. 

Jordan (1999). "Logic models: a tool for telling your program's performance story." Evaluation and Program Planning 22(1): 65-

72. 

6 Appendices B through M provide more information on each program’s multiyear program plan and the inputs, activities, 

milestones, and outputs contained therein. 

7 Some programs derive their outputs through technology-cost simulation models to develop the specific requirements to meet 

overall program cost and performance goals. Specific details of the representation of the program outputs in NEMS-GPRA06, 

MARKAL-GPRA-06, and the underlying program analysis and documentation are found in Chapters 4 and 5 of this report and 

Appendices B through M. 

8

 The level of risk for the programs is assessed qualitatively as part of the Office of Management and Budget (OMB) R&D 

Investment Criteria. EERE is developing a standard approach to assessing technology and program risk. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Introduction (Chapter 1) – Page 1-3 

demand (through efficiency improvements) and by changing the mix of our energy supplies 

(through increased renewable and distributed energy production). EERE incorporates these two 

effects in its primary outcome—the displacement of conventional energy demand. 

Figure 1.1. Generalized EERE Logic Model 

These changes in energy use provide the basis for the economic, environmental, and security 

benefits estimated here. The extent to which a new technology or a deployment effort changes 

energy markets will depend on a variety of external factors. The future demand for energy, its 

price, the development of competing technologies, and other market features (such as consumer 

preferences) all will contribute to the marketability and total sales of a new technology. 

Benefits Framework 

The EERE Benefits Framework addresses the last three columns of the logic model: the link 

between program outputs with resulting outcomes and benefits. The benefits analysis is based on 

the specific program goals or outputs specified by EERE programs in their program plans and 

the EERE budget request, as well as estimated future energy market conditions (external factors). 

EERE estimates its primary outcome—displaced conventional energy consumption—by 

comparing future energy consumption with and without the contributions of its program outputs. 

The market impacts of each of the 11 programs are assessed separately and then combined to 

assess the benefits of EERE’s overall portfolio.9 

 

9 EERE’s benefits analysis, which measures final outcomes due to EERE programs and a host of other external factors as shown 

in Figure 1.1, is distinct from impacts analysis, which determines the portion of outcomes having a causal relationship with 

EERE’s actions. 

Inputs Activities Milestones Final

Outputs

Interim

Outcomes

Final

Outcomes

Research and

Development

• Basic

• Applied

Transforming 

market 

conditions

Weatherization

State & local 

grants

Federal 

funding

Partner 

funding

Employees

Contractors

Partners

Facilities

Knowledge 

• Increased 

knowledge

• Research 

advances

• Prototypes

• Demonstrations

Information 

disseminated

New and existing 

technologies with 

improved cost 

and/or 

performance

Informed public

Market 

introduction 

and penetration 

of advanced 

technology

Enhanced 

market 

penetration of 

advanced 

technologies or 

practices

Economic, 

environment,

security, and 

knowledge 

(realized & 

Partnerships options)

formed

Rulemaking 

notices, public 

hearings, analysis

Audits conducted

New codes & 

standards

Performance Paths

External Factors

• Economic trends

• Demographic trends

• Energy trends

• Government regulations and policies

• Industry economic performance

• Cost/performance of competing technologies

• Energy prices

• Commodity prices

Program Plans Benefits Estimation 

Final 

Program 

outputs 

become 

starting 

point for 

benefits 

estimation
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Introduction (Chapter 1) – Page 1-4 

EERE—along with the offices of Fossil Energy (FE); Nuclear Energy, Science, and Technology 

(NE); and Electricity and Energy Assurance (OE)—is in the process of adopting a framework 

initially developed by the National Research Council (NRC) to assess the benefits associated 

with past EERE research efforts.10 EERE’s annual estimates of prospective benefits have been 

incorporated into an integrated framework addressing the benefits of both existing and future 

program activities. The framework is represented in a matrix, in which the rows distinguish 

among four types of benefits, and the columns represent different elements of time and 

uncertainty. 

This report addresses the three shaded cells of the matrix, reflecting benefits under a business-asusual energy future (Figure 1.2). EERE, FE, NE, and OE currently are developing methods for 

assessing the value to the country of developing technologies that prepare the Nation for 

unexpected energy needs. These results will be in the “option” column in future reports.11

Similarly, EERE is in the process of extending the NRC analysis of realized benefits to include 

its full portfolio. 

 Realized 

Benefits and 

Costs 

Expected 

Prospective Benefits 

and Costs 

Options 

Benefits and 

Costs 

Economic Benefits and Costs 3 

Environmental Benefits and Costs 3

Security Benefits and Costs 3

Knowledge Benefits and Costs 

Figure 1.2. FY 2006 Benefits Metrics Reported 

Completing the cells of this matrix in ways that provide comparable results across programs (and 

DOE offices) poses a number of analytical challenges, especially in light of the varied portfolio 

that EERE maintains: 

• Standard baseline(s) and methodological approaches. EERE uses the Energy 

Information Administration’s (EIA) Annual Energy Outlook 2004 (AEO2004) Reference 

Case as a consistent starting point for analysis of all of its programs.12 A standard set of 

methodological approaches (guidance) is used to assess the incremental improvements to 

energy efficiency and renewable energy production, resultant from realization of EERE 

program goals. This guidance is applicable to all of EERE’s program activities and 

markets. 

 

10 See Energy Research at DOE: Was It Worth It? Energy Efficiency and Fossil Energy Research 1978 to 2000, National 

Research Council (2001) for the original framework. DOE’s offices of Energy Efficiency and Renewable Energy, Fossil Energy, 

Nuclear Energy, and Science cosponsored DOE’s “Estimating the Benefits of Government-Sponsored Energy R&D” conference 

in March 2002 to explore ways of extending this framework to include the prospective benefits of program activities. As a result 

of the conference, the matrix was revised by placing knowledge as a benefit and explicitly showing expected prospective benefits 

and costs in addition to realized benefits and costs. The conference report is available at www.esd.ornl.gov/benefits_conference. 11 For its retrospective study, the NRC defined an option as a technology that is fully developed—but for which existing market 

or policy conditions are not favorable for commercialization. Because current technology choices are known, noncommercial 

(but developed technologies) are options, by default. 

12 See The Annual Energy Outlook 2004 with Projections to 2025, January 2004, DOE/EIA-0383 (2004), available at 

http://www.eia.doe.gov/oiaf/archive/aeo04/index.html. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Introduction (Chapter 1) – Page 1-5 

• Varied markets. Program activities target all end-use markets (buildings, industry, 

transportation, and government) and energy-supply markets (use of renewable energy as 

new sources of liquid and gaseous fuels, and electricity). Because these markets vary 

enormously in structure, regulation, and consumer preferences, a fairly detailed, marketspecific analysis often is needed to gain sufficient understanding of the size and potential 

receptivity of each market to EERE’s activities. EERE strives to incorporate these unique 

market features that are likely to have a significant impact on the resulting benefits. 

• Varied time frames. The analytical time frame extends from a few years to the decades 

that are required for the development of new energy sources, infrastructure, market 

penetration, and product life cycle. This expansive time frame requires a baseline and 

analytical tools that can address energy markets in the short, mid-, and long term. This 

report addresses midterm (5–20 years) and long-term (20–50 years) time frames. 

• Numerous market feedbacks. EERE technology and deployment efforts can have large 

enough effects on their respective energy markets that they generate supply or price 

feedbacks. EERE’s technologies also can interact with each other across their respective 

energy markets. For example, efficiency improvements in end-use markets can be large 

enough to forestall the development of new electricity-generating plants, reducing the 

potential growth of wind and other renewable electricity sources. Past EERE experience 

indicates that failure to reflect market responses tends to overestimate benefit levels. 

EERE utilizes integrated energy-economic models to produce final benefit estimates that 

consider these feedbacks and interactions at the program and portfolio levels. 

Benefits Analysis Team 

This report summarizes program benefits analysis undertaken by experts in energy technology 

programs, energy markets, and energy-economic modeling. The primary team members and their 

areas of responsibility are listed below. 

Report Managers

• EERE 

o Integrated: Brian Unruh 

o Biomass: Tien Nguyen 

o Buildings: Brian Card 

o Distributed Energy Resources (DER): Michael York 

o Federal Energy Management: David Boomsma 

o Geothermal: Cathy Short 

o Hydrogen, Fuel Cells, and Infrastructure Technologies: Jeff Dowd 

o Industry: Peggy Podolak 

o Solar: Tom Kimbis 

o Vehicle Technologies: Phil Patterson 

o Weatherization and Intergovernmental: Michael Gonzalez 

o Wind: Linda Silverman 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Introduction (Chapter 1) – Page 1-6 

• Contractors 

o Project Manager: Doug Norland (NREL) 

o Guidance: Doug Norland (NREL) 

o Appendices: Thomas Jenkin (NREL) 

o Editorial: Michelle Kubik (NREL) 

Benefits Analysis Team

• Energy-Economic Integration–NEMS: Frances Wood, John Holte, Laura Train, Will 

Georgi (OnLocation Inc.) 

• Energy-Economic Integration–MARKAL: Chip Friley, John Lee (BNL) 

• Biomass: Lynn McLarty (TMS), David Andress (DAA), Valerie Reed (OBP) 

• Buildings: Sean McDonald, Dave Anderson, David Belzer, Donna Hostick, Katie Cort, 

Jim Dirks, Doug Elliott (PNNL) 

• DE: Chris Marnay, Kristina LaCommare (LBNL) 

• Federal Energy Management: Daryl Brown, Andrew Nicholls (PNNL) 

• Hydrogen and Fuel Cells: Jeff Dowd (EERE), Margaret Singh (ANL) 

• Geothermal: Dan Entingh (PERI) 

• Green Power: Jim McVeigh (PERI) 

• Industry: Jim Reed (Independent Consultant) 

• Solar: Robert Margolis (NREL), Jim McVeigh (PERI) 

• Vehicle Technologies: Phil Patterson (EERE), Jim Moore (TA Engineering), Margaret 

Singh (ANL) 

• Weatherization and Intergovernmental Programs (WIP): Sean McDonald, David 

Anderson, Nancy Moore (PNNL); Laura Vimmerstedt (NREL) 

• Wind: Tom Schweizer, Joe Cohen, Jim McVeigh (PERI) 

In all cases, these lead analysts drew from the studies and expertise of many others. Much of this 

supporting work can be found in the references provided here and in the appendices. 

Report Organization 

This report is organized into four additional chapters. Chapter 2 describes the process and 

methodology employed by EERE to estimate program and portfolio economic, environmental, 

and security benefits from its RD&D programs. Chapter 3 presents the overall results of the 

savings estimates from the individual programs and from a total EERE portfolio perspective. 

Chapter 4 describes, in detail, the estimated midterm benefits of each program area using 

NEMS-GPRA06. Chapter 5 describes, in detail, the estimated long-term benefits of each 

program area using MARKAL-GPRA06. 

Thirteen appendices are included. Appendix A provides the Baseline Cases. Appendices B 

through M provide program-analysis team inputs for EERE’s programs. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

EERE Benefits-Analysis Process (Chapter 2) – Page 2-1 

CHAPTER 2

EERE BENEFITS-ANALYSIS PROCESS

The Office of Energy Efficiency and Renewable Energy’s (EERE) benefits-analysis process 

involves three major steps (Figure 2.1). In Step 1, EERE’s Office of Planning, Budget, and 

Analysis (PBA) develops a standard baseline and methodological approach (guidance) to help 

ensure consistency in estimates across programs. In Step 2, EERE’s programs develop specific 

technology and market information, which is necessary to understanding the potential roles of 

each program in its target markets. In Step 3, PBA uses this program and market information to 

assess the impacts of each EERE program (as well as the overall EERE portfolio) on energy 

markets in the United States using integrated energy-economic models. 

The process by which the FY04 benefits estimates were developed largely reflects EERE’s prior 

organization, although a few changes in net benefits estimation were adopted in the FY04 

analysis, including an initial reflection of the benefits framework recommendations of the 

National Academy of Science (NAS). 

 

Figure 1.1 EERE Benefits Analysis Process 

Figure 2.1. EERE Program and Portfolio Benefits-Analysis Process

STEP 3 – Program and Portfolio Benefits Estimates 

• Develop estimates of individual program benefits (the Program Cases). 

• Develop estimates of the combined benefits of all programs (the Portfolio Case). 

STEP 1 – Baseline Case and Guidance 

• Create a Baseline Case without EERE RDD&D. 

• Make any necessary updates to EERE’s guidelines on estimating benefits. 

STEP 2 – Program and Market Inputs 

• Review the baseline projections of the timing and rate of adoption of EERE technologies. 

• Assess the potential roles of each program’s performance goals in these future energy 

markets. 

• Develop inputs to Step 3. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

EERE Benefits-Analysis Process (Chapter 2) – Page 2-2 

Step 1: Baseline Case and Guidance 

Baseline Case 

The first step in the benefits analysis process is to establish an appropriate Baseline Case. The 

EERE Baseline Case is a projection intended to represent the future U.S. energy system without 

the effect of EERE programs. This Baseline Case assures that program benefits are estimated 

based on the same initial forecasts for economic growth, energy prices, and levels of energy 

demand. It also ensures that these initial assumptions are consistent with each other; e.g., that the 

level of electricity demand expected under the economic growth assumptions could be met at the 

electricity price assumed. It provides a basis for assessing how well renewable and efficiency 

technologies might be able to compete against future, rather than current, conventional energy 

technologies (e.g., more efficient central power generation). Finally, it helps assure that 

improvements in efficiency and renewable energy, which may occur absent EERE’s RDD&D 

efforts, are not counted as part of the benefits of the EERE programs. 

The most recent Annual Energy Outlook Reference Case is used as the starting point for 

developing the base case. The Energy Information Administration (EIA) Annual Energy Outlook

(AEO) Reference Case provides an independent representation of the likely evolution of energy 

markets. This forecast reflects expected changes in the demand for energy (e.g., to reflect the 

availability of new appliances), technology improvements that might improve the efficiency of 

energy use, and changes in energy resource production costs, including renewable energy. 

Current energy market policies, such as state Renewable Portfolio Standards, which facilitate the 

development and adoption of these technologies, are included in the Baseline Case. This 

approach ensures that EERE’s benefits estimates do not include expected impacts of such 

policies. Neither the EIA Reference Case nor the EERE Baseline Case includes any changes in 

future energy policies. 

In establishing its Baseline Case, EERE makes a number of modifications to the AEO2004

Reference Case (see Table 2.1). Modifications are made to the same model—the National 

Energy Modeling System (NEMS)1—used by EIA in developing the AEO2004. To distinguish it 

from EIA’s version, the model is referred to as NEMS-GPRA06. The AEO2004 Reference Case 

is also the starting point for the long-term (to 2050) benefits modeling using MARKALGPRA06. The Baseline Cases for both NEMS-GPRA06 and MARKAL-GPRA06 are aligned as 

closely as possible, but the two models are different in their internal design.2

Removal of EERE programs. First, several adjustments are made to remove EERE programs 

from the EIA Reference Case. For example, EIA’s estimate of rooftop photovoltaic installations 

resulting from the Million Solar Roofs Initiative were removed for the EERE Baseline. The 

AEO2004 assumption of roughly constant hydroelectric capacity over time was modified to 

 

1

An updated version of NEMS from April 2005 was used that produces similar reference case projections as the Annual Energy 

Outlook 2004 with Projections to 2025, January 2004, DOE/EIA-0383 (2004). See 

http://www.eia.doe.gov/oiaf/archive/aeo04/pdf/0383(2004).pdf. 2

 See Box 4.1 in Chapter 4 for an overview of NEMS and Box 5.1 in Chapter 5 for an overview of MARKAL. General 

information on energy-economy modeling is contained in Projected Benefits of Federal Energy Efficiency and Renewable 

Energy Programs FY2005Budget Request (May 2004), http://www.eere.energy.gov/office_eere/gpra_estimates_fy04.html. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

EERE Benefits-Analysis Process (Chapter 2) – Page 2-3 

reflect the expectation that without more environmentally benign turbine designs some reduction 

in hydro capacity would occur as a result of relicensing requirements. The maximum growth 

rates for cellulosic ethanol production were reduced by a factor of 5 because growth of this new 

industry is expected to be very slow without EERE program involvement. 

Table 2.1. Summary of Baseline Changes from the AEO2004 

AEO2004 GPRA06 Baseline Case 

Removal of EERE Programs 

 Million Solar Roofs 0.4 GW installed 2005 to 2025 Removed 

 Photovoltaic system costs Significant improvement Slower rate of improvement 

 Hydroelectric capacity Roughly constant hydro 

capacity and generation 

2.2 GW reduction for 2010 to 

2025 

 Cellulosic ethanol production 0.6 billion gallons annually by 

2025 

0.12 billion gallons annually by 

2025 

 DG technology improvement Significant improvement Slower rate of improvement 

 Commercial absorption cooling efficiency Constant over time Increase 20 percent by 2020 

 Wind 30 to 44 percent capacity 

factors depending on wind class 

and year 

34 to 50 percent capacity factors 

depending on wind class and 

year

 Geothermal Significant improvement Half the rate of improvement 

Energy Market Updates 

 PV system size 2 kW residential, 25 kW 

commercial 

4 kW residential, 100 kW 

commercial 

 PV maximum market share 30 percent for both residential 

and commercial 

60 percent for residential and 55 

percent for commercial 

 CHP commercial building maximum share 30 percent 50 percent 

 Commercial absorption cooling Included in only 5 building types Included in all 11 building types 

 California PV subsidy Not included Included for residential systems 

 Solar water heat Maximum 20 percent 

replacement market 

New and up to 50 percent 

replacement market 

 Cellulosic conversion efficiency 90 to 103 gallons of ethanol per 

dry ton of biomass 

82 to 101 gallons of ethanol per 

dry ton of biomass 

Structural Changes 

 Wind module One capital cost and resource 

multiplier for all wind classes 

Capital costs and resource 

multipliers by wind classes 

 No offshore wind technology Offshore wind 

 Commercial shell efficiency Index Technology representation 

 Commercial DG algorithms Market share and stock 

accounting modified 

The AEO2004 forecast includes technology improvements in all areas of energy demand and 

supply, and identifying what portion is due to EERE programs is extremely difficult. For 

GPRA2006, selected technology changes were made where the AEO2004 appeared to already 

incorporate the EERE program goals. Technology assumptions that were modified for the 

Baseline include cost and efficiency improvements in distributed combined heat and power 

(CHP) technologies that were assumed to be delayed without an ongoing distributed energy (DE) 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

EERE Benefits-Analysis Process (Chapter 2) – Page 2-4 

program. In addition, the two composite distributed generation technologies in the electricity 

generation sector were modified to reflect baseline values for gas turbines, microturbines, and 

gas engines. The AEO2004 includes a significant improvement in geothermal generation 

technology over time, similar to the program goals. To reflect what might occur without 

continued R&D funding, analysts reduced the cost reduction by half for the GPRA Baseline. 

There are a few EERE technologies that are either not represented in the AEO2004 Reference 

Case or their improvement is less than anticipated by the program in the absence of EERE 

programs. These technology assumptions were also modified for the GPRA06 Baseline. In 

commercial lighting, an advanced electrodeless fluorescent technology was replaced with a 

baseline projection of solid-state lighting characteristics. The efficiency of absorption cooling in 

commercial buildings was assumed to increase slightly, rather than remain constant over time. 

Offshore wind technology characteristics were added, and the onshore wind characteristics were 

modified. The onshore capital costs were increased slightly. More important, the capacity factors 

for each wind class were assumed to be higher than in the AEO2004, although lower than the 

program goals. 

Energy Market Updates. A few other modifications were made to reflect EERE program 

assumptions or updated information about energy markets. These changes affect both the 

Baseline and the Benefits Cases. The size of typical PV systems was increased to 4 kW in 

residential and 100 kW in commercial buildings to reflect recent PV installation experience and 

trends. The maximum market for PV systems was increased from 30 percent to 55 percent in the 

commercial sector and to 60 percent for residential PVs. Similarly, the maximum market share 

for gas-fired distributed generation technologies was increased from 30 percent to 50 percent in 

the commercial sector. California PV credits were incorporated in the Pacific region. Solar water 

heat was added to the slate of technologies for new homes, and the share of the replacement 

market in which it can compete was increased from 20 percent to 50 percent. The conversion 

efficiency of cellulosic ethanol was reduced, because EIA’s assumption appeared too optimistic. 

Structural Changes. In a few cases, analysts made structural changes to improve the model’s 

representation of markets important to EERE technologies. The wind module was modified, so 

that each of the three wind classes is treated more discretely with separate capital costs and 

resource multipliers.3 Offshore wind was added as another technology option with resources 

available in the coastal regions and the regions around the Great Lakes. The shell indices in the 

commercial module were replaced with a technology choice algorithm necessary for later 

representation of EERE shell technologies. In addition, alterations to the distributed generation 

algorithm in the building modules were made to reflect market adoption data gathered by the DE 

program,

4

 to account for buildings that have already installed a DG technology in prior years, 

and to allow greater than an annual 0.5 percent adoption in existing buildings. Absorption 

cooling was allowed to compete in all commercial building types, rather than only a subset, as in 

the AEO2004. 

 

3

 In the AEO2004 version of NEMS, these multipliers are applied to the entire wind resource in each region; whereas, in 

NEMS-GPRA05, they are applied separately by wind class. This latter treatment tends to be more restrictive, because cost 

increases due to resource depletion occur more quickly for the best wind class. 4 Market Trends in the U.S. ESCO Industry: Results from the NAESCO Database Project. Goldman, C., J. Osborn and N. 

Hopper, LBNL, and T. Singer, NAESCO, May 2002, LBNL-49601. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

EERE Benefits-Analysis Process (Chapter 2) – Page 2-5 

Resulting Energy Demand and Energy Intensity. The adjustments for the GPRA06 Baseline 

Case result in an insignificant difference in energy consumption relative to the AEO2004

Reference Case projections. The resulting Baseline Case projects a 33 percent increase in 

conventional energy demand from 2005 to 2025.5

 Energy efficiency and renewable energy 

improvements projected to occur without EERE’s program activities, however, contribute toward 

a 27 percent reduction in conventional energy intensity (energy used per dollar of GDP 

produced) over the same period (Figure 2.2).6

 

EERE benefits estimates do not include any of these efficiency or renewable Baseline Case 

improvements. Rather, the R&D improvements represented in the Baseline Case provide the 

“next best technologies” against which additional EERE improvements are compared. More 

detail from EERE Baseline Case projections is in Appendix A. 

Figure 2.2. U.S. Nonrenewable Energy Demand and Energy Intensity, 1980-2000, 

and Baseline Projections to 2050 

Data Sources: 1980-2000, Energy Information Administration, Annual Energy Review 2003, DOE/EIA-0384 (2003) 

(Washington, D.C., September 7, 2004), Tables 1.3, D1 Web site http://www.eia.doe.gov/emeu/aer/contents.html; 

2005-2025, NEMS-GPRA06; 2030-2050, MARKAL-GPRA06. 

 

5

 Very similar to the AEO2004. 

6

 Energy intensity changes result from a mix of structural changes in the economy (e.g., growing service sector) and efficiency 

improvements. Two recent EERE-sponsored studies provide additional background on understanding the sources of changes to 

our energy intensity: Ortiz and Sollinger, Shaping Our Future by Reducing Energy Intensity in the U.S. Economy; Volume 1: 

Proceedings of the Conference (2003, Rand Corporation) and Bernstein, Fonkych, Loeb, and Loughran, “State-Level Changes in 

Energy Intensity and their National Implications” (2003, Rand Corporation). 

0

50

100

150

200

250

300

350

1980 1990 2000 2010 2020 2030 2040 2050

Nonrenewable Energy Demand (Quadrillion Btu)

0

2

4

6

8

10

12

14

16

Nonrenewable Energy Intensity (1000 Btu/2002$ GDP)

Energy Intensity

(Baseline Case)

Energy Demand

(Constant 2005 Intensity)

Energy Demand

(Baseline Case)
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

EERE Benefits-Analysis Process (Chapter 2) – Page 2-6 

In the Baseline projections, oil prices are projected to fall from 2004 and then gradually rise 

through 2025, as shown in Figure 2.3. Natural gas prices are projected to fall more gradually 

through 2010 and then increase through 2025. Coal prices, on the other hand, are projected to be 

relatively constant in real terms with a very slight decline. Electricity prices are projected to 

experience a slight decrease through 2010. 

Figure 2.3. Projected Energy Prices 

Guidance 

In order to improve the consistency of estimates across EERE’s portfolio, EERE utilizes 

common methodological approaches, definitions, and conversion factors. Prior to the 

reorganization in 2002, EERE communicated these common elements in the form of an annual 

“GPRA Data Call”7

 to the five EERE sectors, which undertook separate analyses based on these 

common guidelines. With the reorganization, the benefits-analysis team utilizes this process 

directly, including: 

Definitions. Common definitions for benefits metrics and related terms are provided. 

Converting nominal dollars to real dollars. The results of EERE’s benefits analysis are 

reported in constant (“real”) dollars as opposed to current/future year (“nominal”) dollars to 

compensate for the effects of inflation over time. In cases where the program or other sources 

provide future expenditures or costs in nominal dollars, these are converted to constant 

dollars based on a forecasted GDP deflator. 

 

7 The guidance used for FY 2006 benefits estimates followed the guidance for FY 2005 (see

http://www.eere.energy.gov/office_eere/ba/gpra_estimates_fy04.html). EERE will continue to maintain standard assumptions 

and methodologies for estimating program benefits.

0.00

2.00

4.00

6.00

8.00

10.00

12.00

1985 1990 1995 2000 2005 2010 2015 2020 2025

2002$ per MMbtu or cents per kWh

Average Electricity

World Oil

Natural Gas

Minemouth Coal
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

EERE Benefits-Analysis Process (Chapter 2) – Page 2-7 

Next best technology. The benefits of EERE technologies are assessed compared to the best 

technologies expected to be available to the market at the time the EERE technologies are 

developed—not compared to the technologies available or installed today. The Baseline Case 

provides the future “next best technologies,” against which EERE technologies will compete. 

In markets where the models do not have explicit technology representation, the “next best 

technology” is reflected in the Baseline Case rates of technology and market improvements. 

In most cases, EERE R&D efforts accelerate the development and introduction of these 

technologies, while its deployment efforts principally accelerate the market penetration of 

technologies once they have reached the market.8

 In specific cases, the RD&D efforts also 

may be directed toward changing the attributes of technologies in the market (e.g., less 

polluting) or of developing technologies that are not reflected in the Baseline Case within the 

timeline of analysis. (See Box 2.1—Impact of EERE Programs). 

Market characteristics and penetration rates. It takes time for new products to reach their 

full market potential, and these market-penetration rates vary considerably by technology and 

market. The Baseline Case includes assumptions about technology-adoption rates for many 

markets, primarily through the use of consumer “hurdle rates” or other representations of the 

trade-off between upfront investment costs and annual operating costs (including energy 

expenses) over time, as well as other attributes in selected cases. Where technologies are not 

explicitly represented, adoption rates are embedded in efficiency trends. Efficiency trends 

may implicitly include capital stock turnover, as well as technology efficiencies and rate of 

uptake of different technologies. Other market characteristics (such as regional markets, 

regulatory constraints, or typical start-up time for new product lines) can influence adoption 

rates and also may be specifically represented in the Baseline Case. For R&D activities, the 

market characteristics and factors affecting adoption rates remain the same for the Program 

Case and the Baseline Case, unless the new technology would fundamentally change the way 

the target markets operate (e.g., accelerate stock turnover or increase consumer acceptance of 

new technologies). For deployment activities, the program output goals provide a basis for 

assessing the expected acceleration of market-penetration rates (or other changes in market 

characteristics), due to the program activities in the Program Case.

Technology performance and cost. For R&D programs, the benefits analysis is based on 

the performance and cost of the technologies being developed or deployed. For each 

technology (or class of technologies), key technology characteristics include: 

• Expected year of technology availability 

• Capital costs 

• Operations and maintenance (O&M) costs 

• Technology product lifetime 

• Technology performance and/or energy displaced/unit by fuel type 

• Other technology features that might affect market acceptance. 

 

8 This is a starting assumption. There may be cases in which EERE’s efforts principally change the characteristics of the 

technologies being marketed (e.g., less polluting) rather than, or in addition to, accelerating market introduction and penetration. 

At times, EERE may be developing technologies that are not expected to be developed by the private sector (i.e., they do not 

show up in the Baseline Case at all). Finally, some research efforts include built-in deployment components that may result in a 

combined accelerated introduction and accelerated penetration effect. These variations on the basic approach described above are 

addressed in the program-level appendices to this report. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

EERE Benefits-Analysis Process (Chapter 2) – Page 2-8 

Box 2.1—Impact of EERE Programs 

For EERE R&D efforts, the initial assumption is that the impact of the program is to accelerate the commercial introduction of a 

technology (see Figure 2.4a). In some cases, that may be the only effect. In other cases, the EERE R&D effort may develop a 

technology with features that can affect the ultimate size of the market, or that otherwise would not have been developed by the 

private sector.*

 For EERE deployment efforts, the initial assumption is that the impact of the program is to accelerate the rate of 

adoption of a technology already developed and introduced to the market (see Figure 2.4b). In some cases, the EERE deployment 

effort also may impact the total size of the market, in addition to the rate of adoption. In such cases, the program affects the 

maximum market share the technology achieves. 

0

1 0

2 0

3 0

4 0

5 0

6 0

7 0

8 0

9 0

100

2005 2015 2025 2035 2045

Market Share (%)

W ith o u t E E R E

W ith E E R E

Figure 2.4a. Potential Impacts of EERE R&D Programs on Technology Introduction 

0

1 0

2 0

3 0

4 0

5 0

6 0

7 0

8 0

9 0

100

2005 2015 2025 2035 2045

Market Share (%)

W ith o u t E E R E

W ith E E R E

Figure 2.4b. Potential Impacts of EERE Deployment Programs on Market Penetration 

*

Assuming the technology, or technological characteristic, would have been developed by the private sector anyway. In some 

cases, technologies are so far from potential commercialization—or so risky—that private-sector firms do not invest in them. In

others, the private sector lacks the market incentive to develop technology features, such as improved load-balancing for home 

appliances (which could improve the reliability of the electricity grid), because the markets do not provide the price signals that 

would generate profits from these public benefits. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

EERE Benefits-Analysis Process (Chapter 2) – Page 2-9 

Two sets of technology characteristics are of interest: Baseline Case and Program Case. The 

EERE Baseline Case already includes expected private-sector advances in efficiency and 

renewable technologies. In many cases, the specific technology characteristics are included 

directly in the NEMS-GPRA06 and MARKAL-GPRA06; while, in other cases, they are 

represented through overall rates of technology improvement—and the characteristics for 

specific technologies must be inferred from these rates. 

For R&D efforts, the Program Case technology characteristics and costs generally reflect the 

program output goals. For deployment efforts, the technology characteristics remain the same 

in the Baseline and Program Cases. 

Calculating direct energy and primary energy displaced. NEMS-GPRA06 and 

MARKAL-GPRA06 provide projections of direct (site) energy savings from end-use 

programs and the corresponding primary energy reductions. Reduced electricity demand 

leads to reduced generation and fuel consumption by electric power producers. The amount 

of fuel consumed (and saved) changes as the marginal efficiency of power production 

increases with the increased efficiency of conventional, central power production. When the 

principal market analysis is performed off-line, the resultant energy savings (expressed in 

direct energy terms) are used as an input to the NEMS-GPRA06 and MARKAL-GPRA06 

models. The two models then compute primary energy savings based on the direct energy 

savings. 

Calculating carbon equivalent emissions reductions. NEMS-GPRA06 and MARKALGPRA06 compute carbon emission reductions based on the amount of coal, oil, and natural 

gas consumed in the Baseline, Program, and Portfolio Cases, as well as the carbon 

coefficients of each energy source. Carbon emissions are computed using NEMS-GPRA06 

and MARKAL-GPRA06. The carbon emissions associated with the displacement of fossilgenerated electricity by efficiency or renewable technologies will vary over time and reflect 

the increasing efficiency of new fossil generators and the dynamic shift in fuel sources. 

EERE’s ability to apply these methodological approaches varies considerably by program, 

depending on the availability and cost of market data, the ability to assess public and privatesector technology contributions, and the capability to reflect specific market conditions in 

energy models available to EERE. 

Step 2: Program and Market Inputs 

In Step 2, program goals and salient target market characteristics are developed as inputs to 

modeling the benefits estimation in Step 3. The effort required under Step 2 varies considerably, 

depending on the form in which programs specify their output or performance goals and how 

NEMS-GPRA06 and MARKAL-GPRA06 utilize this information. It ranges from the 

compilation of technology goals to detailed market analyses that produce technology penetration 

rates—and, in some cases, delivered energy savings. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

EERE Benefits-Analysis Process (Chapter 2) – Page 2-10 

NEMS-GPRA06 and MARKAL-GPRA06 contain detailed technology representations of 

electricity markets, most residential and commercial end uses, and vehicle choice—but use 

trends to represent industrial efficiency improvements and existing residential shell retrofits. For 

programs that address these markets, this step simply requires (1) confirming the adequacy of the 

target market representation in the Baseline Case and (2) providing the program goals in a format 

consistent with the model. Any updated market characteristic information is used to adjust 

NEMS-GPRA06 and MARKAL-GPRA06 for both the Baseline Case and the Program Case to 

avoid ascribing external factors as benefits. Analysts use the program goal information to adjust 

the commercialization date, technology characteristics, or market penetration rate for the 

Program Case. The comparison of market technology introduction and market penetration rates, 

with and without the program goal—and the calculation of the energy displaced—occur within 

NEMS-GPRA06 and MARKAL-GPRA06. 

For much of EERE’s portfolio, additional “off-line” analyses are needed to translate information 

about program technology and market characteristics into usable modeling inputs. This off-line 

Step 2 analysis can range from spreadsheet calculations to the use of market-specific models to 

assess technology or market features that cannot be adequately represented in a broad energyeconomic model, or to translate program goals into the variables used in the modeling. In 

general, analysts perform the most detailed off-line analyses for the Industrial Technologies 

Program, Weatherization and Intergovernmental Program (WIP), Federal Energy Management 

Program (FEMP), and portions of the Building Technologies Program. Analysts tailor these offline analytical approaches to the characteristics of the program and target market being analyzed; 

but, in any case, they are conducted within the overall guidance provided through the GPRA 

benefits estimation process. 

The market applications for EERE technologies are often very specific, and resulting energy 

savings for a given technology can vary significantly from one application to another. For 

example, the impact of upgrading building codes can vary significantly (due to differences in 

climate and in existing building-code standards) and therefore require analysis at the State level. 

The Building, Industrial, and WIP programs are most likely to require tailored analytical 

approaches that address these submarkets. 

Where NEMS-GPRA06 and MARKAL-GPRA06 do not include technology-by-technology 

information (e.g., cost, date of availability), or specific market-penetration rates, it is often 

necessary to translate program goals into the more general rates of technology improvement used 

by the models. This is true for the Industrial Technologies Program and some elements of the 

Building Technologies Program, where numerous specific technology advances or market 

deployment efforts will accelerate overall efficiency improvements in buildings or factories 

specified in the Baseline Case. 

Off-line analysis also can be required for targeted submarkets that are simply not included in 

NEMS-GPRA06 or MARKAL-GPRA06—or for which the resulting technology use is not fully 

market-driven. Examples include the Federal sector (addressed by FEMP) and the Low-Income 

Weatherization Assistance Program, in which the Federal Government directly purchases home 

efficiency improvements. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

EERE Benefits-Analysis Process (Chapter 2) – Page 2-11 

Finally, supporting “off-line” analysis can be required where market functions are not well 

represented in a full energy-economic model. For example, consumer willingness to pay a 

premium for electricity produced by environmentally friendly technologies is not represented 

within the electricity market in NEMS-GPRA06 and MARKAL-GPRA06; and, therefore, 

another model specifically designed to analyze this market provides the input assumptions on 

this market segment. Also, programs designed to help overcome institutional barriers to 

efficiency adoption are often difficult to represent in market-based models. 

Because estimating the benefits of achieving program performance goals requires the ability to 

realistically assess the extent to which future energy markets might adopt the technology and 

market improvements developed by EERE programs, analysts explore the following features in 

these off-line analyses: 

Target Markets. New technologies will not necessarily be well suited to all applications 

served by existing markets. Technologies may occupy niche markets, especially in early 

years. In some cases, initial markets are geographically limited as well. Where integrated 

models do not represent these submarkets explicitly, it may be necessary to develop off-line 

estimates of the applicable market share for the technology being developed, at least in the 

early years. 

Stock Turnover. Modeling stock turnover is crucial to estimating benefits for both new 

technologies and deployment programs. Analyses of the market adoption of new 

technologies must consider the rate at which the specific type of energy-using or -producing 

capital equipment is replaced, in addition to the growth rate of the overall market. Even when 

a technology is suitable and cost-effective for a percentage of a market, it may take a decade 

or more for the capital stock in that portion of the market to retire and be replaced. 

Particularly attractive new technologies might accelerate that turnover. EERE includes this 

potential for early retirement only when market evidence suggests that the technology 

improvement is significant enough to overcome typical hurdle rates to new investment. 

Although stock turnover fluctuates with business cycles, EERE does not incorporate business 

cycles into its Baseline or Program cases. As a result, nearer-term estimates of benefits, in 

particular, do not take into account year-to-year fluctuations in energy use attributable to 

business cycles. 

 

Next Best Technology. Where technology representation is implicit (in a technology 

improvement index, for instance), the Baseline Case improvement must be translated into 

improvement rates for a specific set of technologies. Analysts use this set of baseline 

technologies to assess the specific markets in which the EERE technology might be 

competitive in different time frames. 

Market Penetration. Over time, new technologies typically make their way into markets—

and, therefore, affect energy use—gaining their share of new sales as consumers learn about 

the availability of the product. Manufacturing capacity then grows, and product prices fall 

with economies of scale and learning.9

 While price helps determine whether a product is 

 

9 See Adam B. Jaffe, Richard G. Newell, and Robert N. Stavins, “Energy-Efficient Technologies and Climate Change Policies: 

Issues and Evidence,” Climate Issue Brief No. 19, Resources for the Future, Washington, D.C. (December 1999).
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

EERE Benefits-Analysis Process (Chapter 2) – Page 2-12 

cost-effective, on average, energy prices vary by type of customer and region, so that new 

products may be cost-effective for some customers (a niche market) before they are generally 

cost-effective. Price, or cost-effectiveness, is often not the only aspect of the new technology 

or deployment program that shapes its rate of market uptake. Many non-price or cost factors 

affect consumer behavior. 

As an example, the off-line analysis for the Industrial Technologies Program uses a 

spreadsheet model that provides several possible market-penetration curves. The analyst 

chooses a curve, based on specific information from possible R&D partners, comparison of 

the new technology to similar technologies, or his or her expert judgment. The benefits 

guidance for industrial benefits estimation includes historic penetration curves for 11 

technologies and offers the analyst five choices of penetration curve shapes. The five choices 

are accompanied by detailed data on technology equipment, financial, industry, regulatory, 

and impact characteristics to aid in making the choice. In addition to choosing the shape or 

the penetration curve, the analyst chooses the year—after all pilot testing and demonstration 

phases—the new technology is expected to enter the market. 

Through the use of specialized spreadsheets or other models,10 program analysts produce 

estimates of market penetration and direct energy savings associated with these market sales. 

However, these “off-line” estimates of direct energy savings are not benefits estimates, 

because they do not account for market interactions. Analysts integrate these off-line 

estimates within the NEMS-GPRA06 and MARKAL-GPRA06 models as the final part 

(Step 3) of the process. 

Step 3: Program and Portfolio Benefits Estimates 

The final step for estimating the impacts of EERE’s FY 2006 Budget Request is to analyze all 

EERE’s programs in a consistent economic framework and to account for the interactive effects 

among the various programs. Estimates of individual EERE program energy savings cannot be 

simply summed to create a value for all of EERE, because there are feedback and interactive 

effects resulting from (1) changes in energy prices resulting from lower energy consumption and 

(2) the interaction among programs affecting the mix of generation sources and those affecting 

the demand for electricity. 

The process begins by analysts modeling each EERE program individually within NEMSGPRA06 and MARKAL-GPRA06 to the extent possible. In each NEMS-GPRA06 and 

MARKAL-GPRA06 Program Case, only the modeling assumptions related to the outputs of the 

program being analyzed are changed. The modeling assumptions related to the other EERE 

programs remain as they were in the EERE Baseline Case. Analysts model each program 

separately to derive estimated energy savings without the interaction of the other programs. They 

then compare the results from the NEMS-GPRA06 and MARKAL-GPRA06 Program Cases to 

the Baseline Case to measure the individual benefits of the EERE program being analyzed. 

 

10 In one case (the Building Technologies Program), a portion of NEMS (the buildings module) was used for off-line analysis. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

EERE Benefits-Analysis Process (Chapter 2) – Page 2-13 

For programs modeled using NEMS-GPRA06 and MARKAL-GPRA06 directly, analysts 

compute the Program Case by changing the assumptions representing the program outputs; i.e., 

the goals or performance targets of the program, such as reducing low wind-speed turbine costs 

and improving their performance. The R&D programs are represented in NEMS-GPRA06 and 

MARKAL-GPRA06 through changes in technology characteristics that represent the program 

goals, to the extent possible. Activities designed to stimulate additional market penetration of 

existing technologies generally were modeled through changes in consumer hurdle rates or other 

appropriate market-penetration parameters, with the goal of representing the market share 

targeted by the program. 

In cases where program goals cannot be easily modeled using NEMS-GPRA06 and MARKALGPRA06, analysts estimate benefits using a variety of off-line tools, as described in Step 2. 

These supporting analyses typically provide either estimates of market penetration and per-unit 

energy savings, or total site energy savings, which are then used as inputs to NEMS-GPRA06 

and MARKAL-GPRA06. In cases where the off-line analyses produce a direct estimate of site 

energy savings, analysts adjust this information by an “integration factor” and incorporate it in 

NEMS-GPRA06 and MARKAL-GPRA06 in order to calculate primary energy savings. The 

amount of the integration factor is based on how much program overlap or “integration” was 

captured by the off-line tools. The revision is based on the expert judgment of the benefits 

analysis team. See Chapters 4 and 5 for discussion of program-by-program benefit estimates, 

including such reductions. 

Once each of the programs (or group of programs) is represented individually within NEMSGPRA06 and MARKAL-GPRA06, the benefits of EERE’s portfolio are estimated by combining 

all of the program goals into one EERE Portfolio Case. 

Detailed projections from the EERE Baseline and Portfolio Benefits Case are in Appendix A.
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

FY 2006 Benefits Estimates (Chapter 3) – Page 3-1

CHAPTER 3

FY 2006 BENEFITS ESTIMATES

The Office of Energy Efficiency and Renewable Energy (EERE) estimates expected benefits for 

its overall portfolio and for each of its 11 programs. Benefits for the FY 2006 budget request are 

estimated for the midterm (2010-2025) and long term (2030-2050). Two separate models suited 

to these periods are employed—NEMS-GPRA06 for the midterm and MARKAL-GPRA06 for 

the long term. 

Benefits estimates are intended to reflect the value of program activities from 2006 forward. 

They do not include the impacts of past program success, nor technology development or 

deployment efforts outside EERE’s programs. This distinction is difficult to implement in 

practice, because many research and deployment activities provide continuous improvements 

that build on past success; and because EERE programs are leveraged with private-sector and 

other government efforts (e.g., in addition to the Baseline Case, private-sector improvements). 

Outcomes and Benefits Metrics 

The energy efficiency improvements and additional renewable energy production facilitated by 

EERE’s programs reduce the consumption of traditional energy resources. Reducing energy 

consumption affords the Nation a number of economic, environmental, and energy security 

benefits.1 The extent of these benefits depends on numerous factors including which energy 

sources are reduced, the costs of the new technologies, and the emissions performance of the 

energy technologies used. Different EERE portfolios would produce a different mix of benefits, 

even if the overall level of primary energy savings were the same. 

The public benefits resulting from these reductions in the use of traditional energy resources take 

many forms. Environmental improvements, for instance, can include reductions in local, 

regional, or global air emissions; reduced water pollution; noise abatement, etc. These public 

benefits are typically difficult to measure directly, and some aspects are not quantifiable. EERE 

has developed a set of indicators intended to provide a sense of the magnitude and range of the 

benefits its programs provide the Nation. EERE estimates benefits for the following defined 

metrics: 

Primary Outcome: 

Energy Displaced - the difference in nonrenewable energy consumption with and 

without the technologies and market improvements developed by EERE programs.

 

1

 This is a categorization of EERE’s benefits estimates, based on the framework developed by a National Research Council 

(NRC) committee. The framework is described in more detail in the Introduction. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

FY 2006 Benefits Estimates (Chapter 3) – Page 3-2

Analysts measure energy savings on a primary basis, accounting for the energy consumed in 

producing, transforming, and transporting energy to the final consumer. Energy savings from 

underlying private-sector improvements in technologies are not counted. Energy displaced is 

reported in quadrillion Btus per year (quads/yr). 

Primary Benefits:

Economic Benefits: Economic benefits are the potential for EERE technologies to make 

energy more affordable by reducing expenditures on energy and energy services, increase 

economic productivity and GDP through more efficient production processes, reduce the 

impact of energy price volatility on the U.S. economy by providing more efficient 

technologies and providing alternative energy sources, and improve the balance of trade by 

exporting energy technologies. Of these, EERE currently estimates two aspects of 

affordability—energy-expenditure savings and total system cost savings:2

Energy-expenditure savings – The difference in total consumer energy bills with and 

without the availability of technologies and market improvements developed by 

EERE technologies. This is an estimate of energy bill savings3

 and does not include all 

incremental costs to end users of acquiring the new technology. The EIA NEMS model 

does not currently have the capability to provide net costs in all sectors of the economy. 

Energy-expenditure savings are reported in billions of 2002 dollars per year. 

Total system cost savings – The difference in total systems costs with and without 

the availability of technologies and market improvements developed by EERE 

technologies. Total system cost represents the economic cost to society to produce, 

import, convert, and consume energy. It is calculated as the sum of domestic resourceextraction costs, imported fuel costs, and the annualized capital and operating and 

maintenance costs of energy technologies (including end-use demand devices). Total 

system cost savings is a net estimate of system costs generated by MARKAL-GPRA06, 

which unlike the energy expenditure savings estimates generated by NEMS-GPRA06, 

includes the incremental costs of end-use technologies. Total system cost savings are 

reported in billions of 2002 dollars per year. 

Environmental Benefits: Environmental benefits that can result from use of EERE 

technologies include, among many others, lower carbon, SOx, NOx, and other air emissions. 

Of these, EERE currently estimates only the impacts of its programs on carbon emissions: 

Carbon savings (i.e., emission reductions) – The difference in the level of U.S. 

energy-related carbon emissions with and without the availability of EERE 

technologies and associated market improvements. Carbon emission reductions result 

from the reductions in fossil fuel consumption when these new supply (renewables) and 

 

2 Energy-expenditure savings are calculated through 2025 using the NEMS-GPRA06. Total system cost savings are calculated 

through 2050 using MARKAL-GPRA06. 

3 Energy efficiency improvements and increased use of nonfuel renewable energy (e.g., renewable-generated electricity) reduce 

energy bills in two ways. Consumers who make energy efficiency or renewable energy investments benefit directly through 

reduced purchases of energy (quantity component). In addition, the lower demand for energy reduces the price of energy for all 

consumers (price component).
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

FY 2006 Benefits Estimates (Chapter 3) – Page 3-3

demand (energy-efficient) technologies are used in the market. As with the energysavings metric, emission reductions count the effect of upstream energy savings in 

producing, transforming, and transporting energy to the end user. Carbon savings are 

reported in million metric tons of carbon (mmtc) equivalent per year. 

Security Benefits: Security benefits include improvements in the reliability of fuel and 

electricity deliveries, reduced likelihood of supply disruptions, and reduced impacts from 

potential energy disruptions. EERE contributes to these security gains by reducing U.S. 

reliance on imported fuels, increasing the diversity of domestic energy supplies, increasing 

the flexibility and diversity of the Nation’s energy infrastructure, reducing peak demand 

pressure on that infrastructure, and providing backup energy sources in the event of outages. 

Of these aspects of energy security, EERE has developed indicators related to concerns about 

fuel supplies and the reliability and diversity of electricity supplies:4

Oil savings – The difference in total U.S. oil consumption with and without EERE 

technologies and market improvements. Oil savings are reported in million barrels per 

day (mbpd). 

Natural gas savings – The difference in total U.S. natural gas consumption with and 

without EERE technologies and market improvements. Natural gas savings are 

reported in quadrillion Btu per year (quads/yr). 

Avoided additions to central conventional power – The difference in central 

conventional power additions with and without EERE technologies and market 

improvements. Avoided central conventional power additions result from electricity 

capacity displaced by efficiency improvements; additional distributed generation capacity 

(fossil or renewable); and central renewable power-generating capacity.5 Avoided 

capacity additions are reported in cumulative gigawatts (GW). 

In interpreting these metrics, it is important to remember that while the benefits of efficiency and 

renewable technologies are multifaceted, they are not always distinct or additive. Improvements 

in balance-of-trade or economic productivity, for instance, are contributory to improved GDP 

and not additional to improved GDP. Nonetheless, identifying the various types of economic or 

other contributions can help relate EERE’s portfolio to various economic or other policy 

concerns. 

Each of these metrics is ideally measured as a net benefit (e.g., energy bill savings minus the cost 

to the consumer of investing in the efficient or renewable technology, or including positive and 

negative environmental impacts). Analysts calculate carbon emission reductions, as well as oil 

and natural gas savings, on a net basis, including cases in which EERE programs tend to increase 

rather than decrease use or emissions. While consumer-expenditure estimates calculated by 

 

4

 The inclusion of reliability improvements within the security category was part of the NRC suggestions on how to structure the

types of EERE benefits. 

5 These measures are not additive and are not the same as a measure of peak-load reduction for conventional electricity or of 

improved reliability. Renewable capacity additions are not equivalent to capacity additions avoided because of differences in 

capacity factors and coincidence of renewable generation at system peak (i.e., peak electricity-generation output of wind, for 

example, may not coincide with the peak demand of the utility system to which it supplies power).
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

FY 2006 Benefits Estimates (Chapter 3) – Page 3-4

NEMS-GPRA06 do not reflect the costs to consumers of purchasing more efficient or cleaner 

technologies, MARKAL-GPRA06 is able to provide estimates of net economic costs. 

Portfolio Benefits 

Table 3.1 shows the estimated economic, environmental, and security benefits of EERE’s 

overall portfolio of investments in improved energy-efficient technologies, renewable energy 

technologies, and assistance to consumers in adopting these technologies. Data by five-year 

increments (2010 to 2025) are shown for NEMS-GPRA06 and by 10-year intervals (2020 to 

2050) for MARKAL-GPRA06.6 

Table 3.1. Annual EERE Portfolio Benefits for FY 2006 Budget Request for Selected Years7,8

EERE Midterm Benefits (NEMS-GPRA06) 2010 2015 2020 2025 

Energy Displaced 

• Primary nonrenewable energy savings (quadrillion Btu/yr) 

Economic 

• Energy-expenditure savings (billion 2002 dollars/yr)* 

Environment 

• Carbon dioxide emission reductions (mmtc equivalent/yr) 

Security 

• Oil savings (mbpd) 

• Natural gas savings (quadrillion Btu/yr) 

• Avoided additions to central conventional power (cumulative 

gigawatts) 

1.1 

12 

22 

0.1 

0.5 

5 

3.4 

37 

67 

0.6 

1.1 

49 

7.8 

87 

160 

1.3 

1.9 

96 

12.3 

123 

262 

2.3 

1.8 

137 

EERE Long-Term Benefits (MARKAL-GPRA06) 2020 2030 2040 2050 

Energy Displaced 

• Primary nonrenewable energy savings (quadrillion Btu/yr) 

Economic 

• Energy-system cost savings (billion 2002 dollars/yr)* 

Environment 

• Carbon dioxide emission reductions (mmtc equivalent/yr) 

Security 

• Oil savings (mbpd) 

• Natural gas savings (quadrillion Btu/yr) 

8.0 

43 

152 

1.3 

3.1 

17.7 

102 

364 

4.6 

2.8 

28.2 

188 

568 

9.0 

3.6 

33.6 

282 

699 

11.0 

2.4 

* Midterm energy-expenditure savings only include reductions in consumer energy bills, while long-term energysystem cost savings also include the incremental cost of the advanced energy technology purchased by the 

consumer. 

 

6

 NEMS-GPRA06 runs using one-year intervals, while Markal-GPRA06 runs using five-year intervals. 7 Estimates reflect the annual benefits in each year associated with program activities from FY 2006 to the benefit year, or to 

program completion (whichever is nearer), and are based on program goals developed in alignment with assumptions in the 

President’s Budget. Midterm program benefits were estimated using the GPRA06-NEMS model, based on the Energy 

Information Administration’s (EIA) National Energy Modeling System (NEMS) and using the EIA’s Annual Energy Outlook 

2004 (AEO2004) reference case. Long-term benefits were estimated using the GPRA06-MARKAL model developed by 

Brookhaven National Laboratory. Results can differ among models due to structural differences. The models used in this 

analysis estimate economic benefits in different ways, with MARKAL reflecting the cost of additional investments required to 

achieve reductions in energy bills. 

8 For some metrics, the benefits estimated by MARKAL-GPRA06 do not align well with those reported by NEMS-GPRA06. 

Every attempt is made in the integrated modeling to use consistent baselines, input data and assumptions in both models to 

produce consistent results. However, NEMS and MARKAL are in some respects fundamentally different models (see Boxes 4.1 

and 5.1). Discrepancies in the estimated benefits often occur simply because of these model differences.
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

FY 2006 Benefits Estimates (Chapter 3) – Page 3-5

Energy Displaced: EERE’s portfolio significantly dampens the expected growth in 

nonrenewable energy consumption. Absent the results of EERE’s programs,9

 energy use is 

expected to grow by nearly 32 quads from 2005 to 2025, to about 127 quadrillion Btus of 

energy and by 44 quads from 2005 to 2050. If the goals of EERE’s investment portfolio are 

achieved and the corresponding market outcomes realized, it will reduce nonrenewable 

energy consumption by more than 12 quadrillion Btu by 2025, or about 39 percent of the 

expected incremental growth in energy demand over this time period; and by 34 quadrillion 

Btus by 2050, or about 76 percent of the expected incremental growth in energy demand over 

this time period (see Figure 3.1). This results in a reduction of nonrenewable energy 

consumption starting in 2030 despite a growing economy. 

Figure 3.1. U.S. Nonrenewable Energy Consumption, 1980-2000, and Projections to 2050: 

Baseline, Program, and Portfolio Cases 

Data Source: 1980-2000, Energy Information Administration, Annual Energy Review 2003, DOE/EIA-0384 

(2003) (Washington, D.C., October 2004), Table 1.3, Web site http://www.eia.doe.gov/emeu/aer/contents.html. 

These estimates account for interactions among program results. While some program 

activities reinforce each other to produce larger benefits than would be evident from each 

program’s individual efforts, programs compete for the same markets in other cases. For 

example, the various renewable technology programs compete in the electricity-generation 

market. In addition, activities being funded by some programs reduce the potential market for 

technologies being developed in other programs. As an example, reductions in electricity 

demand due to efficiency improvements reduce the size of the generation market and, 

 

9 See Chapter 1 for information on how EERE’s “no-program” Baseline Case is developed. 

0

20

40

60

80

100

120

140

160

1980 1990 2000 2010 2020 2030 2040 2050

Baseline Case

Portfolio Case

Program Case

Quadrillion Btu
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

FY 2006 Benefits Estimates (Chapter 3) – Page 3-6

therefore, the market opportunity for renewable-generation technologies. The overall effect 

of these interactions is to reduce estimated benefits by about 0.8 quads in 2025 compared to 

the sum of the individual program benefits; and to reduce estimated benefits by about 4.6 

quads in 2050 compared to the sum of the individual program benefits (i.e., Program Case, 

see Figure 3.1). 

Economic Benefits: The energy savings resulting from these efficiency and renewable 

energy contributions are estimated to reduce annual consumer energy expenditures in 2025 

by $123 billion (expressed in real 2002 dollars) relative to the baseline projection of $1,015 

billion (Figure 3.2), or about 12 percent of the nation’s expected energy bill. 

While these energy bill savings appear to be large, they represent both reduced energy 

purchases and lower energy prices resulting from reductions in demand. They also exclude 

incremental costs to end users of acquiring the new technology, because the EIA NEMS 

model does not currently have the capability to determine this in all sectors of the economy. 

Lower energy demand dampens fuel costs and reduces the need for expensive new energy 

infrastructure expenditures. Lower energy prices improve affordability for all consumers, 

including those who make no additional efficiency or renewable investments as a result of 

EERE’s activities. 

Figure 3.2. U.S. Total Energy Expenditure, 1980-2000, and Projections to 2025: 

Baseline, Program, and Portfolio Cases 

Data Source: 1980-2000, Energy Information Administration, Annual Energy Review 2003, DOE/EIA-0384 

(2003) (Washington, D.C., October 2004), Table 3.4 and Table D1, Web site 

http://www.eia.doe.gov/emeu/aer/contents.html. 

0

200

400

600

800

1000

1200

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

Billion 2002 Dollars

Baseline Case

Program Case

Portfolio Case
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

FY 2006 Benefits Estimates (Chapter 3) – Page 3-7

The EERE portfolio also will reduce annual total system energy costs by $282 billion (in real 

2002 dollars) in 2050 (Figure 3.3). This longer-term analysis is done using MARKALGPRA06, which includes the incremental costs to end users of acquiring the new technology.

Figure 3.3. U.S. Total Energy-System Cost Projections to 2050: 

 Baseline, Program, and Portfolio Cases 

Data Source: MARKAL-GPRA06 

Environmental Benefits: Annual carbon emissions are projected to be 262 million metric 

tons (carbon equivalent) less than the 2025 baseline projection of 2,219 million metric tons—

a reduction of about 12 percent (Figure 3.4) or 45 percent of the expected increase from 

2005 to 2025.10 Annual carbon emissions are projected to be 699 million metric tons (carbon 

equivalent) less than the 2050 baseline projection of 2,621 million metric tons—a reduction 

of about 27 percent or 71 percent of the expected increase from 2005 to 2050. 

Although not quantified here, EERE’s portfolio contributes toward improved regional and 

local air quality through reduced SO2 and NOx emissions from fossil energy consumption 

(SO2 reductions in the utility sector are likely to lower permit prices rather than reduce net 

emissions). The portfolio also provides State and local governments with additional options 

for meeting Clean Air Act ambient air quality standards. For instance, the Clean Cities 

activity in the Weatherization and Intergovernmental Program facilitates local purchases of 

alternative-fuel vehicles. 

 

10 To meet the 2012 target under President Bush’s Climate Change Initiative, emissions – from energy consumption – would need 

to be 1,754 mmtce (an 18 percent reduction). The Baseline projection is 1,843 mmtce, which is a 13.8 percent reduction in carbon 

intensity from 2002. With the EERE Portfolio, emissions are projected to be 1,806, or a 15.6 percent intensity reduction from 

2002. The EERE Portoflio, therefore, is expected to contribute 43 percent of the reduction toward the goal. 

7000

9000

11000

13000

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Billion 2002 Dollars

Baseline Case

Program Case

Portfolio Case
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

FY 2006 Benefits Estimates (Chapter 3) – Page 3-8

Figure 3.4. U.S. Carbon Emissions, 1980-2000, and Projections to 2050: 

Baseline, Program, and Portfolio Cases 

Data Source: 1980-2000, Energy Information Administration, Annual Energy Review 2003, DOE/EIA-0384 

(2003) (Washington, D.C., October 2004), Table 12.2, Web site http://www.eia.doe.gov/emeu/aer/contents.html. 

Security Benefits: The EERE portfolio is expected to reduce annual oil consumption by 2.3 

mbpd from the 2025 baseline of 26 mbpd, or about 34 percent of expected growth in oil 

demand between 2005 and 2025 (Figure 3.5). The portfolio is expected to reduce oil 

consumption by 11 mbpd from the 2050 baseline of 30.5 mbpd (about 99 percent of expected 

growth in oil demand between 2005 and 2050). This results in declining oil consumption 

starting in 2030. 

0

500

1000

1500

2000

2500

3000

1980 1990 2000 2010 2020 2030 2040 2050

Million Metric Tons Carbon Equivalent

Baseline Case

Portfolio Case

Program Case
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

FY 2006 Benefits Estimates (Chapter 3) – Page 3-9

Figure 3.5. U.S. Oil Consumption, 1980-2000, and Projections to 2050: 

Baseline, Program, and Portfolio Cases 

Data Source: 1980-2000, Energy Information Administration, Annual Energy Review 2003, DOE/EIA-0384 

(2003) (Washington, D.C., October 2004), Table 1.3, Web site http://www.eia.doe.gov/emeu/aer/contents.html. 

Data were converted from quads to mbpd using conversion factor of 1 quad = 0.472 mbpd. 

While EERE’s portfolio has elements that increase (as well as decrease) natural gas 

consumption; on balance, EERE’s portfolio is expected to reduce annual natural gas 

consumption by about 1.8 quadrillion Btu from the baseline of 32.2 quadrillion Btu in 2025 

and by 2.4 quadrillion Btu from the baseline of 36.6 quadrillion Btu in 2050 (Figure 3.6). 

While EERE does not estimate the portion of natural gas savings attributed to imported 

natural gas supplies, supplies from countries other than the United States and Canada may be 

the marginal sources of natural gas for meeting any future growth in demand. 

EERE’s technology programs also contribute to the security of the Nation’s electricity supply 

by reducing central conventional power plant capacity additions. This is achieved through 

reduced demand for electricity (through improved efficiency or when coincident with 

renewable generation) and central renewable and distributed power additions. By 2025, 

EERE’s portfolio is expected to reduce central conventional capacity additions by 137 

gigawatts—by increasing central renewable and distributed power capacity by 144 gigawatts 

(some of which is intermittent and therefore displaces less conventional capacity). Increased 

efficiency reduces the need for 65 GWs of capacity (based on BT, Industrial, FEMP, and 

WIP programs, which do not take into account the integration effect in the Portfolio Case) 

(Figure 3.7). As shown in Figure 3.8, renewable energy capacity additions (central and 

distributed) are projected to grow by an additional 42 GW compared with the Baseline Case 

in 2025, and 137 GW compared with the Baseline Case in 2050. 

0

5

10

15

20

25

30

35

1980 1990 2000 2010 2020 2030 2040 2050

Million Barrells per Day

Baseline Case

Portfolio Case

Program Case
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

FY 2006 Benefits Estimates (Chapter 3) – Page 3-10

Figure 3.6. U.S. Natural Gas Consumption, 1980-2000, and Projections to 2050: 

Baseline, Program, and Portfolio Cases 

Data Source: 1980-2000, Energy Information Administration, Annual Energy Review 2003, DOE/EIA-0384 

(2003) (Washington, D.C., October 2004), Table 1.3, Web site http://www.eia.doe.gov/emeu/aer/contents.html. 

-200

-150

-100

-50

0

50

100

150

200

2010 2015 2020 2025

Net reduct ions in cent ral convent ional capacit y addit ons Addit ions f rom increased demand Addit ional non-convent ional power

Gigwat t s

Figure 3.7. Impacts on Capacity Projections to 2025: Portfolio Case

Data Source: NEMS-GPRA06 

0

5

10

15

20

25

30

35

40

1980 1990 2000 2010 2020 2030 2040 2050

Quadrillion Btu

Baseline Case

Program Case

Portfolio Case
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

FY 2006 Benefits Estimates (Chapter 3) – Page 3-11

 

Figure 3.8. U.S. Renewable Energy Capacity, 1980-2000, and Projections to 2050: 

Baseline, Program, and Portfolio Cases 

Data Source: 1980-2000, Energy Information Administration, Annual Energy Review 2003, DOE/EIA-0384 

(2003) (Washington, D.C., October 2004), Table 8.11a, Web site http://www.eia.doe.gov/emeu/aer/contents.html. 

0

50

100

150

200

250

300

350

400

450

1980 1990 2000 2010 2020 2030 2040 2050

Gigawatts

Baseline Case

Portfolio Case

Program Case
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

FY 2006 Benefits Estimates (Chapter 3) – Page 3-12

Program Benefits 

The remainder of this chapter is devoted to program-specific information, including program 

budget requests and benefits. See Chapter 4 and Chapter 5 for more specific program-level 

analysis. Figure 3.9 displays the EERE program budget requests for FY 2006. The largest 

program budget is $310 million for the Weatherization and Intergovernmental Program (WIP), 

which includes $225.4 million for Low-Income Weatherization Assistance. 

Figure 3.9. EERE Program FY 2006 Budget Requests

Source: Budget request from FY 2005 Budget-in-Brief, U.S. Department of Energy Office of Energy Efficiency and 

Renewable Energy, http://www.eere.energy.gov/office_eere/pdfs/fy05_budget_in_brief.pdf. 

The FY 2006 estimates of benefits for the individual EERE programs are shown for 2025 and 

2050 in Figures 3.10 through 3.16. The benefits vary widely across EERE’s programs, with 

each program providing a different level and mix of benefits. Often, individual programs target 

different types of benefits. Nonrenewable energy savings in 2025, for example, range from 0.06 

quadrillion British thermal units (Btu) for the Federal Energy Management Program (FEMP) to 

3.98 quadrillion Btu for the Vehicle Technologies Program (Figure 3.10). The differences in 

benefits result from a number of factors: (1) program size and target market; (2) time frames for 

program results and reported benefits; (3) primary types of benefits addressed by each program; 

(4) technical potential achievable within each program beyond the Baseline Case, and (5) ability 

to assess program goals or target markets with current capabilities. Note that these estimates do 

not reflect the relative performance risk associated with these program activities. 

0

50

100

150

200

250

300

350

Biomass Buildings DE FEMP Geothermal HFCIT Industrial Solar Vehicles Wind WIP

Millions Dollars (Nominal)
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

FY 2006 Benefits Estimates (Chapter 3) – Page 3-13

Several EERE programs are targeted toward benefits not well reflected in any of EERE’s 

quantified benefits metrics. For instance, the Distributed Energy (DE) Program focuses on 

improving electricity reliability by developing electricity-generating capacity at or near the point 

of use (Figure 3.16). However, EERE does not currently have the capability of quantifying the 

level or value of improved reliability, or of reflecting the consumer value for reliability in 

estimated future market purchases. Similarly, the State Energy Grant Program funds the 

development of State energy plans, including energy emergency planning. This key component 

of homeland security is not reflected in any of the security metrics in this analysis. In the case of 

the Biomass Program, there has been a substantial redirection of the research toward integrated 

biorefineries that will produce a mix of high-value chemicals, as well as fuels such as ethanol 

and electric power. These are very complex systems, and EERE does not yet have an adequate 

modeling capability for this, as described in Chapters 4 and 5. 

While incomplete, the results indicate both the range and approximate level of benefits available 

to the Nation from funding the efficiency and renewable investments in EERE’s portfolio of 

programs. They indicate a potential for making better use of existing technologies and for 

accelerating technological advances to make significant changes in our energy markets, which 

can drive the Nation to a period of level energy consumption.
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

FY 2006 Benefits Estimates (Chapter 3) – Page 3-14

0

2

4

6

8

10

12

14

16

18

20

Biomass

Buildings

DE

FEMP

Geothermal

HFCIT

Industrial

Solar

Vehicles

Wind

WIP

2025

2050

Figure 3.10. Annual Nonrenewable Energy Savings: 2025 and 2050 (quadrillion Btu) 

Figure 3.11. Annual Energy Expenditure Savings: 2025 (billion 2002 dollars) 

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

Biomass Buildings DE FEMP Geothermal HFCIT Industrial Solar Vehicles Wind WIP
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

FY 2006 Benefits Estimates (Chapter 3) – Page 3-15

Figure 3.12. Annual Energy-System Cost Savings: 2050 (billion 2002 dollars) 

0

50

100

150

200

250

300

350

400

Biomass

Buildings

DE

FEMP

Geothermal

HFCIT

Industrial

Solar

Vehicles

Wind

WIP

2025

2050

Figure 3.13. Annual Carbon Dioxide Savings: 2025 and 2050 (mmt carbon equivalent) 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

FY 2006 Benefits Estimates (Chapter 3) – Page 3-16

0

2

4

6

8

10

Biomass

Buildings

DE

FEMP

Geothermal

HFCIT

Industrial

Solar

Vehicles

Wind

WIP

2025

2050

Figure 3.14. Annual Oil Savings: 2025 and 2050 (mbpd)

-0.5

-0.25

0

0.25

0.5

0.75

1

1.25

1.5

Biomass Buildings DE FEMP Geothermal HFCIT Industrial Solar Vehicles Wind WIP

2025

2050

Figure 3.15. Annual Natural Gas Savings: 2025 and 2050 (quadrillion Btu) 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

FY 2006 Benefits Estimates (Chapter 3) – Page 3-17

0

20

40

60

80

100

120

DE Biomass Geothermal Solar Wind Buildings FEM P Vehicles HFCIT Industrial WIP

2025

2050

DE Renew ables Energy Efficiency

Figure 3.16. Annual Electric Generating Capacity – DE, Renewables, Energy Efficiency: 

 2025 and 2050 (gigawatts)

Note: Capacity for the DE Program includes gas-fired combined heat and power (CHP) systems in commercial and 

industrial applications and non-CHP grid support applications. Renewables include distributed and central station 

capacity. The Biomass Program does not create additional capacity because it is aimed at developing biomass 

refineries. The Buildings, FEMP, Vehicle Technologies, Industrial, and WIP programs do not create additional electric 

generating capacity because they are efficiency programs. Some of the efficiency programs do, however, reduce the 

need for additional capacity. The HFCIT Program includes fuel cell capacity. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-1 

CHAPTER 4

MIDTERM BENEFITS ANALYSIS OF EERE’S PROGRAMS

Introduction 

The results of the Step 2 program and market analyses are incorporated into NEMS-GPRA06 in 

the Program and Portfolio Cases to estimate the midterm (to 2025) benefits for each program and 

for EERE’s overall portfolio. In some cases, NEMS-GPRA06 can directly utilize program 

performance goals (outputs). In other cases, analysts need to make adjustments to the program 

analyses when incorporating them in NEMS-GPRA06. This chapter describes the NEMSGPRA06 analyses for each program. The appendices provide additional information on the 

inputs provided by each program. 

Table 4.1 shows a breakdown by program of the two types of analytical tool employed in its 

benefits analyses—specialized “off-line” tools and NEMS-GPRA06. A description of EIA’s 

NEMS model is provided in Box 4.1 at the end of this chapter. Descriptions of the off-line tools 

are provided in the related program appendix. 

Table 4.1. Program Benefits Modeling by Primary Type of Model Used and Activity Area 

Program Activity Area Off-Line Tool NEMS-GPRA06 

Biomass Bio-based Products 3 

Cellulosic Ethanol 3 3

Technology R&D 3 3 

Regulatory Actions 3 3

Building Technologies 

Market Enhancement 3

DE DE 3

FEMP FEMP 3

Geothermal Geothermal 3

Hydrogen, Fuel Cells, and Fuel Cells 3

Infrastructure Technologies Production and Delivery 3

Industrial Technologies R&D 3

Deployment 3

Solar Water Heaters 3

Photovoltaics 3 3

Solar Energy Technologies 

Concentrated Solar Power 3 

Vehicle Technologies Light Vehicle Hybrid and Diesel 3

Heavy Vehicles 3 3

Weatherization and Intergovernmental Weatherization 3

Domestic Intergovernmental 3

Wind and Hydropower Technologies Wind 3

Required off-line analysis can range from simple verification of program goals to an initial 

calculation of energy savings, depending on the treatment of the target market in NEMSGPRA06 and the nature of the program. Analysts use specialized off-line tools to develop the 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-2 

inputs to NEMS-GPRA06 for each program case. The activity areas listed are groupings of 

activities within each program that share either technology or market features. They do not 

represent actual program-management categories. 

Biomass Program 

The goal of the Biomass Program is the development of biomass refineries (biorefineries), which 

produce a range of products including ethanol and/or other fuels, chemicals, materials, and/or 

power. The biorefinery concept allows the cost of production to be reduced through synergies 

associated with feedstock handling and processing, and the allocation of capital and fixed O&M 

costs across multiple products. The current analysis is based on two types of biorefineries. The 

first type produces chemicals and materials, but not fuels, and the second type produces ethanol 

fuel as the major output. Future analyses could include additional fuels that the program may 

identify in the longer term. 

Bio-based products from nonfuel biorefineries: The use of biomass would displace the use of 

petroleum and natural gas as chemical feedstocks. Because of the multitude of products and the 

complexity of the chemicals industry, NEMS-GPRA06 does not have sufficient detail within its 

representation of this industry to explicitly model bio-based products. Given the lack of a biobased products sector in the model, analysts assessed energy savings off-line. The energy savings 

by fuel type (the largest share was petroleum feedstocks) were implemented in the integrated 

model, by subtracting the estimates from industrial energy consumption otherwise projected by 

NEMS-GPRA06. Analysts then used the model to compute the other benefits of primary energy 

savings, carbon emission reductions, and energy-expenditure savings. 

Cellulosic ethanol from biorefineries dedicated to the production of ethanol, lignin-derived 

electricity, and chemical coproducts: EERE is sponsoring research aimed at reducing the cost 

of producing ethanol from cellulosic biomass.1

 The second type of biorefineries assumed in this 

analysis is one that focuses on producing ethanol, lignin-derived electricity, and a small quantity 

of chemical coproducts. Estimates of future cellulosic ethanol production costs in the AEO2004

and the Baseline Case are comparable. The biomass-to-ethanol conversion efficiencies for both 

the Baseline and Program Cases reflect more updated information than the AEO2004

assumptions. In the AEO2004, EIA assumed that the growth in projected production was 

constrained by a number of factors in addition to ethanol production costs. In the Baseline Case, 

EERE was more conservative in terms of constraining the growth in cellulosic ethanol 

production in the absence of EERE programs, with production at roughly one-fifth of the 

AEO2004 values. EERE’s biofuels analytic model, ELSAS Bioref, was used to estimate the 

growth of ethanol made from agricultural residues (such as corn stover) and other cellulosic 

biomass.2 The two cellulosic ethanol estimates were combined and input into NEMS-GPRA06. 

Petroleum and fossil energy savings occur when the cellulosic ethanol displaces gasoline or corn 

ethanol in the ethanol blend market for gasoline. In the FY 2006 EERE mid-term benefits 

estimates, a large portion of the cellulosic ethanol displaces corn ethanol, which leads to fossil 

 

1 Cellulose and hemi-cellulose that can be converted to ethanol (and other chemicals, materials, and biofuels) are found in 

biomass such as agricultural residues (corn stover, wheat, and rice straw), mill residues, organic constituents of municipal solid 

wastes, wood wastes from forests, future grass, and tree crops dedicated to bio-energy production. 2 For more information on the off-line analysis, see Appendix B.
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-3 

energy and carbon emission savings based on recent EERE life-cycle analysis. Fossil energy 

requirements and carbon emissions to produce ethanol fuels were obtained from EERE’s 

Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. 

The Biomass Program benefits shown in Table 4.2 are the reductions in energy use and carbon 

emissions in the Program Case compared with the Baseline Case. 

Table 4.2. FY06 Benefits Estimates for Biomass Program (NEMS-GPRA06) 

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) ns 0.02 0.06 0.12 

 Cellulosic Ethanol Production (billion gallons/yr) 0.00 0.12 0.26 1.57 

Economic 

 Energy-Expenditure Savings (billion 2002 dollars/yr) ns ns ns ns 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 0.0 0.4 1.6 2.6 

Security 

 Oil Savings (million barrels per day) ns 0.00 0.01 0.01 

 Natural Gas Savings (quadrillion Btu/yr) ns ns ns 0.02 

 Avoided Additions to Central Conventional Power 

 (cumulative gigawatts) ns ns ns ns 

Building Technologies Program 

The activities of the Building Technologies Program can be classified into three general types: 

technology R&D, regulatory actions, and (to a far lesser extent) market enhancement.3

 The 

modeling approach and applicable end uses for the activities that comprise the Building 

Technologies Program are displayed in Table 4.3. Analysts model the technology R&D 

activities by modifying costs and efficiencies of the equipment and shell technology slates. 

Market-enhancement activities and some regulatory activities (such as buildings codes) are 

modeled using penetration rates and energy-savings estimates. A few R&D activities such as 

residential incandescent light fixtures were not modeled, because they represented a small 

segment of the market and are not explicitly represented within NEMS-GPRA06. 

Technology R&D: The technology R&D activities seek to develop new or improved 

technologies that are more energy efficient and more cost-effective than the alternatives currently 

available. The forecast benefits for these are measured by modifying the technology slates from 

those that are available in the Baseline Case to reflect the program goals. Building technologies 

in NEMS-GPRA06 are represented by end use. For most end uses, there are conversion 

technologies (e.g., furnaces and water heaters) that use different fuels and that have several 

different levels of energy efficiency. The Baseline Case incorporates EIA’s estimation of future 

technology improvement that is then modified in the Program Case. 

 

3

 With the reorganization of EERE, the overwhelming majority of the market-enhancement activities are part of the 

Weatherization and Intergovernmental Program. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-4 

Residential shell technologies (such as windows or insulation) for new buildings are represented 

by several packages of technologies with different levels of improvements. Each package is 

characterized by a capital cost and heating and cooling load reductions. The commercial-sector 

shell measures are represented by window and insulation technologies that can be selected 

individually. EIA developed the residential methodology for the AEO2001, while OnLocation 

developed the commercial methodology for EERE. 

Table 4.3. Modeling Approach for Building Technologies Program Activities 

The residential and commercial sectors are each represented by several building types within 

nine Census divisions. NEMS-GPRA06 computes end-use technology choice for each of these 

building types and geographic regions, based on the relative economics and estimations of 

consumer behavior for the technologies. The latter is important to replicate current technology 

market shares. In a few cases where NEMS-GPRA06 has insufficient detail for explicit 

technology representation, analysts computed market penetration using off-line tools, and the 

results were implemented with NEMS-GPRA06 through efficiency factors. 

Regulatory activities: Regulatory activities include setting new appliance standards, based on 

the legislatively mandated schedule and encouraging state adoption of more stringent building 

codes.4

 Representing appliance standards is straightforward. In the year that the program expects 

the new standard to be implemented, all technologies that are less efficient than the standard are 

 

4 The outreach/deployment aspects of the codes process occur with funding provided by the Weatherization and 

Intergovernmental Program. 

Building Technology Project List Resd Comm Heat Cool Water 

Heating Lighting Other

Energy Savings 

and Penetration 

Rates

Equipment 

Technology 

Costs and 

Efficiencies

Shell 

Technology 

Costs and 

Efficiencies

Residential Buildings Team

 Research and Development (Building America) 3 33 3 33 3

 Residential Building Energy Codes 3 33 3

Commercial Buildings Team

 Commercial Research and Development 333 3

 Commercial Building Energy Codes 333 3 3

Standards

 Commercial Unitary AC/HP (EPAct) 333 3

 Distribution Transformers

Analysis Tools and Design Strategies 333 3

Appliances and Engineering Technologies

 Roof top AC 33 3

 Can Lights 3 3

 R-Lamp 3 3

Window Technologies

 Electrochromic Windows 333 3 3

 Superwindows 3 33 3 3

 Low-E Market Accpetance 33 3 3 3

Lighting Research and Development

 Lighting Controls 3 33

 Solid State Lighting 33 3 3

Refrigeration R&D

 Unitary DX System 33 3 3 3

 Remote Fault Detection & Diagnostics 33 3 3 3

 Commercial Refrigeration 3 3

 Ventilation Load Reduction 333 3

Sector End-Use Modeling Approach
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-5 

removed from the market and unavailable for consumer choice. The resulting energy savings 

depend on the difference in the level of efficiency of the standard compared to the technology 

that had been selected in the Baseline Case. 

Market enhancement: Building-code development is primarily a regulatory activity, although it 

also involves outreach to encourage the various states to adopt new and stricter standards. 

Analysts make a spreadsheet computation of average savings using off-line estimates for the 

fraction of buildings within areas that adopt more stringent codes, as well as the heating, cooling, 

and lighting load reductions associated with the new levels of codes. The building shell packages 

in NEMS-GPRA06 are modified to produce the appropriate savings. 

The Building Technologies Program results in energy savings primarily in four end-use 

categories: space heating, space cooling, water heating, and lighting. Table 4.4 demonstrates the 

level of savings from each category. In 2025, cooling and space-heating end uses have the 

highest savings in residential buildings, while the lighting energy-use reduction is the largest in 

commercial buildings. 

Table 4.4. Building Technologies Program Energy Savings by End Use 

Energy Reduction Residential Commercial 

Percentage 2010 2015 2020 2025 2010 2015 2020 2025

Space Heating 0% 2% 3% 5% 0% 1% 2% 2%

Space Cooling 0% 2% 4% 8% 1% 2% 3% 2%

Water Heating 0% 0% 0% 0% 0% 0% 0% 0%

Lighting 0% 0% 0% 1% 0% 1% 2% 10%

Other 0% 0% 0% 0% 0% 0% 0% 0%

Analysts estimate the Building Technologies Program benefits (Table 4.5) within the integrated 

NEMS-GPRA06, so that the electricity-related primary energy savings are directly computed. In 

addition, the estimates include any feedbacks in the buildings or other sectors resulting from 

changes in energy prices that result from the reduced energy consumption. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-6 

Table 4.5. FY06 Benefits Estimates for Building Technologies Program (NEMS-GPRA06) 

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.09 0.31 0.62 1.24 

Economic 

 Energy-Expenditure Savings (billion 2002 dollars/yr) 1.8 5.0 7.5 11.5 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 1.6 6.3 13.6 28.4 

Security 

 Oil Savings (million barrels per day) 0.01 0.02 0.03 0.02 

 Natural Gas Savings (quadrillion Btu/yr) 0.05 0.10 0.14 0.28 

 Avoided Additions to Central Conventional Power (gigawatts) ns 8 19 33 

 Total Electricity Capacity Avoided (cumulative gigawatts) ns 9 19 36 

Distributed Energy Program 

The Distributed Energy (DE) Program encompasses many technologies and markets. The 

benefits were estimated by focusing on several segments of the distributed energy market: gasfired combined heat and power (CHP) systems in commercial building and industrial 

applications, and non-CHP grid support applications. Distributed energy applications that are 

motivated by the need for electric reliability primarily will be systems that produce only 

electricity and are used in backup mode. In the program analysis, these are represented as gridsupport DE for their similar technology characteristics, although the model treats them as though 

they are purchased by electric-power producers rather than electricity-consuming businesses. 

The value of these systems is difficult to capture in the GPRA benefits metrics. They do not 

provide significant energy or emissions savings, because they run for only a few hours per year 

and generally have similar or lower efficiencies than larger central-station peaking facilities. 

They do have the potential to contribute significantly to new electric power-generating capacity. 

The benefit estimates do not account for increased reliability and local Clean Air Act impacts on 

demand. 

Combined heat and power systems produce both useful thermal heat and electricity. Their 

economics depend on the amount of thermal heat needed at the site, the electricity usage at the 

site, the price of the input fuel, and the value of the electricity. If the end-use customer is making 

the investment, the electricity value will depend on the customer-avoided purchases at the 

electricity retail price, and possibly the amount of excess electricity sold off-site at prevailing 

wholesale electricity prices. Using the average electricity price is a simplification that may 

overlook the requirement to continue paying some type of flat distribution charge, even though 

less electricity is purchased from the utility. If a vertically integrated electric utility is making the 

investment, the value is from avoided generation, and transmission and distribution (T&D) costs. 

The distributed systems would be placed strategically in the grid to avoid T&D expansion costs. 

The DE Program facilitates the development of the DE market by improving the technology 

characteristics (lowering costs, improving efficiency, and reducing environmental emissions) and 

by removing barriers to adoption and consumer acceptance. Thus, the benefits are estimated 

based on the impact of improved technology and greater market penetration. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-7 

Baseline adjustments: The AEO2004 Reference Case includes significant DE technological 

advancement. The Baseline Case includes a modified set of technology characteristics that 

represented the absence of continued EERE programs. These modifications were made in all 

three areas in NEMS where distributed technologies are represented: commercial building 

combined heat and power (CHP), industrial CHP, and utility grid support. For most of the 

commercial and industrial CHP technologies, the baseline technology characteristics were 

assumed to be a 10-year lag of the program goals, therefore assuming the baseline technologies 

will catch up with the policy case in 10 years. The technology assumptions for commercial gasfired chillers also were modified, and these chillers were assumed to be applicable to all building 

types; unlike in the AEO2004, where they can be used only in the larger building sizes. 

The adoption rates of distributed technologies in commercial buildings were modified to reflect 

market data gathered by EERE on consumer adoption of energy efficiency projects as a function 

of payback time (Figure 4.1).5 The NEMS-GPRA06 framework uses a cash-flow model to 

evaluate the DE technologies—CHP and photovoltaic (PV) systems—within the building 

sectors. For commercial buildings, debt and interest payments are computed over a loan period 

of 15 years along with associated taxes and tax benefits and assuming a 25 percent down 

payment. Annual fixed maintenance costs also are included. For the gas-fired CHP technologies, 

NEMS-GPRA06 computes fuel costs based on the delivered cost of natural gas and the 

technology efficiency. The value of the useful waste heat produced is netted against the fuel cost, 

based on the delivered natural gas price, the thermal efficiency of the CHP system, and the 

internal thermal load. The value of the electricity produced is then subtracted from these costs to 

determine the cash flow. The value of electricity is equal to the larger of the electricity produced 

and the internal electricity demand, multiplied by the delivered electricity price. Any electricity 

produced in excess of internal needs is assumed to be sold to the grid at the wholesale rate. The 

number of years until positive cash flow is reached determines the market share in new 

buildings. The market share for existing buildings is assumed to be a fraction of the share for 

new. 

Under both the EIA and program assumptions, market share in new buildings decreases sharply 

as the number of years required to achieve positive cash flows increases. This reflects the high 

rates of return generally expected for energy-related projects by commercial-building owners. 

These shares apply to the fraction of commercial buildings assumed to be eligible for an 

installation of distributed CHP. The AEO2004 eligibility fraction assumption of 30 percent was 

increased to 50 percent. These adoption rate changes were made in the Baseline Case as well as 

the Program Case. 

Technology improvements: The program provided characteristics for distributed energy 

systems that reflect the program’s research goals. These included commercial CHP systems (gas 

engines, gas turbines, gas microturbines), commercial gas-fired chillers, industrial CHP (five 

systems sizes for gas-fired engines and turbines), and grid-support DE (base and peaking). 

 

5 Market Trends in the U.S. ESCO Industry: Results from the NAESCO Database Project. Goldman, C., J. Osborn and N. 

Hopper, LBNL, and T. Singer, NAESCO, May 2002, LBNL-49601. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-8 

Figure 4.1. Commercial-Sector DG Adoption Rates 

Because the thermal output of CHP cannot be used for absorption cooling within NEMS, 

analysts made an off-line adjustment based on an exogenous customer payback spreadsheet 

model that determines the impact of absorption cooling on customer payback. In addition, a set 

of exogenous savings was introduced to account for the potential impacts on end-use 

consumption of cooling energy savings from increased absorption penetration and increased 

heating consumption if not all the waste heat was available to meet both the heating and cooling 

loads. 

Market enhancement: The DE Program’s impact on consumer-adoption rates was represented 

by shifting the market adoption rates for industrial CHP systems (Figure 4.2). The effect is 

similar to reducing the acceptance criteria by a year or less. In addition, the penetration rate of 

the amount of economic potential that is implemented each year was increased from 5 percent to 

10 percent for all sizes of gas turbine systems. 

The incremental DE capacity that results from this representation of the DE Program activities is 

shown in Table 4.6, along with the projected total quantities. Of the 64 GW of incremental 

capacity by 2025, roughly 40 percent of the increase is expected to be industrial applications and 

59 percent grid-support systems. 

Commercial Sector DG Adoption Rates

0.0

0.2

0.4

0.6

0.8

1.0

1

3

5

7

9

11

13

15

17

19

Number of Years to Positive Cash Flow

Fraction Adopting

AEO2004

Alternative
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-9 

Figure 4.2. Industrial-Sector Combined Heat and Power Adoption Rates 

In the Baseline Case, by 2025, the industrial sector is projected to satisfy roughly 20 percent of 

its total electricity demand with distributed generation. With the DER Program, the share 

increases to 31 percent. 

Table 4.6. Distributed Energy Capacity (GW) 

2010 2015 2020 2025 

AEO Base 

Buildings 1.6 1.9 2.4 3.5 

Industry* 31.5 35.4 39.6 43.7 

Electric Industry 0.5 2.4 7.6 12.4 

 

Baseline Case 

Buildings 2.1 2.6 4.0 9.3 

Industry* 30.8 34.7 41.1 48.2 

Electric Industry 1.5 1.9 5.0 15.7 

 

Benefits Case 

Buildings 2.1 3.3 4.9 9.8 

Industry* 35.8 47.5 60.6 74.2 

Electric Industry 3.3 22.4 37.6 53.1 

 

Incremental Capacity 

Buildings 0.0 0.6 0.9 0.5 

Industry* 5.0 12.8 19.5 26.0 

Electric Industry 1.9 20.5 32.6 37.5 

Total 6.9 33.9 52.9 63.9 

 * Excludes nontraditional, large qualifying facility cogenerators. 

Industrial CHP Payback Acceptance Curve

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 2 4 6 8 10 12

Payback years

Fraction Meeting Economic Criteria

Baseline Payback

DE Program Payback
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-10 

The DE Program benefits (Table 4.7) are projected within the integrated modeling framework, 

so that the impact of the program will be reflected in the rest of the energy system. As a result of 

increased investments in DE, electricity purchases from the commercial and industrial sectors are 

reduced, and additional electricity is sold wholesale to the grid. The central electricity-generation 

industry responds by reducing production from the most expensive plants operating in each 

region, and over time by building fewer central-station plants in the face of lower demand. 

Retirements are relatively unaffected, with only 3 GW of additional capacity retired by 2025 in 

the Program Case. Almost 60 GW of central-station investments are avoided by the additional 

DE. In the Baseline Case, about half of new central-station capacity additions from 2006 to 2025 

are projected to be natural gas fired, and about three quarters of the avoided central-station 

investments are natural gas-fired turbines and combined-cycle plants. In total, distributed 

generation makes up roughly 15 percent of new capacity additions from 2006 to 2025 in the 

Baseline Case. This share increases to 36 percent in the Program Case. 

The energy- and carbon emission-reduction benefits that stem from distributed generation are 

computed as the decrease in traditional central-station nonrenewable energy consumption and 

associated carbon emissions, net of the energy and emissions from the DE. The central-station 

generation reductions are from a mix of existing plants and avoided new plants. Over time, the 

facilities that are used in the Baseline Case become more efficient as the central station 

generation technologies continue to improve. As a result, the energy and emission savings from 

the central grid decline per kilowatt-hour. 

Table 4.7. FY06 Benefits Estimates for DE (NEMS-GPRA06) 

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.08 0.13 0.28 0.25 

 Generation (gigawatt-hours/yr) 37 106 161 210 

Economic 

 Energy-Expenditure Savings (billion 2002 dollars/yr) 1.6 2.9 ns 1.6 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 2.2 5.5 12.3 11.5 

Security 

 Oil Savings (million barrels per day) ns ns ns ns 

 Natural Gas Savings (quadrillion Btu/yr) -0.01 -0.21 -0.42 -0.42 

 Avoided Additions to Central Conventional Power (gigawatts) 2 26 50 56 

 Program-Specific Electric Capacity Additions 

 (cumulative gigawatts) 7 34 53 64 

Federal Energy Management Program 

The Federal Energy Management Program (FEMP) is an implementation program to increase the 

energy efficiency of Federal Government buildings, which account for about 5 percent of U.S. 

commercial-building energy consumption. FEMP activities support the installation of a variety 

of existing technologies, rather than focusing on the development of specific technologies, as do 

many other EERE programs. Because it encompasses a broad technological scope—while, at the 

same time, targeting a specific market segment—FEMP is difficult to model in an integrated 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-11 

framework such as NEMS-GPRA06. However, there is also less uncertainty associated with 

achieved energy savings because the program tracks changes in Federal energy consumption. 

Delivered energy savings (estimated off-line) are used as inputs for the integrated modeling. 

These projected savings are subtracted from the Baseline Case for commercial-building energy 

consumption. Analysts use the model to compute the other benefits metrics of primary energy 

savings, carbon emission reductions, and energy-expenditure savings (Table 4.8). 

Table 4.8. FY06 Benefits Estimates for FEMP (NEMS-GPRA06) 

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.03 0.04 0.05 0.06 

Economic 

 Energy Expenditure Savings (billion 2002 dollars/yr) 0.2 0.3 0.4 0.5 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 0.5 0.7 0.9 1.2 

Security 

 Oil Savings (million barrels per day) 0.00 0.00 0.01 0.01 

 Natural Gas Savings (quadrillion Btu/yr) 0.02 0.02 0.02 0.02 

 Avoided Additions to Central Conventional Power (cumulative

 gigawatts) ns ns ns ns 

Geothermal Technologies Program 

The primary goal of the Geothermal Technologies Program is to reduce the cost of geothermalgeneration technologies, including both conventional and enhanced geothermal systems (EGS). 

Measuring the benefits involves projecting the market share for these technologies, based on 

their economic and environmental characteristics. 

The NEMS-GPRA06 electricity-sector module performs an economic analysis of alternative 

technologies in each of 13 regions. Within each region, new capacity is selected based on its 

relative capital and operating costs, its operating performance (i.e., availability), the regional 

load requirements, and existing capacity resources. Geothermal capacity is treated in a unique 

manner, due to the specific geographic nature of the resources. The model characterizes 51 

individual sites of known hydrothermal geothermal resources, each with a set of capital and 

operating and maintenance (O&M) costs. For the Program Case, an additional set of EGS sites 

were added to this slate. 

Baseline adjustments: The EIA AEO2004 Reference case includes a significant improvement in 

geothermal generation technology over time, similar to the program goals. To reflect what might 

occur without continued R&D funding, analysts reduced the cost reduction by half for the GPRA 

Baseline.

Technology improvements: The Geothermal Program was represented by reducing the capital 

and O&M costs for all hydrothermal geothermal sites, so that the average of the three lowest-cost 

sites matched the program cost goals. Separate program technology goals were provided for the 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-12 

added EGS sites. In addition, the program was assumed to reduce the risk associated with new 

geothermal development, and the Baseline Case limit on the size of annual developments per 

geothermal site was increased from 25 MW or 50 MW (depending on year) to 100 MW per year. 

Table 4.9 shows the resulting additional geothermal capacity and generation, by region and for 

capacity by technology type. The greatest incremental capacity is in California (CAL) and the 

Northwest (NWP), with less in the Rocky Mountain area (RA). The primary energy, oil, and 

carbon emissions savings stem from geothermal power displacing fossil-fueled generation 

sources. Energy-expenditure savings are measured as the reduction in consumer expenditures for 

electricity and other fuels. Lower-cost renewable generation options reduce the price of 

electricity directly and reduce the pressure on natural gas supply, both of which benefit end-use 

consumers. Table 4.10 shows the overall Geothermal Technologies Program benefits. 

Table 4.9. Geothermal Capacity and Generation 

 2010 2015 2020 2025

GPRA Base Capacity (GW) 

NWP 0.7 1.3 1.6 1.8 

RA 0.5 0.7 0.7 0.8 

CAL 2.7 3.1 3.5 3.9 

Total 3.9 5.0 5.7 6.5 

 

Conventional 3.9 5.0 5.7 6.5 

EGS 0.0 0.0 0.0 0.0 

 

 Program Case Capacity (GW) 

NWP 1.1 2.1 2.7 3.7 

RA 0.5 0.8 0.8 1.6 

CAL 2.9 4.0 4.7 6.0 

Total 4.5 6.9 8.2 11.4 

 

Conventional 4.5 6.6 6.8 6.8 

EGS 0.0 0.3 1.4 4.6 

Total 4.5 6.9 8.2 11.4 

 

Incremental Capacity (GW)

NWP 0.4 0.8 1.1 1.9 

RA 0.0 0.2 0.2 0.8 

CAL 0.2 0.9 1.3 2.2 

Total 0.6 1.9 2.5 4.9 

 

Conventional 0.6 1.6 1.1 0.3 

EGS 0.0 0.3 1.4 4.6 

Total 0.6 1.9 2.5 4.9 

 

Incremental Generation (BkWh) 

NWP 3 6 9 15 

RA 0 1 1 6 

CAL 1 7 10 17 

Total 5 15 20 39 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-13 

Table 4.10. FY06 Benefits Estimates for Geothermal Technologies Program (NEMS-GPRA06) 

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.01 0.09 0.16 0.33 

 Generation (gigawatt-hours/yr) 5 15 20 39 

Economic 

 Energy-Expenditure Savings (billion 2002 dollars/yr) ns ns ns ns 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 0.0 1.9 3.9 8.4 

Security 

 Oil Savings (million barrels per day) ns 0.01 ns ns 

 Natural Gas Savings (quadrillion Btu/yr) 0.02 0.02 0.02 ns 

 Avoided Additions to Central Conventional Power (gigawatts) ns 1 2 3 

 Program-Specific Electric Capacity Additions (cumulative 

 gigawatts) 1 2 3 5 

Hydrogen, Fuel Cells, and Infrastructure Technologies Program 

The Hydrogen, Fuel Cells, and Infrastructure Technologies Program is targeted toward the 

introduction of fuel cells for both stationary and vehicular applications, as well as the production 

and delivery of hydrogen at a reasonable price. NEMS-GPRA06 does not have a representation 

of hydrogen supply options.6

 Therefore, a simple assumption was used that all hydrogen through 

2025 would be derived from natural gas. The hydrogen conversion process was assumed to be 75 

percent efficient and yield a hydrogen price of $1.50 per gallon of gasoline equivalent (excluding 

taxes) when the natural gas price is $4 per MMBtu. 

The stationary fuel cell research is focused on distributed proton-exchange membrane (PEM) 

fuel cells. The program goals for their capital costs and efficiencies were taken from the 

multiyear program plan (MYPP). The MYPP provides goals through 2010, and no further 

improvements were assumed. This conservative assumption most likely understates the benefits 

of these fuel cells. Analysts converted program technology goals into installed costs for 

combined heat and power systems in residential and commercial buildings. 

The fuel cell vehicles were modeled along with the Vehicle Technologies Program. The success 

of fuel cell vehicles is predicated on some of the vehicular improvements being developed under 

the Vehicle Technologies Program, so the fuel cell vehicles could not be treated in isolation. 

Analysts modified the gasoline and hydrogen fuel cell vehicle costs and efficiencies to reflect the 

program goals (see the Vehicle Technologies Program description for more detail about the 

modeling of vehicle choice). In addition, hydrogen availability for vehicle refueling was assumed 

to be 10 percent by 2020 and 25 percent by 2025. The benefits associated with fuel cell vehicles 

were derived by comparing the amount of fuel cell vehicles from the case with “both Hydrogen 

and Vehicle Technologies” to the “Vehicle Technologies only” case. Analysts computed energy 

savings, oil savings, and carbon emission reductions, based on the incremental fuel cell vehicles 

assuming conventional gasoline vehicle displacement (see Figure 4.2). This leads to greater 

 

6

 Hydrogen is represented within the refinery model of NEMS-H2, but for internal use only. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-14 

savings than a simple difference between the cases, while still having smaller savings than would 

be derived by comparing a fuel cell vehicles case with the Baseline Case. Table 4.11 presents the 

overall benefits. 

Table 4.11. FY06 Benefits Estimates for Hydrogen, Fuel Cells, and Infrastructure Technologies 

Program (NEMS-GPRA06) 

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) ns ns ns 0.16 

Economic 

 Energy-Expenditure Savings (billion 2002 dollars/yr) ns ns ns 2.4 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 0.0 0.0 0.0 4.6 

Security 

 Oil Savings (million barrels per day) ns 0.01 0.04 0.23 

 Natural Gas Savings (quadrillion Btu/yr) ns ns -0.01 -0.30 

 Avoided Additions to Central Conventional Power (gigawatts) ns 1 ns 2 

 Program-Specific Electric Capacity Additions (cumulative 

gigawatts) ns 1 ns ns 

Industrial Technologies Program 

The Industrial Technologies Program covers primarily the energy-intensive basic materials 

processing industries, as well as some key technologies that are common across most industries, 

with the objective of increasing energy efficiency. These can be characterized in two categories, 

R&D and deployment. The R&D projects generally apply to specific industries or to specific 

technologies that cut across industries. The R&D projects seek to develop new or improved 

technologies that are more energy efficient and more cost-effective than the alternatives currently 

available. The deployment projects seek to increase the adoption of existing, as well as new, 

energy-efficient technologies. 

The heterogeneity of the program makes it difficult to represent the program activities explicitly 

through technologies in the NEMS-GPRA06 framework. Therefore, analysts perform an off-line 

analysis using detailed spreadsheet models, and use the resulting energy savings by fuel type to 

provide inputs into the integrated model. Because these programs cannot be modeled on an 

economic basis, analysts reduce the off-line energy savings by an “integration factor” before 

putting them into NEMS-GPRA06. This is to account for interactions among programs and 

feedback effects that could not be considered in their original estimation. The amount of the 

integration factor is based on how much program overlap or “integration” was captured by the 

off-line tools and is based on the expert judgment of the benefits analysis team. The crosscutting 

programs, including the Best Practices activity, were reduced by 10 percent. The Industries of 

the Future programs were not reduced because they are relatively specific and not likely to 

experience overlap with other industrial programs. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-15 

Analysts then run the fully integrated NEMS-GPRA06 to compute the benefits metrics of 

primary energy savings, carbon emission reductions, and energy-expenditure savings that are 

associated with the fuel-consumption reductions. 

The resulting estimated primary savings are slightly lower than those targeted because of 

feedback effects that come through the integration with other sectors. The primary feedback 

effect occurs through lower fuel prices. In this case, the lower energy consumption causes lower 

energy prices (although the feedback is small), which causes energy consumption to be higher 

than it otherwise would have been, leading to slightly lower program savings (Table 4.12). 

Table 4.12. FY06 Benefits Estimates for Industrial Technologies Program (NEMS-GPRA06) 

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.25 0.80 1.77 2.16 

Economic 

 Energy-Expenditure Savings (billion 2002 dollars/yr) 2.2 11.2 16.9 12.9 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 4.7 14.8 34.7 44.4 

Security 

 Oil Savings (million barrels per day) 0.03 0.14 0.19 0.15 

 Natural Gas Savings (quadrillion Btu/yr) 0.12 0.30 0.64 0.72 

 Avoided Additions to Central Conventional Power (gigawatts) ns 2 9 8 

 Total Electric Capacity Avoided (cumulative gigawatts) 1 3 8 11 

Solar Energy Technologies Program 

The Solar Energy Technologies Program develops both thermal-heat and electric-solar 

technologies. The solar water-heating component is focused on developing low-cost solar hot 

water and pool heaters to displace fossil-fueled or electric alternatives. Photovoltaics (PVs) are 

being improved for both distributed and central electricity generation applications, and the 

program is working to accelerate PV adoption through the Million Solar Roofs Initiative. The 

Concentrated Solar Power R&D activity is developing better technology for large-scale central 

electricity generation facilities that concentrates solar energy to produce electricity through a 

thermal process. 

The benefits for solar water heat are represented within the residential module of NEMSGPRA06. The solar water heater is a specific technology defined by its capital cost, O&M costs, 

and electrical use. NEMS-GPRA06 was modified to add solar water heat as an option for new 

homes, and the algorithm governing water-heater replacements was modified so that solar water 

heaters could compete in a larger market. In the Program Case, the baseline assumptions were 

modified to reflect the program cost and performance goals. The costs were changed for both 

new and replacement water heaters. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-16 

Three changes were made to the representation of distributed PV systems in the Baseline and 

Program Cases. The size of the typical distributed PV installation was increased to 4 kW per 

home (from 2 kW) and to 100 kW per commercial building (from 25 kW) to reflect literature on 

recent installations. In addition, the fraction of eligible buildings was increased from 30 percent 

to 60 percent for homes and to 55 percent for commercial buildings. The California renewable 

energy credit program, which provides a PV credit of $4000/kW in 2003 declining by $400/kW 

per year, was included for the Pacific region. For the program case, the capital and O&M costs 

were modified to reflect the program’s goals. The regional capacity factors in the Baseline Case 

were similar to those in the program’s goals, so they were left unchanged. 

The improved concentrated solar power (CSP) technology was represented by declining capital 

costs over time and higher capacity factors. The capital costs goals are higher than those used in 

the Baseline but represent systems with significantly more storage and therefore higher electrical 

output. A set of capacity factors by time periods within a year were computed by analysts to 

optimize the timing of solar output for each region within the bounds of the storage potential. 

The capacity factors and capital costs vary by region due to difference in solar insolation and 

resulting storage costs. 

In addition to competing on an economic basis with other electricity-generation technologies, 

PVs may be constructed for their environmental benefits. PERI, using their Green Power Market 

Model, provided an estimate of PV capacity additions in response to the expanding green power 

markets in many places throughout the country. The projections for green power PV installations 

were combined with the Million Solar Roofs Initiative goals to determine the planned PV 

capacity additions that were incorporated into NEMS-GPRA06. Table 4.13 shows the baseline, 

program case, and incremental capacity and electricity generation for the solar technologies. 

Table 4.13. NEMS-GPRA06 Solar Capacity (GW) and Water Heaters 

 Solar Generation Technologies 

2010 2015 2020 2025

GPRA Base 

Solar CSP 0.4 0.5 0.5 0.5 

Central PV 0.2 0.2 0.3 0.4

Distributed PV 0.4 0.4 0.4 3.8 

Total 1.0 1.1 1.2 4.7 

 

Solar Program Case 

Solar CSP 0.4 0.5 0.5 2.2 

Central PV 0.2 0.2 0.3 0.4

Distributed PV 1.2 3.0 6.5 15.2 

Total 1.8 3.7 7.4 17.8 

 

Incremental Capacity 

Solar CSP 0.0 0.0 0.0 1.7 

Central PV 0.0 0.0 0.0 0.0 

Distributed PV 0.8 2.6 6.2 11.4 

Total 0.8 2.6 6.2 13.1 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-17 

Incremental Generation (BkWh) 

Solar CSP 0.0 0.0 0.0 10.8 

Central PV 0.0 0.0 0.0 0.0 

Distributed PV 1.7 5.2 12.6 23.6 

Total 1.7 5.2 12.6 34.4 

Solar Water Heaters 

2010 2015 2020 2025

GPRA Base 

Million 0.61 0.81 1.04 1.40

Share (percent) 0.5% 0.6% 0.8% 1.0%

 

Solar Program Case 

Million 0.92 2.33 3.81 5.73

Share (percent) 0.8% 1.9% 2.9% 4.2%

Estimates of primary energy, oil, and carbon emissions savings result from displacement of 

energy use for water and pool heating, and from electricity demand reductions and PV and CSP 

generation. The savings associated with reduced conventional electricity requirements depend on 

which types of generating plants were built and operated in the Baseline Case. Over time, the 

mix of fuels and efficiencies of power generation vary; and, therefore, the energy savings will as 

well. Energy-expenditure savings are measured as the reduction in consumer expenditures for 

electricity and other fuels. Lower-cost renewable generation options reduce the price of 

electricity directly and reduce the pressure on natural gas supply, both of which benefit end-use 

consumers. Energy savings from water heaters also directly reduce energy expenditures. Overall 

benefits of the Solar Energy Technologies Program are shown in Table 4.14. 

Table 4.14. FY06 Benefits Estimates for Solar Energy Technologies Program (NEMS-GPRA06) 

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.01 0.05 0.12 0.30 

 Generation (gigawatt-hours/yr) 2 5 13 34 

Economic 

 Energy-Expenditure Savings (billion 2002 dollars/yr) ns 1.1 2.7 1.8 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 0.1 1.0 2.4 7.6 

Security 

 Oil Savings (million barrels per day) ns 0.01 0.01 ns 

 Natural Gas Savings (quadrillion Btu/yr) 0.01 0.03 0.03 0.01 

 Avoided Additions to Central Conventional Power (gigawatts) ns 2 4 9 

 Program-Specific Electric Capacity Additions (cumulative 

 gigawatts) 1 3 6 13 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-18 

Vehicle Technologies Program 

The Vehicle Technologies Program consists of research on light-duty vehicle hybrid and diesel 

technologies, heavy vehicle engine/drivetrain and parasitic loss-reduction technologies, and 

lightweight materials for engines and vehicles. In addition, the program includes research in 

advanced petroleum and renewable fuels, the benefits of which are not modeled. 

Light-duty vehicle hybrid and diesel technologies: This research aims to improve engine 

technologies in light-duty vehicles, which include passenger cars and light-duty trucks. NEMSGPRA06 is used to compute benefits estimates for these activities through a process that 

estimates the penetration (sales) of the various technologies in the market for light-duty vehicles 

over time. The amount that each technology penetrates into the market determines the stock of 

these vehicles and the vehicle miles traveled (VMT) associated with each technology. 

Heavy vehicle engine/drivetrain and parasitic loss reduction technologies: Heavy vehicles 

are those that have a gross weight (the weight when fully loaded) of 10,000 pounds or more. This 

program researches multiple technologies including engines/drivetrains, parasitics/accessories, 

aerodynamics, and hybrids. The benefits of this R&D activity are derived from penetration rates 

estimated by the Heavy Truck Energy Balance and Truck 2.0 models developed for the Vehicle 

Technologies Program, using efficiency and technology cost assumptions. 

Lightweight materials for engines and vehicles: The lightweight materials developed under 

this R&D activity are used in both light and heavy vehicles. The effect of these materials are 

included in the projection of vehicle attributes and not modeled separately. 

In the NEMS-GPRA06 integrating model, the light-duty vehicle (LDV) market consists of six 

car classes—mini-compact, subcompact, compact, midsize, large, two-seater—and six light-duty 

truck classes—small and large pickup, small and large van, small and large sport utility vehicle 

(SUV)—in nine Census divisions. For each vehicle type and class and for each region, a number 

of LDV technologies compete against each other in the market for vehicle sales. These include 

conventional gasoline, advanced combustion diesel, gasoline hybrids, diesel hybrids, gasoline 

fuel cell, hydrogen fuel cell, electric, natural gas, and alcohol. Each vehicle technology is 

represented by a number of characteristics that can change over the forecast time horizon and 

that influence the technology’s acceptance in the marketplace (i.e., its sales). These 

characteristics include the vehicle cost, the fuel cost per mile (a combination of the fuel price and 

the vehicle efficiency), the vehicle range, the operating and maintenance cost, the acceleration, 

the luggage space, the fuel availability, and the make and model availability. The NEMSGPRA06 model also includes “calibration” coefficients to calibrate the model to historical data. 

The associated characteristics for all the alternative technologies are specified as relative to those 

for the conventional gasoline vehicle. 

The model estimates the sales-penetration share of each technology in all of the vehicles, classes, 

and regions in each year of the forecast. The various characteristics of the technologies 

determine the technology’s value to consumers and its acceptance in the marketplace, but each 

characteristic has a differing degree of influence.7 The vehicle cost is generally the most 

 

7 The vehicle shares are sensitive to assumptions about consumer preference for each vehicle attribute. In the NEMS-GPRA06 

transportation model, a different set of consumer-choice assumptions is made than those in the NEMS AEO2004 transportation 

model, leading to different rates of technology adoption. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-19 

influential of the characteristics, certainly having a much stronger influence than luggage space, 

for example. The values of all the characteristics are combined to create an overall value. The 

technologies are competed against each other using a nested logit formulation. In a logit 

formulation, the relative size of the overall value for each technology determines the relative 

penetration shares for that technology. Technologies that have higher values are given greater 

sales shares, resulting in a distribution of consumer preferences rather than the technology with 

the highest “utility” receiving 100 percent of the market. The overall sales-penetration results are 

the sum of all the more disaggregated results. 

In the FY 2006 benefits analysis, the Baseline Case for transportation programs is essentially the 

AEO2004 Reference Case, which already includes some small amount of penetration for the 

program vehicle technologies. The Program Case uses the program technology characteristics, 

along with a variety of other assumptions relating to behavioral responses in the underlying logit 

formulation of the NEMS-GPRA06 model. These include moving away from the “calibration” 

coefficients over the forecast period (used by the model for a tie to history), and reworking the 

manner in which the make and model availability coefficients are used. 

Using the fully integrated NEMS-GPRA06 model, the overall sales share for gasoline vehicles in 

2025 falls from 81 percent in the Baseline Case to 40 percent in the Program Case (Figure 4.3). 

This decrease in share is due to the penetration of the alternative technologies. The overall share 

in 2025 for advanced combustion diesel increases from 5 percent to 25 percent, for gasoline 

hybrids from 5 percent to 18 percent, and for diesel hybrids from 1 percent to 12 percent. 

These large vehicle sales shares for advanced technology vehicles in 2025, however, translate 

into much smaller shares of overall vehicle stocks and overall shares of vehicle miles traveled 

(VMT) for each technology. The stock shares depend on the share of sales over time, which only 

gradually increases for the alternative-technology vehicles, and the rate of vehicle replacement 

and growth. The total VMT for gasoline vehicles falls from 3,455 billion miles in 2025 to 2,667 

(just over 60 percent of the VMT) between the two cases (Figure 4.4). The total VMT for 

advanced combustion diesel increases from 180 to 519 billion miles (12 percent), for diesel 

hybrids from 20 to 218 billion miles (5 percent), and for gasoline hybrids from 182 to 710 billion 

miles (16 percent). 

 

Figure 4.3. Sales Shares in 2025

Sales Shares in 2025

0.00

0.20

0.40

0.60

0.80

1.00

Gasoline Adv

Diesel

Gas

Hybrid

Diesel

Hybrid

Other

Technology

Share

Base Sales Shares

GPRA Sales Shares

Figure 4.4. Vehicle Miles Traveled in 2025

Vehicle Miles Traveled in 2025

0

500

1000

1500

2000

2500

3000

3500

4000

Gasoline Adv Diesel Gas Hybd Diesel

Hybd

Other

Technology

Million Miles

Base VM T

GPRA VM T
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-20 

The miles per gallon (MPG) for advanced combustion diesel and for hybrid vehicles is much 

greater than the MPG for conventional gasoline vehicles. As a consequence, since these 

advanced-technology vehicles are substituting for the conventional gasoline vehicles, there is a 

considerable amount of fuel savings. 

 

In these fully integrated NEMS-GPRA06 model runs, the savings are typically somewhat less 

than if they were estimated in a transportation-only model, because of feedback effects that come 

through the integration with other sectors. The primary feedback effect occurs through lower fuel 

prices. In this case, reduced gasoline demand causes lower gasoline prices, which leads to an 

increase in travel and less-efficient vehicle purchases than would otherwise have occurred absent 

the price change. The rebound of gasoline consumption reduces the program savings. At the 

same time, energy-expenditure savings are greater. The small decreases in price apply to the total 

amount of fuel consumed and contribute significant additional expenditure savings. In addition, 

the “rebound” effect is also influenced by the fact that vehicles are more efficient, thereby 

reducing the cost to drive, causing more miles to be driven. Table 4.15 presents the total 

program benefits, including those of heavy trucks. 

Table 4.15. FY06 Benefits Estimates for Vehicle Technologies Program (NEMS-GPRA06) 

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.09 0.73 2.12 3.98 

Economic 

 Energy-Expenditure Savings (billion 2002 dollars/yr) ns 7.3 31.4 60.9 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 1.9 14.6 41.8 76.2 

Security 

 Oil Savings (million barrels per day) 0.04 0.32 0.90 1.80 

 Natural Gas Savings (quadrillion Btu/yr) ns ns ns ns 

 Avoided Additions to Central Conventional Power (cumulative

 gigawatts) ns ns ns ns 

Weatherization and Intergovernmental Program 

The Weatherization and Intergovernmental Program (WIP) encompasses a broad range of 

activities in virtually all demand sectors of the energy economy. These activities generally are 

comprised of market enhancement, rather than R&D. The major components include: 

International, Native American Renewable Initiative (also referred to as Tribal Energy), the 

Renewable Energy Production Incentive (REPI), Weatherization (Assistance), State Energy 

Program Grants, and Gateway Deployment (Energy Star, Clean Cities, Inventions and 

Innovations, Rebuild America, Energy Efficiency Information and Outreach, and Building Codes 

Training and Assistance). The FY 2006 benefits estimate methodologies vary by activity. 

The international activities are currently outside the scope of the integrated modeling framework. 

The Native American renewable initiative also is not being modeled for this year. REPI provides 

payments to publicly owned utilities, such as municipal utilities or rural electric cooperatives, for 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-21 

electricity generation from renewable energy sources that is the public power equivalent of the 

production tax credit for investor-owned renewable generators. Analysts projected the amount of 

new renewable generation that is likely to be stimulated by future REPI payments. Almost all the 

new generation is expected to be wind, based on the eligibility criteria and past experience. 

Analysts then used the NEMS-GPRA06 benefits for the Wind Program to develop the benefits 

metrics for REPI based on the ratio of additional generation. 

Weatherization and State Energy Program grants are implementation programs that lead to 

greater adoption of energy efficiency. They are represented in NEMS-GPRA06 by reducing 

energy consumption in the residential and commercial sectors, based on the program goals. 

The Clean Cities subprogram is represented through an increase in alternative-fuel vehicles and 

an increase in dedicated ethanol (E85) vehicles and fuel usage. For the increase in alternativefuel vehicles, analysts determined the cumulative number of expected vehicles participating in 

Clean Cities through off-line analysis. These were converted to annual vehicle sales and used as 

inputs into NEMS-GPRA06. The incremental sales were allocated to vehicle types, based on 

program information, although the fuel types in the model do not directly correspond in all cases. 

The largest share of vehicles are compressed natural gas, ethanol, and liquefied petroleum gas. 

Electric and methanol vehicle shares are small. For the portion of the program that encourages 

greater ethanol use, analysts determined the change in the fraction of vehicles using E85 over 

time and an increasing fraction of E85 use per vehicle. These were converted to overall fractions 

of E85 use and were then used as inputs to NEMS-GPRA06. 

The Inventions and Innovation (I&I) subprogram savings estimates are based on numerous 

individual technologies receiving grants in the previous year, because this is the most recent year 

of award data available for analysis. For this analysis, the projects with the greatest expected 

energy savings are represented using specific technology characteristics or by targeting the 

energy-savings goals of the individual projects funded. The technologies include two inventions 

involving ethanol production, two types of buildings equipment, and one industrial process. The 

ethanol and industrial process inventions could not be modeled on an economic basis within 

NEMS-GPRA06, so the estimated off-line energy savings were used in the model after being 

discounted by 30 percent to 50 percent to reflect potential interactions with other EERE markets 

and technologies. In the building sector, the electrochromic windows reduce heating and cooling 

loads. Based on an analysis performed by PNNL,8

 the windows were modeled in NEMSGPRA06 based on technology cost and efficiency characteristics. The humidity-control 

invention was modeled using an assumption of air-conditioning savings in homes with 

commercial applications and in the markets where humidity control is important. 

Analysts represented the Energy Star activities of Gateway Deployment by modifying the 

consumer-behavior coefficients, indicating how consumers trade first-cost expenditures for 

annual energy savings. The program goals for market penetration were used to determine the 

degree of change of these parameters. For the compact fluorescent bulb (CFL) activities, the 

target market share was defined as the fraction of lighting demand rather than the fraction of 

bulbs, in order to reflect that CFLs are most likely to be installed in high-use fixtures. 

 

8 See Appendix K on the Weatherization and Intergovernmental Program analysis. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-22 

Other buildings energy-related activities, including Building Codes and Rebuild America, were 

represented in NEMS-GPRA06 based on an offline analysis of penetration rates and efficiency 

improvements. Overall benefits for WIP are shown in Table 4.16. 

Table 4.16. FY06 Benefits Estimates for Weatherization and Intergovernmental Program 

(NEMS-GPRA06) 

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.34 0.61 0.97 1.22 

Economic 

 Energy-Expenditure Savings (billion 2002 dollars/yr) 4.6 10.4 11.9 9.6 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 6.3 12.3 21.0 27.2 

Security 

 Oil Savings (million barrels per day) 0.03 0.08 0.08 0.05 

 Natural Gas Savings (quadrillion Btu/yr) 0.20 0.20 0.26 0.31 

 Avoided Additions to Central Conventional Power (gigawatts) 1 9 16 15 

 Total Electric Capacity Avoided (cumulative gigawatts) 7 11 12 14 

Wind Technologies Program 

The wind component of the Wind Technologies Program seeks to reduce the cost—and improve 

the performance—of wind generation. The FY 2006 benefits are based primarily on projecting 

the market share for wind technologies, based on their economic characteristics. 

Representation of Wind: The NEMS-GPRA06 electricity-sector module performs an economic 

analysis of alternative technologies in each of 13 regions. Within each region, new capacity is 

selected based on its relative capital and operating costs, its operating performance (i.e., 

availability), the regional load requirements, and existing capacity resources. Unlike the 

AEO2004 version of NEMS, NEMS-GPRA06 characterizes wind by three wind classes, which 

each have their own capital costs and resource cost multipliers. For example, wind turbines being 

developed by the program for use in Class 4 winds are expected to be more expensive, but 

deliver more electricity per unit of capacity. The regional resource cost multipliers act to increase 

costs as more of a wind class is developed in a region, and development may move to the next 

most cost-effective wind class. The same resource multipliers are used as in the AEO2004, 

although they are applied at the class level rather than for the entire regional resource. NEMSGPRA06, as in the AEO2004, assumes that the capacity value of wind diminishes with greater 

wind capacity in a region. Finally, another constraint on the growth of wind-resource 

development is how quickly the wind industry can expand before costs increase due to 

manufacturing bottlenecks. The AEO2004 assumption that a cost premium is imposed when new 

orders in a year are 20 percent higher than in the highest of the previous 10 years was maintained 

in the Program Case9

 (see Table 4.17). 

 

9

 In the AEO2004, all generation technologies face similar premiums associated with rapid growth. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-23 

The baseline characterization of wind capital costs and capacity factors were modified to reflect 

a more consistent view relative to the program goals. The Baseline costs were raised slightly and 

distinguished between Class 5 and 6 versus Class 4 costs. The more significant change was an 

increase in capacity factors for all three wind classes. This reduced the benefits attributed to the 

program, but presents a better representation of the impact of the program’s R&D. 

NEMS-GPRA06 also includes a representation of offshore wind that it not in the AEO2004 

version. The offshore wind is represented as a distinct technology that competes with all other 

generation technologies. It is characterized in a similar manner as onshore wind, with three wind 

classes, but also has a distinction between shallow and deep-water sites. The constraints on 

intermittent generation and rapid growth apply similarly to offshore as to onshore wind 

development. The offshore wind does not have the regional resource cost multipliers because 

there is insufficient data on how they might apply. 

Analysts represented the Wind Program R&D activities by reducing the capital and O&M costs 

and increasing the performance of wind capacity to match the program cost goals. In addition to 

competing on an economic basis with other electricity-generation technologies, wind capacity 

may be constructed for its environmental benefit. PERI, using their Green Power Market Model, 

provided an estimate of wind capacity additions in response to the expanding green power 

markets in many places nationwide. Analysts incorporated the projections for green power wind 

installations into NEMS-GPRA06 as planned capacity additions. These are quite small relative to 

the economic additions selected within the model. 

Table 4.18 provides the estimates of primary energy, oil, and carbon emissions savings 

stemming from wind and hydropower displacing fossil-fueled generation sources. Analysts 

measure the energy-expenditure savings as the reduction in consumer expenditures for electricity 

and other fuels. Lower-cost renewable generation options reduce the price of electricity directly 

and reduce the pressure on natural gas supply, both of which benefit end-use consumers. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-24 

Table 4.17. Wind Capacity (GW) 

 2010 2015 2020 2025 

AEO Base 7.9 9.5 12.0 14.0 

GPRA Baseline 

Onshore Class 6 6.1 6.5 6.5 6.5 

 Class 5 4.9 6.2 6.7 7.4 

 Class 4 0.1 0.1 0.1 0.2 

 Subtotal 11.1 12.8 13.3 14.2 

Offshore Class 7 0.0 0.4 1.2 1.4 

 Class 6 0.0 0.0 0.0 0.0 

 Class 4&5 0.0 0.0 0.0 0.0 

 Subtotal 0.0 0.4 1.2 1.4 

Total Total 11.1 13.2 14.5 15.6 

Wind Program Case 

Onshore Class 6 6.1 10.8 11.3 11.3 

 Class 5 7.8 16.5 18.7 20.7 

 Class 4 0.1 14.4 36.3 37.6 

 Subtotal 13.9 41.7 66.4 69.6 

Offshore Class 7 0.0 1.9 16.7 37.2 

 Class 6 0.0 0.0 1.7 2.1 

 Class 4&5 0.0 0.0 0.0 0.0 

 Subtotal 0.0 1.9 18.4 39.2 

Total Total 13.9 43.6 84.8 108.8 

Incremental Capacity 

Onshore Class 6 0.0 4.3 4.8 4.8 

 Class 5 2.8 10.3 12.1 13.2 

 Class 4 0.0 14.2 36.2 37.4 

 Subtotal 2.8 28.8 53.0 55.4 

Offshore Class 7 0.0 1.5 15.5 35.8 

 Class 6 0.0 0.0 1.6 2.0 

 Class 4&5 0.0 0.0 0.0 0.0 

 Subtotal 0.0 1.5 17.2 37.8 

Total Total 2.8 30.3 70.2 93.2 

Table 4.18. FY06 Benefits Estimates for Wind Technologies Program 

(NEMS-GPRA06) 

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Non-Renewable Energy Savings (quadrillion 

 Btu/year) 0.04 0.84 2.29 3.32 

 Generation (gigawatt-hours/year) 11 120 298 416 

Economic 

 Energy Expenditure Savings (billion 2000 dollars/year) ns 5.2 6.8 4.5 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/year) 0.9 18.0 52.4 80.6 

Security 

 Oil Savings (million barrels per day) ns 0.06 0.07 0.06 

 Natural Gas Savings (quadrillion Btu/year) 0.01 0.24 0.52 0.39 

 Avoided Additions to Central Conventional Power (gigawatts) 1 9 19 24 

 Program-Specific Electric Capacity (cumulative gigawatts) 3 30 70 93 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 4) – Page 4-25 

Box 4.1—EIA’s National Energy Modeling System (NEMS)* 

The National Energy Modeling System (NEMS) is an energy-economy modeling system of U.S. energy markets for the 

midterm period through 2025. NEMS projects the production, imports, conversion, consumption, and prices of energy, 

subject to assumptions on macroeconomic and financial factors, world energy markets, resource availability and costs, 

behavioral and technological choice criteria, cost and performance characteristics of energy technologies, and demographics. 

NEMS was designed and implemented by the Energy Information Administration (EIA) of the U.S. Department of Energy 

(DOE). As described in the GPRA Baseline section, the NEMS-GPRA06 version of the model used for the EERE GPRA 

analysis includes minor modifications to the standard EIA NEMS. 

NEMS is designed as a modular system. Four end-use demand modules represent fuel consumption in the residential, 

commercial, transportation, and industrial sectors; subject to delivered fuel prices, macroeconomic influences, and 

technology characteristics. The primary fuel supply and conversion modules compute the levels of domestic production, 

imports, transportation costs, and fuel prices that are needed to meet domestic and export demands for energy; subject to 

resource base characteristics, industry infrastructure and technology, and world market conditions. The modules interact to 

solve for the economic supply and demand balance for each fuel. Because of the modular design, each sector can be 

represented with the methodology and the level of detail (including regional detail) that is appropriate for that sector. 

A key feature of NEMS is the representation of technology and technology improvement over time. Five of the sectors—

residential, commercial, transportation, electricity generation, and refining—include extensive treatment of individual 

technologies and their characteristics, such as the initial capital cost, operating cost, date of availability, efficiency, and other 

characteristics specific to the sector. Technological progress results in a gradual reduction in cost and is modeled as a 

function of time in these end-use sectors. In addition, the electricity sector accounts for technological optimism in the capital 

costs of first-of-a-kind generating technologies and for a decline in cost as experience with the technologies is gained both 

domestically and internationally. In each of these sectors, equipment choices are made for individual technologies as new 

equipment is needed to meet growing demand for energy services or to replace retired equipment. In the other sectors—

industrial, oil and gas supply, and coal supply—the treatment of technologies is more limited, due to a lack of data on 

individual technologies. In the industrial sector, only the combined heat and power and motor technologies are explicitly 

considered and characterized. Cost reductions resulting from technological progress in combined heat and power 

technologies are represented as a function of time as experience with the technologies grows. Technological progress is not 

explicitly modeled for the industrial motor technologies. Other technologies in the energy-intensive industries are 

represented by technology bundles, with technology possibility curves representing efficiency improvement over time. In the 

oil and gas supply sector, technological progress is represented by econometrically estimated improvements in finding rates, 

success rates, and costs. Productivity improvements over time represent technological progress in coal production. 

* Most of this description is taken from The National Energy Modeling System: An Overview 2003, DOE/EIA-0581(2003), 

March 2003. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-1 

CHAPTER 5

LONG-TERM BENEFITS ANALYSIS OF EERE’S

PROGRAMS

Introduction 

This chapter provides an overview of the modeling approach used in MARKAL-GPRA06 to 

evaluate the benefits of EERE R&D programs and technologies. The program benefits reported 

in this section result from comparisons of each Program Case to the Baseline Case, as modeled 

in MARKAL-GPRA06. 

The Baseline Case used to evaluate the impact of the EERE portfolio was benchmarked to EIA’s 

Annual Energy Outlook 2004 (AEO2004) for the period between 2000 and 2025. To the extent 

possible, the same input data and assumptions were used in MARKAL-GPRA06 as were used to 

generate the AEO2004 Reference Case. For example, the macroeconomic projections for GDP, 

housing stock, commercial square footage, industrial output, and vehicle miles traveled were 

taken from the AEO2004. At the sector level, both supply-side and demand-side technologies 

were characterized to reflect the AEO2004 assumptions, in cases where the representation of 

technologies is similar between MARKAL (MARKet ALlocation) and the National Energy 

Modeling System (NEMS). The resulting projections track closely with the AEO2004 at the 

aggregate level, although they do not match exactly at the end-use level. For the period after 

2025, various sources were used to compile a set of economic and technical assumptions. For 

instance, the primary economic drivers of GDP and population were based on the real GDP 

growth rate from the Congressional Budget Office’s Long-Term Budget Outlook and population 

growth rates from the Social Security Administration’s 2003 Annual Report to the Board of 

Trustees. Appendix A provides a more complete discussion of the MARKAL-GPRA06 Baseline 

Case.

For each EERE R&D program, analysts make modifications to the characteristics of the 

technologies involved to generate a Program Case. Program Cases also may include technologies 

not available in the Baseline Case. The modifications made to the model parameters and 

attributes of a technology depend on the nature of the program. They directly affect the 

technology’s competitiveness and market deployment presented in the model. 

Table 5.1 provides a breakdown by program of the two types of analytical methods employed in 

EERE’s long-term benefits analyses—specialized “off-line” tools and MARKAL-GPRA06. The 

activities listed are groupings of activities within each program that share either technology or 

market features. They do not represent actual program-management categories. A description of 

the MARKAL model is provided in Box 5.1 at the end of this chapter. Descriptions of the offline models are provided in the related program appendix. It is important to note that the off-line 

analysis served to feed appropriate parameters and other factors into MARKAL-GPRA06, which 

was then run for all the programs. The indication that the Industrial Technologies Program (or 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-2 

other program areas) was modeled using off-line tools should not be interpreted to mean that the 

Industrial Technologies Program was not included in the MARKAL-GPRA06 modeling, or that 

the results of the Industrial Technologies Program analysis are not impacted by the MARKALGPRA06 modeling. 

Table 5.1. Long-Term Benefits Modeling by Primary Type of Model Used and Activity Area 

Program Activities Off-Line Tools MARKAL-GPRA06

Biomass Bio-based Products 3 

Cellulosic Ethanol 3

Buildings Technologies Residential Sector 3 3

Commercial Sector 3 3

DE DER / CHP 3

FEMP FEMP 3

Geothermal Geothermal 3

Hydrogen, Fuel Cells, and Fuel Cells 3

Infrastructure Technologies Production 3

Industrial Technologies R&D 3

Deployment 3

Solar Energy Technologies Solar Water Heaters 3

Photovoltaics 3 3

Vehicle Technologies Light-Vehicle Hybrid and Diesel 3

Heavy Trucks 3

Weatherization and Intergovernmental Weatherization 3

Domestic Intergovernmental 3

Wind Technologies Wind 3

The following sections summarize how each EERE R&D program is formulated in MARKALGPRA06. In many cases, analysts convert the technological data and their projected market 

potentials in each program directly to MARKAL-GPRA06 input. When this is not feasible, the 

quantitative analyses undertaken in Step 2 are used, in part, to generate the Program Cases. 

Biomass Program 

The goal of the Biomass Program is the development of biomass-based refineries (biorefineries), 

which produce a range of products including cellulosic ethanol and/or other fuels, chemicals, 

materials, and/or electricity. The biorefinery concept allows the cost of production to be reduced 

through synergies associated with feedstock handling and processing, and the allocation of 

capital and fixed O&M costs across multiple products. For the current analysis, we modeled two 

types of biorefineries. The first type produces chemicals and materials, but not fuels, and the 

second type produces ethanol fuel as the major output. Future analyses may include additional 

fuels that the program identifies in the longer term. 

Bio-based products: At this early stage of biorefinery R&D, the output and cost of the nonfuel 

biorefineries (producing only chemicals and materials) are not yet well defined. Program goals 

are estimated off-line and represented in MARKAL-GPRA06 as reductions in petroleum and 

natural gas demand for feedstocks. Off-line projections of the use of petroleum and natural gas as 

chemical feedstock are represented in a highly aggregated manner and include changes in fuel 

requirements for process heat. The off-line energy savings for displaced feedstocks and changes 

in process heat are represented in the MARKAL-GPRA06 model as a conservation curve in the 

amounts shown in Table 5.2. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-3 

Table 5.2. Bio-based Products Energy Savings by Year 

 2010 2020 2030 2040 2050 

Natural Gas (TBtu/yr) 1.13 3.53 11.68 35.66 71.54

Coal (TBtu/yr) -0.15 -0.47 -1.56 -4.75 -9.54

Electricity (Billion kWh/yr) -0.15 -0.47 -1.56 -4.75 -9.54

Distillate (TBtu/yr) 1.13 3.53 11.68 35.66 71.54

Oil Feedstock (TBtu/yr) 2.75 8.62 28.54 87.17 174.86

Total (TBtu/yr) 4.70 13.60 45.03 137.51 275.85

Corn and cellulosic ethanol: EERE is sponsoring research aimed at reducing the cost of 

producing ethanol from cellulosic biomass.1 The second type of biorefineries assumed in this 

analysis is one that focuses on the production of ethanol, lignin-derived electricity and a small 

quantity of chemical coproducts. In the Biomass Program Case, the conversion of corn fiber and 

residual starch to ethanol becomes available for dry mills beginning in 2012 and yields a 20 

percent increase in a dry mill's ethanol output. Corn stover-to-ethanol technology becomes 

available in 2018, whereas sugar-based biorefineries producing ethanol as a major product, along 

with high-value coproducts, from corn stover and other cellulosic wastes and residues, become 

available in 2024. Currently, the MARKAL-GPRA06 model lacks sufficient technical detail to 

properly capture beneficial qualities of ethanol, such as octane enhancement; or the regional 

detail to model niche markets in agricultural states where ethanol/gasoline blends may compete 

on an even basis with traditional gasoline. Therefore, estimates of future ethanol demand from 

biomass-specific models (e.g., ELSAS Bioref) are used for both the Baseline and Program Cases. 

Table 5.3 depicts the production of cellulosic and corn ethanol set in MARKAL-GPRA06, 

which reflects corn and cellulosic ethanol’s penetration if program cost goals are met. 

Table 5.3. Projected Ethanol Demand (million gallons/year) 

 2000 2010 2020 2030 2040 2050 

Corn 1,600 3,733 4,018 3,644 3,531 3,531 

Corn Fiber & 

Residual Starch 

0 0 340 474 459 459 

Cellulosic 0 0 0 3,600 9,000 13,200 

Total 1,600 3,733 4,357 7,718 12,990 17,190 

The benefits of the Biomass Program derived in MARKAL-GPRA06 (Table 5.4) are the results 

of direct substitution of biomass-based energy for fossil fuels. Bio-based products reduce the 

demand for petroleum feedstocks. Cellulosic ethanol displaces an increasing fraction of the 

gasoline used in light-duty vehicles (LDVs) in later periods. The reduction in fossil fuel 

consumption at high marginal cost generates savings both in carbon emissions and energysystem costs. 

 

1 Cellulose and hemi-cellulose that can be converted to ethanol (and other chemicals, materials, and biofuels) are found in 

biomass such as agricultural residues (corn stover, wheat, and rice straw), mill residues, organic constituents of municipal solid 

wastes, wood wastes from forests, future grass and tree crops dedicated to bio-energy production. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-4 

Table 5.4. FY06 Benefits Estimates for Biomass Program (MARKAL-GPRA06) 

 Annual Benefits 2020 2030 2040 2050 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.05 0.23 0.71 1.13

Economic 

 Energy-System Cost Savings (billion 2002 dollars/yr) 0.0 0.2 0.5 0.6

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 1 4 12 19

Security 

 Oil Savings (mbpd) 0.05 0.09 0.29 0.40

 Natural Gas Savings (quadrillion Btu/yr) -0.08 0.02 0.06 0.16

Building Technologies Program 

MARKAL-GPRA06 models technologies and activities in the Buildings Program based on two 

general types of activities: technology R&D and regulatory actions. 

Technology R&D: New and improved technologies are introduced into MARKAL-GPRA06 by 

modifying the technology slates that are available in the Baseline Case. These modifications are 

accomplished by changing any (or all) of the following three parameters to reflect program 

goals: the date of commercialization, capital cost, and efficiency. Building technologies for 

which these parameters can be characterized to meet specific building service demands include 

end-use devices such as heating burners, air conditioners, and water heaters. In instances where 

the market potentials of a technology were estimated off-line, a maximum initial market 

penetration rate was imposed, combined with an annual growth rate limit to replicate these 

potentials in MARKAL-GPRA06. 

Technologies that lower service demand (e.g., building shell technologies, lighting controls) are 

modeled in MARKAL-GPRA06 as conservation supply steps. Each supply step is characterized 

by capital cost, load-reduction potentials expressed as upper bounds of market penetration, 

consumer’s hurdle rate, and technology lifetime. These conservation steps reduce the market size 

or load demand for end-use devices. In the Buildings Program Case, these newly introduced 

technologies compete with the baseline technologies for market share. For example, in future 

time periods, the size of the market for commercial air conditioning is the projected total heat in 

trillion Btus to be removed from the service areas. The new investment opportunity in that time 

period is the difference between the projected service demands in that period and the vintage 

capacities carried over from the previous period. 

Technologies such as solid-state lighting, although available in the Baseline Case, do not have a 

market share initially because of their high consumer hurdle rate (44 percent). These hurdle rates 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-5 

are lowered to 18 percent when running the Buildings Technology Case to reflect consumer 

acceptance of these products with improved performance.2 The 18 percent is an empirical value 

based on observed consumer responses, but is much higher than would be observed if consumers 

were minimizing life-cycle costs. Although the future market potential of new lighting 

technologies is great due to the relatively short life of the equipment, the penetration of these 

technologies modeled in MARKAL-GPRA06 is limited to a sustainable growth path that 

generates a potential market penetration path consistent with the program goals. 

Regulatory activities: Analysts represent new appliance standards and building codes in 

MARKAL-GPRA06 as either new technologies or energy-conservation supply steps. In the time 

period that a new standard becomes effective, the model removes technologies with efficiency 

below the set standard from the market. Regulatory activities primarily affect the performance of 

new energy products for a specific end-use product purchased by consumers in future markets. 

The overall impact of the Buildings Program, therefore, depends on the size of these markets. 

MARKAL-GPRA06 determines the size of these markets by dynamically keeping track of the 

turnover of capital equipment and deriving the new investment needed to meet projected energy 

service demands. Because some end-use devices (e.g., heating equipments) have a long service 

lifetime, the stock turnover constraints modeled in MARKAL-GPRA06 limit near-term energy 

savings. 

In MARKAL-GPRA06, energy savings are achieved when a more efficient and economic (on a 

life-cycle basis) end-use device is selected to substitute for a conventional device competing in 

the same market. For example, a 20 Watt (W) CFL can replace a 75W incandescent light bulb 

and provide the same level of lighting service, but uses much less electricity. The total market 

potential for this substitution in a future time period, however, is constrained by the investment 

opportunity established in MARKAL-GPRA06. 

While the Building Technologies Program conducts research on a variety of technologies and 

applications, the three activities with the highest potential in the GPRA06 analysis are Solid 

State Lighting, Residential and Commercial Unitary DX System, and Building America. For 

solid-state lighting, the Building Technologies Program is conducting research to improve the 

efficiency and reduce the cost of the lamps. Unitary DX Systems research aims to double the 

efficiency of both residential and commercial space cooling and heat pump technologies with 

only a 10 percent increase in cost (by 2020). 

The goal of the Build America Program is to improve efficiency of new and existing homes 

through research, development, demonstrations, and technology transfer strategies using the 

whole buildings approach. This program was modeled as a series of three conservation curves 

for residential space heating and cooling demands. The first conservation curve represents the 

incremental cost to reduce energy consumption by 20 percent, while the next two supply curves 

represent the incremental cost to reduce energy consumption by a further 20 percent and 10 

 

2 The hurdle rates in MARKAL-GPRA06 include factors to reflect both the interest rate available to consumers, as well as 

behavioral and risk premiums that are implicit in consumer decisions. Behavioral premiums would reflect a documented 

consumer bias towards choosing reduced up-front investment costs over longer-term operating cost savings. The behavioral 

premium also incorporates agency issues where the decision maker would not benefit from long-term operating costs and, thus, 

would make decisions based primarily on initial capital costs. Risk premiums would apply to new, unfamiliar products that are 

presumed to be less desirable to consumers due to the lack of familiarity or a track record of successful application. Also, risk 

premiums would be appropriate for modeling situations where technologies may appear to be cost effective on paper, but are not 

chosen by consumers for reasons such as convenience, styling or lack of availability.
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-6 

percent, respectively. The technology assumptions for these activities are shown in Tables 5.5, 

5.6, and 5.7. 

Table 5.5. Solid-State Lighting Technologies

 2010 2020 2030 2040 2050 

Efficacy (lumens/watt) 60 118 153 162 162 

Price ($/kilolumen) $126.93 $9.91 $4.09 $4.00 $4.00 

Table 5.6. Residential and Commercial Unitary DX System Technologies 

 2010 2015 2020 2025 

Incremental Cost 

(percent) 100% 89% 64% 10% 

Incremental Efficiency 

(percent) 100% 100% 100% 100% 

Table 5.7. Building America Building Shell Cost Assumptions (2001$/MMBtu)

 2010 2015 2020 

North 

Step 1 $21.2 $15.9 $10.7 

Step 2 $196 $147 $98 

Step 3 $258 $194 $129 

Midwest 

Step 1 $29 $22 $15 

Step 2 $240 $180 $120 

Step 3 $240.7 $180.6 $120.4 

South 

Step 1 $30 $22 $15 

Step 2 $279.9 $209.9 $140.0 

Step 3 $353 $265 $176 

West 

Step 1 $18.2 $13.6 $9.1 

Step 2 $211 $158 $106 

Step 3 $215 $161 $107 

For information on the other Technologies Program inputs, please refer to Appendix C. 

Tables 5.8 and 5.9 depict the projected delivered energy savings by demand and fuel generated 

from the use of more efficient end-use devices and cost-effective conservation measures covered 

under the Buildings Program. 

In addition to the reduction in delivered primary energy, the reduction in electricity demand in 

buildings also leads to the reduction in gas-fired generation capacity, as well as fuel used for 

generation. Furthermore, building code and envelop improvements reduce both the demand for 

delivered energy and the required output capacity of end-use devices, such as furnaces or air 

conditioners. Thus, consumers see both a reduction in their energy bills, as well as reduced 

capital costs for end-use appliances. This is another factor attributable to the overall reduction in 

energy-system cost in addition to direct energy savings. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-7 

Table 5.8. Residential Delivered Energy Savings by Demand and Fuel

(trillion Btu/year) 

 2010 2020 2030 2040 2050 

 

Reduction by Service Demand 

Space Heating 42 284 502 660 641 

Space Cooling 20 69 132 169 201 

Water Heating 0 0 -1 -16 -28 

Lighting 0 11 56 148 279 

Other 0 0 0 0 0 

Total 62 364 690 961 1,093 

 

Reduction by Fuel 

Petroleum 1 37 92 124 131 

Natural Gas 35 243 380 410 252 

Coal 0 2 0 1 0 

Electricity 27 82 218 425 710 

Total 62 364 690 961 1,093 

Table 5.9. Commercial Delivered Energy Savings by Demand and Fuel (trillion Btu/year) 

 2010 2020 2030 2040 2050 

 

Reduction by Service Demand 

Space Heating 0 38 42 42 36 

Space Cooling 3 24 44 36 30 

Ventilation Equipment 5 19 41 26 2 

Water Heating 0 0 0 0 0 

Lighting 3 61 201 435 781 

Other 0 0 0 0 0 

Total 11 141 327 539 849 

 

Reduction by Fuel 

Petroleum 6 38 18 13 -1 

Natural Gas -12 -18 -41 -37 -30 

Coal 0 0 0 0 0 

Electricity 16 120 350 562 879 

Total 11 141 327 539 849 

Table 5.10. FY06 Benefits Estimates for Building Technologies Program (MARKAL-GPRA06) 

 Annual Benefits 2020 2030 2040 2050 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 1.3 2.4 3.5 4.2

Economic 

 Energy-System Cost Savings (billion 2002 dollars/yr) 12.8 28.7 43.3 62.4

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 23 45 64 92

Security 

 Oil Savings (mbpd) 0.06 0.06 0.10 0.08

 Natural Gas Savings (quadrillion Btu/yr) 0.81 1.42 2.26 1.38

 Electricity Capacity Avoided (gigawatts) 24 62 76 108
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-8 

Distributed Energy Program 

The Distributed Energy (DE) Program covers distributed generation technologies (DG) and 

combined heat and power (CHP). The program focuses on the improvement of these 

technologies (higher efficiency, lower cost, and lower emissions) and removal of market barriers 

for consumer acceptance. 

The DE Program Case in MARKAL-GPRA06 is formulated by the introduction and 

performance improvements in industrial and commercial sector combined heat and power 

technologies and a 1 MW distributed electric utility generator to meet local peaking demands. 

All of these technologies are modeled explicitly as decentralized systems in MARKAL-GPRA06 

and do not require transmission and distribution for their electricity or heat output; and, 

therefore, avoid the associated costs and electricity losses. Implicitly, this improves the electric 

reliability at the end-use locations—although this value to consumers is not reflected in the 

model representation of consumer choices. 

The overall efficiencies and capital costs used to characterize these technologies are assumed to 

become more favorable due to R&D achievements expected from the DE Program. The 

assumptions for commercial, industrial, and distributed electric utility technologies are shown in 

Tables 5.11, 5.12 and 5.13, respectively. 

Table 5.11. Commercial Sector Distributed Generation Technology Assumptions 

2005 2010 2015 2020 2025 

200 kW Gas Engine 

Installed Cost (2001$/kW) $1,112 $793 $729 $729 $729 

Electric Efficiency 32% 39% 39% 39% 39% 

Overall Efficiency 88% 92% 92% 92% 92% 

1 MW Gas Turbine 

Installed Cost (2001$/kW) $1,781 $1,653 $1,597 $1,542 $1,514 

Electric Efficiency 23% 28% 28% 28% 28% 

Overall Efficiency 77% 84% 84% 84% 84% 

100 kW Micro Turbine 

Installed Cost (2001$/kW) $1,595 $1,317 $1,212 $1,212 $1,212 

Electric Efficiency 30% 36% 37% 38% 39% 

Overall Efficiency 71% 80% 81% 81% 81% 

Table 5.12. Industrial Sector Distributed Generation Technology Assumptions 

2005 2010 2015 2020 2025 

1 MW Internal Combustion Engine 

Installed Cost (2001$/kW) $914 $643 $592 $592 $592 

Overall Heat Rate (Btus/kWh) 9,871 8,066 8,066 8,066 8,066 

Overall Efficiency 71% 75% 75% 75% 75% 

 

3 MW Internal Combustion Engine 

Installed Cost (2001$/kW) $909 $639 $588 $588 $588 

Overall Heat Rate (Btus/kWh) 9,538 7,797 7,797 7,797 7,797 

Overall Efficiency 69% 73% 73% 73% 73% 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-9 

1 MW Gas Turbine 

Installed Cost (2001$/kW) $1,881 $1,881 $1,881 $1,881 $1,881 

Overall Heat Rate (Btus/kWh) 15,580 12,030 12,030 12,030 12,030 

Overall Efficiency 65% 73% 73% 73% 73% 

 

5 MW Gas Turbine 

Installed Cost (2001$/kW) $986 $932 $879 $827 $773 

Overall Heat Rate (Btus/kWh) 12,344 9,721 9,721 9,721 9,721 

Overall Efficiency 67% 75% 75% 75% 75% 

 

10 MW Gas Turbine 

Installed Cost (2001$/kW) $900 $860 $821 $778 $738 

Overall Heat Rate (Btus/kWh) 11,551 9,084 9,084 9,084 9,084 

Overall Efficiency 69% 77% 77% 77% 77% 

 

25 MW Gas Turbine 

Installed Cost (2001$/kW) $776 $747 $717 $694 $664 

Overall Heat Rate (Btus/kWh) 9,817 7,679 7,679 7,679 7,679 

Overall Efficiency 70% 78% 78% 78% 78% 

 

40 MW Gas Turbine 

Installed Cost (2001$/kW) $688 $677 $667 $650 $640 

Overall Heat Rate (Btus/kWh) 9,146 7,119 7,119 7,119 7,119 

Overall Efficiency 72% 81% 81% 81% 81% 

 

100 MW Combined Cycle 

Installed Cost (2001$/kW) $677 $667 $658 $645 $635 

Overall Heat Rate (Btus/kWh) 6,894 6,400 6,400 6,400 6,400 

Overall Efficiency 70% 84% 85% 85% 86% 

Table 5.13. Electric Utility Distributed Peaker Technology Assumptions 

 2005 2010 2015 2020 2025 

Installed Cost (2001$/kW) $523 $641 $631 $621 $621 

Heat Rate (Btus/kWh) 10,169 8,348 8,298 8,249 8,249 

Variable O&M (mills/kWh) 19.8 15.9 15.9 15.9 15.9 

In addition to the GPRA Scenario technology assumption changes, the Baseline Case 

assumptions for these technologies were changed from those used to create the 2004 AEO 

projection. The Baseline Case assumptions for distributed technologies were changed such that 

the cost and efficiency of these distributed generation technologies would achieve the same 

levels in the DE GPRA Scenario with a 10-year delay. Thus, the Baseline Case assumptions for 

the industrial sector 1MW gas engine technology in 2025 would be the same as the GPRA 

Scenario assumptions for 2015. This assumption change results in increased penetration of 

distributed generation technologies in the Baseline Case, relative to the penetration of these 

technologies using the 2004 AEO cost and efficiency assumptions. The MARKAL-GPRA06 

results show accelerated market penetration of DE technologies relative to the Baseline Case. 

However, by 2050 the incremental installed capacity is diminished and is primarily the result of 

cumulative capacity investment. The installed distributed generation capacity is shown in 

Table 5.14. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-10 

Table 5.14. Installed Distributed Generation Capacity by Sector and Case

(gigawatts) 

2010 2020 2030 2040 2050 

Baseline Scenario

Buildings 2 5 15 28 29 

Industry 33 43 54 67 78 

Electric Utility 1 11 36 82 72 

Total 36 59 105 177 179 

 

GPRA Scenario

Buildings 2 5 12 26 27 

Industry 35 49 62 75 80 

Electric Utility 1 15 46 97 88 

Total 37 69 121 198 195 

 

Increase

Buildings 0 0 -3 -1 -1 

Industry 1 6 9 8 2 

Electric Utility 0 4 10 14 16 

Total 1 10 16 21 16 

With the increase in distributed generation capacity, MARKAL-GPRA06 directly reduces the 

investment in central gas and coal-fired generators. On the demand side, the heat generated from 

CHP further reduces fuel use for space and water heat in buildings, and for process steam in 

industrial applications. The higher overall efficiency (combined heat and power with no 

transmission loss) of these technologies results in long-term benefits in energy savings, energysystem costs, and carbon emission reductions (Table 5.15). 

Table 5.15. FY06 Benefits Estimates for DE Program (MARKAL-GPRA06) 

 Annual Benefits 2020 2030 2040 2050 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.4 0.4 0.5 0.3

Economic 

 Energy-System Cost Savings (billion 2002 dollars/yr) 1.3 2.1 1.3 1.4

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 8 10 10 4

Security 

 Oil Savings (mbpd) 0.08 0.03 0.05 0.03

 Natural Gas Savings (quadrillion Btu/yr) 0.05 0.03 0.13 0.31

 Capacity (gigawatts) 11 20 22 17

 Total Displaced Need for New Electric Capacity (gigawatts) 9 25 22 17

Federal Energy Management Program 

The Federal Energy Management Program (FEMP) aims to improve the overall energy 

efficiency in Federal Government buildings. As a deployment program, FEMP utilizes a broad 

spectrum of existing technologies and practices for achieving its goal. Therefore, it does not 

provide specific technological information in relating costs and energy savings under its 

activities. The program has a well-documented track record and provided estimates of future 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-11 

savings based on past results and current budgets. The savings by specific energy type projected 

by the program through the year 2030 are depicted in Table 5.16. For the period after 2030, the 

amount of energy displaced was held constant. 

Table 5.16. FEMP Annual Energy Savings Projections 

Year 

Direct 

Electricity 

Displaced 

(billion 

kWh/yr)

Direct 

Natural 

Gas 

Displaced 

(billion 

CF/yr)

Direct 

Petroleum 

Displaced 

(million 

barrels/yr)

Direct 

Coal 

Displaced 

(million 

short 

tons/yr)

2006 0.45 1.37 0.11 0.022 

2007 0.85 2.82 0.20 0.048 

2008 1.22 4.29 0.30 0.071 

2009 1.61 5.61 0.40 0.094 

2010 2.01 6.88 0.49 0.115 

2015 2.41 10.78 0.73 0.171 

2020 2.98 13.80 0.97 0.220 

2025 3.61 16.59 1.18 0.264 

2030 4.09 19.63 1.38 0.307 

In order to quantify the broader benefits of these savings in MARKAL-GPRA06, a single 

energy-conservation supply curve was modeled in the FEMP Case to reduce the energy service 

demands in “miscellaneous” commercial energy demand. The conservation curve was set to 

reflect the program’s estimated delivered energy savings as shown in Table 5.16. Further 

adjustments were made to the case to roughly match the level of delivered energy savings for 

each fuel type. 

The reduction in commercial energy demand effectively leads to lower investment in future 

capacity of demand devices servicing the Federal buildings, resulting in lower energy use in 

these devices. The reduction in electricity demand also leads to a slight drop in the electric 

generation by gas-fired power plants. FEMP also directly reduces fossil fuels used in commercial 

(government) buildings. The long-term systemwide benefits are provided in Table 5.17. 

Table 5.17. FY06 Benefits Estimates for FEMP (MARKAL-GPRA06) 

 Annual Benefits 2020 2030 2040 2050 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.06 0.08 0.07 0.06

Economic 

 Energy-System Cost Savings (billion 2002 dollars/yr) 2.4 3.5 3.5 3.6

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 1 1 0 0

Security 

 Oil Savings (mbpd) 0.00 0.00 0.00 0.01

 Natural Gas Savings (quadrillion Btu/yr) 0.06 0.06 0.05 0.05
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-12 

Geothermal Technologies Program 

The main goals of the Geothermal Technologies Program are to reduce the cost of conventional 

geothermal technologies and to develop Enhanced Geothermal Systems (EGS) as a new source 

of electricity generation. 

The Geothermal Technologies Program Case formulated in MARKAL-GPRA06 reflects the 

program goals for both conventional systems and EGS. For conventional geothermal systems, 

analysts changed the capital and operating and maintenance (O&M) costs to reflect program 

goals. However, EGS represents a new geothermal resource not represented in the MARKALGPRA06 model’s reference case scenario. The program identified three types of potential 

geothermal reservoirs: 

Type I. Improvement prospects in existing commercial reservoirs 

Type II. Identified reservoirs with suboptimal characteristics 

Type III. Prospective sites that are not currently identified as hydrothermal prospects 

Due to program activities, the capital and O&M costs of EGS systems are projected to decline. 

Table 5.18 shows the estimated capital and O&M costs for the three types of EGS systems for 

2000 and 2050. 

The EGS sites projected under the program are grouped into a set of supply steps, and the 

discount rate of these technologies is set at 8 percent (instead of 10 percent for the power 

generation-sector average) to reflect the accelerated depreciation schedule permitted by the 

Internal Revenue Service for renewable-generation technologies. The EGS systems are modeled 

as centralized base-load generation. 

Table 5.18. EGS Generation Assumptions 

 2000 Cost 2050 Cost 

EGS 

Type 

Projected 

Resource 

Capital 

Cost O&M 

Capital 

Cost O&M 

 MWe 2002$/kW 2002$/kW/yr 2002$/kW 2002$/kW/yr 

I 3,400 $2,486 $155 $949 $51 

II 25,000 $2,859 $179 $1,091 $59 

III 60,000 $3,232 $202 $1,233 $67 

Geothermal plants compete directly with fossil fuel-based plants for both electricity generation 

and meeting peak power requirements. In MARKAL-GPRA06, EGS becomes more competitive, 

as its higher capital cost is offset by increased fossil fuel costs for gas and coal-fired generators, 

which increase during the projection period as overall fuel demand increases. 

The improvements in capital and O&M costs lead to increased market penetration for 

conventional geothermal-generation capacity. Furthermore, EGS capacity, which was not 

available in the Baseline Case, shows significant market penetration between 2020 and 2050. 

Table 5.19 shows both Baseline Case and Geothermal Technologies Program Case capacity, 

while Table 5.20 shows geothermal power generation for both cases. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-13 

Table 5.19. Total Geothermal Capacity by Type 

(gigawatts)

 2000 2010 2020 2030 2040 2050 

Baseline Case

Conventional 2.9 3.9 7.7 8.2 11.4 10.9 

EGS 0.0 0.0 0.0 0.0 0.0 0.0 

Total 2.9 3.9 7.7 8.2 11.4 10.9 

Geothermal Program Case

Conventional 2.9 4.3 9.4 11.2 13.5 12.9 

EGS 0.0 0.0 1.0 8.0 23.4 36.1 

Total 2.9 4.3 10.4 19.1 36.9 49.0 

Increase

Conventional 0.0 0.4 1.7 3.0 2.1 1.9 

EGS 0.0 0.0 1.0 8.0 23.4 36.1 

Total 0.0 0.4 2.7 11.0 25.5 38.0 

Table 5.20. Total Geothermal Power Generation by Type 

(billion kilowatt hours/year) 

 2000 2010 2020 2030 2040 2050 

Baseline Case

Conventional 22 30 59 62 87 83 

EGS 0 0 0 0 0 0 

Total 22 30 59 62 87 83 

Conventional 22 33 71 85 103 98 

EGS 0 0 8 68 199 307 

Total 22 33 80 153 302 405 

Increase

Conventional 0 3 13 23 16 15 

EGS 0 0 8 68 199 307 

Total 0 3 21 90 215 322 

The projected market penetration of geothermal generation technologies in MARKALGPRA06’s Geothermal Technologies Program Case directly displaces both natural gas and coalfired generation beginning in 2010. The long-term benefits are shown in Table 5.21. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-14 

Table 5.21. FY06 Benefits Estimates for Geothermal Technologies Program (MARKAL-GPRA06) 

Annual Benefits 2020 2030 2040 2050 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.23 0.82 1.89 2.36

Economic 

 Energy-System Cost Savings (billion 2002 dollars/yr) 0.3 1.4 3.9 5.2

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 5 16 38 59

Security 

 Natural Gas Savings (quadrillion Btu/yr) 0.13 0.43 0.98 0.15

 Capacity (gigawatts) 3 11 25 38

Hydrogen, Fuel Cells, and Infrastructure Technologies Program 

The Hydrogen, Fuel Cells, and Infrastructure Technologies (HFCIT) Program conducts research 

and development activities in hydrogen production, storage, and delivery; and transportation and 

stationary fuel cells. On the demand side, the program’s activities focus on the introduction of 

fuel cells for both stationary and mobile applications. On the supply side, the program goal is to 

lower the production cost of hydrogen to a competitive level against petroleum products. 

The representation of the Hydrogen, Fuel Cells, and Infrastructure Technologies Program in 

MARKAL-GPRA06 requires representation of fuel cell vehicles and transportation markets, 

hydrogen production and distribution infrastructure, and stationary fuel cell applications. 

Fuel cell vehicles and transportation markets: Fuel cell vehicles are projected to compete with 

traditional petroleum and hybrid-electric vehicles for market share in the light-duty vehicle and 

commercial light truck markets. In MARKAL-GPRA06, analysts measure energy service 

demands for road transportation in vehicle miles traveled (VMT). Projected VMTs are taken 

directly from the Annual Energy Outlook 2004 and extended past 2025, based on historical 

relationships between passenger and commercial VMTs and population and economic growth. 

Projected VMTs for cars, light trucks, and commercial light trucks are shown in Table 5.22. 

Table 5.22. LDV and Commercial Light Truck Vehicle Miles Traveled 

(billion VMTs/year) 

 2000 2010 2020 2030 2040 2050 

Total Light-Duty Vehicles 2,355 3,041 3,768 4,507 5,086 5,277 

Cars 1,498 1,686 2,007 2,415 2,600 2,568 

Light Trucks 857 1,355 1,761 2,092 2,485 2,709 

Commercial Light Trucks 69 79 101 129 157 167 

For each time period, these demands are met by a mix of vehicle types selected by the model on 

the basis of total life-cycle costs. The vehicle type is characterized for each model year it is 

available for purchase. The Baseline Case cost and efficiencies of these vehicles were derived 

from the AEO2004 assumptions, with cost and efficiency improvements extrapolated after 2025. 

For the Hydrogen Program Case, capital costs, operation and maintenance costs, and fuel 

efficiency goals were provided by the HFCIT Program for hydrogen fuel cell vehicles from 2015 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-15 

to 2050. As with the Vehicle Technologies Program, these were provided as ratios to 

conventional gasoline-powered vehicles of the same vintage. For example, a 2020 hydrogen-fuel 

cell passenger car with a cost ratio of 1.07 and an efficiency ratio of 2.54 would cost 7 percent 

more than the average 2020 traditional gasoline passenger car and have 154 percent higher fuel 

economy. The cost and efficiency assumptions for passenger cars, sport utility vehicles (SUVs), 

and commercial light trucks are shown in Table 5.23. 

Table 5.23. Cost and Efficiency Assumptions for Fuel Cell Vehicles 

 2010 2020 2030 2040 2050 

 

Passenger Cars 

Cost Ratio to Conventional n.a. 1.07 1.05 1.045 1.04 

Efficiency Ratio to Conventional n.a. 2.54 3.03 3.03 3.03 

 

SUVs & Commercial Light Trucks

Cost Ratio to Conventional n.a. 1.07 1.05 1.045 1.04 

Efficiency Ratio to Conventional n.a. 2.49 2.96 2.96 2.96 

Hydrogen production and distribution infrastructure: The HFCIT Program conducts 

research on developing cost-effective hydrogen production technologies from distributed natural 

gas reformers, as well as a variety of renewable sources, including biomass. For the Hydrogen 

Case, analysts modeled five hydrogen production technologies: distributed natural gas reformers, 

central natural gas reformers, central coal gasification, central biomass gasification, and central 

electrolytic production. Other renewable hydrogen-production technologies were not modeled, 

due to a greater degree of uncertainty in their costs. Nuclear hydrogen production technologies 

were also not represented in the MARKAL-GPRA06 model. We expect that more hydrogen 

production technologies will be modeled in future GPRA analysis, as the data becomes available. 

Carbon sequestration pathways were available for central coal and natural gas hydrogen 

production. However, because no carbon policies were assumed, producers would not have an 

economic incentive to incur the incremental cost to sequester carbon generated from hydrogen 

production activities and, thus, no carbon was sequestered in this Program Case. 

HFCIT Program goals were used to estimate capital and O&M costs and production efficiencies 

for distributed natural gas reformers and central biomass gasifiers and electrolytic production 

technologies. Assumptions for central coal and natural gas production technologies were adapted 

from Hydrogen Production Facilities Plant Performance and Cost Comparisons, Final Report.

3

 

The infrastructure requirements and operating costs for the widespread distribution of hydrogen 

vary widely by distance and method. As a simplifying assumption, a flat cost of $5.28 per 

MMBtu—or $0.65 per gallon of gasoline equivalent (gge)—was assumed for hydrogen 

distribution costs based on published data from NREL.4

 (Please note that one kilogram of 

hydrogen is roughly equivalent in energy content to one gallon of gasoline, and is often referred 

to as a gallon of gasoline equivalent (gge).) As with production technologies, we will be 

enhancing the representation of the distribution and fueling costs for hydrogen in future analysis 

 

3

 Hydrogen Production Facilities Plant Performance and Cost Comparisons, Final Report, March 2002, prepared for NETL by 

Parsons Infrastructure and Technology Group. 4 Amos W.A., Lane J.M., Mann M.K., and Spath P.L. Update of hydrogen from biomass – determination of the delivered cost of 

hydrogen, NREL, 2000. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-16 

as data becomes available. Table 5.24 shows projected hydrogen costs by cost component for the 

Hydrogen Program Case. 

(Please note that due to market factors affecting feedstock costs, the projected costs may not 

match HFCIT Program goals.). 

Table 5.24. Hydrogen Production Costs by Technology and Component 

(2002 $/gge) 

Central Coal 

Unit Costs 2015 2020 2025 2030 2035 2040 2045 2050 

Capital Costs $0.49 $0.49 $0.49 $0.49 $0.49 

O&M $0.27 $0.27 $0.27 $0.27 $0.27 

Feedstock Costs $0.24 $0.24 $0.24 $0.26 $0.27 

Plant Gate $1.00 $1.01 $1.01 $1.03 $1.03 

Distribution, Storage & Tax $1.07 $1.07 $1.07 $1.07 $1.07 

Total $2.06 $2.06 $2.07 $2.08 $2.09 

Distributed Natural Gas Reformer 

Unit Costs 2015 2020 2025 2030 2035 2040 2045 2050 

Capital Costs $0.43 $0.43 $0.43 $0.43 $0.51 

O&M $0.57 $0.57 $0.53 $0.55 $0.54 

Feedstock Costs $1.02 $0.95 $0.90 $0.99 $1.11 

Plant Gate $2.01 $1.95 $1.87 $1.96 $2.14 

Tax $0.40 $0.40 $0.40 $0.40 $0.40 

Total $2.42 $2.35 $2.27 $2.37 $2.55 

Central Natural Gas Reformer** 

Unit Costs 2015 2020 2025 2030 2035 2040 2045 2050 

Capital Costs $0.16 $0.16 $0.16 $0.16 $0.16 

O&M $0.09 $0.08 $0.08 $0.08 $0.08 

Feedstock Costs $0.94 $0.96 $1.08 $1.09 $1.18 

Plant Gate $1.20 $1.21 $1.32 $1.33 $1.42 

Distribution, Storage & Tax $1.07 $1.07 $1.07 $1.07 $1.07 

Total $2.25 $2.28 $2.39 $2.39 $2.49 

Central Biomass 

Unit Costs 2015 2020 2025 2030 2035 2040 2045 2050 

Capital Costs $1.02 $0.99 $0.98 $0.98 $0.96 $0.96 

O&M $0.31 $0.31 $0.31 $0.31 $0.31 $0.31 

Feedstock Costs $0.33 $0.33 $0.33 $0.33 $0.33 $0.33 

Plant Gate $1.66 $1.63 $1.61 $1.61 $1.60 $1.60 

Distribution & Storage* $0.66 $0.66 $0.66 $0.66 $0.66 $0.66 

Total $2.33 $2.29 $2.29 $2.29 $2.27 $2.27 

Central Electrolytic Production** 

Unit Costs 2015 2020 2025 2030 2035 2040 2045 2050 

Capital Costs $0.12 $0.12 $0.12 $0.12 $0.12 

O&M $0.29 $0.29 $0.29 $0.29 $0.29 

Feedstock Costs $1.87 $2.18 $2.07 $2.06 $2.02 

Plant Gate $2.29 $2.60 $2.49 $2.47 $2.43 

Distribution, Storage & Tax $0.66 $0.66 $0.66 $0.66 $0.66 

Total $2.96 $3.26 $3.15 $3.14 $3.10 

* Note: Hydrogen produced from biomass was assumed to receive preferential tax treatment. 

** Central electrolytic and natural gas reformer production technologies did not penetrate in the Hydrogen Case. The 

above costs are based on a separate model run where this technology was required to produce. 

Stationary fuel cell applications: In addition to use in vehicles, fuel cells also may be used for 

distributed electric generation. The HFCIT Program provided cost and performance goals for a 

5kW CHP residential fuel cell system and a 200kW CHP commercial fuel cell system. The cost 

and performance parameters are shown in Tables 5.25 and 5.26
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-17 

Table 5.25. 5 kW Residential Combined Heat and Power System Assumptions 

Year 

CHP 

System 

Efficiency 

Electrical 

Efficiency 

Thermal 

Recovery 

Efficiency 

Equip. Cost 

(2002 $/kW) 

Maint. Cost 

(2002$/kWyr) 

2005 68% 29% 54% $2,274 $261 

2010 72% 32% 59% $1,780 $182 

2015 72% 32% 59% $1,780 $166 

2025 72% 32% 59% $1,780 $166 

Table 5.26. 200 kW Commercial Combined Heat and Power System Assumptions 

Year 

CHP 

System 

Efficiency 

Electrical 

Efficiency 

Thermal 

Recovery 

Efficiency 

Equip. Cost 

(2002 $/kW) 

Maint. Cost 

(2002$/kWyr) 

2005 68% 29% 54% $1,908 $229 

2010 72% 36% 56% $1,414 $127 

2015 72% 36% 56% $1,414 $127 

2025 72% 36% 56% $1,414 $127 

Unlike other program cases, analysts ran the Hydrogen Program Case with both HFCIT and 

Vehicle Technologies Program assumptions. The rationale for this change is that the hydrogen 

fuel cell vehicle assumptions provided by the HFCIT Program assume that the Vehicle 

Technologies Program’s hybrid systems and materials technologies R&D activities are 

successful. The market penetration of hydrogen fuel vehicles is somewhat limited by the 

increased competition from more-advanced hybrid vehicles. The market shares for LDVs are 

shown in Table 5.27. 

Table 5.27. Light-Duty Vehicle Market Shares for the Hydrogen Case (% of VMT) 

 2000 2010 2020 2030 2040 2050 

Gasoline 99% 95% 78% 47% 14% 2% 

Advanced Gasoline 0% 2% 7% 15% 10% 0% 

Gasoline Hybrid 0% 4% 13% 27% 55% 79% 

Diesel Hybrid 0% 0% 1% 5% 7% 1% 

Hydrogen 0% 0% 1% 6% 14% 18% 

Diesel & Other 1% 0% 0% 0% 0% 0% 

Because the Hydrogen Program Case was run with both Hydrogen and Vehicle Technologies 

Programs’ assumptions, analysts could not perform the calculation of benefits through the direct 

comparison of the Hydrogen Program Case and the Baseline Case. Instead, analysts based the 

calculation of oil and carbon benefits for the Hydrogen Program on the relative fuel/carbon 

intensities per vehicle miles traveled (VMTs) of hydrogen fuel cell vehicles. 

To determine petroleum savings, analysts calculated the average consumption of petroleum 

products per billion vehicle miles traveled (oil intensity) for light-duty vehicles and commercial 

light trucks in the Baseline Case. Analysts then multiplied the Baseline Case oil intensity by the 

VMTs traveled by hydrogen fuel cell vehicles in the Hydrogen Program Case to estimate how 

much oil would be consumed if these VMTs were traveled by traditional gasoline vehicles. 

These calculations are shown in Table 5.28. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-18 

Table 5.28. Calculation of Petroleum Savings 

 2010 2020 2030 2040 2050 

Baseline Case Oil Intensities (TBtu/billion VMT)

Light-Duty Vehicles 6.48 5.94 5.70 5.47 5.30 

Light Trucks 10.26 9.35 8.89 8.77 8.14 

 

Hydrogen Vehicle (VMTs/yr)

Light-Duty Vehicles 0 40 271 725 1,009 

Light Trucks 0 0 4 21 55 

Petroleum Savings (TBtu/yr)

Light-Duty Vehicles 0 238 1,543 3,967 5,344 

Light Trucks 0 0 36 182 446 

Total 0 238 1,579 4,150 5,790 

Total (million barrels per day) 0.00 0.11 0.75 1.96 2.74 

Carbon emission reductions accounted for both the reduced carbon emissions from burning 

gasoline, as well as increases in carbon emissions from the production of hydrogen, assuming no 

sequestration. If the hydrogen is produced at central facilities and the resulting carbon is 

sequestered, then the carbon savings will be accordingly larger in the projections below. These 

calculations are shown in Table 5.29. 

Table 5.29. Calculation of Carbon Emission Reduction 

 2010 2020 2030 2040 2050 

Decreased CO2 Emissions from Decline in Gasoline Consumption

Decrease in Gasoline Consumption (TBtu/yr) 0 5 30 77 103 

Carbon Intensity of Gasoline (MT of Carbon 

per MMBtu) 0.0 0.0 0.7 3.5 8.6 

Decline in Carbon (MMT/yr) 0.0 4.6 30.5 80.2 112.0 

 

CO2 Emissions from Hydrogen Production 

Production of Hydrogen (TBtu/yr) 0.0 1 9 36 49 

Carbon Intensity of Hydrogen (MT of Carbon 

per MMBtu) 0.0 0.0 0.2 1.6 3.9 

Increase in Carbon (MMT/yr) 0.0 1.4 9.6 37.6 52.4 

 

Net decrease in Carbon Emissions (MMT/yr) 0.0 3.2 20.9 42.7 59.6 

The carbon intensity of hydrogen varies significantly, because of the varying carbon content and 

market shares of the feedstocks used to produce hydrogen. Hydrogen production by feedstock is 

shown in Table 5.30. It should be noted that this analysis was conducted with a single-region 

MARKAL-GPRA06 model, and that the price of feedstocks and distribution costs are based on 

national averages. There is significant variation in regional fuel costs in the United States, and it 

is likely that during the development of a hydrogen infrastructure, these differences would lead 

to a greater diversity of hydrogen-production technologies than shown below. Furthermore, this 

analysis was conducted with only a subset of the full range of hydrogen-production technologies. 

Thus, this analysis may be biased toward hydrogen production from coal. Future efforts are 

planned to correct for these modeling limitations. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-19 

Table 5.30. Hydrogen Production by Feedstock (% of total hydrogen production)

 2015 2020 2025 2030 2035 2040 2045 2050 

Central Coal 0% 0% 0% 32% 53% 77% 79% 81% 

Remote Natural Gas 0% 100% 85% 46% 28% 10% 0% 0% 

Central Natural Gas 0% 0% 0% 0% 0% 0% 0% 0% 

Central Biomass 0% 0% 15% 22% 19% 13% 21% 19% 

Overall, the Hydrogen, Fuel Cells, and Infrastructure Technologies Program reduces gasoline 

consumption in the transportation sector through the deployment of hydrogen fuel cell LDVs and 

commercial light trucks (Table 5.31). Furthermore, the reduction in petroleum consumption 

leads to reduced carbon emissions. However, as noted above, these reductions in carbon 

emissions are partly offset due to carbon emissions from the production of hydrogen. The 

reductions in total energy-system costs arise from both the reduction in petroleum imports, as 

well as associated refining and distribution capacity. However, this is offset somewhat by the 

cost of establishing the hydrogen-production and -distribution infrastructure. 

Table 5.31. FY06 Benefits Estimates for Hydrogen, Fuel Cells, and Infrastructure Technologies 

Program (MARKAL-GPRA06) 

 Annual Benefits 2020 2030 2040 2050 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.09 1.04 3.03 4.32

Economic 

 Energy-System Cost Savings (billion 2002 dollars/yr) -0.8 1.5 11.2 26.4

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 3 21 43 60

Security 

 Oil Savings (mbpd) 0.11 0.75 1.96 2.73

 Natural Gas Savings (quadrillion Btu/yr) -0.04 -0.27 0.29 0.71

Industrial Technologies Program 

The Industrial Technologies Program (ITP) covers a wide range of technologies, industries, and 

end-use applications. The overall goal of this program is to increase energy efficiency through 

R&D, as well as the deployment of new and improved technologies. The heterogeneity of the 

program’s R&D activities makes it difficult to represent program activities explicitly in the 

MARKAL-GPRA06 framework. Instead, the projected ITP goals by various industries were 

aggregated into MARKAL-GPRA06 industrial energy-use demand categories as a set of 

conservation supply curves. Because this approach does not reflect economic competition nor 

interaction among program technologies, analysts reduced the off-line energy savings by an 

“integration factor” before these supply curves were constructed and input into the model (Table 

5.32). The amount of the integration factor is based on how much program overlap or 

“integration” was captured by the off-line tools. The reduction is based on the expert judgment of 

the benefits analysis team. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-20 

Table 5.32. Industrial Program Integration Factors 

Subprogram 

Integration 

Factor 

Industries of the Future 0% 

Crosscutting R&D 10% 

Industrial Assessment Centers 10% 

Best Practices 0% 

The potential savings represented in these conservation measures yield an overall reduction in 

delivered energy consumption, as shown in Table 5.33. 

The reduction in electricity demand also leads to the reduction in coal and gas-based generation. 

Both conservation and reduction in electricity demand result in less investment in end-use 

devices and electric-generation capacity on the supply side (Table 5.34). 

Table 5.33. Delivered Energy Savings in the Industrial Sector (trillion Btu/year)

 2010 2015 2020 2025 2030 2040 2050 

Petroleum 44 103 140 110 112 90 -48

Natural Gas 135 422 990 1,166 1,062 399 173

Coal 4 29 54 79 105 105 62

Electricity 39 132 243 275 314 172 53

Heat 1 2 11 -19 0 -53 0

Renewable 0 0 0 0 0 0 0

Subtotal 222 687 1,438 1,612 1,593 713 239

 

Petrochemicals 5 31 86 84 69 38 12

 

Total 227 718 1,524 1,696 1,662 751 251

Table 5.34. FY06 Benefits Estimates for Industrial Technologies Program (MARKAL-GPRA06) 

 Annual Benefits 2020 2030 2040 2050 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 2.07 2.26 1.26 0.45

Economic 

 Energy-System Cost Savings (billion 2002 dollars/yr) 13.6 15.7 9.8 3.2

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 34 42 23 8

Security 

 Oil Savings (mbpd) 0.11 0.09 0.06 -0.01

 Natural Gas Savings (quadrillion Btu/yr) 1.48 1.26 0.77 0.29

 Displaced Capacity (gigawatts) 11 20 9 2
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-21 

Solar Energy Technologies Program 

The Solar Energy Technologies Program covers solar water-heating technologies, photovoltaic 

(PV)-based electricity generation and central solar thermal generation with energy storage. The 

program goal is to lower the cost and improve performance of these technologies. 

The Solar Energy Technologies Program Case includes characterization of several solar water 

heaters with backup systems and PV systems for electricity generation. Analysts base the 

characterization of solar water heaters for households on the capital cost reductions and reduced 

reliance on backup fuels as projected in the program objectives. The use of backup fuels is 

modeled as the percentage of total use. Thus, a 2020 solar water heater would rely on its backup 

fuel for 45 percent of the time. Analysts assume the efficiency of the backup system to be the 

efficiency of the least-expensive traditional water heater of the same vintage. Because the 

MARKAL-GPRA06 model assumes that homes will utilize the same fuel for water heat that is 

used for space heat, it was assumed that solar water heaters could use natural gas, electricity, and 

heating oil as the backup fuel. 

Analysts modeled both centralized and decentralized PV power and central solar thermal 

systems. The capital cost and O&M costs for both units are reduced to meet program goals. In 

addition, analysts set the discount rates of these technologies at 8 percent (instead of the 

industrial average of 10 percent) to reflect the accelerated depreciation schedule available for 

renewable-generation technologies. The total installed capacity of the decentralized units reflects 

the Million Solar Roofs installation goals for reducing end-use electricity demand from the 

central grid. Analysts model the centralized PV-generating systems to compete with 

conventional fossil fuel-based power plants. The cost and performance characteristics of the 

Solar Energy Technologies Program Case for water heaters, PV systems, and central thermal 

stations are shown in Table 5.35. 

Solar photovoltaic capacity increases dramatically over the Baseline Case (Table 5.36). By 

2050, the Solar Energy Technologies Program Case shows an additional 36.5 GW of 

photovoltaic capacity over the Baseline Case. Additionally, the Solar Energy Technologies 

Program Case shows an additional 25.4GW of central solar thermal generation. By 2050, the 

improved PV and thermal technologies generate an incremental 268 billion kWh of generation 

over the Baseline Case (Table 5.37). 

Table 5.35. Solar Energy Technologies Program Assumptions 

Photovoltaics

Central Generation Residential Buildings Commercial Buildings 

Year 

Installed 

Price 

(2002$/kW) 

O&M 

(2002$/kW) 

Installed 

Price 

(2002$/kW) 

O&M 

(2002$/kW) 

Installed 

Price 

(2002$/kW) 

O&M 

(2002$/kW) 

2005 $4,845 $51 $9,828 $170 $5,707 $170 

2010 $3,178 $34 $6,224 $40 $4,362 $40 

2015 $2,615 $22 $4,844 $28 $3,667 $28 

2020 $2,054 $10 $3,464 $16 $2,912 $16 

2025 $1,741 $9 $2,783 $10 $2,697 $10 

2030 $1,596 $9 $2,567 $9 $2,481 $9 

2035 $1,450 $8 $2,352 $9 $2,266 $9 

2040 $1,374 $7 $2,235 $8 $2,214 $8 

2050 $1,349 $7 $2,131 $7 $2,110 $7 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-22 

 

Central Solar Thermal 

Year 

Installed 

Price 

(2002$/kW) 

O&M 

(2002$/kW) 

Annual 

Capacity 

Factor 

 

2005 $5,031 $69 53% 

2010 $3,565 $45 65% 

2020 $2,501 $26 72% 

2025 $2,234 $22 72% 

2030 $2,024 $20 72% 

2035 $1,909 $19 72% 

2040 $1,855 $19 72% 

2050 $1,825 $19 72% 

 

Solar Water Heaters

Vintage 

Installed 

Cost 

Backup Fuel 

Use 

 

2000 $2,844 50% 

2010 $1,524 45% 

2020 $1,320 40% 

2030 $1,180 36% 

2040 $2,844 33% 

Table 5.36. Solar-Generation Capacity by Case and Type

(gigawatts) 

 2000 2010 2020 2030 2040 2050 

 

Baseline Case 

Central PV 0.0 0.2 0.3 0.4 0.4 0.4 

Distributed PV 0.1 1.6 4.8 14.6 25.2 26.3 

Central Thermal 0.3 0.5 0.9 1.0 0.8 0.5 

Total 0.4 2.2 6.0 16.0 26.4 27.3 

 

Solar Program Case 

Central PV 0.0 0.1 0.1 0.1 1.0 1.0 

Distributed PV 0.1 1.7 6.7 17.2 36.1 62.3 

Central Thermal 0.3 0.5 2.5 9.4 19.7 25.9 

Total 0.4 2.2 9.3 26.7 56.8 89.1 

 

Increase 

Central PV 0.0 -0.1 -0.3 -0.4 0.6 0.6 

Distributed PV 0.0 0.1 1.9 2.6 11.0 35.9 

Central Thermal 0.0 0.0 1.6 8.4 18.9 25.4 

Total 0.0 0.0 3.2 10.6 30.4 61.9 

Table 5.37. Solar-Generation by Case and Type

(Billion kWh) 

 2000 2010 2020 2030 2040 2050 

 

Baseline Case 

Central PV 0.0 0.4 0.8 0.5 0.9 0.9 

Distributed PV 0.2 3.9 12.1 36.4 62.8 65.8 

Central Thermal 1.1 1.7 3.0 3.5 2.7 1.8 

Total 1.3 6.0 15.9 40.5 66.5 68.5 

 

Solar Program Case 

Central PV 0.0 0.2 0.2 0.1 2.5 2.5 

Distributed PV 0.2 4.1 16.8 42.9 90.2 155.5 

Central Thermal 1.1 1.7 14.3 62.0 133.9 178.4 

Total 1.3 5.9 31.2 105.0 226.6 336.5 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-23 

 2000 2010 2020 2030 2040 2050 

 

Increase 

Central PV 0.0 -0.2 -0.7 -0.5 1.6 1.6 

Distributed PV 0.0 0.2 4.7 6.5 27.4 89.7 

Central Thermal 0.0 0.0 11.3 58.5 131.2 176.7 

Total 0.0 0.0 15.3 64.5 160.2 268.0 

Central and distributed PV and central thermal generation technologies in the Solar Energy 

Technologies Program Case directly displace central gas and coal-fired generation capacity. 

However, because of the PV technologies’ lower availability factor and reduced contribution to 

peak power supply, the total gas and coal capacity replaced is less than the installed solar 

capacity. Benefits estimates for the Solar Energy Technologies Program are shown in 

Table 5.38. 

Table 5.38. FY06 Benefits Estimates for Solar Energy Technologies Program (MARKAL-GPRA06) 

 Annual Benefits 2020 2030 2040 2050 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.18 0.56 1.23 1.71

Economic 

 Energy-System Cost Savings (billion 2002 dollars/yr) 0.4 1.7 2.5 2.3

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 4 11 23 36

Security 

 Oil Savings (mbpd) ns ns ns ns

 Natural Gas Savings (quadrillion Btu/yr) 0.10 0.29 0.71 0.62

 Capacity (gigawatts) 3 11 30 62

Vehicle Technologies Program 

The Vehicle Technologies Program5 consists of Hybrid Systems R&D, Advanced Combustion 

R&D, Heavy Systems R&D, and Materials Technologies R&D. The general goal of these R&D 

activities is to improve the efficiency and lower the cost of road vehicles. 

Energy-service demands for road transportation are measured in vehicle miles traveled (VMT). 

Projected VMTs are taken directly from the Annual Energy Outlook 2004 (AEO 2004) and 

extended past 2025, based on historical relationships between passenger and commercial VMTs, 

and population and economic growth. Projected VMTs for cars, light trucks6

, commercial light 

trucks,7 and heavy trucks are shown in Table 5.39. 

 

5 The Vehicle Technologies Program is run by the Office of FreedomCAR and Vehicle Technologies. 6

 Light trucks include trucks with a gross vehicle weight under 8,500 pounds and may include pickups, vans, or sport utility 

vehicles (SUVs). 

7 Commercial light trucks are light trucks with a gross vehicle weight between 8,500 and 10,000 pounds and may include 

pickups, vans, or SUVs. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-24 

Table 5.39. Projected Vehicle Miles Traveled by Vehicle Class (billion VMTs/year) 

Vehicle Class 2000 2010 2020 2030 2040 2050 

Light-Duty Vehicles 2,355 3,041 3,768 4,507 5,086 5,455 

 Cars 1,498 1,686 2,007 2,415 2,600 2,502 

 Light Trucks 857 1,355 1,761 2,092 2,485 2,953 

 

Commercial Light Trucks 69 79 101 129 157 176 

Heavy Trucks 207 242 313 392 461 506 

For each time period, these demands are met by a mix of vehicle types, selected by the model on 

the basis of total life-cycle costs. The vehicle type is characterized for each model year that it is 

available for purchase. The Baseline Case cost and efficiencies of these vehicles were derived 

from the AEO2004 assumptions, with cost and efficiency improvements extrapolated for periods 

after 2025. 

For the Vehicle Technologies Program Case, the costs and efficiencies for hybrid (HEV) and 

advanced diesel vehicles were changed for passenger cars, sport utility vehicles (SUVs), 

commercial light trucks, and commercial heavy trucks. These changes reflect the results of the 

fuel combustion, hybrid systems, and materials R&D activities. Alternate cost and efficiency 

assumptions were provided for gasoline and diesel hybrid vehicles, as well as advanced diesel 

engines for use in passenger cars, SUVs, and commercial light trucks for the period 2010 to 

2050. Cost and efficiency assumptions for advanced diesel and diesel hybrid Class 3-6 trucks 

and advanced diesel Class 7-8 trucks also were provided for the period 2010 to 2050. The cost 

and efficiency assumptions were provided from the off-line analysis as ratios to conventional 

gasoline or diesel internal combustion engine-powered vehicles of that vintage. 

For example, a 2020 gasoline-hybrid passenger car with a cost ratio of 1.05 and an efficiency 

ratio of 1.96 would cost 5 percent more than the average 2020 traditional gasoline passenger car 

and have 96 percent better fuel economy. MARKAL does not currently distinguish the different 

heavy truck classes and usage profile (i.e. short haul vs. long haul). For the Vehicle 

Technologies Program Case, the analysts created a single advanced truck technology based on 

the cost and efficiency assumptions and market shares for the different truck classes and usage 

profile that were calculated in the off-line analysis. The cost and efficiency assumptions for 

passenger cars, SUVs, and commercial light trucks are shown in Table 5.40, while Table 5.41

shows these assumptions for heavy trucks. 

Table 5.40. Cost and Efficiency Assumptions for Light-Duty Vehicles 

 2010 2020 2030 2040 2050 

Passenger Cars 

Cost Ratio to Conventional in Same Year 

Advanced Gasoline 1.01 1.02 1.03 1.02 1.01 

Gasoline HEV 1.07 1.05 1.03 1.02 1.02 

Advanced Diesel 1.05 1.03 1.03 1.02 1.02 

Diesel HEV 1.10 1.08 1.06 1.04 1.03 

 

Efficiency Ratio to Conventional in Same Year

Advanced Gasoline 1.01 1.09 1.25 1.25 1.25 

Gasoline HEV 1.50 1.96 2.22 2.22 2.22 

Advanced. Diesel 1.40 1.60 1.80 1.80 1.80 

Diesel HEV 1.60 2.05 2.36 2.36 2.36 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-25 

 2010 2020 2030 2040 2050 

Light Trucks and SUVs

Cost Ratio to Conventional in Same Year

Advanced Gasoline 1.01 1.03 1.03 1.02 1.01 

Gasoline HEV 1.07 1.05 1.03 1.02 1.02 

Advanced Diesel 1.04 1.03 1.02 1.02 1.02 

Diesel HEV 1.10 1.08 1.06 1.04 1.03 

 

Efficiency Ratio to Conventional in Same Year

Advanced Gasoline 1.05 1.23 1.37 1.37 1.37 

Gasoline HEV 1.40 1.85 2.00 2.00 2.00 

Advanced Diesel 1.35 1.50 1.70 1.80 1.80 

Diesel HEV 1.50 1.90 2.13 2.13 2.13 

Table 5.41. Cost and Efficiency Assumptions for Heavy Trucks* 

 2010 2020 2030 2040 

Class 7-8 – Diesel 

Efficiency Ratio – Short Haul 1.30 1.45 1.45 1.45 

Efficiency Ratio – 

Intermediate Haul 1.50 1.61 1.61 1.61 

Efficiency Ratio – Long Haul 1.50 1.76 1.76 1.76 

Cost Ratio 1.23 1.10 1.05 1.05 

 

Class 3-6 – Diesel 

Efficiency Ratio 1.02 1.37 1.37 1.37 

Cost Ratio 1.12 1.04 1.04 1.04 

 

Class 3-6 – Diesel Hybrid 

Efficiency Ratio 1.70 1.70 1.70 1.70 

Cost Ratio 1.42 1.12 1.06 1.06 

* Note: Ratios are compared to conventional vehicles in the same year. 

The oil savings generated from the Vehicle Technologies Program are attributable to the market 

penetration of more efficient LDVs and heavy trucks. Table 5.42 shows the market shares for 

traditional gasoline and alternative light-duty vehicles for the Vehicle Technologies Program 

Case, while Table 5.43 shows transportation-sector petroleum consumption for the Baseline and 

Vehicles Technologies Program Case. 

The reduction in transportation-sector petroleum consumption (Table 5.44) is due to both 

increased market share and fuel efficiency of alternative vehicles, particularly hybrid-electric 

vehicles. The reductions in total energy-system costs arise from both the reduction in petroleum 

imports, as well as associated refining and distribution capacity. 

Table 5.42. Light-Duty Vehicle Market Shares for the Vehicles Technologies Program Case

(% of total fleet) 

2000 2010 2020 2030 2040 2050 

Gasoline 99% 95% 79% 53% 25% 9% 

Advanced Gasoline 0% 2% 7% 15% 12% 0% 

Gasoline Hybrid 0% 4% 13% 27% 55% 89% 

Diesel Hybrid 0% 0% 1% 5% 7% 2% 

Diesel & Other 1% 0% 0% 0% 0% 0% 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-26 

Table 5.43. Petroleum Consumption by Vehicle Class and Case

(trillion Btu/year) 

 2000 2010 2020 2030 2040 2050 

Baseline Case 

Light-Duty Vehicles 15,725 19,697 22,376 26,234 28,768 29,871 

Commercial Light Trucks 788 811 944 1,129 1,333 1,383 

Heavy Trucks 4,236 5,102 6,458 7,677 8,805 9,456 

 

Vehicle Technologies Program Case 

Light-Duty Vehicles 15,725 19,564 21,169 21,431 18,583 15,527 

Commercial Light Trucks 788 794 763 682 728 776 

Heavy Trucks 4,236 5,091 5,949 5,879 6,377 6,680 

 

Savings 

Light-Duty Vehicles 0 133 1,207 4,803 10,185 14,344 

Commercial Light Trucks 0 16 181 447 605 608 

Heavy Trucks 0 11 509 1,798 2,428 2,776 

Total Transportation Sector 0 160 1,897 7,047 13,218 17,728 

Table 5.44. FY06 Benefits Estimates for Vehicle Technologies Program (MARKAL-GPRA06) 

 Annual Benefits 2020 2030 2040 2050 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 2.01 7.67 14.17 18.92

Economic 

 Energy-System Cost Savings (billion 2002 dollars/yr) 2.1 28.4 94.6 177.4

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 38 148 272 365

Security 

 Oil Savings (mbpd) 0.97 3.59 6.59 8.77

 Natural Gas Savings (quadrillion Btu/yr) 0.02 0.03 0.16 0.31

Weatherization and Intergovernmental Program 

The Weatherization and Intergovernmental Program (WIP) Case formulated in MARKALGPRA06 focuses on deployment programs that have an impact on the energy consumption in the 

residential sector and vehicle fuel use. Projected program goals of the Weatherization Assistance 

Program, State Energy Program, Rebuild America, and Code Training and Assistance are 

transformed into conservation-supply curves that reduce the heating and cooling loads in 

households benefiting from these programs. Other deployment programs aimed at promoting 

individual technologies were either modeled by adjusting the technologies discount rate or by 

applying lower bounds on the technology investment based on off-line analysis. 

Table 5.45 depicts the projected funds and program goals of the Weatherization Assistance 

Program used to develop the MARKAL-GPRA06 input. Analysts distributed the aggregated 

market potentials for Weatherization Assistance Program energy savings in proportion to 

household savings in the four MARKAL-GPRA06 residential regions: Northeast, Midwest, 

South, and West. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-27 

Table 5.45. Weatherization Assistance Program Projected Budget and Goals8

 

Year 

Cost 

per 

House 

No. Houses 

Weatherized9

Annual 

Total 

Houses 

Weatherized 

SITE 

Energy 

Savings 

(TBtu/yr) 

SingleFamily 

Home 

Savings 

(TBtu/yr) 

Mobile 

Home 

Savings 

(TBtu/yr) 

Multifamily 

Home 

Savings 

(TBtu/yr) 

2006 $2,390 189,650 189,650 5.93 3.80 1.19 0.95 

2010 $2,444 184,267 934,764 29.21 18.70 5.84 4.67 

2015 $2,458 182,983 1,849,681 57.76 36.97 11.55 9.24 

2020 $2,458 182,983 2,764,599 86.31 55.24 17.26 13.81 

2025 $2,458 182,983 2,744,752 85.64 54.81 17.13 13.70 

2030 $2,458 182,983 2,744,752 85.64 54.81 17.13 13.70 

As with the Weatherization Assistance Program, the State Energy Program, Rebuild America, 

Inventions and Innovations, and Code Training and Assistance energy savings goals were 

transformed into conservation-supply curves that reduce the heating and cooling loads in 

households benefiting from these programs. However, due to the potential overlap in target 

markets for these programs, the projected energy savings were reduced by 30 percent. For more 

information about these programs and projected savings, please refer to Appendix K. Analysts 

modeled the Clean Cities Program by applying lower bounds on the investment of alternativefueled vehicles based on program estimates of market penetration, as shown in Table 5.46. 

Table 5.46. Projection of Baseline Case and Clean Cities Program Case 

Alternative-Fueled Vehicles (number of vehicles on the road) 

Cumulative No. 

of Vehicles 

Type of 

Fuel 

% of 

Fleet 

2010 423,178 Natural Gas 44% 

2015 474,185 LPG 17% 

2020 676,235 Ethanol 34% 

2025 856,370 Electric 4% 

Tables 5.47 and 5.48 depict the energy savings by end-use demand and fuel type in the 

residential and commercial sectors respectively, while Table 5.49 reports the reduction in energy 

consumption in the industrial sector due to the I&I Program. 

Table 5.47. Delivered Energy Demand Reductions in the Residential Sector (trillion Btu/year) 

 2010 2020 2030 2040 2050 

Reductions by Demand Service 

Space Heating 53 138 165 162 154 

Space Cooling 2 9 22 21 20 

Water Heating 1 2 -2 0 1 

Lighting 40 168 201 188 133 

Other 0 0 0 0 0 

Total 96 317 386 371 308 

 

Reduction by Fuel 

Petroleum 0 0 1 0 -16 

Natural Gas 87 132 150 158 144 

Coal 1 0 0 -1 1 

Electricity 8 185 235 215 179 

Total 96 317 386 372 308 

 

8 See Appendix K for additional documentation on these goals.

9

 Includes homes weatherized using leveraged state and local funds. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-28 

Table 5.48. Delivered Energy Demand Reductions in the Commercial Sector (trillion Btu/year)

 2010 2020 2030 2040 2050 

Reductions by Demand Service

Space Heating 19 34 40 35 33 

Space Cooling 4 10 11 9 8 

Water Heating 3 4 6 5 5 

Lighting 24 70 99 99 99 

Other 0 0 0 0 0 

Total 50 117 157 148 144 

 

Reduction by Fuel 

Petroleum 5 0 0 5 0 

Natural Gas 13 31 25 1 5 

Coal 0 0 6 7 7 

Electricity 32 87 126 141 132 

Total 50 117 157 154 144 

Table 5.49. Delivered Energy Demand Reductions in the Industrial Sector (trillion Btu/year)

 2010 2020 2030 2040 2050 

Petroleum 3 0 4 8 61 

Natural Gas 16 97 127 130 66 

Coal 0 0 0 0 0 

Electricity 3 31 31 31 31 

Total 22 128 162 169 158 

The reduction in electricity demand in residential space conditioning and lighting also leads to 

the reduction in gas-based generation in the long run. Both conservation and reduction in 

electricity demand result in fewer investments in end-use devices and electric-generation 

capacity on the supply side. This is another factor attributable to the overall reduction in energysystem cost and carbon emissions, in addition to direct energy savings (Table 5.50). 

Table 5.50. FY06 Benefits Estimates for Weatherization and Intergovernmental Program 

(MARKAL-GPRA06) 

Annual Benefits 2020 2030 2040 2050 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 1.23 1.43 1.29 1.10

Economic 

 Energy System Cost Savings (billion 2002 dollars/yr) 11.6 15.8 17.1 17.1

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 22 29 24 23

Security 

 Oil Savings (mbpd) 0.02 0.03 0.03 0.06

 Natural Gas Savings (quadrillion Btu/yr) 0.85 0.66 0.80 0.40

 Displaced Capacity (gigawatts) 12 21 17 16
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-29 

Wind Technologies Program 

The Wind Program R&D aims to reduce capital and O&M costs and improve capacity factors for 

wind turbines. The program goals are represented in the MARKAL-GPRA06 model by changing 

the capital and O&M costs and capacity factors for wind turbines to coincide with the program 

goals as represented in Table 5.51. 

Table 5.51. Wind-Power Assumptions 

2010 2020 2030 2040 2050 

Capital Costs with Contingency Factor (2002 $/kW)

Onshore 

Class 6 893 819 788 767 746 

Class 5 893 819 788 767 746 

Class 4 982 893 866 856 840 

Shallow Offshore 

Class 6 1,129 1,070 1,016 989 962 

Class 4 1,129 1,070 1,016 989 962 

Deep Offshore 

Class 7 1,723 1,177 1,024 977 945 

Class 6 1,723 1,177 1,024 977 945 

Class 4 1,723 1,177 1,024 977 945 

 

Fixed O&M Cost (2002 $/kW/year)

Onshore 20.0 15.0 13.8 13.2 12.8 

Shallow Offshore 47.0 38.0 36.0 34.0 32.0 

Deep Offshore 49.3 41.0 39.0 37.0 36.0 

 

Capacity Factor

Onshore 

Class 6 50% 51% 52% 52% 52% 

Class 5 44% 46% 46% 46% 46% 

Class 4 40% 47% 48% 48% 48% 

Shallow Offshore 

Class 6 45% 50% 50% 50% 50% 

Class 4 36% 40% 40% 40% 40% 

Deep Offshore 

Class 7 55% 60% 60% 60% 60% 

Class 6 45% 50% 50% 50% 50% 

Class 4 36% 40% 40% 40% 40% 

The discount rate for wind generators is set at 8 percent (instead of the utility average of 10 

percent) to reflect the accelerated depreciation schedule available for renewable-generation 

technologies. Wind generators are modeled as centralized plants to compete with fossil fuelbased plants. 

 

The improvements in wind turbines result in a significant increase in installed wind generation 

capacity over the Baseline Case. Total wind generation increases due to both the increase in total 

installed capacity and the increase in capacity factors. The change in wind capacity and 

generation is shown in Table 5.52. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-30 

Table 5.52. Total Wind Capacity and Generation 

 2000 2010 2020 2030 2040 2050 

 

Wind Capacity (GW)

Reference Case 

Onshore 2.5 7.8 13.5 12.8 18.6 14.8 

Offshore 0.0 0.2 1.7 4.9 9.3 13.2 

Total 2.45 8.01 15.13 17.66 27.96 27.97 

GPRA Scenario 

Onshore 2.5 10.9 37.5 80.9 112.6 112.6 

Offshore 0.0 0.2 1.7 4.9 11.7 26.5 

Total 2.5 11.1 39.2 85.7 124.3 139.1 

Increase 

Onshore 0.0 3.0 24.0 68.0 94.0 97.8 

Offshore 0.0 0.0 0.0 0.0 2.4 13.3 

Total 0.0 3.0 24.0 68.0 96.3 111.1 

 

Wind Generation (Billion kWh)

Reference Case 

Onshore 10 31 57 55 83 66 

Offshore 0 1 8 24 49 71 

Total 10 32 65 79 132 137 

GPRA Scenario 

Onshore 10 46 171 372 519 521 

Offshore 0 1 10 28 69 156 

Total 10 47 180 400 588 677 

Increase 

Onshore 0 14 114 316 436 455 

Offshore 0 0 1 5 20 85 

Total 0 14 115 321 457 540 

In the Wind Technologies Program Case, wind generation directly displaces gas-fired and coalbased generation. However, because of wind’s lower availability and reduced contribution to 

peak, the total gas and coal generation capacity replaced is less than the wind capacity installed. 

The estimated benefits for the Wind Program are shown in Table 5.53. 

Table 5.53. FY06 Benefits Estimates for Wind Program (MARKAL-GPRA06) 

Annual Benefits 2020 2030 2040 2050 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 1.14 2.81 3.71 3.66

Economic 

 Energy-System Cost Savings (billion 2002 dollars/yr) 1.2 1.2 3.4 3.7

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 21 60 73 87

Security 

 Oil Savings (mbpd) ns ns ns ns

 Natural Gas Savings (quadrillion Btu/yr) 0.67 1.09 1.99 0.50

 Capacity (gigawatts) 24 68 96 111

 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 5) – Page 5-31 

Box 5.1—The MARKAL Model 

The U.S. MARKAL model is a technology-driven linear optimization model of the U.S. energy system that runs in five-year 

intervals over a 50-year projection period. MARKAL provides a framework to evaluate all resource and technology options 

within the context of the entire energy/materials system, and captures the market interaction among fuels to meet demands 

(i.e., competition between gas and coal for electric generation). The model explicitly tracks the vintage structure of all capital 

stock in the economy that produces, transports, transforms, or uses energy. 

In MARKAL, the entire energy system is represented as a network, based on the reference energy system (RES) concept. The 

RES depicts all possible flows of energy from resource extraction, through energy transformation, distribution, and 

transportation; to end-use devices that satisfy the demands of useful energy services (e.g., vehicle miles traveled, lumensecond in lighting). Figure 5.2 illustrates a simplified RES in graphical form. The U.S. MARKAL has detailed technical 

representations of four end-use sectors (residential, commercial, industrial, and transportation), as well as fossil fuel and 

renewable resources, petroleum refining, power generation, hydrogen production, and other intermediate conversion sectors. 

Cross comparisons of MARKAL outputs provide detailed technical and economic information to use in estimating the 

programs’ benefits. 

Technology choice in the MARKAL framework is based on the present value of the marginal costs of competing technologies 

in the same market sector. On the demand side, the marginal cost of demand devices is a function of levelized capital cost, 

O&M cost, efficiency, and the imputed price of the fuel used by these devices. For a specific energy-service demand and time 

period, the sum of the energy-service output of competing technologies has to meet the projected demand in that period. The 

relative size of the energy-service output (market share) of these technologies depends not only on their individual 

characteristics (technical, economic, and environmental), but also on the availability and cost of the fuels (from the supply 

side) they use. The actual market size of a demand sector in a future time period depends on the growth rate of the demand 

services and the stock turnover rate of vintage capacities. MARKAL dynamically tracks these changes and defines future 

market potentials. Another factor considered in MARKAL, which affects the market penetration of a specific demand device, 

is the sustainability of the expansion in the implied manufacturing capacity to produce these devices. For EERE R&D 

programs that have independently projected the market potentials of their technologies, an initial market penetration 

(combined with an annual growth rate limit) was imposed in MARKAL to replicate these potentials for assessing the benefits 

of these technologies. 

On the supply side, technology choice made in MARKAL is based on the imputed price of the energy products and the 

marginal cost of using these products downstream in the demand sectors. The cost of resource input for production 

(exogenously projected in MARKAL) such as imported oil prices and cost of biomass feedstock, together with the 

characteristics of supply technologies (including electricity generation) determine the market share of a particular fuel type 

(including renewables) and the technology that produces it. The supply-demand balance achieved for all fuels under the least 

energy-system cost represents a partial equilibrium in the energy market. 

Figure 5.1. An Illustrative Reference Energy System 

Resource

Extraction

Refining

and

Conversion

Transport Generation

Transmission

and

Distribution

Utilizing

Device End Use

Misc. Electric -Building

Misc. Electric - Industrial

Office Equipment

Air Conditioning

Water Heat

Space Heat

Electric

Process Heat

Petrochemicals

Renewables

Nuclear

Crude Oil

Natural Gas

Resource

Extraction

Refining

and

Conversion

Transport Generation

Transmission

and

Distribution

Utilizing

Device End Use Resource

Extraction

Refining

and

Conversion

Transport Generation

Transmission

and

Distribution

Utilizing

Device End Use

Misc. Electric -Building

Misc. Electric - Industrial

Office Equipment

Air Conditioning

Water Heat

Space Heat

Electric

Process Heat

Petrochemicals

Renewables

Nuclear

Crude Oil

Natural Gas

Coal

Biomass

Passenger Travel

Other Transportation

Hydrogen Fuel Cell

Hydrogen Fuel Cell LDV

Refined Products

X

X

x 
Appendix A – GPRA06 Benefits Estimates: MARKAL and 

NEMS Model Baseline Cases 

MARKAL Baseline Case Assumptions and Projections 

Economic and Demographic Assumptions 

The reference case projection used to evaluate the impact of the EERE portfolio was 

benchmarked to EIA’s 2004 Annual Energy Outlook (AEO) for the period between 2000 and 

2025. To the extent possible, the same input data and assumptions were used in MARKAL as 

were used to generate the AEO reference case. For example, the macroeconomic projections for 

GDP, housing stock, commercial square footage, industrial output, and vehicle miles traveled 

were taken from the AEO. At the sector level, both supply-side and demand-side technologies 

were characterized to reflect the AEO assumptions, in cases where the representation of 

technologies is similar between MARKAL and the National Energy Modeling System (NEMS). 

The resulting projections track closely with the AEO at the aggregate level, although they do not 

match exactly at the end-use level. For the period after 2025, various sources were drawn upon to 

compile a set of economic and technical assumptions. The primary economic drivers of GDP and 

population were based on the real GDP growth rate from the Congressional Budget Office’s 

Long-Term Budget Outlook and population growth rates from the Social Security 

Administration’s 2003 Annual Report to the Board of Trustees. 

In the reference case, GDP is projected to increase at an average annual rate of 2.8 percent 2000 

to 2025, and then slow to an average annual rate of 2.2 percent from 2025 to 2050. The 

population growth rate is projected to decline from an average annual rate of 0.9 percent between 

2000 and 2025 to 0.5 percent from 2025 to 2050. The reference case macroeconomic 

assumptions are shown in Table 1. 

Table 1. Reference Case Macroeconomic and Demographic Assumptions 

Annual Growth Rates 

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 '00-'25 25-'50 '00-'50 

GDP (Bill. 2002$) $10,163 $11,502 $13,479 $15,592 $17,900 $20,478 $23,169 $26,023 $29,014 $32,021 $35,267 2.8% 2.2% 2.5% 

Population (Million) 275.7 296.8 309.3 321.9 334.6 347.5 359.4 370.4 378.8 384.9 390.4 0.9% 0.5% 0.7% 

Total Households (Million) 105.2 113.7 119.8 126.1 132.0 137.8 143.8 148.1 151.5 153.9 156.2 1.1% 0.5% 0.8% 

Commercial Floorspace (Bill. sq ft) 68.5 77.6 83.8 89.9 95.9 101.8 108.0 114.1 120.2 126.0 131.9 1.6% 1.0% 1.3% 

Industrial Production (2000=100) 100 99 113 128 146 166 186 207 229 250 274 2.0% 2.0% 2.0% 

Light Duty Vehicle Miles Traveled 

(Bill. VMT) 2,355 2,709 3,041 3,409 3,768 4,173 4,507 4,829 5,086 5,277 5,455 2.3% 1.1% 1.7% 

Assumptions on Energy Prices 

Table 2 shows projected energy prices for the reference case. Natural gas prices are projected to 

drop between 2000 and 2005, and then increase at nearly 1 percent per year from 2005 to 2025 

before increasing amounts of arctic gas and LNG imports limit the average annual increase to 1.3 

percent from 2025 to 2050. Crude oil prices are also projected to drop between 2000 and 2005, 

increase at average annual rates of 0.8 percent between 2005 and 2025, and 0.8 percent per year 

thereafter. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-1 
Average mine mouth coal prices are projected to continue to decline by about 0.2 percent a year 

between 2000 and 2025, due to increasing productivity gains and a continued shift to less labor 

intensive Western coal production. However, coal prices are projected to increase at an average 

rate of 1 percent per year after 2025, due to increased demands, gradually increasing mine depths 

and a saturation of labor productivity gains. 

Table 2. Reference Case Energy Prices Annual Growth Rates 

2002 $s 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 '00-'25 25-'50 '00-'50 

World Oil Price ($/bbl) $28.61 $22.95 $24.15 $24.98 $26.25 $26.89 $28.03 $28.80 $29.94 $31.15 $32.42 -0.2% 0.8% 0.3% 

Natural Gas Wellhead Price 

($/Mcf) $3.71 $3.49 $3.38 $4.30 $4.19 $4.17 $3.92 $4.36 $5.10 $5.31 $5.79 0.5% 1.3% 0.9% 

Coal Minemouth Price ($/short 

ton) $17.30 $17.20 $16.65 $16.27 $16.17 $16.54 $17.43 $17.88 $18.89 $19.96 $21.18 -0.2% 1.0% 0.4% 

Average Wholesale Electricity 

Price (¢/kWh) 4.0¢ 4.4¢ 4.9¢ 5.7¢ 6.0¢ 5.7¢ 4.3¢ 5.3¢ 5.0¢ 5.1¢ 5.1¢ 1.4% -0.4% 0.5% 

Primary Energy Consumption 

As a result of slightly increasing energy prices, technology improvements, and shifts within the 

economy, energy demand is projected to increase more slowly than GDP. As shown in Table 3, 

total primary energy use is projected to increase at a rate of 1.3 percent per year from 2000 to 

2025, and at an average annual rate of 0.4 percent between 2025 and 2050. By 2050, total 

primary energy consumption is projected to reach 152 quadrillion Btus (quads). Overall, the 

energy consumption to GDP ratio is projected to decline by 1.6 percent per year from 2000 to 

2050, while total carbon emissions increase by 1.4 percent per year over the same period. 

Table 3. Primary Energy Consumption, Energy Intensity and Carbon Emissions 

Annual Growth Rates 

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 '00-'25 25-'50 '00-'50 

Petroleum 39.8 40.9 44.9 48.5 51.3 54.8 57.6 59.9 61.9 63.4 64.6 1.3% 0.7% 1.0% 

Natural Gas 22.0 24.1 26.6 29.0 30.8 31.5 31.8 33.6 35.1 35.8 36.6 1.4% 0.6% 1.0% 

Coal 22.4 23.5 25.4 26.5 28.3 31.3 33.5 34.3 35.1 35.6 35.9 1.3% 0.5% 0.9% 

Nuclear 8.1 8.4 8.4 8.6 8.6 8.6 8.5 6.6 6.0 4.2 2.7 0.2% -4.5% -2.2% 

Renewables 6.5 7.6 8.0 8.9 9.5 9.8 10.1 11.5 11.7 11.9 12.1 1.7% 0.9% 1.3% 

Total Primary Energy 98.8 104.4 113.3 121.4 128.6 136.0 141.5 145.9 149.8 151.0 151.8 1.3% 0.4% 0.9% 

Energy/GDP (Thos. Btu/ '01$ GDP) 9.8 9.1 8.4 7.8 7.2 6.6 6.1 5.6 5.2 4.7 4.3 -1.6% -1.7% -1.6% 

Carbon Emissions (MMT) 1,594 1,678 1,840 1,965 2,089 2,236 2,346 2,435 2,512 2,562 2,600 1.4% 0.6% 1.0% 

Crude oil’s share of total energy consumption is projected to increase from 40 percent in 2000 to 

nearly 43 percent in 2050. The natural gas share is projected to grow from 22 percent to 24 

percent over the same period. Coal generation is projected to increase slightly from a 23 percent 

share in 2000 to nearly 24 percent in 2050. Almost all existing nuclear generation capacity is 

assumed to retire between 2025 and 2050. However, 29 GW of new nuclear capacity is projected 

to be added between 2025 and 2050. The share of renewable energy is also projected to be 

relatively stable at between seven and eight percent throughout the projection period. 

As the MARKAL reference case projection was calibrated to the 2004 Annual Energy Outlook, 

the natural gas supply assumptions are more pessimistic than in the 2003 AEO. By 2050, LNG 

imports and Arctic gas supplies account for 38 percent of gas supply. Figure 1 shows natural gas 

supplies by source for the reference case. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-2 
Quad. Btus 40 

35 

30 

25 

20 

15 

10 

5 

0 

ic 

ic 

LNG 

Arct

Canadian non-Arct

Lower-48 

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 

Figure 1. Reference Case Natural Gas Supply by Source 

End-Use Energy Demand 

The sectoral breakout of energy use, shown in Figure 2, demonstrates that transportation energy 

demand is projected to increase most rapidly, at 1.2 percent per year, from 2000 to 2050; while 

residential and industrial energy demand increases most slowly, at 0.7 percent per year. 

Commercial energy demand is projected to grow at a rate of 1.1 percent per year. The growth 

rates in energy consumption are a function of the opposing trends of increasing end-use energy 

service demand and improvements in the efficiency of technologies that satisfy this demand, as 

well as macroeconomic shifts toward less energy-intensive industries. This phenomenon is best 

illustrated by examining the energy intensity of the economy. Figure 3 shows the relative energy 

intensity for different end-use and conversion sectors and the economy as a whole. 

0 

20 

40 

60 

80 

100 

120 

140 

160 

180 

2000 2015 2020 2025 2040 2045 2050 

s 

ion 

ial 

ial 

idential 

2005 2010 2030 2035 

Quad. Btu

Transportat

Industr

Commerc

Res

Note: consumption totals include electric generation and distribution losses 

Figure 2. Energy Consumption by Sector 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-3 
i l ion El

 ( ) 

0.00 

0.20 

0.40 

0.60 

0.80 

1.00 

Res dentia Commercial Industry Transportat ectric 

Generation 

Economy 

Index 2000=1

2000 

2010 

2020 

2030 

2040 

2050 

Note: Residential index is primary energy excluding misc. use per household; Commercial index is primary energy use excluding office 

equipment and misc. appliances per square foot; Industrial index is total primary energy per unit output; Transportation index is LDV 

primary energy per mile traveled; Electricity index is non-renewable average heat rate; and Economy index is total primary energy per 

unit GDP. 

Figure 3. Relative Energy Intensity by Sector 

As shown in Figure 3, our reference case projection indicates that the energy intensity of the 

economy (which we’ve defined as total primary energy consumption per $ of GDP) is projected 

to fall by more than half by 2050. This decrease reflects both a continued shift toward a servicebased economy, as well as increases in energy technology efficiency. End-use efficiencies are 

projected to increase throughout the economy over the projection period as new, more efficient 

capital stocks are purchased to replace existing equipment and to meet new demand. The 

reference case technology database includes technologies that are expected to become available 

in the future, as well as those that are currently on the market. For example, more efficient 

electric heat pumps and light-duty vehicles are assumed to become available throughout the 

projection period. The technical and economic data associated with these technologies are 

derived from a variety of sources, but rely most heavily on the NEMS database. 

The residential energy intensity index shows significant improvements in energy use per 

household. However, the residential index excludes “miscellaneous demands,” the fastest 

growing segment of residential energy demand. The miscellaneous demand category includes 

electric devises such as home computers, TVs, microwave ovens, as well as devices such as gas 

lamps and swimming pool heaters. Because these service demands are growing faster than the 

sector as a whole, their energy use per household actually rises over time. Thus, the inclusion of 

miscellaneous demands in the calculation of residential energy intensity would obscure the 

efficiency gains being made in other residential service demands. 

The commercial energy intensity index shows significant improvements in energy use per square 

foot. However, as with the residential sector, this calculation excludes the fastest growing 

demand categories; office equipment and miscellaneous commercial appliances. The inclusion of 

these demand categories would result in relatively constant commercial energy demand per 

square foot. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-4 
The industrial-sector efficiency index shows dramatic declines in energy intensity due to a shift 

from energy-intensive industries to nonenergy-intensive manufacturing, as well as improvements 

in process efficiency. Between 2005 and 2050, nonenergy-intensive manufacturing output is 

expected to grow at twice the rate as energy intensive industrial output. This shift in output 

exaggerates the decline in energy intensity. However, in the transportation sector, consumer 

preferences for more powerful engines and a continued shift from passenger cars to SUVs, limit 

gains in overall efficiency. 

On an individual technology basis, there are several important trends in the reference case 

technology assumptions. Although most technologies’ capital costs are assumed to remain 

constant at their current level in real terms, the costs of a few key technologies are projected to 

decline over time. These include gas combined cycle, integrated coal gasification, and renewable 

technologies, such as wind and PV. Most of these technologies also show improvements in their 

heat rates or performance (e.g. capacity factor) between 2000 and 2050. 

In the power-generation sector, the efficiency of nonrenewable generation is expected to increase 

as older, less efficient fossil steam units retire and new high efficiency gas combined cycle and 

IGCC capacity is built. Electric generation by type is shown in Figure 4. Natural gas-fired 

generation is projected to increase its share of total generation from about 16 percent to 28 

percent over the projection period. Coal-fired generation remains the largest source of electricity 

at 53 to 58 percent of total generation. Due to significant retirements of existing nuclear capacity, 

the share of nuclear generation falls from 21 percent to 3 percent of generation in the projection 

period. Renewable generation is relatively constant at about 10 percent of total generation. Billion kWh 8,000 

7,000 

6,000 

5,000 

4,000 

3,000 

2,000 

1,000 

0 

lear 

l l 

Renewable 

Nuc

Natura Gas & Oi

Coal 

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 

Figure 4. Electricity Generation by Type: Reference Case 

While both natural gas and coal-fired generation show increased efficiency, fossil fuel use for 

electric generation increases by 78 percent during the projection period. Such an increase in coal 

and natural gas demand for power generation is dependant on the availability of these resources. 

However, potential reduction in supply, such as changes in the outlook in natural gas supply, 

would necessitate a significant change fuels used for electric generation. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-5 
NEMS Baseline Case Assumptions and Projections 

Overview 

The Office of Energy Efficiency and Renewable Energy (EERE) programs use integrated energy 

models to analyze the benefits expected from successful implementation of individual programs 

and the EERE portfolio as a whole. The use of integrated models provides a consistent economic 

framework and incorporates the interactive effects among the various programs. Feedback and 

interactive effects result from (1) changes in energy prices resulting from lower energy 

consumption, (2) the interaction between supply programs affecting the mix of generation 

sources and the end-use sector programs affecting the demand for electricity, and (3) additional 

savings from reduced energy production and delivery. 

A modified version of the National Energy Modeling System (NEMS)1

 was one of the models 

used for this benefits analysis. NEMS is an integrated energy model of the U.S. energy system 

that was developed by the Energy Information Administration (EIA) for forecasting and policy 

analysis purposes. NEMS currently provides projection capability to the year 2025 and so is used 

for the midterm benefits analysis. The latest version of NEMS available at the time of the 

benefits analysis was used as the starting point. This is a slightly updated version from Annual 

Energy Outlook 2004 (AEO2004) that was used by EIA for a set of service reports2

. Several 

modifications were made to the model by EERE to enhance its ability to represent the EERE 

programs. The modified version of the model is referred to as NEMS-GPRA06. 

GPRA 2006 Baseline 

The first step in the benefits analysis process is to establish an appropriate Baseline Case. The 

EERE Baseline Case is a projection intended to represent the future U.S. energy system without 

the effect of EERE programs. This Baseline Case assures that program benefits are estimated 

based on the same initial forecasts for economic growth, energy prices, and levels of energy 

demand. It also assures that these initial assumptions are consistent with each other; e.g., that the 

level of electricity demand expected under the economic growth assumptions could be met at the 

electricity price assumed. It provides a basis for assessing how well renewable and efficiency 

technologies might be able to compete against future, rather than current, conventional energy 

technologies (e.g., more efficient central power generation). Finally, it helps assure that 

underlying improvements in efficiency and renewable energy are not counted as part of the 

benefits of the EERE programs. 

The most recent Annual Energy Outlook Reference Case is used as the starting point for the 

developing the base case.3

 The Energy Information Administration (EIA) Annual Energy 

Outlook (AEO) Reference Case provides an independent representation of the likely evolution of 

energy markets. This forecast reflects expected changes in the demand for energy (e.g., to reflect 

the availability of new appliances), technology improvements that might improve the efficiency 

of energy use, and changes in energy resource production costs, including renewable energy. 

1

 The National Energy Modeling System: An Overview 2003, March 2003, DOE/EIA-0581(2003). 2

 Analysis of S. 1844, the Clear Skies Act of 2003; S. 843, the Clean Air Planning Act of 2003; and S. 366, the Clean Power Act 

of 2003” available at http://www.eia.doe.gov/oiaf/servicerpt/csa/pdf/sroiaf(2004)05.pdf. The reference case is similar to the 

AEO2004. 

3

As described above, the updated NEMS produces similar reference case projections as the Annual Energy Outlook 2004 with 

Projections to 2025, January 2004, DOE/EIA-0383 (2004). See http://www.eia.doe.gov/oiaf/archive/aeo04/pdf/0383(2004).pdf. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-6 
Current energy market policies, such as state Renewable Portfolio Standards, which facilitate the 

development and adoption of these technologies, are included in the Base Case. This approach 

ensures that EERE’s benefits estimates do not include expected impacts of such policies. Neither 

the EIA Reference Case nor the EERE Base Case includes any changes in future energy policies. 

The Baseline is constructed starting with EIA’s Annual Energy Outlook Reference Case and then 

any identifiable effects of EERE programs already included are removed. For example, EIA’s 

estimate of rooftop photovoltaic installations resulting from the Million Solar Roofs Initiative 

were removed for the EERE Baseline. The AEO2004 assumption of roughly constant 

hydroelectric capacity over time was modified to reflect the expectation that without more 

environmentally benign turbine designs some reduction in hydro capacity would occur as a result 

of relicensing requirements. The maximum growth rates for cellulosic ethanol production were 

reduced by a factor of 5 because growth of this new industry is expected to be very slow without 

EERE program involvement. 

The AEO forecast includes technology improvements in all areas of energy demand and supply 

and identifying what portion is due to EERE programs is extremely difficult. For GPRA06, 

selected technology changes were made where the AEO appeared to already incorporate the 

EERE program goals. Technology assumptions that were modified for the Baseline include cost 

and efficiency improvements in distributed combined heat and power (CHP) technologies that 

were assumed to be delayed without an ongoing DEER program. In addition, the two composite 

distributed generation technologies in the electricity generation sector were modified to reflect 

baseline values for gas turbines, microturbines, and gas engines. The AEO2004 includes a 

significant improvement in geothermal generation technology over time, similar to the program 

goals. To reflect might what might occur without continued R&D funding, analysts reduced the 

cost reduction by half for the GPRA Baseline. 

There are a few EERE technologies that are either not represented in the AEO2004 Reference 

Case or their improvement is less than anticipated by the program in the absence of EERE 

programs. These technology assumptions were also modified for the GPRA06 Baseline. In 

commercial lighting, an advanced electrodeless fluorescent technology was replaced with a 

baseline projection of solid-state lighting characteristics. The efficiency of absorption cooling in 

commercial buildings was assumed to increase slightly, rather than remain constant over time. 

Offshore wind technology characteristics were added, and the onshore wind characteristics were 

modified. The onshore capital costs were increased slightly. More importantly the capacity 

factors for each wind class were assumed to be higher than in the AEO2004, although lower than 

the program goals. 

A few other modifications were made to reflect EERE program assumptions or updated 

information about energy markets. These changes affect both the Baseline and the Benefits 

Cases. The size of typical PV systems was increased to 4 kW in residential and 100 kW in 

commercial buildings to reflect recent PV installation experience and trends. The maximum 

market for PV systems was increased from 30 percent to 55 percent in the commercial sector and 

to 60 percent for residential PVs. Similarly, the maximum market share for gas-fired distributed 

generation technologies was increased from 30 percent to 50 percent in the commercial sector. 

California PV credits were incorporated in the Pacific region. Solar water heat was added to the 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-7 
slate of technologies for new homes, and the share of the replacement market in which it can 

compete was increased from 20 percent to 50 percent. The conversion efficiency of cellulosic 

ethanol was reduced because EIA’s assumption appeared too optimistic. 

Table 4. Summary of Baseline Changes from the AEO2004 

Preliminary 

AEO2004 GPRA06 Baseline Case 

Representation of EERE Technology 

Characteristics 

 Million Solar Roofs 

 Photovoltaic system costs 

 Hydroelectric capacity 

 Cellulosic ethanol production 

 DG technology improvement 

 Commercial absorption cooling efficiency 

Wind 

Geothermal 

0.4 GW installed 2005 to 2025 

Significant improvement 

Roughly constant hydro 

capacity and generation 

0.6 billion gallons annually by 

2025 

Significant improvement 

Constant over time 

30 to 44 percent capacity 

factors depending on wind class 

and year 

Significant improvement 

Removed 

Slower rate of improvement 

2.2 GW reduction for 2010 to 

2025 

0.12 billion gallons annually by 

2025 

Slower rate of improvement 

Increase 20 percent by 2020 

34 to 50 percent capacity factors 

depending on wind class and 

year

Half the rate of improvement 

Energy Market Updates 

 PV system size 

PV maximum market share 

 CHP commercial building maximum share 

 Commercial absorption cooling 

California PV subsidy 

 Solar water heat 

 Cellulosic conversion efficiency 

2 kW residential, 25 kW 

commercial 

30 percent for both residential 

and commercial 

30 percent 

Included in only 5 building types 

Not included 

Maximum 20 percent 

replacement market 

90 to 103 gallons of ethanol per 

dry ton of biomass 

4 kW residential, 100 kW 

commercial 

60 percent for residential and 55 

percent for commercial 

50 percent 

Included in all 11 building types 

Included for residential systems 

New and up to 50 percent 

replacement market 

82 to 101 gallons of ethanol per 

dry ton of biomass 

Structural Changes 

Wind module 

 Commercial shell efficiency 

 Commercial DG algorithms 

One capital cost and resource 

multiplier for all wind classes 

No offshore wind technology 

Index 

Capital costs and resource 

multipliers by wind classes 

Offshore wind 

Technology representation

Market share and stock 

accounting modified 

In a few cases, structural changes were made to improve the model’s representation of markets 

important to EERE technologies. The wind module was modified, so that each of the three wind 

classes is treated more discretely with separate capital costs and resource multipliers. Offshore 

wind was added as another technology option with resources available in the coastal regions and 

the regions around the Great Lakes. The shell indices in the commercial module were replaced 

with a technology choice algorithm necessary for later representation of EERE shell 

technologies. In addition, alterations to the distributed generation algorithm in the building 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-8 
modules were made to reflect the DEER program’s market adoption data, to account for 

buildings that have already installed a DG technology in prior years, and to allow greater than an 

annual 0.5 percent adoption in existing buildings. Absorption cooling was allowed to compete in 

all commercial building types, rather than only a subset as in the AEO2004. 

A summary of these modifications is provided in Table 4. Greater detail can be found in the 

individual program appendices. 

In the Baseline projections oil prices are projected to fall from 2004 and then gradually rise 

through 2025, as shown in Figure 5. Natural gas prices are projected to fall more gradually 

through 2010 and then increase through 2025. Coal prices, on the other hand, are projected to be 

relatively constant in real terms with a very slight decline. Electricity prices are projected to 

experience a slight decrease through 2010. 

10 

8 

6 

4 

2 

0 

1985 1990 1995 2000 2005 2010 2015 2020 2025 

l

l il 

l

W llh d 

Mi l

2002$ per MMbtu or Cents per kWh 

Average E ectricity 

Wor d O

Natura Gas 

nemouth Coa

Projected 

Figure 5. Projected Energy Prices 

The resulting Baseline Case projects a 33 percent increase in conventional energy demand from 

2005 to 20254

. Energy efficiency and renewable energy improvements, however, contribute 

toward a 25 percent reduction in conventional energy intensity (energy used per dollar of GPD 

produced) over the same period (Figure 2).5

 Between 2005 and 2025, renewable energy 

technology improvements result in increases in electric generation in central and distributed 

applications (in billions of kWh) of 35 for wind, 30 for geothermal, 36 for biomass, 2 for 

municipal solid waste, 8 for photovoltaics, and 0.4 for solar thermal. 

4

 Very similar to the AEO2004. 

5

 Energy-intensity changes result from a mix of structural changes in the economy (e.g., growing service sector) and efficiency

improvements. Two recent EERE-sponsored studies provide additional background on understanding the sources of changes to

our energy intensity: Ortiz and Sollinger, Shaping Our Future by Reducing Energy Intensity in the U.S. Economy; Volume 1:

Proceedings of the Conference (2003, Rand Corporation) and Bernstein, Fonkych, Loeb, and Loughran, “State-Level Changes in

Energy Intensity and their National Implications (2003, Rand Corporation). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-9 
Conventional Energy Demand (Quadrillion Btu) 

130

120

110

100

90

80

70

60

50

16

14

12

10

8

6

4

2

0

Conventional Energy Intensity 

(1000 Btu/1996$ GDP) 

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

Figure 6. U.S. Conventional Energy Demand and Energy Intensity 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-10
EERE NEMS-GPRA06 Baseline Case Tables 

 Table 1. Total Energy Supply and Disposition Summary

 (Quadrillion Btu per Year, Unless Otherwise Noted) 

The Worksheet was generated by ftab 

gA6base35.d010505a 

2010 2015 2020 2025 

Production 

Crude Oil & Lease Condensate 12.60 

Natural Gas Plant Liquids 3.18 

Dry Natural Gas 21.74 

Coal 25.00 

Nuclear Power 8.38 

Renewable Energy 1/ 7.16 

Other 2/ 0.89 

Total 78.94 

11.71 10.52 9.81 

3.21 3.43 3.46 

22.23 24.13 24.58 

26.10 28.13 30.71 

8.52 8.57 8.57 

7.83 8.34 8.83 

0.79 0.81 0.84 

80.39 83.94 86.80 

Imports 

Crude Oil 3/ 24.64 

Petroleum Products 4/ 5.64 

Natural Gas 5.71 

Other Imports 5/ 0.95 

Total 36.95 

29.60 31.49 34.15 

6.09 8.21 10.08 

6.88 7.47 8.18 

1.05 1.12 1.18 

43.63 48.28 53.59 

Exports 

Petroleum 6/ 2.15 

Natural Gas 0.81 

Coal 0.89 

Total 3.85 

2.17 2.13 2.16 

0.79 0.80 0.72 

0.80 0.74 0.55 

3.76 3.67 3.43 

Discrepancy 7/ 0.32 0.46 0.46 0.54 

Consumption 

Petroleum Products 8/ 44.29 

Natural Gas 26.80 

Coal 24.97 

Nuclear Power 8.38 

Renewable Energy 1/ 7.16 

Other 9/ 0.11 

 Total 111.72 

48.56 51.65 55.41 

28.51 30.99 32.24 

26.27 28.47 31.34 

8.52 8.57 8.57 

7.83 8.34 8.83 

0.11 0.07 0.03

119.80 128.09 136.42 

Net Imports - Petroleum 28.14 33.52 37.56 42.08 

Prices (2001 dollars per unit)

 World Oil Price ($ per bbl) 10/ 24.17 

Gas Wellhead Price($ / Mcf) 11/ 3.43 

Coal Minemouth Price ($ / ton) 16.69 

 Aver. Electricity (cents / Kwh) 6.68 

25.07 26.02 27.00 

4.20 4.14 4.39

16.63 16.69 16.56

6.83 6.82 6.84 

1/ Includes grid-connected electricity from conventional hydroelectric; wood and wood waste; landfill gas; 

municipal solid waste; other biomass; wind; photovoltaic and solar thermal sources; non-electric energy 

from renewable sources, such as active and passive solar systems, and wood; and both the ethanol and 

gasoline components of E85, but not the ethanol components of blends less than 85 percent. Excludes 

electricity imports using renewable sources and nonmarketed renewable energy. See Table A18 for selected 

nonmarketed residential and commercial renewable energy. 

2/ Includes liquid hydrogen, methanol, supplemental natural gas, and some domestic inputs to refineries.

3/ Includes imports of crude oil for the Strategic Petroleum Reserve.

4/ Includes imports of finished petroleum products, imports of unfinished oils, alcohols, ethers, and blending components.

5/ Includes coal, coal coke (net), and electricity (net).

6/ Includes crude oil and petroleum products.

7/ Balancing item. Includes unaccounted for supply, losses, gains, net storage withdrawals,

heat loss when natural gas is converted to liquid fuel, and heat loss when coal is converted to liquid fuel. 

8/ Includes natural gas plant liquids, crude oil consumed as a fuel, and nonpetroleum-based liquids for 

blending, such as ethanol. 

9/ Includes net electricity imports, methanol, and liquid hydrogen. 

10/ Average refiner acquisition cost for imported crude oil. 

11/ Represents lower 48 onshore and offshore supplies. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-11 
 Table 2. Energy Consumption by Sector and Source

 (Quadrillion Btu per Year, Unless Otherwise Noted) 

2010 2015 2020 2025 

Energy Consumption

 Residential

 Distillate Fuel 0.92 0.88 0.83 0.79

 Kerosene 0.11 0.11 0.10 0.09

 Liquefied Petroleum Gas 0.56 0.58 0.61 0.64

 Petroleum Subtotal 1.59 1.57 1.55 1.51

 Natural Gas 5.71 5.87 6.15 6.32

 Coal 0.01 0.01 0.01 0.01

 Renewable Energy 1/ 0.40 0.40 0.41 0.40

 Electricity 4.86 5.19 5.57 5.92

 Delivered Energy 12.57 13.05 13.68 14.16

 Electricity Related Losses 10.43 10.85 11.34 11.79

 Total 23.00 23.89 25.03 25.95

 Commercial

 Distillate Fuel 0.63 

 Residual Fuel 0.13 

 Kerosene 0.02 

 Liquefied Petroleum Gas 0.10 

Motor Gasoline 2/ 0.05 

 Petroleum Subtotal 0.92

 Natural Gas 3.56

 Coal 0.10

 Renewable Energy 3/ 0.10

 Electricity 5.02

 Delivered Energy 9.70

 Electricity Related Losses 10.78

 Total 20.47

0.65 0.67 0.69

0.13 0.13 0.13

0.02 0.02 0.02

0.10 0.10 0.10 

0.05 0.05 0.05

0.95 0.97 0.99

3.73 4.01 4.28

0.10 0.10 0.10

0.10 0.10 0.10

5.61 6.21 6.78

10.49 11.39 12.24

11.71 12.65 13.51

22.21 24.03 25.75

 Industrial 4/

 Distillate Fuel 1.18 

 Liquefied Petroleum Gas 2.36 

 Petrochemical Feedstocks 1.35 

 Residual Fuel 0.21 

Motor Gasoline 2/ 0.16 

 Other Petroleum 5/ 4.40 

 Petroleum Subtotal 9.66

Natural Gas 6/ 8.62

 Lease and Plant Fuel 7/ 1.34

Natural Gas Subtotal 6/ 9.96

 Metallurgical Coal 0.65

 Steam Coal 1.43

 Net Coal Coke Imports 0.01

 Coal Subtotal 2.09

 Renewable Energy 8/ 2.00

 Electricity 3.83

 Delivered Energy 27.54

 Electricity Related Losses 8.24

 Total 35.78

1.27 1.34 1.43

2.53 2.74 2.95

1.43 1.54 1.63

0.23 0.22 0.23 

0.17 0.18 0.19

4.72 4.96 5.17

10.36 10.98 11.59 

9.13 9.94 10.76

1.37 1.51 1.55 

10.50 11.46 12.31

0.59 0.53 0.47

1.45 1.47 1.48

0.00 0.00 0.01

2.04 1.99 1.96

2.26 2.48 2.70

4.16 4.46 4.81

29.32 31.37 33.38

8.68 9.08 9.58

38.00 40.45 42.96

 1/ Includes wood used for residential heating. See Table A18 estimates of nonmarketed renewable energy 

consumption for geothermal heat pumps, solar thermal hot water heating, and solar photovoltaic electricity 

generation.

 2/ Includes ethanol (blends of 10 percent or less) and ethers blended into gasoline.

 3/ Includes commercial sector electricity cogenerated by using wood and wood waste, landfill gas, municipal 

solid waste, and other biomass. See Table A18 for estimates of nonmarketed renewable energy 

consumption for solar thermal hot water heating and solar photovoltaic electricity generation.

 4/ Fuel consumption includes consumption for combined heat and power, which produces electricity and other useful 

thermal energy. 

5/ Includes petroleum coke, asphalt, road oil, lubricants, still gas, and miscellaneous petroleum products.

 6/ Includes consumption for combined heat and power; excludes consumption by nonutility generators.

 7/ Represents natural gas used in the field gathering and processing plant machinery.

 8/ Includes consumption of energy from hydroelectric, wood and wood waste, municipal solid waste, and 

other biomass; includes combined heat and power, both for sale to the grid and for own use. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-12
 Table 2. Energy Consumption by Sector and Source (Continued) 

2010 2015 2020 2025

 Transportation

 Distillate Fuel 9/ 6.43 7.27 8.04 8.94

 Jet Fuel 10/ 3.93 4.36 4.69 4.91

 Motor Gasoline 2/ 19.94 21.80 23.37 25.31

 Residual Fuel 0.79 0.80 0.82 0.83

 Liquefied Petroleum Gas 0.06 0.07 0.08 0.09

 Other Petroleum 11/ 0.26 0.27 0.30 0.32

 Petroleum Subtotal 31.41 34.58 37.30 40.40

 Pipeline Fuel Natural Gas 0.72 0.73 0.84 0.87

 Compressed Natural Gas 19/ 0.06 0.08 0.10 0.12

 Renewable Energy (E85) 12/ 0.00 0.00 0.00 0.00

 Liquid Hydrogen 20/ 0.00 0.00 0.00 0.00

 Electricity 0.09 0.10 0.11 0.12

 Delivered Energy 32.27 35.49 38.35 41.51

 Electricity Related Losses 0.19 0.21 0.22 0.24

 Total 32.46 35.70 38.58 41.75

 Electric Generators 15/

 Distillate Fuel 0.22

 Residual Fuel 0.50

 Petroleum Subtotal 0.72

 Natural Gas 6.80

 Steam Coal 22.77

 Nuclear Power 8.38

 Renewable Energy/Other 16/ 4.66

Electricity Imports 17/ 0.11

 Total 43.43

0.52 0.30 0.37

0.59 0.55 0.55

1.10 0.85 0.92

7.59 8.43 8.34

24.12 26.36 29.27

8.52 8.57 8.57

5.07 5.35 5.62

0.11 0.07 0.03

46.51 49.64 52.74 

Total Energy Consumption

 Distillate Fuel 9.37 

 Kerosene 0.16 

 Jet Fuel 10/ 3.93 

Liquefied Petroleum Gas 3.07 

 Motor Gasoline 2/ 20.14 

 Petrochemical Feedstocks 1.35 

 Residual Fuel 1.64 

 Other Petroleum 13/ 4.64 

 Petroleum Subtotal 44.29

 Natural Gas 24.74

 Lease and Plant Fuel 7/ 1.34

 Pipeline Natural Gas 0.72

 Natural Gas Subtotal 26.80

 Metallurgical Coal 0.65

 Steam Coal 24.31

 Net Coal Coke Imports 0.01

 Coal Subtotal 24.97

 Nuclear Power 8.38

 Renewable Energy 18/ 7.16

Electricity Imports 17/ 0.11

 Total 111.72

10.59 11.18 12.22

0.15 0.14 0.13

4.36 4.69 4.91

3.28 3.54 3.77

22.02 23.60 25.55

1.43 1.54 1.63

1.75 1.72 1.74

4.97 5.23 5.47

48.56 51.65 55.41

26.40 28.63 29.81

1.37 1.51 1.55

0.73 0.84 0.87

28.51 30.99 32.24

0.59 0.53 0.47

25.68 27.94 30.86

0.00 0.00 0.01

26.27 28.47 31.34

8.52 8.57 8.57

7.83 8.34 8.83

0.11 0.07 0.03

119.80 128.09 136.42

 2/ Includes ethanol (blends of 10 percent or less) and ethers blended into gasoline.

 7/ Represents natural gas used in the field gathering and processing plant machinery.

 9/ Diesel fuel containing 500 parts per million (ppm) or 15 ppm sulfur.

 10/ Includes only kerosene type.

 11/ Includes aviation gas and lubricants.

 12/ E85 is 85 percent ethanol (renewable) and 15 percent motor gasoline (nonrenewable).

 13/ Includes unfinished oils, natural gasoline, motor gasoline blending compounds, aviation gasoline,

lubricants, still gas, asphalt, road oil, petroleum coke, and miscellaneous petroleum products.

 15/ Includes consumption of energy by electricity-only and combined heat and power (CHP) plants whose primary business 

is to sellelectricity, or electricity and heat, to the public. Includes small power producers and exempt wholesale generators.

 16/ Includes conventional hydroelectric, geothermal, wood and wood waste, municipal solid waste, other 

biomass, petroleum coke, wind, photovoltaic and solar thermal sources. Excludes net electricity imports.

 17/ In 1999 approximately 70 percent of the U.S. electricity imports were provided by renewable sources 

(hydroelectricity); EIA does not project future proportions for the fuel source of imported electricity.

 18/ Includes hydroelectric, geothermal, wood and wood waste, municipal solid waste, other biomass, wind, 

photovoltaic and solar thermal sources. Includes ethanol components of E85; excludes ethanol blends (10 

percent or less) in motor gasoline. Excludes net electricity imports and nonmarketed renewable energy 

consumption for geothermal heat pumps, buildings photovoltaic systems, and solar thermal hot water heaters.

 19/ Includes natural gas for hydrogen production.

 20/ Hydrogen is not reported separately but rather as the fuel feedstock. See note 19.

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-13
 Table 3. Energy Prices by Sector and Source

 (2001 Dollars per Million Btu, Unless Otherwise Noted) 

2010 2015 2020 2025

 Residential 14.24 

Primary Energy 1/ 8.16 

 Petroleum Products 2/ 9.89 

 Distillate Fuel 7.81 

 Liquefied Petroleum Gas 13.85 

 Natural Gas 7.69 

Electricity 23.39 

14.92 14.92 15.24 

8.71 8.62 8.88

10.38 10.88 11.30

8.04 8.38 8.54

14.46 14.82 15.21

8.28 8.07 8.32 

23.83 23.62 23.67

 Commercial 13.81 

Primary Energy 1/ 6.49 

 Petroleum Products 2/ 6.33 

 Distillate Fuel 5.45 

 Residual Fuel 4.13 

 Natural Gas 3/ 6.66 

Electricity 20.51 

14.58 14.73 15.00 

7.03 6.98 7.22

6.51 6.83 7.00

5.63 6.00 6.16

4.27 4.41 4.55

7.30 7.14 7.40 

21.03 21.07 21.15

 Industrial 4/ 6.47 

Primary Energy 5.14 

 Petroleum Products 2/ 6.83 

 Distillate Fuel 5.68 

 Liquefied Petroleum Gas 9.67 

 Residual Fuel 3.74 

 Natural Gas 5/ 4.09 

 Metallurgical Coal 1.96 

 Steam Coal 1.57 

Electricity 13.52 

6.96 7.15 7.39 

5.62 5.82 6.06

7.11 7.56 7.80

5.82 6.24 6.41

10.26 10.66 11.11

3.88 4.03 4.17

4.82 4.74 5.01

1.90 1.84 1.77

1.56 1.55 1.52 

13.91 13.98 14.07

 Transportation 10.52 

Primary Energy 10.49 

 Petroleum Products 2/ 10.49 

 Distillate Fuel 6/ 10.16 

 Jet Fuel 7/ 5.76 

 Motor Gasoline 8/ 11.88 

 Residual Fuel 3.60 

 Liquefied Petroleum Gas9/ 14.92 

 Natural Gas 10/ 8.26

 Ethanol (E85) 11/ 17.21

Electricity 19.76

10.54 10.58 10.75 

10.51 10.56 10.72

10.51 10.56 10.72

10.18 10.10 10.14

5.83 6.09 6.32

11.87 11.91 12.06

3.73 3.87 4.02

15.42 15.54 15.86

9.04 8.89 9.08

17.79 18.58 18.98 

20.39 20.06 19.91 

1/ Weighted average price includes fuels below as well as coal.

2/ This quantity is the weighted average for all petroleum products, not just those listed below.

3/ Excludes independent power producers.

4/ Includes combined heat and power.

5/ Excludes uses for lease and plant fuel.

6/ Diesel fuel containing 500 parts per million (ppm) or 15 ppm sulfur. Price includes Federal and

State taxes while excluding county and local taxes. 

7/ Kerosene-type jet fuel. Price includes Federal and State taxes while excluding county and local taxes. 

8/ Sales weighted-average price for all grades. Includes Federal, State, and local taxes. 

9/ Includes Federal and State taxes while excluding county and local taxes. 

10/ Compressed natural gas used as a vehicle fuel. Price includes estimated motor vehicle fuel taxes. 

11/ E85 is 85 percent ethanol (renewable) and 15 percent motor gasoline (nonrenewable). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-14
 Table 3. Energy Prices by Sector and Source (Continued) 

2010 2015 2020 2025

 Average End-Use Energy 10.25 

 Primary Energy 8.23 

 Electricity 19.58 

10.61 10.70 10.91

8.53 8.62 8.84

20.02 20.00 20.06

 Electric Generators 12/

 Fossil Fuel Average 1.94

 Petroleum Products 4.28

Distillate Fuel 4.94

Residual Fuel 3.99

 Natural Gas 4.11

 Steam Coal 1.22

2.16 2.12 2.12

4.57 4.73 4.96 

5.06 5.50 5.68 

4.14 4.32 4.48

4.80 4.72 4.97

1.22 1.21 1.22

 Average Price to All Users 13/

 Petroleum Products 2/ 9.57

 Distillate Fuel 8.93

 Jet Fuel 5.76

Liquefied Petroleum Gas 10.61

 Motor Gasoline 8/ 11.88

 Residual Fuel 3.78

 Natural Gas 5.30

 Coal 1.24

 Ethanol (E85) 11/ 17.21

 Electricity 19.58

9.65 9.84 10.04

8.95 9.14 9.24

5.83 6.09 6.32

11.19 11.56 11.97

11.87 11.91 12.06

3.93 4.08 4.23

5.95 5.80 6.06

1.24 1.23 1.24

17.79 18.58 18.98

20.02 20.00 20.06 

Non-Renewable Energy Expenditures by Sector 

(billion 2001 dollars)

 Residential 

 Commercial 

 Industrial 

 Transportation 

 Total Non-Renewable Expenditures

 Transportation Renewable Expenditures

 Total Expenditures

173.21 188.68 198.03 209.69

132.57 151.57 166.22 182.12

133.93 153.11 168.24 185.97

331.85 366.20 396.91 436.69

771.56 859.55 929.40 1014.47

0.03 0.04 0.05 0.07

771.59 859.59 929.46 1014.53

 11/ E85 is 85 percent ethanol (renewable) and 15 percent motor gasoline (nonrenewable).

 12/ Includes all electric power generators except combined heat and power, which produce electricity and other useful 

thermal energy. Includes small power producers and exempt wholesale generators.

 13/ Weighted averages of end-use fuel prices are derived from the prices shown in each sector and the 

corresponding sectoral consumption. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-15
 Table 4. Electricity Supply, Disposition, Prices, and Emissions

 (Billion Kilowatthours, Unless Otherwise Noted) 

2010 2015 2020 2025 

Generation by Fuel Type

 Electric Power Sector 1/

 Power Only 2/

 Coal 2170 2313 2581 2934

 Petroleum 67 109 79 82

 Natural Gas 3/ 644 797 922 943

 Nuclear Power 803 816 821 821

 Pumped Storage/Other -9 -9 -9 -9

 Renewable Sources 4/ 400 421 437 452

 Distributed Gen (Natural Gas) 1 1 2 7

 Non-Utility Gen for Own Use -37 -37 -37 -37

 Total 4039 4411 4796 5192

 Combined Heat and Power 5/

 Coal 33 33 33 33

 Petroleum 1 5 2 2

 Natural Gas 177 163 162 150

 Renewable Sources 4 4 4 4

 Non-Utility Gen for Own Use -24 -24 -24 -24

 Total 191 181 178 166

 Net Available to the Grid 4230 4592 4974 5357

 End-Use Sector Generation 6/

 Combined Heat and Power

 Coal 21 21 21 21

 Petroleum 12 15 18 18

 Natural Gas 108 129 176 235

 Other Gaseous Fuels 7/ 9 11 12 13

 Renewable Sources 4/ 39 45 50 54

 Other 8/ 11 11 11 11

 Total 201 233 289 354

 Other End-Use Generators 9/ 5 5 5 12

 Generation for Own Use -158 -175 -206 -251

 Total Sales to the Grid 47 63 88 114

 Net Imports 33 31 21 8

 1/ Includes electricity-only and combined heat and power (CHP) plants whose primary business is to sell electricity, 

or electricity and heat, to the public.

 2/ Includes plants that only produce electricity.

 3/ Includes electricity generation from fuel cells.

 4/ Includes conventional hydroelectric, geothermal, wood, wood waste, municipal solid waste, landfill gas, 

other biomass, solar, and wind power.

 5/ Includes combined heat and power plants whose primary business is to sell electricity and heat to the public 

(i.e., those that report NAICS code 22).

 6/ Includes combined heat and power plants and electricity-only plants in the commercial and industrial sectors.

 7/ Other gaseous fuels include refinery and still gas.

 8/ Other includes batteries, chemicals, hydrogen, pitch, purchased steam, sulfur, and miscellaneous technologies.

 9/ Other end-use generators include small on-site generating systems in the residential, commercial, and industrial sectors 

used primarily for own-use generation, but which may also sell some power to the grid. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-16
Table 4. Electricity Supply, Disposition, Prices, and Emissions (Continued) 

2010 2015 2020 2025 

Electricity Sales by Sector

 Residential 1423 1522 1632 1734

 Commercial 1470 1644 1820 1986

 Industrial 1124 1219 1306 1409

 Transportation 26 29 32 35

 Total 4043 4414 4790 5165 

End-Use Prices 10/ (2001 cents per kilowatthou 

Residential 8.0 8.1 8.1 8.1 

Commercial 7.0 7.2 7.2 7.2 

Industrial 4.6 4.7 4.8 4.8 

Transportation 6.7 7.0 6.8 6.8 

All Sectors Average 6.7 6.8 6.8 6.8 

Prices by Service Category 10/ 

(2001 cents per kilowatthour) 

Generation 4.2 4.4 4.4 4.5 

Transmission 0.6 0.7 0.7 0.7 

Distribution 1.9 1.8 1.7 1.7 

Emissions

 Sulfur Dioxide (million tons) 9.64 9.01 8.95 8.95

 Nitrogen Oxide (million tons) 3.47 3.58 3.65 3.72

 Mercury (tons) 52.39 53.21 54.18 54.46

 10/ Prices represent average revenue per kilowatthour. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-17 
 Table 5. Electricity Generating Capacity

 (Gigawatts) 

2010 2015 2020 2025 

Electric Power Sector 2/

 Power Only 3/

 Coal Steam 302.0 316.9 352.9 402.4

 Other Fossil Steam 4/ 102.1 98.6 97.1 94.9

 Combined Cycle 126.2 158.0 177.0 195.5

 Combustion Turbine/Diesel 128.0 146.5 160.6 170.5

 Nuclear Power 5/ 100.6 102.1 102.6 102.6

 Pumped Storage 20.3 20.3 20.3 20.3

 Fuel Cells 0.1 0.1 0.1 0.1

 Renewable Sources 6/ 98.2 102.0 104.7 108.0

 Distributed Gen (Nat Gas) 7/ 1.5 1.9 5.0 15.7

 Total 878.9 946.4 1020.4 1110.1

 Combined Heat and Power 8/

 Coal Steam 5.2 5.2 5.2 5.2

 Other Fossil Steam 1.1 1.1 1.1 1.1

 Combined Cycle 32.9 32.9 32.9 32.9

 Combustion Turbine/Diesel 5.4 5.4 5.4 5.4

 Renewable Sources 0.3 0.3 0.3 0.3

 Total 44.9 44.9 44.9 44.9

 Total Electric Power Industry 923.8 991.3 1065.2 1154.9 

Cumulative Planned Additions 9/

 Coal Steam 1.1 

 Other Fossil Steam 0.0 

 Combined Cycle 43.5 

 Combustion Turbine/Diesel 8.1 

Nuclear Power 0.0 

 Pumped Storage 0.0 

 Fuel Cells 0.1 

 Renewable Sources 4.3 

 Distributed Generation 0.0 

 Total 57.1 

1.1 1.1 1.1

0.0 0.0 0.0

43.5 43.5 43.5

8.1 8.1 8.1

0.0 0.0 0.0

0.0 0.0 0.0

0.1 0.1 0.1

4.6 4.7 4.8

0.0 0.0 0.0

57.4 57.5 57.6 

Cumulative Unplanned Additions 9/

 Coal Steam 

 Other Fossil Steam 

 Combined Cycle 

 Combustion Turbine/Diesel 

Nuclear Power 

 Pumped Storage 

 Fuel Cells 

 Renewable Sources 

 Distributed Generation 

 Total 

2.5 18.0 55.3 105.9

0.0 0.0 0.0 0.0

6.2 38.1 57.0 75.6

8.8 27.9 44.2 55.6

0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0

4.4 7.9 10.5 13.7

1.5 1.9 5.0 15.7

23.4 93.7 172.0 266.4

 Cumulative Total Additions 80.5 151.1 229.5 324.0

 Cumulative Retirements 10/

 Coal Steam 7.4 

 Other Fossil Steam 28.5 

 Combined Cycle 1.7 

 Combustion Turbine/Diesel 10.8 

Nuclear Power 0.0 

 Pumped Storage 0.0 

 Fuel Cells 0.0 

 Renewable Sources 0.1 

 Total 48.5 

8.0 9.2 10.4

32.0 33.5 35.7

1.7 1.7 1.7

11.4 13.5 15.1

0.0 0.0 0.0

0.0 0.0 0.0

0.0 0.0 0.0

0.1 0.1 0.1

53.2 58.1 62.9

 1/ Net summer capacity is the steady hourly output that generating equipment is expected to supply to 

system load (exclusive of auxiliary power), as demonstrated by tests during summer peak demand.

 2/ Includes electricity-only and combined heat and power (CHP) plants whose primary business is to sell electricity, 

or electricity and heat, to the public.

 3/ Includes plants that only produce electricity. Includes capacity increases (uprates) at existing units.

 4/ Includes oil-, gas-, and dual-fired capacity.

 5/ Nuclear capacity reflects operating capacity of existing units, including 4.3 gigawatts of uprates through 2025.

 6/ Includes conventional hydroelectric, geothermal, wood, wood waste, municipal solid waste, landfill gas, 

other biomass, solar and wind power.

 7/ Primarily peak-load capacity fueled by natural gas.

 8/ Includes combined heat and power plants whose primary business is to sell electricity and heat to the public 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-18
 Table 5. Electricity Generating Capacity (Continued) 

2010 2015 2020 2025 

End-Use Sector Generators 11/

 Combined Heat and Power

 Coal 4.1 4.1 4.1 4.1

 Petroleum 1.6 2.0 2.3 2.4

 Natural Gas 17.6 20.4 26.8 34.8

 Other Gaseous Fuels 2.2 2.4 2.6 2.7

 Renewable Sources 5.6 6.7 7.5 8.3

 Other 0.3 0.3 0.3 0.3

 Total 31.5 35.9 43.7 52.7

 Other End-Use Generators 12/ 0.0 0.0 0.0 0.0

 Renewable Sources 1.4 1.4 1.4 4.8

 Cumulative Additions 9/

 Combined Heat and Power 6.0 10.4 18.2 27.2

 Other End-Use Generators 0.2 0.2 0.2 3.6

 9/ Cumulative additions after December 31, 1999.

 11/ Includes combined heat and power plants and electricity-only plants in the commercial and industrial sectors.

 12/ Other end-use generators include small on-site generating systems in the residential, commercial, and industrial

sectors used primarily for own-use generation, but which may also sell some power to the grid. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-19
 Table 6. Carbon Dioxide Emissions by Sector and Source

 (Million Metric Tons Carbon Equivalent, Unless Otherwise Noted) 

2010 2015 2020 2025

 Residential

 Petroleum 29.9 29.5 28.9 28.1

 Natural Gas 82.2 84.5 88.6 91.0

 Coal 0.3 0.3 0.3 0.3

 Electricity 244.7 258.3 277.2 298.3 

Total 357.0 372.6 395.0 417.7

 Commercial

 Petroleum 18.1 

 Natural Gas 51.3 

 Coal 2.5 

 Electricity 252.7 

Total 324.6 

18.7 19.1 19.5

53.7 57.8 61.6

2.5 2.5 2.5

279.0 309.0 341.7 

354.0 388.4 425.3

 Industrial 1/

 Petroleum 100.1 

 Natural Gas 2/ 141.6 

 Coal 53.1 

 Electricity 193.2 

Total 487.9 

106.6 111.6 116.8

149.2 162.9 175.1

51.7 50.6 49.8

206.8 221.8 242.4 

514.5 546.9 584.1

 Transportation

 Petroleum 3/ 599.5 

 Natural Gas 4/ 11.2 

 Other 5/ 0.0 

 Electricity 4.5 

Total 3/ 615.1 

660.1 712.1 771.5

11.8 13.6 14.3

0.0 0.0 0.0

4.9 5.4 6.1 

676.8 731.2 791.8

 Total by Delivered Fuel

 Petroleum 3/ 747.6 

 Natural Gas 286.1 

 Coal 55.9 

 Other 5/ 0.0 

 Electricity 695.1 

Total 3/ 1784.7 

814.9 871.7 935.9

299.2 322.8 341.9

54.6 53.4 52.6

0.0 0.0 0.0

749.1 813.5 888.5 

1917.8 2061.4 2219.0

 Electric Power Sector 6/

 Petroleum 14.9 

 Natural Gas 97.9 

 Coal 582.2 

Total 695.1 

22.7 17.7 19.0

109.3 121.4 120.0

617.1 674.5 749.5 

749.1 813.5 888.5

 Total by Primary Fuel 7/

 Petroleum 3/ 762.5 

 Natural Gas 384.1 

 Coal 638.1 

 Other 5/ 0.0 

Total 3/ 1784.7 

837.6 889.3 954.9

408.6 444.2 462.0

671.6 727.9 802.1

0.0 0.0 0.0 

1917.8 2061.4 2219.0

 1/ Fuel consumption includes energy for combined heat and power plants, except those plants whose primary business is to sell 

electricity, or electricity and heat, to the public.

 2/ Includes lease and plant fuel.

 3/ This includes international bunker fuel, which by convention are excluded from the international 

accounting of carbon dioxide emissions. In the years from 1990 through 1998, international bunker fuels accounted 

for 25 to 30 million metric tons carbon equivalent of carbon dioxide annually.

 4/ Includes pipeline fuel natural gas and compressed natural gas used as vehicle fuel.

 5/ Includes methanol.

 6/ Includes electricity-only and combined heat and power (CHP) plants whose primary business is to sell electricity, 

or electricity and heat, to the public. Does not include emissions from the nonbiogenic component of municipal solid waste 

because under international guidelines these are accounted for as waste, not energy.

 7/ Emissions from the electric power sector are distributed to the primary fuels. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix A – MARKAL and NEMS – Page A-20
Appendix B – GPRA06 Biomass Program Documentation 

1. Introduction 

This appendix discusses the assumptions and methods employed in the biomass benefits analysis 

that is part of the fiscal year 2006 GPRA benefits analysis for all of the Department of Energy’s 

Energy Efficiency and Renewable Energy (EERE) research and deployment programs. The 

biomass benefits analysis focuses on the benefits of future achievements by the program and 

excludes retrospective benefits and benefits resulting from industry’s own initiative and funding. 

The major program focus is to enable integrated biorefineries producing a slate of products 

including fuels, chemicals, materials, and/or heat and power. The heat and power may be for 

internal biorefinery use, but may also be sold externally. Biorefineries process biomass into 

these products using biochemical processes (such as hydrolysis of biomass to sugars followed by 

fermentation of sugars to fuels and/or chemicals) or thermochemical processes (such as 

gasification of biomass to syngas followed by conversion of syngas to fuels or chemicals). 

Biorefinery configurations may vary as a function of site-specific conditions, including feedstock 

availability and price, local and regional market demand, and other factors. In addition to 

facilitating the development of future biorefineries, the program is collaborating with existing 

ethanol plants in evaluating the feasibility of enhancing their economic viability through 

increasing their ethanol output. This can be achieved with technology that converts the fibrous 

component of the corn kernels to ethanol, and, in the process, also convert the starch that is 

tightly bound by this fiber and so far, not available for conversion to ethanol in existing corn 

ethanol plants (“residual starch”). As an interim step leading to future biorefineries, the program 

is working with ethanol production plants (which are less-advanced, limited-capability 

biorefineries) on near-term technologies aimed at increasing the ethanol that these plants can 

produce from their traditional feedstock (corn kernels), while enhancing the value of the plants’ 

non-ethanol coproducts such as animal feed additives. 

In recent years, the National Renewable Energy Laboratory’s (NREL) design of a cellulosic 

ethanol plant has been a primary source of data for estimates of costs and environmental effects 

related to the conversion of lignocellulosic feedstock to fuels. With respect to nonfuel products, 

the Cargill Dow Company is operating the first U.S. bio-based plastics plant in Blair, Nebraska, 

with the feedstock being the starch from corn kernels. This analysis is consequently based on the 

concept of enhanced corn ethanol plants with the added production of corn fiber ethanol, residual 

starch ethanol, and eventually stover-based ethanol, and two interim concepts of biorefineries. 

The two concepts are “Ethanol Biorefineries” (E-biorefineries) that produce primarily ethanol 

and a small volume of high value coproducts (chemicals and materials), and “nonfuel 

biorefineries” that produce chemicals and/or materials, but not fuels. The Biomass Program is 

conducting midterm and long-term research, development and demonstration (RD&D) that will 

lead to better defined integrated biorefinery concepts. Because it will take some time for RD&D 

work to bear fruit, this analysis does not include the benefits that could be estimated once the 

integrated biorefinery concepts are defined and, therefore, likely underestimates the probable 

benefits of the program. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix B – Biomass Program – Page B-1 
A biorefinery industry is expected to result in biomass displacing most petroleum and natural gas 

feedstocks traditionally used in the production of fuels, chemicals, and materials. The 

biorefinery concept allows the cost of production to be reduced through synergies associated 

with feedstock handling and processing, and the allocation of capital and fixed O&M costs 

across multiple products. While the current analysis assumes that ethanol is the major output of 

E-biorefineries, future analyses could include additional fuels that the program may identify in 

the longer term. 

The E-biorefinery is envisioned to biochemically process cellulosic biomass into ethanol (the 

lignin residues from this process are used to make electricity that will be sold externally, 

reducing the net ethanol cost) and a small quantity of nonfuel chemicals whose economic 

benefits were modeled as a “credit” that reduces the ethanol production cost. EERE used the 

Ethanol Long Range Systems Analysis Spreadsheet with Biorefinery Advantages 

(ELSASBioref) to integrate ethanol supply/demand data with biorefinery synergy credit and 

“current law” tax incentives to estimate market penetration for ethanol. EERE used these results 

as input to the NEMS-GPRA06 and MARKAL-GPRA06 models to estimate benefits. 

In view of the large number of potential chemical products and materials that could be produced 

from biomass, this analysis did not assume specific nonfuel products, but instead assumed a 

category of “generic/composite” product for which EERE made assumptions based on a wide 

range of possible bio-based products (polymers, solvents, etc.). 

Bio-Based Products from Nonfuel Biorefineries 

The FY2006 analysis focuses on the benefits associated with a generic/composite bio-based 

product rather than specific products. This approach was adopted because there are too many 

potential bio-based chemicals and materials, and the markets for those are quite diverse, unlike 

transportation fuels where a few commodities account for most of the national market. 

Target Markets for Nonfuel Biorefineries 

A wide variety of chemicals and materials could potentially be produced at nonfuel biorefineries. 

Sugar and starch products derived through fermentation and thermochemical processes include 

alcohols, acids, starch, xanthum gum, and other products. Oil- and lipid- based products include 

fatty acids, oils, alkyd resins, glycerine, and a variety of vegetable oils. Gum and wood 

chemicals include tall oil, alkyd resins, rosins, pitch, fatty acids, turpentine, and other chemicals. 

Cellulose derivatives, fibers and plastics include products derived from cellulose, including 

cellulose acetate (cellophane) and triacetate, cellulose nitrate, alkali cellulose, and regenerated 

cellulose. Industrial enzymes are used as biocatalysts for a variety of biochemical reactions in 

the production of starch and sugar, alcohols, and oils; and are also used in laundry detergents, 

tanning of leathers and textile sizing. 

Some of these chemicals and materials represent end products, while others represent 

“intermediates” used in the production of other products. The potential target markets are even 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix B – Biomass Program – Page B-2 
more diverse than the list of potential products. Therefore, EERE did not characterize or analyze 

specific target markets for this benefits analysis. 

Key Factors in Shaping Market Adoption for Nonfuel Biorefineries 

Price will be the key factor in market adoption of products from nonfuel biorefineries. Most of 

these chemical and material products are commodity-based; and, as such, price will be the overriding factor in their penetration of their respective markets. In view of the “generic products” 

approach, meaningful comparisons of prices or other market factors for biobased products with 

competing hydrocarbon-based products are not possible. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix B – Biomass Program – Page B-3 
Methodology and Calculations for Nonfuel Biorefineries 

The methodology was based on an “averaged” bio-based product. EERE averaged the energyuse profile from the FY 2004 GPRA estimates to estimate the FY 2006 GPRA profile for the 

average generic bio-based product. The profile, which included a wide range of products 

(polymers, solvents, and other chemicals and materials), was averaged by summing the energy 

savings from the FY 2004 analyses and dividing the total by the volume of products it 

represented. The result is approximately 20,000 Btus of fossil energy displaced per pound of biobased product, with the displaced energy distributed between feedstock and processing 

requirements. This is conservative because it does not include the use of biomass for on-site 

energy generation, which would result in even greater fossil energy displacement. 

Starch/cellulose-based products will involve handling dilute aqueous streams throughout the 

production process, requiring considerable electricity for processes such as separation and 

purification. This fact and the conservative approach previously noted account for the negative 

electricity saving coefficient in Table 2. 

Based on the Biomass Program’s plans, bio-based products will enter the market with the first 

starch-based biorefinery starting up in 2007. The program’s experts assumed that the annual 

starch-based growth rate will be approximately 8% to 9% for the first 28 years, and gradually 

decrease to nearly 6% after 2035. They also assumed technical challenges will delay market 

entry for cellulose-based products until 2018, and that the annual growth rate for cellulosic 

products will be approximately 11% through 2030, 8.5% for 2031-2037, 6.5% for 2038-2044, 

and 5.5% for 2045 and thereafter. Initially high growth rates moderate as the industry matures, 

consistent with what one can observe in other industries. 

Table 1 shows the growth of bio-based products from nonfuel biorefineries. 

Table 1. FY 2006 Bio-Based Products from Nonfuel Biorefineries 

(Billion pounds per year) 

Year 2020 2025 2030 2035 2040 2045 2050 

Products from starch 0.61 0.96 1.44 2.12 2.91 3.99 5.20 

Products from cellulose 0.28 0.47 0.79 1.19 1.68 2.30 3.02 

Table 2 presents energy-related input that the program’s analysts provided to the EERE 

integrating models in the FY 2005 analysis, which serves as the basis for the current analysis1

. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix B – Biomass Program – Page B-4 
Table 2. FY 2005 Input to Integrating Models – 

Energy Savings for Bio-based Products 

(Energy-saving coefficients per billion lbs of bioproducts)

Natural Gas T Btu/billion lbs 4.5 

Coal T Btu/billion lbs -0.6 

B kWh/billion 

Electricity lbs -0.6 

Distillate T Btu/billion lbs 4.5 

Oil 

Feedstock T Btu/billion lbs 11 

Ethanol and Bio-Based Products from Enhanced Corn Ethanol Plants and E-Biorefineries 

The discussion in this section will focus on describing target markets and technical 

characteristics used in the analysis of the market penetration and benefits attributable to 

enhanced corn ethanol plants and E-biorefineries. 

Target Markets for Corn Ethanol Plants and E-Biorefineries 

Corn ethanol plants, both dry mills and wet mills (to learn more about these plants, use search 

feature at www.ethanolrfa.org), currently use corn kernels (no cellulosic feedstock) to produce 

ethanol and some coproducts such as animal feed additives (both dry and wet mills), or corn oil 

and high-fructose corn syrup (wet mills). Ethanol Biorefineries, or E-biorefineries, will use 

cellulosic biomass to produce ethanol fuel, high-value specialty chemicals and materials, and 

combined heat and power. The analysis for this type of biorefinery is limited to the benefits 

associated with ethanol because they will comprise most of the benefits of E-biorefineries. Thus, 

this analysis included only ethanol used as a transportation fuel for light-duty vehicles (passenger 

vehicles, mostly). While it is possible to envision an E-biorefinery using both cellulosic biomass 

and corn kernels as feedstock, it is outside the scope of this benefits analysis. 

In 2004, U.S. ethanol fuel production reached approximately 3.4 billion gallons, an increase of 

21% from the previous year. As of October 2004, 81 ethanol plants were producing and 11 

additional plants were under construction. Ethanol competes in transportation fuel markets for 

light-duty vehicles. In 2003, the U.S. prime supplier sales volume of motor gasoline was 

approximately 134 billion gallons2

. 

EERE targets ethanol technology for the gasoline additive market in the midterm and as a 

gasoline substitute/additive in the longer term. In 2003, approximately 99% of the ethanol 

consumed in the United States was for the gasoline additive market and 1% was for use as a 

gasoline substitute.3

 In 2004, the majority of the ethanol consumed in the additive market is used 

as an oxygenate component (additive) for gasoline, and the remainder is used as a gasoline 

additive to improve octane in conventional gasoline. Within the oxygenate market, in early 2004, 

methyl-tertiary-butyl-ether (MTBE) and ethanol each provided approximately 50% of the 

volume. However, ethanol is expected to take a much larger share of this market because MTBE 

has been or is being phased out in many states due to environmental concerns (see discussion of 

MTBE later in this section for additional detail). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix B – Biomass Program – Page B-5 
The Clean Air Act requires a minimum level of oxygen content in both reformulated gasoline 

(RFG) and oxygenated gasoline. RFG, which is required in ozone nonattainment areas, and 

oxygenated gasoline, which is required in carbon monoxide (CO) nonattainment areas, are not 

the same. Ethanol competes with MTBE in both of these oxygenate market segments. Most of 

the MTBE (and an increasing share of ethanol) are used in RFG, which is the most important 

market segment for oxygenates. Both ethanol and MTBE are used in the smaller oxygenated 

gasoline market segment, with ethanol being the dominant oxygenate. In a third market segment, 

ethanol is blended with conventional gasoline to make gasohol, which is primarily marketed in 

the Midwest. Gasohol consists of 90% gasoline and 10% ethanol by volume, with the ethanol 

serving as an octane enhancer and gasoline extender. 

After adjusting for its Federal excise tax exemption, the price of ethanol has historically tracked 

with the price of gasoline, whereas MTBE is normally priced at a premium relative to gasoline. 

However, MTBE used to be the oxygenate of choice in RFG for most refiners outside the 

Midwest because of its wider availability, more favorable blending characteristics for summer 

Reid Vapor Pressure, and ease of distribution. When blended into gasoline, ethanol raises the 

vapor pressure of the mixture, while adding MTBE to gasoline has only a minor effect on vapor 

pressure. Because ethanol absorbs water, which is typically present in small quantities in the U.S. 

petroleum products pipeline system, ethanol and ethanol blends are not routinely shipped via 

pipeline. Consequently, ethanol is shipped by rail, truck, and/or barge to distribution terminals, 

where it is blended into gasoline. MTBE is blended into gasoline at the refinery, and MTBE 

blends do not require any special handling compared with gasoline that has no MTBE. 

MTBE is currently the subject of environmental concern in several communities, due to its 

leakage and contamination of groundwater. It imparts a turpentine odor to water at low 

concentrations. There have been several efforts at the national level to completely phase out 

MTBE’s use in gasoline. At this time, these efforts have not succeeded. Eighteen states, 

however, have issued their own limits on MTBE use. The states that have enacted MTBE bans 

account for more than 60% of historical MTBE consumption. 

The 2003 production level for ethanol was 2.81 billion gallons per year. The consumption of 

MTBE in 2002 was approximately 4 billion gallons, but MTBE consumption has been declining 

as California, New York, Connecticut and other states transitioned from MTBE to ethanol. A 

national ban on MTBE would increase the demand for ethanol because ethanol, like MTBE, is a 

high-octane content, virtually sulfur-free additive that reduces toxic air emissions. Ethanol also 

will help solve the problem of fuel volume loss that would accompany an MTBE ban because 

oxygenates such as MTBE (or ethanol or other oxygenates), when blended in gasoline, also are 

used by the automobile engine as a fuel. Reformulated gasoline typically contains 11% MTBE. 

Vehicle fleets provide additional demand for ethanol fuel. These include alternative-fuel 

vehicles that have been either modified or manufactured to accommodate the use of E85 (85% 

ethanol and 15% gasoline) or E95 (95% ethanol and 5% gasoline). Many of these vehicles are 

flexible-fuel vehicles, enabling their use with gasoline or E85. The vehicle fleet market is 

dominated by government agencies, but also includes fleets owned by corporate entities and 

other organizations (taxi cabs, utilities, airport authorities, etc.). The use of green fuels in 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix B – Biomass Program – Page B-6 
Federal Government fleets is driven largely by the alternative-fuel vehicle requirements under 

the Energy Policy Act (EPACT) of 1992. The market penetration of E85 has been much lower 

than for E10 because (1) only a limited number or vehicles can use E85 (and fleet rules under 

EPACT do not necessarily require the use of alternative fuels in these vehicles), (2) E85 is 

generally more costly than gasoline on an energy-equivalent basis, and (3) the required 

investment for refueling infrastructure is greater for E85 and E95 than for E10. 

In the longer term, once production technology improvements achieve cost parity between 

ethanol and gasoline, ethanol will compete directly with gasoline in broader automotive fuel 

markets. In this instance, the growth of ethanol consumption eventually will become limited by 

the availability of biomass feedstocks, rather than by ethanol market demand. 

Baseline Technology Improvements for E-Biorefineries 

The degree to which this technology would progress in the absence of EERE’s biomass R&D has 

not been studied. Alternatively, EERE evaluated the technical and market barriers to the 

development of E-Biorefineries using cellulosic feedstock, and concluded that without Federal 

investment in RD&D, the cellulosic ethanol industry would grow at only 20% of the rate 

postulated in the EIA Reference Case for the Annual Energy Outlook 2004. The rationale for 

this conclusion is industry’s reticence to underwrite cellulosic ethanol research because of its risk 

and cost. For example, for a decade, the enzyme industry failed to show interest in partnering 

with EERE to develop low-cost enzymes for cellulosic ethanol production. Only in 2000-2001 

did they make the strategic decision to become key players in the development of the new 

ethanol industry. Feedstock collection infrastructure is another critical area in which industry 

has neglected to invest in the development of new technology. Sustained public/private 

collaboration continues to be needed before cellulose-based chemicals and ethanol can become 

competitive. 

Baseline Market Acceptance for E-Biorefineries 

Gasoline is a mix of both high- and lower-value petroleum-based components, with the highvalue components comprising only a small fraction of the total volume. With current ethanol tax 

incentives and ethanol’s value to refiners due to its environmental and octane characteristics, 

corn-based ethanol is competitive with the small fraction of high-value petroleum-based 

constituents of gasoline that give gasoline acceptable octane and emissions levels. Therefore, a 

small amount of ethanol (10% or less) can be blended with 90% or more gasoline to produce a 

fuel that is competitive with conventional gasoline. However, blending ethanol with gasoline in 

higher concentrations becomes less competitive because a gallon of ethanol has only two-thirds 

the energy of a gallon of gasoline, and it cannot compete with gasoline on an energy-equivalent 

basis. 

Ethanol is already widely used in gasoline and accepted as a component of transportation fuel in 

the target market. As the technology for producing cellulosic ethanol matures in the longer term, 

the retail value of cellulosic ethanol will become competitive with gasoline on an energy basis. 

At that point, fuel markets will likely accept nearly pure ethanol such as E85 because of its 

environmental characteristics and indigenous supply basis. Increases in market penetration for 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix B – Biomass Program – Page B-7 
ethanol will also be affected by competition from other alternative transportation fuels and 

success in overcoming the lack of an established nationwide E85 transportation and distribution 

infrastructure. Eventually, increases in market penetration may be constrained by the availability 

of feedstock, rather than market demand. 

Key Factors in Shaping Market Adoption for E-Biorefineries 

Price - The price of biomass-based fuels is sensitive to biomass feedstock costs, the decrease in 

net ethanol production costs resulting from integration with bio-based products in a biorefinery, 

and prices of competing fuels, such as gasoline. The previous section discussed the value of 

ethanol in the low-blend market (E10) versus the high-blend market (E85 or higher blends). 

Nonprice Factors – These include vehicle compatibility, infrastructure requirements, key 

consumer preferences/values, manufacturing factors, and policy factors. 

Vehicle Compatibility - In the E10 market, virtually all gasoline vehicles can use this low-blend 

ethanol gasoline mixture. For high blends such as E85, automobile manufacturers have 

considerable experience in producing vehicles that meet the Environmental Protection Agency’s 

requirements, due to a few million flex-fuel vehicles that have been sold in the United States; 

these include models of the Ford Taurus, Chevrolet S10 pickup truck, GMC Sonoma pickup 

truck, Isuzu Hombre pickup truck, Chrysler Voyager minivan, Dodge Caravan minivan, 

Chevrolet Silverado, and other models. 

Infrastructure Requirements - A 2002 study4

 on logistics barriers, sponsored by EERE, foresees 

no major infrastructure barriers to a substantial expansion of the ethanol industry in the scenarios 

it analyzes, which include substantial movement of ethanol among and within different regions 

of the country by several different modes of transport. The study reveals that a large number of 

investments in transportation, storage, terminalling, and retailing are possible without 

encountering significant “growing pains.” 

Although petroleum terminal improvements anticipated by the study represent significant capital 

investments for terminal operators, they amount to less than 1 cent per gallon of new ethanol 

volume on an amortized basis. In addition, with some assurance of increased throughput volumes 

at terminals (such as that provided by a Federal renewable fuel standard), terminal operators 

could be expected to make the improvements. 

The volume of product anticipated to be moved by railroad and river barge is a very small 

fraction of products moved by these industries. Furthermore, both the rail freight car-building 

industry and the barge-building industry have the capacity to build equipment that would keep 

pace with the increasing ethanol shipments from new plants. 

There are also operational strategies the ethanol industry could employ that would mitigate risk 

of supply disruptions caused by logistical glitches. Additional inventory levels at terminals and 

other storage locations could act as a cushion against delayed shipments and help ensure the 

smooth functioning of a growing market. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix B – Biomass Program – Page B-8 
While the study did not find any serious logistical impediments to expansion of the ethanol 

industry, it did identify two areas of potential concern that merit further study. These are the 

availability of Jones Act/OPA90-compliant vessels and barge movement in some areas of the 

U.S. inland waterway system as a result of vessel retirements. 

Ships that are used to transport ethanol are subject to various regulations and requirements. The 

Merchant Marine Act of 1920, otherwise known as the Jones Act, requires that all ocean or 

waterway transportation from one U.S. port to another U.S. port be moved in a vessel built in the 

United States, owned by a U.S. person or corporate entity, manned by a certified U.S. crew and 

registered in the United States (U.S. flagged). Tankers meeting these specifications are known as 

Jones Act tonnage. 

Vessels carrying petroleum products between U.S. ports are also subject to the Oil Pollution Act 

of 1990 (OPA90). This would include ethanol because ethanol is normally transported after 

having been “denatured,” with the addition of a small quantity of a petroleum product such as 

gasoline. OPA90 requires the use of double-hulled vessels and further requires the retirement of 

single-hulled vessels from petroleum product service by certain dates, based on their 

manufacture or rebuild date. 

Key Consumer Preferences/Values - Both E10 and E85 are likely to penetrate the market more 

easily in the Midwest where ethanol already is a familiar fuel. In addition, if the trend of 

increasing public awareness and environmental concern continues, this could become a 

significant positive factor in consumer choice in fuel markets in other regions outside of the 

Midwest. 

Manufacturing Factors - Cellulosic ethanol is envisioned as a major product – but not the only 

one – from a biorefinery. While various biorefinery configurations are possible, the two 

fundamental platforms are fermentation (sugar-based) and gasification (syngas-based). EERE is 

working with private industry to further develop these platforms, from which a host of fuels and 

chemicals may be derived. Initial plants will cost more in view of the perceived technical risks. 

As experience is gained with new plants, costs for each subsequent plant will decrease as a result 

of lessons learned and lower cost of capital associated with reduced risk. The Biomass Program 

has historically focused more on the fermentation platform for cellulosic ethanol, as this path 

was seen as a logical extension of the more mature starch-based ethanol process. Consequently, 

NREL and its subcontractors have extensively analyzed the process economics of the 

fermentation pathway. Because the focus on the syngas-based biorefinery is relatively new, our 

understanding of this pathway is not as developed as our understanding of the sugar-based 

pathway. For this reason, our analysis was limited to the sugar-based pathway. 

Policy Factors - In estimating the rate of market adoption, the analysis is based on the 

continuation of existing laws, regulations and policies (such as the ethanol tax incentive) and 

continuing USDA and DOE investment in biomass technologies RD&D at current levels, 

consistent with the Biomass R&D Act of 2000. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix B – Biomass Program – Page B-9 
Methodology and Calculations for E-Biorefineries 

Inputs to Base Case - Tables 3 and 4 show the results of the E-biorefinery analysis documented 

in this report, which serve as input to the integrated benefits analyses. NEMS-GPRA06 analysis 

extends through 2025, while MARKAL-GPRA06 analysis extends through 2050. The 

methodology employed to derive these inputs is described below. 

Table 3. FY 2006 Ethanol from Corn Ethanol Plants and E-Biorefineries 

 (Billion gallons per year) 

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 

Starch-Based 

Ethanol 3.42 3.73 3.97 4.02 3.92 3.64 3.53 3.53 3.53 3.53 

Cellulosic Ethanol 

from Corn Fiber 0.088 0.34 0.51 0.47 0.46 0.46 0.46 0.46 

Cellulosic Ethanol 

from Corn Stover, 

Other Wastes, and 

Energy Crops 1.20 3.60 6.20 9.00 10.2 13.2 

Total 3.42 3.73 4.05 4.36 5.63 7.71 10.2 13.0 14.2 17.2 

Table 4. FY 2006 Bio-Based Products from E-Biorefineries 

Year 2024 2025 2030 2035 2040 2045 2050 

Products as % of cellulosic 

ethanol by weight 1.0 3.0 3.0 5.0 5.0 7.0 7.0 

Products from cellulose in 

billion lbs. per year 0.05 0.24 0.71 2.05 2.97 4.72 6.11 

In Table 3, corn ethanol growth through 2050 is based on our latest assessment of the industry, 

not assuming a Renewable Fuels Standard because this has not been enacted in 2004. For 

cellulosic ethanol based on corn fiber conversion, a near-term technology being developed by the 

program and industry partners, we assumed success for dry mills only. While the other type of 

ethanol plants, wet mills, may also succeed in deploying this technology, the benefits from wet 

mills are not considered in order to make the estimates more conservative. The volume of 

cellulosic ethanol from corn fiber includes the ethanol resulting from converting the fiber in the 

corn kernel and the residual starch that can be converted once liberated from the fiber. The total 

increment in ethanol output would equal 20% of the current dry mill’s ethanol output. As 

previously stated, future E-biorefineries are assumed to use cellulosic biomass such as corn 

stover and energy crops, and not the corn kernel or its fiber as feedstock. 

Also in Table 3, cellulosic ethanol estimates from corn stover, other cellulosic wastes, and 

energy crops resulted from a market equilibrium analysis that competes ethanol versus petroleum 

constituents in the low-blend fuel market (E10) and versus corn ethanol. Future biorefineries 

will produce chemical coproducts from biomass along with the main ethanol output stream to 

enhance their economic viability. Table 4 shows the fraction of bio-based products produced in 

E-biorefineries from cellulosic feedstock. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix B – Biomass Program – Page B-10 
Technical Characteristics - The E-biorefinery analysis is based on a plant whose main product 

is fuel ethanol with coproduction of high-value bio-based products and electricity. This results 

in a reduced cost of ethanol due to the allocation of costs across several products. NREL has 

designed a narrowly defined biorefinery, i.e., a cellulosic ethanol plant whose products are 

ethanol and electricity generated from biomass wastes. To be conservative in the current 

biorefinery analysis, EERE estimated that bio-based coproducts reduce ethanol costs further by a 

modest 3% to 5%. This estimate will be refined when better data are available as a result of 

additional R&D and analysis. 

The analysis is for a biorefinery with a total throughput of 2,000 dry metric tons of feedstock per 

day and with a conversion efficiency increasing from approximately 70 gallons of ethanol per 

dry U.S. ton of feedstock currently to 95 in 2050, as a result of technological advances. 

Technical Potential - The biomass feedstock resources discussed here do not include wood 

waste and black liquor waste from paper mills, an important but captive resource – these 

resources are typically used within the forest and paper products industry. Under favorable R&D 

outcome and market scenarios, the upper bound for ethanol supply from U.S. biomass is 

estimated at 35 billion gallons per year, based strictly on feedstock availability. The farm-gate 

price and supply relationship for biomass used in the ELSASBioref model (for near-term 

conditions) are presented in Table 5. 

Table 5. Farm-gate Biomass Quantities Supplied vs. Price Range 

Excluding Mill Residues and Black Liquor 

(Million dry tons per year) 

Feedstock 

up to 

$20/dt 

up to 

$30/dt 

up to 

$40/dt 

up to 

$50/dt 

Forest Residues 0 12 20 70 

Agricultural Crops Residues 0 1 65 80 

Potential Energy Crops 0 0 80 187 

Other Wastes 0 17 25 35 

Total 0 30 190 372 

The total is 190 million dry tons per year at prices up to $40 per dry ton, and 372 million dry tons 

per year at up to $50 per dry ton, before adding transportation costs to the biorefinery. To be 

realistic, some of the future energy crops are assumed to be used in non-biorefinery applications. 

Therefore, only 66 percent of energy crops will count toward EERE’s benefits. Likewise, some 

of the forest and agricultural residues and other wastes will be used for fiber products and other 

non-biorefinery applications. The fraction of each feedstock available for use in biorefineries is 

shown below. 

While forest residues and some of the “other wastes” may not be optimal for sugar-based ethanol 

production, we recognize that future syngas-based fuels production may use forest residues and 

certain “other wastes” as feedstock. Therefore, this analysis is not deemed to be overly optimistic 

in spite of the assumption that biorefineries are sugar-based. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix B – Biomass Program – Page B-11 
Transportation costs ranging from $7.50 to $15.0 per dry ton (depending on hauling distance) 

were added to farm-gate prices to account for hauling to the biorefinery. After adding these 

costs and applying the factors shown in Table 6, the near-term supply as a function of price per 

dry ton at the biorefinery gate is shown in Table 7. 

Table 6. Fraction of Total Feedstock Assumed 

To Be Available To Biorefineries 

Feedstock Fraction 

Forest Residues 0.4 

Agricultural Crops Residues 0.7 

Potential Energy Crops 0.66 

Other Wastes 0.6 

Table 7. Biorefinery-gate Quantities Supplied vs. Price Range 

Excluding Mill Residues and Black Liquor 

(Million dry tons per year after transportation) 

Feedstock 

Up to 

$27.50/dry ton 

Up to 

$40.00/dry ton 

Up to 

$52.50/dry ton 

Up to 

$65.00/dry ton 

Agricultural Crops Residues 0 0.7 45 56 

Potential Energy Crops 0 0 53 123 

Forest and Other Wastes 0 15 23 49 

Total 0 16 121 228 

The annual quantity available for ethanol production, at up to $65 per dry ton (including costs of 

transportation to the biorefinery), has been reduced from 372 to 228 million dry tons after 

applying the reduction factors from Table 6. In the longer term (2040, for example), crop yields 

increasing at the rate of 1% per year will result in additional feedstock as shown in Table 8. 

Table 8. Long-term Biorefinery-Gate Supply vs. Prices 

Excluding Mill Residues and Black Liquor 

(Million dry tons per year after transportation) 

Feedstock 

Up to 

$27.50/dry ton 

Up to 

$40.00/dry ton 

Up to 

$52.50/dry ton 

Up to 

$65.00/dry ton 

Agricultural Crops Residues 0 1 68 83 

Potential Energy Crops 0 0 78 184 

Forest and Other Wastes 0 15 23 49 

Total 0 16 169 316 

At approximately 93 gallons of ethanol per dry ton of feedstock, the potential supply in the long 

term is 29 billion gallons per year. This potential would increase significantly with appropriate 

incentives such as those aimed at reducing carbon emissions. 

Expected Market Uptake - Although the proposed Renewable Fuels Standard (RFS) is 

expected by many to be enacted, this analysis is limited to existing policies and does not include 

consideration of the RFS. Corn ethanol is projected to continue to expand as a result of various 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix B – Biomass Program – Page B-12 
states’ phase-outs of MTBE, but only to approximately 4 billion gallons/year by 2014 compared 

with approximately 5 billion gallons/year under the proposed RFS. Future cellulosic ethanol 

capacity will slowly replace corn ethanol capacity as the new technology becomes more and 

more competitive relative to corn ethanol. The tables show corn ethanol continuing at a fairly 

stable level through 2050. 

The Biomass Program estimates that, beginning in 2012, corn ethanol plants will deploy the 

technologies for processing corn fiber, a cellulosic feedstock, into ethanol. This would be in 

addition to their continuing production of ethanol from corn starch. Beginning in 2018, a 

number of the ethanol plants will also convert corn stover to ethanol, if R&D is successful. 

Beginning in 2024, the first E-biorefineries producing ethanol as a major product (along with 

high-value coproducts) from biomass wastes and residues will be completed and begin operation. 

Note that a number of nonfuel biorefineries would have started producing before 2024, as 

described in the previous section on bio-based products analysis. Eventually energy crops (e.g., 

fast growing grasses) will supply the biorefineries to augment the residues. 

The analytic tool ELSASBioref was used to estimate ethanol market penetration for cellulosic 

ethanol, including from corn stover, energy crops and other cellulosic residues, but excluding 

corn fiber and residual starch. The market penetration of corn fiber and residual starch-based 

ethanol, small quantities in comparison with the other cellulosic ethanol, was determined 

exogenously with input from the Biomass Program experts. The following section describes 

ELSASBioref and its use for this analysis. 

Methodological Approach - Biomass ethanol market penetration analysis was accomplished 

through the integration of the results of various analyses conducted primarily by national 

laboratory personnel and their subcontractors. ELSASBioref served as the integrating tool. The 

following discussion provides a brief overview of ELSASBioref and the integration 

methodology. 

Integration of Component Analyses - The four principal components integrated with the 

ELSASBioref tool are corn feedstock and conversion cost and performance estimates, cellulosic 

feedstock supply curve information, cellulosic ethanol conversion technology data, and ethanol 

demand curve information. These components are described in greater detail in the following 

sections. 

ELSASBioref is a spreadsheet-based economic equilibrium analysis tool that integrates these 

four sets of data – along with additional technical, economic, and policy variables – to derive 

ethanol supply and demand curves and estimate market penetration (see Figure 1 depicting the 

input and output). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix B – Biomass Program – Page B-13 
( ) 

(

) 

(

ELSAS 

R e g io n a l 

R efin ery/B len d er 

D e m a n d 

F eed sto ck 

S u p p l y C u r v e s 

C a p ita l a n d 

O & M C o sts 

L earn in g C u rve 

P a r a m e t e r s 

O p e r a t i o n a l 

P a r a m e t e r s 

F in an c ia l 

P a r a m e t e r s 

E t h a n o l C o s t 

C urves 

M arket 

P e n e tra tio n 

O i l U s e R e d u c t i o n

E th a n o l F a ir 

M a rk e t P ric e s 

I N P U T S O U T P U T S 

B e n e f it /C o s t 

R a tio s

G r e e n h o u s e G a s 

R e d u c t i o n ) 

Figure 1. ELSASBioref Input and Output Parameter Categories 

The model uses cost projections provided by NREL’s design team to characterize the enzymebased technology used to convert cellulosic feedstocks to sugars and eventually ethanol. The 

model combines this data with cellulosic feedstock supply data to generate the cost and 

incremental supply of ethanol available for a given year. 

For the last year in each five-year increment (to 2050), ELSASBioref balances supply and 

demand of ethanol by establishing a market-clearing price. For supply levels greater than the 

amount of corn starch-based ethanol production, the marginal cost of ethanol supply at each fiveyear increment is determined by cellulosic ethanol production costs and feedstock costs. 

Feedstock costs may increase with increasing demand in accordance with supply curves provided 

by experts at Oak Ridge National Laboratory. 

Quantities demanded at different prices are represented in a demand curve for ethanol. For the 

last year in each five-year increment, supply and demand are balanced through a marketequilibrium price. The production of starch-based ethanol for that year is subtracted from the 

total demand for ethanol to calculate the total volume of cellulosic ethanol produced. Quantities 

of cellulosic ethanol produced in the first four years in the five-year increment are determined by 

interpolation. This process of determining market-equilibrium quantities and prices is performed 

for each five-year increment to 2050. 

Additional details regarding the three primary data-input components and their treatment within 

ELSASBioref are presented below. 

EERE provided estimates of growth rates for conventional corn starch based ethanol supply, and 

these were incorporated as additional input data in the ELSASBioref model. 

Cellulosic Feedstock Supply - Oak Ridge National Laboratory (ORNL) developed cellulosic 

feedstock supply curves with the aid of BIOCOST5

, POLYSYS6, and other regionally detailed 

models. The feedstock supply-curve information shows quantities of different categories of 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix B – Biomass Program – Page B-14 
cellulosic feedstocks available at different prices and time periods. This information is used by 

ELSASBioref at a national level of aggregation. The current GPRA case uses ORNL data 

reported by Arthur D. Little Inc7

. These data were modified based on more recent ORNL work 

on agricultural residue availability and cost8

. 

Within ELSASBioref, cellulosic feedstock costs are adjusted to include transportation charges 

from the farm gate to the conversion facility, and feedstock supplies are allocated among 

different competing uses as described above in the Technical Potential section. In addition, the 

analysis assumes that agricultural residues and bio-energy crops will increase at an annual rate of 

1% during the analysis period, due to increasing agricultural productivity. This assumption yields 

a total U.S. feedstock supply in 2040 approaching 316 million dry tons of agricultural residues, 

forest wastes, energy crops and other biomass wastes, after excluding potential competing uses. 

Ethanol Conversion Costs - NREL, which is partnering with industry and universities to develop 

competitive ethanol production technologies, provided estimates of cellulosic ethanol production 

costs (other than feedstock-related costs) on a per-gallon basis. The NREL estimate of the 

efficiency of converting feedstock into ethanol is input as a function of date, namely the number 

of gallons per dry ton of feedstock increases in the future as a result of R&D success. This allows 

the ORNL-provided feedstock costs to be presented on a per-gallon basis and added to the NREL 

non-feedstock costs to obtain the cost of producing a gallon of cellulosic ethanol. 

Ethanol Demand - Demand curves for ethanol (for use as a blending component with gasoline) 

are developed by ORNL refinery modelers. The value of ethanol to refiners is based on its 

blending characteristics including octane rating, toxics dilution, sulfur dilution, effect on Reid 

vapor pressure in summer reformulated gasoline, etc. The refinery model ORNL-RYM uses this 

value along with crude oil and gasoline price projections, public policy variables, and numerous 

technical and economic factors relating to oil refinery operations. For a given set of input 

assumptions, the ORNL analysis generates quantities of ethanol demanded by refineries for 

blending with gasoline at different prices. Procedures were developed to modify RYM outputs to 

different world oil price scenarios. Ethanol transportation costs also are used in the analysis. 

Benefits Estimation - The factors used by NEMS-GPRA06 and MARKAL-GPRA06 for 

calculating reductions in fossil energy use and carbon emissions were derived from the EERE 

Environmental Benefits Model GREET. The Greenhouse gases, Regulated Emissions, and 

Energy use in Transportation (GREET) model is maintained by Argonne National Laboratory 

and is widely used within EERE, by industry, universities, and other government agencies. 

GREET contains characterizations of several biomass feedstock sources, including herbaceous 

and woody biomass, corn, and soybeans. GREET models many transportation fuels and vehicle 

technologies and includes representations of major electricity generation sources. GREET can 

compare energy and emission changes for alternative technologies, relative to a base technology 

in a unified and consistent way. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix B – Biomass Program – Page B-15 
Sources 

1

 Renewable Fuels Association Press Release of January 22, 2004, accessed on the world wide 

web on 02-09-04 at http://www.ethanolrfa.org/pr040122.html. 2

 Petroleum Marketing Annual 2003, U.S. Energy Information Administration, Table 48. 

3

 Davis, S.C., and S.W. Diegel, “Transportation Energy Data Book.” Oak Ridge National 

Laboratory, Edition 23, October 2003.

4

 Reynolds, R. January 2002. “Infrastructure Requirements for an Expanded Ethanol Industry.” 

http://www.afdc.doe.gov/pdfs/6235.pdf. 

5

 Oak Ridge National Laboratory. BIOCOST: A Tool to Estimate Energy Crops on a PC. 

http://bioenergy.ornl.gov/papers/misc/biocost.html. 

6

 Daryll E. Ray, Daniel G. De La Torre Ugarte, Michael R. Dicks, and Kelly H. Tiller, "The 

Polysis Modeling Framework: A Documentation." Agricultural Policy Analysis Center, 

University of Tennessee, May 1998, http://apacweb.ag.utk.edu/polysys.html

7

 “Aggressive Growth in the Use of Bioderived Energy and Products in the U.S. by 2010” 

Unpublished report prepared by Arthur D. Little Inc. for U.S. Department of Energy, Oct. 2001. 

8

 Graham, R.L, "Key Findings of the Corn Stover Supply Analysis," Oak Ridge National 

Laboratory, unpublished paper, October 15, 2003. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix B – Biomass Program – Page B-16 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-1

Appendix C – GPRA06 Building Technologies (BT) Program 

Documentation 

1.0 Residential Buildings Integration ..................................................................... 3

1.1 Residential Building Energy Codes ..........................................................................3

1.2 Research and Development: Building America .......................................................5

2.0 Commercial Buildings Integration.................................................................... 9

2.1 Commercial Building Energy Codes .........................................................................9

2.2 Technology Research and Development................................................................13

3.0 Equipment Standards and Analysis ............................................................... 16

3.1 Commercial Unitary AC/HP Standards ...................................................................16

3.2 Distribution Transformers........................................................................................18

3.3 Sources......................................................................................................................21

4.0 Emerging Technologies .................................................................................. 21

4.1 Analysis Tools and Design Strategies ....................................................................21

4.2 Appliances and Emerging Technologies R&D .......................................................23

4.3 Envelope Research and Development ....................................................................26

4.4 Lighting Research and Development......................................................................37

4.5 Space Conditioning and Refrigeration R&D...........................................................41
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-2

Introduction 

Table 1 outlines the activities characterized for the GPRA06 Building Technologies Program. 

Characterizations and inputs for these activities were provided to the Department of Energy’s 

(DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) as inputs to EERE’s 

integrated modeling effort. 

Often such analysis requires the development and use of enabling or simplifying assumptions. 

In many cases, no citable sources exist for substantiating assumptions. Therefore, assumptions 

are developed through an iterative process with project managers, project contractors, and 

GPRA analysts. Often, we base these assumptions on project knowledge and experience, as 

there are varying degrees of corroborative studies available on which project information can be 

substantiated, depending on the maturity of the project. Enabling assumptions are sometimes 

relatively crude and should be revisited annually as new and better data are developed. 

Table 1. Building Technologies Subprograms, Projects, and Activities 

Subprogram Project Activity 

Research & Development: 

Building America 

Research & Development: Building 

America Residential Buildings Integration Residential Building Energy 

Codes Residential Building Energy Codes 

Research & Development Research & Development 

Commercial Buildings Integration Commercial Building Energy 

Codes Commercial Building Energy Codes 

Lighting R&D Lighting R&D: Controls 

Solid State Lighting 

Refrigeration R&D: Unitary DX System

Refrigeration R&D: Ventilation Load 

Reduction 

Refrigeration R&D: Commercial 

Refrigeration 

Space Conditioning & 

Refrigeration R&D

Refrigeration R&D: Remote Fault 

Detection and Diagnostics 

Appliances & Emerging Tech R&D: Roof 

Top AC 

Appliances & Emerging Tech R&D: 

Recessed Can Lights 

Appliances & Emerging 

Technologies R&D 

Appliances & Emerging Tech R&D: RLamp 

Window Technologies: Electrochromic 

Windows 

Window Technologies: Superwindows 

Building Envelope R&D: 

Window Technologies 

Window Technologies: Low-E Market 

Acceptance 

Emerging Technologies 

Analysis Tools and Design 

Strategies 

Analysis Tools and Design Strategies 

Standards: Commercial Unitary AC/HP 

Equipment Standards and Analysis 

Equipment Standards and 

Analysis Standards: Distribution Transformers 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-3

1.0 Residential Buildings Integration 

The long-term goal of Residential Buildings Integration is to develop cost-effective designs for 

net Zero Energy Buildings (ZEB)—houses that produce as much energy as they consume on an 

annual basis—by 2020. 

1.1 Residential Building Energy Codes 

1.1.1 Target Market 

Project Description. The Residential Building Energy Codes project improves the minimum 

or baseline energy efficiency of new residential buildings requiring code permits. The project 

promulgates upgraded energy-efficiency requirements for residential buildings. Similarly, the 

project works with model energy code groups to upgrade the energy-efficiency requirements of 

their codes. Federal, state, and local jurisdictions then adopt and implement these upgraded 

federal and model energy codes. The long-term goal is to improve the minimum energy 

efficiency by 20% to 25% in new low-rise residential building construction. 

Market Description. The market includes new residential low-rise buildings three stories or 

less in height and all additions and renovations to buildings requiring code permits. 

Size of Market. Each year, nearly 1.6 million residential building permits are issued, 

approximately 80% of which are single-family dwellings. Although not all jurisdictions 

currently have energy efficiency building codes in place, the Pacific Northwest National 

Laboratory (PNNL) estimates that about half of all new residential construction comes under 

building energy code requirements. Also, consumers spend several billion dollars a year on 

remodeling and renovating projects in private residences, about half of which could be covered 

by an energy code. One market not covered by codes is manufactured homes, which fall under 

Housing and Urban Development (HUD) jurisdiction and regulations. 

Baseline Technology Improvements. Initial compliance with new codes was assumed to be 

lower in the base case, i.e., without the Building Energy Codes project, than with the project. 

For FY06, the percentage of potential savings, in the first year of the single future code, was 

assumed to be approximately 35% for heating and cooling measures without the project. 

Baseline Market Acceptance. Under the baseline scenario, 23 states were assumed to have 

adopted the IECC 2000 or IECC 2003 standard by the end of 2005. The GPRA estimates were 

partly based on states' accelerated schedule of adoption of the IECC 2000 and IECC 2003 

codes. Through the efforts of the Building Energy Codes project, 31 states were assumed to 

have adopted the 2000 or 2003 standard by the end of 2005. The project was assumed to 

accelerate the adoption of the standard by an average of three years nationwide. 

1.1.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. PNNL assumed a five-year payback period on investment to develop incremental 

investment costs (i.e., an annual energy cost savings of $1 implies an initial investment of $5). 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-4

This corresponds to a total incremental cost of approximately $120 million in 2010, $285 

million in 2020, and $300 million in 2030. 

Key Consumer Preferences/Values. The following nonenergy characteristics were not 

considered. 

• Improved environment and more comfortable buildings. 

• Lower home maintenance and repair activities 

• Reduced pollution due to the reduced burning of fossil fuels and electricity generation, 

which improves air quality and mitigates the negative impacts of global warming. 

1.1.3 Methodology and Calculations 

Inputs to Base Case. With respect to codes, it is indeterminate as to whether potential future 

code improvements are incorporated into the National Energy Modeling System (NEMS) base 

case. The NEMS-GPRA06 base case does include some improvements to the building shell 

efficiency; however, the basis for these improvements (e.g., general building practice 

improvements, changes in codes requirements, improvements in materials) is not specified by 

the Energy Information Administration (EIA). Codes that have been issued (but that have not 

gone into effect) may be included in the NEMS-GPRA06 base case, but would not be included 

in the GPRA forecast of savings for the code development activity, because it no longer would 

be funded. Only an estimate of potential future codes is included in the GPRA estimates. 

Therefore, PNNL did not provide inputs to change the base case assumptions for the program 

markets. 

Technical Characteristics. The FY 2006 GPRA estimates are based on increased compliance 

with existing codes, accelerated adoption of the 2000 edition of the International Energy 

Conservation Code (IECC) code (to comply with Section 304 of the Energy Conservation and 

Production Act), and the future development of more stringent building codes. The energysavings methodology was applied at a state level to better link changes in the national codes 

(e.g., IECC 2000) with variations in climate by states (and differences among states) in their 

adoption and enforcement of building codes. This discussion uses national averages of some of 

the key assumptions related to adoption and compliance to help summarize the methodology. 

The principal difference between the 1995 Model Energy Code and the IECC 2000 involves the 

solar heat gain requirements for windows and increased thermal resistance requirements for 

ducts in unconditioned spaces. Based on a series of simulations for various U.S. locations, the 

percentage reduction in cooling load was estimated to be about 15%. This requirement 

increases the heating load by a small amount, about 2% nationally. The requirement itself is 

restricted to the southern tier of states. The GPRA estimates were partly based on states’ 

accelerated schedule of adoption of the IECC 2000 and 2003 codes. Through the efforts of the 

Building Energy Codes project, 31 states were assumed to have adopted the standard by the end 

of 2005. The project was assumed to accelerate the adoption of the standard by an average of 

three years nationwide. 

The IECC's ongoing activities were assumed to lead to more stringent residential standards in 

the future. The Department of Energy (DOE) was assumed to play a major role in developing 

the analytical and economic basis for such standards. For the GPRA process, these activities 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-5

were subsumed in a single upgrade of the IECC standard assumed to become available in the 

latter part of the current decade. Based on discussions with EERE-Building Technologies (BT) 

staff, PNNL assumed that the results of these upgrades were to reduce heating and cooling loads 

in new residential structures by 10%. Without these activities, PNNL assumed that the same 

standard would be adopted, on average, three years later.

Relationship to WIP. EERE’s efforts to support building codes covers two aspects: 1) the 

development of new codes with greater stringency or ease of enforcement and 2) activities 

to improve the compliance with codes and to accelerate adoption of the most recent codes 

by states and localities. The development of new codes is supported by the Building 

Technologies Program, and efforts to improve compliance and accelerate adoption are 

supported in the Weatherization and Intergovernmental Program (WIP). The methodology 

to develop the total effect from these two EERE programs is integrated. The documentation 

below discusses both aspects of EERE activities with regard to energy codes. 

More explicitly for modeling purposes, the GPRA energy savings estimates for BT (in 

regard to codes) is restricted to the development of a single new national residential code, 

expected to be published in the latter part of the current decade. However, with the ongoing 

efforts to promote adoption and compliance, the impact of the published code would be 

modest. However, without development of a new code, activities to promote adoption and 

compliance would be meaningless. Thus, the issue becomes assignment of savings from 

future code between the BT and WIP programs. In the GPRA estimates for 2006, 50% of 

the savings attributable to accelerated adoption and increased compliance of the new code 

were allocated to the BT program. 

Expected Market Uptake. The project's activities also were assumed to improve compliance 

rates for codes currently adopted by states and localities, as well as future building codes. 

Compliance increases through better familiarity with the codes, simplifications to the code 

while maintaining stringency, and the availability and increased use of compliance tools by 

builders and enforcement officials. Compliance rates, with and without the project, were 

estimated for various standards as discussed above. Compliance with the several key provisions 

in the IECC 2001 and 2003 (compared with the 1995 Model Energy Code) was expected to be 

higher from the outset. On average, compliance was estimated to be 68% in the year of the 

adoption. By 2010, compliance rates were assumed to increase to 69% without the project and 

74% with the project. For homes that do not comply with the standard, only half of the 

incremental energy savings were assumed to be achieved by adopting IECC 2001 or 2003. 

The analysis assumed that when states first adopt the new standard (assumed to become 

available in the 2006-2007 time frame), the potential energy savings from moving to the new 

standard would be 85% at the time of adoption, increasing to 90% with the effect of the project 

after the first 10 years. 

1.2 Research and Development: Building America 

1.2.1 Target Market 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-6

Project Description(1,2). The project's long-term goal is to develop integrated cost-effective 

whole-building strategies to enable residential buildings to use up to 70 percent less total energy 

than current code-compliant buildings by 2020 and provide up to 30% in additional energy 

savings through the use of integrated onsite power systems.a

 BT also will develop techniques to 

integrate new home energy efficiency and onsite power technology into existing homes to 

improve the energy efficiency of existing homes by up to 30 percent. In addition, user-friendly 

residential control packages are expected to be designed that interconnect and drive all 

components and reduce summer peak energy consumption by 100 percent when needed and 

annual energy consumption by 10-20 percent, by 2025. 

Market Description (1): The target market primarily includes all new residential homes. The 

new home energy conservation approaches will also be tested and demonstrated in existing 

homes beginning FY 2006, however the impacts on existing homes were not modeled for the 

FY 2006 effort. 

Size of Market(4): Each year about 1.2 million new housing units are built. In 2002, 976,000 

new single-family homes were built. These units are primarily owner occupied. 

Market Introduction: Initial penetration of zero-net energy designs began in the southwest in 

2003 and the design approach is anticipated to expand into the northern climate zones beginning 

in 2008(5). The renewable technologies supported by this project currently exist; however, 

penetration into the general market is expected to continue to be extremely low without DOE 

funding because the technology is currently unaffordable for production home builders. PNNL 

assumed that Building America activities would not occur without DOE funding; therefore, no 

acceleration of market acceptance was modeled. 

Baseline Technology Improvements. For this analysis, PNNL did not suggest any changes in 

technology improvements, apart from the EIA baseline. 

1.2.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Key Consumer Preference/Values – Nonenergy Benefits.

 The cost and performance 

characteristics were used to model this project in NEMS-GPRA06/MARKAL-GPRA06. The 

following nonenergy characteristics were not considered in the model: 

• Improved comfort, durability, and occupant health from better indoor air quality 

• Reduced on-site generated waste 

• Better sustainability 

• Reduced maintenance. 

1.2.3 Methodology and Calculations 

For any one year, the Building America project's energy savings are calculated by multiplying 

the number of homes built with Building America techniques that year multiplied by the percent 

 

a Whole house energy savings are measured relative to the BA Research Benchmark Definition (Building America, Building 

America Research Benchmark Definition, Version 3.1, November 11, 2003, National Renewable Energy Laboratory) which 

consists of the 2000 IECC requirements plus lighting, appliances and plug load energy levels (www.buildingamerica.gov) 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-7

savings per home. Added to this are the energy savings, accrued in that year, for Building 

America homes built in previous years, beginning in 2006. 

Incremental costs for whole-building energy savings were developed with Navigant 

Consulting's Residential Optimization Model (Version 5.7). Cost increments were developed 

for three levels of percentage savings from the baseline: -40%, -60%, and -70%. PNNL 

assumed that half of the costs and corresponding savings for the first level (equivalent to 20% 

savings from the baseline) would occur as a result of other related programs in EERE, namely 

appliance standards, building codes, and Energy Star homes. Thus, the savings percentages 

with Building America are translated to 20%, 40%, and 50% of the baseline unit. 

The ROM simulations were conducted for four cities: Minnesota, Boston, Atlanta, and Phoenix 

(see Table 2). Population weights to develop a national average were assigned in rough fashion 

(see Table 3). Because the NEMS shell module only treats heating and cooling, the energy 

savings from the inputs shown in Table 2 will underestimate the potential savings from BT's 

Residential R&D program. NEMS does produce the number of new homes that are deemed to 

use one of the five shell packages available in the model. Assuming the same cost and 

performance of the technologies not modeled specifically in the shell module, the total savings 

would be roughly three times that shown in the model (i.e., 1.00/0.30 = 3.33). These other 

savings would occur in lighting, water heating, and other appliances in homes building to 

Building America criteria. The challenge for the integrated modeling effort is to try to 

incorporate these additional savings, with a link to the number of homes using shell package 

four or five (as shown in Table 4). 

The Building America program is assumed to result in cost reductions in various residential 

technologies. The impact of this aspect of the program is shown in Shell package #5. Starting 

in 2010, the overall cost of the package is assumed to be 10%, falling by an additional 10% 

every five years. 

Table 2. ROM Simulation Results for Representative Cities 

 Minneapolis Boston Atlanta Phoenix 

 Cost Impact 

 All 

Technologies 

Building 

America Total Cost 

 Delta 

Cost Total Cost 

 Delta 

Cost Total Cost 

 Delta 

Cost Total Cost 

 Delta 

Cost 

 Base $46,499 $25,164 $22,884 $28,384 

20% 

40% 20% $48,297 $899 $27,373 $1,105 $24,818 $967 $29,646 $631 

60% 40% $51,543 $5,044 $30,793 $5,629 $28,376 $5,492 $32,671 $4,287 

70% 50% $62,467 $15,968 $39,880 $14,716 $39,784 $16,900 $40,112 $11,728 

 

 Energy Use MMBtu/HH MMBtu/HH MMBtu/HH MMBtu/HH 

 Base 214.9 191.7 164.2 176.0 

20% 172.0 153.4 131.3 140.8 

40% 129.0 115.0 98.5 105.6 

60% 107.5 95.9 82.1 88.0 

70% 64.5 57.5 49.3 52.8 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-8

Table 3. Population Weights and Incremental Costs for Representative Cities 

 Incremental Costs, Building America 

 City Weight 20% 40% 50% 

 Minneapolis 0.2 $899 $5,044 $15,968 

 Boston 0.3 $1,105 $5,629 $14,716 

 Atlanta 0.3 $967 $5,492 $16,900 

 Phoenix 0.2 $631 $4,287 $11,728 

 Average * $927 $5,203 $15,024 

 HVAC share ** 0.3 $278 $1,561 $4,507 

*Costs for percentage reduction in whole-building energy use 

**Costs for percentage reduction in heating and cooling consumption 

Table 4. Suggested Adjustments to NEMS Shell Factors 

Heating Shell Efficiency Adjustments (multiplicative factors) 

Package 2003 2005 2010 2015 2020 2025

4* 1.00 0.80 0.80 0.60 0.50 0.50

5* 1.00 1.00 0.80 0.60 0.50 0.50

 Cooling Shell Efficiency Adjustments (multiplicative factors) 

Package 2003 2005 2010 2015 2020 2025

4 1.00 0.80 0.80 0.60 0.50 0.50

5 1.00 1.00 0.80 0.60 0.50 0.50

 Shell Cost Adjustment Factors (Amount Subtracted) 

Package 2003 2005 2010 2015 2020 2025

4 0 -$278 -$278 -$1,561 -$4,507 -$4,507

5 -$223 -$1,093 -$2,704 -$2,254

* Packages 4 and 5 represent Building America 

** Costs are incremental, above the baseline 

PNNL refined the target market to reflect new home sale prices because homes built with 

renewable energy technologies will be targeting the higher-end housing market. Based on U.S. 

Census(6,7) data, PNNL assumed that high-end homes would be represented by those new homes 

that sold for at least $200,000 in the South and West regions (about 45% of new homes sold in 

those regions) and Northeast and Midwest regions (about 48% of new homes sold in those 

regions). 

The fundamental premise leading to wide adoption is that existing technologies and projects 

will eventually reduce energy use by about 70% and reduce summer peak loads to zero. This, 

in turn, will result in significantly less solar electric and solar thermal technology needed to 

supply the home’s load, while shaving summer peak loads and thereby alleviating some of the 

need to expand the grid to accommodate system summer peaks. With much improved load 

characteristics, DOE expects zero-net energy houses through 2007 to receive slightly lower 

electric rates or utilize time of use (TOU) rates, and by 2020 will have a zero electric bill in 

return for zero summer peak loads(5). 

Estimates do not include potential applications to manufactured homes or in existing buildings – 

both project goals – as cost estimates for these targeted areas are not yet available. Developers 

are assumed to be more likely to negotiate for favorable electrical service with local utilities – 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-9

based on the zero-net energy home concept. Energy savings resulting from adoption by smaller 

spec builders and one-off builders are not captured but could be significant if utilities offer a 

renewable energy rate to all homeowners. 

1.2.4 Sources 

(1) “Building Technology Program: Research, Development and Demonstration Plan, Planned 

Program Activities for 2004-2010.” Final Draft. U.S. DOE, January 9, 2004. 

(2) Final Draft: Zero Energy Homes’ Opportunities for Energy Savings: Defining the Technology 

Pathways Through Optimization Analysis, U.S. Department of Energy Building Technologies 

Program, October 2003. 

(3) U.S. Department of Energy, Building America Research Benchmark Definition. Version 3.1, 

November 11, 2003. Accessed online March 2004, at 

http://www.eere.energy.gov/buildings/building_america/benchmark_def.html. 

(4) Annual Energy Outlook (AEO) 2003, Department of Energy, Energy Information Administration. 

(5) Information obtained in discussions with the project manager, Lew Pratsch, August/September 2003. 

(6) New Houses Sold, by Region, by Sales Price: Annual Data. U.S. Census Bureau, Manufacturing 

and Construction Division. www.census.gov/const/regsoldbypricea.pdf, accessed August 8, 2003. 

(7) Buildings Energy Databook (July 26, 2003), Table 5.1.1., “2001 Five Largest Residential 

Homebuilders.” 

2.0 Commercial Buildings Integration 

The long-term goal of this subprogram is to develop cost-effective designs for commercial 

buildings that produce as much energy as they use on an annual basis. Research will focus on 

reducing total energy use in a commercial building by 60% to 70%. 

2.1 Commercial Building Energy Codes 

2.1.1 Target Market 

Project Description. The Commercial Building Energy Codes project improves the minimum 

energy efficiency of new commercial and multifamily high-rise buildings and additions and 

alterations to existing buildings requiring code permits. The project promulgates upgraded 

energy-efficiency requirements for federal commercial and high-rise residential building types. 

Similarly, the project works with model energy code groups to upgrade the energy-efficiency 

requirements of their codes. These upgraded national energy standards are then adopted by 

federal, state, and local jurisdictions as part of their building codes. The project's long-term 

goal is to improve minimum energy efficiency by 30% to 35% in new commercial building 

construction. Energy use will be reduced by states and local jurisdictions widely adopting the 

national standards as building energy codes. 

Market Description. The market includes new commercial and multifamily high-rise (above 

three stories) buildings and all additions and renovations to commercial buildings requiring 

code permits. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-10

Size of Market. The commercial market size is about 2 billion square feet of new commercial 

floor space each year. The Federal sector represents nearly 2.3% overall of new commercial 

building construction. 

Baseline Technology Improvements. Initial compliance with new codes was assumed to be 

lower in the base case, i.e., without the Building Energy Codes project. For FY06, the 

percentage of potential savings, in the first year of the single future code, was assumed to be 

approximately 20% for envelope measures and 30% for lighting measures without the project. 

Baseline Market Acceptance. The FY 2006 GPRA estimates are based on increased 

compliance with existing codes, accelerated adoption of the 1999 and 2001 editions of 

ASHRAE 90.1-1999(4) standard (to comply with Section 304 of the Energy Conservation and 

Production Act), and the future development of more stringent building energy codes. Through 

the efforts of the Building Energy Codes project, 21 states were assumed to have adopted the 

standard by the end of 2005. The project was assumed to accelerate the adoption of the 

standard by an average of four years nationwide. 

2.1.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. PNNL developed incremental investment costs by assuming a five-year payback period 

on investment (i.e., an annual energy cost savings of $1 implies an initial investment of $5). 

Key Consumer Preferences/Values. The following nonenergy characteristics were not 

considered. 

• Improved environment and more comfortable buildings. 

• Lower utility bills 

• Lower home maintenance and repair activities 

• Reduced pollution due to the reduced burning of fossil fuels and electricity generation, 

which improves air quality and mitigates the negative impacts of global warming. 

2.1.3 Methodology and Calculations 

Inputs to Base Case. With respect to building codes, it is indeterminate the extent to which 

potential future code improvements are incorporated into the NEMS-GPRA06 base case. The 

NEMS-GPRA06 base case does include some improvements to the building shell efficiency; 

however, the basis for these improvements (e.g., general building practice improvements, 

changes in code requirements, and improvements in materials) is not specified by EIA. The 

impact of accelerated adoption and improved compliance by states of recently issued national 

building standards (e.g., IECC 2003) is included in the GPRA forecast of savings. The GPRA 

savings estimates for WIP also include a portion of the impact of changes in building codes that 

are anticipated within approximately the next 10 years. (A portion of the savings from 

increased stringency of future codes is also allocated to the Building Technologies Program). 

Therefore, PNNL did not provide inputs to change the base case assumptions for the program 

markets. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-11

Technical Characteristics. Energy savings from this project result from some basic 

improvements to the overall energy efficiency of commercial buildings in their space-heating, 

space-cooling, and lighting loads. This project funds research analysis of cost-effective levels 

of energy codes for new commercial and multifamily high-rise buildings. This BT program 

works with the Training and Assistance for Codes project within the Office of Weatherization 

and Intergovernmental Programs, which funds the development of core materials (such as 

compliance tools and training materials) and provision of training and financial and technical 

assistance for states to update and implement their building energy codes. Benefits cannot be 

clearly allocated to either project, thus the benefits estimated are a function of both training and 

deployment as well as development of the commercial building energy codes and standards. 

Savings estimates for commercial codes are based on increased stringency from the combined 

impact of the forthcoming ASHRAE 90.1-2004 code and the “next” code assumed to be 

published in 2007. For FY06, future codes (up through 2010) are assumed to achieve a total 

reduction of 18% in electricity and a 10% reduction in natural gas as compared to 90.1-1999, 

based on a series of simulations for various U.S. locations. Benefits for FY 2006 were assumed 

to be allocated according to the ratio of actual funding levels. 

The project impacts energy consumption through two primary avenues: 1) developing and 

supporting code changes to improve the minimum energy-efficiency requirements for 

commercial and multifamily high-rise buildings and 2) providing technical and financial 

assistance to states to update and implement their building energy codes. The latter includes 

developing tools that can ease the adoption of new codes and through their use, can support 

improvements in compliance and enforcement of code provisions. Tools take the form of code 

compliance software, computer-based training tools for building energy codes, and tools for 

implementing noncomputer-based codes. 

Improvements to building codes are primarily supported by research efforts to review existing 

codes and specific targeted areas of building energy use, as well as the adoption of code 

modifications that promote cost-effective reductions in these energy-use areas. Support for the 

research work has typically taken place in three areas: 

• Upgrading ASHRAE/IES Standard 90.1-1989, "Energy Efficient Design of New Buildings 

Except Low-Rise Residential Buildings"(1) 

• Upgrading the Federal commercial and multifamily high-rise building energy code, 10 CFR 

434, "Energy Code for New Federal Commercial and Multi-Family High Rise Residential 

Buildings"(2) 

• Upgrading the International Energy Conservation Code (IECC).(3)

The FY 2006 GPRA estimates are based on increased compliance with existing codes, 

accelerated adoption of the 1999 and 2001 editions of ASHRAE 90.1(4) standard (to comply 

with Section 304 of the Energy Conservation and Production Act), and the future development 

of more stringent building energy codes. The energy-savings methodology was applied at a 

state level to better link changes in the codes with variations in climates by states and 

differences among states in their adoption and enforcement of building codes. The discussion 

below uses national averages of some of the key assumptions related to adoption and 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-12

compliance to help summarize the methodology, but appropriate state averages were used in the 

analysis. 

The principal differences between the ASHRAE 90.1-1989, 90.1-1999, and 90.1-2001(5)

standards relate to requirements for better windows, reduced installed wattage for lighting, and 

more efficient heating and cooling equipment. The savings from improved equipment are not 

included in the project's savings estimates, because they are reflected in the Equipment 

Standards and Analysis decision unit in this appendix. Based on a series of simulations that 

include various U.S. locations and that were developed specifically to evaluate the two 

ASHRAE standards (often referred to as the “determination” study[6]), the average reduction in 

site energy use was estimated to be about 3.5% or 2 MMBtu/sq ft. The GPRA estimates were 

partly based on states' accelerated adoption schedule of the ASHRAE 90.1-1999 and 90.1-2001 

standards. Through the efforts of the Building Energy Codes project, 21 states were assumed to 

have adopted the standard by the end of 2005. The project was assumed to accelerate the 

adoption of the standard by an average of four years nationwide. 

The ongoing activities of the ASHRAE 90.1 committee were assumed to lead to more stringent 

commercial-building standards in the future. DOE was assumed to play a major role in 

developing the analytical and economic basis for such standards. For the GPRA process, these 

activities were subsumed in a single upgrade of the ASHRAE standard, assumed to become 

available in the latter part of the current decade. The GPRA analysis assumed that the overall 

result of these upgrades is to reduce electricity consumption by 10% and natural gas 

consumption by 10% in new commercial buildings. Many states adopting this standard by 2010 

also depends on the project's continuing activities to assist states in the adoption (and 

compliance) process. Without these activities, the analysis assumed that the same standard 

would be adopted, on average, six years later. 

The project activities also were assumed to improve compliance rates for codes currently 

adopted by states and localities, as well as future building codes. Compliance is increased 

through increased familiarity with the codes, simplifications to the code while maintaining 

stringency, and the availability and increased use of compliance tools by builders and 

enforcement officials. Compliance is effectively measured as the percentage of potential 

savings moving from one code to the next. Compliance rates estimated between the existing 

code (assumed to be 90.1-1989) and a code based on ASHRAE 90.1-1999; and between 90.1-

1999 and a new code discussed above. 

Without the program, the percentage of potential savings is assumed to be modest, as the 

program is directed toward software tools and training that facilitate adherence to the code. In 

this case, on average, PNNL estimated the percentage of potential energy savings for envelope 

measures to be about 20% in the year of adoption. Ten years later, the percentage of potential 

energy savings is assumed to increase to approximately 50%. For lighting, these percentages 

were 30% and 55%, respectively. With the program, the percentage of potential energy savings 

is expected to be higher at the outset and increase more rapidly. For envelope measures, the 

initial potential savings is about 70%, increasing to about 95% 10 years later. For lighting 

measures, the initial percentage of savings is 80%, again increasing to about 95% years later. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-13

Expected Market Uptake. As part of work for an unpublished analysis of the historical 

impacts of Building Energy Codes in August 2003, the assumptions regarding the acceleration 

effect of the program were modified (e.g., program activities leading to states adopting codes 

more rapidly than they would have otherwise). In general, the states were classified into groups 

that: 1) immediately adopted the ASHRAE 90.1-1989 code, 2) would have adopted within five 

years without the building codes project, or 3) would have adopted within 10 years without the 

building codes project. These time periods were then reduced by one year for each successive 

major code cycle after the 1989 code. (For example, a five-year lag for 90.1-1989 is assumed to 

fall to three years for the forthcoming ASHRAE 90.1-2004 code). The overall impact of this 

change was to decrease the average lag between the publication of a new standard and when it is 

adopted – without the project. This modified set of assumptions increases the overall estimate 

of the future energy savings impact from the program. 

2.1.4 Sources 

(1) ASHRAE/IES Standard 90.1-1989, "Energy Efficient Design of New Buildings Except Low-Rise 

Residential Buildings," American Society of Heating, Refrigeration, and Air-Conditioning Engineers 

and Illuminating Engineering Society. 

(2) 10 CFR 434, "Energy Code for New Federal Commercial and Multi-Family High Rise Residential 

Buildings," Code of Federal Regulations, as amended. 

(3) International Energy Conservation Code. 2003. International Code Council, Falls Church, Virginia. 

(4) ASHRAE/IES Standard 90.1-1999, "Energy Standard for Buildings Except Low-Rise Residential 

Buildings," American Society of Heating, Refrigeration, and Air-Conditioning Engineers. 

(5) ASHRAE/IES Standard 90.1-2001, "Energy Standard for Buildings Except Low-Rise Residential 

Buildings," American Society of Heating, Refrigeration, and Air-Conditioning Engineers.

(6) U.S. Department of Energy. March 2002. “Commercial Buildings Determinations, Explanation of 

the Analysis and Spreadsheet (90_1savingsanalysis.xls).” 

http://www.energycodes.gov/implement/determinations_com.stm

2.2 Technology Research and Development 

2.2.1 Target Market 

Project Description. (1) In order to reach net zero conventional energy buildings (ZEB) by 

2025, DOE will employ integrated whole-building strategies to enable commercial buildings to 

be designed and constructed to use 70% less energy. By 2010, the BT goal is to integrate 

design approaches, highly efficient component technologies and controls, improved 

construction and maintenance practices, and operating procedures that will make new and 

existing commercial buildings durable, healthy and safe for occupants, and will reduce energy 

use for new buildings by 50% and by 30% for existing buildings, relative to conventional 

practice. b 

Market Description(1): Although this project does not explicitly exclude any particular 

building type, the types of commercial buildings that will most likely be impacted by the 

technologies developed by this project primarily include small commercial buildings with 

 

b

 Energy savings are measured relative to the 2001 International Energy Conservation Code (IECC). 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-14

relatively higher energy use intensities such as assembly, education, food service, food sales, 

lodging, mercantile and service, and office buildings. 

Baseline Technology Improvements. For this analysis, PNNL did not suggest any changes in 

technology improvements, apart from the EIA baseline. 

Baseline Market Acceptance. In 1998, PNNL conducted a study examining the historical 

market penetration for 10 energy-efficient products related to the buildings sector. The results 

of this study are documented in the PNNL report, Methodological Framework for Analysis of 

Buildings-Related Programs: The GPRA Metrics Effort (2004)(6). The study suggested several 

generic penetration curves based on the type of equipment of interest. PNNL used the curve 

related to design products to model this project. 

2.2.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. 

Cost of Conventional Technology(4): Average of $101/ft2 for the targeted new commercial and 

multifamily; $0 for existing buildings. 

Cost of BT Technology(5): $103.00/ft2

 for new commercial and multifamily; $3/ ft2 (2001 to 

2009), increasing to $4/ ft2

 (2010 to 2030) for existing buildings. 

Incremental Cost(5): 2% above base for new buildings; $3/ ft2 (2005 to 2009), increasing to 

$4/ ft2

 (2010 to 2030) for existing buildings. 

Key Consumer Preference/Values – Nonenergy Benefits. The following nonenergy 

characteristics were not considered in developing energy output estimates: 

• Reduced operation and maintenance expenses 

• Improved indoor environmental quality 

• Increased property asset value 

• Higher tenant satisfaction and retention rates 

• Increased technology sales. 

2.2.3 Methodology and Calculations 

Inputs to Base Case. The base case was developed based on an assortment of sources including 

AEO 2003, CBECS 95, RECS 97, and several other sources, all of which are documented in the 

2004 PNNL report, Methodological Framework for Analysis of Buildings-Related Programs: The 

GPRA Metrics Effort (Elliott et al 2004). 

Technical Characteristics. In concert with the Analysis, Tools, and Design Strategies project, 

the performance goals are to reduce heating and cooling loads by 50% in new small commercial 

construction and by 30% in existing buildings.c

 

c

 The percentage of the load reduction attributed between Commercial R&D and Analysis Tools and Design Strategies is in 

proportion with their respective budget requests. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-15

Expected Market Uptake. The market penetration goal(3) is to accelerate the penetration of 

high-performance building designs, such that 60% of new commercial and multifamily 

construction (Figure 1) and 20% of existing construction incorporates the products supported 

by this project by 2020 (Figure 2). Penetration curves were developed based on market 

diffusion curves developed by PNNL(6). PNNL assumed that this project accelerates the 

adoption of relevant energy-savings products, technologies and designs by 10 years. 

0

10

20

30

40

50

60

70

80

90

100

2005

2010

2015

2020

2025

2030

% Market Share

Accelerated Penetration with DOE

R&D

Penetration without DOE R&D

Net Penetration from DOE R&D

Figure 1. Market-Penetration Curve for Commercial R&D Project Targeting New Buildings 

0

10

20

30

40

50

60

70

80

90

100

2005 2010 2015 2020 2025 2030

Market Share (%)

Accelerated Penetration with DOE R&D

Penetration without DOE R&D

Net Penetration from DOE R&D

Figure 2. Market-Penetration Curve for Commercial R&D Project Targeting Existing Buildings 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-16

2.2.4 Sources 

(1) “Building Technology Program: Research, Development and Demonstration Plan, Planned Program 

Activities for 2004-2010.” Final Draft. U.S. DOE, January 9, 2004. 

(2) Torcellini, Paul, et. al. Lessons Learned from Field Evaluation of Six High-Performance Buildings, 

NREL/CP-550-36290, National Renewable Energy Laboratory, June 2004. 

(3) E-mail correspondence with project manager, Dru Crawley, June 2003. 

(4) RS Means Company, Inc. 2002. “RS MEANS Square Foot Costs.” 23rd Edition, Kingston, MA. 

(5) Kats, Greg (Capital E), et. al. “The Costs and Financial Benefits of Green Buildings,” A Report to 

California’s Sustainable Building Task Force. October 2003. 

(6) Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics Effort. 

PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 

3.0 Equipment Standards and Analysis 

As legislatively mandated, BT will pursue energy efficiency standards for appliances and 

building equipment, setting higher standards where technologically feasible and economically 

justified. 

3.1 Commercial Unitary AC/HP Standards 

3.1.1 Target Market 

Project Description. DOE is required by the Energy Policy and Conservation Act and its 

amendments to consider national energy conservation standards for certain commercial unitary 

air conditioners and heat pumps. 

Market Description: The market includes all residential and commercial equipment covered 

by the appropriate legislation.(1,2) 

Size of Market: The market size includes all applicable residential and commercial equipment 

in the market to which legislation applies (ovens/ranges and medical equipment, for example, 

are not covered). 

Baseline Technology Improvements. For this analysis, PNNL did not suggest any changes in 

technology improvements, apart from the EIA baseline. 

3.1.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. Incremental investment costs were developed assuming a nine-year payback period on 

investment (i.e., an annual energy cost savings of $1 implies an initial investment of $9)d

. This 

 

d

 Screening Analysis for EPACT-Covered Commercial HVAC and Water Heating Equipment (PNNL-1223). P. D-12. 

Payback period for Upgrade Group relative to EPCA 1992 for Central Air Source AC Units, >= 65K, < 135 kBtu/h 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-17

corresponds with a total incremental investment cost of approximately $200 million in 2005, $1 

billion in 2010, $1.4 billion 2020, and $600 million in 2030. 

Key Consumer Preferences/Values. The following nonenergy characteristics were not 

considered in developing energy output estimates: 

• Reduced CO2 and SOX emissions 

• Reduced water consumption from plumbing equipment 

• Increased life of equipment operating at cooler temperatures 

• Reduced first costs that transform new technologies into commodities. 

3.1.3 Methodology and Calculations 

Technical Characteristics. For FY 2006, the energy savings from commercial equipment 

standards activities were based primarily on a PNNL screening analysis conducted in late 1999 

and early 2000(3) to provide preliminary estimates of the potential energy savings from updated 

commercial equipment standards. PNNL used the spreadsheet developed for this study to 

estimate the energy savings from various levels of standards for nearly 40 types of equipment 

covered by the Energy Policy Act (EPAct). The spreadsheet results were used to identify 

technologies that could achieve significant energy savings beyond the efficiency levels set in 

the recent ASHRAE 90.1-1999 publication.(4) 

Based on the spreadsheet EPACT_SA.XLS (essentially identical to the spreadsheet installed on 

the BT Web site for public comment subsequent to the EPAct screening analysis), the tables 

below summarize the efficiency assumptions and energy savings results for technologies that 

EERE-BT will further analyze. The key assumptions and results were summarized for 12 

cooling technologies the Table 5 and for boilers and a high-capacity instantaneous water heater 

in the Table 6. Cumulative savings, shown in the last column in both tables, were based on the 

savings from the effective date of the standards through 2030. 

Table 5. Key Assumptions and Results for Cooling Products 

Efficiency 

(SEER and EER)* 

Energy Savings by Year 

(TBtu) 

Equipment Category EPAct 

New 

Std Eff. Date 2010 2020 2030 Cum. 

3-Phase Single Package, Air Source 

Air Conditioning, <65 kBtu/h 

9.7 12.0 2005 4.6 21.0 26.5 396.0

3-Phase Single Package, Air Source 

Heat Pump, <65 kBtu/h 

9.7 12.0 2005 1.2 3.1 3.4 60.2

3-Phase Split, Air Source Air 

Conditioning, <65 kBtu/h 

9.7 11.0 2005 0.9 4.1 5.2 78.1

3-Phase Split, Air Source Heat Pump, 

<65 kBtu/h 

9.7 12.0 2005 9.1 24.0 26.5 463.0

Central, Water Source Heat Pump, 

>17 and <65 kBtu/h 

9.3 12.5 2008 1.5 7.1 11.1 146.9

Central, Air Source Air Conditioning, 

>=65 and <135 kBtu/h 

8.9 11.0 2008 5.5 25.0 31.6 471.6
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-18

Central, Air Source Air Conditioning, 

>=135 and <240 kBtu/h 

8.5 11.0 2008 5.4 24.6 31.0 463.1

Packaged Terminal Air Conditioning, 

7-10 kBtu/h 

8.6 10.8 2008 0.4 1.8 2.2 33.3

Packaged Terminal Air Conditioning, 

10-13 kBtu/h 

8.1 10.2 2008 0.6 2.6 3.3 49.5

* SEER = seasonal energy efficiency ratio; EER = energy efficiency ratio.

Table 6. Key Assumptions and Results for Boilers and a High-Capacity Instantaneous Water 

Heater 

Efficiency (SEER and EER) Energy Savings by Year 

(TBtu) 

Equipment Category

EPAct 

New Std Eff. 

Date 2010 2020 2030 Cum. 

Pkg'd Boilers, Gas, 400 kBtu/h, Hot 

Water 

75% 78% 2008 0.2 0.9 1.7 19.7 

Pkg'd Boilers, Gas, 800 kBtu/h, Hot 

Water 

75% 78% 2008 0.4 2.0 3.7 43.0 

Pkg'd Boilers, Gas, 1500 kBtu/h, Hot 

Water 

75% 78% 2008 0.1 0.7 1.2 14.2 

Pkg'd Boilers, Gas, 3000 kBtu/h, HW 75% 80% 2008 0.2 0.7 1.3 15.2 

Pkg'd Boilers, Gas, 400 kBtu/h, Steam 72% 76% 2008 0.1 0.6 1.1 12.6 

Pkg'd Boilers, Gas, 800 kBtu/h, Steam 72% 76% 2008 0.4 1.6 3.0 34.5 

Pkg'd Boilers, Gas, 1500 kBtu/h, 

Steam 

72% 79% 2008 0.3 1.2 2.3 26.7 

Pkg'd Boilers, Gas, 3000 kBtu/h, 

Steam 

72% 80% 2008 0.2 0.9 1.7 19.2 

Instantaneous Water Heaters, 1000 

kBtu/h 

80% 83% 2008 1.0 4.4 5.6 83.3 

3.2 Distribution Transformers 

3.2.1 Target Market 

Project Description. Distribution transformers convert high-voltage electricity from 

distribution centers to lower-voltage electricity for use at the household level. During this 

conversion process, a small fraction of heat is lost. The Energy Policy and Conservation Act 

(EPCA) of 1975 established an energy conservation program for major household appliances. 

The National Energy Conservation Policy Act of 1978 amended EPCA to add Part C of Title 

III, which established an energy conservation program for certain industrial equipment. The 

Energy Policy Act of 1992 amended EPCA to add certain commercial equipment, including 

distribution transformers. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-19

BT conducts the program that develops equipment energy conservation standards and has 

overall responsibility for rulemaking activities for distribution transformers in fulfillment of the 

law. 

The first step in developing energy-conservation standards was the secretarial determination in 

1997 that, "Based on its analysis of the information now available, the department has 

determined that energy conservation standards for transformers appear to be technologically 

feasible and economically justified, and are likely to result in significant savings" 62 FR 54809 

(October 22, 1997). 

The department is currently conducting two rulemakings for Distribution Transformers: an 

energy conservation standard and a test procedure. 

 

Baseline Technology Improvements. For this analysis, PNNL did not suggest any changes in 

technology improvements, apart from the EIA baseline. 

3.2.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. PNNL assumed a 10-year payback period on investment to develop incremental 

investment costs (i.e., an annual energy cost savings of $1 implies an initial investment of $10). 

This corresponds to a total incremental investment of approximately $580 million in 2010, $780 

million in 2020, and $230 million in 2030. 

Key Consumer Preferences/Values. The following nonenergy characteristics were not 

considered in developing energy output estimates: 

• Reduced CO2 and SOX emissions 

3.2.3 Methodology and Calculations 

Inputs to Base Case. The base case was developed based on an assortment of sources, 

including AEO 2003, CBECS 95, RECS 97, and several other sources, all of which are 

documented by PNNL(7). 

Technical Characteristics 

Performance Target: Savings estimates for a distribution transformer standard were based on 

the DOE Draft ANOPR Analysis for Distribution Transformers Rulemaking (January 6, 

2004).(5) The analysis assumed the following: 

• Average savings of 140 watts per unit 

• A transformer sales forecast (see Table 5). 

• 0% sales complying with the new level without the standard (this was taken into account 

in calculating the 140 watts average savings) 

• 8,760 annual operating hours per unit 

• 30-year life of equipment. 

PNNL assumed that the distribution transformer standard would not go into effect until 2008, 

based on an internal schedule indicating that the final rule would be issued May 2005, with the 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-20

standard going into effect three years later.(6) The savings estimate of 140 watts per unit 

installed was multiplied by the estimated hours of operation and then by the forecasted number 

of units installed. 

Expected Market Uptake

Table 7. Distribution Transformer Market Penetration 

Year 

Transformer Sales 

Forecast 

2005 1,422,000 

2006 1,452,000 

2007 1,485,000 

2008 1,521,000 

2009 1,549,000 

2010 1,582,000 

2011 1,614,000 

2012 1,646,000 

2013 1,673,000 

2014 1,701,000 

2015 1,729,000 

2016 1,756,000 

2017 1,782,000 

2018 1,810,000 

2019 1,840,000 

2020 1,870,000 

2021 1,898,000 

2022 1,929,000 

2023 1,960,000 

2024 1,994,000 

2025 2,025,000 

2026 2,058,000 

2027 2,090,000 

2028 2,124,000 

2029 2,158,000 

2030 2,192,000 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-21

3.3 Sources 

(1) National Appliance Energy Conservation Act of 1987, Public Law 100-12. 

(2) Energy Policy Act of 1992, Public Law 102-486. 

(3) Somasundaran, S. et al. 2000. Screening Analysis of EPAct-Covered Commercial HVAC and 

Water Heating Equipment. PNNL-13232, Pacific Northwest National Laboratory, Richland, 

Washington. 

(4) ASHRAE 90.1-1999, "Energy Standard for Buildings Except Low-Rise Residential Buildings," 

American Society of Heating, Refrigeration, and Air-Conditioning Engineers. 

(5) DOE Draft ANOPR Analysis for Distribution Transformers Rulemaking, January 6, 2004. 

(6) Internal DOE schedule tracking document for David Garman, Aug. 29, 2003 

(7) Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics Effort. 

PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 

4.0 Emerging Technologies 

The Emerging Technologies subprogram seeks to develop cost effective technologies, e.g., 

lighting, windows, and space heating and cooling, for residential and commercial buildings that 

can reduce the total energy use in buildings by 60% to 70%. The improvement in component 

and system energy efficiency when coupled with research to integrate onsite renewable energy 

supply systems into the commercial building can result in marketable net zero energy designs. 

4.1 Analysis Tools and Design Strategies 

4.1.1 Target Market 

Project Description. (1) The Analysis Tools and Design Strategies project researches the 

interrelationship of energy systems and building energy performance, develops various building 

analysis tools to more accurately model energy use in new and existing buildings, and provides 

recommendations and strategies to cost effectively lower energy use and improve building 

performance. The project focuses on whole-building software tools for evaluating energy 

efficiency and renewable energy. The project also focuses on nonsoftware solutions such as 

improved standards, guidelines, and performance measurements, all of which bring about 

excellence in designing new buildings. The project's long-term goal is to improve energy 

designs for all building types through a number of widely used analytical tools and guidance 

documents. 

Market Description: Although this project does not explicitly exclude any particular building 

type, the types of commercial buildings that most likely will be impacted by the technologies 

developed by this project include those with relatively higher energy use intensities such as 

assembly, education, health care, lodging, and office buildings. 

Market Introduction(1,3): PNNL assumed that this project accelerates the introduction and 

market penetration of the advanced building energy tools and design strategies by 10 years. 

Historically, there have been a number of building energy tools that have been developed 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-22

privately; however, most of these tools use algorithms, code, and modules developed by DOE. 

PNNL assumed that a proportion of these activities (50%) would not occur without DOE 

funding. These assumptions are necessary in the absence of citable sources documenting 

DOE’s influence on building energy tool adoption and algorithm attribution. 

Baseline Technology Improvements. For this analysis, PNNL did not suggest any changes in 

technology improvements, apart from the EIA baseline. 

Baseline Market Acceptance. In 1998, PNNL conducted a study examining the historical 

market penetration for 10 energy-efficient products related to the buildings sector. The results of 

this study are documented by PNNL(5). The study suggested several generic penetration curves 

based on the type of equipment of interest. PNNL used the curve related to design products to 

model this project. 

4.1.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price(3,4). Although the tools supported by this project are distributed free of charge, users must 

invest a certain amount of time to learn the tools. Without a user-friendly interface, 

approximately one person-month is required to be come proficient with the tools. Analysis 

Tools and Design Strategies is currently developing energy-simulation tools without a userfriendly interface, with the idea that the private sector can use these algorithms, codes, and 

modules and design a suitable user-friendly interface. 

Key Consumer Preference/Values – Nonenergy Benefits.

 

 The following nonenergy 

characteristics were not considered in developing energy output estimates: 

• Improved indoor environmental quality, such as thermal comfort and ventilation 

adequacy 

• Improved indoor air quality 

• Fire safety 

• Overall environmental sustainability (i.e., Green Buildings). 

4.1.3 Methodology and Calculations 

Inputs to Base Case. The base case was developed based on an assortment of sources, 

including AEO 2003, CBECS 95, RECS 97, and several other sources, all of which are 

documented by PNNL(5). 

Technical Characteristics(2). In concert with Commercial Buildings R&D project, the 

performance goals are to reduce heating and cooling loads by 50% in new small commercial 

construction and by 30% in existing buildings.e

Expected Market Uptake(3). The market penetration goal is to accelerate the penetration of 

high-performance building design, such that 50% of new commercial and multifamily 

construction and 30% of existing construction incorporates the products supported by this 

 

e The percentage of the load reduction attributed between Commercial R&D and Analysis Tools and Design Strategies is in 

proportion with their respective budget requests. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-23

project by 2020. PNNL assumes that this project accelerates the adoption of relevant energysavings products, technologies and designs by 10 years. The market penetration is shown in 

Figure 3. 

0

10

20

30

40

50

60

70

80

90

100

2005 2010 2015 2020 2025 2030

Market Share (%)

Accelerated Penetration with DOE

R&D

Penetration without DOE R&D

Net Penetration from DOE R&D

Figure 3. Market-Penetration Curve for Analysis Tools and Design Strategies 

4.1.4 Sources 

(1) “Building Technology Program: Research, Development and Demonstration Plan, Planned 

Program Activities for 2004-2010.” Final Draft. U.S. DOE, January 9, 2004. 

(2) Torcellini, Paul, et. al. Lessons Learned from Field Evaluation of Six High-Performance Buildings,

NREL/CP-550-36290, National Renewable Energy Laboratory, June 2004. 

(3) E-mail correspondence with project manager, Dru Crawley, June 2003 and June 2004. 

(4) Kats, Greg (Capital E), et. al. “The Costs and Financial Benefits of Green Buildings,” A Report to 

California’s Sustainable Building Task Force. October 2003. 

(5) Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics Effort. 

PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 

4.2 Appliances and Emerging Technologies R&D 

4.2.1 Target Market 

Project Description. This project helps manufacturers and utilities commercialize highly 

efficient appliances and equipment by providing the following assistance: 

• Technology procurement to bring new technologies to market (late developmental 

work). 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-24

• Independent third-party evaluation and verification of highly efficient technologies 

using field studies and demonstrations increase market share of emerging technologies 

and Energy Star technologies with very low market penetration. 

• R&D on appliances not covered by other projects but offering significant energy-savings 

potential. 

Market Description: The market includes residential and commercial building technologies, 

with emphasis on appliances, water heating, lighting, and building equipment. 

Size of Market: The market size depends on the selected equipment: 

• Rooftop Air Conditioners: One of the most widely used technologies with greatest 

commercial space conditioning energy use; more than a million tons sold in 1998. 

• Residential Can Lights: An estimated 22 million incandescent can fixtures sold in 

2001. 

• Reflector CFLs (R-lamps): Nearly 125 million parabolic/reflector lamps sold to the 

residential market. 

Baseline Technology Improvements. For this analysis, PNNL did not suggest any changes in 

technology improvements, apart from the EIA baseline. 

4.2.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Key Consumer Preferences/Values. The following nonenergy characteristics were not 

considered in developing energy output estimates: 

• Reduced carbon emissions 

• Dehumidification provided by heat-pump water heater. 

• Reduced lamp replacement frequency with R-CFLs and CFL cans. 

4.2.3 Methodology and Calculations 

Rooftop Air Conditioning

The intent of the rooftop air conditioner project is to use competitive procurements of large 

numbers of units to stimulate the production of high-efficiency equipment. The immediate goal 

is to get high-efficiency equipment installed in buildings owned by the federal government and 

other state and local agencies. 

With this long-term goal in mind, PNNL provided costs of high efficiency roof top air 

conditioners to be used in the NEMS-GPRA06 commercial model to reflect the principal 

influence of this project. In NEMS-GPRA06, three air conditioners are specified in the rooftop 

category—a baseline unit (energy efficiency ratio of 8.9), a moderate efficiency unit (energy 

efficiency ratio of 10.2) and a high-efficiency unit (energy efficiency ratio of 14.7). No 

subgroups are distinguished by capacity (e.g., 65 to 135 kBtu/hr vs. 135 to 240 kBtu/hr). 

For this analysis, the incremental cost was reduced by 40%, based on project goals. Given the 

proportion of the market assumed in the NEMS-GPRA06 to display high discount rates in the 

selection of equipment, this cost reduction yielded a 4 to 12% penetration of the higher 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-25

efficiency units through the projection period. By 2025, the proportion of the total rooftop air 

conditioning stock using the higher efficiency units is about 11%. 

Market Introduction: No acceleration of market acceptance was modeled because the impact 

was determined to be negligible. Because the technology has only modest penetration (10%) by 

2020 and only a few percent by 2010, assuming that this project accelerated market acceptance 

would not have a significant impact over the analysis period. 

Performance Target: An efficiency increase from 10.3 to 11.0 energy efficiency ratio for 65 

to 135 kBtu/hr and from 9.7 to 10.8 for 135 to 240 kBtu/hr. 

Lifetime: 15 years. 

Residential Can Lights

The intent of this project is to develop a recessed can light fixture that uses compact fluorescent 

lamps rather than incandescent. 

Market Introduction: These projects were assumed to accelerate the introduction of these 

technologies into the marketplace by seven years. 

Performance Target: Assumed efficacy of 37.5 lumens/wattf

. Actual project requirements 

should be similar to other programs; here, efficacy is expected to improve by a factor of 2.5, 

while R-lamps are expecting an improvement factor of 3.33 and Energy Star CFLs are looking 

to an improvement factor of 3.42. 

Installed Cost: Incremental cost above incandescent cans is $24/can in 2006 declining to 

$20/can by 2011. 

Lifetime: 30 years. 

R-Lamps

The intent of this project is to develop a floodlight or spotlight (lamps using reflector surfaces) 

that can utilize a screw-base compact fluorescent lamp rather than an incandescent lamp. 

Market Introduction: These projects were assumed to accelerate the introduction of these 

technologies into the marketplace by five years. 

Performance Target: Assumed efficacy of 36 lumen/wattg. Actual project requirements 

should be similar to Energy Star (within WIP), as Table 8 shows. 

 

f Actual efficacy is lower than this value. The value of 37.5 assumes an existing technology value of 15 lumens/watt; actual 

incandescent can lights have efficacies significantly lower than this. However, BESET currently assume all incandescent 

lighting to have an efficacy of 15 lumens/watt. The proposed technology, which has the same lumen output as the current 

technology, is rated at 26W, while the existing incandescent technology is rated at 65W. Hence 15 * 65 / 26 = 37.5. g

 Weighting the Energy Star targets 58% for less than 20W and 42% for 20W or more (58% of incandescent lamps in homes 

have Wattages less than 75W and 42% of incandescent lamps in homes have Wattages 75W and greater(1)) yields an average 

lumens/watt of 36. The comparison incandescent lamp, EPACT 65W R-lamp, has approximately 700 lumens or 10.8 

lumens/watt. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-26

Table 8. Performance Targets for R-Lamps 

Lamp Power (watts) and 

Configuration 

Minimum Efficacy: 

Lumens/watt* 

Reflector Lamp: 

Lamp power <20 

Lamp power >=20 

33 

40 

* Based on initial lumen date. 

Installed Cost: Initial cost is about $7/compact fluorescent lamp reflector lamp; which 

represent an initial incremental cost of about $5/unit in 2006 which declines to $1.50/unit by 

2020. 

Lifetime: 8,000 hours 

4.2.4 Sources 

(1) Estimated from http://enduse.lbl.gov/Info/LBNL-39102.pdf, p.19. 

(2) Gordon, K.L., and M.R. Ledbetter. 2001. Technology Procurement Screening Study. Pacific 

Northwest National Laboratory, Richland, Washington. 

(3) The Freedonia Group, Inc. 1999. Lamps in the United States to 2003. Cleveland, Ohio. (See the 

following sections: "Introduction," "Executive Summary," "Market Environment," "Supply and 

Demand," "Incandescent Lamps," "Electrical Discharge," and "Lamp Markets.") 

4.3 Envelope Research and Development 

4.3.1 Target Market 

Project Description(1). Windows typically contribute about 30 percent of overall building 

heating and cooling loads with an annual impact of about 3.7 quads, with an additional potential 

savings of 1 quad from daylight use. The BT approach is to first convert windows from their 

current role as significant thermal losses to the point where they are energy neutral, and then 

move to a higher level of performance, where they contribute to a net energy surplus in a ZEB, 

thus offsetting other energy costs. 

About 60 percent of window sales are to the residential sector and 40 percent to commercial, so 

that this program targets both sectors. Sales are evenly distributed between new construction 

and existing buildings, so both markets are included in the R&D program. Because the energy 

needs of residential differ from commercial, and new construction and renovation/retrofit are 

different, and because all performance is strongly influenced by climate and orientation, 

developing a single “silver bullet R&D solution” that solves all problems is not possible. 

Furthermore, window impacts on building energy use are linked to other building systems. 

Therefore the technical approach of the Windows activity is built around three themes: 

1. The need for a broad portfolio of cost-effective advanced technologies to address the 

disparate heating, cooling and daylighting needs of these different conditions; 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-27

2. Recognition that these advanced glazing and façade technologies will perform best when 

they are optimized as part of fully integrated building systems to address competing 

performance needs as a function of time, climate, building type and orientation; and 

3. The need for decision-support infrastructure to rate and label products, and the tools to 

select and optimize selection and design solutions. For existing energy efficient 

products, rating and labeling an entire suite of products with a strong focus on 

commercial building applications will remove barriers for product specification and 

promotion by industry and non-profit organizations. 

Market Description: The market includes new and existing commercial and residential 

buildings in all climate zones. 

Size of Market: 500 million square feet of windows for commercial buildings and 

approximately 55 million manufactured units sold each year for residential and light 

commercial. 

Baseline Technology Improvements. For this analysis, PNNL did not suggest any changes in 

technology improvements, apart from the EIA baseline. 

4.3.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Key Consumer Preferences/Values. The following nonenergy characteristics were not 

considered in developing energy output estimates: 

• Reduced utility and building peak loads 

• Reduced HVAC Requirements and first costs 

• Improved indoor comfort and aesthetics. 

4.3.3 Methodology and Calculations 

Inputs to Base Case. The base case was developed based on an assortment of sources including 

AEO 2003, CBECS 95, RECS 97, and several other sources, all of which are documented by 

PNNL(2). 

Electrochromic Windows(1)

Windows are capable of providing solar heat when it is needed, rejecting solar gain to reduce 

cooling loads, and offsetting most of a building’s lighting needs during daylight hours. To fully 

accomplish these functions, windows and skylights must continuously and dynamically control 

their transmittance of sunlight and daylight. In commercial buildings the dynamic tradeoffs 

between cooling load reductions and daylight utilization are particularly complex. Glazings 

whose solar optical properties can be varied rapidly over a wide dynamic range are needed to 

address these performance needs. Research activities include development of durable 

chromogenic coatings, emphasizing electrochromic technology for the first generation of 

products and exploring other switchable coating mechanisms with lower cost, faster switching 

and wider dynamic range over time. Work includes fundamental coating technology, 

characterization, durability testing, prototype testing, and controls integration and optimization 

including field-testing. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-28

Market Introduction: 2010; This project was assumed to accelerate the introduction of this 

technology into the marketplace by 10 years. 

Performance Parameters: Performance parameters for Electrochromic Windows are 

presented in Table 9. 

Table 9. Performance Parameters for Electrochromic Windows 

End Use Shading Coefficient U-Value 

Heating 0.6 0.25 Btu/ft2·°F 

Cooling 0.1 0.25 Btu/ft2·°F 

Performance Target: Performance characteristics vary by building type and climate zone. 

The estimated savings per building were determined by simulating residential and commercial 

buildings in all climate zones (see Table 10). Commercial lighting savings are estimated to be 

5% in all regions. 

Table 10. Performance Targets for Electrochromic Windows 

Region Sector End Use New Building 

Savings 

Existing Building 

Savings 

Units 

Heating 1.83 1.61 MMBtu/ksf Northern Commercial Cooling 4.62 4.58 MMBtu/ksf 

Heating 1.88 1.66 MMBtu/ksf North Central Commercial Cooling 5.80 5.52 MMBtu/ksf 

Heating 3.91 4.38 MMBtu/HH Residential Cooling 11.16 11.30 MMBtu/HH 

Heating 0.94 0.88 MMBtu/ksf South Central 

Commercial Cooling 5.75 5.51 MMBtu/ksf 

Heating 3.00 3.61 MMBtu/HH Residential Cooling 7.51 7.76 MMBtu/HH 

Heating 0.56 0.53 MMBtu/ksf Southern 

Commercial Cooling 3.05 2.92 MMBtu/ksf 

Heating 3.65 4.16 MMBtu/HH Residential Cooling 10.13 10.28 MMBtu/HH 

Heating 1.43 1.28 MMBtu/ksf 

Weighted National 

Average (Southern 

and South Central 

for Residential) Commercial Cooling 4.96 4.81 MMBtu/ksf 

Installed Cost:—Incremental Cost Over competing technology (Low-e Double-Pane Windows)

2010 $54.42/ ft2

2011 $44.42/ ft2

2012 $34.42/ ft2

2013 $24.42/ ft2

2014 $19.42/ ft2

2015 $14.42/ ft2

2016 $9.42/ ft2

2017 $7.42/ ft2

2018 $5.42/ ft2

2019 $3.42/ ft2

2020 $1.42/ ft2

Lifetime: 20 years.
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-29

Expected Market Uptake. The goal is to obtain 50% of window sales by 2020 in the 

commercial sector, and 20% of window sales by 2020 in the residential sector. Penetration 

curves were developed and documented based on market diffusion curves developed by 

PNNL(2). The “Accelerated” penetration curve represents the percent of electrochromic window 

sales with the DOE project; the “Net” penetration curve represents the percent of sales 

attributable to DOE, as PNNL assumed that the DOE project would accelerate market 

acceptance by 10 years. See penetration curves in Figures 4 through 7. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

2000 2005 2010 2015 2020 2025 2030 2035

FY06 Accelerated

Penetration from DOE R&D

FY06 Net Penetration with

DOE R&D

Figure 4. Electrochromic Windows – New Commercial Buildings Percent of Sales 

0%

10%

20%

30%

40%

50%

60%

70%

2000 2005 2010 2015 2020 2025 2030 2035

FY06 Accelerated

Penetration from DOE R&D

FY06 Net Penetration with

DOE R&D

Figure 5. Electrochromic Windows – Existing Commercial Buildings Percent of Sales 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-30

0%

5%

10%

15%

20%

25%

30%

35%

2000 2005 2010 2015 2020 2025 2030 2035

FY06 Accelerated

Penetration from DOE R&D

FY06 Net Penetration with

DOE R&D

Figure 6. Electrochromic Windows – New Residential Buildings Percent of Sales 

0%

5%

10%

15%

20%

25%

30%

2000 2005 2010 2015 2020 2025 2030 2035

FY06 Accelerated Penetration

from DOE R&D

FY06 Net Penetration with

DOE R&D

Figure 7. Electrochromic Windows – New Commercial Buildings Percent of Sales 

Superwindows(1)

With heating loads being the largest end-use impact, improving winter performance has the 

potential for large energy savings. Low-E gas-filled windows introduced in the 1980s have now 

captured more than 40% of the residential market. But, heat loss rates for whole windows must 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-31

be reduced by at least a factor of 2 to approach levels needed for zero-energy buildings. Highly 

leveraged competitive R&D will be conducted towards achieving these impacts. Research 

activities will include basic and exploratory research on advanced optical coatings, gas filled 

and evacuated cavities, microporous transparent insulating materials, improved edge and frame 

materials; and applied research to support rating, design tools, and implementation of efficient 

window technologies. 

Technical Characteristics 

Market Introduction: 2007; PNNL assumed that this project would accelerate the introduction 

of this technology into the marketplace by 10 years. 

Performance Parameters: Superwindows have maximum U-value and SHGC for four climate 

zones. These climate zones do not directly correspond to the traditional climate zones used in 

CBECS or RECS; they also do not correspond to the census divisions used in NEMS. These 

new climate zones are based on the eight climate zones that were developed as part of the IECC 

2003 code change cycle or Residential IECC Code Change (RICC). In general, the 

Superwindow zones map from the RICC zones is as follows in Table 11. 

Table 11. Mapping of RICC Zones to Superwindow Zones 

RICC Zone Superwindow Zone 

1 Southern 

2 Southern 

3 South/Central 

4 North/Central 

5 Northern 

6 Northern 

7 Northern 

8 Northern 

To construct the four Superwindow zones there was a fair amount of smoothing required due to 

geo-political boundaries, existing codes, and commercial regions. For example, a strict 

adherence of the eight RICC zones to four Superwindow zones shown above would have 

portions of California in all four Superwindow zones and would result in discontinuities in the 

zones across the country. The final result is that California is wholly within the South/Central 

zone and all four Superwindow zones are continuous across the country. Performance 

parameters are listed in Table 12. 

Table 12. Performance Parameter Maximums for Superwindows 

Region End Use Shading Coefficient U-Value 

Northern Heating 0.6087 0.10 Btu/ft2·°F 

Cooling 0.2609 0.10 Btu/ft2·°F 

North Central Heating 0.6807 0.10 Btu/ft2·°F

Cooling 0.2609 0.10 Btu/ft2·°F

South Central Heating 0.1304 0.20 Btu/ft2·°F

Cooling 0.1304 0.20 Btu/ft2·°F

Southern Heating 0.1304 0.20 Btu/ft2·°F

Cooling 0.1304 0.20 Btu/ft2·°F
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-32

Performance Target: Performance characteristics vary by climate zone. The estimated 

savings per building were determined by simulating residential buildings in all climate zones 

(see Table 13). 

Table 13. Performance Targets for Superwindows 

Region Sector End Use New Building 

Savings 

Existing Building 

Savings 

Units 

Heating 10.80 11.15 MMBtu/HH Northern Residential Cooling 4.29 4.31 MMBtu/HH 

Heating 8.83 9.18 MMBtu/HH North Central Residential Cooling 5.05 5.15 MMBtu/HH 

Heating -0.08 0.02 MMBtu/HH South Central Residential Cooling 10.10 10.32 MMBtu/HH 

Heating 1.64 1.90 MMBtu/HH Southern Residential Cooling 6.32 6.66 MMBtu/HH 

Weighted National Heating 6.24 6.51 MMBtu/HH 

Average Residential Cooling 6.34 6.44 MMBtu/HH 

Installed Cost:—Incremental Cost Over Low-e Double-Pane Windows 

2007: $6.00/ft2

2020: $4.00/ft2

2030: $3.00/ft2

Lifetime: 30 years

Expected Market Uptake. The goal is to obtain 65% of window sales in new buildings and 

33% in existing buildings by 2020. Penetration curves were developed based on market 

diffusion curves developed by PNNL and documented in the 2004 PNNL report, 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics 

Effort (Elliott, et. al). The “Accelerated” penetration curve represents the percent of 

superwindow sales with the DOE project; the “Net” penetration curve represents the percent of 

sales attributable to DOE, as PNNL assumed that the DOE project would accelerate market 

acceptance by 10 years. See penetration curves in Figures 8 and 9. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-33

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2000 2005 2010 2015 2020 2025 2030 2035

FY06 Accelerated

Penetration from DOE R&D

FY06 Net Penetration with

DOE R&D

Figure 8. Superwindows – New Residential Buildings Percent of Sales 

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

2000 2005 2010 2015 2020 2025 2030 2035

FY06 Acclerated

Penetration from DOE R&D

FY06 Net Penetration with

DOE R&D

Figure 9. Superwindows – Existing Residential Buildings Percent of Sales 

Low-Emissivity Glass Acceptance(1)

Low-e windows have at least one surface coated with a thin, nearly invisible, metal oxide or 

semiconductor film that reduces the heat transfer through windows. The conventional windows 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-34

that they replace have no coating. Currently low-e windows represent less than 20% of the 

commercial market and are not the default product for builders in the residential market, 

constituting about 40% of that market. Additional research that supports industry and nonprofit 

energy efficiency programs from FY05 through FY09 can significantly increase the penetration 

of these energy-efficient products. The purpose of the program is to increase the penetration of 

low-e glass from 40% in the residential market and 10% in the commercial market to 100% in 

both markets by 2020. Two programs, Low-e Market Acceptance and Energy Star Windows 

(funded under the Weatherization and Intergovernmental Program), form the joint means to 

achieving the low-e penetration goal; hence, the savings will be split equally. The performance 

of the low-e glass is as described for the Electrochromic and Super Windows baseline. 

Market Introduction: The technology is commercially available. PNNL assumed that this 

project would accelerate the penetration in the marketplace by 10 years. 

Methodology and Calculations 

Technical Characteristics 

Performance Parameters: Low-e Windows have maximum U-value and SHGC for four 

different climate zones. These climate zones do not directly correspond to the traditional 

climate zones used in CBECS or RECS; they also do not correspond to the census divisions 

used in NEMS. These new climate zones are based on the eight climate zones that were 

developed as part of the IECC 2003 code change cycle or Residential IECC Code Change 

(RICC). In general the Low-e zones map from the RICC zones as follows in Table 14. 

Table 14. Mapping of RICC Zones to Low-e Zones 

RICC Zone Low-e Zone 

1 Southern 

2 Southern 

3 South/Central 

4 North/Central 

5 Northern 

6 Northern 

7 Northern 

8 Northern 

To construct the four Low-e zones, there was a fair amount of smoothing required due to geopolitical boundaries, existing codes, and commercial regions. For example, a strict adherence of 

the eight RICC zones to four Low-e zones shown above would have portions of California in all 

four Low-e zones and would result in discontinuities in the zones across the country. The final 

result is that California is wholly within the South/Central zone and all four Low-e zones are 

continuous across the country. Performance parameters are listed in Table 15. 

Table 15. Performance Parameter Maximums for Low-e Windows 

Region Shading Coefficient U-Value 

Northern 0.60 0.35 Btu/ft2·°F 

North Central 0.55 0.40 Btu/ft2·°F 

South Central 0.40 0.40 Btu/ft2·°F 

Southern 0.40 0.65 Btu/ft2·°F 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-35

Performance Target: Performance characteristics vary by building type and climate zone. 

The estimated savings per building were determined by simulating residential and commercial 

buildings in all climate zones (see Table 16). 

Table 16. Performance Targets for Low-e Windows 

Region Sector End Use New Building 

Savings 

Existing Building 

Savings 

Units 

Heating 8.17 8.30 MMBtu/HH Residential Cooling 0.06 0.19 MMBtu/HH 

Heating 6.24 5.73 MMBtu/ksf Northern 

Commercial Cooling -0.45 -0.58 MMBtu/ksf 

Heating 2.88 2.94 MMBtu/HH Residential Cooling 1.72 1.79 MMBtu/HH 

Heating 2.98 2.77 MMBtu/ksf North Central 

Commercial Cooling 0.74 0.68 MMBtu/ksf 

Heating 0.09 0.00 MMBtu/HH Residential Cooling 10.50 10.39 MMBtu/HH 

Heating 0.75 0.66 MMBtu/ksf South Central 

Commercial Cooling 5.91 5.62 MMBtu/ksf 

Heating -1.48 -1.77 MMBtu/HH Residential Cooling 9.18 8.77 MMBtu/HH 

Heating -0.14 -0.14 MMBtu/ksf Southern 

Commercial Cooling 5.21 4.98 MMBtu/ksf 

Heating 3.82 3.82 MMBtu/HH Residential Cooling 4.43 4.42 MMBtu/HH 

Heating 3.36 3.08 MMBtu/ksf 

Weighted National 

Average Commercial Cooling 2.25 2.07 MMBtu/ksf 

Installed Cost:—Incremental Cost Over Conventional Double-Pane Windows 

• 2005: $1.00/ft2

• 2015: $0.50/ft2

Expected Market Uptake. The purpose of the program is to increase the penetration of low-e 

glass from 40% in the residential market and 10% in the commercial market to 100% in the 

residential market by 2020 and in the commercial market by 2025. Both programs, Low-e 

Market Acceptance and Energy Star Windows, form the joint means to achieving the low-e 

penetration goal – the savings are to be split equally. Penetration curves were developed based 

on market diffusion curves developed and documented by PNNL(2). The “Accelerated” 

penetration curve represents the percent of superwindow sales with the DOE project; the “Net” 

penetration curve represents the percent of sales attributable to DOE, as PNNL assumed that the 

DOE project would accelerate market acceptance by 10 years. The penetration rates are shown 

in Figures 10 and 11. For Low-e Market Acceptance/ Energy Star Windows, PNNL assumed 

that these projects would accelerate the acceptance of this technology in the marketplace by 10 

years. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-36

0%

20%

40%

60%

80%

100%

120%

2000 2005 2010 2015 2020 2025 2030 2035

FY06 Accelerated

Penetration from DOE R&D

FY06 Net Penetration with

DOE R&D

Figure 10. FY06 Low-e Windows – Commercial Buildings Percent of Sales 

0%

20%

40%

60%

80%

100%

120%

2000 2005 2010 2015 2020 2025 2030 2035

FY06 Accelerated

Penetration from DOE R&D

FY06 Net Penetration with

DOE R&D

Figure 11. FY06 Low-e Windows – Residential Buildings Percent of Sales 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-37

4.3.4 Sources 

(1) “Building Technology Program: Research, Development and Demonstration Plan, Planned 

Program Activities for 2004-2010.” Final Draft. U.S. DOE, January 9, 2004. 

(2) Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics Effort. 

PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 

4.4 Lighting Research and Development 

4.4.1 Lighting Controls 

4.4.1.1 Target Market 

Project Description. The Lighting R&D project develops and accelerates the introduction of 

advanced lighting technologies. 

Market Description: The market includes all commercial buildings, with some technologies 

being introduced into residential buildings. 

Size of Market: Lighting consumes 26% (3.9 quad) of the primary energy used in commercial 

buildings, which had a building stock of about 69 billion ft2 in 2000(1). 

Baseline Technology Improvements. For this analysis, PNNL did not suggest any changes in 

technology improvements, apart from the EIA baseline. 

4.4.1.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. PNNL assumed a 4-year payback period on investment to develop incremental 

investment costs (i.e., an annual energy cost savings of $1 implies an initial investment of $4). 

Key Consumer Preferences/Values. The following nonenergy characteristics were not 

considered in developing energy output estimates: 

• Develops U.S. leadership in lighting technology 

• Reduces pollution and contributes to U.S. climate-change goals 

• Improves U.S. productivity from better lighting in work environments 

• Responds to an industry-initiated collaborative. 

4.4.1.3 Methodology and Calculations 

Inputs to Base Case. The base case was developed based on an assortment of sources, 

including AEO 2003, CBECS 95, RECS 97, and several other sources, all of which are 

documented by PNNL(3). 

Technical Characteristics. Various field studies(2) have shown a very large energy savings 

potential for lighting controls, primarily using occupancy and daylighting controls. These 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-38

studies have shown that aggressively implementing controls can save 20% to 40% of lighting 

energy use. BT supports the development of more advanced systems—through both research 

and field testing—that will further reduce energy used for lighting in commercial buildings. BT 

support of research to evaluate the interrelationship between human vision and efficient light 

use will also contribute to future energy savings. 

For FY 2006, the impact of the BT activities in lighting controls and efficient lighting practices 

was assumed to yield an incremental 5% reduction in lighting energy use compared with current 

practice. (By incremental, the BT activities are assumed to lead to further savings over and 

above the control technologies that the private sector offers now and are likely to offer.) 

Expected Market Uptake. PNNL assumed that up to 60% of new commercial buildings could 

incorporate these technologies and that 20% of the existing stock could be retrofitted with these 

systems by 2020. A time profile of penetration rates was based on the historical pattern of 

market penetration observed for electronic ballasts. An S-shaped penetration curve was fit to 

historical market shares for electronic ballasts and then applied to project future adoption of 

advanced lighting distribution systems and controls. This curve indicated that nearly 50% of 

the ultimate market penetration was achieved after nine years. 

4.4.1.4 Sources 

(1) Annual Energy Outlook 2002. 2002. Energy Information Administration, Washington, D.C. 

(2) See http://eande.lbl.gov/btp/450gg/publications.html and 

www.cmpco.com/services/pubs/lightingfacts/controls.html

(3) Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics 

Effort. PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 

4.4.2 Solid State Lighting 

4.4.2.1 Target Market 

Project Description. The Solid State Lighting activity develops and accelerates the 

introduction of solid-state lighting and seeks to achieve the following for lighting: 

• Significantly greater efficacy than conventional sources, such as T8 fluorescents 

• Easy integration into building systems of the future 

• Ability to provide the appropriate color and intensity for any application 

• Ability to last 20,000 to 100,000 hours 

• Ability to readily supplement natural sunlight. 

Market Description: The market includes all commercial buildings, with some technologies 

being introduced into residential buildings. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-39

Size of Market(1): Lighting consumes 26% (3.9 QBtu) of the primary energy used in 

commercial buildings, which had building stock of about 69 billion ft2 in 2000.h

Baseline Technology Improvements. For this analysis, PNNL did not suggest any changes in 

technology improvements, apart from the EIA baseline. 

4.4.2.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Key Consumer Preferences/Values. The following nonenergy characteristics were not 

considered in developing energy output estimates: 

• Helps maintain U.S. semiconductor leadership 

• Develops U.S. leadership in lighting technology 

• Reduces pollution and contributes to U.S. climate-change goals 

• Improves U.S. productivity from better lighting in work environments 

• Responds to industry-initiated collaborative. 

4.4.2.3 Methodology and Calculations 

Technical Characteristics. Key assumptions concerning the likely dates of introduction and 

the expected efficacies were influenced by two sources: 1) “The Case for a National Research 

Program on Semiconductor Lighting,”(2) a white paper prepared by Hewlett-Packard and Sandia 

National Laboratories and presented in late 1999 at an industry forum; and 2) a more extended 

study(3) by A.D. Little for BT in early 2001; the study used some of the basic assumptions in 

the white paper(2) in developing some scenarios related to solid-state lighting. 

NEMS characterizes each lighting technology by source efficacy level (lumens/watt), capital 

cost ($/1000 lumens or $/kLumen), and annual maintenance cost of lamps. For new 

technologies, the capital costs can be reduced along a logistic-shaped curve. The NEMS model 

divides the commercial lighting market into four major groups: 1) incandescent CFL (point 

source), 2) 4-foot fluorescent, 3) 8-foot fluorescent, and 4) high-intensity point source (outdoor 

lighting). Solid-state lighting was assumed to penetrate the first three market groupings. 

Given the cost assumptions, the NEMS model chooses among these technologies for each 

building type in each census division. For each group, the market is assumed to be further 

segmented, with each segment characterized by a different discount rate in its decision-making 

criteria. Within each segment, a lighting technology is selected based on minimum annualized 

cost. 

Solid-state lighting was also assumed to be available in the residential lighting market, where it 

competes with conventional incandescent and compact fluorescent options. 

 

h

 According to a recent report completed for DOE by Navigant Consulting (“U.S. Lighting Market Characterization, Volume I: 

National Lighting Inventory and Energy Consumption Estimate,” September 2002), the amount of energy used for lighting is 

greater than EIA has traditionally estimated. The report estimates that commercial lighting requires 4.2 QBtu and residential 

lighting requires 2.2 QBtu. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-40

Table 17 summarizes the cost and performance inputs for the solid state lighting technologies 

used in NEMS-GPRA06 for FY 2006. 

Table 17. Solid-State Lighting Cost and Efficiency Assumptions – FY 2006 GPRA 

 

Year 

Efficacy 

(lumens/watt) 

Price 

($/klm) 

2005 45 160.80 

2006 45 160.80 

2007 45 160.80 

2008 50 152.35 

2009 55 141.11 

2010 60 126.93 

2011 66 110.21 

2012 72 92.00 

2013 78 73.79 

2014 84 57.07 

2015 90 42.89 

2016 96 31.65 

2017 102 23.20 

2018 108 17.11 

2019 113 12.84 

2020 118 9.91 

2021 123 7.93 

2022 128 6.60 

2023 132 5.72 

2024 136 5.13 

2025 140 4.75 

2026 143 4.49 

2027 146 4.32 

2028 148 4.21 

2029 151 4.14 

2030 153 4.09 

4.4.2.4 Sources 

(1) Annual Energy Outlook 2002. 2002. Energy Information Administration, Washington, D.C.. 

(2) Haitz, R., and F. Kish (Hewlitt-Packard Co) and J. Tsao and J. Nelson (Sandia National 

Laboratories). 1997. "Case for a National Research Program on Semiconductor Lighting," White 

paper presented at the 1999 Optoelectronics Industry Development Association forum in 

Washington D.C., October 6, 1999. 

(3) A.D. Little. 2001. Energy Savings Potential of Solid State Lighting in General Lighting 

Applications. Prepared for DOE's Office of Building Technology, State and Community Programs 

by A.D. Little, Cambridge, Massachusetts. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-41

4.5 Space Conditioning and Refrigeration R&D 

4.5.1 General Target Market 

Project Description(1). Over the next five years, space-conditioning activities will focus on the 

following areas: 

1. Developing and demonstrating low-cost, commissioning and remote fault detection and 

diagnostics systems for HVAC systems, including commercial rooftop and residential 

systems. These packages aid in commissioning, and can continuously monitor 

performance and detect faults such as charge leakage, economizer malfunction, heat 

exchanger fouling, burner condition, and controls malfunctions. New sensor, 

electronics, and software technologies that leverage wireless networks, mobile 

computers, and the Internet, will be implemented to provide user-friendly, low-cost 

systems. Potential DOE regulations may ensure factory installation of these systems, 

and DOE efficiency standards may address efficiency degradation over time. 

2. Developing technologies such as intelligent wireless controls, and low cost thermal 

storage to reduce peak electricity demand from HVAC. 

3. Because future efficiency gains in HVAC systems will flow increasingly from electronic 

components, R&D will focus on leveraging advances in electronics, controls and low 

cost computing power to improve system efficiency. Examples of possible research 

topics include: development of variable speed motor and drive technologies with low 

applied costs, using adaptive/fuzzy logic controls to enhance comfort and indoor 

environmental quality while reducing energy consumption, providing real-time feedback 

to consumers on energy consumption in order to change usage patterns, optimizing 

operations based on outdoor and occupancy conditions, or developing modules to be 

integrated with HVAC systems that can respond to price and peak demand signals. 

4. Developing low-cost, high efficiency unitary air conditioners and heat pumps, especially 

in the lower capacities needed for ZEH, and in larger commercial systems where the 

market does not currently focus on part load efficiency. A key element of this effort will 

involve reducing the cost of components to improve part-load efficiency (e.g. variable 

speed motors and drives, new concepts for modulating compressors). 

5. Commercializing low-nergy approaches to reduce ventilation loads, such as natural or 

hybrid ventilation; pre-cooling with cool, dry nighttime air (with integrated energy 

storage); demand control ventilation that is simple to implement, widely accepted, and 

cost-effective; and low-cost air-leaning technologies that allow a reduction in ventilation 

rates. 

6. Creating products and identifying practices that substantially reduce distribution losses 

through better duct installation and sealing techniques, reduction in losses in partially 

conditioned spaces, and consideration of novel distribution approaches, and evaluating 

the energy savings potential of ductless systems. 

Baseline Technology Improvements. For this analysis, PNNL did not suggest any changes in 

technology improvements, apart from the EIA baseline. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-42

4.5.2 Unitary DX System 

4.5.2.1 Target Market 

Project Description(1). Develop prototypes for two-three concepts that have the long-term 

potential to reduce annual HVAC energy consumption by 50%, with a modest installed cost 

premium over conventional systems. 

Market Description. Residential and Commercial Buildings. 

Market Introduction: 2007; this project was assumed to accelerate the introduction of this 

technology into the marketplace by 10 years. 

4.5.2.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. Installed cost initially will be double the cost of conventional systems, declining to less 

than 10% greater than conventional systems by 2025. 

4.5.2.3 Methodology and Calculations 

Inputs to Base Case. The base case was developed based on an assortment of sources, 

including AEO 2003, CBECS 95, RECS 97, and several other sources, all of which are 

documented by PNNL(3). 

Technical Characteristics. 50% reduction in heating and cooling over conventional systems. 

Expected Market Uptake. This activity targets all residential cooling equipment and heat 

pumps, and all commercial DX cooling equipment and heat pumps. 

4.5.3 Ventilation Load Reduction 

4.5.3.1 Target Market 

Project Description. The objective of this project is to apply advanced technologies to reduce 

ventilation energy used in commercial buildings, including both fan energy and conditioning of 

outside air, by 50% in the long term. 

Market Description: Commercial buildings. 

Market Introduction: 2007; this project was assumed to accelerate the introduction of this 

technology into the marketplace by 10 years. 

4.5.3.2 Key Factors in Shaping Market Adoption of EERE Technologies 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-43

Price. Installed cost will be 15%-20% greater than conventional systems 

4.5.3.3 Methodology and Calculations 

Inputs to Base Case. The base case was developed based on an assortment of sources, 

including AEO 2003, CBECS 95, RECS 97, and several other sources, all of which are 

documented by PNNL (3). 

Technical Characteristics. 50% reduction in ventilation and 50% reduction in the 

conditioning (heating and cooling) of outside air (i.e., make-up air)

4.5.4 Commercial Refrigeration 

4.5.4.1 Target Market 

Project Description. DOE is working to improve the efficiency of refrigerated display cases 

and developing methods of recovering reject heat for space conditioning. This project was 

modeled as an advanced supermarket refrigeration system that would target heating, cooling, 

and refrigeration end-use loads in the commercial food sales sector. The heating and cooling 

reductions occur because commercial refrigeration equipment draws a large amount of heat 

from the conditioned space, which must be made up by the heating equipment. In addition, heat 

energy can be recovered and used by the heating equipment, thus reducing the heating energy 

consumption and cost. These end uses comprise about 66% of total building, 67% of electric, 

and 61% of total natural gas end-use energy consumption.

(2)

Displaced Technology: Conventional refrigeration equipment in food-sales buildings. 

Performance Target: Reduced energy for building HVAC and refrigeration equipment during 

the next 15 to 20 years, specifically at least 15% for supermarket refrigeration and HVAC while 

reducing refrigerant needed. For FY 2006, PNNL assumed an overall 22.5% reduction in 

HVAC end-use energy consumption. 

Market Description: All commercial food-sales buildings. 

Market Introduction: 2004; PNNL assumed this project would accelerate the introduction of 

this technology into the marketplace by 10 years. 

4.5.5 Remote Fault Detection and Diagnostics 

4.5.5.1 Target Market 

Project Description(1). This project will develop and demonstrate low-cost commissioning and 

remote fault detection and diagnostics (FDD) systems for unitary HVAC systems, including 

commercial rooftop and residential systems. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix C – Building Technologies Program – Page C-44

Market Description. Residential and commercial space conditioning equipment. 

Market Introduction: 2008 (Commercial), 2009 (Residential); this project was assumed to 

accelerate the introduction of this technology into the marketplace by 10 years. 

4.5.5.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. $50-100 increase in cost per piece of equipment 

4.5.5.3 Methodology and Calculations 

Inputs to Base Case. The base case was developed based on an assortment of sources, 

including AEO 2003, CBECS 95, RECS 97, and several other sources, all of which are 

documented by PNNL (3). 

Technical Characteristics. 20% reduction in Cooling and Heating (heat pumps and integrated 

gas heating – i.e., rooftop package units). 

Expected Market Uptake. The market penetration goal is to enter the commercial market in 

2008 with 10% penetration by 2010 and 50% penetration by 2015; and to enter the residential 

market in 2009 with 10% penetration by 2011 and 50% penetration by 2015. 

4.5.6 Sources 

(1) “Building Technology Program: Research, Development and Demonstration Plan, Planned Program 

Activities for 2004-2010.” Final Draft. U.S. DOE, January 9, 2004. 

(2) Belzer, D.B and L.E. Wrench. 1997. End-Use Consumption Estimates for U.S. Commercial 

Buildings, 1992. PNNL-11514, Pacific Northwest National Laboratory, Richland, Washington. 

(3) Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics Effort. 

PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 
Appendix D – GPRA06 Distributed Energy Program 

Documentation 

Program Objective 

The major programs modeled for DE include: 

Industrial Gas Turbines 

Advanced Microturbines 

Gas-Fired Reciprocating Engines 

Thermally Activated Technologies 

Distributed Energy Systems Applications Integration 

Cooling Heating and Power Integration 

The Technology Base – (Advanced Materials and Sensors is not modeled directly because its 

benefits are represented in the other programs). 

Methodology and Calculations 

Because the time horizon of the Annual Energy Outlook 2004 Reference Case (AEO-4 case) 

version of the National Energy Modeling System (NEMS) is 2025, and the goals of Distributed 

Energy (DE) programs are relatively short term, the approach taken in this GPRA cycle is that 

most of the outputs are captured before that date. However, DE programs are part of a wider 

effort to transform the power system from its current highly centralized form to a more robust 

decentralized paradigm, a transformation with a longer time horizon than NEMS-GPRA 

provides, and are not readily represented in the NEMS-GPRA framework. 

Distributed generation (DG) appears in multiple modules (roughly corresponding to subsectors 

of the full energy sector, i.e. utility, commercial, etc.), which hinders DE program’s 

representation in NEMS-GPRA. Further, only a limited number of technology slots are available 

to represent a broad array of equipment types, sizes, and configurations. For example, the 

reciprocating engines in the commercial sector all have combined heat and power (CHP) heating 

(but not cooling) capability, while those in the utility sector do not, while in some instances, 

engines without CHP might be attractive in the commercial sector and vice-versa. Proper 

representation of DE program goals includes an accurate representation of DE’s technologyadvancement targets, while accounting for the limitations of NEMS. Therefore, in addition to 

changing input assumptions relative to the AEO-4 version of NEMS, other fixes to perceived 

limitations or omissions are also appropriate in both the base and program cases. 

Inputs to Base Case 

Expectations of improvements in technologies embedded in the AEO-4 reference case, which 

presuppose existence of DE programs, need to be eliminated from the base case (referred to as 

the baseline) for comparisons with achievement of program goals (referred to as the program 

case). Two full sets of forecast scenarios are actually needed, with and without DE programs in 

place; and the AEO-4 case is likely, although not certain, to fall between the two. In the FY 2006 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-1 
GPRA (GPRA06), the baseline case generally corresponds to a 10-year lag of the program case, 

though there are exceptions as described below. Estimation of the benefits of the programs is 

based on a comparison of the baseline and program scenarios. In this analysis, both scenarios 

were effectively estimated together as two deviations from the AEO-4 case, so they are presented 

together in the following section. 

NEMS-GPRA Inputs 

NEMS-GPRA input specifications follow by program, and all are summarized in Table 3. 

Inputs for each program are briefly described in the following sections. As a general rule, no 

modifications are made to NEMS-GPRA input data prior to 2006, consistent with the notion that 

FY 2006 benefits begin in year 2006. 

The AEO-4 case and prior GPRA forecasts were compared with the National Renewable Energy 

Laboratory’s (NREL) and Gas Technologies Institute’s Technology Characterizations (TeChars) 

for three technologies: microturbines, gas engines, and industrial gas turbines. When program 

goals were not available from the program office, technology cost and electrical efficiency inputs 

are derived both from the TeChars and from DE program goals.1 

NEMS-GPRA often contains multiple sizes of each DE technology, yet the program goals are 

typically provided for a single representative unit size. As a result, the technology inputs for 

baseline and program cases are scaled using the TeChars to correspond to the units represented 

in NEMS-GPRA. This analysis assumes the DE program goal data represent a 1 MW gas engine, 

5 MW gas turbine, and 100 kW microturbine, which are then scaled to accommodate the various 

sizes. For clarification, a summary table of technology type, module, and nameplate capacities 

represented in the GPRA06 case is included in Table 1. 

Table 1. Summary of Technology Size Representation by Module 

Technology 

Type 

Module Representative Size in NEMS 

Gas Turbine Commercial 1 MW 

Industrial 1 MW, 5 MW, 10 MW 

EMM 2 MW* 

Microturbine Commercial 100 kW 

Gas Engine Commercial 200 kW 

Industrial 800 kW, 3 MW 

EMM 1 MW* 

* The 1 MW peak-load and 2 MW base-load units in the EMM module are composite plants made up of various 

technologies, a portion of which are gas engines and gas turbines, respectively. 

While many of the technology inputs reflect the achievement of DE program goals by 2012, the 

exact replication of this time frame is not always possible because of certain model constraints. 

For example, technological progress for the commercial module absorption chiller, an additional 

technology not explicitly characterized by size, is limited to a step-function advance, and input 

1 Goldstein, Larry, Bruce Hedman, Dave Knowles, Steven I. Freedman, Richard Woods, and Tom Schweizer, (November 2003). 

“Gas-Fired Distributed Energy Resource Technology Characterizations,” NREL/TP-620-34783. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-2 
values are specified in years 2000, 2005, and 2020. Figure 12 represents this limitation. For this 

reason, it is not always possible to exactly replicate program goals in NEMS-GPRA. 

Industrial Gas Turbines 

Gas turbine sizes in NEMS-GPRA range from 1 to 40 MW, appearing explicitly in the 

commercial and industrial demand modules, and as part of a composite plant in the utility 

electricity market module (EMM), where this plant is represented by a mix of different 

technologies and is defined generically as either a base-load or peak-load system. The industrialsector turbines cover a wide size range, but proposed inputs to the GPRA06 process focus on the 

1 MW-, 5MW-, and 10 MW-size systems. Although larger turbines are not the focus of the 

program, the 25 MW and 40 MW gas turbines in the industrial sector are also modified in order 

to maintain consistency across the various sized turbines. The commercial sector contains a 

single representative turbine sized at 1 MW. The inputs for the commercial turbine were scaled 

to 1 MW using the 5 MW representative size gas turbine input data and the TeChars difference 

between these two units. The baseline and program case inputs for the commercial sector 

correspond to the 1 MW system shown in Figures 1-3. Also, a portion of the 2 MW base-load 

EMM generator is represented as a 1 MW gas turbine, which is discussed in a later section. 

The baseline input values for industrial gas turbines reflect a 10-year lag from the program 

electrical and combined efficiencies. There is no cost difference between baseline and program 

cases, which are kept at the AEO-4 levels. 

The program input values represent an improvement in electrical efficiency by 2006. Again, 

costs are kept at AEO-4 levels. The combined efficiency changes reflect target levels for 2006 

and 2008, scaled to the various sized turbines using the TeChars difference from the 5 MW unit 

and kept flat thereafter. The main objective of this program currently is efficiency and 

performance improvement and NOx and CO emissions reduction; but, because emissions 

reductions are not reported metrics, forecasts for these improvements are not included here. 

0 

( )

GPRA06 Gas Turbine Cost Fore cast 

500 

1,000 

1,500 

2,000 

2,500 

2000 2005 2010 2015 2020 2025 

Year 

Cost 2003-$/kW

GPRA06 1M W 

Baseline and Program 

GPRA06 5M W 

Baseline and Program 

GPRA06 10M W 

Baseline and Program 

Figure 1. Industrial Gas Turbine Installed Cost 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-3 
GPRA06 Gas Turbine Ele ctrical Efficie ncy Fore cas t 

0.40 

0.38 

0.35 

Electrical Efficiency 

0.33 

0.30 

0.28 

0.25 

0.23 

0.20

2000 2005 2010 2015 2020 2025

GPRA06 1M W 

Baseline 

GPRA06 5M W 

Baseline 

GPRA06 10M W 

Baseline 

GPRA06 1M W 

Program 

GPRA06 5M W 

Program 

GPRA06 10M W 

Program 

Year 

Figure 2. Industrial Gas Turbine Electric Efficiency 

i i y 

GPRA06 Gas Turbine Combined Efficiency Fore cas t 

0.64 

0.66 

0.68 

0.70 

0.72 

0.74 

0.76 

0.78 

2000 2005 2010 2015 2020 2025 

Year 

Comb ned Eff cienc

GPRA06 1M W 

Baseline 

GPRA06 5M W 

Baseline 

GPRA06 10M W 

Baseline 

GPRA06 1M W 

Program 

GPRA06 5M W 

Program 

GPRA06 10M W 

Program 

Figure 3. Industrial Gas Turbine Combined Efficiency 

Advanced Microturbines 

Microturbines occur only in the commercial module as a representative 100 kW system. Input 

data are therefore directly applied to this system without any scaling of different-sized units. 

The baseline input values for costs, electricity conversion efficiency, and combined efficiency 

shown in Figures 4-6, respectively, represent a 10-year lag of the program input assumptions. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-4 
The program input values for cost are a 25% improvement from 2000 to 2008 with a continued 

trend through 2012. Costs therefore fall from $1,926/kW in 2000 to $1,231/kW by 2012 and are 

flat thereafter. 

Because the AEO-4 microturbine electrical efficiency matches the TeChars projection, the 

program case assumes the AEO-4 with the baseline set to a 10-year lag of the program. 

Combined efficiency values are scaled to agree with the combined efficiency target provided for 

a 5 MW turbine using the relative difference between these two units in the TeChars publication. 

The combined efficiency reaches 71% by 2008 and is flat thereafter. 

0 

( / )

GPRA06 Microturbine Cost Forecast 

500 

1,000 

1,500 

2,000 

2,500 

2000 2005 2010 2015 2020 2025 

Year 

Cost 2003-$ kW

GPRA05 Bas eline 

GPRA05 Program 

GPRA06 Bas eline 

GPRA06 Program 

Figure 4. Microturbine Installed Cost 

i i i

El y 

GPRA06 M croturb ne Electr cal Efficiency Forecast 

0.20 

0.25 

0.30 

0.35 

0.40 

0.45 

2000 2005 2010 2015 2020 2025 

Year 

ectrical Efficienc

GPRA05 Bas eline 

GPRA 05 Program 

GPRA06 Bas eline 

GPRA 06 Program 

Figure 5. Microturbine Electric Efficiency 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-5 
i i i

y

GPRA06 M croturbine Comb ned Eff ciency Forecast 

0.50 

0.55 

0.60 

0.65 

0.70 

0.75 

2000 2005 2010 2015 2020 2025 

Year 

Combined Efficienc

GPRA05 Bas eline 

GPRA 05 Program 

GPRA06 Bas eline 

GPRA 06 Program 

Figure 6. Microturbine Combined Efficiency 

Gas-Fired Reciprocating Engines 

Gas engines appear in several modules in NEMS, in both CHP and simple-cycle configurations, 

but only one or two marker models represent the wide range of available engines (see Table 1). 

The limited number of available technology slots, together with the maturity and clear 

attractiveness of gas engines in many configurations, makes the choice of inputs for this 

technology somewhat complex.2 The commercial module has a marker 200 kW CHP-enabled 

unit, the industrial module has 800 kW and 3 MW CHP-enabled units, and the 1 MW unit that 

appears in the EMM also partially represents a simple-cycle gas engine as a composite plant for 

various technologies 

The baseline input values for costs (see Figure 7), electricity conversion efficiency (see Figure 

8), and combined efficiency (see Figure 9) are a 10-year lag from the program goal assumptions. 

The program input values for the commercial and industrial engines are scaled from the inputs 

provided for a typical engine assumed to be 1 MW in size. However, the 800 kW engine in the 

industrial sector is represented by the 1 MW technology characteristics. The cost is represented 

as a 25% improvement from 2000 to 2008 with a continued trend through 2012, and flat 

thereafter. The electrical efficiency value for a 1 MW gas engine is assumed to be 42% by 2008, 

the cost is $601/kW by 2012, and the combined efficiency is 75% by 2008 and flat thereafter. 

The 200 kW commercial gas engine and 3 MW industrial gas engine are scaled from these 

targets using the relative difference in cost, electrical and combined efficiency in the TeChars. 

2 Heat recovery can be from exhaust gas or jacket coolant, and a promising CHP application is absorption- cycle cooling, which 

is non-existent in NEMS-GPRA. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-6 
400 

500 

600 

700 

800 

900 

1,000 

1,100 

1,200 

2000 2005 2010 2015 2020 2025 

( / ) 

GPRA06 Gas Engine Cost Fore cast 

Year 

Cost 2003-$ kW

GPRA06 200kW 

Comm. Baseline 

GPRA06 800kW 

Ind. Baseline 

GPRA06 3M W 

Ind. Baseline 

GPRA06 200kW 

Comm. Program 

GPRA06 800kW 

Ind. Program 

GPRA06 3M W 

Ind. Program 

Electrical Efficiency 

0.43 

0.40 

0.38 

0.35 

0.33 

0.30 

0.28 

0.25 

2000 2005 2010 2015 2020 2025 

GPRA06 200kW 

Comm. Baseline 

GPRA06 800kW 

Ind. Baseline 

GPRA06 3M W 

Ind. Baseline 

GPRA06 200kW 

Comm. Program 

GPRA06 800kW 

Ind. Program 

GPRA06 3M W 

Ind. Program 

Year 

Figure 7. Gas Engine Installed Cost 

GPRA06 Gas Engine Ele ctrical Efficie ncy Fore cas t 

0.45 

Figure 8. Gas Engine Electric Efficiency 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-7 
GPRA06 Gas Engine Combine d Efficie ncy Fore cas t 

0.85 

0.80 

Combined Efficiency 

0.75 

0.70 

0.65 

0.60 

GPRA06 200kW 

Comm. Baseline 

GPRA06 800kW 

Ind. Baseline 

GPRA06 3M W 

Ind. Baseline 

GPRA06 200kW 

Comm. Program 

GPRA06 800kW 

Ind. Program 

GPRA06 3M W 

Ind. Program 

2000 2005 2010 2015 2020 2025 

Year 

Figure 9. Gas Engine Combined Efficiency 

Technology Representation in the Utility Sector (Electricity Market Module) 

The EMM contains two generic DG technologies: a 2 MW base-load system and a 1 MW peakload system, neither with CHP capability. Baseline and program representation of these 

technologies correspond to a projection based on a composite of various technologies. The 

1 MW peak-load system’s assumed composition is 80% gas engine and 20% microturbine from 

2010 onward. The 2 MW base-load system assumes a make up of 20% gas engine, 20% gas 

turbine, 20% microturbine, and 40% fuel cell from 2010 onward. These modified weightings are 

taken from a study by Joe Iannucci of Distributed Utility Associates.3 The technology 

characteristics of the gas engine, gas turbine and microturbine in these two composite plants are 

replaced with the 1 MW gas engine, 5 MW gas turbine, and 100 kW microturbine assumptions, 

respectively, to match the baseline and program goals. For the baseline, the EMM systems do not 

assume a 10-year lag of the program. Instead, separate trajectories for the baseline and program 

cases based on the Iannnucci study are assumed. Figure 10 shows the cost trajectory and 

Figure 11 illustrates the modified heat rate assumptions. 

Although CHP applications may be attractive to utilities, DG systems in the EMM do not include 

heat-recovery components, and therefore projected technology costs are slightly lower. 

3 “Assessing Market Acceptance and Penetration for Distributed Generation in the United States”, Distributed Utility Associates, 

June 1999. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-8 
Figure 10. Electricity Market Module Installed Cost 

GPRA06 Ele ctricity M arke t M odule 

Electrical Efficiency Fore cas t 0.45 

( / ) 

li

li

GPRA06 Electricity Market Module Cost Fore cast 

200 

300 

400 

500 

600 

700 

800 

900 

2000 2005 2010 2015 2020 2025 

Year 

Cost 2003-$ kW

GPRA06 Baseload 

Sy stem Baseline 

GPRA06 Peakload 

Sy stem Baseline 

GPRA06 Baseload 

Sy stem Program 

GPRA06 Peakload 

Sy stem Program 

GPRA06 Baseload 

Sy stem Base ne 

GPRA06 Peakload 

Sy stem Base ne 

GPRA06 Baseload 

Sy stem Program 

GPRA06 Peakload 

Sy stem Program 

Electrical Efficienc

y

0.40 

0.35 

0.30 

0.25 

0.20 

2000 2005 2010 2015 2020 2025 

Year 

Figure 11. Electricity Market Module Electrical Efficiency 

Advanced Materials 

No separate inputs to represent this program are proposed. The benefits of this activity are 

represented in the preceding technology-development activities. 

Thermally Activated Technologies 

DE’s thermally activated technologies program includes direct-fired absorption chiller 

technologies and desiccant dehumidification systems. Only the former are represented here as 

changes applied to gas-fired absorption chillers in the commercial technology input file. 

The NEMS-GPRA commercial module represents the commercial building stock using 11 

representative building types. Of these, the commercial technology input file restricts gas-fired 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-9 
absorption chillers from being installed in the following building types: food sales, food service, 

small office, warehouse, and other. These restrictions are removed for both the baseline and 

program cases to allow systems to be installed in all buildings. 

The assumptions for the program case inputs include: cost-improvement data taken from 

Resource Dynamics’ study of integrated energy systems

4

 with future cost values (2005+) 

available in 2010; double-effect chillers are approximately 1.5 times the cost of single-effect 

chillers; and technology costs correspond to 50–100 cooling ton5 range. Table 2 and Figure 12 

shows the cost and COP assumptions for this technology. 

The baseline case, based on a double-effect chiller introduced in 2020, uses cost assumptions 

from the AEO-4 with improvement in year 2020 and modest COP improvements in 2005 and 

again in 2020. 

The program case is based on a double-effect chiller introduced in 2005 represented as an 

improvement to the cost and COP with further COP improvement in 2020. The cost 

improvement is introduced in 2005. 

Table 2. GPRA06 Inputs for DE’s Thermally Activated Technologies Program 

Baseline Case Program Case 

Year COP Cost 

($/kBtu/hr) Cost ($/cooling ton) COP Cost 

($/kBtu/hr) Cost ($/cooling ton) 

2000 1.0 78.75 945 1.0 78.75 945 

2005 1.2 78.75 945 1.3 50.00 600 

2010 1.2 78.75 945 1.3 50.00 600 

2020 1.3 50.00 600 1.4 50.00 600 

4 LeMar, P. (August 2002). “Integrated Energy Systems (IES) for Buildings: A Market Assessment,” Resource Dynamics. 5 1 cooling ton is equal to 12,000 Btu/hr or approx 3.5 kW thermal. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-10 
i

40 

45 

50 

55 

60 

65 

70 

75 

80 

85 

( ) 

GPRA06 The rmally Act vate d Te chnology Inputs 

2000 2005 2010 2015 2020 2025 

Year 

Cost 2001-$/kBtu-hr

0.7 

0.8 

0.9 

1.0 

1.1 

1.2 

1.3 

1.4 

1.5 

COP 

GPRA06 TAT 

Cost Baseline 

GPRA06 TAT 

Cost Program 

GPRA06 TAT 

COP Baseline 

GPRA06 TAT 

COP Program 

Figure 12. Thermally Activated Cooling Technology Inputs 

Distributed Energy Systems Applications Integration 

The Distributed Energy Systems Applications Integration (DESAI) Program strives to accelerate 

adoption of DG technologies in certain sectors, especially among the existing building market 

(i.e. through retrofits). The NEMS model calculates DG adoption in existing buildings as a 

predetermined fixed share of the adoption in new buildings, and that share is set at 0.5% in the 

AEO-4 Reference Case. Because the retrofit market is the primary target of the DESAI Program, 

the outputs are represented by an increase in the cap on the share of existing commercial sites 

that can adopt DG. 

The baseline input values are achievements of cost and efficiency targets by 2020. The existing 

building adoption rate is 0.5% of new buildings, equivalent to the AEO-4 value. 

The program input values increase the share of existing buildings eligible to adopt DG from 

0.5% to 10% of new buildings. 

As part of the DG adoption logic fixes described in Section 9, additional changes to the new 

building adoption parameter were made in addition to the DESAI Program representation. 

Cooling Heating and Power Integration 

This program develops improved CHP packages and otherwise supports the market penetration 

of CHP technologies, including indirect-fired absorption chillers. Because NEMS-GPRA does 

not have a representation of indirect-fired absorption chillers, this program is represented by a 

proxy improvement in the payback period of the prime mover technology equivalent to the 

economic benefit of using 25% of the generator waste heat for a cooling end use. 

The baseline input values are a 10-year lag of the program input values. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-11 
The program input values are a target 70% combined efficiency level in 2006 and 75% level in 

2008 assumed to represent a typical 1 MW gas engine or 5 MW gas turbine. The target was 

scaled using the TeChars to the various sized CHP capable technologies in the industrial and 

commercial sectors. Additionally, a commercial module customer payback adjustment is also 

made to incorporate the added benefits of absorption cooling capability in gas engines and 

microturbines. The corresponding energy savings from enhanced absorption cooling deployment 

are also adjusted as impacts to commercial end-use consumption. This adjustment also increases 

heating consumption if not all the waste heat is available to meet the load. 

DG Adoption Logic Fixes 

Two fixes were made to the DG adoption logic of new buildings in the commercial sector of 

NEMS-GPRA for both baseline and program cases. The adoption algorithm for DG in new 

buildings caps the maximum market adoption rate (the penparm parameter) at 30% for a oneyear payback level. The NEMS cap on adoption rates for different paybacks (max pen) decays as 

an inverse function at a rate of 1/years to positive cash flow, and this decay is known as the 

payback acceptance function (shown as equation 1 below). 

penparm max pen = (1) payback 

This approach severely disfavors technologies with paybacks that are moderate but still quite 

acceptable to many building owners—such as in the three- to six-year range—while it allows 

smaller adoption at very long paybacks, such as 15 years. 

First, the cap for new buildings with a one-year payback (represented by the penparm parameter) 

is raised from 30% to 50%. A similar change was made in the GPRA05 analysis. 

Second, the payback acceptance function is changed from an inverse decay function to one based 

on data of observed customer adoption of energy efficiency projects as a function of simple 

payback time6

. These data are shown below for buildings in the institutional sector (n=768) and 

commercial buildings in the private sector (n=108). 

6 Market Trends in the U.S. ESCO Industry: Results from the NAESCO Database Project. Goldman, C., J. Osborn and N. 

Hopper, LBNL, and T. Singer, NAESCO, May 2002, LBNL-49601. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-12 
0 

20 

40 

60 

80 

100 

120 

140 

160 

180 

1 3 5 7 9 

jects

Project Distribution of Simple Payback Time 

11 13 15 17 19 21 23 25 27 29 

Years to Simple Payback 

Number of Pro

Private Sector Commercial 

Institutional Sector 

Figure 13. Distribution of Years to Simple Payback 

To determine a decay function for the max pen based on this data set, the percentage of potential 

adopters from the total sample for each given payback year is calculated. It is assumed that for a 

given payback year, all of the adopters in that year and all adopters of projects with shorter 

payback periods would adopt, i.e. all columns are summed to the right in Figure 13. These data 

show that the relationship between the project payback period and the number of buildings that 

adopt the project is approximately linear for a payback between 1 and 11 years, and then falls off 

sharply at years 13 and higher. For example, all adopters of projects with 29-year paybacks also 

would adopt projects with 27-year paybacks, 25-year paybacks, etc. The resulting customeracceptance curve is shown in Figure 14, along with the mathematical representation of the 

revised curve for input to NEMS-GPRA and the current equation used in the AEO-4. Figure 14 

shows that a maximum of 100% will adopt, and this represents 100% of the sample size; 

however, in NEMS-GPRA, the percentage of the total population that actually will adopt is 

scaled down using the penparm parameter (set at 50% for GPRA06), as discussed above. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-13 
0% 

20% 

40% 

60% 

80% 

100% 

120% 

1 3 5 7 9 

l 

Exi i i

11 13 15 17 19 21 23 25 

simple payback 

Percent Adopting 

Institutional + Private 

Commercia

st ng n NEMS 

Mathematical 

Representation 

Figure 14. Decay Function of the Maxpen 

NEMS-GPRA uses the number of years to positive cash flow7, and not simple payback, as the 

primary metric of DER adoption. Figure 14 converts the data to years to positive cash flow by 

dividing the simple payback time in half. A simple spreadsheet analysis was used to determine 

the relationship between simple payback and years to positive cash flow, assuming the NEMSGPRA financing assumptions. This analysis determined that, with NEMS-GPRA financing 

assumptions in place, the number of years to positive cash flow of a project is approximately half 

of the payback period. Ultimately, the decay above is represented by equation 2 below as a 

function of the payback variable as defined in NEMS-GPRA: 

1.1 penparm (2) 

e

max pen = 24.0 payback 

One additional NEMS-GPRA fix has been implemented in the base and program cases to ensure 

that the changes to the adoption logic described above do not result in an exaggerated number of 

DG adoptions. An internal check is included to ensure that the percentage of existing buildings 

that have DER systems installed does not exceed the cap imposed on new buildings. This will 

prevent a case where the installations in new buildings are not allowed to reach the rate of 

existing buildings. 

The NEMS-GPRA fixes, along with additional minor changes, are summarized in Table 4. 

7 The NEMS payback (or simple payback) variable is defined as the first year in the cash-flow stream for which an investment 

has a positive cumulative net cash flow. (EIA, NEMS Commercial Module Documentation Report 2004, EIA/DOE-MO66(2004)) 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-14 
Market Uptake 

For industrial gas turbines, a modification in the customer acceptance payback distribution was 

made to reflect a market transformation improvement from the DESAI program. This change 

alters the payback distribution, allowing more customers to accept a shorter payback period. As 

well, a change is made to relax the yearly penetration rate for gas turbines from a maximum 5% 

to 10%. The rationale for these modifications was to reflect improvements in the technical 

barriers of CHP adoption. 

No market potential or penetration analyses were done exogenously to NEMS-GPRA for this 

work. The market definition and penetration rates for DG are those that are endogenous to 

NEMS-GPRA, and these are described briefly here for the EMM and the Commercial Demand 

Module. 

In the EMM, the market is driven by the growing electricity-demand forecast and the deferred 

cost of transmission and distribution (T&D) expansion. The two available DER generators (the 

peak and base-load units) compete against the cost of central-station generation and T&D 

upgrades to supply growing demand and replace retiring generating capacity. The total capacity 

of DG is constrained to correspond to a specific level of avoided T&D costs, indicating that there 

is a maximum economic value of T&D deferrals that DG can provide.8 

In the NEMS commercial sector, the market is represented by 11 building types and is 

disaggregated into nine geographic census divisions or regions. Annual penetration into the newbuilding market is determined by the economic attractiveness of on-site generation with heat 

recovery relative to the purchase of electricity and other fuels. The retrofit market is not 

characterized distinctly, and the market adoption is simply proportional to the new-building 

adoption. Distributed generation adoption in the commercial sector is dominated by a few 

building types. The education, lodging, and mercantile/service sectors account for the large 

majority of DG capacity additions from the DE program. Regional DG adoption is distributed 

more evenly among census divisions, though the Pacific and Middle Atlantic regions account for 

a larger share of DG adoption, partly because of the higher electricity demand and prices 

forecasted for those regions. 

Because DG market segments are broadly characterized in NEMS, an accurate representation of 

niche market adoption is difficult to include exogenously in NEMS-GPRA. Several niche market 

segments that contribute to the total market for DG (such as markets for reliability, security, or 

environmental benefits) are not represented in NEMS-GPRA. 

8 Energy Information Administration (2003). “The Electricity Market Module of the National Energy Modeling System: Model 

Documentation Report,” U.S. Department of Energy, Washington, D.C. pg.91. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-15 
Table 3. Summary of DE Program and Baseline Representation in GPRA06 

Technology Development 

DE Program 

Industrial Gas Turbines Advanced Microturbines 

Gas-Fired Reciprocating Engines Technology BasedAdvanced Materials and Sensors 

Program Goals 

- 35% electric efficiency 

HHV 

- 0.5 g/kWh NOx by 2000, 0.4 g/kWh NOx by 2005, 

and 0.2 g/kWh NOx by 2008 

- 32% electric efficiency 

HHV by 2005, 33% electric efficiency HHV by 2008 

- 0.4 g/kWh NOx by 2005, 0.2 g/kWh NOx by 2008 

- 40% electric efficiency 

(HHV) by 2005, 42% electric efficiency (HHV) by 2008 

- $380/kW by 2006, $360/kW 

by 2008 

- 2.1 g/kWh NOx by 2006, 1.1 

g/kWh NOx by 2008 

Advanced material research to assist in other program goals 

Representation in NEMS-GPRA 

Baseline Prog

- 10-year lag of Program

case 

- EMM 1 MW baseload 

composite unit assumes forecast determined from Joe Iannucci report weightings 

- 10-year lag of Program

case 

- 10-year lag of Program case - EMM 1 MW peaker composite 

unit assumes forecast determined from Joe Iannucci report weightings 

No additional changes 

- Industrial module: ele

28%, 35%, and 38% fosystems, respectively thereafter; combined e73%, 75%, and 77% r

- EMM baseload unit c

composite plant withou20% gas turbine make

- Commercial module 

values. 

- 25% cost improveme

and continuing trend threaches 1,231$/kW b

- electric efficiency is k

because it represents 

- 66% and 71% combi

by 2006 and 2008, res

- 200 kW commercial m

electric efficiency by 2087% combined efficienc

- Industrial module 1 MW 

42% and 44% electric ecost improvement from continuing trend througand $597/kW, 75% and efficiencies by 2008, res

- EMM 1 MW peaker un

without CHP capability make-up. 

Included in acceleration 

End-Use Integration pro

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-16 
End-Use Integration 

Representation in NEMS-GPRA DE Program Program Goals Baseline Prog

Thermally Activated Cost and efficiency - COP of 1.2 (2005) and 1.3 - COP of 1.3 (2005) and 

Technologies improvements for direct-fired (2020), $79/kBtu-hr (2005) hr by 2005 

absorption chillers - $51/kBtu-hr (2020); allow - allow installations in a

installations in all building types 

Distributed Energy Demonstration and - Percent of existing buildings - Percent of existing bu

Systems Applications integration projects in that adopt DER set at 0.5% of increased to 10% of new 

Integration industrial sector, high-tech new buildings (same as AEO-4) 

industry, hospitals, and other commercial sectors.9

Cooling Heating and Combined efficiency target of - 10-year lag of Program case - Apply CHP 2006 and 2

Power Integration 70% by 2006 and 75% by - Reduce the payback years for commercial gas engine

2008 commercial gas engines and microturbine and indust

microturbines to account for gas engines and 1MW, enabled absorption chiller turbines and scaling the capability different size units using 

- Reduce the payback y

gas engines and microtenabled absorption chil

9 The National Accounts Energy Alliance focuses on “Fortune 1000, national chain end-users, including the retail, supermarket, food service, hotel, an

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-17 
Table 4. Additional NEMS-GPRA Enhancements for both the Baseline and Program Ca

Change Module Program 

Baseline 

or Implemented in NEMS-GPRA S

Maximum Annual Penetration Commercial Both Penparm parameter currently set to 30%, CCaps for New Buildings change to 50% GMaximum Annual Penetration Commercial Both Remove penetration cap of 0.5% new building ACaps for Existing Buildings penetration im

ovbu

Falloff of Maximum Annual Commercial Both Currently set as an inverse function: MPenetration Caps as a Function of Payback Years simplepayb ack penparm max pen 

=

EfroD

Change to: 

simplepayb ack e 

penparm pen 24.0 1.1 max 

=

GananM

Adjust commercial customer Commercial Both Modify isimplepayback for gas engines and Npayback for gas engines and microturbines by Census Division, building abmicroturbines to include type, and year and corrected for change in absorption cooling capability in energy consumption as a result of increased these technologies absorption cooling. Adjust industrial customer Industrial Program Modify the payback acceptance curve giving a Racceptance payback greater acceptance rate for shorter paybacks. tradistribution and relax the Also increased the yearly penetration rate from adyearly penetration rate for gas 5% to 10%. Cturbines to reflect market transformation improvements with CHP 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix D – Distributed Energy Program – Page D-18 
Appendix E – GPRA06 Federal Energy Management Program 

Documentation 

Introduction 

The mission of the Federal Energy Management Program (FEMP) is to promote energy security, 

environmental stewardship and cost reduction through energy efficiency and water conservation, 

the use of distributed and renewable energy, and sound utility management decisions at Federal 

sites. [FY 2005 Congressional Budget Request, p. 475] 

The Federal Energy Management Program goal is to provide technical and financial assistance to 

Federal agencies and thereby lead the Nation by example in use of energy efficiency and 

renewable energy. Through the Federal Government’s own actions, FEMP’s target is to increase 

Federal renewable energy use to 2.5% of total Federal electrical energy use by 2005, and reduce 

energy intensity in Federal buildings by 30% by 2005 (relative to the 1985 statutory baseline 

level of 138,610 Btu per gross square foot). By 2010, the target is to further reduce energy 

intensity in Federal buildings by 35% (relative to the 1985 statutory baseline level).[FY 2005 

CBR, p. 476] Resource assumptions for FEMP are shown in Table 1. 

Table 1. Resource Assumptions for FEMP, FY 2005 to FY 2010. 

(in millions of nominal dollars) 

FY05 FY06 FY07 FY08 FY09 FY10 

18.4 17.1 17.1 17.1 17.1 17.1 

Introduction to GPRA Metrics Approach 

Pacific Northwest National Laboratory (PNNL) calculates the potential site energy impacts of 

FEMP’s portfolio for DOE/EERE. The details of those mathematical calculations are available 

for review in an annotated Excel spreadsheet, which provides a transparent “A to Z” 

understanding of how the year 2010 impacts are estimated. Individuals interested in the specific 

details should refer to that file, available from PNNL by contacting Daryl Brown 

(daryl.brown@pnl.gov). FEMP’s detailed spreadsheet model is not integrated into the larger FY 

2006 GPRA models (NEMS-GPRA06 and MARKAL-GPRA06). However, to provide source 

energy savings, energy-expenditure savings, and carbon emission reductions attributed to FEMP, 

the outputs of the spreadsheet model are fed into the larger GPRA models exogenously and the 

larger models report these benefits. 

A detailed narrative description of the approach, and a summary of the results, follows below in 

the section Energy Savings Calculation Mechanics. The purpose of this introductory section is to 

provide a general understanding of the approach and assumptions at a higher level. 

There are four key principles governing PNNL’s estimation of GPRA metrics for FEMP. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix E – Federal Energy Management Program – Page E-1 
First, the principal goal examined for metrics development is the 2010 site energy-intensity goal 

for “standard” buildings and facilities described above. PNNL also estimates the impact of the 

Executive Order 13123 goal for energy-intensive operations, which is to reduce energy per 

square foot by 25% in 2010, relative to a 1990 baseline. Both of these goals are stated in terms of 

energy use, per year, per square foot of floor space. It is important to note that FEMP’s mission 

is to assist the 31 Federal agencies in attaining these executive order goals for the Federal 

government. Strictly speaking, these are not goals for FEMP but goals for each individual 

agency, and their involvement is essential. As noted above, the Federal sector also has a 

renewables goal for FY05, but FEMP’s role in helping Federal agencies meet this goal was not 

estimated by PNNL because any impact through FY05 would not be affected by the FY 2006 

budget request. 

Second, to estimate impacts in the Federal marketplace, PNNL treats the entire Federal Energy 

Management Program as one unified deployment program. That is, PNNL takes what is often 

called a “top-down” approach to calculate 2010 energy impacts. The impact of FEMP’s broad 

portfolio of deployment activities – alternative financing, direct technical assistance, training and 

information, publication of the Annual Report to Congress, procurement recommendations – is 

estimated as one combined effect in the market, measured in terms of energy use per square foot 

per year. Put differently, separate impacts for each FEMP activity are not estimated and then 

summed; the approach is not “bottom-up”. 

Third, the target market is the Federal sector, the Nation’s 3.0 billion square feet of federal 

buildings space – military bases, post offices, VA hospitals, Department of Energy (DOE) 

laboratories, courthouses – and the Nation’s Federal energy intensive operations. (Energyintensive operations include, for example, laboratories, check-processing facilities, and linear 

accelerators.) The Federal Government’s actions – via leadership, awards, influence, and raw 

purchasing power – may well influence private-sector and state and local government decisions 

with respect to energy-related decisions, but any such “spillover” impact is not estimated in this 

GPRA process. 

Finally, the question of attribution of impact must be addressed. The mission of FEMP is to 

assist the Department of Defense, GSA, and other Federal agencies in attaining legislative and 

executive order energy goals for those agencies. The analysis needs to determine how much of 

that goal achievement is attributable to FEMP. Very specifically, how much of the site energyintensity reduction in Federal buildings and facilities, from FY 2006 to FY 2010, is attributable 

to the portfolio of FEMP activities funded between FY 2006 and FY 2010, assuming level 

funding? In the GPRA analysis, PNNL assumes that 50% of the progress is attributable to 

FEMP’s leadership and to FEMP’s diverse portfolio. The other 50% is attributable to 

conservation retrofit funding, awareness campaigns at other Federal agencies, as well as to the 

existence of appliance and equipment standards and general technological innovation. 

The 50% estimate was originally derived from analysis performed in support of the Energy 

Savings Performance Contract alternative financing activity within FEMP.1

 An assessment of the 

likely agency markets for alternative-financing products from FEMP (both ESPC and Utility 

1 FEMP Fiscal Year 1999 ESPC Business Strategy Development Summary Report, K. McMordie-Stoughton and D. Hunt, Pacific 

Northwest National Laboratory, March 2000, PNNL-13204. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix E – Federal Energy Management Program – Page E-2 
Programs) produced estimates of FEMP programmatic impact of 35% to 55%, with most of the 

remainder being attributed to the Army Corps’ Huntsville ESPC operation. This estimate did not 

include the likely impacts of the rest of FEMP’s portfolio – direct technical assistance, training, 

and information. Taking the lower-end estimate of 35% and including these other impacts, 

PNNL estimated that a reasonable impact was 50%. 

Energy Savings Calculation Mechanics 

Actual historical and estimated future energy consumption are characterized in terms of fuel 

consumption (MMBtu or million Btu), fuel mix (the fractions of total fuel consumption by fuel 

type), and building floor space (ksf or thousand square feet). A critical derived figure is building 

energy intensity (MMBtu/ksf). The development of these measures is described in the sections 

that follow. 

Historical Federal Agency Energy Consumption and Cost 

Estimates of future Federal agency energy consumption start from the latest data available for 

actual energy consumption. For the analysis of impacts resulting from the FY 2006 Budget 

Request, the latest actual data were for FY 2003. These data were provided by the individual 

Federal agencies to McNeil Technologies, which has the responsibility for collecting and 

managing these data for FEMP. In turn, PNNL receives these data from McNeil. These data are 

eventually documented in the Annual Report to Congress on Federal Government Energy 

Management and Conservation Programs2

 for each fiscal year. As of January 2005, the most 

recent published version of this report covered fiscal year 2001 and was published February 4, 

2004. 

The historical data available for analysis are energy consumption (MMBtu) by fuel type and 

building floor space (ksf). These data are reported by each agency. The fuel type categories are 

electricity, fuel oil, natural gas, liquefied petroleum gas (lpg), coal, purchased steam, and 

“other.” Building energy intensities (MMBtu/ksf) are calculated from these raw data. 

Future Federal Agency Energy Consumption 

Future Federal energy consumption was estimated by combining estimates of future building 

energy intensity, fuel mix, and building floor space. Total energy consumption (MMBtu) is the 

product of building energy intensity (MMBtu/ksf) and building floor space (ksf), as defined by 

Equation 1. Energy consumption by fuel type (MMBtu) is the product of total energy 

consumption and fuel-mix fraction for each fuel type, as defined by Equation 2. 

Total Energy = Building Energy Intensity * Building floor space Eqn. 1. 

Fuel Type “A” Energy = Total Energy * Fuel “A” Mix Fraction Eqn. 2. 

The Department of Defense (DOD), DOE, General Services Administration (GSA), United 

States Postal Service (USPS), and Veterans Affairs (VA) were selected for specific metric 

development because they are the five largest agencies measured by annual energy use, 

consuming nearly 90% of the Federal total in FY2003; DOD alone is nearly two-thirds of total 

2 Available on FEMP’s Web site at http://www.eere.energy.gov/femp/about/annual_report.cfm 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix E – Federal Energy Management Program – Page E-3 
Federal energy use (see Figure 1). Reduction in MMBtu/ksf from FY2000 through FY2010 was 

estimated for each of these five agencies and all other agencies (24 total) grouped together for 

standard buildings. Metrics for energy-intensive operations were developed for the Federal 

government as a whole. The following subsections describe the development of building energy 

intensity, building floor space, and fuel-mix fraction assumptions. In addition, the resulting 

estimates of building energy intensity reductions are provided. 

All Other 

Agencies 11% 

GSA 4% 

DOE 5% 

USPS 7% 

VA 9% DOD 64% 

Figure 1. FY 2003 Federal Agency Standard Building Energy Consumption 

Building Energy Intensity 

Estimates for agency-specific reductions in MMBtu/ksf by FY2010 relative to FY2000 were 

aggregated from estimates due to a) cost-effective retrofits of building energy systems, b) 

replacement of equipment upon failure (with generally more efficient equipment), c) costeffective retrofits of central energy plants and thermal distribution systems (DOD, DOE, and VA 

only), d) construction of new housing (DOD only), and e) improvements in O&M practices. 

These five categories have differing assumptions, and the assumptions for each agency can be 

different within a particular category. The assumptions are discussed in the text below, and are 

based on literature referenced in the text. Table 2 presents the output estimates of energy 

intensity reductions derived from the spreadsheet model by category and agency. 

Table 2. Energy-Intensity Reduction Estimates 

Estimated Reduction in MMBtu/ksf by 2010 from 2000 

Agency 

Reduction Source DOD DOE GSA USPS VA Other 

Building Retrofit 7 11 9 8 8 9 

Replace on Failure 4 4 4 4 4 4 

CEP and Dist Retrofit 2.5 2.5 2.5 

Improved O&M 3 6 2 2 4 3 

New Housing 0.5 

Total 17 23.5 15 14 18.5 16 

FY2000 MMBtu/ksf 105 249 67 74 168 115 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix E – Federal Energy Management Program – Page E-4 
The reduction in MMBtu/ksf for Federal agencies was based primarily on data developed in two 

PNNL reports, Economic Energy Savings Potential in Federal Buildings

3

, and An Assessment of 

Prospective FORSCOM Energy Intensities4. The former was prepared for FEMP by D. Brown, J. 

Dirks, and D. Hunt and is available from PNNL’s Web site at 

http://www.pnl.gov/main/publications/; the latter was prepared for the U.S. Army’s Forces 

Command (FORSCOM) by D. Brown and J. Dirks. 

The report for FEMP specifically examined the retrofit potential based on government financing 

for all government agencies, while the report for FORSCOM examined the retrofit potential for 

their facilities based on either government or alternative-financing mechanisms5

. The report for 

FORSCOM also looked at the impacts of the natural turnover of HVAC and service hot water 

(SHW) equipment (called “replace on failure” in Table 2), improvements to central energy 

plants (CEPs, i.e., boilers and/or chillers) and thermal distribution systems, and housing 

privatization plans (demolition, renovation, and new construction). 

FORSCOM facilities represent about 10% of total DOD floor space and have a mix of buildings 

types generally representative of DOD as a whole. In addition, the retrofit-estimating 

methodology was more robust than that used for the DOD sector in the FEMP report. Therefore, 

the FORSCOM results were used as the basis for estimating retrofit potential for DOD, while the 

FEMP results were used as the basis for other agencies. 

The estimated retrofit potential for non-DOD agencies from the FEMP report was reduced by 

one-third to reflect alternative financing rather than government financing (appropriations). This 

factor is driven by the higher interest rates and shorter financing periods typically seen for 

alternative financing and is based on work by J. Dirks, D. Brown, and J. Currie of PNNL6

. 

Finally, 50% of the estimated potential via alternative financing was assumed captured by 

FY2010. This will approximately occur if the rate of annual alternative-financing investment 

from FY1998 through FY2000 continues through FY2010, with the same ratio of energy savings 

per dollar invested as seen in FY1998 through FY2000. 

Replacement of HVAC and SHW equipment occurs continuously as equipment ages, fails, and 

must be replaced. In general, the efficiency of HVAC and SHW equipment has substantially 

improved because of technology advances, stimulated in part by stricter equipment and appliance 

standards at the national level. Other factors include building energy codes and the forces of 

technological innovation. As a result, replacement equipment will usually consume less energy 

than the equipment being replaced; and, in some cases, much less energy (refrigerators and 

chillers, for example). The estimated energy-intensity reduction from this mechanism was about 

4 MMBtu/ksf in the FORSCOM study; the estimated impact for civilian agencies was judged by 

3

 D.R. Brown, J.A. Dirks, and D.M. Hunt. 2000. Economic Energy Savings Potential in Federal Buildings. PNNL-13332. 

Pacific Northwest National Laboratory. Richland, Washington. 

4 Distribution of the full report is limited by FORSCOM. The following paper, based on the full report, is publicly available. 

D.R. Brown and J.A. Dirks. 2002. “Prospective FORSCOM Energy Intensities.” Proceedings of the 25th World Energy 

Engineering Conference. Association of Energy Engineers. Atlanta, Georgia. 5 Alternative financing includes energy-saving performance contracts (ESPC) and utility energy service contracts (UESC). 

6 J.A. Dirks, D.R. Brown, and J.W. Currie. 1999. Sensitivity of ESPC Projects to Changes in Interest Rates and Energy Prices. 

Pacific Northwest National Laboratory. Richland, Washington. An informal letter report from PNNL to FEMP. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix E – Federal Energy Management Program – Page E-5 
PNNL to be the same, since the phenomenon of improving energy efficiency in new equipment 

and appliances is economy-wide and not restricted to just DOD. 

DOD sites often have large CEPs and accompanying thermal distribution systems. Results from 

the FORSCOM report indicated potential energy savings equivalent to a reduction in building 

energy intensity of 5 MMBtu/ksf. Again, it is unlikely that 100% of the potential will be 

captured. A 50% capture fraction was assumed to be consistent with the building retrofit capture 

fraction assumption. Among the four civilian agencies considered explicitly, only DOE and VA 

have a significant number of sites with CEPs, so this projected savings was only applied to these 

two agencies, in addition to DOD. 

The estimated decrease in MMBtu/ksf from improved O&M practices was developed from data 

presented in Using Targeted Energy Efficiency Programs to Reduce Peak Electrical Demand 

and Address Electric System Reliability Problems by S. Nadel (et al) of American Council for an 

Energy Efficient Economy (ACEEE); and Energy and Comfort Benefits of Continuous 

Commissioning in Buildings by D. Claridge (et al) of Texas A&M University. Specifically, 

Nadel estimated cost-effective energy savings via improved O&M practices to be between 5% 

and 15% of existing energy consumption, with a maximum penetration rate of 50%. To be 

conservative, PNNL used a penetration rate of 25% for the FEMP GPRA analysis. Thus, starting 

from an average potential of 10%, the estimated savings from improved O&M practices was set 

equal to 2.5% of energy consumption in FY2000. 

DOD is unique among the Federal agencies with respect to the housing stock it manages for 

military personnel and their families. About 90% of federal housing stock, or about 600 million 

square feet, resides in the military. All three branches of the military are currently privatizing a 

significant portion of their housing stock. Privatization plans, besides transferring ownership, call 

for significant demolition, new construction, and renovation. The impact of these housing-stock 

changes was estimated (in the FORSCOM report) to reduce FORSCOM’s overall building 

energy intensity by about 3 MMBtu/ksf. This figure was reduced to 0.5 MMBtu/ksf for DOD, as 

a whole, because the energy impacts of housing privatization are concentrated within 

FORSCOM. 

The FY2010 building energy-intensity calculations are defined by Equation 3 for standard 

buildings. To calculate energy intensity for FY2010, the estimated reductions in MMBtu/ksf 

shown in Table 2 are subtracted from the actual energy intensities for each agency in FY2000. 

Although actual FY2003 energy consumption data are now available, the estimated energy 

intensities for FY2010 are based on FY2000 to be consistent with the references (reports for 

FEMP and FORSCOM described above) supporting the figures in Table 2. As described earlier, 

the FY2010 energy intensity for energy-intensive operations was set at the value that exactly 

meets the energy-intensity goal for these types of facilities. 

Building Energy Intensity in FY2010 = 

Building Energy Intensity in FY2000 – 

Building Energy Intensity Reduction Estimate Eqn. 3 

Energy intensities for years between FY2003 and FY2010 were geometrically interpolated 

between these two endpoints. Energy intensities beyond FY2010 were assumed to continue 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix E – Federal Energy Management Program – Page E-6 
declining, with each year 1% less than the previous year. This is a conservative assumption 

compared to the average compounded rate of decline from 1985 through 2003, which was 1.5%. 

Building Floor Space 

Future building floor space was set equal to the FY2003 value, i.e. no change in floor space was 

assumed through FY2030. Note, however, that floor space has been increasing slowly since 

FY1997 at a rate of about 0.4% per year, after declining from FY1985 to FY1997. The decline 

through FY1997 was driven mostly by reductions in DOD, while the increase since FY1997 is 

mostly attributable to USPS. It is not clear whether an increase or decrease in floor space is more 

likely during the next 10 years, let alone the next 30 years; therefore, floor space was assumed to 

remain constant for the duration of the analysis period. 

Fuel Mix 

Since FY1985, total site use of coal and fuel oil has declined significantly, while the use of 

electricity has remained nearly constant and the use of natural gas has declined slightly. As a 

consequence of these changes, the fractions of fuel use associated with electricity (and to a lesser 

extent, natural gas) have increased over time (See Figure 2). EIA forecasts from the Annual 

Energy Outlook 2004 suggest that this trend will continue, with site use of electricity increasing 

relative to other energy forms. 

0 

50 

100 

150 

200 

250 

300 

350 

400 

450 

500 

85 86 87 89 90 91 93 94 95 96 97 98 99 00 01 02 03 

u 

oil 

ng 

88 92 

Fiscal Year 

Trillion Bt

other 

lpg 

steam 

coal 

elec 

Figure 2. Historical Energy Use in Standard Federal Buildings 

Changes in the forecast fuel mix for the commercial sector from EIA’s Annual Energy Outlook 

2004 were applied to the actual Federal fuel mixes in FY2003 to estimate future federal fuel 

mixes. Projected changes for the commercial-sector fuel mix were first normalized relative to the 

existing commercial sector fuel mix in 2003. For example, the normalized electricity fraction in 

the commercial sector grew from 1.0 (by definition) in 2003 to 1.18 in 2030. In contrast, the 

normalized natural gas fraction in the commercial sector fell from 1.0 in 2003 to 0.86 in 2030. 

The normalized fuel fractions for each fuel and each year were multiplied by the actual Federal 

fuel fractions in 2003 for each agency or agency group to estimate future Federal fuel mixes. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix E – Federal Energy Management Program – Page E-7 
This procedure was applied to standard buildings, but not to energy-intensive operations. There, 

it was not so clear what sector (commercial or industrial) would better represent energy-intensive 

operations or whether the year-to-year volatility in reported data for energy-intensive operations 

would invalidate the refined approach. Instead, future fuel mixes for energy-intensive operations 

were assumed to remain as they were in FY2003. 

Federal Agency Energy Consumption Baseline 

The baseline Federal agency energy consumption is the estimated Federal agency energy 

consumption in FY2005. FY2006 is the first possible year that could be affected by the FY2006 

budget, so FY2005 is the logical baseline year. As previously described, the latest actual data are 

from FY2003. Energy consumption by fuel type is estimated for each year after FY2003, 

including the FY2005 baseline year, via the process described above in the section on Future 

Federal Agency Energy Consumption. 

Future Federal Agency Energy Savings 

Annual energy savings were calculated by subtracting the estimated energy consumption in 

FY2005 from the estimated energy consumption for FY2006 and each following year. These 

calculations were done for each fuel type. Implicitly, if not for activities conducted by FEMP and 

the Federal agencies, future energy consumption would remain as estimated for FY2005, and 

there would be no energy savings. Energy savings were summed across agencies and fuel types 

to determine total energy savings. Equations 4-6 define these calculations. 

Fuel Type A Energy Savings for Agency B in FY20XX = 

Fuel Type A Energy Consumption for Agency B in FY20XX – 

Fuel Type A Energy Consumption for Agency B in FY2005 Eqn. 4.

Fuel Type A Federal Energy Savings in FY20XX= 

Σ Fuel Type A Energy Savings across all Agencies in FY20XX Eqn. 5. 

Federal Energy Savings in FY20XX = 

Σ Fuel Type A Federal Energy Savings across all Fuel Types Eqn. 6.

Energy savings by fuel type, measured in MMBtu, were converted to alternative units for 

reporting requirements via the conversion factors listed in Table 3. 

Table 3. Energy Conversion Factors7 

Fuel Oil: 5.825 MMBtu/barrel 

Natural Gas: 1.027 MMBtu/1000 cubic feet 

Coal: 22.489 MMBtu/short ton 

Electricity: 3.412 MMBtu/MWh 

LPG: 3.603 MMBtu/barrel 

7

 Source: Performance Planning Guidance (GPRA Data Call) FY2004-2008 Budget Cycle-Draft. April 1, 2002. 

U.S. Department of Energy. Office of Energy Efficiency and Renewable Energy. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix E – Federal Energy Management Program – Page E-8 
Energy Savings Results 

Estimated annual and cumulative energy savings attributable to FEMP resulting from the FY 

2006 Budget Request are summarized in Table 4 and Table 5. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix E – Federal Energy Management Program – Page E-9 
Table 4. Annual Energy Metrics for Federal Standard Buildings and Energy-Intensive Operatio

 (FY 2006 Budget Request) 

Year Total Site 

Energy 

Displaced 

(TBtu) 

Direct 

Electricity 

Displaced 

(billion kWh) 

Direct 

Natural Gas 

Displaced 

(billion CF) 

Direct 

Petroleum 

Displaced 

(million 

barrels) 

Direct Coal 

Displaced 

(million 

short tons) 

Direct 

Biomass 

Displaced 

(TBtu) 

Direct 

Energy 

Displaced 

from 

Feedstocks 

(TBtu) 

D

E

Dis

from 

(T

2006 4.07 0.453 1.37 0.112 0.0218 0 0 2007 8.04 0.849 2.82 0.204 0.0479 0 0 2008 11.91 1.217 4.29 0.301 0.0711 0 0 2009 15.70 1.612 5.61 0.398 0.0939 0 0 2010 19.40 2.013 6.88 0.489 0.1154 0 0 2015 27.42 2.409 10.78 0.726 0.1705 0 0 2020 35.05 2.975 13.80 0.973 0.2204 0 0 2025 42.30 3.612 16.59 1.181 0.2636 0 0 2030 49.19 4.089 19.63 1.376 0.3067 0 0 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix E – Federal Energy Management Program – Page E-10 
Table 5. Cumulative Energy Metrics for Federal Standard Buildings and Energy-Intensive Operat

(FY 2006 Budget Request) 

Year Total Site 

Energy 

Displaced 

(TBtu) 

Direct 

Electricity 

Displaced 

(billion kWh) 

Direct 

Natural Gas 

Displaced 

(billion CF) 

Direct 

Petroleum 

Displaced 

(million 

barrels) 

Direct Coal 

Displaced 

(million 

short tons) 

Direct 

Biomass 

Displaced 

(TBtu) 

Direct 

Energy 

Displaced 

from 

Feedstocks 

(TBtu) 

D

En

Dis

from 

(T

2006 4.07 0.45 1.4 0.11 0.022 0 0 2007 12.10 1.30 4.2 0.32 0.070 0 0 2008 24.02 2.52 8.5 0.62 0.141 0 0 2009 39.72 4.13 14.1 1.02 0.235 0 0 2010 59.12 6.14 21.0 1.50 0.350 0 0 2015 180.34 17.40 67.0 4.70 1.095 0 0 2020 340.47 31.12 130.2 9.05 2.097 0 0 2025 537.60 47.87 207.9 14.53 3.329 0 0 2030 769.91 67.35 300.1 21.03 4.778 0 0 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix E – Federal Energy Management Program – Page E-11 
Appendix F – GPRA06 Geothermal Technologies Program 

Documentation 

1. Introduction 

The primary goal of the Geothermal Technologies Program is to reduce the cost of geothermal 

generation technologies, including both conventional and enhanced geothermal systems (EGS). 

EGS are defined as geothermal systems where the reservoir requires substantial engineering 

manipulation to make using the reservoir economically feasible. 

Hydrothermal systems (flashed-steam power plants at the hotter reservoirs, and binary plants at 

moderate temperature reservoirs) are near-term commercial realities, based on known sites and 

indicated prospects largely discovered in the 1970s through 1990s. In flashed-steam power 

plants, steam from the reservoir passes directly through the power turbine. In binary plants, the 

geothermal fluid is used to heat a secondary working fluid, e.g., pentane; which, in turn, drives 

the power turbine. 

EGS are expected to weigh in commercially in the longer term, based on resource assessments 

and exploration programs that are just beginning. While the program expects EGS to be 

applicable nationwide in the very long term, the first work on EGS will be concentrated west of 

the Rockies and it is modeled in that manner for GPRA. 

Estimating the GPRA benefits involves projecting the market share for these technologies based 

on their economic and environmental characteristics. Separate estimates of geothermal energy 

physical resources and separate projections of technology improvements are used for 

hydrothermal (Flash and Binary) and EGS. 

1.1 Target Markets 

Geothermal power is expected to penetrate in three market segments: the least-cost power 

market, the state Renewable Portfolio Standards market, and the green power market. Only 

“centrally located” geothermal power plants were considered, although there is emerging 

industry interest in distributed applications. In fact, most geothermal known sites are located at 

some distance from the Western large urban markets, and the projects require substantial 

consideration of where the power will be sold and how it will be delivered. There is also an 

ongoing DOE program to measure the economics of small-scale modular geothermal plant 

technology development (<5 MW), but to date that has not been modeled in the GRPA 

processes. 

• Least-Cost Power 

NEMS-GPRA06 and MARKAL-GPRA06 were run to estimate market penetration into the 

competitive bulk power marketplace for geothermal power technologies. The program goals 

for geothermal technology improvements are modeled directly by incorporating the capital 

and operation and maintenance (O&M) cost reductions. The models also take into account 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix F – Geothermal Technologies Program – Page F-1 
site availability and maximum development per site per year for conventional and EGS 

geothermal capacity. The capital cost reductions for conventional geothermal were computed 

based on the program goals for surface systems and drilling cost improvements. The 

conventional geothermal O&M costs are from the EPRI/DOE Renewable Energy Technology 

Characterizations report [1]. The EGS characteristics were developed by Princeton Energy 

Resources International (PERI) in 2003 [2]. 

The DOE Geothermal Program has recently undertaken a large-scale restructuring of the 

means by which it sets its goals and bases them on detailed estimates of the specific R&D 

subprogram’s (e.g., Exploration, Well Field Construction, Power Conversion, and Enhanced 

Geothermal Systems) likelihood of producing specific technology improvements at various 

levels of funding, and at various points in time. That restructuring will produce improved 

program goals for the FY-2007 GPRA work. 

For the FY-2006 GPRA Baseline, the underlying estimates of cost reductions due to industry 

experience (a.k.a., “learning curve” effects) were modified from those in the AEO 2004 

Reference Case. This was done because the DOE Energy Information Administration (EIA) 

appears to more or less have adopted the Geothermal Program estimates of technology 

improvements as its own estimates when they updated the learning functions for the 

AEO2004. The learning rate estimates in NEMS-GPRA-FY-2006 were set at one half the 

rates used in NEMS-AEO-2005. The resulting learning in the GPRA06 Baseline is very 

similar to that of the AEO2003 and the GPRA05 Baseline from last year. 

• State Renewable Portfolio Standard (RPS) Markets 

While the Geothermal Program believes it has affected entry of geothermal power systems 

into the RPS markets, particularly in Nevada, the program gets no credit for this under GPRA 

assumptions. This is because announced plans for RPS projects are absorbed into the 

category of “Cumulative Planned Additions” in EIA’s AEO tables, and not credited to the 

DOE Programs in the GPRA process. One effect of this is to systematically understate the 

impact of the DOE program’s activities in very near term deployment of geothermal power 

projects. 

• Green Power 

Flash, binary, and EGS technologies were all modeled as potential geothermal power plants 

that could be installed to meet the emerging green power market. Flash and binary 

technologies compete well within the green power market, with flash technology out-gaining 

binary due to its more attractive cost curve. EGS technologies have significant cost penalties 

that restrict capacity additions until after 2015, and even then only a very limited amount of 

EGS power is projected to be built to meet green power demand. Although geothermal plants 

were limited to the western portion of the United States, they were typically one of the leastexpensive options, leading to significant penetration in those two regions. Projections for 

incremental green power geothermal installations were incorporated into the NEMSGPRA06 and MARKAL-GPRA06 models as planned capacity additions. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix F – Geothermal Technologies Program – Page F-2 
1.2 Key Factors in Shaping Market Adoption of Geothermal Technologies 

Most of the geothermal resources in the United States that are currently known lie in two western 

states, California and Nevada. Other potentially useful resources exist in Hawaii, Idaho, Oregon, 

and Utah. The other western states also have geothermal resources, but those are somewhat 

indeterminate with respect to near-term commercial quality. This means that relative few 

electric utilities and government regulators (federal, state, and local) are familiar with the 

promise and issues associated with geothermal energy as a source of electricity. This set of 

factors is being dealt with by the DOE Geothermal Program’s “Geopowering the West” initiative 

[3], which has made substantial strides since 2001 in developing awareness and interest in twelve 

states. Some of the more significant issues are being addressed by the “Geothermal 

Collaborative”; for example, how geothermal power fits into the new state Renewable Portfolio 

Standards [4]. 

Note that commercial development of the Nation’s primary scenic geothermal features, such as 

Yellowstone National Park, Wyoming, is simply off limits, under a number of Federal laws. 

A second important factor is that, although a moderately large amount of geothermal 

hydrothermal resource capable of producing electricity appears to exist, much of that has either 

not been confirmed in detail or is too expensive to compete with conventional fuels. For 

example, costs are too high at some sites because the needed wells are too deep and expensive, 

or because the formation permeability is too low. The detailed estimates of site-specific costs are 

built into the NEMS Geothermal Electricity Submodule. 

A third important factor is that the commercial work to discover and confirm a geothermal 

reservoir (a.k.a., “exploration”) is risky and somewhat expensive. Because of that risk, the 

funding is entirely through equity capital, which is not always easy to procure. The Geothermal 

Program today works to ameliorate some of that risk through its Geothermal Resource 

Exploration and Definition Contracts, which encourage exploration by paying for the testing of 

new exploration methods and tools. That work is not explicitly modeled, but will eventually be, 

through the addition of new geothermal prospect sites to the NEMS-GRPA model, and perhaps 

to the EIA NEMS model. Other efforts to address the exploration issue lie in ongoing combined 

work of the Department of Energy and the Department of the Interior (e.g., Bureau of Land 

Management and the U.S. Geological Survey) to identify the most promising new sites for 

geothermal power development [5]. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix F – Geothermal Technologies Program – Page F-3 
Geothermal Supply Curve

Northwest Region

1.3 Methodology and Calculations 

NEMS-GPRA06 

The NEMS-GPRA06 electricity-sector module performs an economic analysis of alternative 

technologies in each of 13 regions. Within each region, new capacity is selected based on its 

relative capital and operating costs, its operating performance (i.e. availability), the regional load 

requirements, and existing capacity resources. Geothermal capacity is treated in a unique manner 

due to the specific geographic nature of the resources. The model characterizes 51 individual 

sites of known hydrothermal geothermal resources in only three western regions, each with a set 

of capital and O&M costs. For the Program Case, three EGS sites in each of the three regions 

were substituted for the most expensive hydrothermal sites in those regions. 

Conventional Geothermal (Hydrothermal) Systems 

The capital and O&M cost reduction goals of the program are applied to each of the 51 sites. 

Figure 1 illustrates the resulting supply curve of the hydrothermal sites in the Northwest United 

States in 2006 and 2020 that can be developed in each of those years in NEMS-GPRA06. These 

curves reflect the GPRA cost reductions, as well as the financing assumptions from the Annual 

Energy Outlook 2004 (AEO04) Reference Case, and the limit of developing only 100 MW at a 

site each year. The limit of 100 MW development per site per year is an increase from the 

AEO03 assumption of only 25 MW or 50 MW (depending on year). The limit change is made to 

reflect the program's efforts to reduce the risk associated with new geothermal development. The 

lowest part of the curve is not depicted for 2020, because it represents a portion of the capacity 

already developed. 

0 

20 

40 

60 

80

Levelized Cost (2001$/MWh) 100 

120 

140 

160 

180 

2006 

2020 

0 500 1,000 1,500 2,000 2,500 3,000 

Cumulative Capacity (Megawatts) 

Figure 1. Geothermal Supply Curve – Northwest Region 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix F – Geothermal Technologies Program – Page F-4 
Roughly 10 GW of hydrothermal resource in the Northwest and 23 GW in the lower 48 states is 

represented within NEMS-GPRA06. With the GPRA Base Case assumptions, much of this 

resource would be quite expensive to develop; today, an estimated 5 GW might be available at 6 

cents per kWh. 

Enhanced Geothermal Systems (EGS) 

Characteristics for EGS systems were also provided. Nine new EGS sites, were substituted for 

the three most expensive hydrothermal sites in the western regions: Northwest Power Pool 

(NWP, Region 11), Rocky Mountain Power Area, Arizona, New Mexico, and Southern Nevada 

(RA, Region 12), and California (CA, Region 13). Each site represents a Type of EGS resource: 

Type I. A site where EGS would be used to improve an existing commercial hydrothermal 

reservoir. 

Type II. A site where EGS would work to develop economic power from identified sites 

with sub-commercial hydrothermal features. 

Type III. A site where EGS would be used as a longer-term strategy to develop power systems 

in volumes of rock that have not been identified as hydrothermal prospects. 

Similar to the conventional sites, each geothermal site is further specified in four stages of 

increasing costs (Table 1). 

Table 1. EGS Site Characterization for NEMS-GPRA06 

Potential Potential Potential Potential Capacity 

Capacity 1 Capacity 2 Capacity 3 Capacity 4 Factor 

(MW) (MW) (MW) (MW) 

Region 11 EGS Type I 550 550 550 550 0.9 

EGS Type II 2500 2500 2500 2500 0.9 

EGS Type III 5000 5000 5000 5000 0.9 

Region 12 EGS Type I 0 0 0 0 0.9 

EGS Type II 1250 1250 1250 1250 0.9 

EGS Type III 5000 5000 5000 5000 0.9 

Region 13 EGS Type I 300 300 300 300 0.9 

EGS Type II 2500 2500 2500 2500 0.9 

EGS Type III 5000 5000 5000 5000 0.9 

Capital and O&M costs were provided for the initial development at each site and were the same 

for all regions. The EGS and conventional costs are shown below in 2001 dollars (Table 2). 

Hydrothermal flash and binary system costs are shown here for comparison. The relatively high 

O&M costs in Table 2 for the EGS systems is occasioned in part by the need to substantially 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix F – Geothermal Technologies Program – Page F-5 
replace much of the reservoir at about the 15 year point of the 30 year project life. Note also 

that, in the long run, it is reasonable that technology improvements for EGS could drive the 

capital costs of new EGS projects (potentially plentiful) below the cost of newly discovered 

binary systems (which are estimated to become increasingly scarce in the out-years of the 

simulations). 

Table 2. Geothermal Cost Characteristics for NEMS-GPRA06 

2005 2010 2015 2020 2025 

Capital Cost (2001$/kW) 

Flash 1,882 1,784 1,728 1,703 1,681 

Binary 2,807 2,661 2,577 2,539 2,507 

EGS I 2,400 2,132 1,864 1,596 1,328 

EGS II 2,760 2,452 2,144 1,835 1,527 

EGS III 3,120 2,772 2,423 2,075 1,726 

Total O&M Costs (2001$/kW-yr) 

Flash 80.3 71.2 66.6 62.5 60.7 

Binary 84.3 71.7 63.9 56.3 55.3 

EGS I 150.0 132.0 114.0 96.0 78.0 

EGS II 172.5 151.8 131.1 110.4 89.7 

EGS III 195.0 171.6 148.2 124.8 101.4 

Note: Flash and binary costs vary by site. These costs shown here are based on the lowest 

cost-available site in 2005 with technology improvements applied over time. 

While the estimated technology improvements in Table 2 may appear to be somewhat 

aggressive, they are supported by recent detailed research into the historical costs of geothermal 

hydrothermal power systems, between 1980 and 2000 [6]. The 1980s saw extensive 

development of hydrothermal power systems in the United States, and the early 1990s saw that 

extended by American geothermal firms in the Philippines and Indonesia, so there was a useful 

test bed of historical deployment for analyzing costs. The research found that over the 20-year 

period, the real (inflation adjusted) total capital costs of both flash and binary hydrothermal 

systems had decreased by about 45%. The Geothermal Program believes that substantial 

opportunity for technology improvement remains. 

MARKAL-GPRA06 

The geothermal technologies represented in MARKAL-GPRA06 reflect the Geothermal 

Program goals (defined for FY-05, as described above) for both conventional systems and EGS. 

For conventional geothermal systems, the capital and operating and maintenance costs were 

changed to reflect program goals. However, EGS represents a new geothermal resource not 

previously represented in the MARKAL-GPRA06 model. The program identified three separate 

types of potential geothermal reservoirs, as discussed above. 

Due to program activities, the capital and O&M costs of EGS systems are projected to decline 

over time. Table 3 shows the estimated capital and O&M costs for the three types of EGS 

systems for 2000 and 2050. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix F – Geothermal Technologies Program – Page F-6 
Table 3: EGS Generation Assumptions for MARKAL-GPRA06 

EGS Type 

Projected 

Resource 

MWe 

2000 Cost 2050 Cost 

Capital 

Cost O&M 

01$/kW 01$/kW/yr 

Capital 

Cost O&M 

01$/kW 01$/kW/yr 

I 3,400 2,448 153 934 50 

II 25,000 2,815 176 1,074 58 

III 60,000 3,182 199 1,214 66 

The EGS sites projected under the program are grouped into a set of supply steps and the 

discount rate of these technologies is set at 8% (instead of 10% for the industrial average) to 

reflect the accelerated depreciation schedule permitted by the IRS for renewable generation 

technologies. The EGS systems are modeled as centralized base-load generation. 

Geothermal plants compete directly with fossil fuel-based plants for both electricity generation 

and meeting peak power requirements. In MARKAL-GPRA06, EGS becomes more competitive 

as its higher capital cost is offset by increased fossil fuel costs, which increase as demand 

increases. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix F – Geothermal Technologies Program – Page F-7 
GREEN POWER MARKET MODEL 

PERI used the Green Power Market model to project regional green power additions [7]. These 

capacity additions are used by NEMS-GPRA06 and MARKAL-GPRA06 as planned new 

capacity or minimum capacity additions. 

As shown elsewhere in this report, the only two technologies that penetrate the Green Power 

Market, as modeled in the EERE GPRA-2006 exercise, are photovoltaics and wind. The 

Geothermal Program analysts and management are surprised by this, since geothermal 

contributed substantially to the Green Power markets in California when they were first set up in 

the late 1990s [8]. However, the incremental green power projections used for GPRA only 

include additional capacity stimulated by cost reductions through R&D. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix F – Geothermal Technologies Program – Page F-8 
1.4 Sources 

1. “Geothermal Electricity,” in: Renewable Energy Technology Characterizations, EPRI 

& DOE Report TR-109496, 1997. 

2. Entingh, D.J., Model for Enhanced Geothermal Systems in GPRA-NEMS, Princeton 

Energy Resources International, MD, for National Renewable Energy Laboratory and 

DOE Geothermal Technologies Program, Rockville, MD, 2003. 

3. Geopowering The West Program Brochure, Department of Energy and the National 

Renewable Energy Laboratory, 2002. 

4. Wiser, R., et al., Evaluating State Renewables Portfolio Standards: A Focus on 

Geothermal Energy, Wiser Consulting, prepared for National Geothermal 

Collaborative, 92 pp., 2003. 

5. Farhar, B., et al., “Opportunities for Near-Term Geothermal Development on Public 

Lands in the Westen United States,” National Renewable Energy Laboratory, 2003. 

6. Entingh, D.J., and J.F. McVeigh, “Historical Improvements in Geothermal Power 

System Costs,” Geothermal Resources Council Transactions, pp. 533-537, 2003. 

7. Personal communication from J. McVeigh, PERI, to D. Entingh, January 20, 2005. 

8. Anon., “Calpine Receives 2004 Green Power Leadership Award from DOE and 

EPA,” Calpine Corporation News Release, Nov. 9, 2004. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix F – Geothermal Technologies Program – Page F-9 
Appendix G – GPRA06 Hydrogen, Fuel Cells, and 

Infrastructure Technologies (HFCIT) Program 

1. Introduction 

The target markets for the Office of Hydrogen, Fuel Cells, and Infrastructure Technologies 

(HFCIT) program include transportation (cars and light trucks) and stationary (particularly 

residential and commercial) applications. Each will be discussed separately below. 

1.1 Target Market: Fuel Cell Vehicle Market 

The market for fuel cell vehicles (FCVs) includes all cars and light trucks sold for both personal 

and business use. Today, the size of this market is approximately 17 million vehicle sales per 

year. Total car and light truck stock is about 220 million vehicles. EIA projects both sales and 

stock to grow to more than 20 million and 300 million respectively by 2025. Additional growth 

is expected post-2025. 

1.2 Key Factors in Shaping the Market Adoption of FCVs 

Key factors associated with the adoption of new vehicle technologies include how the new 

vehicle technologies compare with the baseline vehicle technologies in terms of the following 

vehicle attributes: 

• Vehicle Price 

• Fuel Economy 

• Range 

• Maintenance Cost 

• Acceleration 

• Top Speed 

• Luggage Space 

Of these, vehicle price and fuel economy are the most important. 

Non-vehicle attributes that are important factors in a consumer’s decision to purchase new 

vehicle technologies include the following: 

• Fuel Price 

• Fuel Availability 

1.3 Methodology and Calculations 

The factors listed above include the factors used in the modeling of new vehicle technology 

penetration by the NEMS and MARKAL models. FCV attributes and other factors are discussed 

below. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix G – Hydrogen, Fuel Cells, and Infrastructure Technologies Program – Page G-1 
1.3.1 FCV Attributes 

FCV attributes were developed based on the HFCIT program goals, discussions with HFCIT 

program managers, Powertrain Systems Analysis Toolkit (PSAT) modeling, and payback 

analysis (Refs. 1-3). The PSAT model is a simulation model used by DOE to evaluate the fuel 

economy and performance of light vehicles using various technologies. Payback analysis was 

used to estimate what the incremental price of FCVs would be when they become cost 

competitive with conventional vehicles, a goal of the program. (The incremental price equals the 

present value of the energy cost reduction achieved by FCVs over three years, assuming a 

hydrogen price of $1.50/gallon gasoline equivalent and 7.5% discount rate.) Other attributes 

were based on a review of past GPRA characterizations (e.g., Ref. 4). 

Because the NEMS and MARKAL models require different levels of detail, two separate vehicle 

characterizations are provided. In both cases, most of the attributes are provided as ratios to the 

vehicle attributes of conventional vehicles. (For NEMS, the $ value of the price increments were 

provided.) The attributes are for new vehicles in the year listed. Table 1 contains the vehicle 

attributes for FCVs provided for input to the NEMS model. Attributes are provided for two car 

size classes and five light-truck classes. NEMS uses six car size classes and six light-truck 

classes, but for the NEMS analysis of FCVs to 2025 fewer classes were deemed sufficient. 

Table 2 contains vehicle attributes for FCVs provided as input to the MARKAL model. 

MARKAL uses only vehicle price and fuel economy attributes. MARKAL does not 

disaggregate cars and light trucks into various classes. 

1.3.2 Hydrogen Price and Availability at Stations 

Hydrogen price and availability assumptions are discussed in Chapters 4 and 5 in the HFCIT 

Program sections. 

1.3.3 FCV Market Penetration Methodology 

Brief descriptions of how the NEMS and MARKAL models project new vehicle technology 

penetration using these vehicle attributes can be found in Chapter 4 (NEMS) and Chapter 5 

(MARKAL). 

1.4 Sources 

1. “Hydrogen, Fuel Cells & Infrastructure Technologies Program: Multi-Year Research, 

Development and Demonstration Plan” (Draft), U.S. Department of Energy, Energy Efficiency 

and Renewable Energy (June 3, 2003). 

2. Phillip Sharer and Aymeric Rousseau, “Fuel Economy of Advanced Technology Vehicles” for 

Phil Patterson, DOE (June 17, 2004). 

3. Payback model developed by Jim Moore, TA Engineering (2003). 

4. “Program Analysis Methodology: Office of Transportation Technologies, Quality Metrics 

2003 Final Repor,” prepared by OTT Analytic Team, for Office of Transportation Technologies, 

U.S. Department of Energy (March 2002). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix G – Hydrogen, Fuel Cells, and Infrastructure Technologies Program – Page G-2 
Table 1. FCV Attributes Input to NEMS 

Small-VAN 

SMALL CARS LARGE CARS and SUV 

Fuel Cell Hydrogen 2022 2025 2018 2023 2025 2020 Incremental Vehicle Price 2010 1605 2264 1582 1477 2462 Range 0.90 0.96 1.00 1.00 1.00 0.90 Maintenance Cost 1.05 1.02 1.05 1.00 0.97 1.10 Acceleration 1.00 1.06 1.00 1.00 1.04 1.00 Top Speed 0.90 0.93 0.85 0.90 0.92 0.90 Luggage Space 0.80 0.86 0.90 1.00 1.00 0.90 Fuel Economy (a) 2.54 2.56 2.54 2.80 2.89 2.49 

Large-VAN and SUV CARGO TRUCK 

Fuel Cell Hydrogen 2012 2018 2023 2025 2024 2025 Incremental Vehicle Price 4500 3140 2086 1975 2886 2656 Range 0.90 1.00 1.00 1.00 0.90 0.93 Maintenance Cost 1.10 1.05 1.00 0.97 1.05 1.04 Acceleration 1.00 1.10 1.10 1.10 1.00 1.00 

Top Speed 0.90 0.90 0.95 0.95 0.90 0.90 Luggage Space 0.90 0.95 1.00 1.00 0.90 0.91 

Fuel Economy (a) 2.00 2.49 2.57 2.73 2.49 2.51 

(a) Gasoline gallon equivalent 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix G – Hydrogen, Fuel Cells, and Infrastructure Technologies Program – Page G-3 
Table 2. FCV Attributes Input to MARKAL 

Vehicle Type Technology Ratio 2010 2015 2020 2025 2030 2040 Car Fuel Cell Cost 1.15 1.07 1.06 1.05 1.045 

MPG 2 2.54 2.8 3.03 3.03 

(a) 

LT Fuel Cell Cost 1.15 1.07 1.06 1.05 1.045 

MPG 2 2.49 2.57 2.96 2.96 

(a) 

(a) Gasoline gallon equivalent 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix G – Hydrogen, Fuel Cells, and Infrastructure Technologies Program – Page G-4 
2.1 Stationary Fuel Cell Market 

Stationary fuel cells are one of a variety of distributed electricity-generation technologies. 

The particular market sectors in which stationary fuel cells are most applicable include 

residential and commercial applications. 

2.2 Key Factors in Shaping the Market Adoption of Stationary Fuel Cells 

Key factors associated with the market penetration of stationary fuel cells include the 

energy efficiency (electrical and combined heat and power), installed cost, and 

maintenance cost of the fuel cells relative to other distributed and traditional electricitygeneration technologies. 

2.3 Methodology and Calculations 

2.3.1 Baseline Assumptions for Stationary Fuel Cells 

The technology assumptions for distributed generation, including stationary fuel cells, 

were updated in AEO2004. A review of these assumptions revealed a few definitional 

differences in how the HFCIT Program goals are stated and how the technology 

characterizations are used within NEMS. There also appeared to be a difference in the 

view of current (or nearly current) technology that might reflect different trade-offs of 

efficiency and costs or may reflect differences in development. In either case, the same 

2005 values should be used for the GPRA Baseline and Program cases so the Baseline 

was modified to reflect the Program view of 2005. As described below, the Program 

values were first adjusted to the same definitions as used in NEMS. By 2010, the 

Baseline returns to the AEO2004 values, with higher efficiencies and also higher costs 

than the values for 2005. Because of their relatively high costs, fuel cells are not costeffective in the early years regardless of which source of data is used. 

Residential 5kW PEMFC Baseline 

AEO2004 Reference Case 

CHP System Electrical Installed Cost Maint. Cost 

Year Efficiency Efficiency (2003 $/kW) (2003$/kW-yr) 

2003 0.690 0.300 5500 264 

2005 0.690 0.300 5500 264 

2010 0.700 0.320 3800 184 

2015 0.710 0.335 3000 168 

2020 0.720 0.350 2200 152 

2025 0.725 0.355 1750 140 

GPRA06 Baseline 

2003 0.630 0.270 3800 264 

2005 0.675 0.288 2300 264 

2010 0.700 0.320 3800 184 

2015 0.710 0.335 3000 168 

2020 0.720 0.350 2200 152 

2025 to 2050 0.725 0.355 1750 140 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix G – Hydrogen, Fuel Cells, and Infrastructure Technologies Program – Page G-5 
Commercial 200kW Fuel Cell Baseline 

AEO2004 Reference Case 

CHP System Electrical Installed Cost Maint. Cost 

Year Efficiency Efficiency (2003 $/kW) (2003$/kW-yr) 

2003 0.750 0.360 5200 232 

2005 0.750 0.360 5200 232 

2010 0.720 0.490 2500 128 

2015 0.720 0.500 2150 124 

2020 0.720 0.510 1800 120 

2025 0.735 0.520 1450 112 

GPRA06 Baseline 

2003 0.630 0.270 3180 232 

2005 0.675 0.288 1930 232 

2010 0.720 0.490 2500 128 

2015 0.720 0.500 2150 124 

2020 0.720 0.510 1800 120 

2025 to 2050 0.735 0.520 1450 112 

2.3.2 Program Case Assumptions for Stationary Fuel Cells 

Assumptions for distributed PEM fuel cells are based on the multiyear program plan 

(Ref.1). Capital costs and efficiencies were provided in the MYPP for the years 2003, 

2005, and 2010. The costs are assumed to be in year 2003 dollars. No values were listed 

for maintenance costs, so the AEO2004 values are used. The costs and efficiencies 

assumed for NEMS by 2025 were held constant through 2050 in MARKAL. 

The program goal capital costs were increased to account for the installation cost that is 

assumed in the Baseline fuel cells costs from the NREL report. In addition, the 

efficiencies in the multiyear plan are expressed in lower heating values and were 

converted to higher heating value efficiencies for use in NEMS. 

Residential 5kW PEMFC Program Case 

HFCIT Goals from Multiyear Plan 

CHP System Electrical Equip. Cost Maint. Cost

Year Efficiency* Efficiency* (2003 $/kW) (2003$/kW-yr)

2003 0.70 0.30 3000 n/a

2005 0.75 0.32 1500 n/a

2010 0.80 0.35 1000 n/a

* based on LHV on input fuel 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix G – Hydrogen, Fuel Cells, and Infrastructure Technologies Program – Page G-6 
Model Inputs for HFCIT Goals 

CHP System Electrical Installed Cost Maint. Cost

 Efficiency Efficiency (2003 $/kW) (2003$/kW-yr) 

Year 

2003 0.630 0.270 3800 264 

2005 0.675 0.288 2300 264 

2010 0.720 0.315 1800 184 

2015 0.720 0.315 1800 168 

2020 0.720 0.315 1800 168 

2025 to 2050 0.720 0.315 1800 168 

Commercial 200kW Fuel Cell Program Case 

HFCIT Goals from Multiyear Plan 

CHP System Electrical 

Efficiency* Efficiency*

Year

2003 0.70 0.30

2005 0.75 0.32

2010 0.80 0.40

Equip. Cost Maint. Cost

(2003 $/kW) (2003$/kW-yr) 

2500 n/a 

1250 n/a 

750 n/a 

* based on LHV on input fuel 

Model Inputs for HFCIT Goals 

CHP System Electrical Installed Cost Maint. Cost

 Efficiency Efficiency (2003 $/kW) (2003$/kW-yr) 

Year 

2003 0.630 0.270 3180 232 

2005 0.675 0.288 1930 232 

2010 0.720 0.360 1430 128 

2015 0.720 0.360 1430 128 

2020 0.720 0.360 1430 128 

2025 to 2050 0.720 0.360 1430 128 

There are no changes for large central-station fuel cells. 

2.4 Sources 

1. “Hydrogen, Fuel Cells & Infrastructure Technologies Program: Multi-Year Research, 

Development and Demonstration Plan” (Draft), U.S. Department of Energy, Energy 

Efficiency and Renewable Energy (June 3, 2003). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix G – Hydrogen, Fuel Cells, and Infrastructure Technologies Program – Page G-7 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-1 

Appendix H – GPRA06 Industrial Technologies Program 

Documentation 

1 Introduction 

The information provided in this appendix is based on the Industrial Technologies Program (ITP) 

report of the GPRA06 process, “GPRA06 Quality Metrics – Methodology and Results,” 

Energetics Inc., October 25, 2004. The report includes additional methodological details and the 

actual off-line energy savings results submitted to the Office of Energy Efficiency and 

Renewable Energy (EERE). 

The GPRA06 calculation of future program impacts was performed separately for each planning 

unit and summed to produce the total ITP program impact. Within planning units, impacts were 

calculated differently for R&D planning units than for Technology Delivery planning units. 

Impacts for R&D planning units were calculated at the project level using a uniform 

methodology embodied in a spreadsheet-based computer tool called the Technology Impact 

Projections Model. Impacts for Industrial Assessment Center (IAC) and Best Practices planning 

units were calculated for subprogram element activities using historical data, estimates, and 

assumptions documented in tabular format; and summed to produce the planning unit impacts. 

ITP’s subprogram structure includes: 

R&D Planning Units 

1. Aluminum Industry Vision 

2. Chemicals Industry Vision 

3. Forest Products Industry Vision 

4. Glass Industry Vision 

5. Metal-Casting Industry Vision 

6. Steel Industry Vision 

7. Mining Industry Vision 

8. Supporting Industry Vision 

9. Industrial Materials Crosscut 

10. Sensors and Automation Crosscut 

11. Combustion Crosscut 

Technology Delivery Planning Units 

1. Industrial Assessment Center (IAC) Program 

2. Best Practices Program 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-2 

1.1 Target Markets (The Baseline Case) 

• Target Market Description 

Advanced industrial energy efficiency technologies under development with program support 

will enter a variety of specialized markets for production equipment, plant energy conversion, 

distribution, heat recovery, and waste-reduction equipment. Underlying fuel prices, the 

electricity generation and distribution fuel mix and heat rates, and sector economic growth rates 

—which were used in the NEMS-GPRA06 runs that produced the ultimate results from ITP’s 

energy-savings inputs—were consistent with the reference case in the Department of Energy’s 

(DOE/EIA) Annual Energy Outlook 2004. ITP’s off-line calculation of fuel and electricity 

savings for individual projects and program-element activities did not refer explicitly to macrobaseline quantities, except that a unique market growth rate was specified in each of the 163 

Technology Impact Projections Model runs. This permitted the analysts to differentiate among 

highly varied market outlooks in the various industries. Except for several chemicals industry 

market targets with short-term growth rates of more than 3.0%, the range of these annual market 

growth rates was from -1.0% to 2.5%, with an average close to 1.5%. 

Due to differences in the analytical framework of the NEMS-GPRA06 model and ITP’s bottomup energy-savings projection methodology, it was not possible to definitively match those 

models’ base-case assumptions with the implicit base case in the GPRA study. NEMS-GPRA06 

addresses the entire industry group in a top-down manner, assigning energy intensities to a 

comprehensive set of activities to project total industry energy use under alternative assumptions. 

The bottom-up ITP GPRA study specified the unit energy savings of a particular set of 163 

advanced technologies, each in comparison to a best-available commercial technology 

alternative. ITP GPRA savings are only those savings attributable to these technologies in their 

primary intended markets. 

The target market for each of 163 R&D technologies included in the ITP study was described 

qualitatively and quantitatively in a spreadsheet-based Technology Impact Projections Model 

run. The technologies were grouped based on common production activity Impact Targets. This 

was done to facilitate the identification of potentially overlapping markets; where potentially 

overlapping markets were found, either the market was split between the two competing 

technologies or only one spreadsheet model run was used to represent both technologies. 

Markets were defined in terms of the total number of technology units potentially in use at the 

year of introduction. This number was reduced to the fraction of those units considered 

technically and economically accessible, and further reduced to the likely achievable technology 

market share accessible to the technology as compared to other advanced technologies. And, 

finally, it was reduced to the savings potential attributable to the program. The market size was 

adjusted annually by the spreadsheet logic, based on the specified annual percentage growth rate. 

• Baseline Technology Improvements 

Continued baseline improvement in energy productivity was accounted for in the ITP 

methodology. ITP’s method essentially subtracted a fixed “next best” baseline technology from a 

fixed advanced technology to obtain unit technology savings. However, the energy savings of a 

new technology were determined by the number of years the technology’s market introduction is 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-3 

accelerated by the Federal program involvement. In particular, the energy savings associated 

with the program were explicitly projected to occur without the EERE R&D after a period of 

years known as the “acceleration period.” Only the slice of energy savings attributable to the 

program’s effort to accelerate technology development was counted as GPRA savings. In this 

way, the methodology incorporated an assumption that the energy intensity of industrial 

production will steadily improve, and that specific Federal interventions in cofunding R&D only 

temporarily accelerate the rate of improvement in the targeted production activities. Acceleration 

periods varying from two years to 42 years were found in the GPRA06 runs, with an average 

close to 10 years. 

Likewise, in the ITP off-line study, the conventional technology with which each new 

technology was compared, was generally the best currently available technology—not a 

projected technology that might exist at the time of market introduction or future sales of the new 

technology, nor the average technology in use. While the industry-level rate of improvement in 

production energy intensity tends to follow fairly smooth curves of monotonic improvement, it is 

very difficult to predict the future energy performance of as-yet unidentified technologies to 

perform specific functions. In addition, the best currently available technology is often not yet 

widely adopted in the market, so that when the ITP technology enters the market, the current 

best-available technology may still represent the next-best decision alternative for many cases. 

Again, taking credit for only that slice of savings due to the presumed acceleration of the new 

technology’s market introduction date was intended to minimize any overestimation of savings 

due to the underlying rate of technology improvement. 

The commercial introduction of a technology normally occurs after a significant demonstration 

or using an operating prototype, and after an adequate test and evaluation period along with 

allowances for the beginnings of production, dissemination of information, initial marketing and 

sales, or other “start-up” factors. To capture this lengthy process, users of the Technology Impact 

Projections Model were asked to indicate the timeline for developing and introducing the 

technology into the market. This includes the years for when an initial prototype, refined 

prototype, and commercial prototype of the technology has or will be completed; and the year 

when the technology will be commercially introduced. An initial prototype is the first prototype 

of the technology. A refined prototype represents changes to the initial prototype but not a 

commercially scaled-up version. A commercial prototype is a commercial-scale version of the 

technology. Commercial introduction is when the first unit beyond the commercial prototype is 

operating. Prototype and commercial introduction years were to be consistent with the 

technology development program plans, and two values for a commercial introduction year were 

requested. One reflects when the technology is projected to be introduced, if the program 

proceeds as expected (“With ITP” case). The other reflects when the technology would have 

entered the market, if the program had not been involved (“Without ITP” case). The difference in 

commercial introduction years for the “With ITP” and “Without ITP” cases is referred to as the 

“acceleration period.” 

• Baseline Market Acceptance 

The rate of market penetration of novel technologies in industrial production markets was 

captured explicitly in the methodology. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-4 

Based on historical data, new technologies normally penetrate a market following a familiar Scurve—the lower end representing the uncertainties overcome by “early adopters.” The curve 

tails off at the far future, where some may never adopt the new technology. The steepest portion 

of the S-curve is where the new technology is most rapidly penetrating the market and producing 

new savings. The rate at which technologies penetrate their markets varies significantly. 

Penetrations of heavy industrial technologies generally occur over decades, while simple process 

or control changes can penetrate much more rapidly. The actual penetration rate varies due to 

economic, environmental, competitive, productivity, regulatory, and other factors. 

In a 1998 study by Arthur D. Little Inc. (“Streamlining of OIT’s GPRA Process (Draft),” Arthur 

D. Little Inc. Reference 33550-01, May 27, 1998), data was presented on a number of actual 

penetration rates of past and present technologies. These penetration rates were analyzed, 

normalized, and grouped into five classes based on a number of characteristics and criteria. 

Users of the ITP Technology Impact Projections Model were asked to complete Table 1 for each 

project by adding the project title in the top row and either a, b, c, d, or e in the right-hand 

column for those characteristics for which they could make a judgment. Based on the strength of 

these characteristic scores, the overall technology market-penetration curve selection was entered 

in the first row at the right under “Score.” The table was copied onto the spreadsheet model run 

at the “Background” tab. Note that the characteristics (rows) are relatively independent, and a 

given technology will likely fit best in different classes for different characteristics. By 

examining the pattern, however, it is possible (based on best judgment and experience) to select 

the most likely class (rate) at which the new technology may penetrate the market. This may be a 

“subjective average” of the characteristics, or it is possible that one or two characteristics are 

expected to dominate future adoption decisions that a particular class of penetration rate is 

justified. There also may be “windows of opportunity,” where significant replacements of 

existing equipment may be expected to occur in the future for other reasons. The user was asked 

to insert into the spreadsheet the class of penetration rate believed most likely—all things 

considered—and provide a narrative of the rationale for selection if not obvious from Table 1. 

For additional context, Table 2 shows actual technologies and the class of their historical 

penetration rates. Comparison of the new technology, by analogy or similarity, with these 

examples provided additional insight into selecting the appropriate penetration rate that might be 

expected for the new technology. The actual technologies’ historical market penetrations are 

shown graphically in Figure 1, falling within the market-penetration rate classes used by the 

model. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-5 

Table 1. Selecting the Market-Penetration Rate Class 

Technology/project Score 

(a,b,c,d,e) 

Characteristic a b c d e 

Time to saturation 5 yrs 10 yrs 20 yrs 40 yrs >40 yrs 

Technology factors 

Payback discretionary <<1 yrs <1 yr 1-3 yrs 3-5 yrs >5 yrs 

Payback nondiscretionary <<1 yr <1 yr 1-2 yrs 2-3 yrs >3 yrs 

Equipment life <5 yrs 5-15 yrs 15-25 yrs 25-40 yrs >40 yrs 

Equipment 

replacement 

none minor unit 

operation 

plant 

section 

entire 

plant 

Impact on product 

quality 

$$ $$ $$ $ 0/- 

Impact on plant 

productivity 

$$ $$ $$ $ 0/- 

Technology 

experience 

new to 

U.S. only 

new to 

U.S. only 

new to 

industry 

new new 

Industry factors 

Growth (% per annum) >5% >5% 2-5% 1-2% <1% 

Attitude to risk open open cautious conservative averse 

External factors forcing forcing driving none none 

Gov’t regulation 

Other 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-6 

Table 2. Examples of Technologies 

Class A B C D E 

Aluminum Treatment of 

used cathode 

liners 

Strip casting, 

VOC 

incinerators 

 

Chemicals New series of 

dehydrogenati

on catalyst 

(incremental 

change) 

CFCs -> 

HCFCs, 

incrementally 

improved 

catalysts, 

membranebaed chloralkali 

Polypropylene 

catalysts, 

solvent to waterbased paints, 

PPE-based AN 

Synthetic 

rubber and 

fibers 

Forest 

Products 

Impulse drying, 

de-inking of 

waste 

newspaper 

Kraft pulping, 

continuous 

paper 

machines 

Glass Lubbers glass 

blowing, 

Pilkington float 

glass 

Particulate 

control, 

regenerative 

melters, 

oxygenase in 

glass furnaces 

Metals 

Casting 

New shop floor 

practice 

Petroleum New series 

HDS catalysts 

Alkylation 

gasoline 

Thermal 

cracking, 

catalytic 

cracking 

Residue 

gasification, 

flexicoking 

Steel Improved EAF 

operating 

practice (e.g. 

modify electric/ 

burner heating 

cycle to 

minimize dust 

generation) 

BOF steel 

making 

Oxyfuel burners 

for steel, Level II 

reheat furnace 

controls, 

continuous 

casting, 

particulate 

control on EAF, 

high-top 

pressure blast 

furnace 

Open-hearth 

technology, 

EAF 

technology 

Other Advanced 

refrigerator 

compressors, 

oxygen flash 

copper 

smelting, 

solvent 

extraction with 

liquid ion 

exchange 

Fluegas 

desulfurization 

(coal-fired 

utilities), low Nox 

industrial 

burners, 

industrial gas 

turbines, ore 

beneficiation 

Dry-kiln 

cement, 

industrial 

ceramic 

recuperators 

Industrial heat 

pumps 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-7 

Data were fitted with formula Data were fitted with formula ( ) 12 1

1

h c t t e f − − + =

A B CD E

0%

20%

40%

60%

80%

100%

0 20 40 60 80

Time (years)

Market Share (f)

Synthetic/Natural 

Rubber

Synthetic/Natural 

Fibers

Open Hearth

 Steels

BOF Steels,

Float glass

Elec Arc Steels

Cracked

 Gasoline

Dry Kiln Cement 

Production

Gasoline 

Alkanation

Glass Blowing

(Lubbers) Polypropylene

Production

Continuous 

Steel

Electronic 

Ballast

Advanced 

Compressor

Figure 1. Market Penetration Rate Classes 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-8 

1.2 Key Factors in Shaping Market Adoption of EERE Technologies 

• Price 

ITP methodology places little emphasis on cost-based estimation of market penetration, because 

useful cost information on industrial technologies in the R&D stage of development is, in nearly 

all cases, impossible to obtain. Instead, relative costs in the form of the expected payback period 

were one of numerous market-driving factors considered in selecting the market-penetration 

schedule best matching each innovative technology (see previous section). These marketpenetration schedules are typical of historical industrial-sector technology innovations, whose 

characteristic payback period, scale, equipment lifetime, impact on product quality, relevant 

experience level, market growth rate, attitude to risk, and other factors were matched to each 

innovative technology to select the best market-penetration schedule. 

• Non-price Factors 

- Key Consumer Preferences/Values. 

Several consumer preference/value issues were incorporated in the ITP market-penetration curve 

selection technique. These include factors such as technology scale, equipment lifetime, impact 

on product quality, etc. listed above. 

- Manufacturing Factors. 

The benefits-estimation approach requested the analyst to estimate the year in which the 

technology is expected to be successfully developed at the successive stages of (1) completion of 

initial R&D, (2) initial system prototype, (3) refined prototype, (4) commercial prototype, and 

finally (5) commercial introduction, given the push provided by the ITP program support. These 

estimates were documented as part of each spreadsheet model run. 

- Policy Factors. 

In the great majority of cases, no policy factors were considered significant to the market 

introduction and acceptance of ITP technologies. However, for cases where a regulation or other 

policy will drive the market to accept a new technology solution, the market-penetration curve 

selection procedure was set up to accept this information and allow it to play a role in the 

analysis. Any such influence was discussed in documentation provided in the spreadsheet model 

run. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-9 

1.3 Methodology and Calculations 

• Changes in Inputs to Base Case 

ITP did not provide inputs that changed the base case assumptions for the industrial markets. 

• Technical Characteristics of the Program Case 

ITP did not provide specific changes to the NEMS-GPRA06 industrial-sector characteristics. 

ITP’s estimates of the energy savings of its advanced technologies were based on information 

provided to the analysts through the proposal review and contracting process, which includes 

industry participation and review, followed by program review of these estimates. ITP analysis 

by sector has focused on assessing where energy is actually consumed and understanding current 

and best practices for each proposed technology. The participation of industry experts in this 

process has been critical to helping refine the estimates. 

• Expected Market Uptake 

 - R&D Planning Units 

GPRA06 energy savings in the ITP off-line study were projected for individual projects within 

planning units and summed to total results for planning units and for ITP as a whole. Active 

projects were selected by the ITP program managers for GPRA06; thus, the FY 2005 program 

portfolio was used as a surrogate for the (as-yet unknown) FY 2006 portfolio. The number of 

study projects in each planning unit was controlled to represent an aggregate nominal funding 

level not greater than 100% of the FY 2005 budget request. 

This prospective assessment was carried out with the aid of an experience-based marketpenetration model designed to estimate the national energy, economic, and environmental 

impacts of innovative industrial technologies. ITP’s off-line calculations for GPRA06 did not 

utilize the model’s capabilities to project environmental and cost impacts, so the results will 

focus only on energy savings. EERE guidance for GPRA06 was to project the energy impacts of 

the FY 2006 program, which subsequently were used by others to specify scenario projections by 

the NEMS-GPRA06. The resulting NEMS-GPRA06 runs (reported elsewhere) produce 

environmental and cost results using integrated demand and supply assumptions consistent 

across the demand sectors. 

The Technology Impact Projections Model was used to estimate the potential energy savings 

resulting from research, development, and demonstration projects funded by the Industrial 

Technologies Program (ITP). Benefit estimates are critical for evaluating projects and presenting 

the merits of both individual projects and the overall RD&D portfolio. 

Proposers responding to a Solicitation or Request for Proposals were asked to use the 

Technology Impact Projections Model to estimate program impacts. Where not provided in 

proposals, principal investigators were asked to provide inputs for their active projects. Use of 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-10 

the model across all projects allows ITP to estimate the impacts of its projects in a consistent 

manner. 

Users were asked to provide their best estimate for each piece of information required for the

spreadsheet model. A description of the advanced technology was required to provide an 

overview of the project/technology. This includes the project name, project number (once project 

is funded), estimates preparer, program manager, planning unit, lab and industry contacts, and 

data sources. A narrative summary of the technology on which benefit estimates are based was 

required. This described what constitutes a typical process unit for the technology, in terms of 

annual output (production capacity times duty factor). For simplicity, the analysis assumed that 

all units in the industry have the same capacity. An average, or typical, unit capacity was chosen, 

particularly for situations where the unit size may vary in different installations. By convention 

and to enable comparisons, units for the new technology and the current state-of-the-art were 

equal in output capacity; even if, in reality, the new technology might have a different unit 

capacity for various reasons. 

The new technology also might not be a physical item of hardware. Rather, it could be a process 

change, a computer model or control system, operational change, or other nonphysical technique. 

In such cases, a unit was defined as the typical or average process or plant that would utilize the 

new technique. The annual energy inputs, based on the expected energy consumption of the 

process or plant with the new technique, were then compared with annual energy consumption 

required by existing techniques. 

Key information was provided on the performance of single installed units or applications of the 

advanced technology. For comparison, information was required on the performance of the bestavailable technology for the application, not the average of all in-place technology units. 

Users were required to provide energy use per year for the new and conventional units, by fuel: 

Electricity - Includes direct electricity. 

Natural Gas - Includes pipeline fuel natural gas and compressed natural gas. 

Petroleum - Includes residual fuel, distillate fuel, and liquid petroleum gas. 

Coal - Includes metallurgical coal, steam coal, and net coal coke imports. 

Feedstock - Includes fossil fuels consumed in nonenergy uses such as process feedstocks. 

Biomass - Includes the use of biomass (for energy or as feedstock). 

Wastes - Includes the use of fuels that are generated as wastes or process byproducts. 

Examples of such fuels are refinery fuel gas, blast furnace gas, hog and bark fuel, and sewage 

sludge. 

Other - Includes any fuels that may not be included in those listed above. 

Total Primary Energy - Is calculated from individual energy inputs. The primary equivalent 

of direct electricity consumption includes losses in electricity generation and distribution. For 

GPRA06, fuel and electricity savings were used as inputs to specify NEMS-GPRA06 runs 

that themselves applied heat rates, etc. varying over time to produce primary energy savings. 

Energy use was entered in physical units (e.g., billion cubic feet of natural gas) or primary units 

(trillion Btu). The exception was electricity use, which has to be entered as site energy 

consumption (either in billion kWh or trillion Btu). 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-11 

To determine the potential impact of the new technology as it becomes adopted, it was necessary 

to estimate the total market for the technology, reduce that to the likely actual market, and 

estimate when—and the rate at which—the new technology will penetrate the market. 

Users were required to estimate the number of installed units in the U.S. market in a specified 

year. That market was defined as narrowly as possible: The smallest group of applications that 

covers all potential applications for which the user may have some data. Users could apply their 

own data on energy use of the state-of-the-art technology. Other potential data sources include 

ITP’s Energy and Environmental Profile for the relevant industry, EIA’s MECS data, or industry 

sources. 

The annual market growth rate was specified by the model user, based on an EIA or industry 

growth projection for the relevant industry and process. A source for the growth rate was called 

for in the comments section. 

Market share was specified as a function of the potential accessible market share and the likely 

market share. The Potential Accessible Market Share was defined as the market that the new 

technology could reasonably access given technical, cost, and other limitations of the 

technology. For example, certain technologies may be applicable only to a certain scale of plant, 

certain temperature-range processes, certain types of existing equipment or subsystems, or only 

certain segments of the industry. A further delimiting fraction was called the Likely Market 

Share. In some instances, in addition to technical and cost factors, the technology may compete 

with other new technology approaches (or with other companies) for the market. The user was 

asked to use current market-share information or base their estimated market share on the 

number of competitors in the market, assuming they are using different technologies not 

resulting from this project. This is different than the possibility of “copycats,” which should not 

be considered as competing. That is, if others adopt essentially the same (or slightly modified) 

technology due to this new technology, that adoption was triggered by the project being 

described and that project should be “credited” with causing that trend. This is potentially the 

case for techniques where the intellectual property cannot be, or is not, protected and becomes 

general knowledge throughout the industry. 

In some instances, a program may be developing a technology in conjunction with another ITP, 

EERE, or DOE program. The analysts were asked in these cases to provide an estimate of the 

percentage of savings that is attributed to the program. The attribution percentage should be 

similar to the percentage of Federal funds provided to the project by the program. A default 

value of 100% was entered in the model. 

To understand how rapidly the potential impact of the technology may be felt, the market 

penetration of the technology must be projected. This is based on two estimates: the technology 

development and commercialization timeline, and the market-penetration curve. 

The technology development and commercialization timeline was first determined. The 

commercial introduction of a technology normally occurs after a significant demonstration or 

operating prototype and after an adequate test-and-evaluation period, along with allowances for 

the beginnings of production, dissemination of information, initial marketing and sales, or other 

“start-up” factors. To capture this lengthy process, the analyst indicated the timeline for 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-12 

developing and introducing the technology into the market. This includes the years for when an 

initial prototype, refined prototype, and commercial prototype of the technology has or will be 

completed, as well as the year when the technology will be commercially introduced. An initial 

prototype is the first prototype of the technology. A refined prototype represents changes to the 

initial prototype but not a commercially scaled-up version. A commercial prototype is a 

commercial-scale version of the technology. Commercial introduction is when the first unit 

beyond the commercial prototype is operating. Prototype and commercial-introduction years 

were to be consistent with the technology-development program plans. Two values for a 

commercial introduction year were requested. One reflected when the technology is projected to 

be introduced, if the program proceeds as expected (“With ITP” case). The other reflected when 

the technology would have entered the market if the program had not been involved (“Without 

ITP” case). If the technology would not have been commercially introduced without the 

program, then a year of 2050 for the “Without ITP” case was entered. The difference in 

commercial introduction years for the “With ITP” and “Without ITP” cases is referred to as the 

acceleration period. Only the slice of energy savings attributable to the program’s effort to 

accelerate technology development was counted as GPRA savings. 

New technologies normally penetrate a market following a familiar S-curve, the lower end 

representing the above uncertainties overcome by “early adopters.” The curve tails off where 

some may never adopt the new technology. The major portion of the S-curve, where the new 

technology is penetrating the market and benefits are being reaped, is most important. The rate at 

which technologies penetrate their markets varies significantly. Penetrations of heavy industrial 

technologies generally take place over decades, while simple process or control changes can 

penetrate much more rapidly. The actual penetration rate varies due to economic, environmental, 

competitive, productivity, regulatory, and other factors. 

Technology impact projections-model runs for individual R&D projects receiving R&D support 

were aggregated to obtain energy savings associated with each R&D planning unit. In 

aggregating the savings, market targets were examined explicitly to avoid double-counting the 

same potential savings in the infrequent instances when the same energy efficiency market is 

clearly addressed by multiple projects. Where possible market overlaps were found, the markets 

were either assigned to only one technology or divided among the competing technologies under 

development. This process increases confidence that any systemic double-counting within 

planning units has been minimized. Nevertheless, some double-counting across planning units 

within ITP or with other EERE programs is assumed to remain. 

The approximate portion of the FY 2005 budget represented by the analysis for each planning 

unit was noted, but the results were not scaled to 100% of the FY 2005 budget. Typically, the 

projects analyzed represented 80% to 99% of the FY 2005 budget for the various planning units. 

Projected benefits for these planning units do not include the effects of R&D projects completed 

prior to the current year. 

The justification for assuming that all of the projects analyzed will succeed is twofold. First, 

projects that fail will likely be replaced with new projects using different technical approaches to 

achieve similar goals. Using this theory, the basic goals will be met by the program in the long 

run and continuously funded. Second, the projects analyzed do not comprise 100% of the FY 

2005 budget, which in itself discounts the aggregated results, equivalent to incorporating some 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-13 

risk of failure into the overall process. In addition, the knowledge benefits of ITP’s R&D 

portfolio are not assessed here; this scientific and technical knowledge can help to underpin 

additional production technology innovations in the future, and spin-off applications in both the 

near and longer terms. 

 - Technology Delivery Planning Units 

The Industrial Analysis Center program and the Best Practices program were assessed, based on 

retrospective analysis of performance data accumulated over a period of years. ITP’s off-line 

Quality Metrics study for these planning units is based on the premise that continuation of the 

programs will result in beneficial impacts proportional to documented experience at historical 

budget levels. These analyses did not count as savings any continuing contributions from prior 

program expenditures, but only assumed that future expenditures will produce results 

proportionate to those reported for past expenditures. 

The approaches for calculating the impacts of the IAC and best-practices planning units were 

similar. In each case, those program activities associated historically with documented energy 

savings were projected into the future based on assumed continuation at the FY 2006 budget 

level. The numbers of assessments, Web site visitors, trained individuals, etc. performed in each 

future year were used to logically arrive at the future energy savings attributable to the activity, 

given continued performance at historical levels of effectiveness. Each quantity and assumption 

was explicitly shown in a tabular format intended to show the contribution of each step of the 

calculation to the final result and to make the entire analytical process repeatable. 

The IAC program benefits were supported by 25 years of actual assessment and implementation 

data. Among other assumptions, the effects of assessments were projected to last for seven years. 

The effects of student training were projected to persist for 11 years. The effects of the Web site 

information activity were projected to last for seven years. 

Best Practices program benefits were based on findings of an Oak Ridge National Laboratory 

study of program effects, and on a FY 2004 peer review that focused on ORNL’s outcome 

evaluation study. The basic methodology used in each of four best-practices activity areas was 

very similar. First, the activity reach was estimated by calculating the number of individuals 

touched by best-practices information. This number was then scaled back to calculate the number 

of plants taking action, due to this information dissemination. The scale-back factors included 

accounting for duplicate “touches” within the same company, the percentage of companies 

actually taking action, and a reduction factor to discount program credit due to it being but one of 

multiple sources of influence. To obtain the total program energy savings, reported rates of 

energy savings were applied to the number of plants estimated to be affected by best-practices 

activities in each future year. 

Best Practices activity areas evaluated for GPRA06 were: Plant-wide Assessments, Training, 

Software Tools Distribution, and Qualified Specialists. Total annual energy savings attributed to 

Best Practices were the sum of the subtotals estimated for these four delivery channels. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-14 

1.4 Sources 

“GPRA06 Quality Metrics – Methodology and Results,” Energetics Inc., October 25, 2004. 

DOE/EIA, Annual Energy Outlook 2004. 

“Streamlining of OIT’s GPRA Process (Draft),”Arthur D. Little Inc. Reference 33550-01, May 

27, 1998. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix I – Solar Energy Technologies Program – Page I-1

Appendix I – GPRA06 Solar Energy Technologies Program 

Documentation 

1. Introduction 

This appendix provides detailed information on the assumptions and methods employed to 

estimate the benefits of EERE’s Solar Energy Technologies Program. The benefits analysis for 

the Solar Program utilized both NEMS and MARKAL as the analytical tools for estimating the 

Program’s benefits. As will be discussed below, a number of assumptions and structural 

modifications to the models were made in order to represent the suite of solar technologies 

funded by the program as accurately as possible (Photovoltaics, Concentrating Solar Power and 

Solar Water Heating). Many of the assumptions used in the FY06 analysis are the same as or 

similar to those employed in the FY05 analysis; however, two key changes are important to 

highlight up-front. First, the AEO2004 analysis used a new set of reference case assumptions 

with respect to photovoltaic technology cost reductions. The new sets of reference case 

assumptions are very similar to the Solar Program’s targets for PV. This shift in assumptions 

necessitated developing a new approach for estimating the baseline (i.e., no program) input 

assumptions for PV. Second, the FY06 analysis included CSP technology benefits – CSP 

benefits were not included in the FY05 analysis. 

The body of this appendix contains two sections. The first discusses the assumptions used to 

construct the GPRA06 Solar Program baseline scenario. The second discusses the modifications 

that were made to this baseline to construct the GPRA06 solar program scenario. 

2. GPRA06 Solar Program Baseline Assumptions 

Several changes from the AEO2004 Reference Case were incorporated into the GPRA06 

Baseline. These changes include the following: 

Revising projected PV cost. The AEO2004 reference case assumptions for PV technology 

costs were based on a recent report published by Navigant Consulting (2003). As shown in 

Figure 1, the cost projections in the new EIA reference case are very close to the Solar 

Program’s targets. Because of this shift in EIA’s reference case assumptions, a new set of 

baseline assumptions for PV technology needed to be developed. In constructing the GPRA06 

baseline, the following approach was used. Between 2005 and 2015, the costs of PV are 

assumed to decline more slowly in the GPRA06 baseline, leading to a five-year lag between the 

GPRA06 baseline and the program’s goals by 2015. Beyond 2015, the GPRA baseline and 

program numbers are assumed to continue to diverge. This approach incorporates a relatively 

conservative estimate of the Solar Program’s impact on the cost of PV technology and captures 

the notion of technological lock-in (Cowan and Kline 1996). 

Increasing the average commercial building system size from 10kW to 100kW. A sample of 

data from 14 PV systems installed by PowerLight Corporation, between July 1999 and March 

2003, reveals that the average commercial system installed by PowerLight during this period was 

381kW (Table 1). 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix I – Solar Energy Technologies Program – Page I-2

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Installed System Cost (2003 $ per Wp)

GPRA06 Baseline

AEO2004

Program Goals

Figure 1. Projected PV Commercial System Costs 

Table 1. Commercial System Size and Surface-Area Requirements 

PowerLight System Installation Location 

Date 

Completed

System Peak 

Capacity (kW)

PV Surface 

Area (sq. ft.) W/sq.ft.

Santa Rita Jail - Alameda County, California Apr-02 1,180 130,680 9.0

Cypress Semiconductor - San Jose, California Jul-02 335 26,100 12.8

Fala Direct Marketing - Farmingdale, New York Nov-02 1,010 102,700 9.8

Fetzer Vineyards, Hopland, California Jul-99 41 3,750 10.9

Franchise Tax Board, Sacramento, California Aug-02 470 50,000 9.4

Greenpoint Manufacturing - Brooklyn, New York Mar-03 115 11,500 10.0

Mauna Lani Resort – Kohala Coast, Hawaii Jan-02 528 43,330 12.2

Naval Base Coronado, California Sep-02 924 81,470 11.3

Neutrogena Corporation - Los Angeles, California Aug-01 229 30,154 7.6

Parker Ranch – Kameula, Hawaii Jan-01 209 20,000 10.5

PSGA/Ortho-McNeil Facility - Pennsylvania Apr-02 75 17,500 4.3

U.S. Coast Guard – Boston, Massachusetts Sep-99 37 3,800 9.7

U.S. Postal Service - Marina del Rey, California Nov-01 127 15,000 8.5

Yosemite National Park - Yosemite, California Oct-01 47 4,500 10.4

Total 5,327 540,484 

Average 381 38,606 10

Source: PowerLight Case Study data sheets, Downloaded from www.powerlight.com, 5/21/03. 

Note: Some of the locations shown in this table have multiple installations. In these cases, the total installed 

capacity is shown above and the most recent installation date is shown in the date-completed column. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix I – Solar Energy Technologies Program – Page I-3

The average space required for these systems was 0.1 sq. ft/W., based on a U.S. average 

commercial building size in 2000 of 14,500 square feet (AEO2003), and assuming a ratio of 

usable roof space to floor space of 0.7. This ratio of usable roof space to floor space was based 

on the “architecturally suitable area” in an International Energy Agency (IEA) report, Table 2, 

examining the potential for integrated photovoltaics in buildings (IEA 2001). Using this 

approximation, the average commercial building could easily accommodate a 100 kW PV 

system, i.e., a 0.7*14,500 sq. ft. = 10,100 sq. ft. PV array. Thus, setting the average system size 

at 100kW is a conservative assumption based on industry trends, as well as the available roof 

space on a large share (50+%) of the commercial building stock. This is a very conservative 

assumption based on the expectations that the efficiency of PV cells will increase; the space 

requirements for a PV system will decrease; and, as system costs decline, facades and other 

spaces (such as parking lots) also could be utilized for PV systems. 

Increasing the maximum share of commercial buildings with solar access from 30% to 

55%. Similar to the preceding ratio of usable roof space to floor space, the share of roof space 

suitable for PV installations was based on the recently published IEA report on integrated 

photovoltaics in buildings (IEA 2001). This report indicates that a reasonable estimate for the 

share of roof space suitable for PV installations is 55%. This estimate includes shading and other 

factors that would limit the use of roof space for PV systems (IEA 2001). 

Increasing the average residential building system size from 2kW to 4kW. A couple of 

years ago, a typical residential rooftop PV system was a 2kW system—this is most likely the 

source for EIA’s 2kW system size in the AEO2004 reference case. However, residential rooftop 

systems being installed in Japan, Europe, and the United States have been growing larger. For 

example, the average Japanese rooftop system size in 2002 was 3.7 kW (Ikki 2003) and the 

average rooftop system size in California in 2004 was 3.6 kW1

. The average home in the United 

States has 1,700 square feet of floor space (this is expected to increase in the future). Using data 

from EIA’s residential energy-consumption survey (EIA 1999, Table HC1-2a) one can estimate 

a floor- to roof-space ratio of 0.7 (based on distribution of one-story, two-story, and 

three-story single-family homes). This is a conservative estimate—most homes have pitched 

roofs, which would increase the total available roof space (yet may make a significant portion of 

the roof oriented away from the sun). If a typical system requires 10 sq. ft./W (as above), then a 

4kW system would require roughly 400 square feet of roof space, which is well below the 

average available space allowing for multiple floors and pitched roofs. Thus, roof space is not a 

constraint for installing residential rooftop PV systems in the 4kW range. Because the efficiency 

of PV cells is likely to improve, a trend toward larger systems on rooftops is likely to continue. 

Thus, based on available roof space and what is happening in the marketplace, setting the 

average system size at 4kW is a conservative assumption.

Increasing the maximum share of residential buildings with solar access from 30% to 60%. 

A maximum share of 60% for residential buildings with solar access was used. This estimate 

 

1

 This estimate was based on data from the California Energy Commission’s Emerging Renewables Program, 

downloaded on 1/27/05 from www.energy.ca.gov/renewables/emerging_renewables.html. Data on small PV 

systems (i.e., with a system size under 10kW) were extracted from the full dataset, indicating that during 2004 a 

total of 15.9 MW of PV was installed in 4,372 small PV systems in California with an average system size of 

3.6kW. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix I – Solar Energy Technologies Program – Page I-4

accounts for the fact that some homes will not be suitable for PV systems due to shading, 

building orientation, roof construction, or other factors. This value was calculated from a 

combination of single-family homes (70%) and multifamily homes (30%), using a 75%–25% 

split between single-family and multifamily homes (EIA 2003, Table A4). Thus, the average 

maximum share was set at 0.7*0.75 + 0.3*0.25 = 0.6. 

Including a declining PV buy-down program in California. This assumed that the California 

renewable energy credit program that provided a PV credit of $4,000/kW in 2003 will continue 

to be available, but will decline by $400/kW per year. This credit was included for the entire 

Pacific region. Given that a number of other local credits were not included in the GPRA 

baseline, applying the California state-level credit to the whole Pacific region is likely to be a 

reasonable approximation. 

Modifying the adoption rate of distributed generation technologies. The modification to the 

adoption rate was based on information provided by the DER program (Figure 2). This applies 

to PV as well as gas-fired CHP technologies. 

0.0

0.2

0.4

0.6

0.8

1.0

1

3

5

7

9

11

13

15

17

19

Number of Years to Positive Cash Flow

Fraction Adopting

AEO2004

Alternative

Figure 2. Commercial-Sector DG Adoption Rates 

These changes lead to increased adoption of PV systems in the baseline. However, the AEO2004 

assumptions about PV installations through the Million Solar Roofs program were removed, so

that there would not be double-counting when these were introduced in the GPRA Program

Case.

One additional NEMS-GPRA06 model modification was made in the residential module. Solar 

water heaters were added as a technology option for new homes, and the algorithm governing 

water heater replacements was modified so that solar water heaters could compete in a larger 

market. 

3. GPRA06 Solar Program Scenario Assumptions 

Three key sets of assumptions were modified to generate the GPRA06 Solar Program scenario. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix I – Solar Energy Technologies Program – Page I-5

Green power additions. Green power additions by region, from Princeton Energy Resources 

International (PERI), were added back into the Solar Program scenario (Table 2). These 

projections take into account the Baseline assumptions of noneconomic capacity additions. This 

capacity is added in NEMS-GPRA06 as exogenous additions in residential and commercial 

buildings. Note that the CSP green power builds were very small (<70MW), so they were not 

include in the GPRA06 analysis. 

Table 2. Incremental Green Power PV Capacity Additions (MW)

Incremental Green Power PV Capacity Additions (MW)

2006-2010 2011-2015 2016-2020 2021-2025 Total 

ECAR 80 183 140 40 443 

ERCT 73 167 127 36 404 

MAAC 70 159 122 35 385 

MAIN 21 47 36 10 113 

MAPP 5 12 9 3 29 

NY 15 35 27 8 84 

NE 20 47 36 10 113 

FL 94 214 164 47 518 

STV 282 641 491 140 1,554 

SPP 76 173 132 38 418 

NWPP 13 31 23 7 74 

RA 24 54 42 12 132 

CNV 0 0 0 0 0 

Total 773 1,761 1,348 385 4,267 

Technology Characteristics. More aggressive technology targets were used for the range of 

solar technologies: concentrating solar power (CSP), central PV systems, distributed PV 

systems, and solar water heating systems. These technology characteristics were based on the 

Solar Program’s most recent Multi-Year Technical Plan (EERE 2004), and the solar baseline 

scenario assumptions contained in Margolis and Wood (2004). 

In order to define a consistent set of long-term targets going out to 2050, a multi-lab, multitechnology team was assembled in 2003. This team produced technology cost projections for 

use in NEMS that are consistent with the Solar Program’s Multi-Year Technical Plan through 

2025 and extended the Solar Program’s targets to 2050 (for details, see Margolis and Woods 

2004). Thus the targets shown in Tables 3, 4 and 5 are consistent with the Multi-Year Technical 

Plan through the 2020-2025 timeframe. Beyond 2025, the targets are increasingly uncertain and 

are likely to be revised as the Solar Program continues to analyze the long-term prospects for PV 

technology cost reductions. Note that, on an annual basis, costs are assumed to decline linearly 

between the years shown in the tables below. 

While the technology assumptions for commercial rooftop PV systems are shown above in 

Figure 1, detailed data for PV systems in the three markets modeled is provided in Table 3. 

Although the costs shown below are for specific years, the costs decline annually between the 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix I – Solar Energy Technologies Program – Page I-6

years shown. Note that in both the GPRA baseline and program scenarios, the AEO2004 

Reference Case assumptions for solar insolation and capacity factors were used. 

Table 3. PV Systems 

 Central Generation Residential Buildings Commercial Buildings 

Year 

Installed 

Price 

(2001$/kW) 

O$M 

(2001$/kW)

Installed 

Price 

(2000$/kW)

O$M 

(2000$/kW)

Installed 

Price 

(2000$/kW) 

O$M 

(2000$/kW)

2003 5,300 60 9,450 160 6,250 160 

2007 3,600 40 6,250 40 4,500 40 

2020 2,000 10 2,800 10 2,800 10 

2025 1,800 9 2,520 9 2,520 9 

2050 1,450 7 2,029 7 2,029 7 

In NEMS, two solar water heaters are represented that have different efficiencies or electric 

backup requirements. In MARKAL, only one type of solar water heater is represented (shown in 

Table 4 at the typical efficiency system). 

Table 4. Residential Solar Water Heat 

 

Best 

(High efficiency) 

Minimum 

(Typical efficiency) 

 Total Total 

First Last Installed Installed 

Year Year Efficiency Cost($01) Efficiency Cost($01) 

1997 2004 2.5 3050 2.0 2800 

2005 2009 2.6 2650 2.1 2500 

2010 2019 2.7 1650 2.2 1500 

2020 2025 3.0 1450 2.5 1300 

2030 n.a n.a n.a 2.8 1162 

2040 n.a. n.a n.a 3.0 1098 

 Notes: n.a. = not applicable. Change in data after 2030 reflects data structure in MARKAL. 

The data for CSP technology shown in Table 5 are for California. The CSP costs are up to 13% 

higher in other regions with less solar insolation to account for greater capacity and storage 

requirements. The annual capacity factors by 2020 range from 49% in MAPP (the Upper 

Midwest) to 74% in the Southwest. The capacity factors by time period were computed by 

Sandia analysts to optimize the timing of solar output for each region within the bounds of the 

storage potential. Note that the AEO2004 Reference Case assumptions include lower-cost CSP 

systems, but with significantly less storage and therefore lower electrical output. 

The future cost assumptions for CSP technology in the Solar Program scenario are based on a 

funding level consistent with the FY06 budget request for FY06 and a funding level 

commensurate with those outlined in the Draft CSP Technology Transition Plan for years 

beyond FY06 (DOE 2004). 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix I – Solar Energy Technologies Program – Page I-7

Table 5. Concentrating Solar Power 

Year 

Installed 

Price 

($/kW) 

O$M 

(mills/kWh)

Capacity 

Factor 

2003 4,953 14.7 53%

2010 3,510 7.8 65%

2020 2,462 4.0 72%

2025 2,199 3.6 72%

2030 1,993 3.2 72%

2035 1,879 3.1 72%

2040 1,826 3.0 72%

2050 1,797 2.9 72%

4. Sources 

Cowan, Robin, and David Kline. 1996. The Implications of Potential “Lock-In” in Markets for 

Renewable Energy. National Renewable Energy Laboratory, Golden, Colorado. NREL/TP-460-

22112. 

DOE. 2004. CSP Technology Transition Plan – Draft. Internal DOE draft document. 

EERE. 2004. Solar Energy Technologies Program: Multi-Year Technical Plan 2003-2007 and 

Beyond, Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy, 

Washington, D.C. DOE/GO-102004-1775. 

EIA (Energy Information Administration). 1999. A Look at Residential Energy Consumption in 

1997. Energy Information Administration, U.S. Department of Energy, Washington, D.C. 

EIA. 2003. Annual Energy Outlook 2003. Energy Information Administration, U.S. Department 

of Energy, Washington, D.C. 

EIA. 2004. Annual Energy Outlook 2004. Energy Information Administration, U.S. Department 

of Energy, Washington, D.C. 

IEA (International Energy Agency). 2001. Potential for Building Integrated Photovoltaics. St. 

Ursen, Switzerland. Report No: IEA - PVPS T7-4. 

Ikki, Osamu. 2003 (May). "PV Activities in Japan.” Resources Total Systems Co. Ltd., Tokyo, 

Japan. 

Margolis, Robert M. and Frances Wood. 2004. “The Role for Solar in the Long-Term Outlook 

of Electric Power Generation in the U.S.” Paper presented at the IAEE North American 

Conference in Washington, DC, July 8–10. 

Navigant Consulting. 2003. The Changing Face of Renewable Energy. Navigant Consulting, 

Burlington, MA. Available from www.navigantconsulting.com. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-1

Appendix J - GPRA06 Vehicle Technologies Program 

1. Introduction 

The target markets for the Office of FreedomCAR and Vehicle Technologies (FCVT) program 

include light vehicles (cars and light trucks) and heavy vehicles (trucks more than 10,000 pounds 

Gross Vehicle Weight). Each will be discussed separately below. 

1.1 Target Market: Alternate Technology Light Vehicle (ATV) Market

The alternate technology light vehicles (ATVs) included in the FCVT program are gasoline 

hybrid vehicles, diesel hybrid vehicles, and advanced diesel vehicles. The market for these 

technologies includes all cars and light trucks sold for both personal and business use. Today, the 

size of this market is approximately 17 million vehicle sales per year. Total car and light truck 

stock is about 220 million vehicles. EIA projects both sales and stock to grow to more than 20 

million and 300 million respectively by 2025. Additional growth is expected post-2025. 

1.2 Key Factors in Shaping the Market Adoption of ATVs 

Key factors associated with the adoption of new vehicle technologies include how the new 

vehicle technologies compare with the baseline vehicle technologies in terms of the following 

vehicle attributes: 

• Vehicle Price 

• Fuel Economy 

• Range 

• Maintenance Cost 

• Acceleration 

• Top Speed 

• Luggage Space 

Of these, vehicle price and fuel economy are the most important. 

Nonvehicle attributes that are important factors in a consumer’s decision to purchase new vehicle 

technologies include the following: 

• Fuel Price 

• Fuel Availability 

1.3 Methodology and Calculations 

The factors listed above include the factors used in the modeling of new vehicle technology 

penetration by the NEMS and MARKAL models. ATV attributes and other factors are discussed 

below. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-2

1.3.1 ATV Attributes 

ATV attributes were developed based on the FCVT program goals, discussions with FCVT 

program managers, Powertrain Systems Analysis Toolkit (PSAT) modeling and payback 

analysis (Refs. 1-3). The PSAT model is a simulation model used by DOE to evaluate the fuel 

economy and performance of light vehicles using various technologies. Payback analysis was 

used to estimate what the incremental price of ATVs would be when they become cost 

competitive with conventional vehicles, a goal of the program. (The incremental price equals the 

present value of the energy cost reduction achieved by ATVs over three years, assuming a fuel 

price of $1.50/gallon gasoline equivalent and 7.5% discount rate.) Other attributes were based 

on a review of past GPRA characterizations (e.g., Ref. 4). 

Because the NEMS and MARKAL models require different levels of detail, two separate vehicle 

characterizations are provided. In both cases, most of the attributes are provided as ratios to the 

vehicle attributes of conventional vehicles. (For NEMS, the $ value of the price increments were 

provided.) The attributes are for new vehicles in the year listed. Table 1 contains the vehicle 

attributes for ATVs provided for input to the NEMS model. Attributes are provided for all six car 

size classes and six light truck classes that NEMS uses.

Table 2 contains vehicle attributes for ATVs provided as input to the MARKAL model. 

MARKAL uses only vehicle price and fuel economy attributes. MARKAL does not disaggregate 

cars and light trucks into various classes. 

1.3.2 Fuel Price and Availability 

Fuel price assumptions are discussed in Chapters 4 and 5. Gasoline and diesel fuel are assumed 

to be widely available. 

1.3.3 ATV Market-Penetration Methodology 

Brief descriptions of how the NEMS and MARKAL models each project new vehicle technology 

penetration using these vehicle attributes can be found in Chapter 4 (NEMS) and Chapter 5 

(MARKAL). 

1.4 Sources 

1. “Strategic Plan,” U.S. Department of Energy Efficiency and Renewable Energy, DOE/GO102002-1649 (October 2002). 

2. Phillip Sharer and Aymeric Rousseau, “Fuel Economy of Advanced Technology Vehicles” for 

Phil Patterson, DOE (June 17, 2004). 

3. Payback model developed by Jim Moore, TA Engineering (2003). 

4. “Program Analysis Methodology: Office of Transportation Technologies, Quality Metrics 

2003 Final Report”, prepared by OTT Analytic Team, for Office of Transportation Technologies, 

U.S. Department of Energy (March 2002). 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-3

Table 1. ATV Attributes Input to NEMS 

2-SEATER MINI-COMPACT SUB-COMPACT 

Advanced Diesel 2014 2019 2024 2025 2018 2023 2025 N/A 2012 2017 2022 2025 2011 Incremental Vehicle Price 1341 988 853 867 1278 946 900 1089 828 718 741 1160 Range 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 Maintenance Cost 1.00 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 

Acceleration 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

Top Speed 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

Luggage Space 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

Fuel Economy 1.50 1.60 1.70 1.72 1.50 1.60 1.64 1.50 1.60 1.70 1.76 1.50 

Diesel Hybrid 2016 2021 2025 N/A 2020 2025 N/A N/A 2016 2021 2025 N/A 2014 Incremental Vehicle Price 1787 1380 1154 1999 1333 1413 1157 935 1433 Range 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 Maintenance Cost 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 

Acceleration 1.00 1.00 1.00 0.90 0.90 0.90 0.90 0.90 0.90 Top Speed 1.00 1.00 1.00 0.90 0.90 0.90 0.90 0.90 0.90 Luggage Space 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 Fuel Economy 1.80 2.10 2.12 2.09 2.12 1.70 2.10 2.12 1.70 

Gasoline Hybrid 2012 2018 2023 2025 2011 2016 2021 2025 2010 2014 2019 2025 2007 Incremental Vehicle Price 1508 1248 1035 1046 1437 1195 1004 1019 1286 1047 858 867 1305 Range 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 Maintenance Cost 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 

Acceleration 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

Top Speed 1.00 1.00 1.00 1.00 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 

Luggage Space 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 Fuel Economy 1.60 1.90 2.00 2.02 1.60 1.90 1.99 2.02 1.60 1.90 1.97 2.02 1.60 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-4

Table 1 (continued) 

MEDIUM CAR LARGE CAR 

Advanced Diesel 2010 2015 2020 2025 2009 2014 2019 2025 Incremental Vehicle Price 1119 845 748 811 1208 898 794 860 Range 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 Maintenance Cost 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 Acceleration 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Top Speed 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Luggage Space 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Fuel Economy 1.40 1.50 1.60 1.70 1.40 1.50 1.60 1.70 Diesel Hybrid 2014 2019 2024 2025 2014 2019 2024 2025 Incremental Vehicle Price 1319 1267 1075 1078 1409 1154 1141 1144 Range 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 Maintenance Cost 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 Acceleration 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 Top Speed 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 Luggage Space 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 Fuel Economy 1.50 2.00 2.17 2.21 1.50 1.75 2.17 2.21 Gasoline Hybrid 2006 2011 2016 2025 2009 2014 2019 2025 Incremental Vehicle Price 1119 950 886 1014 1409 1010 941 1023 Range 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 Maintenance Cost 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 Acceleration 1.00 0.90 0.90 0.90 0.90 0.90 0.90 0.90 Top Speed 0.90 0.90 0.90 0.90 1.00 0.90 0.90 0.90 Luggage Space 0.85 0.95 0.95 0.95 0.85 0.95 0.95 0.95 Fuel Economy 1.40 1.60 1.80 2.06 1.50 1.60 1.80 1.96 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-5

Table 1 (continued) 

MINIVAN LARGE VAN 

Advanced Diesel 2008 2013 2018 2025 2006 2011 2016 2025 Incremental Vehicle Price 1226 931 822 883 1668 1204 1149 1080 Range 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 Maintenance Cost 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 Acceleration 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Top Speed 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Luggage Space 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Fuel Economy 1.40 1.50 1.60 1.70 1.40 1.50 1.70 1.70 Diesel Hybrid 2013 2018 2023 2025 2012 2017 2022 2025 Incremental Vehicle Price 1430 1241 1149 1138 1946 1605 1480 1391 Range 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 Maintenance Cost 1.09 1.05 1.05 1.05 1.09 1.05 1.05 1.05 Acceleration 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 Top Speed 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 Luggage Space 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 Fuel Economy 1.50 1.80 2.10 2.13 1.50 1.80 2.13 2.13 Gasoline Hybrid 2009 2014 2019 2025 2010 2015 2020 2025 Incremental Vehicle Price 1226 1150 1039 1072 1668 1487 1394 1311 Range 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 Maintenance Cost 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 Acceleration 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 Top Speed 0.75 0.90 0.90 0.90 0.90 0.90 0.90 0.90 Luggage Space 0.90 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Fuel Economy 1.40 1.70 1.90 2.00 1.40 1.70 2.00 2.00 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-6

Table 1 (continued) 

SMALL SUV LARGE SUV SMALL TRUCK CA

Advanced Diesel 2008 2013 2018 2025 2007 2012 2017 2025 2008 2013 2018 2025 2006 Incremental Vehicle Price 1155 896 800 868 1563 1163 1019 1074 1112 859 764 830 1457 Range 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 Maintenance Cost 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 

Acceleration 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Top Speed 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

Luggage Space 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Fuel Economy 1.35 1.45 1.55 1.65 1.35 1.45 1.55 1.65 1.35 1.45 1.55 1.65 1.35 Diesel Hybrid 2011 2016 2021 2025 2015 2020 2025 N/A 2012 2017 2022 2025 2016 Incremental Vehicle Price 1485 1283 1098 1169 2024 1665 1399 1425 1239 1050 1118 1874 Range 1.20 1.20 1.20 1.20 1.20 1.20 1.20 0.90 0.90 0.90 0.90 0.90 

Maintenance Cost 1.05 105 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 Acceleration 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 Top Speed 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.90 0.90 0.90 0.90 0.90 Luggage Space 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.80 0.90 0.90 0.90 0.80 Fuel Economy 1.50 1.80 1.95 2.13 1.50 1.80 1.95 1.50 1.80 1.95 2.13 1.50 Gasoline Hybrid 2007 2012 2017 2025 2008 2013 2018 2025 2010 2015 2020 2025 2010 Incremental Vehicle Price 1155 962 966 1102 1563 1249 1231 1363 1112 922 923 1054 1457 Range 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 

Maintenance Cost 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 

Acceleration 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 Top Speed 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.90 0.90 0.90 0.90 0.90 

Luggage Space 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.80 0.95 1.00 1.00 0.80 Fuel Economy 1.35 1.50 1.75 2.00 1.35 1.50 1.75 2.00 1.35 1.50 1.75 2.00 1.35 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-7

Table 2. ATV Attributes for Input to MARKAL 

Ratios to Conventional Vehicles

2005 2010 2015 2020 2025 2030 2035 2040 

CARS 

Advanced Gasoline MPG 1.0 1.0 1.01 1.02 1.03 1.025 1.02 1.015 

 Incremental 

Price 

1.0 1.01 1.05 1.09 1.24 1.25 1.25 1.25 

Diesel MPG 1.3 1.4 1.5 1.6 1.7 1.8 1.8 1.8 

 Incremental 

Price 

1.07 1.05 1.03 1.03 1.03 1.025 1.02 1.02 

Gasoline HEV MPG 1.3 1.5 1.7 1.96 2.06 2.22 2.22 2.22 

 Incremental 

Price 

1.1 1.07 1.05 1.045 1.04 1.03 1.02 1.02 

Diesel HEV MPG NA 1.6 1.8 2.05 2.21 2.36 2.36 2.36 

 Incremental 

Price 

NA 1.1 1.08 1.075 1.07 1.055 1.04 1.035 

LIGHT TRUCKS Advanced Gasoline MPG 1.0 1.01 1.02 1.03 1.03 1.025 1.02 1.015 

 Incremental 

Price 

1.0 1.05 1.17 1.23 1.36 1.37 1.37 1.37 

Diesel MPG 1.3 1.35 1.4 1.5 1.65 1.7 1.8 1.8 

 Incremental 

Price 

1.06 1.04 1.035 1.03 1.025 1.02 1.02 1.02 

Gasoline HEV MPG 1.3 1.4 1.7 1.85 2 2 2 2 

 Incremental 

Price 

1.1 1.07 1.05 1.045 1.04 1.03 1.02 1.02 

Diesel HEV MPG NA 1.5 1.8 1.9 2.13 2.13 2.13 2.13 

 Incremental 

Price 

NA 1.1 1.08 1.075 1.07 1.055 1.04 1.035 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-8

2.0 Heavy Vehicle Benefits Analysis Introduction 

The following sections describe the approach to estimating benefits and analysis results for the 

Heavy Vehicle Technologies activities of the FreedomCAR and Vehicle Technologies Program 

of EERE. The scope of the effort includes: 

• Characterizing baseline and advanced technology vehicles for Class 3–6 and Class 7 and 

8 trucks, 

• Identification of technology goals associated with the DOE EERE programs, 

• Estimating the market potential of technologies that improve fuel efficiency and/or use 

alternative fuels, 

• Determining the petroleum savings associated with the advanced heavy vehicle 

technologies. 

In FY05, the Heavy Vehicles program activity expanded its technical involvement to more 

broadly address various sources of energy loss as compared to focusing more narrowly on engine 

efficiency and alternative fuels. This broadening of focus has continued in the activities planned 

for FY06. These changes are the result of a planning effort that occurred during FY04 and FY05 

(Ref. 1). 

This narrative describes characteristics of the heavy truck market as they relate to the analysis 

and provides a description of the analysis methodology—including a discussion of the models 

used to estimate market potential and benefits. The market penetration of advanced heavy 

vehicle technologies estimated here is then modeled as part of the EERE-wide integrated 

analysis (using NEMS and MARKAL) to provide final benefit estimates reported in the FY06 

Budget Request. 

2.1 Target Market: Heavy Vehicle Target Market 

 “Heavy Vehicles” are defined in this analysis as including Classes 3 through 6 (Medium Trucks) 

and Classes 7 and 8 (Heavy Trucks). The Heavy Truck classes are further subdivided by end-use 

types: i.e., Long-Haul, Intermediate, and Local Use. Vehicle Inventory and Use Survey (VIUS) 

data were examined for all vehicles in use and vehicles 2 years old or less (Ref. 2). The Heavy 

Truck vehicle market was then disaggregated into these three end-use types. The specific vehicle 

configurations grouped in each of the three types have similar usage and annual vehicle mile 

usage patterns. The vehicle type segments are made up of the vehicle configurations listed 

below: 

• Local Use (Type 1) – multistop, step van, beverage, utility, winch, crane, wrecker, 

logging, pipe, garbage collection, dump, and concrete delivery; 

• Intermediate Use (Type 2) – platform, livestock, auto transport, oil-field, grain, and tank; 

• Long-Haul (Type 3) – refrigerated van, drop frame van, open top van, and basic enclosed 

van. 

The lower speed and “stop and start” duty characteristics of Type 1 trucks greatly reduce the 

potential efficiency benefits in that sector compared to Types 2 and 3. For similar reasons, fuel 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-9

economy improvements due to other speed-dependent measures such as improved tires will have 

lower benefit here than in the other two types. 

As compared to long distance, over the road travel, Type 2 vehicles tend to be used in a mix of 

local and regional delivery; and, as a result, will also realize greater fuel economy benefit from 

aerodynamic improvements than Type 1, but not as great as Type 3. Distances traveled by Type 

2 vehicles are typically greater than Type 1, which infers that the typical speeds are higher. 

These characteristics make them a somewhat better market sector for measures that perform in 

relation to speed such as advanced tires. In general, Type 3 vehicles are the best candidates for 

technologies that reduce drivetrain or vehicle losses. 

Refueling characteristics; i.e. central-source refueling or noncentral source also are considered in 

the market characteristics, as centrally refueled vehicles would find an alternative fuel source 

more practical than vehicles that always refuel at road-side facilities. 

Eleven travel distance categories for medium trucks and 21 for heavy trucks are represented in 

the model. These categories were determined using travel distributions developed with the VIUS 

data by ORNL (Refs. 2, 3). 

Exhibit 1 shows the distribution of annual travel for Class 3 through 6 and the three types of 

Class 7 and 8 vehicles. Type 3 vehicles display the greatest amount of annual travel of all heavy 

vehicle classes as is evidenced in part by the curve’s peaking in the 120,000- to 139,000-mile 

segment. 

Exhibit 2 shows the vehicle use pattern for Local or Type 1 Heavy trucks. The distribution based 

both on vehicles and vehicle-miles traveled are indicated. 

The contrast in distribution by type is evident when Exhibits 2 and 3 are compared. Exhibit 3

shows the same information as Exhibit 2—but for Type 3 trucks. For Type 1, the distribution 

peaks in the 20,000- to 39,000-mile segment. For Type 3, the peak distribution shows annual 

travel of 100,000 miles greater than Type 1—120,000 to 139,000 miles. 

Centrally refueled and noncentrally fueled vehicle use characteristics also have been analyzed. 

Centrally refueled travel less per year than noncentrally refueled vehicles. In the noncentrally 

refueled vehicle segment, the majority of travel occurs from 100,000 to 140,000 miles per year. 

In the central refueling segment, the majority of travel occurs in a more even distribution 

between 20,000 and 140,000 miles per year. 

 

Heavy vehicle market characteristics that are pertinent to the analysis are summarized in 

Exhibit 4. In the medium truck market segment (Classes 3 through 6), all vehicle types, with the 

exception of auto transport, on average travel about 20,000 miles per year. Heavy trucks, 

depending on type, travel an average of 40,000 miles to 92,000 miles per year. Based on 

discussions with program personnel, the baseline fuel economy for Type 3 heavy vehicles was 

changed to 7.5 MPG, a reduction from prior years. The fuel economy of this market sector 

remains significantly higher than that attributed to Type 1 heavy vehicles (7.5 vs. 4.55 MPG). 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-10

2.2 Key Factors Shaping Market Adoption of Technology 

Based on a survey conducted by the American Trucking Associations in 1997, energyconservation purchase decisions for this sector are significantly affected by economic viability—

specifically the payback of the investment (Ref. 4). The survey of 224 motor carriers revealed 

that paybacks of one to four years were acceptable for energy-conserving technologies. Based on 

those findings, we model the market acceptance of the various technologies based on payback 

performance. 

The Environmental Protection Agency has initiated regulation of emissions from Heavy Trucks. 

This is changing engine technology and diesel fuel refining. Some reduction in fuel economy 

with the new engines is also expected as the combustion process optimization is addressing 

reduction of emissions. These changes will impose both operating and capital costs on truck 

operators. 

2.3 Methodology and Calculations 

The analysis of the benefits expected from achieving the Heavy Vehicle technologies program 

goals was developed based on four primary reference sources: 

• Technology energy efficiency and fuel-use characteristics—as provided by the managers 

of the technology programs; 

• Vehicle characteristics and use information—as obtained from the 1997 VIUS. This 

provides information on both vehicle performance characteristics, such as fuel economy, 

and vehicle-use patterns such as miles traveled per year (Ref. 2); 

• Truck operator investment requirements—as provided by a survey of Owner-Operators 

performed by the American Trucking Associations in 1995 (Ref. 4); 

• Important “background” information such as energy prices and baseline technology fuel 

economies—as provided in the Annual Energy Outlook (Reference Case) prepared by the 

Energy Information Administration (Ref. 5). 

The methodology involves the definition of the energy conservation or displacement and cost 

attributes of the advanced technologies being fostered by the program, the characterization of the 

markets affected, and the estimation of the benefits. Several models are used. Specifically, initial 

benefits estimates are generated through the linkage of four spreadsheet models: 

• HTEB - Heavy Truck Energy Balance Model 

• TRUCK 2.0 - Heavy Vehicle Market Penetration Model 

• VISION 2.0, and 

• Heavy Truck Summary (HVS) report generator. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-11

The relationship of these four models is indicated in Exhibit 5.1

 Cost estimates are developed 

separately. 

The Heavy Truck Energy Balance Model (HTEB) was developed to assess the overall fuel 

economy effect of several changes to the vehicle involving both the engine and other elements of 

the vehicle. It takes into account energy losses based on user selected inputs of vehicle use. It is a 

steady-state model. It was required as a result of the lack of existence of publicly available 

vehicle simulation tool. The fuel economies of new advanced heavy vehicle technologies 

estimated with the HTEB model are presented in Exhibit 6. 

The cost estimates for these vehicles are also presented in Exhibit 6. The first cost of a 

technology is assumed to reduce to a two-year payback level as a program goal. As an example, 

the cost schedule for the Exhibit 6 technologies in the Long Haul vehicle application is indicated 

in Exhibit 7. This process was replicated for Type 1 and Type 2 vehicles and Medium Trucks to 

develop similar cost estimates. 

The values for fuel economy improvement from HTEB and cost are then input to TRUCK 2.0.

This model was developed to estimate the potential market impacts of new technologies on the 

medium and heavy truck market. The results generated by this model are: 

• Market penetrations, in units of percent of new vehicles sold for each type and class of 

vehicle, and 

• Composite fuel economy rating (new mpg) of the vehicles sold, for each truck type. 

As discussed, the TRUCK 2.0 model estimates market penetration based on cost effectiveness of 

the new technology. Cost effectiveness is measured as the incremental cost of the new 

technology less the expected energy savings of that technology over a specified time period in 

relation to specified payback periods. 

Exhibit 8 shows the payback distribution assumed in the TRUCK model. This payback 

distribution was generated from the American Trucking Association’s survey described above 

(Ref. 4). The survey found that, for example, 16.4% of the truck operators responding require a 

payback of one year on an investment. The TRUCK model market penetration calculation 

method for Class 7 and 8, Type 1 vehicles is described in Exhibit 9. 

The market penetration results are supplied through a link to the VISION 2.0 model (Ref. 6). 

The VISION model is used to estimate preliminary or first order oil/energy use and CO2 

emissions from highway vehicles through 2050. It contains a baseline estimate of heavy vehicle 

energy use to 2050. Through 2025 that baseline is the same as that of the AEO. By inputting the 

 

1 The HTEB was developed by William Shadis and James Moore of TA Engineering. The TRUCK (2.0) Model was developed as 

a collaborative effort, initially by John Maples of Oak Ridge National Laboratory (ORNL), with assistance from James Moore, of 

TA Engineering, Inc. Subsequent enhancements have been performed by Moore (TA Engineering). The Vision (2.0) model 

was developed by Margaret Singh and Anant Vyas of ANL. The Heavy Truck Summary Model is a report 

generating spreadsheet. It was initially developed by Maples, and has subsequently been modified by Analysts at the 

National Renewable Energy Laboratory, and TA Engineering.
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-12

market penetration and fuel economy of the advanced heavy vehicle technologies into the model, 

an alternative estimate of future heavy vehicle energy use is generated and benefits relative to the 

baseline can be estimated. 

Since VISION does not disaggregate Types 1-3 Heavy Trucks or Hybrid-Non-hybrid Medium 

Trucks, the fuel economy multipliers generated by Truck 2.0 are aggregated on both a sales and 

VMT-weighted basis for input to VISION. These aggregated fuel economy multipliers are 

provided in Exhibit 10. They are also adjusted to take into account differences in baseline fuel 

economies provided in VIUS (used in TRUCK 2.0) and the AEO (used in VISION). These 

factors and the market penetration estimates also presented in Exhibit 10 are the factors 

ultimately used in the EERE-wide integrated analysis. 

Finally, the Heavy Truck Summary report generator summarizes the first order benefits for the 

period covering 2000 through 2050. Benefits include the following: 

• Heavy Truck Petroleum Use and Savings, by Class 3-6 and Class 7-8, Million BPD 

• Heavy Truck Petroleum Savings - % 

• Class 7&8 Truck Savings by Program Element (Technology), Million BPD 

• Local Use Truck Savings by Program Element (Technology), Million BPD 

• Intermediate Truck Savings by Program Element (Technology), Million BPD 

• Long-Haul Truck Savings by Program Element (Technology), Million BPD. 

These first order benefits have been generated and will be reported in a forthcoming report. 

2.4 Sources 

1. FreedomCAR and Vehicle Technologies R & D Plan (Draft), August 22, 2003. 

2. “1997 Vehicle Inventory and Use Survey,” EC97TV-US U.S. Bureau of the Census, 

Washington, D. C., 1999. 

3. Personal Communication with Stacy Davis, ORNL, November 2001 

4. “1997 Return on Investment Survey,” American Trucking Association, Arlington 

Va., 1997. 

5. “Annual Energy Outlook 2004, With Projections to 2030,” Energy Information 

Agency, Department of Energy, Washington, D. C., (Web site address: 

http://www.eia.doe.gov/bookshelf.html Library/Archives-Forecasting). 

6. Singh, M.; A. Vyas, and E. Steiner, “VISION Model: Description of Model Used to 

Estimate the Impact of Highway Vehicle Technologies and Fuels on Energy Use and 

Carbon Emissions to 2050,” ANL/ESD/04-1 (Dec. 2003). 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-13

Exhibit 1: Annual Miles Traveled for Four Truck Categories, 1997 

-10%

0%

10%

20%

30%

40%

50%

60%

0-19.9 20-39.9 40-59.9 60-79.9 80-99.9 100-

119.9

120-

139.9

140-

159.9

160-

179.9

180-

199.9

200+

1,000 mi/yr per vehicle

Percent of Vehicles

Type M (Medium-Class 3-6)

Type 1 (Class 7&8)

Type 2 (Class 7&8)

Type 3 (Class 

7&8)
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-14

Exhibit 2: Type 1 Vehicle Use 

Distribution of Type 1 Vehicles and VMT by Annual Miles Per Year

0%

5%

10%

15%

20%

25%

30%

35%

40%

0-19.9 20-39.9 40-59.9 60-79.9 80-99.9 100-

119.9

120-

139.9

140-

159.9

160-

179.9

180-

199.9

200+

Miles Per Year (survey data)

Sample Precentage

Vehicle Miles Distribution

Type 1

Vehicle Distribution
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-15

Exhibit 3: Type 3 Vehicle Use 

Distribution of Type 3 Vehicles and VMT by Annual Miles Per Year

0%

5%

10%

15%

20%

25%

30%

0-19.9 20-39.9 40-59.9 60-79.9 80-99.9 100-

119.9

120-

139.9

140-

159.9

160-

179.9

180-

199.9

200+

Miles Per Year (survey data)

Sample Precentag

e

Vehicle-Miles Distribution

Type 3 

Vehicle Distribution
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-16

Exhibit 4: Heavy Vehicle Characteristics (1997) 

Vehicle Type Average 

Annual Miles

Fuel 

Economy, 

MPG

Percent 

Centrally 

Refueled

Class 3-6 20,126 8.90 40.1%

Class 7 & 8; Type 1 40,043 4.55 59.8%

Class 7 & 8; Type 2 74,066 6.16 41.0%

Class 7 & 8; Type 3 92,434 7.50 42.0%
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-17

VISION 2.0

Heavy Truck 

Summary 

(Benefits Report 

Generator)

HTEB

(Heavy Truck

Energy Balance)

Truck 2.0

(Heavy Truck

Market 

Penetration)

Exhibit 5: Heavy Truck Benefits Analysis Models 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-18

Exhibit 6: Advanced Heavy Vehicle Characterization (New Vehicles) 

Characteristic 2010 2020 2030 2040 2050

1 Fuel Economy Class 7-8, 

Local, mpg Multiplier 1.26 1.46 1.46 1.46 1.46 

2 Fuel Economy Class 7-8, 

Intermediate, mpg Multiplier 1.35 1.64 1.64 1.64 1.64 

3 Fuel Economy Class 7-8, 

Long-Haul, mpg Multiplier 1.47 1.79 1.79 1.79 1.79 

4 Fuel Economy Class 3-6-

Hybrid, mpg Multiplier 1.70 1.70 1.70 1.70 1.70 

5 Fuel Economy Class 3-6-

Non-hybrid, mpg Multiplier 1.20 1.37 1.37 1.37 1.37 

6 Class 7-8, Iincremental Cost, 

$ $ 35,000 $ 15,000 $ 7,500 $ 7,500 $ 7,500 

7 Class 3-6 Hybrid, 

Incremental Cost, $ $ 19,000 $ 5,400 $ 2,700 $ 2,700 $ 2,700 

8 Class 3-6 Nonhybrid, 

Incremental Cost, $ $ 5,400 $ 1,700 $ 1,700 $ 1,700 $ 1,700 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-19

Exhibit 7: Example First Cost and Efficiency Schedule for Advanced Technologies 

Diesel Fuel (only)

Year

Baseline 

Vehicle Cost 

($)

Gross 1st 

Cost ($)

Efficiency 

Ratio

2000 150,000 0 1.000

2005 150,000 45,000 1.500

2010 150,000 35,000 1.500

2015 150,000 25,000 1.760

2020 150,000 15,000 1.760

2025 150,000 11,000 1.760

2030 150,000 7,500 1.760

2035 150,000 7,500 1.760

2040 150,000 7,000 1.760

2045 150,000 7,000 1.760

2050 150,000 7,000 1.760

Enhancement: Program 

Portfolio
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-20

Exhibit 8: ATA Survey Payback Preference Distribution 

Number of 

Years

Percent of 

Motor Carriers

1 16.4%

2 61.7%

3 15.5%

4 6.4%
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-21

Exhibit 9: Truck Payback Algorithm—Type 1 Trucks

Spreadsheet Location Description Comments

Column A Year Identifies year for which values, calcuations and results are representative.

Columns B - F Fuel Economy by Technology Values are developed based on baseline technology mpg assumptions and 

efficiency ratios for advanced technologies.

Column G Cost of Alternative Fuel in $/GGE Links to Fuel Prices Page

Columns H - I Calculates annual savings for 2 alternative 

technologies

For Advanced Diesel: 

(VMT(C10)x$/GGE/Baseline MPG - VMT x $/GGE/Adv. Diesel MPG)

Columns J - M Calculates Net Present Value of Savings for 

'Advanced Diesel' Column J: 1 Year, K: 2 years, L: 3 years; M: 4 years

Columns N - Q Calculates Net Present Value of Savings for 

'Alternative Fuel Technology' Column N: 1 Year, O: 2 years, P: 3 years; Q: 4 years

Columns R - U If-then Statement to determine 'Cost Effectiveness 

Factor' (CEF)

If NPV of savings is > Cost of Technology, cell value is (cost - 

NPVSavings)/Cost; Otherwise cell value is 0. Columns are for paybacks of 1, 2, 

3, and 4 years.

Column V Technology purchase cost 'Alternative Fuel 

Technology' Values are linked to Cost values on 'Inputs' page.

Column W - Z Repeats calcuations in Columns R through U for 

'Alternative Fuel Technology'

Column AA If-then Statement to determine 'Technology 

Adoption Factor' (TAF) for 'Advanced Diesel'

If 'Cost Effectiveness Factor' for Year 1 PB is 0, cell value = 100; Otherwise (100-

((exp(1995 CE Factor-Current Yr. Factor) - 1)/10 x 100)

Column AB Continuation of TAF Calculation for Year 1 Payback 

market If AA<0, cell value is 1; Otherwise the Value is the same as AA.

Columns AC + AD Repeat AA and AB for 2 year payback market

Columns AE + AF Repeat AA and AB for 3 year payback market

Columns AG + AH Repeat AA and AB for 4 year payback market

Columns AI - AP Repeat Columns AA through AH methodology for 

'Alt. Fuel Technology'

Column AQ If-then statement. Start of Market Penetration for 

'Advanced Diesel'

If AB = 100, then cell value is 0; Otherwise cell value is 

(1/(1+Abvalue/exp(-2 x Col. R CEF for 1 Year PB))

Column AR Same as AQ, but for 2 year PB market.

Column AS Same as AQ, but for 3 year PB market.

Column AT Same as AQ, but for 4 year PB market.

Column AU Final, Step 1; Weighted average market penetration 

for year 1 through year 4 markets weighting factors

Weighting factors are based on ATA survey results and are listed at the top of 

Columns AQ-AT.

Column AV

Final, Step 2: Reduces Market Penetration to 

account for market penetration of 'Atl. Fuel 

Technology' and stay below 100% share.

=+(AU+(1-BA)*AU)/2

Columns AW - AZ Same as columns AQ - AT for 'Alterntive fuel 

technology'.

Column BA

Final, Step 1; For 'Alt. Fuel Tech.', weighted average 

market penetration for year 1 through year 4 

markets weighting factors

Column BB

Final, Step 2: Reduces Market Penetration to 

account for market penetration of 'Atl. Fuel 

Technology' and stay below 100% share.

Columns BD - BN Macro Results Array-Centrally Refueled Advanced 

Diesels Central1 Macro results are printed in this part of spreadsheet

BO

Final Step 3: 'Advanced Diesel' (Centrally Refueled) 

Summation of %VMT that is centrally refueled for 

the VMT range (e.g. 0-19.9k)* % Market penetration 

for BD - BN array. 

Results are linked to Market Penetration Page

Columns BQ - CA Macro Results Array-Centrally Refueled Alternative 

Fuels

Macro results are printed in this part of spreadsheet. Alt Fuel technology only 

competes in Centrally Refueled Segment

CB

Final Step 3: 'Alt. Fuel' Summation of %VMT that is 

centrally refueled for the VMT range (e.g. 0-19.9k)* 

% Market penetration for BD - BN array. 

Results are linked to Market Penetration Page

Columns CD - CN Macro Results Array-Non Centrally Refueled 

Advanced Diesels Macro results are printed in this part of spreadsheet

CO

Final Step 3: 'Advanced Diesel' (Non-centrally 

refueled) Summation of %VMT that is centrally 

refueled for the VMT range (e.g. 0-19.9k)* % Market 

penetration for BD - BN array. 

Results are linked to Market Penetration Page
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix J – Vehicle Technologies Program – Page J-22

Exhibit 10: Advanced Heavy Vehicle Market Penetration and Fuel Economy Results for NEMS Modeling

Class 7 & 8 Class 3 - 6

Year

Combined 

Market 

Penetration,

% VMT

Base MPG

(VISION) in 

gasoline 

equivalent 

gallons

Fuel 

Economy for 

All New 

Technology 

Sales,

mpg

Fuel 

Economy 

Multiplier 

only for 

trucks with 

new 

techology 

which 

achieve the 

market 

penetration 

shown in 

Column 2

Combined 

Market 

Penetration, 

% VMT

Base MPG

(VISION) in 

gasoline 

equivalent 

gallons

Fuel 

Economy for 

All New 

Technology 

Sales,

mpg

Fuel 

Economy 

Multiplier 

only for 

trucks with 

new 

techology 

which 

achieve the 

market 

penetration 

shown in 

Column 6

12 3 4 5 6 7 8 9

2005 0% 5.70 10.00 1.75 0% 8.67 11.50 1.33

2006 0% 5.69 10.10 1.78 0% 8.64 11.56 1.34

2007 0% 5.67 10.21 1.80 0% 8.61 11.63 1.35

2008 0% 5.66 10.31 1.82 0% 8.57 11.69 1.36

2009 0% 5.64 10.42 1.85 1% 8.54 11.75 1.38

2010 1% 5.63 10.52 1.87 4% 8.51 11.82 1.39

2011 2% 5.69 10.59 1.86 12% 8.51 11.85 1.39

2012 3% 5.75 10.65 1.85 15% 8.51 11.88 1.40

2013 5% 5.81 10.72 1.84 24% 8.52 11.91 1.40

2014 10% 5.87 10.78 1.84 24% 8.52 11.94 1.40

2015 15% 5.94 10.85 1.83 33% 8.52 11.97 1.40

2016 19% 6.00 10.93 1.82 40% 8.52 12.01 1.41

2017 27% 6.06 11.00 1.82 43% 8.52 12.06 1.41

2018 28% 6.12 11.08 1.81 48% 8.53 12.10 1.42

2019 44% 6.18 11.15 1.81 48% 8.53 12.15 1.42

2020 53% 6.24 11.23 1.80 50% 8.53 12.19 1.43

2021 57% 6.24 11.24 1.80 52% 8.53 12.26 1.44

2022 58% 6.25 11.25 1.80 52% 8.53 12.33 1.45

2023 65% 6.25 11.26 1.80 52% 8.53 12.39 1.45

2024 68% 6.25 11.27 1.80 54% 8.53 12.46 1.46

2025 69% 6.26 11.28 1.80 56% 8.53 12.53 1.47

2026 70% 6.26 11.28 1.80 56% 8.53 12.65 1.48

2027 79% 6.26 11.29 1.80 56% 8.53 12.77 1.50

2028 80% 6.26 11.29 1.80 56% 8.53 12.89 1.51

2029 83% 6.27 11.30 1.80 67% 8.53 13.01 1.53

2030 84% 6.27 11.30 1.80 75% 8.53 13.13 1.54

2031 85% 6.27 11.30 1.80 78% 8.53 13.20 1.55

2032 86% 6.28 11.31 1.80 80% 8.53 13.26 1.55

2033 87% 6.28 11.31 1.80 83% 8.53 13.33 1.56

2034 88% 6.28 11.31 1.80 86% 8.53 13.40 1.57

2035 89% 6.29 11.32 1.80 89% 8.53 13.46 1.58

2036 89% 6.29 11.32 1.80 93% 8.53 13.53 1.59

2037 90% 6.29 11.32 1.80 93% 8.53 13.59 1.59

2038 90% 6.29 11.32 1.80 93% 8.53 13.66 1.60

2039 91% 6.30 11.33 1.80 93% 8.53 13.73 1.61

2040 91% 6.30 11.33 1.80 93% 8.53 13.79 1.62

2041 91% 6.30 11.33 1.80 93% 8.53 13.79 1.62

2042 92% 6.30 11.33 1.80 93% 8.53 13.79 1.62

2043 92% 6.31 11.34 1.80 93% 8.53 13.79 1.62

2044 92% 6.31 11.34 1.80 93% 8.53 13.79 1.62

2045 92% 6.31 11.34 1.80 93% 8.53 13.79 1.62

2046 92% 6.31 11.34 1.80 93% 8.53 13.79 1.62

2047 93% 6.31 11.34 1.80 93% 8.53 13.79 1.62

2048 93% 6.32 11.35 1.80 93% 8.53 13.79 1.62

2049 93% 6.32 11.35 1.80 93% 8.53 13.79 1.62

2050 93% 6.32 11.35 1.80 93% 8.53 13.79 1.62
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-1

Appendix K - GPRA06 Weatherization and Intergovernmental 

Program (WIP) Documentation 

1.0 State Energy Program Grants and State Energy Activities...............................................3

1.1 State Energy Program Grants.......................................................................................3

2.0 Weatherization Assistance Grants ....................................................................................7

2.1 Weatherization Assistance............................................................................................7

3.0 Gateway Deployment......................................................................................................11

3.1 Rebuild America..........................................................................................................11

3.2 Energy Efficiency Information and Outreach...............................................................13

3.3 Building Codes Training and Assistance.....................................................................15

3.4 Energy Star Program...................................................................................................21

3.5 Clean Cities.................................................................................................................36

3.6 Inventions and Innovation ...........................................................................................41

4.0 Intergovernmental Activities ............................................................................................45

4.1 Tribal Energy Activities................................................................................................45

4.2 International Renewable Energy Program ..................................................................45

Appendix A – I&I Market Factor in Technology Impact Projections ............................................46

Appendix B – I&I Energy Savings Results ..................................................................................50
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-2

Introduction 

Table 1 outlines the activities characterized for the GPRA06 Weatherization and 

Intergovernmental Program (WIP). Characterizations and inputs for these activities were 

provided to the Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable 

Energy (EERE) as inputs to EERE’s integrated modeling effort. 

Often such analysis requires the development and use of enabling or simplifying assumptions. In 

many cases, no citable sources exist for substantiating assumptions. Therefore, assumptions are 

developed through an iterative process with project managers, project contractors, and GPRA 

analysts. Often, we base these assumptions on project knowledge and experience, as there are 

varying degrees of corroborative studies available on which project information can be 

substantiated, depending on the maturity of the project. Enabling assumptions are sometimes 

relatively crude and should be revisited annually as new and better data are developed. 

Table 1. WIP Subprograms, Projects, and Activities 

Subprogram Project Activity 

State Energy Program Grants 

and State Energy Activities State Energy Program Grants 

Codes and Standards 

Energy Audits 

Rating and Labeling 

Workshops/Training 

Incentives 

Retrofits 

Loans and Grants 

Technical Assistance 

Weatherization Assistance 

Grants Weatherization Assistance Weatherization Assistance 

Rebuild America Rebuild America Deployment

Energy Efficiency Information 

Outreach Outreach Activities

Building Codes Training and 

Assistance 

Building Codes Training and 

Assistance Deployment 

Energy Star Program 

Clothes Washers 

Refrigerators 

Room Air Conditioners 

Compact Fluorescent Lamps 

Dishwashers 

Windows 

Home Performance 

Clean Cities Clean Cities Deployment 

Gateway Deployment 

Inventions and Innovation Inventions and Innovation 

Tribal Energy Activities Tribal Energy Activities 

International Renewable 

Energy Program 

International Renewable 

Intergovernmental Activities Energy Program 

Renewable Energy 

Production Incentive 

Renewable Energy 

Production Incentive 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-3

1.0 State Energy Program Grants and State Energy 

Activities 

1.1 State Energy Program Grants 

1.1.1 Target Market 

Project Description. The State Energy Program provides financial assistance to States, 

enabling State governments to target their own high priority energy needs and expand clean 

energy choices for their citizens and businesses. With these funds and the resources leveraged 

by them, the State and Territory Energy Offices develop and manage a variety of programs 

designed to increase energy efficiency, reduce energy use and costs, develop alternative energy 

and renewable energy sources, promote environmentally conscious economic development and 

reduce reliance on oil produced outside of the United States.

Market Description. The market includes all markets (including buildings, transportation, 

industry, and power technologies), except new construction and all categories of energy end use. 

Baseline Technology Improvements. For this analysis, the Pacific Northwest National 

Laboratory (PNNL) did not suggest any changes in technology improvements apart from the 

Energy Information Administration (EIA) baseline. 

1.1.2 Methodology and Calculations 

Inputs to Base Case. PNNL did not provide inputs to change the base case assumptions for the 

program markets. PNNL’s calculations were based on a baseline that was developed from the 

Energy Information Administration’s (EIA’s) Commercial Buildings Energy Consumption 

Survey (CBECS), Residential Energy Consumption Survey (RECS), and the Annual Energy 

Outlook (AEO). For more information about the methodology used by PNNL, see 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics 

Effort (2004)(3). 

Technical Characteristics. For the FY06 GPRA metrics, the State Energy Program (SEP) was 

characterized based on the budget request and leveraged funds. Based on the report, Estimating 

Energy and Cost Savings and Emissions Reductions for the State Energy Program Based on 

Enumeration Indicators Data (Schweitzer, et al. 2003)(1), eight activities (referred to in the report 

as program areas) supported by SEP were selected to represent the project. These activities—

Codes and Standards, Energy Audits, Rating and Labeling, Workshops/Training, Incentives, 

Retrofits, Loans and Grants, and Technical Assistance—comprised approximately 98% of the 

total estimated savings reported. Because the Schweitzer et al. study only received responses 

from 20 states (representing about half of the SEP funding), PNNL assumed that the responses 

were representative of the whole program, so all indicators produced were multiplied by two to 

approximate a national total. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-4

Because Schweitzer et al. did not differentiate between funds provided directly by SEP as part of 

the Formula Grants project and those that SEP administers on behalf of other EERE projects 

(e.g., Rebuild America, Training and Assistance for Codes) through the Special Projects grants, 

the methodology was modified in some cases to reduce the likelihood of double-counting the 

savings estimates. Therefore, outputs resulting from Special Project funding should be allocated 

to the originating project for purposes of this effort. As an example, outputs resulting from 

funding that originates in the Training and Assistance for Codes project, but is administered by 

SEP through Special Projects, should be allocated to Training and Assistance for Codes. 

Codes and Standards. Based on the estimated savings contained in Schweitzer et al., PNNL 

determined that the greatest area of potential overlap between Formula Grants and Special 

Projects would come about through the Codes and Standards activities. The Schweitzer report 

provided funding data for each of the activities, with total SEP (Formula Grant and Special 

Project) funding of about $4 million allocated by the responding states to Codes and Standards 

activities. Based on information provided by the Building Energy Codes Project on Special 

Project funding, approximately $1.6 million of that amount would have originated within 

Training and Assistance for Codes. PNNL determined that codes activities are therefore also 

being funded out of the SEP Formula Grants, and that some level of savings should be allocated 

to SEP for codes activities. 

For consistency, PNNL based the estimated savings of the Codes and Standards activities funded 

by the SEP on the savings estimates produced for the Training and Assistance for Codes project. 

The Schweitzer et al. section on Rating and Labeling cited a study (Feldman and Tannenbaum 

2000) indicating that approximately 10% of Energy Star purchases are made as a result of state 

encouragement. PNNL applied this attribution percentage to the estimate developed for Training 

and Assistance for Codes, to allocate 10% to SEP and 90% to Training and Assistance for Codes. 

Energy Audits. In Schweitzer et al., energy-audit calculations were based on three indicators: 

number of audits, square feet retrofit, and reported savings. For this effort, PNNL converted 

these three indicators to number of households and square footage of commercial floor space 

impacted. 

Schweitzer et al provided a savings per audit of 6.8 MMBtu per household and 0.0167 MMBtu 

per square foot of commercial floor space. Based on Tables 1.2.3 and 1.2.4 of the Buildings 

Energy Databook(2), approximately 83 MMBtu/HH/yr are used by residential space heating and 

space cooling, yielding a load reduction of 8% for residential space heating and cooling. Based 

on Tables 1.3.3 and 1.3.4 of the Buildings Energy Databook, approximately 126 kBtu/SF/yr are 

used by commercial space heating, space cooling, and lighting, yielding a load reduction of 13% 

for commercial space heating, space cooling, and lighting. 

To convert the indicators into an estimated number of households, PNNL assumed that each 

residential audit represented one household, divided the total residential square feet retrofit by 

the report’s assumed average square feet per household (1,600), and divided the estimated 

reported annual savings by the 6.8 MMBtu/HH figure. This yielded an estimate of 

approximately 5,500 households impacted by energy audits in any given year. Because the study 

only received responses from 20 states (representing about half of the SEP funding), that number 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-5

was then multiplied by two to provide a crude approximate of the national total in the absence of 

more data. This yielded a total annual estimate of 11,000 households impacted, or 0.014% of 

existing residential single-family buildings, in each year. 

To convert the indicators into an estimated commercial square footage, PNNL assumed that each 

commercial audit represented one building multiplied by the average building size assumed in 

the report (14,500 square feet), used the square footage reported, and divided the estimated 

reported annual savings by the 0.0167 MMBtu/SF figure. This yielded an estimate of 

approximately 0.197 billion square feet impacted by energy audits in any given year. As with 

the residential estimate, the commercial figure was also multiplied by two to approximate a 

crude national total, yielding a total annual estimate of 0.396 billion square feet impacted, or 

1.576% of existing commercial office, education, and health-care floor space, in each year. 

Rating and Labeling. Schweitzer et al provided a national per-device estimate for rating and 

labeling of approximately 895,400 MMBtu per year. While the report allocated these savings to 

states (based on population) to determine an estimate of savings for states reporting estimates, 

the device savings were allocated equally across all states, because no forecast is available for 

determining which states would fund rating and labeling projects in the future. The equivalent 

savings per state is about 17,900 MMBtu per device (the national estimate divided by 50). 

Of the responding states, two states reported that they funded rating and labeling activities for a 

total of 82 devices. To convert to a national representation, PNNL assumed that four states 

would fund rating and labeling activities in any given year, and that each state would cover 

approximately 40 devices, yielding a total of 160 devices saving energy. PNNL assumed that the 

savings would be effective for 15 years, and that they were attributable to electricity. 

Workshops/Training. An estimate of 13.1% HVAC and 8% lighting savings attributable to 

workshops and training was provided by Schweitzer et al. PNNL translated these inputs to a 

13% load reduction for space heating and space cooling, and an 8% load reduction in lighting 

within commercial buildings. According to the report, 19 of 20 states funded workshop and 

training activities, with a total of 5,600 trainees attending and a weighted average of four 

buildings influenced per trainee. To convert this to a national representation, PNNL assumed 

that 40 states would fund workshop/training activities in any given year, yielding approximately 

11,800 trainees impacting a total of 47,000 buildings. There are currently about 4.7 million 

existing commercial buildings in the United States.(3). PNNL assumed that the relationship 

between the number of buildings influenced as a percentage of the total stock would be 

equivalent to the square footage influenced as a percentage of the total commercial square 

footage; therefore, workshops and training were assumed to impact approximately 1% of the 

commercial building stock per year. 

Incentives. According to Schweitzer et al, approximately 0.145 MMBtu are saved per rebate 

dollar. During FY 2000, the ratio of incentive funding to rebate value was approximately 1:39, 

the percentage of SEP funds spent on incentives within the responding states was 0.31%, and the 

amount of leveraged funds received for incentives was $1.78 per dollar of funding. Based on the 

FY 2006 request, PNNL assumed that approximately $368,800 dollars would be spent on 

incentive activities, equating to about $14.3 million in rebates for an annual savings of about 2.0 

TBtu. PNNL assumed that the savings would be in effect for 15 years. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-6

Retrofits. Within Schweitzer et al, retrofit calculations were based on two indicators: number of 

retrofits and square feet retrofit. For this effort, PNNL converted these two indicators to number 

of households and square feet of commercial floor space impacted. 

Schweitzer et al. provided a savings per audit of 14.51 MMBtu per household and 18.8% per 

square foot of commercial floor space. Based on Tables 1.2.3 and 1.2.4 of the Buildings Energy 

Databook, approximately 83 MMBtu/HH/yr are used by residential space heating and space 

cooling, yielding a load reduction of 17% for residential space heating and cooling. PNNL 

applied the 18.8% savings to commercial space heating, space cooling, and lighting. 

To convert the indicators into an estimated number of households, PNNL assumed that each 

residential retrofit represented one household and divided the total residential square feet retrofit 

by the report’s assumed average square feet per household (1,600). This yielded an estimate of 

approximately 20,600 households impacted by retrofits in any given year. Because the study 

only received responses from 20 states (representing about half of the SEP funding), that number 

was multiplied by two to approximate a national total. This yielded a total annual estimate of 

41,000 households impacted, or 0.051% of existing residential single-family buildings, in each 

year. 

To convert the indicators into an estimated commercial square footage, PNNL assumed that each 

commercial retrofit represented one building multiplied by the average building size assumed in 

the report (14,500 square feet) and used the square footage reported. This yielded an estimate of 

approximately 0.028 billion square feet impacted by retrofits in any given year. As with the 

residential estimate, the commercial figure was also multiplied by two to approximate a national 

total, yielding a total annual estimate of 0.056 billion square feet impacted, or 0.222% of existing 

commercial office, education, and health-care floor space, in each year. 

Loans and Grants. According to Schweitzer et al, loans average 0.0164 million source Btu per 

dollar, and grants average 0.0178 million source Btu per dollar. For the GPRA effort, the lower, 

more conservative value was used for this analysis. During FY 2000, the percentage of SEP 

funds spent on incentives within the responding states was 21.7%; and the amount of leveraged 

funds received for incentives was $3.77 per dollar of funding. Based on the FY 2006 request, 

PNNL assumed that approximately $44.0 million dollars would be spent on loans and grants 

activities for an annual savings of about 0.001 TBtu. PNNL assumed that the savings would be 

in effect for 15 years. 

Technical Assistance. Within Schweitzer et al, technical assistance calculations were based on 

the number of recommendations. For this effort, PNNL converted these two indicators to 

number of households and square feet of commercial floor space impacted. 

The report provided a savings per recommendation of 9.0 MMBtu per household and 9.4% per 

square foot of commercial floor space. Based on Tables 1.2.3 and 1.2.4 of the Buildings Energy 

Databook, approximately 83 MMBtu/HH/yr are used by residential space heating and space 

cooling, yielding a load reduction of 11% for residential space heating and cooling. PNNL 

applied the 9.4% savings to commercial space heating, space cooling, and lighting. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-7

To convert the recommendation indicator into an estimated number of households, PNNL 

assumed that each residential recommendation represented one household. This yielded an 

estimate of approximately 18,000 households impacted by technical assistance in any given year. 

Because the study only received responses from 20 states (representing about half of the SEP 

funding), that number was multiplied by two to approximate a national total. This yielded a total 

annual estimate of 36,000 households impacted, or 0.045% of existing residential single-family 

buildings, in each year. 

To convert the recommendation indicator into an estimated commercial square footage, PNNL 

assumed that each commercial recommendation represented one building, and multiplied by the 

average building size assumed in the report (14,500 square feet). This yielded an estimate of 

approximately 0.009 billion square feet impacted by retrofits in any given year. As with the 

residential estimate, the commercial figure was also multiplied by two to approximate a national 

total, yielding a total annual estimate of 0.017 billion square feet impacted, or 0.069% of existing 

commercial office, education, and health-care floor space, in each year. 

1.1.3 Sources 

(1) Schweitzer, M., D.W. Jones, L.G. Berry, and B.E. Tonn. 2003. Estimating Energy and Cost 

Savings and Emissions Reductions for the State Energy Program Based on Enumeration Indicators 

Data. ORNL/CON-487, Oak Ridge National Laboratory, Oak Ridge, TN 

(2) 2003 Buildings Energy Databook. http://www.buildingsdatabook.eren.doe.gov. 

(3) Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics Effort. 

PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 

2.0 Weatherization Assistance Grants 

2.1 Weatherization Assistance 

2.1.1 Target Market 

Project Description. The Weatherization Assistance Project provides cost-effective energyefficiency services to low-income households who otherwise could not afford the investment but 

who would benefit significantly from the cost savings of energy-efficiency technologies. The 

project focuses on households that spend a disproportionate amount of their income for energy, 

giving priority to households with elderly members, persons with disabilities, and children. 

Weatherization Assistance provides technical assistance and formula grants to State and local 

weatherization agencies throughout the United States. A network of approximately 970 local 

agencies provide trained crews to perform weatherization services for eligible low-income 

households in single-family homes, multifamily dwellings, and mobile homes. Of the homes 

weatherized annually, 49 percent are occupied by an elderly person with special needs or a 

person with disabilities. All homes receive a comprehensive energy audit, which is a 

computerized assessment of a home’s energy use and an analysis of which energy conservation 

measures are best for the home and a combination of those energy-saving measures are installed. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-8

Market Description. The market includes households that are eligible for Federal assistance. 

Households are categorized as eligible for federal assistance if the household income is below 

the federal maximum standard of 150% of the poverty line or 60% of Statewide median income, 

whichever is higher. Individual States can also set the standard at a lower level than the federal 

maximum.a Target measures include air sealing; caulking and weather stripping; furnace and 

boiler tune-up, repair, and replacement; cooling system tune-up and repair; replacement of 

windows and doors; addition of storm windows and doors; insulation of building shells; and 

replacement of air conditioners, whole-house fans, evaporative coolers, screening, and window 

films.(2) Weatherization Plus expands this strategy to include water heating, refrigeration, 

lighting, and cooling.(1) 

Size of Market. About 34 million eligible low-income homes are included in the market. 

Baseline Technology Improvements. For this analysis, PNNL did not suggest any changes in 

technology improvements apart from the EIA baseline. 

2.1.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. PNNL employed the average household weatherization cost of $1,830(6); this estimate 

does not include training, technical assistance, and administrative costs. Incremental investment 

beyond this amount for Weatherization Plus homes, estimated at an average of $1,400 by the 

Weatherization project(6), was assumed by the Weatherization Assistance Program to be provided 

by leveraging funds from other organizations. Table 2 shows the estimated total costs by region 

for Plus homes. 

Table 2. Estimated Regional Costs for Weatherization Plus Homes 

Region 

Cost per Plus

Household 

South $2861 

Northeast $3674 

West $1814 

Midwest $3429 

2.1.3 Methodology and Calculations 

Inputs to Base Case. PNNL did not provide inputs to change the base case assumptions for the 

program markets. PNNL’s calculations were based on a baseline that was developed from the 

Energy Information Administration’s (EIA’s) Commercial Buildings Energy Consumption 

Survey (CBECS), Residential Energy Consumption Survey (RECS), and the Annual Energy 

Outlook (AEO). For more information about the methodology used by PNNL, see 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics 

Effort (2004)(7). 

 

a Eligibility requirements for Weatherization Assistance can be found at http://www.eere.energy.gov/weatherization/apply.html
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-9

Technical Characteristics. For the GPRA metrics, this project was characterized based on an 

estimated level of savings per household, cost to weatherize each household, budget request, 

leveraged funds, and an assumed life expectancy of 15 years for weatherization measures. The 

basic assumptions were derived from a spreadsheet provided by the Weatherization project in 

September 2001(6). 

Table 3 shows the savings per household used for each region for the FY 2006 metrics. 

Table 3. Savings Per Household for the Weatherization Assistance Project 

Region 

Regular 

Household 

Savings 

(MMBtu/yr) 

Plus 

Household 

Savings 

(MMBtu/yr) 

South 22.25 24.23 

Northeast 31.20 46.04 

West 19.04 20.31 

Midwest 31.20 49.21 

The figures in the table were calculated based on the 1997 ORNL meta-evaluation report,

(2) the 

ORNL Meeting the Challenge report,

(3) and special tabulations from the 1997 "Residential 

Energy Consumption Survey."(4) 

Of the units weatherized in FY 2006, nearly 50% were assumed by the Weatherization Project(3)

to have the higher savings rates associated with Weatherization Plus. In the Meeting The 

Challenge report,(3) these savings rates were calculated on a regional basis and multiplied by the 

expected number of Plus households in each region. 

To develop energy savings by building type, PNNL evaluated historical Weatherization project 

data in the 1997 ORNL report(2) concerning the types of households weatherized (see Table 4). 

Table 4. Percent of Weatherized Households by Type 

Household 

Type 

% of Weatherized 

Households 

Single Family 64.0% 

Mobile Home 20.0% 

Multi Family 16.0% 

To develop energy savings by fuel type, PNNL also used the historical primary fuel 

Weatherization project data in the 1997 ORNL report(2). Because the GPRA metrics are reported 

for electricity, natural gas, and fuel oil (but not for LPG and kerosene), other fuels were allocated 

within those types based on similarities of emissions. Table 5 shows the allocation approaches 

used. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-10

Table 5. Percent of Weatherized Households by Fuel Type 

Primary Heating Fuel 

% of Weatherized 

Households Categorized As 

Natural Gas 

Liquid Propane Gas 

50.6 

13.2 

Natural Gas 

Fuel Oil 

Kerosene 

Other (includes wood and coal) 

16.0 

3.2 

7.5 

Fuel Oil 

Electricity 9.5 Electricity 

The Department of Energy (DOE) budget and leveraged funding forecasts were used to 

determine the number of households weatherized in each category (regular or Plus) for each of 

the four regions (South, Northeast, West, and Midwest) based on the weatherization costs per 

household and assumptions regarding the use of leveraged funds. Table 6 shows the projection 

for regular and Plus households to be weatherized. PNNL assumed that the number of 

households weatherized for each category would be constant from 2011 through 2030. 

Table 6. Projected Regular and Plus Households to be Weatherized 

2006 2007 2008 2009 2010 2011 

Total Households 189,650 188,286 186,942 185,618 184,267 182,983

Regular South 19,059 18,907 18,758 18,610 18,460 18,318

Regular Northeast 22,694 22,524 22,355 22,189 22,020 21,860

Regular West 24,855 24,758 24,661 24,567 24,470 24,378

Regular Midwest 28,217 27,955 27,697 27,442 27,183 26,936

Plus South 19,059 18,907 18,758 18,610 18,460 18,318

Plus Northeast 22,694 22,524 22,355 22,189 22,020 21,860

Plus West 24,855 24,758 24,661 24,567 24,470 24,378

Plus Midwest 28,217 27,955 27,697 27,442 27,183 26,936

The number of households in each category was multiplied by the estimated savings level for 

each category. The estimated savings level for each household category was further divided by 

household type and then by fuel type. PNNL assumed that savings from each household 

weatherized would be in effect for 15 years; i.e., savings from households weatherized in 2006 

were included in the annual total savings estimates for the years 2006 through 2020. 

2.1.4 Sources 

(1) Weatherization Plus: Opportunities for the 21st Century, April 1999, Millennium Committee Strategy 

Report accessed at http://www.eere.energy.gov/weatherization/pdfs/mcsr.pdf 

(2) Berry, L.G., M.A. Brown, and L.F. Kinney. 1997. Progress Report of the National Weatherization 

Assistance Program, ORNL/CON-450, Oak Ridge National Laboratory, Oak Ridge, Tennessee. 

(3) Schweitzer, M. and J.F. Eisenberg. 2000. Meeting The Challenge: The Prospect of Achieving 30 

Percent Energy Savings Through the Weatherization Assistance Program. ORNL/CON 479, Draft 

Analysis, Oak Ridge National Laboratory, Oak Ridge, Tennessee. 

(4) Eisenberg, J.F., Oak Ridge National Laboratory. 2001. Special tabulations for the Weatherization 

Population derived from the 1997 Residential Energy Consumption Survey. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-11

(5) Brown, M.A., L.G. Bery, R.A. Balzer, and E. Faby. 1993. National Impacts of the Weatherization 

Assistance Program in Single-Family and Small Multifamily Dwellings. ORNL/CON-326, Oak 

Ridge National Laboratory, Oak Ridge, Tennessee. 

(6) Eisenberg, J.F., Oak Ridge National Laboratory. 2001. Projections for the Weatherization 

Assistance Program, provided to PNNL in file “Projections02d230.xls.” 

(7) Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics Effort. 

PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 

3.0 Gateway Deployment 

This effort seeks to accomplish effective delivery of the full menu of efficiency and renewable 

resources aligned with community and customer focus. The activities focus on the end-user 

needs, rather than individual EERE programs, and provide easier access to EERE’s array of 

technologies and resources to ensure they are part of the economic solutions for communities 

across the country. 

3.1 Rebuild America 

3.1.1 Target Market 

Project Description. Rebuild America accelerates energy-efficient improvements in existing 

buildings through community-level partnerships and focuses on K-12 schools, colleges, and 

universities, State and local governments, public and multi-family housing, and commercial 

buildings. Rebuild America connects people, resources, proven ideas, and innovative practices 

to solve problems. The project provides one-stop shopping for information and assistance on 

how to plan, finance, implement, and manage retrofit projects to improve buildings energy 

efficiency and helps communities find other resources on renewable energy applications, 

efficient new building designs, energy education, and other innovative energy conservation 

measures. 

Market Description. The general target market includes new and existing multifamily housing; 

public/assisted single-family residential units; and commercial buildings, particularly new and 

existing assembly, health-care, lodging, office, and education buildings. 

Market Size.(4) The primary market is the commercial-building sector, which includes nearly 68 

billion square feet of building space; however, the five commercial building types that this 

project targets make up a total of nearly 32 billion square feet. The public assistance(5) and 

multifamily housing that this project also targets make up an additional 27 billion square feet. 

Baseline Technology Improvements. For this analysis, PNNL did not suggest any changes in 

technology improvements apart from the EIA baseline. 

3.1.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. 

• Cost of Conventional Technology:(4) Average of $101/ ft2 for new commercial and 

multifamily; $0 for existing buildings. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-12

• Cost of WIP Technology:(7) $103.00/ ft2 for new commercial and multifamily; $3/ ft2

(2006 to 2009), increasing to $4/ ft2 (2010 to 2030) for existing buildings. 

• Incremental Cost: 2% above base for new buildings; $3/ft2 (2006 to 2009), increasing to 

$4/ ft2

 (2010 to 2030) for existing buildings. 

Key Consumer Preference/Values -- Nonenergy Benefits.(5) The following nonenergy 

characteristics were not considered. 

• Revitalized neighborhoods and business districts 

• Improving school facilities 

• Better low-income housing 

• Positive economic impact from keeping dollars locally and increasing property values. 

3.1.3 Methodology and Calculations 

Inputs to Base Case. PNNL did not provide inputs to change the base case assumptions for the 

program markets. 

Technical Characteristics. The project displaces current design/building practices with the 

target of reducing heating, cooling, water heating, and lighting energy use in retrofitted and new 

buildings by 18%/ft2 in 2006 (1) and 25%/ft2 by 2010 (3). 

Expected Market Uptake. PNNL assumed that this activity would not occur in the absence of 

DOE funding, therefore, no acceleration of market acceptance was modeled. The penetration 

rates shown in Table 7 are based on project goals of committing 2.24 billion square feet by 

2010. 

Table 7. Penetration Goals for Rebuild America (2,6) 

Penetration Rate % 

Building Type* 2006 2010 2020 2030 

Targeted 

Commercial 

Buildings & MultiFamily Existing 

0.7 1.9 2.3 2.3 

Targeted 

Commercial 

Buildings & MultiFamily New 

0.7 1.9 0.0 0.0 

Single-Family 

Existing 

0.0 0.02 0.04 0.04 

Single-Family 

New 

0.2 0.24 0.0 0.0 

 * Unless otherwise specified, the building vintage is both new (Post 2006) 

 and existing (2006 and prior construction). 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-13

3.1.4 Sources 

(1) Weatherization and Intergovernmental Activities Funding Profile by Subprogram. FY 2006 Corporate 

Review Budget, U.S.DOE, May 2004. 

(2) DRAFT Weatherization and Intergovernmental Program Multi-Year Program Plan, U.S.DOE, 

September 30, 2003. 

(3) Rebuild America 2002, Rebuild Annual Report, 2002, U.S.DOE, Washington D.C. 

(4) Commercial building and multifamily square footage numbers come from Energy Information 

Administration. 2001. Annual Energy Outlook 2002. DOE/EIA-0383 (2002). U.S. Department of 

Energy, Washington, D.C. 

(5) FY 2002 Budget Request – Data Bucket Report for Rebuild America Program (includes Energy Smart 

Schools and Competitively Selected Community Program) (internal WIP document). 

(6) Rebuild America Key Metric Totals from Oct 2003; Dec 2003; Mar 2004; April 2004; May 2004, 

Spreadsheet used to document key metrics. (internal WIP document). 

(7) RS Means Company, Inc. 2002. “RS MEANS Square Foot Costs,” 23rd Edition. Kingston, MA. 

3.2 Energy Efficiency Information and Outreach 

3.2.1 Target Market 

Project Description. Energy Efficiency Information and Outreach activities will result in 

packaged information on appropriate EERE technologies for key market segments, e.g., 

consumers, homeowners, and school officials. 

Market Description. The targeted market segments are primarily existing residential and 

commercial buildings in all climate zones, with the emphasis in FY 2006 on the residential 

sector, of which there are approximately 100 million existing household units.(1) 

Baseline Technology Improvements. For this analysis, PNNL did not suggest any changes in 

technology improvements apart from the EIA baseline. 

3.2.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. PNNL assumed that the cost of outreach activities (the average price per household) 

would be $1,000, based on discussions with the program manager. The outreach program 

provides information to the residential sector that causes consumers to undertake conservation 

measures that amount to $1,000 that they are assumed to not spend otherwise. 

3.2.3 Methodology and Calculations 

Inputs to Base Case. PNNL did not provide inputs to change the base case assumptions for the 

program markets. PNNL’s calculations were based on a baseline that was developed from the 

Energy Information Administration’s (EIA’s) Commercial Buildings Energy Consumption 

Survey (CBECS), Residential Energy Consumption Survey (RECS), and the Annual Energy 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-14

Outlook 2002 (AEO 2002). For more information about the methodology used by PNNL, see 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics 

Effort (2004)(3). 

Technical Characteristics. Outreach activities were based on funded projects such as the 

Home Energy Saver Web site, where consumers can compare their home’s energy use with that 

of an average home in their area and receive information about possible retrofits for their homes. 

PNNL assumed that consumers visiting such sites and acting on the information were already 

planning to perform some energy-efficient retrofits to their household, so PNNL assumed that 

the average incremental space conditioning and water-heating load reduction would be about 5% 

(e.g., the homeowner was initially interested in replacing the HVAC system, but when provided 

additional information about other cost-effective energy-saving measures, decided also to add 

more insulation to the home). Outreach activities were modeled as an incremental load 

reduction, above what the homeowner would have done in the absence of the information. 

Expected Market Uptake. The penetration rate for Information Outreach Activities was 

developed using a diffusion model based on Fisher and Pry (1971)(2). The equation for 

determining market diffusion over time is: 

( )) ln(81) 1 exp(

( )

m t t

t

N t

− ∆+ −

= κ

Where K = Maximum market share potential 

tm = year in which 50% of potential is reached 

∆t = time to grow from 10% to 90% of potential (years) 

For Outreach Activities, k=0.004%, tm=17, and ∆t=20. These values were developed through 

trial and error to achieve the expected annual household impact in 2006 and in “out” years, based 

on discussions with the program manager. Table 8 displays the resulting estimated number of 

homes impacted based on the penetration curve developed. 

Table 8. FY 2006 Market Penetration for Information Outreach Projects 

Year 

Annual No. Homes – 

Outreach Activities 

2006 11,383

2007 13,998

2008 17,184

2009 21,039

2010 25,684

2011 31,240

2012 37,828

2013 45,574

2014 54,573

2015 64,891

2016 76,550

2017 89,478

2018 103,546
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-15

Year 

Annual No. Homes – 

Outreach Activities 

2019 118,549

2020 134,175

2021 150,060

2022 165,814

2023 181,051

2024 195,428

2025 208,671

2026 220,620

2027 231,149

2028 240,205

2029 247,896

2030 254,283

3.2.4 Sources 

(1) Discussions with Kyle Andrews, Project Manager, August/September 2003. 

(2) Fisher, J.C., and R.H. Pry, (1971) “A Simple Substitution Model of Technological Change.” 

Technological Forecasting and Social Change, 3, 75-88. 

(3) Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics Effort. 

PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 

3.3 Building Codes Training and Assistance 

3.3.1 Target Market 

Project Description. Building Codes Training and Assistance will provide technical and 

financial assistance to States to update and implement their energy codes and train approximately 

2,000 code officials, designers, and builders to implement these codes. The program will work 

with three-five pilot States, builder organizations, and financial institutions to provide a package 

combining builder training, Energy Star promotion, and financing for new and existing homes.

Market Description. The market includes new residential low-rise buildings three stories or less 

in height, new commercial and multifamily high-rise buildings, and all additions and renovations 

to buildings requiring code permits. 

Size of Market. About 2 billion ft2 of new commercial floor space is added each year. The 

Federal sector represents nearly 2.3% overall of new commercial building construction. 

Additionally, each year about 1.4 million residential building permits are issued, of which 1 

million are for single-family dwellings. Although not all jurisdictions currently have energy 

efficiency building codes in place, about half of all new residential construction is conservatively 

estimated to come under building energy code requirements, based on information gathered from 

state and regional offices by the Building Codes Assistance Program (BCAP). Also, consumers 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-16

spend approximately 45 billion dollars a year on remodeling and renovating projects in private 

residences, about half of which could potentially be covered by an energy code.(8) One market 

not covered by codes is manufactured homes, which fall under Housing and Urban Development 

(HUD) jurisdiction and regulations. 

Baseline Technology Improvements. PNNL assumed that initial compliance with new codes 

was lower in the base case, i.e., without building energy codes than with the building energy 

codes project. Compliance in this context is measured as the percentage of potential savings 

from the existing code to the updated code. For FY06, the percentage of potential savings, in the 

first year of the single future code, was assumed by PNNL to be approximately 20% for 

envelope measures and 30% for lighting measures without the building energy codes project. 

Ten years after adoption, compliance rates are assumed to increase to 50% for envelope and 60% 

for lighting. The impact of these compliance percentages vary by state. Some states are 

assumed to update from the ASHRAE 90.1-1989 standard; others from the ASHRAE 90.1-1999 

standard. 

3.3.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. Incremental investment costs were developed assuming a five-year payback period on 

investment (i.e., an annual energy cost savings of $1 implies an initial investment of $5). These 

estimates were based on a series of benefit-cost studies that examined the energy savings and 

first-cost impacts of code improvements on seven building prototypesb

. 

Key Consumer Preferences/Values. The following nonenergy characteristics were not 

considered. 

• Improved environment and more comfortable buildings. 

• Fewer home-maintenance and repair activities 

• Reduced pollution due to the reduced burning of fossil fuels and electricity generation, 

which improves air quality and mitigates the negative impacts of global warming. 

3.3.3 Methodology and Calculations 

Inputs to Base Case. PNNL did not provide inputs to change the base case assumptions for the 

program markets. With respect to codes, it is indeterminate as to whether potential future code 

improvements are incorporated into the NEMS-GPRA06 base case. The NEMS-GPRA06 base 

case does include some improvements to the building shell efficiency; however, the basis for 

these improvements (e.g., general building practice improvements, changes in codes 

requirements, improvements in materials) is not specified by EIA. Codes that have been 

issued—but that have not gone into effect—may be included in the NEMS-GPRA06 base case, 

but would not be included in the GPRA forecast of savings for that activity, because it would no 

longer be funded. Only an estimate of potential future codes is included in the GPRA estimates. 

For more information about the methodology used by PNNL, see Methodological Framework 

for Analysis of Buildings-Related Programs: The GPRA Metrics Effort (2004)(7). 

 

b

 Further information on the series of reports can be found at the Building Energy Codes website: 

http://www.energycodes.gov/implement/tech_assist_reports.stm. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-17

Technical Characteristics: Commercial Buildings. Energy savings from this project result 

from some basic improvements in the overall energy efficiency of commercial buildings. The 

present funding for conducting research activities to establish the cost-effective levels of energy 

codes for new commercial and multifamily high-rise buildings is through the Commercial 

Buildings Integration subprogram within the Building Technologies (BT) Program. The WIP 

Building Codes Training and Assistance project funds the development of core materials (such 

as compliance tools and training materials) and provision of training and financial and technical 

assistance for states to update and implement their building energy codes. Benefits cannot be 

clearly allocated to either project; thus, the benefits estimated are a function of both training and 

deployment as well as development of the commercial building energy codes and standards, and 

the resultant benefits are then allocated between WIP and BT. 

Savings estimates for commercial codes are based on increased compliance and accelerated 

adoption from the ASHRAE 90.1-2004 code and the “next” code assumed to be published in 

2007. For FY06, PNNL assumed future codes (up through 2010) would achieve a potential 

reduction of 18% in electricity and a 10% reduction in natural gas, compared to 90.1-1999. The 

WIP-funded activities are assumed by PNNL to increase the initial compliance with these codes 

to approximately 70% for envelope requirements and 80% for lighting requirements. Adoption 

is accelerated in the range of five to 10 years, depending on the historical experience with 

building codes on each state. Barring future guidance from DOE, PNNL assumed that benefits 

for FY 2006 would be allocated based on the ratio of actual funding levels. 

The project's impact is primarily through two avenues: 1) developing and supporting code 

changes to improve the minimum energy efficiency requirements for commercial and 

multifamily high-rise buildings and 2) providing technical and financial assistance to states to 

update and implement their building energy codes. The latter includes developing tools that can 

ease the adoption of new codes and, through their use, can support improvements in compliance 

and enforcement of code provisions. Tools take the form of code-compliance software, 

computer-based training tools for building energy codes, and tools for implementing 

noncomputer-based codes. 

Improvements to building codes are primarily supported by research efforts to review existing 

codes (conducted by the Building Technologies Program) and specific targeted areas of building 

energy use and the adoption of code modifications that promote cost-effective reductions in these 

energy-use areas. Support for the research work has typically taken place in three areas: 

• Upgrading ASHRAE/IES Standard 90.1-1989, "Energy Efficient Design of New 

Buildings Except Low-Rise Residential Buildings"(1) 

• Upgrading the Federal commercial and multifamily high-rise building energy code, 10 

CFR 434, "Energy Code for New Federal Commercial and Multi-Family High Rise 

Residential Buildings"(2) 

• Upgrading the International Energy Conservation Code (IECC).(3)

The FY 2006 GPRA estimates are based on increased compliance with existing codes, 

accelerated adoption of the 1999 and 2002 editions of ASHRAE 90.1-1999(4) standard (to 

comply with Section 304 of the Energy Conservation and Production Act), and the future 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-18

development of more stringent building energy codes. The energy savings methodology was 

applied at a state level to better link changes in the codes (e.g., IECC 2003) with variations in 

climates by states and differences among states in their adoption and enforcement of building 

codes. The discussion below uses national averages of some of the key assumptions related to 

adoption and compliance to help summarize the methodology, but appropriate state averages

were used in the analysis. 

The principal differences among the ASHRAE 90.1-1989, 90.1-1999, and 90.1-2002(5) standards 

relate to requirements for better windows, reduced installed wattage for lighting, and more 

efficient heating and cooling equipment. The savings from improved equipment are not included 

in the project's savings estimates, because they are reflected in the Equipment Standards and 

Analysis decision unit in this appendix. Based on a series of simulations that include various 

U.S. locations, and that were developed specifically to evaluate the two ASHRAE standards 

(often referred to as the “determination” study[6]), the average reduction in site energy use was 

estimated to be about 3.5% or 2 MMBtu/sq ft. The GPRA estimates were partly based on states' 

accelerated adoption schedule of the ASHRAE 90.1-1999 and 90.1-2002 standards. Through the 

efforts of the Building Energy Codes project, 35 states were assumed by PNNL to have adopted 

the standard by the end of 2005. PNNL assumed that the project would accelerate the adoption 

of the standard by an average of four years nationwide. 

The ongoing activities of the ASHRAE 90.1 committee were assumed by PNNL to lead to more 

stringent commercial-building standards in the future. PNNL assumed that DOE would play a 

major role in developing the analytical and economic basis for such standards. For the GPRA 

process, these activities were subsumed in a single upgrade of the ASHRAE standard, assumed 

by PNNL to become available in the latter part of the current decade. PNNL assumed that the 

overall result of these upgrades is to reduce electricity consumption by 10% and natural gas 

consumption by 2% in new commercial buildings. Successful state adoption of this standard by 

2010 also depends on the project's continuing activities to assist states in the adoption (and 

compliance) process. Without these activities, PNNL assumed that the same standard would be 

adopted, on average, six years later. 

PNNL assumed that the project activities would improve compliance rates for codes currently 

adopted by states and localities, as well as future building codes. Compliance is increased 

through increased familiarity with the codes over time, simplifications to the code while 

maintaining stringency, and the availability and increased use of compliance tools by builders 

and enforcement officials. Compliance rates, with and without the project, were estimated for 

the existing code (a code based on ASHRAE 90.1-1999) and a future standard as discussed 

above. On a national average basis, compliance with existing codes was estimated at 60% in 

2000, increasing to 66% without the project and 79% by 2010 with the project. 

The compliance with several key provisions in ASHRAE 90.1-2001 (compared with 90.1-1999) 

was expected to be higher from the outset. On average, PNNL estimated the compliance to be 

65% in the year of the adoption. Ten years later, compliance rates were assumed by PNNL to 

increase to 67% without the project and 72% with the project. For buildings that do not comply 

with the standard, only half of the incremental energy savings were assumed by PNNL to be 

achieved by adopting the ASHRAE 90.1-2001 standard. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-19

PNNL assumed that the simplifications in the ASHRAE 90.1-1999 and 90.1-2001 standards will 

be extended to the new standard and will result in somewhat higher compliance when states first 

adopt them. PNNL assumed that initial compliance to be about 27% at the time of adoption, 

increasing to 31% without the project and 73% with the project after the first 10 years. The 

energy savings in buildings that do not comply with the new standards were assumed by PNNL 

to be 65% of that in buildings that comply fully with the code. 

Expected Market Uptake: Commercial Buildings. As part of work for an internal analysis of 

the historical impacts of the Building Codes project in August 2003, the assumptions regarding 

the acceleration effect of the program were modified (e.g., program activities leading to states 

adopting codes more rapidly than they would have otherwise). In general, the states were 

classified into groups that: 1) immediately adopted the ASHRAE 90.1-1989 code, 2) would have 

adopted within five years without the project, or 3) would have adopted within 10 years without 

the project. These time periods were then reduced by one year for each successive code after the 

1989 code. (Thus, for example, a five-year lag for 90.1-1989 is assumed to fall to three years for 

the forthcoming ASHRAE 90.1-2004 code). The overall impact of this change was to increase 

the average lag between the publication of a new standard and when it is adopted—without the 

Building Codes project. This modified set of assumptions increases the overall estimate of the 

future energy savings impact from the program. 

Technical Characteristics: Residential Buildings. The FY 2006 GPRA estimates are based 

on increased compliance with existing codes, accelerated adoption of the 2001 and 2003 editions 

of the International Energy Conservation Code (IECC) code (to comply with Section 304 of the 

Energy Conservation and Production Act), and the future development of more stringent 

building codes. The energy-savings methodology was applied at a state level to better link 

changes in the national codes (e.g., IECC 2003) with variations in climate by states and 

differences among states in their adoption and enforcement of building codes. This discussion 

uses national averages of some of the key assumptions related to adoption and compliance to 

help summarize the methodology. 

The principal difference between the 1995 Model Energy Code and the IECC 2001 involves the 

solar heat gain requirements for windows and increased thermal resistance requirements for 

ducts in unconditioned spaces. Based on a series of simulations for various U.S. locations, the 

percentage reduction in cooling load was estimated to be about 15%. This requirement increases 

the heating load by a small amount, about 2% nationally. (The requirement itself is restricted to 

the southern tier of states). The GPRA estimates were partly based on states' accelerated 

schedule of adoption of the IECC 2001 and 2003 codes. Through the efforts of the Building 

Energy Codes project, 31 states were assumed by PNNL to have adopted the standard by the end 

of 2005. PNNL assumed that the project would accelerate the adoption of the standard by an 

average of four years nationwide. 

PNNL assumed that the IECC's ongoing activities would lead to more stringent residential 

standards in the future. PNNL assumed that DOE would play a major role in developing the 

analytical and economic basis for such standards. For the GPRA process, these activities were 

subsumed in a single upgrade of the IECC standard, assumed to become available in the latter 

part of the current decade. Based on discussions with BT staff, PNNL assumed that the results 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-20

of these upgrades were to reduce heating and cooling loads in new residential structures by 10%. 

Without these activities, PNNL assumed that the same standard would be adopted, on average, 

six years later. 

Expected Market Uptake: Residential Buildings. PNNL also assumed the project's activities 

would improve compliance rates for codes currently adopted by states and localities as well as 

future building codes. Compliance increases through increased familiarity with the codes, 

simplifications to the code while maintaining stringency, and the availability and increased use 

of compliance tools by builders and enforcement officials. Compliance rates, with and without 

the project, were estimated for various standards as discussed above. As a national average, 

compliance with existing codes was estimated at 45% in 2003, increasing to 49% without the 

project and 72% by 2010 with the project. 

The compliance with several key provisions in the IECC 2000 and 2003 (compared with the 

1995 Model Energy Code) was expected to be higher from the outset. On average, the 

compliance was estimated to be 68% in the year of the adoption. By 2010, PNNL assumed that 

compliance rates would increase to 69% without the project and 74% with the project. For 

homes that do not comply with the standard, only half of the incremental energy savings were 

assumed by PNNL to be achieved by adopting IECC 2001 or 2003. 

PNNL assumed that when states first adopt the new standard, assumed to become available in the 

2006-2007 time frame, the standard's greater stringency will result in somewhat lower 

compliance. PNNL assumed initial compliance to be about 30% at the time of adoption, 

increasing to 31% without the project and 73% with the project after the first 10 years. For 

IECC 2001 and 2003, the energy savings in units that do not comply were assumed to be 50% of 

that in units that comply fully with the code. 

3.3.4 Sources 

(1) ASHRAE/IES Standard 90.1-1989, "Energy Efficient Design of New Buildings Except Low-Rise 

Residential Buildings," American Society of Heating, Refrigeration, and Air-Conditioning Engineers 

and Illuminating Engineering Society. 

(2) 10 CFR 434, "Energy Code for New Federal Commercial and Multi-Family High Rise Residential 

Buildings," Code of Federal Regulations, as amended. 

(3) International Energy Conservation Code. 2003. International Code Council, Falls Church, Virginia. 

(4) ASHRAE/IES Standard 90.1-1999, "Energy Standard for Buildings Except Low-Rise Residential 

Buildings," American Society of Heating, Refrigeration, and Air-Conditioning Engineers. 

(5) ASHRAE/IES Standard 90.1-2002, "Energy Standard for Buildings Except Low-Rise Residential 

Buildings," American Society of Heating, Refrigeration, and Air-Conditioning Engineers.

(6) U.S. Department of Energy. March 2002. “Commercial Buildings Determinations, Explanation of 

the Analysis and Spreadsheet (90_1savingsanalysis.xls).” 

http://www.energycodes.gov/implement/determinations_com.stm

(7) Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics Effort. 

PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 

(8) U.S. Census Bureau (Census). 2000. “1997 Economic Census Construction Geographic Area Series.” 

U.S. Department of Commerce, March 2000. Washington D.C. Located at the following website: 

http://www.census.gov/epcd/www/97EC23.HTM
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-21

3.4 Energy Star Program 

3.4.1 General Target Market 

Project Description. Energy Star was introduced by the Environmental Protection Agency in 

1992 as a voluntary labeling program designed to identify and promote energy efficient products, 

with the goal of reducing carbon dioxide emissions. Through its partnership with more than 

7,000 private and public sector organizations, Energy Star delivers the technical information and 

tools that organizations and consumers need to choose energy-efficient solutions and best 

management practices.

• Market Description. The market is determined by the project equipment. For FY 2006, 

the following residential equipment is characterized:Clothes washers 

• Refrigerators 

• Room air conditioners 

• Dishwashers 

• Compact Fluorescent Lamps (CFLs) 

• Windows 

• Home Performance 

Baseline Technology Improvements. For this analysis, PNNL did not suggest any changes in 

technology improvements. 

3.4.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Key Consumer Preferences/Values and Manufacturing Factors. The following nonenergy 

characteristics were not considered. 

• Increased comfort for residential homeowners 

• Decreased time spent changing incandescent lamps 

• Water and water-bill savings from higher efficiency dishwashers and clothes washers 

• Increased amenities with clothes washers, also decreased time required for dryer cycle 

• Higher profits for manufacturers. 

3.4.3 General Methodology 

Market transformation projects, such as Energy Star, attempt to accelerate market penetration of 

existing high-efficiency technologies. The information provided by these programs is designed 

to influence the consumer’s awareness of future energy cost savings as compared to the initial 

cost of the technology. From a modeling standpoint, these efforts are assumed to be represented 

by a reduction in the consumer’s implicit discount rate or hurdle rate. The implicit discount rate 

for a technology is assumed to capture the perceived risk in the purchase of new products. For 

Energy Star technologies, most of the costs are incurred at the time the technology is purchased, 

while most of the energy-saving benefits occur in the future. If the implicit discount rate for a 

given technology is particularly high, the value a consumer places on these future energy-saving 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-22

benefits will be low relative to the weight the consumer places on present costs – reflecting the 

consumer’s uncertainty about future benefits. Therefore, to facilitate project modeling, one goal 

of the Energy Star project is to reduce implicit discount rates by providing additional information 

about the potential benefits to the consumer. 

Within NEMS-PNNLc

, the two modeling parameters determining the implicit discount rate are 

labeled Beta1 and Beta2(1). Beta1 is used as multiplicative factor with the initial cost of the 

appliance, and Beta2 is used to multiply the annual energy cost. The sum of the two products 

(i.e., Beta1 * initial cost + Beta2 * operating cost) is used in the logit specification to yield 

market shares for each technology. As a rough approximation, the ratio of Beta1/Beta2 can be 

interpreted as the consumer discount rate for a specific technology. In the residential NEMSPNNL module, the Beta1 and Beta2 coefficients vary among technologies, as do the resulting 

discount rates. For example, the implied discount rate for refrigerators is 16%, while the 

discount rate is estimated to be more than 80% for electric water heaters. 

The modifications to the NEMS input file (RTEKTY)—required to estimate energy savings in 

NEMS-PNNL for each technology in an Energy Star project—are described in the following 

sections. The assumed reduction in the discount rate (from Energy Star support) is modeled by 

reducing the Beta1 parameter. The baseline assumptions made by the EIA, the changes in the 

Beta1 coefficients, and the resulting changes in the market shares for the most energy-efficient 

products are documented by technology. 

General Expected Market Uptake. PNNL modeled clothes washers, refrigerators, electric 

water heaters, gas water heaters, room air conditioners, and dishwashers using input from EIA's 

Annual Energy Outlook 2001,(2) based on a project goal of Energy Star appliances achieving 

20% of the market share by 2010. 

3.4.4 Clothes Washers 

3.4.4.1 Target Market 

Market Description. This project targets new clothes-washer sales. 

3.4.4.2 Methodology and Calculations 

Inputs to Base Case and Technical Characteristics. Modeling the energy savings of clothes 

washers is complex, because energy can be saved by reducing the consumption of the motor, hot 

water use, or dryer energy use. The most efficient new technology is the horizontal-axis design, 

which achieves the bulk of its energy savings by reducing hot water use. 

 

c

 Any modification or alteration to the official NEMS model must be called out as such; for PNNL’s effort, the modified version 

used is referred to as NEMS-PNNL 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-23

The residential NEMS input file (RTEKTY) includes a column of factors that relate to hot water. 

The (unitless) factors can be used to adjust the hot water load associated with clothes washers 

and dishwashers. In preliminary model runs, the values associated with clothes washers 

appeared to be too low compared with the information supplied by Lawrence Berkeley National 

Laboratory (LBNL) in support of an efficiency standard for clothes washers. Therefore, these 

factors were adjusted from 0.67 to 2.00 for vertical-axis machines. The coefficient for the 

horizontal-axis machine was increased from 0.24 to 0.40. The value for the vertical axis 

machine was estimated by making runs of the model with and without any hot water and 

observing the resulting energy consumption. The LBNL analysis (3) suggests that 80% to 90% of 

the energy consumption of clothes washers is attributable to water heating. Table 9 shows the 

original and revised NEMS-PNNL inputs for clothes washers. 

Table 9. Original NEMS and Revised NEMS-PNNL Inputs for Energy Star Clothes Washers 

Original NEMS Inputs 

Technology 

Start 

Yr 

End 

Yr 

Water 

Coeff. 

Energy 

Factor 

Installed 

Cost ($) Type 

1 1997 2020 0.67 2.71 90 V-Axis 

2 1997 2004 0.67 3.88 645 V-Axis 

3 2005 2020 0.67 3.88 590 V-Axis 

4 1997 2020 0.24 4.45 800 H-Axis 

5 2005 2020 0.24 5.27 800 H-Axis 

6 2015 2020 0.24 5.44 800 H-Axis 

NEMS-PNNL Inputs 

1 1997 2020 2.0 2.71 490 V-Axis 

2 1997 2004 2.0 3.88 645 V-Axis 

3 2005 2020 2.0 3.88 590 V-Axis 

4 1997 2020 0.4 4.45 800 H-Axis 

5 2005 2020 0.4 5.27 800 H-Axis 

6 2015 2020 0.4 5.44 800 H-Axis 

Expected Market Uptake. With the support of the Energy Star project, the Beta1 parameter, 

which impacts the resulting market share of each clothes-washer technology, was modified from 

-0.03811 to -0.0101, based on this product's project goals. Table 10 shows the market share 

results of the NEMS-PNNL model runs for clothes washers. 

Table 10. Energy Star Clothes Washer Market Shares by Technology Estimated by NEMS-PNNL 

2005 2010 

Census 

Division Baseline 

Energy 

Star Baseline 

Energy 

Star 

1 0.0000 0.0927 0.0000 0.0923 

2 0.0000 0.0904 0.0000 0.0900 

3 0.0000 0.0814 0.0000 0.0804 

4 0.0000 0.0794 0.0000 0.0794 

5 0.0000 0.0813 0.0000 0.0812 
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Appendix K – Weatherization and Intergovernmental Program – Page K-24

6 0.0000 0.0799 0.0000 0.0797 

7 0.0000 0.0801 0.0000 0.0791 

8 0.0000 0.0831 0.0000 0.0833 

9 0.0000 0.0826 0.0000 0.0830 

Note: Results shown are for new housing units; replacement 

shares are generally within 0.5 % of values shown here. 

3.4.5 Refrigerators 

3.4.5.1 Target Market 

Market Description. This project targets new refrigerator sales. 

3.4.5.2 Methodology and Calculations 

Inputs to Base Case and Technical Characteristics. EIA uses four separate models to 

represent the range of energy efficiencies in the refrigerator market. The first three models are 

conventional top-mount freezer models with a total capacity of 18 cubic feet. The fourth is a 

through-the-door model (for water and ice) and does not compete with the first three models. 

The market share of the through-the-door model is a constant 27% over the forecast horizon. A 

review of Arthur D. Little’s(4) (ADL 1998) efficiency and cost forecasts, as well as a recent 

paper from Oak Ridge National Laboratory(5) (ORNL, Vineyard and Sand 1998), suggests some 

changes to EIA’s assumptions used in the Annual Energy Outlook 2001(2) projection are 

warranted. 

As part of the EIA forecast, the 2001 standard (Model 1) was assumed to yield no increase in 

cost. Table 11 shows the EIA efficiency and cost assumptions, which appear to contradict some 

of the ADL findings. 

Table 11. Refrigerator Efficiency and Costs: Annual Energy Outlook 2001 

Modified NEMSPNNL Inputs 

Model 

Initial 

Year Ending 

Year 

Annual 

Consumption 

(kWh) 

Installed 

Cost 

($1998) 

Retail 

Cost 

($1998) Installed 

Cost 

($1998) 

Retail 

Cost 

($1998) 

1 1997 2001 690 530.0 480.0 530.0 480.0

1 2002 2020 478 530.0 480.0 580.0 480.0

2 1997 2001 660 550.0 500.0 550.0 500.0

2 2002 2020 460 550.0 500.0 600.0 550.0

3 1993 2001 518 850.0 800.0 850.0 800.0

3 2002 2020 460 550.0 500.0 600.0 550.0

3 2005 2020 400 700.0 650.0 700.0 650.0

4 1993 2001 843 1313.8 1313.8 1313.8 1313.8

4 2002 2020 577 1313.8 1313.8 1313.8 1313.8
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Appendix K – Weatherization and Intergovernmental Program – Page K-25

 

The ADL performance/cost characteristics information suggests that a 460-kWh/yr unit would 

have an installed cost of $580 to $700. To be conservative, an installation cost of $600 could be 

assumed. Because a 478-kWh/yr unit is nearly as efficient as the 460-kWh/yr unit, one would 

expect it would be only negligibly less expensive. Using this logic, the cost of the 478-kWh/yr 

unit is assumed to be ~$580. These revised assumptions are included in the shaded columns in 

the table below. 

The ADL report(4) suggests that a 460-kWh/yr model represents a typical model after 2002. A 

high-efficiency model is specified to consume 400 kWh per year. However, this specification is 

for a 20-cubic-foot model rather than 18 cubic feet. ADL suggests a cost differential of $100 to 

$120 between these two models. 

Vineyard and Sand (1998)(5) add some support to this revision in the cost structure. They start 

with a “1996 model baseline unit” of 20 cubic feet, which uses 613 kWh/year. The baseline is 

already 16% more efficient than the 1993 standard (2.01 kWh/day) resulting from the National 

Appliance Energy Conservation Act.(6) From this baseline, they focus on two high-efficiency 

designs. The most aggressive design would reduce energy by 273 kWh/yr at a retail cost 

increase of nearly $270. A more cost-effective unit would consume 1.16 kWh/day (423 kWh/yr) 

at a projected cost increase of $106. 

Based on this information, the resulting estimated cost increase of $100 between the 460- and 

400-kWh/day units appears to be more reasonable (see Table B-8.4 of the ADL report) than 

EIA’s incremental cost of $150. The ORNL baseline unit is less efficient than the 2001 standard 

and achieves a 30% energy reduction with a little more than a $100 cost increase. This suggests 

that the 13% efficiency improvement (460 kWh/day to 400 kWh/day) between models 2 and 3 

could be achieved for $100 or less. 

Expected Market Uptake. The Annual Energy Outlook 2001(2) baseline parameters that 

determined the market share for high-efficiency refrigerators are described as follows: 

19%

0.1207

0.0229

2

1 ≈ = −

− = implicit discount rate

Beta

Beta

PNNL assumed the Energy Star project would increase the market share of the 400-kWh/yr 

refrigerator. With original EIA inputs for refrigerators, sales of the Energy Star unit are only 

about 3,000 per year. The baseline unit's efficiency was changed to 510 kWh/year. The most 

efficient model's cost and efficiency were changed as following discussions with EIA’s John 

Cymbalsky about the new inputs to the NEMS model from Navigant. After baseline change, 

sales of the Energy Star unit were over 30,000 in the reference case with no change in parameters 

reflecting the discount rate. 

In previous years, the beta2 parameter in rtekty was changed to -0.0055 to model this program. 

Given the choices described above, this value yielded the fraction of total sales that met Energy 

Star requirements in the range of 19%. For FY06, PNNL changed the parameter to -0040. This 

yields an Energy Star fraction in the range of 24 to 25%, which is somewhat lower than the 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-26

estimated share of 28% in 2003. However, the Energy Star unit provided by Navigant is more 

efficient than the Energy Star criteria (more than 20% lower than the baseline, rather than the 

required 15%). As a result, the 25% share more accurately reflects the actual energy saving from 

the project. With the support of the Energy Star project, the parameters impacting market share 

were assumed by PNNL to change in the following manner, based on project goals: 

3%

0.1207

0.0040

2

1 ≈ = −

− = −

−

−

E Star

E Star

E Star

implicit discount rate

Beta

Beta

3.4.6 Room Air Conditioners 

3.4.6.1 Target Market 

Market Description. This project targets sales of new room air conditioners. 

3.4.6.2 Methodology and Calculations 

Inputs to Base Case and Technical Characteristics. For 2005, EIA assumes that efficiencies 

of room air conditioners will range from a low of 2.83 COP (seasonal energy efficiency ratio) to 

a high of 3.52 COP. In the Annual Energy Outlook 2001(2) input file for the residential NEMS 

module, two models were at the low end of this range (COP = 2.83, COP = 2.93), while two 

models were at the high end of the range (COP = 3.22, COP = 3.43). To achieve a more realistic 

set of choices, a model with an intermediate efficiency of 3.11 was added and the unit at the 2.93 

(COP) level was dropped. The increase in cost to go from a COP of 2.83 to 2.93 was assumed to 

be $30. Table 12 shows both the original NEMS input data and the revised NEMS-PNNL data. 

Table 12. NEMS-PNNL Input Parameters for Room Air Conditioners 

Technology 

Start 

Year End Year 

Seasonal 

COP SEER* 

Installed 

Cost 

Annual Energy Outlook 2001 and GPRA Baseline 

1 1997 2000 2.55 8.70 $450 

2 2001 2020 2.83 9.66 $450 

3 1997 2004 2.93 10.00 $500 

4 2005 2020 2.93 10.00 $490 

5 1997 2020 3.43 11.71 $760 

6 2005 2020 3.43 11.71 $760 

7 2015 2020 3.22 10.99 $600 

Revised NEMS-PNNL Inputs 

1 1997 2000 2.55 8.70 $450 

2 2001 2020 2.83 9.66 $450 

3 1997 2004 3.11 10.61 $530 

4 2005 2020 3.11 10.61 $520 

5 1997 2020 3.43 11.71 $760 

6 2005 2020 3.52 12.01 $760 

7 2015 2020 3.22 10.99 $600 

*SEER − seasonal energy efficiency ratio. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-27

The high-efficiency units with a COP >3.4 were assumed by PNNL to fall under the Energy Star 

project. In the base case, the combined market share for the units with COPs of 3.43 and 3.52 

were less than 1%. The split in market share between the lowest and intermediate efficiency unit 

(COP = 2.83 and 3.11, respectively) was generally about 75%/25% in favor of the lowestefficiency model. 

Expected Market Uptake. The Annual Energy Outlook 2001(2) baseline parameters that 

determined the market share for high-efficiency room air conditioners are described as follows: 

100%

0.0120

0.0170

2

1 ≈ > −

− = implicit discount rate

Beta

Beta

With the support of the Energy Star project, the parameters impacting market share were 

assumed to change in the following manner, based on project goals: 

58%

0.0120

0.0070

2

1 ≈ = −

− = −

−

−

E Star

E Star

E Star

implicit discount rate

Beta

Beta

Table 13 shows the specific NEMS-PNNL market share results for the high-efficiency model. 

Table 13. NEMS-PNNL Results for Energy Star Room Air Conditioners 

(national market shares for new, single-family homes) 

2005 2010 

Census 

Division Baseline 

Energy 

Star Baseline 

Energy 

Star 

1 0.0083 0.1301 0.0083 0.1299 

2 0.0085 0.1323 0.0085 0.1321 

3 0.0085 0.1319 0.0084 0.1314 

4 0.0084 0.1314 0.0084 0.1312 

5 0.0091 0.1396 0.0091 0.1395 

6 0.0091 0.1402 0.0091 0.1398 

7 0.0101 0.1522 0.0099 0.1501 

8 0.0085 0.1327 0.0085 0.1327 

9 0.0084 0.1314 0.0084 0.1317 

3.4.7 Dishwashers 

3.4.7.1 Target Market 

Market Description. This project targets sales of new dishwashers. 

3.4.7.2 Methodology and Calculations 

Inputs to Base Case and Technical Characteristics. The NEMS baseline (Annual Energy 

Outlook 2001)

(2) data input for 2005 shows three dishwashers, with energy factors 0.46, 0.59, 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-28

and 0.71. Table 14 shows the associated costs of these units. Given the cost structure and logit 

choice parameters, the model suggests that consumers select slightly more than 6% of 

dishwashers with the 0.59 energy factor and virtually none of the very high=efficiency units. 

Table 14. Key NEMS Data Inputs for Dishwashers 

Census 

Division 

Initial 

Yr 

Ending 

Yr 

Water 

Co-Efficiency 

Energy 

Factor 

Installed 

Cost ($) 

1 1997 2020 0.80 0.46 350 

2 1997 2004 0.80 0.59 500 

3 2005 2020 0.80 0.59 450 

4 1997 2004 0.78 0.71 700 

5 2005 2014 0.78 0.71 600 

6 2015 2020 0.78 0.71 500 

7 2015 2020 0.80 0.60 400 

Expected Market Uptake. The Annual Energy Outlook 2001(2) baseline parameters that 

determined the market share for high-efficiency dishwashers are described as follows: 

100%

0.02413

0.02738

2

1 ≈ > −

− = implicit discount rate

Beta

Beta

With the support of the Energy Star project, the parameters impacting market share were 

assumed to change in the following manner, based on project goals: 

55%

0.02413

0.01338

2

1 ≈ = −

− = −

−

−

E Star

E Star

E Star

implicit discount rate

Beta

Beta

Table 15 shows the specific NEMS-PNNL market share results for the two high-efficiency 

models. 

Table 15. NEMS-PNNL Results for Energy Star Project Dishwashers 

(estimated market shares for high-efficiency dishwashers) 

2005 2010 

Census Baseline Energy Star Baseline Energy Star

Division EF=.59 EF=.71 EF=.59 EF=.71 EF=.59 EF=.71 EF=.59 EF=.71 

1 0.0683 0.0012 0.2219 0.0322 0.0682 0.0012 0.2217 0.0321 

2 0.0678 0.0012 0.2207 0.0318 0.0677 0.0012 0.2204 0.0317 

3 0.0659 0.0011 0.2157 0.0305 0.0656 0.0011 0.2151 0.0304 

4 0.0654 0.0011 0.2146 0.0302 0.0654 0.0011 0.2145 0.0304 

5 0.0658 0.0011 0.2156 0.0305 0.0654 0.0011 0.2145 0.0304 

6 0.0655 0.0011 0.2148 0.0303 0.0658 0.0011 0.2156 0.0305 

7 0.0656 0.0011 0.2150 0.0303 0.0653 0.0011 0.2144 0.0302 

8 0.0662 0.0011 0.2166 0.0308 0.0663 0.0012 0.2168 0.0308 

9 0.0661 0.0011 0.2164 0.0307 0.0663 0.0012 0.2169 0.0308 

EF − energy factor. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-29

3.4.8 Energy Star CFLs 

3.4.8.1 Target Market 

Market Description. The target market for this technology is residential non-can and non-RLamp Edison socket lights, which would not otherwise switch to Compact Fluorescent Lamps 

(CFLs). Analysis of Energy Star CFLs was based on the program’s stated goal of converting 

20% of the residential incandescent installed based to high-quality, high-efficiency, Energy Star 

CFLs. 

3.4.8.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. PNNL assumed that the cost of the conventional incandescent technology is $0.75. The 

cost of the Energy Star CFL is assumed by PNNL to decrease over the study period from 

approximately $5 per CFL in 2004 to $3 per CFL in 2030. 

Baseline Market Acceptance. In 1998, PNNL conducted a study examining the historical 

market penetration for 10 energy-efficient products related to the buildings sector. The results of 

this study are documented in the PNNL report, Methodological Framework for Analysis of 

GPRA Metrics: Application to FY04 Projects in BT and WIP (2003, PNNL-14231). The 

resulting data were used to develop a set of generic diffusion curves. These curves were used to 

generate market penetration estimates for projects that do not have a forecast of annual sales 

targets. For the Energy Star CFL activity, the lighting diffusion curve was used. 

3.4.8.3 Methodology and Calculations 

Technical Characteristics. Energy Star-qualified CFLs have the efficacies(7) shown in Table 16.

Table 16. Compact Fluorescent Lamp Efficacies 

Lamp Power (Watts) & Configuration Minimum Efficacy: Lumens/watt (Based 

upon initial lumen data) 

Bare Lamp: 

Lamp power < 15 

Lamp power >= 15 

45 

60 

Covered lamp (no reflector): 

Lamp power <15 

15 >= lamp power < 19 

19 >= lamp power < 25 

Lamp power <= 25 

40 

48 

50 

55 

Reflector Lamp: 

Lamp power < 20 

Lamp power >= 20 

33 

40 

Modeling is based on the bare lamp, because reflector lamps represent only about 6% of the 

shipments of large incandescent lamps, and covered lamps are only a small fraction of the total 

CFL market. CFLs of 15W and greater can replace incandescent lamps at 75W and above, and 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-30

were assumed by PNNL to have an efficacy of 60 lumens/watt. Less than 15W CFLs can 

replace incandescent of less than 75W and were assumed to have an efficacy of 45 lumens/watt. 

About 58% of incandescent lamps in homes have wattages less than 75W and 42% of 

incandescent lamps in homes have wattages 75W and greater(8). The resultant weighted average 

lumens/watt for Energy Star CFLs is 51.3 lumens/watt. 

Expected Market Uptake. PNNL assumed that by 2020, in the residential sector, Energy Star 

CFLs would capture 6.16% of non-can and non-R-lamp incandescent sales (i.e., sales for noncan and non-R-lamp Edison sockets that would not have otherwise converted to CFLs). The 

6.16% is based on a market penetration goal of capturing 20% of the installed base. Energy Star 

CFLs were assumed by PNNL to penetrate both the high-use part of the market, where 76.4% of 

the residential lighting energy is consumed (e.g., rooms such as kitchens and living rooms), and 

the low-use part of the market. PNNL assumed that Energy Star CFLs would be put in high-use 

applications 70% of the time. The sockets in high-use areas (28.4% of the total sockets) will 

consume roughly the same number of the lamps as the low-use sockets. A sales fraction of 

6.16% will yield a long-term installed base of 20% of all sockets with 70% of the Energy Star 

CFLs in high-use sockets and 30% in low-use sockets; the 20% of sockets that convert to Energy 

Star CFLs would be A-line incandescents without the Energy Star program. Penetration curves 

were developed based on market diffusion curves developed by PNNL and documented by 

PNNL(10) (see Figure 1)

d

. 

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

2000 2005 2010 2015 2020 2025 2030 2035

Market Share (Percent of Sales)

Figure 1. Actual Energy Star CFLs Market Penetration Curve – Percent of Sales to Non-Can, NonR-Lamp, Incandescents 

 

d

 The Energy Star CFLs are assumed to compete only against incandescents (not all Edison sockets). Hence, given that 4.0% of 

the Edison sockets are already CFLs by 2005 and that it is expected that by 2020 this will increase to 11% without Energy Star,

the penetration against incandescents only is somewhat higher than the penetration against all Edison sockets. This curve 

compensates for the declining incandescent share of the Edison socket market such that the 20% (of all non-can and non-R-lamp 

Edison sockets that would not have otherwise converted to CFLs) installed base can be achieved. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-31

3.4.9 Windows 

3.4.9.1 Target Market 

Market Introduction. The technology is commercially available. PNNL assumed that this 

project would accelerate the penetration in the marketplace by 10 years. 

3.4.9.2 Methodology and Calculations 

Performance Parameters: Energy Star Windows have maximum U-value and SHGC for four 

different climate zones. These climate zones do not directly correspond to the traditional climate 

zones used in CBECS or RECS; they also do not correspond to the census divisions used in 

NEMS. These new climate zones are based on the eight climate zones that were developed as 

part of the IECC 2003 code change cycle or Residential IECC Code Change (RICC). In general 

the Energy Star zones map from the RICC zones as follows in Table 17. 

Table 17. Mapping of RICC Zones to Energy Star Zones 

RICC Zone Energy Star Zone 

1 Southern 

2 Southern 

3 South/Central 

4 North/Central 

5 Northern 

6 Northern 

7 Northern 

8 Northern 

To construct the four Energy Star zones there was a fair amount of smoothing required due to 

geo-political boundaries, existing codes, and commercial regions. For example, a strict 

adherence of the eight RICC zones to four Energy Star zones shown above would have portions 

of California in all four Energy Star zones and would result in discontinuities in the zones across 

the country. The final result is that California is wholly within the South/Central zone and all 

four Energy Star zones are continuous across the country. Performance parameters are listed in 

Table 18. 

Table 18. Performance Parameter Maximums for Low-e Windows 

Region Shading Coefficient U-Value 

Northern 0.60 0.35 Btu/ft2·°F 

North Central 0.55 0.40 Btu/ft2·°F 

South Central 0.40 0.40 Btu/ft2·°F 

Southern 0.40 0.65 Btu/ft2·°F 

Performance Target: Performance characteristics vary by building type and climate zone. The 

estimated savings per building were determined by simulating residential and commercial 

buildings in all climate zones (see Table 19). 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-32

Table 19. Performance Targets for Low-e Windows 

Region Sector End Use New Building 

Savings 

Existing Building 

Savings 

Units 

Heating 8.17 8.30 MMBtu/HH Residential Cooling 0.06 0.19 MMBtu/HH 

Heating 6.24 5.73 MMBtu/ksf Northern 

Commercial Cooling -0.45 -0.58 MMBtu/ksf 

Heating 2.88 2.94 MMBtu/HH Residential Cooling 1.72 1.79 MMBtu/HH 

Heating 2.98 2.77 MMBtu/ksf North Central 

Commercial Cooling 0.74 0.68 MMBtu/ksf 

Heating 0.09 0.00 MMBtu/HH Residential Cooling 10.50 10.39 MMBtu/HH 

Heating 0.75 0.66 MMBtu/ksf South Central 

Commercial Cooling 5.91 5.62 MMBtu/ksf 

Heating -1.48 -1.77 MMBtu/HH Residential Cooling 9.18 8.77 MMBtu/HH 

Heating -0.14 -0.14 MMBtu/ksf Southern 

Commercial Cooling 5.21 4.98 MMBtu/ksf 

Heating 3.82 3.82 MMBtu/HH Residential Cooling 4.43 4.42 MMBtu/HH 

Heating 3.36 3.08 MMBtu/ksf 

Weighted National 

Average Commercial Cooling 2.25 2.07 MMBtu/ksf 

Installed Cost:—Incremental Cost Over Conventional Double-Pane Windows 

• 2005: $1.00/ft2

• 2015: $0.50/ft2

Expected Market Uptake. The purpose of the program is to increase the penetration of low-e 

glass from 40% in the residential market and 10% in the commercial market to 100% in the 

residential market by 2020 and in the commercial market by 2025. Both programs, Low-e 

Market Acceptance and Energy Star Windows, form the joint means to achieving the low-e 

penetration goal – the savings are to be split equally. Penetration curves were developed based 

on market diffusion curves developed and documented by PNNL(10). The “Accelerated” 

penetration curve represents the percent of superwindow sales with the DOE project; the “Net” 

penetration curve represents the percent of sales attributable to DOE, as PNNL assumed that the 

DOE project would accelerate market acceptance by 10 years. The penetration rates are shown 

in Figures 2 and 3. For Low-e Market Acceptance/ Energy Star Windows, PNNL assumed that 

these projects would accelerate the acceptance of this technology in the marketplace by 10 years. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-33

0%

20%

40%

60%

80%

100%

120%

2000 2005 2010 2015 2020 2025 2030 2035

FY06 Accelerated

Penetration from DOE R&D

FY06 Net Penetration with

DOE R&D

Figure 2. FY06 Low-e Windows – Commercial Buildings Percent of Sales 

0%

20%

40%

60%

80%

100%

120%

2000 2005 2010 2015 2020 2025 2030 2035

FY06 Accelerated

Penetration from DOE R&D

FY06 Net Penetration with

DOE R&D

Figure 3. FY06 Low-e Windows – Residential Buildings Percent of Sales 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-34

3.4.10 Energy Star Home Performance 

3.4.10.1 Target Market 

Home Performance with Energy Star is a joint effort with the Environmental Protection Agency 

to develop and support pilot projects that promote whole-house retrofits for existing homes in 

order to save energy. Home Performance’s three main components include whole-house 

inspections, marketing efforts, and quality assurance. 

Price. PNNL assumed that the cost of Home Performance pilot projects (the average price per 

household) would be $5,000—currently, Pilot Project homeowners are spending between $4,000 

and $6,000 in retrofits through the Pilot Project program.(9) 

3.4.10.2 Methodology and Calculations 

Inputs to Base Case. PNNL did not provide inputs to change the base case assumptions for the 

program markets. PNNL’s calculations were based on a baseline that was developed from the 

Energy Information Administration’s (EIA’s) Commercial Buildings Energy Consumption 

Survey (CBECS), Residential Energy Consumption Survey (RECS), and the Annual Energy 

Outlook (AEO). For more information about the methodology used by PNNL, see 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics 

Effort (2004)(10). 

Technical Characteristics. PNNL assumed that Home Performance with Energy Star activities 

would primarily impact the space conditioning load of existing buildings, as most of the retrofit 

measures involve the building shell (e.g., insulation, windows); however, water heating and 

lighting loads are also reduced. Because these retrofits are occurring due to the programmatic 

builder certification, marketing efforts and financing options, PNNL assumed the activity would 

reap all benefits associated with the retrofits, roughly a 20% load reduction. 

Expected Market Uptake. The penetration rates for Home Performance with Energy Star was 

developed using a diffusion model based on Fisher and Pry (1971)(11). The equation for 

determining market diffusion over time is: 

( )) ln(81) 1 exp(

( )

m t t

t

N t

− ∆+ −

= κ

Where K = Maximum market share potential 

tm = year in which 50% of potential is reached 

∆t = time to grow from 10% to 90% of potential (years) 

For Home Performance with Energy Star, k=0.0002%, tm=17, and ∆t=20. These values were 

developed through trial and error to achieve the expected annual household impact in 2006 and 

in “out” years, based on discussions with the program manager. Table 20 displays the resulting 

estimated number of homes impacted based on the penetration curve developed. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-35

Table 20. FY 2006 Market Penetration for Energy Star Home Performance 

Year Annual No. Homes 

2006 569

2007 700

2008 859

2009 1,052

2010 1,284

2011 1,562

2012 1,891

2013 2,279

2014 2,729

2015 3,245

2016 3,828

2017 4,474

2018 5,177

2019 5,927

2020 6,709

2021 7,503

2022 8,291

2023 9,053

2024 9,771

2025 10,434

2026 11,031

2027 11,557

2028 12,010

2029 12,395

2030 12,714

PNNL assumed that the Energy Star Home Performance activity would not occur without DOE 

funding, because it allocates money for builder training and certification, program marketing 

support, and program-specific financing options; therefore, no acceleration of market acceptance 

was modeled. 

3.4.11 Sources 

(1) Model Documentation Report: Residential Sector Demand Module of the National Energy Modeling 

System. 2003. Energy Information Administration, Washington, D.C. DOE/EIA-M067(2003) 

http://tonto.eia.doe.gov/FTPROOT/modeldoc/m067(2003).pdf

(2) Annual Energy Outlook 2001. 2001. Energy Information Administration, Washington, D.C. 

(3) “Clothes Washer Technical Support Document” source: 

www.eere.energy.gov/buildings/appliance_standards/residential/clwash_0900_r.html. 

(4) Arthur D. Little, Inc. (ADL). 1998. “EIA Technology Forecast Updates – Residential and 

Commercial Building Technologies, Reference Case.” 

(5) Vineyard, E.A. and J.R. Sand. 1998. “Fridge of the Future: Designing a One Kilowatt-Hour/Day 

Domestic Refrigerator Freezer.” In 1998 ACEEE Summer Study Proceedings. 

(6) National Appliance Energy Conservation Act of 1987, Public Law 100-12. 

(7) http://www.energystar.gov/products/cfls/EnergyStarCFLSpecification_Final_8.9.01.pdf p.5. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-36

(8) http://eetd.lbl.gov/btp/papers/43782.pdf Creating Markets For New Products To Replace 

Incandescent Lamps: The International Experience. Presented at the 1998 ACEEE Summer Study 

on Energy Efficiency in Buildings, August 23-28, 1998, Pacific Grove, CA, and published in the 

Proceedings. Figure 2. 

(9) Based on results documented in article, “Energy Star Tackles Existing Homes,” Energy Design 

Update, Volume 23, No. 8, August 2003 as well as discussions with Kyle Andrews, Project 

Manager, June 2004 and Lana Nirk, Project Manager, May, 2004. 

(10) Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics Effort. 

PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 

(11) Fisher, J.C., and R.H. Pry, (1971) “A Simple Substitution Model of Technological Change.” 

Technological Forecasting and Social Change, 3, 75-88. 

3.5 Clean Cities 

3.5.1 Target Market 

Project Description. Clean Cities supports public-private partnerships that advance vehicle 

technologies and practices to reduce petroleum use in the transportation sector. Clean Cities 

works with local businesses and governments to guide them through the process, including goal 

setting, coalition building, and securing commitments.

Market Segment. Clean Cities seeks to reduce petroleum use in the transportation sector by: 

1. Displacing current conventional gasoline and diesel vehicles and fuels with alternativefuel vehicles, fuels, and infrastructure, as well as advanced vehicle technologies; 

2. Increasing the use of fuel blends, such as ethanol and biodiesel; 

3. Enhancing the acceptance of fuel-efficient vehicles and fuel economy practices; 

4. Increasing market penetration of hybrid vehicles; and 

5. Promoting the use of idle reduction technologies in heavy-duty vehicles. 

Thus, Clean Cities targets on-road vehicles that use petroleum fuels, as well as off-road vehicles 

in select niches. 

Market Size. The total conventional light-vehicle stock is 211 millione. Of the total stock, 15.7 

million are fleet vehicles, including Commercial 2B trucksf

. The Clean Cities Program works 

largely with fleet managers and buyers, rather than targeting all private consumers, because of 

the challenges of reaching out to the general public. The market for the Clean Cities Program 

also includes heavy-duty vehicles, such as trucks and buses. 

Base Case Growth: For purposes of an estimate of the number of AFVs attributable to Clean 

Cities, exogenous to NEMS-GPRA06 modeling, this analysis assumed the activity in the 

alternative-fuel vehicle market to be very low - in the absence of the Clean Cities AFVs would 

grow at 1% per year. This assumption was developed in GPRA2005 based on expert judgment in 

consultation with Clean Cities DOE staff, Clean Cities lab analysts, and the Office of Planning, 

 

e

 AEO 2004, Supplemental Table 46 

f

 AEO 2004, Supplemental Table 52; includes only conventional vehicles; table is ambiguous as to whether commercial 2B 

trucks are included in the light truck section or should be added, as they are to calculate this number 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-37

Budget, and Analysis (PBA), and was not changed in GPRA2006. In the presence of the Clean 

Cities Program, Clean Cities DOE staff and Clean Cities lab analysts assumed that the number of 

AFVs would grow at 5% per year in Clean Cities and used in GPRA06 the same GPRA2005 

assumption of 2.9% per year in non-Clean Cities, developed by Clean Cities DOE staff, Clean 

Cities lab analysts, and the Office of Planning, Budget, and Analysis (PBA). The resulting 

difference in AFV numbers was used as an input to NEMS-GPRA06. The NEMS-GPRA06 base 

case growth was not changed. 

Consistency with EERE Baseline. The EERE baseline was used for Clean Cities NEMSGPRA06 modeling. The exogenous calculations to determine a number of vehicles attributable 

to the Clean Cities program use a different baseline. For purposes of calculating the number of 

AFVs attributable to Clean Cities, an AFV growth rate of 1% was assumed to occur in the 

absence of a Clean Cities program. This assumption was developed in GPRA2005 based on 

expert judgment in consultation with Clean Cities DOE staff, Clean Cities lab analysts, and the 

Office of Planning, Budget, and Analysis (PBA), and was not changed in GPRA2006. This 

assumption may be compared with the regulatory requirement of EPAct, and with historical 

growth rates in areas that lack Clean Cities programs. DOE has estimated that the EPAct 

regulatory requirement results in purchases of approximately 30,000 AFVs per yearg

, or about 

0.2% of light-duty vehicle sales. However, AEO2004 shows a 10.7% growth in light-duty AFVs 

stock between 2002 and 2025. This high level of AFVs in AEO2003 is driven in part by EPAct 

regulatory targets that may be higher than expected market performance. Revising this baseline 

was considered for GPRA FY06, but was not performed because a proposed alternative baseline 

was not identified. According to the EIA data used for GPRA FY06, non-Clean Cities showed a 

2.7% growth rate in numbers of AFVs between 1992 and 2001, so a baseline less than that value 

is a logical assumption. 

Baseline Technology Improvements. No changes in technology improvements were attributed 

to Clean Cities for this analysis. Transportation technology changes were assumed by the PBA 

and the GPRA modeling team to occur outside of Clean Cities, as part of modeling of the 

Transportation Technologies R&D, so integrated modeling runs take into account both those 

technology changes and the deployment activities of Clean Cities. 

Baseline Market Acceptance. The literature on consumer choice of vehicle technologies has 

not been reviewed for this project. DOE has developed a variety of detailed models of consumer 

choice of vehicles. These models include factors such as cost, performance, fuel availability, and 

other attributes of vehicles that are generally disadvantageous to AFVs. They are not useful for 

assessing market penetration of technologies whose advantages are primarily environmental, 

macroeconomic, and national security, such as AFVs. Data such as consumer discount rates 

have been reported in that literature with regard to vehicle technologies and fuel savings, 

although there is less specific research on AFVs. For purposes of the Clean Cities baseline 

market acceptance, no changes were recommended to the NEMS-GPRA06 baseline to reflect 

these market-acceptance issues. For purposes of calculating the number of AFV sales 

 

g

 U.S. Department of Energy (2001). “EPAct Fleet Information and Regulations Fact Sheet,” DOE/GO-102001-1306, April 

2001. Accessed at www.ott.doe.gov/ott/pdf/what_is_epact.pdf. DOE estimates that the EPAct regulatory requirement cause 

purchases of 20,000-25,000 AFVs per year, according to FR Vol 68, No 42, March 4, 2003, page 10326, “Office of Energy 

Efficiency and Renewable Energy; Alternative Fuel Transportation Program; Private and Local Government Fleet Determination: 

Notice of Proposed Rulemaking” Accessed online at www.afdc.doe.gov/pdfs/fr_notice_nopr.pdf. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-38

attributable to Clean Cities, Clean Cities DOE staff, Clean Cities lab analysts, and the Office of 

Planning, Budget, and Analysis (PBA) assumed that the number of AFVs would increase by 1% 

per year in the absence of the program, as noted above. 

3.5.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. AFVs are assumed in the baseline to cost more than equivalent conventional vehicles 

throughout the forecast period. Using AEO 2004 estimates, typical price increments for lightduty vehicles are typically $1,000 or less for E85 vehicles, $6,000 - $7,000 for dedicated CNG 

vehicles, and $6,000-$6,000 for dedicated LPG vehicles. Break-even points vary depending on 

vehicle, fuel, duty cycle, subsidies, and discount rate. Break-even timing is highly sensitive to 

fuel-input price. Incremental costs of heavy-duty vehicle technologies were not identified in 

AEO 2004 data tables. For buses, one source suggests typical incremental vehicle costs of about 

$20,000-$40,000h

. Per-mile relative vehicle costs have also been estimated in some studies, and 

these are highly sensitive to fuel cost and other assumptions. 

Key Consumer Preferences/Values. Vehicle-purchase decisions depend on a large number of 

preferences and values. Many of these are represented in the Transportation Sector Model of 

NEMSi

. Some AFV features that may be especially important include: 

1. Emissions performance. 

2. Type or origin of fuel. 

3. Vehicle performance and reliability. 

4. Ease and safety of fueling. 

5. Ease of maintenance. 

6. Regulatory requirements on purchaser. 

Of these, consumer preference for emissions performance and fuel origin do not appear to be 

included in the Transportation Sector Model as consumer values, but are included as regulatory 

effects on vehicle sales. 

Clean Cities lab analysts and the OnLocation modeling team assumed that consumer preference 

for fuel type would change as a result of Clean Cities activities in NEMS-GPRA06. In the 

NEMS reference forecast, only a small fraction of E85 capable vehicles use E85 as the fuel 

(most use gasoline) in the flex-fuel vehicles. One Clean Cities activity is to promote the use of 

E85 use in the capable vehicles, and NEMS-GPRA06 reflects these activities by assuming that 

more vehicles use E85, and each vehicle using E85 uses more of it. 

Manufacturing factors. Manufacturer decisions strongly influence availability of AFVs, and 

depend on factors such as: 

1. Anticipated market size, influenced by extent of fueling infrastructure. 

2. Expected vehicle price. 

3. Estimated manufacturing costs. 

 

h

 General Accounting Office (1999). Mass Transit: Use of Alternative Fuels in Transit Buses. GAO/RCED-00-18. December 

1999. 

i

 U.S. DOE (2003). “The Transportation Sector Model of the National Energy Modeling System: Model Documentation 

Report.” DOE/EIA-M070(2003). Accessed online at www.eia.doe.gov. 
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Appendix K – Weatherization and Intergovernmental Program – Page K-39

4. Maintenance and warranty issues for manufacturer. 

5. Availability of competing investment opportunities. 

6. Regulatory requirements on manufacturer. 

Some manufacturing factors are included in NEMS, though not at this level of detail. None of 

these factors were explicitly considered in developing the estimates of vehicle sales attributable 

to Clean Cities. In addition to vehicle price, NEMS uses maintenance costs, fuel costs, luggage 

space, fuel economy, range, acceleration, etc. as vehicle attributes in which consumers are 

interested. 

Policy factors. Policy factors are a significant consideration that influences AFV markets, 

including: 

1. EPAct (1992) AFV purchase requirements. 

2. EPA vehicle-emissions requirements. 

3. Ethanol tax incentives. 

4. AFV purchase incentives/rebates. 

3.5.3 Methodology and Calculations 

Inputs to Base Case. Clean Cities did not provide inputs to change the base case assumptions 

for the program markets. 

Technical Characteristics. The technical characteristics of alternative fuels and vehicles were 

not changed. 

Expected Market Uptake. In the AEO base case, AFV market penetration is calculated based 

on the Transportation Sector Model. In the Clean Cities case for FY06 GPRA, Clean Cities lab 

analysts and the OnLocation modeling team assumed that additional AFVs attributable to Clean 

Cities would replace conventional vehicles, and this revised vehicle population was modeled. 

This method was unchanged compared to FY05, when it was developed in consultation with 

Clean Cities DOE staff and PBA. The calculation of additional AFVs attributed to Clean Cities 

is based on historical experience with the effect of Clean Cities on AFV markets, and also on a 

survey of Clean Cities coordinators to establish their expectations about future program effects.j

The historical record shows that Clean Cities has been able to achieve growth in the population 

of AFVs in any given urban area of roughly 5%-18%, while areas not under the Clean Cities 

program achieved 2.9% growth. In a survey, Clean Cities coordinators estimated anticipated 

market growth at about 8%. 

For GPRA FY06, the analytic team assumed, as described above, that a Clean Cities program 

would result in a 5% growth rate in AFVs in Clean Cities (starting in 2004) and a 2.9% growth 

rate (the historic growth rate for 1992-2001) in AFVs in non-Clean Cities (starting in 2003). 

Five percent for Clean Cities was selected because it is within the historical range and aligns 

with the reduced program funding assumptions for GPRA06. The non-Clean Cities growth rate 

extends the historical rate. NREL assumed that if the program had never existed, AFVs would 

 

j

 Personal Communication, Elyse Steiner, formerly of NREL, January 29, 2004, describing survey by QSS. 
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have experienced a 1% growth rate starting from 1995. In effect, this assumes that the Clean 

Cities program began to have an influence on non-Clean Cities growth starting in 1996. This is 

based on the idea that some of the historical growth in non-Clean Cities may be attributed to 

Clean Cities, because of the program’s impact on the broader market. The difference in number 

of vehicles between these two cases was used to calculate Clean Cities attributable vehicle stock 

and annual sales numbers, which provided the input to the NEMS-GPRA06 modeling run.k

 

This difference was used starting in 2004, and a 15-year vehicle life was assumed by the On 

Location modeling team. 

The resulting vehicle stocks and sales attributable to Clean Cities are shown in Table 21. 

Because the computation of annual sales from cumulative stock takes into account vehicle 

retirements, the sales values do not smoothly increase. The sales were allocated to vehicle types 

based on program information, and adjusted in the NEMS transportation model to reflect the fuel 

types in the model (they do not directly correspond in all cases). The largest shares of vehicles 

are CNG (44%), ethanol (34%), and LPG (17%). Electric and methanol vehicle shares are small. 

Half of the vehicles were assumed by the OnLocation modeling team to be flex vehicles and the 

other half to be single fuel dedicated vehicles. Table 22 shows the flow of information, from 

program vehicle count to share used in NEMS. 

Table 21. Vehicle Stocks and Sales Attributable to Clean Cities 

Year 

Cumulative Number 

of Vehicles 

Annual Number 

of Vehicles (Sales) 

2004 236,617 

2005 253,653 37,813 

2006 271,438 39,705 

2007 290,004 41,678 

2008 309,383 43,734 

2009 329,611 45,879 

2010 350,723 48,115 

2011 372,758 34,493 

2012 395,756 54,789 

2013 419,757 54,945 

2014 444,805 44,794 

2015 470,945 14,081 

2016 498,224 100,612 

2017 526,692 49,967 

2018 556,400 72,889 

2019 587,402 73,224 

2020 602,718 75,684 

2021 635,730 78,144 

2022 670,181 81,768 

2023 706,133 85,547 

2024 743,652 89,491 

2025 782,806 93,605 

 

k

 Please see spreadsheet, CleanCityInput. This spreadsheet was obtained from John Holte, OnLocation, January 13, 2005. 
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Table 22. Information Flow from Program Vehicle Count to Share Used in NEMS-GPRA06 

Original Mix of Vehicles Allocated to Allocated to NEMS 

(in 2002) Broader Categories Vehicle Types 

Vehicle Type Vehicles Vehicle Type Vehicles Vehicle Type Vehicles Share

Methanol 592 Methanol 592 Methanol Flex 0 0.0000

 Methanol 592 0.0034

Ethanol 37,890 Ethanol 58,868 Ethanol Flex 29,434 0.1708

Biodiesel 20,978 Ethanol 29,434 0.1708

CNG 73,927 NG 76,178 CNG 38,089 0.2211

LNG 2,251 CNG BiFuel 38,089 0.2211

Propane 29,258 LPG 29,258 LPG 14,629 0.0849

 LPG BiFuel 14,629 0.0849

Electric 4,132 Electric 7,387 Electric 7,387 0.0429

N Elec 3,255 

 Diesel Hybrid 0 0.0000

 FC Methanol 0 0.0000

 FC Hydrogen 0 0.0000

 FC Gasoline 0 0.0000

 Gasoline Hybrid 0 0.0000

 Adv Diesel 0 0.0000

 Gasoline 0 0.0000

 Total 172,283 Total 172,283 1.0000

In addition to the assumed increase in number of AFVs, NEMS-GPRA06 incorporates increased 

use of E85 due to Clean Cities activities, based on two projections: 1) an increasing number of 

vehicles using E85 (out of the increasing base of E85 capable vehicles), and; 2) an increasing 

share of E85 use in those vehicles using E85. The projections for these are shown in Table 23. 

Table 23. Projections of E85 Vehicles 

2005 2010 2015 2020 2025

E85 Capable Vehicles (millions) 4.120 8.440 12.690 16.220 18.950

Vehicles Using E85 (millions) 0.152 0.180 0.208 0.236 0.264

Fraction of Vehicles using E85 0.037 0.021 0.016 0.015 0.014

Fraction of E85 Use per Vehicle 0.250 0.300 0.350 0.400 0.400

3.6 Inventions and Innovation 

The Inventions and Innovation Program (I&I) is a Congressional mandated program to help 

inventors and very small businesses develop energy-saving technologies. Historically, I&I 

accepts proposals in two categories. Category 1 proposals are for concept development and have 

a $40K maximum grant. Category 2 proposals are for prototype testing and further technical 

development and have a $200K maximum grant. 
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The I&I program provides an orderly approach to identifying qualified proposals to fund using 

the steps below: 

• Solicitation development 

• Proposal evaluation 

• Program-relevancy review 

• Energy-savings analysis 

• Monitoring and tracking 

• Commercialization assistance 

• Evaluation 

Solicitation Development 

Generally, changes to the solicitation are minor; but some major changes in emphasis have 

occurred over the lifetime of the I&I Program. There is more emphasis on the commercialization 

strategy of the applicant, and each applicant is required to articulate that strategy. Another major 

change has been the increased documentation of energy-savings methodologies and the 

definition of the “commercially available unit of production.” The applicants are now required 

to make comparisons to existing commercially available technologies. 

Proposal Evaluation 

The changes in the solicitation have been designed to make it easier for the reviewer to 

adequately and fairly judge the invention’s energy savings, compared to the savings of existing 

and commercially available technologies. The technical coefficients (fuel use per year) are 

approved by the reviewers. 

These relatively small grants ($40K-$200K) do not call for the same rigorous market analysis 

that would occur on much larger grants or continuing programs. However, all grants do undergo 

a thorough technical and market evaluation. 

Program-Relevancy Review 

As part of the lengthy selection process, the I&I Program requires the designated EERE program 

manager to review every proposal within the office’s technical scope. This review enables the 

I&I DOE project manager to eliminate grant proposals that are outside the scope of EERE. It 

also familiarizes the EERE program managers with potential I&I grants that could potentially 

segue with their ongoing portfolios. 

Energy Savings Analysis 

As I&I conducts a solicitation each year, and the selection of technologies is only bounded by 

EERE program scope, it is impossible to predict the FY06 program at the time of budget 

formulation. As a result, the FY04 program is used to estimate the FY06 savings potential (due 

to the solicitation schedule, results from the FY05 program were not available in time to be 

incorporated). 

For the I&I Program, PNNL analyzes the impact of each selected technology using a model 

developed for DOE-OIT (Technology Impact Projections Model, Energetics Inc.) to be applied 

to industrial technologies considered in the GPRA process. The NEMS-GPRA06 model, used 
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Appendix K – Weatherization and Intergovernmental Program – Page K-43

for most of the analysis in this report, does not have a sufficiently detailed technology- based 

industrial sector. This generally precludes NEMS-GPRA06 from being used to model I&I 

sponsored technologies. 

The DOE-OIT model only considers the market segment appropriate for a given I&I technology. 

However, fuel prices, electrical plant heat rates, and environmental emissions rates are taken 

from EIA forecasts and applied to all technologies. All proposals to I&I contain estimates of 

current technology performance, the expected performance of the proposed technology, and the 

suggested market segment. Markets can be defined in terms of annual sales or manufacturing 

capacity. 

Performance estimates are reviewed with the inventor and adjusted for items such as heat rates 

and fuel mix that differ from the EIA base data. Performance coefficients are prepared for a 

“Technology Unit” in terms of fuel use per year of operation. For example, a technology unit for 

the ethanol industry is a production capability of 10,000,000 gallons per year. Multiplying the 

fuel coefficients by the number of units derives total annual fuel use. 

The market segment size is defined in terms of a number of technical units. Initial segment size 

is based on data from sources such as EIA, trade associations, and DOE industry profiles. Most 

of the inventors have studied the markets for their technology and offer additional sources and 

insights. The sector growth rate is derived from similar sources. The inventor proposes a year 

when the technology would first enter the market, however, when questioned by PNNL, most 

inventors delay the date from the original proposal. 

Maximum market-share limitations are placed on each technology. Factors that limit the share 

are technology issues, such as the technology will only work on motors more than a certain size; 

and market issues, such as the technology will be effective only in certain climates. 

Commercialization plans that use exclusive licensing can limit market share. In a case where 

two inventors are addressing the same market, the maximum market is cut in half. As I&I 

technologies either already have intellectual property protection or are in the process of 

establishing protection, the technology life cycle is set at 15 years. However, to simulate 

continued program funding at current rates, the life cycle is extended to 2030. 

The DOE-OIT model offers four market penetration “S curves.” Each is defined in terms of the 

number of years required to reach 50% of the maximum market share within the defined 

segment. The choice of “S curve” is based on the new technology performance advantage, the 

inventor’s commercialization plan, the market segment characteristics, and experience of the I&I 

tracking program for the same segment or type of technology. An inventor that has a 

development partner who represents a major share of the market segment would be assigned an 

“S curve” implying a shorter time to reach a 50% of maximum share than an inventor with no 

partner. Technologies that require large capital investment are given slower “S curves.” General 

instructions supplied to model users are included in Appendix A. 

Annual estimates of “technology unit” sales and total units installed are made for each 

technology, based on the above inputs. Energy, economic, and environmental consequences are 

derived based on the installed unit forecast. The model results are discussed with each inventor 
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and a signed agreement obtained. Generally, model results show fewer units sold than the 

inventors suggested in their proposals to I&I. A summary of the model results for each 

technology is part of the I&I Fact Sheets available on the I&I Web site.l

 

Calculations Walk-through: 

1) Annual market size is calculated from initial market size in “technical units,” multiplied 

by market limitation fractions, and adjusted for market growth. 

2) Annual market is multiplied by the market share from the selected “S curve” to derive 

annual sales. 

3) Annual installed capacity is the total sales (to date) in technical units. 

4) Energy savings are calculated by fuel type from the difference in performance 

coefficients between the new and current technology’s technical units. 

5) Other impacts are calculated from EIA prices and environmental coefficients multiplied 

by changes in annual fuel use. 

Note: The market share is equal to the “S curve” fraction, multiplied by the market share limit 

fractions. Specific calculation inputs and associated estimates of program benefits are provided 

in Appendix B. 

3.6.2 Target Market 

Project Description. Descriptions of the activities on which outputs are based are included in 

Appendix B. 

Market Description. Market segments are determined using public sources appropriate to each 

I&I technology. OIT’s industry profiles are frequently used. Market limitations are introduced 

to better represent the true target of the technology. EIA forecasts of energy prices and electric 

power fuel mix are used for all cases. 

Baseline Market Acceptance. The tracking of I&I technology acceptance provides an 

important input to the selection of the market penetration “S curve” and limitation of ultimate 

market share. 

3.6.3 Methodology and Calculations 

Inputs to Base Case. Because I&I cannot use NEMS-PNNL, each technology has its own base 

case. The same EIA fuel prices, electric plant fuel mix, and heat rate are used for all cases. 

Technical Characteristics. Technical coefficients of technology performance (i.e. fuel use per

operating unit per year) are provided by the inventor and approved by the proposal reviewers. 

Expected Market Uptake. The market penetration rate and limits consider many factors. The 

DOE-OIT model assumes that a technology with equal technical coefficients appears in the 

 

l

http://www.eere.energy.gov/inventions/
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market at some time after the technology being evaluated is introduced. Depending on the 

strength of intellectual property protection, the time lag is usually 10 to 15 years. 

Calculation Results: 

The FY04 grantees’ energy savings are used to estimate FY06 results. FY04 had included 13 

technologies (grants). Results for six technologies, representing about 85% of program savings, 

are shown in Appendix B to illustrate I&I’s energy-saving impacts. Calculations were made 

using the above-described OIT model. Sources are noted for market size and growth rates. 

Comments on the main factors considered in the “S curve” selection appear after the marketpenetration percentages. 

4.0 Intergovernmental Activities 

4.1 Tribal Energy Activities 

The program potentially could lead to renewables development projects on Tribal land that could 

be significant generation sources for sale to off-reservation markets; however, the current level 

of funding is leading to projects more oriented toward electrification of tribal households on 

reservations. The vast majority of these households are located on the Navajo Reservation in 

Arizona. EIA has identified a maximum electrification potential of at least 13,000 housing units 

using the 1990 Census (EIA Report SR/CNEAF/2000-01). 

Electrification, while an important outcome, in fact increases energy consumption. While new 

renewable supplies may be brought on line, there is no evidence that these resources would lead 

to displacement of existing fossil generation resources. In our assessment this program fills an 

important niche mission for the DOE, but energy savings is not the appropriate metric upon 

which to measure. 

Based on these observations, analysis effort has been focused elsewhere. 

4.2 International Renewable Energy Program 

The program states the goal of developing 1000 MW of new renewables capacity worldwide by 

2010. Even if all of this new generation displaced fossil generation, the savings are insignificant 

– especially on a world scale. In many instances, the new generation that would be created 

would serve to electrify currently unelectrified regions of the world – adding to world energy 

consumption. About 1,000 MW each five years would be equivalent to replacing one moderatesized coal or oil-fired power plant each five years. 

The activities of the program are more consistent with information programs and other outreach 

activities. The difference being that these activities occur with foreign governments. These 

activities could have the effect of placing U.S. technologies in foreign countries for 

demonstration or deployment, which may lead to potential adoption in the United States as a 

result, but this linkage is tenuous at best. 

Based on these observations, analysis effort has been focused elsewhere. 
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Appendix K – Weatherization and Intergovernmental Program – Page K-46

Appendix A – I&I Market Factor in Technology Impact 

Projections 

The Technology Impact Projections model is used to estimate the potential security, economic, 

and environmental benefits resulting from research, development, and demonstration projects 

funded by the Inventions & Innovation Program (I&I). Benefit estimates are critical for 

evaluating projects and presenting the merits of both individual projects and the overall RD&D 

portfolio. 

Market Inputs 

To determine the potential impact of the new technology as it becomes adopted, it is necessary to 

estimate the total market for the technology, reduce that estimate to the likely actual market, and 

estimate when (and the rate at which) the new technology will penetrate the market. 

Total Market 

Total market: the number of units that perform the same task as the proposed technology. Only 

the domestic U.S. market should be included. 

 

Number of Installed Units in U.S. Market

Please define that market as narrowly as possible: i.e. the smallest group of applications 

that covers all potential applications for which you may have some data. You may base 

your estimate on the energy use of the state-of-the-art technology and the energy-use data 

provided in this package. Other potential data sources include OIT’s Energy and 

Environmental Profile for the relevant industry, EIA’s MECS data, or industry sources. 

Annual Market Growth Rate

This should be based on an EIA or industry growth projection for the relevant industry. 

Market Share 

Market share is a function of the potential accessible market share and the likely market share. 

Potential Accessible Market Share 

The accessible market: The market that the new technology could reasonably access 

given technical, cost, and other limitations of the technology. For example, certain 

technologies may be applicable only to a certain scale of plant, certain temperature-range 

processes, certain types of existing equipment or subsystems, or only certain segments of 

the industry. 

Likely Market Share 

In some instances, in addition to technical and cost factors, the technology may compete 

with other new technology approaches, or with other companies, for the market. Please 

estimate the likely market share. Use current market-share information, or base estimated 

market share on the basis of the number of competitors in the market, assuming they are 
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Appendix K – Weatherization and Intergovernmental Program – Page K-47

using different technologies not resulting from this project. This is different than the 

possibility of “copycats,” which should not be considered as competing. That is, if others 

adopt essentially the same, or slightly modified, technology due to this new technology, 

that adoption was triggered by the project being described and that project should be 

“credited” with causing that trend. This is potentially the case for techniques where the 

intellectual property cannot be, or is not, protected and becomes general knowledge 

throughout the industry. 

Market Penetration 

To understand how rapidly the potential impact of the technology will occur, the market 

penetration of the technology must be projected. This is based on two estimates, the technology 

development and commercialization timeline, and the market penetration curve. 

Technology Development & Commercialization Timeline 

The commercial introduction of a technology normally occurs after a significant 

demonstration or operating prototype and after an adequate test-and-evaluation period, 

along with allowances for the beginnings of production, dissemination of information, 

initial marketing and sales, or other “start-up” factors. To capture this lengthy process, 

please indicate the timeline for developing and introducing the technology into the 

market. This includes the years for when an initial prototype, refined prototype, and 

commercial prototype of the technology has or will be completed and the year when the 

technology will be commercially introduced. An initial prototype is the first prototype of 

the technology. A refined prototype represents changes to the initial prototype but not a 

commercially scaled-up version. A commercial prototype is commercial-scale version of 

the technology. Commercial introduction is when the first unit beyond the commercial 

prototype is operating. Prototype and commercial introduction years should be consistent 

with your technology-development program plans. 

Market Penetration Curve (Technology Class)

New technologies normally penetrate a market following a familiar “S curve”, the lower 

end representing the above uncertainties overcome by “early adopters.” The curve tails 

off at the far future, where some may never adopt the new technology. The major portion 

of the “S curve,” here the new technology is penetrating the market and benefits are being 

reaped, is the most important. The rate at which technologies penetrate their markets 

varies significantly: Penetration of heavy industrial technologies generally takes place 

over decades, while simple process or control changes can penetrate much more rapidly. 

The actual penetration rate varies due to many factors including economic, 

environmental, competitive position, productivity, regulatory, and others. 

To assist in “S curve” selection, a large volume of actual penetration rates of past and present 

technologies were analyzed, normalized, and grouped into five classes, based on a number of 

characteristics and criteria. Those criteria have been distilled to the five choices in Table A1. 

Analysts and/or applicants can choose either a, b, c, d, or e as the rate class that best fits a given 

technology. Note that the characteristics (rows) are relatively independent, and a given 

technology will likely fit best in different classes for different characteristics. Selection of the 

most likely “rate class” at which the new technology may penetrate the market is based on best 
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Appendix K – Weatherization and Intergovernmental Program – Page K-48

judgment and experience. This may be a “subjective average” of the characteristics, or it may be 

that one or two characteristics are believed to so dominate future adoption decisions that a 

particular class of penetration rate is justified. There also may be “windows of opportunity” 

where significant replacements of existing equipment may be expected to occur at some point for 

other reasons. 

For additional assistance, Table A2 shows actual technologies and the class of their historical 

penetration rates. Comparison of the new technology (by analogy or similarity) with these 

examples provides additional insight into selecting the appropriate penetration rate that might be 

expected for the new technology. 

Table A1. Selecting the Market-Penetration Rate Class 

Technology/project Score 

(a,b,c,d,e) 

Characteristic a b c d e 

Time to saturation 5 yrs 10 yrs 20 yrs 40 yrs >40 yrs na 

Technology factors 

Payback discretionary <<1 yrs <1 yr 1-3 yrs 3-5 yrs >5 yrs 

Payback nondiscretionary <<1 yr <1 yr 1-2 yrs 2-3 yrs >3 yrs 

Equipment life <5 yrs 5-15 yrs 15-25 yrs 25-40 yrs >40 yrs 

Equipment 

replacement 

none minor unit 

operation 

plant section entire 

plant 

Impact on product 

quality 

$$ $$ $$ $ 0/- 

Impact on plant 

productivity 

$$ $$ $$ $ 0/- 

Technology 

experience 

new to 

U.S. only 

new to 

U.S. 

only 

new to 

industry 

new new 

Industry factors 

Growth (%per annum) >5% >5% 2-5% 1-2% <1% 

Attitude to risk open open Cautious conservative averse 

External factors forcing forcing Driving none none na 

Gov’t regulation 

Other 
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Table A2. Penetration Rate of Technologies. 

Class A B C D E 

Aluminum Treatment of 

used cathode 

liners 

Strip casting, 

VOC 

incinerators 

 

Chemicals New series of 

dehydrogenatio

n catalyst 

(incremental 

change) 

CFCs -> 

HCFCs, 

incrementally 

improved 

catalysts, 

membranebaed chloralkali 

Polypropylene 

catalysts, 

solvent to 

water-based 

paints, PPEbased AN 

Synthetic 

rubber & fibers 

Forest 

Products 

 Impulse drying, 

de-inking of 

waste 

newspaper 

Kraft pulping, 

continuous 

paper 

machines 

Glass Lubbers glass 

blowing, 

Pilkington float 

glass 

Particulate 

control, 

regenerative 

melters, 

oxygenase in 

glass furnaces 

 

Metals 

Casting 

New shop floor 

practice 

 

Petroleum New series 

HDS catalysts 

Alkylation 

gasoline 

Thermal 

cracking, 

catalytic 

cracking 

Residue 

gasification, 

flexicoking 
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Appendix B – I&I Energy Savings Results 

I&I Technology Pulse paper drying Technology Description - Virtually all paper manufacturing equipment worldwide is limited by the evaporative drying stage. The most common air drying process improves efficiency of this process by 59% and speeds overall paper production 21%. Market segment is the paper manufacturing industry - technology unit is a plant producing 44,000 tons/yr Current technology units in operation - 2002 290 Source - DOE - OIT 

technology profile 

Sector annual growth rate 1% Source - DOE - OIT 

technology profile 

New technology Introduction year 2006 Savings per new install unit 235 Billion Btu (Natural gas)/year Year 2008 2010 2015 2020 20Units in service 5 13 78 178 2Annual unit sales 2 4 20 15 4 Primary energy savings 1.2 2.9 18.2 41.6 50(trillion Btu/year) Market Penetration 2% 4% 24% 51% 59Note: Industry is aware of this technology, but waits for the early adapter. Most plants in the industry are owned by a few companies, success will move quickly although the units are expensive. (10yr curve) 
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I&I Technology High Speed/ Low Effluent process for Wet and Dry MTechnology Description - A high speed/low effluent fermentation process based on the BPSC-15 yeast that has the property of forming stable high strength 'pellets'. Very high cell densities are easily attained with this yeast which leads to quick and complete fermentations Energy use reduced by 42% and requires fewer fermenters for the same production rate. Market segment is the ethanol manufacturing industry - technology unit is a plant producing 10,000,000 gal/yr Current technology units in operation - 2001 177 Source - EIA's Annual 

Energy Outlook 2001 

Sector annual growth rate 10% Source - Energy Bill (5 

Billion Gal by 2012) 

New technology Introduction year 2006 Savings per new install unit 228,000 Million Btu (Coal)/year Year 2008 2010 2015 2020 Units in service 6 32 171 284 Units starting operation 4 17 22 26 Primary energy savings 1.4 7.3 39.1 64.8 (trillion Btu/year) Market Penetration 2% 8% 25% 26% Note: Technology can be retrofitted or used with new plants. Retrofit cost are about 5% of original cost, but new plant would see a cost reduction (few fermenting units) in addition to energy savings. (5 yr curve) 
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Appendix K – Weatherization and Intergovernmental Program – Page K-52

I&I Technology Electrochromic Windows - Advanced Processing TecThe project is focused on developing advanced fabrication capabilities for energy-saving electrochromic (EC) smart windows. SAGE EC devices consist of an alt-ceramic stack of thin film coatings on a glass substrate. The window tint can be changed electrically by the application of low voltage DC power. SAGE has developed the basic materials and device technologies and moved operations from laboratory to pilot line. Market segment is residential and commercial windows - technology unit is 1 million Sq-meters of glazing Current technology units in operation - 2001 3000 Source - Implied fro

annual sales 

Sector annual growth rate 3% Source - "Smart 

Windows" an SRI study 

New technology Introduction year 2005 Savings per new install unit 304 billion Btu(gas, oil and Elect) /Year 2005 2010 2015 2020 Units in service 11 106 617 1287 Units sold 11 37 147 103 Primary energy savings 3.6 32.4 182.5 375.3 (trillion Btu/year) Market Penetration 0% 3% 14% 25% Note: Early years sales based on SRI markets study with later years keyed to LBNL saturation estimates referenced by the inventor.(10yr curve) 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-53

I&I Technology Multi-rotor Micro Particle GeneratorThis mechanical generator incorporates a novel approach to continuous emulsification processing of any type of fine particle homogeneous suspensions. Through exceptionally efficient and effective particle size reduction or, in the case of organic materials, cell disruption, thus greater starch exposure. This process eliminates the current Jet Cooking process used to reach the "liquefaction stage" in the production of corn ethanol, saving up to 46% of the related energy costs. Market segment is the ethanol manufacturing industry - technology unit is a plant producing 10,000,000 gal/yr Current technology units in operation - 2001 177 Source - EIA's 

Annual Energy Outlook 2001 

Sector annual growth rate 10% Source - Energy Bi

(5 Billion Gal by 2012) 

New technology Introduction year 2004 Savings per new install unit 37,400 Million Btu (Coal)/year Year 2005 2010 2015 2020 Units in service 2 77 176 284 Units sold 1 28 17 26 Primary energy savings 0.1 2.9 6.7 10.9 (trillion Btu/year) Market Penetration 1% 19% 26% 26% Note: Basic technology exists, but has not been applied to corn. After testing and any necessary modifications units can be sold to new or retrofitted to existing plants. (5yr curve) 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-54

I&I Technology High Efficiency Variable Dehumidification for Air ConThe project goal is to produce a production prototype that will lead industry to a highly marketable improvement in energy efficiency, dehumidification, and maintenance of like-new performance for unitary air-conditioning and dehumidification. Market segment is Commercial and Residential AC - technology unit delivers 20,000 ton-hr/year Current technology units sales - 2002 5.42 

million 

 Source - ADL report foOBT 

Sector annual growth rate 2% Source - ADL report fo

OBT 

New technology Introduction year 2006 Savings per new install unit 142 Million Btu (Electricity)/year Year 2005 2010 2015 2020 Units in service 0 108,19

8 

700,628 1,683,669 

Units sold 0 38,179 186,332 156,923 Primary energy savings 0.0 11.6 68.9 158.1 (trillion Btu/year) Market penetration 2% 10% 22% Note: Technology requires major AC unit design changes, but with result little or no cost increase. Market is limited to regions with high humidity- Southeast and portions of South and Midwest. (10yr curve) 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix K – Weatherization and Intergovernmental Program – Page K-55

I&I Technology Medium Voltage Energy Saving Motor Controller Concept for a medium voltage electric motor controller that costeffectively reduces energy consumption by up to 35% for underloaded medium voltage (2300-4600V) electric motors. While large electric motors comprise only 0.3% of the number of motors used in US manufacturing, they consume 19% of the total motor energy. When a motor is loaded less than 40% of its full load, its efficiency declines quickly. Market segment is Electric motors - technology unit a 1000 HP motor running at part load Current technology units sales - 1997 89,500 Source - DOE Motor 

Challenge data 

Sector annual growth rate 3% Source - DOE Motor 

Challenge data 

New technology Introduction year 2006 Savings per new install unit 4,466 Million Btu (Electricity)/year Year 2005 2010 2015 2020 Units in service 0 1123 3747 11363 Units sold 0 245 787 2149 Primary energy savings 0 5.0 15.3 44.4 (trillion Btu/year) Market Penetration 0% 1% 2% 6% Note: Market is limited to motors over 200HP that operate at less the 40% of full load. The inventor already supplies controllers for smaller motors. Research will develop capability for larger motors. The inventor company knows the industry and provided market forecasts based on his own experience.(20yr curve) 
1 

Appendix L – GPRA06 Wind Technologies Program 

Documentation 

Introduction 

The Wind Technologies Program seeks to reduce the cost and improve the performance of wind 

technology, and to reduce barriers to its use. The GPRA benefits are estimated primarily from 

model projections of the market share for wind technologies, based on their economic 

characteristics. Several models are utilized for this purpose. This document describes the inputs 

and assumptions that are used by the models to calculate those benefits. The most significant 

change from the GPRA05 benefits methodology in assumed program direction and activities for 

the current GPRA06 estimates is the inclusion of offshore wind energy technology development. 

1.1 Target Markets (the Baseline Case) 

Large-scale wind energy is expected to penetrate in two market segments: the least cost 

(competitive bulk power) power market and the green power market. 

In the competitive power markets, wind technology is projected to improve significantly during 

the next decade, in part because of program-sponsored research. This improvement is 

represented in the GPRA06 modeling effort by a declining capital cost trajectory, lower O&M 

costs, and increased performance. The values used for the wind technology cost and performance 

projections are consistent with the program’s 2012 cost of energy goals for low wind-speed 

technology, both onshore and offshore. 

In addition to competing on an economic basis with other electricity-generation technologies, 

wind capacity may be partially valued for its environmental attributes. Princeton Energy 

Resources International (PERI)—using its Green Power Market Model—provided an estimate of 

wind capacity additions in response to the expanding green power markets in many places 

throughout the country. The projections for green power wind installations were incorporated 

exogenously into the OnLocation-modified NEMS (NEMS-GPRA06), and Brookhaven National 

Laboratory-modified MARKAL (MARKAL-GPRA06) models as planned capacity additions. 

Description of Key Elements of the NEMS Approach to Modeling Wind 

The electricity-sector module performs an economic analysis of alternative technologies in each 

of 13 regions. Within each region, new capacity is selected based on its relative capital and 

operating costs, its operating performance (i.e., capacity factor, which reflects energy conversion 

efficiency, and both resource and plant availability), the regional load requirements, and existing 

capacity resources. NEMS-GPRA06 characterizes wind by three wind classes, each with its own 

capital costs and resource cost multipliers. The regional resource cost multipliers increase capital 

costs as increasing portions of a wind class is developed in a given region to reflect 1) declining 

natural resource quality, 2) required transmission network upgrades, 3) competition with other 

market uses, including aesthetic or environmental concerns. As the cost in a particular region 

increases with greater development, at some point it may become more cost-effective to consider 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix L – Wind Technologies Program – Page L-1 
installing wind turbines in areas of lesser wind resource, but with lower ancillary costs and less 

costly access to the grid, as reflected in the model by the capital cost multipliers. These 

multiplier assumptions are viewed as very conservative, and may overestimate the effects of 

actual market dynamics. 

Other key assumptions that can affect projections include a limit on the share of generation in 

each region that can be met with intermittent technologies. NEMS-GPRA06, as in the AEO2004, 

also assumes that the capacity value of wind diminishes with increasing levels of installed wind 

capacity in a region. Finally, another constraint on the growth of wind resource development is 

how quickly the wind industry can expand before costs increase due to manufacturing 

bottlenecks. The AEO2004 assumption that a cost premium is imposed when new orders in a 

year are 20% higher than in the highest of the previous 10 years was maintained in the Program 

Case (the same as applied to all electricity generation technologies) 

Further detail on the representation of wind power in NEMS may be found in Chapter 4 and 

Appendix A. 

Description of Key Elements of the MARKAL Approach to Modeling Wind 

Wind generators are modeled as centralized plants to compete with fossil fuel-based plants. As in 

NEMS, resource cost multipliers were applied to the capital cost of wind generators as additional 

capacity is developed. The resource cost multipliers are based on the assumptions used in the 

AEO2004. However, since MARKAL-GPRA06 is a single region model, these multipliers were 

applied on a national basis. 

To account for the intermittent nature of wind resources, the potential contribution of wind 

systems to meeting peak power demand is limited to a fraction of total wind capacity. This 

factor ranges between 30% and 40% depending on wind class, year, and maximum assumed 

capacity factor. This disadvantages wind generators, compared to fossil fuel generators, as 

additional reserve capacity is required to meet peak power requirements. However, this 

disadvantage is offset by fossil fuel cost savings, as well as the reduction in capital cost and 

performance improvements projected for wind technologies by the program. As a result, wind 

generators near the central grid can be competitive with fossil fuel-based power plants. The 

green power capacity additions are added as a lower bound in the MARKAL-GPRA06 model. 

Developing the GPRA06 Baseline for Wind 

The Baseline, which is used as the benchmark against which to measure the Wind Program’s 

benefits, is developed using NEMS-GPRA06 and some of the technology assumptions in the 

Energy Information Administration’s (EIA) Annual Energy Outlook 2004 (AEO2004) Reference 

Case. The AEO2004 baseline treats wind as a mature technology that experiences, in the future, 

only a limited amount of cost reduction through learning (only 1% reduction in costs is assumed 

for every doubling of capacity). As a result, in the EIA projections, the projected capital costs 

decline only slightly over time. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix L – Wind Technologies Program – Page L-2 
Onshore Wind Class 6 COE

Three changes from the AEO2004 Reference Case were made regarding wind. The AEO2004 

uses short-term (national growth) and longer-term (regional resource) multipliers. These factors 

account for various resource and market phenomena that are intended to reflect increases in the 

cost of deploying technology as cumulative installed capacity increases). In NEMS-GPRA06, the 2002 $/MWh 

resource multipliers are applied by wind class rather than across the entire wind resource in each 

region. This is a more restrictive assumption in that it tends to increase the assumed cost of wind 

technology, and, therefore, to lower projections of installed capacity. 

A second modification is to the capital cost and capacity factor assumptions for current (2005) 

wind technology. The EIA and program assumptions about the expected performance (capacity 

factor) of new wind capacity additions are significantly different. They needed to be reconciled, 

so that the same assumptions would be used for the year 2005 in both the Baseline and Program 

cases. For example, EIA assumes that the capacity factor of wind turbines in Class 6 winds, in 

2005, is 38%, while the program believes it is 49%. A compromise between the two was made 

such that the capacity factor assumptions for 2005 are midway between the two views (e.g., 44% 

for Class 6). The Baseline wind capacity factors then improve over time at the same rate as 

assumed in the AEO2004. The capital costs estimates for 2005 were more similar, and the 

program’s view for 2005 was adopted for the 2005 Baseline, followed by a small decline in cost 

over time at the same rate as in the AEO2004. 

50 

45 

40 

35 

30 

25 

20 

15 Original Onshore Base 

10 Proposed Onshore Base 

5 Onshore Program 

0 

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 

Figure 1. Onshore Wind Class 6 COE 

The third change from the AEO2004 to the Baseline is the addition of offshore wind as a 

separate technology. The offshore wind is represented as a distinct technology that competes 

with all other generation technologies. It is characterized in a similar manner as onshore wind, 

with three wind classes, but also has a distinction between shallow and deep water sites. The 

constraints on intermittent generation and rapid growth apply similarly to offshore as to onshore 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix L – Wind Technologies Program – Page L-3 
wind development. The offshore wind does not have the regional resource cost multipliers 

because there is insufficient data on how they might apply. 

Table 1 provides the complete set of technology assumptions used in developing the Baseline 

estimate for GPRA06. 

Table 1. Baseline Projections for Capital Costs, Capacity Factors, and O&M Costs 

for Onshore and Offshore Wind Systems 

2005 2010 2015 2020 2025 2030 2040 2050 

Capital Costs 

Onshore Class 5 & 6 1050 1048 1045 1042 1039 1036 1031 1025 

Class 4 1103 1087 1084 1076 1053 1050 1045 1039 

Offshore Shallow Water 1243 1241 1234 1225 1212 1196 1155 1102 

Deep Water 1787 1767 1743 1707 1636 1604 1497 1383 

O&M Costs 

Onshore All Classes 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 

Offshore Shallow Water 55.0 49.3 45.0 41.0 40.0 39.0 37.0 35.0 

Deep Water 55.0 51.3 47.0 43.0 42.0 41.0 39.0 37.0 

Capacity Factors 

Onshore Class 6 0.436 0.446 0.458 0.466 0.474 0.482 0.498 0.504 

Class 5 0.398 0.407 0.417 0.426 0.433 0.440 0.453 0.459 

Class 4 0.338 0.345 0.354 0.361 0.366 0.374 0.390 0.395 

Offshore Class 7 0.532 0.537 0.563 0.569 0.569 0.569 0.569 0.569 

Class 6 0.442 0.446 0.468 0.474 0.474 0.474 0.474 0.474 

Class 4 0.351 0.356 0.373 0.380 0.380 0.380 0.380 0.380 

*Includes 1.05 contingency factor for onshore systems and 1.07 for offshore systems. 

Sources: [1] [2] 

1.2 Key factors in shaping market adoption of EERE technologies 

Electricity produced from offshore locations is expected to be of higher value than many onshore 

locations in many cases, because proximity of several major load centers to the coasts could 

reduce transmission constraints and costs facing large-scale onshore power generation. The 

United States has an estimated 60 gigawatts (GW) of shallow water resource and 141 GW of 

deep water resources within 5-20 nautical miles (nm) of the coast, and a further 38 GW of 

shallow water resources and 668 GW of deep water resources located 20-50 nm out. Early 

offshore wind development in Europe, at shallow depths of 5-12m, has relied on the marinisation 

of land-based turbines anchored to the seabed. It is expected, however, that the growth of 

offshore wind technology in the United States will also require new technologies that allow deep 

water development (e.g., floating platforms and power distribution and transmission systems). 

[2] 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix L – Wind Technologies Program – Page L-4 
1.3 Methodology and calculations 

Technical Characteristics of the Program Case for Wind 

The assumptions about capital costs, capacity factors, and O&M costs, which are used as inputs 

into the Program Case, are provided in Table 2. These projections match the program’s 

performance goals, as described in the Wind Energy Multi Year Program Plan For 2005-2010, 

November 2004, available for downloading in PDF format at: 

http://www.nrel.gov/wind_meetings/2003_imp_meeting/pdfs/wind_prog_mypp_15Nov2004.pdf 

So far, the program has only completed preliminary analyses of the costs and performance of 

deep water wind turbines. For this reason, their estimated cost and performance should be 

regarded as tentative, reflecting the best program judgment available at the time of this analysis, 

but being almost certain to change as more knowledge is gained and more comprehensive studies 

are completed. A more detailed examination of cost-reduction potential is being undertaken in 

2005, and will be structured using the wind program’s Technology Improvement Opportunity 

(TIO) pathways framework. 

Table 2. Program Projections for Capital Costs, Capacity Factors, and O&M Costs 

for Onshore and Offshore Wind Systems 

2005 2010 2015 2020 2025 2030 2040 2050 

Capital Costs 

Onshore Class 5 & 6 1050 893 840 819 814 788 767 746 

Class 4 1103 982 919 893 866 866 856 840 

Offshore Shallow Water 1243 1129 1070 1070 1050 1016 989 962 

Deep Water 1787 1723 1370 1177 1050 1024 977 945 

O&M Costs 

Onshore All Classes 25.0 20.0 16.0 15.0 14.2 13.8 13.2 12.8 

Offshore Shallow Water 55.0 47.0 41.0 38.0 37.3 36.0 34.0 32.0 

Deep Water 55.0 49.3 44.0 41.0 40.0 39.0 37.0 36.0 

Capacity Factors 

Onshore Class 6 0.436 0.495 0.507 0.514 0.517 0.519 0.522 0.525 

Class 5 0.398 0.442 0.453 0.457 0.460 0.460 0.462 0.462 

Class 4 0.338 0.404 0.463 0.469 0.472 0.480 0.482 0.483 

Offshore Class 7 0.535 0.546 0.604 0.604 0.604 0.604 0.604 0.604 

Class 6 0.442 0.450 0.501 0.501 0.501 0.501 0.501 0.501 

Class 4 0.352 0.359 0.401 0.401 0.401 0.401 0.401 0.401 

*Includes 1.05 contingency factor for onshore systems and 1.07 for offshore systems.. 

Sources: [2] [3] 

Green Power Market Penetration Projections 

Green power additions (estimated by the PERI Green Power Market Model) were provided by 

region and are included as planned capacity additions. (For more details on the Green Power 

Market Model, see Appendix M). Estimates of the Green Power additions were provided to 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix L – Wind Technologies Program – Page L-5 
2035. After 2035, they remain flat as most of the renewable capacity will likely be introduced 

competitively by then. The same general technology assumptions were made for the green power 

additions as for the economic builds predicted by NEMS or MARKAL. However, none of the 

Green Power additions was modeled as coming from offshore installations, due to their generally 

higher cost in the near term when green power markets are assumed to be most important. 

Table 3 summarizes the green power capacity additions. The green power additions do not 

necessarily lead to greater total wind capacity than those projected for pure economic reasons, 

because development costs are assumed to increase as wind resources are developed. 

Table 3. Green Power Wind-Capacity Additions (MW) for Program Case 

2006- 2011- 2016- 2021- 2026- 2031-

Wind 2010 2015 2020 2025 2030 2035 Total 

1. ECAR 160 164 69 39 27 28 487 

2. ERCT 142 179 91 46 36 38 532 

3. MAAC 316 204 16 18 19 20 592 

4. MAIN 99 101 43 24 16 17 300 

5. MAPP 13 27 21 11 4 4 79 

6. NY 210 136 11 12 13 13 395 

7. NE 231 139 22 20 19 19 450 

8. FL 0 0 0 0 0 0 0 

9. STV 0 0 0 0 0 0 0 

10. SPP 146 192 104 52 38 41 573 

11. NWPP 18 33 23 14 9 10 107 

12. RA 28 47 32 20 14 15 156 

13. CNV 7 18 17 8 3 3 57 

Total 1,372 1,239 448 262 198 210 3,729 

1.4 Sources 

1. Cohen, Joseph M., “Assessment Of Potential Improvements In Large-Scale Low Wind Speed 

Technology,” proceedings of Global Windpower 2004, March 29, 2004, American Wind Energy 

Association, Washington, DC. 

2. Musial, W., and Butterfield, S., Future For Offshore Wind Energy in the United States, June 

2004, NREL.CP-500-46413 http://www.nrel.gov/docs/fy04osti/36313.pdf 

3. Milborrow, D., Offshore Wind Rises to the Challenge, Windpower Monthly, April 2003. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix L – Wind Technologies Program – Page L-6 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-1

Appendix M – GPRA06 Estimate of Penetration of Generating 

Technologies into Green Power Markets 

1. Introduction 

The Green Power Market Model (GPMM or the model) identifies and analyzes the potential 

electric-generating capacity additions that will result from “green power” programs, which are 

not captured in the “least-cost” analyses performed by the National Energy Modeling System 

(NEMS) and the Market Allocation (MARKAL) model. The term "green power" is used to 

define power generated from renewable energy sources, such as wind, solar, geothermal, and 

various forms of biomass. The Green Power market is an increasingly important element of the 

national renewable energy contribution, with changes in the regulatory and legislative 

environment and the recent dramatic changes in natural gas prices slowly altering the size of this 

opportunity. 

1.1 Target Markets 

The GPMM attempts to quantify the size of the green power market opportunity for the GPRA 

Benefits Analysis process. The model projects green power-capacity additions resulting from 

both green power marketing programs available in deregulated markets, and utility green pricing 

programs offered in regulated markets. Electricity markets are now restructured and openly 

competitive in 17 states and the District of Columbia: Arizona, Connecticut, Delaware, Illinois, 

Maine, Maryland, Massachusetts, Michigan, New Hampshire, New Jersey, New York, Ohio, 

Oregon, Rhode Island, Pennsylvania, Texas, Virginia, and Washington D.C. Electric-market 

restructuring has been delayed or repealed in seven states: Arkansas, California, Montana, 

Oklahoma, Nevada, New Mexico, and West Virginia.1 

Of the states with competitive electricity markets, green power marketing products are currently 

being offered in nine states and the District of Columbia: Maine, Maryland, Massachusetts, New 

Jersey, New York, Pennsylvania, Rhode Island, Texas, Virginia, and Washington DC. 2 Green 

power pricing programs are being offered by utilities in 34 states: Alabama, Arizona, California, 

Colorado, Florida, Georgia, Hawaii, Idaho, Illinois, Indiana, Iowa, Kentucky, Massachusetts, 

Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, New Mexico, North Carolina, 

North Dakota, Ohio, Oklahoma, Oregon, South Carolina, South Dakota, Tennessee, Texas, Utah, 

Vermont, Washington, Wisconsin, and Wyoming.3

 

1 The Green Power Network, 2004, Green Power Markets- Green Marketing, U.S. Department of Energy, Office of Energy 

Efficiency and Renewable Energy, available on the Internet at: 

http://www.eere.energy.gov/greenpower/markets/marketing.shtml. 2 The Green Power Network, 2004, Green Power Markets- Green Marketing, U.S. Department of Energy, Office of Energy 

Efficiency and Renewable Energy, available on the Internet at: 

http://www.eere.energy.gov/greenpower/markets/marketing.shtml. 3 The Green Power Network, 2004, Green Power Markets- Green Pricing, U.S. Department of Energy, Office of Energy 

Efficiency and Renewable Energy, available on the Internet at: http://www.eere.energy.gov/greenpower/markets/pricing.shtml. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-2

The GPMM estimates are provided to the National Renewable Energy Laboratory (NREL) and 

the Department of Energy (DOE), in terms of installed capacity (MW) and energy generation 

(kWh) for each renewable power technology, in a format compatible with NEMS. 

The model was first developed in 2000, and many of the assumptions have been revised on an 

annual basis. Additionally, this year a systematic review and revision of the data inputs and 

methodology of the model was undertaken, including: a review of the recent literature on green 

power market developments and status of new and existing programs; an update of current state 

legislation on electricity market restructuring and access to green power programs either through 

regulated green pricing or competitive green marketing programs; an update of input data, from 

EIA and NREL, on demographic and demand data, and renewable energy technology 

characterizations; and conversion of all dollar values from 2000$ to 2004$, including data inputs 

on energy prices and technology costs. 

Baseline Technology Improvements and Market Acceptance 

A “base case” run of the GPMM was established this year for the first time. This version of the 

model uses the technology characterization from the FY2006 GPRA-Base Case run, and serves 

as the baseline reference for how green power marketing and green pricing programs would 

develop in the absence of efforts by the renewable energy programs. After this baseline was set, 

the “program case” was run, using the NREL and DOE projections for technology 

characterizations for renewable technologies that are used in the FY2006 GPRA- Program Case 

run. Additionally, the solar photovoltaic (PV) capacity additions from the Million Solar Roofs 

(MSR) program are included in the program case results of the GPMM. Instead of the entire 

capacity additions and energy generation from the program case run, only the difference between 

the base and program runs is credited to the renewable energy programs for the benefits 

calculations. These incremental results are then used by OnLocation Inc. and added to the 

FY2006 GPRA- Program Case run, to produce the renewable energy program benefits 

calculations. 

1.2 Key Factors in Shaping Market Adoption 

Price is the main factor in most electricity purchases where the customer has a choice in products 

offered. Aside from some reliability issues for a number of industrial and commercial 

customers, price is the one characteristic that is distinguishable between competing electric 

service programs. The electricity provided to the customer is the same regardless of which 

electric provider and package is selected (i.e., the lights come on when the switch is flipped). 

However, green power is one product that offers more than just the electricity to power a home 

or business. Green power is really the combination of the electricity produced by green 

technologies and the attributes associated with that generation (e.g., reduction in pollution and 

emissions, fuel diversity, and price stability). While green power is generally more expensive 

than conventional electricity packages, the additional nonprice factors provide an increased value 

of the product to the customer. Many residential customers choose green power to reduce their 

impacts on the environment and to help support the development of these emerging clean 

technologies. Additionally, a number of reasons exist for businesses to commit to purchasing 

green power, such as fostering good relations with their customers by enhancing their public 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-3

image, offsetting emissions from manufacturing or other business processes, enhancing the 

reliability of their power supply, and limiting exposure to fossil fuel price fluctuations. 

However, the development of green power markets still faces a number of obstacles. Many 

renewable technologies are capital intensive, requiring high up-front costs, which hinder or 

reduce the potential financing mechanisms for development. Also, issues with reliability and 

predictability, as well as potential impacts on transmission system operations present further 

barriers to market adoption. To counter these obstacles, a number of government programs and 

incentives, from the federal down to the local level, have been initiated to stimulate the market. 

Additionally, aggregation of a number of customers, on the residential, commercial, industrial, 

and governmental levels, have allowed these groups to commit to significant purchases of green 

power at favorable rates, which has spurred further development of green power. 

1.3 Methodology and Calculations 

Time frame 

The model projects increased capacity and electricity generated from green technologies for fiveyear periods from 2006 to 2035. 

Technologies 

Thirteen individual technologies, comprising five technology types, were selected as both green 

and commercially viable for this analysis. These are: 

 1) Biomass: - Direct-Fired Biomass 

- Biomass Gasification 

- Landfill Gas 

 2) Geothermal: - Flash Geothermal 

- Binary Geothermal 

- Hot Dry Rock 

 3) Concentrated Solar Power: - Solar Thermal Trough 

- Solar Thermal Dish-Hybrid 

- Solar Central Receiver 

 4) Photovoltaics: - Residential PV (Neighborhood) 

- Central Station PV (Thin Film) 

- Concentrator PV 

 5) Wind: - Onshore Wind Turbines 

Offshore wind technology characterizations are currently being added to the modeling 

capabilities of NEMS and MARKAL. However, the specifications of offshore wind turbines for 

the base case and program case runs were not available for inclusion in this year’s GPMM. It is 

expected that this technology will be included in the FY2007 GPMM base and program runs. 

Although the model was initially designed to distinguish between dispatchable and intermittent 

technologies, more recent versions of the model exclude this distinction. The original distinction 

was accomplished by adding an extra cost to intermittent technologies associated with “firming 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-4

up” the technologies’ ability to provide a constant power supply. However, since green power 

programs only guarantee that a certain number or percentage of total kilowatt-hours generated 

will come from green sources over the course of a year, the developers of new green power do 

not have the incentive to include backup generation to provide a continuous source of power. 

Developers are therefore assumed to build the sites in least-cost fashion (i.e., without backup) 

and take the “green” electrons when and from where they are able. 

Regions 

The model is composed of regional segments, used to capture differences in the costs of 

competing technologies, resource availability, levels of participation in voluntary green 

marketing programs, and electricity demand by sector. U.S. Census regions are used in this 

model, as the availability of regional data often takes this format. Eight regions (the South 

Atlantic and East South Central regions are combined) are modeled independently, and then 

summed to produce national results (see Appendix A). The regions for this analysis are 1) New 

England, 2) Middle Atlantic, 3) East North Central, 4) West North Central, 5) South Atlantic and 

East South Central, 6) West South Central, 7) Mountain, and 8) Pacific. 

This regional breakdown is different from the regional divisions of NEMS, however. In order to 

be hardwired into NEMS, the eight regional capacity projections must be converted to the 13 

divisions used in NEMS. The NEMS divisions are based on the North American Electric 

Reliability Council’s (NERC) regions. The names of these regions, and the conversion formulas 

from the census region breakdown, are documented in the model. Detailed results of the model 

are shown by NEMS Region in Appendix B. 

Assumptions 

The technology cost and performance characterizations for both the base and program cases were 

taken from FY2006 GPRA Base Case and Program Case input data, as provided by Frances 

Wood of OnLocation Inc., and the Assumptions to the AEO2004 document. All technology cost 

figures were converted to 2004$, using GPD price deflators.4

The fraction of each region assumed to have competitively set electricity rates, and therefore 

green marketing program access, is consistent with those used in the Annual Energy Outlook 

2004 (AEO2004), as provided by Frances Wood of OnLocation Inc. The state-by-state access to 

utility green pricing programs, and customer participation rates, for both green marketing and 

green pricing programs, are taken from a 2001 NREL report, Growing the Green Power Market: 

Forecasting the Impacts of Customer Demand for Renewable Energy.

5

 Access to green power 

programs in regulated markets, is assumed to start at 10% and increases by 5% each year to 55%. 

Meanwhile, in competitive markets, 100% of customers are assumed to have access to green 

market programs. Customer participation in regulated markets is assumed to start at 0.75% for 

residential customers in first year, increasing by 0.75% annually to 7.5% in the 10th year; while 

 

4 Office of Management and Budget, Budget of the United States Government: Fiscal Year 2005, Table 10.1—GROSS 

DOMESTIC PRODUCT AND DEFLATORS USED IN THE HISTORICAL TABLES: 1940–2009, available on the Internet at: 

http://www.gpoaccess.gov/usbudget/fy05/sheets/hist10z1.xls. 5 Swezey, Blair, R. Wiser, M. Bolinger, and E. Holt, 2001, Growing the Green Power Market: Forecasting the Impacts of 

Customer Demand for Renewable Energy, National Renewable Energy Laboratory, US Department of Energy, NREL/TP-620-

30101 LBNL-48611. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-5

in competitive markets, customers participation is assumed to start at 1% and increase to 15% in 

the 15th year of open access. Nonresidential (i.e., commercial and industrial) customers are 

assumed to be a constant 25% of residential participation in both regulated and competitive 

markets. Commercial and industrial customers’ participation rates are set at 16.7% and 8.3%, 

respectively, of their residential customers counterparts. A key assumption of the model is that 

all customers continue in the programs, once they have joined. 

These access and participation rates are summed across the regions, and are prorated based on 

the electricity demand in each state compared to the region as a whole. Regional assumptions for 

restructuring, green power access, and customer participation rates are shown in Appendix C. 

Figure 1 shows the combined regional customer participation rates in both green power 

marketing and green pricing programs. 

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

2005200720092011201320152017201920212023202520272029203120332035

year

% of total customers

New England

Mid. Atlantic

E. N. Central

W.N. Central

S. Atl. & E.S. Central

W.S. Central

Mountain

Pacific

Figure 1. Total Regional Customer Participation Rates in Green Power Marketing and Green 

Pricing Programs 

The choice of how to model the pricing premium for participation in green power programs is 

another important modeling assumption. The electricity purchased in a green power program is 

functionally equivalent to any other option offered by a utility or electric service provider. The 

nonelectric attributes of green power (i.e., from renewable energy sources with no or lower 

emissions than conventional fuel types) are the reason that customers are willing to pay a 

premium for this power. A range of payment methods exist for current green programs, with 

some programs charging an additional amount per kilowatt-hour, a fixed amount each month, or 

a percentage of the total bill. PERI has chosen to use the percentage of the total bill, assumed to 

be 10%, to more accurately show the regional energy price variation. The model uses only the 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-6

dollars from new customers joining green programs each year to build the new capacity. Money 

from customers who have joined in prior years is assumed to continue to finance projects built in 

those years. Further, it is assumed that all of the money collected from these programs will go 

toward building additional capacity. 

A very important modeling construct allows the model to build multiple competing technologies 

in a region, not only the least-cost alternative. This approach avoids so-called knife-edge choices, 

and recognizes that single-point estimates of data actually represent a range of values. The 

percentage of funds apportioned to each technology is inversely related to its first-year cost of 

energy (FY COE) through a sharing algorithm (i.e., a logit function), consistent with NEMS 

modeling procedures. The spread of the distribution depends on a scaling factor, lambda, which 

often ranges from 0 to 15. As this factor increases, the lower-cost technologies receive a higher 

percentage of the total distribution. PERI has chosen to set this factor at 3.2. A small sensitivity 

analysis was conducted ranging lambda from 2 to 8 with minor impacts (less than 10%) on the 

resulting totals. 

Another set of assumptions deals with creating regional distinctions in the model by varying the 

resource potential of the technologies. This was done both throughout the entire nation and in 

subsets of the regions, depending on the specific technology characterizations. Landfill gas, for 

example, is limited nationwide by the availability of an economically viable resource base. To 

account for this, a 70 MW capacity limit was instituted in each region for each five-year time 

period. For other technologies, such as CSP and geothermal, resource-based regional 

distinctions were introduced via adjustment factors (AF). For each technology, the capacity 

factors (CF) used in each run are multiplied by the AFs, in order to create the regional 

distinctions. An AF greater than one implies that the resource is more prevalent in a region; and, 

therefore, the cost of producing electricity from that technology is lower. The AFs are based on 

available resource levels as determined from resource maps in the Renewable Energy 

Technology Characterizations report.6

 The AFs for each region, and the subsequent regional 

CFs, are noted in Appendix D. 

Additionally, certain technologies are excluded from regions, due to prohibitively high costs or 

the absence of a resource base, by setting their respective AFs to zero. Table 1 documents these 

exclusions. 

 

6 U.S. Department of Energy/Electric Power Research Institute, 1997, Renewable Energy Technology Characterizations, Office 

of Utility Technologies, Energy Efficiency and Renewable Energy, U.S. Department of Energy, EPRI- TR109496. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-7

Table 1. Regional Exclusions of Green Technologies in the FY06 GPMM 

Technology Region 1 Region 2 Region 3 Region 4 Region 5 Region 6 Region 7 Region 8

Direct-Fired Biomass 

Biomass Gasification 

Landfill Gas 

Flash Geothermal X X X X X X 

Binary Geothermal X X X X X X 

Hot Dry Rock X X X X X X 

Solar Thermal Trough 

Solar Thermal Dish Hybrid 

Solar Central Receiver X X X X 

Residential PV 

Central Station PV 

Concentrator PV 

Onshore Wind Turbines 

X- Indicates regions where technology is assumed to be unavailable. 

Geothermal technologies are restricted to penetrate only in the Pacific and Mountain regions. 

Central receivers are restricted to regions west of the Mississippi, consistent with NEMS 

modeling procedures. Despite the fact that the central receiver technology is the only type of 

CSP technology modeled in NEMS, other CSP technologies (i.e., troughs and dishes) are 

allowed to compete more widely in the GPMM model. Although these CSP technologies are 

available in all regions, they are given substantial penalties in regions with lower solar insolation 

via the AFs. For example, the trough technology has a national average of 33% for its CF; 

however, due to the reductions introduced by the AFs for the New England and Middle Atlantic 

regions, the CF in these regions is about 23%. The reduction in CF also has the effect of 

increasing the COE, making this technology less competitive in these regions. 

Annual Energy Outlook Inputs 

The number of customers by economic sector for each region is determined by the number of 

residential housing units for the residential sector, the amount of commercial floor space for the 

commercial sector, and the industrial gross output for the industrial sector. This data is taken 

from the most recent Energy Information Administration’s (EIA) assumptions for the Annual 

Energy Outlook 2004, DOE/EIA-0383(2004).

7

 The residential housing-units data was updated 

using data provided by EIA.8 The commercial floor space and industrial gross output were 

updated from the AEO2004 supplemental data tables, Tables 22 and 23, respectively. The 

number of commercial establishments is calculated assuming 13,000 square feet per 

establishment; and the number of industrial establishments is calculated assuming $10 million of 

gross output per establishment. The regional energy consumption and prices were taken from 

Tables 1-20 of AEO2004 Supplemental Data Tables. 

 

7

 Energy Information Administration, 2004, Annual Energy Outlook 2004: With Projections to 2025, Office of Integrated 

Analysis and Forecasting, US Department of Energy, DOE/EIA-0383(2004). 

8 Personal contact with EIA, June 25, 2004, John Cymbalsky sent housing data in Excel file “AEO 2004 households data- from J 

Cymbalsky- 6-25-04.xls”. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-8

Other Inputs 

For the program case run, PERI includes additions to the green capacity and generation estimates 

to account for the Million Solar Roofs (MSR) program, as shown in Table 2. However, these 

additions are not included in the base case run, as they are expected to result from program 

actions and would not be expected to occur in the absence of the program. A primary means of 

deployment for PV is expected to be in distributed systems, which are customer-sited and 

customer-owned. This market for distributed systems will be easier for PV to compete in, 

because it allows PV to compete with retail electricity prices, not the very low competitive grid 

prices. The MSR initiative targets this application. The realization of MSR goals for PV (i.e., 

600,000 systems installed by 2010) has formed the basis for the distributed power-penetration 

projections since the FY2001 GPRA benefits reporting. Projections beyond 2010 assume 

declining annual growth rates, as would be expected to occur after the end of a major initiative. 

Table 2. Million Solar Roofs – Incremental Capacity Additions in the GPMM05 

Year Period 

FY2006 MSR Capacity Additions 

(above 2005 Baseline) 

2006-2010 773 

2011-2015 1,761 

2016-2020 1,348 

2021-2025 385 

2026-2030 0 

2031-2035 0 

Total for 2006-2035 4,267 

For both the base case and program case runs, the EIA “floors” builds are subtracted from the 

results of the model. EIA describes the inclusion of “Floors” capacity in the Renewable Fuels 

Module section of the Assumptions to the Annual Energy Outlook 2004, page 137, and in the 

Model Documentation- Renewable Fuels Module of the National Energy Modeling System

report, page 81. “Recognizing that some new solar generating capacity is installed for reasons 

other than represented in the EMM, such as for market testing or unique economic requirements, 

EIA includes estimates of minimal new grid-connected generating capacity using solar 

resources.”9 An additional 332.5 MW of central station PV and 75.5 MW of central station solar 

thermal capacity are “assumed by EIA to be installed for reasons in addition to least-cost 

electricity supply,” during the period 2001 to 2025.10 Table 3 shows the “floors” capacity 

additions, which are prorated for 2006 to 2025 and regionally divided among the regions that 

have capacity additions in these technologies. 

 

9

 Energy Information Administration, 2004, Model Documentation- Renewable Fuels Module of the National 

Energy Modeling System, U.S. Department of Energy, DOE/EIA-M069(2004). 10 Energy Information Administration, 2004, Assumptions to the Annual Energy Outlook 2004- Renewable Fuels 

Module, U.S. Department of Energy, DOE/EIA-0554(2004). 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-9

Table 3. EIA “Floors” Incremental Capacity Additions for PV and Solar Thermal in NEMS 

Year Period 

EIA PV “Floors” Capacity 

Additions (above 2005 Baseline) 

EIA Solar Thermal “Floors” Capacity 

Additions (above 2005 Baseline) 

2006-2010 69.3 15.7 

2011-2015 69.3 15.7 

2016-2020 69.3 15.7 

2021-2025 69.3 15.7 

2026-2030 0.0 0.0 

2031-2035 0.0 0.0 

Total for 2006-2035 277.1 62.9 

These amounts are then subtracted from the residential PV and solar central receiver green power 

builds, respectively, for each region. However, if the prorated regional portion of the "Floors" 

additions was greater than the regional builds in the GPMM, only the amount predicted to be 

built by the GPMM was subtracted (i.e., value reported as zero, no negative numbers reported), 

as shown in Tables 4 and 5, for the base case and reference case, respectively. 

Table 4. EIA “Floors” Incremental Capacity Subtracted from FY2006 GPMM - Base Case 

Year Period 

EIA PV “Floors” 

Capacity Additions 

Subtracted from the FY2006 GPMM 

(above 2005 Baseline) 

EIA Solar Thermal “Floors” 

Capacity Additions 

Subtracted from the FY2006 GPMM 

(above 2005 Baseline) 

2006-2010 0.0 7.4 

2011-2015 0.0 10.9 

2016-2020 0.0 7.9 

2021-2025 0.0 4.8 

2026-2030 0.0 0.0 

2031-2035 0.0 0.0 

Total for 2006-2035 0.0 31.0 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-10

Table 5. EIA “Floors” Incremental Capacity Subtracted from FY2006 GPMM - Reference Case 

Year Period 

EIA PV “Floors” 

Capacity Additions 

Subtracted from the FY2006 GPMM 

(above 2005 Baseline) 

EIA Solar Thermal “Floors” 

Capacity Additions 

Subtracted from the FY2006 GPMM 

(above 2005 Baseline) 

2006-2010 0.0 1.6 

2011-2015 0.0 4.4 

2016-2020 4.3 5.6 

2021-2025 3.8 3.5 

2026-2030 0.0 0.0 

2031-2035 0.0 0.0 

Total for 2006-2035 8.0 15.0 

As can be seen in Tables 4 and 5, not all of the PV or solar thermal “floors” additions in 

Table 3 were subtracted from the FY2006 GPMM model results. No Residential PV capacity is 

built by the model in the base case run, and very little capacity is built in the program case run, 

resulting in almost no PV subtractions in Tables 4 and 5. The solar central receiver technology 

competes better in the base case run, as its COE is more competitive relative to other renewable 

technologies in the base case. In both cases, the subtractions due to the “floors” capacity 

effectively eliminates the solar central receiver builds of the model, until after 2025, when no 

“floors” capacity is accounted for in NEMS. 

Results 

The results of the base case and program case runs of the model, and the incremental capacity 

between these two runs are shown in Tables 6, 7 and 8, respectively. The electricity generated 

from this capacity for the base case, program case runs, and the incremental difference is shown 

in Tables 9, 10 and 11, respectively. It should be noted that technologies with negative results 

in the incremental difference between the program and base cases are treated as zeros for 

inclusion in NEMS (i.e., no capacity additions for the technology due to penetration in the green 

power markets). A detailed accounting of the results for the base case and program cases is 

given by NEMS region, in Appendix B. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-11

Table 6. Green Power Capacity Constructed in the GPMM- Base Case (MW) 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 

Specif

Technol

Total

Direct-Fired Biomass 28 31 16 10 6 7 98 Biomass Gasification 327 316 144 69 41 44 941 Landfill Gas 54 66 63 50 33 35 302 Flash Geothermal 12 17 12 4 2 2 49 Binary Geothermal 12 17 12 4 2 2 49 Enhanced Geothermal Systems 5 14 17 22 13 14 85 Solar Thermal Trough 9 14 9 5 3 3 44 Solar Thermal Dish Hybrid 7 10 7 4 2 3 34 Solar Central Receiver 0 0 0 0 2 3 5 Residential PV 0 0 0 0 0 0 0 Central Station PV 0 0 0 0 0 0 0 Concentrator PV 0 0 0 1 0 1 2 Wind 762 632 256 119 72 77 1,920 GPMM Total 1,217 1,116 537 288 178 192 3,529 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-12

Table 7. Green Power Capacity Constructed in the GPMM- Program Case (MW) 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 

Specif

Technol

Total

Direct-Fired Biomass 27 35 26 11 6 6 111 Biomass Gasification 182 175 90 35 18 19 519 Landfill Gas 60 64 60 47 27 29 287 Flash Geothermal 7 10 6 5 3 3 34 Binary Geothermal 7 10 6 5 3 3 34 Enhanced Geothermal Systems 4 8 9 10 5 6 41.3 Solar Thermal Trough 4 24 30 19 10 11 97 Solar Thermal Dish Hybrid 3 19 23 15 8 8 76 Solar Central Receiver 0 0 0 0 2 2 4 Residential PV 773 1,761 1,348 385 0 0 4,267 Central Station PV 0 0 0 0 1 1 2 Concentrator PV 0 0 4 4 1 1 11 Wind 1,372 1,239 448 262 198 210 3,729 GPMM Total 2,440 3,344 2,051 796 282 300 9,212 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-13

Table 8. Green Power Capacity Constructed in the GPMM- Incremental between Program Case an

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 

Spe

Tech

To

Direct-Fired Biomass -1 4 10 1 0 -1 1Biomass Gasification -145 -141 -54 -34 -23 -25 -4Landfill Gas 6 -2 -3 -3 -6 -6 -Flash Geothermal -5 -7 -6 1 1 1 -Binary Geothermal -5 -7 -6 1 1 1 -Enhanced Geothermal Systems -1 -6 -8 -12 -8 -8 -4Solar Thermal Trough -5 10 21 14 7 8 5Solar Thermal Dish Hybrid -4 9 16 11 6 5 4Solar Central Receiver 0 0 0 0 0 -1 -Residential PV 773 1,761 1,348 385 0 0 4,2Central Station PV 0 0 0 0 1 1 Concentrator PV 0 0 4 3 1 0 Wind 610 607 192 143 126 133 1,GPMM Total 1,224 2,227 1,515 508 106 109 5,
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-14

Table 9. Green Power Generation from Capacity Constructed in the GPMM- Base Case 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 

Specific 

Technolo

Totals 

Direct-Fired Biomass 203,345 226,184 118,849 79,465 47,821 51,252 726,918 Biomass Gasification 2,382,657 2,311,085 1,063,801 517,959 311,251 333,200 6,919,95Landfill Gas 430,441 520,977 505,734 413,013 274,315 292,667 2,437,14Flash Geothermal 103,733 156,729 117,709 49,216 27,331 30,134 484,853 Binary Geothermal 103,733 156,729 117,709 49,216 27,331 30,134 484,853 Enhanced Geothermal Systems 0 0 0 0 0 0 0.0 Solar Thermal Trough 11,655 17,187 12,269 7,502 4,379 4,779 57,771 Solar Thermal Dish Hybrid 11,655 17,187 12,269 7,502 4,379 4,779 57,771 Solar Central Receiver 1,945 2,803 1,949 1,233 4,379 4,779 17,088 Residential PV 0 0 0 0 0 0 0 Central Station PV 0 0 0 0 0 0 0 Concentrator PV 0 0 0 1,845 1,187 1,290 4,321 Wind 2,613,395 2,218,216 927,525 452,599 272,147 292,004 6,775,88GPMM Total 5,862,560 5,627,097 2,877,814 1,579,549 974,519 1,045,019 17,966,55
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-15

Table 10. Green Power Generation from Capacity Constructed in the GPMM- Program Ca

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 

Specific 

Technolo

Totals 

Direct-Fired Biomass 187,902 247,839 180,982 78,120 40,784 44,090 779,718 Biomass Gasification 1,275,579 1,226,983 631,830 242,111 125,630 135,760 3,637,89Landfill Gas 476,017 502,570 470,259 373,657 212,817 230,067 2,265,38Flash Geothermal 60,450 79,644 54,513 41,294 22,225 24,622 282,747 Binary Geothermal 60,450 79,644 54,513 41,294 22,225 24,622 282,747 Enhanced Geothermal Systems 26,650 58,742 66,084 71,331 39,702 44,022 306,531 Solar Thermal Trough 16,109 84,255 107,385 67,047 36,097 39,192 350,084 Solar Thermal Dish Hybrid 16,109 84,255 107,385 67,047 36,097 39,192 350,084 Solar Central Receiver 3,774 12,958 19,760 12,695 16,983 18,659 84,831 Residential PV 1,625,155 3,702,326 2,834,035 809,424 0 0 8,970,94Central Station PV 0 0 16 326 2,510 2,736 5,587 Concentrator PV 0 0 8,986 8,245 2,510 2,736 22,476 Wind 5,622,554 5,265,935 1,928,285 1,164,033 887,251 934,466 15,802,52GPMM Total 9,370,750 11,345,152 6,464,031 2,976,624 1,444,830 1,540,163 33,141,55
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-16

Table 11. Green Power Generation from Capacity Constructed in the GPMM- Incremental between Program Cas

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 

Spe

Techn

To

Direct-Fired Biomass -15,443 21,654 62,133 -1,345 -7,037 -7,162 52,Biomass Gasification -1,107,078 -1,084,101 -431,971 -275,847 -185,620 -197,440 -3,28Landfill Gas 45,576 -18,407 -35,475 -39,355 -61,498 -62,600 -171Flash Geothermal -43,283 -77,085 -63,197 -7,922 -5,107 -5,512 -202Binary Geothermal -43,283 -77,085 -63,197 -7,922 -5,107 -5,512 -202Enhanced Geothermal Systems 26,650 58,742 66,084 71,331 39,702 44,022 306Solar Thermal Trough 4,454 67,068 95,116 59,544 31,718 34,413 292Solar Thermal Dish Hybrid 4,454 67,068 95,116 59,544 31,718 34,413 292Solar Central Receiver 1,829 10,155 17,812 11,463 12,605 13,880 67,Residential PV 1,625,155 3,702,326 2,834,035 809,424 0 0 8,97Central Station PV 0 0 16 326 2,510 2,736 5,5Concentrator PV 0 0 8,986 6,400 1,323 1,446 18,Wind 3,009,158 3,047,719 1,000,759 711,434 615,104 642,462 9,02GPMM Total 3,508,191 5,718,055 3,586,217 1,397,074 470,311 495,144 15,17
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-17

1.4 Sources 

1) The Green Power Network, 2004, Green Power Markets- Green Marketing, U.S. Department 

of Energy, Office of Energy Efficiency and Renewable Energy, available on the Internet at: 

http://www.eere.energy.gov/greenpower/markets/marketing.shtml. 

2) Office of Management and Budget, Budget of the United States Government: Fiscal Year 

2005, Table 10.1—GROSS DOMESTIC PRODUCT AND DEFLATORS USED IN THE 

HISTORICAL TABLES: 1940–2009, available on the Internet at: 

http://www.gpoaccess.gov/usbudget/fy05/sheets/hist10z1.xls. 

3) Swezey, Blair, R. Wiser, M. Bolinger, and E. Holt, 2001, Growing the Green Power Market: 

Forecasting the Impacts of Customer Demand for Renewable Energy, National Renewable 

Energy Laboratory, US Department of Energy, NREL/TP-620-30101 LBNL-48611. 

4) U.S. Department of Energy/Electric Power Research Institute, 1997, Renewable Energy 

Technology Characterizations, Office of Utility Technologies, Energy Efficiency and 

Renewable Energy, U.S. Department of Energy, EPRI- TR109496. 

5) Energy Information Administration, 2004, Annual Energy Outlook 2004: With Projections 

to 2025, Office of Integrated Analysis and Forecasting, US Department of Energy, 

DOE/EIA-0383(2004). 

6) Personal contact with EIA, June 25, 2004, John Cymbalsky sent housing data in Excel file 

“AEO 2004 households data- from J Cymbalsky- 6-25-04.xls”. 

7) Energy Information Administration, 2004, Model Documentation- Renewable Fuels Module 

of the National Energy Modeling System, U.S. Department of Energy, DOE/EIAM069(2004). 

8) Energy Information Administration, 2004, Assumptions to the Annual Energy Outlook 2004- 

Renewable Fuels Module, U.S. Department of Energy, DOE/EIA-0554(2004). 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-a-18

Appendices 

Appendix A: National Results of the GPMM

Base Case

Cumulative Capacity Additions from 2003 Baseline (MW)

* Includes MSR additions for PV Residential and EIA "Floors" subtractions for PV Central 

Station and CSP Troughs 

Green Revenues ($millions/period) 

 2010 2015 2020 2025 2030 2035 

Biomass (incl. LFG) 409 821 1,045 1,174 1,255 1,341 

Geothermal 29 77 118 147 165 184 

CSP 17 41 57 67 74 83 

PV 0 0 0 1 1 2 

Wind 762 1,395 1,651 1,770 1,842 1,920 

Total 1,217 2,334 2,871 3,159 3,337 3,529 

 2010 2015 2020 2025 2030 2035 

Residential 265 259 132 72 42 45 

Commercial 43 40 20 14 10 11 

Industrial 14 13 7 5 4 4 

Total ($millions/year) 322 312 160 91 56 60 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-a-19

Appendix A: National Results of the GPMM (Continued)

Program Case

Cumulative Capacity Additions from 2003 Baseline (MW)

* Includes MSR additions for PV Residential and EIA "Floors" subtractions for PV Central 

Station and CSP Troughs 

Green Revenues ($millions/period) 

 2010 2015 2020 2025 2030 2035 

Biomass (incl. LFG) 269 543 719 812 863 918 

Geothermal 18 45 67 86 97 109 

CSP 7 50 103 136 156 177 

PV 773 2,534 3,886 4,275 4,277 4,280 

Wind 1,372 2,612 3,059 3,321 3,519 3,729 

Total 2,440 5,784 7,835 8,631 8,912 9,212 

 2010 2015 2020 2025 2030 2035 

Residential 265 259 132 72 42 45 

Commercial 43 40 20 14 10 11 

Industrial 14 13 7 5 4 4 

Total ($millions/year) 322 312 160 91 56 60 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-b-20

Appendix B: Capacity Installed by NEMS region 

Capacity Installed by NEMS region- GPMM FY06- Base Case

Direct Fired Biomass 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 3.2 3.9 2.2 1.3 0.8 0.8 12.3 

2 2.7 3.8 2.4 1.4 0.9 1.0 12.3 

3 6.8 5.2 0.9 0.8 0.6 0.7 15.1 

4 1.8 2.1 1.1 0.6 0.4 0.4 6.5 

5 0.2 0.5 0.5 0.3 0.1 0.1 1.8 

6 4.4 3.2 0.3 0.3 0.3 0.3 8.9 

7 4.0 2.8 0.5 0.6 0.5 0.6 9.0 

8 0.3 0.9 1.1 0.6 0.3 0.3 3.6 

9 1.0 2.8 3.2 1.9 0.8 0.9 10.7 

10 2.8 4.1 2.7 1.6 1.0 1.0 13.2 

11 0.2 0.4 0.4 0.3 0.2 0.2 1.6 

12 0.4 0.6 0.5 0.4 0.2 0.3 2.4 

13 0.1 0.2 0.3 0.2 0.1 0.1 0.9 

Total 28 31 16 10 6 7 98 

 

Biomass Gasification 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 41.5 44.0 22.2 10.0 6.0 6.3 130.0 

2 30.6 37.6 21.1 9.1 5.9 6.4 110.7 

3 90.8 61.0 8.9 6.1 4.9 5.1 176.8 

4 24.3 23.9 10.5 4.8 3.1 3.3 69.8 

5 2.9 5.5 4.3 1.9 0.7 0.8 16.2 

6 59.1 37.2 2.5 2.6 2.6 2.7 106.6 

7 23.0 13.5 2.1 2.1 1.8 1.9 44.4 

8 4.3 10.6 10.2 4.4 1.9 2.1 33.6 

9 12.9 31.8 30.6 13.3 5.8 6.4 100.8 

10 31.3 40.3 23.7 10.2 6.2 6.8 118.6 

11 2.1 3.4 2.5 1.4 0.8 0.9 11.1 

12 3.3 4.9 3.3 2.0 1.3 1.4 16.0 

13 0.9 2.0 1.9 0.8 0.3 0.3 6.1 

Total 327 316 144 69 41 44 941 

 

Landfill Gas 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 7.8 8.4 8.4 7.2 4.3 4.5 40.5 

2 5.2 5.2 5.2 5.2 4.9 5.2 31.1 

3 5.5 6.1 5.0 4.4 3.5 3.6 28.2 

4 3.7 3.7 3.7 3.4 2.2 2.3 18.9 

5 1.2 3.2 3.1 1.6 0.6 0.6 10.4 

6 2.5 2.5 1.8 1.8 1.8 1.8 12.2 

7 2.4 2.4 2.4 2.4 2.4 2.4 14.5 

8 3.6 4.8 4.8 3.4 1.5 1.6 19.7 

9 10.7 14.3 14.3 10.2 4.5 4.9 59.0 

10 5.9 7.4 7.4 6.3 5.2 5.6 37.8 

11 2.0 2.4 2.3 1.5 0.8 0.9 9.9 

12 3.0 3.0 3.0 2.1 1.3 1.5 13.9 

13 1.0 2.0 1.9 0.9 0.3 0.3 6.4 

Total 54 66 63 50 33 35 302 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-b-21

Capacity Installed by NEMS region- GPMM FY06- Base Case (Continued)

Binary Geothermal 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

11 4.1 5.7 3.9 1.3 0.8 0.8 16.5 

12 6.4 8.5 5.3 1.8 1.2 1.3 24.6 

13 1.4 2.8 2.5 0.8 0.3 0.3 8.2 

Total 12 17 12 4 2 2 49 

 

Flash Geothermal 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

11 4.1 5.7 3.9 1.3 0.8 0.8 16.5 

12 6.4 8.5 5.3 1.8 1.2 1.3 24.6 

13 1.4 2.8 2.5 0.8 0.3 0.3 8.2 

Total 12 17 12 4 2 2 49 

 

Enhanced Geothermal 

Systems 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

11 1.8 4.6 5.5 7.3 4.4 4.8 28.5 

12 2.8 6.7 7.4 10.5 6.9 7.7 41.9 

13 0.7 2.6 4.1 4.3 1.5 1.6 14.8 

Total 5 14 17 22 13 14 85 

 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-b-22

 

Capacity Installed by NEMS region- GPMM FY06- Base Case (Continued)

Solar Thermal DishHybrid 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2 2.9 3.8 2.4 1.2 0.8 0.9 12.0 

3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

5 0.1 0.3 0.3 0.2 0.0 0.0 0.8 

6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

10 3.0 4.0 2.6 1.3 0.8 0.9 12.6 

11 0.4 0.8 0.6 0.4 0.2 0.2 2.7 

12 0.6 1.0 0.7 0.5 0.3 0.4 3.5 

13 0.2 0.6 0.7 0.3 0.1 0.1 2.1 

Total 7 10 7 4 2 3 34 

 

Solar Central Receiver 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2 0.0 0.0 0.0 0.0 0.8 0.9 1.6 

3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

5 0.0 0.0 0.0 0.0 0.0 0.0 0.1 

6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

10 0.0 0.0 0.0 0.0 0.8 0.9 1.7 

11 0.0 0.0 0.0 0.0 0.2 0.2 0.5 

12 0.0 0.0 0.0 0.0 0.3 0.4 0.7 

13 0 0 0 0 0 0 0 

Total 0 0 0 0 2 3 5 

 

Solar Thermal Trough 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2 3.8 5.0 3.1 1.6 1.0 1.1 15.7 

3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

5 0.1 0.3 0.4 0.2 0.1 0.1 1.1 

6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

10 3.9 5.3 3.4 1.7 1.1 1.2 16.5 

11 0.5 1.0 0.8 0.5 0.3 0.3 3.5 

12 0.8 1.3 1.0 0.7 0.4 0.5 4.6 

13 0.3 0.8 0.9 0.5 0.2 0.2 2.8 

Total 9 14 9 5 3 3 44 

* Includes Subtractions of NEMS "Floor" Capacity Additions 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-b-23

Capacity Installed by NEMS region- GPMM FY06- Base Case (Continued)

PV Residential 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

Total 0.0 0.0 0.0 0.0 0.0 0.00 0.00 

 

Central Station PV 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

Total 0.0 0.0 0.0 0.0 0.0 0.00 0.00 

* Includes Subtractions of NEMS "Floor" Capacity Additions 

 

Concentrator PV 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

2 0.0 0.0 0.0 0.4 0.2 0.3 0.9 

3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

10 0.0 0.0 0.0 0.4 0.2 0.3 0.9 

11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

Total 0 0 0 1 0 1 2 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-b-24

Capacity Installed by NEMS region- GPMM FY06- Base Case (Continued)

Wind 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 89.3 83.3 36.8 16.0 9.6 10.2 245.2 

2 73.2 81.3 39.8 17.0 11.0 12.0 234.3 

3 134.3 81.3 12.3 7.8 5.9 6.1 247.7 

4 50.9 45.9 17.9 7.8 5.0 5.3 132.7 

5 6.1 9.5 6.5 2.9 1.1 1.2 27.3 

6 85.0 48.3 3.0 2.9 2.9 3.0 145.1 

7 175.9 96.9 13.9 8.5 7.2 7.5 309.9 

8 13.6 17.8 15.6 6.8 3.0 3.3 60.1 

9 40.7 53.3 46.9 20.5 9.1 9.9 180.4 

10 74.7 85.6 43.6 18.7 11.6 12.6 246.8 

11 6.3 9.6 6.4 3.4 1.9 2.2 29.8 

12 9.6 13.3 8.1 4.7 3.0 3.3 42.0 

13 2.8 6.3 5.5 2.3 0.8 0.8 18.5 

Total 762 632 256 119 72 77 1,920 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-b-25

Appendix B: Capacity Installed by NEMS region (Continued) 

Capacity Installed by NEMS region- GPMM FY06- Program Case

Direct Fired Biomass 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 3 4 3 1 1 1 13 

2 2 3 2 1 1 1 9 

3 5 5 2 1 1 1 14 

4 2 2 1 0 0 0 5 

5 0 0 0 0 0 0 1 

6 3 2 0 0 0 0 7 

7 4 2 0 0 0 0 8 

8 1 3 3 1 1 1 9 

9 3 9 9 4 2 2 28 

10 2 3 2 1 1 1 10 

11 0 1 1 0 0 0 2 

12 0 1 1 0 0 0 3 

13 0 0 0 0 0 0 1 

Total 27 35 26 11 6 6 111 

 

Biomass Gasification 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 23 22 12 5 2 3 67 

2 15 14 7 3 2 2 42 

3 42 27 7 3 2 2 83 

4 12 9 4 2 1 1 28 

5 1 2 2 1 0 0 6 

6 26 13 1 1 1 1 42 

7 12 5 1 1 0 1 20 

8 8 15 11 4 2 2 42 

9 23 46 34 12 5 5 125 

10 15 15 8 3 2 2 45 

11 2 2 1 1 0 0 6 

12 2 3 2 1 1 1 9 

13 1 1 1 0 0 0 3 

Total 182 175 90 35 18 19 519 

 

Landfill Gas 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 8 8 8 6 3 3 38 

2 5 5 5 4 3 3 26 

3 6 6 4 4 3 3 26 

4 4 4 4 2 1 1 16 

5 1 2 2 1 0 0 7 

6 3 3 1 1 1 1 9 

7 2 2 2 2 1 2 12 

8 5 5 5 5 2 3 24 

9 14 14 14 14 7 8 72 

10 6 7 7 5 3 3 30 

11 2 2 2 1 1 1 9 

12 3 3 3 2 1 1 13 

13 1 2 2 1 0 0 6 

Total 60 64 60 47 27 29 287 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-b-26

Capacity Installed by NEMS region- GPMM FY06- Program Case (Cont.)

Binary Geothermal 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 0 0 0 0 0 0 0 

2 0 0 0 0 0 0 0 

3 0 0 0 0 0 0 0 

4 0 0 0 0 0 0 0 

5 0 0 0 0 0 0 0 

6 0 0 0 0 0 0 0 

7 0 0 0 0 0 0 0 

8 0 0 0 0 0 0 0 

9 0 0 0 0 0 0 0 

10 0 0 0 0 0 0 0 

11 2 3 2 2 1 1 11 

12 4 5 3 2 1 2 16 

13 1 2 2 1 0 0 6 

Total 7 10 6 5 3 3 34 

 

Flash Geothermal 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 0 0 0 0 0 0 0 

2 0 0 0 0 0 0 0 

3 0 0 0 0 0 0 0 

4 0 0 0 0 0 0 0 

5 0 0 0 0 0 0 0 

6 0 0 0 0 0 0 0 

7 0 0 0 0 0 0 0 

8 0 0 0 0 0 0 0 

9 0 0 0 0 0 0 0 

10 0 0 0 0 0 0 0 

11 2 3 2 2 1 1 11 

12 4 5 3 2 1 2 16 

13 1 2 2 1 0 0 6 

Total 7 10 6 5 3 3 34 

 

Enhanced Geothermal 

Systems 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 0 0 0 0 0 0 0 

2 0 0 0 0 0 0 0 

3 0 0 0 0 0 0 0 

4 0 0 0 0 0 0 0 

5 0 0 0 0 0 0 0 

6 0 0 0 0 0 0 0 

7 0 0 0 0 0 0 0 

8 0 0 0 0 0 0 0 

9 0 0 0 0 0 0 0 

10 0 0 0 0 0 0 0 

11 1 3 3 3 2 2 14 

12 2 4 4 5 3 3 20 

13 0 1 2 2 1 1 7 

Total 4 8 9 10 5 6 41 

 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-b-27

 

Capacity Installed by NEMS region- GPMM FY06- Program Case (Cont.)

Solar Thermal DishHybrid 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 0 2 2 2 1 1 8 

2 1 2 2 1 1 1 9 

3 0 2 1 1 1 1 6 

4 0 1 1 1 0 0 3 

5 0 0 0 0 0 0 1 

6 0 1 0 0 0 0 2 

7 0 1 0 0 0 0 3 

8 0 1 2 1 1 1 6 

9 1 4 7 4 2 2 19 

10 1 2 3 2 1 1 9 

11 0 1 1 1 0 0 3 

12 0 1 1 1 1 1 5 

13 0 0 1 1 0 0 2 

Total 3 19 23 15 8 8 76 

 

Solar Central Receiver 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 0 0 0 0 0 0 0 

2 0 0 0 0 1 1 1 

3 0 0 0 0 0 0 0 

4 0 0 0 0 0 0 0 

5 0 0 0 0 0 0 0 

6 0 0 0 0 0 0 0 

7 0 0 0 0 0 0 0 

8 0 0 0 0 0 0 0 

9 0 0 0 0 0 0 0 

10 0 0 0 0 1 1 1 

11 0 0 0 0 0 0 1 

12 0 0 0 0 0 0 1 

13 0 0 0 0 0 0 0 

Total 0 0 0 0 2 2 4 

 

Solar Thermal Trough 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 0 2 3 2 1 1 10 

2 1 3 3 2 1 1 11 

3 0 3 2 1 1 1 8 

4 0 1 1 1 0 0 3 

5 0 0 0 0 0 0 1 

6 0 1 0 0 0 0 3 

7 0 1 1 1 0 0 3 

8 0 2 3 2 1 1 8 

9 1 5 9 5 2 2 24 

10 1 3 3 2 1 1 12 

11 0 1 1 1 1 1 4 

12 0 1 2 1 1 1 6 

13 0 1 1 1 0 0 3 

Total 4 24 30 19 10 11 97 

* Includes Subtractions of NEMS "Floor" Capacity Additions 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-b-28

Capacity Installed by NEMS region- GPMM FY06- Program Case (Cont.)

PV Residential 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 80 183 140 40 0 0 443 

2 73 167 127 36 0 0 404 

3 70 159 122 35 0 0 385 

4 21 47 36 10 0 0 113 

5 5 12 9 3 0 0 29 

6 15 35 27 8 0 0 84 

7 20 47 36 10 0 0 113 

8 94 214 164 47 0 0 518 

9 282 641 491 140 0 0 1,554 

10 76 173 132 38 0 0 418 

11 13 31 23 7 0 0 74 

12 24 54 42 12 0 0 132 

13 0 0 0 0 0 0 0 

Total 773 1,761 1,348 385 0 0 4,267 

* Includes Additions of MSR Capacity Additions 

 

Central Station PV 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 0 0 0 0 0 0 0 

2 0 0 0 0 0 0 0 

3 0 0 0 0 0 0 0 

4 0 0 0 0 0 0 0 

5 0 0 0 0 0 0 0 

6 0 0 0 0 0 0 0 

7 0 0 0 0 0 0 0 

8 0 0 0 0 0 0 0 

9 0 0 0 0 1 1 1 

10 0 0 0 0 0 0 0 

11 0 0 0 0 0 0 0 

12 0 0 0 0 0 0 0 

13 0 0 0 0 0 0 0 

Total 0 0 0 0 1 1 2 

* Includes Subtractions of NEMS "Floor" Capacity Additions 

 

Concentrator PV 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 0 0 0 0 0 0 1 

2 0 0 0 0 0 0 0 

3 0 0 0 0 0 0 1 

4 0 0 0 0 0 0 0 

5 0 0 0 0 0 0 0 

6 0 0 0 0 0 0 0 

7 0 0 0 0 0 0 0 

8 0 0 1 1 0 0 2 

9 0 0 3 2 1 1 6 

10 0 0 0 0 0 0 0 

11 0 0 0 0 0 0 0 

12 0 0 0 0 0 0 0 

13 0 0 0 0 0 0 0 

Total 0 0 4 4 1 1 11 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-b-29

Capacity Installed by NEMS region- GPMM FY06- Program Case (Cont.)

Wind 

2006-

2010 

2011-

2015 

2016-

2020 

2021-

2025 

2026-

2030 

2031-

2035 Total 

1 160 164 69 39 27 28 487 

2 142 179 91 46 36 38 532 

3 316 204 16 18 19 20 592 

4 99 101 43 24 16 17 300 

5 13 27 21 11 4 4 79 

6 210 136 11 12 13 13 395 

7 231 139 22 20 19 19 450 

8 0 0 0 0 0 0 0 

9 0 0 0 0 0 0 0 

10 146 192 104 52 38 41 573 

11 18 33 23 14 9 10 107 

12 28 47 32 20 14 15 156 

13 7 18 17 8 3 3 57 

Total 1,372 1,239 448 262 198 210 3,729 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-c-30

Appendix C: Regional Green Power Access and Market Penetration Parameters 

Fraction of each region assumed to have competitively set electricity rates in the AEO2004 

NEMS Regions 2005 2010 2015 2020 2025 2030 2035

1 ECAR 32% 63% 63% 63% 63% 63% 63%

2 ERCOT 40% 90% 100% 100% 100% 100% 100%

3 Mid Atlantic 70% 100% 100% 100% 100% 100% 100%

4 MAIN 33% 55% 55% 55% 55% 55% 55%

5 MAPP 0% 0% 0% 0% 0% 0% 0%

6 New York 70% 100% 100% 100% 100% 100% 100%

7 New England 70% 100% 100% 100% 100% 100% 100%

8 Florida 0% 0% 0% 0% 0% 0% 0%

9 SERC 7% 13% 13% 13% 13% 13% 13%

10 SPP 12% 33% 41% 41% 41% 41% 41%

11 NWP 10% 22% 25% 25% 25% 25% 25%

12 RA 26% 59% 65% 65% 65% 65% 65%

13 California 5% 5% 5% 5% 5% 5% 5%

Fraction of each region assumed to have competitively set electricity rates in the AEO2004 

Census Regions 2005 2010 2015 2020 2025 2030 2035

1 New England 70% 100% 100% 100% 100% 100% 100%

2 Mid. Atlantic 70% 100% 100% 100% 100% 100% 100%

3 E. N. Central 31% 58% 58% 58% 58% 58% 58%

4 W.N. Central 5% 14% 18% 18% 18% 18% 18%

5 S. Atl. & E.S. Central 14% 24% 24% 24% 24% 24% 24%

6 W.S. Central 26% 62% 71% 71% 71% 71% 71%

7 Mountain 20% 46% 51% 51% 51% 51% 51%

8 Pacific 6% 9% 10% 10% 10% 10% 10%

Percent of All Customers Participating in Green Programs (both marketing and pricing) 

 2005 2010 2015 2020 2025 2030 2035

New England 4.2% 10.7% 13.9% 14.1% 14.2% 14.2% 14.2%

Mid. Atlantic 4.0% 10.6% 14.3% 14.3% 14.3% 14.3% 14.3%

E. N. Central 1.1% 4.2% 6.8% 7.7% 7.9% 7.9% 7.9%

W.N. Central 0.0% 0.3% 1.1% 1.8% 2.0% 2.0% 2.0%

S. Atl. & E.S. 

Central 0.2% 0.8% 1.8% 2.6% 2.9% 2.9% 2.9%

W.S. Central 0.7% 3.9% 7.2% 8.5% 8.8% 8.8% 8.8%

Mountain 0.6% 2.6% 5.1% 6.3% 6.8% 6.8% 6.8%

Pacific 0.0% 0.2% 0.7% 1.1% 1.2% 1.2% 1.2%
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-c-31

Appendix C: Regional Green Power Access and Market Penetration Parameters (Cont.) 

Customer Penetration Yearly Addition- Incremental Penetration 

 2005 2010 2015 2020 2025 2030 2035

New England 1.3% 0.9% 0.7% 0.0% 0.0% 0.0% 0.0%

Mid. Atlantic 1.4% 1.0% 0.7% 0.0% 0.0% 0.0% 0.0%

E. N. Central 0.5% 0.7% 0.5% 0.2% 0.0% 0.0% 0.0%

W.N. Central 0.0% 0.1% 0.2% 0.1% 0.0% 0.0% 0.0%

S. Atl. & E.S. 

Central 0.1% 0.1% 0.2% 0.2% 0.1% 0.0% 0.0%

W.S. Central 0.4% 0.8% 0.6% 0.3% 0.1% 0.0% 0.0%

Mountain 0.3% 0.5% 0.4% 0.3% 0.1% 0.0% 0.0%

Pacific 0.0% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0%
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-d-32

Appendix D: Regional Adjustment Factors and Capacity Factors

Direct-Fired Biomass 2005 2010 2015 2020 2025 2030 2035 

National Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

New England Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

Mid. Atlantic Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

E. N. Central Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

W.N. Central Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

S. Atl. & E.S. Central Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

W.S. Central Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

Mountain Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

Pacific Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

 

Biomass Gasification 2005 2010 2015 2020 2025 2030 2035 

National Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

New England Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

Mid. Atlantic Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

E. N. Central Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

W.N. Central Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

S. Atl. & E.S. Central Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

W.S. Central Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

Mountain Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

Pacific Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 80.00% 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-d-33

Appendix D: Regional Adjustment Factors and Capacity Factors (Continued)

Landfill Gas 2005 2010 2015 2020 2025 2030 2035 

National Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 

New England Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 

Mid. Atlantic Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 

E. N. Central Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 

W.N. Central Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 

S. Atl. & E.S. Central Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 

W.S. Central Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 

Mountain Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 

Pacific Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 90.00% 

 

Flash Geothermal 2005 2010 2015 2020 2025 2030 2035 

National Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

New England Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

Mid. Atlantic Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

E. N. Central Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

W.N. Central Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

S. Atl. & E.S. Central Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

W.S. Central Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

Mountain Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 93.00% 95.00% 95.50% 96.00% 96.00% 96.00% 96.00% 

Pacific Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 93.00% 95.00% 95.50% 96.00% 96.00% 96.00% 96.00% 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-d-34

 

Appendix D: Regional Adjustment Factors and Capacity Factors (Continued)

Binary Geothermal 2005 2010 2015 2020 2025 2030 2035 

National Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 93.00% 95.00% 95.50% 96.00% 96.00% 96.00% 96.00% 

New England Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

Mid. Atlantic Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

E. N. Central Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

W.N. Central Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

S. Atl. & E.S. Central Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

W.S. Central Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

Mountain Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 93.00% 95.00% 95.50% 96.00% 96.00% 96.00% 96.00% 

Pacific Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 93.00% 95.00% 95.50% 96.00% 96.00% 96.00% 96.00% 

 

Enhanced Geothermal Systems 2005 2010 2015 2020 2025 2030 2035 

National Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 82.00% 83.00% 84.00% 85.00% 85.00% 85.00% 85.00% 

New England Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

Mid. Atlantic Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

E. N. Central Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

W.N. Central Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

S. Atl. & E.S. Central Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

W.S. Central Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

Mountain Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 82.00% 83.00% 84.00% 85.00% 85.00% 85.00% 85.00% 

Pacific Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 82.00% 83.00% 84.00% 85.00% 85.00% 85.00% 85.00% 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-d-35

 

Appendix D: Regional Adjustment Factors and Capacity Factors (Continued)

CSP Trough 2005 2010 2015 2020 2025 2030 2035 

National Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 41.70% 51.20% 51.20% 51.20% 51.20% 51.20% 51.20% 

New England Adjustment 0.69 0.69 0.69 0.69 0.69 0.69 0.69 

 Cap Factor 28.84% 35.41% 35.41% 35.41% 35.41% 35.41% 35.41% 

Mid. Atlantic Adjustment 0.70 0.70 0.70 0.70 0.70 0.70 0.70 

 Cap Factor 29.32% 36.00% 36.00% 36.00% 36.00% 36.00% 36.00% 

E. N. Central Adjustment 0.72 0.72 0.72 0.72 0.72 0.72 0.72 

 Cap Factor 29.99% 36.82% 36.82% 36.82% 36.82% 36.82% 36.82% 

W.N. Central Adjustment 0.81 0.81 0.81 0.81 0.81 0.81 0.81 

 Cap Factor 33.90% 41.62% 41.62% 41.62% 41.62% 41.62% 41.62% 

S. Atl. & E.S. Central Adjustment 0.75 0.75 0.75 0.75 0.75 0.75 0.75 

 Cap Factor 31.37% 38.52% 38.52% 38.52% 38.52% 38.52% 38.52% 

W.S. Central Adjustment 0.91 0.91 0.91 0.91 0.91 0.91 0.91 

 Cap Factor 38.09% 46.77% 46.77% 46.77% 46.77% 46.77% 46.77% 

Mountain Adjustment 0.98 0.98 0.98 0.98 0.98 0.98 0.98 

 Cap Factor 40.96% 50.29% 50.29% 50.29% 50.29% 50.29% 50.29% 

Pacific Adjustment 0.84 0.84 0.84 0.84 0.84 0.84 0.84 

 Cap Factor 34.82% 42.76% 42.76% 42.76% 42.76% 42.76% 42.76% 

 

Solar Thermal Dish- Hybrid 2005 2010 2015 2020 2025 2030 2035 

National Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 50.00% 50.00% 50.00% 50.00% 50.00% 50.00% 50.00% 

New England Adjustment 0.91 0.91 0.91 0.91 0.91 0.91 0.91 

 Cap Factor 45.28% 45.28% 45.28% 45.28% 45.28% 45.28% 45.28% 

Mid. Atlantic Adjustment 0.92 0.92 0.92 0.92 0.92 0.92 0.92 

 Cap Factor 45.95% 45.95% 45.95% 45.95% 45.95% 45.95% 45.95% 

E. N. Central Adjustment 0.94 0.94 0.94 0.94 0.94 0.94 0.94 

 Cap Factor 46.94% 46.94% 46.94% 46.94% 46.94% 46.94% 46.94% 

W.N. Central Adjustment 1.06 1.06 1.06 1.06 1.06 1.06 1.06 

 Cap Factor 53.23% 53.23% 53.23% 53.23% 53.23% 53.23% 53.23% 

S. Atl. & E.S. Central Adjustment 0.98 0.98 0.98 0.98 0.98 0.98 0.98 

 Cap Factor 49.08% 49.08% 49.08% 49.08% 49.08% 49.08% 49.08% 

W.S. Central Adjustment 1.19 1.19 1.19 1.19 1.19 1.19 1.19 

 Cap Factor 59.71% 59.71% 59.71% 59.71% 59.71% 59.71% 59.71% 

Mountain Adjustment 1.29 1.29 1.29 1.29 1.29 1.29 1.29 

 Cap Factor 64.28% 64.28% 64.28% 64.28% 64.28% 64.28% 64.28% 

Pacific Adjustment 1.09 1.09 1.09 1.09 1.09 1.09 1.09 

 Cap Factor 54.46% 54.46% 54.46% 54.46% 54.46% 54.46% 54.46% 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-d-36

Appendix D: Regional Adjustment Factors and Capacity Factors (Continued)

Solar Central Receiver 2005 2010 2015 2020 2025 2030 2035 

National Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 44.00% 65.00% 71.00% 77.00% 77.00% 77.00% 77.00% 

New England Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

Mid. Atlantic Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

E. N. Central Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

W.N. Central Adjustment 1.06 1.06 1.06 1.06 1.06 1.06 1.06 

 Cap Factor 46.84% 69.20% 75.59% 81.97% 81.97% 81.97% 81.97% 

S. Atl. & E.S. Central Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

W.S. Central Adjustment 1.19 1.19 1.19 1.19 1.19 1.19 1.19 

 Cap Factor 52.54% 77.62% 84.78% 91.95% 91.95% 91.95% 91.95% 

Mountain Adjustment 1.29 1.29 1.29 1.29 1.29 1.29 1.29 

 Cap Factor 56.57% 83.57% 91.28% 99.00% 99.00% 99.00% 99.00% 

Pacific Adjustment 1.09 1.09 1.09 1.09 1.09 1.09 1.09 

 Cap Factor 47.92% 70.80% 77.33% 83.87% 83.87% 83.87% 83.87% 

 

Residential PV 2005 2010 2015 2020 2025 2030 2035 

National Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 24.00% 24.00% 24.00% 24.00% 24.00% 24.00% 24.00% 

New England Adjustment 0.92 0.92 0.92 0.92 0.92 0.92 0.92 

 Cap Factor 22.10% 22.10% 22.10% 22.10% 22.10% 22.10% 22.10% 

Mid. Atlantic Adjustment 0.93 0.93 0.93 0.93 0.93 0.93 0.93 

 Cap Factor 22.35% 22.35% 22.35% 22.35% 22.35% 22.35% 22.35% 

E. N. Central Adjustment 0.95 0.95 0.95 0.95 0.95 0.95 0.95 

 Cap Factor 22.68% 22.68% 22.68% 22.68% 22.68% 22.68% 22.68% 

W.N. Central Adjustment 1.05 1.05 1.05 1.05 1.05 1.05 1.05 

 Cap Factor 25.12% 25.12% 25.12% 25.12% 25.12% 25.12% 25.12% 

S. Atl. & E.S. Central Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 24.04% 24.04% 24.04% 24.04% 24.04% 24.04% 24.04% 

W.S. Central Adjustment 1.17 1.17 1.17 1.17 1.17 1.17 1.17 

 Cap Factor 28.05% 28.05% 28.05% 28.05% 28.05% 28.05% 28.05% 

Mountain Adjustment 1.22 1.22 1.22 1.22 1.22 1.22 1.22 

 Cap Factor 29.21% 29.21% 29.21% 29.21% 29.21% 29.21% 29.21% 

Pacific Adjustment 1.07 1.07 1.07 1.07 1.07 1.07 1.07 

 Cap Factor 25.79% 25.79% 25.79% 25.79% 25.79% 25.79% 25.79% 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-d-37

 

Appendix D: Regional Adjustment Factors and Capacity Factors (Continued)

Central Station PV 2005 2010 2015 2020 2025 2030 2035 

National Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 24.00% 24.00% 24.00% 24.00% 24.00% 24.00% 24.00% 

New England Adjustment 0.92 0.92 0.92 0.92 0.92 0.92 0.92 

 Cap Factor 22.10% 22.10% 22.10% 22.10% 22.10% 22.10% 22.10% 

Mid. Atlantic Adjustment 0.93 0.93 0.93 0.93 0.93 0.93 0.93 

 Cap Factor 22.35% 22.35% 22.35% 22.35% 22.35% 22.35% 22.35% 

E. N. Central Adjustment 0.95 0.95 0.95 0.95 0.95 0.95 0.95 

 Cap Factor 22.68% 22.68% 22.68% 22.68% 22.68% 22.68% 22.68% 

W.N. Central Adjustment 1.05 1.05 1.05 1.05 1.05 1.05 1.05 

 Cap Factor 25.12% 25.12% 25.12% 25.12% 25.12% 25.12% 25.12% 

S. Atl. & E.S. Central Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 24.04% 24.04% 24.04% 24.04% 24.04% 24.04% 24.04% 

W.S. Central Adjustment 1.17 1.17 1.17 1.17 1.17 1.17 1.17 

 Cap Factor 28.05% 28.05% 28.05% 28.05% 28.05% 28.05% 28.05% 

Mountain Adjustment 1.22 1.22 1.22 1.22 1.22 1.22 1.22 

 Cap Factor 29.21% 29.21% 29.21% 29.21% 29.21% 29.21% 29.21% 

Pacific Adjustment 1.07 1.07 1.07 1.07 1.07 1.07 1.07 

 Cap Factor 25.79% 25.79% 25.79% 25.79% 25.79% 25.79% 25.79% 

 

Concentrator PV 2005 2010 2015 2020 2025 2030 2035 

National Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 24.20% 24.20% 24.20% 24.20% 24.20% 24.20% 24.20% 

New England Adjustment 0.90 0.90 0.90 0.90 0.90 0.90 0.90 

 Cap Factor 21.86% 21.86% 21.86% 21.86% 21.86% 21.86% 21.86% 

Mid. Atlantic Adjustment 0.92 0.92 0.92 0.92 0.92 0.92 0.92 

 Cap Factor 22.23% 22.23% 22.23% 22.23% 22.23% 22.23% 22.23% 

E. N. Central Adjustment 0.94 0.94 0.94 0.94 0.94 0.94 0.94 

 Cap Factor 22.74% 22.74% 22.74% 22.74% 22.74% 22.74% 22.74% 

W.N. Central Adjustment 1.06 1.06 1.06 1.06 1.06 1.06 1.06 

 Cap Factor 25.70% 25.70% 25.70% 25.70% 25.70% 25.70% 25.70% 

S. Atl. & E.S. Central Adjustment 0.98 0.98 0.98 0.98 0.98 0.98 0.98 

 Cap Factor 23.78% 23.78% 23.78% 23.78% 23.78% 23.78% 23.78% 

W.S. Central Adjustment 1.19 1.19 1.19 1.19 1.19 1.19 1.19 

 Cap Factor 28.88% 28.88% 28.88% 28.88% 28.88% 28.88% 28.88% 

Mountain Adjustment 1.28 1.28 1.28 1.28 1.28 1.28 1.28 

 Cap Factor 31.05% 31.05% 31.05% 31.05% 31.05% 31.05% 31.05% 

Pacific Adjustment 1.09 1.09 1.09 1.09 1.09 1.09 1.09 

 Cap Factor 26.40% 26.40% 26.40% 26.40% 26.40% 26.40% 26.40% 

 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix M – Green Power Markets – Page M-d-38

 

Appendix D: Regional Adjustment Factors and Capacity Factors (Continued)

Wind 2005 2010 2015 2020 2025 2030 2035 

National Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 46.68% 46.77% 48.50% 49.14% 50.76% 51.14% 50.90% 

New England Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 46.68% 46.77% 48.50% 49.14% 50.76% 51.14% 50.90% 

Mid. Atlantic Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 46.68% 46.77% 48.50% 49.14% 50.76% 51.14% 50.90% 

E. N. Central Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 46.68% 46.77% 48.50% 49.14% 50.76% 51.14% 50.90% 

W.N. Central Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 46.68% 46.77% 48.50% 49.14% 50.76% 51.14% 50.90% 

S. Atl. & E.S. Central Adjustment 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Cap Factor 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 

W.S. Central Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 46.68% 46.77% 48.50% 49.14% 50.76% 51.14% 50.90% 

Mountain Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 46.68% 46.77% 48.50% 49.14% 50.76% 51.14% 50.90% 

Pacific Adjustment 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

 Cap Factor 46.68% 46.77% 48.50% 49.14% 50.76% 51.14% 50.90% 
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PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION.

1. REPORT DATE (DD-MM-YYYY)

May 2005 

2. REPORT TYPE

Technical Report 

3. DATES COVERED (From - To)

 

5a. CONTRACT NUMBER

DE-AC36-99-GO10337 

5b. GRANT NUMBER

4. TITLE AND SUBTITLE

Projected Benefits of Federal Energy Efficiency and Renewable 

Energy Programs - FY 2006 Budget Request 

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

 NREL/TP-620-37931 

5e. TASK NUMBER

ASA4.4033 

6. AUTHOR(S)

Doug Norland and Thomas Jenkin 

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401-3393 

8. PERFORMING ORGANIZATION 

REPORT NUMBER

NREL/TP-620-37931 

10. SPONSOR/MONITOR’S ACRONYM(S)

NREL 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

11. SPONSORING/MONITORING 

AGENCY REPORT NUMBER

12. DISTRIBUTION AVAILABILITY STATEMENT

National Technical Information Service 

U.S. Department of Commerce 

5285 Port Royal Road 

Springfield, VA 22161 

13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

The Office of Energy Efficiency and Renewable Energy (EERE) of the U.S. Department of Energy (DOE) leads the 

Federal Government’s efforts to provide reliable, affordable, and environmentally sound energy for America, through its 

11 research, development, demonstration, and deployment (RDD&D) programs. EERE invests in high-risk, high-value 

research and development (R&D) that, conducted in partnership with the private sector and other government agencies, 

accelerates the development and facilitates the deployment of advanced clean energy technologies and practices. 

EERE designs its RDD&D activities to improve the Nation’s readiness for addressing current and future energy 

needs.This document summarizes the results of the benefits analysis of EERE’s programs, as described in the FY 2006 

Budget Request. EERE has adopted a benefits framework developed by the National Research Council (NRC) to 

represent the various types of benefits resulting from the energy efficiency technology improvements and renewable 

energy technology development supported by EERE programs. Specifically, EERE’s benefits analysis focuses on three 

main categories of energy-linked benefits—economic, environmental, and security. 

15. SUBJECT TERMS

Government Performance and Results Act; GPRA 06; benefits estimates; portfolio benefits; program benefits; 

benefits analysis; biomass program; building technologies program; distributed energy resources program; federal 

energy management program; geothermal technologies program; hydrogen, fuel cells, and infrastructure 

technologies program; industrial technologies program; solar energy technologies program; vehicle technologies 

program; weatherization and intergovernmental program; wind and hydropower technologies program. 

16. SECURITY CLASSIFICATION OF: 19a. NAME OF RESPONSIBLE PERSON 

a. REPORT 

Unclassified 

b. ABSTRACT 

Unclassified

c. THIS PAGE 

Unclassified

17. LIMITATION 

OF ABSTRACT

UL 

18. NUMBER 

OF PAGES 

19b. TELEPHONE NUMBER (Include area code)

Standard Form 298 (Rev. 8/98) 

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