Renewable Electricity 

Futures Study

End-use Electricity Demand

Volume 3 of 4

Volume 2

PDF

Volume 3

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Volume 1

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Volume 4

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

Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.
Renewable Electricity Futures Study 

Edited By

Hand, M.M.

National Renewable 

Energy Laboratory

Baldwin, S.

U.S. Department of 

Energy

DeMeo, E. 

Renewable Energy 

Consulting Services, Inc.

Reilly, J.

Massachusetts Institute 

of Technology

Mai, T.

National Renewable 

Energy Laboratory 

Arent, D.

Joint Institute for Strategic 

Energy Analysis

Porro, G.

National Renewable 

Energy Laboratory

Meshek, M.

National Renewable 

Energy Laboratory

Sandor, D.

National Renewable 

Energy Laboratory

Suggested Citations

Renewable Electricity Futures Study (Entire Report)

National Renewable Energy Laboratory. (2012). Renewable Electricity Futures Study. Hand, M.M.; 

Baldwin, S.; DeMeo, E.; Reilly, J.; Arent, D.; Porro, G.; Mai, T.; Meshek, M.; Sandor, D. eds. 4 vols. 

NREL/TP-6A20-52409. Golden, CO: National Renewable Energy Laboratory. 

http://www.nrel.gov/analysis/re_futures/. 

Volume 3: End-Use Electricity Demand

Hostick, D.; Belzer, D.B.; Hadley, S.W.; Markel, T.; Marnay, C.; Kintner-Meyer, M. (2012). End-Use 

Electricity Demand. Vol. 3 of Renewable Electricity Futures Study. NREL/TP-6A20-52409-3. Golden, CO: 

National Renewable Energy Laboratory.
Renewable Electricity 

Futures Study 

Volume 3: End-Use 

Electricity Demand 

Donna Hostick,

1 David B. Belzer,1

Stanton W. Hadley,

2 Tony Markel,3

Chris Marnay,

4 and 

Michael Kintner-Meyer1

1 Pacific Northwest National Laboratory

2 Oak Ridge National Laboratory

3 National Renewable Energy Laboratory

4 Lawrence Berkeley National Laboratory
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Renewable Electricity Futures Study 

Volume 3: End-Use Electricity Demand

iii 

Perspective 

The Renewable Electricity Futures Study (RE Futures) provides an analysis of the grid 

integration opportunities, challenges, and implications of high levels of renewable electricity 

generation for the U.S. electric system. The study is not a market or policy assessment. Rather, 

RE Futures examines renewable energy resources and many technical issues related to the 

operability of the U.S. electricity grid, and provides initial answers to important questions about 

the integration of high penetrations of renewable electricity technologies from a national 

perspective. RE Futures results indicate that a future U.S. electricity system that is largely 

powered by renewable sources is possible and that further work is warranted to investigate this 

clean generation pathway. The central conclusion of the analysis is that renewable electricity 

generation from technologies that are commercially available today, in combination with a more 

flexible electric system, is more than adequate to supply 80% of total U.S. electricity generation 

in 2050 while meeting electricity demand on an hourly basis in every region of the United States.

The renewable technologies explored in this study are components of a diverse set of clean 

energy solutions that also includes nuclear, efficient natural gas, clean coal, and energy 

efficiency. Understanding all of these technology pathways and their potential contributions to 

the future U.S. electric power system can inform the development of integrated portfolio 

scenarios. RE Futures focuses on the extent to which U.S. electricity needs can be supplied by 

renewable energy sources, including biomass, geothermal, hydropower, solar, and wind. 

The study explores grid integration issues using models with unprecedented geographic and time 

resolution for the contiguous United States. The analysis (1) assesses a variety of scenarios with 

prescribed levels of renewable electricity generation in 2050, from 30% to 90%, with a focus on 

80% (with nearly 50% from variable wind and solar photovoltaic generation); (2) identifies the 

characteristics of a U.S. electricity system that would be needed to accommodate such levels; 

and (3) describes some of the associated challenges and implications of realizing such a future. 

In addition to the central conclusion noted above, RE Futures finds that increased electric system 

flexibility, needed to enable electricity supply-demand balance with high levels of renewable 

generation, can come from a portfolio of supply- and demand-side options, including flexible 

conventional generation, grid storage, new transmission, more responsive loads, and changes in 

power system operations. The analysis also finds that the abundance and diversity of U.S. 

renewable energy resources can support multiple combinations of renewable technologies that 

result in deep reductions in electric sector greenhouse gas emissions and water use. The study 

finds that the incremental cost associated with high renewable generation is comparable to 

published cost estimates of other clean energy scenarios. Of the sensitivities examined, 

improvement in the cost and performance of renewable technologies is the most impactful lever 

for reducing this incremental cost. Assumptions reflecting the extent of this improvement are 

based on incremental or evolutionary improvements to currently commercial technologies and do 

not reflect U.S. Department of Energy activities to further lower renewable technology costs so 

that they achieve parity with conventional technologies. 
Renewable Electricity Futures Study 

Volume 3: End-Use Electricity Demand

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RE Futures is an initial analysis of scenarios for high levels of renewable electricity in the United 

States; additional research is needed to comprehensively investigate other facets of high 

renewable or other clean energy futures in the U.S. power system. First, this study focuses on 

renewable-specific technology pathways and does not explore the full portfolio of clean 

technologies that could contribute to future electricity supply. Second, the analysis does not 

attempt a full reliability analysis of the power system that includes addressing sub-hourly, 

transient, and distribution system requirements. Third, although RE Futures describes the system 

characteristics needed to accommodate high levels of renewable generation, it does not address 

the institutional, market, and regulatory changes that may be needed to facilitate such a 

transformation. Fourth, a full cost-benefit analysis was not conducted to comprehensively 

evaluate the relative impacts of renewable and non-renewable electricity generation options. 

Lastly, as a long-term analysis, uncertainties associated with assumptions and data, along with 

limitations of the modeling capabilities, contribute to significant uncertainty in the implications 

reported. Most of the scenario assessment was conducted in 2010 with assumptions concerning 

technology cost and performance and fossil energy prices generally based on data available in 

2009 and early 2010. Significant changes in electricity and related markets have already occurred 

since the analysis was conducted, and the implications of these changes may not have been fully 

reflected in the study assumptions and results. For example, both the rapid development of 

domestic unconventional natural gas resources that has contributed to historically low natural gas 

prices, and the significant price declines for some renewable technologies (e.g., photovoltaics) 

since 2010, were not reflected in the study assumptions. 

Nonetheless, as the most comprehensive analysis of U.S. high-penetration renewable electricity 

conducted to date, this study can inform broader discussion of the evolution of the electric 

system and electricity markets toward clean systems. 

The RE Futures team was made up of experts in the fields of renewable technologies, grid 

integration, and end-use demand. The team included leadership from a core team with members 

from the National Renewable Energy Laboratory (NREL) and the Massachusetts Institute of 

Technology (MIT), and subject matter experts from U.S. Department of Energy (DOE) national 

laboratories, including NREL, Idaho National Laboratory (INL), Lawrence Berkeley National 

Laboratory (LBNL), Oak Ridge National Laboratory (ORNL), Pacific Northwest National 

Laboratory (PNNL), and Sandia National Laboratories (SNL), as well as Black & Veatch and 

other utility, industry, university, public sector, and non-profit participants. Over the course of 

the project, an executive steering committee provided input from multiple perspectives to 

support study balance and objectivity. 

RE Futures is documented in four volumes of a single report: Volume 1 describes the analysis 

approach and models, along with the key results and insights; Volume 2 describes the renewable 

generation and storage technologies included in the study; This volume—Volume 3—presents 

end-use demand and energy efficiency assumptions; and Volume 4 discusses operational and 

institutional challenges of integrating high levels of renewable energy into the electric grid. 
Renewable Electricity Futures Study 

Volume 3: End-Use Electricity Demand

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List of Acronyms and Abbreviations

°C degrees Celsius

AEO Annual Energy Outlook 

BA balancing area 

BAU business-as-usual

Btu British thermal unit

CO2 carbon dioxide 

DOE U.S. Department of Energy 

EERE U.S. DOE Office of Energy Efficiency and Renewable Energy

EIA Energy Information Administration

EPA U.S. Environmental Protection Agency 

EPRI Electric Power Research Institute

FERC Federal Energy Regulatory Commission 

FHA Federal Highway Administration

ft2 square foot/feet

GW gigawatt(s)

HVAC heating, ventilation, and air conditioning

IEA International Energy Agency 

kW kilowatt(s)

kWh kilowatt-hour(s) 

LDC load duration curve 

MW megawatt(s)

NAS National Academy of Sciences

NEMS National Energy Modeling System

NERC North American Electric Reliability Corporation

PEV plug-in electric vehicle

PHEV plug-in hybrid electric vehicle

ReEDS Regional Energy Deployment System 

RE Futures Renewable Electricity Futures Study

TES thermal energy storage 

TWh terawatt-hour(s) 

W/ft2

 Watts per square foot

yr year
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Volume 3: End-Use Electricity Demand

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Table of Contents

Perspective ................................................................................................................................................. iii

List of Acronyms and Abbreviations ........................................................................................................ v

Introduction ................................................................................................................................................ xi

Chapter 13. General Assumptions .......................................................................................................13-1

Chapter 14. Potential Impact of Carbon Mitigation Measures and Climate Change on Electricity 

Demand.............................................................................................................................................14-1

Chapter 15. Resulting Scenarios..........................................................................................................15-1

Chapter 16. Building Sector Electricity Demand ................................................................................16-1

16.1 Low-Demand Baseline................................................................................................. 16-1

16.2 High-Demand Baseline ................................................................................................ 16-3

16.3 Comparison of Projected Energy Intensities ................................................................ 16-3

Chapter 17. Industrial Demands and Energy Efficiency ....................................................................17-1

17.1 Low-Demand Baseline................................................................................................. 17-1

17.2 High-Demand Baseline ................................................................................................ 17-2

17.3 Comparison of Projected Energy Intensities ................................................................ 17-3

17.4 Combined Cooling, Heating, and Power within the Industrial Sector ......................... 17-4

Chapter 18. Transportation Demands..................................................................................................18-1

18.1 Low-Demand Baseline................................................................................................. 18-1

18.2 High-Demand Baseline ................................................................................................ 18-3

18.3 Comparison to Other Studies ....................................................................................... 18-3

Chapter 19. Hourly Electricity Demand for 2050.................................................................................19-1

Chapter 20. Supply Curves for Thermal Storage................................................................................20-1

Chapter 21. Demand Response Capability..........................................................................................21-1

Conclusion .............................................................................................................................. Conclusion-1

Appendices ............................................................................................................................ Appendices-1

Appendix G. Comparisons to Other Studies........................................................................................ G-1

Appendix H. Development of Building Load Curves and Hourly Schedules.................................... H-1

Appendix I. Development of Industrial Load Curves and Hourly Schedules ..................................... I-1

Appendix J. Combined Cooling, Heating, and Power within the Industrial Sector........................... J-1

Appendix K. Transportation Electrification Load Development ........................................................ K-1

Appendix L. Cost Functions for Thermal Energy Storage in Commercial Buildings ....................... L-1

Appendix M. Development of Demand Response Amounts .............................................................. M-1

Appendix N. Hourly Load Shapes ......................................................................................................... N-1

References for Volume 3....................................................................................................... References-1
Renewable Electricity Futures Study 

Volume 3: End-Use Electricity Demand

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List of Figures

Figure 15-1. Total electricity demand, 2010–2050 .............................................................. 15-2

Figure 15-2. Total electricity demand, 2010–2050: High Demand (high) and Low Demand 

(low) by sector ............................................................................................................... 15-3

Figure 15-3. Historical and projected electricity demand assumptions in low-demand and 

high-demand scenarios ................................................................................................... 15-3

Figure 16-1. Residential electricity intensities from six studies, including RE Futures ...... 16-4

Figure 16-2. Commercial electricity intensities from six studies, including RE Futures .... 16-4

Figure 17-1. Industrial electricity purchases under the High-Demand Baseline and LowDemand Baseline ........................................................................................................... 17-2

Figure 17-2. Industrial electricity intensities from five studies, including RE Futures ....... 17-4

Figure 18-1. Technology market penetration scenarios ....................................................... 18-3

Figure 19-1. High-demand hourly load profile, 2050 .......................................................... 19-1

Figure 19-2. Low-demand hourly load profile, 2050 .......................................................... 19-3

Figure G-1. Comparison of residential electricity intensities from various studies ............. G-8

Figure G-2. Comparison of commercial electricity intensities from various studies ........... G-9

Figure G-3. Comparison of industrial electricity intensities from various studies ............. G-10

Figure H-1. Electricity intensities for residential building segments in the Low-Demand 

Baseline ........................................................................................................................... H-7

Figure H-2. Electricity intensities for residential buildings: High-Demand Baseline vs. LowDemand Baseline ............................................................................................................ H-7

Figure H-3. Electricity intensities for commercial building segments in the Low-Demand 

Baseline ........................................................................................................................... H-8

Figure H-4. Electricity intensities for commercial sector: High-Demand Baseline vs. LowDemand Baseline ............................................................................................................ H-8

Figure H-5. Aggregate residential sector electricity projections: High-Demand Baseline vs. 

Low-Demand Baseline.................................................................................................. H-10

Figure H-6. Aggregate commercial sector electricity projections: High-Demand Baseline vs. 

Low-Demand Baseline.................................................................................................. H-10

Figure H-7. Electricity market module regions from the National Energy Modeling System, 

also used by the Regional Energy Deployment System ............................................... H-23

Figure I-1. Industrial electricity purchases by sector in the High-Demand Baseline ............. I-2

Figure I-2. Industrial electricity purchases by sector in the Low-Demand Baseline .............. I-2

Figure I-3. Sectors’ electricity purchased per dollar output from the High-Demand Baseline 

(Industries 1–10) ............................................................................................................... I-4

Figure I-4. Sectors’ electricity purchased per dollar output from the High-Demand Baseline 

(Industries 11–19) ............................................................................................................. I-4

Figure I-5. Calculated load duration curves for East Central Area Reliability Coordination 

Agreement in 2050 ............................................................................................................ I-8

Figure I-6. Calculated hourly loads by sector for East Central Area Reliability Coordination 

Agreement in 2050, High-Demand Baseline .................................................................. I-10

Figure J-1. Industrial electricity demands before and after internal generation (TWh) ......... J-1

Figure J-2. Industrial sector electricity generation for High-Demand and Low-Demand 

Baselines (TWh) ............................................................................................................... J-2

Figure J-3. Industrial generation as a fraction of industrial demand ...................................... J-3
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Volume 3: End-Use Electricity Demand

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Figure J-4. Fuels used for industrial on-site generation (TWh) .............................................. J-3

Figure J-5. Fuel sources used for all end-use generation (TWh) ............................................ J-4

Figure J-6. Industrial generation used internally or sold to the grid (TWh) ........................... J-5

Figure K-1. Regional population projections to 2030 (U.S. Census) ................................... K-3

Figure K-2. Historical and projected motor vehicles per capita (FHA 2007) ...................... K-5

Figure K-3. Market penetration for plug-in electric vehicles ............................................... K-6

Figure K-4. Growth trends of stock in plug-in electric vehicles for states with greatest plug-in 

electric vehicle population in 2050 ................................................................................. K-7

Figure K-5. Three fleet charging profiles for plug-in electric vehicles based on 227 driving 

profile vehicle simulation results .................................................................................... K-9

Figure K-6. Transition assumptions from No Utility Control to Opportunity and Valley 

Fill/Managed scenarios ................................................................................................... K-9

Figure K-7. Average daily per vehicle energy demands by charging scenario .................. K-10

Figure K-8. Shape and transition of fixed hourly aggregate load profile for plug-in electric 

vehicles ......................................................................................................................... K-11

Figure K-9. Projected fixed and dynamic annual energy demand plug-in electric vehicles for 

Low-Demand Baseline.................................................................................................. K-12

Figure K-10. Daily fixed energy demand for plug-in electric vehicles by balancing area, 

2050............................................................................................................................... K-12

Figure L-1. Stepped cost curve for North American Electric Reliability Corporation 

Region 1 for 2012 .......................................................................................................... L-10

Figure M-1. Relationship of response of demand response to price for each sector ............ M-5

Figure N-1. Non-PEV hourly demand in 2050, Region 1 (East Central Area Reliability 

Coordination Agreement) ............................................................................................... N-1

Figure N-2. Non-PEV hourly demand in 2050, Region 2 (Electric Reliability Council of 

Texas) .............................................................................................................................. N-2

Figure N-3. Non-PEV hourly demand in 2050, Region 3 (Mid-Atlantic Area Council) ..... N-2

Figure N-4. Non-PEV hourly demand in 2050, Region 4 (Mid-America Interconnected 

Network) ......................................................................................................................... N-3

Figure N-5. Non-PEV hourly demand in 2050, Region 5 (Mid-Continent Area Power 

Pool) ................................................................................................................................ N-3

Figure N-6. Non-PEV hourly demand in 2050, Region 6 (New York) ................................ N-4

Figure N-7. Non-PEV hourly demand in 2050, Region 7 (New England) ........................... N-4

Figure N-8. Non-PEV hourly demand in 2050, Region 8 (Florida Reliability Coordinating 

Council)........................................................................................................................... N-5

Figure N-9. Non-PEV hourly demand in 2050, Region 9 (Southeastern Electric Reliability 

Council)........................................................................................................................... N-5

Figure N-10. Non-PEV hourly demand in 2050, Region 10 (Southwest Power Pool) ........ N-6

Figure N-11. Non-PEV hourly demand in 2050, Region 11 (Northwest Power Pool) ........ N-6

Figure N-12. Non-PEV hourly demand in 2050, Region 12 (Rocky Mountain Power Area, 

Arizona, New Mexico, and Southern Nevada) ............................................................... N-7

Figure N-13. Non-PEV hourly demand in 2050, Region 13 (California) ............................ N-7
Renewable Electricity Futures Study 

Volume 3: End-Use Electricity Demand

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List of Tables

Table 15-1. Comparison of Efficiency Assumptions in 2050: High-Demand Baseline versus 

Low-Demand Baseline................................................................................................... 15-2

Table 15-2. Simple Paybacks for Sectors and Selected Sub-Sectors ................................... 15-5

Table G-1. Effects of Climate Change on Residential Space Heating in U.S. Energy 

Studies ............................................................................................................................. G-1

Table G-2. Effects of Climate Change on Commercial Space Heating in U.S. Energy 

Studies ............................................................................................................................. G-2

Table G-3. Effects of Climate Change on Residential Space Cooling in U.S. Energy 

Studies ............................................................................................................................. G-3

Table G-4. Effects of Climate Change on Commercial Space Cooling in U.S. Energy 

Studies ............................................................................................................................. G-4

Table H-1. Summary of Results from Efficiency Potential Meta-Analysis ......................... H-5

Table H-2. Relative Changes in Cooling Electricity Share for Advanced Medium Office 

Building......................................................................................................................... H-14

Table H-3. Relative Changes in Cooling Electricity Share for Advanced Highway Lodging 

Building......................................................................................................................... H-15

Table H-4. Relative Changes in Cooling Electricity Share for Advanced Retail Building—

Low Plug Load .............................................................................................................. H-16

Table H-5. Relative Changes in Cooling Electricity Share for Advanced Retail Building—

High Plug Load ............................................................................................................. H-16

Table H-6. Relative Changes in Cooling Electricity Share for Advanced Grocery 

Building......................................................................................................................... H-18

Table H-7. Development of Cooling Share Adjustment Factor for Commercial 

Buildings ....................................................................................................................... H-20

Table H-8. Development of Cooling Share Adjustment Factor for Residential 

Buildings ....................................................................................................................... H-22

Table H-9. National Energy Modeling System End Uses by Sector .................................. H-24

Table I-1. Electricity Purchases Annual Growth Rates for Each Industrial Sector, 

2020–2030......................................................................................................................... I-3

Table I-2. Reduction in Low-Demand Purchased Electricity per Shipment Values versus 

High Demand .................................................................................................................... I-5

Table I-3. Load Factors by Sector and Region, 2008 ............................................................. I-6

Table I-4. Load Duration Curve Definition Factors for East Central Area Reliability 

Coordination Agreement, 2050 ......................................................................................... I-7

Table I-5. East Central Area Reliability Coordination Agreement Average Loads for HighDemand Baseline 2050 for Regional Energy Deployment System Time Slices ............ I-11

Table K-1. Ten States with Least and Greatest Percent Change in Population, 

2010–2050....................................................................................................................... K-4

Table K-2. Market Penetration Model Parameter Values for plug-in electric vehicles ........ K-6

Table K-3. United States and Top 15 States with Stock Distribution of plug-in electric 

vehicles (millions) ........................................................................................................... K-7

Table L-1. Segments of Supply Curves for Thermal Energy Storage ................................... L-1

Table L-2. Peak Hour, Average Hourly Cooling Load, and Thermal Energy Storage Capacity 

for Large Office Benchmark, by Location ....................................................................... L-3
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Volume 3: End-Use Electricity Demand

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Table L-3. Estimated Costs for Chilled Water Thermal Energy Storage per Kilowatt 

Shifted .............................................................................................................................. L-5

Table L-4. Cost Assumptions for Thermal Energy Storage, by Step—North American 

Electric Reliability Corporation Region 1 ....................................................................... L-6

Table L-5. Limiting Fractions of Total Cooling Demand, by Step—North American Electric 

Reliability Corporation Region 1 ..................................................................................... L-9

Table M-1. Summary of FERC Estimated Demand Response Potential Savings (MW) in 

2019 for East North Central Region ............................................................................... M-2

Table M-2. Census and Electric Power Research Institute Region Assignments to North 

American Electric Reliability Corporation Regions ....................................................... M-3

Table M-3. Shift of Market Penetration Categories for Demand Response ......................... M-4
Renewable Electricity Futures Study 

Volume 3: End-Use Electricity Demand

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Introduction

The Renewable Electricity Futures Study (RE Futures) is an initial investigation of the extent to 

which renewable energy supply can meet the electricity demands of the contiguous United 

States1 over the next several decades. This study includes geographic and electric system 

operation resolution that is unprecedented for long-term studies of the U.S. electric sector.

The RE Futures study is documented in four volumes: Volume 1 describes the analysis approach 

and models along with the key results and insights from the analysis; Volume 2 documents in 

detail the renewable generation and storage technologies included in the study; Volume 4 

documents the operational and institutional challenges of integrating high levels of renewable 

energy into the electric grid; this Volume—Volume 3—details the end-use electricity demand 

and efficiency assumptions.

The projection of electricity demand is an important consideration in determining the extent to 

which a predominantly renewable electricity future is feasible. Any scenario regarding future 

electricity use must consider many factors, including technological, sociological, demographic, 

political, and economic changes (e.g., the introduction of new energy-using devices; gains in 

energy efficiency and process improvements; changes in energy prices, income, and user 

behavior; population growth; and the potential for carbon mitigation). 

In projecting electricity use, the primary historical drivers for electricity demand (population 

growth and economic growth) are taken into account along with other emerging trends, including 

the green building and supply chain1

 movements, carbon mitigation, policies and legislation 

dealing with codes and standards, research and development in energy efficiency, and foreign 

competition for manufacturing. For the RE Futures, two demand projections were developed to 

represent probable higher and lower electricity trajectories—hereafter referred to as the HighDemand Baseline and the Low-Demand Baseline. The two electricity demand trajectories used 

in RE Futures rely on the same assumptions for population and economic growth, so the 

differences stem from the assumptions regarding other trends.

The emerging trends noted above that lead to increased efficiency motivate the Low-Demand 

Baseline. Based on these, a scenario was developed in which there is an approximately 30% 

reduction in overall electricity intensity2 within the buildings sector, a 50% reduction in 

industrial electricity intensity, and electrification of approximately 40% of the light-duty vehicle 

stock by 2050. 

The High-Demand Baseline is a business-as-usual scenario that assumes trends for the 

residential, commercial, and industrial sectors as forecast to 2030 by the Energy Information 

Administration (EIA) in its Annual Energy Outlook (AEO) (EIA 2009d). Because AEO 2009 

contained only a forecast through 2030, RE Futures extended the AEO trends out to 2050. Under 

this scenario, the overall electricity intensity within the buildings sector remains relatively 

unchanged from 2010 to 2050, and the industrial sector electricity intensity declines by 

approximately 35% during the RE Futures period (2010–2050). 

 

1 As public awareness of environmental issues grows, consumers and retailers are becoming more interested in the 

energy and environmental impacts of the entire manufacturing process, with some retailers (most notably WalMart) 

issuing green supply chain requirements. 2 Definitions of electricity intensity are sector-dependent and are provided below.
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Volume 3: End-Use Electricity Demand

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Chapter 13. General Assumptions

Although there is a growing body of literature dedicated to factors affecting energy demand, 

including behavioral influences, climate change, and new technologies and materials, the explicit 

inclusion of the potential impacts arising from these influences is beyond the scope of RE 

Futures. RE Futures relied on readily available data and projections to the extent possible, and 

attempted to stay within reasonable bounds established by recent literature. RE Futures was 

further constrained by the modeling requirement for hourly load projections through the study 

period. This required the conversion of the estimated projections of electricity consumption into 

regional hourly load profiles. Although studies projecting potential energy consumption futures 

are plentiful, studies that tie those consumption futures to hourly loads are not readily available. 

The basis for the scenarios presented was predominantly drawn from EIA’s AEO for 2009 

(industrial sector) and 2010 (buildings sector), which assumed that the major long-term drivers 

of energy demand—Gross Domestic Product and population—grow at 2.4%/yr and 0.9%/yr, 

respectively, over the period 2008–2035.3 

The fuel prices assumed by the AEO were also taken into consideration in developing the 

scenarios because increasing natural gas prices might lead consumers to change out their natural 

gas heating equipment to equipment fueled by electricity, for example; however, given recent 

prospects to exploit shale gas deposits, the AEO forecast for natural gas prices was believed to 

be too high. As such, RE Futures did not assume more fuel switching from natural gas to electric 

devices for space and water heating than was already assumed in the AEO 2010 Reference Case

(EIA 2010). Given the extreme difficulty of capturing carbon emissions arising from distributed 

use of fossil energy in the buildings sector, however, use of decarbonized electricity to provide 

space and water heating may be an important means for reducing carbon emissions in the future. 

Recent work for the European Union (European Climate Foundation 2010) projected greater 

electrification within the buildings sector; as a consequence, forecasted efficiency gains were 

offset by new electrical demands from transportation and space and water heating. While the 

total electrical demand for RE Futures would be represented by the High-Demand Baseline in 

such a case, the underlying load shapes would not capture the resulting hourly and seasonal 

changes.

Electricity prices also have an impact on the demand for electricity. However, because the 

demand profiles are provided as exogenous inputs to the models used in RE Futures, the 

potential impacts on demand due to changes in electricity prices caused by the various scenarios 

were not considered in developing the demand projections. The interactions that impact 

electricity prices between electricity supply and demand are complex and beyond the scope of 

RE Futures. The efficiency gains are assumed to be cost-effective using today’s electricity prices 

and the current AEO forecasts for electricity prices. 

Within the commercial sector, two additional trends underlie the AEO projections. First, the 

growth in disposable income increases the demand for services that depend on computers and 

other electronic equipment. Also, the growing share of the population over age 65 increases the 

 

3 Full descriptions of the AEO assumptions are detailed in EIA 2009a and EIA 2010.
Renewable Electricity Futures Study 

Volume 3: End-Use Electricity Demand

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demand for health care and assisted-living facilities and the demand for electricity to power 

medical and monitoring equipment at those facilities. Trends in the residential sector include 

population migration into the South and the West;4

 the conversion of older homes from room air 

conditioning to central air conditioning; and the growth in the use of “other” appliances, 

including large-screen televisions and computers. Within the industrial sector, the AEO projects 

that energy-intensive manufacturing industries will show slow growth due to increased foreign 

competition. Additionally, an increase in the use of biofuels in the transportation sector is 

expected to lead to an increase in the conversion of biomass to fuels such as ethanol, diesel, and 

jet fuel. This process creates heat, which can be used for industrial on-site generation.

 

4 Note that changes in these trends, such as reduced migration due to water shortages, were not considered.
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Chapter 14. Potential Impact of Carbon Mitigation Measures and 

Climate Change on Electricity Demand

There is currently much discussion about climate change, emissions, and carbon mitigation 

measures. Potential policies, legislation, and regulation can logically be expected to have an 

impact on the way in which energy is generated, delivered, and used, whether by specific 

controls or through pricing incentives or disincentives. The same drivers that might push the 

United States toward more renewable generation of electricity would also be expected to lead to 

increased energy efficiency—that is, a drive to use less energy to yield the same level of service. 

These drivers act in opposition to other trends, such as population growth and the development 

of new electricity-using devices. Climate change influences another aspect of the energy use 

picture because heating and cooling loads are highly dependent upon outside temperature. 

Although a carbon mitigation policy was not explicitly assumed for RE Futures, implementation 

of a carbon mitigation policy would have an impact on electricity demand. Depending on how 

such a policy might be implemented, one potential outcome is higher prices for fossil energy, 

which could lead to fuel switching in end uses such as space and water heating. The Electric 

Power Research Institute (EPRI) (2009), the National Academy of Sciences (NAS et al. 2009), 

and the Union of Concerned Scientists (Cleetus et al. 2009) all use reference cases from recent 

editions of EIA’s AEO. The extent to which fuel switching occurs in these projections is largely 

due to how EIA models fuel choice in its existing residential and commercial building models. In 

general, some amount of fuel switching (in the sense of the predicted fuel shares of space and 

water heating in new buildings) occurs as a function of projected fuel prices and the menu of 

available energy efficiency technologies for these end uses. With regard to the energy efficiency

scenarios undertaken by these studies, none of them makes any explicit assumptions about any 

fuel switching that would alter the future evolution of electricity growth in buildings.5

 One of the 

results of a greater percentage of renewable generation is that the generation sector would reduce 

its use of natural gas in the longer term. Currently, electricity generation is responsible for 

approximately one-third of the U.S. demand for natural gas;6

 reduced demand could potentially 

lower natural gas prices, countering to some extent the price increases brought about by carbon 

policies. 

Just as climate mitigation policies may impact electricity demand, climate change would also be 

expected to impact both energy supply and energy demand, and numerous studies have been 

conducted to determine the potential impacts of climate change on the U.S. (and world) energy 

picture (e.g., Scott and Huang 2007; Huang 2006; Mansur et al. 2005; Scott et al. 2005). 

Although these studies present varying estimates of the impact on energy demand within the 

United States,7

 they are in general agreement that overall heating consumption is expected to 

 

5 The NAS study is based in part on the Clean Energy Futures report (Interlaboratory Working Group 2000), an 

earlier study that assumed some fuel switching in the direction of increased gas use relative to electricity. The NAS 

study indicates that the estimates of this impact were eliminated in the more recent assessment of future electricity 

consumption.

6 U.S. Department of Energy, Energy Information Administration, Annual Energy Outlook 2010, Table A.2, 

http://www.eia.doe.gov/oiaf/aeo/pdf/appa.pdf (EIA 2010) 7 These estimates depend on assumed change in temperature and year, as well as model differences, regions, and 

other study parameters.
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decrease due to climate change (ranging from -3% to -35% by 2050), while overall cooling 

consumption could increase, ranging from 4% to 90% by 2050 (see Appendix G for 

comparisons). For RE Futures, climate change was not explicitly addressed because the overall 

impacts are subject to a number of assumptions, including temperature change and time frame, 

that are beyond the scope of RE Futures. Generally, if climate change and climate mitigation 

policies were to be taken into account, the demand profiles presented here would most likely be 

underestimating cooling and overestimating heating (although the lower heating demand may 

cause more switching into electricity, e.g. heat pumps that would offset some of the direct effects 

of higher temperatures).
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Chapter 15. Resulting Scenarios

RE Futures selected two energy demand scenarios to represent reasonable bounds for the 

electricity generation requirements through 2050. These two scenarios represent a “higher” level 

of demand (the High-Demand Baseline) and a “lower” level of demand (the Low-Demand 

Baseline). Developing scenarios of energy use 40 years into the future is challenging, and the 

analysis is further complicated by the requirement for detailed hourly system load shapes by 

region, which are needed for the modeling effort. 

The High-Demand Baseline represents a business-as-usual case that assumed that trends within 

the residential, commercial, industrial, and transportation sectors recently forecast by the EIA 

(EIA 2009d and EIA 2010) to 2030 continue through 2050.

8

 This scenario assumed no radical 

changes in available technologies or consumer behavior, although current technologies will 

evolve in terms of cost and efficiency. No new regulations or laws not already enacted are 

included in an AEO Reference Case, and beyond its 2030 horizon, a simple extrapolation is 

made to 2050. The AEO Reference Case was chosen to represent a higher demand trajectory 

because it does not include planned equipment and appliance standards or proposed energy code 

changes, which are expected to lower demand. 

The Low-Demand Baseline assumed a moderately high level of energy efficiency within the 

buildings and industrial sectors. The Low-Demand Baseline assumed that approximately 40% of 

the light-duty vehicle stock becomes electrified by 2050. In the buildings sector, the efficiency 

improvements necessary to achieve ultra-high-efficiency buildings are estimated,9 while in the 

industrial sector, estimated responses to carbon restrictions, based on the Waxman-Markey cap 

and trade provisions, are applied. 

The electricity demand forecasts for buildings, industry, and transportation represent sales 

trajectories. Transmission and distribution losses are not considered as part of these on-site

electricity projections. The electricity sector is expected to deliver these energy quantities 

according to the timing and distribution specified by the corresponding load shapes used in the 

models. 

Table 15-1 compares the two scenarios and their underlying assumptions, which are discussed

more fully in the following sections. 

 

8 Both the 2009 AEO Reference Case (April Stimulus version) (EIA 2009d) and the 2010 AEO Reference Case 

(EIA 2010) were used. The opportunity to use the 2010 AEO for the buildings sectors became available later in the 

Renewable Electricity Futures Study period; compared to the 2009 AEO Reference Case, the 2010 AEO Reference 

Case shows a small reduction in both residential (1.5%) and commercial (3.2%) electricity use in 2030. For the 

commercial sector, the somewhat greater reduction appears to be related to the availability and adoption of more 

efficient lighting, refrigeration, and computer technologies. 

9 Ultra-efficient buildings are designed and operated to generate as much on-site power as the energy they consume.
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Table 15-1. Comparison of Efficiency Assumptions in 2050: High-Demand Baseline versus 

Low-Demand Baseline

Sector High-Demand Baseline Low-Demand Baseline

Residential 2% decline in intensity over 2010 levels 30% decline in intensity over 2010 levels 

Commercial 5% increase in intensity over 2010 levels 32% decline in intensity over 2010 levels

Industrial 35% decline in intensity over 2010 levels 50% decline in intensity over 2010 levels

Transportation <3% plug-in hybrid electric vehicle (PHEV) 

penetration

40% of vehicle sales are plug-in electric 

vehicles (PEVs)

Figure 15-1 illustrates the resulting demand trajectory for the Low-Demand and High-Demand

Baselines through 2050. For comparison, the EIA analysis (EIA 2009b)10 of the American Clean 

Energy and Security Act of 2009 (the Waxman-Markey Climate Bill from 2009)

11 is included, 

using the 2025–2030 trend to extend the analysis to 2050. 

Figure 15-1. Total electricity demand, 2010–2050

 

10 For its analysis, EIA ran a number of cases for the bill. The “EIA Waxman-Markey Analysis” line in this chart 

(Figure 15-1) presents the consumption resulting from its basic case (EIA 2009b). 11 For the full text of the bill (H.R. 2454), see http://www.govtrack.us/congress/bill.xpd?bill=h111-2454. 
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Figure 15-2 illustrates the electricity consumption by sector for the High-Demand Baseline and 

the Low-Demand Baseline. For context, Figure 15-3 contains the historical energy use by 

sector.12

Figure 15-2. Total electricity demand, 2010–2050: 

High Demand (high) and Low Demand (low) by sector 

Figure 15-3. Historical and projected electricity demand assumptions in low-demand and

high-demand scenarios

 

12 The leveling of the industrial sector electricity demand is due in part to reduced production within the 

manufacturing sector. Appendix I contains a breakdown of the changes in the industrial sector due to energy 

efficiency versus declining production for the period 2010–2030.
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Unlike the renewable technologies explored in RE Futures, the reductions in consumption have 

been generated without explicitly considering the investment needed to realize these gains. 

Terms such as cost-effective or cost-competitive are often used in discussing energy efficiency 

measures. These generally mean that the efficiency measures cost less or about the same as their 

less efficient counterparts once the various costs (e.g., energy, operation and maintenance, 

capital) over the lifetime of the measure are considered. Although these investment costs are not 

considered here, findings from other studies are presented to illustrate the approximate cost of 

efficiency gains to provide some perspective.

The National Academy of Sciences (NAS et al. 2009) reported conservation supply curves for 

energy efficiency in the buildings sector that were originally developed by Lawrence Berkeley 

National Laboratory (Brown et al. 2008). These supply curves indicated that energy savings of 

30%–35% could be achieved over Brown et al.’s reported reference case at a cost less than the 

2007 retail cost of energy. For electricity, the conserved cost of energy was found to range from 

less than 1 cent per kWh to about 8 cents per kWh, with an average conserved cost of energy of 

2.7 cents per kWh. Brown et al. (2008) calculated that the cumulative capital investment13

required between 2010 and 2030 to achieve their level of electricity savings14 was approximately 

$299 billion.15 Combined with average annual electricity bill savings of $128 billion in 2030, 

electricity efficiency measures, on average, had a payback of 2.3 years. Additionally, NAS et al. 

(2009) reported that energy savings of 14%–22% could be cost-effectively achieved by 2020 

within the industrial sector. Within the National Academy of Sciences study, cost-effectiveness 

was defined as an internal rate of return of at least 10% or exceeding a company’s cost of capital 

by that company’s defined risk premium; however, no conserved cost of energy was specifically 

reported for the industrial sector.

In another study, McKinsey and Company (Granade et al. 2009) calculated the cost-effective 

energy efficiency potential in the residential, commercial, and industrial sectors.16 Granade et al. 

(2009) calculated the present value of investment costs and annual energy savings for each 

sector, as well as a set of sub-sectors. Table 15-2 contains the simple paybacks calculated for 

selected categories. 

 

13 The cumulative capital investment is the reported capital investment as an incremental investment, including only 

costs above those incurred in the reference case. It includes the full “add-on” cost for new measures or equipment 

(such as additional insulation) as well as the incremental cost of the efficient technology compared with the cost of 

the conventional technology equivalent (e.g., the difference in price between a highly efficient heat pump and an 

electric air conditioner/furnace combination).

14 For reference, Brown et al. (2008) estimated electricity savings within the buildings sector of about 1,270 TWh in 

2030; the Low-Demand Baseline for the Renewable Electricity Futures Study estimated electricity savings in the 

buildings sector of approximately 410 TWh in 2030 and 1,350 TWh in 2050.

15 All dollar amounts presented in this report are presented in 2009 dollars unless noted otherwise; all dollar amounts 

presented in this report are presented in U.S. dollars unless otherwise noted.

16 McKinsey and Company (Granade et al. 2009) estimated the overall energy efficiency potential, defined as net 

present value positive. Total energy savings of 5.45 quadrillion Btu (buildings) and 3.65 quadrillion Btu (industrial) 

in 2020 were calculated, including 900 TWh of electricity savings (buildings) and 190 TWh of electricity savings 

(industrial).
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Table 15-2. Simple Paybacks for Sectors and Selected Sub-Sectorsa

Sector Simple Payback (years)

Residential 5.617

Existing Homes 9.5

New Homes 4.0

Commercial 3.418

Existing Private 6.6

New Private 3.8

Government 5.2

Industrial 2.4

Energy Support Systems 2.0

Energy-Intensive Processes 2.7

Non-Energy-Intensive Processes 2.5 a Granade et al. 2009

Another cost estimate can be drawn from Lazard (2009), which reports a levelized cost of energy 

for energy efficiency measures to range from 0 cents/kWh to 5 cents/kWh, based on utility costs 

as reported in the joint U.S. Environmental Protection Agency and U.S. Department of Energy 

(DOE) National Action Plan for Energy Efficiency Report (DOE/ 2006).

 

17 Includes investment and annual energy savings associated with electrical devices and small appliances, and 

lighting and major appliances (Granade et al. 2009)

18 Includes investment and annual energy savings associated with community infrastructure and office and noncommercial devices (Granade et al. 2009)
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Chapter 16. Building Sector Electricity Demand

The buildings sector dominates overall electricity consumption, representing almost 78% of the 

2030 total in the AEO Reference Case.

19 Additionally, building electrical end uses are highly 

heterogeneous and changing over time. 

16.1 Low-Demand Baseline

Within the buildings sector, trends that can be expected to influence future electricity use include 

the “green” building movement, more stringent building codes, more stringent appliance and 

equipment standards, and research by the DOE and others to develop ultra-efficient buildings. 

Ultra-efficient buildings are designed and operated to generate as much on-site power as the 

energy they consume.

20 Some of the approaches to achieving this ambitious goal include

developing and applying very high-efficiency technologies, finding ways to reduce the cost of 

energy-efficient technologies that have already been developed, and implementing cost-effective 

technologies that are already available.

Because the focus of RE Futures was on the generation of electricity, the lower-demand baseline 

was developed using a more generic, energy-intensity projection, rather than attempting to build 

a projection up from the various technologies and practices available within each of the end uses. 

Although a certain level of energy efficiency gains is possible through the normal adoption of 

new energy-efficient practices and technologies, the larger gains here implicitly require more 

active policy and behavioral change to come to fruition. The efficiency gains within the LowDemand Baseline are assumed to be feasible by sustained efforts on the part of government, 

businesses, and households, and to be cost-effective without major increases in electricity prices 

as a driver. It is implicit to efficiency gains that the measures are properly installed, operated, 

and maintained as intended, and that the resulting savings are not used to increase the level of 

service (e.g., permanently adjusting the thermostat).21

Although a detailed analysis of policy changes remains to be examined, potential policy changes 

could include increased code adoption and enforcement; financial incentives such as tax credits, 

energy efficient mortgages, rebates, and coupons; broadening rating and labeling efforts; and 

encouragement of volume purchase programs. Behavioral changes could include users using 

technologies such as controls and sensors as they were intended, a greater focus on continuous 

building commissioning, and acting on available information on energy use. Other changes 

might include an increased focus on building commissioning so that buildings are built and 

commissioned as designed; more integration between the financial, construction, and associated 

industries to better enable the deployment of highly efficient buildings; and deployment of smart 

meters to provide users with a better understanding of their energy consumption. 

 

19 EIA AEO (EIA 2010) Table 2. 20 Generally, ultra-efficient buildings are assumed to reduce their energy requirements by up to 70%, with the 

remainder of the building load met by local generation sources, such as photovoltaics. For RE Futures, only the 

efficiency improvements were considered as input to the end-use electricity demand. 21 Granade et al. (2009) summarizes the possible impacts on savings in these instances to be in the range of 15% to 

50%. 
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16.1.1 New Building Energy Intensity

The Low-Demand Baseline for RE Futures was based on the energy efficiency vision of DOE’s

ultra-efficient building programs. Consideration of what level of energy consumption intensity 

must be achieved to reach ultra-high efficiency motivates the overall approach to projecting 

future energy demands for the residential and commercial buildings. The electricity demand 

forecast for residential and commercial buildings under the Low-Demand Baseline assumes a 

larger number of buildings to be capable of ultra-high efficiency. Due to the inability to predict 

which technology and end-use consumption areas are most likely to show the greatest 

improvements, the Low-Demand Baseline focused only on the broadest measure of energy 

intensity: energy use per square foot (or per household) at the whole-building level. Accordingly, 

the basic assumption for new buildings is that the average energy intensity in 2050 will be 60% 

below that of new buildings being built today.22 The factors underlying this reduction include 

increased energy code stringency, the continued development and implementation of federal 

energy efficiency standards for equipment and appliances, and research and development efforts 

directed at both the component and system (or whole-building) level. This reduction is also 

assumed to occur along an exponentially declining path. Thus, absolute reductions in energy 

intensity are assumed to be greater in the immediate future than several decades from now. 

Additional detail on the approach and assumptions can be found in Appendix H. 

16.1.2 Existing Building Energy Intensity

Due to the cost to substantially change the building envelope and the heating and cooling system, 

the potential for large efficiency increases for existing buildings is much lower than that for new 

buildings. Moreover, the addition of electrical services in older buildings (e.g., air conditioning) 

tends to increase electricity consumption. However, both research and development 

improvements and federal energy efficiency standards for equipment and appliances will 

contribute to a reduction in electricity use. For the Low-Demand Baseline, intensity in existing 

residential buildings (e.g., pre-2010 homes still in the stock in 2050) was assumed to decline by 

30% by 2050. For commercial buildings, the assumed decline by 2050 is somewhat greater at 

40%. The larger decline for commercial buildings reflects an assumption that the amount of 

miscellaneous electrical uses in commercial buildings will be more amenable to reductions from 

policy and new technology than those in the residential sector. Additional detail on the approach 

and assumptions can be found in Appendix H. 

16.1.3 Retrofits and Renovations

For a study with a long-term time horizon, one cannot assume that the intensity of buildings built 

today and in the near future will remain constant through 2050. Many ultra-high efficiency 

technologies introduced in the latter years (2030–2050) will be adopted, through retrofits and 

renovations, in buildings built over the next 20 years. As a means of accounting for that 

phenomenon, the Low-Demand Baseline assumed that buildings (both residential and 

commercial) built over the next 20 years (2010–2030) will be retrofitted (or renovated) with 

 

22 Best practices guides and case studies that illustrate pathways to achieving 15%–40% whole-building energy 

savings for various residential climate zones are available via DOE’s Building America website 

(http://www1.eere.energy.gov/buildings/building_america/climate_specific_publications.html). Design guides and 

strategies containing pathways to achieving 30%–50% whole-building energy savings for selected building types 

within the commercial sector are available through the U.S. Department of Energy’s Commercial Building Initiative 

website (http://www1.eere.energy.gov/buildings/commercial_initiative/guides.html).
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more efficient equipment in the subsequent 20 years (2030–2050). Operationally, this 

assumption is implemented as a 15% reduction in the electricity intensity for housing units and a 

20% reduction in commercial buildings starting in 2031. In other words, residential and 

commercial buildings built between 2010 and 2030 are “revisited” in the analysis 20 years later

(e.g., buildings built in 2011 are revisited in 2031; buildings built in 2012 are revisited in 2032, 

and so on, through 2050), with the intensity reduction applied in the out years to account for 

improvements in building and equipment practices.

16.2 High-Demand Baseline

The High-Demand Baseline for the residential and commercial sectors uses the AEO Reference 

Case (EIA 2010), as mentioned earlier. The AEO assumed that total electricity consumption 

grows by 0.9%/yr from 2007 to 2030. Commercial growth is approximately 1.4%/yr (with 

commercial electricity intensity in kilowatt-hours per square foot increasing by 0.1%/yr), and 

residential growth is approximately 0.8%/yr (with electricity use per household declining at an 

average annual rate of 0.2%/yr). Documentation of the assumptions is found in the AEO 2009 

and AEO 2010 and supporting materials (EIA 2009a; EIA 2009d; EIA 2010). 

16.3 Comparison of Projected Energy Intensities 

To provide some context for the both the High-Demand and Low-Demand Baselines, the energy 

intensities have been compared to other studies. The comparison studies are from the Union of 

Concerned Scientists (Cleetus et al. 2009), University of California-Davis (McCarthy et al. 

2008), EPRI (2009), EIA’s analysis of Waxman-Markey (EIA 2009b), and NAS et al. (2009). To 

maintain the focus on potential energy efficiency improvement, the comparisons have been made 

in terms of energy intensities. Consistent with EIA’s AEO, the intensity for the residential sector 

is in terms of electricity use per household; for the commercial sector, energy use per square foot 

of floor space is employed as the intensity measure. For comparison purposes, the intensities 

were converted to index numbers normalized to be 1.0 in 2010. 

Figure 16-1 shows the projected intensities for the residential sector, and Figure 16-2 shows the 

scenarios from the same studies for the commercial sector. Within the residential sector, the 

High-Demand Baseline shows the least decline in intensity to the year 2030, while commercial 

sector electricity under this scenario is expected to increase through 2030. The light blue squares 

connected by the solid line in both figures depict the high-efficiency scenario (Low-Demand 

Baseline) developed for RE Futures. As shown in Figures 16-1 and 16-2, this scenario falls 

within the range of intensity forecasts drawn from other studies. Although not included in the 

figures, McKinsey and Company (Granade et al. 2009) conducted another recent study of costeffective energy efficiency potential in the United States. When converted to an index basis, the 

implied intensity indexes (2020 relative to a 2010 base) for the residential and commercial 

sectors are 0.71 and 0.73, respectively. These values are substantially lower than those shown for 

2020; however, due to the shorter time frame and greater emphasis on cost-effective potential 

compared to other studies, these values are not shown in either figure. 
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Figure 16-1. Residential electricity intensities from six studies, including RE Futures

Figure 16-2. Commercial electricity intensities from six studies, including RE Futures 

Sources: EIA 2009b, EPRI 2009, Cleetus et al. 2009, McCarthy et al. 2008, NAS et al. 2009

Appendix G provides additional detail regarding the comparisons of these studies. 
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Chapter 17. Industrial Demands and Energy Efficiency

The industrial sector accounts for approximately 27% of total electricity demand,23 with more 

than 50% used to power electric motors (representing the single largest end use of electricity in 

the United States).24 Within the industrial sector, the chemicals and primary metals industries 

consume the most electricity, representing approximately 35% of electrical consumption.25 The 

sector’s electricity use will be impacted by increases in the efficiency of motor drive systems and 

other processes, a slower growth rate of the more energy-intensive manufacturing industries, and 

a shift toward green supply chains. The sector is also affected by foreign competition.

In projecting industrial loads over the next 40 years, there are a number of unknowns. Industrial 

demands are more difficult to project due to the wider variation in the industrial sector makeup 

over time, as well as large differences in energy intensity across various industries. The extent of 

changes from the energy sector that would feed back in to the industrial sector based on different 

renewable energy scenarios further complicates the task of projecting loads and would ideally 

require iteration. Because RE Futures is a renewable energy study and not an industrial demand 

study, an approximation of the projected industrial demands was deemed sufficient. 

A more detailed analysis of the consequences of some factors of the renewable energy scenarios, 

such as construction of renewable energy technologies and electric vehicle batteries, may result 

in increased load forecasts for some industrial sub-sectors, while other sectors, such as oil 

refining, may see countervailing changes. Possible changes in onshore versus outsourcing of 

manufacturing are also a large unknown and are beyond the scope of RE Futures. In addition,

climate change, new processes for existing products, and new products altogether further 

magnify the uncertainty regarding future industrial loads. 

17.1 Low-Demand Baseline

The Low-Demand Baseline industrial demand trajectory is derived in a quite different manner 

than that used for the buildings sector. It was based on the EIA’s HR2454Cap National Energy 

Modeling System (NEMS) Case,

26 which, as its name suggests, includes a carbon cap and trade 

policy roughly equivalent to the provisions of the Waxman-Markey Climate Bill from 2009. 

Given that this case would evoke a significant efficiency boost (ranging from 6% to 69% in 

2050, depending on the specific industrial sector), RE Futures believed its consumption level 

represents a reasonable energy efficiency scenario and adopted it as the Low-Demand Baseline.

27

Underlying the Low-Demand Baseline are EIA’s assumptions that further standards for 

industrial energy efficiency will be established, an awards program for increasing efficiency in 

the thermal electricity generation process will be created, and the waste-to-heat energy incentives 

in the Energy Independence and Security Act of 2007 will be clarified. Overall, the changes in 

 

23 EIA AEO (EIA 2010), Table 2 24 EIA 2006, Table 5.3 25 EIA 2006, Table 3.1 26 This is the Basic Case described in EIA 2009b. 27 Although RE Futures does not explicitly assume the implementation of any carbon mitigation policy, the 

HR2454Cap case was used as the industrial sector input in the high-efficiency case because the resulting industrial 

loads (and load shapes) were consistent with the higher efficiency targets that characterize the Low-Demand 

Baseline.
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the Low-Demand Baseline result in an approximately 23% decrease in electricity use compared 

to the High-Demand Baseline. Appendix I contains additional information about the derivation 

of the load curves and the implicit energy efficiency gains.

17.2 High-Demand Baseline

The High-Demand Baseline industrial demands provided to the models were based on the 

electricity sales to industry reported in the 2009 AEO Reference Case (EIA 2009d), with a 

simple extrapolation beyond 2030 through 2050. The AEO assumed that total electricity 

consumption grows by 0.9%/yr from 2007 to 2030, with growth in the industrial sector declining 

by 0.2%/yr (industrial electricity intensity in terms of consumption per real dollar of shipments 

declining by 1.3%/yr). Documentation of the assumptions can be found in the AEO 2009 and 

AEO 2010 and supporting materials (EIA 2009a; EIA 2009d; EIA 2010). 

Because the 2009 NEMS version projects energy use only through 2030, the demands for both 

the High-Demand Baseline and Low-Demand Baseline scenarios were extrapolated to 2050 

using the 2020–2030 growth rate (Figure 17-1). Additional calculations explained in Appendix I 

describe the method used to find the results for each North American Electric Reliability Council 

region and convert them to hourly profiles. 

Figure 17-1. Industrial electricity purchases under the High-Demand Baseline and 

Low-Demand Baseline
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17.3 Comparison of Projected Energy Intensities 

As was done for residential and commercial buildings (Section 16.3), a comparison of scenarios 

for the industrial sector was developed and is shown in Figure 17-2.28 For the industrial sector, 

the electricity intensity is computed as the electricity consumption per dollar (in real terms) of 

shipments. For comparison purposes, the intensities were converted to index numbers 

normalized to be 1.0 in 2010. 

Both the High-Demand Baseline and Low-Demand Baseline are taken from EIA model 

simulations. The High-Demand Baseline was based on the 2009 AEO Reference Case (EIA 

2009d). The Low-Demand Baseline was based on the analysis of the Waxman-Markey Climate 

Bill from 2009 performed by EIA (EIA 2009b), whose results here have been extrapolated to 

2050. Figure 17-2 indicates that the Low-Demand Baseline in this instance is reasonably close to 

the projections made by both EPRI (2009) and UC–Davis (McCarthy et al. 2008). The Union of 

Concerned Scientists scenario (Cleetus et al. 2009) is again considerably lower; under its policy 

and research and development scenario, industrial electricity intensity falls to less than half of its 

current value by 2030. Although the results are not included in the figures, McKinsey and 

Company (Granade et al. 2009) conducted another recent study of efficiency potential in the 

United States. When converted to an index basis, the implied intensity index (2020 relative to a 

2010 base) for the industrial sector is 0.68 in 2020. Because of the shorter time frame and greater 

emphasis on cost-effective potential compared to other studies, this value is not shown in 

Figure 17-2. 

 

28 The base year for index numbers was moved back to 2008 for the industrial sector to avoid the distortion in the 

intensities for 2009 and 2010 caused by the economic recession and reflected in the 2009 AEO.
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Figure 17-2. Industrial electricity intensities from five studies, including RE Futures 

Sources: EIA 2009b, EPRI 2009, Cleetus et al. 2009, McCarthy et al. 2008, NAS et al. 2009 

Appendix G provides additional detail regarding the comparison studies. 

17.4 Combined Cooling, Heating, and Power within the Industrial Sector 

The industrial sector can generate some of its own power needs, either through dedicated 

electricity plants or through combined cooling, heating, and power plants. Some of this power 

generated may also be sold to the electric sector for sale to other sectors. Total industrial 

electricity demand is the sum of purchased electricity and electricity generated less electricity 

sold to the grid. For RE Futures, the amount of cooling, heating, and power embedded within the 

High-Demand Baseline and the Low-Demand Baseline was determined, as discussed in 

Appendix J. No additional modifications were made to either increase or decrease the amount of 

cooling, heating, and power within the industrial sector. 
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Chapter 18. Transportation Demands

Although electricity use in the transportation sector is currently negligible (less than 0.2%),

29

plug-in electric vehicles (PEVs) offer the opportunity for the transportation sector to significantly 

reduce petroleum consumption through electrification. Light-duty vehicles currently use more 

than 60% of the energy consumed in transportation, making them the primary focus of an 

electrification effort.

30 Medium- and heavy-duty vehicles are the next largest segment 

(approximately 20%) and, while some portion of these could likely be electrified, it is quite 

difficult to compete with diesel powertrains in these applications. The likely load shape of 

medium- to heavy-duty vehicles is also less certain, making the development and incorporation 

of their associated load shapes into the models difficult. Therefore, RE Futures concentrated the 

transportation electrification effort in the light-duty vehicle market. Additionally, hybrid PEVs 

may have moderately sized energy storage systems and combustion engines that operate over a 

limited range under battery power while retaining the range capability of conventional 

combustion-engine vehicles. Other PEVs may be entirely battery dependent and provide 

complete petroleum displacement for specific vehicle sectors—several manufacturers are slated 

to introduce these types of vehicles to the market over the next several years. Based on past 

technology markets, maturity would likely occur within several decades from introduction. 

The introduction of PEVs also creates new flexible loads that can be integrated into utility 

operations with a high penetration of renewables to achieve a long-term strategy of creating a 

more sustainable transportation system.31 RE Futures developed energy system load 

characteristic forecasts on a regional basis during the study period. The work builds upon past 

travel survey data analyses, regional population forecasts, and charge management scenarios.

18.1 Low-Demand Baseline

Results were generated for 3,109 counties and were consolidated into 134 balancing areas (BAs)

for use in regional generation planning analysis tools. PEV aggregate load profiles from previous 

work were combined with vehicle population data to generate hourly loads on a regional basis. A 

transition from consumer-controlled charging toward utility-controlled charging was assumed 

such that by 2050, approximately 45% of the transportation electricity demands could be 

optimally managed from the utility perspective. No other literature has addressed the potential 

flexibility in energy delivery to electric vehicles in connection with a regional power delivery 

study. This electrified transportation analysis resulted in an estimate for both the flexible load 

and fixed load shapes on a regional basis.

As input to RE Futures, hourly PEV load and energy demand profiles for the fleet of PEVs by 

region over time were developed. The approach was as follows: 

1. Use population growth forecasts and historical vehicle ownership trends to estimate 

vehicle population by region 

 

29 EIA AEO (EIA 2010), Table 2 30 Transportation Energy Data Book, Table 2.6 (DOE EERE 2011) 31 See, for example, Tuffner & Kintner-Meyer 2011a and Tuffner & Kintner-Meyer 2011b
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2. Use a market evolution model to estimate the fraction of vehicles that will be PEVs 

throughout the study period 

3. Develop a vehicle fleet energy demand profile based on those from past studies that 

transitions from market introduction with no utility control to one in which the utility is 

able to manage a portion of the vehicle load. 

Simplifying assumptions, detailed in Appendix K, were made to constrain the study scope. 

A PEV market growth and saturation model was used with the motor vehicle estimates to 

determine the number of PEVs likely to be in use on a county level over the period discussed in

RE Futures. The market model represents a slow ramp toward consistent market growth and a 

final tapering of growth to saturation. PEV sales saturates at a 50% market share after about 50 

years (equivalent to approximately 40% of the total stock by 2050). A comparison of the sales 

rate and vehicle stock is shown in Figure 18-1. The rates in this scenario are consistent with 

results recently developed by Lin and Greene (2010). 

Additional calculations to convert the demand to hourly profiles are explained in Appendix K. 

This appendix also discusses the development and application of three charging profiles: 

• A No Utility Control profile in which the consumer is allowed to plug in and charge 

as soon as the vehicle ends the last trip for the day

• An Opportunity profile in which the charging infrastructure is prevalent and the 

consumer will choose to plug in any time the vehicle is parked 

• A Valley Fill/Managed profile that allows utilities to choose the charging time for 

vehicles

The energy demands of the Valley Fill/Managed scenario are assumed to grow to become a 

flexible load that can be managed by the utility to improve renewable generation asset 

utilization. By 2050, 45% of the total vehicle energy demand of 350 TWh was under managed 

control while the remaining 55% was a fixed load to be planned for and met by utility assets. The 

hourly load profile of the fixed transportation energy demand also shifted over the time period 

from No Utility Control toward Opportunity Charging. 
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Figure 18-1. Technology market penetration scenarios

18.2 High-Demand Baseline

For the High-Demand Baseline, the 2009 AEO Reference Case (EIA 2009d) was used to 

represent the transportation sector. Within the 2009 AEO Reference Case, PHEVs with a 10- to 

40-mile all-electric range represented less than 3% of vehicle sales by 2030 (EIA 2009d), and 

overall electricity use by the transportation sector remains less than 1% by 2030. 

18.3 Comparison to Other Studies

Many analyses have been conducted and papers published that include PEV market projections. 

EPRI and the Natural Resources Defense Council collaborated on a foundational study 

highlighting the nationwide greenhouse gas and pollutant emissions impacts of PEVs on the U.S. 

electrical grid on a regional basis (EPRI 2007). The fleet makeup assumed approximately 40% 

PEVs by 2030 and 60% by 2050. An aggregate hourly load profile was assumed in which 74% 

of the energy was delivered during the off-peak period and 26% during the daytime. Both the 

Pacific Northwest National Laboratory and the Oak Ridge National Laboratory have assessed 

regional PEV penetrations and future load characteristics. The Pacific Northwest National 

Laboratory study (Scott et al. 2007) considered the situation in which all electric vehicle loads 

could be managed and fit into the low points of the daily utility load curve. The Oak Ridge 

National Laboratory study (Hadley and Tsvetkova 2008) considered a variety of charge levels 

and loading scenarios to understand the regional capacity and emissions impacts of the various 

scenarios.
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Chapter 19. Hourly Electricity Demand for 2050

The potential for renewable electricity production to meet a large portion of future electricity 

demand depends not only on the magnitude of that demand but also on its variability regionally, 

seasonally, and diurnally; therefore, RE Futures focuses on load shapes. A central challenge for 

RE Futures was to convert the energy forecasts to the 13 regional hourly system load shapes 

required for the models.32 Within the EIA’s NEMS residential and commercial buildings 

modules, energy consumption by end use is forecast individually and regionally (EIA 2009c). 

These annual energy estimates are then attached to corresponding dimensionless load shapes and 

the results summed up to deliver hourly regional electricity system loads.

Because the High-Demand Baseline is a direct extension of a NEMS run, in this case, the load 

shapes for all sectors can be derived directly from NEMS (see Figure 19-1). This exercise is 

described further in Appendix H. 

Figure 19-1. High-demand hourly load profile, 2050

 

32 As described further in Appendix B, both the ReEDS and NEMS models use a set of representative load shapes to 

represent the 8,760 annual hourly loads.
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The Low-Demand Baseline poses a different challenge. As discussed earlier, due to the 

uncertainties regarding which end-use technologies are likely to show the greatest efficiency 

improvement over the next 40 years, the approach used to project total consumption has taken a 

generally neutral stance on the future composition of end-use consumption. 

There are two exceptions to this approach involving electricity, that used for air conditioning 

(space cooling) and that used for miscellaneous plug loads. These receive special treatment for 

different reasons, of which there are two for cooling. First, because cooling demand plays the 

major role in determining peak electricity demand in almost all regions of the United States, it 

represents a key driver of capacity expansion of the power sector and therefore necessary 

investment in renewable generation. Second, because the Regional Energy Deployment System 

(ReEDS) model is used in RE Futures to determine the deployment of thermal energy storage 

(TES), the potential for cooling load shifting was necessary as a separate load.

The basic assumption of the Low-Demand Baseline is that new technologies (influencing both 

cooling equipment and the building envelope) would keep pace with improvements in other 

technologies affecting other major end uses. The poorly understood, amorphous, and perpetually 

growing plug loads, usually termed “miscellaneous,” are a concern for energy forecasting for 

buildings. If miscellaneous electricity intensity does not decline to the same degree as all other 

major building end-use intensities, the future share of electricity associated with all other end 

uses—including cooling—will decrease in the long run (Figure 19-2).33

The scenarios are generally based on the notion that future technologies will begin to have a 

significant impact on reducing these miscellaneous loads after the next decade. However, the 

inevitable development and adoption of new electricity-using devices, now unforeseen, will 

partially offset improvements in the efficiency of current and future devices. Therefore, in the 

long term, the shares of total electricity consumption devoted to miscellaneous equipment are

expected to increase. Accordingly, these scenarios have the effect of slightly reducing peak loads 

associated with cooling, and thus flattening the building electricity hourly load profile over the 

coming decades. Appendix N contains the non-PEV hourly load shapes for each NEMS 

Electricity Market Module region for the year 2050.

 

33 Appendix H provides some discussion of this uncertainty issue as well as a more detailed presentation of the 

overall approach to projecting the share of electricity used for cooling.
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Figure 19-2. Low-demand hourly load profile, 2050
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Chapter 20. Supply Curves for Thermal Storage

Within buildings, TES offers end users the option to lower peak electricity use by storing energy 

for later use. Although storage technologies exist for both space heating and space cooling, only 

those impacting space cooling were considered for RE Futures. Thermal heat storage was not 

included in the modeling because peak demands are generally not driven by heating and 

therefore would not be anticipated to be selected by the models as an option.34 With respect to 

thermal cooling, pre-cooling using building mass was explored but was not included in the 

modeling effort because, unless additional mass is designed and built for such a purpose, there is 

no capital cost associated with the technology. 

Thermal energy storage for space cooling can be used to lower peak electricity use in buildings 

by drawing upon cooled water or ice to supply some portion of the cooling requirements during 

the hot afternoon and early evening hours. TES is one of a number of storage technologies that 

can be employed to provide more flexibility for how electric utilities can more economically 

meet summer peak electricity loads. For RE Futures, TES was assumed to apply only to the 

commercial buildings sector.

For RE Futures, two types of thermal storage systems were considered, both of which are 

applicable to commercial buildings. For large buildings, the use of chilled water as a storage 

medium may be the most economical system. For smaller buildings, especially those using 

packaged air conditioning units,35 ice storage may be the preferred storage technology. Based on 

a methodology developed in a 2000 “Federal Technology Alert” prepared for the Federal Energy 

Management Program (FEMP 2000) and more recent information from manufacturers and costestimation publications, cost estimates were developed for chilled water storage for a 

prototypical large office building. For ice storage, information from a 2009 E Source technical 

report was employed (Horsey 2009). A detailed description of how these data sources were 

employed is provided in Appendix L, which describes both the derivation of the cost values as 

well as how these values were incorporated in a series of cost curves that could be used by the 

models. 

 

34 If significant fuel switching from natural gas to electricity were to happen in the future, thermal heat storage may 

become a viable method to reduce winter peak loads.

35 A packaged unit is a “unit built and assembled at a factory and installed as a self-contained unit to cool all or a 

portion of a building” (EIA 1989).
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Chapter 21. Demand Response Capability

To determine the available demand response through 2050, the Federal Energy Regulatory 

Commission’s (FERC’s) National Assessment of Demand Response Potential database (FERC 

2009) was used as a basis for the projections in RE Futures. The FERC report identified four 

levels of demand response: Business As Usual, Expanded Business As Usual, Achievable 

Participation, and Full Participation. These four cases were phased in over time, as detailed in 

Appendix M. Based on these demand response capacities, the percentage reductions were 

calculated from the system peak demand for each major end-use sector and census region, and 

then the 2050 system peak was multiplied by the percentage to find the sectoral demand response 

in gigawatts. From this, the price sensitivity curve for each sector was extracted from the FERC 

(2009) database and applied to the 2050 sector electricity price to find the demand response 

supply curve. These supply curves were provided as inputs to the models. The amount of 

demand response for each case was then determined within the models, and was used only to 

meet reserve requirements. A detailed explanation of the development of the demand response 

capability is included in Appendix M. 
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Conclusion

RE Futures developed two “bounding” scenarios and associated load shapes: a High-Demand 

Baseline and a Low-Demand Baseline. In looking at how electricity demand has changed over 

the past 40 years, it is difficult to determine with any certainty how electricity demand might 

evolve over the next 40 years. As such, these scenarios were designed to represent reasonable 

upper and lower boundaries for electricity demand, as opposed to attempting to project an 

“expected” level of demand.
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Appendices 

As input to RE Futures, two projections of electricity demand were produced representing 

reasonable upper and lower boundaries of electricity demand. The models used in RE Futures 

required underlying load shapes, however, so RE Futures also produced load shape data in two 

formats: in 2-year increments for 17 time slices as input to the ReEDS model, and 8760 data for 

the year 2050 for the GridView model.36 The process for developing demand projections and 

load shapes involved many steps: discussion regarding the scenario approach and general 

assumptions, literature reviews to determine readily available data, and development of the 

demand curves and load shapes. Additionally, thermal energy storage and demand response 

within the end use sectors were considered.

These appendices contain the details underlying the electricity use presented in the main report 

as follows: 

• Appendix G contains a comparison to other studies. The first part of the appendix 

addresses the potential impacts of climate change on projections of energy use. The 

second part of the appendix compares the energy intensity of RE Futures to other 

studies.

• Appendix H contains the development of the building sector electricity use and 

underlying load shapes. 

• Appendix I contains the development of the industrial sector electricity use and 

underlying load shapes. 

• Appendix J addresses combined heating, cooling, and power within the industrial 

sector. 

• Appendix K contains the development of the transportation sector electricity use and 

underlying load shapes. 

• Appendix L describes the development of the thermal energy storage cost curves used 

in the modeling effort. 

• Appendix M details the development of the demand response potential within each 

sector.

• Appendix N contains the non-PEV hourly load shapes for each NEMS Electricity 

Market Module region for the year 2050. 

 

36 For information about GridView, see Appendix B (Volume 1).
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Appendix G. Comparisons to Other Studies 

Comparison of Climate Change Impacts on Energy Consumption

The incorporation of climate change into the projections of electricity use was beyond the scope 

of this study. However, a number of studies have been conducted that attempt to estimate the 

change in energy and electricity consumption under different climate change parameters. This 

section of the appendix reproduces comparisons of energy consumption changes in light of 

climate change, as included in Scott and Huang (2007) to provide some context regarding how 

climate change might impact the electricity use projections developed for RE Futures. Four 

tables are included: residential space heating, commercial space heating, residential space 

cooling, and commercial space cooling. 

Table G-1. Effects of Climate Change on Residential Space Heating in U.S. Energy Studiesa

Study

Change in Energy 

Consumption (%)

Temperature Change (°C) and 

Date for Change

National Studies

Rosenthal et al. 1995 -14% +1°C (2010)

Scott et al. 2005 -4% to -20% About +1.7°C median (varies 

from 0.4° to 3.2°C regionally and 

seasonally) (2020)

Mansur et al. 2005 -2.8% for electricity-only 

customers; -2% for gas 

customers, -5.7% for fuel oil 

customers

+1°C January temperatures 

(2050)

Huang 2006 Varies by location and building 

vintage; Average HVAC 

changes:

 -12% heating in 2020

 -24% heating in 2050

 -34% heating in 2080

18 U.S. locations (varies by 

location, month, and time of day)

Average winter temperature 

increases:

+1.3°C in 2020

+2.6°C in 2050

+4.1°C in 2080

Regional Studies

Loveland and Brown 1990 -44 to -73% +3.7°C to +4.7°C (individual 

cities) (no date given)

Amato et al. 2005 

(Massachusetts)

-7 to -14% natural gas

-15% to -20% fuel oil

-15% to -25% natural gas

-15% to -33% fuel oil

-8.6% in heating degree days 

(2020)

-11.5% in heating degree days 

(2030)

Ruth and Lin 2006 (Maryland) -2.5% natural gas

-2.7% fuel oil

+1.7°C to +2.2°C (2025)

a Source: Scott and Huang 2007, Table 2.3
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Table G-2. Effects of Climate Change on Commercial Space Heating in U.S. Energy Studies

Study

Change in Energy 

Consumption (%)

Temperature Change (°C) and 

Date for Change

National Studies

Rosenthal et al. 1995 -16% +1°C (2010)

Belzer et al. 1996 -29% to -35% +3.9°C (2030)

Scott et al. 2005 -5% to -24% About +1.7°C median (varies 

from 0.4° to 3.2°C regionally and 

seasonally) (2020)

Mansur et al. 2005 -2.6% for electricity-only 

customers; -3% for gas 

customers; -11.8% for fuel oil 

customers

+1°C January temperatures 

(2050)

Huang 2006 Varies by location and building 

vintage; Average heating 

changes:

 -12% heating in 2020

 -22% heating in 2050

 -33% heating in 2080

5 U.S. locations (varies by 

location, month, and time of day)

Average winter temperature 

increases:

 +1.3°C in 2020

 +2.6°C in 2050

 +4.1°C in 2080

Regional Studies

Loveland and Brown 1990 -37.3 to -58.8% +3.7°C to +4.7°C (individual 

cities) (no date given)

Scott et al. 1994 (Minneapolis 

and Phoenix)

-26% (Minneapolis)

-43.1% (Phoenix)

+3.9°C (no date given)

Amato et al. 2005 

(Massachusetts)

-7 to -8% 

-8% to -13% 

-8.6% in heating degree days

(2020

-11.5% in heating degree days

(2030)

Ruth and Lin 2006 (Maryland) -2.7% natural gas +1.7°C to +2.2°C (2025)

Source: Scott and Huang 2007, Table 2.3
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Table G-3. Effects of Climate Change on Residential Space Cooling in U.S. Energy Studies

Study

Change in Energy 

Consumption (%)

Temperature Change (°C) and 

Date for Change

National Studies

Rosenthal et al. 1995 +20% +1°C (2010)

Scott et al. 2005 +8% to +39% About +1.7°C median (varies 

from 0.4° to 3.2°C regionally and 

seasonally) (2020)

Mansur et al. 2005 +4% for electricity-only 

customers; +6% for gas 

customers; +15.3% for fuel oil 

customers

+1°C January temperatures 

(2050)

Huang 2006 Varies by location and building 

vintage; Average HVAC 

changes:

+38% in 2020

+89% in 2050

+158% in 2080

18 U.S. locations (varies by 

location, month, and time of day)

Average winter temperature 

increases:

 +1.7°C in 2020

 +3.4°C in 2050

 +5.3°C in 2080

Regional Studies

Loveland and Brown 1990 +55.7% to +146.7% +3.7°C to +4.7°C (individual 

cities) (no date given)

Sailor 2001 +0.9% (New York) to +11.6% 

(Florida) per capita

+2°C (no date given)

Sailor and Pavlova 2003 (four 

states)

+13% to +29% +1°C (no date given)

Amato et al. 2005 

(Massachusetts)

+6.8% in summer

+10% to +40% in summer 

+12.1% in cooling degree days 

(2020

+24.1% in cooling degree days 

(2030)

Ruth and Lin 2006 (Maryland) +2.5% in May–September (high 

energy prices; +24% (low energy 

prices)

+1.7°C to +2.2°C (2025)

Source: Scott and Huang 2007, Table 2.3
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Table G-4. Effects of Climate Change on Commercial Space Cooling in U.S. Energy Studies

Study Change in Energy Consumption (%)

Temperature Change 

(°C) and Date for 

Change

National Studies

Rosenthal et al. 1995 +15% (energy-weighted national averages 

of census division level data)

+1°C (2010)

Belzer et al. 1996 +53.9% +3.9°C (2030)

Scott et al. 2005 +6% to +30% About +1.7°C median 

(varies from 0.4° to 3.2°C 

regionally and

seasonally) (2020)

Mansur et al. 2005 +4.6% for electricity-only customers; 

-2% for gas customers; +13.8% for fuel oil 

customers; a negative effect on electricity 

use for natural gas customers is 

statistically significant at the 10% level, 

but unexplained

+1°C January 

temperatures (2050)

Huang 2006 Varies by location and building vintage; 

Average HVAC changes:

+17% in 2020

+36% in 2050

+53% in 2080

5 U.S. locations (varies 

by location, month, and 

time of day)

Average winter 

temperature increases:

 +1.7°C in 2020

 +3.4°C in 2050

 +5.3°C in 2080

Regional Studies

Loveland and Brown 1990 

(general office buildings in 6 

individual cities)

+34.9% in Chicago; +75% in Seattle +3.7°C to +4.7°C 

(individual cities) (no date 

given)

Scott et al. 1994 (small office 

buildings in specific cities)

+58.4% in Minneapolis; +36.3% in 

Phoenix

+3.9°C (no date given)

Sailor 2001 (7 out of 8 energyintensive states; one state – 

Washington – used electricity 

for space heating)

+1.6% (New York) to +5.0% (Florida) per 

capita

+2°C (no date given)

Amato et al. 2005 

(Massachusetts)

Monthly per employee: 

+2% to +5% in summer

+4% to +10% in summer 

+12.1% in cooling degree 

days (2020

+24.1% in cooling degree 

days (2030)

Ruth and Lin 2006 (Maryland) +10% per employee in April – October +2.2°C (2025)

Source: Scott and Huang 2007, Table 2.3
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Comparison of Electricity Demand Scenarios from Various Studies

This section of the appendix references the electricity demand scenarios made by several 

governmental and other organizations over the past several years, as included in Sections 16.3 

and 17.3. The discussion in this volume helps to provide some perspective regarding how the 

electricity projections in RE Futures compare to these alternative scenarios.

Because the comparisons include studies with different initial electricity intensities, the 

intensities have all been converted to index numbers normalized to the current time frame. 

Indexing the intensities also helps to show more clearly the fractional improvement over time. 

Although the base years of the studies are not all the same, they are all sufficiently recent not to 

significantly distort the differences in the long-run projections. Therefore, the base-year intensity 

index is normalized to be 1.0 in 2010. 

The key studies examined for the comparison are as follows: 

1. Climate 2030: A National Blueprint for a Clean Energy Economy (Union of Concerned 

Scientists [Cleetus et al. 2009])

Supported by a number of groups (such as the Avocet Charitable Lead Unitrust, David 

and Leigh Bangs, The Educational Foundation of America, and The Energy Foundation), 

Rachel Cleetus, Steven Clemmer, and David Friedman from the Union of Concerned 

Scientists’ Climate Program discussed the impacts on carbon emissions and the potential 

savings for consumers and businesses resulting from a series of “smart climate and 

energy policies.” The report explains a series of policies for the United States to reduce 

carbon emissions to 26% below 2005 levels by 2020, and 56% below 2005 by 2030 

through cap-and-trade programs along with other complementary policies. Models in the 

report are largely based on a modified version of the DOE’s NEMS (UCS-NEMS), 

supplemented by an analysis from the American Council for an Energy-Efficient 

Economy on the impact of greater energy efficiency in industry and buildings. The 

reference case assumed no new climate, energy, or transportation policies beyond those 

already in place as of October 2008. 

2. California Energy Demand Scenario Projections to 2050 (University of California–

Davis [McCarthy et al. 2008]) 

The California Energy Demand Scenario Projections to 2050 report was written Ryan 

McCarthy, Christopher Yang, and Joan Ogden of the Institute of Transportation Studies 

at the University of California Davis as a support document for California’s Advanced 

Energy Pathways Project. The report itself described five potential scenarios for the 

future energy demands in California, with the primary objective being to analyze the 

impacts of alternative transportation energy pathways on California’s natural gas and 

electricity sectors between 2005 and 2050. The five scenarios included a minimum 

demand, maximum demand, and three alternative versions of a baseline demand scenario 

(one assuming high-efficiency, one low-efficiency, and the third being a continuation of 

historical and projected trends). All of the scenarios were created using data from the 

California Energy Commission’s 2005 Integrated Energy Policy Report and projecting 

trends through 2050.
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3. Assessment of Achievable Potential from Energy Efficiency and Demand Response 

Programs in the U.S. (2010–2030) (Global Energy Partners and EPRI [EPRI 2009]) 

Sponsored by EPRI and prepared by Global Energy Partners and EPRI,

37 the report, 

Assessment of Achievable Potential from Energy Efficiency and Demand Response 

Programs in the U.S. (2010–2030), examined the potential for electric end-use efficiency 

and demand response programs with the intended effect of reducing projected U.S. 

electricity consumption and summer peak demand through 2030. Specifically, the study 

was intended to advise utilities, electric system operators, and policymakers on efforts to 

develop end-use energy efficiency and demand response programs. Using the 2008 

Annual Energy Outlook, the study assumed annual growth of peak demand in the United 

States would be at a rate of 1.5% from 2008 through 2030. The authors claimed this rate 

could “realistically” be reduced to 0.83% per year, and down to 0.53% per year under 

ideal conditions and aggressive use of energy-efficiency and demand response programs.

4. Energy Market and Economic Impacts of H.R. 2454. (Energy Information 

Administration, Report #: SR-OIAF/2009-05, August 4, 2009. [EIA 2009b]) 

In 2009, the EIA responded to a congressional request to analyze the energy market and 

economic impacts of H.R. 2454, otherwise known as the American Clean Energy and 

Security Act of 2009 or the Waxman-Markey Climate Bill from 2009,38 using the NEMS 

model. If enacted, the act would have regulated emissions of greenhouse gases through 

market-based mechanisms, efficiency programs, and economic incentives. Overall, the 

emissions reductions due to changes in fossil energy use within the residential, 

commercial, industrial, and transportation sectors would account for between 12% and 

20% of the total reduction in energy-related CO2 emissions compared to the 2009 AEO 

Reference Case in 2030. 

The EIA’s analysis of the Waxman-Markey Climate Bill from 2009 (EIA 2009b) 

suggested little or no fuel switching in the residential sector. This result may stem from 

the prediction that residential electricity and natural gas prices would both increase about 

17% in 2030 due to the legislation.

With regard to fuel switching, an examination of space and water heating energy use 

indicated that the response to the electricity and natural gas price increases may be higher 

for electricity than for natural gas. In the EIA simulation output, electricity consumption 

for space heating is lower in 2030 by about 15%, compared to an approximate 10% 

decline for natural gas heating consumption. (On a site energy basis, natural gas used for 

heating is still more than ten times greater than that for electricity.) For water heating, 

total electricity use was lower by 9%, compared to 3% for natural gas. Unfortunately, 

these results cannot be used to address fuel switching directly because EIA did not 

publish tables that would indicate how many households switched from gas to electricity 

(or vice versa) with respect to their primary heating fuels. Clearly, the technical 

opportunities for improved equipment efficiency appear to be greater for electricity than

natural gas, as evidenced by the response to the same relative price change. One can only 

 

37 The principal investigators involved in this study were I. Rohmund, G. Wikler, K. Smith, S. Yoshida, A. Faruqui, 

R. Hledik, and S. Sergici.

38 For the full text of the bill (H.R. 2454), see http://www.govtrack.us/congress/bill.xpd?bill=h111-2454. 
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surmise that in the EIA projections, the number of households that may switch to 

electricity for their primary space or water heating fuel—thus offsetting some of the 

efficiency gains in the aggregate—is relatively small.39 

5. Real Prospects for Energy Efficiency in the United States. (The National Academy of 

Sciences [NAS et al. 2009]) 

In 2007, the National Academy of Sciences and the National Academy of Engineering 

initiated a major study of issues related to the future U.S. energy situation: America’s 

Energy Future: Technology Opportunities, Risks, and Tradeoffs (the Real Prospects for

Energy Efficiency in the United States report). Developed by a panel of industry, 

governmental, and academic experts, the study is one of a series of reports resulting from 

this overall initiative. In the buildings area, the panel decided to build upon the analytical 

framework initially developed in the 2000 study, Scenarios for a Clean Energy Future, 

which was subsequently updated by Brown et al. (2008) and considered specific energy 

efficiency measures for each end use in the residential and commercial building sectors. 

For each end use, a percentage savings estimate was developed based on a “supply 

curve” of energy savings potential at various costs. The study concluded that in both of 

the building sectors, energy consumption could be reduced by about 30%–35% at a cost 

less than current retail prices.

6. Unlocking Energy Efficiency in the U.S. Economy. (McKinsey and Company [Granade 

et al. 2009]) 

Another recent study of efficiency potential in the United States was conducted by 

McKinsey and Company (Granade et al. 2009). This report examined the potential of 

energy efficiency measures in 2020, using the 2008 Annual Energy Outlook projections 

as a business-as-usual case. Because the study focused on implementation of all costeffective technologies (based on positive net present value criteria), the savings are 

considerably greater than the savings shown in the studies shown in Figure G-1 through 

Figure G-3. When converted to an index basis, the implied intensity indexes (2020 

relative to a 2010 base) for the residential and commercial sectors from Granade et al. 

(2009) were 0.71 and 0.73, respectively. For the industrial sector using the 2008 base in 

Figure G-3, the McKinsey-developed index would be 0.68 in 2020. 

Figure G-1 shows the projected intensities for the residential sector. As a business-as-usual case, 

the High-Demand Baseline (based on the Annual Energy Outlook) shows the least decline in 

intensity to the year 2030. Increases in “Other” electricity uses tend to actually increase the 

overall intensities in the last 10 years of the projection period. Shown only for the year 2030, the 

EPRI and cap-and-trade scenarios show a modest reduction in the intensity, on the order of 10%. 

 

39 Taking another approach to evaluating the EIA results, it may be useful to compare the percentage change in 

electricity use to that of heating use. The technical options for improved air conditioning efficiency are roughly 

equivalent to that for electric heating (comparing high-efficiency central air conditioners and heat pumps). In the 

case of the Waxman-Markey Climate Bill from 2009, electricity consumption for cooling declines by 9%, 

considerably less than that for heating. This result supports the notion that any fuel switching toward electricity is 

likely to be small in the simulation results because fuel switching would have tended to reduce the heating impact 

(i.e., offset the reduction). The fact that the heating consumption fell more than cooling consumption belies the 

notion that there was a significant fuel-switching offset to heating.
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The UCS 2030 Blueprint scenario developed by the Union of Concerned Scientists (Cleetus et al. 

2009) shows the greatest decline in intensity, with a potential reduction of approximately 45% by 

2030. 

The minimum demand scenario developed by the University of California Davis team (CA-Min 

Demand Case) for the California Energy Commission extends to 2050 (McCarthy et al. 2008). 

When converted to an intensity, a reduction in the overall residential electricity intensity (again, 

on a household basis) was projected to be approximately 40% by 2050. The near-term intensity 

decline for 2025 was approximately 18%. 

Figure G-1. Comparison of residential electricity intensities from various studies 

Sources: EIA 2009b, EPRI 2009, Cleetus et al. 2009, McCarthy et al. 2008, NAS et al. 2009 

The black squares connected by the solid line depict the high efficiency case developed for RE 

Futures. As shown in Figure G-1, this case falls within the range of intensity forecasts drawn 

from other studies. 

Figure G-2 shows the scenarios from the same studies for the commercial sector. As compared to 

the residential sector, commercial sector electricity is expected to increase through 2030 as part 

of the reference (EIA 2009d) projection developed by EIA and represents the High-Demand 

Baseline. Again, the EPRI and cap-and-trade cases by 2030 showed the most modest reductions 

relative to the reference case. The Union of Concerned Scientists scenario showed the greatest 

decline in intensity, with commercial electricity intensity decreasing 40% by 2030. 
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Figure G-2. Comparison of commercial electricity intensities from various studies 

Sources: EIA 2009b, EPRI 2009, Cleetus et al. 2009, McCarthy et al. 2008, NAS et al. 2009 

In between these studies are the projections for University of California Davis and the highefficiency case for RE Futures. The RE Futures study suggests a somewhat greater decline in 

intensity in the commercial sector than in the residential sector by 2050, in contrast to the 

relative declines projected by University of California Davis. By 2050, electricity intensity under 

the high-efficiency case declines by approximately 32%, compared to the California projection 

of an approximate 30% reduction. 

As was done with the residential and commercial buildings, a comparison of scenarios for the 

industrial sector was developed; it is shown in Figure G-3.40 Both the reference and highefficiency cases are taken from EIA model simulations. As with the building sectors, the 

reference case was based on the 2009 Annual Energy Outlook (EIA 2009d). The high-efficiency 

case was based on the cap-and-trade analysis performed by EIA, whose results here have been 

extrapolated to 2050 (EIA 2009b). Figure G-3 indicates that the high-efficiency case in this 

instance was reasonably close to the projections made by both EPRI and University of California 

Davis. The Union of Concerned Scientists scenario was again considerably lower, with industrial 

electricity intensity falling to less than half of its current value by 2030. The decline in projected 

electricity intensity may be due to the offshoring of energy-intense industries and other structural 

changes in the industrial sector. 

 

40 The base year for index numbers was moved back to 2008 for the industrial sector to avoid the distortion in the 

intensities for 2009 and 2010 caused by the economic recession and reflected in the 2009 Annual Energy Outlook 

(EIA 2009d). 
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Figure G-3. Comparison of industrial electricity intensities from various studies 

Sources: EIA 2009b, EPRI 2009, Cleetus et al. 2009, McCarthy et al. 2008, NAS et al. 2009 
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Appendix H. Development of Building Load Curves and Hourly 

Schedules

Building Sector Electricity Demand

The buildings sector dominates overall electricity consumption, representing almost 78% of the 

2030 total in the AEO Reference Case (EIA 2010). Additionally, buildings electrical end uses are 

highly heterogeneous and changing over time. 

Within the buildings industry, other trends continue to emerge. For new buildings, the “green” 

building movement, exemplified by the Leadership in Energy and Environmental Design 

certification program and more stringent building energy codes (along with more stringent 

appliance and equipment standards) are two of the most prominent factors that could have a 

major influence on energy use. The flurry of activity related to demand-side management 

programs of the 1980s and early 1990s has subsided, but in the past few years, resurgent state, 

local, and utility programs have aimed to significantly reduce energy use in existing buildings. 

Much of the current motivation for these programs is driven by concerns over climate change 

and other environmental issues as well as the desire to reduce the need for new generation 

capacity.

The DOE has been undertaking research programs designed to lead to ultra-efficient buildings. 

This objective for the DOE commercial research and development is specified in the Energy 

Independence and Security Act of 2007 (EISA 2007). Ultra-efficient buildings are designed and 

operated to generate as much on-site power (e.g., through rooftop photovoltaics) as the energy 

they consume. One of the keys to achieving this goal is by developing and applying extreme 

efficiency technologies. Under the DOE’s Building America program,

41 the goal is to develop 

cost-effective solutions that reduce the average energy use of housing by 40% to 70% that, in 

turn, will provide a foundation for ultra-efficient homes. In terms of ultra-efficiency goals, the 

program aims to integrate on-site power systems leading to cost-effective, ultra-efficient homes 

by 2020. For commercial buildings, the DOE’s Commercial Building Initiative42 aims to achieve 

marketable ultra-efficient commercial buildings by 2025. Some states have similar goals (e.g., 

California Assembly Bill 2030 [AB 2030]). Within the context of the Low-Demand Baseline, RE 

Futures considered only the efficiency improvements required to create a building that is capable 

of having its energy needs met by on-site generation—this does not presume that all such ultraefficient buildings will in fact be powered on site; rather, the determination of power provision is 

left to the ReEDS modeling effort.

The DOE programs working toward ultra-efficient buildings are based on a whole-building 

approach to achieve dramatically lower energy use. This perspective views the building as an 

integrated system with each component working together to achieve greater energy efficiency 

and comfort. The whole-building concept, as its name implies, is comprehensive and pays 

special attention to the control systems that operate buildings. It encompasses the specific 

building site; the envelope (roof, walls, and windows); the heating and cooling systems; lighting 

 

41 See http://www1.eere.energy.gov/buildings/building_america/ for more information about this U.S. Department of 

Energy program

42 See http://www1.eere.energy.gov/buildings/commercial_initiative/ for more information about this U.S. 

Department of Energy program
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and equipment (appliances); and controls. By integrating these components, economies can be 

realized in the construction of the building that can offer significant savings in energy use. For 

example, a properly sealed and insulated building that incorporates windows for natural lighting 

will be able to use a smaller, less expensive heating and cooling system. It will also require less 

energy for indoor lighting. 

Electrification within Buildings Sector

Inaccuracy in future electricity demand forecasts arises in part from the considerable uncertainty 

regarding penetration of electric vehicles into the vehicle stock, and also electrification of 

transportation through greater use of mass transit. A similar but less-well recognized uncertainty 

concerns electrification in buildings. Electric vehicles may have dramatically lower carbon 

footprints than their petroleum-fueled counterparts, if the electricity supplied to them is low 

carbon. Likewise, if the electricity supplied to buildings were decarbonized, the use of electricity 

as a fuel would become more attractive. This argument is of particular interest here both because 

the nature of RE Futures directly addresses power generation decarbonization, and because the 

growing worldwide concern over the carbon footprint of the power sector would drive down the 

footprint of buildings. A key dissimilarity between transportation electrification and building 

electrification is that unlike the transportation sector, building energy services typically do not 

rely heavily on imported petroleum, making efficiency and fuel switching less of a national 

imperative from some perspectives.

Currently in a typical building, electrical end uses constitute the largest component of its carbon 

footprint.

43 This situation tends to make local thermal generation with heat recovery attractive 

from a carbon point of view, and likewise substituting direct combustion for electrical end uses 

tends to lower carbon emissions. In other words, avoiding the burden of carbon emissions caused 

by the waste heat of energy conversion at power plants tends to diminish the footprint of the 

whole system, and so does burning gas locally to displace coal combustion at power plants. 

However, if electricity supply is significantly decarbonized, the logic may reverse; that is, highefficiency electrical equipment in buildings could provide energy services with a systemically 

lower carbon footprint than local combustion. Current resistance heating with a site coefficient of 

performance of less than 1 using electricity generated by coal cannot compete when evaluated on 

a carbon basis with direct space heating burning natural gas. However, if efficient heat pumps 

(e.g., ground source) are economically competitive with direct, on-site combustion and the 

electricity supplying them is decarbonized, the low carbon footprint option for heating is the 

electric heat pump.

44 In other words, the high efficiency of electrical end-use devices combined 

with low carbon electricity sources, both from the grid and locally, might further electrify the 

buildings sector, offsetting some fraction of the expected efficiency improvements achieved in 

other energy services. Conversely, direct combustion of sustainably produced biomass, local air 

quality conditions permitting, could also be a very low carbon footprint heating technology. 

Thus, concern about a building’s carbon footprint might encourage on-site combustion options 

when compared to electricity from high carbon sources in the near- to mid-term, but in the longer 

 

43 This assumption takes transmission and distribution losses into account and assumes that electricity is primarily 

being generated from fossil fuels.

44 Depending on regional electricity prices and other factors, heat pumps with a coefficient of performance greater 

than 3 may be competitive on a carbon basis without decarbonized electricity.
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term when electric power is produced by low carbon methods, electrical devices will be

preferred over on-site fossil-energy-based options. In general, the forecasts made for RE Futures 

do not consider these issues. The High-Demand Baseline relies on the standard inter-fuel 

competition, as it exists in NEMS without any additional restriction on carbon emissions. 

Likewise, in the Low-Demand Baseline, the efficiency of buildings is assumed to increase 

significantly without attention to the competition between end-use equipment and the fuels it 

uses. In other words, fuel choice is purely economic. 

Energy Intensity for New Buildings 

The underlying assumptions for the Low-Demand Baseline for RE Futures are informed by the 

energy efficiency vision of the DOE’s ultra-efficient building programs. Consideration of what 

level of energy consumption intensity must be achieved to reach ultra-efficiency motivates the 

overall approach to developing a possible trajectory of future energy demands for the residential 

and commercial buildings. The electricity demand forecast for residential and commercial 

buildings under the Low-Demand Baseline assumes a larger number of buildings to be ultraefficient. At this point, the whole-building perspective suggests that DOE, as well as other 

building research and code-setting organizations, will intensify efforts to increase the energy 

efficiency of all building components and sub-systems, even if historically the most emphasis 

has been placed on heating, cooling, and lighting. This whole-building perspective of these 

efforts suggests that there is no reliable way to predict which technologies (and associated end

uses) will offer the most promise to achieving these overall reductions in building energy use. 

Where future breakthroughs in energy efficiency will occur over the next four decades cannot be 

predicted at this point.

45

Due to the inability to predict which technology and end-use consumption areas are most likely 

to show the greatest improvements, the approach of the Low-Demand Baseline is to focus only 

on the broadest measure of energy intensity, energy use per square foot (or per household) at the 

whole building level. Accordingly, the basic assumption for new buildings is that average energy 

intensity in 2050 will be 60% below that of new buildings being built today. This reduction is 

also assumed to occur along an annual percentage decline rate in energy consumption. Thus, 

absolute reductions in energy intensity are assumed to be greater in the immediate future than 

several decades from now.

For the residential sector, the assumed path of efficiency improvements implies that electricity 

intensity by 2025—the average of new houses built in that year—would be 30% below today’s 

levels. That result may appear conservative in light of DOE’s target of roughly a 50%–70% 

decline in energy intensity by this same year. These near-term reductions were assumed to be 

somewhat slower in commercial new buildings; the 2025 reduction in average new commercial 

building intensity was assumed to be about 22% lower than today.46 Two considerations should 

 

45 For example, for the first time in a number of years, in 2009, the U.S. Department of Energy’s Building 

Technologies Program Office issued several general requests for research and development proposals dealing with 

advanced, building-related efficiency technologies. The range of areas proposed covered advanced insulation and 

glazing materials as well as technologies that would dramatically reduce heating, cooling, and other equipment 

energy use. While not all of the proposals will be successful, a few “winners” may play significant roles in helping 

to achieve the long-term Ultra Efficient Building goals. 46 A key element in reducing electricity growth in commercial buildings relates to the development of technologies 

to slow the growth of miscellaneous electricity (e.g., “plug loads”). Significant improvements in efficiency for this 
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be kept in mind. First, the DOE targets are couched in terms of market-acceptable designs that 

can be used by architect-engineering firms and builders. That is considerably different from 

achieving this level of efficiency in practice, and across all new construction in the United States. 

Moreover, while DOE’s ultra-efficient buildings visions informed the approach in RE Futures, 

considerable research is still needed to find cost-effective and market-acceptable designs that 

will in fact reach these targets, especially when averaged across all types of buildings and 

climate zones. Research in these areas is under way; for example, a series of technical support 

documents are under development to explore the potential design and cost elements of achieving 

50% energy savings compared to the American Society of Heating, Refrigerating, and AirConditioning Engineers 90.1-2004 for a number of commercial building classes (see Bonnema et 

al. 2010, Hale et al. 2008, Jiang et al. 2009, Leach et al. 2009, Leach et al. 2010, Thornton et al. 

2009, and Thornton et al. 2010). Within the residential sector, DOE has funded the production of 

a series of climate-specific “Technology Information Packages” to support the work of its 

Builder’s Challenge initiative (Roberts and Anderson 2009). 

Another perspective is gained from potential goals associated with national building energy 

standards. Several current legislative proposals related to climate change include provisions for 

substantially more stringent building energy codes over the next 15 years. Generally, in the years 

immediately after 2020, both residential and commercial building codes would be mandated to 

be 50% more stringent than the codes that were in effect in the middle of the current decade (i.e., 

2006 residential code and 2004 commercial code). However, even if these goals were met, it 

would not imply that new buildings would use 50% less electricity than the baseline codes just 

cited. First, one needs to distinguish between the development of a new (national model) code 

and having it actually adopted by all state and local jurisdictions. Second, achieving the specified 

energy savings requires that the codes have near full compliance, a situation that is not achieved 

in practice. Finally, but perhaps most importantly, 50% more stringent in terms of reducing total 

energy does not necessarily imply 50% electricity savings. The building codes currently do not 

cover all end uses, and those end uses not covered by codes are predominantly electricity-based. 

Some of these end-use devices, such as refrigerators and some other home appliances, are subject 

to other efficiency mandates, while others are not.

Energy Intensity for Existing Buildings 

Due to the cost to substantially change the building envelope and heating and cooling system, the 

potential for large efficiency increases for existing buildings is much lower than that for new 

buildings. For the Low-Demand Baseline, intensity in existing residential buildings (e.g., pre2010 homes still in the stock in 2050) was assumed to decline by 30% by 2050. For commercial 

buildings, the assumed decline by 2050 is somewhat greater at 40%. The larger decline for 

commercial buildings reflects an assumption that the amount of miscellaneous electrical uses in 

commercial buildings will be more amenable to reductions from policy, appliance and equipment 

standards, and new technology than those in the residential sector. 

 end use is expected to take a decade or longer to achieve. Slower near-term efficiency improvement was also 

assumed for existing commercial buildings as shown in Figure H-3. Another factor assumed to result in relatively 

slower near-term reductions in intensity in the commercial sector relates to the recent step-up in federal policies to 

reduce energy use in the residential sector, namely from appliance rebates and weatherization efforts.
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Several recent “meta-analyses” can be cited to generally support these assumptions. One recent 

study focused on efficiency potential in the south census region of the United States (Chandler 

and Brown 2009). The meta-study included six national studies, four (southern) regional studies, 

and eleven southern state studies. Because these studies covered a variety of time spans, the 

authors normalized the results in terms of percentage changes per year. The time horizons 

considered by the majority of studies ranged between 10 and 20 years. 

A summary table in Chandler and Brown (2009) summarizes the “percent efficiency potential 

per year” across various geographic categories. The broadest category covers the state, regional, 

and national studies examined by Chandler and Brown (2009). Table H-1 shows the results of 

that analysis in terms of the average annual percentage reductions in energy intensity. 

Table H-1. Summary of Results from Efficiency Potential Meta-Analysis

Residential

(percent reduction per year)

Commercial

(percent reduction per year)

Moderate achievable 0.7% (10, 0.4%) 0.7% (8, 0.5%)

Maximum achievable 1.4% (7, 0.7%) 1.5% (7, 0.7%)

Source: Chandler and Brown (2009), Table 4

Numbers in parentheses are number of studies over which average was computed followed by the 

standard deviation of those studies

The Pacific Northwest National Laboratory conducted a second, more limited study for DOE 

(Belzer 2009). This study examined five state-level studies with a focus on efficiency potential 

for existing commercial buildings. Applying the same methodology as Chandler and Brown to 

normalize the efficiency potential across different time periods, the average percentage reduction 

for “economic potential” was 1.5% per year.47 Under the assumption that the maximum 

achievable potential is approximately 80% of economic potential, then the annual percentage 

reduction drops to 1.3% per year. 

Given the assumptions about the magnitude of the 2050 intensities for RE Futures (30% and 

40% for the residential and commercial building sectors, respectively), the implied annual 

reductions are roughly 0.9% for the residential sector and 1.2% for the commercial sector. Thus, 

these changes appear to be roughly consistent with the meta-analyses summarized above.

Retrofits and Renovations

For a study with a long-term time horizon, one cannot assume that the intensity of buildings built 

today and in the near future will remain constant. Many ultra-high-efficiency technologies 

introduced in the latter years (2030–2050) will be adopted, through retrofits and renovations, in 

buildings built over the next 20 years. As a means of accounting for that phenomenon, the LowDemand Baseline assumed that buildings (both residential and commercial) built over the next 

20 years (2010–2030) will be retrofitted, or renovated, with more efficient equipment in the 

subsequent 20 years (2030–2050). Operationally, this assumption is implemented as a 15% 

 

47 The results for Vermont were dropped from this average because the very large potential from refrigeration 

improvement was deemed an outlier. The studies used in the average included those for California, Connecticut, 

Colorado, and the Pacific Northwest (Idaho, Oregon, and Washington). There was no overlap in the states 

considered in the Belzer 2009 study and those considered by Chandler and Brown 2009.
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reduction in the electricity intensity for housing units and a 20% reduction in commercial 

buildings beginning in 2031. In other words, residential and commercial buildings built between 

2010 and 2030 are “revisited” in the analysis 20 years later (e.g., buildings built in 2011 are 

revisited in 2031; buildings built in 2012 are revisited in 2032, and so on, through 2050), with 

the intensity reduction applied in the out years to account for the improvements in building and 

equipment practices. 

As an example, Figure H-1 illustrates the intensity projections for new residential buildings. As 

can be seen on the figure, each point of the “2031–2050 retrofit” line is 15% lower than the 

equivalent point, 20 years earlier, on the “New” line. 

Summary of Intensity Projections

Figure H-1 displays these assumed paths of intensity for the various segments of the future 

residential building stock, all defined in terms of total electricity use per household. The new and 

2031–2050 retrofit intensities are reported as in-year intensities—that is, they represent the 

intensity of the buildings built or retrofit in that year. The pre-2010 intensity is a stock average, 

representing the decline over time as improvements are made to the pre-2010 stock. The higher 

intensity for new homes is largely a result of the larger size (areal square feet) of new homes 

compared to the stock of existing (pre-2010) homes. Over the forecast horizon, new home 

electricity intensity continues to be defined in terms of electricity intensity per household, even if 

new home size (areal square feet) continues to change.48 Figure H-4 compares the overall energy 

intensity across all three categories (new, pre-2010, and 2031–2050 retrofit) for the HighDemand Baseline and the Low-Demand Baseline. By 2030, electricity use per household in the 

Low-Demand Baseline declines by approximately 14%, compared to a 4% decline in the HighDemand Baseline. By 2050, the average electricity intensity in the Low-Demand Baseline is 

reduced by 31% compared to 2010. 

Figure H-3 and Figure H-4 show the same types of intensities for commercial buildings. 

Figure H-3 shows the projected intensities for existing (pre-2010) and new buildings as well as 

the intensities for the 2011–2030 vintage new buildings after undertaking substantial retrofits or 

a major renovation 20 years after initial construction. The increase in the intensities from 2010 to 

2012 largely reflects the improved economic conditions (e.g., lower commercial vacancy rates) 

that will likely increase electricity intensity in the near term. Figure H-4 shows that overall LowDemand Baseline electricity intensity is projected to decline by 10% between 2010 and 2030, in 

contrast to a 4% increase in the High-Demand Baseline. The average intensity in the energy 

efficiency scenario continues to fall over the last 20 years—for a total decline of 32% by 

2050.49,50

 

48 While there has been a steady long-term trend toward larger homes, the average size of new single-family houses 

in 2008 showed the largest year-to-year decline in the 35 years that the U.S. Bureau of Census has collected data 

(from 2,277 to 2,215 square feet). It is therefore conceivable that future increases in average new home size may 

moderate, which would help to attain the intensity goals for the residential sector though increased energy 

efficiency.

49 As cited, these percentage reductions use 2010 as a base year. The reductions are greater (by approximately 4 

percentage points) if the comparison is made with regard to 2012, a year expected to display more normal economic 

activity.
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Figure H-1. Electricity intensities for residential building segments in the Low-Demand Baseline

Figure H-2. Electricity intensities for residential buildings: High-Demand Baseline versus 

Low-Demand Baseline

 50 One additional segment of the commercial sector, not explicitly discussed above, is that for non-building 

electricity use. The largest uses within this segment include street lighting, water and sanitary treatment, and 

communication equipment. Non-building use was estimated to be 8% of overall commercial sector electricity use in 

2009. For projection purposes, this electricity use was also formulated as an intensity (i.e., estimated electricity 

consumption divided by total floor space of commercial buildings). The overall intensity of this segment was 

assumed to decline by 10% by 2050.
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Figure H-3. Electricity intensities for commercial building segments in the Low-Demand Baseline

Figure H-4. Electricity intensities for commercial sector: High-Demand Baseline versus

Low-Demand Baseline

The projected intensities are assumed to be feasible by sustained efforts on the part of 

government, businesses, and households—without major increases in electricity prices as a 

driver. For new buildings, the ultra-efficiency goals of DOE are predicated based on the wholebuilding efficiency improvements being cost effective (e.g., for the federal commercial building 

program, a 5-year payback criterion has been established).
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Projections of Building Electricity Use

The final step in developing projections of total building energy use is to combine the intensity 

projections with those related to future building stock. For the period through 2030, projections 

of the number of households and total commercial floor space match those from the 2009 Annual 

Energy Outlook. Beyond 2030, projections of building stock depend to an approximate degree on 

population projections made by the Bureau of Census (U.S. Census Bureau 2008). Within these 

projections, AEO’s assumptions (EIA 2010) regarding demolitions and other reductions of stock 

were maintained.

For the residential sector, the 2010 Annual Energy Outlook (EIA 2010) projected annual new 

housing units (i.e., new households) to average 1.7 million units between 2033 and 2035. For the 

next 20 years, new housing construction is assumed to be 1.7 million units per year. Given the 

population projections, this level of housing construction maintains a roughly constant household 

size over the study period (2010–2050). As projected in the 2009 AEO (EIA 2009d), households 

comprise an average of 2.67 persons in 2010 and 2.63 persons in 2030. Under the assumed 

construction activity over the subsequent 20 years, household size would grow slightly beyond 

that level to 2.71 persons in 2050. In aggregate terms, the total number of U.S. households would 

increase from 114 million in 2010 to 162 million in 2050. 

The same general methodology was conducted for the commercial sector. The 2010 AEO (EIA 

2010) projects annual new floor space to increase from 2.49 million square feet in 2020 to 2.57 

million square feet in 2035. For RE Futures, annual new construction between 2036 and 2050 is 

assumed to remain constant at the 2035 value (2.57 million square feet annually). The 

projections in the 2010 AEO imply a roughly 8% decline in the total amount of commercial floor 

space per capita between 2010 and 2035. Under the assumed 2035–2050 construction activity, 

that ratio would fall another 3%. These declines in per capita floor space can be rationalized in 

terms of two factors: (1) absorption of often-cited current excess retail space and (2) greater use 

of all types of commercial floor space based on economic pressures, including web commerce, 

and concerns about sustainability and energy efficiency. 

When multiplied by the intensity projections shown in Figure H-2 and Figure H-4, the 

projections of the number of households and commercial floor space then yield values for total 

sector electricity consumption. Figure H-5 compares the aggregate residential electricity

projections from the High-Demand Baseline with the Low-Demand Baseline as constructed here.

Consistent with intensity projections, the High-Demand Baseline and Low-Demand Baseline in 

the residential sector begin to diverge significantly after 2020. By the end of the study period in 

2050, the intensity reductions are sufficient to reduce aggregate residential use by 2% relative to 

2010. Compared to the High-Demand Baseline, total electricity consumption is lower by 29%. 
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Figure H-5. Aggregate residential sector electricity projections:

High-Demand Baseline versus Low-Demand Baseline

For the commercial sector, Figure H-6 shows total electricity consumption under the two 

scenarios. The differences here are more pronounced than the residential sector. In 2050, the 

energy efficiency case is 35% below the reference case. However, compared to the residential 

sector, the assumed declines in intensity for both new and existing buildings are not sufficient to 

keep projected 2050 consumption below the 2010 level. In this case, total electricity 

consumption is about 8% greater in 2050 compared to 2010. 

Figure H-6. Aggregate commercial sector electricity projections: High-Demand Baseline

versus Low-Demand Baseline
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Calculation of Building Load Shapes

The potential for renewable electricity production to meet a large portion of future electricity 

demand depends not only on the magnitude of that demand but also on its variability regionally, 

seasonally, and diurnally; therefore, the focus of RE Futures is on load shapes. The HighDemand Baseline is a simple extrapolation of the AEO Reference Case forecast (EIA 2009d), 

developed by EIA using NEMS, so its load curves could be directly accessed, with some 

complications, as discussed below. The Low-Demand Baseline poses a different challenge. As 

discussed earlier, due to the uncertainties regarding which end-use technologies are likely to 

show the greatest efficiency improvement over a 40-year future, the approach used to project 

total consumption has taken a generally neutral stance on the future composition of end-use 

consumption. 

There are two exceptions to this approach involving electricity used for air conditioning (space 

cooling) and miscellaneous plug loads. These receive special treatment for different reasons. 

First, because cooling demands play the major role in determining peak electricity demand in 

almost all regions of the United States, it represents a key driver of capacity expansion of the 

power sector and therefore a necessary investment in renewable generation. Second, because 

ReEDS was used in RE Futures to determine the deployment of thermal energy storage, the 

potential for cooling load shifting was necessary to input as a separate load. 

The basic assumption of the Low-Demand Baseline is that new technologies (both influencing 

cooling equipment and the building envelope) would keep pace with improvements in other 

technologies affecting other major end uses. The poorly understood, amorphous, and perpetually 

growing plug loads, usually termed “miscellaneous,” are a concern for this approach and for 

buildings energy forecasting in general. If miscellaneous electricity intensity does not decline to 

the same degree as all other major building end-use intensities, then the future share of electricity 

associated with all other end uses—including cooling—will fall, but overall demand will not 

decline as projected. 

Given this framework, the approach used here makes assumptions about the rate of decline of 

electricity for these miscellaneous uses in comparison to all other end uses. For the residential 

sector, average electricity intensity for the miscellaneous end uses (accounting for an estimated 

27% of current household electricity use) was assumed to remain constant through 2020 and then 

decline at 0.3 times the rate for overall electricity use. With this assumption, miscellaneous 

electricity use would increase to 36% of total electricity consumption in 2050. Given that result, 

the share of electricity consumption used for cooling would decline by 12% (as do the shares for 

all other major end uses).

For the commercial sector, a similar approach was followed. Currently, electricity used for 

computers, office equipment, and other miscellaneous equipment is estimated to be about 25% of 

total commercial building electricity use. Following no overall reduction in this intensity over the 

next decade, it is then assumed that it will decline at half the rate for all other end uses. The share 

of total electricity consumption for miscellaneous end use under this scenario would grow to 

33% by 2050. Conversely, the shares of all other end uses, including cooling share, would fall by 

11%. 
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The scenarios are generally based on the notion that future technologies will begin to have a 

significant impact on reducing these miscellaneous loads after the next decade. However, the 

inevitable development and adoption of new electricity-using devices, now unforeseen, will 

partially offset improvements in the efficiency of current and future devices. Therefore, in the 

long term, the shares of total electricity consumption devoted to miscellaneous equipment will 

continue to increase. Accordingly, these scenarios have the effect of slightly reducing peak loads 

associated with cooling, and thus flattening the building electricity hourly load profile over the 

coming decades.

Projected Shares of Electricity for Cooling in Buildings Sector

A key aspect to how the building sector will impact the electricity supply sector in the long term 

is the degree to which the relative importance of weather-related electricity demand will change. 

Because electricity for cooling is the primary influence on summer peak electricity loads, and 

therefore overall electricity capacity requirements, some extended consideration of this issue is 

warranted.

A conventional view is that efficiency improvements in the building envelope (primarily 

insulation and windows) and in lighting and HVAC systems will likely be greater than those 

related to computers, office equipment, home entertainment equipment, and all other types of 

miscellaneous electrical equipment (i.e., plug loads). Moreover, the percentage of total electricity 

consumption from these other uses appears to be growing—primarily as more and more 

electricity-using devices are used by households—and office automation and distributed 

computing are likely to continue to grow in commercial buildings (albeit, at perhaps at a 

somewhat slower pace than during the 1980s and 1990s). The implication of these trends, should 

they continue even at a somewhat slower rate in the future, is that the percentage of electricity 

for air conditioning in buildings will be lower in the future than it is today. Therefore, regardless 

of the annual level of overall consumption, the intensity of summer electricity peaks would 

moderate in the decades to come.

Unfortunately, to estimate the magnitude of this phenomenon is exceedingly difficult because it 

depends on the relative efficiency improvements in building design features and equipment that 

differentially affect cooling and non-cooling end-use consumption. It also depends on any 

changes in weather patterns due to climate change and the expected increase in summer air 

conditioning loads. Near-term studies of efficiency potential in existing residential and 

commercial buildings are of little use because they typically extend only 10–15 years into the

future. A recent meta-analysis of six state and utility-specific studies of electricity efficiency 

potential in existing commercial buildings found that lighting and refrigeration improvements 

represented the largest and most cost-effective sources of electricity savings over the next decade 

or so (Belzer 2009). While some attention was focused on office equipment and computers in 

these studies, the primary energy savings measure involved power management of computer 

networks (including nighttime shutdown of desktop computers). The issue was raised regarding 

how effective these measures could be in the aggregate because nighttime file backup and 

automated software upgrades would appear to be incompatible with power management that 

completely turns off computers.
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Other measures affecting computers and office equipment involve the use of the Energy Star 

program to promote adoption of the most energy-efficient models of such equipment. However, 

current Energy Star criteria provide little guidance regarding how far electricity use could be 

reduced for these types of equipment a quarter century or more into the future. The same issue 

applies to projecting the electricity requirement of home entertainment equipment (televisions, 

audio equipment, gaming consoles, etc.).

For new buildings, speculation about long-term efficiency potential generally does not consider 

how that potential may break out by end use. In DOE’s planning for ultra-efficient residential 

and commercial buildings, the efficiency targets are all couched in terms of reducing overall site 

energy use to the point at which renewable energy (either at the building site, such as rooftop 

photovoltaic, or available elsewhere on the grid) would be able to meet the remaining energy 

requirements.

At least for commercial buildings, recent work sponsored by DOE may provide some hints 

regarding how new, highly efficient building designs may use energy. Over the past year, reports 

dealing with four different types of commercial building have been published that suggest how 

these buildings could reduce overall (site) energy by 50% compared to a recent building energy 

standard (ASHRAE Standard 90.1-2004).51 This work is intended to support potential updates of 

the Advanced Energy Design Guides (AEDG) that have been previously published by the 

American Society of Heating, Refrigerating, and Air-Conditioning Engineers with support from 

DOE.

These studies involve simulations of prototypical buildings in a number of locations across the 

United States. To reach the 50% target, a wide variety of energy efficiency measures were 

considered, affecting energy consumption for all major end uses in the buildings. Pacific 

Northwest National Laboratory examined two building types (medium office [Thornton et al. 

2009] and highway lodging [Jiang et al. 2009]), and the National Renewable Energy Laboratory 

examined two building types (medium box retail [Hale et al. 2008] and grocery stores [Leach et 

al. 2009]). 

A scoping decision made early in RE Futures was to restrict the issue of changing electricity load 

profiles for buildings to that which could be measured by changes in the relative shares of 

cooling; therefore, only the specific results for cooling and all total electricity consumption for 

the baseline and advanced building designs need to be considered. While the studies performed 

simulations for as many as 15 locations across the United States, the brief survey here considers 

only five locations, representative of the largest (in terms of recent building construction activity) 

climate zones used by the building energy code organizations in the United States. 

Medium Office Building

Table H-2 presents the summary results for the medium office building. In this case, two 

different types of HVAC systems are considered. In the base configuration and the initial 

advanced case, a typical variable-air-volume (VAV) HVAC system is modeled. Thornton et al. 

 

51 American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) and Illuminating 

Engineering Society of North America (IESNA). 2004. Energy Standard for Buildings Except Low-Rise Residential 

Buildings. ANSI/ASHRAE/IESNA Standard 90.1-2004, Atlanta, Georgia.
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(2009) indicated that in some locations, it was difficult to be near or meet the 50% target with 

this conventional system. As a result, the packaged VAV system was replaced in a second set of 

simulations by a dedicated outdoor air system in combination with a hydronic radiant cooling 

and heating system (as shown in the third line of each panel, “Radiant-Adv”). 

The results for Houston in the top panel of Table H-2 indicate that the shares of total electricity 

simulated as used for cooling would decline with either type of system. These increases are of 

course relative to a much lower overall level of electricity use (49% for VAV, 53% for the 

radiant system). The particular metric of interest is the relative (or percentage) change in this 

share. The last column shows that the cooling shares of total electricity in these advanced 

building designs would decrease by 11% and 14% for VAV and radiant systems, respectively. 

The results in the lower panels of Table H-2 for the other locations generally show that the 

cooling shares would also decrease for the other locations. The much greater reduction for Las 

Vegas stems from the modeling assumption that an indirect evaporative cooling system is 

employed. This type of system can lead to substantially lower cooling electricity requirements in 

dry, hot climates. 

Table H-2. Relative Changes in Cooling Electricity Share for Advanced Medium Office Building

City System Type

Total 

Electricity 

(MMBtu)

Cooling 

(MMBtu)

Cooling Share 

(fraction)

Percent Change 

in Share

(%)

Houston VAV-Base 2,700 708 0.262 — 

VAV-Adv. 1,385 323 0.233 -11%

Radiant-Adv. 1,257 283 0.225 -14%

Atlanta VAV-Base 2,448 513 0.209 — 

VAV-Adv. 1,303 241 0.185 -12%

Radiant-Adv. 1,187 214 0.180 -14%

Las Vegas VAV-Base 2,527 538 0.213 — 

VAV-Adv. 1,255 140 0.112 -48%

Radiant-Adv. 1,224 250 0.204 -4%

Baltimore VAV-Base 2,443 488 0.200 — 

VAV-Adv. 1,145 255 0.189 -5%

Radiant-Adv. 1,347 176 0.154 -23%

Chicago VAV-Base 2,258 296 0.131 — 

VAV-Adv. 1,291 172 0.133 2%

Radiant-Adv. 1,113 142 0.128 -3%

Highway Lodging Building

Table H-3 shows the summary results for the highway lodging prototypical building. The results 

in the first column present dramatic reductions in cooling electricity consumption. As in all the 

advanced building designs, reductions in cooling energy intensity result from an improved 

building envelope as well as reductions in internal gains from more efficient lighting and reduced 

plug loads. However, as with the medium office building, Modest efficiency improvement for 
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the conventional type of HVAC system in most lodging buildings—room-packaged terminal 

heat pumps—generally inhibited meeting the targeted 50% reductions in overall electricity use 

(Jiang et al. 2009). Accordingly, the advanced designs for this building type incorporate watersource heat pumps. Each zone or space in the building has at least one water-source heat pump, 

which is connected to a two-pipe water loop. The water-source heat pumps either reject heat to, 

or extract heat from, the piping loop depending on whether they are cooling or heating the space.

Table H-3. Relative Changes in Cooling Electricity Share for Advanced Highway Lodging Building

City

Building 

Design

Total 

Electricity 

(MMBtu)

Cooling 

Electricity 

(MMBtu)

Cooling Share

(fraction)

Percent 

Change in 

Share (%)

Houston Base 2,371 551 0.232 — 

 Advanced 1,244 167 0.134 -42%

Atlanta Base 2,330 444 0.191 — 

 Advanced 1,244 151 0.121 -36%

Las Vegas Base 2,318 455 0.196 — 

 Advanced 1,300 209 0.161 -18%

Baltimore Base 2,285 315 0.138 — 

 Advanced 1,211 112 0.092 -33%

Chicago Base 2,283 258 0.113 — 

 Advanced 1,201 96 0.080 -29%

As the first column of Table H-3 shows, the water-source heat pump system, combined with the 

other efficiency measures, can result in cooling energy use as low as one-third of that used in the 

base case. The last column reflects this reduced electricity for cooling. The shares of cooling 

electricity use of total electricity use are roughly between 30% and 40% lower than the base case 

for all locations, with the exception of Las Vegas (with an 18% reduction). 

Retail Buildings

For retail buildings, two reports have been released dealing with medium box retail and grocery 

stores (Hale et al. 2008; Leach et al. 2009). NREL used its recently developed design 

optimization software OptiPlus together with EnergyPlus52 to examine various strategies meeting 

the 50% energy reduction target.

For medium box retail stores, the prototypical buildings were divided into two classes depending 

on the amount of electricity used for plug loads. The low plug load store was considered to have 

an installed electricity density of 1.29 W/ft2

 and the high plug load store 1.32 W/ft2. The low 

plug load level is meant to model stores such as bookstores, which have little to no accent 

lighting or plug-in merchandise. A typical high plug load store would be an electronics store, 

which has a much larger amount of plug-in merchandise and accent lighting.

 

52 For more information, see http://apps1.eere.energy.gov/buildings/energyplus/. 
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Table H-4. Relative Changes in Cooling Electricity Share for Advanced Retail Building— 

Low Plug Load

City

Building 

Design

Total 

Electricity 

(kBtu/ft2)

Cooling 

(kBtu/ft2)

Cooling Share 

(fraction)

Percent 

Change in 

Share (%)

Houston Base 99.4 36.8 0.37 —

 Advanced 40.5 16.8 0.41 12%

Atlanta Base 67.7 16.8 0.25 — 

Advanced 30.0 9.3 0.31 25%

Las Vegas Base 51.1 11.0 0.22 —

 Advanced 21.7 5.7 0.26 22%

Baltimore Base 63.6 13.5 0.21 — 

Advanced 28.1 7.4 0.26 24%

Chicago Base 46.3 7.2 0.16 — 

 Advanced 24.5 4.0 0.16 5%

Table H-5. Relative Changes in Cooling Electricity Share for Advanced Retail Building— 

High Plug Load

City

Building 

Design

Total 

Electricity 

(kBtu/ft2)

Cooling 

(kBtu/ft2)

Cooling Share 

(fraction)

Percent 

Change in 

Share (%)

Houston Base 128.7 41.1 0.32 — 

 Advanced 61.3 20.0 0.33 2%

Atlanta Base 94.0 19.6 0.21 — 

 Advanced 47.0 9.8 0.21 0%

Las Vegas Base 74.1 13.5 0.18 — 

 Advanced 39.9 8.1 0.20 11%

Baltimore Base 88.9 15.8 0.18 — 

 Advanced 45.3 8.3 0.18 3%

Chicago Base 68.6 8.7 0.13 — 

 Advanced 38.0 5.1 0.13 6%
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The simulation results for the low plug load store are given in Table H-4.53 In contrast to the 

previous two building types considered, the shares of electricity for cooling increase in these 

simulated advanced buildings. This result is primarily caused by three factors: 

1. Skylights reduce the consumption of electricity for lighting from 50% to 75%. 

2. Improvements in fan efficiency and ventilation strategy reduce fan energy by roughly 

70% in all locations. 

3. The improvements in HVAC efficiency in the advanced case are relatively modest (i.e., 

the increase in the coefficient of performance is approximately 20%). 

With major end-use loads from both lighting and fans in the base case, the dramatic reduction in 

their electricity consumption (relative to cooling) in the advanced building causes the cooling 

share to increase.

Table H-5 shows the results for the high plug load retail building. Here the cooling shares of total 

electricity also increase for the advanced design, although not to the same degree as in the low 

plug load case. Again, there are dramatic reductions in lighting and fan electricity use compared 

to a much more modest decline in electricity use, which yields an increase in the cooling share. 

However, the presence of much higher plug loads in this case (in both the base and advanced 

designs) results in smaller percentage increases in the cooling shares.

The last commercial building to be considered here is a grocery store (“supermarket”). The 

simulated results for this building are shown in Table H-6. For the baseline grocery store, 

electricity for refrigeration comprises roughly one-half to two-thirds of the total electricity 

consumption, depending on the location considered. For the advanced design, the efficiency 

improvements to this end use are estimated to be on the order of 50%. Because the sharp 

reductions in lighting and fan use (as carried over from the general merchandise stores) are not 

counteracted by the modest assumed efficiency increase in cooling equipment, the overall share 

of electricity used for cooling increases significantly for this building type.54 

 

53 The absolute values in the first two columns are presented in terms of kilowatt-hour per square foot compared to 

total electricity use in million Btu for the entire building in the previous two tables.

54 Examination of the list of efficiency measures for the advanced design in the grocery store technical report did not 

make it clear why the absolute amount of electricity increases for Las Vegas. It is possible that some combination of 

other measures in the optimization process yielded the increase, thus leading to a substantially higher relative change 

in the cooling electricity share compared to other locations.
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Table H-6. Relative Changes in Cooling Electricity Share for Advanced Grocery Building 

City

Building 

Design

Total 

Electricity 

(kBtu/ft2)

Cooling 

(kBtu/ft2)

Cooling Share 

(fraction)

Percent 

Change in 

Fraction (%)

Houston Base 220 35.9 0.163 — 

 Advanced 112 25.5 0.228 40%

Atlanta Base 168.6 13.6 0.081 — 

 Advanced 84.4 11.6 0.137 70%

Las Vegas Base 160 6.3 0.039 — 

 Advanced 75.4 7.2 0.095 143%

Baltimore Base 163.1 10.9 0.067 — 

 Advanced 83.1 9.1 0.110 64%

Chicago Base 158.2 7.3 0.046 — 

 Advanced 78.6 6.6 0.084 82%

Implications of Commercial Building Simulation Results

If these simulation results portend anything in how future high-efficiency buildings may be 

designed, it seems clear that there is considerable uncertainty regarding how the shares of 

electricity used for cooling may trend in the future. The results strongly depend on the type of 

building and the specific assumptions made about the relative improvements that can be 

achieved for equipment and envelope components affecting various end uses. 

As a result, it is clear that to use these simulation results in some rigorous manner would be 

inappropriate in a study extending to the middle of this century. However, they still may perhaps 

give a glimpse of the overall proportion of cooling in advanced design. In qualitative terms, it is 

assumed that the results for the office and lodging buildings may be slightly more representative 

of what may transpire in the wider commercial building stock in the future. Several aspects of 

that assumption can be argued. 

First, the truly low-efficiency building designs of the future may require the types of now 

unconventional HVAC systems as were used in the designs for the office and lodging buildings. 

These systems will permit substantially lower heating, cooling, and ventilation use in these 

buildings. 

Second, the increases in cooling shares for the retail buildings are a result of the fact that the 

50% overall energy efficiency goals could be achieved without advanced HVAC systems. The 

opportunities for skylights (leading to much lower lighting energy use) are particularly good for 

these types of buildings, but probably are not as effective for other types of commercial 

buildings. For grocery stores, the increases in the cooling shares may be anomalous as a result of 

the single, large end use (refrigeration) for which the efficiency improvement was assumed to be 

large. 

Projections of the Aggregate Cooling Share of Electricity—Commercial Buildings 

The previous discussion indicates that the variation in the simulation results does not lead to 

strong guidance regarding how to project the share of cooling electricity for the overall stock of 
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commercial buildings. As such, a “top-down” approach has been conducted to support the 

modeling work for RE Futures. 

The top-down approach is based on an assumed rate of intensity reduction for plug loads and 

miscellaneous electricity uses in commercial buildings compared to an assumption about the 

overall annual decrease in electricity consumption. The approach starts with the estimate from

the 2003 Commercial Buildings Energy Consumption Survey that approximately 25% of the 

total electricity can be attributed to office equipment, computers, and other uses (EIA 2006b). 

(For brevity, the grouping of these end uses will be termed below as “miscellaneous end use.”) 

Lacking more recent data, this fraction is assumed to be the same in 2009. From this point, two 

assumptions are made with regard to how the overall intensity (kWh/ft2

) of these end uses will 

change over time: 

• From 2009 through 2020, the aggregate intensity for miscellaneous end-use category 

will remain roughly the same, as overall end-use intensity declines. 

• From 2021 through 2050, the aggregate intensity for these end uses will decline at 

50% of the rate for all other electricity uses.

The notion behind these assumptions is that any increases in the efficiency (e.g., Energy Star 

promotion of efficient computers and office equipment or improved network management) will 

be largely offset by some continuing increase in the overall penetration of these types of 

equipment in commercial buildings. By 2020, it is anticipated that more focused research—by 

both government and manufacturers—to make more significant efficiency improvements will 

begin to show measurable impact. However, in the long term, the potential for improvement is 

still judged not to be as great as for HVAC systems, lighting, and refrigeration. 

While this approach starts with these miscellaneous uses, it also implicitly assumes that the share 

of cooling relative to all other end-use electricity consumption remains roughly constant. As was 

shown above in the discussion of the advanced building designs, the relative shares of cooling 

will depend on decisions made with regard to unconventional HVAC systems, daylighting (and, 

perhaps, significantly greater lighting equipment based upon light-emitting diode devices in the 

future), and refrigeration efficiency. Dramatic reductions in these end uses are required to meet 

the goals of net zero commercial building in the future. Therefore, in the absence of more 

definitive evidence, it is assumed that the improved HVAC designs will yield reductions in 

cooling electricity use that approximately match the reductions in lighting and other end uses 

(excluding computers, office equipment, and other plug loads). This combination of assumptions 

leads to the projection that cooling electricity use will fall slightly in comparison to total 

electricity growth in the long term (and thus have some rough qualitative congruity to the 

simulations for the office and lodging buildings discussed earlier). 

Table H-7 shows the results of these projections at 10-year intervals from 2010 through 2050. 

The first column shows the projected shares of miscellaneous end-use consumption (again, to 

clarify—office equipment, computers, and other uses). The share for all other major end uses is 

shown in the second column. By assumption, a constant fraction of that share is for cooling, but 

is not explicitly shown in the table. The key metric is the relative increase over time in the share 

of the cooling (as implied from its assumed constant share of the major end uses) compared to
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the base year (2009). This relative increase in the cooling share can be used as an adjustment 

factor for the baseline commercial sector electricity projection. Therefore, for example, the 

adjustment factor for 2020 is 0.964, which is derived by the ratio 0.723/0.750. 

Assuming the rate of intensity reduction for the amalgamated miscellaneous category is smaller 

than for all other end uses (including cooling), then the adjustment factor falls continuously 

throughout the projection period. By 2050, the adjustment factor is approximately 0.89. This 

factor is then applied to the cooling electricity projection in the Low-Demand Baseline. This 

approach assumes that cooling shares in the Low-Demand Baseline will likely be relatively 

constant or rise only slightly.

55 

Table H-7. Development of Cooling Share Adjustment Factor for Commercial Buildings

Year

Electricity Share

for Misc. Equipment

Electricity Share

for Major End Uses

Cooling Share

Adjustment Factor

2009 0.250 0.750 1.000 

2010 0.248 0.752 1.003 

2020 0.277 0.723 0.964 

2030 0.292 0.708 0.944 

2040 0.312 0.688 0.917 

2050 0.333 0.667 0.890 

Projections of the Aggregate Cooling Share of Electricity—Residential Buildings 

DOE’s Building America program has published a Best Practice series of builder guides for 

various climate areas aimed at roughly 30% savings compared to code-compliant homes; thus, 

they do not reflect the more aggressive designs that are anticipated in the future. The residential 

Best Practice guides do not contain simulation results or provide any quantitative estimates about 

how the overall efficiency improvements might break out by end use. This is in contrast to the 

commercial building Advanced Energy Design Guides work for which technical support 

documents with detailed simulation results were produced. 

Most of the work on energy-efficient new homes has been directed at heating, cooling, and water 

heating and to a lesser extent on lighting and appliances. Relatively little attention has been paid 

to reducing energy use for small appliances, computers, televisions, and other uses. For the first 

time, the 2009 edition of the International Energy Conservation Code introduced requirements 

for lighting efficiency. Nevertheless, goals for future stringency changes in the residential energy 

code can generally be taken as applying to space conditioning, water heating, and lighting, 

although Energy Star requirements for major appliances that are often installed by the builder 

 

55 The adjustment factors are designed to be applied separately to each NERC region, each with different shares of 

cooling in the Low-Demand Baseline. Therefore, even after the application of the factors, there may still be an 

offsetting (positive) effect on the national cooling share as a higher fraction of the new buildings continues to be 

built in relatively warmer climate regions. The use of the adjustment factor approach is designed to at least show 

some degree of sensitivity to approximate changes in the cooling shares that might be expected under a low-demand 

or ultra-efficient building scenario. Obviously, a more thorough analysis would be required to better justify the 

absolute levels of cooling electricity use (in the latter years of the projection period) in both the High-Demand and 

Low-Demand Baselines. An alternative approach is to apply the adjustment factors to the 2009 or 2010 ratios of 

cooling electricity consumption in each NERC region. 
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(dishwasher and cooking range) may also come under the scope of future code updates. 

Miscellaneous loads have not been considered because they are viewed as unable to be 

effectively regulated under a building code. This situation is somewhat different from that in the 

commercial sector, where the efforts of DOE and other organizations have explicitly considered 

all energy used in the building as suitable targets for efficiency (or, perhaps more accurately, 

intensity) improvements. 

This lower level of attention to miscellaneous electricity use in the residential sector suggests 

that its relative importance (i.e., share of household electricity consumption) may grow faster 

than the corresponding miscellaneous electricity use in the commercial sector, where it is 

receiving substantial attention, even as overall energy use intensity declines. A similar approach 

to that underlying Table H-7 was employed for the residential sector. In this case, however, the 

reduction in miscellaneous electricity intensity was assumed to be only 0.3 times that of overall 

intensity declines (compared to the 0.5 fraction used for commercial). 

The category of miscellaneous electricity uses was defined from the 2009 AEO as consisting of 

televisions (and set-top boxes), personal computers and related equipment, and other uses (EIA 

2009a). For 2009, the share of electricity consumption for these uses was estimated in the AEO 

to be just under 30%. As for the commercial projection, it was assumed that the overall intensity 

for these uses, electricity use per household, remains constant until 2020.56 From that point 

forward, the assumed reduction in intensity is assumed to be 0.3 times that of the overall 

electricity intensity reduction scenario. 

Based on these assumptions, the second and third columns of Table H-8 display the projections 

of the electricity shares for the miscellaneous category and for the sum of all other major end

uses. By 2050, the share of the miscellaneous category grows from 30% to 40%. Compared to 

the base year (2009), the share of cooling electricity falls by 13.4% (reflecting the 0.866 

adjustment factor shown in the last column of the table.). The higher 2050 adjustment factor in 

the residential sector (13.4% compared to 11% in the commercial sector) reflects the higher 

initial level of miscellaneous electricity use as well as a more conservative assumption about its 

rate of intensity decline over time.

 

56 This assumes that any efficiency improvements (e.g., greater penetration of Energy Star computers and other 

devices) are offset by greater numbers of devices in the average home.
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Table H-8. Development of Cooling Share Adjustment Factor for Residential Buildings

Year

Electricity Share

for Misc. Equipment

Electricity Share 

for Major End Uses

Cooling Share

Adjustment Factor

2009 0.298 0.702 1.000

2010 0.296 0.704 1.002

2020 0.319 0.681 0.970

2030 0.338 0.662 0.943

2040 0.364 0.636 0.905

2050 0.392 0.608 0.866

Load Shape Translation

Once the projections of buildings consumption were determined, the next task was to develop the 

buildings sector loads by NEMS Electricity Market Module regions, shown in Figure H-7. The 

same regional definitions employed by NEMS are used in ReEDS. The buildings sector loads are 

added to the industrial and transportation demand. Whereas the buildings’ load shapes have 

relied on the end-use load shapes used by NEMS, the industrial loads have been developed based 

on prior work using FERC rate class loads. For the buildings sector, RE Futures used Lawrence 

Berkeley National Laboratory’s spreadsheet model, the Building End-Use Loads Forecaster. It 

applies the dimensionless NEMS end-use load shapes extracted from the Electricity Market 

Module together with energy forecasts of the 10 residential and 10 commercial building end uses 

to deliver buildings sector load profiles in the NEMS format for each of the 13 Electricity Market 

Module regions (Figure H-7). One particular source of confusion about NEMS load shapes is the 

correction sometimes called the “squelch” that NEMS uses to calibrate bottom-up results to 

historic reported system loads.57 The squelch is incorporated into the Building End-Use Loads 

Forecaster so the resulting loads are what the Electricity Market Module would see when 

entering its capacity expansion and dispatch simulations. In other words, the Building End-Use 

Loads Forecaster produces the loads the Electricity Market Module would use if faced with a 

similar forecast by building end use made by the respective demand modules within NEMS. 

Within NEMS, however, the squelch is only applied at the system level, so its reliability for this 

application remains unproven.

 

57 See Hamachi-LaCommare et al. (2002)
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Figure H-7. Electricity market module regions from the National Energy Modeling System, 

also used by the Regional Energy Deployment System

1. Northwest Power Pool

2. California

3. Rocky Mountain Power Area—Arizona, 

New Mexico, Colorado, southern Nevada

4. Mid-Continent Area Power Pool

5. Southwest Power Pool

6. Electric Reliability Council of Texas

7. Mid-America Interconnected Network

8. East Central Area Reliability Coordination 

Agreement

9. New England

10. New York

11. Mid-Atlantic Area Council

12. Southeastern Electric Reliability Council

13. Florida Reliability Coordinating Council

Source: EIA 2010
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Applying exogenously derived energy forecasts sacrifices the numerous interactions that NEMS 

accounts for, but it permits modeling of arbitrary end-use energy requirements and forecasting 

beyond 2030. Applying the Building End-Use Loads Forecaster to extreme end-use demands 

(e.g., ones that might characterize diffusion of ultra-efficient buildings) can deliver scenarios that 

would be impossible to implement within the rigid constraints of NEMS itself. 

The High-Demand Baseline is based on a standard EIA NEMS reference case, so the end-use 

energy consumption is available directly from NEMS. The current end uses available in NEMS 

are shown in Table H-9. 

Table H-9. National Energy Modeling System End Uses by Sector

Residential Commercial

Heating (primary) Heating

Cooling Cooling

Water Heating Water Heating

Cooking Ventilation

Clothes Dryers Cooking

Refrigerators Lighting

Freezers Refrigeration

Lighting Office Equipment (PC)

Other Office Equipment (non-PC)

Heating (secondary) Other

The Low-Demand Baseline energy forecast was conducted independently of NEMS. In this case, 

the Building End-Use Loads Forecaster was used to forecast loads for the two end uses to match 

the energy forecast.
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Appendix I. Development of Industrial Load Curves and Hourly 

Schedules

High-demand industrial demands provided to ReEDS were based on the electricity sales to 

industry reported in the AEO Reference Case (EIA 2009d) with a simple extrapolation beyond 

2030. The low-demand industrial demand forecast, however, is derived in a quite different 

manner. It is based on a second HR2454Cap NEMS Case,58 which, as its name suggests, 

includes a carbon cap and trade policy roughly equivalent to the provisions of the WaxmanMarkey Climate Bill from 2009. Given that this case would evoke a significant efficiency boost, 

RE Futures believes its consumption level represents a reasonable energy efficiency trajectory, 

and it is adopted as the Low-Demand Baseline—but it does not include a carbon cap. Because 

NEMS only models through 2030, the demands were extrapolated to 2050 using the 2020–2030 

growth rate. Additional calculations explained within Appendix M found the results for each 

North American Electric Reliability Corporation (NERC) region and converted them to hourly 

profiles.

Determination of Efficiency Change between High-Demand and Low-Demand 

Baselines 

To further understand the changes in industrial electricity demand, RE Futures determined the 

electricity purchases for each of the 19 industrial sectors that NEMS models. Figure I-1 and 

Figure I-2 show the demands for each of the scenarios. In both scenarios, the bulk chemicals 

sector reduces its power purchases significantly over time, while several other smaller markets 

also contribute to the decline. In the Low-Demand Baseline, Paper Products show the most 

significant reductions on a terawatt-hour basis versus the High-Demand Baseline, followed by 

balance of manufacturing, mining, and refining. The sector growth rates between 2010 and 2030 

provide insight into which industrial sectors grow or shrink the most (Figure I-1and Figure I-2).

Table I-1 summarizes the growth rates by sector for the period 2020–2030. 

 

58 This is the Basic Case described in EIA 2009b.
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Figure I-1. Industrial electricity purchases by sector in the High-Demand Baseline 

Figure I-2. Industrial electricity purchases by sector in the Low-Demand Baseline 
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Table I-1. Electricity Purchases Annual Growth Rates for Each Industrial Sector, 2020–2030

Sector

High 

Demand

Low 

Demand Difference

Refining 0.9% 0.2% -0.7%

Food Products 1.2% 0.7% -0.4%

Paper Products 0.1% -3.2% -3.3%

Bulk Chemicals -3.3% -3.9% -0.6%

Glass 0.2% -0.2% -0.4%

Cement Manufacturing 0.0% -0.8% -0.8%

Iron and Steel -2.3% -3.0% -0.7%

Aluminum -1.9% -3.4% -1.5%

Fabricated Metal -2.4% -3.0% -0.5%

Machinery -4.2% -5.0% -0.8%

Computers 1.6% 0.9% -0.7%

Transportation Equipment -0.1% -0.8% -0.7%

Electrical Equipment 0.3% -0.3% -0.6%

Wood Products -0.2% -0.6% -0.4%

Plastics 0.4% -0.1% -0.5%

Balance of Manufacturing 0.4% -0.1% -0.5%

Agriculture 0.7% 0.2% -0.5%

Construction 0.2% -0.4% -0.5%

Mining 0.5% -0.8% -1.3%

Total -0.4% -1.1% -0.7%

While the above analysis shows the change in electricity purchases by each sector, a different 

question arises: How much of this change is due to efficiency and how much is due to reduced 

production? NEMS provides data on shipments from each sector, so evaluating trends in energy 

per dollar of shipment should show the relative efficiency gains for each sector. The two figures 

below show the change in electricity use per dollar output for each of the 19 sectors. All have 

declining lines except Aluminum, which declines early in the period but then rises post-2024 

because the value of shipments drops faster than the electricity amount used. Refining shows a 

smaller increase because its electricity use increases faster than the value of shipments. 

Continuation of the 2020–2030 trend to 2050 further increases its electricity purchases per dollar 

of output. (Values for 2000–2001 are not in the NEMS results for most sectors.) 
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Figure I-3. Sectors’ electricity purchased per dollar output from the High-Demand Baseline

(Industries 1–10)

Figure I-4. Sectors’ electricity purchased per dollar output from the High-Demand Baseline

(Industries 11–19)

The Low-Demand Baseline results show very similar trends to the curves in Figures I-3 and I-4. 

Rather than repeat the curves, Table I-2 compares the ratio of the purchased electricity per 

shipment between the two scenarios. Through 2030, most of the sectors show an improvement in 

efficiency of less than 10% over and above that in the High-Demand Baseline. Paper products 

show the biggest decrease in purchased electricity at 40% by 2030. 
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Table I-2. Reduction in Low-Demand Purchased Electricity per Shipment Values versus 

High Demand

Sector 2010 2020 2030 2040 2050

Refining 0% 1% -3% -7% -11%

Food Products -1% 0% -4% -7% -11%

Paper Products -1% -16% -40% -57% -69%

Bulk Chemicals -1% 0% -6% -11% -16%

Glass 0% -3% -7% -10% -13%

Cement Manufacturing 0% -7% -12% -18% -23%

Iron and Steel 0% -3% -6% -9% -13%

Aluminum -1% -7% -7% -7% -7%

Fabricated Metal -4% -5% -8% -11% -15%

Machinery -9% -9% -13% -17% -21%

Computers -1% -2% -7% -12% -17%

Transportation Equip -1% -1% -5% -10% -14%

Electrical Equipment -4% -5% -8% -11% -14%

Wood Products 0% 0% -2% -4% -6%

Plastics -1% -2% -5% -9% -12%

Balance of Manufacturing -1% -2% -6% -10% -14%

Agriculture 0% -4% -9% -13% -17%

Construction 0% -5% -9% -13% -17%

Mining -1% -5% -10% -15% -19%

Note: Values are calculated as: (MWh/Million $ shipment from Low Demand) / (MWh/Million $ 

shipment from High Demand) 

Industrial and Non-PEV Sector Loads

Several resources were used to create the set of demand curves for each region. The industrial 

and non-PEV transportation loads were calculated through applying all sectoral loads from EIA 

NEMS runs to the regional hourly load shapes from 2006. The resulting industrial and non-PEV 

transportation amounts were kept for the final analysis while the residential and commercial 

sectoral loads were overridden with the results from Appendix H. 

Extrapolation of National Energy Modeling System Reference Run Sectoral 

Demands, 2030–2050

RE Futures compiled and ran the ftab.exe program provided with NEMS archives to generate 

electric power projections for each NERC region (Table I-3). For the High-Demand Baseline, the 

updated 2009 AEO Reference Case, called “stimulus,” was used because it includes the effects 

of recent economic activities and the Recovery Act (EIA 2009d). For the Low-Demand Baseline, 

the basic scenario from the EIA analysis of HR2454 (EIA 2009b) was used. Because NEMS only 

runs to 2030, the annual growth rate was calculated between 2020 and 2030, and that factor was 

applied to extrapolate to 2050. These were applied to three categories of factors: the net energy 

for load, electricity sales by sector, and electricity prices by sector. Because the NEMS report 

shows sectoral sales only after transmission and distribution losses, the values need to be raised 

by the ratio of the net energy for load to the sum of sectoral sales. 
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Use of Hourly Loads from Utilities to Establish North American Electric Reliability 

Corporation Region Hourly Loads

Each year, utilities report their hourly loads for the previous year to FERC in their FERC 714 

report. The 2006 and 2007 data were downloaded and converted for many utilities across the 

United States as part of a study on extended Daylight Saving Time (Belzer et al. 2008). Each 

utility was tagged according to the NERC region to which it belongs and then the utilities’ data 

were summed for each hour for each region. The resulting sum was then “ratioed” to the regional 

net energy for load from the NEMS run for 2006 and then adjusted further by the growth in the 

net energy for load to any given from 2006. This created an adjustment factor for each hour that 

would create an initial hourly system load for each region for any year. 

Determination of Template Load Factors for Each Sector from Electric Power 

Research Institute

The load factor is the ratio of the average load to the peak load. The system load factor can be 

found from the hourly loads determined in the previous section, but the load factors for each enduse demand sector can differ from this value. Typically, the residential sector has the lowest load 

factor and the industrial sector has the highest. Residences are not occupied during much of the 

day and have low appliance demands during the night while industries tend to operate fairly 

constantly over more hours of the day (and night, if multiple shifts.) Regions with lighter

industry may have lower load factors because nighttime use may see lighter loads. Table 3.5 in 

the EPRI report, Assessment of Achievable Potential from Energy Efficiency and Demand 

Response Programs in the U.S. (EPRI 2009) includes a table showing the 2008 residential, 

commercial, and industrial load factors for the four main regions of the United States (Table I-3).

Table I-3. Load Factors by Sector and Region, 2008

Sector Northeast Midwest South West

United 

States

Residential 40% 38% 44% 42% 42%

Commercial 74% 53% 56% 67% 60%

Industrial 49% 87% 69% 59% 68%

Total 53% 53% 53% 54% 53%

Source: EPRI 2009

Conversion of System Load into Load Duration Curve

A load duration curve (LDC) rearranges the hourly loads from highest to lowest so that the curve 

shows the number of hours that demand exceeded the power level shown. For example, demand 

by definition meets or exceeds the minimum load 100% of the year; it exceeds the peak load by 

0%. Figure I-5 shows a representative set of LDCs for the East Central Area Reliability 

Coordination region. The load factor described above is equal to the area below the LDC divided 

by the peak load. For each sector (residential, commercial, industrial, and transportation), the 

load factors above, along with an estimate of the fractions of the system’s demand at minimum 

and at maximum, were used to estimate the LDCs for each demand sector. 
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Use of Excel to Determine Load Curves for Each Sector

As can be seen in Table I-3, the sectors can have different load factors.59 In addition, the system 

load factor for a region may not necessarily match the values shown in the table. Total electricity 

sales for each sector must match the amount from NEMS, and the load factors for each sector 

should approximate the values defined by EPRI, for lack of better data. (The data may not match 

the EPRI results due to different data years and region definitions between the studies.) 

Therefore, first, the fractions at minimum and maximum were set for one of the sectors—in this 

case, the commercial sector—to be equal to one minus the fractions of the other sectors. This 

ensures that the sum of the sectors’ loads equals the system load.

A goal seek function was then used to change the values of the maximum and minimum for each 

of the sectors so that the area under their LDCs equals the total sales for that sector. Then the 

difference of the resulting load factor for each was compared to the target load factor from 

Table I-3. The variance (sum of the square of the difference) of both the load factor and the 

electric sales was determined for each of the sectors and a Solver routine was used to minimize 

that variance by altering the percentages and the minimum and maximum. Lastly, those 

minimum and maximum values were modified so the electric sales match the NEMS values. 

This defined LDCs for each of the end-use sectors that matched both the LDC template from 

2006 and the total sectoral sales from the NEMS run. As an example, Table I-4 shows the factors 

involved for the East Central Area Reliability Coordination region, while Figure I-5 shows the 

resulting LDCs. These factors, along with the system maximum and minimum loads, can be used 

to calculate the hourly demand for the industrial and transportation sectors, based on the 

corresponding system load. 

Table I-4. Load Duration Curve Definition Factors for East Central Area Reliability

Coordination Agreement, 2050

Region 1 Residential Commercial Industrial Transportation

% of Peak 43% 39% 17% 0.568%

% of Min 23% 39% 37% 0.568%

Max Power 60,525 54,749 24,111 796 

Min Power 12,679 21,874 20,737 316 

Calc TWh 255 290 192 4.2 

EPRI Load Factor 38% 53% 87% — 

Calc Load Factor 48% 61% 91% 60%

Note: The calculated load factors may not match the EPRI results due to different data years and 

region definitions between the studies. 

 

59 RE Futures recognizes that the sectoral load factors are not likely to remain constant throughout the study period 

because the relative sectoral mix changes over time; however, annual load factor calculations were beyond the scope 

of this study.
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Figure I-5. Calculated load duration curves for East Central Area Reliability 

Coordination Agreement in 2050 

Sectoral 8760 Hour Load Schedules 

While the calculations described in the previous section provided hourly loads for all four of the 

sectors, the residential and commercial sectors did not use the same methodology. The 

commercial and residential sectors used the load curve methodology from NEMS, as described 

in Appendix H. Using the sectoral hourly loads for each month and region, hourly loads for the 

average weekday (Daytype 1), average weekend (Daytype 2), and peak weekday (Daytype 3) 

were calculated. Values were calculated for 2006, 2008, 2010, and every 5 years thereafter up to 

2050. Different demands were calculated for the High-Demand Baseline and the Low-Demand 

Baseline. 

These average values for weekday and weekend are sufficient to calculate the ReEDS time 

slices. However, they produce the same load shape for every day of the month they represent, 

and there is no variation due to weather. In order to create a semblance of a fluctuating load 

demand based on historical data, algorithms were created to modify the loads for individual days 

depending on the relative system load for the region in that year (using the 2006 load shapes). At 

the same time, the average for the month was formulated to still match the values calculated 

earlier and used in the ReEDS work. 

As an example, the calculations using the residential data set for 8:00 a.m. 7/11/2020 Region 1 

are as follows: 

• Find the average and peak system load (Sa and Sp) for 8:00 a.m. on a weekday in 

July: 

 Sa = Σ (8 a.m. weekday system loads) / 21 weekdays = 71,701 MW and 

 Sp = Max (8 a.m. weekday system loads) = 81,082 MW 
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• Find the average residential load for 8 a.m. on a weekday from the Building dataset: 

Ra = (20 x Daytype 1 from dataset + Daytype3 from dataset) / (20+1) = 20,946 MW

Rp = Daytype 3 for the peak day = 23,490 MW

R = [(S - Sa)/(Sp - Sa)] * (Rp - Ra) + Ra

Where:

R = Adjusted Residential Load 

S = the system load for any weekday hour in July (70,394 MW on 7/11)

Sa = the average system load for that same hour in July (71,701 MW)

Sp = the peak system load for that same hour in July (81,082 MW)

Ra = average residential load for that hour (20,946 MW)

Rp = the peak residential load for that hour (23,490 MW)

So R = [(70,394 -71,701) / (81,082 – 71,701)] * (23,490 - 20,946) + 20,946

= 20,592 MW

The weekend calculations are a bit simpler (but less accurate) because peaks for the weekend 

values for residential and commercial are not available, just the average: 

R = Adjusted Residential*(weekend) = S/Sa * Ra

So for 8 a.m. on 7/9/2020 

R = Adjusted Res = [53,377 / 59,489] * 18,548 = 16,643 MW

Commercial calculations are done similarly.

Because the residential and commercial load shapes from the Buildings analyses do not match 

the actual load shapes within a given day or month, the resulting system loads (residential + 

commercial + industrial + non-PEV transportation) do not necessarily match the 2006 load shape 

template.

Adjustment to Updated National Energy Modeling System Run and Sectoral 

Energy Intensities

Each of the sector energy intensities were modified, as detailed in the corresponding appendices, 

and the load growth from the most recently released AEO report were combined to create new 

expectations of loads for these two regions. Rather than recalculate the daily load shapes for each 

region for the commercial and residential sectors, the ratio of each sector’s new national annual 

load to its previous national annual load was determined. Each hourly load for the sector and 

region was then multiplied by that year’s value. This implies that within the sector, the changes 

apply equally to each hour and to every region. 
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Application of Algorithm to Calculate Hourly Sectoral Demands

Once each sector’s hourly loads were calculated as described above, the hourly system load for 

any year and region could be calculated by summing the individual sectors. Figure I-6 shows the 

representative load shape for the East Central Area Reliability Coordination region in 2050.

Figure I-6. Calculated hourly loads by sector for East Central Area Reliability

Coordination Agreement in 2050, High-Demand Baseline

Average Demands for Each Regional Energy Deployment System Time Slice

The ReEDS model uses only 17 time slices to model the system LDC. Once the hourly loads are 

known for each sector, the average amount of electricity demand in each time slice can be 

calculated. The top 40 hours of the summer peak season are pulled out of the summer afternoon 

slice H3 to make slice H17. Table I-5 shows the resulting time slices for the East Central Area 

Reliability Coordination for 2050. The system peak shows the power level in the highest hour 

and is therefore a bit higher than the average over the top 40 hours. In addition, the PEV loads 

are not included in the transportation sector described here. Rather, PEV loads were calculated 

separately as described in Appendix K. 
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Table I-5. East Central Area Reliability Coordination Agreement Average Loads for High-Demand 

Baseline 2050 for Regional Energy Deployment System Time Slices

Slice Name Hours Residential Commercial Industrial Transportation Total

H1 736 21.1 26.7 21.1 0.4 69.3

H2 644 29.4 34.5 21.5 0.5 86.0

H3 328 39.2 39.5 21.9 0.6 101.2

H4 460 38.5 36.5 21.9 0.6 97.4

H5 489 17.5 23.8 20.7 0.4 62.3

H6 427 24.3 29.0 21.1 0.5 74.9

H7 244 27.1 30.5 21.2 0.5 79.3

H8 305 27.0 29.1 21.3 0.5 77.9

H9 960 20.5 26.9 21.0 0.4 68.8

H10 840 27.1 31.1 21.4 0.5 80.0

H11 480 25.4 29.1 21.3 0.5 76.3

H12 600 27.4 30.1 21.5 0.5 79.5

H13 735 18.0 24.6 20.7 0.4 63.7

H14 1,104 25.6 28.7 21.1 0.5 76.0

H15 368 25.6 29.0 21.1 0.5 76.3

H16 40 49.7 44.4 22.8 0.7 117.6

System Peak (GW) 55.7 46.9 23.4 0.8 123.8

Sector Sales (TWh) 222.9 258.2 185.8 4.1 671.0
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Appendix J. Combined Cooling, Heating, and Power within the 

Industrial Sector

The industrial demands provided to ReEDS are based on the electricity sales to industry reported 

in the NEMS 2009 runs Stimulus (representing the High-Demand Baseline) and HR2454Cap 

(representing the Low-Demand Baseline). These demands represent the sales (not including 

transmission and distribution losses) that the electricity sector makes to the industrial sector. 

Separately, the industrial sector can generate some of its own power needs through dedicated 

electricity plants or the combined cooling, heating, and power plants. Some of this power 

generated may also be sold to the electric sector for sale to other sectors. As with the industrial 

demand for grid-based electricity, NEMS also provides a forecast of industrial end-use (or onsite) generation. This appendix attempts to quantify the NEMS results in more detail. Total 

industrial electricity demand is the sum of purchased electricity and electricity generated less 

electricity sold to the grid. Figure J-1 shows the gross industrial demand (including the 

generation used internally) and the net demand (the amount purchased from the electricity sector 

and used in ReEDS). 

Figure J-1. Industrial electricity demands before and after internal generation (TWh)

Industrial end-use generation (both dedicated and cooling, heating, and power production) is 

calculated for each of the major industrial sectors in NEMS (Figure J-2). The bulk of the 

generation comes from three sectors: refining, paper production, and bulk chemical production. 

Under the Low-Demand Baseline, refining and paper products show some increase in production 

by 2030, but the others show slight decreases. 
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Figure J-2. Industrial sector electricity generation for High-Demand and Low-Demand

Baselines (TWh)

End-use generation as a percentage of total demand increases over time in both scenarios, with a 

larger increase in the Low-Demand Baseline (Figure J-3). The top lines show the total generation 

by industry, but some of this generation is sold to the grid. The bottom set of lines show the 

generation that is used internally, with the difference being the amount sold to the grid. As can 

be seen, both the internally used generation and the amount sold to the grid increase over time, 

with a higher growth under the Low-Demand Baseline. 
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Figure J-3. Industrial generation as a fraction of industrial demand 

The fuels used for the generation are a mixture of fossil energy and renewable resources (Figure 

J-4). NEMS does not break down the renewable resources for the industrial sector alone, but 

does for all end-use sectors (residential, commercial, and industrial) (Figure J-5). In the early 

years, most end-use generation is from industrial sources with only 10 TWh from non-industrial 

sources, but by 2030 about 60 TWh of electricity is produced by the residential and commercial 

sectors. The Low-Demand Baseline has higher end-use generation because that lowers overall 

energy demand (being more efficient) and also lessens grid-based electricity requirements, the 

focus of this study. The ReEDS analysis would create a different mix of fuels to be used for 

generation due to the difference in costs and modeling involved in the two studies. It is also 

important to note that carbon capture and sequestration may be difficult to do for these widely 

dispersed and relatively small-scale uses of fossil energy to generate on-site power. 

Figure J-4. Fuels used for industrial on-site generation (TWh) 
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Figure J-5. Fuel sources used for all end-use generation (TWh)
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The industrial generation that is sold to the grid, either from cooling, heating, and power or from 

their dedicated plants, is not captured in the ReEDS analysis. By 2030, this equals 83 TWh in the 

Low-Demand Baseline. However, this represents less than 2% of total electricity demand in that 

year. The generation used internally is not explicitly captured either. Rather, the industrial 

demands are reduced by this generation within the NEMS results, and only the industrial 

requirements placed on the electricity sector are fed to ReEDS (Figure J-6).

Figure J-6. Industrial generation used internally or sold to the grid (TWh)

Similarly, the generation from the other end-use sectors is not directly captured in ReEDS. Some 

of this may be captured as a reduction in demand on the grid. Of course, the other end-use 

sectors’ demands are not based solely on the NEMS scenarios but rather involve a build-up of 

demands from end uses. Especially, the fuel sources used in the ReEDS analysis will be different 

from the NEMS results due to the different assumptions about and modeling of these 

technologies. 
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Appendix K. Transportation Electrification Load Development 

Electrification of the transportation sector offers the opportunity to significantly reduce 

petroleum consumption. The transportation sector accounts for 70% of U.S. petroleum 

consumption. The transition to electricity as a fuel will create a new load for electricity 

generation. In support of a high renewable electricity generation scenario analysis, regional 

hourly load profiles for electrified vehicles within the time frame of 2010—2050 were created. 

The transportation electrical energy was determined using regional population forecast data, 

historical vehicle per capita data, and market penetration growth functions to determine the 

number of PEVs in each analysis region. To represent a bounding case, a single market 

saturation scenario of 50% of sales being PEVs consuming on average approximately 6 kWh per 

day was considered. Results were generated for 3,109 counties and were consolidated to 134 

BAs for the use in regional generation planning analysis tools. PEV aggregate load profiles from

previous work were combined with vehicle population data to generate hourly loads on a 

regional basis. A transition from consumer-controlled charging toward utility-controlled 

charging was assumed such that by 2050, approximately 45% of the transportation energy 

demands could be delivered across four daily time slices under optimal control from the utility 

perspective. No other literature has addressed the potential flexibility in energy delivery to 

electric vehicles in connection with a regional power delivery study. This electrified 

transportation analysis resulted in an estimate for both the flexible load and fixed load shapes on 

a regional basis.

Introduction

PEVs offer the opportunity for the transportation sector to significantly reduce petroleum 

consumption through electrification. PEVs may have a moderately sized energy storage system 

and a combustion engine to ensure most miles are electrified while retaining the range capability 

of today’s vehicles. Other PEVs may be entirely battery dependent and provide complete 

petroleum displacement for certain vehicle sectors. As of 2010, the timeline for vehicle 

introduction was to start in 2011 with several manufacturers adding to the options over a 2–3-

year period toward market creation. Based on past technology markets, maturity would likely 

occur within 25–30 years from introduction.60

Infrastructure for delivering electricity to these vehicles is also under development. For shortrange vehicles, common 110-volt service outlets would generally suffice while owners of 

vehicles with longer range will likely prefer moderate charge rates from 240-volt service. From a 

utility perspective, 110 volts or level I (typically 1.4 kW) charging has limited impact on 

infrastructure but has less value than a flexible load. Vehicles and infrastructure delivering 240 

volts or level II (typically 6 kW to 7 kW) charging offers more opportunity for load shaping and 

management because individual vehicle needs can be delivered and scheduled optimally to 

match generation opportunities. 

Many analyses have been conducted and papers published that include PEV market projections. 

Electric Power Research Institute and Natural Resources Defense Council collaborated on a 

 

60 Penetration curves of common products can be seen at 

http://people.hofstra.edu/geotrans/eng/ch1en/conc1en/telecomdiffusionUS.html.
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foundational study highlighting the nationwide greenhouse gas and pollutant emissions impacts 

of PEVs on the U.S. electricity grid on a regional basis (EPRI 2007). The fleet makeup assumed 

approximately 40% PEVs by 2030 and 60% by 2050. An aggregate hourly load profile was 

assumed in which 74% of the energy was delivered during the off-peak period and 26% during 

the daytime. Both Scott et al. (2007) and Hadley and Tsvetkova (2008) have assessed regional 

PEV penetrations and future load characteristics. Belzer (2009) considered the situation in which 

all of the electric vehicle loads could be managed and fit into the low points of the daily utility 

load curve (Scott et al. 2007). Hadley and Tsvetkova (2008) considered a variety of charge levels 

and loading scenarios to understand their regional capacity and emissions impacts. This work 

expands upon past activities to uniquely define both a transitioning fixed load for the 

transportation sector and a load portion that can be dynamically managed by utilities for 

integration with high renewable energy integration analyses. 

Approach

As input to RE Futures, an hourly PEV load profile and energy demand for the fleet of PEVs by 

region over time was developed. The approach was as follows: 

1. Use population growth forecasts and historical vehicle ownership trends to estimate 

vehicle population by region. 

2. Use a market evolution model to estimate the fraction of vehicles that will be PEVs 

throughout the study period. 

3. Develop a vehicle fleet energy demand profile based on those from past studies that 

transitions from market introduction with no utility control to one in which the utility is 

able to manage a portion of the vehicle load. 

In developing the electrified transportation loads and energy requirements, the following 

simplifying assumptions were made: 

1. There will be no significant change in transportation mode selection and miles driven. 

Personal vehicles will continue to be the mode of choice. As a result, loads due to mass 

transit are neglected.

2. Transportation electrical energy demands will not vary significantly in amount or timing 

between seasons of the year. PHEVs are expected to make up the majority of the stock, 

meaning that electrical energy is likely to provide the majority of, but not all of, the 

energy needed. The hybrid combustion engine would likely make up any limitations of 

the electric drive system, and would therefore allow the vehicle owner to go beyond the 

electric range without any mileage limitations.

3. The PEV fleet load shapes are based on historical consumer travel survey data and 

assume 110-volt, 1.4-kW charge rates from widespread infrastructure. Level II, 240-volt 

charging, was not considered for the loads not under utility control. Level II charging 

may be necessary to achieve the utility-managed flexible loads assumed.

4. PEV load curves are based on PEVs with 20 miles of electric range and urban power 

capability. On average, this results in approximately 6 kWh of energy per PEV per day. 

Some vehicle designs and some vehicle usage profiles may use more or less energy.
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5. Cars and pickup trucks were assumed to have similar energy needs expressed as mileper-gallon fuel consumption. 

6. No vehicle-to-grid or grid services functions are specifically considered. The utilitycontrolled vehicle loads could be considered controlled as a grid service tool. 

7. It has been assumed that there are no differences in regional penetration rates. 

8. There is no differentiation in the growth rate among counties in the same state. 

Results Analysis 

Data on population growth projections to 2030 by state available from the U.S. Census Bureau 

provide the starting point for projecting the energy demands of electric vehicles. Figure K-1 

shows the consolidated growth rates for the nine census regions (U.S. Census Bureau 2005). The 

projections for each state were fit with either a linear or quadratic function, whichever provided 

the best fit and extended to 2050. Table K-1 highlights the states with the least and greatest 

calculated rates of change in population growth between 2010 and 2050. 

Figure K-1. Regional population projections to 2030 (U.S. Census) 
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Table K-1. Ten States with Least and Greatest Percent Change in Population, 2010–2050

Least Percent Change Greatest Percentage Change

State

Population Change 

2050 Relative to 2010 

(%) State

Population Change 2050 

Relative to 2010 (%)

District of Columbia 65 Nevada 211

West Virginia 77 Arizona 209

Iowa 87 Florida 185

Wyoming 88 Texas 166

North Dakota 94 Utah 163

Ohio 94 Idaho 159

New York 95 North Carolina 159

Pennsylvania 105 Washington 154

South Dakota 108 Georgia 153

Nebraska 108 Oregon 149

Using data from a Polk database query, 2005 county population measurements were extended to 

estimate county population between 2010 and 2050 using the state-level population growth 

trends. It was assumed that all counties within a state grow at the state rate.

The population estimates on a county basis were then scaled to estimate the number of motor 

vehicles on a county basis. Historical data from the Federal Highway Administration present the 

number of motor vehicles per capita between 1960 and 2007 (FHA 2007). The past 20–30 years 

of data fit a logarithmic function. This trend suggests that between 2010 and 2050, the number of 

motor vehicles per capita is likely to grow from just more than 0.8 to a little more than 0.9 motor 

vehicles per person.61 These analyses are summarized in Figure K-2. 

 

61 The possibility of vehicle saturation within the United States was not considered here.
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Figure K-2. Historical and projected motor vehicles per capita (FHA 2007)

A PEV market growth and saturation model was used with the motor vehicle estimates to 

determine the number of PEVs likely to be in use on a county level over the time period of the 

study. The market model represents a slow ramp toward consistent market growth and a final 

tapering of growth to saturation. The model used is represented with the following equation:

 

Where: 

κ = maximum market share potential

Δt = time to grow from 10% to 90% of potential (years) 

tm = year in which 50% of potential is reached 

))( )81ln( exp(1

)(

mtt

t

tN

− ∆−+

= κ
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A single bounding scenario was defined in which the sales of PEVs saturate at a 50% market 

share after approximately 50 years. The parameter values for this scenario are shown in 

Table K-2. A comparison of the sales rates, vehicle stock, and historical hybrid electric vehicle 

sales shifted by introduction year are shown in Table K-2. The rates in this scenario are 

consistent with results recently developed by Zenhong Lin and David Greene (Lin and Greene 

2010). 

Table K-2. Market Penetration Model Parameter Values for Plug-in Electric Vehicles 

Parameters Value

κ 50%

Δt 30

tm 25

Figure K-3. Market penetration for plug-in electric vehicles 

It is possible—using the PEV market models, number of vehicles per capita, and county 

population estimates—to calculate the number of PEVs on a county and state basis. Table K-3 

summarizes the PEV population estimates for several points in time on a state level for those 

states with the greatest number of PEVs. The vehicles in these 15 states highlighted comprise

nearly 70% of the total U.S. PEV population. The PEV population growth trend for these 15 

states is shown in Table K-3. The resulting shape is a function of both population growth and 

PEV market growth and saturation (Figure K-4).
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Table K-3. United States and Top 15 States with Stock Distribution of Plug-in Electric Vehicles 

(millions) 

State 2010 2030 2050

2050

Percent of 

United States

2050

Cumulative 

Percent

United States 0.283 31.07 154.43 100 —

California 0.035 4.01 20.58 13.32 13.32

Texas 0.023 2.85 15.49 10.03 23.36

Florida 0.018 2.45 13.83 8.96 32.32

New York 0.018 1.67 6.88 4.45 36.77

North Carolina 0.009 1.04 5.55 3.59 40.36

Georgia 0.009 1.05 5.55 3.59 43.95

Illinois 0.012 1.17 5.33 3.45 47.41

Arizona 0.006 0.89 5.26 3.41 50.81

Pennsylvania 0.011 1.10 4.93 3.19 54.01

Virginia 0.007 0.85 4.36 2.83 56.83

Michigan 0.010 0.93 4.26 2.76 59.59

Ohio 0.011 0.99 4.08 2.64 62.23

New Jersey 0.008 0.84 4.01 2.60 64.83

Washington 0.006 0.73 3.87 2.51 67.33

Figure K-4. Growth trends of stock in plug-in electric vehicles for states with greatest

plug-in hybrid electric vehicle population in 2050
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Hourly load shapes for PEVs have been presented in several locations (EPRI 2007; Scott et al. 

2007; Hadley and Tsvetkova 2008). RE Futures used three profiles from previous work based on 

detailed vehicle system simulations using second-by-second vehicle speed and trip profile 

characteristics collected using global positioning system units on board a vehicle. These data 

were collected under a periodic household travel survey from the St. Louis metropolitan area and 

were based on 227 24-hour driving profiles for unique vehicles. Three scenarios defined in 

previous work (Parks et al. 2009; Markel et al. 2009) were applied and are shown in Figure K-5. 

In the first case, the consumer is allowed to plug in and charge as soon as the vehicle ends the 

last trip for the day. This case is called No Utility Control because the vehicle load occurs on 

consumer demand and charges until complete or the consumer starts another trip. A second 

scenario is labeled Opportunity, and under this case, it is assumed that charging infrastructure is 

prevalent and the consumer will choose to plug in anytime the vehicle is parked regardless of 

stop duration. This scenario leads to significantly more fuel savings but also increases the 

daytime electric vehicle loads, total energy demands, and potential battery wear. Finally, a 

Valley Fill/Managed scenario is used. Originally, this scenario was implemented such that the 

utility had full control to deliver the daily energy needs as best fit with the evening low point of a 

typical daily utility load curve. However, as it pertains to the high renewable penetration case, it 

may be most beneficial for a utility to charge vehicles at times scattered throughout the day. As a 

result, the Valley Fill/Managed load curve is used to create an energy demand that must be met 

during the day but no constraints have been placed on it regarding when during the day it must 

be delivered. Both the No Utility Control and Opportunity scenarios assumed 110-volt, 1.4-kW 

charge rates while the Valley Fill/Managed curve allowed 3-kW charging to match best the 

energy demands with the utility valley shape.

It was assumed that over the duration of RE Futures, utilities would find managed charging of 

vehicles to be of value to system operations and therefore drivers would be incentivized toward 

participating in a managed charging program. Initially, all consumers would charge at home 

without utility controls and as public infrastructure is created and consumers learn to optimize 

the value of their investment in vehicle technology, the growth of the opportunity charging 

would occur. The transition between the three scenarios over time is summarized in Figure K-6. 

Figure K-7 shows how the average per vehicle energy demand grows slightly over time due to 

the increasing portion of the vehicles that are being Opportunity charged. It also shows that the 

Valley Fill/Managed portion of total vehicle energy demands grows to approximately 45% of the 

total energy demands by 2050. 
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Figure K-5. Three fleet charging profiles for plug-in electric vehicles based on 227 driving profile 

vehicle simulation results 

Figure K-6. Transition assumptions from No Utility Control to Opportunity and Valley 

Fill/Managed scenarios 

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20

Power Demand (kW/Vehicle)

Hour of Day

Valley Fill/Managed Opportunity No Utility Control
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Figure K-7. Average daily per vehicle energy demands by charging scenario

By combining the hourly load profile results from previous work and the transition assumptions 

over the study period, an aggregate per vehicle load profile that changes shape over time is 

generated and shown in Figure K-7. Figure K-7 only shows the fixed portion that is not under the 

control of the utility. This includes the No Utility Control profile and the Opportunity profile. 

The transition from a high percentage of vehicles in the No Utility Control scenario in 2010 to a 

growing percentage of vehicles in the Opportunity scenario by 2050 is observed in Figure K-6 by 

comparing the shape of the 2010 and 2050 curves to those in Figure K-7. 
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Figure K-8. Shape and transition of fixed hourly aggregate load profile for plug-in electric vehicles 

The aggregate load shape in Figure K-8 only represents the fixed load profile. From Figure K-7, 

the dynamic portion under utility control (Valley Fill/Managed) grows from 0% of the load in 

2010 to approximately 45% of the total load in 2050. 

The ReEDS model assesses energy delivery by 134 BAs. Load profiles were generated on a 

county basis. A consolidation from 3,109 counties in the contiguous United States to 134 BAs

was completed.

Figure K-9 shows the total annual energy demand for PEVs. Both the fixed and dynamic 

portions are highlighted. In 2030, the fixed demand is approximately 50 TWh, accounting for 

approximately 80% of the total load. By 2050, the total load grows to 350 TWh, and the fixed 

portion is approximately 180 TWh or 55% of the total load. Lin and Greene (2010) predict the 

total annual PEV energy demand as approximately 100 TWh in 2050 in a PEV Success scenario. 

Several assumptions contribute to these differences: 

• Lin and Greene assumed mostly PHEV10 with a few PHEV40 vehicles, while this 

RE Futures study bases the energy demands and load curves on a PHEV20 design. 

• Lin and Greene assumed a single charge per day, while this RE Futures study 

includes opportunity charging that increases daily electrical energy consumption per 

vehicle.

As a result, the total annual energy for PEVs is approximately three times greater than suggested 

by Lin and Greene’s work. This is approximately 9% of the total electrical demand in the core 

80% RE scenario.
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Figure K-9. Projected fixed and dynamic annual energy demand plug-in electric vehicles for 

Low-Demand Baseline

Figure K-10. Daily fixed energy demand for plug-in electric vehicles by balancing 

area, 2050

Figure K-10 provides a breakdown of the fixed portion of the daily energy demands for PEVs by 

BA region in 2050. The energy shown is approximately 55% of the total load in 2050. PEV 

population growth follows general population growth in RE Futures; therefore, highly populated 

areas are highlighted as PEV load centers.

> 20

15 - 20

10 - 15

5 - 10

2.5 - 5

1.25 - 2.5

< 1.25

2050 PEV Daily Fixed

Energy Demand 

(GWh)
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Conclusion

The introduction of PEVs creates opportunities for the reduction of petroleum and the creation of 

new flexible load that can be integrated in utility operations with a high penetration of 

renewables to achieve a long-term strategy of creating a more sustainable transportation system. 

This work developed energy system load characteristic forecasts on a regional basis during the 

study period for the PEV market penetration scenario to be used in RE Futures. The work builds 

upon past travel survey data analyses, regional population forecasts, and assumptions regarding 

incentivization of charge management scenarios. A single scenario was developed, assuming a 

high penetration of PEVs. This scenario mirrors trends in the historical hybrid electric vehicle

market stock to date. Three PEV charge scenarios were considered, including No Utility Control, 

Opportunity, and Valley Fill/Managed. The energy needed in the Valley Fill/Managed scenario 

was assumed to be flexible in terms of when it needs to be delivered throughout the day and thus 

provides the utility with a flexible load that can be managed to improve renewable generation 

asset utilization. According to the scenario, by 2050, 45% of the total vehicle energy demand of 

350 TWh is under managed control while the remaining 55% is a fixed load to be planned for 

and met by utility assets. The hourly load profile of the fixed transportation energy demand also 

shifted over the time period from mainly No Utility Control toward Opportunity charging. RE 

Futures is the first study to assume that a variety of vehicle load shapes will exist and may 

transition over time, resulting in a unique fixed load and functional flexible load for integration 

into utility operational planning tools. 
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Appendix L. Cost Functions for Thermal Energy Storage in 

Commercial Buildings

TES for space cooling can be used to lower peak electricity use in buildings by drawing upon 

cooled water or ice to supply some portion of the cooling requirements during the hot afternoon 

and early evening hours. These technologies are gaining wider application as building owners 

see the potential to lower electricity costs by allowing electricity-intensive cooling equipment to 

be used to a greater degree in off-peak hours when electricity rates are lower. For RE Futures, 

TES is viewed as one of several technologies that can be used to better align future electricity 

demand with a more renewable-intensive electricity supply. 

Segment Definitions for Supply Curve for Thermal Energy Storage

A stepped supply curve was developed for thermal energy storage based on several key 

assumptions. Four steps of the cost function were defined according to the type of thermal 

storage, type of air conditioning equipment, and building vintage. Table L-1 shows these steps 

and identifies the building segments to which they apply. 

Table L-1. Segments of Supply Curves for Thermal Energy Storage

Cost Segment Type of Storage Building Category

1 Ice storage New buildings with packaged A/C unitsa

2 Ice storage Existing building with packaged A/C units

3 Chilled water New buildings with chillers used for cooling 

4 Chilled water Existing buildings with chillers used for cooling a A packaged A/C unit is a “unit built and assembled at a factory and installed as a selfcontained unit to cool all or portion of a building” (EIA 1989).

Development of Cost Estimates for Thermal Energy Storage

An estimate of the cost per kilowatt shifted was developed for each of the four segments in

Table L-1. For ice storage systems, a recent report by E Source, an energy consulting and 

information company located in Boulder, Colorado, was employed (Horsey 2009). For chilled 

water storage, the costs were derived primarily from the cost functions and methodology 

originally developed for the DOE Federal Energy Management Program under a Federal 

Technology Alert (FEMP 2000). 

Cost Estimates for Ice Storage Systems

A recent resource report by E Source (Horsey 2009) was used to derive the cost estimates for ice 

storage systems. Given the proprietary nature of some of the information in the E Source report, 

only a sketch of the relevant facts is provided here. While ice storage systems can be built up 

from a component level (as described subsequently for chilled water), the market is likely to be 

dominated by manufactured package systems. At present, two U.S. manufacturers are the major 

competitors in this market. The E Source report describes some of the recent studies that have 

examined the first-generation models from these manufacturers as well as the projected cost and 

performance of more advanced models. 
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The technologies used by both of these manufacturers are similar. An extended quote from the 

E Source report (Horsey 2009) provides a summary of the basic technologies involved: 

They [the storage technologies] charge an ice storage system during cooler nighttime 

temperatures and meet the entire daytime cooling load by discharging the stored ice. The 

systems store cooling energy using the ice-on-coil method, where heat transfer coils, 

made of plastic or copper tubing, are arranged in an insulated storage tank and 

surrounded by water. In charging mode, ice forms on the heat transfer surface as 

evaporating refrigerant or a water/glycol solution flows through the coils, freezing up to 

95% of the water. In discharge mode, condensing refrigerant or warm water/glycol 

solution flow through the coils, melting the ice and delivering cooling to the DX unit. 

The differences between the two systems are in the ice-making process and in the choice 

of heat transfer fluid. 

The available cost information for the first generation unit from one of these manufacturers 

comes from a report prepared for the Southern California Public Power Authority. In that report, 

the installed cost per kilowatt across nine different installations ranged between $2,300 and 

$5,000 (with one outlier at more than $8,000). Field tests with the first generation of the second 

manufacturer’s system estimated an installed cost of approximately $2,500/kW (Horsey 2009). 

One of the manufacturers is targeting an installed cost of $2,100 for its second-generation unit. 

However, this cost assumes a market scale in which a large number of units might be installed, 

with promotion by one or more utility incentive programs. As yet, there is no field experience to 

suggest that this target-installed cost is achievable in the near term. One of the key installation 

issues involves the location of the ice-making unit. Because the weight of the unit can approach 

three tons, additional costs may be incurred for roof-mounted systems in some buildings in order 

to provide additional structural support.

For the purposes of RE Futures, it was assumed that the initial cost (in 2012) of a ice-based TES 

system was $2,200/kW for new buildings. This assumption, in essence, tempers the optimism of 

the declared near-term cost target. For existing buildings, the impediments to an optimal design 

(layout) and the potential need to increase the structural integrity of the roof is assumed to add 

another $300/kW to the cost. 

Costs for Chilled Water Systems

The Federal Technology Alert presented a methodology through which a preliminary cost 

estimate could be developed for a chilled water or ice storage system in a commercial facility 

(building) (FEMP 2000). The methodology begins with estimates of the hourly cooling demand 

for the building under peak conditions. Given this profile of cooling demand, a decision must be 

made regarding the degree to which storage will be used to meet the cooling demand during the 

peak afternoon hours of the day. For the analysis here, a “load-leveling” approach was assumed. 

Under this approach, the chiller is assumed to run continuously at the same intensity over a 

24-hour period; during the night hours, its output is used to chill water in a storage tank, and 

during the afternoon, the chiller is employed along with the stored chilled water to meet the peak 

cooling demand. 

Several energy simulations were made of a large office building using DOE’s EnergyPlus 

software. The large office was one of the benchmark buildings developed by the Building 
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Technology Program within the Office of Energy Efficiency and Renewable Energy. The 

benchmark large office is a 12-story building of approximately 500,000 ft2 of floor space. 

Simulations were made for three locations—Chicago, Phoenix, and Houston—to represent cold, 

hot and dry, and hot and humid climates. 

Under the load leveling approach, the key outputs from the simulation are the total amount of 

cooling required over the peak day (converted to ton-hours) and the maximum cooling load for a 

single hour. In a conventional system without TES, the capacity of the chiller must be sized to 

meet the peak hourly load. Using TES, the chiller output can be lower because the chiller only 

needs to meet the average hourly cooling load over the 24 hours of the peak-cooling day. The 

storage capacity of the TES system must be able to provide cooling during those hours of the day 

when the cooling load exceeds the capacity of the chiller. This value can be derived from the 

hourly profile of the hourly cooling loads and is measured in ton-hours. Table L-2 shows the 

cooling capacity to meet the peak hourly load, the cooling capacity to meet average hourly 

cooling load, and the TES capacity for the three locations simulated. 

Table L-2. Peak Hour, Average Hourly Cooling Load, and Thermal Energy Storage Capacity for

Large Office Benchmark, by Location

Location

Peak Hourly Cooling

Load (tons)

Average Hourly Cooling

Load (tons)

TES Capacity

(ton-hours)

Chicago 707 412 3,041

Houston 780 490 2,840

Phoenix 736 465 2,584

Costs for conventional and TES cooling systems for RE Futures were developed with methods 

similar to that described in the 2000 Federal Energy Management Program report. The cost of 

the chilled water storage (basically a steel tank) was represented by the equation: 2,197*TH0.677. 

This cost was increased by 31% to account for additional expenses for design, overhead, and 

miscellaneous auxiliary costs.62 Using Houston as an example, this formulation results in an 

estimated $626,600 for the cost of the TES system. 

The second largest cost component involves the chiller. Based on information from RSMeans 

(2009), Trane (2000), and E Source (Horsey 2009), an updated cost function for a large watercooled centrifugal chiller was developed (updated from the cost function shown in the 2000 

Federal Energy Management Program report). A reasonable representation of current chiller 

costs was judged to follow a linear function: $40,000 + $330/ton. To convert cooling loads to the 

required cooling capacity of the chiller, the cooling loads from the simulations were adjusted 

upward by 10% to represent non-standard operating conditions (i.e., to account for actual 

capacity compared to rated capacity at Air-Conditioning and Refrigeration Institute [ARI] 

conditions). For Houston, these adjustments result in a chiller capacity of 867 tons for the 

conventional system and 544 tons for a TES system. Thus, again using Houston as an example, 

the cost of the chiller in the TES system was estimated to be $40,000 + $330 * 544 = $220,500. 

 

62 The cost equation for the storage tank itself was based on budget estimates provided by Chicago Bridge and Iron 

to Pacific Northwest National Laboratory on June 27, 2006. The costs were adjusted for price changes (downward) 

between 2006 and 2009 by the price index for heat exchangers and tanks from Chemical Engineering magazine.
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For the conventional system with a required capacity of 867 tons for the chiller, the chiller cost 

would be approximately $326,000. 

The final major component of a chiller-based system is a cooling tower. As with the chiller, a 

single cost curve was developed based on examining information from a private sector cost 

database (Richardson’s Cost Data Online, Inc.), Trane, and RSMeans. The resulting cost 

function again took the form of an exponential function to represent economies of scale: C = 

$1,726*T0.62, where T = cooling tower capacity in tons. Continuing the example with respect to 

Houston, where the cooling capacity in the conventional system was estimated to be 780 tons, 

this function yields a cost estimate of $114,000 for the cooling tower (and associated piping 

costs). For the lower overall cooling capacity required with the TES system (544 tons), the 

cooling tower might be sized somewhat smaller with a cost of approximately $86,000. 

To calculate the cost per kilowatt of peak electricity saved, the analysis first compares the total 

cost of a TES system to a conventional system. While the TES system involves the additional 

thermal storage system, the assumed load leveling system results in lower capacity requirements 

for both the chiller and cooling tower. Thus, the savings from these components can be used to 

partially offset the cost of the storage system. Taking this approach in the case for Houston, the 

resulting incremental cost of a TES system over a conventional chiller-only system was 

estimated to be $521,000. 

For the magnitude of the kilowatt peak savings, the capacity output of the chiller at the peak hour 

must first be converted from tons to electricity consumption. Assuming an average coefficient of 

performance of 5.0 for the chiller and auxiliary equipment,63 peak hour electricity consumption 

for the conventional system (on the peak day) is 545 kW, and the average hourly electricity 

consumption of the chiller in the TES system (also during the peak day) is 342 kW. Thus, the 

amount of electricity consumption shifted to off-peak hours is 203 kW. Dividing the incremental 

cost of $521,000 by 203 kW yields a cost per kilowatt shifted of $2,568.These estimates were 

assumed to apply only to new buildings in which the chiller and storage system can be designed 

and sized in an integrated manner. For existing buildings, it was assumed that the TES could be 

installed, but with a cost premium of 10% to cover the additional cost of renovating the building 

or reconfiguring outside space to accommodate an appropriate location for the storage tank. This 

addition of TES was assumed to be coordinated with a chiller replacement, so that the lower 

costs of a new, smaller chiller could be used to offset some of the TES cost. Under these 

assumptions, the cost per kilowatt was calculated to be $2,877. 

The numbers cited in the previous discussion apply to Houston. Table L-3 shows the estimated 

cost per kilowatt for all three locations—for new and existing buildings. The cost estimation 

methodology yields only about a 7% difference in cost between higher and lower cost locations 

(Phoenix and Houston). 

 

63 The coefficient of performance of the chiller itself was assumed to be 7.0 (roughly corresponding to 0.5 kW/ton). 

Fan energy use for the cooling tower was derived from the EnergyPlus simulations. The standard output from 

EnergyPlus does not break out the electricity use for condenser pumps. For RE Futures, it was assumed that the 

electricity use for the pumps would be 10% of the chiller electricity use. As a result, for Houston the cooling tower 

and pumps added an additional 39% to electricity used by the chiller itself. This additional electricity use on a 

percentage basis was somewhat lower for the Chicago and Phoenix simulations.
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Table L-3. Estimated Costs for Chilled Water Thermal Energy Storage per Kilowatt Shifted

Location $/kW (new) $/kW (existing)

Chicago $2,731

$2,568

$3,059

Houston $2,877

Phoenix $2,763 $3,094

Assumed Cost Reductions in the Future

For both chilled water and ice-based storage systems, the RE Futures analysis assumes that costs 

will decline in the future as more of these systems are installed. However, there is no interaction 

between the number of these systems installed, as modeled in ReEDS, and the price (indicating 

cost reduction through learning and economies of scale). Therefore, the cost reductions are based 

on the judgment of the analyst. Because the chilled water storage was deemed a more mature 

technology (in the sense that a major cost item is simply the cost of the tank), future cost 

reductions for chilled water were assumed to be lower than those for ice storage. For the chilled 

water systems, the rate of cost reduction was assumed to be 0.25% per year from 2012 to 2050. 

For ice-based systems, the cost reduction was assumed to be 1.5% per year up to 2020 and 

0.25% per year thereafter.

Table L-4 shows the cost assumptions corresponding to the steps of the cost curve for NERC 

region 1, represented by the Chicago location (as per Table L-3).64 The cost estimates are shown 

for even years, following the 2-year time interval used in ReEDS. While the cost of ice storage 

for new buildings is initially $2,200 in 2012, it falls to approximately $1,950 by 2020. Thus, over 

the next decade it is assumed that the target cost set by one of the manufacturers discussed in a 

previous section, Cost Estimates for Ice Storage Systems, is achieved. By 2050, the cost falls by 

another $150 per kilowatt. 

 

64 Given the sources and methodology used to develop these costs, a natural question is why the costs for ice storage 

per kilowatt shifted may be lower than those for a chilled water system. The relative costs depend on the cost of the 

storage system and the efficiency of the cooling equipment. Other things being equal, the higher the efficiency of the 

equipment, the greater the cost per kilowatt—in part explaining why the chilled water system displays a higher 

overall cost per kilowatt.
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Table L-4. Cost Assumptions for Thermal Energy Storage, by Step—North American Electric 

Reliability Corporation Region 1

Year

Step 1: Ice 

Storage—New

Step 2: Ice 

Storage—

Existing

Step 3: Chilled 

Water—New

Step 4: Chilled 

Water—

Existing

Step 5: All 

Other 

Buildings

2012 $2,200 $2,500 $2,731 $3,059 $10,000

2014 $2,134 $2,425 $2,717 $3,044 $10,000

2016 $2,070 $2,352 $2,704 $3,029 $10,000

2018 $2,008 $2,282 $2,690 $3,014 $10,000

2020 $1,948 $2,213 $2,677 $2,999 $10,000

2022 $1,938 $2,202 $2,663 $2,984 $10,000

2024 $1,928 $2,191 $2,650 $2,969 $10,000

2026 $1,919 $2,180 $2,637 $2,954 $10,000

2028 $1,909 $2,169 $2,624 $2,939 $10,000

2030 $1,899 $2,158 $2,610 $2,924 $10,000

2032 $1,890 $2,148 $2,597 $2,910 $10,000

2034 $1,880 $2,137 $2,584 $2,895 $10,000

2036 $1,871 $2,126 $2,571 $2,881 $10,000

2038 $1,862 $2,116 $2,559 $2,866 $10,000

2040 $1,852 $2,105 $2,546 $2,852 $10,000

2042 $1,843 $2,095 $2,533 $2,838 $10,000

2044 $1,834 $2,084 $2,520 $2,824 $10,000

2046 $1,825 $2,074 $2,508 $2,810 $10,000

2048 $1,816 $2,063 $2,495 $2,795 $10,000

2050 $1,807 $2,053 $2,483 $2,782 $10,000

Note: The cost assumptions above correspond to buildings in steps. Step 1 refers to new buildings with 

packaged A/C units; Step 2 refers to existing buildings with packaged A/C units; Step 3 refers to new 

buildings with chillers used for cooling; Step 4 refers to existing buildings with chillers used for cooling; 

and Step 5 refers to all other buildings.
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Potential for Thermal Energy Storage by Region

The amount and percentages of total thermal storage demand by each step were based on the 

amount of floor space represented by the type of building corresponding to Steps 1 through 5, as 

explained in the note to Table L-4.The estimation procedure simply assumes that, to a first 

approximation, cooling demand per square foot of floor space is the same across all types of 

floor space.65 The procedure would be significantly complicated if an effort had been made to 

distinguish cooling loads by building type. The 2003 Commercial Building Energy Consumption 

Survey was used to derive the amount of floor space served by different cooling systems. The 

Commercial Buildings Energy Consumption Survey indicated that approximately 22% of the 

total commercial floor space employs chillers or district chilled water for cooling. The 

corresponding percentage for packaged air conditioning equipment is 46%. It is assumed that 

these shares remain constant over time and across regions.

These percentages are used to develop the (horizontal) magnitudes for the cost curve in each 

model year. As discussed above, ReEDS operates on even-numbered years to 2050. The first 

(and third) cost segments apply only to new construction. Based on a model run of the NEMS for 

2009, projections of new commercial floor space by census region were obtained. These values 

were converted to fractions of total floor space for the 2-year time step of the model. For Step 1, 

these fractions were multiplied by 0.22 to represent floor space of buildings served by packaged 

air conditioning systems. For Step 3, the new floor space fractions were multiplied by 0.46 to 

represent large buildings using chillers or district chilled water. A conversion matrix was 

constructed to allocate the nine census division estimates to the 13 NERC regions used by 

ReEDS.

For existing buildings, it was assumed that the amount of potential TES storage was constrained 

by the amount of floor space replacing air conditioning equipment. Assuming a 25-year life for 

chillers, an approximation to the percentage of floor space that would replace a chiller would be 

4%. Given the 2-year time step of ReEDS, this percentage is doubled to represent replacements 

over 2 years. A final (judgmental) adjustment of 10% is added to account for the fact that 

potential installations of TES systems are likely to have higher than average cooling loads. 

Combining these factors yields a fraction of 0.088 of existing stock as the maximum fraction of 

floor space (and, thus, peak cooling load) for which a chilled water TES system could be 

installed to shift peak electricity demand. This fraction is multiplied by the percentage of pre2010 buildings in the stock estimated to use chillers (i.e., the 22% value cited previously). This 

same logic is also applied to packaged air conditioning systems, for which a 20-year lifetime is 

assumed to generate the approximate fraction of replacement systems (yielding 11% of floor 

space in an existing building with packaged air conditioning).

One final adjustment is made to account for replacement of systems in buildings constructed 

after 2010. Starting in 2030, the first replacement of chiller or packaged air conditioning system 

 

65 Total cooling demand across various time periods of the day is provided as another separate input to ReEDS. The 

discussion here relates to how total cooling demand can be allocated across the various segments of the TES cost 

curve. For example, one question concerns what percentage of cooling demand can be attributed to new large 

buildings (using chillers) that have been built in the current 2-year time step of the ReEDS model.
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was assumed to be required in 90% of the floor space in these recently built buildings.66 For each 

2-year period beginning in that year, the approach considers the amount of floor space 

construction in the 2-year period 20 years prior (e.g., the period ending 2034 would consider 

floor space built between 2013 and 2014 for replacement). 

The results of these calculations are shown in Table L-5. The table shows the fractions of floor 

space—representing the constraints on the fractions of total cooling demand that could employ 

TES in each ReEDS time period—for each of the four steps of the cost function for NERC 

Region 1. The shares attributable to new construction (Steps 1 and 3) generally fall over time, as 

growth in new buildings moderates over the forecast horizon. 

Table L-5 shows the stepped cost function for 2012 for NERC region 1. To repeat, the fraction 

along the horizontal axis can be interpreted as the total amount of cooling in this region for 

which TES can be applied in the time step ending in 2012. Thus, about 9% of the cooling peak 

cooling demand could be shifted due to TES installations during this period.

 

66 The 90% assumption is designed to roughly measure the fraction of buildings that may be demolished after 20 

years.
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Table L-5. Limiting Fractions of Total Cooling Demand, by Step—North American Electric 

Reliability Corporation Region 1 

Year

Step 1: Ice 

Storage—New

Step 2: Ice 

Storage—

Existing

Step 3: Chilled 

Water—New

Step 4: Chilled 

Water—Existing

2012 0.0170 0.0483 0.0082 0.0187

2014 0.0191 0.0464 0.0092 0.0180

2016 0.0198 0.0446 0.0096 0.0172

2018 0.0192 0.0428 0.0093 0.0165

2020 0.0183 0.0411 0.0088 0.0159

2022 0.0174 0.0396 0.0084 0.0153

2024 0.0172 0.0381 0.0083 0.0147

2026 0.0176 0.0366 0.0085 0.0142

2028 0.0180 0.0351 0.0087 0.0136

2030 0.0168 0.0464 0.0081 0.0192

2032 0.0155 0.0449 0.0075 0.0185

2034 0.0154 0.0452 0.0074 0.0188

2036 0.0153 0.0447 0.0074 0.0187

2038 0.0152 0.0432 0.0074 0.0181

2040 0.0152 0.0415 0.0073 0.0174

2042 0.0151 0.0397 0.0073 0.0166

2044 0.0150 0.0386 0.0073 0.0162

2046 0.0149 0.0378 0.0072 0.0159

2048 0.0149 0.0372 0.0072 0.0157

2050 0.0148 0.0353 0.0072 0.0149

Note: Step 1 refers to new buildings with packaged air conditioning units; Step 2 refers to existing buildings with 

packaged air conditioning units; Step 3 refers to new buildings with chillers used for cooling; Step 4 refers to 

existing buildings with chillers used for cooling.
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Figure L-1. Stepped cost curve for North American Electric Reliability Corporation Region 1, 2012 

One final aspect of the implementation of the cost curve in ReEDS is how to account for 

vintages—installations of TES systems in previous time periods. If TES were cost-effective from 

an electric utility perspective, the inputs shown in Figure L-1 would indicate that 8%–10% of the 

total stock (i.e., total cooling demand) could be augmented with TES in each 2-year time step of 

the model (the sum of the four steps in any given year of Table L-5). Thus, in a limiting case, all 

commercial floor space could employ TES systems after a 20–25-year time frame. Installations 

in subsequent time periods (in existing buildings) would be nonsensical because the resulting 

amount of floor space served by TES would then exceed total floor space. 

As discussed at the outset, the maximum penetration of TES in commercial floor space has been 

assumed to be limited by the fraction of floor space in buildings served by chillers and packaged 

air conditioning equipment. According to the 2003 Commercial Buildings Energy Consumption 

Survey (EIA 2006b), this fraction was approximately 0.68. Thus, for the implementation of TES 

in ReEDS linear programming framework, an overall constraint of 0.7 times the total cooling 

demand in each region was applied in all time steps of the model. More sophisticated procedures 

would be needed to ensure that fractions of cooling demand served by chilled water or ice 

storage were consistent with the allocation by type of cooling system (e.g., chilled water TES 

systems would be limited to approximately 20% of the total cooling demand). 
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Appendix M. Development of Demand Response Amounts

Demand Response Potential

There were a number of steps involved in calculating the demand response potential for each 

end-use sector.

National Assessment of Demand Response Database

In June 2009, the Federal Energy Regulatory Commission released the study A National 

Assessment of Demand Response Potential (FERC 2009). The authors calculated the potential 

amounts for four end-use categories (Residential, Small Commercial & Industrial, Medium 

Commercial & Industrial, and Large Commercial & Industrial). They subdivided the sectors 

based on enabling technologies and the availability of central air conditioning. Five different 

types of demand response were used: dynamic pricing with enabled technology, dynamic pricing 

without enabling technology, automated or direct load control, interruptible tariffs, and other 

demand response (such as capacity bidding). 

They calculated the amount of demand response available for each state for the years 2010–2019 

and used four scenarios: Business-As-Usual (BAU), Expanded BAU, Achievable Participation, 

and Full Participation. The BAU case assumed that existing and planned demand response 

programs would continue unchanged over the next 10 years. Expanded BAU assumed an 

expansion into all states of the current mix of demand response programs, partial deployment of 

an advanced metering infrastructure, and availability of dynamic pricing to customers with 5% 

choosing dynamic pricing. The Achievable Participation case assumed that an advanced 

metering infrastructure was universally deployed, that dynamic pricing tariffs represented the 

default condition, and that other demand response programs were available to those customers 

opting out of dynamic pricing. The Full Participation case assumed that an advanced metering 

infrastructure was universally deployed, that the default tariff was dynamic pricing, and that all 

customers used proven enabling technologies where cost-effective (FERC 2009). Achievable 

Participation amounts were price sensitive to the ratio of the new peak price to the old average 

price. Because RE Futures studied years up to 2050, it was assumed that Full Participation 

amounts were achieved by that time.

The authors released the spreadsheet model they used to calculate the amount of savings. It 

included a summary table that shows the sector demand response savings for each U.S. census 

region for 2019 for the different scenarios and types of demand response. Table M-1 shows the 

amount for the East North Central census region as an example. The model also reports the 

expected system peak for that year, shown in the bottom right corner. 
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Table M-1. Summary of FERC Estimated Demand Response Potential Savings (MW) in 2019 for 

East North Central Region

Scenario/ 

Demand Response Type Residential Small Medium Large Total

BAU

Pricing With Enabling Technology 0.0 0.0 0.0 0.0 0.0

Pricing Without Enabling Technology 0.8 6.0 0.0 119.0 135.1

Automated or Direct Control Demand 

Response 1,008.6 115.8 34.5 0.0 3,453.1

Interruptible Tariffs 0.0 0.0 12.0 2,068.2 1,787.3

Other Demand Response 0.0 0.0 0.2 3,041.4 1,299.3

TOTAL 1,009.4 121.8 46.7 5,228.6 6,406.5

Expanded BAU

Pricing With Enabling Technology 0.0 0.0 0.0 0.0 0.0

Pricing Without Enabling Technology 153.7 8.0 31.8 147.2 135.1

Automated or Direct Control Demand 

Response 2,348.1 136.7 104.5 0.0 3,453.1

Interruptible Tariffs 0.0 0.0 194.6 3,892.3 1,787.3

Other Demand Response 0.0 0.0 0.6 6,417.8 1,299.3

TOTAL 2,501.8 144.8 331.5 10,457.4 13,435.4

Achievable

Pricing With Enabling Technology 1,947.4 787.4 499.6 650.5 3,885.0

Pricing Without Enabling Technology 3,464.6 47.6 444.1 1,184.3 5,140.5

Automated or Direct Control Demand 

Response 1,204.1 121.2 62.3 0.0 1,387.5

Interruptible Tariffs 0.0 0.0 194.6 3,892.3 4,086.9

Other Demand Response 0.0 0.0 0.4 3,928.6 3,929.0

TOTAL 6,616.1 956.1 1,201.0 9,655.7 18,428.9

Full Participation

Pricing With Enabling Technology 4,555.4 1,841.9 1,460.9 1,902.2 9,760.4

Pricing Without Enabling Technology 3,500.1 28.7 347.2 1,534.0 5,410.1

Automated or Direct Control Demand 

Response 1,008.6 115.8 34.5 0.0 1,158.9

Interruptible Tariffs 0.0 0.0 194.6 3,892.3 4,086.9

Other Demand Response 0.0 0.0 0.2 3,041.4 3,041.6

Totals 9,064.1 1,986.4 2,037.4 10,369.9 23,457.9

Region Peak Demand 147002.5

Source: A National Assessment of Demand Response Potential (FERC 2009)

Because the amounts are calculated in the form of megawatt potential, each amount was divided 

by the system peak for 2019 to determine the fraction of peak demand that can be reduced by 
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demand response. These percentages could then be multiplied by the peak demand for a given 

region and sector for a given year. 

The FERC data represent the data based on four rate classes: residential and small, medium and 

large commercial and industrial. To convert these data to data used for RE Futures, the small and 

half of the medium commercial and industrial were assigned to the commercial sector, and the 

large and half of the medium large commercial and industrial were assigned to the industrial 

sector.

Sectoral Demand Response (GW)

The next step was to calculate the gigawatts of demand response available in each region for 

each year. Because the report generated values for the nine census regions instead of the 13 

NERC regions used by NEMS, RE Futures assigned the nine regions to the appropriate NERC 

regions, as shown in Table M-2. The table also shows how the EPRI regions in Table M-2 were 

assigned to the NERC regions. 

Table M-2. Census and Electric Power Research Institute Region Assignments to North American 

Electric Reliability Corporation Regions 

NERC Region Name EPRI Region Census Region

1 ECARa East East North Central

2 ERCOTb Central West South Central

3 MAAC East Middle Atlantic

4 MAIN Central East North Central

5 MAPP Central West North Central

6 NPCCc

/NY East Middle Atlantic

7 NPCC/NE East New England

8 FRCCd East South Atlantic

9 SERCe East South Atlantic + East 

South Central

10 SPPf Central West South Central

11 WECCg

/NWP Pacific Pacific

12 WECC/RMP Mountain Mountain

13 WECC/CA Pacific Pacific a East Central Area Reliability Coordination Agreement b Electric Reliability Council of Texas

c Northeast Power Coordinating Council

d Florida Reliability Coordinating Council

e SERC Reliability Corporation

f

Southwest Power Pool

g Western Electricity Coordinating Council

With an increasing acceptance and penetration of demand response within the utility system, RE 

Futures assumed a change in the level of demand response participation, from BAU, to 

Expanded BAU, to Achievable Participation, to Full Participation. The initial years used 100% 

of the BAU estimates, with a gradual shift to 100% Achievable Participation amounts by 2020. 

After 2030, the amount available through Full Participation was gradually applied such that 
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demand response achieved 100% Full Participation by 2040. These fractions by year are shown 

in Table M-3, with the sum of each row adding up to 100%.

Table M-3. Shift of Market Penetration Categories for Demand Response

Year BAU

Enhanced 

BAU Achievable

Full 

Participation

2006 100% – – – 

2008 100% – – – 

2010 100% – – – 

2012 75% 25% – – 

2014 50% 50% – – 

2016 25% 50% 25% – 

2018 – 50% 50% – 

2020 – – 100% – 

2022 – – 100% – 

2024 – – 100% – 

2026 – – 100% – 

2028 – – 100% – 

2030 – – 100% – 

2032 – – 80% 20%

2034 – – 60% 40%

2036 – – 40% 60%

2038 – – 20% 80%

2040 – – – 100%

2042 – – – 100%

2044 – – – 100%

2046 – – – 100%

2048 – – – 100%

2050 – – – 100%

Price Calculation

The ReEDS model requires prices for the amount of demand response available in order for it to 

compete against other forms of reserve supplies. The first two demand response types use pricing 

as the drivers for the demand response, and the FERC model includes the sensitivity curves for 

each of the end-use sectors. The price-response curves for these, when normalized to the peak 

value at eight times the base price, are the same for each region and are shown in Figure M-1. 

These curves show that if peak prices are eight times the base price, then 100% of the estimated 

demand response is available for that region. If the price ratio is lower, then only a fraction of 

that demand response is realized. RE Futures used the curves for the demand response with 

enabling technologies because it is expected that enabling technologies will be more widespread 

by 2050. 
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Figure M-1. Relationship of demand response to price for each sector

Application of 2050 Sector Electricity Price to Curve to Find Supply Curve for 

Demand Response

The values calculated in Figure M-1 are based on dynamic prices eight times the average price. 

The average prices for each sector are reported in NEMS, so they were used as the basis to 

calculate the price for the price-sensitive demand response for use within ReEDS.

The last three demand response technologies are not explicitly price sensitive in the FERC 

analysis. For these, it may be most appropriate to model them in ReEDS as price takers. Their 

main competition is natural gas–fired combustion turbines, so these were entered into ReEDS at 

the quantities calculated above and with a capital cost and operating cost reflective of the price of 

peaking plants.
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Appendix N. Hourly Load Shapes

This appendix contains graphs (Figure N-1 through Figure N-13) of the resulting non-PEV 

hourly load shapes by region (see Figure H-7 for the region map). The hourly loads from 2006 

were initially used as templates. Load duration curves for each sector and the system as a whole 

were calculated, as described in Appendix I. The 2006 hourly loads were adjusted such that each 

sector’s load requirements were fulfilled and the 2006 pattern of peaks and valleys was

maintained. Commercial and residential load shapes were calculated for the average weekday, 

peak day, and average weekend day for each month for each year. Within each month, the hourly 

loads for each sector were adjusted so that the 2006 pattern was maintained and the average 

weekday, average weekend, and peak day loads were kept. Industrial loads were calculated to 

maintain the overall load factor while mimicking the 2006 system load shape. The underlying 

data, which are further broken out by sector, are available upon request. 

Figure N-1. Non-PEV hourly demand in 2050, Region 1

(East Central Area Reliability Coordination Agreement)
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Figure N-2. Non-PEV hourly demand in 2050, Region 2

(Electric Reliability Council of Texas)

Figure N-3. Non-PEV hourly demand in 2050, Region 3

(Mid-Atlantic Area Council)
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Figure N-4. Non-PEV hourly demand in 2050, Region 4

(Mid-America Interconnected Network)

Figure N-5. Non-PEV hourly demand in 2050, Region 5

(Mid-Continent Area Power Pool)
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Figure N-6. Non-PEV hourly demand in 2050, Region 6

(New York)

Figure N-7. Non-PEV hourly demand in 2050, Region 7

(New England)
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Figure N-8. Non-PEV hourly demand in 2050, Region 8

(Florida Reliability Coordinating Council)

Figure N-9. Non-PEV hourly demand in 2050, Region 9

(Southeastern Electric Reliability Council)
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Figure N-10. Non-PEV hourly demand in 2050, Region 10

(Southwest Power Pool)

Figure N-11. Non-PEV hourly demand in 2050, Region 11

(Northwest Power Pool)
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Figure N-12. Non-PEV hourly demand in 2050, Region 12

(Rocky Mountain Power Area, Arizona, New Mexico, and Southern Nevada)

Figure N-13. Non-PEV hourly demand in 2050, Region 13

(California)
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