### ORNL/TM

- 2012/568



# Estimating the Energy

- Efficiency
Potential in the Eastern Interconnection

January 201 3 (Revised April 2013)

Prepared by

Marilyn A. Brown 1 

### Yu Wang 1

1 School of Public Policy

, Georgia Institute of TechnologyDOCUMENT AVAILABILITY

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- 2012/568

Power and Energy Systems Group

ESTIMATING THE ENERGY

- EFFICIENCY POTENTIAL IN THE EASTERN

INTERCONNECTION

Marilyn A. Brown Yu Wang

School of Public Policy Georgia Institute of Technology

Date Published: January 2013

Prepared by

OAK RIDGE NATIONAL LABORATORY Oak Ridge, Tennessee 37831

- 628 3

managed by

UT

- BATTELLE, LLC

for the

U.S. DEPARTMENT OF ENERGY

under contract DE

- AC05

- 00OR22725iv TABLE OF CONTENTS

Table of Contents

................................ ................................ ................................ ........................... iv List of Figures and Tables

................................ ................................ ................................ ................ v Acknowledgments

................................ ................................ ................................ ........................ v iii Executive Summary

................................ ................................ ................................ ....................... i x

1 Introduction, Objectives, and Background

................................ ................................ ... 1

1.1 Objectives

................................ ................................ ................................ ................. 1

1.2 Review of Energy

- Efficiency Potential

................................ ................................ .... 2

1.3 Barriers, Drivers, and Current Policies

................................ ................................ ..... 6

2 Methodology

................................ ................................ ................................ ..................... 9

2.1 Overview of Approach

................................ ................................ .............................. 9

2.2 National Energy Modeling System

................................ ................................ ........... 9

2.3 Energy Efficiency Policy Levers

................................ ................................ ............ 10

2.4 Calculation of Levelized Cost of Electricity

................................ ........................... 12

2.5 Method for Deriving State

- Specific Estimates

................................ ....................... 13

3 Energy

- Efficiency Potential in Residential Buildings

................................ ................. 15

3.1 Overview of Previous Literature on the Energy

- Efficiency

Potential in Residential Buildings

................................ ................................ ........... 16

3.2 Residential Efficiency Policy Options and Modeling Approach

............................ 17

3.3 Modeling Results for Individual Residential Policies

................................ ........... 21

3.4 Integrated Residential Policy Bundle

................................ ................................ ..... 27

3.5 Summary and Discussion

................................ ................................ ....................... 31

4 Energy

- Efficiency Potential in Commercial Buildings

................................ ............... 33

4.1 Overview of Previous Literature on the Energy

- Efficiency Potential of Commercial Buildings

................................ ................................ ......... 34

4.2 Commercial Efficiency Policy Options and Modeling A pproach

.......................... 35

4.3 Modeling Results for Individual Commercial Building Policies

............................ 38

4.4 Integrated Commercial Policy Bundle

................................ ................................ .... 42

4.5 Conclusion and Discussion

................................ ................................ ..................... 44

5 Energy Efficiency Potential in Industrial Sector

................................ ........................ 45

5.1 Overview of Previous Literature on the Energy

- Efficiency Potential of Industry

................................ ................................ ................................ 47

5.2 Industrial Efficiency Policy Options and Modeling A pproach

............................... 49

5.3 Modeling Results for Industry Policies

................................ ................................ ... 51

5.4 Integrated Industrial Policy Bundle

................................ ................................ ........ 58

5.5 Conclusions and Discussions

................................ ................................ .................. 61

6 Integrated Policy Case

................................ ................................ ................................ ... 63

6.1 The Achievable Potential of Electricity Savings

................................ .................... 6 6

6.2 Policy Impacts on Electricity Rates and the Power Sector

................................ ..... 70v 6.3 Policy Impa cts on Other Fuels and Carbon E missions

................................ ........... 73

6.4 Policy Impacts on Energy Intensity and GDP

................................ ........................ 75

6 .5 Discussion and Conclusion

................................ ................................ ..................... 77

7 References

................................ ................................ ................................ ....................... 79

Appendices

A GT

- NEMS Modeling of Residential Energy Efficiency Policies

................................ .... 85

B GT

- NEMS Modeling of Commercial Energy Efficiency Policies

................................ .. 89

C GT

- NEMS Modeling of Industrial Energy Efficiency Policies

................................ ....... 96

D State Results

................................ ................................ ................................ ..................... 97

LIST OF FIGURES AND TABLES

Figures

ES.1 Total Energy Savings Potential of an Integrated Policy Scenario in the Eastern

Interconnection (in Quads)

................................ ................................ ............................... ix

ES.2 Protential Electricity Savings (all sectors) from the Integrated Policy Scenario in the

Eastern Interconnection

................................ ................................ ................................ ..... x ES.3 Ele ctricity

- Savings Potential by Sector Based on the Integrated Policy Scenario (in TWh)

................................ ................................ ................................ .......................... x i

ES.4 Supply Curve for Electricity Efficiency Policies in 2020 in the Eastern

Interconnection

................................ ................................ ................................ ................ xi i

1.1 Interconnections of the National Elect ricity Reliability Council (top ) and Regions and Divisions of the U.S. Census Bureau (bottom)

................................ ................................ .. 2

3.1 Residential Energy Consumption in the Eastern Interconnection in 2009

...................... 15

3.2 Reference Case Forecast of Residential Consumption in the Eastern Interconnection

... 16

3.3 Building Energy Code Retirement Years by Census Division

................................ ........ 19

3.4 Adoption Statu s of Residen tial Building Energy Codes as of April, 2012

...................... 19

3. 5 States with On

- Bill Financing Programs

................................ ................................ ......... 20

3. 6 Estimated Reduction in Electricity Demand by Individual Residential Policies

............. 22

3. 7 Electricity Savings by Census Division for Individual Policies

................................ ...... 23

3. 8 Residential Electricity Savings Potential in the Eastern Interconnection

........................ 28

4.1 Commercial Energy Consumption in the Eastern Interconnection

................................ .. 33

4.2 Reference Case Forecast of Commercial Electricity Consumption in the Eastern

Interconnection

................................ ................................ ................................ ................ 34

4.3 Mandated Disclosure and Benchmarking Efforts in the United States

............................ 36

4.4 PACE

- Enabled States

................................ ................................ ................................ ...... 37

4. 5 Estimated Reductions in Electricity Demand by Individual Policies

.............................. 38

4. 6 Electricity Savings by Census Division for Individual Policies

................................ ...... 39

4. 7 Commercia l Electricity Savings Potential in the Eastern Interconnection

...................... 42

5.1 Industrial Energy Consumption in the Eastern Interconnection

................................ ...... 45

5.2 Primary Energy Consumption by Manufacturing Industries in 2006, Nationwide

......... 46

5.3 Gross Output by Manufacturing Ind ustries in 2006

................................ ........................ 46vi

5.4 Reference Case Forecast of Industrial Electricity Consumption in the Eastern

Interconnection

................................ ................................ ................................ ................ 47

5.5 Estimated Reductions in Electricity Demand by Individual Policies (Excluding CHP)

. 51

5.6 CHP Generation and Reductions in Electricity Purchase

................................ ................ 52

5.7 Potential for Expanded CHP Generating Capacity in the Eastern Interconnection

......... 53

5.8 Natural Gas Consumption by Industry Sector in the Eastern Interconnection

................ 53

5.9 Electricity Retail Price in the Industry Sector

................................ ................................ .. 54

5.10 El ectricity Savings by Census Division from Motor Rebates , Motor Standard, and Advanced Manufacturing Initiative (West South Central Excluding Texas)

.................. 55

5.11 Electricit y Savings by Census Division fro m CHP (West South Central Excluding Texas)

................................ ................................ ................................ ......................... 55

5.12 U.S. Industrial Consumption by Size of Firm

................................ ................................ .. 58

5.13 Industrial E lectricity Savings Potential in the Eastern Interconnection

........................... 59

5.14 Electricity Savings by Industry Subsectors

................................ ................................ ...... 59

5.15 Increases in Electricity Generation from Industrial CHP

................................ ................ 60

5.16 Natural G as Consumption in the Industry Sector

................................ ............................ 60

5.17 Electricity Retail Price in the Industry Sector

................................ ................................ .. 61

6.1 Supply Curve for Electricity Policies in 2020

................................ ................................ . 65

6.2 Electricity Savings from the Integrated Policy Case in the Eastern Interconnection

...... 67

6.3 Electricity

- Savings Potential by Sector , Based o n the Integrated Policy Case ( in TWh) 67

6. 4 Achievable Potential for Electricity Savings by State in 2020

................................ ........ 69

6.5 Electricity Price Changes by Census Division

................................ ................................ . 71

6.6 Power Generations by Source in the U.S. , in TWh (EE=Energy Efficiency)

................. 73

6.7 Total Energy Savings in the Eastern Interconnection (Quads)

................................ ........ 74

6.8 CO 2 Emissions in the Eastern Interconnection (Million Tonnes)

................................ .... 74

6.9 Forecasted Changes in Energy Intensity by Economic Sectors

................................ ....... 76

A.1 Share of New Houses Built in the Policy Cases

................................ .............................. 87

Tables

ES

- 1 Potential Electricity Savings by Sector Policy Bundles in the Eastern Interconnection

.. xi

1.1 Assessments of Energy

- Efficiency Potential

................................ ................................ ..... 4

1.2 Market Failures and Barriers Inhibiting Energy Efficiency

................................ .............. 7

2.1 Selected Policies for E lectric End

- Use Efficiency

................................ ........................... 11

3.1 GT

- NEMS Lever for Residential Efficiency Policies

................................ ..................... 18

3.2 Natural Gas Savings from Individual Policies

................................ ................................ . 25

3.3 Total Energy Savings from Individual Policies

, Residential Sector

................................ 25

3.4 Cost Esti mations from Appliance Incentives

................................ ................................ ... 26

3.5 Cost Esti mations from Building Codes

................................ ................................ ............ 26

3.6 Cost Estimations from On

- Bill Financing

................................ ................................ ....... 27

3.7 Cost Estimations from Market Priming

................................ ................................ ........... 27

3.8 Cost Estimations from Aggressive Appliance Policy

................................ ...................... 27

3.9 Electr icity Savings Potential by End

- u se in the US

................................ ......................... 29

3.10 Technology Demand Shift from Residential Policies in the Eastern Interconnection

..... 29

3.11 Demand sh are of Electric Heat Pumps for C ooling in the Eastern Interconnection

........ 30

3.12 Potential savings of natural g as and total e nergy in the Eastern Interconnection

............ 30

3.13 Potential Reduction in CO 2 E missions from Residential Policies

................................ ... 30vii

3.14 Comparison of Policy Impacts in the Eastern Interconnection

................................ ........ 31

4.1 GT

- NEMS Lever for Commercial Energy Efficiency Policies

................................ ....... 35

4.2 Natural Gas Savings from Individual Policies

................................ ................................ . 40

4.3 Total Energy Savings from Individual Policies

................................ .............................. 40

4.4 Cost Estimates from Benchmarking

................................ ................................ ................ 41

4.5 Cost Estimates from Financing

................................ ................................ ........................ 41

4.6 Cost Estimates from Building Codes

................................ ................................ ............... 41

4.7 Electricity

- Savings Potential by End

- Use in the US

................................ ........................ 43

4.8 Potential Savings of Natural Gas and Total Energy

................................ ........................ 43

4.9 Potential Reduction in CO 2 Emissions from Commercial Policies

................................ . 43

5.1 GT

- NEMS Lever for Industrial Efficiency Policies

................................ ........................ 50

5.2 Natural Gas Savings from Advance d Manufacturing Initiative

................................ ...... 56

5.3 Cost Estimations from Motor Rebate

................................ ................................ .............. 56

5.4 Cost Estimations from Motor Standard

................................ ................................ ........... 57

5.5 Cost Estimations from CHP Incentive

................................ ................................ ............. 57

5. 6 Cost Estimations from Advanced Manufacturing Initiative

................................ ............ 5 8

5. 7 Reduction s in Carbon Emissions

................................ ................................ ..................... 61

6.1 Savings Potential and Le velized C ost of Electric End

- Use Efficiency, by Policy

.......... 64

6.2 The Levelized Cost of Electricity Efficiency by Type of Policy (in cents/kWh)

............ 66

6. 3 Electricity

- Savings Potential in 2020 , by Census Division (as percentages of Reference case consumption in 2020)

................................ ................................ ............. 68

6. 4 Electricity Rate Changes in 2020, Reference versus Integrated Policy Case

.................. 70

6. 5 Electricity Generation by Source in the U.S. (in TWh), Refer e nce Forecast versus Integrated Policy Cas e

................................ ................................ ................................ ..... 72

6. 6 Energy

- Savings Potential of Natural Gas and Total Energy

................................ ........... 73

6. 7 Reduction in CO 2 Emissions

................................ ................................ ............................ 75

6. 8 GDP Impact

................................ ................................ ................................ ..................... 76

A.1 Most Efficient Home Appliances and Equipment

................................ ........................... 85

A.2 Building Energy C odes Profile for Residential Buildings

................................ ............... 86

A. 3 Residential Technologies Forced Early Retirement

................................ ........................ 88

B.1 Discount Rates Across Scenarios for Space Heating and Lighting in 2015

.................... 90

B.2 Commercial Building Shell Efficiency Improvement*

................................ ................... 91

B.3 Incentivized Technologies in Financing Policy Case

................................ ...................... 91

C.1 Ele ctricity and Natural Gas Saving Estimations from IAC Reports

................................ 96

D.1 Electricity Efficiency Potential By State (TWh)

................................ ............................. 97viii ACKNOWLEDGMENTS

Funding for this research was provided by Oak Ridge National Laboratory and the Department of Energy (DOE). This sponsorship is greatly appreciated.

Bob Pauley of Eastern Interconnection States Planning Council (EISPC) contributed meaningfully to communi cations with EISPC members and the Navigant team regarding the completion of this report. Valuable co mments on earlier drafts of the report were received from Youngson Baek and Stanton Hadley (Oak Ridge National Laboratory). Faculty and

students in the Sch ool of Public Policy at Georgia Tech also provided feedback on this

research , including Professor s Paul Baer and Doug Noonan and PhD students Matt Cox, Gyungwon Kim, Alexander Smith, and Xiaojing Sun. Gyungwon Kim also provided

valuable assistance with the graphics, especially the GIS analysis and maps

.

The authors are grateful for the willingness of these individuals to engage in a dialogue about the potential to expand energy

- efficiency resources.

Any remaining errors are the sole responsibility of the authors.ix EXECUTIVE SUMMARY

Comprehensive and integrated resource planning consider s the potential for increases in energy efficiency to reduce the requirements for new generation and transmission

investments. This study supports such planning efforts by developing robust estimates of the economically achievable potential for improving the energy

- efficiency of homes, commercial buildings, and industrial plants located in the Eastern Interconnection

. The approach of this study involves i dentifying a series of energy

- efficiency policies and examining their impacts

and cost

- effectiveness using Georgia Tech‟s version of the National Energy Modeling System (GT

- NEMS). The project emphasizes the impact s on electricity consumption, the

levelized cost of policy

- driven electricity savings

, and distributive effects at the state and regional level s . Thirty

- six states and the District of Columbia are covered by th is study, and

the time frame extends to 2035.

Using GT

- NEMS, we model t welve policy opti ons designed to stimulate energy

- efficiency

investments and practices by providing better information, more effective regulations, and affordable financing

. These policies are evaluated individually, combined into Policy

Bundles and evaluated for each sector, and evaluated in an Integrated Policy scenario. While the policies are designed to address different market failures and barriers and to focus on distinct market segments and technologies, the level of savings driven by the policies

dimini shes as more policies are combined . We focus on the achievable energy

- efficiency potential, which is the subset of the technically feasible and cost

- effective potential that can be achieved through policy intervention.

Based on GT

- NEMS estimates from the I ntegrated Policy scenario, the twelve policies could significantly drive down energy consumption , reduce energy rates, and generate energy bill savings for end

- users. Figure ES.1 illustrates the projected energy consumption of the region

in the R eference c ase and Integrated Policy case

.

Figure ES.1 Total Energy Savings Potential of an Integrated Policy Scenario in the

Eastern Interconnection (in Quads) 50 55 60 65 70 75 80

2010 2015 2020 2025 2030 2035

Reference Integrated Policy Case 5.2%

3.6%

2.6 quads of savings

in 2020 4.0 quads of savings in

2035 Energy Consumption (Quads)x

In the Eastern Interconnection , elect ricity consumption is forecast to grow at an average rate of 0.77% per year, and to go up to 3,200 TWh in 2035 in the R eferen ce case. With the twelve

energy

- efficiency policies, the growth of electricity consumption slows down to an average rate of 0.35% per year. The combination of tw elve policies could save the region about 190 TWh (6.6%) of electricity in 2020, and 332 TWh (10.3%) of electricity in 2035 (Figure ES.2).

Figure ES.2 P otential Electricity Savings (all sectors) from the Integrated Policy

Scenario in the Eastern Interconnection

The energy

- efficiency potential driven by the twelve policies comes largely from the

residential sector, with additional savings from the commercial and indus trial sectors (Figure

ES.3 ). In the Integrated P olicy case, the residential sector is estimated to save 13.2% of electricity in 2035, which is slightly lower than the estimated savings (15.4% in 2035) from

the residential Policy Bundle . Similarly, the estimated saving s potential from the commercial

sect or is 7.9% in 2035, lower than the estimation (8.4% in 2035) from the commercial sector

P olicy Bundle

. Overlap in the electricity savings identified by the policies underlies these

differences

, and an overlap of 10

- 20% is suggested by these results. In con trast, the industrial sector potential in the Integrated Policy case is estimated to be comparable (10.4% in 2035) to the savings potential identified by the industrial P olicy Bundle . The electricity from industrial CHP generation that is sold back to the grid is not shown in this figure

. An

additional 39 TWh are generated by CHP systems (28 TWh are consumed at the industrial plant and 11 TWh are sold back to the grid).

2,500 2,600 2,700 2,800 2,900 3,000 3,100 3,200 3,300

2010 2015 2020 2025 2030 2035 Delivered Electricity, All Sectors

(TWh)

Reference Integrated Policy Case 10.3%

6.6% 190 TWh of savings in 2020

332 TWh of electricity savings in 2035xi Figure ES.3 Electricity

- Savings Potential by Sector

, Based on the Integrated Policy Scenario (in TWh)

Table ES.1 illustrates the detailed savings potential for electricity end

- use by sector , based on the individual policy bundles

.

Table ES

- 1. Potential Electricity Savings by Sector Policy Bundles in the Eas tern
Interconnection

Residential Sector Commercial Sector Industrial Sector

Potential Savings (TWh) %

Savings Potential

Savings (TWh) %

Savings Potential

Savings (TWh) %

Savings

2020 102 10.1% 60 3.9% 48 6.3%

2035 179 15.4% 153 8.4% 68 10.4%

According to GT

- NEMS modeling supplemented by spreadsheet calculations , the electricity
savings could be achieved at relatively low cost

. Specifically, the levelized cost of electricity
savings estimates in 200 9 $ per kWh range from 0.5

- 0.8 cents/kWh for R esidential Building

Codes and 0.6

- 0.7 cents/kWh for the Aggressive Appliance Policy to 6.7

- 8.0 cents/kWh for
Appliance Incentives and 9.3

- 11.5 for Motor Rebates.

Together with the levelized cost estimations, the electricity

- savings potentials produce a
pol icy supply curve (Figure ES.3). This curve suggests that regulatory policies have
relatively low levelized cost of electricity and financial policies have relatively high LCOEs,

9.3% 12.4% 13.2% 13.2%

4.0% 5.9% 7.1% 7.9%

6.9% 8.2% 9.0% 10.4%

0 50 100 150 200 250

2020

2025 2030

2035 2020

2025 2030

2035 2020

2025 2030

2035

Residential Sector Commercial Sector Industrial Sector Electricity Savings (TWh)

Rest of the

Nation

Eastern

Interconnectio

nxii

with the CHP Incentive as an exception. Taking average electricity rates as be nchmarks, all
twelve policies except for the Motor Rebate are cost

- effective, representing 98% of the
savings potential. Information programs have particularly large energy

- savings potential,
most notably residential Market Priming and commercial Benchmark ing.

Figure ES.3 Supply Curve for Electricity Efficiency Policies in 2020

in the Eastern Interconnection

The electricity savings of energy

- efficiency policies would be accompanied by other benefits,
including natural gas savings and savings in other fuel types. These energy

- efficiency
policies are able to drive down electricity retail prices in most regions and produce sav ings

for consumers in their energy bills. For exa mple, residential customers are estimated to save

about $13 Billion in reduced energy costs in 2020 ; savings would also be experienced by

commercial customers ( $5 Billion ) and industrial customers ($4 Billion), and energy bill
savings for all sectors would expand in later years

. The electric power sector is also affected
by these policies, as a result of slower generation growth . Overall, these policies are
estimated to reduce energy and carbon intensity with out significantly impacting GDP growth.

The sizab le reduction in energy consumption is associated with reductions in carbon dioxide
emissions. The energy efficiency policies trim carbon emissions by 161 million tonnes ofxiii

CO 2 (4.0%) in 2020, increas ing to 246 million tonnes of CO 2 (5.6%) in 2035. The percentage
numbers of carbon emission reductions are slightly higher than the percentage reduction s in
energy consumption, reflecting a shift to less

- intensive energy sources. The energy efficiency
policies are estimated to generate hi gher savings in electricity, which is more carbon
intensive than natural gas in the EIPC region

.

In sum mary, this study shows that ene rgy

- efficiency policies would save the Eastern

Interconnection 332 TWh of electricity in 2035. The primary energy savings would reach 4 .0
Quads if all types of energy are included. The reduced demand for electricity leads to lower
electricity rates, which result in significant energy bill savings for residential, commercial
and industrial consumers. The policies would also r educe the carbon emissions of the region,
with no significant impact on GDP.xiv1 1

. INTRODUCTION, OBJECTIVES, AND BACKGROUND

The potential for improved electric end

- use efficiency has invoked great interest over the past

several decades because the cheapest megawatt hour of electricity is often the one that is not

needed (Croucher, 2011) . In addition, r educing electricity consumption through energy efficiency also helps reduce carbon dioxide emissions , improve air quality, and strengthen grid stability.

Comprehensive and integrated resource planning should consider the potential for increases in energy efficiency to reduce the requirements for new generation and transmission investments. Electricity p lanners have many options at their disposal: supply

- side options, such as central power pla nts, distributed generation, and energy storage; as well as demand

- side options, such as demand response and energy efficiency. What combination of these resources can deliver the

most reliable, affordable

, and clean electricity? Improvem ents to the knowledge base and modeling capabilities surrounding energy

- efficiency resources are critical to the integrity of transmission expansion planning and the optimization of s tate and regional energy policies.

1.1 Objectives

The principal objective of this study is to accurately estimate the achievable potential for improving the energy

- efficiency of homes, commercial buildings, and industrial plants located in

t he Eastern Interconnection

. What are the potential impacts of energy

- effici ency programs and policies in this region? A companion study led by Oak Ridge National Laboratory is examining the potential for greater demand response in this same region (Baek, et al., 2012). The results of

both studies are intended to be use d by the Ea stern Interconnection States Planning Council ( EISPC ) and the Eastern Interconnection Plannin g Collaborative (EIPC) in their transmission planning.

Secondary objectives include:

Reviewing existing assessments of energy

- efficiency potential in the Eastern

Interconnection , and Refining methodologies to construct estimates of energy

- efficiency potential in the

Eastern Interconnection

.

The region served by the EIPC consists of 36 states and the District of Columbia corresponding closely to the coverage of s even census divisions: the East North Central, West North Central, East South Central, West South Central, Middle Atlantic, South Atlantic, and New England

divisions. Th e Eastern Interconnection , including Washington D.C., was home to 211 million

individua ls in 2010, or 68.6% of the nation‟s population of 309 million in in that year.2

Figure 1.1 Interconnections of the National Elect ricity Reliability Council (top ) and

Regions and Divisions of the U.S. Census Bureau (bottom) (Source: http ://www.ercot.com/content/news/mediakit/maps/NERC_Interconnections_color.jpg ) )

1.2 Review of Energy

- Efficiency Potential

1.2 .1 The Energy

- Efficiency Gap

The term “energy

- efficiency gap” refers to the unexploited economic potential for energy efficiency; in other words, it emphasizes the technically feasible energy

- efficiency technologies and practices that are cost

- effective but are not being deployed. The energy

- efficiency gap has3

attracted wide attention among energy policy analysts, since society has forgone many cost

effective i nvestments in energy efficiency . This term was first "coined" by Hirst and Brown (1990) in a paper titled "Closing the Efficien cy Gap: Barriers to the Efficient Use of Energy

. " Many other studies have used similar definitions, such as the International Energy Agency (2007)

and Jaffe and Stavins (1994). The energy

- efficiency gap exists in many sectors, including households, small b usinesses, corporations, and governments (Dietz , 2010 ).

It is difficult to quantify the exact magnitude of the energy

- efficiency gap. One approach to characterizing its size is through modeling. This typically involves enumerating on a technology

by

- techno logy basis the difference between current practice and best practice, where best practice is defined as the utilization of the most cost

- effective energy

- efficient technologies. Keeping in mind the natural rate of equipment turnover through consumer purchases, one can then estimate the size of the gap that exists and that can be reduced by policy efforts

.

1.2 .2 Defining the Potential for Energy Efficiency

We define the “ achievable potential ” for energy efficiency as that portion of the energy

effic iency gap that can be narrowed by the implementation of policies and programs. This terminology has been used by other analysts, in conjunction with the terms “ technical potential ”

and “ economic potential ” (Rufo and Coito, 2002; NYSERDA, 2003). Specificall y :

Technical potential : Assumes that all technically feasible energy

- efficiency measures are adopted. These are also referr ed to as engineering estimates because they do not consider cost s

. In some cases, the technical potential assumes that the technology available changes over time

via innovation.

Economic potential : Generally a subset of technical potential that must pass a cost test. For example, the net present value of total benefits minus costs must be positive, and the rate of

return on the investment must meet a particular threshold. In a utility planning context, this

means that the levelized cost of the electricity saved must be less than the forecast cost of electricity generation that it would offset.

Achievable potential : Genera lly a subset of economic potential that considers what portion of

the cost

- effective efficiency measures could reasonably be achieved by policy efforts; estimate s generally consider a policy incentive of less than 30% of the energy

- efficiency investment ’ s incremental costs.

Among the three types of potential, we focus on estimating the achievable potential for improved energy efficiency in the EIPC region.

1.2.3 A Review of Estimates of Energy

- Efficiency Potential

A large body of literature has focused on the economic potential for energy

- efficiency measures,

as summarized in Table 1.1. The se assessment s of energy

- efficiency potential are derived from theory, simulation, and real

- world practices , and they have been conducted at various geographic scales, covering different time frames

.4 Table 1.1

. Assessments of Energy

- Efficiency Potential

Publication Application Potential Assessment Cost Estimate Policy Relevance

Area End

- use sector Fuel Type Estimation

Brown et al (2001) U.S. Residential

Total

energy Achievable 9

- 20% by 2020 Total net saving: 62

- 108 Billion 1997$ in

2020 Estimation based on

moderate and advanced policy scenarios examining about 50 policy options Commercial 9

- 18% by 2020

Industrial 8

- 17% by 2020

Tonn & Peretz (2007) U.S. Residential and Industry Total

energy Achievable 20

- 30% over a 20

- year period B/C ratio greater than

3:1 Potential achieved by standard energy efficiency

programs

Scott, et al (2008) U.S. Residential and

Commercial Total

energy Achievable 27% by 2030 Impacts of the 2005 Building Technology program

Granade et al. (2009) U.S. Building and Industry Total

energy Economic 23% by 2020 Average annualized cost: $4.4/MMBtu (

Brown, et al. (2010) U.S.

southeast

states Residential,

Commercial and Industry Total

energy Achievable 9

- 12% in 2020;

13

- 18% in 2035 Levelized cost of

electricity: 0.9

- 15

cent/kWh Explores 8 policy options promoting efficiency

Kneifel (2010) 16 cities in

the U.S. Commercial Total

energy Economic 20

- 30% for new buildings

Saygin, et al (2011) U.S. Chemical and petrochemical industry Total

energy Economic 24% with top

- down approach; 10.9% with

bottom

- up approach

Sadineni,et al (2011) U.S. Residential Total

energy Economic 42.5%

Laitner, et al. (2012) U.S. All Sectors Total

energy Economic 42

- 59% by 2050 Technology advances and policy improvements

modeled

McKane and Hasanbeigi (2011) Global Industrial motor

systems Total

energy Technical 27

- 57%

Economic 14

- 49%

Fleiter, et al (2012) Germany Pulp and pap er industry Total

energy Economic 21% by 2035 Electricity Economic 16% by 20355

Scenarios for a Clean Energy Future (Brown, et al., 2001) and a study by McKin sey and
Company ( Granade, et al ., 2009) are national in scope and focus on the 2020 time frame. Brown
et al. (2001) used this technology

- based accounting approach and concluded that removing
obstacles to energy efficiency through policy interventions initiated in the year 2000 could have

reduced the forecasted U.S. ener gy consumption in 2010 by 10%. The McKinsey and Company
study estimates the NPV

- positive potential for energy efficiency savings in non

- transportation
uses of energy. It finds that energy efficiency programs can save the nation 9.1 quadrillion Btu
(23%) in end

- use energy and 18.4 quadrillion Btu in primary energy by 2020. The potential in

the commercial sector is 29% of end

- use energy and 30% of primary energy, which sums up to
$290 billion in total energy savings from 2009

- 2020.

An achievable potential study conducted by the Electric Power Research Institute (EPRI)

forecasts the potential energy saving that would be achieved by energy

- efficiency programs in

2030 to be 398

- 566 billion kWh (8

- 11%), with estimated levelized cost between $0.022 /kWh

and 0.032/kWh (EPRI, 2009)

.

The American Council for an Energy

- Efficient Economy (ACEEE) completed a meta

- review of
energy efficiency potential assessments in the U.S. (Nadel et al., 2004)

. This review concluded

that , across the U.S., the median technical potential is 33%, the median eco no mic potential is

20%, and the media n achievable potential is 24%. The overall median achiev able potential is an annual energy savings of 1.2%, with similar savings from each end

- use sector . The number of
years estimated and the type of potential estimated vary widely from study to study, making the
median numbers relatively unreliable estimation s. Th e ACEEE stud y also summarizes the

electrici ty savings actual l y achieved by utilities in some of the leading states based on historical
data. The leading utilities were estimated to achieve annual electricity savings of 0.5

- 2.0%.

A recent ACEEE study i nvestigates the long

- term efficiency potential associated with
technology advances and policy improvements (Laitner, et al, 2012). By comparing their policy

s cenarios with EIA‟s projection

– the Annual Energy Outlook 2010

– the overall potential of

energy efficiency is estimated to be 42

- 59% by 2050.

Many studies produce potential estimat es at the state level. One of these, by Tonn and Peretz
(2007), es timated that standard residential and industrial energy

- efficiency programs have

energy

- efficiency potentials of 20

- 30% over a 20

- year period. The programs studied in that

review are genera lly cost

- effective

, with benefit

- to

- cost ratios exceeding 3:1. Neuhoff, et al.
(2012 ) conclude that the most aggressive Energy Efficiency Reso urce Standards (requiring
utilities or program administrators to reach specific goals for energy savings) target energy

efficiency savings of about 2% per year, or electricity savings of about 20% in total between
2010 and 2020. Policy instruments , such as subsidies, income tax es
, and carbon tax es can make
efficiency investments more prof itable (Amstalden, et al, 2007), and e nergy labeling can
improve efficiency in hou sehold energy use (Feng, 2010). Recent modeling assessments of6

energy

- efficiency potential have documented a significant achievable potential in the South
(Brown et al., 2010)

, in Appalachia (Brown et al., 2009), and in industry (Brown, et al., 2011).

1.3 Barriers, Drivers, and Current Policies

Numerous obstacles

– includin g market failures and barriers

– contribut e to the energy efficiency
gap (Table 1.2 ). Market failures occur when there are flaws in the way markets operate. The
traditional literature on market failures tends to examine circumstances in which market prices
fail to provide sufficient information or incentives to achieve Pareto optimality within the
context of a perfectly competitive market . Pareto optimality exists when gains from trade are exhausted and when the situation prevails that no individual‟s well

- being can be improved without a worsening of some other individual‟s well

- being.

Market failures are conditions of a market that violate one or more neoclassical economic
assumptions that define a competitive market, such as rational behavior, costless transactions,

a nd perfect information

. Traditionally, m arket failures were attributed to (1) misplaced
incentives; (2) distortionary fiscal and regulatory policies; (3) unpriced costs such as air poll ution;
(4) unpriced goods such as education, training, and technologica l advances ; and (5) monopoly

power

. A more recent literature focuses on information

- based market failures including a general
lack of information, information asymmetries, and price signaling. This literature draws upon

advances in behavioral economic s tha t focus on incentives provided by distributions of
information that are less than perfect, but which are arguably commonplace (Jaffe and Stavins,

1994 ) (Table 1.2)

.

“Market barriers” refer to other obstacles that contribute to the slow diffusion and adop tion of

energy

- efficient innovations (Jaffe and Stavins, 1994, Hirst and Brown, 1990, Levine et al., 1995, and U.S. Department of Energy). To the extent that it is in society‟s best interest to use its energy
more efficiently, improve grid reliability, and reduce emissions from fossil fuel combustion, it is
important to understand the full range of obstacles to energy

- efficient technologies. These
barriers include: (1) the low priority of energy issues among consumers, (2) capital market
imperfections, (3) incomplete markets for energy

- e fficient features and products, and (4)
infrastructure longevity.7 Table 1.2 Market Failures and Barriers Inhibiting Energy Efficiency

Market Failures: Flaws in the way markets
operate Market Barriers: Other factors inhibiting rapid adoption

Misplaced incentives including the “principal

- agent” problem L ow priority of energy issues resulting from the fact that energy is often a small part of the cost of owning and operating a building or factory

Distortionary fiscal and regulatory policies including the preferential tax treatment of capital versus operating

costs , fuel subsidies, and the coupling of electricity sales to profits Capital market barriers due in part to the

lower interest rates available to energy suppliers relative to energy

consumers

Unpriced costs such as air and water pollution and greenhouse gas

emissions Incomplete markets for energy efficiency

, compounded by the complexity of design, construct ion, and operation of buildings making it difficult to characterize their energy efficiency

Unpriced goods such as education, training, and technological advances Infr astructure longevity contributing to the
“lock

- in” of incumbent technologies

Monopoly p ower hindering competition

Insufficient, inaccurate and asymmetric

information leading to suboptimal

decision

- making

Apart from barriers, estimati ons of the achievable potential of energy efficiency and the design
of effective policies must also consider economic and social/institutional drivers of energy
efficiency. The business case for energy efficiency varies across market sectors, types of

households, firm size, and region of the country, and it reflects a variety of motivations for using
energy more wisely. Nevertheless, common motivations emerge from the literature, as summarized in the report on “Strategies for the Commercialization and Deployment of

Greenhouse Ga s Intensity

- Reducing Technologie s and Practices” (CCCSTI, 2009):

Volatile and rising energy prices

– “The sustained pain” of rising oil, coal, natural gas,
and electricity prices is motivating a renewed interest in energy efficiency
Environmental concerns and regulations

– Energy

- efficiency investments can tap into
potentially lucrative revenue streams from NO

x and SO 2 offsets in non

- attainment zones,
RES/EERS compliance requirements, and tradable carbon allowances

Demand charges and demand response incentives

– The ability to cut peak electric
load motivates many utilities to incentivize demand

- side management.8

Collateral benefits

– Increased productivity, improved product quality, reduced labor
costs, and enhanced reliability can r esult from energy

- efficiency investments. In addition,
improved stability of financial performance should lead to lower cost of capital, in terms
of both access and cost of financing.
International competition

– When the cost of energy inputs makes a firm unable to

compete, energy

- efficiency improvements are often sought out. Energy cost reductions
can also be used to offset other rising costs, such as labor.
Corporate sustainability

– Energy efficiency as a climate change mitigation strategy
offers a way t o boost shareholder/investor confidence, profit from future legislation, and

access new markets.
Consumer and shareholder activism, good corporate governance, and reputation

management

– these have proven to be a strong motivators for energy

- efficiency and
other investments in sustainability.
Insurance access and costs, legal compliance, and concerns regarding fiduciary duty

– All represent additional potential drivers.

Most of these drivers were highlighted in the study of Real Prospects for Energy Efficiency in

the United States (National Academies, 2009 ).

N umerous policy levers are available to address traditional market failures and barriers and to leverage drivers for energy efficiency (Geller, 2002; Brown and Sovacool, 2011 ). One succinct typology of policies identifies three ways of exploiting the achievable potential for energy efficiency: (1) financial assistance

, including subsidies, bulk procurements, and loan guarantees ; (2) regulatory requirements

, such as codes

, standards

, and cap a nd trade programs ; and (3) information programs including labeling, education

, R&D support

, and workforce training (Brown et al., 2011) . The costs and benefits of different policy approaches will vary by market sector and technology opportuni ty, as reflected in subsequent chapters.9 2. METHODOLOGY

2.1 Overview of Approach

A portfolio of twelve energy

- efficiency policies is modeled with t he Georgia Insti tute of Technology‟s version of National Energy Modeling Systems ( GT

- NEMS ) to estimate the long

term achievable potential in the Eastern Interconnection . Supplemental spreadsheet analysis is

used to estimate t he levelized cost of electricity (LCOE) that could potentially be saved, based on GT

- NEMS output for each of the financial , regulatory and information policies. Similarly, estimates of carbon dioxide emissions and reductions in fuel consumption for all end

- use sectors
can also be extracted from GT

- NEMS output. Based on this information, the benefit of reduced

carbon emission s and avoided criteria air pollutions will be evaluated for climate change and environmental impacts in a benefit

- cost analysis.

2.2 National Energy Modeling System

GT

- NEMS is the principal modeling tool used in this study, supplemented by spreadsheet

cal culations. Specifically, we employ the version of NEMS that generated EIA‟s Annual Energy

Outlook 2011 (EIA, 2011a), which forecasts energy supply and demand for the nation out to 2035. NEMS models U.S. energy markets and is the principal modeling tool used to forecast future U.S. energy supply and demand. Twelve modules represent supply (oil and gas, coal, and renewable fuels), demand (residential, commercial, industrial, and transportation sectors), energy conversion (electricity and petroleum markets), and macroeconomic and international energy market factors. A thirteenth “integrating” module ensures tha t a general market equilibrium is achieved among the other modules. Beginning with current resource supply and price data and making assumptions about future use patterns and technological development, NEMS carries through the market interactions represent ed by the thirteen modules and solves for the price and quantity of each energy type that balances supply and demand in each sector and region represented (EIA, 2009) . Outputs are intended as forecasts of general trends rather than precise statements of what will happen in the future. As such, NEMS is highly suited to pro jecting how alternative assumptions about resource availability, consumer demand, and policy implementation may impact energy markets over time.

The GT

- NEMS “Reference case” projections are based on federal, state, and local laws and regulations in effect at the time of the analysis. The baseline projections developed by the EIA

via NEMS are published annually in the Annual Energy Outlook , which is regarded as a reliable reference in the field of energy and climate policy. We have used GT

- NEMS to perform sc enario analysis under a consistent modeling framework in order to compare policy options to the Reference case projections.

GT

- NEMS also provides estimates of the carbon intensity of electricity generation based on generation resources over time. The ben efit of reduced CO 2 emissions are estimated by subtracting the emissions in the Reference case from the policy scenario and then multiplying by the “social cost of carbon” (SCC). The SCC is an estimate of the monetized damages caused by a

metric ton of CO 2 emitted in a given year. The social cost of carbon used in this analysis is the10

central value of the U.S. Government Interagency Working Group on the Social Cost of Carbon (EPA, 2010), growing from $23/metric ton in 2011 to $47/metric ton in 2050 (all va lues are in

2008

- $ and account for global avoided damages).

Present

- value calculations for the levelized cost of electricity use a 3% discount rate

, consistent with Office of Management and Bu dget guidelines (OMB, 2002; 2010 )

. The OMB guidelines

recommend the use of 3% discount rates when ev aluating regulatory proposals from a social perspective. This is less than the 10% value used in some other energy

- efficiency studies such as McKinsey and Company‟s analysis (Granade, et al., 2009) , which focus on investment decision

making from a private market perspective

.

2.3 Energy Efficiency Policy Levers

A suite of twelve policies was selected to estimate the achievable potential for energy efficiency: four regulatory policies, five financial policies, and three information policies (Table 2.1) . For residential buildings, five policies are desig ned to reduce the up

- front cost of end

- use technologies and energy

- efficient building envelope s . For commercial buildings, three policies

are designed to expand i nvestments in energy

- efficiency improvements. In the industrial sector, the policies target motor systems and other efficiency improvements in various industrial processes, as well as combined heat and power (“ CHP ”) systems to make use of waste heat.

In the policy scenario, financial incentives are offered to energy

- efficient technologies in the

end

- use sectors. For residential buildings, 25 energy

- efficient home appliances and equipment

were selected from the NEMS technology profile. Financial incentives (either a subsidy or zero

interest loan) were offered to cut down capital costs of these selected technologies. Similarly, 110 commercial building technologies were selected and offered flexible financing options. For industries, energy

- efficient technolo gies include combined heat and power (CHP) systems which utilize waste heat, and electric motor systems which satisfy the Energy Independence and Security Act of 2007 minimum efficiency requirement. Industrial energy efficiency policies provide rebate s and subsid ies for CHP systems and premium motors.

Regulatory policies impose standards and mandates to enhance efficiency improvements. Building energy codes were modeled to represent shell efficiency improvements for residential and commercial buildings. For residential buildings, compliance with new building codes w as assumed implicitly when less stringent codes were forced to gradually retire . In the commercial sector, the whole building stock is assumed to progress in shell efficiency gradually to reach th e efficiency level equivalent to the most recent code, ASHR A E 90.1

- 2010, with 100% compliance

in 2035. Appliance standard s were applied to remove inefficient residential technologies from the market. Motor standard were established to increase efficiency standard for electric motors

used in industries.

In addition, a broad set of information instruments w as explored in the policy scenario. For homes, the Market Priming po licy is a combination of several information options, including mandated disclosure of home energy consumption or performance at the point of sale or lease of

a residential unit , home rating, green labeling, and other technical assistance features such as home energy audits and assistance with green leases, etc. For commercial buildings, the benchmarking policy requires utilities to submit whole building energy consumption data to a1 1

uniform database accessible by building owners

. Studies suggest that provid ing information can

reduce discount rates used in investment decisions from 3% to 22% (Coller and Williams, 1999; Goett, 1983) . Thus, adjusting discount rate was the NEMS lever used for modeling Market Priming and Benchmarking. For industries, Advanced Man ufacturing Initiative is the

information

- based policy that provides information about efficiency opportunities for plant utility upgrades . T he impact of t his information is based on the potential efficiency improvements from the Industrial Assessment Cente rs (IAC) database

.

Table 2.1. Selected Policies for Electric End

- Use Efficiency

Sector Policy Type Policy Scenario Description

Residential Financial Appliance

Incentives Providing a 30% subsidy to cut down capital

costs for the most efficient technologies

Financial On

- Bill

Financing Offering zero

- interest loans for the most

efficient technologies

Regulatory Building Codes Adding four new building codes to improve

shell and equipment efficiency

Regulatory Aggressive

Appliance Policy Accelerate market penetration for energy

efficiency technologies by eliminating the least

efficient ones from the market

Information Market Priming R educ ing high discount rate s (10

- 50%) to 7%

for private investment in efficient technologies

Commercial Financial Financing Offering flexible financing options to lower the

up

- front cost s of highly energy

- efficient equipment

Regulatory Building Codes Requiring h igher building shell efficiency and more stringent standards on space heating and cooling equipment

Information Benchmarking Requiring utilities to submit whole building energy consumption data to a uniform database accessible by building owners

Industrial Financial Motor Rebate Providing a 30 % subsidy for premium motors

that satisf y the minimum efficiency requirement of EISA 2007

Regulatory Motor Standard New motor standard in 2017 requiring 25% efficiency improvement and higher system savings

Financial CHP Incentives Offering a 30% investment tax credit for

industrial CHP systems for 10 years

Information Advanced

Manufacturing

Initiative Promoting plant utility upgrades by identifying

efficiency opportunities with cost assessments and estimations of potential energy savings

.12

All twelve selected policies were modeled in GT

- NEMS with energy consumption forecast s up to 2035. The specific levers used to model each of these policies is described in subsequent chapters and appendices.

2.4 Calculation of Levelized Cost of Electricity

The LCOE of each policy was calculated to estimate the co st of achieving the electricity

- savings potentials in individual policy scenarios. The calculation of LCOE is based on the total resource cost test, where cost s include the incre mental private investment i n energy

- efficient equipment and improvement s , program costs for incentives and loans, and program administrative costs.

We estimate the magnitude of technology investment costs differently for the three end

- use sectors. In the residential sector, costs are an output of the GT

- NEMS model , and are a function of the purchase costs assumed for a range of technologies in different equipment classes

. I n the

commercial sector , the investment costs are estimated separately for new purchase s
, replacements, and retrofits for approximately 350 technologies uniquely defined by technology type, fuel use, purchase price, energy efficiency, and time frame of availability in the marketplace. Total costs are calculated us ing a spreadsheet analys is developed by Georgia
Tech‟s C limate and Energy Policy Laboratory (CEPL) . In each case, the calculation is based on

GT

- NEMS estimates of service demand for energy (SD), costs per unit of SD, and capacity

factor s (see Appendix B). In industry, costs for CHP investments are based on the installed costs per KW of capacity for eight different types of CHP systems

. These costs per kW of installed capacity are specified in GT

- NEMS. Other costs are based on multipliers derived from audit

information produced by DOE‟s Industrial Assessment Centers and the “ Save Energy N ow ”

program as developed by CEPL and described in Brown, et al. (2011)

.

The LCOE is the weighted average cost, calculated by dividing the present value of total costs by

tota l electricity savings , following the methodology described by the Electric Power Research

Institute (EPRI, 2009) . In addition to electricity benefits, natural gas savings are also generated for energy users impacted by energy efficiency policies. We single d out the part of the cost

needed to achieve electricity savings by proportionate total cost to the value of electricity versus natural gas savings through 2035. Present

- value calculations for the levelized cost of electricity

use a 3% discount rate from a social perspective and 7% discount rate for the private

- sector

assessment . This is consistent with Office of Management and Bu dget guidelines (OMB, 2002;

2010 )

, which recommend the use of 3% and 7% discount rates when ev aluating regulatory proposals. Our use of a 7% discount rate for evaluating the private perspective is less than the 10% value used in some other energy

- efficiency studies such as McKinsey and Company‟s analysis (Granade, et al., 2009). Since the social appropriateness of policies is being examined, a sensitivity was conducted where all cos ts were discounted at 3% for LC O E calculation. Other main assumptions in the LCOE calculation include:

A voided transmission and distribution losses are included as part of electricity savings . A multiplier of 1.07 was applied to electricity savings to account for the benefit of avoided electricity related losses. Program admin istrative costs are estimated to be $0.13/MM Btu energy saved , unless specified otherwise (s ee Brown, et al., 2009, for details on these estimates)

.13

We assume the twelve policies start from 2012 and end at 2035. Any costs stimulated from the policies occur through 2035. Electricity savings are then modeled to degrade at a rate of 5% after 2035, such that

benefits from the policy have ended by 2055.

A spreadsheet tool was developed to estimate the LCOE for each of the twelve energy

- efficiency policy options.

2 .5 Method for Deriving State

- Specific Estimates

A “proportioning” methodology is used to produce state specific el ectricity

- demand forecasts for the 39 individual states that comprise the EIPC region. The forecasts are derived from the Annual Energy Outlook 2011 (EIA, 2011) reference projections. The methodology is based on the approach used by Hadley (2003) in a study of the energy efficiency and renewable ene rgy potential in North Carolina.

EIA ’ s Annual Energy Outlook 2011 provides energy consumption and production projections for the nation, its four census regions, and its nine census divisions

. To create stat e estimates, we

combine the electricity demand values from EIA ’ s Annual Energy Outlook 2011 for all states in

each census division and calculate the share of the total that each state constitutes

. The process is described in greater detail through the bulleted list below.

The previous studies that have developed measurement and verification protocols to estimate the energy consumption of individua l states have shown that the energy use per capita o f each state in each sector has been relatively co nstant over the last decade (Brown, et al., 2010) . This previous work supports our assumption that the energy use by state generally increases proportionally to population growth. The methodology used here involves four steps:

First, we calculate the average electricity consumption from 200 5 to 200 9 with the historical energy consumption by sector and by state from the EIA‟s State Energy Data System (EIA, SEDS, 2011).

. Then, we derived the share of each s tate to the total division for residential, commercial, and industrial electricity usages, respectively

. We assume that population distribution will continue with current trend and the state share of electricity consumption can be used to proportionate con sumption to the state

levels. Finally, using the state specific percentages, we allocated out the regional projections of the Annual Energy Outlook 2011 (EIA, 2011) to each state.

A more precise “proportioning” methodology would reflect the fact that stat es with strong

energy

- efficiency policies would not increase their energy consumption in lock

- step with
population growth. While there is an emerging literature that rates the effectiveness of state

- level

energy

- efficiency policies (Foster, et al., 2012), the link to future energy and population growth

rates has not been established.1415



### 3 . ENERGY

- EFFICIENCY POTENTIAL IN RESIDENTIAL BUILDINGS

In 2010, the residential sector in the Eastern Interconnection consumed 1,069 TWh of electricity

, which represents 74.0% of national residential electricity consumption

. T he region is home to 68.6% of the U.S. population, indicating a higher than national average residential electricity consumption per capita. In fact, t he per capita residential electricity consumption in the Eastern

Interconnection was about 5,070 k Wh in 2009 (EIA, SEDS, 2010). In 2009, the households in the region consumed 990 TWh of electricity, slightly l ess than in 2008 due to the country‟s

economic downturn . The 990 TWh of electricity consumption accounts for 68% of the total residential energy consumed in the region in 2009 (15,600 TBtu) (Fig 3.1): delivered electricity consumption accounts for 22 % and electric system ene rgy losses make up another 46%.

Figure 3.1 Residential Ene rgy Consumption in the Eastern Interconnection in 2009 (Source: EIA, SEDS , 2010 )

R esidential electricity demand is forecast to be lower than the 2010 level over the next 10 years, but by 2035, EIA forecasts that residential electricity demand will grow to 1,166 TWh in the region, 9% higher than in 20 10 (Figure 3.2).16

Figure 3.2 Reference Case Forecast of Residential Electricity C onsumption in the Eastern

Inte rconnection (EIA, 2011)

This chapter examines the achievable potential for increased residential energy efficiency from five policies. These policies target electricity consumption from all segments of residential buildings, including the building shell, H VAC equipment, and household appliances for both existing and new buildings. This chapter explores the electricity savings from these policies and examines the regional patterns by comparing the results across Census divisions. The other

benefits from the energy

- efficiency policies, such as natural gas savings, are also investigated. The last section of this chapter examines the impacts of implementing all five residential policies simultaneously

.

3.1 Overview of Previous Literature on the Energy

- Efficiency Potential in Residential Buildings

Numerous assessments of residential energy

- efficiency potential in the U.S. have been conducted,

at various geographic scales and covering different time frames. Some of these have estimated the impact and cost

- effective ness of implementing new or stronger residential energy policies.

A recent study of state and local programs in Arkansas estimates the total energy efficiency potential in the state to be 15% of the electricity demand, 14% of the natural gas demand, and 2 0%

of the peak demand by 2025. The study focuses on the cost

- effective resources in residential buildings in 2025, which is projected to produce 31 trillion Btu of energy savings (37% of

R eference cas e electricity consumption) . The projected potential can be achieved by efficiency improvements in the building shell, HVAC equipment and other appliances in existing buildings with estimated levelized cost of $5.76/ MMBtu s aved (Neubauer et al, 2011). A different study, however, examines the energy efficiency in residential and non

- profit buildings in Ci ncinnati (Mackres, et al, 2011) and finds that the potential energy savings associated with current efficiency programs is 2.8% of electricity demand and 6.9% of natural gas demand in 2030. Its lower estimates reflect the focus on existing energy

- efficiency programs and not the potential of new policies, which is the focus of our study.

For residential and commercial buildings, Laitner, et al. ( 2012 ) considers technology

advance ment as well as p olicy updates (e.g., energy code, appliance and equipment standards)

and estimated efficiency potential to be 49

- 61% in 2050. More specifically, the electric load

reduction in 205 0 is 73

- 87% from building codes and 38

- 47% from appliance standards. The17

sav ings from building codes are quite large compared with the Arkansas study, which models only 1.9

- 2.2% of energy savings from building codes. This difference reflects the different modeling assumptions and time frames used in these two studies.

Numerous st udies have focused specifically on energy

- efficiency measures in the building s ector

. A recent study on energy efficiency in residential buildings estimates the cost

- effective potential to be 42.5%, with encouraging benefit/cost ratios (Sadineni, France, & Boehm, 2011)

. In a different study, DOE estimated the impacts of its energy efficiency programs and found that the 2005 Building Technology program could save 27% of the energy consumed in residential and commercial buildings by 2030. These savings were evaluated to have the potential of inc reasing employment and reducing the need for capital stock in the energy sector (Scott, et al, 2008)

.

These studie s present a wide range of energy

- efficiency potentials due to differences in forecast

methods , and s tate

- and local

- level estimates tend to generate projections slightly smaller than

national

- level studies. The difference calls for the attentions to the ch oice of forecast approaches

and the definition of potential (technical, economic , or achievable potential).

3.2 Residential Efficiency Policy Options and Modeling A pproach

Energy consumption in residential buildings comes from space heating and cooling, wa ter heating, lighting and other end

- uses, which are closely related to the efficiency of building shell, HVAC equipment and household appliances. Many state

- , local

- and utility

- level programs and policies are designed to improve energy efficiency in resid ential buildings. To assess the electricity

- saving s potential from all building components, a policy bundle of f ive efficiency policies was designed, including: Appliance Incentives, National Building Codes, On

- Bill Financing, an Aggressive Appliance policy, and Market Priming. Two of these policies are regulatory (energy codes and the aggressive appliance policy), two involve financial assistance (appliance incentives and on

- bill financing), and one focuses on providing building performance

informatio n (market priming). Table 3.1 summarizes each of these policy scenarios, along with

the GT

- NEMS levers used to model it. Further details are provided in Appendix A.

The Appliance Incentives policy offers a 30% subsidy to reduce the capital costs of the mo st efficient technologies in residential buildings based on the technology inventory in GT

- NEMS

, where the best type of technology in each of the equipment classes is selected (but only when there is more than one type specified in GT

- NEMS) . A list of the 25 incentivized technologies can be found in Table A.1. They cover space heating, space cooling, clothes washing, dishwashing, water heating, cooking, clothes drying, refrigeration, and freezing. Capital costs of these technologies (from the rtekty input f ile in GT

- NEMS) were reduced by 30% in this policy

scenario.

The Aggressive Appliance Policy uses appliance standards to eliminate the least efficient appliances from the marketplace. Appliance standards impose minimum efficiency requirement s

on equipment manufacturers. With the standards, certain appliances and equipment being

manufactured must meet the minimum efficiency requirement s before enter ing the market. EPA

2005 and EIS A 2007 are both examples of federal appliance standards. These two federal rul es cover a broad set of home appliances and building equipment, as well as motor systems for the

industrial sector . Besides the federal rules, 11 states have adopted appliance/equipment standards with efficiency requirements for state selected efficiency t echnologies (DSIRE, 2012)

.18 Table 3.1 GT

- NEMS Lever for Residential Efficiency Policies

Policy type Policy Policy Scenario Description GT

- NEMS Lever

Financial Appliance

Incentives 30% subsidy to reduce the capital costs

for the most efficient equipment Equipment cost in rtekty.txt

Regulatory Aggressive

Appliance Policy Eliminating the least efficient equipment Equipment available years in rtekty.txt

Regulatory National Building

Codes Additional three rounds of improvement

of building codes Building shell profile: rtektyc.txt

Financial On

- bill Financing Offering low interest loans for high efficient appliances through on

- bill financing options Source code + rtekty.txt

Information Market Priming 7% hurdle rate for the most efficient equipment Equipment choice parameters in rtekty.txt

Appliance/equipment standards tackle the first cost sensitivity barriers by preventing consumers from buying the least

- cost, least

- efficient models . The observed savings from adopted federal or state appliance and equipment efficiency standards have been shown to be significant (Geller, 1997; Meier, 1997) . The appliance standard programs are cost

- effective in promoting energy efficiency in many case stu dies (Geller, 1997; Vine, du Pont, & Waide, 2001) . A recent study of high

- efficient appliances from the U.S. Department of Energy's Appliances and Commercial Equipment Standards Program estimates the potential of cost

- effective improvement ranging typically from 15% to 20% (McNeil & Bojda, 2012)

.
Building Energy Codes generally impose efficiency requirements on building shell, HVAC and lighting equipment for new buildings. For residential buildings, states tend to ado pt the International Energy Conservation Code (IECC), a prototypical codes developed and updated periodically by the International Code Council. Most of the states have adopted the IECC 2003 codes (or equivalent) or more stringent codes, with only 9 of the states having no statewide code for residential buildings (Figure 3.4 ) (DOE, 2012) . Maryland is the first state to adopt the most

recent 2012 codes for residential bu ildings (DOE, 2012)

.
This policy adds four more efficient codes to the suite of five less efficient codes available in the Reference case. The policy case also forces early retirement of the less stringent codes, as described in Appendix A. This policy recognizes the regional difference s in code adoption based on historic patterns (Figure 3. 4 ).19

Figure 3.3 Adoption Status of Residential Building Energy Codes as of April, 2012 (Source: DOE, 2012)

Figure 3. 4 Building Energy Code Retirement Years by Census Division20

Building energy codes have been shown to generate large energy savings in various regions

under diverse climates conditions (Chirarattananon, et al, 2010; Fayaz & Kari, 2009) . Studies on building code programs in the U.S . also exhibit evidence of cost

- effectiveness in promoting energy efficiency: generating significant savings in energy expenditure with relatively low costs (Brown, 1993; Nelson, 2012)

. However, c ompared to other energy

- efficiency programs, participation rate s and voluntary complian ce of building codes programs are rela tively low, making it necessary to run assistance tools and supporting programs to increase compliance (Brown, 1993; Reichard & Papamichael, 2005)

.

On

- bill Financing refers to a financial product that is serviced by, or in partnership with , a utility company for energy efficiency improvements in building s , and repaid by homeowner s on their monthly utility bills (Bell, Nadel, and Hayes, 2011). While these programs req uire the participation of a local utility (investor

- owned or municipal), they also often benefit from support of local governments in terms of legal authority and the initial financing. They provide a stream of revenue to the utility or financing institut ion and, as part of the utility bill, are not burdensome

for the customer s where long

- term savings outweigh upfront costs. On

- bill financing opportunities through utilities are also expanding, as shown in Figure 3.5 and, despite a variety of implementation barriers

, energy savings have expanded with minimal defaults (Bell, Nadel, and Hayes, 2011).

Figure 3.5 States with On

- Bill Financing Programs
Source: Bell, Nadel, and Hayes, 2011

The policy modeled here offers zero

- interest loans to upgrade to the most efficient residential appliances and equipment. The technologies eligible for zero

- interest loans are the same technologies that are eligible for appliance subsidies as listed in Table A.1.21

Market Priming programs offer information and education to r educe consumers‟ perception of the uncertainties and investment risks associated with new and innovative energy

- efficient technologies. This policy also targets the set of technologies shown in Table A.1, but with a

different GT

- NEMS lever. In this scenari o, market assi stance programs offer consumers information, technical support, and educational demonstration to help them gain better understanding of the energy

- efficient technologies, and therefore lower their perception of risks

which is manifested in a lower private discount rate for the technologies. We modeled this policy by changing the hurdle rates of these technologies to 7%

.
The achievable potential s of energy efficiency from these policies w ere modeled in GT

- NEMS

, which forecasts the future energy consumption out to 2035 . The results of these policies were then compared to the R eference case forecast from AEO 2011

, and the difference between the

R eference case and policy scenario forecasts is the efficiency potential in residential buildings

. T he a chievable potentials were modeled using the GT

- NEMS High Tech scenario.

The GT

- NEMS modeling levers for these five policies were purposely chosen to be relatively independent in terms of their policy impacts (Table 3.1). T he technology profile from the

R e ference case was used to identify the equipment with the highest and lowest efficiencies of the same types. The most efficient equipment was offered either a 30% subsidy as in the App liance Incentives case, or a zero

- interest loan for 10 years as in the On

- Bill Fina ncing case. The least efficient technologies were eliminated in the Aggressive Appliance Standards case. Building Energy Codes policy would add four new codes to the building shell profile to reflect the anticipation of future updates to building energy code , and also forces the less stringent co des to

retire earlier.

3.3 Modeling Results for Individual Residential Policies

The energy consumption forecast of the each policy case w as compared with the R eference case forecast, and the difference represents the achievable energy

- efficiency potential

. Figure 3

. 6 shows the estimated potential of each policy. The percentage number is the reduction in

residential electricity demand as a percentage of the reference case forecast in each year.

In 2035, the electricity savings from these five policies in the Eastern Interconnection ranges from 19.0

– 119 TWh (1.6%

- 10.2% of reference case consumption), while the total electricity savings potential in the nation ranges from 33.4

- 164 TWh (2.1%

- 10.2% of reference case consumption).

By lowering the hurdle rate for efficiency investment through the Market Priming policy

, the residential sector is able to generate the highest level of electricity savings, amounting to 119

TWh in 2035 in th e Easte rn Interconnection (Fig 3.6). An Aggressive Appliance Policy is also able to produce a significant amount of electricity savings, about 42.3 TWh in 2035 in th e Eastern Interconnection . Comparing to the Market Priming and Aggressive Appliance Policy, the other three residential policies are estimated to have smaller but still meaningful efficiency potentials for residential electricity savings, ranging from 19.0

- 25.6 TWh in 2035 in th e region.22

Figure 3. 6 Estimated Reductions in Electricity Demand by Individual Residential P olicies

(Percentages of Residential Buildings Electricity Consumption)

All five policies, except for the National Building Energy Codes, generate electricity savi ngs in the same proportion in the Eastern Interconnection as in the whole country (Figure 3. 6 ). The percentage of electricity savings from National Building Codes in the Eastern Interconnection is only half (or less) of the national percentage saving

. I n the rest of the country, National Building

Codes are able to generate electricity savings of 3.6% in 2020 , 5.9% in 2025, and 7.2% in 2035.

The reason that National Building Codes benefit the Eastern Interconnection less than the other regions is partly d ue to the differences in technology profiles applied to building shell and HVAC systems in different areas. It also reflects different paces of code retirement: the Pacific region moves rapidly to the four new codes and the East South Central region is ass umed to be

the slowes t to adopt new codes (Figure 3.5 ). GT

- NEMS assumes a five

- year lag in adoption between these two regions.

In the Eastern Interconnection , the cumulative electricity savings (2012

– 2035) generated from Appliance Incentives, Building C odes, On

- bill Financing, Aggressive Appliance Standards, and Market Priming policy case s are 382 TWh, 257 TWh, 366 TWh, 531 TWh, and 2,407 TWh, respectively. In the meanwhile, t he R eference case forecasts that electricity demand in the

residential sector w ill increase from 986 TWh in 2011 to 1,166 TWh in 2035

. The cumulative growth in electricity consumption can be offset by the five policies by 24%, 16%, 23%, 33 %, and 149%, respectively. Since th ese policies deal with separate segment s of energy consumptio n in residential buildings, it is plausible to assume that the demand growth of electricity usage can be completely offset by the combination of residential policies.23

3.3.1 Regional D ifferences

In the individual policy scenarios, each policy is assumed to be applied on the national level with regional deviations in implementation details, for example, the adoption years of Building Energy Codes. Local situations, such as the current state policie s and current consumption levels, vary from state to state. Thus, the same policy would likely to generate different efficiency impacts in different regions. Figure 3.7 illustrates the regional differences in electricity savings from each residential polic y, as estimated by GT

- NEMS. To show the electricity savings in the

Eastern Interconnection , Texas was excluded from the West South Central division, using the proportioning method described in Chapter 2.

Figure 3. 7 Electricity Savings by Census Div ision for Individual Policies

(*West South Central Excluding Texas)24

Appliance Incentives, On

- bill Financing, and Market Priming share similar regional patterns. For each of these policies, the South Atlantic division, which is the largest area with the mos t population, possesses the largest electricity saving potential. The projected savings in the South Atlantic region as a percentage of regional electricity consumption is also higher than the percentage in other regions.

The regional distribution of elec tricity savings from the Aggressive Appliance Policy is more geographically dispersed. While the South Atlantic region has the highest energy savings, the Middle Atlantic and North Central regions also have large savings potential both in absolute

terms an d as a percentage of regional electricity consumption. This more equally distributed pattern may reflect the fact that the energy consumption of appliances is less weather

- dependent than the energy consumption of HVAC equipment.

The National Building Energ y Codes also shows an electricity savings potential that is geographically distributed quite differently from the other residential policies. In 2035, this policy generates 7.9 TWh of electricity savings in the South Atlantic region, and 6.9 TWh of

electri city savings in the West South Central region (excluding Texas). The West South Central region is estimated to reduce 8.2% of its electricity consumption under the national building code policy, which is a much higher percentage than any other region. T he West South Central region receives much high er benefit than other region s due to its unique technology profile. In single

family buildings, the heating and cooling equipment in the WSC region after code implementation generally exhibit s relatively high coo ling efficiency. For example, in houses using heat pumps for air conditioning and heating, the WSC has the highest cooling efficiency at

the lowest cost.

3.3.2 Spillover Benefits

All selected policies aim to promote efficiency in residential buildings, i ncluding the efficient use of electricity and other fuel types. These policies are able to generate significant electricity savings as well as natural gas and fuel oil savings. Table 3.2 illustrates the projected reduction in natural gas consumption that i s estimated to result from each individual policy.25 Table 3.2 Natural Gas Savings from Individual Policies

Reference

Forecast (TBtu) Natural gas Savings (TB tu)

Appliance

Incentives Building

Codes On

- bill

Financing Aggressive

Appliance Policy Market

Priming

2020 EI 4,683 94 39 98 80 230

U S 4,973 127 183 183 138 236

2025 EI 3,612 120 64 134 144 241

U S 4,960 162 245 223 238 353

2030 EI 3,587 144 90 165 201 249

U S 4,953 196 305 302 327 421

2035 EI 3,530 159 110 186 244 245

U S 4,902 218 357 313 397 361

Unlike the policy

- specific estimates of electricity savings potential where Market Priming dominated, Aggressive Appliance Policy is the residential policy with the largest natural gas saving potential in the Eastern Interconnection as well as in the whole nation. The other four policies are also able to generate significant natural gas savings. Building Energy Codes offers the smallest natural gas savings in the Eastern Interconnection , but large savings in other areas of

the nation. The observation of natural gas savings for Building Codes is consistent with its regional distribution of electricity savings.

The policies are also able to generate savings in fuels other than electricity and natural gas, such as fuel oil, diesel, and pr opane. Table 3.3 shows the potential savings in total energy, which is the projected reduction in total residential energy consumption. These savings were calculated without including the avoided electricity related losses. For the saving potential in tota l energy consumption, the difference between Market Priming and other residential policies is not as big as the difference in electricity saving potentials. Market Priming stands out differently from other policies because it imposes a big push for energy savings from the beginning of the program, while energy savings in other policy cases grow steadily with program implementation

time.

Table 3.3 Total Energy Savings from Individual Policies, Residential Sector

(TBtu, avoided electricity related losses not included)

Appliance

Incentives Building

Codes On

- bill
Financing Aggressive

Appliance Policy Market

Priming

2020 210 260 260 210 830

2025 280 350 350 360 910

2030 330 430 420 490 930

2035 370 510 480 600 94026

3.3.3 Cost Estimation

The Appliance Incentives policy would incur two types of costs: the, private investment which is the expenditure spent by residential consumers to purchas e equipment , and the public costs that include the cost of subsidizing the most efficient technologies , and program admin istrative cost s

. T he t otal cost of the policy is the sum of bo th the private and public costs , and it is estimated to
be $ 2.9 billion in 2035. The levelized cost of energy of this policy is estimated to be $0.13/MM Btu energy saved and the levlized cost of electricity (LCOE) in this policy case is estimated at

6.7

- 8.0 cent

/kWh . Details of cost estimations associated with Appliance Incentives are shown in

Table 3.4.

Table 3.4 Cost Estimations from Appliance Incentives

Cost ( Billion $2009) a 2020 2025 2030 20 3 5

Private cost

- 1.37

- 1.01

- 0.73

- 0.53 Subsidy Cost 4.51 4.25 3.83 3.42

Admin istration Cost 0.01 0.01 0.01 0.01

Total 3.15 3.25 3.11 2.90
LCOE (cent/kWh) 6.7 b

- 8.0

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

For Building Energy Codes, the p rivate investment is the incremental cost on equipment expenditure plus the installation cost for improvements to the building shell. By promoting investments in more thermally efficient envelopes, HVAC equipment can be down

- sized, resulting in lower equipment expenditures relative to the Reference case . There is no p ublic cost except for the program admin istrative cost s

. This policy assumes cost associated with building

code enforcement would be represented by the budget of each state hiring their building code officials and inspectors. The administrative costs are based on each state adding one

administrative office run at $150,000 per annum budget and one code official at $75,000 salary

per annum. It also includes two additional building code inspectors for the verification of every 100 million square feet in the state at $75,000 per year (Brown, et al., 2009). The LCOE in this policy case is estimated to be 0.5

- 0.8 cent /kWh . Details of cost estimations associated with Building Codes are shown in Table 3.5.

Table 3. 5 Cost Estimations from Building Codes

Cost ( Billion $2009) a 2020 2025 2030 2035 Equipment Expenditure

- 0.02

- 0.02

- 0.01

- 0.01

Shell Installation C ost 0.33 0.28 0.39 0.25

Administration Cost 0.02 0.02 0.02 0.02

Total 0.33 0.29 0.40 0.26
LCOE (cent/kWh) 0.5

- 0.8 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

In the On

- bill Financing policy, p rivate investment is the expenditure for purchasing equipment

. Loan cost is the initial seed mo ney put into the program for zero

- interest loans . Progr am27

admin istrative cost is estimated as $0.13/MM Btu energy saved . The LCOE in this policy case is

estimated to be 6.6

- 7.4 cent /kWh . Details of cost estimations associated with On

- bill Financing

are shown in Table 3.6.

Table 3. 6 Cost Estimations from On

- Bill Financing

Cost ( Billion $2009) a 2020 2025 2030 2035

Private cost 0.95 0.64 0.40 0.25

Loan Cost 1.48 0.02

- 0.09 0.01

Admin istrative Cost 0.01 0.01 0.01 0.01

Total 2.44 0.67 0.32 0.27
LCOE (cent/kWh) 6.6

- 7.4 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

The Market Priming policy lowers the hurdle rate to 7% for the most efficient equipment types. Private investment is the expenditure for purchasing equipment . There is no p ublic cost except
for program admin istrative cost , estimated as $0.13/MM Btu energy saved. The LCOE in this policy case is estimated to be 2.7

- 3.6 cent /kWh . Details of cost estimations associated with Market Priming are s hown in Table 3.7.

Table 3. 7 Cost Estimations from Market Priming

Cost ( Billion $2009) a 2020 2025 2030 2035

Private cost 6.91 3.76 2.90 1.44

Administration Cost 0.03 0.03 0.02 0.02

Total 6.94 3.79 2.92 1.46
LCOE (cent/kWh) 2.7

- 3.6 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

Similar to the Market Priming policy, the cost estimation for Aggressive Appliance Policy has private cost from the exp enditure for purchasing equipment , and public cost from program

administrative cost s estimated as $0.13/MM Btu energy saved . The LCOE in this policy case is

estimated to be 0.6

- 0.7 cent /kWh . Details of cost estimations associated with Aggressive Appliance Policy are shown in Table 3.8.

Table 3. 8 Cost Estimations from Aggressive Appliance Policy

Cost ( Billion $2009) a 2020 2025 2030 2035

Private cost 0.25 0.16 0.11 0.08

Administration Cost 0.01 0.01 0.01 0.01

Total 0.26 0.18 0.13 0.09
LCOE (cent/kWh) 0.6

- 0.7 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

3.4 Integrated Residential Policy Bundle28

The projected savings change a little when the residential policies were integrated into one policy bundle. Figure 3.8 shows the efficiency potential estimated by the residential policy bundle . In 2035, the savings potential from the integrated policy scen ario reaches 179 TWh (15.4% reduction from the reference consumption) in the Eastern Interconnection

.

Figure 3.8 Residential Electricity Savings Potential in the Eastern Interconnection

With all five energy efficiency policies, homes and residential buildings is estimated to use 987

TWh of electricity in 2035 , which is below the 2010 consumption level. The cumulative savings in electricity is 2,886 TWh from 2011 to 2035, which is 1.8 times the cumulative growth in electricity consumption in the R eference case over the same time period.

For the whole nation, the potential electricity savings from residential policies is 260 TWh (16.1%

of R eference case consumption) in 2035. The cumulative savings residential electricity demand is 4,205 TWh from 2011 to 2035. In the last year of our projection, the total energy consumption per household decreases from 155 million Btu in the reference case to 135 million Btu in our integrated policy scenario.

3.4.1 Electricity Savings Potential by End Use

Most of the potential electricity savings in residential buildings comes from space cooling (46%) and water heating (36%). Space heating has the third highest electricity savings potential, accounting for 9% of electricity savings. Table 3.9 illus trates the potential electricity savings from residential policies by end

- use, which shows that refrigeration, dishwashers and other end

uses are able to generate small amount of electricity savings (2

- 4%) in the nation.

800 850 900 950 1,000 1,050 1,100 1,150 1,200

2010 2015 2020 2025 2030 2035 Electricity Consumption (TWh)

AEO 2011

Residential

Policies 15.4% 179 TWh of

electricity savings in 2035 102 TWh of electricity savings in

202029 Table 3. 9 Electricity Savings Pot ential by End

- use in the US

End

- use Annual Savings (TWh) Cumulative Savings

2020 2025 2030 2035 TWh %

Space Heating 13.0 17.7 21.8 25.2 384.2 9%
Space Cooling 69.0 92.5 108.4 119.3 1,923.2 46%

Water Heating 55.0 77.2 88.5 90.8 1,531.2 36%
Refrigeration 3.0 3.3 3.3 3.2 71.5 2%

Dishwashers 5.8 9.2 11.3 12.0 176.8 4%

Other uses 3.2 5.4 7.1 9.2 118.9 3%

Total 149.0 205.3 240.4 259.7 4205.8

---

The large electricity savings from space cooling and water heating is not driven by technology demand shift between equipment classes, but rather from the shift of different equipment types with different efficiencies within the same equipment class. Table 3.10 shows the technology demand shift of equipment classes in the Eastern Interconnection . There is no significant shift between equipment classes since technology deman d stays almost the same in the R eference case

and the residential policy bundle

.

Tab le 3. 10 Technology Demand Shift from Residential Policies in th e Eastern

Interconnection

End

use Equipment class Reference Case Policy Bundle

2015 2025 2035 2015 2025 2035

Space cooling Central A/C 47% 50% 52% 47% 48% 51%

Electric Heat Pump 11% 12% 13% 11% 13% 13%
Geothermal Heat Pump 1% 1% 2% 1% 1% 2%
Natural Gas Heat Pump 0% 0% 0% 0% 0% 0%
Room A/C 41% 37% 33% 41% 37% 34%

Water heating Distillate Fuel Oil Water Heater 3% 2% 2% 4% 3% 3%

Electric Water Heater 47% 49% 50% 47% 49% 49%

LPG Water Heater 2% 1% 1% 2% 2% 2%

Natural Gas Water Heater 47% 47% 47% 47% 45% 46%

Solar Water Heater 0% 0% 0% 0% 0% 0%

However, technology shifts dramatically among equipment types within the same equipment class. Table 3.11 illustrates the demand shift between four types of electric heat pumps in the

Eastern Interconnection . Type 1 and 2 has relatively low efficiencies, w hile type 4 represents high efficient electric heat pumps. Most of the purchases (about 90%) go to type 1 and 2 in the reference case since they have relatively low costs. However, in the integrated poli cy case, we offer subsidies, no

- interest loans, and market priming programs for the most efficient electric heat pumps, type 4. The result is that, over 95% of the purchase goes to type 4 instead of type 1&2 in the policy bundle scenario. It is clear that the efficient equipment types dominant the market, o ffering great electricity savings in residential buildings.30

Table 3. 11 Demand share of Electric Heat Pumps for Cooli ng in the Eastern

Interconnection

Efficiency Reference Case Policy Bundle

2015 2025 2035 2015 2025 2035

Type 1 2.26

- 2.40 67% 70% 69% 0% 5% 2%

Type 2 2.40

- 2.58 22% 20% 20% 1% 0% 0%

Type 3 2.75

- 2.80 9% 8% 9% 1% 0% 0%

Type 4 3.11

- 3.19 2% 2% 2% 98% 95% 97%

3.4.2 Potentia l for Natural Gas Saving and Carbon Dioxide Emission Reductions

In addition to reducing the demand for electricity, the residential policies are also able to generate significant savings in natural gas and other fuel types. With the policy bundle , the natural gas savings reach 621 TBtu in the Eastern Interconnection , w hile savings in total energy consumption reach 1,291 TBtu in the region (Table 3.12). The savings in the Eastern

Interconnection represents 63% of national savings.

Table 3. 12 Potential savings of na tural gas and total energy in the Eastern Interconnection

Natural gas savings (TBtu) Total Energy Savings (TBtu)

2020 2025 2030 2035 2020 2025 2030 2035

EI 390 515 587 621 761 1,039 1,204 1,291

US 668 870 995 1,069 1,220 1,630 1,880 2,040

Carbon emissions from the consumption of electricity and other fuels will be reduced since the demand of primary energy drops in the integrated policy scenario. Cumulatively, the reduction in carbon emission from electricity consumption is 1,136 million to nnes in the Eastern

Interconnection from 2012 to 2035, while total reduction in emission from primary energy is 1,727 million tonnes in the region (Table 3.13). This represents about 66% of total emission

reductions in the nation.

Table 3. 13 Potential Reduction in CO 2 Emissions from Residential Policies

Carbon Emission Reduction (Million Tonnes)

From electricity From primary energy

2020 2025 2030 2035 2020 2025 2030 2035

EI 39 57 67 72 60 86 100 107

US 56 80 93 100 93 127 148 159
Note: Primary energy includes all fuel types.

3.4.3 Additive Effects of the Residential Policy Bundle

Based on the integrated policy scenario, each of the five residential policies was taken out of the integrated policy scenario one

- at

- a

- time to exam ine their contribution to the estimate of31

efficiency potential, assuming that the policy environment already has implemented the other residential policies. It is plausible that, when only one policy is implemented, the estimate o f potential savings will b e greater than when it is analyzed using this “one

- off” approach. This is because there are overlapped focuses among the policies, and when they interact with each other, the synergetic effect would result in a smaller energy savings than just simple addition of sa vings from individual policies. If this is true, then the electricity savings potential estimated by the integrated GT

- NEMS analysis would be lower than the sum of the savings from the individual policies. For example, bec ause the estimated savings are sig nificantly higher in the policy bundle

, the rebound effect will likely be higher as well, since energy efficiency policies tend to lower electricity prices, which prompt a rebound in overall consumption. In addition, the potential represented by the variou s policies may be duplicative. This duplication is a function of overlap in the end uses and market segments that the policies target.

Table 3.14 shows the impact of each policy when they are taken out of the integration one

- at

- a

time, compared with the im pact when only one of the policies is implemented.

Table 3.14 Comparison of Policy Impacts in the Eastern Interconnection

Policy Electricity savings from individual policies (TWh) Reduction in electricity savings

from policy bundle (TWh)

2020 2025 2030 2035 2020 2025 2030 2035
Appliance Incentives 12.7 19.0 23.0 25.6 6.8 11.5 14.3 16.2
Building Codes 7.9 11.8 15.7 19.0 3.0 3.7 4.5 5.2

On

- bill Financing 13.5 17.6 20.6 22.4 3.9 2.7 1.8 1.5
Aggressive Appliance Policy 16.0 25.6 34.3 40.3 6.2 13.1 20.3 27.3
Market Priming 99.8 113.9 117.0 118.8 62.0 81.2 87.0 83.4 Note: the reduction in electricity savings from integration by each policy is the decrease of savings by turning one policy off, compared with the full integration scenario.

It is clear that the significance of individual policy diminishes when integrated into the integrated residential policy scenario. For example, Appliance Incentives alone can save the region 25.6 TWh of electricity in 2035. But, the all

- but

- Appliance Incen tives scenario only indicates 16.2 TWh savings in 2035. The amount of potential savings from each of the five policies when they are taken out from integration is 30

- 93% lower than the potential savings from each individual policy scenarios. The potential of On

- bill Financing would diminish the most, while Market Priming is successful in retaining 70% of the potential from its individual policy scenario.

3.5 Summary and Discussion

The five polices selected in this chapter target the potential to impro ve the efficiency of energy

use in residential building s . A combination of the five policies can generate significant savings

, more than offsetting the growth in electricity dem and between 2012 and 2035. Each of these policies is associated with relatively lo w levelized costs, ranging from 0.011/kWh for Market Priming, to $0.137/kWh for On

- bill Financing. Three of the policies have LCOEs that are lower than the retail electricity price for the residential sector.

In addition to the electricity savings, the po licies also produce spillover benefits of large savings of natural gas and other fuels. The savings are not distributed in the Eastern Interconnection32

proportionally to area or population. But rather, local situations, such as current state policies and

en ergy consumption, influence the local savings potential

.

Our estimate of electricity efficiency potential is in the middle of the range of potential

assessme nts from a broad set of studies summarized in our review of the previous literature. It should also be acknowledged that our estimation is the achievable potential driven by possible future energy policies, making it smaller than assessments of technical or economic potential

.
Potential estimation in this study is based on GT

- NEMS forecas t of future energy consumption in the U.S. Our estimation is limited by the GT

- NEMS characterization of the U.S. energy market, especially the residential building sector. In fact, GT

- NEMS modeling of residential buildings is rather constrained by a limite d number of technology choices, GT

- NEMS

characterization of market penetration rates for home appliances and equipment, and other model

limitations. The decision

- making process in GT

- NEMS mimics rational choices based on

benefit

- cost analysis, but it has limited modeling power to reflect behavioral constraints and sub

optimal rationality

.
Although estimation is limited by the chosen modeling tool, electricity savings estimated in this study provides a reliabl e and illustrative assessment of the achievable potential, with some analysis of regional impacts, impacts on other fuels and carbon emissions. Findings in this study are able to assist policy

- makers with detailed information about policy options available for promoting electricity end

- use efficiency i n the Easter n Interconnection

.33



### 4. ENERGY

- EFFICIENCY POTENTIAL IN COMMERCIAL BUILDINGS

In 2010, the commercial sector in the Eastern Interconnection consumed 945 TWh, which was 71.1% of national electricity consumption in commercial buildings (compared with 68.6% of the national population) . The 2009 consumption level was slightly lower, at 923 TWh, due to the country‟s economic downturn. Delivered electricity consumpti on (at 25 % ) combined with electric system energy losses (at 51%) make electricity responsible for 76% of the tota l energy consumption attributed to the commercial buildings sector in the Eastern Interconnection in 2009 (12,900 TBtu) (Fig 4.1)

.

Figure 4.1 Commercial Energy Consumption in the Eastern Interconnection (Source: EIA, SEDS , 2010 )

According to the forecast of the Annual Energy Outlook 2011 by EIA (2011), commercial electricity consumption in the Eastern Interconnection will grow steadi ly at the rate of about 1.3% annually. In 2035, EIA forecasts the commercial electricity demand is 1,337 TWh in the region, 41% higher than the 2010 level (Figure 4.2).34

Figure 4.2 Reference Case Forecast of Commercial Electricity C onsumption in the East ern Interconnection ( Source: EIA, AEO 2011)

This chapter ex amines the achievable potential of energy efficiency from selected policies addressing both existing commercial buildings and new construction. The chapter explores the electricity savings from these policies and examines the regional distribution of the estimated savings potential. The other benefits from the energy

- efficiency policies, such as natural gas savings, are also investigated. The last section of this chapter tests the effects and potentials of integrating all commercial policies together.

4 .1 Overview of Previous Literature on the Energy

- Efficiency Potential of Commercial Buildings

E valuation s of energy efficiency potential ha ve been done by studies across multiple scales and

time frames. McKinsey (Granade, et al., 2009) estimates that the NPV

- positive potential for efficiency savings in the commercial sector is 29% in end

- use energy
, which sums up to $29 0 billion of total energy savings from 2009

- 2020. A state

- level study of state and local programs in

Arkansas estimate d the total energy

- efficiency potential to be 15% of electricity demand, 14% of natural gas demand, and 20% of peak demand by 2025. The st udy focuse d on cost

- effective resources, forecasted to produce 4,700 BBtu (9%) of electricity savings and 12,800 (8%) of natural gas savings by 2025 in the commercial sector. The projected potential can be achieved at a relatively low cost where the estima ted levelized cost of electricity is only $0.02/kWh (Neubauer et al, 2011). Another state

- level study, however, examines the energy efficiency in

residential and non

- profit buildings in Cincinnati (Mackres, et al, 2011). This study finds that the potential energy savings associated with current efficiency programs is 2.8% of electricity demand and 6.9% of natural gas demand in 2030.

Many researchers also study energy

- efficiency measures applied to the building sector . A simulation of 12 prototypical buildin gs in 16 cities provides the basis for assessing the energy efficiency potential in commercial buildings, with cost

- effective estimat es of 20

- 30% on average for new buildings and up to over 40% for some building types and locations (Kneifel, 2010)

.

These studies present a wide range of energy

- efficiency potentials due to differences in ti me frames and forecast ing method s. State

- level estimates tend to generate projections slightly35

smaller than national

- level studies. The difference calls for paying attention to the choice of

forecast ing approaches and potential

- type for evaluation (technical, economic , or achievable).

4 .2 Commercial Efficiency Policy Options and Modeling A pproach

Energy consumption in commercial buildings comes from space heating and cooling, water heating, lighting and other end

- uses, which are closely related to the efficiency of building shell, HVAC equipment and other appliances. Many state, local, and utility

- level programs and policies are designed to improve energy efficiency in commercial buildings. To assess the electricity saving potential from all building components, a policy bundle of three efficiency policies was designed, including: Benchmarking (an information policy), Flexible Inn ovative Financing (a financing policy), and National Building Codes (a regulatory policy). Table 4.1 summarizes each of these policy scenarios, along with the GT

- NEMS levers used to model it. Further details are provided in Appendix B.

Table 4.1 GT

- NEMS L ever s for Commercial Energy Efficiency Policies

Policy type Policy Scenario description GT

- NEMS

Lever

Information Benchmarking Lower the time preference premium for commercial technologies

. Kprem.txt

Financial Flexible

Innovative Financing A 30% subsidy was applied to the capit al costs of

the winning technologies under Carbon Tax Ktek.xml

Regulatory National

Building

C odes Revised factor of efficiency improvement in

2035 for new buildings and major renovations KSHEFF.txt

Ktek.xml

The Benchmarking policy focuses on giving building owners in the country access to baseline information on their building‟s energy consumption , as described in Cox, Brown, and Sun (2012)

. This could be accomplished by requiring utilities to submit energy data in a standard format to a widely used database, such as Portfolio Manager, which currently maintains information on hundreds of thousands of buildings in the U.S., submitted by building owners

, managers , and

utilities . Using existing software packages, com bining the meter data

, tenants within a building could provide a “virtual building meter,” allowing for building

- wide analysis. The data would then be available to the building owner and the utility and maintained by the Environmental Protection Agency (EP A).

Benchmarking helps building owners and tenants focus awareness on poor

- performing buildings and subsystems, and enables high

- performing buildings to participate in various certification programs that can lead to higher occupancy rates, rents, and prope rty values. It requires an expenditure of time and effort, but this can be minimized by using Portfolio Manager as the benchmarking tool. In fact, every one of the existing programs, including the international effort between the U.S. and Canada, uses Port folio Manager (Figure 4.3). The Institute for Market

Transformation summarized the recent experiences of nine current U.S. programs (Burr, Keicher,36

and Leipziger, 2011). As a result of program reviews and in

- depth stakeholder discussions, a

series of best practices were recommended for outreach and education, benchmarking, compliance, data quality, energy consumption data, and disclosure. For benchmarking, the main recommendation is to follow EPA guidelines surrounding the use of Portfolio Manager.

Figure 4.3 Mandated Disclosure and Benchmarking Efforts in the United States

Source: www.IMT.org

Benchmarking has the potential to reduce or eliminate information asymmetries in the marketplace and to reduce the discount rates used by consumers in the sector. Thus, we model this policy by lowering the time preference premium for energy

- efficient comme rcial technologies, as described in Appendix B.

Flexible Innovative Financing is a federal policy that would support state and local initiatives via the financially sustainable mechanisms of loan loss reserves, property assessed clean energy (PACE) tax lie ns, revolving loans, performance contracts, and on

- bill programs , as described in Deitchman, Brown, and Wang (2012). The lack of attractive financing options remains one of the most significant barriers to deep energy efficiency upgrades in private commerc ial buildings (Prindle, 2010; Johnson Controls/IFMA, 2010; Kammen, 2009). Recognizing this barrier to energy efficiency investments, many states passed legislation enabling the creation of PACE districts between 2008 and 2010, as shown in Figure 4.4.37 F igure 4.4. PACE

- Enabled States
Source: DSIRE (2012)

A fundamental assumption of this analysis is that flexible innovative financing could be modeled as a subsidy on the first costs of energy

- efficient technologies as firms consider the net present

value o f an investment. Reducing interest rates, entering into ESPCs, or financing through the other mechanisms have the long

- term impact of making upfront investments more economically attractive. We use the results of Brown, Cox, and Sun (2012) to identify the subset of 350 technologies characterized in the GT

- NEMS commercial module that would be modeled with a 30% subsidy. Specifically, we subsidize the 107 technologies that attract greater energy service demand following the introduction of a Carbon Tax. This set of 107 technologies draws on the suite of technologies from EIA‟s “High Tech” side case that assumes higher efficiencies for equipment, and earlier availability of some advanced equipment. Use of the high

- technology equipment menu in GT

- NEMS is predic ated on the presumption that creating flexible innovative financing would facilitate research, development, and the demand for better

- than

- baseline technologies. These technologies are listed in Table B.3.

The National Building Codes policy for commercial buildings would set higher building shell efficiency requirements and more stringent energy efficiency standards for space heating and cooling technologies in both new construction and existing buildings that undergo major

modificati ons, It is modeled after the aggressive building codes policy described in Sun, Brown, Jackson, and Cox (2012), but it does not include requirements for improved lighting and water heating. To maximize code adoption and compliance, DOE would facilitate the establishment of building code compliance assistance programs at the state and local level to help developers and builders analyze and comprehend the code, train code officials and inspectors to oversee the code compliance, provide information and trainin g opportunities to parties involved in the building industry, and engage utilities to promote the adoption and compliance of the codes. The policy would also encourage establishing a building code liability structure under which relevant parties including developers, design companies, builders, building owners, and others have the38

responsibility to ensure their project is in compliance with the building code. Code officials and inspectors at the state and local level would be able to exercise their authorit y established by law to enforce the codes and hold the relevant parties accountable in case of non

- compliance.

Unlike the commercial end

- use technologies that have detailed characteristics profiles, GT

NEMS does not include a building shell technology prof ile. Instead, the shell efficiency improvement over time is indexed by a set of efficiency factors that represent the overall shell efficiency improvement of the U.S. commercial building stock in 2035 over the 2003 level. Table B.2 summarizes the building shell efficiency assumptions of the reference case with the National Building Code policy case examined here.

4 .3 Modeling Results for Individual Commercial Building Policies

The energy consumption forecasts of the policy cases were compared with the refe rence case forecast, and the differences represent the achievable energy efficiency potential

. Figure 4

. 5 shows the estimated potential of each policy. The percentage number is the reduction in electricity demand as a percentage of the reference case forec ast in each year.

In 2035, the ele ctricity savings from these four policies in the Eastern Interconnection is 3.4

- 76 .8 TWh ( 0.3 %

- 5.8 % of reference case consumption), while the total electricity savings in the

nation is 5.0

- 107.0 TWh ( 0.3 %

- 5.7 % of R eference case consumption).

The B enchmarking policy generates the highest level of electricity savings among all four commercial policies. Flexible Innovative Financing also generates significant savings. The deployment of commercial CHP systems, on the other hand, indicates less potential for energy saving, due partly to the slow penetration of CHP facilities in commercial buildings.

Figure 4.5 Estimated Reductions in Electricity Demand by Individual P olicies (Percentages of Commercial Buildings Electr icity Consumption) 2.9% 4.4% 5.4% 5.8%

1.5%

2.1% 3.5% 4.6%

0.7%

1.4% 2.0% 2.6%

0 20 40 60 80 100 120

2020

2025 2030 2035 2020

2025 2030 2035 2020

2025 2030 2035

Benchmarking Financing Building Codes Reduction in Electricity Consumption (TWh)

Rest of the

Nation

Eastern

Interconnection39

4.3.1 Regional D ifferences

Within the Eastern Interconnection , the policies generate different impacts in different census divisions. It is notable that Benchmarking and Flexible and Innovative Financing have a large portion of savings in South Atlantic, East North Central and Middle Atlantic regions. But it is different for Commercial CHP in the pattern of benefit distribution among regions. The largest proportion of electricity savings goes to the Middle Atlantic and East North Central regions (Figure 4.6).

Figure 4.6 Electricity Savings by Census Division for Individual Policies

(*West South Central Excluding Texas)

4.3.2 Spillover Benefits

All selected policies aim to promote efficiency in commercial buildings, including the efficient use of electricity and other fuel types. Table 4.2 illustrates the reduction in natural gas consumption that is estimated to result from each individual policy.

D ifferent from the policy

- specific estimates of electricity

- savings potential where Benchmarking dominated, National Building Codes is the commercial policy with the largest natural gas savings potentials in the Eastern Interconnection and in the whole nati on. The financing policy is similar to the benchmarking policy in its natural gas savings in 2035, but its cumulative savings are greater because its impact is more significant in earlier years. West

South

Central*40 Table 4.2 Natural Gas Savings from Individual Policies

Re ference Demand

Forecast (TB tu) Natural Gas Savings (TBtu)

Benchmarking Financing Building Codes

2020 EI 2,708 60 106 125

US 3,594 75 134 156

2025 EI 2,755 82 112 149

US 3,661 104 145 189

2030 EI 2,838 95 105 165

US 3,775 120 139 208

2035 EI 2,946 116 103 183

US 3,919 146 135 230

The policies are also able to generate savings in fuels other than electricity and natural gas, such as fuel oil, diesel, and propane. Table 4.3 shows the potential savings in total energy, which is the projected reduction in total commercial energy consumption. These savings were calculated without including the avoided electricity related losses. In terms of total energy savings, Benchmarking has the greatest potential. Benchmarking also exerts a bigger push for energy efficiency from the beginning of the program, while for National Building Codes, energy savings start slowly but grow steadily over time.

Table 4.3 Total Energy Savings from Individual Policies

Reference Demand

Forecast (TB tu) Total Energy Savings (TBtu)

Benchmarking Financing Building Codes

2020 EI 14,372 447 354 280

US 20,300 610 450 360

2025 EI 15,220 604 401 395

US 21,460 880 540 520

2030 EI 16,020 720 515 433

US 22,720 1,060 740 600

2035 EI 16,903 857 687 537

US 24,000 1,220 960 730

4.3.3 Cost Estimation

The Benchmarking policy lowers the hurdle rate used by consumers in their equipment

purchasing decisions . Private investment is the expenditure for purchasing equipment . There is

no p ublic cost except for program admin istrative cost s , estimated as $0.13/M M Btu energy saved. The LCOE in this policy case is estimated to be 0.7

- 1.2 cent /kWh . Details of the cost estimations

associated with Benchmarking are shown in Table 4.4.41 Table 4.4 Cost Estimations from Benchmarking

Cost ( Billion $2009) a 2020 2025 2030 2035

Private Cost 0.93 0.85 0.85 0.82 Compliance Cost

- 0.001

- 0.001

- 0.001

- 0.001

Total 0.92 0.85 0.84 0.82
LCOE (cent/kWh) 0.7

- 1.2 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

In the Financing case, private investment is the expenditure for purchasing equipment. Total cost was estimated to be the sum of private cost, the cost of subsidizing the most efficient technologies, and program admin istrative cost s , estimated as $0.13/MM Btu energy saved . The LCOE in this policy case is estimated to be 6.4

- 6.6 cent /kWh . Details of cost estimations associated with Appliance Incentives are shown in Table 4.5.

Table 4.5 Cost Estimations from Financing

Cost ( Billion $2009) a 2020 2025 2030 2035

Private Cost 0.71 0.58 0.49 0.44 Subsidy Cost 9.17 8.07 9.07 8.64

Administration Cost 0.06 0.06 0.07 0.06

Total 9.94 8.71 9.64 9.14
LCOE (cent/kWh) 6.4

- 6.6 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

For Building Codes, p rivate investment is the incremental cost of equipment and building envelope expenditures to meet new building codes . There is no p ublic cost except for program admin istrati on . This policy assumes costs associated with building code enforcement carried out by state building code officials and inspectors. The administrative costs are based on each state‟s adding one administrative office

, operated for $150,000 per annum

, and one code official at $75,000 salary per annum. It also includes two additional buil ding code inspectors for the verification of every 100 million square feet in the state at $75,000 per year (Brown, et al., 2009). The LCOE in this policy case is estimated to be 3.5

- 4.6 cent /kWh . Details of cost estimations associated with Building Codes are shown in Table 4.6.

Table 4.6 Cost Estimations from Building Codes

Cost ( Billion $2009) a 2020 2025 2030 2035

Private Cost 1.08

- 1.91 0.39 0.25 Shell Improvement Cost 0.07

- 0.15 0.05 0.03

Administration Cost 0.04

- 0.10 0.04 0.03

Total 1.19

- 2.16 0.48 0.31
LCOE (cent/kWh) 3.5

- 4.6 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.42

4 .4 Integrated Commercial Policy Bundle

The projected savings change when the commercial policies are evaluated as one policy bundle

. Figure 4.7 shows the efficiency potential resulting from the integrated commercial policy case. The savings potential from the integrated policy scenario is 40 TW h in 2020, expanding to 153 TWh (8.4% of reference consumption) in 2035 in the Eastern Interconnection

.

Figure 4.7 Commercial Electricity Savings Potential in the Eastern Interconnection

4.4.1 Electricity Savings Potential By End Use

Electricity consumption by space cooling , space heating, and ventilation would be reduced significantly by the Commercial Policies bundle (Table 4.7). However, electricity consumption by “other” end

- uses services has increased in the Commercial Policies c ase ; these re present an array of miscellaneous “plug loads” that are not targeted by any of our commercial building energy policies

. 800

900 1,000 1,100 1,200 1,300 1,400

2010 2015 2020 2025 2030 2035 Electricity consumption (TWh)

AEO 2011

Commercial Policies

8.4%

3.7% 40 TWh of savings in 2020 153 TWh of

savings in 203543 Table 4.7 Electricity

- Savings Potential by End

- U se in the US

End Use 2010 2020 2035

Reference Reference Policy

Case Annual

Savings Percent Savings Reference Policy Case Annual Savings Percent Savings

Space Heating 51 50 43 7 15% 52 42 10 18%

Space Cooling 170 160 137 23 14% 180 141 39 22%

Water Heating 27 28 27 1 4% 28 25 3 10%

Ventilation 150 176 102 74 42% 209 107 102 49%

Lighting 300 321 311 10 3% 365 303 62 17%

Refrigeration 115 105 100 5 5% 114 105 9 8%

Personal Computer 62 56 56 0 0% 62 62 0 0%

Other Office Equipment 77 108 108 0 0% 137 137 0 0%

Other Uses 391 518 578

- 60

- 11% 730 802

- 72

- 10%

Total 1343 1522 1462 60 3.9% 1877 1724 153 8.2%

4.4.2 Potential for Natural Gas Savings and Carbon Dioxide Emission Reductions

The commercial policies are also able to generate significant savings in natural gas and other fuel types. In the integrated policy scenario, the natural gas savings reach 368 TBtu in the Eastern

Interconnection , while savings in total energy consumption r each 1,930 TBtu in the region (Table 4.8).

Table 4.8 Potential Savings of N a tural Gas and Total Energy

Natural gas savings (TBtu) Total Energy Savings (TBtu)

2020 2025 2030 2035 2020 2025 2030 2035

EI 187 222 253 288 681 939 1,167 1,375

US 236 283 324 368 900 1,300 1,640 1,930

In addition, carbon emissions from the consumption of electricity and other fuels would be reduced since the demand for primary energy drops in the Integrated Policy scenario (Table 4.9).

Table 4.9 Potential Reduction in CO 2 Emissions from Commercial Policies

Carbon Emission Reduction (Million Tonnes)

From electricity From primary energy

2020 2025 2030 2035 2020 2025 2030 2035

EI 31 44 55 64 42 57 70 81

US 40 56 83 83 54 72 89 103

4.5 Conclusion and Discussion44

The three polices examined in this chapter target the electricity efficiency potential in

commercial building s . A combination of the policies can generate significant savings

, more than offsetting the growth in electricity dem and between 2012 and 2035. Each of these policies is associated with relatively low levelized costs, ranging from $0.017/kWh for Benchmarking, to $0.104/kWh for Flexible Innovative Financing. All three policies have LCOEs that are lower than the retail electricity price for the commercial buildings sector.

The se electricity savings are not distributed in the Eastern Interconnection proportionally to area or population. R ather, local situations, such as state policies and energy consumption, influence

the local savings potential

.

Our estima te of electricity efficiency potential is in the middle of the range of potential

assessments from a broad set of studies summarized in our review of the previous literature. It should also be acknowledged that our estimat e is the achievable potential driv en by selected policies, making it different from assessments of technical or economic potential

.
In addition to saving electricity , the policies also produce spillover benefits of large savings of natural gas and other fuels.

The p otential estimation in this study is based on GT

- NEMS forecast of future energy consumption in the U.S. Our estimation is limited by the GT

- NEMS characterization of the U.S. energy market and the commercial building sector. In fact, GT

- NEMS ‟s modeling of commercial buildings is enhanced by a sizeable number of technology choices . The decision

- making process

in GT

- NEMS mimics rational choices based on benefit

- cost analysis, but it has limited modeling

power to reflect behavioral constraints and sub

- optimal rationality

.45



### 5. E NERGY 

### E FFICIENCY 

### P OTENTIAL IN 

### I NDUSTRIAL 

### S ECTOR

In 2010, the industrial sector in the Eastern Interconnection consumed 700 TWh of electricity

, which was 72.1% of national industrial electricity consumption (compared with 68.6% of the national population) . The 2009 consumption level was higher

, indicating the continued impacts

of the country‟s economic downturn on U.S. manufacturing

. Delivered electricity consumption (at 12 % of total industrial energy use in the region) combined with el ectric system energy losses (at 26%) make electricity responsible fo r 3 8% of the tota l energy consumption attributed to the industry in the Eastern Interconnection in 2009 (18,300 TBtu) (Fig 5.1)

.

Figure 5.1 Industrial Energy Consumption in the Eastern Interconnection (Source: EIA, SEDS , 2010 )

The U.S. industrial sector, which encompasses diverse manufacturing and non

- manufacturing activities, has energy requirements for all kinds of fuel types. As the mo st diversified economic sector, the industrial sector includes manufacturing, agriculture, mining, and construction. Manufacturing industries dominate industrial energy demand. Petroleum and coal products (at 27%) and chemicals (at 23%) are the two most en ergy

- consuming manufacturing industries in terms of primary energy consumption (Figure 5.2), but these same two industries account for lower proportions of manufacturing‟s total value of production (at 11% and 13%, respectively) (Figure 5.3).46

Figure 5.2 Primary Energy Consumption by Manufacturing Industries in 2006, Nationwide (Source: MECS, 2006)

Figure 5.3 Gross Output by Manufacturing Industries in 2006 (Source: DOC, Gross Domestic Product by Industry 1998

- 2007, 2009) Food

6% Wood Products

2%

Paper

11% Plastics and

Rubber Products

3%

Petroleum and

Coal Products

27%

Fabricated Metal

Products

3% Chemicals

23% Nonmetallic

Mineral

Products
5% Primary Metals

10% Transportation Equipment

3% Other

Manufacturing

7%

Food Manufacturing

11% Wood Product

Manufacturing

2%

Paper Manufacturing

3% Plastics and Rubber

Products Manufacturing

4%

Petroleum and Coal

Products Manufacturing

11%

Fabricated Metal

Product Manufacturing

6% Chemical Manufacturing

13% Nonmetallic

Mineral Product Manufacturing

3% Primary Metal Manufacturing
5% Transportation Equipment Manufacturing

14% Other

Manufacturing

28%47

According to the forecast of f uture electricity demand in the industrial sector provided by the
Annual Energy Outlook 2011 , industry reaches a peak in 2021, rising to about 771 TWh in the

Eastern Interconnection . The industrial electricity consumption decreases steadily after 2021
(Figure 5.4). The U.S. as a whole has similar trends as the Eastern Interconnection . The declining
demand in the later years of projection is due to the increasing energy efficiency, wh en electricity requirement for industrial activities drops from 443 Btu/dollar in 2021 to 352
Btu/dollar in 2035 (EIA, 2011).

Figure 5.4 Reference Case Forecast of Industrial E lectrici ty C onsumption in the Eastern

Interconnection (Source: EIA, 2011)

This chapter examines the achievable potential of energy efficiency from selected policies that promote technology advancement and improved energy management practices in industry. The chapter explores the electricity savings from these policies and examines h ow the savings are distributed among regions. The other benefits from the energy

- efficiency policies, such as natural gas savings, are also investigated. The last section of this chapter explores the effects and potentials of concurrently implementing the entire industrial policy bundle

.

5.1 Overview of Previous Literature on the Energy

- Efficiency Potential of Industry

A long

- term efficiency potential assessment done by a recent ACEEE study indicates a 33% electricity

- savings potential in the industrial sec tor in 2050 (Laitner, et al, 2012). This potential can be reached by a set of efficiency improvements and technology advancement in the industrial sector, including recycled feedstock and materials substitution, transformative processes, and

smart manufact uring, and supply chain integration.

The McKinsey report estimates the potential of electricity savings in the industrial sector to be about 190 TWh (18% of baseline demand) in 2020 provided by a combination of energy efficiency programs (Granade, et al., 2009). The industries have great opportunities in various

energy

- efficiency initiatives and CHP deployment, which are able to generate cost

- effective48

energy savings. This study found that 33% of the potential comes from cross

- cutting energy support systems (such as steam systems, motors, and buildings), and the rest resides in the process and assembly steps of industry subsector. It is also found that 61% of the potential can be realized in energy

- intensive industries.

Brown et al. (2011) evaluated seven industrial policies at the national scale. The three policies with the greatest energy savings potential were Output

- Based Emissions Standards (3.4%

s avings in 2020 rising to 9.0% savings in 2035), the Superior Energy Performance program (2.2% savings in 2020 rising to 7.7% savings in 2035), and a 30% Investment Tax Credit for CHP (2.3% savings in 2020 rising to 5.6% savings in 2035 ). These savings are not additive because the policies overlap in their targeted markets, but each of these policies when examined individually was highly cost

- effective.

A state

- level study done in Arkansas estimates the total electric efficiency potential in the industrial sector to be 2,880 GWh (16% of baseline consumption) in 2025 at a levelized cost of about $0.02/kWh (Neubauer et al, 2011). However, the study only est imates the electric efficiency potential for cross

- cutting measures, excluding the efficiency opportunities of process

specific measures. As a result, the percentage potential from the study is lower than the other two

nation

- level assessments mentioned ab ove.

A meta

- review done by Nadel et al (2004) reviews eleven energy

- efficiency potential studies and

summarizes the m by type of potential (technical, economic, and achievable) and by sector (residential, commercial and industrial). It concludes that the me dian achievable potential for electricity savings in the industrial sector is about 14%.

The America‟s Energy Future study by the National Academy of Sciences (2009) examined the potential for increasing efficiency, and estimated the potential to generate 14

- 20% of energy savings by 2020 using financially attractive technologies. The study also forecasted that

industrial CHP could cost

- effectively save approximately 4.4 to 6.8 quadrillion Btu of energy by 2020 (National Academy of Sciences, 2009, Table 4.4 ).

Studies on specific industry subsectors have shown wide range s o f savings potentials. A study of

the pulp and paper industry (Fleiter, et al, 2012) estimate d the savings due to 17 process technologies for improving energy efficiency up to 2035. The economic potential assessed is 16 % for electricity and 21% for all fuels. An assessment based on Best Practice Technology in the chemical and petrochemical industry estimate the potential energy savings for the U.S. to be about 24% with top

- down approach and about 10.9% with bottom

- up app roach (Saygin, et al, 2011) . The energy efficiency for industrial motor systems is estimated for both cost

- effective and technical potential in McKane and Hasanbeigi (2011) ‟s study. The assessment is 14

- 49% as

the economic potential, and 27

- 57% as the technical potential.

These assessments are relatively aggressive comparing with an earlier work, which estimates the

achievable potential for industry to be 7

- 17% by 2020 from different policy scenarios (Worrell & Price, 2001) . A review of assessment studies on the U.S. industr ial sector shows a wide range of estimates of energy efficienc y potential within and across industries: 3

- 18% savings for chemical industry, 5

- 23% for petroleum refining industry, and 6

- 37% for pulp and paper industry in 2020 (Brow n, Cox, & Cortes, 2010)

.49

5.2 Industrial Efficiency Policy Options and Modeling A pproach

Energy consumption in the industrial sector comes from all kinds o f process

- specific technologies and cross

- cutting systems. A policy bundle of four efficiency polic ies were designed to tackle the efficiency potential of industrial processes, motors, and buildings. The industrial policy bundle includes Motor Standards, Motor Rebate, Advanced Manufacturing Initiative, and CHP Incentives.

Approximately two

- thirds of in dustrial electricity consumption is used to operate motors. As a result, two of our four industrial policies are designed to improve the efficiency of industrial motor systems. Over the life of a motor 96% of the expenditure is for electricity, with initi al purchase and equipment maintenance accounting for a small frac tion of the cost (DOE/ITP,

2008 ). Motor replacement is a long

- term endeavor, with an estimated time frame of 15 to 20 years for a 90% turnover in the market (Xnergy Inc., 2002).

The Motor Rebate is a financial policy for promoting the deployment of premium motors that satisfy the efficiency requirement of the Energy Independence and Security Act (EISA) of 2007. EISA upgraded standards on all new motors sold in the U.S. from requirements la id out in the Energy Policy Act of 1992 (EPACT

- 92) to National Electrical Manufacturers Association (NEMA) Premium ® certified levels, but does not offer incentives to encourage firms to replace functional motors that predate EPACT

- 92 standards with more ef ficient models. In fact, an unintended consequence of the new standards policy could be that industrial firms choose to repair instead of replace older, inefficient motors rather than pay the additional costs of new motors under the new regulations (Ellio t, 2007).

This policy would provide industrial firms with rebates for purchases of certified high

- efficiency motors of 25 to 500 horsepower. It is similar to the Motor Efficiency Rebate Program authorized in Section 245 of the American Clean Energy and Se curity (ACES) Act, as passed in the House of Representatives, which would operate for five years. It is modeled here as a 30% subsidy to reduce the capital costs of premium motors and to facilitate the removal of the most inefficient

motors

.

A Motor Standa rd is another policy targeting motor systems in industries, which assumes a new motor standard comes into performance 10 years after EISA 2007, requiring efficiency improvement for premium motors. Also, for systems that use motors, this new motor standard would require higher efficiency, which allows energy usage of these systems to be reduced by

25%. This policy should encourage the development and production of a next generation of premium electric motors.

The Advanced Manufacturing Initiative is a co mbined program of R&D and technology demonstration, which aims at identifying t he most promising opportunities associated with new technologies . This policy can be applied to various industrial processes and sectors, including advanced robotics, nano

- electronics, materials b y design, and biomanufacturing. An assessment for plant utility upgrades for efficiency potential was derived from the Industrial Assessment Centers (IAC) database (see Table C.1 for details)

. Potential energy savings (both electric ity and natural gas savings) from this dataset was modeled in GT

- NEMS to forecast the influence of this policy on industrial electricity demand.50

The CHP Incentive tries to promote the deployment of CHP systems by offering a 30% Investment Tax Credit (ITC) for 10 years across all type of systems ( unlike the current expiring ITC that applies only to systems up to 50 MW in capacity) . This policy aims at decreasing the

up

- front capital costs of CHP equipment, which is more electric efficient by using waste heat to
generate on

- site electricity. If the electricity generated by CHP systems exceeds the demand fro m equipment owners, the excess electricity is allowed to be sold back to the grid, which generates

an extra stream of revenue for equipment owners. With a large potential as a distributed generation re source, CHP is identified as one of the most important clean resources in the most recent proposal of the Clean Energy Standard Act of 2012 (Bingaman Bill, 2012).

Table 5.1 GT

- NEMS Levers for Indusial Efficien cy Policies

Policy type Policy Scenario Description GT

- NEMS

Lever

Financial Motor rebate A 30% subsidy for premium motors which satisfies the minimum efficiency requirement of EISA 2007 Indmotor.xml

Regulatory Motor

standard New motor standard at 2017 which has

efficiency improvement for premium

motor and system savings increase by 25%. Systems include fans, pumps, and compressed air. Source code

R&D and

Demonstration Program Advanced

Manufacturing

Initiative Assessments for Plant Utility Upgrades

for efficiency opportunities. Energy savings potential are based on audit

information in the Industrial Assessment Centers (IAC) dataset. itech.txt

Financial CHP Incentive A 30% ITC for industrial CHP for 10

years indcogen.xml

The GT

- NEMS modeling levers for these four policies were purposely chosen to generate separate policy impacts (Table 5 .1). The Industrial CHP Incentive i s modeled in the co

generation profile of GT

- NEMS, where we add a 30% ITC for 10 years for CHP starting in 2015

. T he modeling of Motor Rebate was done by changing the deal rebate variable for premium motors in the motor profile. Within the same profile, the Motor Standard policy was modeled by modifying two other variables, the efficiency for premium motors by system size, and system savings. These modifications were combined with source code changes to reflect the correct history. To model the Advanced Manufacturing Initiative , t he technology profile from the

R eference case was modified to change the Unit Energy Consumption (UEC) for industry

subsector in different census divisions to reflect the potential energy savings based on the IAC

assessment.

5 .3 Modeling Results for Industry Policies51

The energy consumption forecasts of the policy cases we re compared with the reference case forecast, and the differences represent the achievab le energy

- efficiency potential . Figure 5

. 5 shows the estimated potential from Motor Rebates, Motor Standards, and the Advanced Manufacturing Initiative . The percentage number s are the reduction in electricity demand as a percentage of the R eference case forecast in each year.

In 2035, the reduction in electricity consumption from these three policies in the Eastern

Interconnection ranges from 1.62 TWh (0.2% of reference case consumption ) for the Motor rebate policy to 16.4 TWh ( 2.3% ) for the Advanced Manufacturing Initiative. For the nation as a whole, the electricity savings range from 2.3

- 21.7 TWh (0.3

- 3.1 % of R eference case consumption).

The Motor Rebate policy is the only policy that does not have an expanding level of energy savings over the 20

- year planning horizon. Its decline in energy

- savings potential is a function of

the 5

- year duration of the rebate, which has limited impact in later years.

Figure 5.5 Estimated Reductions in Electricity Demand by Individual P olicies (Excluding

CHP) (Percentages of Industrial Electricity Consumption Savings )

The R efere nce case forecasts a doubling of the capacity of industrial CHP systems in the Eastern

Interconnection from 2012 to 2035. In the Industry CHP scenario, incentives are provided to CHP systems that utilize waste heat to generate electricity for on

- site uses and to sell the excess power back to the grid. In addition to the reduction in electricity demand, the contribution of n ew generation by industrial CHP is significant. Based on our projection, the extended ITC will incentivize 33.8 TWh more generation in 2020, and 39.7 TWh more generation in 2035 in the

Eastern Interconnection (Figure 5.6). This represents a 10 to 15% increase in electricity generating capacity of industrial CHP systems above and beyond the doubling that is forecast to occur without any additional policy intervention (Figure 5.7).52 Figure 5.6 CHP Generation and Reduction in Electricity Pur chase (Percentages of Industrial Electricity Consumption Growth and Reduction in Electricity P urchase)

O f the electricity generated by industrial CHP systems in 2020 , about 78.1% is consumed for industry‟s own use, with the excessive part being sold back to the grid. The percentage of CHP generation sold back to the grid grows to 27.4% in 2035 and represents a significant new revenue stream for manufacturing facilities . The reduction in electricity purchase is the net reduction

from the CHP policy c ompared to the R eference case.

3.4% 3.9% 4.0% 4.1%

1.0%

1.5% 1.5% 1.5% 3.0%

3.9% 4.0% 4.1%

0 5 10 15 20 25 30 35 40 45

2020 2025 2030 2035 2020 2025 2030 2035 2020 2025 2030 2035

Generation for Own

Use Sales back to the Grid Reduction in Electricity

Purchase Electricity Generation (TWh) Rest of the

Nation

Eastern

Interconnecti

on53

Figure 5.7 Potential for Expanded CHP Generating Capacity in the Eastern

Interconnection

The increase in generation is facilitated by the increased industrial CHP capacity (Figure 5.7). Due to the ITC incentives, the deployment of industrial CHP systems keeps increasing at a steady rate. While more CHP systems are installed in the policy scena rio, the consumption of natural gas also increases (Figure 5.8). The usage of natural gas by industrial sector increases by 474 TBtu (22.8%) in 2035.

Figure 5.8 Natural Gas Cons umption by Industry Sector in the Eastern Interconnection

The majority of el ectricity is consumed by industry for its own use, with the rest of generated electricity sold back to the grid. In the CHP policy scenario, one

- fourth to one

- fifth of the on

- site generated electricity is sold back to the grid between 2020 and 2035

. These shifts are able to motivate a small decrease in electricity retail price in the industrial sector (Figure 5.9). The price reduction ranges from 0.5% to 1% across the 20

- year period.54 Figure 5.9 Electricity Retail Price in the Industry Sector

5.3.1 Regio nal D ifferences

The distribution of electricity savings across seven census divisions in the industrial sector is

distinct from the pattern of energy

- efficiency potential in the residential and commercial sector s

. Motor Rebates, Motor Standard, and Advance d Manufacturing Initiative have similar regional
distributions. In absolute savings, the East North Central region ( ENC includes five states:

Indiana , I llinois

, Michigan

, Ohio , and Wisconsin ) enjoys the largest portion of benefits from
these three policies (Figure 5.10). These five states produce a total Gross State Product (GSP) of

about 14.0% of the nation ‟s GDP (DOC, 2010). The ENC region is particularly dominant in its
potential to reduce electricity consumption via the Advanced Manufacturing Initiative.

The distribution of potential from the CHP Incentive is quite different from the other three
industrial policies. The ele ctricity generation and reduction in consumption would mainly occur
in the South Atlantic (SA), the ENC and the East South Central (ESC) regions (Figure 5.11). SA
and ESC could have 7.5% of the electricity consumed for manufacturing be generated by CHP

fac ilities in 2035. SA is the largest census division including nine states and generating 18.5% of the nation‟s GDP. The five states in ENC and the four states in ESC produce 14.0% and 4.7% of
the nation‟s GDP (DOC, 2010).55

Figure 5.10 Electricity Savings by Census Division from Motor Rebates , Motor Standard, and Advanced Manufacturing Initiative (*West South Central Excluding Texas)

Figure 5.11 Electricity Savings by Census Division from CHP

(*West South Central Excluding Texas) 0.4% 0.4% 0.2%

0.2% 0.2% 0.2%

0.2%

0 0.2 0.4 0.6 0.8 1 1.2 1.4 Reduction in Electricity Demand (TWh) Motor Rebate

2020

2025

2030

2035 1.55 1.5% 1.1%

1.0% 1.0% 1.0%

0.8%

0 0.5 1 1.5 2 2.5 3 Reduction in Electricity Demand (TWh) Motor Standard

2020

2025

2030

2035

2.5% 2.4% 2.8%

2.6% 1.9% 1.9%

1.6%

0 1 2 3 4 5 6 7

New England Middle

Atlantic East

North

Central West

North

Central South

Atlantic East

South

Central West

South

Central* Reduction in Electricity Demand (TWh) Advanced Manufacturing

Initiative

2020

2025

2030

2035

4.3% 4.0% 3.0%

2.6% 5.4%

5.4%

4.9%

0 1 2 3 4 5 6 7 8

New England Middle

Atlantic East

North

Central West

North

Central South

Atlantic East

South

Central West

South

Central* Reduction in Electricity Demand (TWh) CHP

2020

2025

2030

2035

6.0% 5.6% 4.1%

3.6% 7.5%

7.5%

6.9%

0 2 4 6 8 10 12

New England Middle

Atlantic East

North

Central West

North

Central South

Atlantic East

South

Central West

South

Central* Electricity Generation by CHP (TWh) CHP

2020

2025

2030

203556

5.3.2 Spillover Benefits

The policies aim to promote energy efficiency in industry, and each of them improves the efficient use of electricity. Their impact on the use of other fuels is highly variable.

CHP systems utilize waste heat to generate electricity; in creasingly these systems are powered by natural gas. As a result, the CHP Incentive policy would produce electricity savings, but natural gas consumption in the industrial sector would increase relative to the reference case.

On the other hand, the two po licies promoting premium motors would have minimal influence on natural gas usage since the only fuel used by motors is electricity. In contrast , the Advanced Manufacturing Initiative is able to generate savings in both electricity and natural gas. Table 5 .2 shows the significant natural gas savings from this policy.

Table 5.2 Natural Gas Savings from Advanced Manufacturing Initiative

Reference

Forecast for

Eastern

Interconnection (TB tu) Natural gas savings (TBtu)

Eastern

Interconnection U

. S

.

2020 9,690 18 20

2025 9,550 31 40

2030 9,550 43 50

2035 9,510 56 60

Total 654 731 Note: the total number is the cumulative savings from 2012

- 2035.

5.3.3 Cost Estimation

The levelized cost of electricity saved from each industrial energy efficiency policy was
calculated to evaluate the cost impacts of these policies. In the Motor Rebate scenario, f ailed

motors can be rewound with relatively low cost or replaced by EISA 2007 premium mot ors with
considerably high costs. A 30% subsidy was applied to premium efficiency motors to lower the
replacement costs. P rivate investment wa s calculated based on the cost of rewound motors and
replaced motors , while subsidy cost was estimated based on mo tor replacement. Program

Administration Cost is estimated as 2.5% of subsidy cost . The LCOE in this policy case is

estimated to be 9 .3

- 11.5 cent /kWh . Details of cost estimations associated with Motor Rebate are

shown in Table 5.3.

Table 5.3 Cost Estimation s from Motor Rebate

Cost ( Billion $2009) a 2020 2025 2030 2035

Private Cost 0.45 0.34 0.25 0.19 Subsidy Cost 0.38 0.35 0.31 0.29

Administration Cost 0.01 0.01 0.01 0.01

Total 0.85 0.69 0.57 0.48
LCOE (cent/kWh) 9 .3

- 11.5 b57

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

Similarly, private costs in the Motor Standard case were estimated based on the cost of
rewinding and replacing failed motors. However, public cost is only the program administrative

cost estimated as $0.13/MMBtu energy saved . The LCOE in this policy case is estimated to be $

- 2.4

- 3.9cent

/kWh . Details of cost estimations in Table 5.4 suggest that Motor Standard is able to
save industries in equipment expenditure. In the policy case, the portion of failed motors got
rewound with relatively low cost are much h igher than the portion of rewound motors in the

reference case. The new motor standard restrains industries in favoring replacing failed motors
with premium motors. By going with the less costly option of rewinding failed motors, the

industries are able to save money in equipment expenditure, thus, leading to a negative LCOE
for this policy.

Table 5.4 Cost Estimations from Motor Standard

Cost ( Billion $2009) a 2020 2025 2030 2035

Private Cost 0.408 0.224 0.182 0.204

Administration Cost 0.001 0.002 0.002 0.002

Total 0.410 0.226 0.184 0.207
LCOE (cent/kWh) 2.4

- 3.9 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

In the CHP Incentive case, p r ivate cost is the investment in CHP systems in the private sector.
Subsidy cost was estimated based on the amount of incremental cost in CHP investments, while
program administrative cost wa s estimated as 2% of subsidy cost . The LCOE in this policy case

is esti mated to be 1.5

- 2.3 cent /kWh . Details of cost estimations associated with CHP Incentive

are shown in Table 5.5.

Table 5.5 Cost Estimations from CHP Incentive

Cost ( Billion $2009) a 2020 2025 2030 2035 Increased Natural Gas Expenditure 1.55 1.15 0.62 0.54

CHP system 0.25

- 0.02

- 0.01 0.00 Subsidy cost 0.56 0.00 0.00 0.00

Administration cost 0.01 0.00 0.00 0.00

Total 2.37 1.13 0.62 0.54
LCOE (cent/kWh) 1.5

- 2.3 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

The Advanced Manufacturing Initiative is a combination of R&D and demonstration programs,
which aims at identifying the most promising opportunities associated with new technologies58

th at can be applied to various industrial processes and sectors. This policy is able to stimulate
volunteer upgrades in plants and firms.

Figure 5.12 U.S. Industrial Consumption by Size of Firm

(Source: Brown, et al., 2011)

To calculate private investment , this study follows the division of industrial plants by Brown et

al (2011) , grouping firms into small, medium and large firms (Figure 5.12). It is assumed that the
private investment is $14/MMBtu energy saved for large firms and $12.6/MMBtu energy saved

for small and medium firms (Brown et al., 2011)

. Public cost includes only the program

administrative cost, estimated as $0.13/MMBtu energy saved

. The LCOE in this po licy case is

estimated to be 3.0

- 4.8 cent /kWh , with investment cost declining from $1. 36 Billion in 2020 to
$ 0.97 Billion in 2035 (in 2009 dollar , Table 5.6 ).

Table 5.6 Cost Estimations from Advanced Manufacturing Initiative

Cost (2009$Billion) a 2020 2025 2030 2035

Private Cost 1.34 1.26 0.89 0.94

Public Cost 0.02 0.03 0.03 0.03

Total 1.36 1.29 0.93 0.97
LCOE (cent/kWh) 3.0

- 4.8 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

5.4 Integrated Industrial Policy Bundle

By evaluating all four industrial energy

- efficiency policies as one policy bundle , the electricity consumption by the Eastern Interconnection drops by about 68.3 TWh (9.7%) in 2035 compared

to the R eference case consumption (Figure 5.13). In the integrated policy bundle , the projected level of consumption in 2035 is 9.4% lower than the current level (700 TWh in 2010). From59

2012 to 2035, the total reduction in electr icity demand is 1,195 TWh in the Eastern

Interconnection . In the integrated policy scenario, the electricity consumption in the industrial sector decreases at an annual rate of 0.5%, which is much higher than the annual decrease rate of

0.05% in the refere nce case.

68 TWh of electricity savings in

2035

Figure 5.13 Industrial Electricity Savings Potential in the Eastern Interconnection

The majority of electricity savings come from the bulk chemical, food, and paper industries. Mining and iron and steel industries also have the potential to generate significant amou nt of savings (Figure 5.14). According to ICF International (2010, Table 5 ) and Brown, Cox and Baer (2012), these same industries have greater opportunities for utilizing waste heat to generate electricity by CHP facilities than other industries.

Figure 5.14 Electricity Savings by Industry Subsectors 0 2 4 6 8 10 12 14 16 Electricity Savings in US (TWh)

2020

2025

2030

203560

Due to the incorporation o f CHP incentives into the policy bundle , the electrici ty generation by

industrial CHP also increases. By comparing the generation in the integrated policy case (Figure 5.15) with the stand

- alone CHP policy case (Figure 5.6) , the amount of incr ease and the trend

over time are almost the same

. This result suggests that the CHP incentives do not interact with other industrial policies in terms of generation. This interpretation is plausible since the other three policies do not address distributed generation issue s in their policy design.

Figure 5.15 Increases in Electricity Generation from Industrial CHP

The increase in CHP capacity precipitates an increase in natural gas consumption in the

industrial sector. However , the amount of increase is off set by the significant potential for natural gas savings from the Advanced Manufacturing Initiative. In the year 2035, the natural gas consumption in the integrated policy case increases by only 154 TBtu (2.8%), which is significantly less than in the stan d

- alone CHP policy case (Figure 5.16).

Figure 5.16 Natural Gas Consumption in the Industry Sector61

Similar to the stand

- al one CHP incentives case, about one

- fifth of the electricity generation (by CHP and other distributed generation technologies) is sold back to the grid each year . Grid sales are able to drive the electricity retail prices down beginning in 2015 (Figure 5.17). However, this

p rice decrease is no bigger than 0.12 cent/kWh through 2035

.

Figure 5.17 Electricity Retail Price in the Industry Sector

The level of carbon dioxide emissions also decreases in the integrated policy case (Table 5. 7 ).

U.S. c arb on emissions are reduced by 54 m illion to n nes in 2035 due to the decrease in electricity

c onsumption. However, the slight increase in natural gas consumption makes the emissions reduction from total primary energy consumption slightly lower than the emission reduction from electricity, which is 43 m illion ton n es in 2035.

Table 5. 7 Reduction s in Carbon Emissions

Carbon Emission Reduction (Million Ton n es)
From electricity From primary energy

2020 2025 2030 2035 2020 2025 2030 2035

EI 37 41 41 44 30 30 29 34

US 45 50 54 54 31 36 37 43

5.5 Conclusions and Discussions

This chapter investigates the energy efficiency potential driven by four industrial policies, including Motor Rebates, Motor Standard, CHP Incentive, and Advanced Manufacturing Initiative. Together these four policies are estimated to generate significant elect ricity savings potential in the Eastern Interconnection

. Each of these policies has levelized costs that are lower than the retail electricity price for industr y . CHP Incentive is the industrial policy with the largest electricity

- savings potential . This p olicy also drives up natural gas consumption while lowering62

electricity pr ices. Other benefits of energy

- efficiency policies include climate change impacts; carbon emissions are projected to be reduced significantly in the integrated policy case.

This s tudy focuses on the achievable potential driven by energy efficiency policies. The

estimation of achievable potential in this study is slightly lower than the estimates summarized in our review of the previous literature . However, it is in the range of pot ential savings assessments

from real

- world practices and programs. Although limited by the modeling tool, this chapter is

able to offer reliable assessment of electric end

- use efficiency potential and cost estimation of industrial energy

- efficiency policie s.63 

### 6. INTEGRATED POLICY CASE

The previous three chapters examined a suite of twelve energy

- efficiency policies, as a means of quantifying the potential for electricity efficiency improvements that are both cost

- effective and

achievable. The results are summarized in Table 6.1. The est imated electricity savings from individual policies sum up to reach 241 TWh in 2020, which is higher than the estimation from the Integrated Policy case (Figure1). This indicates that part of the policy impacts cancels out when all energy efficiency polici es are implemented together. Although modeling levers were chosen purposely to avoid overlap, some of the policies target the same set of technologies. It is quite possible that their ability to promot e energy efficiency diminish es when overlapping policie s co

- exist. A related impact is the rebound effect, where energy usage increases when

consumers save mor e in the Integrated Policy case, because of electricity rate reductions.

Market Priming is the energy efficiency policy with most significant savings po tential and relatively low levelized cost. Information

- based instruments, such as the mandated disclosure of home energy performance with home ratings, green labeling and leasing, home energy audits, etc., are able to promote inclusion of energy efficiency when selling or renting . Efficiency improvements from these policies can generate noticeable home equity premium s ( Nevin & Watson, 1998 ). Because of the potential policy impacts on efficiency improvements and equity value, the Federal Office of Management and Budget (OMB) suggests that policies or measures explicitly designed to alleviate asymmetric information should be given preference over other measures, as a general rule

- of

- thumb (OMB Circular A

- 4)

.
To explore the combined effects of these twelve poli cies, all twelve energy

- efficiency options (five from the residential sector, three from the commercial sector, and four from the industrial sector) are mo deled in the Integrated Policy s cenario. This scenario was prepared to determine whether applying multiple policies at once would enhance or reduce the achievable energy

savings potential. On the one hand, the integrated energy

- savings potential could be less than the sum of the individual policy savings potentials because the policies target overlappi ng technologies, barriers, and energy consumers. If the rebo und effect is strong, the reduction of electricity rates resulting from reduced energy consumption could cause consumers to take back some of their potential savings by using their bill savings to buy more energy services. On the

other hand

, synergistic policy combination s could produce greater energy

- savings potential

. For example, by providing better energy benchmarking data, consumers might be more responsive to an opportunity to secure low

- cost financing to invest in more energy

- efficient equipment. The

resu lts from the Integrated Policy s cenario can help us understand the dynamics among the selected policies and their interactive effects on the energy

- efficiency potential of the Easter n Interco nnection

.64

Table 6. 1 Savings Potential and Levelized Cost of Electric End

- Use Efficiency , by Policy

Sector Policy Type Policy Electricity Efficiency

Potential (TWh) in

2020 Electricity

Efficiency

Potential (TWh) in

2035 Levelized Cost

of Electric End

Use Efficiency ( cent /kWh)

Residential Financial Appliance

Incentives 13 26 6.7

- 8.0

Financial On

- Bill

Financing 13 22 6.6

- 7.4

Regulatory Building

Codes 8 19 0.5

- 0.8

Regulatory Aggressive

Appliance Policy 16 40 0.6

- 0.7

Information Market

Priming 100 119 2.7

- 3.6

Commercial Financial Financing 31 77 6.4

- 6.6

Regulatory Building

Codes 8 34 3.5

- 4.6

Information Benchmarking 16 61 0.7

- 1.2

Industrial Financial Motor Rebate 4 2 9.3

– 11.5

Regulatory Motor

Standard 3 8 2.4

- 3.9

Information Advanced

Manufacturing

Initiative 6 16 3.0

- 4.8

Financial CHP

Incentives 23 29 1.5

- 2.3

Although the target technologies, barriers, and energy consumers may be common to two or

more policies, the modeling of policy integration in GT

- NEMS is straightforward since the GT

NEMS levers for each individual policy have no overlap. T he supply

- side modules

, the

economics and emission modules

, and a ll three end

- use sector modules were used together to incorporate feedback loops between multiple segments of the economy to examine policy impacts

. By using the IHS Global Dynamics general equilibrium model, the GT

- NEMS analysis optimizes energy prices and quantities across energy fuels and across sectors of end

- use demand.65 Figure 6.1 Supply Curve for Electricity Efficiency Policies in 2020

A careful reconciliation of the potential estimations from individual policies and the Integrated Policy scenario reveals the dynamics among energy

- efficiency policies. Together with the

levelized co st estimations, the reconciled electricity

- savings potentials pro duce a policy supply

curve. Supply curves for energy

- efficient equipment have been evaluated since the early 1980‟s (Meier, Wright, and Rosenfeld, 1983; Brown, Levine, Romm, Rosenfeld, and Ko omey, 1998), culminating with the well

- known study by McKinsey & Co. (2009). Supply curves for energy

efficiency policies are a recent extension of this approach.

Figure 6.1 indicates that regulatory policies have relatively low levelized cost s of electric ity and financial pol icies have relatively high LCOE s

. In fact, the weighted average L COE estimations suggests that financing policies have higher levelized cost than regulatory and information policies when considering cumulative electricity savings (Table 6.2). Similarly, a previous analysis of energy efficiency in the U.S. South concluded that the two least cost

- effective policies involved financial subsid ies (Brown, et al., 2010). The CHP Incentive, as an exception, offers subsidies for industrial CHP systems while having very low levelized cost because of its significant electricity

- savings by utilizing waste heat.

Currently, the national average electric ity price for rate payers is approximately 9.0 cent/kWh. Taking the average price as a benchmark, all twelve policies except for Motor Rebate are cost

effective (i.e., having LCOEs lower than the average electricity price) , representing 98% of the66

savings

. All financial policies except for the CHP Incentive have levelized costs higher than the national average electricity price. Benchmarking the policies using sector

- specific retail rates, Motor Rebate is the only energy

- efficiency pol icy that is not cost

- e ffective.

Table 6.2 The Levelized Cost of Electricity Efficiency by Type of Policy (in cents/kWh )

-

EI US EI US

Information Policies 2.1 2.1 2.9 2.9

Regulations 2.8 3.4 3.5 4.1

Financing Policies 4. 6 4.8 4.7 5.0

Information Policies 3. 0 3.3 3.6 3.9

*3% discount rate for public and private costs. **7% discount rate for private costs and 3% discount rate for public costs.

Our estimation of the weighted average LCOE of 3. 0

- 3.6 cent/kWh is in the middle range of cost

estimate s from previous studies (a ll cost estimates are reported in 2009$ )

. An ex ante estimation
by the Electric Power Research Institute (EPRI) found the levelized cost of energy efficiency programs to be $0.022

- 0.032/kWh (EPRI, 2009)

. A nother study developed an average estimation of the cost of saved energy to be $0.026/kWh based on utility and state evaluations and reports
for electricity programs in 14 states (Friedrich et al, 2009). Other ex post estimations have

report ed higher levelized costs for energy efficiency. For example, Arimura, et al. (2011)
estimate that utility

- operated demand

- side management programs between 1992 and 2006 saved
electricity at a program cost averaging $0.05 /kWh using a 5% discount rate, with a 90%

con fidence interval ranging from $0.03 to $0.98 /kWh. Auffhammer, Blumstein and Fowlie
(2008) use utility panel data to construct weighted average cost estimates for demand

- side

management programs. Their findings suggest low cost

- effectiveness

, ranging from $ 0.053 to
$0.151/kWh

.

Cost estimations of energy efficiency depend on accurate assessment s of energy savings, which

can be problematic because of free riders hip (Gellings et al., 2006). Alcott and Greenstone (2009)
question these ex ante estimates of cost

- effectiveness by noting that programs typically reduce
electricity demand by only 1

- 2%, which does not suggest a large energy

- efficiency gap.
Alternatively, it could be that the energy

- efficiency programs were simply underfunded and
unable to complete ly address market failures

.67

6.1 The Achievable Potential of Electricity S avings

In the Eastern Interconnection , the electricity consumption is forecasted to grow at an average
rate of 0.77% per year and to rise to 3,200 TWh in 2035 in the reference case. With the twelve
energy efficiency policies, the growth of electricity consumption slows down to an average rate
of 0.35% per year. The combination of twelve policies could save the region about 190 TWh
(6.6%) of electricity in 2020, and 332 TWh (10.3%) of electricity in 2035 (Figure 6. 2 ).

The energy

- efficiency potential driven by the bundle of twelve policies largely comes from the

residential sector, with significant savings from the commercial and industrial sectors as wel l
(Figure 6. 3 ). In the Integrated P olicy scenario , the residential sector is estimated to save 13.2%
of electricity in 2035, which is slightly lower than the estimated savings (15.4% in 2035) from

the residential Policy Bundle . Similarly, the estimated sav ing s potential from the commercial

sector is 7.9% in 2035 under the Integrated Policy case , lower than the estimation (8.4% in 2035)

f or the commercial sector P olicy Bundle

. In contrast, the industrial sector potential in the
Integrated Policy case is esti mated to be comparable (10.4% in 2035) to the savings potential of

the industrial P olicy Bundle . The electricity from industrial CHP generation that is sold back to
the grid is not shown in Figure 6. 3

. An additional 39 TWh are generated by CHP systems (28
TWh are consumed at the industrial plant and 11 TWh are sold back to the grid).

Figure 6. 2 Electricity Savings from the Integrated Policy Case in the Eastern

Interconnection 2,500 2,600 2,700 2,800 2,900 3,000 3,100 3,200 3,300

2010 2015 2020 2025 2030 2035 Delivered Electricity, All Sectors (TWh)

Reference Integrated Policy Case 10.3%

6.6% 190 TWh of savings in 2020 332 TWh of electricity

savings in 203568 Figure 6. 3 Electricity

- Savings Potential by Sector

, Based on the Integrated Policy Case (in TWh)

Table 6. 3 illustrates the achievable potential for electric ity savings as a percentage of R eference

case c onsumption in 2020. The energy

- efficiency policies are estimated to generate the highest
savings potenti als (as a percentage

- of

- consumption) in the residential and industrial sectors of
the South (i.e., the South Atlantic, East South Central and West South Central regions). For the

commercial sector, the same three Census divisions have the highest percent s avings estimates,

but the rank order is different: with the West South Centra l ; having the highest percent, and the

East South Central division having the third highest estimate of percent savings potential

.

Table 6.3 Electricity

- Savings Potential in 2020 , by Census Division (as percentages of R eference case consumption in 2020 )

Census Division Residential

Sector Commercial

Sector Industrial

Sector Eastern

Interconnection
New England 7.98% 2.88% 5.40%

Middle Atlantic 7.71% 3.27% 5.29%

East North Central 7.81% 4.13% 6.60%

West North

Central 6.40% 3.33% 6.02%

South Atlantic 11.19% 4.54% 8.13%

East South Central 8.52% 3.63% 7.98%

West South

Central 13.39% 4.55% 6.88%

Eastern Intercon

nection 9.3% 4.0% 6.9% 6.6%69

T he electricity

- saving s potential was proporti oned to the state

- level in accordance with the share

of energy consumption each stat e held within its own census division . Figure 6. 4 illustrates the
achievable potential for electricity savings by state in 2020. A large portion of the total savings is
estimated to be available in the South Atlantic region, especially Florida. In that same year, t he
highest savings potentials in the residential and commercial sectors reside in three states: Florida,
North Carolina, and Virginia. The highest savings potentials in the industrial sector reside in in
Ohio, Kentucky, Indiana, and Illinois (see Table D.1 for details).

In contrast, our twelve policies suggest much smaller savings pot ential s in some of the states of
the West North Central and New England regions. Especially low across all three sectors in 2020
are Vermont, Rhode Island, and New Hampshire. The reason for the disproportionate
distribution of electricity savings among states is partially due to differences among state economic structures and partially due to the differences in each state‟s share of the energy

market.

Figure 6. 4 Achievable Potential for Electricity Savings by State in 202070

6.2 Policy Impacts on Electricity Rates and the Power Sector

Generally, the energy

- efficiency policies are projected to reduce electricity retail rates in all three

end

- use sectors (Table 6.3). For retail prices, New England and Middle Atlantic exhibit higher

rates than the other census divisions in the Eastern Interconnection , and large rate decreases

occur in these two divisions . New England has the biggest de crease in commercial and industrial
electricity rates. The biggest decline in residential retail rates happens in the West North Central
division. The East South Central division, on the other hand, experiences essentially no

reductions in either commercia l or industrial electricity rates.

Table 6.4 Electricity Rate Changes in 2020, Reference versus Integrated Policy Case

Census Division Scenario Residential Retail Rate (cent/kWh) Commercial Retail

Rate (cent/kWh) Industrial Retail

Rate (cent/kWh)

New England Reference 17.7 12.0 7.8

Integrated Policy Case 17.6 11.3 7.2 Change

- 0 .1

- 0.7

- 0.6

Middle Atlantic Reference 14.6 11.2 6.3

Integrated Policy Case 14.4 11.0 6.0
Change

- 0.2

- 0. 2

- 0. 3

West North Central Reference 8.5 7.1 5.3

Integrated Policy Case 7.9 7.0 5.2
Change

- 0. 6

- 0 .1

- 0. 1

East North Central Reference 10.0 8.4 5.9

Integrated Policy Case 9.6 8.3 5.8
Change

- 0.4

- 0 .1

- 0.1

West South Central Reference 9.7 7.2 5.4

Integrated Policy Case 9.2 7.0 5.2 Change

- 0. 5

- 0 .2

- 0.2

South Atlantic Reference 10.8 9.0 6.5

Integrated Policy Case 10.5 9.1 6.5 Change

- 0.3 0.1 0

East South Central Reference 8.3 7.9 5.2

Integrated Policy Case 7.9 7.9 5.2
Change

- 0. 4 0 0

Although the absolute value of the rate decrease never exceeds 0.7 cent s /kWh , it is estimated to

result in large energy expenditure savings for customers. With the energy

- efficiency policies,

residential customers are estimated to save about $13 b illion in 2020. Similarly, commercial

customers will save $5 b illion and industrial customers will save $4 b illion in 2020.

Interestingly, the regions with the largest decreases in electricity rates do not correspond to the
regions with the highest electricity sa vings potentials (Tables 6.1 and 6.2). In 2020, New
England is estimated to have the biggest decreases in electricity rate s but only moderate
electricity

- savings potential.71 Figure 6. 5 Electricity Price Changes by Census Division72

On the other hand, the South Atlantic is the only census division with rate increases in 2020 and

it has only a moderate rate decreases in 2035, but it is estimated to have the most electricity savings in the Eastern Interconnection . This suggests that ele ctricity savings dynamics, including the rebound effect, may play an import ant role in this rate change

– consumers tend to demand more electricity services when rates are low.

Similar to the South Atlantic states, the Mountain states experience higher com mercial electricity rates on average in 2020 under the Integrated Policy case. But rates drop across the board in the Integrated Policy case in comparison with the R eference case after 2025. As shown in Figure 6. 5

, the electricity retail rate decrease is u niversal in 2035.

A principal driver of the electricity rate decreases is the decline in consumption from improved

end

- use efficiencies. The electricity market can be treated as a partially competitive market. Consistent with economic theory , energy

- effic iency policies drive down demand, which results in a new equilibrium with lower prices as a result of competition.

In the Integrated Policy case, low consumption levels and low electricity retail rates are estimated to influence the power sector. Table 6.4 suggests that fewer power plants (7.2% fewer in 2020 and 11.4% fewer in 2035) are being built as a result of the energy

- efficiency policies in the Integrated Policy case. Natural gas power plants experience the greatest generation losses

relative to t he reference case as a result of the twelve policies (9.1% less generation in 2020, and 25.2% less generation in 2035). In the Integrated Policy case, electricity generated from renewable sources does not decrease as much as generation from other sources i n 2020. By 2035, however renewables are reduced proportionately more than coal or nuclear (9.6% versus 8.2% and 6.1%), but natural gas generation is offset most dramatically

– by more than 200 TWh (25.2%), when compared with the reference case. If natural gas hydrofracking continues to produce low

- cost gas in the U.S., coal, nuclear and renewables might be further reduced while combined cycle natural gas plants would likely retain more of their market share.

Table 6.5 Electricity Generation by Source in the U.S. (in TWh)

Reference Forecast versus Integrated Policy Case

Fuel Type 2010 2020 2035

Reference

Forecast Reference

Forecast Integrated

Policy

Case %

Change Reference

Forecast Integrated

Policy Case %

Change

Co al 1812 1879 1738

- 7.5% 2082 1912

- 8.2%

Petroleum 39.45 39.02 36.84

- 5.6% 41.32 39.41

- 4.6%

Natural Gas 779.1 696.4 632.7

- 9.1% 914.1 683.6

- 25.2%

Nuclear 802.9 877.3 823.6

- 6.1% 874.4 821.3

- 6.1%

Renewables 371 519.3 494.6

- 4.8% 567.3 512.9

- 9.6%

Total 3804 4013 3726

- 7.2% 4483 3972

- 11.4%
Energy

Efficiency

Potential 287 49173

Figure 6. 6 also illustrates the policy impac ts on the power sector. In the R eference case, the share of power generated from natural gas grows from 17% in 2020 to 20% in 2035. But this growth is

estimated to be largely eliminated by energy

- efficiency policies in the Integrated Policy case

. Although the share of electricity from co al goes up to 48% in the Integrated Policy case, electricity generation from coal grows only slightly. This suggests that most of the new coal power plants with relatively low er carbon technologies will not be built in the policy case.

Figure 6. 6 Power Generation by Source in the U.S. , in TWh (EE=Energy Efficiency)

6.3 Policy Impacts on Other Fuels and Carbon Emissions

Although the twelve energy

- efficiency poli cies aim to shrink electricity consumption, most have spillover benefits that may also cause s ignificant savings in natural gas and other energy sources (Table 6.5). In 2020, the Eastern Interconnection could save 0.5 quadrillion Btus of natural gas with energy

- efficiency policies. The savings potential in natural gas could grow to 1.5 quads in

203 5, which is about 38% of the total energy

- savings potential.

Table 6.6 Energy

- Savings Potential of Natural Gas and Total Energy

Energy Savings (Quads)

Natural Gas Total Energy

2020 2025 2030 2035 2020 2025 2030 2035

EI 0.5 0.9 1. 3 1.5 2.6 3.3 3.6 4.0

U

. S

. 0.9 1.6 2.1 2.3 3.5 4.7 5.3 5.6

Meanwhile, energy

- efficiency policies exhibit significant potential in total energy savings in the

Eastern Interconnection , as well as the other regions of the nation. Table 6.4 suggests that the nation could benefit from these policies, resulting in significant energy savings every year.

Figure 6. 7 illustrates the forecast of total energy consumption in the Eastern Interconnection in the reference case and the Integrat ed Policy case. In 2020, energy efficiency policies are74

estimated to save 2.6 quads (3.6%) of energy, which is four times the energy savings in electricity for that year. In 2035, the potential of savings in total energy will grow to 4.0 quads (5.2%), whi ch is 3.5 times the energy savings in electricity for that year.

Figure 6. 7 Total Energy Savings in the Eastern Interconnection (Quads)

The se sizable reductions in energy consumption are associated with reductions in carbon dioxide emissions. Figu re 6. 8 suggests that the energy

- efficiency policies could reduce carbon emissions by 161 million tonnes of CO 2 (4.0%) in 2020, while the potential for emission reductions

increases to 246 million tonnes of CO 2 (5.6%) in 2035.

Figure 6. 8 CO 2 Emissions in the Eastern Interconnection (Million Tonnes)

The percent of carbon emission reductions is slightly higher than the percent of reductions in energy consumption, which suggests that the energy savings are larger in more carbon

- intensive

50 55 60 65 70 75 80

2010 2015 2020 2025 2030 2035

Reference Integrated Policy Case 5.2%

3.6%

2.6 quads of savings

in 2020 4.0 quads of savings in

2035

3,600 3,800 4,000 4,200 4,400 4,600

2010 2015 2020 2025 2030 2035

Reference Integrated Policy Case 5.6%

4.0% 161 Million tonnes

of reduction in 2020 246 Million tonnes of

CO 2 reduction in 2035 Energy Consumption (Quads)75

energy sources (Table 6.6). The energy efficiency policies are clearly estimated to generate larger savings in electricity, which is more carbon

- in tensive than natural gas.

Table 6.7 Reduction in CO 2 Emissions

Reduction in CO 2 Emissions (Million Tonnes)

2020 2025 2030 2035

Eastern

Interconnection 161 211 221 246

United States 211 270 291 317

E nergy savings throughout the nation would produce carbon emission reductions in a manner

similar to that observed in the Eastern Interconnection . Table 6.5 suggests that the other regions of the country would also experience significant reduction in carbon emissions produced by

energy

- efficiency policies.

6. 4 Policy Impacts on Energy Intensity and GDP

The impact of energy

- efficiency policies on different sector s of the economy can be compared through energy intensity metrics. Residential building energy intensity is measured by primary energy per household, while c ommercial building energy intensity is measured by primary energy use per square footage

.
Two intensity measures were constructed for the industrial sector since electricity represents a relatively small part of industrial ene rgy use. The usual industrial energy intensity is measured by primary energy per dollar of shipment, and the industrial electricity intensity is constructed similarly as electricity per dollar of shipment. T he energy intensity of the whole economy is

repre sented by primary energy use per GDP.

Figure 6. 9 suggests that energy efficiency policies and programs would reduce the energy intensity in residential and commercial buildings more than the energy intensity of the industrial sector or the economy as a whole . For example, in 2020, energy use per household decreases by

10.6 %, and energy use per square footage of commercial building decreases by 4.4%, while energy use per GDP declines by only 3.3 %.76 Figure 6. 9 Forecasted Changes in Energy Intensity by Ec onomic Sectors

To the contrary, industrial energy intensity is much less influenced by the twelve energy

efficiency policies than energy intensity of the nation. Figure 6.8 illustrates that the energy use per dollar of shipment decreases by only 1.2% in 2020, which is barely half of the intensity decline of the nation. However, our policies target onl y the electricity savings potentials in the industrial sector, while electricity represents merely one third of industrial energy consumption. The industrial electricity intensity, measured by electricity use per dollar of shipment, is

estimated to decreas e by 7.1% in 2020, suggesting that our policies could be quite influential in transforming industrial energy systems

.
Table 6.7 suggests that our energy

- efficiency policies have a negligible negative impact on

GDP. The national GDP is estimated by NEMS to grow $15 Billion (0.08%) less in the policy case in 2020, which is equivalent to only 7 hours of delay in GDP growth. In 2035, the GDP is estimated to drop by $47 Billion (0.17%), which is equivalent to about 25 hours of delay in GDP growth.

Table 6.8 GDP Impact

Scenario GDP (Billion 2 009$) 2020 2025 2030 2035

Reference GDP 19,168 22,021 25,000 28,260

Integrated Policy Case GDP 19,152 22,009 24,974 28,213

Change

- 0.08%

- 0.05%

- 0.10%

- 0.17% Delay (hour) 7 7 15 25 *Numbers are percentage change relative to the Reference case ** “Delay” in GDP growth is defined as the number of days in a year required to make up the difference between GDP in the Reference case versus GDP in the Integrated Policy scenario.77

The highe r equipment investments prompted by the twelve policies would divert the capital that

could have been invested in other economic activities. Results from GT

- NEMS suggest that this
reallocation of capital resources would affect the national GDP, albeit to a small extent. In
addition, the policies would reduce energy consumption and production, which also has GDP
consequences. As an energy

- economic model, GT

- NEMS is capable of modeling the
macroeconomic impact of any energy policy by incorporating Global Insight‟s model of the U.S.
economy in its Macroeconomic Activity Module (MAM). Both energy demand and supply sides
interact with MAM through a Cobb

- Douglas production function to calculate the national GDP.
However, the IHS Global Insights model assumes the U.S. economy has a 0.07 energy elasticity,
which means that a 1% decrease in energy supply decreases potential GDP by 0.07% (EIA,
2012), but unlike input

- output models such as IMPLAN, the reduction in energy expenditures is
not recycled back into the e conomy to reflect re

- spending of the energy savings. As a result,
NEMS tends to produce estimates of decreased GDP when energy

- efficiency investments
increase (Laitner, 2013).

6.5 Discussion and Conclusion

With energy

- efficiency policies, we estimate that the Eastern Interconnection could cost

effectively achieve significant savings in electricity consumption by 2035. Driven by policy, this
achievable potential for greater electric end

- use efficiency is rela tively low compared with some
prior assessments of the technical and economic potential. Our review of the literature, however,

indicates that this estimated potential for the Eastern Interconnection is comparable to many
estimates of the achievable potent ial for increased electric end

- use efficiency at various scales of
analysis, ranging from the metropolitan to the national.

The electricity savings benefit of energy

- efficiency policies is accompanied by other benefits,
including natural gas savings, savi ngs in other fuel types, and reduced carbon emissions. In

addition, the twelve energy

- efficiency policies are able to drive electricity retail prices down in
many regions and produce large energy bill savings for consumers. The electric power sector is

als o affected by these policies, in that generation growth is slowed in the Integrated Policy case

, reducing the need for capital

- intensive new generation . Overall, these policies are able to

decrease the energy and carbon intensity of the Eastern Interconne ction with no significant
impact on GDP growth.

In sum, this study offers a reliable assessment of achievable potential and an in

- depth analysis of

the impacts of energy

- efficiency policies in the Eastern Interconnection

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U.S. Department of Energy, Industrial Technologies Program (DOE/ITP). 2008. Improving Motor and Drive System Performance. Washington, DC: DOE. http://www1.eere.energy.gov/in dustry/bestpractices/pdfs/motor.pdf

Vine, E., du Pont, P., & Waide, P. (2001). Evaluating the impact o f appliance efficiency labeling programs and standards: process, impact, and market transformation evaluations. Energy

, 26 (11), 1041

– 1059. doi:10.1016/S0360

- 5442(01)00053

- 6

Worrell, E., & Price, L. (2001). Policy scenarios for e nergy efficiency improvement in industry. Energy Policy

, 29 (April 2001), 1223

– 1241.

Xnergy Inc. 2002. United States Industrial Electric Motor Systems Market Opportun ities.

Assessment. Burlington, MA: Xnergy.86

Appendix A. GT

- NEMS Modeling of Residential Energy Efficiency Policies

To assess the electricity savings potential, energy consumption forecast by GT

- NEMS was modeled with five residential energy efficiency policies, including Appliance Incentives, Aggressive Appliance Policy, Building Energy Codes, On

- Bill Financing and Market Priming.

GT

- NEMS modeling of each of the five policies took distinct approaches in the purpose of separating policy impacts by avoiding modeling overlaps.

Appliance Incentives offer 30% subsidy to reduce the capital cost for the most efficient technologies in residential buildings based on the technology inventory of GT

- NEMS . A list of 25 selected technologies from the major end

- uses eligible for incentives can be found in Table A.1. Capital costs of these technologies (from the rtekty input file) were reduced by 30% in the Appliance Incentives policy scenario.

Table A.1 Mos t Efficient Home Appliances and Equipment

End

- Use Equipment Type Average Cost ($2007) Average

Efficiency Available

Years

Space Heating Fuel Oil Furnace 3 4,983 0.95 2009

- 2023

Fuel Oil Radiator 3 4,513 0.95 2012

- 2022

Electric Heat Pump 4 3,567 3.14 2014

- 2021

Geothermal Heat Pump 2 6,414 5 2010

- 2018

Kerosene Furnace 3 4,983 0.95 2009

- 2023

LPG Furnace 5 2,470 0.96 2009

- 2019

Natural Gas Furnace 5 2,470 0.96 2009

- 2020

Natural Gas Radiator 3 4,513 0.95 2012

- 2022

Space Cooling Central Air Conditioner 4 5,290 6.504 2011

- 2019

Electric Heat Pump 4 3,567 5.325 2014

- 2021

Geothermal Heat Pump 2 5,749 30 2011

- 2021

Room Air Conditioner 3 900 3.52 2012

- 2026
Clothes Washing Clothes Washer 3 958 0.114 2008

- 2022

Dishw ashing Dishwasher 3 1,181 1.1 2010

- 2020

Water Heating Fuel Oil Water Heater 3 2,400 0.68 2012

- 2026

Electric Water Heater 5 1,430 2.4 2009

- 2023

LPG Water Heater 4 852 0.746 2014

- 2022

Natural Gas Water Heater 4 852 0.746 2014

- 2023

Cooking Electric Stove 2 400 601 2006

- 2050

LPG Stove 2 500 0.42 2006

- 2050

Natural Gas Stove 2 500 0.42 2006

- 2050

Clothes Drying Electric Clothes Dryer 2 500 3.74 2009

- 2023

Natural Gas Clothes Dryer 2 515 0.931 2007

- 2028
Refrigeration Refrigerator 4 1,107 399 2009

- 2023

Freezing Freezer 3 626 290 2010

- 2032
Note: The cost, efficiency and available years for each equipment type vary by region. The efficiency for different equipment types are measured by different metrics.87

In the Building Energy Codes policy case, four new codes were added to the residential building codes profile (in the rtektyc input file) to force shell efficiency improvements. These codes were modeled with relatively high heating and cooling shell efficiency, and relatively high

sh ell installation costs , trying to mimic the periodic code updates.

In the R eference case, new residential buildings are built in compliance to five different levels of codes: no code, IECC 2006 code, Energy Star code, forty

- percent above IECC 2006 code, and the most efficient code: PATH code. In this study, the Building Code scen ario were set up based on EIA‟s Expanded Standards and Codes side case, where three new codes were added to the code profile: 'IECC 200 6 +', 'IECC 200 6 ++', and 'IECC 200 6 +++' to reflect code improvements. We managed to add one more code, the „New Code‟ to p ush further shell efficiency improvement. Table A.2 shows the details about the residential building codes in our policy case.

Table A.2 Building Energy Codes Profile for Residential Buildings

Building Codes Average Shell

I nstall ation C ost Average Heating Shell

E ff iciency Factor Average Cooling Shell

E ff iciency Factor

'No IECC' 7 1.21 1.15

'IECC 200 6 ' 5,251 0.81 1.06

'E nergy S tar ' 5,508 0.79 1.03

'FORTY%' 6,797 0.68 0.97

'PATH' 7,868 0.51 0.93

'IECC 200 6 +' 5,580 0.69 0.90

'IECC 200 6 ++' 6,018 0.65 0.85

'IECC 200 6 +++' 6,128 0.61 0.80

'NEW CODE' 7,392 0.56 0.85
Note: The cost and efficiency factors for each building shell type vary by region.

The policy case also forces e arly retirement of less stringent codes . For example, Energy St ar, Forty and IECC 2006+ retire in the same year, which is five years later than IECC 2006‟s

retirement ; IECC 2006++ retires at 2023 for all regions ; IECC 2006+++ retires at 2028 for all
regions ; New Code and PATH , which are the two most efficiency codes, are available for all

years for all regions

.

New houses built in compliance to new codes are able to save energy significantly for home owners due to the high shell efficiency with better insulation and building design. They eventually gain market share, a lthough new codes have higher shell installation costs than existing codes (Figure A.1).88 Figure A.1 Share of New Houses Built in the Policy Case

The On

- bill Financing program offers zero

- interest loans to the most efficient house appliances and equipme nt. The technologies eligible for no interest loans are the same technologies that are eligible for appliance subsidies as listed in Table A.1. In GT

- NEMS modeling, two new parameters for residential technologies were added to the rtekty input file: CAPDIS T assigns interest rate (or discount rate for non

- eligible technologies), and CAPHOR assigns payback time (or time horizon for non

- eligible technologies) for residential technologies.

To realize the input file changes, cost calculation equations in the r esidential module source code were also modified. In the reference case, the life

- cycle costs for residential technologies are calculated as following:

With interest rate option, we changed the life

- cost equation to:

When interest rate equals to 0%, we have,

When interest rate is greater than 0%, we have,

Where, LFCYCLE is the lifecycle costs for appliances ; CAPITAL: the capital costs for appliances ; OPCOST: the operational costs for appliances ; DIST: the discount rate for the operational cost during the life time of the appliances

HORIZON: the appliance life time ANNUALPAY: the annual payment for on

- bill financing equipment89

CAPHOR: the payback time

CAPDIST: the interest rate

In the rtekty input file, the selected technologies with high effi ciencies were assigned with 0%

interest rate and 10

- years payback time, indicating that the life

- cycle costs for these technologies were calculated with the revised equation. Other technologies were assigned with the default setting, and their life

- cycle c osts were calculated with the original equation.

The Market Priming policy also target at the same set of technologies as shown in Table A.1,

but with different GT

- NEMS lever. In this policy scenario, market assistance programs offer

consumers with infor mation, technical support, and educational demonstration to lower the private discount rate for the most efficient technologies. GT

- NEMS modeling of this policy changed the hurdle rates of these technologies to 7% by modifying the beta 2 parameter for the logit model of technology choice in the rtehty input file.

The Aggressive Appliance Policy forces rapid market turnover for residential technologies by retiring the least efficient technologies from the market place. Assuming aggressive appliance

standard s, the policy forces some of the technologies to retire from the market at 2012. In GT

NEMS, the selected technologies were made either unavailable after 2012, or assigned a hurdle rate equals to 100% in the rtehty input file. A list of house appliances an d equipment forced retired in this policy scenario is shown in Table A.3.

Table A.3 Residential Technologies Forced Early Retirement

End

- Use Equipment Type Average Efficiency Available Years

Space Heating Fuel Oil Furnace 1 0.82 2010

- 203 2

Fuel Oil Radiator 1 0.825 2010

- 2031

Electric Heat Pump 1 2.35 2014

- 2028

Kerosene Furnace 1 0.82 2010

- 2032

LPG Furnace 1 0.818 2010

- 2032

Natural Gas Furnace 1 0.818 2010

- 2032

Natural Gas Radiator 1 0.815 2010

- 2031

Space Cooling Central Air Conditioner1 3.899 2009

- 2039

Electric Heat Pump 1 4.003 2014

- 2028

Room Air Conditioner 1 3.103 2013

- 2027
Clothes Washing Clothes Washer 1 0.160 2012

- 2022
Dish Washing Dishwasher 1 0.587 2010

- 2024

Water Heating Fuel Oil Water Heater 1 0.585 2011

- 2032

Electric Water Heater 1 0.925 2011

- 2032

LPG Water Heater 1 0.59 2006

- 2050

Natural Gas Water Heater 1 0.605 2011

- 2032
Refrigeration Refrigerator 1 428. 7 2013

- 2027

Freezing Freezer 1 347.5 2010

- 2032
Note: The efficiency and available years for each equipment type vary by region. The efficiency for different equipment types are measured by different metrics.90

Appendix B. GT

- NEMS Modeling of Commercial Energy Efficiency Policies

We estimate the magnitude of technology investment costs in the commercial sector separately for new purchase s , replacements, and retrofits. In each case, the calculation is based on GT

NEMS estimates of service demand (SD) for energy.

 New Purchases

 SD
new x (Cost/8760) x 1/CF = Investment Cost

 SD
new is a KSDOUT output, as are SD replacement and SD surviving

 CF is the equipment

- specific capacity factor

 Replacements

 SD replacement x (Cost/8760) x 1/CF = Investment Cost

 Retrofits

 SD surviving x (Cost/8760) x 1/CF x 0.022/(SD surviving /SD total )

 Where SD total = SD
new + SD replacement + SD surviving and 0.022 is the average
amount of commercial floorspace undergoing a retrofit

 This proportions the surviving service demand to the commercial sector
retrofit average

In the Benchmarking policy case, GT

- NEMS use s a combination of discount rates and the rate for U.S. government ten

- year Treasury notes to calculate consumer hurdle rates used in making equipment

- purchasing decisions. While the macroeconomic module of GT

- NEMS determines

the rate for ten

- year Treasury notes endogenously, the discount rates are inputs to the model. Modifying these inputs is the primary means of estimating the impact of benchmarking for the commercial sector in this analysis. This is done in two steps: first, by updating the discount rat es to reflect a broader selection of the literature; and second, by adjusting the updated discount rates to account for the effects of a national benchmarking policy.

To illustrate, Table B.1 presents the 2015 hurdle rates used in GT

- NEMS across scenarios for two major end

- uses in the commercial sector, space heating and lighting (these values represent the sum of the Treasury bill rates and the discount rates).91

Table B.1: Discount Rates Across Scenarios for Space Heating and Lighting in 2015

% of Population Discount Rate*

Reference Bench

marking Reference Bench

marking

Space Heating

27 14.2 1005.75 40.4

23 14.3 105.75 19.6

19 14.3 50.75 15.4

18.6 14.3 30.75 12.4

10.7 14.3 20.75 9.8

1.5 14.3 12.25 7.4

0.2 14.3 5.75 4.8

Lighting

27 14.2 1005.75 57.3

23 14.3 105.75 40.8

18.6 14.3 50.75 36.5

18.6 14.3 30.75 33

8.8 14.3 20.75 30.4

1.5 14.3 12.25 26.9

2.5 14.3 5.75 21.7

* Discount rates presented include the projected Treasury bill rate for 2015. Bold numbers represent the median estimate for the specific scenario.

Source: Cox, Brown, and Sun, 2012.

The National Building Code is modeled, in part, by assuming a more rapid rate of commercial shell efficiency improvement, as shown in Table B.2.92 Table B.2 . Commercial Building Shell Efficiency Improvement*

New Construction Existing Buildings

EIA Reference case 14% 6%

EIA High Tech Case 17.4% 7.5%

Building Code Scenario 30% 19%

* Improvement of 2035 efficiency over 2003 efficiency

In the Commercial Financing policy case, a 30% subsidy was provided to 107 technologies, based on a prior analysis of the impact of implementing a carbon tax (Brown, Cox, and Sun, 2012). The subsidized techno logies are listed in Table B.3.

Table B.3 Incentivized Technologie s in Financing Policy Case

End

- use Fuel

type Technology Average efficiency Average

cost First

available

year Last

available

year

Space
Heating Electricity comm_GSHP

- heat 2011 high 4.90 150.00 2011 2052
comm_GSHP

- heat 2011 high 10% ITC w MACRS 4.90 108.00 2011 2016

comm_GSHP

- heat 2011 typ 3.50 120.00 2011 2052
comm_GSHP

- heat 2011 typ

10% ITC w MACRS 3.50 87.00 2011 2016
comm_GSHP

- heat 2020

- 30 typical 4.00 120.00 2020 2052 rooftop_ASHP

- heat 2007 high 3.40 96.67 2003 2052 rooftop_ASHP

- heat 2030 high 3.80 96.67 2030 2052

Natural

Gas gas_boiler 2011 high 0.95 37.08 2011 2052

gas_furnace 2011 high 0.94 9.76 2011 2052
res_type_gasHP

- heat 2020 typical 1.50 150.00 2020 2052
res_type_gasHP

- heat 2030 typical 1.50 141.67 2030 2052

Space Cooling Electricity centrifugal_chiller 2007 high 7.30 43.33 2003 2052 centrifugal_chiller 2007 mid

range 6.90 40.83 2003 2052 centrifugal_chiller 2010 typical 6.40 36.67 2010 2052 centrifugal_chiller 2020 typical 7.00 36.67 2020 2052
centrifugal_chiller ASHRAE 6.10 35.42 2003 205293 90.1

- 2004

comm_GSHP

- cool 2011 high 8.15 150.00 2011 2052
comm_GSHP

- cool 2011 high 10% ITC w MACRS 8.15 108.00 2011 2016

comm_GSHP

- cool 2011 typ 4.10 120.00 2011 2052
comm_GSHP

- cool 2011 typ

10% ITC w MACRS 4.10 87.00 2011 2016
comm_GSHP

- cool 2020

- 30 typical 4.10 120.00 2020 2052 reciprocating_chiller 2007 high 3.52 47.08 2003 2052 reciprocating_chiller 2020 high 3.63 42.08 2020 2052 reciprocating_chiller 2020 typical 3.20 38.75 2020 2052 reciprocating_chiller 2030 high 3.78 42.08 2030 2052 res_type_central_AC 2003

installed base 2.84 47.84 2003 2003 res_type_central_AC 2030 typical 4.40 80.95 2030 2052 res_type_central_AC

NAECA standard

- pre

- 2006 2.93 49.13 2003 2005 rooftop_AC 2003 installed

base 2.70 58.33 2003 2003
rooftop_AC 2007 typical 2.96 65.56 2003 2009
rooftop_AC 2010 high 3.52 80.56 2011 2052
rooftop_AC 2011 typical 3.28 66.67 2011 2052
rooftop_AC 2030 high 3.81 80.56 2030 2040 rooftop_ASHP

- cool 2030 high 3.81 96.67 2030 2040
screw_chiller 2020 high 3.63 42.08 2020 2052
screw_chiller 2030 high 3.91 42.08 2030 2052
scroll_chiller 2007 typical 2.93 36.25 2003 2052

wall

- window_room_AC 2011 typical 3.05 33.81 2011 2052

wall

- window_room_AC 2020 typical 3.22 33.81 2020 2052

Water
Heating Natural

Gas gas_water_heater 2020

high 0.95 26.40 2020 2052

Electricity HP water heater 2011

typical 2.30 225.00 2011 2052

HP water heater 2020 typical 2.30 210.71 2020 2052
Solar water heater 2010 typ 2.50 249.12 2010 205294 south
Solar water heater 2011 typ 30 pct ITC south 2.50 193.76 2011 2016 Solar water heater 2020 typ

south 2.50 205.16 2020 2052 Solar water heater 2030 typ

south 2.50 175.85 2030 2052

Ventilation Electricity CAV_Vent 2008 high 1.10 8833.35 2004 2050
CAV_Vent 2020 typical 0.63 8326.82 2020 2050
CAV_Vent 2030 typical 0.73 8326.82 2030 2050
VAV_Vent 2008 high 1.63 8790.87 2004 2050
VAV_Vent 2020 typical 0.73 8398.71 2020 2050

Cooking Electricity Range, Electric

- in duction, 4
burner, oven, 11 0.80 46.57 2000 2052

Natural

Gas Range, Gas, 4 powe red

burners, convect. oven, 11 0.60 38.92 1995 2052

Lighting Electricity 72W Inc (Halogena Type HIR) 12.21 79.19 2008 2050

F28T5 71.50 31.98 2003 2050
F32T8 Super 65.20 21.71 2003 2050
F96T8 High 95.10 10.96 2003 2050

F96T8HO LB 76.90 18.81 2003 2050

LED 2011

- 2019 Typical for high tech 86.80 196.79 2011 2019

LED 2020

- 2029 Typical 181.00 134.18 2020 2050

Refrigeration Electricity Bevrg_Mchndsr 2008 high 1.87 1674.84 2004 2050
Bevrg_Mchndsr 2008 low 0.88 1102.94 2004 2009 Bevrg_Mchndsr 2011 typical 1.34 1348.04 2011 2050 Bevrg_Mchndsr 2020 typical 1.43 1348.04 2020 2050 Bevrg_Mchndsr 2030 typical 1.54 1348.04 2030 2050 Bevrg_Mchndsr installed

base 0.79 1266.34 2003 2009 Ice_machine 2010 EPACT

standard 0.50 1142.41 2010 2050
Ice_machine 2011

- 2020 typical 0.53 1186.34 2011 2050

Reach

- in_fzr 2008 high 2.26 1270.06 2004 2050

Reach

- in_fzr 2020 typical 1.66 1180.93 2020 2050

Reach

- in_fzr 2030 typical 1.77 1180.93 2030 2050

Reach

- in_fzr installed base 1.23 1136.37 2003 2009

Reach

- in_refrig 2008/2010
high 5.13 898.69 2004 2050

Reach

- in_refrig 2011 typical 3.42 866.01 2011 205095 Reach

- in_refrig 2020 typical 3.67 866.01 2020 2050

Reach

- in_refrig 2030 typical 3.85 866.01 2030 2050

Reach

- in_refrig installed

base 2.03 931.37 2003 2009 Supermkt_compressor_rack 2011 high 3.06 130.72 2011 2050 Supermkt_compressor_rack 2011 typical 2.75 116.71 2011 2050 Supermkt_compressor_rack 2020 high 3.06 130.72 2020 2050 Supermkt_compressor_rack 2020 typical 2.81 116.71 2020 2050 Supermkt_compressor_rack 2030 high 3.06 130.72 2030 2050 Supermkt_compressor_rack 2030 typical 2.87 116.71 2030 2050 Supermkt_condenser 2008 high 27.84 32.25 2004 2050 Supermkt_condenser 2020 typical 22.27 25.80 2020 2050 Supermkt_condenser

installed base 17.82 29.02 2003 2050 Supermkt_display_case 2008 high

- 2012 standard 3.02 436.28 2004 2050 Supermkt_display_case 2011 typical 2.57 303.92 2011 2011 Supermkt_display_case 2020 high 3.42 436.28 2020 2050 Supermkt_display_case

installed base 2.45 303.92 2003 2011
Vend_Machine 2008 low 0.53 2201.69 2004 2012 Vend_Machine 2008

- 10 high

- 2013 standard 1.06 2621.85 2004 2050 Vend_Machine 2008

- 10 typical 0.75 2341.74 2004 2012
Vend_Machine 2011 high 1.17 2621.85 2011 2050
Vend_Machine 2011 typical 0.84 2341.74 2011 2012
Vend_Machine 2020 high 1.24 2621.85 2020 2050
Vend_Machine 2030 high 1.32 2621.85 2030 2050

Walk

- In_fzr 2008 high 1.21 2148.16 2004 2050

Walk

- In_fzr 2009 EISA stnd

- 2010 typical 1.83 2068.59 2009 2050

Walk

- In_fzr 2020 typical 1.86 2068.59 2020 2050

Walk

- In_fzr 2030 typical 1.89 2068.59 2030 2050

Walk

- In_fzr installed base 0.81 1650.90 2003 2008

Walk

- In_refrig 2008 high 6.73 725.11 2004 205096 Walk

- In_refrig 2009 EISA

stnd

- 2010 typical 6.24 710.16 2009 2050

Walk

- In_refrig 2020 typical 6.54 710.16 2020 2050

Walk

- In_refrig 2030 typical 6.94 710.16 2030 2050

Walk

- In_refrig installed

base 2.73 490.52 2003 200897

Appendix C. GT

- NEMS Modeling of Industrial Energy Efficiency Policies

The Motor Rebate policy offers a 30% subsidy for premium motor. GT

- NEMS modeling of this policy was to increase the dealer rebate parameter (in the indmotor.xml input file) from 30% to 60%. The Motor Standard policy assumes efficiency improvement in 2017 that systems using motor have additional 25% savings in energy. In order not to change history, the modification was made effective from 2017 in the industrial source code, ind.f.

In the CHP Incentive scenario, subsidies were applied to industrial CHP sy stems to promote efficient usage of waste heat in various industrial processes. A 10

- year subsidy increasing from 15% in 2012 to 30% was applied to the total installed cost parameter in the indcogen.xml input

file. To reflect the benefit share between cust omers and CHP retailers, a 15% subsidy was applied for the first three years, rising by 5% every year from 2015 and staying at 30% from

2017 to 2021. GT

- NEMS represents CHP as a combination of eight technology systems, including two internal combustion CHP systems (ranging from 1 to 3 MW), five gas turbine CHP systems (3 to 40 MW) and one combined cycle system (with two 40 MW gas turbines and a 20 MW steam turbine). Investment costs are provided for each of these systems.

The Advanced Manufacturing Initiat ive policy mimics the voluntary plant upgrades by the private sector. It took the estimated electricity and natural gas savings from efficiency improvements reported from 2010 to 2012 in the Industrial Assessment Center (IAC) database (Table C.1). The perc entage savings were applied to change the TPC parameter in the itech.txt input file.

Table C.1 Electricity and Natural Gas Saving Estimations from IAC Reports

Industry Electricity savings Natural Gas

Northeast Midwest South West Northeast Midwest South West

311 Food 47.58% 47.98% 37.48% 47.08% 0.58% 25.75% 2.00% 0.00% 322 Paper 29.20% 31.24% 15.06% 11.51% 5.19% 15.98% 11.15% 0.00%

325 Chemicals 62.73% 13.04% 51.98% 35.74%

- 32.64% 25.06%

- 3.76%

- 98.63%

327 Non Metals 9.82% 20.23% 46.18% 37.26% 5.12% 29.45% 0.00%

331 Iron and Steel 15.14% 57.13% 28.02% 5.03% 13.75% 18.44% 3.44% 2.61%

332 Fabricated Metals 29.27% 46.89% 42.70% 28.74%

- 49.03% 16.55% 11.15% N/A

333 Machinery 19.61% 54.08% 45.88% 40.35% 29.22% 53.70% 27.33% N/A
334 Computers and

Electronics 79.76% 58.39% 15.75% 31.06% 28.54% 22.85% 7.08% N/A

336 Transportation Equipment 17.42% 40.24% 56.61% 9.93% 16.85% 4.79% N/A N/A

335 Electrical 8.88% 12.20% 24.35% 36.22% 21.71% 21.81% 3.55% N/A

321 Wood 22.94% 38.47% 34.16% 76.18% 23.14% 3.86% 55.09%

326 Plastics Others 44.69% 26.43% 27.64% 24.39% 15.24% 480.64% 16.78% 10.36%

313 Textile 5.25% 24.03% 4.61% 8.81% 314 Textile product 89.25% 10.83% 13.15% 13.20%

- 11.25% 95.10% 23.81% 29.45%

324 Petroleum and

Coal 13.98% 16.05% 6.64% 12.65% 74.27% 16.57% 20.72% 3.57%98 Appendix D

. State Results

Table D.1

. Electricity Efficiency Potential By State (TWh)

Residential Commercial Industrial

2020 2035 2020 2035 2020 2035

AL 2.85 4.16 0.92 2.39 2.88 3.92

AR 2.51 3.84 0.67 1.48 1.21 1.62

CT 1.04 1.79 0.44 1.23 0.18 0.22

DC 0.22 0.37 0.51 1.27 0.03 0.04

DE 0.50 0.85 0.23 0.57 0.26 0.35

FL 13.25 22.52 4.89 12.23 1.58 2.17

GA 6.07 10.32 2.42 6.04 2.75 3.76

IA 0.91 1.44 0.66 1.50 1.09 1.57

IL 3.21 5.41 2.68 6.30 3.37 4.76

IN 2.28 3.85 1.24 2.90 3.50 4.94

KS 0.90 1.42 0.53 1.21 0.80 1.16

KY 2.40 3.51 0.80 2.07 3.69 5.03

LA 4.21 6.44 1.29 2.88 1.92 2.57

MA 1.58 2.71 0.59 1.65 0.80 1.00

MD 3.17 5.38 1.38 3.46 0.49 0.68

ME 0.34 0.59 0.13 0.38 0.14 0.17

MI 2.42 4.08 1.99 4.67 2.23 3.14

MN 1.45 2.30 0.76 1.73 1.57 2.25

MO 2.26 3.58 1.08 2.46 1.20 1.72

MS 1.62 2.37 0.54 1.40 1.31 1.78

NC 6.17 10.48 2.43 6.07 2.35 3.21

ND 0.26 0.42 0.15 0.33 0.29 0.42

NE 0.63 1.00 0.33 0.76 0.76 1.09

NH 0.34 0.59 0.15 0.41 0.09 0.11

NJ 2.11 4.00 1.43 3.60 0.54 0.67

NY 3.60 6.83 2.82 7.06 0.87 1.10

OH 3.65 6.16 2.39 5.61 4.02 5.68

OK 3.15 4.81 1.06 2.35 1.06 1.43

PA 3.84 7.29 1.70 4.26 2.84 3.56

RI 0.24 0.41 0.12 0.34 0.05 0.06

SC 3.30 5.61 1.13 2.83 2.38 3.25

SD 0.27 0.43 0.14 0.32 0.18 0.26

TN 3.68 5.38 1.20 3.13 2.67 3.64

VA 5.01 8.51 2.42 6.06 1.56 2.14

VT 0.17 0.29 0.07 0.18 0.07 0.08

WI 1.54 2.60 1.11 2.61 1.82 2.57

WV 1.29 2.20 0.41 1.03 1.03 1.41