# ComEd Energy Efficiency

Potential Study Report,

2013–2018

August 20, 2013

Submitted to:

ComEd

3 Lincoln Center

Oakbrook Terrace, IL 60181

Prepared by:

ICF International

9300 Lee Highway

Fairfax, VA 22031

and

Opinion Dynamics Corporation

1000 Winter Street

Waltham, MA 02451

13

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pageICF International i ComEd

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# Executive Summary

This report summarizes the ComEd energy efficiency potential analysis performed by ICF International

and Opinion Dynamics Corporation. The analysis covers the 2013 through 2018 timeframe and the

residential, commercial, and industrial sectors. The study was commissioned by ComEd to comply with

Illinois law, and to provide information useful for ComEd's program plan ning. A bottom

‐ up approach

was used to e s timate economic, program achievable and maximum achievable potential. Total

estimated cumulative economic potential equals 32% of load, or 30 TWh, in 2018; total annual program

achievable potential equals approximately 1.0% of load per year, and; total annual maximum achievable

estimates equal 1.3% of load in 2013, and 2.4% of load in 2018.

Total annual program costs in the program achievable scenario are estimated to equal $125 Million in

2013 and grow to $157 Million in 2018. In the maximum achievable scenario, total program costs equal

$265 Million in 2013 and grow to $527 Million in 2018. Over the six

‐ year timeframe of the study, net

Total Resource Cost ("TRC") benefits are estimated to equal $0.8 Billion in the program achievable

scenario and $2.4 Billion in the maximum achievable scenario. Both the program and maximum

achievable scenarios have TRC benefit

‐ cost ratios of 2.2.



## Study Objectives and Scope

ComEd's objectives for this study were to comply with the provision of the Illinois Public Utility Act

requiring a potential study, and to gain insights for their program planning about additional energy

efficiency savings that could be achieved in a maximum achievable potential scenario.

Estimates developed for this study cover the 2013 through 2018 time horizon (six years) and the

residential, commercial, and industrial sectors in ComEd's service territory.



## Types of Potential Estimated

Three levels of energy efficiency potential were estimated: economic potential, maximum achievable

potential, and program achievable potential. Definitions for each level of potential are below.

 Economic Potential is the amount of savings that would result from replacing all existing equipment

that uses electricity with the most technically

‐ efficient, cost

‐ effective com mercially available

equipment.

 Maximum Achievabl e Po tential is the amount of cost

‐ effective program potential that could be

achieved absent program budget constraints. Incentives are set to 100% of incremental costs in this

scenario.

 Program Achievable Potential is the amount of cost

‐ effective program potential that could be

achieved assuming ComEd is operating und er its current budget cap (app ro ximately 2% of annual

customers’ total electric costs). Incentives in this scenario are consistent with existing program

budgets, and are generally between 25% and 75% of incremental costs.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Executive Summary

ICF International ii ComEd

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‐ 034 © 2013 August 20, 2013 It is important to note that economic potential is a theoretical construct. Economic potential estimates

do not account for customer or other market barriers to energy efficiency, and do not reflect budget

constraints. In addition, economic potential does not inform measure or program market adoption

rates. For these reasons, economic potential estimates are informative at a hig h level, but provide

limited value to program planners conc e rning achievable levels of measure

‐ specific potential. Given

these limitations, this study focuses on the achievable potential estimates, restricting discussion on the

economic estimates to describing the upper limit of cost

‐ effective potential.



## Approach Summary

ICF used a bottom

‐ up approach to estimate energy efficiency potential. "Bottom

‐ up," in the context of

potential studies, refers to an approach that begins with characterizing the eligible stock, screening

measures for cost

‐ effectiveness, estimating savings first at the measure

‐ level, then summing savings at

the end

‐ use, sector, and overall service territory level s. Top

‐ down approaches usually develop sector

level esti mates, which are disaggregate d to end

‐ use or measure estimates.



## Stakeholder Process

Stakeholders were engaged throughout the conduct of ComEd's potential study analysis. They were

provided with draft documents for review and their feedback was considered and incorporated into the

analysis. Draft results were also presented by ICF and Opinion Dynamics in person to the Illinois

Stakeholder Advisory Group ("SAG") in mid

‐ June, 2013, where additional feedback was provided. Some

of this feedback was incorp orated into the final analysis presented in this report.



## Uncertainty

Energy efficiency potential studies are forecasts, and all forecasts have forecast error, or uncertainty.

This study includes thousands of assumptions, including baseline data, measure parameters, avoided

costs, program assumptions, and other inputs. While it is impossible to eliminate uncertainty, it can be

mitigated through certain analytical strategies. The most basic strategy is to use the best information

available at the ti me of the analysis. This study made extensive use of primary and secondary data

specific to ComEd's service territory. Where ComEd

‐ specific da ta was unavailable ICF used the most

accurate proxy data available. Another basic strategy is to use a bottom

‐ up approach.

Finally, it is also important to include multiple persp ectives when developing and reviewing potential

estimates. This helps mini mize confirm ation bias (the estimates seeming accurate because they reflect

one's previous experience). Estimates developed in this study included several viewpoints, including

those of ICF program planners and managers, ComEd program planners and man agers, and ComEd

implementation contra ctors and evaluators. In addition, benchmarking data gathered on program

performance in other jurisdictions was used to help gauge the reasonableness of the estimates.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Executive Summary

ICF International iii ComEd

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## Energy Efficiency Potential

Figure ES

‐ 1 shows the total energy efficiency potential forecasts and total annual program costs.

ICF estimates that, with the current budget cap, ComEd can achieve annual savings equal to 1.0% of load

per year. Without a budget cap (in the maximum achievable scenario) annual savings estimates are

150% higher in 2013 and 250% higher than program achievable savings in 2018. These additional cost

‐

effective savings would co st an additional $1.3 Billion and result in an addi tional $1.6 Billion in net TRC

benefits over the six years.

On a cumulative basis, achievable savings equal 5% of load in 2018 in the program sce nario, and 10% of

load in the maxim u m scenario.

Figure ES

‐ 1. Total Achievable Potential, by Scenario and Year

2013 2014 2015 2016 2017 2018

Cumulative Savings Forecast—GWh

Economic potential 7,610 28,162 28,679 29,161 29,634 30,009

Maximum achievable potential 1,122 2,453 3,767 5,430 7,104 8,693

Program achievable potential 824 1,649 2,294 3,043 3,778 4,387

Cumulative Savings Forecast— % of load

Maximum achievable potential 1% 3% 4% 6% 8% 10%

Program achievable potential 1% 2% 3% 3% 4% 5%

Incremental Savings Forecast—GWh

Maximum achievable potential 1,122 1,438 1,602 1,865 1,956 2,111

Program achievable potential 766 868 827 846 828 846

Incremental Savings Forecast— % of load

Maximum achievable potential 1.3% 1.6% 1.7% 2.1% 2.1% 2.4%

Program achievable potential 0.9% 1.0% 0.9% 1.0% 0.9% 1.0%

Program Costs (Millions, Real 2013$)

Maximum achievable potential $265 $349 $426 $487 $488 $527

Program achievable potential $125 $137 $139 $146 $152 $157

The above estimates include program estimates for both ComEd and Department of Commerce and

Economic Opportunity ("DCEO") programs. Although DCEO conducted its own potential study, ICF

developed independent estimates for DCEO programs funded by ComEd for the purposes of this study.

While important, DCEO programs are not a focus of this report.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Executive Summary

ICF International iv ComEd

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‐ 034 © 2013 August 20, 2013 Savings and cost estimates by rate class are shown in Figure ES

‐ 2, below.

Figure ES

‐ 2. Cumulative Achievable Potential and Total Program Costs, by Rate Class

Residential

Ratepayers C&I Ratepayers

<1 MW

Demand >=1MW

Demand

Cumulative Net GWh Savings (2018)

Maximum Achievable 2,219 3,733 2,741

Program Achievable 1,372 1,697 1,318

Total program Costs, $Millions (2013

‐ 2018)

Maximum Achievable $125 $1,682 $804

Program Achievable $45 $332 $257



## Organization of the Report

The body of this report begins with a detailed discussion on the potential study approach. Next, there

are sections devoted to total energy efficiency potential, and to potential within each sector covered in

this study (residential, commercial, industrial). The conclusion summarizes this study's findings.

The appendices include information on measure and program assumptions, and more de tailed

distributions of the forecasts.ICF International v ComEd

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# Table of Contents

Executive Summary

......................................................................................................................... i

Study Objectives and Scope

............................................................................................................... i

Types of Potential Estimated

.............................................................................................................. i

Approach Summary

............................................................................................................................. ii

Stakeholder Process

........................................................................................................................... ii

Uncertainty

............................................................................................................................................ ii

Energy Efficiency Potential

................................................................................................................ iii

Organization of the Report

................................................................................................................ iv

1 Analysis Approach

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

1.1 Overview of Approach

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

1.2 Data Collection

............................................................................................................................. 8

1.3 Eligible Stock

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

1.4 Measure Analysis

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

1.4.1 Summary

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

1.4.2 Number of Measures Evaluated

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

1.4.3 Treatment of Codes and Standards

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

1.5 Economic Potential Approach

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

1.6 Achievable Potential Approach

............................................................................................... 14

1.6.1 Program Data Review

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

1.6.2 Achievable Potential Workshops

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

1.6.3 ICF Program Manager Review

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

1.6.4 ComEd Implementation Contractor Review

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

1.6.5 Program Performance Benchmarking

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

1.6.6 Additional Steps in the Maximum Achievable Analysis

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

1.7 Participation Examples

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

1.7.1 Example 1: Industrial Sub-Metering & Interval Metering

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

1.7.2 Example 2: Commercial LED Refrigerated Case Lighting

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

1.8 DCEO Programs

........................................................................................................................ 24

2 Total Energy Efficiency Potential

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

2.1 Summary

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

2.2 Total Economic Potential

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

2.3 Total Achievable Potential

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

2.4 Total Benefits and Costs

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

2.5 Portfolio benchmarking

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

3 Residential Energy Efficiency Potential

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

3.1 Summary

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

3.2 Residential Economic Potential

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

3.3 Residential Achievable Potential

............................................................................................. 32

3.3.1 Summary

............................................................................................................................... . 32

3.3.2 Residential Program Savings

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

3.3.3 Savings in 2013

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

3.3.4 Savings in 2018

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

4 Commercial Energy Efficiency Potential

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

4.1 Summary

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

4.2 Commercial Economic Potential

............................................................................................. 37ComEd Energy Efficiency Potential Study Report, 2013

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‐ 034 © 2013 August 20, 2013 4.3 Commercial Achievable Potential

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

4.3.1 Summary

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

4.3.2 Commercial Program Savings

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

4.3.3 Savings in 2013

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

4.3.4 Savings in 2018

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

5 Industrial Energy Efficiency Potential

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

5.1 Summary

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

5.2 Industrial Economic Potential

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

5.3 Industrial Achievable Potential

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

5.3.1 Summary

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

5.3.2 Savings in 2013

..................................................................................................................... 46

5.3.3 Savings in 2018

..................................................................................................................... 48

6 Conclusion

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

7 Appendices

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



## List of Figures

Figure ES-1. Total Achievable Potential, by Scenario and Year

............................................................................................. iii

Figure ES-2. Cumulative Achievable Potentia l and Total Program Costs, by Rate Class

.................................................. iv

Figure 1. Potential Study Approach

............................................................................................................................................. 8

Figure 2. Data Used in Analysis

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

Figure 3. Example Eligible Stock Calculation

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

Figure 4. Measure Characteristics

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

Figure 5. Number of Measures Evaluated and Included

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

Figure 6. Historical Appliance Recycling Program Participation

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

Figure 7. Appliance Recycling Program Performance Benchmarking

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

Figure 8. Industrial Sub-Metering & Interval Metering Measure and Participation Assumptions

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

Figure 9. Industrial Sub-Metering & Interval Metering Participation Curves

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

Figure 10. Commercial LED Refrigerated Case Lighting Measure & Participation Assumptions

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

Figure 11. Commercial LED Refrigerated Case Lighting Participation Curves

................................................................... 24

Figure 12. Alternative Total Load Forecasts

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

Figure 13. Distribution of Total Cumulative Economic Potential, by Sector, 2018

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

Figure 14. Distribution of Total Cumulative Economic Potential by End-Use, 2018

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

Figure 15. Total Achievable Potential, by Scenario and Year

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

Figure 16. Cumulative Achievable Savings by Sector and Scenario, 2018

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

Figure 17. Total Benefits, Costs and Costs-Effectiveness (2013-2018)

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

Figure 19. Alternative Residential Load Forecasts

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

Figure 20. Distribution of Cumulative Residential Economic Potential, by End-use, 2018

............................................... 32

Figure 21. Residential Achievable Potential, by Scenario and Year

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

Figure 22. Annual Residential GWh Savings by Program and Scenario, 2013 and 2018

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

Figure 23. Alternative Commercial Load Forecasts

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

Figure 24. Distribution of cumulative comme rcial economic potential, by end-use, 2018

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

Figure 25. Commercial Achievable Potential, by Scenario and Year

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

Figure 26. Commercial GWh Savings by Program and Scenario, 2013 and 2018

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

Figure 27. Alternative Industrial Load Forecasts

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

Figure 29. Industrial Achievable Potential, by Scenario and Year

........................................................................................ 46

Figure 30. Industrial GWh Savings by End-Use and Scenario, 2013 and 2018

................................................................. 47ICF International 7 ComEd

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# 1 Analysis Approach

1.1 Overview of Approach

ICF used a bottom

‐ up approach to estimate energy efficiency potential. The approach is illustrated in

Figure 1. "Bottom

‐ up," in the context of potential studies, refers to an approach that begins with

characterizing the eligible stock, screening measures for cost

‐ effectiveness, estimating savings first at

the measure

‐ level, then summing savings at the end

‐ use, sector an d overall service territory levels.

This study invo lved extensive collection of primary and secondary baseline, measure and program data.

Primary data included the 2012 ComEd baseline study, ComEd tracking data and evaluation reports, and

customer and trade ally survey data. Utility data, such as customer counts, avoided costs and load

forecasts were acquired from ComEd. Secondary data incl uded information from ICF baseline, measure

and program databases, and program performance research for benchmarking.

Estimating the eligible stock of efficiency options was the first step of the analysis. The eligible stock is

the size of the market for efficien cy measures, in measure uni ts, such as bulbs, tons of cooling, or

homes. ICF estimated the eligible stock for each measure within each end

‐ use and sector. The 2012

ComEd baseline study, conducted by Opinion Dynamics Corporation, was the primary source of

information for this stage in the analysis.

Next, ICF dev eloped a comprehensive measur e datab ase. The database includes all the measures in the

Illinois Technical Reference Manual ("TRM") plus additional measures included based on a gap analysis.

The final database includes commercially available measures covering each relevant savings opportunity

within each end

‐ use and sector. The database includes prescriptive or "deemed" ty pe measures, whole

building options (suc h as commercial custom projects), and behavioral measures (such as residential

Home Energy Report).

ICF then used DSMore to estimate measure cost

‐ effectiveness. Measures with an Illinois Total Resource

Cost Test ("TRC") result of 1.0 or greater were included in the economic potential analysis.

With the eligi ble stock and measures defined, ICF then cal c ulated economic potential. Economic

potential is theoretical maximum level of cost

‐ effective savings. It is the estimated savings that would

result from replacing the entire eligible stock with the most technically

‐ efficient cost

‐ effective measures.

Finally, ICF performed the achievable potential a nalysis. Program achievable potential is the level of

cost

‐ effective savings achievable un der ComEd's tota l program budget cap. Maximum achievable

potential is the level of cost

‐ effective savings absent the budget cap. ICF's approach to estimating

achievable potential involved extensive review of primary and secondary measure and program data,

the conduct of achievable potential wo rkshops, and program performance benchmarki ng.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Analysis Approach

ICF International 8 ComEd

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‐ 034 © 2013 August 20, 2013 Figure 1. Potential Study Approach

In the sub

‐ sections below, we discuss each step in the analysis in further detail.



## 1.2 Data Collection

The sources of information used in the analysis are shown in Figure 2. Every effort was made to use

information that was as current as possible, to use primary data, and to use assumptions specific to

ComEd's service territory. For example:

 Opinion Dynamics provided ICF with data from the 2012 baseline surveys and building audits.

 Current program tracking databases were provided to ICF by ComEd.

 ComEd also provided the most recent draft program evaluation reports as soon as they were

available. 

### Baseline Data

ComEd Baseline Report

ICF Databases



### Measure Analysis

IL TRM

ICF databases & analyses



### Economic Potential

Program Achievable

Potential Maximum Achievable

Potential Measure TRC Test

Program Costs,

Participation,

& NTG Data Collection Eligible Stock

• Review tracking data,

EM&V reports

• Achievable potential

workshops

• Program experience

(ComEd, ICF)

• BenchmarkingComEd Energy Efficiency Potential Study Report, 2013

– 2018 Analysis Approach

ICF International 9 ComEd

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‐ 034 © 2013 August 20, 2013  ICF used the most recently available program reports from other jurisdictions in conducting

benchmarking research.

 The Illinois TRM was the primary source of measure assumptions.

Figure 2. Data Used in Analysis

Data/Information Type Source Type of Data Primary Purpose in Analysis

Utility Information

Avoided costs ComEd Forecast Cost

‐ effectiveness testing

Customer counts ComEd Actual Calculating eligible stock

Load forecast ComEd Forecast Calculating load impacts of EE potential

Retail rates ComEd Actual Achievable potential analysis

Baseline Data

ComEd Baseline Report Opinion Dynamics

Corporation Primary Calculating eligible stock

ICF baseline databases ICF International Secondary Calculating eligible stock

Measure Assumptions

Illinois TRM IL Stakeholder Advisory

Group Measure parameters Measure database development

ICF measure databases ICF International Measure parameters Measure database development

Program Information

Program tracking data ComEd Actual Estimating achievable potential

Program evaluation reports ComEd, DCEO Primary Estimating achievable potential

Program expenditures ComEd Actual Estimating achievable potential

PY4

‐ PY6 Program Plan ComEd Plan Estimating achievable potential

ICF program data ICF International Secondary Estimating achievable potential

Program benchmarking data Program reports Secondary Estimating achievable potential

ComEd customer survey data Opinion Dynamics, ICF Primary Estimating achievable potential

Trade ally survey data Opinion Dynamics, ICF Primary Estimating achievable potential



## 1.3 Eligible Stock

Estimating the eligible stock of efficiency options was the first step of the analysis. The eligible stock is

the size of the market for efficiency measures, in measure units, such as bulbs, tons of cooling, or

homes. ICF estimated the eligible stock for each measure within each end

‐ use and sector. Th e ComEd

baseline study was the primary source of information for th is stage in the analysis. Key data from the

baseline study included items such as:

 The percent of homes with a particular type of equipment (e.g., light bulbs, central air conditioner,

refrigerator),

 Equipment counts (e.g., number of bulbs per home, tons of coolin g per ho me, refrigerators per home),

 Equipment efficiency level (e.g., bulb type, SEER rating, ENERGY STAR Rating), and

 Equipment age.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Analysis Approach

ICF International 10 ComEd

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‐ 034 © 2013 August 20, 2013 A simple example of an eligible stock calculation for residential specialty bulbs is shown below. This

example shows there are 15 million incandescent specialty screw

‐ in bulbs installed in homes in ComEd's

service territory (row g). This is equivalent to 70% of all specialty light bulbs installed (row f), and equals

the total eligible stock for this partic ular opportunity. That is, 70% percent of the existing stock of

residential specialty screw

‐ in bulbs could be replaced with more efficient units (e.g., a specialty CFL or

LED).

Since this is a "replace

‐ on

‐ burnout" measure, the eligible stock must account for stock turnover (ro w h).

Stock turnover is the rate at which existing e quip ment expires and requires replacement. It is the

inverse of equipment age, or one divided by the equipment's effective useful life (EUL). 1 After the

application of the stock turnover rate the total number of specialty bulbs eligible to be replaced in 2013

equals 13.7 million (row i). 2

Figure 3. Example Eligible Stock Calculation

Variable Value Source/Calc.

Efficient unit 15 Watt Specialty CFL

Baseline unit 60W Incandescent Specialty Lamp

a Baseline unit effective useful life 1.1 years IL TRM

b # Residential Customers 3,456,945 ComEd

c # Bulbs per Home 57 ComEd Baseline Study

d % Applicability (% of bulbs that are specialty applications) 11% ComEd Baseline Study

e Efficient unit saturation 30% ComEd Baseline Study

f Not yet adopted rate 70% 1

‐ e

g Total eligible stock in 2013 15,092,676 b*c*d*f

h Annual replacement eligibility (stock turnover rate) 91% 1/a

i Total # bulbs eligible to be replaced in 2013 13,720,615 b*c*d*f*h

For many measures, this information is broken down further in ICF's energy efficiency potential model.

For example, the eligible stock for residential central air conditioners is further broken down by:

 Efficiency rating (SEER level),

 Home heating type (electric or gas), and

 Decision type (replace

‐ on

‐ burnout, retrofit, new con struction).

In summary, calculating th e eligi ble stock is the foundation of the study. It tells us how big the total

market is for each efficiency opportunity in each year. What it does not tell us is the magnitude of the

savings or costs associated with each opportunity. These are accounted for in estimates of economic

and achievable potential.

1 For retrofit measures, annual replacement eligibility equals 100%.

2 ICF's potential model updates the eligible stock in every year of the analysis to account for measures installed in

previous years.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Analysis Approach

ICF International 11 ComEd

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‐ 034 © 2013 August 20, 2013 

## 1.4 Measure Analysis

1.4.1 Summary

ICF developed a comprehensive measure database for this study. The database includes all the

measures in the Illinois Technical Reference Manual ("TRM") plus additional measures included based

on a gap analysis. The final database includes commercially available measures covering each relevant

savings opportunity within each end

‐ use and sector. The database incl udes prescriptive or "deemed"

type measures, whole building options (such as commercial custom projects), and behavioral measures

(such as residential Home Energy Report). 3 Each measure has the characteristics shown in Figure 4.

Figure 4. Measure Characteristics

Measure Characteristic Value*

1. Applicable sector Residential

2. Applicable subsector Single Family

3. Building type Gas

‐ heated

4. End

‐ use Lighting

5. Measure name LED Downlight

6. Measure definition 14 LED Reflector Lamp

7. Baseline definition 35W Incandescent/Halogen MR16/ PAR16 pin

‐ based lamps

8. Measure unit Lamp

9. Measure delivery type Time

‐ of

‐ Sale (Replace

‐ on

‐ burnout)

10. Incremental cost $25.00

11. Baseline unit effective useful life 2 years

12. Efficient unit effective useful life 10 years

13. Incremental (annual) kWh savings 21 kWh

14. Incremental kW savings 0.0021 kW

15. Gas savings

‐ 0.48 therms 4

*Example shown is for LED downlights, also known as reflector lamps.



## 1.4.2 Number of Measures Evaluated

In total, ICF analyzed 192 measure types. An example of a measure type is a residential central air

conditioner ("CAC"). Many measures required permutations for different applications, such as different

building types, lamp wattages, efficiency levels and decision types. For example, there are permutations

of CACs by SEER level, subsector, and building type. As shown in Figure 5, ICF developed a total of 3,799

measure permutations for this study.

3 Retrocommissioning includes some behavior

‐ based measures for the commercial sector, and System measures

include behavior

‐ based options for the industrial sector.

4 For a gas heated home, installing an LED results in an increase in annual gas usage because LEDs produce less

waste heat.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Analysis Approach

ICF International 12 ComEd

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‐ 034 © 2013 August 20, 2013 ICF tested all measures for cost

‐ effectiveness using DSMore, the Integral Analytics software tool used by

Illinois utilities for this purpose. Of the 3,799 measures analyzed, about half, or 1,926 measures had a

measure TRC benefit

‐ cost ratio of 1.0 or higher. 5 This is shown in Figure 5. Of this cost

‐ effective subset

of measures, ICF used 1,452 in calculating economic potential. The number of measures used is less than

were cost

‐ effectiveness because of the definition of economic potential: only the most technically

‐

efficient cost

‐ effective measures were applied. For example, if a florescent lamp and a LE D lamp were

both applicable to a partic ular lighting o pportunity (and both were cost

‐ effective), the LED was applied,

since it has a high lumen per watt rating that the florescent option.

Figure 5. Number of Measures Evaluated and Included

Sector # Measure

Types

Evaluated Total # Measures

Evaluated

(All Measure

Permutations) # with

TRC ≥ 1 Total #

Included in

Economic

Potential Total #

Included in

Achievable

Potential

Residential 52 1,147 266 266 556

Commercial 69 1,392 818 344 868

Industrial 70 1,204 842 842 842

Total 191 3,743 1,926 1,452 2,266

Figure 5 also shows that ICF used 2,266 measures in the achievable potential analysis. This is more than

the number of cost

‐ effective measures. Including non

‐ cost

‐ effective measures was a decision process

that occurred on a case

‐ by

‐ case basis. Most of the non

‐ cost

‐ effective measures included are LED lamps

and residential CACs. Some LED lamps were not cost

‐ effective base d up on current costs. However, there

is ample evidence that LEDs costs will decline rapidly. 6 For this reason ICF included all LED measures in

the achievable potential analysis.

Like LEDs, residential CACs were considered a special case in the measure cost

‐ effectiveness analysis.

Efficient residential CACs (units with a SEER rating of 14.5 or higher) are not cost

‐ effective as standalone

measures. However, they can be cos t

‐ effective when paired wi th an efficient gas furnace in a complete

system replacement ("CSR"). This is the basis of ComEd's Residential CSR program, which ComEd delivers

jointly with the gas utilities. For the purposes of the achievable potential analysis, ICF included residential

CAC measures under the assumption that they wo uld only be installed as par t of a CSR package.

There were also a small number of cases where ICF included non

‐ cost

‐ effective permutations of a

measure when the majority of similar permutations were cost

‐ effective. For example, if a measure was

cost

‐ effective for a majority of, but not all building types, ICF included the measure for all bui lding types

5 Measure TRC benefits are avoided costs. Measure TRC costs are incremental costs. The measure TRC test does

not include program costs.

6 U.S. Department of Energy. Product Snapshot: LED Replacement Lamps. Prepared by D&R International. July

2012.

U.S. Department of Energy. Solid

‐ State Lighting Research & Development: Multi

‐ Year Program Plan. Prepared

by Bardsley Consulting et al. April 2012.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Analysis Approach

ICF International 13 ComEd

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‐ 034 © 2013 August 20, 2013 in the achievable potential analysis. This is because it can be impractical in implementation to exclude

participation in specific building types.

ICF also applied the converse principal in screening measures in a small number of cases. If a measure

was cost

‐ effective for a minority of, but not all measure permutations, ICF excluded all permutations of

the measure in the achievable potential analysis, since it can be impracti cal in implementation to limit

participation to certain building types.



## 1.4.3 Treatment of Codes and Standards

ICF accounted for adopted codes and standards in this study. Key baseline changes are discussed below:

 Residential general service lighting baselines reflect the minimum efficiency standards and schedule

set forth in the Energy Independence and Security Act of 2007 ("EISA 2007") and by the U.S.

Department of Energy ("DOE"). 7 EISA 2007 results in a 30% increase in baseline efficiency for general

service lighting. This is important because standard CFLs account for the largest portion of ComEd's

historical residential savings.

 New construction and retrofit measure baselines reflect prevailing state building codes

(International Energy Conservation Code, or "IECC 2012"). IECC 2012 requires a 15% improve ment in

baseline efficiency over IECC 2009. This is important because it impacts the cost

‐ effectiveness of

retrofit and new constr uction programs, such as Single Family Home Performance, and Commercial

New Construction.

 U.S. DOE rules pertaining to commercial lamps and ballasts are reflected in baselines for line ar

florescent lighting. 8 These rules result in a 20% improvement in baseline efficiency for linear

florescent lamps. 9 This is important because linear florescent retrofits (e.g., replacing T12s with T8s

or T5s) account for the largest portion of ComEd's historical commercial lighting savings.



## 1.5 Economic Potential Approach

Economic potential is the amount of electric energy savings that would result if the entire eligible stock

in ComEd's service territory were replaced with the most technically

‐ efficient, cost

‐ effective energy

efficiency measures.

Calculating economic potential required four steps:

1. Estimating the eligible stock,

2. Defining and testing measures for cost

‐ effectiveness,

3. Estimating savings in 2013, and

4. Estimati ng sa vings in 2014 through 2018.

7 ICF followed the IL TRM specifications for these measures, which reflect EISA 2007.

8 Consistent with the U.S. Energy Policy Act of 2005.

9 The rules specify a switch from magnetic ballast baseline to an electronic ballast baseline.ComEd Energy Efficiency Potential Study Report, 2013

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ICF International 14 ComEd

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‐ 034 © 2013 August 20, 2013 The approaches to estimating the eligible stock and defining and testing measures are discussed above.

Steps three and four are discussed below.

Economic potential is calculated once the eligible stock and cost

‐ effective measures are established. The

remaining steps included:

 Applying all available opportunities to the eligible stock in 2013. In th eory, this means replacing all

electri c ity

‐ using equipment at once with the most technically

‐ efficiency cost

‐ effective options

except in cases where existing equipment is equally or more efficient than such options. Retrofit,

replace

‐ on

‐ burnout and new construction measures were applied in 2013. Savings associated with

these applications in2013 is the "ins tantaneous" economic potentia l.

 For every subsequent year of the analysis (2014 through 2018) all available replace

‐ on

‐ burnout and

new construction measures were applied to the eligible stock. Replace

‐ on

‐ burnout measures were

applied where there is stock turnover. New construction measures were applied to new buildings. 10



## 1.6 Achievable Potential Approach

Achievable potential is the amount of savings that could be realistically achieved by utility programs. ICF

estimated two levels of achievable potential in this study: program and maximum. Program achievable

potential is the amount of savings that could be realistically achieved by ComEd if program spending is

subject to current legislative restrictions (2% of custo mers’ total electric costs per year). Max i mum

achievable potential is the estimated amount of savings that could be attained if there were no program

spending limits. Cost

‐ effectiveness is still a constraint in the maximum achievable scenario.

ICF developed achievable potential estimates on a measure

‐ by

‐ measure basis through a combination of

extensive research, ex p ert input, and program performance benchmarking. Measure

‐ level estimates

were summed to the program or end

‐ use level, then to the sector and service

‐ territory levels for

analysis and reporting.

The ICF approach to estimating achievable potential involved eight steps:

1. Program data review,

2. Achievable potential workshops,

3. ICF program manager review,

4. ComEd i mplementation contractor re view,

5. Program performance benchmarking,

and, for the maximum achievable scenario,

6. Additional review of the eligible stock,

7. Comparative incentive analysis, and

8. Additional benchmarking analysis.

10 A retrofit measure can only be applied in year one of the analysis since the baseline changes to the efficient unit

once it is installed.ComEd Energy Efficiency Potential Study Report, 2013

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ICF International 15 ComEd

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‐ 034 © 2013 August 20, 2013 Each step is discussed in more detail below.



## 1.6.1 Program Data Review

ICF conducted an in depth review of all available information pertaining to ComEd's current energy

efficiency programs. This information included program tracking data, evaluation reports, the PY4

‐ PY6

program plan, and other information. The purpose of this step was to understand historical program

delivery and performance, and to prepare for the achievable potential workshops.

For example, reviewing evaluation repo rts for the Appliance Recycling program provided insight into

historical part icipa tion, savings and net

‐ to

‐ gross levels. Figure 6 shows total historical program

participation and participation impacts as a percentage of the residential customer base.

Figure 6. Historical Appliance Recycling Program Participation

Sources: ComEd evaluation reports and customer data.



### Program Costs

Historical program expenditures were provided by ComEd to ICF. These were used to help guide

program costs for the forecast. For existing programs, ICF's cost forecasts were reviewed by ComEd

program managers. For measures or programs not offered historically by ComEd, ICF developed

program cost estimates based on ICF's program implementation exp erience.



### Net

‐ to

‐ Gross Ratios

ICF used the most currently available ComEd evaluation reports and ComEd planning values as net

‐ to

‐

gross assumptions for this forecast. These values are static for the time horizon of the study. Net

‐ to

‐

gross assumptions are shown in the Appendix. 0.0% 0.2% 0.4% 0.6% 0.8% 1.0% 1.2% 1.4% 1.6%

0 10,000 20,000 30,000 40,000 50,000 60,000

1234UnitsHarvested

Program Year

Units Harvested % of Residential Customer BaseComEd Energy Efficiency Potential Study Report, 2013

– 2018 Analysis Approach

ICF International 16 ComEd

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‐ 034 © 2013 August 20, 2013 

## 1.6.2 Achievable Potential Workshops

ICF conducted achievable potential workshops with ComEd. The purpose of the workshops was to

develop participation estimates for representative measure types. The workshops involved in

‐ depth

discussion and analyses of key measures representing each end

‐ use, program, or sector. ComEd and ICF

program managers and planners attended each workshop. ComEd program evaluators atten ded some

workshops.

Workshop conten t varied depending on whether the measure or program was offered historically by

ComEd, but the general structure of each workshop was as follows:

1. Introduction and purpose

.

2. List of measure

‐ types for discussion in workshop. Since it is impractical to review all cost

‐ ef fective

measures during workshops, representative measur es were selected by ICF for discussion. A

measure was considered "representative" if the market's response to the measure could be

generalized to similar measures. For example, 14 watt CFLs were selected as the representative

"standard CFL" measure for discussion in the residential lighting workshop.

Then, for each representative measure the following was discussed:

3. Meas ure parameters. Savings, costs, lifetime, etc.

4. Measure co st

‐ effectiveness

. Measure TRC results from DSMore were discussed.

5. Level setting (if historical measure). Review of historical program participation savings and costs. 11

6. Market barriers

. Market barriers to participation were identified and discussed.

7. Solutions. Where possible, solutions to overcome each market barrier were identified and

discussed.

8. Achievable participation was then estimated based on the workshop attendees' understanding of

the measure, historical participation, and market barriers and solutions. Program and maximum

achievable participation was estimated for 2013 and 2018. The n, various market penetration curves

were presented by ICF. 12 These curves were discussed and the workshop attendees' selected a curve

they believed would most likely represent the trajectory of the measure.

In total, ICF conducted 10 workshops in the course of the achievable potential analysis.

Note that in order to estimate measure

‐ level participation, a measure first has to be part of a program,

since the program is the vehicle through which savings are delivered. Each program represents a spe c ific

set of strategies and tactics designed to overcome barriers to energy efficiency. Each measure analyzed

11 In some cases it was difficult to interpret program tracking data. ComEd program managers helped clarify

historical program participation and costs.

12 Examples of market penetration curves include: linear, exponential, "S

‐ curves,” and growth

‐ and

‐ decline (where

participation peaks then declines due to the size of the eligible stock or other factors). Custom curves can also

be developed in ICF's potential model to account for factors such as baseline changes.ComEd Energy Efficiency Potential Study Report, 2013

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ICF International 17 ComEd

13

‐ 034 © 2013 August 20, 2013 was assigned to a specific program type. Most of these program types are consistent with ComEd's

current program designs, or what ICF or ComEd thought would be the next generation of a program.

These programs are described in the sector

‐ level energy efficiency potential sections of this report.



## 1.6.3 ICF Program Manager Review

ICF program implementation managers also provided input to the achievable potential analysis. ICF

managers reviewed information on ComEd measure and program market size, existing program

performance, and program design in light of ICF program information in other jurisdictions to inform the

analysis.



## 1.6.4 ComEd Implementation Contractor Review

ComEd's prime commercial program implementation contractor reviewed ICF's draft commercial potential

estimates and added insight into program tracking data, market size and participation estimates.



## 1.6.5 Program Performance Benchmarking

ICF also conducted research on the performance of other utility programs that exhibit best practices, are

ComEd's peers, or both. Benchmarking program performance helped put ComEd program performance

and draft achievable potential estimates into context. For example, Figure 7 shows the performance of

appliance recycling programs in other jurisdictions, and that of Co mEd's program.

Figure 7. Appliance Recycling Program Performance Benchmarking

Administrator State ACEEE

2012 EE

State

Scorecard

Ranking Program

Expen

‐

ditures

($Millions) MWh

Savings

(Net) $ Per 1st

Year

kWh Year Savings

as % of

Residential

Sales

Alliant Energy—Iowa (Interstate P&L) IA 11 $1.6 14,014 $0.11 2011 0.33%

ComEd IL 14 $8.2 72,302 $0.11 PY4 0.26%

DTE Energy MI 12 $2.9 35,109 $0.08 2011 0.22%

PECO PA 20 $3.0 25,908 $0.12 2011 0.19%

Southern California Edison (SCE) CA 2 $12.1 45,982 $0.26 2011 0.16%

Arizona Public Service AZ 12 $1.3 14,168 $0.09 2011 0.11%

Con Edison NY 3 $1.8 6,349 $0.28 2011 0.04%

Xcel Energy—Minnesota MN 9 $0.7 3,717 $0.18 2011 0.04%

Sources: Utility program reports, ACEEE, U.S. EIA.

Energy efficiency program performance benchmarking is a tricky exercise. It is difficult to compare even

similar programs on an apples

‐ to

‐ apples basis due to differences in regulation, codes and standards,

evaluation, electricity costs and retail rates, market size, demographics, and other factors. Because of

such differences "averaging" results across jurisdictions is not parti cularly useful, nor is it reasonable to

assume savings impa cts achieved in one jurisdiction in one particular year in the past could be replicatedComEd Energy Efficiency Potential Study Report, 2013

– 2018 Analysis Approach

ICF International 18 ComEd

13

‐ 034 © 2013 August 20, 2013 in another jurisdiction in future year. Nonetheless, when done carefully benchmarking can shed light on

program performance or savings forecasts.

For the purposes of this study, ICF selected program benchmarks from states exhibiting best practices in

energy efficiency, 13 or that are ComEd's peer in some manner, or both. For example, Xcel Energy

‐ Minnesota

is a ComEd peer because it is a relatively large investor owned utility operating in a similar climate zone.

It is worth noting a few items to help put Figure 7 in context. First, DTE Energy operates in Michigan, which

has a deemed net

‐ to

‐ gross ratio of 0.9 fo r all programs. This is much higher than evaluated net

‐ to

‐ gross

results for most appliance recycling programs. If 0.9 is an overestimate, then DTE's net savings are

overestimated and the cost

‐ effectiveness estimate (8 cents per firs t year kWh) is low. Also, SCE has

operated its re cyc ling program for many years. The relatively high dollar per kWh value for SCE's program

may reflect a depleting eligible stock. That is, most of the very old and inefficient refrigerators and freezers

may have been harvested, and the marginal unit may have be come less cost

‐ effectiv e to harv est.

Compared to the other programs in Figure 7, ComEd's program had one of the biggest impacts (as a % of

residential sales) and was among the more cost

‐ effective programs in that year. Prior to this

benchmarking exercise, ICF already had a strong grasp on ComEd's program design, and un derstood it to

exhibit best practi c es based on our experience as an implementation contractor. The added value of the

benchmarking data is that it helped provide perspective beyond ICF's and ComEd's experience. If

ComEd's program performed poorly relative to the benchmarks ICF would need to dig dee per and figure

out whether this was likel y due to the budge t cap or to other factors.

Program benchmarking data was collected for many program types, and at the portfolio level.



## 1.6.6 Additional Steps in the Maximum Achievable Analysis

The maximum achievable scenario involved increasing all incentives to 100% and reconsidering program

designs. ICF conducted further analyses to help estimate additional savings that could be gained in such

a scenario, including:

 Re

‐ reviewing the eligible stock. While budget is not a constraint under the maximum achievable

scenario, market size is. We carefully reviewed the eligible stoc k to help inform how much more

savings ComEd could gain beyond the program achievable scenario. For example, data from the

baseline study showed the average age of installed chillers is 15 years. The lifetime of a chiller is 20

years. Therefore, the market size for chi llers is fairly limited since most chillers will burnout after the

six year ti me horizon of this study.

 Comparative incentive analysis. Incentive levels in the program achievable potential scenario are

consistent with ComEd's current incentive levels, and are generally between 25% and 75% of

measure incremental cost. All ince ntives in the maximum achievable scenario are 100% of

incremental cost. ICF analyzed the addi ti onal impact increased incentives would have on program

participation. For measures or programs where incentives are less important, the additional

13 Indicated by the ACEEE state scorecard ranking.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Analysis Approach

ICF International 19 ComEd

13

‐ 034 © 2013 August 20, 2013 incentive has little to no impact. This is the case with the commercial New Construction program. In

other cases, the 100% incentive has a large impact, as is the case with the Small Business program.

Where relevant, ICF compared customer payback acceptance for measures 14 in the program and

maximum scenarios. A large increase in payback acceptance could indicate there is large amount of

additional potential for that measure type, all else equal.

 Benchmarking

. In some cases, ICF used data from high performing programs around the country to

help gauge the upper limits of ComEd prog ram performance in the maximu m s cenario.



## 1.7 Participation Examples

As discussed above, ICF assessed achievable participation on a measure

‐ by

‐ measure basis. Because

there is such a wide variety of measures included in this study, we could not apply just one formulaic

approach to estimating program participation for all measures. In order to better describe the steps

taken in this study to forecast parti cipation, we provide two exa m ples, below.

ICF underwent each step outlined above, in Section 1.6, to forecast participation for the below

measures. Note, however, that some forecasting tactics varied by measure. For example, for the first

measure, industrial sub

‐ metering and interval metering, ICF relied largely on data from outside ComEd's

service territory, since this is not a measure offered historically by ComEd, nor is it a deemed measure.

Nor did we consider cus t omer payback a factor in estimating participation for this measure, as we

believe payback is generally not a significant factor in customers decision making processes to install

sub

‐ me ters. On the other hand, LED case lighting is a measure offered hi st orically by ComEd; therefore,

ICF was able to consider program tracking data in forecasting participation. And unlike for sub

‐ metering,

ICF did consider customer payback as one factor in estimating participation for LED case lighting.



## 1.7.1 Example 1: Industrial Sub

‐ Metering & Interval Metering

Industrial facility sub

‐ metering and interval metering is a retrofit measure designed to help industrial

facility managers better manage energy use. Sub

‐ metering is considered a behavioral measure, since

savings result not from the physical installation of the measure, but through actions taken based upon

information gained through sub

‐ metered data (e .g., the identification of equipmen t sch eduling issues,

or sub

‐ optimal equipment performance). This is not a measure offered historically by ComEd, nor is it a

deemed measure. Therefore, ICF relied largely on data from outside ComEd's service territory, including

ICF databases and other sources, 15 along with information gained during the industrial achievable

potential workshop, to develop measure parameters and participation estimates.

14 Customer payback acceptance is the estimated portion of customers that state they will install a measure given

its simple payback. Simple payback is the dollar amount invested by the customer in the measure (the

incremental cost minus the incentive) divided by the annual bill savings due to the measure, expressed in

months or yea rs. Customer paybac k accept ance rates were estimated for residential and non

‐ residential

customers based on self

‐ reported values collected through surveys instrumented in the course of this study.

These surveys are shown in the Appendix.

15 Worrel, E.; Galitsky, C. Energy Efficiency Improvement and Cost Saving Opportunities for Breweries, an Energy

Start Guide for Energy and Plant Managers. Ernest Orlando Lawrence Berkeley National Laboratory. 2003.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Analysis Approach

ICF International 20 ComEd

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‐ 034 © 2013 August 20, 2013 Figure 8 shows key measure assumptions, and participation estimates, for industrial sub

‐ metering and

interval metering. Participation estimates for this measure were developed during the industrial

achievable potential workshop through the process discussed in Section 1.6.2, above. Figure 9 shows the

resulting participation curves for this measure under each achievable potential scenario. Th e consensus

during the workshop was that potentia l growth for this measure is exponential. This reflects the

maturity of the technology, its relatively low current market saturation, and its strong value proposition

for industrial customers.

Figure 8. Industrial Sub

‐ Metering & Interval Metering Measure and Participation Assumptions

Measure Characteristic Value Source

1. Applicable sector Industrial ICF

2. Applicable subsector(s) All ICF

4. End

‐ use(s) All ICF

5. Measure name Sub

‐ metering & interval metering ICF

6. Measure definition Sub

‐ meters installed ICF

7. Baseline definition Existing level of sub

‐ metering ICF

9. Measure delivery type Retrofit ICF

10. Measure unit Facility ICF

11. Incremental cost $363,000 LBNL, ICF

12. Effective useful life 15 years LBNL, ICF

13. Incremental (annual) kWh savings 5% of facility baseline energy use LBNL, ICF

14. Current measure saturation rate 19% LBNL, ICF

15. Incentive, Program Achievable Scenario $0.07 per kWh (29% of

incremental cost) ICF

16. Incentive, Maximum Achievable Scenario $0.24 per kWh (100% of

incremental cost) ICF

17. Annual market acceptance rate in 2013,

Program Achievable Potential Scenario 1.0% Achievable potential workshop

18. Annual market acceptance rate in 2013,

Maximum Achievable Potential Scenario 1.5% Achievable potential workshop

19. Annual market acceptance rate in 2018,

Program Achievable Potential Scenario 27.0% Achievable potential workshop

20. Annual market acceptance rate in 2018,

Maximum Achievable Potential Scenario 53.0% Achievable potential workshop

21. Market penetration curve type Exponential distribution function Achievable potential workshopComEd Energy Efficiency Potential Study Report, 2013

– 2018 Analysis Approach

ICF International 21 ComEd

13

‐ 034 © 2013 August 20, 2013 Figure 9. Industrial Sub

‐ Metering & Interval Metering Participation Curves



## 1.7.2 Example 2: Commercial LED Refrigerated Case Lighting

LED refrigerated case lighting involves retrofitting florescent lighting with LED lamps in refrigerated

display cases in grocery stores and other commercial facilities. Measure savings and costs were sourced

using information in the IL TRM and through ICF research. Current market saturation was sourced from

the ComEd baseline report. LED case lighting is a me asure offered historically by ComEd, and ICF used

ComEd tracking da ta to he lp estimate participation in the first year of the analysis (2013). The forecast 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2013 2014 2015 2016 2017 2018AnnualMarketPenetration

Maximum

Achievable Potential

Program Achievable

PotentialComEd Energy Efficiency Potential Study Report, 2013

– 2018 Analysis Approach

ICF International 22 ComEd

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‐ 034 © 2013 August 20, 2013 for the remaining years of the analysis (2014

‐ 2018) was developed based on secondary research, 16

primary research on C&I customer decision making, 17 and ICF commercial program experience.

Figure 10 shows key measure assumptions, and participation estimates, for LED case lighting. Figure 11

shows the resulting participation curves for this measure under each achievable potential scenario. At

the estimated incentive levels, and based on ICF's understanding of the market for this measure in

ComEd's serv ice territory, and nationwide, ICF believes this measure will move out of the "e arly

‐

adopter" pha se and into the "mid

‐ adopter" phase over the next five years. Our forecast for the diffusion

of LED case lighting in the marketplace is represented by a logistic distribution function; such functions

in ICF's pote ntial model are based in part on well

‐ kn own research cond ucted by Frank Bass, 18 and

others.

Figure 10. Commercial LED Refrigerated Case Lighting Measure & Participation Assumptions

Measure Characteristic Value Source

1. Applicable sector Commercial ICF

2. Applicable subsector(s) All ICF

4. End

‐ use(s) Refrigeration ICF

5. Measure name LED Case Lighting ICF

6. Measure definition LED Refrigerated Case Lighting

(29W or less) ICF

7. Baseline definition T12 Refrigerated Case Lighting IL TRM, ICF

9. Measure delivery type Retrofit IL TRM, ICF

10. Measure unit Linear ft. IL TRM, ICF

11. Incremental cost $43.75 IL TRM, ICF

12. Effective useful life 10 years IL TRM, ICF

13. Incremental (annual) kWh savings 56 kWh IL TRM, ICF

14. Current LED saturation rate 2.0% ComEd 2012 Baseline Study, Opinion

Dynamics Corporation

15. Incentive, Program Achievable Scenario $32.81

(75% of incremental cost) ICF assumption

16 U.S. Department of Energy. Product Snapshot: LED Replacement Lamps. Prepared by D&R International. July 2012.

U.S. Department of Energy. Solid

‐ State Lighting Research & Development: Multi

‐ Year Program Plan. Prepared by

Bardsley Consulting et al. April 2012.

Southern California Edison. The Southern California Edison (SCE) Advanced Light Emitting Diode (LED) Ambient

Light ing Program Customer Preference and Market Pri c ing Trial

. Prepared by Opinion Dynamics Corporation et al.

December 2012.

17 See discussion on comparative incentive analysis in Section 1.6.6, above. Based on ComEd C&I customer survey

data (see Appendix for survey), ICF estimated that two

‐ thirds of customers would accept the payback terms for

LED case lighting under the program achievable scenario (1.7 years); under the maximum achievable scenario,

we estimated 10 0% of customers would accep t the payback terms (0 years). However, since there are many

market barriers to measure adoption, payback acceptance rates were only factor considered in estimating

participation for this measure

18 Bass, Frank M. A New Product Growth Model for Consumer Durables

. Management Science, Vol. 14 No. 5. 1969.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Analysis Approach

ICF International 23 ComEd

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‐ 034 © 2013 August 20, 2013 Measure Characteristic Value Source

16. Incentive, Max. Achievable Scenario $43.75

(100% of incremental cost) Max. achievable scenario assumption

17. Post

‐ incentive simple payback,

Program Achievable Scenario 1.7 years (Incremental Cost

‐ Incentive)/

Customer energy bill savings

18. Post

‐ incentive customer payback

acceptance estimate, Program Achievable

Scenario 66.0% ComEd C&I Customer Adoption Survey,

ICF Calculation

19. Post

‐ incentive simple payback, Maximum

Achievable Scenario 0 years (Incremental Cost

‐ Incentive)/

Customer energy bill savings

20. Post

‐ incentive customer payback

acceptance estimate, Maximum

Achievable Scenario 100.0% ComEd C&I Customer Adoption Survey,

ICF Calculation

21. Annual market acceptance rate in 2013,

Program Achievable Potential Scenario 3.5% ComEd tracking data, ICF estimate

22. Annual market acceptance rate in 2013,

Maximum Achievable Potential Scenario 10.0% ICF estimate based upon research

(U.S. DOE, SCE, ComEd C&I Customer

Adoption Survey), and ICF commercial

program manager experience

23. Annual market acceptance rate in 2018,

Program Achievable Potential Scenario 10.5% ICF estimate based upon research

(U.S. DOE, SCE, ComEd C&I Customer

Adoption Survey), and ICF commercial

program manager experience

24. Annual market acceptance rate in 2018,

Maximum Achievable Potential Scenario 30.0% ICF estimate based upon research

(U.S. DOE, SCE, ComEd C&I Customer

Adoption Survey), and ICF commercial

program manager experience

25. Market penetration curve type Logistic distribution function ICF assumptionComEd Energy Efficiency Potential Study Report, 2013

– 2018 Analysis Approach

ICF International 24 ComEd

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‐ 034 © 2013 August 20, 2013 Figure 11. Commercial LED Refrigerated Case Lighting Participation Curves



## 1.8 DCEO Programs

ICF included estimates of DCEO achievable potential in this study. Estimates for DCEO programs were

developed based upon an analysis of DCEO program evaluation reports and historical program costs.

Average per project savings were developed for each program and adjusted to reflect adopted baseline

changes. Next, program costs per project were esti mated based on historical participat ion and costs.

Participation was then extrapolated for each year according to ComEd's spending cap for DCEO

programs, historical program performance, and ICF's understanding of market size and market barriers

for each program. 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%

2013 2014 2015 2016 2017 2018AnnualMarketPenetration

Maximum

Achievable

Potential

Program

Achievable

PotentialICF International 25 ComEd

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‐ 034 © 2013 August 20, 2013 

# 2 Total Energy Efficiency Potential

This section includes the presentation and analysis of ICF's estimates of total economic and achievable

potential for ComEd's service territory for 2013 through 2018. Total potential is the sum of residential,

commercial, and industrial potential. Electric savings and program cost estimates are shown, as well as

benefit

‐ cost estimates.



## 2.1 Summary

Figure 12 shows ComEd's base case total load forecast, as well as alternative total load forecasts

generated by ICF that account for savings estimated under each scenario in this study. 19 2012 is the base

year for this analysis, and 2013 through 2018 are the study years. In the base case, load grows at an

average rate of 1.7% per year. The program potential scenario savings would cut load grow by over half,

whereas savings under the maximum achievable scenario would completely offset load grow th by 2016,

and would result in a decrease in annual load in 201 7 and 2018.

The load forecast accounting for economic potential has a "hockey stick" shape because all retrofit

measures are applied in 2013, whereas only replace

‐ on

‐ burnout and new construction measures are

applied in 20 14 through 2018. 20 Most measures in the analysis are retrofit in nature.

Note that all estimates shown include values for DCEO programs unless otherwise noted.

Figure 12. Alternative Total Load Forecasts

19 To develop the alternative load forecasts, cumulative savings forecasts were subtracted from the base case

load forecast.

20 Replace

‐ on

‐ burnout and new construction measures are also applied in 2013. 0 20,000 40,000 60,000 80,000 100,000 120,000

2012 2013 2014 2015 2016 2017 2018TotalLoad(GWh) Base case load forecast

Load forecast if program potential achieved

Load forecast if maximum potential achieved

Load forecast reflecting economic potentialComEd Energy Efficiency Potential Study Report, 2013–2018 Total Energy Efficiency Potential

ICF International 26 ComEd

13

‐ 034 © 2013 August 20, 2013 

## 2.2 Total Economic Potential

Figure 13 shows the distribution of economic potential by sector. As discussed above, economic

potential is the amount of savings due to installing the most technically

‐ efficient cost

‐ effective

measures.

The distribution of economic potential reflects three constraints: the portion of load by sector, the size

of the eligible stock by sector, and th e number and type of cost

‐ eff ective measu res by sector. 21

Figure 13. Distribution of Total Cumulative Economic Potential, by Sector, 2018

(30,009 GW h

, 32% of Total Load in 2018)

Figure 14 shows the distribution of total economic potential by end

‐ use. Lighting represents a large

portion, 57%, of cost

‐ effective potential across all sectors. This is because there is a high saturation of

lighting in every sector (i.e., every building has lighting), and because there is still a large eligible stock

for efficient li ghting. For example, about 70% of residential li ghting could be replaced with CFLs or LEDs,

and nearly 60% of commercial linear florescent lighting could be replaced with more efficient T8/T5 or

LED options.

21 ComEd updated its avoided costs in June 2013, following the completion of this analysis. In July 2013, all

measures were re

‐ run in DSMore using the updated avoided costs to examine changes in measure cost

‐

effectiveness. Several additional measures were found to be cost

‐ effective. If these measures are included in

the an alysis of economic pot ential, total cumulative economic potential would increase 2,848 GWh, or 2% in

2018. Load impacts would increase from 32% to 34% in 2018.ComEd Energy Efficiency Potential Study Report, 2013–2018 Total Energy Efficiency Potential

ICF International 27 ComEd

13

‐ 034 © 2013 August 20, 2013 It is also important to note that retrocommissioning ("RCx") is the second largest opportunity, because

RCx is much different than lighting. RCx is a "comprehensive" opportunity involving a commercial

building tune

‐ up and building manager education. Shell measures, such as insulation and air sealing

represent the third largest economic savings potential.

Figure 14. Distribution of Total Cumulative Economic Potential by End

‐ Use, 2018 22



## 2.3 Total Achievable Potential

Total achievable potential is the sum of achievable potential estimated for each measure in the analysis.

Total incremental and cumulative achievable potential estimates are shown in Figure 15, as well as load

impacts. ICF estimates that, with the budget cap, ComEd can achieve annual savings equal to 1.0% of

load per year

. Without a budget cap (in the maxi mu m achievable scenario) annual savings estimates are

150% higher in 2013 and 250% higher than program achievable savings in 2018.

On a cumulative basis, these savings equal 5% of load in 2018 in the program scenario, and 10% of load

in the maximum scenario.

22 Note economic potential does not account for savings due to the residential Home Energy Report

benchmarking program. Appliances

5% Compressors

1% Data Centers

<1% Food

Service

Equipment

1% Hot Water

5%

HVAC

7%

Lighting

57% Motors

2% New

Construction

1% Other

3% Industrial Process

1% Retro

‐

Commissioning

9% Refrigeration

3% Shell

7% Industrial System

1% Ventilation

1%ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Total Energy Efficiency Potential

ICF International 28 ComEd

13

‐ 034 © 2013 August 20, 2013 Figure 15. Total Achievable Potential, by Scenario and Year

2013 2014 2015 2016 2017 2018

Cumulative Savings Forecast—GWh

Economic potential 27,610 28,162 28,679 29,161 29,634 30,009

Maximum achievable potential 1,122 2,453 3,767 5,430 7,104 8,693

Program achievable potential 824 1,649 2,294 3,043 3,778 4,387

Cumulative Savings Forecast— % of load

Maximum achievable potential 1% 3% 4% 6% 8% 10%

Program achievable potential 1% 2% 3% 3% 4% 5%

Incremental Savings Forecast—GWh

Maximum achievable potential 1,122 1,438 1,602 1,865 1,956 2,111

Program achievable potential 766 868 827 846 828 846

Incremental Savings Forecast—% of load

Maximum achievable potential 1.3% 1.6% 1.7% 2.1% 2.1% 2.4%

Program achievable potential 0.9% 1.0% 0.9% 1.0% 0.9% 1.0%

Program Costs (Millions, Real 2013$)

Maximum achievable potential $265 $349 $426 $487 $488 $527

Program achievable potential $125 $137 $139 $146 $152 $157

Figure 16 shows the distribution of cumulative savings by sector for each scenario, for ComEd programs

only (i.e., excluding DCEO program savings). Commercial achievable potential is 11% higher in the

maximum scenario; as discussed in the commercial achievable potential analysis, efficient lighting

constitutes most of the additional commercial potential.

Figure 16. Cumulative Achievable Savings by Sector and Scenario, 2018

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

Prog. Achievable Max. AchievableCumulativeGWhSavings,2018

Industrial

Commercial

Residential 12% 9%

63%

52%

36% 28%ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Total Energy Efficiency Potential

ICF International 29 ComEd

13

‐ 034 © 2013 August 20, 2013 

## 2.4 Total Benefits and Costs

Figure 17 shows total TRC benefits, costs, net benefits, and cost

‐ effectiveness estimated under both

achievable potential scenarios. Net TRC benefits are $0.8 Billion in the program scenario and $2.4 Billion

in the maximum scenario.

Benefits and costs both triple in the maximum scenario; overall, the scenarios are equally cost

‐ effective.

In the maxi mum scenario, increasing incentives to 100% does not im p act cost

‐ effectiveness because

incremental, not incentive costs, count as TRC costs. Further, program experience shows that larger

programs tend to benefit from economies of scale. Therefore, non

‐ incentive costs do not escalate at the

same rate as incentive costs in the max imum scenario. These economies of scale help maintain cost

‐

effectiveness. 23

Figure 17. Total Benefits, Costs and Costs

‐ Effectiveness (2013

‐ 2018)

Sector Program Achievable Max Achievable

Benefits

($Millions) Costs

($Millions) Net

Benefits

($Millions) TRC B/C

Ratio Benefits

($Millions) Costs

($Millions) Net

Benefits

($Millions) TRC B/C

Ratio

Residential $356 $247 $109 1.4 $1,762 $960 $802 1.8

Commercial $963 $394 $569 2.5 $2,213 $900 $1,313 2.5

Industrial $155 $37 $118 4.2 $335 $69 $266 4.8

Total $1,474 $678 $796 2.2 $4,310 $1,930 $2,380 2.2



## 2.5 Portfolio benchmarking

Total estimated savings impacts in 2018 in this study's maximum achievable scenario are higher than the

impacts of some of the top performing portfolios' shown in Figure 18, below. This is most likely due to

the 100% incentive assumption in this study. Program achievable estimates are comparable to, or higher

than ac tual program savings in some top

‐ ranked states. 24

However, as discussed in the approach section of this report, it is very difficult to compare program

performance on an apple

‐ to

‐ apples basis. There are many differences between ComEd's territory and

each territory in Figure 18. For example, ComEd's retail rates are a third or more lower than rates in

most Northeastern states. Higher retail rates generally mean effici ency measures are more financially

attractive to customers. It is hotter in So uthern California than in Illinois. Therefore, more cooling

measures are likely to be cost

‐ effective there than in ComEd's territory.

Further, EISA 2007 went into effect in 2012, after the program reporting periods shown belo w. On the

other hand, the market for LEDs has als o chang ed significantly since 2011.

23 Since all non

‐ incentive program costs are TRC costs a large increase in the share of non

‐ incentive costs would

result in lower cost

‐ effectiveness.

24 Based on ACEEE's state scorecard ranking.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Total Energy Efficiency Potential

ICF International 30 ComEd

13

‐ 034 © 2013 August 20, 2013 Figure 18. Program portfolio benchmarking

Administrator State Year Reporting Type ACEEE

2012 EE

State

Scorecard

Ranking Program

Expen

‐

ditures

($Millions) GWh

Savings

(Net) $ Per

1st

Year

kWh Savings

as % of

Load

Con Edison NY 2011 Program report/actual 3 $119 430 $0.28 0.8%

Connecticut Light & Power CT 2010 Program report/actual 6 $154 591 $0.26 1.2%

DTE Energy MI 2011 Program report/actual 12 $55 472 $0.12 1.0%

Efficiency Vermont VT 2011 Program report/actual 5 $32 101 $0.32 1.8%

National Grid MA 2011 Program report/actual 1 $110 370 $0.30 1.7%

NSTAR MA 2010 Program report/actual 1 $149 362 $0.41 1.7%

PECO PA 2011 Program report/actual 20 $60 356 $0.17 0.9%

Southern California Edison CA 2011 Program report/actual 2 $335 1,087 $0.31 1.3%

Xcel Energy

‐ Minnesota MN 2011 Program report/actual 9 $88 419 $0.21 1.3%

ComEd—PY6 Plan IL PY6

Plan Program plan/forecast 14 $163 775 $0.21 0.9%

ComEd Potential Study

Program Achievable IL 2018 Potential study/forecast 14 $157 966 $0.19 0.9%

ComEd Potential Study

Max Achievable IL 2018 Potential study/forecast 14 $527 2,111 $0.25 2.4%

There are too many differences to describe here, but what we can infer from this benchmarking data is

that even with the current budget cap, ComEd could achieve savings impacts comparable to that of

higher performing administrators' around the country, and that without a budget cap, ComEd's portfolio

impacts could be amongst the hi ghest in the country.

In re ality, we would obviously not expect all program incentive levels to equal 100% of incremental

costs. They are set to this level in potential studies to show an upper limit on incentive impacts. Setting

100% incentive levels on all measures is, in all likelihood, politically infeasible, and significant additional

savings could be gained for many measures at i ncentive levels between current program incentive and

maximum incentive levels. In some cases, 100% incentive levels are economically inefficient and

unnecessary. For example, it is important that most customers have some buy

‐ in to efficiency, as they

are more li kely to take better care of the effici ency assets over th e long

‐ run. For some programs,

incentives are not the primary driver of participation, and 100% incentives may in theory result in dead

weight loss. 25

25 If equal participation could be gained at incentives lower than 100%, the dead weight loss would be the value of

the 100% incentives paid minus the estimated value of the optimal incentives paid, where the optimum level is

an incentive level lower than 100% that results in maximum market acceptance.ICF International 31 ComEd

13

‐ 034 © 2013 August 20, 2013 

# 3 Residential Energy Efficiency Potential

3.1 Summary

Figure 19 shows ComEd's base case residential load forecast, as well as alternative load forecasts

generated by ICF that account for savings estimated under each scenario in this study. 26 2012 is the base

year for this analysis, and 2013 through 2018 are the study years. In the base case load forecast,

residential load grows at an average rate of 1.9% per year. Program potential savings would cut average

load growth by 70%, and savings in the maximum achievable scenario would more than offset load

growth by 20 16.

Note that all estimates shown include values for DCEO programs unless otherwise noted

.

Figure 19. Alternative Residential Load Forecasts



## 3.2 Residential Economic Potential

Figure 20 shows the distribution of residential economic potential by end

‐ use. Lighting comprises about

half the cost

‐ effective potential in homes. CFLs account for most lighting savings. Other significant

measure types include air sealing, duct insulation and sealing, wall and attic insulation, refrigerator

recycling, and specialty CFLs. Together these measures comprise 80% of reside ntial economic potential.

26 To develop the alternative load forecasts, ICF's cumulative savings forecasts were subtracted from the base

case load forecast. ‐ 5,000 10,000 15,000 20,000 25,000 30,000 35,000

2012 2013 2014 2015 2016 2017 2018ResidentialLoad(GWh) Base case load forecast

Load forecast if program potential achieved

Load forecast if maximum potential achieved

Load forecast reflecting economic potentialComEd Energy Efficiency Potential Study Report, 2013–2018 Residential Energy Efficiency Potential

ICF International 32 ComEd

13

‐ 034 © 2013 August 20, 2013 Figure 20. Distribution of Cumulative Residential Economic Potential, by End

‐ use, 2018

(11,978 GWh, 41% of Residential Load)



## 3.3 Residential Achievable Potential

3.3.1 Summary

Residential achievable potential is the sum of achievable potential estimated for each residential

measure in the analysis. Incremental and cumulative achievable potential estimates are shown in Figure

21, as well as load impacts. ICF estimates that, in the program achievable scenario, ComEd residential

programs could reach annual savings equal to 1.3% to 1.5% of re sidential load per year. In the maximum

scenario, esti mated load impacts are 0.2% higher in 2013 and 0.9% higher in 2018.

On a cumulative basis, these savings equal 5% of load in 2018 in the program scenario, and 8% of load in

the maximum scenario. Lighting

49%

HVAC

13% Hot Water

2% Appliances

12%

Shell

18% Other

6%ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Residential Energy Efficiency Potential

ICF International 33 ComEd

13

‐ 034 © 2013 August 20, 2013 Figure 21. Residential Achievable Potential, by Scenario and Year

2013 2014 2015 2016 2017 2018

Cumulative Savings Forecast—GWh

Maximum achievable potential 443 850 1,099 1,535 1,947 2,219

Program achievable potential 352 658 816 1,059 1,272 1,372

Cumulative Savings Forecast— % of residential load

Maximum achievable potential 2% 3% 4% 6% 7% 8%

Program achievable potential 1% 2% 3% 4% 5% 5%

Incremental Savings Forecast—GWh

Maximum achievable potential 443 499 491 575 625 660

Program achievable potential 363 407 380 384 369 366

Incremental Savings Forecast—% of residential load

Maximum achievable potential 1.6% 1.9% 1.8% 2.1% 2.3% 2.4%

Program achievable potential 1.4% 1.5% 1.4% 1.4% 1.3% 1.3%

Program Costs (Millions, Real 2013$)

Maximum achievable potential $92 $104 $123 $132 $115 $125

Program achievable potential $44 $44 $43 $44 $45 $45



## 3.3.2 Residential Program Savings

As discussed in the approach section of this report, measures were assigned to programs for the purposes of

estimating achievable potential. Each program represents a specific set of market interventions designed to

increase uptake of efficiency measures. In most cases, the programs modeled are consistent with ComEd's

program designs. The residential prog rams modeled in this study are described bri e fly below.

 Residential Lighting is a "midstream" lighting program that buys down the cost of efficient lighting

products at the retail level.

 Single Family Home Performance performs diagnostic energy audits of single family homes, directly

installs low

‐ cost measures, provides customer education on fur ther efficiency options, and pays

rebates to customers who agree to in st all additional measures, such as air sealing and attic

insulation. This program is operated jointly with the gas utilities.

 Multifamily Home Performance installs low

‐ cost measures in apartments and pays rebates to

multifamily building owners who agree to have com mon area efficiency projects performed. This

program is o p erated jointly with the gas utilities.

 Residential Complete System Replacement pays rebates to customers who install both a high efficiency

furnace and a high efficiency air conditioner. This program is operated jointly with the gas utilities.

 Appliance recycling pays reba tes to residential custom ers for th e removal and proper disposal of their

secondary and older inefficient but functioning refrigerators, freezers, and room air conditioners.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Residential Energy Efficiency Potential

ICF International 34 ComEd

13

‐ 034 © 2013 August 20, 2013  Residential Benchmarking (Home Energy Report or "HER") provides information to residential

customers on their latest energy usage compared to their historical use, and compared to that of

customers in similar homes.

 Department of Commerce and Economic Opportunity ("DCEO") programs include the Low

‐ Income

Residential Retrofit Program, Public Housing Authority Efficient Li ving, and Energy Efficient

Affordable Ho using Construction.

 Other includes efficient pool pumps.



## 3.3.3 Savings in 2013

Figure 22 27 shows total annual residential savings estimates for ComEd residential programs and the

distribution of savings by program for each achievable scenario in 2013 and 2018.

Estimated Savings in 2013 are 19% higher in the maximum scenario. In absolute terms, lighting savings

increase the most (40 GWh) in the maximum scenario in 2013. In perce ntage terms, Multifamily savings

increases the most (212%) because incentives dou b le to 100% in the maximum scenario, which removes

first

‐ cost and split

‐ incentive barriers. 28

Taken together, Residential Lighting and Multifamily account for 87% of the additional achievable

residential savings in the maximum scenario in 2013.

27 Estimates shown exclude DCEO program savings. DCEO programs are 3% of total estimated annual residential

savings in the program scenario in 2013 and 4% in 2018. They are 4% of total residential savings in the

maximum scenario in 2013, and 6% in 2018.

28 The split incentive barrier exists because renters see direct financial benefits of efficiency upgrades paid for by

owners (i.e., owners do not see a direct financial return on their investments). The split

‐ incentive barrier is a

key market barrier to multifamily program participation.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Residential Energy Efficiency Potential

ICF International 35 ComEd

13

‐ 034 © 2013 August 20, 2013 Figure 22. Annual Residential GWh Savings by Program and Scenario, 2013 and 2018



## 3.3.4 Savings in 2018

Major changes across years and between scenarios illustrated Figure 22 reflect technology baseline

improvements, and other forecasted changes to certain residential programs.

EISA 2007 represents one of the most important impacts to residential savings in this study. The effects of

EISA 2007 impact overall Lighting program savings, as well the program's measures mix. Baseline

improvements required by EISA drop savings per stand ard CFL about 30%, on average. Other factors will also

impact the Lighting program. For example, retailers are stocking fewer CFLs and more LEDs, and prices for

high

‐ quality LEDs, 29 while coming down, are still high. Also, in this analysis, fewer dollars are assumed to be

spent on LEDs in the program scenario than in the maximum scenario. 30 This is because at current LED costs,

the Lighting program cannot afford to spend too much more on LED rebates given the budget cap.

29 Some retailers are also stocking more less

‐ expensive but lower

‐ quality LEDs (those that do not meet Design

Lights Consortium, "DLC," standards) than higher

‐ quality, but pricier DLC

‐ approved models.

30 Cumulative residential LED lighting savings are 214% higher in the maximum scenario than in the program

scenario in 2018. 

### ‐ 100 200 300 400 500 600 700

2013

‐ Prog.

Achievable 2013

‐ Max.

Achievable 2018

‐ Prog.

Achievable 2018

‐ Max.

AchievableIncrementalGWhSavings Other

MF Home Performance

Residential CSR

SF Home Performance

Appliance Recycling

Residential Benchmarking

(HER)

Residential LightingComEd Energy Efficiency Potential Study Report, 2013

– 2018 Residential Energy Efficiency Potential

ICF International 36 ComEd

13

‐ 034 © 2013 August 20, 2013 HER savings are also higher in the program scenario in 2018. This is not because there are more

assumed participants in this scenario, but because average annual savings increases each year for

existing participants. 31 HER savings are substantially higher in 2018 in the maximum scenario than in the

program scenario. This is because ICF assumed that in this scenario, a large group of additional

participants would receive the report beginning in 2016. This additional group would be comprised of

residential customers with above median annual ele ctricity use; current HER recipients are in the top

quartile (25%) of annual hou s ehold consumption.

Third, Multifamily program savings is lower in 2018 than in 2013. ComEd has implemented this program

for several years and has already served many larger multifamily buildings. Therefore, the eligible stock is

depleting and program mar ginal costs per buildi ng ar e increasing. 32 Both factors suggest a downward

trend in savings. This trend also occurs in the maximum scenario. ICF also modeled a common area retrofit

project for this program. Participation in this program element is forecasted to increase over time.

In the maximum achievable scenario, ICF forecasts substantially higher savings for Single Family Home

Performance in 2018. The savings level for this progr am in this scenar i o is commensurate with that of

top performing home performance programs in other jurisdictions, and with what ICF believes to be the

long

‐ term market response to "free" home energy audits and home energy retrofits.

On a cumulative basis, Resi dential Lighting, HER, and SF Home Performance a c count for 83% of

additional savings 33 in the maximum scenario in 2018.

31 Based on the PY4 Home Energy Report evaluation and information provided to ComEd by the HER contractor,

OPower.

32 It takes more effort (marketing, scheduling, traveling) to reach two smaller buildings than one larger building.

33 Savings incremental to program scenario savings in 2018.ICF International 37 ComEd

13

‐ 034 © 2013 August 20, 2013 

# 4 Commercial Energy Efficiency Potential

4.1 Summary

Figure 23 shows ComEd's base case commercial load forecast, as well as alternative load forecasts

generated by ICF that account for savings estimated under each scenario in this study. 34 2012 is the base

year for this analysis, and 2013 through 2018 are the study years. In the base case, commercial load

grows at an average rate of 1.6% per year. The program potential scenario cuts commercial load growth

by about half, whereas savings under the maximum achievable scenario would completely offset load

growth by 20 15, and would result in a decrease in annual co mmer cial load in 2016, 2017 and 2018.

Note that all estimates shown include values for DCEO programs unless otherwise noted.

Figure 23. Alternative Commercial Load Forecasts



## 4.2 Commercial Economic Potential

Figure 24 shows the distribution of commercial economic potential by end

‐ use. Lighting accounts for

68% of cost

‐ effective potential in commercial buildings. The most significant lighting measure types

include lighting occupancy sensors, LED bulbs, and high performance T8/T5s. Other significant measures

types include retrocommissioning, and tankless water heaters. Together these five measures types

comprise 80 % of commercial economic potential.

34 To develop the alternative load forecasts, cumulative savings forecasts were subtracted from the base case

load forecast. ‐ 10,000 20,000 30,000 40,000 50,000 60,000 70,000

2012 2013 2014 2015 2016 2017 2018CommercialLoad(GWh) Base case load forecast

Load forecast reflecting program potential

Load forecast if maximum potential achieved

Load forecast reflecting economic potentialComEd Energy Efficiency Potential Study Report, 2013–2018 Commercial Energy Efficiency Potential

ICF International 38 ComEd

13

‐ 034 © 2013 August 20, 2013 Figure 24. Distribution of cumulative commercial economic potential, by end

‐ use, 2018

(16,339 GWh, 28% of commercial load)



## 4.3 Commercial Achievable Potential

4.3.1 Summary

Commercial achievable potential is the sum of achievable potential estimated for each commercial

measure in the analysis. Incremental and cumulative achievable potential estimates are shown in Figure

25, as well as load impacts. ICF estimates that, in the program achievable scenario, ComEd commercial

programs can gain annual savings equal to 0.8% to 0.9% of com mercial load per year. In the maximu m

scenario, annual load impact estimates are 0.3% higher in 2013 and 1.6% higher in 2018.

On a cumulative basis, these savings equal 5% of load in 2018 in the program scenario, and 11% of load

in the maximum scenario. Food Service

Equipment

1% Hot Water

7% HVAC

2% Refrigeration

5%

New Construction

1%

Data Centers

<1%

RCx

16%

Lighting

68%ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Commercial Energy Efficiency Potential

ICF International 39 ComEd

13

‐ 034 © 2013 August 20, 2013 Figure 25. Commercial Achievable Potential, by Scenario and Year

2013 2014 2015 2016 2017 2018

Cumulative Savings Forecast—GWh

Maximum achievable potential 595 1,420 2,373 3,479 4,621 5,791

Program achievable potential 432 898 1,318 1,744 2,178 2,572

Cumulative Savings Forecast— % of commercial load

Maximum achievable potential 1% 3% 4% 6% 9% 11%

Program achievable potential 1% 2% 2% 3% 4% 5%

Incremental Savings Forecast—GWh

Maximum achievable potential 595 841 998 1,169 1,212 1,303

Program achievable potential 432 479 453 470 481 486

Incremental Savings Forecast— % of commercial load

Maximum achievable potential 1.1% 1.6% 1.8% 2.2% 2.3% 2.5%

Program achievable potential 0.8% 0.9% 0.8% 0.8% 0.9% 0.9%

Program Costs (Millions, Real 2013$)

Maximum achievable potential $161 $231 $286 $337 $354 $378

Program achievable potential $77 $87 $89 $94 $98 $100



## 4.3.2 Commercial Program Savings

As discussed in the approach section of this report, cost

‐ effective measures were assigned to programs

for the purposes of estimating achievable potential. Each program represents a specific set of market

interventions designed to increase uptake of efficiency measures. In most cases, the programs modeled

are consistent with ComEd's program designs. Th e commercial programs modeled in this study are

described briefly below.

 Lighti ng is co mprised of two program elements: prescriptive and midstream. The prescriptive

element offers customers rebates for deemed measures. The midstream element buys

‐ down the

cost of certain light bulb types at the distributor level. 35

 HVAC savings shown in this section 36 are for prescriptive measures, such as VFDs and room air

conditioners, as well as for chillers.

 Refrigeration savings shown in this section 37 were modeled as a prescriptive program element.

 Small Business serves commercial customers with less than 100 kW in peak demand. The program

conducts energy audits, installs free measures, such as CFLs, and provides rebates for retrofit

projects.

 Retrocommissioning provides incentives for building engineering studies, installs building system

optimization measures, and provides building manag er educat ion.

35 The Custom program also includes lighting savings.

36 There are also HVAC savings in the Custom, Retrocommissioning, and Data Center programs.

37 There are also refrigeration savings in the Custom and Small Business programs.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Commercial Energy Efficiency Potential

ICF International 40 ComEd

13

‐ 034 © 2013 August 20, 2013  New construction provides technical assistance and incentives to building designers and architects

for new construction and major retrofit projects that are at least 15% more efficient that code.

 Custom offers incentives to customers for projects that install non

‐ prescriptive measures.

 Data center is a custom program that offers ince ntives for measures installed in new and existing

data centers.

 De pa rtment of Commerce and Economic Opportunity (DCEO) programs include Building Operator

Certification, Public Sector Custom Incentives, Public Sector Retrocommissioning, Public Sector New

Construction, Public Sector Standard Incentives, and Lights for Learning.

 Other includes food service and hot water measures.



## 4.3.3 Savings in 2013

Figure 26 38 shows total annual commercial savings estimates for ComEd programs and the distribution

of savings by program for each achievable scenario in 2013 and 2018.

Estimated annual commercial savings in 2013 are 31% higher in the maximum scenario. Custom,

refrigeration, and lighting savings increase the most, in percentage terms, over their respective 2013

program scenario savings estimates. Par ticipation in the Custom pr ogram could in crease substantially

because incentives in the maximum scenario more than double the program scenario incentives, and

payback acceptance would increase an estimated 50%. 39 Refrigeration measure installations would be

higher in the maximum scenario partly because of the bigger incentive, but also because ICF assumed an

alternative program design could be implemented by a contractor specializing in this end

‐ use. The

lighting measures accounting for the biggest share of additional estimated lighting savings in 2013 in the

maximu m scenar i o include:

 Reduced wattage T8/T5 lamps (through midstream delivery),

 Permanent delamping 40 (through prescriptive delivery), and

 LED bay and recessed lighting applications (through prescriptive delivery).

Some program savings increase very little in the maximum scenario in 2013, Small Business for example.

This is because in the program scenario ICF accounted for an additional $18 Million in approved Illinois

Power Agency ("IPA") funding for Sm all Business in program year six. 41

38 Excludes DCEO programs. DCEO programs account for 22% of total estimated annual commercial savings in the

program scenario in 2013 and 23% in 2018; they account for 26% of total commercial savings in the maximum

scenario in 2013 and 17% in 2018.

39 While custom projects can have long lead times, the "free" nature of the custom projects in the maximum

scenario could motivate customers to implement a certain number of planned projects that were delayed for

financial reasons.

40 Delamping involves permanent removal and existing T12 or T8 systems, and retrofit replacement with high

performanceT8, reduced

‐ wattage T8 or T5/T5 high

‐ output lamps and qualifying ballasts.

41 Small Business spending drops to normal levels in the program scenario after 2014.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Commercial Energy Efficiency Potential

ICF International 41 ComEd

13

‐ 034 © 2013 August 20, 2013 Figure 26. Commercial GWh Savings by Program and Scenario, 2013 and 2018



## 4.3.4 Savings in 2018

Commercial annual savings estimated for 2018 nearly triple in the maximum scenario. In percentage

terms, Small Business is the program with the greatest increase in savings over the program scenario. This

is because the incentives are double the program scenario incentives in 2015 through 2018, and because

the program could continue the mo mentum gained with the add itional IPA funding in 2013 and 2014.

In absolute savings terms, lighting accounts for the greatest increase in additional commercial savings

estimated in the maximum scenario in 2018. This makes sense for several reasons:

1. Lighting accounts for 68% of economic potential;

2. Lighting measures tend to have fewer mark et barriers than other measures; 42

3. Lighting measures are some of the most cost

‐ effective to implement from the perspective of the

utility and of the customer;

4. Lighting programs are easier to scale than most programs; and

42 For example, lighting is easier to understand than most end

‐ uses. And customers can literally see the non

‐

energy benefits of lighting right away (e.g., the improved color spectrum of LEDs over florescent options). 

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

2013

‐ Prog.

Achievable 2013

‐ Max.

Achievable 2018

‐ Prog.

Achievable 2018

‐ Max.

AchievableIncrementalGWhSavings Other

Data Centers

HVAC

New Construction

Refrigeration

Small Business

Commercial Custom

RetroCommissioning

LightingComEd Energy Efficiency Potential Study Report, 2013

– 2018 Commercial Energy Efficiency Potential

ICF International 42 ComEd

13

‐ 034 © 2013 August 20, 2013 5. The 100% incentive under the maximum scenario eliminates first cost and other financial barriers

to LEDs.

The measures with largest share of additional lighting savings in 2018 under the maximum scenario are

the same as those noted above in the analysis of 2013 savings. Note that cumulative commercial LED

lighting savings are 321% higher in the maximum scenar i o than in the program scenario in 2018.

On a cumulative basis, Lighting, Small Business, and Custom account for 88% of additional savings in

2018 in the maximum scenario. 43

ICF would not expect much of a savings increase in the maximum scenario (above the program scenario)

for the New Construction program. This is because this program is driven less by incentives, and more by

the technical support it provides. Further, there is a limited eligible stock of new construction

opportunities. 44 Similarly, the Data Centers program has a small number of facilities it can target.

43 Savings incremental to program scenario savings in 2018.

44 In addition, ICF assumed Illinois will adopt IECC 2015 building codes in 2016. This will like improve the baseline

efficiency for new construction and major retrofits 15% over current adopted codes (IECC 2012).ICF International 43 ComEd

13

‐ 034 © 2013 August 20, 2013 

# 5 Industrial Energy Efficiency Potential

5.1 Summary

Figure 27 shows ComEd's base case industrial load forecast, 45 as well as alternative load forecasts

generated by ICF that account for savings estimated under each scenario in this study. 2012 is the base

year for this analysis, and 2013 through 2018 are the study years. In the base case industrial load grows

at an average rate of 1.6% per year. The program potential sc enario comple tely offsets industrial load

growth in 20 18. Savings under the maximum achievable scenario would completely offset load growth

by 2015, and would result in a decrease in annual industrial load in 2016, 2017, and 2018.

Figure 27. Alternative Industrial Load Forecasts

45 ComEd provided ICF with commercial and industrial customer load by SIC and NAICS code. ICF summed load for

industrial NAICS codes to develop an estimate of total industrial load. Industrial NAICS codes were identified as

those where actual manufacturing takes place within facilities designated by that code. Some NAICS codes

identified as "industrial" are in fact commercial in nature, such as warehouses or distribution ce nters

. Load

associated with these types of NAICS codes was included in commercial load totals. ‐ 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

2012 2013 2014 2015 2016 2017 2018IndustrialLoad(GWh) Base case load forecast

Load forecast if program potential achieved

Load forecast if maximum potential achieved

Load forecast reflecting economic potentialComEd Energy Efficiency Potential Study Report, 2013–2018 Industrial Energy Efficiency Potential

ICF International 44 ComEd

13

‐ 034 © 2013 August 20, 2013 

## 5.2 Industrial Economic Potential

Figure 28 shows the distribution of industrial economic potential by end

‐ use. Motors have the largest

economic potential, followed by compressors and system upgrades. 46

Figure 28. Distribution of Industrial Economic Potential, by End

‐ use, 2013

(1,537 GWh, 25% of Industrial Load)

46 "System" refers to measures that reduce energy consumption in an entire facility, whereas the other categories

affect one end

‐ use only. System measures include Sub

‐ Metering and Interval Metering, and High Efficiency Dry

‐

Type Transformers. Motors

31%

Compressors

15%

Process Heating

3% Ventilation

11% Process

Cooling

9% HVAC

6% Process Specific

4% Lighting

7% System

14%ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Industrial Energy Efficiency Potential

ICF International 45 ComEd

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‐ 034 © 2013 August 20, 2013 Unlike the commercial and residential sectors, industrial measures tend to be specific to applicable

subsectors, therefore there are few single measures comprising large savings levels across industry. Savings

associated with the 11 measures below comprise about two

‐ thirds of industrial economic potential.

End

‐ use Measure Type

Compressors Minimize operating air pressure

Replace compressed air use with mechanical or electrical use

Lighting High Efficiency Light fixtures

Motors Premium Efficiency Control with ASDs

Optimization of pumping system

Impeller Trimming

Process Cooling VSD on chiller compressor

System Sub

‐ Metering and Interval Metering

HE Dry

‐ Type Transformers

Ventilation Premium efficiency ventilation control with VSD

Ventilation Optimization



## 5.3 Industrial Achievable Potential

5.3.1 Summary

Whereas the commercial and residential sectors involve multiple program offerings, most industrial

measures are custom in nature. 47 Therefore, it is more appropriate to discuss industrial achievable

potential by end

‐ use.

Industrial achievable potential is the sum of achievable potential estimated for each industrial measure

in the analysis. Incremental and cumulative achievable potential estimates are shown in Figure 29, as

well as load impacts. ICF estimates that, in the progr am achievable scenario, ComEd indus trial programs

could gain annual savings equal to 0.7% of industrial load per year in 2013, growing to 1.8% 2018. In the

maximum scenario, forecasted load impacts increase 1.0% in 2013 and 0.7% in 2018. Estimated load

impacts are higher in the program scenario for the industrial secto r than for the residential and

commerc ial sectors. This is in part due to historically lower ComEd program participation by industrial

customers; 48 therefore, there are a larger number of opportunities in this sector, all else equal.

47 Meaning that industrial measures are not included in the TRM because measure baseline and upgrade

calculations are unique to each application.

48 Due to the economic recession and other factors.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Industrial Energy Efficiency Potential

ICF International 46 ComEd

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‐ 034 © 2013 August 20, 2013 Figure 29. Industrial Achievable Potential, by Scenario and Year

2013 2014 2015 2016 2017 2018

Cumulative Savings Forecast—GWh

Maximum achievable potential 84 182 295 416 535 683

Program achievable potential 40 94 161 240 329 442

Cumulative Savings Forecast— % of industrial load

Maximum achievable potential 1% 3% 5% 7% 9% 11%

Program achievable potential 1% 2% 3% 4% 5% 7%

Incremental Savings Forecast—GWh

Maximum achievable potential 84 98 113 121 119 148

Program achievable potential 40 53 67 79 89 113

Incremental Savings Forecast— % of industrial load

Maximum achievable potential 1.4% 1.6% 1.9% 2.0% 2.0% 2.5%

Program achievable potential 0.7% 0.9% 1.1% 1.3% 1.4% 1.8%

Program Costs (Millions, Real 2013$)

Maximum achievable potential $12 $14 $17 $19 $19 $24

Program achievable potential $4 $5 $7 $8 $9 $11

Estimated annual industrial savings in 2013 are approximately double program achievable savings in the

maximum scenario. This is largely because incentives are 3.5 times higher in the maximum scenario.

Motor and system measure savings show the highest increases in savings in the maximum scenario over

the program scenario in 2013. In both cases the savings are approximately 150 % higher.

Figure 30 shows total annual industrial savings estima tes for the industrial sector and the distribution of

savings by end

‐ use for each achievable scenario in 2013 and 2018.



## 5.3.2 Savings in 2013

Estimated annual industrial savings in 2013 are approximately double program achievable savings in the

maximum scenario. This is largely because incentives are 3.5 times higher in the maximum scenario.

Motor and system measure savings show the highest increases in savings in the maximum scenario over

the program scenario in 2013. In both cases the savings are approximately 150 % higher.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Industrial Energy Efficiency Potential

ICF International 47 ComEd

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‐ 034 © 2013 August 20, 2013 Figure 30. Industrial GWh Savings by End

‐ Use and Scenario, 2013 and 2018

Compressors, lighting, motors and HVAC account for 90% of the additional savings in the maximum

scenario in 2013. Short

‐ term savings potential can be found primarily in these end

‐ uses due to the

impact of existing ComEd programs, and to the relatively high acceptance of well

‐ known opportunities

within these end

‐ uses. Th e following measures account for 65% of the maximu m potential savings in

2013:

End

‐ use Measure

Compressors Minimize operating air pressure

Eliminate air leaks

Premium efficiency air dryer for compressors

Replace compressed air use with mechanical or electrical

HVAC High

‐ efficiency rooftop AC

Lighting High efficiency light fixtures

High efficiency ballasts for lighting

Motors Impeller trimming or inlet guide vanes

Preventative Motor Maintenance 

### 0 20 40 60 80 100 120 140 160

2013

‐ Prog.

Achievable 2013

‐ Max.

Achievable 2018

‐ Prog.

Achievable 2018

‐ Max.

AchievableIncrementalGWhSavings Ventilation

Process Specific

System

Process Heating

Process Cooling

HVAC

Motors

Lighting

CompressorsComEd Energy Efficiency Potential Study Report, 2013

– 2018 Industrial Energy Efficiency Potential

ICF International 48 ComEd

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‐ 034 © 2013 August 20, 2013 

## 5.3.3 Savings in 2018

Maximum scenario savings in 2018 are 31% greater than the program achievable scenario savings. It is

evident in Figure 30 that there is less difference in program and maximum achievable savings in 2018

when compared to the savings differences between scenarios in 2013. This reflects the adoption curves

that were agreed upon by participa nts in the industrial achievable potential workshops. For certain

measure ty pes it was assumed that with greater incentive levels measure adoption could grow

exponentially. The nature of these exponential adoption curves for these measures would yield more

savings in the earlier years of the analysis than in the later years.

Mo tor and system measures account fo r 55% of the additional savings in 2018 in the maximum scenario.

Motors, system measures, and compressors account for two

‐ thirds of the additional cumulative

industrial savings in 2018. It is expected that motors and compressors account for such a large portion of

the potential savings for th e several reasons:

1. Motors and compressors consume 60% of the total e l ectricity at industrial facilities, therefore they

are obvious end

‐ uses to consider for energy savings;

2. There are many commercially available, economically feasible efficiency opportunities that can be

applied to these end

‐ uses, and;

3. The current market penetration of th ese opportunities is relatively low (especi ally in the case of

motors). Therefore, the eligible stock for these measures is large.

The following measures account for 50% of the maximum potential savings in 2018.

End

‐ use Measure

System Sub

‐ metering and interval metering

Compressors Minimize operating air pressure

Eliminate air leaks

Motors Premium Efficiency Control with ASDs

Impeller trimming or inlet guide vanes

Lighting High efficiency ballasts for lighting

High efficiency light fixtures

Process Cooling VSD on chiller compressor

HVAC High

‐ efficiency rooftop AC

Sub

‐ metering and interval metering is the single largest contributor to the cumulative 2018 maximum

scenario savings potential, accounting for almost 13% of the total savings. The savings for this measure

results from operational changes identified through data provided by sub

‐ metering. To avoid double

counting, sub

‐ metering and interval metering savings does not in clude savings that could potentially be

found if the sub

‐ me tered data leads to the implementation of other equipment measures already

included in this study.ICF International 49 ComEd

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‐ 034 © 2013 August 20, 2013 

# 6 Conclusion

This study involved a detailed bottom

‐ up analysis of energy efficiency potential in ComEd's territory

covering the 2013 through 2018 timeframe and the residential, commercial and industrial sectors. ICF

estimates that with the current budget cap ComEd can achieve annual savings equal to 1.0% of load per

year. Without the budget cap, ICF esti mates ComEd could achieve annual savings equal to 1.3% of load

in 2013, growing to 2.4% of load in 2018. On a cumulative basis, ComEd could achieve an additional 4.3

TWh in savings by 2018 based on assumptions made in the maximum scenario.

On a cumulative basis, Residential Lighting, Home Energy Report, and Single Family Home Per f ormance

account for 83% of additional estimated savings 49 in the maximum achievable scenario in 2018. Lighting,

Small Business, and Custom account for 88% of additional commercial savings in 2018 in the maximum

achievable scenario. And, motors, system measures, and compressors account for two

‐ thirds of the

additional cumulative industrial savings in the maximum achievable scenario in 2018.

49 Savings incremental to program scenario savings in 2018.ComEd Energy Efficiency Potential Study Report, 2013

– 2018 Conclusion

ICF International 50 ComEd

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# 7 Appendices

Appendix A: Program level impacts and costs

Appendix B: Net

‐ to

‐ gross assumptions

Appendix C: Market penetration estimates

Appendix D: Residential measure assumptions

Appendix E: Commercial measure assumptions

Appendix F: Industrial measure assumptions

Appendix G: Adoption rate survey instrumentsComEd Energy Efficiency Potential Study Report, 2013

–

2018 Appendices

ICF International 52 ComEd

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