Appendix H 

Volume 1: Assessment of Energy and Capacity Savings Potential in Iowa

Appendix H includes Volume 1 of the study entitled The Assessment of 

Energy and Capacity Savings Potential in Iowa, prepared for the Iowa Utility 

Association and the Iowa investor-owned utilities by The Cadmus Group, Inc.,

and issued on February 28, 2012. Volume 1 discusses the general approach 

and methodology of the study along with energy efficiency technical, economic 

and market potential. Additionally, demand response potential and assessment 

of the net-to-gross ratio is covered. 

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Assessment of Energy 

and Capacity Savings 

Potential in Iowa 

FINAL REPORT 

February 28, 2012 

Prepared for: 

The Iowa Utility Association 

Prepared by: 

The Cadmus Group, Inc. 

Energy Services Division 

720 SW Washington Street, Suite 400 

Portland, OR 97205 

503.467.7100 

In collaboration with 

Nexant, Inc., and 

First Tracks Consulting 

Appendix H 

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Appendix H 

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Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012 

720 SW Washington Street Corporate Headquarters: 

Suite 400 57 Water Street 

Portland, OR Watertown, MA 02472 

Tel: 503.467.7100 An Employee-Owned Company Tel: 617.673.7000 

Fax: 503.228.3696 www.cadmusgroup.com Fax: 617.673.7001 

Prepared by: 

The Cadmus Group, Inc. 

In collaboration with: 

Nexant, Inc. 

First Tracks Consulting 

 

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Volume 1. Table of Contents 

EXECUTIVE SUMMARY .......................................................................................1

Study Scope ...................................................................................................................... 1

Energy Efficiency .............................................................................................................. 2

Technical and Economic Potentials ........................................................................... 2

Market Potential ......................................................................................................... 4

Comparison to 2008 Assessment .............................................................................. 4

Demand Response ........................................................................................................... 5

Expansion of Legacy Programs ................................................................................. 6

AMI-Enabled Options ................................................................................................. 7

Assessment of the Net-to-Gross Ratio .............................................................................. 7

1. GENERAL APPROACH AND METHODOLOGY .............................................9

Energy Efficiency .............................................................................................................. 9

Base Case Forecasting .............................................................................................. 9

Creating a Database of Energy-Efficiency Measures .............................................. 10

Estimating Technical Potential ................................................................................. 12

Estimating Economic Potential ................................................................................. 13

Assessing Market Potential ...................................................................................... 15

Demand Response ......................................................................................................... 18

Expansion of Legacy Programs ............................................................................... 19

Secondary Data Collection and Analysis ................................................................. 19

Participation Scenario Development ........................................................................ 20

AMI Enabled Demand Response ............................................................................. 21

Freeridership and Spillover ............................................................................................. 21

2. ENERGY EFFICIENCY: TECHNICAL AND ECONOMIC POTENTIAL ........ 23

Scope of Analysis ............................................................................................................ 23

Summary of Results: Electricity ...................................................................................... 24

Summary of Results: Natural Gas ................................................................................... 26

Detailed Results .............................................................................................................. 28

Residential Sector: Electricity ................................................................................... 28

Residential Sector: Natural Gas ............................................................................... 30

Commercial Sector: Electricity ................................................................................. 32

Commercial Sector: Natural Gas ............................................................................. 34

Industrial Sector: Electricity ...................................................................................... 36

Industrial Sector: Natural Gas .................................................................................. 38

3. ENERGY EFFICIENCY: MARKET POTENTIAL ........................................... 41

Effects of Increased Incentives ....................................................................................... 41

Electricity .................................................................................................................. 41

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Natural Gas .............................................................................................................. 45

Effects of Financing Availability ....................................................................................... 47

Effects of Emerging Technologies .................................................................................. 48

4. DEMAND RESPONSE POTENTIAL ............................................................. 50

Potential for Expanding Legacy Programs ...................................................................... 50

Residential DLC ....................................................................................................... 50

Nonresidential Interruptible ...................................................................................... 51

Opportunities With AMI ................................................................................................... 53

Overview of AMI-Enabled Demand Response ......................................................... 53

Program Examples ................................................................................................... 55

5. ASSESSMENT OF THE NET-TO-GROSS RATIO ....................................... 56

Definitions ....................................................................................................................... 56

About Freeridership .................................................................................................. 56

About Spillover ......................................................................................................... 56

About Program-Induced Market Effects ................................................................... 56

Treatment of Freeridership and Spillover ........................................................................ 57

Treatment of NTG Across Jurisdictions ................................................................... 58

Examples of NTG Values ......................................................................................... 59

Measuring Freeridership and Spillover ........................................................................... 60

Statistical Methods ................................................................................................... 60

Self-Report Methods ................................................................................................ 61

Cross-Program Research ........................................................................................ 63

Specific Programs .................................................................................................... 64

Conclusions ..................................................................................................................... 67

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Table of Figures 

Figure 1. General Demand Response Potential Assessment Methodology ............................ 19

Figure 2. Residential Sector Electric Economic Potential by Segment (Cumulative in 2023)

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

Figure 3. Residential Sector Electric Economic Potential by Measure Type ............................ 29

Figure 4. Residential Sector Gas Economic Potential by Segment .......................................... 31

Figure 5. Residential Sector Natural Gas Economic Potential by Measure Type (Cumulative 

in 2023) ........................................................................................................................................ 31

Figure 6. Commercial Sector Electric Economic Potential by Segment (Cumulative in 2023)

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

Figure 7. Commercial Sector Electric Economic Potential by Measure Type (Cumulative in 

2023) ............................................................................................................................................ 33

Figure 8. Commercial Sector Natural Gas Economic Potential by Segment ........................... 35

Figure 9. Commercial Sector Natural Gas Economic Potential by Measure Type (Cumulative 

in 2023) ........................................................................................................................................ 35

Figure 10. Industrial Sector Electric Economic Potential by Segment (Cumulative in 2023)37

Figure 11. Industrial Sector Electric Economic Potential by Measure Type ............................ 37

Figure 12. Industrial Sector Gas Economic Potential by Segment ........................................... 39

Figure 13. Industrial Sector Gas Economic Potential by Measure Type (Cumulative in 2023)

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

Figure 14. Scatter Plot of DSM Savings and DSM Expenditures .............................................. 42

Figure 15. Scatter Plot of Energy-Efficiency Savings and Expenditures................................... 45

Figure 16. Participation Rates for 2010 IOU Residential DLC Programs ................................. 50

Figure 17. Participation Rates for 2010 IOU Nonresidential Interruptible Programs ............. 52

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Volume 2. Table of Contents 

Appendix A. Supplemental Material: Energy Efficiency 

A.1. Base Case Data Inputs 

A.2. Measure Descriptions 

A.2.1. Residential Measures 

A.2.2. Commercial Measures 

A.2.3. Industrial Measures 

A.3. Measure Technical Details 

A.3.1. Residential Electric Measures 

A.3.2. Residential Natural Gas Measures 

A.3.3. Commercial Electric Measures 

A.3.4. Commercial Natural Gas Measures 

A.3.5. Industrial Electric Measures 

A.3.6. Industrial Electric Measures 

A.4 Technical and Economic Potentials 

A.4.1. Residential Electric Measures 

A.4.2. Residential Natural Gas Measures 

A.4.3. Commercial Electric Measures 

A.4.4. Commercial Natural Gas Measures 

A.4.5. Industrial Electric Measures 

A.4.6. Industrial Electric Measures 

A.5 Market Potential 

Appendix B. Supplemental Material: Demand Response 

Appendix C. Supplemental Material: Assessment of Net-to-Gross 

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EXECUTIVE SUMMARY 

Chapter 35 of the 1999 Iowa Administrative Code (199 IAC 35) sets forth the Iowa Utility Board 

(IUB) rules to implement legislation enacted in 1990 and modified in 1996, requiring Iowa’s 

investor-owned utilities to “file with the Board an assessment of the potential for energy and 

capacity savings from actual and projected customer usage by applying commercially available 

technology and improved operating practices to energy-using equipment and buildings.” 

In compliance with this requirement, the Iowa Utility Association selected, through a 

competitive bidding process, The Cadmus Group, Inc., (Cadmus) and its sub-contractors Nexant, 

Inc. (Nexant), and First Tracks Consulting (First Tracks), to assess the remaining potential for 

energy and capacity savings within the service territories of Iowa’s three largest investor-owned 

utilities. Referred to collectively as “the Utilities,” Alliant Energy Corporation (Alliant, 

electricity and natural gas), Black Hills Energy (Black Hills, natural gas only), and MidAmerican 

Energy Company (MidAmerican, electricity and natural gas) serve approximately 72% of Iowa’s 

electric customers and 85% of the state’s natural gas customers. 

Study Scope 

This study builds upon five previous assessments of potential in Iowa, conducted since 1989, 

particularly the most recent (2008) study, led by Cadmus (formerly, Quantec, LLC).1 The 

assessment builds upon the substantial primary data collection activities from the 2008 study, 

updating the data based on recent studies commissioned by the Utilities, DSM achievements of 

the Utilities in the intervening years, and current customer and load forecasts. This information 

was supplemented with data from several secondary data sources. The compiled data provided a 

complete characterization of both the current state of energy consumption in the Utilities’ service 

area and the landscape forecast in the absence of future DSM. 

Although this study addresses the same overall objectives as the 2008 assessment, the two 

studies differ in individual components considered, reflecting the changing landscape of demandside management (DSM), both in Iowa and across the nation. Table 1 shows key components of 

each study. The 2012 study excluded the earlier study’s primary data collection, assessments of 

new non-AMI demand response and renewable resources, and review of code compliance, while 

adding an assessment of market potential for energy efficiency. 

 

1

 Assessment of Energy and Capacity Savings Potential in Iowa, prepared for the Iowa Utility Association, 

prepared by Quantec, LLC, Summit Blue Consulting, Nexant, Inc., A-TEC Energy Corporation, and 

Britt/Makela Group, February, 2008. 

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Table 1. Key Components of 2008 and 2012 Assessments 

Study Component 2008 Assessment 2012 Assessment 

Primary Data Collection 

Energy Efficiency—Technical Potential  

Energy Efficiency—Economic Potential  

Energy Efficiency—Market Potential 

Demand Response—Potential from Expansion of Legacy Programs  

Demand Response—Potential from AMI-Enabled Options  

Demand Response—Potential from New Programs without AMI 

Renewable Resources 

Effects of Free-ridership and Spillover  

Code Compliance 

The resources and technologies considered in this assessment are informed by the Chapter 35 

rules and discussions with the Utilities and stakeholders. Assessments of DSM potential are 

naturally influenced by prevailing rules and considerations, as well as factors such as weather, 

customer demographics, and economic assumptions that will lead to differences between study 

results. Therefore, the results of potential studies may not be readily comparable across 

jurisdictions. The following points related to this study’s scope should be considered in 

comparing results to other potential assessments: 

 Emerging technologies (deemed not commercially available at the time of this study) are 

excluded from technical and economic potentials, but included in market potentials. 

 Early replacement of end-use equipment is not considered in this assessment; equipment 

is assumed to be upgraded at the time of natural replacement. 

 As the assessment covers 10 years, there may be remaining potential for long-lived 

equipment beyond the study’s time horizon. 

 Active generating options, such as renewable and combined heat and power (CHP) are 

excluded from the assessment. 

 The identified technical and economic potentials represent gross savings and some 

measures may not be appropriate for inclusion in utility programs due to potentially high 

freeridership rates. 

Although emerging technologies, equipment early replacement, and on-site generation are 

excluded from the technical and economic potential, this should not preclude the Utilities from 

considering these options in their program offerings. 

Energy Efficiency 

Technical and Economic Potentials 

The energy-efficiency assessment quantified the amount of energy that could be saved in the 

Utilities’ service territories from 2014 to 2023. The assessment included efficient technologies 

and practices widely commercially available at the time of the study,2 accounting for known 

 

2

 The market potential scenario considers emerging technologies. 

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changes in codes and standards, technical limitations (technical potential), and societal costeffectiveness (economic potential). 

Table 2 shows forecasted3

 2023 baseline electric sales and potential by sector. Study results 

indicate 8,446 GWh of technically feasible electric energy-efficiency potential by 2023, the end 

of the 10-year planning horizon, with approximately 6,872 GWh of these resources proving costeffective. Identified economic potential represents a reduction of 19% of forecasted load in 2023. 

The residential sector represents the largest portion of technical and economic potential, at 42% 

and 40%, respectively. The commercial sector represents the second-largest contributor to 

technical and economic potential, at 32% for each, while industrial potential accounts for 26% 

and 28% of technical and economic potential, respectively. 

Table 2. Technical and Economic Electric Energy-Efficiency Potential 

(Cumulative in 2023) by Sector 

Technical Potential Economic Potential 

Sector 

Base Case Sales 

(MWh) MWh 

% of Base 

Sales MWh % of Base Sales 

Residential 9,197,928 3,548,837 39% 2,772,993 30% 

Commercial 7,857,412 2,702,650 34% 2,181,608 28% 

Industrial 18,293,266 2,189,166 12% 1,910,047 10% 

Total 35,348,606 8,440,653 24% 6,864,648 19% 

Table 3 presents 2023 forecasted baseline natural gas sales and potential by sector.4 As shown, 

study results indicate over 37 million therms of technically feasible natural gas energy-efficiency 

potential by 2023. The estimated economic potential of 25.5 million therms amounts to 24% of 

forecasted load in 2023, and over 2 million peak day therms. 

Table 3. Technical and Economic Natural Gas Energy-Efficiency Potential 

(Cumulative in 2023) by Sector 

Sector 

Base Case Sales 

(Thousand therms) 

Technical Potential Economic Potential 

Thousand Therms % of Base Sales Thousand Therms % of Base Sales 

Residential 671,594 274,172 41% 175,823 26% 

Commercial 335,581 92,129 27% 73,649 22% 

Industrial 62,616 5,591 9% 5,280 8% 

Total 1,069,791 371,892 35% 254,752 24% 

As with electric potential, the residential sector represents the largest portion of technical and 

economic potential, at about 74% and 69%, respectively. Almost all remaining potential lies in 

the commercial sector, with a small portion (5.3 million therms) deriving from industrial 

applications. 

 

3

 Forecasted sales have been based on baseline forecasts developed by Cadmus, as described in Section 1, and do 

not necessarily match official utility forecasts. 4

 As specified in the Chapter 35 rules, gas transport customers are excluded from the analysis. 

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Market Potential 

Assessment of market potential, a new component of this study, examined savings that might be 

achievable under an aggressive acquisition scenario where: 

 Utilities offer incentives of 100% of incremental measure costs; 

 Financing is available to further address first-cost barriers; and 

 Additional economic potential becomes available from emerging technologies. 

To address the first aspect, Cadmus analyzed publicly available data on recent energy-efficiency 

experiences for IOUs across the nation, conducting regression analysis to estimate relationships 

between increased incentive spending and savings levels achieved. Based on this analysis, and 

beginning with the Utilities’ 2010 program activity, up to 90% and 65% of electric and natural 

gas economic potential, respectively, may be achievable, over the 10-year study horizon. 

However, acquisition of these resources would require significantly higher utility expenditures 

than those currently occurring in Iowa or elsewhere in the nation. 

To assess financing’s potential effects, Cadmus reviewed available literature regarding the 

success of such programs. It is important to note this financing would only apply to a subset of 

measures included in the economic potential, namely those with full costs differing from 

incremental costs. The research indicates the availability of financing, in addition to 100% 

incentives, likely will not significantly impact measure adoption. 

Finally, Cadmus researched measures not currently widely available commercially, but that are 

expected to become available over the next five to 10 years. In most cases, these measures 

represent incremental improvements over measures already included in the technical and/or 

economic potential identified in this study. The analysis found emerging technologies may 

increase electric market potential by up to 3%, with no impact expected on natural gas potentials. 

Results of the market potential analysis are intended to provide a realistic upper bound to the 

estimates of economic potential and do not necessarily represent “program” potential or utility 

targets. The estimated savings may be realized through market transformation or improved codes 

and standards and may not be available or suitable for inclusion in utility program offerings. For 

example, the electric potential includes a substantial amount of savings from LEDs and CFLs 

replacing minimum standard bulbs. However, if the new lighting standards cause CFLs to 

become the de facto standard, the amount of savings available for utility DSM program 

acquisition could be greatly reduced. 

Comparison to 2008 Assessment 

While the 2008 Assessment utilized the best available information at the time, much has changed 

over the past four years and, thus, many data and assumptions have been updated in this study. 

The key differences are these: 

 Updated utility sales, customer, and avoided cost forecasts; 

 Changes in building codes and equipment standards; and 

 Increased measure saturations due to utility program accomplishments. 

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The 10-year technical and economic electric and natural gas potentials from each study, by 

sector, are presented in Table 4 and Table 5, respectively. As shown, electric technical potentials 

have decreased, largely driven by updated codes and standards, particularly with regard to 

residential lighting. However, due to increased electric avoided costs, the fraction of technical 

potential deemed cost-effective has increased, and system-wide electric economic potentials 

have increased by only 1% above 2008 levels. The natural gas technical potential has similarly 

decreased, with decreased avoided costs contributing to a corresponding decrease in economic 

potential. 

Table 4. Comparison of 10-Year Electric Technical and Economic Potentials 

Technical Potential (GWh) Economic Potential (GWh) 

Sector 2008 Assessment 2012 Assessment 2008 Assessment 2012 Assessment 

Residential 4,937 3,549 3,215 2,773 

Commercial 2,695 2,703 1,563 2,182 

Industrial 2,136 2,189 1,999 1,910 

Total 9,767 8,440 6,777 6,865 

Table 5. Comparison of 10-Year Natural Gas Technical and Economic Potentials 

Sector 

Technical Potential (Thousand Therms) Economic Potential (Thousands Therms) 

2008 Assessment 2012 Assessment 2008 Assessment 2012 Assessment 

Residential 265,320 274,172 186,540 175,823 

Commercial 132,240 92,129 90,130 73,649 

Industrial 8,970 5,591 8,970 5,280 

Total 406,530 371,892 285,640 254,752 

Demand Response 

The 2008 Assessment estimated demand savings potential for a variety of demand-response 

program options, including firm (e.g., residential direct load control [DLC]) and non-firm (e.g., 

critical peak pricing) strategies. In addition to actual potential estimates, the study resulted in two 

key findings: 

1. Large overlap occurs between eligible populations for similar programs, and 

implementing new programs may affect participation in demand-response programs 

currently offered by the two electric utilities. 

2. Regarding billing systems in place in 2008, the study did not allow for implementation of 

price-based options, such as real-time or critical peak pricing. However, these strategies 

could become feasible if and when the Utilities move to an Advanced Metering 

Infrastructure (AMI). 

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Based on these findings, the Utilities have continued to offer their long-running, successful 

Residential DLC and Nonresidential Interruptible programs in their 2009–2013 Energy 

Efficiency Plans (EEPs). Building on the conclusions drawn from the 2008 Assessment, this 

study focused on two questions: 

 What potential exists for expansion of utilities’ current demand response programs? 

 What opportunities would be available if and when utilities implement an AMI? 

Expansion of Legacy Programs 

As both electric utilities have operated successful demand response programs for many years, the 

assessment of demand response potential primarily focused on establishing the upper bounds of 

customer participation, based on the experience of utilities offering similar programs. Cadmus 

gathered data on comparable programs from across the nation to develop possible expansion 

scenarios for each of the current demand response programs. 

The 2010 program accomplishments, 2008 study results, and potential under each scenario for 

Residential Direct Load Control (DLC) and Nonresidential Interruptible programs are shown in 

are shown in Table 6 and Table 7, respectively. 

Table 6. Forecasted Residential DLC Impacts in 2023 (MW) 

10-Year Potential 

2008 Study 2012 Study 

Utility 

2010 Program 

Achievements Base Case 

Base 

Case 

Moderate 

Expansion 

Aggressive 

Expansion 

Alliant 33 53 35 37 46 

MidAmerican 31 72 32 35 43 

Table 7. Forecasted Nonresidential Interruptible Impacts in 2023 (MW)

10-Year Potential 

2008 Study 2012 Study 

Utility 

2010 Program 

Achievements Base Case 

Base 

Case 

Moderate 

Expansion 

Aggressive 

Expansion 

Alliant 264 291 296 304 354 

MidAmerican 193 170 238 422 492 

As shown, based on updated benchmarking data, estimates of available 10-year potential for the 

Residential DLC program have decreased from those presented in the 2008 Assessment. 

Nonresidential Interruptible expansion scenarios indicate potential has increased since the 2008 

Assessment, though it should be recognized that decisions around appropriate levels of load to 

hold under contract are heavily influenced by utilities’ unique objectives and resource needs.

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AMI-Enabled Options 

Analysis of AMI-enabled demand programs was a qualitative exercise, given data quantifying 

impacts of AMI-enabled programs has been drawn almost exclusively from utility pilot 

programs, and may not be appropriate for extrapolation to larger markets. Consequently, 

potential energy and demand savings related to AMI cannot be reliably quantified at this time. 

Nevertheless, this study outlines a number of potential options that may provide viable savings 

sources if Iowa electric utilities implement AMI. 

From initial pilot results, AMI appears to expand demand reduction capabilities of residential 

demand response programs, though the extent of this expansion remains to be seen, as program 

persistence issues have not been thoroughly studied. Additionally, studies of the reliability and 

security of these programs and enabling technologies remain in progress. Further, how AMIenabled programs and traditional programs overlap, and how demand savings may shift, still 

must be understood before specific estimates of demand reduction can be determined. 

From improving operability rates of existing DLC programs to offering new demand response 

programs to customers, who otherwise would not sign up for traditional DLC programs, AMI 

will likely expand utilities’ demand reduction capabilities. 

Assessment of the Net-to-Gross Ratio 

In addition to estimating energy and capacity savings potential, the 2008 assessment investigated 

the use of net-to-gross (NTG) adjustments, specifically freeridership and spillover effects.5 The 

assessment defined the freeridership and spillover concepts, discussed the background and policy 

implications of these concepts, and provided examples of studies that attempted to measure their 

magnitudes. The study concluded with a recommendation that Iowa’s investor-owned utilities 

(IOUs) assume an NTG ratio of 1.0 across all programs for the energy-efficiency plans 

implemented during the 2009–2013 program cycle. 

This report provides additional and more recent information to update findings from the 2008 

study, seeking to determine whether the recommended NTG ratio of 1.0 remains appropriate. 

As part of the current research, Cadmus reviewed treatment of freeridership and spillover in 32 

jurisdictions, relying on regulatory filings, technical planning materials, and evaluation reports. 

The review resulted in the following key findings: 

 Methods for measuring NTG elements are inexact. Despite considerable technical 

progress in measurement techniques for freeridership, spillover, and market effects, 

concerns exists about the potential bias in these methods and the reliability of their 

results. 

 NTG estimates tend to have small impacts on the societal cost test (the basis for 

economic analysis of energy-efficiency programs in Iowa), and, therefore, likely do not 

affect cost-effectiveness of measures and programs. 

 

5

 Assessment of Energy and Capacity Savings Potential in Iowa—Appendix G, prepared for the Iowa Utility 

Association, prepared by Quantec, LLC, Summit Blue Consulting, Nexant, Inc., A-TEC Energy Corporation, 

and Britt/Makela Group, February, 2008. 

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 Many jurisdictions have assumed an NTG ratio of 1.0 at the portfolio level. 

 Of the 32 jurisdictions surveyed, freeridership is considered in most (60%), participant 

spillover in 11 (34%), and nonparticipant spillover in nine (28%). The incidence of cases 

where only freeridership is assessed suggests an asymmetrical treatment of spillover and 

freeridership effects. Should spillover be included, it is likely many NTG ratios will be 

near or greater than 1.0. More than two-thirds of all evaluation studies reviewed in a 

recent best-practices study had a NTG value of approximately 1.0. 

Given these findings, it appears reasonable that gross savings be used as the basis for reporting 

and target compliance. However, utilities should design effective programs that minimize 

freeridership. This entails: (1) regularly monitoring the saturation of measures within their own 

service areas and in other jurisdiction; and (2) using this information to revise their programs and 

their incentive structures periodically. 

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1. GENERAL APPROACH AND METHODOLOGY 

This assessment relies on industry best practices, analytic rigor, and flexible and transparent 

tools to accurately estimate the potential for energy and capacity savings in the Utilities’ service 

territory between 2014 and 2023. This section outlines each step of the assessment process, with 

results presented in the following sections, and supplemental material provided in the 

accompanying appendices. 

Energy Efficiency 

This study distinguishes between three distinct types of energy-efficiency potential: 

 Technical potential6 refers to savings available from adoption of energy-efficiency 

measures and practices, considering physical constraints to installation, but not costeffectiveness or market barriers. Measures must be widely commercially available and 

proven at the time of the study, and the study assumes equipment will be upgraded during 

natural replacement or through new construction. 

 Economic potential serves as a subset of technical potential, containing only measures 

with a benefit-to-cost ratio greater than or equal to 1.0, based on the Iowa Societal Cost 

Test (as defined in the Chapter 35 Rules). 

 Market potential represents a realistic upper bound to potential savings from costeffective efficiency programs that could be achieved offering incentives up to 100% of 

incremental cost, availability of financing to cover additional up-front costs, adoption of 

emerging technologies, and other best practices for efficiency programs. 

This section describes methods and data sources used to estimate each type of potential. 

Base Case Forecasting 

Estimating energy-efficiency potentials begins by establishing an accurate baseline forecast of 

energy sales in the absence of future demand-side management (DSM) activity. While each 

utility officially forecasts sales by rate class, this analysis requires forecasts at an end-use level, 

fully capturing effects of changing codes and standards. As such, utility customer forecasts have 

been combined with detailed end-use level data on equipment saturations, fuel shares, 

penetrations of efficient equipment, equipment replacement rates, and known codes and 

standards, producing alternate baseline forecasts from which to assess potential. 

Characterizing base-case conditions requires extensive data collection. As this assessment did 

not include primary data collection, Cadmus began by cataloguing data collected and developed 

during the 2008 assessment. For end uses and segments, where the Utilities offered rebates for 

efficient equipment, Cadmus used data from the Utilities’ DSM tracking databases to update 

saturations of efficient equipment. For example, the previous assessment included on-site visits 

to count light sockets and measure current compact fluorescent lamp (CFL) saturations. Since 

then, Alliant and MidAmerican have aggressively pursued savings from CFLs, considerably 

 

6

 This definition is analogous to the “phase-in technical potential,” described in the Chapter 35 Rules. 

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increasing this saturation. For each utility, the number of bulbs rebated was used to calculate a 

per-customer increase in saturation. These adjustments to current saturations of efficient 

equipment proved critical to avoid overstating remaining potential. 

Additionally, the importance of accurately accounting for changes in codes and standards over 

the planning horizon cannot be overstated. Not only do these changes affect customers’ energy 

consumption patterns and behaviors, but they establish which energy-efficiency measures will 

continue to produce savings over minimum requirements. This study captures current efficiency 

requirements as well as those enacted, but not yet taking effect. 

The base case forecast particularly accounts for: 

 Iowa’s adoption of the 2009 International Energy Conservation Code (2009 IECC) for 

new construction; 

 Provisions of the Energy Independence and Security Act of 2007 (EISA), affecting 

general service lighting and motors; 

 The Department of Energy’s 2009 rulemaking, setting standards for commercial 

fluorescent lighting, beginning in 2012; and 

 Recent federal standards relating to residential heating, cooling, water heating, and 

appliances. 

Creating a Database of Energy-Efficiency Measures 

To estimate technical, economic, and market potentials for energy efficiency, this study relies on 

an extensive database of efficient equipment and practices. Measures considered in this study 

drew upon: 

 Measures currently offered by the Utilities; 

 Those included in regional and national database (e.g., California DEER and ENERGY 

STAR®); and 

 Cadmus’ internal library, compiled through our extensive experience conducting similar 

studies. 

After compiling the initial list of measures, a qualitative screening process, as specified in the 

Chapter 35 rules, eliminated certain types of measures from consideration. Qualitative screening 

criteria included: 

 Commercial availability; 

 Applicability to Iowa’s climate; and 

 Effects on demand during peak periods. 

 

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The measures qualitatively screened out of the technical and economic potentials assessment, 

along with applicable sector, fuel, and reason for exclusion, are shown in Table 8. Emerging 

technologies were assessed as part of the market potential analysis. 

Table 8. Measures Failing Qualitative Screening 

Sector Fuel Measure Reason for 

Exclusion 

Both Electricity Advanced Modulating HVAC Compressors Emerging technology. 

Both Electricity Heat Pump Dryers Emerging technology. 

Both Electricity Water Heaters - Tankless Increased peak demand 

Commercial Electricity Active Chilled Beam Cooling with DOAS Emerging technology. 

Commercial Electricity LED Replacement of Linear Fluorescent Emerging technology. 

Commercial Electricity Ventilation and Energy Recovery Emerging technology. 

Commercial Electricity Advanced Rooftop Packaged AC Emerging technology. 

Commercial Electricity Hot-Humid Rooftop Unit with Dual Enthalpy Emerging technology. 

Commercial Electricity Liquid Desiccant Hybrid AC Emerging technology. 

Residential Electricity Advanced All-Climate Heat Pump Emerging technology. 

Residential Electricity Hot-Dry Air Conditioners Emerging technology. 

Residential Electricity Multifamily Building Best Practices Emerging technology. 

Residential Electricity On-Demand Recirculation Pumps Emerging technology. 

Residential Electricity Optimized Residential Duct Work Emerging technology. 

Residential Electricity Robust Central Air Conditioners Emerging technology. 

Residential Electricity Water Heaters - Add-On Heat Pump Emerging technology. 

Residential Electricity Water Heaters - Ground Source Heat Pump Emerging technology. 

Residential Electricity Water Heaters - Northern Climate Heat Pump Emerging technology. 

Residential Natural Gas High-Efficiency Gas Fired Rooftop Unit Emerging technology. 

Residential Natural Gas Water Heaters - Condensing Tankless Emerging technology. 

Residential Natural Gas Water Heaters - Non-Condensing Gas Hybrid Emerging technology. 

For each measure passing the qualitative screen, Cadmus compiled several types of data 

necessary to fully characterize each measure. Whenever possible, these data drew upon Iowaspecific sources, such as primary data collection from the 2008 assessment, utility tracking 

databases, or other studies performed by utilities. When Iowa-specific data were not available, 

Cadmus utilized the most appropriate regional and/or national sources, tailoring the data to Iowa, 

when possible. 

Each measure had the following key data elements: 

 Efficient and baseline equipment, labor, and O&M costs; 

 Annual energy savings; 

 Effective useful life; 

 Technical feasibility; and 

 Current saturation. 

 

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For modeling energy-efficiency potential, measures were separated into two distinct classes: 

 Equipment measures save energy by upgrading the efficiency of end-use equipment at 

the time of that equipment’s replacement (e.g., high-efficiency gas furnaces). In the 

absence of early replacement of functional equipment, equipment turnover and 

replacement rates are defined by the equipment’s average effective useful life. In a study 

spanning 10 years, long-lived equipment may not completely turnover during the 

planning horizon, and additional opportunities may exist beyond the study’s close. 

 Retrofit measures save energy by reducing end-use consumption without replacing enduse equipment. Such measures include: insulation, faucet aerators, and lighting controls. 

This study assumes these measures, in existing construction, have been installed in equal 

amounts during each of the 10 years. Retrofit measure installation rates in new 

construction are defined by the utilities’ new construction forecasts. 

Estimating Technical Potential 

Technical potential represents total energy saved from all measures, only adjusting for physical 

constraints. For example, high levels of wall insulation can be placed in a certain percentage of 

homes, and, of those, a certain share may already have this insulation in place. Consequently, 

technical potential would only include technically feasible homes without measures in place. 

Another important technical potential aspect assumes installation of the highest-efficiency 

equipment wherever possible. For example, this study examined SEER 14.5, 15, 16, and 18 

central air conditioners in residential applications, with technical potential assuming that, as 

equipment fails or new homes are built, customers will install SEER 18 units, regardless of costs. 

Competing retrofit measures have been treated the same way, assuming installation of the 

highest-saving measures where technically feasible. 

In estimating technical potential, one cannot merely sum up savings from individual measure 

installations, as significant interactive effects can result from installation of complementary 

measures. For example, upgrading a furnace in a home where insulation measures have already 

been installed can be expected to produce less saving than in an un-insulated home. The analysis 

of technical potential accounts for two types of interaction: 

 Interactions between equipment and non-equipment measures: As equipment burns 

out, technical potential assumes it will be replaced with higher-efficiency equipment, 

which reduces average consumption across all customers. Reduced consumption causes 

non-equipment measures to save less than they would have, had the equipment remained 

at a constant average efficiency. Similarly, as non-equipment measures are installed, 

savings realized by replacing equipment decrease. 

 Interactions between non-equipment measures: Two retrofit measures applying to the 

same end use may not affect each other’s savings. For example, installing a low-flow 

showerhead does not affect savings realized from installing a faucet aerator. Insulating 

hot water pipes, however, would cause the water heater to operate more efficiently, thus 

reducing savings from either measure. The method in this assessment accounted for this 

interaction by “stacking” interactive measures—iteratively reducing baseline 

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consumption as measures are installed, thus lowering the savings from subsequent 

measures. 

While theoretically, all retrofit opportunities in existing construction (often called 

“discretionary” or “instantaneous” resources) could be acquired in the study’s first year, this 

would skew the potential for equipment measures, and provides an inaccurate picture of 

measure-level potential. Therefore, the study assumes realization of these opportunities in equal 

annual amounts over the 10-year planning horizon. Applying this assumption, natural equipment 

turnover rates, and other adjustments described above, annual incremental and cumulative 

potential is estimated by utility, fuel, sector, segment, construction vintage, end use, and 

measure. 

Estimating Economic Potential 

Economic potential represents the subset of technical potential that is deemed cost effective. 

Consistent with Chapter 35’s definition of the Societal Cost Test,7 a measure can be deemed 

cost-effective if its present-value benefits meet or exceed its present-value costs. The measure’s 

cost results simply from the difference in upfront costs between the measure and the baseline 

technology. In some cases (such as retrofits), the cost used equals the measure’s full cost. 

Calculating a measure’s societal benefits proves far more complex, relying on significant 

economic and load data such as: 

 End-use load shapes. End-use consumption patterns by costing period are applied to 

electric and natural gas measures, capturing the time-differentiated value of energy 

savings and determining the amount of savings during peak periods. 

 Externality factors. As specified in the Rules, an externality factor is applied to avoided 

energy and capacity costs, accounting for societal costs of supplying energy. This factor 

adds an additional 10% to electric avoided energy and capacity benefits, and an 

additional 7.5% to natural gas energy and capacity benefits.

 Line losses. Line losses represent energy lost between the generator and the customer 

meter. Thus, energy and capacity savings at the customer meter are grossed up, capturing 

the true value of savings. Such values vary by utility, fuel, and sector, and may differ for 

energy and demand.

 Societal discount rate. As specified in the Rules, the societal discount rate equals the 

12-month average of the 10-year and 30-year Treasury Bonds rates at the time of this 

study, which uses a nominal discount rate of 5.63% for all utilities.

 Utility avoided energy costs are utility-specific projections of energy generating or 

purchasing costs. Electric costs are analyzed by season, weekday/weekend, and on- and 

off-peak periods, whereas natural gas costs are assessed monthly. 

 

7

 This study did not assess other standard cost-effectiveness tests. The Utilities will consider these perspectives in 

developing the 2014–2018 Energy Efficiency Plans. 

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 Utility avoided capacity costs are utility-specific projections of the cost of supplying 

energy during peak periods, which is assumed to be the system peak hour for an electric 

utility, and the system peak day for a natural gas utility. 

 Values of other resources. Some measures save non-energy resources, such as water or 

detergent. Value for these resources have been determined and applied consistently 

across utilities. 

These data have been combined with measure-level data to calculate a variety of benefits for 

each measure. The benefits, described as follows, have been added and compared to the 

measure’s costs to determine whether the measure proved cost-effective from the societal 

perspective: 

 Energy benefits: The present value of conserved energy over a measure’s life, calculated 

by applying the appropriate line loss and externality factor to avoided energy forecasts, 

spreading over the measure’s load shape, and discounting back to present terms using the 

societal discount rate. For measures saving electricity and natural gas (e.g., insulation in 

homes with a gas furnace and central air conditioner), benefits from both fuels have been 

considered. 

 Capacity benefits: The present value of conserved capacity over a measure’s life, 

calculated by applying the appropriate line loss and externality factor to avoided capacity 

forecasts, and multiplying by the measure’s savings in the peak period, and discounting 

back to present terms using the societal discount rate. As with energy benefits, for 

measures saving electricity and natural gas (e.g., insulation in homes with a gas furnace 

and central air conditioner), benefits from both fuels have been considered. 

 Non-energy benefits: The value of applicable non-energy benefits, such as water or 

detergent, considered over the measure’s life, and discounted back to present terms using 

the societal discount rate.

As evident from the information sources and methods used to quantify societal benefits, the 

measures’ cost-effectiveness varies between utilities, based on projections of energy and capacity 

costs and line loss values. As such, this study calculated cost-effectiveness separately for each 

utility, leading to differences in economic potential, presented later in this report.8

Based on the results of the cost-effectiveness analysis, and using the same method described in 

the technical potential section, above, an alternate sales forecast the annual incremental and 

cumulative potential for each cost-effective measure has been calculated. 

 

8

 Differences in economic potential across utilities are a function of customer characteristics and current 

saturations of end uses and efficiency measures. 

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Assessing Market Potential 

Market—or achievable—potential generally is defined as the portion of economic potential 

expected to be reasonably achievable over the course of the planning horizon, given certain 

assumptions regarding market barriers and behavioral factors that may inhibit consumers’ 

participation in utility-sponsored energy-efficiency programs. In this assessment, market 

potential is defined more narrowly, as the amount of savings that might be achieved, assuming: 

incentive payments up to 100% of incremental measure cost; financing availability; exemplary 

program design and implementation practices; and emergence of new technologies, currently not 

widely available in the marketplace. 

Methods for estimating achievable potential vary across potential assessment studies. These 

methods fall into three general categories. 

1. The first group of methods (such as those used in assessments of energy-efficiency 

potential in California) is based on a conventional market diffusion model, and assumes 

first-cost as the primary participation barrier. In this approach, market potential is 

hypothesized to depend on the return from energy-efficiency investments, and the effects 

of incentives on enhancing that return. Due to limited data available to establish the 

empirical relationship between consumers’ expectations about returns on investments, 

this relationship often must be hypothesized. 

2. The second group of methods typically rely on self-reports to determine consumers’ 

willingness to participate in energy-efficiency programs. The approach involves asking a 

representative sample of potential participants about their willingness to adopt a measure 

or participate in a program, under given incentive amounts—generally expressed as a 

fraction of the incremental measure cost. These studies result in a demand curve for 

conservation measures, which relates willingness to participate as a function of 

respondents’ shares of incremental measure costs. 

3. Benchmarking, used in this assessment, provides the third method for determining market 

potential. This method incorporates certain elements from the first two method groups, 

but primarily relies on historical market penetration achieved by a representative sample 

of relevant programs to determine what might be achievable over a longer term. 

In this assessment, Cadmus relied on the empirical statistical relationship between program 

expenditures (both incentive and non-incentive) and energy savings, based on historical 

performance data for a representative sample of utility-sponsored electric and natural gas 

programs in various jurisdictions. Cadmus used analysis results to estimate the likely maximum 

market potential for utility-sponsored electric and natural gas programs in Iowa under the study’s 

specific assumptions. Data sources and analytic methods follow below, with results presented in 

Section 3. 

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The Effects of Increased Incentives 

Form 861 of the Energy Information Administration (EIA) served as the primary data source for 

assessing electric market potential, providing energy savings, program expenditures, revenues, 

and retail sales reported by approximately 75 investor-owned utilities from 2004 through 2010.9

As natural gas utilities do not report energy-efficiency program results in universal datasets 

similar to EIA Form 861, Cadmus compiled publicly available documents from utilities and 

other program administrators reporting annual energy-efficiency results to create a comparable 

dataset. Performance data for 2010 programs for 14 portfolios were included in the analysis. The 

14 selected portfolios represented those most relevant for informing market potential for Iowa 

utilities, using the following criteria: 

 Portfolios operating for at least three years. 

 Serving territories with at least 10 quadrillion Btu of annual sales. 

 Portfolios in “Northern Tier” states, with climates most similar to Iowa. (As a practical 

matter, this criterion eliminated only two southern California utilities and one New 

Mexico utility from the data set.) 

 Portfolios providing publicly available data, which, at a minimum, included the following 

information: 

 Natural gas spending separated from electric spending; 

 Spending differentiated between incentives and other costs; and 

 Annual energy savings. 

Given these criteria, Cadmus developed a list of 14 portfolios spanning nine states, as shown in 

Table 9, below. 

For each utility and program administrator listed in Table 9, data on natural gas sales, revenue, 

and average rates were collected from EIA Form 176, normalizing savings and spending across 

service areas of different sizes. 

Using these data, Cadmus developed regression equations to estimate effects increased 

incentives would have on portfolio-level electric and natural gas savings for Iowa utilities. Data 

on current program activity and incentive spending were derived from the utilities’ 2010 Annual 

Reports. 

 

9

 Although Form 861 contains data for a larger number of utilities and a longer time series, back to 1999, the 

information tends to be incomplete and lack some variables of interest for this study. 

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Table 9. Natural Gas Utility Portfolios 

Included in the Benchmarking Analysis

Utility/Program Administrator State 

Pacific Gas & Electric California 

Public Service Co. of Colorado Colorado 

Yankee Gas Services Connecticut 

Connecticut Natural Gas Connecticut 

Southern Connecticut Gas Connecticut 

Avista Corp Idaho 

National Grid Massachusetts 

NStar Massachusetts 

Northern States Power Massachusetts 

Questar Utah 

Puget Sound Energy Washington 

Avista Corp Washington 

Cascade Natural Gas Washington 

Wisconsin Focus on Energy Wisconsin 

Effects of Financing Availability 

Offering incentives covering full incremental costs may not be sufficient to offset first-cost 

barriers for all measures. For example, if a customer with low insulation levels chooses to 

upgrade to insulation exceeding minimum building code, a utility incentive may only cover costs 

above and beyond code-required levels. In this case, remaining cost could be substantial. 

Cadmus reviewed secondary literature on the success of financing programs to quantify the 

effect this option could have on market potential. 

It should be noted that, in many cases, measures have the same full and incremental costs. 

Moreover, for equipment replacement, the study assumes equipment would be upgraded per its 

natural replacement cycles, and baseline costs would be incurred, regardless of whether an 

efficient unit would be installed. 

Effects of Emerging Technologies 

As specified in the Chapter 35 Rules, only measures commercially available were included in the 

technical and economic potential. However, the market potential is designed to include measures 

expected to become commercially available and cost-effective within the next five to 10 years, as 

these measures could provide savings over the course of the next round of EEPs. While these 

measures will increase available potential, their effects cannot merely be added to the identified 

economic potential for two reasons: 

 First, many of these measures will supplant existing technologies; so only the incremental 

increase in efficiency creates new potential. 

 Second, due to interactive effects, these measures will reduce potential from other 

measures included in the technical and economic potential. That is, emergence of a more 

efficient heat pump not only supplants the potential attributed to currently available 

technologies, but will reduce the potential attributable to shell measures. 

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To determine impacts on market potential, Cadmus developed a list of emerging measures drawn 

from secondary sources, such as DOE and ACEEE, providing estimates of efficiency levels and 

savings. Cadmus then determined how these measures overlapped with measures already 

considered in the study, and estimated incremental savings and potential from the emerging 

technologies. For measures without a complementary choice within the measure list, Cadmus 

apportioned estimates of national long-term potential to the Utilities’ territory. 

Demand Response 

The 2008 Assessment estimated demand savings potential for a variety of demand-response 

program options, including firm (e.g., residential direct load control [DLC]) and non-firm (e.g., 

critical peak pricing) strategies. In addition to actual potential estimates, the study resulted in two 

key findings: 

1. Large overlap occurs between eligible populations for similar programs, and 

implementing new programs may affect participation in demand-response programs 

currently offered by the two electric utilities. 

2. Billing systems in place during the 2008 study did not allow implementation of pricebased options, such as real-time or critical peak pricing. However, these strategies could 

become feasible if and when the utilities move to an Advanced Metering Infrastructure 

(AMI). 

Based on these findings, the utilities continued to offer their long-running, successful Residential 

DLC and Nonresidential Interruptible programs in their 2009–2013 Energy Efficiency Plans 

(EEPs). Building on the conclusions drawn from the 2008 Assessment, this study focused on two 

questions: 

 What potential exists for expansion of utilities’ current demand response programs? 

 What opportunities would be available if and when utilities implement an AMI? 

The methodology for assessing these questions follows this section. 

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Expansion of Legacy Programs 

As both electric utilities have operated successful demand response programs for many years, the 

assessment of demand response potential primarily focused on establishing the upper bounds of 

customer participation, based on the experience of utilities offering similar programs. After 

developing a database of participation data from other, comparable utilities, a bottom-up 

methodology established the estimated market potential. Figure 1 illustrates the general process. 

Figure 1. General Demand Response Potential Assessment Methodology 

Secondary Data Collection and Analysis 

The key metrics used to compare utility programs (and, subsequently, to estimate remaining 

potential) were current participation levels. As customer counts, peak loads, and program 

impacts can vary greatly across utilities, identified metrics sought to normalize for these effects. 

For residential DLC programs, “participation” was defined as the percentage of eligible 

customers (for example, residential customers with central air conditioners) currently enrolled in 

the program. Nonresidential interruptible programs used a metric of the percentage of 

nonresidential demand during the system peak under contract. 

While calculating these participation rates for Iowa utilities proved relatively straightforward, 

greater difficulty resulted in collecting data on other utilities’ program achievements due to 

reporting differences. Data on utility program achievements, customer counts, and peak demand 

derived from an array of sources, including: 

 Federal Energy Regulatory Commission (FERC) 2010 Assessment of Demand Response 

and Advanced Metering Demand Response Survey Data; 

 United States Energy Information Administration (EIA) Database; 

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 Utility integrated resource plans (IRP); 

 Utility annual reports; and 

 Utility demand response program evaluation reports. 

Based on data collected from these sources, participation rates, defined above, were calculated 

for each utility program, based on 2010 data (with the most recent data available, in most cases). 

Participation Scenario Development 

Data collected indicated wide ranges of participation rates across utilities. The programs at the 

extremes (extremely high or low participation) were reviewed in additional depth, and some 

were removed as outliers, based on unique program differences or unreliable data. 

Calculated participation rates allowed establishment of two potential expansion scenarios: 

1. Moderate expansion: The amount of potential available if Iowa utilities’ participation 

rates increased to the upper quartile of the reviewed utilities. 

2. Aggressive expansion: The amount of potential available if Iowa utilities’ participation 

rates increased to industry-leading participation rates. 

These scenarios were compared to a “baseline” scenario, where Iowa utilities continued at 

current participation levels. 

Under each expansion scenario, assumed participation rates were applied to Iowa utility 

customers, loads, and per-participant impacts to identify demand savings to be realized. 

Though demand response strategies primarily focus on reducing demand during peak periods, 

reduced demand can also translate into energy savings. However, such energy savings cannot be 

calculated by merely multiplying demand impacts by an event’s duration, as this neglects some 

or all demand may have shifted to off-peak periods, rather than be avoided entirely. 

For example, in a central air conditioning DLC program, energy savings occur during the 

curtailment event, but temperatures in homes rise, and units must work harder after the event to 

achieve the desired temperatures. Similarly, if a commercial customer sheds load by temporarily 

adjusting processes (such as slowing production or shutting down some portion of a facility), it 

may have to increase production or use more of its facilities following an event. This 

phenomenon, commonly called “snapback,” must be captured to reliably quantify energy savings 

attributable to these programs. 

Quantifying snapback for a given demand response program proves quite difficult, as variations 

between demand response strategies and differences in participating customers can greatly 

impact snapback effects. For example, a region’s temperature fluctuation greatly affects 

snapback. In climates where temperatures remain relatively high after the end of an event, a 

demand response program generally experiences a higher snapback amounts. If an event ends at 

a time of day when temperatures begin to decline, the snapback would be lower. Similarly, 

customers enrolled in nonresidential interruptible programs may retain on-site generation 

capabilities, and experience no snapback effects. 

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Consequently, Cadmus reviewed secondary data on observed and assumed snapback effects for 

utilities across the nation, providing estimates of the likely energy-savings range that could be 

realized from these demand response strategies. 

AMI Enabled Demand Response 

While the prevalence of AMIs has increased significantly since 2008’s Assessment, few utilities 

have established AMI-specific demand response programs. Existing offerings primarily remain 

in pilot forms, and do not offer data that could be reliably extrapolated to quantify available 

potential for large-scale programs. Thus, this study presents a qualitative assessment of how 

utilities currently use AMI to reduce system peaks, and anticipates opportunities that may 

emerge in the next several years, if Iowa utilities implement AMI. 

Freeridership and Spillover 

This task largely updated the 2008 Assessment, reviewing current practices for assessing 

freeridership and spillover, and determining how jurisdictions across the country accounted for 

these effects. To provide a robust and complete assessment for use in future decisions regarding 

treatment of NTG in Iowa, and to provide the Utilities with recommendations for methods 

regarding mitigation of freerider effects, Cadmus conducted a thorough review of commission 

orders, legislative mandates, energy-efficiency program evaluations, and assumed values from 

jurisdictions across the nation, as described in Section 5. 

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2. ENERGY EFFICIENCY: TECHNICAL AND 

ECONOMIC POTENTIAL 

Scope of Analysis 

The assessment of energy-efficiency resources primarily sought to produce reasonable estimates 

of savings available in each utility’s service territory over a 10-year planning horizon (2014-

2023), thus informing creation of the 2014–2018 EEPs. Technical and economic potential for 

residential, commercial, and industrial10 sectors were assessed separately for each utility, divided 

by fuel type. Within each utility’s sector-level assessment, the study further distinguished among 

market segments or industry types, and their respective applicable end uses. Analysis included: 

10 residential segments (existing and new construction for single-family, multifamily, 

manufactured, low-income single-family, and low-income multifamily); 24 commercial 

segments (12 building types within existing and new construction); and 18 industrial segments. 

Analysis began by assessing the technical potential for 359 unique electric and 155 unique gas 

energy-efficiency measures passing the qualitative screening process, as described in Section 1 

(and shown in Table 10), representing a comprehensive set of electric and natural gas energyefficiency measures applicable to Iowa’s climate and customer characteristics. 

Table 10. Energy-Efficiency Measure Counts 

Sector Electric Measure Counts Natural Gas Measure Counts 

Residential 132 unique, 632 permutations 61 unique, 281 permutations 

Commercial 164 unique, 1,580 permutations 71 unique, 657 permutations 

Industrial 63 unique, 255 permutations 23 unique, 92 permutations 

This list included measures analyzed in the 2008 Assessment (which may be active in current 

utility programs), and new measures that have become commercially available over the past five 

years. Considering all permutations of these measures across applicable customer sectors, market 

segments, fuels, and end uses, resulted in customized data, compiled and analyzed for over 4,000 

measures. Appendix A.2 describes all measures analyzed, and Appendix A.3 presents technical 

details and economic potential for all permutations.11

The remainder of this section is organized into two parts: 

 A summary of resource potentials by fuel; and 

 Detailed sector-level results. 

 

10 The industrial sector includes sales and potential for agriculture and street lighting. 

11 Economic potential in Appendix A.3 has been aggregated to the state level. 

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Summary of Results: Electricity 

Table 11 and Table 12 show forecasted12 2023 baseline electric sales and potential by utility and 

sector, respectively. Study results indicate 8,446 GWh of technically feasible electric energyefficiency potential by 2023, the end of the 10-year planning horizon, with approximately 

6,872 GWh of these resources cost-effective. Identified economic potential amounts to 19% of 

forecasted load in 2023. 

Savings have been based on forecasts of future consumption, absent utility program activities. 

While consumption forecasts account for past savings each utility has acquired, estimated 

potential is inclusive of—not in addition to—current or forecasted program savings. 

As shown in Table 11, though utility-specific technical and economic potential are a function of 

baseline sales, they are roughly comparable, when analyzed in percentage terms. Differences in 

technical potential as a percent of baseline sales are driven by differences in distributions of 

customers by segment, and other utility-specific customer characteristics. In addition to these 

differences, economic potential varies due to differences in utility avoided energy and capacity 

costs. 

Table 11. Technical and Economic Electric Energy-Efficiency Potential 

(Cumulative in 2023) by Utility 

Utility 

Base Case Sales 

(MWh) 

Technical Potential Economic Potential 

MWh 

% of Base 

Sales MW MWh 

% of Base 

Sales MW 

Alliant 15,465,326 3,839,043 25% 926 3,294,806 21% 803 

MidAmerican 19,883,278 4,601,610 23% 1,110 3,569,842 18% 885 

Total 35,348,604 8,440,653 24% * 6,864,648 19% * 

* Due to differences in timing of utility system peaks, demand impacts could not be aggregated across utilities. 

 

 

12 Forecasted sales have been based on baseline forecasts developed by Cadmus, as described in Section 1, and do 

not necessarily match official utility forecasts. 

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Table 12 provides each sector’s technical and economic potentials. The residential sector 

represents the largest portion of technical and economic potential, at 42% and 40%, respectively. 

The commercial sector represents the second-largest contributor to technical and economic 

potential, at 32% of each, while industrial potential accounts for 26% and 28% of technical and 

economic potential, respectively. 

Table 12. Technical and Economic Electric Energy-Efficiency Potential 

(Cumulative in 2023) by Sector 

Sector 

Base Case Sales 

(MWh) 

Technical Potential Economic Potential 

MWh 

% of Base 

Sales MWh 

% of Base 

Sales 

Residential 9,197,928 3,548,837 39% 2,772,993 30% 

Commercial 7,857,412 2,702,650 34% 2,181,608 28% 

Industrial 18,293,266 2,189,166 12% 1,910,047 10% 

Total 35,348,606 8,440,653 24% 6,864,648 19% 

Table 13 shows the electric measures with the highest expected 10-year technical potential, and 

whether each is cost-effective in all, some, or no applications. 

Table 13. Top Electric Technical Measures and Cost-Effectiveness Results13

Sector Measure Name Cost-Effective Applications 

Residential LED All 

Commercial Fluorescent Reduced Wattage Some 

Industrial Integrated Plant Energy Management All 

Residential TV - ENERGY STAR Some 

Residential ECM Motor - Air Conditioner/Electric/Gas Furnace All 

Industrial High Bay Fluorescent High Output Packages All 

Commercial Daylighting Controls Some 

Commercial LED Lamp Package All 

Commercial Induction Lighting Package Some 

Commercial Retro-Commissioning Some 

Cost-effectiveness varies by utility due to differences in avoided costs, but can also differ by 

segment or construction vintage due to differences in savings and/or incremental costs. As 

shown, residential and commercial lighting measures represent six of the top 10 electric technical 

measures, with additional large savings opportunities for industrial plan energy management, 

ENERGY STAR televisions, efficient motors, and retro-commissioning. All of these measures 

were deemed cost-effective in at least some applications, with half economic in all instances. 

 

 

13 Measure-by-measure economic potential is provided in Appendix A.3. 

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Table 14 compares identified 10-year technical and economic electric potentials to results from 

the 2008 Assessment. 

Table 14. Comparison of 10-Year Electric Technical and Economic Potentials 

Sector 

Technical Potential (GWh) Economic Potential (GWh) 

2008 Assessment 2012 Assessment 2008 Assessment 2012 Assessment 

Residential 4,937 3,549 3,215 2,775 

Commercial 2,695 2,703 1,563 2,182 

Industrial 2,136 2,195 1,999 1,916 

Total 9,767 8,446 6,777 6,872 

Residential potentials, both technical and economic, have declined, primarily driven by utility 

program activity as well as changes in minimum building codes and equipment standards. While 

the commercial sector has seen increased efficiency requirements, technical potentials have risen 

marginally compared to the 2008 Assessment due to availability of new advanced technologies, 

such as LED lighting. Economic potentials saw greater increases, driven by increased electric 

avoided costs and declining measure costs for certain measures. Industrial technical potential 

also increased in the 2012 assessment, while economic potential showed a marginal decrease. 

Summary of Results: Natural Gas 

Table 15 and Table 16 present 2023 forecasted baseline sales and potential by sector and utility, 

respectively.14 As shown, study results indicate over 37 million therms of technically feasible 

natural gas energy-efficiency potential by 2023, the end of the 10-year planning horizon. The 

identified economic potential of 25.5 million therms amounts to 24% of forecasted load in 2023 

and over 2 million peak day therms. 

As with electric potential, technical and economic potential result as a function of baseline sales, 

and are roughly comparable across utilities when analyzed in percentage terms. Again, 

differences are driven by utility customer characteristics and avoided costs. 

Table 15. Technical and Economic Gas Energy-Efficiency Potential 

(Cumulative in 2023) by Utility 

Utility 

Base Case Sales 

(Thousand 

therms) 

Technical Potential Economic Potential 

Thousand 

Therms 

% of 

Base 

Sales 

Peak Day 

Thousand 

Therms 

Thousand 

Therms 

% of 

Base 

Sales 

Peak Day 

Thousand 

Therms 

Alliant 267,040 90,767 34% 732 61,574 23% 515 

Black Hills 169,983 60,754 36% 486 42,507 25% 348 

MidAmerican 632,769 220,371 35% 1,785 150,670 24% 1,262 

Total 1,069,791 371,892 35% 3,003 254,751 24% 2,125 

 

 

14 As specified in the Chapter 35 rules, gas transport customers are excluded from the analysis. 

Appendix H 

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Table 16 provides each sector’s technical and economic potentials. As with electric potential, the 

residential sector represents the largest portion of technical and economic potential, at about 74% 

and 69%, respectively. Almost all remaining potential lies in the commercial sector, with a small 

portion (5.3 million therms) from industrial applications. 

Table 16. Technical and Economic Natural Gas Energy-Efficiency Potential 

(Cumulative in 2023) by Sector 

Sector 

Base Case Sales 

(Thousand therms) 

Technical Potential Economic Potential 

Thousand Therms % of Base SalesThousand Therms % of Base Sales 

Residential 671,594 274,172 41% 175,823 26% 

Commercial 335,581 92,129 27% 73,649 22% 

Industrial 62,616 5,591 9% 5,280 8% 

Total 1,069,791 371,892 35% 254,752 24% 

Table 17 shows the natural gas measures with the highest estimated 10-year technical potential, 

and whether each is cost-effective in all, some, or no applications. 

Table 17. Top Natural Gas Technical Measures and 

Cost-Effectiveness Results 

Sector Measure Name Cost-Effective Applications 

Residential Duct Sealing Some 

Residential Window Upgrades None 

Commercial Retro-Commissioning Some 

Residential Infiltration Reduction All 

Residential Insulation - Basement Wall All 

Residential Insulation - Attic/Ceiling Some 

Residential Insulation – Floor None 

Residential Home Energy Management System Some 

Residential Water Heater - Tankless None 

Commercial Green Roof None 

Cost-effectiveness varies by utility due to differences in avoided costs, but can also differ by 

segment or construction vintage due to differences in savings and/or incremental costs. As 

shown, most of the top measures are improvements to residential building shell, with commercial 

retro-commissioning also representing a large amount of technical potential. Only two of the top 

10 measures are cost-effective in all applications, whereas four do not pass the economic screen 

in any instance. 

 

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Table 18 compares identified 10-year technical and economic natural gas potentials to results of 

the 2008 Assessment. 

Table 18. Comparison of 10-Year Natural Gas Technical and Economic Potentials 

Sector 

Technical Potential (Thousand Therms) Economic Potential (Thousands Therms) 

2008 Assessment 2012 Assessment 2008 Assessment 2012 Assessment 

Residential 265,320 274,172 186,540 175,823 

Commercial 132,240 92,129 90,130 73,649 

Industrial 8,970 5,591 8,970 5,280 

Total 406,530 371,892 285,640 254,752 

Economic potentials for all sectors have decreased in this assessment, largely due to significantly 

lower avoided energy costs. 

Detailed Results 

Residential Sector: Electricity 

Residential customers in Iowa account for about one-quarter of forecasted electricity retail sales. 

The single-family, manufactured, multifamily, and low-income dwellings comprising this sector 

present a variety of potential savings sources, including: equipment efficiency upgrades (e.g., air 

conditioning, refrigerators); improvements to building shells (e.g., insulation, windows, air 

sealing); and increases in lighting efficiency (e.g., CFLs, LED interior lighting). 

As shown in Table 19, based on resources included in this assessment, residential sector electric 

economic potential is estimated at 2,775 GWh over 10 years, corresponding to a 30% reduction 

(33% for Alliant and 28% for MidAmerican) in 2023 residential consumption,/ 

Table 19. Residential Sector Electric Energy-Efficiency Potential by Utility 

(Cumulative in 2023) 

Utility Base Case Sales (MWh) 

Technical Potential Economic Potential 

MWh % of Base Sales MW MWh % of Base Sales MW 

Alliant 3,852,109 1,485,069 39% 443 1,275,181 33% 399 

MidAmerican 5,345,819 2,063,768 39% 615 1,497,812 28% 497 

Total 9,197,928 3,548,837 39% * 2,772,993 30% * 

* Due to differences in timing of utility system peaks, demand impacts cannot be aggregated across utilities. 

As shown in Figure 2, single-family homes represent 71% of total economic residential potential, 

followed by low-income, multifamily, and manufactured homes. Each home type’s proportion of 

baseline sales serve as the primary drivers, but other factors, such as heating fuel sources, play 

important roles in determining potential. For example, manufactured homes typically have 

higher electric heating saturations than other home types, increasing their relative shares of the 

potential. Conversely, lower-use per customer for multifamily units decreases this potential, as 

some measures may not be cost-effective at lower consumption levels. 

Appendix H 

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Figure 2. Residential Sector Electric Economic Potential by Segment 

(Cumulative in 2023) 

Figure 3 presents the distribution of electric economic potential by measure type. 

Figure 3. Residential Sector Electric Economic Potential by Measure Type 

The largest portion of economic potential in the residential sector (41%) results from heating and 

cooling savings achieved through shell measures. Cooling measures account for nearly 50% of 

HVAC shell measure savings while ventilation and heating measures account for approximately 

25% and 20%, respectively. A small amount of shell measure savings comes from homes with 

heat pumps. ECM motors, duct sealing, infiltration reduction, radiant barriers, and whole-house 

Single Family

71%

Low Income SF

13%

Multi Family

6%

Manufactured

6%

Low Income MF

5%

Refrigerator: 3%, Freezer: 2%, Dryer: <1%, Pool Pump: <1%, Heat Pump: <1%

Note: 'Other End Uses' includes:

HVAC Shell Measure

41%

Lighting

21%

Plug Load

15%

Water Heat

11%

Cooling

5%

Other End Uses

7%

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fans, account for over 60% of the identified shell measure savings. Lighting measures, primarily 

LED and CFL bulbs, account for the next largest slice (21%), followed by various plug load end 

uses and water heating. Table 20 provides technical and economic potentials by end-use 

category. 

Table 20. Residential Sector Electric Energy-Efficiency Potential 

by End-Use Category (Cumulative in 2023) 

End Use Base Case Sales (GWh) 

Technical Potential Economic Potential 

GWh % of Base Sales GWh % of Base Sales 

Computer 210 64 30% 64 30% 

Cooking 296 33 11% 0 0% 

Cooling 1,456 817 56% 699 48% 

Dehumidifier 283 26 9% 26 9% 

Dryer 596 59 10% 22 4% 

Heat Pump 168 94 56% 85 50% 

Heating 787 380 48% 221 28% 

Lighting 817 588 72% 588 72% 

Other Plug Load 1,164 191 16% 104 9% 

Pool Pump 20 10 51% 10 51% 

Refrigerators and Freezers 981 221 22% 148 15% 

Set Top Box 206 113 55% 113 55% 

Television 699 278 40% 116 17% 

Ventilation and Circulation 682 273 40% 273 40% 

Water Heat 834 403 48% 305 37% 

Total 9,199 3,550 39% 2,774 30% 

Residential Sector: Natural Gas 

As shown in Table 21, based on resources included in this assessment, natural gas economic 

potential in the residential sector is estimated at about 176 million therms over the 10-year 

planning horizon, corresponding to a 26% reduction (27% for Alliant, 27% for Black Hills, and 

26% for MidAmerican) in 2023 residential consumption. 

Table 21. Residential Sector Natural Gas Energy-Efficiency Potential 

by Utility (Cumulative in 2023) 

Utility 

Base Case 

Sales 

(Thousands 

of therms) 

Technical Potential Economic Potential 

Thousand 

Therms 

% of Base 

Sales 

Peak Day 

Thousand 

Therms 

Thousand 

Therms 

% of Base 

Sales 

Peak Day 

Thousand 

Therms 

Alliant 142,565 62,444 44% 531 37,922 27% 345 

Black Hills 105,983 44,238 42% 376 28,891 27% 258 

MidAmerican 423,046 167,490 40% 1,422 109,010 26% 974 

Total 671,594 274,172 41% 2,329 175,823 26% 1,578 

 

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As shown in Figure 4, single-family homes represent 73% of total economic residential potential, 

followed by low-income, multifamily, and manufactured homes, with results extremely similar 

to electric potential, with manufactured homes representing a smaller percentage due to lower 

saturations of gas heating equipment. 

Figure 4. Residential Sector Gas Economic Potential by Segment 

Figure 5 presents distributions of natural gas economic potential by measure type. The largest 

portion of economic potential in the residential sector (88%) comes from shell measures, 

followed by water heating (10%). Duct sealing, infiltration reduction, basement and attic 

insulation, and home energy management systems account for nearly 75% of shell measure 

savings. 

Figure 5. Residential Sector Natural Gas Economic Potential 

by Measure Type (Cumulative in 2023) 

Manufactured: 5%, Multi Family: 4%, Low Income MF: 4%

Note: 'Other Segments' includes:

Single Family

73%

Low Income SF

15%

Other Segments

13%

Heat Central Furna: 2%, Pool Heat: <1%

Note: 'Other End Uses' includes:

HVAC Shell Measure

88%

Water Heat

10%

Other End Uses

2%

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Table 22 provides technical and economic potential by end-use category. 

Table 22. Residential Sector Natural Gas Energy-Efficiency Potential 

by End-Use Category (Cumulative in 2023) 

End Use 

Base Case Sales 

(Thousand 

Therms) 

Technical Potential Economic Potential 

Thousand 

Therms 

% of Base 

Sales 

Thousand 

Therms 

% of Base 

Sales 

Cooking 15,526 1,579 10% 0 0% 

Dryer 6,591 654 10% 0 0% 

Heat Central—Boiler 24,758 9,889 40% 6,028 24% 

Heat Central—Furnace 452,542 218,107 48% 152,577 34% 

Other 64,002 0 0% 0 0% 

Pool Heat 1,513 377 25% 340 23% 

Water Heat 106,662 43,565 41% 16,877 16% 

Total 671,594 274,171 41% 175,822 26% 

Commercial Sector: Electricity 

As shown in Table 23, based on resources included in this assessment, electric economic 

potential in the commercial sector is estimated at just over 2,180 GWh over the 10-year planning 

horizon, corresponding to a 28% reduction (29% for Alliant and 27% for MidAmerican) of 

forecasted 2023 commercial consumption. 

Table 23. Commercial Sector Electric Energy-Efficiency Potential 

by Utility (Cumulative in 2023) 

Utility 

Base Case 

Sales 

(MWh) 

Technical Potential Economic Potential 

MWh 

% of Base 

Sales MW MWh 

% of Base 

Sales MW 

Alliant 3,969,210 1,377,058 35% 358 1,148,549 29% 292 

MidAmerican 3,888,201 1,325,592 34% 343 1,033,059 27% 257 

Total 7,857,411 2,702,650 34% * 2,181,608 28% * 

* Due to differences in timing of utility system peaks, demand impacts cannot be aggregated across utilities. 

As shown in Figure 6, miscellaneous buildings and warehouses represent the largest shares (23% 

and 21%, respectively) of economic potential in the commercial sector. The miscellaneous 

segment combines customers not fitting into one of the other categories and those that would, but 

do not having sufficient information to be classified. The commercial sector also provides 

considerable savings opportunities in offices (14%), retail (11%), and grocery (7%) segments. 

Moderate savings amounts are expected to be available in education, health, restaurants, and 

lodging facilities. 

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Figure 6. Commercial Sector Electric Economic Potential by Segment 

(Cumulative in 2023) 

Figure 7 presents distributions of electric economic potential by measure types. The largest 

portion of economic potential in the commercial sector (62%) comes from lighting, followed by 

HVAC shell measures (22%). Cooling and ventilation each account for about one-third of shell 

measure savings, with heat pumps and electric heating accounting for 24% and 6%, respectively. 

Retro-commissioning, variable frequency drives, ECM motors, variable refrigerant flow systems 

for heat pumps, and programmable thermostats account for nearly 73% of the shell measure 

savings. 

Figure 7. Commercial Sector Electric Economic Potential by Measure Type 

(Cumulative in 2023) 

Small Office: 3%, Convenience: 3%, Lodging: 3%, Small Retail: 2%

Note: 'Other Segments' includes:

Other Commercial

23% Warehouse

21%

Large Office

11%

Large Retail

9%

Grocery

7%

Health

6%

Education

6%

Restaurant

5%

Other Segments

10%

Plug Load: 5%, Water Heat: 4%, Cooling: 1%, Heat Pump: <1%, Other: <1%, Cooking: <1%, Dryer: <1%

Note: 'Other End Uses' includes:

Lighting

62%

HVAC Shell Measure

22%

Refrigeration

5%

Other End Uses

10%

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Table 24 provides technical and economic potential by end-use category. 

Table 24. Commercial Sector Electric Energy-Efficiency Potential 

by End-Use Category (Cumulative in 2023) 

End Use Base Case Sales (GWh) 

Technical Potential Economic Potential 

GWh % of Base Sales GWh % of Base Sales 

Cooking 73 4 5% 1 2% 

Cooling 844 304 36% 205 24% 

Dryer 226 0 0% 0 0% 

Heat Pump 366 153 42% 124 34% 

Heating 352 82 23% 30 9% 

Lighting 3,540 1,605 45% 1,353 38% 

Other 25 2 8% 1 5% 

Plug Load 974 121 12% 104 11% 

Refrigeration 584 150 26% 115 20% 

Ventilation and Circulation 680 191 28% 162 24% 

Water Heat 192 90 47% 87 45% 

Total 7,856 2,702 34% 2,182 28% 

Commercial Sector: Natural Gas 

The commercial sector represents about one-third of both technical and economic gas energyefficiency potential. The 73.6 million therms of economic potential over 10 years, corresponds to 

a 22% reduction (23% for Alliant and Black Hills and 21% for MidAmerican) of forecasted 2023 

commercial consumption, as shown in Table 25. 

Table 25. Commercial Sector Natural Gas Energy-Efficiency Potential 

by Utility (Cumulative in 2023) 

Utility 

Base Case 

Sales 

(Thousand 

Therms) 

Technical Potential Economic Potential 

Thousand 

Therms 

% of Base 

Sales 

Peak Day 

Thousand 

Therms 

Thousand 

Therms 

% of Base 

Sales 

Peak Day 

Thousand 

Therms 

Alliant 90,558 25,191 28% 193 20,683 23% 162 

Black Hills 57,302 15,941 28% 109 13,076 23% 89 

MidAmerican 187,721 50,997 27% 358 39,890 21% 283 

Total 335,581 92,129 27% 660 73,649 22% 534 

As shown in Figure 8, miscellaneous buildings and education facilities represent the largest 

shares of economic potential in the commercial sector (24% and 19%, respectively). As with the 

commercial electric sector, the miscellaneous segment is composed of a combination of 

customers not fitting into one of the other categories and those that would fit, but have 

insufficient enough information to be classified. Considerable savings opportunities are expected 

in the commercial sector’s retail (15%), office (15%), and warehouse (12%) segments. Moderate 

savings amounts can be expected in health, restaurants, and lodging, and grocery facilities. 

Appendix H 

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Figure 8. Commercial Sector Natural Gas Economic Potential by Segment 

Figure 9 presents distributions of natural gas economic potential by measure type. The largest 

portion of economic potential in the commercial sector (64%) comes from HVAC shell 

measures, followed by water heating (26%). More than 63% of the shell measure savings comes 

from furnace applications, with the remainder attributable to boiler measures. Retrocommissioning, demand controlled ventilation systems, variable air-volume systems, boiler reset 

controls, and infiltration control account for nearly 95% of shell measure savings. 

Figure 9. Commercial Sector Natural Gas Economic Potential 

by Measure Type (Cumulative in 2023) 

Small Office: 3%, Restaurant: 3%, Small Retail: 2%, Grocery: 2%, Lodging: 2%, Convenience: <1%

Note: 'Other Segments' includes:

Other Commercial

24% Education

19%

Large Retail

15%

Warehouse

12%

Large Office

12%

Health

6%

Other Segments

12%

Boiler: 5%, Cooking: <1%, POOL HEAT: <1%

Note: 'Other End Uses' includes:

HVAC Shell Measure

64%

Water Heat

26%

Heating

5%

Other End Uses

6%

Appendix H 

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Table 26 provides technical and economic potential by end-use category. 

Table 26. Commercial Sector Gas Energy-Efficiency Potential 

by End Use Category (Cumulative in 2023) 

End Use 

Baseline 

Sales 

(Thousand 

Therms) 

Technical Potential Economic Potential 

Thousand 

Therms 

% of Base 

Sales 

Thousand 

Therms 

% of Base 

Sales 

Boiler 71,649 23,222 32% 20,644 29% 

Cooking 14,149 556 4% 556 4% 

Dryer 948 0 0% 0 0% 

Heating 179,088 47,624 27% 33,527 19% 

Pool Heat 240 34 14% 34 14% 

Water Heat 69,507 20,692 30% 18,888 27% 

Total 335,581 92,128 27% 73,649 22% 

Industrial Sector: Electricity 

Technical and economic energy-efficiency potentials were estimated for major end uses within 

18 major industries, including agriculture and street lighting.15 Across all industries, economic 

potential totals approximately 1,916 GWh over 10 years, corresponding to a 10% reduction (11% 

for Alliant and 10% for MidAmerican) of forecasted 2023 industrial consumption, as shown in 

Table 27. 

Table 27. Industrial Sector Electric Energy-Efficiency Potential 

by Utility (Cumulative in 2023) 

Utility 

Base Case 

Sales 

(MWh) 

Technical Potential Economic Potential 

MWh 

% of Base 

Sales MW MWh 

% of Base 

Sales MW 

Alliant 7,644,007 976,916 13% 125 871,076 11% 112 

MidAmerican 10,649,258 1,212,250 11% 152 1,038,971 10% 131 

Total 18,293,265 2,189,166 12% * 1,910,047 10% * 

* Due to differences in timing of utility system peaks, demand impacts cannot be aggregated across utilities. 

As shown in Figure 10, food processing and primary metal manufacturing facilities represent 

approximately one-half of the economic potential in the industrial sector (34% and 16%, 

respectively). Considerable savings opportunities are also expected in the industrial sector’s 

chemical manufacturing segment (10%). 

 

15 Industries analyzed varied by utility, based on customer and sales distributions 

Appendix H 

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Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012 

The Cadmus Group, Inc. / Energy Services 37 

Figure 10. Industrial Sector Electric Economic Potential 

by Segment (Cumulative in 2023) 

 

The majority of electric economic potential in the industrial sector (41%) can be attributed to 

gains in process efficiency (such as heating, cooling, and compressed air), followed by lighting 

improvements (24%) and motor system improvements (mainly fans and pumps). As shown in 

Table 28 and Figure 11, a small amount of additional potential exists for other facility 

improvements. 

Figure 11. Industrial Sector Electric Economic Potential by Measure Type 

Furniture Mfg: <1%

Miscellaneous Mfg: 1%, Electrical Equipment Mfg: <1%, Agriculture: <1%, Mining: <1%

Transportation Equipment Mfg: 2%, Fabricated Metal Products: 1%, Instruments: 1%, Wood Product Mfg: 1%

Note: 'Other Segments' includes:

Food Mfg

34%

Primary Metal Mfg 16%

Chemical Mfg

10%

Industrial Machinery

10%

Paper Mfg

6%

Nonmetallic Mineral Products

5%

Street Lighting

4%

Plastics Rubber Products

4%

Printing Related Support

2%

Other Segments

9%

, ,

HVAC: 4%, Other: <1%

Note: 'Other End Uses' includes:

Process

41%

Lighting 24%

Motors Other

13%

Pumps

10%

Fans

8%

Other End Uses

4%

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Table 28. Industrial Sector Electric Energy-Efficiency Potential 

by End-Use Category (Cumulative in 2023) 

End Use 

Baseline 

Sales (GWh) 

Technical Potential Economic Potential 

(GWh 

% of Base 

Sales GWh 

% of Base 

Sales 

Fans 1,056 162 15% 148 14% 

HVAC 1,655 170 10% 77 5% 

Indirect Boiler 219 0 0% 0 0% 

Lighting 1,379 577 42% 463 34% 

Motors Other 3,485 288 8% 251 7% 

Other 585 2 0% 2 0% 

Process—Air Compressor 1,099 248 23% 248 23% 

Process—Electro Chemical 1,860 0 0% 0 0% 

Process—Heat 2,676 70 3% 68 3% 

Process—Other 234 3 1% 3 1% 

Process—Refrigeration and Cooling 2,426 463 19% 461 19% 

Pumps 1,622 206 13% 190 12% 

Total 18,296 2,189 12% 1,911 10% 

Industrial Sector: Natural Gas 

Most industrial processes and end uses rely on electricity; therefore, the industrial sector 

represents an extremely small portion of natural gas baseline sales and potential. As shown in 

Table 29, across all industries, economic potential totals approximately 5.3 million therms over 

10 years, corresponding to an 8% reduction (9% for Alliant, 8% for Aquila, and 8% for 

MidAmerican) in forecasted 2023 industrial consumption. 

Table 29. Industrial Sector Natural Gas Energy-Efficiency Potential 

by Utility (Cumulative in 2023) 

Utility 

Base Case 

Sales 

(Thousand 

Therms) 

Technical Potential Economic Potential 

Thousand 

Therms 

% of Base 

Sales 

Peak Day 

Thousand 

Therms 

Thousand 

Therms 

% of Base 

Sales 

Peak Day 

Thousand 

Therms 

Alliant 33,917 3,132 9% 8 2,969 9% 8 

Black Hills 6,697 575 9% 1 540 8% 1 

MidAmerican 22,002 1,884 9% 5 1,770 8% 5 

Total 62,616 5,591 9% 14 5,279 8% 14 

 

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IPL Energy Efficiency Plan January 25, 2013
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The Cadmus Group, Inc. / Energy Services 39 

Due to the composition of industries using natural gas in Iowa, over 67% of the economic 

potential lies in the food processing (35%) and chemical manufacturing (32%) segments. As 

shown in Figure 12, substantial savings opportunities also exist in agriculture (19%) and 

nonmetallic mineral products (6%). 

Figure 12. Industrial Sector Gas Economic Potential by Segment 

Almost all baseline consumption occurs in boilers and process heating (87%); thus, these end 

uses account for 97% of the economic potential. As shown in and Figure 13, the remaining 

potentials result in HVAC improvements and other (non-heating) process improvements. 

Figure 13. Industrial Sector Gas Economic Potential by Measure Type 

(Cumulative in 2023) 

Miscellaneous Mfg: <1%, Electrical Equipment Mfg: <1%, Furniture Mfg: <1%, Instruments: <1%

Fabricated Metal Produc ts: <1%, Transportation Equipment Mfg: <1%, Plastics Rubber Produc ts: <1%, Wood Produc t Mfg: <1% Industrial Machinery: 2%, Primary Metal Mfg: 2%, Paper Mfg: 1%, Printing Related Support: <1%

Note: 'Other Segments' includes:

Food Mfg

35%

Chemical Mfg

32%

Agriculture

19%

Nonmetallic Mineral Products

6%

Other Segments

7%

Process Heat

50%

Indirect Boiler

47%

HVAC

3%

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Table 30. Industrial Sector Natural Gas Energy-Efficiency Potential 

by End-Use Category (Cumulative in 2023) 

End Use 

Baseline 

Sales 

(Thousand 

Therms) 

Technical Potential Economic Potential 

Thousand 

Therms 

% of Base 

Case 

Thousand 

Therms 

% of Base 

Case 

HVAC 3,694 210 6% 157 4% 

Indirect Boiler 32,829 2,506 8% 2,506 8% 

Other 1,919 0 0% 0 0% 

Process—Heat 21,063 2,874 14% 2,616 12% 

Process—Other 3,110 0 0% 0 0% 

Total 62,615 5,590 9% 5,279 8% 

 

Appendix H 

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The Cadmus Group, Inc. / Energy Services 41 

3. ENERGY EFFICIENCY: MARKET POTENTIAL 

Market potential, as defined in this study, represents savings that might be achievable under an 

aggressive acquisition scenario, assuming: incentive payments up to 100% of incremental 

measure costs; financing availability; exemplary program design and implementation practices; 

and emergence of new technologies, currently not widely available in the marketplace. This 

section presents research results in each of these areas, and examines its implications regarding 

realistic market potential levels in Iowa. 

The results of the market potential analysis are intended to provide context to the estimates of 

economic potential and do not necessarily represent utility targets or “program potential.” These 

savings may be realized through market transformation or improved codes and standards and 

may not be available or appropriate for utility programs. For example, the electric potential 

includes a substantial amount of savings from LEDs and CFLs replacing minimum standard 

bulbs. However, if the new lighting standards cause CFLs to become the de facto standard, the 

amount of savings available for utility DSM program acquisition could be greatly reduced. 

Effects of Increased Incentives 

Due to key differences in measure characteristics and customer demographics, and the differing 

composition of programs and portfolios, one must separately assess incentives’ effects on 

measure adoption for each fuel. While using similar methods, the two analyses adopted rely on 

fuel-specific potential and benchmarking data. The analyses’ results follow. 

Electricity 

As described in Section 1, analysis quantifying the amount of electric market potential available, 

given incentives covering the entire incremental measure cost, has been based on portfolio-level 

data derived from EIA Form 861. Figure 14 shows relationships from 2004 to 2010 between 

savings (as a fraction of retail sales) and incentive payments (as a fraction of annual retail 

revenues) for the 75 utilities in the dataset. The figure suggests a generally linear relationship, 

with relationships that can be examined using regression analysis. The center of the larger red 

square indicates the average spending and savings for Iowa’s electric IOUs in 2010. 

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Figure 14. Scatter Plot of DSM Savings and DSM Expenditures 

The following regression equation estimated the statistical relationship between incentives and 

savings: 

logሺ%	Savingsሻ ൌβ0൅	β1 log ൬

Incentive

Revenue ൰ ൅β2 log ൬

Other	Costs

Revenue ൰ ൅β3logሺRateሻ൅β4LogሺTimeሻ		

This formulation states energy-efficiency savings is a function of: incentive payments 

(Incentive); non-incentive program expenditures (Other Costs), including program 

administration, marketing, and operating expenses; average per-unit cost of delivered energy 

(Rate); and time (Time). The rate term included in the equation accounts for the propensity to 

conserve energy and can be expected to run higher in jurisdictions with high rates. The time 

variable captures trends resulting from exogenous factors affecting program activity from 2004 

to 2010. The equation parameters were estimated using a logarithmic specification with the panel 

data shown in Figure 14. 

The analysis shows a relatively strong overall relationship between savings and the explanatory 

variables, indicated by a coefficient of determination (R2

) of 0.6, meaning 60% of the savings 

variation can be explained by the equation’s explanatory variable (see Table 31). All estimated 

parameters have the correct sign, and are statistically significant at the 90% or higher level of 

statistical confidence, indicating a probability less than 10% that results might be due to chance. 

Coefficients for the incentive term and other expenditures are statistically significant at the 99% 

confidence level. 

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0%

Annual Savings (Percentof Annual Sales)

Annual Expenditures on Energy Efficiency (Percent of Annual Revenue )

2004 2005 2006 2007 2008 2009 2010

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Table 31. Electric Model Terms and Coefficients 

Model Term Coefficient Standard Error P-Value 

Intercept 0.94 0.54 0.08 

Log (Incentive / Revenue) 0.44 0.05 < 0.01 

Log (Other Costs / Revenue) 0.57 0.07 < 0.01 

Log (Rate) 0.32 0.21 0.12 

Log (Time) -0.26 0.12 0.03 

As the equation’s terms are expressed in logarithmic form, estimated coefficients for each term 

in the equation represent the elasticity of savings with respect to that term. For example, as seen 

in Table 31, the estimated coefficient of incentives as a percent of revenue is 0.44, suggesting a 

1% increase in incentives will likely lead to a 0.44% increase in savings. Using this parameter, 

one can estimate the maximum market potential achievable if incentives increase to 100% of 

incremental measure cost. 

As the estimated coefficient on incentive amount measures the marginal impacts of higher 

incentives, a starting point for incentive amounts must be assumed. Available information on 

Iowa’s electric utilities in 2010 indicates, on average, incentives covered approximately 40% of 

incremental measure costs across the energy-efficiency programs in their portfolios. A scenario 

assuming incentives at 100% of incremental costs thus requires a 150% increase 

([100% - 40%] / 40%) increase in current incentive outlays. 

Non-incentive expenditures, such as marketing, outreach, planning, and administration, have 

traditionally been assumed to be relatively fixed. This study’s findings indicate this might not be 

the case. Indeed, the 0.57 estimated elasticity for non-incentive expenditures (shown in Table 31) 

suggests a positive and statistically significant correlation between non-expenditures and market 

penetration, and that these expenditures may even be more effective in expanding the market 

potential than incentives. 

This finding is not surprising, given that first-cost is not necessarily the primary barrier in all 

sectors, and highlights that success in effectively promoting energy-efficiency programs depends 

on the total marketing effort, consisting not only of incentives, but of effective communication, 

education, and dissemination of information. Program administrators must examine and choose 

an appropriate mix of these investments, based on the unique characteristics of their service 

territories, customer needs, and characteristics of programs and products they offer. 

In further analyzing EIA data, Cadmus found a statistically significant positive correlation 

between incentive payments and non-incentive expenditures of approximately 20%. That is, as 

incentives increase, so do non-incentive expenditures, and one cannot consider a scenario with 

drastically increased incentive payments without considering an accompanying rise in nonincentive costs. 

Using 2010 reported portfolio savings and expenditures, revenues, and retail sales for the two 

electric utilities, Cadmus estimates that, if incentives for electric programs increase to 100% of 

incremental measure costs, up to 90% of estimated statewide economic electric potential will 

likely be achievable (see Table 32). As shown, however, budgets would need to increase by more 

than twofold at these incentive levels. As discussed, this increase in incentive spending would 

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likely lead to additional spending on program administration, further increasing program budgets 

to over $113 million annually. 

Table 32. Expected Electric Market Potential If Incentives Increase 

to 100% of Incremental Costs 

Data Value Statewide Value (2010) 

Total Energy Efficiency Program Expenditures $53,975,612 

Total Energy Efficiency Program Expenditures % of Revenue 2.2% 

Incentive % of Incremental Measure Cost 40% 

Actual Savings % of Retail Sales 1.12% 

Estimated Elasticity of Savings Relative to Incentives 0.44% 

Actual Energy Efficiency Savings (MWh) 378,578 

Change in % Savings at Incentives of 100% of Incremental Cost 66% 

Projected Annual Energy Efficiency Savings (MWh) 628,440 

Projected Annual Program Expenditures $113,292,323 

Estimated Annual Economic Potential (MWh) 687,221 

Market Potential % of Economic Potential 91% 

The analysis further shows the associated electric energy savings would likely produce statewide 

life-cycle benefits of approximately $450 million. The estimated costs and benefits do not 

account for potential future decreases in measure costs as energy-efficient technologies improve 

over time. 

A market potential up to 90% of economic potential is extremely high, compared to results of 

other potential studies and market potential levels deemed achievable in other jurisdictions. 

Given economic potentials, relative to technical potentials, are also higher than in most 

jurisdictions, the identified market potential may not be realistically achievable. 

A review of over 100 electric energy-efficiency potential studies completed since 2000, across 

37 states, shows the estimates of economic potential exceeded 80% of technical potential (as 

seen in this study) in only 10 cases. These 10 studies estimate a maximum achievable potential 

of less than 60% of economic potential, a level significantly below that estimated in this study. 

Planning study results in several regions with long histories of aggressive energy-efficiency 

resource acquisition programs also supports the supposition that, relative to the identified 

potential in this study, market potential up to 90% might be exaggerated. 

In the Pacific Northwest, for example, 85% of economic potential has been considered a 

maximum feasible level, which is consistent with findings of potential studies in California. In 

that state, a 2003 study of statewide electric energy-efficiency potentials estimated that, under 

the most aggressive scenario, assuming incentives of 100% of measures costs and total market 

awareness, 73% of the economic potential identified in the study would be achievable.16

 

16 California Statewide Commercial-Sector Energy Efficiency Potential Study, Xenergy Inc, 2002. 

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Natural Gas 

Due to the lack of centralized natural gas energy-efficiency portfolio data, Cadmus compiled 

information on 14 natural gas energy-efficiency portfolios, based on the criteria presented in 

Section 1. Cadmus used these data, presented in Figure 15, to perform a similar regression 

analysis (as described in the electric section, above). 

Figure 15. Scatter Plot of Energy-Efficiency Savings and Expenditures 

Cadmus specified a regression equation similar to that for electricity to estimate relationships 

between natural gas savings and incentives. As data were limited to 2010 results, the equation 

has no “Time” term: 

logሺ%	Savingsሻ ൌβ0൅	β1 log ൬

Incentive

Revenue ൰ ൅β2 log ൬

Other	Costs

Revenue ൰ ൅β3logሺRateሻ	

As shown in Table 34, estimated coefficients for the incentive and other expenditure terms are 

positive, while the coefficient for the rate term has a negative sign, which appears counterintuitive. This coefficient, however, also has a large margin of error and is statistically 

insignificant. 

Table 33. Natural Gas Model Terms and Coefficients 

Model Term Coefficient Standard Error P-Value 

Intercept -0.89 1.15 0.453 

Log (Incentive / Revenue) 0.49 0.16 0.009 

Log (Other Costs / Revenue) 0.15 0.17 0.394 

Log (Rate) -0.62 0.54 0.272 

PG&E

PSE

Avista‐WA

Cascade

Avista‐ID

Questar‐UT

Xcel‐CO

Xcel‐MN

YGS

CNG

SoCT

NGrid‐MA

NStar‐MA

WFOE

Iowa

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

1.4%

0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0%

Gross Annual Savings (% of Annual Sales)

Annual Expenditures on Energy Efficiency (% of Revenue)

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Of the three estimated coefficients, only the incentive term (the critical term in the equation) is 

statistically significant at the 90% confidence level. The weaker overall performance of the 

estimated relationship for natural gas (as compared to electric) in the regression model is largely 

a result of the significantly smaller sample size. 

As shown in Table 34, the estimated coefficient of incentives as a percent of revenue is 0.49, 

suggesting a 1% increase in incentive spending can be associated with a 0.49% increase in 

savings, a result generally consistent with the results found in the electric analysis. The 

coefficient for other spending is much smaller (and statistically less significant) than the electric 

result, suggesting, while savings also increase with other costs, first costs may be the primary 

barrier. 

Table 34. Natural Gas Model Terms and Coefficients 

Model Term Coefficient Standard Error P-Value 

Intercept -0.89 1.15 0.453 

Log (Incentive / Revenue) 0.49 0.16 0.009 

Log (Other Costs / Revenue) 0.15 0.17 0.394 

Log (Rate) -0.62 0.54 0.272 

Available information on Iowa’s electric utilities in 2010 indicates incentives covered 

approximately 42% of incremental measure costs across all programs in the three utility’s 

portfolios. A scenario assuming incentives at 100% of incremental costs thus requires an increase 

of 138% ([100% - 42%] / 42%) in current incentive outlays. 

Using 2010 energy-efficiency program savings and expenditures, revenues and retail sales for 

the three natural gas utilities, Cadmus estimates that, if incentives for natural gas programs 

increase to 100% of incremental measure costs, the achievable fraction of economic potential 

might increase to approximately 65% of the estimated economic potential (see Table 35). 

Table 35. Expected Achievable Natural Gas Market Potential 

If Incentives Increase to 100% of Incremental Costs 

Data Value Statewide Value (2010) 

Total Energy Efficiency Program Expenditures $37,851,535 

Total Energy Efficiency Program Expenditures % of Revenue 4.1% 

Incentive % of Incremental Measure Cost 42% 

Actual Savings % of Retail Sales 0.92% 

Estimated Elasticity of Savings Relative to Incentives 0.49% 

Actual Energy Efficiency Savings (thousand therms) 9,682 

Change in % Savings at Incentives of 100% of Incremental Cost 62% 

Projected Annual Energy Efficiency Savings (thousand therms) 15,661 

Projected Annual Program Expenditures $74,951,818 

Estimated Annual Economic Potential (thousand therms) 25,475 

Market Potential % of Economic Potential 65% 

As annual statewide savings relative to retail sales are currently lower for natural gas than 

electricity, the analysis projects a lower share of the economic potential as achievable, given it 

would be more difficult for natural gas programs to ramp up to maximal savings levels. As 

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shown, however, budgets would need to increase twofold at these incentive levels. As discussed, 

this increase in incentive spending would likely lead to additional spending on program 

administration, further increasing program budgets to $75 million dollars annually. The analysis 

further shows the associated natural gas energy savings would likely produce statewide life-cycle 

benefits of over $100 million. 

Effects of Financing Availability 

Market potential depends on a number of factors, including retail energy rates, energy-efficiency 

measure costs, and the program’s ability to overcome a host of market barriers recognized in the 

energy-efficiency literature to impede adoption of energy-efficiency measures and practices by 

consumers, including high first costs. These barriers tend to vary in severity, depending on 

customer sectors, local energy market conditions, and other, hard-to-quantify factors. Ultimately, 

market potential is a function of consumers’ willingness and ability to participate in programs. 

Financing options (in the form of loan programs) are mechanisms used to help mitigate effects 

from lack of capital—or high-cost financing—on consumers’ ability to participate in energyefficiency programs. Studies of financing and loan programs, including two recent reports by 

ACEEE, have found energy-efficiency loan programs have minimal effects on consumers’ 

participation in energy-efficiency programs. 

The findings of one ACEEE study17 suggest participation rates tend to be generally low across 

programs. Compared to numbers of eligible customers in classes served by these programs, more 

than half the programs had participation rates below 0.5%. The highest participation rate was 

reported at 3%, experienced by only two surveyed programs. The report concludes these 

programs generally have not successfully achieved appreciable market penetration, and, 

importantly, sound program design does not appear to guarantee success. 

A survey of on-bill financing programs found similar results. In a 2011 report, ACEEE examined 

19 of 31 on-bill financing programs, structured as on-bill loans or on-bill tariffs in 20 states.18

The study found less than 1%19 of the eligible customers participated in these programs, despite 

several of these programs having been available for nearly 20 years.20 

In light of extremely high economic potential levels assumed available under a 100% incentive 

scenario, and the performance of financing programs to date, it is unlikely availability of 

financing would increase market potential beyond that achievable assuming a 100% incentive. 

 

17 Hays, Sara, et. al., What Have We Learned From Energy Efficiency Financing Programs, ACEEE, Report 

Number U115, September 2011. 18 Bell, Catherine J., et. al., On-Bill Financing for Energy Efficiency Improvements: A Review of Current Program 

Challenges, Opportunities, and Best Practices, Report Number E118, December 2011. 19 This number represents the average found by ACEEE for the programs reviewed. There have been cases where 

individual utilities have achieved higher penetration rates for on-bill financing programs, such as Cedar Falls 

Utility in Iowa. 20 See also Byrd, D.J. and R.S. Cohen, A Roadmap to Energy Efficiency Loan Financing, Memorandum to U.S. 

Department of Energy, April 2011. 

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Effects of Emerging Technologies 

In addition to commercially available technologies included in the assessment of technical and 

economic potentials, Cadmus considered the potential for emerging technologies in the context 

of market potential. Emerging energy-efficient technologies are those expected to become 

commercially available and cost-effective within the next five to 10 years. 

The primary sources used to identify potential measures and corresponding savings data were 

reports published by ACEEE. Since the mid-1990s, ACEEE has published reports on Emerging 

Energy-Saving Technologies and Practices in the Building Sector.

21 In 2009 and 2011, reports 

focused on HVAC and hot water systems, respectively. ACEEE currently is investigating 

emerging lighting technologies, but, as results of this research are not available at this time, 

Cadmus referenced work conducted through DOE’s CALiPER program.22 

Generally, these technologies are higher-efficiency replacements for measures already included 

in the assessment. For example, Advanced Northern Heat Pumps (SEER 16/HSPF 9.6) are a 

more efficient variant of SEER 16/HSPF 9.0 heat pumps, already included. Active Chilled Beam 

Cooling with DOAS (dedicated outdoor air system) proves the exception: this measure 

represents an alternate building design, replacing standard duct systems with integrated features, 

combining lighting, water-cooled convective heat exchange surfaces, and ventilation. In short, it 

utilizes pumps to deliver cool water instead of fans to blow cold air. 

The analysis assumes replacement measures for existing, cost-effective measures will, in turn, 

become cost-effective over the planning horizon. In these cases, Cadmus estimated additional 

potential savings for these measures relative to the comparable measure’s economic potential. 

That is, using the example measure above, additional potential for the SEER 16/HSPF 9.6 heat 

pump is incremental to the SEER 16/HSPF 9.0 unit. However, if the measure supplanted by this 

emerging technology does not pass the economic screen, no additional economic potential is 

assumed for the emerging technology. That is, existing technology would first need to become 

economically feasible before being supplanted by an emerging technology. For example, as 

existing natural gas tankless water heaters do not pass the economic screen, it is assumed 

condensing tankless water heaters will not pass either. 

Measures identified through this research, along with applicable sectors, fuels, and end uses, are 

listed in Table 36. Though ACEEE reports addressed more measures, only those in Table 36 

achieved efficiency levels greater than economic measures already in the measure list. 

 

21 http://www.aceee.org/topics/emerging-technologies-and-practices 22 http://www1.eere.energy.gov/buildings/ssl/caliper.html 

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Table 36. Emerging Technologies 

Sector Fuel End Use Technology 

Additional Market 

Potential (MWh or 

thousand therms) 

Residential Electric Water Heating Add-On Heat Pump Water Heater 27,426 

Residential Electric HVAC Optimized Residential Duct Work 763 

Residential Electric Water Heating Singe Family On-Demand Recirculation Pumps 1,615 

Residential Electric HVAC Multifamily Building Best Practices 43,599 

Residential Gas Water Heating Condensing Tankless Water Heater 0 

Commercial Electric Lighting LED Replacement of Linear Fluorescent 62,915 

Commercial Electric HVAC Active Chilled Beam Cooling with DOAS 2,338 

Commercial Electric HVAC Ventilation and Energy Recovery 34,214 

The additional market potential from the emerging technologies is estimated at 73,403 MWh in 

the residential sector and 99,468 MWh in the commercial sector, assuming 90% of economic 

potential is achievable (given 100% incentives). If realized, these additional savings would 

increase the electric market potential, shown in Table 32, by about 3%. Cadmus did not identify 

additional natural gas potential from emerging technologies. Appendix A.5 describes each 

measure included in the analysis. 

 

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4. DEMAND RESPONSE POTENTIAL 

Potential for Expanding Legacy Programs 

Residential DLC 

As discussed in Section 1, the key metric for the residential DLC analysis was the fraction of 

eligible customers currently participating, with eligible customers defined as those with 

residential electric service and central air conditioners. Based on 2010 program activity, 

residential customer counts, and saturation data from the 2007 Residential Appliance Saturation 

Survey, Cadmus estimated similar currently participation rates for Iowa DLC programs: 19% 

and 18% for Alliant and MidAmerican, respectively. 

Based on secondary data collected, Cadmus calculated participation rates for an additional 51 

residential DLC programs for investor-owned utilities (IOUs) from across the nation. Figure 16 

shows the calculated participation for each of these utilities in 2010, with Iowa utilities shown in 

red. 

Figure 16. Participation Rates for 2010 IOU Residential DLC Programs 

As program participation serves as the key driver of residential DLC impacts, Cadmus 

established three scenarios to quantify available potential for Iowa utilities, based on differing 

program participation levels. Participation levels in the moderate and aggressive expansion 

scenarios have been based on average participation in the upper-tier and industry-leading IOU 

programs, respectively. 

0%

10%

20%

30%

40%

50%

60%

Percent of Eligible Customers Signed UP

IOUs

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Assumed participation rates are: 

 Baseline: maintaining current program participation levels. 

 Moderate expansion: achieving 20% program participation. 

 Aggressive expansion: achieving 25% program participation. 

To estimate peak demand impacts under each scenario, Cadmus multiplied participation rates by 

eligible customer forecasts for each utility, calculating the number of participating customers, 

then multiplying this number by per-participant values currently used by Iowa utilities, to 

calculate program-level demand impacts. Table 37 compares estimated 10-year potential under 

each scenario to the 2008 Assessment and each utility’s 2010 accomplishments. As shown in 

Table 37identified potential is lower than in the 2008 Assessment, based on updated data on 

actual program achievements. 

Table 37. Forecasted Residential DLC Impacts in 2023 (MW) 

10-Year Potential 

2008 Study 2012 Study 

Utility 

2010 Program 

Achievements Base Case 

Base 

Case 

Moderate 

Expansion 

Aggressive 

Expansion 

Alliant 33 53 35 37 46 

MidAmerican 31 72 32 35 43 

Secondary research into snapback effects indicated residential DLC programs typically see 

energy savings reductions of 40% to 70% due to snapback.23 Actual energy saved by these 

programs is a function not only of demand under contract, but also of the duration and frequency 

of events. However, based on the secondary literature, Cadmus expect per-hour MWh potential 

to be roughly half of the MW values presented in Table 37. 

Nonresidential Interruptible 

Participation in interruptible programs will vary greatly across utilities due to the following: 

 The value of capacity savings; 

 Eligibility requirements; 

 Utility incentives; 

 Prevalence of standby generation; and 

 Who implements the program (utility vs. third-party aggregator). 

These caveats aside, Cadmus collected data on IOU programs similar to those offered in Iowa to 

assess opportunities for program growth. 

Unlike the residential sector, due to large differences in demand between nonresidential 

customers, the percent of eligible load enrolled (rather than customers) serves as the key metric 

in assessing program participation. However, as data on eligible loads by utility are not readily 

 

23 Appendix B lists programs reviewed. 

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available for most utilities, Cadmus used total nonresidential demand during the system peak 

hour as a proxy. Using this metric and based on 2010 programs, Alliant and MidAmerican have 

currently enrolled 14% and 8% of eligible load, respectively. Figure 17 shows similar 

information collected for an additional 30 IOUs with similar programs, with Iowa utilities 

appearing as red bars. 

Figure 17. Participation Rates for 2010 IOU Nonresidential Interruptible Programs 

As program participation serves as the key driver of nonresidential interruptible impacts, 

Cadmus established three scenarios to quantify available potential for Iowa utilities, based on 

differing program participation levels. Participation levels in the moderate and aggressive 

expansion scenarios have been based on average participation in the upper-tier and industryleading IOU programs, respectively, with the assumed participation rates: 

 Baseline: maintaining current program participation levels. 

 Moderate expansion: achieving 15% program participation. 

 Aggressive expansion: achieving 17.5% program participation. 

For each scenario, the percent increase in participation over 2010 activity has been used to 

calculate each utility’s potential. As noted, utilities must consider their current and projected 

resource needs to determine whether these program participation levels are desirable and 

prudent. 

0%

5%

10%

15%

20%

25%

30%

Percent of C&I Peak Participating

IOUs

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Table 38 compares estimated 10-year potential under each scenario to the 2008 Assessment and 

each utility’s 2010 accomplishments. 

Table 38. Forecasted Nonresidential Interruptible Impacts in 2023 (MW) 

10-Year Potential 

2008 Study 2012 Study 

Utility 

2010 Program 

Achievements Base Case 

Base 

Case 

Moderate 

Expansion 

Aggressive 

Expansion 

Alliant 264 291 296 304 354 

MidAmerican 193 170 238 422 492 

As in the residential sector, snapback effects, and thus energy savings attributable to demand 

response strategies, can vary greatly across utilities. Though literature on the likely snapback 

effects for nonresidential programs is limited, available data indicate that the effect may be 

around 50%. 

Opportunities With AMI 

Analysis of AMI-enabled demand programs was a qualitative exercise, given data quantifying 

impacts of AMI-enabled programs has been drawn almost exclusively from utility pilot 

programs, and may not be appropriate for extrapolation to larger markets. Consequently, 

potential energy and demand savings related to AMI cannot be reliably quantified at this time. 

Nevertheless, this study outlines a number of potential options that may provide viable savings 

sources if Iowa electric utilities implement AMI. 

Overview of AMI-Enabled Demand Response 

At the highest level, AMI’s addition enables two-way communication for the mass-market of 

utility customers. Such two-way communication enables two primary opportunities. First, 

collection of near real-time interval meter data becomes possible as smart meters record interval 

meter reads, and send data back to the utility. Second, AMI enables communication from the 

utility to the customer, with the utility sending signals to the customer’s meter, which can be 

used to specify changes in dynamic pricing or to control various appliances. 

AMI technology does not present a new idea: many utilities have installed similar systems 

strategically for their larger C&I customers. Its strategic aspect arises regarding costeffectiveness, as non-AMI systems have been inappropriate for installation in some situations, 

due to costs outweighing benefits. Such systems have often relied on dedicated Internet 

connections and advanced metering. 

AMI enables a much lower per-meter cost for such advanced capabilities. By deploying systemwide communication networks, AMI systems reduce communication costs, and open doors to 

more cost-effective smart meter installations. Thus, as the C&I market has utilized various forms 

of advanced metering, the residential and small commercial market will likely realize much 

greater impacts from AMI. 

Consequently, our research focused on residential, AMI-enabled opportunities. AMI can 

automate load reductions within a home or business through use of demand response enabling 

technology, which can be remotely signaled when utilities call demand response events, thus 

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reducing an appliance’s load through control strategies established by the utility or the customer. 

AMI-enabled demand response technologies include the following: 

 Smart thermostats: Devices similar to programmable thermostats, but receiving and 

reacting to utility pricing and signals. Customers using smart thermostats typically 

program devices to react in specific ways when demand response events occur. For 

example, a customer may choose to raise the temperature set point by four degrees during 

an event to reduce load. Smart thermostats automate this process. 

 Smart appliances: Smart appliances typically are very efficient versions of traditional 

appliances, equipped with AMI communication capabilities. They can receive event 

notifications or pricing signals, modifying operations to reduce demand during demand 

response events. For example, a smart refrigerator, when signaled with a relatively high 

electric price, may cycle its refrigerant compressor to reduce peak consumption. Other 

smart appliances include: water heaters, lighting, clothes washers and dryers, and 

dishwashers. 

 Load control devices: AMI load control devices resemble traditional load control 

devices, except they communicate over AMI systems, and have an added benefit of 

communicating their status; so non-operable devices can be more readily repaired. 

 Home energy management systems (HEMS): In advanced homes utilizing HEMS to 

control operations such as HVAC, lighting, appliances and security, adding AMI allows 

HEMS’ to control systems to reduce demand when signaled through an AMI network. 

In addition to demand response enabling technologies, other AMI-enabled technologies improve 

communication of energy usage from the utility to consumer. Traditionally, customers have 

received monthly utility bills that report consumption and charge customers for their aggregate 

monthly consumption, a system that somewhat disconnects customers from immediate 

connections between their actions and energy consumption. However, AMI enables near realtime feedback, informing customers of their energy consumption much more quickly. Examples 

of enhanced communication devices include the following: 

 Personal Web portals: These portals offer customized Websites customers can use to 

monitor interval consumption. Such systems allow customers to analyze their 

consumption over time periods they choose to view. Increasingly, these systems employ 

advanced analytics to provide customers with even more useful information. For 

example, some systems allow customers to benchmark their performance against those of 

neighbors with similar homes. Some systems allow customers to specify what they wish 

their utility bills to be, and the portal provides recommended actions they should take to 

meet these goals. 

 In-home displays (IHD): These are standalone devices, typically communicating with 

smart meters to show customers their energy consumption and current utility pricing. 

These devices allow customers to better understand their energy consumption. 

 Energy Orb: These standalone devices, which change color as energy rates change or as 

demand response events are called, signal customers to take appropriate actions to reduce 

their electric demand. 

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Program Examples 

AMI Enabled DLC 

As noted, AMI adoption creates opportunities to control appliances within customers’ homes and 

businesses. In some ways, they differ little from current DLC programs: individual load 

reductions from activities such as cycling central air conditioners may not differ from load 

reductions resulting from currently deployed DLC programs. However, AMI improves upon 

DLC by implementing two-way communication. Most residential DLC programs experience 

lower demand reduction capabilities due to malfunctioning DLC devices. As traditional systems 

cannot communicate their status to the utility, these devices often remain inoperable until 

discovered through inspections. Utilities typically experience 10% to 20% losses due to nonoperable DLC devices. With AMI-enabled DLC, non-operable devices can be more readily 

detected, and inoperability rates can typically be decreased to between 2% and 5%. 

AMI Enabled Dynamic Pricing 

Dynamic pricing has encountered a limiting factor in that traditional utility meters cannot record 

or transmit the interval data required to reconcile customer consumption. However, as AMI 

enables such communication, it allows implementation of dynamic pricing programs. To date, 

the majority of dynamic pricing data have resulted from pilot evaluations, which have been 

plagued with potential bias, stemming from early adopters’ reporting results, as these individuals 

may use AMI capabilities more than average customers. Nevertheless, preliminary pilot results 

have been somewhat promising. 

The Brattle Group recently synthesized results of 109 AMI-enabled dynamic pricing pilots, 

finding the majority of pilots resulted in load reductions of up to 16%, with a 12% median 

demand reduction.24 The majority of these pilots relied on customers taking action when 

prompted through signaling techniques such as telephone calls, e-mails, and text messages. 

Brattle also examined 39 AMI-enabled dynamic pricing programs, utilizing various 

combinations of enabling technologies. These programs showed consistently higher savings than 

programs without enabling technologies, with a median demand reduction of 23%. 

Summary of AMI-Enabled Demand Response Opportunities 

From initial pilot results, AMI appears to expand demand reduction capabilities of residential 

demand response programs, though the extent of this expansion remains to be seen, as program 

persistence issues have not been thoroughly studied. Additionally, studies of the reliability and 

security of these programs and enabling technologies remain in progress. Further, how AMIenabled programs and traditional programs overlap, and how demand savings may shift, still 

must be understood before specific estimates of demand reduction can be determined. 

From improving operability rates of existing DLC programs to offering new demand response 

programs to customers, who otherwise would not sign up for traditional DLC programs, AMI 

will likely expand utilities’ demand reduction capabilities. 

 

24 Ahmed Faruqui and Palmer, J. “Dynamic Pricing of Electricity and its Discontents.” The Brattle Group. August 

2011. 

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5. ASSESSMENT OF THE NET-TO-GROSS RATIO 

Definitions 

Net-to-gross (NTG) assessments primarily seek to determine energy savings attributable to 

energy-efficiency programs by explicitly accounting for freeridership (energy savings likely to 

have occurred in the program’s absence) and spillover (energy savings induced but not 

subsidized by the program). Savings resulting from this calculation are the “net” program 

savings, and the ratio of net program savings to gross savings is the NTG ratio. 

About Freeridership 

Freeridership subtracts from gross energy savings likely to have occurred through adoption of 

energy-efficiency measures by participants, independent of the program. That is, participants are 

considered freeriders if they would have adopted the same energy-saving measures at the same 

time, in the same quantity, and at the same efficiency level, had the program not existed. 

About Spillover 

Spillover adjustment adds energy savings from adoption of high-efficiency measures outside the 

program, but likely induced by the program. These additional energy savings are assumed to 

derive from greater knowledge and awareness of energy-efficient options resulting directly from 

the program’s availability and influence. 

Spillover can occur within participant and nonparticipant populations. For example, participants 

in a program may be motivated to adopt high-efficiency measures beyond those subsidized by a 

program. Simultaneously, the knowledge, awareness, and availability of measures caused by a 

program may induce nonparticipants to adopt the same energy-efficient measures. 

For most programs, the number of eligible nonparticipants far outnumbers participants; thus, 

potential exists for large spillover impacts within this population. 

About Program-Induced Market Effects 

A third possible adjustment is program-induced market effects25―that is, any change the 

program causes to operations of supply chains in energy-efficiency markets. For example, the 

programs may result in: 

 Manufacturers changing the efficiency of their products; 

 Wholesalers and retailers changing their stocking decisions, reacting to shifts in demand 

for more efficient goods caused by IOU programs; and/or 

 Architects and builders adopting energy-efficient practices. 

 

 

25 Note that some literature includes nonparticipant spillover as part of market effects. 

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These market effects can be significant, especially in upstream programs implemented through 

point-of-sale discounts. Such transformational market effects are, arguably, the ideal 

achievements of energy-efficiency programs, and can have long-lasting impacts. However, it is 

broadly accepted that these impacts can be difficult to measure for at least two reasons: 

 Identifying these consumers in the larger populations can be difficult, as they may not be 

aware they participated in a program. 

 A large number of factors may influence consumers’ purchasing decisions. 

Thus, measuring and attributing these effects to particular energy-efficiency programs has been a 

significant measurement and evaluation challenge. 

Treatment of Freeridership and Spillover 

Depending on the relative magnitudes of freeridership and spillover, NTG may be less than, 

greater than, or equal to 1.0. However, in jurisdictions where freeridership is the only measured 

effect, NTG never takes a value greater than 1.0. 

Applying NTG also affects the cost-effectiveness of IOU programs. The Iowa Chapter 35 rules 

specify the method and assumptions for cost-effectiveness tests, including the Societal Cost Test 

(SCT), the standard for determination of cost-effectiveness in Iowa. The rules have been based 

on the Standard Practice Manual (SPM) for Economic Analysis of Demand-Side Management 

Programs, established by the California Public Utilities Commission (CPUC).26 

In calculating benefits for the Total Resource Cost (TRC) test, the CPUC observed: 

“...ratepayers, through the energy-efficiency revenue requirements collected to fund these 

programs, incur a cost for freerider participants that must not be ignored in the formulation of the 

TRC test.”27 (The same observation applies to the SCT, which is a variant of the TRC.) 

Due to ambiguity regarding how to fold in freerider considerations on the equation’s cost side, 

the CPUC (in its 2007 Clarification Memo) modified the original method for calculating TRC 

costs by adding a transfer incentive (INC) recapture term to the initial TRC cost equation, as 

follows: 

TRC Costs = PRC + NTG*PC + UIC + (1.0-NTG)*INC 

Where, 

PRC = program administrator costs 

PC = participant device costs (before INC is received) 

UIC = (for fuel substitution programs) utility increase supply costs 

 

26 The SPM describes procedures for determining cost-effectiveness of energy-efficiency programs from five 

perspectives: resource allocation efficiency (Total Resource Cost); the utility (Utility Cost Test); participants 

(Participant Cost Test); society (Societal Cost Test); and ratepayers (Rate Impact Measure). 27 2007 SPM Clarification Memo, D.07-09-043, pages 154-158, California Public Utilities Commission, 2007.

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NTG = net-to-gross ratio 

INC = incentive costs, restricted to include only dollar benefits. 

According to the CPUC, adding the INC term to the TRC formulation ensures removal of 

freerider costs does not remove program costs that become utility-revenue requirements, 

consistent with the test’s intent and purpose. Given administrative costs normally represent only 

a small percentage of total resource costs, freeridership impacts on TRC (and SCT) results tend 

to be small. 

Treatment of NTG Across Jurisdictions 

The definition, measurement, and treatment of freeridership―and of NTG in general―vary 

across jurisdictions in the United States. Some jurisdictions include both freeridership and 

spillover in defining net savings, while others allow only freeridership to be counted. In several 

cases, freeridership and spillover are measured separately, and incorporated in NTG, while other 

jurisdictions estimate NTG without specifying freeridership and spillover individually. Finally, 

in some cases, measurement of NTG—or its components—may not be required. Instead, gross 

savings, adjusted for actual installation rates, are used as the measure of program impacts. This is 

also the case with regional transmission organization (RTOs), such as the New England 

independent system operator (ISO-NE), where verified gross savings serve as the basis for 

verification of energy-efficiency bids into the forward energy market. 

Cadmus compiled data on 32 jurisdictions active in energy efficiency to determine how NTG is 

defined, and whether it is used as an adjustment to gross savings. The survey established the 

following highlights: 

 All but six of these jurisdictions (81%) have energy-efficiency resource standards 

(EERS) in place, setting minimum performance requirements, either as legislative or 

regulatory mandates or voluntary goals. 

 No requirements exist for NTG calculations in 12 jurisdictions (38%). 

 In 17 jurisdictions (53%), freeridership is included in determination of program savings. 

In seven of these jurisdictions (41%), freeridership is applied at the measure level.28

 In 10 jurisdictions (31%), NTG calculations include freeridership and either participant or 

nonparticipant spillover effects. 

 In the majority of cases where NTG is calculated, it is applied prospectively for planning 

purposes. In these jurisdictions, utilities rely on adjusted gross savings for reporting 

compliance with targets, but are required to use deemed freeridership values in their 

program plans. 

 Participant spillover is measured in 12 jurisdictions (37%) in the sample, while 

nonparticipant spillover is taken into account in 10 (31%). 

 The incidence of cases only assessing freeridership suggests asymmetrical treatment of 

spillover and freeridership effects. 

 

28 New Jersey applies freeridership only to appliance recycling programs. 

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For a list of jurisdictions reviewed, and the NTG activity in each, see Appendix C. 

Examples of NTG Values 

Table 39 lists deemed NTG values adopted by the CPUC for the 2009–2011 program cycle. 

Although these NTG values do not include spillover effects, the CPUC allowed evaluations of 

the 2006–2008 energy-efficiency programs to contain an examination and estimation of 

participant spillover. As seen, NTG estimates vary widely across market sectors and measures. 

On average, NTG ratios are lower in the residential sector than in the commercial and industrial 

sectors, mainly due to the high freeridership in upstream programs. 

Table 39. California Program Deemed NTG Ratios 

Program Average NTG Maximum NTG (Measure) Minimum NTG (Measure) 

Residential 

Lighting 0.78 0.85 (Multiple) 0.60 (CFL ≤30 watt) 

Appliance Replacement 0.70 0.85 (Clothes washer 15% above 

standard) 0.41 (Dishwasher EF>0.58) 

Appliance Recycling 0.66 0.702 (Freezer) 0.614 (Refrigerator) 

Water Heating 0.76 0.85 (multiple) 0.58 (Water Heater EF>0.62) 

HVAC 0.67 0.85 (Programmable thermostat with 

direct install) 

0.49 (Programmable thermostat with 

prescriptive rebate) 

Multifamily 0.84 1.0 (Boiler controls) 0.76 (Lighting) 

New Construction 0.53 0.62 (Lighting) 0.48 (Whole building single family 

RNC) 

Residential Audits 0.80 N/A N/A 

Default Values 0.78 0.85 (New measures with <5% market 

share) 

0.70 (New measures with ≥5% market 

share) 

Nonresidential 

Lighting 0.78 0.85 (Multiple) 0.60 (CFL ≤30 watt) 

HVAC 0.74 0.85 (Multiple) 0.50 (Multiple) 

Refrigeration 0.68 0.82 (Refrigeration in NRNC) 0.46 (Strip door curtains) 

Motors 0.84 N/A N/A 

Water Heating 0.64 0.82 (Water heating in new 

construction) 

0.46 (Water heating in existing 

buildings) 

Building Shell 0.93 N/A N/A 

Whole Building 0.70 N/A N/A 

Custom 0.75 0.85 (Multiple) 0.64 (Multiple) 

Agricultural 0.50 0.75 (Vacuum pump VSD) 0.26 (Plate cooler) 

Audits 0.41 0.48 (Lighting/cooling 20 to 100 kW) 0.29 (Lighting/cooling less than 20 kW) 

Retrocommissioning 0.95 1.0 (Gas measures) 0.90 (Electric measures) 

Local Govt Partnerships 0.68 N/A N/A 

Default Values 0.78 0.85 (New measures with <5% 

market share) 

0.70 (New measures with ≥5% 

market share) 

Source: 2008 Database for Energy-Efficient Resources 

(http://www.deeresources.com/deer0911planning/downloads/DEER2008_NTG_ValuesAndDocumentation_080530.zip) 

Version 2008.2.05 December 16, 2008 

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To date, only one evaluation (NYSERDA)29 has estimated spillover effects for a new 

construction program. The evaluation showed a 46% freeridership rate (consistent with 

Table 39), and a combined participant-and-nonparticipant spillover rate of 54%, more than 

offsetting the freeridership estimate. 

Measuring Freeridership and Spillover 

A variety of methods and analytic techniques have been used to measure or to account for 

freeridership and/or NTG in general. Despite apparent differences, these methods and techniques 

tend to fall into one of two categories: statistical and self-report. 

Statistical Methods 

Statistical methods are based on the general difference-in-differences approach, where actual 

energy consumption is measured for program participants and a comparable group of 

nonparticipants in two time periods: before and after program implementation. Using statistical 

methods: 

 Participants are exposed to program treatment in the second period, but not in the first. 

 The comparison (nonparticipant) group is not exposed to treatment during either period. 

Implemented properly, with a well-chosen control group, this approach removes potential biases 

related to the unique characteristics of participants, and biases from comparisons over time, 

which could result from non-program related trends (so-called “naturally occurring 

conservation”). Net program impacts are then calculated by subtracting the average change in 

nonparticipants’ consumption from the average change in the participant group. 

This approach is sometimes implemented within an econometric framework for the following 

reasons: (1) controlling for the residual difference between the two groups; (2) evaluating the 

sensitivity of savings to various factors; and (3) estimating savings for bundles of measures. It 

cannot, however, be used for measuring NTG for individual measures. Moreover, this approach 

does not provide estimates for the individual NTG components―freeridership, spillover, and 

market effects. 

The approach is also not well suited to estimating NTG in large commercial and industrial 

energy-efficiency programs. Due to the heterogeneity of these customers, it often can be 

impractical to identify an appropriately comparable group of nonparticipants. Also, as energy 

savings in these programs are often a small fraction of total consumption, it can be difficult to 

isolate consumption changes resulting from implementation of energy-efficiency measures. 

Moreover, this method is not recommended for upstream programs or new construction 

programs (where the lack of a pre-program period limits the effectiveness of the approach). 

 

29 New Construction Program (NCP) Market Characterization and Assessment, prepared for New York State 

Energy Research and Development Authority, prepared by Summit Blue Consulting, LLC, August, 2008. 

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Self-Report Methods 

Studies relying on self-reporting are more common than those relying on statistical methods. At 

a basic level, these methods directly involve asking participants questions about what they would 

have done in the program’s absence. Responses are then scaled, weighted, and combined to 

produce a composite freeridership score (or index) for each respondent. Scores are then weighted 

(by savings) and averaged to produce a program-level freeridership fraction. 

The self-report approach does not produce an NTG ratio. The other NTG components―spillover 

and market effects―must be estimated separately, and then be factored into the calculations. 

Surveys for determining spillover effects within groups of participants or nonparticipants are 

especially sensitive to variations in spillover scores. Small fractions multiplied by very large 

numbers of customers can dramatically boost savings. 

Using surveys to assess freeridership raises concerns about response bias, particularly biases 

involving social desirability (the tendency of respondents to gauge their responses to conform to 

socially acceptable values). This well-recognized issue in social sciences has been discussed in a 

vast body of academic and professional literature. 

Due to social desirability, respondents tend to offer what they think is the right answer, resulting 

in freeridership overstatement. Also, as some evaluation experts have noted, people have internal 

reasons―as explained by social psychology’s attribution theory―motivating them to make 

certain decisions. 

Another aspect is called the construct validity. This issue stems from the fact that while survey 

respondents―by virtue of their participation in the program―are predisposed to conservation, 

the extent that their responses have been conditioned by the psychological effects of the 

conservation program remains unclear. Thus, what surveys measure may be the program’s effect 

rather than what would have happened in its absence.30 In areas with long histories of 

conservation programs and activities, it can be difficult to determine who is a freerider and who 

has been influenced by the program.31

In recent years, research methods have become more sophisticated, resulting in development of a 

series of questions and incremental answers designed to understand partial freeriders. 

 In general, freerider questions ask interviewees about actions they would have taken had 

the program not been in place. 

 For spillover, recent survey-based studies have focused mainly on participant and 

nonparticipant spillover. Participant surveys elicit responses about whether customers 

 

30 See Peters, Jane S. and Marjorie McRae., Freeridership Measurement Is Out of Sync with Program 

Logic...or, We’ve Got the Structure Built, but What’s Its Foundation? Proceedings, ACEEE Summer 

Study Monterey, CA, August 2008. 31 Friedman, Rafael, Maximizing Societal Uptake of Energy Efficiency in the New Millennium: Time for Net-toGross to Get Out of the Way? Proceedings, International Energy Program Evaluation Conference, Chicago, 

August 2007. 

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have purchased additional energy-efficient measures of the same type without financial 

assistance. 

 Nonparticipant spillover surveys ask customers if they purchased efficiency measures due 

to their awareness of the program. 

 These developments have resulted in more systematic and transparent approaches, but 

results remain sensitive to evaluators’ subjective assumptions. 

Recall presents another problem, especially regarding spillover. Studies have found interviewees 

have difficulty self-reporting details such as usage, size, and efficiency levels. 

Partly due to inherent biases, NTG results can vary sharply, based on the method selected. For 

example, two studies completed in the mid-1990s found self-reported freeridership estimates can 

be more than 50% higher than discrete choice approaches.32 On the other hand, a recent study of 

several small commercial-sector programs in California found results, derived from more 

advanced statistical models (based on a nested logit model specification), were nearly identical to 

those obtained from self reports33 (see Table 40). 

Table 40. Freeridership Rates Differences Based on Research Approach 

Discrete Choice Self-Reported 

2010 California Small Commercial Programs 77% 78% 

1995 Commercial Lighting Study 22% 32% to 38% 

1994 PG&E Commercial Rebate 27% 42% 

For these reasons, some experts have argued estimating freeridership and spillover can be too 

expensive, given considerable uncertainty about the results.34

 

32 Train, K. and E. Paquette, “A Discrete Choice Method to Estimate Freeridership, Net-to-Gross Ratios, and the 

Effect of Program Advertising,” Energy Services Journal, Vol. 1, No. 1, 1995. 33 Grover, Stephen, et. al., Free to Choose? A Comparison of a Nested Logit Model with a Billing Regression 

Model and Self-Report Analysis in a Commercial Impact Evaluation, Proceedings, International Energy 

Program Evaluation Conference, Boston, August 2011. 34 Saxonis, William P., Freeridership and Spillover: A Regulatory Dilemma, Proceedings, Energy Program 

Evaluation Conference, Chicago, August 2007. 

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Cross-Program Research 

The National Energy Efficiency Best Practices Study, an ongoing project sponsored by the 

CPUC, provides some insight into how the NTG issue has been handled in programs across the 

country.35 The project seeks to identify best practices, and to communicate findings to program 

administrators for enhancing design of their programs. 

In-depth interviews were conducted with managers of more than 100 programs in 2004 and 

2005. Based on these interviews, program profiles were developed, and best practices were 

identified. Information was also provided regarding whether a program included a NTG 

adjustment, and whether this adjustment was based solely on freeridership, or if it also included 

spillover. Table 41 summarizes NTG values reported. 

Table 41. NTG Values Identified Through the Best Practices Project 

Program Area NTG Value(s) Freeridership Value(s) Spillover Value(s) 

Residential 

Lighting 0.57, 0.8, 1.27 5.7%, 6% 9.8%, 15% 

Air Conditioning 0.8 N/A N/A 

Single Family Comprehensive 0.89, 0.93, 0.94, 0.97 3%, 4.4% 0% 

Multifamily Comprehensive 0.78, 0.89 0%, 3% N/A 

New Construction 0.8, 1.0, 1.16 0%, 20% N/A 

Nonresidential 

Lighting 0.96, 1.0 N/A N/A 

HVAC 0.85, 0.96, 1.0 0%, 15% N/A 

Large Comprehensive 0.7, 0.8, 1.0, 1.06 N/A N/A 

New Construction 0.65, 0.67, 0.75, 0.81, 0.93 7%, 33%, 40% N/A 

See the Best Practices Website for detailed reports: http://www.eebestpractices.com/index.asp 

More than 50% of studies reviewed either assumed or calculated an NTG value of 0.9 or greater. 

(In most cases, NTG values only included freeridership, or were based on a deemed NTG 

assumption.) Reported freeridership values varied significantly, even within program groups. 

Spillover effects were reported very infrequently. 

Another cross-program study reviewed evaluation efforts of 50 resource acquisition programs 

and 31 information-only programs from the 2002–2003 California energy-efficiency programs.36

That study found only 23 evaluations took freeridership into consideration. 

Far fewer studies included efforts to account for spillover effects: three measured participant 

spillover, and three measured nonparticipant spillover. 

 

35 This study is managed by Pacific Gas and Electric Company under the auspices of the California Public Utility 

Commission in association with the California Energy Commission, San Diego Gas and Electric, Southern 

California Edison, and Southern California Gas Company. The website address is: 

http://www.eebestpractices.com/index.asp 36 California 2002-2003 Portfolio Energy Efficiency Program Effects and Evaluation Summary Report, prepared 

for Southern California Edison and the Project Advisory Group by TecMarket Works, January 16, 2006. 

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Although the study stated freeridership and spillover were important considerations that should 

be included in evaluation research, it provided no guidelines as to which effects may have greater 

impacts, or whether it was appropriate to assume freeridership and spillover effects essentially 

cancelled each other out. However, some specific program evaluation efforts were identified, 

which will be reviewed in the next section of this report. 

Specific Programs 

This section examines measurement results for specific program types, based on data available 

from evaluation reports assessing both freeridership and spillover. Selection of program types 

was based on their expected savings potential in Iowa. 

Lighting Programs 

Table 42 lists results from four evaluation efforts that assessed lighting freeridership and 

spillover effects.37 The majority of these programs have an estimated NTG value is 1.0 or higher, 

as spillover estimates are higher than freeridership estimates. 

Table 42. Residential and Commercial Lighting Programs with Spillover Estimates 

Sponsoring Organization NTG Values Freeridership Values Spillover Values 

Residential 

Efficiency Vermont* 1.19 6% 25% 

Energy Trust of Oregon** 0.75 51% 26% 

Efficiency Maine*** 1.10 20% 30% 

Nonresidential 

NYSERDA**** 1.10 39% 80% 

* Final Report: Phase 2 Evaluation of the Efficiency Vermont Residential Programs, prepared for the Vermont Department of 

Public Service, prepared by KEMA, Inc, December 2005 

** Process and Impact Evaluation of the 2007-2008 Energy Trust of Oregon Home Energy Solutions Program Volume 2, 

prepared for the Energy Trust of Oregon, prepared by Opinion Dynamics Corporation, January, 2010. 

*** Process and Impact Evaluation of the Efficiency Main Lighting Program, prepared for Efficiency Main, prepared by Nexus 

Market Research, Inc., and RLW Analytics, Inc., 2007. 

**** New York's System Benefits Charge Program Evaluation and Status Report—Year Ending December 31, 2010, prepared for 

the New York Public Service Commission, prepared by NYSERDA, March, 2011. 

 

37 Note: the NYSERDA NTG value does not equal (1 - freeridership + spillover), which is the formula used by 

most programs, but uses (1-freeridership) * (1 + spillover). Note also that the efficiency Vermont values 

represent a more recent study than that identified in Table 39. 

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Nonresidential Large Comprehensive Programs 

Programs in this category promote procurement and installation of high-efficiency energy 

technologies by providing incentive payments and design/audit assistance, in some cases, to 

partially offset incremental equipment costs. Customers can receive incentives for customized 

projects based on calculating the amount of kWh saved, or based on a measurement-andverification procedure. Providing incentives to shorten payback periods and assistance to 

quantify equipment performance increases the adoption of new technologies (see Table 43). 

Table 43. Nonresidential Large Comprehensive Programs with Spillover Effects 

Sponsoring Organization NTG Values Freeridership Values Spillover Values 

Wisconsin Power & Light* 0.91 44% 34% 

NYSERDA** 1.23 35% 58% 

CA Standard Performance Contract 0.7 30% N/A 

* Shared Savings Decision-Making Process Evaluation Research Results, prepared for Wisconsin Power & Light by Summit Blue 

Consulting, April 11 2006 

** Commercial and Industrial Performance Program (CIPP) Market Characterization, Market Assessment and Causality 

Evaluation, prepared for New York State Energy Research and Development Authority, prepared by Summit Blue Consulting, 

LLC, May, 2007. 

The SPC program in California has a relatively low NTG value of 0.7. However, this NTG 

estimate contains adjustments only for freeriders, and does not include spillover effects. 

Cadmus also reviewed evaluations estimating spillover effects from two similar programs. Much 

like the California SPC program, freeridership is large, with values of 35% for NYSERDA and 

44% for Wisconsin. However, these high freeridership values are largely offset by large spillover 

estimates, with an adjusted NTG of 0.91 for Wisconsin and 1.23 for NYSERDA. 

Refrigerator and Freezer Recycling Programs 

NTG estimates for appliance recycling programs tend to be well below 1.0. As shown in 

Table 44, these estimates in California are 0.61 for refrigerators and 0.7 for freezers. This type of 

program likely does not lend itself to much (if any) spillover effect, as it is unlikely many 

participants or nonparticipants would dispose of additional qualified refrigerators and freezers 

beyond those they dispose of within the program. Therefore, these low NTG values may be 

appropriate. 

Numerous studies investigating NTG ratios for refrigerator and freezer recycling programs have 

been completed recently. The results from these evaluations indicate consistently sub-1.0 NTG 

ratios, ranging from 0.31 to 0.79 for refrigerators, and from 0.38 to 0.82 for freezers (see 

Table 44). 

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Table 44. Reported NTG Ratios for Appliance Recycling Programs 

Study 

Study 

Year 

Refrigerator 

NTG Ratio 

Freezer 

NTG Ratio 

Rocky Mountain Power Wyoming, The Cadmus Group 2011 0.57 0.58

Ameren Illinois, The Cadmus Group 2010 0.79 0.82

Pacific Gas & Electric, The Cadmus Group 2010 0.51 N/A

Ontario Power Authority, The Cadmus Group 2008 0.48 0.52

Statewide Residential Appliance Recycling Program, ADM Associates, Inc. 2008 0.61 0.71 

Wisconsin Residential Appliance Turn-In Program, PA Consulting Group, 2008 0.57 N/A 

Washington Refrigerator and Freezer Recycling Program, PacifiCorp, KEMA 2007 0.31 0.56 

California Statewide Residential Appliance Recycling Program, KEMA-Xenergy 2004 0.35 0.54 

Sacramento Municipal Utility District, Heschong Mahone Group 2003 0.55 0.68 

Southern California Edison, Xenergy 1998 0.53 0.57 

Southern California Edison, Xenergy 1996 0.42 0.38 

Energy-Efficient Residential Clothes Washers 

Many utilities offer programs promoting ENERGY STAR residential appliances, such as clothes 

washers. In recent years, however, evidence has appeared that the market for energy-efficient 

clothes washers is being transformed, with resulting low NTG estimates. Attribution for this 

market transformation may lie with the ENERGY STAR program, and not with local utility 

financial incentive programs. If so, this would indicate very little spillover (especially 

nonparticipant spillover) from this program. 

Efficiency Vermont38 has evaluated energy-efficient clothes washers as part of its portfolio of 

energy-efficient appliances, offered under the efficient products portion of its residential 

program. In 2001, Efficiency Vermont estimated the NTG ratio for this program element as only 

0.38. In 2004, Efficiency Vermont re-estimated NTG, and results showed an even lower value 

of 0.17. 

These studies did not specifically address spillover. However, the evaluation report noted the 

high saturation of ENERGY STAR clothes washers in the marketplace not as a local 

phenomenon, but as a national phenomenon, inferring attribution for spillover would require a 

national rather than local effort. 

Despite this very low NTG value, Efficiency Vermont plans to continue administering rebates 

for ENERGY STAR clothes washers to maintain the good relationships with retailer channels 

built up over many years. 

 

38 Final Report: Phase 2 Evaluation of the Efficiency Vermont Residential Programs, prepared for the Vermont 

Department of Public Service, prepared by KEMA, Inc, December 2005 

Appendix H 

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Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012 

The Cadmus Group, Inc. / Energy Services 67 

Conclusions 

Cadmus’ examination of the methods, assumptions, and policies used to address NTG resulted in 

these key findings. 

 Methods for measuring NTG elements, particularly spillover, are imprecise. The 

methods for calculating freerider and spillover effects exhibit considerable limitations, 

and little consensus exists among evaluation experts on best methods. Methods used to 

calculate NTG have inherent biases, particularly those based on self-reporting (the most 

common approach). These biases can significantly affect NTG analysis results. 

 NTG estimates would have a small impact on the societal benefit test. If the benefit-cost 

tests were run with net impacts, programs with an NTG ratio of less than one would have 

administrative costs spread over fewer participants. Given administrative costs normally 

represent only a small percentage of program expenditures, this impact would be minor. 

 Many states have assumed a NTG ratio of 1.0. A review of NTG methods and 

application of NTG in 32 jurisdictions conducted by Cadmus found that 13 (40%) did not 

adjust savings for freeridership. In a recent decision by the CPUC, IOUs will report gross 

savings as the measure for compliance. 

 A study of best-practices programs found more than two-thirds of all identified 

programs had an NTG value of approximately 1.0. Approximately half of the studies 

(49%) either assumed or calculated a NTG value of 1.0, and 68% of the studies had NTG 

values between 0.9 and 1.0. In most cases, NTG values, when used by a program, were 

only based on freeridership values. Consequently, an even higher percentage of programs 

would have a NTG ratio of approximately 1.0 if spillover were examined. 

 Assuming a NTG ratio of 1.0 may be conservative in certain cases. Research indicates 

some programs, particularly those for lighting, routinely achieve NTG ratios well over 

1.0 when spillover is examined. Even in programs where high freeridership is reported, 

spillover effects are largely ignored. If properly accounted for, spillover effects may 

offset freeridership to a large extent. 

Given these findings, it appears reasonable that gross savings be used as the basis for reporting 

and target compliance. However, utilities should make efforts to design effective programs that 

minimize freeridership through the following techniques: 

1. Regularly track the saturation of measures within their own service areas and in other 

jurisdictions. For example, ENERGY STAR clothes washers continue to gain market 

share throughout the country, and freeridership will likely increase, resulting in an NTG 

of less than 1.0. 

2. Carefully monitor market responses to particular programs, and set incentive levels 

that minimize freeridership. As programs mature and market shares for efficiency 

measures increase, program administrators may be inclined to reduce incentive levels. 

Paradoxically, however, freeridership tends to be higher in programs with low incentives, 

as lower incentives are less likely to motivate customers to adopt efficiency measures. 

Thus, incentive levels should be carefully reviewed and set at values that motivate a 

substantial number of participants to install efficiency measures. 

Appendix H 

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IPL Energy Efficiency Plan January 25, 2013