### 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

.

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

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

The Cadmus Group Inc. / Energy Services i 

## 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: MA RKET POTENTIAL ........................................... 41
Effects of Increased Incentives ....................................................................................... 41 Electricity .................................................................................................................. 41Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group Inc. / Energy Services ii 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 NE T-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 ................................................................................................................... .. 67Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group Inc. / Energy Services iii 

## Table of Figures

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

Figure 2. Residential Sector Electric Econom ic 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 Econom ic Potential by Segmen t (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 Econom ic Potential by Measure Type (Cumulative in 2023) ...................................................................................................................... .................. 35

Figure 10. Industrial Sector Electric Econom ic 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 Pote ntial 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 No nresidential Interruptible Programs ............. 52Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group Inc. / Energy Services iv 

## Volume 2. Table of Contents

Appendix A. Supplemental Ma terial: 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 Materi al: Assessment of Net-to-GrossVolume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 1 

## 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 Boar d an assessment of the potential for energy and capacity savings from actual and projected cust omer usage by applying commercially available technology and improved operating practices to energy-using equipm ent and buildings.”

In compliance with this requi rement, the Iowa Utility Asso ciation selected, through a competitive bidding process, The Cadmus Group, In c., (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 terri tories of Iowa’s three largest investor-owned utilities. Referred to collectively as “the Ut ilities,” Alliant Energy Corporation (Alliant, electricity and natural gas), Black Hills Energy (B lack Hills, natural ga s only), and MidAmerican Energy Company (MidAmerican, electricity and natu ral 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 assessmen ts of potential in Io wa, 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 activit ies from the 2008 study, updating the data based on recent studies commissi oned 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 second ary data sources. The compiled data provided a complete characterization of both the current stat e 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 twostudies differ in individual components considered, reflecting the changing landscape of demandside management (DSM), both in Iowa and acro ss 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 resour ces, 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 2 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 th is assessment are informed by the Chapter 35 rules and discussions with the Utilities and st akeholders. 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 read ily comparable across jurisdictions. The following points related to th is study’s scope shoul d 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 potentia ls, but included in market potentials.

 Early replacement of end-use equipment is not considered in this assessment; equipment is assumed to be upgraded at th e 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 renewabl e 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 fr om 2014 to 2023. The assessment included efficient technologies and practices widely co mmercially available at the time of the study, 2 accounting for known

2 The market potential scenario considers emerging technologies.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 3 changes in codes and standards, technical limita tions (technical potentia l), and societal costeffectiveness (economic potential).

Table 2 shows forecasted 3 2023 baseline electric sales and potential by sector. Study results indicate 8,446 GWh of t echnically feasible electric energy- efficiency potential by 2023, the end of the 10-year planning horizon, with approxima tely 6,872 GWh of these resources proving costeffective. Identified economic pot ential represents a reduction of 19% of forecasted load in 2023. The residential sector represents the largest por tion of technical and economic potential, at 42% and 40%, respectively. The commercial sector re presents the second-largest contributor to technical and economic potential, at 32% for ea ch, while industrial potential accounts for 26% and 28% of technical and econom ic potential, respectively.

Table 2. Technical and Economic Elect ric 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 na tural gas sales and potential by sector. 4 As shown, study results indicate over 37 million therms of t echnically feasible natural gas energy-efficiency potential by 2023. The estimated economic potentia l 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 Natura l 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%, resp ectively. Almost all rema ining 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 tr ansport customers are excluded from the analysis.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 4 

### Market Potential Assessment of market potential, a new component of this study, ex amined 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 pub licly available data on recent energy-efficiency experiences for IOUs across the nation, conducting re gression analysis to estimate relationships between increased incentive spending and saving s levels achieved. Base d on this analysis, and beginning with the Utilities’ 2010 program activit y, 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 re quire significantly highe r utility expenditures than those currently occurring in Io wa 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 onl y apply to a subset of measures included in the economic potential, namely those with full costs differing from incremental costs. The research indicates the av ailability of financing, in addition to 100% incentives, likely will not signi ficantly impact measure adoption.

Finally, Cadmus researched measures not curren tly 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 exp ected on natural gas potentials.

Results of the market potential analysis are intended to provide a re alistic upper bound to the estimates of economic potential and do not necessa rily represent “program ” potential or utility targets. The estimated savings may be realized through market transfor mation or improved codes and standards and may not be avai lable or suitable for inclusion in utility program offerings. For example, the electric potentia l 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 fo r 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 a nd equipment standards; and

 Increased measure saturations due to utility program accomplishments.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 5 The 10-year technical and economic electric a nd 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 a nd 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 natura l gas technical potential has similarly decreased, with decreased avoided costs contri buting to a corresponding decrease in economic potential.

Table 4. Comparison of 10-Year Electri c 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 Potentia l (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 pot ential for a variety of demand-response program options, including firm (e.g., residential direct load control [D LC]) 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 pa rticipation in demand-response programs currently offered by the two electric utilities.

2. Regarding billing systems in place in 2008, th e study did not allow for implementation of price-based options, such as real-time or critic al peak pricing. However, these strategies could become feasible if and when the Ut ilities move to an Advanced Metering Infrastructure (AMI).Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 6 Based on these findings, the Utilities have c ontinued to offer their long-running, successful Residential DLC and Nonresidential Interruptible programs in their 2009–2013 Energy Efficiency Plans (EEPs). Building on the conc lusions 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 respons e 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 si milar programs. Cadmus gathered data on comparable pr ograms 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 Contro l (DLC) and Nonresidential Inte rruptible programs are shown in are shown in Table 6 an d 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, estim ates of available 10-ye ar potential for the Residential DLC program have decreased from those presen ted in the 2008 Assessment. Nonresidential Interruptible expansion scenarios indicate potential has increased since the 2008 Assessment, though it should be re cognized that decisions around appr opriate levels of load to hold under contract are heavily in fluenced by utilities’ unique objectives and resource needs.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 7 

### AMI-Enabled Options Analysis of AMI-enabled demand programs was a qualitative exercise, given data quantifying impacts of AMI-enabled programs has been dr awn almost exclusively from utility pilot programs, and may not be appropriate for extr apolation 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 po tential options that may provide viable savings sources if Iowa electric utilities implement AMI.

From initial pilot results, AMI appears to expa nd demand reduction capabilities of residential demand response programs, though the extent of th is expansion remains to be seen, as program persistence issues have not b een thoroughly studied. Additionally, studies of the reliability and security of these programs and enabling techno logies remain in progress. Further, how AMIenabled programs and traditional programs overl ap, and how demand savings may shift, still must be understood before specific estimat es 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 spill over concepts, discussed the background and policy implications of these concepts, and provided exampl es 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 whet her 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 te st (the basis for economic analysis of energy-efficiency progr ams 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 8  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 nonpar ticipant spillover in nine (28%). The incidence of cases where only freeridership is assessed suggests an asymmetrical treat ment of spillover and freeridership effects. Should spillover be incl uded, 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 r easonable that gross savings be used as the basis for reporting and target compliance. However, utilities sh ould design effective programs that minimize freeridership. This entails: (1) regularly monito ring the saturation of meas ures within their own service areas and in other jurisdic tion; and (2) using this informa tion to revise their programs and their incentive struct ures periodically.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 9 

## 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 potential 6 refers to savings available fr om adoption of energy-efficiency measures and practices, consid ering 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 gr eater 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 in centives 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 standa rds. As such, utility customer forecasts have been combined with detailed end-use level data on equipment saturations, fuel shares, penetrations of efficient equipment, equi pment replacement rates, and known codes and standards, producing alternate baseline forecasts from which to assess potential.

Characterizing base-case conditions requires extensive data coll ection. As this assessment did not include primary data collection, Cadmus began by cataloguing data co llected and developed during the 2008 assessment. For en d 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 co mpact fluorescent lamp (C FL) 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 10 increasing this saturation. For each utility, the number of bulbs rebated was used to calculate a per-customer increase in saturation. These adju stments to current saturations of efficient equipment proved critical to avoi d overstating rema ining potential.

Additionally, the importance of accurately accoun ting for changes in codes and standards over the planning horizon cannot be overstated. Not onl y do these changes affect customers’ energy consumption patterns and behaviors, but they es tablish which energy-efficiency measures will continue to produce savings over minimum require ments. This study captures current efficiency requirements as well as those enac ted, but not yet taking effect.

The base case forecast particularly accounts for:

 Iowa’s adoption of the 2009 International Energy Conser vation 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 rulema king, setting standards for commercial fluorescent lighting, beginning in 2012; and

 Recent federal standards relating to reside ntial heating, cooling, water heating, and appliances. 

### Creating a Database of Energy-Efficiency Measures To estimate technical, economic, and market potentia ls for energy efficiency, this study relies on an extensive database of efficient equipment a nd practices. Measures cons idered in this study drew upon:

 Measures currently offered by the Utilities;

 Those included in regional and national da tabase (e.g., California DEER and ENERGY STAR ® ); and

 Cadmus’ internal library, compiled through our extensive experien ce conducting similar studies.

After compiling the initial list of measures, a qual itative screening process, as specified in the Chapter 35 rules, eliminated certain types of me asures from consideration. Qualitative screening criteria included:

 Commercial availability;

 Applicability to Iowa’s climate; and

 Effects on demand during peak periods.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 11 The measures qualitatively screen ed out of the tec hnical and economic pot entials assessment, along with applicable sector, fu el, and reason for exclusion, ar e 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 E nergy Recovery Emerging technology. Commercial Electricity Advanced R ooftop Packaged AC Emerging technology. Commercial Electricity Hot-Humid Rooftop Un it with Dual Enthalpy Emerging technology. Commercial Electricity Liquid Desicc ant Hybrid AC Emerging technology. Residential Electricity Advanced All-Cl imate Heat Pump Emerging technology. Residential Electricity Hot-Dry Ai r Conditioners Emerging technology. Residential Electricity Multifamily Build ing Best Practices Emerging technology. Residential Electricity On-Demand Reci rculation Pumps Emerging technology. Residential Electricity Optimized Resid ential Duct Work Emerging technology. Residential Electricity Robust Centra l Air Conditioners Emerging technology. Residential Electricity Water Heaters - Add-On Heat Pump Emerging technology. Residential Electricity Water Heaters - Gr ound Source Heat Pump Emerging technology. Residential Electricity Water Heaters - Nort hern 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 - N on-Condensing Gas Hybrid Emerging technology.

For each measure passing the qualitative scree n, Cadmus compiled several types of data necessary to fully characterize each measure. Whenever possibl e, these data drew upon Iowaspecific sources, such as primary data collect ion 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 12 For modeling energy-efficiency potential, measures were separated into two distinct classes:

 Equipment measure s save energy by upgrading the effi ciency 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 functi onal equipment, equipment turnover and replacement rates are defined by the equipment’ s average effective us eful life. In a study spanning 10 years, long-liv ed equipment may not comp letely 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: insu lation, 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 in stallation rates in new construction are defined by the utili ties’ new construction forecasts. 

### Estimating Technical Potential Technical potential represents total energy saved from all meas ures, only adjusting for physical constraints. For example, high levels of wall insu lation can be placed in a certain percentage of homes, and, of those, a certain share may alrea dy have this insulation in place. Consequently, technical potential would only include technically feasible homes without measures in place.

Another important technical pote ntial 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 resi dential applications, with technical potential assuming that, as equipment fails or new homes are built, customers wi ll install SEER 18 units, regardless of costs. Competing retrofit measures have been trea ted the same way, assuming installation of the highest-saving measures where technically feasible.

In estimating technical potential, one cannot me rely 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 sa ving than in an un-insula ted 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 re placed with higher-effi ciency equipment, which reduces average consumption across a ll customers. Reduced consumption causes non-equipment measures to save less than th ey would have, had the equipment remained at a constant average efficiency. Similarl y, 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 sa vings. For example, installing a low-flow showerhead does not affect savi ngs realized from installing a faucet aerator. Insulating hot water pipes, however, would cause the wate r heater to operate mo re efficiently, thus reducing savings from either measure. The me thod in this assessmen t accounted for this interaction by “stacking” interactive measures—iteratively reducing baselineVolume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 13 consumption as measures are installed, t hus lowering the savings from subsequent

measures.
While theoretically, all retrofit opportunitie s in existing constr uction (often called “discretionary” or “instantaneous ” resources) could be acquired in the study’s first year, this would skew the potential for eq uipment measures, and provides an inaccurate picture of measure-level potential. Therefor e, the study assumes realization of these opportunities in equal annual amounts over the 10-year planning horizon. A pplying this assumption, natural equipment turnover rates, and other adjustments descri bed 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 po tential that is deem ed cost effective. Consistent with Chapter 35’s definition of the Societal Cost Test, 7 a measure can be deemed cost-effective if its present-valu e benefits meet or exceed its pr esent-value costs. The measure’s cost results simply from the difference in upf ront costs between the measure and the baseline technology. In some cases (such as retrofits), th e cost used equals the measure’s full cost.

Calculating a measure’s societal benefits prov es 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, capturi ng the time-differentiated value of energy savings and determining the amount of savings during peak periods.

 Externality factors. As specified in the Rules, an extern ality factor is applied to avoided energy and capacity costs, accounting for societ al costs of supplying energy. This factor adds an additional 10% to electric avoi ded energy and capacity benefits, and an additional 7.5% to natural ga s energy and capacity benefits.

 Line losses. Line losses represent energy lost betw een 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 projectio ns of energy generating or purchasing costs. Electric costs are analy zed 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-effectivene ss tests. The Utilities will cons ider these perspectives in developing the 2014–2018 Energy Efficiency Plans.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 14  Utility avoided capacity costs are utility-specific projectio ns 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-leve l data to calculate a variety of benefits for each measure. The benefits, described as foll ows, have been added and compared to the measure’s costs to determine whether the meas ure 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 exte rnality 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 el ectricity 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 appropr iate line loss and externalit y 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-ener gy 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 me thods used to quantify societal benefits, the measures’ cost-effectiveness varies between utiliti es, based on projections of energy and capacity costs and line loss values. As such, this study cal culated cost-effectivene ss 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 usi ng the same method described in the technical potential section, above, an alte rnate 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 15 

### Assessing Market Potential Market—or achievable—potential generally is defined as the portion of economic potential expected to be reasonably achie vable over the course of the planning horizon, given certain assumptions regarding market barriers and beha vioral factors that may inhibit consumers’ participation in utility-sponsored energy-efficiency programs. In this assessment, market potential is defined more narrowly, as the amou nt of savings that might be achieved, assuming: incentive payments up to 100% of incremental m easure 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 va ry across potential assessment studies. These methods fall into three general categories.

1. The first group of methods (such as those us ed in assessments of energy-efficiency potential in California) is based on a conve ntional market diffusion model, and assumes first-cost as the primary participation barri er. In this approach, market potential is hypothesized to depend on the return from ener gy-efficiency investments, and the effects of incentives on enhancing that return. Due to limited data available to establish the empirical relationship between consumers’ e xpectations 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-efficien cy programs. The approach involves asking a representative sample of poten tial participants about their w illingness 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 willin gness to participate as a function of respondents’ shares of incr emental measure costs.

3. Benchmarking, used in this assessment, provid es the third method for determining market potential. This method incorporates certain elements from the first two method groups, but primarily relies on historical market pe netration 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 empi rical statistical relati onship between program expenditures (both incentive a nd non-incentive) and energy savi ngs, 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 me thods follow below, with results presented in Section 3.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 16 The Effects of Increased Incentives Form 861 of the Energy Information Administration (EIA) served as the primary data source for assessing electric market potenti al, providing energy sa vings, program expenditures, revenues, and retail sales reported by approximately 75 investor-owned utilities from 2004 through 2010. 9

As natural gas utilities do not re port energy-efficiency program results in universal datasets similar to EIA Form 861, Cadmus compiled public ly available documents from utilities and other program administrators reporting annual ener gy-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 rele vant 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” st ates, with climates most similar to Iowa. (As a practical matter, this criterion eliminated only tw o 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 separate d from electric spending;  Spending differentiated between in centives and other costs; and  Annual energy savings.

Given these criteria, Cadmus deve loped a list of 14 portf olios 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 Fo rm 176, normalizing savings and spending across service areas of different sizes.

Using these data, Cadmus developed regressi on 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 in centive 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 17 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 cost s may not be sufficient to offset first-cost barriers for all measures. For example, if a cu stomer with low insulation levels chooses to upgrade to insulation exceeding minimum building c ode, a utility incentive may only cover costs above and beyond code-required levels. In this case, remaining cost c ould 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, measur es 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 Emer ging Technologies As specified in the Chapter 35 Rules, only measur es commercially available were included in the technical and economic potential. However, the mark et 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 c ourse of the next roun d of EEPs. While these measures will increase available potential, their e ffects 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 economi c potential. That is, emergence of a more efficient heat pump not only supplants the po tential attributed to currently available technologies, but will reduce the poten tial attributable to shell measures.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 18 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 m easures overlapped with measures already considered in the study, and estimated incremen tal savings and potentia l from the emerging technologies. For measures without a complement ary choice within the measure list, Cadmus apportioned estimates of national long-ter m potential to the U tilities’ territory. 

## Demand Response The 2008 Assessment estimated demand savings pot ential for a variety of demand-response program options, including firm (e.g., residential direct load control [D LC]) 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 pa rticipation 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-tim e or critical peak pricing. Ho wever, these strategies could become feasible if and when the utilities m ove to an Advanced Metering Infrastructure (AMI).

Based on these findings, the utilities continued to offer their long-running, successful Residential DLC and Nonresidential Inte rruptible programs in their 2009–2013 Energy Efficiency Plans (EEPs). Building on the conclusi ons drawn from the 2008 Assessmen t, 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 que stions follows this section.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 19 

### Expansion of Legacy Programs As both electric utilities have operated successful demand respons e 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 si milar programs. After developing a database of participation data from other, comparable utilities, a bottom-up methodology established the estimated market potenti al. Figure 1 illustrate s the general process.

Figure 1. General Demand Response Potential Assessment Methodology



### Secondary Data Coll ection and Analysis The key metrics used to compare utility prog rams (and, subsequently, to estimate remaining potential) were current participation levels. As customer count s, peak loads, and program impacts can vary greatly across ut ilities, identifie d metrics sought to norma lize 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 progr ams used a metric of the percentage of nonresidential demand during the system peak under contract.

While calculating these participation rates for Io wa utilities proved rela tively straightforward, greater difficulty resulted in collecting data on other utilities’ program achievements due to reporting differences. Data on utility program achie vements, customer counts, and peak demand derived from an array of sources, including:

 Federal Energy Regulatory Commission (F ERC) 2010 Assessment of Demand Response and Advanced Metering Demand Response Survey Data;

 United States Energy Information Administration (EIA) Database;Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 20  Utility integrated resource plans (IRP);

 Utility annual reports; and

 Utility demand response program evaluation reports.

Based on data collected from these sources, partic ipation 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 particip ation 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 establis hment of two potential expansion scenarios:

1. Moderate expansion: The amount of potential available if Iowa utilities’ participation rates increased to the upper quart ile of the reviewed utilities.

2. Aggressive expansion: The amount of potential available if Iowa utilities’ participation rates increased to industry-l eading participation rates.

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

Under each expansion scenario, assumed particip ation rates were applied to Iowa utility customers, loads, and per-participant impact s to identify demand savings to be realized.

Though demand response strategies primarily focus on reducing demand during peak periods, reduced demand can also translat e 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 ha rder after the event to achieve the desired temperatures. Similarly, if a commercial customer sheds load by temporarily adjusting processes (such as slow ing production or shutting down so me 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 differenc es in participating customers can greatly impact snapback effects. For example, a re gion’s temperature fluctuation greatly affects snapback. In climates where temperatures remain relatively high after th e end of an event, a demand response program generally experiences a hi gher snapback amounts. If an event ends at a time of day when temperatures begin to declin e, the snapback would be lower. Similarly, customers enrolled in nonresiden tial interruptible programs may retain on-site generation capabilities, and experience no snapback effects.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 21 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 si gnificantly since 2008’s A ssessment, few utilities have established AMI-specific demand response pr ograms. Existing offerings primarily remain in pilot forms, and do not offer data that coul d 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 curre nt 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, a nd to provide the Utilities w ith recommendations for methods regarding mitigation of freerider effects, Ca dmus 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 23 

## 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 EEP s. Technical and economic potential for residential, commercial, and industrial 10 sectors were assessed separately for each utility, divided by fuel type. Within each utility ’s sector-level assessment, th e study further distinguished among market segments or industry types, and their resp ective 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 compre hensive 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 200 8 Assessment (which may be active in current utility programs), and new measures that have b ecome commercially available over the past five years. Considering all permutations of these measur es across applicable customer sectors, market segments, fuels, and end uses, resulted in cust omized 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 pot entials 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 24 

## Summary of Results: Electricity
Table 11 and Table 12 show forecasted 12 2023 baseline electric sales an d potential by utility and sector, respectively. Study results indicate 8,446 GWh of technicallyfeasible electric energyefficiency potential by 2023, the end of the 10- year planning horizon, with approximately 6,872 GWh of these resources cost-effective. Id entified 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 sa vings 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 tec hnical and economic potential are a function of baseline sales, they are roughly comparable, when analyzed in pe rcentage 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 cu stomer characteristics. In addition to these differences, economic potential varies due to di fferences in utility avoided energy and capacity
costs.

Table 11. Technical and Economic Elect ric 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 pea ks, 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 25 Table 12 provides each sector’s technical and economic potential s. 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 pote ntial accounts for 26% and 28% of technical and economic potential, respectively.

Table 12. Technical and Economic Elect ric 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 hi ghest expected 10-year technical potential, and whether each is cost-effective in all, some, or no applications.

Table 13. Top Electric Technical Measu res and Cost-Effect iveness Results 13
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 Condi tioner/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 differen ces 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 opportun ities for industrial plan energy management, ENERGY STAR televisions, efficient motors, a nd retro-commissioning. A ll 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 26 Table 14 compares identified 10-ye ar technical and economic electri c potentials to results from the 2008 Assessment.

Table 14. Comparison of 10- Year Electric Technical a nd 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 econom ic, 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 meas ure 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 th e 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, tec hnical 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 custom er characteristics and avoided costs.

Table 15. Technical and Economic Ga s 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 tr ansport customers are excluded from the analysis.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 27 Table 16 provides each sector’s tech nical and economic potentials. As with electric potential, the residential sector represents th e largest portion of t echnical and economic potential, at about 74% and 69%, respectively. Almost all re maining potential lies in the commercial sector, with a small portion (5.3 million therms) from industrial applications.

Table 16. Technical and Economic Natura l 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%

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

The Cadmus Group, Inc. / Energy Services 28 Table 18 compares identified 10-ye ar technical and economic natura l gas potentials to results of the 2008 Assessment.

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

Sector Technical Potentia l (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 el ectricity retail sales. The single-family, manufactured, multifamily, and low-income dwellings comprising this sector present a variety of potential savings sources, in cluding: equipment efficiency upgrades (e.g., air conditioning, refrigerators); im provements 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 MidAmeri can) in 2023 residential consumption,/

Table 19. Residential Sector Electric En ergy-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 repres ent 71% of total economi c residential potential, followed by low-income, multifamily, and manufact ured homes. Each home type’s proportion of baseline sales serve as the primary drivers, but ot her 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, increasi ng 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 29 Figure 2. Residential Sector Electric Economic Potential by Segment (Cumulative in 2023)

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

Figure 3. Residential Sector Electric Economic Potential by Measure Type

The largest portion of economic poten tial 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 measur es account for approximately 25% and 20%, respectively. A small amount of sh ell measure savings comes from homes with heat pumps. ECM motors, duct s ealing, infiltration reduction, ra diant 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%Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 30 fans, account for over 60% of the identified she ll measure savings. Lighting measures, primarily LED and CFL bulbs, account for th e next largest slice (21%), fo llowed by various plug load end uses and water heating. Table 20 provides technical and economic potentials by end-use category.

Table 20. Residential Sector Electr ic 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 se ctor 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% 1,578Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 31 As shown in Figure 4, single-family homes repres ent 73% of total economi c 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 seali ng, infiltration reduction, basement and attic insulation, and home energy management system s 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%Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 32 Table 22 provides technical and econ omic 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 ov er 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, miscellane ous buildings and warehouses repr esent 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 a nd those that would, but do not having sufficient information to be cla ssified. The commercial sector also provides considerable savings opportunities in offices (14 %), retail (11%), and grocery (7%) segments. Moderate savings amounts are expe cted to be available in edu cation, health, restaurants, and lodging facilities.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 33 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 s ector (62%) comes from lighting, followed by HVAC shell measures (22%). Cooling and ventil ation 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 fre quency drives, ECM motors, variab le refrigerant flow systems for heat pumps, and programmable thermostats account for nearly 73% of the shell measure savings.

Figure 7. Commercial Sector Electric Ec onomic 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%Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 34 Table 24 provides technical and econ omic 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 represen ts about one-third of both tec hnical 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 th e 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, re staurants, and lodging, a nd grocery facilities.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 35 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 remainde r attributable to bo iler measures. Retrocommissioning, demand controlled vent ilation systems, variable ai r-volume systems, boiler reset controls, and infiltra tion 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%Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 36 Table 26 provides technical and econ omic 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-effi ciency potentials were estimated for major end uses within 18 major industries, including ag riculture and street lighting. 15 Across all industries, economic potential totals approximately 1,916 GWh over 10 ye ars, corresponding to a 10% reduction (11% for Alliant and 10% for MidAme rican) 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 primar y metal manufacturing facilities represent approximately one-half of the economic potentia l in the industrial s ector (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 distributionsVolume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 37 Figure 10. Industrial Sector Elec tric Economic Potential by Segment (Cumulative in 2023)

The majority of electric economic potential in the industrial sect or (41%) can be attributed to gains in process efficiency (s uch as heating, cooling, and co mpressed 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 add itional potential exists for other facility improvements.

Figure 11. Industrial Sector Electric Ec onomic 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%Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 38 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 en d uses rely on electricity; therefore, the industrial sector represents an extremely small portion of natural gas baseline sa les 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% 14Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 39 Due to the composition of industries using na tural gas in Iowa, ove r 67% of the economic potential lies in the food processing (35%) an d chemical manufacturing (32%) segments. As shown in Figure 12, substantial savings opport unities also exist in agriculture (19%) and nonmetallic minera l products (6%).

Figure 12. Industrial Sector Gas Ec onomic Potential by Segment

Almost all baseline consumption occurs in boile rs 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 ot her (non-heating) pr ocess improvements.

Figure 13. Industrial Sector Gas Econ omic 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%Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 40 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%Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 41 

## 3. ENERGY EFFICIENCY: MARKET POTENTIAL

Market potential, as defined in this study, repr esents savings that might be achievable under an aggressive acquisition scenario, assuming: in centive payments up to 100% of incremental measure costs; financing availability; exempl ary program design and implementation practices; and emergence of new technologie s, currently not widely available in the marketplace. This section presents research results in each of thes e areas, and examines its implications regarding realistic market potential levels in Iowa.

The results of the market potential analysis are intended to provide contex t to the estimates of economic potential and do not necessarily represen t utility targets or “program potential.” These savings may be realized through market transf ormation 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 lightin g standards cause CFLs to beco me 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 characteristic s and customer demogra phics, and the differing composition of programs and portfolios, one must separately assess incentives’ effects on measure adoption for each fuel. While using simila r methods, the two analyses adopted rely on fuel-specific potential and benchmarking data. The analyses’ results follow. 

### Electricity As described in Section 1, analys is quantifying the amount of electri c market potential available, given incentives covering the entire incrementa l 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 in centive payments (as a fraction of annual retail revenues) for the 75 utilities in the dataset. Th e figure suggests a genera lly 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 42 Figure 14. Scatter Plot of DSM Savings and DSM Expenditures

The following regression equation estimated the statistical relationship between incentives and savings:

log ሺ %	Savings ሻ ൌβ 0൅ β 1 log ൬ Incentive
Revenue ൰ ൅β 2 log ൬ Other	Costs
Revenue ൰ ൅β 3 logሺRateሻ൅β 4 LogሺTimeሻ

This formulation states energy-efficiency sa vings is a function of : incentive payments (Incentive); non-incentive program expenditu res (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 expect ed to run higher in jurisdicti ons with high rates. The time variable captures trends resu lting from exogenous factors aff ecting program activity from 2004 to 2010. The equation parameters were estimated us ing 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 (R 2 ) of 0.6, meaning 60% of the savings variation can be explained by the equation’s expl anatory variable (see Table 31). All estimated parameters have the correct sign, and are statisti cally 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%AnnualSavings(PercentofAnnualSales)

Annual Expenditures on Energy Efficiency (Percent of Annual Revenue )

2004 2005 2006 2007 2008 2009 2010Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 43 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 logarith mic form, estimated coefficients for each term in the equation represent the elasticity of savings w ith respect to that term. For example, as seen in Table 31, the estimated coefficient of incentiv es 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 potentia l achievable if incentives increas e 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 amount s must be assumed. Available information on Iowa’s electric utilities in 2010 indicates, on average, incentives covere d approximately 40% of incremental measure costs across the energy-efficien cy programs in their portfolios. A scenario assuming incentives at 100% of incrementa l costs thus require s a 150% increase ([100% - 40%] / 40%) increase in current incentive outlays.

Non-incentive expenditures, such as marketin g, outreach, planning, a nd administration, have traditionally been assumed to be relatively fixed. This study’s findings indicate this might not be the case. Indeed, the 0.57 estimat ed elasticity for non-in centive expenditures (shown in Table 31) suggests a positive and statistically significant correlation between non-expenditures and market penetration, and that these expenditures may ev en 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 effectiv ely promoting energy-effi ciency programs depends on the total marketing effort, c onsisting not only of incentives, but of effective communication, education, and dissemination of information. Progr am administrators must examine and choose an appropriate mix of these investments, base d on the unique characteris tics of their service territories, customer needs, and characteris tics of programs and products they offer.

In further analyzing EIA data, Cadmus found a statistically significant positive correlation between incentive payments and non-incentive e xpenditures of approximately 20%. That is, as incentives increase, so do non-incentive expenditure s, and one cannot consid er a scenario with drastically increased incentive payments without considering an accompanying rise in nonincentive costs.

Using 2010 reported port folio savings and expenditures, reve nues, and retail sales for the two electric utilities, Cadmus estimates that, if incen tives for electric progra ms increase to 100% of incremental measure costs, up to 90% of estim ated statewide economic electric potential will likely be achievable (see Table 32). As shown, how ever, budgets would need to increase by more than twofold at these incentive levels. As disc ussed, this increase in incentive spending wouldVolume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 44 likely lead to additional spending on program ad ministration, 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 electr ic energy savings would likely produce statewide life-cycle benefits of approximately $450 milli on. The estimated costs and benefits do not account for potential future decreas es in measure costs as energy- efficient technologies improve over time.

A market potential up to 90% of economic potentia l is extremely high, compared to results of other potential studies and mark et 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 potent ial exceeded 80% of t echnical potential (as seen in this study) in only 10 cases. These 10 studies estimate a maximum achievable potential of less than 60% of econo mic potential, a level si gnificantly below that es timated in this study. Planning study results in several regions with l ong histories of aggressive energy-efficiency resource acquisition programs also supports the supposition that, relativ e to the identified potential in this study, market poten tial 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 en ergy-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 iden tified in the study w ould be achievable. 16

16 California Statewide Commercial-Sector Energy Effi ciency Potential Study, Xenergy Inc, 2002.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 45 

### Natural Gas Due to the lack of centralized natural gas en ergy-efficiency portfolio data, Cadmus compiled information on 14 natural gas energy-efficiency por tfolios, 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 th e electric sec tion, above).

Figure 15. Scatter Plot of Energy-Effi ciency 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:

log ሺ %	Savings ሻ ൌβ 0൅ β 1 log ൬ Incentive
Revenue ൰ ൅β 2 log ൬ Other	Costs
Revenue ൰ ൅β 3 logሺRateሻ

As shown in Table 34, estimated coefficients fo r the incentive and other expenditure terms arepositive, 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%GrossAnnualSavings(%ofAnnualSales)

Annual Expenditures on Energy Efficiency (% of Revenue)Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 46 Of the three estimated coefficients, only the incentiv e 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 significant ly 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 cons istent with the resu lts found in the elec tric 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 util ities in 2010 indicates incentives covered approximately 42% of incremental measure cost s 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 cost s, the achievable fraction of economic potential might increase to approximately 65% of the estimated economic potential (see Table 35).

Table 35. Expected Achievable Na tural 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 Effici ency 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 relati ve to retail sales are current ly 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. AsVolume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 47 shown, however, budgets would need to increase twof old at these incentive levels. As discussed, this increase in incentive spending would li kely lead to additional spending on program administration, further increasi ng program budgets to $75 million dollars annually. The analysis further shows the associated natural gas energy savings would likely prod uce 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 overc ome 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 ba rriers 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’ will ingness and ability to par ticipate 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 a nd loan programs, including two recent reports by ACEEE, have found energy-efficiency loan pr ograms have minimal effects on consumers’ participation in energy-efficiency programs.

The findings of one ACEEE study 17 suggest participation rates te nd to be generally low across programs. Compared to numbers of eligible cust omers 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 be en 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 Impr ovements: 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 48 

## Effects of Emerging Technologies In addition to commercially available technologies included in the assess ment of technical and economic potentials, Cadmus considered the pot ential for emerging technologies in the context of market potential. Emerging energy-efficient technologies ar e 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 mi d-1990s, ACEEE has published reports on Emerging Energy-Saving Technologies and Pr actices in the Building Sector

. 21 In 2009 and 2011, reports focused on HVAC and hot water systems, resp ectively. ACEEE current ly is investigating emerging lighting technologies, but, as results of this research are not available at this time, Cadmus referenced work conducte d through DOE’s CALiPER program. 22

Generally, these technologies are higher-efficiency replacements for meas ures already included in the assessment. For example, Advanced No rthern Heat Pumps (S EER 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 sy stem) proves the exception: this measure represents an alternate building design, replacing st andard duct systems with integrated features, combining lighting, water-cooled convective heat exchange surfaces, and ventilation. In short, it utilizes pumps to deliver cool water in stead 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 meas ures relative to the comparab le measure’s economic potential. That is, using the example measure above, addi tional potential for th e SEER 16/HSPF 9.6 heat pump is incremental to the SEER 16/HSPF 9.0 un it. 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, ex isting technology would first need to become economically feasible before being supplante d 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 heat ers will not pass either.

Measures identified through this re search, along with applicable sect ors, fuels, and end uses, are listed in Table 36. Though ACEEE reports addresse d more measures, only those in Table 36 achieved efficiency levels great er than economic measures al ready in the measure list.

21 http://www.aceee.org/topics/emerg ing-technologies-and-practices

22 http://www1.eere.energy.gov/buildings/ssl/caliper.htmlVolume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 49 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 Chi lled Beam Cooling with DOAS 2,338 Commercial Electric HVAC Ventilation and Energy Recovery 34,214

The additional market potential from the emer ging technologies is estimated at 73,403 MWh in the residential sector and 99,468 MWh in the commercial sector, assu ming 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 a bout 3%. Cadmus did not identify additional natural gas potential from emerging technologies. A ppendix A.5 describes each measure included in the analysis.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 50 

## 4. DEMAND RESPONSE POTENTIAL
Potential for Expanding Legacy Programs

Residential DLC As discussed in Section 1, the key metric for th e residential DLC analysis was the fraction of eligible customers currently pa rticipating, with eligible cust omers defined as those with residential electric service and central ai r conditioners. Based on 2010 program activity, residential customer counts, and saturation data from the 2007 Residential Appliance Saturation Survey, Cadmus estimated similar currently pa rticipation rates for Iowa DLC programs: 19% and 18% for Alliant and MidAmerican, respectively.

Based on secondary data collected, Cadmus calcu lated participation rate s for an additional 51 residential DLC programs for i nvestor-owned utilities (IOUs) from across the nation. Figure 16 shows the calculated part icipation for each of th ese 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. Pa rticipation levels in the mode rate and aggressive expansion scenarios have been based on average particip ation in the upper-tier and industry-leading IOU programs, respectively. 0% 10% 20% 30% 40% 50% 60%PercentofEligibleCustomersSignedUP



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

The Cadmus Group, Inc. / Energy Services 51 Assumed participation rates are:

 Baseline: maintaining current program participation levels.

 Moderate expansion: achieving 20% program pa rticipation.

 Aggressive expansion: achievi ng 25% program participation.

To estimate peak demand impacts under each scen ario, Cadmus multiplied participation rates by eligible customer forecasts for each utility, calc ulating the number of pa rticipating customers, then multiplying this number by per-participant values currently used by Iowa utilities, to calculate program-level demand impacts. Tabl e 37 compares estimated 10-year potential under each scenario to the 2008 Assessment and each utility’s 2010 accomplishments. As shown in Table 37identified poten tial 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 i ndicated 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 cont ract, but also of the duration and frequency of events. However, based on the secondary li terature, 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 sta ndby 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 di fferences in demand be tween nonresidential customers, the percent of eligible load enrolled (rather than customer s) 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 52 available for most utilities, Cadmus used tota l 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 nonresid ential interruptible impacts, Cadmus established three scenarios to quantify available potential for Iowa utilities, based on differing program participation levels. Participa tion levels in the moderate and aggressive expansion scenarios have been based on averag e participation in th e upper-tier and industryleading IOU programs, respectively, with the assumed participation rates:

 Baseline: maintaining current program participation levels.

 Moderate expansion: achieving 15% program pa rticipation.

 Aggressive expansion: achievi ng 17.5% program participation.

For each scenario, the percent increase in part icipation 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 pr ogram participation levels are desirable and prudent. 0% 5% 10% 15% 20% 25% 30%PercentofC&IPeakParticipating

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

The Cadmus Group, Inc. / Energy Services 53 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 gr eatly across utilities. Though li terature on the likely snapback effects for nonresidential programs is limited, avai lable 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 dr awn almost exclusively from utility pilot programs, and may not be appropriate for extr apolation 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 po tential 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 communicati on 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 ba ck 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 prici ng 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 ina ppropriate for installati on in some situations, due to costs outweighing benefits. Such system s 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 re duce 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 bus iness through use of demand response enabling technology, which can be remote ly signaled when utilities cal l demand response events, thusVolume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 54 reducing an appliance’s load thr ough control strategies established by the utility or the customer. AMI-enabled demand response tech nologies include the following:

 Smart thermostats: Devices similar to programmabl e thermostats, but receiving and reacting to utility pricing a nd 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 ther mostats automate this process.

 Smart appliances: Smart appliances typically are very efficient versions of traditional appliances, equipped with AMI communicatio n capabilities. They can receive event notifications or pricing signals, modifyi ng operations to reduce demand during demand response events. For example, a smart refriger ator, when signaled with a relatively high electric price, may cycle its refrigerant co mpressor to reduce peak consumption. Other smart appliances include: water heaters, lighting, clothes wash ers and dryers, and dishwashers.

 Load control devices: AMI load control devices resemble traditional load control devices, except they communicate over AMI sy stems, 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, a ppliances and security, adding AMI allows HEMS’ to control systems to reduce dema nd when signaled through an AMI network.

In addition to demand response enabling technol ogies, other AMI-enabled technologies improve communication of energy usage from the utilit y to consumer. Traditionally, customers have received monthly utility bills th at report consumption and charge customers for their aggregate monthly consumption, a system that somewh at disconnects customers from immediate connections between their actions and energy c onsumption. 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 be nchmark their performance against those of neighbors with similar homes. Some systems al low customers to spec ify what they wish their utility bills to be, and the portal provides recomme nded 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 ener gy consumption and current utility pricing. These devices allow customers to bette r understand their energy consumption.

 Energy Orb: These standalone devices, which change color as energy rates change or as demand response events are called, signal custom ers to take appropriate actions to reduce their electric demand.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 55 

### Program Examples

AMI Enabled DLC As noted, AMI adoption creates opp ortunities to c ontrol 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 deploye d DLC programs. However, AMI improves upon DLC by implementing two-way communication. Mo st residential DLC programs experience lower demand reduction capabilities due to malf unctioning DLC devices. As traditional systems cannot communicate their status to the utility, these devices often remain inoperable until discovered through inspections. Utilities typica lly experience 10% to 20% losses due to nonoperable DLC devices. With AMI-enabled DLC, non-operable devices can be more readily detected, and inoperability ra tes can typically be decrea sed 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 reco ncile customer consumption. However, as AMI enables such communication, it allows implemen tation 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 cust omers. Nevertheless, preliminary pilot results have been somewhat promising.

The Brattle Group recently synt hesized results of 109 AMI-ena bled 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 dyna mic pricing programs, utilizing various combinations of enabling technologies. These progr ams showed consistently higher savings than programs without enabling technologies, with a median dema nd reduction of 23%.
Summary of AMI-Enabled De mand Response Opportunities From initial pilot results, AMI appears to expa nd demand reduction capabilities of residential demand response programs, though the extent of th is expansion remains to be seen, as program persistence issues have not b een thoroughly studied. Additionally, studies of the reliability and security of these programs and enabling techno logies remain in progress. Further, how AMIenabled programs and traditional programs overl ap, and how demand savings may shift, still must be understood before specific estimat es 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 Elect ricity and its Discontents.” The Brattle Group. August 2011.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 56 

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

Definitions Net-to-gross (NTG) assessments primarily seek to determine energy savi ngs 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 savi ngs 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, indepe ndent 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 ef ficiency level, had the program not existed. 

### About Spillover Spillover adjustment adds energy savings from adoption of hi gh-efficiency measures outside the program, but likely induced by the program. Thes e 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-e fficiency 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 no nparticipants far outnumbers participants; thus, potential exists for large spillove r impacts within this population. 

### About Program-Induced Market Effects A third possible adjustment is program-induced market effects 25

― 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 effi ciency of their products;

 Wholesalers and retailers changing their stocki ng decisions, reacting to shifts in demand for more efficient goods cause d 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 57 These market effects can be significant, especi ally in upstream programs implemented through point-of-sale discounts. Such transformationa l 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 popula tions 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 th ese effects to particular energy- efficiency programs has been a significant measurement and evaluation challenge. 

## Treatment of Freeridership and Spillover Depending on the relative magnit udes of freeridership and spil lover, NTG may be less than, greater than, or equal to 1.0. However, in jurisd ictions where freeridership is the only measured effect, NTG never takes a value greater than 1.0.

Applying NTG also affects the co st-effectiveness of IOU programs. The Iowa Chapter 35 rules specify the method and assumptions for cost-effectiv eness tests, including the Societal Cost Test (SCT), the standard for determination of cost-e ffectiveness in Iowa. The rules have been based on the Standard Practice Manual (SPM) for Economic Analysis of Demand-Side Management Programs, established by the Californi a Public Utilities Commission (CPUC). 26

In calculating benefits for the Total Reso urce Cost (TRC) test, the CPUC observed: “...ratepayers, through the ener gy-efficiency revenue requirem ents 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 freer ider considerations on the equation’s cost side, the CPUC (in its 2007 Clarification Memo) modi fied the original method for calculating TRC costs by adding a transfer incentive (INC) recaptur e 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 program s) utility increase supply costs

26 The SPM describes procedures for determining cost-effectiveness of energy-efficiency programs from five perspectives: resource allocation efficiency (Total Resour ce 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 58 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, freer idership 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 wit hout specifying freeridership and spillover individually. Finally, in some cases, measurement of NTG—or its co mponents—may not be required. Instead, gross savings, adjusted for actual insta llation rates, are used as the measure of program impacts. This is also the case with regional transmission or ganization (RTOs), such as the New England independent system operator (ISO-NE), where verified gross savings serve as the basis for verification of energy-effi ciency bids into the forward energy market.
Cadmus compiled data on 32 jurisd ictions active in energy efficien cy 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 calculati ons in 12 jurisdictions (38%).

 In 17 jurisdictions (53%), freeridership is in cluded 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 incl ude freeridership and either participant or nonparticipant spillover effects.

 In the majority of cases where NTG is calcula ted, 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 ju risdictions (37%) in the sample, while nonparticipant spillover is taken into account in 10 (31%).

 The incidence of cases only as sessing freeridership suggests asymmetrical treatment of spillover and freeridership effects.

28 New Jersey applies freeridership on ly to appliance recycling programs.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 59 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 in clude spillover effects, the CPUC allowed evaluations of the 2006–2008 energy-efficiency programs to c ontain 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 residentia l sector than in the commercial and industrial sectors, mainly due to the high freer idership 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, 2008Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 60 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 gene ral. Despite apparent differen ces, these methods and techniques tend to fall into one of two categories: statistica l and self-report.



### Statistical Methods Statistical methods are based on the general diffe rence-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 pr ogram 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 re moves potential biases related to the unique characteristics of partic ipants, and biases from comparisons over time, which could result from non-program related trends (so-called “n aturally occurring conservation”). Net program impacts are then calc ulated by subtracting the average change in nonparticipants’ consumption from the aver age change in the participant group.

This approach is sometimes implemented with in an econometric framework for the following reasons: (1) controlling for the residual differe nce between the two groups; (2) evaluating the sensitivity of savings to various factors; and (3) estimating savi ngs for bundles of measures. It cannot, however, be used for measuring NTG for i ndividual 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 es timating NTG in large commercial and industrial energy-efficiency programs. Due to the heter ogeneity of these customers, it often can be impractical to identify an appr opriately comparable group of nonparticipants. Also, as energy savings in these programs are often a small fractio n of total consumption, it can be difficult to isolate consumption changes resulting from im plementation of energy-efficiency measures. Moreover, this method is not recommended for upstream programs or new construction programs (where the lack of a pre-program peri od 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, prepar ed by Summit Blue Consulting, LLC, August, 2008.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 61 

### Self-Report Methods Studies relying on self-reporting ar e more common than those rely ing on statistical methods. At a basic level, these methods dire ctly involve asking participants questions about what they would have done in the program’s absence. Responses are then scaled, weight ed, and combined to produce a composite freeridership score (or index) for each responde nt. Scores are then weighted (by savings) and averaged to produce a program-level freeri dership 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 variati ons in spillover scores. Small fractions multiplied by very large numbers of customers can dramatically boost savings.

Using surveys to assess freeridership raises con cerns about response bias , particularly biases involving social desirability (the tendency of respondents to ga uge their responses to conform to socially acceptable values). This well-recognized i ssue in social sciences has been discussed in a vast body of academic and professional literature.

Due to social desirability, respondents tend to o ffer what they think is the right answer, resulting in freeridership overstatement. Al so, as some evaluation experts ha ve noted, people have internal

reasons
― as explained by social ps ychology’s attribution theory

― motivating them to make certain decisions.

Another aspect is called the construct validity . This issue stems from th e fact that while survey respondents

― by virtue of their partic ipation in the program

― are predisposed to conservation, the extent that their responses have been co nditioned by the psychological effects of the conservation program remains unclear. Thus, what surveys measure may be the program’s effect rather than what would ha ve happened in its absence. 30 In areas with long histories of conservation programs and activities, it can be di fficult 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 a bout 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 Ener gy Efficiency in the New Millennium: Time for Net-toGross to Get Out of the Way? Proceedings, Internatio nal Energy Program Evalua tion Conference, Chicago, August 2007.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 62 have purchased additional energy-efficient m easures 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 eval uators’ subjectiv e assumptions.

Recall presents another problem, especially regard ing 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 freeri dership 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), we re nearly identical to those obtained from self reports 33 (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 esti mating 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 63 

### Cross-Program Research The National Energy Efficiency Best Practic es 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 practi ces, 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 free ridership, or were based on a deemed NTG assumption.) Reported freeridership values vari ed significantly, even within program groups. Spillover effects were repor ted very infrequently.

Another cross-program study revi ewed 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.eebestprac tices.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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 64 Although the study stated freeridersh ip and spillover were important considerations that should be included in evaluation research, it provided no guidelines as to which e ffects may have greater impacts, or whether it was appropriate to assume freeridership and spill over effects essentially cancelled each other out. However, some specifi c program evaluation efforts were identified, which will be reviewed in the ne xt section of this report. 

### Specific Programs This section examines measurement results for specific program types, based on data available from evaluation reports assessing both freeridersh ip and spillover. Selection of program types was based on their expected savings potential in Iowa.
Lighting Programs Table 42 lists results from four evaluation e fforts 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 Evaluati on 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 - fr eeridership + 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.Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 65 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 Proc ess Evaluation Research Results, prepared for Wisconsin Power & Light by Summit Blue Consulting, April 11 2006

** Commercial and Industrial Performance Program (CIPP) Mark et Characterization, Market Assessment and Causality Evaluation , prepared for New York State Energy Research and Deve lopment Authority, prepared by Summit Blue Consulting, LLC, May, 2007.

The SPC program in California has a relativel y 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 sp illover 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 freeridersh ip 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 r ecycling programs tend to be we ll below 1.0. As shown in Table 44, these estimates in California are 0.61 for re frigerators 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 qualif ied 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 c onsistently sub-1.0 NTG ratios, ranging from 0.31 to 0.79 for refriger ators, and from 0.38 to 0.82 for freezers (see Table 44).Volume 1. Assessment of Energy and Capacity Savings Potential in Iowa February 28, 2012

The Cadmus Group, Inc. / Energy Services 66 Table 44. Reported NTG Ratios fo r 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 & Electr ic, 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 resu lting 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 Vermont 38 has evaluated energy-efficient clothes washers as part of its portfolio of energy-efficient appliances, offered under the e fficient 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, inferri ng attribution for spillover would require a national rather than local effort.

Despite this very low NTG value, Efficiency Ve rmont plans to continue administering rebates for ENERGY STAR clothes washers to maintain the good relationships w ith 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 2005Volume 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 spillove r effects exhibit considerable limitations, and little consensus exists among evaluation experts on best methods. Methods used to calculate NTG have inherent biases, particul arly those based on self-reporting (the most common approach). These biases can signi ficantly 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 partic ipants. 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 c onducted 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 calcu lated 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. Consequent ly, an even higher percentage of programs would have a NTG ratio of approximate ly 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 ligh ting, 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 pr operly accounted for, spillover effects may offset freeridership to a large extent.

Given these findings, it appears r easonable 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 wi thin their own service areas and in other jurisdictions. For example, ENERGY STAR clothes washers continue to gain market share throughout the country, and freeridershi p will likely increase, resulting in an NTG of less than 1.0.

2. Carefully monitor market responses to par ticular 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 motiv ate 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.