## The Best Value for America’s Energy Dollar: A National Review of the Cost of Utility Energy Efficiency Programs

Maggie Molina
March 2014 Report Number U1402

© American Council for an Energy

- Efficient Economy
529 14 th Street NW, Suite 600, Washington, DC 20045
Phone: (202) 507

- 4000  Twitter: @ACEEEDC Facebook.com/myACEEE  www. aceee.orgB EST V AL UE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

i 

## Contents Acknowledgments

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

Executive Summary

................................ ................................ ................................ ........................ ii iii

Introduc tion

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

Measuring Cost Effectiveness: Practices and Challenges

................................ .............................. 3

Energy Efficiency Costs

................................ ................................ ................................ .................. 3

Evaluation, Measurement, and Verification (EM&V) of Energy Savings

............................... 5

Levelized Costs Versus First

- Year Costs

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

Cost

- Effectiveness Tests

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

Energy Efficiency Valuation in Integ rated Resource Planning

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

Methodology

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

Data Collection and Processing

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

Challenges and Caveats

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

Calculations and Assumptions

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

Results

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

Electricity

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

Natural Gas

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

Discussion and R ecommendations

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

Energy Efficiency Costs Compared to Supply

- Side Options

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

Consistency and Transparency in Energy Efficiency Reporting

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

Further Research

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

Conclusion

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

References

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

Appendix A. Data Sources by State and Program Administrator

................................ .............. 43B EST V AL UE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

ii 

## Acknowledgments Thank you to ACEEE’s funders for supporting this work. Many individuals at state utility commissions and utilities as well as non

- program administrators were invaluable in sending data and answering questions about the data. Thank you to colleagues at ACEEE who reviewed an earlier draft of this report : Jim Barrett, Annie Downs, Neal Elliott, Marty Kushler, Steve Nadel, Max Neubauer, and Dan York; and thank you to the following individuals who reviewed a draft of the report and provided comments : Megan Billingsley (Lawrence Berkeley National Laboratory), Joe Bryson ( U.S. Environmental Protection Agency)
, Tom Eckman (Northwest Power and Conservation Council), Nick Mark (Centerpoint Energy), Cecily McChalicher ( Northeast Energy Efficiency Partnerships
, NEEP )
, Jen Miller (Sierra Club)
, Chris Neme (Energy Futures Group)
, Wally Nixon (Arkansas Public Service Commission ), and Kenji Takahashi (Synapse Energy Economics)

. Finally, thank you to the following individuals who helped with the editing, production
, and communications for this report: from ACEEE, Fred G r ossberg, Patri ck Kiker, Eric Schwass
, and Glee Murray , and from Resource Media, Debbie Slobe

.B EST V AL UE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

iii 

## Executive Summary

After a decades

- long history
, U.S. e nergy efficiency programs have expanded rapidly in recent years . A s program administrators face rising energy efficiency targets that require more comprehensive portfolios, they have an increasing concern about the impact on program costs. This creates the need for high

- quality, comprehensive
, and consistent data metrics on energy efficiency program costs and cost effectiveness. To this end, the American Council for an Energy

- Efficient Economy (ACEEE) has undertake n an assessment of utility

sector energy efficiency program costs and cost effectiveness in 2009

- 2012

.

The results of our analysis clearly demonstrate th at e nergy efficiency programs are holding steady as the least

- cost energy resource option that provides the best value for America’s energy dollar . D ata from a large number of diverse jurisdictions across the nation show that energy efficiency has remained the lowest

- cost resource even as the amount of energy efficiency being captured has increased significantly

. At an average cost of 2.8 cents per kilowatt hour ( kWh ) , electricity efficiency programs are one half to one third the cost of alternative new ele ctricity resource options such as building new power plants. Natural gas energy efficiency programs also remain a least

- cost option at an average cost of 35 cents per therm as compared to the national average natural gas commodity price of 49 cents per the rm in 2013 . In addition, b oth electricity and natural gas efficiency costs have remained consistent over the past decade. This consistency shows the reliab ility of efficiency as a long

term resource.



### M ETHODOLOGY The goal of th e current ACEEE analysis is t o collect and aggregate recent data on energy efficiency program costs and cost effectiveness from jurisdictions across the U nited S tates

. Our focus is on the costs to utilities or other program administrators to run efficiency programs, but we also includ e some data on the broader costs and benefits to participants and to society. We do not aim to compare one state’s efficiency portfolio results to others, but instead to present overall results.

We collected data for 20 states for electricity programs and 10 states for natural gas efficiency programs from 2009 to 2012, pulling from utilities’ and other program administrators’ program results. We collected the necessary data (annual program costs, net energy savings, and measure lifetime) to calculate the l evelized utility cost of saved energy ( CSE ). By levelized we mean that upfront costs are amortized over the lifetime of a measure at an assumed real discount rate. The levelized CSE is the best measure for compar ing energy efficiency to other energy resource options.

Our definition of utility energy efficiency costs includes

 D irect program costs incurred by administrators, includ ing incentives to participants and all non

- incentive costs such as the direct installation of measures, progr am design and administration, marketing, education, and evaluation

 S hareholder incentives or performance fees, which reflect the rate of return utilities earn in some states to meet or exceed certain thresholds of energy savings levelsB EST V AL UE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

iv We also collected s ome data on participant costs ; however these data are much more sparsely reported and therefore the data set includes only seven states.

Our task of data collection and comparison was complicated by numerous challenges, including inconsistent reporting for mat s , nomenclature, and frequency ; variation in energy savings evaluation approaches and in the accounting of demand response programs ; and structural differences in program portfolios. W e tried to make the data as consistent as possible in the face of the se challenges

. W e consistently calculated the CSE based on a 5% real discount rate, we used net energy savings values and measure lifetimes as reported by the program administrator, and we used energy savings reported at the meter rather than at generation

. We converted a ll data to real 2011 dollars

.


### R ESULTS

A s shown in f igure S 1 , t he CSE for electricity energy efficiency programs ranged from $0.013 to $0.056 per kWh across the 20 states from 2009 to 2012.

Figure S1. Electricity energy efficiency program CSE by year. Each dot represents average costs for each state in a given year. 2011$ per levelized net kWh at meter. Assumes 5% real discount rate.

We calculated four

- year averages (2009

- 2012) for each of t he 20 jurisdictions (and 10 jurisdictions for gas programs), and display the average, median, minimum, and maximum for the dataset in t able S1. The simple average utility CSE was $0.028 per kWh for electricity programs and $0.35 per therm for gas programs.B EST V AL UE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

v Table S1. Summary of results for four

- year averages (2009

- 2012) for all states in dataset

Electricity pr ograms ($ per kWh) Natural g as p rograms ($ per therm)
Average $0.028 $0.35

Median $0.026 $0.37

Minimum $0.016 $0.10

Maximum $0.048 $0.59

2011 $ per levelized net kWh or therm at meter

. 5% real discount rate

. E ach state’s four

- year average i s a distinct data point

. T he complete data set for individual years (Figure S1) has lower minimum and higher maximum values

. We also reviewed energy savings and CSE by customer class. Among the 17 states that readily reported electricity savings by customer class, the average portfolio included 45% savings from residential customers and 55% from business (commercial and industrial)
customers. We calculated ele ctricity CSE values by customer class for nine states (complete data was not readily available for the other jurisdictions), and identified an average CSE of $0.037/kWh for residential portfolios and $0.027/kWh for business portfolios.

While this study fo cused on the utility costs to deliver energy efficiency programs
, we also examined some results of the total resource cost (TRC) test, which involves a system

- wide perspective. 1 TRC test results from nine states show benefit

- cost ratios rang ing from 1.24 t o 4.0 for electricity portfolios. In other words, in these jurisdictions
, each dollar invested by utilities and participants in energy efficiency measures yields $1.24 to $4.00 in benefits.

M any analysts have hypothesized that program CSE will increase ov er time as administrators increase energy savings levels. An initial correlation analysis in this study finds only a very weak correlation between CSE values and energy savings levels

. This analysis casts doubt on the claim that higher savings levels are a ssociated with higher costs.

While comparisons of efficiency program costs to current levelized costs for new electricity resource options or natural gas commodity prices provide useful context, they do not tell the complete cost

- effectiveness story for e nergy efficiency. For example, in addition to the avoided energy

- and capacity

- related costs to all customers, energy efficiency programs also result in utility benefits such as avoided transmission and distribution (T&D) costs, peak demand benefits, price mitigation effects in wholesale markets, and reduced pollution. Program participants can also benefit from lower water and fuel usage and improved comfort. In addition, energy efficiency programs result in reinvestment of local dollars in local jobs and i ndustries

. Also, these indicators of current avoided energy costs do not reflect future expected avoided energy costs and future price volatility. Inc luding higher levels of low

- cost energy efficiency in long

- term planning can hedge against volatile and /or rising costs of supply resources

. In summary, t he results of this analysis clearly demonstrate that energy efficiency programs are the least

- cost resource option available to utilities. As shown in f igure S2, electricity

1 A complete and balanced TRC test should include benefits both to participants and to the system.B EST V AL UE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

vi efficiency programs, at a range of about 2 to 5 cents per kWh and an average of 2.8 cents per kWh, are about one half to one third the levelized cost of alternative new electricity resource options.

Figure S2 . Levelized costs of electricity resource opt ions. Source: Energy efficiency data represent the results of this analysis for utility program costs (range of four

- year averages for 2009

- 2012); supply costs are from Lazard 2013.



### R ECOMMENDATIONS AND 

### F URTHER 

### R ESEARCH Given the inconsistency in efficiency program report formatting, nome nclature, and frequency
, w e recommend that utilities, regulators
, and program administrators in each
state discuss these issues, perhaps also at a regional and national level, and work toward adopting best reporting practices. We offer several specific rec ommendations to improve consistency and transparency in reporting.

In this review we discuss numerous metrics that may have a direct impact on the cost of efficiency, e.g., the share of savings by customer class, or the types of programs offered. Further research is needed on the relative impact of these different variables on CSE values and on the broader set of benefits to customers. T rends in CSE over time may be another fruitful area of study. C orrelation analyses of CSE trends over time across jurisdi ctions are difficult and may pro duce incomplete results because of differences among program portfolio structures and reporting consistency. Further research should delve into this question, perhaps examining individual jurisdictions or regions.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

1 

## Introduction

The energy future of the United States has entered an era of increasing change and uncertainty

. While oil and gas production have increased in recent years, ongoing challenges include the environmental impacts of power generation
, difficul ty in siting new energy facilities and infrastructure as well as their high capital cost, and the continuing risk of fuel price volatility

. In the face of these challenges and the need for economic development strategies, an increasing number of states have turn ed to energy efficiency as a significant component of their long

- term energy resource planning.

Energy effic iency has long been demonstrated to be a low

- cost and low

- risk strategy

. The American Council for an Energy

- Efficient Economy (ACEEE) has conducted two reviews of utility

- sector energy efficiency program s to document their cost effectiveness (Kushler, York and Witte, 2004; Friedrich, Eldridge, and York 2009). Both studies found that energy efficiency programs are the least

- cost resource option compared to supply

- side energy options.

ACEEE’s 2004 and 2009 efficiency program cost reviews found the following:

 Examining data from seven states, the 2004 review identifie d a range of levelized cost of saved energy (CSE) from $0.023 to $0.044 per kilowatt hour ( kWh ) , with a median value of $0.03 per kWh (Kushler, York

, and Witte, 2004).

 The 2009 review of 14 states identified CSEs ranging from $0.016 to $0.033 per kWh, with an average cost of $0.025 per kWh. Six natural gas efficiency program portfolios covered in the report had an average CSE of $0.37 per therm (Friedrich, Eldridge

, and York 2009).

U tility

- sector energy efficiency programs have a decades

- long history in the U.S., but have expanded significantly in recent years, which means the availability of new data sets and the increasing visibility of efficiency. 1 A s states face rising energy savi ngs targets, some stakeholders are concerned that energy efficiency p rogram s’ cost of saved energy will increase as they ramp up to hit their targets. These recent trends and concerns suggest the need for an updated and expanded review of energy efficiency program costs.

From 2006 to 2011 , national annual electricity efficiency program spending tripled from $1.6 billion to about $4.8 billion (Downs et al

. 2013). Although b udgets for natural gas efficiency

1 This report focuses exclusively on utility

- sector energy efficiency programs that aim to reduce overall customer
energy usage. We do not include demand response programs, which aim to reduce or shift energy usage only during times of peak demand. By “ utility

- sector e nergy efficiency programs, ” we mean programs funded through utility rates (whether embedded in rates or as a separate tariff rider or surcharge) or through associated public benefits charges and administered by utilities, government agencies
, or third

- part y organizations

.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

2 programs have been much smaller
, they have gained popularity in recent years and in creased from $0.3 billion in 2006 to $1.1 billion in 2011

. This rapid growth stems largely from the increasing adoption of energy efficiency resource standard s (EERS) and other regulatory mechanisms that reduce barriers to efficiency and encourage utilities to pursue it cost effectively. 2 States have also increasingly recognized energy efficiency as a low

- cost economic development tool that attracts new business es , creates jobs, and stimulates the economy. Twent y

- six states now have EERS policies, and many others have other short

- term energy efficiency planning processes.

Th ese recent trends open up a wider set of utilities and states that collect data on efficiency program costs. Similarly, a s efficiency progr ams g ain traction as a true resource that planners can use in long

- term forecasting, the need increases in step for high

- quality and uniform data metrics on energy efficiency program costs and benefits

.

Numerous utilities and statewide program administrat ors are now facing rising energy efficiency targets as part of their EERS policies, and they must hit these targets within firm
cost

- effectiveness requirements. Some stakeholders are concerned that the cost of efficiency
programs is rising and that it is becoming more difficult to realize savings as federal appliance and equipment standards raise the baseline

. It is true that p rogram costs increase in the short term a s programs target the uptake of higher

- cost technologies, e.g. , as they

mov e from CFLs to LEDs

. But , continuing with this example, costs are quickly declining for LEDs, which can counterbalance the initial higher program costs. Similarly, utilities are developing new , highly cost

- effective program strategies such as large customer reverse aucti on s and strategic energy management . T hey are also identifying market gaps such as multifamily buildings

. Still, much uncertainty and many misconception s remain about the costs and cost trends of efficiency programs.

The goal of this analysis is to collec t and aggregate recent data on energy efficiency program costs and cost

- effectiveness metrics from jurisdictions across the U.S. as a comprehensive source of information for stakeholders. Our primary focus is on the costs incurred by utilities or other pro gram administrators to run efficiency programs, but we also include some data on the broader costs and benefits to participants and society. We collect ed data by review ing utilities’ and other program administrators’ program results to calculate CSE values

. We do not aim to compare states ’ efficiency portfolio results, but instead to present overall results. We also would like to advance the discussion on how to improve protocols and consistency in the reporting of efficiency programs.

2 EERS policies establish specific, long

- term (3+ y ears) energy efficiency targets that utilities or non

- utility program administrators must meet through customer energy efficiency programs

. See http://www.aceee.org/topics/eers for more information.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

3 

## Measuring Cost Effectiveness: Practices and Challenges
Since the 1980s, e nergy efficiency programs have gained traction as a true resource option that utilities and states can scale up and rely on within their resource plans

. A s a demand

side resource, however, efficienc y is fundamentally different from supply options such as power plants and wind turbines

. This difference calls for a unique set of methodologies to quantify efficiency as a resource by measur ing its energy savings and cost effectiveness.

Since the 1970s a nd 80s, regulators have adopted particular practices to evaluate the costs and energy savings from efficiency programs. States have adopted these practices with varying degrees of consistency, and this creates a challenge for reviewing efficiency program costs across states. S ome regions of the country have begun to coordinate methodologies and reporting guidelines through efforts such as the Regional Technical Forum in the Pacific Northwest and the Nort heast Energy Efficiency Partnership (NEEP) Regional Evaluation, Measurement, and Verification Forum

. 3 However most states continue to use a diverse set of methodologies and reporting structures.

This section presents our approach to review ing the costs , sa vings, and cost effectiveness of efficiency programs, addressing the following questions :
1) What is typically included in the definition of energy efficiency program costs? 2) How are energy savings evaluated, measured, and verified (EM&V)? 3) How are costs expre ssed relative to energy savings ? For example, what is the relationship between levelized costs , first

- year costs, and measure lifetimes ? 4) How are energy efficiency cost

- effectiveness tests currently applied ?

We discuss practices and challenges for each topi c in this background section, as well as the approach we took to these issues in our review. The subsequent section on methodology goes into further detail on our approach to some of these topics.



### E NERGY 

### E FFICIENCY 

### C OSTS Program Costs
What types of values should be attributed to the cost of deliver ing energy efficiency as a utility resource ? We include two broad categories of energy efficiency resource costs: ( 1) d irect program costs and ( 2) shareholder incentives (also called performance incentives or performance fees) earned by utilities or third

- party program administrators for reaching or exceeding certain energy savings thresholds. 4 From a utility or program administrator perspective, t he sum of efficiency program costs and performance incentives comprise s the total cost of energy efficiency resources

.

3 For more information, see http://rtf.nwcouncil.org/ and http://neep.org/emv

- forum/

.

4 For more information on performance incentives, see http://aceee.org/sector/state

- policy/toolkit/utility

programs/performance

- incentives

.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

4 The following types of costs are commonly incurred by energy efficiency program administrators as the direct costs to administer programs :

a. direct rebates or incentives to customers b. engineering or technical support

c. program administration, planning , and delivery d. evaluation
, measurement , and verification (EM&V)

e. marketing and education 5

The second general category is p erformance incentives , which are either utility shareholder incentives or performance management fees for non

- utility program administrators. Both are typically established as a way to encourage greater levels of efficiency, and typically they are earned only if certain thresholds of energy sav ings are met or exceeded. While utilities earn the incentives for good performance and may not perceive them as a direct cost of efficiency programs, ratepayers foot the bill for performance incentives, so they need to be accounted for in calculating the o verall cost of deliveri ng energy efficiency resources. N ot all jurisdictions
, however, adopt performance incentives : c urrently 28 states have them in place for at least one major utility (Downs et al

. 2013)

. We have chose n to include
p erformance incentives as a cost component of delivering energy efficiency resources because they are a direct way to encourag e energy efficiency performance
, and they are equivalent to a rate of return that utilities would earn on a supply

- side investment

. 6

Participant Costs In addition to the program costs incurred by administrators, program participants may spend additional money to purchase or install energy efficiency upgrades

. Depending on the type of cost

- effectiveness test used ( as discussed later in this section )
, part icipant costs may or may not be included as a component in cost

- effectiveness screening

. The t otal

r esource c ost (TRC) test, for example, includes participant costs, while the utility or program administrator cost test (UCT /PACT ) assumes the perspective of the utility planner and so does not take participant costs into account.

The best way to directly compare efficiency costs to supply

- side options is to take the perspective of the UCT/PACT and focus on the cost of energy efficiency prog rams as a resource option to utility planners

. Since this is how we focused our analysis, we did not conduct an extensive review of participant costs. Although w e did collect some limited data, most annual program administrator reports do not include parti cipant cost estimates and benefits

. Participant costs are used as an input to the TRC calculations , however, and therefore embedded in the results of any TRC test. S ee the M ethodology and R esults sections for further details on participant cost estimates a nd TRC test results

.

5 For all of these cost types, the nomenclature and reporting vary across jurisdictions.

6 I ncluding this factor in comparisons with the cost of supply

- side resources is only appropriate if those supply cost estimates include all associated utility “incentives” (e.g., rate of return).B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

5 Decoupling and Lost Fixed Cost Recovery Symmetrical decoupling is a way to remove the throughput incentive to utilities, which otherwise links utility profits to increased energy sales. While the decoupling mechanism is a critical component of a regulatory approach that puts efficiency on a level playing field with supply resources, it should not be considered a cost of delivering energy efficiency
programs. Rather, it is used to improve regulatory certainty in ratemaking

. Decoupling was widely adopted in the gas utility industry, for example, during the era of declining energy sales.

Mechanisms to directly compensate utilities for lost fixed cost are a different approach than decoupling

. 7 These mechanisms allow utilities to recover fixed

- cost revenues that are “ lost” due to energy savings from efficiency, but they do not adjust rates down ward if revenues are greater than authorized.

Neither decoupling nor lost fixed cost adjustments are costs of delivering efficiency ser vices, because they do not increase total revenue requirements. Rather, they are rate tools designed to reallocate fixed costs in different ways , i.e.
, to recover the same fixed costs that would have been recovered anyway

. For these reasons, neither mechanism is included in

our analysis of efficiency costs

. It is noteworthy that these policy mechanisms are being used as a way to improve the business case for energy efficiency. Currently 13 states have full revenue decoupling for at least one major el ectric utility in the state
, and 19 states have lost fixed cost mechanisms for at least one utility (Downs et al

. 2013)

. Recent literature has explored the impact of decoupling on rates and found that most rate adjustments (64%) are within plus or minus 2% of the retail energy rate, which amounts to about $2.30 for the average electric residential consumer (Morgan 2012 ).



### E VALUATION



### , M EASUREMENT



### , AND 

### V ERIFICATION 

### (EM&V) OF 

### E NERGY 

### S AVINGS Dating back to the 1970s and 1980 s, EM&V of energy efficiency results aims to assess the performance and implementation of programs, document and measure their effects, help program planners improve performance, and ensure that programs are cost effective. The State and Local Energy Efficiency Acti on Network (SEE Action) defines EM&V as the

c ollection of approaches for determining and documenting energy and non

- energy benefits resulting from end

- use energy efficiency activities and programs. Effective EM&V can confirm energy savings, verify cost

- e ffectiveness, and guide future energy efficiency investment decisions

. 8

Various international, national, and regional groups have been working to improve consistency and standardization in the EM&V process. For example, t he International

7 These are often called lost revenue adjustment mechanisms (LRAM) or lost contribution to fixed costs (LCFC).

8 See http://www1.eere.energy.gov/seeaction/evaluation.html

.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

6 Performance Measurement and Verification Protocol (IPMVP), published by the Energy Valuation Organization , defines standard terms and provides a framework for verifying project

- specific energy efficiency savings. In the U nited S tates , the Department of Ene rgy’s SEE Action network, the NEEP EM&V Forum, and the Regional Technical Forum (RTF) of the Northwest Power and Conservation Council all have initiatives to develop common standards and approaches to verify and evaluate efficiency savings.

While efforts to improve consistency in energy savings EM&V have expanded , in practice states still use a diverse set of methods to document savings. Not only do they use a variety of cost

- effectiveness tests, as discussed below , but they also have various approaches to the energy savings calculations themselves. For example, whereas most states use deemed savings (i.e.
, predetermined engineering estimates of savings per measure , or savings estimates verified in past EM&V studies ), some states use different methodologies to calculate savings after measures are installed

. 9 Similarly, states have different approaches to achieving consistency in evaluation. Many adopt their own t echnical r esource m anual (TRM) as a way to specify engineering calculations for estimating savings . Other s in regions such as the N ortheast or Northwest may share resources, and still others do not have standard methodologies.

Net Versus Gross Savings Another key methodolog ical diffe rence among states in evaluating energy savings is whether they estimate net or gross energy savings impacts from efficiency programs (or both)

. T he definition of these terms, methodology used, and application for use also vary

. G ross energy savings impact s are “changes in energy consumption that result directly from
program

- related actions taken by participants in an energy efficiency program, regardless of why they participated ” (NREL 2013). Net energy savings are “ changes in energy use attributable to a particular energy efficiency program. These changes may implicitly or explicitly include the effects of factors such as freeridership
, participant and non

- participant spillover, and induced market effects” (NREL 2013). 10 In practice, n et savings calculation s typically account for freeridership, but only sometimes account for spillover and induced market effects.

A recent national review by ACEEE examines and documents state practices
, precedents, and issues regarding net and gross savings (Kushler , Nowak, a nd Witte 2014). T he study finds that the majority (54%) of the 43 states that responded to the survey estimate net energy savings using specific values for programs, another 5 states apply a uniform net

- to

gross (NTG) ratio, another 4 states estimate both net and gross, and the final 11 states estimate gross savings only. The study’s review of states and national experts makes it clear that both net and gross savings can serve useful purposes

. F or example
, estimates of net savings help program improvement a s they provide information toward minimizing

9 ACEEE’s 2012 survey of EM&V practices found that 36 of the 42 states that responded used deemed savings values to calculate energy savings (Kushler , Nowak, and Witte 2012).

10 Freeriders are participants who would have adopted efficiency measures in the absence of the program.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

7 freeriders, while gross savings are more straightforward and less expensive to estimate. Overall, there is a need and often regulatory pressure to understand the net impacts attributable to programs, especially as a way to calculate things like cost effectiveness and lost revenue adjustments in order to protect ratepayer interests.

For our CSE calculations
, we chose to use net energy savings figures, which most jurisdictions reported. We recognize that metho dologies for calculating net savings can vary by state and jurisdiction, mak ing it difficult to directly compare results. However, because the focus of this review is on energy efficiency as a resource for utility planners, and since stakeholders by and la rge are most interested in the net impacts of efficiency programs on
energy usage, we decided to focus on cost effectiveness based on net savings.

Electricity Savings at Site or Generation Level Another variation in reporting of energy savings (for electr icity only) is that some entities report “at

- site” savings
, i.e.
, at the customer meter, whereas others report “ at

- generation” savings, which add in estimated transmission and distribution (T&D) line losses that are avoided

. The at

- generation approach is an attempt to directly compare the energy savings to the electric generation that would otherwise be needed to offset the efficiency gains

. It is useful in integrated resource planning (IRP) because it puts efficiency on a level playing field with supp ly

- side resource s

.

At

- generation savings are most appropriate for comparing efficiency costs to electric supply

resources , and perhaps the appropriate framework for this analysis. However a t

- site savings

are mo re useful for comparing efficiency gains to o verall electricity sales , and they are the most common and longstanding approach to measuring and evaluating energy savings

. M oreover most state EERS are established as a percentage of retail sales. For these reasons, this analysis presents energy savings data at site or meter

. In the R esults section, we also examine the implications of using generation

- level energy efficiency savings.

While this range of diversity in methodology among states makes it challenging to compare cost values, our review tries to make the differences across states transparent

. See the M ethod ology section for more details.



### L EVELIZED 

### C OSTS 

### V

ERSUS 

### F IRST



### - Y EAR 

### C OSTS Program managers and regulators typically use two general approaches to express the costs of energy efficiency portfolios relative to energy savings : ( 1) levelized CSE and ( 2) first

- year

“ acquisition ” costs. Since b oth approaches provide meaningful information to planners, we review them both . H owever , ACEEE finds that levelized costs are the best way to compare efficiency program costs to supply options , and therefore we place more emphasis on this metric

. By levelized, we mean that upfront investments are annualized over the life of the investment assuming a real discount rate.

Energy pl anners commonly use levelized costs as a way to express the costs of long

- term energy supply investments. For electricity generation technologies, for example, the levelized cost represents the per

- kWh cost expressed in real dollars of building andB EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

8 operati ng a power plant over an assumed financial life
, duty cycle , and capacity factor

. 11 Similarly, levelized cost is an appropriate metric for energy efficiency resources, which continue to save energy over several years of their effective useful lifetime. A fu ll description of the cost

- of

- saved energy approach is included in the M ethodology section.

A second approach to expressing efficiency program costs relative to their savings is to use f irst

- year costs , which are representative of the annual costs to admin ister an efficiency portfolio divided by the energy savings in the first year only

. These are sometimes called energy efficiency acquisition costs , and they can be useful for program budgeting purposes. P rogram administrators and regulators tend to focus on first

- year costs when they are faced with one

- year savings targets. These costs , however, do not take into account the full value of efficiency investments because they capture only the first

- year savings
, wh ereas the measures continue saving energy thr oughout their useful lifetime. 12 (We present data on typical measure lifetime i n the R esults section. ) In other words, higher first

- year costs do not necessarily mean higher levelized CSE values. First

- year costs thus misrepresent the full benefits of effic iency. Furthermore, supply

- s ide investments are not typically assessed ba sed on the full upfront costs.



### C OST



### - E FFECTIVENESS 

### T

ESTS

R egulators typically predicate energy efficiency programs on the fact that they are cost

effective compared to the ir avoided c osts

. 13 This adds a layer of rigor to the requirements for energy efficiency program review, necessitating detailed analysis to evaluate how efficiency costs and benefits accrue to various parties with different perspectives.

Utilities and other program administrators use some combination of various cost

effectiveness tests . The se tests have evolved from the first California Standard Practice Manual in 1983, which ha s been periodically revised since then, most recently in 2011. Represen ting various perspectives, t he five standard cost

- effectiveness tests are

11 See http://www.eia.gov/forecasts/aeo/er/electricity_generation.cfm

12 For example, two measures can have identical levelized costs, while the first

- year cost for a measure with a shorter lifetime (e.g
., CFLs) appears lower than that of a measure with a much longer lifetime (e.g.
, insulation).

13 The term “avoided costs” originated with federal laws designed to encourage independent power production

. They refer to the costs that utilities would incur to produce one more unit of electricity (kWh) and/or capacity (kW) or one more unit of natural gas (therm). For energy efficiency cost

- effectiveness evaluation, avoided costs refer to the energy

- related and capacity

- relate d costs that would have been incurred by utilities if the energy efficiency measures had not been adopted

. Thus they are used as a reference point against which efficiency programs are compared. The methodology for calculating avoided costs can vary signif icantly across jurisdictions.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

9  Utility /Program Administrator Cost Test (UCT /PACT )

 Total Resource Cost ( TRC ) test

 Societal Cost Test (SCT)

 Participant Cost Test (PCT)

 Ratepayer Impact Measure (RIM) test

Numerous resources are available on the topic of cost

- effectiveness tests (e.g.
, National Action Plan for Energy Efficiency 2008 ; Woolf et al

. 2012 ; Kushler , Nowak, and Witte 2012)

. 14 A recent national review by ACEEE found that most states use a combination of the tests, with the TRC being the most widely used as the primary test and the RIM rarely being used (Kushler , Nowak, and Witte 2012)

. For information on each state’s approach to cost

effectiveness tests, see the ACEEE State Energy Eff iciency Policy Database

. 15

From a utility resource planning perspective, the UCT is the preferred approach for evaluating energy efficiency as a resource for utility planners, and thus is the focus of this report. A handful of states use the UCT as their pr imary test: Connecticut, Michigan, New Mexico, Texas, and Utah. The TRC, although most widely used as the primary test
, can be cha llenging to implement properly because it takes a system

- based approach that requires

a ll costs and benefits to be fully accou nted. While costs to utilities and customers are relatively straightforward to count, the benefits are less straightforward, particularly for customers, and as a result they are often underreport ed (Kushler and Neme 2010)

. Given the diversity of cost

- effectiveness tests used across the states a s well as methodological differences such as discount rates, the results of these tests can be difficult to compare across jurisdictions. While the focus of this review is on the cost of saved energy, we als o collected the benefit/cost (B/C) ratio results of the TRC when they were available.



### E NERGY 

### E FFICIENCY 

### V

ALUATION IN 

### I NTEGRATED 

### R ESOURCE 

### P LANNING Energy efficiency program costs are typically evaluated differently than other energy

resources: they are eval uated against the avoided costs of supply options. In other words, regulators want to know h ow the cost of procuring additional energy efficiency compare s to the prevailing cost of the next marginal unit of supply that would otherwise be incurred

. Efficien cy resources that cost less than avoided costs are deemed cost effective.

A s efficiency gains traction as a resource option
, efficiency programs should be incorporated into integrated resource plans (IRP) and other planning tools that truly optimize effic iency

as a nalogous to a supply

- resource option. Although m any states began to do this in the 1980s and many continue today
, efficiency is typically treated through scenarios of the demand curv e rather than as a n explicit resource option. Improved analysis of energy efficiency program costs and impacts in terms of procured energy (kWh) and demand (kW)

14 Resources are available from the Regulatory Assistance Project (RAP), the SEE Action Network (and its predecessor the National Action Plan for Energy Efficiency), and ACEEE.

15 http://www.aceee.org/sector/state

- policyB EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

10 savings will be crucial to the incorporation of efficiency into multi

- objective resource planning tools

. For more information, see the resources offe red by the Regulatory Assistance Project ( RAP ) on best practices for the incorporation of energy efficiency into IRP
processes ( e.g. , RAP 2013)

. 

## Methodology This section describes the data collection process for this study
, the challenges and caveats
atten dant on processing the data, and the various calculations used to estimate the C SE and first

- year acquisition costs.



### D ATA 

### C OLLECTION AND 

### P ROCESSING
For this review, we collected data on energy efficiency program costs and energy savings from secondary so urces including annual reports
, EM&V reports, and in some cases individual requests to contacts at public utility commissions (PUCs), utilities, and state agencies. ACEEE’s 2009 review collected data from 14 states. Now, with more states developing comprehensive energy efficiency portfolios and reporting their results, we were able to collect data for 20 states for electricity programs , as shown in t able 1 , and 10 states for natural gas efficiency programs, as shown i n t able 2. We chose t he states for two reasons : ( 1) they were included in the last study and thus were good candidates to include again
, and/or ( 2) they ha d readily available cost data in consistent reporting formats

. Other states or utilities may have had data on energy efficiency program cost e ffectiveness, but if they did not have consistent and transparent metrics reported in a common location
, they were not good candidate s for this study. ACEEE hopes to continue conducting reviews of this sort and to e xpand th e data set in the next update.

Table 1

. States and program administrators covered in the review: electricity p rograms

State Program

administrator covered State Program administrator covered

1 Arizona Arizona Public

Service Company (APS) 11 New Mexico Public Service of New Mexico

2 California IOUs 12 New York NYSERDA

3 Colorado Xcel Energy 13 Nevada NV Energy

4 Connecticut CEEF (all IOUs) 14 Oregon Energy Trust of Oregon

5 Hawaii Hawaii Energy 15 Pennsylvania IOUs

6 Illinois Ameren and Com

- Ed 16 Rhode Island National Grid

7 Iowa IOUs 17 Texas IOUs

8 Massachusetts IOUs 18 Utah Rocky Mountain Power

9 Michigan All utilities 19 Vermont Efficiency VermontB EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

11 State Program

administrator covered State Program administrator covered

10 Minnesota Xcel Energy 20 Wisconsin Focus on Energy

IOUs are i nvestor

- o wned u tilities

.

Table 2

. States and program administrators covered in the review: natural gas pr ograms

State Program administrator

covered

1 California IOUs

2 Colorado Xcel Energy

3 Connecticut CEEF

4 Iowa IOUs

5 Massachusetts IOUs

6 Michigan All utilities

7 Minnesota Xcel Energy

8 Oregon Energy Trust of Oregon

9 Rhode Island National Grid

10 Wisconsin Focus on Energy

IOUs are i nvestor

- o wned u tilities

.

We collected 10 data points for 2009 to 2012 annual program years as available. Some states did not yet have 2012 data available, and others only had one or two years available. Not a ll data points were available for all states

. N ote that in many cases we had to manipulate the data to permit consistent comparison among prog rams , e.g. , by subtracting out demand response or renewable energy program costs. We provide some details here for each of the data points collected and processed , and we further discuss key challenges and caveats in the next section.

1. Annual t otal p rogram costs by program year

. We included e nergy efficiency program portfolio costs only, not renewable energy or demand response.

2. Annual p rogram costs by customer c lass (residential and business)

. M ost states categorize classes by residential and bus iness, whereas only a couple of jurisdictions disaggregate business customers into commercial and industrial. Low

- income programs are often categorized separately ; however we chose to include these programs in the residential category for convenience in re porting.

3. Shareholder or performance incentives awarded annually as applicable

. We collected data for those states with performance incentives that ha d been approved for the applicable programB EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

12 year. In a couple of cases ( e.g. , Wisconsin )
, performance incentives are awarded on a cumulative

- year basis, and so they were not yet approved and were not included in our estimates.

4. Annual costs by type (customer incentives, non

- incentive program costs, and shareholder/performance incentives)

. Several of the jurisdictions in our review reported customer incentives as a distinct category, and some report ed numerous other categories of spending such as administrative, research and development, education, and marketing. In these cases, we combined all non

- incentive costs into one category. Other states may have reported program costs as distinct from administrative or EM&V ; however it was unclear whether the definition of program costs included both customer incentives and other
program

- related cos ts.

5. Annual participant cost estimates

. Only a handful of states explicitly and readily report participant contributions to energy efficiency measures, or at least report full incremental
measure costs

. It is possible to derive participant cost contribu tions by subtracting incentives from full incremental measure costs

.

6. Gross and net energy (kWh and therms) savings reported , both total and by customer class

. We collected both gross and net electricity and natural gas savings from efficiency programs, and by customer class if available. For electricity savings, we noted whether savings were reported at site or at generation. We also collected some data on elec tricity demand (kW) impacts, but not comprehensively enough to report here.

7. Applicable electricity sales within jurisdiction

. We collected electricity sales for the jurisdictions included in the state for 2010, which is the most readily accessible data point from the Energy Information Administration ( EIA ) for all jurisdictions. We collected electricity sales data in order to normalize savings as a percentage of sales and to compare this metric to CSE values.

8. Measure lifetimes by customer class

. As available, we collected measure lifetimes by customer class and in aggregate for the entire portfolio as an input to CSE calculations.

9. Cost

- effectiveness test ratios

. We collected these for TRC tests and UCT/PAC T , as available

.

10. Weighted average cost of capital assumed in cost

- effectiveness calculations

. In some cases, program administrators reported their own utility CSE values

. We collected the weighted average cost of capital (WACC) that w as assumed for these calculations, along with aggregate measure lifetimes, in order to derive first

- year cost assumptions. Also, we were interested in this metric in general to compare to our own assumptions, and so we also collected WACC and social discount r ate assumptions for other states as available.



### C HA LLENGES AND 

### C

AVEATS As previously discussed, there are a number of challenges involved in the collection and comparative review of national energy efficiency cost

- effectiveness data

. In light of these, the goal of this report is not to compare one state’s efficiency portfolio results to others, butB EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

13 to present overall information and trends in energy efficiency program costs and cost effectiveness

— and also to improve the transparency of key data issues

. This section explores several of the challenges and discuss es our approach to improv ing consistency within the dataset

.

Variation In Reporting Formats, Frequency , And Timin g Most utilities and program administrators prepare annual repor ts on the impacts of efficiency programs
, and some prepare reports more frequently
, e.g. , semiannual ly or quarterly. T he frequency and formats are usually based on regulator requirements ; however this varies by state. I n some limited cases , annual reports present only cumulative data for multiple years and not distinct annual results

.

The location and consistent availability of reports var y significantly by state

. Some utilities and program administrators post reports on their own websites, others file them on commission websites or within commission dockets, some states have a separate website hosted by an advisory group , or there may be combination of these approaches. Program administrators who do not produce reports that are readily available in a co mmon location were less likely to be included in this study.

Most program administrators are required by their regulators to calculate cost and energy impacts separately for electricity and natural gas programs (and they may also present combined results). A few states combine electricity and natural gas programs in their reporting

. In some cases, on request, program administrators suggest ed a methodology f or disaggregating program costs ; in other cases we were unable to disaggregate the data and

so these j urisdictions were not good candidates for this type of review. Similarly, renewable
energy programs or demand response programs were sometimes combined in the overall reporting of cost

- effectiveness metrics. In these cases w e backed out these program cost s and savings impacts to isolate the energy efficiency programs.

Variation In Reporting of Net or Gross Energy Savings, and At

- Site or At

- Generation Electricity Savings As discussed earlier, we chose to calculate and report CSE values based on net energy savings. The great majority of jurisdictions in this review reported net energy savings values

. A couple of states or program administrators explicitly assume that net and gross savings are equivalent, i.e.
, that there is a 100% NTG ratio. Only a couple of states in our data set (Minnesota and Nevada) do not estimate net savings ; in those cases we adjusted gross savings figures by a n NTG ratio of 0.9 to estimate net savings and make them more comparable to net savings figures reported by other stat es

. 16 A n NTG ratio of 0.9 falls within the range of factors used by several states in calculating net energy efficiency savings.

As discussed earlier, some states report electricity savings at the customer or site level while others report savings at the ge neration level. At

- site saving s appear to be the most common and longstanding approach to measuring and evaluating energy savings ; moreover most

16 This methodology is consistent with ACEEE’s State Energy Efficiency Scorecard (Downs et al. 2013).B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

14 state EERS are established as a percentage of retail sales. For these reasons, this review presents at

- site ene rgy savings data

. In some cases we converted at

- generation savings to at

site savings assuming the same line loss factor used by the reporting entity.

Demand R esponse Many program administrators combine their reporting of energy efficiency (EE) and demand response (DR) programs in their overall demand

- side management reports and evaluations. 17 While EE aims to reduce overall energy usage (kWh), DR aims to curtail demand (kW) only during peak hours or to shift usage from peak hours to off

- peak hours. As demand

- side resources, EE and DR have documented synergies ; for example
, EE can contribute on

- peak demand reductions
, and DR can produce some kWh savings

. However they remain fundamentally different resources. This review focuses exclusively on EE costs and energy (kWh) savings. In some cases we had to subtract out DR costs from reported spending to focus on EE costs and benefits.

Structural D ifferences in Program Portf olios In addition to variations in reporting and evaluation methods, numerous differe nces in the structure of efficiency programs can affect efficiency costs. T he type of programs offered, the relative share of program savings from different customer classes, and the range of eligible efficiency measures can all affect program cost effecti venes s. These factors can also impact the balance of incentive versus non

- incentive program costs

. While this study does not tease
out costs by program type , we do classify costs by customer class and try to identify trends. For trends in costs at the prog ram level, readers should review a n analysis by the Lawrence Berkeley National Laborator y (LBNL 2014 ).

Differences In Program Cost Type Definition s As discussed earlier, energy efficiency program administrators commonly incur the following types of costs as the direct costs to administer programs, with the nomenclature and reporting often inconsistent across jurisdictions:

a. direct rebates or incentives to customers b. engineering or technical support

c. program administration, planni ng, and delivery d. EM&V

e. marketing and education

In particular, the definition of cost categories such as “ administrative ” var ies by state, and sometimes categories are not disaggregated

. Nor is this an exhaustive list of current or future cost types. E mergi ng programs such as behavioral and loan programs may require
new cost

- type definitions.

In addition, whether a program administrator earns a shareholder or performance incentive can increase the cost of energy efficiency resources. Also, we note that some states have an

17 Demand response is a l so referred to as load management ; h owever “demand response” is the more modern
term.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

15 incentive mechanism in place but they award it on a cumulative basis. If the award had not yet been finalized for program years 2009

- 2012 , we did not include it in this analysis. Future work on CSE values could develop a methodology for est imating average annual values in such instances.



### C ALCULATIONS AND 

### A SSUMPTIONS After the data collection process, we first converted all cost data by program year to 2011$ using GDP deflators from the Bureau of Economic Analysis (BEA).

Utility CSE
In the 2009 review, ACEEE presented t he CSE as reported by the state in many cases, while calculat ing the CSE for some states. Reported values have the limitation that input assumptions may not be clear, which create inconsistencies in the data set. For this update
, to attempt a more consistent review and methodology , we instead calculate the CSE for each
state , as shown below

.
To calculate the CSE, we multiply annual energy efficiency program costs (C) by a capital recovery factor and then divide by the ann ual energy savings (D)

. The calculation for the capital recovery factor , which is used as a way to levelize or spread the costs over a specified period of time and assumed interest rate , is shown below

. For consistency, we use the same real discount rate ( A) for all jurisdictions

. We use each state’s estimated measure lifetime (B), program costs (C)
, and net energy savings (D)

. We discuss each of these elements in further detail below

.

The CSE c alculation is:

CSE in $/kWh = (C) x (capital recovery factor)/(D)

where: A = Real d iscount rate (5%) B = Estimated measure life in years C = Total annual program cost in 2011$ D = Incremental net annual energy (kWh or therms) saved by energy efficiency program s Capital recovery factor = [A*(1+A)^(B)]/[(1+A)^(B)

- 1]

D ISCOUNT R ATE T he discount rate for energy efficiency cost

- benefit analysis depends on the cost

effectiveness test used. For the utility cost test and TRC
, jurisdictions typical ly us e the utility’s WACC

. The SCT take s a societal perspective and should use a lower social discount rate to appropriately value long

- term societal perspective. We c ollected some utility data on WACC rates , and found that they ranged from 7 % to 8% over the 2009

- 2012 period

. It was not always c lear whether these values were nominal or real ; however w e presume d them to be nominal rates because they were used for annual reporting and in some cases they wereB EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

16 confirmed as nominal

. 18 We also collected some data on assumed social discount rates used fo r cost

- effectiveness screening and found they ranged widely from about 1 .2 % to 6 .0 % (real)

.

The current practice of assuming the WACC for energy efficiency cost

- effectiveness screening, however, has been criticized as undervaluing the reduced risk of energy efficiency program expenditures v ersus supply

- side investments (Woolf et al. 2012). To reflect th e lower financial risk of efficiency investments, s ome jurisdictions have adopted alternative discount rates for energy efficiency valuation in the UCT and TRC tests , such as a societal discount rate or a risk

- adjusted discount rate

. In Massachusetts, for example, regulators have acknowledged that energy efficiency resources are a low

- risk investment and that a low

- risk discount rate is most appropriate f or the TRC test (Woolf et al

. 2013). In the Northwest, the preferred approach is to use a risk

- free discount rate for both supply resource and energy efficiency, and then to explicitly model resource risk ( i.e., fuel price, environmental regulation, capita l cost, and so forth ) in the analysis of resource options ( Northwest Power and Conservation Council 2010 ). This approach improves transparency by requiring that the type and magnitude of risk estimates for each resource are displayed.

For this analysis, we assume a real discount rate of 5% (value A in our CSE calculation) for the overall presentation of the results. 19 This is meant to be fairly consistent with the weighted average utility cost of capital in real terms, and is consistent with the approach in the 2009 ACEEE review of energy efficiency costs

. We also report the aggregate CSE values (for all states) in the R esults section using a 3% real discount rate and 7% real discount rate to show the impact of this assumption on the results.

M EASURE L IFETIME The estimated measure lifetime in years (B) is based on data from the program administrator, if available. For some states ( Colorado, Illinois, Michigan, Nevada, Pennsylvania
, and Texas )
, we were unable to track down average measure lifetime estimates for the entire portfolio

. In some cases t hese states did report program

- or measure

specific measure lifetimes ; however
, due to time constraints
, we were unable to go through all program data to develop an average portfolio

- wide estimate ourselves. Instead, f or these
states we assumed an overall 11

- year meas ure lifetime, which was the average of states that did provide data

. Similarly, to estimate CSE values by customer class, if state

- specific data were not available
, we assumed an 8

- year measure lifetime for the residential class and 12.5 years for the business class , which were the average values for states that did provide data

.

C OSTS AND S AVINGS Total program costs (C) and incremental net annual energy savings (D) are based on data collected from the progr am administrators, as previously discussed and defined. Note that we used net savings (D) as available. Some states assume that net savings

18 Real discount rates do not include inflation, whereas nominal discount rates do. Assuming 1% inflation, these nominal WACC rates of 7

- 8% would range from 6 % to 7% in real dollar terms. Assuming 2% inflat ion, they range from 5 % to 6%.

19 In deciding whether to use a nominal or real discount rate, the key is consistency. This analysis examines energy efficiency program costs in real (2011$) terms, and therefore we apply a real discount rate.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

17 equal gross savings (i.e.
, a 100% NTG ratio); a couple of states do not estimate net savings, in which case we estimated net savings using a n NTG ratio of 0.9.

First

- Year Acquisition Costs
We also calculate the first

- year acquisition costs ($ per kWh

- net or $ per therm

- net)
, as shown below:

First

- year cost in $/kWh net or $ per therm net = C / D

where: C = Total an nual program cost in 2011$ D = Incremental net annual energy (kWh or therms) saved by energy efficiency program s

Energy Savings Relative to Sales

F or the electricity data set, we collected data on actual electricity sales in each applicable jurisdiction fo r one year (2010). We then were able to calculate energy savings as a percentage of applicable energy sales in the given jurisdiction. This allowed for a direct comparison of energy savings thresholds to energy costs.



## Results This section presents the r esults of the review

. Data sources for each state can be found in Appendix A.



### E LECTRICITY
The review includes electricity energy efficiency program data for the 20 states listed in
t able 1 above

.

Cost of Saved Energy
O ur results are focused on the CSE values as presented in f igure 1 and t able 3 below . We emphasize again that the goal is not to compare results among states, but to present an overall picture of the range and typical values acro ss many different jurisdictions, each of which has its own factors that bear on the costs of efficiency programs. For example, n ote that th e costs in figure 1 range from $0.01 3 /kWh to $0.05 6 /kWh , a spread of about $0.04 2 /kWh.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

18 Figure 1. Electricity energy efficiency program CSE by year. Each dot represents ave rage costs for each state in a given year. 2011$ per levelized net kWh at meter. Assumes 5% real discount rate.

Table 3 presents the average for each state for each year, and the state’s four

- year average from 2009 to 2012. We were unable to calculate data for every state for each year due to missing data points , which means that the overall average for each year represents a varying number of jurisdictions.

Table 3

. CSE in $ per levelized net kWh at meter

State 2009 2010 2011 2012 4

- y ear

a verage (2009

- 2012)

Arizona $0.016 $0.019 $0.020 $0.021 $0.019

California $0.039 $0.041 $0.056 n/a $0.045

Colorado $0.023 $0.029 $0.027 $0.027 $0.027

Connecticut $0.037 $0.050 $0.045 $0.047 $0.045

Hawaii $0.025 $0.024 $0.033 $0.040 $0.031

Illinois n/a n/a $0.019 n/a $0.019

Iowa $0.019 $0.018 $0.020 $0.018 $0.019

Massachusetts $0.056 $0.048 $0.037 $0.051 $0.048
Michigan $0.017 $0.016 $0.017 $0.018 $0.017

Minnesota $0.021 $0.027 $0.029 $0.026 $0.026

New Mexico $0.025 $0.024 $0.022 $0.018 $0.022

Nevada $0.013 $0.014 $0.016 $0.020 $0.016B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

19 State 2009 2010 2011 2012 4

- y ear

a verage (2009

- 2012)

New York $0.020 $0.020 $0.020 n/a $0.020
Oregon $0.028 $0.025 $0.029 $0.026 $0.027
Pennsylvania n/a n/a $0.017 n/a $0.017

Rhode Island n/a $0.040 $0.044 $0.050 $0.045

Texas $0.025 $0.026 $0.028 n/a $0.026

Utah $0.029 $0.033 $0.024 $0.029 $0.029

Vermont $0.043 $0.041 $0.042 $0.037 $0.041

Wisconsin n/a n/a $0.022 $0.015 $0.019
Average $0.027 $0.029 $0.028 $0.030 $0.028

Median $0.025 $0.026 $0.026 $0.026 $0.026

Minimum $0.013 $0.014 $0.016 $0.015 $0.016

Maximum $0.056 $0.050 $0.056 $0.051 $0.048

2011$

. 5% real discount rate

. N/A means that we were unable to track down sufficient data for the calculation. Average for each year represents a varying number of states , so they are not directly comparable.

For the four

- year average values in the column furthest to the right, we find an overall national average of $0.028/kWh, and a range of $0.016 to $0.048/kWh. As pointed out in the D iscussion section, these typical efficiency program costs compare very favorably to the typical costs of new el ectricity generation.

The v alues in t able 3 vary among states due to numerous factors such as structural differences in program types and share of savings by customer class. For example, portfolios with a larger share of savings from residential programs or low

- income programs tend to have higher overall CSE values

. ( W e present some data by customer class for several states later in this section

. ) On the other hand, p rogram portfolios that rely heavily on low

- cost ligh ting programs, or that have lower shares of savings from low

- income programs, tend to have lower CSE values. A n analysis by LBNL provides further insight in to specific CSE values and ranges for different types of programs (LBNL 2014).

In addition, the eli gibility and attribution of non

- electricity savings from programs differ by jurisdiction, which can affect the cost of saved electricity. For example in Massachusetts, electric ratepayer funds are used to support investments in oil and propane energy savin gs. Because the values in t able 3 are developed using total electric spending ( without adjusting for spending on oil and propane savings) and total electric savings, the cost per unit of electricity savings appear s higher than it would if spending were adj usted for non

- electricity savings.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

20 The CSE values in f igure 1 and t able 3 represent costs per net electricity savings, and they assume a 5% real discount rate

. This rate is meant to be roughly consistent with the typical nominal utility WACC of about 7%

. We also wanted to calculate and compare the values under different real discount rate assumptions

. Table 4 presents values for 3%, 5%, and 7% real discount rates.

Table 4 . CSE values under various real discount rate assum ptions

3% real discount rate 5% real discount rate 7% real discount rate
Average $0.025 $0.028 $0.031

Median $0.023 $0.026 $0.029

Minimum $0.014 $0.015 $0.018

Maximum $0.043 $0.048 $0.054

These values represent aggregate 4

- year averages (2009

- 2012) for all states

.

Table 4 shows that a difference of 2% in the discount rate assumption can impact the CSE values by about 10

- 12%, which is minimal compared to the wide margin between energy efficiency portfolios and alternative energy options. For specific programs on the margins, howev er, the assumed rate can have an impact on whether programs are deemed cost effective.

From a utility resource planning perspective, it is important that analysts use appropriate discount rates for energy efficiency and supply side resources, considering their relative risks and other characteristics, in any levelized cost analyses. As discussed earlier, planners should also consider explicitly modeling resource risk in their analysis of resource options.

All these results reflect energy savings reported a t the meter, which is how most states report energy efficiency savings

. H owever , as discussed earlier, the more appropriate metric for comparing costs to supply

- side resources may be savings at the generator level , which
account for T&D line losses that ar e avoided by efficiency. EIA estimates a national average line loss factor of 7%. 20 If we convert the savings values at the meter level to the generator level, the average CSE value of $0.028/kWh would decrease by 7% to $0.026/kWh. No matter which method is chosen, the most important thing is that the assumptions be transparent and that avoided T&D line losses be factored into the cost

- effectiveness analysis in some way.

First

- Year Acquisition Costs
In addition to the CSE values, in f igure 2 and t able 5 we present the first

- year program costs (non

- amortized) , which are often called acquisition costs. As noted earlier, first

- year costs

20 See http://www.eia.gov/tools/faqs/faq.cfm?id=105&t=3B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

21 can be useful for program budgeting purposes, but we caution that this metric is not reflective of t he full resource value of efficiency

.

Figure 2. Electricity energy efficiency program first

- year acquisition costs by year

Table 5 . First

- year acquisition cos ts

State 2009 2010 2011 2012 4

- year

ave rage (2009

- 2012)

Arizona $0.13 $0.16 $0.18 $0.15 $0.15

California $0.28 $0.28 $0.39 n/a $0.32

Colorado $0.20 $0.26 $0.24 $0.24 $0.24

Connecticut $0.31 $0.35 $0.30 $0.37 $0.33

Hawaii $0.17 $0.19 $0.22 $0.30 $0.22

Illinois n/a n/a $0.16 n/a $0.16

Iowa $0.15 $0.15 $0.17 $0.15 $0.16

Massachusetts $0.49 $0.42 $0.36 $0.43 $0.42

Michigan $0.14 $0.13 $0.14 $0.15 $0.14

Minnesota $0.23 $0.30 $0.31 $0.28 $0.28

New Mexico $0.18 $0.17 $0.15 $0.13 $0.16

Nevada $0.11 $0.12 $0.13 $0.17 $0.13

New York $0.21 $0.21 $0.21 n/a $0.21
Oregon $0.24 $0.21 $0.24 $0.23 $0.23

Pennsylvania n/a n/a $0.14 n/a $0.14

Rhode Island n/a $0.35 $0.37 $0.41 $0.38B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

22 State 2009 2010 2011 2012 4

- year

ave rage (2009

- 2012)

Texas $0.20 $0.20 $0.22 n/a $0.20

Utah $0.27 $0.28 $0.20 $0.23 $0.24

Vermont $0.35 $0.33 $0.34 $0.31 $0.33

Wisconsin n/a n/a $0.19 $0.13 $0.16

Average $0.23 $0.24 $0.23 $0.24 $0.23

Median $0.21 $0.21 $0.21 $0.23 $0.21

Minimum $0.11 $0.12 $0.13 $0.13 $0.13

Maximum $0.49 $0.42 $0.39 $0.43 $0.42

2011 $ per first

- year net kWh at meter

. N/A means that we were unable to track down sufficient data for the calculation. Average for each year represents a varying number of states , so they are not directly comparable. Values vary among states due to numerous factors such as structural differenc es in program types and share of savings by customer class

.
Participant Costs Program administrators use e stimates of participant s’ costs for energy efficiency measures

as inputs to the TRC test and the SCT. 21 P rogram administrators typically estimate participant costs either through deemed measure costs or for custom

- based programs through actual reporting by customers.

Unfortunately most program administrators do not explicitly include participant cost estimates or participant benefits in their annua l reporting

. H owever they are implicit in their TRC outcomes. It might be possible to use TRC values as compared to UCT values to derive estimates of participant costs, but this approach has obvious caveats : cost

- benefit tests do not make transparent all t he annual values or discount rate assumptions, and they use a net present value basis. Further work should more fully explo re participant cost and associated participant benefit estimates

.

Several program administrators did report estimates of annual parti cipant contributions
, or made it possible to derive participant costs as the difference between incremental measure costs and incentives paid to participants

. Although limited, w e report these estimates along with program costs for the states listed in t able 6 below , but we caution that there are significant caveats

.

21 Participant cost s are the additional costs incurred by program participants net of any incentives paid to them by program administrators.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

23 Table 6 shows that t he ratio of participant costs to program costs varies significantly by
state

. The ratio depend s largely on the nature of the programs, e.g., the level of participant reba tes versus non

- financial incentives including technical assistance and marketing.

Table 6

. Combined program and participant cost esti mates

State Program cost

estimate Ratio of participant

costs to program cost s Participant cost

estimate Combined program and

participant cost estimate

Hawaii $0.033 149% $0.049 $0.076

Illinois $0.016 115% $0.018 $0.041

Iowa $0.019 159 % $0.0 30 $0.049

New York $0.020 262% $0.053 $0.073

Pennsylvania $0.018 159% $0.029 $0.043

Rhode Island $0.045 25% $0.011 $0.056

Wisconsin $0.019 118% $0.022 $0.041
Average for this dataset $0.024 141 % $0.03 0 $0.054

2011$ per kWh levelized
For these seven states, the ratio of participant costs to program costs ranges from 25% to 262%, and the simple average is 141%

. I n other words
, for every $1 invested by the program administrator, participants are estimated to spend on average an additional $1.41 on efficiency upgrades

. The sum of program costs and participant costs on average for these states is $0.054 per kWh levelized. However, given the limited dataset, t his figure is highly uncertain and does
not represent a national average

. It is also important to recognize that this metric is not an appropriate comparison to the utility cost of supply

- side resources, because it cap tures participant costs which are not incurred by utilities. 22

The 2009 ACEEE review similarly collected participant costs for about six program portfolios and found that on average participants contributed $0.83 for every $1 invested by program administrat ors. Again, these values varied significantly across jurisdictions. The states included in the 2009 review were different from the ones in this review (only three states were included in both reviews), which explains the large difference between the 2009

r esults and those presented in t able 6. Overall, much caution is warranted in making comparisons among jurisdictions about participant costs.

22 T he inclusion of participant costs in a cost

- effectiveness test also requires incorporating participant non

- energy benefits for a systematically balanced calculation. For example, see http://www.nhpci.org/publications/NHPC_EE

- Screening

- Coalition

- Position

- Paper

- final_20131118.pdf

.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

24 Benefit

- Cost Ratios Next we present the benefit

- cost (B/C) ratios as reported by program administrators. While the CSE values represent only the cost side of the cost

- effectiveness equation, the B/C ratios represent a more complete picture of how program costs compare to program benefits. It is important to note that the benefits side of the equation can also vary sig nificantly from state to state. Benefits include avoided energy
, capacity , and T&D costs for the UCT, as well as participant and other system

- wide non

- energy benefits for the TRC test. Moreover, as noted earlier, implementation of the TRC test is incomplet e in many states, i.e.
, the range of benefits calculated can vary significantly.

As in our review of CSE values, our goal is not to directly compare B/C ratios, but to present overall trends. Although m ost states conduct the TRC test when evaluating energy efficiency cost effectiveness
, the results are not always presented clearly in reports. Therefore t he TRC results presented in t able 7 reflect only 9 of the 20 states. The results show that energy efficiency benefits in these states exceed costs by a factor of 1.24 to 4.0

. In other words, each dollar invested by program administrators and customers in energy efficiency measures yields $1.24 to $ 4.00 in benefits to all customers.

Table 7 . Benefit

- c ost r atios for TRC t ests

State 2009 2010 2011 2012

California 1.83 1.61 1.24 n/a

Colorado 3.66 2.87 2.47 2.09

Hawaii n/a 1.40 1.60 2.60

Illinois 2.15 2.84 2.24 n/a

Iowa 2.54 2.06 2.10 2.34

Massachusetts 3.28 3.11 4.00 3.50

New Mexico 1.57 2.20 1.78 2.63

Utah 1.99 1.68 1.95 2.00

Wisconsin n/a n/a 2.84 3.26

Minimum 1.57 1.40 1.24 2.00

Maximum 3.66 3.11 4.00 3. 50 Savings by Customer Class Figure 3 displays the results of electricity savings in several jurisdiction s by customer class for 2009

- 2012

. (1 7 states readily reported savings by customer class

. 23 ) J urisdictions with the highest share of savings from residential customers are on the left , and those with the highest share of savings from business customers are on the right.

23 Some of t he other jurisdictions reported savings at the program level, but did not aggregate by customer class.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

25 Figure 3. Electricity savings by customer class

. Program administrators often report low

- income programs as a separate category ; we include these with residential savings. Most jurisdictions report savings in aggregate for residential customers and for business customers

. O nly Utah and Oregon readily reported commercial and industrial customer categories separately.
As shown in the column furthest to the right, t he average savings by customer class amount
to 45% from residential customers and 55% from business customers. Howeve r th is ratio varies significantly by state ; for example
, the share of savings from residential programs ranges from 60% to 26%. There are several likely reasons for this variation

. For example, the relative size of energy savings potential by customer clas s itself can differ from state to state, or regulators may require that a specific share of savings come from specific customer classes.

I n general it appears that jurisdictions that are newer to broad

- scale energy efficiency portfolios (e.g., Pennsylvania , Illinois, Arizona, and New Mexico) have a higher share of savings from residential customers, while states with more mature portfolios (e.g., Minnesota, Massachusetts, Oregon, and Rhode Island) have a higher share of savings from business customers. N ew program development tends to start with a large portion of funding to mass residential lighting and appliance programs, and a smaller portion to business programs, before launching into more comprehensive programs for business
customers.

There is no optim al mix of savings by customer class because it may vary significantly by jurisdiction. Also, stakeholders must consider a number of factors in addition to cost effectiveness (e.g., equity ) to ensure that all customer segments benefit from efficiency
progra ms. In sum, these data show that the portion of savings by customer class can vary significantly by state, and this is a likely factor in the overall average CSE values

. S ee the section on Costs by Customer Class for further discussion

.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

26 Measure Lifetime Es timates Energy efficiency upgrades continue saving energy over the lifetime of the measure installed. The estimate of measure lifetime is an important factor in calculating the cost of energy efficiency resources. As with many metrics, states vary in thei r explicit reporting of this figure . Table 8 presents the average electricity measure lifetimes by customer class (if available) for several jurisdictions, either as they were explicitly reported or as we derived them by dividing lifetime energy savings estimates by annual energy savings estimates. For these jurisdictions, the average measure lifetime for residential programs is 8 years
, for business programs, about 13 years
, and for the overall portfolio, about 11 years

.

Table 8

. Average electricity meas ure lifetimes by state and customer c lass

State Residential Commercial/ b usiness Industrial All s ectors

Arizona 7.3 13.4 n/a 9.8

California n/a n/a n/a 9.1

Connecticut 6.5 12.8 n/a 9.6

Hawaii 6.7 12.3 n/a 9.2

Massachusetts 8 13 n/a 11.6

Minnesota n/a n/a n/a 13.8

New Mexico 8 10 n/a 8.9
Oregon 10.6 13.5 9.5 11.2

Rhode Island 9.1 12.3 n/a 11.1

Utah n/a n/a n/a 11.3

Vermont 7.7 13.1 n/a 11

Wisconsin 9 12.4 n/a 11.4
Average 8.1 12.5 9.5 10.6

V alues for each state typically represent the average over the 2009

- 2012 program period, although data were not available for all years in each state

. Costs by Customer Class We can discern some trends from the CSE results of electricity efficiency resources by customer class. First

- year costs are comparable for both residential and business (commercial and industrial) programs at about $0.22/kW h. H owever , because business energy efficiency measures tend to have longer measure lifetimes ( an average of 12.5 years in this electricity data s et) than residential measures (8.1 years), the levelized CSE is on average lower for business program portfolios than for residential portfolios. We calculated electricity CSE values by customer class for 9 states as shown in f igure 4 and t able 9 , andB EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

27 identified an average CSE of $0.037/kWh for residential portfolios and $0.027/kWh for business portfolios. 24

Figure 4. Electricity CSE by state and customer class, and avera ge

Table 9 . Electricity CSE by state and customer c lass

State Residential Busi ness All Sectors

Arizona $0.026 $0.016 $0.019

Connecticut $0.062 $0.038 $0.045

Iowa $0.028 $0.016 $0.019

New Mexico $0.022 $0.022 $0.022
Oregon $0.032 $0.025 $0.027

Rhode Island $0.063 $0.037 $0.045

Hawaii $0.033 $0.028 $0.031

Wisconsin $0.032 $0.016 $0.019

Vermont $0.039 $0.044 $0.041
Average $0.037 $0.027 $0.030

$ per kWh levelized

We selected states that had readily available data for all three components of this calculation: savings, costs, and measure lifetime by customer class. While the average difference between customer class portfolio costs is about $0.01/kWh higher for residential
programs, f igure 4 demonstrates that it can vary significantly by state. A couple of states

24 Not e that the overall average CSE for this limited set of states is $0.030/kWh, which is slightly higher than the complete data set average value of $0.028/kWh.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

28 have cost differences of about $0.02/kWh higher for re sidential programs, while other states exhibit very negligible differences, and a few states have business programs that cost more than residential portfolios. Note also that the residential portfolio includes low

- income programs, which tend to have higher CSE values and therefore ( depending on the size of the programs ) will have an impact on the overall residential CSE values.

We did not review individual program CSE values ; however it is worth noting that there is significant variation in CSE value by pr ogram type. A report by LBNL provides information at the program level (LBNL 2014). These results again demonstrate the significant variation among jurisdictions in CSE trends by customer class. The estimates of measure lifetime values in particular are a large factor in determining CSE values.

Costs by Type Figure 5 break s down efficiency program costs by type, including customer incentives, performance incentives, and non

- incentive program costs such as marketing, EM&V, and administrative costs

.

Figure 5. Electricity energy efficiency program costs by type. Some states did not explicitly define costs as customer incentives ; for example
, California uses the term “ d irect i mplementation c osts” and Connecticut
, “ d irect p rogram c osts

. ” H owever we took these to mean customer incentives.
Since definitions of cost types vary from state to state, there is significant uncertainty in directly comparing states. In particular, the types of costs included in the non

- incentive program category can vary significan tly. For the 8 states shown in figure 5, for instance,

non

- incentive program costs range from about 15% to 40%. One example that might help explain this range is mass marketing

- based programs. As programs ramp up marketing and outreach as a way to increase participation and spur market transformation, this type of spending would fall into non

- incentive costs. However it might have the same if not higherB EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

29 energy savings impact as spending on direct incentives. Spending categories may need to shift as n ext

- generation efficiency programs develop.

CSE Relative to Electricity Savings Thresholds

T he hypothesis that programs with higher savings also have higher CSE values has been suggested but not readily demonstrated

. This idea is especially relevant becaus e as program administrators face increasing energy savings targets, they fear that program costs will rise as they go after higher savings. To test this hypothesis, we compare CSE values for each jurisdiction with relative electricity savings thresholds , i .e. , savings as a percentage of applicable retail electricity sales. Figure 6 shows the scatter plot of these results, where each dot represents an individual jurisdiction for an individual year. Note that we were not able to present these data for all sta tes.

Figure 6. CSE values relative to electricity savings as a percentage of sale s

We calculated t he Pearson correlation coefficient (r) for this data set in Excel. This correlation coefficient is a measure of how well two data arrays are linearly relat ed or dependent

. Correlation tests do not indicate a causal relationship between two variables ; rather, they measures the strength of a linear association. The correlation coefficient may range from +1.0 to

- 1.0, where 1 is a total positive correlation, 0 is no correlation, and

- 1 is a total negative correlation. An r value of greater than 0.7 is generally regarded as strong, whereas an r value of less than 0.3 is generally regarded as weak

. V alues in between are considered moderate. However these general g uidelines should not be regarded as strict rules; the strength ascribed to a particular value depends on the context and purpose of the calculation. Studies that use scientific data, for example, may require much higher values than social science data to i ndicate strength in correlation.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

30 The r value for th e data set in f igure 6 is 0.27, which indicat es a positive, but low or weak correlation between CSE and electricity savings as a percentage of sales. These findings cast doubt on the hypothesis that programs with higher electricity savings levels are associated with higher CSE values. In fact, other analysis suggests that CSE values may decrease as savings levels increase, due to factors such as economies of scale (Takahashi and Nichols 2008). Our fin dings indicate that many robust program portfolios can exceed and are exceeding 1% or 1.5% savings as a percentage of sales while maintaining a cost

- effective portfolio.

While these general findings are notable, there are many differences in the data point s

. For example, individual jurisdictions may have different program types or share of savings by customer class. Future work should examine trends over time for individual jurisdictions and within regions

.


### N ATURAL 

### G

AS
The review includes natural gas energy efficiency program data for 10 states as shown in
t able 2 above

.
Cost of Saved Energy Figure 7 shows CSE results by jurisdiction and year

.

Figure 7. Natural g as CSE r esults by y ear. Each dot represents average costs for each state in a given year. 2011$ per levelized net therm at site

. Assumes a 5% real discount rate.

Table 10 shows CSE values by state for each year, as well as the average, median, minimum, and maximum values for each year across the 10 jurisdictions, and for the average of 2009

- 2012. The CSE ranges from $0.15 per therm to $0.71 per therm across the time period, with a four

- year average of $0. 35 per therm.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

31 Table 1 0 . Natural gas effic iency CSE results by s tate

State 2009 2010 2011 2012 4

- y ear

a verage (2009

- 2012)

Colorado $0. 39 $0. 42 $0.37 $0. 29 $0. 37

Connecticut $0.37 $0.42 $0.35 $0.38 $0.38

California $0.32 $0.52 $0.49 n/a $0.44

Iowa $0.32 $0.34 $0.38 $0.34 $0. 34

Massachusetts $0.43 $0.58 $0.71 $0.64 $0.59

Michigan $0.26 $0.2 5 $0.22 n/a $0.25

Minnesota $0.15 $0.22 $0.22 $0.20 $0.20
Oregon $0.47 $0.32 $0.34 $0.36 $0.37

Rhode Island n/a $0.38 $0.42 $0.56 $0.45

Wisconsin n/a n/a $0.11 $0.09 $0.10
Average $0.34 $0.38 $0.36 $0.3 6 $0.35

Median $0.34 $0.38 $0.36 $0.35 $0.37

Minimum $0.15 $0.22 $0.11 $0.09 $0.10

Maximum $0.47 $0.58 $0.71 $0.64 $0.59

2011$ per therm at site . 5% real discount rate. N/A means that we were unable to track down sufficient data for the calculation. Average for each year represents a varying number of states , so they are not directly comparable. Values vary among states due to numerous factors such as structural differences in program types and share of savings by customer class.

First

- Year Acquisition Costs Figure 8 shows the results of the first

- year costs by jurisdiction and year

.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

32 Figure 8. Natural gas first

- year acquisition costs by ye ar

. Each dot represents average costs for each state in a given year.

Table 1 1 shows the average, median, minimum
, and maximum values for each year across the 9 jurisdictions, and for the average of 2009

- 2012. The first

- year acquisition cost ranges from $1.37 per therm to $6.97 per therm across the time period, with an overall average of $3.73 per therm.

Table 1 1

. Nat ural gas efficiency first

- year cost re sults

2009 2010 2011 2012 4

- y ear

a verage (200 9

- 2012)

Colorado $4.26 $4.56 $4.00 $3.1 $3.98

Connecticut $4.11 $4.55 $3.92 $4.6 $4.30

California $2.20 $1.86 $1.76 n/a $1.94

Iowa $3.45 $3.64 $4.12 $3.74 $3.74

Massachusetts $4.30 $6.25 $6.97 $5.99 $5.88
Michigan $2.84 $2.75 $2.40 n/a $2.66

Minnesota $1.37 $1.99 $2.08 $2.01 $1.86
Oregon $6.37 $4.16 $3.95 $4.17 $4.66

Rhode Island n/a $4.00 $4.07 $5.69 $4.59

Wisconsin n/a n/a $1.13 $0.95 $1.04
Average $3.61 $3.75 $3.70 $4.19 $3.73B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

33 2009 2010 2011 2012 4

- y ear

a verage (200 9

- 2012)

Median $3.78 $4.00 $3.95 $4.17 $3.98

Minimum $1.37 $1.86 $1.76 $2.01 $1.86

Maximum $6.37 $6.25 $6.97 $5.99 $5.88

$ per therm. N/A means that we were unable to track down sufficient data for the calculation. Average for each year represents a varying number of states, so they are not directly comparable. Values vary among states due to numerous factors such as structural differences in program types and share of savings by customer class.

Measure Lifetimes We also collected gas efficiency measure lifetimes overall and by customer class, as presented in t able 1 2

.

Table 1 2 . Average natural gas measure lifetimes by state and customer c lass

State Residential Commercial/ b usiness Industrial All s ectors

California n/a n/a n/a 17.6

Connecticut 18.0 13.9 n/a 17.1

Massachusetts 13.2 12.9 n/a 13.1

Minnesota n/a n/a n/a 13.2
Oregon 23.1 18.2 14.0 19.8

Rhode Island 19.1 12.1 n/a 14.4

Vermont 18.1 17.6 n/a 18.0

Wisconsin 24.2 13.3 n/a 15.4
Average 19.3 14.7 14.0 16.1

Average values for each state typically represent the average over the 2009

- 2012 program period, although data were not available for all years in each state. F or example Massachusetts data represent 2012 only
, and Wisconsin data represent 2011

- 2012 averag e.
The average measure lifetime is about 16 years overall. Unlike electricity measures, which tend to have longer lifetimes for business than for residential measures, natural gas efficiency measure lifetimes tend to be longer for residential measures

. Thi s is likely due to the prevalence of equipment replacement and residential building shell measures for residential programs.

Due to the limited available data, we did not calculate CSE values by customer class for natural gas efficiency programs.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

34 

## Discussi on and Recommendations The results of this review provide a large data set that we can draw on for a discussion of energy efficiency program costs. First we discuss program costs in terms of cost effectiveness and as compared to supply

- side options

. Secon d, we discuss issues related to the consistency and transparency of energy efficiency reporting, and we make recommendations for improvements

. Third, we discuss areas for further research that can build upon the findings of this review

.


### E NERGY 

### E FFICIENCY 

### C OSTS 

### C OMPARED TO 

### S UPPLY



### - S IDE 

### O PTIONS
This review finds that energy efficiency programs are clearly the least

- cost resource option compared to new energy supply resources . Here we discuss the results of our efficiency program cost review compared to typica l costs for supply

- side resource. A couple of important caveats are worth noting. First, we do not try to conduct a new cost

- benefit analysis here ; rather
, we aim to provide a high

- level discussion. Energy efficiency offers multi ple benefits to utilities a nd program administrators

— as well as to society and to participants

— which we do not analyze for this study

. Second, this discussion compares efficiency program costs to indicators of current avoided energy

- and capacity

- related costs. A complete cost

- benef it analysis compare s the costs of efficiency programs to forecast avoided energy costs
, because efficiency measures continue to provide energy savings over their useful lifetimes. Examining forecasted avoided energy costs would show additional benefits if avoided energy costs are expected to increase in future years.

Electricity Figure 9 shows the CSE results from this analysis alongside data from Lazard, an energy industry analysis firm, for national averages of new electricity generation options (Lazard 2013)

. On a levelized cost basis, new electricity energy efficiency programs cost about one

half to one

- third as much as new electricity generation resources

.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

35 Figure 9. Levelized costs of electricity resource opt ions. Source: Energy efficiency data represent the results of this analysis for utility program costs (range of four

- year averages for 2009

- 2012); supply costs are from Lazard 2013.
The costs for all resources in figure 9 are presented as a range, which is indicative of the variability and uncertainty implicit in any energy resource option for new electricity generation. The results of our energy efficiency program cost review may at first seem to display an overly wide variation across states; however, when seen next to supply

- side options, this variation is not unlike what we find in other resource options.

Comparing efficiency program costs to other new electricity resource options on a levelized cost basis provides useful context

. H owever it does not tell the complete cost

- effectiveness story for energy efficiency

. When done properly, efficiency c ost

- benefit analysis should be

more comprehensive

. The utility cost test, for example, compares efficiency costs to the utility’s avoided energy

- related costs and capacity

- related costs (as well as avoided T&D and other benefits to utilities)

. States use different methodologies for calculati ng avoided
costs. Due to differences in methodology, economics, and market structures, avoided costs can vary significantly by jurisdic tion
, and may represent various mix es of the resources shown in f igure 9

. A complete utility cost

- test analysis should consider a dditional benefits to utilities such as avoided T&D, wholesale price mitigation impacts, avoided environmental compliance costs , and other non

- energy benefits

.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

36 In addition, levelized annual costs as shown in f igure 9 do not reflect the added value of energy efficiency resources at certain periods of time during the year

. For example, avoided energy costs c an vary significantly b etween seasons and between peak and non

- peak hours. Energy efficiency measures that reduce demand during peak periods can result in higher benefits.

The TRC and s ocietal cost

- effectiveness tests also include the broader benefits that efficiency provides t o participants and to society , which are significant and present an even more complete view of the benefits of efficiency

. ( H owever as discussed earlier, in practice the TRC test is often incomplete when it does not include full participant benefits

. ) Our review of the TRC ratios reported by several states finds that the benefits of efficiency exceed costs by a factor of about 1.2 to 4.0

. These results further demonstrate that the benefits of efficiency far exceed the costs.

Natural Gas Average natural gas commodity prices have fallen significantly in recent years, which has put pressure on gas program administrators to keep costs below avoided costs. Our analysis finds that natural gas energy efficiency programs remain a low

- cost and cost

- effective

res ource at an average portfolio cost of $0.35/therm across 10 states

. This average value is lower than the average citygate price of natural gas of $0.49/therm nationally in 2013 (EIA 2014). 25 However t he avoided gas commodity cost does not tell the complete story of gas energy efficiency benefits

. In addition to the commodity cost of gas, a voided costs to utilities can also include avoided distribution and transmission costs
, peak demand benefits, hedging against fuel price volatility, and environmental benef its. Adding these benefits of efficiency savings further tilt s the scale in favor of efficiency as a cost

- effective resource.

In addition, n atural gas avoided costs vary significantly across the country due to methodology and market structure differences
, and they are subject to the uncertainty around future gas prices. For example, we collected a sample of recent (2012 and 2013) avoided natural gas costs, both current values and forecasts, for a handful of jurisdictions across the country. We identified a range of $0.37/therm to $1.019/therm for current and forecasted avoided gas costs. In comparison, we identified a range of natural gas efficiency portfolios of about $0.10 to $0.70/therm , very favorable values compared to avoided costs

. And l ooking at the average gas efficiency program CSE of $0.35/therm, we can see that energy efficiency remains cost effective compared to average gas prices

.
Efficiency C ost Trends Energy efficiency program costs appear to be holding steady as the least

- cos t resource. The
average utility CSE value in this review ($0.028/kWh) is only slightly higher than the average CSE 2009 review value ($0.025/kWh) , and slightly lower than the 2004 value ($0.030/kWh)

. Similarly, the average natural gas efficiency program co st in the current data set ($0.35/therm) is comparable to the 2009 review value of $0.37/therm. Figure 10 displays

25 Per EIA, t he citygate price is the “point or measuring station at which a distributing gas utili ty receives gas from a natural gas pipeline company or transmission system

. ”B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

37 the annual results from the 2009 review and from this analysis for average, minimum, and maximum CSE values . A nnual results from the 2004 rev iew were not available.

Figure 10. Utility cost of saved energ y 2005

- 2012. Source: Data for 2005

- 2008 are from Friedrich et al. 2009 (designated by unfilled markers). Data for 2009

- 2012 are from this analysis.
Some caution is warranted in drawing direct comparisons between the results of the two studies, since they used different data sets (i.e., the number and specific jurisdictions included ) and slightly different methodolog ies . For example, the 2009 study did not review whether the CSE captured net or gross energy savings, and it did not include utility shareholder incentives, both of which we addressed in the current analysis. In addition, the
current review calculates all CSE values, whereas the 2009 study r elied on a combination of reported and calculated values. As discussed in the next section, there is a need for further analysis on CSE trends.

What is clear, however, is that the available data re fute the claim s that the low

- hanging fruit has been picked and that the future availability and cost effectiveness of energy efficiency

are in doubt. D ata from a large number of diverse jurisdictions across the nation show that energy efficiency has consistently remained the lowest

- cost resource for the pas t decade, even as the amount of captured energy efficiency has increased significantly.



### C ONSISTENCY AND 

### T

RANSPARENCY IN 

### E

NERGY 

### E FFICIENCY 

### R EPORTING Throughout this report we have discussed the challenges in energy efficiency reporting around the country

. A ll states should take steps toward improv ing consistency and transparency in reporting

. To this end, we recommend that utilities, regulators and program administrators in a given state (and perhaps also at a regional and national level) discuss these issue s and work toward adopting best reporting practices

. Guidelines are already available for program administrators interested in improving the transparency and consistency of their reporting metrics

. For stakeholders interested in detailed guidelines andB EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

38 tem plates for reporting, we suggest the NEEP Common Statewide Energy Efficiency Reporting Guidelines and Regional Energy Efficiency Database (REED) (NEEP 2010
, 2013 )
, and Energy Efficiency Program Typology and Data Metrics: Enabling Multi

- State Analyses through the Use of Common Terminology (LBNL 2013)

.

Rather than offering detailed guidelines, the following section makes recommendations around several common issues that p rogram administrators should address to improve consistency and transparency

.
Regularize Location and F requency of R eporting
First and foremost , annual program reports and evaluations should be easily accessible on a common website . They should a lso follow a consistent annual schedule if possible, or provide public notification of schedule and availability

. The website may be an individual program administrator’s site, a common docket established by the c ommission, or an independent advisory group website. R egulators or advisory groups should require at least

some minimum threshold of reporting and provide sample templates that build on best practices such as those laid out by NEEP

. In cases where there are multiple utilities or program administrators report ing , it makes sense and is in the interest of all stakeholders to have one dedicated entity to aggregate key metrics across all territories

.
Improve Transparency of Energy Efficiency Metrics and Assumptio ns To improve overall transparency, we recommend that program administrators and regulators adopt or improv e on measures such as the following:

 R eport energy efficiency program portfolio spending and impacts separately from demand response and renewable energy impacts

.

 S eparate electricity and natural gas program spending and savings

. F or combined programs, develop methodologies for attributing spending and savings to gas or electric

.

 R eport estimated participant costs by customer class

.

 I ndicate whether electricity savings are reported at site or at g eneration

. I f at generation
, make clear the assumption of T&D line losses so they can be converted to site

.

 I dentify whether energy savings are net or gross , and what assumptions are used

.

 P rovide a succinct but transparent description of the methodologie s used to estimate gross and/or net savings, with links to more detailed information

.

 I f the emphasis is on cumulative (i.e.
, multiyear) energy savings and cost

effectiveness impacts, provide incremental annual impacts to indicate trends over time and facilitate comparisons with other jurisdictions

.
Expand Reporting and Disaggregation of Key M etrics More often than not, energy efficiency reporting le aves out critical metrics or assumptions that are necessary to calculate the cost of saved energy, or at least d oes not report aggregate values across customer classes . We recommend reporting measures such as the following:B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

39  R eport both net and gross energy savings values

.

 R eport measure lifetime estimates

.

 D isaggregate all data by customer class (e.g., resi dential, commercial, and industrial) . Most jurisdictions currently disaggregate business customers into commercial and industrial ; however we recommend that programs disaggregat e data in a way that furthers program development (e.g.
, commercial v ersus indu strial
customers , or small v ersus large business customers).

 Disaggregate cost data at least by the following: customer incentives, non

- incentive program costs, and performance/shareholder incentives

.


### F URTHER 

### R ESEARCH This review presents a large quantitat ive dataset combined with qualitative findings on energy efficiency cost

- effectiveness metrics and reporting practices

. However we offer only

a very limited initial statistical analysis of trends. Further analysis is needed to discern trends in CSE values over time and the relative impact of various metrics on CSE values. As for trends over time, many analysts have hypothesized that the cost of saved energy for
programs will increase as program administrators raise energy savings levels. Yet a n initial correlation analysis in this study (for electricity programs only) finds only a weak correlation between CSE values and electricity savings levels and therefore casts doubt on th e broad notion that high savings are associated with high CSE values

. 26 However

c orrelation analys e s of CSE trends over time across jurisdictions are difficult and may provide incomplete results because of fundamental differences among program portfolio
structure s and reporting consistency. Further research should delve into this que stion, perhaps examining individual jurisdictions or regions

.

The relative impact of different variables on CSE values is also an important area for further statistical and qualitative research. In this review w e present numerous metrics that may have a d irect impact on the cost of efficiency, e.g.
, the share of savings by customer class, or the types of programs offered

. Also of interest is the impact of avoided costs on CSE within a jurisdiction , which we hypothesize should be a significant indicator of CSE values . (We did not conduct this analysis because we did not collect avoided costs data. ) If program administrators must pass cost

- effectiveness screening up to the point that efficiency programs cost more than the marginal unit of energy supply ( i.e.
, avoided costs), that would allow for a higher ceiling on program costs in jurisdictions with higher avoided costs

. Similarly, labor and capital costs may have a direct influence on the cost of energy efficiency
programs.



## Conclusion This analysis finds tha t energy efficiency is clearly holding steady as the least

- cost energy option that provides the best value for America’s energy dollar. At an average cost of 2.8 cents per kWh, electricity efficiency programs are one half to one third the cost of the

26 Note that this is different from the notion that total program dollar costs will increase to meet higher savings levels. Total program costs may increase, but the levelized CSE for the efficiency resources can hold steady.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

40 alter native of building new power plants. Natural gas energy efficiency programs also remain a least

- cost option at an average cost of 35 cents per therm, which is less than the average natural gas commodity price of 49 cents per therm in 2013. These data repre sent a large number of diverse jurisdictions across the nation

.

The data show that energy efficiency has remained consistent as the lowest

- cost resource over the past decade even as the amount of energy efficiency being captured has increased significantly

. Energy efficiency also provides additional benefits beyond avoided energy costs, including reductions in water and fuel usage, avoided T&D costs, price mitigation effects in wholesale markets, and non

- energy benefits to society such as reduced pollution and job creation. As utility and state planners face increasing uncertainty and rising supply costs in their long

- term planning (including fuel

- price volatility and the need to address the environmental impacts of power generation ) , they should look to ene rgy efficiency as a reliable and consistent “ first fuel ” in their loading order of energy options.

The need increases for high

- quality and consistent data across the country a s efficiency gains even wider adoption and traction as the least

- cost energy res ource option

. In this analysis we found that jurisdictions collect and report a wealth of data and information on efficiency programs

. H owever we also found that the collecti on and compari son of energy efficiency cost data across the nation face numerous challenges
, including variation in reporting format s , nomenclature, and frequency. All states should take steps toward improving consistency and transparency in reporting

. Finally, further work should explore the relative impact of different variables on C SE values
, as well as trends over time for individual jurisdictions.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

41 

## References
Downs, A., A

. Chittum, S

. Hayes, M

. Neubauer, S

. Nowak, S

. Vaidyanathan, K

. Farley, and C

. Cui . 2013. The 2013 State Energy Efficiency Scorecard

. Washington, DC: American Council for an Energy

- Efficient Economy. http://aceee.org/research

- report/e13k

.

EIA ( U.S. Energy Information Administration )

. 2014. “Natural Gas Prices.” Accessed March 2014

. http://www.eia.gov/dnav/ng/ng_pri_sum_dcu_nus_a.htm )

Friedrich, K., M. Eldridge, and D. York. 2009. Saving Energy Cost

- Effectively : A National Review

. Washington
, DC: ACEEE.

Kushler, M. and C. Neme. 2010. “ Is it Time to Ditch the TRC? Examining Concerns with Current Practice in Benefit

- Cost Analysis. ” In Proceedings of the 2010 ACEEE Summer Study on Energy Efficiency in Buildings. Washington, DC: ACEEE.

Kushler, M., S. Nowak, and P. Witte. 20 12. A National Survey of State Policies and Practices for the Evaluation of Ratepayer

- Funded Energy Efficiency Programs. Washington, DC: ACEEE.

——— . 2014

. Examining the Net Savings Issue: A National Survey of State Policies and Practices in the Evaluation of Ratepayer

- Funded Energy Efficiency Programs. Washington, DC: ACEEE.

Kushler, M., D. York , and P. Witte

. 2004

. Five Years In: An Examination of the First Half

- Decade of Public Benefits Energy Efficiency Policies. Washington, DC: ACEEE.

Lazard. 2013. "Levelized Cost of Energy Analysis : Version 7.0." New York: Lazard.

LBNL (Lawrence Berkeley National Laboratory). 2013

. Energy Efficiency Program Typology and Data Metrics: Enabling Multi

- State Analyses through the Use of Common Terminology. Berkeley: LBNL.

——— . 2014

. The Program Administrator Cost of Saved Energy for Utility Customer

- Funded Energy Efficiency Programs in the United States. Berkeley: LBNL.

Morg an, P

. 2012. A Decade of Decoupling for US Energy Utilities: Rate Impacts, Designs, and Observations. Revised February 2013. Accessed March 2014. http://switchboard.nrdc.org/blogs/rcavanagh/Decoupling%20report%20Final%20Feb %202013%20

- %20pdf%20%282%29.pdf

National Action Plan fo r Energy Efficiency. 2008. Understanding Cost

- Effectiveness of Energy Efficiency Programs: Best Practices, Technical Methods, and Emerging Issues for Policy

- Makers. Prepared by Energy and Environmental Economics, Inc. and Regulatory Assistance Project. http://www.epa.gov/cleanenergy/documents/suca/cost

- effectiveness.pdf

NEEP (Northeast Energy Efficiency Partnership). 2010. Common Statewide Energy Efficiency Reporting Guidel ines. Version 1.0. Lexington, MA: NEEP. http://www.neep.org/Assets/uploads/files/emv/emv

products/EMV%20Forum_Statewide_EE_ Reporting_Guidelines_12

- 30

- 10.pdfB EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

42 ——— . 2013. Regional Energy Efficiency Database (REED). Accessed October 2013. http://www.neep

- reed.org/Disclaimer.aspx ?Source=http://www.neep

reed.org/default.aspx

Northwest Power and Conservation Council. 2010. Sixth Northwest Conservation and Electric Power Plan. Appendix N. Accessed March 2014. http://www.nwcouncil.org/media/6332/SixthPowerPlan_Appendix_N.pdf

NREL ( National Renewable Energy Laboratory ) . 2013. The Uniform Methods Project: Methods for Determining Energy Efficiency Savings for Specific Measures. Golden, C O .: N REL

. RAP ( Regulatory Assistance Project ) . 2013. The Treatment of Energy Efficiency in Integrated Resource Planning (IRP). Montpelier, VT: Regulatory Assistance Project. http://www.raponline.org/docum ent/download/id/6368

Takahashi, K. and D. Nichols. 2008. “The Sustainability and Costs of Increasing Efficiency Impacts: Evidence from Experience to Date.” In Proceedings of the 2008 ACEEE Summer Study on Energy Efficiency in Buildings. Washington, DC: A CEEE

. Woolf, T., E. Malone, J. Kallay, and K. Takahashi. 2013. Energy Efficiency Cost

- Effectiveness Screening in the Northeast and Mid

- Atlantic States. Cambridge, M A : Synapse Energy Economics.

Woolf, T., E. Malone, K. Takahashi, and W. Steinhurst. 2012. B est Practices in Energy Efficiency Program Screening: How to E nsure that the Value of Energy Efficiency is Properly Accounted For . Prepared for the National Home Performance Council by Synapse Energy Economics. Cambridge, MA.: Synapse Energy Economics.B EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

43 

## A ppendix A. Data Sources by State and Program Administrator

State and a dministrator Data s ources

Arizona: Arizona Public Service Company (APS) DSM s emi a nnual r eports with annual data for 2009, 2010, 2011, and 2012

. Published in March each year.

California : Southern California Gas; Southern California Edison; Pacific Gas & Electric; San Diego Gas & Electric Utility annual reports available for 2009, 2010, and 2011 from http://eega.cpuc.ca.gov/

. Published in May.

Colorado : Xcel Energy Xcel Energy Colorado Demand

- Side Management (DSM) Annual Status Reports

. Published in April. http://www.xcelenergy.com/About_Us/R ates_&_Regulations/Regulato ry_Filings/CO_DSM

Connecticut: Connecticut Energy Efficiency Fund CEEF Annual Legislative Reports for 2009, 2010, 2011, and 2012

. Published from February to April. http://www.ctenergyinfo.com/about/eeboard/annualreports

Hawaii : Hawaii Energy Hawaii Energy PY12 Annual Report, October 1, 2013

. a lso PY11, PY10, and PY9 reports. http://www.hawaiienergy.com/information

reports

Illinois: Ameren Illinois Utilities; ComEd, and DCEO Ameren: ActOnEnergy Energy Efficiency and Demand Response Results (PY 1)

. Portfolio Cost

- Effectiveness Evaluation (PY 3)

. ComEd: Evaluation Reports Prepared by Summit Blue/Navigant for P Y 1 (2009), PY 2 (2010), and PY 3 (2011) (savings data and TRC ratios)

. ComEd PY3 annual report to ICC (cost data for 2011). Some reports available at http://ilsag.org/

. Iowa : MidAmerican Energy and Alliant Energy/Intrastate Power & Light Energy Efficiency Annual reports for 2009, 2010, 2011, and 2012. Published in May. http://www.state.ia.us/government/com/util/energy/energy_efficienc y/ee_plans_reports.html

Massachusetts: All investor

- owned utilities 2011 data: North east Energy Efficiency Partnership’s Regional Energy Efficiency Database (REED)

. 2012, 2010

, and 2009 data: i ndividual utility reports available from

www.ma

- eeac.org 2011 and 2012 TRC results: “Statewide Electric Results Master Summary” spreadsheets. www.ma

- eeac.org
Michigan: All utilities Michigan PSC. 2012

. Report on the Implementation of P.A. 295 Utility Energy Optimization Programs. November 30, 2012

. Data for 2009, 2010, and 2011

. Michigan PSC. 2013 Report on the Implementation of P.A. 295 Utility Energy Optimization Programs

. November 26, 2013

. Minnesota: Xcel Energy Xcel Energy annual Status Report s available from https://www.edockets.state.mn.us/EFiling/home.jsp for 2009, 2010, 2011, and 2012 program yearsB EST V ALUE FOR A MERICA ’ S E NERGY D OLLAR © ACEEE

44 State and a dministrator Data s ources

New Mexico: Public Service Company of New Mexico (PNM) and Southwestern Public Service Company (SPS) PNM: PNM Energy Efficiency Program Annual Report (available for 2009, 2010, 2011, and 2012), published annually from March

- June.
SPS: SPS 2009 Energy Efficiency and Load Management Annual Report (August 1, 2010)

. O nly 2009 report was readily available

. Note: New Mexico’s TRC results reported only for PNM

. Nevada: NV Energy (merger of Nevada Power Company (NPC) & Sierra Pacific Power Company (SPPC); but separate EE reporting still) NPC: DSM Status Update Reports for 2009, 2010, 2011

, and 2012 SPPC: DSM Status Update Reports for 2009, 2010, 2011, and 2012

New York: NYSERDA Cumulative data for 2006

- 2011: NYSERDA

, New York's System Benefits Charge Programs Evaluation and Status Report : Year Ending December 31, 2011 Report to the Public Service Commission (March 2012) (Revised April 2012)
Oregon: Energy Trust of Oregon ETO Annual Reports for 2009, 2010, 2011, and 2012 http://energytrust.org/About/policy

- and

- reports/Reports.aspx
Pennsylvania: Electric Distribution Companies (EDCs) Act 129 Market Potential Study, with impacts for PY 1 (June 2009

- May 2010) and PY 2 (June 2010

– May 2011) http://www.puc.pa.gov/electric/pdf/Act129/Act129

- PA_Market_Potential_Study051012.pdf

Rhode Island: National Grid Electric and Natural Gas National Grid Year

- End Reports for 2010, 2011, an d 2012

. Also for 2011 data: Northeast Energy Efficiency Partnership Regional Energy Efficiency Database (REED)

.

Texas: Electric IOUs Frontier Associates; Energy Efficiency Accomplishments of Texas Investor

- Owned Utilities

. Annual reports for 2009, 2010, and 2011 http://www.texasefficiency.com/index.php/publications/reports

Utah: Rocky Mountain Power (PacifiCorp) PacifiCorp, Annual Energy Efficiency and Peak Reduction Report : Utah

. Annual reports for 2009, 2010, 2011

, and 2012

Vermont: Efficiency Vermont (Vermont Energy Investment Corporation) Annual Savings Claim Reports http://www.efficiencyvermont.com/about_us/information_reports/ann ual_reports.aspx

Wisconsin : Focus on Energy Focus on Energy evaluation reports for 2011 and 2012: Calendar Year 2011 Evaluation Report (October 31, 2012)

, Calendar Year 2012 Evaluation Report (April 30, 2013)