## From: Higgins, Cortney Sent: Monday, May 05, 2014 5:35 PM To: Wayland, Robertj Cc: Elman, Barry Subject: RE: Comments on TSDs for RIN 2060-AR33

Hi Bob,
Here are interagency comments on the remaining GHG Abatement TSD Chapters (5-7).
Regards, Cortney

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## Chapter 5: Demand-side Energy Efficiency (EE)

Introduction This chapter provides information on dema nd-side energy efficiency (EE) as an abatement measure for reducing carbon dioxide (CO 2 

### ) emissions from fossil fuel-fired electric generating units (EGUs). Specifically, this chapte r addresses EE as a component of both the “best system of emission reduction” (BSER) and state goals, and the inclusion of EE within the impacts assessment. Support is provided in this chapter for the discussion of the EE abatement measure throughout the preamble (most extensively in these sections: Building Blocks for Setting State Goals and Considerations, State Goal s, State Plans, and Impacts of the Proposed Rule) and its representation within the RIA. Resu lts from this chapter feed into the technical support document (TSD) on Goal Computation. EE is also addressed in TSDs on state plan considerations and projecting emissions performance.

This chapter is organized as follows:

 Background

– EE Technologies and Practices

– Barriers to EE Investment

– EE Policies

– EE Programs

 The EE Opportunity

– Rapid Growth in EE

– EE Program Impacts

– EE Potential

– Costs and Cost-Effectiveness of State EE Policies

– EE as an Abatement Measure

 State Goal Setting

– Approach

– Inputs

– Calculations

– Results

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 Impacts Assessment

– Approach

– Inputs

– Calculations

– Results

 Appendices

 References

Background As discussed in the State Plan Considerations TSD (Appendix: “Survey of Existing State Policies and Programs that Reduce Power Sector CO 2 

### Emissions”), demand-side energy efficiency policies and programmatic efforts have existed for decades and are now used in all 50 states. These strategies are intended to help states achieve energy savings goals, reduce the environmental impacts (including carbon dioxide (CO 2 

### ) emissions) of meeting energy service needs, save energy and money for consumers, and provide a significant resource for meeting power system capacity requirements. EE policies curr ently in place are considered by states to be cost-effective strategies for contributing to these policy objectives 1 

### . Moreover, states – through their utilities, primarily – have been rapidly in creasing their funding of EE programs in recent years, more than tripling budgets in the five years from 2006 to 2011, from $1.6 billion to $5.9
billion i 

### . In 2012, the cumulative impacts of these programs represented a 3.7% reduction in national electricity demand ii 

### . And, EE spending is projected to continue to grow at a substantial rate. A recent study by Lawrence Berkeley National Laboratory (LBNL) projects EE program spending to reach $8.1 billion to $12.2 billion (“Medium Case” an d “High Case,” respectively) in 2025 even “without considering possible major new policy developments,” such as requirements under Clean Air Act, Section 111(d) 2iii



### .

1 See below for discussion of cost-effectiveness and relate d cost tests used by states to evaluate EE programs.
2 Specifically, the LBNL study states: “By virtue of limiting the analysis to current energy efficiency policies, we do not consider the potential impact of major new federal (or state) policy initiatives (e.g., a national energy efficiency resource standard, clean energy standard, or carbon policy) that could result in customer-funded energy efficiency program spending and savings that ex ceed the values in our High Case.” Comment [A1]: Suggest including citation.

Comment [A2]: Calls into question the need for BB 4??

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This section provides relevant background for the subsequent sections that address the EE opportunity, EE as a component of BSER, EE within state goal setting, and the integration of EE within the benefit, cost, and impacts assessments as reported in the RIA and elsewhere. This section begins with a discussion of EE technol ogies and practices, and then describes the market
failures that limit cost-effective EE investment s. We then summarize EE policy objectives and discuss policy types, their relative impacts, and discuss in more detail the key strategy of employing EE programs.

EE Technologies and Practices

Energy efficiency is using less energy to provide the same or greater level of service. Demand-side energy efficiency refers to an extensive array of technologies, practices and measures that are applied throughout all sectors of the economy to reduce energy demand while providing the same, and sometimes better, level and quality of service. Utilities ’ employ a large array of strategies in implementing energy efficiency programs, these include financial incentives such as rebates and loans, technical services such as audits and retrofits, and educational campaigns about the benefits of energy efficiency improvements. The purpose of these EE programs is to induce EE investments a nd practices that would not otherwise occur. In the residential sector, examples of EE activities include the purchase of more efficient products and equipment (e.g., ENERGY STAR labeled), the upgrading of insulation in attics and walls, sealing of air leaks, and undertaking home ener gy audits leading to customized whole home retrofits. Opportunities for cost-effective EE in commercial buildings include optimization of heating, ventilation, and air conditioning (H VAC) systems, upgrades of windows, and use of more efficient office equipment at replacement. In the industrial sector key EE strategies include motor upgrades and maintenance programs, recovery of waste heat streams, and optimization of processes through modern instrumentation and controls systems. The opportunity presented for economic invest ment in EE is dynamic, growing over time as technologies and practices advance, as popul ations grow, and as investment occurs in the construction of new homes, buildings, and industrial facilities. As new policies are enacted, leading to the acceleration of investment in EE, an additional portion of the expanding opportunity is realized. After decades of experience implementing policies to accelerate Comment [A3]: Can EPA explain why the EE intervention is needed?

Comment [A4]: Although Figure 5.1 some pages ahead does some of this, this would be a good opportunity to bulletize real world examples of EE actions, like windows, light bulbs, etc., with their cost and efficiency and gains, even maybe a graph showing how EE are the lowest cost GHG actions. Otherwise, the next few pages read like a philosophical discussion of an abstract concept.

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investment in cost-effective energy efficiency, states are finding renewed opportunities as they develop more sophisticated and effective strate gies, evolving from a focus on individual enduses and products to whole-building and systems-based strategies that account for the interactions between the many energy end-uses in buildings and industry iv 

### . As will be discussed, the experience in the U.S. has been that on balance, a persistent and large potential for achievable and cost-effective EE has remained even as the impact of past and ongoing efforts
have accumulated.

Barriers to EE Investment

Despite the persistent and large potential for electricity savings through investment in EE technologies and practices, market failures, as well as non-market failures, limit the realization of the many benefits of these invest ments. Several market failures that lead to inefficiencies in energy use are well recognized by analysts and practitioners, and are discussed extensively in the
economic literature. v 

### Some of the most common examples of these market failures include:

 Pollution externalities. Energy consumption is associated with negative externalities, such as
emissions of CO 2 

### , SO 2 

### , and NO x 

### that cause human health and environmental damages. Energy prices that do not correctly reflect th ese externalities lead to investments in energy efficiency below the socially optimal levels.

 Imperfect information. Energy users often lack accurate information about energy savings and other attributes of energy efficient pro ducts or practices to understand the costs and benefits of EE investments. Market failure due to information imperfection leads to underinvestment in energy efficiency by consumers.

 Split incentives (or the “pri ncipal-agent problem”). Incentives of individuals who make EE investment decisions are not always aligned with incentives of those who use and pay for energy. Examples include misalignment between landlords and tenants, and between builders and homeowners. Split incentives also persist with in organizations and institutions that lead
to underinvestment in EE in both public and the private entities. vi



###  Credit constraints . Limited access to credit may prevent some consumers, especially lowincome consumers, from making cost-effective EE improvement decisions due to the higher upfront cost of energy efficient products or practices.

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 Under-provision of research and development (R&D). Because of the public good nature of knowledge, technology innovation invested by one firm likely spills over to other firms. As a result, firms involved in technology developmen t may be less willing to invest in R&D, leading to sub-optimal levels of EE investments from a social perspective. vii



###  Supply market imperfections. Market for energy efficient products is incomplete. Manufacturers do not have perfect information about consumer preferences and may supply limited menu of products to consumers. High start-up costs and the existence of patents may create barriers to entry in markets and result in oligopolistic or monopolistic behavior. Supply chains of EE products is fragmented, leading to underinvestment in innovation and energy efficiency by suppliers. In addition, supply chain fragmentation may also add complexity to the purchase an d installation of otherwise econo mically rational investments, thereby slowing the adoption of EE technologies.

 Behavioral impediments . Behavioral economics and psychology have identified potential behavioral phenomena that lead to consumers to deviate from the standard theory of welfare maximizing in consumption and other decisions, including energy efficiency investments. Behavioral economics posits possible explanations, including bounded rationality, heuristic decision-making, and non-standard preference and belief. viii 

### In the presence of market failures, users of electricity, or those making energy efficiency investments, face prices or incentives that prevent them from weighing the social benefits and
costs of their investments and thus under-invest in approaches to reduce electricity consumption. The behavioral impediments discussed above ex plain why individuals do not always make energy efficiency investments that are seemingly in their own best intere st to reduce their total expenditure, given prevailing electricity prices.
In addition to market failures and behavioral impediments, other factors, such as hidden costs, risk and uncertainty experienced by both consumers and suppliers of energy efficient products, and heterogeneity among consumers, producers and markets, also influence EE
investment decisions 3 

### . Examples of such factors include:

3 It has been recognized that there is a difference between cost-effective energy efficiency investment levels, based on cost-minimizing consideration, and observed levels of energy efficiency. This phenom enon, also termed ‘energy paradox,’ or ‘energy efficiency gap,’ has been studied extens ively in the literature. See, for example, Jaffe, AB, and

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 Risk and uncertainty . Adopting an unfamiliar, typically more expensive EE technology can be an uncertain undertaking given the lack of credible information on product performance and future energy prices, and the irreversibility of the investment. Imperfect or asymmetric information can exacerbate the perceived risk of energy efficiency investments and help explain why consumers and firms do not always invest in EE measures. Suppliers also face risk and uncertainty, without perfect inform ation of consumer preferences for energy efficiency. In the presence of risk and uncertainties, consumers and suppliers alike will
underinvest in EE.

 Transaction costs . Consumers face transaction costs in searching, assessing and acquiring energy efficient technologies and services . It can be time-consuming and difficult for consumers to estimate lifetime operating costs of a product. The complexity of the search process puts many efficient products at a disadvan tage relative to less-efficient products with lower upfront costs.

 Capital market barriers . Consumers sometimes face higher interest rates to finance EE investments compared to other investments. Le nders can be reluctant to invest in EE loan portfolios in part because energy efficiency loans may lack standardization and financial markets have difficulty ascertaining the likely payoff from such investments. EE policies and programs can play an important role in correcting market failures and addressing the barriers to the investment and adoption of socially beneficial energy efficiency opportunities. Examples of effective EE policie s and programs include public funding of R&D, information programs (such as energy labeling, the voluntary ENERGY STAR Program, and consumer education), rebates for high-efficiency products, product energy performance standards, financing and loan programs, and technical assistance.

EE Policies 4

RN Stavins. 1994. “The Energy Paradox and the Diffusion of Conservation Technology.” Resource and Energy Economics 16(2): 91–122; Sanstad, A. H. and R. B. Howarth. 1994. ‘Normal’ markets, market imperfections and energy efficiency, Energy Policy , 22: 811-818; DeCanio 1998; DeCanio, SJ and WE Watkins. 2008. Investment in Energy Efficiency: Do the Char acteristics of Firms Matter? The Review of Economics and Statistics , 80: 95-107; Allcott, H, and M. Greenstone. 2012. Is There an Energy Efficiency Gap? Journal of Economic Perspectives 26 (1):3-28. 4 Existing state EE policies are described extensively in the State Plan Considerations TSD.

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Objectives and Role in Reducing CO 2 

### Emissions from the Power Sector EE policies are implemented by states to meet a number of closely related policy goals ix 

### including:

- Reducing costs to electricity customers

- Providing a significant resource for meeting power system capacity needs

- Meeting energy savings goals

- Stimulating local economic development and new jobs

- Reducing the environmental impacts of meeting electricity service needs EE policies currently in place are considered by states to be cost-effective strategies for contributing to each of these policy objectives x 

### . While each of these objectives, and others, contribute to the motivation of state policymakers to pursue EE policies, reducing energy costs over the long term is the leading objective in pur suing these policies. In addition, EE policies are central to meeting state objectives for reducing CO 2 

### emissions from the power sector. As noted in the State Plan Considerations TSD, EE policies are a leading tool for achieving CO 2 

### reductions from power plants, accounting for 35% to 70% of reductions of sector emissions in

ten states 5 

### with statutory requirements for greenhouse gas reductions. Economy-wide studies of climate mitigation scenar ios confirm that energy efficiency plays a critical role in reducing the costs and enhancin g the flexibility of meeting long-term climate stabilization targets xi 

### . Analysis by the International Energy Agency (IEA) suggested that in order to stabilize carbon concentration in the atmosphere at 450 ppm, as much as 44% of the estimated global abatement potential in 2035 derives fro m greater energy efficiency in the world

economy xii 

### . Several recent Energy Modeling Forum (EMF) studies have investigated the role of technology in achieving climate policy objectives in the U.S. (“EMF 24” and “EMF 25” studies) and globally (“EMF 27” study). 6 

### These studies concluded that compared to business-as-usual energy efficiency, improvements in energy effi ciency in various economic sectors would slow

5 States with GHG reduction laws include: California, Connecticut, Hawaii, Maine, Maryland, Massachusetts, Minnesota, New Jersey, Oregon, and Washington. 6 Energy Modeling Forum (EMF) is a consortium of energy economists and energy economic modeling teams that

was established in 1976. Through ad hoc working groups, the EMF has focused on a series of energy and environmental topics that are of interest to policy decisions. In recent years, the EMF is recognized for its contribution to the advancement of economics of climate change and the reports of the Intergovernmental Panel

on Climate Change (IPCC). Comment [A5]: Rebound effect??

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the increases of GHG emissions in the short run, substantially reduce the costs of GHG mitigation (on average, by about 50% 7 

### ), and ease the technology transformation pathways to achieve long-term carbon reduction goals xiii,xiv



### . Several economic studies (including EMF25 studies) examined the role of energy efficiency policies (such as energy efficiency standards and subsidies) in relation to other climate policy instruments (such as carbon taxes). These studies found that when energy efficiency policies address market failures, they are welfare improving and can complement climate policy. xv 

### In addition, EE policies are recognized to be an appropriate response to demonstrated market failures and behavioral impediments, particularly in contexts where these failures have broader societal implications such as environmental externalities. xvi 

### In addition to providing cost-effective opport unities for reducing GHG emissions, energy efficiency is recognized to provide other co-benefits, including air quality and public health benefits, waste reduction from energy generation, energy security, energy system reliability, community economic and social development, and consumer amenities. xvii 

### Energy efficiency investments and policies are also found to spur productivity growth, technology learning and
innovation. xviiixix 

### Recently, more attention has been paid to developing methods for recognizing these co-benefits and integrating them into the cost-benefit analysis framework used by state utility commissions and administrators of EE programs xx 

### . These co-benefits have not been fully accounted for in the EPA analysis.

Policy Types EE policies come in many forms. The most prominent and impactful EE policies in most states include those that drive development and funding of EE programs 8 

### , and building energy codes. Other policies that are leading to si gnificant impacts include state appliance and equipment standards, building energy disclosure requirements, innovative financing strategies (e.g., Property Assessed Clean Energy or “PACE”), state tax policies, and “lead by example” strategies targeting energy use in state operations. Comparing the relative impact (achieved or

7 It should be noted that these energy

‐ economy modeling studies do not typically include the costs of implementing energy efficiency measures or would treat such costs as exogenous. 8 EE programs are described in more detail in the follo wing section of this chapter and in the State Plan Considerations TSD.

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potential) of the different policy types is challenging, particularly to do so comprehensively, across all states, and at the national level. EE programs are the only state EE approach that has comprehensive and detailed reporting of impacts, costs, and other characteristics from all 50

states 9 

### . This information is generally based upon measurement and verification studies submitted annually, most commonly to state utility commis sions, and reported to the Energy Information Administration (EIA) for all program administrato r types (all utility types, third-parties, and government agencies). EE program data reported to EIA includes incremental and cumulative energy and peak demand savings, program costs broken down by component, and composition by end-use sector (residential, commercial, industrial). In 201 2, utilities and other program administrators in 48 states reported savings fr om EE programs to EIA through form EIA-861. At a national level, the EPA is not aware of a comprehensive dataset reported by states of the achieved impacts of strategies other than those that lead to investment in EE programs. However, state and regional-level information does ex ist. For example, the Northwest Power and Conservation Council (NPCC) has been compiling the impacts of EE policies (including utility and third-party EE programs, state building energy codes, and federal appliance standards) across their member states (ID, MT, OR, WA) for more than three decades. For the past decade, EE programs have accounted for more than 75% of the cumulative energy savings from state EE policies for NPCC, with building energy co des accounting for th e remaining savings xxi



### . Another representation of the relative opp ortunity provided by different state EE strategies is presented by evaluations of EE achievable potential or projections of the impacts of EE policies. The results from two recent evaluations at a national level are presented in Table 5- 1. EE programs account for 77% and 82% of achievable savings in ACEEE xxii 

### and Georgia
Tech xxiii 

### studies, respectively. These studies indicate that the substantial majority of potential savings from state EE efforts are available through EE programs, and that state and local building energy codes can make a significant additional contribution. Massachusetts provides a state example of the impacts of EE programs relative to other state EE policies. The

9 This is a reference to data collected by the Energy Information Administration (EIA) through Form EIA-861. In 2011, EIA began collecting data from third-party administ rators of programs. Prior to 2011, this was a significant shortcoming in the breadth of the data collected. The br eadth and quality of information collected through Form EIA-861 has improved over time, however, outside entities (e .g., ACEEE) have found that the data can be improved through expert review and supplementation with other data sources. While now fairly comprehensive, the EIA data can be improved further with regard s to data quality and consistency.

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Massachusetts Global Warming Solutions Ac t of 2008 established statewide limits on greenhouse gas (GHG) emissions of 25 percent below 1990 levels by 2020. To achieve this target, Massachusetts is relying upon an integrated portfolio of clean energy policies. State EE policies are expected to provide the largest cont ribution to meeting the 25 percent target with utility sponsored EE programs and state build ing energy codes accounting for 76% and 17%, respectively, of those policies xxiv 

### . In their 2013 progress report, Massachusetts indicates that they are generally on track for meeting or exceeding these projections. xxv

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TABLE 5-1 Relative Opportunities Provided by Key EE Programs and Building Codes Study Year EE Programs Building Codes Other
ACEEE 2030 77% 13% 10% Georgia Tech 2035 82% 18% 0%

The full range of EE policies are addressed in greater detail (including designs, authority, obligated parties, measurement and verification (M&V), penalties for non-compliance, and implementation status) in the State Plan C onsiderations TSD. Because EE programs have provided the majority of state EE-policy electrici ty savings to-date and offer the majority of potential savings going forward, we next summarize key characteristics of this strategy.

EE Programs

EE programs (actually portfolios of programs) are comprised of numerous measures and measure types that are applied across all sectors of electricity end-users. Figure 5-1 xxvi 

### illustrates the multi-level composition and breadth of EE program portfolios. The diversity represented by a typical portfolio of EE programs implemented by a utility (or other program administrator) is an important characteristic relevant to analysis of EE policies. Every detailed program type (as illustrated in the lower half of the figure) repr esents a unique set of ch aracteristics including costs of energy saved, ratio of program to pa rticipant costs, investment life, scale, M&V approach, etc. 10

10 See following sections for discussion of these factors. Formatted: Not Highlight

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FIGURE 5-1 Energy Efficiency Program Portfolio

Administrators

EE programs are admini stered by a variety of entities (“program administrators”)including utilities of all ownership types (investor-owned, municipals, and cooperatives), nonprofit and for-profit third-parties (e.g., Vermon t Energy Investment Corporation), and state and local government agencies (e.g., NYSERDA). Most EE programs (including all investor-owned
utilities which account for more th an 75% of reported savings xxvii 

### ) are overseen by state utility commissions, which review and approve program plans, projected impacts, and associated budgets; and establish annual reporting and M&V requirements. Comment [A6]: The placement of EM&V in Figure 5

‐ 1 as separate from the actual programs is misleading. Each program has EM&V associated with it, so showing it as a separate activity is inaccurate and possibly confusing.

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Policy Drivers

EE programs result from a number of different policy approaches or “drivers.” 11 

### These include energy efficiency resource standards (EERS) (26 states) xxviii 

### , system benefit charges (14 states), integrated resource planning (IRP) requ irements (34 states), demand-side management plan or multi-year energy efficiency budget (28 states), and statutory re quirement to acquire “allcost-effective EE” (6 states) xxix12 

### . EERS is a more recently developed strategy and has quickly become the leading driver of the rapid growth in EE programs due to their clear goals and proven success as a policy tool xxx 

### . These policy drivers lead to the evaluation, planning, and adoption of EE programs and associated budgets, which are supported through different funding
mechanisms.

Funding Sources

Funding sources for EE programs are varied but for most states are dominated by revenues collected from ratepayers through electricity surcharges, typically ranging from $1 to $4 per megawatt-hour xxxi 

### . More recently adopted funding sources include proceeds from the auction of allowances in the Regional Greenhouse Gas Initiative (RGGI) states and from EE resources bid into the forward capacity market operated by the New England Independent System Operator (NE-ISO). Ratepayer-funding accounts for more than 90% of total EE program support nationally.

The EE Opportunity

As discussed, states are employing a number of EE strategies with EE programs yielding the most significant impacts both historically as well as in terms of future potential. Furthermore, EE programs are unique among state EE strategies in the comprehensiveness and transparency of their reported impacts, funding, and other charact eristics. In this section we address the rapid

11 These policies are discussed in depth in State Plan Considerations TSD. 12 The number of EERS states is from ACEEE (see endnote) and includes states with explicit EERS, those with long-term energy savings targets for individual program ad ministrators, and those with EE incorporated as an eligible resource in a renewable portfolio standard. The numbers for the other policy approaches are from LBNL (see endnote).

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growth in EE programs, estimated impacts of EE programs to-date and projections of the impacts of existing EE programs and trends, and the el ectricity savings potential achievable throughexpanded use of EE policies and programs. Finally, we will discuss the costs and costeffectiveness of EE programs, specifically.

Rapid Growth in EE

Funding for EE programs has increased rapidly in recent years driven by recent policy innovations and increasing evidence of the eff ectiveness of these new strategies. Table 5-2 presents levels of EE program funding in the U.S. since 2006 xxxii 

### . In the previous five years, funding increased by more than 200%, from $1.6 billion in 2006 to $5.9 billion in 2011.

TABLE 5-2 U.S. Electric Utility EE Program Funding (2006-2011)

Year
2006 2007 2008 2009 2010 2011 Electric Efficiency Program Budgets (billions of $s, nominal) 1.6 2.2 2.6 3.4 4.6 5.9

Key new state policies that have helped to drive these rapid increases in EE program funding include EERS, electricity savings goals, and “all cost-effective energy efficiency” requirements. The adoption of EERS, in particular, increased through this period and clearly has been the primary driving force behind the increasing success of and investment in EE programs. Table 5-3 shows the number of states adopting EERS by year xxxiii



### .

TABLE 5-3 U.S. State Adoption of Energy Efficiency Resource Standards

Year States Adopting an EERS Total
1997-2004 California, Hawaii, Texas, Vermont 4 Comment [A7]: Please update this table to include the most recent numbers

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2005 Nevada, Pennsylvania 2
2006 Rhode Island, Washington 2
2007 Colorado, Connecticut, Illinois, Minnesota, North Carolina 5
2008 Maryland, Michigan, New Mexico, New Year, Ohio 5
2009 Arizona, Indiana, Iowa, Maine, Massachusetts 5
2010 Arkansas, Oregon 2
2011 Wisconsin 1
1997-2011 26

EE Program Impacts

Impacts to-date

The primary sources for EE program information (including costs and impacts) are annual EE program reports required by utility co mmissions, or cooperative or municipal utility boards of directors. These reports are based on M&V studies of individual EE programs within the program portfolio. The Energy Information Admi nistration (EIA) has been collecting data on EE programs for more than three decades with an increasing degree of breadth and detail over time. For example, third-party-administered pr ograms were not initially required to report but were added beginning in 2011. Data fields have been added over the years to reflect industry trends (e.g., EE programs are now reported separately from load management programs). Outside organizations have taken the EIA data, supplemented it with additional sources including surveys of utility commissions and program administrators, and published their own annual reports that capture EE program impacts xxxivxxxv



### . The EPA has relied on the EIA dataset for identifying historic impacts of EE programs by state. EIA data is reported by program administrator (e.g., utility, third-party, or state agency) and requires the disaggregation of reported data by state for administrators with programs in multiple states (e.g., multi-state investor-owned utilities). Program administrators in 48 states reported savings in 2012. The EPA has compiled th is information and aggreg ated key data to the state level. Table 5-4 provides a summary of this data by state for the 2012 reporting year, the Comment [A8]: As noted elsewhere in the comments, this dataset can be problematic.

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most recent available. At the national level, incremental electricity savings 13 

### in 2012 was 0.58%
of retail sales with individual state values ra nging from 0.00% to 2.19%. Cumulative electricity savings 14 

### (representing the remaining impacts of pr ograms from all prior years) reported at the national level for 2012 represent 3.74% of retail sales with individual state values ranging from
0.0% to 15.44%.

TABLE 5-4 2012 Reported Electricity Savings by State

State Incremental Savings as a % of Retail Sales (2012) Cumulative Savings as a % of Retail Sales (2012)

Alabama 0.07% 0.78%
Arizona 1.61% 5.39%
Arkansas 0.11% 0.39%
California 1.24% 13.67%
Colorado 0.84% 4.67%
Connecticut 1.05% 13.37%

Delaware 0.00% 0.00%
District of Columbia 0.00% 0.57%
Florida 0.27% 3.60% Georgia 0.18% 0.67%

Idaho 0.79% 6.20%

Iowa 1.05% 7.80%
Illinois 0.93% 2.15%
Indiana 0.58% 1.72%

Kansas 0.02% 0.24% Kentucky 0.23% 1.04%

13 Incremental savings (also known as first-year savings) represent the reduction in electricity use in a given year associated with EE activities in that same year. 14 Cumulative savings (also known as annual savings) represen t the reduction in electricity use in a given year from EE activities in that year and all preceding years, ta king into account the lifetimes of installed measures. Comment [A9]: EPA needs to provide much

more detail about how this data was derived.

Comment [A10]: EPA used EIA 861 data

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Louisiana 0.00% 0.00%
Maine 1.96% 5.42% Maryland 0.89% 2.47%

Massachusetts 0.94% 6.27% Michigan 1.01% 2.77%
Minnesota 1.12% 13.10% Mississippi 0.08% 0.50%
Missouri 0.12% 0.55%

Montana 0.66% 5.85%

Nebraska 0.30% 0.99%
Nevada 0.54% 6.19% New Hampshire 0.48% 4.90% New Jersey 0.03% 1.04%
New Mexico 0.60% 1.86%
New York 0.93% 6.89%
North Carolina 0.37% 1.26%
North Dakota 0.07% 0.22%
Ohio 0.87% 3.20%
Oklahoma 0.21% 0.70% Oregon 1.09% 7.72% Pennsylvania 1.06% 3.08%

Rhode Island 0.78% 11.22%
South Carolina 0.35% 1.12%
South Dakota 0.13% 0.33%

Tennessee 0.31% 1.76%

Texas 0.19% 1.54%
Utah 0.74% 6.59%
Vermont 2.19% 15.44% Virginia 0.03% 0.30%

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Washington 0.93% 7.37% West Virginia 0.18% 0.20%
Wisconsin 1.05% 6.61% Wyoming 0.14% 0.71%
Continental U.S. Total 0.58% 3.75%
Alaska 0.02% 0.10%
Hawaii 0.04% 0.25%
U.S. Total 0.58% 3.74%

Projected Spending and Savings from EE Programs

In 2013, Lawrence Berkeley National Laboratory (LBNL) published an update to a 2009 analysis and projected future spending levels and savings through 2025 from energy efficiency programs funded by electric and gas utility customers in the United States under three scenarios (high, medium, and low cases) xxxvi 

### . The scenarios represent “a range of potential outcomes under the current policy environment” and were based on detailed, bottom-up analysis of existing state energy efficiency policies. Significantly, the study presumes no new major policy developments such as a “national energy efficiency standard , clean energy standard, or carbon policy” and specifies that such policy changes could “result in customer-funded energy efficiency program spending and savings that exceed the values in our High Case.” The study concludes that efficiency programs are “poised for dramatic growth over the course of the next 10 to 15 years” with the most significant increases occurring in regions with lower levels of program spending, historically, including the Midwest and South. For example, under the medium scenario total U.S. spending on electric efficiency programs increase by 40% to $8.1 billion in 2025 from 2012 levels. Under the high scenario, spending more than doubles from 2012 levels to $12.2 billion in 2025. Incremental savings levels grow commensurately, to 0.8% and 1.1% of sales under the medium and high scenarios, respectively. The study results indicate that under the high scenario 20 states would be achieving 1.5% or higher levels of

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incremental savings, with 11 of those reaching or exceeding 2.0% 15 

### . Table 5-5 summarizes the results of the LBNL analysis.

15 LBNL provided these unpublished results from their analysis. Comment [A11]: So what does this imply about BB #4? Why does EPA put into the baseline?

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Table 5-5 Summary of Impacts: Scenarios of Future Utility Customer-Fu nded Energy Efficiency Programs

Case 2025 Incremental Savings (% of Sales) Program Costs (billions of $, nominal) Programs Costs (% of Revenues)

Low 0.5% 5.5 1.1%
Medium 0.8% 8.1 1.7% High 1.1% 12.2 2.7%

EE Potential

Evaluations of EE Potential

Energy efficiency potential studies are a common tool for informing the development of EE program plans and budgets, as well as supporting th e development of electricity savings targets, required savings levels under an EERS, or “all cost-effective” EE requirement. In conducting these studies, states and utilities have developed a standardized methodology that is often described as a “bottom-up, engineering-based” approach xxxvii 

### . EE potential studies are conducted at various geographic scopes (national, regional, st ate, and utility service territory level) and at different degrees of aggregation (e.g., economy-wi de, sectoral, and program), and can be broadly grouped into a few types: technical, economic, market, and program. 16



###  Technical potential represents the theoretical maximum amount of energy use that could be displaced by efficiency, without regard to non-engineering constraints such as costs and the willingness of energy consumers to adopt the efficiency measures. It often assumes immediate implementation of all technologically feasible energy saving measures, with additional efficiency oppo rtunities assumed as they arise.

16 The definitions discussed below largely follow that outlined in the Guide for Conducting Energy Efficiency Potential Studies (NAPEE 2007) but the variations in definition are also discussed (e.g., Sathaye and Murtishaw 2004; Huntington 2011).

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 Economic potential refers to the subset of the technical potential that is economically cost-effective. Definition of “economic potenti al” can vary to some degree by study. Some estimate economic potential by evaluatin g technology upfront cost, operating costs that considers energy prices, product lifetime and discount rate, compared to a conventional alternative or the supply-side energy resources. Others incorporate consideration of consumer preferences in addition to consumers’ out-of-pocket expenditure when evaluating the economic potential. Both technical and economic potential estimates assume immediate implementation of efficiency measures without regard to technology adoption process or r eal-life program implemen tation. In addition, these estimates do not always reflect market failures or barriers that impede energy efficiency and often fail to capture transaction costs (e.g., administration, marketing, analysis, etc.) beyond the costs of efficiency measures.

 Market potential (or “achievable” potential ) refers to the subset of economic potential that reflects the estimated amount of energy savings that can realistically be achieved, taking into account factors such as technology adoption process, market failures or barriers that inhibit technology adoption, transaction costs, consumer preferences, social and institutional constraints, and possibly the capability of programs and administrators to ramp up program activity over time.

 Program potential refers to the subset of market potential that can be realized given specific program funding levels and designs. Program potential studies can consider scenarios ranging from a single program to a full portfolio of programs 17



### . As mentioned, the EE industry standard for potential studies is the bottom-up, engineering evaluation of energy efficiency potential of individual end-use technologies and

measures xxxviii 

### . Bottom-up analyses all employ a similar methodology but can vary significantly in key assumptions (e.g., breadth of sectors and end-uses considered, study period, discount rate, pattern of technology penetration, whether economically justified early replacement of

17 Each subsequent potential estimate described above is a subs et of the previous potential estimate, e.g., the market potential is a subset of the economic potential, and the economic potential is a subset of the technical potential.

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technologies is allowed for, and whether continue d improvement in efficiency of technology is provided for). As a result, estimated efficiency potential can vary significantly among studies 18



### .

Overview of Results

Studies of energy efficiency potential are numerous. In recent years, dozens of studies have been conducted at regional, state, and util ity levels. This section reviews recent studies and presents a summary of findings. We first address meta-analyses that summarize results from multiple utility, state, and regional studies, and th en we address the few national studies that have been conducted. To normalize results of analyses addressing different study periods, we present average annual achievable potential by dividing cumulative percentage savings in the last year of the study by the duration (in years) of the study period. This is a common method of normalization for energy efficiency potential studies. At the regional and state level, two meta-analyses, Sreedharan (2013) xxxix 

### and Eldridge et al. (2008) xl 

### , captured numerous studies conducted between 2001 and 2009. The meta-analysis conducted by Sreedharan (2013) presents average annual values of 4.1% per year in technical potential, 2.7% per year in economic potential, and 1.2% per year in maximum achievable potential. In comparison, Eldridge et al. (2008) estimated average annual values of 2.3% per year in technical potential, 1.8% per year in economic potential, and 1.5% per year in achievablepotential. To supplement these studies with more recent data, the EPA has conducted a metaanalysis of twelve studies conducted between 2010 and 2014 at the utility, state or regional level (see Appendix 5-1). The EPA review indicates an average annual achievable potential of 1.5% per year across the reviewed studies. See Appendix 5-2 (Summary of Recent (2010-2014) Electric Energy Efficiency Potential Studies) for complete results from the EPA research.

18 Because of the complex consumer behavior, energy market and macroeconomic drivers of energy use and energy efficiency, and in some cases due to the lack of consistent data, quantifying energy efficiency potential and energy savings from policies and programs remains a challenging analytical task. Assumptions about consumer technology adoption behavior, market barriers and failures, and how technology diffusion occurs can also affect estimated potential. Comment [A12]: Font is different in footnote

Comment [A13]: Assuming these studies are correct, what, if any, policies are needed to realized the achievable potential of 1.2%

‐ 1.5%?

Comment [A14]: Make sure to check this carefully!!

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Table 5-6 presents a summary of these three meta-analyses of EE potential.

TABLE 5-6 Summary of Meta-Analyses of EE Potential at Utility, State, and Regional Levels Study Dates of Studies Number of
Studies Average Annual Achievable
Potential Sreedharan (2013) 2001-2009 10 1.2%/year

Eldridge (2008) 2001-2007 20 1.5%/year

EPA (2014) 20010-2014 12 1.5%/year

In addition to the numerous studies conducted at the utility, state, or regional levels since 2001, a number of studies have evaluated efficiency potential at the national level, applying a generally consistent methodology and employing a common data set, across all regions of the country. Sreedharan (2013) evaluated four major energy efficiency potential studies at the national level, namely, McKinsey and Co. (2007), McKinsey and Co. (2009), EPRI (2009), and AEO (2008) Energy Efficiency Side Case. All f our studies used the AEO 2008 reference case as the baseline but differed in other key respects (e.g., breadth of end-uses, assumed technology improvement over time, and definition of cost test for economic potential screening). These studies suggest technical electricity savings potential in the range of 25-40% and economic potential in the range of 10-25%, as a percentage of total demand in 2020. Of these studies, only EPRI provided an estimate of achievable potential . On a normalized basis, the EPRI 2009 study provides an achievable annualized potential range of 0.2-0.4% per year (realistically achievable and maximum achievable potentia l, respectively) through 2030 at the national level. Two more recent studies also provide national estimates of achievable EE potential: EPRI (2014) xli 

### updates their 2009 analysis, using a conventional bottom-up engineering approach, and ACEEE (2014) xlii 

### , using a top-down, policy-based approach derived from state experience and their evaluated results. EPRI (2014) results show an average annual achievable potential range of 0.5% to 0.6% per year (achievable and high achievable potential, respectively). ACEEE found average annual achievable potential of 1.5% per year. The results of
the EPRI and ACEEE studies are summarized in Table 5-7. Comment [A15]: Seems like too few studies to do a valid meta

‐ analysis.

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TABLE 5-7 Summary of National EE Potential Studies Study Study Type Average Annual Achievable
Potential

EPRI (2009) Bottom-up, engineering 0.2%-0.4%/year

(realistic to maximum achievable)

EPRI (2014) Bottom-up, engineering 0.5%-0.6%/year

(achievable to high achievable)

ACEEE (2014) Top-down, policy-based 1.6%/year

Notably, each of these national potential st udies show significant potential in every region of the country including regions with lowe r electricity prices like the southeast, regions with historically high levels of EE program budge ts like the northeast and west coast, and across regions with varied sectoral composition (e.g ., higher manufacturing regions like the midwest and south, as well as higher service industry regions like the northeast and California). Both
EPRI studies illustrate the substantial and si milar scale opportunity across all regions. For instance, EPRI (2014) shows achievable potential ra nging from 8% to 14% relative to baseline in 2035 across the thirteen regions of their analys is as well as significant opportunity in the residential, commercial and industrial sectors in every region. The ACEEE (2014) study also shows consistently large potential across all states and regions through 2030, with an average potential of 24% and a range of 20% to 36% across 50 states.

Costs and Cost-Effectiveness of State EE Policies

EE Cost-Effectiveness States enact EE policies to meet multiple policy objectives including reduction of customer electricity bills, lower costs of meeting electrici ty supply needs, energy reduction, environment Comment [A16]: This raises the question of peer review. Have these been independently peer reviewed? If so, state it. If not, is EPA planning to do so? Comment [A17]: If these are the average achievables, is it really feasible to go for 1.5% for all states in each year?

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and health benefits, and local economic development benefits xliii 

### . Most states evaluate their EE policy options through the application of cost te sts, weighing the projected benefits with the costs of the energy efficiency technologies and practices xliv xlv 

### . Each state determines their own policies for the specific costs and benefits to include in these tests. The costs and benefits are compared on an equal footing by using present value analysis. This is necessary because EE typically requires primarily upfront expenditure s (e.g., a whole home retrofit) while the economic benefits (e.g., electricity bill savings) accrue over the life of the investment (“measure life”) which can range from a few to twenty or more years. As such, the choice of discount rate and the estimation of measure life are significant determinants of the cost-effectiveness results. Most states employ multiple tests, adjustin g cost and benefit categories depending upon the economic perspective of interest (e.g., utility, ratepayer, program participant, society), and consider the results from each one, usually with an identified primary test type xlvi 

### . Policies that
are selected are those that are found to be cost-e ffective, with benefits greater than costs, as determined by the utility applying methods de fined by their state utility commission. There are five primary cost-effective tests used in the U.S.: (1) Participant cost test from the perspective of the customer installing the measure. Costs may include incremental equipment and installation costs; benefits include incentive payments, bill savings, and applicable tax credits or incentives. (2) Utility/program administrator cost test from the perspective of utility, government agency or third-party implementing the program. Costs may include program incentive, installation, and overhead costs; benefits may include avoided energy and capacity costs - including generation, transmission and distribution - by the utility. (3) Ratepayer impact measure test from the perspective of utilit y ratepayers not participating in available energy efficiency programs. This text includes the costs and benefits that will affect utility rates, including program and administration costs, as well as “lost revenues” to the utility; benefits include avoided energy and capacity costs, and additional resource savings. (4) Total resource cost test from the perspective of all utility customers in the service area. Costs may include the full incremental cost of the measure, program installation and overhead costs; benefits may include avoided energy and capacity costs, and additional resource savings.

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(5 ) Societal cost test from the social perspective. In addition to benefits considered in total resource cost test, may also include non-monetized benefits such as environmental and health
benefits. While many states consider more than one cost test in evaluating EE programs, the most commonly used (29 states) primary test is the total resources cost test. This test is considered to
be the best measure of the interests of all utilit y customers. The utility and societal cost tests are the next most commonly used primary tests, used by five states each. The utility cost test is considered to be the most comparable metric to compare with supply-side resource investments from a utility resource planning perspective. Economic and modeling analyses of climate change policy suggests that energy efficiency presents a large potential in reducin g greenhouse gas emissions and plays a critical role in offsetting the costs and enhancing the flexibility to achieve long-term GHG reduction targets xlvii 

### . Consistently, evaluations of the economic potential for carbon dioxide reductions from the United States’ power sector identify demand-side energy efficiency as the lowest cost strategy (typically, as noted above, with positive net present value) as well as the strategy having the greatest reduction potential xlviii 

### . For example, McKinsey (2007) xlix 

### found that EE accounted for more than 60% of their mid-range potential for greenhouse gas reductions from the U.S. power sector and that it was available at positiv e net present value if “persistent barriers to market efficiency” could be addressed.

Costs of Saved Energy A common metric for comparing alternative el ectricity resource options within utility resource plans is the levelized cost of energy (LCOE) or, for EE resources, the levelized cost of saved energy (LCSE) l 

### . LCSE EE is often compared favora bly with LCOE of alternative new generation sources such as fossil-fueled or nucl ear power plants, or renewable energy resources like wind or solar-power generation. The energy efficiency analysis literature reports average LCSE in the range of 1-6 cents/kWh based on program administrator cost. 19 

### A recent review by

19 Unless otherwise noted, estimates of LCSE discussed in this section refer to program administrator cost (also known as utility cost). The discount rates, average measur e lives, and other assumptions affecting the calculation of LCSE were not consistent or not reported in all studies. Comment [A18]: These LCSE numbers by most metrics would be considered very competitive and sometimes even lowest

‐ cost. The methodology presented later, based on program costs, ends up providing numbers that seem high. Please consider revisiting the methodology if appropriate.

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ACEEE (2014) examined studies across 20 stat es between 2009 and 2012, and estimated LCSE for electricity energy efficiency programs in the range of 1.3-5.6 cents/kWh, with a mean value
of 2.8 cents/kWh. li 

### Earlier reviews of utility EE programs identified a similar range of LCSE. Friedrich et al. (2009) reviewed 14 utility studi es of LCSE and found a range from 1.6 to 3.3 cents/kWh, with a mean value of 2.5 cents/kWh. lii 

### An earlier ACEEE study (2004) reviewed cost-effectiveness analysis results in nine states and suggested that reported utility LCSE ranged between 2.3-4.4 cents/kWh, with a mean value of 3 cents/kWh. liii



### The economic literature also evaluates th e LCSE from EE measures using various techniques (e.g., empirical econometrical, top-down modeling). Some recent analyses suggest estimated LCSE in a similar range as in the energy efficiency analysis literature. Gillingham et al. (2004) estimated an average cost of 3.4 cents per kWh saved from utility EE programs. liv

### Using a top-down approach that evaluates the savings potential of EE investments using stateand region-specific price elasticit y, Paul et al. (2011) estimated that electricity savings were available at an average utility LCSE of 2.5-3.5 cents/kWh. lv 

### In an econometric analysis of utility rate-payer funded demand-side management and energy efficiency programs between 1992 and 2006, Arimura et al. (2009) found that the estimated energy savings in electricity consumption were achieved at an expected average cost to utilities of approximately 5 cents/kWh. lvi 

### A number of analytical and data considera tions related to LCSE estimation are also discussed in the literature, including the issue of “free riders” in EE programs lvii 

### , and the accuracy of utility reported costs and energy savings lviii 

### . Energy efficiency practitioners also recognize the need to consider “free rider” an d “spillover” effects in program evaluation. A slight majority of states adjust for free riders hip in energy savings estimates, leading to higher
LCSE values than otherwise would be the case. A smaller number of states adjust for spillover
effects which reduce LCSE values when addressed. lix



### EE as an Abatement Measure

Demand-side energy efficiency is a technically viable, broadly applicable, and generally cost-effective measure for achieving significant reductions in utilization of affected EGUs. Moreover, this measure has been adopted by ne arly every state and most utilities across the country, typically through multiple policy approaches. Increased use of, and impacts from, state Comment [A19]: “ Under the scenarios modeled, electricity savings of between 1 and 3 percent are achievable at a marginal cost of $50 per megawatt hour (MWh) and a corresponding average cost of $25–$35/MWh. ”

Comment [A20]: Implies $50/MWh and coal emits appox. 1 metric ton/MWh which is valued at

an SCC of approximately $40. Am I missing something? I guess the co

‐ benefits make it economically viable?

Comment [A21]: Suggest making a table comparing the estimated LCSE for these studies.

Comment [A22]: EPA needs to provide data to show how DSM is cost

‐ effective relative to other abatement techniques (e.g. increased NGCC capacity and generation)

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energy efficiency policies is a leading industry trend over recent years and the trend of increasing investment in EE programs is projected to continue for the next decade, at least. These findings support the inclusion of demand-side en ergy efficiency as an abatement measure for reducing carbon dioxide (CO 2 

### ) emissions from fossil fuel-fired electric generating units (EGUs). In the next section, we address the setting of state-specific goals for electricity savings levels resulting from state demand-side energy efficiency efforts. In the final section, the integration of these goals into the impacts assessment is presented and we consider the cost-effectiveness of this building block within the context of the broader proposal.

State Goal Setting Approach

To estimate the potential CO 2 

### reductions at affected EGUs that could be achieved through implementation of demand-side energy efficiency policies as a part of state goals, the EPA developed a “best practices” demand-side energy efficiency scenario. This scenario provides an estimate of the potential for states to implement policies that increase investment in cost-effective demand-side energy efficiency tec hnologies and practices, and projects the annual impacts of the scenario for each state. It does not represent an EPA forecast of business-as-usual impacts of state energy efficiency policies or an EPA estimate of the full potential of end-use energy efficiency available to the power system, but rather is intended to represent a feasible policy scenario showing the reductions of CO 2 

### emissions from fossil fuel-fired EGUs resulting from accelerated use of energy efficiency policies in all states, generally consistent with ongoing industry trends. The scenario uses : 1) a level of performance that has already been demonstrated or required by policies (e.g., energy efficiency resource standards) of many leading states ,; 2) considers each state’s unique existing level of performance ; , and 3) allows appropriate time for
each state to increase from their current level of performance to the identified best practices
level. The best practices scenario is derived from state experience with, and reliance on, policies that drive investment in energy efficiency programs, and the energy savings that result from those efforts. We focus on energy efficiency programs for several reasons: Comment [A23]: 1) If it is projected to increase then why is it not in the baseline? And 2) If it does increase then the cost of achieving incremental energy savings will be increasing.

Comment [A24]: Does it include, though, BAU for states that should be in the baseline?

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 EE programs have achieved significant levels of savings and are being used in almost every state,

 EE program spending and savings levels are reported by utility or other program administrator, by state, and compiled nationally, using standardized elements and definitions, and

 EE program savings are projected and evaluated under requirements established and overseen by state utility commissions, and by municipal and cooperative utility boards of
directors.

While the approach is derived from information about energy efficiency programs overseen by state utility commissions, other stat e energy efficiency policies are available to realize a state’s goals 20 

### such as building energy codes, appliance standards, and building energy benchmarking requirements. All policies included in a state plan will need to meet established requirements or guidance for EM&V 21



### . The following steps were taken to establish the inputs for development of the best practices scenario for each state:

 Step 1: Determine current level of performance

 Step 2: Determine best prac tices level of performance

 Step 3: Determine start year for state efforts

 Step 4: Determine start year level of performance

 Step 5: Determine pace at which states improve from start year to best practices level of performance

 Step 6: Determine average portfolio measur e life and distribution of measure lives

 Step 7: Determine sustainability of be st practices level of performance

Inputs Step 1: Determine Current Level of Performance

20 See State Plan Considerations TSD. 21 See State Plan Considerations TSD.

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A fundamental indicator of the level of energy efficiency program performance is incremental annual savings as a percent of retail sales. This is a common metric defining savings levels for energy efficiency resource standards and is readily calculated from EIA Form 861 data for each state. Incremental annual savings are also more directly estimated and evaluated than are cumulative savings 22 

### . For the best practices scenario, we aggregated the most recent year of EIA Form 861 data to the state level to establish each state’s current level of performance. These results were presented previously in Table 5-4.

22 Estimates of cumulative savings impacts in a given year are derived from incremental savings values and information on measure lives. Information on measure lives is less consistently gathered than is information on incremental savings values.

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Step 2: Determine Best Practices Level of Performance

As discussed previously, achievable demand-si de energy efficiency potential exists at significant and comparable levels (on the basis of total cumulative potential over a period of ten to twenty years) in all regions of the country. While varied regional characteristics (e.g., avoided power system costs, economic growth, sectoral mix, climate, and level of past energy efficiency efforts) affect estimates of achievable poten tial, ongoing improvements in energy-efficient technologies and practices, economic growth, po pulation increases, and continually improving strategies for program delivery have resulted in persistent and substantial levels of achievable potential regardless of specific regional characteristics. A direct indicator of the achievable incremental levels of energy savings performance is provided by past performance at the state and utili ty levels, and by requirements states have put in place for levels of savings to be achieved by 2020. As discussed, these requirements are typically in the form of energy efficiency res ource standards or similar savings goals that are applied to utilities in the state 23



### . Table 5-8 summarizes incremental savings levels as a percentage of retail sales from EIA Form 861 (2012) data lx 

### , aggregated to the state level, and categorized into four ranges of savings levels (> 0.5%, 0.5% to 0.99%, 1.0% to 1.49%, and >= 1.5%). As shown, three states achieved the highest level of performance (> 1.5%) and an additional eight states achieved the second highest level of performance (1.0% to 1.49%).

TABLE 5-8 2012 Reported State Levels of Incremental Annual Savings Incremental Savings as % of
Retail Sales # of States States

>= 1.5% 3 AZ, ME, VT
1.0% to 1.49% 8 CA, CT, IA, MI, MN, OR, PA, WI
0.5% to 0.99% 14
< 0.5% 25

23 See State Plan Considerations TSD for more information. Comment [A25]: What and where is this superscript? Comment [A26]: Should this be < 0.5?

Comment [A27]: What evidence does EPA have that 1) these states achieved this level of EE due to the DSM policies they have in place; 2) these policies will continue to have this level of effect in the future; and 3) that demonstrates that these DSM will be able to push the remaining states to achieve the same level of success with these policies?

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Table 5-9 summarizes incremental savings levels required by state policy on or before 2020 and categorized into the same four ranges lxi 

### . Eleven states are required to achieve the highest level of performance (> 1.5%) and an additional five states are required to achieve the next highest level of performance (1.0% to 1.49%).

TABLE 5-9 Levels of Incremental Savings Required by State Policy on or before 2020 Incremental Savings as %
of Retail Sales # of States States

>= 1.5% 11 AZ, CO, IL, IN, MA, MN, NY, OH, RI, VT, WA
1.0% to 1.49% 5 HI, IA, ME, MI, OR
0.5% to 0.99% 3 AR, CA, WI
< 0.5% 1 TX

For the best practices level of performance for Option 1 24 

### , the EPA has chosen 1.5% incremental savings as a percentage of retail sale s. This level was achieved by three states (AZ, ME, and VT) in 2012 and an additional nine states (CO, IL, IN, MA, MN, NY, OH, RI, and WA), accounting for overlap, are expected to achieve this level by 2020. Thus, twelve states have either achieved or are required to achieve this level of performance by 2020. For Option 2 25 

### , the EPA has chosen 1.0% incremental savings as a percentage of retail sales as the best practices level of performance for this alternate approach. This level was achieved by eleven states in 2012 and an additiona l twelve states are expected to achieve this level by 2020. In total, twenty states (accounti ng for duplication between th e two sets of states) have either achieved or are required to achieve this level of performance by 2020. Step 3: Determine Start Year for State Efforts

24 See Preamble and Goal Computation TSD for description of Option 1. 25 See Preamble and Goal Computation TSD for description of Option 2. Comment [A28]: Indicates that all these states should be in the baseline and not the policy scenariors.

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For construction of the best practices scenario, the EPA has used 2017, the year following the required state plan submittal 26 

### , as the first year for state efforts. Step 4: Determine Start Year Level of Performance

For construction of the best practices scenario, the EPA has set each state’s level of performance (incremental savings) in the start year (2017) to its current level of performance (aggregated to the state-level fr om reported EIA Form 861 data). This approach reflects neither improvement nor decline in performance between 2012 and 2017. Any improvement in EE savings performance between 2012 and 2017 will benefit a state in meeting its state EE goals for the 2020-2029 interim compliance period 27



### .

Step 5: Determine Pace at Which States Improve from Start Year to Best Practices Level of Performance

To determine a trajectory of incremental savi ngs levels from the 2017 level to the best practices level, the EPA considered past perf ormance of individual program administrators 28 

### as well as requirements of existing state energy efficiency resource standards. For the past performance of individual program administrators, we first screened the data and divided them into moderate and high performing sub-groups. The moderate group (47 entities) was defined as programs that achieved from 0.8% to 1.5% maximum incremental savings levels and the high group (26 entities) was defined as programs that achieved greater than 1.5% maximum incremental savings levels. We then calculated the rate at which each entity had increased savings over time and calculated average values for each sub-group. For the moderate group, the average rate of improvement of incremental annual savings rate was 0.30% per year. For the high group, the average rate of improvement of incremental annual savings rate was 0.38% per year. See Appendix 5-3 for supporting data and analysis. The EPA also considered requirements of ex isting state EERS and evaluated the rate at which their incremental savings levels increase ove r time. For several EERS , we were unable to

26 See Preamble and State Plan Considerations TSD for descriptions of the schedule for state plan submittals. 27 See Preamble for description of interim and final compliance periods. 28 EIA 861 was the primary data source; however, we supplem ented EIA 861 data with data for third-party program administrators because prior to 2011 EIA did not co llect data from third-party program administrators. Comment [A29]: In the preamble, it is noted that states can have an additional year to submit a final state plan, or two additional years if submitting

a multi

‐ state plan. How is this reflected in terms of determining the start year for state efforts?

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clearly identify ramp-up schedules. We identified ten states with clear schedules and calculated the average rate of improvement for each. The average rate of improvement of incremental annual savings rate required for these ten states is 0.21% per year s . See Appendix 5-3 for supporting data and analysis. Based on these results, for the best practices rate of improvement the EPA has chosen 0.2% per year and 0.15% per year for Options 1 and 2, respectively. These values are conservative by comparison with our analysis of past state performance and future state requirements.

Step 6: Determine Average Portfolio Measur e Life and Distribution of Measure Lives

The next step in defining the best practices scenario requires projecting the cumulative future impacts of the annual incremental savings levels for each state. The incremental savings impacts reflect the savings from EE measures put in place in that year, driven by EE program activities in that year. The cumulative annual savings represent the total impacts of all EE measures put in place in that year and all prior years, due to EE program activities. The cumulative savings account for the continuing impacts of energy efficiency measures that remain in place for a period of time (the “measure life”) before being replaced. For example, the purchase of a high-efficiency refrigerator may lead to savings for twelve years, before being replaced with a new model. To estimate cumulative impacts of a series of years of incremental savings, the industry uses the concept of an average measure life for the entire portfolio of EE programs. Rather than use a single, average measure life to represent a diverse portfolio of programs, that range in measure lives from as little as a few years (e.g., certain lighting technologies and applications) to as long as fifteen or twenty years (e.g., adding insulation to an attic), the EPA is assuming a distribution of measure lives around the average to account for future impacts of incremental savings levels lxii



### . In 2014, ACEEE updated their 2004 and 2009 national reviews of EE program costs and related program characteristics, including measur e lives. They reviewed electricity EE program data from 20 states and summarized average measure lifetimes by state and customer class. Table 5-10 summarizes the results from the ACEEE study and shows an average across all sectors for these states of 10.6 years. Comment [A30]: Again if the improvements are not driven by 111(d) then they should be in the baseline.

Comment [A31]: What are the costs?

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TABLE 5-10 Average Electricity Measure lifetimes by state and customer class
Sector
All Sectors Residential Commercial/Business Industrial Average 8.1 12.5 9.5 10.6

Other studies have found slightly higher valu es for average measure life for EE portfolios, ranging from 10 to 13 years lxiii 

### . Our assumption of 10 years is conservative by comparison and leads to lower cumulative impacts over time and correspondingly lower state goals. To approximate a distribution of measure lives across an EE portfolio, consistent with an average measure life of ten years, we have assumed an even distribution from one year in length to two times the average measure life (twenty years) in length. Our approach is generally supported by the substantial range in measure lives reviewed and summarized in a 2014 study by LBNL which shows an interquartile range fro m five to 25 years across twelve program categories (e.g., low income, resi dential new construction, commer cial/industrial custom, etc.). Our approach represents a first-order approximati on of the distribution of measure lives across a diverse portfolio of programs. The more common approach in other studies is to assume a portfolio with no diversity of measure lives whatsoever, with the entirety of incremental savings being realized in each year from the first through the full average measure life and then dropping to zero in the following year. Our approach is a conservative one, leading to the same quantity of total energy savings, but with a greater portion of the savings occurring in later years than occurs with the more common, and simpler, approach. This results in lower cumulative impacts in earlier years and correspondingly lower state goals through 2030.

Step 7: Determine Sustainability of Best Practices Level of Performance

For construction of the best practices scenar io, once a state achieves the best practices level of performance, the EPA has kept the level of performance constant through 2030. For states with lower levels of current performance (and, hence, later achievement of the best practices level of performance – as late as 2025 in some instances), this requires sustaining the target level for as little as five years. For states currently at or above the best practices level of Comment [A32]: Consider referencing other

sources in addition to ACEEE data, including a forthcoming DOE technical memo and Northwest Regional Forum documents.

Comment [A33]: The distribution of measure lives is not uniform, but if you do want to use a uniform distribution, it should be longer, since LBNL has shown that some measures last 25 years. (Based

on data, the best estimate of the top end of a uniform distribution is the maximum of the observations.) If you use a uniform distribution from 0 to 25 years, and take the midpoint as your estimate, moving from 10 to 12.5 years would have

a significant impact.

Comment [A34]: Does this mean that they no longer make any improvements after the reach the best practices level of performance

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performance, this reflects an ability to sustain the target level for thirteen years (2017 through 2030). Limited empirical data suggests the reasonableness of this approach; however, comprehensive data, across all regions and stat es, does not exist because these levels of performance have not been achieved and sustained nationwide previously. The Northwest Power Conservation Council (NPCC) provides one such example. NPCC has been conducting the most consistent and long-running series of evaluations of achievable cost-effective potential in the country, updated every five years, as part of their five-state 29 

### regional energy resource plans lxiv



### . These analyses have become more detailed, reliable, and purposeful over time. Since 1998, NPCC’s estimates of achievable potential have mo re than tripled even as evaluated electricity savings from energy efficiency programs have increased rapidly, more than quadrupling between 1998 and 2010 (while levelized costs of saved energy achieved have remained flat), and exceeding plan targets every year since 2005. A study of the NPCC results concludes: “our research shows that when programs invest in hi gher levels of efficiency, this helps drive measurement improvements and technical innovation, resulting in larger and more reliable conservation supply estimates.” lxv 

### Table 5-11 summarizes the NPCC’s achievable potential estimates and evaluated savings since 1998 lxvi



### .

TABLE 5-11 NPCC Achievable EE Potential and Achieved Incremental Savings (1998-2010)

Year
1998 2005 2010

Achievable Potential over 20 Years (GWh) 13,447 24,651 51,684

Achieved Incremental Savings from EE Programs (GWh) 547 1,184 2,248

29 NPCC’s resource plans cover Idaho, Oregon, and Washingt on in their entirety, and western regions of Montana and Wyoming. Comment [A35]: EPA could reference the 12

‐ 13 year average measure life. This matches very well with the sustained period from 2017 to 2030.

Comment [A36]: Not peer

‐ reviewed. It would be good for EPA to provide peer

‐ reviewed evidence

on the efficacy of these types of DSM programs.

Formatted: Not Highlight

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Additional substantiation of this approach is provided by average annual achievable rates from reviewed studies, as discussed previously, and comparison of those w ith the rates resulting from the best practices scenario. We address this in a later section, Results in Context , after presenting those results.

Summary of Best Practices Scenarios Construction Table 5-12 provides a summary of inputs for the best practices scenarios for Options 1 and 2. The pace of improvements, average measure life, and distribution of measure lives are each conservative and, therefore, contribute lower state goals than would otherwise result. Similarly, the best practices level of performance, being based solely on results from and requirements of EE programs, is less stringent than a level would be that accounted for potential impacts of other state EE policies such as building energy codes, building energy benchmarking requirements, and state appliance standards. The use of 2012 level of performance for the 2017 start year, allows states that increase their use of effective EE policies prior to submitting their implementation plan to benefit.

Table 5-12 Summary of EE Best Practices Scenario Inputs Input Option 1 Option 2
Current Level of Performance (incremental savings as % of retail sales) Data from 2012 EIA 861 (2012) Data from 2012 EIA 861 (2012)

Best Practices Level of
Performance (incremental savings as % of retails sales) 1.5% 1.0%

Start Year 2017 2017
Start Year Level of
Performance Data from 2012 EIA 861 (2012) Data from 2012 EIA 861 (2012) Pace of Improvement 0.20% per year 0.15% per year

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(increase in incremental

savings rate per year) Average Measure Life and
Distribution of Measure Lives 10 years; evenly distributed across 20 years 10 years; evenly distributed across 20 years

Continued Performance Once achieved, best practices level sustained through 2030 Once achieved, best practices level sustained through 2025

Calculations
This section addresses the calculations fo r determining cumulative savings levels (cumulative savings as a percentage of baseline sales) for each state, for each year of the interim and final compliance periods for Options 1 and 2. The cumulative savings levels are derived based upon the key inputs summarized in Table 5-12. These levels represent the demand-side EE component of the state goals for each state. See the Goal Computation TSD for a detailed description of how the demand-side EE component is used as one of several inputs to the calculation of interim and final state emission rate goals. Calculating the net cumulative savings as a perc ent of electricity sales for each state involves six steps. For each state, for each year (2017-2030 for Option 1 and 2017-2025 for Option 2) the following steps are taken:

1. Determine annual business as usual (BAU) sales
2. Determine annual incremental EE savings as a percentage of sales
3. Determine annual incremental EE savings (GWh) and sales after net EE (GWh)
4. Determine annual expiring EE savings (GWh)

5. Determine net cumulative EE savings (GWh)
6. Determine net cumulative EE saving s as a percentage of BAU sales

To illustrate these calculations, each step is described and results provided for one state (using South Carolina as an example) for 2017 through 2025 for Option 1. We

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truncate the results at 2025 for simplicity, but full results are presented for all states in the
section.

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Step 1: Determine the Annual Business as Usual (BAU) Sales BAU sales are derived by taking 2012 sales from EIA Form 861 data for the state and increasing them for each subsequent year by the average annual growth rate from the AEO 2013 Reference Case for the region corresponding to the state. For South Carolina the corresponding region is SERC and the average annual growth rate from 2012 to 2040 is 1.10% per year. The resulting values are summarized in Table 5-13 for South Carolina.

TABLE 5-13
BAU Sales for South Carolina

Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
BAU Sales

(GWh) 82,451 83,359 84,278 85,206 86,145 87,094 88,054 89,024 90,005

Step 2: Determine Annual Incremental EE Savings as a Percentage of Sales As discussed, the 2017 value for annual incremental EE savings as a percentage of sales is set at the 2012 value based upon EIA-861 reported data. For South Carolina that value is 0.34%. This value is then increased by the pace of improvement of 0.2% per year (for Option 1) until the goal level of 1.50% (for Option 1) is reached and then held constant. The resulting values are summarized in Table 5-14 for South Carolina.

TABLE 5-14 Annual Incremental EE Savings as a Percentage of Sales for South Carolina

Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
Annual
Incremental EE Savings

(% sales) 0.34% 0.54% 0.74% 0.94% 1.14% 1.34% 1.50% 1.50% 1.50%

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Step 3: Determine Annual Incremental EE Savings (GWh) and Sales after net EE
Annual incremental EE savings are calculated by multiplying the annual incremental savings as a percentage of sales times the prior year sales after net EE. Sales after net EE are calculated by subtracting ne t cumulative savings from BAU sales. The resulting values are summarized in Table 5-15 for South Carolina.

TABLE 5-15 Annual Incremental EE Savings and Sales after Net EE Savings for South Carolina

Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
Annual
Incremental EE Savings

(GWh) 274 440 608 777 945 1,113 1,250 1,249 1,249

Sales after

Net EE (GWh) 82,177 82,660 83,008 83,229 83,333 83,329 83,258 83,264 83,346

Step 4: Determine Annual Expiring EE Savings (GWh) Expiring EE savings are calculated as the sum of all expired savings in a given year from all prior years’ incremental (first-year) savings based upon an average measure life of 10 years and a linear decline in first-year savings over twenty years. As an example, Figure 2 illustrates the decline in first-year savings from EE measures installed in 2017. The resulting values for expiring EE savings are summarized in Table 5-16 for
South Carolina.

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FIGURE 1 Generalized Distribution of First-Year Savings over Time.

TABLE 5-16 Expiring EE Savings for South Carolina

Year
2017 2018 2019 2020 2021 2022 2023 2024 2025 Expiring EE Savings

(GWh) 0 14 38 70 110 160 219 285 350

Step 5: Determine the Net Cumulative EE Savings (GWh) Net cumulative EE savings in a given year are equal to annual incremental savings for that year minus total expiring savings for that year plus net cumulative savings for the prior year. The resulting values are summarized for South Carolina in
Table 5-17.

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TABLE 5-17 Net Cumulative EE Savings for South Carolina

Year
2017 2018 2019 2020 2021 2022 2023 2024 2025

Net
Cumulative EE Savings

(GWh) 274 700 1,270 1,977 2,812 3,765 4,796 5,760 6,659

Step 6: Determine the Net Cumulative EE Savings as a Percentage of BAU Sales Net cumulative EE savings as a percenta ge of BAU sales are equal to net cumulative savings divided by BAU sales. Th e resulting values are summarized for South
Carolina in Table 5-18.

TABLE 5-18 Net Cumulative EE Savings as a Percentage of BAU Sales for South Carolina

Year
2017 2018 2019 2020 2021 2022 2023 2024 2025

Net
Cumulative EE Savings

as % of
BAU Sales 0.33% 0.84% 1.51% 2.32% 3.26% 4.32% 5.45% 6.47% 7.40%

Summary of General Formulas and Re sults by Step for South Carolina Tables 5-19 and 5-20 provide summaries of the generic formulas and results for South Carolina for each step.

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TABLE 5-19 Summary of Calculation Formulas by Step

Step Result Formula

1 BAU Sales (GWh) BAU Sales year i 

### = BAU Sale year i-1 

### * Annual Average Sales Growth Rate

2 Annual Incremental EE Savings (% of Sales) Annual Incremental EE Savings 2017 

### = Annual Incremental EE Savings 2012 

### ; Annual Incremental EE Savings year i 

### = Annual Incremental EE Savings year

i-1 

### + annual pace of improvement (until goal level is reached)

3 Annual Incremental EE Savings (GWh) Annual Incremental Savings year i 

### = Annual Incremental Savings as a
Percent of Sales year i 

### * Sales After Net EE year i-1



### 3 Sales after Net EE (GWh) Sales After Net EE year i 

### = BAU Sales year i 

### – Net Cumulative Savings year i



### 4 Expiring EE Savings (GWh) Expiring Savings year i 

### = Σ Expiring measures from all prior program years (10-year average measure life with linearly decline over 20 years)

5 Net Cumulative Savings (GWh) Net Cumulative Savings year i 

### = Σ Annual Incremental Savings YTD 

### – Expiring Savings year i



### 6 Net Cumulative Savings (% of Sales) Net Cumulative Savings year i 

### = Net Cumulative Savings year i 

### / BAU Sales

year i



### TABLE 5-20 Summary of Results by Step for South Carolina.

Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
BAU Sales

(GWh) 82,451 83,359 84,278 85,206 86,145 87,094 88,054 89,024 90,005

Annual
Incremental 0.34% 0.54% 0.74% 0.94% 1.14% 1.34% 1.50% 1.50% 1.50%

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EE Savings

(% sales)
Annual
Incremental EE Savings

(GWh) 274 440 608 777 945 1,113 1,250 1,249 1,249

Sales after

Net EE (GWh) 82,177 82,660 83,008 83,229 83,333 83,329 83,258 83,264 83,346

Expiring EE Savings (GWh) 0 14 38 70 110 160 219 285 350

Net
Cumulative EE Savings

(GWh) 274 700 1,270 1,977 2,812 3,765 4,796 5,760 6,659

Net
Cumulative EE Savings
as % of BAU
Sales 0.33% 0.84% 1.51% 2.32% 3.26% 4.32% 5.45% 6.47% 7.40%

Results Summary of Results As discussed, the EE goals for each state are represented as cumulative savings as a percentage of retail sales by year for each option. Table 5-21 summarizes these values for the first and last year of the interim compliance period for Options 1 and 2. See Appendix 5-4 for comprehensive results by state, for years 2017 through 2030, including both annual incremental and cumulative savings as a percentage of retail sales, for each options.

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TABLE 5-21 Summary of State EE Goals for Options 1 and 2

State EE State Goal Cumulative Savings as a % of Retail Sales

Option 1 Option 2

2020 2029 2020 2024

Alabama 1.36% 9.48% 1.07% 3.86%


### Arizona 1.52% 9.71% 1.24% 4.10%


### Arkansas 5.24% 11.42% 3.52% 5.98%


### California 4.95% 11.56% 3.55% 6.08%


### Colorado 3.92% 11.01% 3.32% 5.87%


### Connecticut 4.71% 11.88% 3.61% 6.25%


### Delaware 1.14% 9.47% 0.86% 3.59%


### District of Columbia 1.14% 9.47% 0.86% 3.59%


### Florida 2.03% 9.98% 1.75% 4.65% 

### Georgia 1.76% 9.83% 1.48% 4.36%


### Idaho 4.36% 11.63% 3.52% 6.15%


### Iowa 3.80% 11.10% 3.28% 5.88%


### Illinois 3.20% 11.11% 2.89% 5.70%


### Indiana 4.65% 11.66% 3.58% 6.17%


### Kansas 1.22% 9.52% 0.94% 3.70% 

### Kentucky 1.91% 10.02% 1.63% 4.55%


### Louisiana 1.14% 9.33% 0.85% 3.56%


### Maine 4.43% 11.77% 3.55% 6.21% 

### Maryland 4.21% 11.51% 3.47% 6.10%


### Massachusetts 5.37% 12.13% 3.61% 6.25% 

### Michigan 4.59% 11.77% 3.59% 6.22%

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Minnesota 4.80% 11.72% 3.58% 6.17% 

### Mississippi 1.58% 9.92% 1.29% 4.20%


### Missouri 1.40% 9.59% 1.12% 3.93%


### Montana 3.36% 10.90% 3.01% 5.69%


### Nebraska 2.84% 11.00% 2.56% 5.49%


### Nevada 2.37% 10.26% 2.09% 4.98% 

### New Hampshire 1.25% 9.58% 0.96% 3.74% 

### New Jersey 3.10% 10.60% 2.81% 5.50%


### New Mexico 4.42% 11.76% 3.54% 6.20%


### New York 1.39% 9.71% 1.11% 3.95%


### North Carolina 2.20% 10.40% 1.91% 4.89%


### North Dakota 2.95% 10.69% 2.67% 5.45%


### Ohio 4.17% 11.56% 3.47% 6.12%


### Oklahoma 1.86% 9.97% 1.57% 4.49% 

### Oregon 4.66% 11.41% 3.55% 6.06% 

### Pennsylvania 4.67% 11.69% 3.58% 6.18%


### Rhode Island 3.90% 11.56% 3.35% 6.06%


### South Carolina 2.32% 10.23% 2.04% 4.94%


### South Dakota 1.60% 9.91% 1.32% 4.22%


### Tennessee 2.21% 10.26% 1.93% 4.86%


### Texas 1.78% 9.91% 1.50% 4.40%


### Utah 3.62% 11.03% 3.19% 5.82%


### Vermont 1.23% 9.33% 0.95% 3.67% 

### Virginia 5.37% 12.13% 3.61% 6.25% 

### Washington 4.24% 11.26% 3.45% 6.00% 

### West Virginia 4.68% 11.79% 3.60% 6.22%


### Wisconsin 1.77% 10.11% 1.49% 4.44% 

### Wyoming 1.61% 9.73% 1.33% 4.19%


### Continental U.S. 3.05% 10.66% 2.44% 5.18%

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Alaska 1.22% 9.45% 0.94% 3.69%


### Hawaii 1.29% 9.52% 1.01% 3.79%


### U.S. Total 3.04% 10.65% 2.43% 5.17%



### Results in Context

To provide context for state cumulative savings results presented in Table 5-21 and Appendix 5-4, the average annual savings were calculated for each state through 2025 and 2030, starting from 2017. Table 5-22 summarizes the results.

TABLE 5-22 Summary of Average Annual Savings Rates of Best Practices Scenario

Option Years Number
of Years Range of Cumulative Savings (% of Sales)
across States in Last

Year (2025/2030) Range of Average Annual Savings
Rates across States National Average Annual

Savings Rate

1 2017-
2030 13 9.9% to 12.5% 0.76%/year to 0.96%/year 0.86% per year

2 2017-
2025 8 4.3% to 6.8% 0.54%/year to 0.85%/year 0.72% per year

The state range and national values for the average annual savings rate represented in the EE best practices scenario are below the range of values found in recent utility, state, and regional studies (1.2% to 1.5% per year) as summarized in Table 5-6, and within the range of values found in the 2014 national studies from EPRI and ACEEE (0.5%/0.6% to 1.6% per year) as summarized in Table 5-7. These results provide additional support for the feasibility of the EE best practices scenario and asso ciated state-specific EE goals.

Impacts Assessment Approach

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In the Goal Computation TSD, state-specific EE goals from the previous section are integrated with the other building blocks and used to set state-specific emission rate goals for the interim and final compliance periods. These state emission rate goals are then represented as requirements within the power sector modeling for the Regulatory Impacts Analysis (RIA). In addition, the EE state goals, resulting from the EE best practices scenario, are used to adjust electricity demand levels used as exogenous i nputs to power sector modeling for the illustrative compliance scenarios. In other words, the degree to which EE is employed as an abatement resource is not determined endogenously within the power sector modeling based upon optimization of costs but, rather, “hard wired” into the illustrative compliance scenarios. Thisapproach is taken because the EPA has determined, as discussed previously, that EE is costeffective at the established EE goal levels. The EE goal levels were constrained by practical considerations of state EE policy implementati on, specifically, the current levels of EE performance and the pace at which states can feasibly improve their levels of performance over
time. The EE goals represented in the illustrative compliance scenarios lead to substantial reductions in power system costs due to the reductions in specified electricity demand. Since EE is not represented endogenously as an abatement measure within the power sector modeling, the costs associated with the EE best practice scenario must be estimated outside of the power sector modeling and integrated with the results from that modeling. These EE cost estimates, their basis, and calculations are addressed in the following sections.

Inputs The following steps were taken to establish the inputs for development of the EE cost
estimates for each state.

 Step 1: Determine state-specifi c electricity savings by year

 Step 2: Determine first-year program costs of saved energy

 Step 3: Determine the ratio of program to participant costs

 Step 4: Determine the escalation rate of EE costs

Step 1: Determine State-Specific Electricity Savings by Year Comment [A37]: This section creates inputs that feed directly through to end costs. These costs are external to the IPM modeling and are simply added to the “rest of system” costs produced by IPM. The ultimate program costs are therefore sensitive to these input parameters. Highlighted below are several places where it seems EPA may have been overly conservative in its choice of parameters. Using other numbers that are cited in this report it is possible to deduce input costs that are only half

as high as what EPA uses. Even a more moderate calculation reduces input costs by roughly 20

‐ 30%. Since this rule BSER relies on high EE, even a few percentage points of reduction in these EE inputs may lead to significant changes in the RIA cost calculations. Please consider revisiting EE cost inputs.

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Results from the previous section, State Goal Setting, provide the starting point for estimation of EE costs. From those results, st ate-specific annual incremental savings (MWh) and yearly distribution of associated continuing savings (MWh) in future years are used as inputs to
the cost estimation calculations.

Step 2: Determine First-Year Program Costs of Saved Energy First-year program costs refer to the full costs (e.g., administration, incentive payments, marketing, information to consumers, etc.) incu rred by a utility or other administrator of EE programs in a given year that lead to EE measures (technologies and practices) put in place in that year and resulting in reductions in electricity demand in that and future years (driven by the mix of measure lives across the portfolio of EE programs employed). Unlike participant costs, program costs are readily known by the administrator of EE programs and are, therefore, an appropriate starting point for EE program cost analysis. In 2009, ACEEE conducted a national review of data on EE program costs from program annual reports, evaluation reports, and information compiled from contacts at program administers in 14 states. Compiled data was sourced from multiple EE program administrators in each state and over multiple years of data for each administrator. ACEEE found average first-year net 30 

### costs of $275/MWh (2011$). The EPA has used this value for our analysis. Two recent national analyses have found slightly substantially (16-36%) lower program costs than the 2009 ACEEE study. In 2014, ACEEE updated their analysis from 2009, expanding the number of states to 20, and incl uding a greater number of program administrators and years. In this analysis ACEEE found average first-year net costs of $230/MWh (2011$). In 2014, an LBNL study presented results from a uniquely comprehensive study of EE program costs. The LBNL analysis reviewed program-level data from over 100 program administrators in
31 states. Data w asere collected from over 1,700 individual programs for up to three years (2009-2011), covering more than 4,000 individual program years of data points. Because of the broad scope of their study and the lack of net savings information for many programs, LBNL

30 “Net costs” refers to costs per electricity saved after accounting for effects of free-ridership on those savings. Depending upon the state, spillover effects may also be accounted for in net costs. Comment [A38]: Some commenters recommend using either the most recent ACEEE analysis, with a first

‐ year net cost of $230/MWh or the recent LBNL study, with a calculated first

‐ year net cost of $175/MWh. If used, these numbers would lead to a cost of saved energy more in line with the available literature than what EPA is currently proposing.

Comment [A40]: Suggest EPA analysis use these

more recent and more robust numbers. Comment [A39]: The RFF discussion paper “Supply Curves for Conserved Electricity” at http://www.rff.org/documents/RFF

‐ DP

‐ 11

‐ 11.pdf has cost estimates that could be included: “Under the scenarios modeled, electricity savings of between 1 and 3 percent are achievable at a marginal cost of $50 per megawatt hour (MWh) and

a corresponding average cost of $25–$35/MWh.”

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focused on gross 31 

### , rather than net, savings values. LBNL found national average first-year cost of gross savings of $162/MWh (2012$). Applying an average net-to-gross ratio of 0.9 and deflating costs at 3%, results in an estimated national average first-year cost of net savings of $175 (2011$). The up-to-date, more comprehensive results from the ACEEE and LBNL studies, indicate that the value of $275/MWh used for this analysis is conservative, resulting in comparatively higher total costs than would be the case based upon the newer studies.

Step 3: Determine the Ratio of Program to Participant Costs As noted above, while program costs are readily known and consistently reported by the program administrator, participant costs requir e significant effort to estimate, and are less consistently estimated and reported. The ratio between program and participant costs will vary significantly from one program to the next with in a utility’s portfolio. The EPA has used a generic approach to estimate the ratio of program to participant costs across an entire portfolio, thus providing for the estimation of total costs once program costs are determined. To derive the ratio, the EPA reviewed 2012 EE annual reports from program administrators in 22 states identified as leaders in EE programs 32 

### based upon their magnitude of savings or their savings as a percentage of retail sales. Complete inform ation on full portfolio participant costs were
available for nine of the 22 states. Across the nine states, the average program and participant costs as a percentage of total costs were 53% and 47%, respectively. See Appendix 5-3 for the data and analysis documenting this review. Based on this review, the EPA has taken a slightly conservative approach 33 

### and used a ratio of 1:1 between program and participant costs. We use this ratio to derive participant and total costs based upon program costs. Starting from program CSE of $275/MWh and applying the 1:1 ratio, we estimate participant CSE of $275/MWh and total CSE of $550/MWh (all values 2011$).

Step 4: Determine the escalation rate of EE costs

31 “Gross savings” refers to electricity savings before an y accounting for effects of free-ridership or spillover. 32 Leaders were identified using re sults from the 2013 ACEEE State Energy E fficiency Scorecard based on energy savings as a percentage of retail sales or total savings. 33 If we had used the 53% and 47% values, starting from prog ram costs, total costs would have been slightly lower than calculated with the 1:1 split used. Comment [A41]: It may not be necessary to take the most conservative assumption particularly when there have been subsequent refinements to the study that may create more reliable numbers. In addition, it may not be appropriate to use this old number as a single point estimate to calculate EE program costs for this rule. Please consider revisiting if appropriate.

Comment [A42]: A commenters suggest DOE may have updated participant cost data.

Comment [A43]: If possible, please provide further support for using a number different than EPA’s calculations. If the derived number is used (53/47), the total program costs would drop by about 6%. This again inflates EE costs relative to what the empirical evidence suggests they would be.

Comment [A44]: EPA seems to have taken a conservative approach to costs that may not be justified by the empirical literature. Please consider revisiting if appropriate.

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The level of EE program impacts represented in the state EE goals are substantial and represent a scenario that has not previously been achieved and sustained at a national level in the U.S. Thus, even though the EPA has taken a conservative approach (i.e., leading to higher estimates of costs) to the development of the EE st ate goals as well as to other factors that affect the EE cost estimates, we have also chosen to take a cautious approach to the escalation of EE costs at higher levels of performance (i.e., as states improve from their historic levels of incremental savings to the best practices level of 1.5% of retails sales). Economic theory suggests two mechanisms that would change EE costs as higher levels of performance are achieved. Economies of scale in the operation of larger EE programs and larger portfolios of EE programs, and learning and expertise gained over time from the continued implementation of programs, are two factors that would lower costs as programs scale up and expand to realize higher levels of performance. However, the limited supply of EE abatement measures and the need to employ higher cost measures, over time, to reach higher levels of performance, and to sustain high levels of performance, are factors that would increase costs as higher levels of performance are achieved. Analysis based upon limited empirical data does provide support for significant economies of scale and/or cost reduc tions over time as learning and expertise are gained lxvii 

### . “Supply curves” of EE as an energy resource, as well as EE as a measure represented within a GHG abatement curve, provide support fo r escalating costs as higher levels of savings
are realized lxviii 

### . In a recent analysis, Lawrence Berkeley National Laboratory (LBNL) adopted an approach that generically represented both effects discussed above lxix 

### . LBNL changed EE costs (first-year program costs) as a function of EE savings levels, decreasing costs at savings levels up to 0.5%, leaving costs constant at the base level at savings levels from 0.5% to 1.5%, and increasing costs at savings levels above 1.5 %. Another recent analysis, by ACEEE, provides weak statistical support for a cost increase of 20% when going from 0.5% to 1.0% savings rate and an additional cost increase of 20% when going from 1.0% to 1.5% savings rate lxx



### . In consideration of the above discussion, th e EPA has chosen to escalate EE costs over three steps as a function of incremental savings (as a percentage of electricity sales) at the state level. Until a state reaches a 0.5% savings level, their costs are set at the base level; for savings levels between 0.5% and 1.0%, state costs are escalated to 120% of the base level; and for savings levels over 1.0%, state costs are escalated to 140% of the base level. This approach leads Comment [A45]: On the timescales of this rule (16 years) we can expect a reasonable amount of technological change to be occurring over that period as well, which would offset some of the cost escalation from extracting new supply. Please consider incorporating or accounting for this. Comment [A46]: Please consider incorporating literature on learning and cost reductions over time

across many technologies, and particularly in many energy technologies. Comment [A47]: EPA methodology seems to select only the supply curve dimension and not the cost improvement dimension. DOE now uses cost improvements as part of their efficiency rulemaking

so it seems appropriate to apply that principle here. Please consider revisiting if appropriate. Comment [A48]: This is quite different from the approach EPA chose. The LBNL study takes into consideration more programs and more driving factors and seems to be a robust study. Please consider revisiting EPA’s approach if appropriate. Comment [A49]: Also came in lower than the approach EPA chose. Please consider revisiting approach.

Comment [A50]: Here is a third example of possibly overly conservative assumptions. Reasonable alternates are to use 100% to base level and then an adder, say 120%, above 1.5. LBNL actually discounts from 0 to 0.5 level. If a cost escalation to 140% is justified, please consider doing it after 1.25 of base.

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to higher costs relative to the one used by LBNL when applied to EPA’s EE best practices
scenario.

Summary of Inputs for EE Cost Analysis Table 5-23 provides a summary of inputs for the EE cost analysis including first-year costs of saved energy, ratio of program to participant costs, and escalation of costs as a function of the rate of incremental savings. Each of these factors incorporates some level of conservatism, leading to higher costs than would otherwise result.

Table 5-23 Summary of EE Cost Analysis Inputs Input Source or Assumption State-Specific Electricity Savings by Year Results from state goal setting First-Year Program Cost of Saved Energy $275/MWh (2011$)

Ratio of Program to Participant Costs 1:1 First-Year Participant Cost of Saved Energy $275/MWh (2011$) First-Year Total Cost of Saved Energy $550/MWh (2011$)

Escalation of Costs Incremental savings rate
0.5% - 1.0% > 1.0%
120% of base costs: $660/MWh (2011$) 140% of base costs: $770/MWh (2011$)

Summary of Alternate EE Cost Anal ysis Inputs based on ACEEE 2014 Input Source or Assumption State-Specific Electricity Savings by Year Results from state goal setting First-Year Program Cost of Saved Energy $230/MWh (2011$)

Ratio of Program to Participant Costs 53:47 First-Year Participant Cost of Saved Energy $204/MWh (2011$) First-Year Total Cost of Saved Energy $434/MWh (2011$)
Escalation of Costs Incremental savings rate Comment [A51]: Shown below that the EE cost inputs can, under defensible and reasonable alternate assumptions, range from 34% to 50% lower; and are very likely to be at least 10

‐ 15% lower, than the numbers in this original Table 5

‐ 23. Suggest that EPA revisit the set of assumptions and modify accordingly.

Comment [A56]: To clarify, we are not suggesting these are redline additions, but instead

are providing the information for illustrative purposes. Comment [A52]: Using only information provided in the preceding paragraphs about cost inputs, derived below are two alternate price inputs,

one based on the LBNL study and one based on the updated ACEEE numbers.

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0.5% - 1.0% > 1.0%
100% of base costs: $434/MWh (2011$) 120% of base costs: $520/MWh (2011$)

Summary of EE Cost Analysis Inputs based on LBNL Input Source or Assumption State-Specific Electricity Savings by Year Results from state goal setting First-Year Program Cost of Saved Energy $175/MWh (2011$)

Ratio of Program to Participant Costs 53:47 First-Year Participant Cost of Saved Energy $155/MWh (2011$) First-Year Total Cost of Saved Energy $330/MWh (2011$)

Escalation of Costs Incremental savings rate
0.5% - 1.0% > 1.0%
100% of base costs: $330/MWh (2011$) 120% of base costs: $396/MWh (2011$)

Calculations
This section addresses the calculations for esti mating the costs associated with the statespecific EE goals discussed above. The results of these calculations are then used within the RIA and preamble. Specifically, three values are calculated (annual first-year costs, levelized cost of saved energy (LCSE), and annualized costs); for each, program and participant components are then calculated using the 1:1 ratio (i.e., 50% of total for each) derived above. Specific results from prior sections on state goal setting and im pacts assessment inputs are used as inputs for these calculations. For each state, the following steps are taken for each year (2017-2030) and for each option. Calculations for steps 2 and 3 are done using real discount rates of 3% and 7%. The steps are:
1. Calculate annual first-year costs Comment [A53]: As stated above this escalation

can be treated in a couple of ways based on the cited literature. Using the following:

0 To 1 = 100% 1 To 1.5 = 120%

Alternate suggestion (or some variation thereon): 0 to 0.5 = 80% 0.5 to 1 = 100% 1 to 1.25 = 120%

1.25 to 1.5 = 140%

Comment [A54]: This uses LBNL seed data plus 100/120 escalation.

Comment [A55]: Please compare these final numbers to the original calculation of 660/770 $/MWh and consider revisiting approach. Comment [A57]:

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2. Calculate levelized cost of saved energy (LCSE)
3. Calculate annualized costs To illustrate these calculations, each step is described and results are provided for one state (using South Carolina as an example) for 2017 through 2025 for Option 1. The results are truncated at 2025 for simplicity, but full national results (through 2030) are presented below.

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Step 1: Calculate Annual First-Year Costs Annual total first-year costs are calculate d by multiplying annual total incremental savings (MWh) (from Table 5-15) by the firs t-year total CSE (from Table 5-23 with escalation based upon results from Table 5-14). Program and participant portions of the first-year costs are then calculated as 50% of total first-year costs for each per Table 5-23. The resulting values are summarized for South Carolina in Table 5-24.

TABLE 5-24
Calculation of Annual First-Year Costs for South Carolina

Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
Annual
Incremental

Savings (GWh) 274 440 608 777 945 1,113 1,250 1,249 1,249

First-Year
Total Cost of Saved Energy (2011$/MWh) $550 $660 $660 $660 $770 $770 $770 $770 $770

First-Year
Total Cost (millions 2011$) 151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7

First-Year Program (millions of

2011$) 75.3 145.3 200.7 256.3 363.9 428.5 481.2 480.8 480.9

First-Year Participant (millions of 75.3 145.3 200.7 256.3 363.9 428.5 481.2 480.8 480.9

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2011$)

Step 2: Calculate Levelized Cost of Saved Energy Levelized costs of saved energy (LCSE) are based on levelization of all savings (first and future years) resulting from EE activities in a given year. The levelization algorithm is based on the 2002 California Standard Practice Manual lxxi 

### . The net present value of all savings from a single year’s EE act ivities (i.e., over the entire distribution of program lifetimes) is calculated using the real discount rate. The levelized cost of saved energy is then calculated by dividing the annu al first-year costs (from Table 5-24) by the levelized savings. The resulting values are summarized for South Carolina in Table 5-25.

TABLE 5-25 Levelized Cost of Saved Energy for South Carolina (at 3% discount rate)

Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
Levelized Savings (GWh) 2,313 3,720 5,139 6,563 7,987 9,405 10,562 10,553 10,554

First-Year
Total Cost (millions 2011$) 151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7

Total
LCSE 6.51 7.81 7.81 7.81 9.11 9.11 9.11 9.11 9.11

Program

LCSE 3.25 3.91 3.91 3.91 4.56 4.56 4.56 4.56 4.56

Participant
LCSE 3.25 3.91 3.91 3.91 4.56 4.56 4.56 4.56 4.56

Step 3: Calculate Annualized Costs Comment [A58]: There seems to be an error in the calculation of LCSE in Table 5

‐ 25 below (factor of 10 difference). Please check.

Comment [A59]: Might want to check calculation

– seems there is a factor of 10 difference?

Comment [A60]: Unit?

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Annualized costs are calculated by multip lying the LCSE for each year by the remaining savings in each year through the full distribution of measure lifetimes. For each year in the analysis, the annualized costs resulting from all current and past investments are summed to calculate the total annualized costs. The resulting values are
summarized for South Carolina in Table 5-26.

TABLE 5-26
Annualized Costs for South Carolina

Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
Annualized
Total Costs

(millions 2011$) 17.8 51.3 96.0 151.4 229.1 317.6 413.2 502.7 586.2

Annualized

Program

Costs (millions 2011$) 8.9 25.6 48.0 75.7 114.6 158.8 206.6 251.3 293.1

Annualized Participant
Costs (millions of

2011$) 8.9 25.6 48.0 75.7 114.6 158.8 206.6 251.3 293.1

Summary of General Formulas and Re sults by Step for South Carolina Tables 5-27 and 5-28 provide summaries of the generic formulas and results for South Carolina for each step.

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TABLE 5-27 Summary of Calculation Formulas by Step

Step Result Formula

1 Annual FirstYear Costs

(2011$) Annual First-Year Costs year i 

### = Annual Incremental Savings x First-Year CSE year i 

### First-Year CSE = f(incremental savings rate) per Table 5-23

2 Levelized Savings (GWh) Levelized Savings i 

### = NPV [Lifetime Savings from EE Investments year i 

### at Discount Rate]

2 LCSE (2011$/MWh) Levelized Cost of Saved Energy year i 

### = Incremental Cost year i 

### / Levelized Savings year i



### 3 Annualized Costs (2011$) Annualized Cost of Saved Energy year i 

### = (LCSE year i 

### x Remaining Savings year i 

### ) + (Levelized Cost of Saved Energy year i-1 

### x Remaining Savings year i-1 

### ) + ... + (Levelized Cost of Saved Energy year i-n 

### x Remaining Savings year i-n 

### )

TABLE 5-28 Summary of Results by Step for South Carolina.

Year
2017 2018 2019 2020 2021 2022 2023 2024 2025
Total FirstYear Costs (millions 2011$) 151.6 290.5 401.4 512.6 727.8 857.1 962.5 961.6 961.7

Total LCSE (2011$/MWh) 65.1 78.1 78.1 78.1 91.1 91.1 91.1 91.1 91.1

Annualized
Total Costs (millions 2011$) 17.8 51.3 96.0 151.4 229.1 317.6 413.2 502.7 586.2

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Results Summary of National Results Tables 5-29 and 5-31 summarize the national first-year and annualized EE costs for Option 1 for 2018, 2020, 2025, and 2030. Table 5-30 summarizes national LCSE for Option 1 for the same years. Each of the three tables in cludes values for program , participant, and total

costs. TABLE 5-29 First-Year EE Costs (3% discount rate, billions 2011$) (Continental U.S.)

Year
2018 2020 2025 2030 Program 10.2 15.4 21.8 21.8 Participant 10.2 15.4 21.8 21.8

Total 20.5 30.7 43.6 43.5

TABLE 5-30 Levelized Cost of Saved Energy (3% discount rate, 2011$/MWh) (Continental U.S.)

Year
2018 2020 2025 2030 Program 42 43 45 45 Participant 42 43 45 45

Total 83 85 89 90

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TABLE 5-31 Annualized EE Costs (3% discount rate, billions 2011$) (Continental U.S.)

Year
2018 2020 2025 2030 Program 2.0 5.1 14.4 21.4 Participant 2.0 5.1 14.4 21.4

Total 4.1 10.2 28.9 42.7

See Appendix 5-4 for comprehensive data sheet s of EE cost results at the national level by year for Options 1 and 2, and at discount rates of 3% and 7%. These data sheets provide results of LCSE (total, program and participant), first-year costs (total, program and participant), and annualized costs (total, program and participant).
Results in Context To provide context for the pace of increase in EE program spending levels represented by Option 1, we consider the compound annual growth rate (CAGR) of the recent rapid increase in historic investment (2006 to 2011) and the CAGR from 2011 through 2018, 2020, and 2025 represented by Option 1 program costs. Historic data is from Table 5-2 and Option 1 data is from Table 5-29. Table 5-32 provides a summary of the results. The CAGRs represented by Option 1 through 2018, 2020, and 2025 vary from 8% to 11%. The historic growth rate reflecting the rapid recent growth in EE program spending is 30%, roughly three times the Option 1 values.
TABLE 5-32 Comparison of Historic and Projected (Option 1) Annual Growth Rates

Time Period (Years) Compound Average Growth Rate Historic (2006-2011) 29.8% Option 1 (2011-2018) 8.1% Option 1 (2011-2020) 11.3% Option 1 (2011-2025) 9.8%

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## Appendices

Appendix 5-1: Summary of Recent (2010-2014) Electric Energy Efficiency Potential Studies Appendix 5-2: Incremental Electricity Savings Pace of Improvement Analysis Appendix 5-3: Review of the Ratio of Program to Participant Costs Appendix 5-4: Comprehensive Results: State Goal Setting and Impacts Assessment

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## Appendix 5-1 Summary of Recent (2010-2014) Electric Energy Efficiency Potential Studies The following table summarizes estimates of economic and achievable energy efficiency potential from a number of recent studies (2010-2014) for states, utilities, and other agencies across the U.S. Study periods ranged from five to twenty-one years in length. As Table 1 shows, across the eleven studies that reported achievable potential, results for average annual achievable potential range from 0.8% per year to 2.9% per year (of baseline sales) with an average of 1.5%

per year.

TABLE 1 Summary of Recent (2010-2014) Electric Energy Efficiency Potential Studies

State Client Analyst Study
Year Study
Period End-year Projected
Potential as % of

Baseline Sales Average Annual Projected
Potential as % of Baseline
Sales

Economic Achievable Economic Achievable

Arizona Salt River Project Cadmus Group 2010 2012-
2020 29% 20% 3.2% 2.2%

California California Energy
Commission California Energy
Commission 2013 2014-
2024 Not reported 9.6% N/A 0.9%

Colorado Xcel Energy Kema, Inc. 2010 2010-
2020 20% 15% 1.8% 1.4%

Delaware Delaware
DNR/DEC Optimal Energy,

Inc. 2013 2014-
2025 26.3% Not reported 2.2% N/A

Illinois ComEd ICF International 2013 2013-
2018 32% 10% 5.3% 1.7%

Michigan Michigan PSC GDS Associates 2013 2013-
2023 33.8% 15% 3.1% 1.4%

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New Jersey Rutgers University EnerNOC Utility
Solutions 2012 2010-
2016 12.8% 5.90% 1.8% 0.8%

New Mexico State of New
Mexico Global Energy

Partners 2011 2012-
2025 14.7% 11.1% 1.1% 0.8%

New York ConEd Global Energy

Partners 2010 2010-
2018 26% 15% 2.9% 1.7%

Pacific
Northwest

(Idaho, Montana, Oregon, Washington) US Department of Energy Lawrence Berkeley
National Laboratory 2014 2011-
2021 11% Not reported 1.9% Not reported

Pennsylvania Pennsylvania
PUC GDS Associates
and Nexant 2012 2013-
2018 27.2% 17.3% 4.5% 2.9%

Tennessee Tennessee Valley Authority Global Energy

Partners 2011 2009-
2030 24.8% 19.8% 1.1% 0.9%

Range 0.8% - 2.9%

per year

Average 1.5% Per year



### References Arizona: The Cadmus Group, Inc. 2010. Salt Ri ver Project 2012-2017 Energy Efficiency Plan – Final Report. Prepared for Salt River Project, April 16.

Colorado: Kema, Inc. 2010. Colorado DSM Ma rket Potential Assessment – Final Report. Prepared for Xcel Energy, March 12.

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Delaware: Optimal Energy, Inc. 2013. Delawa re Economic Energy Efficiency Potential. Prepared for the Delaware Department of Natural Resources and Environmental Control, May
24.

Illinois: ICF International. 2013. ComEd Energy Efficiency Potential Study Report, 2013-2018. August 20.

Michigan: GDS Associates, Inc. 2013. Michigan Electric and Natural Gas Energy Efficiency Potential Study – Final Report. Prepared for the Michigan Public Service Commission,
November 1.

New Jersey: EnerNOC Utility Solutions Consulting. 2012. New Jersey Energy Efficiency Market Potential Assessment, Volume 1: Executive Summary. Report Number 1401, Prepared for Rutgers, The State University of New Jersey, October 17, 2012.

New Mexico: Global Energy Partners. 2011. Energy Efficiency Potential Study for the State of New Mexico, Volume 2: Electric Energy Efficiency Analysis. June 30.

New York: Global Energy Partners, LLC. 2010. Energy Efficiency Potential Study for Consolidated Edison Company of New York, Inc., Volume 2: Electric Potential Report. March.

Pacific Northwest states (combined): Barbose, Galen, and Alan Sanstad, Charles Goldman, Stuart McMenamin, Andy Sukenik. 2014. Incorporating Energy Efficiency into Western Interconnection Transmission Planning. Draft report, Lawrence Berkeley National Laboratory, January.

Pennsylvania: GDS Associates, Inc., and Nexant. 2012. Electric Energy Efficiency Potential for Pennsylvania – Final Report. Prepared for the Pennsylvania Public U tilities Commission, May
10.

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Tennessee: Global Energy Partners. 2011. Tennessee Valley Authority Potential Study, Final Report, Volume 1: Executive Summary. Report Number 1360, December 21.

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## Appendix 5-2 Incremental Electricity Savings Pace of Improvement Analysis

This appendix summarizes and analyzes data to characterize the pace of improvement of incremental (or first-year) savings as a percentage of retail sales for electricity energy efficiency (EE) programs. We considered two different pers pectives: 1) historical data reflecting achieved savings of EE programs and 2) requirements of existing state energy efficiency resource standards (EERS). For the historical perspective, we reviewed data from the Energy Information Administration’s Form EIA-861 on EE program el ectricity savings (supplemented as needed with program administrator reports) and identi fied the pace at which entities reaching higher savings levels have historically increased energy savings over time. 34 

### Specifically, we reviewed the historical savings data in the following two groups of energy efficiency program
administrators.

1. Top saver 1% - a group with 47 entities that achieved a maximum first-year savings level
of 0.8% to 1.5%.

2. Top saver 2% - a group with 26 entities that achieved a maximum first-year savings level
of 1.5% to 3.0%. 35



### For the existing state requirements perspective, we reviewed energy savings ramp-up schedules established under EERS for states that provide clear ramp-up schedules. According to ACEEE’s 2013 State Energy Efficiency Scorecard lxxii 

### , there are a total of 26 states that have

34 The EIA 861 was the main data source. However, we ha ve supplemented the EIA 861 with third-party program administrator data because the EIA 861 just started to collect third-party administrator data in 2011. The third-party entities included in our analysis are Efficiency Vermont, En ergy Trust of Oregon, Efficiency Maine Trust, and Cape Light Compact. In addition, we supplemented the EIA 861 da tabase with additional data for two utilities that we found achieved high energy savings, but did not report savings data in the EIA 861 data for one or two years. These entities are Burlington Electric and Massachusetts Electric Company (now part of National Grid). 35 In addition to these maximum first-year savings threshol ds, we screened program administrators for the following conditions: (a) the maximum savings levels occurs after the minimum savings levels; (b) sufficient amounts of increase in first-year savings are provided to evaluate reas onable ramp-up schedules to gain an incremental 1% firstyear savings; and (c) savings data series are continuous between the years for the minimum and maximum savings levels. Formatted: Not Highlight

Comment [A61]: Some commenters recommend referencing DSIRE as the data source on which states have EERSs rather than ACEEE scorecard.

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mandatory EERS policies. 36 

### Our analysis contains 10 states for which clear ramp-up schedules
were identifiable.

Our research findings on historical savings performance are:

 The “Top Saver 1%” group (savings between 0.8% and 1.5%) exhibits a trend that these
entities took or would take about 3.4 years on average to increase first-year savings by 1% (with a range of 1.6 years to 10 years) (see Table 1 ). The entities in this group have increased the level of first-year savings by 0.30% per year on average from their minimum to their maximum first-year savings levels (with a range of 0.10% per year to 0.63% per year). 37



###  The “Top Saver 2%” group (savings between 1.5% and 3%) exhibits a trend that took or
would take about 2.6 years on average to increase savings by 1% (with a range of 0.8 years to 7.3 years) (see Table 1 ). The entities in this group have increased the level offirst-year savings by 0.38% per year on average from the minimum to the maximum firstyear savings levels (with a range of 0.14% per year to 1.28% per year). 38



### Table 1. Energy savings ramp-up trends in first-year savings for “Top Saver 1%” and “Top Saver 2%” groups 39



### Top Saver 1% Top Saver 2%

36 ACEEE, 2013 State Energy Efficiency Scorecard, Appendix B, November 2013, 37 This is a simple average estimate of the annual averag e increase in first-year savi ngs from each entity in this group. 38 This is the simple average estimate of the annual average increase in firs t-year savings from each entity in this group. 39 Data sources: EIA-861; ISO New England, “Compilation of raw PA data for the 2013 EE Forecast,” Jun 21, 2013; energy efficiency program administrators’ 2012 energy efficiency annual reports for the following entities: Burlington Electric Department, Efficiency Vermont, Effi ciency Maine, Energy Trust of Oregon, Cape light Compact, and Wisconsin Focus on Energy.

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Average
Annual
First-Year

Savings
Increase Estimated
Years to

Gain
Incremental
1% Average
Annual
First-Year

Savings
Increase Estimated
Years to

Gain
Incremental
1% Average 0.30% 3.4 0.38% 2.6
Median 0.29% 3.4 0.34% 3.0

Max 0.63% 1.6 1.28% 0.8
Min 0.10% 10 0.14% 7.3 # of sample entities 47 26

Our research findings on incremental electrici ty savings ramp-up based on existing state EERS policies are:

 The states with EERS policies which exhi bit savings ramp-up schedules are requiring increases in first-year energy savings at a pace that ranges from 0.11% (Colorado and Oregon) to 0.40% (Rhode Island) as shown in Table 2.

 The first-year savings pace of increase averag es 0.21% per year across the 10 states. This savings level translates to about 4.7 years to achieve an incremental 1% first-year savings
increase.

Table 2. First-Year Energy Savings Ramp-up Review of State EERS Policies 40



### State Minimum Target Maximum Target Climb
Time (years) Annual Average %

Increase Years to
Achieve 1%

Increase Min Year Max Year a b c d e=d-b f=(c-a)/e g=1/f
Arizona 1.25% 2011 2.5% 2016 5 0.25% 4.0

40 Data sources: ACEEE, 2013 State Energy Efficiency Scorecard, Appendix B, November 2013, Arkansas Public Service Commission, Docket Nos. 13-002-U. Order No. 7, September 9, 2013.

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Arkansas 0.25% 2011 0.9% 2015 4 0.16% 6.2
Colorado 0.80% 2011 1.7% 2019 8 0.11% 9.3

Illinois 0.20% 2008 2.0% 2015 7 0.26% 3.9
Indiana 0.30% 2010 2.0% 2019 9 0.19% 5.3
Massachusetts 1.4% 2010 2.6% 2015 5 0.24% 4.2

Michigan 0.3% 2009 1.0% 2012 3 0.23% 4.3

Ohio 0.3% 2009 1.2% 2019 10 0.17% 5.9 Oregon 0.8% 2010 1.0% 2013 3 0.07% 15.0
Rhode Island 1.7% 2011 2.5% 2013 2 0.40% 2.5

Average 0.21% 4.8

References MidAmerican Energy Company, "Energy Efficiency Plan Docket No. EEP-08-2, 2012 Annual Report to the Iowa Utilities Boar d," May 1, 2013, available at: http://www.state.ia.us/government/com/util/energy/energy_efficiency/ee_plans_reports.html Efficiency Maine Trust, "2012 Annual Report of the Efficiency Maine Trust," November 30, 2012, revised February 12, 2013, available at: http://www.efficiencymaine.com/docs/2012- Annual-Report.pdf National Grid Electric, "2013-2015 Massachusetts Joint Statewide Three-Year Electric and Gas Energy Efficiency Plan," Docket No. 12-109, November 2, 2012, available at: http://www.env.state.ma.us/dpu/docs /electric/13-120/8113ngptapb.xlsx PECO Energy Company, "Final Annual Report for the Pennsylvania Public Utility Commission, for the Period June 2012 through May 2013, Program Year 4," Docket No. M-2009-2093215, November 15, 2013, available at: http://www.puc.state.pa.us//pcdocs/1260111.pdf Efficiency Vermont, "Annual Report 2012," October 18, 2013, available at: http://www.efficiencyvermont.com/docs/about_efficiency_vermont/annual_reports/EfficiencyVermont-Annual-Report-2012.pdf

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## Appendix 5-3 Review of the Ratio of Program to Participant Costs

Introduction and Summary This appendix summarizes and analyzes data on EE costs (program and participant) to develop a ratio to enable the estimation of participant costs from known program costs. We reviewed cost data from leading EE program admi nistrators in 22 states. Our research findings
are as follows:

 A 1:1 ratio between program and participant costs is a reasonable and slightly conservative (i.e., slightly higher total costs) basis for estimating participant costs from known program costs.

 Reported data was reviewed from 22 states; however, program administrator reports from only nine states contained sufficient inform ation (participant costs across entire portfolio of EE programs) to inform the analysis.

 Participant cost data from ten program admini strators in nine states indicate that the weighted average and simple average participant costs were 47 percent of total costs.

Participant Cost Analysis We first identified states having high incremental electricity savings rates or high absolute savings levels based upon 2013 ACEEE State Energy Efficiency Scorecard. 41 

### These states represent a large portion of total EE savings in the U.S. We identified 22 states meeting these criteria and collected publicly available EE program reports for major program
administrators within each state. From thes e program reports we identified 10 program administrators across nine states where we were able to identify both program administrator and participant costs across their full portfolio of EE programs. The table below provides the 2012 portfolio-level program administrator and par ticipant costs for the nine states. Program

41 For the purpose of this research, we have defined leadi ng or high impact states as the top 15 states in the 2013 ACEEE State Energy Efficiency Scorecard in terms of incremen tal savings as a percentage of retail sales or absolute annual energy savings in terms of total annual MWh savings. These criteria resulted in a total of 22 states which include Arizona, California, Connecticut, Florida, Hawaii, Illinois, Indiana, Iowa, Maine, Massachusetts, Michigan, Minnesota, New Jersey, New York, North Carolina, Ohio, Oregon, Pennsylvania, Rhode Island, Texas, Vermont, and Washington.

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administrator costs represent the program administrator’s program development and implementation costs, and the participant costs represent the customer costs to partake in the program. Total program costs are the sum of both costs. Each state’s program administrator and participant costs are presented as a percentage of total program costs in Table 3. The weighted and simple average program and participant cost s across all nine states are presented as a percentage of total program costs. The weighted average cost shares were based on each program’s spending by administrator and participants.

Table 1 2012 Participant and Program Cost Information from Reported Entities

2012 Portfolio Costs

State Program Administrator Program Costs (Million $s) Participant Costs (Million $s) Total Costs (Million $s) Program Costs as Percent of Total Cost (%) Participant Costs as Percent of Total Cost (%) A b c = a + b a / c b / c

California Southern California

Edison $ 316 $ 269 $ 585 54.0% 46.0% Hawaii Hawaii Energy $ 31 $ 37 $ 68 45.6% 54.4%

Iowa MidAmerican Energy Company $ 50 $ 70 $ 120 41.5% 58.5%

Maine Efficiency Maine Trust $ 24 $ 36 $ 60 39.8% 60.2% Massachusetts National Grid $ 173 $ 54 $ 227 76.3% 23.7%

Minnesota Xcel Energy $ 53 $ 98 $ 151 35.1% 64.9% Pennsylvania PECO $ 68 $ 109 $ 178 38.5% 61.5% Rhode Island National Grid $ 63 $ 13 $ 75 83.2% 16.8%

Vermont Efficiency Vermont; Burlington

Electric Department $ 34 $ 23 $ 57 59.3% 40.7% Weighted Average 53.4% 46.6%

Simple Average 52.6% 47.4%

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In our analysis, the weighted average program and participant costs are 53.4% and 46.6%, respectively, of total costs. On a simple average basis, program and participant costs are 52.6% and 47.4%, respectively, of total costs. Participant cost results range from a low of 17% of total costs (National Grid in Rhode Island) to a high of 65% (Xcel Energy in Minnesota). When deriving participant costs from program costs, using a ratio of 1:1 is consistent with these results and slightly conservative, leading to slightly hi gher total costs than the precise average values would provide.

References MidAmerican Energy Company, "Energy Efficiency Plan Docket No. EEP-08-2, 2012 Annual Report to the Iowa Utilities Boar d," May 1, 2013, available at: http://www.state.ia.us/government/com/util/energy/energy_efficiency/ee_plans_reports.html Efficiency Maine Trust, "2012 Annual Report of the Efficiency Maine Trust," November 30, 2012, revised February 12, 2013, available at: http://www.efficiencymaine.com/docs/2012- Annual-Report.pdf National Grid Electric, "2013-2015 Massachusetts Joint Statewide Three-Year Electric and Gas Energy Efficiency Plan," Docket No. 12-109, November 2, 2012, available at: http://www.env.state.ma.us/dpu/docs /electric/13-120/8113ngptapb.xlsx PECO Energy Company, "Final Annual Report for the Pennsylvania Public Utility Commission, for the Period June 2012 through May 2013, Program Year 4," Docket No. M-2009-2093215, November 15, 2013, available at: http://www.puc.state.pa.us//pcdocs/1260111.pdf Efficiency Vermont, "Annual Report 2012," October 18, 2013, available at: http://www.efficiencyvermont.com/docs/about_efficiency_vermont/annual_reports/EfficiencyVermont-Annual-Report-2012.pdf Burlington Electric Department, " 2012 Energy Efficiency Annual Report," 2012, available at: https://www.burlingtonelectric.com/ELBO/a ssets/2012%20DSM%20Annual%20Report%20Mas ter.pdf Hawaii Energy, “Annual Report Program Year 2012 July 1, 2012 - June 30, 2013,” 2013,
available at http://www.hawaiienergy. com/information-reports

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Xcel Energy, "2012 Status Report & Associated Compliance Filings Minnesota Electric and Natural Gas Conservation Improvement Program Docket No. E,G002/CIP-09-198, " April 1, 2013, available at http://www.xcelenergy.com/staticfiles/xe/Regulatory/Regulatory%20PDFs/MN-DSM-CIP-2012- Status-Report.pdf National Grid, "2012 Energy Efficiency Year -End Report," May 31, 2013, available at http://www.nationalgridus.com/non_html /eer/ri/4295-YearEnd%20Rept%20(PUC%205-31- 13).pdf Southern California Edison, "2013 Energy Efficiency Annual Report," June 2013, available at http://eega.cpuc.ca.gov/Documents.aspx http://eega.cpuc.ca.gov/Documents.aspx

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## Appendix 5-4 Comprehensive Results: State Goal Setting and Impacts Assessment

See attached file, “Abatement Measures TSD Appendix 5-5.xlsx,” containing the following:

Goal Setting Sheets

 Option 1 – Incremental Savings as % of Sales by State (2017-2030)

 Option 1 – Cumulative Savings as % of Sales by State (2017-2030)

 Option 2 – Incremental Savings as % of Sales by State (2017-2030)

 Option 2 – Cumulative Savings as % of Sales by State (2017-2030)

Impacts Assessment Sheets

 Option 1 – National Costs at 3% Discount Rate (2017-2030)

o Levelized Cost of Saved Energy, First-year Costs, and Annualized Costs

 Option 1 – National Costs at 7% Discount Rate (2017-2030)

o Levelized Cost of Saved Energy, First-year Costs, and Annualized Costs

 Option 2 – National Costs at 3% Discount Rate (2017-2030)

o Levelized Cost of Saved Energy, First-year Costs, and Annualized Costs

 Option 2 – National Costs at 7% Discount Rate (2017-2030)

o Levelized Cost of Saved Energy, First-year Costs, and Annualized Costs

Formatted: Not Highlight

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References

i American Council for an Energy-Efficient Economy (AC EEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard

. ii U.S. Energy Information Administration Form EIA-861 data files. 2012. Available at http://www.eia.gov/electricity/data/eia861/

. iii Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-FundedEnergy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility -customer-funded-energy-efficiency-programsunited-states-projected-spend

. iv Seth Nowak, Martin Kushler, Patti Witte, and Dan York . Leaders of the Pack: ACEEE’ s Third National Review of Exemplary Energy Efficiency Programs. American Council for an Energy-Efficient Economy (ACEEE). Research Report U132. Available at http://www.aceee.org/research-report/u132

. v See reviews of market failures and barriers related to en ergy efficiency in Gillingham, K, R Newell, and K Palmer. 2009. Energy Efficiency Economics and Policy. Annua l Review of Resource Economics. Annual Review of Resource Economics 1: 597-619 and Gillingham and Palmer (2013). “Bridging the Energy Efficiency Gap: insights for policy from economic theory and empirical analysis,” Resources for the Future DP 13-02. vi For example, see DeCanio, S. 1998. The efficiency paradox: bureaucratic and organizational barriers to profitable energy-saving investments. Energy Policy 26(5): 441-458; McKinsey & Co and The Conference Board. 2007. Reducing U.S. Greenhouse Gas Emissions: How Much at What Cost? pp. (52-53). vii See discussion in Jaffe, A.B., R.G. Newell, and R.N. Stavins. 2003. Technological Change and the Environment. Chapter 11 in the Handbook of Environmental Economics. Volume 1, Edited by K.-G. Maler and J.R. Vincent. Elsevier Science B.V. 461-516. viii See discussion in Gillingham, K and K Palmer. 2014. Bridging the Energy Efficiency Gap: Policy Insights from Economic Theory and Empirical Analysis. Review of Environmental Economics & Policy, 8(1): 18-38. ix U.S. EPA and U.S. DOE. July 2006. National Ac tion Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/napee_report.pdf

. x U.S. EPA and U.S. DOE. July 2006. National Action Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/napee_report.pdf

. xi Kriegler, E., J. P. Weyant, G. J. Blanford et al. 2014. The role of technology for achieving climate policy objectives: overview of the EMF 27 study on global technology and climate policy strategies. Climatic Change

. January 2014; Clarke, L, A Fawcett, J Weyant et al. Tech nology and U.S. Emissions Reductions Goals: Results of the EMF 24 Modeling Exercise. [forthcoming] xii International Energy Agency (IEA). 2012. World Energy Outlook 2012. Paris. xiii Kriegler, E., J. P. Weyant, G. J. Blanford et al. 201 4. The role of technology for achieving climate policy objectives: overview of the EMF 27 study on global technology and climate policy strategies. Climatic Change

. January 2014. xiv Kyle P., L. Clarke, S. Smith et al. 2011. The Value of Advanced End-Use Energy Technologies in Meeting U.S. Climate Policy Goals. The Energy Journal, 32: 61-87.

xv See, for example, Comstock, O, and E Boedecker. 2011. Energy and Emissions in the Building Sector: A Comparison of Three Policies and Their Combinations. The Energy Journal , 32: 23-41; Fischer, C. (2005) “On the importance of the supply side in demand side management." in Energy Economics , 27: 165-180; Fischer, C. 2010. Imperfect Competition, Consumer Behavior, and the Pr ovision of Fuel Efficiency in Light-Duty Vehicles. Resources for the Future DP 10-60. Washington, DC. xvi E.g., Gillingham, K, R Newell, and K Palmer. 2009. Ener gy Efficiency Economics and Policy. Annual Review of Resource Economics. Annual Review of Resource Economics 1: 597-619. xvii Woolf, T. W. Steinhurst, E. Malone, K. Takahashi. 20 12. “Energy Efficiency Cost-Effectiveness Screening: How to Properly Account for ‘Other Program Impacts’ and Environmental Compliance Costs,” Report prepared by RAP and Synapse Energy Economics. xviii Boyd, GA and JX Pang (2000). “Estimating the linkage between energy efficiency and productivity,” Energy Policy , 28: 289-296; Worrell, E. (2011). “Productivity benefits of industrial energy efficiency measures.” Lawrence Berkeley National Laboratory Paper LBNL-52727.

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xix

Van Buskirk, R, C. Kantner, B. Gerke et al. The bene fits of energy efficiency standards and how policies may accelerate declines in appliance costs. Proceeding of National Academy of Sciences. [forthcoming]

xx Woolf, T. W. Steinhurst, E. Malone, K. Takahashi. 20 12. “Energy Efficiency Cost-E ffectiveness Screening: How to Properly Account for ‘Other Program Impacts’ and Environmental Compliance Costs,” Report prepared by RAP and Synapse Energy Economics. Available at http://www.raponline.org/event/the- importance-of-effective-energyefficiency-cost-effectiveness

. xxi Sixth Northwest Electric Power and Conservation Plan, Northwest Power and Conservation Council. February 2010. Council Document 2010-09. http://www.nwcouncil.org/media/6284/SixthPowerPlan.pdf xxii American Council for an Energy-Efficient Economy (ACEE E). April 2004. Select State-Level Energy Efficiency Policy Opportunities 2016-2030. [forthcoming] xxiii Yu Wang and Marilyn A. Brown. February 2013. Policy Drivers for Improving Electricity End-Use Efficiency in the U.S.: An Economic-Engineer ing Analysis. Energy Efficiency. xxiv Ian A. Bowles. December 29, 2010. Massachusetts Cl ean Energy and Climate Plan for 2020. Available at http://www.mass.gov/eea/waste-mgnt-recycling/air-quality/ green-house-gas-and-climat e-change/climate-changeadaptation/mass-clean-energ y-and-climate-plan.html

. xxv

Commonwealth of Massachusetts. Global Warming Solu tions Act: 5-Year Progress Report. December 2013. Available at http://www.mass.gov/eea/docs/eea/gwsa/m a-gwsa-5yr-progres s-report-1-6-14.pdf

. xxvi Ian M. Hoffman, Megan A. Billingsley, Steven R. Schiller, Charles A. Goldman and Elizabeth Stuart. Lawrence Berkeley National Laboratory. August 28, 2013. Energy Efficiency Program Typology and Data Metrics: Enabling Multi-State Analyses Through the Use of Common Terminology. LBNL-6370E. Available at http://eetd.lbl.gov/news/article/56865/new-policy-brief-energy-efficie. xxvii U.S. Energy Information Administration Form EIA-861 data files (2012). Available at http://www.eia.gov/electricity/data/eia861/

. xxviii American Council for an Ener gy-Efficient Economy (ACEEE). Nove mber 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard

. xxix Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-FundedEnergy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/fut ure-utility-customer-funded-e nergy-efficiency-programsunited-states-projected-spend.

xxx

Sciortino, M., et. al. American Council for an Energy-Efficient Economy (ACEEE). June 2011. Energy Efficiency Resources Standards: A Progress Report on State Experience. Report Number U112. Available at http://www.aceee.org/research-report/u112

. xxxi American Council for an Energy-Efficient Econ omy (ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard

. xxxii American Council for an Ener gy-Efficient Economy (ACEEE). Nove mber 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard

. xxxiii American Council for an Energy-Efficient Economy (ACEEE). February 24, 2014. State Energy Efficiency Resource Standard (EERS) Activity Policy Brief. Available at www.aceee.org/files/pdf/ policy-brief/eers-02- 2014.pdf

. xxxiv Consortium for Energy Efficiency. March 28, 2013. 2012 State of the Efficiency Program Industry. Available at http://library.cee1.org/content/2012-state-efficiency-program-industry-report/

. xxxv American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard

. xxxvi Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-FundedEnergy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility -customer-funded-energy-efficiency-programsunited-states-projected-spend

. xxxvii U.S. EPA and U.S. DOE. November 2007. Guide for Conducting Energy Efficiency Potential Studies: a Resource of the National Action Plan fo r Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/potential_guide.pdf

.

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xxxviii U.S. EPA and U.S. DOE. November 2007. Guide for Conducting Energy Efficiency Potential Studies: a Resource of the National Action Plan fo r Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/potential_guide.pdf

. xxxix Sreedharan, P. 2013. Recent estimates of energy ef ficiency potential in the USA. Energy Efficiency. xl Eldridge et. al. 2008. State-Level Energy Efficiency Analysis: Goals, Methods, and Lessons Learned. 2008 ACEEE Summer Study on Energy Efficiency in Buildings. Available at http:// xli Electric Power Research Institute (EPRI). April 2014. U. S. Energy Efficiency Potential Analysis through 2035. [forthcoming] xlii American Council for an Energy-Efficient Economy (AC EEE). April 2014. Select State-Level Energy Efficiency Policy Opportunities 2016-2035. [forthcoming] xliii U.S. EPA and U.S. DOE. July 2006. National Ac tion Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/napee_report.pdf

. xliv

U.S. EPA and U.S. DOE. November 2008. Understanding Cost-Effectiveness of Energy Efficiency Programs (Best Practices, Technical Methods, and Emerging Issues for Policy-Makers): a Resource of the National Action Plan for Energy Effici ency. Available at http://www.epa.gov/cleanenergy/docum ents/suca/cost-effectiveness.pdf

. xlv

Woolf, T., et. al. November 2012. Regulatory Assi stance Project, “Energy Efficiency Cost-Effectiveness Screening. Available at http://ww.raponline.org/document/download/id/6149

. xlvi U.S. EPA and U.S. DOE. November 2008. Understanding Cost-Effectiveness of Energy Efficiency Programs (Best Practices, Technical Methods, and Emerging Issues for Policy-Makers): a Resource of the National Action Plan for Energy Effici ency. Available at http://www.epa.gov/cleanenergy/docum ents/suca/cost-effectiveness.pdf

. xlvii See, for instance, Kriegler, E., J. P. Weyant, G. J. Blanford et al. 2014. The role of technology for achieving climate policy objectives: overview of the EMF 27 study on global technology and climate policy strategies. Climatic Change, January 2014; Kyle P., L. Clarke, S. Sm ith et al. 2011. The Value of Advanced End-Use Energy Technologies in Meeting U.S. Climate Policy Goals. The Energy Journal, 32: 61-87. xlviii U.S. EPA and U.S. DOE. September 2009. Energy Efficiency as a Low-Cost Resource for Achieving Carbon Emissions Reductions: a Resource of the National Ac tion Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/documents/suca/ee_and_carbon.pdf

. xlix

McKinsey & Company. December 2007. Reducing U.S. Greenhouse Gas Emissions: How Much at What Cost? Available at http://www.mckinsey.com/client_service/sustainability/l atest_thinking/reducing_us _greenhouse_gas_emissions

. l U.S. EPA and U.S. DOE. November 2007. Guide for Reso urce Planning with Energy Efficiency: a Resource of the National Action Plan for Energy Efficiency. Available at http://www.epa.gov/cleanenergy/docum ents/suca/resource_planning.pdf

. li Molina, M. 2014. The Best Value for America’s Energy Do llar: A National Review of the Cost of Utility Energy Efficiency Programs. ACEEE Report No. U1402. Washington, DC. Available at http://www.aceee.org/researchreport/u1402

. lii Friedrich, K., M. Eldridge, D. York et al. 2009. Saving En ergy Cost-Effectively: A National Review of the Cost of Energy Saved Through Utility-Sector Energy Efficiency Programs,” ACEEE Report No. U092. Available at http://www.aceee.org/research-report/u092

. liii Kushler, M., D. York, and P. Witte. American Coun cil for an Energy-Efficient Economy (ACEEE). 2004. Five Years In: An Examination of the First Half-Decade of Public Benefits Energy Efficiency Policies. liv

Gillingham, K., R. Newell, K. Palmer. 2004. Retrosp ective Examination of Demand-Side Energy Efficiency Policies. Resources for the Future Working Paper DP 04-19 REV. Washington, DC. lv Paul, A., K. Palmer and M. Woerman. 2011. Supply Curves for Conserved Electricity. Resources for the Future Discussion Paper 11-11. Washington, DC. lvi Arimura, T., S. Li, R. Newell, K. Palmer. 2012. Cost-E ffectiveness of Electricity Energy Efficiency Programs. The Energy Journal 33(2): 63-99. lvii Trains, K. 1994. Estimation of Net Savings from Energy-Conservation Programs. Energy 19 (4):423-441. lviii Joskow, P., and D. Marron. 1992. What Does a Negawatt Really Cost? Evidence from Utility Conservation Programs. The Energy Journal 13 (4):41-74.

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lix

U.S. EPA and U.S. DOE. November 2008. Understanding Cost-Effectiveness of Energy Efficiency Programs (Best Practices, Technical Methods, and Emerging Issues for Policy-Makers): a Resource of the National Action Plan for Energy Effici ency. Available at http://www.epa.gov/cleanenergy/docum ents/suca/cost-effectiveness.pdf

. lx U.S. Energy Information Administration Form EIA-861 data files (2012). Available at http://www.eia.gov/electricity/data/eia861/

. lxi American Council Energy-Efficient Economy (ACEEE). February 24, 2014. State Energy Efficiency Resource Standard (EERS) Activity Policy Brief. Available at www.aceee.org/files/pdf/polic y-brief/eers-02-2014.pdf

. lxii Billingsley, Megan A., I. M. Hoffman, E. Stuart, S. R. Schiller, C. A. Goldman, K. LaCommare, Lawrence Berkeley National Laboratory. March 201 4. The Program Administrator Cost of Saved Energy for Utility CustomerFunded energy Efficiency Programs. http://emp.lbl.gov/sites/all/files/cost-of-saved-energy-for-ee-programs.pdf

. lxiii Billingsley, Megan A., I. M. Hoffman, E. Stuart, S. R. Schiller, C. A. Goldman, K. LaCommare, Lawrence Berkeley National Laboratory. March 201 4. The Program Administrator Cost of Saved Energy for Utility CustomerFunded energy Efficiency Programs. http://emp.lbl.gov/sites/all/files/cost-of-saved-energy-for-ee-programs.pdf

.

lxiv

Northwest Power & Conservation Council (NPCC). February 1, 2010. “Sixth Northwest Conservation and Electric Power Plan,” Council Document 2010-09. Available at www.nwcouncil.org/energy/powerplan/6/plan/

. lxv

Gordon, Fred, Lakin Garth, Tom Eckman, and Charle s Grist, “Beyond Supply Curves,” Proceedings of 2008 ACEEE Summer Study on Energy Efficiency in Buildings, August 17, 2008 available at: http://aceee.org/files/proceedings /2008/data/pape rs/8_419.pdf

. lxvi Northwest Power & Conservation Council (NPCC). February 1, 2010. “Sixth Northwest Conservation and Electric Power Plan,” Council Document 2010-09. Available at www.nwcouncil.org/energy/powerplan/6/plan/

. lxvii Kenji Takahashi and David Nichols, Synapse Energy Econ omics, Inc. 2008. The Sustainability and Costs of Increasing Efficiency Impacts: Eviden ce from Experience to Date. ACEEE Summer Study on Energy Efficiency in Buildings. http://www.aceee.org/files/proceedin gs/2008/data/p apers/8_434.pdf



### . lxviii For example: Northwest Power & Conservation Coun cil (NPCC). February 1, 2010. “Sixth Northwest Conservation and Electric Power Plan,” Council Document 2010-09. Available at www.nwcouncil.org/energy/powerplan/6/plan/ ; and McKinsey & Company. December 2007. Reducing U.S. Greenhouse Gas Emissions: How Much at What Cost? Available at http://www.mckinsey.com/client_service/sustainability/l atest_thinking/reducing_us _greenhouse_gas_emissions

. lxix

Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-FundedEnergy Efficiency Programs in the United States: Projected Spending and Savings to 2025. January 2013. LBNL5803E. Available at http://emp.lbl.gov/publications/future-utility -customer-funded-energy-efficiency-programsunited-states-projected-spend

. lxx

Molina, M. 2014. The Best Value for America’s Energy Do llar: A National Review of the Cost of Utility Energy Efficiency Programs. ACEEE Repo rt No. U1402. Available at http://www.aceee.org/research-report/u1402

. lxxi State of California Governor’s Office of Planning and Re search. July 2002. California Standard Practice Manual: Economic Analysis of Demand-Side Programs and Projects. Available at http://www.calmac.org/events/SPM_9_20_02.pdf

. lxxii American Council for an Energy-Efficient Econom y (ACEEE). November 2013. The 2013 State Energy Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard

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1 

## Chapter 6: Fuel Switching

Coal-to-Natural Gas Switching

Introduction
Firing natural gas in a boiler designed for coal-fired generation is one approach to reducing the output-based CO 2 

### emissions rate (lbs/MWh) in these boilers. The CO 2 

### emission rate is reduced when natural gas is substituted for coal because the gas has a much higher percentage of hydrogen and a lower percentage of carbon than the coal it replaces. When quantities of gas and coal are burned with oxygen from air to produce the same amounts of heat, the higher hydrogen content of natural gas produces more water vapor (H 2 

### O) than coal, but far less CO 2 

### . The discussion below focuses solely on the conversion of an existing coal-fired boiler to burn natural gas instead of, or along with, coal. There are other technical options for gas substitution in an existing coal-steam EGU that are not examined in any detail here. They include:

 Repowering an existing coal EGU by providing heat input to the boiler from the exhaust of a newly installed gas turbine generator; and,

 Gasification of coal, producing substitute natural gas (SNG) that provides heat input to the existing coal-fired boiler.
These other options have higher capital cost and thus would not be as economic as the direct substitution of natural gas in an ex isting coal boiler, for reasons th at will become apparent in this analysis.
This chapter evaluates the cost-effectiveness of widespread adoption of coal-to-gas switching at a national level for the purpose of setting CO 2 

### emissions goals consistently in each state.
Description of Technology
Engineering Considerations
Most existing coal-fired EGU boilers can be modified to switch to 100% gas input, or to co-fire gas with coal in any desired proportion. This tr ansition typically requires at least some plant modifications and might have some negative imp act on the efficiency of the unit as described later in this chapter.

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2 A conversion from coal to gas firing first requires either an existing natural gas delivery system to the boiler or the installation of a new gas pipeline to serve the boiler. While it is sometimes assumed that a need to install a new gas pipeline would render the conversion uneconomic, this analysis will show that the cost of a new gas pipeli ne is not likely to be the determining factor for the project’s economic merit, given the significance of the change to the cost of fuel for generation from the converted boiler.
Conversion to natural gas firing in a coal-fired boiler typically involves installation of new gas burners and supply piping, modifications to combustion air ducts and control dampers, and possibly modifications to the boiler’s steam superheater, reheater, and economizer heating surfaces that transfer heat from the hot flue gas exiting the boiler furnace. The conversion may
also involve some modification of downstrea m air pollution emission control equipment. Engineering studies are performed to assess ch anges in furnace heat absorption and exit gas temperature; material changes affecting heat tran sfer surfaces; the need for sizing of flue gas recirculation fans; and operational changes to soot blowers, spray flows, air heaters, and emission
controls.
Whether co-firing with coal or switching complete ly to natural gas, boilers will become less efficient due to the high hydrogen content of natural gas. When combusted, the additional hydrogen yields increased moisture content (water vapor) in the flue gas. The increased moisture content, in turn, results in additional he at lost up the stack in stead of being directed towards electricity generation. Additionally, depending on the design of the boiler and extent of modifications, some boilers may in cur some derate (reduction in generating capacity) in order to maintain steam temperatures at or within design limits, or for other technical reasons.
Even with a decrease in boiler efficiency, the over all net output efficiency of a coal-steam boiler EGU that switches from coal to natural gas firing may change only slightly, depending on how much auxiliary load is converted to net output by avoiding the need to run coal pulverizers, conveyors, ash sluice pumps, and relevant air pollution control equipment (e.g., PM and SO 2 

### controls).

Fuel Considerations
Delivery of natural gas via pipeline is critical for conversion of a coal-fired boiler to a gas-fired boiler. Some coal boilers are connected to the natural gas pipeline network for purposes of using Comment [A1]: Would there also be a reduction in emissions equipment (and possible removal of baghouses, electrostatic precipitators, and scrubbers.).If so, consider making this point.

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3 gas as a startup fuel, or are located at facilities wi th onsite gas-fired generators. These boilers are likely able to co-fire to some degr ee with gas (at least 10% total output 1 

### ) without constructing additional gas pipeline capacity. For purposes of this analysis, the EPA conservatively assumed that gas use of 10% or greater at these boilers, or any gas use at boilers without an existing gas pipeline, would require construction of additional pipeline capacity.
Unlike coal, natural gas cannot be stored in quanti ties sufficient for sustained utilization on site.To the extent that firm (uninterruptible) gas supply is contractually unavailable or costprohibitive, any potential interruption in gas su pply could impact the ability of the unit to continue operating without increasing its CO 2 

### emissions rate (since it would likely need to
substitute more CO 2 

### -intensive fuel for the unavailable natural gas). Additionally, for boilers that switch to 100% gas, interruption in gas impacts the ability of the unit to continue generating at all if gas is unavailable. For these reasons, an EGU switching to a large percentage of gas use may elect to install more than one new gas supply pipeline from separate sources. Although the EPA assumes the addition of one gas pipeline in the simplified cost analysis presented below, it will be seen that pipeline cost will generally not be the main driver of economic feasibility.
Cost and Performance Impacts of Coal-to-Gas Switching
The analysis described in this section presents a hypothetical conversion of a boiler from burning 100% coal to burning varying proportions of gas (10%, 50%, and 100%). The capital cost of modifying a coal boiler to switch to natural gas includes the new gas burners and piping, combustion air ductwork and control damper modifications, air heater upgrades, gas recirculating fans, control systems modifications, and other site-specific modifications, as well as any pipeline installation costs that would be necessary to supply the unit’s assumed level of gas combustion following the conversion.
For this analysis, the EPA assumes capital costs for pulverized coal (PC) and cyclone boiler
modifications are as follows 2 

### : $/kW = 267*(75/MW) 0.35 

### (pulverized coal)

1 Based on assumed use of Class 1 igniters (10% of burner capacity) as defined in NFPA 85 Boiler and Combustion Systems Hazards Code.
2 EPA assumptions on costing and performance associated with coal

‐ to

‐ gas conversion and pipeline additions in this analysis are generally consistent with assumptions presented and discussed in EPA’s power sector modeling documentation, Chapter 5.7, at: http://www.epa.gov/airmarkets/progsregs/epa

‐ ipm/docs/v513/Chapter_5.pdf

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4 $/kW = 374*(75/MW) 0.35 

### (cyclone)
Based on the above formula, a 500 MW pulverized coal unit would have a capital cost of $137/kW to convert the boiler such that it could burn any proportion of natural gas. For this illustrative example, to support 100% gas combustion we assume that a 50-mile gas pipeline 3 

### at $50 million, 4 

### or $100/kW for a 500 MW unit, is also required, which raises the unit’s total capital cost for conversion to $237/kW. Black & Veatch also used a similar cost level in a recent case study. 5 

### At a 14.3% capital charge rate 6 

### and 75% annual capacity factor, the total capital cost in this example equates to an annualized capital cost of about $5/MWh. This $/MWh capital cost is relatively insignificant compared to the increase in fuel cost discussed later.
Due to a reduced need for operators, maintenance materials, and maintenance staff, fixed O&M costs are reduced by 33% as a result of switching from coal to 100% gas. Similarly, variable O&M costs are reduced by 25% due to reduced waste disposal, reduced auxiliary power requirement, and miscellaneous other costs.
EPA also assumed for this analysis that there would be no derate in the net EGU output, and estimated that the impact on net heat rate for an average unit would be a 3% increase for a switch from coal to 100% natural gas firing. The assumed 3% increase in net heat rate is conservative compared to the 2% assumption used by Black & Veatch in their previously mentioned case study.
Cost of Fuel
For this analysis, the EPA uses base case projections for delivered gas prices that are about double projected delivered coal prices on average ($2.62/MMBTU for coal, $5.36/MMBTU for

3 Based on EPA analysis, the majority of existing coal units would require less than 50 miles of new gas pipelines to switch fuels from coal to 100% natural gas. See Chapter 5 and Table 5

‐ 22 of Documentation for EPA Base Case v.5.13 Using the Integrated Planning Model, available at: http://www.epa.gov/air markets/progsregs/epa

‐ ipm/BaseCasev513.html
4 For those plants that require additional pipeline capacity, the average capital cost of constructing new pipelines is assumed to be approximately $1 million per mile of pipeline built, which is consistent with assumptions used in EPA’s IPM modeling.
5 A Case Study on Coal to Natural Gas Fuel Switch, Black & Veatch, Power

‐ Gen International, December 2012, available at http://bv.com/redirects

. 6 Capital charge rate at 14.3% is the average of the regulated utility and unregulated merchant rates as used in IPMv5.13 for environmental retrofits having a 15 year book life. Comment [A2]: Are there any references to cite for fixed O&M and variable O&M cost reductions?

Comment [A3]: Suggest providing further support for this choice. What is the sensitivity to conclusions from this decision?

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5 gas). 7 

### As a result, the fuel cost for a typical converted boiler burning 100% gas is expected to be at least double its prior fuel cost on an outpu t basis as well ($27/MWh for coal, $57/MWh for gas). 8,9 

### Compared to the estimated $5/MWh capital cost impact presented above, a $30/MWh
increase in fuel cost would make the difference in fuel costs the most significant driver of project economics when switching from coal to gas in a coal boiler. 10 

### This difference would increase with higher gas prices, which would be projected to result from an increase in overall gas demand caused by widespread adoption of gas co-firing.

Emission Reduction Potential

The CO 2 

### reduction potential is directly related to the amount of gas co-fired, and is due largely to
the different carbon intensities of each fuel. More reductions in CO 2 

### rate are achieved at higher levels of gas co-firing as shown in Table 6-1. At 10% gas co-firing, the net emissions rate (lbs/MWh net) of a typical unit would decrease by approximately 4%. At 100% gas co-firing, the net emissions rate (lbs/MWh net) of a typical unit would decrease by approximately 40%.

Table 6-1. CO 2 

### Rates at Various Levels of Natural Gas Co-Firing

Case Heat Rate (Btu/kWh) CO 2 

### Rate (lbs/MWh net) Reduction in CO 2 

### Rate from 100% Coal (lbs/MWh net) 100% Coal 10,340 2,108 N/A 10% Gas 10,370 2,021 4% 50% Gas 10,490 1,673 21% 100% Gas 10,640 1,239 41%


### In addition to reducing CO 2 

### emissions, natural gas co-firing at a coal-fired steam EGU will generally also reduce criteria air pollution. Reducing CO 2 

### and criteria air pollution will result in

7 EPA Base Case 5.13, projections for 2020 8 This estimated fuel cost also accounts for the decrease in efficiency that results from switching from coal to gas in

a boiler, as well as the decrease in parasitic power consumption. 9 Combusting natural gas using combined

‐ cycle turbine technology can remain economically attractive notwithstanding these types of fuel price differentials because combined cycle turbine technology converts a substantially higher share of the fuel’s heat input into electricity output as compared to boiler technology. The $/MWh impact of a higher gas price in that instance is significantly mitigated by higher MWh output produced for

a given amount of heat input from the fuel purchased.
10 This demonstration assumes that the converting boiler in question remains a “price taker” in the fuel marketplace, such that the projected gas and coal prices would be unaffected by this hypothetical unit’s potential decision to convert. However, if enough other units might be expected to make similar conversions, the aggregate increased demand for natural gas would be likely to further increase the price differential between coal and gas, making fuel costs an even more influential factor in the evaluation of such a project’s economic merit. Comment [A4]: Would it be worth mentioning the potential volatility of natural gas prices (vs. coal prices)? 

### 1. http://www.eia.gov/naturalgas/ http://www.eia.gov/coal/

Comment [A5]: Suggest inclusion of heat rate units.

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6 climate benefits and human health co-benefits. The impacts of these pollutants on the environment and health are discussed in detail in Chapter 5 of the RIA for this proposed rule.
For this analysis, EPA estimated the PM 2.5 

### -related human health co-benefits of SO 2 

### , NO X 

### , and
direct-PM 2.5 

### emission reductions attributable to a ra nge of natural gas co-firing levels at an
illustrative coal steam unit burn ing bituminous coal in 2020. 11 

### The estimated monetized cobenefits do not include climate benefits or health effects from direct exposure to NO 2 

### , SO 2 

### , and HAP; ecosystem effects; or visibility impairment. Only the unit-level emissions of SO 2 

### , NO X 

### and
direct-PM 2.5 

### are considered in this illustrative ex ercise. Additionally, emissions from the extraction and transport of the fuels used by these technologies are not considered. Furthermore, there may be differences in upstream greenhouse gas emissions (in particular, methane) from different technologies but those were not quantified for this assessment. The estimated avoided emissions under 10% gas co-firing and a 100% switch to gas are presented Table 6-2.

Table 6-2. Avoided Emissions at Various Leve ls of Co-Firing, based on Illustrative Unit (lbs/MWh net)

10% Gas 100% Gas SO 2 0.3 3.1 NO X 0.2 2.04 PM 2.5 0.02 0.2



### To estimate human health co-benefits for this illustrative coal steam unit, the EPA used PM 2.5 

### - related benefit-per-ton estimates for SO 2 

### , NO X 

### , and direct-PM 2.5 

### emission reductions described in detail in Chapter 5 of the RIA for this proposa l. To estimate the benefits associated with cofiring, we determine the emission reductions for co-firing in Table 6-2 and apply the 2020 social benefit values discussed in Chapter 5 of the RIA for this proposal. Specifically, we multiply the
reduction in SO 2 

### , NO X 

### , and direct-PM 2.5 

### emissions by the PM 2.5 

### -related benefit per-ton estimates, and add those values to get a measure of 2020 benefits. Table 6-3 shows the PM 2.5 

### -related benefits expected based on the estimated emission reductions that would occur in this illustrative example. These estimates are purely illustrative as the EPA does not assert a specific location for

11 The illustrative unit in this analysis was assumed to be a 500 MW coal

‐ steam unit burning bituminous coal with a heat rate of 10,339 btu/kWh (net) operating at 75% capacity factor. Furthermore, this unit was assumed to operate a wet scrubber, cold

‐ side ESP, and SNCR.

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7 the illustrative electricity generation technol ogies and is therefore unable to specifically determine the population that would be affected by their emissions. Therefore, the benefits for any specific unit can be different than the estimates shown here. Table 6-3. Rounded PM2.5-related Co-benefits ($/MWh net) of Gas Co-firing (2011$)

Health Co

‐ benefit Discount Rate 3% Discount Rate 7% Discount Rate
Gas Co

‐ firing 10% $6.5 to $15 $5.9 to $13 Gas Co

‐ firing 100% $67 to $150 $61 to $140

Note: All estimates are rounded to two significant figures. Co

‐ benefits are based on national benefit

‐ per

‐ ton estimates for directly emitted PM 2.5 and PM 2.5 precursors, SO 2 and NO X . It is important to note that the monetized health co

‐ benefits do not include reduced health effects from ozone or direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairment. Emissions of directly emitted particles are disaggregated into EC+OC or crustal components using the method discussed in Appendix. 12 5A of the RIA for this proposal. The health co

‐ benefits reflect the sum of the PM2.5 co

‐ benefits and reflect the range based on adult mortality functions (e.g., from Krewski et al. (2009) to Lepeule et al. (2012)).



### The precise incremental health co-benefits associated with lower emissions would depend primarily on the location of the co-firing unit, the specific types of coals that natural gas would replace, and the pollution controls installed on that unit. This illustrative assessment is unable to account for these characteristics. However, these factors will not change the qualitative conclusion. There will always be incremental huma n health co-benefits associated with co-firing natural gas in an existing coal steam boiler, independent of the location, coal type, and operating pollution controls.
A related beneficial use of natural gas in existing coal boilers can be via gas reburning, a NOx reduction technology. 13 

### Gas reburning involves firing natural gas (between 10 and 25% of total heat input) above the primary combustion zone in the boiler furnace. This upper-level firing creates a slightly fuel-rich zone. NOx produced in the primary zone of the furnace is "reburned" in this zone and converted to molecular nitrogen and other reduced nitrogenous species. Overfire

12 Krewski D.; M. Jerrett; R.T. Burnett; R. Ma; E. Hughes; Y. Shi, et al. 2009. Extended Follow

‐ Up and Spatial Analysis of the American Cancer Society Study Linking Particulate Air Pollution and Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA. Lepeule, J.; F. Laden; D. Dockery; J. Schwartz. 2012. “Chronic Exposure to Fine Particles and Mortality: An Extended Follow

‐ Up of the Harvard Six Cities Study from 1974 to 2009.” Environmental Health Perspectives, July, 120(7):965

‐ 70. 13 DOE/NETL 2001, Evaluation of Gas Reburning and Low

‐ NOx Burners on a Wall

‐ Fired Boiler, DOE/NETL

‐ 2001/1143, February 2001, available at http://www.netl.doe.gov/File%20Library/Research/Coal /major%20demonstrations/cctdp/Round3/GRLNBPPA.pdf

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8 air is injected downstream of the reburn zone to burn out the remaining combustibles and convert the reduced nitrogenous species to molecular nitrogen. The heat input from gas would approximately substitute for a similar heat input from coal, thus reducing CO 2 

### and other coal emissions in a manner similar to gas co-firing as discussed above.

Cost of Reductions and Cost Effectiveness This analysis examines the average $/tonne 14 

### cost of avoided CO 2 

### that results from applying a range of natural gas co-firing levels to a typical baseload coal boiler. We capture the capital costs of boiler modifications and new pipeli ne construction (assuming 50 miles of new pipeline), 15 

### decreased FOM and VOM costs, and incremental fuel costs (based on IPMv5.13 Base Case average delivered fuel price projections for coal and gas in 2020). For a typical coal boiler at current base case fuel prices, the average cost of avoided CO 2 

### ranges from $83/tonne for 100% gas switch to $150/tonne for co-firing at 10% (see Table 6-4).
Table 6-4. Average Cost of Avoided CO 2 

### and CO 2 

### Emission Rate Reductions from 100% Coal at Various Levels of Natural Gas Co-Firing at Base Case Projected Gas Price ($5.36/MMBtu)

Case Average Cost of Avoided CO 2 

### ($/tonne) Change in CO 2 

### Rate from 100% Coal (lbs/MWh net) 100% Coal N/A N/A 10% Gas 150 4% 50% Gas 91 21% 100% Gas 83 41% Note: Based on a typical 500 MW bituminous coal steam unit operating at 75% capacity factor. Assumes construction of new 50

‐ mile pipeline. EPA estimated reduced total capital costs for the 50% and 10% gas cases; for example, total capital cost for 10% gas was estimated to be about one

‐ half of the capital costs for the 100% gas
case. 

### However, widespread adoption of gas co-firing would increase overall gas demand and place upward pressure on the natural gas price, which would consequently increase the average cost of
avoided CO 2 

### of a potential boiler conversion.

14 This document uses “tonne” to refer to a metric tonne. All control costs in this analysis are presented in dollars per metric tonne, or “$/tonne.”
15 Based on EPA analysis, the majority of existing coal units would require less than 50 miles of new gas pipelines to switch fuels from coal to 100% natural gas. See Chapter 5 and Table 5

‐ 22 of Documentation for EPA Base Case v.5.13 Using the Integrated Planning Model, available at: http://www.epa.gov/airmarkets/progsregs/epa

‐ ipm/BaseCasev513.html Comment [A6]: What is the sensitivity to your gas price assumptions?

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9 Conclusion
Switching from coal to gas is a relatively costly approach to CO 2 

### reductions at existing coal steam boilers when compared to other measures such as heat rate improvements and re-dispatch of generation supply to other existing capacity with lower CO 2 

### emission rates. Moreover, this analysis shows that coal-to-gas conversion of an ex isting boiler is less efficient than constructing a new natural gas combined cycle (NGCC) turbine in its place. For example, replacing the coal steam plant discussed above with a new NGCC facility would reduce the net CO 2 

### emission rate of the generating capacity by 62% at a cost of about $50/tonne of avoided CO 2 

### under the base case projected gas price and about $81/tonne of avoided CO 2 

### at a future gas price 50% higher than the base case projection. [INSERT CROSS-REFERENCE TO LOCATION IN RULE
MATERIALS WHERE WE EXPLAIN WHY WE ARE NOT CONSIDERING NEW NGCC
POTENTIAL TO REPLACE EXISTING COAL AS A REDUCTION MEASURE FOR CRAFTING STATE GOALS.]
The EPA is considering cost-effectiveness at a na tional level for the purpose of setting emissions goals consistently in each state. While this an alysis suggests that cost-effective reductions of
CO 2 

### are not available on a national basis from widespread adoption of natural gas co-firing, it does not preclude the potential for individual E GUs to utilize co-firing as a way to reduce CO 2 

### and other emissions, nor does it preclude states from factoring in that unit-level potential into the design of state plans for compliance with the 111( d) standard. EPA notes that there are utilities that see merit in converting some existing coal un its to burn 100% gas, and several are currently doing so. 16,17

16 Reuters 2014, “Southern to repower three Alabama coal power plants with natgas,” Reuters U.S. Edition, January 16, 2014 , available at http://www.reuters.com/article/2014/01/16/utilities

‐ southern

‐ alabama

‐ idUSL2N0KP1WA20140116 17 Dominion 2012, “Dominion Virginia Power Proposes To Convert Bremo Power Station From Coal To Natural Gas,” Dominion News, September 5, 2012, available at http://dom.mediaroom.com/2012

‐ 09

‐ 05

‐ Dominion

‐ Virginia

‐ Power

‐ Proposes

‐ To

‐ Convert

‐ Bremo

‐ Power

‐ Station

‐ From

‐ Coal

‐ To

‐ Natural

‐ Gas Comment [A7]: Is there any reference to cite for these numbers?

Comment [A8]: please fix.

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10 Biomass Co-firing

Introduction
Co-firing biomass in existing boilers designed fo r coal-fired generation is another approach to potentially reduce the output-based CO 2 

### emissions rate (lbs/MWh) of these boilers. In the analysis presented in this technical support document, the physical CO 2 

### emissions rate at the boiler stack could increase or decrease, depending on the amount of coal energy replaced by biomass energy and differences in the properties of a selected biomass and the coal it replaces. 18



### There are many possible combinations of coals and biomass types that could be co-fired. Sitespecific economics and accessibility would determine which combinations might actually be feasible. This TSD analysis does not attempt to estimate an economically feasible national average increase or decrease in CO 2 

### emission rate via biomass co-firing. Instead, this analysis simply employs one reasonably representative case to evaluate the cost effectiveness of biomass energy substitution in re ducing the physical CO 2 

### emission rate based only on the CO 2 

### coming from coal. This analysis indicates that while th e co-firing of biomass with coal is technically feasible as a means of reducing the coal-based CO 2 

### emission rate due to the substitution of biomass for coal, it has limited eco nomic feasibility due to the generally higher cost of energy from biomass as compared to coal. This general finding largely explains the very limited amount of biomass co-firing currently practiced in the U.S. It is also consistent with findings by others, including a study by the State of Maryland 19 

### that concluded as follows:
“Due to the higher cost of biomass fuels wh en compared to coal, cofiring with biomass
will lead to an increase in fuel costs. Without consideration for any environmental benefits, it is unlikely that any Maryland coal -fired facility would make the investments required to cofire with a more expensive and less efficient fuel.”
Based on the basic analysis of the cost effectiv eness of biomass energy substitution in reducing the physical CO 2 

### emission rate based only on the CO 2 

### coming from coal presented below, the

18 Fuel properties particularly affecting relative CO 2 emission rates are: higher heating value, carbon and hydrogen contents, and as

‐ fired moisture content. 19 The Potential for Biomass Cofiring in Maryland, Maryland Department of Natural Resources, March 2006, (pg 53) http://esm.versar.com/pprp/bibl iography/PPES_06_02/PPES_06_02.pdf Comment [A9]: You might also mention coal plants that have been retired (i.e. decommissioned)

or smaller facilities that have been converted entirely to biomass (i.e. Schiller, NH and others).

Comment [A10]: Maryland is not representative of biomass co

‐ firing across the United States, neither is a 2006 study. For example, limiting to this

case ignores coal plants that have been retired or facilities that have been converted entirely to biomass (i.e. Schiller, NH and others). Comment [A11]: : Biomass will be more economically feasible with an implicit carbon tax on fossil emissions, as well as increased recognition of the other air quality benefits (e.g. lowered toxic emissions, etc.) that could be realized by cofiring. Indeed the co

‐ firing of biomass is much more prevalent than is CCS, which EPA has described in

more generous terms in the 111(b) rulemaking. It also ignores one of the major economic barriers to biomass co

‐ firing which has been the difficulty/lack of desire/cost to open up Clean Air Act permits to allow biomass co

‐ firing where that was not part of the original permitting proposal.

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11 EPA concludes in this TSD that biomass co-firing would not be a cost-effective measure on which to base state goals. 20



### This TSD’s analysis indicates that while the co-firing of biomass with coal is technically feasible, and in some cases it can yield increases in the stack emission rate of CO 2 

### (lb/MWh ). In the absence of this rulemaking or a program that puts an implicit price on fossil carbon biomass co-firing ) and has limited economic feasibility given th e delivered cost of available biomass
versus the cost of coal. However, the use of co-firing biomass to help meet the 111(d) state goals may be economically feasible if the cost of reducing fossil carbon emissions is high. For example, Europe is currently co-firing substantial amounts of biomass to help lower carbon emissions of their fossil fuel fired electricity s ector (Ehrig and Behrendt 2013). That study notes that co-firing biomass is one of the lower cost options to reach the EU-2020 targets.
Description of Technology
Engineering/Economic Considerations
The technical feasibility of biomass co-firing in existing coal-fired boilers has been thoroughly investigated in many research and engineering studies, as well as in test burns at coal power plants in the U.S. and globally. 21 

### It has been demonstrated that the boiler and related systems of almost any existing coal-fired EGU can accept or be modified to support co-firing of at least some small percentage of biomass. In some cases, major modifications can be made to support a
switch to 100% biomass.
A decision to actually modify an existing coal-fired boiler for biomass co-firing at any percentage level depends on numerous technical and economic factors, including reliable

20 This analysis does not include evaluation of stack biogenic CO 2 emissions relative to the net landscape and process

‐ related carbon fluxes associated with the production and use of the biogenic feedstocks combusted. Issues related to methods for assessing biogenic CO 2 emissions from stationary sources are currently being evaluated by the EPA. In general, the overall net atmospheric contribution of CO 2 resulting from the use of a biogenic feedstock by a stationary source, such as an EGU, will ultimately depend on the stationary source process and the type of feedstock used, as well as the conditions under which that feedstock is grown and harvested. In September 2011, the EPA submitted a draft Accounting Framework to the Science Advisory Board (SAB) Biogenic Carbon Emissions (BCE) Panel for peer review. The SAB BCE Panel delivered its Peer Review Advisory to the EPA on September 28, 2012. In its Advisory, the SAB recommended revisions to the EPA's proposed accounting approach, and also noted that biomass cannot be considered carbon neutral a priori, without an evaluation of the carbon cycle effects related to the use of the type of biomass being considered.
21 See Partial Bibliography

– Biomass Co

‐ firing at end of this section. Comment [A12]: For example, Schiller, NH facility.

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12 availability of suitable biomass at an economic cost; adequate onsite space for biomass receiving, storage, preparation, and handling systems; potential corrosive effects of biomass ash in the boiler furnace; potential impacts of co-firing on boiler efficiency even at low biomass percentages, and the likely reduction (derate) in unit generating output at very high biomass percentages.
There are considerable physical differences between coal and biomass that will generally limit the extent to which biomass can be reasonably used to replace coal in a boiler. For example, compared to most coals, many solid biomass fuels have both a significantly higher as-fired moisture content and a significantly lower heati ng value per unit of weight. Most solid biomass fuels are also significantly less dense than most coals. For example, a typical biomass might have twice the moisture, half the heating value, and less than half the density of coal. 22 

### Important consequences of these physical differences are that the weight of biomass needed to provide a given amount of heat energy could be twice the weight of the coal it replaces, and the volume (cubic feet) of biomass needed could be four-to-eight times the volume of coal replaced.
Biomass requires space for storage after delivery to a facility, and the length of time that the biogenic material would remain on site prior to use can differ. For example, wood chips could be delivered year-round while crop residue delivery would follow specific seasons in which the crop was grown. As noted above, the four-fold or greater increase in volume occupied by
biomass relative to coal means that the n ecessary additional storage space could be large. However, if pelletized or torrefied biomass is used, these concerns may not apply, recognizing that such pre-preperations of the feedstock could entail additional costs. Stored biomass can be at even greater risk of spontaneous combustion than stored coal; this may limit the safe height of biomass piles and further increase storage area requirements. 23



### The volumetric differences alone can have other unexpected consequences. For this analysis, experienced EPA engineering staff estimated that a 500 MW baseload coal plant co-firing 10% biomass and receiving biomass deliveries 10 hours per day and 5 days per week would require a 20-ton truck delivery to the plant every 10 minutes, in addition to the ongoing coal deliveries.

22 Biomass Energy Data Book ‐ Edition 4, October 2012, DOE

‐ EERE

‐ ORNL, http://cta.ornl.gov/bedb/index.shtml 23 Properties of Wood Waste Stored for Energy Production, Purdue University, 2011, http://www.extension.purdue.edu/extmedia/ID/ID

‐ 421

‐ W.pdf Comment [A13]: A commenter suggest that EPA could do the following here: elaborate on the challenges of preparing biomass fuel for cofiring in pulverized systems (i.e. in reducing biomass fuel particle size to close to pulverized coal size). consider the influence of biomass on the potential markets for flyash in concrete. mention the need for fuel drying systems for many types of biomass.

Comment [A14]: There is extensive effort and additional research being done to mitigate these

concerns through pre

‐ preparation of the feedstock. Wood pellets and low rank coal may have approximately the same energy density, and depending on the cost structure of the feedstock supply chain, the reduced transportation and storage benefits could outweigh the preparation costs, particularly for cases like torrefaction that may produce economic co

‐ products.

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13 Limiting traffic issues may arise in some situati ons. Also, because of the low energy density of biomass and its relatively higher transportation cost per unit of delivered energy, it may only be economically viable to transport biomass a limited distance from where it is grown. This could limit the both the percentage of biomass co-firing in a single boiler and the maximum MW output from biomass at a single site. New technologies under development, such as torrefaction of biomass, but could m itigate some of these transportation, storage and energy content concerns, but are not yet commercially available.
The relatively higher moisture content and lower heat content of biomass reduces boiler efficiency, and typically requires a derating in un it generation at very high co-firing percentages as furnace volume and boiler fan capacities become inadequate.
For all of the above reasons, the EPA assumed for this analysis that coal-steam EGU boilers will generally only co-fire with biomass to a limited degree. While the actual level at which any plant can co-fire with biomass is highly site-speci fic, this analysis adopts the assumption used in EPA’s fleet wide IPM modeling of the electric power sector: a reasonable average limit on biomass co-firing is up to 10% on any single boiler, not to exceed 50 MW total biomass powered output at an individual plant site. This amount of co-firing has been used as representative practical limit in other studies as well. 24 

### But EPA notes that higher percentages of co-firing and at higher levels may be available in other cases as have been noted in many other studies.
For example, Europe’s 2nd biggest coal-fired power plant is being converted to burn wood pellets imported from the United States and Canada to power half of the plants 4 gigawatt capacity. The 700 million Euro project will “transfo rm Drax from the largest carbon emitter by site in UK to one of the largest clean-energy plants in the world.” (Financial Times, August 14, 2013). Similarly, in Canada the potential to reduce coal generation through co-firing is assumed to be 20 to 30%, much higher than IPM’s assumptions (See Canadian Clean Power Coalition http://www.canadiancleanpowercoalition.com/files/7813/2621/8340/Append ix%20C.pdf). As such EPA specifically requests for comment on data to support higher levels of biomass co-firing in the EPA’s IPM model and for this rulemaking.
Costs and Performance Impacts of Retrofitted Biomass Co-firing

24 The Potential for Biomass Cofiring in Maryland, Maryland Department of Natural Resources, March 2006, http://esm.versar.com/pprp/biblio graphy/PPES_06_02/PPES_06_02.pdf Comment [A15]: The use of sawmill residues, when avaialabe, could provide a high quality feedstock at potentially lower moisture content (i.e. planer shavings). A high quality feedstock could justify greater transportation distances (perhaps 75+ miles)

Comment [A16]: : Might mention, for perspective, that 50MW is in line with some of the largest standalone biomass powered electrical facilities in the US. Agreed that 50 MW upper limit is realistic. However if EU cofiring practices were adopted in the US, this could exceed 100MW at an individual site.

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14 For this analysis the EPA adopted capital and O&M costs, and performance impacts for retrofitted biomass co-firing capab ility that are approximately repr esentative of EPA assumptions used in its IPM modeling and discussed in the documentation for IPM v.5.13. 25



### EPA estimated that the capital cost to install 50 MW of biomass co-firing capability would be at least $10 million. 26 

### As applied to a 500 MW coal unit, the minimum cost of this 10% co-firing capability would then be $20/kW. Fixed O&M cost was estimated by EPA engineering staff to be 10% greater than with coal alone, and variable O&M cost was estimated to remain unchanged.
The heat rate impact (Btu/kWh) of 10% biomass co-firing as estimated by EPA engineering staff for this analysis was an increase of slightly more than 1% compared to coal alone. At low biomass cofiring rates, this factor slightly affects calculated biomass fuel consumption and any
associated CO 2 

### emission from biomass.
Cost of Fuel For this analysis, the EPA uses a delivered biomass cost of $4/MMBtu, representative of delivered woody crops grown specifically for energy-generating combustion, 27 

### and roughly 50% greater than IPM projected 2020 average delivered coal costs. 28 

### This analysis also considers a sensitivity scenario assuming a higher $6/MMBtu biomass price.
The EPA recognizes that the cost of biomass is hi ghly site-specific, and in some cases could be largely comprised of collection and transportation cost (as is the case for opportunity fuels with little to no other market value). The transporta tion component depends primarily on the distance that biomass needs to be transported. For exampl e, the EPA engineering staff estimate that for a one-way distance of 50 miles with a 20-ton semi-trailer truck, transportation costs could be $10- 20/ton. For biomass at a total delivered price of $4/MMBtu with an indicative heating value of 5,000 Btu/lb (higher heating value (HHV) basis), transportation cost in this example case could

25 See Sec 5.3, pg 5

‐ 19 at: http://www.epa.gov/air markets/progsregs/epa

‐ ipm/docs/v513/Chapter_5.pdf
26 Generally consistent with EPA assumptions in IPM modeling; also see the following source using the same retrofit capital cost assumption: Cofiring Biomass and Coal for Fossil Fuel Reduction and Other Benefits

– Status of North American Facilities in 2010, USDA, August 2012, http://www.fs.fed.us/pnw/pubs/pnw_gtr867.pdf
27 Average biomass price as projected by EPA modeling in IPMv5.13 Base Case 28 EIA, Electric Power Annual 2012

– Electricity (Table 7.4) http://www.eia.gov/electricit y/annual/html/epa_07_04.html Comment [A17]: Should this number be $200/kW? ($200/kW) x (1,000 kW/MW) x (50 MW)

= $10 million

Comment [A18]: There are some assumptions in this section that state “estimated by EPA staff”. Suggest providing further support if possible.

Comment [A19]: This is intriguing, and it might be worthwhile providing more detail about this (perhaps in a separate Table) ‐ especially given the emphasis on heat rates in chapter 2.

Comment [A20]: It might be worth distinguishing transportation costs for plantation grown biomass vs. forest harvesting residues vs. wood products manufacturing residues.

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15 account for 25-50 percent of the total delivered biom ass cost. In any case, it is the total delivered price of biomass on a $/MMBtu basis that will primarily determine the economic feasibility of biomass co-firing.

Emission Reduction Potential

The CO 2 

### reduction potential of biomass co-firing is di rectly related to the amount and type of
biomass co-fired and is due to the differen ce in heating value, moisture content and hydrogen/carbon ratios 29 

### for a selected biomass fuel compared to the particular coal it replaces. The types of biomass typically available to EGUs in the United States include woody-based feedstocks such as wood chips, forest industry byproducts, and to a lesser degree agricultural crop residues, as well as emerging dedicated en ergy crops such as switchgrass and short-rotation woody crops. 30 

### In general, when comparing coal-only versus co-firing coal with biomass, cofiring may result in either an increase or decrease in the stack CO 2 

### emission rate. The extent to which biogenic emissions can reduce fossil emission s is being considered in EPA’s current study on the framework for accounting for biogenic emissions. European studies show that biomass can significantly displace fossil emissions in a co-fired plant. EPA specifically requests for comment on the degree to which biomass can be co -fired to reduce fossil emissions in the United
States.

Cost of Reductions and Cost Effectiveness
In order to evaluate cost-effectiveness of potentia l reductions, the EPA first estimated the cost of
avoided coal CO 2 

### emissions in a hypothetical scenario where biomass CO 2 

### emissions are not
included in total stack CO 2 

### emissions (in effect, biogenic CO 2 

### emissions are subtracted from
total CO 2 

### emissions measured at the stack). The estimated results presented below are based on a reasonably representative case using a baseload bituminous coal-fired boiler with a net heat rate of 10,340 btu/kWh that shifts from 100% bituminous coal to 90% coal and 10% biomass (assuming fuel prices of $2.62/MMBtu for coal in 2020 as projected in IPMv5.13 Base Case and $4/MMBtu for biomass as explained above). When biogenic stack emissions are not counted as

29 IFRF Combustion Handbook, Combustion File No. 23, What is Biomass? (Van Krevelen Diagram), http://www.handbook.ifrf.ne t/handbook/cf.html?id=23
30 Biomass Combined Heat and Power Catalog of Technologies, U.S. EPA, September 2007, http://www.epa.gov/chp/docume nts/biomass_chp_catalog.pdf Comment [A22]: Shouldn’t this at least include a discussion of the potential net/lifecycle emissions benefits of utilizing biomass. Comment [A21]: However, isn’t it safe to say that most studies indicate a decrease in CO2 emissions with increasing biomass amounts. Example ‐ Mann and Spath 2001. http://link.springer.com/article/10.1007%2Fs10098 0100109

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16 part of total emissions, the cost of avoided CO 2 

### for a “typical” baseload coal boiler co-firing 10% biomass is $30/tonne. This estimated cost of avoided coal CO 2 

### emissions would increase if some portion of the biogenic CO 2 

### emissions from the co-fired biomass were included. At higher delivered fuel price differentials, the cost of avoided coal CO 2 

### emissions would increase (for example, at a biomass price of $6/MMBtu, cost of avoided CO 2 

### is $80/tonne if CO 2 

### emissions from biomass are not counted). Similarly, the costs of avoided coal CO2 emissions would decrease at lower delivered biomass prices associat ed with waste and residue materials, at higher coal prices than assumed here, or if other means of GHG reductions were less available.

Conclusion
Replacing some coal with low levels of biomass co-firing may can result in stack CO 2 

### emission
increases and the degree to which combined heat and power are accounted for in energy
demand

. 31 

### Even if biogenic CO 2 

### emissions are not counted as part of stack emissions,
bBiomassbiomass co-firing can be i s a relatively costly cost-effective approach to CO 2 

### reductions at existing coal steam boilers in certain circumstances, such as availability of inexpensive biomass supplies, costly alternatives to improve heat rates, and inability to use redispatch of generation supply. In all cases, t hough, an efficient policy would seek to equalize marginal costs of abatement and not average costs of abatement. As such some level of biomass co-firing will likely be efficient where feedst ock supply costs are below the average stated herewhen compared to other measures such as heat rate improvements and re-dispatch of generation supply to other existing capacity with lower CO 2 

### emission rates.
The EPA is considering cost-effectiveness at a na tional level for the purpose of setting emissions goals consistently in each state. While this analysis concludes that cost-effective reductions of
CO 2 

### are not available on a national basis from wide spread adoption of biomass co-firing, it does not preclude the potential for individual EGUs to utilize co-firing as a way to reduce overall CO 2

31 Depending on biogenic feedstocks used and whether or not an assessment system is applied that evaluates biogenic CO 2 emissions from the stack in relation to the terrestrial carbon cycling associated with the production and use of that biogenic feedstock.

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17 emissions, nor does it preclude states from factoring in that unit-level poten tial into the design of state plans for compliance with the 111(d) standard. 32



### Partial Bibliography fo r Technical Feasibility of Biomass Co-firing
(a) Briggs, J. and J. M. Adams, Biomass Combus tion Options for Steam Generation, Presented at Power-Gen 97, Dallas, TX, December 9 – 11, 1997.
(b) Grusha, J and S. Woldehanna, K. McCarthy, and G. Heinz, Long Term Results from the First US Low NOx Conversion of a Tangential Lignite Fired Unit, presented at 24th International Technical Conference on Coal & Fuel System s, Clearwater, FL., March 8 – 11, 1999.
(c) EPRI, Biomass Co-firing: Field Test Results : Summary of Results of the Bailly and Seward Demonstrations, Palo Alto, CA, supported by U.S. Department of Energy Division of Energy Efficiency and Renewable Energy, Washington D.C.; U.S. Department of Energy Division Federal Energy Technology Center, Pittsburgh PA; Northern Indiana Public Service Company, Merrillville, IN; and GPU Generation, Inc., Johnstown, PA: 1999. TR-113903.

(d) Laux S., J. Grusha, and D. Tillman, Co-firi ng of Biomass and Opportunity Fuels in Low NOx Burners, PowerGen 2000 - Orlando, FL
(e) Tillman, D. A., Co-firing Biomass for Gr eenhouse Gas Mitigation, presented at Power-Gen 99, New Orleans, LA, November 30 – December 1, 1999.
(f) Tillman, D. A. and P. Hus, Blending Oppo rtunity Fuels with Coal for Efficiency and Environmental Benefit, presented at 25th International Technical Conference on Coal Utilization & Fuel Systems, Clearwater, FL., March 6 – 9, 2000
(g) Tillman D A, Harding N S (2004) Fuels of opportunity: characteristics and uses in combustion systems. Elsevier, Oxford, UK
(k) http://esm.versar.com/pprp/bib liography/PPES_06_ 02/PPES_06_02.pdf
(h) Tillman D, Conn R, Duong D (2010) Coal char acteristics and biomass cofiring in pulverized
coal boilers. In: Electric Power, Baltimore, MD, USA, 18 - 20 May, 2010

32 EPA notes that states will need to consider any future EPA finding regarding an assessment framework that considers carbon fluxes on the biogenic feedstock production landscape applied when evaluating net stack CO 2 emissions from biomass co

‐ firing. Comment [A23]: Suggest the following 2 references: Spath, P.L.; Mann, M.K. Life Cycle Assessment of a Natural Gas Combined

‐ Cycle Power Generation System http://www.nrel.gov/docs/fy00osti/27715.pdf Mann, M.K.; Spath, P.L. A Life Cycle Assessment of biomass cofiring in a coal fired power plant. https://www.bioenergykdf.net/sites/default/files/z pdfzholderz/NREL_Dat a/KC_091102094512.pdf

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18 (i) Renewable and Alternative Energy Fact Sheet – Co-firing Biomass with Coal, Pennsylvania State University, 2010, http://pubs.cas.psu.edu/Free Pubs/PDFs/ub044.pdf
(j) Fernando R, Cofiring High Ratios of Bioma ss with Coal, IEA Clean Coal Centre (CCC/194), January 2012
(k) DOE-EERE-ORNL, Biomass Energy Da ta Book- Edition 4, October 2012, http://cta.ornl.gov/bedb/index.shtml

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## Chapter 7: Carbon Capture & Storage
Introduction Another possible approach for reducing CO 2 

### emissions from existing fossil fuel-fired EGUs is through the application of carbon capture and storage technology (CCS; sometimes also referred to as carbon capture and sequestration). In the recently proposed standards of performance for new fossil fuel-fired EGUs (79 FR 1430), the EPA proposed to find that the best system of emission reduction for new fossil fu el-fired boilers and IGCC units is partial application of CCS. In that proposal, the EPA found that, for new units, partial CCS has been adequately demonstrated; it is technically feasib le; it can be implemented at reasonable costs; it provides meaningful emission reductions; and its implementation will serve to promote further development and deployment of the technology. This chapter examines the potential for implementation of CCS technology at existing fo ssil fuel-fired utility boilers and IGCC units. Carbon Capture Options for Existing Fossil Fuel-fired EGUs In general, CO 2 

### capture technologies applicable to existing fossil fuel-fired powergeneration can be categorized into three approaches – (1) post-combustion capture; (2) precombustion capture; and (3) oxy-combustion. Each of these is described and discussed in more
detail below. Post-combustion Capture
Post-combustion CO 2 

### capture refers to removal of CO 2 

### from a combustion flue gas prior to discharging to the atmosphere. Separating CO 2 

### from such a gas stream can be challenging for
a number of reasons. Because CO 2 

### is a dilute fraction of the combustion flue gas – typically 13- 15 % in coal-fired systems and 3-4 % in natural gas-fired systems – a large volume of flue gas must be treated. The flue gas from typical combustion systems is usually at near atmospheric pressure. Therefore, most of the available capture systems rely on chemical absorption (chemisorption) options (e.g., amines) that require added energy to release the captured CO 2 

### and regenerate the solvent. Many of the chemical solvents require a flue gas stream that is free of or has very low quantities of components – such as SO 2 

### , NO X 

### , and HCl – that can degrade the solvent. The captured CO 2 

### must then be compressed from near atmospheric pressure to much higher pipeline pressures (about 2,000 psia).

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2 Pre-combustion Capture Pre-combustion capture systems are applicable to fossil fuel gasification power plants (i.e., IGCC units) where coal or other solid foss il fuel (e.g., pet coke ) is converted into a synthesis gas (or “syngas”) by applying heat under pressure in the presence of steam and limited
O 2 

### . The product syngas contains primarily H 2 

### and CO – and, depending on the fuel and gasification system – some lesser amount of CO 2 

### . The amount of CO 2 

### in the resulting syngas stream can be increased by “shifting” the composition via the catalytic water-gas shift (WGS) reaction. This process involves the catalytic reaction of steam (“water”) with CO (“gas”) to form

H 2 

### and CO 2 

### . The resulting CO 2 

### contained in the syngas is then captured before combustion of
the H 2 

### -enriched syngas for power generation in a combined cycle system. Contrary to the postcombustion capture flue gas, the IGCC syngas can contain a high volume of CO 2 

### and is pressurized. This allows the use of physical absorbents (e.g., SelexolTM, Rectisol®) that require much less added energy to release the captured CO 2 

### and require less compression to get to pipeline standards. Oxy-combustion Oxy-combustion systems for CO 2 

### capture rely on combusting coal or other fuels with relatively pure O 2 

### diluted with recycled CO 2 

### or CO 2 

### /steam mixtures. Under these conditions, the primary products of combustion are water and CO 2 

### , with the CO 2 

### purified by condensing the water. Challenges associated with oxy-combustion include the capital cost and energy consumption for a cryogenic air separation unit (ASU) to produce oxygen, introduction of N 2 

### via boiler air infiltration, and excess O 2 

### in the CO 2 

### product stream.
CO 2 

### Transportation and Storage
CO 2 

### Pipeline Infrastructure Carbon dioxide has been transported via pipelines in the U.S. for nearly 40 years. Approximately 50 million metric tons of CO 2 

### are transported each year through 3,600 miles of pipelines. Moreover, a review of the 500 largest CO 2 

### point sources in the U.S. shows that 95 percent are within 50 miles of a possible geologic sequestration site, which would lower transportation costs. There are multiple fact ors that contribute to the cost of CO 2 

### transportation via pipelines including but not limited to: availa bility and acquisition of rights-of-way for new pipelines, capital costs, operating costs, length a nd diameter of pipeline, terrain, flow rate of
CO 2 

### , and the number of sources utilizing the pipeline.

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4 Geologic Storage Existing project and regulatory experience, research, and analogs (e.g. naturally existing
CO 2 

### sinks, natural gas storage, and acid gas inject ion), indicate that geologic sequestration is a viable long term CO 2 

### storage option. The viability of geologic sequestration of CO 2 

### is based on a demonstrated understanding of the fate of CO 2 

### in the subsurface. Geologic storage potential for
CO 2 

### is widespread and available throughout the U.S. and Canada. Nearly every state in the U.S. has or is in close proximity to formations wi th carbon storage potential including vast areas
offshore. Estimates based on DOE studies indicat e that areas of the U.S. with appropriate geology have a storage potential of 2,300 billion to more than 20,000 billion metric tons of CO 2 

### in deep saline formations, oil and gas reservoirs and un-mineable coal seams. 1 

### Other types of geologic formations such as organic rich shale and basalt may also have the ability to store CO 2 

### ; and the DOE is currently evaluating their potential storage capacity. Further evidence of the widespread availability of CO 2 

### storage reserves in the U.S. comes from the Department of Interior’s U.S. Geological Survey (USGS) which has recently completed a comprehensive evaluation of the technically accessible storage resource for carbon storage for 36 sedimentary basins in the onshore areas and State waters of the United
States. 2 

### The USGS assessment estimates a mean of 3,000 billion metric tons of subsurface CO 2 

### storage potential across the United States. For comparison, this amount is 500 times the 2011 annual U.S. energy-related CO 2 

### emissions of 5.5 Gigatons (Gt). 3 

### Enhanced Oil Recovery (EOR) Geologic storage options also include use of CO 2 

### in EOR, which is the injection of fluids into a reservoir to increase oil production effici ency. EOR is typically conducted at a reservoir after production yields have decreased from primary production. EOR using CO 2 

### , sometimes
referred to as ’CO 2 

### flooding’ or CO 2 

### -EOR, involves injecting CO 2 

### into an oil reservoir to help mobilize the remaining oil and make it avai lable for recovery. The crude oil and CO 2 

### mixture is produced, and sent to a separator where the crude oil is separated from the gaseous hydrocarbons

1 The United States 2012 Carbon Utilization and Storage Atlas, Fourth Edition, U.S Department of Energy, Office of Fossil Energy, National Energy Technology Laboratory (NETL). 2 U.S. Geological Survey Geologic Carbon Dioxide Storage Resources Assessment Team, 2013, National assessment of geologic carbon dioxide storage resources – Results: U.S. Geological Survey Circular 1386, 41 p., http://pubs.usgs.gov/fs/2013/1386/

. 3 U.S. Geological Survey Geologic Carbon Dioxide Storage Resources Assessment Team, 2013, National assessment of geologic carbon dioxide storage resources – Summary: U.S. Geological Survey Factsheet 2013-3020, 6p.http://pubs.usgs.gov/fs/2013/3020/.

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5 and CO 2 

### . The gaseous CO 2 

### -rich stream then is typically dehydrated, purified to remove hydrocarbons, recompressed, and re-injected into the oil or natural ga s reservoir to further enhance recovery.
CO 2 

### -EOR has been successfully used at many production fields throughout the U.S. to increase oil recovery. The oil and natural gas industry in the United States has over 40 years of experience of injection and monitoring of CO 2 

### in the deep subsurface for the purposes of enhancing oil and natural gas production. This experience provides a strong foundation for the injection and monitoring technol ogies that will be needed for successful deployment of CCS. Evaluation of Retrofit CCS as BSER for Existing Fossil Fuel-fired EGUs Technical Feasibility In evaluating partial CCS as the BSER for new fossil fuel-fired boilers and IGCC units, the EPA determined that the technology is feas ible and adequately demonstrated for new units because the major components of CCS – the capture, the transportation, and the storage – are all proven technologies that have been demonstrated at large scale. While the EPA found that partial CCS is technically feasible for new fossil fuel -fired boilers and IGCC units, it is much more
difficult to make that determination for the entire fleet of existing fossil fuel-fired EGUs. Developers of new generating facilities can select a physical location that is more amenable to CCS – such as a site that is near an existing CO 2 

### pipeline or an existing oil field. Existing sources do not have the advantage of pre-selecting an appropriate location. Some existing facilities are
located in areas where CO 2 

### storage is not geologically favorable and are not near an existing CO 2 

### pipeline. Developers of new facilities also have the advantage of integrating the partial CCS system into the original design of the new facility. Integrating a retrofit CCS system into an existing facility is much more challenging. Some existing sources have a limited footprint and may not have the land available to add partial CCS system. Integration of the existing steam system with a retrofit CCS system can be particularly challenging. Partial CCS has been demonstrated at existing EGUs. It has been demonstrated at a pilotscale at Southern Company’s Plant Barry; it is being installed for large-scale demonstration at NRG’s WA Parish facility; and it will very soon be applied at commercial-scale as a retrofit at SaskPower’s Boundary Dam coal-fired EGU in Ca nada. However, all of these facilities are located in areas that are either near an existing oil field or in an area that is geologically
favorable for CO 2 

### storage. Thus, at some existing facil ities, the implementation of partial CCS

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6 may be a viable GHG mitigation option and some utilities may choose to pursue that option. However, the EPA does not believe that it can serve as the best system of emission reduction for a broadly applicable GHG mitigation program. Ther efore, the EPA does not propose to find that CCS is a component of the best system of emission reduction for CO 2 

### emissions from existing
fossil fuel-fired EGUs.
Reasonableness of Cost In the proposed standard of performance fo r new fossil fuel-fired EGUs (79 FR 1430), the EPA found that the costs to implement partia l CCS (to a level to meet the proposed emission standard of 1,100 lb/MWh-gross) were consistent with costs for other non-natural gas-fired generating technologies – such as nuclear, biomass and ge othermal – that utilities are considering for new intermediate and base load generating capacity. The EPA also noted in the proposal, that most of the relatively few new projects that are in the development phase are already planning to implement CCS; and, as a result, the standard would not have a significant impact on nationwide energy prices. In contrast, the EPA noted that identifying CCS as BSER for new natural gas-fired stationary combustion turbines, would likely result in higher nationwide electricity prices an d could adversely affect the supply of electricity since the majority of new fossil-fuel fired pow er generation is projected to be natural gas combined cycle (NGCC). An emission standard for existing units based on CCS (or even partial CCS) would most certainly have an even more si gnificant effect on nationwide electricity prices and could affect the reliability of the supply of electricity. Therefore, we do not find that the cost to implement existing source emission standards to be reasonable, which further supports the determination that CCS is not an appropriate comp onent of the best system of emission reduction
for CO 2 

### emissions from existing fossil fuel-fired EGUs. Emission Reductions and Promotion of Advanced Technology An emission standard for existing units based on CCS (or even partial CCS) would clearly result in significant emission reductions and would certainly serve to promote further deployment, development and improvement in the most advanced technology. However, the EPA has determined that such an emission standard is not technically feasible in all cases and
cannot be done at a reasonable cost at this time. Comment [A1]: This comment is quite problematic and incorrect, and not consistent with the language agreed to in the NSPS discussions. Recommend EPA cut and paste the NSPS language here related to NG CCS not currently being the best system of emission reduction at present, but not this other text regarding nationwide energy prices and grid reliability.