From: Vasu, Amy Sent: Monday, July 27, 2015 7:38 PM To: Frey, Nathan J. Cc: Szabo, Aaron; Harvey, Reid; Tsirigotis, Peter; Culligan, Kevin Subject: Additional CPP TSDs for interagency review

Nathan,
Attached are the following four CPP technical documents: 1) GHG Mitigation Measures TSD 2) Demand-Side Energy Efficiency TSD 3) Resource and Reliability Analysis TSD 4) Stranded assets memorandum
Thank you.
Amy

~~~~~~~~~~~~~~~~~~~~~~~ Amy B. Vasu U.S. EPA / OAQPS Sector Policies and Programs Division email: vasu.amy@epa.gov phone: 919.541.0107

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### Technical Support Document (TSD) for Carbon Pollution Guidelines for Existing Power Plants: Emission Guidelines for Greenhouse Gas Emissi ons from Existing Stationary Sources: Electric Utility Generating Units
Docket ID No. EPA-HQ-OAR-2013-0602



# Greenhouse Gas Mitigation Measures

U.S. Environmental Protection Agency Office of Air and Radiation
August XX, 2015

### TABLE OF CONTENTS

Chapter 1: Introduction ....................................................................................................... ......1-1 Chapter 2: Heat Rate Improvement at Exis ting Coal-fired Electric Generating Units .............2-1 Chapter 3: Generation Shifts and CO 2 

### Reduction amongst Affected Electric Generating Units

............................................................................................................................... ....................3-1 Chapter 4: Cleaner Generation Sources ....................................................................................4-1 Chapter 5: Carbon Capture & Storage ......................................................................................5-1

### 3 - 1 

## Chapter 1: Introduction

Under the authority of Clean Air Act (CAA) se ction 111(d), the EPA is establishing final
CO 2 

### emission guidelines for existing fossil fuel-f ired electric generating units (EGUs) – the Clean Power Plan. More specifically, the EP A is establishing: 1) carbon dioxide (CO 2 

### ) emission performance rates based on the best system of emission reduction (BSER) for two subcategories of existing fossil fuel-fired EGUs, fossil fuel-fired electric utility steam generating units and stationary combustion turbines, and 2) gui delines for the development, submittal and implementation of state plans that implement the CO 2 

### emission performance rates. The emission performance rates reflect the “best system of emission reductions ... adequately demonstrated”
for CO 2 

### emissions from the EGU source category. The guidelines also provide for the development, submittal and implementation of state plans that implement the CO 2 

### emission performance rates, either direc tly by means of source-specific re quirements or through measures that achieve equivalent CO 2 

### reductions from the same group of EGUs. This technical support document (TSD) provides additional informa tion, data, and analysis to support EPA’s assessment and application of BSER.
In the June 2014 proposal, the EPA proposed to determine that the best system of emission reduction adequately demonstrated for reducing CO 2 

### emissions from existing EGUs was a combination of measures: (1) increasing th e operational efficiency of existing coal-fired steam EGUs, (2) substituting increased generatio n at existing NGCC units for generation at existing steam EGUs, (3) substituting generatio n from low- and zero-ca rbon generating capacity for generation at existing fossil fuel-fired E GUs, and (4) increasing demand-side energy efficiency to reduce the amount of fossil fuel-fired generation. Th ese activities were categorized as four “building blocks.”
The final rule BSER incorporates certain ch anges from the proposed rule, reflecting the
EPA’s consideration of comments received and fu rther analysis. The principal changes are the
exclusion from the BSER of emission reduc tions achievable thr ough demand-side energy efficiency (building block 4) and through nuc lear generation; a revised approach to
determination of emission reductions achievable through increased renewable energy generation; a consistent approach to dete rmination of emission reductions achievable through all the building blocks that better reflects the regional nature of the electricity system; and a revised interim goal

### 3 - 2 period of 2022 to 2029 (instead of the proposed interim period of 2020 to 2029). These changes to the BSER and the building blocks are discusse d in more detail in the preamble for the CPP
Final Rule. Further information can be found in the Legal Memorandum for the CPP Final Rule,
the CO 2 

### Emission Performance Rate and Goal Computation TSD for the CPP Final Rule, and the Response to Comments document, all of which are available in the docket.
This TSD includes chapters with detailed information and EPA’s evaluation of each building block included in BSER, and also in cludes a chapter on car bon capture and storage technology (CCS). 1 

### A separate TSD includes supplement al information on demand-side energy efficiency (Demand-side Energy Efficiency TSD for CPP Final Rule), wh ich is not part of BSER, but supports the Regul atory Impact Analysis.
While evaluating each measure, the EPA considered the technical feasibility, applicability and use, applicati on level appropriate for BSER, and cost effectiveness associated with reducing GHG emissions at EGUs. The app lication of the building blocks as BSER is consistent with current trends in the electric power sector and with strategies that companies and states are already taking to re duce GHG emissions. The applicati on of the building blocks as BSER supports achieving cost-effective a nd technically feasible reductions of CO 2 

### , consistent
with the final CPP rule.

1 The proposed rule GHG Abatement Measures TSD (docket ID #XX) also included a chapter on fuel switching at affected EGUs. This mitigation strategy was not included as BSER in the proposal, nor is it included in the final rule.

### 2-1 

## Chapter 2: CO 2 

## intensity reductions through heat rate improvements at existing coal fired elect ricity generating units
This Technical Support Document (TSD) inco rporates by reference the materials found in the Clarified Technical Support Document (TSD) fo r Carbon Pollution Guidelines for Existing Power Plants: Emission Guidelines for Gree nhouse Gas Emissions from Existing Stationary Sources: Electric Utility Genera ting Units: GHG Abatement Measures as well as the Dataset related to Chapter 2 of the GHG Abatement Measures TSD , including inter alia the description of the data and methodology, analytical results, illustrative equipment upgrades, costs, and datasets. 1



### 2.0 Overview The heat rate of coal-fired electricity ge nerating units (EGUs) is the amount of energy required to generate each kilowatt-hour (kWh) of electricity. The more efficiently an EGU converts the energy embedded in coal into elec tricity, the lower the EGU’s heat rate and, correspondingly, the lower its CO 2 

### intensity (CO 2 

### lb/kWh). Because heat rate improvements reduce fuel consumption, improving coal-fired E GUs’ heat rates can be a cost-effective method of reducing the CO 2 

### intensity of the power system as a whole. 2 

### In addition, it can provide additional economic benefits from reduced fuel costs. To assess the potential heat rate improve ments of existing coal-fired EGUs, EPA analyzed historical gross heat rate data from 2002 to 2012 for 884 coal-fired EGUs that reported both heat input and gross electr icity output to the agency in 2012. The agency grouped the EGUs by regional interconnections – Western, Texas, and Eastern – and analyzed potential heat rate improvements within each interc onnection using three analytical approaches. The approaches compare each EGU’s “best” or “benchmark” hist orical gross heat rates against the EGU’s 2012gross heat rate. By founding the methodology on unitspecific comparisons between an EGU’s historical and baseline performances, the analyses control for variations in EGU design a nd largely obviate the need for subcategorization. By using the 2012 gross heat rate as the baseline, the analyses account for actions that individual EGUs have taken to improve gross heat rate. Accordingly, the results identify the overall heat rate improvements that will result if the coal-fired EGUs simply returned to their demonstrated and achievable past performance.

1 See Clarified Technical Support Do cument (TSD) for Carbon Pollution Guidelines for Existing Power Plants: Emission Guidelines for Greenhouse Gas Emissions from Existing Stationary Sources: Electric Utility Generating Units; GHG Abatement Measures, Docket No. EPA-HQ-OAR-2013-0602-17180 (Sept. 16, 2014), available at http://www.regulations.gov/#!documen tDetail;D=EPA-HQ-OAR-2013-0602-17180 ; Dataset related to Chapter 2 of the GHG Abatement Measures TSD, Docket No. EPA-HQ-OAR-2013-0602-0238 (June 8, 2014), available at http://www.regulations.gov/#!documen tDetail;D=EPA-HQ-OAR-2013-0602-0238

. 2 Improving heat rate can result in a “rebound effect” th at undermines the emissions reductions a coal-fired EGU would otherwise achieve. For a detailed discussion of the consequences of the rebound effect for building block 1 and the best system of emission reductions, see sections V.A and V.C of the final rule preamble of the CPP. 

### Abbreviations Btu British thermal unit CONUS contiguous U.S. EGU electricity generating unit kWh kilowatt hour MW megawatt

### 2-2 The first approach, “best historical average performance,” compared each EGU’s best 1- and 2-year average gross heat rates to the E GU’s 2012 gross heat rate. For the second and third approach, the agency controlled for select conditions beyond the EGU operator’s control – hourly capacity factor and hourly ambient air te mperature. These two conditions account for approximately 26 percent of vari ation in gross heat rate for the 884 coal-fired EGUs between 2002 and 2012. To control for these conditions, the agency grouped data by hours with similar hourly capacity factors (using 10 percent increm ents) and hourly ambient air temperature conditions (using 10° F ranges). In the second a pproach, “best historical average performance under similar conditions,” the agency compared each EGU’s best 1- and 2-year average gross heat rates in each capacity-temperature group ag ainst the EGU’s 2012 gross heat rate for the capacity-temperature group. For the third appro ach, “efficiency and consistency improvements under similar conditions,” the agency identified “benchmarks” for each EGU equal to the EGU’s lowest hourly gross heat rate (c haracterized as the 10th percen tile value over 1- and 2-year periods) for each capacity-temperature group. 3 

### Then the agency determined the heat rate that could be achieved if the EGU implemented measur es to operate more consistently nearer the “benchmark.” The results of these three approaches indicate that there is significant potential for heat rate improvement ranging from 4.0 to 6.6 percent na tionally if coal-fired EGUs return to their best past performance between 2002 to 2012. The mo st conservative approach that is supported by the weight of evidence yields average potential heat rate improvements of 2.1 percent in the Western Interconnection, 2.6 percent in the Texas Interconnection, and 4.3 pe rcent in the Eastern Interconnection.

2.1 Introduction Heat rate, a measure of efficiency for foss il fuel-fired EGUs, is commonly expressed as the amount of heat input in British thermal un its (Btu) required to generate one kWh of electricity (Btu/kWh). As a coal-fired EGU’s h eat rate improves (i.e., e fficiency improves), less coal or other fuel is required to produce the same amount of electricity. Because less fuel is combusted, the amount of CO 2 

### and other emissions (e.g., mercury, NO X 

### , PM, SO 2 

### ) released to the environment also declines. In addition to th e clear environmental be nefits of heat rate improvements, there are important economic co-ben efits attributable to reduced fuel costs and coal combustion residue manage ment and disposal costs. Heat rates of existing coal-fired EGUs in th e U.S. vary substantia lly. The variation in heat rates among EGUs with similar design char acteristics (e.g., boiler ty pe, fuel type, size, cooling system) as well as year-to-year variation in heat rate at indivi dual EGUs indicate that there is potential for broad efficiency improvements that can yield significant CO 2 

### emission reductions and fuel savings for the existing co al-fired EGU fleet as a whole. This chapter presents (1) the characteristics of existing co al-fired EGUs in the US, including heat rate performance over time, (2) the impact that design characteristics and other factors may have on

3 The third approach uses hourly values to determine a heat rate benchmark for each EGU for periods with similar hourly capacity factor and hourly am bient temperature conditions. Therefor e, the agency determined it was appropriate to select the 10th percentile value to (1) cont rol for unusually low hourly values and (2) set a benchmark with sufficient hourly measurements at or below the benchmark. The first and second approaches use annual averages so the agency did not determine it necessary to remove unusually low or unusually high heat rate values as the low and high values will generally balance out.

### 2-3 EGU heat rates, (3) additional equipment upgrades and practices that can improve efficiency at existing coal-fired EGUs, (4) the potential heat rate improvements from ex isting coal-fired EGUs that, when realized, would reduce CO 2 

### emission intensity, and (5) responses to key comments about building block 1 of the proposed rule.

2.2 Coal-fired EGUs Several different coal combusti on configurations and technol ogies are used at existing coal-fired EGUs in the U.S. Nevertheless, they all commonly follow the same process for converting the energy stored within coal into electricity. Figure 2-1 depicts a pulverized coalfired EGU, the most widely used configuration in the U.S. 4 

### In general, coal is ground into small pieces or a fine powder, and then fed into a fu rnace where it is burned. The heat from coal combustion boils preheated water creating high- pressure high-temperature steam that is transported to a steam turbine-generator where the steam passes through, forcing the blades of the steam turbine to rotate, which in turn drives a generator to generate electricity. The steam drops in pressure and expands in volume as its energy is transferred to the turbine. After the steam has expanded, the exhaust steam is cooled in a heat exchanger (i.e., condenser). The water is then returned to the boiler to repeat the cy cle. Coal combustion wast e gases (i.e., flue gas), including various air emissions (e.g., CO 2 

### , mercury, NO X 

### , PM, SO 2 

### ), are drawn downstream from the furnace by fans and pass through pollution control devices before they are released from the stack to the atmosphere.

Figure 2-1 Coal-fired EGU schematic
Note: Used by permission. Courtesy of the Babcock & Wilcox Company.


### 2.2.1 Design characteristics and site-speci fic factors that affect heat rate

4 For information about coal combustion configurations at coal-fired EGUs, see supra note 1.

### 2-4 A variety of design characteri stics, site-specific factors, and operating conditions can affect the heat rate of existing coal-fired EGUs. Some of these factors are influenced by or are under the direct control of the EGU operator, but other factors may be beyond the operator’s control, such as: Thermodynamic cycle . The thermodynamic steam cycle of a coal-fired EGU indicates the range of steam temperatures and pressures used in th e boiler and steam turbine. There are two common steam cycles: subcritical and supercritical. In general, the higher the steam temperature and pressure, the higher the efficiency of the EGU a nd the lower the heat rate. Existing subcritical boilers that operate below the critical pressure of water (approximately 22.1 MPa or 3,206 psia) generally have higher heat rate s with design thermal efficienci es of around 35 percent while supercritical and ultrasupercriti cal EGUs that operate above the critical pressure of water may have design thermal efficiencies of 40 to 45 percen t. In any case, actual thermal efficiencies in daily operation are usually lo wer than design values. Boiler and steam turbine size . In general as the size of the boi ler and the steam turbine increase, heat rate decreases. Larger coal-fired EGUs ge nerally have lower heat rates for technical and economic reasons. Furthermore, relative to smaller EGUs, the larger EGUs typically have lower percentages of draft loss, heat loss, and seal leakage, a nd also tend to use more advanced technologies. They also have economies of scal e for investment, operation, and maintenance, and have higher capacity factors becau se they are often dispatched before smaller, less-efficient units. Cooling system. The design of the cooling system for existing coal-fired EGUs – once-through, recirculating (i.e., evaporative), or dry – is la rgely dependent on an EGU’s proximity to water resources, ecological concerns, and regulatory requirements. The cooling system design directly affects the steam turbine’s ability to extract ener gy from the steam and affects overall efficiency. The amount of energy that the st eam turbine can extract from the steam depends on the steam temperature and pressure, as stated above, as we ll as condenser pressure (affected by the cooling system design, condenser cleanliness, EGU load, a nd ambient cooling water or air temperature). At a lower condenser pressure (w ithin design limits), more energy can be extracted by the steam turbine, with a resulting lower EGU heat rate. Auxiliary equipment . Auxiliary equipment, such as pum ps, fans, pulverizers, and pollution control devices, require power to run, thereby consuming so me of the electricity generation onsite and reducing the amount of el ectricity output available for us e on the electricity grid. These systems vary in efficiency ba sed on design, age, and usage. Geographic location . Ambient conditions at the plan t location (e.g., seasonal ambient temperatures, cooling water temperatures) affect an EGU’s heat rate. Ambient air is mixed with the fuel prior to combustion, and a fraction of energy released duri ng combustion heats the incoming air, increasing heat rate. On the othe r hand, cooler water temperatures improve the efficiency of the cooling system , thereby reducing heat rate. Cold er cooling water (or ambient air for dry cooling systems) in the condenser reduces the back pressure in the condenser, allowing for more energy to be extracted by the steam turbine. Coal rank and quality . Higher rank coals (e.g., bituminous) tend to have higher energy contents (i.e., carbon content), burn more intensely, and have a lower moisture content than mid-rank (e.g., subbituminous) and low-rank (e.g., lignite) coals. In general, th e lower the moisture content of coal, the less energy that is needed to evapor ate the moisture and, therefore, the more energy

### 2-5 that is available for producing steam, thus lowering heat rate. The ash content of the coal can also affect heat rate. All coal fuels produce ash that accumulates on heat transfer surfaces (i.e., metallic tubes) and impedes energy transfer to the steam. While systems are designed to remove this “soot,” the quantity present between surface cl eanings hinders energy transfer and results in higher heat rates. All EGU’s are designed to burn a specific, range of fuels, often within a single rank – deviating from the intended design can nega tively affect heat rate. However, some coalfired EGUs efficiently burn ble nds of different coal types.

2.2.2 Regional electricity interconnections As discussed in sections V.A and V.C of the preamble for the final CPP rule and the associated CO 2 

### Emission Performance Rate and G oal Computation Technical Support Document , EPA assessed the potential CO 2 

### reductions achievable th rough each of the building blocks that comprise the best system of em ission reduction. The agency conducted assessment at the level of the three large regi onal electricity interc onnections: Western, Texas, and Eastern (see Figure 2-2). The Western Interc onnection is a major power grid that comprises one North American Electric Reliability Corporation (NERC) region – the Western El ectricity Coordinating Council (WECC). The Western Interc onnection encompasses all or po rtions of 14 western states. The Texas Interconnection includes the portion of Texas managed by the Electric Reliability Council of Texas (ERCOT). The Eastern Interconnec tion is the other major regional power grid, comprising six NERC regions – Florida Relia bility Coordinating Council (FRCC), Midwest Reliability Organization (MRO), Northeast Power Coordinati ng Council (NPCC), Reliability First Corporation (RFC), SERC Reliability Co rporation (SERC), and Southwest Power Pool (SPP) – covering all or portions of 39 eastern and midwestern states. Each interconnection is a highly-connected electricity grid in which, during normal conditions, power generated in one part of the interconnection can help balance power generation and load in all other parts of the system. Therefore, each interc onnection must carefully manage electricity generation and load to ensure safe and reliable electricity service. For the proposal, EPA analyzed the potential heat rate improveme nts from coal-fired EGUs at the national level. Some commenters sugge sted that EPA should ad just the analysis and develop state-specific estimates of potential heat rate improvements from coal-fired EGUs. While the agency carefully evaluated these comme nts, the agency determined that due to the highly interconnected nature of th e electricity system, and as furt her discussed in section V.C.3 of the final rule preamble, it is more appropriate in this rule to assess potential for heat rate improvements from existing coal-fired EGUs at the regional interconnec tion level than at the national or state level.

2.2.3 Coal-fired EGUs in operation in 2012 In 2012, there were 884 coal-fired EGUs 5 

### at 367 power plants in the contiguous U.S. (CONUS) that reported hourly heat input and gross electricity genera tion to EPA’s Clean Air

5 Excludes cogeneration, integrated gasification and co mbined cycle (IGCC), retired, and non-operational EGUs. EGUs that reported steam co-generation to EPA or EIA are excluded because only a portion of heat input is used to generate electricity while the remainder is used to genera te steam, making it difficult to meaningfully compare these EGUs’ heat rates. IGCC units are exclud ed because they are not conventional steam boilers and may regularly use pipeline natural gas in lieu of syngas from coal gasification. EGUs retired prior to 2012 or EGUs that did not operate

### 2-6 Markets Division. These EGUs are spread across the CONUS (see Figure 2- 2) in each of the three regional electricity interconnects. Of the 884 coal-fired EGUs, 93 (10.5 percent) are located in the Western Interconnection, 32 (3.5 percent) are located in the Texas Interconnection, and 759 (86 percent) are located in the Eastern Interconnection.

Figure 2-2 Power plants with coal-fired EGUs reporting heat input and gross electricity generation in 2012 The population of 884 coal-fired EGUs in cludes units with a variety of design configurations and operating char acteristics that can affect an EGU’s heat rate, such as thermodynamic steam cycle, coal rank and coal qua lity, boiler type, boiler size (i.e., maximum hourly heat input), and genera tor power output capacity. At the end of 2012, the age of the 884 coal-fir ed EGUs varied widely, ranging from less than 1 year to 68 years. Nationally, the averag e age was 42 years, but this differed among the interconnections. The Texas Interc onnection had the lowest averag e age of existing coal-fired EGUs at 27 years followed by the Western Interc onnection at 34 years. The average age of the coal-fired EGUs in the Eastern Interc onnection was 44 years (see Figure 2-3). Similarly, electricity generating capacity of the 884 coal-fired EGUs varied widely within and between the interconnectio ns. In 2012, the capacity ranged from 25 MW to nearly 1,500 MW with a national average capacity of 385 MW. 6 

### In the Western Interconnection the average generator capacity of coal-fired EGUs was 390 MW, in th e Texas Interconnec tion the average was 689 MW, and in the Eastern Interconnectio n the average was 372 MW (see Figure 2-4).

in 2012 are also excluded because they do not have 2012 operating data necessary to estimate potential heat rate improvement.
6 EGU operators report maximum hourly load data to EPA/CAMD. This value may differ from the EGU’s nameplate capacity and summer net capacity.

### 2-7 Figure 2-3 Age (years) distribution of c oal-fired EGUs in 2012, by interconnection

Figure 2-4 Generator capacity (MW) dist ribution of coal-fi red EGUs in 2012, by interconnection

2.2.3.1 Historical gross heat rate trends of coal-fired EGUs Between 2002 and 2012, the average gross heat rates of the 884 coal-fired EGUs have fluctuated from year to year. Nationally, the average gross heat rate 7 

### declined between 2002 and 2008 to a low of 9,645 Btu/kWh in 2008 and 2009 (s ee Figure 2-5). However, after 2009, the

7 EPA used hourly heat input (Btu) and hourly gross electricity generation (kWh) to calculate the hourly gross heat rate (Btu/kWh). As described later in this chapter, gr oss generation was the only complete and accurate hourly dataset available to EPA. Therefore, unless otherwise noted, re ferences to heat rate in this chapter refer to gross heat rate.

### 2-8 average annual gross heat rate of the 884 coal-fir ed EGUs began to deteriorate, climbing to 9,753 Btu/kWh in 2012. The variation for each of the interconnection regions was more pronounced. The average annual gross heat rate of the 93 coal-fired EGUs in the Western Interconnection declined from nearly 10,458 Btu/kWh in 2002 to 9,888 Btu/kWh in 2012, just slightly hi gher than the lowest gross heat rate in 2010. The larger and rela tively younger 32 coal-fired EGUs in the Texas Interconnection saw less change than those in th e Western Interconnection. The lowest average gross heat rate for the Texas Interconnection occurred in 2009 and was only slightly higher in 2012 (9,760 Btu/kWh and 9,789 Btu/kWh in 2009 and 2012, respectively). In the Eastern Interconnection, where 86 percent of the coal-f ired EGUs are located, gross heat rates have steadily increased since 2008, rising from 9,567 Bt u/kWh to 9,700 Btu/kWh.

Figure 2-5 Gross heat rate trends of coal-fired EGUs, 2002-2012 by interconnection
Notes: red dotted line represents the 11-year arithmetic average; axis does not start at zero to better illustrate trends in gross heat rate 

### Commenters have offered a variety of opinions in an attempt to explain these gross heat rate trends over time, such as shifts from low-moisture bituminous coals to higher-moisture subbituminous coals from the Powder River Basin, installation of air pollu tion control devices in response to federal and state regulations, retirements of le ss-efficient coal-fired EGUs, fluctuations in precipitation a nd weather conditions, deterioration of plant equipment or deferral of equipment maintenance, or changes in dispatch patterns and capacity factors. EPA acknowledges that there are many fa ctors that can affect the heat rate of coal-fired EGUs, some of which are within the EGU operator’s control and some that are not. In the analysis of potential heat rate improvements described below, the agen cy attempts to limit the influence of some of the factors outside the EGU operator’s control.

2.3 Heat rate improvement practices and equipment upgrades at coal-fired EGUs In the proposal TSD, EPA provided examples of best practices and equipment upgrades that may be available to existing coal-fired EGUs to improve heat rate and, as a result, reduce the

### 2-9 EGU’s CO 2 

### emissions intensity. 8 

### Several commenters also provided extensive information about additional best practices and equipment upgrades th at may be available for coal-fired EGUs to improve heat rate. Commenters also noted that many coal-fired EGUs have already implemented heat rate improvement measures for economic reasons and requiring EGUs to implement the measures again might not yield substantia l benefits or might not be feasible. As explained in section 2.5 below and in pream ble sections V.A and V.C, the agency has decided not to estimate a se parate potential for heat rate improvements from equipment upgrades. As stated in the proposal TSD, the ag ency lacks reliable and complete information about the unit-level upgrades that coal-fired EGUs have alre ady implemented and it is not practical for EPA to conduct engineering analyses at each coal-fired EGU for the purpose of answering the question about whet her existing coal-fired EGUs ar e already taking advantage of all opportunities to improve heat rate. Nonetheless, the agency re mains confident that there are additional opportunities for coal-f ired EGUs to achieve heat rate improvements by implementing some combination of best practices and equipm ent upgrades, including th e illustrative best practices and equipment upgrades shown below. Th e agency recognizes that the types and extent of capital improvements and opera tional and maintenance improveme nts available to individual coal-fired EGUs will vary. For this and other reasons stated below, the agency is therefore not basing the estimate of potential he at rate improvement on a specifi c proportion of best practices or equipment upgrades. The lists below are intended to provide examples of equipment upgrades and best practices to improve heat rates. While the ag ency believes these equipment upgrades and best practices are highly cost-effective, the agency recognizes that some EGUs (e.g., small EGUs) may not find each of the measures to be cost e ffective. In addition, the agency recognizes that not all measures are applicable to each and every EGU. Therefore, these lists are intended only to demonstrate the types of actions that EGUs might be able to implement or maintain to improve heat rate.

2.3.1 Example equipment upgrades fo r heat rate improvement

 Install intelligent sootblowing system

 Replace feed water pump steam turbine seals

 Overhaul high pressure feedwater pumps

 Upgrade main steam turbine seals

 Upgrade steam turbine internals

 Install variable frequency drives for motors

 Retube or expand the condenser

 Install sorbent injection system to reduce flue gas sulfuric acid to allow lower temperature exhaust gas

 Upgrade air heater baskets fo r lower temperature operation

 Upgrade and repair flue gas desulfurization systems

 Refurbish the economizer

 Upgrade ESP components to lower auxiliary power consumption

8 See Clarified Technical Support Document, supra note 1 (discussing in greater detail some of the different types of equipment upgrades and best practices that coal -fired EGUs can employ to improve heat rate).

### 2-10  Improve SCR and FGD system co mponents to lower draft loss

2.3.2 Example best practices fo r heat rate improvement

 Adopt training for O&M staff on heat rate improvements

 Perform on-site appraisals to identify areas for improved heat rate performance

 Install neural network software for com bustion/optimization with monitoring system for heat rate optimization

 Repair steam and water leaks – replace leaking valves and steam traps

 Replace / repair worn air heater seals

 Manage feed water quality

 Chemical clean boiler to remove scale build-up from water side

 Install and operate condens er tube cleaning system

 Repair boiler furnace and ductwork crack s to prevent boiler air in-leakage

 Clean air preheater coils to restore performance

 Adopt sliding pressure operation to reduce turbine throttling losses

 Reduce activation of attemperator whic h compensates for over-firing the unit

 Remove deposits on turbine blades

2.3.3 Heat rate improvement measures identified by external studies There are a number of engineering studies that explore opportuni ties for improving the efficiency of existing coal-fired power plan ts, including studies by Sargent & Lundy and NETL that were referenced in the pr oposal TSD. Some of the practi ces and equipment upgrades have the potential to improve gross heat rate while others will not affect an E GU’s gross heat rate, but they will reduce the auxiliary load from on-site equipment, thereby improving the net heat rate (i.e., making more net electricity available for th e same amount of fuel input). The following potential efficiency improvements in the boiler, turbine, flue gas syst em, air pollution control equipment, and the water treatm ent system are discussed below. 9



### 2.3.3.1 Boiler systems

2.3.3.1.1 Materials handling Materials handling includes the handling and tran sport of coal, the prep aration of coal for combustion, and handling of combustion residues:

 Coal-handling equipment requires about 0.07 percent (7 Btu/kWh) of an EGU’s gross electricity generation. Installation of energy- efficient motors and variable frequency drives can reduce auxiliary power requirements.

 Coal pulverizers require about 0.6 percen t (60 Btu/kWh) of an EGU’s gross electricity generation to prepare the coal for combustion. Upgrading the pulverizers to provide more consistent size and finer coal particles can improve the EGU’s combustion efficiency, produce less unburned co al, and consequently reduce fuel cost and gross heat rate.

9 Chapter 2 of the proposal TSD provides a detailed description of numerous well-proven measures for improving heat rate at coal-fired EGUs. See supra note 1.

### 2-11  Bottom ash handling equipment requires a bout 0.1 percent (10 Btu/kWh) of an EGU’s gross electricity generation. Sw itching from a water-sluicing bottom ash system to a dry system can reduce the a uxiliary requirements and reduce the amount of wastewater to the water treatment plant.

2.3.3.1.2 Boiler control system The boiler control system has a large impact on the gross heat rate of the unit. Commonly referred to as the DCS (Distributed Control Syst em), this computer controls and displays many parameters affecting boiler operation. A neural ne twork is an advanced co ntrol system utilizing artificial intelligence and insta lled in addition to the plant DCS. The neural network evaluates many parameters of the EGU’s operation and make s adaptive tuning based on statistical analysis to predict performance for incr eased efficiency. Many vendors o ffer neural network systems to improve the overall efficiency. These systems can offer gross heat rate improvements up to 150 Btu/kWh.

2.3.3.1.3 Steam turbine The steam turbine extracts energy from the steam and rotates a generator to produce electricity. Modern developments allow the stea m turbine to extract more energy from the same amount of steam than was possible with equipm ent from decades ago. Replacing existing steam turbines with advanced designs or, where prac tical, upgrading turbines with improved internals can reduce gross heat rate by 100-300 Btu/kWh. As one commenter pointed out, Leyzerovich explains that upgrading and retrofitting a turbin e at a 20-year old 500 to 700 MW coal-fired EGU can reduce heat rate by a total of about 4 percent. 10



### 2.3.3.1.4 Feedwater heaters Boiler feedwater heaters pre-heat the water going to the boiler. The hotter the water entering the boiler, the less energy it takes to conver t it to steam. Like any heat exchanger, EGUs can improve efficiency by removing built-up scale or increasing heat transfer surface area. Chemical cleaning removes scale and re stores heat transfer efficiency.

2.3.3.1.5 Condenser Condensers are subject to fou ling and plugging, which directly impact the heat transfer efficiency and water quality. Closed cooling wate r systems can be used to provide better control over water quality and tube cleaning can be performed as needed. Condenser upgrades and maintenance typically improve gr oss heat rate by 30-70 Btu/kWh.

2.3.3.1.6 Cooling tower The temperature of cooling water affects c ondenser performance. Supplying cold water allows the condenser to operate at lower pressures, which allows the steam turbine to extract more energy for power generation. As the coo ling towers become fouled over time, the water returned to the condenser becomes warmer and negatively affects steam turbine performance and reduces gross electricity genera ted. Routine maintenance on the cooling towers might include

10 See AS Leyzerovich, 2007. Steam Turbines for Mode rn Fossil-Fuel Power Plants. CRC Press. ISBN 13: 978- 1420061024.

### 2-12 discarding accumulated debris, removing biolog ic growth, replacing defective fill material, ensuring uniform water distributi on, correcting for uneven air dist ribution, verifying water flow rates, and replaci ng missing louvers

2.3.3.1.7 Boiler feed water pumps Boiler feed pumps require a large amount of auxiliary power to pump large amounts of boiler feedwater through the heater s and the boiler. Due to thei r continuous operation, the pumps wear over time, lose efficiency, and require more energy to move the sa me volume of water. A pump overhaul restores efficiency, ensures reli ability, and can reduce th e net heat rate by 25-50 Btu/kWh.

2.3.3.1.8 Water Treatment System The quality of boiler water affect s the rate of scale build-up on boiler tubes. Scale inhibits heat transfer and the EGU operator typically compensates by increasing the firing-rate to overcome this effect. Scale can reduce heat transf er up to 10 percent in severe cases. However, the higher the quality of water us ed in the boiler, the slower the scale build-up and thus the higher the efficiency of the EGU. Enhanced wate r treatment can reduce scale. In addition, scale can be removed through chemical cleaning to restor e heat transfer and lo wer gross heat rate. The cooling water system can also suffer from poor water quality for the same reason – scale build-up fouls the heat transfer surfaces in the condenser, causing higher condenser temperatures and reducing the capability of th e turbine to extract energy from the steam. Chemical cleaning and better water quality can mitigate the issue.

2.3.3.1.9 Variable frequency drives Replacing fixed speed electric motors with va riable frequency drives reduces auxiliary power requirements by reducing th e drives’ speed (i.e., rpm) when maximum speed is not required. The net heat rate impr ovements can be 10-150 Btu/kWh.

2.3.3.2 Flue gas and emission control technologies Equipment to move flue gases and control ai r pollution typically re quire large amounts of auxiliary power to properly ope rate equipment. Even small upgrades to the equipment can decrease auxiliary power requirements wh ile maintaining emission reduction levels.

2.3.3.2.1 Economizer The economizer extracts energy from the combus tion gases as they exit the furnace. This energy is used to preheat the water returning to the boiler. This heat exchanger improves EGU efficiency by reducing the amount of fuel requi red to convert water into steam. The replacement or refurbishment of the economizer can lead to gross heat rate improvements of around 50-100 Btu/kWh.

2.3.3.2.2 Sootblowers Coal combustion produces ash that builds up on tubing surfaces, reduc ing heat transfer for steam generation and resulting in higher gros s heat rates. Sootblowers remove built-up coal ash to restore performance of th e heat transfer surfaces. Intelligent sootblowers monitor system

### 2-13 conditions and activate when boile r operation indicates the need to remove ash rather than activating at arbitrary time intervals set by opera tors. These systems use real-time furnace data to identify specific areas requiring ash removal. They can improve gross heat rates 30-150 Btu/kWh.

2.3.3.2.3 Air heaters Air heaters transfer heat between the inco ming pre-combustion air and the exiting flue gas. By preheating the combustion ai r, less fuel is required to conve rt pre-heated water to steam. Due to their design, leakage can occur and, as a result, reduce th e pre-combustion air temperature, requiring greater coal consumption. Leakage also increases auxiliary power requirements since equipment must now handle the increased volume of air from the in-leakage. Properly maintained air heaters limit leakage to below 6 percent. Reducing leakage can reduce the auxiliary power requirement s and, therefore, the net he at rate by 10-40 Btu/kWh. Another method to improve heat rate is to ex tract more energy acro ss the air heater to raise pre-combustion air temperatur e. Typically, the air heater ou tlet is maintained at 20-30°F above the sulfuric acid dew point to prevent corrosion. Depending on the air heater’s size, major modifications may be required to capture more energy, but the modifications might reduce gross heat rate by 50-120 Btu/kWh.

2.3.3.2.4 Induced draft fans and forced draft fans Induced draft fans and forced draft fans typi cally operate at consta nt speed with dampers to control flue gas flow rate. Using dampers is the conventional choice to control flow while the fan motor runs at constant speed. However, newer methods are available to control flow for more efficient operation while reducing auxiliary load (e.g., variable fr equency drives, variable pitch blades). These upgrades or replacements provide net heat rate improvements of 10-50 Btu/kWh.

2.3.3.2.5 Flue gas desulfurization A variety of flue gas desulfur ization systems are used at existing coal-fired EGUs. Some EGUs still employ outdated venturi-type designs th at create large pressure drops and require greater auxiliary power to operate the induced draf t fans. Upgraded designs, such as co-current spray tower quenchers, can replace the outda ted designs and enhance net heat rate. A more common flue gas desu lfurization technology installe d throughout the fleet is a spray tower absorber, also known as a wet scr ubber. Technology upgrades , including installing turning vanes to reduce pressure drop and impr ove gas flow, can reduces induced fan auxiliary power requirements and enhance net heat rate.

2.3.3.2.6 Electrostatic precipitator Efficient electrostatic precipitator operation involves maintaining the maximum applied voltage, without arcing. Arcing re presents lost energy unusable for particulate removal and requires the field be re-energized, consumi ng additional auxiliary power. Electrostatic precipitator upgrades improve pe rformance by eliminating arcing and reducing auxiliary power consumption.

2.3.3.2.7 Selective catalytic reduction

### 2-14 Innovations in selective catalytic reducti on systems have reduced auxiliary energy requirements, such as the use of hot secondary air or auxiliary steam for heating system equipment (instead of electrically heating ambi ent air). Catalyst design can lower pressure drop across the reactor and reduce induced fan auxiliary power requirements.

2.4 External studies of heat rate improvemen t potential at existing coal-fired EGUs There are a number of techni cal and statistical studies th at explore opportunities to improve heat rate at existing co al-fired EGUs in the U.S. In the proposal TSD, EPA summarized five studies that found potential heat rate improvements of between 8.7 and 15 percent from existing coal-fired EGUs. Commenters provide d information on additional studies, including government and independent research reports that assess potential heat rate improvements from a large study population of coal-fired EGUs. In general, the concep ts for improving heat rate or reducing CO 2 

### emissions presented in these studies fo cus on: reducing heat losses, extracting more energy from the cycle, replacing worn or degraded equipment, installing equipment upgrades, increasing data measurement and tr acking to enhance operation and efficiency, conducting routine maintenance to su stain peak performance, hiring staff dedicated to heat rate improvement and management, and instituting a corporate culture that places emphasis on efficiency improvements. The studies use a variety of approaches to estimate potential heat rate improvement and CO 2 

### emission reductions. For example, some studies used engineering assessments, some subcategorized EGUs by specific design charact eristics or operati ng conditions and then compared EGUs in each subcategory against one a nother, and others compared each EGU’s heat rate performance or CO 2 

### emission rate over time. Summaries of several of the studies referenced in the proposal TSD are presented below. NETL produced a number of reports and workshop summaries about improving efficiency at existing coal-fired EGUs. In a 2009 workshop to “explore op portunities to improve the thermal efficiency of existing and future coal-fired power plants,” NETL brought together 18 leading industry experts repres enting EGU operators, equipment vendors, energy consultants, and industry associations to analyze the technical and non-technical issues affecting efficiency. 11 

### The analysis identified a wide variation in efficiency levels across the EGU fleet, but found numerous opportunities to improve efficiency exist, such as: (1 ) decreasing excess oxygen to the boiler, (2) installing va riable speed motors, (3) utilizing waste heat for coal drying, and (4) employing solar systems to heat feed water. The report identifies several barriers that limit the adoption of measures to improve heat rate, includ ing: (1) lack of economi c incentives to improve efficiency because of fuel adjustment clauses that enable electricity generating companies to “pass through” changes in fuel costs directly to customers, (2 ) lack of management commitment for efficiency programs, (3) focus on optimizing profitability and ensuri ng EGU availability over efficiency, and (4) lack of on-site performance e ngineers dedicated to heat rate improvement. In general, the report found that while heat rate improvements and efficiency are important,

11 See J Eisenhauer and R Scheer, 2009. Opportunities to Improve the Efficiency of Existing Coal-fired Power Plants. NETL Workshop Report. 2009 July 15-16 in Rosemont, IL. Available at http://www.netl.doe.gov/File%20Library /Research/Energy%20Analysis/Publica tions/OpportImproveEfficExistCFP P-ReportFinal.pdf

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### 2-15 potential fuel cost savings represent only a sm all portion of total costs and are “of secondary concern when compared to needs for maintaining system reliability.” A second NETL workshop was conducted with 53 leading industry experts to explore technical opportunities to improve thermal efficienc y, identify barriers, and identify initiatives to enhance efficiency across the fleet. 12 

### The workshop participants deve loped a list of more than 50 distinct opportunities to improve thermal efficiency, many of which are broadly applicable “across most of the fleet of co al-fired [EGUs],” including bett er sensing, measurement, and control; better and more frequent maintenance; improved fuel handling; and capital upgrades and improvements. In 2010, NETL published a report th at estimated existing coal-fired EGUs could enhance efficiency from a fleetwide generation-wei ghted efficiency of 32.5 percent in 2008 to 35.2 percent, equivalent to a heat ra te reduction of about 9 percent. 13 

### To develop the estimate, NETL segmented coal-fired EGUs into 13 groups based on steam cycle, coal type, and capacity. Using data from 1998 to 2008, the researchers identified the 90 th 

### percentile EGU, 14 

### ranked by efficiency, within each of the 13 segments and calculated the pot ential efficiency improvements if all EGUs below the 90 th 

### percentile improved their average efficiency to the level of the 90 th 

### percentile EGU in the segment. Aside from the impact of steam cycle, coal type, and capacity, the NETL study found that other design characte ristics, location, and emission air pollution controls did not account for vari ations in plant efficiency. N ETL also conducted an alternate analysis that evaluated existing coal-fired EGUs’ past performance between 1998 and 2008 and found that if each EGU returned to its own best hi storical heat rate, overa ll fleetwide heat rate would improve more than 6 percent. Lehigh University’s Energy Research Center (ERC) developed techniques for improving heat rate, and a 2009 update stated improvements in net heat rate of up to 15 percent for existing coal-fired EGUs burning Powder River Basin subbituminous coal and lignite coal, and 10 percent for bituminous coal were possible if improvements could be made “in all possible areas.” 15 

### However, the authors note that it would not be possible to take full advantage of all opportunities at all EGUs, but the information demonstrates that there are substantial opportunities to improve heat rate. The rese arch includes improvement opportunities from process optimization, more aggressive ma intenance practice, and equipment design modifications.

12 See R Brindle, J Eisenhauer, A Greene, M Justiniano, L Kishter, M Munderville, R Scheer, 2010. Improving the Thermal Efficiency of Coal-Fired Powe r Plants in the United States. Workshop Report. 2010 February 24-25 in Baltimore, MD. Available at http://www.netl.doe.gov/File%20Library /Research/Energy%20Analysis/Publica tions/ThermalEfficCoalFiredPower Plants-TechWorkshopRpt.pdf

. 13 See P DiPietro and K Krulla, 2010. Improving the Efficiency of Coal-F ired Power Plants for Near Term Greenhouse Gas Emissions Reductions. NETL Report DOE/NETL-2010/1411. Available at http://www.netl.doe.gov/File%20Libra ry/Research/Energy%20Analysis/P ublications/DOE-NETL-2010-1411- ImpEfficCFPPGHGRdctns-0410.pdf

. 14 NETL ranked heat rate in the opposite order of many of the other studies discussed below. Therefore, the 90 th percentile in the NETL study is methodologically equivalent to the 10 th percentile performer in the studies discussed below.
15 See Lehigh University Energy Research Center, 2009. “Reducing Heat Rates of Coal-fired Power Plants,” Lehigh Energy Update, 27(1). Available at http://www.lehigh.edu/~inenr/leu/leu_61.pdf

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### 2-16 The Congressional Research Service (CRS) published a report summarizing NETL and other analyses. 16 

### The report notes that as coal-fired EGUs age, the EGUs lose efficiency due to mechanical wear; minimizing energy losses pres ents the greatest opportunity for regaining efficiency through equipment refurbishment, improved operations, and dedicated maintenance programs. For example, the top 10 percent of EGU’s operate on average 3.5 percentage points more efficiently than the remaining fleet. Cons equently, there exists room for improvement. Lashof et al. of the Natural Resources De fense Council (NRDC) an alyzed the potential heat rate improvements from coal-fired EGUs from a suite of heat rate improvement measures at EGUs with the highest heat rates for their thermodynamic steam cycle category. 17 

### The study ranked coal-fired EGUs by their full-load gross h eat rate. EGUs that ranked higher than the 10 th 

### percentile for their category, but lower than the median (i.e., 50 th 

### percentile) were assumed to have potential heat rate improvements of 300 Btu/kWh and EGUs ranked greater than the median for their category were assumed to have potential heat rate improvements of 600 Btu/kWh. The 600 Btu/kWh heat rate improvement is based on measures outlined in the NRDC report (e.g., combustion optimization software an d controls, upgrading economizers, cleaning and tuning steam condensers). Linn et al. examined annual heat rate data from approximately 1,000 coal-fired EGUs between 1985 and 2009, grouping plants by boiler t ype, age, size, and other characteristics. 18 

### The RFF study estimates that if coal-fired EGUs in each category improved heat rate to the level of the EGU with the 10th percentile heat rate in th e category, overall gross heat rate would decline by 5.5 percent. Sargent & Lundy, LLC (S&L) developed for the EPA an engineering study of potentially applicable heat rate improvement methods for coal-steam EGUs. The S&L 2009 study report, cited in the proposal TSD, presents techni cal descriptions of numerous well proven, commercially available heat rate improvement techniques. The study also includes indicative ranges of potential heat rate improvement (Btu/n et kWh) with associat ed indicative ranges of capital and O&M costs at three unit sizes (200, 500, and 900 MW). Several leading energy sector engineering consultants pr oduced conference papers and presentations that summarize real-world projects and/or the consultants’ experience with heat rate improvement projects. In a conference paper, Storm indicated that there were at least 22 O&M heat rate factors that are controllable by the EGU operator, includ ing (1) reducing air inleakage into the boiler; (2) improving fineness of fuel by optimizing coal pulverizers; (3) reducing air heater leakage; (4) optimizing primary, overfire air, and secondary air flow measurement and control; and (5) balancing fuel and air distribution into the burner belt. The consultant estimates that the average larg e coal-fired EGU has about 300 to 500 Btu/kWh

16 See RJ Campbell, 2013. Increasing the Efficiency of Existing Coal-Fired Power Plants. CRS Report R43343. Available at http://fas.org/sgp/crs/misc/R43343.pdf

. 17 See DA Lashof, S Yeh, D Doniger, S Carter, L Johnson, 2013. Closing the Power Plant Carbon Pollution Loophole: Smart Ways the Clean Air Act Can Clean Up America’s Biggest Climate Polluters. Available at: http://www.nrdc.org/air/pollution-standar ds/files/pollution-standards-report.pdf

. 18 See J Linn, E Mastrangelo, and D Burtraw, 2014. “Regulating Greenhouse Gases from Coal Power Plants under the Clean Air Act,” Journal of the Association of Environmental and Resource Economists. 1(1/2): 97-134. (DOI: 10.1086/676038). First published as 2013 working paper RFF-DP-13-05 available at http://www.rff.org/rff/Documents/RFF-DP-13-05.pdf

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### 2-17 potential heat rate improvement (i.e., about 3 to 5 percent for an EGU with a heat rate of 10,000 Btu/kWh). The consultant goes on to say that “ex acerbating the challenge [t o improve heat rate] is the fact that often major plant overhauls ha ve been changed from 6 months between boiler scheduled outages to sometimes more than 2 years.” 19 

### Commenters submitted independent studies to inform EPA’s analysis of potential heat rate improvement, including engineering analyses , statistical analyses, and case studies. The results of the commenters’ studies provide si milar results to EPA’s potential heat rate improvement analyses described in the fo llowing section. The commenters’ studies are summarized below. Sierra Club and Earthjustice submitted an i ndependent study comparing each coal-fired EGU’s best rolling 365-day gross heat rate between 2001 and 2012 against the EGU's own average gross heat rate. 20 

### The study included 52 randomly select ed coal-fired EGUs designed to represent the coal-fired EGU fl eet as a whole (based on 2012 el ectricity generation). The study uses rolling 365-day averages of daily gross heat rates in order to mini mize the influence of weather and electricity generati on, and to represent long-term tr ends. For each EGU, the EGU’s lowest and 95 th 

### percentile rolling 365-day average gross heat rate were compared to the EGU’s 12-year average gross heat rate (2001-2012). The results of the anal ysis indicate that, on average, the coal-fired EGUs in the study achieved and ma intained gross heat ra tes over a 365-day period that were 7.6 or 6.1 percent lower at the lowest and 95 th 

### percentile gross heat rate, respectively. The General Electric Company (GE) submitte d an assessment of technical, economic, and market potential for heat rate improvements at coal-fired EGUs. 21 

### The assessment identified equipment upgrades and best prac tices that, individually, could im prove heat rate between 0 and 2.9 percent. The study estimated th e average technical potential fo r gross heat rate improvements to be 5.8 for large coal-fired EGUs to 6.8 percen t for smaller coal-fired EGUs. To calculate the economic potential, GE estimated the penetration rate for equipment upgrades and best practices by establishing a “best in class” full load net heat rate benchmark (9,400 Btu/kWh), irrespective of EGU design characteristics, a nd compared each EGU’s full-load ne t heat rate to the “best in class” benchmark to determine potential heat rate improvements. The study then constrained equipment upgrades and best practices to those with costs below the social cost of carbon, resulting in estimated heat rate improvement of 6.3 percent fo r large coal-fired EGUs to 5.3 percent for small coal-fired EGUs. Finally, the study includes an assessment of market potential based on results from the North American Electricity & Environmental Model (NEEM). For the modeling study, each state was constrained to the CO 2 

### emission rate from EPA’s proposal; coalfired EGUs for which the study assumed some heat rate improvement measures were implemented were only offered the option to use the most expensive heat rate improvements (on the assumption that the lower price options were already implemented at the EGU.) Using the

19 See D Storm, 2009. Applying the Fundamentals for Be st Heat Rate Performance of Pulverized Coal Fueled Boilers. EPRI 2009 Heat Rate Conference Paper. 2009 February 3-5 in Albuquerque, NM. Available at http://www.stormeng.com/pdf/EPRI 2009HeatRateConference%20FINAL.pdf

. 20 See Comments of Sierra Club and Earthjustice. Available at http://www.regulations.gov/#!docum entDetail;D=EPA-HQ-OAR-2013-0602-24029. Also, see B Buckheit and N Spiegel, 2014. Sierra Club 52-Unit Study: Data Files. Available at http://www.regulations.gov/#!documen tDetail;D=EPA-HQ-OAR-2013-0602-25475

. 21 See Comments of The General Electric Company. Available at http://www.regulations.gov/#!documen tDetail;D=EPA-HQ-OAR-2013-0602-22971

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### 2-18 least-cost electricity utility planning framework of NEEM, th e modeling study estimates that only 1 percent heat rate improvement w ill be implemented and the remaining CO 2 

### reductions will come from other acti ons within each state. In addition to the reports described above , POWER Magazine published an article on heat rate improvement opport unities at coal-fired EGUs. 22 

### Korellis summarized an Electric Power Research Institute (EPRI) report of heat ra te improvement case studies at five large coalfired EGUs with electricity generating capaci ties of 95 to 650 MW, combusting Powder River Basin subbituminous (3 EGUs) and bituminous (2 EGUs) coal, and utilizing different thermodynamic steam cycles (2 su percritical and 3 subcritical). Through a combination of best practices and equipment upgrades, f our of the five EGUs were able to improve net heat rate by 3 to 5 percent (see Table 2-1). Co mmon issues at the EGUs prior to the heat rate improvement project included: (1) problems related to combus tion and high air heater/s tack gas temperatures, (2) shortage of heat rate data, (3) lack of training and understa nding of controllable losses, (4) lack of performance testing, (5 ) problems with feedwater heat er train performance, and (6) buildup of soot on heat transfer surfaces. The ar ticle provides a list of general and EGU-specific heat rate improvement recommendations with esti mated reductions in heat input. The article also provides a summary of a utility ’s “fleetwide” heat rate improvement assessment. The engineering study indicated that the 12 coal-fired EGUs in a member utility’s fleet could improve net heat rate by approximately 5.3 percen t at a cost of approximately $40 per ton of CO 2 

### reduced. The summary of the full report states, “[ r]educing a power plant's heat rate can lower emissions, fuel consumption, and costs, thus cont ributing to the plant's bottom line. A significant improvement in heat rate can often be achie ved with a re-commitment to best operating practices, which can minimize the need for capital expenditures on new technology. However, because the current fleet of coal-fired plants has endured age-related degradation; changed operational requirements such as fuel quality, lo w emissions, and flexible operations; and made physical modifications, achievable heat rates may be significantly different from initial design values. As a result, power plant owners and opera tors are unable to assess the range of possible heat rate improvements in many cases.”

22 See S Korellis, 2014. “Coal-Fired Power Plant Heat Rate Improvement Options, Part 2,” Power Magazine. Available at http://www.powermag.com/coal-fired-power-plant-heat-rate-improvement-options-part-2 . For the full report, see EPRI, 2014. Range and Applicability of H eat Rate Improvements. Technical Update 3002003457. Available at http://www.epri.com/abstracts/Pages/ProductAbstract.aspx?ProductId=000000003002003457

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### 2-19 Table 2-1 Net heat rate improvement case study at five coal-fired EGUs 

### EGU Project type Net heat rate improvement
A Implementing multi-level heat rate teams, training, managing excess air 279 Btu/kWh

B Aligning cycles, reducing air inleakage, chemically cleaning turbine 557 Btu/kWh

C Maintaining steam path and replaci ng feedwater heater 400 Btu/kWh
D Reducing air inleakage, replac ing feedwater heater, cleaning condenser and feedwater tubes 500 Btu/kWh

E [Not available] Heat rate increased (350 Btu/kWh)

These studies provide technical information about potential heat rate improvement at existing coal-fired EGUs, including available be st practices and equipment upgrades. As several commenters noted, these studies demonstrate that heat rate improvements are both readily available and achievable. The studies and comm enters’ suggestions informed EPA about the different approaches and analytical techniques that are available to assess potential heat rate improvements at existing coal-fired EGUs. The agency used the information to enhance the analytical approaches, but decided not to adop t any one of the studies described above. The agency believes its analyses, described in the next section, are more a ppropriate for the purpose of determining potential heat rate improvement for this final rule because the agency’s approach: (1) uses more recent data on nearly all existing coal-fired EGUs in the CONUS; (2) compares each EGU’s 2012 performance against the EGU’s ow n best performance, largely obviating the need for subcategorization or a “ one-size-fits-all” heat rate or CO 2 

### emission rate; and (3) controls for key ambient and operating conditions outside the EGU operators’ control.

2.5 EPA study of heat rate improvement pot ential at existing coal-fired EGUs To assess potential heat rate improvements at coal-fired EGUs, EPA began by looking at each coal-fired EGU’s past gross heat rate perfor mance. EPA used three analytical approaches to assess potential heat rate impr ovement for each coal-fired EGU by comparing the EGU’s best historical gross heat rate between 2002 and 2012 against the EGU’s own 2012 gross heat rate. The agency based this approach on the reasonable expectation that, in th e general sense, if a coal-fired EGU was able to demonstrate and achie ve specific heat rates in the past the EGU should be able to achieve that same heat rate agai n. The advantages of such an approach are (1) it compares each EGU against the EGU’s own past performance, thereby controlling for many of the design characteristics that vary among EGUs but are constant or nearly constant over time at individual EGUs (e.g., generator capacity, ther modynamic steam cycle, coal type, cooling system type); (2) it accounts for heat rate improvement measures employed during the study period by using 2012 as the baseline, (3) it is not significantly affect ed by the addition of pollution control devices during the study period because the auxiliary power consumption from

### 2-20 these devices has negligible imp act on gross electricity generation; 23 

### and (4) the historical unitlevel gross heat rate is by definition demonstr ated and achievable by the respective coal-fired EGU. In the June 2014 proposal, EPA proposed a 6 per cent potential heat rate improvement for the coal-fired EGU fleet for the purpose of es tablishing state emission goals. The potential heat rate improvement was based on tw o components: (1) a statistical analysis that determined 4 percent improvement in the fleetwide coal-fired EGU heat rate is possibl e through best practices (e.g., improved operation and maintenance), and (2 ) an estimate that a 2 percent heat rate improvement is possible through additional equipment upgrades. Several commenters suggested these values we re too conservative, suggesting the agency should require greater heat rate improvements from the fleet on average or from each and every coal-fired EGU. Some of these commenters provi ded studies and analyses showing larger heat rate improvements are feasible for coal-fired EGUs. EPA carefully reviewed the comments and supplemental materials before refining the pot ential heat rate improvement analysis. As explained below, the analysis for this final rule does not include a separate estimate of the potential from equipment upgrades. Conversely, several commenters suggested EP A’s proposed heat rate improvement was too optimistic, providing engineeri ng studies, statistical evaluations, and data describing heat rate improvement measures that had already been implemented at specific coal-fired EGUs. Commenters also expressed concern that EPA im properly characterized the potential heat rate improvements from equipment upgrades because: (1 ) the Sargent & Lundy (S &L) report cited in the proposal TSD is based on “conceptual” equipm ent upgrades, (2) plant ope rators indicate they have already implemented many of the heat rate improvement measures identified in the S&L report, (3) heat rate improvements from the list of equipment upgrades and best practices in the S&L report may not be additive (i.e., cumulativ e), and (4) the costs of equipment upgrades are highly site specific. The EPA generally agrees wi th the four points noted above, and alluded to these considerations in the proposal TSD, s ection 2.6.1 Heat Rate Improvement Capital Cost Assumptions. The points are also discussed in more detail below. The agency does not agree that the EPA impr operly characterized or misused information from the S&L report. The S&L study 24 

### was prepared for the EPA, and senior engineering staff with previous experience in fossil power plant engineering, op erations, and management were

23 In addition to the parasitic load from add-on pollution c ontrols, another effect of some add-on controls is worth mentioning here. The installation of wet flue gas desulfuriz ation (i.e., wet FGD or scrubb er) technologies can affect heat rate because the chemic al process of capturing SO 2 creates CO 2 that is measured in the continuous emission monitoring systems. This results in higher calculated heat input (see infra 29 ). However, the typical increase in heat rate is less than 1 percent and may be offset by heat rate improvements resulting from switching to lower-moisture, higher-sulfur bituminous coal. Furthermore, the impact on calculated heat input is only relevant to EGUs that added WFGD during the study period because the analytical appro aches described below evaluate the step change in heat rates. Assuming consistent operation and fuel sulfur cont ent, an EGU with a WFGD du ring the entire study period would not have a heat rate st ep change due to the WFGD CO 2 . Circulating fluidized bed (CFB) boilers also have higher CO 2 emissions from the chemical process of limestone injection to capture SO 2 , thereby affecting the heat input calculations. However, because CFBs generally injected limestone at all times thro ughout the study period, the EGUs would not have heat rate step changes. See section 2.6.3 for more information about the impact of CO 2 emissions from SO 2 pollution control devices.
24 Coal-fired Power Plant Heat Ra te Reductions, Sargent & Lundy LLC, January 2009. Available at http://www.epa.gov/airmarkets/ documents/ipm/coalfired.pdf

### 2-21 involved in the scoping and review of the study re port. The agency recogn ized the limits of the study and considered the S&L caveats/limitations when using information from the study, including the following st atements from the study:

 “The primary intent of the study was to fo cus on methods that have been successfully implemented by the utility industry.”

 “This study identifies specific plant systems a nd equipment where efficiency improvements can be realized either through new installations or modifications, and provides estimates of the resulting net plant heat rate reductions and the order-of-magnitude costs for implementation. To conduct the study presented in this report, S& L surveyed available literature, spoke with technology manufacturers, and used its engineering expertise as the basis.”

 “This report discusses potential efficiency improvement concepts and the resulting heat rate reductions that can be implemented in various sy stems of a typical coal-fired power plant. All estimated capital and installation costs are refere nced from work in progress and vendor quotes as of the year 2008.”

 “S&L cautions that the costs presented herein are not indicative of those that may be expected for a specific facility due to variables such as equi pment, material, and labor market conditions and site specifications. However, these cost estimate s provide valuable information for comparative purposes when evaluating the advantages and disa dvantages of the various concepts. The costs should not be used as a basis for project budgeting or financing purposes.”



### In consideration of these caveats/limitations , the agency used the average conceptual performance values from the S&L report to deve lop an estimated 4 per cent potential heat rate improvement that the average coal-fired EGU could make from only a few of the higher-cost equipment upgrades. The agency then discounted that potential by 50 percent to obtain the 2 percent potential heat rate impr ovement estimate attributable to equipment upgrades that was included in the proposal. The 50 percent discount wa s applied to account for site-specific factors that could limit the potential heat rate improvement , including the fact that the cumulative effect of installing multiple measures can be less than th e sum of their individual effects. The agency similarly used averages of S&L order-of-magnitude costs for thirteen of th e fifteen S&L heat rate improvement measures 25 

### to develop an indicative conceptu al cost estimate ($100/kW) for an overall 6 percent heat rate improvement. That es timate was conservative because 4 percent was projected to come from relatively inexpensive best practices while only 2 percent was projected to come from a small number of more expensiv e “new installations or modifications” described by the S&L report. The average $100/kW cost, derived from S&L estimates for unit sizes ranging from 200 to 900 MW inherently reflects th e inverse relationship of $/kW cost to MW unit size, and was thus appropriate ly applied in the agency’s estimate of the fleet wide average cost of heat rate improvement. The agency was also mindful of the 2008 time frame of the S&L cost estimates. EPA considered the 2008 cost valu es to be applicable as 2011 values because, through a coincidence of timing, the 2008 study reflect ed the highest cost levels experienced by the power sector up to that point in time. Th e economic recession that commenced in late 2008 caused costs to decrease sharply before beginn ing a slow recovery. The agency therefore considered the S&L cost values to quite reasonably represen t the costs in 2011. 26

25 Three mutually exclusive fan and fan drive modifications were discussed by S&L. Accordingly, EPA selected the fan-related measure that included both a fan upgrade and variable frequency drive for use in the cost estimate because the selected measure had th e highest estimated capital cost.
26 Cost estimates for implementation of the final rule are based on 2011 dollars.

### 2-22 In addition to the S&L report, EPA referred to several other studies in the proposal TSD that present similar information on equipment upgr ades to the S&L report. As demonstrated by the materials provided by commenters expressing a preference for larger heat rate improvements, there are further analyses that show a greate r than 6 percent heat rate improvement is demonstrated and achievable. One commenter also noted that many existing smaller coal-fired EGUs have recently retired or will be retiri ng over the next decade (see section 2.6.5 below for more information about retirements), leaving more of the larger, well-contro lled coal-fired EGUs that are similar to the example EGUs used in the S&L report. Furthermore, the equipment upgrades and best practices identified in the pr oposal TSD and supporting reports are intended to serve as illustrative lists of meas ures that coal-fired EGUs might employ to improve heat rate. In the refined heat rate analyses described below for this final rule, the results are not based on heat rate improvements that would be achieved by implementation of specific measures. Instead, the heat rate analyses are base d on each coal-fired EGU’s best historical heat rate performance relative to its 2012 heat rate performance. It re flects the heat rate improvements that would occur if each EGU performed at leve ls that the EGU has already demonstrated are achievable. As one commenter phrased it, EPA is simply basing building block 1 on a continuation of the types of good maintenance an d operating practices that are necessary for EGUs overall to maintain the better heat rates they have previously achieved. Some commenters also noted th at if a coal-fired EGU implemented equipment upgrades during the study period (2002–2012), improvements to gross heat rate would show up in the historical gross heat rate data and could result in double counting be cause the heat rate improvements would affect the statistical results and would still be expected from the equipment upgrade component. Similarly, many commenter s provided lists of equipment upgrades undertaken at specific coal-fir ed EGUs, arguing that the equipment upgrade component of the agency’s potential heat rate improvements does not account for upgrades th at coal-fired EGUs have already implemented. EPA acknowledges that there could be overlap between the two components of the potential heat rate results, but notes that the ag ency was not requiring individual coal-fired EGUs to achieve specifi c heat rate improvements. However, for this analysis the agency no longer uses the two components to identify potential heat rate improvements. Instead, the agency conducts the st atistical analyses described below to compare each EGU’s best historical performance to its 2012 performance. The results reflect the overall heat rate improvement for coal-fired EGUs in each regional interconnection if those EGUs had returned to their demonstrated best historical gro ss heat rates from the baseline year of 2012. This backward-looking approach is inherently conservative because it does not reflect opportunities for heat rate improvements from ad ditional best practices and equipment upgrades that could have been (and still can be) cost -effectively implemented at coal-fired EGUs. In the proposal TSD, EPA’s statistical anal ysis compared each E GU’s best historical gross heat rate to the EGU’s 11-year average gros s heat rate. Commenters stated that some coalfired EGUs made significant investments to impr ove heat rate during the 11-year study period and the use of an 11-year baseline fails to recognize those investments. These commenters suggested the agency could adjust the potential heat rate improve ments to reflect any upgrades already implemented at coal-fired EGUs. In the revised analyses described below, in order to account for actions undertaken to improve gross heat rates at coal-fired EGUs, the agency refined the methodology to compare each EGU’s best heat rate performance to its 2012 gross heat rate. Therefore, any actions an EGU impl emented to improve gross heat rate during or

### 2-23 before 2012 (and maintained in 2012) would be ac counted for in the analysis. In addition, the selection of 2012 as a ba seline is conservative because many commenters noted that coal-fired EGUs experienced greater cycling and lower cap acity factors in the later years of the study period (2002-2012). Several commenters suggested th at EPA should replace the stat istical analysis with unitspecific engineering studies to establish un it-level heat rate improvement goals. These commenters contended that such studies are th e only method for accurately identifying what types of equipment upgrades and best practices (a nd therefore, potential heat rate improvements) are feasible and cost effective at each coal-fired EGU. As discussed in sections V.A and V.C of the preamble and the response to comments, c onducting engineering studies to determine the potential for improving heat rate through best practices and equi pment upgrades at every coalfired EGU would be unnecessarily lengthy and expens ive, and would not be consistent with the broader goal-setting purp ose of this rulemaking. However, the agency does believe that EGU owners and operators that conduct such st udies will discover addi tional cost-effective opportunities to improve heat rate at their E GUs. Commenters also suggested that as an alternative to unit-level engineer ing studies, EPA could identify uni t-level heat rate improvement goals. EPA’s methodology does assess the poten tial heat rate improvement for each interconnection by comparing an individual EGU’s best demonstrated and achievable historical gross heat rate relative to th e EGU’s own 2012 gross heat rate. However, as several commenters note, and the agency explained in the proposal, this rule does not establish mandatory federal heat rate performance requirements for coal-fired EGUs or mandate heat rate improvement as a compliance strategy. The agency uses the results of the heat rate improvement analysis for each regional interconnec tion (i.e., the electricity system) al ong with emission reductions from the other building blocks of the best system of em ission reductions to establish state-level goals. Each state has the flexibility to identify measures in its state pl an to achieve the state goals and, if the state chooses, it may develop unit-specific heat rate improvement targets – or entirely ignore potential heat ra te improvement. Commenters also st ated that the proposal expressly anticipates fleetwide averaging of CO 2 

### emission intensity improvements, thereby offering greater opportunities to achieve the state goals at lower cost relative to controlling emissions or heat rate at the plant or unit level. Theref ore, the agency believes it is a ppropriate in this rulemaking to assess potential heat rate improvement at a scope broa der than the unit-level. Commenters also recommended that EPA s ubcategorize coal-fired EGUs and develop specific heat rate improvement goals for each subcategory to account for the influence of different design characteris tics and fuel choices on an EGU’s heat rate. However, as discussed above and noted in the proposal TSD, the agen cy is not establishing mandatory heat rate improvements that must be achieved at each and every coal-fired EGU. Instead, the agency is attempting to determine a reasonable heat rate improvement that, on average, the coal-fired electricity generating fleet can achieve, and to us e the average potential heat rate to calculate state goals. In addition, the agency does not belie ve there is a need to control for the design characteristics and fuel choices because the ag ency compared each EGU’s performance to the EGU’s own past performance. Because EPA comp ared each EGU to itself, the full analyses inherently account for design characteristics that do not change from year to year. Commenters also highlighted the role of age and coal rank on an EGU’s heat rate. The average age of coal-fired E GUs in the study population was approximately 42 years in 2012. To assess the impact of age, EPA grouped the 884 coal-f ired EGUs by 10-year age increments (as of

### 2-24 December 2012). While there is a difference in median and average annual gross heat rates between coal-fired EGUs less than 10 years of age and those greater than 10 years, there was no statistical difference between gross heat rates of coal-fired EGUs older than 10 years of age (i.e., range of performance of an EGU 10-19 years of age is approximately the same as one 30-39 years of age). In addition, it is likely that the gross heat rate differences for younger units is due to other factors (e.g., advanced technology, larger boiler and/or steam turb ine size) that do not change as the EGU ages. Therefore, the influence of age is unlikely to play an important role for an individual coal-fired EGU. An EGU’s primary coal rank may ch ange, but changing coal ranks at a coal-fired EGU requires significant effo rt. Generally, coal-fired EGUs – especially pulverized coal boilers and their corresponding fu el handling equipment, combustion systems, and pollution control devices – are designed for th e characteristics of a specific type of coal. Significant deviations from thos e coal characteristics can affect the EGU’s performance and reliability. Changes to coal type might require plant modifications to maintain maximum output and efficiency (e.g., pulverizer capacity, fan capac ity, heating surface). Commenters noted that changing coal rank is likely not practical at ma ny EGUs because coal rank is determined by the design of the EGU and environmental control systems. Although it might be a significant engineering endeavor, some coal-fired EGUs have switched coal types or blended different coal types. Furthermore, some EGUs might have the option of changing coals in the future, potentially improving their heat ra te. Commenters also discussed the impact that coal quality (e.g., higher heating value, moisture content, ash content) can have on an EGU’s heat rate. While EPA recognizes the impact that co al rank and quality can have on an EGU’s heat rate, the agency also recognizes that EGU operators and coal supplie rs typically set requirem ents for coal quality in purchase contracts, providing co al-fired EGUs with some level of control over the quality of fuel they purchase. Therefore, EPA did not control for coal rank or coal quality in the analysis. In summary, the agency feels the analyti cal approaches described below, which are founded on comparisons between each coal-fired EGU’s historical performance and its 2012 performance, account for nearly all design charac teristics because these characteristics do not vary for a particular EGU between years or are accounted for by the use of 2012 as the baseline. Other factors, (i.e., ambient temperature and hourly capacity factor) that are beyond the EGU operator’s control are explicitly accounted for in so me of the agency’s analyses presented below. Finally, for all of these analyses, by using long-term averages of heat rate (i.e., at least a year) or grouping large numbers of hourly values and accoun ting for outliers, EPA implicitly accounts for some variation that might be beyond the EGU operator’s control. EPA used three approaches to calculate th e potential heat rate improvement: (1) best historical performance, (2) best historical performance under similar conditions, and (3) efficiency and consistency improvements under similar conditions. Each of these approaches is described in more detail below.

2.5.1 Heat rate, capacity factor, and temperature data Under 40 CFR Part 75, most fossil fuel-fired EGUs report hourly emission and operation data to EPA’s Clean Air Markets Division (EPA/CAMD). In 2012, 884 coal-fired EGUs

### 2-25 reported heat input and gross elect ricity generation data to EPA. 27 

### These 884 EGUs account for approximately 1.61 billion tons of CO 2 

### emissions in 2012, 96 percent of the 1.67 billion tons of CO 2 

### emitted by the national coal-fired EGU fleet in 2012. 28 

### Therefore, EPA considers these 884 coal-fired EGUs representative of th e coal-fired EGU fleet as a whole. Some commenters suggested th at the agency’s inclusion of coal-fired EGUs near retirement inflates the results of the heat rate improvement analysis b ecause the soon-to-retire EGUs may have more variability in gross heat ra te and higher gross heat rates due to frequent cycling, deferred or canceled maintenance, and termination of good heat rate practices. EPA’s analysis included the 884 coal-fired EGUs th at reported heat input and gross electricity generation in 2012, including those EGUs that ar e expected to retire. While the agency recognizes the commenter’s concern that soon-to -retire EGU that operate with poor maintenance and operating practices can increase the potential heat rate improve ments identified by the heat rate improvement analysis, the agency does not belie ve this artificially inflates the potential heat rate improvements. Any coal-fired EGU th at stops implementing good maintenance and operations practices to maintain heat rates will have a similar type of impact on the results regardless of whether it is scheduled to retire to not. Also, the agency believes that the inclusion of coal-fired EGUs that are near retirement is conservative for two reasons: (1) for states with mass-equivalent goals based on 2012 baseline leve ls, after soon-to-retire EGUs cease operation they cease emitting CO 2 

### contributing to attainment of the st ate goal, and (2) for states with CO 2 

### intensity goals based on 2012 baselin e levels, the retirement of co al-fired EGUs with high heat rates in 2012 will contribute to achievement of the state goals. 40 CFR Part 75 requires that most coal-fired EGUs continuously measure emissions of CO 2 

### , NO X 

### , and SO 2 

### , and report those hourly emissions along with hourly heat input 29 

### and gross electricity generation to EPA/CAMD at the end of each calendar quarter. The monitoring regulation requires regular quality assurance/quality control (QA/ QC) of the monitoring systems, including daily calibrations and semi-a nnual or annual relati ve accuracy tests. 30 

### When EGU operators submit the hourly emi ssion and operation data to EP A/CAMD, a responsible company official must certify that the data are true, accurate, and comp lete. In addition, the data undergo thousands of automated quality assurance tests as well as statistical analyses and EPA staff audits. Therefore, the agency believes these high- quality data are the best available information for assessing coal-fired EGUs’ performance over time. The monitoring regulation includes provisions to accommodate time periods when emission data are not available due to monitoring system malfuncti ons or maintenance, technical challenges, or missed QA/QC tests. When data are not available or deemed invalid (e.g., when a QA/QC test was not performed as required), the EGU is required to use specified data substitution methods that are designed to be cons ervative (i.e., overestimate emissions). This

27 This population does not include coal-fired EGUs that did not operate in 2012, some of which retired before 2012 and others which may have resumed operation after 2012. See supra note 5 for a discussion about treatment of cogeneration EGUs.
28 CO 2 emissions for the national coal-fired EGU fleet are taken from Table 12.6 Carbon dioxide emissions from energy consumption: electric power sector . EIA, 2015. Monthly Energy Review. Dated 2015 May 22 available at http://www.eia.gov/totalenergy/data/monthly/#environment.
29 Heat input is derived using CO 2 or O 2 concentration and calculations detailed in 40 CFR Part 75 Appendix F. As a result, heat rate (Btu/gross kWh) is proportional to CO 2 rate (CO 2 /gross kWh).
30 The quality assurance requirements fo r monitoring systems are described in 40 CFR Part 75 Appendix B.

### 2-26 conservative bias is intended to create an incentive for better emission measurement – the overestimate incurs an economic penalty because at the end of the compliance period an EGU must surrender allowances equal to total reported emissions. Because of this conservative bias and the impact it would have on the results of this study, the agency excluded substitute data reported by EGUs from the dataset. These substitute data represen t approximately two percent of all reported operating hours for the 884 coal-fired EGUs during the study period. In addition, the agency excluded partial hours of operation that occu r during the first hour of startup and the last hour of shutdown, and hourly gross heat rate valu es that were greater than or less than 2.6 standard deviations from an EGU’ s annual average gross heat rate. 31 

### Both of these conditions tend to occur during very low gross electricity generation. Th e hourly values ±2.6 standard deviations from the EGU’s annual average gro ss heat rate may be due to unusual operating conditions or misreported gr oss electricity generation. 32 

### These outliers account for approximately 1.9 percent of hourly data records. The monitoring provisions of 40 CFR Part 75 require regular QA/QC testing of the monitoring systems to ensure they are r eading accurately. Comment ers hypothesized that discrepancies between the monitoring system and the reference tests could explain much of the variation in heat rates. EPA assessed the mon itoring systems’ relative accuracy and the findings indicate that the relative accura cy does not significantly affect the results of the agency’s analyses. In general, coal-fired EGU operators are required to perform relative accuracy test audits (RATA) once per year if the difference between the monitoring system and RATA results is less than or equal to 7.5 per cent, and twice per year if the di fference is greater than 7.5 percent but less than 10 percent. 33 

### If the flow monitoring system read ings are statistically-significantly lower than the RATA results, the EGU operator must apply a bias adjustment factor (BAF) to all subsequent flow measurements to reduce pot ential underreporting of flow. There is no BAF applied for CO 2 

### measurements. To assess the relative accuracy of the monitoring systems during the study period, EPA reviewed the EGU-level RA TA results for the study period and then conservatively calculated the average annual gene ration-weighted relative accuracy within each of the interconnection regions. As shown in Table 2-2, the av erage (i.e., mean) relative accuracy 34 

### for both CO 2 

### and flow monitoring systems are reasonable – the difference between the reference tests and measured values is appr oximately 1.5 to 3.0 percent – and varies little from year to year within each interconnection.

31 Assuming a normal distribution, 99 percent of all values will be within 2.6 standard deviations.
32 40 CFR Part 75 requires operators to apportion heat in put to all EGUs sharing a common stack. For this reason, EGUs that are not generating electricity during startup or shutdown periods may report a small amount of gross electricity generation (e.g., 1 MW) to avoid errors in th e apportionment calculations. However, this can result in unusually large gross heat rates.
33 More information about the quality assurance provisions, including RATAs, can be found in 40 CFR Part 75 Appendix B.
34 Relative accuracy is expressed as the absolute value of the difference between the refe rence method measurements and the EGU’s pollutant concentration or flow measurements. However, the aver ages at the interconnection level are overstated because the relative accuracy is expressed as an absolute value. As a simple example, if the difference between the measurements at one unit is -2 percent and at another unit the di fference is 2 percent, the arithmetic average is 0 [(-2+2)/2] while the average of the absolute values is 2 [(|-2| + |2 |)/2]. EPA’s calculation is based on the absolute value.

### 2-27 Table 2-2 Generation-weighte d relative accuracy of CO 2 

### and flow monitoring systems, by interconnection

Western Texas Eastern
CO 2 

### Flow CO 2 

### Flow CO 2 

### Flow
2003 2.57 2.70 2.27 2.30 2.52 2.22 2004 3.17 2.51 2.63 1.64 2.51 2.11 2005 3.21 2.95 2.82 2.25 2.69 2.10 2006 3.06 2.81 2.85 1.33 2.56 1.99 2007 3.15 2.59 2.25 1.50 2.61 2.06 2008 3.41 2.86 3.68 1.59 2.75 2.12 2009 2.95 2.97 2.47 1.76 2.55 1.97 2010 2.65 3.07 2.86 2.00 2.45 2.05 2011 2.40 2.59 2.56 1.69 2.63 2.02 2012 2.33 2.56 2.46 1.53 2.41 2.13

Notes: All values ar e generation weighted; Due to data management and archive policies, 2002 data were not available at the time of this analysis 

### While the average relative accuracy at the E GU-level varies from 1 to 3 percent, the uncertainty becomes significantly less when thes e differences are aggregated over many EGUs – the relevant scope of analysis for building bl ock 1 as a whole. EPA evaluated the combined effect of CO 2 

### and flow measurement differences betw een the RATA and measurement systems at 685 coal-fired EGUs between 2009 and 2012. The results show that the combined differences across the 685 EGUs amount to a fleetwi de standard “error” of 0.07 percent. 35 

### Therefore the agency believes the impact of monitoring system relative accuracy does not significantly influence the results of the potential heat rate improvement analyses. EPA also used data from NOAA’s Integr ated Surface Data (ISD) and the USGS’s WaterQualityWatch (WQW). The NOAA dataset pr ovides hourly ambient air temperature for over 20,000 weather stations worldwide. 36 

### Since coal-fired EGU heat rates can be sensitive to ambient air temperature and barometric pressure, the agency collected meteorological data from stations that are reasonably clos e to each coal-fired EGU's loca tion and elevation to account for the impact of ambient conditions. For each of th e 884 coal-fired EGUs, the agency identified the nearest weather station 37 

### that reported a minimum of 8,400 hourly observations in the calendar

35 The average combined difference between the RATA and monitoring system for flow and CO 2 measurements, using the directional difference (e.g., positive and negative va lues), is 0.5 percent and the standard deviation is 3.4 percent. To calculate the standard error, EPA used the following equation: ௌ௧௔௡ௗ௔௥ௗ	ௗ௘௩௜௔௧௜௢௡ √

௡ 36 NOAA’s Integrated Surf ace Data are available at http://www.ncdc.noaa.gov/data -access/land-based-stationdata/land-based-datasets/inte grated-surface-database-isd

. 37 EPA established two constraints on distance between the weather station and the EGU: (1) less than 100 miles, and (2) less than 1,000 feet in elevation.

### 2-28 year (i.e., greater than 95 percent of the hours in a year). Because of the constraint that a station must report at least 8,400 meas urements in a calendar year, most of the 884 EGUs were associated with meteorological data from the two closest stations over the study period (2002- 2012). The average distance between the EGUs and the weather stations was 22 miles and the average difference in elevation was only 366 feet. 38 

### For at least one year of the study period (2002-2012), nine power plants did not have weat her stations that met the three constraints described above. For these plants, the agency us ed its judgement and assigned nearby weather stations with as few as 7,000 hourly observations , but within 100 miles and 1,000 feet elevation. In response to commenters’ concerns that EP A’s analysis did not account for cooling water temperature, the agency added data from the USGS WQW. 39 

### After excluding coal-fired EGUs that did not report heat input and gross electricity generation in 2012 and coal-fired cogeneration EGUs that reported steam generation in one or more years during the study peri od, and excluding the data desc ribed above in subsection 2.5.1, EPA developed a dataset of hourly heat input, gross electricity generation, gross heat rate, 40 

### capacity factor, 41 

### and ambient air temperature for the years 2002 through 2012 for all 884 coalfired EGUs. This dataset, whic h was included in the proposal, 42 

### contained more than 61,848,500 hourly records. Commenters also suggested that EPA should use fewer years of data in its analysis, noting that the power sector is very different today than it wa s in 2002. EPA recognizes that the power sector went through many changes be tween 2002 and 2012, and is likely to go through many more changes between 2012 and 2022 – the fi rst year of the interim implementation period. Nevertheless, the agency be lieves it is appropriate and bene ficial to use the full 11 years of data. First, this time period includes a va riety of national and regional economic conditions, fuel prices, electricity resour ce mixes, weather conditions ( e.g. , precipitation), and demand levels. As a result, it is more likely that future economic and weather conditions will be represented by data periods between 2002 and 2012. Second, commenters specifically expressed concern that many coal-fired reti red during the study period and might bias the results. However, EGUs that retired prior to 2012 are not included in the dataset. Only coal-fired EGUs that reported both heat input and gross electricity ge neration are included in the dataset. Third, the foundation of EPA’s methodology involves comparisons of each coal-fired EGU’s best gross heat rate performance between 2002 and 2012 ag ainst the EGU’s own 2012 gross heat rate. Therefore, if a coal-fired EGU employed better practices for maintenance and operation in the past than it did in 2012, as charact erized by a lower gross heat ra te, the agency believes it is reasonable to base the potential heat rate improvement for the population of EGUs in the interconnection region on their collectively more efficient operations – whether for individual

38 The NOAA data set is the most complete and comprehens ive ambient air temperature data available to EPA. The hourly ambient air temperature of the nearest weather station is used only for the purpose of grouping hourly heat rate into temperature bins by 10° F increments. Therefor e, the agency believes conditi ons at the nearest weather station provide a reasonable approximation of te mperature conditions at the coal-fired EGU.
39 The USGS WaterQualityWatch dataset provides hourly surface wa ter temperature from a pproximately 2,000 sites around the U.S. The data are available at http://waterwatch.usgs.gov/wqwatch

. 40 EPA calculated gross heat rate as heat in put (Btu) / gross electricity output (kWh).
41 EPA calculated capacity factor as gross elect ricity output (kWh) / generator capacity (kWh).
42 See supra note 1.

### 2-29 EGUs those operations were in 2012 or as early as 2002. 43 

### This longer time period better reflects various operating practices and investment prior ities for the sector. As one power-sector trade magazine stated, “[R]elatively cheap coal a nd a focus on plant emi ssions controls has, unfortunately, taken the focu s away from maintaining and improving plant heat rate.” 44 

### EPA’s analysis seeks to identify the potential for EGUs to re-achieve their better heat rates. Commenters also noted that the state goals are based on CO 2 

### intensity using net electricity generation, but EPA based its potential heat rate improvement analysis on gross heat rate. 45 

### The commenters suggested that EPA should change the analys is to use net heat rate. While EPA acknowledges the discrepancy between the unit of measure for the state goals and the gross heat rate improvement analysis, the agency uses hourly gross heat rate for the analysis because it is the only comprehensive, quality assu red dataset available to the agency. The agency also believes the use of gross he at rate is inherently conserva tive for two reasons: (1) it does not reflect additional opportunities th at coal-fired EGUs may have to reduce auxiliary energy use (i.e., parasitic load), 46 

### and (2) gross electricity generation is not affected by auxiliary energy use required to power the significant capacity of ai r pollution control devices that were installed between 2002 and 2012. If net heat rate data were available and the agency used these data for the analytical approaches described below, th e auxiliary energy use of air pollution control devices installed during the study period could possibly inflate the potential heat rate improvement.

2.5.2 Influence of ambient conditions and ca pacity factor on gross heat rate Ambient conditions can affect a coal-fired EGU’s gross heat rate. EPA evaluated three ambient conditions – hourly capac ity factor, hourly air temperatur e, and hourly surface water temperature – to assess the influence these cond itions can have on coal-fired EGU’s gross heat rate. The assessment results indicate that each of the ambient conditions has significant influence on the variation in hourly gross heat rate of the 884 coal-fired EGUs during the study period (2002–2012). The agency chose to control only for the influence of hourly capacity factor and hourly ambient air temperature in two of the three analyses described below because: (1) comprehensive water temperature data from mo nitoring locations nearby and upstream of each of the 884 coal-fired EGUs were not available, and (2) water temperature data are highly correlated with ambient air temperature data so, as described below, the additional influence of water temperature on heat rates is implicitly included.

43 Beginning in reporting year 2002, the agency introduced numerous quality assurance checks on hourly emission data, enhancing the data quality. Therefore, for the purpose of the potential heat rate improvement analyses, the agency chose not to incl ude data prior to 2002.
44 See U Nowling, 2015. “Understanding coal power plant heat rate and efficiency,” Power. Available at http://www.powermag.com/unders tanding-coal-pow er-plant-heat-rat e-and-efficiency
45 For an explanation of why EPA used gross heat rate in this heat rate improvement analysis, section V.C.3 of the final rule preamble of the CPP.
46 See EPRI, 2011. Opportunities to Enhance Electric Energy Efficiency in the Production and Delivery of Electricity. Technical Report 1024651. Available at http://www.pserc.wis c.edu/documents/publications/special_interest_ publications/EPRI_Electricity_Use_Report_Fin al_1024651.pdf . The report explores the auxiliary electricity consumption at EG Us and finds that auxiliary energy use (and transmission and distribution losses) can be reduced by 15 percent through available technology options, including variable speed drives.

### 2-30 2.5.2.1 Capacity factor Coal-fired EGUs are designed to operate most efficiently near full cap acity. In general, as an EGU’s hourly capacity factor d eclines, the EGU’s hourly gross he at rate increases. Across the full range of capacity factors, this trend is not linear. This relationship between hourly gross heat rate and capacity factor is illustrated in Figure 2-6. Each dot represents the average hourly gross heat rate for all measurements at a specific hourly capacity factor fo r four “sister units” 47 

### in 2012. The bottom chart shows the number of hour ly measurements at each hourly capacity factor. The figure illustrates three effects that are broadly applicab le to the 884 coal-fired EGUs: (1) average hourly gross heat ra te is higher and shows more va riation, or “scatter,” from the average at lower hourly capacity factors, (2) the gross heat rate trend flattens at around 60 – 70 percent hourly capacity factor, and (3) the majority of opera ting hours are at higher hourly capacity factors.

Figure 2- 6 Average hourly gross he at rates at partial load levels

Notes: 2012 data are from four 650 MW coal-fired “sister units” at JM Stuart in Ohio; cumulative number of meas urements exceeds 8,784 hours because it includ es measurements from four “sister units”; the trend line is a power function in the form of y=c×x b ; average hourly gross heat rate axis does not start at ze ro to better illustrate changes in gross heat rate



### For each EGU, EPA used a least-squares li near regression to assess the relationship between hourly gross heat rate s and hourly capacity factor. 48 

### The hourly capacity factor explained about 18 percent of the variation in gross heat rate for the 884 coal-fired EGUs between 2002 and 2012. These results, however, con ceal considerable variability. At some coalfired EGUs hourly capacity factor has a greater in fluence on hourly gross heat rate. For example, hourly capacity factor can explain nearly 50 percent of variation in gross heat rate at some loadfollowing EGUs. The analysis indicates that as EGUs move from base load to load following

47 Four 650 MW cell burner coal-fired EG Us at J M Stuart Station in Ohio.
48 For each EGU, the annual coefficient of determination (i.e., the square of the correlation coefficient, or R 2 ) values were weighted using the EGU’s total annual gross elect ricity generation resulting in an EGU-specific generationweighted R 2 . This approach controlled for any differences in av erage gross heat rates betw een years. The weighted R 2 values were averaged across all 88 4 coal-fired EGUs, resulting in an R 2 value of 0.179.

### 2-31 (i.e., from high to lower hourly cap acity factors), hourly capacity f actor tends to have a larger influence on gross heat rate. Several commenters noted that the power sect or saw dramatic changes during the 11-year study period, including significant declines in an nual capacity factors at coal-fired EGUs. As discussed in the proposal TSD, it can be difficult to interpret the relationship between annual capacity factors and hourly capacity factors as used in the agency’s analyses. EGUs with high annual capacity factors (e.g., greater than 90 percent) must operate at high levels for most hours in a year. However, it is not possible to make such a generalization at lower annual capacity factors. For example, if a hypothetical coal-fir ed EGU operated at 90 percent capacity for six months and was offline for the remaining six m onths of the year, the EGU’s annual capacity factor would be only 45 percent – equivalent to an EGU that might have operated below 50 percent capacity factor for the entire year. Ho wever, to assess the commenters’ concerns, the agency calculated two annual capacity factors for the 884 coal -fired EGUs – annual capacity factor and annual capacity factor during hours of operation. The latter represents the average annual capacity factor for the 884 coal-fired E GUs excluding hours in which the EGU did not operate, such as downtime for maintenance or du e to low electricity demand. This latter capacity factor reflects the average capacity levels when EGUs are operating. As shown in Table 2-3 and Figure 2-7, the annua l average capacity factor of the 884 coalfired EGUs declined by approximately 10 perc ent in 2009 and, after a small increase in 2010, continued to decline through 2012. However, the annual capac ity factor during operating hours (i.e., excluding non-operating hours ) declined after 2010, but by a much smaller amount. This illustrates that during the 11-year study period, coal-fired EGUs operated fewer hours in later years, but during the hours th ey operated, they maintained high hourly capacity factors on average.

Table 2-3 Annual capacity factors for 884 coal-fired EGUs
Year Annual capacity factor Operating hours capacity factor
2002 68 77 2003 69 79 2004 70 79 2005 71 80 2006 70 80 2007 71 81 2008 70 80 2009 62 77 2010 65 78 2011 61 76 2012 53 72

### 2-32 Figure 2-7 Change in annual capacity fact ors for 884 coal-fired EGUs relative to 2002

Note: capacity factors are relative to 2002 capacity factor, the first year of the study period



### 2.5.2.2 Ambient air temperature Ambient air temperature can affect gross heat rate in three ways: (1) ambient air (primary air) is mixed with fuel prior to combustion a nd a fraction of energy re leased during combustion heats the incoming air, (2) in many regions of th e country temperature a ffects electricity demand and, as a result, some EGUs’ hourly capacity fa ctors may increase when ambient temperatures are high, and (3) ambient air temperature influenc es cooling water temperatures, affecting an EGU’s efficiency. The relationship between hourl y ambient air temperature and hourly capacity factor is complex. Generally, peak electricity demand in most pa rts of the U.S. occurs on the hottest days of the year. However, each E GU responds differently depending on design, duty cycle, meteorological conditions, electricity dema nd, and other factors. For example, a baseload plant may operate at high hourly capacity factor s regardless of temperature. EPA assessed the correlation between hourly ambient air temperat ure and hourly capacity factor during the study period (2002–2012) and found the correlation to be very low. 49 

### For each EGU, EPA used a least-squares li near regression to assess the relationship between hourly gross heat rates a nd hourly ambient air temperature. 50 

### The hourly ambient air temperature explained about 8 perc ent of the variation in gross heat rate. At approximately onefourth of the 884 coal-fired EGUs ambient air temperature had a greater influence on hourly gross heat rate than the influence of hourly cap acity factor. At some individual EGUs, ambient air temperature may explain up to 30 percent of the vari ation in hourly gross heat rate. These are

49 The Pearson correlation coefficient fo r hourly ambient air temperature and h ourly capacity factor from 2002–2012 is -0.048.
50 For each EGU, the annual coefficient of determination (i.e., the square of the correlation coefficient, or R 2 ) values were weighted using the EGU’s total annual gross elect ricity generation resulting in an EGU-specific generationweighted R 2 . This approach controlled for any differences in av erage gross heat rates betw een years. The weighted R 2 values were averaged across all 88 4 coal-fired EGUs, resulting in an R 2 value of 0.083.

### 2-33 typically, but not exclusively, units with fres hwater once-through coolin g systems. Identifying temperature-responsive coal-fired EGUs allows us to understand why h eat rate may increase during periods of peak demand. These are the EGUs where the ambient temp erature ‘signal’ is an important variable. Gross hourly heat rates at a temperature -responsive EGU may increase as much 10 percent during high temper ature days in summer months.

2.5.2.3 Cooling water temperature As discussed earlier in this chapter and not ed by commenters, cooling water temperature can have a significant influence on a coal-fired EGU’ s heat rate. The availability of a cold heat sink in the condenser is important to maximize the EGU’s efficiency. The design of the heat exchanger, type of cooling system, availability of water, and temperature of water all have an impact on performance. Higher water temperatur es typically reduce the effectiveness of the cooling system and, therefore, overall plant efficiency. Commenters suggested EPA should account for the influence of cooling water temperature in addition to ambient air temperat ure. The agency did not have comprehensive cooling water temperature data to assess the in fluence on gross heat ra te at the time of the proposal. However, in response to commenters’ suggestions, the agency compiled hourly surface water temperature data for 45 coal-fired EGUs at 19 power plants in the Eastern Interconnection. For each EGU and each year with available data, EPA used a least-squares linear regression to assess the relationship between hourly gross he at rates and hourly water temperatures. 51 

### The variation in hourly water temperat ure explained about 7 percent of the variation in gross heat rate. When EPA applied the same approach for the 45 coal-fired EGUs over the same time period using hourly ambient air temperature in stead of hourly water temperature, the air temperatures explained approximately 6 perc ent of the variation in gross heat rate. 52 

### The agency also examined relationships between hourly gross heat rate and several time-averaged temperatures (air and water temperatures) and found comparable results to the hourly ambient temperature values. For example, the rolling 24- hour water temperatures and rolling 24-hour ambient air temperatures explained 7 percent of the variation in hour ly gross heat rate. 53 

### Not surprisingly, the air temperature and water temperature ar e strongly related. 54 

### Although the surface water data in this analysis were from the Eastern Interconnection, where 86 percent of the 884 coal-fired EGUs ar e located, the agency has no reason to believe, nor have commenters suggested, th at this correlation would not hold elsewhere in the country. Studies suggest that ambient air temperature is similarly correlated with water temperature

51 For each EGU, the annual coefficient of determination (i.e., the square of the correlation coefficient, or R 2 ) values were weighted using each EGU’s total annual gross elect ricity generation resulting in an EGU-specific generationweighted R 2 . This approach controlled for any differences in av erage gross heat rates betw een years. The weighted R 2 values were averaged across all 45 EGUs, resulting in an R 2 value of 0.072.
52 For each EGU, the annual coefficient of determination (i.e., the square of the correlation coefficient, or R 2 ) values were weighted using each EGU’s total annual gross elect ricity generation resulting in an EGU-specific generationweighted R 2 . This approach controlled for any differences in av erage gross heat rates betw een years. The weighted R 2 values were averaged across all 45 EGUs, resulting in an R 2 value of 0.057.
53 The R 2 value for rolling 24-hour water temperature and hourly gross heat rate was 0.071 and the R 2 for rolling 24- hour ambient temperature and hourly gross heat rate was 0.072.
54 The Pearson correlation coefficient for hourly water temperature and 24-hou r ambient air temperature is 0.74.

### 2-34 elsewhere in the country. See, for example, a report by the New Mexico Environment Department notes that “based on data for approximately 300 New Mexico streams, air temperature is highly correlated with stream water temperature...” 55 

### ; Kaushal et al. found “[l]ong-term increases in stream water temperatures were typically correlated with increases in air temperatures”; 56 

### and Webb et al. found there is usually a strong correlation between ambient air temperatures above 0° C and stream wate r temperatures (although th e relationship may be non-linear at higher temperatures). 57 

### Based on these results, the agency believes th at hourly ambient air temperature is the appropriate variable to use in the potential heat rate improvement analyses because: (1) available hourly water temperature data are limited to a por tion of the fleet, (2) air and water temperatures are highly correlated (i.e., simultaneously contro lling for air temperature and water temperature appears highly redundant), and (3) the results from all of the agency’s an alyses described above demonstrate that ambient air temperature and wate r temperature explained similar amounts of the variability in hourly gross heat rate.

2.5.2.4 Capacity factor and ambient air temperature In addition to the regression analyses di scussed above, EPA conducted a least-squares linear regression analysis to assess the relatio nship between hourly gross heat rate and the combination of hourly capacity factor and hourly ambient air temperature. 58 

### For the 884 coalfired EGUs, the combination of hourly capacity factor and hourly ambient air temperature explained about 26 percent of variation in gross heat rate between 2002 and 2012.

2.5.2.5 Accounting for the influence of factors outside coal-fired EGU operators’ control The objective of EPA’s analysis is to identi fy the potential heat rate improvements at coal-fired EGUs for the purpose of calculating st ate goals. Therefore, the agency attempted to control for many of the factors that influence gr oss heat rate but are be yond the control of the coal-fired EGU operator. For example, as stated above, EPA’s analysis controls for EGU design characteristics such as boiler type and size, age, generator size, coal t ype, and cooling system type because it compares each coal-fired EGU’s historical performance against the EGU’s own 2012 performance. Because these de sign characteristics are typically constant over time, they do not influence the results of the EGU-level anal yses. As mentioned earlie r, age does not have a significant effect on gross heat ra te. EPA controlled for the inst allation of air pollution control devices by analyzing gross heat rate. Because the impact of air pollution control devices’ auxiliary energy consumption does not affect gross electricity gene ration, the addition of controls does not influence the results of the gross heat rate analyses.

55 See New Mexico Environment Department, 2011. Ai r-Water Temperature Corr elation. Available at https://www.env.nm.gov/swqb/docum ents/swqbdocs/MAS/Protocols/Ai r-Water/Air-Water 08-01-2011.pdf

. 56 See SS Kaushal, GE Likens, NA Jaworski, ML Pace, AM Sides, D Seekell, KT Belt, DH Secor, and RL Wingate, 2010. “Rising stream and river temperatures in the United States,” Frontiers in Ecology and the Environment. 8:461- 466. (DOI: 10.1890/090037 ).
57 See BW Webb, DM Hanna, RD Moore, LE Brown and F Nobilis, 2008. “Recent advances in stream and river temperature research,” Hydrological Processes. 22: 902-918 (DOI: 10.1002/hyp.6994).
58 For each EGU, the annual coefficient of determination (i.e., the square of the correlation coefficient, or R 2 ) values were weighted using the EGU’s total annual gross elect ricity generation resulting in an EGU-specific generationweighted R 2 . This approach controlled for any differences in av erage gross heat rates betw een years. The weighted R 2 values were averaged across all 88 4 coal-fired EGUs, resulting in an R 2 value of 0.255.

### 2-35 In two of the three analytical approaches di scussed below, EPA also controlled for hourly capacity factor and hourly ambient air temper ature because these two environmental and operating factors are considered to be beyond the EGU operators’ control. To limit the influence of these two factors, the agency grouped each EGU’s hourly gross heat rate values into unitspecific “capacity-temperature bins” based on h ourly capacity factor and hourly ambient air temperature. This enabled the agency to compar e gross heat rate values for each coal-fired EGU during hours with similar capacity factor and am bient air temperature conditions, effectively controlling for a large influen ce on the variation in an EGU’ s hourly gross heat rates. To create the capacity-temperature bins, the agency divided the ra nge of hourly capacity factors into 12 groups, each se parated by 10 percent of an E GU’s capacity (i.e., 0-9, 10-19, 20- 29, ... , 90-99, 100-109, greater than or equal to 110 percent), and the ambient air temperatures into 14 groups, each separated by 10° F (i.e., below

- 

### 10°,
- 

### 10-
- 

### 1°, 0-9°, ... , 90-99°, 100-109°, greater than or equal to 110° F). The result is 168 capacity-temperature bins (12 X 14) for each of the 884 coal-fired EGUs. However, some cap acity-temperature bins for an EGU might not have any data. At least one commenter expre ssed concern that the hourly cap acity factor ranges were too large. They pointed to informa tion that showed heat rate can vary significantly at low hourly capacity factors, resulting in differences of as much as 4 percent from a 30 percent hourly capacity factor to a 40 percent hourly capacity f actor. Figure 2-6 illustrates this concept. At higher hourly capacity factors the ho urly gross heat rate declines . However, the trend is more pronounced at low capacity factors, as noted by the commenter, and less prominent at higher capacity factors. EPA chose to maintain the si ze of the hourly capacity factor ranges for three reasons: (1) approximately 70 perc ent of the hourly values are gr eater than 70 pe rcent capacity factor in each of the three inte rconnection regions and nationally (see Figure 2-8), (2) the data are skewed toward the higher end of each range (e.g., more hourly data points range from 75-79 percent range than from 70-74 pe rcent), and (3) reducing the range of the hourly capacity factor groups would double the number of capacity-tempera ture bins, resulting in fewer measurements in each bin and more data excluded from the analysis. 59 

### EPA’s analyses do not indicate that using a smaller range for capacity factor woul d have a significant im pact on the results.

59 As described below, in the efficiency and consistency an alysis EPA excluded any capacity-temperature bins that had fewer than 15 hourly measurements for a single coal-fired EGU.

### 2-36 Figure 2- 8 Distribution of hourly capacity factors by interconnection Several commenters noted that EPA’s analysis does not account for all the factors that affect a coal-fired EGU’s heat rate. Specifica lly, these commenters mention the influence of geographic differences (e.g., seasonality, altitude), coal rank and quality, equipment degradation, and operating conditions. As stated above, the agency made every effort to control for design characteristics and ambient conditions that the ag ency believes to be ou tside the EGU operator’s control. The agency also controlled for capac ity factor, which some commenters argued is partially under the operator’s control. However, the agency lacks sufficient information to determine the portion of capacity f actor that is under the direct control of influence of the operator. Therefore, the agency chose to reasonabl y and conservatively cont rol for the full effect of hourly capacity factor based on the result s of the analysis described above. Regarding commenters’ assertion about the role of a coal-fired EGU’s lo cation (i.e., geography) on heat rate, the agency believes that the approaches described below do control for these conditions because coal-fired EGUs are not considered mobile sources so by comparing each EGU’s performance from 2002 to 2012 against the E GU’s own performance in 2012 the agency controlled for both seasonal differences and locati on characteristics (e.g., altitude). Commenters also noted that coal-fired EGUs that cha nged coal rank during the study period might have significantly different heat ra tes depending on the rank of coal burned. The agency recognizes that: (1) different coal ranks ha ve different heating values and moisture content, and (2) coalfired EGUs are generally designed for a specific coal rank and coal quality and deviations from that coal rank can adversely affect heat rate. Ho wever, the agency believes that for most coalfired EGUs coal rank and coal quality are controlled by the EGU operator through purchase contracts with fuel suppliers. Therefore, the agen cy chose not to control for coal rank or coal quality. Commenters also stated that equipment degrades over time, increasing heat rate. As discussed further in section 2.6.8 below, EPA r ecognizes that all equipment will degrade over time. However, some of the best practices that can be employed to impr ove heat rate involve properly operating and maintaining equipment to minimize the degradation and, when necessary, replacing equipment to maintain efficient operati on of the plant. Therefore, the agency did not control for the effect of equipment degradati on in the analyses described below because EGU

### 2-37 operators can limit equipment degradation through best practices for operation and maintenance. 60 

### Similarly, the agency believes operating c onditions at each coal-fired EGU are largely under the control of the EGU operator so it is appropriate for the agency to include variability due to operating conditions (w ith the exception of capacity factor.)

2.5.3 Heat rate improvement approach “best historical performance” In the proposal TSD, EPA included an assessm ent of potential heat rate improvement based on the difference between each coal-fired E GU’s best rolling annual gross heat rate and its 11-year (2002–2012) gross heat rate. Based on commenters’ suggestions and heat rate improvement analyses provided by commenters, the agency refined the approach and calculated 1- and moving 2-year gross heat rates for each of the 884 coal-fired EGUs in the study for the years 2002–2012. The agency removed the 3-year averaging period discussed in the proposal TSD because, as noted by commenters, the differences in temperature profiles and loads from one year to the next are relative ly small and not large enough to ju stify a 3-year ro lling average. Commenters suggested that 1 year is sufficient because the agency already controls for capacity factor and temperature in some of the analytical approaches. However, the agency chose to conservatively include a 2-year rolling average to acc ount for atypical annual operations due to factors such as extended outages, equipment fa ilures, cooling water av ailability, or unusual demand patterns. The advantage of using the moving 2-year average is that it can smooth out the peaks and troughs and reduce the influence of a single unusual year (e.g., low utilization, unusual weather or climate conditions), while not dilutin g the results so much that it would obscure significant changes to annual gross heat rate. For each EGU, the agency compared the EGU’s best (i.e., lowest) 1- and moving 2-year gross heat rates to the EGU’s own 2012 average heat rate. As described above, this approach controls for design characteristics that remain c onstant and is not affect ed by the installation and operation of air pollution control devices. It does not, however, cont rol for the variation in hourly gross heat rate attributable to hourly capaci ty factor and hourly ambient air temperature.

2.5.3.1 Methodology The procedures for calculating the po tential heat rate improvement are:

1. For each coal-fired EGU, calculate the 1- and moving 2-year average gross heat rate.

2. Identify each EGU’s best 1- and moving 2-year gross heat rate.

3. Calculate each EGU-specific potential heat rate improvement – the difference between the best 1- and moving 2-year gross h eat rate and the 2012 gross heat rate.

4. Calculate the generation-weighted potentia l heat rate improvement based on 1- and moving 2-year gross heat rate wi thin each interconnection region.

60 See section 2.6.8 for more information about equipment degradation and maintaining heat rate performance.2-382.5.3.2ExampleStep 1: The annual data for an examplecoal-fired EGU are shown in Table 2-4.Table 2-4 1- and moving 2-year heatrates for an example coal-fired EGU2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 20121-year gross heat rate (Btu/kWh)9,615 10,146 10,078 10,076 10,186 9,994 9,157 9,502 10,341 10,549 9,8642-year gross heat rate (Btu/kWh)N/A 9,883 10,108 10,077 10,134 10,101 9,528 9,311 9,962 10,441 10,244Step 2: The lowest 1-year gross heat rate occurred in 2008; the lowest moving 2-year gross heat rate occurred in 2009 (2008-2009data).Step 3: The potential heat rate improvements for the example EGU are:Based on best 1-year gross heat rate:1െቆଽଵହ଻ಳ೟ೠೖೈ೓ଽ଼଺ସಳ೟ೠೖೈ೓ቇ ൌ 7.2	݌݁ݎܿ݁݊ݐBased on best 2-year gross heat rate:1െቆଽଷଵଵಳ೟ೠೖೈ೓ଽ଼଺ସಳ೟ೠೖೈ೓ቇ ൌ 5.6	݌݁ݎܿ݁݊ݐ

### 2-39 2.5.3.3 Results This approach calculates the potential heat rate improvement by determining what would have been possible if each of the 884 coal-fir ed EGUs had reproduced its demonstrated and achievable best performance from 2002 to 2012 in the year 2012. These results are conservative because they only account for a return to each E GU’s historical best annual performance and do not account for the full extent of heat rate improvement availa ble through additional equipment upgrades and best practices. After calculating the difference between each EGU’s best 1- and moving 2-year gross heat and its 2012 gross heat rate, the agency calculated the average potential heat rate improvement for each interconnection region by weighting the EGU-specific potential improvements by each EGU’s 2012 gross electricity generation. The results of the analytical approach are presented in Table 2-5. Table 2-5 Potential heat rate improvement ba sed on best historic al performance, by interconnection Performance period Western Texas Eastern Nationally 61 

### Best 1-year gross heat rate (%) 4.1 4.2 6.3 5.9 Best moving 2-year gross heat rate (%) 2.6 3.1 4.9 4.6

2.5.4 Heat rate improvement approach “best historical performance under similar conditions” The second analytical approach – best hist orical performance under similar conditions – is an outgrowth of the first and third analytic al approaches based on comments and analyses provided by commenters. The second approach is similar to the first approach described above, but in this approach EPA controlled for each EGU’s hourly capacity factor and hourly ambient air temperature as described above. Within each capacity-temperature bin for each coal-fired EGU, EPA calculated the bin-spec ific 1-year and moving 2-year gross heat rates and compared the EGU’s best gross heat rate w ithin that capacity-temperature bin to its 2012 gross heat rate within the same bin. In other words, the agency compared only the operating hours when capacity factor and ambient air temperature were similar for an EGU. The difference between an EGU’s best gross heat rate and the 2012 gross heat rate, expressed as a percentage, represents the potential heat rate improvement for that EGU in that capacity-temperature bin. EPA weighted the results on 2012 electricity generation to calculate the potential heat rate improvement for the coal-fired EGU fleet in each interconnection. One a dvantage of this approach is that it controls for factors that explain a signifi cant portion of differences in an EGU’s gross heat rate and is based on each EGU’s historical mid-term (i .e., 1- or moving 2-year) performance.

2.5.4.1 Methodology The procedures for calculating the potential heat rate improvement using this approach are the same as those shown above with one exception: in the first approach, the agency calculated a single potential heat rate improvement for each E GU then calculated a generationweighted average potential heat rate improvement within each interconnection region. In this second approach, the agency calculated a potenti al heat rate improvement for each of the 168

61 EPA has provided nationwide results for purposes of comparison; they are not part of the methodology.

### 2-40 capacity-temperature bins for each coal-fired E GU, and then calculated a generation-weighted average potential heat rate improvement within each interconnection region.

2.5.4.2 Results The results of this second anal ysis are shown in Table 2-6. Table 2-6 Potential heat rate improvement ba sed on best historic al performance under similar conditions, by interconnection Performance period Western Texas Eastern Nationally 62 

### Best 1-year gross heat rate (%) 4.7 4.9 6.9 6.6 Best moving 2-year gross heat rate (%) 3.1 3.5 5.3 5.0

One reason this approach, which controls for the influence of hourly capacity factor and hourly ambient air temperature, yi elds interconnection-level results that are higher than the first approach is that it relies on the lowest 1- a nd moving 2-year gross heat rates within each capacity-temperature bin. Therefore, unlike the fi rst approach where the “best” performance for each coal-fired EGU is from one or two year s across all capacity f actor and temperature conditions, this second approach may have diffe rent “best” years for the different capacitytemperature bins. This analytical approach determines the potential heat rate improvement if each EGU reproduced its best demonstrated and achieva ble 1- and moving 2-year heat rates while controlling for the influence of hourly capacity factor and hourly ambient temperatures. These results are conservative because they account only for potential heat rate improvements if each coal-fired EGU returned to its best performance; it does not account for th e full extent of heat rate improvements available through additiona l equipment upgrades and best practices.

2.5.5 Heat rate improvement approach “effi ciency and consistency under similar conditions” The two approaches described above are based on the general princi ple that coal-fired EGUs are capable of returning to historically demonstrated and achievable best annual average heat rates (over a 1- or m oving 2-year period). The third approach, which was described extensively in the proposal TSD, uses a diffe rent methodology to determ ine the potential heat rate improvement. This third approach is based on the principle that coal-fired EGUs are capable of operating more consistently with their histor ically demonstrated and achievable best hourlyheat rates. For this approach, the agency grouped each coal-fired EGU’s data by capacitytemperature bin and determined a 1- and m oving 2-year gross heat rate “benchmark.” 63 

### This benchmark was based on each coal-fired EGU’s 10th percentile hourly gross heat rate during each 1- and moving 2-year period for each capacit y-temperature bin. The 10th percentile value means 10 percent of all hourly valu es for the 1- or 2-year period were below the selected value (i.e., the benchmark is demonstrated by 10 perc ent of all measurements in each capacitytemperature bin). The agency chose the 10th perc entile because it represents a demonstrably

62 EPA has provided nationwide results for purposes of comparison; they are not part of the methodology.
63 EPA excluded capacity-temperature bins that had fewer th an 15 hourly measurements fo r a single coal-fired EGU.

### 2-41 achievable gross heat rate indi cating efficient operation of the EGU, but ignores unusually low outlier values. 64 

### Furthermore, the 10th percentile is a commonly used statistical level for determining if particular va lues are unusually low relative to the average value. 65


### EPA did not assume a coal-fired EGU could regularly achieve the benchmark gross heat rate under similar hourly capacity factor and hour ly ambient air temperature conditions. Instead, the agency calculated the potenti al heat rate improvements from greater consistency (i.e., less variability of heat rate) such that each EGU’s hour ly gross heat rates that were greater than the benchmark for the capacity-temperature bin were adjusted downward by a specific percentage (consistency factor) to represent more consistently efficient operation of the EGU. Using this conservative approach, the hourly gross heat rate va lues adjusted most in absolute terms were the highest values (i.e., less-efficien t hours of operation) that indicat e a relative failure to fully employ heat rate improvement measures. The net re sult is that the post-adj ustment distribution of hourly heat rate values has a smaller variability in heat rate, with the average value nearer the benchmark value, while the lowest heat rate values remain unchanged (see Figure 2-9 and example below). This is based on the principle that a coal-fired EGU following best practices should be able to consistently operate closer to the demonstrated and achievable benchmark heat rate. In the proposal TSD, EPA assessed several di fferent consistency f actors ranging from 10 to 50 percent and proposed 30 percent because th e results were similar to results achieved through other analytical approaches (e.g., 3-year rolling averages relative to the 11-year average), accorded with EPA experts’ engineer ing judgment, and becaus e 30 percent was in the middle of the reasonable range evaluated by the ag ency. Several commenters suggested that this choice of values was arbitrary a nd could have easily been higher or lower had EPA selected the middle of a different range of values. To identif y a consistency factor w ith more precision for this analytical approach, EPA analyzed the EGUs’ historically demonstrated level of consistency (i.e., variability) for hourly gro ss heat rate over 1-year and moving 2-year periods between 2002 and 2012 for each capacity-temperature bin. The agency calculated the mean-centered standard deviation of hourly gross heat rate values for each 1- and 2-year period for the 884 coal-fired EGUs for all capacity-temperature bins. 66 

### The lowest standard deviation – a measure of dispersion – for the 1- and moving 2-year periods were then compared to the mean-centered standard deviation of 11-year gross heat rate data . The ratio of the 1- a nd moving 2-year standard deviations to the 11-year standard deviation re present the potential cons istency improvement at each interconnection. In other words, this ap proach compares the EGU’s most consistent

64 During the development of the dataset the agency excluded extreme outliers – hourly gross heat rate values that were greater than ±2.6 standard deviatio ns from the EGU’s mean gross heat rate.
65 Common statistical levels include the 10th and 90th percentiles, the 5th and 95th percentiles, and the 1st and 99th percentiles. EPA considered these other levels but select ed the 10th percentile because it is a common statistical level, is conservative, and is substantially less likely to ha ve been an outlying value than the 5th or 1st percentile. If EPA had selected the 5th percentile as the benchmark, the facility would have half as many hours of historical operation at or below that level and the overall resulting potential heat rate improvement would have been substantially larger.
66 The mean-centered standard deviation shows the extent of variation relative to the mean and allows for meaningful comparison of variability from coal-fired EGUs that can have widely different average gross heat rates. The mean-centered standard deviation, al so called the coefficient of variation, is calculated by dividing each coalfired EGU’s standard deviation for the capacity-temperatu re bin by the EGU’s capacity-temperature bin’s average gross heat rate. It is reasonable to use the standard deviation when evaluating the potential to reduce variability because standard deviation is the most common measure of the spread, or variation, of values within a dataset.

### 2-42 performance (i.e., least variability) for each capac ity-temperature bin over a 1- or moving 2-year period to the EGU’s consistency (i.e., va riability) over the st udy period (2002-2012). As described above at the beginning of s ection 2.5, EPA replaced the 11-year baseline used in the proposal TSD with a 2012 baseline in this analysis. Th e refined approach – comparing each EGU’s best performance against its 2012 baseline performance – recognizes actions that improve gross heat rate performan ce because better practices that result in lower gross heat rates in 2012 reduce the estim ated potential heat rate improvement. Several commenters noted that reduced va riability does not nece ssarily equate to improved heat rates. EPA agrees with commenters that reducing variabilit y alone does not result in lower heat rates and, therefore, lower CO 2 

### emissions. For example, if an EGU were to operate more consistently at a higher heat rate (i.e., less efficient), the variability would decrease, but the heat rate would increase. However, the agency is not simply asking for less variability in operation at existing coal-fired EGUs. Instead, the agency is as sessing the average heat rate improvement that would result if coal-fired E GUs had operated more cons istently nearer their gross heat rate benchmarks. Both the benchmark gross heat rates and consistency factors were demonstrated and achieved by the EGUs fr om 2002–2012 during hours with similar hourly capacity factors and hourly ambient air temperatures.

2.5.5.1 Methodology The procedures for calculating the consistency factor are:
1. For each coal-fired EGU, calculate the 1-yearaverage gross heat rate for each capacitytemperature bin.

2. For each coal-fired EGU, calculate the differe nce, as a percentage, between every hourly gross heat rate value and the relevant 1-yearaverage gross heat rate within each capacitytemperature bin.

3. Within each interconnection, calculate the m ean-centered standard deviation of the differences calculated in step 2 for all coal-f ired EGUs and capacity-temperature bins for each 1-, moving 2-, and 11-year period.

4. Within each interconnection, calculate the cons istency factor by comparing the lowest 1- and 2-year standard deviation and the 11- year standard deviation from step 3.

The procedures for calculating the po tential heat rate improvement are:
1. For each coal-fired EGU, determine the gross heat rate benchmarks – the 10th percentile hourly gross heat rate – for each 1- and moving 2-year period within each capacitytemperature bin.

2. Identify each EGU’s best 1- and moving 2-year gross heat rate benchmark within each capacity-temperature bin.

3. For each coal-fired EGU, calculate the adju sted gross heat rate for each capacitytemperature bin:

### 2-43 a. For each hourly gross heat rate value greate r than the 1- or moving 2-year benchmark calculate the difference between the hour ly value and the relevant benchmark; b. Multiply the difference by the relevant 1- or moving 2-year interconnection-specific consistency factor; and

c. Subtract the result from the hourly gross heat rate value.

4. Within each interconnection, calculate the 11-year generation-weighted adjusted heat rate average for the 1- and moving 2-year benchmark using the adjusted gross heat rate values from step 3.

5. Calculate the generation-weighted potential heat rate improvement by comparing the 11- year adjusted heat rate average for the 1- and moving 2-year benchmarks from step 4 against the 2012 generation-we ighted gross heat rate.

One commenter noted that EPA’s analysis in the proposal TSD used heat input instead of gross heat rate to assess poten tial heat rate improvements. The commenter noted that EPA’s assumption of an equivalent level of gross el ectricity generation acro ss the full range of the capacity factor bin skewed the results. EPA agrees with the commenter that using the gross heat rate for the analysis provides more accurate resu lts. Therefore, the agency changed the approach to use hourly gross heat rate, cal culated as heat input divided by gross electricity generation.

2.5.5.2 Example Step 1: The 1- and moving 2-year gross heat rate benchmarks (i.e ., 10th percentile values) for the 90–99 percent hourly capacity factor/70–79° F temp erature bin for an example coal-fired EGU are shown in Table 2-7.

Table 2- 7 1- and moving 2-year 10th percentile benchmark gross heat rate for an example

coal-fired EGU in capacity factor rang e 90-99 percent and temperature range 70-79° F
(Btu/kWh) 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 1-year benchmark 9166 9978 9659 9745 9839 9497 8625 9005 9544 9729 9780 2-year benchmark N/A 9253 9774 9696 9786 9554 8743 8728 9142 9661 9769



### Step 2: The lowest 1-year gro ss heat rate benchmark is from 2008; the lowest moving 2-year gross heat rate benchmark occurred in 2009 (2008–2009 data). There are 284 hours of operation in the capacity-temperature bin with a gross he at rate lower than the 1-year benchmark of 8,625 Btu/kWh. For the moving 2-year benchmark, 434 hour s of operation in the capacity-temperature bin had a gross heat rate lower than 8,728 Btu/kWh.

Step 3: The EGU’s adjusted gross heat rates fo r the capacity-temperature bin calculated with the 1-year benchmark and the Eastern interconnecti on’s 1-year consistenc y factor (.455), and

### 2-44 moving 2-year benchmark and the Eastern interco nnection’s 2-year consis tency factor (.381), are shown in Figure 2-9.

Figure 2-9 Frequency of hourly gross heat rates measured and adjusted, 2002–2012 for an example coal-fired EGU in capacity factor ra nge 90–99 percent and temperature range 70–79 

### ° F Step 4: The 11-year measured and adjusted gr oss heat rate using the 1- and moving 2-year benchmarks and consistency factors for the ex ample EGU in the capacity-temperature bin are listed in Table 2-8.

Table 2-8 Measured and adjusted heat rate based on efficiency and consistency under

similar conditions for one capacity-temper ature bin for an example coal-fired EGU Measured/adjusted 2002–2012 gross heat rate (Btu/kWh) Measured gross heat rate 9,968 Adjusted gross heat rate – 1-year benchmark and consistency factor 9,353 Adjusted gross heat rate – 2-year benchmark and consistency factor 9,492

Step 5: The potential heat rate improvement for the example EGU in the capacity-temperature bin based is:
Based on 1-year benchmark and consistency factor: 1െቆ ଽଷହଷ ಳ೟ೠ ೖೈ೓ ଽଽ଺଼ ಳ೟ೠ ೓ೖೈ 

### ݐܿ݁݊ݎ݁݌	6.2 ൌ ቇ

Based on 2-year benchmark and consistency factor: 1െቆ ଽସଽଶ ಳ೟ೠ ೖೈ೓ ଽଽ଺଼ ಳ೟ೠ ೓ೖೈ 

### ݐܿ݁݊ݎ݁݌	4.8 ൌ ቇ

### 2-45 2.5.5.3 Results This approach calculates the potential heat rate improvement by determining what would be possible if each of the 884 coal-fired EGUs had operated more efficiently and consistently nearer their historically demonstrated and achie vable “best” gross heat rate from 2002 to 2012. First, EPA calculated the ratio of the leas t variability in the period 2002 to 2012, defined by standard deviation, to the average variability to establish interconnection-specific consistency factors (see Table 2-9). Next, the agency determined the average pot ential heat rate improvement for each interconnection by calculating an adjusted heat rate for each EGU based on the EGU’s benchmark and the relevant inte rconnection consistency factor, and weighting the EGU-specific potential improvements by the EGU’s 2012 gross electricity generation. The results of the analytical approach are presented in Table 2-10. Table 2-9 Consistency factors based on be st historical performance under similar conditions, by interconnection Performance period Western Texas Eastern Nationally Best 1-year consistency 46.9 46.6 45.5 45.8 Best 2-year consistency 38.4 37.1 38.1 38.2

Table 2-10 Potential heat rate improvement based on efficiency and consistency under similar conditions, by interconnection Performance period Western Texas Eastern Nationally 67 

### Best 1-year benchmark and consistency (%) 3.5 3.7 5.6 5.3 Best 2-year benchmark and consistency (%) 2.1 2.3 4.3 4.0

2.5.6 Results Each of the three analytical approaches described above is based on unit-specific comparisons between individual EGUs’ more efficient and baseline performances. In part for this reason, each approach constitutes a reasonable means of assessing the potential for heat rate improvements by affected coal-fired EGUs with in the regional interconnections. Because the analyses are based on actual histor ical gross heat rate data for each of the 884 coal-fired EGUs in the study population, the EPA is confident that th e potential heat rate improvements identified on the regional basis through these approaches are feasible. Although each of these three analytical appr oaches is independently reasonable, EPA’s overall methodology adopts as “building block 1” the most conservative value identified by any approach. For each analytical approach, EPA calculated potential heat rate improvements based on the best 1-year and moving 2-year performa nce for each EGU. The results from the moving

67 EPA has provided nationwide results for purposes of comparison; they are not part of the methodology.

### 2-46 2-year analyses will always be more conservative than the 1-year analyses. 68 

### Therefore, to be conservative and ensure that the potential heat rate improvements are suppor ted by the weight of evidence, EPA compared the results of the m oving 2-year analyses shown in Table 2-11. 69



### Table 2-11 Potential heat rate improvement for best moving 2-year period, by interconnection Analytical approach Western Texas Eastern Nationally 70 

### Best historical performance 2.6 3.1 4.9 4.6 Best historical performance under similar conditions 3.1 3.5 5.3 5.0

Efficiency and consistency under similar conditions 2.1 2.3 4.3 4.0

For the final rule, the “efficiency and c onsistency under similar conditions” approach provides the most conservative result for all three regions. Accordingly, the tripartite methodology adopts those results for purpos es of calculating the relevant CO 2 

### emission performance rates and accompanying state goals. 71 

### Specifically, a well-supported and conservative estimate of the potential heat rate improvements (and accompanying reductions in CO 2 

### emission rates) that affected coal-fired EGUs in the Western interconn ection is 2.1 percent, in the Texas interconnection is 2.3 percent, and in the Eastern in terconnection is 4.3 percent. The agency believes these values are conservative an d that EGUs have demonstrated these gross heat rates were achieved du ring the study period. It is important to note that these potentia l heat rate improvements are based on the difference between coal-fired EGUs’ past perfor mance and their 2012 performance; they do not reflect additional opportunities fo r heat rate improvements from best practices and equipment upgrades that could have been (and still can be ) cost-effectively implemented at coal-fired EGUs. The agency decided not to pursue a separa te heat rate improvement target for specific equipment upgrades due to (1) insufficient info rmation about the current deployment of the technologies specified in the proposal TSD, (2) th e availability of data necessary to conduct the statistical analyses to identify potential heat ra te improvement, and (3) potential impact of past deployments of heat rate improvement technology at some EGUs that might already be reflected in the results of the analyses described above.

68 The “best” gross heat rate over a moving 2-year period w ill always be higher than the best gross heat rate over a 1-year period because the moving 2-year period includes at least one non-best y ear (i.e., a year with higher average gross heat rate values).
69 The results of EPA’s inherently less conservative 1-year analyses can be found in section V.C of the final rule preamble of the CPP. 70 EPA has provided nationwide results for purposes of comparison, they are not part of the methodology.
71 The “best historical performance” an d “best historical performance under si milar conditions” approaches identify greater potential heat rate improvemen t in each region. Accordingly, they i nherently support th e feasibility of the more conservative values identified by the “efficien cy and consistency under similar conditions” approach.

### 2-47 2.6 Assessment of key comments EPA received more than 1,500 comments on bu ilding block 1, including the methodology and assumptions for calculating potential heat rate improvements. Several of the comments are addressed above and below, and in the preamble with the remaining comments addressed in the response to comments (RTC). EPA appreciates the information and insights that commenters shared about the proposal. The information assist s the agency in developing a stronger, more effective rule. Below are several key comments re lated to potential heat rate improvement and the additional analyses conducted by EPA to respond to those comments.

2.6.1 Effect of heat rate improvement implemented during study period Several commenters expressed concern that m easures implemented at specific EGUs to improve heat rate between 2002 and 2012 would result in greater variability of heat rate and, therefore, inflate the po tential heat rate improvements. EPA acknowledges variability from heat rate improvements would affect the results of the approach used for the proposal. However, by changing the methodology to use a 2012 baseline for the analytical approaches described in this chapter, any heat rate improvements made during the study period and maintained through 2012 are accounted for and do not result in addi tional potential heat rate improvement.

2.6.2 Impact of air pollution controls installed during study period Commenters described many of the air polluti on control devices that were installed at coal-fired EGUs between 2002 and 2012, noting that these devices can have significant auxiliary energy requirements (i.e., parasitic load) that reduce the amount of elect ricity available for distribution on the regional interc onnection. As a result of lowe r net electricity generation, the EGU’s net heat rate increases. Many commenters recommended that EPA consider the auxiliary energy requirements of these controls when asse ssing the potential heat rate improvement. EPA recognizes that air pollution control devices have auxiliary power require ments and a significant amount of coal-fired EGUs were retrofitted with air pollution controls during the study period. However, EPA rejects the recommendation to cr edit the auxiliary power requirements in the analysis because the agency’s analysis is based on gross heat rate and, ther efore, is not affected by auxiliary power requirement s of air pollution controls. 72



### 2.6.3 Impact of CO 2 

### emissions from SO 2 

### pollution control devices Commenters noted that the addition of wet flue gas de sulfurization (WFGD or wet scrubber) technologies resu lt in increased process CO 2 

### emissions because of the dissolution of limestone to capture SO 2



### . 73 

### Because heat input, the numerator for calculating heat rate, is based on an EGU’s measured CO 2 

### emissions, the additional process CO 2 

### emissions result in artificial increases in calculated gross heat rate . Commenters suggest that process CO 2 

### emissions can be “several percentage points.” To assess the range of potential impacts on EPA’s analysis from process CO 2 

### emissions, the agency estimated the incremental CO 2 

### from the WFGD chemical reaction. The results indicate that retrofit WFGD installed on ex isting coal-fired EGUs between

72 See supra note 45. 73 The reaction in the WFGD absorber is summarized by SO 2 +CaCO 3 +1⁄2H 2 O → CaSO 3 +1⁄2H 2 O+CO 2 (see RK Srivastava, 2000. Controlling SO 2 emissions: a review of technologies. EPA report EPA/600/R-00/093. Available at http://nepis.epa.gov/Adobe/PDF/P1007IQM.pdf

.

### 2-48 2002 and 2012 increased 2012 CO 2 

### emissions (and calculated heat input) by an average of no more than 0.7 percent for those EGUs in 2012. To calculate the CO 2 

### emissions, the agency first iden tified the 121 existing coal-fired EGUs that installed retrofit limestone WFGD between 2002 and 2012. Collectively these EGUs have approximately 59 GW of el ectricity generating cap acity. Next, EPA revi ewed monthly fuel transactions and calculated the weighted average su lfur content of fuel deliveries to the relevant power plants. 74 

### Finally, the agency calculated the process CO 2 

### emissions that could result at 85 and 95 percent removal efficiencies (see Table 2- 11). Due to data limitations – EGU-level fuel consumption data are not available – the agency assumed that: (1) fuel delivered to the power plant in 2012 was representative of coal burne d in the WFGD-equipped coal-fired EGU, and (2) EGUs operated the WFGD to remove 85 to 95 per cent of the sulfur. The resulting process CO 2 

### emission increase at coal-fired EGUs that inst alled retrofit limestone WFGD ranges from 0.2 percent (Texas) to 1.0 per cent (Eastern). It should be note d, however, that this is a very conservative estimate for the following reasons: (1 ) some of the sulfur is bound in pyrites and well-maintained coal pulverizers/grinders shoul d remove much of the pyritic-bound sulfur, (2) a small amount of the combustion CO 2 

### might be absorbed in the WFGD slurry, and (3) in 2012 many EGUs did not operate their WFGD at full capacity.

Table 2-11 Increase in 2012 CO 2 measurements due to retrofit limestone wet flue gas desulfurization 

### Western Texas Eastern Nationally
Number of retrofit limestone WFGD 6 6 109 121 Average sulfur content (% ) of 2012 coal deliveries 0.55 0.43 1.82 1.63 Total quantity (1,000 tons) of 2012 coal deliveries 12,375 11,637 148,631 172,644 Potential process CO 2 

### increase (%) from WFGD 0.3 0.2 0.9-1.0 0.8-0.9

Note: Interconnection-level totals may not add up to national-le vel totals due to rounding; Potential process CO 2 emissions are based on 85 and 95 percent removal ra tes, Western and Texas values round to the same quantity for 85 and 95 percent removal rates; Retrofits are for limestone WFGD only and do not acc ount for other WFGD that do not produce process CO 2 emissions


### 2.6.4 Impact of future retrofit air pollution co ntrols to comply with EPA and state environmental requirements This rule establishes CO 2 

### intensity goals based on net elect ricity generation for each state using the state’s 2012 net electr icity generation baseline. Many commenters noted that air pollution controls installed afte r 2012, many of which ar e required to comply with other federal and state environmental regulations, will require auxiliary power and therefore will increase EGUs’ net heat rate and CO 2 

### intensity. These commenters su ggested that EPA should account for the addition of new air pollution controls when calculating the state goals. EPA evaluated the potential impact of the additional pollution contro ls that are expected to be installed between 2013 and 2025 to comply with rules such as th e Mercury and Air Toxi cs Standards (MATS), Clean Air Interstate Rule (CAIR), Cross-State Air Pollution Rule (CSAPR), Regional Haze Rule,

74 Monthly fuel transactions are reported at the plant level on form EIA-923.

### 2-49 as well as consent decrees and state policies. Th e agency’s analysis, described below, indicates that the potential impact on net heat rate and, therefore, CO 2 

### intensity is small – approximately 0.3 percent – in 2025. To assess the anticipated impact of new retr ofit air pollution contro ls on net heat rate, EPA used data and information from EP A’s Power Sector Modeling Platform v.5.15. 75 

### The Base Case is a projection of power s ector activity, taking in to account existing fe deral and state air emission laws and regulations. The Base Case in cludes projected installa tions of air pollution controls and EGU-level net electricity gene ration in the years 2017, 2018, and 2025. The agency compared the Base Case 2025 installations of air pollution controls to the 2012 installations listed in the National Electric Energy Data System (NEEDS) v.5.15. Specifically, the agency identified predicted additions of WFGD, dry flue gas desulfurization (DFGD), selective catalytic reduction (SCR), and selective non-catalytic reduction (SNCR) (see Table 2-12).

Table 2-12 Predicted additional capacity (MW) with air pollution controls installed by 2025, by interconnection Air pollution control Western Texas Eastern WFGD: wet flue gas desulfurization (MW) 283 0 18,214 DFGD: dry flue gas desulfurization (MW) 505 584 15,388 SCR: selective catalytic reduction (MW) 11,194 0 13,618 SNCR: selective non-catalytic reduction (MW) 0 391 4,014

To estimate the impact on heat rates, the ag ency used the heat rate penalties (i.e., auxiliary power requirements) from EPA’s Po wer Sector Modeling Platform v.5.13 (see Table 2- 13). 76 

### Each EGU’s 2012 net heat rate was adjusted for the assumed heat rate penalties and multiplied by the EGU’s projected 2025 net electri city generation to estimate the incremental heat input required to op erate the retrofit air pollution contro ls. The incremental heat input at EGUs with additional air pollution controls was aggregated to the interconnection region and then divided by the region’s pre-control heat input. 77 

### The anticipated impact s of the retrofit air pollution controls on net heat rate in 2025 are 0.31 percent in the Western Interconnection, 0.06 percent in the Texas Interconnection, and 0.31 percent in the East ern Interconnection.

Table 2-13 Assumed heat rate pena lty for air pollution controls Air pollution control Heat rate penalty (%)

75 More information about the Power Sector Modeling Platform, including data, assumptions, and documentation is available at http://www.epa.gov/airmarkets/programs/ipm/psmodel515.html

. 76 Information on air pollution controls is taken from Chapter 5 available at http://www.epa.gov/airmarkets/documents/ipm/Chapter_5.pdf

. 77 The pre-control heat input is calculated as 2012 net h eat rate (before additional air pollution controls) multiplied by 2025 net electricity generation.

### 2-50 WFGD: wet flue gas desulfurization 1.70 DFGD: dry flue gas desulfurization 1.33 SCR: selective catalytic reduction 0.56 SNCR: selective non-catalytic reduction 0.78

This estimate is conservative for at leas t two reasons: (1) EPA’s cost performance assumptions tend to overestimate the auxiliary energy requirements fr om individual retrofit controls, and (2) at some EGUs these newer pollution control devices will replace existing pollution control devices. Accordingly, for these EGUs , the increase in net heat rate due to power required to operate new controls will be at least partially offset by the decrease in net heat rate caused by removal of the control devices currently in place. The retrofit of air pollution control devices also present opportunitie s to improve net heat rate by replacing some equipment (e.g., fans, pumps, air heaters, economizer s) with more efficient equipment. 78 

### In addition, future technological innovations or advances in emissi on control practices may reduce the need for retrofit emission controls. Therefore, for the reasons described above, the agency has determined that when calculating overarching state goals, it is not necessary to account for the net heat rate impact of predicted installations of controls. However, EPA recognizes that states may choose to account for the installation of pollution controls at co al-fired EGUs after 2012 when developing their state plans.

2.6.5 Impact of cycling operations and lo wer capacity factors on heat rate Several commenters expressed concern that th e application of the other building blocks that make up the best system of emissions reduc tions will result in coal-f ired EGUs operating at lower capacity factors and cycling more fre quently during the interim and full compliance periods. Commenters noted that at lower hourly capacity factors e fficiency decreases and heat rate increases (for example, see Figure 2-6) ma king improving heat rate even more challenging. EPA disagrees that the application of the other bu ilding blocks from the best system of emission reductions necessarily results in more cycling at existing coal-fired EGUs for the following reasons: (1) many coal-fired EGUs are expect ed to retire between 2012 and 2020, reducing the remaining capacity of coal-fired EGUs during th e interim and full compliance periods; (2) EPA modeling of electricity generation in 2030 indicate that the remain ing coal-fired EGUs will have higher annual capacity factors than the 884 co al-fired EGUs in 2012 – the baseline for the agency’s heat rate improvement analyses; and (3 ) states have flexibility when developing their state plans and may choose policie s that do not require heat rate improvements at specific EGUs. A significant amount of coal-fired EGU capacity has retired since 2012 and even more is expected to retire by 2020. For th e 884 coal-fired EGUs in the agency’s analyses, more than 53,000 MW of generator capacity is e xpected to retire. This repres ents approximately 16 percent of the capacity from the 884 coal-fired EGUs acr oss the nation. In the We stern Interconnection, coal-fired EGU owners and operators have officially announced 6,000 MW of capacity, 16

78 For example, see D Osowski, 2014. “Bagging Two Birds with One Economizer,” Power Engineering. Available at http://www.power-eng.com /articles/print/volume-118/issu e-11/departments/c learing-the-air/bagging-two-birdswith-one-economizer.html

.

### 2-51 percent of total capacity, will retire in 2020 or earlier (see Figure 2-10). The agency does not have information about any offi cial planned retirements in th e Texas Interconnection. In the Eastern Interconnection, EGU owne rs and operators have officially announced more than 47,000 MW of capacity, 17 percent of to tal capacity, will retire in 2020 or earlier (s ee Figure 2-11). Because less coal-fired electricity generati ng capacity will be available for dispatch, the remaining coal-fired EGUs are expected to operate at higher a nnual capacity factors.

Figure 2- 10 Official completed and planned retirements of coal-fired EGUs in Western Interconnection, 2012–2020

Note: Generator capacity scale is 1 / 10 of scale in Eastern Interconnection (Figure 2-11)

### 2-52 Figure 2- 11 Official completed and planned retirements of coal-fired EGUs in Eastern Interconnection, 2012–2020 

### EPA's power sector modeling for this rule pr edicts that annual capacity factors for coalfired EGUs in 2030, with the rule in full effect, wi ll be higher than the EGUs ’ capacity factors in 2012. As noted above, the increased utilization of existing coal-fired EGUs that remain in operation is due in large part to significant retirements of coal -fired EGUs that have already occurred or are expected to o ccur as well as growth in electri city demand. Nationally, net annual capacity factors – net electricity generation divi ded by net summertime capacity – at coal-fired EGUs is predicted to be approximately 70 perc ent under a rate-based go al and 75 percent under a mass-based goal while the 2012 net annual capacity factor averaged 55 percent. 79 

### The values for the interconnections are shown in Table 2-14. While these values repr esent annual capacity factors and not hourly capacity factors, the subs tantial increase in annu al capacity factors at existing coal-fired EGUs in all interconnections except Texas leads to a reasonable conclusion that the remaining coal-fired EGUs as a whole wi ll operate at higher levels in 2030 than they did in 2012, the baseline for this heat rate impr ovement study. For the Texas interconnection, the capacity factors are anticipated to be appr oximately the same under a mass-based policy approach and lower under a rate-based approach . The state of Texas has the flexibility to consider this projected change in annual capacity factors when de signing the state plan.

Table 2-14 Annual net capacity factors (p ercent) in 2012 and 2030, by interconnection

Interconnection 2012 2030 mass goal 2030 rate goal
Western 69.5 80.5 79.5

79 The 2012 net annual capacity factor s were calculated from the 2012 baseline data using the eGRID methodology. For the 947 coal steam EGUs in the CONUS, the capacity factor was calcu lated as net electricity generation (MWh)/[8784 hours X net summertime capacity (MW)].

### 2-53 Texas 66.5 66.5 55.8
Eastern 52.8 74.9 69.5 CONUS 55.3 74.7 69.5

2.6.6 Influence of flow monitoring method on historical heat rate data To calculate heat input at a coal-fired EGU, the operator must measure the flow rate of flue gases as they pass through th e stack (or duct). Initially, all coal-fired fired EGUs used EPA Reference Method 2 to measure stack gas flow. 80 

### However, in 1999 EPA introduced new reference methods to address angu lar flow (Methods 2F and 2G) a nd the effect of the stack walls on gas flow (Method 2H). In general, these alte rnative measurement methods reduce or eliminate the over estimation of stack gas fl ow that results from the use of method 2 when the specific flow conditions (e.g., angular flow) are pr esent in the stack. Generally, th e alternative methods lead to lower flow rates and as a result, lower heat input. After the introducti on of these new methods, many coal-fired EGUs adopted the alternative methods to m easure flow and calculate mass emissions. 81 

### However, coal-fired EGUs are not requi red to use the alte rnative measurement methods and they may change methods when conducting a RATA. Commenters have hypothesized that much of the variability in gr oss heat great is caused by coal-fired EGUs changing from one flow measurement method to a nother. EPA carefully considered commenters concerns and conducted additional analyses to explore the potent ial impact of flow method on heat rate variability. Evaluation of flow measurement methods is complex because coal-fired EGUs may have one or more stacks, including common stacks th at are shared with other EGUs at the power plant, and might use different methods for up to three different load levels in each stack. To assess the potential influence of flow measur ement methods on heat rate variability, EPA reviewed the reported flow measurement methods for the 884 coal-fired EGUs between 2002 and 2012. Of the 884 EGUs, 531 EGUs modified one or more flow measurement method for at least one load level during the 11-year st udy period (2002–2012). If, as commenters suggested, changes to flow measurement methods were resp onsible for much of the variability in EPA’s data, the analytical results should show that E GUs with a change of flow measurement method have higher indicated po tential heat rate improvements. Ho wever, as shown in Table 2-15, the EGUs that changed flow method during the study period (2002-2012) have lower potential heat rate improvements in the Western and Texas in terconnections than for the full group of coalfired EGUs in the interconnections. In the Ea stern interconnection, the results between the two groups – EGUs that changed flow measurement me thod(s) and EGUs that have not changed flow measurement methods – are nearly identical.

80 For more information about Method 2, see 40 CFR Part 60 Appendix A.
81 Coal-fired EGUs use continuous emission monitoring sy stems to measure pollutant concentrations and stack gas flow. The two measured values are used to calculate mass emissions.

### 2-54 Table 2-15 Potential heat rate improvement base d on the 2-year efficiency and consistency under similar conditions analytical approach fo r all coal-fired EGUs and coal-fired EGUs that changed flow measurement method, by interconnection

Interconnection 884 coal-fired EGUs Coal-fired EGUs with flow measurement method changes
Western 2.1 2.0
Texas 2.3 0.0
Eastern 4.3 4.3 CONUS 4.0 3.8

Note: The Texas interconnection has a small number of EGUs, so small changes to the population can result in large changes in potential heat ra te improvement 

### Because the potential heat rate improveme nt for the 884 coal-fired EGUs and the 531 coal-fired EGUs that changed one or more fl ow measurement methods are approximately the same, EPA disagrees with commenters’ statemen t that changes to flow measurement methods were responsible for much of the variation in gross heat rates.

2.6.7 Inability of individual coal-fired EGUs to achieve the heat rate improvement targets Several commenters stated that specific coal-f ired EGUs are not able to achieve the heat rate improvements identified in the proposal TSD. They provide a variety of reasons, including: (1) fuel type (e.g., lignite has low energy value and high moisture content, making it difficult to improve heat rate), (2) specific EGUs have alre ady implemented efficiency measures and are not capable of improving heat rates beyond their curre nt levels, (3) soon-to-retire EGUs would be forced to make additional efficiency upgrades to improve heat rate, resulting in stranded assets, and (4) small coal-fired EGUs do not have th e same economies of scale as larger EGUs

. The commenters argue that requiring every coal-fir ed EGU to meet a mandatory heat rate improvement target or standard would force EGU retirements and, as a result, strand assets and affect electricity consumers. First, EPA disagrees with commenters that this rule establishes a mandatory “one-size-fits-all” heat rate improveme nt standard. Rather, the heat rate improvement is one part of the best system of emission reducti on and must be viewed in the context of the full best system of emission reductions. As noted earlie r, states have the flexibility to establish policies in the state plan that are reasonable and appropriate for that stat e; these policies may or may not include heat rate improvement goals . Second, EPA disagrees with the commenters’ presumption that each coal-fired EGU must i ndependently achieve the potential heat rate improvements identified in this an alysis. The results of the analys es described above provide an estimate of potential heat rate improvement s that, on average, are possible for each interconnection region based on each coal-fired E GU’s best historical heat rate between 2002 and 2012 and the EGU’s own 2012 heat rate. Because the results represent the average potential heat rate improvement for an in terconnection, some coal-fired E GUs can make greater heat rate improvements while other EGUs might make less. Third, the agency’s analysis is based on coalfired EGUs' historical heat rate s relative to 2012 and is therefor e demonstrated and achievable.

### 2-55 Commenters also noted that the interim and full compliance periods do not begin for several years and new technologies and practices for improvi ng heat rate may be available in the future. With regard to soon-to-retire EGUs, it is not cl ear why EGUs retiring before the beginning of the interim compliance period in 2022 would make additional investments in heat rate improvements. However, if a soon-to-retire EGU did make such investments, those investments might serve to extend the useful life of the EGU.

2.6.8 Impact of equipment degradation Several commenters stated that EPA’s asse ssment of potential heat rate improvement from equipment upgrades was unrealistic because it did not account for degradation of the equipment over time. First, it is important to note that: (1) EPA is no longer basing potential heat rate improvements on specific upgrad es and (2) the rule does not establish a federal requirement for coal-fired EGUs to meet specific heat ra te improvement targets (see section 2.6.7). Second, the agency disagrees with commenters; the agen cy believes EGU operators can control the rate of degradation through proper maintenance and, wh en necessary, replacement or refurbishment of equipment. This is suppor ted by information provided by a commenter referring to National Petroleum Council (NPC) and In ternational Energy Agency (IEA) reports that discuss deterioration and enhanced maintenance practices at coal-fired EGUs. The NPC report stated: Deterioration naturally occurs, and if left unchecked it can become substantial. Therefore, some amount of deterioration, normal deterioration, will always be present and non-controllable. Most of the normal dete rioration can be recovered with regularly scheduled maintenance intervals... There is a gradual increase in the unrecoverable portion as the unit ages which would require a replacement rather than a refurbishment to eliminate. Poor maintenance practices re garding the timing of the intervals and the amount of refurbishment may result in excessi ve deterioration and is controllable [by the EGU operator]. 82 

### The IEA report stated: [I]mprovements can not only restore ageing pl ants to their original performance, but also can often improve them further by avoiding the limitations of the prevailing technology at the time of original plant design...Not all of th ese areas have been considered worthy of attention previously, and a conclusion from that is that there may be other unrecognized areas of losses in ef ficiency which could be worth identifying. 83



### 2.6.9 Limited adoption of low- and z ero-cost heat rate improvements Commenters suggested that if there were low- or no-cost op tions to improve heat rate and, therefore, reduce fuel cost, coal-fired EGUs would have already implemented those measures because they have economic incentives to do so. However, research from NETL, RFF, and others show that there ar e a number of reasons that exis ting coal-fired EGUs might not implement low- and no-cost heat rate improve ment practices, includi ng: (1) concerns or

82 See National Petroleum Council (NPC), 2007. Electricity Generation Efficiency. NPC Working Document; Topic Paper #4. Available at http://www.npc.org/Study_Topic_Pape rs/4-DTG-ElectricEfficiency.pdf

. 83 See C Henderson, 2013. Upgrading and efficiency improvement in coal-fired power plants. International Energy Agency (IEA) Clean Coal Cent re Report. CCC/221. ISBN 978-92-9029-541-9. Available at http://bookshop.ieacoal.org/reports/ccc-221/83186

.

### 2-56 uncertainties about New Source Review impacts; (2) lack of economic incentives to improve efficiency because of fuel adjustment clauses that enable electricity generating companies to “pass through” changes in fuel costs directly to customers; (3 ) lack of management commitment for efficiency programs; (4) focus on optimizing pr ofitability and ensuring availability; (5) lack of on-site performance engineers dedicated to he at rate improvement; (6) potential fuel cost savings represent only a sma ll portion of total costs and are “of secondary concern when compared to needs for maintaining system reliabili ty”; and (7) lack of regulatory certainty about future environmental regulations that lead to operators deferr ing significant upgrades on older coal-fired EGUs. 84



### 2.7 Costs

2.7.1 Breakeven costs In the proposal TSD the EPA used an estimat ed average cost of $100/kW for a potential national average 6 percent heat rate improvement by coal-fired EGUs. Four percentage points of that potential improvement was projected to come from relatively inexpensive low- or no-cost best practices. Thus, most of the estimated $100/kW average cost actually represented the cost of relatively more expensive equipment and system upgrades that EPA expected would provide the remaining 2 percent improvement. For this final rule, as explained above, EPA ha s refined the estimate of potential national average net heat rate improvement by coal-fir ed EGUs to between 2.1 to 4.3 percent for each interconnection, or about 4 percent nationally, wi th potentially all of that improvement coming from best practices. The agency no longer relies on an additi onal and distinct 2 percent improvement contribution from equipment upgrades . The agency expects, however, that some EGUs will in fact choose to make equipment upgrades for heat rate improvement, and that at least some small fraction of the overall heat rate improvement will come from equipment upgrades, and for at least a few EGUs most of the improvement may come from equipment upgrades. Overall, the agency expects the cost of heat rate improvement to be significantly less than $100/kW at the new level of heat rate im provement. However, because of the uncertainty associated with what fractions of a potential national average 4 percent heat rate improvement may come from no- or low-cost versus higher cost measures at individual EGUs, and considering commenter concerns that EPA’s pr evious estimated cost was too optimistic, the agency assessed the economic effects of a heat rate improveme nt cost that might range from $50 to $100/kW. The agency acknowledges here, as in the propos al TSD, that the te chnical applicability and efficacy of heat rate improvement measures , the cost of implementing them, and delivered coal cost, all depend upon site specific factors and can vary widely from site to site. Nonetheless, the agency finds it reasonable to expect that a fleet wide average cost for a national fleetwide average improvement of 4 percent, based primaril y on low- and no-cost best practices, would be significantly less than $100/kW. However, as a c onservative measure, and because the cost of heat rate improvement is one of the least costly and least impactful measures considered in the best system of emission reductions for this rule, the agency has decided to continue to use the

84 See supra notes 11 and 18. Also see Comments of Natural Reso urces Defense Council (NDRC). Available at http://www.regulations.gov/#!documen tDetail;D=EPA-HQ-OAR-2013-0602-26818

.

### 2-57 previously estimated $100/kW cost value for IP M modeling and the RIA estimates of the total cost of this final rule. The brief assessment presented here is intende d only to illustrate the approximate effects of coal cost and heat rate improvement capit al cost on the economic breakeven point between coal cost savings and the capital cost of im plementing heat rate improvement. The resulting order-of-magnitude cost range for CO 2 

### removal ($ per ton) is also discussed. EPA does not suggest in the proposal or in this final rule that heat rate improvement measures should meet a particular economic criterion (e.g., pay for themselves through reduced fuel costs) in order to be applied in state plans for comp liance with this rule. Figure 2-12 presents a simplified economic break even analysis for a 4 percent nationwide average heat rate improvement, assuming there is no CO 2 

### emission allowance cost involved. Additional assumptions used in the breakeven calculation include: capita l charge rate of 14.3 percent, as used in the IPM base case modeling for this final rule; 2030 annual capacity factor of 78 percent, derived from the IPM modeling; co al fleet generation weighted average 2030 net heat rate of 10,250 Btu/kWh before heat rate im provement, also derived from the IPM modeling.

Figure 2-12 Breakeven economics of a 4 percent heat rate improvement



### Figure 2-12 shows that at a cap ital cost of $50/kW, the aver age fleetwide 2030 savings in coal cost would become greater than the annua lized capital cost of an average 4 percent reduction in net heat rate when the average fl eetwide coal cost exceeds about $2.50/mmBtu. For comparison, the average U.S. power sector de livered cost of coal was $2.38/mmBtu in 2012 85


### , and is projected to be about $2.70/mmBtu in 2030. 86 

### For different assumptions, the heat rate improvement economic breakeven point would change directionally as follows:

85 EIA, Electricity DataTable 7.4, Av erage Weighted Cost of Fossil Fuels for the Electric Power Industry 2002– 2012. Available at http://www.eia.gov/electric ity/annual/html/epa_07_04.html

. 86 2030 projected fuel costs are derived from IPM Base Case.

### 2-58 If the heat rate improvement capital cost were on averag e less than $50/kW, 4 percent heat rate improvement would then “become economic” (i.e., pay for itself) at lower coal costs. For example, if the average capital cost were actually $25/kW, a fleetwide 4 percent heat rate improvement would become economic at an aver age coal cost of about $1.25/mmBtu, which is comparable to the U.S. power sector averag e costs of $2.38/mmBtu for all coal ranks and $2.89/mmBtu for bituminous coals in 2012. 87 

### This sensitivity indicates that fuel cost savings alone would make it economic for some of t hose EGUs currently using the higher cost bituminous coals to make heat rate improvement investments. At an EGU net heat rate that is higher than the IPM projected 2030 average value of 10,250 Btu/kWh, 4 percent heat rate improvement c ould be economic at coal costs lower than the values mentioned above. If there were additional heat rate impr ovement savings due to avoided future CO 2 

### emission costs, heat rate improvement could beco me economic at lower coal costs, or at higher capital costs, or at lower heat rate reduction percentages.

2.7.2 Estimated fleetwide CO 2 

### reduction from and cost for heat rate improvementsEPA derived the following illustrative order-of-magnitude estimate of the fleetwide costeffectiveness of a 4 percent heat rate im provement from the IPM base case modeling:

2.7.2.1 Fleetwide 2030 assumptions derived from IPM Base Case for this final rule

 Coal fleet capacity applying combined heat rate improvement methods: 213,000 MW

 Average net heat rate: 10,250 Btu/kWh net, before 4 percent heat rate improvement

 Average capacity factor: 78 percent

 Heat rate improvement Btu and CO 2 

### reduction: 4 percent

 Heat rate improvement capita l cost: $50/kW (assumed)

 Annual capital charge rate: 14.3 percent

 Average coal cost: $2.70/mmBtu

2.7.2.2 Estimated fleetwide results

 Fleetwide CO 2 

### reduction via heat rate improvement: 62 million ton/yr

 Total heat rate improvement capital cost: $10.7 billion

 Annualized heat rate improveme nt capital cost: $1.5 billion

 Annual coal cost savings: $1.6 billion

 Annual net savings: $0.09 billion

 Annual net savings for CO 2 

### reduction: $1.4/ton

2.7.3 Conclusion - heat rate improvement economics This necessarily simplified analysis s upports the following summary conclusions:

87 See supra note 85.

### 2-59 1. Some degree of heat rate improvement is very likely already economic (paying for itself) for high heat rate – high coal cost EGUs, and will remain so in 2030
2. If a fleetwide average 4 percent heat rate im provement were implemented, it would also pay for itself on the basis of fuel savings alone, before consideration of the value of the
associated CO 2 

### emission reductions, on a fleetwide basis at projected 2030 coal prices if the associated average capital co st is about $50/kW or less.
3. If a fleetwide average 4 percent heat rate impr ovement were implemented, and the associated average capital cost is as much as $100/kW, 4 percent heat rate improvement would not likely pay for itself based on fuel cost savings alone.
4. Even at a capital cost of $100/kW and an IPM projected 2030 coal price of $2.70/MMBtu, the average fleetwide cost of CO 2 

### reduction via 4 percent heat rate improvement would be a still relatively low $23/ton.
Thus, although there is currently some uncertain ty associated with the costs of achieving a particular fleetwide amount of h eat rate improvement, it is clear that heat rate improvement is an available low-cost approach to CO 2 

### reduction for existing coal-fired EGUs.

### 3 - 1 

## Chapter 3: CO 2 

## Reduction Potential from Gene ration Shifts Among Existing Units

Overview
This chapter explores the dynamics of power s ector dispatch and the cost-effectiveness of lowering the carbon dioxide (CO 2 

### ) emissions intensity of the power sector by substituting generation from the most carbon-int ensive existing EGUs and increa sing utilization, to the extent possible, of less carbon-intensive existing fossi l fuel-fired EGUs. More specifically, the examination focuses on opportunities to improve em issions intensity by increasing the utilization of existing natural gas combined cycle units. The TSD provides background on existing power plants, power system operation, and the economics of electricity productio n and delivery in the
context of cost-effective CO 2 

### emission reduction opportunities.

Introduction
Electric system dispatch is typically defined as “the operation of ge neration facilities to produce energy at the lowest cost to reliably serve consumers, recognizing any operational limits of generation and transmission facilities.” 1 

### Electricity demand varies across geography and time in response to numerous conditions , such that electricity generato rs are constantly responding to changes to meet demand in the most re liable and cost-effective manner possible. The nation’s EGUs are connected by transmi ssion grids that extend over large regions. Through these interconnections, EGU balancing author ities treat the product (i.e., electricity) of EGUs as fungible, calling for el ectricity generation supply to m eet demand usually by deploying the least expensive power source first, subjects to the constraints of reliable system operation. 2 

### EGU operators and balancing authorities must take into account se veral constraints in dispatch, including transmission and other operati ng constraints as well as emission control programs and other environmental requirements. Such programs and requirements can change the relative cost of generating electricity among plants and/or limit the nu mber of hours that a plant can run. For many years, EGU operators throughout the country have considered the

1 Energy Policy Act of 2005
2 A balancing authority is the responsible entity that integrates resource plans ahead of time, maintains the balance between supply, demand, and generation within a balancing authority area, and supports interconnection frequency in real-time. http://www.nerc.com/files/glossary_of_terms.pdf

### 3 - 2 emissions implications for pollutants such as SO 2 

### and NO x 

### when scheduling unit dispatch, in response to costs and regulatory requirements . For example, EGU operators in 10 states participating in the Regional Greenhouse Gas Ini tiative have several year s of experience with factoring CO 2 

### emissions limits directly into bids for economic dispatch. The electric system’s carbon intensity can be lowered by shifting gene ration among existing EGUs, particularly by shifting generation from coal-fired units to natural gas combined cycle (NGCC) units.

Power Sector Background
Electric Dispatch Electricity generation conforms to the principle of least-cost economic dispatch, which is “the operation of generation faci lities to produce energy at the lo west cost to reliably serve consumers, recognizing any operational limits of generation and tr ansmission facilities.” 3 

### The cost of operating electric generato rs varies based on a number of f actors, such as fuel used and generator efficiency. Regional Transmission Or ganizations (RTOs) a nd Independent System Operators (ISOs) help coordinate economic dispatch over larger ar eas to help keep the cost of meeting electricity demand as low as possi ble, subject to opera tional constraints. The decision by balancing authorities to call upon, or dispatch, any particular generating unit is driven by the relative operating cost, or marg inal cost, of generating electricity to meet the
last increment of electric demand. These cost s change over time depending upon a variety of factors like fuel prices, weather conditions, and overall demand levels. Since the fixed cost of power plants is a sunk cost, plant operators bid into electricity ma rkets such that their variable
costs are covered. For fossil fuel-fired electric generating units, variable costs are dominated by the cost of the fuel, although coal-fired units often also have considerable variable costs associated with running pollution controls. 4 

### Other generating technol ogies, like renewables, hydroelectric, and nuclear, have lit tle or no variable costs and ar e generally dispatched to the extent possible. In order to maintain least-cost dispatch, the units with the lowest variable costs will be called upon first, then other units (wit h higher variable costs) will be called upon

3 Federal Energy Regulatory Commission, 2005. Economic Dispatch: Concepts, Practices, and Issues
4 In addition to fuel costs, variable costs also include costs associated operating and maintenance, and costs of operating a pollution control and/or the price of allowances to emit certain pollutants.

### 3 - 3 sequentially, such that total system demand is met. The economic orde r in which units are dispatched to meet demand, at any particular point in time, is commonly called a dispatch
“curve.” 5



### Balancing Authorities In states with cost-of-service regulation of vertically-integra ted utilities who own generation, transmission, and distribution infrastructu re, the utilities themselves are often also the balancing authorities who determin e unit dispatch. Such utilities ar e presumed to dispatch their units in a cost-minimizing fashion (seeking the lo west marginal cost), and they can arrange to buy and sell power with othe r balancing authorities.
In states that have restruct ured regulation to allow for co mpetition between generators, RTOs and ISOs are generally responsible for movi ng electricity across larger areas in the most efficient (least-cost) manner possible. 6 

### RTOs and ISOs coordinate, control, and monitor electricity transmission systems to ensure cost-e ffective and reliable delive ry of power, and they are independent from market participants. IS Os grew out of the Fe deral Energy Regulatory Commission (FERC) requirements for existing power pools to satisfy the requirement of providing non-discriminatory access to transmission. Subsequently, FERC encouraged the voluntary formation of RTOs to administer the transmission grid on a regional basis throughout North America (including Canada). RTOs and ISOs administer wholesale power markets, which match the generation of electricity with the purchase of el ectricity (and ancillary services) prior to delivery to end-users. Companies that provide retail electricity (e.g., utilities and energy service companies) procure power through these wholesal e electricity markets.

State Public Utility Commissions (PUCs) 7 

### Each state has a governing body that is tasked with regulating retail electricity rates and electric services to protect the public interest, en sure efficient and reliable delivery of electricity, and plan appropriately for the short and long te rm energy needs of the state and consumers.

5 http://www.eia.gov/todayinenergy/detail.cfm?id=7590
6 http://www.ferc.gov/industrie s/electric/indus-act/rto.asp
7 These entities are sometimes called Utilities Commissions , Utility Regulatory Commissions, or Public Service Commissions

.

### 3 - 4 Depending on market structure, PUCs also al locate costs among customers, design price structures and set price levels, set service qua lity standards, approve capital expenditure by utilities, and arbitrate disputes among relevant pa rties and stakeholders. In restructured markets, the PUC’s authority is generally applicable to the transmission and distribution system, since the generation and dispatch component is governed by RTOs and ISOs. In cost-of-service states, the PUC also has oversight of the genera tion and capacity planning components

.

Wholesale Power Markets RTOs and ISOs operate spot markets for w holesale power supply and demand for their designated area, including both day-ahead and real -time markets (using hourly or shorter time periods). These markets are based on bids for su pply and demand and operate according to rules established by FERC. The RTOs and ISOs use these markets for balancing power supply and load in their area and typical ly serve as the balancing au thority for the same area. For areas not administered by RTOs and ISOs, dispatch is scheduled on both a day-ahead and an hourly basis, but it is typically driv en by the power supply costs and schedules of traditional utilities. This dispatch will depe nd to a certain degree on spot markets for power, since utilities will dispatch pur chased power from other suppliers when that power can be obtained at a cost savings. There is an active wholesale market for this power in the spot market, from individual sales and from exchanges. Th ese markets typically sell power on a day-ahead basis rather than on an hourly basis, and they can also sell power for longer periods of time, such as weekly or monthly. However, the actual dispat ch and balancing of power is conducted by the utility based on its own scheduling, and purchasing protocols and varies considerably from one utility to the next.

Reliability As a balancing authority, the RTO or u tility will balance demand, generation, and imports/exports in real time while maintaining system frequency and ensuring that the next hour’s demand, or load, is met. In addition, the tr ansmission system is constantly monitored to ensure reliability limits are met, voltage levels are appropriate, and appropriate corrective action
is taken when needed. As reliability coordinators, balancing author ities are responsible for the reliable operation of the bulk electric system. The bulk electric syst em refers to a large interconnected electrical

### 3 - 5 system made up of generation and transmission faci lities and their control systems. To ensure reliability, system operators continuously analyz e real-time and forecasted load and transmission conditions to ensure that scheduled generation di spatch can meet load without adverse impacts. If the scheduled dispatch is not feasible within the limits of the transmission system, it must be adjusted by the system operator. The North Amer ican Electric Reliability Corporation (NERC) develops and enforces the procedures to ensure reliability, in accordance with Federal laws and regulations, and with FERC oversight. 8



### Generation Shifting: Technical Potential
In 2012, average CO 2 

### emission rates 9 

### across the following technology types on a net generation basis were:

 Coal Steam: 2,217 lbs/MWh

 Oil/Gas (O/G) Steam: 1,435 lbs/MWh

 NGCC: 905 lbs/MWh

Coal- and oil/gas-fired boilers are consid erably higher-emitting sources than NGCCs, on average. Therefore, shifting ge neration from the average existing fossil fuel-fired boiler to an average existing NGCC will result in notable CO 2 

### emission reductions. The lower emission rate of NGCC conveys the potential of generati on shifting to reduce GHG emissions. However, the actual potential to realize emission reductions through this technology depends on the availability and capacity factors of the existing NGCC fleet. In order to shift generation from existing fossil fuel-fired boilers to existing NGCC, there needs to be some existing unused generation potential in the current NGCC fleet that could replace generation from more CO 2 

### -intensive generating resources. For purposes of economic dispatc h, most NGCCs have historical ly been operated to serve
base load or intermediate demand due to their hi gh efficiency and flexib ility of operation, with

8 http://www.nerc.com/
9 Emission rates in this document are shown on a net gene ration basis and reflect Hawaii and Alaska sources, as well as units that commenced construction in 2012. See “2012 unit-level data using the eGrid methodology” file provided in the docket

### 3 - 6 national average annual capacity f actors in the range of 40-50%. 10 , 11 

### However, NGCCs are designed for, and are demonstrably capable of, reliable and efficient operation at much higher annual capacity factors, as shown in observed historical data for particular units and their design and engineering specifications. The capability of NGCCs to operate is i ndicated, in part, by data on the average availability factor of NGCCs. 12 

### The term “availability” is a common engineering term used in the power sector that reflects the percentage of period hours that a plan t is available to produce electricity (a period being 1 y ear, or 8,784 hours in 2012 since that year included a leap day). The unavailable period is generally attribut able to scheduled maintenance, unplanned maintenance, and unplanned outages. For mode ling purposes, the EPA assumes that NGCC has an availability of 87%. 13 

### Other reports suggest that NGCC av ailability factors may reach as high
as 92%. 14 , 15 

### Many modern NGCCs have demonstrat ed availability factors of over 95%. According to one NGCC manufacturer, these highl y efficient units already represent over 15 percent of total installed capaci ty nationwide, including all elec tric generating sources (as of 2010). 16 

### These high-efficiency and high-availab ility NGCC units were first introduced around 1995 and have consistently reported availabi lity factors of 90 to 92 percent to NERC. 17 

### These availability factors are substant ially above the 2012 NGCC generati on level, indicating that there is a significant potential for increasing generation from unutilized existing NGCC capacity. To evaluate potential opportunities for sh ifting generation to unused NGCC capacity, the EPA reviewed recent operating data across all NGCCs to determine NGCC capacity factors in 2012. For GHG abatement purposes, generation sh ifting would require one net MWh of generation from a lower-emitting technology operati ng in place of one net MWh of generation

10 EIA, Today In Energy, January 15, 2014, http://www.eia.gov/todayine nergy/detail.cfm?id=14611 (for recent data)
11 EIA, Electric Power Annual 2009, http://www.eia.gov/electric ity/annual/archive/03482009.pdf (Table 5-2 for 2009 and earlier data)
12 NERC, 2008-2012 Generating Unit Statis tical Brochure – All Units Reporting, http://www.nerc.com/pa/RAPA/ gads/Pages/Reports.aspx
13 See Chapter 3, Table 3-18 at http://www.epa.gov/powersectormodeling/BaseCasev513.html
14 http://www.power-eng.com/a rticles/print/volume-115/issue-2/featur es/higher-availab ility-of-gas-turbinecombined-cycle.html
15 Power Engineering, Negotiating Availability Gu arantees for Gas Turbine Plants, 03/01/2001, http://www.powereng.com/articles/print/volume-105/issue-3/features/negotia ting-availability-guarantees-for-gas-turbine-plants.html
16 http://site.ge-energy.com/corporate/n etwork/downloads/7FA_Evolution.pdf
17 GE Power Systems submitted to U.S. Department of Energy, 2000. Utility Advanced Turbine Systems Technology Readiness Testing Phase 3 Restructured. DOE Cooperative Agreement No. DE-FC21-95MC31176— 30.

### 3 - 7 from a higher-emitting technology. Therefore, the EP A calculated capacity factors as the ratio of a unit’s reported net generation to its potential net generation, th e latter of which is determined by the unit’s net generating capaci ty. Net generating capacity refl ects a reduction from nameplate capacity due to on-site electricity use (e.g., station service or aux iliaries) and local temperature conditions. The EPA used the net summer capac ity reported for units in the EIA Form 860
because it controls for local conditions and is th us a consistent metric for evaluating dependable capacity nationwide. Potential net generati on was calculated as the net summer generation capacity of a unit multiplied by the number of hours in a year. Therefore, the equation for calculating capacity factors is:

Reported generation in 2012 ሺ Net summer capacity ሻ x (hours in 2012)

This calculation of capacity factor provides an indication of how mu ch net generation a unit is providing as a percent of its total net generating capacity. Th e capacity factor refers to the
actual utilization of that source on an annual basis, while that sour ce’s availability refers to its potential maximum utilization level. The EPA surveyed 2012 data for 439 NGCC plants and
observed that the NGCC fleet had an av erage capacity factor of 46% in 2012. 18 

### Since the fleetwide capacity factor in 2012 was less than the technical genera tion potential available for the technology, the historical data sugge sts that there is a significan t potential for sh ifting generation from higher CO 2 

### emitting resources to lower CO 2 

### emitting NGCC generation.

Availability for NGCC fleet ........................................... 87% to 92% 2012 Capacity Factor for NGCC fleet ....................................... 46%

To quantify the GHG reduction potential from generation shifting, the EPA considered alternative capacity factor le vels at which the NGCC fleet c ould be dispatched. Although the availability for NGCC units is assumed to be 87% to 92%, the EPA did not assume that the NGCC fleet could collectively operate at this level on an annual ba sis. To determine a reasonable

18 EIA 860 and 923.

### 3 - 8 average capacity factor for the NGCC fleet as part of BSER, the EPA consid ered historical data and modeling projections de scribing NGCC characteristics and operating behavior. As seen in Table 3-1, the existing NGCC fleet is relatively young. More than 80% of the capacity has come online in the last 15 years. 19 

### Of this capacity, almost all are highly efficient NGCCs that are able to achieve high availability factors.

Table 3-1: Existing NGCC Capacity, by Age 20



### Online Year Capacity (Name Plate Capacity – MW) Percentage of Total Existing NGCC
Fleet
Pre 1950 103 0%
1950-1959 1,769 0.7%
1960-1969 3,087 1.3%
1970-1979 6,909 2.8%
1980-1989 7,658 3.1%
1990-1999 28,467 11.7%
2000-2009 174,947 71.7%
2010+ 21,068 8.6%
Total 244,008 100%

Of 439 NGCC plants generating in 2012, the EP A observed that 67 plants (more than 15% of NGCC plants) had a capacity factor of 75% or greater (see Figures 3-1 and 3-2). 21



### Figure 3-1: NGCC Plant Distributio n by Capacity Factors (2012)

19 See the National Electricity Ener gy Data Systems (NEEDS) file at http://www.epa.gov/powersectormodeling/BaseCasev513.html
20 See “Operable” worksheet in “Generator Y2012” Workbook in 2012 Zip file at http://www.eia.gov/electricity/data/eia860/
21 See “2012 unit-level data using the eGrid methodology” file provided in the docket

### 3 - 9 Source: EIA forms 860, 923 (2012), Combined Cycl e Steam Part (CA) and Combined Cycle Combustion Turbine Part (C T) prime mover categories

Table 3-2: Plant Distributio n of Existing NGCCs (2012) Capacity Factor

(net summer) # of NGCC plants % of NGCC Plants Less than 5% 32 7.3% 5%-9% 23 5.2% 10%-14% 17 3.9% 15%-19% 23 5.2% 20%-24% 22 5.0% 25%-29% 13 3.0% 30%-34% 21 4.8% 35%-39% 23 5.2% 40%-44% 26 5.9% 45%-49% 25 5.7% 50%-54% 29 6.6% 55%-59% 30 6.8% 60%-64% 27 6.2% 65%-69% 38 8.7% 70%-74% 23 5.2% 0 5 10 15 20 25 30 35 40NumberofNGCCPlants

2012 Capacity Factor

### 3 - 10 75%-79% 30 6.8% 80-84% 19 4.3% 85-89% 7 1.6% Greater than 90% 11 2.5%

In 2012, more than 15% of NGCC plants operated at an annual capacity factor of 75% or higher on a net summer basis. This subset of NGCCs was largely di spatched to provide base load power. While only 15% of plants op erated at 75% or higher capacity factor on an annual basis, the fleet of NGCC units was relied upon heavily during certain periods of time, in response to higher demand. On a seasonal basis, a significant number of units achi eved capacity factors greater than 50%, and even up to 80%. During periods where demand levels are typically lower, some NGCCs were idled or operated at lower cap acity factors. Nonetheless, a notable number of existing NGCCs have demonstrated the ability to achieve a 75% or highe r capacity factor for extended periods of time. These units achieved high capacity factors with out adverse effects on the electric system. Hourly data reported to EPA 22 

### , particularly from the peak summer months of July and August, also show that most individual NGCC units can operate at high capacity factors. Capacity factors from the hourly unit level da ta reported to EPA are based on heat input and input capacity; these calcul ations correspond closely to the capaci ty factor calculations described above (that are based on data reported to EIA representing longer periods of time) and enable consideration of capacity factors at the unit and hourly level. Using EIA and EPA data from 2012 for the same facilities, capaci ty factor averages from EIA nameplate values and EPA unit level data from 2012 data are both 46%, while net summer capacity factors from EIA are 52%. 23 

### On average, then, net capacity factors are appr oximately 5% or more above nameplate capacity factors. Analysis of these hourly data demonstr ates several important point about the feasibility of running NGCC units at capacity factors above 70% nameplate – or 75% net summer – levels. To examine historical potential capacity f actors for NGCC units, EPA calculated nameplate capacity factors for daily peak operation from 7 AM to 7 PM during the months of July and August, a sustained period that demonstrates both th e ability of individual un its to operate at high

22 Air Markets Program Data (at http://ampd.epa.gov/ampd/ ).
23 The EPA Air Markets data is unit level data from 411 facilities; EIA data surveys are from 439 facilities.

### 3 - 11 capacity factors and of the power grid to support regional opera tion of the NGCC fleet at much higher levels than current averages. This analysis shows the following key points:

 Most individual units are capable of high levels of operation: 88% of NGCC units operated at a nameplate capacity factor of 70% or above for at least one day in July and August of 2012; 67% operated at 80% or above for at least one day.

 The interconnected power grids can support region-wide NGCC operation at much higher than average capacity fact ors. Each NERC region operated at a nameplate capacity 70% or above for at least one day, as shown below:

o ERCOT 75%

o WECC 74%

o Eastern Interconnection Regions

 RFC 75%

 MRO 76%

 SERC 73%

 FRCC 75%

 SPP 71%

 NPCC 70%
These observations demonstrate the technical ability to operate existing NGCC capacity at or above a 70% nameplate capac ity factor, and therefore at l east at a 75% net summer capacity factor or higher.

Historical Trend Over the last several years, advances in th e production of natural gas have helped reduce natural gas prices and improved the competitive pos ition of gas-fired units relative to coal-fired units. As a result, operators have shifted significant quantities of generation from coal units to NGCCs, absent any federal CO 2 

### requirements. 2012 net generation from NGCC units grew to 966 TWh, up from 783 TWh in 2011 (23% growth in one year). 24 

### An analysis of historical generation across the fleet of coal and NGCC units for 2011 and 2012 provides some implicit measures of the cost dynamics between the two technologies. For

24 EIA Form 860, 923 (2011)

### 3 - 12 example, one is able to look at th e change in the prices of coal a nd gas to gauge the relative costs of generating for each technology. While there are wide-ranging co sts at the unit level, an aggregated assessment of the relative economics is informative and can provide a metric for assessing the implications of this generation pattern as it relates to emissions of CO 2. 

### The potential for shifting generation from CO 2 

### intensive sources to less CO 2 

### intensive
sources is evidenced in histor ical data. EIA form 860 and 923 da ta demonstrate an increase in NGCC generation and fuel use between 2011 a nd 2012 of more than 20% (even though the NGCC fleet capacity rose by just 3%). As NGCC generation rose by approximately 183 TWh, coal generation fell by approximately 217 TWh. The si gnificant shift from coal to gas generation over just a one year period demonstrates the abil ity for a quick shift in generation patterns in response to market or economic drivers. The increase in NGCC utilization was in large part driven by a decrease in natural gas prices (see Table 3-3). Electric power sector delivered natural ga s prices averaged $4.89/Mcf in 2011 and $3.54/Mcf in 2012. 25 

### This price decline of $1.35/TCF created an additional incentive for decreasing coal generati on and increasing NGCC generati on relative to 2011 electricity production economics. This historic al data also shows a sharp in crease in the NGCC fleet’s net summer capacity factor from approximately 40% on average in 2011 to approximately 46% on average in 2012. The historical trend of shifting generation to NGCC is expected to continue, as dry natural gas production is expected to grow as much as 23% by 2022 relative to its 2012 level. 26



### Table 3-3: 2011 and 2012 Gas and Coal Generation 27



### Year NGCC Net Summer Capacity

(GW) NGCC Net Generation (TWh) NGCC Capacity

Factor Electric Power Sector Natural Gas Price ($/TCF) Coal Net Generation (TWh)

2011 204 783 40% $4.89 1,718 2012 211 966 46% $3.54 1,501

25 EIA Natural Gas Prices, http://www.eia.gov/dnav/ng/ ng_pri_sum_dcu_nus_a.htm
26 AEO 2015
27 EIA form 860 and EIA form 923 (NGCC), and EIA Monthly Energy Review, Table 7.2b (coal)

### 3 - 13 Table 3-4. Electric Power Sector Electricit y Net Generation from Natural Gas, 1990-2012

Year Net Generation: Natural Gas (million KWh) Percent Increase from Previous Year 1990 309,486 1991 317,773 3% 1992 334,274 5% 1993 342,222 2% 1994 385,689 13% 1995 419,179 9% 1996 378,757 -10% 1997 399,596 6% 1998 449,293 12% 1999 472,996 5% 2000 517,978 10% 2001 554,940 7% 2002 607,683 10% 2003 567,303 -7% 2004 627,172 11% 2005 683,829 9% 2006 734,417 7% 2007 814,752 11% 2008 802,372 -2% 2009 841,006 5% 2010 901,389 7% 2011 926,290 3% 2012 1,132,791 22% 

### Source: EIA Monthly Energy Review, Table 7.2b (multiple years)

Phase-In

The application of an increase in NGCC ope ration equivalent to a 75% net summer
utilization to the base line data from 2012 would yield an increase of less than 11% in NGCC generation, from 966 TWh to 1,070 TWh. To allow time for potentia l infrastructure improvements to facilitate increased utilization of existing NGCC plants, the EPA is finalizing a gradual phase over th e interim period, consistent with the hi storical data presented above. The

### 3 - 14 phase-in schedule is based on two parameters. The first parameter defines an amount of generation shift to existing NGCC capacity thatis feasible by 2022. This parameter is conservatively defined as a percen t increase from 2012 to 2022 that is equal to the largest single largest annual increase in power sector gas-fi red generation since 1990. It is reasonable to assume that the same increase in NGCC utilization that occurred in one year could be implemented again by 2022.
The increase in NGCC utilization over the re maining years of the interim period is limited by the average annual grow th in gas-fired generation in the power sector between 1990 and 2012, or approximately 5 percent per year. Sin ce this is the average annual increase in gas
utilization that has occu rred over recent history, it is reas onable to assume that this increase could occur throughout the interim period, up to the full technical potential of 75% utilization on
a net summer basis. In the performance rate calculation met hodology, these two parameters constrain the annual rate at which building block 2 shifts ge neration from fossil steam units to NGCC units. As discussed in the preamble, the interim perf ormance rate is an average of annual rates calculated over the 2022-2029 period. The two pa rameters above limit the extent to which NGCC generation is assumed to increase and replace fossil steam generation in each year of the interim period. From historical data, we can obs erve that the single la rgest annual increase in power sector gas-fired generation since 1990 oc curred between 2011 and 2012 and is equal to 22 percent. Therefore, in the first year, NGCC ge neration is limited to a maximum of a 22 percent increase from 2012 levels in each region. In each subsequent year, regi onal NGCC generation is limited to a maximum of a 5 percent increase from the previous year. This phase-in continues in the performance rate-setting methodology until th e BSER level of shifting from fossil steam generation to NGCC generation is completed. Under this ap proach, building block 2 is completely phased into the performance rates of all regions by the end of the interim period. Table 3-5 summarizes the quantity of NGCC ge neration that represents building block 2 potential as incorporated in the emission perfor mance rate calculation in each region and year.

Table 3-5. BSER Maximum NGCC Generation by Region and Year (TWh).

NGCC Generation (TWh) Region BSER Maximum

### 3 - 15 Maximum Potential

at 75% 2012 (adjusted) 2022 2023 2024 2025 2026 2027 2028 2029 2030 Limit -- -- 22% 5% 5% 5% 5% 5% 5% 5% 5% Eastern Interconn ection 988 735 896 941 988 988 988 988 988 988 988 Western Interconn ection 306 198 242 254 267 280 294 306 306 306 306 Texas Interconn ection 204 137 167 176 185 194 203 204 204 204 204



### Natural Gas Supply
The EPA expects that the gradual increase in generation from exis ting NGCC as defined
above to be feasible and consistent with domes tic natural gas supplies in each year of the 2022- 2029 phase-in. Increases in the natural gas resource base have led to fundamental changes in the outlook for natural gas. There is general agreemen t that recoverable natura l gas resources will be substantially higher for the foreseeable future than previously anticipated, exerting downward pressure on natural gas prices. 28 

### According to EIA, natural gas proved reserves have doubled between 2000 and 2012. 29 

### Domestic dry gas production has increased by 25% over that same timeframe (from 19.2 TCF to 24.0 TCF). EIA’s Annual Energy Outlook for 2015 projects that production will further increase to 29.5 TCF by 2022, due to increased supplies and favorable market conditions. For comparison, building block 2 assumes NGCC genera tion growth of 340 TWh from 2012 to reach the level assumed for 2022, and that NGCC generation growth w ould result in increased gas consumption of less than 2 TCF for the electrici ty sector, which is le ss than EIA’s projected increase in natural gas production of 5.5 TCF from 2012 to 2022. 30 

### The National Petroleum Council (NPC), a privately funded advisory committee established by the Secretary of Energy, recently updated a major resource study and concluded

28 National Petroleum Council. 2011. Prudent Development: Realizing the Potential of North America's Abundant Natural Gas and Oil Resources. http://www.npc.org /reports/rd.html (see Figure 1.2 on p. 47). 29 http://www.eia.gov/cfapps/ipdbprojec t/IEDIndex3.cfm?tid=3&pid=3&aid=6 30 Assuming 1,028 Btu/cubic foot and 7,615 Btu/KWh

### 3 - 16 that “the potential supply of North American natu ral gas is far bigger th an was thought even a few years ago,” after large increases in shale resource es timates had already been realized. 31



### Figure 3-2: U.S. Natural Gas Technicall y Recoverable Resources (from NPC, 2011) 32



### Natural Gas Deliverability To examine the potential for increases in pi peline deliverability, the EPA analyzed the pipeline flow data from the Energy Informati on Administration. These data provide pipeline capacity for inflows and outflows by region (s ee Figure 3-4). However, we cannot measure natural gas deliverability solely by considering a region’s inflow capacity, since that region may need to allow some of that inflowing natural ga s to become outflow to other regions (effectively passing that natural gas through the region to cons umers in other regions). Consequently, net capacity – the difference between in flow capacity and outflow capacity -- is a better metric for a region’s ability to increase deliverability of na tural gas supply, because natural gas using such

31 National Petroleum Council, 2012 (h ttp://www.npc.org/PD _update-80112.pdf) 32 http://www.npc.org/reports/rd.html

### 3 - 17 pipeline net capacity must be consumed within the region (as there would be no more outflow capacity for it to leave the region). The regions used by EIA for measuring regional natural gas deliverability are shown in Figure 3-4. Our assess ment focuses on areas that would experience increases in natural gas consumption in line w ith building block 2’s quantified potential for increased utilization of NGCC; in other words, these areas have most of the unutilized NGCC capacity. These are the Northeast, Southeast, Mi dwest and Western regions in Figure 3-4. The net pipeline capacity for thes e regions from 2005 to 2011 is shown in Table 3-5 below.

Table 3-6. Natural Gas Pipeline Net Capacity by Region, 2005 - 2011 by Gas Consuming Area 33 

### Region 2005 2006 2007 2008 2009 2010 2011

Capacity in MMCF/day
Midwest 17,102 17,232 17,452 17,302 18,714 18,564 18,414
Northeast 11,199 11,219 11,384 11,929 12,079 12,229 12,379
Southeast 12,921 12,901 12,736 15,741 18,241 20,797 20,797
Western 11,882 11,882 12,496 12,496 12,496 12,641 14,407
All Areas 53,104 53,234 54,068 57,468 61,530 64,231 65,997 Percent Change from 2005
Midwest 0.0% 0.8% 2.0% 1.2% 9.4% 8.5% 7.7%
Northeast 0.0% 0.2% 1.7% 6.5% 7.9% 9.2% 10.5%
Southeast 0.0% -0.2% -1.4% 21.8% 41.2% 61.0% 61.0%
Western 0.0% 0.0% 5.2% 5.2% 5.2% 6.4% 21.3%
All Areas 0.0% 0.2% 1.8% 8.2% 15.9% 21.0% 24.3%

As a conservative assumption, the increase fr om the period 2005 to 2011 can be used as an estimator of the potential increase in pipeline capacity between 2016 and 2030 in the IPM projections for the BB2 BSER scenario where exis ting natural gas units we re required to operate at the BSER capacity factor. Increased use of natu ral gas in existing units can be largely met with expansions to existing pipeline fa cilities and corridors, so that the types of capacity expansion

33 Source: Energy Information Administration, http://www.eia.gov/naturalgas/data.cfm

### 3 - 18 required will be less expensive and take less tim e than new pipelines. Over 2005-2011, the total gas deliverability in gas cons uming areas increased by 24%. In the BB2 BSER scenario, power sector natural gas consumption increased 36% over the base case, and 35% over estimated 2016 consumption. However, pipeline deliverability in creases apply to all sector consumptions, and the total consumption in the BSER scenario we re projected to increa se by 13% over the base case and 18% over 2016 consumption. Since this hi storical increase in pi peline capacity over the 2005 to 2011 period is 24.3%, the histor ical data indicate that pipe line capacity will be adequate to support the potential increased utilization of natural gas power plants quantified for building block 2 in the BSER modeling.

Figure 3-3. Energy Information Administra tion Natural Gas Pipeline Regions

Source: Energy Information Administration.

Recent pipeline construction continued to de monstrate the pipeline network’s ability to accommodate increasing demand for natural gas. A ccording to information released in April,

### 3 - 19 2014 by the EIA, 34 

### 118 pipeline projects were completed and placed into service from 2010 to 2014, totaling 4,699 miles of pipe, and 44,107 MMcf pe r day of additional pipeline capacity. For projects expected to be in service from April, 2014 through 2016, EIA reports 47 projects, with planned capacity additi ons of 20,505 MMcf per day and 1,567 miles of pipe. These projects cover all major gas consuming areas of the US, and include both new pipeline construction, such as the Spectra Energy’s NEXUS Gas Transmission project in the Upper Midwest and pipeline expansions such as the Tennessee Gas Pipeline Proj ect in Connecticut. The ability of the pipeline system to acco mmodate the increased utilization of NGCC quantified for building block 2 is reinforced by a recent DOE study of pipeline system expansion. 35 

### This study examined two cases of pi peline expansion through 2030, one with a 25% increase in overall natural gas demand in 20 30 and one with a 46% increase compared to a reference case projection. In a key finding, the study concluded that, in both scenarios, compared to historical pipeline infrastructure expansion, “Inter mediate interstate natural gas pipeline infrastructure needs in [t he assessed scenarios] are projecte d to be modest relative to the
Reference Case”. 36 

### The levels of increa sed natural gas demand assessed in that DOE study are substantially above the 15% incr ease in natural gas demand project ed in the EPA’s power sector modeling of a scenario that would realize the building block 2 NGCC utilization level (described below).

Cost-Effectiveness of Shifting Generation to NGCC
To evaluate the cost-effectiveness and techni cal capability of increasing utilization of existing NGCC as quantified for building block 2, EPA employed the Integrated Planning Model (IPM), a multi-regional, dynamic, deterministic lin ear programming model of the U.S. electric power sector that the EPA has used for over tw o decades to evaluate the economic and emission impacts of prospective environmental policies. IPM provides a wide array of projections related to the electric power sector and its related ma rkets (including least cost capacity expansion and electricity dispatch projections) while meeting fu el supply, transmission, dispatch, and reliability
constraints.

34 See www.eia.gov/naturalgas/pipelines/E IA-NaturalGasPipe lineProjects.xls

. 35 U.S. Department of Energy, Natural Gas Infrastructure Implications of Increased Demand from the Electric Power Sector, February 2015.
36 Ibid. Page vi.

### 3 - 20 Natural gas supply, demand, transportation, st orage, and related costs are modeled directly in IPM through the incor poration of a natural gas module. The module includes a detail rich representation of the natural gas pipeline ne twork inclusive of discount curves that represent the marginal value of gas transmission as a func tion of the pipeline’s load factor. IPM’s natural gas module has the capability to expand pipeline capacity on an economic basis. At the unit level, IPM contains a detailed representation of new and existing resource options, inclusive of key operational limitations . For example, turn down constraints are designed to account for the cycli ng capabilities of EGUs to ensure that the model properly reflects the distinct operating ch aracteristics of peaking, cycl ing, and base load units. EPA believes IPM represents a powerful tool to evaluate the technical feasibility of requiring increasing levels of re-dispatch from higher to lower-emitting EGUs.
The EPA used IPM to model a series of s cenarios representing replacement of fossil steam generation in each interconne ction with increases of averag e NGCC net capacity factors in the same interconnection of up to 70, 75, and 80 percent. For example, in the 70 percent net capacity factor scenario, total fossil fuel-fired generation at existing sources across each
interconnection is held constant with the level projected in the base case, while the model increases existing NGCC utilization in each interconnec tion either to a 70 percent capacity factor on average, or to a lower utilization level if such a level would deliver the same amount of fossil fuel-fired generation projected from existing so urces in that interconnection in the base case. 37



### The costs and economic impacts of these scen arios were evaluated by comparing the total
costs and emissions from each scenario to the co sts and emissions from the base case. For the scenarios reflecting a 70, 75, and 80 percent NGCC utilization rate, comparison to the base case indicates that the average cost of CO 2 

### reductions was $15, $24, and $33 per short ton of CO 2 

### , respectively, over the 2022-2030 period.

Table 3-7: IPM Results

37 The existing source fossil fuel

‐ fired generation described here includes generation from existing NGCC, coal steam, IGCC and oil/gas steam boilers greater than 25 MW.

### 3 - 21 Existing NGCC Average Capacity Factor Average Cost ($ per short ton, 2022-2030)

Base Case N/A 70% $15 75% $24 80% $33

Natural Gas Price Impacts
The extent of generation shifti ng estimated in this building block can be achieved without causing significant economic impacts, even with out the gradual phase-in of this building block that was not represented in this analysis. For ex ample, in the 75% scenario, delivered natural gas price projections increase by approximately 7 percent in the 2022-2030 period, which is well within the range of historical natural gas price volatility. Fo r example, the year-to-year percentage difference in Henry Hub prices reported by the En ergy Information Administration averaged 18.5% over the period from 1981 to 2012. 38 

### Projected wholesale electricity price increases over the same period were about 4 pe rcent in the 75% case, which is similarly well within the range of histor ic electric price variability. For exam ple, the average year-to-year price change for all of the ISOs and RTOs in the East was about 20 percent over the period from 2000
to 2013. 39



### Table 3-8: National Average De livered Natural Gas Price, Power Sector (Average 2022-2030) Existing NGCC Average Capacity Factor Price ($/mmBtu) % Change from Base Case Base Case $5.45 N/A 70% $5.70 5% 75% $5.85 7% 80% $5.99 10%

38 http://www.eia.gov/dnav/ng/hist/rngwhhdA.htm 39 ISO Real-Time data for all hours, from Ventx Velocity Suite data across Eastern ISOs (PJM, NYISO,ISO-NE and Midcontinent ISO).

### A - 14.1 Introduction

Renewable energy is a cost

‐ effective approach for reducing carbon dioxide (CO2) emissions from fossil fuel

‐ fired electric generating units (EGUs) through the replacement of electricity generated from affected EGUs by renewable resources, referred to in this document as renewable energy (RE). The portfolio of available RE sources encompasses a wide variety of technologies fueled by the sun, wind, water, organic matter, and other resources regularly replenished by physical and biological cycles. To integrate the rapidly increasing and evolving portfolio of RE into the Best System of Emission Reductions (BSER), the EPA has developed a methodology that is based on historical data.

This chapter serves to complement the description of incremental RE as a component of the BSER in section V.E of the preamble and is organized into several sections:

 Section 4.2 provides data and examples to support the seven steps described in the preamble to quantifying RE generation potential for the BSER

 Section 4.3 describes the interaction between the building block 3 generation levels and the goal

‐ setting methodology

 Section 4.4 compares the final building block 3 generation level against the proposed levels

 Section 4.5 details the NREL’s 2015 Annual Technology Baseline mid

‐ case estimates

 Section 4.6 explores the technical feasibility and cost effectiveness of the building block 3 generation level

4.2 Approach to Quantifying RE Generation Potential for the BSER

The methodology used to quantify building block 3 generation for the BSER, as described in section V.E.6 of the preamble, contains seven steps:

1. Identify historical maximum capacity change and average capacity change from year to year over the past five years (2010

– 2014) for utility

‐ scale solar photovoltaic (PV), concentrating solar power (CSP), onshore wind, hydropower, and geothermal technologies. 2. Assign each RE technology an annual capacity factor that represents expected generation from each megawatt installed in the future. 3. Use the data from steps 1 and 2 to produce annual generation levels associated with the historical average and historical maximum RE capacity changes for each technology. 4. Establish an initial level of incremental RE generation from each technology that could be expected by 2022 even in the absence of the rule, using the EPA’s IPM Base Case. 5. Add the generation associated with the historical average capacity change to the initial level to obtain the building block 3 generation level for 2022. To that 2022 level, add the generation associated with the historical average capacity change to obtain the building block 3 generation level for 2023. 6. For each subsequent year, add the generation associated with the historical maximum capacity change to the building block 3 generation level calculated for the preceding year, in order to obtain the building block 3 generation level for each year from 2024 through 2030.7. Apportion the national totals calculated in steps 5 and 6 to each of the three interconnections for inclusion in the BSER. 1
Step 1

The EPA relied on numerous data sources to compile the most recent five years of capacity deployment data for each of the RE technologies that contribute to building block 3 generation. 2 The totals in Table X

‐ X represent the net change in capacity from year to year, inclusive of declines in operating capacity.

Table X

‐ X: Annual Capacity Change by RE Technology (MW)

RE Technology 2010 2011 2012 2013 2014 Average Maximum

Solar PV 3 267 784 1,803 2,847 3,934 1,927 3,934 CSP 78 0 0 410 767 251 767 Onshore Wind 5,112 6,816 13,131 1,087 4,854 6,200 13,131

Geothermal 15 138 147 407 4 142 407 Hydropower 294 ‐ 10 47 216 158 141 294 Note: All values are rounded to the nearest MW.
Step 2

The NREL Annual Technology Baseline (ATB) estimates serve as the basis for the expected future generation by MW of installed capacity for each RE technology: 4

 Solar PV

– Simple average of all three annual capacity factor values presented in the ATB (14%, 20%, and 28%). 5

 CSP

– Simple average of representative plant performance with six hours of thermal electric storage across Solar Class 1, 3, and 5 (28%, 37%, and 38%, respectively).

 Onshore Wind

– Simple average of annual capacity factors for each of the five techno

‐ resource groups (32.0%, 37.9%, 44.0%, 46.3%, and 49.0%).

 Geothermal

– Simple average of annual capacity factors for both geothermal technology types (90% flash; 80% binary)

 Hydropower

– Simple average of annual capacity factors for each technology (non

‐ power dams and new stream

‐ reach development) and resource combination (58% ‐ 67%).

1 Unless otherwise indicated, “national” in this chapter refers to the contiguous U.S.
2 Energy Information Administration, Geothermal Energy Association, Lawrence Berkley National Laboratory, Solar Energy Industries Association/GTM Research, Interstate Renewable Energy Council, American Wind Energy Association (2014 onshore wind), and Federal Energy Regulatory Commission (2014 hydropower)
3 Solar PV capacity totals are for utility

‐ scale solar PV installations only and are expressed in terms of MW DC 4 http://www.nrel.gov/analysis/data_tech_baseline.html

5 The capacity factor values presented for solar PV account for the range of latitude across the contiguous U.S. Solar PV capacity factors are influenced by hourly solar profile, technology type, axis type, inverter losses due to conversion from DC to AC power, and other factors.The capacity factors displayed in Table X

‐ X below are assumed to be constant over time for the purposes of calculating building block 3 generation totals from 2022

‐ 2030.

Table X

‐ X: Annual Net Capacity Factor by Technology Type

RE Technology Capacity Factor Solar PV 20.7% CSP 34.3% Onshore Wind 41.8% Geothermal 85.0% Hydropower 63.8%

Step 3

The generation totals, expressed in MWhs, associated with the average and maximum capacity changes are the result of multiplying the corresponding capacity values by 8,760 hours/year and the net annual capacity factor for each technology, as shown in Table X

‐ X.

Table X

‐ X: Annual Generation Totals Associated with Average and Maximum RE Capacity Changes
Average Capacity Change (MW) Capacity Factor Generation (MWh, Average Capacity Change) Maximum Capacity Change (MW) Capacity Factor Generation (MWh, Maximum Capacity Change) Solar PV 6 1,927 20.7% 3,494,268 3,934 20.7% 7,133,601 CSP 251 34.3% 754,175 767 34.3% 2,304,590 Onshore Wind 6,200 41.8% 22,702,416 13,131 41.8% 48,081,520 Geothermal 142 85.0% 1,057,332 407 85.0% 3,030,522 Hydropower 141 63.8% 788,032 294 63.8% 1,643,131 Total Generation (MWh) 28,796,222 62,193,363

Step 4

Initial incremental RE generation totals for utility

‐ scale solar PV, CSP, onshore wind, geothermal, and hydropower are taken from the EPA IPM Base Case. 7 Initial incremental RE represents generation from existing projects currently in commercial operation with an online year of 2013 or later; projects that are not currently operating but are firmly anticipated to be operational in the future and have either initiated construction or secured financing; and capacity that is projected to deploy as an

6 Solar PV capacity totals are for utility

‐ scale solar PV installations only and are expressed in terms of MW DC 7 Documentation and modeling files are available in the docket for this rulemaking and online at www.epa.gov/powersectormodeling.economic resource to meet load. All of this generation is represented in the EPA IPM Base Case projections.

To quantify an initial generation value, the EPA interpolated between the EPA IPM Base Case’s 2020 and 2025 projections, assuming each calendar year accounts for 20% of the difference in those projections (i.e., a linear interpolation between run years). Given that the projected incremental RE generation across the contiguous U.S. in 2020 is 202,219,818 MWh, and the corresponding projected value in 2025 is 256,541,353 MWh, the initial generation value is calculated as: 256,541,353 െ 	202,219,818 5 ൅ 202,219,818 ൌ 213,084,125

This initial level represents expected incremental RE generation before the assumed deployment of additional RE capacity in 2022 and beyond, as described in the following steps.

Step 5

The national building block 3 generation total in 2022 is the initial level (step 4) plus the generation associated with the historical average change in capacity (step 3):

 2022 National Building Block 3 Generation = 213,084,125 MWh + 28,796,222 MWh = 241,880,347 MWh

The 2023 national building block 3 generation value is the sum of the 2022 value plus the generation associated with the historical average change in capacity:

 2023 National Building Block 3 Generation = 241,880,347 MWh + 28,796,222 MWh = 270,676,570 MWh 8

Step 6

The 2024 national building block 3 generation value is the sum of the 2023 value plus the generation associated with the historical maximum change in capacity (step 3):

2024 National Building Block 3 Generation = 270,676,570 MWh + 62,193,363 MWh = 332,869,933 MWh

For each subsequent year through 2030, the national building block 3 generation total is the previous year’s value plus the generation associated with the historical maximum change in capacity (62,193,363 MWh). The annual values for all years are presented below in Table X

‐ X:

Table X

‐ X: National Building Block 3 Generation Totals (MWh)

Year Building Block 3 Generation 2022 241,880,347 2023 270,676,570

8 Values may not sum due to rounding; refer to GHG Mitigation Measures spreadsheet for unrounded values.2024 332,869,933 2025 395,063,296 2026 457,256,659 2027 519,450,023 2028 581,643,386 2029 643,836,749 2030 706,030,112

Step 7

To apportion the national building block 3 generation totals calculated in steps 5 and 6, the EPA considered the geographic pattern of RE deployment in an IPM modeling scenario projecting national

‐ level incremental generation for 2025 and 2030 at levels similar to the building block 3 generation totals. 9 The scenario included a representation of utility

‐ scale solar PV, CSP, onshore wind, geothermal, and hydropower generation. The geographic pattern by interconnection of incremental RE generation totals for 2020, 2025, and 2030 are displayed below:

Table X

‐ X: Incremental RE Generation by Interconnection from Building Block Assignment Analysis (MWh)

Interconnection 2020 2025 2030
Eastern 165,133,240 272,168,303 449,874,640
Western 53,558,376 76,325,304 165,543,643

Texas 20,146,141 63,283,451 109,380,771

The absolute generation levels in this projection do not substitute for the national building block 3 generation totals in Table X

‐ X; instead, this step is only assessing the most economic location by interconnection of this general level of incremental RE generation potential. To facilitate this geographic assignment of the building block 3 generation totals in Table X

‐ X, the modeled generation totals for each interconnection are converted into a percentage of the modeled national total such that these interconnection

‐ specific percentage values, or “shares”, can be applied to apportion the building block 3 generation levels in Table X

‐ X. The interconnection shares by model projection year are shown below in Table X

‐ X.

Table X

‐ X: Incremental RE Generation Shares by Interconnection from Building Block Assignment Analysis

Interconnection 2020 2025 2030
Eastern 69.2% 10 66.1% 62.1%
Western 22.4% 18.5% 22.8%

Texas 8.4% 15.4% 15.1%

9 The IPM run ‘Building Block 3 Generation Assignment’ is available in the docket
10 Value rounded up to ensure all interconnections sum to 100%The model run
‐ year values are then converted into values for each year from 2022 through 2030 using a linear interpolation of the model

‐ projected 2020 and 2030 generation share values. The resulting shares by interconnection for each year are shown in Table X

‐ X below.

Table X

‐ X: Building Block 3 Generation Shares by Interconnection

Eastern Western Texas 2022 67.8% 22.5% 9.7% 2023 67.1% 22.5% 10.4% 2024 66.4% 22.6% 11.1% 2025 65.7% 22.6% 11.8% 2026 64.9% 22.6% 12.4% 2027 64.2% 22.7% 13.1% 2028 63.5% 22.7% 13.8% 2029 62.8% 22.8% 14.4% 2030 62.1% 22.8% 15.1%

Applying the generation shares in Table X

‐ X to the national building block 3 generation totals in Table X

‐ X produces an annual incremental generation value for each interconnection in each year, as shown in Table X

‐ X.

Table X

‐ X: Annual Average Incremental Generation Values, Eastern Interconnection (MWh) 11

National Building Block 3 Generation Total Eastern Interconnection Generation

Share (%) Annual Generation Values Annual Incremental Generation

Values 12

2022 241,880,347 67.8% 163,946,499 12,983,007 2023 270,676,570 67.1% 181,542,775 17,596,276 2024 332,869,933 66.4% 220,892,487 39,349,712 2025 395,063,296 65.7% 259,359,054 38,466,566 2026 457,256,659 64.9% 296,942,475 37,583,421 2027 519,450,023 64.2% 333,642,749 36,700,275 2028 581,643,386 63.5% 369,459,879 35,817,129 2029 643,836,749 62.8% 404,393,862 34,933,983 2030 706,030,112 62.1% 438,444,700 34,050,838

As shown in the steps above, this approach considers annual generation levels associated with the historical average RE capacity change to inform building block 3 levels in 2022 and 2023, and the

11 Table X

‐ X displays the Eastern Interconnection; refer to the GHG Mitigation Measures spreadsheet for the Western and Texas Interconnections calculations:
12 These amounts are in addition to the initial generation value for the Eastern Interconnection, which is 150,963,493 MWh.historical maximum RE capacity change to inform building block 3 levels in 2024 through 2030. In keeping with that schedule, the annual incremental generation values in Table X

‐ X are averaged for 2022

‐ 2023 and for 2024

‐ 2030 to create an interconnection

‐ specific analog to the national values presented above in step 3. For example, the 2022

‐ 2023 average annual incremental generation value for the Eastern Interconnection (using the values in Table X

‐ X) is 15,289,641 MWh, which is the Eastern Interconnection’s share of the national annual incremental generation value of 28,796,222 MWh (based

on the historical average RE capacity change) presented in step 3. Following the same process, the 2024

‐ 2030 average annual incremental generation value for the Eastern Interconnection is 36,700,275 MWh, which is the Eastern Interconnection’s share of the national annual incremental generation value of 62,193,363 MWh (based on the historical maximum RE capacity change) presented in step 3.

The final step in calculating interconnection

‐ level building block 3 generation totals is to apply each region’s average incremental generation to its share of the initial generation level presented in step 4. To continue the Eastern Interconnection example from Table X

‐ X:

 2022 Building Block 3 Generation Level = Initial Generation Level + 2022

‐ 2023 Average Incremental Generation = 150,963,493 MWh + 15,289,641 MWh = 166,253,134 MWh

 2023 Building Block 3 Generation Level = 2022 Building Block 3 Generation Level + 2022

‐ 2023 Average Incremental Generation = 166,253,134 MWh + 15,289,641 MWh = 181,542,775 MWh

For all subsequent years, 2024 through 2030, the building block generation level for the Eastern Interconnection is the previous year’s generation level plus the 2024

‐ 2030 average incremental generation level of 36,700,275 MWh. The calculations for all years and interconnections are available in the GHG Mitigation Measures spreadsheet.

The building block 3 generation levels used in the BSER for each interconnection are displayed below in Table X

‐ X:

Table X

‐ X: Building Block 3 Generation Levels by Interconnection (MWh)

Eastern Interconnection Western Interconnection Texas Interconnection Total

2022 166,253,134 56,663,541 18,963,672 241,880,347 2023 181,542,775 60,956,363 28,177,431 270,676,570 2024 218,243,050 75,244,721 39,382,162 332,869,933 2025 254,943,325 89,533,078 50,586,893 395,063,296 2026 291,643,600 103,821,436 61,791,623 457,256,659 2027 328,343,875 118,109,793 72,996,354 519,450,023 2028 365,044,150 132,398,151 84,201,085 581,643,386 2029 401,744,425 146,686,508 95,405,816 643,836,749 2030 438,444,700 160,974,866 106,610,547 706,030,112

4.3 Building Block 3 Generation Required by the Goal

‐ Setting

As discussed in section V.A.3.f and section VI of the preamble, the BSER reflects the degree of emission limitation achieved through application of the building blocks in the least stringent region.Therefore, by definition, there is an amount of building block 3 generation in each year that is not required to achieve the goal rates. Through the application of a minimization routine described in the CO 2 Emission Performance Rate and Goal Computation TSD, the EPA has solved for the minimum amount of building block 3 generation necessary for affected EGUs to achieve the source category

‐ specific emission performance rates.

Table X

‐ X: Building Block 3 Generation Necessary to Achieve Source Category

‐ Specific Emission Performance Rates (MWh)

Year Building Block 3 Generation Building Block 3 Generation Required to Achieve Goal Rates
2022 241,880,347 146,904,585 2023 270,676,570 179,963,324 2024 332,869,933 239,903,903 2025 395,063,296 292,428,842 2026 457,256,659 346,222,749 2027 519,450,023 405,981,689 2028 581,643,386 449,706,611 2029 643,836,749 493,669,241 2030 706,030,112 539,774,619

The building block 3 generation levels, both total and required to achieve the goal rates, are presented together with the projections of incremental RE from the EPA’s IPM Base Case:
Figure X

‐ X: Building Block 3 Generation Levels, Relative to Base Case Incremental RE (MWh)

‐ 100,000,000 200,000,000 300,000,000 400,000,000 500,000,000 600,000,000 700,000,000 800,000,000

2022 2023 2024 2025 2026 2027 2028 2029 2030

Base Case Incremental RE Building Block 3 Generation Building Block 3 Generation Necessary to Achieve Goal RatesFigure X

‐ X displays how the combination of building block 3 and the goal

‐ setting methodology

serve to significantly reduce compliance pressure on affected EGUs in the near
‐ term, insulating near
‐ term building block 3 generation levels against adjustments to RPS requirements and fluctuations in market conditions, while still establishing an historically

‐ based deployment pathway that achieves significantly greater amounts of RE through 2030 than what is projected in the Base Case. The interaction between the building block 3 generation level, goal

‐ setting methodology and the technical feasibility and cost

‐ effectiveness of incremental RE is explored in greater detail in section 4.6.

4.4 Building Block 3 Generation Levels, Relative to Proposed Levels

The final methodology, which is adapted from the proposal’s alternative approach, produces greater incremental RE generation than the proposed levels. For example, the 2030 building block 3 generation level of 706,030 GWh in the final rule is 370,660 GWh greater than the incremental RE contained in the proposal’s alternative approach at a constant $/ton cost

‐ effectiveness. 13 This difference is comprised primarily of two components

– removal of the benchmark development rate 14 from the proposed approach and updating RE cost and performance data to the most recent NREL data. The magnitude of these components is shown below:
Figure X

‐ X: Proposed and Final Building Block 3 Generation Levels, by Component in 2030

The EPA invited comment at proposal on RE levels in excess of the final building block 3 generation level. For example, the Alternative RE Approach TSD cited NREL values for incremental

13 The proposed RE levels were associated with a $36/ton cost; the final building block 3 generation levels produce

an average cost of $37/ton (see section 4.6).
14 The benchmark development rate set limits on the projected economic deployment of RE if a state had achieved deployment levels that met a benchmark deployment rate (total capacity/total resource) equal to the top

‐ third of states. See the response to comment document XXX for more detail.onshore wind, solar and geothermal technologies of 687,600 GWh. 15 When combined with the proposed levels of incremental hydropower (102,524 GWh), the resulting generation level of 790,124 GWh exceeds the building block 3 generation level in the final rule by more than 80 TWh in 2030. Additionally, the goal

‐ setting methodology in the final rule does not require all of the building block 3 generation level to achieve the goal rates (see Section 4.3). In evaluating the proposed and final levels of building block 3 generation, it is appropriate to compare the amount of building block 3 generation levels necessarily reflected in the final goal rates: Figure X

‐ X: Block 3 Generation Levels Necessary to Achieve Goal Rates and Alternative NREL Values, in 2030

It should be noted that the building block 3 generation levels from the proposal and the final rule produce consistent estimates of cost

‐ effectiveness ‐ $36/ton at proposal versus $37/ton in the final rule. Preserving a consistent cost

‐ effectiveness determination at increased levels of RE penetration reflects the final rule’s adoption of updated cost and performance assumptions from the 2015 NREL Annual Technology Baseline (ATB). This update is described in detail in section 4.5 below.

4.5 NREL’s 2015 Annual Technology Baseline Mid

‐ Case Estimates The EPA’s approach for determining the building block 3 component of the BSER is based in part on revised cost and performance estimates for onshore wind and solar technologies. As discussed in section V.E.6 of the preamble to final rule, commenters provided data demonstrating that the cost and performance estimates relied on in the proposed rule did not reflect the decline in cost and increase in performance that have been demonstrated by current projects, particularly in regards to wind and solar. As a result, the agency revised its data for onshore wind and solar technologies in the final rule. The revision

15 http://www2.epa.gov/c leanpowerplan/clean

‐ power
‐ plan

‐ proposed

‐ rule

‐ alternative

‐ renewable

‐ energy
‐ approachincluded the mid

‐ case estimates from the National Renewable Energy Laboratory’s (NREL) 2015 Annual Technology Baseline (ATB).

The EPA selected the NREL 2015 ATB mid

‐ case estimates based on the quality of its data and consistency with recent RE cost and performance trends. The agency used the draft 2015 ATB version, which was available at the time of the analysis. Recently, the final 2015 version was posted. 16 It is particularly notable that the final 2015 version’s cost estimates are either unchanged or lowered. 17 Despite the recent availability of the final 2015 version, the agency believes its data is well supported by the draft 2015 version’s cost and performance data.

In comparing the two data sets, the EPA found AEO2013 installation cost assumptions for onshore wind in 2014 are 30% higher than the ATB mid

‐ case cost assumptions for the same year. 18 Moreover, the AEO2013 assumes onshore wind installation costs in 2030 at $2,100/kW, which is almost 30% higher than installation costs in the ATB mid

‐ case in that year. Installation costs for solar PV in 2013 for the AEO2013 are 15% higher than those assumed in the ATB mid

‐ case. Similarly, installation cost assumptions for solar PV in 2030 are almost 75% higher in the AEO2013 cost assumptions when compared to the NREL ATB Mid RE Case.

Table X. Comparing Onshore Wind and Utility Solar PV Cost Estimates in AEO2013 and ATB Mid

‐ Case

In comparing the two data sets with current project costs, recent trends, and a reasonable expectation of the future, the EPA found that the ATB mid

‐ case estimates are more in line with current costs and recent market analysis and projections than the AEO2013 costs.
The rapid cost declines for wind and solar have been well documented. Lazard’s recent Levelized Cost of Energy Analysis found that wind and solar PV became increasingly competitive over the last five years. During that time period, the average percentage decrease of high and low of the LCOE ranges for

16 Available at http://www.nrel.gov/analysis/data_tech_baseline.html

. 17 NREL lowered current and projected overnight capital cost values for the ATB mid-case solar PV projection to reflect significant changes in solar mark et prices that has occurred over th e last year. NREL, Annual Technology Baseline (ATB) Spreadsheet, July 2015, s ee “Solar – PV” worksheet, Available at: http://www.nrel.gov/docs/fy15osti/64077-DA.xlsm

. 18 EIA capital costs sourced from Assumptions to the Annual Energy Outlook 2013, Table 8.2 (pp 103), Available at: http://www.eia.gov/forecasts/aeo/ assumptions/pdf/ 0554(2013).pdf. Cost projections for future years were obtained from personal communications with EIA. Technology Report 2013 2014 2015 2020 2025 2030 Onshore Wind AEO 2013 $2,246 $2,274 $2,330 $2,293 $2,192 $2,106 ATB - TRG 3 Mid $1,758 $1,729 $1,721 $1,674 $1,643 $1,630 Utility Solar PV AEO 2013 ($/kW-DC) $3,144 $2,932 $2,881 $2,658 $2,501 $2,363 ATB - TRG 3 Mid ($/kWDC) $2,673 $2,522 $2,369 $1,603 $1,470 $1,337 Notes Capital Costs (2013$/kW)

1. The AEO2013 costs are in 2011$, whereas in ATB they are in 2013$. For the comparison, the AEO2013 costs were inflated using a net inflation of 3.28% to make them directly comparable. 2. The costs for solar PV in AEO are in $/kW-AC, whereas in ATB they are in $/kW-DC. For the comparison, AEO costs were expressed in $/kW-DC, using EIA's assumed 1.25:1 ratio. (See p. 24-2 in EIA's Updated Capital Cost Estimates for Utility Scale Electricity Generating Plants.)wind and utility

‐ scale solar PV were 58 percent and 78 percent, respectively. 19 Lazard’s capital costs for wind and utility

‐ scale solar PV were $1,400

‐ $1,800/kW and $1,500

‐ $1,750/kW DC , respectively. 20 These
cost estimates were significantly lower than near term and longer term AEO forecasts.
The EPA also observed solar PV costs from other data sources that are considerably lower than those reported in the AEO2013, and more consistent with those seen in ATB mid

‐ case. For instance, the U.S. Solar Market Insight report published for Q1 2015 shows recent utility

‐ scale solar installed costs below $2,000/kW DC , whereas the AEO2013 assumes costs above $2,800/kW DC for the year 2015. 21 Similarly, a recent report by Lawrence Berkley National Lab (LBNL) shows that utility

‐ scale solar PV projects were installed in 2013 at average costs of $3,000/kW DC . 22 While these recent projects may have represented best

‐ in

‐ class installations for the years 2013

‐ 2014, it is reasonable to assume that average costs by 2020 will generally reflect these best

‐ in

‐ class installations. In contrast, the AEO2013 assumes a
more conservative cost decline, and is close to $2,600/kW DC by 2020, and about $2,300/kW DC by 2030. Table X. Comparing Utility Solar PV Cost Estimates

The ATB near
‐ term capital cost estimates for concentrating solar power (CSP) with storage are comparable with Lazard’s. The ATB assumes a CSP trough with six and twelve hours of storage ($6,777

‐ $8,532/kW) and Lazard examines a tower with ten to eighteen hours of storage ($7,000

‐ $9,800/kW). While the configurations that serve as the basis for the estimates differ, they are close enough to validate the ATB cost estimates for CSP. Additionally, the projections were based on SunShot Vision study and vetted with solar industry participants. Similarly, the recent wind costs estimates are also much lower than those assumed in the AEO2013. The LBNL 2013 Wind Technologies Report showed 2013 capacity

‐ weighted average installed
costs for onshore wind at $1,630/kW, whereas the AEO2013 assumes costs close to $2,250/kW (2013$)

19 Lazard, Levelized Cost of Energy Analysis-Version 8.0 , September 2014, p. 9, Available at: http://www.lazard.com/media/1777/levelized_cost_of_energy_-_version_80.pdf

. 20 Levelized Cost of Energy Analysis-Version 8.0 , Lazard, September 2014, pp. 16-17, Available at: http://www.lazard.com/media/1777/levelized_cost_of_energy_-_version_80.pdf

. 21 U.S. Solar Market Insights Report , Solar Energy Industries Association/ GTM Research, Q1 2015, figure 2.4, Available at: http://www.seia.org/research-resources/solar-market-insight-report-2015-q1

. 22 Is $50/MWh Solar for Real? Falling Project Prices and Rising Capacity Factors Drive Utility-Scale PV Toward Economic Competitiveness (2015). LBNL. Available at: http://emp.lbl.gov/sites/all/files/lbnl-183129.pdf (referenced figure 4). Technology Report 2013 2014 2015 2020 2025 2030 Utility Solar PV AEO 2013 ($/kWDC) $3,144 $2,932 $2,881 $2,658 $2,501 $2,363 ATB - TRG 3 Mid ($/kWDC) $2,673 $2,522 $2,369 $1,603 $1,470 $1,337 LBNL ($/kWDC) $3,000 SEIA/GTM ($/kWDC) $2,000 Lazard (2014$) $1,500-$1,750 Notes Capital Costs (2013$/kW)

1. The AEO2013 costs are in 2011$, whereas in ATB they are in 2013$. For the comparison, the AEO2013 costs were inflated using a net inflation of 3.28% to make them directly comparable. 2. The costs for solar PV in AEO are in $/kW-AC, whereas in ATB they are in $/kW-DC. For the comparison, AEO costs were expressed in $/kW-DC, using EIA's assumed 1.25:1 ratio. (See p. 24-2 in EIA's Updated Capital Cost Estimates for Utility Scale Electricity Generating Plants.)for the same year. 23 Furthermore, the AEO2013 wind cost in 2030, which assumes continued cost reductions, is about $2,100/kW. Yet, this wind cost in 2030 is almost 30 percent higher than observed wind costs in 2013. 24

Table X. Comparing Onshore Wind Cost Estimates

Commenters (see RTC Chapter 3.3.7, Cost Effectiveness of RE) said that the proposal did not capture the full potential of RE and that the final rule needs to reflect the dramatic cost reductions that have occurred in recent years. There was support for the principle of using conservative assumptions, but significant concern about the use of cost and performance assumptions that are more pessimistic than currently observable and accurate data on the cost of RE deployment. They also added that EPA's analysis for the proposed rule using AEO2013 did not account for the cost reductions that can reasonably be expected to continue. For projecting future economics and performance, commenters recommended reliance on credible projections available from the DOE and National Laboratories. They asserted that recent cost declines are also consistent with the projected declines expected as an industry experiences significant growth, which the RE industry has seen in the U.S. and abroad.

Commenters provided a number of references to data from a variety of sources (e.g., LBNL, NREL and Lazard) to support their arguments that the agency needs to change cost assumptions. A number of commenters noted NRDC’s recent analysis that used a number of these sources to show AEO2013's cost estimates for wind and solar energy are 46 percent above current average costs. 25 Commenters also contrasted Lazard’s current range of levelized costs of energy (LCOE) for onshore wind without subsidies of $37

‐ $81/MWh with AEO2013's 2019 estimates of $70

‐ 90/MWh. 26 Regarding solar PV, commenters highlight DOE and national laboratory reports that have observed the cost of building utility

‐ scale solar projects has declined by about 50 percent between 2010 and 2014. The commenters said these declines are consistent with NREL's modeled prices using its bottom

‐ up modeling methodology ‐ NREL estimates that the price of solar declined to $1800/kW DC in Q4 2013. Commenters also highlight forecasted solar PV cost estimates and found no reason to believe that the declines in cost

23 2013 Wind Technologies Market Report , LBNL, August 2014, Available at: http://emp.lbl.gov/sites/all/files/2013_Wind_Technologies_Market_Report_Final3.pdf (referenced Figure 39 on Page 49).
24 Although the LBNL sample size is small for 2013, the decline in wind installed costs in 2013 from 2012 is in keeping with the decline in wind tu rbine prices, which is a leading indicator of installed cost trends. 2013 Wind Technologies Market Report , LBNL, August 2014, Available at: http://emp.lbl.gov/sites/all/files/2013_Wind_Technologies_Market_Report_Final3.pdf

. 25 NRDC, Issue Brief: The EPA's Clean Power Plan Could Save Up to $9 Billion in 2030 , November 2014, Available at: http://www.nrdc.org/air/pollution-standards/f iles/clean-power-plan-energy-savings-IB.pdf

. 26 Lazard, Levelized Cost of Energy Analysis-Version 8.0 , September 2014, p. 15, Available at: http://www.lazard.com/media/1777/leveliz ed_cost_of_energy_-_version_80.pdf. Technology Report 2013 2014 2015 2020 2025 2030 Onshore Wind AEO 2013 $2,246 $2,274 $2,330 $2,293 $2,192 $2,106 ATB - TRG 3 Mid $1,758 $1,729 $1,721 $1,674 $1,643 $1,630 Lazard $1,400-$1,800 LBNL $1,630 Notes Capital Costs (2013$/kW)

1. The AEO2013 costs are in 2011$, whereas in ATB they are in 2013$. For the comparison, the AEO2013 costs were inflated using a net inflation of 3.28% to make them directly comparable.will not continue. Referencing the SunShot Vision study’s roadmap for continued cost declines in solar PV technologies, they observed that estimates of solar system costs dropping 75% between 2010 and 2020. They added that NREL recently predicted in its 2014 update on Solar PV pricing trends that solar PV costs are still on track to meet the SunShot Initiative goal of $1,000/kW DC by 2020 for utility

‐ scale PV.

It should also be noted that the NREL ATB cost report was published in 2015, whereas the AEO costs were published in 2013. Commenters noted that the LBNL found that the AEO2013 assumptions

were based on projects completed in 2012, and the installed price data for those projects may reflect
contract terms agreed to a number of years prior to project completion. 27

NREL’s ATB and Standard Scenarios were developed with the objectives of creating consistent and normalized technology cost and performance assumptions as well as defining a conceptual and consistent scenario framework that can be used in future analyses.

In general, the ATB cost and performance estimates represents typical U.S. RE plants by reflecting a range of resources and groupings of RE technologies, such as wind technology resource groups or TRGs based on wind resource and turbine technologies. The future cost projections are compared with data in published literature and generally within the bounds of those perspectives. These literature estimates were normalized to a common starting point. Projections for each technology were developed independently using distinct methods, but the starting points were compared with available market data to provide consistent baseline methodology. 28

The wind cost projections derived from literature reviews for onshore and offshore wind, including numerous projection scenarios from many independently published studies, and vetted with a consortium of National Laboratory, DOE and wind industry experts. The results are three different projections developed for scenario modeling as bounding levels, based generally on maximum, median, and minimal annual cost reductions based on literature. These are reflected as the low, mid, and high
cost cases, respectively. 29

The solar cost projections are based on SunShot Vision study (2012) and vetted broadly with solar industry participants. Similar to wind projections, three cost projections are developed for scenario modeling as bounding levels. These low, mid, and high cases for utility

‐ scale solar PV are based changing
cost reduction trajectories. The concentrating solar power (CSP) cases are based on differing evolutionary paths for CSP design and deployment. These projections are compared to range of available analyst projections from BNEF, EIA, and EREC. 30

4.6 Technical Feasibility and Cost

‐ Effectiveness of the Building Block 3 Generation Levels

27 Barbose, et al., Tracking the Sun VII , LBNL, September 2014, Available at: http://emp.lbl.gov/publications/tracking-sun-vii-historical -summary-installed-price-photovoltaics-united-states1998-20

. 28 ATB Summary Presentation, NREL, March 2015, Available at: http://www.nrel.gov/analysis /data_tech_baseline.html

. 29 ATB Summary Presentation, NREL, March 2015, Available at: http://www.nrel.gov/analysis /data_tech_baseline.html

. 30 ATB Summary Presentation, NREL, March 2015, Available at: http://www.nrel.gov/analysis /data_tech_baseline.html

.This section addresses the technical feasibility of the RE requirements described in the previous
sections for this building block. The current deployment and operation of RE resources are discussed first, followed by analysis of the future deployment potential for RE and related needs such as available

transmission capacity to deliver RE.

Historical Deployment

The major incremental additions to RE capacity over the past two decades have come from wind
and solar, although there have also been some additions to hydro and geothermal resources. After constituting two or three percent of the total RE capacity in the 1990s, wind and solar capacity have
increased dramatically to over 50 percent of total RE today, as shown in Figure X

‐ X:

Figure X

‐ X Growth in Utility

‐ Scale U.S. Renewable Resources

Many states are already integrating significant levels of Variable Generation (VG) RE resources.
For wind and solar, there were 21 states in 2014 where RE exceeded 5% of the total annual generation,
and 10 states where RE exceeded 10%. 31 Several states were considerably higher: Iowa at 28.5% from

31 EIA, Form EIA

‐ 923 and EIA

‐ 860 reports. 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015SummerCapacity(MW)

Source: EIA Geothermal Solar(Thermal+PV) Windwind and solar, Kansas 21.7% and South Dakota at 25.3% were all above 20 %, in 2014 with Idaho not far
below at 18.6%. If Geothermal is included, California is also close with 18.3%.

The current grid can reliably integrate much higher levels of over short time intervals of an hour

or a day. ERCOT met 40 percent of demand on 3/31/14 with wind power; SPP met 33 percent on 4/6/13, and Xcel Energy Colorado 60 percent on 5/2/13. Moreover, many states also demonstrate that high levels of wind and solar VG can be sustained for longer periods. Looking at the maximum monthly
percentage of wind and solar in 2014, nine states are already above 20% and 18 are above 10% of total generation from wind and solar for at least one month in 2014. Percentages are as high as 42.3 in Iowa,
37.1 in South Dakota, and 33.7 in Kansas. These figures indicate that the current configuration of the
electric power system is already capable of integrating very high levels of renewable variable generation
for sustained periods of time.

Deployment Projections

The EPA believes the historically grounded Building Block 3 generation levels constitute
moderate estimates of the future deployment of RE that can be achieved. The moderate nature of
these levels is demonstrated by:

 The EPA’s analysis of building block 3 generation levels conducted using IPM; and

 A multitude of external studies and reports that demonstrate the feasibility of higher
percentages of total generation than are associated with the building block 3 generation
levels

The IPM scenarios support building block 3 generation levels in two ways ‐ by apportioning the
national

‐ level generation totals calculated from national

‐ level deployment, and validating the building
block 3 generation levels as technically feasible and cost

‐ effective. 32 It is important to note that the IPM
model was not the basis for the overall setting of the Building Block 3 generation levels; as described above, those levels are based on historical additions that have actually been achieved. The IPM model, however, is a useful tool for analyzing the cost and impact of deploying these historically

‐ based generation levels. IPM is a power sector planning model with a detailed representation of current and

32 Supporting IPM analysis, including ‘BB3: Generation Assignment’ and ‘BB3: Cost

‐ Effectiveness’ are available in the docket.potential generating resources, a large number of regions, interregional transmission constraints based

on current limits, and regional reserve margins based on current NERC standards. IPM does not attempt

to perform a detailed power flow analysis, or to project new transmission additions; however, it
captures key variables such as costs associated with connecting new renewable resource to the grid
within a region and will include additional costs related to interregional transmission congestion from

constraints in the current grid. For these reasons, IPM is an appropriate tool for analyzing the generation levels assumed for RE and for reviewing RE penetration levels and the impact on the overall generation mix. 33

To specifically assess the cost effectiveness and technical feasibility of building block 3, the EPA imposed minimum generation constraints in IPM at levels consistent with the building block 3 generation levels. The only technologies made available to satisfy these constraints were the RE technologies associated with building block 3

– utility

‐ scale solar PV, CSP, onshore wind, geothermal,
and hydropower with an online year of 2013 or later. 34 To meet these levels in a manner that reflects
the BSER, every incremental MWh of qualifying RE the model deployed necessitated that one MWh of
affected EGU generation be reduced. This requirement was necessary to reflect that building block 3 is premised on the replacement of affected EGU generation by incremental RE. The cost effectiveness of meeting this constraint, expressed as the average $/ton of CO 2 emissions reductions, is shown below in
Table XX3: 35

Table X

‐ X: Cost Effectiveness of Building Block 3

Average Annual Cost (Billion $) Average CO2 Emissions (Million
Short Tons) Avoided

Cost per
Short Ton

Year Base Building
Block 3 Incremental

Cost Base Building
Block 3 Reduced

Emissions

2022

‐ 2030 36 184.7 193.7 9.1 2184.8 1940.9 243.9 $37

33 For further documentation detail on the IPM model, visit http://www.epa.gov/powersectormodeling.
34 This same procedure was used to apportion the national

‐ level building block 3 generation totals to each of the three interconnections. See the IPM analysis ‘BB3: Generation Assignment’ in the docket for further details.
35 Refer to ‘BB3: Cost

‐ Effectiveness’ IPM analysis, available in the docket, for further detail.
36 The cost

‐ effectiveness calculation is based on the 2022

‐ 2030 time period, reflecting the entirety of the interim compliance period through achievement of the final building block 3 generation level in 2030. The resultsThe EPA believes the 2022

‐ 2030 average cost of $37/ton to be reasonable.

The feasibility of these generation levels is further confirmed by the results of other industry projections of RE, as well as many industry studies of the technical feasibility of even higher levels of RE penetration. The percentage of total RE generation projected in 2025 and 2030 is shown in Table XX1.

As the table shows, the highest level of penetration from renewables is 19 percent. Virtually all recent
studies of achievable penetration level of RE in the power grid are above 20 percent, for the simple

reason that 20 percent has been shown to be feasible

– as indicated by the current RE levels discussed
above

– and studies are directed at assessing the impact of higher levels. A good review of the main integration studies is provided in a report by the National Renewable Energy Laboratory. 37 The

summary table from this report is included as an Appendix to this TSD. The table summarizes a range of regional and national studies that analyze renewable penetration levels up to 50 percent.

Table X

‐ X:

2025 (TWh) 2030 (TWh)

Scenario Total RE RE% Total RE RE

Base Case 4328 394 9 4467 426 10 Building Block 3 4326 535 12 4473 832 19

Another important reference point for understanding the Building Block 3 generation levels is
found in projections of future renewables by independent industry evaluations. Two example of such
evaluations have been recently conducted by IHS Energy and by UBS Securities LLC. 38 In each case,
these projections are presented in terms of the additions of RE capacity that are expected to be deployed. Comparable levels of capacity for Building Block 3 are available from the IPM results for the
base and Building Block 3 levels and are shown in Table XX2 along with the projections from IHS Energy.

presented reflect a weighted average of the 2020, 2025, and 2030 run years; specifically, one calendar year from IPM’s 2020 run year (2022), five calendar years from IPM’s 2025 run year (2023

– 2027), and three calendar years from IPM’s 2030 run year (2028

– 2030).
37 NREL, Relevant Studies for NERC’s Analysis of EPA’s Clean Power Plan 111(d) Compliance.
38 HIS Energy, North American Renewables Outlook: The rise of solar and the uncertainty of policy, 2 June 2015; UBS Securities LLC, US Solar & Alternative Energy, Sizing Up the US Solar Market.This table shows Building Block 3 potential RE capacity additions are somewhat above a midpoint, but
well within the range of industry expectations.

In the UBS projections cited, UBS estimates a solar market of 90GW. This compares closely with
the Building Block 3 projection of 93GW of total solar capacity. 39 Together these comparisons show that
the assumed generation levels are within the range of expected feasible future expectations of independent industry analysts. Moreover, it is important to realize that these results from the IPM
model are not projections of any expected future scenario of compliance with the final rule, but rather
results designed to show the potential impact in key variables of modeling a potential future based on
demonstrated historical performance. In this context it is also important to note that compliance with
the final rule may be achieved with far lower overall levels of RE generation. In the illustrative scenario
for the rate

‐ base compliance, for example, the total renewable capacity additions were 95 GW in

2030 40
, only slightly above the 87 GW in the IHS Energy Low case.

Table X

‐ X: Total RE Capacity Additions from 2015 (GW)

Scenario Through 2025 Through 2030

EPA Base Case 48 62

EPA Building Block 3 121 233

IHS Energy ‐ Low 64 87

IHS Energy ‐ Mid 126 192

IHS Energy ‐ High 222 352

Transmission

Compliance with the final rule is not expected to add significant transmission requirements. The

RIA projects that the final rule will add only 20 GW of renewable generation capacity beyond the base

case; this is a small increment that can be accommodated in the normal transmission planning process.
Even the larger levels of new generation for Building Block 3 considered here should not raise concerns about the reasonableness of the costs associated with the full build out of renewables. There are
several reasons for this:

39 UBS includes some distributed solar as well as utility scale.
40 See the RIA, Table 3

‐ 14. Although it is reasonable to assume that some proposed renewable scenarios will require specific patterns of new transmission requirements, 41 transmission will be required for any new generation under all future scenario, with or without significant additions of renewables. Although the location

‐ specific nature of renewables will place different requirements that can
add to some transmission needs, it will also mean that generation and flows on the grid will shift, resulting in reduced flows and constraints in some areas. Since the additional renewable capacity projected for Building Block three occurs over a 15 year period, and rises to a level of only 20 percent renewables, these additions should be manageable in the normal planning and expenditure process for transmission.

 EEI members alone are expected to increase transmission expenditures substantially over the

next five years to approximately 20 billion dollars per year, nearly doubling the historical

investment over the average of the past five years. 42 By contrast, DOE’s SunShot scenario,
which increases utility

‐ scale PV to 180 GW by 2030, anticipated spending of $60 billion through

2050. This level is approximately double the level of solar assumed in this building block and
still amounts to less than $2 Billion per year.

 Using the Eastern Interconnection as an example, the additional costs required for transmission expansion are almost all either for generation interconnection or for constraint relief. 43 For
renewable generation, many of these costs are already captured in the cost estimates from the

IPM modeling. For example, the cost of connecting wind to the existing grid are reflected in the

cost classes assigned to different categories of wind available for deployments. The IPM model
considers the location of generating resources and the existing limits of interregional transfer of capacity in costs of developing new capacity and delivering it to load, so where increased interregional congestion arises from delivering power from new remote supply, the cost of
from re
‐ routing the flows to avoid congestion are captured in the total cost of modeled

scenarios.

41 An example of these requirements can be found in the Eastern Interconnection Planning Collaborative (EIPC) Phase 2 Report: Interregional Transmission Development and Analysis for Three Stateholder Selected Scenarios (December, 2012). These studies have been used to analyze the feasibility of a 30 percent wind and solar scenario by the Eastern Renewable Generation Integration Study (ERGIS).
42 Edison Electric Institute, Transmission Projects at a Glance, March 2015, Page v. 43 The Eastern Interconnection is a key region, because it is the limiting region for purposes of setting final rule goals for 2030. See the EIPC study, Section 5.1, Table 5

‐ 9, where over 95 percent of the scenario costs for Transmission related costs arise from these two sources.Appendix: System Integration Studies from NREL Report

### Chapter 7: Carbon Capture & Storage

Introduction Another possible approach for reducing CO 2 

### emissions from existing fossil fuel-fired steam generating EGUs is through the appli cation of carbon capture and storage technology (CCS; sometimes also referred to as carbon capture and sequestration). In the final standards of performance for new fossil fuel-fired EGUs, the EPA has determined that the best system of emission reduction (BSER) for new fossil fuel-fir ed steam generating units is implementation of partial CCS (i.e., capturing a portion of the CO 2 

### from the emission stream – in contrast to “full CCS” which would involve cap turing > 90% of the CO 2 

### from the emissions). The EPA has determined that that, for new steam generating EGUs (utility boilers and IGCC units), partial CCS has been adequately demonstrated; it is technically feasible; it can be implemented at reasonable costs; it provides meaningful emissi on 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 fossil fuel-fired steam generating
EGUs.

Carbon Capture Options for Existing Fossil Fuel-fired EGUs In general, CO 2 

### capture technologies a pplicable to existing fo ssil fuel-fired power generation can be categorized into three appr oaches – (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 co mbustion 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 fr om typical combustion systems is usually at near atmospheric pressure. Therefore, most of the available capture systems rely on chemical absorption (chemisorption) options (e.g., using amines) that require added energy to release the captured
CO 2 

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

### and HCl – that can degrade the

### solvent. The captured CO 2 

### , if it is to be transported via pipeli ne for storage or use in enhanced oil recovery (EOR), must then be compressed from near atmospheric pressure to much higher pipeline pressures (about 2,000 psia).

Pre-combustion Capture Pre-combustion capture systems are applicable to fossil fuel gasification power plants (i.e., IGCC units) where coal or other solid fossil fuel (e.g., petroleum coke) is converted into a synthesis gas (or “syngas”) by applying heat under pressure in the pres ence 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 com position via the catalytic water-gas shift (WGS) reaction. This process involves the catalytic reaction of steam (“wate r”) 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 co mbined 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 ab sorbents (e.g., SelexolTM, R ectisol®) 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. Unde r 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-com bustion include the capit al cost and energy consumption for a cryogenic air separation unit (ASU) to produce oxyge n, 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 pipe lines 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 re view of the 500 largest CO 2 

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

### transportation via pipelines including but not li mited to: availability and acqui sition of rights-of-way for new pipelines, capital costs, operating costs, length and diameter of pipeline, terrain, flow rate of
CO 2 

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

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

### sinks, natural gas storage, and acid gas injec tion), indicate that geol ogic 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 with carbon storage potenti al including vast areas
offshore. Estimates based on DOE studies indicate that areas of the U.S. with appropriate geology have a storage potential of 2,300 billion to more than 20,000 billio n 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 an d basalt may also have the ability to store CO 2 

### ; and the DOE is currently evaluating their potential storage capacity.
Further evidence of the wide spread 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 tech nically accessible storag e 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 Un ited States. For comparison, this amount is 500 times the 2011 annual U.S. energy-related CO 2 

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

1 The United States 2012 Carbon Utilization and Storage Atla s, 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 Su rvey Factsheet 2013-3020, 6p.http://pubs.usgs.gov/fs/2013/3020/.

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

### in EOR, which is th e injection of fluids
into a reservoir to incr ease oil production efficiency. 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 ava ilable for recovery. The crude oil and CO 2 

### mixture is produced, and sent to a separato r where the crude oil is separa ted from the gaseous hydrocarbons
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 gas 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 indu stry in the United States has over 40 years of experience of injecti on and monitoring of CO 2 

### in the deep subsurface for the purposes of enhancing oil and natural gas production. This experience provid es a strong foundation for the injection and monitoring technologies 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 feasib le 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-fir ed boilers and IGCC units, it is 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 amen able to CCS – such as a site that is near an existing CO 2 

### pipeline or an existin g oil field. Existing sources do not have the advantage of pre-selecting an appropriate locati on. Some existing facilitie s are located in areas
where CO 2 

### storage is not geologically favorab le and are not near an existing CO 2 

### pipeline. Developers of new facilities also have the advant age of integrating the partial CCS system into the original design of the new facility. Integratin g a retrofit CCS system into an existing facility

### can be more challenging. Some existing sources have a limited footprint and may not have the land or space available to add the equipment needed to implement CO 2 

### capture, compression, and transportation. Integration of the existing st eam 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 Plan t Barry and it is being installed for large-scale demonstration at NRG’s WA Parish facility. Sa skPower’s Boundary Dam Unit #3, a 110 MW coal-fired utility boiler in Saskatchewan, began operations in October 2014. The facility has retrofit postcombustion carbon capture technology that is ca pturing approximately 90 % of the CO2 from flue gas stream. All of these facili ties 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 facilities, the implementation of partial CCS may be a viab le 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 as a broadly appl icable GHG mitigation program. Therefore, the EPA did not propose to find that CCS is a compone nt 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 proposed that the costs to implement pa rtial CCS (to a level to meet the proposed emission standard of 1,100 lb/MWh-gross) were c onsistent with costs for other non-natural gasfired 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 pr ojects that are in the development phase are already planning to implement CCS; and, as a re sult, the standard woul d not have a significant impact on nationwide energy prices . In issuing the final NSPS for new fossil fuel-fired EGUs, the EPA – in response to public comments and update d cost information – reevaluated the cost to implement partial CCS and adjusted the final emission limitation to a standard of 1,400 lb/MWhgross. In contrast, the EPA did not propose to iden tify full or partial CCS as BSER for new natural gas-fired stationary combustion turbines noting technical challenges to implementation of

### CCS at NGCC units as compared to implementati on at new solid fossil fuel-fired sources. The EPA also noted that, because virtually all new foss il fuel-fired generating capacity is projected to use NGCC technology, requiring full or partial CCS would have more of an impact on the price of electricity than the few proj ected coal plants with CCS a nd the number of projects would make it difficult to implement in the short term. An emission standard for existing units base d on CCS (or even partial CCS) would most certainly have an even more significant effect on nationwide electricity pr ices and could affect the reliability of the supply of electricity. Therefor e, as stated earlier, the EPA did not find that the cost to implement partial CCS at existing so urce emission standards to be reasonable, which further supports the determinati on that CCS is not an appropriate component of the best system
of emission reduction for CO 2 

### emissions from existing fossil fuel-fired EGUs.

Emission Reductions and Prom otion 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 sta ndard may not be technically or logistically
feasible in a number of cases and cannot be broadly implemente d at a reasonable cost at this
time.

CCS and CCU in State Plans Affected EGUs may utilize retrofit CCS technology to reduce reported stack CO 2 

### emissions from the EGU. 4 

### Affected EGUs that apply CCS under a state plan must meet the same monitoring, recordkeeping and reporti ng requirements for sequestered CO 2 

### as new units with CCS capability subject to the final C AA section 111(b) rule for new EGUs. 5 

### Specifically, the final CAA section 111(b) rule for new sources requi res that, if a new affected EGU uses CCS to meet the applicable CO 2 

### emission limit, the EGU must repor t in accordance with 40 CFR part 98

4 Addition of retrofit CCS technology should not trigger CAA section 111(b) applicability for modified or reconstructed sources. Pollution control projects do not trigger NSPS modifications and addition of CCS technology does not count toward the capital costs of reconstruction. 5 Standards of Performance for Greenhouse Gas Emissions from New, Modified, and Reconstructed Stationary Sources: Electric Utility Generating Units.

### subpart PP (Suppliers of Carbon Dioxide), and the captured CO 2 

### must be injected at a facility or facilities that report in accordance with 40 CFR part 98 s ubpart RR (Geologic Sequestration of Carbon Dioxide). 6,7 

### See 40 CFR part 60.46Da(h)(5) and part 60.5555(d). Taken together, these requirements ensure that the amount of captured and sequestered CO 2 

### will be tracked as appropriate at project- a nd national-levels, and that the status of the CO 2 

### in its sequestration site will be monitored, including air-s ide monitoring and reporting. As detailed in the preamble for the CAA section 111(b) standards for new EGUs, the EPA is convinced that there is ample evidence that CCS is technically feasible and that partial CCS can be implemented at a new fossil fuel-fired steam generating EGU at a cost that is consistent with the cost of other dispatchable, non-NGCC generating options. The EPA received comments suggesting that carbon capture and utilization (CCU) technologies should also be allowed as a CO 2 

### emission rate adjustment measure for affected EGUs. Potential alternatives to storing CO 2 

### in geologic formations are emerging and these relatively new potential alternatives may offer the opportuni ty to offset the cost of CO 2 

### capture. For example, captured anthropogenic CO 2 

### may be stored in solid ca rbonate materials such as precipitated calcium carbonate (PCC) or magne sium or calcium carbonate, bauxite residue carbonation, and certain types of cement through mineralizati on. The carbonate materials produced can be tailored to optimize perfor mance in specific industrial and commercial applications. For example, these carbonate materi als have been used in the construction industry and, more recently and innovatively, in cement pr oduction processes to re place Portland cement. The Skyonics Skymine® project, which opene d its demonstration project in October 2014, is an example of captured CO 2 

### being used in the production of carbonate products. This plant converts CO 2 

### into commercial products. It captures over 75,000 tons of CO 2 

### annually from a San Antonio, Texas, cement plant and converts the CO 2 

### into other products including sodium

6 The final CAA section 111(b) rule finalizes amendments to subpart PP reporting requirements, specifically requiring that the following pieces of information be reported: (1) the electr onic GHG Reporting Tool identification (e-GGRT ID) of the EGU facility from which CO 2 was captured, and (2) the e-GGRT ID(s) for, and mass of CO 2 transferred to, each GS site reporting under subpart RR. As noted, the final 11 1(b) rule also re quires that any affected EGU unit that captures CO 2 to meet the applicable emissions limit must transfer the captured CO 2 to a facility that reports under 40 CFR part 98 subpart RR.
7 Under final requirements in the C AA 111(b) NSPS, any well receiving CO 2 captured from an affected source, be it a Class VI or Class II well, must report under subpart RR. A UIC Class II well’s regulatory status does not change because it receives such CO 2 , nor does it change by virtue of reporting under subpart RR.

### carbonate and sodium bicarbonate. 8 

### Other companies – including Calera 9 

### and New Sky 10 

### – also offer commercially available technology for the beneficial use of captured CO 2 

### . These processes can be utilized in a variety of i ndustrial applications – including at fossil fuel -fired power plants. However, consideration of how these emergi ng alternatives could be used to meet CO 2 

### emission performance rates or state CO 2 

### emission goals would require a better understanding of the ultimate fate of the captured CO 2 

### and the degree to which the method permanently isolates the captured CO 2 

### or displaces other CO 2 

### emissions from the atmosphere. Several commenters also suggested that al gae-based CCU (i.e., the use of algae to convert captured CO 2 

### to useful products – es pecially biofuels) should be recognized for its potential to reduce emissions fr om existing fossil-fueled EGUs. Unlike geologic sequestration, there are curre ntly no uniform mon itoring and reporting mechanisms to demonstrate that these alternative end uses of captured CO 2 

### result in overall
reductions of CO 2 

### emissions to the atmosphere. As these alternative technol ogies are developed, the EPA is committed to work collaboratively with stakeholders to evaluate the efficacy of
alternative utiliza tion technologies, to addre ss any regulatory hurdles, an d to develop appropriate monitoring and reporting protocols to demonstrate CO 2 

### reductions. In the meantime, the EPA has specified that state plans may allow affected EGUs to use qualifying CCU technologies to reduce CO 2 

### emissions that are subject to an emission standard,
or those that are counted when de monstrating achievement of the CO 2 

### emission performance
rates or a state rate-based or mass-based CO 2 

### emission. Approvable state plans must include analysis supporting how the proposed qualifying CCU technology results in CO 2 

### emission mitigation and provide monitoring, reporting, and verification requirements to demonstrate the reductions. The EPA would then review the appr opriateness and basis for the analysis and the verification requirements in the course of its review of the state plan.

8 http://skyonic.com/technologies/skymine.
9 http://www.calera.com/benefici al-reuse-of-co2/process.html. 10 http://www.newskyenergy.com/i ndex.php/products/carboncycle.

## Demand-Side Energy Efficiency Technical Support Documentii 

## Contents
Page List of Figures ............................................................................................................... ..................v List of Tables ................................................................................................................ ..................v 1. Introduction .................................................................................................................. ...........1 2. Background .................................................................................................................... .........1 2.1 EE Technologies and Practices ................................................................................2 2.2 Barriers to EE Investment ........................................................................................3 2.3 EE Policies ...............................................................................................................5 2.3.1 Objectives and Role in Reducing CO 2 

### Emissions from the Power Sector .......................................................................................................... 5 2.3.2 Policy Types................................................................................................ 7 3. History of EE Policies in the United States ..........................................................................9 4. EE Programs ................................................................................................................... ......15 4.1 EE Programs ..........................................................................................................15 4.1.1 Administrators ........................................................................................... 16 4.1.2 Policy Drivers ........................................................................................... 16 4.1.3 Funding Sources........................................................................................ 17 4.2 The Demand-Side EE Opportunity ........................................................................17 4.2.1 Rapid Growth in Demand-Side EE ........................................................... 17 4.2.2 EE Program Impacts ................................................................................. 18 4.2.2.1 Impacts to-date .................................................................................... 18 4.2.2.2 Projected Impacts ................................................................................ 22 

### 4.2.3 EE Potential .............................................................................................. 23 4.2.3.1 Evaluations of EE Potential ................................................................ 23 4.2.3.2 Overview of Results ............................................................................ 25 

### 4.3 Costs and Cost-Effectiveness of State EE Policies ................................................26 4.3.1 EE Cost-Effectiveness .............................................................................. 26 4.3.2 Costs of Saved Energy .............................................................................. 28 5. Other EE Strategies ........................................................................................................... ...30 5.1 Building Energy Codes ..........................................................................................30 5.1.1 Overview ................................................................................................... 30 5.1.2 Authority / Obligated Parties .................................................................... 31 5.1.3 Building Code Regulations Over Time ..................................................... 31 5.1.4 Remaining Market Potential ..................................................................... 33 5.2 State Appliance Standards .....................................................................................34

## Contents (Continued)
Page

iii 

### 5.2.1 Overview ................................................................................................... 34 5.2.2 Authority / Obligated Parties .................................................................... 34 5.2.3 Appliance Standards over Time ................................................................ 35 5.2.4 Remaining Market Potential ..................................................................... 36 5.3 Energy Service Performance Contracting ..............................................................37 5.3.1 Overview ................................................................................................... 37 5.3.2 History of ESPC Programs and the ESCO Business Model ..................... 38 5.3.3 Remaining Market Potential ..................................................................... 39 5.4 Volt/VAR Optimization .........................................................................................40 5.4.1 Overview ................................................................................................... 40 5.4.2 History of VVO Programs ........................................................................ 42 5.4.3 VVO Market Potential .............................................................................. 43 6. Utility Regulatory Models for Supporting Demand-Side EE ...........................................43 7. Energy Efficiency Certificates .............................................................................................46 8. Demand-Side EE Plan Scenario: Magn itude and Timing of Savings ..............................49 8.1 Approach ................................................................................................................49 8.2 Inputs......................................................................................................................50 8.2.1 Step 1: Determine Current Level of Demand-Side EE Performance ....... 50 8.2.2 Step 2: Determine EE Plan Scenario Level of Performance ..................... 51 8.2.3 Step 3: Determine Start Year for EE Plan Scenario ................................. 54 8.2.4 Step 4: Determine Start Year Level of Performance ................................ 54 8.2.5 Step 5: Determine Pace of Improveme nt from Start Year to EE Plan Scenario Level of Performance ................................................................. 54 8.2.6 Step 6: Determine Average Portfo lio Measure Life and Distribution of Measure Lives ....................................................................................... 55 8.2.7 Step 7: Determine Sustainability of EE Plan Scenario Level of Performance .............................................................................................. 57 8.2.8 Summary of the Demand-Side EE Pl an Scenario Construction ............... 58 8.3 Calculations............................................................................................................59 8.3.1 Step 1: Determine Annual Busi ness as Usual (BAU) Sales ..................... 59 8.3.2 Step 2: Determine Annual Incremental EE Savings as a Percentage of Sales .......................................................................................................... 60 8.3.3 Step 3: Determine Annual Incremental EE Savings (GWh) ..................... 60 8.3.4 Step 4: Determine Annual Expiring EE Savings (GWh) .......................... 61 8.3.5 Step 5: Determine Net Cumulative EE Savings (GWh) ........................... 61 8.3.6 Step 6: Determine Sales after Net EE (GWh) ........................................... 61

## Contents (Continued)
Page

iv 

### 8.3.7 Step 7: Determine Net Cumulative EE Savings as a Percentage of BAU Sales ................................................................................................. 62 8.3.8 Summary of Calculation Formulas and Results by Step for South Carolina ..................................................................................................... 62 8.4 Results ....................................................................................................................63 8.4.1 Summary of Results .................................................................................. 63 8.4.2 Results in Context ..................................................................................... 65 9. Demand-Side EE Plan Scenario: Assessment of Costs ......................................................66 9.1 Approach ................................................................................................................66 9.2 Inputs......................................................................................................................67 9.2.1 Step 1: Determine Incremental Electricity Savings .................................. 67 9.2.2 Step 2: Determine First-Year Program Costs and Cost Factors ............... 67 9.2.3 Step 3: Determine the Ratio of Program to Participant Costs .................. 69 9.2.4 Summary of Inputs for Demand- Side EE Cost Analysis .......................... 70 9.3 Calculations............................................................................................................71 9.3.1 Step 1: Calculate Annua l First-Year Costs ............................................... 71 9.3.2 Step 2: Calculate Levelized Cost of Saved Energy .................................. 72 9.3.3 Step 3: Calculate Annualized Costs .......................................................... 73 9.3.4 Summary of General Formulas and Results by Step for South Carolina ..................................................................................................... 73 9.4 Results ....................................................................................................................74 9.4.1 Summary of National Results ................................................................... 74 9.4.2 Results in Context ..................................................................................... 75 9.4.3 Costs per Tonne CO 2 

### Reduced ................................................................. 75 10. Analysis Considerations ....................................................................................................... 76 10.1 State EE Policies in the Ba seline Electricity Demand ...........................................76 10.2 Energy Information Administration Form EIA-861 as Data Source .....................77 References .................................................................................................................... .................86


## Appendices

Appendix 1: Summary of Recent Energy Effi ciency Potential Studies (2009-2014) ................................. 81 Appendix 2: Incremental Electricity Savi ngs Pace of Improvement Analysis ........................................... 82 Appendix 3: Demand-Side Energy Efficiency Plan Scenario: Data, Calculations and Results .................. 85v 

## List of Figures Page Figure 1. EE Program Portfolio ................................................................................................ .................. 16 Figure 2. Residential Energy Buildi ng Code Adoption (2005 to 2014) ..................................................... 33 Figure 3. Remaining Market Potential for ESCOs................................................................................. ..... 40 Figure 4. Hypothetical Voltage Profiles with and without VVO Technologies ......................................... 42 Figure 5. The Four-Tier Distribution of Demand-Side EE Program Measure Lifetime ............................. 56



## List of Tables Page Table 1. Relative Opportunities Provided by Key EE Programs and Building Codes ................................. 9 Table 2. U.S. Electric Utility EE Program Funding (2006-2013) ............................................................... 1 8 Table 3. U.S. State Adoptio n of EE Resource Standards ......................................................................... ... 18 Table 4. Aggregate Electricity Savings by State, Tribal Area and Territory .............................................. 20 Table 5. Summary of Impacts: Scenarios of Future Utility Customer-Funded Electric EE Programs ...... 23 Table 6. Summary of EE Potential Studies (2009 to 2014) ....................................................................... . 26 Table 7. Estimated Energy Savings Poten tial of State Appliance Standards .............................................. 36 Table 8. Summary of State Regulat ory Frameworks: December 2014 ....................................................... 44 Table 9. International Examples of Trading Energy Savings ..................................................................... 48 Table 10. 2013 State Levels of Incremental Annual Savings ..................................................................... 52 Table 11. 2013 Incremental Annual Sa vings – Investor-Owned Utilities .................................................. 52 Table 12. 2013 Incremental Annual Sa vings – Cooperative-Owned Utilities ............................................ 52 Table 13. 2013 Incremental Annual Sa vings – Municipal-Owned Utilities ............................................... 53 Table 14. Levels of Incremental Savings Required by State EERS ........................................................... 53 Table 15. NPCC Achievable EE Potential and Ac hieved Incremental Savings (1998-2010) .................... 58 Table 16. Summary of Demand-Side EE Plan Scenario Inputs .................................................................. 58 Table 17. BAU Sales for South Carolina ........................................................................................ ............ 60 Table 18. Annual Incremental EE Savings as a Pe rcentage of Sales for South Carolina ........................... 60 Table 19. Annual Incremental EE Savi ngs (GWh) for South Carolina ...................................................... 60 Table 20. Expiring EE Savings for South Carolina .............................................................................. ...... 61 Table 21. Net Cumulative EE Savings for South Carolina ........................................................................ . 61 Table 22. Sales after Net EE Savings for South Carolina ....................................................................... .... 62 Table 23. Net Cumulative EE Savings as a Percen tage of BAU Sales for South Carolina ........................ 62 Table 24. Summary of Results by Step for South Carolina ....................................................................... . 62 Table 25. Demand-Side EE Plan Scenario: Magnit ude and Timing of Net Cumulative Savings ............. 63

## List of Tables (Continued) Page

vi Table 26. Average Annual Savings Rates of Demand-Side EE Plan Scenario ........................................... 65 Table 27. Summary of EE Cost Analysis Inputs .................................................................................. ...... 70 Table 28. Calculation of Annual First- Year Costs for South Carolina ....................................................... 71 Table 29. Levelized Cost of Saved Energy for South Carolina (at 3% discount rate) ................................ 72 Table 30. Annualized Cost s for South Carolina ................................................................................. ........ 73 Table 31. Summary of Results by Step for South Carolina. ...................................................................... . 73 Table 32. First-Year Demand-Side EE Cost s (billions 2011$) (Continental U.S.) .................................... 74 Table 33. Annualized Demand-Side EE Costs (3% disc ount rate, billions 2011$) (Continental U.S.) ...... 74 Table 34. Levelized Cost of Saved Energy (3% discount rate, 2011$/MWh) (Continental U.S.) .............. 74
Table 35. Annual Growth Rates of Demand-Side EE Program Spending – Historic and DemandSide EE Plan Scenario ......................................................................................................... ....................... 751 

## 1. Introduction This chapter provides information on demand-si de energy efficiency (EE) as a measure for reducing carbon dioxide (CO 2 

### ) emissions from fossil fuel-fired electric generating units (EGUs). Specifically, this chapter supports th e discussion of demand-side EE as a non-BSER compliance measure in section V, “The Best System of Emission Reduction (BSER) and Associated Building Blocks,” and section VIII, “State Plans,” of the preamble. In addition, support is provided for the magnitude, timing, and cost of demand-side EE measures included in the illustrative demand-side EE plan scenario developed as a basis fo r the Regulatory Impact Analysis (RIA) and related discussion in secti on X, “Impacts of this Ac tion,” of the preamble.
The chapter is organized as follows:
2. Background
3. History of EE Policies in the United States
4. EE Programs
5. Other EE Strategies
6. Utility Business Models
7. Experience with EE Certificates

8. Demand-Side EE Plan Scenario: Magnitude and Timing of Savings

9. Demand-Side EE Plan Scenario: Assessment of Costs
10. Analysis Considerations
Sections 2 through 7 provide support for both the preamble sections cited above as well as background for the illustrative demand-side EE plan scenario presented in sections 8 and 9. Sections 8 and 9 provide details on the appro ach, inputs, calculations, and results for the magnitude and timing of savings and the assessment of associated costs for the demand-side EE plan scenario. Lastly, section 10 discusses c onsiderations relevant to sections 8 and 9.



## 2. Background Demand-side EE policies and programmatic effo rts have existed for decades and are now used in all 50 states. These strategies are inte nded to help states achieve energy savings goals,2 

### reduce the environmental impacts (including CO 2 

### emissions) of meeting energy service needs, save energy and money for consumers, and provi de a significant reso urce for meeting power system capacity requirements (EPA 2015). EE policies currently in pl ace are considered by states to be cost-effective strategies for contributing to thes e policy objectives. 1 

### Moreover, states – through their utilities, primarily – have been rapidly increasi ng 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 (ACEEE 2014c). In 2012, the cumulativ e impacts of these programs represented a
3.7% reduction in national elect ricity demand (EIA 2012a). And, EE spending is projected to continue to grow at a substantial rate. A r ecent study by Lawrence Berkeley National Laboratory (LBNL) projects EE program spending to reac h $8.1 billion to $12.2 billion (“Medium Case” and “High Case,” respective ly) in 2025 (LBNL 2013c).

2.1 EE Technologies and Practices EE is using less energy to provide the same or greater level of service. Demand-side EE refers to an extensive array of technologies, practices and measures th at are applied throughout all sectors of the economy to reduce energy de mand while providing the same, and sometimes better, level and quality of serv ice. Utilities employ an array of strategies in implementing EE programs, these include financial in centives such as rebates and loan s, technical services such as audits and retrofits, and educational campaigns about the benefits of EE improvements. The purpose of these EE programs is to induce EE investments and practices that would not otherwise occur in the presence of market failures and behavioral impediments. 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 atti cs and walls, sealing of air leaks, and undertaking home en ergy audits leading to custom ized whole home retrofits. Opportunities for cost-effective EE in commercia l buildings include opt imization of heating, ventilation, and air conditioning (HVAC) system s, upgrades of windows, and use of more efficient office equipment at replacement. In the industrial sector key EE st rategies include motor upgrades and maintenance program s, recovery of waste heat streams, and optimization of processes through modern instrume ntation and controls systems.

1 See below for discussion of cost-effectiveness and related cost tests used by states to evaluate EE programs.3 

### The opportunity presented for economic invest ment in EE is dynamic, growing over time as technologies and practices advance, as populat ions grow, and as investment occurs in the construction of new homes, buildings, and indus trial facilities. As ne w 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 investment in cost-effective EE, states are findi ng renewed opportunities as they develop more sophisticated and effective stra tegies, evolving from a focus on i ndividual end-uses and products to whole-building and systems-ba sed strategies that account for the interactions between the many energy end-uses in buildings and industr y (ACEEE 2013). As will be discussed, the experience in the U.S. has been that, on balance, a persistent and large po tential for achievable
and cost-effective EE has remained even as the impact of past and ongoing efforts have
accumulated.

2.2 Barriers to EE Investment Despite the persistent and larg e potential for elec tricity savings through investment in EE technologies and practices, market failures, as well as non-market barr iers and behavioral impediments, limit the realization of the many be nefits of these invest ments. Several market
failures that lead to ineffici encies in energy use are wide ly recognized by analysts and practitioners, and are discussed exte nsively in the literature (Levine et al. 1995; Gillingham et al. 2009; Helfand and Wolverton 2011). In the presence of market failures, us ers of electricity, or those making EE investments, face prices or incen tives that prevent them from weighing the full
social benefits and costs of their investments and thus under-invest in approaches to reduce electricity consumption. The behavioral impedi ments explain why individuals do not always make EE investments that are seemingly in thei r own best interest. Some of the most common examples of these mark et failures include:

 Pollution externalities. Fossil 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 reflec t the externalities associated with energy use would lead to investments in EE below the socially optimal levels.4 

###  Imperfect information. Energy users often lack accurate information about energy savings and other attributes of energy efficient products or practices to understand the
costs and benefits of EE investments.

 Split incentives (or the “principal-agent problem”). Incentives of individuals who
make EE investment decisions are not always aligned w ith incentives of those who use and pay for energy. Examples include misalignment between landlords and tenants, between builders and homeowners, a nd within organizations and institutions (DeCanio 1998; McKinsey 2007).

 Credit constraints . Limited access to credit may prevent some consumers, especially low-income consumers, from making cost -effective EE improvement with higher upfront cost.

 Under-provision of research and development (R&D). Because of the public good nature of knowledge, firms involved in t echnology development may be less willing to invest in R&D, leading to sub-optimal levels of EE investments from a social perspective (Jaffe et al. 2003).

 Supply market imperfections. Market for energy efficient pr oducts is incomplete and fragmented, leading to unde rinvestment in innovation a nd limited EE supplied by the manufacturers. In addition, supply chain fragmentation may also add complexity to the purchase and installation of otherwise ec onomically rational investments, thereby slowing the adoption of EE technologies (Fischer 2005).

 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 consum ption and other decisions, including EE investments (Gillingham and Palmer 2014).

Other factors, such as hidden costs, risk and uncertainty experienced by both consumers and suppliers of energy efficient products, and heterogeneity among consumers, also influence
EE investment decisions. 2 

### Examples of such barriers include:

2 It has been recognized that there is a difference between cost-effective energy efficiency investment levels, based on costminimizing consideration, and observed leve ls of energy efficiency. This phenomenon, also termed ‘energy paradox,’ or ‘energy5 

###  Risk and uncertainty . Adopting an unfamiliar, typically more expensive EE technology can be an uncertain undertaking gi ven the lack of credible information on product performance and future energy pr ices, and the irreve rsibility of the investment. Imperfect or asymmetric inform ation can exacerbate the perceived risk of EE investments and help expl ain why consumers and firms do not always invest in EE measures. Suppliers also face risk and un certainty, without perfect information of consumer preferences for EE. In the presen ce 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 disadvantage 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. Lenders can be reluctant to invest in EE loan portfolios in part because EE 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 a doption of socially bene ficial EE opportunities. Examples of effective EE policies and program s include public funding of R&D, information programs (such as energy labeling, the ENERGY STAR program, and consumer education), rebates for high-efficiency produc ts, product energy performance st andards, financing and loan programs, and technical assistance.

2.3 EE Policies

2.3.1 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, including:

efficiency gap,’ has been studied extensiv ely in the literature. See, for example, Jaffe and Stavin s 1994, DeCanio 1998, Sansta d and Howarth 1994, DeCanio and Watkins 2008, and Allcott and Greenstone 2012.6 

###  Reducing costs to elec tricity customers,

 Providing a significant resource for mee ting power system capacity needs,

 Meeting energy savings goals,

 Stimulating local economic de velopment and new jobs, and

 Reducing the environmental impacts of mee ting electricity service needs (EPA and DOE 2006; EPA 2015).

EE policies currently in place are considered by states to be cost-eff ective strategies for contributing to each of these policy objectives (EPA and DOE 2006). While each of these objectives, and others, contribute to the motivation of policymakers and utilities to pursue EE policies programs, reducing energy costs to consum ers over the long term is a primary purpose in pursuing these policies. In a ddition, EE policies are central to meeting state objectives for reducing CO 2 

### emissions from the power sector. EE polic ies 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 with statutory requirements for greenhouse gas reductions (EPA 2015). 3 

### Consistently, evaluations of the economic pot ential for carbon dioxide reducti ons from the United States’ power sector identify demand-side EE as the lowest cost strategy (typically, as noted above, with positive net present value) as well as the strate gy having the greatest reduction potential. For example, McKinsey found that EE accounted for mo re than 60% of their mid-range potential for greenhouse gas reductions from the U.S. power sect or and that it was av ailable at positive net present value if “persistent barr iers to market efficiency” co uld be addressed (McKinsey 2007).
Similarly, economy-wide studies of climate m itigation scenarios confirm that EE plays a critical role in reducing the costs and enhanci ng the flexibility of meeting long-term climate stabilization targets (Clarke et al. 2014; Kriegler et al. 2014). Analysis by the International Energy Agency (IEA) suggested that in order to stabilize carbon concentrat ion in the atmosphere at 450 ppm, as much as 44% of the estimated global abatement potential in 2035 derives from greater EE in the world economy (IEA 2012b). Se veral recent Energy Modeling Forum (EMF) studies have investigated the ro le of technology in achieving climat e policy objectives in the U.S.

3 States with GHG reduction laws include: California, Connecticut, Ha waii, Maine, Maryland, Mass achusetts, Minnesota, New Jersey, Oregon, and Washington.7 

### (“EMF 24” and “EMF 25” studies ) and globally (“EMF 27” study). 4 

### These studies concluded that compared to business-as-usual EE, improve ments in EE in various economic sectors would
slow the increases of GHG emissions in the sh ort run, substantially re duce the costs of GHG mitigation (on average, by about 50%), and ea se the technology transformation pathways to achieve long-term carbon reduction goals (K riegler et al. 2014; Kyle et al. 2011).
Several economic studies (including EMF25 studies) examined the role of EE policies (such as EE standards and subsid ies) in relation to other clim ate policy instruments (such as carbon taxes). These studies found that when EE policies address market failures, they are welfare improving and can complement climate policy (Comstock and Boedecker 2011; Fischer 2005; RFF 2010). In addition, EE policies are rec ognized to be an appropriate response to demonstrated market failures and behavioral impe diments, particularly in contexts where these
failures have broader societal im plications such as environmenta l externalities (G illingham et al. 2009).
In addition to providing cost-effective oppor tunities for reducing GHG emissions, EE is recognized to provide other co-b enefits, including air quality and public health benefits, waste reduction from energy generation, energy securi ty, energy system reliability, community economic and social development, and cons umer amenities (RAP 2012). EE investments and policies are also found to spur productivity growth, techno logy learning and innovation (Boyd and Pang 2000; Worrell et al. 2003). Recently, more attention has been paid to developing methods for recognizing these co-b enefits and integrating them in to the cost-benefit analysis framework used by state utility commissions a nd administrators of EE programs. These cobenefits have not been fully accounted for in the EPA analysis.

2.3.2 Policy Types EE policies come in many forms. The most pr ominent and impactful EE policies in most
states include those that drive de velopment and funding of EE programs, and building energy codes. Other policies that ar e leading to significant impact s include state appliance and equipment standards, “lead by example” strategi es targeting energy us e in state operations

4 Energy Modeling Forum (EMF) is a consortium of energy economists and energy econom ic 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 economic s of climate change and the reports of the Inter governmental Panel on Climate Change (IPCC).8 

### through energy services performance contracting, and “volt-VAR optimization.” See section 5 for further description of these policies, thei r history, and potential fo r achieving significant electricity savings. Comparing the relative impact (potential or ach ieved) of the different policy types is challenging, particularly to do so comp rehensively, 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. 5 

### This information is generally based upon measurement and veri fication studies submitted annually, most commonly to state utility commissions, and reported to the Ener gy Information Administration (EIA) for all program administrator types (including all utility types, third-parties, an d government agencies). EE program data reported to EIA in cludes: incremental savings (als o referred to as “first-year” savings), cumulative savings, peak demand savi ngs, program costs broken down by component, and composition by end-use sector (residential, commercial, industrial) . As of 2013 EIA ended
their collection of cumulative savi ngs data. In 2013, util ities and other program administrators in
all 50 states and the District of Columbia st ates reported savings from EE programs to EIA through form EIA-861. At a national level, the EP A is not aware of a comprehensive dataset reported by states of the achieved im pacts of strategies other than t hose that lead to investment in EE programs. However, state and regional-level information does exist. For example, the Northwest Power and Conservation Council (NPC C) has been compiling the impacts of EE policies (including utility and third-party EE prog rams, 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, w ith building energy codes accounting for the remaining savings (NPCC 2010).
Another representation of the relative oppor tunity provided by different state EE strategies is presented by evalua tions 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 1. EE programs account for 77% and 82% of achieva ble savings in studie s by ACEEE and Georgia

5 In 2011, EIA began collecting data from third-party administrators of programs. Prior to 2011, this was a significant shortcoming in the breadth of the data collected. The breadth and quality of information collected through Form EIA-861 has improved over time, however, outside entities (e.g., ACEEE and CEE) have found that the data can be improved through expert review and supplementation with other data sources. While now fairly comprehens ive, the EIA data can be improved further with regards to data quality and consistency. See section 10 for further discussion.9 

### Tech, respectively (ACEEE 2014a; Wang and Brow n 2013). 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 ex ample of the impacts of EE programs relative to other state EE policies. The Massachusetts Glob al Warming Solutions Act of 2008 established statewide limits on greenhouse gas (GHG) emi ssions of 25 percent below 1990 levels by 2020. To achieve this target, Massachus etts is relying upon an integr ated portfolio of clean energy policies. State EE policies are expected to provi de the largest contribution to meeting the 25 percent target with u tility sponsored EE programs and stat e building energy codes accounting for 76% and 17%, respectively, of those policie s (Massachusetts EOEEA 2010). In their 2013 progress report, Massachusetts indicates that they are gene rally on track for meeting or exceeding these projections (Massachusetts EOEEA 2013).

Table 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% Source: ACEEE 2014a, and Wang and Brown 2013



### The next section reviews the le ngthy history of EE policies in the U.S. In the following section, the focus returns to the EE programs highli ghted here for an in-depth review of this important strategy.



## 3. History of EE Policies in the United States Demand-side EE has been repeatedly suppor ted through federal po licies and market innovations over the past four decades, in rec ognition of its multiple benefits, including increased energy security, reduced emissions from the power sector, lower customer energy bills, enhanced economic development and job creation, a nd improved reliability and resiliency of the electricity system. Following the 1973 Organiza tion of Petroleum Exporting Countries (OPEC) oil embargo, the Nixon administrated establishe d the Federal Energy Administration, imposed oil price regulations, and created Project Independence to put the U.S. back on the path toward improved energy security.10 

### The 94 th 

### Congress took the first major U.S. le gislative action on en ergy through the 1975 Energy Policy and Conservation Act (EPCA). Am ong other provisions, EPCA authorized the creation of vehicle fuel economy standards, and labeling requirements and efficiency standards for major household appliances (Congress 1975). Also in 1975, the American Society of Heating, Refrigerating, and Air-conditioning Engin eers (ASHRAE) developed the first national model building energy code, 90-75 “Energy C onservation in New Bu ilding Design,” for residential and commercial buildings (BCAP n.d.).
Continuing this trend of energy policy initiatives, Congress passed the Energy
Conservation and Production Act in 1976, which authorized the cr eation of mandatory national building energy performance standards (BEPS) wh ich were repealed in the 1980s, provided loan guarantees for saving energy in public and commercia l buildings, authorized grants to states for low-income weatherization and state energy conservation programs, and encouraged the development of electric utility rate structures designed to save energy.
Federal energy efficiency policy action c ontinued during the Ca rter administration.
President Carter’s 1977 National Energy Plan included energy effi ciency as “the quickest, cheapest, and most practical source of energy In th e same year President Carter also issued the first executive order on energy efficiency, Ener gy Policy and Conservation E.O. 12003 requiring federal agencies to develop energy conservati on plans (Woolley and Peters n.d.). Other policy
actions in 1977 included the creat ion of the U.S. Department of Energy, thereby consolidating multiple federal agencies and programs, includi ng many that support energy efficiency, into one cabinet-level department.
In 1978, partly in response to the Administ ration’s new energy plan, Congress enacted the National Energy Act, including the Nationa l Energy Conservation Policy Act (NECPA), which made EPCA’s provisions for federal effici ency standards mandatory and pre-emptive of state law; established the Reside ntial Conservation Service (RCS ), which required utilities to provide home energy audits; and authorized th e Institutional Conservation Program (ICP), a grant program for schools and hospitals. NECPA also provides the core authority for the federal government’s energy management goals and re quirements, which have been updated and amended since 1978 (Congress 1978a). Congress also passed the Public Utility Regulatory Policies Act (PURPA) in 1978 which, among othe r provisions, introduced competition into11 

### generation markets, and required state utility co mmissions to consider ra temaking practices that encourage energy efficiency (Congress 1978b). As state commissions pursued PURPA-related requirements as well as their own related policy in terests, integrated reso urce planning became more widely used, and the concept of demand-side management (DSM) emerged, in which utilities implement programs to influence cust omer loads through energy efficiency and other demand-side initiatives (RAP 2011).
The energy service company (ESCO) busines s model also emerged during the 1970s and 1980s. Leaders in the Federal Energy Administra tion, such as Roger Sant, defined the term “energy services” in the 1970s as the services people want from energy, such as lighting and comfort, and distinct from energy commodities such as fuels and electricity. This concept gave rise to the energy services bus iness, in which ESCOs could re duce energy service costs through energy efficiency. The ESCO industry’s growth was supported by utilities paying ESCOs for services such as utility-supported energy audits , and by helping federal agencies implementing federal energy management goals (NAESCO 2011). ESCOs also succeeded in contracting with state and local and institutional customers, whos e limited ability to finance energy improvements matched up well with ESCOs’ ability to bring pr ivate capital to energy improvement projects.
Federal policy support for energy efficiency declined dramatically in the 1980s under the Reagan Administration as energy supplies impr oved, prices stabilize d, and a less favorable climate for federal regulation emerged. The BE PS and RCS programs were repealed, and a “nostandard” standard was issued under NECPA applianc e standards authority. Some states acted to fill these policy vacuums, enacting their own build ing codes, appliance standards, and utility programs. Appliance manufactur ers, concerned about the prolif eration of multiple, different state standards, supported a compromised effort that resulted in th e 1987 National Appliance Energy Conservation Act (NAECA). NAECA, whic h was further amended in 1988, established the first national home appliance efficiency standards by amending NECPA (NAECA 1987). During the 1980s, state and local residential and commercial bu ilding energy codes were also adopted to achieve greater energy savi ngs in new construction (BCAP n.d.).
As integrated resource pla nning and DSM practices continue d to spread in the 1980s, and electric utilities began to o ffer customers DSM programs under formal regulatory processes, including programs to incentivize more effici ent technologies and practices. These DSM12 

### programs sometimes resembled utility cons ervation programs launched in the 1970s, but signified a larger shift to integrate energy effici ency into broader utility planning and operations (ACEEE 2012b). The rapid growth of DSM in the 1980s was the result of the 1970s’ increased energy prices, energy security concerns, and th e environmental conseque nces of electricity generation. Rising power plant capital and fuel costs resulted in ra te increases, rising customer dissatisfaction, and new requirements for utilitie s to pursue IRP to manage electricity demand, supply and risk (LBNL 1996). By the end of the decade, 41 states had made some progress towards implementing a rule to require integrated resource planning by elec tric utilities (Synapse 2011). Utilities also played a key role in s upporting ESCO projects into the 1990s by providing incentives that offset energy effi ciency project costs, as inte grated resource plans found that investments in energy efficiency were lo wer-cost alternative to new power plants.
The 1990s began with the Clean Air Act Am endments and the Pollution Prevention Act of 1990, each of which in their own ways recognized the role energy efficiency can play in protecting human health and the environment. The Clean Air Act amendments provided the authority to use demand-side energy efficiency as a compliance option that gives utilities flexibility to obtain need ed sulfur dioxide emission reductions through programs that encourage customers to conserve energy. In 1992, EPA introduced the ENERGY STAR label for office products which manufacturers volunta rily design to perform at supe rior levels of energy savings. President Clinton signed Execu tive Order 12845 in 1993 requiring federal agencies to purchase ENERGY STAR qualified products. The Pollution Pr evention Act gave EP A new authority to reduce emissions via voluntary and other approaches that reduce pollution sources.
The Energy Policy Act of 1992 (EPAct 1992) also advanced energy efficiency through a broad suite of policies, incl uding federal appliance and equi pment efficiency standards, requirements for states and the Department of Energy on model building codes, new requirements for federal energy management, new provisions on least-cost planning for federal electric utilities, and new research and deve lopment authorizations. EPAct 1992 also included provisions to encourage states to adopt policies to support energy savings through electric utility regulatory reforms (EPAct 1992). In 1996, DO E and EPA signed a joint Memorandum of Understanding to cooperate in delivering the growing suite of ENERGY STAR programs, increasing the number of products and markets c overed and leveraging expertise and resources across the federal government.13 

### During the early 1990s, electric utility DSM programs grew rapidly, with energy savings exceeding 50 billion kilowatt-hours (kWh) and utility spending at $2.7 billion in 1994 (ACEEE 2000). About 95 percent of the reported energy sa vings, though only slight ly over half of the spending, was attributed to programs reported as targeting energy e fficiency (ACEEE 2000). These utility programs accelerated the commerci alization of advanced energy efficiency technologies, often by partnering with federa l energy efficiency programs like ENERGY STAR (ENERGY STAR 2012a, 2012b). By the late 1990s, funding for utility-sponsored energy efficiency programs fell to less than one-third of its 1994 peak, as about half of the states from a wave of electricity restructur ing policies, accompanied by in creased political and regulatory pressures to hold down electricity prices (EPA and DOE 2006). Some restructured electric utilities shifted their focus away from regulat ed DSM programs to unregulated power marketing and ESCO businesses to deliver energy sales and se rvices directly to cu stomers. However, the
business model for these market-based ESCOs limite d their ability to reach all but the largest industrial, commercial and inst itutional customers. Recognizing the limits of private-market delivery of EE, and seeking to preserve the be nefits of EE programs, as the more states restructured their electricity markets in the 1990s , some 20 states adopted public benefit funds (PBFs) to ensure continuati on of ratepayer-funded programs. Several states and local governments also expanded the authorized use of ESCO services in public facilities via lead-byexample policies.
Following the year 2000, energy efficiency policies and programs gained increased attention and, supported by decades of implem entation experience, began to be widely recognized as a legitimate, measureable, and lo w-cost option to serve the nation’s energy and environmental goals. Congress displayed a strong commitment to energy efficiency in the Energy Policy Act of 2005 (EPAct 2005), wh ich included sweeping energy efficiency provisions: it authorized $250 million over five years to provide energy efficiency appliance rebates, $1.8 billion over three years for weatherizati on services, included di rect enactment of federal efficiency standards, and authorized a national consumer education campaign on energy efficiency (EPAct 2005). At the same time, ho wever, the ESCO market declined due to an authorization lapse for the federal Energy Sa vings Performance Contracting (ESPC) program
and loss of market confidence for industrial fi nancing mechanisms (NAESCO 2011). This trend
was soon reversed as ESCOs increased focus on st ate and local public buildings and innovated in14 

### order to provide customers new technologies and financial models . Eventual resolution of the ESPC authorization program, and executive orde rs by both President Bush and President Obama have also required more aggressive energy sa vings in federal facilities (White House 2007, 2009, 2015).
By the mid-2000s, the electric utility and region al electricity market s also solidified the validity of rate-payer funded en ergy efficiency programs as system resources. In 2006, the National Action Plan for Energy Efficiency Leader ship Group of more th an 60 leading gas and electric utilities, state agencies, energy cons umers, energy service providers, environmental groups, and energy efficiency organizations rele ased five policy reco mmendations recognizing energy efficiency as a high-priority energy resour ce. States, utilities, and key stakeholders across 49 states joined in making aggressive commit ments to energy efficiency and endorsing the
Action Plan recommendati ons (EPA and DOE 2006).
In the years following the Action Plan’s effo rts, about half the states enacted Energy Efficiency Resource Standards (EERS), which ar e long-term, quantified energy savings targets designed to drive ratepayer-funded program plans. These policy dr ivers lead to the evaluation, planning, and adoption of EE programs and a ssociated budgets, which are supported through different funding mechanisms. Funding for utility electricity energy efficiency programs has in turn increased rapidly, from $1.6 billion in 2006 to $6.3 billion in 2013 (ACEEE 2014c).
At the regional level, the wholesale electric ity market operator in New England began to allow energy efficiency to participate in regi onal capacity markets as part of a 2006 settlement agreement with their regulator, the Federal Energy Regulatory Commi ssion (FERC). Since 2009, energy efficiency has been compensated for its measureable contri bution to meeting New England’s regional capacity needs, helping to ensu re sufficient electricity will be available in the future to reliably meet demand across the region. PJM, the wholesale elect ricity market operator
across 13 states around the Mid-Atla ntic region, also integrated energy efficiency into this capacity market rules in 2009 (Booz Allen 2012).
In 2007, the Energy Independence and Securi ty Act (EISA) passed due to growing concerns over both energy pri ces and climate change. EISA included several new, and strengthened existing, appliance and equipment standards including a major incandescent lamp efficiency standard, authorized industrial energy efficiency pr ograms, and included provision for15 

### states to consider adopting policies to support gr eater energy efficiency. EISA also enacted the federal agency savings goals of President Bush’s 2007 Executive Order 13423, Strengthening Federal Environmental, Energy and Transporta tion Management, which required even more aggressive building energy savings than previous Executive Orders.
The American Recovery and Reinvestment Act of 2009 (ARRA 2009) recognized energy efficiency for its economic benefits, incl uding job creation, bill savings and economic development. ARRA 2009 allocated DOE around $16 billion for energy efficiency programs, including appliance rebates, gr ants to local governments, state energy programs, weatherization assistance, and smart gr id grants (DOE 2012c). These investments brought results: for example, ARRA’s weatherization program helped more than 650,000 low-income families nationwide, exceeding the original target of 600,000 homes. Th ese retrofits improved the energy efficiency of the homes, saving families an average of $437 a year on their energy bills (DOE 2012c).



## 4. EE Programs

4.1 EE Programs Portfolios of EE programs are comprised of nu merous measures and measure types that are applied across all sectors of electricity end-users. Figure 1 illustrates the multi-level composition and breadth of EE program portfolio s (LBNL 2013b). The diversity represented by a typical portfolio of EE programs implemented by a utility (or other program administrator) is an important characteristic releva nt to analysis of EE policies. Every detailed program type (as illustrated in the lower half of the figure) represents a unique set of characteristics including costs of energy saved, ratio of program to part icipant costs, investment life, scale, M&V approach, etc. 6

6 See following sections for discussion of these factors.16 

### Figure 1. EE Program Portfolio 7

Source: LBNL 2013b



### 4.1.1 Administrators EE programs are administered by a variety of entities (“program administrators”) including utilities of all owne rship types (investor-owned, mu nicipals, and cooperatives), nonprofit and for-profit third-partie s (e.g., Vermont Energy Investment Corporation), and state and local government agencies (e.g., NYSERDA). Most EE programs (including all investor-owned utilities which account for more than 75% of reported savings 8 

### ) are overseen by state utility commissions, which review and approve program plans, projected impacts, and associated budgets; and establish annual reporti ng and M&V requirements (EIA 2013).

4.1.2 Policy Drivers EE programs result from a number of different policy approaches or “drivers.” These include EE resource standards (EERS) (26 states), system benefit charges (14 states), integrated

7 The “EM&V” box is not comparable to the other program types and is not relevant to this discussion. It was included in the referenced source to indicate that EM&V is a key activity within a program portfolio. 8 EPA calculation using EIA-861 2013 data.17 

### resource planning (IRP) requireme nts (34 states), demand-side management plan or multi-year EE budget (28 states), and statutor y requirement to acquire “all- cost-effective EE” (6 states) (ACEEE 2014c; LBNL 2013c). 9 

### EERS is a more recently developed strategy and has quickly become the leading driver of the rapid growth in EE programs due to thei r clear goals and proven success as a policy tool (ACEEE 2011). These po licy drivers lead to the evaluation, planning, and adoption of EE programs and associated budgets, which are supported through different funding mechanisms.

4.1.3 Funding Sources Funding sources for EE programs are varied but for most states are dominated by revenues collected from ratepaye rs through electricity surcharges , typically ranging from $1 to $4 per megawatt-hour (ACEEE 2014c). More recently adopted funding sources include proceeds from the auction of allowances in the Regi onal Greenhouse Gas Initia tive (RGGI) states and from EE resources bid into the forward cap acity market operated by the New England Independent System Operator (NE-ISO). Ratepa yer-funding accounts for more than 90% of total EE program support nationally.

4.2 The Demand-Side EE Opportunity As discussed, states are employing a number of EE strategies with EE programs yielding the most significant impacts both hi storically 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, a nd other characteristics. In this section we address the rapid 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.

4.2.1 Rapid Growth in Demand-Side EE Funding for EE programs has increased rapidl y in recent years driven by recent policy innovations and increasing evidence of the effectiveness of these new strategies. Table 2 presents

9 The number of EERS states is from ACEEE 2014c and includes stat es with explicit EERS, those with long-term energy savings targets for individual program administrators , and those with EE incorporated as an eligible resource in a renewable portfolio standard. The numbers for the other policy approaches are from LBNL 2013c.18 

### levels of EE program funding in the U.S. since 2006 (ACEEE 2014c). In the previous five years, funding increased by more than 250%, from $1.6 billion in 2006 to $5.9 billion in 2011.

Table 2. U.S. Electric Utility EE Program Funding (2006-2013)

Year 2006 2007 2008 2009 2010 2011 2012 2013 Electric Efficiency Program Budgets (billions of $s, nominal) 1.6 2.2 2.6 3.4 4.6 5.9 5.9 6.3

Source: ACEEE 2014c



### Key new state policies that ha ve helped to drive these ra pid increases in EE program funding include EERS, electricity savings goals, and “all cost-effective EE” requirements. The adoption of EERS, in particular, increased throug h this period and clearl y has been the primary driving force behind the increasing success of and investment in EE programs. Table 3 shows the number of states adoptin g EERS by year (ACEEE 2014c).

Table 3. U.S. State Adoption of EE Resource Standards

Year States Adopting an EERS Total 1997-2004 California, Hawaii, Texas, Vermont 4 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

Source: ACEEE 2014c



### 4.2.2 EE Program Impacts

4.2.2.1 Impacts to-date The primary sources for EE program inform ation (including costs and impacts) are annual EE program reports required by utility commi ssions, or cooperative or municipal utility boards of directors. Program impact results pr ovided in these reports are based on evaluation, measurement, and verification (E M&V) procedures and methods us ed to estimate the electricity19 

### savings from EE programs within the program portf olio. The EIA has been collecting data on EE programs through Form 861, “Annual Electric Power Industry Report,” for more than three
decades. 10 

### The data collection reflects an increasing degree of breadth and detail over time. For example, third-party-administered programs 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 ad ditional sources including surveys of utility commissions and program administ rators, and published their own annual reports that capture EE program impacts (ACEEE 2014c; CEE 2015).

The EPA has relied on the EIA Form 861 datase t for identifying historical impacts of EE programs by state, tribe and terr itory. Specifically, the reported sales data and incremental electricity savings in the 2013 EIA 861 dataset ar e used to estimate electricity EE impacts by

state. 11 

### EIA data is reported by program administra tor (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 util ities). Program administrators in 50 states and the District of Columbia reported savings in 2013. The EPA has compiled this information to derive aggregate electricity sale s and incremental electricity savi ngs at the state, tribal and
territorial levels. 12


### Three tribal areas (Navajo, Ute, and Fort Mo jave) and two territories (Puerto Rico and Guam) have affected EGUs under the final rule. The 2013 EIA form 861 data contain information to identify historical electricity sa les and EE activities in these tribal areas and
territories. The tribes are located in three stat es – Arizona, New Mexic o, and Utah. Electricity
sales within the tribal areas are taken out of the total sales within these three states and reported separately. For Arizona, this includes utility sa les in the “Navajo Trib al Utility Authority” (Navajo tribe) and the “Aha Macav Power Servi ce” (Fort Mojave tribe). For New Mexico, this includes utility sales in the “N avajo Tribal Utility Authority” (Navajo tribe). For Utah, this

10 More information on EIA Form 861 can be found at http://www.eia.gov/el ectricity/data/eia861/

. 11 The analysis uses 2013 EIA 861 data, available at http://www.eia.gov/electricity/data/eia861/

. 12 The sub-form “Retail Sales 2013” is used to calculate electric ity sales. The sub-form contains details on utility electricity sales by service type (energy, delivery, and bundled) and by sector, within each state and territory. In addition, while EIA’s 861 su rvey encompasses most of each state’s utility sales, EIA makes an ad justment to account for the sma ll remainder of data not included in each utility’s entry. The ad justed data for “delivery” and “bundled” sales fro m all sectors are used to calculate sales at t he state and territory levels.20 

### includes utility sales in the “Nav ajo Tribal Utility Authority” (N avajo tribe) and the “Moon Lake Electric Assn, Inc.” (Ute tribe).
Table 4 provides a summary of the estimated el ectricity savings data by state, tribal area and territory for the latest year of available data . At the national level, incremental electricity savings 13 

### in 2013 was 0.66% of retail sale s with individual state valu es ranging from 0.00% to 2.00%. Cumulative electricity savings 14 

### (representing the remaining impacts of programs from all prior years) reported at th e national level for 2012 repres ent 3.73% of retail sales with
individual state values ranging from 0.0% to 15.44%. 15 

### 2012 was the most recent year that EIA Form 861 included cumulative savings data, whic h has been discontinued in the 2013 version of
the form.

Table 4. Aggregate Electricity Savings by State, Tribal Area and Territory

State Incremental Savings as a Percent of Retail Sales (%) 2013 Cumulative Savings as a Percent of Retail Sales (%) 2012 Alabama 0.10% 0.78%
Arizona 1.59% 5.43%
Arkansas 0.49% 0.39%
California 1.16% 13.67%
Colorado 0.92% 4.67%
Connecticut 1.00% 13.39%

Delaware 0.01% 0.00%
District of Columbia 0.45% 0.57%
Florida 0.17% 3.60% Georgia 0.26% 0.67%

Idaho 0.54% 6.20%
Illinois 1.06% 2.15%
Indiana 0.93% 1.72%

Iowa 1.18% 7.80%

13 Incremental savings (also known as first-ye ar savings) represent the reduction in electr icity use in a given year associated w ith new EE activities in that same year, either new participants in DSM programs that already existed in the previous years, or new DSM programs that existed for the first time in the current year. 14 Cumulative savings (also known as annual sa vings) represent the reduction in electric ity use in a given year from EE activitie s in that year and all preceding years, taking into account the lifetimes of installed measures. 15 EPA recognizes concerns associat ed with consistency and quality of 861 data th at different reporting en tities may have used different methodologies to estimate savings and the EIA 861 da ta are self-reported. Over time, there has been increased standardization in data reporting. We belie ve his dataset remains the most comprehensive publically available dataset. See sect ion 3.10 for further discussion.21 State Incremental Savings as a Percent of Retail Sales (%) 2013 Cumulative Savings as a Percent of Retail Sales (%) 2012 Kansas 0.01% 0.24% Kentucky 0.38% 1.04%
Louisiana 0.03% 0.00%

Maine 1.14% 5.42% Maryland 1.07% 2.47%

Massachusetts 1.06% 6.27% Michigan 1.15% 2.77%
Minnesota 1.13% 13.10% Mississippi 0.23% 0.50%

Missouri 0.49% 0.55%

Montana 0.54% 5.85%

Nebraska 0.15% 0.99%

Nevada 0.48% 6.19% New Hampshire 0.15% 4.90% New Jersey 0.70% 1.04%
New Mexico 0.57% 1.87%

New York 1.20% 6.89%
North Carolina 0.60% 1.26%

North Dakota 0.02% 0.22%
Ohio 1.04% 3.20%
Oklahoma 0.26% 0.70%

Oregon 1.20% 7.72% Pennsylvania 0.91% 3.08%

Rhode Island 2.00% 11.22%
South Carolina 0.46% 1.12%
South Dakota 0.13% 0.33%

Tennessee 0.31% 1.76%

Texas 0.20% 1.54%

Utah 0.82% 6.70%

Vermont 1.77% 15.44% Virginia 0.02% 0.30% Washington 1.00% 7.37% West Virginia 0.25% 0.20%

Wisconsin 1.32% 6.61%22 State Incremental Savings as a Percent of Retail Sales (%) 2013 Cumulative Savings as a Percent of Retail Sales (%) 2012 Wyoming 0.17% 0.71%

Navajo 0.00% 0.00%

Ute 0.00% 0.00% Fort Mojave 0.00% 0.00%
Continental U.S. Total 0.66% 3.76%
Alaska 0.05% 0.15%
Hawaii 0.04% 0.33%
Puerto Rico 0.00% 0.00%
Guam 0.00% 0.00%
U.S. Total and Territories 0.66% 3.73%

Source: EPA calculation based EIA 2013 and EIA 2012a



### 4.2.2.2 Projected Impacts In 2013, Lawrence Berkeley National Laborator y (LBNL) published an update to a 2009 analysis and projected future spending le vels and savings through 2025 from EE programs funded by electric and gas utility customers in the United Stat es under three scenarios (high, medium, and low cases) (LBNL 2013c). The scen arios represent “a rang e of potential outcomes under the current policy envir onment” and were based on de tailed, bottom-up analysis of existing state EE policies. Signi ficantly, the study presumes no new major policy developments such as a “national EE standard, clean energy sta ndard, or carbon policy” a nd specifies that such policy changes could “result in customer-funde d EE program spending and savings that exceed the values in our High Case.”
The study concludes that effici ency programs are “poised for dramatic growth over the course of the next 10 to 15 years” with the mo st significant increases o ccurring in regions with lower levels of program spending, historicall y, including the Midwest an d 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 th e high scenario, spending more than doubles from 2012 levels to $12.2 billion in 2025. Incremen tal savings levels grow commensurately, to
0.8% and 1.1% of sales under the medium and hi gh scenarios, respectively. The study results indicate that under the high scen ario 20 states would be achie ving 1.5% or higher levels of23 

### incremental savings, with 11 of those reaching or exceeding 2.0%. 16 

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

Table 5. Summary of Impacts: Scenarios of Future Utility Customer-Funded Electric EE 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%
Source: LBNL 2013c



### 4.2.3 EE Potential

4.2.3.1 Evaluations of EE Potential EE potential studies are a common tool for informing the development of EE program plans and budgets, as well as suppo rting the development of electri city savings targets, required savings levels under an EERS, or “all cost-e ffective” EE requirement. In conducting these studies, states and utilities ha ve developed a methodology that is often described as a “bottomup, engineering-based” approach (EPA and DOE 2007a). EE potential studies are conducted at various geographic scopes (nationa l, regional, state, 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: techni cal, economic, market, and program. 17



###  Technical potential represents the theoretical maximum amount of energy use that could be displaced by efficiency, without re gard 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 opportunities assumed as they arise.

 Economic potential refers to the subset of the technical potential that is economically cost-effective. Definition of “economic pot ential” can vary to some degree by study. Some estimate economic potential by ev aluating technology upfr ont cost, operating

16 LBNL provided these unpublished results from their analysis. 17 The definitions discussed below largely follow that outlined in EPA and DOE 2007a.24 

### costs that considers energy pr ices, product lifetime and discount rate, compared to a
conventional alternative or the supply-side energy res ources. Others incorporate consideration of consumer preferences in addition to consumers’ out-of-pocket expenditure when evaluati ng the economic potential. Bo th technical and economic potential estimates assume immediate implem entation of efficiency measures without regard to technology adoption process or real-life program implementation. In addition, these estimates do not always reflect market failures or ba rriers that impede EE and often fail to capture transacti on costs (e.g., administration, marketing, analysis, etc.) beyond the costs of effi ciency measures. Another key factor determining economic potential is the le vel of aggregation at which the costeffectiveness evaluation is applied. Applyi ng the cost test at lower levels of aggregation (e.g., at the measure or program le vel, rather than the sector level) will typically lead to lower economic potential when evaluating a portfolio of programs.

 Market potential (or “achievable” potential ) refers to the subset of economic potential that reflects the es timated amount of energy saving s that can realistically be achieved, taking into account factors such as technology adoption process, market
failures or barriers that inhibit technol ogy adoption, transaction costs, consumer preferences, social and institutional cons traints, and possibly the capability of programs and administrators to ra mp up program activity over time.

 Program potential refers to the subset of market pot ential that can be realized given specific program funding levels and designs. Program potenti al studies can consider scenarios ranging from a single progra m to a full portfolio of programs. 18



### As mentioned, the EE industry standard for potential studie s is the bottom-up, engineering evaluation of EE poten tial of individual end-use t echnologies and measures (EPA and DOE 2007a). Bottom-up analyses all employ a similar methodology but can vary significantly in key assumptions (e.g., breadth of sectors and e nd-uses considered, aggregation level at which cost tests are applied, st udy period, discount rate, pattern of technology penetration, whether economically ju stified early replacement of t echnologies is allowed for, and

18 Each subsequent poten tial estimate described above is a subset of the previous potential estimate, e.g., the market potential is a subset of the economic pot ential, and the economic potential is a subset of the technical potential.25 

### whether continued improvement in efficiency of technology is provided for). As a result, estimated efficiency potential can vary significantly among studies. 19



### 4.2.3.2 Overview of Results EE potential studies have been conducted fo r decades and in recent years numerous
studies have been conducted at ut ility, state, regional, and national levels. This section presents a summary of more than 50 recent studies (2009 through 2014) collected by EPA. Further detail on the collection of studies is provided in Appendi x 1, including a database of study sources and results. Studies were identified by reviewing three recent meta-studies (Sreedharan 2013; ACEEE 2014b; and EPA 2014a) and supplementing thos e reviewed studies with more recent or overlooked studies. By focusing on studies conducted after 20 08, the potentially significant effects of the lighting provisions of the Energy Independence and Security Act of 2007 (Congress 2007) are accounted for. The collected studies cover all major end-use sectors (residential, commercial and industria l) and all regions of the U.S.
To normalize results of analyses addressing different study periods, we present average annual achievable potential by dividing cumulative pe rcentage savings in th e last year of the study by the duration (in years) of the study pe riod. This is a common method of normalization for EE potential studies and allows for comparison with results from the EPA’s demand-side EE plan scenario later in this chapter (section 8) (Sreedharan 2013; ACEEE 2008, 2014b). Many studies provided multiple estimates of achievabl e potential. Some studies use the terms “high,”
“medium” and “low” achievable potential, while others used te rms such as “high achievable” and “achievable” potential or, in some cases “achievable” and “programmatic” potential. For studies that provided multiple levels of achievabl e potential, the highest value was classified as high potential and the lowest va lue was classified as low poten tial. If a study provided just a single value, it was used as both the high and low potential value for that study. In many instances, low potential values in the studies were arrived at using assumptions of limited budgets or other limiting factors. To be conserva tive, the EPA has included these low values but

19 Because of the complex consumer behavior , energy market and macroe conomic drivers of energy use and energy efficiency, and in some cases due to the lack of consistent data, quantify ing energy efficiency potential and energy savings from policies and programs remains a challenging analytical task. Assumptions a bout consumer technology adoption behavior, market barriers and failures, and how technology diffusion occu rs can also affect estimated potential.26 

### generally consider the high achieva ble values to be the more appr opriate point of comparison in evaluating the magnitude and timing of savings repr esented in the illustrative EE plan scenario.
Table 6 provides summary results of EPA’s review of recent EE potential studies. Across all sectors for all studies, aver age annual achievable potential ra nges from 0.9% to 1.3% per year for low to high potential estimates, respectively. Results vary between en d-use sectors with the industrial sector showing less potential than residential and co mmercial sectors. Results for all sectors indicate very substantial opportunity for achievable EE savings.

Table 6. Summary of EE Potential Studies (2009 to 2014) 20

Sector Average Annual Achievable Potential (% per year) High Low Median Mean Median Mean Residential 1.0%/year 1.2%/year 0.7%/year 0.8%/year Commercial 1.3%/year 1.3%/year 0.8%/year 0.9%/year

Industrial 0.9%/year 1.0%/year 0.5%/year 0.7%/year All 1.3%/year 1.2%/year 0.9%/year 0.9%/year Source: Appendix 1



### 4.3 Costs and Cost-Effectiveness of State EE Policies

4.3.1 EE Cost-Effectiveness States enact EE policies to meet multiple policy objectives including reduction of customer electricity bills, lo wer costs of meeting electricity supply need s, energy reduction, environment and health benefits, and local economic development benefits (EPA and DOE 2006). Most states evaluate th eir EE policy options through the application of cost tests, weighing the projected benefits with the costs of the EE technol ogies and practices (EPA and DOE 2008; RAP 2012). Each state determines thei r 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 expenditures (e.g., a whole home retrofit) while the economic benefits (e.g., electricity bill savings) accrue over the lif e of the investment (“measure life”) which can range from a few to twenty or more years. As such, the choice of di scount rate and the estima tion of measure life are

20 All 53 of the studies reported results for all sectors but many did not report results for one or more of the individual sector s. At least 30 studies reported results for each sector shown in the table.27 

### significant determinants of the cost-effectivene ss results. Most states employ multiple tests, adjusting cost and benefit categories depending upon the economic perspective of interest (e.g., utility, ratepayer, program partic ipant, society), and consider th e results from each one, usually with an identified primary test t ype. Policies that are selected are those that are found to be costeffective, with benefits greater than costs, as determined by the utility applying methods defined 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 cu stomer installing the measure. Costs may include incremental equipment a nd installation costs; benefits include incentive payments, bill savings, and a pplicable tax credits or incentives.

2. Utility/program administrator cost test from the perspective of utility, government agency or third-party implementing th e program. Costs may include program incentive, installation, and overhead costs; benefits may include avoided energy and capacity costs - including generation, transm ission and distributi on - by the utility.

3. Ratepayer impact measure test from the perspective of utility ratepayers not participating in available EE programs. This text includes the costs and benefits that will affect utility rates, including program a nd administration costs, as well as “lost revenues” to the utility; be nefits include avoided ener gy and capacity costs, and additional resource savings.

4. Total resource cost test from the perspective of all ut ility customers in the service area. Costs may include the full incremental co st of the measure, program installation and overhead costs; benefits may include avoided energy and capacity costs, and additional resource savings.

5. Societal cost test from the social perspective. In addition to benefits considered in total resource cost test, may also in clude non-monetized benefits such as
environmental and health benefits. While many states consider more than one co st test in evaluating EE programs, the most commonly used (29 states) primary te st is the total resources cost test. This test is considered to
be the best measure of the interest s of all utility customers. The u tility and societal cost tests are the next most commonly used primary tests, used by five states each. The utility cost test is28 

### 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 ch ange policy suggests th at EE presents a large potential in reducing gree nhouse gas emissions and plays a cr itical role in offsetting the costs and enhancing the flexibility to achieve long-term GHG reduction targets (Kriegler et al. 2014; Kyle et al. 2011). Consiste ntly, evaluations of the econo mic potential for carbon dioxide reductions from the United States’ power sector identify demand-side EE as the lowest cost strategy (typically, as noted above , with positive net present value) as well as the strategy having the greatest reduction potentia l (EPA and DOE 2009). For exam ple, McKinsey and Co. (2007)
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 positive net present value if “persistent barriers to market efficiency” could be addressed.

4.3.2 Costs of Saved Energy A common metric for comparing alternative electricity reso urce options within utility resource plans is the levelized cost of energy (L COE) or, for EE resources, the levelized cost of saved energy (LCSE) (EPA and DOE 2007b). LCSE EE is often compared favorably with LCOE of alternative new generation sources such as foss il-fueled or nuclear power plants, or renewable energy resources like wind or solar-power genera tion. In these comparisons , typically only utility (or program) costs are considered, not the total co sts of saved energy that are discussed later in this chapter. The EE analysis literature report s average LCSE in the range of 1-6 cents/kWh based on program administrator cost. 21 

### A recent review by ACEEE examined studies across 20 states between 2009 and 2012, and estimated LCSE for electricity EE programs in the range of 1.3-5.6 cents/kWh, with a mean value of 2.8 cents/kWh (ACEEE 2014d). Earlier reviews of utility EE programs identified a similar range of LCSE. In 2009 ACEEE reviewed 14 utility studies of LCSE and found a range from 1.6 to 3.3 cents/kWh, with a mean value of 2.5 cents/kWh (ACEEE 2009). An earlier ACEEE study re viewed cost-effectiven ess analysis results in nine states and suggested that reported u tility LCSE ranged between 2.3-4.4 cents/kWh, with a mean value of 3 cents/kWh (ACEEE 2004). In 2014, an LBNL analyzed the program cost of

21 Unless otherwise noted, es timates of LCSE discussed in this section refer to program admini strator cost (also known as utility cost). The discount rates, average measure lives, and other assumptions affecting the calculation of LCSE were not always consistent or reported in all studies.29 

### saved energy based on data from their DSM Pr ogram Impacts Database. The database includes program results reported to state regulators by more than 100 program administrators in 31 state, primarily for the years 2009-2011 (LBNL 2014b). LB NL found a national average LCSE of 2.1 cents/kWh of gross savings.

The economic literature also evaluates the LCSE from EE measures using other techniques (e.g., econometrics, top-down modeli ng), although this body of studies is much smaller compared to the bottom-up, engineeri ng-based analysis. The economic literature has varying treatment of the free ridership, EE program endogeneity, and the rebound effect. The different assumptions used in these analyses make direct comparison challenging, but overall these empirical analyses present a wider range of estimates of co st of saved energy. For example, a 2008 study in the Energy Journal examining utility DSM programs estimated the average utility cost of saved energy in the range of 5.1 to 14.6 cents per kWh (Auffhammer et al. 2008). Some other studies in the economic literature sugg est estimated LCSE in a similar range as from the bottom-up analyses. RFF estimated an average cost of 3.4 cents per kWh saved from utility EE programs (RFF 2004). A 2012 econometric analysis of utility rate-payer funded demand-side management and EE programs between 1992 and 2006 found that the estimated energy savings in electricity consumption were achieved at an expected average cost to utilities of approximately 5 cents/kWh (Arimura et al. 2012 ). In 2011, using a top-down approach that evaluates the savings potential of EE investments using state- and region-spe cific price elasticity, an RFF study estimated that electricity savings of 1 to 3 percent were available at a marginal cost of 5 cents/kWh and a corresponding aver age cost of 2.5-3.5 cents/kWh (RFF 2011).
A number of analytical and data considera tions related to LCSE estimation are also
discussed in the literatu re, including the issue of “free ride rs” in EE programs, and the accuracy of utility reported costs and energy savi ngs (Train 1994; Joskow and Marron 1992). EE practitioners also recognize the ne ed to consider “free rider” a nd “spillover” effects in program evaluation. A slight majority of states adjust for free ridership in energy savings estimates, leading to higher LCSE values than otherw ise would be the case (ACEEE 2012a). A smaller number of states adjust for spillover effects which reduce LCSE values when addressed .

Another consideration relate d to LCSE estimation is th e rebound effect. The economic literature has extensive discussion of the pot ential rebound effect, mark et interactions and30 

### economy-wide response of EE policies and i nvestments. An improvement in EE would effectively reduce the cost of a service or production input, pot entially boosting its demand or production output thus increasi ng energy use (“direct” rebound). In addition, money saved from EE can be used for consumption or investment that can increase energy consumption in other markets of the economy and lower energy prices as a result of EE improvement may increase energy consumption (two forms of “indirect” re bound). Reviews suggest th at both direct and
indirect rebound effects exist and the size of such effects varies among different studies, technologies, sectors and income groups (UKE RC 2007). Overall, however, rebound effects are found to be relatively modest compared to the im portance of EE as an effective way of reducing energy consumption and carbon emissions (Greening et al. 2000; Davis 2008; Gillingham et al. 2013; UKERC 2007).



## 5. Other EE Strategies In addition to ratepayer-funded EE programs, many other strategies are available to
increase investment in EE and realize associated energy savings, emissi ons reductions and other
benefits. In this section we address a few of the more signi ficant opportunities for realizing additional EE potential: building energy codes, state appliance sta ndards, energy service performance contracting, and volt/VAR optimization.

5.1 Building Energy Codes

5.1.1 Overview Building energy codes establish minimum effi ciency requirements for new and renovated residential and commercial buildings. Building energy codes lock in long-term energy savings at a low cost during the building design and cons truction phase; some of which would not be feasible or cost-effective as building retrofits. The primary policy rationale for energy codes is the need to overcome large and persistent market barriers, the largest of which is the concern over split-incentives or “principal -agents,” in which builders’ in centives to minimize the initial costs of constructing a building are misaligned with occupants’ incentives to minimize total occupancy costs. Building energy codes are designed to eliminat e inefficient technologies and building practices, minimizing total ownership a nd operating costs over the life of the building. Additional co-benefits of implementing build ing energy codes include pollution prevention, improved electric system reliability, avoidance of new energy supply investment, and improved31 

### occupant comfort and health. Energy building co de requirements are also designed to overcome the complexity of advanced code s, lack of local-level implemen tation resources, and a shortage of empirical data on the cost s and benefits of codes.
Building energy codes specify thermal performance 22 

### criteria for building thermal envelope components such as wa lls, ceilings, floors, and windows, and also set air leakage and duct leakage standards. They also address li ghting system efficien cy, hot water system efficiency, and heating and cooling equipment effi ciency in cases where federal standards do not cover the affected equipment or system types. Fo r the residential sector, the International Energy Conservation Code (IECC) is the prevailing mo del code, and ASHRAE 90.1 is the model code for the commercial sector (EPA 2014b). Various ve rsions of these model codes are adopted and enforced at the state and local government levels.

5.1.2 Authority / Obligated Parties Model energy building codes are typically de veloped at the nationa l or international level, adopted at the state or local level, and administered and enforced locally. Local building industry parties, such as developers and propert y owners requiri ng building permits, are the most common obligated parties (EPA 2014b). Federal au thority affecting state code adoption was established in the Energy Po licy Act of 1992 (EPAct 1992); its provisions require the U.S. Department of Energy to review each new version of the IECC and ASHRAE 90.1, released about every three years, to determ ine whether they save energy compared to the previous version (EPA 2015). When DOE concludes that a new version of the code would improve energy efficiency, states must review and consider adopting the new code. However, since EPAct 1992, not all states have adopted or updated residential and commercia l building energy codes per the
current DOE determination.

5.1.3 Building Code Regulations Over Time Federal authority for buildi ng codes was established in Energy Conservation and Production Act of 1976 (ECPA), which authorized DOE to create national Building Energy Performance Standards (BEPS) th at would be legally binding nationwide. The BEPS provision was repealed by Congress in the early 1980s. St ates, however, continued to develop energy

22 The thermal performance of a building refers to how well the temperature of a building is insulated from the outside environment.32 

### codes. In 1978, California became the first state to include energy requirements in its code (EPA 2015). Other states, such as New York, Massachusetts, Florida, and Minnesota, developed their own energy codes during the 1980s.
In response to the EPAct 1992 requirements, most states adopted various versions of the IECC and ASHRAE 90.1. Over the last decade, th e number of states adopting residential and commercial energy codes has remained relativel y stable at about 40 (EPA 2006, 2015). Figure 2 shows this consistency for residential energy codes, as the states with th e lightest shading are the ones that had either not adopted or recently updated their energy c odes. However, the stringency of these codes has been updated multiple times sin ce 2005 in most states. Eleven of the 40 states
with residential build ing codes are using the 2012 IECC ve rsion, which DOE has determined would improve the energy efficiency of resi dential buildings by a pproximately 30 percent compared to the 2006 IECC (EPA 2015). A recen t study estimated that upgrading the energy efficiency of a typical new home in Arizona from the 2006 IECC would save homeowners an average of $3,245 over 30 years with the 2009 IECC and $6,550 over 30 years with the 2012 IECC (DOE 2012b). DOE estimates that currently- adopted building energy codes across the country will result in a fina ncial benefit of nearly $2 b illion annually by 2015 and over $15 billion annually by 2030 (DOE 2014).33 

### Figure 2. Residential Energy Buildin g Code Adoption (2005 to 2014)

Source: EPA 2006 and EPA 2015



### 5.1.4 Remaining Market Potential There is still significant poten tial for savings from buildi ng energy code adoption. From 1992 to 2012, building energy codes in the U.S. saved a cumulative 4 quadrillion Btu and reduced greenhouse gas emissions by 300 million me tric tons. From 2013 to 2040, the continued adoption of increasingly stringen t building energy codes in the U.S. is expected to save 40.1 quadrillion Btu (ACEEE 2015a). A recent study by ACEEE also found that for residential and commercial building codes, the potential fo r energy savings by 2030 was 155 million megawatthours (MWh) annually, or 4.2 percent of annual electricity consump tion relative to 2012 (ACEEE 2014a).34 

### 5.2 State Appliance Standards

5.2.1 Overview State appliance standards establish mini mum energy efficiency levels for those appliances and other energy-consuming products that are not already covered by the federal government. These standards typically prohibit the sa le of less efficient mo dels within a state. States are finding that appliance standards o ffer a cost-effective strategy for improving energy efficiency and lowering energy costs for busines ses and consumers, though these standards are superseded when federal standards are en acted for new product categories (EPA 2014b).
The key objectives of appliance efficiency standards are to:

 Raise the efficiency of a range of resi dential and commercial energy-consuming products, where cost-effective.

 Overcome market barriers, such as split incentives between homebuilders and homebuyers and between landlords and tenants, and panic-purchase 23 

### situations in which appliances break and must be replaced on an emergency basis.

 Ensure energy use reductions to preven t criteria air pollu tion and greenhouse emissions, improve electric system reliability, and reduce consumer energy bills (EPA 2015).

While state appliance standards can be useful in testing and explori ng the effectiveness of standards for new products, many state appliance standards have been preempted or superseded by existing federal standards. States may apply to DOE for a waiver to implement more stringent standards. This is sometimes granted if a cer tain period of time has passed since the federal standard has been updated (EPA 2014b).

5.2.2 Authority / Obligated Parties Establishing efficiency standa rds in a state typically requir es enabling legislation from the state legislature. However, once legislation is enacted, it may allow an executive agency to
set further standards ad ministratively (EPA 2015).

23 In a panic purchase, customers usually do not have the time to consider a range of models, features, and efficiency levels, an d the full range may not be available from all suppliers.35 

### State energy offices, which typically administ er the federal state energy program funds, have generally acted as the administrative l ead for implementing standards. In contrast, inspection and enforcement of appliance standa rds regulations has typically involved selfpolicing among industry. Manufacturers of products being sold in a give n state are typically obligated to ensure their appliances meet th e appropriate energy efficiency standards (EPA 2014b).

5.2.3 Appliance Standards over Time California’s appliance standards program da tes to the 1970s, when the state began to pursue standards before the enactment of federa l legislation. When the federal government opted
not to issue standards under it s legislative mandate in 1982, other states joined California in developing state standards. Thes e state initiatives helped create the impetus for new federal legislation in 1987 (the National Appliance En ergy Conservation Act or NAECA), the Energy Policy Acts (EPAct) in 1992 and 2005, and the Ener gy Independence and Security Act (EISA) in 2007 (EPA 2015). These federal laws have established appliance e fficiency standards for more than 50 products, representing about 90% of home energy use, 60% of commercial building energy use, and about 30% of industrial ener gy use (DOE 2015a). Bolstered by new standards contained in the EPAct 2005, EISA 2007, and additional DOE rules, 24 

### these savings rose to 278 terawatt-hours (TWh), or 7 % of U.S. electric ity use in 2010. By 2025, total electricity savings from existing federal standards are projected to increase to 682 TWh pe r year, or 14% of the projected annual U.S. electricity use. Net savings to consumers and businesses are also expected to increase from $27 billion in 2010 to $61 billion in 2025 (ASAP 2012).
While the NAECA effectively preempted st ate action on federally covered consumer products, California has continue d to develop efficiency stan dards for other products and technologies that aren’t included in the Act. California’s standa rds program has contributed to substantial improvements in energy efficienc y. Since its inception, the program has saved consumers over $75 billion on el ectricity bills (CEC 2013).
Since 2001, 15 states and the District of Colu mbia have enacted appliance efficiency
standards. While most of these individual standa rds have been superseded by federal standards,

24 DOE is required to review federal appliance standards and test procedures periodically, and initiate a process to set new standards provided that improvements to en ergy efficiency are "technically feasible and economically justified" (DOE 2015b).36 

### as of February 2014, 12 states 25 

### and the District of Columbia have one or more appliance efficiency standards for products not covered by federal standards (ASAP 2014).

5.2.4 Remaining Market Potential While federal appliance standards conti nue to expand, the direct economic and
environmental benefits of state standards are sti ll substantial. California draft regulations for 15 new appliance standards are expected to save 50 billion gallons of water, 1,400 MW of peak electricity, 9.8 TWh of electricity, and 162 millio n therms of natural gas per year. This is expected to result in annual savings of $2 billion (CEC 2014).
From a national perspective, there is still untapped potential for st ates to move into product areas not yet covered by federal standards. Table 7 shows potential energy savings from products that do not currently have federal stan dards. The three products with the greatest potential in the residential, commercial and i ndustrial, and lighting sect ors are shown, along with a summary line for each sector. Some of the greatest potential savi ngs in 2035 are in the residential sector, specifically improvements in water heater efficiency. Implementing all of these standards through federal regulations woul d result in annual savings of 212 TWh in 2025, or 5% of projected national electri city consumption (ASAP 2012; EIA 2015).

Table 7. Estimated Energy Savings Pote ntial of State Appliance Standards Annual savings in 2025 Annual savings in 2035 Electricity

savings Peak demand Natural gas Electricity savings Peak demand Natural gas Products (TWh) (GW) (Tbtu) (TWh) (GW) (Tbtu) RESIDENTIAL STANDARDS Water heaters 18.2 2.5 — 43.0 5.9 — Set top boxes and digital communication equipment 14.7 2.0 — 14.7 2.0 — Air handlers 13.7 5.6 — 29.1 11.9 — Total (14 products) 98.5 16.8 51.6 142.3 27.0 51.6 COMMERCIAL AND INDUSTRIAL STANDARDS Walk-in coolers and freezers 14.7 3.4 — 14.7 3.4 — Distribution transformers 10.9 1.5 — 22.4 3.1 — Electric motors 9.0 1.4 — 18.6 2.9 — Total (13 products) 62.4 15.5 74.2 98.5 24.5 139.9

25 These states include Arizona, Californi a, Connecticut, Georgia, Maryland, Neva da, New Hampshire, New York, Oregon, Rhode Island, Texas, and Washington.37 Annual savings in 2025 Annual savings in 2035 Electricity

savings Peak demand Natural gas Electricity savings Peak demand Natural gas Products (TWh) (GW) (Tbtu) (TWh) (GW) (Tbtu) LIGHTING STANDARDS Incandescent reflector lamps 20.2 5.0 — 20.2 5.0 — Outdoor lighting fixtures 10.3 0.7 — 26.1 1.8 — General service fluorescent lamps 6.9 1.7 — 6.9 1.7 — Total (7 products) 50.8 9.3 — 65.6 15.6 — ALL PRODUCTS 212 42 126 306 67 235
Source: ASAP 2012



### 5.3 Energy Service Performance Contracting

5.3.1 Overview Energy service performance contracting (ESPC ) uses cost savings from reduced energy consumption to repay the cost of installing en ergy conservation measures (HUD 2014). Under an EPC program, an Energy Service Company (ESCO) first conducts an en ergy audit. The ESCO then designs and constructs a project that achi eves the building owner’s energy efficiency needs and arranges for the project’s financing, usually through a third party. The third party is repaid by the building owner/operator from the savings in their energy costs. In this type of ESPC arrangement, the builder owner/operator does no t need to incur upfront expenses, and will experience the benefits of the upgrades, including monetary savings once the financer’s costs have been repaid (ICF 2007).
ESPC programs have been used extensively by st ate, federal, and local facilities to reduce utility and operating costs and to help meet environmental and energy efficiency goals, which can include the use of combined heat and power (CHP). Forty-nine states have implemented performance contracting activities, primarily through legisl ation, covering a combination of entities that include public agencies, school di stricts, municipalitie s, state colleges and universities, counties, or th e state as a whole (ORNL 2015). 26 

### While ESPC programs are already widespread, states have found that they can furt her utilize this approach by extending eligibility to all public facilities in the state (EPA 2015).

26 Wyoming currently has no perform ance contracting legislation.38 

### While ESCOs deliver ESPC projects, and the E SPC market is the main driver for ESCOs, the two markets do not overlap completely. Since 2006, the share of the ESCO market consisting of ESPC projects has remained stable at abou t 70 percent (ICF 2007; LBNL 2014a). The other 30 percent of the market is comprised of desi gn/build services; engine ering, procurement, and construction services; consulting services; and operation and maintenance contracts. These
services are not considered to be EPC projects since the project fi nancial performance risk is not incurred by the ESCO or potential third party lenders (ICF 2007).

5.3.2 History of ESPC Programs and the ESCO Business Model The ESCO business model emerged during th e 1970s, supported in part by utilities paying ESCOs a fee for services such as energy audits provided to customers via their electric utilities and implementing federal energy mana gement goals (NAESCO 2011). EPC activities emerged in the mid-1980s as power plants became more expensive to build and utilities were ordered to produce Integrated Resource Plans (IRP s) where energy efficiency measures could be used to meet projected demand. ESCOs were us ed to deliver energy savings from projects implemented at industrial and in stitutional sites, relying on a dvances in energy efficiency monitoring and verificati on techniques (ICF 2007).
By the late 1990s, funding for utility-sponsor ed energy efficiency programs was reduced
in about half of the states due to changed regulatory structur es and increased political and regulatory pressures to maintain lower electricit y prices (EPA 2006). Many restructured electric
utilities shifted their focus aw ay from sponsoring programs to starting new affiliated ESCOs to deliver energy services directly to customers. However, the business model for these marketbased ESCOs was limited in reach to mainly la rger industrial, comme rcial and institutional customers. Several states and local governments al so turned to EPC activities to provide services in public facilities via lead-by- example policies (see se ction 3). These governments also saw the benefits of addressing ca pital equipment and maintenance projects that would not have otherwise been addressed through the capital budget proces s (ICF 2007). Commercial lenders also became involved in EPC funding, loweri ng the cost of projects throu gh competition and paving the way for new financing vehicles (ICF 2007).
During the early 2000s, however, the growth of the EPC and ESCO market declined due to loss of both performance contracting in fede ral buildings and market confidence for industrial39 

### financing mechanisms (NAESCO 2011). This tr end reversed in 2005 as the EPC and ESCO market increased focus on state and local public buildings and innovated program offerings to provide customers new technologies and fina ncial models (see section 3 and ICF 2007).

5.3.3 Remaining Market Potential The market potential for EPC programs and ESCOs remain significant. Annual revenues for the U.S. ESCO market was expected to increase from $5.3 billion in 2011 to about $6.4 billion in 2013 (LBNL 2014a). An analysis by Lawrence Berkeley National Lab estimated the investment opportunity for ESCOs ranged betw een $71 billion and $133 billion (LBNL 2013a). Figure 3 shows a breakdown of investment opportunity by market segment. Commercial buildings, K-12 schools, and the he alth and hospital sectors have the highest remaining potential for ESCOs. Commercial buildings in particular have high ba rriers to implementing energy efficiency under EPC activities, as the private sector is generally aver se to financing energy efficiency activities and has more stringent re quirements for a lower payback period on projects (LBNL 2013a). While the two markets do not ov erlap completely, the potential for ESCO investments provide a strong indicator for th e remaining potential of EPC activities.40 

### Figure 3. Remaining Market Potential for ESCOs

Source: LBNL 2013a



### 5.4 Volt/VAR Optimization

5.4.1 Overview Volt/VAR optimization (VVO) refers to coordi nated efforts by utilities to manage and improve the delivery of real and reactive power 27 

### in order to increase the efficiency of electricity distribution. VVO is accomplished primarily th rough the implementation of Smart Grid technologies that improve the real-time response to the dema nd for real and reactive power. Technologies for VVO include load tap changers a nd voltage regulators, which can help manage voltage levels, as well as capacitor banks, whic h allow for the delivery of reactive power. By using local capacitor banks to respond to reactiv e power demand, rather th an adjusting the output of remote generators, systems can achieve reduc tions in transmission line loss. The U.S. Energy Information Administration (EIA) estimates that on average, 7.5% of genera ted electricity is lost

27 Reactive power is experienced in alternating current systems, and is the result of a phase shift between the current and volta ge. Reactive power does no work on resistive loads, but is necessary for circuits that include magnetic loads, most notably motors. It is measured in Volt-Ampere Reactive (VAR).41 

### in transmission and distribution, a number that could be reduced by effective use of VVO (EIA 2012b).
VVO efforts are often closely related to cons ervation voltage reduction (CVR), which are actions taken to reduce initial delivered voltage levels in feeder transmission lines while remaining within the 114 volt to 126 volt range re quired at the customer meter (based on ANSI C84.1 standards). Like VVO, CVR relies on the use of Smart Grid technologies in order to more effectively manage voltage levels across all loads in a system. A simple presentation of these benefits are shown in Figure 4, where the implem entation of VVO technologies allows electricity
distributors to deliver electricit y initially at a lower voltage, thus decreasing the generation fuel requirements, while still delivering within the acce ptable range to all 7 loads. The combination of VVO and CVR can lead to a range of benefits, in cluding reductions in pe ak electricity demand, reductions in energy requirements necessary to meet a certain level of demand, and a reduction
in line loss.42 

### Figure 4. Hypothetical Voltage Profiles with and without VVO Technologies

Source: DOE 2011



### 5.4.2 History of VVO Programs
VVO and CVR efforts were first introduced with partial implementation in California in 1977 (UNLV 2014). In 1987, the Snohomish Public Utility District instituted a pilot CVR project that demonstrated the possibility of reducing voltage and energy requirements by approximately 2.1% (NRECA 2013). More recently, the advent of Smart Grid technology with increased availability of energy use data, has led to a re newed interest in VVO and CVR as the potential for increased efficiency has become more appare nt. Of the 99 projects funded by the Smart Grid Investment Grant program under the American Recovery and Reinvestment Act of 2009, 2643 

### included the implementation of VVO technologies. These projects, which target reduced voltage during peak demand, electricity c onservation due to reduced volta ge, and reduced losses from feeder lines, are expected to achieve a 1% re duction in energy consumption for every 1% reduction in voltage level (DOE 2012a).
There is no formal state requirement direc ting implementation of Volt/VAR optimization. However, in 2012, the National Association of Regulatory Utility Commissioners published a resolution in support of VVO impl ementation at the state-level. The resolution encouraged public utility commissions to work with stat e legislature to include deployment of VVO technologies as part of en ergy efficiency resource standards (NARUC 2012).

5.4.3 VVO Market Potential Large market potential currently exists for the implementation of VVO. The Pacific Northwest National Laboratory estimates that implementing VVO nationwide could potentially yield as much as a 3.2% annual reduction in energy consumption (PNNL 2012). The Bonneville
Power Administration estimates that the cost-b enefit ratio for transmi ssion and distribution optimization is positive and well understood, and th at CVR is a key component of these positive effects (BPA n.d.). McKinsey estimates that VVO could provide $43 billion in gross annual benefits in 2019 in the U.S., with CVR comprising the largest share of th ese benefits (McKinsey 2010). IHS Technology estimates that the ship ments of VVO technol ogy devices will double between 2013 and 2018, with a quadrupling of revenue over the same time period (IHS 2013). The Institute of Electrical and Electronics Engineers estimates that CVR has the potential to save approximately 750 trillion BTUs of electricity, equivalent to 5 .4% of total U.S. electricity generation, at a total cost of $3.3 billion dollars (IEEE 2014). While these estimates vary in level of impact, they display the strong market potential that exists for VVO projects.



## 6. Utility Regulatory Models for Supporting Demand-Side EE The electricity sector widely recognizes that state utility regulatory models for supporting energy efficiency are key to expanding utility investments and highe r energy savings from programs (IEI 2014). Further, all states which regul ate investor-owned utilities have adopted or modified one or more policies in order to better align utility incentives with the delivery of costeffective energy efficiency (IEI 2014). States with the highe st per capita spending on energy44 

### efficiency programs have the most comprehensiv e regulatory models for energy efficiency in place (NRDC 2012).
The most common regulatory models for incentiv izing energy efficien cy through electric utilities include decoupling reve nue from sales volumes, ensuri ng program cost recovery, and providing shareholder incentives li nked to program performance. The Edison Foundation reports on the adoption of these policies across states through its Institute for Elec tric Innovation, as
shown in Table 8.

Table 8. Summary of State Regulatory Frameworks: December 2014 Energy Efficiency In centive Mechanism Number of States Pending Fixed-Cost Recovery Mechanisms Lost Revenue Recovery 19 0 Revenue Decoupling 14 1 Performance Incentives 29 2 Program Cost Recovery 46 0
Source: IEI 2014



### These approaches have been widely used for over a decade, with performance incentives approved by Connecticut as early as 1988 (IEI 20 14). In 2006, the need for such policies was recognized by the National Action Plan for Ener gy Efficiency Leadership Group of gas and electric utilities, state agencies, energy cons umers, energy service providers, environmental groups, and energy efficiency organizations. Over 100 states, utilities, and key stakeholders across 49 states made aggressive commitments to energy efficiency under the Action Plan and endorsed the Action Plan five key policy reco mmendations which included modifying utility incentives (EPA 2006). In 2014, the Edison Electric Institute (EEI) and the Natural Resources Defense Council (NRDC) issued a joint statemen t urging state utility regulators to adopt a number of policies to sustain utility financial he alth given the greater investment being made in both energy efficiency and customer-sit ed electricity generation (NRDC 2014).
The need for states to adopt utility financia l incentive policies is due in part to how traditional ratemaking allows utilities to recovery costs based on the volume of electricity sold.
This creates an incentive to maximize the volume of sales (often referred to as the “throughput incentive”), which in turn creates a disincentiv e to invest in energy efficiency since such programs will reduce sales volume. A decoupling policy counteracts the financial incentive to45 

### increase sales by fixing a utility’s revenue for a specific term, in order to match the amount of anticipated costs incurred plus an appropriate profit. If the utility can redu ce its costs during the term through energy efficiency, the utility will be able to increase its profits. Alternately, lost revenue adjustment mechanisms may be used to diminish the throughput incentive by allowing utilities to directly recover th e lost revenue associated with not selling energy due to energy efficiency programs.
Reduced electricity sales may affect util ity finances in additional ways, including reducing the utility’s opportunity to earn additional profit from th e regulated rate of return on new capital projects which would ot herwise be needed to meet hi gher load growth, such as new generators and transformers upgrades. As an alte rnative, performance incentive policies are used to allow utilities to earn a prof it for achieving high levels of energy savings, recognizing that multiple benefits of energy efficiency resources that are not otherwise explicitly accounted for in traditional regulatory models. Performance incentive policies come in different forms, such as earning bonuses and allowing a utility to earn a higher rate of return on capital invested in energy efficiency.
The level of capital investment made by a uti lity is often informed by electricity resource planning. Most states require one or more forms of electricity re source planning, such processes used to inform long-term integrated resources plans (IRPs) and regulat ory approvals for large projects often referred to as Ce rtificates of Public Convenience and Necessity (CPCNs). IRPs are required or present in more th an 30 states, including most verti cally integrated states, and at least 19 states have some form of CPCN (EPA 2015). Incorporating en ergy efficiency into electricity resource planning enables the utility to consider a broad range of electricity resource options and avoid capital investment in more expensive electricity supply or delivery
infrastructure.
Utilities are also increasing seeing energy effi ciency as part of their business model. According to a 2015 survey of utility executives, 71% of utilities are developing new business models for energy efficiency and demand res ponse (UtilityDIVE 2015). A significant number of the utilities surveyed se e their business model changing from a traditional vertically integrated utility, to an energy services or smart integrator businesses model. Further, states such as New
York and Hawaii have initiated actions at th eir public utility commissions to examine and46 

### encourage changes to the utility business model. 28 

### Both of these state ar e experiencing growth in distributed electricity generation and can build upon their existing energy efficiency regulatory frameworks which include energy efficiency program cost recovery, decoupling, and performance incentives.



## 7. Energy Efficiency Certificates In the U.S., extensive cons ideration of tradable ener gy efficiency credits (EEC) developed almost a decade ago, most often framed as a potential compliance mechanism for state energy efficiency resource standards (EERS), a widely adopted and effective policy mechanism for achieving cost-effective electricity savi ngs through utility energy efficiency programs. 29 

### State EERS as a key policy strategy and the use of EECs for compliance we re concepts borrowed from the earlier adoption of renewable portfolio st andards (RPS) and the use of renewable energy credits (RECs) for compliance.
Building from the successful experience with EERS/RECs, several significant policy papers discussed how the genera tion, tracking, and trading of en ergy efficiency credits could work as a means for supporting compliance with EERS or as a mechanism for achieving greenhouse gas reductions (NREL 2 008; CRS 2007; IEA 2012a). A few states put regulations in place that allowed for the use of EECs for EERS or RPS compliance. 30 

### Consistent with these developments, several of the regional tracking syst ems developed to support the use of RECs for compliance with state RPS also included a capability to perform the same functions for the use of EECs for potential compliance with state policies. 31 

### However, these capabilities have yet to be widely used as significant leve ls of EEC transactions have not developed in the U.S.
There are, however, some voluntary markets for energy savings credits. For example, North American Renewable Registry extende d its certificate tracking system to energy efficiency, and registered the first voluntary en ergy efficiency certificates for IBM Corporation

28 For more information, see the New Yo rk Reforming Energy Vision proceeding ( http://www3.dps.ny.gov/W/PSCWeb.nsf/Al l/26BE8A93967E604785257CC40066B91A?OpenDocument ) and Summary from Hawaii PUC of the Commissions Inclinations on the Future of Hawaii’s Electric Utilities ( http://puc.hawaii.gov/wpcontent/uploads/2014/04/Commi ssions-Inclinations.pdf ). 29 Many terms have been used or suggested including energy effi ciency certificates, energy savi ngs certificates, white tags, and white certificates. 30 These states include Connecticut , Michigan, and North Carolina. 31 NEPOOL Generation Information System issues energy efficiency certificates for CT. PJM Generation Attribute Tracking System issues energy efficiency certificates for PA. NC Renewable Energy Tracking System allows energy efficiency to count towards compliance with the NC RPS.47 

### in March 2010 (NARR 2010). Sterling Planet, a retailer of RECs and carbon offsets, also offers White Tags® trading instrument s that recognize the meter-measur ed, performance-based results of energy efficiency projects. In June 2012, St erling Planet completed the first White Tags transaction by purchasing Michig an’s Selfridge Air National Gu ard Base’s 2011 White Tags in order to fulfill energy efficiency compliance requir ements for a Michigan utility (Sterling Planet 2012). The Climate Registry has a program ca lled “Cool Planet Energy Efficiency” which provides assistance to utility company business customers with measuring and managing their energy and carbon outputs (TCR 2015).
Even though experience with EECs in the U.S. is limited, their use internationally has been significant. Nine energy efficiency polic ies across seven countries outside of the United States allow for trading of the energy savings used for compliance (IEA 2012a). Trading has been incorporated in the design of these policies to help identify cost-effective opportunities from a broader pool of participating projects an d parties. Table 9 summarizes the types of energy savings and trading included in policy de sign. Over half of these policies allow for trading through the creati on and sale of EECs.48 

### Table 9. International Examples of Trading Energy Savings Country (Jurisdiction) Eligible Energy savings Type of Trading
Australia (New South Wales) Preapproved projects implemented byobligated and accredited nonobligated parties EECs

Australia (South Australia) Activities undertaken by, or on behalf of, obligated energy retailer Limited transfer of credits among obligated parties
Australia (Victoria) Installation of preapproved energy efficiency products by obligated and accredited non-oblig ated parties EECs

China Achieved by grid companies and energy service companies subsidiaries of grid companies Obligated parties may purchase savings from customers and ESCOs under bilateral contracts

Denmark Distributors must engage third parties to achieve energy savings across energy types Energy savings may only be traded among obligated energy distributors

France Produced by obligated parties, local authorities, and social housing landlords Over-the-counter trading of EECs

Italy Produced by obligated distributors ad accredited energy service providers Over-the-counter and spot market trading of EECs
Poland Projects selected through annual auctions EECs fully tradable on the Polish Power Exchange
United Kingdom Projects implemente d by obligated parties or their contractors Transfers of emissions reductions and trading of obligations allowed among obligated parties
Source: IEA 2012a



### Two good international examples are provide d by programs in Italy and in Australia (New South Wales) that have been trading EEC s successfully and at significant scale over the past decade (Pavan 2008; NSW 2015). Italy uses EECs as part of its White Certificates system, a key policy supporting their interna tional climate commitments (Italy 2014). In implementing this policy, electricity and gas distri bution utilities use EECs to m eet established energy savings targets. These utilities may eith er generate their own EECs or buy from third parties through the market. Approximately one-third of EECs are gene rated by third parties and subsequently trade on the open market. EECs are also exchanged betw een energy efficiency providers and utilities through bilateral contracts. 32 

### The majority of energy savings from the Italian White Certificates system are realized from reduc ing electricity usage through a se t of pre-established eligible project types (IEA 2012a).

32 From 2005-2009, electricity savings met 77% of the overall en ergy savings targets, as reported in The Fifth National Communication under the UN Framework Convention on Clim ate Change, submitted by Italy in November 2009.49 

### In Australia, the state of New South Wales ini tially incorporated EEC trading as part of their Greenhouse Gas Emissions Trading sche me. In 2009, New South Wales began trading EECs via the Energy Savings Scheme recognizing that unique barriers to energy efficiency persisted. Obligated parties, whic h include electric uti lities, are required to acquire and surrender EECs to meet the energy savings targets of the Energy Savings Scheme. Ot her organizations also use EECs to voluntarily reduce their greenhouse gas emissions. Energy efficiency projects across residential, commercial and i ndustrial sectors are used to cr eate EECs in New South Wales.
Active trading of EECs also exists in New South Wales. Trading markets have been established by the private sector with some organizations, such as the Australian Financial
Markets Association and individual brokers of environmental commodities, providing regular updates on EEC wholesale market prices (NSW 2015). Even though most EECs are traded via bilateral contracts, trading of EECs has also developed across spot, forward, and options contracts. Since any party who is an “Accredit ed Certificate Provider” may create EECs, the Energy Savings Scheme policy has fostered the de velopment of a robust energy services industry of firms implementing energy efficiency projects supported in part by revenues from the sale of
associated EECs.



## 8. Demand-Side EE Plan Scenario: M agnitude and Timing of Savings

8.1 Approach Chapter 3 of the Regulatory Impacts Analysis (RIA) presents illustrative analysis of the final rule by making assumptions about the appro aches that states might pursue as they develop their state plans. These appro aches are embodied in the illustra tive plan scenarios developed by EPA for the RIA and used to estimate the costs, benefits, and impacts of the final rule. The final CPP provides the states the flexibility to use demand reductions resulting from demand-side EE as a component of their state plan strategy, eith er directly recognized to wards compliance with a rate-based goal or as a complimentary appro ach for achieving a mass-based goal (see section VIII, State Plans, of the final rule preamble for details). The EPA has included in the illustrative plan scenarios (both rate- and mass-based) a le vel of demand reduction th at could be achieved,
and the associated costs incurr ed, through implementation of dema nd-side EE measures. In this section, the EPA provides the basis for the illust rative demand-side EE pl an scenario that is50 

### included as a component of the plan scenarios presented in the RIA and, then, in the following section, the basis for associated EE cost estimates are provided.
The demand-side EE plan scenario is derive d from state and utility experience with ratepayer-funded EE programs and the resulting energy savings, as described in preceding sections of this TSD. Demand-side EE is included in the illustrative analysis contained in the RIA for several reasons including the current use of such programs in every state, the substantial savings relative to othe r opportunities to reduce CO 2 

### emissions from the power sector, overall cost-effectiveness, and the quality of available in formation on their impacts and associated costs. The ratepayer-funded EE programs are the basis for the levels of demand-side EE that are included as part of the RIA, a lthough there are other strategies and measures that states may employ for purposes of the final CPP. For example, th e final rule provides states the flexibility to use building codes, appliance standards, and en ergy service performance contracting (to name a few examples), and those strate gies also offer substantial, low-cost opportunities to reduce CO 2 

### emissions from affected EGUs.

8.2 Inputs The following steps were taken to establish th e inputs for development of the illustrative demand-side EE plan scenario for each state, tribal area, and territory:

 Step 1: Determine current leve l of demand-side EE performance

 Step 2: Determine EE plan sce nario level of performance

 Step 3: Determine start year for EE plan scenario

 Step 4: Determine start year level performance

 Step 5: Determine pace of improvement from start year to EE plan scenario level of performance

 Step 6: Determine average portfolio measur e life and distributi on of measure lives

 Step 7: Determine sustainabilit y of EE plan scenario level of performance

8.2.1 Step 1: Determine Current Level of Demand-Side EE Performance
A fundamental indicator of th e level of EE program performance is incremental annual savings (also known as first-year savings) as a percent of retail sa les. This is a common metric51 

### defining savings levels for state EERS and is readily calculated from EIA Form 861 data for each state, tribal area and territory (h ereafter, just referred to as “sta tes”). Incremental annual savings are also more directly estimated and evaluated than are cumulative savings. 33 

### To determine the current level of demand-side EE performance, th e EPA aggregated the mo st recent (2013) EIA
Form 861 data to the state leve l to establish their current le vel of performance. Estimated incremental savings results are presented in Table 4.

8.2.2 Step 2: Determine EE Plan Scenario Level of Performance As discussed previously (s ection 4.2.3), achievable demand- side EE 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 regi onal characteristics (e.g., avoided power system costs, economic grow th, sectoral mix, climate, and le vel of past EE efforts) affect estimates of achievable potential, ongoing impr ovements in energy-efficient technologies and practices, economic growth, populat ion increases, and continually improving strategies for program delivery have resulted in persistent and substantial levels of achievable potential regardless of specific regional characteristics.

Direct indicators of the achievable incremen tal levels of energy savings performance is provided by past performance at th e state and utility levels, and by requirements states have put in place for levels of savings to be achieved by 2020. As discussed, thes e requirements are often in the form of EERS or similar savings goals that are applied to utilities in the state.
Table 10 summarizes incremental savings levels as a percentage of retail sales from EIA Form 861 (2013) data, 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%, a nd >= 1.5%). As shown, three states achieved the highest level of performance (> 1.5%) and an additional fourteen st ates achieved the second highest level of perfor mance (1.0% to 1.49%).

33 Estimates of cumulative savings impacts in a given year ar e derived from incremental savings values and information on measure lives. Information on measure lives is less consistently gather ed than is information on incremental savings values.52 

### Table 10. 2013 State Levels of Incremental Annual Savings
Incremental Savings as % of Retail Sales # of States States

>= 1.5% 3 AZ, RI, VT 1.0% to 1.49% 14 CA, CT, IL, IA, ME, MD, MA, MI, MN, NY, OH, OR, WA, WI 0.5% to 0.99% 9 CO, ID, IN, MT, NJ, NM, NC, PA, UT < 0.5% 25 AL, AK, AR, DE, DC, FL, GA, HI, KS, KY, LA, MS, MO, NE, NV, NH, ND, OK, SC, SD, TN, TX, VA, WV, WY Source: EPA calculation using EIA 2013



### In addition to the state levels of performance represented in Table 10, numerous utilities of all ownership types and in al l regions of the country have achieved significant levels of incremental savings. Tables 11, 12, and 13 provide examples of investor-, cooperative-, and municipal-owned utilities and their incrementa l savings levels for 2013 based on EIA Form 861
data.

Table 11. 2013 Incremental Annual Savings – Investor-Owned Utilities

Investor-Owned Utility State Incremental Savings as % of Retail Sales Arizona Public Service Company AZ 1.6% Commonwealth Edison Company IL 1.2% DTE Electric Company MI 1.3% Niagara Mohawk Power Corporation NY 2.2%

Pacific Gas & Electric Company CA 1.7% Potomac Edison Company MD 1.8% Source: EPA calculation using EIA 2013



### Table 12. 2013 Incremental Annual Savings – Cooperative-Owned Utilities

Cooperative-Owned Utility State Incremental Savings as % of Retail Sales French Broad Electric Membership Corporation NC 2.2% Jackson Electric Cooperative Inc. WI 2.6%

KEM Electric Cooperative Inc. ND 1.6% Southern Maryland Electric Cooperative Inc. MD 1.4%

Southern Pine Electric Power Association MS 2.4% Umatilla Electric Cooperative Association OR 1.5% Source: EPA calculation using EIA 201353 

### Table 13. 2013 Incremental Annual Saving s – Municipal-Owned Utilities

Municipal-Owned Utility State Incremental Savings as % of Retail Sales Austin Energy TX 0.9% City of Fort Collins CO 2.2%

City of Oberlin OH 2.6% City of Seattle WA 1.5% City of Saint Peter MN 2.1% Jacksonville Electric Authority FL 0.8% Los Angeles Department of Water & Power CA 0.9% Source: EPA calculation using EIA 2013



### Table 14 summarizes incremental savings leve ls required by state EERS and categorized into the same four ranges as Table 10 (ACEEE 2015b). Seven states are required to achieve the highest level of performance (> 1.5%) and an ad ditional eight states are required to achieve the next highest level of pe rformance (1.0% to 1.49%).

Table 14. Levels of Incremental Savings Required by State EERS
Incremental Savings as % of Retail Sales # of States States

>= 1.5% 7 AZ, ME, MA, MN, RI, VT, WA 1.0% to 1.49% 8 CO, CT, HI, IA, MD, MI, NY, OR 0.5% to 0.99% 4 AR, CA, IL, WI < 0.5% 1 TX
Source: ACEEE 2015b



### For the illustrative demand-side EE plan s cenario level of performance, the EPA has determined that a 1.0% incremental savings, as a per centage of retail sales, is appropriate. This level was achieved by seventeen states and by nu merous utilities of all ownership types in 2013. An additional two states (CO and HI), accounting fo r overlap, are expected to achieve this level by 2020 as shown in Table 14. Thus, nineteen states have either achieved or are required to achieve this level of performance.54 

### 8.2.3 Step 3: Determine Start Ye ar for EE Plan Scenario For construction of the EE plan scenari o, the EPA has determined that 2020 is appropriate as the first year fo r state demand-side EE efforts. Th e EPA believes this is reasonable for the illustrative plan scen ario as efforts are ramped up in advance of the 2022-2029 interim plan performance period. This allows sufficient tim e – for than four years – after issuance of the final rule for planning of these efforts. This re flects a three year delay from the 2017 start year used for the proposal.

8.2.4 Step 4: Determine Start Ye ar Level of Performance For construction of the best practices scen ario, the EPA has set each state’s level of performance in the start year (2020) to the level of performa nce observed in 2013, based on the aggregate state-level incremental electricity sa vings based on EIA Form 861 data (Table 4).

8.2.5 Step 5: Determine Pace of Improvement fr om Start Year to EE Plan Scenario Level of Performance To determine a reasonable trajectory of incr emental savings levels from the 2020 level to the EE plan level of performance, the EPA cons idered past performance of individual program
administrators 34 

### as well as requirements of existing st ate EERS. For the past performance of individual program administrato rs, the EPA first screened the data and divided them into moderate and high performing sub-groups. The m oderate group (47 entities) was defined as programs that achieved from 0.8% to 1.5% maxi mum incremental saving s levels and the high group (26 entities) was defined as programs that achieved greater than 1.5% maximum incremental savings levels. The EPA then calculat ed 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 2 for s upporting data and analysis.
EPA also considered requirements of existi ng state EERS and evaluated the rate at which their incremental savings levels increase over time. For several EERS, the EPA was unable to clearly identify ramp-up schedules. The EPA id entified ten states with clear schedules and

34 EIA 861 was the primary data source; however, EIA 861 da ta was supplemented with da ta for third-party program administrators. Prior to 2011 EIA did not collect data from third-party program administrators.55 

### calculated the average rate of improvement fo r each. The average rate of improvement of incremental annual savings rate required for th ese ten states is 0.21% per year. See Appendix 2 for supporting data and analysis.
Based on these results, the EPA has chosen 0.2% per year for the demand-side EE plan scenario rate of improvement. These values are at the low end of the range in comparison with our analysis of past state perf ormance and state requirements.

8.2.6 Step 6: Determine Average Portfolio Measure Life and Distribution of Measure Lives The next step in defining the EE plan scenar io entails projecting the cumulative future impacts of the annual incremental savings levels for each state. The incremental (or first-year) savings impacts in a year reflect the savings fr om EE measures put in place in that year. The cumulative incremental savings in a given year represents the total impacts of all EE measures, those put in place in that year a nd all prior years, that still have remaining savings impacts in the given year. The cumulative savings account for th e continuing impacts of EE measures that remain in place for a period of time (the “measur e life”) before being replaced. For example, the purchase of a high-efficiency refrigerator may le ad to savings for twelve years, before being replaced with a new model. To estimate cumulativ e impacts of a series of years of incremental savings, the industry uses the c oncept of an average measure lif e for the entire portfolio of EE programs being administered. Rather than use a single, average measure life to represent a diverse portfolio of programs, that range in measure lives from less than ten years (e.g., commercial lighting technologies and applications, residential behavi oral feedback) to as long as twenty years or more (e.g., residential HVAC, residential building in sulation), the EPA is assuming a distribution of measur e lives around the average to account for future impacts of incremental savings levels.
To approximate a distribution of measur e lives across a portfolio of EE programs represented by the EE plan scenario, EPA relied on a recent analysis by the Lawrence Berkeley National Laboratory (LBNL) of the distributi on of EE program lifetimes (LBNL 2015a). The analysis was based on the LBNL DSM Program Da tabase. The database consists of programlevel data, including first-year savings and program savings lifetimes, reported directly by the EE program administrators since 2009. Across the data base of nearly 6,000 program years of data,56 

### program savings lifetimes are available for a bout 1,600 program years (27% of the program years). More than 50 utilities a nd other EE program administrators in 25 states contributed to those data through their regulatory filings, statew ide databases, and other sources. The weighted average of EE measure lifetimes for the entire population in the LBNL analysis is 10.2 years. The LBNL analysis further partitions the measur e lifetimes of the EE programs into four tiers based on a cluster analysis – a statistical appro ach for grouping values based on their similarity. 35


### The four-tier distributio n of EE program measure lifetimes, as well as the associated weightedaverage measure lifetime and the shares of first-year savings of each tier, are used in the analysis. Figure 5 presents LBNL’s four-tier distributi on of the EE program measure lifetimes, and the associated weighted average measure lifetime a nd share of first-year savings for each tier.
Figure 5. The Four-Tier Distribution of Demand-Side EE Program Measure Lifetime

Source: LBNL 2015a

35 The method used for the cluster analysis is the k -means approach. The method starts with assignment of each data point to a cluster so as to minimize the distance of cluster members from the center of the cluster, which is designated randomly. In essence, the method seeks to minimize differe nces within each cluster and maximize diffe rences among the clusters. In this case
, the programs within each cluster would have similar lifetimes and program types.57 

### The weighted-average EE program measur e lifetime of 10.2 year s for the entire population of data in the LBNL analysis is roughly consistent with a 2014 ACEEE study that found an average measure life of 10.6 years ba sed on electricity EE program data across all sectors in 20 states (ACEEE 2014d) . A more common approach in ot her studies is to assume a portfolio with no diversity of measure lives, 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. By comparison, the appro ach used here is a c onservative one, leading to the same quantity of total energy savings, but w ith a greater portion of the savings occurring in later years than occur with the more common, simpler approach of using a single average measure life to represent the entire portfolio of EE programs.

8.2.7 Step 7: Determine Sustainability of EE Plan Scenario 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 leve l of performance consta nt through the analysis period. Limited empirical data suggests the reasonableness of this approach; however, comprehensive data, across all regions and states, does not exist because these levels of performance have not been achieved and sustai ned 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 36 

### regional energy resource plans (NPCC 2010). These analyses have become more detailed, reliable, and purposeful over time. Since 1998, NPCC’s estimates of achievable potential have more than tripled even as evaluated electricity savings from EE progr ams have increased rapidly, mo re than quadrupling between 1998 and 2010 (while levelized co sts 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 innovati on, resulting in larger and more reliable conservation supply estimates” (NPCC 2008). Ta ble 15 summarizes the NPCC’s achievable potential estimates and evaluate d savings since 1998 (NPCC 2010).

36 NPCC’s resource plans cover Idaho, Oregon, and Washington in their entirety, and western regions of Montana and Wyoming.58 

### Table 15. 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

Source: NPCC 2010



### 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 scenari o. We address this in a later section, 8.4.2, after presenting those
results.

8.2.8 Summary of the Demand-Side EE Plan Scenario Construction Table 16 provides a summary of inputs for th e demand-side EE plan scenario. The pace of improvements, average measure life, and distri bution of measure lives are each conservative and, therefore, lead to lower cumulative savings than would otherwise result. Similarly, the EE plan scenario, being based solely on results from and requirements of EE programs, is less stringent than a level would be th at accounted for potential impacts of other EE strategies such as building energy codes, state app liance standards, or energy serv ices performance contracting.

Table 16. Summary of Demand-Si de EE Plan Scenario Inputs
Input Demand-Side EE Plan Scenario Current Level of Performance (incremental savings as % of retail sales) Calculated using data from EIA 861 (2013)

EE Plan Scenario Level of Performance (incremental savings as % of retails sales) 1.0%

Start Year 2020 Start Year Level of Performance 2013 level of performance Pace of Improvement (increase in incremental savings rate per year) 0.20% per year

Average Measure Life and Distribution of Measure Lives 10.2 years with a 4-tier distribution of measure lifetimes (LBNL database and analysis)

Continued Performance Once achieved, EE plan scenario savings level is sustained

Source: Section 8.259 

### 8.3 Calculations This section presents the calculations for dete rmining cumulative savings levels for each state, for each year based on the demand-side EE plan scenario. The cumulative savings levels are derived based upon the key i nputs summarized in Table 16.
Calculating the net cumulative savings of th e demand-side EE plan scenario involves six steps. For each state, for each year (2 020-2030) the following steps are taken:

1. Determine annual business- as-usual (BAU) sales

2. Determine annual incremental EE savi ngs as a percentage of sales

3. Determine annual incremental EE savings (GWh)

4. Determine annual expiring EE savings (GWh)

5. Determine net cumulative EE savings (GWh)

6. Determine sales after net EE (GWh)

7. Determine net cumulative EE savings 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 2020 through 2027. We truncate the results at 2027 for simplicity, but all calculations and full results ar e presented for all states for all years in the spreadsheet file included as Appendix 3.

8.3.1 Step 1: Determine Annual Business as Usual (BAU) Sales BAU sales are derived by aggregating 2013 sale s to the state level using EIA Form 861 data and increasing the value for each subseque nt year by the average annual growth rate (AAGR) from the AEO 2015 Reference Case fo r the region corresponding to the state. Specifically, the AAGR from AEO 2015 for 2013- 2040 is used to derive BAU sales for 2014 through 2040, and the AAGR from AEO 2015 for 2030- 2040 is used to derive BAU sales for 2041 through 2050. For South Carolina the corre sponding region is SRVC and the AAGR from 2013-2040 is 0.88% per year and from 2030-2040 is 0.76% per year. The resulting values are provided in Table 17 for South Carolina.60 

### Table 17. BAU Sales for South Carolina

Year 2020 2021 2022 2023 2024 2025 2026 2027

BAU Sales

(GWh) 83,601 84,341 85,087 85,840 86,599 87,365 88,138 88,918



### 8.3.2 Step 2: Determine Annual Incremental EE Savings as a Percentage of Sales Next, the 2020 value for annual incremental EE sa vings as a percentage of sales is set at the 2013 value based upon aggregated EIA-861 repor ted data (see Table 4). For South Carolina
that value is 0.46%. This value is then increase d by the pace of improvement of 0.2% per year until the goal level of 1.0% is reached and then held constant . The resulting values are provided
in Table 18 for South Carolina.

Table 18. Annual Incremental EE Savings as a Percentage of Sales for South Carolina

Year 2020 2021 2022 2023 2024 2025 2026 2027

Annual Incremental EE Savings

(% sales) 0.46% 0.66% 0.86% 1.00% 1.00% 1.00% 1.00% 1.00%



### 8.3.3 Step 3: Determine Annual Incremental EE Savings (GWh) Annual incremental EE savings (also known as first-year savings) are calculated by multiplying the annual incremental savings as a pe rcentage of sales times the prior year sales after net EE. The resulting values are provided in Table 19 for South Carolina.

Table 19. Annual Incremental EE Savings (GWh) for South Carolina

Year

2020 2021 2022 2023 2024 2025 2026 2027

Annual Incremental EE Savings

(GWh) 385 553 721 834 833 833 832 83261 

### 8.3.4 Step 4: Determine Annual Expiring EE Savings (GWh) As discussed above (section 8.2.6), the annual incremental (or first- year) EE savings of the portfolio decline (or expire) over time according to the four-tier distribution of measure lives. Expiring EE savings are calculated as the sum of all expired savings in a given year from all prior years’ incremental (first -year) savings. The resulting valu es for expiring EE savings are provided in Table 20 for South Carolina.

Table 20. Expiring EE Savings for South Carolina

Year 2020 2021 2022 2023 2024 2025 2026 2027 Expiring EE Savings (GWh) 0 0 0 0 0 0 69 165



### 8.3.5 Step 5: Determine Net Cu mulative EE Savings (GWh) Net cumulative EE savings in a given year ar e equal to annual incremental savings for that year minus total expiring savings for that ye ar plus net cumulative savings for the prior year. The resulting values are provided for South Carolina in Table 21.

Table 21. Net Cumulative EE Savings for South Carolina

Year 2020 2021 2022 2023 2024 2025 2026 2027 Net Cumulative EE Savings

(GWh) 385 938 1,659 2,493 3.327 4,159 4,922 5,589



### 8.3.6 Step 6: Determine Sales after Net EE (GWh) Sales after net EE are calculated by subtrac ting net cumulative savings from BAU sales. The resulting values are provided in Table 22 for South Carolina.62 

### Table 22. Sales after Net EE Savings for South Carolina

Year 2020 2021 2022 2023 2024 2025 2026 2027 Sales after

Net EE Savings (GWh) 83,216 83,403 83,428 83,346 83,272 83,206 83,216 83,329



### 8.3.7 Step 7: Determine Net Cumulative EE Savings as a Percentage of BAU Sales Net cumulative EE savings as a percentage of BAU sales are equal to net cumulative savings divided by BAU sales. The resulting valu es are summarized for South Carolina in Table
23.

Table 23. Net Cumulative EE Savings as a Percentage of BAU Sales for South Carolina

Year 2020 2021 2022 2023 2024 2025 2026 2027 Net Cumulative EE Savings

as % of BAU Sales 0.46% 1.11% 1.95% 2.90% 3.84% 4.76% 5.58% 6.29%



### 8.3.8 Summary of Calculation Formulas and Results by Step for South Carolina Table 24 provides summaries of the results for South Carolina for each step, respectively.

Table 24. Summary of Results by Step for South Carolina

Year 2020 2021 2022 2023 2024 2025 2026 2027 BAU Sales

(GWh) 83,601 84,341 85,087 85,840 86,599 87,365 88,138 88,918

Annual Incremental EE Savings

(% sales) 0.46% 0.66% 0.86% 1.00% 1.00% 1.00% 1.00% 1.00%

Annual Incremental EE Savings

(GWh) 385 553 721 834 833 833 832 83263 Year 2020 2021 2022 2023 2024 2025 2026 2027 Annual Expiring EE Savings (GWh) 0 0 0 0 0 0 69 165

Net Cumulative EE Savings

(GWh) 385 938 1,659 2,493 3.327 4,159 4,922 5,589

Sales after

Net EE (GWh) 83,216 83,403 83,428 83,346 83,272 83,206 83,216 83,329

Net Cumulative EE Savings as % of BAU Sales 0.46% 1.11% 1.95% 2.90% 3.84% 4.76% 5.58% 6.29%



### 8.4 Results

8.4.1 Summary of Results As discussed, the EE plan scenario results in reduced electricity demand in each state, in each year of the plan scenario. Table 25 provides the results for 2020, 2025, and 2030, as electricity demand reduced in gigawatt-hours and as a percentage of BAU sales for the year. Full results are presented for all states for all years in the spreadsheet file included as Appendix 3.

Table 25. Demand-Side EE Plan Scenario: Magnitude and Timing of Net Cumulative Savings

States Net Cumulative Savings 2020 2025 2030

GWh % of BAU Sales GWh % of BAU Sales GWh % of BAU Sales Alabama 91 0.10% 3,287 3.37% 7,258 7.13% Arizona 797 0.99% 4,786 5.65% 7,195 8.09% Arkansas 241 0.48% 2,519 4.81% 4,279 7.80% California 2,735 0.99% 16,310 5.70% 24,310 8.19% Colorado 524 0.91% 3,367 5.58% 5,116 8.06% Connecticut 305 1.00% 1,805 5.77% 2,658 8.34%

Delaware 1 0.01% 352 2.98% 849 7.06% District of Columbia 51 0.45% 556 4.81% 938 7.99% Florida 389 0.17% 9,054 3.69% 18,673 7.31% Georgia 357 0.26% 5,889 4.07% 11,314 7.48%64 States Net Cumulative Savings 2020 2025 2030

GWh % of BAU Sales GWh % of BAU Sales GWh % of BAU Sales Idaho 137 0.54% 1,327 4.98% 2,195 7.91% Illinois 1,458 1.00% 8,633 5.76% 12,742 8.31% Indiana 1,006 0.92% 6,347 5.69% 9,459 8.29%

Iowa 484 0.99% 2,878 5.73% 4,270 8.25% Kansas 2 0.01% 1,265 2.95% 3,080 6.97% Kentucky 337 0.37% 4,232 4.50% 7,544 7.68% Louisiana 23 0.02% 2,905 3.02% 6,983 6.92%

Maine 121 1.00% 717 5.77% 1,056 8.34% Maryland 632 1.00% 3,729 5.78% 5,482 8.36% Massachusetts 566 1.00% 3,344 5.77% 4,925 8.34%

Michigan 1,055 1.00% 6,232 5.77% 9,176 8.34% Minnesota 712 0.99% 4,230 5.73% 6,275 8.25% Mississippi 119 0.23% 2,159 3.95% 4,244 7.40%

Missouri 420 0.49% 4,324 4.89% 7,230 7.97% Montana 79 0.53% 770 4.98% 1,274 7.91% Nebraska 49 0.15% 1,205 3.65% 2,500 7.35% Nevada 176 0.47% 1,862 4.81% 3,162 7.84% New Hampshire 17 0.15% 425 3.67% 875 7.42%

New Jersey 531 0.69% 4,202 5.40% 6,514 8.24% New Mexico 138 0.56% 1,300 5.02% 2,140 7.87%

New York 1,477 1.00% 8,637 5.86% 12,537 8.51% North Carolina 814 0.59% 7,385 5.12% 11,984 7.95% North Dakota 3 0.02% 517 3.00% 1,244 7.00% Ohio 1,545 1.00% 9,150 5.76% 13,506 8.31% Oklahoma 168 0.26% 2,744 4.07% 5,276 7.45%

Oregon 500 0.99% 2,986 5.69% 4,460 8.17% Pennsylvania 1,351 0.90% 8,677 5.70% 12,915 8.33% Rhode Island 80 1.00% 471 5.77% 693 8.34% South Carolina 385 0.46% 4,159 4.76% 7,117 7.79% South Dakota 16 0.13% 464 3.54% 986 7.28%

Tennessee 320 0.31% 4,586 4.26% 8,503 7.57%

Texas 801 0.20% 16,359 3.82% 32,959 7.32% Utah 257 0.81% 1,823 5.53% 2,789 8.12% Vermont 57 1.00% 338 5.77% 498 8.34% Virginia 28 0.02% 3,707 3.02% 8,907 6.94% Washington 975 0.99% 5,822 5.69% 8,696 8.17%65 States Net Cumulative Savings 2020 2025 2030

GWh % of BAU Sales GWh % of BAU Sales GWh % of BAU Sales West Virginia 79 0.24% 1,347 4.06% 2,580 7.59%

Wisconsin 710 1.00% 4,201 5.76% 6,197 8.31% Wyoming 31 0.17% 700 3.73% 1,433 7.33%

Navajo 0 0.00% 21 2.90% 53 6.85%

Ute 0 0.00% 17 2.91% 41 6.90% Fort Mojave 0 0.00% 2 2.90% 4 6.85% Continental U.S. 23,150 0.59% 194,126 4.81% 327,092 7.83% Alaska 2 0.04% 161 3.13% 376 7.04% Hawaii 3 0.04% 284 3.10% 668 7.03% Puerto Rico 0 0.00% 528 2.97% 1,271 7.14% Guam 0 0.00% 46 2.97% 112 7.14% U.S. Total 23,156 0.59% 195,145 4.80% 329,518 7.82% Source: Appendix 3



### 8.4.2 Results in Context To provide context for state cumulative savi ngs results provided in Table 26, the average annual savings were calculated for each state through 2030, starting from 2020. Table 22
summarizes the results.

Table 26. Average Annual Savings Rates of Demand-Side EE Plan Scenario

Years Number of Years Range of Cumulative Savings (% of Sales) across States in 2030 Range of Average Annual Savings Rates across States National Average Annual Savings Rate

2020- 2030 11 6.92% to 8.36% 0.63%/year to 0.76%/year 0.71% per year

Source: EPA calculations based on Table 25



### The state range and national va lue for the average annual savi ngs rate represented in the demand-side EE plan scenario are below the rang e of values found in recent utility, state, and regional studies (0.9% to 1.3% per year) as summarized in Table 6. These results provide additional support for the reasonableness and feasib ility of the demand-side EE plan scenario and associated state-specific results.66 

### 8.4.3 Representation of Demand-Side EE in Modeli ng of the Illustrative Plan Scenario To reflect the implementation of the illustrative energy efficien cy plan scenario in power sector modeling, as discussed in the RIA (Chapt er 3), the IPM base case electricity demand was adjusted exogenously to reflect the estimated future-year demand reductions described and
summarized above. State-level demand reductions were scaled up to account for transmission losses and applied to base case generation demand in each model year to derive adjusted demand for each state, reflecting the energy efficiency plan scenario energy reductions. The demand adjustments were applied proporti onally across all segments (peak and non-peak) of the load duration curve. This approach to allocating annua l electricity reductions is consistent with the significant levels of savings repr esented in the illustrative demand- side plan scenario. Achieving 1.0% incremental savings typically require r obust EE program portfolios, employing a diverse
set of measures across all sectors and most end-use categories. The impacts of such comprehensive portfolios occur across the full an nual load duration curve with higher periods of demand providing proportionally greater EE pot ential. In other words, potential demand reductions through EE measures tend to mirror the underlying load duration curve which, in turn,
is formed from the demands from all end uses , across all end-use sectors. Appendix 1 provides summary results from more than 50 recent EE potential studies – conducted between 2009 and
2014 – from across the U.S. The deta iled results of these studies indi cate the breadth of end-uses, across all end-use sectors, which provide cost-effective EE potential.



## 9. Demand-Side EE Plan Scenario: Assessment of Costs

9.1 Approach This section provides the basis for estimating the costs that would be incurred to achieve the magnitude and timing of EE savings represente d by the EE plan scenario. Chapter 3 of the Regulatory Impacts Analysis (RIA) presents illust rative analysis of the final rule by showing approaches that states might pursue as they deve lop their state plans. Th e scenarios used in the RIA are the basis for costs, bene fits, and other power sector impact s of the final rule. The final
rule allows states to use demand-side EE as a component of their stat e plan strategy, either directly recognized towards complia nce with a rate-based goal or as a complimentary approach for achieving a mass-based goal. The EPA has incl uded in the illustrative plan scenarios (both rate- and mass-based) a level of demand reduction that could be achieved, and the associated67 

### costs incurred, through implementation of demand-s ide EE measures as shown in this TSD. In this section, the EPA provides th e basis for the costs of the illustrative demand-side EE plan scenario that is included as a component of the plan scenarios presented in the RIA.
The electricity savings resulting from th e demand-side EE methodology was used to adjust electricity demand levels in an exogenous manner, applied to the power sector modeling for the illustrative plan scenarios of the RIA. In other words, the degree to which EE is employed is not determined endogenously within the pow er sector modeling based upon optimization of costs but, rather, “hard wired” in to the illustrative plan scen arios. The electricity savings represented in the demand-side EE plan scenario l ead to substantial reductions in power system
costs due to the reductions in specified electricity dema nd. Since EE is not represented endogenously as an abatement measure within th e power sector modeling, the costs associated with the EE plan scenario must be estimated outsi de 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.

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

 Step 1: Determine incremental electricity savings

 Step 2: Determine first-year program costs and cost factors

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

9.2.1 Step 1: Determine Increme ntal Electricity Savings Results from section 8 provide the starting basis for estima tion of EE costs used in the RIA. The resulting incremental savings (MWh) by state and by year provide the first step in calculating EE plan scenario costs.

9.2.2 Step 2: Determine First-Year Program Costs and Cost Factors First-year program costs refer to the full co sts (e.g., incentive paym ents, administration, technical support, marketing, evaluation, measurem ent and verification (EM&V), information to consumers, etc.) incurred by a utility or other ad ministrator of EE programs in a given year that68 

### lead to EE measures (technologies and practices ) put in place in that year, and result in reductions in electricity demand in that and fu ture years (driven by the mix of measure lives across the portfolio of EE programs employed). Firs t-year program costs are represented as these costs divided by the incremental (o r first-year) savings for that ye ar. Unlike participant costs, program costs are readily known by the administrator of EE programs and are, therefore, an appropriate starting point for cost analysis of the dema nd-side EE plan scenario.
Recent studies have collected and analyzed firs t-year program cost data directly from EE program administrators from across the U.S. In 2009, ACEEE conducted a national review of data on EE program costs from program annual reports, evaluation repor ts, and information compiled from contacts at program administers in 14 states (ACEEE 2009). The program cost data were compiled from multiple EE program admi nistrators over multiple years in each state. ACEEE found average first-year net 37 

### program costs of $275/MWh (2011$). Two newer national analyses have found lower program costs th an 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 (ACEEE 2014d). In this analysis ACEEE found average first-year net program costs of $230/MWh ( 2011$). In 2014, an LBNL study presented results from a uniquely comprehensive study of EE pr ogram costs (LBNL 2014b). The LBNL analysis reviewed program-level data fr om over 100 program administrato rs in 31 states. Data were collected from over 1,700 individu al programs for up to three y ears (2009-2011), covering more than 4,000 individual program-years data points. B ecause of the broad scope of their study and the lack of net savings information for many prog rams, LBNL focused on gross, rather than net, savings values. 38 

### LBNL found national average first-year program cost of gross savings of $162/MWh (2012$). Applying an aver age net-to-gross ratio of 0.9 and deflating costs at 1.8%, results in an estimated national average fi rst-year cost of net savings of $177 (2011$). 39 

### The results cited from these three studies repres ent average first-year costs from program
administrators across the U.S.

37 “Net costs” refers to costs per electricity saved after acc ounting for effects of free-ridership on those savings. Depending u pon the state, spillover effects may also be accounted for in net costs. 38 “Gross savings” refers to electricity savings before an y accounting for effects of free-ridership or spillover. 39 Source: BEA National Income and Product Accounts Table 1.5. 4 Price Indexes for Gross Dome stic Product (version revised on March 27, 2015).69 

### There are two factors that would influence first-year EE costs as higher levels of performance are achieved. Economies of scale in the operation of la rger EE programs and larger portfolios of EE programs, and learning and expe rtise 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 (Jaffe et al. 2003) . 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 ach ieved. Several analyses of EE program costs and savings based on empirical data ar e consistent with the economic theory, indicating significantly declining costs up to incremental savings levels of 1.0% or higher, and then level or increasing costs as higher savings le vels are achieved (Synapse 2008; GEEG 2012; LBNL 2015b). Based on analysis like these, a recent LBNL study adopt ed an approach that generically represented declining costs at lower savings levels and increasing costs at higher savings levels (LBNL 2013c). Counter to these studies, other analysis of empirical da ta has indicated only steadily rising costs as savings levels increase even from levels below 0.5% (ACEEE 2014d).
In consideration of the above discussion, the EPA has chosen to use a three-tier approach to generically represent declining first-year program costs through the maximum savings level of 1.0% represented in the EE plan scen ario. The first tier is used fo r incremental savings levels less than 0.5%; the second tier is used for incremental savings levels equal to or greater than 0.5% and less than 1.0%; and the third tier is used for incremental savings levels of 1.0% (the highest level assumed for demand-side EE plan scenario). To reflect the significantly higher costs found at the lowest savings levels, program costs for th e first tier are set at tw o times the level found in the 2009 ACEEE study. This results in first-year program costs of $550/MWh ($2011) for the first tier. For the second and third tiers, thes e costs are reduced by 20% and 40%, respectively, resulting in first-year program costs of $440/MWh and $330/MWh. The approach results in
reasonable but conservative cost assumptions for analyzing the demand-side EE plan scenario.

9.2.3 Step 3: Determine the Ratio of Program to Participant Costs As noted above, while program costs are r eadily known and consistently tracked and reported by program administrators, particip ant costs are not eas ily known, and are less consistently estimated and reported. The ratio be tween program and participant costs will vary70 

### significantly from one program to the next within a utility’s portfolio. To determine an appropriate ratio for the impacts assessment of the CPP proposed rule, EPA conducted research and analysis of industry data (annual EE program reports from ad ministrators in 22 states) and found that on average program costs represente d 53% of total costs (w ith participant costs representing the remaining 47%) (EPA 2014a). Base d on this analysis, the EPA used a ratio of 1- to-1 for program to participan t costs for the EE cost estimates contained in the CPP proposed rule. In April 2015 the Lawrence Berkeley Nati onal Laboratory (LBNL) published a technical brief that analyzed program and participant costs for EE programs based on their extensive LBNL DSM Program Database (LBNL 2015b). The database contains information on approximately 1,700 individual EE programs across multiple years from 34 states. The LBNL summary findings indicate a ratio of program to participant costs of 51% to 49%. These results
are consistent with the earlier analysis and support th e use of the 1-to-1 ratio for the current analysis. This ratio is used to derive partic ipant and total costs based upon program costs.

9.2.4 Summary of Inputs for Dema nd-Side EE Cost Analysis Table 27 provides a summary of inputs for the EE cost analysis including incremental electricity savings, first-year costs and cost factor s, and ratio of program to participant costs.

Table 27. Summary of EE Cost Analysis Inputs
Input Source or Value Incremental Electricity Savings Results from demand-side EE plan scenario First-Year Program Cost $550/MWh (2011$)

Ratio of Program to

Participant Costs 1:1
First-Year Participant Cost $550/MWh (2011$)
First-Year Total Cost $1100/MWh (2011$)

Cost Factors and Total Costs by Level of Incremental Savings Incremental Savings Rate

< 0.5% 0.5% to < 1.0% 1.0% 100% of first-year costs

$1100/MWh (2011$) 80% of base costs: $880/MWh (2011$) 60% of base costs: $660/MWh (2011$)

Source: Section 9.271 

### 9.3 Calculations This section addresses the methodology for cal culating the cost associated with the EE
levels included in the illustrative plan s cenarios. Specifically, three va lues are calculated (annual first-year costs, levelized cost of saved ener gy (LCSE), and annualized co sts); 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 sectio ns on timing and magnitude of savings, and costs inputs are used for these calculations. For each stat e, the following steps are taken for each year:

1. Calculate annual first-year costs of saved energy

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) fo r 2020 through 2027 (truncated for simplicity). The full national results (for 2020, 2025, and 2030) are presented following the step-by-step example
for South Carolina.

9.3.1 Step 1: Calculate A nnual First-Year Costs Annual total first-year costs are calculat ed by multiplying annual total incremental savings (MWh) (from Table 19) by the first-year total cost of saved energy (from Table 27, using the corresponding incremental savings rate). Prog ram and participant portions of the first-year
costs are then calculated as 50% of total first-year costs for each per Table 27.
The resulting values are summarized for South Carolina in Table 28.

Table 28. Calculation of Annual First-Year Costs for South Carolina

Year 2020 2021 2022 2023 2024 2025 2026 2027 Annual Incremental

Savings (GWh) 385 553 721 834 833 833 832 832

First-Year Total Cost of Saved Energy (2011$/MWh) 1100 880 880 660 660 660 660 66072 Year 2020 2021 2022 2023 2024 2025 2026 2027 First-Year Total Cost (millions

2011$) 423.4 486.6 634.5 550.6 550.1 549.6 549.2 549.2

First-Year Program (millions of

2011$) 211.7 243.3 317.3 275.3 275.0 274.8 274.6 274.6

First-Year Participant (millions of

2011$) 211.7 243.3 317.3 275.3 275.0 274.8 274.6 274.6



### 9.3.2 Step 2: Calculate Levelized Cost of Saved Energy The LCSE calculation algorithm is based on the 2002 California Standard Practice Manual (California GOPR 2002). This approach was adopted in order to a ppropriately allocate the cost of a single year EE program over declini ng lifetime savings. The net present value of all savings from a single year’s EE activities (i.e., ov er the entire distributio n of program lifetimes) is calculated using the real disc ount rate rates of 3% and 7%. Th e levelized cost of saved energy is then calculated by dividing the annual first-year costs (from Ta ble 28) by the net present value of the savings. The resulting values are su mmarized for South Carolina in Table 29.

Table 29. Levelized Cost of Saved Energy fo r South Carolina (at 3% discount rate)

Year 2020 2021 2022 2023 2024 2025 2026 2027 Levelized Savings (GWh) 3,279 4,710 6,141 7,105 7,099 7,092 7,087 7,087

First-Year Total

Cost (millions 2011$) 423.4 486.6 634.5 550.6 550.1 549.6 549.2 549.2

Total LCSE (2011$/MWh) 129.2 103.3 103.3 77.5 77.5 77.5 77.5 77.5
Program LCSE

(2011$/MWh) 64.6 51.7 51.7 38.7 38.7 38.7 38.7 38.7
Participant LCSE

(2011$/MWh) 64.6 51.7 51.7 38.7 38.7 38.7 38.7 38.773 

### 9.3.3 Step 3: Calculate Annualized Costs Annualized costs are calculated by multiplyi ng the LCSE for each ye ar by the estimated savings of the demand-side EE plan scenario in th at year through the full distribution of measure lifetimes. For a given year in the analysis, the total annualized costs resulting from all current and past investments are summed to calculate the total annuali zed costs for that year. The resulting values are summarized for South Carolina in Table 30.

Table 30. Annualized Costs for South Carolina

Year 2020 2021 2022 2023 2024 2025 2026 2027 Annualized Total Costs

(millions

2011$) 49.7 106.9 181.4 246.0 310.6 375.1 430.7 476.4

Annualized

Program

Costs (millions

2011$) 24.9 53.4 90.7 123.0 155.3 187.6 215.3 238.2

Annualized Participant Costs (millions of

2011$) 24.9 53.4 90.7 123.0 155.3 187.6 215.3 238.2



### 9.3.4 Summary of General Formulas and Results by Step for South Carolina Table 31 provides results for South Carolina for each step.

Table 31. Summary of Results by Step for South Carolina.

Year 2020 2021 2022 2023 2024 2025 2026 2027Total FirstYear Costs

(millions

2011$) 423.4 486.6 634.5 550.6 550.1 549.6 549.2 549.2

Total LCSE (2011$/MWh) 129.2 103.3 103.3 77.5 77.5 77.5 77.5 77.5

Annualized Total Costs

(millions

2011$) 49.7 106.9 181.4 246.0 310.6 375.1 430.7 476.474 

### 9.4 Results

9.4.1 Summary of National Results Tables 32 and 33 provide the national first- year and annualized EE costs for the best practices EE scenario for 2020, 2025, and 2030. Table 34 provides the estimated national LCSE for the same years. Each of the three tables includes values fo r program, participant, and total

costs.

Table 32. First-Year Demand-Side EE Costs (billions 2011$) (Continental U.S.)

Year 2020 2025 2030 Program 9.1 12.7 12.7 Participant 9.1 12.7 12.7 Total 18.1 25.4 25.3



### Table 33. Annualized Demand-Side EE Cost s (3% discount rate, billions 2011$) (Continental U.S.)

Year 2020 2025 2030 Program 1.1 8.4 13.2 Participant 1.1 8.4 13.2 Total 2.1 16.7 26.3



### Table 34. Levelized Cost of Saved Energy (3% discount rate, 2011$/MWh) (Continental U.S.)

Year 2020 2025 2030 Program 46.0 43.1 40.3 Participant 46.0 43.1 40.3 Total 92.0 86.3 80.5



### Full calculations and results are presented for al l states for all years in the spreadsheet file included as Appendix 3. The file includes result s for LCSE (total, program and participant, discounted at 3% and 7%), first- year costs (total, program and pa rticipant), and annualized costs (total, program and participant, discounted at 3% and 7%).75 

### 9.4.2 Results in Context To provide context for the pace of increase in EE program spending levels represented by the demand-side EE plan scenario, we consider the compound annual growth rate (CAGR) of the recent rapid increase in historic investment (2006 to 2011) and the CAGR from 2013 (the most recent historic value) through 2020, 2025, and 2030 represented by program costs of the plan
scenario. Historic data is from Table 2 and EE plan scenario data is from Table 32. Table 35 provides a summary of the results. The CAGRs represented by the EE plan scenario through 2020, 2025, and 2030 vary from 4% to 6%. The historic growth rate reflec ting the rapid recent growth in EE program spending is 30%, roughly si x times the EE plan scenario values. The pace of increase in EE program spending represented by the demand-side EE plan scenario is modest and much lower than recently achieved performance.

Table 35. Annual Growth Rates of Demand- Side EE Program Spending – Historic and Demand-Side EE Plan Scenario
Time Period (Years) Compound Average Growth Rate Historic (2006-2011) 29.8% Demand-Side EE Plan Scenario (2013-2020) 5.4% Demand-Side EE Plan Scenario (2013-2025) 6.0% Demand-Side EE Plan Scenario (2013-2030) 4.2%



### 9.4.3 Costs per Tonne CO 2 

### Reduced For the CPP proposed rule, the EPA analyzed a scenario incorporat ing the reductions in electricity demand associated with the proposal’s “EE scenario” and comp ared the results with the base case scenario (EPA 2014a). Both analyses were conducted using the Integrated Planning Model (IPM) described in RIA. Combining the resulting power syst em cost reductions with the energy efficiency cost estimates associated with the EE scenario, net cost impacts were derived for 2020, 2025, and 2030. Dividing these net cost impacts by the associated CO 2 

### reductions for each year, the average cost of the CO 2 

### reductions achieved were found to range from $16 to $24 per metric tonne of CO 2 

### . These results confirme d the general cost-effec tiveness of demand-side EE at the levels and costs of the proposal, and rela tive to the IPM base ca se used at that time (since updated). The final rule incorporates changes to the IPM base case, and the levels (significantly lower) and costs (higher in 2020, lower in 2025 and 2030) of demand-side EE incorporated into the illustrative plan scenario. These changes are likely to result in similar or76 

### lower average costs per tonne of CO2 achieved th rough the demand-side EE plan scenario in the
final rule.



## 10. Analysis Considerations Two considerations are worth no ting in regards to the analysis described in the previous two sections (9 and 10) which characterize the energy savings and costs of the demand-side EE plan scenario: 1) the implicit re presentation of demand-side EE po licies and programs in the base case electric demand forecast and 2) Form EIA-861 as a data source.

10.1 State EE Policies in the Ba seline Electricity Demand The AEO 2015 reference case provides the basis for the base case electric demand forecast used for EPA’s power sector modeling of the final rule (as discussed in the RIA) and
also serves as a basis for estimates of electric ity savings for the demand-side EE plan scenario. The AEO 2015 reference case does not explic itly represent existing EE programs, the continuation of EE programs or policies, or futu re requirements (e.g., EERS) to achieve savings through such programs. For example, existing st ate EERS are not evaluated and represented in the AEO 2015 reference case. However, to some degree, AEO 2015 may implicitly reflect a continuation of the effects of past or existing EE programs in the electricity demand projections represented in the reference case. This implicit representation is captu red in part through a calibration process that is affect ed by several historic factors in cluding reported electricity sales and sectoral energy consumption surveys.
As noted, the estimates of electricity savi ngs for the demand-side EE plan scenario do rely on AEO 2015 as a basis for base case el ectricity demand projections. The underlying
mechanics of the calculations are such that mode st changes in base case demand growth rates lead to minor impacts on cumulative savings levels relative to other input s such as 2020 starting savings levels (based on 2013 EIA 861 data), the target savings level (i.e., 1.0% incremental annual savings), and the pace of improvement from the starting to target savings levels (i.e., 0.2% per year). Any implicit repr esentation of the effects of existing EE programs in the base case demand forecast would also, to some degree, aff ect estimates of the impacts of the final rule
in the RIA. The effects of these consideratio ns would have some degree of influence on the assessment of demand-side EE, however, for purposes of the RIA, the methodology for77 

### determining levels of demand-side EE, and associat ed costs, is appropriate in the context of the
final rule.

10.2 Energy Information Administration Form EIA-861 as Data Source Comprehensive national data on electricity savings are limited for evaluating and comparing energy efficiency programs and their eff ectiveness at the utility, state, and national
scale. Issues related to the l ack of standardized definitions and reporting, and data quality are noted to limit evaluation of EE programs ( MJ Bradley 2011). The EIA Form 861, “Annual Electric Power Industry Report,” remains the most comprehensive effort that collects data annually on demand-side management (DSM) pr ograms, including their spending and energy savings impacts, nationally. 40 

### The form is requested for elec tric utilities, electric power producers, energy service provi ders, wholesale power market ers, and all DSM program managers and entities responsible to estimate the DSM activity for the reporting year using their best available data, including costs and elec tricity savings from EE and load management

programs. This analysis uses only two EIA-861 data va riables. Specificall y, we use the 2013 sales data and reported incremental annual electricit y savings of EE programs to estimate the current performance of EE programs (aggregated to the st ate level) to inform the illustrative demandside EE plan scenario disc ussed in this chapter.
EPA notes potential concerns associated w ith consistency and qua lity of reported DSM program data in Form EIA-861. Specifically, the data are self-reported by utilities and other DSM program administrators. The definitions and data categories may not be consistently applied across different program ad ministrators, utilities, and stat es, and may vary by data year. Over time, however, the data quality has im proved significantly and there is increased standardization in data reporti ng and more detailed and up-to-d ate data categories are being reported. For instance, in 2011, EIA began collecti ng data from third-party administrators of programs and now requires that entities operati ng programs in multiple states report results disaggregated to the state level. While now comp rehensive, outside entiti es have found that the

40 More information on EIA Form 861 can be found at http://www.eia.gov/el ectricity/data/eia861/

.78 

### EIA-861 data can be improved through supplem entation with publicly available annual EE program reports. 41

41 See, for example, ACEEE 2014c and CEE 2015.79 

## Appendices

Appendix 1: Summary of Recent Energy Efficiency Potential Studies (2009-2014)
Appendix 2: Incremental Electricity Savings Pace of Improvement Analysis
Appendix 3: Demand-Side Energy Efficiency Plan Scen ario: Data, Calculations and Results81 

## Appendix 1: Summary of Recent Energy Efficiency Potential Studies (2009-2014)

The EPA conducted an extensive review of energy efficiency pot ential studies of electricity savings. Studies were identified fr om recent published meta-analyses (Sreedharan 2013; ACEEE2014b) and supplemented with add itional studies identified through research. Comprising results from more than 50 poten tial studies conducted between 2009 and 2014, the resulting database of results is one of the most comprehensive of its kind.

See the attached file for detailed inform ation on the sources, data, calculations, and
results:

 GHG Abatement TSD – Demand-Side EE Potential Studies.xlsx82 

## Appendix 2: Incremental Electricity Saving s 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 tw o different perspectives: 1) hist orical data reflecting achieved savings of EE programs and 2) requirements of existing state energy efficiency resource standards (EERS). For the historic al perspective, we reviewed da ta from the Energy Information Administration’s Form EIA-861 on EE program elect ricity savings (supplemented as needed with program administrator reports) and identified the pace at which entities reaching higher savings levels have historically increased energy savings over time. 42 

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

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

2. Top saver 2% - a group with 26 entities th at achieved a maximum first-year savings
level of 1.5% to 3.0%. 43 

### For the existing state requirements perspec tive, we reviewed energy savings ramp-up
schedules established under EERS for states that provi de clear ramp-up schedules. According to ACEEE’s 2013 State Energy Efficiency Scorecard, there are a total of 26 states that have mandatory EERS policies (ACEEE 2013a). Our analysis contains 10 states for which clear rampup schedules were identifiable.
Our research findings on historic al 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 averag e to increase first-year

42 The EIA 861 was the main data source (EIA 2012a). However, we have supplemented the EIA 861 with third-party program administrator data because the EIA 861 just started to collect third-pa rty administrator data in 2011. The third- party entities included in our analysis are Efficiency Vermont, Energy Trust of Oregon, Efficiency Maine Trust, and Cape Light Compact. In addition, we supplemented the EIA 861 database 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). 43 In addition to these maximum first-year savings thresholds, we screened program administrato rs for the following conditions: (a) the maximum savings levels occurs afte r the minimum savings levels; (b) sufficien t amounts of increase in first-year saving s are provided to evaluate reasonable ramp-up schedules to gain an incremental 1% first- year savings; and (c) savings data series are continuous between the years for th e minimum and maximu m savings levels.83 

### savings by 1% (with a range of 1.6 years to 10 years) (see Table 2- 1). The entities in this group have increased the level of firs t-year savings by 0.30% per year on average
from their minimum to their maximum firs t-year savings levels (with a range of 0.10% per year to 0.63% per year). 44



###  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 2-1). The en tities in this group have increased the level of first-year savings by 0.38% per ye ar on average from the minimum to the maximum first-year savings levels (with a range of 0.14% per year to 1.28% per year). 45



### Table 2-1. Energy savings ramp-up trends in first-year savings for “Top Saver 1%” and “Top Saver 2%” groups
Top Saver 1% Top Saver 2% 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 Sources: EIA 2012a and third-party program administrator data 46



### Our research findings on incremental electric ity savings ramp-up based on existing state EERS policies are:

44 This is a simple average estimate of the annual average in crease in first-year savings from each entity in this group. 45 This is the simple average estimate of the annual average in crease in first-year savings fro m each entity in this group. 46 Data sources: The EIA 861 was the main data source (EIA 2012a). However, we ha ve supplemented the EIA 861 with thirdparty program administrator data because th e EIA 861 just started to collect third-pa rty administrator data in 2011. The thirdparty entities included in our anal ysis are Efficiency Vermont, Energy Trust of Or egon, Efficiency Maine Trust, and Cape Light Compact. In addition, we supplem ented the EIA 861 database with additional data for two utilities that we found achieved high energy savings, but did not report savings data in the EIA 861 da ta for one or two years. Thes e entities are Burlington Electri c and Massachusetts Electric Company (now part of National Grid).84 

###  The states with EERS policies which exhibi t savings ramp-up schedules are requiring increases in first-year ener gy savings at a pace that ra nges from 0.11% (Colorado and Oregon) to 0.40% (Rhode Island ) as shown in Table 2-2.

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

Table 2-2. First-Year Energy Savings Ra mp-up Review of State EERS Policies

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 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
Sources: ACEEE 2014c and APSC 201385 

## Appendix 3: Demand-Side Energy Efficiency Plan Scenario: Data, Calculations and Results
See attached files for comprehensive data , calculations, and results supporting the illustrative demand-side energy efficiency (EE) plan scenario. These files address the magnitude and timing of savings, and the asse ssment of costs of the demand- side energy efficiency included in the illustrative plan scenario of the Regul atory Impacts Assessment (RIA). The two files
reflect costs discounted at 3% and 7%, respectively:

 GHG Abatement Measures TSD – Demand-Side EE @ 3%.xlsx

 GHG Abatement Measures TSD – Demand-Side EE @ 7%.xlsx86 

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### Technical Support Document: Resource Adequacy and Reliability Analysis

This document describes resource adequacy an d reliability impacts of the two illustrative state plan scenarios that are refl ected in the Regulatory Impact Analysis (RIA) that supports the final rule emission guidelines is sued under section 111( d) of the Clean Air Act, also known as the Clean Power Plan. As used here, the term resource adequacy is defined as the provision for adequate generating resources to meet projected load and generating reserve requirements in each power region 1 

### , while reliability includes the ability to delive r the resources to the loads, such that the overall power grid remains stable. Th e focus of the analysis is on the impact of the illustrative scenarios, in the context of a base cas e (absent the rule requirements) that is assumed to be adequate and reliable. In this framewor k, the emphasis is on the incremental changes in the power system that are projected to occur under the pres ence of the rule. 2 

### The EPA uses the Integrated Planning Model (IPM) to project likely future electricity market conditions with and without the proposed rule. 3



### It is important to recognize that the final rule provides flexib ility in the context of state plan development that preserve s the ability of res ponsible authorities to maintain electric reliability, as well as a provision to ensure that el ectric reliability is adequa tely maintained in the case of extreme circumstances, amongst other changes to the final rule that will assist states and
other authorities with en suring adequate supplies of electricity and maintain electric reliability.
For more detail on how the final CPP addresses re liability, see Section VIII of the final rule preamble. The results presented in this document show that power system impacts of the final rule on system operations, under conditions preserving resource adequacy, are modest and manageable.

1 As analyzed in this document, power regions correspond to aggregates of IPM regions corresponding to NERC assessment areas. 2 Both the base and policy cases start from input data on the expected state of the fleet of power plants in 2016 and assume certain planned retirements and additions happen by the end of 2015; the analysis focuses on the impacts of retirements that are projected by the IPM model in the 2025 and 2030. See the documentation of the NEEDS data base at epa.gov/powersectormodeling for information on what retirements and additions are assumed to occur by the end of 2015.
3 See final rule Regulatory Impact Analysis for more detail on the power sector impacts of the final CPP.2 

### IPM is a multi-regional, dynamic, determinis tic linear programming model of the U.S. electric power sector. It provide s forecasts of least cost capacity expansion, electricity dispatch, and emission control strategies while meeting energy demand and environmental, transmission, dispatch, and reliability constraints. The model is designed to reflect electricity markets as accurately as possible. The EPA uses the best av ailable information from utilities, industry experts, gas and coal market experts, financial institutions, and government statistics as the basis for the detailed power sector modeling in IPM. The model documentation provides additional information on the assumptions discussed here as well as all other model assumptions and inputs. 4



### IPM’s least-cost dispatch solution, under a ny scenario, will ensure generation resource adequacy, either by using existing resources or through the construction of new resources. IPM addresses reliable delivery of generation resource s for the delivery of electricity between the 64 IPM regions, based on current and planned tran smission capacity, by setting limits to the ability to transfer power between regions using the bul k power transmission system. Within each model region, IPM assumes that adequate transmission cap acity exists to delive r any resources located in, or transferred to, the regi on. This document focusses on key regional results important to management of the power system. For a more co mplete presentation of the broad power sector impacts of the proposed rule, see the Regulatory Impact Analysis.

Overview

In the final rule, the EPA is establishing emission guidelines for states in developing plans to address greenhouse gas emissions from existing fossil fuel-fir ed electric generating units. Specifically, the EPA is proposing category-specific performance rates for CO 2 

### emissions from power plants, as well goals fo r states to use in developing pl ans to meet the guidelines . See final rule preamble for more detail on the final CPP structure and ru le requirements. This TSD uses the same scenarios and years of analysis contained in the RIA. 5 

### The scenarios include a base case (no CPP), a rate-based state plan scenario and a mass-based state plan scenario. For

4 Detailed information and documentation of EPA’s Base Case using IPM (v5.13), including all the underlying assumptions, data sources, and architecture parameters can be found on EPA’s website at: http://www.epa.gov/powersectormodeling/BaseCasev513.html
5 See Chapter 3 of the RIA for additional detail on the scenarios analyzed.3 

### purposes of the resource adequacy and reliability assessment, estimates and projections are taken from the same scenarios and years as shown in the RIA (2020, 2025, and 2030).



## CPP Rate-Based Scenarios

Summary of Changes in Operational Capacity

Total operational capacity is lower in the C PP scenario, primarily from reduced need for existing and new capacity as a result of increases in energy efficiency. These increases in energy efficiency make possible increases in retirements compared to the base case. Since most regions currently have capacity above th eir target reserve margins, mo st of these retirements are absorbed by a reduction in excess reserves in the early years. For illustrative purposes, the 2025 projection period, the first period to fall entirely within the compliance period of, is discussed first, followed by discussion of 2020 a nd 2030. Operational generating capacity 6 

### changes from
the base case in 2025 are summarized below:

Table 1. Operational Capacity Summary in 2025 Base case operational capacity (MW) 1,037,223

Minus Retirements in CPP Rate Case: (-) Coal -22,984 (-) Oil-Gas Steam -9,264 (-) Combustion Turbine -1,942 (-) Combined Cycle -1,643 (-) Nuclear -1,128 Plus New Capacity Change from EE Reduction -12,626
Equals Policy Case Operational Capacity 988,764 7



### Since the model must maintain adequate rese rves in each region, a portion of the reduced operational capacity in the CPP case is taken from reduced need for reserves compared to the base case. In order to maintain resource adequ acy in each region where ex isting resources retire,

6 Operational capacity is any existing, new or retrofitted capacity that is not retired.
7 Numbers in this table may not sum to numbers in Table A1 due to independent rounding and small classification differences between the base and policy cases.4 

### the model relies on any excess reserve that are available from continuing to operate existing capacity, additions of new capacit y, reduced total resource requireme nts from increases in energy efficiency, and the ability to shift transmi ssion among regions as the generating capacity mix changes. As the table shows, the reduced resour ce needs permit lower capacity additions even though there are substantial increases in retir ements. Each of these CPP case changes is
discussed further below.

Reduction in Reserve Requirements and Excess Reserves

IPM uses a target reserve margin in each region 8 

### as the basis for determining how much capacity to keep operational in or der to preserve resource adequac y. IPM retires capacity if it is no longer needed to provide energy for load or to provide capacity to meet reserve margin during the planning horizon of the projections. Since curre nt regional reserves ar e generally higher than the target reserve margin for the region, and incr eased energy efficiency will reduce the need for reserves, IPM may retire reserve capacity in 2025 if it is not economic to use it to maintain adequate reserve margins. Existing resources may also be more expensive, compared to alternatives such as building new capacity or transferring capacity from another region. As a result, many of the plants that are projected to retire in 2025 will not need to be replaced. Because existing plants eventually retire in most regions, and IPM builds no more than what it needs to maintain a target reserve margin in each region, the actual reserve margins tend to approach the target rese rve margins over time.

Table 1 above shows that operational generating capacity is reduced by 48,459MW (4.7 percent) nationwide in 2025 under the policy. 9 

### The majority of this reduction is the result of decreases the reserve requirements from ener gy efficiency under the fi nal rule; in 2025 new energy efficiency under the final rule contributes 45,085 MW to reserve capacity 10 

### (see Table B1 for regional detail). Moreover, th ese reductions are from energy effici ency that is available in all hours, not just at peak, so it can substitute for existing or new baseload capacity. A reduction of

8 Reserve margin targets are generally based on the NERC 201 0 10 Year Assessments for the region, except in cases where there are more stringent stat e requirements or other exceptions.
9 Regional data on operational capacity is shown in Table A1 of the Appendix.
10 The reserve contribution to reserve capacity requirements from energy efficiency is determined by the reduced peak demand and the target reserve margin in each region. For example, if peak demand in reduced by 100 MW and the reserve margin percentage in a region is 15%, the reduction in reserve capacity requirements is 115MW.5 

### 4.7 percent in 2025 will therefore have little overall impact, particularly given the length of time available to plan for any system changes. More over, retirements are di stributed throughout the power grid, so there will be only small impacts at the regional level. 11



### Although there are substantial ex isting regional variations in reserve margin, IPM adjusts regional operating capacities in 2025 to meet the specific target reserve margin in each region, through changes in the level of retirements, constr uction of new generating capacity, or transfers of capacity among regions to meet the specific reserve margin in each region. Each of these adjustments in the 2025 projecti ons is described below.

Changes in Retirements and New Capaci ty Additions in the CPP Scenarios 12



### There are significant levels of fossil steam – coal and oil/gas – reti ring in 2025 in both the
base and CPP scenario. The incremental final ru le case retirements are shown above in Table 1; the 36,931 MW of retirements are in additi on to 69,254MW of coal and 12,973MW of oil/gas retirements already occurring in the base case.

By 2025, the increased level of energy efficiency in the CPP case, compared to the base case, leads to lower levels of new overall ne w capacity additions (S hown regionally in Table A5). Renewable additions are approximately the same in both the base and policy cases. The largest decreases are in NGCC (7,808MW) and CT (3,131MW). Although there can be local grid reliability issues in replac ing some of the retiri ng units shown in Table 1 above and in Table A4, these can be managed within the normal reliability planni ng and management time frames provided by the flexible resource options and time frames in the rule. These retirements and additions in the projections ar e the result of economic planning for energy and capacity needs modeled in the projections, they are not forced on individual units. In particular, new additions in a base case scenario might that do not occu r in the policy scenario projections might, in reality, be retained under a policy if local reliability conditions re ndered this the most appropriate

11 See maps of IPM regions and NERC Assessment Regions, and the table of target and projected reserve margins in the Appendix C. IPM regions are based on the regions NERC uses for regional assessments. These Assessment Regions are used for the Appendix tables in this document.
12 Retirement and additions in this section are all incremen tal to the base case in 2025; the MW values represent model projections of responses to the imposition of the polic y, not currently announced re tirements or additions that are currently planned or under construction.6 

### choice. This rule does not prevent generation owners from shifting retirements and additions among specific sources to ensure reliability in such circumstances.

Reserve Transfers

In cases where it is economic to transfer reserves from a neighboring region, rather than supply reserves from within a region, IPM will tr ansfer reserves, subject to summer and winter limits that are designed to ensure that these reserves can be transf erred reliably. The transfer of reserves can occur, for example, if a region retires capacity that was used in the base case to meet reserve requirements, but a neighbo ring region has lower cost reserves that are not needed for its own reserve requirements. To examine these tran sfers, the EPA analyzed the change in net transfers from each region, where the net transfer for the base and policy cases is measured by the reserves sent to neighboring regions. In these cases, a positive value signifies the reserve capacity sent to other regions is larger than the reserve capacity received from other regions (sending and receiving regions can be different ), while a negative value signifies that the capacity received is larger than the capacity se nt. Thus, the value measures the degree to which resources in the region were reserved for use by other regions (positive value), or where the capacity to meet load in the region was served by resources in other regions (negative value). In each case these reserve transfers represent the use of the transmission system on a firm basis for
at least a season.

To look at the impact of the CPP case on tran sfers, the measure used was the change in the summer reserves sent in the policy case comp ared to the base case. To develop a relative measure of the impact of the policy, the change in reserves was measured as a percentage of load in the sending region. This percentage gives an indication of the significance of the policy for changes in the grid. In general, the percentage ch anges in the final rule are below the changes in the in the proposed rule, and all are below 5%. 13



### Using this measure, the largest percentage changes in reserve transfers are in the Northwest (4.3%), SERC-North (-3.5%), and FRCC (3.1%). The change in the Northwest is attributable primarily to change in transfers fr om the Pacific Northwest IPM region to California,

13 See the Resource Adequacy and Reliability Analysis for the proposed rule. A level of 5% was used as a screen in that analysis; there were three regions above 5% identified in the proposed rule TSD.7 

### where an additional 2000MW are transferred to Northern California, including 1000MW shifted from Southern California (LADWP). The net ch ange of 1,134(MW) occurs, in part, as a result of additional operating capacity made available by increased energy efficiency in the policy case compared to the base case. This shift does not indicate any reliability challenges, as the total transfers of 3,499MW in the policy case remain substantially below the transfer limit of 4,200MW.

The SERC regions and FRCC have shifts around three percent, with some areas increasing net transfers and others decreasing. Th ese areas also saw shifts in transfers in the modeling for the proposed rule, but the changes in this final rule have reduced this shifts to modest transfers compared to th e shifts under the proposal. In ge neral, the shifts under the final
rule show transfers from the Southeastern area s with greater natural gas resources available – FRCC and SERC-SE – toward northern areas with greater amounts of coal -- TVA and Kentucky. These transfers in the modeling remain well below the interreg ional transfers limits and 3 percent or less of the regional load in each case, and it is reasonable to expect them to be manageable, if needed, given the length of time available.

CPP Rate Scenario in 2020 and 2030

There are other model projection periods that include years that fall in the period for compliance with the rule. The 2020 projections include the years 2019 through 2022, so one of the years in the period falls in the compliance pe riod. EPA examined the information for the rate scenario for 2020 corresponding to the rate scenar io in 2025. This information is shown the tables in Appendix A, which correspond to the ta bles for the 2025 case in Appendix B. Since the projections are representative of the entire four year period, it is difficult to draw conclusions with regard to firm impacts on re liability resulting from the rule. This is because most of the period is outside the compliance pe riod of the final rule, and there can be no direct conflict with the rule except in the very last year of the projection period. Th e results be compared below with the 2025 results for purposes of illustration.

The differences between 2020 and 2025 in the CPP rate scenario are driven by the fact that IPM modeling retires capacity from the start of the modeling period in 2016, based on projections that assume complete information a bout the future and precise economic planning. It8 

### thus assumes that capacity retires at the earliest possible moment, so that excess reserves are used up and reserve margins can fall to target levels earlier than e xpected in practice. Nevertheless, in 2020, seven regions maintain ma rgins above their leve ls in 2025 (See Table A3). This is consistent with the lower leve l of overall retirements in 2020 compared to 2025 (24,567MW compared to 36,961MW in 2025), so that the modeling confir ms that the major potential resource adequacy pressures from the fi nal rule are not seen un til the projections for
2025.

Results for the rate scenario in 2030 are cont ained in Appendix C. Reserve margins in the policy case in 2030 are essentially unchanged (l ess than 0.5% differen ce in all regions) from 2025, reflecting the fact that most regions were already at their target margins in 2025 rate scenario. Base case margins decline from 2025 to 2030, as margins above targets are reduced, so that the base and policy overall national margins are less than 1 percent different (Table C3).
There continue to be more retirements in the policy case relative to the base case in 2030 (Incremental retirements of 41,009MW in 2030 co mpared to 36,961MW in 2025, an increase of 5,048 MW). These retirements are offset by a corresponding combination of new energy efficiency and changes in new capacity. In 2025, there were 12,626 MW fewer new capacity additions in the CPP case; in 2030 there were 16,964 MW fewer: a net decrease new of an incremental 4,338 MW (over the base case) in the 2030 case compared to the comparable 2025 figure. However, the increase in the contribution of energy effici ency to reserve margin capacity was considerably greater than the combined d ecrease in new capacity additions and increase in
retirements in 2030: the contribution of new en ergy efficiency to reserve margin in 2030 was 77,741 MW compared to 45,085 MW in 2025 (See Tables B1 and C1 in the Appendices), an increase of 22,656 MW. Given the long planning horizon of 2030, there will be adequate time to plan for potential shifts in the mix of demand a nd supply resources as these evolve over time. None of these results suggest there will be reas ons for concern over the management of resource adequacy



## CPP Mass-Based Scenarios9 

### The EPA also examined the mass scenario modeling results to identify differences with rate scenario potential impacts. The results for the CPP mass-based scen arios are contained in Appendices D through F for the projection y ears 2020, 2025 and 2030. These tables correspond
to the tables for the CPP Rate-Based scenarios in Appendices A through C.

As expected, rate and mass cases showed ve ry similar patterns in total operating and reserve capacity, since the IPM model must serve the same loads and ensure the same reserve margins in each case. In 2025, to tal operating capacity between rate and mass cases differs by less than one percent and regional operating capacities are within 5 percent except for the BASN regions, where the percentage difference is 8.6%, th e result of retirement of a resource (coal) in Utah, which is offset by increased transmission capacity transfers. Reserve capacity and projected reserve margins for the mass scenario (Tables E2 and E3) follow a pattern similar to operating capacity: totals are w ithin 1% of the projections fo r the corresponding outputs rate scenario and regional reserve cap acities are under 5 percent.

Differences between the scenario s develop in the types of capac ity that are retired and in the amount and types of new capacity built in 2025. The mass scenario results show more incremental retirements of coal capacity ( 29,319 MW compared to 22,984 MW in the rate scenario) and in Oil-Gas capacity (10,421 MW comp ared to 9,264 MW). The mass scenario also results in more new NGCC and CT capacity, an d less new wind and solar capacity. The mass scenario still is projected to have a decrease ne w capacity compared to the base case, but the decrease is only 9,971 MW compared to the 12,626 MW in the rate scenario. No reliability concerns are raised by these differences.

The patterns of transfers are also similar. In the mass scenario, there is somewhat more
variation in net transfers than in the rate case. In particular there are tw o regions with shifts of
more than 5% in the mass scenario: the Northw est and BASN, both in WECC. These two shifts
are linked. The Northwest reserve transfers include the transfers in the rate scenario discussed above, plus additional transfers from the Nort hwest to BASN to compensate for the coal
retirements in BASN discussed above. The two cases with the largest percentage shift examined above are reduced in magnitude. These transf ers in the mass scenario remain below the interregional transfer limits in the modeling and do not appear to present significant reliability
related concerns.10 

### The same general patterns noted here fo r 2025 are also present in 2020 and 2025: only minor variations in total opera ting capacity, somewhat higher fo ssil steam retirements, more NGCC and CT capacity. These differences do not raise further issues of resource adequacy. Details can be found in the Appendices.11 

### Appendix A: Tables by IPM Region for CPP Rate-Based Scenario in 2020

A1. Projected Operational Capacity

A2. . Summary of Summer Peak Loads and Reserve Capacity

A3. Summary of Target and Projected Reserve Margins

A4. Policy Case Retired Capacity Incremental to Base Case

A5. New Capacity in Policy Case Incremental to Base Case

A6. Net Reserves Sent by NERC Assessment Region12 

### A4. Policy Case Retired Capacity Incremental to the Base Case (MW) Year= 2020

CC Coal CT NU OG Steam Total Basin (BASN) 180

‐ 179 0 0 108 109 Desert Southwest (DS W 616 162 0 0 513 1,290 ERCOT 0 0 0 0 0 0 FRCC 59 317 0 0 288 664 ISO

‐ NE 0 0 0 0 1,725 1,725 MAPP 0 679 0 0 0 679 MISO 0 2,822 0 0 0 2,822 Northern California (C A 0 0 1,898 0 0 1,898 Northwest (NORW) 1,465 52 0 867 0 2,383 NYISO 81

‐ 68 0 0 1,674 1,687 PJM 0 408 0 0 29 437 Rockies (Rock) 39 963 0 0 55 1,057 SERC

‐ E 0 434 0 0 0 434 SERC

‐ N 0 1,443 0 0 0 1,443 SERC

‐ SE 0 2,171 0 0 0 2,171 SERC

‐ W

‐ 434 1,328 0 0

‐ 92 802 Southern California (C A 0 0 43 0 2,440 2,484 SPP

‐ 362 2,619 0 0 226 2,483 Grand Total 1,643 13,149 1,942 867 6,966 24,56713 

### A2. Summer Peak Loads and Reserve Capacity in 2020 Projected Reserve Margins

Assessment Re g ion Peak Demand

Base Peak Demand

Polic y Reserve Capacity Base Reserve Capacity

Polic y Basin (BASN) 11,953 11,880 13,926 13,567 Desert Southwest ( 29,612 29,352 34,074 33,314 ERCOT 73,948 73,806 84,116 83,955 FRCC 44,324 44,253 56,200 55,193 ISO-NE 26,475 26,248 30,446 30,185 MAPP 5,330 5,324 6,603 6,123 MISO 90,719 89,895 106,505 104,548 Northern California 21,282 21,090 25,399 24,193 Northwest (NORW ) 25,414 25,175 39,507 34,859 NYISO 30,058 29,744 34,868 34,503 PJM 150,553 149,437 173,738 172,450 Rockies (Rock) 14,156 14,042 17,609 16,654 SERC-E 42,195 41,966 48,524 48,261 SERC-N 38,518 38,398 44,296 44,158 SERC-SE 48,110 48,014 55,327 55,217 SERC-W 29,095 29,052 37,890 34,798 Southern California 28,108 27,797 33,588 32,085 SPP 51,995 51,865 63,790 60,946 US T otal 761,845 757,338 906,407 885,00814 A3. Summary of Target and Projected Reserve Margins in 2020 Projected Reserve Margins

NERC Assessment Re g ion Target Reserve

Mar g i n Base Case Polic y Case Policy % A bove Mar g in Policy Change from Base Basin (BASN) 12.60% 16.51% 14.21% 1.6% -2.3% Desert Southwest (DSW) 13.50% 15.07% 13.50% 0.0% -1.6% ERCOT 13.75% 13.75% 13.75% 0.0% 0.0%

FRCC 19.25% 26.79% 24.72% 5.5% -2.1% ISO-NE 15.00% 15.00% 15.00% 0.0% 0.0% MAPP 15.00% 23.87% 15.00% 0.0% -8.9% MISO 16.30% 17.40% 16.30% 0.0% -1.1% Northern California ( CALN ) 14.71% 19.35% 14.71% 0.0% -4.6% Northwest (NORW) 17.90% 31.61% 25.25% 7.4% -6.4% NYISO 16.00% 16.00% 16.00% 0.0% 0.0%

PJM 15.40% 15.40% 15.40% 0.0% 0.0% Rockies (Rock) 14.65% 24.39% 18.60% 3.9% -5.8% SERC-E 15.00% 15.00% 15.00% 0.0% 0.0% SERC-N 15.00% 15.00% 15.00% 0.0% 0.0% SERC-SE 15.00% 15.00% 15.00% 0.0% 0.0% SERC-W 15.00% 30.23% 19.78% 4.8% -10.5% S outhern California ( CALS ) 15.14% 19.50% 15.42% 0.3% -4.1% SPP 13.60% 22.69% 17.51% 3.9% -5.2% Grand Total 15.30% 18.98% 16.86% 1.6% -2.1%15 

### A4. Policy Case Retired Capacity Incremental to the Base Case (MW) Year= 2020

CC Coal CT NU OG Steam Total Basin (BASN) 180

‐ 179 0 0 108 109 Desert Southwest (DS W 616 162 0 0 513 1,290 ERCOT 0 0 0 0 0 0 FRCC 59 317 0 0 288 664 ISO

‐ NE 0 0 0 0 1,725 1,725 MAPP 0 679 0 0 0 679 MISO 0 2,822 0 0 0 2,822 Northern California (C A 0 0 1,898 0 0 1,898 Northwest (NORW) 1,465 52 0 867 0 2,383 NYISO 81

‐ 68 0 0 1,674 1,687 PJM 0 408 0 0 29 437 Rockies (Rock) 39 963 0 0 55 1,057 SERC

‐ E 0 434 0 0 0 434 SERC

‐ N 0 1,443 0 0 0 1,443 SERC

‐ SE 0 2,171 0 0 0 2,171 SERC

‐ W

‐ 434 1,328 0 0

‐ 92 802 Southern California (C A 0 0 43 0 2,440 2,484 SPP

‐ 362 2,619 0 0 226 2,483 Grand Total 1,643 13,149 1,942 867 6,966 24,56716 

### A5. New Capacity Policy Case Incremental to Base Case(MW) Year= 2020 Region CC CT Wind Solar Other Total Basin (BASN) 0 0 0 0 0 0 Desert Southwest (DSW) 0 0 3 0 0 3 ERCOT

‐ 196 0 323 0 0 127 FRCC 0 0 0 0 0 0 ISO

‐ NE 0 0 334 0 0 334 MAPP 0 0 247 0 0 247 MISO 320 0

‐ 38

‐ 6 305 581 Northern California (CALN) 0 0 12 0 0 12 Northwest (NORW) 0 0 10 0 0 10 NYISO 0 0

‐ 386 0

‐ 213

‐ 599 PJM 767

‐ 1,692 175 12

‐ 115

‐ 852 Rockies (Rock) 0 0 0

‐ 54 0

‐ 54 SERC

‐ E 612 0 0

‐ 29 0 583 SERC

‐ N 1,203 0 3 0 23 1,229 SERC

‐ SE 0 0 0 0 0 0 SERC

‐ W 0 0 0 00 0 Southern California (CALS) 0 0

‐ 270 0 0

‐ 270 SPP 0 0 1,110 0 0 1,110 Grand Total 2,705

‐ 1,692 1,523

‐ 78 0 2,45917 

### A6. Net Reserves Sent by NERC Assessment Region(MW) i n 2020

NERC Assessment Region Base Policy Change from Base to Policy Change as Percent of Summer Peak Basin (BASN) -1,772 -1,522 251 2.1% Desert Southwest (DS W 3,869 3,340 -529 -1.8% ERCOT -1,942 -1,942 0 0.0% FRCC 0 342 342 0.8% ISO-NE -1,823 -3,212 -1,388 -5.2% MISO -2,775 -3,249 -474 -0.5% Northern California (CAL 0 -692 -692 -3.3% Northwest (NORW) 407 2,675 2,268 8.9% NYISO 320 -1,303 -1,623 -5.4% PJM -4,614 -4,702 -88 -0.1% Rockies (Rock) 1,417 1,298 -118 -0.8% SERC-E -981 -547 434 1.0% SERC-N 1,502 1,435 -67 -0.2% SERC-SE 3,800 1,739 -2,061 -4.3% SERC-W 1,112 3,416 2,304 7.9% Southern California (CA L -3,761 -4,884 -1,124 -4.0% SPP 2,191 2,872 682 1.3%18 

### Appendix B: Tables by IPM Region for CPP Rate-Based Scenario in 2025

B1. Projected Operational Capacity

B2 . Summary of Summer Peak Loads and Reserve Capacity

B3. Summary of Target and Projected Reserve Margins

B4. Policy Case Retired Capacity Incremental to Base Case

B5. New Capacity in Policy Case Incremental to Base Case

B6. Net Reserves Sent by NERC Assessment Region19 

### B1. Projected Operational Capacity Year= 2025

All Generation Sources EE Capacity Overall Coal Only Region Base Policy Contribution Change Base Policy Basin (BASN) 14,293 13,888 687 282 4,802 4,685 Desert Southwest (DSW) 42,265 40,855 2,300 890 8,380 8,096 ERCOT 99,864 96,751 3,345 232 17,937 17,647 FRCC 56,336 55,672 2,080 1,417 7,479 7,162 ISO

‐ NE 32,685 31,294 1,644 252 0 0 MAPP 9,627 8,993 207

‐ 428 3,007 2,190 MISO 125,625 118,165 6,127

‐ 1,333 45,036 39,567 Northern California (CALN ) 29,389 27,503 1,321

‐ 565 71 71 Northwest (NORW) 51,181 48,711 2,036

‐ 434 2,406 2,354 NYISO 38,484 36,082 2,173

‐ 229 0 0 PJM 188,212 179,396 9,367 551 50,144 44,546 Rockies (Rock) 23,663 22,284 1,041

‐ 339 9,216 8,363 SERC

‐ E 55,904 52,022 2,556

‐ 1,326 11,045 9,478 SERC

‐ N 50,217 46,755 2,051

‐ 1,411 13,141 11,557 SERC

‐ SE 57,354 56,571 2,192 1,409 11,655 10,871 SERC

‐ W 40,012 37,823 1,069

‐ 1,121 4,733 3,418 Southern California (CALS) 43,405 39,826 2,159

‐ 1,420 57 50 SPP 78,704 75,121 2,733

‐ 850 22,966 19,113 Grand Total 1,037,223 987,713 45,085

‐ 4,425 212,075 189,16920 

### B2. Summer Peak Loads and Reserve Capacity in 2025 Projected Reserve Margins

Assessment Re g ion Peak Demand

Base Peak Demand

Polic y Reserve Capacity Base Reserve Capacity

Polic y Basin (BASN) 12,466 11,856 14,036 13,349 Desert Southwest ( 31,887 29,861 36,192 33,892 ERCOT 77,606 74,665 88,276 84,932 FRCC 46,355 44,611 55,279 53,199 ISO-NE 26,701 25,272 30,706 29,063 MAPP 5,539 5,360 6,370 6,164 MISO 93,625 88,356 108,885 102,758 Northern California 22,305 21,155 25,592 24,301 Northwest (NORW ) 26,489 24,955 36,672 33,098 NYISO 30,051 28,179 34,860 32,687 PJM 154,841 146,724 178,686 169,319 Rockies (Rock) 15,223 14,315 17,676 16,412 SERC-E 44,263 42,040 50,902 48,346 SERC-N 40,450 38,666 46,517 44,466 SERC-SE 50,430 48,524 57,994 55,802 SERC-W 30,589 29,660 35,644 34,109 Southern California 29,473 27,605 34,006 31,922 SPP 54,332 51,926 62,673 58,988 US T otal 792,624 753,693 920,969 872,80921 

### B3. Summary of Target and Projected Reserve Margins in 2025 Projected Reserve Margins

NERC Assessment Re g io n Target Reserve

Mar g i n Base Case Polic y Case Policy % Above Mar g i n Basin (BASN) 12.60% 12.60% 12.60% 0.0% Desert Southwest (DSW) 13.50% 13.50% 13.50% 0.0% ERCOT 13.75% 13.75% 13.75% 0.0%

FRCC 19.25% 19.25% 19.25% 0.0% ISO-NE 15.00% 15.00% 15.00% 0.0% MAPP 15.00% 15.00% 15.00% 0.0% MISO 16.30% 16.30% 16.30% 0.0% Northern California ( CALN ) 14.71% 14.74% 14.87% 0.2% Northwest (NORW) 17.90% 27.05% 25.71% 7.8% NYISO 16.00% 16.00% 16.00% 0.0%

PJM 15.40% 15.40% 15.40% 0.0% Rockies (Rock) 14.65% 16.12% 14.65% 0.0% SERC-E 15.00% 15.00% 15.00% 0.0% SERC-N 15.00% 15.00% 15.00% 0.0% SERC-SE 15.00% 15.00% 15.00% 0.0% SERC-W 15.00% 16.52% 15.00% 0.0% S outhern California ( CALS ) 15.14% 15.38% 15.64% 0.5% SPP 13.60% 15.35% 13.60% 0.0% Grand Total 15.29% 16.19% 15.80% 0.5%22 

### B4. Policy Case Retired Capacity Incremental to the Base Case (MW) Year= 2025

CC Coal CT NU OG Steam Total Basin (BASN) 180 118 0 0 108 405 Desert Southwest (DS W 616 284 0 0 513 1,412 ERCOT 0 291 0 0 0 291 FRCC 59 317 0 0 288 664 ISO

‐ NE 0 0 0 0 1,725 1,725 MAPP 0 817 0 0 0 817 MISO 0 5,484 0 0 0 5,484 Northern California (C A 0 0 1,898 0 0 1,898 Northwest (NORW) 1,465 52 0 867 0 2,383 NYISO 81 0 0 261 1,674 2,016 PJM 0 5,601 0 0 19 5,620 Rockies (Rock) 39 855 0 0 55 949 SERC

‐ E 0 1,566 0 0 0 1,566 SERC

‐ N 0 1,603 0 0 0 1,603 SERC

‐ SE 0 783 0 0 0 783 SERC

‐ W

‐ 434 1,328 0 0 1,309 2,203 Southern California (C A 0 7 43 0 2,514 2,565 SPP

‐ 362 3,878 0 0 1,060 4,576 Grand Total 1,643 22,984 1,942 1,128 9,264 36,96123 

### B5. New Capacity Policy Case Incremental to Base Case(MW) Year= 2025 Region CC CT Wind Solar Other Total Basin (BASN) 0 0 0 0 0 0 Desert Southwest (DSW) 0 0 3 0 0 3 ERCOT

‐ 2,003

‐ 1,142 323 0 0

‐ 2,823 FRCC 0 0 0 0 0 0 ISO

‐ NE 0 0 334 0 0 334 MAPP 0 0 182 0 0 182 MISO

‐ 1,534 0

‐ 404

‐ 53 0

‐ 1,991 Northern California (CALN) 0 0 12 0 0 12 Northwest (NORW) 0 0 10 0

‐ 96

‐ 86 NYISO 0 0

‐ 386 0 0

‐ 386 PJM

‐ 382

‐ 1,988

‐ 696

‐ 133 0

‐ 3,199 Rockies (Rock) 0 0 0

‐ 432 0

‐ 432 SERC

‐ E

‐ 2,008 0 0

‐ 308 0

‐ 2,316 SERC

‐ N

‐ 1,881 0 3 0 0

‐ 1,878 SERC

‐ SE 0 0 0 0 0 0 SERC

‐ W 0 0 0 00 0 Southern California (CALS) 0 0

‐ 270

‐ 706

‐ 39

‐ 1,015 SPP 0 0 965 4 0 969 Grand Total

‐ 7,808

‐ 3,131 75

‐ 1,628

‐ 135

‐ 12,62624 

### B6. Net Reserves Sent by NERC Assessment Region(MW) in 2025

NERC Assessment Region Base Policy Change from Base to Policy Change as Percent of Summer Peak Basin (BASN) -2,052 -1,769 283 2.3% Desert Southwest (DS W 1,846 2,734 888 2.8% ERCOT -1,931 -1,942 -11 0.0% FRCC 838 2,254 1,417 3.1% ISO-NE -2,009 -1,999 10 0.0% MISO -2,502 -4,017 -1,515 -1.5% Northern California (CAL -1,490 -2,095 -606 -2.7% Northwest (NORW) 3,345 4,480 1,134 4.3% NYISO 249 296 47 0.2% PJM -6,877 -5,637 1,240 0.8% Rockies (Rock) 1,419 1,619 201 1.3% SERC-E -402 -1,492 -1,091 -2.5% SERC-N 2,141 728 -1,413 -3.5% SERC-SE -1,213 196 1,409 2.8% SERC-W 3,806 3,149 -657 -2.1% Southern California (CA L -3,309 -4,091 -782 -2.7% SPP 3,631 3,059 -572 -1.1%25 

### Appendix C: Tables by IPM Region for CPP Rate-Based Scenario in 2030

C1. Projected Operational Capacity

C2. Summary of Summer Peak Loads and Reserve Capacity

C3. Summary of Target and Projected Reserve Margins

C4. Policy Case Retired Capacity Incremental to Base Case

C5. New Capacity in Policy Case Incremental to Base Case

C6. Net Reserves Sent by NERC Assessment Region26 

### C1. Projected Operational Capacity Year= 2030

All Generation Sources EE Capacity Overall Coal Only Region Base Policy Contribution Change Base Policy Basin (BASN) 14,346 13,942 1,135 731 4,802 4,685 Desert Southwest (DSW) 42,265 40,855 3,911 2,501 8,380 8,096 ERCOT 107,231 111,864 6,856 11,490 17,937 17,339 FRCC 61,471 55,847 4,357

‐ 1,267 7,414 6,325 ISO

‐ NE 32,688 31,294 2,518 1,124 0 0 MAPP 9,627 9,322 464 159 3,007 2,190 MISO 126,164 119,045 9,389 2,269 45,036 39,567 Northern California (CALN ) 29,389 27,503 1,976 90 71 71 Northwest (NORW) 51,415 49,030 3,171 786 2,406 2,354 NYISO 39,032 35,323 3,220

‐ 489 0 0 PJM 203,408 185,630 15,258

‐ 2,519 50,129 43,483 Rockies (Rock) 24,105 22,666 1,777 337 9,216 8,363 SERC

‐ E 59,039 53,920 4,304

‐ 814 11,045 7,952 SERC

‐ N 52,505 48,091 3,916

‐ 498 13,141 11,084 SERC

‐ SE 60,658 60,265 4,470 4,076 11,655 10,692 SERC

‐ W 40,602 40,391 2,247 2,037 4,733 3,418 Southern California (CALS) 49,260 44,898 3,238

‐ 1,124 57 50 SPP 79,238 74,663 5,534 958 22,506 18,653 Grand Total 1,082,444 1,024,551 77,741 19,847 211,536 184,32227 

### C2. Summer Peak Loads and Reserve Capacity in 2030 Projected Reserve Margins

Assessment Re g ion Peak Demand

Base Peak Demand

Polic y Reserve Capacity Base Reserve Capacity

Polic y Basin (BASN) 12,961 11,953 14,594 13,459 Desert Southwest ( 34,589 31,144 39,259 35,348 ERCOT 82,455 76,428 93,793 86,936 FRCC 49,145 45,491 58,606 54,248 ISO-NE 27,433 25,243 31,547 29,029 MAPP 5,732 5,329 6,592 6,128 MISO 96,428 88,354 112,145 102,756 Northern California 23,372 21,650 26,810 24,834 Northwest (NORW ) 27,515 25,023 33,947 31,852 NYISO 30,694 27,918 35,605 32,385 PJM 160,273 147,051 184,955 169,696 Rockies (Rock) 16,419 14,869 18,824 17,047 SERC-E 46,801 43,058 53,821 49,517 SERC-N 42,204 38,799 48,534 44,619 SERC-SE 52,944 49,057 60,886 56,416 SERC-W 32,436 30,482 37,301 35,054 Southern California 30,929 28,127 35,684 32,494 SPP 57,092 52,221 64,857 59,323 US T otal 829,421 762,176 957,760 881,14328 C3. Summary of Target and Projected Reserve Margins in 2030 Projected Reserve Margins

NERC Assessment Re g ion Target Reserve

Mar g i n Base Case Polic y Case Policy % A bove Mar g in Policy Change from Base Basin (BASN) 12.60% 12.60% 12.60% 0.0% 0.0% Desert Southwest (DSW) 13.50% 13.50% 13.50% 0.0% 0.0% ERCOT 13.75% 13.75% 13.75% 0.0% 0.0%

FRCC 19.25% 19.25% 19.25% 0.0% 0.0% ISO-NE 15.00% 15.00% 15.00% 0.0% 0.0% MAPP 15.00% 15.00% 15.00% 0.0% 0.0% MISO 16.30% 16.30% 16.30% 0.0% 0.0% Northern California ( CALN ) 14.71% 14.71% 14.71% 0.0% 0.0% Northwest (NORW) 17.90% 23.38% 25.07% 7.2% 1.7% NYISO 16.00% 16.00% 16.00% 0.0% 0.0%

PJM 15.40% 15.40% 15.40% 0.0% 0.0% Rockies (Rock) 14.65% 14.65% 14.65% 0.0% 0.0% SERC-E 15.00% 15.00% 15.00% 0.0% 0.0% SERC-N 15.00% 15.00% 15.00% 0.0% 0.0% SERC-SE 15.00% 15.00% 15.00% 0.0% 0.0% SERC-W 15.00% 15.00% 15.00% 0.0% 0.0% S outhern California ( CALS ) 15.14% 15.37% 15.53% 0.4% 0.2% SPP 13.60% 13.60% 13.60% 0.0% 0.0% Grand Total 15.28% 15.47% 15.61% 0.3% 0.1%29 

### C4. Policy Case Retired Capacity Incremental to the Base Case (MW) Year= 2030

CC Coal CT NU OG Steam Total Basin (BASN) 180 118 0 0 108 405 Desert Southwest (DS W 616 284 0 0 513 1,412 ERCOT 0 600 0 0 0 600 FRCC 59 1,089 0 0 288 1,436 ISO

‐ NE 0 0 0 0 1,725 1,725 MAPP 0 817 0 0 0 817 MISO 0 5,484 0 0 0 5,484 Northern California (C A 0 0 1,898 0 0 1,898 Northwest (NORW) 1,465 52 0 867 0 2,383 NYISO 81 0 0 1 1,674 1,756 PJM 0 6,652 0

‐ 1 19 6,669 Rockies (Rock) 39 855 0 0 55 949 SERC

‐ E 0 3,093 0 0 0 3,093 SERC

‐ N 0 2,076 0 0 0 2,076 SERC

‐ SE 0 962 0 0 0 962 SERC

‐ W

‐ 434 1,328 0 0 1,309 2,203 Southern California (C A 0 7 43 0 2,514 2,565 SPP

‐ 362 3,878 0 0 1,060 4,576 Grand Total 1,643 27,294 1,942 867 9,264 41,00930 

### C5. New Capacity Policy Case Incremental to Base Case(MW) Year= 2030 Region CC CT Wind Solar Other Total Basin (BASN) 0 0 1 0 0 1 Desert Southwest (DSW) 0 0 3 0 0 3 ERCOT

‐ 6,330

‐ 4,182 323 15,421 0 5,232 FRCC

‐ 4,189 0 0 0 0

‐ 4,189 ISO

‐ NE 0 0 331 0 0 331 MAPP 0 0 497 0 15 511 MISO

‐ 1,234 0

‐ 330

‐ 86 0

‐ 1,650 Northern California (CALN) 0 0 12 0 0 12 Northwest (NORW) 0 0

‐ 2 00

‐ 2 NYISO

‐ 1,567 0

‐ 386 0 0

‐ 1,953 PJM

‐ 6,179

‐ 2,899

‐ 864

‐ 1,172 0

‐ 11,114 Rockies (Rock) 0 0 0

‐ 529 37

‐ 492 SERC

‐ E

‐ 1,838 0 0

‐ 188 0

‐ 2,026 SERC

‐ N

‐ 2,360 0 3 0 0

‐ 2,357 SERC

‐ SE

‐ 3,304 0 0 3,873 0 569 SERC

‐ W

‐ 589 0 0 2,569 0 1,979 Southern California (CALS)

‐ 1,603 0

‐ 270 79

‐ 3

‐ 1,797 SPP

‐ 887 0 859 4 0

‐ 24 Grand Total

‐ 30,080

‐ 7,081 177 19,970 50

‐ 16,96431 

### C6. Net Reserves Sent by NERC Assessment Region(MW) in 2030

NERC Assessment Region Base Policy Change from Base to Policy Change as Percent of Summer Peak Basin (BASN) -2,563 -1,826 738 5.7% Desert Southwest (DS W -1,245 1,269 2,514 7.3% ERCOT -40 -389 -349 -0.4% FRCC 2,646 1,379 -1,267 -2.6% ISO-NE -2,848 -1,949 900 3.3% MISO -5,473 -3,076 2,397 2.3% Northern California (CAL -2,709 -2,630 80 0.3% Northwest (NORW) 6,225 5,945 -279 -1.0% NYISO 26 -63 -90 -0.3% PJM -5,123 -6,100 -977 -0.6% Rockies (Rock) 399 1,097 698 4.3% SERC-E -506 -1,177 -670 -1.4% SERC-N 2,412 1,912 -500 -1.2% SERC-SE -800 326 1,126 2.1% SERC-W 2,732 2,844 112 0.3% Southern California (CA L -2,124 -3,120 -997 -3.2% SPP 1,933 2,271 337 0.6%32 

### Appendix D: Tables by IPM Region for CPP Mass-Based Scenario in 2020

D1. Projected Operational Capacity

D2. Summary of Summer Peak Loads and Reserve Capacity

D3. Summary of Target and Projected Reserve Margins

D4. Policy Case Retired Capacity Incremental to Base Case

D5. New Capacity in Policy Case Incremental to Base Case

D6. Net Reserves Sent by NERC Assessment Region33 

### D1. Projected Operational Capacity Year= 2020

All Generation Sources EE Capacity Overall Coal Only Region Base Policy Contribution Change Base Policy Basin (BASN) 14,448 14,475 84 111 5,064 5,244 Desert Southwest (DSW) 42,114 40,168 296

‐ 1,650 8,380 7,501 ERCOT 95,496 95,339 162 4 17,937 17,550 FRCC 56,419 55,636 88

‐ 695 7,479 7,131 ISO

‐ NE 32,558 30,383 262

‐ 1,914 0 0 MAPP 9,545 9,022 7

‐ 516 3,007 2,545 MISO 122,247 118,757 958

‐ 2,533 45,045 39,926 Northern California (CALN ) 30,690 28,522 220

‐ 1,948 71 71 Northwest (NORW) 51,014 49,408 330

‐ 1,276 2,406 2,354 NYISO 38,484 36,626 365

‐ 1,494 0 0 PJM 183,187 181,488 1,288

‐ 411 50,144 49,784 Rockies (Rock) 23,250 22,246 135

‐ 869 9,327 8,407 SERC

‐ E 52,103 52,109 262 269 11,045 11,176 SERC

‐ N 47,143 47,672 138 667 13,722 12,696 SERC

‐ SE 59,679 57,662 110

‐ 1,907 14,035 12,018 SERC

‐ W 39,074 38,826 52

‐ 196 4,733 3,918 Southern California (CALS) 40,576 38,137 358

‐ 2,082 57 57 SPP 77,768 75,481 153

‐ 2,135 22,966 20,139 Grand Total 1,015,797 991,956 5,267

‐ 18,575 215,416 200,51534 

### D2. Summer Peak Loads and Reserve Capacity in 2020 Projected Reserve Margins

Assessment Re g ion Peak Demand

Base Peak Demand

Polic y Reserve Capacity Base Reserve Capacity

Polic y Basin (BASN) 11,953 11,878 13,926 13,375 Desert Southwest ( 29,612 29,352 34,074 33,314 ERCOT 73,948 73,806 84,116 83,955 FRCC 44,324 44,253 56,200 55,045 ISO-NE 26,475 26,248 30,446 30,185 MAPP 5,330 5,324 6,603 6,123 MISO 90,719 89,895 106,505 104,548 Northern California 21,282 21,090 25,399 24,193 Northwest (NORW ) 25,414 25,181 39,507 35,702 NYISO 30,058 29,744 34,868 34,503 PJM 150,553 149,437 173,738 172,450 Rockies (Rock) 14,156 14,045 17,609 16,994 SERC-E 42,195 41,966 48,524 48,261 SERC-N 38,518 38,398 44,296 44,158 SERC-SE 48,110 48,014 55,327 55,217 SERC-W 29,095 29,053 37,890 35,696 Southern California 28,108 27,797 33,588 32,053 SPP 51,995 51,864 63,790 60,300 US T otal 761,845 757,343 906,407 886,07235 D3. Summary of Target and Projected Reserve Margins in 2020 Projected Reserve Margins

NERC Assessment Re g ion Target Reserve

Mar g i n Base Case Polic y Case Policy % A bove Mar g in Policy Change from Base Basin (BASN) 12.60% 16.51% 12.60% 0.0% -3.9% Desert Southwest (DSW) 13.50% 15.07% 13.50% 0.0% -1.6% ERCOT 13.75% 13.75% 13.75% 0.0% 0.0%

FRCC 19.25% 26.79% 24.39% 5.1% -2.4% ISO-NE 15.00% 15.00% 15.00% 0.0% 0.0% MAPP 15.00% 23.87% 15.00% 0.0% -8.9% MISO 16.30% 17.40% 16.30% 0.0% -1.1% Northern California ( CALN ) 14.71% 19.35% 14.71% 0.0% -4.6% Northwest (NORW) 17.90% 31.61% 28.79% 10.9% -2.8% NYISO 16.00% 16.00% 16.00% 0.0% 0.0%

PJM 15.40% 15.40% 15.40% 0.0% 0.0% Rockies (Rock) 14.65% 24.39% 21.00% 6.4% -3.4% SERC-E 15.00% 15.00% 15.00% 0.0% 0.0% SERC-N 15.00% 15.00% 15.00% 0.0% 0.0% SERC-SE 15.00% 15.00% 15.00% 0.0% 0.0% SERC-W 15.00% 30.23% 22.87% 7.9% -7.4% S outhern California ( CALS ) 15.14% 19.50% 15.31% 0.2% -4.2% SPP 13.60% 22.69% 16.27% 2.7% -6.4% Grand Total 15.30% 18.98% 17.00% 1.7% -2.0%36 

### D4. Policy Case Retired Capacity Incremental to the Base Case (MW) Year= 2020

CC Coal CT NU OG Steam Total Basin (BASN) 43

‐ 179 0 0 108

‐ 28 Desert Southwest (DS W 553 881 0 0 513 1,948 ERCOT 0 391 0 0 0 391 FRCC 148 348 0 0 288 784 ISO

‐ NE 0 0 0 0 1,910 1,910 MAPP 0 462 0 0 0 462 MISO 0 5,146 0

‐ 1,112 0 4,034 Northern California (C A 141 0 1,898 0 0 2,039 Northwest (NORW) 675 52 0 860 0 1,588 NYISO 81 0 0 0 1,330 1,411 PJM 0 363 0 0 377 740 Rockies (Rock) 0 921 0 0 0 921 SERC

‐ E 0

‐ 132 0 0 0

‐ 132 SERC

‐ N 0 1,042 0 0 0 1,042 SERC

‐ SE 0 2,017 0 0 0 2,017 SERC

‐ W

‐ 434 823 0 0

‐ 92 297 Southern California (C A 2 0 75 0 2,942 3,019 SPP

‐ 206 2,845 0

‐ 554 98 2,184 Grand Total 1,004 14,981 1,974

‐ 806 7,474 24,62737 

### D5. New Capacity Policy Case Incremental to Base Case(MW) Year= 2020 Region CC CT Wind Solar Other Total Basin (BASN) 0 0

‐ 1 00

‐ 1 Desert Southwest (DSW) 0 0

‐ 1 00

‐ 1 ERCOT 233 0

‐ 3 0 0 230 FRCC 0 0 0 0 0 0 ISO

‐ NE 0 0

‐ 265 0 0

‐ 265 MAPP 0 0

‐ 62 0 0

‐ 62 MISO 905 0

‐ 688

‐ 6 305 516 Northern California (CALN) 0 0

‐ 19 0

‐ 110

‐ 128 Northwest (NORW) 0 0

‐ 19 0 0

‐ 19 NYISO 0 0

‐ 448 0 0

‐ 448 PJM 1,380

‐ 1,797

‐ 292

‐ 16

‐ 237

‐ 962 Rockies (Rock) 0 0 0

‐ 84 0

‐ 84 SERC

‐ E 0 00

‐ 125 0

‐ 125 SERC

‐ N 1,554 0 0 0 0 1,554 SERC

‐ SE 0 0 0 0 0 0 SERC

‐ W 0 0 0 0 41 41 Southern California (CALS) 834 0

‐ 255 0 0 580 SPP 0 0

‐ 121 0 0

‐ 121 Grand Total 4,906

‐ 1,797

‐ 2,172

‐ 231 0 70638 

### D6. Net Reserves Sent by NERC Assessment Region(MW) i n 2020

NERC Assessment Region Base Policy Change from Base to Policy Change as Percent of Summer Peak Basin (BASN) -1,772 -1,194 579 4.8% Desert Southwest (DS W 3,869 2,684 -1,185 -4.0% ERCOT -1,942 -1,942 0 0.0% FRCC 0 371 371 0.8% ISO-NE -1,823 -3,534 -1,710 -6.5% MISO -2,775 -3,958 -1,183 -1.2% Northern California (CAL 0 -909 -909 -4.3% Northwest (NORW) 407 2,617 2,210 8.7% NYISO 320 -906 -1,226 -4.1% PJM -4,614 -4,697 -84 -0.1% Rockies (Rock) 1,417 1,091 -325 -2.3% SERC-E -981 -616 365 0.9% SERC-N 1,502 2,169 667 1.7% SERC-SE 3,800 1,893 -1,907 -4.0% SERC-W 1,112 3,043 1,931 6.6% Southern California (CA L -3,761 -4,543 -782 -2.8% SPP 2,191 3,440 1,250 2.4%39 

### Appendix E: Tables by IPM Region for CPP Mass-Based Scenario in 2020

E1. Projected Operational Capacity

E2. Summary of Summer Peak Loads and Reserve Capacity

E3. Summary of Target and Projected Reserve Margins

E4. Policy Case Retired Capacity Incremental to Base Case

E5. New Capacity in Policy Case Incremental to Base Case

E6. Net Reserves Sent by NERC Assessment Region40 

### E1. Projected Operational Capacity Year= 2025

All Generation Sources EE Capacity Overall Coal Only Region Base Policy Contribution Change Base Policy Basin (BASN) 14,293 12,687 687

‐ 919 4,802 3,345 Desert Southwest (DSW) 42,265 40,374 2,300 409 8,380 7,501 ERCOT 99,864 96,691 3,345 171 17,937 16,950 FRCC 56,336 55,647 2,080 1,391 7,479 7,131 ISO

‐ NE 32,685 30,497 1,644

‐ 545 0 0 MAPP 9,627 9,028 207

‐ 393 3,007 2,407 MISO 125,625 118,290 6,127

‐ 1,209 45,036 37,169 Northern California (CALN ) 29,389 27,278 1,321

‐ 791 71 17 Northwest (NORW) 51,181 48,752 2,036

‐ 393 2,406 1,513 NYISO 38,484 36,517 2,173 205 0 0 PJM 188,212 178,002 9,367

‐ 843 50,144 43,013 Rockies (Rock) 23,663 21,906 1,041

‐ 717 9,216 7,797 SERC

‐ E 55,904 52,622 2,556

‐ 727 11,045 10,907 SERC

‐ N 50,217 48,098 2,051

‐ 68 13,141 11,405 SERC

‐ SE 57,354 56,885 2,192 1,722 11,655 11,185 SERC

‐ W 40,012 38,007 1,069

‐ 936 4,733 3,918 Southern California (CALS) 43,405 39,745 2,159

‐ 1,501 57 57 SPP 78,704 74,286 2,733

‐ 1,685 22,966 18,522 Grand Total 1,037,223 985,310 45,085

‐ 6,828 212,075 182,83641 

### E2. Summer Peak Loads and Reserve Capacity in 2025 Projected Reserve Margins

Assessment Re g ion Peak Demand

Base Peak Demand

Polic y Reserve Capacity Base Reserve Capacity

Polic y Basin (BASN) 12,466 11,856 14,036 13,349 Desert Southwest ( 31,887 29,861 36,192 33,892 ERCOT 77,606 74,665 88,276 84,932 FRCC 46,355 44,611 55,279 53,199 ISO-NE 26,701 25,272 30,706 29,063 MAPP 5,539 5,360 6,370 6,164 MISO 93,625 88,356 108,885 102,758 Northern California 22,305 21,155 25,592 24,301 Northwest (NORW ) 26,489 24,909 36,672 32,084 NYISO 30,051 28,179 34,860 32,687 PJM 154,841 146,724 178,686 169,319 Rockies (Rock) 15,223 14,315 17,676 16,412 SERC-E 44,263 42,040 50,902 48,346 SERC-N 40,450 38,666 46,517 44,466 SERC-SE 50,430 48,524 57,994 55,802 SERC-W 30,589 29,660 35,644 34,109 Southern California 29,473 27,604 34,006 31,897 SPP 54,332 51,927 62,673 58,997 US T otal 792,624 753,649 920,969 871,77742 E3. Summary of Target and Projected Reserve Margins in 2025 Projected Reserve Margins

NERC Assessment Re g ion Target Reserve

Mar g i n Base Case Polic y Case Policy % A bove Mar g in Policy Change from Base Basin (BASN) 12.60% 12.60% 12.60% 0.0% 0.0% Desert Southwest (DSW) 13.50% 13.50% 13.50% 0.0% 0.0% ERCOT 13.75% 13.75% 13.75% 0.0% 0.0%

FRCC 19.25% 19.25% 19.25% 0.0% 0.0% ISO-NE 15.00% 15.00% 15.00% 0.0% 0.0% MAPP 15.00% 15.00% 15.00% 0.0% 0.0% MISO 16.30% 16.30% 16.30% 0.0% 0.0% Northern California ( CALN ) 14.71% 14.74% 14.87% 0.2% 0.1% Northwest (NORW) 17.90% 27.05% 25.96% 8.1% -1.1% NYISO 16.00% 16.00% 16.00% 0.0% 0.0%

PJM 15.40% 15.40% 15.40% 0.0% 0.0% Rockies (Rock) 14.65% 16.12% 14.65% 0.0% -1.5% SERC-E 15.00% 15.00% 15.00% 0.0% 0.0% SERC-N 15.00% 15.00% 15.00% 0.0% 0.0% SERC-SE 15.00% 15.00% 15.00% 0.0% 0.0% SERC-W 15.00% 16.52% 15.00% 0.0% -1.5% S outhern California ( CALS ) 15.14% 15.38% 15.55% 0.4% 0.2% SPP 13.60% 15.35% 13.62% 0.0% -1.7% Grand Total 15.29% 16.19% 15.67% 0.4% -0.5%43 

### E4. Policy Case Retired Capacity Incremental to the Base Case (MW) Year= 2025

CC Coal CT NU OG Steam Total Basin (BASN) 43 1,458 0 0 108 1,609 Desert Southwest (DS W 553 881 0 0 513 1,948 ERCOT 0 991 0 0 0 991 FRCC 148 348 0 0 288 784 ISO

‐ NE 0 0 0 0 1,910 1,910 MAPP 0 600 0 0 0 600 MISO 0 7,895 0

‐ 1,112 0 6,783 Northern California (C A 141 54 1,898 0 0 2,093 Northwest (NORW) 675 893 0 860 0 2,429 NYISO 81 0 0 109 1,330 1,520 PJM 0 7,134 0 0 367 7,501 Rockies (Rock) 0 1,421 0 0 0 1,421 SERC

‐ E 0 137 0 0 0 137 SERC

‐ N 0 1,753 0 0 0 1,753 SERC

‐ SE 0 470 0 0 0 470 SERC

‐ W

‐ 434 823 0 0 1,957 2,346 Southern California (C A 2 0 75 0 3,016 3,093 SPP

‐ 206 4,462 0

‐ 554 932 4,635 Grand Total 1,004 29,319 1,974

‐ 697 10,421 42,02144 

### E5. New Capacity Policy Case Incremental to Base Case(MW) Year= 2025 Region CC CT Wind Solar Other Total Basin (BASN) 0 0 3 0 0 3 Desert Southwest (DSW) 0 0 55 0 0 55 ERCOT

‐ 52

‐ 2,134 0 0 0

‐ 2,185 FRCC 94 0 0 0 0 94 ISO

‐ NE 0 0

‐ 265 0

‐ 13

‐ 278 MAPP 0 0 0 0 0 0 MISO

‐ 948 0 416

‐ 48 0

‐ 581 Northern California (CALN) 0 0

‐ 19 0 0

‐ 19 Northwest (NORW) 0 0 73 0

‐ 73 0 NYISO 0 0

‐ 448 0 0

‐ 448 PJM 1,447

‐ 2,094

‐ 1,933

‐ 133 0

‐ 2,713 Rockies (Rock) 0 0 0

‐ 375 37

‐ 338 SERC

‐ E

‐ 2,620 0 0

‐ 525 0

‐ 3,145 SERC

‐ N

‐ 383 0 0 0 0

‐ 383 SERC

‐ SE 0 0 0 0 0 0 SERC

‐ W 333 0 0 0 0 333 Southern California (CALS) 834 0

‐ 255

‐ 1,114

‐ 32

‐ 567 SPP 0 0

‐ 66

‐ 1 267 200 Grand Total

‐ 1,295

‐ 4,228

‐ 2,439

‐ 2,197 187

‐ 9,97145 

### E6. Net Reserves Sent by NERC Assessment Region(MW) in 2025

NERC Assessment Region Base Policy Change from Base to Policy Change as Percent of Summer Peak Basin (BASN) -2,052 -2,971 -919 -7.4% Desert Southwest (DS W 1,846 2,219 372 1.2% ERCOT -1,931 -1,760 172 0.2% FRCC 838 2,229 1,391 3.0% ISO-NE -2,009 -2,345 -337 -1.3% MISO -2,502 -4,422 -1,920 -1.9% Northern California (CAL -1,490 -2,298 -808 -3.6% Northwest (NORW) 3,345 5,486 2,141 8.1% NYISO 249 834 585 1.9% PJM -6,877 -6,098 779 0.5% Rockies (Rock) 1,419 1,175 -243 -1.6% SERC-E -402 -727 -325 -0.7% SERC-N 2,141 2,074 -68 -0.2% SERC-SE -1,213 509 1,722 3.4% SERC-W 3,806 3,330 -476 -1.6% Southern California (CA L -3,309 -3,860 -552 -1.9% SPP 3,631 2,846 -785 -1.4%46 

### Appendix F: Tables by IPM Region for CPP Mass-Based Scenario in 2020

F1. Projected Operational Capacity

F2. Summary of Summer Peak Loads and Reserve Capacity

F3. Summary of Target and Projected Reserve Margins

F4. Policy Case Retired Capacity Incremental to Base Case

F5. New Capacity in Policy Case Incremental to Base Case

F6. Net Reserves Sent by NERC Assessment Region47 

### F1. Projected Operational Capacity Year= 2030

All Generation Sources EE Capacity Overall Coal Only Region Base Policy Contribution Change Base Policy Basin (BASN) 14,346 13,573 1,135 361 4,802 3,172 Desert Southwest (DSW) 42,265 40,844 3,911 2,490 8,380 7,115 ERCOT 107,231 113,686 6,856 13,312 17,937 16,619 FRCC 61,471 57,489 4,357 375 7,414 6,324 ISO

‐ NE 32,688 30,510 2,518 340 0 0 MAPP 9,627 9,288 464 125 3,007 2,171 MISO 126,164 117,707 9,389 931 45,036 35,626 Northern California (CALN ) 29,389 27,278 1,976

‐ 136 71 17 Northwest (NORW) 51,415 48,834 3,171 589 2,406 1,332 NYISO 39,032 35,605 3,220

‐ 207 0 0 PJM 203,408 185,441 15,258

‐ 2,709 50,129 39,667 Rockies (Rock) 24,105 24,184 1,777 1,855 9,216 7,657 SERC

‐ E 59,039 53,149 4,304

‐ 1,585 11,045 10,405 SERC

‐ N 52,505 49,374 3,916 785 13,141 10,953 SERC

‐ SE 60,658 56,487 4,470 298 11,655 10,221 SERC

‐ W 40,602 38,739 2,247 385 4,733 3,918 Southern California (CALS) 49,260 46,137 3,238 114 57 57 SPP 79,238 75,215 5,534 1,510 22,506 18,036 Grand Total 1,082,444 1,023,537 77,741 18,833 211,536 173,29148 

### F2. Summer Peak Loads and Reserve Capacity in 2030 Projected Reserve Margins

Assessment Re g ion Peak Demand

Base Peak Demand

Polic y Reserve Capacity Base Reserve Capacity

Polic y Basin (BASN) 12,961 11,953 14,594 13,459 Desert Southwest ( D 34,589 31,144 39,259 35,348 ERCOT 82,455 76,428 93,793 86,936 FRCC 49,145 45,491 58,606 54,248 ISO-NE 27,433 25,243 31,547 29,029 MAPP 5,732 5,329 6,592 6,128 MISO 96,428 88,354 112,145 102,756 Northern California 23,372 21,652 26,810 24,867 Northwest (NORW ) 27,515 25,057 33,947 32,335 NYISO 30,694 27,918 35,605 32,385 PJM 160,273 147,051 184,955 169,696 Rockies (Rock) 16,419 14,869 18,824 17,047 SERC-E 46,801 43,058 53,821 49,517 SERC-N 42,204 38,799 48,534 44,619 SERC-SE 52,944 49,057 60,886 56,416 SERC-W 32,436 30,482 37,301 35,054 Southern California 30,929 28,127 35,684 32,494 SPP 57,092 52,245 64,857 59,639 US T otal 829,421 762,235 957,760 881,97549 F3. Summary of Target and Projected Reserve Margins in 2030 Projected Reserve Margins

NERC Assessment Re g ion Target Reserve

Mar g i n Base Case Polic y Case Policy % A bove Mar g in Policy Change from Base Basin (BASN) 12.60% 12.60% 12.60% 0.0% 0.0% Desert Southwest (DSW) 13.50% 13.50% 13.50% 0.0% 0.0% ERCOT 13.75% 13.75% 13.75% 0.0% 0.0%

FRCC 19.25% 19.25% 19.25% 0.0% 0.0% ISO-NE 15.00% 15.00% 15.00% 0.0% 0.0% MAPP 15.00% 15.00% 15.00% 0.0% 0.0% MISO 16.30% 16.30% 16.30% 0.0% 0.0% Northern California ( CALN ) 14.71% 14.71% 14.85% 0.1% 0.1% Northwest (NORW) 17.90% 23.38% 25.23% 7.3% 1.9% NYISO 16.00% 16.00% 16.00% 0.0% 0.0%

PJM 15.40% 15.40% 15.40% 0.0% 0.0% Rockies (Rock) 14.65% 14.65% 14.65% 0.0% 0.0% SERC-E 15.00% 15.00% 15.00% 0.0% 0.0% SERC-N 15.00% 15.00% 15.00% 0.0% 0.0% SERC-SE 15.00% 15.00% 15.00% 0.0% 0.0% SERC-W 15.00% 15.00% 15.00% 0.0% 0.0% S outhern California ( CALS ) 15.14% 15.37% 15.53% 0.4% 0.2% SPP 13.60% 13.60% 14.15% 0.6% 0.6% Grand Total 15.28% 15.47% 15.71% 0.4% 0.2%50 

### F4. Policy Case Retired Capacity Incremental to the Base Case (MW) Year= 2030

CC Coal CT NU OG Steam Total Basin (BASN) 43 1,630 0 0 108 1,782 Desert Southwest (DS W 553 1,267 0 0 513 2,334 ERCOT 0 1,321 0 0 0 1,321 FRCC 148 1,089 0 0 288 1,525 ISO

‐ NE 0 0 0 0 1,910 1,910 MAPP 0 836 0 0 0 836 MISO 0 9,437 0

‐ 521 0 8,916 Northern California (C A 141 54 1,898 0 0 2,093 Northwest (NORW) 675 1,074 0 860 0 2,610 NYISO 81 0 0 1 1,330 1,412 PJM 0 10,469 0

‐ 1 367 10,835 Rockies (Rock) 0 1,561 0 0 0 1,561 SERC

‐ E 0 639 0 0 0 639 SERC

‐ N 0 2,205 0 0 0 2,205 SERC

‐ SE 0 1,433 0 0 0 1,433 SERC

‐ W

‐ 434 823 0 0 1,957 2,346 Southern California (C A 2 0 75 0 3,016 3,093 SPP

‐ 206 4,488 0

‐ 554 932 4,660 Grand Total 1,004 38,328 1,974

‐ 215 10,421 51,51251 

### F5. New Capacity Policy Case Incremental to Base Case(MW) Year= 2030 Region CC CT Wind Solar Other Total Basin (BASN) 479 0 6 523 0 1,008 Desert Southwest (DSW) 0 0 57 853 0 911 ERCOT

‐ 4,378

‐ 5,173 0 17,325 0 7,773 FRCC

‐ 2,458 0 0 0 0

‐ 2,458 ISO

‐ NE 0 0

‐ 268 0 0

‐ 268 MAPP 0 0 497 0 0 497 MISO

‐ 543 0 1,054

‐ 80 0 431 Northern California (CALN) 0 0

‐ 19 0 0

‐ 19 Northwest (NORW) 0 0 28 0 0 28 NYISO

‐ 1,567 0

‐ 448 0 0

‐ 2,015 PJM

‐ 325

‐ 3,004

‐ 2,207

‐ 1,602 0

‐ 7,139 Rockies (Rock) 0 0 0 1,601 37 1,638 SERC

‐ E

‐ 4,418 0 0

‐ 833 0

‐ 5,250 SERC

‐ N

‐ 943 0 0 0 0

‐ 943 SERC

‐ SE

‐ 2,739 0 0 0 0

‐ 2,739 SERC

‐ W 476 0 0 0 0 476 Southern California (CALS) 117 0

‐ 255 79 29

‐ 30 SPP

‐ 505 0 859

‐ 2 267 619 Grand Total

‐ 16,804

‐ 8,177

‐ 696 17,864 334

‐ 7,47952 

### F6. Net Reserves Sent by NERC Assessment Region(MW) in 2030

NERC Assessment Region Base Policy Change from Base to Policy Change as Percent of Summer Peak Basin (BASN) -2,563 -2,563 1 0.0% Desert Southwest (DS W -1,245 631 1,877 5.4% ERCOT -40 260 301 0.4% FRCC 2,646 3,021 375 0.8% ISO-NE -2,848 -2,282 567 2.1% MISO -5,473 -5,470 2 0.0% Northern California (CAL -2,709 -2,864 -154 -0.7% Northwest (NORW) 6,225 5,246 -979 -3.6% NYISO 26 301 275 0.9% PJM -5,123 -4,891 232 0.1% Rockies (Rock) 399 1,114 715 4.4% SERC-E -506 -1,455 -948 -2.0% SERC-N 2,412 3,197 785 1.9% SERC-SE -800 -503 298 0.6% SERC-W 2,732 3,122 390 1.2% Southern California (CA L -2,124 -1,859 264 0.9% SPP 1,933 2,389 456 0.8%53 

### Appendix C: Maps

C1. IPM Regions

C2. . NERC Assessment Regions54C1: IPM v5.13 Regions55C2:NERCAssessmentAreasinLongTermReliabilityAssessment.Source:NERC2012LongTermReliabilityAssessment567/26/2015 draft

1 Memorandum

To: Clean Power Plan Docket
Subject: Stranded assets analysis

Date: July 2015

The EPA performed a stranded assets analysis for purposes of responding to comments on the proposed Clean Power Plan. Some commenters said that the proposed state goals were so stringent that states would have no choice but to adopt requirements that would result in retirements of coal

‐ fired capacity that had been built relatively recently or had recently made pollution control investments, resulting in stranded assets. For the final rule, the EPA revised the goal

‐ setting methodology. The analysis in this

memo demonstrates that the final performance rates address the stranded asset concerns raised by
commenters. The final rule’s goal

‐ setting approach (now referred to as “emission performance rate calculation”) represents a level of generation and emissions across each interconnection that is consistent with the ability to operate affected EGUs through the end of their book life, considering both the age of each facility and recently installed capital

‐ intensive pollution control retrofits. This memo describes the analysis assumptions, methodology and findings.
Assumptions

In this context, the EPA is considering a potential “stranded asset” to be a coal

‐ fired EGU that retires before it (or a capital

‐ intensive pollution control that it has installed) is fully depreciated. We use “book life” to represent the period of time over which investment in a coal

‐ fired EGU is fully depreciated.

The EPA estimated the book life of coal

‐ fired EGUs to be 40 years, and conservatively assumed a book life of pollution control retrofits at 20 years. Note that the book life of coal

‐ fired EGU (coal steam and IGCC) is twice as long as the debt life and the depreciation schedule used for federal tax purposes. The book life was estimated based on researching financial statements of utility and merchant generation companies in filings to the Security and Exchange Commission. 1 These statements typically list the period over which long

‐ lived assets are depreciated for financial reporting purposes. Additionally, note that while the book life for environmental retrofits is often 15 years, this analysis conservatively

assumes 20 years.
While coal steam facilities have been retrofitted with various pollution controls, this analysis focuses on wet and dry scrubbers, as well as SCRs, since these are the most capital

‐ intensive controls, and as such represent longer

‐ term investments. Methodology

This analysis compares the amount of coal generation included in the final rule’s national emission performance rate calculation 2 in each interconnection to the amount of coal generation from coal

‐ fired EGUs that we assume are not fully depreciated in 2025 and/or 2030. If the amount of coal generation remaining after the BSER calculation is greater than the amount of generation from EGUs that we

1 IPM Documentation Chapter 8, available at: http://www.epa.gov/powersectormodeling/

2 For more information on how the national emission performance rates were determined, please see the final rule preamble.7/26/2015 draft

2 assume are not fully depreciated in a given year, it demonstrates that the final rule preserves flexibility for operators to determine the level of generation at these units.

The emission performance rate calculation described in the final rule preamble applies all three building blocks to their full potential in the region that sets the national rate (the region where the emission performance rate calculation results in the highest emissions rate for each subcategory). In the other regions, full application of the building blocks would result in a rate much lower than the national emission performance rate. For these regions, this analysis assumes that building blocks two and three

are not applied at their full potential such that the regional rate is equal to the national emissions performance rate for each category. This makes the analysis consistent with the emission performance
rate calculation approach in the final rule.

The analysis identifies the coal

‐ fired EGUs included in the emission performance rate calculation that will be less than 40 years old in 2025 and/or 2030. The analysis also identifies the coal

‐ fired EGUs included in the emission performance rate calculation that have pollution control retrofits that will be less than 15 years old in 2025 and/or 2030, including retrofits projected in the base case.
This analysis includes all coal

‐ fired capacity that is included in the emission performance rate calculation. The analysis does not reduce that capacity to account for recent retirement announcements or future projected closures.

The analysis is conducted using the three regions that were used in the emission performance rate calculation: The Eastern, Western, and Texas Interconnections.

Results

In both 2025 and 2030, the amount of 2012 coal generation included in the final guidelines’ emission performance rate calculation after application of the building blocks is greater than the amount of 2012 coal generation that, under the book life assumptions in this analysis, is not fully depreciated in 2025 or 2030. The analysis results are provided below.

Table 1. 2012 Coal

‐ Fired Generation Assumed in National Emission Performance Rate Calculation (TWh)

2025 2030

From All

Coal From Undepreciated Coal From All Coal From Undepreciated Coal

Eastern Interconnection 762 750 578 431

Western Interconnection 158 131 116 103

Texas Interconnection 70 61 70 387/26/2015 draft

3 As explained above, for the region that sets the national emission performance rate for steam in the given year (which for fossil steam is the Eastern Interconnection in 2025 and in 2030), the coal

‐ fired generation included in this table represents all coal fired generation that is included in that emissions performance rate calculation. In the other regions that did not set that rate in that year, the coal

‐ fired generation in this table is higher than the level shown for that region’s emissions performance rate calculation; the difference represents the building block potential in those regions that was not captured in establishing the national emission performance rate. As a result, the national emission performance
rates for steam do not assume that steam generation would be reduced below the levels shown in this table.

The total amount of coal generation shown in this table decreases between 2025 and 2030 consistent with how the emission performance rate calculation captures the gradual phasing

‐ in of building blocks
two and three. For more information on how the national emission performance rates were determined, please see the final rule preamble.

Conclusion

This analysis shows that the national EGU performance rates in the final guidelines can be met without the retirement of affected EGUs before the end of their book life, and without the retirement of affected EGUs before the end of the book life of capital

‐ intensive pollution control retrofits installed on those EGUs. Thus, state plans can be designed to achieve the EGU performance rates and statewide performance goals without requiring the retirement of these units.