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 

<< GHG Mitigation TSD_Intro_7-27.docx >> 

<< GHG Mitigation TSD_BB1_7-27.docx >> 

<< GHG Mitigation TSD_BB2_7-27.docx >> 

<< GHG Mitigation TSD_BB3_7-27.docx >> 

<< GHG Mitigation TSD_CCS_7-27.docx >> 

<< Demand-Side EE TSD_7-27.docx >> 

<< Demand-Side Energy Efficiency TSD - at 3%.xlsx >> 

<< Demand-Side Energy Efficiency TSD - at 7%.xlsx >> 

<< Demand-Side Energy Efficiency TSD - Potential Studies.xlsm >> 

<< Resource Adequacy TSD_7-27.docx >> 

<< Stranded assets docket memo_7-27.docx >> 

 
Technical Support Document (TSD) for 

Carbon Pollution Guidelines for Existing Power Plants: 

Emission Guidelines for Greenhouse Gas Emissions 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 Existing Coal-fired Electric Generating Units .............2-1 

Chapter 3: Generation Shifts and CO2 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) section 111(d), the EPA is establishing final 

CO2 emission guidelines for existing fossil fuel-fired electric generating units (EGUs) – the 

Clean Power Plan. More specifically, the EPA is establishing: 1) carbon dioxide (CO2) 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) guidelines for the development, submittal and 

implementation of state plans that implement the CO2 emission performance rates. The emission 

performance rates reflect the “best system of emission reductions ... adequately demonstrated” 

for CO2 emissions from the EGU source category. The guidelines also provide for the 

development, submittal and implementation of state plans that implement the CO2 emission 

performance rates, either directly by means of source-specific requirements or through measures 

that achieve equivalent CO2 reductions from the same group of EGUs. This technical support 

document (TSD) provides additional information, 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 CO2 emissions from existing EGUs 

was a combination of measures: (1) increasing the operational efficiency of existing coal-fired 

steam EGUs, (2) substituting increased generation at existing NGCC units for generation at 

existing steam EGUs, (3) substituting generation from low- and zero-carbon generating capacity 

for generation at existing fossil fuel-fired EGUs, and (4) increasing demand-side energy 

efficiency to reduce the amount of fossil fuel-fired generation. These activities were categorized 

as four “building blocks.” 

The final rule BSER incorporates certain changes from the proposed rule, reflecting the 

EPA’s consideration of comments received and further analysis. The principal changes are the 

exclusion from the BSER of emission reductions achievable through demand-side energy 

efficiency (building block 4) and through nuclear generation; a revised approach to 

determination of emission reductions achievable through increased renewable energy generation; 

a consistent approach to determination 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 discussed 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 CO2 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 includes a chapter on carbon capture and storage 

technology (CCS). 1 A separate TSD includes supplemental information on demand-side energy 

efficiency (Demand-side Energy Efficiency TSD for CPP Final Rule), which is not part of 

BSER, but supports the Regulatory Impact Analysis. 

While evaluating each measure, the EPA considered the technical feasibility, 

applicability and use, application level appropriate for BSER, and cost effectiveness associated 

with reducing GHG emissions at EGUs. The application 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 reduce GHG emissions. The application of the building blocks as 

BSER supports achieving cost-effective and technically feasible reductions of CO2, 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: CO2 intensity reductions through heat rate improvements at 

existing coal fired electricity generating units 

This Technical Support Document (TSD) incorporates by reference the materials found 

in the Clarified Technical Support Document (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 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 generating 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 electricity, the lower the EGU’s heat rate and, 

correspondingly, the lower its CO2 intensity (CO2 lb/kWh). Because heat rate improvements 

reduce fuel consumption, improving coal-fired EGUs’ heat rates can be a cost-effective method 

of reducing the CO2 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 improvements 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 electricity 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 interconnection using three analytical approaches. The approaches 

compare each EGU’s “best” or “benchmark” historical gross heat rates against the EGU’s 2012 

gross 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 and 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 Document (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/#!documentDetail;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/#!documentDetail;D=EPA-HQ-OAR-2013-0602-0238. 2

 Improving heat rate can result in a “rebound effect” that 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 EGU’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 temperature. These two conditions account for 

approximately 26 percent of variation 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 increments) and hourly ambient air temperature 

conditions (using 10° F ranges). In the second approach, “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 against the EGU’s 2012 gross heat rate for the 

capacity-temperature group. For the third approach, “efficiency and consistency improvements 

under similar conditions,” the agency identified “benchmarks” for each EGU equal to the EGU’s 

lowest hourly gross heat rate (characterized as the 10th percentile 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 measures 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 nationally if coal-fired EGUs return to their 

best past performance between 2002 to 2012. The most 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 percent in the Eastern 

Interconnection. 

2.1 Introduction 

Heat rate, a measure of efficiency for fossil fuel-fired EGUs, is commonly expressed as 

the amount of heat input in British thermal units (Btu) required to generate one kWh of 

electricity (Btu/kWh). As a coal-fired EGU’s heat rate improves (i.e., efficiency improves), less 

coal or other fuel is required to produce the same amount of electricity. Because less fuel is 

combusted, the amount of CO2 and other emissions (e.g., mercury, NOX, PM, SO2) released to 

the environment also declines. In addition to the clear environmental benefits of heat rate 

improvements, there are important economic co-benefits attributable to reduced fuel costs and 

coal combustion residue management and disposal costs. 

Heat rates of existing coal-fired EGUs in the U.S. vary substantially. The variation in 

heat rates among EGUs with similar design characteristics (e.g., boiler type, fuel type, size, 

cooling system) as well as year-to-year variation in heat rate at individual EGUs indicate that 

there is potential for broad efficiency improvements that can yield significant CO2 emission 

reductions and fuel savings for the existing coal-fired EGU fleet as a whole. This chapter 

presents (1) the characteristics of existing coal-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 ambient temperature conditions. Therefore, the agency determined it was 

appropriate to select the 10th percentile value to (1) control 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 existing coal-fired EGUs 

that, when realized, would reduce CO2 emission intensity, and (5) responses to key comments 

about building block 1 of the proposed rule. 

2.2 Coal-fired EGUs 

Several different coal combustion configurations and technologies 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 furnace 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 cycle. Coal combustion waste gases (i.e., flue gas), 

including various air emissions (e.g., CO2, mercury, NOX, PM, SO2), 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-specific 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 characteristics, 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 the 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 and 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 rates with design thermal efficiencies of around 35 percent while 

supercritical and ultrasupercritical EGUs that operate above the critical pressure of water may 

have design thermal efficiencies of 40 to 45 percent. In any case, actual thermal efficiencies in 

daily operation are usually lower than design values. 

Boiler and steam turbine size. In general as the size of the boiler and the steam turbine increase, 

heat rate decreases. Larger coal-fired EGUs generally 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, and also tend to use more advanced 

technologies. They also have economies of scale for investment, operation, and maintenance, and 

have higher capacity factors because 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 largely 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 energy from the steam and affects overall efficiency. 

The amount of energy that the steam turbine can extract from the steam depends on the steam 

temperature and pressure, as stated above, as well as condenser pressure (affected by the cooling 

system design, condenser cleanliness, EGU load, and ambient cooling water or air temperature). 

At a lower condenser pressure (within design limits), more energy can be extracted by the steam 

turbine, with a resulting lower EGU heat rate. 

Auxiliary equipment. Auxiliary equipment, such as pumps, fans, pulverizers, and pollution 

control devices, require power to run, thereby consuming some of the electricity generation onsite and reducing the amount of electricity output available for use on the electricity grid. These 

systems vary in efficiency based on design, age, and usage. 

Geographic location. Ambient conditions at the plant 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 during combustion heats the 

incoming air, increasing heat rate. On the other hand, cooler water temperatures improve the 

efficiency of the cooling system, thereby reducing heat rate. Colder 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, the lower the moisture content 

of coal, the less energy that is needed to evaporate 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 cleanings 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 negatively affect heat rate. However, some coalfired EGUs efficiently burn blends 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 CO2 Emission Performance Rate and Goal Computation Technical Support 

Document, EPA assessed the potential CO2 reductions achievable through each of the building 

blocks that comprise the best system of emission reduction. The agency conducted assessment at 

the level of the three large regional electricity interconnections: Western, Texas, and Eastern (see 

Figure 2-2). The Western Interconnection is a major power grid that comprises one North 

American Electric Reliability Corporation (NERC) region – the Western Electricity Coordinating 

Council (WECC). The Western Interconnection encompasses all or portions of 14 western states. 

The Texas Interconnection includes the portion of Texas managed by the Electric Reliability 

Council of Texas (ERCOT). The Eastern Interconnection is the other major regional power grid, 

comprising six NERC regions – Florida Reliability Coordinating Council (FRCC), Midwest 

Reliability Organization (MRO), Northeast Power Coordinating Council (NPCC), Reliability 

First Corporation (RFC), SERC Reliability Corporation (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 interconnection must carefully manage 

electricity generation and load to ensure safe and reliable electricity service. 

For the proposal, EPA analyzed the potential heat rate improvements from coal-fired 

EGUs at the national level. Some commenters suggested that EPA should adjust the analysis and 

develop state-specific estimates of potential heat rate improvements from coal-fired EGUs. 

While the agency carefully evaluated these comments, the agency determined that due to the 

highly interconnected nature of the electricity system, and as further 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 interconnection 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 EGUs5 at 367 power plants in the contiguous U.S. 

(CONUS) that reported hourly heat input and gross electricity generation to EPA’s Clean Air 

 5

 Excludes cogeneration, integrated gasification and combined 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 generate steam, making it difficult to meaningfully compare these 

EGUs’ heat rates. IGCC units are excluded 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 includes units with a variety of design 

configurations and operating characteristics that can affect an EGU’s heat rate, such as 

thermodynamic steam cycle, coal rank and coal quality, boiler type, boiler size (i.e., maximum 

hourly heat input), and generator power output capacity. 

At the end of 2012, the age of the 884 coal-fired EGUs varied widely, ranging from less 

than 1 year to 68 years. Nationally, the average age was 42 years, but this differed among the 

interconnections. The Texas Interconnection had the lowest average age of existing coal-fired 

EGUs at 27 years followed by the Western Interconnection at 34 years. The average age of the 

coal-fired EGUs in the Eastern Interconnection was 44 years (see Figure 2-3). 

Similarly, electricity generating capacity of the 884 coal-fired EGUs varied widely within 

and between the interconnections. 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 the Texas Interconnection the average 

was 689 MW, and in the Eastern Interconnection 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 coal-fired EGUs in 2012, by interconnection 

Figure 2-4 Generator capacity (MW) distribution of coal-fired 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 rate7

 declined between 2002 and 

2008 to a low of 9,645 Btu/kWh in 2008 and 2009 (see 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, gross generation was the only complete and accurate hourly 

dataset available to EPA. Therefore, unless otherwise noted, references to heat rate in this chapter refer to gross heat 

rate. 
2-8 

 

average annual gross heat rate of the 884 coal-fired 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 higher than the lowest 

gross heat rate in 2010. The larger and relatively younger 32 coal-fired EGUs in the Texas 

Interconnection saw less change than those in the 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-fired EGUs are located, gross heat rates have 

steadily increased since 2008, rising from 9,567 Btu/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 pollution control devices in 

response to federal and state regulations, retirements of less-efficient coal-fired EGUs, 

fluctuations in precipitation and 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 factors 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 agency 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 CO2 emissions intensity.8

 Several commenters also provided extensive information about 

additional best practices and equipment upgrades that 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 substantial benefits or might not be feasible. 

As explained in section 2.5 below and in preamble sections V.A and V.C, the agency has 

decided not to estimate a separate potential for heat rate improvements from equipment 

upgrades. As stated in the proposal TSD, the agency lacks reliable and complete information 

about the unit-level upgrades that coal-fired EGUs have already 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 whether existing coal-fired EGUs are already taking advantage of 

all opportunities to improve heat rate. Nonetheless, the agency remains confident that there are 

additional opportunities for coal-fired EGUs to achieve heat rate improvements by implementing 

some combination of best practices and equipment upgrades, including the illustrative best 

practices and equipment upgrades shown below. The agency recognizes that the types and extent 

of capital improvements and operational and maintenance improvements 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 heat rate improvement on a specific 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 agency 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 effective. 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 for 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 for 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 components to lower draft loss 

2.3.2 Example best practices for 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 combustion/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 condenser tube cleaning system 

 Repair boiler furnace and ductwork cracks 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 which 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 opportunities for improving the 

efficiency of existing coal-fired power plants, including studies by Sargent & Lundy and NETL 

that were referenced in the proposal TSD. Some of the practices and equipment upgrades have 

the potential to improve gross heat rate while others will not affect an EGU’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 the same amount of fuel input). The following 

potential efficiency improvements in the boiler, turbine, flue gas system, air pollution control 

equipment, and the water treatment system are discussed below.9

2.3.3.1 Boiler systems 

2.3.3.1.1 Materials handling 

Materials handling includes the handling and transport of coal, the preparation 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 percent (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 coal, 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 about 0.1 percent (10 Btu/kWh) of an 

EGU’s gross electricity generation. Switching from a water-sluicing bottom ash 

system to a dry system can reduce the auxiliary 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 System), this computer controls and displays many 

parameters affecting boiler operation. A neural network is an advanced control system utilizing 

artificial intelligence and installed in addition to the plant DCS. The neural network evaluates 

many parameters of the EGU’s operation and makes adaptive tuning based on statistical analysis 

to predict performance for increased efficiency. Many vendors offer 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 steam turbine to extract more energy from the same 

amount of steam than was possible with equipment from decades ago. Replacing existing steam 

turbines with advanced designs or, where practical, 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 turbine 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 convert 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 restores heat transfer efficiency. 

2.3.3.1.5 Condenser 

Condensers are subject to fouling and plugging, which directly impact the heat transfer 

efficiency and water quality. Closed cooling water 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 gross heat rate by 30-70 Btu/kWh. 

2.3.3.1.6 Cooling tower 

The temperature of cooling water affects condenser 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 cooling towers become fouled over time, the water 

returned to the condenser becomes warmer and negatively affects steam turbine performance and 

reduces gross electricity generated. Routine maintenance on the cooling towers might include 

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

1420061024. 
2-12 

 

discarding accumulated debris, removing biologic growth, replacing defective fill material, 

ensuring uniform water distribution, correcting for uneven air distribution, verifying water flow 

rates, and replacing 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 heaters and the boiler. Due to their continuous operation, the pumps 

wear over time, lose efficiency, and require more energy to move the same volume of water. A 

pump overhaul restores efficiency, ensures reliability, and can reduce the net heat rate by 25-50 

Btu/kWh. 

2.3.3.1.8 Water Treatment System 

The quality of boiler water affects 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 transfer up to 10 percent in severe cases. However, 

the higher the quality of water used in the boiler, the slower the scale build-up and thus the 

higher the efficiency of the EGU. Enhanced water treatment can reduce scale. In addition, scale 

can be removed through chemical cleaning to restore heat transfer and lower 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 the 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 variable frequency drives reduces auxiliary 

power requirements by reducing the drives’ speed (i.e., rpm) when maximum speed is not 

required. The net heat rate improvements can be 10-150 Btu/kWh. 

2.3.3.2 Flue gas and emission control technologies 

Equipment to move flue gases and control air pollution typically require large amounts of 

auxiliary power to properly operate equipment. Even small upgrades to the equipment can 

decrease auxiliary power requirements while maintaining emission reduction levels. 

2.3.3.2.1 Economizer 

The economizer extracts energy from the combustion 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 required 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, reducing heat transfer 

for steam generation and resulting in higher gross heat rates. Sootblowers remove built-up coal 

ash to restore performance of the heat transfer surfaces. Intelligent sootblowers monitor system 
2-13 

 

conditions and activate when boiler operation indicates the need to remove ash rather than 

activating at arbitrary time intervals set by operators. 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 incoming pre-combustion air and the exiting flue 

gas. By preheating the combustion air, less fuel is required to convert pre-heated water to steam. 

Due to their design, leakage can occur and, as a result, reduce the 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 requirements and, therefore, the net heat rate by 10-40 Btu/kWh. 

Another method to improve heat rate is to extract more energy across the air heater to 

raise pre-combustion air temperature. Typically, the air heater outlet 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 typically operate at constant 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 frequency 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 desulfurization systems are used at existing coal-fired EGUs. Some 

EGUs still employ outdated venturi-type designs that create large pressure drops and require 

greater auxiliary power to operate the induced draft fans. Upgraded designs, such as co-current 

spray tower quenchers, can replace the outdated designs and enhance net heat rate. 

A more common flue gas desulfurization technology installed throughout the fleet is a 

spray tower absorber, also known as a wet scrubber. Technology upgrades, including installing 

turning vanes to reduce pressure drop and improve 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 represents lost energy unusable for particulate removal and 

requires the field be re-energized, consuming additional auxiliary power. Electrostatic 

precipitator upgrades improve performance by eliminating arcing and reducing auxiliary power 

consumption. 

2.3.3.2.7 Selective catalytic reduction 
2-14 

 

Innovations in selective catalytic reduction 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 ambient air). Catalyst design can lower pressure drop 

across the reactor and reduce induced fan auxiliary power requirements. 

2.4 External studies of heat rate improvement potential at existing coal-fired EGUs 

There are a number of technical and statistical studies that explore opportunities to 

improve heat rate at existing coal-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 provided 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 concepts for improving heat rate or 

reducing CO2 emissions presented in these studies focus on: reducing heat losses, extracting 

more energy from the cycle, replacing worn or degraded equipment, installing equipment 

upgrades, increasing data measurement and tracking to enhance operation and efficiency, 

conducting routine maintenance to sustain 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 

CO2 emission reductions. For example, some studies used engineering assessments, some 

subcategorized EGUs by specific design characteristics or operating conditions and then 

compared EGUs in each subcategory against one another, and others compared each EGU’s heat 

rate performance or CO2 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 opportunities to improve 

the thermal efficiency of existing and future coal-fired power plants,” NETL brought together 18 

leading industry experts representing 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 variable 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, including: (1) 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, (2) lack of management commitment 

for efficiency programs, (3) focus on optimizing profitability and ensuring EGU availability over 

efficiency, and (4) lack of on-site performance engineers 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/Publications/OpportImproveEfficExistCFP

P-ReportFinal.pdf. 
2-15 

 

potential fuel cost savings represent only a small 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 efficiency, identify barriers, and identify initiatives to 

enhance efficiency across the fleet.12 The workshop participants developed a list of more than 50 

distinct opportunities to improve thermal efficiency, many of which are broadly applicable 

“across most of the fleet of coal-fired [EGUs],” including better sensing, measurement, and 

control; better and more frequent maintenance; improved fuel handling; and capital upgrades and 

improvements. 

In 2010, NETL published a report that estimated existing coal-fired EGUs could enhance 

efficiency from a fleetwide generation-weighted efficiency of 32.5 percent in 2008 to 35.2 

percent, equivalent to a heat rate 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 90th percentile EGU,14 ranked by 

efficiency, within each of the 13 segments and calculated the potential efficiency improvements 

if all EGUs below the 90th percentile improved their average efficiency to the level of the 90th

percentile EGU in the segment. Aside from the impact of steam cycle, coal type, and capacity, 

the NETL study found that other design characteristics, location, and emission air pollution 

controls did not account for variations in plant efficiency. NETL 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 historical heat rate, overall 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 research includes improvement opportunities from 

process optimization, more aggressive maintenance 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 Power 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/Publications/ThermalEfficCoalFiredPower

Plants-TechWorkshopRpt.pdf. 13 See P DiPietro and K Krulla, 2010. Improving the Efficiency of Coal-Fired Power Plants for Near Term 

Greenhouse Gas Emissions Reductions. NETL Report DOE/NETL-2010/1411. Available at 

http://www.netl.doe.gov/File%20Library/Research/Energy%20Analysis/Publications/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 90th

percentile in the NETL study is methodologically equivalent to the 10th 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. 
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 presents 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. Consequently, there exists room for improvement. 

Lashof et al. of the Natural Resources Defense Council (NRDC) analyzed 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 heat rate. EGUs that ranked higher than the 10th

percentile for their category, but lower than the median (i.e., 50th 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 and 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 type, 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 the 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 technical descriptions of numerous well proven, 

commercially available heat rate improvement techniques. The study also includes indicative 

ranges of potential heat rate improvement (Btu/net kWh) with associated indicative ranges of 

capital and O&M costs at three unit sizes (200, 500, and 900 MW). 

Several leading energy sector engineering consultants produced 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, including (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 large 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-standards/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. 
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 “exacerbating the challenge [to improve heat rate] 

is the fact that often major plant overhauls have 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 similar results to EPA’s potential heat rate 

improvement analyses described in the following section. The commenters’ studies are 

summarized below. 

Sierra Club and Earthjustice submitted an independent 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 selected coal-fired EGUs designed to 

represent the coal-fired EGU fleet as a whole (based on 2012 electricity generation). The study 

uses rolling 365-day averages of daily gross heat rates in order to minimize the influence of 

weather and electricity generation, and to represent long-term trends. For each EGU, the EGU’s 

lowest and 95th 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 analysis indicate that, on average, 

the coal-fired EGUs in the study achieved and maintained gross heat rates over a 365-day period 

that were 7.6 or 6.1 percent lower at the lowest and 95th percentile gross heat rate, respectively. 

The General Electric Company (GE) submitted an assessment of technical, economic, 

and market potential for heat rate improvements at coal-fired EGUs.21 The assessment identified 

equipment upgrades and best practices that, individually, could improve heat rate between 0 and 

2.9 percent. The study estimated the average technical potential for gross heat rate improvements 

to be 5.8 for large coal-fired EGUs to 6.8 percent 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, and compared each EGU’s full-load net 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 for 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 CO2 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 Best 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/EPRI2009HeatRateConference%20FINAL.pdf. 20 See Comments of Sierra Club and Earthjustice. Available at 

http://www.regulations.gov/#!documentDetail;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/#!documentDetail;D=EPA-HQ-OAR-2013-0602-25475. 21 See Comments of The General Electric Company. Available at 

http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OAR-2013-0602-22971. 
2-18 

 

least-cost electricity utility planning framework of NEEM, the modeling study estimates that 

only 1 percent heat rate improvement will be implemented and the remaining CO2 reductions 

will come from other actions within each state. 

In addition to the reports described above, POWER Magazine published an article on 

heat rate improvement opportunities at coal-fired EGUs.22 Korellis summarized an Electric 

Power Research Institute (EPRI) report of heat rate improvement case studies at five large coalfired EGUs with electricity generating capacities of 95 to 650 MW, combusting Powder River 

Basin subbituminous (3 EGUs) and bituminous (2 EGUs) coal, and utilizing different 

thermodynamic steam cycles (2 supercritical and 3 subcritical). Through a combination of best 

practices and equipment upgrades, four of the five EGUs were able to improve net heat rate by 3 

to 5 percent (see Table 2-1). Common issues at the EGUs prior to the heat rate improvement 

project included: (1) problems related to combustion and high air heater/stack gas temperatures, 

(2) shortage of heat rate data, (3) lack of training and understanding of controllable losses, (4) 

lack of performance testing, (5) problems with feedwater heater train performance, and (6) 

buildup of soot on heat transfer surfaces. The article provides a list of general and EGU-specific 

heat rate improvement recommendations with estimated 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 percent at a cost of approximately $40 per ton of CO2

reduced. The summary of the full report states, “[r]educing a power plant's heat rate can lower 

emissions, fuel consumption, and costs, thus contributing to the plant's bottom line. A significant 

improvement in heat rate can often be achieved 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, low 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 operators 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 Heat Rate Improvements. Technical Update 3002003457. 

Available at http://www.epri.com/abstracts/Pages/ProductAbstract.aspx?ProductId=000000003002003457. 
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 replacing feedwater heater 400 Btu/kWh 

D Reducing air inleakage, replacing 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 best practices and equipment upgrades. As several 

commenters noted, these studies demonstrate that heat rate improvements are both readily 

available and achievable. The studies and commenters’ 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 adopt any one of the studies described above. The 

agency believes its analyses, described in the next section, are more appropriate 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 own best performance, largely obviating the 

need for subcategorization or a “one-size-fits-all” heat rate or CO2 emission rate; and (3) controls 

for key ambient and operating conditions outside the EGU operators’ control. 

2.5 EPA study of heat rate improvement potential 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 performance. EPA used three analytical approaches to 

assess potential heat rate improvement 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 the general sense, if a 

coal-fired EGU was able to demonstrate and achieve specific heat rates in the past the EGU 

should be able to achieve that same heat rate again. 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, thermodynamic 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 affected by the addition of 

pollution control devices during the study period because the auxiliary power consumption from 
2-20 

 

these devices has negligible impact on gross electricity generation;23 and (4) the historical unitlevel gross heat rate is by definition demonstrated and achievable by the respective coal-fired 

EGU. 

In the June 2014 proposal, EPA proposed a 6 percent potential heat rate improvement for 

the coal-fired EGU fleet for the purpose of establishing state emission goals. The potential heat 

rate improvement was based on two components: (1) a statistical analysis that determined 4 

percent improvement in the fleetwide coal-fired EGU heat rate is possible 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 were 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 provided 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 potential 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 EPA’s proposed heat rate improvement was 

too optimistic, providing engineering 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 improperly 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” equipment upgrades, (2) plant operators 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., cumulative), and (4) the costs of equipment upgrades are 

highly site specific. The EPA generally agrees with the four points noted above, and alluded to 

these considerations in the proposal TSD, section 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 improperly characterized or misused information 

from the S&L report. The S&L study24 was prepared for the EPA, and senior engineering staff 

with previous experience in fossil power plant engineering, operations, and management were 

 23 In addition to the parasitic load from add-on pollution controls, another effect of some add-on controls is worth 

mentioning here. The installation of wet flue gas desulfurization (i.e., wet FGD or scrubber) technologies can affect 

heat rate because the chemical process of capturing SO2 creates CO2 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 approaches described below evaluate the step change in heat 

rates. Assuming consistent operation and fuel sulfur content, an EGU with a WFGD during the entire study period 

would not have a heat rate step change due to the WFGD CO2. Circulating fluidized bed (CFB) boilers also have 

higher CO2 emissions from the chemical process of limestone injection to capture SO2, thereby affecting the heat 

input calculations. However, because CFBs generally injected limestone at all times throughout the study period, the 

EGUs would not have heat rate step changes. See section 2.6.3 for more information about the impact of CO2

emissions from SO2 pollution control devices. 24 Coal-fired Power Plant Heat Rate 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 report. The agency recognized the limits of the 

study and considered the S&L caveats/limitations when using information from the study, 

including the following statements from the study: 

 “The primary intent of the study was to focus on methods that have been successfully 

implemented by the utility industry.” 

 “This study identifies specific plant systems and 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 systems of a typical coal-fired power plant. All 

estimated capital and installation costs are referenced 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 equipment, material, and labor market conditions and 

site specifications. However, these cost estimates provide valuable information for comparative 

purposes when evaluating the advantages and disadvantages 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 develop an estimated 4 percent 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 improvement estimate attributable to equipment upgrades that was 

included in the proposal. The 50 percent discount was 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 the sum of their individual effects. The agency 

similarly used averages of S&L order-of-magnitude costs for thirteen of the fifteen S&L heat rate 

improvement measures25 to develop an indicative conceptual cost estimate ($100/kW) for an 

overall 6 percent heat rate improvement. That estimate 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 expensive “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 the inverse relationship of $/kW cost to MW 

unit size, and was thus appropriately 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 values to be applicable as 2011 values because, 

through a coincidence of timing, the 2008 study reflected the highest cost levels experienced by 

the power sector up to that point in time. The economic recession that commenced in late 2008 

caused costs to decrease sharply before beginning a slow recovery. The agency therefore 

considered the S&L cost values to quite reasonably represent 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 the 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 upgrades 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 greater 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 retiring over the next decade (see section 2.6.5 below for 

more information about retirements), leaving more of the larger, well-controlled 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 proposal TSD and supporting reports are intended to 

serve as illustrative lists of measures 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 based on each coal-fired EGU’s best historical heat 

rate performance relative to its 2012 heat rate performance. It reflects the heat rate improvements 

that would occur if each EGU performed at levels 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 and operating practices that are necessary for 

EGUs overall to maintain the better heat rates they have previously achieved. 

Some commenters also noted that 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 because the heat rate 

improvements would affect the statistical results and would still be expected from the equipment 

upgrade component. Similarly, many commenters provided lists of equipment upgrades 

undertaken at specific coal-fired EGUs, arguing that the equipment upgrade component of the 

agency’s potential heat rate improvements does not account for upgrades that 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 agency was not requiring 

individual coal-fired EGUs to achieve specific 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 statistical 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 gross 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 additional 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 analysis compared each EGU’s best historical 

gross heat rate to the EGU’s 11-year average gross heat rate. Commenters stated that some coalfired EGUs made significant investments to improve 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 improvements 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 implemented to improve gross heat rate during or 
2-23 

 

before 2012 (and maintained in 2012) would be accounted for in the analysis. In addition, the 

selection of 2012 as a baseline is conservative because many commenters noted that coal-fired 

EGUs experienced greater cycling and lower capacity factors in the later years of the study 

period (2002-2012). 

Several commenters suggested that EPA should replace the statistical analysis with unitspecific engineering studies to establish unit-level heat rate improvement goals. These 

commenters contended that such studies are the only method for accurately identifying what 

types of equipment upgrades and best practices (and 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, conducting engineering studies to determine the 

potential for improving heat rate through best practices and equipment upgrades at every coalfired EGU would be unnecessarily lengthy and expensive, and would not be consistent with the 

broader goal-setting purpose of this rulemaking. However, the agency does believe that EGU 

owners and operators that conduct such studies will discover additional cost-effective 

opportunities to improve heat rate at their EGUs. Commenters also suggested that as an 

alternative to unit-level engineering studies, EPA could identify unit-level heat rate improvement 

goals. EPA’s methodology does assess the potential heat rate improvement for each 

interconnection by comparing an individual EGU’s best demonstrated and achievable historical 

gross heat rate relative to the 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 interconnection (i.e., the electricity system) along with emission reductions from the 

other building blocks of the best system of emission reductions to establish state-level goals. 

Each state has the flexibility to identify measures in its state plan to achieve the state goals and, 

if the state chooses, it may develop unit-specific heat rate improvement targets – or entirely 

ignore potential heat rate improvement. Commenters also stated that the proposal expressly 

anticipates fleetwide averaging of CO2 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. Therefore, the agency believes it is appropriate in this rulemaking to 

assess potential heat rate improvement at a scope broader than the unit-level. 

Commenters also recommended that EPA subcategorize coal-fired EGUs and develop 

specific heat rate improvement goals for each subcategory to account for the influence of 

different design characteristics and fuel choices on an EGU’s heat rate. However, as discussed 

above and noted in the proposal TSD, the agency 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 use the average potential heat rate to calculate 

state goals. In addition, the agency does not believe there is a need to control for the design 

characteristics and fuel choices because the agency compared each EGU’s performance to the 

EGU’s own past performance. Because EPA compared 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 EGUs in the study population was approximately 42 years in 2012. To 

assess the impact of age, EPA grouped the 884 coal-fired 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 turbine 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 change, but changing coal ranks 

at a coal-fired EGU requires significant effort. Generally, coal-fired EGUs – especially 

pulverized coal boilers and their corresponding fuel handling equipment, combustion systems, 

and pollution control devices – are designed for the characteristics of a specific type of coal. 

Significant deviations from those 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 capacity, heating surface). Commenters noted that 

changing coal rank is likely not practical at many 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 rate. 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 coal rank and quality can have on an EGU’s heat rate, the agency 

also recognizes that EGU operators and coal suppliers typically set requirements for coal quality 

in purchase contracts, providing coal-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 analytical 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 characteristics 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 some 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 accounting for outliers, EPA implicitly accounts for 

some variation that might be beyond the EGU operator’s control. 

EPA used three approaches to calculate the 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 electricity generation data to EPA.27 These 884 EGUs account for 

approximately 1.61 billion tons of CO2 emissions in 2012, 96 percent of the 1.67 billion tons of 

CO2 emitted by the national coal-fired EGU fleet in 2012.28 Therefore, EPA considers these 884 

coal-fired EGUs representative of the coal-fired EGU fleet as a whole. 

Some commenters suggested that the agency’s inclusion of coal-fired EGUs near 

retirement inflates the results of the heat rate improvement analysis because the soon-to-retire 

EGUs may have more variability in gross heat rate 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 that reported heat input and gross electricity 

generation in 2012, including those EGUs that are 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 improvements identified by the heat 

rate improvement analysis, the agency does not believe this artificially inflates the potential heat 

rate improvements. Any coal-fired EGU that 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 levels, after soon-to-retire EGUs cease operation 

they cease emitting CO2 contributing to attainment of the state goal, and (2) for states with CO2

intensity goals based on 2012 baseline levels, the retirement of coal-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 

CO2, NOX, and SO2, and report those hourly emissions along with hourly heat input29 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-annual or annual relative accuracy tests.30 When EGU 

operators submit the hourly emission and operation data to EPA/CAMD, a responsible company 

official must certify that the data are true, accurate, and complete. 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 malfunctions 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 conservative (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 CO2 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 CO2 or O2 concentration and calculations detailed in 40 CFR Part 75 Appendix F. As a 

result, heat rate (Btu/gross kWh) is proportional to CO2 rate (CO2/gross kWh). 30 The quality assurance requirements for 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 represent 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 occur during the first hour of startup and the last 

hour of shutdown, and hourly gross heat rate values 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. The hourly values ±2.6 standard 

deviations from the EGU’s annual average gross heat rate may be due to unusual operating 

conditions or misreported gross 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 reading accurately. Commenters hypothesized that 

discrepancies between the monitoring system and the reference tests could explain much of the 

variation in heat rates. EPA assessed the monitoring systems’ relative accuracy and the findings 

indicate that the relative accuracy 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 percent, and twice per year if the difference is greater than 7.5 percent 

but less than 10 percent.33 If the flow monitoring system readings 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 potential underreporting of flow. There is no BAF 

applied for CO2 measurements. To assess the relative accuracy of the monitoring systems during 

the study period, EPA reviewed the EGU-level RATA results for the study period and then 

conservatively calculated the average annual generation-weighted relative accuracy within each 

of the interconnection regions. As shown in Table 2-2, the average (i.e., mean) relative 

accuracy34 for both CO2 and flow monitoring systems are reasonable – the difference between 

the reference tests and measured values is approximately 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 input 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 the 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 reference method measurements 

and the EGU’s pollutant concentration or flow measurements. However, the averages 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 difference 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-weighted relative accuracy of CO2 and flow monitoring systems, by 

interconnection 

 Western Texas Eastern 

CO2 Flow CO2 Flow CO2 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 are 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 EGU-level varies from 1 to 3 percent, the 

uncertainty becomes significantly less when these differences are aggregated over many EGUs – 

the relevant scope of analysis for building block 1 as a whole. EPA evaluated the combined 

effect of CO2 and flow measurement differences between 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 fleetwide 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 Integrated Surface Data (ISD) and the USGS’s 

WaterQualityWatch (WQW). The NOAA dataset provides 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 close to each coal-fired EGU's location and elevation to account for 

the impact of ambient conditions. For each of the 884 coal-fired EGUs, the agency identified the 

nearest weather station37 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 CO2 measurements, 

using the directional difference (e.g., positive and negative values), 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 Surface Data are available at http://www.ncdc.noaa.gov/data-access/land-based-stationdata/land-based-datasets/integrated-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 measurements 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 weather stations that met the three constraints 

described above. For these plants, the agency used 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 EPA’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 period, and excluding the data described 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, which 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 was in 2002. EPA recognizes that the 

power sector went through many changes between 2002 and 2012, and is likely to go through 

many more changes between 2012 and 2022 – the first year of the interim implementation 

period. Nevertheless, the agency believes it is appropriate and beneficial to use the full 11 years 

of data. First, this time period includes a variety of national and regional economic conditions, 

fuel prices, electricity resource 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 retired 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 generation 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 against 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 characterized by a lower gross heat rate, 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 comprehensive 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. Therefore, the agency believes conditions at the nearest weather 

station provide a reasonable approximation of temperature conditions at the coal-fired EGU. 39 The USGS WaterQualityWatch dataset provides hourly surface water temperature from approximately 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 input (Btu) / gross electricity output (kWh). 41 EPA calculated capacity factor as gross electricity 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 priorities for the sector. As one power-sector trade 

magazine stated, “[R]elatively cheap coal and a focus on plant emissions controls has, 

unfortunately, taken the focus 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 CO2 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 analysis 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 assured dataset available to the agency. The agency 

also believes the use of gross heat rate is inherently conservative for two reasons: (1) it does not 

reflect additional opportunities that 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 air 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, the 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 capacity factor on gross heat rate 

Ambient conditions can affect a coal-fired EGU’s gross heat rate. EPA evaluated three 

ambient conditions – hourly capacity factor, hourly air temperature, and hourly surface water 

temperature – to assess the influence these conditions 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 monitoring 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 include data prior to 2002. 44 See U Nowling, 2015. “Understanding coal power plant heat rate and efficiency,” Power. Available at 

http://www.powermag.com/understanding-coal-power-plant-heat-rate-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.wisc.edu/documents/publications/special_interest_publications/EPRI_Electricity_Use_Report_Fin

al_1024651.pdf. The report explores the auxiliary electricity consumption at EGUs 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 capacity. In general, as 

an EGU’s hourly capacity factor declines, the EGU’s hourly gross heat 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 for four “sister units”47 in 

2012. The bottom chart shows the number of hourly measurements at each hourly capacity 

factor. The figure illustrates three effects that are broadly applicable to the 884 coal-fired EGUs: 

(1) average hourly gross heat rate is higher and shows more variation, 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 operating hours are at higher hourly 

capacity factors. 

Figure 2- 6 Average hourly gross heat 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 measurements exceeds 8,784 hours because it includes measurements from four “sister units”; 

the trend line is a power function in the form of y=c×xb

; 

average hourly gross heat rate axis does not start at zero to better illustrate changes in gross heat rate 

For each EGU, EPA used a least-squares linear regression to assess the relationship 

between hourly gross heat rates 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, conceal considerable variability. At some coalfired EGUs hourly capacity factor has a greater influence 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 EGUs 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 R2

) values 

were weighted using the EGU’s total annual gross electricity generation resulting in an EGU-specific generationweighted R2

. This approach controlled for any differences in average gross heat rates between years. The weighted 

R2

 values were averaged across all 884 coal-fired EGUs, resulting in an R2 value of 0.179. 
2-31 

 

(i.e., from high to lower hourly capacity factors), hourly capacity factor tends to have a larger 

influence on gross heat rate. 

Several commenters noted that the power sector saw dramatic changes during the 11-year 

study period, including significant declines in annual 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-fired EGU operated at 90 percent capacity for six 

months and was offline for the remaining six months 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. However, 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 EGUs excluding hours in which the EGU did not 

operate, such as downtime for maintenance or due 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 annual average capacity factor of the 884 coalfired EGUs declined by approximately 10 percent in 2009 and, after a small increase in 2010, 

continued to decline through 2012. However, the annual capacity 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 they 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 factors 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 and a fraction of energy released during combustion 

heats the incoming air, (2) in many regions of the country temperature affects electricity demand 

and, as a result, some EGUs’ hourly capacity factors may increase when ambient temperatures 

are high, and (3) ambient air temperature influences cooling water temperatures, affecting an 

EGU’s efficiency. The relationship between hourly ambient air temperature and hourly capacity 

factor is complex. Generally, peak electricity demand in most parts of the U.S. occurs on the 

hottest days of the year. However, each EGU responds differently depending on design, duty 

cycle, meteorological conditions, electricity demand, and other factors. For example, a baseload 

plant may operate at high hourly capacity factors regardless of temperature. EPA assessed the 

correlation between hourly ambient air temperature 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 linear regression to assess the relationship 

between hourly gross heat rates and hourly ambient air temperature.50 The hourly ambient air 

temperature explained about 8 percent 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 capacity factor. At some individual EGUs, ambient 

air temperature may explain up to 30 percent of the variation in hourly gross heat rate. These are 

 49 The Pearson correlation coefficient for hourly ambient air temperature and hourly 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 R2

) values 

were weighted using the EGU’s total annual gross electricity generation resulting in an EGU-specific generationweighted R2

. This approach controlled for any differences in average gross heat rates between years. The weighted 

R2

 values were averaged across all 884 coal-fired EGUs, resulting in an R2 value of 0.083. 
2-33 

 

typically, but not exclusively, units with freshwater once-through cooling systems. Identifying 

temperature-responsive coal-fired EGUs allows us to understand why heat rate may increase 

during periods of peak demand. These are the EGUs where the ambient temperature ‘signal’ is 

an important variable. Gross hourly heat rates at a temperature-responsive EGU may increase as 

much 10 percent during high temperature days in summer months. 

2.5.2.3 Cooling water temperature 

As discussed earlier in this chapter and noted 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 temperatures 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 temperature. The agency did not have comprehensive 

cooling water temperature data to assess the influence on gross heat rate 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 heat rates and hourly water temperatures.51 The 

variation in hourly water temperature 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 instead of hourly water temperature, the air 

temperatures explained approximately 6 percent 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 hourly gross heat rate.53 Not 

surprisingly, the air temperature and water temperature are 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 are located, the agency has no reason to believe, 

nor have commenters suggested, that 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 R2

) values 

were weighted using each EGU’s total annual gross electricity generation resulting in an EGU-specific generationweighted R2

. This approach controlled for any differences in average gross heat rates between years. The weighted 

R2

 values were averaged across all 45 EGUs, resulting in an R2 value of 0.072. 52 For each EGU, the annual coefficient of determination (i.e., the square of the correlation coefficient, or R2

) values 

were weighted using each EGU’s total annual gross electricity generation resulting in an EGU-specific generationweighted R2

. This approach controlled for any differences in average gross heat rates between years. The weighted 

R2

 values were averaged across all 45 EGUs, resulting in an R2 value of 0.057. 53 The R2

 value for rolling 24-hour water temperature and hourly gross heat rate was 0.071 and the R2 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-hour 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 water temperatures (although the relationship may be 

non-linear at higher temperatures).57

Based on these results, the agency believes that 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 portion of the fleet, (2) air and water temperatures 

are highly correlated (i.e., simultaneously controlling for air temperature and water temperature 

appears highly redundant), and (3) the results from all of the agency’s analyses described above 

demonstrate that ambient air temperature and water 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 discussed above, EPA conducted a least-squares 

linear regression analysis to assess the relationship 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 identify the potential heat rate improvements at 

coal-fired EGUs for the purpose of calculating state goals. Therefore, the agency attempted to 

control for many of the factors that influence gross heat rate but are beyond 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 type, and cooling system 

type because it compares each coal-fired EGU’s historical performance against the EGU’s own 

2012 performance. Because these design characteristics are typically constant over time, they do 

not influence the results of the EGU-level analyses. As mentioned earlier, age does not have a 

significant effect on gross heat rate. EPA controlled for the installation 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 generation, the addition of controls 

does not influence the results of the gross heat rate analyses. 

 55 See New Mexico Environment Department, 2011. Air-Water Temperature Correlation. Available at 

https://www.env.nm.gov/swqb/documents/swqbdocs/MAS/Protocols/Air-Water/Air-Water08-01-2011.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 R2

) values 

were weighted using the EGU’s total annual gross electricity generation resulting in an EGU-specific generationweighted R2

. This approach controlled for any differences in average gross heat rates between years. The weighted 

R2

 values were averaged across all 884 coal-fired EGUs, resulting in an R2 value of 0.255. 
2-35 

 

In two of the three analytical approaches discussed below, EPA also controlled for hourly 

capacity factor and hourly ambient air temperature 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 hourly capacity factor and hourly ambient air 

temperature. This enabled the agency to compare gross heat rate values for each coal-fired EGU 

during hours with similar capacity factor and ambient air temperature conditions, effectively 

controlling for a large influence on the variation in an EGU’s hourly gross heat rates. 

To create the capacity-temperature bins, the agency divided the range of hourly capacity 

factors into 12 groups, each separated by 10 percent of an EGU’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 capacity-temperature bins for an EGU might not 

have any data. 

At least one commenter expressed concern that the hourly capacity factor ranges were too 

large. They pointed to information 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 factor. Figure 2-6 illustrates this concept. At 

higher hourly capacity factors the hourly 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 size of the hourly capacity factor ranges for three 

reasons: (1) approximately 70 percent of the hourly values are greater than 70 percent capacity 

factor in each of the three interconnection 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 percent), and (3) reducing the range of the hourly capacity factor 

groups would double the number of capacity-temperature 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 would have a significant impact on the results. 

 59 As described below, in the efficiency and consistency analysis 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. Specifically, 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 agency believes to be outside the EGU operator’s 

control. The agency also controlled for capacity factor, which some commenters argued is 

partially under the operator’s control. However, the agency lacks sufficient information to 

determine the portion of capacity factor that is under the direct control of influence of the 

operator. Therefore, the agency chose to reasonably and conservatively control for the full effect 

of hourly capacity factor based on the results of the analysis described above. Regarding 

commenters’ assertion about the role of a coal-fired EGU’s location (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 EGU’s own performance in 2012 the agency 

controlled for both seasonal differences and location characteristics (e.g., altitude). Commenters 

also noted that coal-fired EGUs that changed coal rank during the study period might have 

significantly different heat rates depending on the rank of coal burned. The agency recognizes 

that: (1) different coal ranks have 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. However, 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 agency 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 recognizes that all equipment will degrade over 

time. However, some of the best practices that can be employed to improve heat rate involve 

properly operating and maintaining equipment to minimize the degradation and, when necessary, 

replacing equipment to maintain efficient operation of the plant. Therefore, the agency did not 

control for the effect of equipment degradation 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 conditions 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 (with the exception of capacity factor.) 

2.5.3 Heat rate improvement approach “best historical performance” 

In the proposal TSD, EPA included an assessment of potential heat rate improvement 

based on the difference between each coal-fired EGU’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 relatively small and not large enough to justify a 3-year rolling 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 account for atypical annual operations due to 

factors such as extended outages, equipment failures, cooling water availability, 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 diluting 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 constant and is not affected by the installation and 

operation of air pollution control devices. It does not, however, control for the variation in hourly 

gross heat rate attributable to hourly capacity factor and hourly ambient air temperature. 

2.5.3.1 Methodology 

The procedures for calculating the potential 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 heat rate and the 2012 gross heat rate. 

4. Calculate the generation-weighted potential heat rate improvement based on 1- and 

moving 2-year gross heat rate within each interconnection region. 

 60 See section 2.6.8 for more information about equipment degradation and maintaining heat rate performance. 
2-38 

2.5.3.2 Example Step 1: The annual data for an example coal-fired EGU are shown in Table 2-4. 

Table 2-4 1- and moving 2-year heat rates for an example coal-fired EGU 

 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2011-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 9,82-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 10,2

Step 2: The lowest 1-year gross heat rate occurred in 2008; the lowest moving 2-year gross heat rate occurred in 

data). Step 3: The potential heat rate improvements for the example EGU are: 

Based on best 1-year gross heat rate: 1െቆଽଵହ଻ ಳ೟ೠ ೖೈ೓ ଽ଼଺ସ ಳ೟ೠ ೖೈ೓ቇ ൌ 7.2	݌݁ݎܿ݁݊ݐ

Based on best 2-year gross heat rate: 1െቆଽଷଵଵ ಳ೟ೠ ೖೈ೓ ଽ଼଺ସ ಳ೟ೠ ೖೈ೓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-fired 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 EGU’s historical best annual performance and do 

not account for the full extent of heat rate improvement available 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 based on best historical performance, by 

interconnection 

Performance period Western Texas Eastern Nationally61

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 historical performance under similar conditions – 

is an outgrowth of the first and third analytical 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-specific 1-year and moving 2-year gross heat rates and compared 

the EGU’s best gross heat rate within 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 advantage of this approach is that it controls 

for factors that explain a significant 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 EGU then calculated a generationweighted average potential heat rate improvement within each interconnection region. In this 

second approach, the agency calculated a potential 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 EGU, 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 analysis are shown in Table 2-6. 

Table 2-6 Potential heat rate improvement based on best historical performance under 

similar conditions, by interconnection 

Performance period Western Texas Eastern Nationally62

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, yields interconnection-level results that are higher than the first 

approach is that it relies on the lowest 1- and moving 2-year gross heat rates within each 

capacity-temperature bin. Therefore, unlike the first approach where the “best” performance for 

each coal-fired EGU is from one or two years across all capacity factor and temperature 

conditions, this second approach may have different “best” years for the different capacitytemperature bins. 

This analytical approach determines the potential heat rate improvement if each EGU 

reproduced its best demonstrated and achievable 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 the full extent of heat 

rate improvements available through additional equipment upgrades and best practices. 

2.5.5 Heat rate improvement approach “efficiency and consistency under similar 

conditions” 

The two approaches described above are based on the general principle that coal-fired 

EGUs are capable of returning to historically demonstrated and achievable best annual average 

heat rates (over a 1- or moving 2-year period). The third approach, which was described 

extensively in the proposal TSD, uses a different methodology to determine 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 historically demonstrated and achievable best hourly 

heat rates. For this approach, the agency grouped each coal-fired EGU’s data by capacitytemperature bin and determined a 1- and moving 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 capacity-temperature bin. The 10th percentile value 

means 10 percent of all hourly values for the 1- or 2-year period were below the selected value 

(i.e., the benchmark is demonstrated by 10 percent of all measurements in each capacitytemperature bin). The agency chose the 10th percentile 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 than 15 hourly measurements for a single coal-fired EGU. 
2-41 

 

achievable gross heat rate indicating 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 values 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 hourly ambient air temperature conditions. Instead, 

the agency calculated the potential heat rate improvements from greater consistency (i.e., less 

variability of heat rate) such that each EGU’s hourly 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 values adjusted most in absolute terms were the 

highest values (i.e., less-efficient hours of operation) that indicate a relative failure to fully 

employ heat rate improvement measures. The net result is that the post-adjustment 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 different consistency factors ranging from 10 

to 50 percent and proposed 30 percent because the 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’ engineering judgment, and because 30 percent was in the 

middle of the reasonable range evaluated by the agency. Several commenters suggested that this 

choice of values was arbitrary and could have easily been higher or lower had EPA selected the 

middle of a different range of values. To identify a consistency factor with more precision for 

this analytical approach, EPA analyzed the EGUs’ historically demonstrated level of consistency 

(i.e., variability) for hourly gross 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- and moving 2-year standard 

deviations to the 11-year standard deviation represent the potential consistency improvement at 

each interconnection. In other words, this approach 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 deviations 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 selected the 10th percentile because it is a common statistical 

level, is conservative, and is substantially less likely to have 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, also called the coefficient of variation, is calculated by dividing each coalfired EGU’s standard deviation for the capacity-temperature 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 capacity-temperature bin over a 1- or moving 2-year 

period to the EGU’s consistency (i.e., variability) over the study period (2002-2012). 

As described above at the beginning of section 2.5, EPA replaced the 11-year baseline 

used in the proposal TSD with a 2012 baseline in this analysis. The refined approach – 

comparing each EGU’s best performance against its 2012 baseline performance – recognizes 

actions that improve gross heat rate performance because better practices that result in lower 

gross heat rates in 2012 reduce the estimated potential heat rate improvement. 

Several commenters noted that reduced variability does not necessarily equate to 

improved heat rates. EPA agrees with commenters that reducing variability alone does not result 

in lower heat rates and, therefore, lower CO2 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 assessing the average heat rate 

improvement that would result if coal-fired EGUs had operated more consistently nearer their 

gross heat rate benchmarks. Both the benchmark gross heat rates and consistency factors were 

demonstrated and achieved by the EGUs from 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-year average gross heat rate for each capacitytemperature bin. 

2. For each coal-fired EGU, calculate the difference, as a percentage, between every hourly 

gross heat rate value and the relevant 1-year average gross heat rate within each capacitytemperature bin. 

3. Within each interconnection, calculate the mean-centered standard deviation of the 

differences calculated in step 2 for all coal-fired EGUs and capacity-temperature bins for 

each 1-, moving 2-, and 11-year period. 

4. Within each interconnection, calculate the consistency 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 potential 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 adjusted gross heat rate for each capacitytemperature bin: 
2-43 

 

a. For each hourly gross heat rate value greater than the 1- or moving 2-year benchmark 

calculate the difference between the hourly 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-weighted gross heat rate. 

One commenter noted that EPA’s analysis in the proposal TSD used heat input instead of 

gross heat rate to assess potential heat rate improvements. The commenter noted that EPA’s 

assumption of an equivalent level of gross electricity generation across 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 results. Therefore, the agency changed the approach 

to use hourly gross heat rate, calculated 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 temperature 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 range 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 gross 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 heat rate lower than the 1-year benchmark of 8,625 

Btu/kWh. For the moving 2-year benchmark, 434 hours 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 for the capacity-temperature bin calculated with the 

1-year benchmark and the Eastern interconnection’s 1-year consistency factor (.455), and 
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moving 2-year benchmark and the Eastern interconnection’s 2-year consistency 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 range 90–99 percent and temperature range 70–79° F

Step 4: The 11-year measured and adjusted gross heat rate using the 1- and moving 2-year 

benchmarks and consistency factors for the example 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-temperature 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: 1െቆଽଷହଷ ಳ೟ೠ

ೖೈ೓

ଽଽ଺଼ ಳ೟ೠ

ݐܿ݁݊ݎ݁݌	6.2 ൌ ቇ೓ೖೈ

Based on 2-year benchmark and consistency factor: 1െቆଽସଽଶ 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 achievable “best” gross heat rate from 2002 to 2012. 

First, EPA calculated the ratio of the least 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 potential heat rate improvement 

for each interconnection by calculating an adjusted heat rate for each EGU based on the EGU’s 

benchmark and the relevant interconnection 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 best 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 Nationally67

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 within the regional interconnections. Because the 

analyses are based on actual historical gross heat rate data for each of the 884 coal-fired EGUs in 

the study population, the EPA is confident that the potential heat rate improvements identified on 

the regional basis through these approaches are feasible. 

Although each of these three analytical approaches 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 performance 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. 
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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 supported by the weight of 

evidence, EPA compared the results of the moving 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 Nationally70

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 consistency under similar conditions” approach 

provides the most conservative result for all three regions. Accordingly, the tripartite 

methodology adopts those results for purposes of calculating the relevant CO2 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 

CO2 emission rates) that affected coal-fired EGUs in the Western interconnection is 2.1 percent, 

in the Texas interconnection is 2.3 percent, and in the Eastern interconnection is 4.3 percent. The 

agency believes these values are conservative and that EGUs have demonstrated these gross heat 

rates were achieved during the study period. 

It is important to note that these potential heat rate improvements are based on the 

difference between coal-fired EGUs’ past performance and their 2012 performance; they do not 

reflect additional opportunities for 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 separate heat rate improvement target for specific 

equipment upgrades due to (1) insufficient information about the current deployment of the 

technologies specified in the proposal TSD, (2) the availability of data necessary to conduct the 

statistical analyses to identify potential heat rate 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 will 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 year (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” and “best historical performance under similar conditions” approaches identify 

greater potential heat rate improvement in each region. Accordingly, they inherently support the feasibility of the 

more conservative values identified by the “efficiency and consistency under similar conditions” approach. 
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2.6 Assessment of key comments 

EPA received more than 1,500 comments on building 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 assists the agency in developing a stronger, more 

effective rule. Below are several key comments related 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 measures implemented at specific EGUs to 

improve heat rate between 2002 and 2012 would result in greater variability of heat rate and, 

therefore, inflate the potential 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 additional potential heat rate improvement. 

2.6.2 Impact of air pollution controls installed during study period 

Commenters described many of the air pollution 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 electricity available for 

distribution on the regional interconnection. As a result of lower net electricity generation, the 

EGU’s net heat rate increases. Many commenters recommended that EPA consider the auxiliary 

energy requirements of these controls when assessing the potential heat rate improvement. EPA 

recognizes that air pollution control devices have auxiliary power requirements and a significant 

amount of coal-fired EGUs were retrofitted with air pollution controls during the study period. 

However, EPA rejects the recommendation to credit the auxiliary power requirements in the 

analysis because the agency’s analysis is based on gross heat rate and, therefore, is not affected 

by auxiliary power requirements of air pollution controls.72

2.6.3 Impact of CO2 emissions from SO2 pollution control devices 

Commenters noted that the addition of wet flue gas desulfurization (WFGD or wet 

scrubber) technologies result in increased process CO2 emissions because of the dissolution of 

limestone to capture SO2.

73 Because heat input, the numerator for calculating heat rate, is based 

on an EGU’s measured CO2 emissions, the additional process CO2 emissions result in artificial 

increases in calculated gross heat rate. Commenters suggest that process CO2 emissions can be 

“several percentage points.” To assess the range of potential impacts on EPA’s analysis from 

process CO2 emissions, the agency estimated the incremental CO2 from the WFGD chemical 

reaction. The results indicate that retrofit WFGD installed on existing coal-fired EGUs between 

 72 See supra note 45. 73 The reaction in the WFGD absorber is summarized by SO2+CaCO3+1⁄2H2O → CaSO3+1⁄2H2O+CO2 (see RK 

Srivastava, 2000. Controlling SO2 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 CO2 emissions (and calculated heat input) by an average of no 

more than 0.7 percent for those EGUs in 2012. 

To calculate the CO2 emissions, the agency first identified the 121 existing coal-fired 

EGUs that installed retrofit limestone WFGD between 2002 and 2012. Collectively these EGUs 

have approximately 59 GW of electricity generating capacity. Next, EPA reviewed monthly fuel 

transactions and calculated the weighted average sulfur content of fuel deliveries to the relevant 

power plants.74 Finally, the agency calculated the process CO2 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 burned in the WFGD-equipped coal-fired EGU, and (2) 

EGUs operated the WFGD to remove 85 to 95 percent of the sulfur. The resulting process CO2

emission increase at coal-fired EGUs that installed retrofit limestone WFGD ranges from 0.2 

percent (Texas) to 1.0 percent (Eastern). It should be noted, 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 should remove much of the pyritic-bound sulfur, (2) a 

small amount of the combustion CO2 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 CO2 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 CO2 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-level totals due to rounding; 

Potential process CO2 emissions are based on 85 and 95 percent removal rates, 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 account for other WFGD that do not produce process CO2 emissions 

2.6.4 Impact of future retrofit air pollution controls to comply with EPA and state 

environmental requirements 

This rule establishes CO2 intensity goals based on net electricity generation for each state 

using the state’s 2012 net electricity generation baseline. Many commenters noted that air 

pollution controls installed after 2012, many of which are required to comply with other federal 

and state environmental regulations, will require auxiliary power and therefore will increase 

EGUs’ net heat rate and CO2 intensity. These commenters suggested 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 controls that are expected to be installed between 

2013 and 2025 to comply with rules such as the Mercury and Air Toxics 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. The agency’s analysis, described below, indicates 

that the potential impact on net heat rate and, therefore, CO2 intensity is small – approximately 

0.3 percent – in 2025. 

To assess the anticipated impact of new retrofit air pollution controls on net heat rate, 

EPA used data and information from EPA’s Power Sector Modeling Platform v.5.15.75 The Base 

Case is a projection of power sector activity, taking into account existing federal and state air 

emission laws and regulations. The Base Case includes projected installations of air pollution 

controls and EGU-level net electricity generation 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 agency used the heat rate penalties (i.e., 

auxiliary power requirements) from EPA’s Power 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 electricity generation to estimate the incremental 

heat input required to operate the retrofit air pollution controls. 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 impacts 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 Eastern Interconnection. 

Table 2-13 Assumed heat rate penalty 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 heat rate (before additional air pollution controls) multiplied 

by 2025 net electricity generation. 
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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 least two reasons: (1) EPA’s cost performance 

assumptions tend to overestimate the auxiliary energy requirements from 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 opportunities to improve net heat rate by replacing some equipment (e.g., 

fans, pumps, air heaters, economizers) with more efficient equipment.78 In addition, future 

technological innovations or advances in emission 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 coal-fired EGUs after 2012 when developing their 

state plans. 

2.6.5 Impact of cycling operations and lower capacity factors on heat rate 

Several commenters expressed concern that the application of the other building blocks 

that make up the best system of emissions reductions will result in coal-fired EGUs operating at 

lower capacity factors and cycling more frequently during the interim and full compliance 

periods. Commenters noted that at lower hourly capacity factors efficiency decreases and heat 

rate increases (for example, see Figure 2-6) making improving heat rate even more challenging. 

EPA disagrees that the application of the other building 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 expected to retire between 2012 and 2020, reducing the 

remaining capacity of coal-fired EGUs during the interim and full compliance periods; (2) EPA 

modeling of electricity generation in 2030 indicate that the remaining coal-fired EGUs will have 

higher annual capacity factors than the 884 coal-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 policies 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 the 884 coal-fired EGUs in the agency’s analyses, more than 

53,000 MW of generator capacity is expected to retire. This represents approximately 16 percent 

of the capacity from the 884 coal-fired EGUs across the nation. In the Western 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/issue-11/departments/clearing-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 official planned retirements in the Texas Interconnection. In the 

Eastern Interconnection, EGU owners and operators have officially announced more than 47,000 

MW of capacity, 17 percent of total capacity, will retire in 2020 or earlier (see Figure 2-11). 

Because less coal-fired electricity generating capacity will be available for dispatch, the 

remaining coal-fired EGUs are expected to operate at higher annual 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 predicts that annual capacity factors for coalfired EGUs in 2030, with the rule in full effect, will 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 occur as well as growth in electricity demand. Nationally, net annual 

capacity factors – net electricity generation divided by net summertime capacity – at coal-fired 

EGUs is predicted to be approximately 70 percent under a rate-based goal 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 represent annual capacity 

factors and not hourly capacity factors, the substantial increase in annual 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 will operate at higher levels in 2030 than they did 

in 2012, the baseline for this heat rate improvement study. For the Texas interconnection, the 

capacity factors are anticipated to be approximately 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 designing the state plan. 

Table 2-14 Annual net capacity factors (percent) 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 factors were calculated from the 2012 baseline data using the eGRID methodology. 

For the 947 coal steam EGUs in the CONUS, the capacity factor was calculated as net electricity generation 

(MWh)/[8784 hours X net summertime capacity (MW)]. 
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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 the 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 angular flow (Methods 2F and 2G) and the effect of the stack walls 

on gas flow (Method 2H). In general, these alternative measurement methods reduce or eliminate 

the over estimation of stack gas flow that results from the use of method 2 when the specific flow 

conditions (e.g., angular flow) are present in the stack. Generally, the alternative methods lead to 

lower flow rates and as a result, lower heat input. After the introduction of these new methods, 

many coal-fired EGUs adopted the alternative methods to measure flow and calculate mass 

emissions.81 However, coal-fired EGUs are not required to use the alternative measurement 

methods and they may change methods when conducting a RATA. Commenters have 

hypothesized that much of the variability in gross heat great is caused by coal-fired EGUs 

changing from one flow measurement method to another. EPA carefully considered commenters 

concerns and conducted additional analyses to explore the potential 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 that 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 measurement 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 study period (2002–2012). If, as commenters suggested, 

changes to flow measurement methods were responsible for much of the variability in EPA’s 

data, the analytical results should show that EGUs with a change of flow measurement method 

have higher indicated potential heat rate improvements. However, 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 interconnections than for the full group of coalfired EGUs in the interconnections. In the Eastern interconnection, the results between the two 

groups – EGUs that changed flow measurement method(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 systems 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 based on the 2-year efficiency and consistency 

under similar conditions analytical approach for 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 rate improvement 

Because the potential heat rate improvement for the 884 coal-fired EGUs and the 531 

coal-fired EGUs that changed one or more flow measurement methods are approximately the 

same, EPA disagrees with commenters’ statement 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-fired 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 already implemented efficiency measures and are not 

capable of improving heat rates beyond their current 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 the same economies of scale as larger EGUs. The 

commenters argue that requiring every coal-fired 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 improvement standard. Rather, the heat rate improvement 

is one part of the best system of emission reduction and must be viewed in the context of the full 

best system of emission reductions. As noted earlier, states have the flexibility to establish 

policies in the state plan that are reasonable and appropriate for that state; these policies may or 

may not include heat rate improvement goals. Second, EPA disagrees with the commenters’ 

presumption that each coal-fired EGU must independently achieve the potential heat rate 

improvements identified in this analysis. The results of the analyses described above provide an 

estimate of potential heat rate improvements that, on average, are possible for each 

interconnection region based on each coal-fired EGU’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 interconnection, some coal-fired EGUs can make greater heat rate 

improvements while other EGUs might make less. Third, the agency’s analysis is based on coalfired EGUs' historical heat rates relative to 2012 and is therefore 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 improving heat rate may be available in the future. 

With regard to soon-to-retire EGUs, it is not clear 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 assessment 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 upgrades and (2) the rule does not establish a federal requirement 

for coal-fired EGUs to meet specific heat rate improvement targets (see section 2.6.7). Second, 

the agency disagrees with commenters; the agency believes EGU operators can control the rate 

of degradation through proper maintenance and, when necessary, replacement or refurbishment 

of equipment. This is supported by information provided by a commenter referring to National 

Petroleum Council (NPC) and International 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 deterioration 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 regarding the timing of the intervals and the 

amount of refurbishment may result in excessive deterioration and is controllable [by the 

EGU operator].82 

The IEA report stated: 

[I]mprovements can not only restore ageing plants 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 these areas have been 

considered worthy of attention previously, and a conclusion from that is that there may 

be other unrecognized areas of losses in efficiency which could be worth identifying.83

2.6.9 Limited adoption of low- and zero-cost heat rate improvements 

Commenters suggested that if there were low- or no-cost options 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 are a number of reasons that existing coal-fired EGUs might not 

implement low- and no-cost heat rate improvement practices, including: (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_Papers/4-DTG-ElectricEfficiency.pdf. 83 See C Henderson, 2013. Upgrading and efficiency improvement in coal-fired power plants. International Energy 

Agency (IEA) Clean Coal Centre 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 profitability and ensuring availability; (5) lack 

of on-site performance engineers dedicated to heat rate improvement; (6) potential fuel cost 

savings represent only a small portion of total costs and are “of secondary concern when 

compared to needs for maintaining system reliability”; and (7) lack of regulatory certainty about 

future environmental regulations that lead to operators deferring significant upgrades on older 

coal-fired EGUs.84 

2.7 Costs 

2.7.1 Breakeven costs 

In the proposal TSD the EPA used an estimated 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 has refined the estimate of potential national 

average net heat rate improvement by coal-fired EGUs to between 2.1 to 4.3 percent for each 

interconnection, or about 4 percent nationally, with potentially all of that improvement coming 

from best practices. The agency no longer relies on an additional 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 improvement. 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 previous estimated cost was too optimistic, the agency assessed 

the economic effects of a heat rate improvement cost that might range from $50 to $100/kW. 

The agency acknowledges here, as in the proposal TSD, that the technical 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 primarily on low- and no-cost best practices, would be 

significantly less than $100/kW. However, as a conservative 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 Resources Defense Council (NDRC). Available at 

http://www.regulations.gov/#!documentDetail;D=EPA-HQ-OAR-2013-0602-26818. 
2-57 

 

previously estimated $100/kW cost value for IPM modeling and the RIA estimates of the total 

cost of this final rule. 

The brief assessment presented here is intended only to illustrate the approximate effects 

of coal cost and heat rate improvement capital cost on the economic breakeven point between 

coal cost savings and the capital cost of implementing heat rate improvement. The resulting 

order-of-magnitude cost range for CO2 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 compliance with this rule. 

Figure 2-12 presents a simplified economic breakeven analysis for a 4 percent nationwide 

average heat rate improvement, assuming there is no CO2 emission allowance cost involved. 

Additional assumptions used in the breakeven calculation include: capital 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; coal fleet generation weighted average 2030 net 

heat rate of 10,250 Btu/kWh before heat rate improvement, 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 capital cost of $50/kW, the average fleetwide 2030 savings in 

coal cost would become greater than the annualized capital cost of an average 4 percent 

reduction in net heat rate when the average fleetwide coal cost exceeds about $2.50/mmBtu. For 

comparison, the average U.S. power sector delivered cost of coal was $2.38/mmBtu in 201285, 

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, Average Weighted Cost of Fossil Fuels for the Electric Power Industry 2002–

2012. Available at http://www.eia.gov/electricity/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 average 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 average coal cost of about $1.25/mmBtu, which is 

comparable to the U.S. power sector average 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 those 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 could be economic at coal costs lower than 

the values mentioned above. 

If there were additional heat rate improvement savings due to avoided future CO2

emission costs, heat rate improvement could become economic at lower coal costs, or at higher 

capital costs, or at lower heat rate reduction percentages. 

2.7.2 Estimated fleetwide CO2 reduction from and cost for heat rate improvements 

EPA derived the following illustrative order-of-magnitude estimate of the fleetwide costeffectiveness of a 4 percent heat rate improvement 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 CO2 reduction: 4 percent 

 Heat rate improvement capital 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 CO2 reduction via heat rate improvement: 62 million ton/yr 

 Total heat rate improvement capital cost: $10.7 billion 

 Annualized heat rate improvement capital cost: $1.5 billion 

 Annual coal cost savings: $1.6 billion 

 Annual net savings: $0.09 billion 

 Annual net savings for CO2 reduction: $1.4/ton 

2.7.3 Conclusion - heat rate improvement economics 

This necessarily simplified analysis supports the following summary conclusions: 

 87See 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 improvement were implemented, it would also pay 

for itself on the basis of fuel savings alone, before consideration of the value of the 

associated CO2 emission reductions, on a fleetwide basis at projected 2030 coal prices if the 

associated average capital cost is about $50/kW or less. 

3. If a fleetwide average 4 percent heat rate improvement 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 CO2 reduction via 4 percent heat rate improvement would be a 

still relatively low $23/ton. 

Thus, although there is currently some uncertainty associated with the costs of achieving 

a particular fleetwide amount of heat rate improvement, it is clear that heat rate improvement is 

an available low-cost approach to CO2 reduction for existing coal-fired EGUs. 
3 - 1 

Chapter 3: CO2 Reduction Potential from Generation Shifts Among Existing 

Units 

Overview 

This chapter explores the dynamics of power sector dispatch and the cost-effectiveness of 

lowering the carbon dioxide (CO2) emissions intensity of the power sector by substituting 

generation from the most carbon-intensive existing EGUs and increasing utilization, to the extent 

possible, of less carbon-intensive existing fossil fuel-fired EGUs. More specifically, the 

examination focuses on opportunities to improve emissions 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 production and delivery in the 

context of cost-effective CO2 emission reduction opportunities. 

Introduction 

Electric system dispatch is typically defined as “the operation of generation 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 generators are constantly responding to 

changes to meet demand in the most reliable and cost-effective manner possible. 

The nation’s EGUs are connected by transmission grids that extend over large regions. 

Through these interconnections, EGU balancing authorities treat the product (i.e., electricity) of 

EGUs as fungible, calling for electricity generation supply to meet 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 several constraints in 

dispatch, including transmission and other operating 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 number 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 SO2 and NOx when scheduling unit dispatch, in 

response to costs and regulatory requirements. For example, EGU operators in 10 states 

participating in the Regional Greenhouse Gas Initiative have several years of experience with 

factoring CO2 emissions limits directly into bids for economic dispatch. The electric system’s 

carbon intensity can be lowered by shifting generation 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 facilities to produce energy at the lowest cost to reliably serve 

consumers, recognizing any operational limits of generation and transmission facilities.”3 The 

cost of operating electric generators varies based on a number of factors, such as fuel used and 

generator efficiency. Regional Transmission Organizations (RTOs) and Independent System 

Operators (ISOs) help coordinate economic dispatch over larger areas to help keep the cost of 

meeting electricity demand as low as possible, subject to operational constraints. 

The decision by balancing authorities to call upon, or dispatch, any particular generating 

unit is driven by the relative operating cost, or marginal cost, of generating electricity to meet the 

last increment of electric demand. These costs 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 markets 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 technologies, like renewables, 

hydroelectric, and nuclear, have little or no variable costs and are 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 (with 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 order 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-integrated utilities who own 

generation, transmission, and distribution infrastructure, the utilities themselves are often also the 

balancing authorities who determine unit dispatch. Such utilities are presumed to dispatch their 

units in a cost-minimizing fashion (seeking the lowest marginal cost), and they can arrange to 

buy and sell power with other balancing authorities. 

In states that have restructured regulation to allow for competition between generators, 

RTOs and ISOs are generally responsible for moving 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-effective and reliable delivery of power, and they 

are independent from market participants. ISOs grew out of the Federal 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 electricity (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 wholesale 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, ensure efficient and reliable delivery of electricity, 

and plan appropriately for the short and long term energy needs of the state and consumers. 

                                                            

5

 http://www.eia.gov/todayinenergy/detail.cfm?id=7590 

6

 http://www.ferc.gov/industries/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 allocate costs among customers, design price 

structures and set price levels, set service quality standards, approve capital expenditure by 

utilities, and arbitrate disputes among relevant parties 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 generation and capacity planning components. 

Wholesale Power Markets 

RTOs and ISOs operate spot markets for wholesale 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 supply 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 typically serve as the balancing authority 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 driven by the power supply costs and schedules of 

traditional utilities. This dispatch will depend to a certain degree on spot markets for power, 

since utilities will dispatch purchased 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. These 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 dispatch 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 utility 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 transmission 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 authorities are responsible for the reliable operation 

of the bulk electric system. The bulk electric system refers to a large interconnected electrical 
3 - 5 

system made up of generation and transmission facilities and their control systems. To ensure 

reliability, system operators continuously analyze real-time and forecasted load and transmission 

conditions to ensure that scheduled generation dispatch 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 American 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 CO2 emission rates9 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 considerably higher-emitting sources than NGCCs, on 

average. Therefore, shifting generation from the average existing fossil fuel-fired boiler to an 

average existing NGCC will result in notable CO2 emission reductions. 

The lower emission rate of NGCC conveys the potential of generation 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 CO2-intensive generating resources. 

For purposes of economic dispatch, most NGCCs have historically been operated to serve 

base load or intermediate demand due to their high efficiency and flexibility of operation, with 

                                                            

8

 http://www.nerc.com/ 

9

 Emission rates in this document are shown on a net generation 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 factors 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 indicated, 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 plant is available to produce 

electricity (a period being 1 year, or 8,784 hours in 2012 since that year included a leap day). 

The unavailable period is generally attributable to scheduled maintenance, unplanned 

maintenance, and unplanned outages. For modeling purposes, the EPA assumes that NGCC has 

an availability of 87%.13 Other reports suggest that NGCC availability factors may reach as high 

as 92%.14,15 Many modern NGCCs have demonstrated availability factors of over 95%. 

According to one NGCC manufacturer, these highly efficient units already represent over 15 

percent of total installed capacity nationwide, including all electric generating sources (as of 

2010).16 These high-efficiency and high-availability NGCC units were first introduced around 

1995 and have consistently reported availability factors of 90 to 92 percent to NERC.17 These 

availability factors are substantially above the 2012 NGCC generation level, indicating that there 

is a significant potential for increasing generation from unutilized existing NGCC capacity. 

To evaluate potential opportunities for shifting 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 shifting would require one net MWh of 

generation from a lower-emitting technology operating in place of one net MWh of generation 

                                                            

10 EIA, Today In Energy, January 15, 2014, http://www.eia.gov/todayinenergy/detail.cfm?id=14611 (for recent data) 11 EIA, Electric Power Annual 2009, http://www.eia.gov/electricity/annual/archive/03482009.pdf (Table 5-2 for 

2009 and earlier data) 

12 NERC, 2008-2012 Generating Unit Statistical 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/articles/print/volume-115/issue-2/features/higher-availability-of-gas-turbinecombined-cycle.html 

15 Power Engineering, Negotiating Availability Guarantees for Gas Turbine Plants, 03/01/2001, http://www.powereng.com/articles/print/volume-105/issue-3/features/negotiating-availability-guarantees-for-gas-turbine-plants.html 16 http://site.ge-energy.com/corporate/network/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 EPA calculated capacity factors as the ratio of 

a unit’s reported net generation to its potential net generation, the latter of which is determined 

by the unit’s net generating capacity. Net generating capacity reflects a reduction from nameplate 

capacity due to on-site electricity use (e.g., station service or auxiliaries) and local temperature 

conditions. The EPA used the net summer capacity reported for units in the EIA Form 860 

because it controls for local conditions and is thus a consistent metric for evaluating dependable 

capacity nationwide. Potential net generation 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

ሺNet summer capacityሻ x (hours in 2012)

This calculation of capacity factor provides an indication of how much net generation a 

unit is providing as a percent of its total net generating capacity. The capacity factor refers to the 

actual utilization of that source on an annual basis, while that source’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 average capacity factor of 46% in 2012.18 Since the fleetwide capacity factor in 2012 was less than the technical generation potential available for the 

technology, the historical data suggests that there is a significant potential for shifting generation 

from higher CO2 emitting resources to lower CO2 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 levels at which the NGCC fleet could 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 basis. To determine a reasonable 

                                                            

18 EIA 860 and 923. 
3 - 8 

average capacity factor for the NGCC fleet as part of BSER, the EPA considered historical data 

and modeling projections describing 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 Age20

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 EPA 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 Distribution by Capacity Factors (2012) 

                                                            

19 See the National Electricity Energy Data Systems (NEEDS) file at 

http://www.epa.gov/powersectormodeling/BaseCasev513.html 

20 See “Operable” worksheet in “GeneratorY2012” 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 Cycle Steam Part (CA) and Combined Cycle 

Combustion Turbine Part (CT) prime mover categories 

 

Table 3-2: Plant Distribution 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

40

Number of NGCC Plants

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 dispatched to provide base load 

power. While only 15% of plants operated 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 achieved 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 capacity factors. Nonetheless, a notable number of 

existing NGCCs have demonstrated the ability to achieve a 75% or higher capacity factor for 

extended periods of time. These units achieved high capacity factors without adverse effects on 

the electric system. 

Hourly data reported to EPA22, 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 data reported to EPA are based on heat input and 

input capacity; these calculations correspond closely to the capacity 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, capacity 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 approximately 5% or more above nameplate capacity 

factors. Analysis of these hourly data demonstrates 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 factors 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 the ability of individual units 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 operation 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 factors. 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 capacity factor, and therefore at least at a 75% net summer capacity 

factor or higher. 

Historical Trend 

Over the last several years, advances in the production of natural gas have helped reduce 

natural gas prices and improved the competitive position 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 CO2 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 the change in the prices of coal and gas to gauge the relative costs 

of generating for each technology. While there are wide-ranging costs 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 CO2. 

The potential for shifting generation from CO2 intensive sources to less CO2 intensive 

sources is evidenced in historical data. EIA form 860 and 923 data demonstrate an increase in 

NGCC generation and fuel use between 2011 and 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 significant shift from coal to gas generation 

over just a one year period demonstrates the ability 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 gas 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 generation and increasing NGCC generation relative to 2011 electricity 

production economics. This historical data also shows a sharp increase 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 Generation27

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 Electricity 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 operation equivalent to a 75% net summer 

utilization to the baseline 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 potential infrastructure 

improvements to facilitate increased utilization of existing NGCC plants, the EPA is finalizing a 

gradual phase over the interim period, consistent with the historical 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 that is feasible by 2022. This parameter is 

conservatively defined as a percent increase from 2012 to 2022 that is equal to the largest single 

largest annual increase in power sector gas-fired 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 remaining years of the interim period is 

limited by the average annual growth in gas-fired generation in the power sector between 1990 

and 2012, or approximately 5 percent per year. Since this is the average annual increase in gas 

utilization that has occurred over recent history, it is reasonable 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 methodology, these two parameters constrain the 

annual rate at which building block 2 shifts generation from fossil steam units to NGCC units. 

As discussed in the preamble, the interim performance rate is an average of annual rates 

calculated over the 2022-2029 period. The two parameters 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 observe that the single largest annual increase in 

power sector gas-fired generation since 1990 occurred between 2011 and 2012 and is equal to 22 

percent. Therefore, in the first year, NGCC generation is limited to a maximum of a 22 percent 

increase from 2012 levels in each region. In each subsequent year, regional 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 the BSER level of shifting from fossil steam 

generation to NGCC generation is completed. Under this approach, 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 generation that represents building block 2 

potential as incorporated in the emission performance 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 existing NGCC as defined 

above to be feasible and consistent with domestic 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 agreement that recoverable natural 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 generation growth of 340 TWh from 2012 to reach 

the level assumed for 2022, and that NGCC generation growth would result in increased gas 

consumption of less than 2 TCF for the electricity sector, which is less 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/ipdbproject/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 natural gas is far bigger than was thought even a 

few years ago,” after large increases in shale resource estimates had already been realized.31 

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

Natural Gas Deliverability 

To examine the potential for increases in pipeline deliverability, the EPA analyzed the 

pipeline flow data from the Energy Information Administration. These data provide pipeline 

capacity for inflows and outflows by region (see 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 gas to become outflow to other regions (effectively 

passing that natural gas through the region to consumers in other regions). Consequently, net 

capacity – the difference between inflow capacity and outflow capacity -- is a better metric for a 

region’s ability to increase deliverability of natural gas supply, because natural gas using such 

                                                            

31 National Petroleum Council, 2012 (http://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 assessment focuses on areas that would experience 

increases in natural gas consumption in line with 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, Midwest and Western regions in Figure 3-4. The 

net pipeline capacity for these 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 

Area33

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 from 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 existing natural gas units were required to operate 

at the BSER capacity factor. Increased use of natural gas in existing units can be largely met with 

expansions to existing pipeline facilities 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 time than new pipelines. Over 2005-2011, the total 

gas deliverability in gas consuming 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 increases apply to all sector consumptions, and 

the total consumption in the BSER scenario were projected to increase by 13% over the base 

case and 18% over 2016 consumption. Since this historical increase in pipeline capacity over the 

2005 to 2011 period is 24.3%, the historical data indicate that pipeline 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 Administration Natural Gas Pipeline Regions

Source: Energy Information Administration. 

Recent pipeline construction continued to demonstrate the pipeline network’s ability to 

accommodate increasing demand for natural gas. According 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 per day of additional pipeline capacity. 

For projects expected to be in service from April, 2014 through 2016, EIA reports 47 projects, 

with planned capacity additions 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 Project in Connecticut. 

The ability of the pipeline system to accommodate 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 pipeline expansion through 2030, one with a 

25% increase in overall natural gas demand in 2030 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, “Intermediate interstate natural gas 

pipeline infrastructure needs in [the assessed scenarios] are projected to be modest relative to the 

Reference Case”.36 The levels of increased natural gas demand assessed in that DOE study are 

substantially above the 15% increase in natural gas demand projected 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 technical 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 linear programming model of the U.S. electric 

power sector that the EPA has used for over two 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 markets (including least cost capacity expansion and 

electricity dispatch projections) while meeting fuel supply, transmission, dispatch, and reliability 

constraints. 

                                                            

34 See www.eia.gov/naturalgas/pipelines/EIA-NaturalGasPipelineProjects.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, storage, and related costs are modeled 

directly in IPM through the incorporation of a natural gas module. The module includes a detail 

rich representation of the natural gas pipeline network inclusive of discount curves that represent 

the marginal value of gas transmission as a function 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 cycling capabilities of EGUs to ensure that the model properly 

reflects the distinct operating characteristics of peaking, cycling, 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 scenarios representing replacement of fossil 

steam generation in each interconnection with increases of average 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 interconnection 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 sources in that interconnection in the base case.37 

 

 The costs and economic impacts of these scenarios were evaluated by comparing the total 

costs and emissions from each scenario to the costs 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 CO2 reductions was $15, $24, and $33 per short ton of CO2, 

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 shifting estimated in this building block can be achieved without 

causing significant economic impacts, even without the gradual phase-in of this building block 

that was not represented in this analysis. For example, 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. For example, the year-to-year 

percentage difference in Henry Hub prices reported by the Energy 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 percent in the 75% case, which is similarly well 

within the range of historic electric price variability. For example, 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 Delivered 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 - 1 
4.1 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 PV3 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 MWDC

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 PV6 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 MWDC

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 MWh8

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

Values12

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

CO2 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 Rates
Figure 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 rate14

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 EstimatesThe 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/cleanpowerplan/clean‐power‐plan‐proposed‐rule‐alternative‐renewable‐energy‐

approach
included 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 market prices that has occurred over the last year. NREL, Annual Technology 

Baseline (ATB) Spreadsheet, July 2015, see “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/kWDC, 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/kWDC, whereas the AEO2013 assumes costs above $2,800/kWDC 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/kWDC.

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/kWDC by 2020, and about $2,300/kWDC 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/kWDC 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 turbine 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/files/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/levelized_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/kWDC 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 2015

Summer Capacity (MW)

Source: EIA

Geothermal Solar(Thermal+PV) Wind
wind 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 CO2 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‐

203036

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 results
The 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

203040, 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 CO2 emissions from existing fossil fuel-fired 

steam generating EGUs is through the application 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-fired steam generating units is implementation of 

partial CCS (i.e., capturing a portion of the CO2 from the emission stream – in contrast to “full 

CCS” which would involve capturing > 90% of the CO2 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 emission reductions; and its implementation will serve 

to promote further development and deployment of the technology. This chapter examines the 

potential for implementation of CCS technology at existing fossil fuel-fired steam generating 

EGUs. 

Carbon Capture Options for Existing Fossil Fuel-fired EGUs 

In general, CO2 capture technologies applicable to existing fossil fuel-fired power 

generation can be categorized into three approaches – (1) post-combustion capture; (2) precombustion capture; and (3) oxy-combustion. Each of these is described and discussed in more 

detail below. 

Post-combustion Capture 

Post-combustion CO2 capture refers to removal of CO2 from a combustion flue gas prior 

to discharging to the atmosphere. Separating CO2 from such a gas stream can be challenging for 

a number of reasons. Because CO2 is a dilute fraction of the combustion flue gas – typically 13-

15 % in coal-fired systems and 3-4 % in natural gas-fired systems – a large volume of flue gas 

must be treated. The flue gas from typical combustion systems is usually at near atmospheric 

pressure. Therefore, most of the available capture systems rely on chemical absorption 

(chemisorption) options (e.g., using amines) that require added energy to release the captured 

CO2 and regenerate the solvent. Many of the chemical solvents require a flue gas stream that is 

free of or has very low quantities of components – such as SO2 and HCl – that can degrade the 
solvent. The captured CO2, if it is to be transported via pipeline 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 presence of steam and limited 

O2. The product syngas contains primarily H2 and CO – and, depending on the fuel and 

gasification system – some lesser amount of CO2. The amount of CO2 in the resulting syngas 

stream can be increased by “shifting” the composition via the catalytic water-gas shift (WGS) 

reaction. This process involves the catalytic reaction of steam (“water”) with CO (“gas”) to form 

H2 and CO2. The resulting CO2 contained in the syngas is then captured before combustion of 

the H2-enriched syngas for power generation in a combined cycle system. Contrary to the postcombustion capture flue gas, the IGCC syngas can contain a high volume of CO2 and is 

pressurized. This allows the use of physical absorbents (e.g., SelexolTM, Rectisol®) that require 

much less added energy to release the captured CO2 and require less compression to get to 

pipeline standards. 

Oxy-combustion 

Oxy-combustion systems for CO2 capture rely on combusting coal or other fuels with 

relatively pure O2 diluted with recycled CO2 or CO2/steam mixtures. Under these conditions, the 

primary products of combustion are water and CO2, with the CO2 purified by condensing the 

water. Challenges associated with oxy-combustion include the capital cost and energy 

consumption for a cryogenic air separation unit (ASU) to produce oxygen, introduction of N2 via 

boiler air infiltration, and excess O2 in the CO2 product stream. 

CO2 Transportation and Storage 

CO2 Pipeline Infrastructure 

Carbon dioxide has been transported via pipelines in the U.S. for nearly 40 years. 

Approximately 50 million metric tons of CO2 are transported each year through 3,600 miles of 
pipelines. Moreover, a review of the 500 largest CO2 point sources in the U.S. shows that 95 

percent are within 50 miles of a possible geologic sequestration site, which would lower 

transportation costs. There are multiple factors that contribute to the cost of CO2 transportation 

via pipelines including but not limited to: availability and acquisition of rights-of-way for new 

pipelines, capital costs, operating costs, length and diameter of pipeline, terrain, flow rate of 

CO2, and the number of sources utilizing the pipeline. 

Geologic Storage 

 Existing project and regulatory experience, research, and analogs (e.g. naturally existing 

CO2 sinks, natural gas storage, and acid gas injection), indicate that geologic sequestration is a 

viable long term CO2 storage option. The viability of geologic sequestration of CO2 is based on a 

demonstrated understanding of the fate of CO2 in the subsurface. Geologic storage potential for 

CO2 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 potential 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 billion metric tons of CO2

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

 Other types of 

geologic formations such as organic rich shale and basalt may also have the ability to store CO2; 

and the DOE is currently evaluating their potential storage capacity. 

 Further evidence of the widespread availability of CO2 storage reserves in the U.S. 

comes from the Department of Interior’s U.S. Geological Survey (USGS) which has recently 

completed a comprehensive evaluation of the technically accessible storage resource for carbon 

storage for 36 sedimentary basins in the onshore areas and State waters of the United 

States.2

 The USGS assessment estimates a mean of 3,000 billion metric tons of subsurface CO2

storage potential across the United States. For comparison, this amount is 500 times the 2011 

annual U.S. energy-related CO2 emissions of 5.5 Gigatons (Gt).3

                                                            

1

 The United States 2012 Carbon Utilization and Storage Atlas, Fourth Edition, U.S Department of Energy, Office of 

Fossil Energy, National Energy Technology Laboratory (NETL). 2

 U.S. Geological Survey Geologic Carbon Dioxide Storage Resources Assessment Team, 2013, National 

assessment of geologic carbon dioxide storage resources – Results: U.S. Geological Survey Circular 1386, 41 p., 

http://pubs.usgs.gov/fs/2013/1386/. 3

 U.S. Geological Survey Geologic Carbon Dioxide Storage Resources Assessment Team, 2013, National 

assessment of geologic carbon dioxide storage resources – Summary: U.S. Geological Survey Factsheet 2013-3020, 

6p.http://pubs.usgs.gov/fs/2013/3020/.
Enhanced Oil Recovery (EOR) 

Geologic storage options also include use of CO2 in EOR, which is the injection of fluids 

into a reservoir to increase oil production efficiency. EOR is typically conducted at a reservoir 

after production yields have decreased from primary production. EOR using CO2, sometimes 

referred to as ’CO2 flooding’ or CO2-EOR, involves injecting CO2 into an oil reservoir to help 

mobilize the remaining oil and make it available for recovery. The crude oil and CO2 mixture is 

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

and CO2. The gaseous CO2-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. 

CO2-EOR has been successfully used at many production fields throughout the U.S. to 

increase oil recovery. The oil and natural gas industry in the United States has over 40 years of 

experience of injection and monitoring of CO2 in the deep subsurface for the purposes of 

enhancing oil and natural gas production. This experience provides a strong foundation for the 

injection and monitoring 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 feasible and adequately demonstrated for new units 

because the major components of CCS – the capture, the transportation, and the storage – are all 

proven technologies that have been demonstrated at large scale. While the EPA found that partial 

CCS is technically feasible for new fossil fuel-fired boilers and IGCC units, it is more difficult to 

make that determination for the entire fleet of existing fossil fuel-fired EGUs. Developers of new 

generating facilities can select a physical location that is more amenable to CCS – such as a site 

that is near an existing CO2 pipeline or an existing oil field. Existing sources do not have the 

advantage of pre-selecting an appropriate location. Some existing facilities are located in areas 

where CO2 storage is not geologically favorable and are not near an existing CO2 pipeline. 

Developers of new facilities also have the advantage of integrating the partial CCS system into 

the original design of the new facility. Integrating a retrofit CCS system into an existing facility 
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 CO2 capture, compression, 

and transportation. Integration of the existing steam system with a retrofit CCS system can be 

particularly challenging. 

Partial CCS has been demonstrated at existing EGUs. It has been demonstrated at a pilotscale at Southern Company’s Plant Barry and it is being installed for large-scale demonstration at 

NRG’s WA Parish facility. SaskPower’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 capturing approximately 90 % of the CO2 from 

flue gas stream. All of these facilities are located in areas that are either near an existing oil field 

or in an area that is geologically favorable for CO2 storage. Thus, at some existing facilities, the 

implementation of partial CCS may be a viable GHG mitigation option and some utilities may 

choose to pursue that option. However, the EPA does not believe that it can serve as the best 

system of emission reduction as a broadly applicable GHG mitigation program. Therefore, the 

EPA did not propose to find that CCS is a component of the best system of emission reduction 

for CO2 emissions from existing fossil fuel-fired EGUs. 

Reasonableness of Cost 

In the proposed standard of performance for new fossil fuel-fired EGUs (79 FR 1430), 

the EPA proposed that the costs to implement partial CCS (to a level to meet the proposed 

emission standard of 1,100 lb/MWh-gross) were consistent with costs for other non-natural gasfired generating technologies – such as nuclear, biomass and geothermal – that utilities are 

considering for new intermediate and base load generating capacity. The EPA also noted in the 

proposal, that most of the relatively few new projects that are in the development phase are 

already planning to implement CCS; and, as a result, the standard would not have a significant 

impact on nationwide energy prices. In issuing the final NSPS for new fossil fuel-fired EGUs, 

the EPA – in response to public comments and updated 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 identify 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 implementation at new solid fossil fuel-fired sources. The 

EPA also noted that, because virtually all new fossil 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 projected coal plants with CCS and the number of projects would 

make it difficult to implement in the short term. 

An emission standard for existing units based on CCS (or even partial CCS) would most 

certainly have an even more significant effect on nationwide electricity prices and could affect 

the reliability of the supply of electricity. Therefore, as stated earlier, the EPA did not find that 

the cost to implement partial CCS at existing source emission standards to be reasonable, which 

further supports the determination that CCS is not an appropriate component of the best system 

of emission reduction for CO2 emissions from existing fossil fuel-fired EGUs. 

Emission Reductions and Promotion of Advanced Technology 

An emission standard for existing units based on CCS (or even partial CCS) would 

clearly result in significant emission reductions and would certainly serve to promote further 

deployment, development and improvement in the most advanced technology. However, the 

EPA has determined that such an emission standard may not be technically or logistically 

feasible in a number of cases and cannot be broadly implemented at a reasonable cost at this 

time. 

CCS and CCU in State Plans 

Affected EGUs may utilize retrofit CCS technology to reduce reported stack CO2

emissions from the EGU.4

 Affected EGUs that apply CCS under a state plan must meet the same 

monitoring, recordkeeping and reporting requirements for sequestered CO2 as new units with 

CCS capability subject to the final CAA section 111(b) rule for new EGUs.5

 Specifically, the 

final CAA section 111(b) rule for new sources requires that, if a new affected EGU uses CCS to 

meet the applicable CO2 emission limit, the EGU must report 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 CO2 must be injected at a facility or 

facilities that report in accordance with 40 CFR part 98 subpart 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 CO2 will be tracked as 

appropriate at project- and national-levels, and that the status of the CO2 in its sequestration site 

will be monitored, including air-side 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 CO2 emission rate adjustment measure for affected 

EGUs. Potential alternatives to storing CO2 in geologic formations are emerging and these 

relatively new potential alternatives may offer the opportunity to offset the cost of CO2 capture. 

For example, captured anthropogenic CO2 may be stored in solid carbonate materials such as 

precipitated calcium carbonate (PCC) or magnesium or calcium carbonate, bauxite residue 

carbonation, and certain types of cement through mineralization. The carbonate materials 

produced can be tailored to optimize performance in specific industrial and commercial 

applications. For example, these carbonate materials have been used in the construction industry 

and, more recently and innovatively, in cement production processes to replace Portland cement. 

The Skyonics Skymine® project, which opened its demonstration project in October 

2014, is an example of captured CO2 being used in the production of carbonate products. This 

plant converts CO2 into commercial products. It captures over 75,000 tons of CO2 annually from 

a San Antonio, Texas, cement plant and converts the CO2 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 electronic GHG Reporting Tool identification 

(e-GGRT ID) of the EGU facility from which CO2 was captured, and (2) the e-GGRT ID(s) for, and mass of CO2

transferred to, each GS site reporting under subpart RR. As noted, the final 111(b) rule also requires that any 

affected EGU unit that captures CO2 to meet the applicable emissions limit must transfer the captured CO2 to a 

facility that reports under 40 CFR part 98 subpart RR. 

7

 Under final requirements in the CAA 111(b) NSPS, any well receiving CO2 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 CO2, nor does it change by virtue of reporting under subpart RR.
carbonate and sodium bicarbonate.8

 Other companies – including Calera9 and New Sky10 – also 

offer commercially available technology for the beneficial use of captured CO2. These processes 

can be utilized in a variety of industrial applications – including at fossil fuel-fired power plants. 

However, consideration of how these emerging alternatives could be used to meet CO2

emission performance rates or state CO2 emission goals would require a better understanding of 

the ultimate fate of the captured CO2 and the degree to which the method permanently isolates 

the captured CO2 or displaces other CO2 emissions from the atmosphere. 

Several commenters also suggested that algae-based CCU (i.e., the use of algae to 

convert captured CO2 to useful products – especially biofuels) should be recognized for its 

potential to reduce emissions from existing fossil-fueled EGUs. 

Unlike geologic sequestration, there are currently no uniform monitoring and reporting 

mechanisms to demonstrate that these alternative end uses of captured CO2 result in overall 

reductions of CO2 emissions to the atmosphere. As these alternative technologies are developed, 

the EPA is committed to work collaboratively with stakeholders to evaluate the efficacy of 

alternative utilization technologies, to address any regulatory hurdles, and to develop appropriate 

monitoring and reporting protocols to demonstrate CO2 reductions. 

In the meantime, the EPA has specified that state plans may allow affected EGUs to use 

qualifying CCU technologies to reduce CO2 emissions that are subject to an emission standard, 

or those that are counted when demonstrating achievement of the CO2 emission performance 

rates or a state rate-based or mass-based CO2 emission. Approvable state plans must include 

analysis supporting how the proposed qualifying CCU technology results in CO2 emission 

mitigation and provide monitoring, reporting, and verification requirements to demonstrate the 

reductions. The EPA would then review the appropriateness 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/beneficial-reuse-of-co2/process.html. 10 http://www.newskyenergy.com/index.php/products/carboncycle.
Demand-Side Energy Efficiency Technical Support Document
ii

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 CO2 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: Magnitude 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 Improvement from Start Year to EE Plan 

Scenario Level of Performance ................................................................. 54

8.2.6 Step 6: Determine Average Portfolio 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 Plan Scenario Construction ............... 58

8.3 Calculations............................................................................................................59

8.3.1 Step 1: Determine Annual Business 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 Annual 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 CO2 Reduced ................................................................. 75

10. Analysis Considerations .......................................................................................................76

10.1 State EE Policies in the Baseline Electricity Demand ...........................................76

10.2 Energy Information Administration Form EIA-861 as Data Source .....................77

References .....................................................................................................................................86

Appendices 

Appendix 1: Summary of Recent Energy Efficiency Potential Studies (2009-2014) ................................. 81

Appendix 2: Incremental Electricity Savings Pace of Improvement Analysis ........................................... 82

Appendix 3: Demand-Side Energy Efficiency Plan Scenario: Data, Calculations and Results .................. 85
v

List of Figures 

Page 

Figure 1. EE Program Portfolio .................................................................................................................. 16

Figure 2. Residential Energy Building 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) ............................................................... 18

Table 3. U.S. State Adoption 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 Potential of State Appliance Standards .............................................. 36

Table 8. Summary of State Regulatory 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 Savings – Investor-Owned Utilities .................................................. 52

Table 12. 2013 Incremental Annual Savings – Cooperative-Owned Utilities ............................................ 52

Table 13. 2013 Incremental Annual Savings – Municipal-Owned Utilities ............................................... 53

Table 14. Levels of Incremental Savings Required by State EERS ........................................................... 53

Table 15. NPCC Achievable EE Potential and Achieved 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 Percentage of Sales for South Carolina ........................... 60

Table 19. Annual Incremental EE Savings (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 Percentage 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: Magnitude 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 Costs for South Carolina ......................................................................................... 73

Table 31. Summary of Results by Step for South Carolina. ....................................................................... 73

Table 32. First-Year Demand-Side EE Costs (billions 2011$) (Continental U.S.) .................................... 74

Table 33. Annualized Demand-Side EE Costs (3% discount 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 ................................................................................................................................ 75
1 

1. Introduction 

This chapter provides information on demand-side energy efficiency (EE) as a measure 

for reducing carbon dioxide (CO2) emissions from fossil fuel-fired electric generating units 

(EGUs). Specifically, this chapter supports the 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 for the Regulatory Impact 

Analysis (RIA) and related discussion in section X, “Impacts of this Action,” 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 approach, 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 considerations relevant to sections 8 and 9. 

2. Background 

Demand-side EE policies and programmatic efforts have existed for decades and are now 

used in all 50 states. These strategies are intended to help states achieve energy savings goals, 
2 

reduce the environmental impacts (including CO2 emissions) of meeting energy service needs, 

save energy and money for consumers, and provide a significant resource for meeting power 

system capacity requirements (EPA 2015). EE policies currently in place are considered by states 

to be cost-effective strategies for contributing to these policy objectives.1

 Moreover, states – 

through their utilities, primarily – have been rapidly increasing 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 cumulative impacts of these programs represented a 

3.7% reduction in national electricity demand (EIA 2012a). And, EE spending is projected to 

continue to grow at a substantial rate. A recent study by Lawrence Berkeley National Laboratory 

(LBNL) projects EE program spending to reach $8.1 billion to $12.2 billion (“Medium Case” 

and “High Case,” respectively) 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 that are applied throughout 

all sectors of the economy to reduce energy demand while providing the same, and sometimes 

better, level and quality of service. Utilities employ an array of strategies in implementing EE 

programs, these include financial incentives such as rebates and loans, 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 attics and walls, sealing of air 

leaks, and undertaking home energy audits leading to customized whole home retrofits. 

Opportunities for cost-effective EE in commercial buildings include optimization of heating, 

ventilation, and air conditioning (HVAC) systems, upgrades of windows, and use of more 

efficient office equipment at replacement. In the industrial sector key EE strategies include motor 

upgrades and maintenance programs, recovery of waste heat streams, and optimization of 

processes through modern instrumentation and controls systems. 

                                                            

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 investment in EE is dynamic, growing over time 

as technologies and practices advance, as populations grow, and as investment occurs in the 

construction of new homes, buildings, and industrial facilities. As new policies are enacted, 

leading to the acceleration of investment in EE, an additional portion of the expanding 

opportunity is realized. After decades of experience implementing policies to accelerate 

investment in cost-effective EE, states are finding renewed opportunities as they develop more 

sophisticated and effective strategies, evolving from a focus on individual end-uses and products 

to whole-building and systems-based strategies that account for the interactions between the 

many energy end-uses in buildings and industry (ACEEE 2013). As will be discussed, the 

experience in the U.S. has been that, on balance, a persistent and large potential for achievable 

and cost-effective EE has remained even as the impact of past and ongoing efforts have 

accumulated. 

2.2 Barriers to EE Investment 

Despite the persistent and large potential for electricity savings through investment in EE 

technologies and practices, market failures, as well as non-market barriers and behavioral 

impediments, limit the realization of the many benefits of these investments. Several market 

failures that lead to inefficiencies in energy use are widely recognized by analysts and 

practitioners, and are discussed extensively in the literature (Levine et al. 1995; Gillingham et al. 

2009; Helfand and Wolverton 2011). In the presence of market failures, users of electricity, or 

those making EE investments, face prices or incentives 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 impediments explain why individuals do not always 

make EE investments that are seemingly in their own best interest. Some of the most common 

examples of these market failures include: 

 Pollution externalities. Fossil energy consumption is associated with negative 

externalities, such as emissions of CO2, SO2, and NOx that cause human health and 

environmental damages. Energy prices that do not correctly reflect 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 with incentives of those who 

use and pay for energy. Examples include misalignment between landlords and 

tenants, between builders and homeowners, and 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 technology 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 products is incomplete and 

fragmented, leading to underinvestment in innovation and limited EE supplied by the 

manufacturers. In addition, supply chain fragmentation may also add complexity to 

the purchase and installation of otherwise economically 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 consumption 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 levels of energy efficiency. This phenomenon, also termed ‘energy paradox,’ or ‘energy 
5 

 Risk and uncertainty. Adopting an unfamiliar, typically more expensive EE 

technology can be an uncertain undertaking given the lack of credible information on 

product performance and future energy prices, and the irreversibility of the 

investment. Imperfect or asymmetric information can exacerbate the perceived risk of 

EE investments and help explain why consumers and firms do not always invest in 

EE measures. Suppliers also face risk and uncertainty, without perfect information of 

consumer preferences for EE. In the presence of risk and uncertainties, consumers 

and suppliers alike will underinvest in EE. 

 Transaction costs. Consumers face transaction costs in searching, assessing and 

acquiring energy efficient technologies and services. It can be time-consuming and 

difficult for consumers to estimate lifetime operating costs of a product. The 

complexity of the search process puts many efficient products at a 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 adoption of socially beneficial EE opportunities. 

Examples of effective EE policies and programs include public funding of R&D, information 

programs (such as energy labeling, the ENERGY STAR program, and consumer education), 

rebates for high-efficiency products, product energy performance standards, financing and loan 

programs, and technical assistance. 

2.3 EE Policies 

2.3.1 Objectives and Role in Reducing CO2 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 extensively in the literature. See, for example, Jaffe and Stavins 1994, DeCanio 1998, Sanstad 

and Howarth 1994, DeCanio and Watkins 2008, and Allcott and Greenstone 2012. 
6 

 Reducing costs to electricity customers, 

 Providing a significant resource for meeting power system capacity needs, 

 Meeting energy savings goals, 

 Stimulating local economic development and new jobs, and 

 Reducing the environmental impacts of meeting electricity service needs (EPA and 

DOE 2006; EPA 2015). 

EE policies currently in place are considered by states to be cost-effective 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 consumers over the long term is a primary purpose in 

pursuing these policies. In addition, EE policies are central to meeting state objectives for 

reducing CO2 emissions from the power sector. EE policies are a leading tool for achieving CO2

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 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 potential. For 

example, McKinsey 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 (McKinsey 2007). 

Similarly, economy-wide studies of climate mitigation scenarios confirm that EE plays a 

critical role in reducing the costs and enhancing 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 concentration 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). Several recent Energy Modeling Forum (EMF) 

studies have investigated the role of technology in achieving climate policy objectives in the U.S. 

                                                            

3 States with GHG reduction laws include: California, Connecticut, Hawaii, Maine, Maryland, Massachusetts, 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, improvements in EE in various economic sectors would 

slow the increases of GHG emissions in the short run, substantially reduce the costs of GHG 

mitigation (on average, by about 50%), and ease the technology transformation pathways to 

achieve long-term carbon reduction goals (Kriegler et al. 2014; Kyle et al. 2011). 

Several economic studies (including EMF25 studies) examined the role of EE policies 

(such as EE standards and subsidies) in relation to other climate 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 recognized to be an appropriate response to 

demonstrated market failures and behavioral impediments, particularly in contexts where these 

failures have broader societal implications such as environmental externalities (Gillingham et al. 

2009). 

In addition to providing cost-effective opportunities for reducing GHG emissions, EE is 

recognized to provide other co-benefits, including air quality and public health benefits, waste 

reduction from energy generation, energy security, energy system reliability, community 

economic and social development, and consumer amenities (RAP 2012). EE investments and 

policies are also found to spur productivity growth, technology learning and innovation (Boyd 

and Pang 2000; Worrell et al. 2003). Recently, more attention has been paid to developing 

methods for recognizing these co-benefits and integrating them into the cost-benefit analysis 

framework used by state utility commissions and administrators of EE programs. 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 prominent and impactful EE policies in most 

states include those that drive development and funding of EE programs, and building energy 

codes. Other policies that are leading to significant impacts include state appliance and 

equipment standards, “lead by example” strategies targeting energy use in state operations 

                                                            

4 Energy Modeling Forum (EMF) is a consortium of energy economists and energy economic modeling teams that was 

established in 1976. Through ad hoc working groups, the EMF has focused on a series of energy and environmental topics that 

are of interest to policy decisions. In recent years, the EMF is recognized for its contribution to the advancement of economics of 

climate change and the reports of the Intergovernmental Panel on Climate Change (IPCC). 
8 

through energy services performance contracting, and “volt-VAR optimization.” See section 5 

for further description of these policies, their history, and potential for achieving significant 

electricity savings. Comparing the relative impact (potential or achieved) of the different policy 

types is challenging, particularly to do so comprehensively, across all states, and at the national 

level. EE programs are the only state EE approach that has comprehensive and detailed reporting 

of impacts, costs, and other characteristics from all 50 states.5 This information is generally 

based upon measurement and verification studies submitted annually, most commonly to state 

utility commissions, and reported to the Energy Information Administration (EIA) for all 

program administrator types (including all utility types, third-parties, and government agencies). 

EE program data reported to EIA includes: incremental savings (also referred to as “first-year” 

savings), cumulative savings, peak demand savings, program costs broken down by component, 

and composition by end-use sector (residential, commercial, industrial). As of 2013 EIA ended 

their collection of cumulative savings data. In 2013, utilities and other program administrators in 

all 50 states and the District of Columbia states reported savings from EE programs to EIA 

through form EIA-861. At a national level, the EPA is not aware of a comprehensive dataset 

reported by states of the achieved impacts of strategies other than those that lead to investment in 

EE programs. However, state and regional-level information does exist. For example, the 

Northwest Power and Conservation Council (NPCC) has been compiling the impacts of EE 

policies (including utility and third-party EE programs, state building energy codes, and federal 

appliance standards) across their member states (ID, MT, OR, WA) for more than three decades. 

For the past decade, EE programs have accounted for more than 75% of the cumulative energy 

savings from state EE policies for NPCC, with building energy codes accounting for the 

remaining savings (NPCC 2010). 

Another representation of the relative opportunity provided by different state EE 

strategies is presented by evaluations of EE achievable potential or projections of the impacts of 

EE policies. The results from two recent evaluations at a national level are presented in Table 1. 

EE programs account for 77% and 82% of achievable savings in studies 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 comprehensive, 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 Brown 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 example of the impacts of EE programs relative to 

other state EE policies. The Massachusetts Global Warming Solutions Act of 2008 established 

statewide limits on greenhouse gas (GHG) emissions of 25 percent below 1990 levels by 2020. 

To achieve this target, Massachusetts is relying upon an integrated portfolio of clean energy 

policies. State EE policies are expected to provide the largest contribution to meeting the 25 

percent target with utility sponsored EE programs and state building energy codes accounting for 

76% and 17%, respectively, of those policies (Massachusetts EOEEA 2010). In their 2013 

progress report, Massachusetts indicates that they are generally 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 lengthy history of EE policies in the U.S. In the following 

section, the focus returns to the EE programs highlighted 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 supported through federal policies and market 

innovations over the past four decades, in recognition of its multiple benefits, including 

increased energy security, reduced emissions from the power sector, lower customer energy bills, 

enhanced economic development and job creation, and improved reliability and resiliency of the 

electricity system. Following the 1973 Organization of Petroleum Exporting Countries (OPEC) 

oil embargo, the Nixon administrated established 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 94th Congress took the first major U.S. legislative action on energy through the 1975 

Energy Policy and Conservation Act (EPCA). Among 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 Engineers (ASHRAE) developed the first national 

model building energy code, 90-75 “Energy Conservation in New Building 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 creation of mandatory national 

building energy performance standards (BEPS) which were repealed in the 1980s, provided loan 

guarantees for saving energy in public and commercial 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 continued during the Carter administration. 

President Carter’s 1977 National Energy Plan included energy efficiency as “the quickest, 

cheapest, and most practical source of energy In the same year President Carter also issued the 

first executive order on energy efficiency, Energy Policy and Conservation E.O. 12003 requiring 

federal agencies to develop energy conservation plans (Woolley and Peters n.d.). Other policy 

actions in 1977 included the creation of the U.S. Department of Energy, thereby consolidating 

multiple federal agencies and programs, including many that support energy efficiency, into one 

cabinet-level department. 

In 1978, partly in response to the Administration’s new energy plan, Congress enacted 

the National Energy Act, including the National Energy Conservation Policy Act (NECPA), 

which made EPCA’s provisions for federal efficiency standards mandatory and pre-emptive of 

state law; established the Residential Conservation Service (RCS), which required utilities to 

provide home energy audits; and authorized the 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 requirements, which have been updated and 

amended since 1978 (Congress 1978a). Congress also passed the Public Utility Regulatory 

Policies Act (PURPA) in 1978 which, among other provisions, introduced competition into 
11 

generation markets, and required state utility commissions to consider ratemaking practices that 

encourage energy efficiency (Congress 1978b). As state commissions pursued PURPA-related 

requirements as well as their own related policy interests, integrated resource planning became 

more widely used, and the concept of demand-side management (DSM) emerged, in which 

utilities implement programs to influence customer loads through energy efficiency and other 

demand-side initiatives (RAP 2011). 

The energy service company (ESCO) business model also emerged during the 1970s and 

1980s. Leaders in the Federal Energy Administration, 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 business, in which ESCOs could reduce 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, whose limited ability to finance energy improvements 

matched up well with ESCOs’ ability to bring private capital to energy improvement projects. 

Federal policy support for energy efficiency declined dramatically in the 1980s under the 

Reagan Administration as energy supplies improved, prices stabilized, and a less favorable 

climate for federal regulation emerged. The BEPS and RCS programs were repealed, and a “nostandard” standard was issued under NECPA appliance standards authority. Some states acted to 

fill these policy vacuums, enacting their own building codes, appliance standards, and utility 

programs. Appliance manufacturers, concerned about the proliferation of multiple, different 

state standards, supported a compromised effort that resulted in the 1987 National Appliance 

Energy Conservation Act (NAECA). NAECA, which 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 building energy codes were also 

adopted to achieve greater energy savings in new construction (BCAP n.d.). 

As integrated resource planning and DSM practices continued to spread in the 1980s, and 

electric utilities began to offer customers DSM programs under formal regulatory processes, 

including programs to incentivize more efficient technologies and practices. These DSM 
12 

programs sometimes resembled utility conservation programs launched in the 1970s, but 

signified a larger shift to integrate energy efficiency 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 the environmental consequences of electricity 

generation. Rising power plant capital and fuel costs resulted in rate increases, rising customer 

dissatisfaction, and new requirements for utilities 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 electric utilities (Synapse 

2011). Utilities also played a key role in supporting ESCO projects into the 1990s by providing 

incentives that offset energy efficiency project costs, as integrated resource plans found that 

investments in energy efficiency were lower-cost alternative to new power plants. 

The 1990s began with the Clean Air Act Amendments 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 needed 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 voluntarily design to perform at superior levels of energy savings. 

President Clinton signed Executive Order 12845 in 1993 requiring federal agencies to purchase 

ENERGY STAR qualified products. The Pollution Prevention Act gave EPA 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, including federal appliance and equipment 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 development 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, DOE 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 covered 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 savings, though only slightly over half of the 

spending, was attributed to programs reported as targeting energy efficiency (ACEEE 2000). 

These utility programs accelerated the commercialization of advanced energy efficiency 

technologies, often by partnering with federal 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 restructuring policies, accompanied by increased political and regulatory 

pressures to hold down electricity prices (EPA and DOE 2006). Some restructured electric 

utilities shifted their focus away from regulated DSM programs to unregulated power marketing 

and ESCO businesses to deliver energy sales and services directly to customers. However, the 

business model for these market-based ESCOs limited their ability to reach all but the largest 

industrial, commercial and institutional customers. Recognizing the limits of private-market 

delivery of EE, and seeking to preserve the benefits of EE programs, as the more states 

restructured their electricity markets in the 1990s, some 20 states adopted public benefit funds 

(PBFs) to ensure continuation 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 implementation experience, began to be widely 

recognized as a legitimate, measureable, and low-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), which 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 weatherization services, included direct enactment of 

federal efficiency standards, and authorized a national consumer education campaign on energy 

efficiency (EPAct 2005). At the same time, however, the ESCO market declined due to an 

authorization lapse for the federal Energy Savings Performance Contracting (ESPC) program 

and loss of market confidence for industrial financing mechanisms (NAESCO 2011). This trend 

was soon reversed as ESCOs increased focus on state and local public buildings and innovated in 
14 

order to provide customers new technologies and financial models. Eventual resolution of the 

ESPC authorization program, and executive orders by both President Bush and President Obama 

have also required more aggressive energy savings in federal facilities (White House 2007, 2009, 

2015). 

By the mid-2000s, the electric utility and regional electricity markets also solidified the 

validity of rate-payer funded energy efficiency programs as system resources. In 2006, the 

National Action Plan for Energy Efficiency Leadership Group of more than 60 leading gas and 

electric utilities, state agencies, energy consumers, energy service providers, environmental 

groups, and energy efficiency organizations released five policy recommendations recognizing 

energy efficiency as a high-priority energy resource. States, utilities, and key stakeholders across 

49 states joined in making aggressive commitments to energy efficiency and endorsing the 

Action Plan recommendations (EPA and DOE 2006). 

In the years following the Action Plan’s efforts, about half the states enacted Energy 

Efficiency Resource Standards (EERS), which are long-term, quantified energy savings targets 

designed to drive ratepayer-funded program plans. These policy drivers lead to the evaluation, 

planning, and adoption of EE programs and associated 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 electricity market operator in New England began to 

allow energy efficiency to participate in regional capacity markets as part of a 2006 settlement 

agreement with their regulator, the Federal Energy Regulatory Commission (FERC). Since 2009, 

energy efficiency has been compensated for its measureable contribution to meeting New 

England’s regional capacity needs, helping to ensure sufficient electricity will be available in the 

future to reliably meet demand across the region. PJM, the wholesale electricity market operator 

across 13 states around the Mid-Atlantic region, also integrated energy efficiency into this 

capacity market rules in 2009 (Booz Allen 2012). 

In 2007, the Energy Independence and Security Act (EISA) passed due to growing 

concerns over both energy prices 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 programs, and included provision for 
15 

states to consider adopting policies to support greater energy efficiency. EISA also enacted the 

federal agency savings goals of President Bush’s 2007 Executive Order 13423, Strengthening 

Federal Environmental, Energy and Transportation 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, including job creation, bill savings and economic 

development. ARRA 2009 allocated DOE around $16 billion for energy efficiency programs, 

including appliance rebates, grants to local governments, state energy programs, weatherization 

assistance, and smart grid 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. These 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 numerous 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 portfolios (LBNL 2013b). The diversity represented by 

a typical portfolio of EE programs implemented by a utility (or other program administrator) is 

an important characteristic relevant to analysis of EE policies. Every detailed program type (as 

illustrated in the lower half of the figure) represents a unique set of characteristics including 

costs of energy saved, ratio of program to participant costs, investment life, scale, M&V 

approach, etc.6

                                                            

6 See following sections for discussion of these factors. 
16 

Figure 1. EE Program Portfolio7

Source: LBNL 2013b 

4.1.1 Administrators 

EE programs are administered by a variety of entities (“program administrators”) 

including utilities of all ownership types (investor-owned, municipals, and cooperatives), nonprofit and for-profit third-parties (e.g., 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 savings8

) are overseen by state utility 

commissions, which review and approve program plans, projected impacts, and associated 

budgets; and establish annual reporting and M&V requirements (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) requirements (34 states), demand-side management plan or multi-year 

EE budget (28 states), and statutory 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 their clear goals and proven 

success as a policy tool (ACEEE 2011). These policy 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 ratepayers 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 Regional Greenhouse Gas Initiative (RGGI) states and 

from EE resources bid into the forward capacity market operated by the New England 

Independent System Operator (NE-ISO). Ratepayer-funding accounts for more than 90% of total 

EE program support nationally. 

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 historically as well as in terms of future potential. Furthermore, 

EE programs are unique among state EE strategies in the comprehensiveness and transparency of 

their reported impacts, funding, and other 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 electricity savings potential achievable through 

expanded 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 rapidly 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 states 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 have helped to drive these rapid increases in EE program 

funding include EERS, electricity savings goals, and “all cost-effective EE” requirements. The 

adoption of EERS, in particular, increased through this period and clearly has been the primary 

driving force behind the increasing success of and investment in EE programs. Table 3 shows the 

number of states adopting 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 information (including costs and impacts) are 

annual EE program reports required by utility commissions, or cooperative or municipal utility 

boards of directors. Program impact results provided in these reports are based on evaluation, 

measurement, and verification (EM&V) procedures and methods used to estimate the electricity 
19 

savings from EE programs within the program portfolio. 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 additional sources including surveys of utility 

commissions and program administrators, and published their own annual reports that capture 

EE program impacts (ACEEE 2014c; CEE 2015).

The EPA has relied on the EIA Form 861 dataset for identifying historical impacts of EE 

programs by state, tribe and territory. Specifically, the reported sales data and incremental 

electricity savings in the 2013 EIA 861 dataset are used to estimate electricity EE impacts by 

state.11 EIA data is reported by program administrator (e.g., utility, third-party, or state agency) 

and requires the disaggregation of reported data by state for administrators with programs in 

multiple states (e.g., multi-state investor-owned utilities). Program administrators in 50 states 

and the District of Columbia reported savings in 2013. The EPA has compiled this information to 

derive aggregate electricity sales and incremental electricity savings at the state, tribal and 

territorial levels.12 

Three tribal areas (Navajo, Ute, and Fort Mojave) 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 sales and EE activities in these tribal areas and 

territories. The tribes are located in three states – Arizona, New Mexico, 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 sales in the “Navajo Tribal Utility Authority” 

(Navajo tribe) and the “Aha Macav Power Service” (Fort Mojave tribe). For New Mexico, this 

includes utility sales in the “Navajo Tribal Utility Authority” (Navajo tribe). For Utah, this 

                                                            

10 More information on EIA Form 861 can be found at http://www.eia.gov/electricity/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 electricity 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 survey 

encompasses most of each state’s utility sales, EIA makes an adjustment to account for the small remainder of data not included

in each utility’s entry. The adjusted data for “delivery” and “bundled” sales from all sectors are used to calculate sales at the state 

and territory levels. 
20 

includes utility sales in the “Navajo Tribal Utility Authority” (Navajo tribe) and the “Moon Lake 

Electric Assn, Inc.” (Ute tribe). 

Table 4 provides a summary of the estimated electricity savings data by state, tribal area 

and territory for the latest year of available data. At the national level, incremental electricity 

savings13 in 2013 was 0.66% of retail sales with individual state values ranging from 0.00% to 

2.00%. Cumulative electricity savings14 (representing the remaining impacts of programs from 

all prior years) reported at the national level for 2012 represent 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, which 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-year savings) represent the reduction in electricity use in a given year associated with 

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 savings) represent the reduction in electricity use in a given year from EE activities 

in that year and all preceding years, taking into account the lifetimes of installed measures. 15 EPA recognizes concerns associated with consistency and quality of 861 data that different reporting entities may have used 

different methodologies to estimate savings and the EIA 861 data are self-reported. Over time, there has been increased 

standardization in data reporting. We believe his dataset remains the most comprehensive publically available dataset. See section 

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 Laboratory (LBNL) published an update to a 2009 

analysis and projected future spending levels and savings through 2025 from EE programs 

funded by electric and gas utility customers in the United States under three scenarios (high, 

medium, and low cases) (LBNL 2013c). The scenarios represent “a range of potential outcomes 

under the current policy environment” and were based on detailed, bottom-up analysis of 

existing state EE policies. Significantly, the study presumes no new major policy developments 

such as a “national EE standard, clean energy standard, or carbon policy” and specifies that such 

policy changes could “result in customer-funded EE program spending and savings that exceed 

the values in our High Case.” 

The study concludes that efficiency programs are “poised for dramatic growth over the 

course of the next 10 to 15 years” with the most significant increases occurring in regions with 

lower levels of program spending, historically, including the Midwest and South. For example, 

under the medium scenario total U.S. spending on electric efficiency programs increase by 40% 

to $8.1 billion in 2025 from 2012 levels. Under the high scenario, spending more than doubles 

from 2012 levels to $12.2 billion in 2025. Incremental savings levels grow commensurately, to 

0.8% and 1.1% of sales under the medium and high scenarios, respectively. The study results 

indicate that under the high scenario 20 states would be achieving 1.5% or higher levels of 
23 

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 supporting the development of electricity savings targets, required 

savings levels under an EERS, or “all cost-effective” EE requirement. In conducting these 

studies, states and utilities have developed a methodology that is often described as a “bottomup, engineering-based” approach (EPA and DOE 2007a). EE potential studies are conducted at 

various geographic scopes (national, regional, state, and utility service territory level) and at 

different degrees of aggregation (e.g., economy-wide, sectoral, and program), and can be broadly 

grouped into a few types: technical, economic, market, and program.17

 Technical potential represents the theoretical maximum amount of energy use that 

could be displaced by efficiency, without regard to non-engineering constraints such 

as costs and the willingness of energy consumers to adopt the efficiency measures. It 

often assumes immediate implementation of all technologically feasible energy 

saving measures, with additional efficiency opportunities assumed as they arise. 

 Economic potential refers to the subset of the technical potential that is economically 

cost-effective. Definition of “economic potential” can vary to some degree by study. 

Some estimate economic potential by evaluating technology upfront 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 prices, product lifetime and discount rate, compared to a 

conventional alternative or the supply-side energy resources. Others incorporate 

consideration of consumer preferences in addition to consumers’ out-of-pocket 

expenditure when evaluating the economic potential. Both technical and economic 

potential estimates assume immediate implementation of efficiency measures without 

regard to technology adoption process or real-life program implementation. In 

addition, these estimates do not always reflect market failures or barriers that impede 

EE and often fail to capture transaction costs (e.g., administration, marketing, 

analysis, etc.) beyond the costs of efficiency measures. Another key factor 

determining economic potential is the level of aggregation at which the costeffectiveness evaluation is applied. Applying the cost test at lower levels of 

aggregation (e.g., at the measure or program level, 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 estimated amount of energy savings that can realistically be 

achieved, taking into account factors such as technology adoption process, market 

failures or barriers that inhibit technology adoption, transaction costs, consumer 

preferences, social and institutional constraints, and possibly the capability of 

programs and administrators to ramp up program activity over time. 

 Program potential refers to the subset of market potential that can be realized given 

specific program funding levels and designs. Program potential studies can consider 

scenarios ranging from a single program to a full portfolio of programs.18 

As mentioned, the EE industry standard for potential studies is the bottom-up, 

engineering evaluation of EE potential of individual end-use technologies 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 end-uses considered, aggregation 

level at which cost tests are applied, study period, discount rate, pattern of technology 

penetration, whether economically justified early replacement of technologies is allowed for, and 

                                                            

18 Each subsequent potential estimate described above is a subset of the previous potential estimate, e.g., the market potential is a 

subset of the economic potential, and the economic potential is a subset of the technical potential. 
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 for decades and in recent years numerous 

studies have been conducted at utility, 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 Appendix 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 those reviewed studies with more recent or 

overlooked studies. By focusing on studies conducted after 2008, 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 industrial) 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 percentage savings in the last year of the 

study by the duration (in years) of the study period. This is a common method of normalization 

for 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 achievable potential. Some studies use the terms “high,” 

“medium” and “low” achievable potential, while others used terms such as “high achievable” 

and “achievable” potential or, in some cases “achievable” and “programmatic” potential. For 

studies that provided multiple levels of achievable potential, the highest value was classified as 

high potential and the lowest value was classified as low potential. 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 conservative, the EPA has included these low values but 

                                                            

19 Because of the complex consumer behavior, energy market and macroeconomic drivers of energy use and energy efficiency, 

and in some cases due to the lack of consistent data, quantifying energy efficiency potential and energy savings from policies and 

programs remains a challenging analytical task. Assumptions about consumer technology adoption behavior, market barriers and 

failures, and how technology diffusion occurs can also affect estimated potential. 
26 

generally consider the high achievable values to be the more appropriate point of comparison in 

evaluating the magnitude and timing of savings represented 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, average annual achievable potential ranges from 0.9% to 1.3% per year 

for low to high potential estimates, respectively. Results vary between end-use sectors with the 

industrial sector showing less potential than residential and commercial 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, lower costs of meeting electricity supply needs, energy reduction, 

environment and health benefits, and local economic development benefits (EPA and DOE 

2006). Most states evaluate their EE policy options through the application of cost tests, 

weighing the projected benefits with the costs of the EE technologies and practices (EPA and 

DOE 2008; RAP 2012). Each state determines their own policies for the specific costs and 

benefits to include in these tests. The costs and benefits are compared on an equal footing by 

using present value analysis. This is necessary because EE typically requires primarily upfront 

expenditures (e.g., a whole home retrofit) while the economic benefits (e.g., electricity bill 

savings) accrue over the life of the investment (“measure life”) which can range from a few to 

twenty or more years. As such, the choice of discount rate and the estimation of measure life are 

                                                            

20All 53 of the studies reported results for all sectors but many did not report results for one or more of the individual sectors. At 

least 30 studies reported results for each sector shown in the table. 
27 

significant determinants of the cost-effectiveness results. Most states employ multiple tests, 

adjusting cost and benefit categories depending upon the economic perspective of interest (e.g., 

utility, ratepayer, program participant, society), and consider the results from each one, usually 

with an identified primary test type. 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 customer installing the measure. 

Costs may include incremental equipment and installation costs; benefits include 

incentive payments, bill savings, and applicable tax credits or incentives. 

2. Utility/program administrator cost test from the perspective of utility, government 

agency or third-party implementing the program. Costs may include program 

incentive, installation, and overhead costs; benefits may include avoided energy and 

capacity costs - including generation, transmission and distribution - by the utility. 

3. Ratepayer impact measure test from the perspective of utility ratepayers not 

participating in available EE programs. This text includes the costs and benefits that 

will affect utility rates, including program and administration costs, as well as “lost 

revenues” to the utility; benefits include avoided energy and capacity costs, and 

additional resource savings. 

4. Total resource cost test from the perspective of all utility customers in the service 

area. Costs may include the full incremental cost of the measure, program installation 

and overhead costs; benefits may include avoided energy and capacity costs, and 

additional resource savings. 

5. Societal cost test from the social perspective. In addition to benefits considered in 

total resource cost test, may also include non-monetized benefits such as 

environmental and health benefits. 

While many states consider more than one cost test in evaluating EE programs, the most 

commonly used (29 states) primary test is the total resources cost test. This test is considered to 

be the best measure of the interests of all utility customers. The utility and societal cost tests are 

the next most commonly used primary tests, used by five states each. The utility cost test is 
28 

considered to be the most comparable metric to compare with supply-side resource investments 

from a utility resource planning perspective. 

Economic and modeling analyses of climate change policy suggests that EE presents a 

large potential in reducing greenhouse gas emissions and plays a critical role in offsetting the 

costs and enhancing the flexibility to achieve long-term GHG reduction targets (Kriegler et al. 

2014; Kyle et al. 2011). Consistently, evaluations of the economic 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 potential (EPA and DOE 2009). For example, 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 resource options within utility 

resource plans is the levelized cost of energy (LCOE) 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 fossil-fueled or nuclear power plants, or renewable 

energy resources like wind or solar-power generation. In these comparisons, typically only utility 

(or program) costs are considered, not the total costs of saved energy that are discussed later in 

this chapter. The EE analysis literature reports 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 reviewed cost-effectiveness analysis results 

in nine states and suggested that reported utility LCSE ranged between 2.3-4.4 cents/kWh, with a 

mean value of 3 cents/kWh (ACEEE 2004). In 2014, an LBNL analyzed the program cost of 

                                                            

21 Unless otherwise noted, estimates of LCSE discussed in this section refer to program administrator 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 Program 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). LBNL 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 modeling), although this body of studies is much 

smaller compared to the bottom-up, engineering-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 cost 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 suggest 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-specific 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 average cost of 2.5-3.5 cents/kWh (RFF 2011). 

A number of analytical and data considerations related to LCSE estimation are also 

discussed in the literature, including the issue of “free riders” in EE programs, and the accuracy 

of utility reported costs and energy savings (Train 1994; Joskow and Marron 1992). EE 

practitioners also recognize the need to consider “free rider” and “spillover” effects in program 

evaluation. A slight majority of states adjust for free ridership in energy savings estimates, 

leading to higher LCSE values than otherwise would be the case (ACEEE 2012a). A smaller 

number of states adjust for spillover effects which reduce LCSE values when addressed . 

Another consideration related to LCSE estimation is the rebound effect. The economic 

literature has extensive discussion of the potential rebound effect, market interactions and 
30 

economy-wide response of EE policies and investments. An improvement in EE would 

effectively reduce the cost of a service or production input, potentially boosting its demand or 

production output thus increasing 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” rebound). Reviews suggest that both direct and 

indirect rebound effects exist and the size of such effects varies among different studies, 

technologies, sectors and income groups (UKERC 2007). Overall, however, rebound effects are 

found to be relatively modest compared to the importance 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, emissions reductions and other 

benefits. In this section we address a few of the more significant opportunities for realizing 

additional EE potential: building energy codes, state appliance standards, energy service 

performance contracting, and volt/VAR optimization. 

5.1 Building Energy Codes 

5.1.1 Overview 

Building energy codes establish minimum efficiency 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 construction 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’ incentives 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 eliminate inefficient technologies and 

building practices, minimizing total ownership and operating costs over the life of the building. 

Additional co-benefits of implementing building energy codes include pollution prevention, 

improved electric system reliability, avoidance of new energy supply investment, and improved 
31 

occupant comfort and health. Energy building code requirements are also designed to overcome 

the complexity of advanced codes, lack of local-level implementation resources, and a shortage 

of empirical data on the costs and benefits of codes. 

Building energy codes specify thermal performance22 criteria for building thermal 

envelope components such as walls, ceilings, floors, and windows, and also set air leakage and 

duct leakage standards. They also address lighting system efficiency, hot water system 

efficiency, and heating and cooling equipment efficiency in cases where federal standards do not 

cover the affected equipment or system types. For the residential sector, the International Energy 

Conservation Code (IECC) is the prevailing model code, and ASHRAE 90.1 is the model code 

for the commercial sector (EPA 2014b). Various versions 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 developed at the national or international 

level, adopted at the state or local level, and administered and enforced locally. Local building 

industry parties, such as developers and property owners requiring building permits, are the most 

common obligated parties (EPA 2014b). Federal authority affecting state code adoption was 

established in the Energy Policy 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 determine 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 commercial building energy codes per the 

current DOE determination. 

5.1.3 Building Code Regulations Over Time 

Federal authority for building codes was established in Energy Conservation and 

Production Act of 1976 (ECPA), which authorized DOE to create national Building Energy 

Performance Standards (BEPS) that would be legally binding nationwide. The BEPS provision 

was repealed by Congress in the early 1980s. States, 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, the number of states adopting residential and 

commercial energy codes has remained relatively stable at about 40 (EPA 2006, 2015). Figure 2 

shows this consistency for residential energy codes, as the states with the lightest shading are the 

ones that had either not adopted or recently updated their energy codes. However, the stringency 

of these codes has been updated multiple times since 2005 in most states. Eleven of the 40 states 

with residential building codes are using the 2012 IECC version, which DOE has determined 

would improve the energy efficiency of residential buildings by approximately 30 percent 

compared to the 2006 IECC (EPA 2015). A recent 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 financial benefit of nearly $2 billion annually by 2015 and over $15 

billion annually by 2030 (DOE 2014). 
33 

Figure 2. Residential Energy Building Code Adoption (2005 to 2014) 

Source: EPA 2006 and EPA 2015 

5.1.4 Remaining Market Potential 

There is still significant potential for savings from building 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 metric tons. From 2013 to 2040, the continued 

adoption of increasingly stringent 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 for energy savings by 2030 was 155 million megawatthours (MWh) annually, or 4.2 percent of annual electricity consumption relative to 2012 

(ACEEE 2014a). 
34 

5.2 State Appliance Standards 

5.2.1 Overview 

State appliance standards establish minimum 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 sale of less efficient models within a state. 

States are finding that appliance standards offer a cost-effective strategy for improving energy 

efficiency and lowering energy costs for businesses and consumers, though these standards are 

superseded when federal standards are enacted for new product categories (EPA 2014b). 

The key objectives of appliance efficiency standards are to: 

 Raise the efficiency of a range of residential 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-purchase23 situations in 

which appliances break and must be replaced on an emergency basis. 

 Ensure energy use reductions to prevent criteria air pollution and greenhouse 

emissions, improve electric system reliability, and reduce consumer energy bills 

(EPA 2015). 

While state appliance standards can be useful in testing and exploring 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 certain period of time has passed since the federal 

standard has been updated (EPA 2014b). 

5.2.2 Authority / Obligated Parties 

Establishing efficiency standards in a state typically requires enabling legislation from 

the state legislature. However, once legislation is enacted, it may allow an executive agency to 

set further standards administratively (EPA 2015). 

                                                            

23 In a panic purchase, customers usually do not have the time to consider a range of models, features, and efficiency levels, and 

the full range may not be available from all suppliers. 
35 

State energy offices, which typically administer the federal state energy program funds, 

have generally acted as the administrative lead for implementing standards. In contrast, 

inspection and enforcement of appliance standards regulations has typically involved selfpolicing among industry. Manufacturers of products being sold in a given state are typically 

obligated to ensure their appliances meet the appropriate energy efficiency standards (EPA 

2014b). 

5.2.3 Appliance Standards over Time 

California’s appliance standards program dates to the 1970s, when the state began to 

pursue standards before the enactment of federal legislation. When the federal government opted 

not to issue standards under its legislative mandate in 1982, other states joined California in 

developing state standards. These state initiatives helped create the impetus for new federal 

legislation in 1987 (the National Appliance Energy Conservation Act or NAECA), the Energy 

Policy Acts (EPAct) in 1992 and 2005, and the Energy Independence and Security Act (EISA) in 

2007 (EPA 2015). These federal laws have established appliance efficiency standards for more 

than 50 products, representing about 90% of home energy use, 60% of commercial building 

energy use, and about 30% of industrial energy 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. electricity use in 2010. By 2025, total electricity savings 

from existing federal standards are projected to increase to 682 TWh per 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 state action on federally covered consumer 

products, California has continued to develop efficiency standards for other products and 

technologies that aren’t included in the Act. California’s standards program has contributed to 

substantial improvements in energy efficiency. Since its inception, the program has saved 

consumers over $75 billion on electricity bills (CEC 2013).

Since 2001, 15 states and the District of Columbia have enacted appliance efficiency 

standards. While most of these individual standards 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 energy efficiency are "technically feasible and economically justified" (DOE 2015b). 
36 

as of February 2014, 12 states25 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 continue to expand, the direct economic and 

environmental benefits of state standards are still 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 million 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 states 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 standards. The three products with the greatest 

potential in the residential, commercial and industrial, and lighting sectors are shown, along with 

a summary line for each sector. Some of the greatest potential savings in 2035 are in the 

residential sector, specifically improvements in water heater efficiency. Implementing all of 

these standards through federal regulations would result in annual savings of 212 TWh in 2025, 

or 5% of projected national electricity consumption (ASAP 2012; EIA 2015). 

Table 7. Estimated Energy Savings Potential 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, California, Connecticut, Georgia, Maryland, Nevada, 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 energy conservation measures (HUD 2014). Under an 

EPC program, an Energy Service Company (ESCO) first conducts an energy audit. The ESCO 

then designs and constructs a project that achieves 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 not 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 state, 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 legislation, covering a combination of 

entities that include public agencies, school districts, municipalities, state colleges and 

universities, counties, or the state as a whole (ORNL 2015).26 While ESPC programs are already 

widespread, states have found that they can further utilize this approach by extending eligibility 

to all public facilities in the state (EPA 2015). 

                                                            

26 Wyoming currently has no performance contracting legislation. 
38 

While ESCOs deliver ESPC projects, and the ESPC 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 about 70 percent (ICF 2007; LBNL 2014a). The other 

30 percent of the market is comprised of design/build services; engineering, procurement, and 

construction services; consulting services; and operation and maintenance contracts. These 

services are not considered to be EPC projects since the project financial 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 the 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 management 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 (IRPs) where energy efficiency measures could be 

used to meet projected demand. ESCOs were used to deliver energy savings from projects 

implemented at industrial and institutional sites, relying on advances in energy efficiency 

monitoring and verification techniques (ICF 2007). 

By the late 1990s, funding for utility-sponsored energy efficiency programs was reduced 

in about half of the states due to changed regulatory structures and increased political and 

regulatory pressures to maintain lower electricity prices (EPA 2006). Many restructured electric 

utilities shifted their focus away 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 larger industrial, commercial and institutional 

customers. Several states and local governments also turned to EPC activities to provide services 

in public facilities via lead-by-example policies (see section 3). These governments also saw the 

benefits of addressing capital equipment and maintenance projects that would not have otherwise 

been addressed through the capital budget process (ICF 2007). Commercial lenders also became 

involved in EPC funding, lowering the cost of projects through 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 federal buildings and market confidence for industrial 
39 

financing mechanisms (NAESCO 2011). This trend 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 financial 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 between $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 health and hospital sectors have the highest remaining potential 

for ESCOs. Commercial buildings in particular have high barriers to implementing energy 

efficiency under EPC activities, as the private sector is generally averse to financing energy 

efficiency activities and has more stringent requirements for a lower payback period on projects 

(LBNL 2013a). While the two markets do not overlap completely, the potential for ESCO 

investments provide a strong indicator for the 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 coordinated efforts by utilities to manage and 

improve the delivery of real and reactive power27 in order to increase the efficiency of electricity 

distribution. VVO is accomplished primarily through the implementation of Smart Grid 

technologies that improve the real-time response to the demand for real and reactive power. 

Technologies for VVO include load tap changers and voltage regulators, which can help manage 

voltage levels, as well as capacitor banks, which allow for the delivery of reactive power. By 

using local capacitor banks to respond to reactive power demand, rather than adjusting the output 

of remote generators, systems can achieve reductions in transmission line loss. The U.S. Energy 

Information Administration (EIA) estimates that on average, 7.5% of generated electricity is lost 

                                                            

27 Reactive power is experienced in alternating current systems, and is the result of a phase shift between the current and voltage. 

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 conservation 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 required 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 implementation of VVO technologies allows electricity 

distributors to deliver electricity initially at a lower voltage, thus decreasing the generation fuel 

requirements, while still delivering within the acceptable range to all 7 loads. The combination of 

VVO and CVR can lead to a range of benefits, including reductions in peak 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 renewed interest in VVO and CVR as the potential 

for increased efficiency has become more apparent. Of the 99 projects funded by the Smart Grid 

Investment Grant program under the American Recovery and Reinvestment Act of 2009, 26 
43 

included the implementation of VVO technologies. These projects, which target reduced voltage 

during peak demand, electricity conservation due to reduced voltage, and reduced losses from 

feeder lines, are expected to achieve a 1% reduction in energy consumption for every 1% 

reduction in voltage level (DOE 2012a). 

There is no formal state requirement directing implementation of Volt/VAR optimization. 

However, in 2012, the National Association of Regulatory Utility Commissioners published a 

resolution in support of VVO implementation at the state-level. The resolution encouraged 

public utility commissions to work with state legislature to include deployment of VVO 

technologies as part of energy 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-benefit ratio for transmission and distribution 

optimization is positive and well understood, and that 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 these benefits (McKinsey 

2010). IHS Technology estimates that the shipments of VVO technology 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 higher energy savings from 

programs (IEI 2014). Further, all states which regulate 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 highest per capita spending on energy 
44 

efficiency programs have the most comprehensive regulatory models for energy efficiency in 

place (NRDC 2012). 

The most common regulatory models for incentivizing energy efficiency through electric 

utilities include decoupling revenue from sales volumes, ensuring program cost recovery, and 

providing shareholder incentives linked to program performance. The Edison Foundation reports 

on the adoption of these policies across states through its Institute for Electric Innovation, as 

shown in Table 8. 

Table 8. Summary of State Regulatory Frameworks: December 2014

Energy Efficiency Incentive 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 2014). In 2006, the need for such policies was 

recognized by the National Action Plan for Energy Efficiency Leadership Group of gas and 

electric utilities, state agencies, energy consumers, 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 recommendations which included modifying utility 

incentives (EPA 2006). In 2014, the Edison Electric Institute (EEI) and the Natural Resources 

Defense Council (NRDC) issued a joint statement urging state utility regulators to adopt a 

number of policies to sustain utility financial health given the greater investment being made in 

both energy efficiency and customer-sited electricity generation (NRDC 2014). 

The need for states to adopt utility financial 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 disincentive to invest in energy efficiency since such 

programs will reduce sales volume. A decoupling policy counteracts the financial incentive to 
45 

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 reduce 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 the lost revenue associated with not selling energy due to energy 

efficiency programs. 

Reduced electricity sales may affect utility finances in additional ways, including 

reducing the utility’s opportunity to earn additional profit from the regulated rate of return on 

new capital projects which would otherwise be needed to meet higher load growth, such as new 

generators and transformers upgrades. As an alternative, performance incentive policies are used 

to allow utilities to earn a profit 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 utility is often informed by electricity resource 

planning. Most states require one or more forms of electricity resource planning, such processes 

used to inform long-term integrated resources plans (IRPs) and regulatory approvals for large 

projects often referred to as Certificates of Public Convenience and Necessity (CPCNs). IRPs 

are required or present in more than 30 states, including most vertically integrated states, and at 

least 19 states have some form of CPCN (EPA 2015). Incorporating energy 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 efficiency 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 response (UtilityDIVE 2015). A significant number of 

the utilities surveyed see 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 their public utility commissions to examine and 
46 

encourage changes to the utility business model.28 Both of these state are 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 consideration of tradable energy 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 savings through utility energy efficiency programs.29 State 

EERS as a key policy strategy and the use of EECs for compliance were concepts borrowed from 

the earlier adoption of renewable portfolio standards (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 generation, tracking, and trading of energy efficiency credits could 

work as a means for supporting compliance with EERS or as a mechanism for achieving 

greenhouse gas reductions (NREL 2008; 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 systems 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 levels 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 extended its certificate tracking system to energy 

efficiency, and registered the first voluntary energy efficiency certificates for IBM Corporation 

                                                            

28 For more information, see the New York Reforming Energy Vision proceeding 

(http://www3.dps.ny.gov/W/PSCWeb.nsf/All/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/Commissions-Inclinations.pdf). 

29 Many terms have been used or suggested including energy efficiency certificates, energy savings 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 instruments that recognize the meter-measured, performance-based results 

of energy efficiency projects. In June 2012, Sterling Planet completed the first White Tags 

transaction by purchasing Michigan’s Selfridge Air National Guard Base’s 2011 White Tags in 

order to fulfill energy efficiency compliance requirements for a Michigan utility (Sterling Planet 

2012). The Climate Registry has a program called “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 policies 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 and parties. Table 9 summarizes the types of 

energy savings and trading included in policy design. Over half of these policies allow for 

trading through the creation 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 by 

obligated 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-obligated 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 implemented 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 provided by programs in Italy and in Australia 

(New South Wales) that have been trading EECs 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 international climate commitments (Italy 2014). In implementing this 

policy, electricity and gas distribution utilities use EECs to meet established energy savings 

targets. These utilities may either generate their own EECs or buy from third parties through the 

market. Approximately one-third of EECs are generated by third parties and subsequently trade 

on the open market. EECs are also exchanged between energy efficiency providers and utilities 

through bilateral contracts.32 The majority of energy savings from the Italian White Certificates 

system are realized from reducing electricity usage through a set of pre-established eligible 

project types (IEA 2012a). 

                                                            

32 From 2005-2009, electricity savings met 77% of the overall energy savings targets, as reported in The Fifth National 

Communication under the UN Framework Convention on Climate Change, submitted by Italy in November 2009. 
49 

In Australia, the state of New South Wales initially incorporated EEC trading as part of 

their Greenhouse Gas Emissions Trading scheme. In 2009, New South Wales began trading 

EECs via the Energy Savings Scheme recognizing that unique barriers to energy efficiency 

persisted. Obligated parties, which include electric utilities, are required to acquire and surrender 

EECs to meet the energy savings targets of the Energy Savings Scheme. Other organizations also 

use EECs to voluntarily reduce their greenhouse gas emissions. Energy efficiency projects across 

residential, commercial and industrial sectors are used to create 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 “Accredited Certificate Provider” may create EECs, the 

Energy Savings Scheme policy has fostered the development 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: Magnitude 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 approaches that states might pursue as they develop 

their state plans. These approaches are embodied in the illustrative 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, either directly recognized towards compliance with a 

rate-based goal or as a complimentary approach 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 level of demand reduction that could be achieved, 

and the associated costs incurred, through implementation of demand-side EE measures. In this 

section, the EPA provides the basis for the illustrative demand-side EE plan scenario that is 
50 

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 derived 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 other opportunities to reduce CO2 emissions from the power sector, overall 

cost-effectiveness, and the quality of available information 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, although there are other strategies and measures that states may 

employ for purposes of the final CPP. For example, the final rule provides states the flexibility to 

use building codes, appliance standards, and energy service performance contracting (to name a 

few examples), and those strategies also offer substantial, low-cost opportunities to reduce CO2

emissions from affected EGUs. 

8.2 Inputs 

The following steps were taken to establish the inputs for development of the illustrative 

demand-side EE plan scenario for each state, tribal area, and territory: 

 Step 1: Determine current level of demand-side EE performance 

 Step 2: Determine EE plan scenario 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 measure life and distribution of measure lives 

 Step 7: Determine sustainability of EE plan scenario level of performance 

8.2.1 Step 1: Determine Current Level of Demand-Side EE Performance 

A fundamental indicator of the level of EE program performance is incremental annual 

savings (also known as first-year savings) as a percent of retail sales. This is a common metric 
51 

defining savings levels for state EERS and is readily calculated from EIA Form 861 data for each 

state, tribal area and territory (hereafter, just referred to as “states”). 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, the EPA aggregated the most recent (2013) EIA 

Form 861 data to the state level to establish their current level 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 (section 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 regional characteristics (e.g., avoided 

power system costs, economic growth, sectoral mix, climate, and level of past EE efforts) affect 

estimates of achievable potential, ongoing improvements in energy-efficient technologies and 

practices, economic growth, population 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 incremental levels of energy savings performance is 

provided by past performance at the state and utility levels, and by requirements states have put 

in place for levels of savings to be achieved by 2020. As discussed, these 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%, and >= 1.5%). As shown, three states achieved 

the highest level of performance (> 1.5%) and an additional fourteen states achieved the second 

highest level of performance (1.0% to 1.49%). 

                                                            

33 Estimates of cumulative savings impacts in a given year are derived from incremental savings values and information on 

measure lives. Information on measure lives is less consistently gathered than is information on incremental savings values. 
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 all 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 incremental 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 2013 
53 

Table 13. 2013 Incremental Annual Savings – 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 levels 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 additional eight states are required to achieve the 

next highest level of performance (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 scenario level of performance, the EPA has 

determined that a 1.0% incremental savings, as a percentage of retail sales, is appropriate. This 

level was achieved by seventeen states and by numerous utilities of all ownership types in 2013. 

An additional two states (CO and HI), accounting for 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 Year for EE Plan Scenario 

For construction of the EE plan scenario, the EPA has determined that 2020 is 

appropriate as the first year for state demand-side EE efforts. The EPA believes this is reasonable 

for the illustrative plan scenario as efforts are ramped up in advance of the 2022-2029 interim 

plan performance period. This allows sufficient time – for than four years – after issuance of the 

final rule for planning of these efforts. This reflects a three year delay from the 2017 start year 

used for the proposal. 

8.2.4 Step 4: Determine Start Year Level of Performance 

For construction of the best practices scenario, the EPA has set each state’s level of 

performance in the start year (2020) to the level of performance observed in 2013, based on the 

aggregate state-level incremental electricity savings based on EIA Form 861 data (Table 4). 

8.2.5 Step 5: Determine Pace of Improvement from Start Year to EE Plan Scenario Level 

of Performance 

To determine a reasonable trajectory of incremental savings levels from the 2020 level to 

the EE plan level of performance, the EPA considered past performance of individual program 

administrators34 as well as requirements of existing state EERS. For the past performance of 

individual program administrators, the EPA first screened the data and divided them into 

moderate and high performing sub-groups. The moderate group (47 entities) was defined as 

programs that achieved from 0.8% to 1.5% maximum incremental savings levels and the high 

group (26 entities) was defined as programs that achieved greater than 1.5% maximum 

incremental savings levels. The EPA then calculated the rate at which each entity had increased 

savings over time and calculated average values for each sub-group. For the moderate group, the 

average rate of improvement of incremental annual savings rate was 0.30% per year. For the 

high group, the average rate of improvement of incremental annual savings rate was 0.38% per 

year. See Appendix 2 for supporting data and analysis. 

EPA also considered requirements of existing 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 identified ten states with clear schedules and 

                                                            

34 EIA 861 was the primary data source; however, EIA 861 data was supplemented with data 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 for each. The average rate of improvement of 

incremental annual savings rate required for these 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 performance 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 scenario 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 from 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 and all prior years, that still have remaining savings impacts in the 

given year. The cumulative savings account for the continuing impacts of EE measures that 

remain in place for a period of time (the “measure life”) before being replaced. For example, the 

purchase of a high-efficiency refrigerator may lead to savings for twelve years, before being 

replaced with a new model. To estimate cumulative impacts of a series of years of incremental 

savings, the industry uses the concept of an average measure life for the entire portfolio of EE 

programs 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 behavioral feedback) to as long as 

twenty years or more (e.g., residential HVAC, residential building insulation), the EPA is 

assuming a distribution of measure lives around the average to account for future impacts of 

incremental savings levels. 

To approximate a distribution of measure 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 distribution of EE program lifetimes (LBNL 2015a). The 

analysis was based on the LBNL DSM Program Database. 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 database of nearly 6,000 program years of data, 
56 

program savings lifetimes are available for about 1,600 program years (27% of the program 

years). More than 50 utilities and other EE program administrators in 25 states contributed to 

those data through their regulatory filings, statewide 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 measure lifetimes of the EE programs into four tiers 

based on a cluster analysis – a statistical approach for grouping values based on their similarity.35

The four-tier distribution 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 distribution of the EE program measure lifetimes, and the 

associated weighted average measure lifetime and 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 differences within each cluster and maximize differences among the clusters. In this case, 

the programs within each cluster would have similar lifetimes and program types. 
57 

The weighted-average EE program measure lifetime of 10.2 years 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 based on electricity EE program data across all 

sectors in 20 states (ACEEE 2014d). A more common approach in other 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 approach used here is a conservative one, leading 

to the same quantity of total energy savings, but with a greater portion of the savings occurring in 

later years than 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 scenario, once a state achieves the best practices 

level of performance, the EPA has kept the level of performance constant 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 sustained nationwide previously. The Northwest Power 

Conservation Council (NPCC) provides one such example. NPCC has been conducting the most 

consistent and long-running series of evaluations of achievable cost-effective potential in the 

country, updated every five years, as part of their five-state36 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 programs have increased rapidly, more than quadrupling between 

1998 and 2010 (while levelized costs of saved energy achieved have remained flat), and 

exceeding plan targets every year since 2005. A study of the NPCC results concludes: “our 

research shows that when programs invest in higher levels of efficiency, this helps drive 

measurement improvements and technical innovation, resulting in larger and more reliable 

conservation supply estimates” (NPCC 2008). Table 15 summarizes the NPCC’s achievable 

potential estimates and evaluated 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 with the rates resulting 

from the best practices scenario. 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 the demand-side EE plan scenario. The pace 

of improvements, average measure life, and distribution 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 that accounted for potential impacts of other EE strategies such as 

building energy codes, state appliance standards, or energy services performance contracting. 

Table 16. Summary of Demand-Side 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 8.2 
59 

8.3 Calculations 

This section presents the calculations for determining 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 inputs summarized in Table 16. 

Calculating the net cumulative savings of the demand-side EE plan scenario involves six 

steps. For each state, for each year (2020-2030) the following steps are taken: 

1. Determine annual business-as-usual (BAU) sales 

2. Determine annual incremental EE savings as a percentage of sales 

3. Determine annual incremental EE savings (GWh) 

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 are 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 sales to the state level using EIA Form 861 

data and increasing the value for each subsequent year by the average annual growth rate 

(AAGR) from the AEO 2015 Reference Case for 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 corresponding 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 savings as a percentage of sales is set at 

the 2013 value based upon aggregated EIA-861 reported data (see Table 4). For South Carolina 

that value is 0.46%. This value is then increased 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 percentage 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) 

385 553 721 834 833 833 832 832 
61 

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 values 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 Cumulative EE Savings (GWh) 

Net cumulative EE savings in a given year are equal to annual incremental savings for 

that year minus total expiring savings for that year plus net cumulative savings for the prior year. 

The resulting values are 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 subtracting 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 values 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) 

385 553 721 834 833 833 832 832 
63 

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 savings 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 value for the average annual savings rate represented in the 

demand-side EE plan scenario are below the range 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 feasibility of the demand-side EE plan scenario and 

associated state-specific results. 
66 

8.4.3 Representation of Demand-Side EE in Modeling of the Illustrative Plan Scenario 

To reflect the implementation of the illustrative energy efficiency plan scenario in power 

sector modeling, as discussed in the RIA (Chapter 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 proportionally across all segments (peak and non-peak) of the load 

duration curve. This approach to allocating annual electricity reductions is consistent with the 

significant levels of savings represented in the illustrative demand-side plan scenario. Achieving 

1.0% incremental savings typically require robust 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 annual load duration curve with higher periods of 

demand providing proportionally greater EE potential. 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 detailed results of these studies indicate 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 represented by the EE plan scenario. Chapter 3 of the 

Regulatory Impacts Analysis (RIA) presents illustrative analysis of the final rule by showing 

approaches that states might pursue as they develop their state plans. The scenarios used in the 

RIA are the basis for costs, benefits, and other power sector impacts of the final rule. The final 

rule allows states to use demand-side EE as a component of their state plan strategy, either 

directly recognized towards compliance with a rate-based goal or as a complimentary approach 

for achieving a mass-based goal. The EPA has included in the illustrative plan scenarios (both 

rate- and mass-based) a level of demand reduction that could be achieved, and the associated 
67 

costs incurred, through implementation of demand-side EE measures as shown in this TSD. In 

this section, the EPA provides the 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 the 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 power sector modeling based upon optimization of 

costs but, rather, “hard wired” into the illustrative plan scenarios. The electricity savings 

represented in the demand-side EE plan scenario lead to substantial reductions in power system 

costs due to the reductions in specified electricity demand. Since EE is not represented 

endogenously as an abatement measure within the power sector modeling, the costs associated 

with the EE plan scenario must be estimated outside of the power sector modeling and integrated 

with the results from that modeling. These EE cost estimates, their basis, and calculations are 

addressed in the following sections. 

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 Incremental Electricity Savings 

Results from section 8 provide the starting basis for estimation 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 costs (e.g., incentive payments, administration, 

technical support, marketing, evaluation, measurement and verification (EM&V), information to 

consumers, etc.) incurred by a utility or other administrator of EE programs in a given year that 
68 

lead to EE measures (technologies and practices) put in place in that year, and result in 

reductions in electricity demand in that and future years (driven by the mix of measure lives 

across the portfolio of EE programs employed). First-year program costs are represented as these 

costs divided by the incremental (or first-year) savings for that year. 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 demand-side EE plan scenario. 

Recent studies have collected and analyzed first-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 reports, and information 

compiled from contacts at program administers in 14 states (ACEEE 2009). The program cost 

data were compiled from multiple EE program administrators over multiple years in each state. 

ACEEE found average first-year net37 program costs of $275/MWh (2011$). Two newer national 

analyses have found lower program costs than the 2009 ACEEE study. In 2014, ACEEE updated 

their analysis from 2009, expanding the number of states to 20, and including 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 program costs (LBNL 2014b). The LBNL analysis 

reviewed program-level data from over 100 program administrators in 31 states. Data were 

collected from over 1,700 individual programs for up to three years (2009-2011), covering more 

than 4,000 individual program-years data points. Because of the broad scope of their study and 

the lack of net savings information for many programs, 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 average net-to-gross ratio of 0.9 and deflating costs at 1.8%, 

results in an estimated national average first-year cost of net savings of $177 (2011$).39 The 

results cited from these three studies represent average first-year costs from program 

administrators across the U.S. 

                                                            

37 “Net costs” refers to costs per electricity saved after accounting for effects of free-ridership on those savings. Depending upon 

the state, spillover effects may also be accounted for in net costs. 

38 “Gross savings” refers to electricity savings before any accounting for effects of free-ridership or spillover. 39 Source: BEA National Income and Product Accounts Table 1.5.4 Price Indexes for Gross Domestic 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 larger EE programs and larger 

portfolios of EE programs, and learning and expertise gained over time from the continued 

implementation of programs, are two factors that would lower costs as programs scale up and 

expand to realize higher levels of performance (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 achieved. Several analyses of EE program costs and 

savings based on empirical data are 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 levels are achieved (Synapse 2008; GEEG 2012; LBNL 2015b). Based 

on analysis like these, a recent LBNL study adopted 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 data 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 scenario. The first tier is used for 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 the first tier are set at two 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, these 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 readily known and consistently tracked and 

reported by program administrators, participant costs are not easily known, and are less 

consistently estimated and reported. The ratio between program and participant costs will vary 
70 

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 administrators in 22 states) and 

found that on average program costs represented 53% of total costs (with participant costs 

representing the remaining 47%) (EPA 2014a). Based on this analysis, the EPA used a ratio of 1-

to-1 for program to participant costs for the EE cost estimates contained in the CPP proposed 

rule. In April 2015 the Lawrence Berkeley National 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 the use of the 1-to-1 ratio for the current 

analysis. This ratio is used to derive participant and total costs based upon program costs. 

9.2.4 Summary of Inputs for Demand-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 factors, 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 9.2 
71 

9.3 Calculations 

This section addresses the methodology for calculating the cost associated with the EE 

levels included in the illustrative plan scenarios. Specifically, three values are calculated (annual 

first-year costs, levelized cost of saved energy (LCSE), and annualized costs); for each, program 

and participant components are then calculated using the 1:1 ratio (i.e., 50% of total for each) 

derived above. Specific results from prior sections on timing and magnitude of savings, and costs 

inputs are used for these calculations. For each state, 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) for 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 Annual First-Year Costs 

Annual total first-year costs are calculated 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). Program 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 880 880 660 660 660 660 660 
72 

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 appropriately allocate 

the cost of a single year EE program over declining lifetime savings. The net present value of all 

savings from a single year’s EE activities (i.e., over the entire distribution of program lifetimes) 

is calculated using the real discount rate rates of 3% and 7%. The levelized cost of saved energy 

is then calculated by dividing the annual first-year costs (from Table 28) by the net present value 

of the savings. The resulting values are summarized for South Carolina in Table 29. 

Table 29. Levelized Cost of Saved Energy for 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 38.7 
73 

9.3.3 Step 3: Calculate Annualized Costs 

Annualized costs are calculated by multiplying the LCSE for each year by the estimated 

savings of the demand-side EE plan scenario in that 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 annualized 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 2027 

Total 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$) 

49.7 106.9 181.4 246.0 310.6 375.1 430.7 476.4 
74 

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 for 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 Costs (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 all states for all years in the spreadsheet file 

included as Appendix 3. The file includes results for LCSE (total, program and participant, 

discounted at 3% and 7%), first-year costs (total, program and participant), 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 reflecting the rapid recent 

growth in EE program spending is 30%, roughly six 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 CO2 Reduced 

For the CPP proposed rule, the EPA analyzed a scenario incorporating the reductions in 

electricity demand associated with the proposal’s “EE scenario” and compared 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 system 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 CO2 reductions for 

each year, the average cost of the CO2 reductions achieved were found to range from $16 to $24 

per metric tonne of CO2. These results confirmed the general cost-effectiveness of demand-side 

EE at the levels and costs of the proposal, and relative to the IPM base case 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 or 
76 

lower average costs per tonne of CO2 achieved through the demand-side EE plan scenario in the 

final rule. 

10. Analysis Considerations 

Two considerations are worth noting 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 representation of demand-side EE policies 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 Baseline 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 electricity savings for the demand-side EE plan scenario. 

The AEO 2015 reference case does not explicitly represent existing EE programs, the 

continuation of EE programs or policies, or future requirements (e.g., EERS) to achieve savings 

through such programs. For example, existing state 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 captured in part through a 

calibration process that is affected by several historic factors including reported electricity sales 

and sectoral energy consumption surveys. 

As noted, the estimates of electricity savings for the demand-side EE plan scenario do 

rely on AEO 2015 as a basis for base case electricity demand projections. The underlying 

mechanics of the calculations are such that modest changes in base case demand growth rates 

lead to minor impacts on cumulative savings levels relative to other inputs 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 representation of the effects of existing EE programs in the base 

case demand forecast would also, to some degree, affect estimates of the impacts of the final rule 

in the RIA. The effects of these considerations would have some degree of influence on the 

assessment of demand-side EE, however, for purposes of the RIA, the methodology for 
77 

determining levels of demand-side EE, and associated 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 effectiveness at the utility, state, and national 

scale. Issues related to the lack 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) programs, including their spending and energy 

savings impacts, nationally.40 The form is requested for electric utilities, electric power 

producers, energy service providers, wholesale power marketers, 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 electricity savings from EE and load management 

programs. 

This analysis uses only two EIA-861 data variables. Specifically, we use the 2013 sales 

data and reported incremental annual electricity savings of EE programs to estimate the current 

performance of EE programs (aggregated to the state level) to inform the illustrative demandside EE plan scenario discussed in this chapter. 

EPA notes potential concerns associated with consistency and quality 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 administrators, utilities, and states, and may vary by data year. 

Over time, however, the data quality has improved significantly and there is increased 

standardization in data reporting and more detailed and up-to-date data categories are being 

reported. For instance, in 2011, EIA began collecting data from third-party administrators of 

programs and now requires that entities operating programs in multiple states report results 

disaggregated to the state level. While now comprehensive, outside entities have found that the 

                                                            

40 More information on EIA Form 861 can be found at http://www.eia.gov/electricity/data/eia861/. 
78 

EIA-861 data can be improved through supplementation 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 Scenario: Data, Calculations and Results 

81

Appendix 1: 

Summary of Recent Energy Efficiency Potential Studies (2009-2014) 

The EPA conducted an extensive review of energy efficiency potential studies of 

electricity savings. Studies were identified from recent published meta-analyses (Sreedharan 

2013; ACEEE2014b) and supplemented with additional studies identified through research. 

Comprising results from more than 50 potential 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 information on the sources, data, calculations, and 

results: 

 GHG Abatement TSD – Demand-Side EE Potential Studies.xlsx 
82

Appendix 2: 

Incremental Electricity Savings Pace of Improvement Analysis 

This appendix summarizes and analyzes data to characterize the pace of improvement of 

incremental (or first-year) savings as a percentage of retail sales for electricity energy efficiency 

(EE) programs. We considered two different perspectives: 1) historical data reflecting achieved 

savings of EE programs and 2) requirements of existing state energy efficiency resource 

standards (EERS). For the historical perspective, we reviewed data from the Energy Information 

Administration’s Form EIA-861 on EE program electricity 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 following two groups of energy efficiency program 

administrators. 

1. Top saver 1% - a group with 47 entities that achieved a maximum first-year savings 

level of 0.8% to 1.5%. 

2. Top saver 2% - a group with 26 entities that achieved a maximum first-year savings 

level of 1.5% to 3.0%.43

For the existing state requirements perspective, we reviewed energy savings ramp-up 

schedules established under EERS for states that provide clear ramp-up schedules. According to 

ACEEE’s 2013 State Energy Efficiency Scorecard, 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 historical savings performance are: 

 The “Top Saver 1%” group (savings between 0.8% and 1.5%) exhibits a trend that 

these entities took or would take about 3.4 years on average to increase first-year 

                                                            

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-party 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 administrators for the following conditions: 

(a) the maximum savings levels occurs after the minimum savings levels; (b) sufficient amounts of increase in first-year savings 

are provided to evaluate reasonable ramp-up schedules to gain an incremental 1% first-year savings; and (c) savings data series

are continuous between the years for the minimum and maximum 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 first-year savings by 0.30% per year on average 

from their minimum to their maximum first-year savings levels (with a range of 

0.10% per year to 0.63% per year).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 entities in this group have increased the 

level of first-year savings by 0.38% per year 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 electricity savings ramp-up based on existing state 

EERS policies are: 

                                                            

44 This is a simple average estimate of the annual average increase in first-year savings from each entity in this group. 45 This is the simple average estimate of the annual average increase in first-year savings from each entity in this group. 46 Data sources: The EIA 861 was the main data source (EIA 2012a). However, we have supplemented the EIA 861 with thirdparty program administrator data because the EIA 861 just started to collect third-party administrator data in 2011. The thirdparty 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). 
84

 The states with EERS policies which exhibit savings ramp-up schedules are requiring 

increases in first-year energy savings at a pace that ranges from 0.11% (Colorado and 

Oregon) to 0.40% (Rhode Island) as shown in Table 2-2. 

 The first-year savings pace of increase averages 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 Ramp-up Review of State EERS Policies 

State 

Minimum Target Maximum Target Climb 

Time 

(years) 

Annual 

Average % 

Increase 

Years to 

Achieve 1% 

Min Year Max Year Increase 

 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 2013 
85

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 assessment of costs of the demand-side energy efficiency included 

in the illustrative plan scenario of the Regulatory 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%.xlsx 
86

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1

Technical Support Document: 

Resource Adequacy and Reliability Analysis 

This document describes resource adequacy and reliability impacts of the two illustrative 

state plan scenarios that are reflected in the Regulatory Impact Analysis (RIA) that supports the 

final rule emission guidelines issued 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 region1

, while reliability includes the ability to deliver the resources to the loads, 

such that the overall power grid remains stable. The focus of the analysis is on the impact of the 

illustrative scenarios, in the context of a base case (absent the rule requirements) that is assumed 

to be adequate and reliable. In this framework, the emphasis is on the incremental changes in the 

power system that are projected to occur under the presence 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 flexibility in the context of state 

plan development that preserves the ability of responsible authorities to maintain electric 

reliability, as well as a provision to ensure that electric reliability is adequately maintained in the 

case of extreme circumstances, amongst other changes to the final rule that will assist states and 

other authorities with ensuring adequate supplies of electricity and maintain electric reliability. 

For more detail on how the final CPP addresses reliability, 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, deterministic linear programming model of the U.S. 

electric power sector. It provides 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 available 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 any scenario, will ensure generation resource 

adequacy, either by using existing resources or through the construction of new resources. IPM 

addresses reliable delivery of generation resources for the delivery of electricity between the 64 

IPM regions, based on current and planned transmission capacity, by setting limits to the ability 

to transfer power between regions using the bulk power transmission system. Within each model 

region, IPM assumes that adequate transmission capacity exists to deliver any resources located 

in, or transferred to, the region. This document focusses on key regional results important to 

management of the power system. For a more complete 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-fired electric generating 

units. Specifically, the EPA is proposing category-specific performance rates for CO2 emissions 

from power plants, as well goals for states to use in developing plans to meet the guidelines . See 

final rule preamble for more detail on the final CPP structure and rule 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 CPP 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 their target reserve margins, most 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 and 2030. Operational generating capacity6

 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 988,7647

Since the model must maintain adequate reserves 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 adequacy in each region where existing 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 capacity, reduced total resource requirements from increases in energy 

efficiency, and the ability to shift transmission among regions as the generating capacity mix 

changes. As the table shows, the reduced resource needs permit lower capacity additions even 

though there are substantial increases in retirements. 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 region8 as the basis for determining how much 

capacity to keep operational in order to preserve resource adequacy. 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 current regional reserves are generally higher than 

the target reserve margin for the region, and increased 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 reserve 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 energy efficiency under the final rule; in 2025 new 

energy efficiency under the final rule contributes 45,085 MW to reserve capacity10 (see Table B1 

for regional detail). Moreover, these reductions are from energy efficiency 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 2010 10 Year Assessments for the region, except in cases 

where there are more stringent state 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. Moreover, retirements are distributed throughout the 

power grid, so there will be only small impacts at the regional level.11 

Although there are substantial existing 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, construction 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 projections is described below. 

Changes in Retirements and New Capacity Additions in the CPP Scenarios12

There are significant levels of fossil steam – coal and oil/gas – retiring in 2025 in both the 

base and CPP scenario. The incremental final rule case retirements are shown above in Table 1; 

the 36,931 MW of retirements are in addition 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 new capacity additions (Shown 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 replacing some of the retiring units shown in Table 1 above and in Table 

A4, these can be managed within the normal reliability planning and management time frames 

provided by the flexible resource options and time frames in the rule. These retirements and 

additions in the projections are 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 occur in the policy scenario projections might, in 

reality, be retained under a policy if local reliability conditions rendered 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 incremental to the base case in 2025; the MW values represent 

model projections of responses to the imposition of the policy, not currently announced retirements 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 transfer reserves, subject to summer and winter 

limits that are designed to ensure that these reserves can be transferred 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 neighboring region has lower cost reserves that are not needed for its 

own reserve requirements. To examine these transfers, 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 sent. 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 transfers, the measure used was the change in 

the summer reserves sent in the policy case compared 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 changes 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 from 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 change 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. These 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 the shifts under the proposal. In general, the shifts under the final 

rule show transfers from the Southeastern areas 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 interregional 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 period. EPA examined the information for the rate 

scenario for 2020 corresponding to the rate scenario in 2025. This information is shown the 

tables in Appendix A, which correspond to the tables 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 reliability resulting from the rule. This is because most of the 

period is outside the compliance period of the final rule, and there can be no direct conflict with 

the rule except in the very last year of the projection period. The 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 about the future and precise economic planning. It 
8

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 expected in practice. 

Nevertheless, in 2020, seven regions maintain margins above their levels in 2025 (See Table 

A3). This is consistent with the lower level of overall retirements in 2020 compared to 2025 

(24,567MW compared to 36,961MW in 2025), so that the modeling confirms that the major 

potential resource adequacy pressures from the final rule are not seen until the projections for 

2025. 

 Results for the rate scenario in 2030 are contained in Appendix C. Reserve margins in 

the policy case in 2030 are essentially unchanged (less than 0.5% difference 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 compared 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 efficiency to reserve margin capacity 

was considerably greater than the combined decrease in new capacity additions and increase in 

retirements in 2030: the contribution of new energy 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 and supply resources as these evolve over time. 

None of these results suggest there will be reasons for concern over the management of resource 

adequacy 

 

CPP Mass-Based Scenarios 
9

The EPA also examined the mass scenario modeling results to identify differences with 

rate scenario potential impacts. The results for the CPP mass-based scenarios are contained in 

Appendices D through F for the projection years 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 very 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, total 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%, the 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 within 1% of the projections for the corresponding outputs rate 

scenario and regional reserve capacities are under 5 percent. 

Differences between the scenarios develop in the types of capacity 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 compared to 9,264 MW). The mass scenario also 

results in more new NGCC and CT capacity, and less new wind and solar capacity. The mass 

scenario still is projected to have a decrease new 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 two regions with shifts of 

more than 5% in the mass scenario: the Northwest 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 Northwest 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 transfers 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 for 2025 are also present in 2020 and 2025: only 

minor variations in total operating capacity, somewhat higher fossil 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 Region 
12

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 (DSW 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 (CA 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 (CA 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 24,567
13

A2. Summer Peak Loads and Reserve Capacity in 2020

Projected Reserve Margins

Assessment 

Region

Peak 

Demand 

Base

Peak 

Demand 

Policy

Reserve 

Capacity 

Base

Reserve 

Capacity 

Policy

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 761,845 757,338 906,407 885,008
14

A3. Summary of Target and Projected Reserve Margins in 2020

Projected Reserve Margins

NERC Assessment 

Region

Target 

Reserve 

Margin Base Case Policy Case

Policy % 

Above Margin

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%

Southern 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 (DSW 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 (CA 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 (CA 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 24,567
16

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 2,459
17

A6.  Net Reserves Sent  by NERC Assessment Region(MW) in2020

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 (DSW 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 (CAL -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 Region 
19

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 ‐4,425 212,075 189,169
20

B2. Summer Peak Loads and Reserve Capacity in 2025

Projected Reserve Margins

Assessment 

Region

Peak 

Demand 

Base

Peak 

Demand 

Policy

Reserve 

Capacity 

Base

Reserve 

Capacity 

Policy

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 792,624 753,693 920,969 872,809
21

B3. Summary of Target and Projected Reserve Margins in 2025

Projected Reserve Margins

NERC Assessment 

Region

Target 

Reserve 

Margin Base Case Policy Case

Policy % 

Above Margin

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%

Southern 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 (DSW 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 (CA 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 (CA 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 36,961
23

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 ‐12,626
24

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 (DSW 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 (CAL -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 Region 
26

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 19,847 211,536 184,322
27

C2. Summer Peak Loads and Reserve Capacity in 2030

Projected Reserve Margins

Assessment 

Region

Peak 

Demand 

Base

Peak 

Demand 

Policy

Reserve 

Capacity 

Base

Reserve 

Capacity 

Policy

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 829,421 762,176 957,760 881,143
28

C3. Summary of Target and Projected Reserve Margins in 2030

Projected Reserve Margins

NERC Assessment 

Region

Target 

Reserve 

Margin Base Case Policy Case

Policy % 

Above Margin

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%

Southern 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 (DSW 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 (CA 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 (CA 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 41,009
30

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 ‐16,964
31

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 (DSW -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 (CAL -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 Region 
33

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 ‐18,575 215,416 200,515
34

D2. Summer Peak Loads and Reserve Capacity in 2020

Projected Reserve Margins

Assessment 

Region

Peak 

Demand 

Base

Peak 

Demand 

Policy

Reserve 

Capacity 

Base

Reserve 

Capacity 

Policy

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 761,845 757,343 906,407 886,072
35

D3. Summary of Target and Projected Reserve Margins in 2020

Projected Reserve Margins

NERC Assessment 

Region

Target 

Reserve 

Margin Base Case Policy Case

Policy % 

Above Margin

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%

Southern 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 (DSW 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 (CA 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 (CA 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 24,627
37

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 0 706
38

D6.  Net Reserves Sent  by NERC Assessment Region(MW) in2020

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 (DSW 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 (CAL -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 Region 
40

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 ‐6,828 212,075 182,836
41

E2. Summer Peak Loads and Reserve Capacity in 2025

Projected Reserve Margins

Assessment 

Region

Peak 

Demand 

Base

Peak 

Demand 

Policy

Reserve 

Capacity 

Base

Reserve 

Capacity 

Policy

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 792,624 753,649 920,969 871,777
42

E3. Summary of Target and Projected Reserve Margins in 2025

Projected Reserve Margins

NERC Assessment 

Region

Target 

Reserve 

Margin Base Case Policy Case

Policy % 

Above Margin

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%

Southern 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 (DSW 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 (CA 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 (CA 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 42,021
44

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 ‐9,971
45

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 (DSW 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 (CAL -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 Region 
47

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 18,833 211,536 173,291
48

F2. Summer Peak Loads and Reserve Capacity in 2030

Projected Reserve Margins

Assessment 

Region

Peak 

Demand 

Base

Peak 

Demand 

Policy

Reserve 

Capacity 

Base

Reserve 

Capacity 

Policy

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 829,421 762,235 957,760 881,975
49

F3. Summary of Target and Projected Reserve Margins in 2030

Projected Reserve Margins

NERC Assessment 

Region

Target 

Reserve 

Margin Base Case Policy Case

Policy % 

Above Margin

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%

Southern 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 (DSW 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 (CA 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 (CA 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 51,512
51

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 ‐7,479
52

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 (DSW -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 (CAL -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 Regions 
54

C1: IPM v5.13 Regions
55

C2: NERC Assessment Areas in Long Term Reliability Assessm

Source: NERC 2012 Long Term Reliability Assessment
56
7/26/2015 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 calculation2 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 38
7/26/2015 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.