Technical Support Document (TSD) for Carbon Pollution Guidelines for Existing Power Plan ts: Emission Guidelines for Greenhouse Gas Emissions fr om 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 3, 2015TABLE OF CONTENTS

Chapter 1: Introduction ...........................

................................................... ........................................1-1 Chapter 2: Heat Rate Improvement at Existing Coal-f ired Electric Generating Units .................... ..2-1 Chapter 3: Generation Shifts and CO 2 Reduction amongst Affected Electric Generating Uni ts ......3-1 Chapter 4: Cleaner Generation Sources .............

................................................... .............................4-1 Chapter 5: Carbon Capture & Storage ...............

................................................... .............................5-11 – 1



### CHAPTER 1: INTRODUCTION

Under the authority of Clean Air Act (CAA) section 111(d), EPA is establishing final CO 2 emission guidelines for existing fossil fuel-fired electric generating units (EGUs) – the Clean Power Plan (CPP). More specifically, EPA is establi shing: (1) carbon dioxide (CO 2 ) emission performance rates based on the best system of emiss ion reduction (BSER) for two subcategories of existing fossil fuel-fired EGUs, fossil fuel-fir ed electric utility steam generating units and stationary combustion turbines, and (2) guidelines for the development, submittal and implementation of state plans that implement the CO 2 emission performance rates. The emission performance rates reflect the “best system of emiss ion reductions ... adequately demonstrated” for CO 2 emissions from the EGU source category. The guidel ines also provide for the development, submittal and implementation of state plans that im plement the CO 2 emission performance rates, either directly by means of source-specific require ments or through measures that achieve equivalent CO 2 reductions from the same group of EGUs. This techn ical support document (TSD) provides additional information, data, and analysis to support EPA’s assessment and application of BSER.
In the June 2014 proposal, EPA proposed to determin e that the best system of emission reduction adequately demonstrated for reducing CO 2 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-carb on 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 activ ities were categorized as four “building blocks.”
The final rule BSER incorporates certain changes fr om the proposed rule, reflecting EPA’s consideration of comments received and further anal ysis. The principal changes are the exclusion from the BSER of emission reductions achievable thr ough demand-side energy efficiency (building block 4) and through nuclear generation; a revised approach to determination of emission reductions achievable through increased renewable e nergy generation; a consistent approach to determination of emission reductions achievable thr ough all the building blocks that better reflects the regional nature of the electricity system; and a revised interim goal 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 fo r the CPP Final Rule, the CO 2 Emission Performance Rate and Goal Computation TSD for the C PP Final Rule, and the Response to Comments document, all of which are available in th e docket.1 – 2

This TSD includes chapters with detailed informatio n and EPA’s evaluation of each building block included in BSER, and also includes a chapter on ca rbon capture and storage technology (CCS). 1 A separate TSD includes supplemental information on demand-side energy efficiency (Demandside Energy Efficiency TSD for CPP Final Rule), whi ch is not part of BSER, but supports the Regulatory Impact Analysis.
While evaluating each measure, EPA considered the t echnical feasibility, applicability and use, application level appropriate for BSER, and cost ef fectiveness 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 strate gies that companies and states are already taking to reduce GHG emissions. The application of the bui lding blocks as BSER supports achieving cost-effective and technically feasible reductions of CO 2 , consistent with the final CPP rule.

1 The proposed rule GHG Mitigation Measures TSD (doc ket ID #EPA-HQ-OAR-2013-0602-0437) also included a chapter on fuel switching at affected EGUs. This mi tigation strategy was not included as BSER in the p roposal, nor is it included in the final rule.2 – 1



### CHAPTER 2: CO 2 

### INTENSITY REDUCTIONS THROUGH HEAT RATE IMPROVEMENTS AT EXISTING COAL FIRED ELECTRICITY GENERATING UNITS

This is Technical Support Document (TSD) incorporat es by reference the materials found in the Clarified Technical Support Document (TSD) for Carb on Pollution Guidelines for Existing Power Plants: Emission Guidelines for Greenhouse Gas Emis sions from Existing Stationary Sources: Electric Utility Generating Units: GHG Abatement Me asures 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 e quipment 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. T he more efficiently an EGU converts the energy embedded in coal into electricity, the lower the EG U’s heat rate and, correspondingly, the lower its CO 2 intensity (CO 2 lb/kWh). Because heat rate improvements reduce fue l consumption, improving coal-fired EGUs’ heat rates can be a cost -effective method of reducing the CO 2 intensity of the power system as a whole. 2 In addition, it can provide additional economic be nefits from reduced fuel costs.
To assess the potential heat rate improvements of e xisting coal-fired EGUs, EPA analyzed historical gross heat rate data from 2002 to 2012 f or 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 – an d analyzed potential heat rate improvements within each interconnection using three analytical approaches. The approaches compare EGUs’ “best” or “benchmark” historical gross heat rates a gainst EGUs’ 2012 gross heat rate. By founding the methodology on comparisons between EGUs’ histor ical and baseline performances, the analyses control for variations in EGU design and l argely obviate the need for subcategorization. By using the 2012 gross heat rate as the baseline, the analyses account for actions that individual EGUs have already taken to improve and maintain gro ss heat rate. Accordingly, the results identify the overall heat rate improvements that will result if 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 Pla nts: Emission Guidelines for Greenhouse Gas Emissions fr om Existing Stationary Sources: Electric Utility Ge nerating Units; GHG Abatement Measures, Docket No. EPA-HQ-OA R-2013-0602-17180 (Sept. 16, 2014), available at http://www.regulations.gov/#!documentDetail;D=EPA-H Q-OAR-2013-0602-17180 ; and Dataset related to Chapter 2 of the GHG Abatement Measures TSD, Docket No. EPA-H Q-OAR-2013-0602-0238 (June 8, 2014), available at http://www.regulations.gov/#!documentDetail;D=EPA-H Q-OAR-2013-0602-0238

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

.2 – 2

In the first approach, “best historical average per formance,” the agency selected each EGU’s best ( i.e., lowest) 1- and moving 2-year gross heat rates, cal culated the generation weighted average across all units, and compared that to the generati on-weighted 2012 average gross heat rate across all units. For the second and third approach, the a gency 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 coalfired EGUs between 2002 and 2012. To control for th ese conditions, the agency grouped, or “binned,” data by hours with similar hourly capacit y 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 condit ions,” within each capacity-temperature bin for each coal-fired EGU, EPA calculated the bin-spe cific 1- and moving 2-year gross heat rates, selected the lowest 1- and moving 2-year gross heat rate within that capacity-temperature bin, calculated the generation weighted average across a ll bins and units, and compared that to the generation weighted 2012 average gross heat rat e. 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 rat e (characterized as the 10th percentile value over 1- and 2-year periods) for each capacity-tempe rature bin. 3 Then the agency determined the heat rate that could be achieved if the EGU impleme nted measures to operate more consistently nearer the lowest “benchmark,” and compared that to the actual gross heat rate.
The results of these three approaches indicate that there is significant potential for heat rate improvement ranging from 4.0 to 6.6 percent nationa lly if coal-fired EGUs return to their best past performance between 2002 to 2012. The most conserva tive approach that is supported by the weight of evidence yields average potential heat ra te improvements of 2.1 percent in the Western Interconnection, 2.3 percent in the Texas Interconn ection, 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) requir ed 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 CO 2 and other emissions ( e.g., mercury, NO X , PM, SO 2 ) released to the environment also declines. In addition to the clear environmental benefits of heat rate improvements, there are important

3 The third approach uses hourly values to determine a heat rate benchmark for each EGU for periods wit h similar hourly capacity factor and hourly ambient temperatu re 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 bench mark 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 a nd high values will generally balance out.2 – 3

economic co-benefits attributable to reduced fuel c osts 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 i ndividual EGUs indicate that there is potential for efficiency improvements that can yield significant CO 2 emission reductions and fuel savings for the existing coal-fired EGU fleet as a whole. This chapter presents (1) the characteristics of existin g coal-fired EGUs in the US, including heat rate perf ormance over time, (2) the impact that design characteristics and other factors may have on EGU h eat rates, (3) additional equipment upgrades and practices that can improve efficiency at existi ng coal-fired EGUs, (4) the potential heat rate improvements from existing coal-fired EGUs that, wh en realized, would reduce CO 2 emission intensity, and (5) responses to key comments about building block 1 of the proposed rule.

2.2 Coal-fired EGUs
Several different coal combustion configurations an d technologies are used at existing coal-fired EGUs in the U.S. Nevertheless, they all commonly fo llow the same process for converting the energy stored within coal into electricity. Figure 2-1 depicts a pulverized coal-fired 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 turbinegenerator where the steam passes through, forcing t he 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 boil er to repeat the cycle. Coal combustion waste gases ( i.e., flue gas), including various air emissions ( e.g., CO 2 , mercury, NO X , PM, SO 2 ), are drawn downstream from the furnace by fans an d pass through pollution control devices before they are released from the stack to the atmosphere.

4 For information about coal combustion configuratio ns at coal-fired EGUs, see supra note 1.2 – 4 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 th at affect heat rate
A variety of design characteristics, site-specific factors, and operating conditions can affect the heat rate of existing coal-fired EGUs. Some of thes e factors are influenced by or are under the direct control of the EGU operator, but other facto rs 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 gen eral, 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 therma l efficiencies of up to 35 percent while supercritical and ultrasupercritical EGUs that oper ate 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 stea m turbine increase, heat rate decreases. Larger coal-fired EGUs general ly 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 leak age, and also tend to use more advanced2 – 5

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 requ irements. The cooling system design directly affects the steam turbine’s ability to extract ener gy from the steam and affects overall efficiency. The amount of energy that the steam turbine can ext ract from the steam depends on the steam temperature and pressure as well as condenser press ure (affected by the cooling system design, condenser cleanliness, EGU load, and ambient coolin g water or air temperature). At lower condenser pressures (within design limits), more en ergy can be extracted by the steam turbine, with a resulting lower EGU heat rate.
Auxiliary equipment . Auxiliary equipment, such as pumps, fans, pulveri zers, and pollution control devices, require power to run, thereby consuming so me of the electricity generation on-site 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 ( e.g., seasonal ambient temperatures, cooling water temperatures) at the power plant location 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. Cooler water temperature s improve the efficiency of the cooling system, thereby reducing heat rate. Colder cooling water (o r ambient air for dry cooling systems) in the condenser reduces the back pressure in the condense r, 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 l ower 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 t he moisture and, therefore, the more energy that is available for producing steam, thus lowering hea t rate. The ash content of the coal can also affect heat rate by accumulating on heat transfer surfaces ( i.e., metallic tubes) and impeding energy transfer to the steam. While systems are designed t o remove this “soot,” the quantity present between surface cleanings hinders energy transfer a nd results in higher heat rates.

2.2.2 Regional electricity interconnections
As discussed in sections V.A and V.C of the preambl e for the final CPP rule and the associated CO 2 Emission Performance Rate and Goal Computation Tec hnical Support Document for CPP Final Rule , EPA assessed the potential CO 2 reductions achievable through each of the building blocks that comprise the best system of emission re duction. The agency conducted assessments at the level of the three large regional electricity i nterconnections: Western, Texas, and Eastern (see2 – 6

Figure 2-2). The Western Interconnection is a major power grid that comprises one North American Electric Reliability Corporation (NERC) re gion – the Western Electricity Coordinating Council (WECC). The Western Interconnection encompa sses all or portions of 14 western states. The Texas Interconnection includes the portion of T exas managed by the Electric Reliability Council of Texas (ERCOT). The Eastern Interconnecti on is the other major regional power grid, comprising six NERC regions – Florida Reliability C oordinating Council (FRCC), Midwest Reliability Organization (MRO), Northeast Power Coo rdinating Council (NPCC), Reliability First Corporation (RFC), SERC Reliability Corporation (SE RC), and Southwest Power Pool (SPP) – covering all or portions of 39 eastern and midweste rn states.
Each interconnection is a highly-connected electric ity 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 inte rconnection must carefully manage electricity generation and load to ensure safe and reliable ele ctricity service.
For the proposal, EPA analyzed the potential heat r ate improvements from coal-fired EGUs at the national level. Some commenters suggested that EPA should adjust the analysis and develop statespecific estimates of potential heat rate improveme nts from coal-fired EGUs. While the agency carefully evaluated these comments, the agency dete rmined that due to the highly interconnected nature of the electricity system, and as further di scussed in section V.C.3 of the final rule preamble
, it is more appropriate in this rule to assess poten tial for heat rate improvements from existing coalfired EGUs at the regional interconnection level th an at the national or state level.

2.2.3 Coal-fired EGUs in operation in 2012
In 2012, there were 884 coal-fired EGUs at 367 powe r plants in the contiguous U.S. (CONUS) that reported hourly heat input and gross electrici ty generation to EPA’s Clean Air Markets Division. 5 These EGUs are spread across the CONUS (see Figure 2-2) in each of the three regional electricity interconnects. Of the 884 coal-fired EG Us, 93 (10.5 percent) are located in the Western Interconnection, 32 (3.5 percent) are located in th e Texas Interconnection, and 759 (86 percent) are located in the Eastern Interconnection.

5 Excludes cogeneration, integrated gasification and combined cycle (IGCC), retired, and non-operationa l EGUs. EGUs that reported steam co-generation to EPA or EI A 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 meaningfull y compare these EGUs’ heat rates. IGCC units are excluded because t hey are not conventional steam boilers and may regu larly use pipeline natural gas in lieu of syngas from coal ga sification. EGUs retired prior to 2012 or EGUs that did not operate in 2012 are also excluded because they do not have 2012 operating data necessary to estimate potential heat rate improvement using the methodology described later i n this document.2 – 7 Figure 2-2 Power plants with coal-fired EGUs report ing heat input

and gross electricity generation in 2012
The population of 884 coal-fired EGUs includes unit s 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 siz e ( i.e., maximum hourly heat input), and generator power output capacity.
At the end of 2012, the age of the 884 coal-fired E GUs varied widely, ranging from less than 1 year to 68 years. Nationally, the average age was 4 2 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 Interconne ction 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 8 84 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 generat or capacity of coal-fired EGUs was 390 MW, in the Texa s Interconnection the average was 689 MW, and in the Eastern Interconnection the average was 372 MW (see Figure 2-4).

6 EGU operators report maximum hourly load data to E PA/CAMD. This value may differ from the EGU’s namep late capacity and summer net capacity.2 – 8

Figure 2-3 Age (years) distribution of coal-fired E GUs in 2012, by interconnection

Figure 2-4 Generator capacity (MW) distribution of coal-fired EGUs in 2012, by interconnection2 – 9

2.2.3.1 Historical gross heat rate trends of coal-fired EGU s
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 he at rate 7 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 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 regio ns was more pronounced. The average annual gross heat rate of the 93 coal-fired EGUs in the We stern Interconnection declined from nearly 10,458 Btu/kWh in 2002 to 9,888 Btu/kWh in 2012, ju st 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 Interconn ection. 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 h eat 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 EGU s, 2002-2012 by interconnection

Notes: red dotted line represents the 11-year arith metic average; axis does not start at zero to better illustrate tr ends 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 bitumin ous coals to higher-moisture subbituminous coals from the Powder River Basin, installation of air pollution control devices in response to

7 EPA used hourly heat input (Btu) and hourly gross electricity generation (kWh) to calculate the hourl y gross heat rate (Btu/kWh). As described later in this chapter, gross generation was the only complete and accurat e hourly dataset available to EPA. Therefore, unless otherwise noted , references to heat rate in this chapter refer to gross heat rate.2 – 10

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 ca pacity factors. EPA acknowledges that there are many factors that can affect the heat rate of c oal-fired EGUs, some of which are within the EGU operator’s control and some that are not. In th e analysis of potential heat rate improvements described below, the agency attempts to limit the i nfluence of some of the factors outside the EGU operator’s control.

2.3 Heat rate improvement practices and equipment upgra des 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 EGU’s CO 2 emissions intensity. 8 Several commenters also provided extensive informa tion about additional best practices and equipment upgrades th at may be available for coal-fired EGUs to improve heat rate. Commenters also noted that many coal-fired EGUs have already implemented heat rate improvement measures for economic reasons and requiring EGUs to implement the measures again might not yield substantial benefits or might not be feasible.
As explained in section 2.5 below and in preamble s ections 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 implemen ted 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 tha t there are additional opportunities for coalfired EGUs to achieve heat rate improvements by imp lementing some combination of best practices and equipment upgrades, including the ill ustrative best practices and equipment upgrades shown below. The agency recognizes that the types a nd 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 b est 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 s ome 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 to improve or maintain heat rate.

8 See Clarified Technical Support Document, supra note 1.2 – 11

2.3.1 Example equipment upgrades for heat rate improvemen t

• Install intelligent sootblowing system

• Replace feed water pump steam turbine seals

• Overhaul high pressure feed water 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 op eration

• Upgrade and repair flue gas desulfurization systems

• Refurbish the economizer

• Upgrade ESP components to lower auxiliary power con sumption

• Improve SCR and FGD system components to lower draf t loss

2.3.2 Example best practices for heat rate improvement

• Adopt training for O&M staff on heat rate improveme nts

• Perform on-site appraisals to identify areas for im proved heat rate performance

• Install neural network software for combustion/opti mization with monitoring system for heat rate optimization

• Repair steam and water leaks – replace leaking valv es 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 preven t 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 extern al studies
There are a number of engineering studies that expl ore opportunities for improving the efficiency of existing coal-fired power plants, including stud ies by Sargent & Lundy and NETL that were referenced in the proposal TSD. Some of the practic es and equipment upgrades have the potential to improve gross heat rate while others will not af fect an EGU’s gross heat rate, but they will2 – 12

reduce the auxiliary load from on-site equipment, t hereby 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 syste m, 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 transp ort of coal, the preparation of coal for combustion, and handling of combustion residues:

• Coal-handling equipment: Installation of energy-eff icient motors and variable frequency drives can reduce auxiliary power requirements.

• Coal pulverizers: Upgrading the pulverizers to prov ide more consistent size and finer coal particles can improve the EGU’s combustion efficien cy, produce less unburned coal, and consequently reduce fuel cost and gross heat rate.

• Bottom ash handling equipment: Switching from a wat er-sluicing bottom ash system to a dry system can reduce the auxiliary requirements an d 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 co mputer 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 adaptiv e 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 o ffer gross heat rate improvements up to 150 Btu/kWh.

2.3.3.1.3 Steam turbine
The steam turbine extracts energy from the steam an d rotates a generator to produce electricity. Modern developments allow the steam turbine to extr act 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 tur bines with improved internals can reduce gross heat rate by 100-300 Btu/kWh. As one commenter poin ted out, Leyzerovich explains that

9 Chapter 2 of the proposal TSD provides a detailed description of numerous well-proven measures for im proving heat rate at coal-fired EGUs. See supra note 1.2 – 13

upgrading and retrofitting a turbine at a 20-year o ld 500 to 700 MW coal-fired EGU can reduce heat rate by a total of about 4 percent while also increasing output by approximately 5 percent. 10

2.3.3.1.4 Feed water heaters
Boiler feed water 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 s team. 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, whi ch 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 neede d. 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 allo ws the steam turbine to extract more energy for power generation. As the cooling towers foul, the w ater returned to the condenser becomes warmer and negatively affects steam turbine performance an d reduces gross electricity generated. Routine maintenance on the cooling towers might include dis carding accumulated debris, removing biologic growth, replacing defective fill material, ensuring uniform water distribution, correcting for uneven air distribution, verifying water flow r ates, and replacing missing louvers

2.3.3.1.7 Boiler feed water pumps
Boiler feed pumps require a large amount of auxilia ry power to pump large amounts of boiler feed water through the heaters and the boiler. Due to th eir continuous operation, the pumps wear over time, lose efficiency, and require more energy to m ove 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 sca le 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 per cent 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

10 See page 477 in AS Leyzerovich, 2007. Steam Turbin es for Modern Fossil-Fuel Power Plants. CRC Press. ISBN 13: 978-1420061024.2 – 14

of the EGU. Enhanced water treatment can reduce sca le. In addition, scale can be removed through chemical cleaning to restore heat transfer and lowe r gross heat rate.
The cooling water system can also suffer from poor water quality for the same reason – scale buildup fouls the heat transfer surfaces in the condense r, causing higher condenser temperatures and reducing the capability of the turbine to extract e nergy 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 pollut ion typically require large amounts of auxiliary power to properly operate equipment. Even small upg rades to the equipment can decrease auxiliary power requirements while maintaining emission reduc tion 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 boile r. 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 h eat rate improvements of around 50-100 Btu/kWh.

2.3.3.2.2 Sootblowers
Coal combustion produces ash that builds up on tubi ng 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. Intellig ent sootblowers monitor system conditions and activate when boiler operation indicates the need t o 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 require d to convert pre-heated water to steam. Due to their design, leakage can occur and, as a result, r educe the pre-combustion air temperature, requiring greater coal consumption. Leakage also in creases auxiliary power requirements since2 – 15

equipment must now handle the increased volume of a ir from the in-leakage. Properly maintained air heaters limit leakage to below 6 percent. Reduc ing leakage can reduce the auxiliary power requirements and, therefore, the net heat rate by 1 0-40 Btu/kWh.
Another method to improve heat rate is to extract m ore energy across the air heater to raise precombustion air temperature. Typically, the air heat er outlet is maintained at 20-30°F above the sulfuric acid dew point to prevent corrosion. Depen ding on the air heater’s size, major modifications may be required to capture more energ y, 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 convention al 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 rat e improvements of 10-50 Btu/kWh.

2.3.3.2.5 Flue gas desulfurization
A variety of flue gas desulfurization systems are u sed at existing coal-fired EGUs. Some EGUs still employ outdated venturi-type designs that cre ate 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 enh ance net heat rate.
A more common flue gas desulfurization technology i nstalled 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 r educes induced fan auxiliary power requirements and enhance net heat rate.

2.3.3.2.6 Electrostatic precipitator
Efficient electrostatic precipitator operation invo lves maintaining the maximum applied voltage, without arcing. Arcing represents lost energy unusa ble for particulate removal and requires the field be re-energized, consuming additional auxilia ry power. Electrostatic precipitator upgrades can improve performance by eliminating arcing and r educing auxiliary power consumption.

2.3.3.2.7 Selective catalytic reduction
Innovations in selective catalytic reduction system s have reduced auxiliary energy requirements, such as the use of hot secondary air or auxiliary s team 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 – 16

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

There are a number of technical and statistical stu dies 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 betw een 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 conc epts for improving heat rate or reducing CO 2 emissions presented in these studies focus on: redu cing heat losses, extracting more energy from the steam cycle, replacing worn or degraded equipme nt, installing equipment upgrades, increasing data measurement and tracking to enhance operation and efficiency, conducting routine maintenance to sustain peak performance, hiring sta ff dedicated to heat rate improvement and management, and instituting a corporate culture tha t places emphasis on efficiency improvements.
The studies use a variety of approaches to estimate potential heat rate improvement and CO 2 emission reductions. For example, some studies used engineering assessments, some subcategorized EGUs by specific design characterist ics or operating conditions and then compared EGUs in each subcategory against one another, and o thers compared each EGU’s heat rate performance or CO 2 emission rate over time. Summaries of several of t he studies referenced in the proposal TSD are presented below.
NETL produced a number of reports and workshop summ aries about improving efficiency at existing coal-fired EGUs. In a 2009 workshop to “ex plore opportunities to improve the thermal efficiency of existing and future coal-fired power plants,” NETL brought together 18 leading industry experts representing EGU operators, equipm ent vendors, energy consultants, and industry associations to analyze the technical and non-techn ical issues affecting power plant efficiency. 11 The analysis identified a wide variation in efficie ncy 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) u tilizing waste heat for coal drying, and (4) employing solar systems to heat feed water. The rep ort identifies several barriers that limit the adoption of measures to improve heat rate, includin g: (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 customer s, (2) lack of management commitment for efficiency programs, (3) focus on optimizing profit ability and ensuring EGU availability over efficiency, and (4) lack of on-site performance eng ineers dedicated to heat rate improvement. In general, the report found that while heat rate impr ovements and efficiency are important, potential

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

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fuel cost savings represent only a small portion of total costs and are “of secondary concern when compared to needs for maintaining system reliabilit y.”
A second NETL workshop was conducted with 53 leadin g industry experts to explore technical opportunities to improve efficiency, identify barri ers, and identify initiatives to enhance efficiency across the fleet. 12 The workshop participants developed a list of more than 50 distinct opportunities to improve efficiency, many of which are broadly ap plicable “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 up grades and improvements.
In 2010, NETL published a report that estimated exi sting coal-fired EGUs could enhance efficiency from a fleet wide generation-weighted ef ficiency of 32.5 percent in 2008 to 35.2 percent, equivalent to a heat rate improvement of about 9 pe rcent. 13 To develop the estimate, NETL segmented coal-fired EGUs into 13 groups based on s team cycle, coal type, and capacity. Using data from 1998 to 2008, the researchers identified the 90 th percentile EGU, 14 ranked by efficiency, within each of the 13 segments and calculated the p otential efficiency improvements if all EGUs below the 90 th percentile improved their average efficiency to th e level of the 90 th percentile EGU in the segment. Aside from the impact of steam cycl e, 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 condu cted 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 fleet wide heat rate would improve more than 6 percent.
Lehigh University’s Energy Research Center (ERC) co mpiled techniques for improving heat rate, and a 2009 update stated improvements in net heat r ate 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 t ake full advantage of all opportunities at all EGUs
, but the information demonstrates that there are sub stantial opportunities to improve heat rate. The

12 See R Brindle, J Eisenhauer, A Greene, M Justinian o, L Kishter, M Munderville, R Scheer, 2010. Improv ing the Thermal Efficiency of Coal-Fired Power Plants in th e United States. Workshop Report. 2010 February 24- 25 in Baltimore, MD. Available at http://www.netl.doe.gov/File%20Library/Research/Ene rgy%20Analysis/Publications/ThermalEfficCoalFiredPo wer Plants-TechWorkshopRpt.pdf

. 13 See P DiPietro and K Krulla, 2010. Improving the E fficiency 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/Ene rgy%20Analysis/Publications/DOE-NETL-2010-1411- ImpEfficCFPPGHGRdctns-0410.pdf

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

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research includes improvement opportunities from pr ocess optimization, more aggressive maintenance practice, and equipment design modifica tions.
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, improve d 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. Conseque ntly, there exists room for improvement.

Lashof et al. of the Natural Resources Defense Coun cil (NRDC) analyzed the potential heat rate improvements from coal-fired EGUs from a suite of h eat rate improvement measures at EGUs with the highest heat rates for their thermodynamic steam cycle category. 17 The study ranked coalfired EGUs by their full-load gross heat rate. EGUs that ranked higher than the 10 th percentile for their category, but lower than the median ( i.e., 50 th percentile) were assumed to have potential heat rate improvements of 300 Btu/kWh and EGUs ranked gr eater than the median for their category were assumed to have potential heat rate improvemen ts of 600 Btu/kWh. The 600 Btu/kWh heat rate improvement is based on measures outlined in t he NRDC report ( e.g., combustion optimization software and controls, upgrading econo mizers, cleaning and tuning steam condensers).
Linn et al. examined annual heat rate data from app roximately 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, over all gross heat rate would improve by 5.5 percent.
Sargent & Lundy, LLC (S&L) developed an engineering study for EPA of potentially applicable heat rate improvement methods for coal-steam EGUs. The S&L study, cited in the proposal TSD, presents technical descriptions of numerous well pr oven, commercially available heat rate improvement techniques. The study also includes ind icative ranges of potential heat rate improvement (Btu/net kWh) with associated indicativ e ranges of capital and O&M costs at three unit sizes (200, 500, and 900 MW).

16 See RJ Campbell, 2013. Increasing the Efficiency o f Existing Coal-Fired Power Plants. CRS Report R433 43. Available at http://fas.org/sgp/crs/misc/R43343.pdf

. 17 See DA Lashof, S Yeh, D Doniger, S Carter, L Johns on, 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/p ollution-standards-report.pdf

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

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Several leading energy sector engineering consultan ts produced conference papers and presentations that summarize real-world projects an d/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 in-leakage into the boiler; (2) improving fineness of fuel by optimizing coal pulverizers; (3) reducing air heater leakage; (4) optimizing primary, overfire ai r, and secondary air flow measurement and control; and (5) balancing fuel and air distributio n into the burner belt. The consultant estimates that the average large coal-fired EGU has about 300 to 500 Btu/kWh potential heat rate improvement ( i.e., about 3 to 5 percent for an EGU with a heat rate o f 10,000 Btu/kWh). The consultant goes on to say that “exacerbating the ch allenge [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, statis tical 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 independe nt study comparing each coal-fired EGU’s best rolling 365-day gross heat rate between 2001 a nd 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 elec tricity 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 95 th percentile rolling 365-day average gross heat rate were compar ed to the EGU’s 12-year average gross heat rate (2001-2012). The results of the analysis indic ate 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 95 th percentile gross heat rate, respectively.
The General Electric Company (GE) submitted an asse ssment 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, cou ld 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 co al-fired EGUs. To calculate the economic

19 See D Storm, 2009. Applying the Fundamentals for B est 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/EPRI2009HeatRateConfere nce%20FINAL.pdf

. 20 See Comments of Sierra Club and Earthjustice. Avai lable at http://www.regulations.gov/#!documentDetail;D=EPA-H Q-OAR-2013-0602-24029 . Also, see B Buckheit and N Spiegel, 2014. Sierra Club 52-Unit Study: Data File s. Available at http://www.regulations.gov/#!documentDetail;D=EPA-H Q-OAR-2013-0602-25475

. 21 See Comments of The General Electric Company. Avai lable at http://www.regulations.gov/#!documentDetail;D=EPA-H Q-OAR-2013-0602-22971

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potential, GE estimated the penetration rate for eq uipment upgrades and best practices by establishing a “best in class” full load net heat r ate 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 improvem ents. The study then constrained equipment upgrades and best practices to those with costs bel ow 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 ass essment of market potential based on results from the North American Electricity & Environmental Model (NEEM). For the modeling study, each state was constrained to the CO 2 emission rate from EPA’s proposal; coal-fired EGUs for which the study assumed some heat rate improvement measures were implemented were only offered the option to use the most expensive heat r ate improvements (on the assumption that the lower price options were already implemented at the EGU.) Using the least-cost electricity utility planning framework of NEEM, the modeling study esti mates that only 1 percent heat rate improvement will be implemented and the remaining C O 2 reductions will come from other actions within each state.
In addition to the reports described above, POWER M agazine 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 ca se studies at five large coal-fired EGUs with electricity generating capacities of 95 to 650 MW, combusting Powder River Basin subbituminous (3 EGUs) and bituminous (2 EGUs) coal, and utilizin g different thermodynamic steam cycles (2 supercritical and 3 subcritical). Through a combina tion 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 improveme nt projects included: (1) problems related to combustion and high air heater/stack gas temperatur es, (2) shortage of heat rate data, (3) lack of training and understanding of controllable losses, (4) lack of performance testing, (5) problems with feed water heater train performance, and (6) b uildup 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 als o provides a summary of a utility’s “fleet wide” heat rate improvement assessment. The engineering s tudy 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 CO 2 reduced. The summary of the full report states, “[ r]educing a power plant's heat rate can lower emissions, fuel c onsumption, and costs, thus contributing to the plant's bottom line. A significant improvement in h eat rate can often be achieved with a re-

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 Im provements. Technical Update 3002003457. Available at http://www.epri.com/abstracts/Pages/ProductAbstract .aspx?ProductId=000000003002003457 . This supplementary study expands upon and confirms the large quantitie s of similar information presented in this TSD.2 – 21

commitment to best operating practices, which can m inimize the need for capital expenditures on new technology.”

Table 2-1 EPRI 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 feed water hea ter 400 Btu/kWh

D Reducing air inleakage, replacing feed water heater , cleaning condenser and feed water tubes 500 Btu/kWh

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

A report by the Energy Information Agency (EIA) use d data from 1,027 coal-fired EGUs to estimate potential net heat rate improvements. 23 The coal-fired EGUs were grouped into quartiles based on the difference between reported and predic ted net heat rates. Those EGUs with the least difference between the reported and predicted net h eat rates ( i.e., 1 st quartile) were assumed to have the least potential for net heat rate improvem ent while those with the largest difference ( i.e., 4 th quartile) were assumed to have the most potential for net heat rate improvement. Using a matrix of 29 heat rate improvement measures (out of 56 ide ntified in the study), the study determined that net heat rate for the 1,027 coal-fired EGUs could b e improved, on average, by approximately 2.0 to 5.5 percent at capital costs of approximately $3 00/kW of capacity.
These studies provide technical information about p otential heat rate improvement at existing coalfired EGUs, including available best practices and equipment upgrades. As several commenters noted, these studies demonstrate that heat rate imp rovements are both readily available and achievable. The studies and commenters’ suggestions informed EPA about the different

23 See EIA, 2015. Analysis of Heat Rate Improvement P otential at Coal-Fired Power Plants. Available at http://www.eia.gov/analysis/studies/powerplants/hea trate/pdf/heatrate.pdf . This supplementary study expands upon and confirms the large quantities of similar inform ation presented in this TSD.2 – 22

approaches and analytical techniques that are avail able to assess potential heat rate improvements at existing coal-fired EGUs. The agency used the in formation to enhance the analytical approaches, but decided not to adopt any one of the studies des cribed 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 ag ency’s approach: (1) uses more recent data on nearly all existing coal-fired EGUs in the CONUS; ( 2) compares EGUs’ 2012 performance against the EGUs’ best performance, largely obviating the n eed for subcategorization or a “one-size-fitsall” heat rate or CO 2 emission rate; and (3) controls for key ambient an d operating conditions outside the EGU operators’ control.

2.5 EPA study of heat rate improvement potential at exi sting 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 interconne ction by comparing the EGUs’ best historical gross heat rates between 2002 and 2012 against the EGUs’ own 2012 gross heat rate. The agency based this approach on the reasonable expectation t hat, in the general sense, if coal-fired EGUs in an interconnection were able to demonstrate and ach ieve specific heat rates in the past, the EGUs should be able to achieve similar heat rates again. The advantages of such an approach are (1) it compares each EGU against the EGU’s own past perfor mance, 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, coa l type, cooling system type); (2) it accounts for heat rate improvement me asures employed during the study period by using 2012 as the baseline, (3) it is not significa ntly affected by the addition of pollution control devices during the study period because the auxilia ry power consumption from these devices has negligible impact on gross electricity generation; 24 and (4) the historical unit-level 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 coalfired EGU fleet for the purpose of establishing sta te emission goals. The potential heat rate improvement was based on two components: (1) a stat istical analysis that determined 4 percent

24 In addition to the parasitic load from add-on poll ution controls, another effect of some add-on contr ols is worth mentioning here. The installation of wet flue gas d esulfurization ( i.e., wet FGD or scrubber) technologies can affect heat rate because the chemical process of capturing SO 2 creates CO 2 that is measured in the continuous emission monitoring systems. This results in higher calculat ed heat input (see infra note 30). 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 imp act on calculated heat input is only relevant to EG Us that added WFGD during the study period because the analytical approaches described below evaluate the change in heat rates. Assuming consistent operation and fuel sulfur conte nt, an EGU with a WFGD during the entire study peri od would not have a heat rate change due to the WFGD CO 2 . Circulating fluidized bed (CFB) boilers also have higher CO 2 emissions from the chemical process of limestone in jection to capture SO 2 , 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 changes due to limestone injection. See section 2.6.3 for more information about the im pact of CO 2 emissions from SO 2 pollution control devices.2 – 23

improvement in the fleet wide coal-fired EGU heat r ate is possible through best practices ( e.g., improved operation and maintenance), and (2) an est imate 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 fle et 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 (see summaries in section 2.4). EPA carefully reviewed the comments and supplemental materials be fore refining the potential heat rate improvement analysis. As explained below, the analy sis for this final rule does not include a separate estimate of the potential from equipment u pgrades.
Conversely, several commenters suggested EPA’s prop osed heat rate improvement was too optimistic, providing engineering studies 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 hea t 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 th ey have already implemented many of the heat rate improvement measures identified in the S&L rep ort, (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 c onsiderations 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 c haracterized or misused information from the S&L study. 25 The S&L study was prepared for EPA, and agency sen ior engineering staff with previous experience in fossil power plant engineeri ng, operations, and management were 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 i nformation from the study, including the following statements from the S&L study:

1. The primary intent of the study was to focus on met hods that have been successfully implemented by the utility industry. 2. This study identifies specific plant systems and eq uipment where efficiency improvements can be realized either through new ins tallations or modifications, and provides estimates of the resulting net plant heat rate reductions and the order-ofmagnitude costs for implementation. To conduct the study presented in this report, S&L

25 See Sargent & Lundy, 2009. Coal-fired Power Plant Heat Rate Reductions. Available at http://www.epa.gov/airmarkets/documents/ipm/coalfir ed.pdf.2 – 24

surveyed available literature, spoke with technolog y manufacturers, and used its engineering expertise as the basis. 3. This report discusses potential efficiency improvem ent 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. 4. S&L cautions that the costs presented herein are no t indicative of those that may be expected for a specific facility due to variables s uch as equipment, material, and labor market conditions and site specifications. However, these cost estimates provide valuable information for comparative purposes when evaluatin g the advantages and disadvantages of the various concepts. The costs should not be us ed 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 study to develop an estimated 4 percent potential heat rate improvement that the average coal-fired EGU could make from only a f ew of the higher-cost equipment upgrades. The agency then discounted that potential by 50 per cent to obtain the 2 percent potential heat rate improvement estimate attributable to equipment upgr ades 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 effect s. The agency similarly used averages of S&L order-of-magnitude costs for thirteen of the fiftee n S&L heat rate improvement measures 26 to develop an indicative conceptual cost estimate ($10 0/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 modi fications” described in the S&L study. The average $100/kW cost, derived from S&L estimates fo r unit sizes ranging from 200 to 900 MW inherently reflects the inverse relationship of $/k W cost to MW unit size, and was thus appropriately applied in the agency’s estimate of t he fleet-wide average cost of heat rate improvement. The agency was also mindful of the 200 8 time frame of the S&L cost estimates. EPA considered the 2008 cost values to be applicabl e 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 reces sion that commenced in late 2008 caused costs to decrease sharply before beginning a slow recover y. The agency therefore considered the S&L cost values to quite reasonably represent the costs in 2011. 27

26 Three mutually exclusive fan and fan drive modific ations were discussed in the S&L study. Accordingly , EPA conservatively selected the fan-related measure tha t included both a fan upgrade and variable frequenc y drive for use in the cost estimate because the selected measure h ad the highest estimated capital cost.
27 Cost estimates for implementation of the final rul e are based on 2011 dollars.2 – 25

In addition to the S&L report, EPA referred to seve ral other studies in the proposal TSD that present similar information on equipment upgrades t o the S&L report. As demonstrated by the materials provided by commenters expressing a prefe rence for larger heat rate improvements, there are further analyses that show a greater than 6 per cent heat rate improvement is achievable at coalfired EGUs. One commenter also noted that many exis ting smaller coal-fired EGUs have recently retired or will be retiring over the next decade (s ee section 2.6.5 below for more information about retirements), leaving more of the larger, well-cont rolled coal-fired EGUs that are similar to the example EGUs used in the S&L report. Furthermore, t he equipment upgrades and best practices identified in the proposal TSD and supporting repor ts are only intended to serve as illustrative lists of measures that coal-fired EGUs might employ to im prove heat rate.
In the refined heat rate analyses for this final ru le, the results are not based on heat rate improvements that would be achieved by implementati on of specific measures. Instead, the heat rate analyses are based on each coal-fired EGU’s be st historical heat rate performance as well as its 2012 heat rate performance. It reflects the hea t rate improvements that would occur if EGUs performed at heat rates that they have already demo nstrated are achievable. As one commenter phrased it, EPA is simply basing building block 1 o n a continuation of the types of good maintenance and operating practices that are necess ary for EGUs overall to maintain the better heat rates they have previously achieved.
Some commenters noted that if a coal-fired EGU impl emented equipment upgrades during the study period (2002–2012), improvements to gross hea t 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 equipm ent upgrades undertaken at specific coalfired EGUs, arguing that the equipment upgrade comp onent of the agency’s potential heat rate improvements does not account for upgrades that coa l-fired EGUs have already implemented. EPA acknowledges that there could be overlap between th e two components of the potential heat rate results, but notes that the agency was not requirin g 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. Inste ad, the agency conducts the statistical analyses described below to compare each EGU’s best historic al performance to its 2012 performance. The results reflect the overall heat rate improvement f or 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-looki ng 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 st ill can be) cost-effectively implemented at coal-fired EGUs.
In the proposal TSD, EPA’s statistical analysis com pared each EGU’s best historical gross heat rate to the EGU’s 11-year average gross heat rate. Commenters stated that some coal-fired EGUs2 – 26

made significant investments to improve heat rate d uring the 11-year study period and the use of an 11-year baseline fails to recognize those invest ments. These commenters suggested the agency could adjust the potential heat rate improvements t o 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-fire d EGUs, the agency refined the methodology to compare each EGU’s best heat rate performance to it s 2012 gross heat rate. Therefore, any actions an EGU implemented to improve gross heat rate durin g or before 2012 (and maintained in 2012) would be accounted for in the analysis and would re sult in a reduction in the estimated potential heat rate improvement. In addition, the selection o f 2012 as a baseline is conservative because many commenters noted that coal-fired EGUs experien ced greater cycling and lower capacity factors in the later years of the study period (200 2-2012).
Several commenters suggested that EPA should replac e the statistical analysis with unit-specific engineering studies to establish unit-level heat ra te 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, potenti al heat rate improvements) are feasible and cost effective at each coal-fired EGU. As discussed in s ections V.A and V.C of the preamble and the response to comments, conducting engineering studie s to determine the potential for improving heat rate through best practices and equipment upgr ades at every coal-fired EGU would be unnecessarily lengthy and expensive, and would not be consistent with the broader goal-setting purpose of this rulemaking. However, the agency doe s 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 al ternative 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 interconne ction by comparing an individual EGU’s best demonstrated historical gross heat rate relative to the EGU’s own 2012 gross heat rate. However, as several commenters note, and the agency explaine d in the proposal, this rule does not establish mandatory federal heat rate performance requirement s for coal-fired EGUs or mandate heat rate improvement as a compliance strategy. The agency us es 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 be st system of emission reductions to establish state-level goals. Each state has the flexibility t o identify measures in its state plan to achieve th e state goals and, if the state chooses, it may devel op unit-specific heat rate improvement targets – or entirely ignore potential heat rate improvement. Commenters also stated that the proposal expressly anticipates fleet wide averaging of CO 2 emission intensity improvements, thereby offering greater opportunities to achieve the state goals at lower cost relative to controlling emissions or heat rate at the plant or unit level. Therefore, the agency believes it is appropriate in this rulemaking to assess potential heat rate impro vement 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 design2 – 27

characteristics and fuel choices on an EGU’s heat r ate. However, as discussed above and noted in the proposal TSD, the agency is not establishing ma ndatory 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 potential heat rate improve ment to calculate state goals. In addition, the agency does not believe there is a need to subcateg orize by design characteristics because the heat rate improvement potential is based on analyses of each EGU’s past performance. Because heat rate improvement is assessed using each EGU’s best historical and 2012 performance, the full analyses inherently account for design characterist ics that do not change from year to year.
Commenters also highlighted the role of age and coa l 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 December 2012). While there is a difference in median and av erage annual gross heat rates between coalfired EGUs less than 10 years of age and those grea ter than 10 years, there was no statistical difference in gross heat rates for the different gr oupings of coal-fired EGUs older than 10 years of age ( i.e., the range of performance of EGUs 10-19 years of ag e is approximately the same as EGUs 30-39 years of age). This indicates that operation and maintenance practices to manage degradation plays an important part in ensuring the efficiency of coal-fired EGUs. In addition, it is likely that the gross heat rate differences for younger units i s due to other factors ( e.g., advanced technology, larger boiler and/or steam turbine size) that do no t change as the EGU ages. Therefore, the influence of age is unlikely to play an important r ole 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 – es pecially pulverized coal boilers and their corresponding fuel handling equipment, combustion s ystems, 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 an d reliability. Changes to coal type might require plant modifications to maintain maximum out put and efficiency ( e.g., pulverizer capacity, fan capacity, heating surface). Commenters noted th at changing coal rank is likely not practical at many EGUs because coal rank is determined by the de sign of the EGU and environmental control systems. Although it might be a significant enginee ring 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 conten t) can have on an EGU’s heat rate. While EPA recognize s the impact that coal rank and quality can have on an EGU’s heat rate, the agency also recogni zes that EGU operators and coal suppliers typically set requirements for coal quality in purc hase 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.2 – 28

In summary, the agency feels the analytical approac hes described below, which are founded on each coal-fired EGU’s historical performance and it s 2012 performance, account for nearly all design characteristics because these characteristic s do not vary for a particular EGU between years or are accounted for by the use of 2012 as the base line. 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 belo w. 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 hour ly values and accounting for outliers, EPA implicitly account s for some of the variation that might be beyond the EGU operator’s control. Accordingly, subcategor ization among affected coal-fired EGUs is not appropriate under building block 1.
EPA used three approaches to calculate the potentia l 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 r eport hourly emission and operation data to EPA’s Clean Air Markets Division (EPA/CAMD). In 201 2, 884 coal-fired EGUs reported heat input and gross electricity generation data to EPA. 28 These 884 EGUs account for approximately 1.61 billion tons of CO 2 emissions in 2012, 96 percent of the 1.67 billion tons of CO 2 emitted by the national coal-fired EGU fleet in 2012. 29 Therefore, EPA considers these 884 coal-fired EGUs representative of the coal-fired EGU fleet as a who le.

EPA’s dataset includes the 884 coal-fired EGUs that reported heat input and gross electricity generation in 2012, including those EGUs that are n ear retirement. 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 EGU s may have more variability in gross heat rate and higher gross heat rates due to frequent cy cling, deferred or canceled maintenance, and termination of good heat rate practices. While the agency recognizes the commenters’ concern that soon-to-retire EGUs that operate with poor maintena nce 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 potentia l 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, th e

28 This population does not include coal-fired EGUs t hat did not operate in 2012, some of which retired before 2012 and others which may have resumed operation after 2 012. See supra note 5 for a discussion about treatment of cogeneration EGUs.
29 CO 2 emissions for the national coal-fired EGU fleet ar e 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/#enviro nment

.2 – 29

agency believes that the inclusion of coal-fired EG Us that are near retirement is conservative for two reasons: (1) for states with mass-equivalent go als based on 2012 baseline levels, after soonto-retire EGUs cease operation they cease emitting CO 2 contributing to attainment of the state goal, and (2) for states with CO 2 intensity goals based on 2012 baseline levels, the retirement of coalfired EGUs with high heat rates in 2012 will contri bute to achievement of the state goals.
The data used in the analysis include hourly gross heat rate values. 40 CFR Part 75 requires that most coal-fired EGUs continuously measure emissions of CO 2 , NO X , and SO 2 , and report those hourly emissions along with hourly heat input 30 and gross electricity generation to EPA/CAMD at the end of each calendar quarter. The monitoring re gulation requires regular quality assurance/quality control (QA/QC) of the monitoring systems, including daily calibrations and semi-annual or annual relative accuracy tests. 31 When EGU operators submit the hourly emission and operation data to EPA/CAMD, a responsible compa ny 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 info rmation for assessing coal-fired EGUs’ performance over time.
The monitoring regulation includes provisions to ac commodate time periods when emission data are not available due to monitoring system malfunct ions 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 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 bi as 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 additio n, the agency excluded partial hours of operation that occur during the first hour of start up and the last hour of shutdown, and hourly gross heat rate values that were greater than or less tha n 2.6 standard deviations from an EGU’s annual average gross heat rate. 32 Both of these conditions tend to occur during very low gross electricity generation. The hourly values ±2.6 standard deviati ons from the EGU’s annual average gross heat

30 Heat input is derived using CO 2 or O 2 concentration and calculations detailed in 40 CFR Part 75 Appendix F. As a result, heat rate (Btu/gross kWh) is proportional t o CO 2 rate (CO 2 /gross kWh).
31 The quality assurance requirements for monitoring systems are described in 40 CFR Part 75 Appendix B.
32 For datasets with a normal distribution, 99 percen t of all values will be within 2.6 standard deviati ons of the mean.2 – 30

rate may be due to unusual operating conditions or misreported gross electricity generation. 33 These outliers account for approximately 1.9 percen t 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. Comm enters hypothesized that discrepancies between the monitoring system and the reference tes ts could explain much of the variation in heat rates. EPA assessed the monitoring systems’ relativ e 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 relativ e accuracy test audits (RATA) once per year if the difference between the monitoring system and RA TA results is less than or equal to 7.5 percent, and twice per year if the difference is greater tha n 7.5 percent but less than 10 percent. 34 If the flow monitoring system readings are statistically-signif icantly 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 a pplied for CO 2 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 calculat ed the average annual generation-weighted relative accuracy within each of the interconnectio n regions. As shown in Table 2-2, the average ( i.e., mean) relative accuracy 35 for both CO 2 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 int erconnection.

Table 2-2 Generation-weighted relative accuracy of CO 2 and flow monitoring systems, by interconnection

Western Texas Eastern

CO 2 Flow CO 2 Flow CO 2 Flow
2003 2.57 2.70 2.27 2.30 2.52 2.22
2004 3.17 2.51 2.63 1.64 2.51 2.11
2005 3.21 2.95 2.82 2.25 2.69 2.10

33 40 CFR Part 75 requires operators to apportion hea t input to all EGUs sharing a common stack. For thi s reason, EGUs that are not generating electricity during sta rtup or shutdown periods may report a small amount of gross electricity generation ( e.g., 1 MW) to avoid errors in the apportionment calcula tions. However, this can result in unusually large gross heat rates.
34 More information about the quality assurance provi sions, including RATAs, can be found in 40 CFR Part 75 Appendix B.
35 Relative accuracy is expressed as the absolute val ue of the difference between the reference method m easurements and the EGU’s pollutant concentration or flow measu rements. However, the averages at the interconnecti on level are overstated because the relative accuracy is express ed as an absolute value. As a simple example, if th e difference between the measurements at one unit is -2 percent and at another unit the difference is 2 percent, th e arithmetic average is 0 [(-2+2)/2] while the average of the ab solute values is 2 [(|-2| + |2|)/2]. EPA’s calculat ion is based on the absolute value.2 – 31

Western Texas Eastern

CO 2 Flow CO 2 Flow CO 2 Flow
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 d ata were not available at the time of this analysis
While the average relative accuracy at the EGU-leve l varies from 1 to 3 percent, the uncertainty becomes significantly less when these differences a re aggregated over many EGUs – the relevant scope of analysis for building block 1 as a whole. EPA evaluated the combined effect of CO 2 and flow measurement differences between the RATA and m easurement systems at 685 coal-fired EGUs between 2009 and 2012. 36 The results show that the combined differences acr oss the 685 EGUs over four years amount to a fleet wide standar d “error” – an estimate of the uncertainty expected in fleet-wide emissions – of 0.08 percent. 37 Therefore the agency believes the impact of monitoring system relative accuracy does not signif icantly influence the results of the potential heat rate improvement analyses.
EPA also used data from NOAA’s Integrated Surface D ata (ISD) and the USGS’s WaterQualityWatch (WQW). The NOAA dataset provides hourly ambient air temperature for over 20,000 weather stations worldwide. 38 Since coal-fired EGU heat rates can be sensitive t o 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-fir ed EGUs, the agency identified the nearest

36 Coal-fired EGUs with complex stacks ( i.e., common stacks or multiple stacks) were excluded fr om the analysis. Coal-fired EGUs that converted to or from complex s tacks between 2009 and 2012 are included only for y ears during which the EGUs did not use complex stacks.
37 The average combined difference between the RATA a nd monitoring system for flow and CO 2 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 follo wing equation: 		
 √  where n is 3 years × 685 EGUs.

38 NOAA’s Integrated Surface Data are available at http://www.ncdc.noaa.gov/data-access/land-based-sta tiondata/land-based-datasets/integrated-surface-databas e-isd

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weather station 39 that reported a minimum of 8,400 hourly observatio ns in the calendar year ( i.e., greater than 95 percent of the hours in a year). Be cause of the constraint that a station must report at least 8,400 measurements in a calendar year, mos t of the 884 EGUs were associated with meteorological data from the two closest stations o ver 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. 40 For at least one year of the study period (2002-20 12), nine power plants did not have weather stations that met the t hree constraints described above. For these plants, the agency used its judgement and assigned nearby w eather stations that were within 100 miles and 1,000 feet elevation, but had as few as 7,000 h ourly observations. In response to commenters’ concerns that EPA’s analysis did not account for co oling water temperature, the agency added data from the USGS WaterQualityWatch (WQW). 41

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, EPA developed a dataset of hourly heat input, gross electricity generation, gross heat rat e, 42 capacity factor, 43 and ambient air temperature for the years 2002 through 2012 for all 884 coal-fi red EGUs. This dataset, which was included in the proposal, 44 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, an d is likely to go through many more changes between 2012 and 2022 – the first year of t he interim implementation period. Nevertheless, the agency believes it is appropriate and beneficia l to use the full 11 years of data. First, this tim e 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 expr essed 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 t hat reported both heat input and gross electricity generation in 2012 are included in the dataset. Thi rd, the foundation of EPA’s methodology uses

39 EPA established two constraints on the locational differences of the weather station and the EGU: (1) less than 100 miles in distance, and (2) less than 1,000 feet in elevation.
40 The NOAA dataset is the most complete and comprehe nsive ambient air temperature data available to EPA . The hourly ambient air temperature of the nearest weath er station is used only for the purpose of grouping hourly heat rate into temperature bins by 10° F increments. Therefor e, the agency believes conditions at the nearest we ather station provide a reasonable approximation of temperature c onditions at the coal-fired EGU.
41 The USGS WQW 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

. 42 EPA calculated gross heat rate as heat input (Btu) / gross electricity output (kWh).
43 EPA calculated capacity factor as gross electricit y output (kWh) / hourly gross electricity generatin g capacity (kWh).
44 See supra note 1.2 – 33

each coal-fired EGU’s best gross heat rate performa nce between 2002 and 2012 and the EGU’s own 2012 gross heat rate. Therefore, if a coal-fire d EGU employed better practices for maintenance and operation in the past than it did i n 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 more efficient operations – whether for individual EGUs those operations were in 2012 or as early as 2002. 45 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 maintainin g and improving plant heat rate.” 46 EPA’s analysis seeks to identify the potential for EGUs t o re-achieve their better heat rates.
Commenters also noted that the state goals are base d on CO 2 intensity using net electricity generation, but EPA based its potential heat rate i mprovement analysis on gross heat rate. 47 The commenters suggested that EPA should change the ana lysis to use net heat rate. While EPA acknowledges the discrepancy between the unit of me asure for the state goals and the gross heat rate improvement analysis, the agency uses hourly g ross heat rate for the analysis because it is the only comprehensive, quality assured hourly dataset available to the agency. The agency also believes the use of gross heat rate is inherently c onservative for two reasons: (1) it does not reflec t additional opportunities that coal-fired EGUs may h ave to reduce auxiliary energy use ( i.e., parasitic load), 48 and (2) gross electricity generation is not affect ed by auxiliary energy use required to power the significant capacity of air p ollution control devices that were installed between 2002 and 2012. If net heat rate data were a vailable and the agency used these data for the analytical approaches described below, the auxiliar y energy use of air pollution control devices installed during the study period could possibly in flate 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 gr oss heat rate. EPA evaluated three ambient conditions – hourly capacity factor, hourly air tem perature, and, at the suggestion of commenters, hourly surface water temperature – to assess the in fluence these conditions can have on coal-fired EGU’s gross heat rate. The assessment results indic ate that each of the ambient conditions has

45 Beginning in reporting year 2002, the agency intro duced numerous quality assurance checks on hourly e mission data, enhancing the data quality. Therefore, for th e purpose of the potential heat rate improvement an alyses, the agency chose not to include data prior to 2002.
46 See U Nowling, 2015. “Understanding coal power pla nt heat rate and efficiency,” Power. Available at http://www.powermag.com/understanding-coal-power-pl ant-heat-rate-and-efficiency

. 47 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.
48 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/sp ecial_interest_publications/EPRI_Electricity_Use_Re port_Fin al_1024651.pdf . The report explores the auxiliary electricity con sumption at EGUs and finds that auxiliary energy us e (and transmission and distribution losses) can be r educed by 15 percent through available technology o ptions, including variable speed drives.2 – 34

significant influence on the variation in hourly gr oss heat rate of the 884 coal-fired EGUs during the study period (2002–2012). The agency chose to c ontrol 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 f rom monitoring locations nearby and upstream of each of the 884 coal-fired EGUs were no t 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 imp licitly included.

2.5.2.1 Capacity factor
Coal-fired EGUs are designed to operate most effici ently near full capacity. In general, as an EGU’s hourly capacity factor declines, the EGU’s ho urly 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. E ach dot represents the average hourly gross heat rate for all measurements at a specific hourly capa city factor for four “sister units” 49 in 2012. The bottom chart shows the number of hourly measurement s at each hourly capacity factor. The figure illustrates three effects that are broadly applicab le to the 884 coal-fired EGUs: (1) average hourly gross heat rate is higher and shows more variation, or “scatter,” from the average at lower hourly capacity factors, (2) the gross heat rate trend fla ttens 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 parti al load levels
Notes: 2012 data are from four 650 MW coal-fired “s ister units” at JM Stuart in Ohio; cumulative number of measurements exceeds 8,784 hou rs because it includes measurements from four EGUs; the trend line is a power function in the form of y=c×x b ; average hourly gross heat rate axis does not start at zero to better ill ustrate changes in gross heat rate

49 Four 650 MW cell burner coal-fired EGUs at J M Stu art Station in Ohio.2 – 35

For each EGU, EPA used a least-squares linear regre ssion to assess the relationship between hourly gross heat rates and hourly capacity factor. 50 The hourly capacity factor explained about 18 perc ent of the variation in gross heat rate for the 884 coa l-fired EGUs between 2002 and 2012. These results, however, conceal considerable variability. At some coal-fired 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 a t some load-following EGUs. The analysis indicates that as EGUs move from base load to load following ( i.e., cycling operation), 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 ca pacity factors at coal-fired EGUs. As discussed in the proposal TSD, it can be difficult to interpr et 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 leve ls 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 ag ency reviewed two annual capacity factors for the 884 coal-fired EGUs – annual capacity facto r 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, s uch 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 av erage capacity factor of the 884 coal-fired EGUs declined by approximately 10 percent in 2009 a nd, after a small increase in 2010, continued to decline through 2012. However, the annual capaci ty 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 EG Us operated fewer hours in later years, but maintained high hourly capacity factors, on average , during the hours they operated.

50 For each EGU, the annual coefficient of determinat ion ( i.e., the square of the correlation coefficient, or R 2 ) values were weighted using the EGU’s total annual gross el ectricity generation resulting in an EGU-specific g enerationweighted R 2 . This approach controlled for any differences in a verage gross heat rates between years. The weighted R 2 values were averaged across all 884 coal-fired EGUs , resulting in an R 2 value of 0.179.2 – 36 Table 2-3 Annual capacity factors for 884 coal-fire d 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

Figure 2-7 Change in annual capacity factors for 88 4 coal-fired EGUs relative to 2002
Note: capacity factors are relative to 2002 capacit y factor, the first year of the study period2 – 37

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 te mperature affects electricity demand and, as a result, some EGUs’ hourly capacity factors may incr ease when ambient temperatures are high, and (3) ambient air temperature influences cooling wate r temperatures, affecting an EGU’s efficiency. The relationship between hourly ambient air tempera ture 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 temperat ure. EPA assessed the correlation between hourly ambient air temperature and hourly capacity factor during the study period (2002–2012) to determine and found the correlation to be very low. 51

For each EGU, EPA used a least-squares linear regre ssion to assess the influence that hourly ambient air temperature has on the variability of h ourly gross heat rates. 52 The hourly ambient air temperature explained about 8 percent of the variat ion in gross heat rate across the entire fleet. At approximately one-fourth of the 884 coal-fired EGUs ambient air temperature had a greater influence on hourly gross heat rate than the influe nce 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 typically, but not exclusively, uni ts 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 t he EGUs where the ambient temperature ‘signal’ is an important variable. Gross hourly hea t 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 c ommenters, 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 effici ency. The design of the heat exchanger, type of cooling system, availability of water, and tempe rature of water all have an impact on performance. Higher water temperatures typically re duce the effectiveness of the cooling system and, therefore, overall plant efficiency.

51 The Pearson correlation coefficient for hourly amb ient air temperature and hourly capacity factor fro m 2002–2012 is -0.048.
52 For each EGU, the annual coefficient of determinat ion ( i.e., the square of the correlation coefficient, or R 2 ) values were weighted using the EGU’s total annual gross el ectricity generation resulting in an EGU-specific g enerationweighted R 2 . This approach controlled for any differences in a verage gross heat rates between years. The weighted R 2 values were averaged across all 884 coal-fired EGUs , resulting in an R 2 value of 0.083.2 – 38

Commenters suggested EPA should account for the inf luence 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 h eat rate at the time of the proposal. However, in response to commenters’ suggestions, the agency com piled hourly surface water temperature data for 45 coal-fired EGUs in the Eastern Interconnecti on. For each EGU and each year with available data, EPA used a least-squares linear regression to assess the influence that hourly water temperatures had on the variability of hourly gross heat rates. 53 The variation in hourly water temperature explained about 7 percent of the variat ion 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 wa ter temperature, the air temperatures explained approximately 6 percent of the variation in gross h eat rate. 54 The agency also examined the influence of several time-averaged temperatures (ai r and water temperatures) and found comparable results to the hourly ambient temperatur e 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. 55 Not surprisingly, the air temperature and water te mperature are strongly related. 56

Although the surface water data in this analysis we re 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 n ot hold elsewhere in the country. Studies suggest that ambient air temperature is similarly c orrelated with surface water temperature elsewhere in the country. See, for example, a repor t 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...” 57 ; Kaushal et al. found “[l]ong-term increases in stream water temperatures were typically correlated with increases in air temperatures”; 58 and Webb et al. found there is usually a strong correla tion between ambient air temperatures above 0°

53 For each EGU, the annual coefficient of determinat ion ( i.e., the square of the correlation coefficient, or R 2 ) values were weighted using each EGU’s total annual gross e lectricity generation resulting in an EGU-specific generationweighted R 2 . This approach controlled for any differences in a verage gross heat rates between years. The weighted R 2 values were averaged across all 45 EGUs, resulting in an R 2 value of 0.072.
54 For each EGU, the annual coefficient of determinat ion ( i.e., the square of the correlation coefficient, or R 2 ) values were weighted using each EGU’s total annual gross e lectricity generation resulting in an EGU-specific generationweighted R 2 . This approach controlled for any differences in a verage gross heat rates between years. The weighted R 2 values were averaged across all 45 EGUs, resulting in an R 2 value of 0.057.
55 The R 2 value for rolling 24-hour water temperature and ho urly gross heat rate was 0.071 and the R 2 for rolling 24- hour ambient temperature and hourly gross heat rate was 0.072.
56 The Pearson correlation coefficient for hourly wat er temperature and 24-hour ambient air temperature is 0.74.
57 See New Mexico Environment Department, 2011. Air-W ater Temperature Correlation. Available at https://www.env.nm.gov/swqb/documents/swqbdocs/MAS/ Protocols/Air-Water/Air-Water08-01-2011.pdf

. 58 See SS Kaushal, GE Likens, NA Jaworski, ML Pace, A M Sides, D Seekell, KT Belt, DH Secor, and RL Winga te, 2010. “Rising stream and river temperatures in the United States,” Frontiers in Ecology and the Enviro nment. 8:461- 466. (DOI: 10.1890/090037).2 – 39

C and stream water temperatures (although the relat ionship may be non-linear at higher temperatures). 59

Based on these results, the agency believes that ho urly ambient air temperature is the appropriate variable to use in the potential heat rate improvem ent analyses because: (1) available hourly water temperature data are limited to a portion of the fl eet, (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 ab ove, EPA conducted a least-squares linear regression analysis to assess the influence that th e combination of hourly capacity factor and hourly ambient air temperature had on the variabili ty of hourly gross heat rate. 60 For the 884 coalfired EGUs, the combination of hourly capacity fact or and hourly ambient air temperature explained about 26 percent of variation in gross he at rate between 2002 and 2012.

2.5.2.5 Accounting for the influence of factors outside coa l-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. Th erefore, 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 analy sis controls for EGU design characteristics such as boiler type and size, generator size, coal type, and cooling system type because it compares each coal-fired EGU’s historical performance agains t 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 d oes not appear to have a significant effect on gross heat rate. In addition, EPA controlled for th e installation of air pollution control devices by analyzing gross heat rate – because the impact of a ir pollution control devices’ auxiliary energy consumption does not affect gross electricity gener ation, the addition of controls does not influence the results of the gross heat rate analyses.
In two of the three analytical approaches discussed below, EPA also controlled for hourly capacity factor and hourly ambient air temperature because t hese two environmental and operating factors are considered to be beyond the EGU operators’ cont rol. To limit the influence of these two factors,

59 See BW Webb, DM Hanna, RD Moore, LE Brown and F No bilis, 2008. “Recent advances in stream and river temperature research,” Hydrological Processes. 22: 902-918 (DOI: 10.1002/hyp.6994).
60 For each EGU, the annual coefficient of determinat ion ( i.e., the square of the correlation coefficient, or R 2 ) values were weighted using the EGU’s total annual gross el ectricity generation resulting in an EGU-specific g enerationweighted R 2 . This approach controlled for any differences in a verage gross heat rates between years. The weighted R 2 values were averaged across all 884 coal-fired EGUs , resulting in an R 2 value of 0.255.2 – 40

the agency grouped each EGU’s hourly gross heat rat e values into unit-specific “capacitytemperature bins” based on hourly capacity factor a nd hourly ambient air temperature. This enabled the agency to compare gross heat rate value s 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 electricity generating capacity ( i.e., 0- 9, 10-19, 20-29, ... , 90-99, 100-109, greater than o r 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 res ult is 168 capacity-temperature bins (12 X 14) for each of the 884 coal-fired EGUs. However, not a ll capacity-temperature bins for an individual EGU have data ( e.g., coal-fired EGUs in Florida are unlikely to experie nce hourly ambient temperatures below -10° F).
At least one commenter expressed concern that the h ourly capacity factor ranges were too large. They pointed to information that showed heat rate c an vary significantly at low hourly capacity factors, resulting in differences of as much as 4 p ercent 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. Howeve r, the trend is more pronounced at low capacity factors, as noted by the commenter, and less promin ent at higher capacity factors. EPA chose to maintain the size of the hourly capacity factor ran ges for three reasons: (1) approximately 70 percent of the hourly values are greater than 70 pe rcent 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 r ange 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 me asurements in each bin and more data excluded from the analysis. 61 EPA’s assessment does not indicate that using a sm aller range for capacity factor would have a significant impact on the resul ts.

61 As described below, in the efficiency and consiste ncy analysis EPA excluded any capacity-temperature bins that had fewer than 15 hourly measurements for a single coal-fired EGU.2 – 41 Figure 2-8 Distribution of hourly capacity factors by interconnection
Several commenters noted that EPA’s analysis does n ot account for all the factors that affect a coal-fired EGU’s heat rate. Specifically, these com menters mention the influence of geographic differences ( e.g., seasonality, altitude), coal rank and quality, equ ipment degradation, and operating conditions. As stated above, the agency m ade every effort to control for design characteristics and ambient conditions that the age ncy believes to be outside the EGU operator’s control. The agency also controlled for capacity fa ctor, which some commenters argued is partially under the operator’s control. However, the agency l acks sufficient information to determine the portion of hourly capacity factor that is under the direct control of influence of the EGU owner or operator. Therefore, the agency chose to reasonably and conservatively control for the full effect of hourly capacity factor based on the results of t he analysis described above. Regarding commenters’ assertion about the role of a coal-fire d EGU’s location ( i.e., geography) on heat rate, the agency believes that the approaches described b elow do control for these conditions because coal-fired EGUs are not considered mobile sources s o by comparing each EGU’s performance from 2002 to 2012 against the EGU’s 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 re cognizes that: (1) different coal ranks have different heating values and moisture content, and (2) coal-fired EGUs are generally designed for a specific coal rank and coal quality and deviation s from that coal rank can adversely affect heat rate. However, the agency believes that for most co al-fired EGUs coal rank and coal quality are controlled by the EGU operator through purchase con tracts with fuel suppliers. Therefore, the agency chose not to control for coal rank or coal q uality. Commenters also stated that equipment degrades over time, increasing heat rate. As discus sed further in section 2.6.8 below, EPA recognizes that all equipment will degrade over tim e. However, some of the best practices that can2 – 42

be employed to improve heat rate involve properly o perating and maintaining equipment to minimize the degradation and, when necessary, repla cing equipment to maintain efficient operation of the plant. Therefore, the agency did n ot control for the effect of equipment degradation in the analyses described below because EGU operato rs can limit equipment degradation through best practices for operation and maintenance. 62 Similarly, the agency believes operating condition s at each coal-fired EGU are largely under the contro l of the EGU operator so it is reasonable and appropriate for the agency to include variability d ue to operating conditions (with the exception of capacity factor.)

2.5.3 Heat rate improvement approach “best historical per formance”
In the proposal TSD, EPA included an assessment of potential heat rate improvement based on the difference between each coal-fired EGU’s best rolli ng annual gross heat rate and its 11-year (2002– 2012) gross heat rate. Based on commenters’ suggest ions and heat rate improvement analyses provided by commenters, the agency refined the appr oach and calculated 1- and moving 2-year average gross heat rates for each of the 884 coal-f ired EGUs in the study for the years 2002–2012. The agency removed the 3-year averaging period disc ussed in the proposal TSD because, as noted by commenters, the differences in temperature profi les 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 alread y controls for capacity factor and temperature in some of the analytical approaches. However, the age ncy chose to conservatively include a 2-year rolling average to account for atypical annual oper ations 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 condit ions), while not diluting the results so much that it would obscure significant changes to annual gross heat rate.
The agency selected each EGU’s best ( i.e., lowest) 1- and moving 2-year average gross heat ra tes, calculated the generation-weighted average across a ll EGUs in each interconnection, and compared the results to the interconnection’s gener ation-weighted 2012 average gross heat rate. As described above, this approach controls for desi gn characteristics that remain constant and is not affected by the installation and operation of a ir pollution control devices.

2.5.3.1 Methodology
The procedures for calculating the potential heat r ate improvement are:

1. For each coal-fired EGU, calculate the 1- and movin g 2-year average gross heat rate. 2. Identify each EGU’s best 1- and moving 2-year gross heat rate.

62 See section 2.6.8 for more information about equip ment degradation and maintaining heat rate performa nce.2 – 43

3. Calculate the generation-weighted average heat rate based on the lowest 1- and moving 2-year average gross heat rate and the generation o ver the time period within each interconnection region. 4. For each interconnection, calculate the 2012 averag e gross heat rate weighted by the 2012 generation. 5. For each interconnection, calculate the potential h eat rate improvement – the percent difference between the best 1- and moving 2-year av erage gross heat rate from step 3 and the 2012 gross heat rate from step 4.

2.5.3.2 Results
This approach identifies the potential heat rate im provement by determining what would have been possible if each of the 884 coal-fired EGUs had rep roduced its demonstrated best performance from 2002 to 2012. These results are conservative b ecause 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. The results of the analytical approach are presented in Table 2-4.

Table 2-4 Potential heat rate improvement based on best historical performance, by interconnection
Performance period Western Texas Eastern Nat ionally 63

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 per formance under similar conditions”
The second analytical approach – best historical pe rformance under similar conditions – is an outgrowth of the first and third analytical approac hes based on comments and analyses provided by commenters. The second approach is similar to th e first approach described above, but in this approach EPA controlled for each EGU’s hourly capac ity factor and hourly ambient air temperature as described above. Within each capacit y-temperature bin for each coal-fired EGU, EPA calculated the bin-specific 1-year and moving 2 -year average gross heat rates, selected the lowest gross heat rate within that capacity-tempera ture bin, calculated the generation weighted average across all bins and units, and compared tha t to the generation weighted 2012 average gross

63 EPA has provided nationwide results for purposes o f comparison; they are not part of the methodology.2 – 44

heat rate. One advantage of this approach is that i t controls for factors that explain a significant portion of variation in EGUs’ gross heat rate.

2.5.4.1 Methodology
The procedures for calculating the potential heat r ate improvement using this approach are the same as those shown above with one exception: the a gency partitioned the hourly data by hourly capacity factor and temperature as described in the following steps.

1. For each coal-fired EGU and capacity-temperature bi n, 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 in each temperature and capacity factor bin. 3. Calculate the generation-weighted average heat rate using the lowest 1- and moving 2- year average gross heat rates from step 2 and the g eneration over the time period for all the EGUs and temperature and capacity factor bins i n each interconnection region 4. Within each interconnection, calculate the 2012 ave rage gross heat rate weighted by the 2012 generation. 5. For each interconnection, calculate the potential h eat rate improvement – the percent difference between the best 1- and moving 2-year av erage gross heat rate from step 3 and the 2012 gross heat rate from step 4.

2.5.4.2 Results
This approach identifies the potential heat rate im provement by determining what would have been possible if each of the 884 coal-fired EGUs had rep roduced its demonstrated best performance from 2002 to 2012 while controlling for the influen ce of hourly capacity factor and hourly ambient temperatures. These results are conservative becaus e 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 thr ough additional equipment upgrades and best practices.
One reason this approach yields interconnection-le vel results that are higher than the first approach is that it relies on the lowest 1- and mov ing 2-year average gross heat rates within each capacity-temperature bin. Therefore, unlike the fir st 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” year s for the different capacity-temperature bins.
The results of this second analysis are shown in Ta ble 2-5.2 – 45 Table 2-5 Potential heat rate improvement based on best historical performance under similar condition s, by interconnection
Performance period Western Texas Eastern Nationally 64

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

2.5.5 Heat rate improvement approach “efficiency and cons istency 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 h eat rates ( e.g., best annual average over a 1- or moving 2-year period). The third approach, which wa s described extensively in the proposal TSD, uses a different approach to determine the potentia l heat rate improvement. This third approach is based on the principle that coal-fired EGUs are cap able of operating more consistently with their historically demonstrated “best” hourly gross heat rates. For this approach, the agency grouped each coal-fired EGU’s data by capacity-temperature bin and determined a 1- and moving 2-year gross heat rate “benchmark.” 65 This benchmark was based on each coal-fired EGU’s 10th percentile hourly gross heat rate during each 1- an d moving 2-year period for each capacitytemperature 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 capacity-temperature bin). The agency chose the 10th percentile because it represents a demonstrably achievable gross heat rate indicating efficient operation of the EGU, but ignores unusually low outlier values. 66 Furthermore, the 10th percentile is a commonly use d statistical level for determining if particular val ues are unusually low relative to the average value. 67

In this approach, EPA assumed that a coal-fired EGU could achieve the benchmark with the same frequency in the future as in the past while also a ssuming that for the remaining time it could achieve heat rates that were closer to the benchmar k under similar hourly capacity factor and hourly ambient air temperature conditions. The agen cy calculated the potential heat rate

64 EPA has provided nationwide results for purposes o f comparison; they are not part of the methodology.
65 EPA excluded capacity-temperature bins that had fe wer than 15 hourly measurements for a single coal-f ired EGU.
66 During the development of the dataset the agency e xcluded extreme outliers – hourly gross heat rate values that were greater than ±2.6 standard deviations from the EGU’s mean gross heat rate.
67 Common statistical levels include the 10th and 90t h percentiles, the 5th and 95th percentiles, and th e 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 t o have been an outlying value than the 5th or 1st p ercentile. If EPA had selected the 5th percentile as the benchmark, e ach EGU would have half as many hours of historical operation at or below the benchmark and the overall resulting po tential heat rate improvement would have been subst antially larger.2 – 46

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 – a cons istency factor – to represent more consistently efficient operation of the EGU. Using this approach , the hourly gross heat rate values that were adjusted most in absolute terms were the highest va lues ( i.e., less-efficient hours of operation) that indicate a relative failure to fully employ heat ra te improvement measures. The result is that the post-adjustment distribution of hourly heat rate va lues 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). This is based on the principle th at a coal-fired EGU following best practices should be able to consistently operate closer to th e demonstrated and achievable benchmark heat
rate.

Figure 2-9 Frequency of hourly gross heat rates mea sured and adjusted, 2002–2012 for an

example coal-fired EGU in a single capacity-tempera ture bin
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 av erage), accorded with EPA experts’ engineering judgment, and because 30 p ercent was in the middle of the reasonable range evaluated by the agency. Several commenters s uggested that this choice of values was arbitrary and could have easily been higher or lowe r had EPA selected the middle of a different range of values. To identify a consistency factor w ith more precision for this analytical approach, EPA analyzed EGUs’ historically demonstrated level of consistency ( i.e., variability) for hourly gross heat rate over 1-year and moving 2-year perio ds between 2002 and 2012 for each capacitytemperature bin. The agency calculated the mean-cen tered standard deviation of hourly gross heat rate values for each 1- and 2-year period for the p opulation of EGUs in each interconnection for2 – 47

all capacity-temperature bins. 68 The lowest standard deviations – a measure of vari ability – 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 percent difference o f the 1- and moving 2-year standard deviations and the 11-year standard deviation represent the po tential consistency improvement at each interconnection. In other words, this approach comp ares the EGUs’ most consistent performance ( i.e., least variability) for each capacity-temperature b in over a 1- or moving 2-year period to the EGUs’ average 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. For the population of EGUs in each interconnection, the refined approach – compares th e generation weighted average heat rate across all bins and units adjusted to be “efficient and co nsistent”, with the generation weighted 2012 average gross heat rate.
Several commenters noted that reduced variability d oes not necessarily equate to improved heat rates. EPA agrees with commenters that reducing var iability alone does not result in lower heat rates and, therefore, lower CO 2 emissions. For example, if an EGU were to operate more consistently at a higher heat rate ( i.e., less efficient), the variability would decrease, b ut the heat rate would increase. However, the agency is not sim ply asking for less variability in operation at existing coal-fired EGUs. Instead, the agency is as sessing the average heat rate improvement that would result if coal-fired EGUs had operated more c onsistently nearer their gross heat rate benchmarks. Both the benchmark gross heat rates and reduced variability in heat rate were demonstrated by the EGUs from 2002–2012 during hour s with similar hourly capacity factors and hourly ambient air temperatures.

68 The mean-centered standard deviation shows the ext ent of variation relative to the mean and allows fo r meaningful comparison of variability from coal-fired EGUs that can have different average gross heat rates. The m ean-centered standard deviation, also called the coefficient of variation, is calculated by dividing each coal-fire d EGU’s standard deviation for the capacity-temperature bin by the E GU’s capacity-temperature bin’s average gross heat rate. It is reasonable to use the standard deviation when evalu ating the potential to reduce variability because s tandard deviation is the most common measure of the spread, or variat ion, of values within a dataset.2 – 48

2.5.5.1 Methodology
The procedures for calculating the consistency fact or are:

1. For each coal-fired EGU, calculate the 1-year avera ge gross heat rate for each capacity-temperature bin. 69 2. For each coal-fired EGU, calculate the difference, as a percentage, between every hourly gross heat rate value and the value from ste p 1 within each capacitytemperature bin. 3. Within each interconnection, calculate the mean-cen tered deviation of all the values derived from step 2 for each 1-, moving 2-, and 11- year period. 70 4. Within each interconnection, calculate the consiste ncy factor as the percent difference between the lowest 1- and 2-year standard deviation s and the 11-year standard deviation from step 3.
The procedures for calculating the potential heat r ate improvement are:

1. For each coal-fired EGU, determine the gross heat r ate benchmarks – the 10th percentile hourly gross heat rate – for each 1- and moving 2-y ear 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 hourly heat rate value, calculate the adju sted gross heat rate:

a. For each hourly gross heat rate value greater than the 1- or moving 2-year benchmark calculate the difference between the hour ly value and the relevant benchmark; b. Multiply the difference by the relevant 1- or movin g 2-year interconnectionspecific 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 average gross heat rate representing “efficient and consistent” operation using the adjusted gross heat rate values from step 3. 5. For each interconnection, calculate the 2012 averag e gross heat rate weighted by the 2012 generation.

69 The agency did not calculate 2-year average gross heat rates because any change in average heat rate from one year to the next could lead to overestimates in the amou nt of variability. The method used here accounts fo r changes in average heat rate from one year to the next, so tha t only the relative variability is being evaluated.
70 This approach identifies the variability of the be st 1 or 2-year time period relative to the 11-year average variability. As described above, mean-centering the data account s for changes in heat rate over the time period. Be cause the analysis accounts for changes in heat rate, the ave rage variability is more-representative than the va riability of any particular year ( e.g., 2012).2 – 49

6. For each interconnection, calculate the genera tion-weighted potential heat rate improvement by calculating the percent difference b etween the 11-year adjusted heat rate average from step 4 and the 2012 generation-we ighted gross heat rate from step 5.

One commenter noted that EPA’s analysis in the prop osal TSD used heat input instead of gross heat rate to assess potential heat rate improvement s. The commenter noted that EPA’s assumption of an equivalent level of gross electricity generat ion across the full range of the capacity factor bi n skewed the results. EPA agrees with the commenter t hat using the gross heat rate for the analysis provides more accurate results. Therefore, the agen cy changed the approach to use hourly gross heat rate, calculated as heat input divided by gros s electricity generation.

2.5.5.2 Results
This approach calculates the potential heat rate im provement by determining what would be possible if each of the 884 coal-fired EGUs had ope rated more efficiently and consistently nearer their historically demonstrated “best” gross heat r ate from 2002 to 2012 while controlling for the influence of hourly capacity factor and hourly ambi ent temperatures. The consistency factors calculated for this analytical approach are present ed in Table 2-6 and the potential heat rate improvement results are presented in Table 2-7.

Table 2-6 Consistency factors based on best histori cal performance

under similar conditions, by interconnection
Performance period Western Texas Eastern Nationally 71

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 2-7 Potential heat rate improvement based on efficiency and consistency under similar conditions, by interconne ction
Performance period Western Texas Eastern Nationally 72

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

71 EPA has provided nationwide results for purposes o f comparison; they are not part of the methodology.
72 EPA has provided nationwide results for purposes o f comparison; they are not part of the methodology.2 – 50

2.5.6 Summary of results
Each of the three analytical approaches described a bove 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, EPA is confident that the potential heat rate improvements identified on the regional basis throu gh these approaches are feasible.
Although each of these three analytical approaches is a reasonable definition of heat rate improvement potential under building block 1, EPA’s overall methodology adopts the most conservative value identified by any of the approac hes. Under each of the three analytical approaches, EPA separately calculated potential hea t rate improvements based on the best 1-year and moving 2-year performance for each EGU in each region. In order to further increase our confidence in the region-wide results, EPA’s conser vative methodology compared the six resulting values for each region and adopted the lowest value identified by any analytical approach. Because the results from the moving 2-year analyses will al ways be more conservative than those from the 1-year analyses, 73 and thus the 1-year analyses cannot provide the mo st conservative outcome as a whole, only the results of the moving 2-year anal yses are presented in Table 2-8. 74

Table 2-8 Potential heat rate improvement for best moving 2-year period, by interconnection Analytical approach Western Texas Eastern Nationally 75 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. Acc ordingly, the tripartite methodology adopts those results for purposes of calculating the relevant CO 2 emission performance rates and accompanying

73 The “best” gross heat rate over a moving 2-year pe riod will always be higher than the best gross heat rate over a 1- year period because the moving 2-year period includ es at least one non-best year ( i.e., a year with higher average gross heat rate values).
74 The results of EPA’s inherently less conservative 1-year analyses can be found in section V.C.1.a of the final rule preamble of the CPP, as well as in the discussion a bove.
75 EPA has provided nationwide results for purposes o f comparison, they are not part of the methodology.2 – 51

state goals. 76 Specifically, a well-supported and conservative es timate of the potential heat rate improvements (and accompanying reductions in CO 2 emission rates) that affected coal-fired EGUs in the Western interconnection is 2.1 percent, in t he Texas interconnection is 2.3 percent, and in the Eastern interconnection is 4.3 percent. The age ncy believes these values are conservative and that EGUs have demonstrated these gross heat rates during the study period.
It is important to note that these potential heat r ate 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-effect ively implemented at coal-fired EGUs. The agency decided not to pursue a separate heat rate i mprovement 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 improvemen t, and (3) potential impact of past deployments of heat rate improvement technology at some EGUs th at might already be reflected in the results of the analyses described above.

2.6 Assessment of key comments
EPA received more than 1,500 comments on building b lock 1 of the best system of emission reductions, including comments on the methodology a nd assumptions for calculating potential heat rate improvements. Several of the comments are addr essed above and below, and in the preamble with the remaining comments addressed in the respon se to comments (RTC). EPA appreciates the information and insights that commenters shared abo ut the proposal. The information assists the agency in developing a stronger, more effective rul e. 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 gre ater variability of heat rate and, therefore, inflate the potential heat rate improvements. EPA a cknowledges variability from heat rate improvements would affect the results of the approa ch 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 p otential heat rate improvement.

76 The “best historical performance” and “best histor ical performance under similar conditions” approach es identify greater potential heat rate improvement in each reg ion. Accordingly, they inherently support the feasi bility of the more conservative values identified by the “efficiency a nd consistency under similar conditions” approach.2 – 52

2.6.2 Impact of air pollution controls installed during s tudy period
Commenters described many of the air pollution cont rol devices that were installed at coal-fired EGUs between 2002 and 2012, noting that these devic es can have significant auxiliary energy requirements ( i.e., parasitic load) that reduce the amount of electric ity available for distribution on the regional interconnection. As a result of lower net electricity generation, an EGU’s net heat rate increases. Many commenters recommended that EPA con sider the auxiliary energy requirements of these controls when assessing the potential heat rate improvement. EPA recognizes that air pollution control devices have auxiliary power requ irements and a significant amount of coal-fired EGUs were retrofitted with air pollution controls d uring the study period (2002-2012). However, EPA rejects the recommendation to credit the auxili ary power requirements in the analysis because the agency’s analysis is based on gross heat rate a nd, therefore, is not affected by auxiliary power requirements of air pollution controls. 77

2.6.3 Impact of CO 2 emissions from SO 2 pollution control devices
Commenters noted that the addition of wet flue gas desulfurization (WFGD, or wet scrubber) technologies result in increased process CO 2 emissions because of the dissolution of limestone to capture SO 2
. 78 Because heat input, the numerator for calculating heat rate, is based on an EGU’s measured CO 2 emissions, the additional process CO 2 emissions result in artificial increases in calculated gross heat rate. Commenters suggest that process CO 2 emissions can be “several percentage points.” To assess the range of potentia l impacts on EPA’s analysis from process CO 2 emissions, the agency estimated the incremental CO 2 from the WFGD chemical reaction. The results indicate that retrofit WFGD installed on ex isting coal-fired EGUs between 2002 and 2012 increased 2012 CO 2 emissions (and calculated heat input) by an averag e of no more than 0.7 percent for those EGUs in 2012.

To calculate the CO 2 emissions, the agency first identified the 121 exi sting coal-fired EGUs that installed retrofit limestone WFGD between 2002 and 2012. Collectively these EGUs have approximately 59 GW of electricity generating capac ity. Next, EPA reviewed monthly fuel transactions and calculated the weighted average su lfur content of fuel deliveries to the relevant power plants. 79 Finally, the agency calculated the process CO 2 emissions that could result at 85 and 95 percent removal efficiencies (see Table 2-9) . Due to data limitations – EGU-level fuel consumption data are not available – the agency ass umed that: (1) coal delivered to the power plant in 2012 was representative of coal burned in the WF GD-equipped coal-fired EGU, and (2) EGUs operated the WFGD to remove 85 to 95 percent of the sulfur. The resulting process CO 2 emission increase at coal-fired EGUs that installed retrofit limestone WFGD ranges from 0.2 percent (Texas)

77 See supra note 47.
78 The reaction in the WFGD absorber is summarized by SO 2 +CaCO 3 +1⁄2H 2 O → CaSO 3 +1⁄2H 2 O+CO 2 (see RK Srivastava, 2000. Controlling SO 2 emissions: a review of technologies. EPA report EP A/600/R-00/093. Available at http://nepis.epa.gov/Adobe/PDF/P1007IQM.pdf

. 79 Monthly fuel transactions are reported at the plan t level on form EIA-923.2 – 53

to 1.0 percent (Eastern). It should be noted, howev er, that this is a 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 pyri tic-bound sulfur, (2) a small amount of the combustion CO 2 might be absorbed in the WFGD slurry, and (3) in 2 012 some EGUs did not operate their WFGD at full capacity.

Table 2-9 Increase in 2012 CO 2 measurements due to retrofit limestone wet flue gas desulfurization

Western Texas Eastern Nationally

Number of retrofit limestone WFGD 6 6 109 121
Average sulfur content (%) of 2012 coal deliveries 0.55 0.43 1.82 1.63
Total quantity (1,000 tons) of 2012 coal deliveries 12,375 11,637 148,631 172,644
Potential process CO 2 increase (%) from WFGD 0.3 0.2 0.9-1.0 0.8-0.9
Note: Interconnection-level totals may not add up t o national-level totals due to rounding; Potential process CO 2 emissions are based on 85 and 95 percent removal r ates, 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 ac count for other WFGD that do not produce process CO 2 emissions

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

This rule establishes CO 2 intensity goals based on net electricity generatio n for each state using the state’s 2012 net electricity generation baselin e. Many commenters noted that air pollution controls installed after 2012, many of which are re quired to comply with other federal and state environmental regulations, will require auxiliary p ower and therefore will increase EGUs’ net heat rate and CO 2 intensity. These commenters suggested that EPA sho uld account for the addition of future air pollution controls when calculating the state goals. EPA evaluated the potential impact of the additional pollution controls that are expec ted to be installed between 2013 and 2025 to comply with rules such as the Mercury and Air Toxic s Standards (MATS), Clean Air Interstate Rule (CAIR), Cross-State Air Pollution Rule (CSAPR) , Regional Haze Rule, as well as consent decrees and state policies. The agency’s analysis, described below, indicates that the potential impact on national net heat rate and, therefore, CO 2 intensity is small – approximately 0.3 percent – in 2025.2 – 54

To assess the anticipated impact of new retrofit ai r pollution controls on net heat rate, EPA used data and information from EPA’s Power Sector Modeli ng Platform v.5.15. 80 The Base Case is a projection of power sector activity, taking into ac count existing federal and state air emission laws and regulations. The Base Case includes projected i nstallations of air pollution controls and EGUlevel net electricity generation in the years 2017, 2018, and 2025. The agency compared the Base Case 2025 installations of air pollution controls t o the 2012 installations listed in the National Electric Energy Data System (NEEDS) v.5.15. Specifi cally, the agency identified predicted additions of WFGD, dry flue gas desulfurization (DF GD), selective catalytic reduction (SCR), and selective non-catalytic reduction (SNCR) (see Table 2-40).

Table 2-40 Predicted additional nameplate capacity (MW) with air pollution controls installed by 2025, by i nterconnection
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 us ed the heat rate penalties ( i.e., auxiliary power requirements) from EPA’s Power Sector Modeling Plat form v.5.13 (see Table 2-11). 81 Each EGU’s 2012 net heat rate was adjusted for the assum ed heat rate penalties and multiplied by the EGU’s projected 2025 net electricity generation in the Base Case to estimate the incremental heat input required to operate the retrofit air pollutio n controls. The incremental heat input at EGUs with additional air pollution controls was aggregat ed to the interconnection region and then divided by the region’s pre-control heat input. 82 The anticipated impacts of the retrofit air pollut ion 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.

80 More information about the Power Sector Modeling P latform, including data, assumptions, and documenta tion is available at http://www.epa.gov/airmarkets/programs/ipm/psmodel5 15.html

. 81 Information on air pollution controls is taken fro m Chapter 5 available at http://www.epa.gov/airmarkets/documents/ipm/Chapter _5.pdf

. 82 The pre-control heat input is calculated as 2012 n et heat rate (before additional air pollution contr ols) multiplied by 2025 net electricity generation.2 – 55 Table 2-11 Assumed heat rate penalty for air pollut ion controls
Air pollution control Heat rate penalty (%)
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 reas ons: (1) EPA’s cost performance assumptions tend to overestimate the auxiliary energy requireme nts from individual retrofit controls, and (2) at some EGUs these newer pollution control devices wil l replace existing pollution control devices. Accordingly, for these EGUs, the increase in net he at rate due to auxiliary power requirements to operate new controls will be at least partially off set by the decrease in net heat rate caused by removal of current control devices. 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. 83 In addition, future technological innovations or advances in emission control practices may reduce t he need for retrofit emission controls.
Therefore, for the reasons described above, the age ncy has determined that when calculating overarching state goals, it is not necessary to acc ount 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 EG Us after 2012 when developing their state plans.

2.6.5 Impact of cycling operations and lower capacity fac tors on heat rate
Several commenters expressed concern that the appli cation of the other building blocks that make up the best system of emissions reductions will res ult 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 fact ors, heat rate increases ( e.g., see Figure 2-6) making improving heat rate even more challenging. E PA disagrees that the application of the other building blocks from the best system of emission re ductions 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 remainin g capacity of existing coal-fired EGUs during the interim and full compliance periods; (2) EPA mo deling of electricity generation in 2030

83 For example, see D Osowski, 2014. “Bagging Two Bir ds with One Economizer,” Power Engineering. Availab le at http://www.power-eng.com/articles/print/volume-118/ issue-11/departments/clearing-the-air/bagging-two-b irdswith-one-economizer.html

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indicate that the remaining coal-fired EGUs will ha ve higher annual capacity factors than the 884 coal-fired EGUs in 2012 – the baseline for the agen cy’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 t he agency’s analyses, more than 53,000 MW of capacity is expected to retire. This represents app roximately 16 percent of the capacity from the 884 coal-fired EGUs across the nation. In the Weste rn Interconnection, coal-fired EGU owners and operators have officially announced 6,000 MW of capacity, 16 percent of total capacity, will retire in 2020 or earlier (see Figure 2-10). The ag ency does not have information about any official planned retirements in the Texas Interconnection. I n the Eastern Interconnection, EGU owners and operators have officially announced more than 47,00 0 MW of capacity, 17 percent of total capacity, will retire in 2020 or earlier (see Figure 2-11). B ecause less coal-fired electricity generating capacity will be available for dispatch, the remain ing coal-fired EGUs are expected to operate at higher annual capacity factors.

Figure 2-10 Official completed and planned retireme nts of coal-fired EGUs in Western Interconnection, 2012–2020
Note: Capacity scale is 1 / 10 of scale in Eastern Interconnection (Figure 2-11)2 – 57

Figure 2-11 Official completed and planned retireme nts of coal-fired EGUs in Eastern Interconnection, 2012–2020
EPA's power sector modeling for this rule predicts that annual capacity factors for coal-fired EGUs in 2030, with the rule in full effect, will be high er 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-fi red 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 summert ime capacity – at coal-fired EGUs is predicted to be approximately 70 percent under a rate-based g oal and 75 percent under a mass-based goal while the 2012 net annual capacity factor averaged 55 percent. 84 The values for the interconnections are shown in Table 2-52. While the se values represent annual capacity factors and not hourly capacity factors, the substantial in crease in annual capacity factors at existing coalfired EGUs in all interconnections except Texas lea ds to a reasonable conclusion that the remaining coal-fired EGUs as a whole will operate at higher l evels in 2030 than the coal-fired EGU fleet did in 2012, the baseline for this heat rate improvemen t study. For the Texas interconnection, the capacity factors are anticipated to be approximatel y the same under a mass-based policy approach and lower under a rate-based approach. The state of Texas has the flexibility to consider this projected change in annual capacity factors when de signing the state plan.

84 The 2012 net annual capacity factors were calculat ed from the 2012 baseline data using the eGRID meth odology. For the 947 coal steam EGUs in the CONUS, the capac ity factor was calculated as net electricity genera tion (MWh)/[8784 hours X net summertime capacity (MW)]. The difference between the values in Table 2-12 and the values in Table 2-3 are attributable to the metric – Table 2-12 lists net capacity factor while Table 2-3 lists gross capacity factors.2 – 58

Table 2-52 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

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 h eat rate data
To calculate heat input at a coal-fired EGU, the op erator 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. 85 However, in 1999 EPA introduced new reference meth ods 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 meth ods 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 alte rnative 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. 86 However, coalfired EGUs are not required to use the alternative measurement methods and they may change methods when conducting a RATA. Commenters have hyp othesized that much of the variability in gross heat great is caused by coal-fired EGUs ch anging from one flow measurement method to another. EPA considered this possibility and discus sed some of the issues in the proposed TSD. The agency evaluated the commenters’ concerns and c onducted additional analyses to explore the potential impact of flow method on heat rate variab ility.
.Evaluation of flow measurement methods is complex because coal-fired EGUs may have one or more stacks, including common stacks that are share d with other EGUs at the power plant, and might use different flow measurement methods for up to three different load levels in each stack. To assess the potential influence of flow measureme nt 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 mor e flow measurement method for at least

85 For more information about Method 2, see 40 CFR Pa rt 60 Appendix A.
86 Coal-fired EGUs use continuous emission monitoring systems to measure pollutant concentrations and st ack gas flow. The two measured values are used to calculate mass emissions.2 – 59

one load level during the 11-year study period (200 2–2012). 87 If, as commenters suggested, changes to flow measurement methods were responsibl e 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. To test this theory, the agency conducted the “efficiency and consistency” analysis using only th e 531 EGUs that changed flow measurement methodology. 88 However, as shown in

Table 2-6 3, the EGUs that changed flow method during the stu dy period (2002–2012) have lower potential heat rate improvements in the Western and Texas interconnections than for the full group of coal-fired EGUs in the interconnections. In the Eastern interconnection, the results between the two groups – EGUs that changed flow measurement met hod(s) and all coal-fired EGUs – are nearly identical. 89 This demonstrates that although changes in flow me asurement method might have implications for an individual EGU’s reported heat rate, the changes do not adversely affect the overall interconnection-level results of EPA’s potential heat rate improvement analyses.
Table 2-63 Potential heat rate improvement (percent ) based on the 2-year efficiency and consistency under similar conditions analytical app roach for all coal-fired EGUs and coalfired 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

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 meth ods are approximately the same in the

87 The flow measurement methods for these 531 coal-fi red EGUs are available in the dataset titled Supplemental data and analysis for potential heat rate improvement fo r the CPP final rule

. 88 EPA did not reassess the other analyses of the tri partite approach for EGUs that changed flow measure ment methodology because the “efficiency and consistency ” approach was the most conservative approach ( i.e., resulted in the lowest potential heat rate improvement).
89 This is particularly noteworthy given that the bes t system of emission reductions for the Eastern Int erconnection sets the best system of emission reductions for the entire CONUS.2 – 60

Western and Eastern Interconnection, EPA disagrees with commenters’ concern that changes to flow measurement methods were responsible for much of the variation in gross heat rates. In the Texas Interconnection, only six of 32 coal-fired EG Us reported changing flow measurement methodology during the study period (2002–2012). Co llectively, the six EGUs in the Texas interconnection that changed flow measurement metho ds had performed better in 2012 and/or had lower variability than the population of Texas Inte rconnection coal-fired EGUs.
In the year 2012, 193 of the 884 coal-fired EGUs us ed Method 2 for all load levels, 663 coal-fired EGUs used alternative flow measurement methods for all load levels, and 28 coal-fired EGUs used a combination of methods.
For the reasons described above, changes in monitor ing methodology at some coal-fired EGUs does not alter our confidence in the regional resul ts that constitute the potential heat rate improvement under building block 1.

2.6.7 Inability of individual coal-fired EGUs to achieve the heat rate improvements and concerns about stranded assets
Several commenters stated that specific coal-fired EGUs are not able to achieve the heat rate improvements identified in the proposal TSD. They p rovide a variety of reasons, including: (1) the EGU’s fuel type ( e.g., lignite has low energy value and high moisture con tent) makes it difficult to improve heat rate, (2) specific EGUs have alread y implemented efficiency measures and are not capable of improving heat rates beyond their curren t levels, and (3) small coal-fired EGUs do not have the same economies of scale as larger EGUs. Co mmenters also expressed concern that (1) soon-to-retire EGUs would be forced to make additio nal efficiency upgrades to improve heat rate, resulting in stranded assets, and (2) mandatory hea t rate targets might force retirements, resulting in stranded assets. 90

First, EPA disagrees with commenters that this rule establishes a mandatory “one-size-fits-all” heat rate improvement standard. Rather, the heat ra te improvement is one part of the best system of emission reduction and must be viewed in the con text of the full best system of emission reductions. As noted earlier, states have the flexi bility to establish policies in the state plan that are reasonable and appropriate for that state; these po licies may or may not include heat rate improvement goals. Second, EPA disagrees with the c ommenters’ presumption that each coalfired EGU must independently achieve the potential heat rate improvements identified in this analysis. The results of the analyses described abo ve provide an estimate of potential heat rate improvements that, on average, are possible within each interconnection region based on each coal-fired EGU’s best historical heat rate between 2002 and 2012 and the EGUs’ 2012 heat rate.

90 For more information about EPA’s analysis of the s tranded assets question, see preamble section VIII. G.1, the legal memorandum, and the July 2015 docket memo titled “S tranded assets analysis.”2 – 61

Because the results represent the average potential heat rate improvement for an interconnection, some coal-fired EGUs can make greater heat rate imp rovements while other EGUs might make less. 91 Third, the agency’s analysis is based on coal-fire d EGUs’ historical heat rates and is therefore demonstrated. Commenters also noted that the interim and full compliance periods do not begin for several years and new or enhanced tec hnologies and practices for improving heat rate might be available in the future if there is a stronger incentive to improve efficiency. With regard to soon-to-retire EGUs, the interim complian ce period does not begin until 2022 so the agency does not expect there would be significant r equirements of those EGUs to make additional investments in heat rate improvements if a state ch ose to pursue heat rate improvements in its state plan. However, if a soon-to-retire EGU did make suc h 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 n ot 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 improvem ent targets (see section 2.6.7). Second, the agency believes EGU operators can control the rate of degradation through proper maintenance and, when necessary, replacement or refurbishment o f equipment. This is supported by information provided by a commenter referring to National Petro leum 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 uncheck ed 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 r egularly scheduled maintenance intervals... There is a gradual increase in the unrecoverable po rtion as the unit ages which would require a replacement rather than a refurbishment to eliminat e. Poor maintenance practices regarding the timing of the intervals and the amount of refurbish ment may result in excessive deterioration and is controllable [by the EGU operator]. 92

91 The final CPP provides states the flexibility to d evelop emission standards for individual EGUs that more precisely take into account that EGU’s potential heat rate im provement – should states desire to consider potent ial heat rate improvement at all in establishing emission standar ds. Furthermore, if an affected EGU cannot meet the particular emission standard solely because it has below-avera ge potential to reduce emissions through heat rate improvements, then in instances where the EGU’s state plan allows emissions trading, the EGU can acquire credits or allowances from affected EGUs that have above-average potentia l.
92 See National Petroleum Council (NPC), 2007. Electr icity Generation Efficiency. NPC Working Document; Topic Paper #4. Available at http://www.npc.org/Study_Topic_Papers/4-DTG-Electri cEfficiency.pdf

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The IEA report stated:
[I]mprovements can not only restore ageing plants t o their original performance, but also can often improve them further by avoiding the limitati ons of the prevailing technology at the time of original plant design...Not all of these areas have b een considered worthy of attention previously, and a conclusion from that is that there may be oth er unrecognized areas of losses in efficiency which could be worth identifying. 93

2.6.9 Limited adoption of low- and zero-cost heat rate im provements
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. How ever, research from NETL, RFF, and others show that there are a number of reasons that existi ng coal-fired EGUs might not implement lowand no-cost heat rate improvement practices, includ ing: (1) concerns or uncertainties about New Source Review impacts; (2) lack of economic incenti ves to improve efficiency because of fuel adjustment clauses that enable electricity generati ng companies to “pass through” fuel costs directly to customers; (3) lack of management commi tment 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 f uel cost savings represent only a small portion of total costs and are “of secondary concern when c ompared 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 c oal-fired EGUs. 94

2.7 Costs

2.7.1 Breakeven costs
In the proposal TSD, EPA used an estimated average cost of $100/kW for a potential national average 6 percent heat rate improvement by coal-fir ed EGUs. Four percentage points of that potential improvement was projected to come from re latively inexpensive low- or no-cost best practices. Thus, most of the estimated $100/kW aver age cost actually represented the cost of relatively more expensive equipment and system upgr ades that EPA expected would provide the remaining 2 percent improvement.

93 See C Henderson, 2013. Upgrading and efficiency im provement in coal-fired power plants. International Energy Agency (IEA) Clean Coal Centre Report. CCC/221. ISB N 978-92-9029-541-9. Available at http://bookshop.ieacoal.org/reports/ccc-221/83186

. 94 See supra notes 11 and 18. Also see Comments of Natural Resources D efense Council (NDRC). Available at http://www.regulations.gov/#!documentDetail;D=EPA-H Q-OAR-2013-0602-26818

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For this final rule, as explained above, EPA has re fined the estimate of potential national average net heat rate improvement by coal-fired EGUs to bet ween 2.1 to 4.3 percent for each interconnection, or about 4 percent nationally, wit h 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. T he 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 improv ement 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 improvemen t to be significantly less than $100/kW at the new level of heat rate improvement. However, becaus e 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 optimist ic, the agency assessed the economic effects of a heat rate improvement cost that might range fr om $50 to $100/kW.
The agency acknowledges here, as in the proposal TS D, that the technical applicability and efficacy of heat rate improvement measures, the cos t 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 w ide average cost for a national fleet wide average improvement of 4 percent, based primarily on low- a nd no-cost best practices, would be significantly less than $100/kW. However, as a cons ervative measure, and because the cost of heat rate improvement is one of the least costly and lea st impactful measures considered in the best system of emission reductions for this rule, the ag ency has decided to conservatively use the previously estimated $100/kW cost value for IPM mod eling and the RIA estimates of the total cost of this final rule.
The brief assessment presented here is intended onl y to illustrate the approximate effects of coal cost and heat rate improvement capital cost on the economic breakeven point between coal cost savings and the capital cost of implementing heat r ate improvement. The resulting order-ofmagnitude cost range for CO 2 removal ($ per ton) is also discussed. EPA does no t suggest in the proposal nor in this final rule that heat rate impr ovement 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 breakeve n analysis for a 4 percent nationwide average heat rate improvement, assuming there is no CO 2 emission allowance cost involved. Additional assumptions used in the breakeven calculation inclu de: 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 IPM modeling; coal fleet generation-we ighted average 2030 net heat rate of 10,250 Btu/kWh before heat rate improvement, also derived from IPM modeling.2 – 64 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 fleet wide 2030 savings in coal cost would become greater than the annualized capit al cost of an average 4 percent reduction in net heat rate when the average fleet wide coal cost exceeds about $2.50/mmBtu. For comparison, the average U.S. power sector delivered cost of coa l was $2.38/mmBtu in 2012, 95 and is projected to be about $2.70/mmBtu in 2030. 96 For different assumptions, the heat rate improveme nt economic breakeven point would change directionally as follows:
If the heat rate improvement capital cost were on a verage 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 fleet wide 4 percent heat rate improvement would become economic at an average coal cost of ab out $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. 97 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.

95 EIA, Electricity Data Table 7.4, Average Weighted Cost of Fossil Fuels for the Electric Power Industr y 2002–2012. Available at http://www.eia.gov/electricity/annual/html/epa_07_0 4.html

. 96 2030 projected fuel costs are derived from IPM Bas e Case.
97 See supra note 953.2 – 65

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 e conomic at coal costs lower than the values mentioned above.
If there were additional heat rate improvement savi ngs due to avoided future CO 2 emission costs, heat rate improvement could become economic at lowe r coal costs, or at higher capital costs, or at lower heat rate reduction percentages.

2.7.2 Estimated fleet wide CO 2 reduction from and cost for heat rate improvements
EPA derived the following illustrative order-of-mag nitude estimate of the fleet wide costeffectiveness of a 4 percent heat rate improvement from the IPM Base Case modeling:

2.7.2.1 Fleet wide 2030 assumptions derived from IPM Base C ase for this final rule

• Coal fleet capacity applying combined heat rate imp rovement methods: 213,000 MW

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

• Average capacity factor: 78 percent

• Heat rate improvement Btu and CO 2 reduction: 4 percent

• Heat rate improvement capital cost: $50/kW (assumed )

• Annual capital charge rate: 14.3 percent

• Average coal cost: $2.70/mmBtu

2.7.2.2 Estimated fleet wide results

• Fleet wide CO 2 reduction via heat rate improvement: 62 million to n/year

• Total heat rate improvement capital cost: $10.7 bil lion

• 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 CO 2 reduction: $1.4/ton

2.7.3 Conclusion - heat rate improvement economics
This necessarily simplified analysis supports the f ollowing summary conclusions:

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 fleet wide average 4 percent heat rate improve ment were implemented, it would also pay for itself on the basis of fuel savings al one, before consideration of the value2 – 66 of the associated CO 2 emission reductions, on a fleet wide basis at proj ected 2030 coal prices if the associated average capital cost is ab out $50/kW or less.

3. If a fleet wide average 4 percent heat rate improve ment 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 o n fuel cost savings alone.

4. Even at a capital cost of $100/kW and an IPM projec ted 2030 coal price of $2.70/mmBtu, the average fleet wide cost of CO 2 reduction via 4 percent heat rate improvement would be a still relatively low $23/ton

. Thus, although there is currently some uncertainty associated with the costs of achieving a particular fleet wide amount of heat rate improveme nt, it is clear that heat rate improvement is an available low-cost approach to CO 2 reduction for existing coal-fired EGUs.3 – 1



### CHAPTER 3: CO 2 

### REDUCTION POTENTIAL FROM GENERATION SHIFTS AMONG EXISTING UNITS

3.0 Overview
This chapter explores the dynamics of power sector dispatch and the cost-effectiveness of lowering the carbon dioxide (CO 2 ) emissions intensity of the power sector by substi tuting generation from the most carbon-intensive existing EGUs and increas ing utilization, to the extent possible, of less carbon-intensive existing fossil fuel-fired EGUs. M ore specifically, the examination focuses on opportunities to improve emissions intensity by inc reasing the utilization of existing natural gas combined cycle units. The TSD provides background o n existing power plants, power system operation, and the economics of electricity product ion and delivery in the context of cost-effective CO 2 emission reduction opportunities.

3.1 Introduction
Electric system dispatch is typically defined as “t he operation of generation facilities to produce energy at the lowest cost to reliably serve consume rs, recognizing any operational limits of generation and transmission facilities.” 1 Electricity demand varies across geography and tim e in response to numerous conditions, such that electric ity generators are constantly responding to changes to meet demand in the most reliable and cos t-effective manner possible.
The nation’s EGUs are connected by transmission gri ds that extend over large regions. Through these interconnections, EGU balancing authorities t reat 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, subject to the constr aints of reliable system operation. 2
EGU operators and balancing authorities must take i nto account several constraints in dispatch, including transmission and other operating constrai nts 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 ha ve considered the emissions implications for pollutants such as SO 2 and NO x when scheduling unit dispatch, in response to cost s and regulatory requirements. For example, EGU operators in 10 states participating in the Regional Greenhouse Gas Initiative have several years of exp erience with factoring CO 2 emissions limits directly into bids for economic dispatch. The elect ric system’s carbon intensity can be lowered by

1 Energy Policy Act of 2005
2 A balancing authority is the responsible entity th at integrates resource plans ahead of time, maintai ns the balance between supply, demand, and generation within a bal ancing authority area, and supports interconnection frequency in real-time. http://www.nerc.com/files/glossary_of _terms.pdf3 – 2

shifting generation among existing EGUs, particular ly by shifting generation from coal-fired units to natural gas combined cycle (NGCC) units.

3.2 Power Sector Background
3.2.1 Electric Dispatch
The operation of electricity generation accords to the principle of security-constrained economic dispatch, which is “the operation of generation fac ilities to produce energy at the lowest cost to reliably serve consumers, recognizing any operation al limits of generation and transmission facilities.” 3 The cost of operating electric generators varies b ased on a number of factors, such as fuel used and generator efficiency. In many regions of the country, System Operators help coordinate economic dispatch over large areas to he lp keep the cost of meeting electricity demand as low as possible, subject to operational constrai nts.
The decision by balancing authorities to call upon, or dispatch, any particular generating unit is generally driven by the relative operating cost, or marginal cost, of generating electricity to meet the last increment of electricity demand. These cos ts change over time depending upon a variety of factors like fuel prices, weather conditions, an d overall demand levels. Since the fixed cost of power plants is a sunk cost, plant operators usuall y bid into electricity markets such that their variable costs are covered. For fossil fuel-fired e lectric generating units, variable costs are dominated by the cost of the fuel, although coal-fi red 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 varia ble costs and are generally dispatched to the exten t possible. Under economic dispatch, the units with t he lowest variable costs will be called upon first, then other units (with higher variable costs ) will be called upon sequentially, such that total system demand is met. The merit order in which unit s are dispatched to meet demand, at any particular point in time, is commonly called a disp atch or supply “curve.” 5
3.2.2 Balancing Authorities
In states with cost-of-service regulation of vertic ally-integrated utilities who own generation, transmission, and distribution infrastructure, the utilities themselves are often also the balancing authorities who determine unit dispatch. Such utili ties are presumed to dispatch their units in a cost-minimizing fashion (seeking the lowest cost), and they can arrange to buy and sell power with other balancing authorities.

3 Federal Energy Regulatory Commission, 2005. Econom ic Dispatch: Concepts, Practices, and Issues
4 In addition to fuel costs, variable costs also inc lude costs associated with operation and maintenanc e, and costs of operating a pollution control and/or the price of a llowances to emit certain pollutants.
5 http://www.eia.gov/todayinenergy/detail.cfm?id=759 03 – 3

In states that have restructured regulation to allo w for competition between generators, RTOs and ISOs are generally responsible for moving electrici ty across larger areas in the most efficient (least

cost) manner possible. 6 RTOs and ISOs coordinate, control, and monitor the operation of the electric power system 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 p ools to satisfy the requirement of providing non-discriminatory access to transmission. Subseque ntly, FERC encouraged the voluntary formation of RTOs to administer the transmission gr id on a regional basis throughout North America (including Canada).
RTOs and ISOs administer wholesale power markets, w hich match the generation of electricity with the purchase of electricity (and ancillary ser vices) prior to delivery to end-users. Companies that provide retail electricity (e.g., utilities an d energy service companies) procure power through these wholesale electricity markets.
3.2.3 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 eff icient and reliable delivery of electricity, and pl an appropriately for the short and long term energy ne eds of the state and consumers. Depending on market structure, PUCs also allocate costs among cu stomers, design price structures and set price levels, set service quality standards, approve capi tal expenditure by utilities, and arbitrate dispute s among relevant parties and stakeholders. In restruc tured markets, the PUC’s authority is generally applicable to the transmission and distribution sys tem, since the revenues from generation (and sometimes capacity) are earned through markets gove rned by RTOs and ISOs. In cost-of-service states, the PUC also has oversight of the generatio n and capacity planning components

.
3.2.4 Wholesale Power Markets
RTOs and ISOs operate spot markets for wholesale po wer supply and demand for their designated area, including both day-ahead and real-time market s (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 balan cing power supply and load in their area, serve as the reliability coordinator for their area and typically serve as the balancing authority for the same area.
For areas not administered by RTOs and ISOs, dispat ch is scheduled on both a day-ahead and an hourly basis, but it is typically driven by the pow er supply costs and schedules of traditional utilities. This dispatch will depend to a certain d egree on spot markets for power, since utilities

6 http://www.ferc.gov/industries/electric/indus-act/ rto.asp 7 These entities are sometimes called Utilities Comm issions, Utility Regulatory Commissions, or Public Service Commissions.3 – 4

will dispatch purchased power from other suppliers when that power can be obtained at a cost savings. There is an active wholesale market for th is power in the spot market, from individual sales and from exchanges. These markets typically s ell power on a day-ahead basis rather than on an hourly basis, and they can also sell power for l onger 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 con siderably from one utility to the next.
3.2.5 Reliability
As a balancing authority, the ISO/RTO or utility wi ll balance demand, generation, and imports/exports in real time while ensuring that th e next hour’s demand, or load, is met. As this happens, the electric power system is constantly mo nitored load is dispatched to ensure reliability limits are met, voltage levels are appropriate, sys tem frequency is maintained and appropriate corrective action is taken when needed.
A reliability coordinator is responsible for the ge neration-demand balance within its reliability coordination area of the bulk power system, and may direct a balancing authority within its reliability coordinator area to take whatever actio n is necessary to ensure that this balance does not adversely impact reliability. The bulk power system refers to a large interconnected electrical system made up of generation and transmission facil ities and their control systems. To ensure reliability, system operators continuously analyze real-time and forecasted load and transmission conditions to ensure that scheduled generation disp atch can meet load without adverse impacts. If the scheduled dispatch is not feasible within the l imits 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 reli ability, in accordance with Federal laws and regulations, and with FERC oversight. 8
3.3 Generation Shifting: Technical Potential
In 2012, national average CO 2 emission rates 9 across the following technology types on a net generation basis were:

• Coal Steam: 2,217 lbs/MWh

• Oil/Gas (O/G) Steam: 1,435 lbs/MWh

• NGCC: 905 lbs/MWh

8 http://www.nerc.com/
9 Emission rates in this document are shown on a net generation basis and reflect Hawaii and Alaska sou rces, as well as units that commenced construction in 2012. See “ 2012 unit-level data using the eGrid methodology” f ile provided in the docket3 – 5

Coal- and oil/gas-fired boilers are considerably hi gher-emitting sources than NGCCs, on average. Therefore, shifting generation from the average exi sting fossil fuel-fired boiler to an average existing NGCC will result in notable CO 2 emission reductions.
The lower emission rate of NGCC conveys the potenti al 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 fle et that could replace generation from more CO 2 -intensive generating resources.
Given their cost structure, for purposes of economi c dispatch, most NGCCs have historically operated as base load or intermediate load 10 due to their high efficiency and flexibility of op eration, with national average annual capacity factors in th e range of 40-50%. 11 , 12 However, NGCCs are designed for, and are demonstrably capable of, reli able and efficient operation at much higher annual capacity factors, as shown in observed histo rical 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. 13 The term “availability” is a common engineering te rm used in the power sector that reflects the percentage of period hours that a plant is available to produ ce electricity (a period being 1 year, or 8,784 hours in 2012 since that yea r included a leap day). The unavailable period is generally attributable to scheduled maintenance, unplanned maintenance, and unplanned outages. For modeling purposes, EPA assumes that NG CC has an availability of 87%. 14 Other reports suggest that NGCC availability factors may reach as high as 92%. 15 , 16 Many modern NGCCs have demonstrated availability factors of ove r 95%. According to one NGCC manufacturer, these highly efficient units already represent over 15 percent of total installed capacity nationwide, including all electric generat ing sources (as of 2010). 17 These high-efficiency and high-availability NGCC units were first introdu ced around 1995 and have consistently

10 Unlike baseload power, intermediate load is cycled to follow the changing demand for electricity, but it is not cycled as often as peaking supply which operates on much shorter timescales.
11 EIA, Today In Energy, January 15, 2014, http://www.eia.gov/todayinenergy/detail.cfm?id=1461 1 (for recent data)
12 EIA, Electric Power Annual 2009, http://www.eia.gov/electricity/annual/archive/03482 009.pdf (Table 5-2 for 2009 and earlier data)
13 NERC, 2008-2012 Generating Unit Statistical Brochu re – All Units Reporting, http://www.nerc.com/pa/RAPA/gads/Pages/Reports.aspx
14 See Chapter 3, Table 3-18 at http://www.epa.gov/po wersectormodeling/BaseCasev513.html
15 http://www.power-eng.com/articles/print/volume-115 /issue-2/features/higher-availability-of-gas-turbin ecombined-cycle.html
16 Power Engineering, Negotiating Availability Guaran tees 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
17 http://site.ge-energy.com/corporate/network/downlo ads/7FA_Evolution.pdf3 – 6

reported availability factors of 90 to 92 percent t o NERC. 18 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 ge neration to unused NGCC capacity, EPA reviewed recent operating data across all NGCCs to determine NGCC capacity factors in 2012. For GHG abatement purposes, generation shifting wou ld require one net MWh of generation from a lower-emitting technology operating in place of o ne net MWh of generation from a higheremitting technology. Therefore, EPA calculated capa city 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 reflec ts a reduction from nameplate capacity due to on-site electricity use (e.g., station service or a uxiliaries) and local temperature conditions. 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 eval uating dependable capacity nationwide. Potential net generation was calculated as the net summer gen eration 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  Net summer capacity x (hours in 2012)

This calculation of capacity factor provides an ind ication 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, whil e that source’s availability refers to its potentia l maximum utilization level. EPA surveyed 2012 data f or 439 NGCC plants and observed that the NGCC fleet had an average capacity factor of 46% in 2012. 19 Since the fleet-wide capacity factor in 2012 was less than the technical generation pote ntial available for the technology, the historical data suggests that there is a significant potential for shifting generation from higher CO 2 emitting resources to lower CO 2 emitting NGCC generation.

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

18 GE Power Systems submitted to U.S. Department of E nergy, 2000. Utility Advanced Turbine Systems Technology Readiness Testing Phase 3 Restructured. DOE Cooperative Agreement No. DE-FC21-95MC31176— 30.
19 EIA 860 and 923.3 – 7

To quantify the GHG reduction potential from genera tion shifting, EPA considered alternative capacity factor levels at which the NGCC fleet coul d be dispatched. Although the availability for NGCC units is assumed to be 87% to 92%, EPA did not assume that the NGCC fleet could collectively operate at this level on an annual bas is. To determine a reasonable average capacity factor for the NGCC fleet as part of BSER, EPA cons idered historical data and modeling projections describing NGCC characteristics and ope rating behavior.
As seen in Table 3-1, the existing NGCC fleet is re latively young. More than 80% of the capacity has come online in the last 15 years. 20 Of this capacity, almost all are highly efficient NGCCs that are able to achieve high availability factors.

Table 3-1: Existing NGCC Capacity, by Age 21

Online Year Capacity

(Name Plate Capacity – MW) Percentage of
Total Existing NGCC Fleet, by Capacity

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%

20 See the National Electricity Energy Data Systems ( NEEDS) file at http://www.epa.gov/powersectormodeling/BaseCasev513 .html
21 See “Operable” worksheet in “GeneratorY2012” Workb ook in 2012 Zip file at http://www.eia.gov/electricity/data/eia860/3 – 8

Of 439 NGCC plants generating in 2012, EPA observed that 67 plants (more than 15% of NGCC plants) had a capacity factor of 75% or greater (se e Figures 3-1 and 3-2). 22

Figure 3-1: NGCC Plant Distribution by Capacity Fac tors (2012)

Source: EIA forms 860, 923 (2012), Combined Cycle S team Part (CA) and Combined Cycle Combustion Turbine Part (CT) prime mover categories

22 See “2012 unit-level data using the eGrid methodol ogy” file provided in the docket 0 5 10 15 20 25 30 35 40Number of NGCC Plants

2012 Capacity Factor3 – 9 Table 3-2: Plant Distribution of Existing NGCCs (20 12)

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%

75%-79% 30 6.8%

80-84% 19 4.3%

85-89% 7 1.6%

Greater than 90% 11 2.5%3 – 10

In 2012, more than 15% of NGCC plants operated at a n annual capacity factor of 75% or higher on a net summer basis. This subset of NGCCs was lar gely 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 certai n periods of time, in response to higher demand. On a seasonal basis, a significant number o f units achieved capacity factors greater than 50%, and even up to 80%. During periods where deman d levels are typically lower, some NGCCs were idled or operated at lower capacity factors. N onetheless, a notable number of existing NGCCs have demonstrated the ability to achieve a 75% or h igher capacity factor for extended periods of time. These units achieved high capacity factors wi thout adverse effects on the electric system.
Hourly data reported to EPA 23 , 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 base d on heat input and input capacity; these calculations correspond closely to the capacity fac tor 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 EI A are 52%. 24 On average, then, net capacity factors are approximately 5% or more above nameplat e 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 – l evels. To examine historical potential capacity factors for NGCC units, EPA calculated nam eplate capacity factors for daily peak operation from 7 AM to 7 PM during the months of Ju ly and August, a sustained period that demonstrates both the ability of individual units t o operate at high capacity factors and of the power grid to support regional operation of the NGCC flee t 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 a bove 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-w ide NGCC operation at much higher than average capacity factors. Each NERC reg ion 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%

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

.3 – 11  SERC 73%

 FRCC 75%

 SPP 71%

 NPCC 70%

These observations demonstrate the technical abilit y to operate existing NGCC capacity at or above a 70% nameplate capacity factor, and therefor e at least at a 75% net summer capacity factor or higher. These observations also demonstrate the ability of the natural gas transmission system to support this level of generation.

3.4 Historical Trend
Over the last several years, advances in the produc tion 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 quanti ties of generation from coal units to NGCCs, absent any federal CO 2 requirements. 2012 net generation from NGCC units grew to 966 TWh, up from 783 TWh in 2011 (23% growth in one year). 25
An analysis of historical generation across the fle et of coal and NGCC units for 2011 and 2012 provides some implicit measures of the cost dynamic s between the two technologies. For 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 wid e-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 relat es to emissions of CO 2. The potential for shifting generation from CO 2 intensive sources to less CO 2 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 mo re 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 shif t from coal to gas generation over just a one year period demonstrates the ability for a quick sh ift 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. 26 This price decline of $1.35/TCF created an additio nal incentive for decreasing coal generation and increasing NGCC gene ration relative to 2011 electricity production economics. This historical data also shows a sharp increase in the NGCC fleet’s net summer

25 EIA Form 860, 923 (2011)
26 EIA Natural Gas Prices, http://www.eia.gov/dnav/ng /ng_pri_sum_dcu_nus_a.htm3 – 12

capacity factor from approximately 40% on average i n 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 20 22 relative to its 2012 level. 27

Table 3-3: 2011 and 2012 Gas and Coal Generation 28

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

27 AEO 2015
28 EIA form 860 and EIA form 923 (NGCC), and EIA Mont hly Energy Review, Table 7.2b (coal)3 – 13

Table 3-4. Electric Power Sector Electricity Net Ge neration 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 (mult iple years)3 – 14

3.5 Phase-In
The application of an increase in NGCC operation eq uivalent to a 75% net summer utilization to the baseline data from 2012 would yield an increase of about 55% in NGCC generation, from 966 TWh to 1,498 TWh. To allow time for potential infra structure improvements to facilitate increased utilization of existing NGCC plants, EPA is finaliz ing a gradual phase over the interim period, consistent with the historical data presented above . The phase-in schedule is based on two parameters. The first parameter defines an amount o f generation shift to existing NGCC capacity that is feasible by 2022. This parameter is conserv atively 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 t hat 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 th e 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 techn ical potential of 75% utilization on a net summer basis.
In the performance rate calculation methodology, th ese two parameters constrain the annual rate at which building block 2 shifts generation from fo ssil 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 th e extent to which NGCC generation is assumed to increase and replace fossil steam genera tion in each year of the interim period. From historical data, we can observe that the single lar gest annual increase in power sector gas-fired generation since 1990 occurred between 2011 and 201 2 and is equal to 22 percent. Therefore, in the first year of the interim period, NGCC generati on is limited to a maximum of a 22 percent increase from 2012 levels in each region. In each s ubsequent year, regional NGCC generation is limited to a maximum of a 5 percent increase from t he 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 t his 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 rep resents building block 2 potential as incorporated in the emission performance rate calcu lation in each region and year.3 – 15

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

NGCC Generation (TWh)

Region Maximu

m Potential at 75% 2012

(adjusted) BSER Maximum

202 2 202 3 202 4 202 5 202 6 202 7 202 8 202 9 2030 Limit -- -- 22% 5% 5% 5% 5% 5% 5% 5% 5% EasternInterconnection 988 735 896 941 988 988 988 988 988 988 988 WesternInterconnection 306 198 242 254 267 280 294 306 306 306 306 TexasInterconnection 204 137 167 176 185 194 203 204 204 204 204 Source: Preamble, Section V.D.

3.6 Natural Gas Supply
EPA expects that the gradual increase in generatio n from existing NGCC as defined above to be feasible and consistent with domestic natural gas s upplies 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 recove rable natural gas resources will be substantially higher for the foreseeable future than previously a nticipated, exerting downward pressure on natural gas prices. 29
According to EIA, natural gas proved reserves have doubled between 2000 and 2012. 30 Domestic dry gas production has increased by 25% over that s ame 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 ma rket conditions. For comparison, building

29 National Petroleum Council. 2011. Prudent Developm ent: Realizing the Potential of North America's Abu ndant Natural Gas and Oil Resources. http://www.npc.org/r eports/rd.html (see Figure 1.2 on p. 47).
30 http://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cf m?tid=3&pid=3&aid=63 – 16

block 2 assumes NGCC generation growth of 340 TWh f rom 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. 31
The National Petroleum Council (NPC), a privately f unded advisory committee established by the Secretary of Energy, recently updated a major resou rce study and concluded 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 alr eady been realized. 32
Figure 3-2: U.S. Natural Gas Technically Recoverabl e Resources (from NPC, 2011) 33

31 Assuming 1,028 Btu/cubic foot and 7,615 Btu/KWh
32 National Petroleum Council, 2012 (http://www.npc.o rg/PD_update-80112.pdf)
33 http://www.npc.org/reports/rd.html3 – 17

3.6.1 Natural Gas Deliverability
To examine the potential for increases in pipeline deliverability, EPA analyzed the pipeline flow data from the Energy Information Administration. Th ese data provide pipeline capacity for inflows and outflows by region (see Figure 3-3). However, w e cannot measure natural gas deliverability solely by considering a region’s inflow capacity, s ince that region may need to allow some of that inflowing natural gas to become outflow to other re gions (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 natur al gas using such pipeline net capacity must be consumed within the region (as there would be no mo re outflow capacity for it to leave the region). The regions used by EIA for measuring regional natu ral gas deliverability are shown in Figure 3- 3. Our assessment focuses on areas that would exper ience increases in natural gas consumption in line with building block 2’s quantified potential f or increased utilization of NGCC; in other words, these areas have most of the unutilized NGCC capaci ty. These are the Northeast, Southeast, Midwest and Western regions in Figure 3-3. The net pipeline capacity for these regions from 2005 to 2011 is shown in Table 3-6 below.

Table 3-6. Natural Gas Pipeline Net Capacity by Reg ion, 2005 - 2011 by Gas Consuming Area 34 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%

34 Source: Energy Information Administration, http://w ww.eia.gov/naturalgas/data.cfm3 – 18

As a conservative assumption, the increase from the period 2005 to 2011 can be compared with the longer period of the projected increase in pipe line capacity between 2016 and 2030 in the IPM projections for the BB2 BSER scenario where existin g natural gas units were required to operate at the BSER capacity factor. Increased use of natur al gas in existing units can be largely met with expansions to existing pipeline facilities and corr idors, so that the types of capacity expansion 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 t he base case, and 35% over estimated 2016 consumption. However, pipeline deliverability incre ases apply to all sector consumptions, and the total consumption in the BSER scenario were project ed to increase by 13% over the base case and 18% over 2016 consumption. Since this historical in crease in pipeline capacity over the 2005 to 2011 period is 24.3%, the historical data indicate that pipeline capacity can expand sufficiently to support the potential increased utilization of natu ral gas power plants quantified for building block 2 in the BSER modeling.
Recent pipeline construction continued to demonstra te the pipeline network’s ability to accommodate increasing demand for natural gas. Acco rding to information released in April, 2014 by the EIA, 35 118 pipeline projects were completed and placed in to service from 2010 to 2014, totaling 4,699 miles of pipe, and 44,107 MMcf per d ay 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 Uppe r Midwest and pipeline expansions such as the Tennessee Gas Pipeline Project in Connecticu t.

35 See www.eia.gov/naturalgas/pipelines/EIA-NaturalGasPipe lineProjects.xls

.3 – 19

Figure 3-3. Energy Information Administration Natur al Gas Pipeline Regions

Source: Energy Information Administration.

The ability of the pipeline system to accommodate t he increased utilization of NGCC quantified for building block 2 is reinforced by a recent DOE study of pipeline system expansion. 36 This study examined two cases of pipeline expansion through 20 30, one with a 25% increase in overall natural gas demand in 2030 and one with a 46% increase comp ared to a reference case projection. In a key finding, the study concluded that, in both scen arios, compared to historical pipeline infrastructure expansion, “Intermediate interstate natural gas pipeline infrastructure needs in [the assessed scenarios] are projected to be modest rela tive to the Reference Case”. 37 The levels of increased natural gas demand assessed in that DOE s tudy are substantially above the 15% increase in natural gas demand projected in EPA’s power sect or modeling of a scenario that would realize the building block 2 NGCC utilization level (descri bed below).

36 U.S. Department of Energy, Natural Gas Infrastruct ure Implications of Increased Demand from the Elect ric Power Sector, February 2015.
37 Ibid. Page vi.3 – 20

3.7 Cost-Effectiveness of Shifting Generation to NG CC

To evaluate the cost-effectiveness and technical ca pability of increasing utilization of existing NGCC as quantified for building block 2, EPA employ ed the Integrated Planning Model (IPM), a multi-regional, dynamic, deterministic linear progr amming model of the U.S. electric power sector that 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 lea st cost capacity expansion and electricity dispatch projections) while meeting fuel supply, tr ansmission, dispatch, and reliability constraints.
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 di scount 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 econom ic basis.
At the unit level, IPM contains a detailed represen tation of new and existing resource options, inclusive of key operational limitations. For examp le, turn down constraints are designed to account for the cycling capabilities of EGUs to ens ure 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.
EPA used IPM to model a series of scenarios repres enting replacement of fossil steam generation in each interconnection with increases of average N GCC net capacity factors in the same interconnection of up to 70, 75, and 80 percent. Fo r example, in the 70 percent net capacity factor scenario, total fossil fuel-fired generation at exi sting 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 interconnec tion in the base case. 38
The costs and economic impacts of these scenarios w ere evaluated by comparing the total costs and emissions from each scenario to the costs and e missions from the base case. For the scenarios reflecting a 70, 75, and 80 percent NGCC utilizatio n rate, comparison to the base case indicates that the average cost of CO 2 reductions was $15, $24, and $33 per short ton of CO 2 , respectively, over the 2022-2030 period.

38 The existing source fossil fuel-fired generation d escribed here includes generation from existing NGC C, coal steam, IGCC and oil/gas steam boilers greater than 25 MW.3 – 21 Table 3-7: IPM Results

Existing NGCC Average Capacity Factor Average Cost ($

per short ton, 2022-2030)

Base Case N/A

70% $15

75% $24

80% $33

3.8 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 grad ual phase-in of this building block that was not represented in this analysis. For example, in t he 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. F or example, the year-to-year percentage difference in Henry Hub prices reported by the Energy Informat ion Administration averaged 18.5% over the period from 1981 to 2012. 39 Projected wholesale electricity price increases ov er the same period were about 4 percent in the 75% case, which is simi larly 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 fr om 2000 to 2013. 40

39 http://www.eia.gov/dnav/ng/hist/rngwhhdA.htm
40 ISO Real-Time data for all hours, from Ventx Veloc ity Suite data across Eastern ISOs (PJM, NYISO,ISO- NE and Midcontinent ISO).3 – 22 Table 3-8: National Average Delivered Natural Gas P rice,

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%4 – 1



### CHAPTER 4: CLEANER GENERATION SOURCE

4.1 Introduction
Renewable energy is a cost-effective approach for r educing 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 encompa sses a wide variety of technologies fueled by the sun, wind, water, organic matter, and other res ources regularly replenished by physical and biological cycles. To integrate the rapidly increas ing and evolving portfolio of RE into the Best System of Emission Reductions (BSER), EPA has devel oped a methodology that is based on historical data.
This chapter serves to complement the description o f incremental RE as a component of the BSER in section V.E of the preamble and is organized int o several sections:

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

• Section 4.3 describes the interaction between the b uilding block 3 generation levels and the goal-setting methodology

• Section 4.4 compares the final building block 3 gen eration level against the proposed levels

• Section 4.5 details the NREL’s 2015 Annual Technolo gy 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 Potentia l for the BSER
The methodology used to quantify building block 3 g eneration for the BSER, as described in section V.E.6 of the preamble, contains seven steps :

1. Identify historical maximum capacity change and ave rage capacity change from year to year over the past five years (2010 – 2014) for uti lity-scale solar photovoltaic (PV), 1 concentrating solar power (CSP), onshore wind, hydr opower, and geothermal technologies. 2. Assign each RE technology an annual capacity factor that represents expected generation from each megawatt installed in the future.

1 In their Solar Market Insight reports, Greentech M edia and SEIA define utility-scale by offtake arran gement rather than by project size: any project owned by or that sells electricity directly to a utility (rather tha n consuming it on site) is considered a “utility-scale” project. This definition includes even relatively small projects (e.g., 100 kW) that sell electricity through a feed-in tariff (“FIT”) o r avoided cost contract.”(See Bolinger, M.; Weaver, S.; "Utility Scale Solar 2013." Defining "Utility-Scale," LBNL, Sept. 2014. Page 3. http://emp.lbl.gov/public ations/utility-scalesolar-2013-empirical-analysis-project-cost-performa nce-and-pricing-trends)4 – 2

3. Use the data from steps 1 and 2 to produce the annu al generation change associated with the historical average and historical maximum RE ca pacity changes for each technology. 4. Establish an initial level of incremental generatio n from the building block 3 RE technologies that could be expected by 2022 even in the absence of the rule, using EPA’s IPM Base Case. 5. Add the generation associated with the historical a verage capacity change to the initial level to obtain the building block 3 generation lev el for 2022. To that 2022 level, add the generation associated with the historical average c apacity change to obtain the building block 3 generation level for 2023. 6. For each subsequent year, add the generation associ ated 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. 2

4.2.1 Step 1
EPA relied on numerous data sources to compile the most recent five years of capacity deployment data for each of the RE technologies that contribut e to building block 3 generation. 3 The totals in Table 4-1 represent the net change in capacity from year to year, inclusive of declines in operating capacity.
Table 4-1: Annual Capacity Change by RE Technology (MW)

RE Technology 2010 2011 2012 2013 2014 Average Maximum

Solar PV 4 267 784 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.

2 Unless otherwise indicated, “national” in this cha pter refers to the contiguous U.S.
3 Energy Information Administration, Geothermal Ener gy Association, Lawrence Berkley National Laborator y, Solar Energy Industries Association/GTM Research, I nterstate Renewable Energy Council, American Wind E nergy Association (2014 onshore wind), and Federal Energy Regulatory Commission (2014 hydropower)
4 Solar PV capacity totals are for utility-scale sol ar PV installations only and are expressed in terms of MW DC4 – 3

4.2.2 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: 5

• Solar PV – Simple average of all three annual capacity fact or values presented in the ATB (14%, 20%, and 28%). 6

• CSP – Simple average of representative plant performanc e 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 ea ch of the five technoresource groups (32.0%, 37.9%, 44.0%, 46.3%, and 49 .0%).

• Geothermal – Simple average of annual capacity factors for bot h geothermal technology types (90% flash; 80% binary)

• Hydropower – Simple average of annual capacity factors for each technology (nonpower dams and new stream-reach development) and re source combination (58% - 67%).
The capacity factors displayed in Table 4-2 below a re assumed to be constant over time for the purposes of calculating building block 3 generation totals from 2022-2030.

Table 4-2: 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%

5 http://www.nrel.gov/analysis/data_tech_baseline.ht ml
6 The capacity factor values presented for solar PV account for the range of latitude across the contig uous U.S. Solar PV capacity factors are influenced by hourly solar profile, technology type, axis type, inverter losse s due to conversion from DC to AC power, and other factors.4 – 4

4.2.3 Step 3
The change in generation, expressed in MWhs, associ ated with the average and maximum capacity changes are the result of multiplying the correspon ding capacity values by 8,760 hours/year and the net annual capacity factor for each technology, as shown in Table 4-3.

Table 4-3: Annual Generation Change Associated with Average and Maximum RE Capacity Changes
Average Capacity Change (MW) Capacity

Factor Generation

(MWh, Average Capacity Change) Maximum

Capacity Change (MW) Capacity

Factor Generation

(MWh, Maximum Capacity Change) Solar PV 7 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

4.2.4 Step 4
Initial incremental RE generation totals for utilit y-scale solar PV, CSP, onshore wind, geothermal, and hydropower are taken from EPA IPM Base Case. 8 Initial incremental RE represents generation from existing projects currently in commercial oper ation with an online year of 2013 or later; projects that are not currently operating but are f irmly anticipated to be operational in the future and have either initiated construction or secured f inancing; and capacity that is projected to deploy as an economic resource to meet load. All of this g eneration is represented in EPA IPM Base Case projections.
To quantify an initial generation value, EPA interp olated between EPA IPM Base Case’s 2020 and 2025 projections, assuming each calendar year accou nts for 20% of the difference in those projections (i.e., a linear interpolation between r un years). Given that the projected incremental

7 Solar PV capacity totals are for utility-scale sol ar PV installations only and are expressed in terms of MW DC 8 Documentation and modeling files are available in the docket for this rulemaking and online at www.epa.gov/powersectormodeling.4 – 5

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 ini tial 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 de scribed in the following steps.
4.2.5 Step 5
The national building block 3 generation total in 2 022 is the initial level (step 4) plus the generati on associated with the historical average change in ca pacity (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 c hange in capacity:

• 2023 National Building Block 3 Generation = 241,880 ,347 MWh + 28,796,222 MWh = 270,676,570 MWh 9

4.2.6 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 c hange 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 associate d with the historical maximum change in capacity (62,193,363 MWh). The annual values for al l years are presented below in Table 4-4:

9 Values may not sum due to rounding; refer to GHG M itigation Measures spreadsheet for unrounded values

.4 – 6 Table 4-4: National Building Block 3 Generation Tot als (MWh)

Year Building Block 3

Generation 2022 241,880,347 2023 270,676,570 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

4.2.7 Step 7
To apportion the national building block 3 generati on totals calculated in steps 5 and 6, 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. 10 The scenario included a representation of utility- scale solar PV, CSP, onshore wind, geothermal, and hydropower generation. The ge ographic pattern by interconnection of incremental RE generation totals for 2020, 2025, an d 2030 are displayed below:

Table 4-5: Incremental RE Generation by Interconnec tion 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 d o not substitute for the national building block 3 generation totals in Table 4-4; instead, this step is only assessing the most economic location by interconnection of this general level of incrementa l RE generation potential. To facilitate this geographic assignment of the building block 3 gener ation totals in Table 4-4, the modeled generation totals for each interconnection are conv erted into a percentage of the modeled national total such that these interconnection-specific perc entage values, or “shares”, can be applied to

10 The IPM run ‘Building Block 3 Generation Assignmen t’ is available in the docket4 – 7

apportion the building block 3 generation levels. T he interconnection shares by model projection year are shown below in Table 4-6.

Table 4-6: Incremental RE Generation Shares by Inte rconnection

from Building Block Assignment Analysis

Interconnection 2020 2025 2030 Eastern 69.2% 11 66.1% 62.1% Western 22.4% 18.5% 22.8% Texas 8.4% 15.4% 15.1%

The model run-year values are then converted into v alues 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 a re shown in Table 4-7 below.

Table 4-7: Building Block 3 Generation Shares by In terconnection

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 4-7 to the national building block 3 generation totals in Table 4-4 produces an annual incremental generation value for each interconnection in each year, as shown in Table 4-8.

11 Value rounded up to ensure all interconnections su m to 100%4 – 8 Table 4-8: Annual Average Incremental Generation Va lues,

Eastern Interconnection (MWh) 12

National

Building Block 3 Generation Total Eastern

Interconnection Generation Share (%) Eastern

Interconnection Generation Share (MWh) Annual

Increase in Generation Values 13 Multi-Year

Average Annual Increase in Generation Values 2022 241,880,347 67.8% 163,946,499 12,983,007 15,289,641 2023 270,676,570 67.1% 181,542,775 17,596,276 2024 332,869,933 66.4% 220,892,487 39,349,712

36,700,275 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 consider s annual generation levels associated with the historical average RE capacity change to inform bui lding block 3 levels in 2022 and 2023, and the historical maximum RE capacity change to inform bui lding block 3 levels in 2024 through 2030. In keeping with that schedule, multi-year average a nnual incremental generation values are presented in Table 4-8 for 2022-2023 and for 2024-2 030 to create an interconnection-specific analog to the national values presented above in st ep 3. For example, the 2022-2023 average annual incremental generation value for the Eastern Interc onnection (using the values in Table 4-8) is 15,289,641 MWh, which is the Eastern Interconnectio n’s share of the national annual incremental generation value of 28,796,222 MWh (based on the hi storical average RE capacity change) presented in step 3. Following the same process, th e 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 incr emental 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:

12 Table 4-8 displays the Eastern Interconnection; re fer to the GHG Mitigation Measures spreadsheet for the Western and Texas Interconnections calculations:
13 These amounts are in addition to the initial gener ation value for the Eastern Interconnection, which is 150,963,493 MWh.4 – 9

• 2022 Building Block 3 Generation Level = Initial Ge neration 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 Build ing 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 bu ilding block generation level for the Eastern Interconnection is the previous year’s generation l evel plus the 2024-2030 average incremental generation level of 36,700,275 MWh. The calculation s for all years and interconnections are available in the GHG Mitigation Measures spreadshee t.
The building block 3 generation levels used in the BSER for each interconnection are displayed below in Table 4-9:
Table 4-9: Building Block 3 Generation Levels by In terconnection (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 61,791,623 457,256,65 9 2027 328,343,875 118,109,793 72,996,354 519,450,02 3 2028 365,044,150 132,398,151 84,201,085 581,643,38 6 2029 401,744,425 146,686,508 95,405,816 643,836,74 9 2030 438,444,700 160,974,866 106,610,547 706,030,1 12

4.3 Building Block 3 Generation Required by the Go al-Setting

As discussed in section V.A.3.f and section VI of t he 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 amoun t of building block 3 generation in each year that is not required to achieve the goal rates. Thr ough the application of a minimization routine described in the CO 2 Emission Performance Rate and Goal Computation TSD , EPA has solved for the minimum amount of building block 3 generation n ecessary in each interconnection for affected EGUs to achieve the source category-specific emissi on performance rates.4 – 10

Table 4-10: National Building Block 3 Generation Ne cessary to Achieve Source CategorySpecific 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 incremen tal RE from EPA’s IPM Base Case:
Figure 4-1: Building Block 3 Generation Levels, Rel ative 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 Go al Rates4 – 11

Figure 4-1 displays how the combination of building block 3 and the goal-setting methodology serve to significantly reduce compliance pressure o n affected EGUs in the near-term, insulating near-term building block 3 generation levels agains t adjustments to RPS requirements and fluctuations in market conditions, while still esta blishing 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 t o Proposed Levels
The final methodology, which is adapted from the pr oposal’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. 14 This difference is comprised primarily of two components – removal of the benchmark development
rate 15 from the proposed approach and updating RE cost an d performance data to the most recent NREL data. The magnitude of these components is sho wn below:
Figure 4-2: Proposed and Final Building Block 3 Gen eration Levels, by Component in 2030

14 The proposed RE levels were associated with a $36/ ton cost; the final building block 3 generation lev els produce an average cost of $37/ton (see section 4.6).
15 The benchmark development rate set limits on the p rojected economic deployment of RE if a state had a chieved deployment levels that met a benchmark deployment r ate (total capacity/total resource) equal to the to p-third of states. See the response to comment document for mo re detail.4 – 12

EPA invited comment at proposal on RE levels in exc ess of the final building block 3 generation level. For example, the Alternative RE Approach TSD cited NREL values for incremental onshore wind, solar and geothermal technologies of 687,600 GWh. 16 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 th e final rule by more than 80 TWh in 2030.
Additionally, the goal-setting methodology in the f inal 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 4-3: Block 3 Generation Levels Necessary to Achieve Goal Rates

and Alternative NREL Values, in 2030

It should be noted that the building block 3 genera tion 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 de termination 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-Cas e Estimates
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, commen ters provided data demonstrating that the cost and performance estimates relied on in the proposed rule did not reflect the decline in cost and

16 http://www2.epa.gov/cleanpowerplan/clean-power-pla n-proposed-rule-alternative-renewable-energy-approa ch4 – 13

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 included the mid-ca se estimates from the National Renewable Energy Laboratory’s (NREL) 2015 Annual Technology Baseline (ATB).

EPA selected the NREL 2015 ATB mid-case estimates b ased on the quality of its data and consistency with recent RE cost and performance tre nds. The ATB capital cost estimates cover the same scope of power plant envelope (equipment, inst allation, developer costs, etc.) as used for AEO inputs used in the proposal. The agency used th e draft 2015 ATB version, which was available at the time of the analysis. Recently, th e final 2015 version was posted. 17 It is particularly notable that the final 2015 version’s cost estimate s are either unchanged or lowered. 18 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 AEO2013 data set used in the propo sal to the NREL ATB data set used in the final rule, EPA found AEO2013 installation cost ass umptions for onshore wind in 2014 are 30% higher than the ATB mid-case cost assumptions for t he same year. 19 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 AT B mid-case. 20 Similarly, installation cost assumptions for solar PV in 2030 are almost 75% hig her in the AEO2013 cost assumptions when compared to the NREL ATB Mid RE Case.

17 Available at http://www.nrel.gov/analysis/data_tech_baseline.htm l

. 18 NREL lowered current and projected overnight capit al cost values for the ATB mid-case solar PV projec tion 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

. 19 EIA capital costs sourced from Assumptions to the Annual Energy Outlook 2013, Table 8.2 (pp 103), Ava ilable at: http://www.eia.gov/forecasts/aeo/assumptions/pd f/0554(2013).pdf. Cost projections for future years were obtained from personal communications with EIA.
20 The AEO2015 contains updated capital costs for win d and solar PV which are lower than the AEO2013, re flecting the decrease in costs over the previous two years. Despite the lower capital costs in AEO2015, the NRE L 2015 ATB estimates were lower and more consistent with recen t RE cost and performance trends.4 – 14 Table 4-11: Comparison of Onshore Wind and Utility- Scale 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, EPA found that the ATB m id-case estimates are more in line with current costs and recent market analysis and projections th an the AEO2013 costs.
The rapid cost declines for wind and solar have bee n well documented. Lazard’s recent Levelized Cost of Energy Analysis found that wind and solar P V became increasingly competitive over the last five years. During that time period, the avera ge percentage decrease of high and low of the LCOE ranges for wind and utility-scale solar PV wer e 58 percent and 78 percent, respectively. 21 Lazard’s capital costs for wind and utility-scale s olar PV were $1,400-$1,800/kW and $1,500- $1,750/kW DC , respectively. 22 These cost estimates were significantly lower than near term and longer term AEO forecasts.

EPA also observed solar PV costs from other data so urces that are considerably lower than those reported in the AEO2013, and more consistent with t hose seen in ATB mid-case. For instance, the U.S. Solar Market Insight report published for Q1 2 015 shows recent utility-scale solar installed costs below $2,000/kW DC , whereas the AEO2013 assumes costs above $2,800/kW DC for the year 2015. 23 Similarly, a recent report by Lawrence Berkley Nat ional Lab (LBNL) shows that utilityscale solar PV projects were installed in 2013 at a verage costs of $3,000/kW DC
. 24 While these recent projects may have represented best-in-class installations for the years 2013-2014, it is

21 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

. 22 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

. 23 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

. 24 Is $50/MWh Solar for Real? Falling Project Prices a nd Rising Capacity Factors Drive Utility-Scale PV T oward 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 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 $2,88 1 $2,658 $2,501 $2,363

ATB - 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$, whereas in ATB t hey are in 2013$. For the comparison, the AEO2013 costs were inflated using a net inflati on of 3.28% to make them directly comparable. 2. The costs for solar PV in AEO are in $/kW-AC, wh ereas in ATB they are in $/kW-DC. For the comparison, AEO costs were expressed in $/kW-DC, us ing EIA's assumed 1.25:1 ratio, which was presented as a rounded value in EIA's Updated Capit al Cost Estimates for Utility Scale Electricity Generating Plants (See p. 24-2), according to email communications with EIA staff.4 – 15

reasonable to assume that average costs by 2020 wil l generally reflect these best-in-class installations. In contrast, the AEO2013 assumes a m ore conservative cost decline, and is close to $2,600/kW DC by 2020, and about $2,300/kW DC by 2030.

Table 4-12: Comparison of Utility-Scale Solar PV Co st Estimates

The ATB near-term capital cost estimates for CSP wi th 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 stor age ($7,000-$9,800/kW). While the configurations that serve as the basis for the esti mates differ, they are close enough to validate the ATB cost estimates for CSP. Additionally, the proje ctions 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 sho wed 2013 capacity-weighted average installed costs for onshore wind at $1,630/kW, wher eas the AEO2013 assumes costs close to $2,250/kW (2013$) for the same year. 25 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. 26

25 2013 Wind Technologies Market Report , LBNL, August 2014, Available at: http://emp.lbl.gov/sites/all/files/2013_Wind_Techno logies_Market_Report_Final3.pdf (referenced Figure 39 on Page 49).
26 Although the LBNL sample size is small for 2013, t he decline in wind installed costs in 2013 from 201 2 is in keeping with the decline in wind turbine prices, wh ich 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_Techno logies_Market_Report_Final3.pdf

. 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 - 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$, whereas in ATB t hey are in 2013$. For the comparison, the AEO2013 costs were inflated using a net inflati on of 3.28% to make them directly comparable.

3. While all sources are reported as ‘2013’, there is a difference what each source defines as ‘year’. For example, the LBNL report and the ATB represent the projects completed installation in 2013 while the AEO numbers are for projects that were initiated in 2013. 2. The costs for solar PV in AEO are in $/kW-AC, wh ereas in ATB they are in $/kW-DC. For the comparison, AEO costs were expressed in $/kW-DC, us ing EIA's assumed 1.25:1 ratio, which was presented as a rounded value in EIA's Updated Capit al Cost Estimates for Utility Scale Electricity Generating Plants (See p. 24-2), according to email communications with EIA staff.4 – 16 Table 4-13: Comparison of Onshore Wind Capital Cos t Estimates

Commenters (see RTC Chapter 3.3.7, Cost Effectivene ss 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 suppo rt 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 accu rate data on the cost of RE deployment. They also added that EPA's analysis for the proposed rul e using AEO2013 did not account for the cost reductions that can reasonably be expected to conti nue. For projecting future economics and performance, commenters recommended reliance on cre dible projections available from the DOE and National Laboratories. They asserted that recen t cost declines are also consistent with the projected declines expected as an industry experien ces 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 age ncy needs to change cost assumptions. A number of commenters noted NRDC’s recent analysis t hat used a number of these sources to show AEO2013's cost estimates for wind and solar energy are 46 percent above current average costs. 27 Commenters also contrasted Lazard’s current range o f levelized costs of energy (LCOE) for onshore wind without subsidies of $37-$81/MWh with AEO2013's 2019 estimates of $70- 90/MWh. 28 Regarding solar PV, commenters highlight DOE and n ational laboratory reports that have observed the cost of building utility-scale so lar projects has declined by about 50 percent between 2010 and 2014. The commenters said these de clines are consistent with NREL's modeled prices using its bottom-up modeling methodology - N REL estimates that the price of solar declined to $1800/kW DC in Q4 2013. Commenters also highlight forecasted s olar PV cost estimates and found no reason to believe that the declines in cos t will not continue. Referencing the SunShot

27 NRDC, Issue Brief: EPA's Clean Power Plan Could Save Up t o $9 Billion in 2030 , November 2014, Available at: http://www.nrdc.org/air/pollution-standards/files/c lean-power-plan-energy-savings-IB.pdf

. 28 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 $1,630 Lazard (2014$) $1,400-$1,800 LBNL $1,630
Notes

2. While all sources are reported as ‘2013’, there is a difference what each source defines as ‘year’. For example, the LBNL report and the ATB represent the projects completed installation in 2013 while the AEO numbers are for projects that were initiated in 2013. Capital Costs (2013$/kW)

1. The AEO2013 costs are in 2011$, whereas in ATB t hey are in 2013$. For the comparison, the AEO2013 costs were inflated using a net inflati on of 3.28% to make them directly comparable.4 – 17

Vision study’s roadmap for continued cost declines in solar PV technologies, they observed that estimates of solar system costs dropping 75% betwee n 2010 and 2020. They added that NREL recently predicted in its 2014 update on Solar PV p ricing trends that solar PV costs are still on track to meet the SunShot Initiative goal of $1,000 /kW DC by 2020 for utility-scale PV.
It should also be noted that the NREL ATB cost repo rt was published in 2015, whereas the AEO costs were published in 2013, and that the most rec ent AEO2015 includes significant cost reductions of wind and solar PV as compared to AEO2 013. 29 Commenters noted that the LBNL found that the AEO2013 assumptions were based on pr ojects 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. 30

NREL’s ATB and Standard Scenarios were developed wi th the objectives of creating consistent and normalized technology cost and performance assu mptions 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 technologi es, such as wind technology resource groups or TRGs based on wind resource and turbine technolo gies. The future cost projections are compared with data in published literature and gene rally within the bounds of those perspectives. These literature estimates were normalized to a com mon starting point. Projections for each technology were developed independently using disti nct methods, but the starting points were compared with available market data to provide cons istent baseline methodology. 31

The wind cost projections derived from literature r eviews for onshore and offshore wind, including numerous projection scenarios from many independent ly published studies, and vetted with a consortium of National Laboratory, DOE and wind ind ustry experts. The results are three different projections developed for scenario modeling as boun ding 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. 32

The solar cost projections are based on SunShot Vis ion 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 hi gh cases for utility-scale solar PV are based changing cost reduction trajectories. The concentra ting solar power (CSP) cases are based on

29 http://www.eia.gov/forecasts/aeo/assumptions/pdf/ta ble_8.2.pdf
30 Barbose, et al., Tracking the Sun VII , LBNL, September 2014, Available at: http://emp.lbl.gov/publications/tracking-sun-vii-hi storical-summary-installed-price-photovoltaics-unit ed-states1998-20

. 31 ATB Summary Presentation, NREL, March 2015, Availa ble at: http://www.nrel.gov/analysis/data_tech_baseline.htm l

. 32 ATB Summary Presentation, NREL, March 2015, Availa ble at: http://www.nrel.gov/analysis/data_tech_baseline.htm l

.4 – 18

differing evolutionary paths for CSP design and dep loyment. These projections are compared to range of available analyst projections from BNEF, E IA, and EREC. 33

4.6 Technical Feasibility and Cost-Effectiveness of the Building Block 3 Generation Levels
This section addresses the technical feasibility of the RE requirements described in the previous sections for this building block. The current deplo yment 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.
4.6.1 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 addit ions to hydro and geothermal resources. After constituting two or three percent of the total RE c apacity in the 1990s, wind and solar capacity have increased dramatically to over 50 percent of t otal RE today, as shown in Figure 4-4:

33 ATB Summary Presentation, NREL, March 2015, Availa ble at: http://www.nrel.gov/analysis/data_tech_baseline.htm l . The cost projection references include: Greenpeac e/EREC (2014). Energy [R]evolution: A Sustainable USA Ener gy Outlook. http://www.greenpeace.org/usa/Global/usa/planet3/PD Fs/Solutions/Energy-Revolution-2014.pdf (utility-sc ale only); International Energy Agency. (2013). World Energy O utlook 2013. http://www.worldenergyoutlook.org/publications/weo- 2013/. (New Policy & 450 Scenarios for utility-scal e & commercial-scale); Bloomberg New Energy Finance (20 14). Q2 PV Market Outlook; and, United States Energ y Information Administration (EIA). (2014a). Annual E nergy Outlook 2014 with Projections to 2040. DOE/EI A0383(2014). Washington, D.C.: U.S. Department of En ergy Office of Integrated and International Energy Analysis. http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf4 – 19 Figure 4-4: Growth in Utility-Scale U.S. Renewable Resources 34

Many states are already integrating significant lev els of Variable Generation (VG) RE resources. For wind and solar, there were 21 states in 2014 wh ere RE exceeded 5% of the total annual generation, and 10 states where RE exceeded 10%. 35 Several states were considerably higher: Iowa at 28.5% from wind and solar, Kansas 21.7% and Sout h Dakota at 25.3% were all above 20 %, in 2014 with Idaho not far below at 18.6%. If Geotherm al 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 sustai ned 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, along with many other studies 36 looking at the integration of variable generation RE, indicate that the current

34 Source: http://www.eia.gov/electricity/data.cfm#ge neration
35 EIA, Form EIA-923 and EIA-860 reports.
36 References to include: Cochran, J., Denholm, P., S peer, B., and Miller, M. Grid Integration and the C arrying Capacity of the U.S. Grid to Incorporate Variable R enewable Energy. NREL/TP-6A20-62607, 2015. (http://www.nrel.gov/docs/fy15osti/62607.pdf); Hand , M.M.; Baldwin, S.; DeMeo, E.; Reilly, J.M.; Mai, T.; Arent, D.; Porro, G.; Meshek, M.; Sandor, D. eds. 4 vols. (2012). Renewable Electricity Futures Study. NREL/T P-6A20- 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 201 2 2013 2014 2015Summer Capacity (MW)

Geothermal Solar(Thermal+PV) Wind4 – 20

configuration of the electric power system is alrea dy capable of integrating very high levels of renewable variable generation for sustained periods of time.
4.6.2 Deployment Projections
EPA believes the historically grounded Building Blo ck 3 generation levels constitute moderate estimates of the future deployment of RE that can b e achieved. The moderate nature of these levels is demonstrated by:

• EPA’s analysis of building block 3 generation level s conducted using IPM

• A multitude of external studies and reports that de monstrate 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 generati on levels in two ways - by apportioning the national-level generation totals calculated from na tional-level deployment, and validating the building block 3 generation levels as technically f easible and cost-effective. 37 It is important to note that the IPM model was not the basis for the o verall 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 potential generating resources, a large number of regions, interregional transmission constraints based on cur rent 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 congest ion from constraints in the current grid. For these reasons, IPM is an appropriate tool for analyzing t he generation levels assumed for RE and for reviewing RE penetration levels and the impact on t he overall generation mix. 38

To specifically assess the cost effectiveness and t echnical feasibility of building block 3, EPA imposed minimum generation constraints in IPM at le vels consistent with the building block 3

52409. Golden, CO. (http://www.nrel.gov/analysis/re _futures/); Bloom, A., Townsend, A., Palchak, D., K ing, J., Ibanez, E., Barrows, C., Hummon, M., Draxl, C., (20 15). Eastern Renewable Generation Integration Study

. NREL/TP-6A20-64472. Golden, CO. In press, http://ww w.nrel.gov/electricity/transmission/eastern_renewab le.html; Lew, D., Brinkman, G., Ibanez, E., Florita, A., Hea ney, M., Hodge, B.-M., Hummon, M., Stark, G., King, J., Lefton, S.A., Kumar, N., Agan, D., Jordan, G., Venkataraman , S., (2013). The Western Wind and Solar Integratio n Study Phase 2. NREL/TP-5500-55588. Golden, CO. (http://ww w.nrel.gov/docs/fy13osti/55588.pdf); Miller, N.W., Shao, M., Pajic, S., D’Aquila, R., (2014). Western Wind a nd Solar Integration Study Phase 3 – Frequency Resp onse and Transient Stability. NREL/SR-5D00-62906. Golden; (h ttp://www.nrel.gov/docs/fy15osti/62906.pdf); PJM Renewable Integration Study. 2014. (http://www.pjm. com/committees-and-groups/subcommittees/irs/pris.as px)
37 Supporting IPM analysis, including ‘BB3: Generatio n Assignment’ and ‘BB3: Cost-Effectiveness’ are ava ilable in the docket.
38 For further documentation detail on the IPM model, visit http://www.epa.gov/powersectormodeling.4 – 21

generation levels. The only technologies made avail able to satisfy these constraints were the RE technologies associated with building block 3 – uti lity-scale solar PV, CSP, onshore wind, geothermal, and hydropower with an online year of 2 013 or later. 39 To meet these levels in a manner that reflects the BSER, every incremental MW h of qualifying RE the model deployed necessitated that one MWh of affected EGU generatio n be reduced. 40 This requirement was necessary to reflect that building block 3 is premi sed on the replacement of affected EGU generation by incremental RE. The cost effectivenes s of meeting this constraint, expressed as the average $/ton of CO 2 emissions reductions, is shown below in Table 4-14 : 41

Table 4-14: Cost Effectiveness of Building Block 3
Average Annual Cost (Billion $) Average CO2 Emissi ons (Million Short Tons) Avoided Cost per Short Ton Year Base Building

Block 3 Incremental

Cost Base Building

Block 3 Reduced

Emissions 2022- 2030 42 184.7 193.7 9.1 2184.8 1940.9 243.9 $37

EPA believes the 2022-2030 average cost of $37/ton to be reasonable. 43

The feasibility of these generation levels is furth er confirmed by the results of other industry projections of RE, as well as many industry studies of the technical feasibility of even higher levels

39 This same procedure was used to apportion the nati onal-level building block 3 generation totals to ea ch of the three interconnections. See the IPM analysis ‘BB3: Generation Assignment’ in the docket for further de tails.
40 To model 1:1 replacement, incremental RE replaced affected EGU generation from levels projected by EP A IPM Base Case, rather than from historical levels. All other resources – non-building block 3 RE, other ex isting nonaffected sources, and new sources - were available to meet projected demand growth in the analysis. In contrast, and to reflect the flexibilities inherent in compliance , the ‘BB3: Generation Assignment’ analysis did not include constraints necessitating 1:1 replacement of affect ed EGU generation.
41 Refer to ‘BB3: Cost-Effectiveness’ IPM analysis, a vailable in the docket, for further detail.
42 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 resu lts presented reflect a weighted average of the 2020, 2025, and 2 030 run years; specifically, one calendar year from IPM’s 2020 run year (2022), five calendar years from IPM’s 202 5 run year (2023 – 2027), and three calendar years from IPM’s 2030 run year (2028 – 2030).
43 The wind and solar resource potential in IPM was d eveloped with the assistance of NREL data and methodologies, which includes appropriate resource exclusions and constraints to recognize land uses t hat are incompatible with RE development. The level of RE d eployment required by the building block 3 analyses represents a small amount of the total resource est imates, which, together with the regional nature of the BSER, effectively addresses the various examples of land use incompatibilities raised by commenters. For exa mple, in 2030 the total onshore wind capacity projected to be dev eloped is only 1.5% of potential capacity (153 GW o ut of a potential of 10,468 GW). Likewise, the total solar PV potential developed in the building block 3 anal ysis is only 0.1% of potential (154 GW out of a potential of 194 ,490 GW). Consequently, the level of RE deployment associated with achieving building block 3 remains technically feasible and cost-effective even under conservativ e land use assumptions.4 – 22

of RE penetration. The percentage of total RE gener ation associated with building block 3 projected in 2025 and 2030 is shown in Table 4-15. 44 As the table shows, the highest nationallevel share of generation from renewables is 20 per cent. 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 leve ls. A good review of the main integration studies is provided in a report by the National Renewable E nergy Laboratory. 45 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 4-15: EPA IPM Analysis of Generation Mix

2025 (TWh) 2030 (TWh) Scenario Total RE RE% Total RE RE

EPA IPM Base Case 4328 441 10 4467 472 11

Building Block 3 Analysis 46 4326 582 13 4473 894 20

Another important reference point for understanding the Building Block 3 generation levels is found in projections of future renewables by indepe ndent industry evaluations. Two example of such evaluations have been recently conducted by IH S Energy and by UBS Securities LLC. 47 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 B uilding Block 3 are available from the IPM results for the base and Building Block 3 levels an d are shown in Table 4-16 along with the projections from IHS Energy. This table shows Build ing 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 so lar capacity. 48 Together these comparisons show that the assumed generation levels are within the range of expected feasible future expectations of independent industry analysts. More over, it is important to realize that these results

44 For purposes of Table 4-15, RE is comprised new an d existing wind, utility-scale solar, geothermal, a nd new hydropower.
45 NREL, Relevant Studies for NERC’s Analysis of EPA’ s Clean Power Plan 111(d) Compliance, Table ES-1, a t http://www.nrel.gov/docs/fy15osti/63979.pdf.
46 Available in the docket as IPM run ‘BB3: Cost-Effe ctiveness.’
47 HIS Energy, North American Renewables Outlook: The rise of solar and the uncertainty of policy, 2 Jun e 2015; UBS Securities LLC, US Solar & Alternative Energy, Sizing Up the US Solar Market.
48 UBS includes some distributed solar as well as uti lity scale.4 – 23

from the IPM model are not projections of any expec ted 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 p erformance. 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 ra te-base compliance, for example, the total renewable capacity additions were 95 GW in 2030 49 , only slightly above the 87 GW in the IHS Energy Low case.

Table 4-16: Total RE Capacity Additions from 2015 ( GW)
Scenario Through 2025 Through 2030 EPA Base Case 48 62 EPA Building Block 3 50 121 233 IHS Energy -Low 64 87 IHS Energy -Mid 126 192 IHS Energy -High 222 352

4.6.3 Transmission
Compliance with the final rule is not expected to a dd significant transmission requirements. The RIA projects that the final rule will add only 20 G W of renewable generation capacity beyond the base case; this is a small increment that can be ac commodated in the normal transmission planning process. Even the larger levels of new generation f or Building Block 3 considered here should not raise concerns about the reasonableness of the cost s associated with the full build out of renewables. There are several reasons for this:

• Although it is reasonable to assume that some propo sed renewable scenarios will require specific patterns of new transmission requirements, 51 transmission will be required for any new generation under all future scenario, with or w ithout significant additions of renewables. Although the location-specific nature o f renewables will place different requirements that can add to some transmission need s, it will also mean that generation and flows on the grid will shift, resulting in redu ced flows and constraints in some areas. Since the additional renewable capacity projected f or Building Block three occurs over a 15 year period, and rises to a level of only 20 per cent renewables, these additions should be manageable in the normal planning and expenditur e process for transmission.

49 See the RIA, Table 3-14.
50 Available in the docket as IPM run ‘BB3: Cost-Effe ctiveness.’
51 An example of these requirements can be found in t he Eastern Interconnection Planning Collaborative ( EIPC) Phase 2 Report: Interregional Transmission Developm ent and Analysis for Three Stateholder Selected Sce narios (December, 2012). These studies have been used to a nalyze the feasibility of a 30 percent wind and sol ar scenario by the Eastern Renewable Generation Integration Study (ERGIS).4 – 24

• EEI members alone are expected to increase transmis sion expenditures substantially over the next five years to approximately 20 billion dol lars per year, nearly doubling the historical investment over the average of the past five years. 52 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, th e additional costs required for transmission expansion are almost all either for ge neration interconnection or for constraint relief. 53 For renewable generation, many of these costs are already captured in the cost estimates from the IPM modeling. For examp le, 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 o f capacity in costs of developing new capacity and delivering it to load, so where increa sed interregional congestion arises from delivering power from new remote supply, the cost o f from re-routing the flows to avoid congestion are captured in the total cost of modele d scenarios.
4.6.4 Ownership and Co-Location
The availability of co-owned RE that is proximate t o affected EGUs allows owners and operators to increase RE and reduce CO2 emissions at their af fected EGUs. In addition to the examples of owners and operators of affected EGUs that own RE i n section V.E.4 of the preamble to the final rule, there are examples of owners and operators ofRE and affected EGUs in which they are colocated or located close to each other (e.g., withi n power control areas or balancing authorities).
EPA has observed that companies, such as NRG, NextE ra, Constellation, Sempra and Exelon, offer examples of co-owned RE and affected EGUs tha t are at least within the same power control area. NRG’s asset map include examples, such as wes tern Pennsylvania wind and coal plants, Texas wind, coal and natural gas plants and souther n California solar and natural gas plants. 54 NextEra Energy Resources, a subsidiary of NextEra E nergy, owns solar PV in Gloucester County, NJ a short distance from its two Marcus Hook NGCC p lant in PA, across the Delaware River. They also own two NGCC facilities in Texas along wi th more than ten wind plants. 55 Constellation Energy has an ownership interest in a concentrating solar thermal plant in CA along with several

52 Edison Electric Institute, Transmission Projects a t a Glance, March 2015, Page v.
53 The Eastern Interconnection is a key region, becau se it is the limiting region for purposes of settin g final rule goals for 2030. See the EIPC study, Section 5.1, Ta ble 5-9, where over 95 percent of the scenario cost s for Transmission related costs arise from these two sou rces. 54 NRG, Asset Map, Available at: http://maps.nrg.com/ , accessed July 2015
55 NextEra Energy Resources, Facilities Map, Availabl e at: http://www.nexteraenergyresources.com/where/us_map. shtml?type=Hydro, Accessed July 20154 – 25

small coal plants. 56 Sempra Energy has neighboring natural gas and sola r PV projects in both Boulder City, NV and Arlington, AZ. 57

These owners and operators have a direct path to re ducing higher-emitting generation with RE that is either in close proximity or within the same gri d regions regardless of their organizational type and regardless of whether they operate in a cost-of -service framework or in a competitive, organized market.

56 Constellation Energy, Generation Assets Portfolio, Available at: http://www.constellation.com/documents/constellatio n_energy_pdf_documents_corpcontent_1286210749525/as setmap.pdf, Accessed July 2015
57 Sempra Energy, Our Energy Assets, Available at: ht tp://www.sempra.com/about/our-companies/, Accessed July 20155– 1



### CHAPTER 5: CARBON CAPTURE & STORAGE

5.1 Introduction
Another possible approach for reducing CO 2 emissions from existing fossil fuel-fired steam generating EGUs is through the application of carbo n capture and storage technology (CCS; sometimes also referred to as carbon capture and se questration). In the final standards of performance for new fossil fuel-fired EGUs, EPA has determined that the best system of emission reduction (BSER) for new fossil fuel-fired steam ge nerating units is implementation of partial CCS (i.e., capturing a portion of the CO 2 from the emission stream – in contrast to “full CC S” which would involve capturing > 90% of the CO 2 from the emissions). 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 implementat ion will serve to promote further development and deployment of the technology. This chapter exam ines the potential for implementation of CCS technology at existing fossil fuel-fired steam gene rating EGUs.

5.2 Carbon Capture Options for Existing Fossil Fuel -fired EGUs
In general, CO 2 capture technologies applicable to existing fossil fuel-fired power generation can be categorized into three approaches: (1) post-comb ustion capture; (2) pre-combustion capture; and (3) oxy-combustion. Each of these is described and discussed in more detail below.

5.2.1 Post-combustion Capture

Post-combustion CO 2 capture refers to removal of CO 2 from a combustion flue gas prior to discharging to the atmosphere. Separating CO 2 from such a gas stream can be challenging for a number of reasons. Because CO 2 is a dilute fraction of the combustion flue gas – typically 13-15 % in coal-fired systems and 3-4 % in natural gas-fi red systems – a large volume of flue gas must be treated. The flue gas from typical combustion sy stems is usually at near atmospheric pressure. Therefore, most of the available capture systems re ly on chemical absorption (chemisorption) options (e.g., using amines) that require added ene rgy to release the captured CO 2 and regenerate the solvent. Many of the chemical solvents require a flue gas stream that is free of or has very low quantities of components – such as SO 2 and HCl – that can degrade the solvent. The captur ed CO 2 , 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 muc h higher pipeline pressures (about 2,000 psia).5– 2

5.2.2 Pre-combustion Capture
Pre-combustion capture systems are applicable to fo ssil 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 th e presence of steam and limited O 2 . The product syngas contains primarily H 2 and CO – and, depending on the fuel and gasificati on system – some lesser amount of CO 2 . The amount of CO 2 in the resulting syngas stream can be increased by “shifting” the composition via the catalytic water- gas shift (WGS) reaction. This process involves the catalytic reaction of steam (“water”) with CO ( “gas”) to form H 2 and CO 2 . The resulting CO 2 contained in the syngas is then captured before com bustion of the H 2 -enriched syngas for power generation in a combined cycle system. Contrary to the post-combustion capture flue gas, the IGCC syngas can contain a high volume of CO 2 and is pressurized. This allows the use of physica l absorbents (e.g., SelexolTM, Rectisol®) that require much less added energy to release the captured CO 2 and require less compression to get to pipeline st andards.

5.2.3 Oxy-combustion
Oxy-combustion systems for CO 2 capture rely on combusting coal or other fuels wit h relatively pure O 2 diluted with recycled CO 2 or CO 2 /steam mixtures. Under these conditions, the primar y products of combustion are water and CO 2 , with the CO 2 purified by condensing the water. Challenges associated with oxy-combustion include t he capital cost and energy consumption for a cryogenic air separation unit (ASU) to produce oxyg en, introduction of N 2 via boiler air infiltration, and excess O 2 in the CO 2 product stream.

5.3 CO 2 Transportation and Storage

5.3.1 CO 2 Pipeline Infrastructure
Carbon dioxide has been transported via pipelines i n the U.S. for nearly 40 years. Approximately 50 million metric tons of CO 2 are transported each year through 3,600 miles of p ipelines. Moreover, a review of the 500 largest CO 2 point sources in the U.S. shows that 95 percent ar e within 50 miles of a possible geologic sequestratio n site, which would lower transportation costs. There are multiple factors that contribute to the c ost of CO 2 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, t errain, flow rate of CO 2 , and the number of sources utilizing the pipeline.

5.3.2 Geologic Storage
Existing project and regulatory experience, researc h, and analogs (e.g. naturally existing CO 2 sinks, natural gas storage, and acid gas injection) , indicate that geologic sequestration is a viable long term CO 2 storage option. The viability of geologic sequestr ation of CO 2 is based on a5– 3

demonstrated understanding of the fate of CO 2 in the subsurface. Geologic storage potential for CO 2 is widespread and available throughout the U.S. an d Canada. Nearly every state in the U.S. has or is in close proximity to formations with car bon storage potential including vast areas offshore. Estimates based on DOE studies indicate t hat areas of the U.S. with appropriate geology have a storage potential of 2,300 billion to more t han 20,000 billion metric tons of CO 2 in deep saline formations, oil and gas reservoirs and un-mi neable coal seams. 1 Other types of geologic formations such as organic rich shale and basalt ma y also have the ability to store CO 2 ; and the DOE is currently evaluating their potential storage capacity.
Further evidence of the widespread availability of CO 2 storage reserves in the U.S. comes from the Department of Interior’s U.S. Geological Survey (USGS) which has recently completed a comprehensive evaluation of the technically accessi ble storage resource for carbon storage for 36 sedimentary basins in the onshore areas and State w aters of the United States. 2 The USGS assessment estimates a mean of 3,000 billion metric tons of subsurface CO 2 storage potential across the United States. For comparison, this amou nt is 500 times the 2011 annual U.S. energyrelated CO 2 emissions of 5.5 Gigatons (Gt). 3

5.3.3 Enhanced Oil Recovery (EOR)
Geologic storage options also include use of CO 2 in EOR, which is the injection of fluids into a reservoir to increase oil production efficiency. EO R is typically conducted at a reservoir after production yields have decreased from primary produ ction. EOR using CO 2 , sometimes referred to as ‘CO 2 flooding’ or CO 2 -EOR, involves injecting CO 2 into an oil reservoir to help mobilize the remaining oil and make it available for recovery. T he crude oil and CO 2 mixture is produced, and sent to a separator where the crude oil is separate d from the gaseous hydrocarbons and CO 2 . The gaseous CO 2 -rich stream then is typically dehydrated, purified to remove hydrocarbons, recompressed, and re-injected into the oil or natur al gas reservoir to further enhance recovery.

CO 2 -EOR has been successfully used at many production fields throughout the U.S. to increase oil recovery. The oil and natural gas industry in t he United States has over 40 years of experience of injection and monitoring of CO 2 in the deep subsurface for the purposes of enhanci ng oil and natural gas production. This experience provides a strong foundation for the injection and monitoring technologies that will be needed for suc cessful deployment of CCS.

1 The United States 2012 Carbon Utilization and Stor age Atlas, Fourth Edition, U.S Department of Energy , Office of Fossil Energy, National Energy Technology Laborator y (NETL).
2 U.S. Geological Survey Geologic Carbon Dioxide Sto rage Resources Assessment Team, 2013, National assessment of geologic carbon dioxide storage resou rces – Results: U.S. Geological Survey Circular 138 6, 41 p., http://pubs.usgs.gov/fs/2013/1386/.
3 U.S. Geological Survey Geologic Carbon Dioxide Sto rage Resources Assessment Team, 2013, National assessment of geologic carbon dioxide storage resou rces – Summary: U.S. Geological Survey Factsheet 20 13-3020, 6p.http://pubs.usgs.gov/fs/2013/3020/.5– 4

5.4 Evaluation of Retrofit CCS as BSER for Existing Fossil Fuel-fired EGUs
5.4.1 Technical Feasibility
In evaluating partial CCS as the BSER for new fossi l fuel-fired boilers and IGCC units, EPA determined that the technology is feasible and adeq uately demonstrated for new units because the major components of CCS – the capture, the transpor tation, and the storage – are all proven technologies that have been demonstrated at large s cale. While EPA found that partial CCS is technically feasible for new fossil fuel-fired boil ers 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 locatio n that is more amenable to CCS – such as a site that is near an existing CO 2 pipeline or an existing oil field. Existing source s do not have the advantage of pre-selecting an appropriate location. Some existing facilities are located in areas where CO 2 storage is not geologically favorable and are not near an existing CO 2 pipeline. Developers of new facilities also have the advantag e 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 l imited footprint and may not have the land or space available to add the equipment needed to impl ement CO 2 capture, compression, and transportation. Integration of the existing steam s ystem with a retrofit CCS system can be particularly challenging.

Partial CCS has been demonstrated at existing EGUs. It has been demonstrated at a pilot-scale at Southern Company’s Plant Barry and it is being inst alled 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 post-combustion carbon capture technology that is capturing approximately 90 % of the CO 2 from flue gas stream. All of these facilities are located in areas that are eith er near an existing oil field or in an area that is geologically favorable for CO 2 storage. Thus, at some existing facilities, the im plementation of partial CCS may be a preferred compliance option. H owever, for the reasons stated above, CCS as a system of emission reduction would be much more e asily implemented at some existing plants relative to others, and for some would be precluded due to limited space. Therefore, identification of CCS as the best system of emission reduction for existing fossil fuel fired EGUs would require sub-categorization of EGUs based on such factors an d EPA does not have adequate information to appropriately perform such a sub-categorization.

5.4.2 Reasonableness of Cost
In the proposed standard of performance for new fos sil fuel-fired EGUs (79 FR 1430), EPA proposed that the costs to implement partial CCS (t o a level to meet the proposed emission standard of 1,100 lb CO 2 /MWh-gross) were consistent with costs for other no n-natural gas-fired generating technologies – such as nuclear, biomass and geother mal – that utilities are considering for new5– 5

intermediate and base load generating capacity. EPA also noted in the proposal, that most of the relatively few new projects that are in the develop ment 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 fu el-fired EGUs, 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 standar d of 1,400 lb CO 2 /MWh-gross.

In contrast, EPA did not propose to identify full o r partial CCS as BSER for new natural gas-fired stationary combustion turbines noting technical cha llenges to implementation of CCS at NGCC units as compared to implementation at new solid fo ssil fuel-fired sources. EPA also noted that, because virtually all new fossil fuel-fired generat ing capacity is projected to use NGCC technology, requiring full or partial CCS would hav e more of an impact on the price of electricity than the few projected coal plants with CCS, and th e number of projects would make it difficult to implement in the short term.

An emission standard for existing units based on CC S (or even partial CCS) would most certainly have an even more significant effect on nationwide electricity prices. Importantly, the approach identified as the best system of emission reduction will achieve equivalent or greater emission reductions at a far lower cost and provide sources and states with greater flexibility to ensure reliability. Therefore, as stated earlier, EPA did not find partial CCS to be an appropriate component of the best system of emission reduction for CO 2 emissions from existing fossil fuelfired EGUs.

5.4.3 Emission Reductions and Promotion of Advanced Technology
An emission standard for existing units based on CC S (or even partial CCS) would clearly result in significant emission reductions and would certai nly serve to promote further deployment, development and improvement in the most advanced te chnology. However, EPA has determined that such an emission standard may not be feasible in a number of cases and would involve higher costs (and less flexibility) than the approach iden tified as the best system of emission reduction.

5.4.4 CCS and CCU in State Plans
Affected EGUs may utilize retrofit CCS technology t o reduce reported stack CO 2 emissions from the EGU. 4 Affected EGUs that apply CCS under a state plan mu st meet the same monitoring, recordkeeping and reporting requirements for seques tered CO 2 as new units with CCS capability

4 Addition of retrofit CCS technology should not tri gger CAA section 111(b) applicability for modified or reconstructed sources. Pollution control projects d o not trigger NSPS modifications and addition of CC S technology does not count toward the capital costs of reconstr uction.5– 6

subject to the final CAA section 111(b) rule for ne w 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 CO 2 emission limit, the EGU must report in accordance with 40 CFR part 98 subpart PP (Suppliers of Carbon Dioxide), and the captured CO 2 must be injected at a facility or facilities that report in accordance with 40 CFR part 98 subpart RR (Geologic Sequestration of Carbon Dioxide). 6,7 See 40 CFR part 60.46Da(h)(5) and part 60.5555(d). Take n together, these requirements ensure that the amount of captured and sequestered CO 2 will be tracked as appropriate at project- and nat ionallevels, and that the status of the CO 2 in its sequestration site will be monitored, inclu ding air-side monitoring and reporting. As detailed in the preamb le for the CAA section 111(b) standards for new EGUs, EPA is convinced that there is ample evid ence 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.
EPA received comments suggesting that carbon captur e and utilization (CCU) technologies should also be allowed as a CO 2 emission rate adjustment measure for affected EGUs . Potential alternatives to storing CO 2 in geologic formations are emerging and these rela tively new potential alternatives may offer the opportunity to offset th e cost of CO 2 capture. For example, captured anthropogenic CO 2 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 carbona te materials produced can be tailored to optimize performance in specific industrial and com mercial applications. For example, these carbonate materials have been used in the construct ion industry and, more recently and innovatively, in cement production processes to rep lace Portland cement.
The Skyonics Skymine® project, which opened its dem onstration project in October 2014, is an example of captured CO 2 being used in the production of carbonate products . This plant converts CO 2 into commercial products. It captures over 75,000 tons of CO 2 annually from a San Antonio, Texas, cement plant and converts the CO 2 into other products including sodium carbonate and sodium bicarbonate. 8 Other companies – including Calera 9 and New Sky 10 – also offer commercially available technology for the beneficia l use of captured CO 2 . These processes can be

5 Standards of Performance for Greenhouse Gas Emissi ons from New, Modified, and Reconstructed Stationar y Sources: Electric Utility Generating Units.
6 The final CAA section 111(b) rule finalizes amendm ents to subpart PP reporting requirements, specific ally 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 CO 2 was captured, and (2) the e-GGRT ID(s) for, and ma ss of CO 2 transferred to, each GS site reporting under subpar t RR. As noted, the final 111(b) rule also requires that any affected EGU unit that captures CO 2 to meet the applicable emissions limit must transf er the captured CO 2 to a facility that reports under 40 CFR part 98 subpart RR.
7 Under final requirements in the CAA 111(b) NSPS, a ny well receiving CO 2 captured from an affected source, be it a Class VI or Class II well, must report under subp art RR. A UIC Class II well’s regulatory status doe s not change because it receives such CO 2 , nor does it change by virtue of reporting under s ubpart RR.
8 http://skyonic.com/technologies/skymine.
9 http://www.calera.com/beneficial-reuse-of-co2/proc ess.html.
10 http://www.newskyenergy.com/index.php/products/car boncycle.5– 7

utilized in a variety of industrial applications – including at fossil fuel-fired power plants.
However, consideration of how these emerging altern atives could be used to meet CO 2 emission performance rates or state CO 2 emission goals would require a better understandin g of the ultimate fate of the captured CO 2 and the degree to which the method permanently iso lates the captured CO 2 or displaces other CO 2 emissions from the atmosphere.
Several commenters also suggested that algae-based CCU (i.e., the use of algae to convert captured CO 2 to useful products – especially biofuels) should b e 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 en d uses of captured CO 2 result in overall reductions of CO 2 emissions to the atmosphere. As these alternative technologies are developed, EPA is committed to work collaboratively with stake holders to evaluate the efficacy of alternative utilization technologies, to address any regulatory hurdles, and to develop appropriate monitoring and reporting protocols to demonstrate CO 2 reductions.
In the meantime, EPA has specified that state plans may allow affected EGUs to use qualifying CCU technologies to reduce CO 2 emissions that are subject to an emission standard , or those that are counted when demonstrating achievement of the C O 2 emission performance rates or a state rate-based or mass-based CO 2 emission. Approvable state plans must include anal ysis supporting how the proposed qualifying CCU technology results in CO 2 emission mitigation and provide monitoring, reporting, and verification requirement s to demonstrate the reductions. EPA would then review the appropriateness and basis for the a nalysis and the verification requirements in the course of its review of the state plan.