From: Wayland, Robertj [mailto:Wayland.Robertj@epa.gov] 

Sent: Thursday, May 22, 2014 4:22 PM 

To: Cortney Higgins 

Cc: Culligan, Kevin; Srivastava, Ravi 

Subject: Revised HRI Chapter 2 - GHG Abatement TSD 

Cortney – 

Attached is a revised version of the Heat Rate Improvement Chapter 2 from the GHG Abatement 

Measures TSD. Please let either Kevin, Ravi or me know if you have questions/concerns. 

thanx! 

bob 

Robert J. Wayland, Ph.D. 

Leader, Energy Strategies Group 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Sector Policies and Programs Division 

Mail Code D243-01 

Research Triangle Park, NC 27709 

Office: (919) 541-1045 

Cell: (919) 306-2290 

Fax: (919) 541-5450 

Email: wayland.robertj@epa.gov 

************************************* 

 DRAFT/Deliberative Document - FOIA Exempt 

************************************* 

<< GHG Abatement Measures - Chapter 2 05-10-2014_InteragencyCommentsunderEO12866_ 

EPA Edits Responses_05-22-2014.docx >> 

<< GHG Abatement Measures - Chapter 2 05-10-2014_InteragencyCommentsunderEO12866_ 

EPA Edits Responses_05-22-2014_clean.docx >> 
DRAFT May 22, 2014 

Draft Deliberative

Deleted: April 

Deleted: 14

Chapter 2: Heat Rate Improvement at Existing Coal-fired EGUs 

2.0 Introduction 

Based on the range of operating efficiencies for existing coal-fired electric generating 

units (EGUs), it is evident that EGUs are generally less efficient at converting fuel into 

electricity than is technically and economically possible. For example, the difference in operating 

efficiency of EGUs with similar design characteristics and the year to year variability in 

individual EGU efficiency indicates that there is potential for broadly applicable efficiency 

improvements through cost-effective operational and maintenance practices. These improved 

efficiencies would result in corresponding reductions in greenhouse gas emissions. 

This chapter presents an overview of existing coal-fired EGUs, design factors that 

influence efficiency, technologies to improve efficiency, previous studies estimating potential 

efficiency improvements, the proposed EPA approach to calculate efficiency improvements, and 

the estimated capital costs of those improvements. 

2.1 Overview of U.S. Existing Coal-Fired Electric Generating Units 

Coal in the United States is predominately used for electric power generation. Most coalfired EGUs in the United States burn either bituminous or subbituminous coals. The largest 

sources of bituminous coals burned in coal-fired EGUs are mines in regions along the 

Appalachian Mountains and in southern Illinois, western Kentucky and Indiana. Additional 

bituminous coals are supplied from mines in Utah and Colorado. The vast majority of 

subbituminous coals are supplied from mines in Wyoming and Montana, with many coal-fired 

EGUs burning subbituminous coals from the Powder River Basin (PRB) region in Wyoming. In 

general, the burning of lignite by U.S. electric utilities is limited to coal-fired EGUs that are 

located near the mines that supply the lignite in Texas, Louisiana, Mississippi, Montana, and 

North Dakota. At a few power plant locations in the Eastern United States, recovered anthracite 

coal or coal refuse is burned in limited quantities. 

Existing coal-fired EGUs in the U.S. electric utility fleet use one of five basic coal 

combustion configurations: (1) pulverized coal (PC) combustion, (2) fluidized-bed combustion 

(FBC), (3) gasified coal combustion, (4) cyclone furnace combustion, or (5) stoker-fired coal 

combustion. Table 2-1 presents a summary of the operating characteristics of each of these coal 

combustion configurations. 

Deleted: can be presumed 

Comment [A1]: This section read quite weakly as

initially written.   

EPA response: Thank you for the edit.

Comment [A2]: MATS Compliance.  MATS

compliance will lead to significant installations of

pollution control equipment (PCE) over the 2013 to

2016 period on capacity that owners intend to keep

in service beyond the MATS compliance deadline

but would not be MATS‐compliant without

augmenting the PCE that was in place during 2012.  

The PCE systems likely to be installed on such units

include FGD systems that may consume significant

power and therefore, all else equal, increase energy

input per net kilowatt‐hour of generation.  It would

be helpful to understand if or how additions of PCE

for MATS compliance are reflected in the analysis of

HRI in the draft TSD.

EPA response: The results of our statistical analysis

(described later) are based on gross heat rates and

will not change with installation of MATS controls,

which will add additional parasitic load

requirements and thereby have an impact on the

net heat rates. However, in this TSD chapter, we

explain that EPA conservatively considers fleet‐wide

net heat rate improvement potential to be the same

as that indicated for the fleet‐wide gross heat rate.

And based on this consideration, IPM modeling and

state goals calculations used 6% improvement on a

net heat rate basis. So we also need to speak to the

fleet‐wide net heat rate. That being said, MATS

controls are expected to lead to a small increase in

auxiliary power use and therefore will not have a

significant effect on the fleet‐wide net heat rate.

Here is what we projected (using IPM) for the final

MATS policy case.

Technology                       Capacity         Parasitic Load

New wet FGD                    0 GW               1.5‐2%

New SCR                             0 GW               0.5%       

New dry FGD (SDA)          22 GW              1‐1.5%

New DSI                             43 GW              0.5%  

                                                          (assumed same as

ACI + FF)

New ACI                             141 GW            0.5%

New FF                               102 GW             0.5%

Upgrade of existing ESP            34 GW                   

Upgrade of existing wet FGD   63 GW                   

So MATS requirements would increase parasitic load

from 0% (for a unit that can meet the standards

with existing equipment) up to 2 % for units that

install a SDA and implement ACI + FF   [Note: a unit

would implement either SDA or DSI but not both].

In any case, EPA’s IPM v 5.13 Base Case modelling

for this proposal assumes that MATS is in effect and

that such controls as are needed for compliance are ... [1]

Deleted: and 

Deleted: .
DRAFT May 22, 2014 

2 

Deleted: April 

Deleted: 14

Table 2-1. Characteristics of coal-firing configurations used for U.S. EGUs 

Coal-firing 

Configuration 

Coal Combustion Process 

Description Distinctive Design/Operating Characteristics 

Pulverized 

Coal (PC)

Combustion 

Coal is ground to a fine powder that 

is pneumatically fed to a burner 

where it is mixed with combustion 

air and then blown into the furnace. 

The pulverized-coal particles burn in 

suspension in the furnace. Unburned 

and partially burned coal particles 

are carried off with the flue gas. 

Wall-fired 

An array of burners fire into the 

furnace horizontally, and can be 

positioned on one wall or opposing 

walls depending on furnace design. 

Tangential-fired 

(Corner-fired) 

Multiple burners are positioned in 

opposite corners of the furnace 

producing a fireball that moves in a 

cyclonic motion and expands to fill 

the furnace. 

Fluidized-bed 

Combustion 

(FBC) 

Coal is crushed into fine particles. 

The coal particles are suspended in a 

fluidized bed by upward-blowing jets 

of air creating a turbulent mixing of 

combustion air with the coal 

particles. Typically, the coal is mixed 

with a sorbent such as limestone (for 

SO2 emission control). FBC have a 

greater fuel flexibility than PC EGUs 

and can be designed for combustion 

within the bed to occur at 

atmospheric or elevated pressures. 

FBC operating temperatures are in 

the range of 1,500 to 1,650F (800 to 

900o

C). 

Bubbling 

fluidized bed 

(BFB) 

Operates at relatively low gas stream 

velocities and with coarse-bed size 

particles. Air in excess of that 

required to fluidize the bed passes 

through the bed in form of bubbles. 

Circulating 

fluidized bed 

(CFB) 

Operates at higher gas stream 

velocities and with finer-bed size 

particles. No defined bed surface. 

Must use high-volume, hot cyclone 

separators to recirculate entrained 

solid particles in flue gas to maintain 

the bed and achieve high combustion 

efficiency. 

Integrated Coal 

Gasification 

Combined 

Cycle (IGCC) 

Synthetic combustible gas (“syngas”) 

derived from an on-site coal 

gasification process is burned in a 

combustion turbine. The hot exhaust 

gases from the combustion turbine 

pass through a heat recovery steam 

generator to produce steam for 

driving a steam turbine/generator 

unit. 

Coal gasification units are unique among coal-firing 

configurations because a gaseous fuel (synfuel or syngas) 

is burned instead of solid coal because the combustion 

and power generation process and combines the Rankine 

and Brayton thermodynamic cycles as is the case for a 

combined cycle power plant. 

Cyclone 

Furnace 

Combustion 

Coal is crushed into small pieces and 

fed through a burner into the cyclone 

furnace. A portion of the combustion 

air enters the burner tangentially 

creating a whirling motion to the 

incoming coal. 

Designed to burn coals with low-ash fusion temperatures 

that are difficult to burn in PC boilers. The majority of 

the ash is retained in the form of a molten slag. 

Stoker-fired 

Coal 

Combustion 

Coal is crushed into large lumps and 

burned in a fuel bed on a moving, 

vibrating, or stationary grate. Coal is 

fed to the grate by a mechanical 

device called a “stoker.” 

One of three types of stoker mechanisms can be used that 

ether feed the coal by pushing, dropping, or flipping coal 

unto the grate. 

The three technologies currently used for new coal-fired power plants are pulverized 

coal, fluidized bed (FBC), and integrated coal gasification combined cycle (IGCC). Pulverized 

coal combustion is the coal-firing configuration predominately used at existing EGUs. The most 

Comment [A3]: We would suggest reducing the

amount of text in tables (perhaps as bullet points).

EPA response: Thank you for the observation.

Deleted: FBC

Comment [A4]: It might be helpful to indicate

the typical size (MW) and capital cost ($/installed

MW) here.  

EPA response: Thank you for the observation, but it 

is unclear how the suggested edit would relate to the 

analysis.

Deleted: involves pulverizing the coal into a fine 

powder, which allows the coal to be burned more 

easily and efficiently. This 

Deleted: , and is the most frequently selected 

technology for new coal-fired EGU projects

Deleted: However, FBC EGUs are becoming 

more prevalent since they have a greater fuel 

flexibility.

Comment [A5]: No one is building coal plants

currently in the U.S., so the future tense is not

appropriate.  Who is building coal plants using FBC?  

China?  If so, we should specify, or else delete.

EPA response: The text has been deleted.
DRAFT May 22, 2014 

3 

Deleted: April 

Deleted: 14

recent coal technology combustion development involves the integration of coal gasification 

technologies with the combined cycle electric generation process. The efficiency of an IGCC 

power plant is comparable to the latest advanced PC-fired and FBC EGU designs using 

supercritical steam cycles. The advantages of using IGCC technology can include greater fuel 

flexibility (e.g., capability to use a wider variety of coal ranks), potential improved control of 

PM, SO2 emissions, and other air pollutants, the need for fewer post-combustion control devices 

(e.g., almost all of the sulfur and ash in the coal can be removed once the fuel is gasified and 

prior to combustion), generation of less solid waste, reduced water consumption, and the 

chemical process that creates a concentrated CO2 stream that is more amenable to carbon capture 

processes. 

Older combustion technologies, namely cyclone furnaces and stoker-fired coal 

combustion, have been replaced at new coal-fired EGUs by more efficient methods that provide 

superior coal combustion efficiency and other advantages. However, a few remaining old stokerfired EGUs and cyclone furnaces still remain in service for a small number of existing EGUs in 

the U.S. electric utility market. 

2.2 Influence of Heat Rate on Coal-Fired EGU CO2 Emission Rate 

Heat rate is a common way to measure EGU efficiency. As the efficiency of a coal-fired 

EGU is increased, less coal is burned per kilowatt-hour (kWh) generated by the EGU resulting in 

a corresponding decrease in CO2 and other air emissions. Heat rate is expressed as the number of 

British thermal units (Btu) or kilojoules (kJ) required to generate a kilowatt-hour (kWh) of 

electricity. Lower heat rates are associated with more efficient coal-fired EGUs. 

The electric energy output for an EGU can be expressed as either as “gross output” or 

“net output.” The gross output of an EGU is the total amount of electricity generated at the 

generator terminal. The net output of an EGU is the gross output minus the total amount of 

auxiliary (or parasitic) electricity used to operate the EGU (e.g., electricity to power fuel 

handling equipment, pumps, fans, pollution control equipment, and other on-site electricity 

needs), and thus is a measure of the electricity delivered to the transmission grid for distribution 

and sale to customers. Some EGUs also produce part of their useful output in the form of useful 

thermal output (e.g., steam for heating purposes). These types of facilities are called combined 

heat and power, or CHP, facilities. 

Deleted: and 

Formatted: Subscript

Deleted: .

Comment [A6]: One of the primary benefits of

IGCC is the concentrated CO2 stream and ability to

use in CCS.  Needs to be highlighted.

EPA response: Thank you for the observation and

the suggested text, which has been included.

Deleted: . 
DRAFT May 22, 2014 

4 

Deleted: April 

Deleted: 14

A variety of factors must be considered when comparing the effectiveness of heat rate 

improvement technologies to increase the efficiency of a given coal-fired EGU. The actual 

overall efficiency that a given coal-fired EGU achieves is determined by the interaction of a 

combination of site-specific factors that impact efficiency to varying degrees. Examples of the 

factors affecting EGU efficiency at a given facility include: 

 EGU thermodynamic cycle – EGU efficiency can be significant improved by using a 

supercritical or ultra-supercritical steam cycle. Supercritical and ultra-supercritical boilers 

operate above the critical point of water (approximately 374°C (705°F) and 22.1 MPa 

(3,210 psia)). As a general guideline, the thermal design efficiencies for subcritical EGUs 

are in the range of 35% to 37%, supercritical EGUs are in the range of 39% to 40%, and 

ultra-supercritical EGUs in the range of 42% to 45%. However, actual operating 

efficiencies are lower than design efficiencies. 

 EGU coal rank and quality – EGUs burning higher quality coals (e.g., bituminous) tend 

to be more efficient than EGUs burning lower quality coals with a greater moisture 

content (e.g., lignite). Bituminous coals have higher heating values of greater than 10,500 

British thermal units per pound and lignite coals have higher heating values of less than 

8,300 British thermal units per pound. 

 EGU plant size –EGU efficiency generally increases with size because the boiler and 

steam turbine losses are lower for larger equipment compared to smaller equipment. 

However, the EPA is not aware of sufficient data to quantify the relationship. 

 EGU pollution control systems – The electric power consumed by air pollution control 

equipment reduces the overall efficiency of the EGU.

 EGU operating and maintenance practices – The specific practices used by an individual 

electric utility company for combustion optimization, equipment maintenance, etc. can 

affect EGU efficiency. 

 EGU cooling system – The temperature of the cooling water entering the condenser can 

have impacts on steam turbine performance. Once-through cooling systems can have an 

efficiency advantage over recirculating cooling systems (e.g., cooling towers). However, 

once-though cooling systems typically have larger water related ecological concerns than 

recirculating cooling systems. 

Deleted: The majority of existing PC-fired 

EGUs in the U.S. electric fleet use subcritical 

boilers

Comment [A7]: Could indicate the approximate

range (BTUs / lb) of the different coal types, as well

as the influence of moisture.

EPA response: The ranges have been added.

Deleted: However, 

Deleted: as equipment size increases the 

differences in these losses start to taper off

Comment [A8]: : Could indicate approximate

turbine size / size class here.  

EPA response: Edits made to address the comment.
DRAFT May 22, 2014 

5 

Deleted: April 

Deleted: 14

 EGU geographic location and ambient conditions – The elevation and seasonal ambient 

temperatures at the facility site potentially may have an impact on EGU efficiency. At 

higher elevations, air pressure is lower and less oxygen is available for combustion per 

unit volume of ambient air than at lower elevations. Cooler ambient temperatures 

theoretically could increase the overall EGU efficiency by increasing the draft pressure of 

the boiler flue gases and the condenser vacuum, and by increasing the efficiency of the

cooling system. Also, geographic location influences the type of cooling system that can 

be used (e.g., EGUs located in arid locations often cannot use once through cooling)

 EGU load generation flexibility requirements – Operating an EGU as a baseload unit is 

more efficient than operating an EGU as a load following unit to respond to fluctuations 

in customer electricity demand. 

 EGU plant components – EGUs using the optimum number of feedwater heaters, highefficiency electric motors, variable speed drives, better materials for heat exchangers, etc. 

tend to be more efficient. 

2.3 Technologies to Improve Existing Coal-Fired EGU Heat Rate 

A number of studies have been conducted involving literature reviews of published 

articles and technical papers identifying potential efficiency improvement techniques applicable 

to existing coal-fired EGUs.1

 For example, a summary of the findings from one study conducted 

by the Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) is 

presented in Table 2-2. The efficiency percentages were converted to a common basis so that all 

of the data can be compared. All of the improvement technologies presented in Table 2-2 cannot 

necessarily be implemented at every existing coal-fired EGU facility in the U.S. electric utility 

fleet. The existing EGU design configuration and other site-specific factors may prevent the 

technical feasibility of using a given technology. 

Typically, these studies share as a common basis the estimated potential efficiency 

improvement percentages and costs from the engineering study originally completed by Sargent 

and Lundy in 2009 titled “Coal-Fired Power Plant Heat Rate Reductions.” It describes numerous 

well known and technically proven methods to improve efficiency of coal-fired EGUs. The study 

                                                            

1

 See HRI Partial Bibliography at the end of this chapter. 

Deleted: a

Deleted: measureable 

Deleted: a condenser recirculating

Comment [A9]: Geographic location could also

influence cooling water issues, mentioned earlier

(i.e. coal plants in the arid west).

EPA response: Text has been added to address the

comment.  

Deleted: cycling 

Comment [A10]: Suggest using “load following”

for clarity

EPA response: Done.

Deleted: <#>EGU equipment manufacturers

– The efficiency specifications of major EGU 

components such as boilers, turbines, and 

electrical generators provided by equipment 

manufacturers can affect EGU efficiency.¶

Comment [A11]: This seems less about the

manufacturers and more about the specific

technology choice. Suggest describing this as “EGU

technology choice” and, if necessary, note that

efficiency can vary by manufacturer or by product.

EPA Response: Deleted since this is not relevant to

the discussion.
DRAFT May 22, 2014 

6 

Deleted: April 

Deleted: 14

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

equipment and the water treatment system. Each of these main areas are expanded upon below. 

2.3.1 Boiler 

The systems to focus on for improving heat input within the boiler area include the 

materials handling, combustion system, boiler control system, sootblowers and the air heaters. 

2.3.1.1 Materials Handling2

The coal handling portion of materials handling typically requires about 0.07% (7 

Btu/kWh) of the gross electrical output of a power plant. Depending on the state of the motors 

and drives, replacing them with energy-efficient motors and variable frequency drives can 

reduce the auxiliary power requirements. The variable frequency drives also limit the stress and 

strain on the other equipment. 

Coal pulverizers typically require about 0.6% (60 Btu/kWh) of the gross electrical output 

and can be upgraded to provide more consistent size and finer coal particles. The fine particles 

improve combustion efficiency, consequently reducing fuel cost and heat rate. The costs for 

changes to the pulverizer system are significant, and historically, the projects have improved the 

heat rate justifiably only when the existing equipment has degraded. 

The bottom ash handling system may be a source for heat rate improvement. Switching 

from a water-sluicing bottom ash system to a dry drag chain system can reduce the auxiliary 

requirements and reduce the amount of water to the water treatment plant. The typical power 

requirements are about 0.1% (10 Btu/kWh) of the plant’s gross output. 

2.3.1.2 Economizer 

An economizer is a heat exchanger that improves the efficiency of an EGU by recovering 

energy from the exhaust gases to preheat the boiler feedwater. The replacement of the 

economizer can lead substantial heat rate improvements around 50-100 Btu/kWh, but is large 

capital investment (~$2-8M). Due to this high cost, economizers upgrades are not generally 

performed unless the existing equipment has degraded or a replacement is necessary due to the 

installation of new control equipment. 

                                                            

2 The Sargent and Lundy report did not provide potential savings for material handling operations. Energy use in 

Btu/kWh has been provided to compare the energy use of materials handling relative to the potential energy savings 

from other efficiency activities.

Deleted: (VFDs)

Deleted: VFDs

Deleted: VFDs

Comment [A12]: Did you want to include an

estimate of pulverizer electric demand?

EPA response: An approximate electric demand has

been added.

Comment [A13]: For this part of the discussion,

some efficiency gains are given in percent, and

others in Btu/kWh. It would be helpful to keep

either one common unit (suggest Btu/kWh) or

provide both metrics.

EPA response: See footnote.  

Deleted: 1

Comment [A14]: Please add a sentence on what

an economizer is and does.

EPA response:  Added an introductory sentence.
DRAFT May 22, 2014 

7 

Deleted: April 

Deleted: 14

2.3.1.3 Boiler Control System 

The boiler control system has a large impact on the heat rate of the unit. The process 

control capabilities can control and evaluate many aspects of the plant’s operations. Commonly 

referred to as the Neural Network, computer models are able to control the plant’s processes by 

predicting performance during static and dynamic changes. Many vendors offer Neural Network

systems to improve the overall efficiency. Neural network systems are typically around 

$550,000-$750,000 and offer heat rate reductions up to 150 Btu/kWh. 

2.3.1.4 Sootblowers 

Intelligent sootblowers may be installed to improve system efficiency. The intelligent 

sootblowers system monitors the furnace exhaust gas temperatures and steam temperatures. 

Other readings may be incorporated into the intelligent sootblowers system which also 

communicates with the boiler control system. This system uses real-time data to identify which 

areas need sootblowing. Boiler efficiency improvements have ranges from 30-(150 Btu/kWh)

with capital costs around $300,000-$500,000 and $50,000/year for fixed operating and 

maintenance costs. 

2.3.1.5 Air Heaters 

Air heaters operate to transfer heat between the incoming pre-combustion air and the 

effluent flue gas. These systems are critical to maintain an efficient power plant. In order to 

maintain these systems to operate most efficiently, air heater leakages must be maintained below 

6% of incoming air flow. Most leakage is due to the pre-combustion air leaking across the 

rotating section and leaving with the flue gas. This increases the flue gas volume going through 

the forced draft and induced draft fans to the emissions controls and avoids capturing the heat 

transferred between the flue gas and pre-combustion gas. The increased volume requires more 

power to pump more flue gas. Additionally, improvements to seals on the air heaters also reduce 

the leakages. Improvements to reduce air heater and duct leakages generally reduces the heat rate 

by 10-40 Btu/kWh with capital costs between $0.3-1.2M. 

A second method to improve the heat rate is to lower the air heater outlet temperature by 

controlling the acid dew point. Typically the air heater outlet is maintained at 20-30°F above the 

sulfuric acid dew point to prevent corrosion of cold-end baskets. Sorbent injections such as 

Trona or hydrated lime can be used to lower the dew point. Depending on the sizing of the air 

Deleted: 2

Deleted: (NN)

Deleted: NN

Deleted: NN

Deleted: 3

Deleted: (ISB)

Deleted: ISB

Deleted: ISB

Deleted: 0.3

Deleted: %

Deleted: .

Deleted: %

Deleted: 4
DRAFT May 22, 2014 

8 

Deleted: April 

Deleted: 14

heater, the air heater may need to be modified in order to optimize the lower outlet temperature. 

The capital costs can range from $1.5-18M for heat rate reductions of 50-120 Btu/kWh. 

2.3.2 Turbine 

The systems within the turbine area for which to focus heat rate improvements are the 

turbine, the feedwater heaters, the condenser, and the turbine drive and motor-driven feed 

pumps. 

2.3.2.1 Turbine 

Replacements or overhauls of existing steam turbines with advanced turbine designs 

improves the efficiency of converting the energy in the steam to electrical energy. The capital 

costs for these projects ranges from $2-25M with heat rate reductions of 100-300 Btu/kWh.

2.3.2.2 Feedwater Heaters 

The feedwater heaters are heat exchangers to heat the boiler feedwater by extracting heat 

from the steam leaving the turbine section. The efficiency can be increased by improving heat 

transfer surface area. This entails adding heat exchange surfaces to the existing heaters or adding 

additional heaters. The costs relative to the heat rate improvement associated with these projects 

typically prohibit the advancement of the project unless the feedwater heaters are in need of 

repair. 

2.3.2.3 Condenser 

To obtain the most efficiency from the condenser section, the most effective operation 

would have the steam from the turbine to reach the lowest temperature possible before entering 

the condenser. This allows for the turbine to extract as much energy from the steam as possible. 

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

water quality. To improve water quality, closed cooling water systems provide better control of 

the water quality and tube cleaning can be performed. Heat rate reductions observed from the 

condenser upgrades and maintenances are 30-70 Btu/kWh with annual fixed costs of $30,000-

$80,000. 

2.3.2.4 Boiler Feed Pumps 

Boiler feed pumps require a large amount of auxiliary power to pump massive amounts 

of boiler feedwater through the heaters and boiler. Due to the high use of these pumps, 

Deleted: Generally, a 40°F reduction in the air 

heater outlet results in 1% efficiency improvement. 

These systems are costly and depend on whether the 

air heater is adequate, needs modifications or needs a 

complete replacement. 

Deleted: The performance and longevity of 

turbines is constantly increasing with advances in 

technology. Most turbines in the industry are 

maintained so that the efficiency improvements are 

1-3%; however, some extremely degraded turbines 

may benefit up to 10% with a turbine overhaul. 

Comment [A15]: Not clear whether the 1‐3%

efficiency improvement refers to improvement

resulting from an overhaul. It might be helpful to

indicate what size of turbines correspond to these

costs.  

EPA response: the paragraph has been modified.
DRAFT May 22, 2014 

9 

Deleted: April 

Deleted: 14

maintenance is extremely important to ensure reliability and the most efficient operation. As the 

pumps wear and operate less efficiently, a pump overhaul may be required. The overhaul can 

reduce the heat rate by 25-50 Btu/kWh with capital costs around $250,000-$800,000. 

2.3.3 Flue Gas System 

Two aspects of the flue gas system that offer potential improvements to the plant 

efficiency are to improve the forced draft and induced draft fan efficiencies and implementation 

of variable frequency drives. 

2.3.3.1 Induced Draft Fans 

One of the most important features in the fans is being able to control the flue gas flow. 

Many fans have dampers, which are the least effective and efficient option. There are many 

methods, such as variable inlet vanes , variable frequency drives, and variable pitch blades, 

available to control the flue gas flow allowing highly efficient fan performance. These upgrades 

or replacements provide a heat rate reduction of 10-50 Btu/kWh and cost between $6-$16M. 

2.3.3.2 Variable Frequency Drives 

It is apparent with the control that a variable frequency drives offers, the plant is able to 

reduce the auxiliary load immensely. Considering upgrading all drives, the capital costs are $6-

16M with heat rate reductions between 10-150 Btu/kWh. 

2.3.4 Emission Control Technologies 

With the passage of environmental regulations, additional emission control devices have 

been and must be implemented in the power plant. These systems typically require large amounts 

of auxiliary power with their benefit being improved air quality. Even small upgrades can 

decrease the power requirements significantly while at least maintaining the same level of 

emissions reduction. The three technologies discussed are the flue gas desulfurization, the 

electrostatic precipitator, and the selective catalytic reduction systems. 

2.3.4.1 Flue Gas Desulfurization

Coal-fired power plants contain many variances between their flue gas desulfurization

systems. A few common technology improvements are discussed further in this section. Older 

units typically contained a venturi throat that increased the velocity of the fluid, but resulted in a 

large pressure drop and greater power to operate the induced draft fans. To improve this 

Deleted: (FD)

Deleted: (ID) 

Deleted: VFDs

Comment [A16]: Please spell out acronym –

even if it has been spelled out earlier, I have

forgotten and couldn’t find the location on looking

back.

EPA response: Spelled out acronyms.

Deleted: ID

Comment [A17]: For all these subtitles, it

wouldn’t hurt to spell out the acronym in the

section title.

EPA response: Spelled out acronyms.

Deleted: (VIV)

Deleted: VFD

Deleted: (VPB)

Deleted: VFD

Deleted: (VFD)

Deleted: VFD

Deleted: (FGD)

Deleted: (ESP)

Deleted: (SCR) 

Deleted: FGD

Deleted: FGD

Deleted: FGD

Deleted: ID 
DRAFT May 22, 2014 

10 

Deleted: April 

Deleted: 14

operation, a co-current spray tower quencher may replace the unit. The capital cost is about 

$2.5M with heat rate reductions around 13 Btu/kWh. 

Another technology upgrade affects the vanes and distribution plate into an absorber. The 

improvement of the gas flow coming into contact with the absorber sorbent increases SO2

capture, reduces maintenance due to erosion, and reduces the amount of energy required from the 

induced draft fan. Turning vanes and a perforated gas distribution plate improve gas distribution. 

The cost of the vanes is around $250,000 with heat rate reductions of 1-2 Btu/kWh. 

In a wet flue gas desulfurization multiple spray levels are installed to deliver the limestone 

slurry. If a power plant is operating with SO2 levels below its permit limit, turning off one spray 

level will reduce the auxiliary power necessary. If this is possible, a unit heat rate reduction of 16 

Btu/kWh may be available. 

2.3.4.2 Electrostatic Precipitator

The best operation for an electrostatic precipitator involves maintaining the maximum 

applied voltage, but below the level in which spark-over occurs. Electrostatic precipitator energy 

management system upgrades often help improve the electrostatic precipitator performance by 

maintaining the optimal performance and lowering power consumption. The installation for this 

technology can be from minimal to $0.8M and can lower heat rate by 5 Btu/kWh. 

2.3.4.3 Selective Catalytic Reduction

For the last 15 years, selective catalytic reduction systems have been in use to reduce 

NOX emissions from power plants. Extensive modeling was performed to achieve the necessary 

reduction with minimum ammonia slip. The results showed that reducing pressure drop and 

using secondary air as dilution for the ammonia vaporizer can reduce the auxiliary power 

necessary. The heat rate reduction is 0-10 Btu/kWh and capital costs between $0.5-$2M with 

fixed and variable costs up to $100,000 each. 

2.3.5 Water Treatment System 

The water treatment system heat rate improvements can occur for the boiler water 

treatment and the cooling tower improvements. The boiler water is one of the most important 

aspects of the power plant. The quality of the water plays a huge impact on the scale buildup on 

the boiler tubes, which reduces the heat transfer in the tubes or can cause tube failures. Proper 

use of chemicals to maintain pure water is key. Also, high-quality water can reduce the 

Deleted: ID 

Deleted: FGD

Deleted: ESP

Deleted: ESP

Deleted: ESP

Deleted: e

Deleted: ESP

Deleted: (EMS) 

Deleted: ESP

Deleted: SCR

Deleted: SCR

Deleted: SCR
DRAFT May 22, 2014 

11 

Deleted: April 

Deleted: 14

blowdowns required, which allows for more steam in the turbine cycle. If the water is not 

properly maintained, heat transfer may be reduced up to 10%. 

Similar to the boiler water, the cooling towers are also affected by the water quality. 

Fouling and scaling remain issues for heat transfer and purity of the water. By maintaining the 

cooling water system efficiently, the overall water quality is improved, which branches into other 

aspects already mentioned. 
DRAFT May 22, 2014 

12 

Table 2-2. Existing coal-fired EGU efficiency improvements reported for actual efficiency improvement projects 

Efficiency 

Improvement 

Technology

Description 

Reported 

Efficiency 

Increasea

Combustion 

Control 

Optimization 

Combustion controls adjust coal and air flow to optimize steam production for the steam turbine/generator set. However, combustion control for a coal-fired EGU is complex and impacts a number of important operating parameters including combustion efficiency, steam temperature, furnace slagging and fouling, and NOX

formation. The technologies include instruments that measure carbon levels in ash, coal flow rates, air flow rates, CO levels, oxygen levels, slag deposits, and burner metrics as well as advanced coal nozzles and plasma assisted coal combustion. 

0.15 to 0.84% 

Cooling System 

Heat Loss 

Recovery 

Recover a portion of the heat loss from the warm cooling water exiting the steam condenser prior to its circulation thorough a cooling tower or discharge to a water body. The identified technologies include replacing the cooling tower fill (heat transfer surface) and tuning the cooling tower and condenser. 

0.2 to 1% 

Flue Gas Heat 

Recovery 

Flue gas exit temperature from the air preheater can range from 250 to 350°F depending on the acid dew point temperature of the flue gas, which is dependent on the concentration of vapor phase sulfuric acid and moisture. For power plants equipped with wet FGD systems, the flue gas is further cooled to approximately 125°F as it is sprayed with the FGD reagent slurry. However, it may be possible to recover some of this lost energy in the flue gas to preheat boiler feedwater via use of a condensing heat exchanger. 

0.3 to 1.5% 

Low-rank Coal 

Drying 

Subbituminous and lignite coals contain relatively large amounts of moisture (15 to 40%) compared to bituminous coal (less than 10%). A significant amount of the heat released during combustion of low-rank coals is used to evaporate this moisture, rather than generate steam for the turbine. As a result, boiler efficiency is typically lower for plants burning low-rank coal. The technologies include using waste heat from the flue gas and/or cooling water systems to dry low-rank coal prior to combustion. 

0.1 to 1.7% 

Sootblower 

Optimization 

Sootblowers intermittently inject high velocity jets of steam or air to clean coal ash deposits from boiler tube surfaces in order to maintain adequate heat transfer. Proper control of the timing and intensity of individual sootblowers is important to maintain steam temperature and boiler efficiency. The identified technologies include 

intelligent or neural-network sootblowing (i.e., sootblowing in response to real-time conditions in the boiler) and detonation sootblowing. 

0.1 to 0.65% 

Steam Turbine 

Design 

There are recoverable energy losses that result from the mechanical design or physical condition of the steam turbine. For example, steam turbine manufacturers have improved the design of turbine blades and steam seals which can increase both efficiency and output (i.e., steam turbine dense pack technology). 

0.84 to 2.6 

Source: National Energy Technology Laboratory (NETL), 2008. Reducing CO2 Emissions by Improving the Efficiency of the Existing Coal-fired Power Plant 

Fleet, DOE/NETL-2008/1329. U.S. Department of Energy, National Energy Technology Laboratory, Pittsburgh, PA. July 23, 2008. Available at: <http://www.netl.doe.gov/energy-analyses/pubs/CFPP%20Efficiency-FINAL.pdf>. a Reported efficiency improvement metrics adjusted to common basis by conversion methodology assuming individual component efficiencies for a reference 

plant as follows: 87% boiler efficiency, 40% turbine efficiency, 98% generator efficiency, and 6% auxiliary load. Based on these assumptions, the reference power plant has an overall efficiency of 32% and a net heat rate of 10,600 Btu/kWh. As a result, if a particular efficiency improvement method was reported to achieve a 1% point increase in boiler efficiency, it would be converted to a 0.37 % point increase in overall efficiency. Likewise, a reported 100 Btu/kWh decrease in net heat rate would be converted to a 0.30% point increase in overall efficiency. 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

13 

Deleted: 14

2.4 Previous Studies on Heat Rate Improvements 

A number of studies using varying approaches have been performed to determine 

potential efficiency improvements and associated resulting CO2 emission reductions. These 

approaches include characterizing the current U.S. coal-fired EGU fleet, identifying potential 

efficiency improvements, and applying improvement actions to existing EGUs. The approach 

taken within each study varies. Five studies are briefly summarized and compared in Table 2-3. 

The NETL studies used a benchmarking approach that evaluated the design factors that 

are known to influence efficiency and grouped EGUs based on similar design characteristics. 

The studies categorized the industry based on fuel type, location, steam cycle, and age of the 

boilers. Potential efficiency improvements were calculated based on an assumption that the 

lower-performing EGUs in each group should to be able to do as well as the better performing 

EGUs in that group. Specifically, the goal for potential improvement: for each subcategory was 

that the bottom 90% of EGUs in each group improved their heat rate to the average performance 

of top 10% in that group. While the studies are different in the level of detail and assumptions, 

the results of these studies overall suggest that a U.S. coal-fired EGU fleet-wide improvement 

ranging from 9% to 15% is theoretically possible. The Lehigh study used a less detailed 

approach and evaluated technologies applicable to bituminous and subbituminous coals to 

estimate potential fleet wide reductions. 

An alternative approach to evaluate heat rate improvement is used by Resources for the 

Future. This study focused on the operating efficiency (synonymous with heat rate) of the entire 

existing U.S. coal-fired EGU fleet. The authors evaluated decades of data from industrial 

responses to economic factors such as demand, coal price and energy policies. This approach 

sought to estimate overall changes in industry fleet efficiency in response to changes in fuel 

prices or carbon prices. In this specific example, the coal price was assumed to be a 10% 

increase and the CO2 emissions tax at $1.64 per ton for heat rate reductions of 0.3 to 0.9%. 

The National Resources Defense Council approach considered the fleet of coal-fired 

EGUs and assumes a target heat rate in order for the EGUs to comply with an inferred standard. 

As opposed to the above studies that determined by how much the efficiencies can improve, this 

approach estimated how the industry will meet any imposed standards and calculated the heat 

Deleted: Studies 1 and 2

Comment [A19]: To avoid ambiguity it might be

helpful to cite (footnote) studies when referring to

them.  

EPA response: Added study names.

Deleted: Study 3

Deleted: for Study 4.

Deleted: This approach 

Deleted: determined that given specific changes in 

the coal price and a carbon tax, the industry will 

make heat rate improvements on their own

Comment [A20]: Suggest an alternate phrasing

such as: This approach sought to estimate overall

changes in industry fleet efficiency in response to

changes in fuel prices or carbon prices.

EPA response: The language has been edited as

suggested.

Deleted: Study 5
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

14 

Deleted: 14

rates necessary to meet a standard. As it has not been determined with regard to how the CO2

emissions limit will be averaged, the paper discusses many potential options that coal-fired 

EGUs may have to meet new standards. These options include additional options such as adding 

renewable, natural gas, and combined-cycle sources. 

The EPA observed that existing HRI studies using a benchmarking approach have relied 

on a single year annual average heat rate data, whereas many of the operational impacts on heat 

rate (diurnal temperatures, load following, etc) become more apparent in the variability of hourly 

performance data. We further recognized that an examination of heat rate data over a multiple 

year period, perhaps a decadal time frame might reveal patterns of performance that should also 

inform estimates of HRI potential. For these reasons, the EPA has developed in this TSD an 

additional assessment of HRI potential that draws on multiyear historical hourly data. While we 

understand that engineering judgment remains essential to a proper interpretation of the results, 

the EPA intends that this assessment be a more substantial basis for estimating the fleet-wide 

HRI potential for coal-fired EGUs. 

Table 2-3. Summary comparison of previous studies on EGU heat rate improvements 

Study 

ID 

Study Title 

Author 

Factors Used for 

Industry Grouping 

Key Study 

Assumptions 

Relevant HRI 

Results 

1 “Reducing CO2 

Emissions by 

Improving the 

Efficiency of the 

Existing Coal-fired 

Power Plant Fleet” 

NETL 

• Plant design: age and 

steam cycle 

• Category improvement is 

the bottom 90% of EGUs in 

each group improving their 

heat rate to the average 

performance of top 10% in 

that category 

• Units with capacity factors 

under 50% were removed 

from dataset 

• 15% reduction in 

overall heat rate of 

coal-fired EGUs 

2 “Improving the 

Efficiency of CoalFired Power Plants 

for Near Term 

Greenhouse Gas 

Emissions 

Reductions” 

NETL 

• Plant design: coal type, 

steam cycle, and size 

• Category improvement is 

the bottom 90% of EGUs in 

each group improving their 

heat rate to the average 

performance of top 10% in 

that category • Units with 

anomalous data, capacity 

factors under 10%, using 

less than 97% coal, and 

gasification plants were 

removed from dataset 

• Low pressure subcritical 

units and 0-200 MW 

subbituminous units 

• 8.7% reduction in 

overall heat rate of 

coal-fired EGUs 

Deleted: s

Deleted: The EPA observed that most of the 

existing studies develop fleet-wide average HRI 

potentials/goals that are higher than values 

considered feasible and sustainable by the power 

sector engineers with whom the EPA has informally 

discussed this subject. 

Comment [A21]: The reasons for considering

the existing studies unrealistic could be discussed

more explicitly. Perhaps, for example, there is a

more explicit argument to be made about specific

interventions and timescales.

EPA response: The text has been deleted.

Deleted: also noted

Deleted: all 

Comment [A22]: Not certain this is correct ‐ the

RFF study abstract states: “This paper examines the

operation of coal‐fired generating units over 25

years to estimate the marginal costs and potential

magnitude of emissions reductions from improving

their efficiency.” Please correct if appropriate.

EPA response: The text has been clarified to refer to

studies that used a benchmarking type approach to

estimate potential reductions.  

Deleted: oo

Deleted: understood

Deleted: would 

Deleted: still be 

Deleted: ed

Comment [A23]: Please check on the tense –

past or present?

EPA response: Thank you for the observation.

Deleted: Study

Deleted: 

Deleted: ¶
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

15 

Deleted: 14

Study 

ID 

Study Title 

Author 

Factors Used for 

Industry Grouping 

Key Study 

Assumptions 

Relevant HRI 

Results 

assumed retired for goal 

• Lost generation made up by 

more efficient coal-fired 

EGUs 

3 “Reducing Heat 

Rates of Coal-Fired 

Power Plants” 

Lehigh Energy 

Update 

 All possible heat rate 

improvements are made 

including drying of high 

moisture coal: 

• 10% improvement 

for bituminous coalfired EGU 

• 15% improvement 

for subbituminous 

coal-fired EGU 

4 “Regulating 

Greenhouse Gases 

from Coal Power 

Plants under the 

Clean Air Act” 

Resources for the 

Future 

• Analyze the actual 

operating efficiency of the 

entire fleet of U.S. coalfired EGUs 

• Assess abatement 

opportunities and costs by 

observing how coal plants 

respond to market and 

regulatory incentives to 

improve energy efficiency 

Overall efficiency 

improvements of 2 to 5%, 

from other literature studies 

10% coal price 

increase, 

corresponding to a 

tax on CO2

emissions of about 

$1.64 per ton, 

improving heat rates 

by 0.3 to 0.9 % 

5 “Closing the Power 

Plant Carbon 

Pollution Loophole: 

Smart Ways the 

Clean Air Act Can 

Clean Up 

America’s Biggest 

Climate Polluters” 

NRDC 

• Plant design: coal type 

and steam cycle 

Improvements are broken into 

3 group 

• Top 10% for each 

subcategory, no change 

• Top 11% to 49%, improve 

heat rate by 50% of the 

difference between facility 

heat rate and performance of 

top 10% in class or 600 

Btu/kWh, whichever is less 

• Bottom 50%, improve heat 

rate by 100% of the difference 

between facility heat rate and 

performance of top 10% in 

class or 600 Btu/kWh, 

whichever is less 

 Broader analysis 

not strictly focused 

on heat rate 

improvements. 11% 

reduction on overall 

fossil fuel-fired EGU 

emission rates; 

includes switching 

from coal to natural 

gas. 

2.5 EPA’s Heat Rate Improvement Assessment 

This EPA assessment of fleet-wide HRI potential looks at historical data from coal-fired 

EGUs in the U.S. to identify changes to EGUs’ heat rates – the amount of heat input required, on 

average, to generate 1 kWh of electricity – that can be attributed to operation and maintenance 

practices and equipment upgrades. These heat rate changes are analyzed to determine their 

applicability to the rest of the coal-fired EGU fleet and to determine the potential heat rate 

improvement that, on average, could be achieved by the fleet. 

Deleted: Study

Deleted: 

Deleted: ¶

Comment [A24]: Please include text regarding

the peer review to which all these reports have

been subject. If not, please consider subjecting this

concept to technical peer review in the period

following proposal release.

EPA response: We will check on the peer review

status of the studies.  

Comment [A25]: Is there a reason there is no

comment in this box?

EPA response: Text has been added.
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

16 

Deleted: 14

This data analysis portion of the study relies on unit-level heat input and gross generation 

data reported to the EPA by owners or operators of EGUs to assess in detail the changes in gross 

heat rates. Potential changes in net heat rates are then addressed later in this section. Unit-level 

evaluations allowed the EPA to recognize the significant heterogeneity of coal-fired EGUs; even 

‘sister’ units, units built at the same time at a given facility, may display different operating 

profiles and may have different equipment, controls, fuel mixes or cooling systems. 

Based on literature reviews; informal interviews with engineering experts, vendors, and 

plant operators; and historical information collected by the EPA, we believe EGUs achieve heat 

rate improvements by: 1) operating under recommended operation and maintenance conditions 

(best practices), and 2) installing and using equipment upgrades. Best practices include no-cost 

or low-cost methods such as the installation or more frequent tuning of control systems and the 

like-kind replacement of worn existing components. Upgrades often involve higher costs and 

greater downtime, such as, extensive overhaul or upgrade of major equipment (turbine or boiler) 

or replacing existing components with improved versions. 

The EPA developed unit-level statistics from over 60 million rows of hourly data. We 

evaluate each unit on its individual performance using heat rate variability as an indicator of the 

application of best practices and potential for improvement. To estimate heat rate improvement 

through equipment upgrades we survey engineering studies, examine year-to-year trends, and 

research EGUs where such methods were applied. 

2.5.1 Study Population and Data 

The EGU study population consists of 884 coal- and petroleum coke-fired EGUs that 

reported both heat input3

 and electrical output to the EPA’s Clean Air Markets Division in 2012.4

It includes a wide range of configurations, from 24 to 1,500 MW nameplate capacities, super and 

subcritical thermodynamic cycles, between 1 and 69 years old, and different coal ranks. It 

excludes any EGUs at any facility that reported cogeneration to the EPA or the EIA. These units 

are excluded because a portion of the heat input was used to generate electricity and/or steam 

                                                            

3 Sources calculate heat input using an ‘F factor’ for the carbon content of the fuel being combusted and the average 

hourly measurements of CO2 flow and concentration. 4

 Information on the Clean Air Markets Division data is available at http://ampd.epa.gov/ampd/
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

17 

Deleted: 14

heat. Therefore, it is difficult for the EPA, using available data, to make a meaningful 

comparison of these units’ heat rates. 

The EPA performed this study using hourly heat input (Btu), and electricity output 

(MWh) data from the Clean Air Markets Division and meteorological data from NOAA’s 

National Climatic Data Center for the years 2002-2012. As described later in this section, these 

meteorological data were used to account for temperature impacts on heat rates. The eleven year 

study period is representative of a wide range of conditions, including growth and recessionary 

economic conditions, changing electricity generation from renewable and natural gas, and 

different regulatory constraints. 

The hourly heat input and generation data used in this study is collected under the 

authority of 40 CFR part 75 (hereafter, Part 75). The EPA designed Part 75 to encourage 

complete and accurate emission measurement and reporting to support emission trading 

programs, including the Acid Rain Program and Clean Air Interstate Rule (CAIR). However, the 

EPA recognized that there will be times when emission data are not available due to monitoring 

system malfunctions or maintenance, technical challenges, or missed quality assurance/quality 

control (QA/QC) tests. When data are not available or deemed invalid (e.g., when a QA/QC test 

was not performed as required), the EPA has specified data substitution methods that are 

designed to 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 bias and the impact it would have on the results of this study, the 

EPA excluded substitute data reported by EGUs from this study’s dataset. These substitute data 

represent approximately 2% of all reported operating hours. In addition, we excluded partial 

hours of operation that occur during the first hour of startup and the last hour of shutdown. 

We also excluded 40 unit-years (0.5% of records) with atypical annual heat rates less 

than 6,500 or greater than 15,000 Btu/kWh resulting from a variety of factors including firing of 

natural gas, very low operating time, or errors in reported gross load. Table 2-4 summarizes the 

heat input, electric generation, heat rate and unit counts by year for the study population used in 

this work. This population corresponds to 9,388 unit-years of data at 884 distinct EGUs. Figure 

2-1 displays the study population average gross heat rate by year and the 11-year average.

Comment [A26]: IS QA/QC defined in the

abbreviations?

EPA response: It is defined in the sentence.

Deleted: reporting 

Deleted: 50350

Moved (insertion) [1]

Deleted: 2

Deleted: annual 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

18 

Deleted: 14
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

19 

Deleted: 14

Table 2-4. Study Population Annual Heat Input, Generation, Heat Rate and Unit Count 

2002 - 2012 5

Year 

Heat Input 

(million 

MMBtu)

Electric 

Generation 

(million MWhgross) 

Heat Rate 

(Btu/kWhgross) Unit Count 

2002 18,601 1,874 9,924 839

2003 18,428 1,864 9,886 834

2004 18,405 1,875 9,819 836

2005 18,665 1,910 9,774 838

2006 18,644 1,914 9,743 848

2007 18,704 1,920 9,740 846

2008 18,459 1,914 9,643 852

2009 16,588 1,719 9,649 864

2010 17,693 1,831 9,662 869

2011 16,934 1,744 9,708 878

2012 14,947 1,536 9,732 884

Figure 2-1. Study Population Average Gross Heat Rate by Year 

NOAA’s Integrated Surface Data (ISD) product provides hourly temperature for over 

20,000 weather stations worldwide.7

 Since EGU heat rate performance is sensitive to air 

temperature and barometric pressure, which vary with elevation, we use meteorological data 

from stations that are reasonably close to the EGU's location and elevation to account for the 

                                                            

5 The study population for each year includes those EGUs that reported both heat input and electric generation. 7 Temperature data is from NOAA’s Integrated Surface Data at http://www.ncdc.noaa.gov/data-access/land-basedstation-data/land-based-datasets/integrated-surface-database-isd.

9,500

9,550

9,600

9,650

9,700

9,750

9,800

9,850

9,900

9,950

10,000

20022003200420052006200720082009201020112012

Heat Rate (Btu/kWh)

Annual Heat Rate

11-yr Avg Heat Rate

Deleted: Study Population Unit Count by Year

Comment [A27]: Change in unit counts due to

using conditions that match preceding paragraph.   

Deleted: 833

Deleted: 828

Deleted: 833

Deleted: 836

Deleted: 845

Deleted: 844

Deleted: 851

Deleted: 860

Deleted: 865

Deleted: 876

Deleted: 879

Comment [A28]: PHK: corrected unit caps  in

chart legend

Deleted: 2

Deleted: p

Deleted: 6
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

20 

Deleted: 14

impact of ambient conditions. For each of the plants in this study’s database, we identified the 

nearest stations that reported a minimum of 8,400 hourly observations in the calendar year (i.e., 

greater than 95% of the hours in a year). Generally, each plant was associated with 

meteorological data from the two closest stations over the eleven-year study period. The average 

distance between the plant and the nearest station was 22 miles and the average difference in 

elevation was 366 feet. At nine plants we used data with as few as 7,000 observations in order to 

keep the maximum difference in elevation under 1,000 feet. The EPA believes that nearby 

weather station measurements are a good approximation of ambient meteorological conditions at 

each facility. Joining the trimmed heat rate and hourly temperature datasets resulted in 

61,848,580 hourly records for the study population of EGUs (see Table 2-4). 

The results of this study are based on analyses of data from the population of coal-fired 

EGUs shown in Table 2-4. These units emitted 1,605 million tons of CO2 in 2012. In contrast, 

emissions of CO2 from coal-fired EGUs in the entire U.S. electric power sector in 2012 were at 

1,669 million tons according preliminary data from the EIA.8

 Since the study population of 

EGUs accounted for over 96% of CO2 emissions from the fleet of U.S. coal-fired EGUs, the 

EPA considers the results of this study to be applicable to the coal-fired fleet at large. 

2.5.2 Subcategorization 

In this analysis, units are not categorized by unit specific design characteristics or fuel 

because: (1) EGU-specific detailed design information on all factors that influence heat rate is 

not available, and (2) certain design characteristics are not easily categorized (e.g., EGUs use a 

large range of steam conditions). Several other studies do categorize EGUs broadly by capacity, 

thermodynamic cycle, and/or fuel rank. Although the EPA believes grouping by categories can 

provide a useful way of understanding the operating profile of an EGU and the fleet, the range of 

heat rates for the broad categories has significant overlap (see box and whisker chart in Figure 2-

2) and therefore makes it challenging to develop appropriate categorization. The figure below 

displays available information on coal-fired EGUs considered in this work for the years 2009-

2011 in typical subcategories of capacity, fuel rank, and thermodynamic cycle. As the figure 

reflects, the means are clustered and the ranges of heat rates overlap. 

                                                            

8

 Preliminary 2012 results from http://www.eia.gov/tools/faqs/faq.cfm?id=77&t=3.    

Deleted: 1000

Deleted: 6

Deleted: to 

Deleted: 1

Deleted: Revised 2012 results will be available in 

March, 2014. The somewhat lower 2012 CO2

emissions total in Table 2-5 below is due to the 

exclusion of substitute data.
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

21 

Deleted: 14

 

Figure 2-2. Three-Year Average Heat Rates by Subcategory9

2.5.3 Observed Trends in the Period 2002-2012 

Three trends are notable for the study population during the period 2002-2012. 

Comparing the averages of the first three years (2002-2004) to the last three years (2010-2012), 

electric generation and gross heat rate declined by 9% and 2%, respectively. Capacity factor for 

the study population fell by 14% comparing the same time periods (see Table 2-5). The decrease 

in coal-fired generation and capacity factor may be because of reduced demand for electricity 

resulting from the recession starting in late 2008 and greater use of natural gas and renewables to 

generate electricity. 

The 11-year average annual gross heat rate for the study population of coal-fired EGUs 

(see Table 2-4) was 9,754 (Btu/kWh). The decrease in study population annual heat rate between 

2002 and 2012 may be due to several factors. Unit efficiency may have improved or units with 

lower heat rates may have taken up a larger share of generation. In addition, changes in reporting 

methodology described later in this chapter may be partly responsible. The minimum annual heat 

rate (9,643 (Btu/kWh)) occurred in 2008 and was approximately 1% below the 11-year average. 

                                                            

9 Abbreviations in the figure: BIT means bituminous, SUB means subbituminous, PC means petroleum coke, LIG 

means lignite, SUPER means supercritical, OVER/UNDER means greater/less than indicated MW capacity. Unit 

counts (n) by category: BIT SUPER, n=80; SUB SUPER, n=30; BIT OVER 200, n=196; PC, n=2; BIT 100 to 200, 

n=140; SUB OVER 100, n=299; LIG OVER 100, n=20; BIT UNDER 100, n=68; SUB UNDER 100, n=56; LIG 

UNDER 100, n=2. Total unit count is 893.

6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000

BIT SUPER

SUB SUPER

BIT OVER 200

PC

BIT 100 to 200

SUB OVER 100

LIG OVER 100

BIT UNDER 100

SUB UNDER 100

LIG UNDER 100

3-Year (2009-2011) Average Heat Rate (Btu/kWh)

Subcategory

Deleted: 1. 

Comment [A29]: Suggest including on the figure

the number of plants in each category (N=? for each

category).

EPA response: added counts to footnote.

Deleted: 3

Comment [A30]: The footnote uses SUB twice

for different terms.  Please clarify/fix.

EPA response: Done

Deleted: , capacity factor, 

Deleted: , 21%,

Deleted: UsingC the same time periods c

Deleted: 63

Deleted: 650 

Deleted: , near peak generation for the years under 

consideration, 

Deleted: SUB means subcritical 

Formatted: Highlight

Deleted: ,
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

22 

Deleted: 14

Table 2-5. Reported Annual Capacity Factor 2002-201210

Year Capacity 

Factor (%) 

2002 68

2003 69

2004 70

2005 71

2006 70

2007 71

2008 70

2009 62

2010 65

2011 61

2012 53

2.5.4 Startups and Shutdowns - Impact On the Results of This Study 

During periods of startup and shutdown, EGUs are known to operate at higher heat rates. 

Therefore, we evaluated the potential impact of such events in our study. A startup event, as 

defined here, occurs when an EGU begins combusting fossil fuel and generates some measurable 

amount of electricity. Table 2-6 summarizes the study population average, maximum and total 

starts by year. On average, coal-fired EGUs start combusting fuel and generating electricity 11 

times per year. The total number (approximately 9,000) of starts for the study population of 

EGUs has remained stable over the study period. Our data reflects that some coal-fired units 

operate in a load following capacity and may report upwards of 200 starts in a single year, but 

these units tend to have low annual capacity factors. The subset of EGUs with more than 20 

annual startup and shutdown events is responsible for less than 4% of total generation in any 

study year. Therefore while the number of starts is an important variable at a small number of 

EGUs, its impact on heat rate performance evaluated in this study is considered to be marginal. 

                                                            

10 Table 2-5 shows data as reported to EPA as of May 8, 2014. 

Deleted: Table 2-5 displays the annual heat input, 

electricity generation, capacity factor, and heat rate 

for the study population of EGUs as reported to 

EPA. 

Moved up [1]: Figure 2-2 displays the average 

annual gross heat rate by year and the 11-year 

average.

Deleted: Heat Input, Generation, 

Deleted: and Heat Rate Annual for the 

PeriodTrends 

Deleted: 10

Comment [A31]: Suggest making the Table title

more descriptive.  

Also could present fewer signficant  digits in table

...i.e. heat input‐ MM BTU x 106  ‐‐  ... [2]

Deleted: 2102

Deleted: 64

Deleted: 65

Deleted: 65

Deleted: 66

Deleted: 65

Deleted: 66

Deleted: 64

Deleted: 54

Deleted: 58

Deleted: 53

Deleted: 44

Comment [A32]: This capacity factor decline in

2012 is very different from the CF used later, see... [3]

Deleted: 

9,500

9,550

9,600

9,650

9,700

9,750

9,800

9,850

9,900

9,950

10,000

200220032004

Heat Rate (btu/kwh)

... [4]

Deleted: on 

Deleted: evaluation

Deleted: 

Comment [A33]: At about this point, the

chapter transitions into the quantitative analysis. ... [5]
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

23 

Deleted: 14

Table 2-6. EGU Start Count by Year 

Year Average Maximum (at 

any single EGU) Total 

2002 11.1 209 9,363 

2003 10.6 194 8,936

2004 10.7 134 9,081 

2005 11.0 183 9,265 

2006 10.5 139 8,859 

2007 10.5 164 8,908 

2008 10.4 134 8,880

2009 10.3 178 8,902 

2010 10.5 211 9,110 

2011 10.6 206 9,295 

2012 9.9 119 8,805 

To understand the potential for heat rate improvement available with existing coal-fired 

steam EGUs, the EPA conducted a number of quantitative analyses. These included: (1) 

regression analyses to understand the impact of capacity factor and ambient temperature; (2) 

using a bin model to determine the potential from best practices; and, (3) evaluating available 

data and information to assess the potential from equipment upgrades. These analyses are 

described in the following sections. 

2.5.5 Impact of capacity factor and ambient temperature 

Two important factors that affect heat rate at an EGU are hourly capacity factor and 

ambient temperature. In this section, we examine the impact of these two variables on heat rates 

of the EGUs in the study population. Power plant operators today typically use digital control 

systems to capture hundreds of data points in near real-time that are summarized in the unit heat 

rate statistic. EPA has access to a small fraction of that information. A key reason this study used 

capacity factor and ambient temperature as independent variables is that both were available as 

hourly data. Preliminary analyses of heat rate at higher time increments, such as month, were 

useful to describe aspects such as seasonality but we determined hourly data was necessary to 

understand how heat rate was responding to constantly changing operating conditions. We tested 

for collinearity between capacity factor and ambient temperature using a zero-order correlation 

Deleted: 

Comment [A34]: After this sentence, suggest

adding a sentence stating why this is important to

do, and a sentence summarizing the approach.

(EPA) see updated text  
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

24 

Deleted: 14

matrix on the entire hourly data set. The correlation between the independent variables was -.048 

– well below an indication of collinearity. 

We also considered fixed unit characteristics such as unit type, fuel rank and age as 

independent variables. As noted above in the discussion on subcategorization, these factors can 

be helpful to understanding the heat rate performance of EGUs. The purpose of this study, 

however, is to find the potential for heat rate improvement across the fleet. We use heat rate 

variability as a key statistic to measure this potential. The correlation between the potential for 

heat rate improvement and fixed characteristics is typically low.11 

Coal-fired units are designed to operate most efficiently at full capacity. As a unit drops 

below this level, in general, heat rate will increase. The average capacity factor over 11 years for 

the study population is 67%, but as noted above, has moved markedly over the study period. This 

study looks at utilization level at both hour and year time scales. The two are related but reveal 

different information about how an EGU is operating. For example, for a unit to achieve a high 

annual capacity factor (e.g., over 90%) it must operate at a high load for most hours in a year. At 

lower capacity factors interpreting the relationship between hourly and yearly utilization levels 

becomes more complex. For example, an EGU may run at an annual 60% capacity factor by 

operating 8 months at near full capacity and generating no electricity the rest of the year, or it 

may run at lower utilization levels for most hours of the year in response to weather, generation 

cost, and transmission constraints. 

Ambient temperature can affect heat rate in two ways: 1) the efficiency of the 

thermodynamic steam cycle12 and, 2) in many regions of the country, as temperatures increase 

electricity demand and capacity factor follow. Figure 2-3 shows the average monthly capacity 

factor in 2012 alongside the climate normal monthly temperature.13 The lines intersect in the 

spring as temperatures begin to rise and the need for cooling drives electricity demand. 

                                                            

11 For example, the correlation between annual unit heat rate variability (discussed below as relative standard 

deviation) and unit nameplate capacity (MW) is in the -0.1 range. 12 The availability of a cold heat sink in the condenser is a key factor in that cycle. The design of the heat exchanger, 

type of cooling system and availability of water all have an impact on performance. An increase in ambient air 

temperature, and consequent increase in water temperature, typically lower the effectiveness of the cooling system, 

the condenser, and, therefore, overall plant efficiency.

13 Climate normal is the average of temperature (or other measure) over a prescribed 30-year interval and location. 

The chart shows the 1981-2010 climate normal monthly temperature at Baltimore-Washington Airport, MD.

Deleted: ¶

Comment [A35]: Suggest discussing explicitly

the choice of CF as an independent variable – why is

it reasonable to think of it as an independent

element? Are there better factors that EPA could

have used but was unable to because of information

availability?

EPA response: see the preceding paragraph

Deleted: s
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

25 

Deleted: 14

Generally, peak capacity factor and generation in most parts of the U.S. occur on the hottest days 

of the year. Yet, the relationship between ambient temperature and capacity factor is complex. 

Each plant responds differently depending on design, meteorological conditions and electricity 

demand. For example, a base load plant may operate at a high capacity factor seven days a week 

regardless of temperature. As noted above, the collinearity between these two variables is low.

Figure 2-3. Monthly Capacity Factor, 2012. 

2.5.5.1 Regression Analysis to Assess Impact of Capacity Factor and Ambient Temperature on 

Heat Rate

Using the hourly data set, the EPA performed three regression analyses for each unityear: heat rate onto capacity factor, heat rate onto ambient temperature and heat rate onto 

capacity factor and ambient temperature. Since this analysis seeks to evaluate heat rate under 

normal operating conditions, we removed records with hourly heat rate values outside of +/- 2.6 

standard deviations (1.9% of records) before performing the regressions.14 Similarly to partial 

operating hours, these outliers tend to occur during low load conditions. The records trimmed 

amount to one-fourth of a percent of the total study population generation. Regression results 

describe the goodness of fit for the model and are expressed as the coefficient of determination 

or ‘r-squared’. To represent the relative contribution of varying unit capacities all results are 

generation-weighted.15 The average study population r-squared for the multivariate regression is 

26%. This means that hourly ambient temperature and capacity factor together explain 26% of 

                                                            

14 The 2.6 standard deviation bound is used in other EPA regulatory analyses. 15 In a weighted average, each component is multiplied by a factor reflecting its importance. In this case, generationweighted r-squared is the sum of r-squared for each unit multiplied by its annual generation divided by the sum of 

generation for all years.

0

20

40

60

80

100

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Electric generation monthly 2012 capacity 

factor and normal temperature

Capacity Factor

(Monthly %, 2012)

Climate Normal

(BWI, MD)

Deleted: P

Deleted: 7

Deleted: 

Deleted: ere is little 

Deleted: Analysis

Comment [A36]: Could more granularity on unit

characteristic/type of fuel/design be included as

part of the regression analysis?  

EPA response: As noted earlier in the TSD, fixed unit

characteristics such as unit type, fuel rank and age

can be useful for understanding the heat rate

performance of EGUs.  The purpose of the study,

however, was to find the potential for heat rate

improvement. We found very little correlation

between potential for heat rate improvement and

fixed characteristics.  For example, the correlation

between the unit heat rate variability (annual

relative standard deviation) and unit capacity (MW)

is typically in the ‐.1 range.   

Comment [A37]: It is sometimes hard to follow

the outline of the chapter – currently uses bold,

bold italic, underline.  Suggest using headings like

1.1, 1.1.2, etc. or other indicators.

EPA response: Done ‐ thanks for the suggestion.

Deleted: a

Deleted: assess i

Deleted: FCF

Deleted: h

Deleted: r

Comment [A38]: Capacity factor is also

influenced by temperature – any considerations for

collinearity effects in the regression?

EPA response: We tested for collinearity between

capacity factor and ambient temperature using a

zero‐order correlation matrix on the entire hourly

data set. The correlation between the independent

variables was ‐.048 – well below an indication of

collinearity.    

Deleted: The single variable analyses evaluations 

help to examine the relative influence of capacity 

factor and ambient temperature on heat rate. 

Deleted: issquared

Deleted: average 

Deleted: multipliedmultipliedbymultiplied by

Deleted: 

Deleted: unit
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

26 

Deleted: 14

the change in heat rate for the study population over the study period. The average study 

population r-squared from the single variable analysis of capacity factor is 16%; the 

corresponding result for temperature is 10%. This means that approximately 16% of the change 

in hourly heat rate is attributable to capacity factor and 10% to ambient temperature. These 

results, however, conceal considerable variability. Some EGUs, typically load-following, have 

an 11-year average r-squared for capacity factor exceeding 50%. At those EGUs, the capacity 

factor is a key variable influencing changes in heat rate. 

At approximately one-fourth of the study population the response to ambient temperature 

is larger than the response to capacity factor. At some individual EGUs, temperature may explain 

up to 30% of the change in heat rate. These are typically, but not exclusively, units with oncethrough, fresh water, cooling systems. Identifying temperature-responsive EGUs allows us to 

understand why heat rate may increase during periods of peak demand. These are the EGUs 

where the ambient temperature ‘signal’ is an important variable. At a typical EGU, summer 

month heat rates may increase by 2-4% compared to winter months, but at a temperatureresponsive EGU that figure may be as high as 10%. 

The multivariate partial regression coefficients show a similar pattern. The average unityear temperature coefficient (non-standardized) is 2, i.e., each degree F increase in temperature 

increases the heat rate by 2 Btu/kWh. Put another way, over a typical 40 degree F seasonal range 

(see Figure 2-3 the average change in heat rate that can be attributed to temperature is 80 

Btu/kWh, i.e., less than 1%. But the temperature coefficient at the individual unit level varies 

widely. For example, the average study population top quintile of temperature coefficient 

(descending) was 9, over four times greater than the study population mean. The average unityear coefficient (non-standardized) for capacity factor is -15, i.e., each 1% increase in capacity 

factor decreases the heat rate by 15 Btu/kWh. This means that on average, when a study 

population EGU increased capacity factor from 60% to 80% the heat rate decreased by 300 

Btu/kWh, i.e., approximately 3%. Although capacity factor is the more important explanatory 

variable, this is by no means universally true across the fleet or even year to year at the same 

EGU.

Our analysis indicates that as EGUs moved from base load to load following, capacity 

factor tended to have a larger effect on heat rate. Since 2008, the study population capacity 

Formatted: Highlight

Deleted: regression 

Deleted: below)

Deleted: as a whole

Formatted: Highlight

Deleted: Similarly, 

Formatted: Highlight

Formatted: Font: Italic, Highlight

Formatted: Highlight

Deleted: , generally,

Deleted: 

Deleted: results 

Deleted: found 

Deleted: cyclecycled more, i.e., move

Deleted: will tend
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

27 

Deleted: 14

factors moved from the top load bin into lower load bins. This can be seen from Figure 2-4, 

which compares the study population duty cycles in 2008 to 2012. A significant share of 2008 

generation occurred at EGUs running at greater than 84% annual capacity factor. In 2012, little 

generation took place in that bin or above, and generation was reduced by about half in the next 

lower bin (78-83% capacity factor). 

Figure 2-4. Change in Annual Duty-Cycle 2008 and 2012 

2.5.6 Heat Rate Variability and Indication of Improvement Potential 

This study examines heat rate variability from the standpoint of statistical process 

control, which is utilized throughout the power industry. Several years ago, the EPA introduced 

process control charts for auditing emissions data reported under Part 75. Sources and vendors 

adopted the EPA methodology to identify potential problems early, before they significantly 

affect emission measurements. Heat rate lends itself to process control since it is the principal 

indicator that defines the quality of the electric generation process.16,17 Therefore, in general, 

high variability in heat rate values would reflect opportunities for process improvement. 

                                                            

16 “The principal indicator that defines the quality of the process is heat rate.” (Fredrick & Todd, 1993. Statistical 

Process Control Methods in Performance Monitoring. Available at 

famos.scientech.us/Papers/1993/1993section11.pdf) 

Deleted: 5

Deleted: 9

Deleted: 4

Deleted: 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

28 

Deleted: 14

We use the relative standard deviation (RSD) of the hourly heat rates to evaluate each 

unit against its past performance and to compare with the study population. Each unit has up to 

eleven RSD values, i.e., one for each operating year between 2002 and 2012. The generationweighted mean RSD for the study population across 11 years is 5.4%. Table 2-8 summarizes 

eleven years of results for the study population by quartiles ordered by the 11-year generationweighted RSD average (ascending).18 The RSD of the top quartile (3.5%) is significantly lower 

than the study population generation-weighted mean RSD of 5.4%. Notably, the EGUs in the top 

quartile are not outliers; they report a third of all generation – the most of any segment. The 

results display a wide range of heat rate variability in the study population and thereby indicate 

the potential for heat rate improvement. 

Table 2-8. RSD in reported heat rate (generation weighted) 

Quartile RSD 

Average 

RSD 

Minimum 

RSD 

Maximum 

Share of 

Generation 

1 3.5 1.6 4.2 33

2 4.8 4.2 5.3 26

3 6.1 5.3 7.0 24

4 9.8 7.1 25.2 16

The study also examined EGU heat rate variability using the residual heat rate. The 

residual in a regression analysis is the difference between the observed value of the dependent 

variable (heat rate) and the predicted value. The intercept is the value where the linear regression 

crosses the y-axis. For each EGU, we calculated the residual heat rate by summing the residual 

for each hour to the intercept value. The standard deviation of the residual heat rate statistic is 

used to understand the amount of variability that is not explained by capacity factor and 

temperature. 

The average RSD corresponding to residual heat rate variability for each EGU is the 

generation-weighted average of up to eleven annual values. The study population generation-

                                                                                                                                                                                                17 Since accurate measurement is essential to process control the introduction of increasingly sophisticated digital 

control systems (DCS) presents new opportunities for finding inefficiencies. Vendors (ABB, Siemens, Emerson) 

claim heat rate improvements of 2-5 percent can result from upgrading to a modern DCS and advanced control 

technologies. The improvement can be even higher if system-wide real-time optimization is included. 18 Nine EGU RSD values exceeded 2.6 standard deviations above the mean and were removed from the results in the 

table.

Deleted: 7

Deleted: 0

Deleted: Seimens

Deleted: e
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

29 

Deleted: 14

weighted mean RSD over the study period is 4.5%. This percentage represents the total 

variability across the study population that our analysis could not explain by hourly capacity 

factor or ambient temperature. Possible causes of this variability include changes in plant 

equipment, operating procedures and maintenance, fuels (particularly coal rank), reporting 

methodology, and unexplained factors. There is no temporal trend evident in the RSD. Table 2-9 

summarizes the study population generation-weighted average RSD of residual heat rate over the 

study period in quartiles ordered by the 11-year generation-weighted RSD average (ascending). 

Table 2-9. RSD of residual heat rate (generation weighted)19

Quartile RSD 

average 

RSD 

Minimum 

RSD 

Maximum 

1 2.7 0.0 3.2

2 3.6 3.2 4.1 

3 4.7 4.1 5.3 

4 6.9 5.3 10.4 

The weighted average RSD of the top quartile is 2.7% – well below the study population 

average of 4.5 %. This means that the residual hourly heat rates of these units generally stay in a 

narrow range within a given year. The maximum RSD in the top quartile is 3.2%. From the 

statistical process control point of view, these units appear to have low variability. 

The weighted average RSD of the bottom quartile is 6.9%, which is over twice that of the top 

quartile. This spread indicates that there is likely room for improvement in study population 

operation to reduce variability and heat rate. 

2.5.6.1 Heat rate variability and performance 

To examine the association between heat rate variability and heat rate performance this 

study examined the RSD for unit-year heat rates calculated from reported data. The study 

population generation-weighted annual RSD ranges between 5% and 6% during the study period. 

Figure 2-5 below summarizes the results of regressing RSD of heat rate onto annual heat rate.20

                                                            

19 This table excludes nine units where RSD exceeded 2.6 standard deviations from the mean. 20 The regression analysis was performed on 9,388 unit-years of study data which were trimmed to remove values 

outside 2.6 standard deviations.

Comment [A39]: This Table (and others like it)

might be more clearly presented as a simple line

graph.  

EPA response: Thank you.

Deleted: This table excludes nine units where RSD 

exceeded 2.6 standard deviations from the mean.¶

d

Deleted: 4
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

30 

Deleted: 14

The r-squared result is 57%. These results indicate that, other factors held equal, if an EGU 

reduces heat rate variability, generally heat rate performance will improve. 

Figure 2-5. Regression analysis of reported heat rate RSD onto annual heat rate 

2.5.7 Units with large heat rate changes 

Over one third of the study population (355 units) reported at least one year-to-year 

change in heat rate greater than +/- 8.5%.21 We consider this magnitude to be in the upper range 

of what would be expected due to changes in fuel rank, operations and maintenance, or plant 

equipment. Table 2-10 presents the counts by three categories: EGUs with at least one year-toyear decrease in heat rate > 8.5%; units with at least one year-to-year increase in heat rate > 

8.5%; and, units with both. We examined whether the large heat rate changes were due to `yearto-year changes in capacity factor and found no correlation between the year-to-year changes in 

heat rate and capacity factor for any of the three groups in Table 2-10.22 This would indicate that 

other factors account for these large changes to heat rate. The EPA’s research found that 

approximately two-thirds of the large decreases in heat rate can be associated with changes in 

reporting method implemented to provide more accurate heat input data.23 The large changes 

                                                            

21 After removing unit-years where annual capacity factor fell under 50 percent the count is 313 EGUs. 22 The correlation remains weak even when limited to cases where capacity factor changed more than 30 percent. 23 EPA Reference method 2 specifies the normal procedure for measuring stack gas volumetric flow rate during a 

relative accuracy test audit. Methods 2F, 2G, 2H and CTM-041 are approved alternatives. Methods 2F and 2G 

correct measured flow rates for angular (non-axial) flow, Method 2H (for circular stacks) and conditional test 

method CTM-041 (method J, for rectangular stacks and ducts) are used to correct measured flow rates for velocity 

decay near the stack wall, using a “wall effects adjustment factor”. These alternative methodologies are optional. 

Therefore, given the additional complexity and cost of using these alternatives a source is likely to use them only if 

y = 0.002x - 14.254

R2 = 0.5704

5.0

5.2

5.4

5.6

5.8

6.0

6.2

9600 9650 9700 9750 9800 9850 9900 9950

RSD heat rate % 

(generation weighted)

Heat Rate

Deleted: a 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

31 

Deleted: 14

noted at the remaining one-third could not be explained by changes in reporting methodology. 

Moreover, we found no correlation between changes in reporting method and heat rate RSD. 

Table 2-10. Year-to-Year Heat Rate Change 

Description Count 

Correlation of 

heat rate to 

capacity factor 

Units with only year-to-year heat rate decrease > 8.5% 166 .1 

Units with only year-to-year heat rate increase > 8.5% 80 .1 

Units with both year-to-year heat rate decrease and increase > 8.5% 355 .1 

The breakdown of the ‘large decrease’ EGUs by quartile, in Table 2-11 below, is consistent with 

the study population results shown in Table 2-8. This does not imply that the changes associated 

with a large year-to-year decrease, which may include operations and maintenance, more 

accurate reporting methods, or new equipment, do not affect heat rate variability. If they occur as 

part of an engineering effort to improve efficiency, heat rate variability may also be reduced. 

Table 2-11. RSD in reported heat rate of 166 ‘large 

decrease’ EGUs (generation weighted) 

Quartile RSD 

Average 

RSD 

Minimum 

RSD 

Maximum 

1 4.0 2.3 4.8 

2 5.3 4.8 5.9 

3 6.6 5.9 7.5 

4 10.5 7.6 26.5 

2.5.8 Assessment of heat rate improvement potential via best practices 

As mentioned before, across the study period, the effects of hourly capacity factor and 

ambient temperature explain a generation-weighted average of 26% of the change in study 

population heat rate.24 This means that on average 74% of the change remains unexplained after 

controlling for those factors. The residual heat rate analysis determined there is significant 

variation in the operation of EGUs. Since lower heat rate variability is associated with lower heat 

                                                                                                                                                                                               

the results are significantly lower volumetric stack gas flow. The EPA was unable to draw conclusions about the 

effect of changes in flow reporting methods on study population heat rate performance. 24 The 26% result is the generation weighted average of r-squared values from the multivariate regression analysis.

Formatted: Highlight

Deleted: fleet 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

32 

Deleted: 14

rate, other factors held equal, the range of variation indicates that significant potential for heat 

rate improvement is available through the application of best practices. 

To control for known factors, the EPA constructed a model that groups each EGU’s 

hourly heat rate data into 14 temperature bins and 12 capacity factor bins, resulting in a 12 by 14 

matrix of 168 bins.25 For a given EGU, a temperature and capacity factor bin will have all the 

relevant hourly values over the eleven-year study period. For each bin with 15 or more values the 

model finds the reported hourly heat input value corresponding to the 10th percentile (p10).26

This means that approximately 90% of the heat input values in that bin exceed p10. For each 

unit, the model reduces the reported hourly heat input greater than p10 by a percentage of the 

distance between the reported value and p10 (e.g., 50% of the difference). The same statistical 

procedure is applied to every hour of heat input data in each bin. These reduced hourly heat input 

values are then used to calculate a reduced 11-year average heat rate for each unit. The percent 

difference between a unit’s reported 11-year average heat rate and the heat rate that corresponds 

to reduced heat inputs is the potential heat rate improvement for that unit.27 Using this approach, 

those units with the lowest variability (e.g., in the top quartile of residual heat rate variability) 

take proportionally smaller reductions. 

Table 2-12 below shows the model results with options of 10% to 50% stringency. For 

example, reducing reported heat input 10 % of the distance to the p10 value achieves a 1.3% 

study population wide reduction. Alternatively, a 50% reduction will result in a 6.7% study 

population wide improvement in heat rate. In effect, the model proportionately reduces heat rate 

variability and improves performance for each unit while controlling for temperature and 

capacity factor. The heat rate improvement for the study population is derived from the 

performance of each individual EGU as compared to its own record. 

                                                            

25 The matrix provides up to 168 bins but only 164 contained hourly values. Temperature bins ranged from -20 to 

greater than 110 with 10 degrees F in each. Capacity factor bins ranged from 0% to greater than 110% with 10% in 

each. 

26 Performing the calculation with a minimum of 30 values in each bin has a modest effect on the results in Table 2-

12. For example, a 30% reduction obtains a 3.9% fleet wide improvement in heat rate (rather than 4.0%). 27 Heat rate is calculated as the sum of heat input (btu, reported or reduced) divided by the sum of generation (kWh) 

for the given population and time period.  

Deleted: ¶

Deleted: ,
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

33 

Deleted: 14

Table 2-12. Assessment of heat rate improvement potential via best practices 

% Reduction from 

reported heat input 

to p10 

Study population 

heat rate 

(Btu(kWh-gross)28

Reduced study 

population heat 

rate (Btu/kWhgross) 

Study population 

heat rate 

improvement (%)

10 

9,755 

9,625 1.3 

20 9,495 2.7 

30 9,365 4.0 

40 9,235 5.3 

50 9,105 6.7 

2.5.9 Assessment of potential heat rate improvement via equipment upgrades 

The EPA inspected the study population to find examples of EGUs that made significant 

year-to-year improvements in heat rate. After filtering out those cases that may have been the 

result of changes in capacity factor, reporting method, or other events, we identified 16 EGUs 

that reported a single year-to-year heat rate improvement of 3-8%. In two of these cases we were 

able to identify equipment upgrades responsible for 2-3% heat rate improvement using the 

applicable estimates from the Sargent & Lundy 2009 study. Similarly, in the other cases, while 

our research was unable to confirm specific equipment upgrades, based on the elimination of 

other possible explanations we believe that equipment upgrades were the most likely cause of 

some of the observed heat rate improvements.

Two other sources provide information about heat rate improvements after equipment 

upgrades at existing plants. EPA Region 7 provided data with details on specific equipment 

modifications for seven coal-fired units at three anonymous plants. These included turbine 

efficiency and condenser performance upgrades, installation of variable frequency drive fans, 

reducing boiler air in-leakage and others. Together, these measures achieved from 0.25% to 

3.5% heat rate improvement at the seven EGUs. 

An EPA study (SRA, 2001) describes WEPCO’s two-phase efficiency program at four 

coal-fired plants over a ten-year period. In the first phase, 1990 – 1994, WEPCO installed 

                                                            

28 Fleet heat rate for study population as described in Table 2-4 is 9,754; the 9,755 value is derived from the dataset 

that includes hourly temperature values.

Comment [A40]: This technical analysis of the

possibility for heat rate improvements should be

peer reviewed.  Has EPA run this by the SAB, or does

EPA intend to do so after release of the proposal?

EPA response: Thanks for your comment

Deleted: one or more 

Comment [A41]: Do you really mean in a single

year to year change, as an indicator of a functional

change in the equipment?  Otherwise, 8% annual

improvement year after year is immense.  

Suggested edit, but change at EPA’s discretion.

EPA response: We meant one time – thank you.

Deleted: s

Deleted: 

Comment [A42]: Is it possible to give any more

information about this or provide further support?  

EPA response: More information has been added.

Formatted: Highlight

Deleted: with details on specific equipment 

modifications

Deleted: entire 

Deleted: 63
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

34 

Deleted: 14

equipment upgrades that included retractable turbine packing, variable speed drives on the forced 

and induced draft fans, feed water heater replacements and new performance monitoring 

instrumentation. The four units reported heat rate improvements ranging from 2.3% – 4.1% as a 

result of the equipment upgrades. In the second phase, 1995-2000, WEPCO implemented 

changes that would generally fall into the best practices category: equipment control and 

metering upgrades, boiler cleaning, feed water heater improvements and reduced condenser air 

in-leakage and thermal losses. These gained an additional 0.5% per year heat rate improvement 

(for a total of 2.5%). 

The EPA also reviewed the engineering studies available in the literature and selected the 

Sargent & Lundy 2009 study as the basis for our assessment of heat rate improvement potentials 

from equipment and system upgrades. We focused on some thirteen heat rate improvement 

methods discussed by Sargent & Lundy, seen in Table 2-13. We used the average of the 

estimated $/kW costs for each method to develop the cost-ranked list of heat rate improvement 

methods (lowest cost at the top, highest at the bottom) shown in Table 2-13. The first nine items 

in Table 2-13 contribute about 15 percent of the total average $/kW cost for all items. We believe 

it is reasonable to consider those nine no-cost and low-cost heat rate improvement methods as 

belonging in the category of what has been described above as best practices. The remaining four 

methods are higher cost heat rate improvement items that we believe properly fall into the 

category discussed here as upgrades. Using an average of the ranges of potential Btu 

improvements estimated by Sargent & Lundy for the four upgrade methods, upgrades, as defined 

here, could provide a 4% heat rate improvement if all were applied on an EGU that has not 

already made these upgrades. 

Table 2-13. Sargent & Lundy Heat Rate Improvement Methods 

No-Cost and Low-Cost Options 

Condenser Cleaning 

Intelligent Soot Blowers 

ESP Modification 

Boiler Feed Pump Rebuild 

Air Heater and Duct Leakage Control 

Neural Network 

SCR System Modification 

FGD System Modification 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

35 

Deleted: 14

Cooling Tower Advanced Packing 

Higher Cost Options 

Economizer Replacement 

Acid Dew Point Control 

Combined VFD and Fan 

Turbine Overhaul 

We also examined the annual heat rate trend line for each unit using the method of least 

squares. Using the slope of the trend line, 2002-2012, as an indicator of the heat rate 

performance of an EGU, a negative slope would indicate that the heat rate has improved. The 

annual trend line incorporates performance due to operating conditions (capacity factor, 

temperature), coal rank, maintenance, reporting method changes, equipment upgrades, and other 

factors. Over 40% of units have a positive slope. This would imply that equipment maintenance 

and upgrades at a significant fraction of the study population have not been sufficient even to 

maintain the status quo. 

2.5.10 Combined study population results 

The EPA’s analysis finds that a total of 6% heat rate improvements for the coal study 

population can be achieved through two types of changes: best practices that have the potential 

to improve heat rate by 4% and equipment upgrades that have the potential to improve heat rate 

by 2%. 

The best practices results are supported by the variability analysis using 11 years of 

hourly data applied to each unit. This analysis found that the top quartile of EGUs reported 

significantly lower heat rate variability than the study population average. Reducing heat rate 

variability will generally also improve heat rate performance, other factors held equal. We found 

that a 4% improvement is determined by conservatively reducing heat input by 30% of the 

difference between the reported value and p10 in each unit’s capacity factor and ambient 

temperature bins. 

The 30% approach is in the middle of the range of options shown in Table 2-12 and is 

comparable to other approaches for measuring potential fleet heat rate improvement. For 

example, if each unit achieved heat rate performance equal to its best three-year moving average, 

the study population as a whole would post a 3.9% heat rate improvement. The best two-year 

Formatted: Highlight

Deleted: developed 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

36 

Deleted: 14

moving average would achieve nearly a 5% improvement and the best single year over 6%. EPA 

believes that the minimum three-year moving average heat rate is a reasonable target for the 

improvement potential from applying best practices. Single year results could be due to unusual 

conditions, such as, an extended outage or weather. Using three consecutive years tends to 

smooth out the effect of equipment maintenance cycles and unusual meteorological patterns. 

The adjustment of heat rate using the 30% approach is approximately equal to improving 

the heat rate variability performance of the lower ranked EGUs by one quartile (see Table 2-8 

above). The top quartile has the lowest heat rate variability with an average RSD of 3.5% over a 

range of 1.6% to 4.2%. The bottom (fourth) quartile has an average RSD of 9.8%. The 30% 

approach proportionately trims the hourly heat input for each EGU in a given temperature and 

capacity factor bin. Using the bounds in Table 2-8, applying the 30% approach to the study 

population has the effect of nearly doubling the number of EGUs in the top quartile and reducing 

the count of those in the fourth quartile to 58 (of 218). These results appear reasonable. In effect, 

is the approach amounts to asking the lower ranked EGUs to improve heat rate variability to a 

level comparable to that which the top quartile of EGUs have maintained over an 11-year period. 

The equipment upgrades results are supported by numerous studies29 and by the EPA’s 

analysis of the costs and associated improvements in heat rate that can be attributed to equipment 

and system upgrades. We considered that a 4% reduction in heat rate might be achieved on a 

coal-steam unit by applying the four higher cost upgrade actions described in Table 2-13 above. 

However, because details of current actual unit configurations are unknown, and some units may 

have applied at least some of the upgrades, we conservatively estimate the heat rate improvement 

potential for upgrades at 2%. The EPA considers the results of this study to be applicable to the 

U.S. coal-fired fleet at large since the study population of EGUs accounted for over 96% of 2012 

electric sector CO2 emissions from coal. 

2.5.11 Sensitivity Analysis Removing Planned or Announced EGU Retirements 

                                                            

29 See discussion in Table 2-3 above, and the HRI Partial Bibliography at the end of this section 

Deleted: it is
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

37 

Deleted: 14

The EPA’s research found 233 coal-fired, non-cogeneration EGUs that have announced 

they will retire before 2016.30 A sensitivity analysis was applied to the EGUs in the study 

population that plan to operate through 2015. The results are identical to the full population – 

both achieve a heat rate reduction of 4% under the 30% approach described in best practices. 

2.6 Heat Rate Improvement – Economics 

Most of the methods that can be applied to achieve a sustained Heat Rate Improvement 

(HRI) on a coal-steam EGU will entail a capital cost. These HRI capital costs can be economic to 

incur if they yield sufficient reductions in other current or potential costs, particularly reductions 

in coal fuel cost and any cost related to CO2 emissions. For the purpose of this TSD analysis, it is 

assumed that HRI can be economic if the annualized net savings (coal cost savings plus CO2

emission cost savings minus capital cost) is positive: 

Annual Net HRI Savings = Coal Cost Savings + CO2 Emission Cost Savings – Capital Cost31

2.6.1 Heat Rate Improvement Capital Cost Assumption 

The 2009 Sargent & Lundy study describes numerous well known and technically proven 

HRI methods for coal-steam EGUs. The study includes an estimated min-max range of heat rate 

improvement, and the min-max range of associated capital cost for each HRI method, for units 

ranging in size from 200 MW to 900 MW. If these methods and unit sizes are combined, as 

though they were all applied on a single EGU, the following range of Sargent & Lundy estimated 

Btu reductions and associated range of capital costs are obtained: 

Combined Min-Max HRI Btu Reduction: 415-1205 Btu 

Combined Min-Max HRI Capital Cost: $40-150/kW32

EPA Assumed Combined HRI Capital Cost: $100/kW 

                                                            

30 IPM documentation includes a list of the announced retirements. See Table 4-36 of IPM documentation: 

http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/v513/Chapter_4.pdf and 

http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/v513/NEEDS_v513.xlsx 31 This TSD analysis assesses a broadly combined application of multiple HRI methods. As estimated in the 2009 

Sargent & Lundy study most HRI-related O&M costs are sufficiently small relative to the associated annualized 

capital costs, such that they do not materially affect the economics of broadly combined HRI methods. The analysis 

therefore does not consider the small economic impact of HRI-related O&M costs. 32 Note that highest cost does not necessarily align with greatest heat rate improvement. A low cost HRI method

can have a large HRI potential (eg: upgraded digital control system, neural network). Also, economy of scale causes

most HRI methods to be more costly ($/kW) on smaller unit sizes.

Deleted: 0

Deleted: 230

Comment [A43]: This is an important statement,

that is accorded little explanation.  I presume these

are coal EGUs? And is it plants or separate units?  

How did these anticipated requirements factor into

the state goal setting, and then into the IPM

modeling?   

EPA response: See updated footnote with IPM

source.

Formatted: Highlight

Deleted: percent difference option

Deleted: The EPA research was derived from 

Ventyx Velocity Suite New Entrants database 

(proprietary) and other sources,

Formatted: Highlight

Deleted: D
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

38 

Deleted: 14

The wide ranges of estimated HRI Btu and costs are indicative of the wide range of real 

differences in the many details of site specific EGU designs, fuel types, age, size, ambient 

conditions, current physical condition, etc. This TSD analysis therefore assumes $100/kW as a 

representative combined HRI capital cost to achieve whatever HRI Btu reduction is possible at 

an average site. The effect of a lower HRI cost is also examined. 

2.6.2 Heat Rate Reduction Assumption 

The weighted average annual net heat rate of the U.S. coal-steam EGU fleet in 2020 is 

projected at 10,450 Btu/kWh in the EPA’s IPMv5.13 Base Case modeling. As indicated by the 

Sargent & Lundy estimates given above, HRI methods could possibly reduce this average coal 

fleet heat rate by about 400 to 1200 Btu/kWh, or by about 4% to 12% of the projected 2020 

average, provided that all units were able to apply all of the combined HRI methods. The proviso 

is important to this analysis because the EPA expects that a significant fraction of the coal fleet 

has already applied some or many of the available HRI methods.33 

The EPA does not have sufficient site specific information to accurately estimate what 

percentage of the fleet has adopted various HRI methods, nor how effectively, and is not aware 

of any other investigator having sufficient information. HRI potential can therefore not be 

estimated at this time through analysis of the current equipment configurations of the coal steamEGU fleet. The EPA therefore analyzed 11 years of historical heat rate data and the literature on 

HRI methods, as discussed earlier in this TSD, to estimate that the U.S. coal-steam EGU fleet 

might reasonably be expected to reduce its annual average gross heat rate by about 6%. 

The EPA understands that any HRI method that reduces gross heat rate will also reduce 

net heat rate, and that some HRI methods reduce net heat rate without reducing gross heat rate. 

We expect that the HRI potential on a net output basis is somewhat greater than on a gross output 

basis, primarily through upgrades that result in reductions in auxiliary loads. For purposes of this 

TSD the EPA conservatively assumes that the coal fleet average net heat rate can be reduced by 

6%. 

                                                            

33 Based on the EPA informal discussions with Sargent & Lundy and other power sector engineering firms. The 

EPA has found no comprehensive data set on the extent to which specific HRI methods have already been applied at 

individual EGUs. The EPA believes that many EGU owners consider such information to be confidential. 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

39 

Deleted: 14

2.6.3 Heat Rate Improvement Breakeven Economic Analysis 

Figure 2-6 presents a simple breakeven economic analysis for combined HRI methods 

using the assumptions described above, also assuming there is no CO2 emission cost that is 

reduced via HRI. 

Figure 2-6. HRI Breakeven Economics 

Notes: 

1. Capital cost S/MWh assumes the following: HRI capital cost = $100/kW; capital charge rate 

= 14.3%; IPM projected 2020 annual capacity factor = 78% 

2. Coal fleet average 2020 net heat rate = 10,450 Btu/kWh; heat rate reduction = 6% 

Figure 2-6 shows that the average fleet-wide savings in coal cost would become greater 

than the annualized capital cost of an average 6% reduction in heat rate when the average fleetwide coal cost exceeds about $3.25/MMBtu. For comparison, the average U.S. power sector 

delivered cost of coal in 2020 is projected in EPA’s IPMv5.13 Base Case modeling at 

$2.62/MMBtu. For different assumptions, the HRI economic breakeven point would change 

directionally as follows: 

 If the HRI capital cost were on average less than the assumed $100/kW, 6% HRI would 

then become economic at lower coal costs. For example, if the average capital cost were 

actually $75/kW, a fleet-wide 6% HRI would become economic at an average coal cost 

of about $2.50/MMBtu, which is comparable to the U.S. power sector average costs of 

0.00

0.50

1.00

1.50

2.00

2.50

3.00

1.00 2.00 3.00 4.00

$/MWh‐net

Coal Cost  $/MMBtu

Coal Cost Savings   $/MWh‐net Capital Cost  $/MWh‐net

HRI Capital Cost = $100/kW
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

40 

Deleted: 14

$2.38/MMBtu for all coal ranks and $2.89/MMBtu for bituminous coals in 2012.34 This 

sensitivity indicates that fuel cost savings alone would make it economic for some of 

those EGUs currently using high cost bituminous coals to make HRI investments. 

 At an EGU net heat rate that is higher than the IPM projected 2020 average value of 

10,450 Btu/kWh, 6% HRI could be economic at coal costs lower than the values 

mentioned above. 

 If the average heat rate reduction were only 4% instead of the assumed 6%, at a cost of 

$100/kW, average coal costs would have to exceed $4/MMBtu for 4% HRI to be 

economic fleet wide, 

 But, if the average heat rate reduction were 4% at a cost of $50/kW, HRI could become 

economic at an average coal cost of about $2.50/MMBtu. 

 If there were additional HRI savings due to avoided future CO2 emission costs, HRI 

could become economic at lower coal costs, or at higher capital costs, or at lower heat 

rate reduction percentages. 

2.6.4 U.S. Coal-steam EGUs – Estimated Fleet-wide CO2 Reduction and Cost via HRI 

It is possible to make an order-of-magnitude estimate of the fleet-wide extent and costeffectiveness of HRI using reasonable assumptions as in the following example: 

Fleet-wide 2020 Assumptions (basis: similar to IPMv5.13 Base Case): 

 Coal fleet capacity applying combined HRI methods = 244,000 MW 

 Average CO2 emission rate = 0.976 tonne/MWh net 

 Average net heat rate = 10,450 Btu/kWh net 

 Average capacity factor = 78% 

 Pre-HRI CO2 emissions = 1.62 billion tonne/yr (calculated) 

 HRI Btu and CO2 reduction = 6% 

 HRI capital cost = $100/kW 

                                                            

34 EIA, Electricity DataTable 7.4, Average Weighted Cost of Fossil Fuels for the Electric Power Industry 2002-

2012, http://www.eia.gov/electricity/annual/html/epa_07_04.html 

Deleted: Estimated Fleet-wide 

Deleted: 

Deleted: EGU

Comment [A44]: Throughout the paper, it might

help to indicate the “fleet‐ wide” data refer to the

national fleet (i.e. all the coal facilities in the U.S.) if

this is the case.

  

EPA response: Thank you for the suggestion.  See

new section heading above.

Comment [A45]: It was ostensibly 44% in 2012 –

see Table 2‐5, page 19 above – or am I missing

something?  Please clarify.  Is this assuming a lot of

coal plant closures to get the capacity factor up?  If

so, please clarify the discrepancy/change over time

in the text and how this might affect state goal

setting,  especially building block 2?

EPA response: Good catch. See revised Table 2‐5

and associated text.  Also see the RIA and other TSD

discussions regarding IPM Base Case assumptions,

resulting 2020 coal capacity, etc.
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

41 

Deleted: 14

 Annual capital charge rate = 14.3% 

 Average coal cost = $2.62/MMBtu 

Estimated Fleet-wide Results: 

 Fleet-wide CO2 reduction via HRI = 97 million tonne/yr 

 Total HRI capital cost = $24 billion 

 Annualized HRI capital cost = $3.5 billion 

 Annual coal cost savings (cost) = $2.7 billion 

 Annual net savings (cost) = ($0.8 billion) 

 Annual net savings (cost) of CO2 reduction = ($7.7/tonne) 

2.6.5 Conclusion - HRI Economics 

This necessarily simplified HRI economic analysis supports the following summary conclusions: 

 Some degree of HRI is already economic for high heat rate – high coal cost EGUs 

 If a fleet-wide average 6% HRI is technically feasible, it would also be economic on the 

basis of fuel savings alone, before consideration of the value of the associated CO2

emission reductions, on a fleet-wide basis at today’s coal prices if the associated average 

capital cost is about $75/kW or less. 

 If a fleet-wide average 6% HRI is technically feasible, and the associated average capital 

cost is as much as $100/kW, 6% HRI could become economic on the basis of fuel 

savings alone, before consideration of the value of the associated CO2 emission 

reductions, if/when average coal prices rise to about $3.25/MMBtu (IPM projects coal at 

$2.62/MMBtu in 2020). 

 Even at a capital cost of $100/kW and an IPM projected 2020 coal price of 

$2.62/MMBtu, the fleet-wide cost of CO2 reduction via 6% HRI would be a relatively 

low $7.7/tonne. 

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

particular fleet-wide amount of HRI, it is clear that HRI is an available low-cost approach to CO2

reduction for existing coal-fired EGUs 

Definitions/Abbreviations

Btu British thermal unit 

capacity factor electricity generation expressed as a percentage of maximum 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

42 

Deleted: 14

electricity generation (i.e., actual generation / maximum potential 

generation) 

CO2 carbon dioxide 

EGU electric generating unit 

EIA Energy Information Administration 

EPA Environmental Protection Agency 

heat input amount of energy consumed in a combustion unit (e.g., boiler) 

expressed in Btu 

heat rate improvement decrease in the amount of heat input required to generate 1 kWh of 

electricity 

heat rate gross heat input required to generate 1 kWh of electricity, expressed 

in gross Btu/kWh. 

MMBtu million Btu 

MW megawatt 

PC pulverized coal (boiler) 

RSD Relative standard deviation 

S & L report 

Sargent & Lundy engineering study on the potential heat rate 

improvement from equipment upgrades (EPA 2009 version) 

[Available at: 

http://www.epa.gov/airmarket/resource/docs/coalfired.pdf] 

start 

a startup event in which an EGU begins combusting fossil fuel and 

generates some measurable amount of electricity before ceasing fossil 

fuel combustion 

unit-year data for one EGU over a one year period 

Docket Datasets 

Name Description 

hour_QA_data.txt 2002-2012 hourly dataset 

hour_QA_regression_data.txt 2002-2012 hourly dataset for regression analysis 

units_885.txt List of study units and characteristics 

year_bin_10_50_data.txt 2002-2012 unit-year binned results at 10 – 50% difference 

options 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

43 

Deleted: 14

HRI Partial Bibliography 

PowerEng 2002, “Heat Rate Optimization Pays Dividends,” Power Engineering, January 1, 

2002, available at http://www.power-eng.com/articles/print/volume-106/issue-1/features/heatrate-optimization-pays-dividends.html

NETL 2008, Reducing CO2 Emissions by Improving the Efficiency of the Existing Coal-fired 

Power Plant Fleet, DOE/NETL-2008/1329, July 2008 

NETL 2009, Opportunities to Improve the Efficiency of Existing Coal-fired Power Plants, 

Workshop Report, NETL July 2009, available at http://www.netl.doe.gov/energyanalyses/pubs/NETL%20Power%20Plant%20Efficiency%20Workshop%20Report%20Final.pdf

NETL 2010, Improving the Thermal Efficiency of Coal-Fired Power Plants in the United States, 

DOE/NETL Technical Workshop Report, February 2010, available at 

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

ficCoalFiredPowerPlants-TechWorkshopRpt.pdf

NETL 2010a, Improving the Efficiency of Coal-Fired Power Plants for Near Term GHG 

Emissions Reductions, DOE/NETL-2010/1411, April 2010, available at 

http://www.alrc.doe.gov/energy-analyses/refshelf/PubDetails.aspx?Action=View&PubId=307

Sargent & Lundy 2009, Coal-Fired Power Plant Heat Rate Reductions, SL-009597, Final Report, 

January 2009, available at http://www.epa.gov/airmarkets/resource/docs/coalfired.pdf

Storm 2009, “Applying the Fundamentals for Best Heat Rate Performance of Pulverized Coal 

Fueled Boilers, Storm Technologies, Inc, EPRI 2009 Heat Rate Conference, available at 

http://www.stormeng.com/pdf/EPRI2009HeatRateConference%20FINAL.pdf

NRDC 2013, Closing the Power Plant Carbon Pollution Loophole, NRDC Report R:12-11-A, 

March 2013 available at http://www.nrdc.org/air/pollution-standards/files/pollution-standardsreport.pdf

Lehigh 2009, Reducing Heat Rates Of Coal-Fired Power Plants, Lehigh Energy Update, January 

2009, available at http://www.lehigh.edu/~inenr/leu/leu_61.pdf

ESC/OnLocation, Efficient Heat Rate Benchmarks for Coal-Fired Generating Units, draft, B. 

Roberts, Economic Sciences Corporation, L Goudarzi, OnLocation, Inc., 1998, available at 

http://www.onlocationinc.com/heatratepaper.pdf

CRS 2013, Increasing the Efficiency of Existing Coal-Fired Power Plants, Congressional 

Research Service, December 2013, available at http://www.fas.org/sgp/crs/misc/R43343.pdf

Evonik/VGB 2008, “Power Plant Performance Reporting and Improvement under the Provision 

of the Indian Energy Conservation Act – Output 1.1”, Evonik/VGB 2008, available at 

http://www.emt-india.net/PowerPlantComponent/Output1.1/Output1.1.pdf

IEA 2010, Power Generation from Coal - Measuring and Reporting Efficiency Performance and 

CO2 Emissions, OECD/IEA-CIAB 2010, available at 

http://www.iea.org/ciab/papers/power_generation_from_coal.pdf

EPRI 2009, Renewed Interest in Reducing Heat Rate, EPRI Journal, Spring 2009, available at 

http://mydocs.epri.com/docs/CorporateDocuments/EPRI_Journal/2009-

Spring/1019287_Heat%20Rate.pdf
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

44 

Deleted: 14

EPRI/Korellis 2011, Identifying Ways to Improve Plant Heat Rate, Energy-Tech Magazine, 

March 2011, available at http://www.energy-tech.com/article.cfm?id=30528

EPRI 2011a , Opportunities to Enhance Electric Energy Efficiency in the Production and 

Delivery of Electricity, EPRI Technical Report 1024651, November 2011, available at 

http://www.google.com/url?sa=t&rct=j&q=&esrc=s&frm=1&source=web&cd=1&ved=0CCsQF

jAA&url=http%3A%2F%2Fwww.pserc.wisc.edu%2Fdocuments%2Fpublications%2Fspecial_in

terest_publications%2FEPRI_Electricity_Use_Report_Final_1024651.pdf&ei=Qo9yUvmwMY

Xb4AOZz4GYAw&usg=AFQjCNElzekbtoSNCR5SKFwkfbKx83p0Uw&bvm=bv.55819444,d.

dmg

RFF 2013, Regulating Greenhouse Gases from Coal Power Plants under the Clean Air Act, RFF 

DP 13-05, February 2013, available at http://www.rff.org/RFF/documents/RFF-DP-13-05.pdf
Page 1: [1] Comment [A2] Author 

MATS Compliance.  MATS compliance will lead to significant installations of pollution control

equipment (PCE) over the 2013 to 2016 period on capacity that owners intend to keep in

service beyond the MATS compliance deadline but would not be MATS‐compliant without

augmenting the PCE that was in place during 2012.  The PCE systems likely to be installed on

such units include FGD systems that may consume significant power and therefore, all else

equal, increase energy input per net kilowatt‐hour of generation.  It would be helpful to

understand if or how additions of PCE for MATS compliance are reflected in the analysis of HRI

in the draft TSD.

EPA response: The results of our statistical analysis (described later) are based on gross heat rates and

will not change with installation of MATS controls, which will add additional parasitic load requirements

and thereby have an impact on the net heat rates. However, in this TSD chapter, we explain that EPA

conservatively considers fleet‐wide net heat rate improvement potential to be the same as that

indicated for the fleet‐wide gross heat rate. And based on this consideration, IPM modeling and state

goals calculations used 6% improvement on a net heat rate basis. So we also need to speak to the fleet‐

wide net heat rate. That being said, MATS controls are expected to lead to a small increase in auxiliary

power use and therefore will not have a significant effect on the fleet‐wide net heat rate.

Here is what we projected (using IPM) for the final MATS policy case.

Technology                       Capacity         Parasitic Load

New wet FGD                    0 GW               1.5‐2%

New SCR                             0 GW               0.5%       

New dry FGD (SDA)          22 GW              1‐1.5%

New DSI                             43 GW              0.5%  

                                                          (assumed same as ACI + FF)

New ACI                             141 GW            0.5%

New FF                               102 GW             0.5%

Upgrade of existing ESP            34 GW                   

Upgrade of existing wet FGD   63 GW                   

So MATS requirements would increase parasitic load from 0% (for a unit that can meet the standards

with existing equipment) up to 2 % for units that install a SDA and implement ACI + FF   [Note: a unit

would implement either SDA or DSI but not both].

In any case, EPA’s IPM v 5.13 Base Case modelling for this proposal assumes that MATS is in effect and

that such controls as are needed for compliance are added before 111(d) policy options are evaluated in

policy runs.  

Page 22: [2] Comment [A31] Author 

Suggest making the Table title more descriptive.  

Also could present fewer signficant  digits in table ...i.e. heat input‐ MM BTU x 106  ‐‐ 

EPA response: changed units and title.

Page 22: [3] Comment [A32] Author 
This capacity factor decline in 2012 is very different from the CF used later, see comment on page p. 35, i.e., 78% in

2020.  Which is correct?  And, did coal CF at 44% factor into the EPA modeling?

EPA response: The table has been updated to use all reported data (rather than a trimmed data set). This changes

the capacity factor in 2012 to 53%. Note the capacity factor of 78% is for the coal‐fired steam fleet in base case,

2020. Note also that the study population includes a significant number of EGUs that IPM lists as retiring prior to

2016.

Page 22: [4] Deleted Author 

Figure 2-2. Study population Average Gross Heat Rate by Year 

Page 22: [5] Comment [A33] Author 

At about this point, the chapter transitions into the quantitative analysis. Suggest highlighting that in some way.

There seem to be several components of the quantitative analysis: 1. Regressing HR onto CF and Temp; 2. Looking

at residual heat rates relative to model prediction; 3. The “bin model” for assessing HRI potential via best practice;

4. The assessment of HRI potential via equipment upgrades. Suggest presenting those more clearly either here or

even forward in the chapter in summary form.

EPA response: Thank you. We have added a paragraph introducing more‐quantitative analyses.

9,500

9,550

9,600

9,650

9,700

9,750

9,800

9,850

9,900

9,950

10,000

20022003200420052006200720082009201020112012

Heat Rate (btu/kwh)

Annual Heat Rate

11-yr Avg Heat Rate
DRAFT May 22, 2014 

Draft Deliberative 

Chapter 2: Heat Rate Improvement at Existing Coal-fired EGUs 

2.0 Introduction 

Based on the range of operating efficiencies for existing coal-fired electric generating 

units (EGUs), it is evident that EGUs are generally less efficient at converting fuel into 

electricity than is technically and economically possible. For example, the difference in operating 

efficiency of EGUs with similar design characteristics and the year to year variability in 

individual EGU efficiency indicates that there is potential for broadly applicable efficiency 

improvements through cost-effective operational and maintenance practices. These improved 

efficiencies would result in corresponding reductions in greenhouse gas emissions. 

This chapter presents an overview of existing coal-fired EGUs, design factors that 

influence efficiency, technologies to improve efficiency, previous studies estimating potential 

efficiency improvements, the proposed EPA approach to calculate efficiency improvements, and 

the estimated capital costs of those improvements. 

2.1 Overview of U.S. Existing Coal-Fired Electric Generating Units 

Coal in the United States is predominately used for electric power generation. Most coalfired EGUs in the United States burn either bituminous or subbituminous coals. The largest 

sources of bituminous coals burned in coal-fired EGUs are mines in regions along the 

Appalachian Mountains and in southern Illinois, western Kentucky and Indiana. Additional 

bituminous coals are supplied from mines in Utah and Colorado. The vast majority of 

subbituminous coals are supplied from mines in Wyoming and Montana, with many coal-fired 

EGUs burning subbituminous coals from the Powder River Basin (PRB) region in Wyoming. In 

general, the burning of lignite by U.S. electric utilities is limited to coal-fired EGUs that are 

located near the mines that supply the lignite in Texas, Louisiana, Mississippi, Montana, and 

North Dakota. At a few power plant locations in the Eastern United States, recovered anthracite 

coal or coal refuse is burned in limited quantities. 

Existing coal-fired EGUs in the U.S. electric utility fleet use one of five basic coal 

combustion configurations: (1) pulverized coal (PC) combustion, (2) fluidized-bed combustion 

(FBC), (3) gasified coal combustion, (4) cyclone furnace combustion, or (5) stoker-fired coal 

combustion. Table 2-1 presents a summary of the operating characteristics of each of these coal 

combustion configurations. 

Comment [A1]: This section read quite weakly as

initially written.   

EPA response: Thank you for the edit.

Comment [A2]: MATS Compliance.  MATS

compliance will lead to significant installations of

pollution control equipment (PCE) over the 2013 to

2016 period on capacity that owners intend to keep

in service beyond the MATS compliance deadline

but would not be MATS‐compliant without

augmenting the PCE that was in place during 2012.  

The PCE systems likely to be installed on such units

include FGD systems that may consume significant

power and therefore, all else equal, increase energy

input per net kilowatt‐hour of generation.  It would

be helpful to understand if or how additions of PCE

for MATS compliance are reflected in the analysis of

HRI in the draft TSD.

EPA response: The results of our statistical analysis

(described later) are based on gross heat rates and

will not change with installation of MATS controls,

which will add additional parasitic load

requirements and thereby have an impact on the

net heat rates. However, in this TSD chapter, we

explain that EPA conservatively considers fleet‐wide

net heat rate improvement potential to be the same

as that indicated for the fleet‐wide gross heat rate.

And based on this consideration, IPM modeling and

state goals calculations used 6% improvement on a

net heat rate basis. So we also need to speak to the

fleet‐wide net heat rate. That being said, MATS

controls are expected to lead to a small increase in

auxiliary power use and therefore will not have a

significant effect on the fleet‐wide net heat rate.

Here is what we projected (using IPM) for the final

MATS policy case.

Technology                       Capacity         Parasitic Load

New wet FGD                    0 GW               1.5‐2%

New SCR                             0 GW               0.5%       

New dry FGD (SDA)          22 GW              1‐1.5%

New DSI                             43 GW              0.5%  

                                                          (assumed same as

ACI + FF)

New ACI                             141 GW            0.5%

New FF                               102 GW             0.5%

Upgrade of existing ESP            34 GW                   

Upgrade of existing wet FGD   63 GW                   

So MATS requirements would increase parasitic load

from 0% (for a unit that can meet the standards

with existing equipment) up to 2 % for units that

install a SDA and implement ACI + FF   [Note: a unit

would implement either SDA or DSI but not both].

In any case, EPA’s IPM v 5.13 Base Case modelling

for this proposal assumes that MATS is in effect and

that such controls as are needed for compliance are

added before 111(d) policy options are evaluated in

policy runs.  
DRAFT May 22, 2014 

2 

Table 2-1. Characteristics of coal-firing configurations used for U.S. EGUs 

Coal-firing 

Configuration 

Coal Combustion Process 

Description Distinctive Design/Operating Characteristics 

Pulverized 

Coal (PC) 

Combustion 

Coal is ground to a fine powder that 

is pneumatically fed to a burner 

where it is mixed with combustion 

air and then blown into the furnace. 

The pulverized-coal particles burn in 

suspension in the furnace. Unburned 

and partially burned coal particles 

are carried off with the flue gas. 

Wall-fired 

An array of burners fire into the 

furnace horizontally, and can be 

positioned on one wall or opposing 

walls depending on furnace design. 

Tangential-fired 

(Corner-fired) 

Multiple burners are positioned in 

opposite corners of the furnace 

producing a fireball that moves in a 

cyclonic motion and expands to fill 

the furnace. 

Fluidized-bed 

Combustion 

(FBC) 

Coal is crushed into fine particles. 

The coal particles are suspended in a 

fluidized bed by upward-blowing jets 

of air creating a turbulent mixing of 

combustion air with the coal 

particles. Typically, the coal is mixed 

with a sorbent such as limestone (for 

SO2 emission control). FBC have a 

greater fuel flexibility than PC EGUs 

and can be designed for combustion 

within the bed to occur at 

atmospheric or elevated pressures. 

FBC operating temperatures are in 

the range of 1,500 to 1,650F (800 to 

900o

C). 

Bubbling 

fluidized bed 

(BFB) 

Operates at relatively low gas stream 

velocities and with coarse-bed size 

particles. Air in excess of that 

required to fluidize the bed passes 

through the bed in form of bubbles. 

Circulating 

fluidized bed 

(CFB) 

Operates at higher gas stream 

velocities and with finer-bed size 

particles. No defined bed surface. 

Must use high-volume, hot cyclone 

separators to recirculate entrained 

solid particles in flue gas to maintain 

the bed and achieve high combustion 

efficiency. 

Integrated Coal 

Gasification 

Combined 

Cycle (IGCC) 

Synthetic combustible gas (“syngas”) 

derived from an on-site coal 

gasification process is burned in a 

combustion turbine. The hot exhaust 

gases from the combustion turbine 

pass through a heat recovery steam 

generator to produce steam for 

driving a steam turbine/generator 

unit. 

Coal gasification units are unique among coal-firing 

configurations because a gaseous fuel (synfuel or syngas) 

is burned instead of solid coal because the combustion 

and power generation process and combines the Rankine 

and Brayton thermodynamic cycles as is the case for a 

combined cycle power plant. 

Cyclone 

Furnace 

Combustion 

Coal is crushed into small pieces and 

fed through a burner into the cyclone 

furnace. A portion of the combustion 

air enters the burner tangentially 

creating a whirling motion to the 

incoming coal. 

Designed to burn coals with low-ash fusion temperatures 

that are difficult to burn in PC boilers. The majority of 

the ash is retained in the form of a molten slag. 

Stoker-fired 

Coal 

Combustion 

Coal is crushed into large lumps and 

burned in a fuel bed on a moving, 

vibrating, or stationary grate. Coal is 

fed to the grate by a mechanical 

device called a “stoker.” 

One of three types of stoker mechanisms can be used that 

ether feed the coal by pushing, dropping, or flipping coal 

unto the grate. 

The three technologies currently used for new coal-fired power plants are pulverized 

coal, fluidized bed (FBC), and integrated coal gasification combined cycle (IGCC). Pulverized 

coal combustion is the coal-firing configuration predominately used at existing EGUs. The most 

Comment [A3]: We would suggest reducing the

amount of text in tables (perhaps as bullet points).

EPA response: Thank you for the observation.

Comment [A4]: It might be helpful to indicate

the typical size (MW) and capital cost ($/installed

MW) here.  

EPA response: Thank you for the observation, but it 

is unclear how the suggested edit would relate to the 

analysis.

Comment [A5]: No one is building coal plants

currently in the U.S., so the future tense is not

appropriate.  Who is building coal plants using FBC?  

China?  If so, we should specify, or else delete.

EPA response: The text has been deleted.
DRAFT May 22, 2014 

3 

recent coal technology combustion development involves the integration of coal gasification 

technologies with the combined cycle electric generation process. The efficiency of an IGCC 

power plant is comparable to the latest advanced PC-fired and FBC EGU designs using 

supercritical steam cycles. The advantages of using IGCC technology can include greater fuel 

flexibility (e.g., capability to use a wider variety of coal ranks), potential improved control of 

PM, SO2 emissions, and other air pollutants, the need for fewer post-combustion control devices 

(e.g., almost all of the sulfur and ash in the coal can be removed once the fuel is gasified and 

prior to combustion), generation of less solid waste, reduced water consumption, and the 

chemical process that creates a concentrated CO2 stream that is more amenable to carbon capture 

processes. 

Older combustion technologies, namely cyclone furnaces and stoker-fired coal 

combustion, have been replaced at new coal-fired EGUs by more efficient methods that provide 

superior coal combustion efficiency and other advantages. However, a few remaining old stokerfired EGUs and cyclone furnaces still remain in service for a small number of existing EGUs in 

the U.S. electric utility market. 

2.2 Influence of Heat Rate on Coal-Fired EGU CO2 Emission Rate 

Heat rate is a common way to measure EGU efficiency. As the efficiency of a coal-fired 

EGU is increased, less coal is burned per kilowatt-hour (kWh) generated by the EGU resulting in 

a corresponding decrease in CO2 and other air emissions. Heat rate is expressed as the number of 

British thermal units (Btu) or kilojoules (kJ) required to generate a kilowatt-hour (kWh) of 

electricity. Lower heat rates are associated with more efficient coal-fired EGUs. 

The electric energy output for an EGU can be expressed as either as “gross output” or 

“net output.” The gross output of an EGU is the total amount of electricity generated at the 

generator terminal. The net output of an EGU is the gross output minus the total amount of 

auxiliary (or parasitic) electricity used to operate the EGU (e.g., electricity to power fuel 

handling equipment, pumps, fans, pollution control equipment, and other on-site electricity 

needs), and thus is a measure of the electricity delivered to the transmission grid for distribution 

and sale to customers. Some EGUs also produce part of their useful output in the form of useful 

thermal output (e.g., steam for heating purposes). These types of facilities are called combined 

heat and power, or CHP, facilities. 

Comment [A6]: One of the primary benefits of

IGCC is the concentrated CO2 stream and ability to

use in CCS.  Needs to be highlighted.

EPA response: Thank you for the observation and

the suggested text, which has been included.
DRAFT May 22, 2014 

4 

A variety of factors must be considered when comparing the effectiveness of heat rate 

improvement technologies to increase the efficiency of a given coal-fired EGU. The actual 

overall efficiency that a given coal-fired EGU achieves is determined by the interaction of a 

combination of site-specific factors that impact efficiency to varying degrees. Examples of the 

factors affecting EGU efficiency at a given facility include: 

 EGU thermodynamic cycle – EGU efficiency can be significant improved by using a 

supercritical or ultra-supercritical steam cycle. Supercritical and ultra-supercritical boilers 

operate above the critical point of water (approximately 374°C (705°F) and 22.1 MPa 

(3,210 psia)). As a general guideline, the thermal design efficiencies for subcritical EGUs 

are in the range of 35% to 37%, supercritical EGUs are in the range of 39% to 40%, and 

ultra-supercritical EGUs in the range of 42% to 45%. However, actual operating 

efficiencies are lower than design efficiencies. 

 EGU coal rank and quality – EGUs burning higher quality coals (e.g., bituminous) tend 

to be more efficient than EGUs burning lower quality coals with a greater moisture 

content (e.g., lignite). Bituminous coals have higher heating values of greater than 10,500 

British thermal units per pound and lignite coals have higher heating values of less than 

8,300 British thermal units per pound. 

 EGU plant size –EGU efficiency generally increases with size because the boiler and 

steam turbine losses are lower for larger equipment compared to smaller equipment. 

However, the EPA is not aware of sufficient data to quantify the relationship. 

 EGU pollution control systems – The electric power consumed by air pollution control 

equipment reduces the overall efficiency of the EGU.

 EGU operating and maintenance practices – The specific practices used by an individual 

electric utility company for combustion optimization, equipment maintenance, etc. can 

affect EGU efficiency. 

 EGU cooling system – The temperature of the cooling water entering the condenser can 

have impacts on steam turbine performance. Once-through cooling systems can have an 

efficiency advantage over recirculating cooling systems (e.g., cooling towers). However, 

once-though cooling systems typically have larger water related ecological concerns than 

recirculating cooling systems. 

Comment [A7]: Could indicate the approximate

range (BTUs / lb) of the different coal types, as well

as the influence of moisture.

EPA response: The ranges have been added.

Comment [A8]: : Could indicate approximate

turbine size / size class here.  

EPA response: Edits made to address the comment.
DRAFT May 22, 2014 

5 

 EGU geographic location and ambient conditions – The elevation and seasonal ambient 

temperatures at the facility site potentially may have an impact on EGU efficiency. At 

higher elevations, air pressure is lower and less oxygen is available for combustion per 

unit volume of ambient air than at lower elevations. Cooler ambient temperatures 

theoretically could increase the overall EGU efficiency by increasing the draft pressure of 

the boiler flue gases and the condenser vacuum, and by increasing the efficiency of the 

cooling system. Also, geographic location influences the type of cooling system that can 

be used (e.g., EGUs located in arid locations often cannot use once through cooling) 

 EGU load generation flexibility requirements – Operating an EGU as a baseload unit is 

more efficient than operating an EGU as a load following unit to respond to fluctuations 

in customer electricity demand. 

 EGU plant components – EGUs using the optimum number of feedwater heaters, highefficiency electric motors, variable speed drives, better materials for heat exchangers, etc. 

tend to be more efficient. 

2.3 Technologies to Improve Existing Coal-Fired EGU Heat Rate 

A number of studies have been conducted involving literature reviews of published 

articles and technical papers identifying potential efficiency improvement techniques applicable 

to existing coal-fired EGUs.1

 For example, a summary of the findings from one study conducted 

by the Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) is 

presented in Table 2-2. The efficiency percentages were converted to a common basis so that all 

of the data can be compared. All of the improvement technologies presented in Table 2-2 cannot 

necessarily be implemented at every existing coal-fired EGU facility in the U.S. electric utility 

fleet. The existing EGU design configuration and other site-specific factors may prevent the 

technical feasibility of using a given technology. 

Typically, these studies share as a common basis the estimated potential efficiency 

improvement percentages and costs from the engineering study originally completed by Sargent 

and Lundy in 2009 titled “Coal-Fired Power Plant Heat Rate Reductions.” It describes numerous 

well known and technically proven methods to improve efficiency of coal-fired EGUs. The study 

                                                            

1

 See HRI Partial Bibliography at the end of this chapter. 

Comment [A9]: Geographic location could also

influence cooling water issues, mentioned earlier

(i.e. coal plants in the arid west).

EPA response: Text has been added to address the

comment.  

Comment [A10]: Suggest using “load following”

for clarity

EPA response: Done.
DRAFT May 22, 2014 

6 

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

equipment and the water treatment system. Each of these main areas are expanded upon below. 

2.3.1 Boiler 

The systems to focus on for improving heat input within the boiler area include the 

materials handling, combustion system, boiler control system, sootblowers and the air heaters. 

2.3.1.1 Materials Handling2

The coal handling portion of materials handling typically requires about 0.07% (7 

Btu/kWh) of the gross electrical output of a power plant. Depending on the state of the motors 

and drives, replacing them with energy-efficient motors and variable frequency drives can 

reduce the auxiliary power requirements. The variable frequency drives also limit the stress and 

strain on the other equipment. 

Coal pulverizers typically require about 0.6% (60 Btu/kWh) of the gross electrical output 

and can be upgraded to provide more consistent size and finer coal particles. The fine particles 

improve combustion efficiency, consequently reducing fuel cost and heat rate. The costs for 

changes to the pulverizer system are significant, and historically, the projects have improved the 

heat rate justifiably only when the existing equipment has degraded. 

The bottom ash handling system may be a source for heat rate improvement. Switching 

from a water-sluicing bottom ash system to a dry drag chain system can reduce the auxiliary 

requirements and reduce the amount of water to the water treatment plant. The typical power 

requirements are about 0.1% (10 Btu/kWh) of the plant’s gross output. 

2.3.1.2 Economizer 

An economizer is a heat exchanger that improves the efficiency of an EGU by recovering 

energy from the exhaust gases to preheat the boiler feedwater. The replacement of the 

economizer can lead substantial heat rate improvements around 50-100 Btu/kWh, but is large 

capital investment (~$2-8M). Due to this high cost, economizers upgrades are not generally 

performed unless the existing equipment has degraded or a replacement is necessary due to the 

installation of new control equipment. 

                                                            

2 The Sargent and Lundy report did not provide potential savings for material handling operations. Energy use in 

Btu/kWh has been provided to compare the energy use of materials handling relative to the potential energy savings 

from other efficiency activities.

Comment [A11]: Did you want to include an

estimate of pulverizer electric demand?

EPA response: An approximate electric demand has

been added.

Comment [A12]: For this part of the discussion,

some efficiency gains are given in percent, and

others in Btu/kWh. It would be helpful to keep

either one common unit (suggest Btu/kWh) or

provide both metrics.

EPA response: See footnote.  

Comment [A13]: Please add a sentence on what

an economizer is and does.

EPA response:  Added an introductory sentence.
DRAFT May 22, 2014 

7 

2.3.1.3 Boiler Control System 

The boiler control system has a large impact on the heat rate of the unit. The process 

control capabilities can control and evaluate many aspects of the plant’s operations. Commonly 

referred to as the Neural Network, computer models are able to control the plant’s processes by 

predicting performance during static and dynamic changes. Many vendors offer Neural Network 

systems to improve the overall efficiency. Neural network systems are typically around 

$550,000-$750,000 and offer heat rate reductions up to 150 Btu/kWh. 

2.3.1.4 Sootblowers 

Intelligent sootblowers may be installed to improve system efficiency. The intelligent 

sootblowers system monitors the furnace exhaust gas temperatures and steam temperatures. 

Other readings may be incorporated into the intelligent sootblowers system which also 

communicates with the boiler control system. This system uses real-time data to identify which 

areas need sootblowing. Boiler efficiency improvements have ranges from 30-(150 Btu/kWh) 

with capital costs around $300,000-$500,000 and $50,000/year for fixed operating and 

maintenance costs. 

2.3.1.5 Air Heaters 

Air heaters operate to transfer heat between the incoming pre-combustion air and the 

effluent flue gas. These systems are critical to maintain an efficient power plant. In order to 

maintain these systems to operate most efficiently, air heater leakages must be maintained below 

6% of incoming air flow. Most leakage is due to the pre-combustion air leaking across the 

rotating section and leaving with the flue gas. This increases the flue gas volume going through 

the forced draft and induced draft fans to the emissions controls and avoids capturing the heat 

transferred between the flue gas and pre-combustion gas. The increased volume requires more 

power to pump more flue gas. Additionally, improvements to seals on the air heaters also reduce 

the leakages. Improvements to reduce air heater and duct leakages generally reduces the heat rate 

by 10-40 Btu/kWh with capital costs between $0.3-1.2M. 

A second method to improve the heat rate is to lower the air heater outlet temperature by 

controlling the acid dew point. Typically the air heater outlet is maintained at 20-30°F above the 

sulfuric acid dew point to prevent corrosion of cold-end baskets. Sorbent injections such as 

Trona or hydrated lime can be used to lower the dew point. Depending on the sizing of the air 
DRAFT May 22, 2014 

8 

heater, the air heater may need to be modified in order to optimize the lower outlet temperature. 

The capital costs can range from $1.5-18M for heat rate reductions of 50-120 Btu/kWh. 

2.3.2 Turbine 

The systems within the turbine area for which to focus heat rate improvements are the 

turbine, the feedwater heaters, the condenser, and the turbine drive and motor-driven feed 

pumps. 

2.3.2.1 Turbine 

Replacements or overhauls of existing steam turbines with advanced turbine designs 

improves the efficiency of converting the energy in the steam to electrical energy. The capital 

costs for these projects ranges from $2-25M with heat rate reductions of 100-300 Btu/kWh.

2.3.2.2 Feedwater Heaters 

The feedwater heaters are heat exchangers to heat the boiler feedwater by extracting heat 

from the steam leaving the turbine section. The efficiency can be increased by improving heat 

transfer surface area. This entails adding heat exchange surfaces to the existing heaters or adding 

additional heaters. The costs relative to the heat rate improvement associated with these projects 

typically prohibit the advancement of the project unless the feedwater heaters are in need of 

repair. 

2.3.2.3 Condenser 

To obtain the most efficiency from the condenser section, the most effective operation 

would have the steam from the turbine to reach the lowest temperature possible before entering 

the condenser. This allows for the turbine to extract as much energy from the steam as possible. 

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

water quality. To improve water quality, closed cooling water systems provide better control of 

the water quality and tube cleaning can be performed. Heat rate reductions observed from the 

condenser upgrades and maintenances are 30-70 Btu/kWh with annual fixed costs of $30,000-

$80,000. 

2.3.2.4 Boiler Feed Pumps 

Boiler feed pumps require a large amount of auxiliary power to pump massive amounts 

of boiler feedwater through the heaters and boiler. Due to the high use of these pumps, 

Comment [A14]: Not clear whether the 1‐3%

efficiency improvement refers to improvement

resulting from an overhaul. It might be helpful to

indicate what size of turbines correspond to these

costs.  

EPA response: the paragraph has been modified.
DRAFT May 22, 2014 

9 

maintenance is extremely important to ensure reliability and the most efficient operation. As the 

pumps wear and operate less efficiently, a pump overhaul may be required. The overhaul can 

reduce the heat rate by 25-50 Btu/kWh with capital costs around $250,000-$800,000. 

2.3.3 Flue Gas System 

Two aspects of the flue gas system that offer potential improvements to the plant 

efficiency are to improve the forced draft and induced draft fan efficiencies and implementation 

of variable frequency drives. 

2.3.3.1 Induced Draft Fans 

One of the most important features in the fans is being able to control the flue gas flow. 

Many fans have dampers, which are the least effective and efficient option. There are many 

methods, such as variable inlet vanes , variable frequency drives, and variable pitch blades, 

available to control the flue gas flow allowing highly efficient fan performance. These upgrades 

or replacements provide a heat rate reduction of 10-50 Btu/kWh and cost between $6-$16M. 

2.3.3.2 Variable Frequency Drives 

It is apparent with the control that a variable frequency drives offers, the plant is able to 

reduce the auxiliary load immensely. Considering upgrading all drives, the capital costs are $6-

16M with heat rate reductions between 10-150 Btu/kWh. 

2.3.4 Emission Control Technologies 

With the passage of environmental regulations, additional emission control devices have 

been and must be implemented in the power plant. These systems typically require large amounts 

of auxiliary power with their benefit being improved air quality. Even small upgrades can 

decrease the power requirements significantly while at least maintaining the same level of 

emissions reduction. The three technologies discussed are the flue gas desulfurization, the 

electrostatic precipitator, and the selective catalytic reduction systems. 

2.3.4.1 Flue Gas Desulfurization 

Coal-fired power plants contain many variances between their flue gas desulfurization 

systems. A few common technology improvements are discussed further in this section. Older 

units typically contained a venturi throat that increased the velocity of the fluid, but resulted in a 

large pressure drop and greater power to operate the induced draft fans. To improve this 

Comment [A15]: Please spell out acronym –

even if it has been spelled out earlier, I have

forgotten and couldn’t find the location on looking

back.

EPA response: Spelled out acronyms.

Comment [A16]: For all these subtitles, it

wouldn’t hurt to spell out the acronym in the

section title.

EPA response: Spelled out acronyms.
DRAFT May 22, 2014 

10 

operation, a co-current spray tower quencher may replace the unit. The capital cost is about 

$2.5M with heat rate reductions around 13 Btu/kWh. 

Another technology upgrade affects the vanes and distribution plate into an absorber. The 

improvement of the gas flow coming into contact with the absorber sorbent increases SO2

capture, reduces maintenance due to erosion, and reduces the amount of energy required from the 

induced draft fan. Turning vanes and a perforated gas distribution plate improve gas distribution. 

The cost of the vanes is around $250,000 with heat rate reductions of 1-2 Btu/kWh. 

In a wet flue gas desulfurization multiple spray levels are installed to deliver the limestone 

slurry. If a power plant is operating with SO2 levels below its permit limit, turning off one spray 

level will reduce the auxiliary power necessary. If this is possible, a unit heat rate reduction of 16 

Btu/kWh may be available. 

2.3.4.2 Electrostatic Precipitator 

The best operation for an electrostatic precipitator involves maintaining the maximum 

applied voltage, but below the level in which spark-over occurs. Electrostatic precipitator energy 

management system upgrades often help improve the electrostatic precipitator performance by 

maintaining the optimal performance and lowering power consumption. The installation for this 

technology can be from minimal to $0.8M and can lower heat rate by 5 Btu/kWh. 

2.3.4.3 Selective Catalytic Reduction 

For the last 15 years, selective catalytic reduction systems have been in use to reduce 

NOX emissions from power plants. Extensive modeling was performed to achieve the necessary 

reduction with minimum ammonia slip. The results showed that reducing pressure drop and 

using secondary air as dilution for the ammonia vaporizer can reduce the auxiliary power 

necessary. The heat rate reduction is 0-10 Btu/kWh and capital costs between $0.5-$2M with 

fixed and variable costs up to $100,000 each. 

2.3.5 Water Treatment System 

The water treatment system heat rate improvements can occur for the boiler water 

treatment and the cooling tower improvements. The boiler water is one of the most important 

aspects of the power plant. The quality of the water plays a huge impact on the scale buildup on 

the boiler tubes, which reduces the heat transfer in the tubes or can cause tube failures. Proper 

use of chemicals to maintain pure water is key. Also, high-quality water can reduce the 
DRAFT May 22, 2014 

11 

blowdowns required, which allows for more steam in the turbine cycle. If the water is not 

properly maintained, heat transfer may be reduced up to 10%. 

Similar to the boiler water, the cooling towers are also affected by the water quality. 

Fouling and scaling remain issues for heat transfer and purity of the water. By maintaining the 

cooling water system efficiently, the overall water quality is improved, which branches into other 

aspects already mentioned. 
DRAFT May 22, 2014 

12 

Table 2-2. Existing coal-fired EGU efficiency improvements reported for actual efficiency improvement projects 

Efficiency 

Improvement 

Technology

Description 

Reported 

Efficiency 

Increasea

Combustion 

Control 

Optimization 

Combustion controls adjust coal and air flow to optimize steam production for the steam turbine/generator set. However, combustion control for a coal-fired EGU is complex and impacts a number of important operating parameters including combustion efficiency, steam temperature, furnace slagging and fouling, and NOX

formation. The technologies include instruments that measure carbon levels in ash, coal flow rates, air flow rates, CO levels, oxygen levels, slag deposits, and burner metrics as well as advanced coal nozzles and plasma assisted coal combustion. 

0.15 to 0.84% 

Cooling System 

Heat Loss 

Recovery 

Recover a portion of the heat loss from the warm cooling water exiting the steam condenser prior to its circulation thorough a cooling tower or discharge to a water body. The identified technologies include replacing the cooling tower fill (heat transfer surface) and tuning the cooling tower and condenser. 

0.2 to 1% 

Flue Gas Heat 

Recovery 

Flue gas exit temperature from the air preheater can range from 250 to 350°F depending on the acid dew point temperature of the flue gas, which is dependent on the concentration of vapor phase sulfuric acid and moisture. For power plants equipped with wet FGD systems, the flue gas is further cooled to approximately 125°F as it is sprayed with the FGD reagent slurry. However, it may be possible to recover some of this lost energy in the flue gas to preheat boiler feedwater via use of a condensing heat exchanger. 

0.3 to 1.5% 

Low-rank Coal 

Drying 

Subbituminous and lignite coals contain relatively large amounts of moisture (15 to 40%) compared to bituminous coal (less than 10%). A significant amount of the heat released during combustion of low-rank coals is used to evaporate this moisture, rather than generate steam for the turbine. As a result, boiler efficiency is typically lower for plants burning low-rank coal. The technologies include using waste heat from the flue gas and/or cooling water systems to dry low-rank coal prior to combustion. 

0.1 to 1.7% 

Sootblower 

Optimization 

Sootblowers intermittently inject high velocity jets of steam or air to clean coal ash deposits from boiler tube surfaces in order to maintain adequate heat transfer. Proper control of the timing and intensity of individual sootblowers is important to maintain steam temperature and boiler efficiency. The identified technologies include 

intelligent or neural-network sootblowing (i.e., sootblowing in response to real-time conditions in the boiler) and detonation sootblowing. 

0.1 to 0.65% 

Steam Turbine 

Design 

There are recoverable energy losses that result from the mechanical design or physical condition of the steam turbine. For example, steam turbine manufacturers have improved the design of turbine blades and steam seals which can increase both efficiency and output (i.e., steam turbine dense pack technology). 

0.84 to 2.6 

Source: National Energy Technology Laboratory (NETL), 2008. Reducing CO2 Emissions by Improving the Efficiency of the Existing Coal-fired Power Plant 

Fleet, DOE/NETL-2008/1329. U.S. Department of Energy, National Energy Technology Laboratory, Pittsburgh, PA. July 23, 2008. Available at: <http://www.netl.doe.gov/energy-analyses/pubs/CFPP%20Efficiency-FINAL.pdf>. a Reported efficiency improvement metrics adjusted to common basis by conversion methodology assuming individual component efficiencies for a reference 

plant as follows: 87% boiler efficiency, 40% turbine efficiency, 98% generator efficiency, and 6% auxiliary load. Based on these assumptions, the reference power plant has an overall efficiency of 32% and a net heat rate of 10,600 Btu/kWh. As a result, if a particular efficiency improvement method was reported to achieve a 1% point increase in boiler efficiency, it would be converted to a 0.37 % point increase in overall efficiency. Likewise, a reported 100 Btu/kWh decrease in net heat rate would be converted to a 0.30% point increase in overall efficiency. 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

13 

2.4 Previous Studies on Heat Rate Improvements 

A number of studies using varying approaches have been performed to determine 

potential efficiency improvements and associated resulting CO2 emission reductions. These 

approaches include characterizing the current U.S. coal-fired EGU fleet, identifying potential 

efficiency improvements, and applying improvement actions to existing EGUs. The approach 

taken within each study varies. Five studies are briefly summarized and compared in Table 2-3. 

The NETL studies used a benchmarking approach that evaluated the design factors that 

are known to influence efficiency and grouped EGUs based on similar design characteristics. 

The studies categorized the industry based on fuel type, location, steam cycle, and age of the 

boilers. Potential efficiency improvements were calculated based on an assumption that the 

lower-performing EGUs in each group should to be able to do as well as the better performing 

EGUs in that group. Specifically, the goal for potential improvement: for each subcategory was 

that the bottom 90% of EGUs in each group improved their heat rate to the average performance 

of top 10% in that group. While the studies are different in the level of detail and assumptions, 

the results of these studies overall suggest that a U.S. coal-fired EGU fleet-wide improvement 

ranging from 9% to 15% is theoretically possible. The Lehigh study used a less detailed 

approach and evaluated technologies applicable to bituminous and subbituminous coals to 

estimate potential fleet wide reductions. 

An alternative approach to evaluate heat rate improvement is used by Resources for the 

Future. This study focused on the operating efficiency (synonymous with heat rate) of the entire 

existing U.S. coal-fired EGU fleet. The authors evaluated decades of data from industrial 

responses to economic factors such as demand, coal price and energy policies. This approach 

sought to estimate overall changes in industry fleet efficiency in response to changes in fuel 

prices or carbon prices. In this specific example, the coal price was assumed to be a 10% 

increase and the CO2 emissions tax at $1.64 per ton for heat rate reductions of 0.3 to 0.9%. 

The National Resources Defense Council approach considered the fleet of coal-fired 

EGUs and assumes a target heat rate in order for the EGUs to comply with an inferred standard. 

As opposed to the above studies that determined by how much the efficiencies can improve, this 

approach estimated how the industry will meet any imposed standards and calculated the heat 

Comment [A18]: To avoid ambiguity it might be

helpful to cite (footnote) studies when referring to

them.  

EPA response: Added study names.

Comment [A19]: Suggest an alternate phrasing

such as: This approach sought to estimate overall

changes in industry fleet efficiency in response to

changes in fuel prices or carbon prices.

EPA response: The language has been edited as

suggested.
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

14 

rates necessary to meet a standard. As it has not been determined with regard to how the CO2

emissions limit will be averaged, the paper discusses many potential options that coal-fired 

EGUs may have to meet new standards. These options include additional options such as adding 

renewable, natural gas, and combined-cycle sources. 

The EPA observed that existing HRI studies using a benchmarking approach have relied 

on a single year annual average heat rate data, whereas many of the operational impacts on heat 

rate (diurnal temperatures, load following, etc) become more apparent in the variability of hourly 

performance data. We further recognized that an examination of heat rate data over a multiple 

year period, perhaps a decadal time frame might reveal patterns of performance that should also 

inform estimates of HRI potential. For these reasons, the EPA has developed in this TSD an 

additional assessment of HRI potential that draws on multiyear historical hourly data. While we 

understand that engineering judgment remains essential to a proper interpretation of the results, 

the EPA intends that this assessment be a more substantial basis for estimating the fleet-wide 

HRI potential for coal-fired EGUs. 

Table 2-3. Summary comparison of previous studies on EGU heat rate improvements 

Study 

ID 

Study Title 

Author 

Factors Used for 

Industry Grouping 

Key Study 

Assumptions 

Relevant HRI 

Results 

1 “Reducing CO2 

Emissions by 

Improving the 

Efficiency of the 

Existing Coal-fired 

Power Plant Fleet” 

NETL 

• Plant design: age and 

steam cycle 

• Category improvement is 

the bottom 90% of EGUs in 

each group improving their 

heat rate to the average 

performance of top 10% in 

that category 

• Units with capacity factors 

under 50% were removed 

from dataset 

• 15% reduction in 

overall heat rate of 

coal-fired EGUs 

2 “Improving the 

Efficiency of CoalFired Power Plants 

for Near Term 

Greenhouse Gas 

Emissions 

Reductions” 

NETL 

• Plant design: coal type, 

steam cycle, and size 

• Category improvement is 

the bottom 90% of EGUs in 

each group improving their 

heat rate to the average 

performance of top 10% in 

that category • Units with 

anomalous data, capacity 

factors under 10%, using 

less than 97% coal, and 

gasification plants were 

removed from dataset 

• Low pressure subcritical 

units and 0-200 MW 

subbituminous units 

• 8.7% reduction in 

overall heat rate of 

coal-fired EGUs 

Comment [A20]: Not certain this is correct ‐ the

RFF study abstract states: “This paper examines the

operation of coal‐fired generating units over 25

years to estimate the marginal costs and potential

magnitude of emissions reductions from improving

their efficiency.” Please correct if appropriate.

EPA response: The text has been clarified to refer to

studies that used a benchmarking type approach to

estimate potential reductions.  

Comment [A21]: Please check on the tense –

past or present?

EPA response: Thank you for the observation.
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

15 

Study 

ID 

Study Title 

Author 

Factors Used for 

Industry Grouping 

Key Study 

Assumptions 

Relevant HRI 

Results 

assumed retired for goal 

• Lost generation made up by 

more efficient coal-fired 

EGUs 

3 “Reducing Heat 

Rates of Coal-Fired 

Power Plants” 

Lehigh Energy 

Update 

 All possible heat rate 

improvements are made 

including drying of high 

moisture coal: 

• 10% improvement 

for bituminous coalfired EGU 

• 15% improvement 

for subbituminous 

coal-fired EGU 

4 “Regulating 

Greenhouse Gases 

from Coal Power 

Plants under the 

Clean Air Act” 

Resources for the 

Future 

• Analyze the actual 

operating efficiency of the 

entire fleet of U.S. coalfired EGUs 

• Assess abatement 

opportunities and costs by 

observing how coal plants 

respond to market and 

regulatory incentives to 

improve energy efficiency 

Overall efficiency 

improvements of 2 to 5%, 

from other literature studies 

10% coal price 

increase, 

corresponding to a 

tax on CO2

emissions of about 

$1.64 per ton, 

improving heat rates 

by 0.3 to 0.9 % 

5 “Closing the Power 

Plant Carbon 

Pollution Loophole: 

Smart Ways the 

Clean Air Act Can 

Clean Up 

America’s Biggest 

Climate Polluters” 

NRDC 

• Plant design: coal type 

and steam cycle 

Improvements are broken into 

3 group 

• Top 10% for each 

subcategory, no change 

• Top 11% to 49%, improve 

heat rate by 50% of the 

difference between facility 

heat rate and performance of 

top 10% in class or 600 

Btu/kWh, whichever is less 

• Bottom 50%, improve heat 

rate by 100% of the difference 

between facility heat rate and 

performance of top 10% in 

class or 600 Btu/kWh, 

whichever is less 

 Broader analysis 

not strictly focused 

on heat rate 

improvements. 11% 

reduction on overall 

fossil fuel-fired EGU 

emission rates; 

includes switching 

from coal to natural 

gas. 

2.5 EPA’s Heat Rate Improvement Assessment 

This EPA assessment of fleet-wide HRI potential looks at historical data from coal-fired 

EGUs in the U.S. to identify changes to EGUs’ heat rates – the amount of heat input required, on 

average, to generate 1 kWh of electricity – that can be attributed to operation and maintenance 

practices and equipment upgrades. These heat rate changes are analyzed to determine their 

applicability to the rest of the coal-fired EGU fleet and to determine the potential heat rate 

improvement that, on average, could be achieved by the fleet. 

Comment [A22]: Please include text regarding

the peer review to which all these reports have

been subject. If not, please consider subjecting this

concept to technical peer review in the period

following proposal release.

EPA response: We will check on the peer review

status of the studies.  

Comment [A23]: Is there a reason there is no

comment in this box?

EPA response: Text has been added.
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

16 

This data analysis portion of the study relies on unit-level heat input and gross generation 

data reported to the EPA by owners or operators of EGUs to assess in detail the changes in gross 

heat rates. Potential changes in net heat rates are then addressed later in this section. Unit-level 

evaluations allowed the EPA to recognize the significant heterogeneity of coal-fired EGUs; even 

‘sister’ units, units built at the same time at a given facility, may display different operating 

profiles and may have different equipment, controls, fuel mixes or cooling systems. 

Based on literature reviews; informal interviews with engineering experts, vendors, and 

plant operators; and historical information collected by the EPA, we believe EGUs achieve heat 

rate improvements by: 1) operating under recommended operation and maintenance conditions 

(best practices), and 2) installing and using equipment upgrades. Best practices include no-cost 

or low-cost methods such as the installation or more frequent tuning of control systems and the 

like-kind replacement of worn existing components. Upgrades often involve higher costs and 

greater downtime, such as, extensive overhaul or upgrade of major equipment (turbine or boiler) 

or replacing existing components with improved versions. 

The EPA developed unit-level statistics from over 60 million rows of hourly data. We 

evaluate each unit on its individual performance using heat rate variability as an indicator of the 

application of best practices and potential for improvement. To estimate heat rate improvement 

through equipment upgrades we survey engineering studies, examine year-to-year trends, and 

research EGUs where such methods were applied. 

2.5.1 Study Population and Data 

The EGU study population consists of 884 coal- and petroleum coke-fired EGUs that 

reported both heat input3

 and electrical output to the EPA’s Clean Air Markets Division in 2012.4

It includes a wide range of configurations, from 24 to 1,500 MW nameplate capacities, super and 

subcritical thermodynamic cycles, between 1 and 69 years old, and different coal ranks. It 

excludes any EGUs at any facility that reported cogeneration to the EPA or the EIA. These units 

are excluded because a portion of the heat input was used to generate electricity and/or steam 

                                                            

3 Sources calculate heat input using an ‘F factor’ for the carbon content of the fuel being combusted and the average 

hourly measurements of CO2 flow and concentration. 4

 Information on the Clean Air Markets Division data is available at http://ampd.epa.gov/ampd/
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

17 

heat. Therefore, it is difficult for the EPA, using available data, to make a meaningful 

comparison of these units’ heat rates. 

The EPA performed this study using hourly heat input (Btu), and electricity output 

(MWh) data from the Clean Air Markets Division and meteorological data from NOAA’s 

National Climatic Data Center for the years 2002-2012. As described later in this section, these 

meteorological data were used to account for temperature impacts on heat rates. The eleven year 

study period is representative of a wide range of conditions, including growth and recessionary 

economic conditions, changing electricity generation from renewable and natural gas, and 

different regulatory constraints. 

The hourly heat input and generation data used in this study is collected under the 

authority of 40 CFR part 75 (hereafter, Part 75). The EPA designed Part 75 to encourage 

complete and accurate emission measurement and reporting to support emission trading 

programs, including the Acid Rain Program and Clean Air Interstate Rule (CAIR). However, the 

EPA recognized that there will be times when emission data are not available due to monitoring 

system malfunctions or maintenance, technical challenges, or missed quality assurance/quality 

control (QA/QC) tests. When data are not available or deemed invalid (e.g., when a QA/QC test 

was not performed as required), the EPA has specified data substitution methods that are 

designed to 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 bias and the impact it would have on the results of this study, the 

EPA excluded substitute data reported by EGUs from this study’s dataset. These substitute data 

represent approximately 2% of all reported operating hours. In addition, we excluded partial 

hours of operation that occur during the first hour of startup and the last hour of shutdown. 

We also excluded 40 unit-years (0.5% of records) with atypical annual heat rates less 

than 6,500 or greater than 15,000 Btu/kWh resulting from a variety of factors including firing of 

natural gas, very low operating time, or errors in reported gross load. Table 2-4 summarizes the 

heat input, electric generation, heat rate and unit counts by year for the study population used in 

this work. This population corresponds to 9,388 unit-years of data at 884 distinct EGUs. Figure 

2-1 displays the study population average gross heat rate by year and the 11-year average. 

Comment [A24]: IS QA/QC defined in the

abbreviations?

EPA response: It is defined in the sentence.
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

18 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

19 

Table 2-4. Study Population Annual Heat Input, Generation, Heat Rate and Unit Count 

2002 - 2012 5

Year 

Heat Input 

(million 

MMBtu) 

Electric 

Generation 

(million MWhgross) 

Heat Rate 

(Btu/kWhgross) Unit Count 

2002 18,601 1,874 9,924 839 

2003 18,428 1,864 9,886 834 

2004 18,405 1,875 9,819 836 

2005 18,665 1,910 9,774 838 

2006 18,644 1,914 9,743 848 

2007 18,704 1,920 9,740 846 

2008 18,459 1,914 9,643 852 

2009 16,588 1,719 9,649 864 

2010 17,693 1,831 9,662 869 

2011 16,934 1,744 9,708 878 

2012 14,947 1,536 9,732 884 

Figure 2-1. Study Population Average Gross Heat Rate by Year 

NOAA’s Integrated Surface Data (ISD) product provides hourly temperature for over 

20,000 weather stations worldwide.6

 Since EGU heat rate performance is sensitive to air 

temperature and barometric pressure, which vary with elevation, we use meteorological data 

from stations that are reasonably close to the EGU's location and elevation to account for the 

                                                            

5 The study population for each year includes those EGUs that reported both heat input and electric generation. 6 Temperature data is from NOAA’s Integrated Surface Data at http://www.ncdc.noaa.gov/data-access/land-basedstation-data/land-based-datasets/integrated-surface-database-isd.

9,500

9,550

9,600

9,650

9,700

9,750

9,800

9,850

9,900

9,950

10,000

20022003200420052006200720082009201020112012

Heat Rate (Btu/kWh)

Annual Heat Rate

11-yr Avg Heat Rate

Comment [A25]: Change in unit counts due to

using conditions that match preceding paragraph.   

Comment [A26]: PHK: corrected unit caps  in

chart legend
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

20 

impact of ambient conditions. For each of the plants in this study’s database, we identified the 

nearest stations that reported a minimum of 8,400 hourly observations in the calendar year (i.e., 

greater than 95% of the hours in a year). Generally, each plant was associated with 

meteorological data from the two closest stations over the eleven-year study period. The average 

distance between the plant and the nearest station was 22 miles and the average difference in 

elevation was 366 feet. At nine plants we used data with as few as 7,000 observations in order to 

keep the maximum difference in elevation under 1,000 feet. The EPA believes that nearby 

weather station measurements are a good approximation of ambient meteorological conditions at 

each facility. Joining the trimmed heat rate and hourly temperature datasets resulted in 

61,848,580 hourly records for the study population of EGUs (see Table 2-4). 

The results of this study are based on analyses of data from the population of coal-fired 

EGUs shown in Table 2-4. These units emitted 1,605 million tons of CO2 in 2012. In contrast, 

emissions of CO2 from coal-fired EGUs in the entire U.S. electric power sector in 2012 were at 

1,669 million tons according preliminary data from the EIA.7

 Since the study population of 

EGUs accounted for over 96% of CO2 emissions from the fleet of U.S. coal-fired EGUs, the 

EPA considers the results of this study to be applicable to the coal-fired fleet at large. 

2.5.2 Subcategorization 

In this analysis, units are not categorized by unit specific design characteristics or fuel 

because: (1) EGU-specific detailed design information on all factors that influence heat rate is 

not available, and (2) certain design characteristics are not easily categorized (e.g., EGUs use a 

large range of steam conditions). Several other studies do categorize EGUs broadly by capacity, 

thermodynamic cycle, and/or fuel rank. Although the EPA believes grouping by categories can 

provide a useful way of understanding the operating profile of an EGU and the fleet, the range of 

heat rates for the broad categories has significant overlap (see box and whisker chart in Figure 2-

2) and therefore makes it challenging to develop appropriate categorization. The figure below 

displays available information on coal-fired EGUs considered in this work for the years 2009-

2011 in typical subcategories of capacity, fuel rank, and thermodynamic cycle. As the figure 

reflects, the means are clustered and the ranges of heat rates overlap. 

                                                            

7

 Preliminary 2012 results from http://www.eia.gov/tools/faqs/faq.cfm?id=77&t=3.    
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

21 

 

Figure 2-2. Three-Year Average Heat Rates by Subcategory8

2.5.3 Observed Trends in the Period 2002-2012 

Three trends are notable for the study population during the period 2002-2012. 

Comparing the averages of the first three years (2002-2004) to the last three years (2010-2012), 

electric generation and gross heat rate declined by 9% and 2%, respectively. Capacity factor for 

the study population fell by 14% comparing the same time periods (see Table 2-5). The decrease 

in coal-fired generation and capacity factor may be because of reduced demand for electricity 

resulting from the recession starting in late 2008 and greater use of natural gas and renewables to 

generate electricity. 

The 11-year average annual gross heat rate for the study population of coal-fired EGUs 

(see Table 2-4) was 9,754 (Btu/kWh). The decrease in study population annual heat rate between 

2002 and 2012 may be due to several factors. Unit efficiency may have improved or units with 

lower heat rates may have taken up a larger share of generation. In addition, changes in reporting 

methodology described later in this chapter may be partly responsible. The minimum annual heat 

rate (9,643 (Btu/kWh)) occurred in 2008 and was approximately 1% below the 11-year average. 

                                                            

8 Abbreviations in the figure: BIT means bituminous, SUB means subbituminous, PC means petroleum coke, LIG 

means lignite, SUPER means supercritical, OVER/UNDER means greater/less than indicated MW capacity. Unit 

counts (n) by category: BIT SUPER, n=80; SUB SUPER, n=30; BIT OVER 200, n=196; PC, n=2; BIT 100 to 200, 

n=140; SUB OVER 100, n=299; LIG OVER 100, n=20; BIT UNDER 100, n=68; SUB UNDER 100, n=56; LIG 

UNDER 100, n=2. Total unit count is 893.

6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000

BIT SUPER

SUB SUPER

BIT OVER 200

PC

BIT 100 to 200

SUB OVER 100

LIG OVER 100

BIT UNDER 100

SUB UNDER 100

LIG UNDER 100

3-Year (2009-2011) Average Heat Rate (Btu/kWh)

Subcategory

Comment [A27]: Suggest including on the figure

the number of plants in each category (N=? for each

category).

EPA response: added counts to footnote.

Comment [A28]: The footnote uses SUB twice

for different terms.  Please clarify/fix.

EPA response: Done
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

22 

Table 2-5. Reported Annual Capacity Factor 2002-20129

Year Capacity 

Factor (%) 

2002 68 

2003 69 

2004 70 

2005 71 

2006 70 

2007 71 

2008 70 

2009 62 

2010 65 

2011 61 

2012 53 

2.5.4 Startups and Shutdowns - Impact On the Results of This Study 

During periods of startup and shutdown, EGUs are known to operate at higher heat rates. 

Therefore, we evaluated the potential impact of such events in our study. A startup event, as 

defined here, occurs when an EGU begins combusting fossil fuel and generates some measurable 

amount of electricity. Table 2-6 summarizes the study population average, maximum and total 

starts by year. On average, coal-fired EGUs start combusting fuel and generating electricity 11 

times per year. The total number (approximately 9,000) of starts for the study population of 

EGUs has remained stable over the study period. Our data reflects that some coal-fired units 

operate in a load following capacity and may report upwards of 200 starts in a single year, but 

these units tend to have low annual capacity factors. The subset of EGUs with more than 20 

annual startup and shutdown events is responsible for less than 4% of total generation in any 

study year. Therefore while the number of starts is an important variable at a small number of 

EGUs, its impact on heat rate performance evaluated in this study is considered to be marginal. 

                                                            

9 Table 2-5 shows data as reported to EPA as of May 8, 2014. 

Comment [A29]: Suggest making the Table title

more descriptive.  

Also could present fewer signficant  digits in table

...i.e. heat input‐ MM BTU x 106  ‐‐ 

EPA response: changed units and title.

Comment [A30]: This capacity factor decline in

2012 is very different from the CF used later, see

comment on page p. 35, i.e., 78% in 2020.  Which is

correct?  And, did coal CF at 44% factor into the EPA

modeling?

EPA response: The table has been updated to use all

reported data (rather than a trimmed data set). This

changes the capacity factor in 2012 to 53%. Note

the capacity factor of 78% is for the coal‐fired steam

fleet in base case, 2020. Note also that the study

population includes a significant number of EGUs

that IPM lists as retiring prior to 2016.

Comment [A31]: At about this point, the

chapter transitions into the quantitative analysis.

Suggest highlighting that in some way. There seem

to be several components of the quantitative

analysis: 1. Regressing HR onto CF and Temp; 2.

Looking at residual heat rates relative to model

prediction; 3. The “bin model” for assessing HRI

potential via best practice; 4. The assessment of HRI

potential via equipment upgrades. Suggest

presenting those more clearly either here or even

forward in the chapter in summary form.

EPA response: Thank you. We have added a

paragraph introducing more‐quantitative analyses.
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

23 

Table 2-6. EGU Start Count by Year 

Year Average Maximum (at 

any single EGU) Total 

2002 11.1 209 9,363 

2003 10.6 194 8,936

2004 10.7 134 9,081 

2005 11.0 183 9,265 

2006 10.5 139 8,859 

2007 10.5 164 8,908 

2008 10.4 134 8,880

2009 10.3 178 8,902 

2010 10.5 211 9,110 

2011 10.6 206 9,295 

2012 9.9 119 8,805 

To understand the potential for heat rate improvement available with existing coal-fired 

steam EGUs, the EPA conducted a number of quantitative analyses. These included: (1) 

regression analyses to understand the impact of capacity factor and ambient temperature; (2) 

using a bin model to determine the potential from best practices; and, (3) evaluating available 

data and information to assess the potential from equipment upgrades. These analyses are 

described in the following sections. 

2.5.5 Impact of capacity factor and ambient temperature 

Two important factors that affect heat rate at an EGU are hourly capacity factor and 

ambient temperature. In this section, we examine the impact of these two variables on heat rates 

of the EGUs in the study population. Power plant operators today typically use digital control 

systems to capture hundreds of data points in near real-time that are summarized in the unit heat 

rate statistic. EPA has access to a small fraction of that information. A key reason this study used 

capacity factor and ambient temperature as independent variables is that both were available as 

hourly data. Preliminary analyses of heat rate at higher time increments, such as month, were 

useful to describe aspects such as seasonality but we determined hourly data was necessary to 

understand how heat rate was responding to constantly changing operating conditions. We tested 

for collinearity between capacity factor and ambient temperature using a zero-order correlation 

Comment [A32]: After this sentence, suggest

adding a sentence stating why this is important to

do, and a sentence summarizing the approach.

(EPA) see updated text  
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

24 

matrix on the entire hourly data set. The correlation between the independent variables was -.048 

– well below an indication of collinearity. 

We also considered fixed unit characteristics such as unit type, fuel rank and age as 

independent variables. As noted above in the discussion on subcategorization, these factors can 

be helpful to understanding the heat rate performance of EGUs. The purpose of this study, 

however, is to find the potential for heat rate improvement across the fleet. We use heat rate 

variability as a key statistic to measure this potential. The correlation between the potential for 

heat rate improvement and fixed characteristics is typically low.10 

Coal-fired units are designed to operate most efficiently at full capacity. As a unit drops 

below this level, in general, heat rate will increase. The average capacity factor over 11 years for 

the study population is 67%, but as noted above, has moved markedly over the study period. This 

study looks at utilization level at both hour and year time scales. The two are related but reveal 

different information about how an EGU is operating. For example, for a unit to achieve a high 

annual capacity factor (e.g., over 90%) it must operate at a high load for most hours in a year. At 

lower capacity factors interpreting the relationship between hourly and yearly utilization levels 

becomes more complex. For example, an EGU may run at an annual 60% capacity factor by 

operating 8 months at near full capacity and generating no electricity the rest of the year, or it 

may run at lower utilization levels for most hours of the year in response to weather, generation 

cost, and transmission constraints. 

Ambient temperature can affect heat rate in two ways: 1) the efficiency of the 

thermodynamic steam cycle11 and, 2) in many regions of the country, as temperatures increase 

electricity demand and capacity factor follow. Figure 2-3 shows the average monthly capacity 

factor in 2012 alongside the climate normal monthly temperature.12 The lines intersect in the 

spring as temperatures begin to rise and the need for cooling drives electricity demand. 

                                                            

10 For example, the correlation between annual unit heat rate variability (discussed below as relative standard 

deviation) and unit nameplate capacity (MW) is in the -0.1 range. 11 The availability of a cold heat sink in the condenser is a key factor in that cycle. The design of the heat exchanger, 

type of cooling system and availability of water all have an impact on performance. An increase in ambient air 

temperature, and consequent increase in water temperature, typically lower the effectiveness of the cooling system, 

the condenser, and, therefore, overall plant efficiency.

12 Climate normal is the average of temperature (or other measure) over a prescribed 30-year interval and location. 

The chart shows the 1981-2010 climate normal monthly temperature at Baltimore-Washington Airport, MD.

Comment [A33]: Suggest discussing explicitly

the choice of CF as an independent variable – why is

it reasonable to think of it as an independent

element? Are there better factors that EPA could

have used but was unable to because of information

availability?

EPA response: see the preceding paragraph
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

25 

Generally, peak capacity factor and generation in most parts of the U.S. occur on the hottest days 

of the year. Yet, the relationship between ambient temperature and capacity factor is complex. 

Each plant responds differently depending on design, meteorological conditions and electricity 

demand. For example, a base load plant may operate at a high capacity factor seven days a week 

regardless of temperature. As noted above, the collinearity between these two variables is low. 

Figure 2-3. Monthly Capacity Factor, 2012. 

2.5.5.1 Regression Analysis to Assess Impact of Capacity Factor and Ambient Temperature on 

Heat Rate

Using the hourly data set, the EPA performed three regression analyses for each unityear: heat rate onto capacity factor, heat rate onto ambient temperature and heat rate onto 

capacity factor and ambient temperature. Since this analysis seeks to evaluate heat rate under 

normal operating conditions, we removed records with hourly heat rate values outside of +/- 2.6 

standard deviations (1.9% of records) before performing the regressions.13 Similarly to partial 

operating hours, these outliers tend to occur during low load conditions. The records trimmed 

amount to one-fourth of a percent of the total study population generation. Regression results 

describe the goodness of fit for the model and are expressed as the coefficient of determination 

or ‘r-squared’. To represent the relative contribution of varying unit capacities all results are 

generation-weighted.14 The average study population r-squared for the multivariate regression is 

26%. This means that hourly ambient temperature and capacity factor together explain 26% of 

                                                            

13 The 2.6 standard deviation bound is used in other EPA regulatory analyses. 14 In a weighted average, each component is multiplied by a factor reflecting its importance. In this case, generationweighted r-squared is the sum of r-squared for each unit multiplied by its annual generation divided by the sum of 

generation for all years.

0

20

40

60

80

100

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Electric generation monthly 2012 capacity 

factor and normal temperature

Capacity Factor

(Monthly %, 2012)

Climate Normal

(BWI, MD)

Comment [A34]: Capacity factor is also

influenced by temperature – any considerations for

collinearity effects in the regression?

EPA response: We tested for collinearity between

capacity factor and ambient temperature using a

zero‐order correlation matrix on the entire hourly

data set. The correlation between the independent

variables was ‐.048 – well below an indication of

collinearity.    
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

26 

the change in heat rate for the study population over the study period. The average study 

population r-squared from the single variable analysis of capacity factor is 16%; the 

corresponding result for temperature is 10%. This means that approximately 16% of the change 

in hourly heat rate is attributable to capacity factor and 10% to ambient temperature. These 

results, however, conceal considerable variability. Some EGUs, typically load-following, have 

an 11-year average r-squared for capacity factor exceeding 50%. At those EGUs, the capacity 

factor is a key variable influencing changes in heat rate. 

At approximately one-fourth of the study population the response to ambient temperature 

is larger than the response to capacity factor. At some individual EGUs, temperature may explain 

up to 30% of the change in heat rate. These are typically, but not exclusively, units with oncethrough, fresh water, cooling systems. Identifying temperature-responsive EGUs allows us to 

understand why heat rate may increase during periods of peak demand. These are the EGUs 

where the ambient temperature ‘signal’ is an important variable. At a typical EGU, summer 

month heat rates may increase by 2-4% compared to winter months, but at a temperatureresponsive EGU that figure may be as high as 10%. 

The multivariate partial regression coefficients show a similar pattern. The average unityear temperature coefficient (non-standardized) is 2, i.e., each degree F increase in temperature 

increases the heat rate by 2 Btu/kWh. Put another way, over a typical 40 degree F seasonal range 

(see Figure 2-3 the average change in heat rate that can be attributed to temperature is 80 

Btu/kWh, i.e., less than 1%. But the temperature coefficient at the individual unit level varies 

widely. For example, the average study population top quintile of temperature coefficient 

(descending) was 9, over four times greater than the study population mean. The average unityear coefficient (non-standardized) for capacity factor is -15, i.e., each 1% increase in capacity 

factor decreases the heat rate by 15 Btu/kWh. This means that on average, when a study 

population EGU increased capacity factor from 60% to 80% the heat rate decreased by 300 

Btu/kWh, i.e., approximately 3%. Although capacity factor is the more important explanatory 

variable, this is by no means universally true across the fleet or even year to year at the same 

EGU. 

Our analysis indicates that as EGUs moved from base load to load following, capacity 

factor tended to have a larger effect on heat rate. Since 2008, the study population capacity 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

27 

factors moved from the top load bin into lower load bins. This can be seen from Figure 2-4, 

which compares the study population duty cycles in 2008 to 2012. A significant share of 2008 

generation occurred at EGUs running at greater than 84% annual capacity factor. In 2012, little 

generation took place in that bin or above, and generation was reduced by about half in the next 

lower bin (78-83% capacity factor). 

Figure 2-4. Change in Annual Duty-Cycle 2008 and 2012 

2.5.6 Heat Rate Variability and Indication of Improvement Potential 

This study examines heat rate variability from the standpoint of statistical process 

control, which is utilized throughout the power industry. Several years ago, the EPA introduced 

process control charts for auditing emissions data reported under Part 75. Sources and vendors 

adopted the EPA methodology to identify potential problems early, before they significantly 

affect emission measurements. Heat rate lends itself to process control since it is the principal 

indicator that defines the quality of the electric generation process.15,16 Therefore, in general, 

high variability in heat rate values would reflect opportunities for process improvement. 

                                                            

15 “The principal indicator that defines the quality of the process is heat rate.” (Fredrick & Todd, 1993. Statistical 

Process Control Methods in Performance Monitoring. Available at 

famos.scientech.us/Papers/1993/1993section11.pdf) 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

28 

We use the relative standard deviation (RSD) of the hourly heat rates to evaluate each 

unit against its past performance and to compare with the study population. Each unit has up to 

eleven RSD values, i.e., one for each operating year between 2002 and 2012. The generationweighted mean RSD for the study population across 11 years is 5.4%. Table 2-8 summarizes 

eleven years of results for the study population by quartiles ordered by the 11-year generationweighted RSD average (ascending).17 The RSD of the top quartile (3.5%) is significantly lower 

than the study population generation-weighted mean RSD of 5.4%. Notably, the EGUs in the top 

quartile are not outliers; they report a third of all generation – the most of any segment. The 

results display a wide range of heat rate variability in the study population and thereby indicate 

the potential for heat rate improvement. 

Table 2-8. RSD in reported heat rate (generation weighted) 

Quartile RSD 

Average 

RSD 

Minimum 

RSD 

Maximum 

Share of 

Generation 

1 3.5 1.6 4.2 33

2 4.8 4.2 5.3 26

3 6.1 5.3 7.0 24

4 9.8 7.1 25.2 16

The study also examined EGU heat rate variability using the residual heat rate. The 

residual in a regression analysis is the difference between the observed value of the dependent 

variable (heat rate) and the predicted value. The intercept is the value where the linear regression 

crosses the y-axis. For each EGU, we calculated the residual heat rate by summing the residual 

for each hour to the intercept value. The standard deviation of the residual heat rate statistic is 

used to understand the amount of variability that is not explained by capacity factor and 

temperature. 

The average RSD corresponding to residual heat rate variability for each EGU is the 

generation-weighted average of up to eleven annual values. The study population generation-

                                                                                                                                                                                                16 Since accurate measurement is essential to process control the introduction of increasingly sophisticated digital 

control systems (DCS) presents new opportunities for finding inefficiencies. Vendors (ABB, Siemens, Emerson) 

claim heat rate improvements of 2-5 percent can result from upgrading to a modern DCS and advanced control 

technologies. The improvement can be even higher if system-wide real-time optimization is included. 17 Nine EGU RSD values exceeded 2.6 standard deviations above the mean and were removed from the results in the 

table.
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

29 

weighted mean RSD over the study period is 4.5%. This percentage represents the total 

variability across the study population that our analysis could not explain by hourly capacity 

factor or ambient temperature. Possible causes of this variability include changes in plant 

equipment, operating procedures and maintenance, fuels (particularly coal rank), reporting 

methodology, and unexplained factors. There is no temporal trend evident in the RSD. Table 2-9 

summarizes the study population generation-weighted average RSD of residual heat rate over the 

study period in quartiles ordered by the 11-year generation-weighted RSD average (ascending). 

Table 2-9. RSD of residual heat rate (generation weighted)18

Quartile RSD 

average 

RSD 

Minimum 

RSD 

Maximum 

1 2.7 0.0 3.2

2 3.6 3.2 4.1 

3 4.7 4.1 5.3 

4 6.9 5.3 10.4 

The weighted average RSD of the top quartile is 2.7% – well below the study population 

average of 4.5 %. This means that the residual hourly heat rates of these units generally stay in a 

narrow range within a given year. The maximum RSD in the top quartile is 3.2%. From the 

statistical process control point of view, these units appear to have low variability. 

The weighted average RSD of the bottom quartile is 6.9%, which is over twice that of the top 

quartile. This spread indicates that there is likely room for improvement in study population 

operation to reduce variability and heat rate. 

2.5.6.1 Heat rate variability and performance 

To examine the association between heat rate variability and heat rate performance this 

study examined the RSD for unit-year heat rates calculated from reported data. The study 

population generation-weighted annual RSD ranges between 5% and 6% during the study period. 

Figure 2-5 below summarizes the results of regressing RSD of heat rate onto annual heat rate.19

                                                            

18 This table excludes nine units where RSD exceeded 2.6 standard deviations from the mean. 19 The regression analysis was performed on 9,388 unit-years of study data which were trimmed to remove values 

outside 2.6 standard deviations.

Comment [A35]: This Table (and others like it)

might be more clearly presented as a simple line

graph.  

EPA response: Thank you.
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

30 

The r-squared result is 57%. These results indicate that, other factors held equal, if an EGU 

reduces heat rate variability, generally heat rate performance will improve. 

Figure 2-5. Regression analysis of reported heat rate RSD onto annual heat rate 

2.5.7 Units with large heat rate changes 

Over one third of the study population (355 units) reported at least one year-to-year 

change in heat rate greater than +/- 8.5%.20 We consider this magnitude to be in the upper range 

of what would be expected due to changes in fuel rank, operations and maintenance, or plant 

equipment. Table 2-10 presents the counts by three categories: EGUs with at least one year-toyear decrease in heat rate > 8.5%; units with at least one year-to-year increase in heat rate > 

8.5%; and, units with both. We examined whether the large heat rate changes were due to `yearto-year changes in capacity factor and found no correlation between the year-to-year changes in 

heat rate and capacity factor for any of the three groups in Table 2-10.21 This would indicate that 

other factors account for these large changes to heat rate. The EPA’s research found that 

approximately two-thirds of the large decreases in heat rate can be associated with changes in 

reporting method implemented to provide more accurate heat input data.22 The large changes 

                                                            

20 After removing unit-years where annual capacity factor fell under 50 percent the count is 313 EGUs. 21 The correlation remains weak even when limited to cases where capacity factor changed more than 30 percent. 22 EPA Reference method 2 specifies the normal procedure for measuring stack gas volumetric flow rate during a 

relative accuracy test audit. Methods 2F, 2G, 2H and CTM-041 are approved alternatives. Methods 2F and 2G 

correct measured flow rates for angular (non-axial) flow, Method 2H (for circular stacks) and conditional test 

method CTM-041 (method J, for rectangular stacks and ducts) are used to correct measured flow rates for velocity 

decay near the stack wall, using a “wall effects adjustment factor”. These alternative methodologies are optional. 

Therefore, given the additional complexity and cost of using these alternatives a source is likely to use them only if 

y = 0.002x - 14.254

R2 = 0.5704

5.0

5.2

5.4

5.6

5.8

6.0

6.2

9600 9650 9700 9750 9800 9850 9900 9950

RSD heat rate % 

(generation weighted)

Heat Rate
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

31 

noted at the remaining one-third could not be explained by changes in reporting methodology. 

Moreover, we found no correlation between changes in reporting method and heat rate RSD. 

Table 2-10. Year-to-Year Heat Rate Change 

Description Count 

Correlation of 

heat rate to 

capacity factor 

Units with only year-to-year heat rate decrease > 8.5% 166 .1 

Units with only year-to-year heat rate increase > 8.5% 80 .1 

Units with both year-to-year heat rate decrease and increase > 8.5% 355 .1 

The breakdown of the ‘large decrease’ EGUs by quartile, in Table 2-11 below, is consistent with 

the study population results shown in Table 2-8. This does not imply that the changes associated 

with a large year-to-year decrease, which may include operations and maintenance, more 

accurate reporting methods, or new equipment, do not affect heat rate variability. If they occur as 

part of an engineering effort to improve efficiency, heat rate variability may also be reduced. 

Table 2-11. RSD in reported heat rate of 166 ‘large 

decrease’ EGUs (generation weighted) 

Quartile RSD 

Average 

RSD 

Minimum 

RSD 

Maximum 

1 4.0 2.3 4.8 

2 5.3 4.8 5.9 

3 6.6 5.9 7.5 

4 10.5 7.6 26.5 

2.5.8 Assessment of heat rate improvement potential via best practices 

As mentioned before, across the study period, the effects of hourly capacity factor and 

ambient temperature explain a generation-weighted average of 26% of the change in study 

population heat rate.23 This means that on average 74% of the change remains unexplained after 

controlling for those factors. The residual heat rate analysis determined there is significant 

variation in the operation of EGUs. Since lower heat rate variability is associated with lower heat 

                                                                                                                                                                                               

the results are significantly lower volumetric stack gas flow. The EPA was unable to draw conclusions about the 

effect of changes in flow reporting methods on study population heat rate performance.

23 The 26% result is the generation weighted average of r-squared values from the multivariate regression analysis.
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

32 

rate, other factors held equal, the range of variation indicates that significant potential for heat 

rate improvement is available through the application of best practices. 

To control for known factors, the EPA constructed a model that groups each EGU’s 

hourly heat rate data into 14 temperature bins and 12 capacity factor bins, resulting in a 12 by 14 

matrix of 168 bins.24 For a given EGU, a temperature and capacity factor bin will have all the 

relevant hourly values over the eleven-year study period. For each bin with 15 or more values the 

model finds the reported hourly heat input value corresponding to the 10th percentile (p10).25

This means that approximately 90% of the heat input values in that bin exceed p10. For each 

unit, the model reduces the reported hourly heat input greater than p10 by a percentage of the 

distance between the reported value and p10 (e.g., 50% of the difference). The same statistical 

procedure is applied to every hour of heat input data in each bin. These reduced hourly heat input 

values are then used to calculate a reduced 11-year average heat rate for each unit. The percent 

difference between a unit’s reported 11-year average heat rate and the heat rate that corresponds 

to reduced heat inputs is the potential heat rate improvement for that unit.26 Using this approach, 

those units with the lowest variability (e.g., in the top quartile of residual heat rate variability) 

take proportionally smaller reductions. 

Table 2-12 below shows the model results with options of 10% to 50% stringency. For 

example, reducing reported heat input 10 % of the distance to the p10 value achieves a 1.3% 

study population wide reduction. Alternatively, a 50% reduction will result in a 6.7% study 

population wide improvement in heat rate. In effect, the model proportionately reduces heat rate 

variability and improves performance for each unit while controlling for temperature and 

capacity factor. The heat rate improvement for the study population is derived from the 

performance of each individual EGU as compared to its own record. 

                                                            

24 The matrix provides up to 168 bins but only 164 contained hourly values. Temperature bins ranged from -20 to 

greater than 110 with 10 degrees F in each. Capacity factor bins ranged from 0% to greater than 110% with 10% in 

each. 

25 Performing the calculation with a minimum of 30 values in each bin has a modest effect on the results in Table 2-

12. For example, a 30% reduction obtains a 3.9% fleet wide improvement in heat rate (rather than 4.0%). 26 Heat rate is calculated as the sum of heat input (btu, reported or reduced) divided by the sum of generation (kWh) 

for the given population and time period.  
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

33 

Table 2-12. Assessment of heat rate improvement potential via best practices 

% Reduction from 

reported heat input 

to p10 

Study population 

heat rate 

(Btu(kWh-gross)27

Reduced study 

population heat 

rate (Btu/kWhgross) 

Study population 

heat rate 

improvement (%)

10 

9,755 

9,625 1.3 

20 9,495 2.7 

30 9,365 4.0 

40 9,235 5.3 

50 9,105 6.7 

2.5.9 Assessment of potential heat rate improvement via equipment upgrades 

The EPA inspected the study population to find examples of EGUs that made significant 

year-to-year improvements in heat rate. After filtering out those cases that may have been the 

result of changes in capacity factor, reporting method, or other events, we identified 16 EGUs 

that reported a single year-to-year heat rate improvement of 3-8%. In two of these cases we were 

able to identify equipment upgrades responsible for 2-3% heat rate improvement using the 

applicable estimates from the Sargent & Lundy 2009 study. Similarly, in the other cases, while 

our research was unable to confirm specific equipment upgrades, based on the elimination of 

other possible explanations we believe that equipment upgrades were the most likely cause of 

some of the observed heat rate improvements. 

Two other sources provide information about heat rate improvements after equipment 

upgrades at existing plants. EPA Region 7 provided data with details on specific equipment 

modifications for seven coal-fired units at three anonymous plants. These included turbine 

efficiency and condenser performance upgrades, installation of variable frequency drive fans, 

reducing boiler air in-leakage and others. Together, these measures achieved from 0.25% to 

3.5% heat rate improvement at the seven EGUs. 

An EPA study (SRA, 2001) describes WEPCO’s two-phase efficiency program at four 

coal-fired plants over a ten-year period. In the first phase, 1990 – 1994, WEPCO installed 

                                                            

27 Fleet heat rate for study population as described in Table 2-4 is 9,754; the 9,755 value is derived from the dataset 

that includes hourly temperature values.

Comment [A36]: This technical analysis of the

possibility for heat rate improvements should be

peer reviewed.  Has EPA run this by the SAB, or does

EPA intend to do so after release of the proposal?

EPA response: Thanks for your comment

Comment [A37]: Do you really mean in a single

year to year change, as an indicator of a functional

change in the equipment?  Otherwise, 8% annual

improvement year after year is immense.  

Suggested edit, but change at EPA’s discretion.

EPA response: We meant one time – thank you.
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

34 

equipment upgrades that included retractable turbine packing, variable speed drives on the forced 

and induced draft fans, feed water heater replacements and new performance monitoring 

instrumentation. The four units reported heat rate improvements ranging from 2.3% – 4.1% as a 

result of the equipment upgrades. In the second phase, 1995-2000, WEPCO implemented 

changes that would generally fall into the best practices category: equipment control and 

metering upgrades, boiler cleaning, feed water heater improvements and reduced condenser air 

in-leakage and thermal losses. These gained an additional 0.5% per year heat rate improvement 

(for a total of 2.5%). 

The EPA also reviewed the engineering studies available in the literature and selected the 

Sargent & Lundy 2009 study as the basis for our assessment of heat rate improvement potentials 

from equipment and system upgrades. We focused on some thirteen heat rate improvement 

methods discussed by Sargent & Lundy, seen in Table 2-13. We used the average of the 

estimated $/kW costs for each method to develop the cost-ranked list of heat rate improvement 

methods (lowest cost at the top, highest at the bottom) shown in Table 2-13. The first nine items 

in Table 2-13 contribute about 15 percent of the total average $/kW cost for all items. We believe 

it is reasonable to consider those nine no-cost and low-cost heat rate improvement methods as 

belonging in the category of what has been described above as best practices. The remaining four 

methods are higher cost heat rate improvement items that we believe properly fall into the 

category discussed here as upgrades. Using an average of the ranges of potential Btu 

improvements estimated by Sargent & Lundy for the four upgrade methods, upgrades, as defined 

here, could provide a 4% heat rate improvement if all were applied on an EGU that has not 

already made these upgrades. 

Table 2-13. Sargent & Lundy Heat Rate Improvement Methods 

No-Cost and Low-Cost Options 

Condenser Cleaning 

Intelligent Soot Blowers 

ESP Modification 

Boiler Feed Pump Rebuild 

Air Heater and Duct Leakage Control 

Neural Network 

SCR System Modification 

FGD System Modification 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

35 

Cooling Tower Advanced Packing 

Higher Cost Options 

Economizer Replacement 

Acid Dew Point Control 

Combined VFD and Fan 

Turbine Overhaul 

We also examined the annual heat rate trend line for each unit using the method of least 

squares. Using the slope of the trend line, 2002-2012, as an indicator of the heat rate 

performance of an EGU, a negative slope would indicate that the heat rate has improved. The 

annual trend line incorporates performance due to operating conditions (capacity factor, 

temperature), coal rank, maintenance, reporting method changes, equipment upgrades, and other 

factors. Over 40% of units have a positive slope. This would imply that equipment maintenance 

and upgrades at a significant fraction of the study population have not been sufficient even to 

maintain the status quo. 

2.5.10 Combined study population results 

The EPA’s analysis finds that a total of 6% heat rate improvements for the coal study 

population can be achieved through two types of changes: best practices that have the potential 

to improve heat rate by 4% and equipment upgrades that have the potential to improve heat rate 

by 2%. 

The best practices results are supported by the variability analysis using 11 years of 

hourly data applied to each unit. This analysis found that the top quartile of EGUs reported 

significantly lower heat rate variability than the study population average. Reducing heat rate 

variability will generally also improve heat rate performance, other factors held equal. We found 

that a 4% improvement is determined by conservatively reducing heat input by 30% of the 

difference between the reported value and p10 in each unit’s capacity factor and ambient 

temperature bins. 

The 30% approach is in the middle of the range of options shown in Table 2-12 and is 

comparable to other approaches for measuring potential fleet heat rate improvement. For 

example, if each unit achieved heat rate performance equal to its best three-year moving average, 

the study population as a whole would post a 3.9% heat rate improvement. The best two-year 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

36 

moving average would achieve nearly a 5% improvement and the best single year over 6%. EPA 

believes that the minimum three-year moving average heat rate is a reasonable target for the 

improvement potential from applying best practices. Single year results could be due to unusual 

conditions, such as, an extended outage or weather. Using three consecutive years tends to 

smooth out the effect of equipment maintenance cycles and unusual meteorological patterns. 

The adjustment of heat rate using the 30% approach is approximately equal to improving 

the heat rate variability performance of the lower ranked EGUs by one quartile (see Table 2-8 

above). The top quartile has the lowest heat rate variability with an average RSD of 3.5% over a 

range of 1.6% to 4.2%. The bottom (fourth) quartile has an average RSD of 9.8%. The 30% 

approach proportionately trims the hourly heat input for each EGU in a given temperature and 

capacity factor bin. Using the bounds in Table 2-8, applying the 30% approach to the study 

population has the effect of nearly doubling the number of EGUs in the top quartile and reducing 

the count of those in the fourth quartile to 58 (of 218). These results appear reasonable. In effect, 

is the approach amounts to asking the lower ranked EGUs to improve heat rate variability to a 

level comparable to that which the top quartile of EGUs have maintained over an 11-year period. 

The equipment upgrades results are supported by numerous studies28 and by the EPA’s 

analysis of the costs and associated improvements in heat rate that can be attributed to equipment 

and system upgrades. We considered that a 4% reduction in heat rate might be achieved on a 

coal-steam unit by applying the four higher cost upgrade actions described in Table 2-13 above. 

However, because details of current actual unit configurations are unknown, and some units may 

have applied at least some of the upgrades, we conservatively estimate the heat rate improvement 

potential for upgrades at 2%. The EPA considers the results of this study to be applicable to the 

U.S. coal-fired fleet at large since the study population of EGUs accounted for over 96% of 2012 

electric sector CO2 emissions from coal. 

2.5.11 Sensitivity Analysis Removing Planned or Announced EGU Retirements 

                                                            

28 See discussion in Table 2-3 above, and the HRI Partial Bibliography at the end of this section 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

37 

The EPA’s research found 233 coal-fired, non-cogeneration EGUs that have announced 

they will retire before 2016.29 A sensitivity analysis was applied to the EGUs in the study 

population that plan to operate through 2015. The results are identical to the full population – 

both achieve a heat rate reduction of 4% under the 30% approach described in best practices. 

2.6 Heat Rate Improvement – Economics 

Most of the methods that can be applied to achieve a sustained Heat Rate Improvement 

(HRI) on a coal-steam EGU will entail a capital cost. These HRI capital costs can be economic to 

incur if they yield sufficient reductions in other current or potential costs, particularly reductions 

in coal fuel cost and any cost related to CO2 emissions. For the purpose of this TSD analysis, it is 

assumed that HRI can be economic if the annualized net savings (coal cost savings plus CO2

emission cost savings minus capital cost) is positive: 

Annual Net HRI Savings = Coal Cost Savings + CO2 Emission Cost Savings – Capital Cost30

2.6.1 Heat Rate Improvement Capital Cost Assumption 

The 2009 Sargent & Lundy study describes numerous well known and technically proven 

HRI methods for coal-steam EGUs. The study includes an estimated min-max range of heat rate 

improvement, and the min-max range of associated capital cost for each HRI method, for units 

ranging in size from 200 MW to 900 MW. If these methods and unit sizes are combined, as 

though they were all applied on a single EGU, the following range of Sargent & Lundy estimated 

Btu reductions and associated range of capital costs are obtained: 

Combined Min-Max HRI Btu Reduction: 415-1205 Btu 

Combined Min-Max HRI Capital Cost: $40-150/kW31

EPA Assumed Combined HRI Capital Cost: $100/kW 

                                                            

29 IPM documentation includes a list of the announced retirements. See Table 4-36 of IPM documentation: 

http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/v513/Chapter_4.pdf and 

http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/v513/NEEDS_v513.xlsx 30 This TSD analysis assesses a broadly combined application of multiple HRI methods. As estimated in the 2009 

Sargent & Lundy study most HRI-related O&M costs are sufficiently small relative to the associated annualized 

capital costs, such that they do not materially affect the economics of broadly combined HRI methods. The analysis 

therefore does not consider the small economic impact of HRI-related O&M costs. 31 Note that highest cost does not necessarily align with greatest heat rate improvement. A low cost HRI method

can have a large HRI potential (eg: upgraded digital control system, neural network). Also, economy of scale causes

most HRI methods to be more costly ($/kW) on smaller unit sizes.  

Comment [A38]: This is an important statement,

that is accorded little explanation.  I presume these

are coal EGUs? And is it plants or separate units?  

How did these anticipated requirements factor into

the state goal setting, and then into the IPM

modeling?   

EPA response: See updated footnote with IPM

source.
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

38 

The wide ranges of estimated HRI Btu and costs are indicative of the wide range of real 

differences in the many details of site specific EGU designs, fuel types, age, size, ambient 

conditions, current physical condition, etc. This TSD analysis therefore assumes $100/kW as a 

representative combined HRI capital cost to achieve whatever HRI Btu reduction is possible at 

an average site. The effect of a lower HRI cost is also examined. 

2.6.2 Heat Rate Reduction Assumption 

The weighted average annual net heat rate of the U.S. coal-steam EGU fleet in 2020 is 

projected at 10,450 Btu/kWh in the EPA’s IPMv5.13 Base Case modeling. As indicated by the 

Sargent & Lundy estimates given above, HRI methods could possibly reduce this average coal 

fleet heat rate by about 400 to 1200 Btu/kWh, or by about 4% to 12% of the projected 2020 

average, provided that all units were able to apply all of the combined HRI methods. The proviso 

is important to this analysis because the EPA expects that a significant fraction of the coal fleet 

has already applied some or many of the available HRI methods.32 

The EPA does not have sufficient site specific information to accurately estimate what 

percentage of the fleet has adopted various HRI methods, nor how effectively, and is not aware 

of any other investigator having sufficient information. HRI potential can therefore not be 

estimated at this time through analysis of the current equipment configurations of the coal steamEGU fleet. The EPA therefore analyzed 11 years of historical heat rate data and the literature on 

HRI methods, as discussed earlier in this TSD, to estimate that the U.S. coal-steam EGU fleet 

might reasonably be expected to reduce its annual average gross heat rate by about 6%. 

The EPA understands that any HRI method that reduces gross heat rate will also reduce 

net heat rate, and that some HRI methods reduce net heat rate without reducing gross heat rate. 

We expect that the HRI potential on a net output basis is somewhat greater than on a gross output 

basis, primarily through upgrades that result in reductions in auxiliary loads. For purposes of this 

TSD the EPA conservatively assumes that the coal fleet average net heat rate can be reduced by 

6%. 

                                                            

32 Based on the EPA informal discussions with Sargent & Lundy and other power sector engineering firms. The 

EPA has found no comprehensive data set on the extent to which specific HRI methods have already been applied at 

individual EGUs. The EPA believes that many EGU owners consider such information to be confidential. 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

39 

2.6.3 Heat Rate Improvement Breakeven Economic Analysis 

Figure 2-6 presents a simple breakeven economic analysis for combined HRI methods 

using the assumptions described above, also assuming there is no CO2 emission cost that is 

reduced via HRI. 

Figure 2-6. HRI Breakeven Economics 

Notes: 

1. Capital cost S/MWh assumes the following: HRI capital cost = $100/kW; capital charge rate 

= 14.3%; IPM projected 2020 annual capacity factor = 78% 

2. Coal fleet average 2020 net heat rate = 10,450 Btu/kWh; heat rate reduction = 6% 

Figure 2-6 shows that the average fleet-wide savings in coal cost would become greater 

than the annualized capital cost of an average 6% reduction in heat rate when the average fleetwide coal cost exceeds about $3.25/MMBtu. For comparison, the average U.S. power sector 

delivered cost of coal in 2020 is projected in EPA’s IPMv5.13 Base Case modeling at 

$2.62/MMBtu. For different assumptions, the HRI economic breakeven point would change 

directionally as follows: 

 If the HRI capital cost were on average less than the assumed $100/kW, 6% HRI would 

then become economic at lower coal costs. For example, if the average capital cost were 

actually $75/kW, a fleet-wide 6% HRI would become economic at an average coal cost 

of about $2.50/MMBtu, which is comparable to the U.S. power sector average costs of 

0.00

0.50

1.00

1.50

2.00

2.50

3.00

1.00 2.00 3.00 4.00

$/MWh‐net

Coal Cost  $/MMBtu

Coal Cost Savings   $/MWh‐net Capital Cost  $/MWh‐net

HRI Capital Cost = $100/kW
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

40 

$2.38/MMBtu for all coal ranks and $2.89/MMBtu for bituminous coals in 2012.33 This 

sensitivity indicates that fuel cost savings alone would make it economic for some of 

those EGUs currently using high cost bituminous coals to make HRI investments. 

 At an EGU net heat rate that is higher than the IPM projected 2020 average value of 

10,450 Btu/kWh, 6% HRI could be economic at coal costs lower than the values 

mentioned above. 

 If the average heat rate reduction were only 4% instead of the assumed 6%, at a cost of 

$100/kW, average coal costs would have to exceed $4/MMBtu for 4% HRI to be 

economic fleet wide, 

 But, if the average heat rate reduction were 4% at a cost of $50/kW, HRI could become 

economic at an average coal cost of about $2.50/MMBtu. 

 If there were additional HRI savings due to avoided future CO2 emission costs, HRI 

could become economic at lower coal costs, or at higher capital costs, or at lower heat 

rate reduction percentages. 

2.6.4 U.S. Coal-steam EGUs – Estimated Fleet-wide CO2 Reduction and Cost via HRI 

It is possible to make an order-of-magnitude estimate of the fleet-wide extent and costeffectiveness of HRI using reasonable assumptions as in the following example: 

Fleet-wide 2020 Assumptions (basis: similar to IPMv5.13 Base Case): 

 Coal fleet capacity applying combined HRI methods = 244,000 MW 

 Average CO2 emission rate = 0.976 tonne/MWh net 

 Average net heat rate = 10,450 Btu/kWh net 

 Average capacity factor = 78% 

 Pre-HRI CO2 emissions = 1.62 billion tonne/yr (calculated) 

 HRI Btu and CO2 reduction = 6% 

 HRI capital cost = $100/kW 

                                                            

33 EIA, Electricity DataTable 7.4, Average Weighted Cost of Fossil Fuels for the Electric Power Industry 2002-

2012, http://www.eia.gov/electricity/annual/html/epa_07_04.html 

Comment [A39]: Throughout the paper, it might

help to indicate the “fleet‐ wide” data refer to the

national fleet (i.e. all the coal facilities in the U.S.) if

this is the case.

  

EPA response: Thank you for the suggestion.  See

new section heading above.

Comment [A40]: It was ostensibly 44% in 2012 –

see Table 2‐5, page 19 above – or am I missing

something?  Please clarify.  Is this assuming a lot of

coal plant closures to get the capacity factor up?  If

so, please clarify the discrepancy/change over time

in the text and how this might affect state goal

setting,  especially building block 2?

EPA response: Good catch. See revised Table 2‐5

and associated text.  Also see the RIA and other TSD

discussions regarding IPM Base Case assumptions,

resulting 2020 coal capacity, etc.
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

41 

 Annual capital charge rate = 14.3% 

 Average coal cost = $2.62/MMBtu 

Estimated Fleet-wide Results: 

 Fleet-wide CO2 reduction via HRI = 97 million tonne/yr 

 Total HRI capital cost = $24 billion 

 Annualized HRI capital cost = $3.5 billion 

 Annual coal cost savings (cost) = $2.7 billion 

 Annual net savings (cost) = ($0.8 billion) 

 Annual net savings (cost) of CO2 reduction = ($7.7/tonne) 

2.6.5 Conclusion - HRI Economics 

This necessarily simplified HRI economic analysis supports the following summary conclusions: 

 Some degree of HRI is already economic for high heat rate – high coal cost EGUs 

 If a fleet-wide average 6% HRI is technically feasible, it would also be economic on the 

basis of fuel savings alone, before consideration of the value of the associated CO2

emission reductions, on a fleet-wide basis at today’s coal prices if the associated average 

capital cost is about $75/kW or less. 

 If a fleet-wide average 6% HRI is technically feasible, and the associated average capital 

cost is as much as $100/kW, 6% HRI could become economic on the basis of fuel 

savings alone, before consideration of the value of the associated CO2 emission 

reductions, if/when average coal prices rise to about $3.25/MMBtu (IPM projects coal at 

$2.62/MMBtu in 2020). 

 Even at a capital cost of $100/kW and an IPM projected 2020 coal price of 

$2.62/MMBtu, the fleet-wide cost of CO2 reduction via 6% HRI would be a relatively 

low $7.7/tonne. 

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

particular fleet-wide amount of HRI, it is clear that HRI is an available low-cost approach to CO2

reduction for existing coal-fired EGUs 

Definitions/Abbreviations

Btu British thermal unit 

capacity factor electricity generation expressed as a percentage of maximum 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

42 

electricity generation (i.e., actual generation / maximum potential 

generation) 

CO2 carbon dioxide 

EGU electric generating unit 

EIA Energy Information Administration 

EPA Environmental Protection Agency 

heat input amount of energy consumed in a combustion unit (e.g., boiler) 

expressed in Btu 

heat rate improvement decrease in the amount of heat input required to generate 1 kWh of 

electricity 

heat rate gross heat input required to generate 1 kWh of electricity, expressed 

in gross Btu/kWh. 

MMBtu million Btu 

MW megawatt 

PC pulverized coal (boiler) 

RSD Relative standard deviation 

S & L report 

Sargent & Lundy engineering study on the potential heat rate 

improvement from equipment upgrades (EPA 2009 version) 

[Available at: 

http://www.epa.gov/airmarket/resource/docs/coalfired.pdf] 

start 

a startup event in which an EGU begins combusting fossil fuel and 

generates some measurable amount of electricity before ceasing fossil 

fuel combustion 

unit-year data for one EGU over a one year period 

Docket Datasets 

Name Description 

hour_QA_data.txt 2002-2012 hourly dataset 

hour_QA_regression_data.txt 2002-2012 hourly dataset for regression analysis 

units_885.txt List of study units and characteristics 

year_bin_10_50_data.txt 2002-2012 unit-year binned results at 10 – 50% difference 

options 
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

43 

HRI Partial Bibliography 

PowerEng 2002, “Heat Rate Optimization Pays Dividends,” Power Engineering, January 1, 

2002, available at http://www.power-eng.com/articles/print/volume-106/issue-1/features/heatrate-optimization-pays-dividends.html

NETL 2008, Reducing CO2 Emissions by Improving the Efficiency of the Existing Coal-fired 

Power Plant Fleet, DOE/NETL-2008/1329, July 2008 

NETL 2009, Opportunities to Improve the Efficiency of Existing Coal-fired Power Plants, 

Workshop Report, NETL July 2009, available at http://www.netl.doe.gov/energyanalyses/pubs/NETL%20Power%20Plant%20Efficiency%20Workshop%20Report%20Final.pdf

NETL 2010, Improving the Thermal Efficiency of Coal-Fired Power Plants in the United States, 

DOE/NETL Technical Workshop Report, February 2010, available at 

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

ficCoalFiredPowerPlants-TechWorkshopRpt.pdf

NETL 2010a, Improving the Efficiency of Coal-Fired Power Plants for Near Term GHG 

Emissions Reductions, DOE/NETL-2010/1411, April 2010, available at 

http://www.alrc.doe.gov/energy-analyses/refshelf/PubDetails.aspx?Action=View&PubId=307

Sargent & Lundy 2009, Coal-Fired Power Plant Heat Rate Reductions, SL-009597, Final Report, 

January 2009, available at http://www.epa.gov/airmarkets/resource/docs/coalfired.pdf

Storm 2009, “Applying the Fundamentals for Best Heat Rate Performance of Pulverized Coal 

Fueled Boilers, Storm Technologies, Inc, EPRI 2009 Heat Rate Conference, available at 

http://www.stormeng.com/pdf/EPRI2009HeatRateConference%20FINAL.pdf

NRDC 2013, Closing the Power Plant Carbon Pollution Loophole, NRDC Report R:12-11-A, 

March 2013 available at http://www.nrdc.org/air/pollution-standards/files/pollution-standardsreport.pdf

Lehigh 2009, Reducing Heat Rates Of Coal-Fired Power Plants, Lehigh Energy Update, January 

2009, available at http://www.lehigh.edu/~inenr/leu/leu_61.pdf

ESC/OnLocation, Efficient Heat Rate Benchmarks for Coal-Fired Generating Units, draft, B. 

Roberts, Economic Sciences Corporation, L Goudarzi, OnLocation, Inc., 1998, available at 

http://www.onlocationinc.com/heatratepaper.pdf

CRS 2013, Increasing the Efficiency of Existing Coal-Fired Power Plants, Congressional 

Research Service, December 2013, available at http://www.fas.org/sgp/crs/misc/R43343.pdf

Evonik/VGB 2008, “Power Plant Performance Reporting and Improvement under the Provision 

of the Indian Energy Conservation Act – Output 1.1”, Evonik/VGB 2008, available at 

http://www.emt-india.net/PowerPlantComponent/Output1.1/Output1.1.pdf

IEA 2010, Power Generation from Coal - Measuring and Reporting Efficiency Performance and 

CO2 Emissions, OECD/IEA-CIAB 2010, available at 

http://www.iea.org/ciab/papers/power_generation_from_coal.pdf

EPRI 2009, Renewed Interest in Reducing Heat Rate, EPRI Journal, Spring 2009, available at 

http://mydocs.epri.com/docs/CorporateDocuments/EPRI_Journal/2009-

Spring/1019287_Heat%20Rate.pdf
DRAFT May 22, 2014 

INTERAGENCY WORKING COMMENTS ON DRAFT LANGUAGE UNDER EO12866

INTERAGENCY REVIEW. SUBJECT TO FURTHER POLICY REVIEW.

44 

EPRI/Korellis 2011, Identifying Ways to Improve Plant Heat Rate, Energy-Tech Magazine, 

March 2011, available at http://www.energy-tech.com/article.cfm?id=30528

EPRI 2011a , Opportunities to Enhance Electric Energy Efficiency in the Production and 

Delivery of Electricity, EPRI Technical Report 1024651, November 2011, available at 

http://www.google.com/url?sa=t&rct=j&q=&esrc=s&frm=1&source=web&cd=1&ved=0CCsQF

jAA&url=http%3A%2F%2Fwww.pserc.wisc.edu%2Fdocuments%2Fpublications%2Fspecial_in

terest_publications%2FEPRI_Electricity_Use_Report_Final_1024651.pdf&ei=Qo9yUvmwMY

Xb4AOZz4GYAw&usg=AFQjCNElzekbtoSNCR5SKFwkfbKx83p0Uw&bvm=bv.55819444,d.

dmg

RFF 2013, Regulating Greenhouse Gases from Coal Power Plants under the Clean Air Act, RFF 

DP 13-05, February 2013, available at http://www.rff.org/RFF/documents/RFF-DP-13-05.pdf