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

Sent: Wednesday, May 28, 2014 3:29 PM 

To: Cortney Higgins 

Cc: Culligan, Kevin; Stenhouse, Jeb 

Subject: Revised Chapters 2 & 3 of the GHG Abatement TSD 

Cortney – 

Attached please find revised versions of Chapters 2 and 3 of the GHG Abatement TSD. Regarding 

the heat rate improvement chapter (Chapter 2), we accepted all of DOE’s edits/formatting 

suggestions in the revised version. We also considered the recent RFF paper (provided by OMB) 

to include and reference in this chapter, but decided to not do so because: (1) the paper does not 

speak to the levels of improvements that may be practical, and (2) the cost effectiveness numbers 

in the paper appear to be inconsistent with our understanding of such numbers. 

Please let Kevin, Jeb, Ravi or me know if you have any 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 

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

combustion configurations. 

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. 
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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 

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 
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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. 

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 can be 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 higher moisture contents 

(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 size –EGU efficiency generally increases somewhat with size (e.g., from 200 MW 

to 800 MW) because: a) the boiler and steam turbine losses are lower for larger 

equipment compared to smaller equipment, b) larger units tend to be younger 

incorporating improvements from advanced technologies, and c) the economy of scale of 

larger units allows the use of higher cost improvements to be more economic. 

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

equipment reduces the overall efficiency of the EGU.
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 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. 

 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 

                                                            

1

 See HRI Partial Bibliography at the end of this chapter. 
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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 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. 

                                                            

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.
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The bottom ash handling system may be a candidate 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 to substantial heat rate improvements around 50-100 Btu/kWh, but is large 

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

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

installation of new control equipment. 

2.3.1.3 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 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 sootblower system, which also 

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

areas need sootblowing. Boiler efficiency improvements range from 30-150 Btu/kWh with 

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

costs. 
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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. For 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 and avoids capturing the heat transferred between the flue gas 

and pre-combustion air. The increased volume requires more power to move more flue gas. 

Improvements to seals on the air heaters reduce the leakages. Improvements to reduce air heater 

and duct leakages generally reduce 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. Injection of sorbents such as 

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

heater, it 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 on 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 

Replacement or overhaul 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 used to heat the boiler feedwater by extracting 

heat from the steam leaving the turbine section. The EGU efficiency can be increased by 
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improving the 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 can be used to provide 

better control over water quality and tube cleaning can be performed as needed. Heat rate 

reductions observed from 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 large amounts of 

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

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 can contribute to improvements in the plant heat 

rate are: (1) improve the forced draft and induced draft fan efficiencies, and (2) implement 

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 efficient option. There are many other methods, 

such as variable inlet vanes, variable frequency drives, and variable pitch blades, available to 
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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 

Variable frequency drives facilitate more efficient plant operation by reducing the 

auxiliary load significantly. The capital costs for upgrading all drives at an EGU can be $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 

sometimes decrease the power requirements significantly while maintaining the level of 

emissions reduction desired. The three technologies discussed below 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 use many types of flue gas desulfurization systems. 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 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 in 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 for 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 system, 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 required. If this is possible, a unit heat rate 

reduction of 16 Btu/kWh may be available. 
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2.3.4.2 Electrostatic Precipitator 

The best operation for an electrostatic precipitator involves maintaining the maximum 

applied voltage, but below the level at 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 boiler water is one of the most important aspects of the power plant. The quality of 

the water is a key factor affecting 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 blowdowns required, which allows for more steam 

in the turbine cycle. If the water is not properly maintained, heat transfer may be reduced by 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. 
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Table 2-2. Existing coal-fired EGU efficiency improvements reported for actual efficiency improve

Efficiency 

Improvement 

Technology

Description 

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 ratCO levels, oxygen levels, slag deposits, and burner metrics as well as advanced coal nozzles and plasma assistcoal combustion. 

Cooling System 

Heat Loss 

Recovery 

Recover a portion of the heat loss from the warm cooling water exiting the steam condenser prior to its circulathorough a cooling tower or discharge to a water body. The identified technologies include replacing the coolin

tower fill (heat transfer surface) and tuning the cooling tower and condenser. 

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 moistureFor power plants equipped with wet FGD systems, the flue gas is further cooled to approximately 125°F as it isprayed with the FGD reagent slurry. However, it may be possible to recover some of this lost energy in the flugas to preheat boiler feedwater via use of a condensing heat exchanger. 

Low-rank Coal 

Drying 

Subbituminous and lignite coals contain relatively large amounts of moisture (15 to 40%) compared to bitumincoal (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 low

for plants burning low-rank coal. The technologies include using waste heat from the flue gas and/or cooling wsystems to dry low-rank coal prior to combustion. 

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 inclu

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

detonation sootblowing. 

Steam Turbine 

Design 

There are recoverable energy losses that result from the mechanical design or physical condition of the st

turbine. For example, steam turbine manufacturers have improved the design of turbine blades and steam s

which can increase both efficiency and output (i.e., steam turbine dense pack technology). 

Source: National Energy Technology Laboratory (NETL), 2008. Reducing CO2 Emissions by Improving the Efficiency of the Existin

Fleet, DOE/NETL-2008/1329. U.S. Department of Energy, National Energy Technology Laboratory, Pittsburgh, PA. July 23, 200

<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 e

plant as follows: 87% boiler efficiency, 40% turbine efficiency, 98% generator efficiency, and 6% auxiliary load. Based on these a

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 improvem
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to achieve a 1% point increase in boiler efficiency, it would be converted to a 0.37 % point increase in overall efficiency. Likewisedecrease in net heat rate would be converted to a 0.30% point increase in overall efficiency. 
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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 one 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 
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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 

• 8.7% reduction in 

overall heat rate of 

coal-fired EGUs 
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Study 

ID 

Study Title 

Author 

Factors Used for 

Industry Grouping 

Key Study 

Assumptions 

Relevant HRI 

Results 

subbituminous units 

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. 
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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/
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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. 
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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 

                                                            

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

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-

                                                            

7

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

 

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 

                                                            

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
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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. 

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.
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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 

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

– well below an indication of collinearity. 
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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. 

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. 

                                                            

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.
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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 

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

                                                            

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 units.

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)
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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%. 

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 

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). 
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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. 

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 

                                                            

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) 

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. 
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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 generationweighted 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 

                                                            

17 Nine EGU RSD values exceeded 2.6 standard deviations above the mean and were removed from the results in the 

table.
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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

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. 

                                                            

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.
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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 

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 fleet heat rate performance.

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
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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 

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. 

                                                            

23 The 26% result is the generation weighted average of r-squared values from the multivariate regression analysis.
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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. 

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/kWhStudy population 

heat rate 

improvement (%) 

                                                            

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.   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.
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gross) 

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 for seven coal-fired units at three 

anonymous plants with details on specific equipment modifications. 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 

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 
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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 

Cooling Tower Advanced Packing 

Higher Cost Options 

Economizer Replacement 

Acid Dew Point Control 

Combined VFD and Fan 

Turbine Overhaul 
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We also examined the annual heat rate trend line for each unit developed 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 

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. 
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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 

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 percent difference option 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

                                                            

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

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. 
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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 

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 

                                                            

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 (e.g., upgraded digital control system, neural network). Also, economy of scale causes 

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

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. 
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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%. 

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: 

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
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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 

$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. 

                                                            

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 
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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 

 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. 
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 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 

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 
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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 
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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
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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
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GHG Abatement Measures Technical Support Document 

Chapter 3: CO2 Reduction Potential from Re-Dispatch of Existing Units 

Table of Contents 

Overview.......................................................................................................................................................2

Introduction ..................................................................................................................................................2

Power Sector Background............................................................................................................................. 3

Historical Context..........................................................................................................................................6

Natural Gas Supply......................................................................................................................................13

Technical Considerations............................................................................................................................ 14

Analysis of Cost‐Effectiveness..................................................................................................................... 21

Natural Gas Price Impacts........................................................................................................................... 27

 

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Overview 

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

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

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

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

examination focuses on opportunities to improve emissions intensity by increasing the utilization 

of existing natural gas combined cycle units. The TSD provides background on existing power 

plants, power system operation, and the economics of electricity production and delivery in the 

context of cost-effective CO2 emission reduction opportunities. 

Introduction

Electric system dispatch is typically defined as “the operation of generation facilities to 

produce energy at the lowest cost to reliably serve consumers, recognizing any operational limits 

of generation and transmission facilities.”1

 Electricity demand varies across geography and time 

in response to numerous conditions, such that electricity generators are constantly responding to 

changes in demand and “re-dispatching” to meet demand in the most reliable and cost-effective 

manner possible. 

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

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

EGUs as fungible, calling for electricity generation supply to meet demand usually by deploying 

the least expensive power source first.2

 

EGU operators and balancing authorities must take into account several constraints in 

dispatch, including transmission constraints as well as emission control programs and other 

environmental requirements. Such programs and requirements can change the relative cost of 

generating electricity among plants and/or limit the number of hours that a plant can run. For 

many years, EGU operators throughout the country have considered the emissions implications 

for pollutants such as SO2 and NOx when scheduling unit dispatch, in response to costs and 

                                                            

1

 Energy Policy Act of 2005 

2

 A balancing authority is the responsible entity that integrates resource plans ahead of time, maintains the balance 

between supply, demand, and generation within a balancing authority area, and supports interconnection frequency 

in real-time. http://www.nerc.com/files/glossary_of_terms.pdf 

Comment [A1]: General Comments:

1)The section on electric markets contains a

number of over‐simplifications. It’s not clear why

the section is necessary.

2)It should be made clear in introductory

sections (and throughout TSD) that the analysis

of building block 2/re‐dispatch is based on

dispatch to existing NGCC.   

Comment [A2]: EPA Response:

We feel that some context is useful for this chapter,

but we are open to specific suggestions for

improvements.

Clarifications also added for existing NGCC.

Deleted: reducing utilization of

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regulatory requirements. For example, EGU operators in 10 states participating in the Regional 

Greenhouse Gas Initiative have several years of experience with factoring CO2 emissions limits 

directly into bids for economic dispatch. The electric system’s carbon intensity can be lowered 

through re-dispatch among existing EGUs, particularly by shifting generation from coal-fired 

units to natural gas combined cycle (NGCC) units. 

Power Sector Background

 Electric Dispatch 

Electricity generation conforms to the principle of least-cost economic dispatch, which is 

“the operation of generation facilities to produce energy at the lowest cost to reliably serve 

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

 The 

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

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

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

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

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

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

increment of electric demand. These costs change over time depending upon a variety of factors 

like fuel prices, weather conditions, and overall demand levels. Since the fixed cost of power 

plants is a sunk cost, plant operators bid into electricity markets such that their variable costs are 

covered. For fossil fuel-fired electric generating units, variable costs are dominated by the cost of 

the fuel, although coal-fired units often also have considerable variable costs associated with 

running pollution controls.4

 Other generating technologies, like renewables, hydroelectric, and 

nuclear, have little or no variable costs and are generally dispatched to the extent possible. In 

order to maintain least-cost dispatch, the units with the lowest variable costs will be called upon 

first, then other units (with higher variable costs) will be called upon sequentially, such that total 

                                                            

3

 Federal Energy Regulatory Commission, 2005. Economic Dispatch: Concepts, Practices, and Issues 

4

 In addition to fuel costs, variable costs also include costs associated operating and maintenance, and costs of 

operating a pollution control and/or emission allowance charges. 

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system demand is met. The economic order in which units are dispatched to meet demand, at any 

particular point in time, is commonly called a dispatch “curve.”5 Balancing Authorities 

In states with cost-of-service regulation of vertically-integrated utilities who own 

generation, transmission, and distribution infrastructure, the utilities themselves often form the 

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

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

buy and sell power with other balancing authorities. 

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

RTOs and ISOs are generally responsible for moving electricity across larger areas in the most 

efficient and least-cost manner possible.6

 They coordinate, control, and monitor electricity 

transmission systems to ensure cost-effective and reliable delivery of power, and they are 

independent from market participants. ISOs grew out of the Federal Energy Regulatory 

Commission (FERC) requirements for existing power pools to satisfy the requirement of 

providing non-discriminatory access to transmission. Subsequently, FERC encouraged the 

voluntary formation of RTOs to administer the transmission grid on a regional basis throughout 

North America (including Canada). 

RTOs and ISOs administer wholesale power markets, which match the generation of 

electricity with the purchase of electricity (and ancillary services) prior to delivery to end-users. 

Companies that provide retail electricity (e.g., utilities and energy service companies) procure 

power through these wholesale electricity markets. 

State Public Utility Commissions (PUCs)7

Each state has a governing body that is tasked with regulating retail electricity rates and 

electric services to protect the public interest, ensure efficient and reliable delivery of electricity, 

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

Depending on market structure, PUCs also allocate costs among customers, design price 

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

                                                            

5

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

6

 http://www.ferc.gov/industries/electric/indus-act/rto.asp 

7

 These entities are sometimes called Utilities Commissions, Utility Regulatory Commissions, or Public Service 

Commissions.

Deleted: ¶

Page Break

¶

Comment [A3]: It’s probably more appropriate

to say that vertically‐integrated utilities seek the

lowest total cost, not the lowest marginal cost.

Comment [A4]: EPA Response:

It is EPA’s understanding that least‐cost dispatch

(system operation) is generally decided on a

marginal cost basis regardless of the type of utility.  

EPA recognizes that vertically‐integrated utilities

consider total costs in the context of longer‐term

capacity planning, but this document is focused on

operational dispatch patterns across existing units,

for which marginal cost is the relevant metric.
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utilities, and arbitrate disputes among relevant parties and stakeholders. In restructured markets, 

the PUC’s authority is generally applicable to the transmission and distribution system, since the 

generation and dispatch component is governed by RTOs and ISOs. In cost-of-service states, the 

PUC also has oversight of the generation and capacity planning components. 

Spot and Day-Ahead Markets 

RTOs and ISOs operate spot markets for wholesale power supply and demand for their 

designated area, including both day ahead and real-time (hourly, or shorter time periods). These 

markets are based on bids for supply and demand and operate according to rules established by 

FERC. The RTOs and ISOs use these markets for balancing power supply and load in their area 

and typically serve as the balancing authority for the same area. 

For areas not administered by RTOs and ISOs, dispatch is scheduled both day ahead and 

hourly, but is typically driven by the power supply costs and schedules of traditional utilities. 

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

dispatch purchased power from other suppliers when that power can be obtained at a cost 

savings. There is an active wholesale market for this power in the spot market, from individual 

sales and from exchanges. These markets typically sell power day-ahead but not hourly, and also 

sell power for longer periods, such as weekly or monthly. However, the actual dispatch and 

balancing of power is conducted by the utility based on its own scheduling and purchasing 

protocols and varies considerably from one utility to the next. 

As a balancing authority, the RTO or utility will balance demand, generation, and 

imports/exports in real time while maintaining system frequency and ensuring that the next 

hour’s demand, or load, is met. In addition, the transmission system is constantly monitored to 

ensure reliability limits are met, voltage levels are appropriate, and appropriate corrective action 

is taken when needed. 

Reliability 

As reliability coordinators, balancing authorities are responsible for the reliable operation of the 

bulk electric system. The bulk electric system refers to a large interconnected electrical system 

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

system operators continuously analyze real-time and forecasted load and transmission conditions 

to ensure that scheduled generation dispatch can meet load without adverse impacts. If the 
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scheduled dispatch is not feasible within the limits of the transmission system, it must be 

adjusted by the system operator. The North American Electric Reliability Corporation (NERC) 

develops and enforces the procedures to ensure reliability, in accordance with Federal laws and 

regulations, and with FERC oversight.8

 

Historical Context

In 2012, average CO2 emission rates9 across all the following technology categories on a net 

generation basis were: 

 Coal Steam - 2,220 lbs/MWh 

 Oil/Gas (O/G) Steam - 1,463 lbs/MWh 

 NGCC – 907 lbs/MWh 

Coal- and oil/gas-fired boilers are considerably higher-emitting sources than NGCCs, on 

average. Therefore, the replacement, or re-dispatch, of each megawatt-hour (MWh) from the 

average fossil fuel-fired boiler with each MWh from an average NGCC will result in notable 

CO2 emission reductions. 

The lower emission rate of NGCC conveys the potential of re-dispatch to reduce GHG 

emissions. However, the actual potential to realize emission reductions through this technology 

depends on the availability and capacity factors of the existing NGCC fleet. In order to redispatch from existing fossil fuel-fired boilers to existing NGCC, there needs to be some existing 

unused generation potential in the current NGCC fleet that could displace generation from more 

CO2 intensive generating resources. The term “availability” is a common engineering term used 

in the power sector, which reflects the percentage of period hours that a plant is available to 

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

day). The unavailable period is generally attributed to scheduled maintenance, unplanned 

                                                            

8

 http://www.nerc.com/ 

9

 Emission rates in this document are shown on a net generation basis and reflect Hawaii and Alaska sources. See 

“2012 eGrid Data” file provided in the docket 

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maintenance, and unplanned outages. The EPA assumes that NGCC has an availability of 87%.10 

Other reports suggest that NGCC availability factors may reach as high as 92%.11 

If the existing NGCC fleet was already operating at a level of 87% to 92%, there would 

not be any additional generation potential in the existing generating system for re-dispatch to 

those units. To evaluate re-dispatch opportunities to unused NGCC generation potential in the 

system, the EPA reviewed recent NGCC fleet operating data to determine capacity factors. 

Redispatch for GHG abatement purposes would require one net MWh of a lower emitting 

technology displacing one net MWh of generation from higher emitting technology. Therefore, 

when the EPA was assessing capacity factor it used the net generation of a given NGCC unit as 

the numerator. The EPA was interested in the relationship of a unit’s total net generation relative 

to its net generating capacity (i.e., capacity factor). Net generating capacity is a function of 

weather/temperature conditions at the site, which varies throughout the year. While some units 

may model actual weather adjusted capacity by the hour/minute, these data are not reported for 

the fleet. Therefore, the EPA used the nameplate capacity reported for units. The net generation 

was divided into the nameplate generation capacity of a unit multiplied by the number of hours 

in a year. This calculation of capacity factor provides an indication of how much net generation 

a unit is providing as a percent of its total generating capacity. Whereas availability refers to the 

maximum amount of generation that could be expected from a given source, the capacity factor 

refers to the actual utilization of that source on an annual basis. The EPA surveyed 2012 data for 

over 1800 NGCC units and observed that the NGCC fleet had an average capacity factor in the 

44% to 46% range for 2012.12 Since the fleet-wide capacity factor in 2012 was less than the 

availability assumed for the technology, the historical data suggests that there is a significant 

potential for re-dispatch from higher CO2 emitting resources to lower emitting NGCC 

generation. 

Availability for NGCC fleet................................87% to 92% 

2012 Capacity Factor for NGCC fleet.....................44% -46% 

                                                            

10 See Chapter 3, Table 3-18 at http://www.epa.gov/powersectormodeling/BaseCasev513.html 

11 http://www.power-eng.com/articles/print/volume-115/issue-2/features/higher-availability-of-gas-turbinecombined-cycle.html 

12 See “2012 eGrid Data” file provided in the docket for 44% figure. See EIA 860 and 923 for 46% value. 

Deleted: 

Comment [A5]: This is incorrect. Other forms of

generation are dispatchable. Please clarify.

Comment [A6]: EPA Response:

Clarification added.

Deleted: i

Deleted: this

Deleted: i

Deleted: sis

Deleted: reported 

Deleted: (84% to 89%)13
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To quantify the GHG reduction potential from re-dispatch, the EPA considered 

alternative capacity factor levels at which the NGCC fleet could be dispatched. Although the 

availability for NGCC units is assumed to be in the mid to high 80s, the EPA did not assume that 

each state’s NGCC fleet could collectively operate at this level on an annual basis. To determine 

reasonable average capacity factor ceilings for a state’s NGCC fleet as part of BSER, the EPA 

considered historical data and modeling projections describing NGCC characteristics and 

operating behavior. 

As seen in Table 3-1, the existing NGCC fleet is relatively young. More than 80% of the 

capacity has come online in the last 15 years.14 Of this capacity, almost all are a highly efficient 

class of NGCCs that are able to achieve high availability factors. 

Table 3-1: Existing NGCC Capacity, by Age15

Online Year Capacity 

(Name Plate Capacity – MW) 

Percentage of 

Total Existing NGCC 

Fleet 

Pre 1950 103 0% 

1950-1959 1,769 0.7% 

1960-1969 3,087 1.3% 

1970-1979 6,909 2.8% 

1980-1989 7,658 3.1% 

1990-1999 28,467 11.7% 

2000-2009 174,947 71.7% 

2010+ 21,068 8.6% 

Total 244,008 100% 

                                                            

14 See the National Electricity Energy Data Systems (NEEDS) file at 

http://www.epa.gov/powersectormodeling/BaseCasev513.html 

15 See “Operable” worksheet in “GeneratorY2012” Workbook in 2012 Zip file at 

http://www.eia.gov/electricity/data/eia860/ 
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Of 464 NGCC plants generating in 2012 and greater than 25 MW, the EPA observed that 

50 plants (more than 10% of NGCC plants) had a net generation value that was greater than or 

equal to its nameplate capacity x 8784 hours * 70%. That is, a capacity factor that was 70% or 

greater (see Figures 3-1 and 3-2).16 

Figure 3-2: NGCC Plant Distribution by Capacity Factors (2012) 

17 

Table 3-X: Plant Distribution of Existing NGCCs (2012) 

Capacity Factor # of NGCC 

plants 

% of NGCC 

Plants 

Less than 5% 40 8.62% 

5%-9% 26 5.60% 

10%-14% 23 4.96% 

15%-19% 25 5.39% 

20%-24% 16 3.45% 

25%-29% 18 3.88% 

30%-34% 27 5.82% 

35%-39% 38 8.19% 

40%-44% 30 6.47% 

45%-49% 36 7.76% 

50%-54% 33 7.11% 

                                                            

16 See “2012 eGrid Data” file provided in the docket 

17 EIA Forms 860 and 923. CA and CT Prime Mover categories

Deleted: 1,812

Deleted: NGCC

Deleted: units operating

Deleted: 179

Deleted: units

Deleted: 10

Deleted: units

Deleted: Figure 3-1: Cumulative NGCC 

Capacity Factor vs. Percentage of Units¶ ... [1]

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0

20

40

60

80

100

120

140

160

180

200

# of Units

Unit Distr

... [2]

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55%-59% 39 8.41% 

60%-64% 36 7.76% 

65%-69% 27 5.82% 

70%-74% 20 4.31% 

75%-79% 13 2.80% 

80-84% 10 2.16% 

85-89% 4 0.86% 

Greater than 90% 3 0.65% 

In 2012, more than 10% of NGCC plants operated at an annual capacity factor of 70% or 

higher. This subset of NGCCs was largely dispatched to provide base load power. While only 

10% of plants operated at 70% or higher capacity factor on an annual basis, the fleet of NGCC 

units was relied upon heavily during certain periods of time, in response to higher demand. On a 

seasonal basis, a significant number of units achieved capacity factors greater than 50%, and 

even up to 80%. Using data reported to the EPA,18 and looking more closely at data during the 

summer and winter peak electricity demand timeframes nationwide, more than 10% of NGCCs

were operated at a capacity factor greater than 70%.19 In fact, 19% of NGCCs achieved 70% 

capacity factor during the winter of 2011/2012 and 20% hit that level or higher during the 

summer.20 During periods where demand levels are typically lower, some NGCCs were idled or 

operated at lower capacity factors. Nonetheless, a notable number of existing NGCCs have 

demonstrated the ability to achieve a 70% capacity factor for extended periods of time. These 

units achieved high capacity factors without adverse effects on the electric system. While many 

units demonstrated an ability to deliver net generation that was more than 70% of their 

nameplate capacity, the EPA assumed that 70% was a reasonable fleet-wide ceiling for each 

state. It should also be noted, roughly 6% of units (107 units) operated at a 75% capacity factor, 

or higher, in 2012. In addition, 16% of units (291 units) operated at 65%, or higher.

Over the last several years, advances in the production of natural gas have helped reduce 

natural gas prices and improved the competitive position of gas-fired units relative to coal-fired 

units. As a result, operators have shifted significant quantities of generation from coal units to 

NGCCs, absent any federal CO2 requirements. 2012 net generation from NGCC units grew to 

                                                            

18 Air Markets Program Data (at http://ampd.epa.gov/ampd/). 

19 Summer defined as June, July and August. Winter defined as December, January, and February. Estimates are 

for units for which data was provided to EPA. 

20 Air Markets Program Data (http://ampd.epa.gov/ampd/). Winter includes December of 2011, January and 

February of 2012. Summer includes June, July, and August. 

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Comment [A7]: The use of historical data has

little bearing on the capability of the entire fleet to

reach a given level of dispatch. It only shows the

ability of individual units to reach high levels, but it

is already well‐established (and noted elsewhere)

that these plants are designed to operate at yearly

capacity factors in excess of 85%. The main

constraints on reaching high capacity factors for

existing NGCC generation are system constraints

including the need for dispatchable generation to

meet intermediate load. These are addressed later

in the TSD.

Comment [A8]: EPA Response:

We agree with the comment, but it is important to

show all evidence where it exists, supporting the

conclusion that higher capacity factors can be met.  

By using historical data, we show that this is not

merely a theoretical or engineering argument, but

actually happens, in practice, fairly routinely at

individual units. The other arguments mentioned in

the comment are addressed throughout this TSD.

Comment [A9]: EPA Response:

We agree with the comment, but it is important to

show all evidence where it exists, supporting the

conclusion that higher capacity factors can be met.  

By using historical data, we show that this is not

merely a theoretical or engineering argument, but

actually happens, in practice, fairly routinely at

individual units. The other arguments mentioned in

the comment are addressed throughout this TSD.
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981 TWh, up from 796 TWh in 2011 (22% growth in one year). The extent of this capability 

varies by region, based on factors such as the mix of EGU types and the amount of available 

NGCC capacity. 

An analysis of historical dispatch across the generating fleet of coal and NGCC units for 

2011 and 2012 provides some implicit measures of the cost dynamics between the two 

technologies. For example, one is able to look at the change in the prices of coal and gas to 

gauge the relative costs of generating, or dispatch, for each technology. While there are wideranging costs at the unit level, an aggregated assessment of the relative economics is informative 

and can provide a metric for assessing the implications of dispatch as it relates to emissions of 

CO2. 

The potential for redispatch from CO2 intensive sources to less CO2 intensive sources is 

evidenced in historical data. EIA form 860 and 923 data demonstrate an increase in NGCC 

generation and fuel use between 2011 and 2012 of more than 20% (even though the NGCC fleet 

capacity rose by just 3%). As NGCC generation rose by approximately 185 TWh, coal 

generation fell by approximately 216 TWh. The significant redispatch from coal to gas over just 

a one year period demonstrates the ability for the quick re-dispatch in response to market or 

economic drivers. 

The increase in the NGCC utilization was in large part driven by the decrease in natural 

gas prices to historic lows (see Table 3-2). Henry Hub natural gas prices averaged $4.00/mmBtu 

in 2011 and $2.76/mmBtu in 2012. This $1.24/mmBtu creates an additional incentive for 

redispatch from coal generation to NGCC relative to 2011 dispatch economics. The fuel 

advantage is similar to the incentive that a $15/metric ton of CO2 price signal would create.21 

This historical data also shows a sharp increase in the NGCC fleet’s capacity factor from the 

high 30s to the mid 40s. During that same period, net coal generation dropped by an amount 

similar to the increase observed in NGCC net generation. Furthermore, natural gas supply is 

                                                            

21 Assumes 11,000 Btu/KWh heat rate and 2354 lb/MWh emision rate for coal, 8000 Btu/KWh and 926 lbs/MWh 

for NGCC (based of “2012 eGrid file”) 
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expected to grow more than 20% by 2020 relative to its 2012 levels, creating more fuel resources 

to foster the potential continued and increasing redispatch to NGCC generating technology.22

Table 3-2: 2011 and 2012 Gas and Coal Generation23

Year 

NGCC 

Name Plate 

Capacity 

(GW) 

NGCC Heat 

Input for 

Electricity 

(TBtu) 

NGCC Net 

Generation 

(TWh) 

NGCC 

Capacity 

Factor 

Henry Hub

Natural Gas 

Price 

($/mmBtu 

Coal Net 

Generation 

(TWh) 

2011 239 5,912 796 38% $4.00 1,719 

2012 244 7,224 981 46% $2.76 1,503 

The demonstrated ability of the NGCC plants to consistently operate at levels greater 

than 70% of their nameplate capacity (e.g., this was the utilization level of the ~ 90th percentile 

plant), the historic evidence supporting quick and significant redispatch to NGCC, and the costeffectiveness of high NGCC utilization demonstrated later in this TSD all supported the notion of 

a NGCC fleet capacity factor of 70% as a reasonable ceiling in the EPA’s BSER approach. 

For purposes of establishing state goals, historical electric generation data (2012) was 

used to apply each building block and develop each state’s goal (expressed as an emissions rate, 

lbs/MWh). In 2012, electric generation from existing NGCC units likely subject to the 111(d) 

applicability criteria was 959 TWh.24 After the application of NGCC re-dispatch to the 70% 

level,25 these same existing sources were calculated to collectively generate 1,390 TWh. Adding 

in the existing sources that were not yet online in 2012 (under construction) increases total 

NGCC generation calculated in the goal setting to 1,444 TWh. 

Although, states may choose to comply with state goals through other abatement 

measures, the EPA believes that upwards of 1,400 TWh from existing and under construction 

NGCCs is achievable. As a reference point, NGCC generation increased by approximately 430 

TWh (an 81% increase) between 2005 and 2012. EPA is calculating that NGCC generation in 

                                                            

22 http://www.eia.gov/oiaf/aeo/tablebrowser/#release=AEO2014ER&subject=0-AEO2014ER&table=13-

AEO2014ER®ion=0-0&cases=ref2014er-d102413a 

23 EIA form 860 and EIA form 923 

24 For covered sources. 25 This dispatch level is a ceiling dependent upon available existing steam generation that can be decreased.  As a

result, not all states achieve the assumed 70% re‐dispatch for purposes of goal setting (see Goal Setting chapter).

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2020 could increase by approximately 47% form today’s levels. This reflects a smaller ramp rate 

in NGCC generation than has been observed from 2005 to 2012. 

Table 3-3: Historic and Assumed Generation Patterns for State Goal Setting 

 

NGCC 

Name Plate 

Capacity 

(GW) 

NGCC Net 

Generation 

(TWh) 

Growth in 

NGCC 

Generation 

from 2005 to 

2012 

Growth in 

NGCC 

Generation 

from 2012 to 

2020 

Nationwide 

NGCC 

Capacity 

Factor 

2005 199 551 NA NA 32% 

2012 244 981 81% NA 46% 

2020 State Goal Calculation 256 1,444 47% 64% 

Natural Gas Supply 

The EPA expects the growth in NGCC generation assumed in goal setting to be feasible 

and consistent with domestic natural supplies. Increases in the natural gas resource base have led 

to fundamental changes in the outlook for natural gas. There is general agreement that 

recoverable natural gas resources will be substantially higher for the foreseeable future than 

previously anticipated, exerting downward pressure on natural gas prices.26

According to EIA, natural gas proved reserves have doubled between 2000 and 2012.27 

Domestic production has increased by 32% over that same timeframe (from 19.2 TCF to 25.3 

TCF). EIA’s Annual Energy Outlook for 2014 projects that production will further increase to 

29.1 TCF, due to increased supplies and favorable market conditions. For comparison, NGCC 

generation growth of 450 TWh (calculated in goal setting) would result in increased gas 

consumption of roughly 3.5 TCF for the electricity sector.28 

                                                            

26 National Petroleum Council. 2011. Prudent Development: Realizing the Potential of North America's Abundant  

Natural Gas and Oil Resources. http://www.npc.org/reports/rd.html (see Figure 1.2 on p. 47). 27 http://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=3&pid=3&aid=6 28 Assuming 1,024 Btu/cubic foot and 10,000 Btu/KWh

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The National Petroleum Council (NPC), a privately funded advisory committee 

established by the Secretary of Energy, recently updated a major resource study and concluded 

that “the potential supply of North American natural gas is far bigger than was thought even a 

few years ago,” after large increases in shale resource estimates.29 

Figure 3-3: U.S. Natural Gas Technically Recoverable Resources (from NPC, 2011)30

Technical Considerations 

Emission reductions through re-dispatch are largely determined by the ability to change 

the utilization of existing generating units, relative to current utilization levels. Other influences 

include physical limitations of the electric transmission system and considerations for reliability, 

timing, and cost. 

NGCC Availability 

                                                            

29 National Petroleum Council, 2012 (http://www.npc.org/PD_update‐80112.pdf) 30 http://www.npc.org/reports/rd.html

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

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

national average annual capacity factors in the range of 40-50%.31,32 However, NGCCs are 

designed for, and are demonstrably capable of, reliable and efficient operation at much higher 

annual capacity factors, as shown in observed historical data for particular units and their design 

and engineering specifications. 

The capability of NGCCs to operate at capacity factors of 70% and greater is indicated, in 

part, by statistics on the average availability factor of NGCCs. 33 Annual availability is the ratio 

of annual hours that an EGU is operating or considered able to operate (not in a forced or 

maintenance outage) to the hours in a year. The average availability factor for NGCCs in the 

U.S. generally exceeds 85%, and can exceed 90% for selected groups, as reported to NERC.34,35 

Advanced NGCCs being built today have availability factors of over 95%. According to one 

NGCC manufacturer, these highly efficient units already represent over 15 percent of total 

installed capacity nationwide, including all electric generating sources (as of 2010).36

These high-efficiency and high-availability NGCC units were first introduced around 

1995 and have consistently reported availability factors of 90 to 92 percent to NERC (compared 

to 95 percent or greater availabilities reported by current vintage F class and H class turbines 

from General Electric Power Systems).37 Data reported to NERC from NGCC units greater than 

50 MW in 1994 through 1998 shows similar availability factors (generally exceeding 89 

percent). 

Natural Gas Pipeline and Electricity Transmission 

                                                            

31 EIA, Today In Energy, January 15, 2014, http://www.eia.gov/todayinenergy/detail.cfm?id=14611 (for recent data) 32 EIA, Electric Power Annual 2009, http://www.eia.gov/electricity/annual/archive/03482009.pdf (Table 5-2 for 

2009 and earlier data) 

33 NERC, 2008-2012 Generating Unit Statistical Brochure – All Units Reporting, 

http://www.nerc.com/pa/RAPA/gads/Pages/Reports.aspx

34 Power Engineering, Negotiating Availability Guarantees for Gas Turbine Plants, 03/01/2001, http://www.powereng.com/articles/print/volume-105/issue-3/features/negotiating-availability-guarantees-for-gas-turbine-plants.html 35 Power Engineering, Higher Availability of Gas Turbine Combined Cycle 02/01/2011, http://www.powereng.com/articles/print/volume-115/issue-2/features/higher-availability-of-gas-turbine-combined-cycle.html 36 http://site.ge-energy.com/corporate/network/downloads/7FA_Evolution.pdf 

37 GE Power Systems submitted to U.S. Department of Energy, 2000. Utility Advanced Turbine Systems 

Technology Readiness Testing Phase 3 Restructured. DOE Cooperative Agreement No. DE-FC21-95MC31176—

30. 
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The EPA believes that the natural gas pipeline and electricity transmission networks can 

support aggregate operation of the NGCC fleet at up to a 70% capacity factor on average, either 

as they currently exist or with modifications that can be reasonably expected in the time frame 

for compliance with this rule. Existing NGCCs are already connected to both the power and 

natural gas networks and, while constraints to specific unit operations can occur in either or both 

networks during peak pipeline flow or electricity use, the rule allows for emission rate averaging 

across multiple units and across time for compliance. As a consequence of this averaging 

flexibility, constraints that occur at peak times are unlikely to be a barrier to achieving 

compliance with the rule, because these peak times are only a small percentage of the year and 

will constrain only a limited percentage of the state-wide NGCC fleet. The ability for the current 

fleet to ramp up significantly to meet changes in demand can be seen from the increased use of 

natural gas that occurred in 2012 in response to historically low natural gas prices. Power plant 

use of natural gas use in 2012 increased by 20% over 201138 and resulted in a national average 

capacity factor for NGCC of 45.8% on average, and higher in some states.39

During the peak hours of the day (which vary by region and season), NGCC capacity 

factors are typically well above average capacity factors.40 The pattern of capacity utilization by 

hour for 2005 to 2010 is shown in Figure 3-4. In this figure, capacity factors in 2010 are 

approximately 50% from the Hour 11 to Hour 21.41 The persistence of this hourly pattern across 

years shows the pattern to be stable. Since the average capacity factor for combined cycle units 

in 2010 from the same information source was 39%42, this indicates that the current system can 

support levels of approximately 11% above the average capacity factor. These peak hours are the 

period when there are most likely to be constraints on the pipeline or electricity transmission 

networks; during other hours of the day, continued NGCC operation at equal, or higher levels, 

are technically feasible but may be limited by economic considerations (e.g., whether NGCCs 

can offer least-cost electricity compared to other sources at those times). As a result, the current 

system is already able to support national average capacity factors in the mid to high 50’s for 

                                                            

38 Energy Information Administration, U.S. Natural Gas Consumption by End Use, 

http://www.eia.gov/dnav/ng/ng_cons_sum_dcu_nus_a.htm. 

39 Source: Air Markets Program Data (AMPD), ampd.epa.gov, EPA, 2014 

40 Energy Information Administration, Today in Energy, July 9, 2011. Average utilization of the nation’s natural gas 

combined-cycle power plant fleet is rising. http://www.eia.gov/todayinenergy/detail.cfm?id=1730# 41 In this figure, hour 11 is the hour ending at 11 AM, and similarly for other hours. 

42 Air Markets Program Data (AMPD), ampd.epa.gov, EPA, 2014

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NGCC for peak. It is reasonable to expect that average capacity factors could be extended to 

higher levels at all hours without experiencing technical feasibility barriers from either pipeline 

supplies or electricity transmission. 

Figure 3-4. Average Utilization of Natural Gas Combined Cycle Power Plant Fleet 

Although there can be site-specific constraints on utilization at some NGCC facilities, 

several factors support the ability of the power and natural gas pipeline systems to respond 

effectively with increases in infrastructure when needed to alleviate these barriers. For example, 

in recent years, the power transmission system has responded with increased transmission 

infrastructure when needed to allow the retirement of uneconomic coal plants.43 This rule 

provides for flexible implementation that will permit efficient scheduling of infrastructure 

upgrades as needed. Upgrades to pipeline and transmission infrastructure potentially needed to 

meet additional use of existing facilities will generally be less extensive than upgrades of that 

infrastructure potentially needed for siting of new capacity. In addition, this proposed rule is 

                                                            

43 See http://www.pjm.com/~/media/about-pjm/newsroom/2013-releases/20131211-pjm-board-authorizes-4.6-

billion-in-chnages-to-regional-electric-grid.ashx for an example of short term transmission upgrades performed to 

facilitate environmental compliance. For technical description of these upgrades, see: 

http://www.pjm.com/~/media/committees-groups/committees/teac/20131211/20131211-december-2013-pjm-boardapproval-of-rtep-whitepaper.ashx. 

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expected to result in significantly higher levels of end-use energy efficiency, which will reduce 

the load on the electricity transmission and natural gas pipeline infrastructure, while also 

providing other system wide benefits, such as decreased need for new generating units and 

reduced peak demands. 

In addition, natural gas pipeline capacity is regularly added in response to increased gas 

demand and supply, such as the addition of large amounts of new NGCC capacity in 2001 to 

2003, or the delivery to market of unconventional gas supplies since 2008.44 These pipeline 

capacity increases have added significant deliverability to the natural gas pipeline network to 

meet the potential demands from increased use of existing NGCCs. Over a longer time period, 

much more significant pipeline expansion is possible. In previous studies, when the pipeline 

system was expected to face very large demands for natural gas use by electric utilities about 10 

years ago, increases of up to 30% in total deliverability out of the pipeline system were judged to 

be possible by the pipeline industry.45 There have also been notable capacity expansions over 

the past five years, in response to increased natural gas supply estimates and advances in drilling 

techniques.46 

To examine the potential for increases in pipeline deliverability, the EPA analyzed the 

pipeline flow data from the Energy Information Administration. These data provide pipeline 

capacity for inflows and outflows by state. However, since the natural gas pipeline system is a 

network for flows into, across, and out of states and broader area, the level of gas supply that can 

be firmly delivered to a particular region depends on the amount of natural gas the will be 

required to be delivered out of the region to other regions. Consequently, it is important to focus 

on the net capacity – the difference between inflow capacity and outflow capacity -- in the 

relevant areas. The regions used by EIA for measuring regional natural gas deliverability are 

shown in Figure 3-5. Of these regions, the key regions for the analysis of the potential impact of 

the proposed rule are those natural gas consuming areas where there could be increases in natural 

gas consumption as a result of re-dispatch to comply with the proposed rule. These are the 

                                                            

44 Energy Information Administration, Today in Energy, Natural Gas Pipeline Additions in 2011. Additions 

averaged around 20Bcf per day from 2008 to 2011. 

45 Pipeline and Storage Infrastructure Requirements for a 30 Tcf Market, INGAA Foundation, 1999 (Updated July, 

2004); U.S. gas groups confident of 30-tcf market, Oil and Gas Journal, 1999. 

46 http://www.eia.gov/naturalgas/data.cfm#pipelines 

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Northeast, Southeast, Midwest and Western regions in Figure 35. The net pipeline capacity for 

these regions from 2005 to 2011 is shown in Table 3-4 below. 

Table 3-4. Natural Gas Pipeline Net Capacity by Region, 2005 - 2011 by Gas Consuming 

Area47

Region 2005 2006 2007 2008 2009 2010 2011

Capacity in MMCF/day 

Midwest 17,102 17,232 17,452 17,302 18,714 18,564 18,414 

Northeast 11,199 11,219 11,384 11,929 12,079 12,229 12,379 

Southeast 12,921 12,901 12,736 15,741 18,241 20,797 20,797 

Western 11,882 11,882 12,496 12,496 12,496 12,641 14,407 

All Areas 53,104 53,234 54,068 57,468 61,530 64,231 65,997 

Percent Change from 2005 

Midwest 0.0% 0.8% 2.0% 1.2% 9.4% 8.5% 7.7%

Northeast 0.0% 0.2% 1.7% 6.5% 7.9% 9.2% 10.5%

Southeast 0.0% -0.2% -1.4% 21.8% 41.2% 61.0% 61.0%

Western 0.0% 0.0% 5.2% 5.2% 5.2% 6.4% 21.3%

All Areas 0.0% 0.2% 1.8% 8.2% 15.9% 21.0% 24.3%

As a conservative assumption, the increase from the period 2005 to 2010 can be used as 

an estimator of the potential increase in pipeline capacity to accommodate compliance with the 

rule between 2015 and 2020. This is an extremely conservative assumption, since compliance is 

measured over a longer period and is not limited to re-dispatch approaches. Moreover, increased 

use of natural gas in existing facilities can be largely met with expansions to existing pipeline 

facilities and corridors, so that the types of capacity expansion required will be less expensive 

and take less time than new pipelines. Over 2005-2010, the total gas deliverability in gas 

consuming areas increased by 21%. Since the power sector currently uses approximately 30% of 

the total national natural gas consumption, and gas usage in other sectors is expected to be 

essentially flat through 2020, this historical increase indicates that pipeline capacity will be 

adequate to support any compliance changes in re-dispatch of natural gas power plants. 

                                                            

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

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Figure 3-5. Energy Information Administration Natural Gas Pipeline Regions

Source: Energy Information Administration. 

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Recent pipeline construction has continued to support the increasing need for natural gas. 

According to information released in April, 2014 by the EIA,48 118 pipeline projects were 

completed and placed into service from 2010 to 2014, totaling 4,699 miles of pipe, and 44,107 

MMcf per day of additional pipeline capacity. 

For projects expected to be in service from April, 2014 through 2016, EIA reports 47 

projects, with planned capacity additions of 20,505 MMcf per day and 1,567 miles of pipe. 

These projects cover all major gas consuming areas of the US, and include both new pipeline 

construction, such as the Spectra Energy’s NEXUS Gas Transmission project in the Upper 

Midwest and pipeline expansions such as the Tennessee Gas Pipeline Project in Connecticut. 

The electric transmission system has also been expanded over the past few years, and 

continued investment is expected. As of 2012, The EIA reports 187 thousand circuit miles of 

high voltage transmission in the US at 100 kV. There are 8 thousand miles of planned expansion 

in 2013, with a total 26 thousand miles proposed from 2013 to 2018.49

According to the Edison Electric Institute,50 member companies are planning over 170 

projects through 2024, totaling approximately $60.6 billion (this is only a portion of the total 

transmission investment anticipated). Approximately 75 percent of the reported projects are high 

voltage (345 kV and higher), representing over 13,000 line miles. 

Analysis of Cost-Effectiveness 

To further evaluate the technical capability of the natural gas supply and delivery system 

to provide increased quantities of natural gas and the capability of the electricity transmission 

system to accommodate shifting generation patterns, EPA employed the Integrated Planning 

Model (IPM), a multi-regional, dynamic, deterministic linear programming model of the U.S. 

electric power sector that the EPA has used for over two decades to evaluate the economic and 

emission impacts of prospective environmental policies. IPM provides a wide array of 

                                                            

48 See www.eia.gov/naturalgas/pipelines/EIA‐NaturalGasPipelineProjects.xls.   49 http://www.eia.gov/electricity/annual/

50

http://www.eei.org/issuesandpolicy/transmission/Documents/Trans_Project_lowres_bookmarked.pdf

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projections related to the electric power sector and its related markets (including least cost 

capacity expansion and electricity dispatch projections) while meeting fuel supply, transmission, 

dispatch, and reliability constraints. 

Natural gas supply, demand, transportation, storage, and related costs are modeled 

directly in IPM through the incorporation of a natural gas module. The module includes a detail 

rich representation of the natural gas pipeline network inclusive of discount curves that represent 

the marginal value of gas transmission as a function of the pipeline’s load factor. IPM’s natural 

gas module has the capability to expand pipeline capacity on an economic basis. 

At the unit level, IPM contains a detailed representation of new and existing resource 

options, inclusive of key operational limitations. For example, turn down constraints are 

designed to account for the cycling capabilities of EGUs to ensure that the model properly 

reflects the distinct operating characteristics of peaking, cycling, and base load units. EPA 

believes IPM represents a powerful tool to evaluate the technical feasibility of requiring 

increasing levels of re-dispatch from higher to lower-emitting EGUs. 

The EPA has conducted extensive analysis to quantify the opportunity to reduce CO2

emissions through re-dispatch. As part of this effort, the EPA conducted an initial set of analyses 

utilizing the Integrated Planning Model (IPM) to provide a framework for understanding the 

broader economic and emissions implications of shifting generation from coal-fired steam EGUs 

to NGCC units within defined areas.51 In the most restrictive scenarios, re-dispatch was 

simulated only between EGUs located in the same state. These scenarios were designed to 

consider, even under a restrictive interpretation of the degree of re-dispatch that might constitute 

a component of BSER under CAA section 111(d),52 to what extent existing NGCC units could 

increase their dispatch cost-effectively taking into account the impact of that behavior on prices 

of natural gas and electricity. To evaluate how EGU operators and balancing authorities could 

                                                            

51 IPM is a multi-regional, dynamic, deterministic linear programming model of the U.S. electric power sector. It 

provides forecasts of least cost capacity expansion, electricity dispatch, and emission control strategies while

meeting energy demand and environmental, transmission, dispatch, and reliability constraints. Full documentation 

of the IPM model can be found at http://www.epa.gov/powersectormodeling 52 In practice, unit dispatch does not respect state boundaries because least-cost supply must be balanced with 

demand in real time over grid interconnects which span multiple states (with the exception of the Electric Reliability 

Council of Texas interconnect). The design of this modeling scenario assumes artificial constraints on re-dispatch to 

force such behavior to respect state boundaries, given the context of this rulemaking’s quantification of individual 

state goals. These state boundary constraints necessarily forgo cost-effective opportunities to re-dispatch units in 

different states; as a result, costs and prices in this analysis are overstated. 
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respond to a state’s goal by incentivizing re-dispatch from more carbon-intensive to less carbonintensive EGUs, the EPA introduced two additional elements to the IPM framework: 

1. The application of a CO2 charge to the variable cost of dispatch for all existing coal 

steam boilers, IGCC units, and oil/gas steam boilers greater than 25 MW and with a CO2

emissions rate greater than 1,100 lbs/MWh.53

2. Generation constraints that maintain the sum of state-level generation in the Base Case54

from existing NGCC of any size, plus existing coal steam, IGCC and oil/gas steam 

boilers greater than 25 MW and with a CO2 emissions rate >1,100 lbs/MWh. 

These elements test the economic and technical potential for re-dispatch by: (1) increasing 

dispatch costs for affected coal steam, IGCC, and O/G steam EGUs within each state, and (2) 

requiring that any reduction in output from those sources be offset in its entirety by an increase 

in output from that state’s existing NGCC capacity. Utilizing IPM to conduct this analysis 

ensures an integrated, least-cost, technically feasible solution subject to power sector system 

reliability constraints, fuel market impacts, natural gas transmission and distribution networks, 

electric power transmission constraints, and unit-specific characteristics (e.g., operating and 

maintenance costs, heat rate, turndown/cycling behavior). 

Cost and Availability of Economic Re-Dispatch Opportunities

In executing this analysis, the EPA conducted a number of scenarios to quantify the 

relationship between the amount and cost of re-dispatch. Figure 3-6 below presents the projected 

national average capacity factor for NGCCs in 2020 (the first year of the assumed re-dispatch 

incentive) and the associated average $/tonne of CO2 reduced by comparing emissions and costs 

against the EPA’s Base Case (the difference in total system cost divided by the difference in 

power sector emissions). While the charge is applied exogenously in IPM, the average $/tonne 

shown below is calculated from the modeling results and projections by dividing the increase in 

total system costs (compared to the base case) by the total change in CO2 emissions (compared to 

the base case). 

                                                            

53 The addition of CO2 costs represents a simple analytic approach to estimating the cost-effective CO2 reductions 

under this building block and acts as a proxy for some existing state policies that shift dispatch. In actual plan 

implementation, states would be free to select any policy approach that has the net effect of reducing the carbon 

intensity of generation and/or reducing overall emissions from affected sources. 

54 http://epa.gov/powersectormodeling/BaseCasev513.html

Comment [A10]: By imposing a carbon price,

one is modeling the most economically efficient

method of abatement, not the costs associated with

an increase in NGCC capacity factors. The former

has little bearing on the latter.

Comment [A11]: EPA Response:

EPA believes the commenter has mischaracterized

this particular analysis.  The charge is applied to a

certain set of sources to explore the concept of

redispatch and assess the potential impacts, to

directly support BSER.  The combination of the

charge on steam boilers with the constraint to

maintain total generation across fossil fuel‐fired

steam boilers and NGCCs is specifically designed to

analyze the costs associated with an increase in

NGCC capacity factors.

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Comment [A12]: EPA Comment:

While latter portions of this section have heavy

redline, it largely reflects reordering, and maintains

the same context (for the most part).

Deleted: ¶

Deleted: 5

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Figure 3-6: NGCC National Capacity Factor, 2020 Initial Analyses 

The EPA believes average $/tonne – which is distinct from the $/tonne CO2 cost imposed in 

these analytic scenarios on affected coal steam, IGCC, and O/G steam EGUs – to be the most 

relevant metric in evaluating cost-effectiveness. System cost changes necessarily encompass all 

elements of cost across power, transmission, and fuel markets and therefore provide the most 

comprehensive perspective regarding cost-effectiveness. 

 

58%

62%

65%

68% 71%

74% 75%

0%

10%

20%

30%

40%

50%

60%

70%

80%

 ‐  10 20 30 40 50 60

Average Capacity Factor

Average $/tonne

Projected Capacity Factor for Existing NGCCs (2020)

Deleted: 5

Comment [A13]: $/tonne costs seem high –

would be helpful to have more detail on

methodology/analysis used to derive these costs?  

Comment [A14]: EPA Response:

Some clarifying language was added to distinguish

between the “charge” and the avg $/tonne, and

how it is calculated.

Deleted: 

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Projected Ca

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Table 3-5: Power Sector Emission Reductions Due to Re-Dispatch 

CO2 Cost Level 

Imposed 

National Average 

NGCC 

Capacity Factor 

Average Cost 

($/tonne) 

2020 Power Sector CO2

Emissions 

(MMT / % Change from 

2020) 

Base Case 52% N/A 2,161 

$10/tonne 58% $17 2,038 / -6% 

$15/tonne 62% $18 1,997 / -8% 

$20/tonne 65% $21 1,961 / -9% 

$25/tonne 68% $27 1,928 / -11% 

$30/tonne 71% $34 1,901 / -12% 

$40/tonne 74% $44 1,866 / -14% 

$50/tonne 75% $50 1,852 / -14% 

 

Although the EPA views this estimated range of average $/tonne costs as reasonable, we 

expect the costs of implementing this requirement in a compliance55 setting will be considerably 

lower for several reasons: 

 Analytic construct used to simulate re-dispatch incentive: As described earlier in this 

chapter, the EPA’s initial analyses utilized CO2 charges on the variable cost of dispatch 

for existing coal steam, IGCC, and O/G steam with emission rates greater than 1,100 

lbs/MWh and a capacity greater than 25 MW, as an analytic construct to induce redispatch behavior in the model to existing NGCC facilities. The CO2 charge was applied 

uniformly to all states in order to quantify the ultimate amount of in-state re-dispatch 

opportunities available as that charge is increased across scenarios. In the initial analyses, 

low levels of CO2 charges produce cost-effective re-dispatch opportunities relative to the 

Base Case in almost all states. However, as the CO2 costs are increased to higher levels, 

economic re-dispatch, opportunities within some states may eventually plateau – a point 

                                                            

55 The Regulatory Impact Analysis supporting the proposal examines, in an illustrative manner, how the power

sector could respond to the state goals that are calculated from all of the building blocks in a cost effective

manner.

Deleted: 4

Comment [A15]: Can this be quantified?

Comment [A16]: EPA Response:

Footnote added.  This is shown, in an illustrative

manner, in the RIA.
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clearly illustrated in the declining slope of the best-fit line in Figure 3-6. A uniform 

application of the same rising CO2 charge in all states produces an outcome for many 

states where the additional CO2 costs imposed on affected coal steam, IGCC, and O/G 

steam are not able to produce incremental economic re-dispatch at units within that state; 

therefore, the additional costs imposed by these higher CO2 charges overstate the actual 

$/tonne necessary to induce achievable re-dispatch in each state. 

 Potential for multi-state compliance: The EPA also analyzed scenarios where shifting of 

generation among EGUs was not limited by state boundaries. In one set of analyses, redispatch was allowed to occur across the multi-state regions defined by NERC 

assessment areas (subject to other real-world constraints specified in the model, including 

transmission limits). In these scenarios with greater re-dispatch flexibility, the system 

was able to achieve 8% greater CO2 emission reductions at an identical CO2 charge

(relative to a scenario where it was limited on a state basis), demonstrating that the main 

analysis’s imposition of artificial re-dispatch boundaries on state borders overstates the 

cost-effectiveness of re-dispatch potential. 

To evaluate how EGU owners and grid operators could respond to a state plan’s possible 

requirements, signals, or incentives to re-dispatch from more carbon-intensive to less carbonintensive EGUs, the EPA also analyzed an additional series of scenarios in which the fleet of 

NGCC units nationwide was required, on average, to achieve a specified annual utilization rate. 

Specifically, the scenarios required average NGCC unit utilization rates of 65, 70, and 75 

percent. For each scenario, dispatch decisions are allowed such that electricity demand is met at 

the lowest total cost, subject to all other specified operating and reliability constraints for the 

scenario, including the aforementioned state-by-state generation levels from the base case. This 

constraint effectively requires states that decrease coal generation to offset, in equal amounts, 

NGCC generation. Collectively, states must achieve the required capacity factor for NGCCs. 

 

 The costs and economic impacts of the various scenarios were evaluated by comparing 

the total costs and emissions from each scenario to the costs and emissions from a business-asusual scenario. For the scenarios reflecting a 65, 70, and 75 percent NGCC utilization rate, 

comparison to the business-as-usual case indicates that the average cost of the CO2 reductions 

achieved over the 2020-2029 period was $21, $30, and $40 per metric ton of CO2, respectively. 

Deleted: 5

Comment [A18]: EPA Response:

Clarification added.

Comment [A17]: Can you clarify if this 8%

greater emission reduction is relative to base case

or relative to reductions achieved from redispatch

without regional scenario?

Deleted: , 

Comment [A19]: Also, can this be stated

alternately as a drop in cost for a given CO2

reduction level?

Comment [A20]: EPA Response:

The commenter’s intuition is correct, but we do not

have analytic scenarios that can quantify this

concept because we did not model “a given CO2

reduction level” between state and regional re‐

dispatch scenarios.

Comment [A21]: It would be helpful 

to have the results – can EPA 

provide this? Will it be in the 

docket?

Comment [A22]: EPA Response:  EPA will

provide this modeling in the docket for the

proposal.

Deleted: The costs and economic impacts of the 

various scenarios were evaluated by comparing the 

total costs and emissions from each scenario to the 

costs and emissions from a business-as-usual 

scenario. For the scenario reflecting a 70 percent 

NGCC utilization rate and a constraint prohibiting 

interstate re-dispatch opportunities, comparison to 

the business-as-usual case indicates that the average 

cost of the CO2 reductions achieved ranged from 

[$xx] to [$xx] per metric ton of CO2.

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However, we also note that the costs just described are higher than we would expect to actually 

occur in real-world compliance with this proposal’s goals. This is because only 29 state goals are 

premised on the existing NGCC fleet achieving an average capacity factor of 70 percent. 

Consequently, a 70 percent utilization rate target for the existing NGCC fleet requires an average 

national capacity factor of 63 percent. 

The EPA also analyzed dispatch-only scenarios where shifting of generation among 

EGUs was limited by state boundaries. In these scenarios with less re-dispatch flexibility, the 

cost of achieving the quantity of CO2 reductions corresponding to a nationwide average NGCC 

unit utilization of 70% was $33 per ton. 

Table 3-6: IPM Results from Re-Dispatch Scenarios 

Existing NGCC 

Average 

National 

Capacity 

Factor 

Re-Dispatch 

Constraint 

Average Cost 

($/tonne, 2020-

2029) 

Average CO2

Emissions 

(MMT, 2020-

2029) 

Reductions 

from Base Case 

(%, 2020-2029) 

Base Case NA NA 2,215 NA 

65% Regional $21 2,022 9% 

70% Regional $30 1,969 11% 

75% Regional $40 1,915 14% 

65% State $22 2,024 9% 

70% State $33 1,971 11% 

Natural Gas Price Impacts 

The extent of re-dispatch estimated in this building block can be achieved without 

causing significant economic impacts. For example, in neither of the 70 percent NGCC unit 

utilization rate scenarios – re-dispatch limited to regional or state boundaries – did delivered 

natural gas price projections increase by more than 10 percent in the 2020-2029 period, which is 

Deleted: These scenarios include constraints such 

that there will be no cost-effective re-dispatch 

opportunities among EGUs in different states, 

contrary to the reality that interstate flows of 

electricity routinely change in response to economic 

conditions. As a result, this case assumes no use of 

interstate electricity trading as a cost reduction 

mechanism, and thus overstates the impacts of redispatch. ¶

In the 70 percent NGCC unit utilization rate 

scenario with restrictions on interstate re-dispatch, 

natural gas prices were projected to increase by a 

range of from [x] percent to [x] percent, which is ... [40]

Comment [A23]: A lot of text has been devoted

to demonstrating how the NGCC fleet will cope with

increase dispatch from the coal fleet.  Analysis

should also be included of how the coal fleet will

respond to various levels of redispatch, and if there

is information on how coal plants can dial down

output when demand is reduced without having to

switch off.

Comment [A24]: EPA Response:

Some additional detail that addresses this comment

was added earlier, in the context of the modeling

framework.

Comment [A25]: Can the actual numbers on

costs be presented in a table or other format, other

than just stating “notably lower”?

Comment [A26]: A table has been added.

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well within the range of historical natural gas price volatility. For example, the year-to-year 

percentage difference in Henry Hub prices reported by the Energy Information Administration 

averaged 18.5% over the period from 1981 to 2012.56 Projected wholesale electricity price 

increases over the same period were less than 7 percent in both cases, which similarly is well 

within the range of historic electric price variability. For example, the average year-to-year price 

change for the PJM region was 19.5 percent over the period from 2000 to 2013. For all the ISOs 

in the East, the variation is virtually unchanged from the PJM example (at 19.6%).57 

However, for the reasons previously discussed with respect to estimated costs per ton of 

CO2, the actual implementation is expected to result in notably lower economic impacts, 

including natural gas price impacts, and are considerably larger than would actually occur in 

real-world compliance with this rule’s proposed goals. 

Table 3-X: National Average Delivered Natural Gas Price, 

Power Sector (Average 2020-2029) 

Existing NGCC 

Average National 

Capacity Factor 

Re-Dispatch 

Constraint Price ($/mmBtu) % Change 

Base Case NA $5.94 

65% Regional $6.36 7% 

70% Regional $6.53 10% 

75% Regional $6.69 13% 

65% State $6.37 7% 

70% State $6.52 10% 

Table 3-X: National Average Delivered Natural Gas Price, 

 Power Sector (Average 2020-2029) 

Price ($/mmBtu) % Change 

Base Case $6.05 

$10/tonne $6.21 3% 

$15/tonne $6.33 5% 

                                                            

56 http://www.eia.gov/dnav/ng/hist/rngwhhdA.htm

57 ISO Real‐Time data for all hours, from Ventx Velocity Suite data across Eastern ISOs (PJM, NYISO,ISO‐NE and

Midcontinent ISO).

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$20/tonne $6.42 6% 

$25/tonne $6.57 9% 

$30/tonne $6.61 9% 

$40/tonne $6.78 12% 

$50/tonne $6.90 14% 

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Figure 3-1: Cumulative NGCC Capacity Factor vs. Percentage of Units

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These scenarios include constraints such that there will be no cost-effective re-dispatch 

opportunities among EGUs in different states, contrary to the reality that interstate flows of 

electricity routinely change in response to economic conditions. As a result, this case assumes no 

use of interstate electricity trading as a cost reduction mechanism, and thus overstates the 

impacts of re-dispatch. 

 In the 70 percent NGCC unit utilization rate scenario with restrictions on 

interstate re-dispatch, natural gas prices were projected to increase by a range of from [x] percent 
to [x] percent, which is within the range of historic natural gas price variability. Projected 

wholesale electricity price increases ranged from [x] to [x] [A1][A2]percent, which similarly is 

within the range of historic electric price variability. 

The EPA also analyzed dispatch-only scenarios where shifting of generation among EGUs was 

not limited by state boundaries. In one set of analyses, re-dispatch was simulated to occur across 

the multi-state regions overseen by independent system operators (ISOs) and other regional 

transmission organizations (RTOs), while in another set of analyses, national re-dispatch was 

simulated (subject to other constraints specified in the model, including transmission limits). In 

these scenarios with greater re-dispatch flexibility, the cost of achieving the quantity of CO2

reductions corresponding to a nationwide average NGCC unit utilization of 70% was notably 

lower. [A3]

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