## From: Wayland, Robertj [mailto:Wayland.Robertj@epa.gov] Sent: Thursday, May 22, 2014 4:22 PM To: Cortney Higgins Cc: Culligan, Kevin; Srivastava, Ravi Subject: Revised HRI Chapter 2 - GHG Abatement TSD

Cortney –
Attached is a revised version of the Heat Rate Improvement Chapter 2 from the GHG Abatement Measures TSD. Please let either Kevin, Ra vi or me know if you have questions/concerns.

thanx!

bob
Robert J. Wayland, Ph.D. Leader, Energy Strategies Group U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Sector Policies and Programs Division Mail Code D243-01 Research Triangle Park, NC 27709 Office: (919) 541-1045 Cell: (919) 306-2290 Fax: (919) 541-5450 Email: wayland.robertj@epa.gov

************************************* DRAFT/Deliberative Document - FOIA Exempt *************************************

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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 character istics 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 ex isting coal-fired EGUs, design factors that influence efficiency, technologies to improve e fficiency, 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 us ed 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 co al-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 U nited States , recovered anthracite coal or coal refuse is burned in limited quantities.
Existing coal-fired EGUs in the U.S. electri c utility fleet use one of five basic coal combustion configurations: (1) pulverized coal (PC) combustion, (2) fluidized-bed combustion (FBC), (3) gasified coal combustion, (4) cyclone furnace combustion, or (5) stoker-fired coal combustion. Table 2-1 presents a summary of the operating characteristics of each of these coal combustion configurations. Deleted: can be presumed Comment [A1]: This section read quite weakly as initially written.

EPA response: Thank you for the edit. Comment [A2]: MATS Compliance. MATS compliance will lead to significant installations of pollution control equipment (PCE) over the 2013 to 2016 period on capacity that owners intend to keep in service beyond the MATS compliance deadline but would not be MATS

‐ compliant without augmenting the PCE that was in place during 2012. The PCE systems likely to be installed on such units include FGD systems that may consume significant power and therefore, all else equal, increase energy input per net kilowatt

‐ hour of generation. It would be helpful to understand if or how additions of PCE for MATS compliance are reflected in the analysis of HRI in the draft TSD.

EPA response: The results of our statistical analysis (described later) are based on gross heat rates and will not change with installation of MATS controls, which will add additional parasitic load requirements and thereby have an impact on the net heat rates. However, in this TSD chapter, we explain that EPA conservatively considers fleet

‐ wide net heat rate improvement potential to be the same
as that indicated for the fleet

‐ wide gross heat rate. And based on this consideration, IPM modeling and state goals calculations used 6% improvement on a net heat rate basis. So we also need to speak to the fleet

‐ wide net heat rate. That being said, MATS controls are expected to lead to a small increase in auxiliary power use and therefore will not have a significant effect on the fleet

‐ wide net heat rate.

Here is what we projected (using IPM) for the final MATS policy case. Technology Capacity Parasitic Load New wet FGD 0 GW 1.5

‐ 2% New SCR 0 GW 0.5% New dry FGD (SDA) 22 GW 1

‐ 1.5% New DSI 43 GW 0.5% (assumed same as ACI + FF) New ACI 141 GW 0.5% New FF 102 GW 0.5%

Upgrade of existing ESP 34 GW Upgrade of existing wet FGD 63 GW

So MATS requirements would increase parasitic load from 0% (for a unit that can meet the standards with existing equipment) up to 2 % for units that install a SDA and implement ACI + FF [Note: a unit would implement either SDA or DSI but not both].

In any case, EPA’s IPM v 5.13 Base Case modelling for this proposal assumes that MATS is in effect and that such controls as are needed for compliance are ... [1] Deleted: and

Deleted:

.

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## 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 SO 2 

### 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 900 o 

### C). Bubbling fluidized bed (BFB) Operates at relatively low gas stream velocities and with coarse-bed size particles. Air in excess of that required to fluidize the bed passes through the bed in form of bubbles.

Circulating fluidized bed (CFB) Operates at higher gas stream velocities and with finer-bed size particles. No defined bed surface. Must use high-volume, hot cyclone separators to recirculate entrained solid particles in flue gas to maintain the bed and achieve high combustion efficiency.

Integrated Coal

Gasification Combined Cycle (IGCC) Synthetic combustible gas (“syngas”) derived from an on-site coal gasification process is burned in a combustion turbine. The hot exhaust gases from the combustion turbine pass through a heat recovery steam generator to produce steam for driving a steam turbine/generator unit. Coal gasification units are unique among coal-firing configurations because a gaseous fuel (synfuel or syngas) is burned instead of solid coal because the combustion and power generation process and combines the Rankine and Brayton thermodynamic cycles as is the case for a combined cycle power plant.

Cyclone Furnace Combustion Coal is crushed into small pieces and fed through a burner into the cyclone furnace. A portion of the combustion air enters the burner tangentially creating a whirling motion to the incoming coal. Designed to burn coals with low-ash fusion temperatures that are difficult to burn in PC boilers. The majority of the ash is retained in the form of a molten slag.

Stoker-fired Coal Combustion Coal is crushed into large lumps and burned in a fuel bed on a moving, vibrating, or stationary grate. Coal is fed to the grate by a mechanical device called a “stoker.” One of three types of stoker mechanisms can be used that ether feed the coal by pushing, dropping, or flipping coal unto the grate.



## The three technologies currently used for new coal-fired power plants are pulverized coal, fluidized bed (FBC), and integrated coal gasification combined cycle (IGCC). Pulverized coal combustion is the coal-firing configuration predominately used at existing EGUs. The most Comment [A3]: We would suggest reducing the amount of text in tables (perhaps as bullet points).

EPA response: Thank you for the observation.

Deleted: FBC

Comment [A4]: It might be helpful to indicate the typical size (MW) and capital cost ($/installed MW) here.

EPA response: Thank you for the observation, but it is unclear how the suggested edit would relate to the analysis.

Deleted: involves pulverizing the coal into a fine powder, which allows the coal to be burned more easily and efficiently. This

Deleted: , and is the most frequently selected technology for new coal-fired EGU projects

Deleted: However, FBC EGUs are becoming more prevalent since they have a greater fuel flexibility. Comment [A5]: No one is building coal plants currently in the U.S., so the future tense is not appropriate. Who is building coal plants using FBC? China? If so, we should specify, or else delete.

EPA response: The text has been deleted.

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## 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, SO 2 

## 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 CO 2 

## 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 providesuperior 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 CO 2 

## Emission Rate
Heat rate is a common way to measure EGU ef ficiency. As the efficiency of a coal-fired EGU is increased, less coal is burned per kilowa tt-hour (kWh) generated by the EGU resulting in a corresponding decrease in CO 2 

## 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 op erate the EGU (e.g., elect ricity to power fuel handling equipment, pumps, fans, pollution control equipment, and other on-site electricity needs), and thus is a measure of the electricity de livered to the transmission grid for distribution and sale to customers. Some EGUs also produce part of their useful output in the form of useful thermal output (e.g., steam for heating purposes). These types of facilities are called combined heat and power, or CHP, facilities. Deleted: and

Formatted: Subscript

Deleted:

. Comment [A6]: One of the primary benefits of IGCC is the concentrated CO2 stream and ability to

use in CCS. Needs to be highlighted.

EPA response: Thank you for the observation and the suggested text, which has been included.

Deleted:

.

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## A variety of factors must be considered when comparing the effectiveness of heat rate improvement technologies to increase the effici ency 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 ultr a-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 th ermal design efficiencies for subcritical EGUs are in the range of 35% to 37%, supercritical EGUs are in the range of 39% to 40%, and ultra-supercritical EGUs in the range of 42% to 45%. However, actual operating efficiencies are lower than design efficiencies.

 EGU coal rank and quality – EGUs burning higher quality coals (e.g., bituminous) tend
to be more efficient than EG Us burning lower quality coals with a greater moisture

content (e.g., lignite). Bituminous coals have higher heating values of greater than 10,500 British thermal units per pound and lignite coals have higher heating values of less than 8,300 British thermal units per pound.

 EGU plant size –EGU efficiency generally increases with size because the boiler and steam turbine losses are lower for larger equipment compared to smaller equipment. However, the EPA is not aware of suffic ient data to quan tify the relationship

.

 EGU pollution control systems – The electric power consumed by air pollution control equipment reduces the overall efficiency of the EGU.

 EGU operating and maintenance practices – The specific practices used by an individual electric utility company for combustion optimization, equipment maintenance, etc. can affect EGU efficiency.

 EGU cooling system – The temperature of the cooling water entering the condenser can have impacts on steam turbine performance. Once-through cooling systems can have an efficiency advantage over recirculating coolin g systems (e.g., cooling towers). However, once-though cooling systems typically have larger water related ecological concerns than recirculating cooling systems. Deleted: The majority of existing PC-fired EGUs in the U.S. electric fleet use subcritical boilers

Comment [A7]: Could indicate the approximate range (BTUs / lb) of the different coal types, as well

as the influence of moisture.

EPA response: The ranges have been added.

Deleted: However,
Deleted: as equipment size increases the differences in these losses start to taper off Comment [A8]: : Could indicate approximate turbine size / size class here.

EPA response: Edits made to address the comment.

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##  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 driv es, 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 potentia l efficiency improvement techniques applicable to existing coal-fired EGUs. 1 

## For example, a summary of the findings from one study conducted by the Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) is presented in Table 2-2. The efficiency percentages were converted to a common basis so that all of the data can be compared. All of the improvement technologies presented in Table 2-2 cannot necessarily be implemented at every existing coal -fired EGU facility in the U.S. electric utility fleet. The existing EGU design configuration and other site-specific factors may prevent the technical feasibility of using a given technology.
Typically, these studies share as a common basis the estimated potential efficiency improvement percentages and costs from the engineering study originally completed by Sargent and Lundy in 2009 titled “Coal-Fired Power Plant Heat Rate Reductions.” It describes numerous well known and technically proven methods to im prove efficiency of coal-fired EGUs. The study

1 

### See HRI Partial Bibliography at the end of this chapter. Deleted: a Deleted: measureable

Deleted: a condenser recirculating

Comment [A9]: Geographic location could also influence cooling water issues, mentioned earlier (i.e. coal plants in the arid west).

EPA response: Text has been added to address the comment.

Deleted: cycling Comment [A10]: Suggest using “load following” for clarity

EPA response: Done.

Deleted: 

## <#> EGU equipment manufacturers – The efficiency specifications of major EGU components such as boilers, turbines, and electrical generators provided by equipment manufacturers can affect EGU efficiency.¶ Comment [A11]: This seems less about the manufacturers and more about the specific technology choice. Suggest describing this as “EGU technology choice” and, if necessary, note that efficiency can vary by manufacturer or by product.

EPA Response: Deleted since this is not relevant to the discussion.

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## 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 Handling 2



## 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 redu cing fuel cost and heat rate. The costs for changes to the pulverizer system are significant, and historically, the projects have improved the heat rate justifiably only when the existing equipment has degraded.
The bottom ash handling system may be a source for heat rate improvement. Switching from a water-sluicing bottom ash system to a dr y 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 improv es the efficiency of an EGU by recovering energy from the exhaust gases to preheat the boiler feedwater. The replacement of the economizer can lead substantial heat rate improvements around 50-100 Btu/kWh, but is large capital investment (~$2-8M). Due to this high cost, economizers upgrades are not generally performed unless the existing equipment has degraded or a replacement is necessary due to the installation of new control equipment.

2 

### The Sargent and Lundy report did not provide potential sa vings for material handling operations. Energy use in Btu/kWh has been provided to compare the energy use of materials handling relative to the potential energy savings from other efficiency activities. Deleted: (VFDs)

Deleted: VFDs
Deleted: VFDs

Comment [A12]: Did you want to include an estimate of pulverizer electric demand?

EPA response: An approximate electric demand has been added.

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

some efficiency gains are given in percent, and others in Btu/kWh. It would be helpful to keep either one common unit (suggest Btu/kWh) or provide both metrics.

EPA response: See footnote.

Deleted: 1

Comment [A14]: Please add a sentence on what

an economizer is and does.

EPA response: Added an introductory sentence.

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## 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 ma ny aspects of the plan t’s operations. Commonly referred to as the Neural Network, computer models are able to control the plant’s processes by predicting performance during static and dynamic changes. Many vendors offer Neural Network systems to improve the overall efficiency. Neural network systems are typically around $550,000-$750,000 and offer heat rate reductions up to 150 Btu/kWh.

2.3.1.4 Sootblowers
Intelligent sootblowers may be installe d to improve system efficiency. The intelligent
sootblowers system monitors the furnace exhaust gas temperatures and steam temperatures. Other readings may be incorporated into the intelligent sootblowers system which also communicates with the boiler control system. This system uses real-time data to identify which areas need sootblowing. Boiler efficiency improvements have ranges from 30

- ( 15 0 Btu/kWh) with capital costs around $300,000-$500,000 and $50,000/year for fixed operating and
maintenance costs.

2.3.1.5 Air Heaters
Air heaters operate to transfer heat between the incoming pre-combustion air and the effluent flue gas. These systems are critical to maintain an efficient power plant. In order to maintain these systems to operate most efficiently , air heater leakages must be maintained below 6% of incoming air flow. Most leakage is due to the pre-combustion air leaking across the rotating section and leaving with the flue gas. This increases the flue gas volume going through the forced draft and induced draft fans to the emissions controls and avoids capturing the heat transferred between the flue gas and pre-combustion gas. The increased volume requires more power to pump more flue gas. Ad ditionally, improvements to seal s on the air heaters also reduce the leakages. Improvements to reduce air heater and duct leakages generally reduces the heat rate by 10-40 Btu/kWh with capital costs between $0.3-1.2M.
A second method to improve the heat rate is to lower the air heater outlet temperature by controlling the acid dew point. Typically the air heater outlet is maintained at 20-30°F above the sulfuric acid dew point to prevent corrosion of cold-end baskets. Sorbent injections such as Trona or hydrated lime can be used to lower the dew point. Depending on the sizing of the air Deleted: 2

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## heater, the air heater may need to be modified in order to optimize the lower outlet temperature. The capital costs can range from $1.5-18M for heat rate reductions of 50-120 Btu/kWh.

2.3.2 Turbine
The systems within the turbine area for which to focus heat rate improvements are the turbine, the feedwater heaters, the condenser , and the turbine drive and motor-driven feed

pumps.

2.3.2.1 Turbine
Replacements or overhauls of existing steam turbines with advanced turbine designs improves the efficiency of converting the energy in the steam to electrical energy. The capital costs for these projects ranges from $2-25M with heat rate reductions of 100-300 Btu/kWh.

2.3.2.2 Feedwater Heaters
The feedwater heaters are heat exchangers to heat the boiler feedwater by extracting heat from the steam leaving the turbine section. The efficiency can be increased by improving heat transfer surface area. This entails adding heat ex change 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 extr act as much energy from the steam as possible. Condensers are subject to fouling and plugging whic h directly impact the heat transfer rates and water quality. To improve water quality, closed cooling water systems provide better control of the water quality and tube cleaning can be performed. Heat rate reductions observed from the condenser upgrades and maintenances are 30-70 Btu/kWh with annual fixed costs of $30,000- $80,000.

2.3.2.4 Boiler Feed Pumps
Boiler feed pumps require a large amount of auxiliary power to pump massive amounts of boiler feedwater through the heaters and boiler. Due to the high use of these pumps, Deleted: Generally, a 40°F reduction in the air heater outlet results in 1% efficiency improvement. These systems are costly and depend on whether the air heater is adequate, needs modifications or needs a complete replacement.

Deleted: The performance and longevity of turbines is constantly increasing with advances in technology. Most turbines in the industry are maintained so that the efficiency improvements are 1-3%; however, some extremely degraded turbines may benefit up to 10% with a turbine overhaul. Comment [A15]: Not clear whether the 1

‐ 3% efficiency improvement refers to improvement resulting from an overhaul. It might be helpful to indicate what size of turbines correspond to these costs.

EPA response: the paragraph has been modified.

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## maintenance is extremely important to ensure relia bility and the most efficient operation. As the pumps wear and operate less efficiently, a pump overhaul may be required. The overhaul can reduce the heat rate by 25-50 Btu/kWh with capital costs around $250,000-$800,000.

2.3.3 Flue Gas System
Two aspects of the flue gas system that offer potential improvements to the plant efficiency are to improve the forced draft and induced draft fan efficiencies and implementation
of variable frequency drives

. 2.3.3.1 Induced Draft Fans
One of the most important features in the fans is being able to control the flue gas flow. Many fans have dampers, which are the least effective and efficient option. There are many methods, such as variable inlet vanes , variable frequency drives , and variable pitch blades, available to control the flue gas flow allowing highly efficient fan performance. These upgrades or replacements provide a heat rate reduction of 10-50 Btu/kWh and cost between $6-$16M.
2.3.3.2 Variable Frequency Drives
It is apparent with the control that a variable frequency drives offers, the plant is able to reduce the auxiliary load immensely. Consider ing upgrading all drives, the capital costs are $6-
16M with heat rate reductions between 10-150 Btu/kWh.

2.3.4 Emission Control Technologies
With the passage of environmental regulations, additional emission control devices have been and must be implemented in the power plant. These systems typically require large amounts of auxiliary power with their benefit being im proved air quality. Even small upgrades can decrease the power requirements significantly while at least maintaining the same level of emissions reduction. The three technologies discussed are the flue gas desulfurization, the electrostatic precipitator, and the selective catalytic reduction systems.
2.3.4.1 Flue Gas Desulfurization
Coal-fired power plants contain many variances between their flue gas desulfurization systems. A few common technology improvements are discussed further in this section. Older units typically contained a venturi throat that increased the velocity of the fluid, but resulted in a large pressure drop and greater power to operate the induced draft fans. To improve this Deleted: (FD)
Deleted: (ID)

Deleted: VFDs Comment [A16]: Please spell out acronym – even if it has been spelled out earlier, I have forgotten and couldn’t find the location on looking back.

EPA response: Spelled out acronyms.

Deleted: ID Comment [A17]: For all these subtitles, it wouldn’t hurt to spell out the acronym in the section title.

EPA response: Spelled out acronyms.

Deleted: (VIV)
Deleted: VFD
Deleted: (VPB)

Deleted: VFD
Deleted: (VFD)
Deleted: VFD

Deleted: (FGD)

Deleted: (ESP)
Deleted: (SCR)

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## 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 an d distribution plate into an absorber. The improvement of the gas flow coming into contact with the absorber sorbent increases SO 2 

## capture, reduces maintenance due to erosion, and reduces the amount of energy required from the
induced draft fan. Turning vanes and a perforated gas di stribution plate improve gas distribution. The cost of the vanes is around $250,000 with heat rate reductions of 1-2 Btu/kWh.

In a wet flue gas desulfurization multiple spray levels are installed to deliver the limestone slurry. If a power plant is operating with SO 2 

## levels below its permit li mit, turning off one spray level will reduce the auxiliary power necessary. If this is possible, a unit heat rate reduction of 16 Btu/kWh may be available.
2.3.4.2 Electrostatic Precipitator
The best operation for an electrostatic precipitator involves maintaining the maximum applied voltage, but below the level in which spark-over occurs. Electrostatic precipitator energy management system upgrades often help improve the electrostatic precipitator performance by maintaining the optimal performance and lowering power consumption. The installation for this technology can be from minimal to $0.8M and can lower heat rate by 5 Btu/kWh.
2.3.4.3 Selective Catalytic Reduction
For the last 15 years, selective catalytic reduction systems have been in use to reduce
NO X 

## emissions from power plants. Extensive modeling was performed to achieve the necessary reduction with minimum ammonia slip. The resu lts showed that reducing pressure drop and using secondary air as dilution for the ammonia vaporizer can reduce the auxiliary power necessary. The heat rate reduction is 0-10 Btu/kWh and capital costs between $0.5-$2M with fixed and variable costs up to $100,000 each.

2.3.5 Water Treatment System
The water treatment system heat rate improvements can occur for the boiler water treatment and the cooling tower improvements. Th e boiler water is one of the most important aspects of the power plant. The quality of the water plays a huge impact on the scale buildup on the boiler tubes, which reduces the heat transfer in the tubes or can cause tube failures. Proper use of chemicals to maintain pure water is key. Also, high-quality water can reduce the Deleted: ID

Deleted: FGD

Deleted: ESP
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Deleted: e Deleted: ESP
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Deleted: SCR
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## blowdowns required, which allows for more stea m in the turbine cycle. If the water is not properly maintained, heat transfer may be reduced up to 10%.
Similar to the boiler water, the cooling towers are also affected by the water quality. Fouling and scaling remain issues for heat transf er 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.DRAFTMay 22, 201412Deleted:AprilDeleted:14Table 2-2. Existing coal-fired EGU efficiency improvementsreported for actual efficiency improvement projectsEfficiencyImprovementTechnologyDescriptionReportedEfficiencyIncreaseaCombustionControlOptimizationCombustion 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 operatingparameters including combustion efficiency, steam temperature, furnace slagging and fouling, and NOXformation. The technologies include instruments that measure carbon levels in ash, coal flow rates, air flow rates,CO levels, oxygen levels, slag deposits, and burner metricsas well as advanced coal nozzles and plasma assistedcoal combustion.0.15 to 0.84%Cooling SystemHeat LossRecoveryRecover a portion of the heat loss from the warm cooling water exiting the steam condenser prior to its circulationthorough a cooling tower or discharge to a water body. The identified technologies include replacing the coolingtower fill (heat transfer surface) and tuning the cooling tower and condenser.0.2 to 1%Flue Gas HeatRecoveryFlue gas exit temperature from the air preheater can range from 250 to 350°F depending on the acid dew pointtemperature of the flue gas, which is dependent on the concentration of vapor phase sulfuric acid and moisture.For power plants equipped with wet FGD systems, the flue gas is further cooled to approximately 125°F as it issprayed with the FGD reagent slurry. However, it may be possible to recover some of this lost energy in the fluegas to preheat boiler feedwater via use of a condensing heat exchanger.0.3 to 1.5%Low-rank CoalDryingSubbituminous and lignite coals contain relatively large amounts of moisture (15 to 40%) compared to bituminouscoal (less than 10%). A significant amount of the heatreleased during combustion of low-rank coals is used toevaporate this moisture, rather than generate steam for the turbine. As a result, boiler efficiency is typically lowerfor plants burning low-rank coal. The technologies include using waste heat from the flue gas and/or cooling watersystems to dry low-rank coal prior to combustion.0.1 to 1.7%SootblowerOptimizationSootblowers intermittently inject high velocity jets of steam or air to clean coal ash deposits from boiler tubesurfaces in order to maintain adequate heat transfer. Proper control of the timing and intensity of individualsootblowers is important to maintain steam temperature and boiler efficiency.The identified technologies includeintelligent or neural-network sootblowing (i.e., sootblowing in response to real-time conditions in the boiler) anddetonation sootblowing.0.1 to 0.65%Steam TurbineDesignThere are recoverable energy losses that result from the mechanical design or physical condition of the steamturbine. For example, steam turbine manufacturers haveimproved the design of turbine blades and steam sealswhich can increase both efficiency and output (i.e., steam turbine dense pack technology).0.84 to 2.6Source: National Energy Technology Laboratory (NETL), 2008.Reducing CO2Emissions by Improving the Efficiency of the Existing Coal-fired Power PlantFleet, DOE/NETL-2008/1329. U.S. Department of Energy, National Energy Technology Laboratory, Pittsburgh, PA. July 23, 2008. Available at:<http://www.netl.doe.gov/energy-analyses/pubs/CFPP%20Efficiency-FINAL.pdf>.aReported efficiency improvement metrics adjusted to common basis by conversion methodology assuming individual component efficiencies for a referenceplant as follows: 87% boiler efficiency, 40% turbine efficiency, 98% generator efficiency, and 6% auxiliary load. Based on these assumptions, the referencepower plant has an overall efficiency of 32% and a net heat rate of 10,600 Btu/kWh. As a result, if a particular efficiency improvement method was reportedto achieve a 1% point increase in boiler efficiency, it would be converted to a 0.37 % point increase in overall efficiency. Likewise, a reported 100 Btu/kWhdecrease in net heat rate would be converted to a 0.30% point increase in overall efficiency.Comment [A18]:Themathinthisfootnoteisuntraceable.Suggestaddingalittlemoreexplanation–anequationexample‐‐ofhowthismathleadstothatpercentageorheatrate.EPAresponse:FootnoteisdirectlyfromthecitedNETLreport.

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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 CO 2 

## 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 EG Us 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 fl eet efficiency in response to changes in fuel prices or carbon prices . In this specific example, the coal price was assumed to be a 10%
increase and the CO 2 

## 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 fo r the EGUs to comply w ith an inferred standard. As opposed to the above studies that determined by how much the efficiencies can improve, this approach estimated how the industry will meet any imposed standards and calculated the heat Deleted: Studies 1 and 2 Comment [A19]: To avoid ambiguity it might be helpful to cite (footnote) studies when referring to them.

EPA response: Added study names.

Deleted: Study 3

Deleted: for Study 4.

Deleted: This approach

Deleted: determined that given specific changes in the coal price and a carbon tax, the industry will make heat rate improvements on their own Comment [A20]: Suggest an alternate phrasing such as: This approach sought to estimate overall changes in industry fleet efficiency in response to changes in fuel prices or carbon prices.

EPA response: The language has been edited as suggested.

Deleted: Study 5

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## rates necessary to meet a standard. As it has not been determined with regard to how the CO 2 

## emissions limit will be averaged, the paper disc usses many potential options that coal-fired EGUs may have to meet new standards. These op tions 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, wh ereas 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 r easons, the EPA has developed in this TSD an additional assessment of HRI potential that draw s on multiyear historical hourly data. While we
understand that engineering judgment remains essential to a proper interpretation of the results,
the EPA intend s 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 CO 2 

### 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 theEfficiency of CoalFired Power Plants for Near Term Greenhouse Gas Emissions Reductions” NETL • Plant design: coal type, steam cycle, and size • Category improvement is the bottom 90% of EGUs in each group improving their heat rate to the average performance of top 10% in that category • Units with anomalous data, capacity factors under 10%, using less than 97% coal, and gasification plants were removed from dataset • Low pressure subcritical

units and 0-200 MW subbituminous units • 8.7% reduction in overall heat rate of coal-fired EGUs Deleted: s

Deleted: The EPA observed that most of the existing studies develop fleet-wide average HRI potentials/goals that are higher than values considered feasible and sustainable by the power sector engineers with whom the EPA has informally discussed this subject. Comment [A21]: The reasons for considering the existing studies unrealistic could be discussed

more explicitly. Perhaps, for example, there is a
more explicit argument to be made about specific interventions and timescales.

EPA response: The text has been deleted.

Deleted: also noted
Deleted: all Comment [A22]: Not certain this is correct ‐ the RFF study abstract states: “This paper examines the operation of coal

‐ fired generating units over 25 years to estimate the marginal costs and potential magnitude of emissions reductions from improving their efficiency.” Please correct if appropriate.

EPA response: The text has been clarified to refer to studies that used a benchmarking type approach to estimate potential reductions.

Deleted: oo Deleted: understood
Deleted: would
Deleted: still be
Deleted: ed Comment [A23]: Please check on the tense

– past or present?

EPA response: Thank you for the observation.

Deleted: Study
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### Study

ID Study Title Author Factors Used for Industry Grouping Key Study Assumptions Relevant HRI Results assumed retired for goal • Lost generation made up by

more efficient coal-fired EGUs 3 “Reducing Heat Rates of Coal-Fired Power Plants” Lehigh Energy Update All possible heat rate improvements are made including drying of high moisture coal: • 10% improvementfor 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 theentire 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 CO 2 

### 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 potentia l looks at historical data from coal-fired EGUs in the U.S. to identify changes to EGUs’ h eat rates – the amount of heat input required, on average, to generate 1 kWh of electricity – that can be attributed to operation and maintenance practices and equipment upgrades. These heat rate changes are analyzed to determine their applicability to the rest of the coal-fired EGU fleet and to determine the potential heat rate improvement that, on average, could be achieved by the fleet. Deleted: Stud y Deleted:

Deleted: ¶

Comment [A24]: Please include text regarding the peer review to which all these reports have been subject. If not, please consider subjecting this concept to technical peer review in the period following proposal release.

EPA response: We will check on the peer review status of the studies. Comment [A25]: Is there a reason there is no comment in this box?

EPA response: Text has been added.

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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 sign ificant 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 collect ed 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 eq uipment 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 input 3 

## and electrical output to the EPA’s Clean Air Markets Division in 2012. 4 

## It includes a wide range of configurations, fr om 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 CO 2 

### 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 Cl ean Air Interstate Rule (CAIR). However, the EPA recognized that there will be times when em ission 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 sp ecified 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 fr om this study’s dataset. These substitute data represent approximately 2% of all reported ope rating 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 exclude d 40 unit-years (0.5% of records) with atypical annual heat rates less than 6,500 or greater th an 15,000 Btu/kWh resulting from a vari ety of factors incl uding firing of natural gas, very low operating time, or errors in reported gross load. Table 2-4 summarizes the heat input, electric generation, heat rate and unit counts by year for the study population used in this work. This population corresponds to 9, 388 unit-years of data at 884 distinct EGUs. Figure
2- 1 displays the study population average gross heat rate by year and the 11-year average. Comment [A26]: IS QA/QC defined in the abbreviations?

EPA response: It is defined in the sentence.

Deleted: reporting

Deleted: 50350 Moved (insertion) [1]

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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. 7 

## Since EGU heat rate performance is sensitive to air temperature and barometric pressure, which vary with elevation, we use meteorological data from stations that are reasonably close to the EGU's location and elevation to account for the

5 

### The study population for each year includes those EGUs that reported both heat input and electric generation.
7 

### Temperature data is from NOAA’s Integrated Surface Da ta at http://www.ncdc.no aa.gov/data-acce ss/land-basedstation-data/land-based-datasets/i ntegrated-surface-database-isd. 9,500 9,550 9,600 9,650 9,700 9,750 9,800 9,850 9,900 9,950 10,00020022003200420052006200720082009201020112012Heat Rate (Btu/kWh)



### Annual Heat Rate 11-yr Avg Heat Rate Deleted: Study Population Unit Count by Year Comment [A27]: Change in unit counts due to using conditions that match preceding paragraph.

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Comment [A28]: PHK: corrected unit caps in chart legend

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## 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 approxima tion 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 1,60 5 million tons of CO 2 

## in 2012. In contrast,
emissions of CO 2 

## from coal-fired EGUs in the entire U.S. electric power sector in 2012 were at 1,669 million tons according preliminary data from the EIA. 8 

## Since the study population of
EGUs accounted for over 96% of CO 2 

## 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 characteristi cs are not easily categorized (e.g., EGUs use a large range of steam conditions). Several other studies do categorize EGUs broadly by capacity, thermodynamic cycle, and/or fuel rank. Although the EPA believes grouping by categories can provide a useful way of understanding the operating profile of an EGU and the fleet, the range of heat rates for the broad categories has significant overlap (see box and whisker chart in Figure 2-
2 ) and therefore makes it challenging to develop appropriate categorization. The figure below displays available information on coal-fired EGUs considered in this work for the years 2009- 2011 in typical subcategories of capacity, fuel rank, and thermodynamic cycle. As the figure reflects, the means are clustered and the ranges of heat rates overlap.

8 

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

. Deleted: 1000

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Deleted: Revised 2012 results will be available in March, 2014. The somewhat lower 2012 CO 2 emissions total in Table 2-5 below is due to the exclusion of substitute data

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### Figure 2- 2. Three -Year Average Heat Rates by Subcategory 9



## 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% comparin g 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 9,7 54 (Btu/kWh). The decrease in study population annual heat rate between 2002 and 2012 may be due to several factors. Unit efficiency may have improved or units with lower heat rates may have taken up a larger share of generation. In addition, changes in reporting methodology described later in this chapter may be partly responsible. The minimum annual heat rate (9, 643 (Btu/kWh)) occurred in 2008 and was approximately 1% below the 11-year average.

9 

### Abbreviations in the figure: BIT means bituminous, SUB means subbituminous, PC means petroleum coke, LIG means lignite, SUPER means supercritical, OVER/UNDE R means greater/less than indicated MW capacity. Unit counts (n) by category: BIT SUPER, n=80; SUB SUPER, n=30; BIT OVER 200, n=196; PC, n=2; BIT 100 to 200, n=140; SUB OVER 100, n=299; LIG OVER 100, n=20 ; BIT UNDER 100, n=68; SUB UNDER 100, n=56; LIG UNDER 100, n=2. Total unit count is 893. 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 BIT SUPER SUB SUPER BIT OVER 200 PC BIT 100 to 200 SUB OVER 100 LIG OVER 100 BIT UNDER 100 SUB UNDER 100 LIG UNDER 100

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

Deleted: 1. Comment [A29]: Suggest including on the figure the number of plants in each category (N=? for each category).

EPA response: added counts to footnote.

Deleted: 3 Comment [A30]: The footnote uses SUB twice for different terms. Please clarify/fix.

EPA response: Done

Deleted: , capacity factor,
Deleted: , 21%,
Deleted: UsingC the same time periods c

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Deleted: 650
Deleted: , near peak generation for the years under consideration,

Deleted: SUB means subcritical

Formatted: Highlight
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## Table 2-5. Reported Annual Capacity Factor 2002- 2012 10



### 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 ar e known to operate at higher heat rates. Therefore, we evaluated the potential impact of such events in our study . A startup event, as defined here, occurs when an EGU begins combusting fossil fuel and generates some measurable amount of electricity. Table 2-6 summarizes the study population average, maximum and total starts by year. On average, coal-fired EGUs start combusting fuel and generating electricity 11 times per year. The total number (approximately 9,000) of starts for the study population of EGUs has remained stable over the study period. Our data reflects that some coal-fired units operate in a load following capacity and may report upwards of 200 starts in a single year, but these units tend to have low annual capacity factors. The subset of EGUs with more than 20 annual startup and shutdown events is responsible for less than 4% of total generation in any study year. Therefore while the number of starts is an important variable at a small number of EGUs, its impact on heat rate performance evaluated in this study is considered to be marginal.

10 

### Table 2-5 shows data as reported to EPA as of May 8, 2014. Deleted: Table 2-5 displays the annual heat input, electricity generation, capacity factor, and heat rate for the study population of EGUs as reported to EPA. Moved up [1]: Figure 2-2 displays the average annual gross heat rate by year and the 11-year average.

Deleted: Heat In p ut, Generation,
Deleted: and Heat Rate Annual for the PeriodTrends
Deleted: 10 Comment [A31]: Suggest making the Table title

more descriptive. Also could present fewer signficant digits in table ...i.e. heat input ‐ MM BTU x 106 ‐‐ ... [2]

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Deleted: 44 Comment [A32]: This capacity factor decline in 2012 is very different from the CF used later, see ... [3]

D e l eted : 

### 9,500 9,550 9,600 9,650 9,700 9,750 9,800 9,850 9,900 9,950 10,000200220032004Heat Rate (btu/kwh)

... [4] Deleted: on Deleted: evaluation
Deleted: Comment [A33]: At about this point, the chapter transitions into the quantitative analysis. ... [5]

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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 conducte d 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 consta ntly changing operating conditions. We tested for collinearity between capacity factor and ambient temperature using a zero-order correlation Deleted:

Comment [A34]: After this sentence, suggest adding a sentence stating why this is important to do, and a sentence summarizing the approach. (EPA) see updated text

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## matrix on the entire hourly data set. The correlation between the independent variables was -.048 – well below an indication of collinearity. We also considered fixed unit characteristics such as unit type, fuel rank and age as independent variables. As noted above in the discussion on subcategorization, these factors can be helpful to understanding the heat rate performance of EGUs. The purpose of this study, however, is to find the potential for heat rate improvement across the fleet. We use heat rate variability as a key statistic to measure this potential. The correlation between the potential for heat rate improvement and fixed characteristics is typically low. 11 

## Coal-fired units are designed to operate most efficiently at full capacity. As a unit drops below this level, in general, heat rate will increase. The average capacity factor over 11 years for the study population is 67%, but as noted above, has moved markedly over the study period. This study looks at utilization level at both hour and year time scales. The two are related but reveal different information about how an EGU is operating. For example, for a unit to achieve a high annual capacity factor (e.g., over 90%) it must operate at a high load for most hours in a year. At lower capacity factors interpreting the relationship between hourly and yearly utilization levels becomes more complex. For example, an EGU may run at an annual 60% capacity factor by operating 8 months at near full capacity and generating no electricity the rest of the year, or it may run at lower utilization levels for most hours of the year in response to weather, generation cost, and transmission constraints.
Ambient temperature can affect heat rate in two ways: 1) the efficiency of the thermodynamic steam cycle 12 

## and, 2) in many regions of the country, as temperatures increase electricity demand and capacity factor follow. Figure 2-3 shows the average monthly capacity factor in 2012 alongside the climate normal monthly temperature. 13 

## The lines intersect in the spring as temperatures begin to rise and the need for cooling drives electricity demand.

11 

### For example, the correlation between annual unit heat rate variability (d iscussed below as relative standard deviation) and unit nameplate capacity (MW) is in the -0.1 range. 12 

### The availability of a cold heat sink in the condenser is a key factor in that cycle. The design of the heat exchanger, type of cooling system and availability of water all ha ve an impact on performance. An increase in ambient air temperature, and consequent increase in water temperature, typically lower the effectiveness of the cooling system, the condenser, and, therefore, overall plant efficiency. 13 

### Climate normal is the average of temperature (or other me asure) over a prescribed 30-year interval and location. The chart shows the 1981-2010 climate normal monthl y temperature at Baltimore-Washington Airport, MD. Deleted: ¶

Comment [A35]: Suggest discussing explicitly the choice of CF as an independent variable

– why is it reasonable to think of it as an independent element? Are there better factors that EPA could have used but was unable to because of information availability?

EPA response: see the preceding paragraph

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## Generally, p eak capacity factor and generation in most parts of the U.S. occur on the hottest days of the year. Yet, the relationship between ambient temperature and capacity factor is complex. Each plant responds differently depending on design, meteorological conditions and electricity demand. For example, a base load plant may operate at a high capacity factor seven days a week regardless of temperature. As noted above, the collinearity between these two variables is low.

Figure 2-3. Monthly Capacity Factor, 2012. 

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

Heat Rate
Using the hourly data set, the EPA performed three regression analyses for each unityear: heat rate onto capacity factor, heat rate onto ambient temperature and heat rate onto capacity factor and ambient temperature. Since this analysis seeks to evaluate heat rate under normal operating conditions, we removed records with hourly heat rate values outside of +/- 2.6 standard deviations (1.9% of records) before performing the regressions. 14 

## Similarly to partial operating hours, these outliers tend to occur during low load conditi ons. 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 cont ribution of varying unit capacities all results are generation-weighted. 15 

## The average study population r-squared for the multivariate regression is 26%. This means that hourly ambient temperature and capacity factor together explain 26% of

14 

### The 2.6 standard deviation bound is used in other EPA regulatory analyses. 15 

### In a weighted average, each component is multiplied by a factor reflecting its importance. In this case, generationweighted r- squared is the sum of r-squared for each unit multiplied by its annual generation divided by the sum of generation for all years

. 0 20 40 60 80 100JanFebMarAprMayJunJulAugSepOctNovDec 

## Electric generation monthly 2012 capacity factor and normal temperature

Capacity Factor (Monthly %, 2012) Climate Normal (BWI, MD) Deleted: P

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Deleted: Analysis Comment [A36]: Could more granularity on unit characteristic/type of fuel/design be included as part of the regression analysis?

EPA response: As noted earlier in the TSD, fixed unit characteristics such as unit type, fuel rank and age
can be useful for understanding the heat rate performance of EGUs. The purpose of the study, however, was to find the potential for heat rate improvement. We found very little correlation between potential for heat rate improvement and fixed characteristics. For example, the correlation between the unit heat rate variability (annual relative standard deviation) and unit capacity (MW) is typically in the ‐ .1 range. Comment [A37]: It is sometimes hard to follow the outline of the chapter

– currently uses bold, bold italic, underline. Suggest using headings like 1.1, 1.1.2, etc. or other indicators.

EPA response: Done ‐ thanks for the suggestion.

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Deleted: r Comment [A38]: Capacity factor is also influenced by temperature

– any considerations for collinearity effects in the regression?

EPA response: We tested for collinearity between capacity factor and ambient temperature using a
zero

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

– well below an indication of collinearity.

Deleted: The single variable analyses evaluations help to examine the relative influence of capacity factor and ambient temperature on heat rate.
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## the change in heat rate for the study population over the study period. The average study population r-squared from the single variable analysis of capacity factor is 16%; the corresponding result for temperature is 10%. This means that approximately 16% of the change in hourly heat rate is attributable to capac ity 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 respon se to capacity factor. At some individual EGUs, temperature may explainup 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 pe riods of peak demand. These are the EGUs where the ambient temperature ‘signal’ is an important variable. At a typical EGU, summermonth heat rates may increase by 2-4% compared to winter months, but at a temperatureresponsive EGU that figure may be as high as 10%.
The multivariate partial regression coefficien ts show a similar pattern. The average unityear temperature coefficient (non-standardized) is 2, i.e., each degree F increase in temperature increases the heat rate by 2 Btu/kWh. Put anothe r way, over a typical 40 degree F seasonal range (see Figure 2-3 the average change in heat rate that can be attributed to temperature is 80 Btu/kWh, i.e., less than 1%. But the temperature coefficient at the individual unit level varies widely. For example, the average study popula tion top quintile of temperature coefficient(descending) was 9, over four times greater than the study population mean. The average unityear coefficient (non-standardized) for capacity factor is -15, i.e., each 1% increase in capacity
factor decreases the heat rate by 15 Btu/kWh. This means that on average, when a study population EGU increased capacity factor from 60% to 80% the heat rate decreased by 300 Btu/kWh, i.e., approximately 3%. Although capacity factor is the more important explanatory variable, this is by no means universally true across the fleet or even year to year at the same
EGU.

Our analysis indicates that as EGUs moved from base load to load following, capacity
factor tended to have a larger effect on heat rate. Since 2008, the study population capacity Formatted: Highlight
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## factors moved from the top load bin into lower lo ad 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 8 4 % 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 (7 8 -8 3 % capacity factor).

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

2.5.6 Heat Rate Variability and Indication of Improvement Potential
This study examines heat rate variability from the standpoint of statistical process control, which is utilized throughout the power industry. Several years ago, the EPA introduced process control charts for auditing emissions da ta 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 itse lf to process control si nce it is the principal indicator that defines the quality of the electric generation process. 16,17 

## Therefore, in general, high variability in heat rate values would reflect opportunities for process improvement.

16 

### “The principal indicator that defines the quality of the process is heat rate.” (Fredrick & Todd, 1993. Statistical Process Control Methods in Performance Monitoring. Available at famos.scientech.us/Papers/1993/1993section11.pdf) Deleted: 5

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## 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 toeleven RSD values, i.e., one for each operating year between 2002 and 2012. The generationweighted mean RSD for the study population acr oss 11 years is 5.4%. Table 2-8 summarizeseleven years of results for the study population by quartiles ordered by the 11-year generationweighted RSD average (ascending). 18 

## The RSD of the top quartile (3.5%) is significantly lower than the study population generation-weighted mean RSD of 5.4%. Notably, the EGUs in the top quartile are not outliers; they report a third of all generation – the most of any segment. The results display a wide range of heat rate variab ility 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 de viation of the residual heat rate statistic is used to understand the amount of variability that is not explained by capacity factor and temperature. The average RSD corresponding to residual heat rate variability for each EGU is the generation-weighted average of up to eleven annual values. The study population generation-

17 

### Since accurate measurement is essentia l to process control the introduction of increasingly sophisticated digital control systems (DCS) presents new opportunities for finding inefficiencies. Vendors (ABB, Siemens , Emerson) claim heat rate improvements of 2-5 percent can result from upgrading to a modern DCS and advanced control technologies. The improvement can be even higher if system-wide real-time optimization is included. 18 

### Nine EGU RSD values exceeded 2.6 standard deviations ab ove the mean and were removed from the results in the table. Deleted: 7

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## weighted mean RSD over the study period is 4.5%. This percentage represents the total variability across the study population that our analysis could not explain by hourly capacity factor or ambient temperature. Possible causes of this variability include changes in plant equipment, operating procedures and maintenance, fuels (particularly coal rank), reporting methodology, and unexplained factors. There is no temporal trend evident in the RSD. Table 2-9 summarizes the study population generation-weighted average RSD of residual heat rate over the study period in quartiles ordered by the 11-year generation-weighted RSD average (ascending).
Table 2-9. RSD of residual heat rate (generation weighted) 19



### Quartile RSD average RSD Minimum RSD Maximum

1 2.7 0.0 3.2 2 3.6 3.2 4.1 3 4.7 4.1 5.3 4 6.9 5.3 10.4


## The weighted average RSD of the top quartile is 2.7% – well below the study population average of 4.5 %. This means that the residual hourly heat rates of these units generally stay in a narrow range within a given year. The maximum RSD in the top quartile is 3.2%. From the statistical process control point of view, thes e units appear to have low variability.
The weighted average RSD of the bottom quartile is 6.9%, which is over twice that of the top quartile. This spread indicates that there is likely room for improvement in study population operation to reduce variability and heat rate.

2.5.6.1 Heat rate variability and performance
To examine the association between heat rate variability and heat rate performance this study examined the RSD for unit-year heat rates calculated from reported data. The study population generation-weighted annual RSD ranges between 5% and 6% during the study period. Figure 2-5 below summarizes the results of regressing RSD of heat rate onto annual heat rate. 20

19 

### This table excludes nine units where RSD exceeded 2.6 standard deviations from the mean. 20 

### The regression analysis was performed on 9,388 unit-years of study data which were trimmed to remove values outside 2.6 standard deviations. Comment [A39]: This Table (and others like it) might be more clearly presented as a simple line graph.

EPA response: Thank you.

Deleted: This table excludes nine units where RSD exceeded 2.6 standard deviations from the mean.¶ d
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## The r-squared result is 57%. These results indicate that, other factors held equal, if an EGU reduces heat rate variability, generally heat rate performance will improve.

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



## 2.5.7 Units with large heat rate changes
Over one third of the study population (355 units) reported at least one year-to-year change in heat rate greater than +/- 8.5%. 21 

## We consider this magnitude to be in the upper range of what would be expected due to changes in fuel rank, operations and maintenance, or plant equipment. Table 2-10 presents the counts by th ree categories: EGUs with at least one year-toyear decrease in heat rate > 8.5%; units with at least one year-to-year increase in heat rate > 8.5%; and, units with both. We examined whether the large heat rate changes were due to ` yearto-year changes in capacity factor and found no correlation between the year-to-year changes in heat rate and capacity factor for any of the three groups in Table 2-10. 22 

## This would indicate that other factors account for these large changes to heat rate. The EPA’s research found that approximately two-thirds of the large decreases in heat rate can be associated with changes in reporting method implemented to provide more accurate heat input data. 23 

## The large changes

21 

### After removing unit-years where annual capacity factor fell under 50 percent the count is 313 EGUs.
22 

### The correlation remains weak even when limited to cases where capacity fact or changed more than 30 percent.
23 

### EPA Reference method 2 specifies the normal procedure fo r measuring stack gas volumetric flow rate during a relative accuracy test audit. Methods 2F, 2G, 2H a nd CTM-041 are approved alternat ives. Methods 2F and 2G correct measured flow rates for angular (non-axial) flow , Method 2H (for circular stacks) and conditional test method CTM-041 (method J, for rectangular stacks and ducts) are used to correct measured flow rates for velocity decay near the stack wall, using a “wall effects adjustment factor”. These alternative methodologies are optional. Therefore, given the additional complexity and cost of using these alternatives a source is likely to use them only if y = 0.002x - 14.254 R2 = 0.5704 5.0 5.2 5.4 5.6 5.8 6.0 6.2

9600 9650 9700 9750 9800 9850 9900 9950RSD 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. Th is does not imply that the changes associated with a large year-to-year decrease, which may include operations and maintenance, more accurate reporting methods, or new equipment, do not affect heat rate variability. If they occur as part of an engineering effort to improve efficiency, heat rate variability may also be reduced.
Table 2-11. RSD in reported heat rate of 166 ‘large decrease’ EGUs (generation weighted)

Quartile RSD Average RSD Minimum RSD Maximum

1 4.0 2.3 4.8 2 5.3 4.8 5.9 3 6.6 5.9 7.5 4 10.5 7.6 26.5



## 2.5.8 Assessment of heat rate improvement potential via best practices
As mentioned before, across the study period, the effects of hourly capacity factor and ambient temperature explain a generation-weighted average of 26% of the change in study population heat rate. 24 

## This means that on average 74% of the change remains unexplained after controlling for those factors. The residual heat rate analysis determined there is significant variation in the operation of EGUs. Since lower heat rate variability is associated with lower heat

the results are significantly lower volumetric stack gas flow. The EPA was unable to draw conclusions about the effect of changes in flow reporting methods on study population heat rate performance. 24 

### The 26% result is the generation weighted average of r-squared values from the multivariate regression analysis. Formatted: Highlight
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## rate, other factors held equal, the range of variation indicates that significant potential for heat rate improvement is available through the application of best practices.
To control for known factors, the EPA constructed a model that groups each EGU’s hourly heat rate data into 14 temperature bins an d 12 capacity factor bins, resulting in a 12 by 14
matrix of 168 bins. 25 

## For a given EGU, a temperature and capacity factor bin will have all the relevant hourly values over the eleven-year study period. For each bin with 15 or more values the model finds the reported hourly heat input value corresponding to the 10th percentile (p10). 26 

## This means that approximately 90% of the heat input values in that bin exceed p10. For each unit, the model reduces the reported hourly heat input greater than p10 by a percentage of the distance between the reported value and p10 ( e.g., 50% of the difference ) . The same statistical procedure is applied to every hour of heat input data in each bin. These reduced hourly heat input values are then used to calculate a reduced 11-ye ar average heat rate for each unit. The percent difference between a unit’s reported 11-year average heat rate and the heat rate that corresponds to reduced heat inputs is the potential heat rate improvement for that unit. 27 

## Using this approach, those units with the lowest variability (e.g., in th e 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 eff ect, the model proportionately reduces heat rate variability and improves performance for each unit while controlling for temperature and capacity factor. The heat rate improvement for the study population is derived from the performance of each individual EGU as compared to its own record.

25 

### The matrix provides up to 168 bins but only 164 contained hourly values. Temperature bins ranged from -20 to greater than 110 with 10 degrees F in each. Capacity factor bins ranged from 0% to greater than 110% with 10% in each. 26 

### Performing the calculation with a minimum of 30 values in each bin has a modest effect on the results in Table 2- 12. For example, a 30% reduction obtains a 3.9% fleet wi de improvement in heat rate (rather than 4.0%). 27 

### Heat rate is calculated as the sum of heat input (btu , reported or reduced) divided by the sum of generation (kWh) for the given population and time period. Deleted: ¶

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

% Reduction from reported heat input to p10 Study population heat rate (Btu(kWh-gross) 28 

### Reduced study population heatrate (Btu/kWhgross) Study population heat rate improvement (%)

10

9,755 9,625 1.3 20 9,495 2.7 30 9,365 4.0 40 9,235 5.3 50 9,105 6.7



## 2.5.9 Assessment of potential heat rate improvement via equipment upgrades
The EPA inspected the study population to find examples of EGUs that made significant year-to-year improvements in heat rate. After filtering out those cases that may have been the result of changes in capacity factor, reporting me thod, 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 responsi ble 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 provid ed data with details on specific equipment modifications for seven coal-fired units at three anonymous plants. These included turbine efficiency and condenser performance upgrades, installation of variable frequency drive fans, reducing boiler air in-leakage and others. Together, these measures achieved from 0.25% to 3.5% heat rate improvement at the seven EGUs.
An EPA study (SRA, 2001) describes WEPCO’s two-phase efficiency program at four coal-fired plants over a ten-year period. In the first phase, 1990 – 1994, WEPCO installed

28 

### Fleet heat rate for study population as described in Table 2-4 is 9,7 54 ; the 9,755 value is derived from the dataset that includes hourly temperature values. Comment [A40]: This technical analysis of the possibility for heat rate improvements should be peer reviewed. Has EPA run this by the SAB, or does EPA intend to do so after release of the proposal?

EPA response: Thanks for your comment

Deleted: one or more Comment [A41]: Do you really mean in a single year to year change, as an indicator of a functional change in the equipment? Otherwise, 8% annual improvement year after year is immense. Suggested edit, but change at EPA’s discretion.

EPA response: We meant one time

– thank you.

Deleted: s

Deleted: Comment [A42]: Is it possible to give any more information about this or provide further support?

EPA response: More information has been added.

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## equipment upgrades that included retractable turbine packing, variable speed drives on the forced and induced draft fans, feed water heater replacements and new performance monitoring instrumentation. The four units reported heat rate improvements ranging from 2.3% – 4.1% as a result of the equipment upgrades. In the second phase, 1995-2000, WEPCO implemented changes that would generally fall into the best practices category: equipment control and metering upgrades, boiler cleaning, feed water heater improvements and reduced condenser air in-leakage and thermal losses. These gained an additional 0.5% per year heat rate improvement (for a total of 2.5%).
The EPA also reviewed the engineering studies available in the literature and selected the Sargent & Lundy 2009 study as the basis for our assessment of heat rate improvement potentials from equipment and system upgrades. We focused on some thirteen heat rate improvement methods discussed by Sargent & Lundy, seen in Table 2-13. We used the average of the estimated $/kW costs for each method to develop the cost-ranked list of heat rate improvement methods (lowest cost at the top, highest at the bottom) shown in Table 2-13. The first nine items in Table 2-13 contribute about 15 percent of the total average $/kW cost for all items. We believe it is reasonable to consider those nine no-cost and low-cost heat rate improvement methods as belonging in the category of what has been described above as best practices. The remaining four methods are higher cost heat rate improvement items that we believe properly fall into the category discussed here as upgrades. Using an average of the ranges of potential Btu improvements estimated by Sargent & Lundy for the four upgrade methods, upgrades, as defined here, could provide a 4% heat rate improvement if all were applied on an EGU that has not already made these upgrades.

Table 2-13. Sargent & Lundy Heat Rate Improvement Methods No-Cost and Low-Cost Options Condenser Cleaning Intelligent Soot Blowers ESP Modification Boiler Feed Pump Rebuild Air Heater and Duct Leakage Control Neural Network SCR System Modification FGD System Modification

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## Cooling Tower Advanced Packing
Higher Cost Options Economizer Replacement Acid Dew Point Control Combined VFD and Fan Turbine Overhaul
We also examined the annual heat rate trend line for each unit using the method of least squares. Using the slope of the trend line, 2002-2012, as an indicator of the heat rate performance of an EGU, a negative slope would indicate that the heat rate has improved. The annual trend line incorporates performance du e to operating conditions (capacity factor, temperature), coal rank, maintenance, reporting method changes, equipment upgrades, and other factors. Over 40% of units have a positive slop e. 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 th e study population average. Reducing heat rate variability will generally also improve heat rate performance, other factors held equal. We found that a 4% improvement is determined by conservatively reducing heat input by 30% of the difference between the reported value and p10 in each unit’s capacity factor and ambient temperature bins.
The 30% approach is in the middle of the range of options shown in Table 2-12 and is comparable to other approaches for measuring potential fleet heat rate improvement. For example, if each unit achieved heat rate performance equal to its best three-year moving average, the study population as a whole would post a 3.9% heat rate improvement. The best two-year Formatted: Highlight
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## 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 practic es. Single year results could be due to unusual conditions, such as, an extended outage or weather. Using th ree consecutive years tends to smooth out the effect of equipment maintenance cycles and unusual meteorological patterns.
The adjustment of heat rate using the 30% approach is approximately equal to improving the heat rate variability performance of the lo wer ranked EGUs by one quartile (see Table 2-8 above). The top quartile has the lowest heat rate variability with an average RSD of 3.5% over a range of 1.6% to 4.2%. The bottom (fourth) quartile has an average RSD of 9.8%. The 30% approach proportionately trims the hourly heat input for each EGU in a given temperature and capacity factor bin. Using the bounds in Table 2-8, applying the 30% approach to the study population has the effect of nearly doubling the number of EGUs in the top quartile and reducing the count of those in the fourth quartile to 58 (of 218). These results appear reasonable. In effect, is the approach amounts to asking the lower ranked EGUs to improve heat rate variability to a level comparable to that which the top quartile of EGUs have maintained over an 11-year period.
The equipment upgrades results are supported by numerous studies 29 

## 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 CO 2 

## emissions from coal.

2.5.11 Sensitivity Analysis Removing Planned or Announced EGU Retirements

29 

### See discussion in Table 2-3 above, and the HRI Partial Bibliography at the end of this section Deleted: it is

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## The EPA’s research found 233 coal-fired, non-cogeneration EGUs that have announced they will retire before 2016. 30 

## A sensitivity analysis was applied to the EGUs in the study population that plan to operate through 2015. The results are identical to the full population –
both achieve a heat rate reduction of 4% under the 30 % approach described in best practices.

2.6 Heat Rate Improvement – Economics
Most of the methods that can be applied to achieve a sustained Heat Rate Improvement (HRI) on a coal-steam EGU will entail a capital cost . These HRI capital costs can be economic to incur if they yield sufficient reductions in other current or potential costs, particularly reductions in coal fuel cost and any cost related to CO 2 

## emissions. For the purpose of this TSD analysis, it is
assumed that HRI can be economic if the annua lized net savings (coal cost savings plus CO 2 

## emission cost savings minus capital cost) is positive:

Annual Net HRI Savings = Coal Cost Savings + 

## CO 2 

### Emission Cost Savings – Capital Cost 31



## 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 Ca pital Cost: $40-150/kW 32


## EPA Assumed Combined HRI Capital Cost: $100/kW

30 IPM documentation includes a list of the announced retirements. See Table 4-36 of IPM documentation: http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/v513/Chapter_4.pdf and http://www.epa.gov/airmarkets/progsregs /epa-ipm/docs/v513/NEEDS_v513.xlsx 31 

### This TSD analysis assesses a broadly combined application of multiple HRI methods. As estimated in the 2009 Sargent & Lundy study most HRI-related O&M costs are suff iciently small relative to the associated annualized capital costs, such that they do not materially affect th e economics of broadly combined HRI methods. The analysis therefore does not consider the small econo mic impact of HRI-related O&M costs. 32 

### Note that highest cost does not necessarily align with greatest heat rate improvement. A low cost HRI method

can have a large HRI potential (eg: upgraded digital control system, neural network). Also, economy of scale causes most HRI methods to be more costly ($/kW) on smaller unit sizes. Deleted: 0
Deleted: 230 Comment [A43]: This is an important statement, that is accorded little explanation. I presume these

are coal EGUs? And is it plants or separate units? How did these anticipated requirements factor into the state goal setting, and then into the IPM modeling?

EPA response: See updated footnote with IPM

source. Formatted: Highlight
Deleted: percent difference option

Deleted: The EPA research was derived from Ventyx Velocity Suite New Entrants database (proprietary) and other sources,

Formatted: Highlight
Deleted: D

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## The wide ranges of estimated HRI Btu and costs are indicative of the wide range of real differences in the many details of site specifi c 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 ach ieve 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 e xpects that a significant fraction of the coal fleet has already applied some or many of the available HRI methods. 33



## The EPA does not have sufficient site specific information to accurately estimate what percentage of the fleet has adopted various HRI methods, nor how effectively, and is not aware of any other investigator having sufficient information. HRI potential can therefore not beestimated at this time through analysis of the current equipment configurations of the coal steamEGU fleet. The EPA therefore analyzed 11 years of hi storical heat rate data and the literature on HRI methods, as discussed earlie r 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 re ductions in auxiliary loads. For purposes of this TSD the EPA conservatively assumes that the coal fleet average net heat rate can be reduced by
6%.

33 

### Based on the EPA informal discussions with Sargent & Lundy and other power sector engineering firms. The EPA has found no comprehensive data set on the extent to which specific HRI methods have already been applied at individual EGUs. The EPA believes that many EGU owners consider such information to be confidential.

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## 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 CO 2 

## emission cost that is
reduced via HRI.

Figure 2-6. HRI Breakeven Economics

Notes: 1. Capital cost S/MWh assumes the following: HRI capital cost = $100/kW; capital charge rate = 14.3%; IPM projected 2020 annual capacity factor = 78% 2. Coal fleet average 2020 net heat rate = 10,450 Btu/kWh; heat rate reduction = 6%
Figure 2-6 shows that the average fleet-wide savings in coal cost would become greaterthan the annualized capital cost of an average 6% reduction in heat rate when the average fleetwide coal cost exceeds about $3.25/MMBtu. For comparison, the average U.S. power sector delivered cost of coal in 2020 is projected in EPA’s IPMv5.13 Base Case modeling at $2.62/MMBtu. For different assumptions, the HRI economic breakeven point would change directionally as follows:

 If the HRI capital cost were on average less than the assumed $100/kW, 6% HRI would then become economic at lower coal costs. For example, if the average capital cost were actually $75/kW, a fleet-wide 6% HRI would become economic at an average coal cost of about $2.50/MMBtu, which is comparable to the U.S. power sector average costs of 0.00 0.50 1.00 1.50 2.00 2.50 3.00

1.00 2.00 3.00 4.00$/MWh‐net



### Coal Cost $/MMBtu Coal Cost Savings $/MWh

‐ net Capital Cost $/MWh

‐ net



### HRI Capital Cost = $100/kW

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## $2.38/MMBtu for all coal ranks and $2.89/MMBtu for bituminous coals in 2012. 34 

## This sensitivity indicates that fuel cost savings alone would make it economic for some of those EGUs currently using high cost bituminous coals to make HRI investments.

 At an EGU net heat rate that is higher than the IPM projected 2020 average value of 10,450 Btu/kWh, 6% HRI could be economic at coal costs lower than the values
mentioned above.

 If the average heat rate reduction were only 4% instead of the assumed 6%, at a cost of $100/kW, average coal costs would have to exceed $4/MMBtu for 4% HRI to be economic fleet wide,

 But, if the average heat rate reduction were 4% at a cost of $50/kW, HRI could become economic at an average coal cost of about $2.50/MMBtu.

 If there were additional HRI sa vings due to avoided future CO 2 

## emission costs, HRI could become economic at lower coal costs, or at higher capital costs, or at lower heat rate reduction percentages.

2.6.4 U.S. Coal -steam EGUs – Estimated Fleet-wide CO 2 

## 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 CO 2 

## emission rate = 0.976 tonne/MWh net

 Average net heat rate = 10,450 Btu/kWh net

 Average capacity factor = 78%

 Pre-HRI CO 2 

## emissions = 1.62 billion tonne/yr (calculated)

 HRI Btu and CO 2 

## reduction = 6%

 HRI capital cost = $100/kW

34 

### EIA, Electricity DataTable 7.4, Average Weighted Cost of Fossil Fuels for the Electric Power Industry 2002- 2012, http://www.eia.gov/electricity/annual/html/epa_07_04.html Deleted: Estimated Fleet-wide
Deleted:

Deleted: EGU

Comment [A44]: Throughout the paper, it might help to indicate the “fleet ‐ wide” data refer to the national fleet (i.e. all the coal facilities in the U.S.) if this is the case.

EPA response: Thank you for the suggestion. See

new section heading above.

Comment [A45]: It was ostensibly 44% in 2012

– see Table 2

‐ 5, page 19 above

– or am I missing something? Please clarify. Is this assuming a lot of coal plant closures to get the capacity factor up? If so, please clarify the discrepancy/change over time in the text and how this might affect state goal setting, especially building block 2?

EPA response: Good catch. See revised Table 2

‐ 5 and associated text. Also see the RIA and other TSD discussions regarding IPM Base Case assumptions, resulting 2020 coal capacity, etc.

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##  Annual capital charge rate = 14.3%

 Average coal cost = $2.62/MMBtu
Estimated Fleet-wide Results:

 Fleet-wide CO 2 

## 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 CO 2 

## 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 CO 2 

## emission reductions, on a fleet-wide basis at toda y’s coal prices if the associated average capital cost is abou t $75/kW or less.

 If a fleet-wide average 6% HRI is technically feasible, and the associated average capital cost is as much as $100/kW, 6% HRI coul d become economic on the basis of fuel savings alone, before consideration of the value of the associated CO 2 

## 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 CO 2 

## 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 CO 2 

## reduction for existing coal-fired EGUs

Definitions/Abbreviations Btu British thermal unit capacity factor electricity generation expressed as a percentage of maximum

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### electricity generation (i.e., actual generation / maximum potential generation) CO 2 

### 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 genera te 1 kWh of electricity, expressed in gross Btu/kWh. MMBtu million Btu MW megawatt PC pulverized coal (boiler) RSD Relative standard deviation

S & L report Sargent & Lundy engineering study on the potential heat rate improvement from equipment upgrades (EPA 2009 version) [Available at: http://www.epa.gov/airmarket/resource/docs/coalfired.pdf]

start a startup event in which an EGU begins combusting fossil fuel and generates some measurable amount of electricity before ceasing fossil fuel combustion unit-year data for one EGU over a one year period



### Docket Datasets

Name Description hour_QA_data.txt 2002-2012 hourly dataset hour_QA_regression_data.txt 2002-2012 hourly dataset for regression analysis units_885.txt List of study units and characteristics year_bin_10_50_data.txt 2002-2012 unit-year binned results at 10 – 50% difference options

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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/pri nt/volume-106/issue-1/features/heatrate-optimization-pays-dividends.html NETL 2008, Reducing CO 2 

## 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 Effi ciency of Existing Coal-fired Power Plants, Workshop Report, NETL July 2009, available at http://www.netl.doe.gov/energyanalyses/pubs/NETL%20Power%20Plant%20E fficiency%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/Resear ch/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/airmarket s/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-st andards/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 Exis ting Coal-Fired Power Plants, Congressional Research Service, December 2013, available at http://www.fas.org/s gp/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/PowerPlan tComponent/Output1.1/Output1.1.pdf IEA 2010, Power Generation from Coal - Measuring and Reporting Efficiency Performance and CO 2 

## 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 Electri c Energy Efficiency in the Production and Delivery of Electricity, EPRI Technical Re port 1024651, November 2011, available at http://www.google.com/url?sa=t&rct=j&q=&esrc=s&frm=1&source=web&cd=1&ved=0CCsQF jAA&url=http%3A%2F%2Fwww.pserc.wisc.edu% 2Fdocuments%2Fpublications%2Fspecial_in terest_publications%2FEPRI_Electricity_Use _Report_Final_1024651.pdf&ei=Qo9yUvmwMY Xb4AOZz4GYAw&usg=AFQjCNElzekbtoSNCR5SKFwkfbKx83p0Uw&bvm=bv.55819444,d. dmg RFF 2013, Regulating Greenhouse Gases from Coal Power Plants under the Clean Air Act, RFF DP 13-05, February 2013, available at http://www.rff.org/RFF/documents/RFF-DP-13-05.pdf

### Page 1: [1] Comment [A2] Author 

# MATS Compliance. MATS compliance will lead to significant installations of pollution control equipment (PCE) over the 2013 to 2016 period on capacity that owners intend to keep in service beyond the MATS compliance deadline but would not be MATS

‐ compliant without augmenting the PCE that was in place during 2012. The PCE systems likely to be installed on such units include FGD systems that may consume significant power and therefore, all else equal, increase energy input per net kilowatt

‐ hour of generation. It would be helpful to understand if or how additions of PCE for MATS compliance are reflected in the analysis of HRI in the draft TSD.

EPA r esponse 

# : The results of our statistical analysis (described later) are based on gross heat rates and will not change with installation of MATS controls, which will add additional parasitic load requirements and thereby have an impact on the net heat rates. However, in this TSD chapter, we explain that EPA conservatively considers fleet

‐ wide net heat rate improvement potential to be the same as that indicated for the fleet

‐ wide gross heat rate. And based on this consideration, IPM modeling and state goals calculations used 6% improvement on a net heat rate basis. So we also need to speak to the fleet

‐ wide net heat rate. That being said, MATS controls are expected to lead to a small increase in auxiliary power use and therefore will not have a significant effect on the fleet

‐ wide net heat rate.

Here is what we projected (using IPM) for the final MATS policy case. Technology Capacity Parasitic Load New wet FGD 0 GW 1.5

‐ 2% New SCR 0 GW 0.5% New dry FGD (SDA) 22 GW 1

‐ 1.5% New DSI 43 GW 0.5% (assumed same as ACI + FF) New ACI 141 GW 0.5% New FF 102 GW 0.5%

Upgrade of existing ESP 34 GW Upgrade of existing wet FGD 63 GW

So MATS requirements would increase parasitic load from 0% (for a unit that can meet the standards with existing equipment) up to 2 % for units that install a SDA and implement ACI + FF [Note: a unit would implement either SDA or DSI but not both].

In any case, EPA’s IPM v 5.13 Base Case modelling for this proposal assumes that MATS is in effect and that such controls as are needed for compliance are added before 111(d) policy options are evaluated in policy runs.

Page 22: [2] Comment [A31] Author 

## Suggest making the Table title more descriptive. Also could present fewer signficant digits in table ...i.e. heat input ‐ MM BTU x 106 ‐‐ 

# EPA response: changed units and title.
Page 22: [3] Comment [A32] Author

## This capacity factor decline in 2012 is very different from the CF used later, see comment on page p. 35, i.e., 78% in 2020. Which is correct? And, did coal CF at 44% factor into the EPA modeling?

EPA response: The table has been updated to use all reported data (rather than a trimmed data set). This changes the capacity factor in 2012 to 53%. Note the capacity factor of 78% is for the coal

‐ fired steam fleet in base case, 2020. Note also that the study population includes a significant number of EGUs that IPM lists as retiring prior to 2016. Page 22: [4] Deleted Author



# Figure 2-2. Study population Aver age Gross Heat Rate by Year
Page 22: [5] Comment [A33] Author 

## At about this point, the chapter transitions into the quantitative analysis. Suggest highlighting that in some way. There seem to be several components of the quantitative analysis: 1. Regressing HR onto CF and Temp; 2. Looking at residual heat rates relative to model prediction; 3. The “bin model” for assessing HRI potential via best practice; 4. The assessment of HRI potential via equipment upgrades. Suggest presenting those more clearly either here or
even forward in the chapter in summary form.

EPA response: Thank you. We have added a paragraph introducing more

‐ quantitative analyses. 9,500 9,550 9,600 9,650 9,700 9,750 9,800 9,850 9,900 9,950 10,00020022003200420052006200720082009201020112012Heat Rate (btu/kwh)



## Annual Heat Rate 11-yr Avg Heat Rate

## DRAFT May 22, 2014 Draft Deliberative 

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

2.0 Introduction
Based on the range of operating efficiencies for existing coal-fired electric generating units (EGUs), it is evident that EGUs are generally less efficient at converting fuel into electricity than is technically and economically possible. For example, the difference in operating efficiency of EGUs with similar design character istics 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 ex isting coal-fired EGUs, design factors that influence efficiency, technologies to improve e fficiency, 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 us ed 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 co al-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. electri c utility fleet use one of five basic coal combustion configurations: (1) pulverized coal (PC) combustion, (2) fluidized-bed combustion (FBC), (3) gasified coal combustion, (4) cyclone furnace combustion, or (5) stoker-fired coal combustion. Table 2-1 presents a summary of the operating characteristics of each of these coal combustion configurations. Comment [A1]: This section read quite weakly as initially written.

EPA response: Thank you for the edit. Comment [A2]: MATS Compliance. MATS compliance will lead to significant installations of pollution control equipment (PCE) over the 2013 to 2016 period on capacity that owners intend to keep in service beyond the MATS compliance deadline but would not be MATS

‐ compliant without augmenting the PCE that was in place during 2012. The PCE systems likely to be installed on such units include FGD systems that may consume significant power and therefore, all else equal, increase energy input per net kilowatt

‐ hour of generation. It would be helpful to understand if or how additions of PCE for MATS compliance are reflected in the analysis of HRI in the draft TSD.

EPA response: The results of our statistical analysis (described later) are based on gross heat rates and will not change with installation of MATS controls, which will add additional parasitic load requirements and thereby have an impact on the net heat rates. However, in this TSD chapter, we explain that EPA conservatively considers fleet

‐ wide net heat rate improvement potential to be the same
as that indicated for the fleet

‐ wide gross heat rate. And based on this consideration, IPM modeling and state goals calculations used 6% improvement on a net heat rate basis. So we also need to speak to the fleet

‐ wide net heat rate. That being said, MATS controls are expected to lead to a small increase in auxiliary power use and therefore will not have a significant effect on the fleet

‐ wide net heat rate.

Here is what we projected (using IPM) for the final MATS policy case. Technology Capacity Parasitic Load New wet FGD 0 GW 1.5

‐ 2% New SCR 0 GW 0.5% New dry FGD (SDA) 22 GW 1

‐ 1.5% New DSI 43 GW 0.5% (assumed same as ACI + FF) New ACI 141 GW 0.5% New FF 102 GW 0.5%

Upgrade of existing ESP 34 GW Upgrade of existing wet FGD 63 GW

So MATS requirements would increase parasitic load from 0% (for a unit that can meet the standards with existing equipment) up to 2 % for units that install a SDA and implement ACI + FF [Note: a unit would implement either SDA or DSI but not both].

In any case, EPA’s IPM v 5.13 Base Case modelling for this proposal assumes that MATS is in effect and that such controls as are needed for compliance are added before 111(d) policy options are evaluated in policy runs.

## DRAFT May 22, 2014

2 Table 2-1. Characteristics of coal-firing configurations used for U.S. EGUs
Coal-firing Configuration Coal Combustion Process Description Distinctive Design/Operating Characteristics

Pulverized Coal (PC) Combustion Coal is ground to a fine powder that is pneumatically fed to a burner where it is mixed with combustion air and then blown into the furnace. The pulverized-coal particles burn in suspension in the furnace. Unburned and partially burned coal particles are carried off with the flue gas. Wall-fired An array of burners fire into the furnace horizontally, and can be positioned on one wall or opposing walls depending on furnace design.

Tangential-fired (Corner-fired) Multiple burners are positioned in opposite corners of the furnace producing a fireball that moves in a cyclonic motion and expands to fill the furnace.

Fluidized-bed Combustion

(FBC) Coal is crushed into fine particles. The coal particles are suspended in a fluidized bed by upward-blowing jets of air creating a turbulent mixing of combustion air with the coal particles. Typically, the coal is mixed with a sorbent such as limestone (for SO 2 

### 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 900 o 

### C). Bubbling fluidized bed (BFB) Operates at relatively low gas stream velocities and with coarse-bed size particles. Air in excess of that required to fluidize the bed passes through the bed in form of bubbles.

Circulating fluidized bed (CFB) Operates at higher gas stream velocities and with finer-bed size particles. No defined bed surface. Must use high-volume, hot cyclone separators to recirculate entrained solid particles in flue gas to maintain the bed and achieve high combustion efficiency.

Integrated Coal

Gasification Combined Cycle (IGCC) Synthetic combustible gas (“syngas”) derived from an on-site coal gasification process is burned in a combustion turbine. The hot exhaust gases from the combustion turbine pass through a heat recovery steam generator to produce steam for driving a steam turbine/generator unit. Coal gasification units are unique among coal-firing configurations because a gaseous fuel (synfuel or syngas) is burned instead of solid coal because the combustion and power generation process and combines the Rankine and Brayton thermodynamic cycles as is the case for a combined cycle power plant.

Cyclone Furnace Combustion Coal is crushed into small pieces and fed through a burner into the cyclone furnace. A portion of the combustion air enters the burner tangentially creating a whirling motion to the incoming coal. Designed to burn coals with low-ash fusion temperatures that are difficult to burn in PC boilers. The majority of the ash is retained in the form of a molten slag.

Stoker-fired Coal Combustion Coal is crushed into large lumps and burned in a fuel bed on a moving, vibrating, or stationary grate. Coal is fed to the grate by a mechanical device called a “stoker.” One of three types of stoker mechanisms can be used that ether feed the coal by pushing, dropping, or flipping coal unto the grate.



## The three technologies currently used for new coal-fired power plants are pulverized coal, fluidized bed (FBC), and integrated coal gasification combined cycle (IGCC). Pulverized coal combustion is the coal-firing configuration predominately used at existing EGUs. The most Comment [A3]: We would suggest reducing the amount of text in tables (perhaps as bullet points).

EPA response: Thank you for the observation.

Comment [A4]: It might be helpful to indicate the typical size (MW) and capital cost ($/installed MW) here.

EPA response: Thank you for the observation, but it is unclear how the suggested edit would relate to the analysis. Comment [A5]: No one is building coal plants currently in the U.S., so the future tense is not appropriate. Who is building coal plants using FBC? China? If so, we should specify, or else delete.

EPA response: The text has been deleted.

## DRAFT May 22, 2014

3 recent coal technology combustion development involves the integration of coal gasification technologies with the combined cycle electric generation process. The efficiency of an IGCC power plant is comparable to the latest advanced PC-fired and FBC EGU designs using supercritical steam cycles. The advantages of using IGCC technology can include greater fuel flexibility (e.g., capability to use a wider variety of coal ranks), potential improved control of PM, SO 2 

## 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 CO 2 

## 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 providesuperior 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 CO 2 

## Emission Rate
Heat rate is a common way to measure EGU ef ficiency. As the efficiency of a coal-fired EGU is increased, less coal is burned per kilowa tt-hour (kWh) generated by the EGU resulting in a corresponding decrease in CO 2 

## 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 op erate the EGU (e.g., elect ricity to power fuel handling equipment, pumps, fans, pollution control equipment, and other on-site electricity needs), and thus is a measure of the electricity de livered to the transmission grid for distribution and sale to customers. Some EGUs also produce part of their useful output in the form of useful thermal output (e.g., steam for heating purposes). These types of facilities are called combined heat and power, or CHP, facilities. Comment [A6]: One of the primary benefits of IGCC is the concentrated CO2 stream and ability to

use in CCS. Needs to be highlighted.

EPA response: Thank you for the observation and the suggested text, which has been included.

## DRAFT May 22, 2014

4 A variety of factors must be considered when comparing the effectiveness of heat rate improvement technologies to increase the effici ency 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 ultr a-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 th ermal design efficiencies for subcritical EGUs are in the range of 35% to 37%, supercritical EGUs are in the range of 39% to 40%, and ultra-supercritical EGUs in the range of 42% to 45%. However, actual operating efficiencies are lower than design efficiencies.

 EGU coal rank and quality – EGUs burning higher quality coals (e.g., bituminous) tend to be more efficient than EGUs burning lo wer quality coals with a greater moisture content (e.g., lignite). Bituminous coals have higher heating values of greater than 10,500 British thermal units per pound and lignite coals have higher heating values of less than 8,300 British thermal units per pound.

 EGU plant size –EGU efficiency generally increases with size because the boiler and steam turbine losses are lower for larger equipment compared to smaller equipment. However, the EPA is not aware of suffic ient data to quantif y the relationship.

 EGU pollution control systems – The electric power consumed by air pollution control equipment reduces the overall efficiency of the EGU.

 EGU operating and maintenance practices – The specific practices used by an individual electric utility company for combustion optimization, equipment maintenance, etc. can affect EGU efficiency.

 EGU cooling system – The temperature of the cooling water entering the condenser can have impacts on steam turbine performance. Once-through cooling systems can have an efficiency advantage over recirculating coolin g systems (e.g., cooling towers). However, once-though cooling systems typically have larger water related ecological concerns than recirculating cooling systems. Comment [A7]: Could indicate the approximate range (BTUs / lb) of the different coal types, as well

as the influence of moisture.

EPA response: The ranges have been added.

Comment [A8]: : Could indicate approximate turbine size / size class here.

EPA response: Edits made to address the comment.

## DRAFT May 22, 2014

5  EGU geographic location and ambient conditions – The elevation and seasonal ambient temperatures at the facility site potentially may have an impact on EGU efficiency. At higher elevations, air pressure is lower and less oxygen is available for combustion per unit volume of ambient air than at lower elevations. Cooler ambient temperatures theoretically could increase the overall EGU efficiency by increasing the draft pressure of the boiler flue gases and the condenser vacuum, and by increasing the efficiency of the cooling system. Also, geographic location influences the type of cooling system that can be used (e.g., EGUs located in arid loca tions 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 driv es, 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 potentia l efficiency improvement techniques applicable to existing coal-fired EGUs. 1 

## For example, a summary of the findings from one study conducted by the Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) is presented in Table 2-2. The efficiency percentages were converted to a common basis so that all of the data can be compared. All of the improvement technologies presented in Table 2-2 cannot necessarily be implemented at every existing coal -fired EGU facility in the U.S. electric utility fleet. The existing EGU design configuration and other site-specific factors may prevent the technical feasibility of using a given technology.
Typically, these studies share as a common basis the estimated potential efficiency improvement percentages and costs from the engineering study originally completed by Sargent and Lundy in 2009 titled “Coal-Fired Power Plant Heat Rate Reductions.” It describes numerous well known and technically proven methods to im prove efficiency of coal-fired EGUs. The study

1 

### See HRI Partial Bibliography at the end of this chapter. Comment [A9]: Geographic location could also influence cooling water issues, mentioned earlier (i.e. coal plants in the arid west).

EPA response: Text has been added to address the comment.

Comment [A10]: Suggest using “load following” for clarity

EPA response: Done.

## DRAFT May 22, 2014

6 lists possible efficiency improvements in the boiler , turbine, flue gas system, air pollution control equipment and the water treatment system. Each of these main areas are expanded upon below.

2.3.1 Boiler
The systems to focus on for improving heat input within the boiler area include the materials handling, combustion system, boiler control system, sootblowers and the air heaters.
2.3.1.1 Materials Handling 2



## The coal handling portion of materials handling typically requires about 0.07% (7 Btu/kWh) of the gross electrical output of a pow er 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 variab le 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 redu cing fuel cost and heat rate. The costs for changes to the pulverizer system are significant, and historically, the projects have improved the heat rate justifiably only when the existing equipment has degraded.
The bottom ash handling system may be a source for heat rate improvement. Switching from a water-sluicing bottom ash system to a dr y 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 improv es the efficiency of an EGU by recovering energy from the exhaust gases to preheat the boiler feedwater. The replacement of the economizer can lead substantial heat rate improvements around 50-100 Btu/kWh, but is large capital investment (~$2-8M). Due to this high cost, economizers upgrades are not generally performed unless the existing equipment has degraded or a replacement is necessary due to the installation of new control equipment.

2 

### The Sargent and Lundy report did not provide potential sa vings for material handling operations. Energy use in Btu/kWh has been provided to compare the energy use of materials handling relative to the potential energy savings from other efficiency activities. Comment [A11]: Did you want to include an estimate of pulverizer electric demand?

EPA response: An approximate electric demand has been added.

Comment [A12]: For this part of the discussion,

some efficiency gains are given in percent, and others in Btu/kWh. It would be helpful to keep either one common unit (suggest Btu/kWh) or provide both metrics.

EPA response: See footnote.

Comment [A13]: Please add a sentence on what

an economizer is and does.

EPA response: Added an introductory sentence.

## DRAFT May 22, 2014

7 2.3.1.3 Boiler Control System
The boiler control system has a large impact on the heat rate of the unit. The process control capabilities can control and evaluate ma ny aspects of the plan t’s operations. Commonly referred to as the Neural Network, computer models are able to control the plant’s processes by predicting performance during static and dynamic changes. Many vendors offer Neural Network systems to improve the overall efficiency. Neural network systems are typically around $550,000-$750,000 and offer heat rate reductions up to 150 Btu/kWh.

2.3.1.4 Sootblowers
Intelligent sootblowers may be installed to improve system efficiency. The intelligent sootblowers system monitors the furnace exhaus t gas temperatures and steam temperatures. Other readings may be incorpor ated into the intelligent so otblowers system which also communicates with the boiler control system. This system uses real-time data to identify which areas need sootblowing. Boiler efficiency improvements have ranges from 30-(150 Btu/kWh) with capital costs around $300,000-$500,000 and $50,000/year for fixed operating and
maintenance costs.

2.3.1.5 Air Heaters
Air heaters operate to transfer heat between the incoming pre-combustion air and the effluent flue gas. These systems are critical to maintain an efficient power plant. In order to maintain these systems to operate most efficiently , air heater leakages must be maintained below 6% of incoming air flow. Most leakage is due to the pre-combustion air leaking across the rotating section and leaving with the flue gas. This increases the flue gas volume going through the forced draft and induced draft fans to the emissions controls and avoids capturing the heat transferred between the flue gas and pre-combustion gas. The increased volume requires more power to pump more flue gas. Ad ditionally, improvements to seal s on the air heaters also reduce the leakages. Improvements to reduce air heater and duct leakages generally reduces the heat rate by 10-40 Btu/kWh with capital costs between $0.3-1.2M.
A second method to improve the heat rate is to lower the air heater outlet temperature by controlling the acid dew point. Typically the air heater outlet is maintained at 20-30°F above the sulfuric acid dew point to prevent corrosion of cold-end baskets. Sorbent injections such as Trona or hydrated lime can be used to lower the dew point. Depending on the sizing of the air

## DRAFT May 22, 2014

8 heater, the air heater may need to be modified in order to optimize the lower outlet temperature. The capital costs can range from $1.5-18M for heat rate reductions of 50-120 Btu/kWh.

2.3.2 Turbine
The systems within the turbine area for which to focus heat rate improvements are the turbine, the feedwater heaters, the condenser , and the turbine drive and motor-driven feed

pumps.

2.3.2.1 Turbine
Replacements or overhauls of existing steam turbines with advanced turbine designs improves the efficiency of converting the energy in the steam to electrical energy. The capital costs for these projects ranges from $2-25M with heat rate reductions of 100-300 Btu/kWh.

2.3.2.2 Feedwater Heaters
The feedwater heaters are heat exchangers to heat the boiler feedwater by extracting heat from the steam leaving the turbine section. The efficiency can be increased by improving heat transfer surface area. This entails adding heat ex change 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 extr act as much energy from the steam as possible. Condensers are subject to fouling and plugging whic h directly impact the heat transfer rates and water quality. To improve water quality, closed cooling water systems provide better control of the water quality and tube cleaning can be performed. Heat rate reductions observed from the condenser upgrades and maintenances are 30-70 Btu/kWh with annual fixed costs of $30,000- $80,000.
2.3.2.4 Boiler Feed Pumps
Boiler feed pumps require a large amount of auxiliary power to pump massive amounts of boiler feedwater through the heaters and boiler. Due to the high use of these pumps, Comment [A14]: Not clear whether the 1

‐ 3% efficiency improvement refers to improvement resulting from an overhaul. It might be helpful to indicate what size of turbines correspond to these costs.

EPA response: the paragraph has been modified.

## DRAFT May 22, 2014

9 maintenance is extremely important to ensure relia bility and the most efficient operation. As the pumps wear and operate less efficiently, a pump overhaul may be required. The overhaul can reduce the heat rate by 25-50 Btu/kWh with capital costs around $250,000-$800,000.
2.3.3 Flue Gas System
Two aspects of the flue gas system that offer potential improvements to the plant efficiency are to improve the forced draft and induced draft fan efficiencies and implementation of variable frequency drives.
2.3.3.1 Induced Draft Fans
One of the most important features in the fans is being able to control the flue gas flow. Many fans have dampers, which are the least effective and efficient option. There are many methods, such as variable inlet vanes , variable frequency drives, and variable pitch blades, available to control the flue gas flow allowing highly efficient fan performance. These upgrades or replacements provide a heat rate reduction of 10-50 Btu/kWh and cost between $6-$16M.
2.3.3.2 Variable Frequency Drives
It is apparent with the control that a variable frequency drives offers , the plant is able to reduce the auxiliary load immensely. Consider ing upgrading all drives, the capital costs are $6-
16M with heat rate reductions between 10-150 Btu/kWh.
2.3.4 Emission Control Technologies
With the passage of environmental regulations, additional emission control devices have been and must be implemented in the power plant. These systems typically require large amounts of auxiliary power with their benefit being im proved air quality. Even small upgrades can decrease the power requirements significantly while at least maintaining the same level of emissions reduction. The three technologies discussed are the flue gas desulfurization, the electrostatic precipitator, and the selective catalytic reduction systems.
2.3.4.1 Flue Gas Desulfurization
Coal-fired power plants contain many varian ces between their flue gas desulfurization systems. A few common technology improvements are discussed further in this section. Older units typically contained a venturi throat that increased the velocity of the fluid, but resulted in a large pressure drop and greater power to operate the induced draft fans. To improve this Comment [A15]: Please spell out acronym – even if it has been spelled out earlier, I have forgotten and couldn’t find the location on looking back.

EPA response: Spelled out acronyms. Comment [A16]: For all these subtitles, it wouldn’t hurt to spell out the acronym in the section title.

EPA response: Spelled out acronyms.

## DRAFT May 22, 2014

10 operation, a co-current spray tower quencher may replace the unit. The capital cost is about $2.5M with heat rate reductions around 13 Btu/kWh.
Another technology upgrade affects the vanes an d distribution plate into an absorber. The improvement of the gas flow coming into contact with the absorber sorbent increases SO 2 

## capture, reduces maintenance due to erosion, and reduces the amount of energy required from the induced draft fan. Turning vanes and a perforated gas distribution plate im prove gas distribution. The cost of the vanes is around $250,000 with heat rate reductions of 1-2 Btu/kWh.
In a wet flue gas desulfurization multiple spray levels are installed to deliver the limestone slurry. If a power plant is operating with SO 2 

## levels below its permit li mit, turning off one spray level will reduce the auxiliary power necessary. If this is possible, a unit heat rate reduction of 16 Btu/kWh may be available.
2.3.4.2 Electrostatic Precipitator
The best operation for an electrostatic pr ecipitator involves maintaining the maximum applied voltage, but below the level in which spark-over occurs. Electrostatic precipitator energy management system upgrades often help improve the electrostatic precipitator performance by maintaining the optimal performance and lowering power consumption. The installation for this technology can be from minimal to $0.8M and can lower heat rate by 5 Btu/kWh.
2.3.4.3 Selective Catalytic Reduction
For the last 15 years, selective catalytic reduction systems have been in use to reduce
NO X 

## emissions from power plants. Extensive modeling was performed to achieve the necessary reduction with minimum ammonia slip. The resu lts showed that reducing pressure drop and using secondary air as dilution for the ammonia vaporizer can reduce the auxiliary power necessary. The heat rate reduction is 0-10 Btu/kWh and capital costs between $0.5-$2M with fixed and variable costs up to $100,000 each.
2.3.5 Water Treatment System
The water treatment system heat rate improvements can occur for the boiler water treatment and the cooling tower improvements. Th e boiler water is one of the most important aspects of the power plant. The quality of the water plays a huge impact on the scale buildup on the boiler tubes, which reduces the heat transfer in the tubes or can cause tube failures. Proper use of chemicals to maintain pure water is key. Also, high-quality water can reduce the

## DRAFT May 22, 2014

11 blowdowns required, which allows for more stea m in the turbine cycle. If the water is not properly maintained, heat transfer may be reduced up to 10%.
Similar to the boiler water, the cooling towers are also affected by the water quality. Fouling and scaling remain issues for heat transf er and purity of the water. By maintaining the cooling water system efficiently, the overall water quality is improved, which branches into other aspects already mentioned.DRAFT May 22, 201412Table 2-2. Existing coal-fired EGU efficiency improvements reported for actual efficiency improvement projectsEfficiencyImprovementTechnologyDescriptionReportedEfficiencyIncreaseaCombustionControlOptimizationCombustion 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 operatingparameters including combustion efficiency, steam temperature, furnace slagging and fouling, and NOXformation. The technologies include instruments that measure carbon levels in ash, coal flow rates, air flow rates,CO levels, oxygen levels, slag deposits, and burner metricsas well as advanced coal nozzles and plasma assistedcoal combustion.0.15 to 0.84%Cooling SystemHeat LossRecoveryRecover a portion of the heat loss from the warm cooling water exiting the steam condenser prior to its circulationthorough a cooling tower or discharge to a water body. The identified technologies include replacing the coolingtower fill (heat transfer surface) and tuning the cooling tower and condenser.0.2 to 1%Flue Gas HeatRecoveryFlue gas exit temperature from the air preheater can range from 250 to 350°F depending on the acid dew pointtemperature of the flue gas, which is dependent on the concentration of vapor phase sulfuric acid and moisture.For power plants equipped with wet FGD systems, the flue gas is further cooled to approximately 125°F as it issprayed with the FGD reagent slurry. However, it may be possible to recover some of this lost energy in the fluegas to preheat boiler feedwater via use of a condensing heat exchanger.0.3 to 1.5%Low-rank CoalDryingSubbituminous and lignite coals contain relatively large amounts of moisture (15 to 40%) compared to bituminouscoal (less than 10%). A significant amount of the heatreleased during combustion of low-rank coals is used toevaporate this moisture, rather than generate steam for the turbine. As a result, boiler efficiency is typically lowerfor plants burning low-rank coal. The technologies include using waste heat from the flue gas and/or cooling watersystems to dry low-rank coal prior to combustion.0.1 to 1.7%SootblowerOptimizationSootblowers intermittently inject high velocity jets of steam or air to clean coal ash deposits from boiler tubesurfaces in order to maintain adequate heat transfer. Proper control of the timing and intensity of individualsootblowers is important to maintain steam temperature and boiler efficiency.The identified technologies includeintelligent or neural-network sootblowing (i.e., sootblowing in response to real-time conditions in the boiler) anddetonation sootblowing.0.1 to 0.65%Steam TurbineDesignThere are recoverable energy losses that result from the mechanical design or physical condition of the steamturbine. For example, steam turbine manufacturers haveimproved the design of turbine blades and steam sealswhich can increase both efficiency and output (i.e., steam turbine dense pack technology).0.84 to 2.6Source: National Energy Technology Laboratory (NETL), 2008.Reducing CO2Emissions by Improving the Efficiency of the Existing Coal-fired Power PlantFleet, DOE/NETL-2008/1329. U.S. Department of Energy, National Energy Technology Laboratory, Pittsburgh, PA. July 23, 2008. Available at:<http://www.netl.doe.gov/energy-analyses/pubs/CFPP%20Efficiency-FINAL.pdf>.aReported efficiency improvement metrics adjusted to common basis by conversion methodology assuming individual component efficiencies for a referenceplant as follows: 87% boiler efficiency, 40% turbine efficiency, 98% generator efficiency, and 6% auxiliary load. Based on these assumptions, the referencepower plant has an overall efficiency of 32% and a net heat rate of 10,600 Btu/kWh. As a result, if a particular efficiency improvement method was reportedto achieve a 1% point increase in boiler efficiency, it would be converted to a 0.37 % point increase in overall efficiency. Likewise, a reported 100 Btu/kWhdecrease in net heat rate would be converted to a 0.30% point increase in overall efficiency.Comment [A17]:Themathinthisfootnoteisuntraceable.Suggestaddingalittlemoreexplanation–anequationexample‐‐ofhowthismathleadstothatpercentageorheatrate.EPAresponse:FootnoteisdirectlyfromthecitedNETLreport.

## DRAFT May 22, 2014

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13 2.4 Previous Studies on Heat Rate Improvements
A number of studies using varying approaches have been performed to determine potential efficiency improvements and associated resulting CO 2 

## 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 approa ch that evaluated the design factors that are known to influence efficiency and grouped EG Us 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 fl eet efficiency in response to changes in fuel prices or carbon prices. In this specific exam ple, the coal price was assumed to be a 10%
increase and the CO 2 

## 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 fo r the EGUs to comply w ith an inferred standard. As opposed to the above studies that determined by how much the efficiencies can improve, this approach estimated how the industry will meet any imposed standards and calculated the heat Comment [A18]: To avoid ambiguity it might be helpful to cite (footnote) studies when referring to them.

EPA response: Added study names.

Comment [A19]: Suggest an alternate phrasing such as: This approach sought to estimate overall changes in industry fleet efficiency in response to changes in fuel prices or carbon prices.

EPA response: The language has been edited as suggested.

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14 rates necessary to meet a standard. As it has not been determined with regard to how the CO 2 

## emissions limit will be averaged, the paper disc usses many potential options that coal-fired EGUs may have to meet new standards. These op tions 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, wh ereas 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 r easons, the EPA has developed in this TSD an additional assessment of HRI potential that draw s on multiyear historical hourly data. While we understand that engineering judgment remains essen tial 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 CO 2 

### 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 theEfficiency of CoalFired Power Plants for Near Term Greenhouse Gas Emissions Reductions” NETL • Plant design: coal type, steam cycle, and size • Category improvement is the bottom 90% of EGUs in each group improving their heat rate to the average performance of top 10% in that category • Units with anomalous data, capacity factors under 10%, using less than 97% coal, and gasification plants were removed from dataset • Low pressure subcritical

units and 0-200 MW subbituminous units • 8.7% reduction in overall heat rate of coal-fired EGUs Comment [A20]: Not certain this is correct ‐ the RFF study abstract states: “This paper examines the operation of coal

‐ fired generating units over 25 years to estimate the marginal costs and potential magnitude of emissions reductions from improving their efficiency.” Please correct if appropriate.

EPA response: The text has been clarified to refer to studies that used a benchmarking type approach to estimate potential reductions.

Comment [A21]: Please check on the tense

– past or present?

EPA response: Thank you for the observation.

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15 Study

ID Study Title Author Factors Used for Industry Grouping Key Study Assumptions Relevant HRI Results assumed retired for goal • Lost generation made up by

more efficient coal-fired EGUs 3 “Reducing Heat Rates of Coal-Fired Power Plants” Lehigh Energy Update All possible heat rate improvements are made including drying of high moisture coal: • 10% improvementfor 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 theentire 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 CO 2 

### 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 potentia l looks at historical data from coal-fired EGUs in the U.S. to identify changes to EGUs’ h eat rates – the amount of heat input required, on average, to generate 1 kWh of electricity – that can be attributed to operation and maintenance practices and equipment upgrades. These heat rate changes are analyzed to determine their applicability to the rest of the coal-fired EGU fleet and to determine the potential heat rate improvement that, on average, could be achieved by the fleet. Comment [A22]: Please include text regarding the peer review to which all these reports have been subject. If not, please consider subjecting this concept to technical peer review in the period following proposal release.

EPA response: We will check on the peer review status of the studies. Comment [A23]: Is there a reason there is no comment in this box?

EPA response: Text has been added.

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16 This data analysis portion of the study relies on unit-level heat input and gross generation data reported to the EPA by owners or operators of EGUs to assess in detail the changes in gross heat rates. Potential changes in net heat rates are then addressed later in this section. Unit-level evaluations allowed the EPA to recognize the sign ificant 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 collect ed 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 eq uipment 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 input 3 

## and electrical output to the EPA’s Clean Air Markets Division in 2012. 4 

## It includes a wide range of configurations, fr om 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 CO 2 

### flow and concentration. 4 

### Information on the Clean Air Markets Division data is available at http://ampd.epa.gov/ampd/

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17 heat. Therefore, it is difficult for the EPA, using available data, to make a meaningful comparison of these units’ heat rates.
The EPA performed this study using hourly heat input (Btu), and electricity output (MWh) data from the Clean Air Markets Division and meteorological data from NOAA’s National Climatic Data Center for the years 2002-2012. As described later in this section, these meteorological data were used to account for temperature impacts on heat rates. The eleven year study period is representative of a wide range of conditions, including growth and recessionary economic conditions, changing electricity generation from renewable and natural gas, and different regulatory constraints.
The hourly heat input and generation data used in this study is collected under the authority of 40 CFR part 75 (hereafter, Part 75). The EPA designed Part 75 to encourage complete and accurate emission measurement and reporting to support emission trading programs, including the Acid Rain Program and Cl ean Air Interstate Rule (CAIR). However, the EPA recognized that there will be times when em ission 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 sp ecified 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 fr om this study’s dataset. These substitute data represent approximately 2% of all reported ope rating 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 th an 15,000 Btu/kWh resulting from a vari ety of factors incl uding 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 un it-years of data at 884 distinct EGUs. Figure 2-1 displays the study population average gross heat rate by year and the 11-year average. Comment [A24]: IS QA/QC defined in the abbreviations?

EPA response: It is defined in the sentence.

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18

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19

Table 2-4. Study Population Annual Heat Input, Generation, Heat Rate and Unit Count 2002 - 2012 5



### Year Heat Input (million MMBtu) Electric Generation(million MWhgross) Heat Rate(Btu/kWhgross) Unit Count

2002 18,601 1,874 9,924 839 2003 18,428 1,864 9,886 834 2004 18,405 1,875 9,819 836 2005 18,665 1,910 9,774 838 2006 18,644 1,914 9,743 848 2007 18,704 1,920 9,740 846 2008 18,459 1,914 9,643 852 2009 16,588 1,719 9,649 864 2010 17,693 1,831 9,662 869 2011 16,934 1,744 9,708 878 2012 14,947 1,536 9,732 884



## Figure 2-1. Study Population Average Gross Heat Rate by Year
NOAA’s Integrated Surface Data (ISD) product provides hourly temperature for over 20,000 weather stations worldwide. 6 

## Since EGU heat rate performance is sensitive to air temperature and barometric pressure, which vary with elevation, we use meteorological data from stations that are reasonably close to the EGU's location and elevation to account for the

5 

### The study population for each year includes those EGUs that reported both heat input and electric generation. 6 

### Temperature data is from NOAA’s Integrated Surface Da ta at http://www.ncdc.no aa.gov/data-access/land-basedstation-data/land-based-datasets/i ntegrated-surface-database-isd. 9,500 9,550 9,600 9,650 9,700 9,750 9,800 9,850 9,900 9,950 10,00020022003200420052006200720082009201020112012Heat Rate (Btu/kWh)



### Annual Heat Rate 11-yr Avg Heat Rate Comment [A25]: Change in unit counts due to using conditions that match preceding paragraph.

Comment [A26]: PHK: corrected unit caps in chart legend

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20 impact of ambient conditions. For each of the plants in this study’s database, we identified the nearest stations that reported a minimum of 8, 400 hourly observations in the calendar year (i.e., greater than 95% of the hours in a year). Generally, each plant was associated with meteorological data from the two closest stations over the eleven-year study period. The average distance between the plant and the nearest stati on 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 approxima tion 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 CO 2 

## in 2012. In contrast,
emissions of CO 2 

## 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 CO 2 

## 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 characteristi cs are not easily categorized (e.g., EGUs use a large range of steam conditions). Several other studies do categorize EGUs broadly by capacity, thermodynamic cycle, and/or fuel rank. Although the EPA believes grouping by categories can provide a useful way of understanding the operating profile of an EGU and the fleet, the range of heat rates for the broad categories has significant overlap (see box and whisker chart in Figure 2- 2) and therefore makes it challenging to develop appropriate categorization. The figure below displays available information on coal-fired EGUs considered in this work for the years 2009- 2011 in typical subcategories of capacity, fuel rank, and thermodynamic cycle. As the figure reflects, the means are clustered and the ranges of heat rates overlap.

7 

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

.

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21 Figure 2-2. Three-Year Average Heat Rates by Subcategory 8



## 2.5.3 Observed Trends in the Period 2002-2012
Three trends are notable for the study population during the period 2002-2012. Comparing the averages of the first three years (2002-2004) to the last three years (2010-2012), electric generation and gross heat rate declined by 9% and 2%, respectively. Capacity factor for the study population fell by 14% comparing the same time periods (see Table 2-5). The decrease in coal-fired generation and capacity factor may be because of reduced demand for electricity resulting from the recession starting in late 2008 and greater use of natural gas and renewables to generate electricity.
The 11-year average annual gross heat rate for the study population of coal-fired EGUs (see Table 2-4) was 9,754 (Btu/kWh). The decrease in study population annual heat rate between 2002 and 2012 may be due to several factors. Unit efficiency may have improved or units with lower heat rates may have taken up a larger share of generation. In addition, changes in reporting methodology described later in this chapter may be partly responsible. The minimum annual heat rate (9,643 (Btu/kWh)) occurred in 2008 and was approximately 1% below the 11-year average.

8 

### Abbreviations in the figure: BIT means bituminous, SUB means subbituminous, PC means petroleum coke, LIG means lignite, SUPER means supercritical, OVER/UNDER means greater/less than in dicated MW capacity. Unit counts (n) by category: BIT SUPER, n=80; SUB SUPER, n=30; BIT OVER 200, n=196; PC, n=2; BIT 100 to 200, n=140; SUB OVER 100, n=299; LIG OVER 100, n=20 ; BIT UNDER 100, n=68; SUB UNDER 100, n=56; LIG UNDER 100, n=2. Total unit count is 893. 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000 BIT SUPER SUB SUPER BIT OVER 200 PC BIT 100 to 200 SUB OVER 100 LIG OVER 100 BIT UNDER 100 SUB UNDER 100 LIG UNDER 100

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

Comment [A27]: Suggest including on the figure the number of plants in each category (N=? for each category).

EPA response: added counts to footnote. Comment [A28]: The footnote uses SUB twice for different terms. Please clarify/fix.

EPA response: Done

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22 Table 2-5. Reported Annual Capacity Factor 2002-2012 9



### 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 ar e known to operate at higher heat rates. Therefore, we evaluated the potential impact of such events in our study. A startup event, as defined here, occurs when an EGU begins combusting fossil fuel and generates some measurable amount of electricity. Table 2-6 summarizes the study population average, maximum and total starts by year. On average, coal-fired EGUs start combusting fuel and generating electricity 11 times per year. The total number (approximately 9,000) of starts for the study population of EGUs has remained stable over the study period. Our data reflects that some coal-fired units operate in a load following capacity and may report upwards of 200 starts in a single year, but these units tend to have low annual capacity factors. The subset of EGUs with more than 20 annual startup and shutdown events is responsible for less than 4% of total generation in any study year. Therefore while the number of starts is an important variable at a small number of EGUs, its impact on heat rate performance evaluated in this study is considered to be marginal.

9 

### Table 2-5 shows data as reported to EPA as of May 8, 2014. Comment [A29]: Suggest making the Table title

more descriptive. Also could present fewer signficant digits in table ...i.e. heat input ‐ MM BTU x 106 ‐‐ EPA response: changed units and title.

Comment [A30]: This capacity factor decline in 2012 is very different from the CF used later, see comment on page p. 35, i.e., 78% in 2020. Which is correct? And, did coal CF at 44% factor into the EPA modeling?

EPA response: The table has been updated to use all reported data (rather than a trimmed data set). This changes the capacity factor in 2012 to 53%. Note the capacity factor of 78% is for the coal

‐ fired steam fleet in base case, 2020. Note also that the study population includes a significant number of EGUs that IPM lists as retiring prior to 2016.

Comment [A31]: At about this point, the chapter transitions into the quantitative analysis. Suggest highlighting that in some way. There seem to be several components of the quantitative analysis: 1. Regressing HR onto CF and Temp; 2. Looking at residual heat rates relative to model prediction; 3. The “bin model” for assessing HRI potential via best practice; 4. The assessment of HRI potential via equipment upgrades. Suggest presenting those more clearly either here or even forward in the chapter in summary form.

EPA response: Thank you. We have added a paragraph introducing more

‐ quantitative analyses.

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23 Table 2-6. EGU Start Count by Year

Year Average Maximum (at any single EGU) Total
2002 11.1 209 9,363 2003 10.6 194 8,936 2004 10.7 134 9,081 2005 11.0 183 9,265 2006 10.5 139 8,859 2007 10.5 164 8,908 2008 10.4 134 8,880 2009 10.3 178 8,902 2010 10.5 211 9,110 2011 10.6 206 9,295 2012 9.9 119 8,805



## To understand the potential for heat rate improvement available with existing coal-fired steam EGUs, the EPA conducte d 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 consta ntly changing operating conditions. We tested for collinearity between capacity factor and ambient temperature using a zero-order correlation Comment [A32]: After this sentence, suggest adding a sentence stating why this is important to do, and a sentence summarizing the approach. (EPA) see updated text

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24 matrix on the entire hourly data set. The correlation between the independent variables was -.048 – well below an indication of collinearity. We also considered fixed unit characteristics such as unit type, fuel rank and age as independent variables. As noted above in the discussion on subcategorization, these factors can be helpful to understanding the heat rate performance of EGUs. The purpose of this study, however, is to find the potential for heat rate improvement across the fleet. We use heat rate variability as a key statistic to measure this potential. The correlation between the potential for heat rate improvement and fixed characteristics is typically low. 10 

## Coal-fired units are designed to operate most efficiently at full capacity. As a unit drops below this level, in general, heat rate will increase. The average capacity factor over 11 years for the study population is 67%, but as noted above, has moved markedly over the study period. This study looks at utilization level at both hour and year time scales. The two are related but reveal different information about how an EGU is operating. For example, for a unit to achieve a high annual capacity factor (e.g., over 90%) it must operate at a high load for most hours in a year. At lower capacity factors interpreting the relationship between hourly and yearly utilization levels becomes more complex. For example, an EGU may run at an annual 60% capacity factor by operating 8 months at near full capacity and generating no electricity the rest of the year, or it may run at lower utilization levels for most hours of the year in response to weather, generation cost, and transmission constraints.
Ambient temperature can affect heat rate in two ways: 1) the efficiency of the thermodynamic steam cycle 11 

## and, 2) in many regions of the country, as temperatures increase electricity demand and capacity factor follow. Figure 2-3 shows the average monthly capacity factor in 2012 alongside the climate normal monthly temperature. 12 

## The lines intersect in the spring as temperatures begin to rise and the need for cooling drives electricity demand.

10 

### For example, the correlation between annual unit heat rate variability (d iscussed 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 ha ve 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 me asure) over a prescribed 30-year interval and location. The chart shows the 1981-2010 climate normal monthl y temperature at Baltimore-Washington Airport, MD. Comment [A33]: Suggest discussing explicitly the choice of CF as an independent variable

– why is it reasonable to think of it as an independent element? Are there better factors that EPA could have used but was unable to because of information availability?

EPA response: see the preceding paragraph

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25 Generally, peak capacity factor and generation in most parts of the U.S. occur on the hottest days of the year. Yet, the relationship between ambient temperature and capacity factor is complex. Each plant responds differently depending on design, meteorological conditions and electricity demand. For example, a base load plant may operate at a high capacity factor seven days a week regardless of temperature. As noted above, the collinearity between these two variables is low.

Figure 2-3. Monthly Capacity Factor, 2012. 

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

Heat Rate
Using the hourly data set, the EPA performed three regression analyses for each unityear: heat rate onto capacity factor, heat rate onto ambient temperature and heat rate onto capacity factor and ambient temperature. Since this analysis seeks to evaluate heat rate under normal operating conditions, we removed records with hourly heat rate values outside of +/- 2.6 standard deviations (1.9% of records) before performing the regressions. 13 

## Similarly to partial operating hours, these outliers tend to occur during low load conditi ons. 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 cont ribution of varying unit capacities all results are generation-weighted. 14 

## The average study population r-squared for the multivariate regression is 26%. This means that hourly ambient temperature and capacity factor together explain 26% of

13 

### The 2.6 standard deviation bound is used in other EPA regulatory analyses. 14 

### In a weighted average, each component is multiplied by a factor reflecting its importance. In this case, generationweighted r-squared is the sum of r-squared for each unit mult iplied by its annual generation divided by the sum of generation for all years. 0 20 40 60 80 100JanFebMarAprMayJunJulAugSepOctNovDec 

## Electric generation monthly 2012 capacity factor and normal temperature

Capacity Factor (Monthly %, 2012) Climate Normal (BWI, MD)

Comment [A34]: Capacity factor is also influenced by temperature

– any considerations for collinearity effects in the regression?

EPA response: We tested for collinearity between capacity factor and ambient temperature using a
zero

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

– well below an indication of collinearity.

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26 the change in heat rate for the study population over the study period. The average study population r-squared from the single variable analysis of capacity factor is 16%; the corresponding result for temperature is 10%. This means that approximately 16% of the change in hourly heat rate is attributable to capac ity 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 explainup 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 pe riods of peak demand. These are the EGUs where the ambient temperature ‘signal’ is an important variable. At a typical EGU, summermonth heat rates may increase by 2-4% compared to winter months, but at a temperatureresponsive EGU that figure may be as high as 10%.
The multivariate partial regression coefficien ts show a similar pattern. The average unityear temperature coefficient (non-standardized) is 2, i.e., each degree F increase in temperature increases the heat rate by 2 Btu/kWh. Put anothe r way, over a typical 40 degree F seasonal range (see Figure 2-3 the average change in heat rate that can be attributed to temperature is 80 Btu/kWh, i.e., less than 1%. But the temperature coefficient at the individual unit level varies widely. For example, the average study popula tion top quintile of temperature coefficient(descending) was 9, over four times greater than the study population mean. The average unityear coefficient (non-standardized) for capacity factor is -15, i.e., each 1% increase in capacity
factor decreases the heat rate by 15 Btu/kWh. This means that on average, when a study population EGU increased capacity factor from 60% to 80% the heat rate decreased by 300 Btu/kWh, i.e., approximately 3%. Although capacity factor is the more important explanatory variable, this is by no means universally true across the fleet or even year to year at the same
EGU.
Our analysis indicates that as EGUs moved from base load to load following, capacity factor tended to have a larger effect on heat rate. Since 2008, the study population capacity

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27 factors moved from the top load bin into lower lo ad bins. This can be seen from Figure 2-4, which compares the study population duty cycles in 2008 to 2012. A significant share of 2008 generation occurred at EGUs running at greater than 84% annual capacity factor. In 2012, little generation took place in that bin or above, and generation was reduced by about half in the next lower bin (78-83% capacity factor).

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

2.5.6 Heat Rate Variability and Indication of Improvement Potential
This study examines heat rate variability from the standpoint of statistical process control, which is utilized throughout the power industry. Several years ago, the EPA introduced process control charts for auditing emissions da ta 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 itse lf to process control si nce it is the principal indicator that defines the quality of the electric generation process. 15,16 

## Therefore, in general, high variability in heat rate values would reflect opportunities for process improvement.

15 

### “The principal indicator that defines the quality of the process is heat rate.” (Fredrick & Todd, 1993. Statistical Process Control Methods in Performance Monitoring. Available at famos.scientech.us/Papers/1993/1993section11.pdf)

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28 We use the relative standard deviation (RSD) of the hourly heat rates to evaluate each unit against its past performance and to compare with the study population. Each unit has up toeleven RSD values, i.e., one for each operating year between 2002 and 2012. The generationweighted mean RSD for the study population acr oss 11 years is 5.4%. Table 2-8 summarizeseleven 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 de viation of the residual heat rate statistic is used to understand the amount of variability that is not explained by capacity factor and temperature. The average RSD corresponding to residual heat rate variability for each EGU is the generation-weighted average of up to eleven annual values. The study population generation-

16 

### Since accurate measurement is essential to process contro l the introduction of increasingly sophisticated digital control systems (DCS) presents new opportunities for finding inefficiencies. Vendors (ABB, Siemens, Emerson) claim heat rate improvements of 2-5 percent can result from upgrading to a modern DCS and advanced control technologies. The improvement can be even higher if system-wide real-time optimization is included. 17 

### Nine EGU RSD values exceeded 2.6 standard deviations ab ove the mean and were removed from the results in the table.

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29 weighted mean RSD over the study period is 4.5%. This percentage represents the total variability across the study population that our analysis could not explain by hourly capacity factor or ambient temperature. Possible causes of this variability include changes in plant equipment, operating procedures and maintenance, fuels (particularly coal rank), reporting methodology, and unexplained factors. There is no temporal trend evident in the RSD. Table 2-9 summarizes the study population generation-weighted average RSD of residual heat rate over the study period in quartiles ordered by the 11-year generation-weighted RSD average (ascending).
Table 2-9. RSD of residual heat rate (generation weighted) 18



### Quartile RSD average RSD Minimum RSD Maximum

1 2.7 0.0 3.2 2 3.6 3.2 4.1 3 4.7 4.1 5.3 4 6.9 5.3 10.4


## The weighted average RSD of the top quartile is 2.7% – well below the study population average of 4.5 %. This means that the residual hourly heat rates of these units generally stay in a narrow range within a given year. The maximum RSD in the top quartile is 3.2%. From the statistical process control point of view, thes e units appear to have low variability.
The weighted average RSD of the bottom quartile is 6.9%, which is over twice that of the top quartile. This spread indicates that there is likely room for improvement in study population operation to reduce variability and heat rate.
2.5.6.1 Heat rate variability and performance

To examine the association between heat rate variability and heat rate performance this study examined the RSD for unit-year heat rates calculated from reported data. The study population generation-weighted annual RSD ranges between 5% and 6% during the study period. Figure 2-5 below summarizes the results of regressing RSD of heat rate onto annual heat rate. 19

18 

### This table excludes nine units where RSD exceeded 2.6 standard deviations from the mean. 19 

### The regression analysis was performed on 9,388 unit-years of study data which were trimmed to remove values outside 2.6 standard deviations. Comment [A35]: This Table (and others like it) might be more clearly presented as a simple line graph.

EPA response: Thank you.

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30 The r-squared result is 57%. These results indicate that, other factors held equal, if an EGU reduces heat rate variability, generally heat rate performance will improve.

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


## 2.5.7 Units with large heat rate changes
Over one third of the study population (355 units) reported at least one year-to-year change in heat rate greater than +/- 8.5%. 20 

## We consider this magnitude to be in the upper range of what would be expected due to changes in fuel rank, operations and maintenance, or plant equipment. Table 2-10 presents the counts by th ree 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 fact or changed more than 30 percent.
22 

### EPA Reference method 2 specifies the normal procedure fo r measuring stack gas volumetric flow rate during a relative accuracy test audit. Methods 2F, 2G, 2H a nd CTM-041 are approved alternat ives. Methods 2F and 2G correct measured flow rates for angular (non-axial) flow , Method 2H (for circular stacks) and conditional test method CTM-041 (method J, for rectangular stacks and ducts) are used to correct measured flow rates for velocity decay near the stack wall, using a “wall effects adjustment factor”. These alternative methodologies are optional. Therefore, given the additional complexity and cost of using these alternatives a source is likely to use them only if y = 0.002x - 14.254 R2 = 0.5704 5.0 5.2 5.4 5.6 5.8 6.0 6.2

9600 9650 9700 9750 9800 9850 9900 9950RSD heat rate %(generation weighted)



### Heat Rate

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31 noted at the remaining one-third could not be explained by changes in reporting methodology. Moreover, we found no correlation between changes in reporting method and heat rate RSD.
Table 2-10. Year-to-Year Heat Rate Change

Description Count Correlation of heat rate to capacity factor Units with only year-to-year heat rate decrease > 8.5% 166 .1 Units with only year-to-year heat rate increase > 8.5% 80 .1 Units with both year-to-year heat rate decrease and increase > 8.5% 355 .1



## The breakdown of the ‘large decrease’ EGUs by quartile, in Table 2-11 below, is consistent with the study population results shown in Table 2-8. Th is does not imply that the changes associated with a large year-to-year decrease, which may include operations and maintenance, more accurate reporting methods, or new equipment, do not affect heat rate variability. If they occur as part of an engineering effort to improve efficiency, heat rate variability may also be reduced.
Table 2-11. RSD in reported heat rate of 166 ‘large decrease’ EGUs (generation weighted)

Quartile RSD Average RSD Minimum RSD Maximum

1 4.0 2.3 4.8 2 5.3 4.8 5.9 3 6.6 5.9 7.5 4 10.5 7.6 26.5


## 2.5.8 Assessment of heat rate improvement potential via best practices
As mentioned before, across the study period, the effects of hourly capacity factor and ambient temperature explain a generation-weighted average of 26% of the change in study population heat rate. 23 

## This means that on average 74% of the change remains unexplained after controlling for those factors. The residual heat rate analysis determined there is significant variation in the operation of EGUs. Since lower heat rate variability is associated with lower heat

the results are significantly lower volumetric stack gas flow. The EPA was unable to draw conclusions about the effect of changes in flow reporting methods on study population heat rate performance. 23 

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

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32 rate, other factors held equal, the range of varia tion indicates that signifi cant potential for heat rate improvement is available through the application of best practices.
To control for known factors, the EPA constructed a model that groups each EGU’s hourly heat rate data into 14 temperature bins an d 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-ye ar 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 th e 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 eff ect, the model proportionately reduces heat rate variability and improves performance for each unit while controlling for temperature and capacity factor. The heat rate improvement for the study population is derived from the performance of each individual EGU as compared to its own record.

24 

### The matrix provides up to 168 bins but only 164 contained hourly values. Temperature bins ranged from -20 to greater than 110 with 10 degrees F in each. Capacity factor bins ranged from 0% to greater than 110% with 10% in each. 25 

### Performing the calculation with a minimum of 30 values in each bin has a modest effect on the results in Table 2- 12. For example, a 30% reduction obtains a 3.9% fleet wi de 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.

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

% Reduction from reported heat input to p10 Study population heat rate (Btu(kWh-gross) 27 

### Reduced study population heatrate (Btu/kWhgross) Study population heat rate improvement (%)

10

9,755 9,625 1.3 20 9,495 2.7 30 9,365 4.0 40 9,235 5.3 50 9,105 6.7



## 2.5.9 Assessment of potential heat rate improvement via equipment upgrades
The EPA inspected the study population to find examples of EGUs that made significant year-to-year improvements in heat rate. After filtering out those cases that may have been the result of changes in capacity factor, reporting me thod, 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 responsi ble 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 provid ed data with details on specific equipment modifications for seven coal-fired units at three anonymous plants. These included turbine efficiency and condenser performance upgrades, installation of variable frequency drive fans, reducing boiler air in-leakage and others. Together, these measures achieved from 0.25% to 3.5% heat rate improvement at the seven EGUs.
An EPA study (SRA, 2001) describes WEPCO’s two-phase efficiency program at four coal-fired plants over a ten-year period. In the first phase, 1990 – 1994, WEPCO installed

27 

### Fleet heat rate for study population as described in Tabl e 2-4 is 9,754; the 9,755 value is derived from the dataset that includes hourly temperature values. Comment [A36]: This technical analysis of the possibility for heat rate improvements should be peer reviewed. Has EPA run this by the SAB, or does EPA intend to do so after release of the proposal?

EPA response: Thanks for your comment

Comment [A37]: Do you really mean in a single year to year change, as an indicator of a functional change in the equipment? Otherwise, 8% annual improvement year after year is immense. Suggested edit, but change at EPA’s discretion.

EPA response: We meant one time

– thank you.

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34 equipment upgrades that included retractable turbine packing, variable speed drives on the forced and induced draft fans, feed water heater replacements and new performance monitoring instrumentation. The four units reported heat rate improvements ranging from 2.3% – 4.1% as a result of the equipment upgrades. In the second phase, 1995-2000, WEPCO implemented changes that would generally fall into the best practices category: equipment control and metering upgrades, boiler cleaning, feed water heater improvements and reduced condenser air in-leakage and thermal losses. These gained an additional 0.5% per year heat rate improvement (for a total of 2.5%).
The EPA also reviewed the engineering studies available in the literature and selected the Sargent & Lundy 2009 study as the basis for our assessment of heat rate improvement potentials from equipment and system upgrades. We focused on some thirteen heat rate improvement methods discussed by Sargent & Lundy, seen in Table 2-13. We used the average of the estimated $/kW costs for each method to develop the cost-ranked list of heat rate improvement methods (lowest cost at the top, highest at the bottom) shown in Table 2-13. The first nine items in Table 2-13 contribute about 15 percent of the total average $/kW cost for all items. We believe it is reasonable to consider those nine no-cost and low-cost heat rate improvement methods as belonging in the category of what has been described above as best practices. The remaining four methods are higher cost heat rate improvement items that we believe properly fall into the category discussed here as upgrades. Using an average of the ranges of potential Btu improvements estimated by Sargent & Lundy for the four upgrade methods, upgrades, as defined here, could provide a 4% heat rate improvement if all were applied on an EGU that has not already made these upgrades.

Table 2-13. Sargent & Lundy Heat Rate Improvement Methods No-Cost and Low-Cost Options Condenser Cleaning Intelligent Soot Blowers ESP Modification Boiler Feed Pump Rebuild Air Heater and Duct Leakage Control Neural Network SCR System Modification FGD System Modification

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35 Cooling Tower Advanced Packing
Higher Cost Options Economizer Replacement Acid Dew Point Control Combined VFD and Fan Turbine Overhaul
We also examined the annual heat rate trend line for each unit using the method of least squares. Using the slope of the trend line, 2 002-2012, 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 du e to operating conditions (capacity factor, temperature), coal rank, maintenance, reporting method changes, equipment upgrades, and other factors. Over 40% of units have a positive slop e. 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 th e 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

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36 moving average would achieve nearly a 5% improvement and the best single year over 6%. EPA believes that the minimum three-year moving average heat rate is a reasonable target for the improvement potential from applying best practic es. Single year results could be due to unusual conditions, such as, an extended outage or weather. Using th ree consecutive years tends to smooth out the effect of equipment maintenance cycles and unusual meteorological patterns.
The adjustment of heat rate using the 30% approach is approximately equal to improving the heat rate variability performance of the lo wer ranked EGUs by one quartile (see Table 2-8 above). The top quartile has the lowest heat rate variability with an average RSD of 3.5% over a range of 1.6% to 4.2%. The bottom (fourth) quartile has an average RSD of 9.8%. The 30% approach proportionately trims the hourly heat input for each EGU in a given temperature and capacity factor bin. Using the bounds in Table 2-8, applying the 30% approach to the study population has the effect of nearly doubling the number of EGUs in the top quartile and reducing the count of those in the fourth quartile to 58 (o f 218). These results appear reasonable. In effect, is the approach amounts to asking the lower ranked EGUs to improve heat rate variability to a level comparable to that which the top quartile of EGUs have maintained over an 11-year period.
The equipment upgrades results are supported by numerous studies 28 

## 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 CO 2 

## emissions from coal.
2.5.11 Sensitivity Analysis Removing Planned or Announced EGU Retirements

28 

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

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37 The EPA’s research found 233 coal-fired, non-cogeneration EGUs that have announced they will retire before 2016. 29 

## A sensitivity analysis was applied to the EGUs in the study population that plan to operate through 2015. The results are identical to the full population – both achieve a heat rate reduction of 4% under the 30% approach described in best practices.
2.6 Heat Rate Improvement – Economics
Most of the methods that can be applied to achieve a sustained Heat Rate Improvement (HRI) on a coal-steam EGU will entail a capital cost . These HRI capital costs can be economic to incur if they yield sufficient reductions in other current or potential costs, particularly reductions in coal fuel cost and any cost related to CO 2 

## emissions. For the purpose of this TSD analysis, it is
assumed that HRI can be economic if the annua lized net savings (coal cost savings plus CO 2 

## emission cost savings minus capital cost) is positive:

Annual Net HRI Savings = Coal Cost Savings + 

## CO 2 

### Emission Cost Savings – Capital Cost 30



## 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 associat ed 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 Ca pital Cost: $40-150/kW 31


## EPA Assumed Combined HRI Capital Cost: $100/kW

29 IPM documentation includes a list of the announced retirements. See Table 4-36 of IPM documentation: http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/v513/Chapter_4.pdf and http://www.epa.gov/airmarkets/progsregs /epa-ipm/docs/v513/NEEDS_v513.xlsx 30 

### This TSD analysis assesses a broadly combined application of multiple HRI methods. As estimated in the 2009 Sargent & Lundy study most HRI-related O&M costs are suff iciently small relative to the associated annualized capital costs, such that they do not materially affect th e economics of broadly combined HRI methods. The analysis therefore does not consider the small econo mic impact of HRI-related O&M costs. 31 

### Note that highest cost does not necessarily align with greatest heat rate improvement. A low cost HRI method

can have a large HRI potential (eg: upgraded digital control system, neural network). Also, economy of scale causes most HRI methods to be more costly ($/kW) on smaller unit sizes. Comment [A38]: This is an important statement, that is accorded little explanation. I presume these

are coal EGUs? And is it plants or separate units? How did these anticipated requirements factor into the state goal setting, and then into the IPM modeling?

EPA response: See updated footnote with IPM

source.

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38 The wide ranges of estimated HRI Btu and costs are indicative of the wide range of real differences in the many details of site specifi c 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 ach ieve 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 e xpects that a significant fraction of the coal fleet has already applied some or many of the available HRI methods. 32



## The EPA does not have sufficient site specific information to accurately estimate what percentage of the fleet has adopted various HRI methods, nor how effectively, and is not aware of any other investigator having sufficient information. HRI potential can therefore not beestimated at this time through analysis of the current equipment configurations of the coal steamEGU fleet. The EPA therefore analyzed 11 years of hi storical 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 re ductions in auxiliary loads. For purposes of this TSD the EPA conservatively assumes that the coal fleet average net heat rate can be reduced by
6%.

32 

### Based on the EPA informal discussions with Sargent & Lundy and other power sector engineering firms. The EPA has found no comprehensive data set on the extent to which specific HRI methods have already been applied at individual EGUs. The EPA believes that many EGU owners consider such information to be confidential.

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39 2.6.3 Heat Rate Improvement Breakeven Economic Analysis
Figure 2-6 presents a simple breakeven economic analysis for combined HRI methods using the assumptions described above, also assuming there is no CO 2 

## emission cost that is
reduced via HRI.

Figure 2-6. HRI Breakeven Economics

Notes:
1. Capital cost S/MWh assumes the following: HRI capital cost = $100/kW; capital charge rate = 14.3%; IPM projected 2020 annual capacity factor = 78% 2. Coal fleet average 2020 net heat rate = 10,450 Btu/kWh; heat rate reduction = 6%
Figure 2-6 shows that the average fleet-wide savings in coal cost would become greaterthan the annualized capital cost of an average 6% reduction in heat rate when the average fleetwide coal cost exceeds about $3.25/MMBtu. For comparison, the average U.S. power sector delivered cost of coal in 2020 is projected in EPA’s IPMv5.13 Base Case modeling at $2.62/MMBtu. For different assumptions, the HRI economic breakeven point would change directionally as follows:

 If the HRI capital cost were on average less than the assumed $100/kW, 6% HRI would then become economic at lower coal costs. For example, if the average capital cost were actually $75/kW, a fleet-wide 6% HRI would become economic at an average coal cost of about $2.50/MMBtu, which is comparable to the U.S. power sector average costs of 0.00 0.50 1.00 1.50 2.00 2.50 3.00

1.00 2.00 3.00 4.00$/MWh‐net



### Coal Cost $/MMBtu Coal Cost Savings $/MWh

‐ net Capital Cost $/MWh

‐ net



### HRI Capital Cost = $100/kW

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40 $2.38/MMBtu for all coal ranks and $2.89/MMBtu for bituminous coals in 2012. 33 

## This sensitivity indicates that fuel cost savings alone would make it economic for some of those EGUs currently using high cost bitumin ous 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 CO 2 

## emission costs, HRI could become economic at lower coal costs, or at higher capital costs, or at lower heat rate reduction percentages.

2.6.4 U.S. Coal-steam EGUs – Estimated Fleet-wide CO 2 

## 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 CO 2 

## emission rate = 0.976 tonne/MWh net

 Average net heat rate = 10,450 Btu/kWh net

 Average capacity factor = 78%

 Pre-HRI CO 2 

## emissions = 1.62 billion tonne/yr (calculated)

 HRI Btu and CO 2 

## reduction = 6%

 HRI capital cost = $100/kW

33 

### EIA, Electricity DataTable 7.4, Average Weighted Cost of Fossil Fuels for the Electric Power Industry 2002- 2012, http://www.eia.gov/electricity/annual/html/epa_07_04.html Comment [A39]: Throughout the paper, it might help to indicate the “fleet ‐ wide” data refer to the national fleet (i.e. all the coal facilities in the U.S.) if this is the case.

EPA response: Thank you for the suggestion. See

new section heading above.

Comment [A40]: It was ostensibly 44% in 2012

– see Table 2

‐ 5, page 19 above

– or am I missing something? Please clarify. Is this assuming a lot of coal plant closures to get the capacity factor up? If so, please clarify the discrepancy/change over time in the text and how this might affect state goal setting, especially building block 2?

EPA response: Good catch. See revised Table 2

‐ 5 and associated text. Also see the RIA and other TSD discussions regarding IPM Base Case assumptions, resulting 2020 coal capacity, etc.

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41  Annual capital charge rate = 14.3%

 Average coal cost = $2.62/MMBtu
Estimated Fleet-wide Results:

 Fleet-wide CO 2 

## 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 CO 2 

## 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 CO 2 

## emission reductions, on a fleet-wide basis at toda y’s coal prices if the associated average capital cost is abou t $75/kW or less.

 If a fleet-wide average 6% HRI is technically feasible, and the associated average capital cost is as much as $100/kW, 6% HRI coul d become economic on the basis of fuel savings alone, before consideration of the value of the associated CO 2 

## 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 CO 2 

## 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 CO 2 

## reduction for existing coal-fired EGUs

Definitions/Abbreviations Btu British thermal unit capacity factor electricity generation expressed as a percentage of maximum

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42 electricity generation (i.e., actual generation / maximum potential generation) CO 2 

### 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 genera te 1 kWh of electricity, expressed in gross Btu/kWh. MMBtu million Btu MW megawatt PC pulverized coal (boiler) RSD Relative standard deviation

S & L report Sargent & Lundy engineering study on the potential heat rate improvement from equipment upgrades (EPA 2009 version) [Available at: http://www.epa.gov/airmarket/resource/docs/coalfired.pdf]

start a startup event in which an EGU begins combusting fossil fuel and generates some measurable amount of electricity before ceasing fossil fuel combustion unit-year data for one EGU over a one year period



### Docket Datasets

Name Description hour_QA_data.txt 2002-2012 hourly dataset hour_QA_regression_data.txt 2002-2012 hourly dataset for regression analysis units_885.txt List of study units and characteristics year_bin_10_50_data.txt 2002-2012 unit-year binned results at 10 – 50% difference options

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43 HRI Partial Bibliography PowerEng 2002, “Heat Rate Optimization Pays Dividends,” Power Engineering, January 1, 2002, available at http://www.power-eng.com/articles/pri nt/volume-106/issue-1/features/heatrate-optimization-pays-dividends.html NETL 2008, Reducing CO 2 

## 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 Effi ciency of Existing Coal-fired Power Plants, Workshop Report, NETL July 2009, available at http://www.netl.doe.gov/energyanalyses/pubs/NETL%20Power%20Plant%20E fficiency%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/Resear ch/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/airmarket s/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-st andards/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 Exis ting Coal-Fired Power Plants, Congressional Research Service, December 2013, available at http://www.fas.org/s gp/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/PowerPlan tComponent/Output1.1/Output1.1.pdf IEA 2010, Power Generation from Coal - Measuring and Reporting Efficiency Performance and CO 2 

## 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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44 EPRI/Korellis 2011, Identifying Ways to Improve Plant Heat Rate, Energy-Tech Magazine, March 2011, available at http://www.energy-tech.com/article.cfm?id=30528 EPRI 2011a , Opportunities to Enhance Electri c Energy Efficiency in the Production and Delivery of Electricity, EPRI Technical Re port 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