### From: Wayland, Robertj [mailto:Wayland.Robertj@epa.gov] Sent: Wednesday, May 28, 2014 3:29 PM To: Cortney Higgins Cc: Culligan, Kevin; Stenhouse, Jeb Subject: Revised Chapters 2 & 3 of the GHG Abatement TSD

Cortney –
Attached please find revised versions of Chapters 2 and 3 of the GHG Abatement TSD. Regarding the heat rate improvement chapter (Chapter 2), we accepted all of DOE’s edits/formatting suggestions in the revised version. We also co nsidered the recent RFF paper (provided by OMB) to include and reference in this chapter, but decided to not do so because: (1) the paper does not speak to the levels of improvements that may be practical, and (2) the cost effectiveness numbers in the paper appear to be inconsistent with our understanding of such numbers.
Please let Kevin, Jeb, Ravi or me know if you have any questions/concerns.

thanx!

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

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

2.0 Introduction
Based on the range of operating efficiencies for existing coal-fir ed electric generating units (EGUs), it is evident that EGUs are gene rally 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 charac teristics and the year-t o-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 exis ting coal-fired EGUs, design factors that influence efficiency, technologies to improve efficiency, previ ous studies estimating potential efficiency improvements, the proposed EPA appro ach to calculate efficiency improvements, and the estimated capital costs of those improvements.
2.1 Overview of U.S. Existing Coal-F ired 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 coal-f ired EGUs are mines in regions along the Appalachian Mountains and in southern Illinois, western Ke ntucky 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 bur ned in limited quantities.
Existing coal-fired EGUs in the U.S. electric utility fleet use one of five basic coal combustion configurations: (1) pu lverized coal (PC) combusti on, (2) fluidized-bed combustion (FBC), (3) gasified coal combustion, (4) cyclone furnace combustion, or (5) stoker-fired coal

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

Table 2-1. Characteristics of coal-firin g 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 gase ous fuel (synfuel or syngas) is burned instead of solid coal because the combustion and power generation process and combines the Rankine and Brayton thermodynamic cycles as is the case for a combined cycle power plant.

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

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

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3 The three technologies currently used for ne w coal-fired power plants are pulverized coal, fluidized bed (FBC), and integrated coal gasification combined cycle (IGCC). Pulverized
coal combustion is the coal-firi ng configuration predominately us ed at existing EGUs. The most recent coal technology combustion development i nvolves the integration of coal gasification technologies with the combined cycle electric generation proce ss. The efficiency of an IGCC power plant is comparable to the latest a dvanced 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 poll utants, the need for fewer post-combustion control devices (e.g., almost all of the sulfur a nd ash in the coal can be remove d once the fuel is gasified and prior to combustion), generation of less soli d 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 provide superior coal combustion effici ency and other advantages. Howe ver, a few remaining old stokerfired EGUs and cyclone furnaces still remain in se rvice 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 effi ciency. As the efficiency of a coal-fired EGU is increased, less coal is burned per kilo watt-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) electri city used to operate the EGU (e.g., electricity to power fuel handling equipment, pumps, fans, pollution contro l equipment, and other on-site electricity

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4 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.
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 de grees. Examples of the factors affecting EGU efficiency at a given facility include:

 EGU thermodynamic cycle – EGU efficiency can be si gnificant improved by using a supercritical or ultra-su percritical steam cycle. Supercriti cal and ultra-supercritical boilers operate above the critical point of wa ter (approximately 374°C (705°F) and 22.1 MPa (3,210 psia)). As a general guideline, the therma l design efficiencies for subcritical EGUs are in the range of 35% to 37%, supercritical EGUs are in the range of 39% to 40%, and ultra-supercritical EGUs in the range of 42% to 45%. However, actual operating
efficiencies can be lower than design efficiencies.

 EGU coal rank and quality – EGUs burning higher quality coals (e.g., bituminous) tend to be more efficient than EGUs burning lower quality coals with higher moisture contents (e.g., lignite). Bituminous coals have higher he ating values of great er than 10,500 British thermal units per pound and lignite coals have higher heating values of less than 8,300 British thermal units per pound.

 EGU size –EGU efficiency generally increases somewhat with size (e.g., from 200 MW to 800 MW) because: a) the boiler and steam turbine losses are lower for larger equipment compared to smaller equipmen t, b) larger units tend to be younger incorporating improvements from advanced te chnologies, and c) the economy of scale of larger units allows the use of higher co st improvements to be more economic.

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

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

 EGU cooling system – The temperature of the cooli ng water entering the condenser can have impacts on steam turbine performance. Once-through cooling systems can have an efficiency advantage over recirculating coo ling systems (e.g., cooling towers). However, once-though cooling systems typically have larg er water related ecological concerns than recirculating cooling systems.

 EGU geographic location and ambient conditions – The elevation and seasonal ambient temperatures at the facility site potentially may have an impact on EGU efficiency. At higher elevations, air pressure is lower a nd less oxygen is availa ble for combustion per
unit volume of ambient air th an at lower elevations. Cooler ambient temperatures theoretically could increase the overall EGU e fficiency by increasing the draft pressure of the boiler flue gases and the condenser vac uum, 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 lo ad following unit to respond to fluctuations in customer electricity demand.

 EGU plant components – EGUs using the optimum numberof 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 Existi ng Coal-Fired EGU Heat Rate

A number of studies have been conducted involving literature reviews of published articles and technical papers identifying potential efficiency improvement techniques applicable to existing coal-fired EGUs. 1 

## For example, a summary of the findings from one study conducted by the Department of Energy’s (DOE) Nati onal Energy Technology Laboratory (NETL) is

1 

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

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6 presented in Table 2-2. The efficiency percentage s were converted to a co mmon basis so that all of the data can be compared. All of the improve ment technologies presented in Table 2-2 cannot necessarily be implemented at every existing coal-f ired 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 engi neering study originally completed by Sargent and Lundy in 2009 titled “Coal-Fired Power Plant H eat Rate Reductions.” It describes numerous well-known and technically proven methods to im prove efficiency of coal-fired EGUs. The study lists possible efficiency improvements in the boiler, turbine, flue gas system, air pollution control equipment and the water treatment syst em. 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 contro l 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 freq uency 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% (6 0 Btu/kWh) of the gro ss electrical output and can be upgraded to provide more consistent size and finer coal partic les. The fine particles improve combustion efficiency, consequently reduc ing fuel cost and heat rate. The costs for changes to the pulverizer system are significant, and, historicall y, the projects have improved the heat rate justifiably only when th e existing equipment has degraded.

2 

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

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7 The bottom ash handling system may be a candidate for heat rate improvement. Switching from a water-sluicing bo ttom ash system to a dry drag chain system can reduce the auxiliary requirements and reduce the amount of water to the wate r treatment plant. The typical power requirements are about 0.1% (10 Bt u/kWh) of the plant’s gross output.

2.3.1.2 Economizer
An economizer is a heat exchanger that impr oves the efficiency of an EGU by recovering energy from the exhaust gases to preheat th e boiler feedwater. The replacement of the
economizer can lead to substa ntial heat rate improvements around 50-100 Btu/kWh, but is large capital investment (~$2-8M). Due to this hi gh cost, economizer upgrad es are not generally performed unless the existing equipment has degrad ed or a replacement is necessary due to the installation of new control equipment.
2.3.1.3 Boiler Control System
The boiler control system has a large impact on the heat ra te of the unit. The process control capabilities can control a nd evaluate many aspects of th e plant’s operations. Commonly referred to as Neural Network, computer models are able to control the plant’s processes by predicting performance during static and dynamic changes. Many vendors offer Neural Network systems to improve the overall efficiency. Neural network system s are typically around $550,000-$750,000 and offer heat rate re ductions 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 incorporated into th e intelligent sootblower system, which also communicates with the boiler control system. This system uses real-time data to identify which areas need sootblowing. Boiler efficiency improvements range from 30-150 Btu/kWh with capital costs around $300,000-$500,000 and $50,000/year for fixed operating and maintenance

costs.

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8 2.3.1.5 Air Heaters
Air heaters operate to transfer heat betw een the incoming pre-combustion air and the effluent flue gas. These systems are critical to maintain an efficient power plant. For these systems to operate most efficiently, air heater leakages must be maintained below 6% of incoming air flow. Most leakage is due to th e 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 fa ns and avoids capturing the heat transferred between the flue gas and pre-combustion air. The increased volume re quires more power to move more flue gas. Improvements to seals on the air heaters reduce the leakages. Improvements to reduce air heater and duct leakages generally reduce the heat ra te 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. Typi cally the air heater outlet is maintained at 20-30°F above the sulfuric acid dew point to prev ent corrosion of cold-end baskets. Injection of sorbents such as Trona or hydrated lime can be used to lower the dew point. Depending on the sizing of the air heater, it may need to be modified in order to optimize the lower outlet temperature. The capital costs can range from $1.5-18M for heat rate reductions of 50-120 Btu/kWh.

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

pumps.
2.3.2.1 Turbine
Replacement or overhaul of existing steam tu rbines 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 reduc tions of 100-300 Btu/kWh.

2.3.2.2 Feedwater Heaters
The feedwater heaters are heat exchangers us ed to heat the boiler feedwater by extracting heat from the steam leaving the turbine sect ion. The EGU efficiency can be increased by

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9 improving the heat transfer surface area. This en tails adding heat exchange surfaces to the existing heaters or adding additi onal heaters. The costs relative to the heat rate improvement associated with these projects typically prohi bit 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 c ondenser 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 th e turbine to extract as much en ergy from the steam as possible. Condensers are subject to fouling and plugging, which directly impact the heat transfer rates and water quality. To improve water qua lity, closed cooling water syst ems can be used to provide
better control over water quality and tube cleaning can be performed as needed. Heat rate reductions observed from condenser upgrades an d maintenances are 30-70 Btu/kWh with annual fixed costs of $30,000-$80,000.
2.3.2.4 Boiler Feed Pumps
Boiler feed pumps require a large amount of auxiliary power to pump large amounts of boiler feedwater through the heat ers and the boiler. Due to the high use of these pumps, maintenance is extremely important to ensure reli ability and the most efficient operation. As the pumps wear and operate less e fficiently, a pump overhaul may be required. The overhaul can reduce the heat rate by 25-50 Btu/ kWh with capital costs around $250,000-$800,000.
2.3.3 Flue Gas System
Two aspects of the flue gas system that can contribute to improvements in the plant heat rate are: (1) improve the forced draft and i nduced draft fan efficiencies, and (2) implement variable frequency drives.
2.3.3.1 Induced Draft Fans
One of the most important features in the fans is being able to control the flue gas flow. Many fans have dampers, which are the least efficient option. There are many other methods, such as variable inlet vanes, variable frequenc y drives, and variable pi tch blades, available to

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10 control the flue gas flow allowing highly e fficient fan performance. These upgrades or replacements provide a heat rate reduction of 10-50 Btu/kWh and cost between $6-$16M.
2.3.3.2 Variable Frequency Drives
Variable frequency drives facilitate more efficient plant operation by reducing the auxiliary load significantly. The capital costs for upgrading all driv es at an EGU can be $6-16M
with heat rate reducti ons 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 plan t. These systems typically require large amounts of auxiliary power with their benefit being improved air quality. Even small upgrades can sometimes decrease the power requirements si gnificantly while maintaining the level of
emissions reduction desired. Th e three technologies discu ssed below are the flue gas desulfurization, the elect rostatic precipitator, and the selective cataly tic reduction systems.
2.3.4.1 Flue Gas Desulfurization
Coal-fired power plants use many types of fl ue gas desulfurization systems. Older units typically contained a venturi throat that increased the velocity of the fluid, but resulted in a large pressure drop and greater power to operate the induced draft fans. To improve this operation, a co-current spray tower quencher may replace the unit. The capital cost is about $2.5M with heat
rate reductions around 13 Btu/kWh.
Another technology upgrade affects the vanes an d distribution plate in an absorber. The improvement of the gas flow coming into cont act with the absorber sorbent increases SO 2 

## capture, reduces maintenance due to erosion, an d reduces the amount of energy required for the induced draft fan. Turning vanes and a perforated gas distributi on plate improve gas distribution. The cost of the vanes is around $250,000 with heat rate reductions of 1-2 Btu/kWh.
In a wet flue gas desulfurization system, multip le spray levels are installed to deliver the limestone slurry. If a powe r plant is operating with SO 2 

## levels below its permit limit, turning off one spray level will reduce the auxiliary power re quired. If this is possible, a unit heat rate reduction of 16 Btu/kWh may be available.

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11 2.3.4.2 Electrostatic Precipitator
The best operation for an electrostatic pr ecipitator involves main taining the maximum applied voltage, but below the level at which sp ark-over occurs. Electrosta tic precipitator energy management system upgrades often help improve the electrostatic prec ipitator performance by maintaining the optimal performance and lowering power consumption. The installation for this technology can be from minimal to $0.8M a nd can lower heat rate by 5 Btu/kWh.
2.3.4.3 Selective Catalytic Reduction
For the last 15 years, selectiv e catalytic reduction systems ha ve been in use to reduce
NO X 

## emissions from power plants. Extensive mode ling was performed to achieve the necessary reduction with minimum ammonia slip. The result s showed that reducing pressure drop and using secondary air as dilution for the amm onia vaporizer can reduce the auxiliary power necessary. The heat rate reduc tion is 0-10 Btu/kWh and capita l costs between $0.5-$2M with fixed and variable costs up to $100,000 each.
2.3.5 Water Treatment System
The boiler water is one of the most important aspects of the power pl ant. The quality of the water is a key factor affecting the scale bu ildup 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 blow downs required, which allows for more steam in the turbine cycle. If the water is not properl y maintained, heat transfer may be reduced by up
to 10%.
Similar to the boiler water, the cooling towe rs 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 wate r quality is improved, whic h branches into other aspects already mentioned.DRAFT DELIBERATIVE May 27, 2014INTERAGENCYWORKINGCOMMENTS ONDRAFTLANGUAGE UNDEREO12866INTERAGENCYREVIEW.SUBJECT TOFURTHERPOLICYREVIEW.12Table 2-2. Existing coal-fired EGU efficiency improvements reported for actualefficiency 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 metrics as 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 fluegas 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 heat released during combustion of low-rank coals is used toevaporate this moisture, rather than generate steam for theturbine. 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 adequateheat transfer. Proper control of the timing and intensity of individualsootblowers is important to maintainsteam 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 designor physical condition of the steamturbine. For example, steam turbine manufacturers have improved 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 Efficiencyof 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 reportedDRAFT DELIBERATIVE May 27, 2014INTERAGENCYWORKINGCOMMENTS ONDRAFTLANGUAGE UNDEREO12866INTERAGENCYREVIEW.SUBJECT TOFURTHERPOLICYREVIEW.13to achieve a 1% point increase in boiler efficiency, it would beconverted to a 0.37 % point increase in overall efficiency. Likewise, a reported 100 Btu/kWhdecrease in net heat rate would be converted toa 0.30% point increase in overall efficiency.

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14 2.4 Previous Studies on Heat Rate Improvements
A number of studies using varying appro aches have been performed to determine potential efficiency improvement s and associated resulting CO 2 

## emission reductions. These approaches include characterizi ng the current U.S. coal-fired EGU fleet, identifying potential efficiency improvements, and applying improveme nt actions to existing EGUs. The approach taken within each study varies. Five studies are briefly summarized and co mpared in Table 2-3.
The NETL studies used a benchmarking approach that evaluated the design factors that are known to influence efficiency and grouped E GUs based on similar design characteristics. The studies categorized the indus try 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 improve d 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 th eoretically possible. The Lehi gh study used a less detailed approach and evaluated technol ogies applicable to bituminous and subbituminous coals to estimate potential flee t wide reductions.
An alternative approach to evaluate heat ra te improvement is used by Resources for the Future. This study focused on the operating efficien cy (synonymous with heat rate) of the entire existing U.S. coal-fired EGU fl eet. The authors evaluated decad es of data from industrial responses to economic factors such as demand, co al price and energy polic ies. This approach sought to estimate overall change s in industry fleet efficiency in response to changes in fuel prices or carbon prices. In one specific exampl e, the coal price was assumed to be a 10%
increase and the CO 2 

## emissions tax at $1.64 per ton for h eat rate reductions of 0.3 to 0.9%.
The National Resources Defense Council approa ch considered the fleet of coal-fired EGUs and assumes a target heat rate in order fo r the EGUs to comply with an inferred standard. As opposed to the above studies that determined by how much the efficiencies can improve, this approach estimated how the industry will meet any imposed standards and calculated the heat

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15 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 he at rate data, whereas many of the operational impacts on heat rate (diurnal temperatures, load following, etc) b ecome 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 re veal patterns of performance that should also inform estimates of HRI potential. For these re asons, 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 essent ial to a proper interpre tation 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 • 8.7% reduction in overall heat rate of coal-fired EGUs

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16 Study

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

more efficient coal-fired EGUs 3 “Reducing Heat Rates of Coal-Fired Power Plants” Lehigh Energy Update All possible heat rate improvements are made including drying of high moisture coal: • 10% 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 potential 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 ra te changes are analyzed to determine their applicability to the rest of the coal-fired EGU fleet and to determine the potential heat rate improvement that, on average, could be achieved by the fleet.

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17 This data analysis po rtion of the study relies on unit-le vel 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 se ction. Unit-level
evaluations allowed the EPA to r ecognize the significant heterogene ity of coal-fired EGUs; even ‘sister’ units, units built at the same time at a given facility, may disp lay different operating profiles and may have different equipment, c ontrols, fuel mixes or cooling systems.
Based on literature reviews; informal interv iews 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 reco mmended operation and maintenance conditions (best practices), and 2) instal ling and using equipment upgrades. Best practices include no-cost
or low-cost methods such as the installation or more frequent tuning of control systems and the like-kind replacement of worn existing component s. Upgrades often involve higher costs and greater downtime, such as, extensive overhaul or upgrade of major equipment (turbine or boiler) or replacing existing component s with improved versions.
The EPA developed unit-level statistics fr om over 60 million rows of hourly data. We
evaluate each unit on its individual performance using heat rate vari ability as an indicator of the application of best practices a nd potential for improvement. To estimate heat rate improvement through equipment upgrades we survey engineeri ng 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 c onfigurations, from 24 to 1,500 MW nameplate capacities, super and subcritical thermodynamic cycles, between 1 a nd 69 years old, and diffe rent 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 th e 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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18 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 he at input (Btu), and electricity output (MWh) data from the Clean Air Markets Divi sion 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 temp erature impacts on heat ra tes. The eleven year study period is representative of a wide range of conditions, including gr owth and recessionary economic conditions, changing electricity genera tion from renewable and natural gas, and different regulatory constraints.
The hourly heat input and ge neration 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 measuremen t 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 emission data are not available due to monitoring system malfunctions or maintenance, technical challenges, or missed qua lity assurance/quality control (QA/QC) tests. When data are not available or deemed invalid (e.g., when a QA/QC test was not performed as required), the EPA has specified data substitution methods that are designed to overestimate emissions. This conservative bias is intended to create an incentive for
better emission measurement – the overestimate incu rs an economic penalty because, at the end of the compliance period, an EGU must surrender allowances equa l to total reported emissions. Because of this conservative bias and the impact it would have on the results of this study, the EPA excluded substitute data reported by EGUs from this study’s dataset. These substitute data represent approximately 2% of all reported operating hours. In addition, we excluded partial hours of operation that occur during the first hour of startup and the last hour of shutdown.
We also excluded 40 unit-years (0.5% of reco rds) with atypical a nnual heat rates less than 6,500 or greater than 15,000 Btu/kWh resulting fr om a variety of factors including firing of natural gas, very low operating time, or errors in reported gro ss load. Table 2-4 summarizes the heat input, electric generation, heat rate and un it 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.

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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 Aver age Gross Heat Rate by Year
NOAA’s Integrated Surface Data (ISD) pr oduct 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 us e meteorological data

5 

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

### Temperature data is from NOAA’s Integrated Surface Data at http://www.ncdc.noaa. gov/data-access/land-basedstation-data/land-based-datasets /integrated-surface-database-isd. 9,500 9,550 9,600 9,650 9,700 9,750 9,800 9,850 9,900 9,950 10,00020022003200420052006200720082009201020112012Heat Rate (btu/kwh)



### Annual Heat Rate 11-yr Avg Heat Rate

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20 from stations that are reasonably close to th e EGU's location and elevation to account for the impact of ambient conditions. For each of the plants in this st udy’s database, we identified the nearest stations that reported a minimum of 8,4 00 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 statio ns over the eleven-year study period. The average distance between the plant and the nearest sta tion was 22 miles and the average difference in elevation was 366 feet. At nine plants we used da ta 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 approxim ation of ambient meteorological conditions at each facility. Joining the trimmed heat rate a nd hourly temperature datasets resulted in 61,848,580 hourly records for the study popula tion of EGUs (see Table 2-4).
The results of this study are based on analys es 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 stu dy 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 th e 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 de sign characteristics are not eas ily categorized (e.g., EGUs use a large range of steam conditions). Several othe r studies do categorize E GUs broadly by capacity, thermodynamic cycle, and/or fuel rank. Alt hough the EPA believes grouping by categories can provide a useful way of understand ing the operating profile of an E GU and the fleet, the range of heat rates for the broad categories has significa nt overlap (see box and wh isker chart in Figure 2- 2) and therefore makes it challenging to deve lop appropriate categoriz ation. The figure below displays available information on coal-fired E GUs considered in this work for the years 2009-

7 

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

.

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21 2011 in typical subcategories of capacity, fuel rank, and thermodynamic cycle. As the figure reflects, the means are clustered and the ranges of heat rates overlap.

Figure 2-2. Three-Year Average Heat Rates by 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 ye ars (2002-2004) to the last three years (2010-2012), electric generation and gross heat rate declined by 9% and 2%, re spectively. Capacity factor for the study population fell by 14% comp aring the same time periods (see Table 2-5). The decrease in coal-fired generation and cap acity factor may be because of reduced demand for electricity resulting from the recession starti ng 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 shar e of generation. In addi tion, changes in reporting

8 

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

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

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22 methodology described later in this chapter may be partly responsible. The minimum annual heat rate (9,643 (Btu/kWh)) occurred in 2008 and was a pproximately 1% below the 11-year average.
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 are known to operate at higher heat rates. Therefore, we evaluated the poten tial impact of such events in our study. A startup event, as defined here, occurs when an EGU begins combus ting fossil fuel and generates some measurable amount of electricity. Table 2-6 summarizes th e study population average, maximum and total starts by year. On average, coal-fired EGUs st art combusting fuel and generating electricity 11 times per year. The total number (approximately 9,000) of star ts for the study population of EGUs has remained stable over the study period. Ou r data reflects that some coal-fired units operate in a load following capacity and may repor t upwards of 200 starts in a single year, but these units tend to have low annual capacity fact ors. 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 numbe r of starts is an important va riable at a small number of EGUs, its impact on heat rate performance evaluated in this study is consider ed to be marginal.

9 

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

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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 h eat rate improvement availabl e with existing coal-fired steam EGUs, the EPA conducted a number of quantitative analyses. These included: (1) regression analyses to understa nd the impact of capaci ty factor and ambient temperature; (2) using a bin model to determine the potential fr om 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 a ffect 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 typicall y use digital control systems to capture hundreds of data points in n ear 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 independe nt 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 c onditions. We tested for collinearity between capacity factor and ambi ent 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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24 We also considered fixed unit characteristic s 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 perf ormance 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 in crease. 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 opera ting. 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 year ly utilization levels becomes more complex. For example, an EGU may run at an annual 60% capacity factor by operating 8 months at near full cap acity 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 c ountry, as temperatures increase electricity demand and capacity factor follow. Figure 2-3 shows the average monthly capacity factor in 2012 alongside the clim ate normal monthly temperature. 12 

## The lines intersect in the spring as temperatures begin to rise and the need for cooling drives electricity demand. Generally, peak capacity factor a nd generation in most parts of the U.S. occur on the hottest days of the year. Yet, the relationship between ambi ent temperature and capacity factor is complex.

10 

### For example, the correlation between annual unit heat rate variability (discussed below as relative standard deviation) and unit nameplate capacity (MW) is in the -0.1 range. 11 

### The availability of a cold heat sink in the condenser is a ke y factor in that cycle. The design of the heat exchanger, type of cooling system and availability of water all have an impact on performance. An increase in ambient air temperature, and consequent increase in water temperature, typically lower the effectiv eness of the cooling system, the condenser, and, therefor e, overall plant efficiency. 12 

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

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25 Each plant responds differently depending on de sign, 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 perfor med three regression analyses for each unityear: heat rate onto capacity factor, heat rate onto ambient temperat ure and heat rate onto capacity factor and ambient temper ature. Since this analysis seek s to evaluate heat rate under normal operating conditions, we re moved records with hourly heat rate values outside of +/- 2.6 standard deviations (1.9% of record s) before performing the regressions. 13 

## Similarly to partial operating hours, these outliers tend to occur during low load conditions. The records trimmed amount to one-fourth of a percen t of the total study population generation. Regression results describe the goodness of fit for the model and are expressed as the coefficient of determination or ‘r-squared’. To represent the relative contribution of varyi ng 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 toge ther explain 26% of the change in heat rate for the study populat ion over the study period. The average study

13 

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

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

## Electric generation monthly 2012 capacity factor and normal temperature

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

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26 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 attribut able to capacity factor and 10% to ambient temperature. These results, however, conceal consid erable 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 influe ncing changes in heat rate.
At approximately one-fourth of the study popul ation the response to ambient temperature is larger than the response to capacity factor. At some individual EGUs, temperature may explain up to 30% of the change in heat rate. These ar e typically, but not excl usively, units with oncethrough, fresh water, cooling systems. Identifyi ng 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, summer month heat rates may increase by 2-4% compared to winter months, but at a temperatureresponsive EGU that figure may be as high as 10%.
Our analysis indicates that as EGUs moved from base lo ad to load following, capacity factor tended to have a larg er effect on heat rate. Since 2008, the study population capacity factors moved from the top load bin into lower load bins. This can be seen from Figure 2-4, which compares the study population duty cycl es in 2008 to 2012. A significant share of 2008 generation occurred at EGUs running at greate r than 84% annual capacit y factor. In 2012, little generation took place in that bin or above, and ge neration was reduced by about half in the next lower bin (78-83% cap acity factor).

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27 Figure 2-4. Change in Annual Duty-Cycle 2008 and 2012
2.5.6 Heat Rate Variability and Indica tion of Improvement Potential
This study examines heat rate variability from the standpoint of statistical process control, which is utilized th roughout 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 earl y, before they significantly
affect emission measurements. Heat rate lends its elf to process control since it is the principal indicator that defines the quality of the electric generation process. 15,16 

## Therefore, in general, high variability in heat rate values would reflect opport unities for process improvement.
We use the relative standard deviation (RSD ) of the hourly heat rates to evaluate each unit against its past performance and to comp are with the study population. Each unit has up to eleven RSD values, i.e., one for each opera ting year between 2002 and 2012. The generationweighted mean RSD for the study population across 11 years is 5.4%. Table 2-8 summarizes

15 

### “The principal indicator that defines the quality of the process is heat rate.” (Fredrick & Todd, 1993. Statistical Process Control Methods in Performance Monitoring. Available at famos.scientech.us/Papers/1993/1993section11.pdf) 16 

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

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28 eleven years of results for the study populati on 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 m ean RSD of 5.4%. Notably, the EGUs in the top quartile are not outliers; they re port 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 vari ability using the resi dual 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 st udy population generationweighted mean RSD over the study period is 4.5%. This perc entage represents the total variability across the study populat ion 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 te mporal trend evident in the RSD. Table 2-9

17 

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

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29 summarizes the study population gene ration-weighted average RSD of residual heat rate over the study period in quartiles ordere d by the 11-year generation-weight ed 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 quart ile is 2.7% – well below the study population average of 4.5 %. This means that the residual hour ly heat rates of these un its generally stay in a narrow range within a given year. The maximu m RSD in the top quartile is 3.2%. From the statistical process control point of view, these units appear to have low variability.
The weighted average RSD of the bottom quartil e is 6.9%, which is over twice that of the top quartile. This spread indica tes that there is likely room fo r improvement in study population operation to reduce variab ility 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 rate s calculated from reported data. The study population generation-weighted an nual RSD ranges between 5% and 6% during the study period. Figure 2-5 below summarizes the re sults of regressing RSD of heat rate onto annual heat rate. 19 

## The r-squared result is 57%. These results indica te that, other factors he ld equal, if an EGU reduces heat rate variability, generally heat rate performance will improve.

18 

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

### The regression analysis was performed on 9,388 unit-years of study data which were trimmed to remove values outside 2.6 standard deviations.

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30 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 leas t 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 thr ee categories: EGUs with at least one year-toyear decrease in heat rate > 8.5%; units with at least one year-to-year in crease in heat rate > 8.5%; and, units with both. We examined whether th e large heat rate chan ges were due to `yearto-year changes in capac ity factor and found no correlation betw een the year-to-year changes in heat rate and capacity factor for a ny 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 annu al capacity factor fell under 50 percent the count is 313 EGUs.
21 

### The correlation remains weak even when limited to cases wh ere capacity factor changed more than 30 percent.
22 

### EPA Reference method 2 specifies the normal procedure for measuring stack gas volumetric flow rate during a relative accuracy test audit. Methods 2F, 2G, 2H and CTM-041 are approved alternativ es. 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 usin g these alternatives a source is likely to use them only if the results are significantly lower volumetric stack gas flow. The EPA was unable to draw conclusions about the effect of changes in flow reporting methods on fleet heat rate performance. y = 0.002x - 14.254 R2 = 0.5704 5.0 5.2 5.4 5.6 5.8 6.0 6.2

9600 9650 9700 9750 9800 9850 9900 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 qua rtile, 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 d ecrease, 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 efficien cy, 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 improve ment potential via best practices
As mentioned before, across the study period, the effects of hourly capacity factor and ambient temperature explain a generation-weight ed 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 ra te analysis determined there is significant variation in the operation of EGUs. Since lower heat rate variability is associated with lower heat rate, other factors held equal, th e range of variation i ndicates that significant potential for heat rate improvement is available through the application of best practices.

23 

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

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32 To control for known factors, the EPA c onstructed a model that groups each EGU’s hourly heat rate data into 14 temperature bins and 12 capacity factor bins, resulting in a 12 by 14
matrix of 168 bins. 24 

## For a given EGU, a temperature and capacity factor bin will have all the relevant hourly values over the eleven-year study period. For each bin with 15 or more values the model finds the reported hourly heat input va lue 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 repor ted hourly heat input greater th an p10 by a percentage of the distance between the reported va lue and p10 (e.g., 50% of the diffe rence). 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 vari ability (e.g., in the top quartile of residual heat rate variability) take proportionally smaller reductions.

Table 2-12 below shows the model results wi th 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. Al ternatively, a 50% reduction will result in a 6.7% study population wide improvement in heat rate. In eff ect, the model proportionate ly reduces heat rate variability and improves performance for each unit while controlling for temperature and capacity factor. The heat rate improvement for the study population is derived from the performance of each individual EGU as compared to its own record.
Table 2-12. Assessment of heat rate impr ovement potential via best practices

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

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

24 

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

### Performing the calculation with a minimu m of 30 values in each bin has a mode st effect on the results in Table 2- 12. For example, a 30% reduction obtains a 3.9% fleet wide improvement in heat rate (rather than 4.0%). 26 

### Heat rate is calculated as the sum of heat input (Btu, reported or reduced) divided by the sum of generation (kWh) for the given population and time period. 27 

### Fleet heat rate for study population as described in Table 2-4 is 9,754; the 9,755 value is derived from the dataset that includes hourly temperature values.

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33 gross)
10

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


## 2.5.9 Assessment of potential heat rate improvement via equipment upgrades
The EPA inspected the study population to find examples of EGUs that made significant year-to-year improvements in heat rate. After fi ltering 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 responsible for 2-3% heat rate improvement using the applicable estimates from the Sargent & Lundy 2009 study. Similarly, in the other cases, while our research was unable to confirm specific eq uipment upgrades, based on the elimination of other possible explanations we believe that equipment upgrades were the most likely cause of some of the observed heat rate improvements.
Two other sources provide information about heat rate improvements after equipment upgrades at existing plants. EPA Region 7 provided data for seve n coal-fired units at three anonymous plants with details on specific equi pment modifications. These included turbine efficiency and condenser performance upgrades, in stallation 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) de scribes WEPCO’s two-phase e fficiency program at four coal-fired plants over a ten-year period. In the first phase, 1990 – 1994, WEPCO installed equipment upgrades that included re tractable turbine pack ing, variable speed drives on the forced and induced draft fans, feed water heater replacements and new performance monitoring instrumentation. The four units reported heat ra te improvements ranging from 2.3% – 4.1% as a result of the equipment upgrades. In th e second phase, 1995-2000, WEPCO implemented changes that would generally fall into the be st practices category: equipment control and metering upgrades, boiler cleaning, feed water he ater improvements and reduced condenser air

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34 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 engi neering studies available in th e literature and selected the Sargent & Lundy 2009 study as the basis for our asse ssment 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 bo ttom) shown in Table 2-13. The first nine items in Table 2-13 contribute about 15 percent of the to tal average $/kW cost fo r 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 be lieve properly fall into the category discussed here as upgr ades. Using an average of the ranges of potential Btu improvements estimated by Sargent & Lundy for the four upgrade methods, upgrades, as defined here, could provide a 4% heat rate improvement if all were applied on an EGU that has not already made these upgrades.

Table 2-13. Sargent & Lundy Heat Rate Improvement Methods No-Cost and Low-Cost Options Condenser Cleaning Intelligent Soot Blowers ESP Modification Boiler Feed Pump Rebuild Air Heater and Duct Leakage Control Neural Network SCR System Modification FGD System Modification Cooling Tower Advanced Packing
Higher Cost Options Economizer Replacement Acid Dew Point Control Combined VFD and Fan Turbine Overhaul

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35 We also examined the annual heat rate trend line for each unit developed using the method of least squares. Using the slope of the trend line, 2002-2012, as an indicator of the heat rate performance of an EGU, a negative slope would indicate that the heat rate has improved. The annual trend line incorporates performan ce due to operating conditions (capacity factor, temperature), coal rank, maintenance, reporting method changes, equipment upgrades, and other factors. Over 40% of units have a positive slope. This would imply that equipment maintenance and upgrades at a significant fraction of the study population have not been sufficient even to maintain the status quo.
2.5.10 Combined study population results
The EPA’s analysis finds that a total of 6% heat rate improvements for the coal study population can be achieved through two types of cha nges: best practices th at 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 ar e supported by the va riability analysis using 11 years of hourly data applied to each un it. This analysis found that th e top quartile of EGUs reported significantly lower heat rate va riability than the st udy 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 cons ervatively 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 meas uring potential fleet heat rate improvement. For example, if each unit achieved heat rate performance equal to its best three-year moving average, the study population as a whole would post a 3.9% heat rate improvement. The best two-year moving average would achieve nearly a 5% improve ment and the best single year over 6%. EPA believes that the minimum three-year moving averag e heat rate is a reasonable target for the improvement potential from applying best practice s. Single year results could be due to unusual conditions, such as, an extended outage or weat her. Using three consecu tive years tends to smooth out the effect of equipment maintenan ce cycles and unusual mete orological patterns.

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36 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 un it configurations are unknown, and some units may have applied at least some of the upgrades, we co nservatively 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 st udy population of EGUs accounted for over 96% of 2012
electric sector CO 2 

## emissions from coal.
2.5.11 Sensitivity Analysis Removing Pl anned or Announced EGU Retirements
The EPA’s research found 233 coal-fired, non- cogeneration EGUs that have announced they will retire before 2016. 29 

## A sensitivity analysis was applied to the EGUs in the study population that plan to operate through 2015. The results are identical to the full population –
both achieve a heat rate reducti on of 4% under the 30 percent diffe rence option described in best practices.
2.6 Heat Rate Improvement – Economics
Most of the methods that can be applied to achieve a sustained Heat Rate Improvement (HRI) on a coal-steam EGU will entail a capital co st. These HRI capital co sts can be economic to incur if they yield sufficient redu ctions in other current or potentia l 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

28 

### See discussion in Table 2-3 above, and the HRI Pa rtial Bibliography at the end of this section 29 IPM documentation includes a list of the announced retirements. See Table 4-36 of IPM Documentation: http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/v513/Chapter_4.pdf and http://www.epa.gov/airmarkets/progsregs /epa-ipm/docs/v513/NEEDS_v513.xlsx 30 

### This TSD analysis assesses a broadly combined application of multiple HRI met hods. As estimated in the 2009 Sargent & Lundy study most HRI-related O&M costs are sufficiently small relative to the associated annualized capital costs, such that they do not ma terially affect the economics of broadl y combined HRI methods. The analysis therefore does not consider the small econom ic impact of HRI-related O&M costs.

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37 2.6.1 Heat Rate Improvement Ca pital Cost Assumption The 2009 Sargent & Lundy study describes numer ous 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 thes e methods and unit sizes are combined, as though they were all applied on a single EGU, th e following range of Sargent & Lundy estimated Btu reductions and associated range of capital cost s are obtained:

Combined Min-Max HRI Btu Reduction: 415-1205 Btu Combined Min-Max HRI Capital Cost: $40-150/kW 31


## EPA Assumed Combined HRI Capital Cost: $100/kW
The wide ranges of estimated HRI Btu and costs are indicative of the wide range of real differences in the many details of site 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 cap ital cost to achieve whatever HRI Btu reduction is possible at an average site. The effect of a lower HRI cost is also examined.
2.6.2 Heat Rate Reduction Assumption
The weighted average annual net heat rate of the U.S. coal-steam EGU fleet in 2020 is projected at 10,450 Btu/kWh in the EPA’s IPM v5.13 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 fr action of the coal fleet has already applied some or ma ny of the available HRI methods. 32

31 

### Note that highest cost does not necessarily align with greatest heat rate improvement. A low cost HRI method can have a large HRI potential (e.g., upgraded digital control system, neural network). Also, economy of scale causes most HRI methods to be more costly ($/kW) on smaller unit sizes. 32 

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

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38 The EPA does not have sufficient site speci fic information to accurately estimate what percentage of the fleet has adopted various HR I methods, nor how effectively, and is not aware of any other investigator havi ng sufficient information. HRI po tential can therefore not be estimated at this time through analysis of the cu rrent equipment configura tions of the coal steamEGU fleet. The EPA therefore analyzed 11 years of historical heat rate da ta 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 a nnual 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 HR I methods reduce net heat rate w ithout 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 th at result in reductions in auxiliary loads. For purposes of this TSD the EPA conservatively assumes that the coal fleet average net heat rate can be reduced by
6%.
2.6.3 Heat Rate Improvement Brea keven Economic Analysis
Figure 2-6 presents a simple breakeven ec onomic analysis for combined HRI methods using the assumptions described a bove, also assuming there is no CO 2 

## emission cost that is
reduced via HRI.

Figure 2-6. HRI Breakeven Economics
Notes: 0.00 0.50 1.00 1.50 2.00 2.50 3.00

1.00 2.00 3.00 4.00$/MWh‐net



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

‐ net Capital Cost $/MWh

‐ net



### HRI Capital Cost = $100/kW

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39 1. Capital cost S/MWh assumes the following: HRI capital cost = $100/kW; capital charge rate = 14.3%; IPM projected 2020 annual capacity factor = 78% 2. Coal fleet average 2020 net heat rate = 10,450 Btu/kWh; heat rate reduction = 6%
Figure 2-6 shows that the average fleet-wide savings in coal cost would become greater than the annualized capital cost of an average 6% reduction in heat rate when the average fleetwide coal cost exceeds about $3.25/MMBtu. Fo r 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 le ss than the assumed $100/kW, 6% HRI would
then become economic at lower coal costs. Fo r example, if the aver age capital cost were actually $75/kW, a fleet-wide 6% HRI would become economic at an average coal cost of about $2.50/MMBtu, which is comparable to the U.S. power sector average costs of $2.38/MMBtu for all coal ranks and $2.89/ MMBtu for bituminous coals in 2012. 33 

## This sensitivity indicates that fuel cost savings alone would make it economic for some of those EGUs currently using high cost bitumi nous 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 reduc tion were only 4% instead of th e 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 cost s, or at lower heat
rate reduction percentages.

33 

### EIA, Electricity DataTable 7.4, Av erage Weighted Cost of Fossil Fuels for the Electric Power Industry 2002- 2012, http://www.eia.gov/electri city/annual/html/epa_07_04.html

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40 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 a ssumptions as in the following example:
Fleet-wide 2020 Assumpti ons (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

 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 analys is supports the following summary conclusions:

 Some degree of HRI is already economic fo r high heat rate – high coal cost EGUs

 If a fleet-wide average 6% HR I is technically feasible, it would also be economic on the basis of fuel savings alone, before consid eration of the value of the associated CO 2 

## emission reductions, on a fleet-wid e basis at today’s coal prices if the associated average capital cost is about $75/kW or less.

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41  If a fleet-wide average 6% HRI is technically feasible, and the associ ated 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 an d 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 uncertain ty 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 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/res ource/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 EG U over a one year period

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42 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 E ngineering, January 1, 2002, available at http://www.power-eng.com/art icles/print/volume-106 /issue-1/features/heatrate-optimization-pays-dividends.html NETL 2008, Reducing CO 2 

## Emissions by Improving the Efficien cy of the Existing Coal-fired Power Plant Fleet, DOE/NETL-2008/1329, July 2008 NETL 2009, Opportunities to Improve the Efficien cy of Existing Coal-fired Power Plants, Workshop Report, NETL July 2009, available at http://www.netl.doe.gov/energyanalyses/pubs/NETL%20Power%20Plant%20Eff iciency%20Workshop%20Report%20Final.pdf NETL 2010, Improving the Thermal Efficiency of Co al-Fired Power Plants in the United States, DOE/NETL Technical Workshop Repor t, February 2010, available at http://www.netl.doe.gov/File%20Lib rary/Research/Energy%20Anal ysis/Publications/ThermalEf ficCoalFiredPowerPlants-TechWorkshopRpt.pdf NETL 2010a, Improving the Efficiency of Coal -Fired Power Plants for Near Term GHG Emissions Reductions, DOE/NETL-2 010/1411, April 2010, available at http://www.alrc.doe.gov/energy-analyses/ref shelf/PubDetails.aspx?Action=View&PubId=307 Sargent & Lundy 2009, Coal-Fired Power Plant H eat Rate Reductions, SL-009597, Final Report, January 2009, available at http://www.epa.gov/airmarkets /resource/docs/coalfired.pdf Storm 2009, “Applying the Fundamentals for Best H eat 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 Po llution 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 Existi ng Coal-Fired Power Plants, Congressional Research Service, December 2013, available at http://www.fas.org/sgp/crs/misc/R43343.pdf Evonik/VGB 2008, “Power Plant Performance Re porting and Improvement under the Provision of the Indian Energy Conservation Act – Output 1.1”, Evonik/ VGB 2008, available at http://www.emt-india.net/Power PlantComponent/Output1.1/Output1.1.pdf IEA 2010, Power Generation from Coal - Measuri ng and Reporting Efficiency Performance and CO 2 

## Emissions, OECD/IEA-CIAB 2010, available at http://www.iea.org/ciab/paper s/power_generation_from_coal.pdf EPRI 2009, Renewed Interest in Reducing Heat Rate, EPRI Jour nal, Spring 2009, available at http://mydocs.epri.com/docs/Corpor ateDocuments/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 Electr ic Energy Efficiency in the Production and Delivery of Electricity, EPRI Technical Report 1024651, November 2011, available at http://www.google.com/url?sa=t&rct=j&q=& esrc=s&frm=1&source= web&cd=1&ved=0CCsQF jAA&url=http%3A%2F%2Fwww.pserc.wisc.edu%2 Fdocuments%2Fpublica tions%2Fspecial_in terest_publications%2FEPRI_Electricit y_Use_Report_Final_1024651.pdf&ei=Qo9yUvmwMY Xb4AOZz4GYAw&usg=AFQjCNElzekbt oSNCR5SKFwkfbKx83p0Uw&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.pdfI NTERAGENCY W ORKING C OMMENTS ON D RAFT L ANGUAGE UNDER EO12866 I NTERAGENCY R EVIEW
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1 GHG Abatement Measures Technical Support Document Chapter 3: CO 2 Reduction Potential from Re-Dispatch of Existing Units

Table of Contents Overview

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

............................................................................................................................... ................... 2

Power Sector Background

............................................................................................................................. 3
Historical Context

............................................................................................................................... ........... 6
Natural Gas Supply

............................................................................................................................... ....... 13
Technical Considerations

............................................................................................................................ 14 Analysis of Cost

‐ Effectiveness

..................................................................................................................... 21
Natural Gas Price Impacts

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2 Overview This chapter explores the dynamics of power sector dispatch and the cost-effectiveness of lowering the carbon dioxide (CO 2 ) emissions intensity of the power sector by substituting generation from the most carbon-intensive existing EGUs and increasing utilization, to the extent possible, of less carbon-intensive existing fossil fuel-fired EGUs. More specifically, the examination focuses on opportunities to improve emissions intensity by increasing the utilization of existing natural gas combined cycle units. The TSD provides background on existing power plants, power system operation, and the economics of electricity production and delivery in the
context of cost-effective CO 2 emission reduction opportunities.

Introduction

Electric system dispatch is typically defined as “the operation of generation facilities to produce energy at the lowest cost to reliably serve consumers, recognizing any operational limits of generation and transmission facilities.” 1 Electricity demand varies across geography and time in response to numerous conditions, such that elec tricity generators are constantly responding to changes in demand and “re-dispatching” to meet demand in the most reliable and cost-effective manner possible.

The nation’s EGUs are connected by transmission grids that extend over large regions. Through these interconnections, EGU balancing auth orities treat the product (i.e., electricity) of EGUs as fungible, calling for electricity generation supply to meet demand usually by deploying the least expensive power source first. 2

EGU operators and balancing authorities must take into account several constraints in dispatch, including transmission constraints as well as emission control programs and other environmental requirements. Such programs and requirements can change the relative cost of generating electricity among plants and/or limit the number of hours that a plant can run. For many years, EGU operators throughout the country have considered the emissions implications for pollutants such as SO 2 and NO x when scheduling unit dispatch, in response to costs and

1 Energy Policy Act of 2005 2 A balancing authority is the responsible entity that inte grates resource plans ahead of time, maintains the balance between supply, demand, and generation within a balanci ng authority area, and supports interconnection frequency in real-time. http://www.nerc.com/files/glossary_of_terms.pdf Comment [A1]: General Comments: 1) The section on electric markets contains a number of over
‐ simplifications. It’s not clear why the section is necessary. 2) It should be made clear in introductory sections (and throughout TSD) that the analysis of building block 2/re

‐ dispatch is based on dispatch to existing NGCC. Comment [A2]: EPA Response:

We feel that some context is useful for this chapter, but we are open to specific suggestions for improvements.

Clarifications also added for existing NGCC.
Deleted: reducing utilization of

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3 regulatory requirements. For example, EGU operat ors in 10 states partic ipating in the Regional Greenhouse Gas Initiative have several years of experience with factoring CO 2 emissions limits directly into bids for economic dispatch. The electric system’s carbon intensity can be lowered through re-dispatch among existing EGUs, particularly by shifting generation from coal-fired units to natural gas combined cycle (NGCC) units.

Power Sector Background

Electric Dispatch Electricity generation conforms to the principl e of least-cost economic dispatch, which is “the operation of generation facilities to produce energy at the lowest cost to reliably serve consumers, recognizing any operational limits of generation and transmission facilities.” 3 The cost of operating electric generators varies based on a number of factors, such as fuel used and generator efficiency. Regional Transmission Or ganizations (RTOs) and Independent System Operators (ISOs) help coordinate economic dispatch over larger areas to help keep the cost of meeting electricity demand as low as possi ble, subject to operational constraints.

The decision by balancing authorities to call upon, or dispatch, any particular generating unit is driven by the relative operating cost, or marginal cost, of generating electricity to meet the last increment of electric demand. These costs change over time depending upon a variety of factors like fuel prices, weather conditions, and overall demand levels. Since the fixed cost of power plants is a sunk cost, plant operators bid into elec tricity markets such that their variable costs are covered. For fossil fuel-fired electric generating un its, variable costs are dominated by the cost of the fuel, although coal-fired units often also have considerable variable costs associated with running pollution controls. 4 Other generating technologies, like renewables, hydroelectric, and nuclear, have little or no variable costs and are ge nerally dispatched to the extent possible. In order to maintain least-cost disp atch, the units with the lowest variable costs will be called upon first, then other units (with higher variable costs) will be called upon sequentially, such that total

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4 system demand is met. The economic order in which units are dispatched to meet demand, at any particular point in time, is commonly called a dispatch “curve.” 5 Balancing Authorities

In states with cost-of-service regulation of vertically-integrated utilities who own generation, transmission, and distribution infrastru cture, the utilities themselves often form the balancing authorities who determine unit dispatch. Such utilities are presumed to dispatch their units in a cost-minimizing fashion (seeking the lo west marginal cost), and they can arrange to buy and sell power with other balancing authorities.

In states that have restructured regulation to allow for competition between generators, RTOs and ISOs are generally responsible for moving electricity across larger areas in the most efficient and least-cost manner possible. 6 They coordinate, control, and monitor electricity transmission systems to ensure cost-effective and reliable delivery of power, and they are independent from market participants. ISOs grew out of the Federal Energy Regulatory Commission (FERC) requirements for existing po wer pools to satisfy the requirement of providing non-discriminatory access to transmission. Subsequently, FERC encouraged the voluntary formation of RTOs to administer the transmission grid on a regional basis throughout North America (including Canada).

RTOs and ISOs administer wholesale power markets, which match the generation of electricity with the purchase of electricity (and an cillary services) prior to delivery to end-users. Companies that provide retail electricity (e.g., utilities and energy service companies) procure power through these wholesale electricity markets.

State Public Utility Commissions (PUCs) 7
Each state has a governing body that is tasked with regulating retail electricity rates and electric services to protect the public interest, en sure efficient and reliable delivery of electricity, and plan appropriately for the short and long term energy needs of the state and consumers. Depending on market structure, PUCs also allocate costs among customers, design price structures and set price levels, set service quality standards, approve capital expenditure by

5 http://www.eia.gov/todayinenergy/detail.cfm?id=7590 6 http://www.ferc.gov/industries/electric/indus-act/rto.asp 7 These entities are sometimes called Utilities Commissions, Utility Regulatory Commissions, or Public Service Commissions

. Deleted: ¶ Page Break ¶

Comment [A3]: It’s probably more appropriate to say that vertically

‐ integrated utilities seek the lowest total cost, not the lowest marginal cost. Comment [A4]: EPA Response:

It is EPA’s understanding that least

‐ cost dispatch (system operation) is generally decided on a marginal cost basis regardless of the type of utility. EPA recognizes that vertically

‐ integrated utilities consider total costs in the context of longer

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5 utilities, and arbitrate disputes among relevant pa rties and stakeholders. In restructured markets, the PUC’s authority is generally applicable to the transmission and distribution system, since the generation and dispatch component is governed by RTOs and ISOs. In cost-of-service states, the PUC also has oversight of the generation and capacity planning components

. Spot and Day-Ahead Markets RTOs and ISOs operate spot markets for wholesale power supply and demand for their designated area, including both day ahead and re al-time (hourly, or shorter time periods). These markets are based on bids for supply and demand and operate according to rules established by FERC. The RTOs and ISOs use these markets for balancing power supply and load in their area and typically serve as the balancing authority for the same area.

For areas not administered by RTOs and ISOs, dispatch is scheduled both day ahead and hourly, but is typically driven by the power supply costs and schedules of traditional utilities. This dispatch will depend, to a certain degree, on spot markets for power, since utilities will dispatch purchased power from other suppliers when that power can be obtained at a cost savings. There is an active wholesale market for this power in the spot market, from individual sales and from exchanges. These markets typically sell power day-ahead but not hourly, and also sell power for longer periods, such as weekly or monthly. However, the actual dispatch and balancing of power is conducted by the utili ty based on its own sche duling and pu rchasing protocols and varies considerably from one utility to the next.

As a balancing authority, the RTO or utility will balance demand, generation, and imports/exports in real time while maintaining system frequency and ensuring that the next hour’s demand, or load, is met. In addition, the transmission system is constantly monitored to ensure reliability limits are met, voltage levels are appropriate, and appropriate corrective action
is taken when needed.

Reliability As reliability coordinators, balancing authorities are responsible for the reliable operation of the bulk electric system. The bulk electric system refe rs to a large interconnected electrical system made up of generation and transmission facilities and their control systems. To ensure reliability, system operators continuously an alyze real-time and forecasted lo ad and transmission conditions to ensure that scheduled generation dispatch can meet load without adverse impacts. If theI NTERAGENCY W ORKING C OMMENTS ON D RAFT L ANGUAGE UNDER EO12866 I NTERAGENCY R EVIEW
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6 scheduled dispatch is not feasible within the limits of the transmission system, it must be adjusted by the system operator. The North Am erican Electric Reliability Corporation (NERC) develops and enforces the procedures to ensure reliability, in accordance with Federal laws and regulations, and with FERC oversight. 8

Historical Context

In 2012, average CO 2 emission rates 9 across all the following technology categories on a net generation basis were:

 Coal Steam - 2, 220 lbs/MWh

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

 NGCC – 9 07 lbs/MWh

Coal- and oil/gas-fired boilers are consider ably higher-emitting sources than NGCCs, on average. Therefore, the replacement, or re-dispatch, of each megawatt-hour (MWh) from the average fossil fuel-fired boiler with each MWh from an average NGCC will result in notable
CO 2 emission reductions.

The lower emission rate of NGCC conveys th e potential of re-dispatch to reduce GHG emissions. However, the actual potential to realize emission reductions through this technologydepends on the availability and capacity factors of the existing NGCC fleet. In order to redispatch from existing fossil fuel-fired boilers to existing NGCC, there needs to be some existing unused generation potential in the current NGCC fleet that could displace generation from more
CO 2 intensive generating resources. The term “availability” is a common engineering term used in the power sector, which reflects the percentage of period hours that a plant is available to produce electricity (a period being 1 year, or 8,7 84 hours in 2012 since that year included a leap day). The unavailable period is generally attributed to scheduled maintenance, unplanned

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7 maintenance, and unplanned outages. The EPA assumes that NGCC has an availability of 87%. 10 Other reports suggest that NGCC availab ility factors may reach as high as 92%. 11

If the existing NGCC fleet was already opera ting at a level of 87% to 92%, there would not be any additional generation potential in the existing generating system for re-dispatch to those units

. To evaluate re-dispatch opportunities to unused NGCC generation potential in the system, the EPA reviewed recent NGCC fleet oper ating data to determine capacity factors. Redispatch for GHG abatement purposes woul d require one net MWh of a lower emitting technology displacing one net MWh of generation from higher emitting technology. Therefore, when the EPA was assessing capacity factor it us ed the net generation of a given NGCC unit as
the numerator. The EPA was interested in the relationship of a unit’s total net generation relative to its net generating capacity (i.e., capacity f actor). Net generating capacity is a function of weather/temperature conditions at the site, which varies throughout the year. While some units may model actual weather adjusted capacity by the hour/minute, these data are not reported for
the fleet. Therefore, the EPA used the nameplate capacity reported for units. The net generation was divided into the nameplate generation capacity of a unit multiplied by the number of hours in a year. This calculation of capacity factor provides an indication of how much net generation a unit is providing as a percent of its total generating capacity. Whereas availability refers to the maximum amount of generation that could be expected from a given source, the capacity factor
refers to the actual utilization of that source on an annual basis. The EPA surveyed 2012 data for over 1800 NGCC units and observed that the NGCC fleet had an average capacity factor in the 44% to 46% range for 2012. 12 Since the fleet-wide capacity factor in 2012 was less than the availability assumed for the technology, the histor ical data suggests that there is a significant potential for re-dispatch from higher CO 2 emitting resources to lower emitting NGCC generation.

Availability for NGCC fleet................................87% to 92% 2012 Capacity Factor for NGCC fleet.....................44% -46%

10 See Chapter 3, Table 3-18 at http://www.epa.gov/powersectormodeling/BaseCasev513.html 11 http://www.power-eng.com/articles/print/volume-115/is sue-2/features/higher-availability-of-gas-turbinecombined-cycle.html 12 See “2012 eGrid Data” file provided in the docket for 44% figure. See EIA 860 and 923 for 46% value. Deleted: Comment [A5]: This is incorrect. Other forms of generation are dispatchable. Please clarify. Comment [A6]: EPA Response:

Clarification added.

Deleted: i
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8 To quantify the GHG reductio n potential from re-dispatch, the EPA considered alternative capacity factor levels at which the NGCC fleet could be dispatched. Although the availability for NGCC units is assu med to be in the mid to high 80s, the EPA did not assume that each state’s NGCC fleet could collectively operate at this level on an annual basis. To determine reasonable average capacity fact or ceilings for a stat e’s NGCC fleet as part of BSER, the EPA considered historical data and modeling proj ections describing NGCC characteristics and operating behavior.

As seen in Table 3-1, the existing NGCC fleet is relatively young. More than 80% of the capacity has come online in the last 15 years. 14 Of this capacity, almost all are a highly efficient
class of NGCCs that are able to ach ieve high availability factors.

Table 3-1: Existing NGCC Capacity, by Age 15

Online Year Capacity (Name Plate Capacity – MW) Percentage of Total Existing NGCC
Fleet
Pre 1950 103 0%
1950-1959 1,769 0.7%
1960-1969 3,087 1.3%
1970-1979 6,909 2.8%
1980-1989 7,658 3.1%
1990-1999 28,467 11.7%
2000-2009 174,947 71.7%
2010+ 21,068 8.6%
Total 244,008 100%

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9 Of 464 NGCC plants generating in 2012 and greater than 25 MW , the EPA observed that
50 plants ( more than 10 % of NGCC plants ) had a net generation value that was greater than or equal to its nameplate capacity x 8784 hours * 70%. That is, a capacity factor that was 70% or greater (see Figures 3-1 and 3-2). 16

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

17 Table 3-X: Plant Distribution of Existing NGCCs (2012)
Capacity Factor # of NGCC plants % of NGCC Plants Less than 5% 40 8.62% 5%-9% 26 5.60% 10%-14% 23 4.96% 15%-19% 25 5.39% 20%-24% 16 3.45% 25%-29% 18 3.88% 30%-34% 27 5.82% 35%-39% 38 8.19% 40%-44% 30 6.47% 45%-49% 36 7.76% 50%-54% 33 7.11%

16 See “2012 eGrid Data” file provided in the docket 17 EIA Forms 860 and 923. CA and CT Prime Mover categories Deleted: 1,812
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10 55%-59% 39 8.41% 60%-64% 36 7.76% 65%-69% 27 5.82% 70%-74% 20 4.31% 75%-79% 13 2.80% 80-84% 10 2.16% 85-89% 4 0.86% Greater than 90% 3 0.65%

In 2012, more than 10% of NGCC plants operated at an annual capacity factor of 70% or higher. This subset of NGCCs was largely dispat ched to provide base load power. While only
10% of plants operated at 70% or higher capacity factor on an annual basis, the fleet of NGCC units was relied upon heavily during certain periods of time, in response to higher demand. On a seasonal basis, a significant number of units achieved capacity factors greater than 50%, and even up to 80%. Using data reported to the EPA, 18 and looking more closely at data during the summer and winter peak electricity demand timeframes nationwide, more than 10% of NGCC s were operated at a capacity factor greater than 70%. 19 In fact, 19% of NGCCs achieved 70% capacity factor during the winter of 2011/2012 a nd 20% hit that level or higher during the

summer. 20 During periods where demand levels are typically lower, some NGCCs were idled or operated at lower capacity factors. Nonetheless, a notable number of existing NGCCs have demonstrated the ability to achieve a 70% capacity factor for extended periods of time. These units achieved high capacity factors without adve rse effects on the electric system. While many units demonstrated an ability to deliver net generation that was more than 70% of their nameplate capacity, the EPA assumed that 70 % was a reasonable fleet-wide ceiling for each

state. It should also be noted, roughly 6% of units (107 units) operated at a 75% capacity factor, or higher, in 2012. In addition, 16% of units (291 units) operated at 65%, or higher. Over the last several years, advances in the production of natural gas have helped reduce natural gas prices and improved the competitive po sition of gas-fired units relative to coal-fired units. As a result, operators have shifted signifi cant quantities of generatio n from coal units to NGCCs, absent any federal CO 2 requirements. 2012 net generation from NGCC units grew to

18 Air Markets Program Data (at http://ampd.epa.gov/ampd/). 19 Summer defined as June, July and August. Winter defi ned as December, January, and February. Estimates are for units for which data was provided to EPA. 20 Air Markets Program Data ( http://ampd.epa.gov/ampd/ ). Winter includes December of 2011, January and February of 2012. Summer in cludes June, July, and August. Formatted: Indent: First line: 0"

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Comment [A7]: The use of historical data has little bearing on the capability of the entire fleet to reach a given level of dispatch. It only shows the ability of individual units to reach high levels, but it is already well

‐ established (and noted elsewhere) that these plants are designed to operate at yearly capacity factors in excess of 85%. The main constraints on reaching high capacity factors for existing NGCC generation are system constraints including the need for dispatchable generation to meet intermediate load. These are addressed later in the TSD. Comment [A8]: EPA Response:

We agree with the comment, but it is important to show all evidence where it exists, supporting the conclusion that higher capacity factors can be met. By using historical data, we show that this is not merely a theoretical or engineering argument, but actually happens, in practice, fairly routinely at individual units. The other arguments mentioned in the comment are addressed throughout this TSD. Comment [A9]: EPA Response:

We agree with the comment, but it is important to show all evidence where it exists, supporting the conclusion that higher capacity factors can be met. By using historical data, we show that this is not merely a theoretical or engineering argument, but actually happens, in practice, fairly routinely at individual units. The other arguments mentioned in the comment are addressed throughout this TSD.I NTERAGENCY W ORKING C OMMENTS ON D RAFT L ANGUAGE UNDER EO12866 I NTERAGENCY R EVIEW
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11 981 TWh, up from 796 TWh in 2011 (22% growth in one year). The extent of this capability varies by region, based on factors such as the mix of EGU types and the amount of available NGCC capacity.

An analysis of historical dispatch across th e generating fleet of coal and NGCC units for 2011 and 2012 provides some implicit measures of the cost dynamics between the two technologies. For example, one is able to look at the change in the prices of coal and gas togauge the relative costs of generating, or dispatch, for each technology. While there are wideranging costs at the unit level, an aggregated a ssessment of the relative economics is informative and can provide a metric for assessing the implicatio ns of dispatch as it relates to emissions of
CO 2.
The potential for redispatch from CO 2 intensive sources to less CO 2 intensive sources is
evidenced in historical data. EIA form 860 an d 923 data demonstrate an increase in NGCC generation and fuel use between 2011 and 2012 of more than 20% (even though the NGCC fleet capacity rose by just 3%). As NGCC generation rose by approximately 185 TWh, coal generation fell by approximately 216 TWh. The significant redispatch from coal to gas over just a one year period demonstrates the ability for th e quick re-dispatch in response to market or
economic drivers.

The increase in the NGCC utilization was in la rge part driven by the decrease in natural gas prices to historic lows (see Table 3-2). He nry Hub natural gas prices averaged $4.00/mmBtu in 2011 and $2.76/mmBtu in 2012. This $1. 24/mmBtu creates an ad ditional incentive for redispatch from coal generation to NGCC relative to 2011 dispatch economics. The fuel advantage is similar to the incentive that a $15/metric ton of CO 2 price signal would create. 21 This historical data also shows a sharp increas e in the NGCC fleet’s capacity factor from the high 30s to the mid 40s. During that same period, net coal generation dropped by an amount similar to the increase observed in NGCC net gene ration. Furthermore, natural gas supply is

21 Assumes 11,000 Btu/KWh heat rate and 2354 lb/MWh emision rate for coal, 8000 Btu/KWh and 926 lbs/MWh for NGCC (based of “2012 eGrid file”)I NTERAGENCY W ORKING C OMMENTS ON D RAFT L ANGUAGE UNDER EO12866 I NTERAGENCY R EVIEW
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12 expected to grow more than 20% by 2020 relative to its 2012 levels, creating more fuel resources to foster the potential contin ued and increasing redispatch to NGCC generating technology. 22

Table 3-2: 2011 and 2012 Gas and Coal Generation 23

Year NGCC Name Plate

Capacity

(GW) NGCC Heat Input for Electricity

(TBtu) NGCC Net Generation

(TWh) NGCC Capacity

Factor H enry H ub Natural Gas Price ($/mmBtu Coal Net Generation

(TWh)
2011 23 9 5,912 796 3 8 % $4.00 1,719
2012 24 4 7,224 981 46% $2.76 1,503

The demonstrated ability of the NGCC plants to consistently operate at levels greater than 70% of their nameplate capacity (e.g., this was the utilization level of the ~ 90 th percentile plant ), the historic evidence supporting quick and significant redispatch to NGCC, and the costeffectiveness of high NGCC utilization demonstrated later in this TSD all supported the notion of a NGCC fleet capacity factor of 70% as a reasonable ceiling in the EPA’s BSER approach.

For purposes of establishing state goals, hi storical electric generation data (2012) was used to apply each building block and develop each state’s goal (expressed as an emissions rate, lbs/MWh). In 2012, electric generation from existing NGCC units likely subject to the 111(d) applicability criteria was 959 TWh. 24 After the application of NGCC re-dispatch to the 70% level, 25 these same existing sources were calculated to collectively generate 1,390 TWh. Adding in the existing sources that were not yet onlin e in 2012 (under construction) increases total NGCC generation calculated in the goal setting to 1,444 TWh.

Although, states may choose to comply with state goals through other abatement measures, the EPA believes that upwards of 1,400 TWh from existing and under construction NGCCs is achievable. As a reference point, NGCC generation increased by approximately 430 TWh (an 81% increase) between 2005 and 2012. EPA is calculating that NGCC generation in

22 http://www.eia.gov/oiaf/aeo/tablebrowser/#release=AEO2014ER&subject=0-AEO2014ER&table=13- AEO2014ER®ion=0-0&cases=ref2014er-d102413a 23 EIA form 860 and EIA form 923 24 For covered sources. 25 This dispatch level is a ceiling dependent upon available existing steam generation that can be decreased. As a result, not all states achieve the assumed 70% re
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13 2020 could increase by approximately 47% form today’s levels. This reflects a smaller ramp rate in NGCC generation than has been observed from 2005 to 2012.

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

NGCC Name Plate

Capacity

(GW) NGCC Net Generation

(TWh) Growth in NGCC Generation from 2005 to 2012 Growth in NGCC Generation from 2012 to 2020 Nationwide NGCC Capacity

Factor

2005 199 551 NA NA 32%

2012 244 981 81% NA 46%

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

Natural Gas Supply

The EPA expects the growth in NGCC generation assumed in goal setting to be feasible and consistent with domestic natural supplies. Increases in the natural gas resource base have led to fundamental changes in the outlook for natural gas. There is general agreement that recoverable natural gas resources will be substa ntially higher for the foreseeable future than previously anticipated, exerting downward pressure on natural gas prices. 26

According to EIA, natural gas proved reserves have doubled between 2000 and 2012. 27 Domestic production has increased by 32% over that same timeframe (from 19.2 TCF to 25.3 TCF). EIA’s Annual Energy Outlook for 2014 pr ojects that production will further increase to 29.1 TCF, due to increased supplies and favorable market conditions. For comparison, NGCC generation growth of 450 TWh (calculated in goal setting) would result in increased gas consumption of roughly 3.5 TCF for the electricity sector. 28

26 National Petroleum Council. 2011. Prudent Development: Realizing the Potential of North America's Abundant Natural Gas and Oil Resources. http://www.npc.org /reports/rd.html (see Figure 1.2 on p. 47).
27 http://www.eia.gov/cfapps /ipdbproject/IEDIndex3. cfm?tid=3&pid=3&aid=6 28 Assuming 1,024 Btu/cubic foot and 10,000 Btu/KWh Deleted: 5-45
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14 The National Petroleum Council (NPC), a privately funded advisory committee established by the Secretary of Energy, recently updated a major resource study and concluded that “the potential supply of No rth American natural gas is far bigger than was thought even a few years ago,” after large increases in shale resource estimates. 29

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

Technical Considerations
Emission reductions through re-dispatch are largely determined by the ability to change the utilization of existing generating units, relative to current utilization levels. Other influences include physical limitations of th e electric transmission system and considerations for reliability, timing, and cost.

NGCC Availability

29 National Petroleum Council, 2012 (http://www.npc.org/PD_update

‐ 80112.pdf)
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15 For purposes of economic dispatch, most NGCCs have historically been operated to serve
base load or intermediate demand due to their hi gh efficiency and flexibility of operation, with national average annual capacity factors in the range of 40-50%. 31,32 However, NGCCs are designed for, and are demonstrably capable of, reliable and efficient operation at much higher annual capacity factors, as shown in observed hist orical data for particular units and their design and engineering specifications.

The capability of NGCCs to operate at capacity factors of 70% and greater is indicated, in part, by statistics on the average availability factor of NGCCs. 33 Annual availability is the ratio of annual hours that an EGU is operating or considered able to operate (not in a forced or maintenance outage) to the hours in a year. The average availability factor for NGCCs in the U.S. generally exceeds 85%, and can exceed 90% for selected groups, as reported to NERC. 34,35 Advanced NGCCs being built today have availability factors of over 95%. According to one NGCC manufacturer, these highly efficient units already represent over 15 percent of total installed capacity nationwide, including all electric generating sources (as of 2010). 36

These high-efficiency and high-availability NGCC units were first introduced around 1995 and have consistently report ed availability factors of 90 to 92 percent to NERC (compared to 95 percent or greater availabilities reported by current vintage F class and H class turbines from General Electric Power Systems). 37 Data reported to NERC from NGCC units greater than 50 MW in 1994 through 1998 shows similar availability factors (generally exceeding 89 percent).

Natural Gas Pipeline and Electricity Transmission

31 EIA, Today In Energy, January 15, 2014, http://www.eia.gov/todayine nergy/detail.cfm?id=14611 (for recent data) 32 EIA, Electric Power Annual 2009, http://www.eia.gov/electricity/annual/archive/03482009.pdf (Table 5-2 for 2009 and earlier data) 33 NERC, 2008-2012 Generating Unit Statistical Brochure – All Units Reporting, http://www.nerc.com/pa/RAPA/gads/Pages/Reports.aspx 34 Power Engineering, Negotiating Availability Guarantees for Gas Turbine Plants, 03/01/2001, http://www.powereng.com/articles/print/volume-105/issu e-3/features/negotiating-availability-guarantees-for-gas-turbine-plants.html 35 Power Engineering, Higher Availability of Gas Turbine Combined Cycle 02/01/2011, http://www.powereng.com/articles/print/volume-115/is sue-2/features/higher-availability-o f-gas-turbine-combined-cycle.html 36 http://site.ge-energy.com/corporate/network/downloads/7FA_Evolution.pdf 37 GE Power Systems submitted to U.S. Department of Energy, 2000. Utility Advanced Turbine Systems Technology Readiness Testing Phase 3 Restructured. DOE Cooperative Agreement No. DE-FC21-95MC31176— 30.I NTERAGENCY W ORKING C OMMENTS ON D RAFT L ANGUAGE UNDER EO12866 I NTERAGENCY R EVIEW
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16 The EPA believes that the natural gas pipeline and electricity transmission networks can support aggregate operation of the NGCC fleet at up to a 70% capacity factor on average, either as they currently exist or with modifications that can be reasonably expected in the time frame for compliance with this rule. Existing NGCCs are already connected to both the power and natural gas networks and, while constraints to specific unit operations can occur in either or both networks during peak pipeline flow or electricity us e, the rule allows for emission rate averaging across multiple units and across time for compliance. As a consequence of this averaging flexibility, constraints that occur at peak times are unlikely to be a barrier to achieving compliance with the rule, because these peak times are only a small percentage of the year and will constrain only a limited percentage of the state-wide NGCC fleet. The ability for the current fleet to ramp up significantly to meet changes in demand can be seen from the increased use of natural gas that occurred in 2012 in response to historically low natural gas prices. Power plant use of natural gas use in 2012 increased by 20% over 2011 38 and resulted in a national average capacity factor for NGCC of 4 5.8 % on average, and higher in some states. 39

During the peak hours of the day (which vary by region and season), NGCC capacity factors are typically well above average capacity factors. 40 The pattern of capacity utilization by hour for 2005 to 2010 is shown in Figure 3- 4 . In this figure, capacity factors in 2010 are approximately 50% from the Hour 11 to Hour 21. 41 The persistence of this hourly pattern across years shows the pattern to be stable. Since the average capacity factor for combined cycle units
in 2010 from the same information source was 39% 42 , this indicates that the current system can support levels of approximately 11% above the average capacity factor. These peak hours are the period when there are most likely to be constr aints on the pipeline or electricity transmission networks; during other hours of the day, continued NGCC operation at equal, or higher levels, are technically feasible but may be limited by economic considerations (e.g., whether NGCCs can offer least-cost electricity compared to other sources at those times). As a result, the current system is already able to support national average capacity factors in the mid to high 50’s for

38 Energy Information Administration, U.S. Natural Gas Consumption by End Use, http://www.eia.gov/dnav/ng/ng_cons_sum_dcu_nus_a.htm. 39 Source: Air Markets Program Data (AMPD), ampd.epa.gov, EPA, 2014 40 Energy Information Administration, Today in Energy, July 9, 2011. Average utilization of the nation’s natural gas combined-cycle power plant fleet is rising. http://www.eia.gov/todayinenergy/detail.cfm?id=1730# 41 In this figure, hour 11 is the hour ending at 11 AM, and similarly for other hours. 42 Air Markets Program Data (AMPD), ampd.epa.gov, EPA, 2014 Deleted: 9

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17 NGCC for peak. It is reasonable to expect that average capacity factors could be extended to higher levels at all hours without experiencing t echnical feasibility barriers from either pipeline supplies or electricity transmission.

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

Although there can be site-specific constraint s on utilization at some NGCC facilities, several factors support the ability of the power and natural gas pipeline systems to respond effectively with increases in infrastructure when needed to alleviate these barriers. For example, in recent years, the power transmission system has responded with increased transmission infrastructure when needed to allow the retirement of uneconomic coal plants. 43 This rule provides for flexible implementation that will permit efficient scheduling of infrastructure upgrades as needed. Upgrades to pipeline and transmission infrastructure potentially needed to meet additional use of existing facilities will generally be less extensive than upgrades of that infrastructure potentially needed for siting of new capacity. In addition, this proposed rule is

43 See http://www.pjm.com/~/media/about-pjm/newsroom/ 2013-releases/20131211-pjm-board-authorizes-4.6- billion-in-chnages-to-regional-electric-grid.ashx for an example of short term transmission upgrades performed to facilitate environmental compliance. For tec hnical description of these upgrades, see:http://www.pjm.com/~/media/committees-groups/committees/teac/20131211/20131211-december-2013-pjm-boardapproval-of-rtep-whitepaper.ashx

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18 expected to result in significantly higher levels of end-use energy efficiency, which will reduce the load on the electricity transmission and natural gas pipeline infrastructure, while also providing other system wide benefits, such as decreased need for new generating units and reduced peak demands.

In addition, natural gas pipeline capacity is regularly added in response to increased gas demand and supply, such as the addition of large amounts of new NGCC capacity in 2001 to 2003, or the delivery to market of unconventional gas supplies since 2008. 44 These pipeline capacity increases have added significant deliver ability to the natural gas pipeline network to meet the potential demands from increased use of existing NGCCs. Over a longer time period, much more significant pipeline expansion is possible. In previous studies, when the pipeline system was expected to face very large demands for natural gas use by electric utilities about 10 years ago, increases of up to 30% in total deliverab ility out of the pipeline system were judged to be possible by the pipeline industry. 45 There have also been notable capacity expansions over the past five years, in response to increased natural gas supply estimates and advances in drilling techniques. 46

To examine the potential for in creases in pipeline deliverab ility, the EPA analyzed the pipeline flow data from the Energy Information Administration. These data provide pipeline capacity for inflows and outflows by state. Howeve r, since the natural gas pipeline system is a network for flows into, across, and out of states and broader area, the level of gas supply that can be firmly delivered to a particular region depends on the amount of natural gas the will be required to be delivered out of the region to other regions. Consequently, it is important to focus on the net capacity – the difference between inflow capacity and outflow capacity -- in the relevant areas. The regions used by EIA for measuring regional natural gas deliverability are shown in Figure 3- 5 . Of these regions, the key regions for the analysis of the potential impact of the proposed rule are those natural gas consuming areas where there could be increases in natural gas consumption as a result of re-dispatch to comply with the proposed rule. These are the

44 Energy Information Administration, Today in Energy , Natural Gas Pipeline Additions in 2011. Additions averaged around 20Bcf per day from 2008 to 2011. 45 Pipeline and Storage Infrastructure Requirements for a 30 Tcf Market, INGAA Foundati on, 1999 (Updated July, 2004); U.S. gas groups confident of 30-tcf market, Oil and Gas Journal, 1999. 46 http://www.eia.gov/naturalgas/data.cfm#pipelines Deleted: 4I NTERAGENCY W ORKING C OMMENTS ON D RAFT L ANGUAGE UNDER EO12866 I NTERAGENCY R EVIEW
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19 Northeast, Southeast, Midwest and Western regions in Figure 3 5 . The net pipeline capacity for these regions from 2005 to 2011 is shown in Table 3- 4 below.

Table 3- 4 . Natural Gas Pipeline Net Capacity by Region, 2005 - 2011 by Gas Consuming Area 47
Region 2005 2006 2007 2008 2009 2010 2011

Capacity in MMCF/day
Midwest 17,102 17,232 17,452 17,302 18,714 18,564 18,414
Northeast 11,199 11,219 11,384 11,929 12,079 12,229 12,379
Southeast 12,921 12,901 12,736 15,741 18,241 20,797 20,797
Western 11,882 11,882 12,496 12,496 12,496 12,641 14,407
All Areas 53,104 53,234 54,068 57,468 61,530 64,231 65,997 Percent Change from 2005
Midwest 0.0% 0.8% 2.0% 1.2% 9.4% 8.5% 7.7%
Northeast 0.0% 0.2% 1.7% 6.5% 7.9% 9.2% 10.5%
Southeast 0.0% -0.2% -1.4% 21.8% 41.2% 61.0% 61.0%
Western 0.0% 0.0% 5.2% 5.2% 5.2% 6.4% 21.3%
All Areas 0.0% 0.2% 1.8% 8.2% 15.9% 21.0% 24.3%

As a conservative assumption, the increase from the period 2005 to 2010 can be used as an estimator of the potential increase in pipeline capacity to accommodate compliance with the rule between 2015 and 2020. This is an extremely conservative assumption, since compliance is measured over a longer period and is not limited to re-dispatch approaches. Moreover, increased use of natural gas in existing facilities can be largely met with expansions to existing pipeline facilities and corridors, so that the types of capacity expansion required will be less expensive and take less time than new pipelines. Over 2005-2010, the total gas deliverability in gas consuming areas increased by 21%. Since the power sector currently uses approximately 30% of the total national natural gas consumption, and gas usage in other sectors is expected to be essentially flat through 2020, this historical increase indicates that pipeline capacity will be adequate to support any compliance changes in re-dispatch of natural gas power plants.

47 Source: Energy Information Administrati on, http://www.eia.gov/naturalgas/data.cfm Deleted:

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

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21 Recent pipeline construction has continued to s upport the increasing need for natural gas. According to information releas ed in April, 2014 by the EIA, 48 118 pipeline projects were completed and placed into service from 2010 to 2014, totaling 4,699 miles of pipe, and 44,107 MMcf per day of additional pipeline capacity.

For projects expected to be in service from April, 2014 through 2016, EIA reports 47 projects, with planned capacity additions of 20,505 MMcf per day and 1,567 miles of pipe. These projects cover all major gas consuming areas of the US, and include both new pipeline construction, such as the Spectra Energy’s NEXUS Gas Transmission project in the Upper Midwest and pipeline expansions such as the Tennessee Gas Pipeline Project in Connecticut.

The electric transmission system has also been expanded over the past few years, and continued investment is expected. As of 2012, The EIA reports 187 thousand circuit miles of high voltage transmission in the US at 100 kV. There are 8 thousand miles of planned expansion in 2013, with a total 26 thousand miles proposed from 2013 to 2018. 49

According to the Edison Electric Institute, 50 member companies are planning over 170 projects through 2024, totaling ap proximately $60.6 billion (this is only a portion of the total transmission investment anticipated). Approximatel y 75 percent of the reported projects are high voltage (345 kV and higher), representing over 13,000 line miles.

Analysis of Cost-Effectiveness To further evaluate the technical capability of the natural gas supply and delivery system to provide increased quantities of natural gas an d the capability of the electricity transmission system to accommodate shifting generation patterns, EPA employed the Integrated Planning Model (IPM), a multi-regional, dynamic, deterministic linear programming model of the U.S. electric power sector that the EPA has used for over two decades to evaluate the economic and emission impacts of prospective environmental policies. IPM provides a wide array of

48 See www.eia.gov/naturalgas/pipelines/EIA

‐ NaturalGasPipelineProjects.xls. 49 http://www.eia.gov/electricity/annual/

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22 projections related to the electric power sector and its related markets (including least cost capacity expansion and electricity dispatch project ions) while meeting fuel supply, transmission, dispatch, and reliability constraints.

Natural gas supply, demand, transportation, storage, and related costs are modeled directly in IPM through the incorporation of a natural gas module. The module includes a detail rich representation of the natural gas pipeline network inclusive of discount curves that represent the marginal value of gas transmission as a functi on of the pipeline’s load factor. IPM’s natural gas module has the capability to expand pipeline capacity on an economic basis.

At the unit level, IPM contains a detailed representation of new and existing resource options, inclusive of key operational limitations. For example, turn down constraints are designed to account for the cycling capabilities of EGUs to ensure that the model properly reflects the distinct operating characteristics of peaking, cycling, and base load units. EPA believes IPM represents a powerful tool to ev aluate the technical f easibility of requiring increasing levels of re-dispatch from higher to lower-emitting EGUs.

The EPA has conducted extensive analysis to quantify the opportunity to reduce CO 2 emissions through re-dispatch. As part of this ef fort, the EPA conducted an initial set of analyses utilizing the Integrated Planning Model (IPM) to provide a framework for understanding the broader economic and emissions implications of shifting generation from coal-fired steam EGUs
to NGCC units within defined areas. 51 In the most restrictive scenarios, re-dispatch was simulated only between EGUs located in the same state. These scenarios were designed to consider, even under a restrictiv e interpretation of the degree of re-dispatch that might constitute a component of BSER under CAA section 111(d), 52 to what extent existing NGCC units could increase their dispatch cost-effectively taking into account the impact of that behavior on prices of natural gas and electricity. To evaluate how EGU operators and balancing authorities could

51 IPM is a multi-regional, dynamic, deterministic linear programming model of the U.S. electric power sector. It provides forecasts of least cost capac ity expansion, electricity dispatch, and emission control strategies while meeting energy demand and environmental, transmission, di spatch, and reliability constraints. Full documentation of the IPM model can be found at http://www.epa.gov/powersectormodeling 52 In practice, unit dispatch does not respect state boundari es because least-cost supply must be balanced with demand in real time over grid interconnects which span mul tiple states (with the exception of the Electric Reliability Council of Texas interconnect). The design of this modeling scenario assumes artificial constraints on re-dispatch to force such behavior to respect state boundaries, given the context of this rulemaking’s quantification of individual state goals. These state boundary constraints necessarily forgo cost-effective opportunities to re-dispatch units in different states; as a result, costs and prices in this analysis are overstated.I NTERAGENCY W ORKING C OMMENTS ON D RAFT L ANGUAGE UNDER EO12866 I NTERAGENCY R EVIEW
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23 respond to a state’s goal by in centivizing re-dispatch from more carbon-intensive to less carbonintensive EGUs, the EPA introduced two addi tional elements to the IPM framework:

1. The application of a CO 2 charge to the variable cost of dispatch for all existing coal steam boilers, IGCC units, and oil/gas steam boilers greater than 25 MW and with a CO 2 emissions rate greater than 1,100 lbs/MWh. 53

2. Generation constraints that maintain the sum of state-level generation in the Base Case 54 from existing NGCC of any size, plus existing coal steam, IGCC and oil/gas steam boilers greater than 25 MW and with a CO 2 emissions rate >1,100 lbs/MWh.

These elements test the economic and technical potential for re-dispatch by: (1) increasing dispatch costs for affected coal steam, IGCC, and O/G steam EGUs within each state, and (2) requiring that any reduction in output from those sources be offset in its entirety by an increase in output from that state’s existing NGCC capac ity. Utilizing IPM to conduct this analysis ensures an integrated, least-cost, technically feas ible solution subject to power sector system reliability constraints, fuel market impacts, na tural gas transmission and distribution networks, electric power transmission cons traints, and unit-specific characteristics (e.g., operating and maintenance costs, heat rate, turndown/cycling behavior).

Cost and Availability of Economic Re-Dispatch Opportunities In executing this analysis, the EPA conducted a number of scenarios to quantify the relationship between the amount and cost of re-dispatch. Figure 3- 6 below presents the projected national average capacity factor for NGCCs in 2020 (the first year of the assumed re-dispatch incentive) and the associated average $/tonne of CO 2 reduced by comparing emissions and costs against the EPA’s Base Case (the difference in total system cost divided by the difference in power sector emissions). While the charge is applied exogenously in IPM, the average $/tonne shown below is calculated from the modeling results and projections by dividing the increase in total system costs (compared to the ba se case) by the total change in CO 2 emissions (compared to the base case).

53 The addition of CO 2 costs represents a simple analytic a pproach to estimating the cost-effective CO 2 reductions under this building block and acts as a proxy for some exis ting state policies that shift dispatch. In actual plan implementation, states would be free to select any polic y approach that has the net effect of reducing the carbon intensity of generation and/or reducing overall emissions from affected sources. 54 http://epa.gov/powersectormodeling/BaseCasev513.html Comment [A10]: By imposing a carbon price,

one is modeling the most economically efficient method of abatement, not the costs associated with

an increase in NGCC capacity factors. The former has little bearing on the latter. Comment [A11]: EPA Response:

EPA believes the commenter has mischaracterized this particular analysis. The charge is applied to a certain set of sources to explore the concept of redispatch and assess the potential impacts, to directly support BSER. The combination of the charge on steam boilers with the constraint to maintain total generation across fossil fuel

‐ fired steam boilers and NGCCs is specifically designed to analyze the costs associated with an increase in NGCC capacity factors.
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While latter portions of this section have heavy redline, it largely reflects reordering, and maintains the same context (for the most part).

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

The EPA believes average $/tonne – wh ich is distinct from the $/tonne CO 2 cost imposed in these analytic scenarios on affected coal stea m, IGCC, and O/G steam EGUs – to be the most relevant metric in evaluating cost-effectiveness. System cost changes necessarily encompass all elements of cost across power, transmission, a nd fuel markets and therefore provide the most comprehensive perspective regarding cost-effectiveness. 58%

62% 65% 68% 71% 74% 75%

0% 10% 20% 30% 40% 50% 60% 70% 80%

‐ 10 20 30 40 50 60AverageCapacityFactor

Average $/tonne 

### Projected Capacity Factor for Existing NGCCs (2020)

Deleted: 5 Comment [A13]: $/tonne costs seem high

– would be helpful to have more detail on methodology/analysis used to derive these costs? Comment [A14]: EPA Response:

Some clarifying language was added to distinguish between the “charge” and the avg $/tonne, and how it is calculated.

D e l eted : 0% 10% 20% 30% 40% 50% 60% 70% 80%

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### Projected C a

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

CO 2 Cost Level Imposed National Average
NGCC Capacity Factor Average Cost ($/tonne) 2020 Power Sector CO 2 Emissions (MMT / % Change from 2020) Base Case 52% N/A 2,161 $10/tonne 58% $17 2,038 / -6% $15/tonne 62% $18 1,997 / -8% $20/tonne 65% $21 1,961 / -9% $25/tonne 68% $27 1,928 / -11% $30/tonne 71% $34 1,901 / -12% $40/tonne 74% $44 1,866 / -14% $50/tonne 75% $50 1,852 / -14%

Although the EPA views this estimated range of average $/tonne costs as reasonable, we expect the costs of implementing this requirement in a compliance 55 setting will be considerably
lower for several reasons:

 Analytic construct used to simulate re-dispatch incentive: As described earlier in this chapter, the EPA’s initial analyses utilized CO 2 charges on the variable cost of dispatch for existing coal steam, IGCC, and O/G steam with emission rates greater than 1,100lbs/MWh and a capacity greater than 25 MW, as an analytic construct to induce redispatch behavior in the model to existing NGCC facilities. The CO 2 charge was applied uniformly to all states in or der to quantify the ultimate am ount of in-state re-dispatch opportunities available as that charge is incr eased across scenarios. In the initial analyses,
low levels of CO 2 charges produce cost-effective re-dispatch opportunities relative to the Base Case in almost all states. However, as the CO 2 costs are increased to higher levels, economic re-dispatch, opportunities within some states may eventually plateau – a point

55 The Regulatory Impact Analysis supporting the proposal examines, in an illustrative manner, how the power sector could respond to the state goals that are calculated from all of the building blocks in a cost effective

manner. Deleted: 4

Comment [A15]: Can this be quantified? Comment [A16]: EPA Response:

Footnote added. This is shown, in an illustrative manner, in the RIA.I NTERAGENCY W ORKING C OMMENTS ON D RAFT L ANGUAGE UNDER EO12866 I NTERAGENCY R EVIEW
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26 clearly illustrated in the declining slop e of the best-fit line in Figure 3- 6 . A uniform application of the same rising CO 2 charge in all states produces an outcome for many
states where the additional CO 2 costs imposed on affected coal steam, IGCC, and O/G steam are not able to produce incremental economic re-dispatch at units within that state; therefore, the additional cost s imposed by these higher CO 2 charges overstate the actual $/tonne necessary to induce achievable re-dispatch in each state.

 Potential for multi-state compliance: The EPA also analyzed scenarios where shifting ofgeneration among EGUs was not limited by state boundaries. In one set of analyses, redispatch was allowed to occur across th e multi-state regions defined by NERC assessment areas (subject to other real-world constraints specified in the model, including transmission limits). In these scenarios with greater re-dispatch flexibility, the system was able to achieve 8% greater CO 2 emission reductions at an identical CO 2 charge (relative to a scenario where it was limited on a state basis), demonstrating that the main analysis’s imposition of artificial re-dispatc h boundaries on state borders overstates the
cost-effectiveness of re -dispatch potential.

To evaluate how EGU owners and grid operat ors could respond to a state plan’s possiblerequirements, signals, or incentives to re-dispatch from more carbon-intensive to less carbonintensive EGUs, the EPA also analyzed an additional series of scenarios in which the fleet of NGCC units nationwide was required, on average, to achieve a specified annual utilization rate. Specifically, the scenarios required average NGCC unit utilization rates of 65, 70, and 75 percent. For each scenario, dispatch decisions are allowed such that electricity demand is met at the lowest total cost, subject to all other speci fied operating and reliability constraints for the scenario, including the aforementioned state-by-sta te generation levels from the base case. This constraint effectively requires states that decr ease coal generation to offset, in equal amounts, NGCC generation. Collectively, states must ach ieve the required capacity factor for NGCCs.

The costs and economic impacts of the various scenarios were evaluated by comparing
the total costs and emissions from each scenario to the costs and emissions from a business-asusual scenario. For the scenarios reflecting a 65, 70, and 75 percent NGCC utilization rate, comparison to the business-as-usual case indicates that the average cost of the CO 2 reductions achieved over the 2020-2029 period was $21, $30, and $40 per metric ton of CO 2 , respectively. Deleted: 5

Comment [A18]: EPA Response:

Clarification added. Comment [A17]: Can you clarify if this 8% greater emission reduction is relative to base case
or relative to reductions achieved from redispatch without regional scenario?

Deleted:
, Comment [A19]: Also, can this be stated alternately as a drop in cost for a given CO2 reduction level? Comment [A20]: EPA Response:

The commenter’s intuition is correct, but we do not have analytic scenarios that can quantify this concept because we did not model “a given CO2 reduction level” between state and regional re
‐ dispatch scenarios.

Comment [A21]: It would be helpful to have the results – can EPA provide this? Will it be in the docket? Comment [A22]: EPA Response: EPA will provide this modeling in the docket for the proposal.

Deleted: The costs and economic impacts of the various scenarios were evaluated by comparing the total costs and emissions from each scenario to the costs and emissions from a business-as-usual scenario. For the scenario reflecting a 70 percent NGCC utilization rate and a constraint prohibiting interstate re-dispatch opportunities, comparison to the business-as-usual case indicates that the average cost of the CO 2 reductions achieved ranged from [$xx] to [$xx] per metric ton of CO 2 . Formatted: Subscript
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27 However, we also note that the costs just desc ribed are higher than we would expect to actually occur in real-world compliance with this proposal’s goals. This is because only 29 state goals are premised on the existing NGCC fleet achieving an average capacity factor of 70 percent. Consequently, a 70 percent utiliza tion rate target for the existing NGCC fleet requires an average national capacity factor of 63 percent.

The EPA also analyzed dispatch-only scenarios where shifting of generation among EGUs was limited by state boundaries. In these scenarios with less re-dispatch flexibility, the cost of achieving the quantity of CO 2 reductions corresponding to a nationwide average NGCC unit utilization of 70% was $33 per ton.

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

Existing NGCC Average National Capacity Factor Re-Dispatch Constraint Average Cost ($/tonne, 2020- 2029) Average CO 2 Emissions (MMT, 2020- 2029) Reductions from Base Case (%, 2020-2029)

Base Case NA NA 2,215 NA

65% Regional $21 2,022 9%

70% Regional $30 1,969 11%

75% Regional $40 1,915 14%

65% State $22 2,024 9%

70% State $33 1,971 11%

Natural Gas Price Impacts

The extent of re-dispatch estimated in this building block can be achieved without causing significant economic impacts. For exampl e, in neither of the 70 percent NGCC unit utilization rate scenarios – re-dispatch limited to regional or state boundaries – did delivered natural gas price projections increase by more than 10 percent in the 2020-2029 period, which is Deleted: These scenarios include constraints such that there will be no cost-effective re-dispatch opportunities among EGUs in different states, contrary to the reality that interstate flows of electricity routinely change in response to economic conditions. As a result, this case assumes no use of interstate electricity trading as a cost reductionmechanism, and thus overstates the impacts of redispatch. ¶

In the 70 percent NGCC unit utilization rate scenario with restrictions on interstate re-dispatch, natural gas prices were projected to increase by a range of from [x] percent to [x] percent, which is ... [40] Comment [A23]: A lot of text has been devoted to demonstrating how the NGCC fleet will cope with increase dispatch from the coal fleet. Analysis should also be included of how the coal fleet will respond to various levels of redispatch, and if there is information on how coal plants can dial down output when demand is reduced without having to switch off. Comment [A24]: EPA Response:

Some additional detail that addresses this comment
was added earlier, in the context of the modeling framework. Comment [A25]: Can the actual numbers on costs be presented in a table or other format, other than just stating “notably lower”? Comment [A26]: A table has been added.

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28 well within the range of historical natural ga s price volatility. For exam ple, the year-to-year percentage difference in Henry Hub prices reported by the Energy Information Administration averaged 18.5% over the period from 1981 to 2012. 56 Projected wholesal e electricity price increases over the same period were less than 7 percent in both cases, which similarly is well within the range of historic elect ric price variability. For example, the average year-to-year price change for the PJM region was 19.5 percent over the period from 2000 to 2013. For all the ISOs in the East, the variation is virtually unchanged from the PJM example (at 19.6%). 57

However, for the reasons previously discussed with respect to estimated costs per ton of
CO 2 , the actual implementation is expected to result in notably lower economic impacts, including natural gas price impacts, and are considerably larger than would actually occur in real-world compliance with this rule’s proposed goals.

Table 3-X: National Average Delivered Natural Gas Price, Power Sector (Average 2020-2029)

Existing NGCC Average National Capacity Factor Re-Dispatch Constraint Price ($/mmBtu) % Change Base Case NA $5.94 65% Regional $6.36 7% 70% Regional $6.53 10% 75% Regional $6.69 13% 65% State $6.37 7% 70% State $6.52 10%

Table 3-X: National Average Delivered Natural Gas Price, Power Sector (Average 2020-2029)

Price ($/mmBtu) % Change Base Case $6.05 $10/tonne $6.21 3% $15/tonne $6.33 5%

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

57 ISO Real

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

‐ NE and Midcontinent ISO). Deleted:
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## Figure 3-1: Cumulative NGCC Capacity Factor vs. Percentage of Units

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### 0% 20% 40% 60% 80% 100%0%4%8%12%16%20%24%28%32%36%40%44%48%52%56%60%65%69%73%77%81%85%89%93%97%CapacityFactor



### % of Units 

# Cumulative Capacity Factor

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## These scenarios include constraints such that there will be no cost-effective re-dispatch opportunities among EGUs in differen t states, contrary to the reality that interstate flows of electricity routinely change in response to ec onomic conditions. As a result, this case assumes no use of interstate electricity trading as a co st reduction mechanism, and thus overstates the impacts of re-dispatch.

In the 70 percent NGCC unit utilization rate scenario with restrictions on interstate re-dispatch, natu ral gas prices were projected to incr ease by a range of from [x] percent

## to [x] percent, which is within the range of historic natural gas price variability. Projected wholesale electricity price increases ranged from [x] to [x] [A1][A2] 

## percent, which similarly is within the range of historic electric price variability.

The EPA also analyzed dispatch-only scenario s where shifting of generation among EGUs was not limited by state boundaries. In one set of analyses, re-dispatc h was simulated to occur across the multi-state regions overseen by independent system operators (ISOs) and other regional transmission organizations (RTO s), while in another set of analyses, national re-dispatch was simulated (subject to other constraints specified in the model, including transmission limits). In these scenarios with greater re -dispatch flexibility, the cost of achieving the quantity of CO 2 

## reductions corresponding to a nationwide averag e NGCC unit utilization of 70% was notably
lower. [A3]

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